From 21882c9d179d2e25b8c9f1b4c262de534867da30 Mon Sep 17 00:00:00 2001 From: paxcema Date: Mon, 18 Mar 2024 12:16:38 +0000 Subject: [PATCH] Rebuilt the docs --- .../custom_cleaner/custom_cleaner.ipynb.txt | 136 +- .../custom_encoder_rulebased.ipynb.txt | 178 +-- .../custom_explainer.ipynb.txt | 1397 +++++++++-------- .../custom_mixer/custom_mixer.ipynb.txt | 270 ++-- .../custom_splitter/custom_splitter.ipynb.txt | 228 +-- .../tutorial_data_analysis.ipynb.txt | 144 +- .../tutorial_time_series.ipynb.txt | 439 +++--- .../tutorial_update_models.ipynb.txt | 336 ++-- searchindex.js | 2 +- tutorials/custom_cleaner/custom_cleaner.html | 56 +- tutorials/custom_cleaner/custom_cleaner.ipynb | 136 +- .../custom_encoder_rulebased.html | 90 +- .../custom_encoder_rulebased.ipynb | 178 +-- .../custom_explainer/custom_explainer.html | 1316 ++++++++-------- .../custom_explainer/custom_explainer.ipynb | 1397 +++++++++-------- tutorials/custom_mixer/custom_mixer.html | 221 ++- tutorials/custom_mixer/custom_mixer.ipynb | 270 ++-- .../custom_splitter/custom_splitter.html | 148 +- .../custom_splitter/custom_splitter.ipynb | 228 +-- .../tutorial_data_analysis.html | 40 +- .../tutorial_data_analysis.ipynb | 144 +- .../tutorial_time_series.html | 353 +++-- .../tutorial_time_series.ipynb | 439 +++--- .../tutorial_update_models.html | 278 ++-- .../tutorial_update_models.ipynb | 336 ++-- 25 files changed, 4490 insertions(+), 4270 deletions(-) diff --git a/_sources/tutorials/custom_cleaner/custom_cleaner.ipynb.txt b/_sources/tutorials/custom_cleaner/custom_cleaner.ipynb.txt index e7d529c36..3c6be2ceb 100644 --- a/_sources/tutorials/custom_cleaner/custom_cleaner.ipynb.txt +++ b/_sources/tutorials/custom_cleaner/custom_cleaner.ipynb.txt @@ -31,10 +31,10 @@ "id": "happy-wheat", "metadata": { "execution": { - "iopub.execute_input": "2024-03-18T10:32:04.246435Z", - "iopub.status.busy": "2024-03-18T10:32:04.245945Z", - "iopub.status.idle": "2024-03-18T10:32:06.798108Z", - "shell.execute_reply": "2024-03-18T10:32:06.797291Z" + "iopub.execute_input": "2024-03-18T12:13:41.624054Z", + "iopub.status.busy": "2024-03-18T12:13:41.623553Z", + "iopub.status.idle": "2024-03-18T12:13:44.391003Z", + "shell.execute_reply": "2024-03-18T12:13:44.390294Z" } }, "outputs": [ @@ -42,14 +42,14 @@ "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2632:No torchvision detected, image helpers not supported.\u001b[0m\n" + "\u001b[32mINFO:lightwood-2806:No torchvision detected, image helpers not supported.\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2632:No torchvision/pillow detected, image encoder not supported\u001b[0m\n" + "\u001b[32mINFO:lightwood-2806:No torchvision/pillow detected, image encoder not supported\u001b[0m\n" ] } ], @@ -87,10 +87,10 @@ "id": "recognized-parish", "metadata": { "execution": { - "iopub.execute_input": "2024-03-18T10:32:06.801294Z", - "iopub.status.busy": "2024-03-18T10:32:06.800939Z", - "iopub.status.idle": "2024-03-18T10:32:07.802600Z", - "shell.execute_reply": "2024-03-18T10:32:07.801886Z" + "iopub.execute_input": "2024-03-18T12:13:44.394866Z", + "iopub.status.busy": "2024-03-18T12:13:44.394215Z", + "iopub.status.idle": "2024-03-18T12:13:47.386061Z", + "shell.execute_reply": "2024-03-18T12:13:47.385334Z" } }, "outputs": [ @@ -221,10 +221,10 @@ "id": "chicken-truth", "metadata": { "execution": { - "iopub.execute_input": "2024-03-18T10:32:07.805528Z", - "iopub.status.busy": "2024-03-18T10:32:07.805118Z", - "iopub.status.idle": "2024-03-18T10:32:23.116609Z", - "shell.execute_reply": "2024-03-18T10:32:23.115969Z" + "iopub.execute_input": "2024-03-18T12:13:47.389003Z", + "iopub.status.busy": "2024-03-18T12:13:47.388624Z", + "iopub.status.idle": "2024-03-18T12:14:03.044309Z", + "shell.execute_reply": "2024-03-18T12:14:03.043698Z" } }, "outputs": [ @@ -232,98 +232,98 @@ "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2632:Dropping features: ['url_legal', 'license', 'standard_error']\u001b[0m\n" + "\u001b[32mINFO:lightwood-2806:Dropping features: ['url_legal', 'license', 'standard_error']\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2632:Analyzing a sample of 2478\u001b[0m\n" + "\u001b[32mINFO:type_infer-2806:Analyzing a sample of 2478\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2632:from a total population of 2834, this is equivalent to 87.4% of your data.\u001b[0m\n" + "\u001b[32mINFO:type_infer-2806:from a total population of 2834, this is equivalent to 87.4% of your data.\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2632:Infering type for: id\u001b[0m\n" + "\u001b[32mINFO:type_infer-2806:Infering type for: id\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2632:Doing text detection for column: id\u001b[0m\n" + "\u001b[32mINFO:type_infer-2806:Doing text detection for column: id\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2632:Column id has data type categorical\u001b[0m\n" + "\u001b[32mINFO:type_infer-2806:Column id has data type categorical\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2632:Infering type for: excerpt\u001b[0m\n" + "\u001b[32mINFO:type_infer-2806:Infering type for: excerpt\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2632:Doing text detection for column: excerpt\u001b[0m\n" + "\u001b[32mINFO:type_infer-2806:Doing text detection for column: excerpt\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2632:Infering type for: target\u001b[0m\n" + "\u001b[32mINFO:type_infer-2806:Infering type for: target\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2632:Column target has data type float\u001b[0m\n" + "\u001b[32mINFO:type_infer-2806:Column target has data type float\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[33mWARNING:type_infer-2632:Column id is an identifier of type \"Hash-like identifier\"\u001b[0m\n" + "\u001b[33mWARNING:type_infer-2806:Column id is an identifier of type \"Hash-like identifier\"\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2632:Starting statistical analysis\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2806:Starting statistical analysis\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2632:Dropping features: ['id']\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2806:Dropping features: ['id']\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2632:Finished statistical analysis\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2806:Finished statistical analysis\u001b[0m\n" ] } ], @@ -360,10 +360,10 @@ "id": "designed-condition", "metadata": { "execution": { - "iopub.execute_input": "2024-03-18T10:32:23.119408Z", - "iopub.status.busy": "2024-03-18T10:32:23.119198Z", - "iopub.status.idle": "2024-03-18T10:32:23.123971Z", - "shell.execute_reply": "2024-03-18T10:32:23.123305Z" + "iopub.execute_input": "2024-03-18T12:14:03.047471Z", + "iopub.status.busy": "2024-03-18T12:14:03.047072Z", + "iopub.status.idle": "2024-03-18T12:14:03.051562Z", + "shell.execute_reply": "2024-03-18T12:14:03.050874Z" } }, "outputs": [ @@ -434,7 +434,7 @@ " \"unbias_target\": true,\n", " \"seconds_per_mixer\": 21384.0,\n", " \"seconds_per_encoder\": 85536.0,\n", - " \"expected_additional_time\": 15.30086874961853,\n", + " \"expected_additional_time\": 15.644360542297363,\n", " \"time_aim\": 259200,\n", " \"target_weights\": null,\n", " \"positive_domain\": false,\n", @@ -518,10 +518,10 @@ "id": "325d8f1b", "metadata": { "execution": { - "iopub.execute_input": "2024-03-18T10:32:23.126476Z", - "iopub.status.busy": "2024-03-18T10:32:23.126282Z", - "iopub.status.idle": "2024-03-18T10:32:23.131477Z", - "shell.execute_reply": "2024-03-18T10:32:23.130857Z" + "iopub.execute_input": "2024-03-18T12:14:03.054794Z", + "iopub.status.busy": "2024-03-18T12:14:03.054271Z", + "iopub.status.idle": "2024-03-18T12:14:03.060074Z", + "shell.execute_reply": "2024-03-18T12:14:03.059395Z" } }, "outputs": [ @@ -646,10 +646,10 @@ "id": "f030f8ca", "metadata": { "execution": { - "iopub.execute_input": "2024-03-18T10:32:23.133852Z", - "iopub.status.busy": "2024-03-18T10:32:23.133652Z", - "iopub.status.idle": "2024-03-18T10:32:23.137057Z", - "shell.execute_reply": "2024-03-18T10:32:23.136533Z" + "iopub.execute_input": "2024-03-18T12:14:03.062892Z", + "iopub.status.busy": "2024-03-18T12:14:03.062488Z", + "iopub.status.idle": "2024-03-18T12:14:03.065997Z", + "shell.execute_reply": "2024-03-18T12:14:03.065454Z" } }, "outputs": [], @@ -711,10 +711,10 @@ "id": "floating-patent", "metadata": { "execution": { - "iopub.execute_input": "2024-03-18T10:32:23.139665Z", - "iopub.status.busy": "2024-03-18T10:32:23.139226Z", - "iopub.status.idle": "2024-03-18T10:32:23.351781Z", - "shell.execute_reply": "2024-03-18T10:32:23.351122Z" + "iopub.execute_input": "2024-03-18T12:14:03.068815Z", + "iopub.status.busy": "2024-03-18T12:14:03.068356Z", + "iopub.status.idle": "2024-03-18T12:14:03.298497Z", + "shell.execute_reply": "2024-03-18T12:14:03.297746Z" } }, "outputs": [ @@ -795,7 +795,7 @@ " \"unbias_target\": True,\n", " \"seconds_per_mixer\": 21384.0,\n", " \"seconds_per_encoder\": 85536.0,\n", - " \"expected_additional_time\": 15.30086874961853,\n", + " \"expected_additional_time\": 15.644360542297363,\n", " \"time_aim\": 259200,\n", " \"target_weights\": None,\n", " \"positive_domain\": False,\n", @@ -1449,10 +1449,10 @@ "id": "violent-guard", "metadata": { "execution": { - "iopub.execute_input": "2024-03-18T10:32:23.354693Z", - "iopub.status.busy": "2024-03-18T10:32:23.354305Z", - "iopub.status.idle": "2024-03-18T10:32:23.361980Z", - "shell.execute_reply": "2024-03-18T10:32:23.361356Z" + "iopub.execute_input": "2024-03-18T12:14:03.301712Z", + "iopub.status.busy": "2024-03-18T12:14:03.301155Z", + "iopub.status.idle": "2024-03-18T12:14:03.310184Z", + "shell.execute_reply": "2024-03-18T12:14:03.309472Z" } }, "outputs": [], @@ -1467,10 +1467,10 @@ "id": "closing-episode", "metadata": { "execution": { - "iopub.execute_input": "2024-03-18T10:32:23.364591Z", - "iopub.status.busy": "2024-03-18T10:32:23.364125Z", - "iopub.status.idle": "2024-03-18T10:32:23.499702Z", - "shell.execute_reply": "2024-03-18T10:32:23.499147Z" + "iopub.execute_input": "2024-03-18T12:14:03.313258Z", + "iopub.status.busy": "2024-03-18T12:14:03.312845Z", + "iopub.status.idle": "2024-03-18T12:14:03.450976Z", + "shell.execute_reply": "2024-03-18T12:14:03.450274Z" }, "scrolled": false }, @@ -1479,70 +1479,70 @@ "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2632:Starting statistical analysis\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2806:Starting statistical analysis\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2632:Dropping features: ['id']\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2806:Dropping features: ['id']\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2632:Finished statistical analysis\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2806:Finished statistical analysis\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:lightwood-2632: `analyze_data` runtime: 0.05 seconds\u001b[0m\n" + "\u001b[37mDEBUG:lightwood-2806: `analyze_data` runtime: 0.05 seconds\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2632:Cleaning the data\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2806:Cleaning the data\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2632:Dropping features: ['id']\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2806:Dropping features: ['id']\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2632:Cleaning column =excerpt\u001b[0m\n" + "\u001b[32mINFO:lightwood-2806:Cleaning column =excerpt\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2632:Cleaning column =target\u001b[0m\n" + "\u001b[32mINFO:lightwood-2806:Cleaning column =target\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2632:Converted target into strictly non-negative\u001b[0m\n" + "\u001b[32mINFO:lightwood-2806:Converted target into strictly non-negative\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:lightwood-2632: `preprocess` runtime: 0.07 seconds\u001b[0m\n" + "\u001b[37mDEBUG:lightwood-2806: `preprocess` runtime: 0.08 seconds\u001b[0m\n" ] }, { @@ -1632,10 +1632,10 @@ "id": "major-stake", "metadata": { "execution": { - "iopub.execute_input": "2024-03-18T10:32:23.502447Z", - "iopub.status.busy": "2024-03-18T10:32:23.502082Z", - "iopub.status.idle": "2024-03-18T10:32:23.506941Z", - "shell.execute_reply": "2024-03-18T10:32:23.506294Z" + "iopub.execute_input": "2024-03-18T12:14:03.453743Z", + "iopub.status.busy": "2024-03-18T12:14:03.453330Z", + "iopub.status.idle": "2024-03-18T12:14:03.458416Z", + "shell.execute_reply": "2024-03-18T12:14:03.457734Z" } }, "outputs": [ diff --git a/_sources/tutorials/custom_encoder_rulebased/custom_encoder_rulebased.ipynb.txt b/_sources/tutorials/custom_encoder_rulebased/custom_encoder_rulebased.ipynb.txt index 9367addcc..914b2a036 100644 --- a/_sources/tutorials/custom_encoder_rulebased/custom_encoder_rulebased.ipynb.txt +++ b/_sources/tutorials/custom_encoder_rulebased/custom_encoder_rulebased.ipynb.txt @@ -43,10 +43,10 @@ "id": "raising-adventure", "metadata": { "execution": { - "iopub.execute_input": "2024-03-18T10:31:45.964816Z", - "iopub.status.busy": "2024-03-18T10:31:45.964618Z", - "iopub.status.idle": "2024-03-18T10:31:48.543190Z", - "shell.execute_reply": "2024-03-18T10:31:48.542464Z" + "iopub.execute_input": "2024-03-18T12:13:22.285628Z", + "iopub.status.busy": "2024-03-18T12:13:22.285375Z", + "iopub.status.idle": "2024-03-18T12:13:25.085461Z", + "shell.execute_reply": "2024-03-18T12:13:25.084685Z" } }, "outputs": [ @@ -54,14 +54,14 @@ "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2551:No torchvision detected, image helpers not supported.\u001b[0m\n" + "\u001b[32mINFO:lightwood-2722:No torchvision detected, image helpers not supported.\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2551:No torchvision/pillow detected, image encoder not supported\u001b[0m\n" + "\u001b[32mINFO:lightwood-2722:No torchvision/pillow detected, image encoder not supported\u001b[0m\n" ] } ], @@ -93,10 +93,10 @@ "id": "technical-government", "metadata": { "execution": { - "iopub.execute_input": "2024-03-18T10:31:48.546568Z", - "iopub.status.busy": "2024-03-18T10:31:48.545997Z", - "iopub.status.idle": "2024-03-18T10:31:48.750333Z", - "shell.execute_reply": "2024-03-18T10:31:48.749609Z" + "iopub.execute_input": "2024-03-18T12:13:25.089135Z", + "iopub.status.busy": "2024-03-18T12:13:25.088792Z", + "iopub.status.idle": "2024-03-18T12:13:25.619155Z", + "shell.execute_reply": "2024-03-18T12:13:25.618419Z" } }, "outputs": [ @@ -243,10 +243,10 @@ "id": "absent-maker", "metadata": { "execution": { - "iopub.execute_input": "2024-03-18T10:31:48.753282Z", - "iopub.status.busy": "2024-03-18T10:31:48.752879Z", - "iopub.status.idle": "2024-03-18T10:31:59.613412Z", - "shell.execute_reply": "2024-03-18T10:31:59.612794Z" + "iopub.execute_input": "2024-03-18T12:13:25.622051Z", + "iopub.status.busy": "2024-03-18T12:13:25.621631Z", + "iopub.status.idle": "2024-03-18T12:13:36.720794Z", + "shell.execute_reply": "2024-03-18T12:13:36.719966Z" } }, "outputs": [ @@ -254,161 +254,161 @@ "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2551:Analyzing a sample of 6920\u001b[0m\n" + "\u001b[32mINFO:type_infer-2722:Analyzing a sample of 6920\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2551:from a total population of 10668, this is equivalent to 64.9% of your data.\u001b[0m\n" + "\u001b[32mINFO:type_infer-2722:from a total population of 10668, this is equivalent to 64.9% of your data.\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2551:Using 3 processes to deduct types.\u001b[0m\n" + "\u001b[32mINFO:type_infer-2722:Using 3 processes to deduct types.\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2551:Infering type for: price\u001b[0m\n" + "\u001b[32mINFO:type_infer-2722:Infering type for: year\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2551:Infering type for: year\u001b[0m\n" + "\u001b[32mINFO:type_infer-2722:Infering type for: price\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2551:Column price has data type integer\u001b[0m\n" + "\u001b[32mINFO:type_infer-2722:Column year has data type integer\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2551:Column year has data type integer\u001b[0m\n" + "\u001b[32mINFO:type_infer-2722:Column price has data type integer\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2551:Infering type for: transmission\u001b[0m\n" + "\u001b[32mINFO:type_infer-2722:Infering type for: transmission\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2551:Infering type for: mileage\u001b[0m\n" + "\u001b[32mINFO:type_infer-2722:Infering type for: mileage\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2551:Infering type for: model\u001b[0m\n" + "\u001b[32mINFO:type_infer-2722:Infering type for: model\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2551:Column mileage has data type integer\u001b[0m\n" + "\u001b[32mINFO:type_infer-2722:Column mileage has data type integer\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2551:Infering type for: fuelType\u001b[0m\n" + "\u001b[32mINFO:type_infer-2722:Infering type for: fuelType\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2551:Column transmission has data type categorical\u001b[0m\n" + "\u001b[32mINFO:type_infer-2722:Column transmission has data type categorical\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2551:Infering type for: tax\u001b[0m\n" + "\u001b[32mINFO:type_infer-2722:Infering type for: tax\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2551:Column tax has data type integer\u001b[0m\n" + "\u001b[32mINFO:type_infer-2722:Column tax has data type integer\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2551:Infering type for: mpg\u001b[0m\n" + "\u001b[32mINFO:type_infer-2722:Infering type for: mpg\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2551:Column mpg has data type float\u001b[0m\n" + "\u001b[32mINFO:type_infer-2722:Column mpg has data type float\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2551:Infering type for: engineSize\u001b[0m\n" + "\u001b[32mINFO:type_infer-2722:Infering type for: engineSize\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2551:Column engineSize has data type float\u001b[0m\n" + "\u001b[32mINFO:type_infer-2722:Column engineSize has data type float\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2551:Column model has data type categorical\u001b[0m\n" + "\u001b[32mINFO:type_infer-2722:Column model has data type categorical\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2551:Column fuelType has data type categorical\u001b[0m\n" + "\u001b[32mINFO:type_infer-2722:Column fuelType has data type categorical\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2551:Starting statistical analysis\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2722:Starting statistical analysis\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2551:Finished statistical analysis\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2722:Finished statistical analysis\u001b[0m\n" ] } ], @@ -437,10 +437,10 @@ "id": "coastal-paragraph", "metadata": { "execution": { - "iopub.execute_input": "2024-03-18T10:31:59.616597Z", - "iopub.status.busy": "2024-03-18T10:31:59.616070Z", - "iopub.status.idle": "2024-03-18T10:31:59.620828Z", - "shell.execute_reply": "2024-03-18T10:31:59.620223Z" + "iopub.execute_input": "2024-03-18T12:13:36.723741Z", + "iopub.status.busy": "2024-03-18T12:13:36.723516Z", + "iopub.status.idle": "2024-03-18T12:13:36.728384Z", + "shell.execute_reply": "2024-03-18T12:13:36.727737Z" } }, "outputs": [ @@ -545,7 +545,7 @@ " \"unbias_target\": true,\n", " \"seconds_per_mixer\": 21384.0,\n", " \"seconds_per_encoder\": 85536.0,\n", - " \"expected_additional_time\": 10.848296403884888,\n", + " \"expected_additional_time\": 11.086917877197266,\n", " \"time_aim\": 259200,\n", " \"target_weights\": null,\n", " \"positive_domain\": false,\n", @@ -643,10 +643,10 @@ "id": "e03db1b0", "metadata": { "execution": { - "iopub.execute_input": "2024-03-18T10:31:59.623535Z", - "iopub.status.busy": "2024-03-18T10:31:59.623175Z", - "iopub.status.idle": "2024-03-18T10:31:59.628498Z", - "shell.execute_reply": "2024-03-18T10:31:59.627897Z" + "iopub.execute_input": "2024-03-18T12:13:36.731032Z", + "iopub.status.busy": "2024-03-18T12:13:36.730666Z", + "iopub.status.idle": "2024-03-18T12:13:36.735792Z", + "shell.execute_reply": "2024-03-18T12:13:36.735161Z" } }, "outputs": [ @@ -766,10 +766,10 @@ "id": "e30866c1", "metadata": { "execution": { - "iopub.execute_input": "2024-03-18T10:31:59.630984Z", - "iopub.status.busy": "2024-03-18T10:31:59.630777Z", - "iopub.status.idle": "2024-03-18T10:31:59.634186Z", - "shell.execute_reply": "2024-03-18T10:31:59.633618Z" + "iopub.execute_input": "2024-03-18T12:13:36.738409Z", + "iopub.status.busy": "2024-03-18T12:13:36.738073Z", + "iopub.status.idle": "2024-03-18T12:13:36.741539Z", + "shell.execute_reply": "2024-03-18T12:13:36.740938Z" } }, "outputs": [], @@ -828,10 +828,10 @@ "id": "elementary-fusion", "metadata": { "execution": { - "iopub.execute_input": "2024-03-18T10:31:59.636762Z", - "iopub.status.busy": "2024-03-18T10:31:59.636339Z", - "iopub.status.idle": "2024-03-18T10:31:59.639596Z", - "shell.execute_reply": "2024-03-18T10:31:59.639007Z" + "iopub.execute_input": "2024-03-18T12:13:36.744078Z", + "iopub.status.busy": "2024-03-18T12:13:36.743688Z", + "iopub.status.idle": "2024-03-18T12:13:36.746940Z", + "shell.execute_reply": "2024-03-18T12:13:36.746313Z" } }, "outputs": [], @@ -857,10 +857,10 @@ "id": "inappropriate-james", "metadata": { "execution": { - "iopub.execute_input": "2024-03-18T10:31:59.642016Z", - "iopub.status.busy": "2024-03-18T10:31:59.641636Z", - "iopub.status.idle": "2024-03-18T10:31:59.986759Z", - "shell.execute_reply": "2024-03-18T10:31:59.986037Z" + "iopub.execute_input": "2024-03-18T12:13:36.749283Z", + "iopub.status.busy": "2024-03-18T12:13:36.749096Z", + "iopub.status.idle": "2024-03-18T12:13:37.131539Z", + "shell.execute_reply": "2024-03-18T12:13:37.130834Z" } }, "outputs": [], @@ -891,10 +891,10 @@ "id": "palestinian-harvey", "metadata": { "execution": { - "iopub.execute_input": "2024-03-18T10:31:59.989829Z", - "iopub.status.busy": "2024-03-18T10:31:59.989414Z", - "iopub.status.idle": "2024-03-18T10:32:01.137819Z", - "shell.execute_reply": "2024-03-18T10:32:01.137143Z" + "iopub.execute_input": "2024-03-18T12:13:37.134854Z", + "iopub.status.busy": "2024-03-18T12:13:37.134380Z", + "iopub.status.idle": "2024-03-18T12:13:38.313702Z", + "shell.execute_reply": "2024-03-18T12:13:38.312990Z" } }, "outputs": [ @@ -902,133 +902,133 @@ "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2551:Starting statistical analysis\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2722:Starting statistical analysis\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2551:Finished statistical analysis\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2722:Finished statistical analysis\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:lightwood-2551: `analyze_data` runtime: 0.43 seconds\u001b[0m\n" + "\u001b[37mDEBUG:lightwood-2722: `analyze_data` runtime: 0.44 seconds\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2551:Cleaning the data\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2722:Cleaning the data\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:lightwood-2551: `preprocess` runtime: 0.13 seconds\u001b[0m\n" + "\u001b[37mDEBUG:lightwood-2722: `preprocess` runtime: 0.13 seconds\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2551:Splitting the data into train/test\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2722:Splitting the data into train/test\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:lightwood-2551: `split` runtime: 0.01 seconds\u001b[0m\n" + "\u001b[37mDEBUG:lightwood-2722: `split` runtime: 0.01 seconds\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:dataprep_ml-2551:Preparing sequentially...\u001b[0m\n" + "\u001b[37mDEBUG:dataprep_ml-2722:Preparing sequentially...\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:dataprep_ml-2551:Preparing encoder for year...\u001b[0m\n" + "\u001b[37mDEBUG:dataprep_ml-2722:Preparing encoder for year...\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:dataprep_ml-2551:Preparing encoder for mileage...\u001b[0m\n" + "\u001b[37mDEBUG:dataprep_ml-2722:Preparing encoder for mileage...\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:dataprep_ml-2551:Preparing encoder for tax...\u001b[0m\n" + "\u001b[37mDEBUG:dataprep_ml-2722:Preparing encoder for tax...\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:dataprep_ml-2551:Preparing encoder for mpg...\u001b[0m\n" + "\u001b[37mDEBUG:dataprep_ml-2722:Preparing encoder for mpg...\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:dataprep_ml-2551:Preparing encoder for engineSize...\u001b[0m\n" + "\u001b[37mDEBUG:dataprep_ml-2722:Preparing encoder for engineSize...\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2551:Categories Detected = 1\u001b[0m\n" + "\u001b[32mINFO:lightwood-2722:Categories Detected = 1\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2551:Categories Detected = 1\u001b[0m\n" + "\u001b[32mINFO:lightwood-2722:Categories Detected = 1\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2551:Categories Detected = 1\u001b[0m\n" + "\u001b[32mINFO:lightwood-2722:Categories Detected = 1\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:lightwood-2551: `prepare` runtime: 0.02 seconds\u001b[0m\n" + "\u001b[37mDEBUG:lightwood-2722: `prepare` runtime: 0.02 seconds\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2551:Featurizing the data\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2722:Featurizing the data\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:lightwood-2551: `featurize` runtime: 0.56 seconds\u001b[0m\n" + "\u001b[37mDEBUG:lightwood-2722: `featurize` runtime: 0.58 seconds\u001b[0m\n" ] } ], @@ -1063,10 +1063,10 @@ "id": "silent-dealing", "metadata": { "execution": { - "iopub.execute_input": "2024-03-18T10:32:01.140734Z", - "iopub.status.busy": "2024-03-18T10:32:01.140293Z", - "iopub.status.idle": "2024-03-18T10:32:01.149459Z", - "shell.execute_reply": "2024-03-18T10:32:01.148884Z" + "iopub.execute_input": "2024-03-18T12:13:38.316708Z", + "iopub.status.busy": "2024-03-18T12:13:38.316438Z", + "iopub.status.idle": "2024-03-18T12:13:38.326322Z", + "shell.execute_reply": "2024-03-18T12:13:38.325705Z" } }, "outputs": [ @@ -1168,10 +1168,10 @@ "id": "superior-mobility", "metadata": { "execution": { - "iopub.execute_input": "2024-03-18T10:32:01.152221Z", - "iopub.status.busy": "2024-03-18T10:32:01.151819Z", - "iopub.status.idle": "2024-03-18T10:32:01.155027Z", - "shell.execute_reply": "2024-03-18T10:32:01.154373Z" + "iopub.execute_input": "2024-03-18T12:13:38.329017Z", + "iopub.status.busy": "2024-03-18T12:13:38.328782Z", + "iopub.status.idle": "2024-03-18T12:13:38.332766Z", + "shell.execute_reply": "2024-03-18T12:13:38.331960Z" } }, "outputs": [ diff --git a/_sources/tutorials/custom_explainer/custom_explainer.ipynb.txt b/_sources/tutorials/custom_explainer/custom_explainer.ipynb.txt index 58f1c570e..ed151869b 100644 --- a/_sources/tutorials/custom_explainer/custom_explainer.ipynb.txt +++ b/_sources/tutorials/custom_explainer/custom_explainer.ipynb.txt @@ -30,10 +30,10 @@ "execution_count": 1, "metadata": { "execution": { - "iopub.execute_input": "2024-03-18T10:30:56.638469Z", - "iopub.status.busy": "2024-03-18T10:30:56.638271Z", - "iopub.status.idle": "2024-03-18T10:31:00.698957Z", - "shell.execute_reply": "2024-03-18T10:31:00.698246Z" + "iopub.execute_input": "2024-03-18T12:12:28.047614Z", + "iopub.status.busy": "2024-03-18T12:12:28.047078Z", + "iopub.status.idle": "2024-03-18T12:12:32.339044Z", + "shell.execute_reply": "2024-03-18T12:12:32.338214Z" } }, "outputs": [ @@ -49,14 +49,14 @@ "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2205:No torchvision detected, image helpers not supported.\u001b[0m\n" + "\u001b[32mINFO:lightwood-2394:No torchvision detected, image helpers not supported.\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2205:No torchvision/pillow detected, image encoder not supported\u001b[0m\n" + "\u001b[32mINFO:lightwood-2394:No torchvision/pillow detected, image encoder not supported\u001b[0m\n" ] }, { @@ -95,10 +95,10 @@ "execution_count": 2, "metadata": { "execution": { - "iopub.execute_input": "2024-03-18T10:31:00.701828Z", - "iopub.status.busy": "2024-03-18T10:31:00.701393Z", - "iopub.status.idle": "2024-03-18T10:31:00.729658Z", - "shell.execute_reply": "2024-03-18T10:31:00.729152Z" + "iopub.execute_input": "2024-03-18T12:12:32.342349Z", + "iopub.status.busy": "2024-03-18T12:12:32.341649Z", + "iopub.status.idle": "2024-03-18T12:12:32.371750Z", + "shell.execute_reply": "2024-03-18T12:12:32.371146Z" } }, "outputs": [], @@ -124,17 +124,17 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2024-03-18T10:31:00.732051Z", - "iopub.status.busy": "2024-03-18T10:31:00.731690Z", - "iopub.status.idle": "2024-03-18T10:31:00.735666Z", - "shell.execute_reply": "2024-03-18T10:31:00.735032Z" + "iopub.execute_input": "2024-03-18T12:12:32.374741Z", + "iopub.status.busy": "2024-03-18T12:12:32.374330Z", + "iopub.status.idle": "2024-03-18T12:12:32.378803Z", + "shell.execute_reply": "2024-03-18T12:12:32.378124Z" } }, "outputs": [ { "data": { "text/plain": [ - "<__main__.ModelCorrelationHeatmap at 0x75d4e00a3370>" + "<__main__.ModelCorrelationHeatmap at 0x78710515c520>" ] }, "execution_count": 3, @@ -160,10 +160,10 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2024-03-18T10:31:00.738215Z", - "iopub.status.busy": "2024-03-18T10:31:00.737801Z", - "iopub.status.idle": "2024-03-18T10:31:00.741541Z", - "shell.execute_reply": "2024-03-18T10:31:00.740988Z" + "iopub.execute_input": "2024-03-18T12:12:32.381594Z", + "iopub.status.busy": "2024-03-18T12:12:32.381079Z", + "iopub.status.idle": "2024-03-18T12:12:32.385092Z", + "shell.execute_reply": "2024-03-18T12:12:32.384388Z" } }, "outputs": [], @@ -192,10 +192,10 @@ "execution_count": 5, "metadata": { "execution": { - "iopub.execute_input": "2024-03-18T10:31:00.744058Z", - "iopub.status.busy": "2024-03-18T10:31:00.743673Z", - "iopub.status.idle": "2024-03-18T10:31:00.747371Z", - "shell.execute_reply": "2024-03-18T10:31:00.746834Z" + "iopub.execute_input": "2024-03-18T12:12:32.387865Z", + "iopub.status.busy": "2024-03-18T12:12:32.387387Z", + "iopub.status.idle": "2024-03-18T12:12:32.391340Z", + "shell.execute_reply": "2024-03-18T12:12:32.390752Z" } }, "outputs": [], @@ -238,10 +238,10 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2024-03-18T10:31:00.749823Z", - "iopub.status.busy": "2024-03-18T10:31:00.749468Z", - "iopub.status.idle": "2024-03-18T10:31:00.753920Z", - "shell.execute_reply": "2024-03-18T10:31:00.753302Z" + "iopub.execute_input": "2024-03-18T12:12:32.394089Z", + "iopub.status.busy": "2024-03-18T12:12:32.393556Z", + "iopub.status.idle": "2024-03-18T12:12:32.398390Z", + "shell.execute_reply": "2024-03-18T12:12:32.397810Z" } }, "outputs": [ @@ -335,10 +335,10 @@ "execution_count": 7, "metadata": { "execution": { - "iopub.execute_input": "2024-03-18T10:31:00.756605Z", - "iopub.status.busy": "2024-03-18T10:31:00.756191Z", - "iopub.status.idle": "2024-03-18T10:31:00.914784Z", - "shell.execute_reply": "2024-03-18T10:31:00.914234Z" + "iopub.execute_input": "2024-03-18T12:12:32.401222Z", + "iopub.status.busy": "2024-03-18T12:12:32.400839Z", + "iopub.status.idle": "2024-03-18T12:12:32.718388Z", + "shell.execute_reply": "2024-03-18T12:12:32.717725Z" } }, "outputs": [ @@ -346,126 +346,126 @@ "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2205:Analyzing a sample of 222\u001b[0m\n" + "\u001b[32mINFO:type_infer-2394:Analyzing a sample of 222\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2205:from a total population of 225, this is equivalent to 98.7% of your data.\u001b[0m\n" + "\u001b[32mINFO:type_infer-2394:from a total population of 225, this is equivalent to 98.7% of your data.\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2205:Infering type for: Population\u001b[0m\n" + "\u001b[32mINFO:type_infer-2394:Infering type for: Population\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2205:Column Population has data type integer\u001b[0m\n" + "\u001b[32mINFO:type_infer-2394:Column Population has data type integer\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2205:Infering type for: Area (sq. mi.)\u001b[0m\n" + "\u001b[32mINFO:type_infer-2394:Infering type for: Area (sq. mi.)\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2205:Column Area (sq. mi.) has data type integer\u001b[0m\n" + "\u001b[32mINFO:type_infer-2394:Column Area (sq. mi.) has data type integer\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2205:Infering type for: Pop. Density \u001b[0m\n" + "\u001b[32mINFO:type_infer-2394:Infering type for: Pop. Density \u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2205:Column Pop. Density has data type float\u001b[0m\n" + "\u001b[32mINFO:type_infer-2394:Column Pop. Density has data type float\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2205:Infering type for: GDP ($ per capita)\u001b[0m\n" + "\u001b[32mINFO:type_infer-2394:Infering type for: GDP ($ per capita)\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2205:Column GDP ($ per capita) has data type integer\u001b[0m\n" + "\u001b[32mINFO:type_infer-2394:Column GDP ($ per capita) has data type integer\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2205:Infering type for: Literacy (%)\u001b[0m\n" + "\u001b[32mINFO:type_infer-2394:Infering type for: Literacy (%)\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2205:Column Literacy (%) has data type float\u001b[0m\n" + "\u001b[32mINFO:type_infer-2394:Column Literacy (%) has data type float\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2205:Infering type for: Infant mortality \u001b[0m\n" + "\u001b[32mINFO:type_infer-2394:Infering type for: Infant mortality \u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2205:Column Infant mortality has data type float\u001b[0m\n" + "\u001b[32mINFO:type_infer-2394:Column Infant mortality has data type float\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2205:Infering type for: Development Index\u001b[0m\n" + "\u001b[32mINFO:type_infer-2394:Infering type for: Development Index\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2205:Column Development Index has data type categorical\u001b[0m\n" + "\u001b[32mINFO:type_infer-2394:Column Development Index has data type categorical\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2205:Starting statistical analysis\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2394:Starting statistical analysis\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2205:Finished statistical analysis\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2394:Finished statistical analysis\u001b[0m\n" ] } ], @@ -506,10 +506,10 @@ "execution_count": 8, "metadata": { "execution": { - "iopub.execute_input": "2024-03-18T10:31:00.917488Z", - "iopub.status.busy": "2024-03-18T10:31:00.917032Z", - "iopub.status.idle": "2024-03-18T10:31:00.921256Z", - "shell.execute_reply": "2024-03-18T10:31:00.920656Z" + "iopub.execute_input": "2024-03-18T12:12:32.721556Z", + "iopub.status.busy": "2024-03-18T12:12:32.721301Z", + "iopub.status.idle": "2024-03-18T12:12:32.726465Z", + "shell.execute_reply": "2024-03-18T12:12:32.725724Z" } }, "outputs": [ @@ -540,10 +540,10 @@ "execution_count": 9, "metadata": { "execution": { - "iopub.execute_input": "2024-03-18T10:31:00.923961Z", - "iopub.status.busy": "2024-03-18T10:31:00.923505Z", - "iopub.status.idle": "2024-03-18T10:31:06.601212Z", - "shell.execute_reply": "2024-03-18T10:31:06.600632Z" + "iopub.execute_input": "2024-03-18T12:12:32.729356Z", + "iopub.status.busy": "2024-03-18T12:12:32.728918Z", + "iopub.status.idle": "2024-03-18T12:12:38.734385Z", + "shell.execute_reply": "2024-03-18T12:12:38.733753Z" }, "scrolled": false }, @@ -552,182 +552,182 @@ "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2205:[Learn phase 1/8] - Statistical analysis\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2394:[Learn phase 1/8] - Statistical analysis\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2205:Starting statistical analysis\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2394:Starting statistical analysis\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2205:Finished statistical analysis\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2394:Finished statistical analysis\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:lightwood-2205: `analyze_data` runtime: 0.02 seconds\u001b[0m\n" + "\u001b[37mDEBUG:lightwood-2394: `analyze_data` runtime: 0.02 seconds\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2205:[Learn phase 2/8] - Data preprocessing\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2394:[Learn phase 2/8] - Data preprocessing\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2205:Cleaning the data\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2394:Cleaning the data\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:lightwood-2205: `preprocess` runtime: 0.01 seconds\u001b[0m\n" + "\u001b[37mDEBUG:lightwood-2394: `preprocess` runtime: 0.01 seconds\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2205:[Learn phase 3/8] - Data splitting\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2394:[Learn phase 3/8] - Data splitting\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2205:Splitting the data into train/test\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2394:Splitting the data into train/test\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:lightwood-2205: `split` runtime: 0.01 seconds\u001b[0m\n" + "\u001b[37mDEBUG:lightwood-2394: `split` runtime: 0.01 seconds\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2205:[Learn phase 4/8] - Preparing encoders\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2394:[Learn phase 4/8] - Preparing encoders\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:dataprep_ml-2205:Preparing sequentially...\u001b[0m\n" + "\u001b[37mDEBUG:dataprep_ml-2394:Preparing sequentially...\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:dataprep_ml-2205:Preparing encoder for Population...\u001b[0m\n" + "\u001b[37mDEBUG:dataprep_ml-2394:Preparing encoder for Population...\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:dataprep_ml-2205:Preparing encoder for Area (sq. mi.)...\u001b[0m\n" + "\u001b[37mDEBUG:dataprep_ml-2394:Preparing encoder for Area (sq. mi.)...\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:dataprep_ml-2205:Preparing encoder for Pop. 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"\u001b[37mDEBUG:lightwood-2205: `prepare` runtime: 0.01 seconds\u001b[0m\n" + "\u001b[37mDEBUG:lightwood-2394: `prepare` runtime: 0.01 seconds\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2205:[Learn phase 5/8] - Feature generation\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2394:[Learn phase 5/8] - Feature generation\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2205:Featurizing the data\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2394:Featurizing the data\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:lightwood-2205: `featurize` runtime: 0.05 seconds\u001b[0m\n" + "\u001b[37mDEBUG:lightwood-2394: `featurize` runtime: 0.05 seconds\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2205:[Learn phase 6/8] - Mixer training\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2394:[Learn phase 6/8] - Mixer training\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2205:Training the mixers\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2394:Training the mixers\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[33mWARNING:lightwood-2205:XGBoost running on CPU\u001b[0m\n" + "\u001b[33mWARNING:lightwood-2394:XGBoost running on CPU\u001b[0m\n" ] }, { @@ -742,7 +742,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "[10:31:01] WARNING: ../src/learner.cc:339: No visible GPU is found, setting `gpu_id` to -1\n" + "[12:12:33] WARNING: ../src/learner.cc:339: No visible GPU is found, setting `gpu_id` to -1\n" ] }, { @@ -754,63 +754,63 @@ "Consider using one of the following signatures instead:\n", "\taddcmul_(Tensor tensor1, Tensor tensor2, *, Number value) (Triggered internally at ../torch/csrc/utils/python_arg_parser.cpp:1630.)\n", " exp_avg_sq.mul_(beta2).addcmul_(1 - beta2, grad, grad)\n", - "\u001b[32mINFO:lightwood-2205:Loss of 18.69619858264923 with learning rate 0.0001\u001b[0m\n" + "\u001b[32mINFO:lightwood-2394:Loss of 18.69619858264923 with learning rate 0.0001\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2205:Loss of 16.93891429901123 with learning rate 0.0005\u001b[0m\n" + "\u001b[32mINFO:lightwood-2394:Loss of 16.93891429901123 with learning rate 0.0005\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2205:Loss of 16.197376608848572 with learning rate 0.001\u001b[0m\n" + "\u001b[32mINFO:lightwood-2394:Loss of 16.197376608848572 with learning rate 0.001\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2205:Loss of 16.06481909751892 with learning rate 0.002\u001b[0m\n" + "\u001b[32mINFO:lightwood-2394:Loss of 16.06481909751892 with learning rate 0.002\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2205:Loss of 16.472004413604736 with learning rate 0.003\u001b[0m\n" + "\u001b[32mINFO:lightwood-2394:Loss of 16.472004413604736 with learning rate 0.003\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2205:Loss of 18.28026556968689 with learning rate 0.005\u001b[0m\n" + "\u001b[32mINFO:lightwood-2394:Loss of 18.28026556968689 with learning rate 0.005\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2205:Loss of 26.746760368347168 with learning rate 0.01\u001b[0m\n" + "\u001b[32mINFO:lightwood-2394:Loss of 26.746760368347168 with learning rate 0.01\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2205:Loss of 101.83524441719055 with learning rate 0.05\u001b[0m\n" + "\u001b[32mINFO:lightwood-2394:Loss of 101.83524441719055 with learning rate 0.05\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2205:Found learning rate of: 0.002\u001b[0m\n" + "\u001b[32mINFO:lightwood-2394:Found learning rate of: 0.002\u001b[0m\n" ] }, { @@ -819,3906 +819,3906 @@ "text": [ "/opt/hostedtoolcache/Python/3.9.18/x64/lib/python3.9/site-packages/torch/cuda/amp/grad_scaler.py:126: UserWarning: torch.cuda.amp.GradScaler is enabled, but CUDA is not available. 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"\u001b[32mINFO:lightwood-2205:Mixer: XGBoostMixer got accuracy: 1.0\u001b[0m\n" + "\u001b[32mINFO:lightwood-2394:Mixer: XGBoostMixer got accuracy: 1.0\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2205:Mixer: RandomForest got accuracy: 1.0\u001b[0m\n" + "\u001b[32mINFO:lightwood-2394:Mixer: RandomForest got accuracy: 1.0\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2205:Picked best mixer: RandomForest\u001b[0m\n" + "\u001b[32mINFO:lightwood-2394:Picked best mixer: RandomForest\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:lightwood-2205: `fit` runtime: 5.0 seconds\u001b[0m\n" + "\u001b[37mDEBUG:lightwood-2394: `fit` runtime: 5.24 seconds\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2205:[Learn phase 7/8] - Ensemble analysis\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2394:[Learn phase 7/8] - Ensemble analysis\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2205:Analyzing the ensemble of mixers\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2394:Analyzing the ensemble of mixers\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2205:The block ICP is now running its analyze() method\u001b[0m\n" + "\u001b[32mINFO:lightwood-2394:The block ICP is now running its analyze() method\u001b[0m\n" ] }, { @@ -5027,142 +5034,142 @@ "output_type": "stream", "text": [ "/opt/hostedtoolcache/Python/3.9.18/x64/lib/python3.9/site-packages/sklearn/preprocessing/_encoders.py:975: FutureWarning: `sparse` was renamed to `sparse_output` in version 1.2 and will be removed in 1.4. `sparse_output` is ignored unless you leave `sparse` to its default value.\n", - " warnings.warn(\n", - "\u001b[32mINFO:lightwood-2205:The block ConfStats is now running its analyze() method\u001b[0m\n" + " warnings.warn(\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2205:The block AccStats is now running its analyze() method\u001b[0m\n" + "\u001b[32mINFO:lightwood-2394:The block ConfStats is now running its analyze() method\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2205:The block PermutationFeatureImportance is now running its analyze() method\u001b[0m\n" + "\u001b[32mINFO:lightwood-2394:The block AccStats is now running its analyze() method\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2205:[PFI] Using a random sample (1000 rows out of 22).\u001b[0m\n" + "\u001b[32mINFO:lightwood-2394:The block PermutationFeatureImportance is now running its analyze() method\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2205:[PFI] Set to consider first 10 columns out of 6: ['Population', 'Area (sq. mi.)', 'Pop. Density ', 'GDP ($ per capita)', 'Literacy (%)', 'Infant mortality '].\u001b[0m\n" + "\u001b[32mINFO:lightwood-2394:[PFI] Using a random sample (1000 rows out of 22).\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2205:The block ModelCorrelationHeatmap is now running its analyze() method\u001b[0m\n" + "\u001b[32mINFO:lightwood-2394:[PFI] Set to consider first 10 columns out of 6: ['Population', 'Area (sq. mi.)', 'Pop. Density ', 'GDP ($ per capita)', 'Literacy (%)', 'Infant mortality '].\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:lightwood-2205: `analyze_ensemble` runtime: 0.2 seconds\u001b[0m\n" + "\u001b[32mINFO:lightwood-2394:The block ModelCorrelationHeatmap is now running its analyze() method\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2205:[Learn phase 8/8] - Adjustment on validation requested\u001b[0m\n" + "\u001b[37mDEBUG:lightwood-2394: `analyze_ensemble` runtime: 0.23 seconds\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2205:Updating the mixers\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2394:[Learn phase 8/8] - Adjustment on validation requested\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "/opt/hostedtoolcache/Python/3.9.18/x64/lib/python3.9/site-packages/torch/cuda/amp/grad_scaler.py:126: UserWarning: torch.cuda.amp.GradScaler is enabled, but CUDA is not available. Disabling.\n", - " warnings.warn(\n" + "\u001b[32mINFO:dataprep_ml-2394:Updating the mixers\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2205:Loss @ epoch 1: 0.033697554686417185\u001b[0m\n" + "/opt/hostedtoolcache/Python/3.9.18/x64/lib/python3.9/site-packages/torch/cuda/amp/grad_scaler.py:126: UserWarning: torch.cuda.amp.GradScaler is enabled, but CUDA is not available. 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"\u001b[32mINFO:lightwood-2205:Loss @ epoch 6: 0.03466159128583968\u001b[0m\n" + "\u001b[32mINFO:lightwood-2394:Loss @ epoch 6: 0.03466159128583968\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2205:Loss @ epoch 7: 0.03769115870818496\u001b[0m\n" + "\u001b[32mINFO:lightwood-2394:Loss @ epoch 7: 0.03769115870818496\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2205:XGBoost mixer does not have a `partial_fit` implementation\u001b[0m\n" + "\u001b[32mINFO:lightwood-2394:XGBoost mixer does not have a `partial_fit` implementation\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:lightwood-2205: `adjust` runtime: 0.06 seconds\u001b[0m\n" + "\u001b[37mDEBUG:lightwood-2394: `adjust` runtime: 0.07 seconds\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:lightwood-2205: `learn` runtime: 5.36 seconds\u001b[0m\n" + "\u001b[37mDEBUG:lightwood-2394: `learn` runtime: 5.64 seconds\u001b[0m\n" ] } ], @@ -5187,10 +5194,10 @@ "execution_count": 10, "metadata": { "execution": { - "iopub.execute_input": "2024-03-18T10:31:06.604127Z", - "iopub.status.busy": "2024-03-18T10:31:06.603680Z", - "iopub.status.idle": "2024-03-18T10:31:07.405807Z", - "shell.execute_reply": "2024-03-18T10:31:07.405111Z" + "iopub.execute_input": "2024-03-18T12:12:38.737511Z", + "iopub.status.busy": "2024-03-18T12:12:38.737048Z", + "iopub.status.idle": "2024-03-18T12:12:39.637965Z", + "shell.execute_reply": "2024-03-18T12:12:39.637072Z" } }, "outputs": [ diff --git a/_sources/tutorials/custom_mixer/custom_mixer.ipynb.txt b/_sources/tutorials/custom_mixer/custom_mixer.ipynb.txt index 288ab230b..718ea6fc5 100644 --- a/_sources/tutorials/custom_mixer/custom_mixer.ipynb.txt +++ b/_sources/tutorials/custom_mixer/custom_mixer.ipynb.txt @@ -46,10 +46,10 @@ "execution_count": 1, "metadata": { "execution": { - "iopub.execute_input": "2024-03-18T10:31:39.165227Z", - "iopub.status.busy": "2024-03-18T10:31:39.165033Z", - "iopub.status.idle": "2024-03-18T10:31:39.173257Z", - "shell.execute_reply": "2024-03-18T10:31:39.172655Z" + "iopub.execute_input": "2024-03-18T12:13:14.798659Z", + "iopub.status.busy": "2024-03-18T12:13:14.798214Z", + "iopub.status.idle": "2024-03-18T12:13:14.807631Z", + "shell.execute_reply": "2024-03-18T12:13:14.806928Z" } }, "outputs": [ @@ -133,10 +133,10 @@ "execution_count": 2, "metadata": { "execution": { - "iopub.execute_input": "2024-03-18T10:31:39.209970Z", - "iopub.status.busy": "2024-03-18T10:31:39.209477Z", - "iopub.status.idle": "2024-03-18T10:31:41.899985Z", - "shell.execute_reply": "2024-03-18T10:31:41.899281Z" + "iopub.execute_input": "2024-03-18T12:13:14.846502Z", + "iopub.status.busy": "2024-03-18T12:13:14.846021Z", + "iopub.status.idle": "2024-03-18T12:13:17.928216Z", + "shell.execute_reply": "2024-03-18T12:13:17.927465Z" } }, "outputs": [ @@ -144,238 +144,238 @@ "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2520:No torchvision detected, image helpers not supported.\u001b[0m\n" + "\u001b[32mINFO:lightwood-2694:No torchvision detected, image helpers not supported.\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2520:No torchvision/pillow detected, image encoder not supported\u001b[0m\n" + "\u001b[32mINFO:lightwood-2694:No torchvision/pillow detected, image encoder not supported\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2520:Analyzing a sample of 298\u001b[0m\n" + "\u001b[32mINFO:type_infer-2694:Analyzing a sample of 298\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2520:from a total population of 303, this is equivalent to 98.3% of your data.\u001b[0m\n" + "\u001b[32mINFO:type_infer-2694:from a total population of 303, this is equivalent to 98.3% of your data.\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2520:Infering type for: age\u001b[0m\n" + "\u001b[32mINFO:type_infer-2694:Infering type for: age\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2520:Column age has data type integer\u001b[0m\n" + "\u001b[32mINFO:type_infer-2694:Column age has data type integer\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2520:Infering type for: sex\u001b[0m\n" + "\u001b[32mINFO:type_infer-2694:Infering type for: sex\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2520:Column sex has data type binary\u001b[0m\n" + "\u001b[32mINFO:type_infer-2694:Column sex has data type binary\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2520:Infering type for: cp\u001b[0m\n" + "\u001b[32mINFO:type_infer-2694:Infering type for: cp\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2520:Column cp has data type categorical\u001b[0m\n" + "\u001b[32mINFO:type_infer-2694:Column cp has data type categorical\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2520:Infering type for: trestbps\u001b[0m\n" + "\u001b[32mINFO:type_infer-2694:Infering type for: trestbps\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2520:Column trestbps has data type integer\u001b[0m\n" + "\u001b[32mINFO:type_infer-2694:Column trestbps has data type integer\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2520:Infering type for: chol\u001b[0m\n" + "\u001b[32mINFO:type_infer-2694:Infering type for: chol\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2520:Column chol has data type integer\u001b[0m\n" + "\u001b[32mINFO:type_infer-2694:Column chol has data type integer\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2520:Infering type for: fbs\u001b[0m\n" + "\u001b[32mINFO:type_infer-2694:Infering type for: fbs\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2520:Column fbs has data type binary\u001b[0m\n" + "\u001b[32mINFO:type_infer-2694:Column fbs has data type binary\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2520:Infering type for: restecg\u001b[0m\n" + "\u001b[32mINFO:type_infer-2694:Infering type for: restecg\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - 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"\u001b[32mINFO:type_infer-2520:Infering type for: oldpeak\u001b[0m\n" + "\u001b[32mINFO:type_infer-2694:Infering type for: oldpeak\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2520:Column oldpeak has data type float\u001b[0m\n" + "\u001b[32mINFO:type_infer-2694:Column oldpeak has data type float\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2520:Infering type for: slope\u001b[0m\n" + "\u001b[32mINFO:type_infer-2694:Infering type for: slope\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2520:Column slope has data type categorical\u001b[0m\n" + "\u001b[32mINFO:type_infer-2694:Column slope has data type categorical\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2520:Infering type for: ca\u001b[0m\n" + "\u001b[32mINFO:type_infer-2694:Infering type for: ca\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2520:Column ca has data type categorical\u001b[0m\n" + "\u001b[32mINFO:type_infer-2694:Column ca has data type categorical\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2520:Infering type for: thal\u001b[0m\n" + "\u001b[32mINFO:type_infer-2694:Infering type for: thal\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2520:Column thal has data type categorical\u001b[0m\n" + "\u001b[32mINFO:type_infer-2694:Column thal has data type categorical\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2520:Infering type for: target\u001b[0m\n" + "\u001b[32mINFO:type_infer-2694:Infering type for: target\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2520:Column target has data type binary\u001b[0m\n" + "\u001b[32mINFO:type_infer-2694:Column target has data type binary\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2520:Starting statistical analysis\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2694:Starting statistical analysis\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2520:Finished statistical analysis\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2694:Finished statistical analysis\u001b[0m\n" ] }, { @@ -502,7 +502,7 @@ " \"unbias_target\": true,\n", " \"seconds_per_mixer\": 42768.0,\n", " \"seconds_per_encoder\": null,\n", - " \"expected_additional_time\": 0.06839251518249512,\n", + " \"expected_additional_time\": 0.07393026351928711,\n", " \"time_aim\": 259200,\n", " \"target_weights\": null,\n", " \"positive_domain\": false,\n", @@ -571,10 +571,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2024-03-18T10:31:41.902933Z", - "iopub.status.busy": "2024-03-18T10:31:41.902517Z", - "iopub.status.idle": "2024-03-18T10:31:41.905841Z", - "shell.execute_reply": "2024-03-18T10:31:41.905210Z" + "iopub.execute_input": "2024-03-18T12:13:17.931147Z", + "iopub.status.busy": "2024-03-18T12:13:17.930695Z", + "iopub.status.idle": "2024-03-18T12:13:17.934544Z", + "shell.execute_reply": "2024-03-18T12:13:17.933897Z" } }, "outputs": [], @@ -603,10 +603,10 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2024-03-18T10:31:41.908451Z", - "iopub.status.busy": "2024-03-18T10:31:41.908026Z", - "iopub.status.idle": "2024-03-18T10:31:42.240157Z", - "shell.execute_reply": "2024-03-18T10:31:42.239529Z" + "iopub.execute_input": "2024-03-18T12:13:17.937482Z", + "iopub.status.busy": "2024-03-18T12:13:17.937008Z", + "iopub.status.idle": "2024-03-18T12:13:18.301601Z", + "shell.execute_reply": "2024-03-18T12:13:18.300927Z" } }, "outputs": [], @@ -622,10 +622,10 @@ "execution_count": 5, "metadata": { "execution": { - "iopub.execute_input": "2024-03-18T10:31:42.243248Z", - "iopub.status.busy": "2024-03-18T10:31:42.242700Z", - "iopub.status.idle": "2024-03-18T10:31:42.863413Z", - "shell.execute_reply": "2024-03-18T10:31:42.862885Z" + "iopub.execute_input": "2024-03-18T12:13:18.304835Z", + "iopub.status.busy": "2024-03-18T12:13:18.304383Z", + "iopub.status.idle": "2024-03-18T12:13:18.945519Z", + "shell.execute_reply": "2024-03-18T12:13:18.944829Z" } }, "outputs": [ @@ -633,308 +633,308 @@ "name": "stderr", "output_type": "stream", "text": [ - 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"\u001b[37mDEBUG:lightwood-2520: `fit` runtime: 0.13 seconds\u001b[0m\n" + "\u001b[37mDEBUG:lightwood-2694: `fit` runtime: 0.13 seconds\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2520:[Learn phase 7/8] - Ensemble analysis\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2694:[Learn phase 7/8] - Ensemble analysis\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2520:Analyzing the ensemble of mixers\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2694:Analyzing the ensemble of mixers\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2520:The block ICP is now running its analyze() method\u001b[0m\n" + "\u001b[32mINFO:lightwood-2694:The block ICP is now running its analyze() method\u001b[0m\n" ] }, { @@ -943,35 +943,35 @@ "text": [ "/opt/hostedtoolcache/Python/3.9.18/x64/lib/python3.9/site-packages/sklearn/preprocessing/_encoders.py:975: FutureWarning: `sparse` was renamed to `sparse_output` in version 1.2 and will be removed in 1.4. `sparse_output` is ignored unless you leave `sparse` to its default value.\n", " warnings.warn(\n", - "\u001b[32mINFO:lightwood-2520:The block ConfStats is now running its analyze() method\u001b[0m\n" + "\u001b[32mINFO:lightwood-2694:The block ConfStats is now running its analyze() method\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2520:The block AccStats is now running its analyze() method\u001b[0m\n" + "\u001b[32mINFO:lightwood-2694:The block AccStats is now running its analyze() method\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2520:The block PermutationFeatureImportance is now running its analyze() method\u001b[0m\n" + "\u001b[32mINFO:lightwood-2694:The block PermutationFeatureImportance is now running its analyze() method\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2520:[PFI] Using a random sample (1000 rows out of 31).\u001b[0m\n" + "\u001b[32mINFO:lightwood-2694:[PFI] Using a random sample (1000 rows out of 31).\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2520:[PFI] Set to consider first 10 columns out of 10: ['age', 'sex', 'cp', 'trestbps', 'chol', 'fbs', 'restecg', 'thalach', 'exang', 'oldpeak'].\u001b[0m\n" + "\u001b[32mINFO:lightwood-2694:[PFI] Set to consider first 10 columns out of 10: ['age', 'sex', 'cp', 'trestbps', 'chol', 'fbs', 'restecg', 'thalach', 'exang', 'oldpeak'].\u001b[0m\n" ] }, { @@ -987,42 +987,36 @@ "output_type": "stream", "text": [ "/opt/hostedtoolcache/Python/3.9.18/x64/lib/python3.9/site-packages/sklearn/metrics/_classification.py:2399: UserWarning: y_pred contains classes not in y_true\n", - " warnings.warn(\"y_pred contains classes not in y_true\")\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\u001b[37mDEBUG:lightwood-2520: `analyze_ensemble` runtime: 0.27 seconds\u001b[0m\n" + " warnings.warn(\"y_pred contains classes not in y_true\")\n", + "\u001b[37mDEBUG:lightwood-2694: `analyze_ensemble` runtime: 0.28 seconds\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2520:[Learn phase 8/8] - Adjustment on validation requested\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2694:[Learn phase 8/8] - Adjustment on validation requested\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2520:Updating the mixers\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2694:Updating the mixers\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:lightwood-2520: `adjust` runtime: 0.04 seconds\u001b[0m\n" + "\u001b[37mDEBUG:lightwood-2694: `adjust` runtime: 0.04 seconds\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:lightwood-2520: `learn` runtime: 0.62 seconds\u001b[0m\n" + "\u001b[37mDEBUG:lightwood-2694: `learn` runtime: 0.64 seconds\u001b[0m\n" ] } ], @@ -1042,10 +1036,10 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2024-03-18T10:31:42.866072Z", - "iopub.status.busy": "2024-03-18T10:31:42.865679Z", - "iopub.status.idle": "2024-03-18T10:31:42.985005Z", - "shell.execute_reply": "2024-03-18T10:31:42.984402Z" + "iopub.execute_input": "2024-03-18T12:13:18.948374Z", + "iopub.status.busy": "2024-03-18T12:13:18.948128Z", + "iopub.status.idle": "2024-03-18T12:13:19.076130Z", + "shell.execute_reply": "2024-03-18T12:13:19.075363Z" } }, "outputs": [ @@ -1053,35 +1047,35 @@ "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2520:[Predict phase 1/4] - Data preprocessing\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2694:[Predict phase 1/4] - Data preprocessing\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2520:Cleaning the data\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2694:Cleaning the data\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:lightwood-2520: `preprocess` runtime: 0.01 seconds\u001b[0m\n" + "\u001b[37mDEBUG:lightwood-2694: `preprocess` runtime: 0.01 seconds\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2520:[Predict phase 2/4] - Feature generation\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2694:[Predict phase 2/4] - Feature generation\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2520:Featurizing the data\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2694:Featurizing the data\u001b[0m\n" ] }, { @@ -1104,91 +1098,91 @@ " outputs = ufunc(*inputs)\n", "/opt/hostedtoolcache/Python/3.9.18/x64/lib/python3.9/site-packages/numpy/lib/function_base.py:2455: RuntimeWarning: invalid value encountered in _none_fn (vectorized)\n", " outputs = ufunc(*inputs)\n", - "\u001b[37mDEBUG:lightwood-2520: `featurize` runtime: 0.02 seconds\u001b[0m\n" + "\u001b[37mDEBUG:lightwood-2694: `featurize` runtime: 0.02 seconds\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2520:[Predict phase 3/4] - Calling ensemble\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2694:[Predict phase 3/4] - Calling ensemble\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:lightwood-2520: `_timed_call` runtime: 0.01 seconds\u001b[0m\n" + "\u001b[37mDEBUG:lightwood-2694: `_timed_call` runtime: 0.01 seconds\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2520:[Predict phase 4/4] - Analyzing output\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2694:[Predict phase 4/4] - Analyzing output\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2520:The block ICP is now running its explain() method\u001b[0m\n" + "\u001b[32mINFO:lightwood-2694:The block ICP is now running its explain() method\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2520:The block ConfStats is now running its explain() method\u001b[0m\n" + "\u001b[32mINFO:lightwood-2694:The block ConfStats is now running its explain() method\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2520:ConfStats.explain() has not been implemented, no modifications will be done to the data insights.\u001b[0m\n" + "\u001b[32mINFO:lightwood-2694:ConfStats.explain() has not been implemented, no modifications will be done to the data insights.\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2520:The block AccStats is now running its explain() method\u001b[0m\n" + "\u001b[32mINFO:lightwood-2694:The block AccStats is now running its explain() method\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2520:AccStats.explain() has not been implemented, no modifications will be done to the data insights.\u001b[0m\n" + "\u001b[32mINFO:lightwood-2694:AccStats.explain() has not been implemented, no modifications will be done to the data insights.\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2520:The block PermutationFeatureImportance is now running its explain() method\u001b[0m\n" + "\u001b[32mINFO:lightwood-2694:The block PermutationFeatureImportance is now running its explain() method\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2520:PermutationFeatureImportance.explain() has not been implemented, no modifications will be done to the data insights.\u001b[0m\n" + "\u001b[32mINFO:lightwood-2694:PermutationFeatureImportance.explain() has not been implemented, no modifications will be done to the data insights.\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:lightwood-2520: `explain` runtime: 0.01 seconds\u001b[0m\n" + "\u001b[37mDEBUG:lightwood-2694: `explain` runtime: 0.01 seconds\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:lightwood-2520: `predict` runtime: 0.05 seconds\u001b[0m\n" + "\u001b[37mDEBUG:lightwood-2694: `predict` runtime: 0.06 seconds\u001b[0m\n" ] }, { diff --git a/_sources/tutorials/custom_splitter/custom_splitter.ipynb.txt b/_sources/tutorials/custom_splitter/custom_splitter.ipynb.txt index 1775b9344..78b93a84d 100644 --- a/_sources/tutorials/custom_splitter/custom_splitter.ipynb.txt +++ b/_sources/tutorials/custom_splitter/custom_splitter.ipynb.txt @@ -28,10 +28,10 @@ "id": "interim-discussion", "metadata": { "execution": { - "iopub.execute_input": "2024-03-18T10:32:26.642823Z", - "iopub.status.busy": "2024-03-18T10:32:26.642629Z", - "iopub.status.idle": "2024-03-18T10:32:29.510740Z", - "shell.execute_reply": "2024-03-18T10:32:29.509930Z" + "iopub.execute_input": "2024-03-18T12:14:06.775523Z", + "iopub.status.busy": "2024-03-18T12:14:06.775322Z", + "iopub.status.idle": "2024-03-18T12:14:09.903896Z", + "shell.execute_reply": "2024-03-18T12:14:09.903162Z" } }, "outputs": [ @@ -39,14 +39,14 @@ "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2670:No torchvision detected, image helpers not supported.\u001b[0m\n" + "\u001b[32mINFO:lightwood-2840:No torchvision detected, image helpers not supported.\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2670:No torchvision/pillow detected, image encoder not supported\u001b[0m\n" + "\u001b[32mINFO:lightwood-2840:No torchvision/pillow detected, image encoder not supported\u001b[0m\n" ] } ], @@ -87,10 +87,10 @@ "id": "foreign-orchestra", "metadata": { "execution": { - "iopub.execute_input": "2024-03-18T10:32:29.514196Z", - "iopub.status.busy": "2024-03-18T10:32:29.513818Z", - "iopub.status.idle": "2024-03-18T10:32:34.219711Z", - "shell.execute_reply": "2024-03-18T10:32:34.219020Z" + "iopub.execute_input": "2024-03-18T12:14:09.907258Z", + "iopub.status.busy": "2024-03-18T12:14:09.906900Z", + "iopub.status.idle": "2024-03-18T12:14:16.385696Z", + "shell.execute_reply": "2024-03-18T12:14:16.384954Z" } }, "outputs": [ @@ -316,10 +316,10 @@ "id": "cathedral-mills", "metadata": { "execution": { - "iopub.execute_input": "2024-03-18T10:32:34.222771Z", - "iopub.status.busy": "2024-03-18T10:32:34.222257Z", - "iopub.status.idle": "2024-03-18T10:32:34.583402Z", - "shell.execute_reply": "2024-03-18T10:32:34.582727Z" + "iopub.execute_input": "2024-03-18T12:14:16.388631Z", + "iopub.status.busy": "2024-03-18T12:14:16.388201Z", + "iopub.status.idle": "2024-03-18T12:14:16.758868Z", + "shell.execute_reply": "2024-03-18T12:14:16.758208Z" } }, "outputs": [ @@ -374,10 +374,10 @@ "id": "medieval-zambia", "metadata": { "execution": { - "iopub.execute_input": "2024-03-18T10:32:34.586342Z", - "iopub.status.busy": "2024-03-18T10:32:34.585854Z", - "iopub.status.idle": "2024-03-18T10:33:44.164777Z", - "shell.execute_reply": "2024-03-18T10:33:44.164066Z" + "iopub.execute_input": "2024-03-18T12:14:16.761890Z", + "iopub.status.busy": "2024-03-18T12:14:16.761276Z", + "iopub.status.idle": "2024-03-18T12:15:26.682541Z", + "shell.execute_reply": "2024-03-18T12:15:26.681823Z" } }, "outputs": [ @@ -385,469 +385,469 @@ "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2670:Analyzing a sample of 18424\u001b[0m\n" + "\u001b[32mINFO:type_infer-2840:Analyzing a sample of 18424\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2670:from a total population of 284807, this is equivalent to 6.5% of your data.\u001b[0m\n" + "\u001b[32mINFO:type_infer-2840:from a total population of 284807, this is equivalent to 6.5% of your data.\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2670:Using 3 processes to deduct types.\u001b[0m\n" + "\u001b[32mINFO:type_infer-2840:Using 3 processes to deduct types.\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2670:Infering type for: Time\u001b[0m\n" + "\u001b[32mINFO:type_infer-2840:Infering type for: Time\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2670:Infering type for: V3\u001b[0m\n" + "\u001b[32mINFO:type_infer-2840:Infering type for: V3\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2670:Infering type for: V6\u001b[0m\n" + "\u001b[32mINFO:type_infer-2840:Infering type for: V6\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2670:Column Time has data type integer\u001b[0m\n" + "\u001b[32mINFO:type_infer-2840:Column Time has data type integer\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - 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"\u001b[32mINFO:type_infer-2670:Column V21 has data type float\u001b[0m\n" + "\u001b[32mINFO:type_infer-2840:Column V20 has data type float\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2670:Infering type for: V22\u001b[0m\n" + "\u001b[32mINFO:type_infer-2840:Infering type for: V21\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2670:Column V19 has data type float\u001b[0m\n" + "\u001b[32mINFO:type_infer-2840:Column V14 has data type float\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2670:Infering type for: V20\u001b[0m\n" + "\u001b[32mINFO:type_infer-2840:Column V17 has data type float\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2670:Column V17 has data type float\u001b[0m\n" + "\u001b[32mINFO:type_infer-2840:Infering type for: V24\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2670:Infering type for: V24\u001b[0m\n" + "\u001b[32mINFO:type_infer-2840:Infering type for: V27\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2670:Column V22 has data type float\u001b[0m\n" + "\u001b[32mINFO:type_infer-2840:Column V21 has data type float\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2670:Infering type for: V23\u001b[0m\n" + "\u001b[32mINFO:type_infer-2840:Infering type for: V22\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2670:Column V20 has data type float\u001b[0m\n" + "\u001b[32mINFO:type_infer-2840:Column V24 has data type float\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2670:Infering type for: V27\u001b[0m\n" + "\u001b[32mINFO:type_infer-2840:Infering type for: V25\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2670:Column V23 has data type float\u001b[0m\n" + "\u001b[32mINFO:type_infer-2840:Column V27 has data type float\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2670:Infering type for: Class\u001b[0m\n" + "\u001b[32mINFO:type_infer-2840:Infering type for: V28\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2670:Column V24 has data type float\u001b[0m\n" + "\u001b[32mINFO:type_infer-2840:Column V25 has data type float\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2670:Infering type for: V25\u001b[0m\n" + "\u001b[32mINFO:type_infer-2840:Infering type for: V26\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2670:Column Class has data type binary\u001b[0m\n" + "\u001b[32mINFO:type_infer-2840:Column V22 has data type float\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2670:Column V27 has data type float\u001b[0m\n" + "\u001b[32mINFO:type_infer-2840:Infering type for: V23\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2670:Infering type for: V28\u001b[0m\n" + "\u001b[32mINFO:type_infer-2840:Column V26 has data type float\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2670:Column V25 has data type float\u001b[0m\n" + "\u001b[32mINFO:type_infer-2840:Infering type for: Class\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2670:Infering type for: V26\u001b[0m\n" + "\u001b[32mINFO:type_infer-2840:Column Class has data type binary\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2670:Column V28 has data type float\u001b[0m\n" + "\u001b[32mINFO:type_infer-2840:Column V28 has data type float\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2670:Infering type for: Amount\u001b[0m\n" + "\u001b[32mINFO:type_infer-2840:Infering type for: Amount\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2670:Column V26 has data type float\u001b[0m\n" + "\u001b[32mINFO:type_infer-2840:Column V23 has data type float\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2670:Column Amount has data type float\u001b[0m\n" + "\u001b[32mINFO:type_infer-2840:Column Amount has data type float\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2670:Starting statistical analysis\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2840:Starting statistical analysis\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2670:Finished statistical analysis\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2840:Finished statistical analysis\u001b[0m\n" ] } ], @@ -901,10 +901,10 @@ "id": "4411ee53", "metadata": { "execution": { - "iopub.execute_input": "2024-03-18T10:33:44.167938Z", - "iopub.status.busy": "2024-03-18T10:33:44.167724Z", - "iopub.status.idle": "2024-03-18T10:33:44.173216Z", - "shell.execute_reply": "2024-03-18T10:33:44.172671Z" + "iopub.execute_input": "2024-03-18T12:15:26.685990Z", + "iopub.status.busy": "2024-03-18T12:15:26.685721Z", + "iopub.status.idle": "2024-03-18T12:15:26.691571Z", + "shell.execute_reply": "2024-03-18T12:15:26.690891Z" } }, "outputs": [ @@ -996,10 +996,10 @@ "id": "34092d12", "metadata": { "execution": { - "iopub.execute_input": "2024-03-18T10:33:44.175771Z", - "iopub.status.busy": "2024-03-18T10:33:44.175404Z", - "iopub.status.idle": "2024-03-18T10:33:44.178602Z", - "shell.execute_reply": "2024-03-18T10:33:44.178067Z" + "iopub.execute_input": "2024-03-18T12:15:26.694541Z", + "iopub.status.busy": "2024-03-18T12:15:26.694114Z", + "iopub.status.idle": "2024-03-18T12:15:26.697712Z", + "shell.execute_reply": "2024-03-18T12:15:26.697149Z" } }, "outputs": [], @@ -1055,10 +1055,10 @@ "id": "alleged-concentrate", "metadata": { "execution": { - "iopub.execute_input": "2024-03-18T10:33:44.181235Z", - "iopub.status.busy": "2024-03-18T10:33:44.180788Z", - "iopub.status.idle": "2024-03-18T10:33:44.552667Z", - "shell.execute_reply": "2024-03-18T10:33:44.551983Z" + "iopub.execute_input": "2024-03-18T12:15:26.700467Z", + "iopub.status.busy": "2024-03-18T12:15:26.700050Z", + "iopub.status.idle": "2024-03-18T12:15:27.095902Z", + "shell.execute_reply": "2024-03-18T12:15:27.095206Z" } }, "outputs": [ @@ -1139,7 +1139,7 @@ " \"unbias_target\": True,\n", " \"seconds_per_mixer\": 42768.0,\n", " \"seconds_per_encoder\": None,\n", - " \"expected_additional_time\": 69.56466770172119,\n", + " \"expected_additional_time\": 69.90523362159729,\n", " \"time_aim\": 259200,\n", " \"target_weights\": None,\n", " \"positive_domain\": False,\n", @@ -1902,10 +1902,10 @@ "id": "organic-london", "metadata": { "execution": { - "iopub.execute_input": "2024-03-18T10:33:44.555398Z", - "iopub.status.busy": "2024-03-18T10:33:44.555057Z", - "iopub.status.idle": "2024-03-18T10:33:44.563230Z", - "shell.execute_reply": "2024-03-18T10:33:44.562720Z" + "iopub.execute_input": "2024-03-18T12:15:27.098742Z", + "iopub.status.busy": "2024-03-18T12:15:27.098311Z", + "iopub.status.idle": "2024-03-18T12:15:27.107127Z", + "shell.execute_reply": "2024-03-18T12:15:27.106588Z" } }, "outputs": [], @@ -1920,10 +1920,10 @@ "id": "fabulous-prime", "metadata": { "execution": { - "iopub.execute_input": "2024-03-18T10:33:44.565670Z", - "iopub.status.busy": "2024-03-18T10:33:44.565232Z", - "iopub.status.idle": "2024-03-18T10:34:05.084019Z", - "shell.execute_reply": "2024-03-18T10:34:05.083387Z" + "iopub.execute_input": "2024-03-18T12:15:27.109742Z", + "iopub.status.busy": "2024-03-18T12:15:27.109328Z", + "iopub.status.idle": "2024-03-18T12:15:47.870683Z", + "shell.execute_reply": "2024-03-18T12:15:47.870045Z" } }, "outputs": [ @@ -1931,28 +1931,28 @@ "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2670:Cleaning the data\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2840:Cleaning the data\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:lightwood-2670: `preprocess` runtime: 18.89 seconds\u001b[0m\n" + "\u001b[37mDEBUG:lightwood-2840: `preprocess` runtime: 18.63 seconds\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2670:Splitting the data into train/test\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2840:Splitting the data into train/test\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:lightwood-2670: `split` runtime: 1.63 seconds\u001b[0m\n" + "\u001b[37mDEBUG:lightwood-2840: `split` runtime: 2.12 seconds\u001b[0m\n" ] } ], @@ -1968,10 +1968,10 @@ "id": "suspended-biography", "metadata": { "execution": { - "iopub.execute_input": "2024-03-18T10:34:05.086748Z", - "iopub.status.busy": "2024-03-18T10:34:05.086529Z", - "iopub.status.idle": "2024-03-18T10:34:06.382420Z", - "shell.execute_reply": "2024-03-18T10:34:06.381657Z" + "iopub.execute_input": "2024-03-18T12:15:47.873560Z", + "iopub.status.busy": "2024-03-18T12:15:47.873132Z", + "iopub.status.idle": "2024-03-18T12:15:49.329385Z", + "shell.execute_reply": "2024-03-18T12:15:49.328650Z" } }, "outputs": [ diff --git a/_sources/tutorials/tutorial_data_analysis/tutorial_data_analysis.ipynb.txt b/_sources/tutorials/tutorial_data_analysis/tutorial_data_analysis.ipynb.txt index 5d12c6f82..161b40864 100644 --- a/_sources/tutorials/tutorial_data_analysis/tutorial_data_analysis.ipynb.txt +++ b/_sources/tutorials/tutorial_data_analysis/tutorial_data_analysis.ipynb.txt @@ -35,10 +35,10 @@ "execution_count": 1, "metadata": { "execution": { - "iopub.execute_input": "2024-03-18T10:31:30.957410Z", - "iopub.status.busy": "2024-03-18T10:31:30.956859Z", - "iopub.status.idle": "2024-03-18T10:31:31.284834Z", - "shell.execute_reply": "2024-03-18T10:31:31.284162Z" + "iopub.execute_input": "2024-03-18T12:13:05.545255Z", + "iopub.status.busy": "2024-03-18T12:13:05.545047Z", + "iopub.status.idle": "2024-03-18T12:13:05.926141Z", + "shell.execute_reply": "2024-03-18T12:13:05.925446Z" } }, "outputs": [ @@ -175,10 +175,10 @@ "execution_count": 2, "metadata": { "execution": { - "iopub.execute_input": "2024-03-18T10:31:31.321665Z", - "iopub.status.busy": "2024-03-18T10:31:31.321247Z", - "iopub.status.idle": "2024-03-18T10:31:33.536309Z", - "shell.execute_reply": "2024-03-18T10:31:33.535656Z" + "iopub.execute_input": "2024-03-18T12:13:05.964928Z", + "iopub.status.busy": "2024-03-18T12:13:05.964337Z", + "iopub.status.idle": "2024-03-18T12:13:08.437191Z", + "shell.execute_reply": "2024-03-18T12:13:08.436484Z" } }, "outputs": [ @@ -186,14 +186,14 @@ "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2482:No torchvision detected, image helpers not supported.\u001b[0m\n" + "\u001b[32mINFO:lightwood-2657:No torchvision detected, image helpers not supported.\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2482:No torchvision/pillow detected, image encoder not supported\u001b[0m\n" + "\u001b[32mINFO:lightwood-2657:No torchvision/pillow detected, image encoder not supported\u001b[0m\n" ] } ], @@ -215,10 +215,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2024-03-18T10:31:33.539401Z", - "iopub.status.busy": "2024-03-18T10:31:33.539136Z", - "iopub.status.idle": "2024-03-18T10:31:33.544458Z", - "shell.execute_reply": "2024-03-18T10:31:33.543810Z" + "iopub.execute_input": "2024-03-18T12:13:08.440807Z", + "iopub.status.busy": "2024-03-18T12:13:08.440196Z", + "iopub.status.idle": "2024-03-18T12:13:08.445917Z", + "shell.execute_reply": "2024-03-18T12:13:08.445190Z" } }, "outputs": [ @@ -270,10 +270,10 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2024-03-18T10:31:33.547310Z", - "iopub.status.busy": "2024-03-18T10:31:33.546748Z", - "iopub.status.idle": "2024-03-18T10:31:33.573011Z", - "shell.execute_reply": "2024-03-18T10:31:33.572417Z" + "iopub.execute_input": "2024-03-18T12:13:08.449281Z", + "iopub.status.busy": "2024-03-18T12:13:08.448775Z", + "iopub.status.idle": "2024-03-18T12:13:08.477079Z", + "shell.execute_reply": "2024-03-18T12:13:08.476441Z" } }, "outputs": [ @@ -281,112 +281,112 @@ "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2482:Analyzing a sample of 222\u001b[0m\n" + "\u001b[32mINFO:type_infer-2657:Analyzing a sample of 222\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2482:from a total population of 225, this is equivalent to 98.7% of your data.\u001b[0m\n" + "\u001b[32mINFO:type_infer-2657:from a total population of 225, this is equivalent to 98.7% of your data.\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2482:Infering type for: Population\u001b[0m\n" + "\u001b[32mINFO:type_infer-2657:Infering type for: Population\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2482:Column Population has data type integer\u001b[0m\n" + "\u001b[32mINFO:type_infer-2657:Column Population has data type integer\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2482:Infering type for: Area (sq. mi.)\u001b[0m\n" + "\u001b[32mINFO:type_infer-2657:Infering type for: Area (sq. mi.)\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2482:Column Area (sq. mi.) has data type integer\u001b[0m\n" + "\u001b[32mINFO:type_infer-2657:Column Area (sq. mi.) has data type integer\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2482:Infering type for: Pop. Density \u001b[0m\n" + "\u001b[32mINFO:type_infer-2657:Infering type for: Pop. Density \u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2482:Column Pop. Density has data type float\u001b[0m\n" + "\u001b[32mINFO:type_infer-2657:Column Pop. Density has data type float\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2482:Infering type for: GDP ($ per capita)\u001b[0m\n" + "\u001b[32mINFO:type_infer-2657:Infering type for: GDP ($ per capita)\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2482:Column GDP ($ per capita) has data type integer\u001b[0m\n" + "\u001b[32mINFO:type_infer-2657:Column GDP ($ per capita) has data type integer\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2482:Infering type for: Literacy (%)\u001b[0m\n" + "\u001b[32mINFO:type_infer-2657:Infering type for: Literacy (%)\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2482:Column Literacy (%) has data type float\u001b[0m\n" + "\u001b[32mINFO:type_infer-2657:Column Literacy (%) has data type float\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2482:Infering type for: Infant mortality \u001b[0m\n" + "\u001b[32mINFO:type_infer-2657:Infering type for: Infant mortality \u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2482:Column Infant mortality has data type float\u001b[0m\n" + "\u001b[32mINFO:type_infer-2657:Column Infant mortality has data type float\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2482:Infering type for: Development Index\u001b[0m\n" + "\u001b[32mINFO:type_infer-2657:Infering type for: Development Index\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2482:Column Development Index has data type categorical\u001b[0m\n" + "\u001b[32mINFO:type_infer-2657:Column Development Index has data type categorical\u001b[0m\n" ] }, { @@ -421,10 +421,10 @@ "execution_count": 5, "metadata": { "execution": { - "iopub.execute_input": "2024-03-18T10:31:33.575615Z", - "iopub.status.busy": "2024-03-18T10:31:33.575224Z", - "iopub.status.idle": "2024-03-18T10:31:33.579328Z", - "shell.execute_reply": "2024-03-18T10:31:33.578714Z" + "iopub.execute_input": "2024-03-18T12:13:08.480056Z", + "iopub.status.busy": "2024-03-18T12:13:08.479564Z", + "iopub.status.idle": "2024-03-18T12:13:08.484517Z", + "shell.execute_reply": "2024-03-18T12:13:08.483762Z" } }, "outputs": [ @@ -474,10 +474,10 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2024-03-18T10:31:33.581881Z", - "iopub.status.busy": "2024-03-18T10:31:33.581527Z", - "iopub.status.idle": "2024-03-18T10:31:33.608327Z", - "shell.execute_reply": "2024-03-18T10:31:33.607793Z" + "iopub.execute_input": "2024-03-18T12:13:08.487525Z", + "iopub.status.busy": "2024-03-18T12:13:08.487081Z", + "iopub.status.idle": "2024-03-18T12:13:08.516522Z", + "shell.execute_reply": "2024-03-18T12:13:08.515803Z" } }, "outputs": [ @@ -485,14 +485,14 @@ "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2482:Starting statistical analysis\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2657:Starting statistical analysis\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2482:Finished statistical analysis\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2657:Finished statistical analysis\u001b[0m\n" ] } ], @@ -520,10 +520,10 @@ "execution_count": 7, "metadata": { "execution": { - "iopub.execute_input": "2024-03-18T10:31:33.610868Z", - "iopub.status.busy": "2024-03-18T10:31:33.610441Z", - "iopub.status.idle": "2024-03-18T10:31:33.615023Z", - "shell.execute_reply": "2024-03-18T10:31:33.614403Z" + "iopub.execute_input": "2024-03-18T12:13:08.519606Z", + "iopub.status.busy": "2024-03-18T12:13:08.519165Z", + "iopub.status.idle": "2024-03-18T12:13:08.524187Z", + "shell.execute_reply": "2024-03-18T12:13:08.523481Z" } }, "outputs": [ @@ -569,10 +569,10 @@ "execution_count": 8, "metadata": { "execution": { - "iopub.execute_input": "2024-03-18T10:31:33.617561Z", - "iopub.status.busy": "2024-03-18T10:31:33.617181Z", - "iopub.status.idle": "2024-03-18T10:31:33.621308Z", - "shell.execute_reply": "2024-03-18T10:31:33.620693Z" + "iopub.execute_input": "2024-03-18T12:13:08.526971Z", + "iopub.status.busy": "2024-03-18T12:13:08.526551Z", + "iopub.status.idle": "2024-03-18T12:13:08.531002Z", + "shell.execute_reply": "2024-03-18T12:13:08.530306Z" } }, "outputs": [ @@ -612,10 +612,10 @@ "execution_count": 9, "metadata": { "execution": { - "iopub.execute_input": "2024-03-18T10:31:33.623812Z", - "iopub.status.busy": "2024-03-18T10:31:33.623493Z", - "iopub.status.idle": "2024-03-18T10:31:33.627977Z", - "shell.execute_reply": "2024-03-18T10:31:33.627346Z" + "iopub.execute_input": "2024-03-18T12:13:08.534224Z", + "iopub.status.busy": "2024-03-18T12:13:08.533746Z", + "iopub.status.idle": "2024-03-18T12:13:08.538987Z", + "shell.execute_reply": "2024-03-18T12:13:08.538298Z" }, "scrolled": false }, @@ -673,10 +673,10 @@ "execution_count": 10, "metadata": { "execution": { - "iopub.execute_input": "2024-03-18T10:31:33.630489Z", - "iopub.status.busy": "2024-03-18T10:31:33.630170Z", - "iopub.status.idle": "2024-03-18T10:31:33.634186Z", - "shell.execute_reply": "2024-03-18T10:31:33.633604Z" + "iopub.execute_input": "2024-03-18T12:13:08.542241Z", + "iopub.status.busy": "2024-03-18T12:13:08.541677Z", + "iopub.status.idle": "2024-03-18T12:13:08.546474Z", + "shell.execute_reply": "2024-03-18T12:13:08.545783Z" } }, "outputs": [ @@ -700,10 +700,10 @@ "execution_count": 11, "metadata": { "execution": { - "iopub.execute_input": "2024-03-18T10:31:33.636639Z", - "iopub.status.busy": "2024-03-18T10:31:33.636196Z", - "iopub.status.idle": "2024-03-18T10:31:33.640738Z", - "shell.execute_reply": "2024-03-18T10:31:33.640098Z" + "iopub.execute_input": "2024-03-18T12:13:08.549300Z", + "iopub.status.busy": "2024-03-18T12:13:08.548787Z", + "iopub.status.idle": "2024-03-18T12:13:08.553922Z", + "shell.execute_reply": "2024-03-18T12:13:08.553224Z" }, "scrolled": false }, @@ -786,10 +786,10 @@ "execution_count": 12, "metadata": { "execution": { - "iopub.execute_input": "2024-03-18T10:31:33.643295Z", - "iopub.status.busy": "2024-03-18T10:31:33.642925Z", - "iopub.status.idle": "2024-03-18T10:31:33.646473Z", - "shell.execute_reply": "2024-03-18T10:31:33.645803Z" + "iopub.execute_input": "2024-03-18T12:13:08.556686Z", + "iopub.status.busy": "2024-03-18T12:13:08.556451Z", + "iopub.status.idle": "2024-03-18T12:13:08.560876Z", + "shell.execute_reply": "2024-03-18T12:13:08.560184Z" } }, "outputs": [ @@ -841,10 +841,10 @@ "execution_count": 13, "metadata": { "execution": { - "iopub.execute_input": "2024-03-18T10:31:33.649181Z", - "iopub.status.busy": "2024-03-18T10:31:33.648813Z", - "iopub.status.idle": "2024-03-18T10:31:36.233826Z", - "shell.execute_reply": "2024-03-18T10:31:36.233128Z" + "iopub.execute_input": "2024-03-18T12:13:08.564014Z", + "iopub.status.busy": "2024-03-18T12:13:08.563369Z", + "iopub.status.idle": "2024-03-18T12:13:11.366294Z", + "shell.execute_reply": "2024-03-18T12:13:11.365542Z" }, "scrolled": false }, diff --git a/_sources/tutorials/tutorial_time_series/tutorial_time_series.ipynb.txt b/_sources/tutorials/tutorial_time_series/tutorial_time_series.ipynb.txt index 666208a50..03ddc61b1 100644 --- a/_sources/tutorials/tutorial_time_series/tutorial_time_series.ipynb.txt +++ b/_sources/tutorials/tutorial_time_series/tutorial_time_series.ipynb.txt @@ -24,10 +24,10 @@ "execution_count": 1, "metadata": { "execution": { - "iopub.execute_input": "2024-03-18T10:31:10.675092Z", - "iopub.status.busy": "2024-03-18T10:31:10.674541Z", - "iopub.status.idle": "2024-03-18T10:31:11.090125Z", - "shell.execute_reply": "2024-03-18T10:31:11.089404Z" + "iopub.execute_input": "2024-03-18T12:12:43.212199Z", + "iopub.status.busy": "2024-03-18T12:12:43.211949Z", + "iopub.status.idle": "2024-03-18T12:12:43.771553Z", + "shell.execute_reply": "2024-03-18T12:12:43.770829Z" } }, "outputs": [ @@ -162,10 +162,10 @@ "execution_count": 2, "metadata": { "execution": { - "iopub.execute_input": "2024-03-18T10:31:11.127376Z", - "iopub.status.busy": "2024-03-18T10:31:11.126896Z", - "iopub.status.idle": "2024-03-18T10:31:13.412768Z", - "shell.execute_reply": "2024-03-18T10:31:13.412026Z" + "iopub.execute_input": "2024-03-18T12:12:43.810740Z", + "iopub.status.busy": "2024-03-18T12:12:43.810255Z", + "iopub.status.idle": "2024-03-18T12:12:46.271817Z", + "shell.execute_reply": "2024-03-18T12:12:46.271020Z" } }, "outputs": [ @@ -173,14 +173,14 @@ "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2349:No torchvision detected, image helpers not supported.\u001b[0m\n" + "\u001b[32mINFO:lightwood-2526:No torchvision detected, image helpers not supported.\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2349:No torchvision/pillow detected, image encoder not supported\u001b[0m\n" + "\u001b[32mINFO:lightwood-2526:No torchvision/pillow detected, image encoder not supported\u001b[0m\n" ] } ], @@ -193,10 +193,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2024-03-18T10:31:13.416144Z", - "iopub.status.busy": "2024-03-18T10:31:13.415631Z", - "iopub.status.idle": "2024-03-18T10:31:13.419412Z", - "shell.execute_reply": "2024-03-18T10:31:13.418797Z" + "iopub.execute_input": "2024-03-18T12:12:46.275639Z", + "iopub.status.busy": "2024-03-18T12:12:46.274895Z", + "iopub.status.idle": "2024-03-18T12:12:46.279199Z", + "shell.execute_reply": "2024-03-18T12:12:46.278569Z" } }, "outputs": [], @@ -223,10 +223,10 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2024-03-18T10:31:13.421879Z", - "iopub.status.busy": "2024-03-18T10:31:13.421525Z", - "iopub.status.idle": "2024-03-18T10:31:13.425477Z", - "shell.execute_reply": "2024-03-18T10:31:13.424875Z" + "iopub.execute_input": "2024-03-18T12:12:46.281788Z", + "iopub.status.busy": "2024-03-18T12:12:46.281581Z", + "iopub.status.idle": "2024-03-18T12:12:46.286226Z", + "shell.execute_reply": "2024-03-18T12:12:46.285517Z" } }, "outputs": [ @@ -261,10 +261,10 @@ "execution_count": 5, "metadata": { "execution": { - "iopub.execute_input": "2024-03-18T10:31:13.428172Z", - "iopub.status.busy": "2024-03-18T10:31:13.427791Z", - "iopub.status.idle": "2024-03-18T10:31:17.491379Z", - "shell.execute_reply": "2024-03-18T10:31:17.490539Z" + "iopub.execute_input": "2024-03-18T12:12:46.289099Z", + "iopub.status.busy": "2024-03-18T12:12:46.288679Z", + "iopub.status.idle": "2024-03-18T12:12:50.587952Z", + "shell.execute_reply": "2024-03-18T12:12:50.587366Z" } }, "outputs": [ @@ -272,49 +272,49 @@ "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2349:Analyzing a sample of 2467\u001b[0m\n" + "\u001b[32mINFO:type_infer-2526:Analyzing a sample of 2467\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2349:from a total population of 2820, this is equivalent to 87.5% of your data.\u001b[0m\n" + "\u001b[32mINFO:type_infer-2526:from a total population of 2820, this is equivalent to 87.5% of your data.\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2349:Infering type for: Month\u001b[0m\n" + "\u001b[32mINFO:type_infer-2526:Infering type for: Month\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2349:Column Month has data type date\u001b[0m\n" + "\u001b[32mINFO:type_infer-2526:Column Month has data type date\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2349:Infering type for: Sunspots\u001b[0m\n" + "\u001b[32mINFO:type_infer-2526:Infering type for: Sunspots\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2349:Column Sunspots has data type float\u001b[0m\n" + "\u001b[32mINFO:type_infer-2526:Column Sunspots has data type float\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2349:Starting statistical analysis\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2526:Starting statistical analysis\u001b[0m\n" ] }, { @@ -323,7 +323,7 @@ "text": [ "/opt/hostedtoolcache/Python/3.9.18/x64/lib/python3.9/site-packages/dataprep_ml/cleaners.py:163: UserWarning: The argument 'infer_datetime_format' is deprecated and will be removed in a future version. A strict version of it is now the default, see https://pandas.pydata.org/pdeps/0004-consistent-to-datetime-parsing.html. You can safely remove this argument.\n", " result = pd.to_datetime(element,\n", - "\u001b[32mINFO:dataprep_ml-2349:Finished statistical analysis\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2526:Finished statistical analysis\u001b[0m\n" ] } ], @@ -360,10 +360,10 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2024-03-18T10:31:17.494768Z", - "iopub.status.busy": "2024-03-18T10:31:17.494475Z", - "iopub.status.idle": "2024-03-18T10:31:19.509975Z", - "shell.execute_reply": "2024-03-18T10:31:19.509394Z" + "iopub.execute_input": "2024-03-18T12:12:50.591170Z", + "iopub.status.busy": "2024-03-18T12:12:50.590744Z", + "iopub.status.idle": "2024-03-18T12:12:52.722053Z", + "shell.execute_reply": "2024-03-18T12:12:52.721367Z" } }, "outputs": [ @@ -371,14 +371,14 @@ "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2349:[Learn phase 1/8] - Statistical analysis\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2526:[Learn phase 1/8] - Statistical analysis\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2349:Starting statistical analysis\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2526:Starting statistical analysis\u001b[0m\n" ] }, { @@ -387,28 +387,28 @@ "text": [ "/opt/hostedtoolcache/Python/3.9.18/x64/lib/python3.9/site-packages/dataprep_ml/cleaners.py:163: UserWarning: The argument 'infer_datetime_format' is deprecated and will be removed in a future version. A strict version of it is now the default, see https://pandas.pydata.org/pdeps/0004-consistent-to-datetime-parsing.html. You can safely remove this argument.\n", " result = pd.to_datetime(element,\n", - "\u001b[32mINFO:dataprep_ml-2349:Finished statistical analysis\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2526:Finished statistical analysis\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:lightwood-2349: `analyze_data` runtime: 0.07 seconds\u001b[0m\n" + "\u001b[37mDEBUG:lightwood-2526: `analyze_data` runtime: 0.06 seconds\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2349:[Learn phase 2/8] - Data preprocessing\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2526:[Learn phase 2/8] - Data preprocessing\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2349:Cleaning the data\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2526:Cleaning the data\u001b[0m\n" ] }, { @@ -417,133 +417,133 @@ "text": [ "/opt/hostedtoolcache/Python/3.9.18/x64/lib/python3.9/site-packages/dataprep_ml/cleaners.py:163: UserWarning: The argument 'infer_datetime_format' is deprecated and will be removed in a future version. A strict version of it is now the default, see https://pandas.pydata.org/pdeps/0004-consistent-to-datetime-parsing.html. You can safely remove this argument.\n", " result = pd.to_datetime(element,\n", - "\u001b[32mINFO:dataprep_ml-2349:Transforming timeseries data\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2526:Transforming timeseries data\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:lightwood-2349: `preprocess` runtime: 0.1 seconds\u001b[0m\n" + "\u001b[37mDEBUG:lightwood-2526: `preprocess` runtime: 0.09 seconds\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2349:[Learn phase 3/8] - Data splitting\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2526:[Learn phase 3/8] - Data splitting\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2349:Splitting the data into train/test\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2526:Splitting the data into train/test\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:lightwood-2349: `split` runtime: 0.0 seconds\u001b[0m\n" + "\u001b[37mDEBUG:lightwood-2526: `split` runtime: 0.0 seconds\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2349:[Learn phase 4/8] - Preparing encoders\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2526:[Learn phase 4/8] - Preparing encoders\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:dataprep_ml-2349:Preparing sequentially...\u001b[0m\n" + "\u001b[37mDEBUG:dataprep_ml-2526:Preparing sequentially...\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:lightwood-2349: `prepare` runtime: 0.05 seconds\u001b[0m\n" + "\u001b[37mDEBUG:lightwood-2526: `prepare` runtime: 0.05 seconds\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2349:[Learn phase 5/8] - Feature generation\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2526:[Learn phase 5/8] - Feature generation\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2349:Featurizing the data\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2526:Featurizing the data\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:lightwood-2349: `featurize` runtime: 0.05 seconds\u001b[0m\n" + "\u001b[37mDEBUG:lightwood-2526: `featurize` runtime: 0.05 seconds\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2349:[Learn phase 6/8] - Mixer training\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2526:[Learn phase 6/8] - Mixer training\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2349:Training the mixers\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2526:Training the mixers\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[33mWARNING:lightwood-2349:XGBoost running on CPU\u001b[0m\n" + "\u001b[33mWARNING:lightwood-2526:XGBoost running on CPU\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[33mWARNING:lightwood-2349:XGBoost running on CPU\u001b[0m\n" + "\u001b[33mWARNING:lightwood-2526:XGBoost running on CPU\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[33mWARNING:lightwood-2349:XGBoost running on CPU\u001b[0m\n" + "\u001b[33mWARNING:lightwood-2526:XGBoost running on CPU\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[33mWARNING:lightwood-2349:XGBoost running on CPU\u001b[0m\n" + "\u001b[33mWARNING:lightwood-2526:XGBoost running on CPU\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[33mWARNING:lightwood-2349:XGBoost running on CPU\u001b[0m\n" + "\u001b[33mWARNING:lightwood-2526:XGBoost running on CPU\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[33mWARNING:lightwood-2349:XGBoost running on CPU\u001b[0m\n" + "\u001b[33mWARNING:lightwood-2526:XGBoost running on CPU\u001b[0m\n" ] }, { @@ -558,12 +558,12 @@ "name": "stdout", "output_type": "stream", "text": [ - "[10:31:17] WARNING: ../src/learner.cc:339: No visible GPU is found, setting `gpu_id` to -1\n", - "[10:31:17] WARNING: ../src/learner.cc:339: No visible GPU is found, setting `gpu_id` to -1\n", - "[10:31:17] WARNING: ../src/learner.cc:339: No visible GPU is found, setting `gpu_id` to -1\n", - "[10:31:17] WARNING: ../src/learner.cc:339: No visible GPU is found, setting `gpu_id` to -1\n", - "[10:31:17] WARNING: ../src/learner.cc:339: No visible GPU is found, setting `gpu_id` to -1\n", - "[10:31:17] WARNING: ../src/learner.cc:339: No visible GPU is found, setting `gpu_id` to -1\n" + "[12:12:50] WARNING: ../src/learner.cc:339: No visible GPU is found, setting `gpu_id` to -1\n", + "[12:12:50] WARNING: ../src/learner.cc:339: No visible GPU is found, setting `gpu_id` to -1\n", + "[12:12:50] WARNING: ../src/learner.cc:339: No visible GPU is found, setting `gpu_id` to -1\n", + "[12:12:50] WARNING: ../src/learner.cc:339: No visible GPU is found, setting `gpu_id` to -1\n", + "[12:12:50] WARNING: ../src/learner.cc:339: No visible GPU is found, setting `gpu_id` to -1\n", + "[12:12:50] WARNING: ../src/learner.cc:339: No visible GPU is found, setting `gpu_id` to -1\n" ] }, { @@ -575,63 +575,63 @@ "Consider using one of the following signatures instead:\n", "\taddcmul_(Tensor tensor1, Tensor tensor2, *, Number value) (Triggered internally at ../torch/csrc/utils/python_arg_parser.cpp:1630.)\n", " exp_avg_sq.mul_(beta2).addcmul_(1 - beta2, grad, grad)\n", - "\u001b[32mINFO:lightwood-2349:Loss of 9.051180630922318 with learning rate 0.0001\u001b[0m\n" + "\u001b[32mINFO:lightwood-2526:Loss of 9.051180630922318 with learning rate 0.0001\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2349:Loss of 9.014871209859848 with learning rate 0.0005\u001b[0m\n" + "\u001b[32mINFO:lightwood-2526:Loss of 9.014871209859848 with learning rate 0.0005\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2349:Loss of 8.969509482383728 with learning rate 0.001\u001b[0m\n" + "\u001b[32mINFO:lightwood-2526:Loss of 8.969509482383728 with learning rate 0.001\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2349:Loss of 8.879052013158798 with learning rate 0.002\u001b[0m\n" + "\u001b[32mINFO:lightwood-2526:Loss of 8.879052013158798 with learning rate 0.002\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2349:Loss of 8.788950502872467 with learning rate 0.003\u001b[0m\n" + "\u001b[32mINFO:lightwood-2526:Loss of 8.788950502872467 with learning rate 0.003\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2349:Loss of 8.611965209245682 with learning rate 0.005\u001b[0m\n" + "\u001b[32mINFO:lightwood-2526:Loss of 8.611965209245682 with learning rate 0.005\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2349:Loss of 8.195775926113129 with learning rate 0.01\u001b[0m\n" + "\u001b[32mINFO:lightwood-2526:Loss of 8.195775926113129 with learning rate 0.01\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2349:Loss of 6.255893141031265 with learning rate 0.05\u001b[0m\n" + "\u001b[32mINFO:lightwood-2526:Loss of 6.255893141031265 with learning rate 0.05\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2349:Found learning rate of: 0.05\u001b[0m\n" + "\u001b[32mINFO:lightwood-2526:Found learning rate of: 0.05\u001b[0m\n" ] }, { @@ -640,105 +640,105 @@ "text": [ "/opt/hostedtoolcache/Python/3.9.18/x64/lib/python3.9/site-packages/torch/cuda/amp/grad_scaler.py:126: UserWarning: torch.cuda.amp.GradScaler is enabled, but CUDA is not available. Disabling.\n", " warnings.warn(\n", - "\u001b[32mINFO:lightwood-2349:Loss @ epoch 1: 0.5818348675966263\u001b[0m\n" + "\u001b[32mINFO:lightwood-2526:Loss @ epoch 1: 0.5818348675966263\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2349:Loss @ epoch 2: 0.4797109067440033\u001b[0m\n" + "\u001b[32mINFO:lightwood-2526:Loss @ epoch 2: 0.4797109067440033\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2349:Loss @ epoch 3: 0.48386093974113464\u001b[0m\n" + "\u001b[32mINFO:lightwood-2526:Loss @ epoch 3: 0.48386093974113464\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2349:Loss @ epoch 4: 0.49511992931365967\u001b[0m\n" + "\u001b[32mINFO:lightwood-2526:Loss @ epoch 4: 0.49511992931365967\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2349:Loss @ epoch 5: 0.39475560188293457\u001b[0m\n" + "\u001b[32mINFO:lightwood-2526:Loss @ epoch 5: 0.39475560188293457\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2349:Loss @ epoch 6: 0.39592696726322174\u001b[0m\n" + "\u001b[32mINFO:lightwood-2526:Loss @ epoch 6: 0.39592696726322174\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2349:Loss @ epoch 7: 0.3622782379388809\u001b[0m\n" + "\u001b[32mINFO:lightwood-2526:Loss @ epoch 7: 0.3622782379388809\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2349:Loss @ epoch 8: 0.38170479238033295\u001b[0m\n" + "\u001b[32mINFO:lightwood-2526:Loss @ epoch 8: 0.38170479238033295\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2349:Loss @ epoch 9: 0.5138543993234634\u001b[0m\n" + "\u001b[32mINFO:lightwood-2526:Loss @ epoch 9: 0.5138543993234634\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2349:Loss @ epoch 10: 0.6360723078250885\u001b[0m\n" + "\u001b[32mINFO:lightwood-2526:Loss @ epoch 10: 0.6360723078250885\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2349:Loss @ epoch 1: 0.29868809472430835\u001b[0m\n" + "\u001b[32mINFO:lightwood-2526:Loss @ epoch 1: 0.29868809472430835\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2349:Loss @ epoch 2: 0.30318967591632495\u001b[0m\n" + "\u001b[32mINFO:lightwood-2526:Loss @ epoch 2: 0.30318967591632495\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:lightwood-2349: `fit_mixer` runtime: 0.93 seconds\u001b[0m\n" + "\u001b[37mDEBUG:lightwood-2526: `fit_mixer` runtime: 1.0 seconds\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2349:Started fitting LGBM models for array prediction\u001b[0m\n" + "\u001b[32mINFO:lightwood-2526:Started fitting LGBM models for array prediction\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2349:Started fitting XGBoost model\u001b[0m\n" + "\u001b[32mINFO:lightwood-2526:Started fitting XGBoost model\u001b[0m\n" ] }, { @@ -752,14 +752,14 @@ "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2349:A single GBM iteration takes 0.1 seconds\u001b[0m\n" + "\u001b[32mINFO:lightwood-2526:A single GBM iteration takes 0.1 seconds\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2349:Training XGBoost with 57023 iterations given 7127.98668384552 seconds constraint\u001b[0m\n" + "\u001b[32mINFO:lightwood-2526:Training XGBoost with 57023 iterations given 7127.98410153389 seconds constraint\u001b[0m\n" ] }, { @@ -871,7 +871,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2349:Started fitting XGBoost model\u001b[0m\n" + "\u001b[32mINFO:lightwood-2526:Started fitting XGBoost model\u001b[0m\n" ] }, { @@ -885,14 +885,14 @@ "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2349:A single GBM iteration takes 0.1 seconds\u001b[0m\n" + "\u001b[32mINFO:lightwood-2526:A single GBM iteration takes 0.1 seconds\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2349:Training XGBoost with 57023 iterations given 7127.9880249500275 seconds constraint\u001b[0m\n" + "\u001b[32mINFO:lightwood-2526:Training XGBoost with 57023 iterations given 7127.986747741699 seconds constraint\u001b[0m\n" ] }, { @@ -997,7 +997,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2349:Started fitting XGBoost model\u001b[0m\n" + "\u001b[32mINFO:lightwood-2526:Started fitting XGBoost model\u001b[0m\n" ] }, { @@ -1011,14 +1011,14 @@ "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2349:A single GBM iteration takes 0.1 seconds\u001b[0m\n" + "\u001b[32mINFO:lightwood-2526:A single GBM iteration takes 0.1 seconds\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2349:Training XGBoost with 57023 iterations given 7127.988857269287 seconds constraint\u001b[0m\n" + "\u001b[32mINFO:lightwood-2526:Training XGBoost with 57023 iterations given 7127.986978292465 seconds constraint\u001b[0m\n" ] }, { @@ -1074,7 +1074,14 @@ "name": "stdout", "output_type": "stream", "text": [ - "[7]\tvalidation_0-rmse:19.00714\n" + "[7]\tvalidation_0-rmse:19.00714" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n" ] }, { @@ -1088,35 +1095,49 @@ "name": "stdout", "output_type": "stream", "text": [ - "[9]\tvalidation_0-rmse:19.12589\n" + "[9]\tvalidation_0-rmse:19.12589" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "[10]\tvalidation_0-rmse:19.34977\n" + "\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "[11]\tvalidation_0-rmse:19.43217\n" + "[10]\tvalidation_0-rmse:19.34977" ] }, { - "name": "stderr", + "name": "stdout", + "output_type": "stream", + "text": [ + "\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[11]\tvalidation_0-rmse:19.43217" + ] + }, + { + "name": "stdout", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2349:Started fitting XGBoost model\u001b[0m\n" + "\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "[0]\tvalidation_0-rmse:44.19079" + "[12]\tvalidation_0-rmse:19.48230" ] }, { @@ -1130,14 +1151,28 @@ "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2349:A single GBM iteration takes 0.1 seconds\u001b[0m\n" + "\u001b[32mINFO:lightwood-2526:Started fitting XGBoost model\u001b[0m\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[0]\tvalidation_0-rmse:44.19079\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[32mINFO:lightwood-2526:A single GBM iteration takes 0.1 seconds\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2349:Training XGBoost with 57023 iterations given 7127.9874176979065 seconds constraint\u001b[0m\n" + "\u001b[32mINFO:lightwood-2526:Training XGBoost with 57023 iterations given 7127.987501621246 seconds constraint\u001b[0m\n" ] }, { @@ -1231,11 +1266,18 @@ "[12]\tvalidation_0-rmse:20.83998\n" ] }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[13]\tvalidation_0-rmse:20.77980\n" + ] + }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2349:Started fitting XGBoost model\u001b[0m\n" + "\u001b[32mINFO:lightwood-2526:Started fitting XGBoost model\u001b[0m\n" ] }, { @@ -1249,14 +1291,14 @@ "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2349:A single GBM iteration takes 0.1 seconds\u001b[0m\n" + "\u001b[32mINFO:lightwood-2526:A single GBM iteration takes 0.1 seconds\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2349:Training XGBoost with 57023 iterations given 7127.987009763718 seconds constraint\u001b[0m\n" + "\u001b[32mINFO:lightwood-2526:Training XGBoost with 57023 iterations given 7127.986826181412 seconds constraint\u001b[0m\n" ] }, { @@ -1319,56 +1361,63 @@ "name": "stdout", "output_type": "stream", "text": [ - "[8]\tvalidation_0-rmse:22.21348\n" + "[8]\tvalidation_0-rmse:22.21348" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "[9]\tvalidation_0-rmse:22.10747\n" + "\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "[10]\tvalidation_0-rmse:22.20352\n" + "[9]\tvalidation_0-rmse:22.10747" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "[11]\tvalidation_0-rmse:22.25761\n" + "\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "[12]\tvalidation_0-rmse:22.25308\n" + "[10]\tvalidation_0-rmse:22.20352\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "[13]\tvalidation_0-rmse:22.31415\n" + "[11]\tvalidation_0-rmse:22.25761\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[12]\tvalidation_0-rmse:22.25308\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "[14]\tvalidation_0-rmse:22.31000\n" + "[13]\tvalidation_0-rmse:22.31415\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2349:Started fitting XGBoost model\u001b[0m\n" + "\u001b[32mINFO:lightwood-2526:Started fitting XGBoost model\u001b[0m\n" ] }, { @@ -1382,14 +1431,14 @@ "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2349:A single GBM iteration takes 0.1 seconds\u001b[0m\n" + "\u001b[32mINFO:lightwood-2526:A single GBM iteration takes 0.1 seconds\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2349:Training XGBoost with 57023 iterations given 7127.988016605377 seconds constraint\u001b[0m\n" + "\u001b[32mINFO:lightwood-2526:Training XGBoost with 57023 iterations given 7127.986331701279 seconds constraint\u001b[0m\n" ] }, { @@ -1424,7 +1473,14 @@ "name": "stdout", "output_type": "stream", "text": [ - "[4]\tvalidation_0-rmse:23.09943\n" + "[4]\tvalidation_0-rmse:23.09943" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n" ] }, { @@ -1490,123 +1546,130 @@ "[13]\tvalidation_0-rmse:21.68890\n" ] }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[14]\tvalidation_0-rmse:21.70025\n" + ] + }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:lightwood-2349: `fit_mixer` runtime: 0.5 seconds\u001b[0m\n" + "\u001b[37mDEBUG:lightwood-2526: `fit_mixer` runtime: 0.54 seconds\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2349:Ensembling the mixer\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2526:Ensembling the mixer\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2349:Mixer: NeuralTs got accuracy: 0.875\u001b[0m\n" + "\u001b[32mINFO:lightwood-2526:Mixer: NeuralTs got accuracy: 0.875\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[33mWARNING:lightwood-2349:This model does not output probability estimates\u001b[0m\n" + "\u001b[33mWARNING:lightwood-2526:This model does not output probability estimates\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2349:Mixer: XGBoostArrayMixer got accuracy: 0.869\u001b[0m\n" + "\u001b[32mINFO:lightwood-2526:Mixer: XGBoostArrayMixer got accuracy: 0.869\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2349:Picked best mixer: NeuralTs\u001b[0m\n" + "\u001b[32mINFO:lightwood-2526:Picked best mixer: NeuralTs\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:lightwood-2349: `fit` runtime: 1.48 seconds\u001b[0m\n" + "\u001b[37mDEBUG:lightwood-2526: `fit` runtime: 1.59 seconds\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2349:[Learn phase 7/8] - Ensemble analysis\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2526:[Learn phase 7/8] - Ensemble analysis\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2349:Analyzing the ensemble of mixers\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2526:Analyzing the ensemble of mixers\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2349:The block ICP is now running its analyze() method\u001b[0m\n" + "\u001b[32mINFO:lightwood-2526:The block ICP is now running its analyze() method\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2349:The block ConfStats is now running its analyze() method\u001b[0m\n" + "\u001b[32mINFO:lightwood-2526:The block ConfStats is now running its analyze() method\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2349:The block AccStats is now running its analyze() method\u001b[0m\n" + "\u001b[32mINFO:lightwood-2526:The block AccStats is now running its analyze() method\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2349:The block PermutationFeatureImportance is now running its analyze() method\u001b[0m\n" + "\u001b[32mINFO:lightwood-2526:The block PermutationFeatureImportance is now running its analyze() method\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[33mWARNING:lightwood-2349:Block 'PermutationFeatureImportance' does not support time series nor text encoding, skipping...\u001b[0m\n" + "\u001b[33mWARNING:lightwood-2526:Block 'PermutationFeatureImportance' does not support time series nor text encoding, skipping...\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:lightwood-2349: `analyze_ensemble` runtime: 0.17 seconds\u001b[0m\n" + "\u001b[37mDEBUG:lightwood-2526: `analyze_ensemble` runtime: 0.18 seconds\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2349:[Learn phase 8/8] - Adjustment on validation requested\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2526:[Learn phase 8/8] - Adjustment on validation requested\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2349:Updating the mixers\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2526:Updating the mixers\u001b[0m\n" ] }, { @@ -1615,77 +1678,77 @@ "text": [ "/opt/hostedtoolcache/Python/3.9.18/x64/lib/python3.9/site-packages/torch/cuda/amp/grad_scaler.py:126: UserWarning: torch.cuda.amp.GradScaler is enabled, but CUDA is not available. Disabling.\n", " warnings.warn(\n", - "\u001b[32mINFO:lightwood-2349:Loss @ epoch 1: 0.29626286526521045\u001b[0m\n" + "\u001b[32mINFO:lightwood-2526:Loss @ epoch 1: 0.29626286526521045\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2349:Loss @ epoch 2: 0.2954987535874049\u001b[0m\n" + "\u001b[32mINFO:lightwood-2526:Loss @ epoch 2: 0.2954987535874049\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2349:Updating array of LGBM models...\u001b[0m\n" + "\u001b[32mINFO:lightwood-2526:Updating array of LGBM models...\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2349:XGBoost mixer does not have a `partial_fit` implementation\u001b[0m\n" + "\u001b[32mINFO:lightwood-2526:XGBoost mixer does not have a `partial_fit` implementation\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2349:XGBoost mixer does not have a `partial_fit` implementation\u001b[0m\n" + "\u001b[32mINFO:lightwood-2526:XGBoost mixer does not have a `partial_fit` implementation\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2349:XGBoost mixer does not have a `partial_fit` implementation\u001b[0m\n" + "\u001b[32mINFO:lightwood-2526:XGBoost mixer does not have a `partial_fit` implementation\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2349:XGBoost mixer does not have a `partial_fit` implementation\u001b[0m\n" + "\u001b[32mINFO:lightwood-2526:XGBoost mixer does not have a `partial_fit` implementation\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2349:XGBoost mixer does not have a `partial_fit` implementation\u001b[0m\n" + "\u001b[32mINFO:lightwood-2526:XGBoost mixer does not have a `partial_fit` implementation\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2349:XGBoost mixer does not have a `partial_fit` implementation\u001b[0m\n" + "\u001b[32mINFO:lightwood-2526:XGBoost mixer does not have a `partial_fit` implementation\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:lightwood-2349: `adjust` runtime: 0.09 seconds\u001b[0m\n" + "\u001b[37mDEBUG:lightwood-2526: `adjust` runtime: 0.09 seconds\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:lightwood-2349: `learn` runtime: 2.01 seconds\u001b[0m\n" + "\u001b[37mDEBUG:lightwood-2526: `learn` runtime: 2.13 seconds\u001b[0m\n" ] } ], @@ -1707,10 +1770,10 @@ "execution_count": 7, "metadata": { "execution": { - "iopub.execute_input": "2024-03-18T10:31:19.512963Z", - "iopub.status.busy": "2024-03-18T10:31:19.512366Z", - "iopub.status.idle": "2024-03-18T10:31:19.744570Z", - "shell.execute_reply": "2024-03-18T10:31:19.743883Z" + "iopub.execute_input": "2024-03-18T12:12:52.725282Z", + "iopub.status.busy": "2024-03-18T12:12:52.724752Z", + "iopub.status.idle": "2024-03-18T12:12:52.958333Z", + "shell.execute_reply": "2024-03-18T12:12:52.957658Z" } }, "outputs": [ @@ -1718,20 +1781,20 @@ "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2349:[Predict phase 1/4] - Data preprocessing\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2526:[Predict phase 1/4] - Data preprocessing\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "/tmp/1f926d4632fedc27db202c5ff831e365e4fa0b9b785a349717107578774839697.py:584: SettingWithCopyWarning: \n", + "/tmp/3b68360fcd1f78fb3b4ddb152a98e8448a8364ced0f8a5001710763970581605.py:584: SettingWithCopyWarning: \n", "A value is trying to be set on a copy of a slice from a DataFrame.\n", "Try using .loc[row_indexer,col_indexer] = value instead\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", " data[col] = [None] * len(data)\n", - "\u001b[32mINFO:dataprep_ml-2349:Cleaning the data\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2526:Cleaning the data\u001b[0m\n" ] }, { @@ -1739,120 +1802,126 @@ "output_type": "stream", "text": [ "/opt/hostedtoolcache/Python/3.9.18/x64/lib/python3.9/site-packages/dataprep_ml/cleaners.py:163: UserWarning: The argument 'infer_datetime_format' is deprecated and will be removed in a future version. A strict version of it is now the default, see https://pandas.pydata.org/pdeps/0004-consistent-to-datetime-parsing.html. You can safely remove this argument.\n", - " result = pd.to_datetime(element,\n", - "\u001b[32mINFO:dataprep_ml-2349:Transforming timeseries data\u001b[0m\n" + " result = pd.to_datetime(element,\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[32mINFO:dataprep_ml-2526:Transforming timeseries data\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:lightwood-2349: `preprocess` runtime: 0.02 seconds\u001b[0m\n" + "\u001b[37mDEBUG:lightwood-2526: `preprocess` runtime: 0.03 seconds\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2349:[Predict phase 2/4] - Feature generation\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2526:[Predict phase 2/4] - Feature generation\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2349:Featurizing the data\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2526:Featurizing the data\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:lightwood-2349: `featurize` runtime: 0.01 seconds\u001b[0m\n" + "\u001b[37mDEBUG:lightwood-2526: `featurize` runtime: 0.01 seconds\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2349:[Predict phase 3/4] - Calling ensemble\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2526:[Predict phase 3/4] - Calling ensemble\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:lightwood-2349: `_timed_call` runtime: 0.09 seconds\u001b[0m\n" + "\u001b[37mDEBUG:lightwood-2526: `_timed_call` runtime: 0.09 seconds\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2349:[Predict phase 4/4] - Analyzing output\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2526:[Predict phase 4/4] - Analyzing output\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2349:The block ICP is now running its explain() method\u001b[0m\n" + "\u001b[32mINFO:lightwood-2526:The block ICP is now running its explain() method\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2349:The block ConfStats is now running its explain() method\u001b[0m\n" + "\u001b[32mINFO:lightwood-2526:The block ConfStats is now running its explain() method\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2349:ConfStats.explain() has not been implemented, no modifications will be done to the data insights.\u001b[0m\n" + "\u001b[32mINFO:lightwood-2526:ConfStats.explain() has not been implemented, no modifications will be done to the data insights.\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2349:The block AccStats is now running its explain() method\u001b[0m\n" + "\u001b[32mINFO:lightwood-2526:The block AccStats is now running its explain() method\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2349:AccStats.explain() has not been implemented, no modifications will be done to the data insights.\u001b[0m\n" + "\u001b[32mINFO:lightwood-2526:AccStats.explain() has not been implemented, no modifications will be done to the data insights.\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2349:The block PermutationFeatureImportance is now running its explain() method\u001b[0m\n" + "\u001b[32mINFO:lightwood-2526:The block PermutationFeatureImportance is now running its explain() method\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2349:PermutationFeatureImportance.explain() has not been implemented, no modifications will be done to the data insights.\u001b[0m\n" + "\u001b[32mINFO:lightwood-2526:PermutationFeatureImportance.explain() has not been implemented, no modifications will be done to the data insights.\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:lightwood-2349: `explain` runtime: 0.1 seconds\u001b[0m\n" + "\u001b[37mDEBUG:lightwood-2526: `explain` runtime: 0.09 seconds\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:lightwood-2349: `predict` runtime: 0.23 seconds\u001b[0m\n" + "\u001b[37mDEBUG:lightwood-2526: `predict` runtime: 0.23 seconds\u001b[0m\n" ] } ], @@ -1872,10 +1941,10 @@ "execution_count": 8, "metadata": { "execution": { - "iopub.execute_input": "2024-03-18T10:31:19.747589Z", - "iopub.status.busy": "2024-03-18T10:31:19.747091Z", - "iopub.status.idle": "2024-03-18T10:31:19.758976Z", - "shell.execute_reply": "2024-03-18T10:31:19.758329Z" + "iopub.execute_input": "2024-03-18T12:12:52.961444Z", + "iopub.status.busy": "2024-03-18T12:12:52.961022Z", + "iopub.status.idle": "2024-03-18T12:12:52.973201Z", + "shell.execute_reply": "2024-03-18T12:12:52.972469Z" } }, "outputs": [ @@ -1980,10 +2049,10 @@ "execution_count": 9, "metadata": { "execution": { - "iopub.execute_input": "2024-03-18T10:31:19.761609Z", - "iopub.status.busy": "2024-03-18T10:31:19.761234Z", - "iopub.status.idle": "2024-03-18T10:31:20.163813Z", - "shell.execute_reply": "2024-03-18T10:31:20.163154Z" + "iopub.execute_input": "2024-03-18T12:12:52.976262Z", + "iopub.status.busy": "2024-03-18T12:12:52.975865Z", + "iopub.status.idle": "2024-03-18T12:12:53.399503Z", + "shell.execute_reply": "2024-03-18T12:12:53.398790Z" } }, "outputs": [], @@ -1996,10 +2065,10 @@ "execution_count": 10, "metadata": { "execution": { - "iopub.execute_input": "2024-03-18T10:31:20.166951Z", - "iopub.status.busy": "2024-03-18T10:31:20.166343Z", - "iopub.status.idle": "2024-03-18T10:31:20.357226Z", - "shell.execute_reply": "2024-03-18T10:31:20.356518Z" + "iopub.execute_input": "2024-03-18T12:12:53.402849Z", + "iopub.status.busy": "2024-03-18T12:12:53.402310Z", + "iopub.status.idle": "2024-03-18T12:12:53.602066Z", + "shell.execute_reply": "2024-03-18T12:12:53.601246Z" } }, "outputs": [ diff --git a/_sources/tutorials/tutorial_update_models/tutorial_update_models.ipynb.txt b/_sources/tutorials/tutorial_update_models/tutorial_update_models.ipynb.txt index b74cfc2b1..6927fe7f8 100644 --- a/_sources/tutorials/tutorial_update_models/tutorial_update_models.ipynb.txt +++ b/_sources/tutorials/tutorial_update_models/tutorial_update_models.ipynb.txt @@ -21,10 +21,10 @@ "execution_count": 1, "metadata": { "execution": { - "iopub.execute_input": "2024-03-18T10:31:23.547591Z", - "iopub.status.busy": "2024-03-18T10:31:23.546962Z", - "iopub.status.idle": "2024-03-18T10:31:26.071211Z", - "shell.execute_reply": "2024-03-18T10:31:26.070500Z" + "iopub.execute_input": "2024-03-18T12:12:57.043269Z", + "iopub.status.busy": "2024-03-18T12:12:57.043006Z", + "iopub.status.idle": "2024-03-18T12:12:59.864287Z", + "shell.execute_reply": "2024-03-18T12:12:59.863434Z" } }, "outputs": [ @@ -32,14 +32,14 @@ "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2398:No torchvision detected, image helpers not supported.\u001b[0m\n" + "\u001b[32mINFO:lightwood-2576:No torchvision detected, image helpers not supported.\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2398:No torchvision/pillow detected, image encoder not supported\u001b[0m\n" + "\u001b[32mINFO:lightwood-2576:No torchvision/pillow detected, image encoder not supported\u001b[0m\n" ] } ], @@ -53,10 +53,10 @@ "execution_count": 2, "metadata": { "execution": { - "iopub.execute_input": "2024-03-18T10:31:26.074489Z", - "iopub.status.busy": "2024-03-18T10:31:26.074012Z", - "iopub.status.idle": "2024-03-18T10:31:26.190366Z", - "shell.execute_reply": "2024-03-18T10:31:26.189797Z" + "iopub.execute_input": "2024-03-18T12:12:59.867639Z", + "iopub.status.busy": "2024-03-18T12:12:59.867254Z", + "iopub.status.idle": "2024-03-18T12:13:00.088594Z", + "shell.execute_reply": "2024-03-18T12:13:00.087866Z" } }, "outputs": [ @@ -98,10 +98,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2024-03-18T10:31:26.192914Z", - "iopub.status.busy": "2024-03-18T10:31:26.192528Z", - "iopub.status.idle": "2024-03-18T10:31:27.603098Z", - "shell.execute_reply": "2024-03-18T10:31:27.602461Z" + "iopub.execute_input": "2024-03-18T12:13:00.091440Z", + "iopub.status.busy": "2024-03-18T12:13:00.091021Z", + "iopub.status.idle": "2024-03-18T12:13:01.674571Z", + "shell.execute_reply": "2024-03-18T12:13:01.673937Z" }, "scrolled": true }, @@ -110,364 +110,364 @@ "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2398:Analyzing a sample of 979\u001b[0m\n" + "\u001b[32mINFO:type_infer-2576:Analyzing a sample of 979\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2398:from a total population of 1030, this is equivalent to 95.0% of your data.\u001b[0m\n" + "\u001b[32mINFO:type_infer-2576:from a total population of 1030, this is equivalent to 95.0% of your data.\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2398:Using 3 processes to deduct types.\u001b[0m\n" + "\u001b[32mINFO:type_infer-2576:Using 3 processes to deduct types.\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2398:Infering type for: slag\u001b[0m\n" + "\u001b[32mINFO:type_infer-2576:Infering type for: cement\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2398:Infering type for: cement\u001b[0m\n" + "\u001b[32mINFO:type_infer-2576:Infering type for: slag\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2398:Column cement has data type float\u001b[0m\n" + "\u001b[32mINFO:type_infer-2576:Column slag has data type float\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2398:Column slag has data type float\u001b[0m\n" + "\u001b[32mINFO:type_infer-2576:Column cement has data type float\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2398:Infering type for: flyAsh\u001b[0m\n" + "\u001b[32mINFO:type_infer-2576:Infering type for: flyAsh\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2398:Infering type for: water\u001b[0m\n" + "\u001b[32mINFO:type_infer-2576:Infering type for: water\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2398:Column flyAsh has data type float\u001b[0m\n" + "\u001b[32mINFO:type_infer-2576:Column flyAsh has data type float\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2398:Infering type for: superPlasticizer\u001b[0m\n" + "\u001b[32mINFO:type_infer-2576:Column water has data type float\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - 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"\u001b[32mINFO:type_infer-2398:Column concrete_strength has data type float\u001b[0m\n" + "\u001b[32mINFO:type_infer-2576:Column fineAggregate has data type float\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2398:Column id has data type integer\u001b[0m\n" + "\u001b[32mINFO:type_infer-2576:Column concrete_strength has data type float\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2398:Starting statistical analysis\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2576:Starting statistical analysis\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2398:Finished statistical analysis\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2576:Finished statistical analysis\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2398:[Learn phase 1/8] - Statistical analysis\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2576:[Learn phase 1/8] - 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"\u001b[37mDEBUG:lightwood-2398: `prepare` runtime: 0.01 seconds\u001b[0m\n" + "\u001b[37mDEBUG:lightwood-2576: `prepare` runtime: 0.01 seconds\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2398:[Learn phase 5/8] - Feature generation\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2576:[Learn phase 5/8] - Feature generation\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2398:Featurizing the data\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2576:Featurizing the data\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:lightwood-2398: `featurize` runtime: 0.06 seconds\u001b[0m\n" + "\u001b[37mDEBUG:lightwood-2576: `featurize` runtime: 0.06 seconds\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2398:[Learn phase 6/8] - Mixer training\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2576:[Learn phase 6/8] - Mixer training\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2398:Training the mixers\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2576:Training the mixers\u001b[0m\n" ] }, { @@ -487,63 +487,63 @@ "Consider using one of the following signatures instead:\n", "\taddcmul_(Tensor tensor1, Tensor tensor2, *, Number value) (Triggered internally at ../torch/csrc/utils/python_arg_parser.cpp:1630.)\n", " exp_avg_sq.mul_(beta2).addcmul_(1 - beta2, grad, grad)\n", - "\u001b[32mINFO:lightwood-2398:Loss of 39.99637508392334 with learning rate 0.0001\u001b[0m\n" + "\u001b[32mINFO:lightwood-2576:Loss of 39.99637508392334 with learning rate 0.0001\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2398:Loss of 21.826460361480713 with learning rate 0.0005\u001b[0m\n" + "\u001b[32mINFO:lightwood-2576:Loss of 21.826460361480713 with learning rate 0.0005\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2398:Loss of 15.12899512052536 with learning rate 0.001\u001b[0m\n" + "\u001b[32mINFO:lightwood-2576:Loss of 15.12899512052536 with learning rate 0.001\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2398:Loss of 15.062753021717072 with learning rate 0.002\u001b[0m\n" + "\u001b[32mINFO:lightwood-2576:Loss of 15.062753021717072 with learning rate 0.002\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2398:Loss of 26.490495562553406 with learning rate 0.003\u001b[0m\n" + "\u001b[32mINFO:lightwood-2576:Loss of 26.490495562553406 with learning rate 0.003\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2398:Loss of 33.6572003364563 with learning rate 0.005\u001b[0m\n" + "\u001b[32mINFO:lightwood-2576:Loss of 33.6572003364563 with learning rate 0.005\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2398:Loss of 303.60721158981323 with learning rate 0.01\u001b[0m\n" + "\u001b[32mINFO:lightwood-2576:Loss of 303.60721158981323 with learning rate 0.01\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2398:Loss of nan with learning rate 0.05\u001b[0m\n" + "\u001b[32mINFO:lightwood-2576:Loss of nan with learning rate 0.05\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2398:Found learning rate of: 0.002\u001b[0m\n" + "\u001b[32mINFO:lightwood-2576:Found learning rate of: 0.002\u001b[0m\n" ] }, { @@ -552,161 +552,161 @@ "text": [ "/opt/hostedtoolcache/Python/3.9.18/x64/lib/python3.9/site-packages/torch/cuda/amp/grad_scaler.py:126: UserWarning: torch.cuda.amp.GradScaler is enabled, but CUDA is not available. Disabling.\n", " warnings.warn(\n", - "\u001b[32mINFO:lightwood-2398:Loss @ epoch 1: 0.11838734149932861\u001b[0m\n" + "\u001b[32mINFO:lightwood-2576:Loss @ epoch 1: 0.11838734149932861\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2398:Loss @ epoch 2: 0.4641949534416199\u001b[0m\n" + "\u001b[32mINFO:lightwood-2576:Loss @ epoch 2: 0.4641949534416199\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2398:Loss @ epoch 3: 0.3976145386695862\u001b[0m\n" + "\u001b[32mINFO:lightwood-2576:Loss @ epoch 3: 0.3976145386695862\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2398:Loss @ epoch 4: 0.3706841468811035\u001b[0m\n" + "\u001b[32mINFO:lightwood-2576:Loss @ epoch 4: 0.3706841468811035\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2398:Loss @ epoch 5: 0.2367912232875824\u001b[0m\n" + "\u001b[32mINFO:lightwood-2576:Loss @ epoch 5: 0.2367912232875824\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2398:Loss @ epoch 6: 0.22560915350914001\u001b[0m\n" + "\u001b[32mINFO:lightwood-2576:Loss @ epoch 6: 0.22560915350914001\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2398:Loss @ epoch 7: 0.12089195847511292\u001b[0m\n" + "\u001b[32mINFO:lightwood-2576:Loss @ epoch 7: 0.12089195847511292\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:lightwood-2398: `fit_mixer` runtime: 0.58 seconds\u001b[0m\n" + "\u001b[37mDEBUG:lightwood-2576: `fit_mixer` runtime: 0.64 seconds\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2398:Ensembling the mixer\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2576:Ensembling the mixer\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2398:Mixer: Neural got accuracy: 0.238\u001b[0m\n" + "\u001b[32mINFO:lightwood-2576:Mixer: Neural got accuracy: 0.238\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2398:Picked best mixer: Neural\u001b[0m\n" + "\u001b[32mINFO:lightwood-2576:Picked best mixer: Neural\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:lightwood-2398: `fit` runtime: 0.58 seconds\u001b[0m\n" + "\u001b[37mDEBUG:lightwood-2576: `fit` runtime: 0.65 seconds\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2398:[Learn phase 7/8] - Ensemble analysis\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2576:[Learn phase 7/8] - Ensemble analysis\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2398:Analyzing the ensemble of mixers\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2576:Analyzing the ensemble of mixers\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2398:The block ICP is now running its analyze() method\u001b[0m\n" + "\u001b[32mINFO:lightwood-2576:The block ICP is now running its analyze() method\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2398:The block ConfStats is now running its analyze() method\u001b[0m\n" + "\u001b[32mINFO:lightwood-2576:The block ConfStats is now running its analyze() method\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2398:The block AccStats is now running its analyze() method\u001b[0m\n" + "\u001b[32mINFO:lightwood-2576:The block AccStats is now running its analyze() method\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2398:The block PermutationFeatureImportance is now running its analyze() method\u001b[0m\n" + "\u001b[32mINFO:lightwood-2576:The block PermutationFeatureImportance is now running its analyze() method\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2398:[PFI] Using a random sample (1000 rows out of 10).\u001b[0m\n" + "\u001b[32mINFO:lightwood-2576:[PFI] Using a random sample (1000 rows out of 10).\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2398:[PFI] Set to consider first 10 columns out of 9: ['id', 'cement', 'slag', 'flyAsh', 'water', 'superPlasticizer', 'coarseAggregate', 'fineAggregate', 'age'].\u001b[0m\n" + "\u001b[32mINFO:lightwood-2576:[PFI] Set to consider first 10 columns out of 9: ['id', 'cement', 'slag', 'flyAsh', 'water', 'superPlasticizer', 'coarseAggregate', 'fineAggregate', 'age'].\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:lightwood-2398: `analyze_ensemble` runtime: 0.15 seconds\u001b[0m\n" + "\u001b[37mDEBUG:lightwood-2576: `analyze_ensemble` runtime: 0.16 seconds\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2398:[Learn phase 8/8] - Adjustment on validation requested\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2576:[Learn phase 8/8] - Adjustment on validation requested\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2398:Updating the mixers\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2576:Updating the mixers\u001b[0m\n" ] }, { @@ -714,28 +714,22 @@ "output_type": "stream", "text": [ "/opt/hostedtoolcache/Python/3.9.18/x64/lib/python3.9/site-packages/torch/cuda/amp/grad_scaler.py:126: UserWarning: torch.cuda.amp.GradScaler is enabled, but CUDA is not available. Disabling.\n", - " warnings.warn(\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\u001b[32mINFO:lightwood-2398:Loss @ epoch 1: 0.1678172747294108\u001b[0m\n" + " warnings.warn(\n", + "\u001b[32mINFO:lightwood-2576:Loss @ epoch 1: 0.1678172747294108\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:lightwood-2398: `adjust` runtime: 0.03 seconds\u001b[0m\n" + "\u001b[37mDEBUG:lightwood-2576: `adjust` runtime: 0.03 seconds\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:lightwood-2398: `learn` runtime: 0.86 seconds\u001b[0m\n" + "\u001b[37mDEBUG:lightwood-2576: `learn` runtime: 0.95 seconds\u001b[0m\n" ] } ], @@ -772,10 +766,10 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2024-03-18T10:31:27.606418Z", - "iopub.status.busy": "2024-03-18T10:31:27.605734Z", - "iopub.status.idle": "2024-03-18T10:31:27.746155Z", - "shell.execute_reply": "2024-03-18T10:31:27.745504Z" + "iopub.execute_input": "2024-03-18T12:13:01.677744Z", + "iopub.status.busy": "2024-03-18T12:13:01.677327Z", + "iopub.status.idle": "2024-03-18T12:13:01.826253Z", + "shell.execute_reply": "2024-03-18T12:13:01.825506Z" } }, "outputs": [ @@ -783,126 +777,126 @@ "name": "stderr", "output_type": "stream", "text": [ - 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"\u001b[37mDEBUG:lightwood-2398: `featurize` runtime: 0.03 seconds\u001b[0m\n" + "\u001b[37mDEBUG:lightwood-2576: `featurize` runtime: 0.03 seconds\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2398:[Predict phase 3/4] - Calling ensemble\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2576:[Predict phase 3/4] - Calling ensemble\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:lightwood-2398: `_timed_call` runtime: 0.03 seconds\u001b[0m\n" + "\u001b[37mDEBUG:lightwood-2576: `_timed_call` runtime: 0.03 seconds\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2398:[Predict phase 4/4] - Analyzing output\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2576:[Predict phase 4/4] - Analyzing output\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2398:The block ICP is now running its explain() method\u001b[0m\n" + "\u001b[32mINFO:lightwood-2576:The block ICP is now running its explain() method\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2398:The block ConfStats is now running its explain() method\u001b[0m\n" + "\u001b[32mINFO:lightwood-2576:The block ConfStats is now running its explain() method\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2398:ConfStats.explain() has not been implemented, no modifications will be done to the data insights.\u001b[0m\n" + "\u001b[32mINFO:lightwood-2576:ConfStats.explain() has not been implemented, no modifications will be done to the data insights.\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2398:The block AccStats is now running its explain() method\u001b[0m\n" + "\u001b[32mINFO:lightwood-2576:The block AccStats is now running its explain() method\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2398:AccStats.explain() has not been implemented, no modifications will be done to the data insights.\u001b[0m\n" + "\u001b[32mINFO:lightwood-2576:AccStats.explain() has not been implemented, no modifications will be done to the data insights.\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2398:The block PermutationFeatureImportance is now running its explain() method\u001b[0m\n" + "\u001b[32mINFO:lightwood-2576:The block PermutationFeatureImportance is now running its explain() method\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2398:PermutationFeatureImportance.explain() has not been implemented, no modifications will be done to the data insights.\u001b[0m\n" + "\u001b[32mINFO:lightwood-2576:PermutationFeatureImportance.explain() has not been implemented, no modifications will be done to the data insights.\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:lightwood-2398: `explain` runtime: 0.05 seconds\u001b[0m\n" + "\u001b[37mDEBUG:lightwood-2576: `explain` runtime: 0.05 seconds\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:lightwood-2398: `predict` runtime: 0.13 seconds\u001b[0m\n" + "\u001b[37mDEBUG:lightwood-2576: `predict` runtime: 0.13 seconds\u001b[0m\n" ] }, { @@ -1096,10 +1090,10 @@ "execution_count": 5, "metadata": { "execution": { - "iopub.execute_input": "2024-03-18T10:31:27.748907Z", - "iopub.status.busy": "2024-03-18T10:31:27.748459Z", - "iopub.status.idle": "2024-03-18T10:31:27.854192Z", - "shell.execute_reply": "2024-03-18T10:31:27.853569Z" + "iopub.execute_input": "2024-03-18T12:13:01.829389Z", + "iopub.status.busy": "2024-03-18T12:13:01.828926Z", + "iopub.status.idle": "2024-03-18T12:13:01.949500Z", + "shell.execute_reply": "2024-03-18T12:13:01.948852Z" } }, "outputs": [ @@ -1107,35 +1101,35 @@ "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2398:Cleaning the data\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2576:Cleaning the data\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:lightwood-2398: `preprocess` runtime: 0.02 seconds\u001b[0m\n" + "\u001b[37mDEBUG:lightwood-2576: `preprocess` runtime: 0.02 seconds\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2398:Cleaning the data\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2576:Cleaning the data\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:lightwood-2398: `preprocess` runtime: 0.01 seconds\u001b[0m\n" + "\u001b[37mDEBUG:lightwood-2576: `preprocess` runtime: 0.01 seconds\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2398:Updating the mixers\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2576:Updating the mixers\u001b[0m\n" ] }, { @@ -1143,15 +1137,21 @@ "output_type": "stream", "text": [ "/opt/hostedtoolcache/Python/3.9.18/x64/lib/python3.9/site-packages/torch/cuda/amp/grad_scaler.py:126: UserWarning: torch.cuda.amp.GradScaler is enabled, but CUDA is not available. Disabling.\n", - " warnings.warn(\n", - "\u001b[32mINFO:lightwood-2398:Loss @ epoch 1: 0.10915952424208324\u001b[0m\n" + " warnings.warn(\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[32mINFO:lightwood-2576:Loss @ epoch 1: 0.10915952424208324\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:lightwood-2398: `adjust` runtime: 0.1 seconds\u001b[0m\n" + "\u001b[37mDEBUG:lightwood-2576: `adjust` runtime: 0.12 seconds\u001b[0m\n" ] } ], @@ -1164,10 +1164,10 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2024-03-18T10:31:27.856839Z", - "iopub.status.busy": "2024-03-18T10:31:27.856418Z", - "iopub.status.idle": "2024-03-18T10:31:27.992119Z", - "shell.execute_reply": "2024-03-18T10:31:27.991478Z" + "iopub.execute_input": "2024-03-18T12:13:01.952937Z", + "iopub.status.busy": "2024-03-18T12:13:01.952459Z", + "iopub.status.idle": "2024-03-18T12:13:02.101103Z", + "shell.execute_reply": "2024-03-18T12:13:02.100367Z" } }, "outputs": [ @@ -1175,126 +1175,126 @@ "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2398:[Predict phase 1/4] - Data preprocessing\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2576:[Predict phase 1/4] - Data preprocessing\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2398:Cleaning the data\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2576:Cleaning the data\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:lightwood-2398: `preprocess` runtime: 0.01 seconds\u001b[0m\n" + "\u001b[37mDEBUG:lightwood-2576: `preprocess` runtime: 0.01 seconds\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2398:[Predict phase 2/4] - Feature generation\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2576:[Predict phase 2/4] - Feature generation\u001b[0m\n" ] }, { "name": "stderr", "output_type": 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"\u001b[32mINFO:dataprep_ml-2576:[Predict phase 4/4] - Analyzing output\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2398:The block ICP is now running its explain() method\u001b[0m\n" + "\u001b[32mINFO:lightwood-2576:The block ICP is now running its explain() method\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2398:The block ConfStats is now running its explain() method\u001b[0m\n" + "\u001b[32mINFO:lightwood-2576:The block ConfStats is now running its explain() method\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2398:ConfStats.explain() has not been implemented, no modifications will be done to the data insights.\u001b[0m\n" + "\u001b[32mINFO:lightwood-2576:ConfStats.explain() has not been implemented, no modifications will be done to the data insights.\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2398:The block AccStats is now running its explain() method\u001b[0m\n" + "\u001b[32mINFO:lightwood-2576:The block AccStats is now running its explain() method\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2398:AccStats.explain() has not been implemented, no modifications will be done to the data insights.\u001b[0m\n" + "\u001b[32mINFO:lightwood-2576:AccStats.explain() has not been implemented, no modifications will be done to the data insights.\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2398:The block PermutationFeatureImportance is now running its explain() method\u001b[0m\n" + "\u001b[32mINFO:lightwood-2576:The block PermutationFeatureImportance is now running its explain() method\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2398:PermutationFeatureImportance.explain() has not been implemented, no modifications will be done to the data insights.\u001b[0m\n" + "\u001b[32mINFO:lightwood-2576:PermutationFeatureImportance.explain() has not been implemented, no modifications will be done to the data insights.\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:lightwood-2398: `explain` runtime: 0.05 seconds\u001b[0m\n" + "\u001b[37mDEBUG:lightwood-2576: `explain` runtime: 0.05 seconds\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:lightwood-2398: `predict` runtime: 0.12 seconds\u001b[0m\n" + "\u001b[37mDEBUG:lightwood-2576: `predict` runtime: 0.13 seconds\u001b[0m\n" ] }, { @@ -1458,10 +1458,10 @@ "execution_count": 7, "metadata": { "execution": { - "iopub.execute_input": "2024-03-18T10:31:27.994662Z", - "iopub.status.busy": "2024-03-18T10:31:27.994461Z", - "iopub.status.idle": "2024-03-18T10:31:28.000217Z", - "shell.execute_reply": "2024-03-18T10:31:27.999577Z" + "iopub.execute_input": "2024-03-18T12:13:02.104430Z", + "iopub.status.busy": "2024-03-18T12:13:02.103755Z", + "iopub.status.idle": "2024-03-18T12:13:02.110215Z", + "shell.execute_reply": "2024-03-18T12:13:02.109494Z" } }, "outputs": [ diff --git a/searchindex.js b/searchindex.js index 3b7ba2b8c..a448a92af 100644 --- a/searchindex.js +++ b/searchindex.js @@ -1 +1 @@ -Search.setIndex({"docnames": ["analysis", "api", "api/dtype", "api/encode", "api/high_level", "api/json_ai", "api/predictor", "api/types", "data", "encoder", "ensemble", "helpers", "index", "lightwood_philosophy", "mixer", "tutorials", "tutorials/README", "tutorials/custom_cleaner/custom_cleaner", "tutorials/custom_encoder_rulebased/custom_encoder_rulebased", "tutorials/custom_explainer/custom_explainer", "tutorials/custom_mixer/custom_mixer", "tutorials/custom_splitter/custom_splitter", "tutorials/tutorial_data_analysis/tutorial_data_analysis", "tutorials/tutorial_time_series/tutorial_time_series", "tutorials/tutorial_update_models/tutorial_update_models"], "filenames": ["analysis.rst", "api.rst", "api/dtype.rst", "api/encode.rst", "api/high_level.rst", "api/json_ai.rst", "api/predictor.rst", "api/types.rst", "data.rst", "encoder.rst", "ensemble.rst", "helpers.rst", "index.rst", "lightwood_philosophy.rst", "mixer.rst", "tutorials.rst", "tutorials/README.md", "tutorials/custom_cleaner/custom_cleaner.ipynb", "tutorials/custom_encoder_rulebased/custom_encoder_rulebased.ipynb", "tutorials/custom_explainer/custom_explainer.ipynb", "tutorials/custom_mixer/custom_mixer.ipynb", "tutorials/custom_splitter/custom_splitter.ipynb", "tutorials/tutorial_data_analysis/tutorial_data_analysis.ipynb", "tutorials/tutorial_time_series/tutorial_time_series.ipynb", "tutorials/tutorial_update_models/tutorial_update_models.ipynb"], "titles": ["Analysis", "API", "Data Types (dtypes)", "Encode your data", "JSON-AI Config", "JSON-AI Config", "Predictor Interface", "Lightwood API Types", "Data", "Encoders", "Ensemble", "Helpers", "Lightwood", "Lightwood Philosophy", "Mixers", "Tutorials", "How to make a tutorial notebook?", "Using your own pre-processing methods in Lightwood", "Custom Encoder: Rule-Based", "Tutorial - Implementing a custom analysis block in Lightwood", "Tutorial - Implementing a custom mixer in Lightwood", "Build your own training/testing split", "Tutorial - Introduction to Lightwood\u2019s statistical analysis", "Tutorial - Time series forecasting", "Introduction"], "terms": {"analys": 0, "mixer": [0, 6, 7, 9, 10, 12, 13, 15, 17, 18, 19, 21, 22, 23, 24], "ensembl": [0, 6, 7, 12, 13, 14, 17, 19, 20, 21, 23, 24], "extract": [0, 7, 8, 13, 17, 21], "static": [0, 7], "insight": [0, 7, 8, 17, 19, 20, 21, 22, 23, 24], "train": [0, 4, 5, 6, 7, 8, 9, 12, 14, 15, 17, 18, 19, 20, 22], "predict": [0, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 17, 18, 19, 20, 21, 24], "time": [0, 7, 8, 9, 11, 12, 13, 14, 17, 19, 21], "model": [0, 5, 6, 7, 8, 9, 10, 14, 15, 17, 18, 19, 20, 21, 22, 23], 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Date: 2021.10.07 
-INFO:lightwood-2632:No torchvision detected, image helpers not supported.
+INFO:lightwood-2806:No torchvision detected, image helpers not supported.
@@ -133,7 +133,7 @@

Date: 2021.10.07 
-INFO:lightwood-2632:No torchvision/pillow detected, image encoder not supported
+INFO:lightwood-2806:No torchvision/pillow detected, image encoder not supported

@@ -257,7 +257,7 @@

2) Create a JSON-AI default object 
-INFO:lightwood-2632:Dropping features: ['url_legal', 'license', 'standard_error']
+INFO:lightwood-2806:Dropping features: ['url_legal', 'license', 'standard_error']
@@ -265,7 +265,7 @@

2) Create a JSON-AI default object 
-INFO:type_infer-2632:Analyzing a sample of 2478
+INFO:type_infer-2806:Analyzing a sample of 2478

@@ -273,7 +273,7 @@

2) Create a JSON-AI default object 
-INFO:type_infer-2632:from a total population of 2834, this is equivalent to 87.4% of your data.
+INFO:type_infer-2806:from a total population of 2834, this is equivalent to 87.4% of your data.

@@ -281,7 +281,7 @@

2) Create a JSON-AI default object 
-INFO:type_infer-2632:Infering type for: id
+INFO:type_infer-2806:Infering type for: id

@@ -289,7 +289,7 @@

2) Create a JSON-AI default object 
-INFO:type_infer-2632:Doing text detection for column: id
+INFO:type_infer-2806:Doing text detection for column: id

@@ -297,7 +297,7 @@

2) Create a JSON-AI default object 
-INFO:type_infer-2632:Column id has data type categorical
+INFO:type_infer-2806:Column id has data type categorical

@@ -305,7 +305,7 @@

2) Create a JSON-AI default object 
-INFO:type_infer-2632:Infering type for: excerpt
+INFO:type_infer-2806:Infering type for: excerpt

@@ -313,7 +313,7 @@

2) Create a JSON-AI default object 
-INFO:type_infer-2632:Doing text detection for column: excerpt
+INFO:type_infer-2806:Doing text detection for column: excerpt

@@ -321,7 +321,7 @@

2) Create a JSON-AI default object 
-INFO:type_infer-2632:Infering type for: target
+INFO:type_infer-2806:Infering type for: target

@@ -329,7 +329,7 @@

2) Create a JSON-AI default object 
-INFO:type_infer-2632:Column target has data type float
+INFO:type_infer-2806:Column target has data type float

@@ -337,7 +337,7 @@

2) Create a JSON-AI default object 
-WARNING:type_infer-2632:Column id is an identifier of type "Hash-like identifier"
+WARNING:type_infer-2806:Column id is an identifier of type "Hash-like identifier"

@@ -345,7 +345,7 @@

2) Create a JSON-AI default object 
-INFO:dataprep_ml-2632:Starting statistical analysis
+INFO:dataprep_ml-2806:Starting statistical analysis

@@ -353,7 +353,7 @@

2) Create a JSON-AI default object 
-INFO:dataprep_ml-2632:Dropping features: ['id']
+INFO:dataprep_ml-2806:Dropping features: ['id']

@@ -361,7 +361,7 @@

2) Create a JSON-AI default object 
-INFO:dataprep_ml-2632:Finished statistical analysis
+INFO:dataprep_ml-2806:Finished statistical analysis

Lightwood, as it processes the data, will provide the user a few pieces of information.

@@ -449,7 +449,7 @@

2) Create a JSON-AI default object6) Call python to run your code and see your preprocessed outputs 
-INFO:dataprep_ml-2632:Starting statistical analysis
+INFO:dataprep_ml-2806:Starting statistical analysis
@@ -1405,7 +1405,7 @@

6) Call python to run your code and see your preprocessed outputs 
-INFO:dataprep_ml-2632:Dropping features: ['id']
+INFO:dataprep_ml-2806:Dropping features: ['id']

@@ -1413,7 +1413,7 @@

6) Call python to run your code and see your preprocessed outputs 
-INFO:dataprep_ml-2632:Finished statistical analysis
+INFO:dataprep_ml-2806:Finished statistical analysis

@@ -1421,7 +1421,7 @@

6) Call python to run your code and see your preprocessed outputs 
-DEBUG:lightwood-2632: `analyze_data` runtime: 0.05 seconds
+DEBUG:lightwood-2806: `analyze_data` runtime: 0.05 seconds

@@ -1429,7 +1429,7 @@

6) Call python to run your code and see your preprocessed outputs 
-INFO:dataprep_ml-2632:Cleaning the data
+INFO:dataprep_ml-2806:Cleaning the data

@@ -1437,7 +1437,7 @@

6) Call python to run your code and see your preprocessed outputs 
-INFO:dataprep_ml-2632:Dropping features: ['id']
+INFO:dataprep_ml-2806:Dropping features: ['id']

@@ -1445,7 +1445,7 @@

6) Call python to run your code and see your preprocessed outputs 
-INFO:lightwood-2632:Cleaning column =excerpt
+INFO:lightwood-2806:Cleaning column =excerpt

@@ -1453,7 +1453,7 @@

6) Call python to run your code and see your preprocessed outputs 
-INFO:lightwood-2632:Cleaning column =target
+INFO:lightwood-2806:Cleaning column =target

@@ -1461,7 +1461,7 @@

6) Call python to run your code and see your preprocessed outputs 
-INFO:lightwood-2632:Converted target into strictly non-negative
+INFO:lightwood-2806:Converted target into strictly non-negative

@@ -1469,7 +1469,7 @@

6) Call python to run your code and see your preprocessed outputs 
-DEBUG:lightwood-2632: `preprocess` runtime: 0.07 seconds
+DEBUG:lightwood-2806: `preprocess` runtime: 0.08 seconds

diff --git a/tutorials/custom_cleaner/custom_cleaner.ipynb b/tutorials/custom_cleaner/custom_cleaner.ipynb index e7d529c36..3c6be2ceb 100644 --- a/tutorials/custom_cleaner/custom_cleaner.ipynb +++ b/tutorials/custom_cleaner/custom_cleaner.ipynb @@ -31,10 +31,10 @@ "id": "happy-wheat", "metadata": { "execution": { - "iopub.execute_input": "2024-03-18T10:32:04.246435Z", - "iopub.status.busy": "2024-03-18T10:32:04.245945Z", - "iopub.status.idle": "2024-03-18T10:32:06.798108Z", - "shell.execute_reply": "2024-03-18T10:32:06.797291Z" + "iopub.execute_input": "2024-03-18T12:13:41.624054Z", + "iopub.status.busy": "2024-03-18T12:13:41.623553Z", + "iopub.status.idle": "2024-03-18T12:13:44.391003Z", + "shell.execute_reply": "2024-03-18T12:13:44.390294Z" } }, "outputs": [ @@ -42,14 +42,14 @@ "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2632:No torchvision detected, image helpers not supported.\u001b[0m\n" + "\u001b[32mINFO:lightwood-2806:No torchvision detected, image helpers not supported.\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2632:No torchvision/pillow detected, image encoder not supported\u001b[0m\n" + "\u001b[32mINFO:lightwood-2806:No torchvision/pillow detected, image encoder not supported\u001b[0m\n" ] } ], @@ -87,10 +87,10 @@ "id": "recognized-parish", "metadata": { "execution": { - "iopub.execute_input": "2024-03-18T10:32:06.801294Z", - "iopub.status.busy": "2024-03-18T10:32:06.800939Z", - "iopub.status.idle": "2024-03-18T10:32:07.802600Z", - "shell.execute_reply": "2024-03-18T10:32:07.801886Z" + "iopub.execute_input": "2024-03-18T12:13:44.394866Z", + "iopub.status.busy": "2024-03-18T12:13:44.394215Z", + "iopub.status.idle": "2024-03-18T12:13:47.386061Z", + "shell.execute_reply": "2024-03-18T12:13:47.385334Z" } }, "outputs": [ @@ -221,10 +221,10 @@ "id": "chicken-truth", "metadata": { "execution": { - "iopub.execute_input": "2024-03-18T10:32:07.805528Z", - "iopub.status.busy": "2024-03-18T10:32:07.805118Z", - "iopub.status.idle": "2024-03-18T10:32:23.116609Z", - "shell.execute_reply": "2024-03-18T10:32:23.115969Z" + "iopub.execute_input": "2024-03-18T12:13:47.389003Z", + "iopub.status.busy": "2024-03-18T12:13:47.388624Z", + "iopub.status.idle": "2024-03-18T12:14:03.044309Z", + "shell.execute_reply": "2024-03-18T12:14:03.043698Z" } }, "outputs": [ @@ -232,98 +232,98 @@ "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2632:Dropping features: ['url_legal', 'license', 'standard_error']\u001b[0m\n" + "\u001b[32mINFO:lightwood-2806:Dropping features: ['url_legal', 'license', 'standard_error']\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2632:Analyzing a sample of 2478\u001b[0m\n" + "\u001b[32mINFO:type_infer-2806:Analyzing a sample of 2478\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2632:from a total population of 2834, this is equivalent to 87.4% of your data.\u001b[0m\n" + "\u001b[32mINFO:type_infer-2806:from a total population of 2834, this is equivalent to 87.4% of your data.\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2632:Infering type for: id\u001b[0m\n" + "\u001b[32mINFO:type_infer-2806:Infering type for: id\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2632:Doing text detection for column: id\u001b[0m\n" + "\u001b[32mINFO:type_infer-2806:Doing text detection for column: id\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2632:Column id has data type categorical\u001b[0m\n" + "\u001b[32mINFO:type_infer-2806:Column id has data type categorical\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2632:Infering type for: excerpt\u001b[0m\n" + "\u001b[32mINFO:type_infer-2806:Infering type for: excerpt\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2632:Doing text detection for column: excerpt\u001b[0m\n" + "\u001b[32mINFO:type_infer-2806:Doing text detection for column: excerpt\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2632:Infering type for: target\u001b[0m\n" + "\u001b[32mINFO:type_infer-2806:Infering type for: target\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2632:Column target has data type float\u001b[0m\n" + "\u001b[32mINFO:type_infer-2806:Column target has data type float\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[33mWARNING:type_infer-2632:Column id is an identifier of type \"Hash-like identifier\"\u001b[0m\n" + "\u001b[33mWARNING:type_infer-2806:Column id is an identifier of type \"Hash-like identifier\"\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2632:Starting statistical analysis\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2806:Starting statistical analysis\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2632:Dropping features: ['id']\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2806:Dropping features: ['id']\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2632:Finished statistical analysis\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2806:Finished statistical analysis\u001b[0m\n" ] } ], @@ -360,10 +360,10 @@ "id": "designed-condition", "metadata": { "execution": { - "iopub.execute_input": "2024-03-18T10:32:23.119408Z", - "iopub.status.busy": "2024-03-18T10:32:23.119198Z", - "iopub.status.idle": "2024-03-18T10:32:23.123971Z", - "shell.execute_reply": "2024-03-18T10:32:23.123305Z" + "iopub.execute_input": "2024-03-18T12:14:03.047471Z", + "iopub.status.busy": "2024-03-18T12:14:03.047072Z", + "iopub.status.idle": "2024-03-18T12:14:03.051562Z", + "shell.execute_reply": "2024-03-18T12:14:03.050874Z" } }, "outputs": [ @@ -434,7 +434,7 @@ " \"unbias_target\": true,\n", " \"seconds_per_mixer\": 21384.0,\n", " \"seconds_per_encoder\": 85536.0,\n", - " \"expected_additional_time\": 15.30086874961853,\n", + " \"expected_additional_time\": 15.644360542297363,\n", " \"time_aim\": 259200,\n", " \"target_weights\": null,\n", " \"positive_domain\": false,\n", @@ -518,10 +518,10 @@ "id": "325d8f1b", "metadata": { "execution": { - "iopub.execute_input": "2024-03-18T10:32:23.126476Z", - "iopub.status.busy": "2024-03-18T10:32:23.126282Z", - "iopub.status.idle": "2024-03-18T10:32:23.131477Z", - "shell.execute_reply": "2024-03-18T10:32:23.130857Z" + "iopub.execute_input": "2024-03-18T12:14:03.054794Z", + "iopub.status.busy": "2024-03-18T12:14:03.054271Z", + "iopub.status.idle": "2024-03-18T12:14:03.060074Z", + "shell.execute_reply": "2024-03-18T12:14:03.059395Z" } }, "outputs": [ @@ -646,10 +646,10 @@ "id": "f030f8ca", "metadata": { "execution": { - "iopub.execute_input": "2024-03-18T10:32:23.133852Z", - "iopub.status.busy": "2024-03-18T10:32:23.133652Z", - "iopub.status.idle": "2024-03-18T10:32:23.137057Z", - "shell.execute_reply": "2024-03-18T10:32:23.136533Z" + "iopub.execute_input": "2024-03-18T12:14:03.062892Z", + "iopub.status.busy": "2024-03-18T12:14:03.062488Z", + "iopub.status.idle": "2024-03-18T12:14:03.065997Z", + "shell.execute_reply": "2024-03-18T12:14:03.065454Z" } }, "outputs": [], @@ -711,10 +711,10 @@ "id": "floating-patent", "metadata": { "execution": { - "iopub.execute_input": "2024-03-18T10:32:23.139665Z", - "iopub.status.busy": "2024-03-18T10:32:23.139226Z", - "iopub.status.idle": "2024-03-18T10:32:23.351781Z", - "shell.execute_reply": "2024-03-18T10:32:23.351122Z" + "iopub.execute_input": "2024-03-18T12:14:03.068815Z", + "iopub.status.busy": "2024-03-18T12:14:03.068356Z", + "iopub.status.idle": "2024-03-18T12:14:03.298497Z", + "shell.execute_reply": "2024-03-18T12:14:03.297746Z" } }, "outputs": [ @@ -795,7 +795,7 @@ " \"unbias_target\": True,\n", " \"seconds_per_mixer\": 21384.0,\n", " \"seconds_per_encoder\": 85536.0,\n", - " \"expected_additional_time\": 15.30086874961853,\n", + " \"expected_additional_time\": 15.644360542297363,\n", " \"time_aim\": 259200,\n", " \"target_weights\": None,\n", " \"positive_domain\": False,\n", @@ -1449,10 +1449,10 @@ "id": "violent-guard", "metadata": { "execution": { - "iopub.execute_input": "2024-03-18T10:32:23.354693Z", - "iopub.status.busy": "2024-03-18T10:32:23.354305Z", - "iopub.status.idle": "2024-03-18T10:32:23.361980Z", - "shell.execute_reply": "2024-03-18T10:32:23.361356Z" + "iopub.execute_input": "2024-03-18T12:14:03.301712Z", + "iopub.status.busy": "2024-03-18T12:14:03.301155Z", + "iopub.status.idle": "2024-03-18T12:14:03.310184Z", + "shell.execute_reply": "2024-03-18T12:14:03.309472Z" } }, "outputs": [], @@ -1467,10 +1467,10 @@ "id": "closing-episode", "metadata": { "execution": { - "iopub.execute_input": "2024-03-18T10:32:23.364591Z", - "iopub.status.busy": "2024-03-18T10:32:23.364125Z", - "iopub.status.idle": "2024-03-18T10:32:23.499702Z", - "shell.execute_reply": "2024-03-18T10:32:23.499147Z" + "iopub.execute_input": "2024-03-18T12:14:03.313258Z", + "iopub.status.busy": "2024-03-18T12:14:03.312845Z", + "iopub.status.idle": "2024-03-18T12:14:03.450976Z", + "shell.execute_reply": "2024-03-18T12:14:03.450274Z" }, "scrolled": false }, @@ -1479,70 +1479,70 @@ "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2632:Starting statistical analysis\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2806:Starting statistical analysis\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2632:Dropping features: ['id']\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2806:Dropping features: ['id']\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2632:Finished statistical analysis\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2806:Finished statistical analysis\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:lightwood-2632: `analyze_data` runtime: 0.05 seconds\u001b[0m\n" + "\u001b[37mDEBUG:lightwood-2806: `analyze_data` runtime: 0.05 seconds\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2632:Cleaning the data\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2806:Cleaning the data\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2632:Dropping features: ['id']\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2806:Dropping features: ['id']\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2632:Cleaning column =excerpt\u001b[0m\n" + "\u001b[32mINFO:lightwood-2806:Cleaning column =excerpt\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2632:Cleaning column =target\u001b[0m\n" + "\u001b[32mINFO:lightwood-2806:Cleaning column =target\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2632:Converted target into strictly non-negative\u001b[0m\n" + "\u001b[32mINFO:lightwood-2806:Converted target into strictly non-negative\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:lightwood-2632: `preprocess` runtime: 0.07 seconds\u001b[0m\n" + "\u001b[37mDEBUG:lightwood-2806: `preprocess` runtime: 0.08 seconds\u001b[0m\n" ] }, { @@ -1632,10 +1632,10 @@ "id": "major-stake", "metadata": { "execution": { - "iopub.execute_input": "2024-03-18T10:32:23.502447Z", - "iopub.status.busy": "2024-03-18T10:32:23.502082Z", - "iopub.status.idle": "2024-03-18T10:32:23.506941Z", - "shell.execute_reply": "2024-03-18T10:32:23.506294Z" + "iopub.execute_input": "2024-03-18T12:14:03.453743Z", + "iopub.status.busy": "2024-03-18T12:14:03.453330Z", + "iopub.status.idle": "2024-03-18T12:14:03.458416Z", + "shell.execute_reply": "2024-03-18T12:14:03.457734Z" } }, "outputs": [ diff --git a/tutorials/custom_encoder_rulebased/custom_encoder_rulebased.html b/tutorials/custom_encoder_rulebased/custom_encoder_rulebased.html index 6af83b075..c1463efbf 100644 --- a/tutorials/custom_encoder_rulebased/custom_encoder_rulebased.html +++ b/tutorials/custom_encoder_rulebased/custom_encoder_rulebased.html @@ -126,7 +126,7 @@

Custom Encoder: Rule-Based 
-INFO:lightwood-2551:No torchvision detected, image helpers not supported.
+INFO:lightwood-2722:No torchvision detected, image helpers not supported.

@@ -134,7 +134,7 @@

Custom Encoder: Rule-Based 
-INFO:lightwood-2551:No torchvision/pillow detected, image encoder not supported
+INFO:lightwood-2722:No torchvision/pillow detected, image encoder not supported

@@ -282,7 +282,7 @@

2) Generate JSON-AI Syntax 
-INFO:type_infer-2551:Analyzing a sample of 6920
+INFO:type_infer-2722:Analyzing a sample of 6920
@@ -290,7 +290,7 @@

2) Generate JSON-AI Syntax 
-INFO:type_infer-2551:from a total population of 10668, this is equivalent to 64.9% of your data.
+INFO:type_infer-2722:from a total population of 10668, this is equivalent to 64.9% of your data.

@@ -298,7 +298,7 @@

2) Generate JSON-AI Syntax 
-INFO:type_infer-2551:Using 3 processes to deduct types.
+INFO:type_infer-2722:Using 3 processes to deduct types.

@@ -306,7 +306,7 @@

2) Generate JSON-AI Syntax 
-INFO:type_infer-2551:Infering type for: price
+INFO:type_infer-2722:Infering type for: year

@@ -314,7 +314,7 @@

2) Generate JSON-AI Syntax 
-INFO:type_infer-2551:Infering type for: year
+INFO:type_infer-2722:Infering type for: price

@@ -322,7 +322,7 @@

2) Generate JSON-AI Syntax 
-INFO:type_infer-2551:Column price has data type integer
+INFO:type_infer-2722:Column year has data type integer

@@ -330,7 +330,7 @@

2) Generate JSON-AI Syntax 
-INFO:type_infer-2551:Column year has data type integer
+INFO:type_infer-2722:Column price has data type integer

@@ -338,7 +338,7 @@

2) Generate JSON-AI Syntax 
-INFO:type_infer-2551:Infering type for: transmission
+INFO:type_infer-2722:Infering type for: transmission

@@ -346,7 +346,7 @@

2) Generate JSON-AI Syntax 
-INFO:type_infer-2551:Infering type for: mileage
+INFO:type_infer-2722:Infering type for: mileage

@@ -354,7 +354,7 @@

2) Generate JSON-AI Syntax 
-INFO:type_infer-2551:Infering type for: model
+INFO:type_infer-2722:Infering type for: model

@@ -362,7 +362,7 @@

2) Generate JSON-AI Syntax 
-INFO:type_infer-2551:Column mileage has data type integer
+INFO:type_infer-2722:Column mileage has data type integer

@@ -370,7 +370,7 @@

2) Generate JSON-AI Syntax 
-INFO:type_infer-2551:Infering type for: fuelType
+INFO:type_infer-2722:Infering type for: fuelType

@@ -378,7 +378,7 @@

2) Generate JSON-AI Syntax 
-INFO:type_infer-2551:Column transmission has data type categorical
+INFO:type_infer-2722:Column transmission has data type categorical

@@ -386,7 +386,7 @@

2) Generate JSON-AI Syntax 
-INFO:type_infer-2551:Infering type for: tax
+INFO:type_infer-2722:Infering type for: tax

@@ -394,7 +394,7 @@

2) Generate JSON-AI Syntax 
-INFO:type_infer-2551:Column tax has data type integer
+INFO:type_infer-2722:Column tax has data type integer

@@ -402,7 +402,7 @@

2) Generate JSON-AI Syntax 
-INFO:type_infer-2551:Infering type for: mpg
+INFO:type_infer-2722:Infering type for: mpg

@@ -410,7 +410,7 @@

2) Generate JSON-AI Syntax 
-INFO:type_infer-2551:Column mpg has data type float
+INFO:type_infer-2722:Column mpg has data type float

@@ -418,7 +418,7 @@

2) Generate JSON-AI Syntax 
-INFO:type_infer-2551:Infering type for: engineSize
+INFO:type_infer-2722:Infering type for: engineSize

@@ -426,7 +426,7 @@

2) Generate JSON-AI Syntax 
-INFO:type_infer-2551:Column engineSize has data type float
+INFO:type_infer-2722:Column engineSize has data type float

@@ -434,7 +434,7 @@

2) Generate JSON-AI Syntax 
-INFO:type_infer-2551:Column model has data type categorical
+INFO:type_infer-2722:Column model has data type categorical

@@ -442,7 +442,7 @@

2) Generate JSON-AI Syntax 
-INFO:type_infer-2551:Column fuelType has data type categorical
+INFO:type_infer-2722:Column fuelType has data type categorical

@@ -450,7 +450,7 @@

2) Generate JSON-AI Syntax 
-INFO:dataprep_ml-2551:Starting statistical analysis
+INFO:dataprep_ml-2722:Starting statistical analysis

@@ -458,7 +458,7 @@

2) Generate JSON-AI Syntax 
-INFO:dataprep_ml-2551:Finished statistical analysis
+INFO:dataprep_ml-2722:Finished statistical analysis

Let’s take a look at our JSON-AI and print to file.

@@ -572,7 +572,7 @@

2) Generate JSON-AI Syntax 
-INFO:dataprep_ml-2551:Starting statistical analysis
+INFO:dataprep_ml-2722:Starting statistical analysis
@@ -852,7 +852,7 @@

5) Generate code and your predictor from JSON-AI 
-INFO:dataprep_ml-2551:Finished statistical analysis
+INFO:dataprep_ml-2722:Finished statistical analysis

@@ -860,7 +860,7 @@

5) Generate code and your predictor from JSON-AI 
-DEBUG:lightwood-2551: `analyze_data` runtime: 0.43 seconds
+DEBUG:lightwood-2722: `analyze_data` runtime: 0.44 seconds

@@ -868,7 +868,7 @@

5) Generate code and your predictor from JSON-AI 
-INFO:dataprep_ml-2551:Cleaning the data
+INFO:dataprep_ml-2722:Cleaning the data

@@ -876,7 +876,7 @@

5) Generate code and your predictor from JSON-AI 
-DEBUG:lightwood-2551: `preprocess` runtime: 0.13 seconds
+DEBUG:lightwood-2722: `preprocess` runtime: 0.13 seconds

@@ -884,7 +884,7 @@

5) Generate code and your predictor from JSON-AI 
-INFO:dataprep_ml-2551:Splitting the data into train/test
+INFO:dataprep_ml-2722:Splitting the data into train/test

@@ -892,7 +892,7 @@

5) Generate code and your predictor from JSON-AI 
-DEBUG:lightwood-2551: `split` runtime: 0.01 seconds
+DEBUG:lightwood-2722: `split` runtime: 0.01 seconds

@@ -900,7 +900,7 @@

5) Generate code and your predictor from JSON-AI 
-DEBUG:dataprep_ml-2551:Preparing sequentially...
+DEBUG:dataprep_ml-2722:Preparing sequentially...

@@ -908,7 +908,7 @@

5) Generate code and your predictor from JSON-AI 
-DEBUG:dataprep_ml-2551:Preparing encoder for year...
+DEBUG:dataprep_ml-2722:Preparing encoder for year...

@@ -916,7 +916,7 @@

5) Generate code and your predictor from JSON-AI 
-DEBUG:dataprep_ml-2551:Preparing encoder for mileage...
+DEBUG:dataprep_ml-2722:Preparing encoder for mileage...

@@ -924,7 +924,7 @@

5) Generate code and your predictor from JSON-AI 
-DEBUG:dataprep_ml-2551:Preparing encoder for tax...
+DEBUG:dataprep_ml-2722:Preparing encoder for tax...

@@ -932,7 +932,7 @@

5) Generate code and your predictor from JSON-AI 
-DEBUG:dataprep_ml-2551:Preparing encoder for mpg...
+DEBUG:dataprep_ml-2722:Preparing encoder for mpg...

@@ -940,7 +940,7 @@

5) Generate code and your predictor from JSON-AI 
-DEBUG:dataprep_ml-2551:Preparing encoder for engineSize...
+DEBUG:dataprep_ml-2722:Preparing encoder for engineSize...

@@ -948,7 +948,7 @@

5) Generate code and your predictor from JSON-AI 
-INFO:lightwood-2551:Categories Detected = 1
+INFO:lightwood-2722:Categories Detected = 1

@@ -956,7 +956,7 @@

5) Generate code and your predictor from JSON-AI 
-INFO:lightwood-2551:Categories Detected = 1
+INFO:lightwood-2722:Categories Detected = 1

@@ -964,7 +964,7 @@

5) Generate code and your predictor from JSON-AI 
-INFO:lightwood-2551:Categories Detected = 1
+INFO:lightwood-2722:Categories Detected = 1

@@ -972,7 +972,7 @@

5) Generate code and your predictor from JSON-AI 
-DEBUG:lightwood-2551: `prepare` runtime: 0.02 seconds
+DEBUG:lightwood-2722: `prepare` runtime: 0.02 seconds

@@ -980,7 +980,7 @@

5) Generate code and your predictor from JSON-AI 
-INFO:dataprep_ml-2551:Featurizing the data
+INFO:dataprep_ml-2722:Featurizing the data

@@ -988,7 +988,7 @@

5) Generate code and your predictor from JSON-AI 
-DEBUG:lightwood-2551: `featurize` runtime: 0.56 seconds
+DEBUG:lightwood-2722: `featurize` runtime: 0.58 seconds

The splitter creates 3 data-splits, a “train”, “dev”, and “test” set. The featurize command from the predictor allows us to convert the cleaned data into features. We can access this as follows:

diff --git a/tutorials/custom_encoder_rulebased/custom_encoder_rulebased.ipynb b/tutorials/custom_encoder_rulebased/custom_encoder_rulebased.ipynb index 9367addcc..914b2a036 100644 --- a/tutorials/custom_encoder_rulebased/custom_encoder_rulebased.ipynb +++ b/tutorials/custom_encoder_rulebased/custom_encoder_rulebased.ipynb @@ -43,10 +43,10 @@ "id": "raising-adventure", "metadata": { "execution": { - "iopub.execute_input": "2024-03-18T10:31:45.964816Z", - "iopub.status.busy": "2024-03-18T10:31:45.964618Z", - "iopub.status.idle": "2024-03-18T10:31:48.543190Z", - "shell.execute_reply": "2024-03-18T10:31:48.542464Z" + "iopub.execute_input": "2024-03-18T12:13:22.285628Z", + "iopub.status.busy": "2024-03-18T12:13:22.285375Z", + "iopub.status.idle": "2024-03-18T12:13:25.085461Z", + "shell.execute_reply": "2024-03-18T12:13:25.084685Z" } }, "outputs": [ @@ -54,14 +54,14 @@ "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2551:No torchvision detected, image helpers not supported.\u001b[0m\n" + "\u001b[32mINFO:lightwood-2722:No torchvision detected, image helpers not supported.\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2551:No torchvision/pillow detected, image encoder not supported\u001b[0m\n" + "\u001b[32mINFO:lightwood-2722:No torchvision/pillow detected, image encoder not supported\u001b[0m\n" ] } ], @@ -93,10 +93,10 @@ "id": "technical-government", "metadata": { "execution": { - "iopub.execute_input": "2024-03-18T10:31:48.546568Z", - "iopub.status.busy": "2024-03-18T10:31:48.545997Z", - "iopub.status.idle": "2024-03-18T10:31:48.750333Z", - "shell.execute_reply": "2024-03-18T10:31:48.749609Z" + "iopub.execute_input": "2024-03-18T12:13:25.089135Z", + "iopub.status.busy": "2024-03-18T12:13:25.088792Z", + "iopub.status.idle": "2024-03-18T12:13:25.619155Z", + "shell.execute_reply": "2024-03-18T12:13:25.618419Z" } }, "outputs": [ @@ -243,10 +243,10 @@ "id": "absent-maker", "metadata": { "execution": { - "iopub.execute_input": "2024-03-18T10:31:48.753282Z", - "iopub.status.busy": "2024-03-18T10:31:48.752879Z", - "iopub.status.idle": "2024-03-18T10:31:59.613412Z", - "shell.execute_reply": "2024-03-18T10:31:59.612794Z" + "iopub.execute_input": "2024-03-18T12:13:25.622051Z", + "iopub.status.busy": "2024-03-18T12:13:25.621631Z", + "iopub.status.idle": "2024-03-18T12:13:36.720794Z", + "shell.execute_reply": "2024-03-18T12:13:36.719966Z" } }, "outputs": [ @@ -254,161 +254,161 @@ "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2551:Analyzing a sample of 6920\u001b[0m\n" + "\u001b[32mINFO:type_infer-2722:Analyzing a sample of 6920\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2551:from a total population of 10668, this is equivalent to 64.9% of your data.\u001b[0m\n" + "\u001b[32mINFO:type_infer-2722:from a total population of 10668, this is equivalent to 64.9% of your data.\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2551:Using 3 processes to deduct types.\u001b[0m\n" + "\u001b[32mINFO:type_infer-2722:Using 3 processes to deduct types.\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2551:Infering type for: price\u001b[0m\n" + "\u001b[32mINFO:type_infer-2722:Infering type for: year\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2551:Infering type for: year\u001b[0m\n" + "\u001b[32mINFO:type_infer-2722:Infering type for: price\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2551:Column price has data type integer\u001b[0m\n" + "\u001b[32mINFO:type_infer-2722:Column year has data type integer\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2551:Column year has data type integer\u001b[0m\n" + "\u001b[32mINFO:type_infer-2722:Column price has data type integer\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2551:Infering type for: transmission\u001b[0m\n" + "\u001b[32mINFO:type_infer-2722:Infering type for: transmission\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2551:Infering type for: mileage\u001b[0m\n" + "\u001b[32mINFO:type_infer-2722:Infering type for: mileage\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2551:Infering type for: model\u001b[0m\n" + "\u001b[32mINFO:type_infer-2722:Infering type for: model\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2551:Column mileage has data type integer\u001b[0m\n" + "\u001b[32mINFO:type_infer-2722:Column mileage has data type integer\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2551:Infering type for: fuelType\u001b[0m\n" + "\u001b[32mINFO:type_infer-2722:Infering type for: fuelType\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2551:Column transmission has data type categorical\u001b[0m\n" + "\u001b[32mINFO:type_infer-2722:Column transmission has data type categorical\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2551:Infering type for: tax\u001b[0m\n" + "\u001b[32mINFO:type_infer-2722:Infering type for: tax\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2551:Column tax has data type integer\u001b[0m\n" + "\u001b[32mINFO:type_infer-2722:Column tax has data type integer\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2551:Infering type for: mpg\u001b[0m\n" + "\u001b[32mINFO:type_infer-2722:Infering type for: mpg\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2551:Column mpg has data type float\u001b[0m\n" + "\u001b[32mINFO:type_infer-2722:Column mpg has data type float\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2551:Infering type for: engineSize\u001b[0m\n" + "\u001b[32mINFO:type_infer-2722:Infering type for: engineSize\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2551:Column engineSize has data type float\u001b[0m\n" + "\u001b[32mINFO:type_infer-2722:Column engineSize has data type float\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2551:Column model has data type categorical\u001b[0m\n" + "\u001b[32mINFO:type_infer-2722:Column model has data type categorical\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2551:Column fuelType has data type categorical\u001b[0m\n" + "\u001b[32mINFO:type_infer-2722:Column fuelType has data type categorical\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2551:Starting statistical analysis\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2722:Starting statistical analysis\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2551:Finished statistical analysis\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2722:Finished statistical analysis\u001b[0m\n" ] } ], @@ -437,10 +437,10 @@ "id": "coastal-paragraph", "metadata": { "execution": { - "iopub.execute_input": "2024-03-18T10:31:59.616597Z", - "iopub.status.busy": "2024-03-18T10:31:59.616070Z", - "iopub.status.idle": "2024-03-18T10:31:59.620828Z", - "shell.execute_reply": "2024-03-18T10:31:59.620223Z" + "iopub.execute_input": "2024-03-18T12:13:36.723741Z", + "iopub.status.busy": "2024-03-18T12:13:36.723516Z", + "iopub.status.idle": "2024-03-18T12:13:36.728384Z", + "shell.execute_reply": "2024-03-18T12:13:36.727737Z" } }, "outputs": [ @@ -545,7 +545,7 @@ " \"unbias_target\": true,\n", " \"seconds_per_mixer\": 21384.0,\n", " \"seconds_per_encoder\": 85536.0,\n", - " \"expected_additional_time\": 10.848296403884888,\n", + " \"expected_additional_time\": 11.086917877197266,\n", " \"time_aim\": 259200,\n", " \"target_weights\": null,\n", " \"positive_domain\": false,\n", @@ -643,10 +643,10 @@ "id": "e03db1b0", "metadata": { "execution": { - "iopub.execute_input": "2024-03-18T10:31:59.623535Z", - "iopub.status.busy": "2024-03-18T10:31:59.623175Z", - "iopub.status.idle": "2024-03-18T10:31:59.628498Z", - "shell.execute_reply": "2024-03-18T10:31:59.627897Z" + "iopub.execute_input": "2024-03-18T12:13:36.731032Z", + "iopub.status.busy": "2024-03-18T12:13:36.730666Z", + "iopub.status.idle": "2024-03-18T12:13:36.735792Z", + "shell.execute_reply": "2024-03-18T12:13:36.735161Z" } }, "outputs": [ @@ -766,10 +766,10 @@ "id": "e30866c1", "metadata": { "execution": { - "iopub.execute_input": "2024-03-18T10:31:59.630984Z", - "iopub.status.busy": "2024-03-18T10:31:59.630777Z", - "iopub.status.idle": "2024-03-18T10:31:59.634186Z", - "shell.execute_reply": "2024-03-18T10:31:59.633618Z" + "iopub.execute_input": "2024-03-18T12:13:36.738409Z", + "iopub.status.busy": "2024-03-18T12:13:36.738073Z", + "iopub.status.idle": "2024-03-18T12:13:36.741539Z", + "shell.execute_reply": "2024-03-18T12:13:36.740938Z" } }, "outputs": [], @@ -828,10 +828,10 @@ "id": "elementary-fusion", "metadata": { "execution": { - "iopub.execute_input": "2024-03-18T10:31:59.636762Z", - "iopub.status.busy": "2024-03-18T10:31:59.636339Z", - "iopub.status.idle": "2024-03-18T10:31:59.639596Z", - "shell.execute_reply": "2024-03-18T10:31:59.639007Z" + "iopub.execute_input": "2024-03-18T12:13:36.744078Z", + "iopub.status.busy": "2024-03-18T12:13:36.743688Z", + "iopub.status.idle": "2024-03-18T12:13:36.746940Z", + "shell.execute_reply": "2024-03-18T12:13:36.746313Z" } }, "outputs": [], @@ -857,10 +857,10 @@ "id": "inappropriate-james", "metadata": { "execution": { - "iopub.execute_input": "2024-03-18T10:31:59.642016Z", - "iopub.status.busy": "2024-03-18T10:31:59.641636Z", - "iopub.status.idle": "2024-03-18T10:31:59.986759Z", - "shell.execute_reply": "2024-03-18T10:31:59.986037Z" + "iopub.execute_input": "2024-03-18T12:13:36.749283Z", + "iopub.status.busy": "2024-03-18T12:13:36.749096Z", + "iopub.status.idle": "2024-03-18T12:13:37.131539Z", + "shell.execute_reply": "2024-03-18T12:13:37.130834Z" } }, "outputs": [], @@ -891,10 +891,10 @@ "id": "palestinian-harvey", "metadata": { "execution": { - "iopub.execute_input": "2024-03-18T10:31:59.989829Z", - "iopub.status.busy": "2024-03-18T10:31:59.989414Z", - "iopub.status.idle": "2024-03-18T10:32:01.137819Z", - "shell.execute_reply": "2024-03-18T10:32:01.137143Z" + "iopub.execute_input": "2024-03-18T12:13:37.134854Z", + "iopub.status.busy": "2024-03-18T12:13:37.134380Z", + "iopub.status.idle": "2024-03-18T12:13:38.313702Z", + "shell.execute_reply": "2024-03-18T12:13:38.312990Z" } }, "outputs": [ @@ -902,133 +902,133 @@ "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2551:Starting statistical analysis\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2722:Starting statistical analysis\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2551:Finished statistical analysis\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2722:Finished statistical analysis\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:lightwood-2551: `analyze_data` runtime: 0.43 seconds\u001b[0m\n" + "\u001b[37mDEBUG:lightwood-2722: `analyze_data` runtime: 0.44 seconds\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2551:Cleaning the data\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2722:Cleaning the data\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:lightwood-2551: `preprocess` runtime: 0.13 seconds\u001b[0m\n" + "\u001b[37mDEBUG:lightwood-2722: `preprocess` runtime: 0.13 seconds\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2551:Splitting the data into train/test\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2722:Splitting the data into train/test\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:lightwood-2551: `split` runtime: 0.01 seconds\u001b[0m\n" + "\u001b[37mDEBUG:lightwood-2722: `split` runtime: 0.01 seconds\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:dataprep_ml-2551:Preparing sequentially...\u001b[0m\n" + "\u001b[37mDEBUG:dataprep_ml-2722:Preparing sequentially...\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:dataprep_ml-2551:Preparing encoder for year...\u001b[0m\n" + "\u001b[37mDEBUG:dataprep_ml-2722:Preparing encoder for year...\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:dataprep_ml-2551:Preparing encoder for mileage...\u001b[0m\n" + "\u001b[37mDEBUG:dataprep_ml-2722:Preparing encoder for mileage...\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:dataprep_ml-2551:Preparing encoder for tax...\u001b[0m\n" + "\u001b[37mDEBUG:dataprep_ml-2722:Preparing encoder for tax...\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:dataprep_ml-2551:Preparing encoder for mpg...\u001b[0m\n" + "\u001b[37mDEBUG:dataprep_ml-2722:Preparing encoder for mpg...\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:dataprep_ml-2551:Preparing encoder for engineSize...\u001b[0m\n" + "\u001b[37mDEBUG:dataprep_ml-2722:Preparing encoder for engineSize...\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2551:Categories Detected = 1\u001b[0m\n" + "\u001b[32mINFO:lightwood-2722:Categories Detected = 1\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2551:Categories Detected = 1\u001b[0m\n" + "\u001b[32mINFO:lightwood-2722:Categories Detected = 1\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2551:Categories Detected = 1\u001b[0m\n" + "\u001b[32mINFO:lightwood-2722:Categories Detected = 1\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:lightwood-2551: `prepare` runtime: 0.02 seconds\u001b[0m\n" + "\u001b[37mDEBUG:lightwood-2722: `prepare` runtime: 0.02 seconds\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2551:Featurizing the data\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2722:Featurizing the data\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:lightwood-2551: `featurize` runtime: 0.56 seconds\u001b[0m\n" + "\u001b[37mDEBUG:lightwood-2722: `featurize` runtime: 0.58 seconds\u001b[0m\n" ] } ], @@ -1063,10 +1063,10 @@ "id": "silent-dealing", "metadata": { "execution": { - "iopub.execute_input": "2024-03-18T10:32:01.140734Z", - "iopub.status.busy": "2024-03-18T10:32:01.140293Z", - "iopub.status.idle": "2024-03-18T10:32:01.149459Z", - "shell.execute_reply": "2024-03-18T10:32:01.148884Z" + "iopub.execute_input": "2024-03-18T12:13:38.316708Z", + "iopub.status.busy": "2024-03-18T12:13:38.316438Z", + "iopub.status.idle": "2024-03-18T12:13:38.326322Z", + "shell.execute_reply": "2024-03-18T12:13:38.325705Z" } }, "outputs": [ @@ -1168,10 +1168,10 @@ "id": "superior-mobility", "metadata": { "execution": { - "iopub.execute_input": "2024-03-18T10:32:01.152221Z", - "iopub.status.busy": "2024-03-18T10:32:01.151819Z", - "iopub.status.idle": "2024-03-18T10:32:01.155027Z", - "shell.execute_reply": "2024-03-18T10:32:01.154373Z" + "iopub.execute_input": "2024-03-18T12:13:38.329017Z", + "iopub.status.busy": "2024-03-18T12:13:38.328782Z", + "iopub.status.idle": "2024-03-18T12:13:38.332766Z", + "shell.execute_reply": "2024-03-18T12:13:38.331960Z" } }, "outputs": [ diff --git a/tutorials/custom_explainer/custom_explainer.html b/tutorials/custom_explainer/custom_explainer.html index 6230556a5..f86cedd93 100644 --- a/tutorials/custom_explainer/custom_explainer.html +++ b/tutorials/custom_explainer/custom_explainer.html @@ -127,7 +127,7 @@

Objective 
-INFO:lightwood-2205:No torchvision detected, image helpers not supported.
+INFO:lightwood-2394:No torchvision detected, image helpers not supported.
@@ -135,7 +135,7 @@

Objective 
-INFO:lightwood-2205:No torchvision/pillow detected, image encoder not supported
+INFO:lightwood-2394:No torchvision/pillow detected, image encoder not supported

@@ -188,7 +188,7 @@

Step 1: figuring out what we need 
-<__main__.ModelCorrelationHeatmap at 0x75d4e00a3370>
+<__main__.ModelCorrelationHeatmap at 0x78710515c520>

Right now, our newly created analysis block doesn’t do much, apart from returning the info and insights (row_insights and global_insights) exactly as it received them from the previous block.

@@ -344,7 +344,7 @@

Step 4: Final test run 
-INFO:type_infer-2205:Analyzing a sample of 222
+INFO:type_infer-2394:Analyzing a sample of 222
@@ -352,7 +352,7 @@

Step 4: Final test run 
-INFO:type_infer-2205:from a total population of 225, this is equivalent to 98.7% of your data.
+INFO:type_infer-2394:from a total population of 225, this is equivalent to 98.7% of your data.

@@ -360,7 +360,7 @@

Step 4: Final test run 
-INFO:type_infer-2205:Infering type for: Population
+INFO:type_infer-2394:Infering type for: Population

@@ -368,7 +368,7 @@

Step 4: Final test run 
-INFO:type_infer-2205:Column Population has data type integer
+INFO:type_infer-2394:Column Population has data type integer

@@ -376,7 +376,7 @@

Step 4: Final test run 
-INFO:type_infer-2205:Infering type for: Area (sq. mi.)
+INFO:type_infer-2394:Infering type for: Area (sq. mi.)

@@ -384,7 +384,7 @@

Step 4: Final test run 
-INFO:type_infer-2205:Column Area (sq. mi.) has data type integer
+INFO:type_infer-2394:Column Area (sq. mi.) has data type integer

@@ -392,7 +392,7 @@

Step 4: Final test run 
-INFO:type_infer-2205:Infering type for: Pop. Density 
+INFO:type_infer-2394:Infering type for: Pop. Density

@@ -400,7 +400,7 @@

Step 4: Final test run 
-INFO:type_infer-2205:Column Pop. Density  has data type float
+INFO:type_infer-2394:Column Pop. Density  has data type float

@@ -408,7 +408,7 @@

Step 4: Final test run 
-INFO:type_infer-2205:Infering type for: GDP ($ per capita)
+INFO:type_infer-2394:Infering type for: GDP ($ per capita)

@@ -416,7 +416,7 @@

Step 4: Final test run 
-INFO:type_infer-2205:Column GDP ($ per capita) has data type integer
+INFO:type_infer-2394:Column GDP ($ per capita) has data type integer

@@ -424,7 +424,7 @@

Step 4: Final test run 
-INFO:type_infer-2205:Infering type for: Literacy (%)
+INFO:type_infer-2394:Infering type for: Literacy (%)

@@ -432,7 +432,7 @@

Step 4: Final test run 
-INFO:type_infer-2205:Column Literacy (%) has data type float
+INFO:type_infer-2394:Column Literacy (%) has data type float

@@ -440,7 +440,7 @@

Step 4: Final test run 
-INFO:type_infer-2205:Infering type for: Infant mortality 
+INFO:type_infer-2394:Infering type for: Infant mortality

@@ -448,7 +448,7 @@

Step 4: Final test run 
-INFO:type_infer-2205:Column Infant mortality  has data type float
+INFO:type_infer-2394:Column Infant mortality  has data type float

@@ -456,7 +456,7 @@

Step 4: Final test run 
-INFO:type_infer-2205:Infering type for: Development Index
+INFO:type_infer-2394:Infering type for: Development Index

@@ -464,7 +464,7 @@

Step 4: Final test run 
-INFO:type_infer-2205:Column Development Index has data type categorical
+INFO:type_infer-2394:Column Development Index has data type categorical

@@ -472,7 +472,7 @@

Step 4: Final test run 
-INFO:dataprep_ml-2205:Starting statistical analysis
+INFO:dataprep_ml-2394:Starting statistical analysis

@@ -480,7 +480,7 @@

Step 4: Final test run 
-INFO:dataprep_ml-2205:Finished statistical analysis
+INFO:dataprep_ml-2394:Finished statistical analysis

We can take a look at the respective Json AI key just to confirm our newly added analysis block is in there:

@@ -520,7 +520,7 @@

Step 4: Final test run 
-INFO:dataprep_ml-2205:[Learn phase 1/8] - Statistical analysis
+INFO:dataprep_ml-2394:[Learn phase 1/8] - Statistical analysis
@@ -528,7 +528,7 @@

Step 4: Final test run 
-INFO:dataprep_ml-2205:Starting statistical analysis
+INFO:dataprep_ml-2394:Starting statistical analysis

@@ -536,7 +536,7 @@

Step 4: Final test run 
-INFO:dataprep_ml-2205:Finished statistical analysis
+INFO:dataprep_ml-2394:Finished statistical analysis

@@ -544,7 +544,7 @@

Step 4: Final test run 
-DEBUG:lightwood-2205: `analyze_data` runtime: 0.02 seconds
+DEBUG:lightwood-2394: `analyze_data` runtime: 0.02 seconds

@@ -552,7 +552,7 @@

Step 4: Final test run 
-INFO:dataprep_ml-2205:[Learn phase 2/8] - Data preprocessing
+INFO:dataprep_ml-2394:[Learn phase 2/8] - Data preprocessing

@@ -560,7 +560,7 @@

Step 4: Final test run 
-INFO:dataprep_ml-2205:Cleaning the data
+INFO:dataprep_ml-2394:Cleaning the data

@@ -568,7 +568,7 @@

Step 4: Final test run 
-DEBUG:lightwood-2205: `preprocess` runtime: 0.01 seconds
+DEBUG:lightwood-2394: `preprocess` runtime: 0.01 seconds

@@ -576,7 +576,7 @@

Step 4: Final test run 
-INFO:dataprep_ml-2205:[Learn phase 3/8] - Data splitting
+INFO:dataprep_ml-2394:[Learn phase 3/8] - Data splitting

@@ -584,7 +584,7 @@

Step 4: Final test run 
-INFO:dataprep_ml-2205:Splitting the data into train/test
+INFO:dataprep_ml-2394:Splitting the data into train/test

@@ -592,7 +592,7 @@

Step 4: Final test run 
-DEBUG:lightwood-2205: `split` runtime: 0.01 seconds
+DEBUG:lightwood-2394: `split` runtime: 0.01 seconds

@@ -600,7 +600,7 @@

Step 4: Final test run 
-INFO:dataprep_ml-2205:[Learn phase 4/8] - Preparing encoders
+INFO:dataprep_ml-2394:[Learn phase 4/8] - Preparing encoders

@@ -608,7 +608,7 @@

Step 4: Final test run 
-DEBUG:dataprep_ml-2205:Preparing sequentially...
+DEBUG:dataprep_ml-2394:Preparing sequentially...

@@ -616,7 +616,7 @@

Step 4: Final test run 
-DEBUG:dataprep_ml-2205:Preparing encoder for Population...
+DEBUG:dataprep_ml-2394:Preparing encoder for Population...

@@ -624,7 +624,7 @@

Step 4: Final test run 
-DEBUG:dataprep_ml-2205:Preparing encoder for Area (sq. mi.)...
+DEBUG:dataprep_ml-2394:Preparing encoder for Area (sq. mi.)...

@@ -632,7 +632,7 @@

Step 4: Final test run 
-DEBUG:dataprep_ml-2205:Preparing encoder for Pop. Density ...
+DEBUG:dataprep_ml-2394:Preparing encoder for Pop. Density ...

@@ -640,7 +640,7 @@

Step 4: Final test run 
-DEBUG:dataprep_ml-2205:Preparing encoder for GDP ($ per capita)...
+DEBUG:dataprep_ml-2394:Preparing encoder for GDP ($ per capita)...

@@ -648,7 +648,7 @@

Step 4: Final test run 
-DEBUG:dataprep_ml-2205:Preparing encoder for Literacy (%)...
+DEBUG:dataprep_ml-2394:Preparing encoder for Literacy (%)...

@@ -656,7 +656,7 @@

Step 4: Final test run 
-DEBUG:dataprep_ml-2205:Preparing encoder for Infant mortality ...
+DEBUG:dataprep_ml-2394:Preparing encoder for Infant mortality ...

@@ -664,7 +664,7 @@

Step 4: Final test run 
-DEBUG:lightwood-2205:Encoding UNKNOWN categories as index 0
+DEBUG:lightwood-2394:Encoding UNKNOWN categories as index 0

@@ -672,7 +672,7 @@

Step 4: Final test run 
-DEBUG:lightwood-2205: `prepare` runtime: 0.01 seconds
+DEBUG:lightwood-2394: `prepare` runtime: 0.01 seconds

@@ -680,7 +680,7 @@

Step 4: Final test run 
-INFO:dataprep_ml-2205:[Learn phase 5/8] - Feature generation
+INFO:dataprep_ml-2394:[Learn phase 5/8] - Feature generation

@@ -688,7 +688,7 @@

Step 4: Final test run 
-INFO:dataprep_ml-2205:Featurizing the data
+INFO:dataprep_ml-2394:Featurizing the data

@@ -696,7 +696,7 @@

Step 4: Final test run 
-DEBUG:lightwood-2205: `featurize` runtime: 0.05 seconds
+DEBUG:lightwood-2394: `featurize` runtime: 0.05 seconds

@@ -704,7 +704,7 @@

Step 4: Final test run 
-INFO:dataprep_ml-2205:[Learn phase 6/8] - Mixer training
+INFO:dataprep_ml-2394:[Learn phase 6/8] - Mixer training

@@ -712,7 +712,7 @@

Step 4: Final test run 
-INFO:dataprep_ml-2205:Training the mixers
+INFO:dataprep_ml-2394:Training the mixers

@@ -720,7 +720,7 @@

Step 4: Final test run 
-WARNING:lightwood-2205:XGBoost running on CPU
+WARNING:lightwood-2394:XGBoost running on CPU

@@ -737,7 +737,7 @@

Step 4: Final test run 
-[10:31:01] WARNING: ../src/learner.cc:339: No visible GPU is found, setting `gpu_id` to -1
+[12:12:33] WARNING: ../src/learner.cc:339: No visible GPU is found, setting `gpu_id` to -1

@@ -750,7 +750,7 @@

Step 4: Final test runINFO:lightwood-2205:Loss of 18.69619858264923 with learning rate 0.0001 +INFO:lightwood-2394:Loss of 18.69619858264923 with learning rate 0.0001

@@ -758,7 +758,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss of 16.93891429901123 with learning rate 0.0005
+INFO:lightwood-2394:Loss of 16.93891429901123 with learning rate 0.0005

@@ -766,7 +766,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss of 16.197376608848572 with learning rate 0.001
+INFO:lightwood-2394:Loss of 16.197376608848572 with learning rate 0.001

@@ -774,7 +774,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss of 16.06481909751892 with learning rate 0.002
+INFO:lightwood-2394:Loss of 16.06481909751892 with learning rate 0.002

@@ -782,7 +782,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss of 16.472004413604736 with learning rate 0.003
+INFO:lightwood-2394:Loss of 16.472004413604736 with learning rate 0.003

@@ -790,7 +790,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss of 18.28026556968689 with learning rate 0.005
+INFO:lightwood-2394:Loss of 18.28026556968689 with learning rate 0.005

@@ -798,7 +798,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss of 26.746760368347168 with learning rate 0.01
+INFO:lightwood-2394:Loss of 26.746760368347168 with learning rate 0.01

@@ -806,7 +806,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss of 101.83524441719055 with learning rate 0.05
+INFO:lightwood-2394:Loss of 101.83524441719055 with learning rate 0.05

@@ -814,7 +814,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Found learning rate of: 0.002
+INFO:lightwood-2394:Found learning rate of: 0.002

@@ -824,7 +824,7 @@

Step 4: Final test run
 /opt/hostedtoolcache/Python/3.9.18/x64/lib/python3.9/site-packages/torch/cuda/amp/grad_scaler.py:126: UserWarning: torch.cuda.amp.GradScaler is enabled, but CUDA is not available.  Disabling.
   warnings.warn(
-INFO:lightwood-2205:Loss @ epoch 1: 1.319209337234497
+INFO:lightwood-2394:Loss @ epoch 1: 1.319209337234497

@@ -832,7 +832,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 2: 1.3220206499099731
+INFO:lightwood-2394:Loss @ epoch 2: 1.3220206499099731

@@ -840,7 +840,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 3: 1.3063435554504395
+INFO:lightwood-2394:Loss @ epoch 3: 1.3063435554504395

@@ -848,7 +848,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 4: 1.2932535409927368
+INFO:lightwood-2394:Loss @ epoch 4: 1.2932535409927368

@@ -856,7 +856,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 5: 1.2823516130447388
+INFO:lightwood-2394:Loss @ epoch 5: 1.2823516130447388

@@ -864,7 +864,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 6: 1.2705544233322144
+INFO:lightwood-2394:Loss @ epoch 6: 1.2705544233322144

@@ -872,7 +872,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 7: 1.2418551445007324
+INFO:lightwood-2394:Loss @ epoch 7: 1.2418551445007324

@@ -880,7 +880,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 8: 1.2208324670791626
+INFO:lightwood-2394:Loss @ epoch 8: 1.2208324670791626

@@ -888,7 +888,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 9: 1.197828769683838
+INFO:lightwood-2394:Loss @ epoch 9: 1.197828769683838

@@ -896,7 +896,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 10: 1.1781431436538696
+INFO:lightwood-2394:Loss @ epoch 10: 1.1781431436538696

@@ -904,7 +904,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 11: 1.161504864692688
+INFO:lightwood-2394:Loss @ epoch 11: 1.161504864692688

@@ -912,7 +912,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 12: 1.1442031860351562
+INFO:lightwood-2394:Loss @ epoch 12: 1.1442031860351562

@@ -920,7 +920,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 13: 1.1058541536331177
+INFO:lightwood-2394:Loss @ epoch 13: 1.1058541536331177

@@ -928,7 +928,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 14: 1.0935649871826172
+INFO:lightwood-2394:Loss @ epoch 14: 1.0935649871826172

@@ -936,7 +936,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 15: 1.0802721977233887
+INFO:lightwood-2394:Loss @ epoch 15: 1.0802721977233887

@@ -944,7 +944,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 16: 1.0696258544921875
+INFO:lightwood-2394:Loss @ epoch 16: 1.0696258544921875

@@ -952,7 +952,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 17: 1.0607414245605469
+INFO:lightwood-2394:Loss @ epoch 17: 1.0607414245605469

@@ -960,7 +960,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 18: 1.0493905544281006
+INFO:lightwood-2394:Loss @ epoch 18: 1.0493905544281006

@@ -968,7 +968,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 19: 1.020617961883545
+INFO:lightwood-2394:Loss @ epoch 19: 1.020617961883545

@@ -976,7 +976,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 20: 1.0081787109375
+INFO:lightwood-2394:Loss @ epoch 20: 1.0081787109375

@@ -984,7 +984,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 21: 0.9943330883979797
+INFO:lightwood-2394:Loss @ epoch 21: 0.9943330883979797

@@ -992,7 +992,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 22: 0.9842473268508911
+INFO:lightwood-2394:Loss @ epoch 22: 0.9842473268508911

@@ -1000,7 +1000,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 23: 0.9762145280838013
+INFO:lightwood-2394:Loss @ epoch 23: 0.9762145280838013

@@ -1008,7 +1008,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 24: 0.9653865098953247
+INFO:lightwood-2394:Loss @ epoch 24: 0.9653865098953247

@@ -1016,7 +1016,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 25: 0.9380742311477661
+INFO:lightwood-2394:Loss @ epoch 25: 0.9380742311477661

@@ -1024,7 +1024,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 26: 0.9271669387817383
+INFO:lightwood-2394:Loss @ epoch 26: 0.9271669387817383

@@ -1032,7 +1032,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 27: 0.9147073030471802
+INFO:lightwood-2394:Loss @ epoch 27: 0.9147073030471802

@@ -1040,7 +1040,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 28: 0.9064992070198059
+INFO:lightwood-2394:Loss @ epoch 28: 0.9064992070198059

@@ -1048,7 +1048,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 29: 0.900122344493866
+INFO:lightwood-2394:Loss @ epoch 29: 0.900122344493866

@@ -1056,7 +1056,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 30: 0.8903173208236694
+INFO:lightwood-2394:Loss @ epoch 30: 0.8903173208236694

@@ -1064,7 +1064,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 31: 0.8648637533187866
+INFO:lightwood-2394:Loss @ epoch 31: 0.8648637533187866

@@ -1072,7 +1072,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 32: 0.8549227118492126
+INFO:lightwood-2394:Loss @ epoch 32: 0.8549227118492126

@@ -1080,7 +1080,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 33: 0.8434366583824158
+INFO:lightwood-2394:Loss @ epoch 33: 0.8434366583824158

@@ -1088,7 +1088,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 34: 0.8365358114242554
+INFO:lightwood-2394:Loss @ epoch 34: 0.8365358114242554

@@ -1096,7 +1096,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 35: 0.831310510635376
+INFO:lightwood-2394:Loss @ epoch 35: 0.831310510635376

@@ -1104,7 +1104,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 36: 0.8222441673278809
+INFO:lightwood-2394:Loss @ epoch 36: 0.8222441673278809

@@ -1112,7 +1112,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 37: 0.7981722950935364
+INFO:lightwood-2394:Loss @ epoch 37: 0.7981722950935364

@@ -1120,7 +1120,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 38: 0.789170503616333
+INFO:lightwood-2394:Loss @ epoch 38: 0.789170503616333

@@ -1128,7 +1128,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 39: 0.7787192463874817
+INFO:lightwood-2394:Loss @ epoch 39: 0.7787192463874817

@@ -1136,7 +1136,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 40: 0.7730110287666321
+INFO:lightwood-2394:Loss @ epoch 40: 0.7730110287666321

@@ -1144,7 +1144,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 41: 0.7687097787857056
+INFO:lightwood-2394:Loss @ epoch 41: 0.7687097787857056

@@ -1152,7 +1152,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 42: 0.7602015137672424
+INFO:lightwood-2394:Loss @ epoch 42: 0.7602015137672424

@@ -1160,7 +1160,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 43: 0.7373268604278564
+INFO:lightwood-2394:Loss @ epoch 43: 0.7373268604278564

@@ -1168,7 +1168,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 44: 0.7292225956916809
+INFO:lightwood-2394:Loss @ epoch 44: 0.7292225956916809

@@ -1176,7 +1176,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 45: 0.7197889685630798
+INFO:lightwood-2394:Loss @ epoch 45: 0.7197889685630798

@@ -1184,7 +1184,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 46: 0.7151773571968079
+INFO:lightwood-2394:Loss @ epoch 46: 0.7151773571968079

@@ -1192,7 +1192,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 47: 0.7117206454277039
+INFO:lightwood-2394:Loss @ epoch 47: 0.7117206454277039

@@ -1200,7 +1200,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 48: 0.7038285136222839
+INFO:lightwood-2394:Loss @ epoch 48: 0.7038285136222839

@@ -1208,7 +1208,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 49: 0.682073175907135
+INFO:lightwood-2394:Loss @ epoch 49: 0.682073175907135

@@ -1216,7 +1216,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 50: 0.674643874168396
+INFO:lightwood-2394:Loss @ epoch 50: 0.674643874168396

@@ -1224,7 +1224,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 51: 0.6659626364707947
+INFO:lightwood-2394:Loss @ epoch 51: 0.6659626364707947

@@ -1232,7 +1232,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 52: 0.6620772480964661
+INFO:lightwood-2394:Loss @ epoch 52: 0.6620772480964661

@@ -1240,7 +1240,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 53: 0.6590715646743774
+INFO:lightwood-2394:Loss @ epoch 53: 0.6590715646743774

@@ -1248,7 +1248,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 54: 0.6515910625457764
+INFO:lightwood-2394:Loss @ epoch 54: 0.6515910625457764

@@ -1256,7 +1256,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 55: 0.6308077573776245
+INFO:lightwood-2394:Loss @ epoch 55: 0.6308077573776245

@@ -1264,7 +1264,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 56: 0.6241987347602844
+INFO:lightwood-2394:Loss @ epoch 56: 0.6241987347602844

@@ -1272,7 +1272,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 57: 0.6163835525512695
+INFO:lightwood-2394:Loss @ epoch 57: 0.6163835525512695

@@ -1280,7 +1280,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 58: 0.6131908297538757
+INFO:lightwood-2394:Loss @ epoch 58: 0.6131908297538757

@@ -1288,7 +1288,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 59: 0.6106155514717102
+INFO:lightwood-2394:Loss @ epoch 59: 0.6106155514717102

@@ -1296,7 +1296,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 60: 0.6036757826805115
+INFO:lightwood-2394:Loss @ epoch 60: 0.6036757826805115

@@ -1304,7 +1304,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 61: 0.5848420262336731
+INFO:lightwood-2394:Loss @ epoch 61: 0.5848420262336731

@@ -1312,7 +1312,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 62: 0.5793871879577637
+INFO:lightwood-2394:Loss @ epoch 62: 0.5793871879577637

@@ -1320,7 +1320,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 63: 0.5726662278175354
+INFO:lightwood-2394:Loss @ epoch 63: 0.5726662278175354

@@ -1328,7 +1328,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 64: 0.5703645348548889
+INFO:lightwood-2394:Loss @ epoch 64: 0.5703645348548889

@@ -1336,7 +1336,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 65: 0.5684641003608704
+INFO:lightwood-2394:Loss @ epoch 65: 0.5684641003608704

@@ -1344,7 +1344,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 66: 0.5622180104255676
+INFO:lightwood-2394:Loss @ epoch 66: 0.5622180104255676

@@ -1352,7 +1352,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 67: 0.5449516773223877
+INFO:lightwood-2394:Loss @ epoch 67: 0.5449516773223877

@@ -1360,7 +1360,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 68: 0.5401747226715088
+INFO:lightwood-2394:Loss @ epoch 68: 0.5401747226715088

@@ -1368,7 +1368,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 69: 0.5341063141822815
+INFO:lightwood-2394:Loss @ epoch 69: 0.5341063141822815

@@ -1376,7 +1376,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 70: 0.5322306752204895
+INFO:lightwood-2394:Loss @ epoch 70: 0.5322306752204895

@@ -1384,7 +1384,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 71: 0.5305425524711609
+INFO:lightwood-2394:Loss @ epoch 71: 0.5305425524711609

@@ -1392,7 +1392,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 72: 0.5246548056602478
+INFO:lightwood-2394:Loss @ epoch 72: 0.5246548056602478

@@ -1400,7 +1400,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 73: 0.5083626508712769
+INFO:lightwood-2394:Loss @ epoch 73: 0.5083626508712769

@@ -1408,7 +1408,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 74: 0.5040708184242249
+INFO:lightwood-2394:Loss @ epoch 74: 0.5040708184242249

@@ -1416,7 +1416,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 75: 0.49863335490226746
+INFO:lightwood-2394:Loss @ epoch 75: 0.49863335490226746

@@ -1424,7 +1424,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 76: 0.49717265367507935
+INFO:lightwood-2394:Loss @ epoch 76: 0.49717265367507935

@@ -1432,7 +1432,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 77: 0.49564701318740845
+INFO:lightwood-2394:Loss @ epoch 77: 0.49564701318740845

@@ -1440,7 +1440,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 78: 0.4900944232940674
+INFO:lightwood-2394:Loss @ epoch 78: 0.4900944232940674

@@ -1448,7 +1448,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 79: 0.47473227977752686
+INFO:lightwood-2394:Loss @ epoch 79: 0.47473227977752686

@@ -1456,7 +1456,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 80: 0.4708785116672516
+INFO:lightwood-2394:Loss @ epoch 80: 0.4708785116672516

@@ -1464,7 +1464,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 81: 0.46578508615493774
+INFO:lightwood-2394:Loss @ epoch 81: 0.46578508615493774

@@ -1472,7 +1472,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 82: 0.4644494950771332
+INFO:lightwood-2394:Loss @ epoch 82: 0.4644494950771332

@@ -1480,7 +1480,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 83: 0.4629424810409546
+INFO:lightwood-2394:Loss @ epoch 83: 0.4629424810409546

@@ -1488,7 +1488,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 84: 0.4576236307621002
+INFO:lightwood-2394:Loss @ epoch 84: 0.4576236307621002

@@ -1496,7 +1496,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 85: 0.44295966625213623
+INFO:lightwood-2394:Loss @ epoch 85: 0.44295966625213623

@@ -1504,7 +1504,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 86: 0.4393518269062042
+INFO:lightwood-2394:Loss @ epoch 86: 0.4393518269062042

@@ -1512,7 +1512,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 87: 0.4346559941768646
+INFO:lightwood-2394:Loss @ epoch 87: 0.4346559941768646

@@ -1520,7 +1520,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 88: 0.43358123302459717
+INFO:lightwood-2394:Loss @ epoch 88: 0.43358123302459717

@@ -1528,7 +1528,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 89: 0.43231165409088135
+INFO:lightwood-2394:Loss @ epoch 89: 0.43231165409088135

@@ -1536,7 +1536,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 90: 0.42753666639328003
+INFO:lightwood-2394:Loss @ epoch 90: 0.42753666639328003

@@ -1544,7 +1544,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 91: 0.41425880789756775
+INFO:lightwood-2394:Loss @ epoch 91: 0.41425880789756775

@@ -1552,7 +1552,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 92: 0.4113253951072693
+INFO:lightwood-2394:Loss @ epoch 92: 0.4113253951072693

@@ -1560,7 +1560,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 93: 0.4074642062187195
+INFO:lightwood-2394:Loss @ epoch 93: 0.4074642062187195

@@ -1568,7 +1568,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 94: 0.40698081254959106
+INFO:lightwood-2394:Loss @ epoch 94: 0.40698081254959106

@@ -1576,7 +1576,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 95: 0.4062195420265198
+INFO:lightwood-2394:Loss @ epoch 95: 0.4062195420265198

@@ -1584,7 +1584,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 96: 0.4020974636077881
+INFO:lightwood-2394:Loss @ epoch 96: 0.4020974636077881

@@ -1592,7 +1592,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 97: 0.3904534876346588
+INFO:lightwood-2394:Loss @ epoch 97: 0.3904534876346588

@@ -1600,7 +1600,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 98: 0.3879585564136505
+INFO:lightwood-2394:Loss @ epoch 98: 0.3879585564136505

@@ -1608,7 +1608,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 99: 0.38447296619415283
+INFO:lightwood-2394:Loss @ epoch 99: 0.38447296619415283

@@ -1616,7 +1616,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 100: 0.3841344118118286
+INFO:lightwood-2394:Loss @ epoch 100: 0.3841344118118286

@@ -1624,7 +1624,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 101: 0.3833732604980469
+INFO:lightwood-2394:Loss @ epoch 101: 0.3833732604980469

@@ -1632,7 +1632,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 102: 0.37956172227859497
+INFO:lightwood-2394:Loss @ epoch 102: 0.37956172227859497

@@ -1640,7 +1640,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 103: 0.36883822083473206
+INFO:lightwood-2394:Loss @ epoch 103: 0.36883822083473206

@@ -1648,7 +1648,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 104: 0.3664571940898895
+INFO:lightwood-2394:Loss @ epoch 104: 0.3664571940898895

@@ -1656,7 +1656,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 105: 0.36335548758506775
+INFO:lightwood-2394:Loss @ epoch 105: 0.36335548758506775

@@ -1664,7 +1664,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 106: 0.36316758394241333
+INFO:lightwood-2394:Loss @ epoch 106: 0.36316758394241333

@@ -1672,7 +1672,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 107: 0.3625164330005646
+INFO:lightwood-2394:Loss @ epoch 107: 0.3625164330005646

@@ -1680,7 +1680,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 108: 0.359012246131897
+INFO:lightwood-2394:Loss @ epoch 108: 0.359012246131897

@@ -1688,7 +1688,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 109: 0.34912365674972534
+INFO:lightwood-2394:Loss @ epoch 109: 0.34912365674972534

@@ -1696,7 +1696,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 110: 0.34696850180625916
+INFO:lightwood-2394:Loss @ epoch 110: 0.34696850180625916

@@ -1704,7 +1704,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 111: 0.3441202938556671
+INFO:lightwood-2394:Loss @ epoch 111: 0.3441202938556671

@@ -1712,7 +1712,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 112: 0.34398093819618225
+INFO:lightwood-2394:Loss @ epoch 112: 0.34398093819618225

@@ -1720,7 +1720,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 113: 0.3432472050189972
+INFO:lightwood-2394:Loss @ epoch 113: 0.3432472050189972

@@ -1728,7 +1728,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 114: 0.3399496376514435
+INFO:lightwood-2394:Loss @ epoch 114: 0.3399496376514435

@@ -1736,7 +1736,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 115: 0.3308461308479309
+INFO:lightwood-2394:Loss @ epoch 115: 0.3308461308479309

@@ -1744,7 +1744,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 116: 0.3289041221141815
+INFO:lightwood-2394:Loss @ epoch 116: 0.3289041221141815

@@ -1752,7 +1752,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 117: 0.32627207040786743
+INFO:lightwood-2394:Loss @ epoch 117: 0.32627207040786743

@@ -1760,7 +1760,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 118: 0.3261372447013855
+INFO:lightwood-2394:Loss @ epoch 118: 0.3261372447013855

@@ -1768,7 +1768,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 119: 0.32546231150627136
+INFO:lightwood-2394:Loss @ epoch 119: 0.32546231150627136

@@ -1776,7 +1776,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 120: 0.3224080502986908
+INFO:lightwood-2394:Loss @ epoch 120: 0.3224080502986908

@@ -1784,7 +1784,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 121: 0.314008891582489
+INFO:lightwood-2394:Loss @ epoch 121: 0.314008891582489

@@ -1792,7 +1792,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 122: 0.31220486760139465
+INFO:lightwood-2394:Loss @ epoch 122: 0.31220486760139465

@@ -1800,7 +1800,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 123: 0.3098214566707611
+INFO:lightwood-2394:Loss @ epoch 123: 0.3098214566707611

@@ -1808,7 +1808,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 124: 0.30979809165000916
+INFO:lightwood-2394:Loss @ epoch 124: 0.30979809165000916

@@ -1816,7 +1816,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 125: 0.3090403079986572
+INFO:lightwood-2394:Loss @ epoch 125: 0.3090403079986572

@@ -1824,7 +1824,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 126: 0.30612480640411377
+INFO:lightwood-2394:Loss @ epoch 126: 0.30612480640411377

@@ -1832,7 +1832,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 127: 0.29819032549858093
+INFO:lightwood-2394:Loss @ epoch 127: 0.29819032549858093

@@ -1840,7 +1840,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 128: 0.29648637771606445
+INFO:lightwood-2394:Loss @ epoch 128: 0.29648637771606445

@@ -1848,7 +1848,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 129: 0.2943042516708374
+INFO:lightwood-2394:Loss @ epoch 129: 0.2943042516708374

@@ -1856,7 +1856,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 130: 0.29420480132102966
+INFO:lightwood-2394:Loss @ epoch 130: 0.29420480132102966

@@ -1864,7 +1864,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 131: 0.2934538424015045
+INFO:lightwood-2394:Loss @ epoch 131: 0.2934538424015045

@@ -1872,7 +1872,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 132: 0.2907505929470062
+INFO:lightwood-2394:Loss @ epoch 132: 0.2907505929470062

@@ -1880,7 +1880,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 133: 0.2835786044597626
+INFO:lightwood-2394:Loss @ epoch 133: 0.2835786044597626

@@ -1888,7 +1888,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 134: 0.28203263878822327
+INFO:lightwood-2394:Loss @ epoch 134: 0.28203263878822327

@@ -1896,7 +1896,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 135: 0.2801313102245331
+INFO:lightwood-2394:Loss @ epoch 135: 0.2801313102245331

@@ -1904,7 +1904,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 136: 0.2801584303379059
+INFO:lightwood-2394:Loss @ epoch 136: 0.2801584303379059

@@ -1912,7 +1912,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 137: 0.27946653962135315
+INFO:lightwood-2394:Loss @ epoch 137: 0.27946653962135315

@@ -1920,7 +1920,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 138: 0.2770102620124817
+INFO:lightwood-2394:Loss @ epoch 138: 0.2770102620124817

@@ -1928,7 +1928,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 139: 0.2705138921737671
+INFO:lightwood-2394:Loss @ epoch 139: 0.2705138921737671

@@ -1936,7 +1936,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 140: 0.2689667046070099
+INFO:lightwood-2394:Loss @ epoch 140: 0.2689667046070099

@@ -1944,7 +1944,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 141: 0.26713839173316956
+INFO:lightwood-2394:Loss @ epoch 141: 0.26713839173316956

@@ -1952,7 +1952,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 142: 0.26722976565361023
+INFO:lightwood-2394:Loss @ epoch 142: 0.26722976565361023

@@ -1960,7 +1960,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 143: 0.26659274101257324
+INFO:lightwood-2394:Loss @ epoch 143: 0.26659274101257324

@@ -1968,7 +1968,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 144: 0.26436734199523926
+INFO:lightwood-2394:Loss @ epoch 144: 0.26436734199523926

@@ -1976,7 +1976,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 145: 0.2585783302783966
+INFO:lightwood-2394:Loss @ epoch 145: 0.2585783302783966

@@ -1984,7 +1984,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 146: 0.25718021392822266
+INFO:lightwood-2394:Loss @ epoch 146: 0.25718021392822266

@@ -1992,7 +1992,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 147: 0.25569674372673035
+INFO:lightwood-2394:Loss @ epoch 147: 0.25569674372673035

@@ -2000,7 +2000,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 148: 0.25572293996810913
+INFO:lightwood-2394:Loss @ epoch 148: 0.25572293996810913

@@ -2008,7 +2008,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 149: 0.254925012588501
+INFO:lightwood-2394:Loss @ epoch 149: 0.254925012588501

@@ -2016,7 +2016,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 150: 0.25273722410202026
+INFO:lightwood-2394:Loss @ epoch 150: 0.25273722410202026

@@ -2024,7 +2024,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 151: 0.24740025401115417
+INFO:lightwood-2394:Loss @ epoch 151: 0.24740025401115417

@@ -2032,7 +2032,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 152: 0.24594709277153015
+INFO:lightwood-2394:Loss @ epoch 152: 0.24594709277153015

@@ -2040,7 +2040,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 153: 0.2445458471775055
+INFO:lightwood-2394:Loss @ epoch 153: 0.2445458471775055

@@ -2048,7 +2048,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 154: 0.2445453554391861
+INFO:lightwood-2394:Loss @ epoch 154: 0.2445453554391861

@@ -2056,7 +2056,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 155: 0.24368290603160858
+INFO:lightwood-2394:Loss @ epoch 155: 0.24368290603160858

@@ -2064,7 +2064,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 156: 0.2416052669286728
+INFO:lightwood-2394:Loss @ epoch 156: 0.2416052669286728

@@ -2072,7 +2072,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 157: 0.23683445155620575
+INFO:lightwood-2394:Loss @ epoch 157: 0.23683445155620575

@@ -2080,7 +2080,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 158: 0.23561015725135803
+INFO:lightwood-2394:Loss @ epoch 158: 0.23561015725135803

@@ -2088,7 +2088,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 159: 0.2342897355556488
+INFO:lightwood-2394:Loss @ epoch 159: 0.2342897355556488

@@ -2096,7 +2096,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 160: 0.2342986762523651
+INFO:lightwood-2394:Loss @ epoch 160: 0.2342986762523651

@@ -2104,7 +2104,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 161: 0.2334001511335373
+INFO:lightwood-2394:Loss @ epoch 161: 0.2334001511335373

@@ -2112,7 +2112,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 162: 0.23141320049762726
+INFO:lightwood-2394:Loss @ epoch 162: 0.23141320049762726

@@ -2120,7 +2120,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 163: 0.22705355286598206
+INFO:lightwood-2394:Loss @ epoch 163: 0.22705355286598206

@@ -2128,7 +2128,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 164: 0.22583813965320587
+INFO:lightwood-2394:Loss @ epoch 164: 0.22583813965320587

@@ -2136,7 +2136,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 165: 0.22455710172653198
+INFO:lightwood-2394:Loss @ epoch 165: 0.22455710172653198

@@ -2144,7 +2144,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 166: 0.2245052307844162
+INFO:lightwood-2394:Loss @ epoch 166: 0.2245052307844162

@@ -2152,7 +2152,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 167: 0.22359803318977356
+INFO:lightwood-2394:Loss @ epoch 167: 0.22359803318977356

@@ -2160,7 +2160,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 168: 0.22173909842967987
+INFO:lightwood-2394:Loss @ epoch 168: 0.22173909842967987

@@ -2168,7 +2168,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 169: 0.2178291231393814
+INFO:lightwood-2394:Loss @ epoch 169: 0.2178291231393814

@@ -2176,7 +2176,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 170: 0.21670299768447876
+INFO:lightwood-2394:Loss @ epoch 170: 0.21670299768447876

@@ -2184,7 +2184,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 171: 0.21559178829193115
+INFO:lightwood-2394:Loss @ epoch 171: 0.21559178829193115

@@ -2192,7 +2192,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 172: 0.21557293832302094
+INFO:lightwood-2394:Loss @ epoch 172: 0.21557293832302094

@@ -2200,7 +2200,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 173: 0.21463343501091003
+INFO:lightwood-2394:Loss @ epoch 173: 0.21463343501091003

@@ -2208,7 +2208,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 174: 0.21291333436965942
+INFO:lightwood-2394:Loss @ epoch 174: 0.21291333436965942

@@ -2216,7 +2216,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 175: 0.20953477919101715
+INFO:lightwood-2394:Loss @ epoch 175: 0.20953477919101715

@@ -2224,7 +2224,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 176: 0.20840951800346375
+INFO:lightwood-2394:Loss @ epoch 176: 0.20840951800346375

@@ -2232,7 +2232,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 177: 0.20733794569969177
+INFO:lightwood-2394:Loss @ epoch 177: 0.20733794569969177

@@ -2240,7 +2240,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 178: 0.20730628073215485
+INFO:lightwood-2394:Loss @ epoch 178: 0.20730628073215485

@@ -2248,7 +2248,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 179: 0.20635393261909485
+INFO:lightwood-2394:Loss @ epoch 179: 0.20635393261909485

@@ -2256,7 +2256,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 180: 0.20470596849918365
+INFO:lightwood-2394:Loss @ epoch 180: 0.20470596849918365

@@ -2264,7 +2264,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 181: 0.2016059160232544
+INFO:lightwood-2394:Loss @ epoch 181: 0.2016059160232544

@@ -2272,7 +2272,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 182: 0.2004680186510086
+INFO:lightwood-2394:Loss @ epoch 182: 0.2004680186510086

@@ -2280,7 +2280,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 183: 0.1995442509651184
+INFO:lightwood-2394:Loss @ epoch 183: 0.1995442509651184

@@ -2288,7 +2288,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 184: 0.1995476931333542
+INFO:lightwood-2394:Loss @ epoch 184: 0.1995476931333542

@@ -2296,7 +2296,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 185: 0.1985597461462021
+INFO:lightwood-2394:Loss @ epoch 185: 0.1985597461462021

@@ -2304,7 +2304,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 186: 0.19704405963420868
+INFO:lightwood-2394:Loss @ epoch 186: 0.19704405963420868

@@ -2312,7 +2312,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 187: 0.19429439306259155
+INFO:lightwood-2394:Loss @ epoch 187: 0.19429439306259155

@@ -2320,7 +2320,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 188: 0.1931215077638626
+INFO:lightwood-2394:Loss @ epoch 188: 0.1931215077638626

@@ -2328,7 +2328,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 189: 0.19224728643894196
+INFO:lightwood-2394:Loss @ epoch 189: 0.19224728643894196

@@ -2336,7 +2336,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 190: 0.1922168731689453
+INFO:lightwood-2394:Loss @ epoch 190: 0.1922168731689453

@@ -2344,7 +2344,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 191: 0.19120150804519653
+INFO:lightwood-2394:Loss @ epoch 191: 0.19120150804519653

@@ -2352,7 +2352,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 192: 0.1897118091583252
+INFO:lightwood-2394:Loss @ epoch 192: 0.1897118091583252

@@ -2360,7 +2360,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 193: 0.187192901968956
+INFO:lightwood-2394:Loss @ epoch 193: 0.187192901968956

@@ -2368,7 +2368,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 194: 0.18604235351085663
+INFO:lightwood-2394:Loss @ epoch 194: 0.18604235351085663

@@ -2376,7 +2376,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 195: 0.18525990843772888
+INFO:lightwood-2394:Loss @ epoch 195: 0.18525990843772888

@@ -2384,7 +2384,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 196: 0.18517257273197174
+INFO:lightwood-2394:Loss @ epoch 196: 0.18517257273197174

@@ -2392,7 +2392,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 197: 0.1841844767332077
+INFO:lightwood-2394:Loss @ epoch 197: 0.1841844767332077

@@ -2400,7 +2400,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 198: 0.18275843560695648
+INFO:lightwood-2394:Loss @ epoch 198: 0.18275843560695648

@@ -2408,7 +2408,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 199: 0.18052375316619873
+INFO:lightwood-2394:Loss @ epoch 199: 0.18052375316619873

@@ -2416,7 +2416,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 200: 0.1795222908258438
+INFO:lightwood-2394:Loss @ epoch 200: 0.1795222908258438

@@ -2424,7 +2424,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 201: 0.17878693342208862
+INFO:lightwood-2394:Loss @ epoch 201: 0.17878693342208862

@@ -2432,7 +2432,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 202: 0.17881926894187927
+INFO:lightwood-2394:Loss @ epoch 202: 0.17881926894187927

@@ -2440,7 +2440,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 203: 0.17786364257335663
+INFO:lightwood-2394:Loss @ epoch 203: 0.17786364257335663

@@ -2448,7 +2448,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 204: 0.17654098570346832
+INFO:lightwood-2394:Loss @ epoch 204: 0.17654098570346832

@@ -2456,7 +2456,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 205: 0.17458021640777588
+INFO:lightwood-2394:Loss @ epoch 205: 0.17458021640777588

@@ -2464,7 +2464,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 206: 0.17358489334583282
+INFO:lightwood-2394:Loss @ epoch 206: 0.17358489334583282

@@ -2472,7 +2472,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 207: 0.1728101521730423
+INFO:lightwood-2394:Loss @ epoch 207: 0.1728101521730423

@@ -2480,7 +2480,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 208: 0.17278429865837097
+INFO:lightwood-2394:Loss @ epoch 208: 0.17278429865837097

@@ -2488,7 +2488,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 209: 0.17180292308330536
+INFO:lightwood-2394:Loss @ epoch 209: 0.17180292308330536

@@ -2496,7 +2496,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 210: 0.17050613462924957
+INFO:lightwood-2394:Loss @ epoch 210: 0.17050613462924957

@@ -2504,7 +2504,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 211: 0.16873842477798462
+INFO:lightwood-2394:Loss @ epoch 211: 0.16873842477798462

@@ -2512,7 +2512,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 212: 0.1677248477935791
+INFO:lightwood-2394:Loss @ epoch 212: 0.1677248477935791

@@ -2520,7 +2520,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 213: 0.16707710921764374
+INFO:lightwood-2394:Loss @ epoch 213: 0.16707710921764374

@@ -2528,7 +2528,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 214: 0.1671123504638672
+INFO:lightwood-2394:Loss @ epoch 214: 0.1671123504638672

@@ -2536,7 +2536,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 215: 0.16612616181373596
+INFO:lightwood-2394:Loss @ epoch 215: 0.16612616181373596

@@ -2544,7 +2544,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 216: 0.16487975418567657
+INFO:lightwood-2394:Loss @ epoch 216: 0.16487975418567657

@@ -2552,7 +2552,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 217: 0.16339382529258728
+INFO:lightwood-2394:Loss @ epoch 217: 0.16339382529258728

@@ -2560,7 +2560,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 218: 0.1624278575181961
+INFO:lightwood-2394:Loss @ epoch 218: 0.1624278575181961

@@ -2568,7 +2568,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 219: 0.16172048449516296
+INFO:lightwood-2394:Loss @ epoch 219: 0.16172048449516296

@@ -2576,7 +2576,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 220: 0.16165515780448914
+INFO:lightwood-2394:Loss @ epoch 220: 0.16165515780448914

@@ -2584,7 +2584,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 221: 0.16061937808990479
+INFO:lightwood-2394:Loss @ epoch 221: 0.16061937808990479

@@ -2592,7 +2592,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 222: 0.1594206690788269
+INFO:lightwood-2394:Loss @ epoch 222: 0.1594206690788269

@@ -2600,7 +2600,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 223: 0.15802235901355743
+INFO:lightwood-2394:Loss @ epoch 223: 0.15802235901355743

@@ -2608,7 +2608,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 224: 0.15704363584518433
+INFO:lightwood-2394:Loss @ epoch 224: 0.15704363584518433

@@ -2616,7 +2616,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 225: 0.15640243887901306
+INFO:lightwood-2394:Loss @ epoch 225: 0.15640243887901306

@@ -2624,7 +2624,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 226: 0.15635541081428528
+INFO:lightwood-2394:Loss @ epoch 226: 0.15635541081428528

@@ -2632,7 +2632,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 227: 0.15536457300186157
+INFO:lightwood-2394:Loss @ epoch 227: 0.15536457300186157

@@ -2640,7 +2640,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 228: 0.154209703207016
+INFO:lightwood-2394:Loss @ epoch 228: 0.154209703207016

@@ -2648,7 +2648,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 229: 0.15291643142700195
+INFO:lightwood-2394:Loss @ epoch 229: 0.15291643142700195

@@ -2656,7 +2656,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 230: 0.15191468596458435
+INFO:lightwood-2394:Loss @ epoch 230: 0.15191468596458435

@@ -2664,7 +2664,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 231: 0.15118129551410675
+INFO:lightwood-2394:Loss @ epoch 231: 0.15118129551410675

@@ -2672,7 +2672,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 232: 0.151133194565773
+INFO:lightwood-2394:Loss @ epoch 232: 0.151133194565773

@@ -2680,7 +2680,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 233: 0.1501670926809311
+INFO:lightwood-2394:Loss @ epoch 233: 0.1501670926809311

@@ -2688,7 +2688,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 234: 0.14912192523479462
+INFO:lightwood-2394:Loss @ epoch 234: 0.14912192523479462

@@ -2696,7 +2696,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 235: 0.1481197327375412
+INFO:lightwood-2394:Loss @ epoch 235: 0.1481197327375412

@@ -2704,7 +2704,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 236: 0.14712536334991455
+INFO:lightwood-2394:Loss @ epoch 236: 0.14712536334991455

@@ -2712,7 +2712,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 237: 0.14660944044589996
+INFO:lightwood-2394:Loss @ epoch 237: 0.14660944044589996

@@ -2720,7 +2720,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 238: 0.14649781584739685
+INFO:lightwood-2394:Loss @ epoch 238: 0.14649781584739685

@@ -2728,7 +2728,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 239: 0.145524799823761
+INFO:lightwood-2394:Loss @ epoch 239: 0.145524799823761

@@ -2736,7 +2736,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 240: 0.14443959295749664
+INFO:lightwood-2394:Loss @ epoch 240: 0.14443959295749664

@@ -2744,7 +2744,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 241: 0.14341002702713013
+INFO:lightwood-2394:Loss @ epoch 241: 0.14341002702713013

@@ -2752,7 +2752,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 242: 0.14249812066555023
+INFO:lightwood-2394:Loss @ epoch 242: 0.14249812066555023

@@ -2760,7 +2760,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 243: 0.14185366034507751
+INFO:lightwood-2394:Loss @ epoch 243: 0.14185366034507751

@@ -2768,7 +2768,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 244: 0.1419423222541809
+INFO:lightwood-2394:Loss @ epoch 244: 0.1419423222541809

@@ -2776,7 +2776,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 245: 0.1409326195716858
+INFO:lightwood-2394:Loss @ epoch 245: 0.1409326195716858

@@ -2784,7 +2784,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 246: 0.1399424970149994
+INFO:lightwood-2394:Loss @ epoch 246: 0.1399424970149994

@@ -2792,7 +2792,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 247: 0.13920661807060242
+INFO:lightwood-2394:Loss @ epoch 247: 0.13920661807060242

@@ -2800,7 +2800,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 248: 0.13832959532737732
+INFO:lightwood-2394:Loss @ epoch 248: 0.13832959532737732

@@ -2808,7 +2808,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 249: 0.13784818351268768
+INFO:lightwood-2394:Loss @ epoch 249: 0.13784818351268768

@@ -2816,7 +2816,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 250: 0.13769637048244476
+INFO:lightwood-2394:Loss @ epoch 250: 0.13769637048244476

@@ -2824,7 +2824,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 251: 0.1367887407541275
+INFO:lightwood-2394:Loss @ epoch 251: 0.1367887407541275

@@ -2832,7 +2832,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 252: 0.13588252663612366
+INFO:lightwood-2394:Loss @ epoch 252: 0.13588252663612366

@@ -2840,7 +2840,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 253: 0.13521170616149902
+INFO:lightwood-2394:Loss @ epoch 253: 0.13521170616149902

@@ -2848,7 +2848,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 254: 0.13428467512130737
+INFO:lightwood-2394:Loss @ epoch 254: 0.13428467512130737

@@ -2856,7 +2856,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 255: 0.13384407758712769
+INFO:lightwood-2394:Loss @ epoch 255: 0.13384407758712769

@@ -2864,7 +2864,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 256: 0.13372991979122162
+INFO:lightwood-2394:Loss @ epoch 256: 0.13372991979122162

@@ -2872,7 +2872,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 257: 0.13274942338466644
+INFO:lightwood-2394:Loss @ epoch 257: 0.13274942338466644

@@ -2880,7 +2880,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 258: 0.13186557590961456
+INFO:lightwood-2394:Loss @ epoch 258: 0.13186557590961456

@@ -2888,7 +2888,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 259: 0.13135230541229248
+INFO:lightwood-2394:Loss @ epoch 259: 0.13135230541229248

@@ -2896,7 +2896,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 260: 0.13046588003635406
+INFO:lightwood-2394:Loss @ epoch 260: 0.13046588003635406

@@ -2904,7 +2904,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 261: 0.12980172038078308
+INFO:lightwood-2394:Loss @ epoch 261: 0.12980172038078308

@@ -2912,7 +2912,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 262: 0.12982229888439178
+INFO:lightwood-2394:Loss @ epoch 262: 0.12982229888439178

@@ -2920,7 +2920,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 263: 0.12882845103740692
+INFO:lightwood-2394:Loss @ epoch 263: 0.12882845103740692

@@ -2928,7 +2928,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 264: 0.12798714637756348
+INFO:lightwood-2394:Loss @ epoch 264: 0.12798714637756348

@@ -2936,7 +2936,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 265: 0.12758088111877441
+INFO:lightwood-2394:Loss @ epoch 265: 0.12758088111877441

@@ -2944,7 +2944,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 266: 0.12660843133926392
+INFO:lightwood-2394:Loss @ epoch 266: 0.12660843133926392

@@ -2952,7 +2952,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 267: 0.1261577606201172
+INFO:lightwood-2394:Loss @ epoch 267: 0.1261577606201172

@@ -2960,7 +2960,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 268: 0.1260918229818344
+INFO:lightwood-2394:Loss @ epoch 268: 0.1260918229818344

@@ -2968,7 +2968,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 269: 0.12515920400619507
+INFO:lightwood-2394:Loss @ epoch 269: 0.12515920400619507

@@ -2976,7 +2976,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 270: 0.12436933070421219
+INFO:lightwood-2394:Loss @ epoch 270: 0.12436933070421219

@@ -2984,7 +2984,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 271: 0.12405422329902649
+INFO:lightwood-2394:Loss @ epoch 271: 0.12405422329902649

@@ -2992,7 +2992,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 272: 0.12305409461259842
+INFO:lightwood-2394:Loss @ epoch 272: 0.12305409461259842

@@ -3000,7 +3000,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 273: 0.12272939085960388
+INFO:lightwood-2394:Loss @ epoch 273: 0.12272939085960388

@@ -3008,7 +3008,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 274: 0.12267134338617325
+INFO:lightwood-2394:Loss @ epoch 274: 0.12267134338617325

@@ -3016,7 +3016,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 275: 0.12182944267988205
+INFO:lightwood-2394:Loss @ epoch 275: 0.12182944267988205

@@ -3024,7 +3024,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 276: 0.12103450298309326
+INFO:lightwood-2394:Loss @ epoch 276: 0.12103450298309326

@@ -3032,7 +3032,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 277: 0.12083345651626587
+INFO:lightwood-2394:Loss @ epoch 277: 0.12083345651626587

@@ -3040,7 +3040,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 278: 0.11998103559017181
+INFO:lightwood-2394:Loss @ epoch 278: 0.11998103559017181

@@ -3048,7 +3048,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 279: 0.11937755346298218
+INFO:lightwood-2394:Loss @ epoch 279: 0.11937755346298218

@@ -3056,7 +3056,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 280: 0.1195112019777298
+INFO:lightwood-2394:Loss @ epoch 280: 0.1195112019777298

@@ -3064,7 +3064,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 281: 0.1185888797044754
+INFO:lightwood-2394:Loss @ epoch 281: 0.1185888797044754

@@ -3072,7 +3072,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 282: 0.11789504438638687
+INFO:lightwood-2394:Loss @ epoch 282: 0.11789504438638687

@@ -3080,7 +3080,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 283: 0.11783000081777573
+INFO:lightwood-2394:Loss @ epoch 283: 0.11783000081777573

@@ -3088,7 +3088,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 284: 0.11681754887104034
+INFO:lightwood-2394:Loss @ epoch 284: 0.11681754887104034

@@ -3096,7 +3096,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 285: 0.11649196594953537
+INFO:lightwood-2394:Loss @ epoch 285: 0.11649196594953537

@@ -3104,7 +3104,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 286: 0.11648327857255936
+INFO:lightwood-2394:Loss @ epoch 286: 0.11648327857255936

@@ -3112,7 +3112,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 287: 0.11562418192625046
+INFO:lightwood-2394:Loss @ epoch 287: 0.11562418192625046

@@ -3120,7 +3120,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 288: 0.11489420384168625
+INFO:lightwood-2394:Loss @ epoch 288: 0.11489420384168625

@@ -3128,7 +3128,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 289: 0.11485717445611954
+INFO:lightwood-2394:Loss @ epoch 289: 0.11485717445611954

@@ -3136,7 +3136,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 290: 0.11407709866762161
+INFO:lightwood-2394:Loss @ epoch 290: 0.11407709866762161

@@ -3144,7 +3144,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 291: 0.11348505318164825
+INFO:lightwood-2394:Loss @ epoch 291: 0.11348505318164825

@@ -3152,7 +3152,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 292: 0.11358898878097534
+INFO:lightwood-2394:Loss @ epoch 292: 0.11358898878097534

@@ -3160,7 +3160,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 293: 0.11268813163042068
+INFO:lightwood-2394:Loss @ epoch 293: 0.11268813163042068

@@ -3168,7 +3168,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 294: 0.11207651346921921
+INFO:lightwood-2394:Loss @ epoch 294: 0.11207651346921921

@@ -3176,7 +3176,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 295: 0.11220688372850418
+INFO:lightwood-2394:Loss @ epoch 295: 0.11220688372850418

@@ -3184,7 +3184,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 296: 0.11118005961179733
+INFO:lightwood-2394:Loss @ epoch 296: 0.11118005961179733

@@ -3192,7 +3192,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 297: 0.11089354008436203
+INFO:lightwood-2394:Loss @ epoch 297: 0.11089354008436203

@@ -3200,7 +3200,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 298: 0.11088859289884567
+INFO:lightwood-2394:Loss @ epoch 298: 0.11088859289884567

@@ -3208,7 +3208,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 299: 0.1100316271185875
+INFO:lightwood-2394:Loss @ epoch 299: 0.1100316271185875

@@ -3216,7 +3216,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 300: 0.1093800961971283
+INFO:lightwood-2394:Loss @ epoch 300: 0.1093800961971283

@@ -3224,7 +3224,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 301: 0.10955681651830673
+INFO:lightwood-2394:Loss @ epoch 301: 0.10955681651830673

@@ -3232,7 +3232,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 302: 0.10869839787483215
+INFO:lightwood-2394:Loss @ epoch 302: 0.10869839787483215

@@ -3240,7 +3240,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 303: 0.10815789550542831
+INFO:lightwood-2394:Loss @ epoch 303: 0.10815789550542831

@@ -3248,7 +3248,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 304: 0.10832306742668152
+INFO:lightwood-2394:Loss @ epoch 304: 0.10832306742668152

@@ -3256,7 +3256,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 305: 0.10742544382810593
+INFO:lightwood-2394:Loss @ epoch 305: 0.10742544382810593

@@ -3264,7 +3264,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 306: 0.10682710260152817
+INFO:lightwood-2394:Loss @ epoch 306: 0.10682710260152817

@@ -3272,7 +3272,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 307: 0.10698221623897552
+INFO:lightwood-2394:Loss @ epoch 307: 0.10698221623897552

@@ -3280,7 +3280,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 308: 0.10616409033536911
+INFO:lightwood-2394:Loss @ epoch 308: 0.10616409033536911

@@ -3288,7 +3288,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 309: 0.10568025708198547
+INFO:lightwood-2394:Loss @ epoch 309: 0.10568025708198547

@@ -3296,7 +3296,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 310: 0.10574078559875488
+INFO:lightwood-2394:Loss @ epoch 310: 0.10574078559875488

@@ -3304,7 +3304,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 311: 0.10493995994329453
+INFO:lightwood-2394:Loss @ epoch 311: 0.10493995994329453

@@ -3312,7 +3312,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 312: 0.10438455641269684
+INFO:lightwood-2394:Loss @ epoch 312: 0.10438455641269684

@@ -3320,7 +3320,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 313: 0.10449497401714325
+INFO:lightwood-2394:Loss @ epoch 313: 0.10449497401714325

@@ -3328,7 +3328,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 314: 0.10372297465801239
+INFO:lightwood-2394:Loss @ epoch 314: 0.10372297465801239

@@ -3336,7 +3336,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 315: 0.10320515185594559
+INFO:lightwood-2394:Loss @ epoch 315: 0.10320515185594559

@@ -3344,7 +3344,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 316: 0.10330332815647125
+INFO:lightwood-2394:Loss @ epoch 316: 0.10330332815647125

@@ -3352,7 +3352,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 317: 0.10239537805318832
+INFO:lightwood-2394:Loss @ epoch 317: 0.10239537805318832

@@ -3360,7 +3360,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 318: 0.10185065120458603
+INFO:lightwood-2394:Loss @ epoch 318: 0.10185065120458603

@@ -3368,7 +3368,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 319: 0.10217708349227905
+INFO:lightwood-2394:Loss @ epoch 319: 0.10217708349227905

@@ -3376,7 +3376,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 320: 0.10135672241449356
+INFO:lightwood-2394:Loss @ epoch 320: 0.10135672241449356

@@ -3384,7 +3384,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 321: 0.10087659955024719
+INFO:lightwood-2394:Loss @ epoch 321: 0.10087659955024719

@@ -3392,7 +3392,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 322: 0.10087589174509048
+INFO:lightwood-2394:Loss @ epoch 322: 0.10087589174509048

@@ -3400,7 +3400,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 323: 0.10005565732717514
+INFO:lightwood-2394:Loss @ epoch 323: 0.10005565732717514

@@ -3408,7 +3408,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 324: 0.09949999302625656
+INFO:lightwood-2394:Loss @ epoch 324: 0.09949999302625656

@@ -3416,7 +3416,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 325: 0.09970266371965408
+INFO:lightwood-2394:Loss @ epoch 325: 0.09970266371965408

@@ -3424,7 +3424,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 326: 0.09918338060379028
+INFO:lightwood-2394:Loss @ epoch 326: 0.09918338060379028

@@ -3432,7 +3432,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 327: 0.09840800613164902
+INFO:lightwood-2394:Loss @ epoch 327: 0.09840800613164902

@@ -3440,7 +3440,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 328: 0.09882311522960663
+INFO:lightwood-2394:Loss @ epoch 328: 0.09882311522960663

@@ -3448,7 +3448,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 329: 0.09775345772504807
+INFO:lightwood-2394:Loss @ epoch 329: 0.09775345772504807

@@ -3456,7 +3456,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 330: 0.09729817509651184
+INFO:lightwood-2394:Loss @ epoch 330: 0.09729817509651184

@@ -3464,7 +3464,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 331: 0.09763044863939285
+INFO:lightwood-2394:Loss @ epoch 331: 0.09763044863939285

@@ -3472,7 +3472,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 332: 0.0967596173286438
+INFO:lightwood-2394:Loss @ epoch 332: 0.0967596173286438

@@ -3480,7 +3480,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 333: 0.09642492234706879
+INFO:lightwood-2394:Loss @ epoch 333: 0.09642492234706879

@@ -3488,7 +3488,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 334: 0.09656761586666107
+INFO:lightwood-2394:Loss @ epoch 334: 0.09656761586666107

@@ -3496,7 +3496,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 335: 0.09573261439800262
+INFO:lightwood-2394:Loss @ epoch 335: 0.09573261439800262

@@ -3504,7 +3504,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 336: 0.09523642063140869
+INFO:lightwood-2394:Loss @ epoch 336: 0.09523642063140869

@@ -3512,7 +3512,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 337: 0.09568659961223602
+INFO:lightwood-2394:Loss @ epoch 337: 0.09568659961223602

@@ -3520,7 +3520,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 338: 0.09509280323982239
+INFO:lightwood-2394:Loss @ epoch 338: 0.09509280323982239

@@ -3528,7 +3528,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 339: 0.09460369497537613
+INFO:lightwood-2394:Loss @ epoch 339: 0.09460369497537613

@@ -3536,7 +3536,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 340: 0.09476538747549057
+INFO:lightwood-2394:Loss @ epoch 340: 0.09476538747549057

@@ -3544,7 +3544,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 341: 0.09388881921768188
+INFO:lightwood-2394:Loss @ epoch 341: 0.09388881921768188

@@ -3552,7 +3552,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 342: 0.09349637478590012
+INFO:lightwood-2394:Loss @ epoch 342: 0.09349637478590012

@@ -3560,7 +3560,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 343: 0.09398090839385986
+INFO:lightwood-2394:Loss @ epoch 343: 0.09398090839385986

@@ -3568,7 +3568,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 344: 0.09314301609992981
+INFO:lightwood-2394:Loss @ epoch 344: 0.09314301609992981

@@ -3576,7 +3576,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 345: 0.09281699359416962
+INFO:lightwood-2394:Loss @ epoch 345: 0.09281699359416962

@@ -3584,7 +3584,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 346: 0.09290202707052231
+INFO:lightwood-2394:Loss @ epoch 346: 0.09290202707052231

@@ -3592,7 +3592,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 347: 0.09209518879652023
+INFO:lightwood-2394:Loss @ epoch 347: 0.09209518879652023

@@ -3600,7 +3600,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 348: 0.09171803295612335
+INFO:lightwood-2394:Loss @ epoch 348: 0.09171803295612335

@@ -3608,7 +3608,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 349: 0.09221566468477249
+INFO:lightwood-2394:Loss @ epoch 349: 0.09221566468477249

@@ -3616,7 +3616,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 350: 0.09150414168834686
+INFO:lightwood-2394:Loss @ epoch 350: 0.09150414168834686

@@ -3624,7 +3624,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 351: 0.0910501629114151
+INFO:lightwood-2394:Loss @ epoch 351: 0.0910501629114151

@@ -3632,7 +3632,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 352: 0.09118885546922684
+INFO:lightwood-2394:Loss @ epoch 352: 0.09118885546922684

@@ -3640,7 +3640,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 353: 0.09043896198272705
+INFO:lightwood-2394:Loss @ epoch 353: 0.09043896198272705

@@ -3648,7 +3648,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 354: 0.09006913751363754
+INFO:lightwood-2394:Loss @ epoch 354: 0.09006913751363754

@@ -3656,7 +3656,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 355: 0.09049264341592789
+INFO:lightwood-2394:Loss @ epoch 355: 0.09049264341592789

@@ -3664,7 +3664,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 356: 0.0898597463965416
+INFO:lightwood-2394:Loss @ epoch 356: 0.0898597463965416

@@ -3672,7 +3672,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 357: 0.08943390846252441
+INFO:lightwood-2394:Loss @ epoch 357: 0.08943390846252441

@@ -3680,7 +3680,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 358: 0.0896739661693573
+INFO:lightwood-2394:Loss @ epoch 358: 0.0896739661693573

@@ -3688,7 +3688,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 359: 0.08882326632738113
+INFO:lightwood-2394:Loss @ epoch 359: 0.08882326632738113

@@ -3696,7 +3696,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 360: 0.08850156515836716
+INFO:lightwood-2394:Loss @ epoch 360: 0.08850156515836716

@@ -3704,7 +3704,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 361: 0.08897048979997635
+INFO:lightwood-2394:Loss @ epoch 361: 0.08897048979997635

@@ -3712,7 +3712,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 362: 0.08849596232175827
+INFO:lightwood-2394:Loss @ epoch 362: 0.08849596232175827

@@ -3720,7 +3720,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 363: 0.08790712803602219
+INFO:lightwood-2394:Loss @ epoch 363: 0.08790712803602219

@@ -3728,7 +3728,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 364: 0.08821234852075577
+INFO:lightwood-2394:Loss @ epoch 364: 0.08821234852075577

@@ -3736,7 +3736,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 365: 0.08732891082763672
+INFO:lightwood-2394:Loss @ epoch 365: 0.08732891082763672

@@ -3744,7 +3744,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 366: 0.08704856038093567
+INFO:lightwood-2394:Loss @ epoch 366: 0.08704856038093567

@@ -3752,7 +3752,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 367: 0.08765564113855362
+INFO:lightwood-2394:Loss @ epoch 367: 0.08765564113855362

@@ -3760,7 +3760,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 368: 0.08696923404932022
+INFO:lightwood-2394:Loss @ epoch 368: 0.08696923404932022

@@ -3768,7 +3768,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 369: 0.08649873733520508
+INFO:lightwood-2394:Loss @ epoch 369: 0.08649873733520508

@@ -3776,7 +3776,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 370: 0.08676613122224808
+INFO:lightwood-2394:Loss @ epoch 370: 0.08676613122224808

@@ -3784,7 +3784,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 371: 0.08599219471216202
+INFO:lightwood-2394:Loss @ epoch 371: 0.08599219471216202

@@ -3792,7 +3792,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 372: 0.08565033972263336
+INFO:lightwood-2394:Loss @ epoch 372: 0.08565033972263336

@@ -3800,7 +3800,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 373: 0.08618329465389252
+INFO:lightwood-2394:Loss @ epoch 373: 0.08618329465389252

@@ -3808,7 +3808,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 374: 0.08559156954288483
+INFO:lightwood-2394:Loss @ epoch 374: 0.08559156954288483

@@ -3816,7 +3816,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 375: 0.08509930223226547
+INFO:lightwood-2394:Loss @ epoch 375: 0.08509930223226547

@@ -3824,7 +3824,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 376: 0.08543801307678223
+INFO:lightwood-2394:Loss @ epoch 376: 0.08543801307678223

@@ -3832,7 +3832,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 377: 0.084554523229599
+INFO:lightwood-2394:Loss @ epoch 377: 0.084554523229599

@@ -3840,7 +3840,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 378: 0.08425222337245941
+INFO:lightwood-2394:Loss @ epoch 378: 0.08425222337245941

@@ -3848,7 +3848,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 379: 0.08496475219726562
+INFO:lightwood-2394:Loss @ epoch 379: 0.08496475219726562

@@ -3856,7 +3856,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 380: 0.08428442478179932
+INFO:lightwood-2394:Loss @ epoch 380: 0.08428442478179932

@@ -3864,7 +3864,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 381: 0.08389458060264587
+INFO:lightwood-2394:Loss @ epoch 381: 0.08389458060264587

@@ -3872,7 +3872,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 382: 0.08416417241096497
+INFO:lightwood-2394:Loss @ epoch 382: 0.08416417241096497

@@ -3880,7 +3880,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 383: 0.08331726491451263
+INFO:lightwood-2394:Loss @ epoch 383: 0.08331726491451263

@@ -3888,7 +3888,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 384: 0.08304726332426071
+INFO:lightwood-2394:Loss @ epoch 384: 0.08304726332426071

@@ -3896,7 +3896,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 385: 0.0837259590625763
+INFO:lightwood-2394:Loss @ epoch 385: 0.0837259590625763

@@ -3904,7 +3904,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 386: 0.08301664143800735
+INFO:lightwood-2394:Loss @ epoch 386: 0.08301664143800735

@@ -3912,7 +3912,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 387: 0.08279375731945038
+INFO:lightwood-2394:Loss @ epoch 387: 0.08279375731945038

@@ -3920,7 +3920,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 388: 0.08285657316446304
+INFO:lightwood-2394:Loss @ epoch 388: 0.08285657316446304

@@ -3928,7 +3928,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 389: 0.0822003185749054
+INFO:lightwood-2394:Loss @ epoch 389: 0.0822003185749054

@@ -3936,7 +3936,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 390: 0.08189017325639725
+INFO:lightwood-2394:Loss @ epoch 390: 0.08189017325639725

@@ -3944,7 +3944,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 391: 0.08244460821151733
+INFO:lightwood-2394:Loss @ epoch 391: 0.08244460821151733

@@ -3952,7 +3952,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 392: 0.08176209777593613
+INFO:lightwood-2394:Loss @ epoch 392: 0.08176209777593613

@@ -3960,7 +3960,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 393: 0.08143384009599686
+INFO:lightwood-2394:Loss @ epoch 393: 0.08143384009599686

@@ -3968,7 +3968,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 394: 0.08153267949819565
+INFO:lightwood-2394:Loss @ epoch 394: 0.08153267949819565

@@ -3976,7 +3976,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 395: 0.08074252307415009
+INFO:lightwood-2394:Loss @ epoch 395: 0.08074252307415009

@@ -3984,7 +3984,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 396: 0.0804641842842102
+INFO:lightwood-2394:Loss @ epoch 396: 0.0804641842842102

@@ -3992,7 +3992,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 397: 0.08112648874521255
+INFO:lightwood-2394:Loss @ epoch 397: 0.08112648874521255

@@ -4000,7 +4000,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 398: 0.0804068073630333
+INFO:lightwood-2394:Loss @ epoch 398: 0.0804068073630333

@@ -4008,7 +4008,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 399: 0.08000007271766663
+INFO:lightwood-2394:Loss @ epoch 399: 0.08000007271766663

@@ -4016,7 +4016,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 400: 0.08030638843774796
+INFO:lightwood-2394:Loss @ epoch 400: 0.08030638843774796

@@ -4024,7 +4024,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 401: 0.07946185022592545
+INFO:lightwood-2394:Loss @ epoch 401: 0.07946185022592545

@@ -4032,7 +4032,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 402: 0.07926557213068008
+INFO:lightwood-2394:Loss @ epoch 402: 0.07926557213068008

@@ -4040,7 +4040,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 403: 0.07995376735925674
+INFO:lightwood-2394:Loss @ epoch 403: 0.07995376735925674

@@ -4048,7 +4048,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 404: 0.07914069294929504
+INFO:lightwood-2394:Loss @ epoch 404: 0.07914069294929504

@@ -4056,7 +4056,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 405: 0.07901032269001007
+INFO:lightwood-2394:Loss @ epoch 405: 0.07901032269001007

@@ -4064,7 +4064,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 406: 0.07910943776369095
+INFO:lightwood-2394:Loss @ epoch 406: 0.07910943776369095

@@ -4072,7 +4072,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 407: 0.07840055227279663
+INFO:lightwood-2394:Loss @ epoch 407: 0.07840055227279663

@@ -4080,7 +4080,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 408: 0.07814037799835205
+INFO:lightwood-2394:Loss @ epoch 408: 0.07814037799835205

@@ -4088,7 +4088,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 409: 0.07874786853790283
+INFO:lightwood-2394:Loss @ epoch 409: 0.07874786853790283

@@ -4096,7 +4096,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 410: 0.07819069921970367
+INFO:lightwood-2394:Loss @ epoch 410: 0.07819069921970367

@@ -4104,7 +4104,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 411: 0.07780887931585312
+INFO:lightwood-2394:Loss @ epoch 411: 0.07780887931585312

@@ -4112,7 +4112,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 412: 0.07802116870880127
+INFO:lightwood-2394:Loss @ epoch 412: 0.07802116870880127

@@ -4120,7 +4120,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 413: 0.0772867277264595
+INFO:lightwood-2394:Loss @ epoch 413: 0.0772867277264595

@@ -4128,7 +4128,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 414: 0.07709880918264389
+INFO:lightwood-2394:Loss @ epoch 414: 0.07709880918264389

@@ -4136,7 +4136,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 415: 0.0776868537068367
+INFO:lightwood-2394:Loss @ epoch 415: 0.0776868537068367

@@ -4144,7 +4144,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 416: 0.07716330885887146
+INFO:lightwood-2394:Loss @ epoch 416: 0.07716330885887146

@@ -4152,7 +4152,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 417: 0.07688125967979431
+INFO:lightwood-2394:Loss @ epoch 417: 0.07688125967979431

@@ -4160,7 +4160,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 418: 0.07698465138673782
+INFO:lightwood-2394:Loss @ epoch 418: 0.07698465138673782

@@ -4168,7 +4168,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 419: 0.0762372612953186
+INFO:lightwood-2394:Loss @ epoch 419: 0.0762372612953186

@@ -4176,7 +4176,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 420: 0.07603802531957626
+INFO:lightwood-2394:Loss @ epoch 420: 0.07603802531957626

@@ -4184,7 +4184,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 421: 0.07675285637378693
+INFO:lightwood-2394:Loss @ epoch 421: 0.07675285637378693

@@ -4192,7 +4192,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 422: 0.07623977214097977
+INFO:lightwood-2394:Loss @ epoch 422: 0.07623977214097977

@@ -4200,7 +4200,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 423: 0.07567108422517776
+INFO:lightwood-2394:Loss @ epoch 423: 0.07567108422517776

@@ -4208,7 +4208,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 424: 0.07615751028060913
+INFO:lightwood-2394:Loss @ epoch 424: 0.07615751028060913

@@ -4216,7 +4216,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 425: 0.07526733726263046
+INFO:lightwood-2394:Loss @ epoch 425: 0.07526733726263046

@@ -4224,7 +4224,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 426: 0.07509555667638779
+INFO:lightwood-2394:Loss @ epoch 426: 0.07509555667638779

@@ -4232,7 +4232,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 427: 0.07569493353366852
+INFO:lightwood-2394:Loss @ epoch 427: 0.07569493353366852

@@ -4240,7 +4240,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 428: 0.07537294924259186
+INFO:lightwood-2394:Loss @ epoch 428: 0.07537294924259186

@@ -4248,7 +4248,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 429: 0.07467805594205856
+INFO:lightwood-2394:Loss @ epoch 429: 0.07467805594205856

@@ -4256,7 +4256,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 430: 0.07528648525476456
+INFO:lightwood-2394:Loss @ epoch 430: 0.07528648525476456

@@ -4264,7 +4264,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 431: 0.07435967028141022
+INFO:lightwood-2394:Loss @ epoch 431: 0.07435967028141022

@@ -4272,7 +4272,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 432: 0.07422596961259842
+INFO:lightwood-2394:Loss @ epoch 432: 0.07422596961259842

@@ -4280,7 +4280,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 433: 0.07503972947597504
+INFO:lightwood-2394:Loss @ epoch 433: 0.07503972947597504

@@ -4288,7 +4288,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 434: 0.07434249669313431
+INFO:lightwood-2394:Loss @ epoch 434: 0.07434249669313431

@@ -4296,7 +4296,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 435: 0.07409335672855377
+INFO:lightwood-2394:Loss @ epoch 435: 0.07409335672855377

@@ -4304,7 +4304,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 436: 0.07420685887336731
+INFO:lightwood-2394:Loss @ epoch 436: 0.07420685887336731

@@ -4312,7 +4312,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 437: 0.0735834538936615
+INFO:lightwood-2394:Loss @ epoch 437: 0.0735834538936615

@@ -4320,7 +4320,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 438: 0.07333341240882874
+INFO:lightwood-2394:Loss @ epoch 438: 0.07333341240882874

@@ -4328,7 +4328,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 439: 0.07391082495450974
+INFO:lightwood-2394:Loss @ epoch 439: 0.07391082495450974

@@ -4336,7 +4336,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 440: 0.07348911464214325
+INFO:lightwood-2394:Loss @ epoch 440: 0.07348911464214325

@@ -4344,7 +4344,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 441: 0.07308389991521835
+INFO:lightwood-2394:Loss @ epoch 441: 0.07308389991521835

@@ -4352,7 +4352,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 442: 0.07328886538743973
+INFO:lightwood-2394:Loss @ epoch 442: 0.07328886538743973

@@ -4360,7 +4360,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 443: 0.0725550651550293
+INFO:lightwood-2394:Loss @ epoch 443: 0.0725550651550293

@@ -4368,7 +4368,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 444: 0.07240220904350281
+INFO:lightwood-2394:Loss @ epoch 444: 0.07240220904350281

@@ -4376,7 +4376,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 445: 0.07308465242385864
+INFO:lightwood-2394:Loss @ epoch 445: 0.07308465242385864

@@ -4384,7 +4384,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 446: 0.07288312911987305
+INFO:lightwood-2394:Loss @ epoch 446: 0.07288312911987305

@@ -4392,7 +4392,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 447: 0.0722663402557373
+INFO:lightwood-2394:Loss @ epoch 447: 0.0722663402557373

@@ -4400,7 +4400,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 448: 0.07264856994152069
+INFO:lightwood-2394:Loss @ epoch 448: 0.07264856994152069

@@ -4408,7 +4408,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 449: 0.07182618230581284
+INFO:lightwood-2394:Loss @ epoch 449: 0.07182618230581284

@@ -4416,7 +4416,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 450: 0.07167533785104752
+INFO:lightwood-2394:Loss @ epoch 450: 0.07167533785104752

@@ -4424,7 +4424,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 451: 0.07241341471672058
+INFO:lightwood-2394:Loss @ epoch 451: 0.07241341471672058

@@ -4432,7 +4432,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 452: 0.07208056002855301
+INFO:lightwood-2394:Loss @ epoch 452: 0.07208056002855301

@@ -4440,7 +4440,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 453: 0.07154601812362671
+INFO:lightwood-2394:Loss @ epoch 453: 0.07154601812362671

@@ -4448,7 +4448,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 454: 0.07190731167793274
+INFO:lightwood-2394:Loss @ epoch 454: 0.07190731167793274

@@ -4456,7 +4456,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 455: 0.0710812360048294
+INFO:lightwood-2394:Loss @ epoch 455: 0.0710812360048294

@@ -4464,7 +4464,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 456: 0.07096673548221588
+INFO:lightwood-2394:Loss @ epoch 456: 0.07096673548221588

@@ -4472,7 +4472,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 457: 0.0718337818980217
+INFO:lightwood-2394:Loss @ epoch 457: 0.0718337818980217

@@ -4480,7 +4480,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 458: 0.07134897261857986
+INFO:lightwood-2394:Loss @ epoch 458: 0.07134897261857986

@@ -4488,7 +4488,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 459: 0.07083813846111298
+INFO:lightwood-2394:Loss @ epoch 459: 0.07083813846111298

@@ -4496,7 +4496,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 460: 0.07124733179807663
+INFO:lightwood-2394:Loss @ epoch 460: 0.07124733179807663

@@ -4504,7 +4504,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 461: 0.0705094262957573
+INFO:lightwood-2394:Loss @ epoch 461: 0.0705094262957573

@@ -4512,7 +4512,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 462: 0.07036501169204712
+INFO:lightwood-2394:Loss @ epoch 462: 0.07036501169204712

@@ -4520,7 +4520,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 463: 0.07111788541078568
+INFO:lightwood-2394:Loss @ epoch 463: 0.07111788541078568

@@ -4528,7 +4528,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 464: 0.07069509476423264
+INFO:lightwood-2394:Loss @ epoch 464: 0.07069509476423264

@@ -4536,7 +4536,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 465: 0.07026039808988571
+INFO:lightwood-2394:Loss @ epoch 465: 0.07026039808988571

@@ -4544,7 +4544,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 466: 0.07056906819343567
+INFO:lightwood-2394:Loss @ epoch 466: 0.07056906819343567

@@ -4552,7 +4552,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 467: 0.06981150805950165
+INFO:lightwood-2394:Loss @ epoch 467: 0.06981150805950165

@@ -4560,7 +4560,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 468: 0.06967213749885559
+INFO:lightwood-2394:Loss @ epoch 468: 0.06967213749885559

@@ -4568,7 +4568,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 469: 0.0704450011253357
+INFO:lightwood-2394:Loss @ epoch 469: 0.0704450011253357

@@ -4576,7 +4576,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 470: 0.07002224773168564
+INFO:lightwood-2394:Loss @ epoch 470: 0.07002224773168564

@@ -4584,7 +4584,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 471: 0.06954890489578247
+INFO:lightwood-2394:Loss @ epoch 471: 0.06954890489578247

@@ -4592,7 +4592,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 472: 0.07001929730176926
+INFO:lightwood-2394:Loss @ epoch 472: 0.07001929730176926

@@ -4600,7 +4600,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 473: 0.06918215751647949
+INFO:lightwood-2394:Loss @ epoch 473: 0.06918215751647949

@@ -4608,7 +4608,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 474: 0.06905678659677505
+INFO:lightwood-2394:Loss @ epoch 474: 0.06905678659677505

@@ -4616,7 +4616,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 475: 0.06994140148162842
+INFO:lightwood-2394:Loss @ epoch 475: 0.06994140148162842

@@ -4624,7 +4624,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 476: 0.06957031041383743
+INFO:lightwood-2394:Loss @ epoch 476: 0.06957031041383743

@@ -4632,7 +4632,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 477: 0.06890591233968735
+INFO:lightwood-2394:Loss @ epoch 477: 0.06890591233968735

@@ -4640,7 +4640,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 478: 0.06942413747310638
+INFO:lightwood-2394:Loss @ epoch 478: 0.06942413747310638

@@ -4648,7 +4648,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 479: 0.068662129342556
+INFO:lightwood-2394:Loss @ epoch 479: 0.068662129342556

@@ -4656,7 +4656,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 480: 0.0685315951704979
+INFO:lightwood-2394:Loss @ epoch 480: 0.0685315951704979

@@ -4664,7 +4664,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 481: 0.06919320672750473
+INFO:lightwood-2394:Loss @ epoch 481: 0.06919320672750473

@@ -4672,7 +4672,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 482: 0.06884051114320755
+INFO:lightwood-2394:Loss @ epoch 482: 0.06884051114320755

@@ -4680,7 +4680,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 483: 0.06852498650550842
+INFO:lightwood-2394:Loss @ epoch 483: 0.06852498650550842

@@ -4688,7 +4688,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 484: 0.06881336867809296
+INFO:lightwood-2394:Loss @ epoch 484: 0.06881336867809296

@@ -4696,7 +4696,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 485: 0.0681278333067894
+INFO:lightwood-2394:Loss @ epoch 485: 0.0681278333067894

@@ -4704,7 +4704,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 486: 0.06801153719425201
+INFO:lightwood-2394:Loss @ epoch 486: 0.06801153719425201

@@ -4712,7 +4712,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 487: 0.0688665509223938
+INFO:lightwood-2394:Loss @ epoch 487: 0.0688665509223938

@@ -4720,7 +4720,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 488: 0.06848578155040741
+INFO:lightwood-2394:Loss @ epoch 488: 0.06848578155040741

@@ -4728,7 +4728,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 489: 0.0680362805724144
+INFO:lightwood-2394:Loss @ epoch 489: 0.0680362805724144

@@ -4736,7 +4736,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 490: 0.0685308426618576
+INFO:lightwood-2394:Loss @ epoch 490: 0.0685308426618576

@@ -4744,7 +4744,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 491: 0.06770123541355133
+INFO:lightwood-2394:Loss @ epoch 491: 0.06770123541355133

@@ -4752,7 +4752,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 492: 0.06760372221469879
+INFO:lightwood-2394:Loss @ epoch 492: 0.06760372221469879

@@ -4760,7 +4760,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 493: 0.06856502592563629
+INFO:lightwood-2394:Loss @ epoch 493: 0.06856502592563629

@@ -4768,7 +4768,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 494: 0.0679614394903183
+INFO:lightwood-2394:Loss @ epoch 494: 0.0679614394903183

@@ -4776,7 +4776,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 495: 0.0675961971282959
+INFO:lightwood-2394:Loss @ epoch 495: 0.0675961971282959

@@ -4784,7 +4784,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 496: 0.06795072555541992
+INFO:lightwood-2394:Loss @ epoch 496: 0.06795072555541992

@@ -4792,7 +4792,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 497: 0.06731095910072327
+INFO:lightwood-2394:Loss @ epoch 497: 0.06731095910072327

@@ -4800,7 +4800,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 498: 0.06714644283056259
+INFO:lightwood-2394:Loss @ epoch 498: 0.06714644283056259

@@ -4808,7 +4808,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 499: 0.06786693632602692
+INFO:lightwood-2394:Loss @ epoch 499: 0.06786693632602692

@@ -4816,7 +4816,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 500: 0.06758256256580353
+INFO:lightwood-2394:Loss @ epoch 500: 0.06758256256580353

@@ -4824,7 +4824,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 501: 0.06698315590620041
+INFO:lightwood-2394:Loss @ epoch 501: 0.06698315590620041

@@ -4832,7 +4832,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 502: 0.06747950613498688
+INFO:lightwood-2394:Loss @ epoch 502: 0.06747950613498688

@@ -4840,7 +4840,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 503: 0.06655343621969223
+INFO:lightwood-2394:Loss @ epoch 503: 0.06655343621969223

@@ -4848,7 +4848,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 504: 0.06652842462062836
+INFO:lightwood-2394:Loss @ epoch 504: 0.06652842462062836

@@ -4856,7 +4856,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 505: 0.06745205074548721
+INFO:lightwood-2394:Loss @ epoch 505: 0.06745205074548721

@@ -4864,7 +4864,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 506: 0.0668550580739975
+INFO:lightwood-2394:Loss @ epoch 506: 0.0668550580739975

@@ -4872,7 +4872,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 507: 0.06666403263807297
+INFO:lightwood-2394:Loss @ epoch 507: 0.06666403263807297

@@ -4880,7 +4880,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 508: 0.06683854013681412
+INFO:lightwood-2394:Loss @ epoch 508: 0.06683854013681412

@@ -4888,7 +4888,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 509: 0.06626935303211212
+INFO:lightwood-2394:Loss @ epoch 509: 0.06626935303211212

@@ -4896,7 +4896,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 510: 0.06613652408123016
+INFO:lightwood-2394:Loss @ epoch 510: 0.06613652408123016

@@ -4904,7 +4904,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 511: 0.06672576069831848
+INFO:lightwood-2394:Loss @ epoch 511: 0.06672576069831848

@@ -4912,7 +4912,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 512: 0.0666651502251625
+INFO:lightwood-2394:Loss @ epoch 512: 0.0666651502251625

@@ -4920,7 +4920,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 513: 0.06582488119602203
+INFO:lightwood-2394:Loss @ epoch 513: 0.06582488119602203

@@ -4928,7 +4928,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 514: 0.06652247160673141
+INFO:lightwood-2394:Loss @ epoch 514: 0.06652247160673141

@@ -4936,7 +4936,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 515: 0.06558185815811157
+INFO:lightwood-2394:Loss @ epoch 515: 0.06558185815811157

@@ -4944,7 +4944,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 516: 0.0655498206615448
+INFO:lightwood-2394:Loss @ epoch 516: 0.0655498206615448

@@ -4952,7 +4952,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 517: 0.06624851375818253
+INFO:lightwood-2394:Loss @ epoch 517: 0.06624851375818253

@@ -4960,7 +4960,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 518: 0.06601088494062424
+INFO:lightwood-2394:Loss @ epoch 518: 0.06601088494062424

@@ -4968,7 +4968,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 519: 0.06545697897672653
+INFO:lightwood-2394:Loss @ epoch 519: 0.06545697897672653

@@ -4976,7 +4976,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 520: 0.0659414529800415
+INFO:lightwood-2394:Loss @ epoch 520: 0.0659414529800415

@@ -4984,7 +4984,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 521: 0.06516807526350021
+INFO:lightwood-2394:Loss @ epoch 521: 0.06516807526350021

@@ -4992,7 +4992,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 522: 0.06501934677362442
+INFO:lightwood-2394:Loss @ epoch 522: 0.06501934677362442

@@ -5000,7 +5000,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 523: 0.06574487686157227
+INFO:lightwood-2394:Loss @ epoch 523: 0.06574487686157227

@@ -5008,7 +5008,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 524: 0.06553597748279572
+INFO:lightwood-2394:Loss @ epoch 524: 0.06553597748279572

@@ -5016,7 +5016,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 525: 0.06504649668931961
+INFO:lightwood-2394:Loss @ epoch 525: 0.06504649668931961

@@ -5024,7 +5024,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 526: 0.06540416181087494
+INFO:lightwood-2394:Loss @ epoch 526: 0.06540416181087494

@@ -5032,7 +5032,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 527: 0.06479271501302719
+INFO:lightwood-2394:Loss @ epoch 527: 0.06479271501302719

@@ -5040,7 +5040,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 528: 0.06469936668872833
+INFO:lightwood-2394:Loss @ epoch 528: 0.06469936668872833

@@ -5048,7 +5048,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 529: 0.0654490739107132
+INFO:lightwood-2394:Loss @ epoch 529: 0.0654490739107132

@@ -5056,7 +5056,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 530: 0.06509881466627121
+INFO:lightwood-2394:Loss @ epoch 530: 0.06509881466627121

@@ -5064,7 +5064,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 531: 0.06460769474506378
+INFO:lightwood-2394:Loss @ epoch 531: 0.06460769474506378

@@ -5072,7 +5072,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 532: 0.06506450474262238
+INFO:lightwood-2394:Loss @ epoch 532: 0.06506450474262238

@@ -5080,7 +5080,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 533: 0.06425388902425766
+INFO:lightwood-2394:Loss @ epoch 533: 0.06425388902425766

@@ -5088,7 +5088,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 534: 0.06419297307729721
+INFO:lightwood-2394:Loss @ epoch 534: 0.06419297307729721

@@ -5096,7 +5096,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 535: 0.06507144123315811
+INFO:lightwood-2394:Loss @ epoch 535: 0.06507144123315811

@@ -5104,7 +5104,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 536: 0.06475593149662018
+INFO:lightwood-2394:Loss @ epoch 536: 0.06475593149662018

@@ -5112,7 +5112,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 537: 0.0640476867556572
+INFO:lightwood-2394:Loss @ epoch 537: 0.0640476867556572

@@ -5120,7 +5120,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 538: 0.06452148407697678
+INFO:lightwood-2394:Loss @ epoch 538: 0.06452148407697678

@@ -5128,7 +5128,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 539: 0.063988097012043
+INFO:lightwood-2394:Loss @ epoch 539: 0.063988097012043

@@ -5136,7 +5136,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 540: 0.06390102207660675
+INFO:lightwood-2394:Loss @ epoch 540: 0.06390102207660675

@@ -5144,7 +5144,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 541: 0.06427431106567383
+INFO:lightwood-2394:Loss @ epoch 541: 0.06427431106567383

@@ -5152,7 +5152,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 542: 0.06461699306964874
+INFO:lightwood-2394:Loss @ epoch 542: 0.06461699306964874

@@ -5160,7 +5160,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 543: 0.06366197764873505
+INFO:lightwood-2394:Loss @ epoch 543: 0.06366197764873505

@@ -5168,7 +5168,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 544: 0.06439769268035889
+INFO:lightwood-2394:Loss @ epoch 544: 0.06439769268035889

@@ -5176,7 +5176,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 545: 0.06354749947786331
+INFO:lightwood-2394:Loss @ epoch 545: 0.06354749947786331

@@ -5184,7 +5184,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 546: 0.06346575170755386
+INFO:lightwood-2394:Loss @ epoch 546: 0.06346575170755386

@@ -5192,7 +5192,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 547: 0.06415951251983643
+INFO:lightwood-2394:Loss @ epoch 547: 0.06415951251983643

@@ -5200,7 +5200,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 548: 0.06416907906532288
+INFO:lightwood-2394:Loss @ epoch 548: 0.06416907906532288

@@ -5208,7 +5208,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 549: 0.06350232660770416
+INFO:lightwood-2394:Loss @ epoch 549: 0.06350232660770416

@@ -5216,7 +5216,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 1: 0.03389815576374531
+INFO:lightwood-2394:Loss @ epoch 1: 0.03389815576374531

@@ -5224,7 +5224,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 2: 0.033698095567524435
+INFO:lightwood-2394:Loss @ epoch 2: 0.033698095567524435

@@ -5232,7 +5232,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 3: 0.0372611828148365
+INFO:lightwood-2394:Loss @ epoch 3: 0.0372611828148365

@@ -5240,7 +5240,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 4: 0.0382374182343483
+INFO:lightwood-2394:Loss @ epoch 4: 0.0382374182343483

@@ -5248,7 +5248,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 5: 0.03677316829562187
+INFO:lightwood-2394:Loss @ epoch 5: 0.03677316829562187

@@ -5256,7 +5256,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 6: 0.04194173291325569
+INFO:lightwood-2394:Loss @ epoch 6: 0.04194173291325569

@@ -5264,7 +5264,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 7: 0.04046095162630081
+INFO:lightwood-2394:Loss @ epoch 7: 0.04046095162630081

@@ -5272,7 +5272,7 @@

Step 4: Final test run 
-DEBUG:lightwood-2205: `fit_mixer` runtime: 4.73 seconds
+DEBUG:lightwood-2394: `fit_mixer` runtime: 5.01 seconds

@@ -5280,7 +5280,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Started fitting XGBoost model
+INFO:lightwood-2394:Started fitting XGBoost model

@@ -5296,7 +5296,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:A single GBM iteration takes 0.1 seconds
+INFO:lightwood-2394:A single GBM iteration takes 0.1 seconds

@@ -5304,7 +5304,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Training XGBoost with 131 iterations given 16.482705808877945 seconds constraint
+INFO:lightwood-2394:Training XGBoost with 131 iterations given 16.48267477631569 seconds constraint

@@ -5502,9 +5502,17 @@

Step 4: Final test run
+
+
+[24]    validation_0-mlogloss:0.01305
+
+

+
+
+
-INFO:lightwood-2205:XGBoost mixer does not have a `partial_fit` implementation
+INFO:lightwood-2394:XGBoost mixer does not have a `partial_fit` implementation
@@ -5512,7 +5520,7 @@

Step 4: Final test run 
-DEBUG:lightwood-2205: `fit_mixer` runtime: 0.04 seconds
+DEBUG:lightwood-2394: `fit_mixer` runtime: 0.05 seconds

@@ -5520,7 +5528,7 @@

Step 4: Final test run 
-WARNING:dataprep_ml-2205:Exception: Unspported categorical type for regression when training mixer: <lightwood.mixer.regression.Regression object at 0x75d4df6f4490>
+WARNING:dataprep_ml-2394:Exception: Unspported categorical type for regression when training mixer: <lightwood.mixer.regression.Regression object at 0x787104727790>

@@ -5528,7 +5536,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Started fitting RandomForest model
+INFO:lightwood-2394:Started fitting RandomForest model

@@ -5536,7 +5544,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:RandomForest based correlation of (train data): 1.0
+INFO:lightwood-2394:RandomForest based correlation of (train data): 1.0

@@ -5544,7 +5552,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:RandomForest based correlation of (dev data): 1.0
+INFO:lightwood-2394:RandomForest based correlation of (dev data): 1.0

@@ -5552,7 +5560,7 @@

Step 4: Final test run 
-DEBUG:lightwood-2205: `fit_mixer` runtime: 0.13 seconds
+DEBUG:lightwood-2394: `fit_mixer` runtime: 0.12 seconds

@@ -5560,7 +5568,7 @@

Step 4: Final test run 
-INFO:dataprep_ml-2205:Ensembling the mixer
+INFO:dataprep_ml-2394:Ensembling the mixer

@@ -5568,7 +5576,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Mixer: Neural got accuracy: 0.922
+INFO:lightwood-2394:Mixer: Neural got accuracy: 0.922

@@ -5576,7 +5584,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Mixer: XGBoostMixer got accuracy: 1.0
+INFO:lightwood-2394:Mixer: XGBoostMixer got accuracy: 1.0

@@ -5584,7 +5592,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Mixer: RandomForest got accuracy: 1.0
+INFO:lightwood-2394:Mixer: RandomForest got accuracy: 1.0

@@ -5592,7 +5600,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Picked best mixer: RandomForest
+INFO:lightwood-2394:Picked best mixer: RandomForest

@@ -5600,7 +5608,7 @@

Step 4: Final test run 
-DEBUG:lightwood-2205: `fit` runtime: 5.0 seconds
+DEBUG:lightwood-2394: `fit` runtime: 5.24 seconds

@@ -5608,7 +5616,7 @@

Step 4: Final test run 
-INFO:dataprep_ml-2205:[Learn phase 7/8] - Ensemble analysis
+INFO:dataprep_ml-2394:[Learn phase 7/8] - Ensemble analysis

@@ -5616,7 +5624,7 @@

Step 4: Final test run 
-INFO:dataprep_ml-2205:Analyzing the ensemble of mixers
+INFO:dataprep_ml-2394:Analyzing the ensemble of mixers

@@ -5624,7 +5632,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:The block ICP is now running its analyze() method
+INFO:lightwood-2394:The block ICP is now running its analyze() method

@@ -5634,7 +5642,6 @@

Step 4: Final test run
 /opt/hostedtoolcache/Python/3.9.18/x64/lib/python3.9/site-packages/sklearn/preprocessing/_encoders.py:975: FutureWarning: `sparse` was renamed to `sparse_output` in version 1.2 and will be removed in 1.4. `sparse_output` is ignored unless you leave `sparse` to its default value.
   warnings.warn(
-INFO:lightwood-2205:The block ConfStats is now running its analyze() method

@@ -5642,7 +5649,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:The block AccStats is now running its analyze() method
+INFO:lightwood-2394:The block ConfStats is now running its analyze() method

@@ -5650,7 +5657,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:The block PermutationFeatureImportance is now running its analyze() method
+INFO:lightwood-2394:The block AccStats is now running its analyze() method

@@ -5658,7 +5665,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:[PFI] Using a random sample (1000 rows out of 22).
+INFO:lightwood-2394:The block PermutationFeatureImportance is now running its analyze() method

@@ -5666,7 +5673,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:[PFI] Set to consider first 10 columns out of 6: ['Population', 'Area (sq. mi.)', 'Pop. Density ', 'GDP ($ per capita)', 'Literacy (%)', 'Infant mortality '].
+INFO:lightwood-2394:[PFI] Using a random sample (1000 rows out of 22).

@@ -5674,7 +5681,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:The block ModelCorrelationHeatmap is now running its analyze() method
+INFO:lightwood-2394:[PFI] Set to consider first 10 columns out of 6: ['Population', 'Area (sq. mi.)', 'Pop. Density ', 'GDP ($ per capita)', 'Literacy (%)', 'Infant mortality '].

@@ -5682,7 +5689,7 @@

Step 4: Final test run 
-DEBUG:lightwood-2205: `analyze_ensemble` runtime: 0.2 seconds
+INFO:lightwood-2394:The block ModelCorrelationHeatmap is now running its analyze() method

@@ -5690,7 +5697,7 @@

Step 4: Final test run 
-INFO:dataprep_ml-2205:[Learn phase 8/8] - Adjustment on validation requested
+DEBUG:lightwood-2394: `analyze_ensemble` runtime: 0.23 seconds

@@ -5698,7 +5705,7 @@

Step 4: Final test run 
-INFO:dataprep_ml-2205:Updating the mixers
+INFO:dataprep_ml-2394:[Learn phase 8/8] - Adjustment on validation requested

@@ -5706,8 +5713,7 @@

Step 4: Final test run 
-/opt/hostedtoolcache/Python/3.9.18/x64/lib/python3.9/site-packages/torch/cuda/amp/grad_scaler.py:126: UserWarning: torch.cuda.amp.GradScaler is enabled, but CUDA is not available.  Disabling.
-  warnings.warn(
+INFO:dataprep_ml-2394:Updating the mixers

@@ -5715,7 +5721,9 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 1: 0.033697554686417185
+/opt/hostedtoolcache/Python/3.9.18/x64/lib/python3.9/site-packages/torch/cuda/amp/grad_scaler.py:126: UserWarning: torch.cuda.amp.GradScaler is enabled, but CUDA is not available.  Disabling.
+  warnings.warn(
+INFO:lightwood-2394:Loss @ epoch 1: 0.033697554686417185

@@ -5723,7 +5731,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 2: 0.033981192080924906
+INFO:lightwood-2394:Loss @ epoch 2: 0.033981192080924906

@@ -5731,7 +5739,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 3: 0.037426896315688886
+INFO:lightwood-2394:Loss @ epoch 3: 0.037426896315688886

@@ -5739,7 +5747,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 4: 0.04428015494098266
+INFO:lightwood-2394:Loss @ epoch 4: 0.04428015494098266

@@ -5747,7 +5755,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 5: 0.061086510928968586
+INFO:lightwood-2394:Loss @ epoch 5: 0.061086510928968586

@@ -5755,7 +5763,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 6: 0.03466159128583968
+INFO:lightwood-2394:Loss @ epoch 6: 0.03466159128583968

@@ -5763,7 +5771,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:Loss @ epoch 7: 0.03769115870818496
+INFO:lightwood-2394:Loss @ epoch 7: 0.03769115870818496

@@ -5771,7 +5779,7 @@

Step 4: Final test run 
-INFO:lightwood-2205:XGBoost mixer does not have a `partial_fit` implementation
+INFO:lightwood-2394:XGBoost mixer does not have a `partial_fit` implementation

@@ -5779,7 +5787,7 @@

Step 4: Final test run 
-DEBUG:lightwood-2205: `adjust` runtime: 0.06 seconds
+DEBUG:lightwood-2394: `adjust` runtime: 0.07 seconds

@@ -5787,7 +5795,7 @@

Step 4: Final test run 
-DEBUG:lightwood-2205: `learn` runtime: 5.36 seconds
+DEBUG:lightwood-2394: `learn` runtime: 5.64 seconds

Finally, we can visualize the mixer correlation matrix:

diff --git a/tutorials/custom_explainer/custom_explainer.ipynb b/tutorials/custom_explainer/custom_explainer.ipynb index 58f1c570e..ed151869b 100644 --- a/tutorials/custom_explainer/custom_explainer.ipynb +++ b/tutorials/custom_explainer/custom_explainer.ipynb @@ -30,10 +30,10 @@ "execution_count": 1, "metadata": { "execution": { - "iopub.execute_input": "2024-03-18T10:30:56.638469Z", - "iopub.status.busy": "2024-03-18T10:30:56.638271Z", - "iopub.status.idle": "2024-03-18T10:31:00.698957Z", - "shell.execute_reply": "2024-03-18T10:31:00.698246Z" + "iopub.execute_input": "2024-03-18T12:12:28.047614Z", + "iopub.status.busy": "2024-03-18T12:12:28.047078Z", + "iopub.status.idle": "2024-03-18T12:12:32.339044Z", + "shell.execute_reply": "2024-03-18T12:12:32.338214Z" } }, "outputs": [ @@ -49,14 +49,14 @@ "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2205:No torchvision detected, image helpers not supported.\u001b[0m\n" + "\u001b[32mINFO:lightwood-2394:No torchvision detected, image helpers not supported.\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2205:No torchvision/pillow detected, image encoder not supported\u001b[0m\n" + "\u001b[32mINFO:lightwood-2394:No torchvision/pillow detected, image encoder not supported\u001b[0m\n" ] }, { @@ -95,10 +95,10 @@ "execution_count": 2, "metadata": { "execution": { - "iopub.execute_input": "2024-03-18T10:31:00.701828Z", - "iopub.status.busy": "2024-03-18T10:31:00.701393Z", - "iopub.status.idle": "2024-03-18T10:31:00.729658Z", - "shell.execute_reply": "2024-03-18T10:31:00.729152Z" + "iopub.execute_input": "2024-03-18T12:12:32.342349Z", + "iopub.status.busy": "2024-03-18T12:12:32.341649Z", + "iopub.status.idle": "2024-03-18T12:12:32.371750Z", + "shell.execute_reply": "2024-03-18T12:12:32.371146Z" } }, "outputs": [], @@ -124,17 +124,17 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2024-03-18T10:31:00.732051Z", - "iopub.status.busy": "2024-03-18T10:31:00.731690Z", - "iopub.status.idle": "2024-03-18T10:31:00.735666Z", - "shell.execute_reply": "2024-03-18T10:31:00.735032Z" + "iopub.execute_input": "2024-03-18T12:12:32.374741Z", + "iopub.status.busy": "2024-03-18T12:12:32.374330Z", + "iopub.status.idle": "2024-03-18T12:12:32.378803Z", + "shell.execute_reply": "2024-03-18T12:12:32.378124Z" } }, "outputs": [ { "data": { "text/plain": [ - "<__main__.ModelCorrelationHeatmap at 0x75d4e00a3370>" + "<__main__.ModelCorrelationHeatmap at 0x78710515c520>" ] }, "execution_count": 3, @@ -160,10 +160,10 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2024-03-18T10:31:00.738215Z", - "iopub.status.busy": "2024-03-18T10:31:00.737801Z", - "iopub.status.idle": "2024-03-18T10:31:00.741541Z", - "shell.execute_reply": "2024-03-18T10:31:00.740988Z" + "iopub.execute_input": "2024-03-18T12:12:32.381594Z", + "iopub.status.busy": "2024-03-18T12:12:32.381079Z", + "iopub.status.idle": "2024-03-18T12:12:32.385092Z", + "shell.execute_reply": "2024-03-18T12:12:32.384388Z" } }, "outputs": [], @@ -192,10 +192,10 @@ "execution_count": 5, "metadata": { "execution": { - "iopub.execute_input": "2024-03-18T10:31:00.744058Z", - "iopub.status.busy": "2024-03-18T10:31:00.743673Z", - "iopub.status.idle": "2024-03-18T10:31:00.747371Z", - "shell.execute_reply": "2024-03-18T10:31:00.746834Z" + "iopub.execute_input": "2024-03-18T12:12:32.387865Z", + "iopub.status.busy": "2024-03-18T12:12:32.387387Z", + "iopub.status.idle": "2024-03-18T12:12:32.391340Z", + "shell.execute_reply": "2024-03-18T12:12:32.390752Z" } }, "outputs": [], @@ -238,10 +238,10 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2024-03-18T10:31:00.749823Z", - "iopub.status.busy": "2024-03-18T10:31:00.749468Z", - "iopub.status.idle": "2024-03-18T10:31:00.753920Z", - "shell.execute_reply": "2024-03-18T10:31:00.753302Z" + "iopub.execute_input": "2024-03-18T12:12:32.394089Z", + "iopub.status.busy": "2024-03-18T12:12:32.393556Z", + "iopub.status.idle": "2024-03-18T12:12:32.398390Z", + "shell.execute_reply": "2024-03-18T12:12:32.397810Z" } }, "outputs": [ @@ -335,10 +335,10 @@ "execution_count": 7, "metadata": { "execution": { - "iopub.execute_input": "2024-03-18T10:31:00.756605Z", - "iopub.status.busy": "2024-03-18T10:31:00.756191Z", - "iopub.status.idle": "2024-03-18T10:31:00.914784Z", - "shell.execute_reply": "2024-03-18T10:31:00.914234Z" + "iopub.execute_input": "2024-03-18T12:12:32.401222Z", + "iopub.status.busy": "2024-03-18T12:12:32.400839Z", + "iopub.status.idle": "2024-03-18T12:12:32.718388Z", + "shell.execute_reply": "2024-03-18T12:12:32.717725Z" } }, "outputs": [ @@ -346,126 +346,126 @@ "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2205:Analyzing a sample of 222\u001b[0m\n" + "\u001b[32mINFO:type_infer-2394:Analyzing a sample of 222\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2205:from a total population of 225, this is equivalent to 98.7% of your data.\u001b[0m\n" + "\u001b[32mINFO:type_infer-2394:from a total population of 225, this is equivalent to 98.7% of your data.\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2205:Infering type for: Population\u001b[0m\n" + "\u001b[32mINFO:type_infer-2394:Infering type for: Population\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2205:Column Population has data type integer\u001b[0m\n" + "\u001b[32mINFO:type_infer-2394:Column Population has data type integer\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2205:Infering type for: Area (sq. mi.)\u001b[0m\n" + "\u001b[32mINFO:type_infer-2394:Infering type for: Area (sq. mi.)\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2205:Column Area (sq. mi.) has data type integer\u001b[0m\n" + "\u001b[32mINFO:type_infer-2394:Column Area (sq. mi.) has data type integer\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2205:Infering type for: Pop. Density \u001b[0m\n" + "\u001b[32mINFO:type_infer-2394:Infering type for: Pop. Density \u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2205:Column Pop. Density has data type float\u001b[0m\n" + "\u001b[32mINFO:type_infer-2394:Column Pop. Density has data type float\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2205:Infering type for: GDP ($ per capita)\u001b[0m\n" + "\u001b[32mINFO:type_infer-2394:Infering type for: GDP ($ per capita)\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2205:Column GDP ($ per capita) has data type integer\u001b[0m\n" + "\u001b[32mINFO:type_infer-2394:Column GDP ($ per capita) has data type integer\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2205:Infering type for: Literacy (%)\u001b[0m\n" + "\u001b[32mINFO:type_infer-2394:Infering type for: Literacy (%)\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2205:Column Literacy (%) has data type float\u001b[0m\n" + "\u001b[32mINFO:type_infer-2394:Column Literacy (%) has data type float\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2205:Infering type for: Infant mortality \u001b[0m\n" + "\u001b[32mINFO:type_infer-2394:Infering type for: Infant mortality \u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2205:Column Infant mortality has data type float\u001b[0m\n" + "\u001b[32mINFO:type_infer-2394:Column Infant mortality has data type float\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2205:Infering type for: Development Index\u001b[0m\n" + "\u001b[32mINFO:type_infer-2394:Infering type for: Development Index\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2205:Column Development Index has data type categorical\u001b[0m\n" + "\u001b[32mINFO:type_infer-2394:Column Development Index has data type categorical\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2205:Starting statistical analysis\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2394:Starting statistical analysis\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2205:Finished statistical analysis\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2394:Finished statistical analysis\u001b[0m\n" ] } ], @@ -506,10 +506,10 @@ "execution_count": 8, "metadata": { "execution": { - "iopub.execute_input": "2024-03-18T10:31:00.917488Z", - "iopub.status.busy": "2024-03-18T10:31:00.917032Z", - "iopub.status.idle": "2024-03-18T10:31:00.921256Z", - "shell.execute_reply": "2024-03-18T10:31:00.920656Z" + "iopub.execute_input": "2024-03-18T12:12:32.721556Z", + "iopub.status.busy": "2024-03-18T12:12:32.721301Z", + "iopub.status.idle": "2024-03-18T12:12:32.726465Z", + "shell.execute_reply": "2024-03-18T12:12:32.725724Z" } }, "outputs": [ @@ -540,10 +540,10 @@ "execution_count": 9, "metadata": { "execution": { - "iopub.execute_input": "2024-03-18T10:31:00.923961Z", - "iopub.status.busy": "2024-03-18T10:31:00.923505Z", - "iopub.status.idle": "2024-03-18T10:31:06.601212Z", - "shell.execute_reply": "2024-03-18T10:31:06.600632Z" + "iopub.execute_input": "2024-03-18T12:12:32.729356Z", + "iopub.status.busy": "2024-03-18T12:12:32.728918Z", + "iopub.status.idle": "2024-03-18T12:12:38.734385Z", + "shell.execute_reply": "2024-03-18T12:12:38.733753Z" }, "scrolled": false }, @@ -552,182 +552,182 @@ "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2205:[Learn phase 1/8] - Statistical analysis\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2394:[Learn phase 1/8] - Statistical analysis\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2205:Starting statistical analysis\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2394:Starting statistical analysis\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2205:Finished statistical analysis\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2394:Finished statistical analysis\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:lightwood-2205: `analyze_data` runtime: 0.02 seconds\u001b[0m\n" + "\u001b[37mDEBUG:lightwood-2394: `analyze_data` runtime: 0.02 seconds\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2205:[Learn phase 2/8] - Data preprocessing\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2394:[Learn phase 2/8] - Data preprocessing\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2205:Cleaning the data\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2394:Cleaning the data\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:lightwood-2205: `preprocess` runtime: 0.01 seconds\u001b[0m\n" + "\u001b[37mDEBUG:lightwood-2394: `preprocess` runtime: 0.01 seconds\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2205:[Learn phase 3/8] - Data splitting\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2394:[Learn phase 3/8] - Data splitting\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2205:Splitting the data into train/test\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2394:Splitting the data into train/test\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:lightwood-2205: `split` runtime: 0.01 seconds\u001b[0m\n" + "\u001b[37mDEBUG:lightwood-2394: `split` runtime: 0.01 seconds\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2205:[Learn phase 4/8] - Preparing encoders\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2394:[Learn phase 4/8] - Preparing encoders\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:dataprep_ml-2205:Preparing sequentially...\u001b[0m\n" + "\u001b[37mDEBUG:dataprep_ml-2394:Preparing sequentially...\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:dataprep_ml-2205:Preparing encoder for Population...\u001b[0m\n" + "\u001b[37mDEBUG:dataprep_ml-2394:Preparing encoder for Population...\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:dataprep_ml-2205:Preparing encoder for Area (sq. mi.)...\u001b[0m\n" + "\u001b[37mDEBUG:dataprep_ml-2394:Preparing encoder for Area (sq. mi.)...\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:dataprep_ml-2205:Preparing encoder for Pop. 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"\u001b[37mDEBUG:lightwood-2205: `prepare` runtime: 0.01 seconds\u001b[0m\n" + "\u001b[37mDEBUG:lightwood-2394: `prepare` runtime: 0.01 seconds\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2205:[Learn phase 5/8] - Feature generation\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2394:[Learn phase 5/8] - Feature generation\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2205:Featurizing the data\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2394:Featurizing the data\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:lightwood-2205: `featurize` runtime: 0.05 seconds\u001b[0m\n" + "\u001b[37mDEBUG:lightwood-2394: `featurize` runtime: 0.05 seconds\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2205:[Learn phase 6/8] - Mixer training\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2394:[Learn phase 6/8] - Mixer training\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2205:Training the mixers\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2394:Training the mixers\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[33mWARNING:lightwood-2205:XGBoost running on CPU\u001b[0m\n" + "\u001b[33mWARNING:lightwood-2394:XGBoost running on CPU\u001b[0m\n" ] }, { @@ -742,7 +742,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "[10:31:01] WARNING: ../src/learner.cc:339: No visible GPU is found, setting `gpu_id` to -1\n" + "[12:12:33] WARNING: ../src/learner.cc:339: No visible GPU is found, setting `gpu_id` to -1\n" ] }, { @@ -754,63 +754,63 @@ "Consider using one of the following signatures instead:\n", "\taddcmul_(Tensor tensor1, Tensor tensor2, *, Number value) (Triggered internally at ../torch/csrc/utils/python_arg_parser.cpp:1630.)\n", " exp_avg_sq.mul_(beta2).addcmul_(1 - beta2, grad, grad)\n", - "\u001b[32mINFO:lightwood-2205:Loss of 18.69619858264923 with learning rate 0.0001\u001b[0m\n" + "\u001b[32mINFO:lightwood-2394:Loss of 18.69619858264923 with learning rate 0.0001\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2205:Loss of 16.93891429901123 with learning rate 0.0005\u001b[0m\n" + "\u001b[32mINFO:lightwood-2394:Loss of 16.93891429901123 with learning rate 0.0005\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2205:Loss of 16.197376608848572 with learning rate 0.001\u001b[0m\n" + "\u001b[32mINFO:lightwood-2394:Loss of 16.197376608848572 with learning rate 0.001\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2205:Loss of 16.06481909751892 with learning rate 0.002\u001b[0m\n" + "\u001b[32mINFO:lightwood-2394:Loss of 16.06481909751892 with learning rate 0.002\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2205:Loss of 16.472004413604736 with learning rate 0.003\u001b[0m\n" + "\u001b[32mINFO:lightwood-2394:Loss of 16.472004413604736 with learning rate 0.003\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2205:Loss of 18.28026556968689 with learning rate 0.005\u001b[0m\n" + "\u001b[32mINFO:lightwood-2394:Loss of 18.28026556968689 with learning rate 0.005\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2205:Loss of 26.746760368347168 with learning rate 0.01\u001b[0m\n" + "\u001b[32mINFO:lightwood-2394:Loss of 26.746760368347168 with learning rate 0.01\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2205:Loss of 101.83524441719055 with learning rate 0.05\u001b[0m\n" + "\u001b[32mINFO:lightwood-2394:Loss of 101.83524441719055 with learning rate 0.05\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2205:Found learning rate of: 0.002\u001b[0m\n" + "\u001b[32mINFO:lightwood-2394:Found learning rate of: 0.002\u001b[0m\n" ] }, { @@ -819,3906 +819,3906 @@ "text": [ "/opt/hostedtoolcache/Python/3.9.18/x64/lib/python3.9/site-packages/torch/cuda/amp/grad_scaler.py:126: UserWarning: torch.cuda.amp.GradScaler is enabled, but CUDA is not available. 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"\u001b[32mINFO:lightwood-2205:XGBoost mixer does not have a `partial_fit` implementation\u001b[0m\n" + "\u001b[32mINFO:lightwood-2394:XGBoost mixer does not have a `partial_fit` implementation\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:lightwood-2205: `fit_mixer` runtime: 0.04 seconds\u001b[0m\n" + "\u001b[37mDEBUG:lightwood-2394: `fit_mixer` runtime: 0.05 seconds\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[33mWARNING:dataprep_ml-2205:Exception: Unspported categorical type for regression when training mixer: \u001b[0m\n" + "\u001b[33mWARNING:dataprep_ml-2394:Exception: Unspported categorical type for regression when training mixer: \u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2205:Started fitting RandomForest model\u001b[0m\n" + "\u001b[32mINFO:lightwood-2394:Started fitting RandomForest model\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2205:RandomForest based correlation of (train data): 1.0\u001b[0m\n" + "\u001b[32mINFO:lightwood-2394:RandomForest based correlation of (train data): 1.0\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2205:RandomForest based correlation of (dev data): 1.0\u001b[0m\n" + "\u001b[32mINFO:lightwood-2394:RandomForest based correlation of (dev data): 1.0\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:lightwood-2205: `fit_mixer` runtime: 0.13 seconds\u001b[0m\n" + "\u001b[37mDEBUG:lightwood-2394: `fit_mixer` runtime: 0.12 seconds\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2205:Ensembling the mixer\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2394:Ensembling the mixer\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2205:Mixer: Neural got accuracy: 0.922\u001b[0m\n" + "\u001b[32mINFO:lightwood-2394:Mixer: Neural got accuracy: 0.922\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2205:Mixer: XGBoostMixer got accuracy: 1.0\u001b[0m\n" + "\u001b[32mINFO:lightwood-2394:Mixer: XGBoostMixer got accuracy: 1.0\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2205:Mixer: RandomForest got accuracy: 1.0\u001b[0m\n" + "\u001b[32mINFO:lightwood-2394:Mixer: RandomForest got accuracy: 1.0\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2205:Picked best mixer: RandomForest\u001b[0m\n" + "\u001b[32mINFO:lightwood-2394:Picked best mixer: RandomForest\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:lightwood-2205: `fit` runtime: 5.0 seconds\u001b[0m\n" + "\u001b[37mDEBUG:lightwood-2394: `fit` runtime: 5.24 seconds\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2205:[Learn phase 7/8] - Ensemble analysis\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2394:[Learn phase 7/8] - Ensemble analysis\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2205:Analyzing the ensemble of mixers\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2394:Analyzing the ensemble of mixers\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2205:The block ICP is now running its analyze() method\u001b[0m\n" + "\u001b[32mINFO:lightwood-2394:The block ICP is now running its analyze() method\u001b[0m\n" ] }, { @@ -5027,142 +5034,142 @@ "output_type": "stream", "text": [ "/opt/hostedtoolcache/Python/3.9.18/x64/lib/python3.9/site-packages/sklearn/preprocessing/_encoders.py:975: FutureWarning: `sparse` was renamed to `sparse_output` in version 1.2 and will be removed in 1.4. `sparse_output` is ignored unless you leave `sparse` to its default value.\n", - " warnings.warn(\n", - "\u001b[32mINFO:lightwood-2205:The block ConfStats is now running its analyze() method\u001b[0m\n" + " warnings.warn(\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2205:The block AccStats is now running its analyze() method\u001b[0m\n" + "\u001b[32mINFO:lightwood-2394:The block ConfStats is now running its analyze() method\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2205:The block PermutationFeatureImportance is now running its analyze() method\u001b[0m\n" + "\u001b[32mINFO:lightwood-2394:The block AccStats is now running its analyze() method\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2205:[PFI] Using a random sample (1000 rows out of 22).\u001b[0m\n" + "\u001b[32mINFO:lightwood-2394:The block PermutationFeatureImportance is now running its analyze() method\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2205:[PFI] Set to consider first 10 columns out of 6: ['Population', 'Area (sq. mi.)', 'Pop. Density ', 'GDP ($ per capita)', 'Literacy (%)', 'Infant mortality '].\u001b[0m\n" + "\u001b[32mINFO:lightwood-2394:[PFI] Using a random sample (1000 rows out of 22).\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2205:The block ModelCorrelationHeatmap is now running its analyze() method\u001b[0m\n" + "\u001b[32mINFO:lightwood-2394:[PFI] Set to consider first 10 columns out of 6: ['Population', 'Area (sq. mi.)', 'Pop. Density ', 'GDP ($ per capita)', 'Literacy (%)', 'Infant mortality '].\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:lightwood-2205: `analyze_ensemble` runtime: 0.2 seconds\u001b[0m\n" + "\u001b[32mINFO:lightwood-2394:The block ModelCorrelationHeatmap is now running its analyze() method\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2205:[Learn phase 8/8] - Adjustment on validation requested\u001b[0m\n" + "\u001b[37mDEBUG:lightwood-2394: `analyze_ensemble` runtime: 0.23 seconds\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2205:Updating the mixers\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2394:[Learn phase 8/8] - Adjustment on validation requested\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "/opt/hostedtoolcache/Python/3.9.18/x64/lib/python3.9/site-packages/torch/cuda/amp/grad_scaler.py:126: UserWarning: torch.cuda.amp.GradScaler is enabled, but CUDA is not available. Disabling.\n", - " warnings.warn(\n" + "\u001b[32mINFO:dataprep_ml-2394:Updating the mixers\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2205:Loss @ epoch 1: 0.033697554686417185\u001b[0m\n" + "/opt/hostedtoolcache/Python/3.9.18/x64/lib/python3.9/site-packages/torch/cuda/amp/grad_scaler.py:126: UserWarning: torch.cuda.amp.GradScaler is enabled, but CUDA is not available. Disabling.\n", + " warnings.warn(\n", + "\u001b[32mINFO:lightwood-2394:Loss @ epoch 1: 0.033697554686417185\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2205:Loss @ epoch 2: 0.033981192080924906\u001b[0m\n" + "\u001b[32mINFO:lightwood-2394:Loss @ epoch 2: 0.033981192080924906\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2205:Loss @ epoch 3: 0.037426896315688886\u001b[0m\n" + "\u001b[32mINFO:lightwood-2394:Loss @ epoch 3: 0.037426896315688886\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2205:Loss @ epoch 4: 0.04428015494098266\u001b[0m\n" + "\u001b[32mINFO:lightwood-2394:Loss @ epoch 4: 0.04428015494098266\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2205:Loss @ epoch 5: 0.061086510928968586\u001b[0m\n" + "\u001b[32mINFO:lightwood-2394:Loss @ epoch 5: 0.061086510928968586\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2205:Loss @ epoch 6: 0.03466159128583968\u001b[0m\n" + "\u001b[32mINFO:lightwood-2394:Loss @ epoch 6: 0.03466159128583968\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2205:Loss @ epoch 7: 0.03769115870818496\u001b[0m\n" + "\u001b[32mINFO:lightwood-2394:Loss @ epoch 7: 0.03769115870818496\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2205:XGBoost mixer does not have a `partial_fit` implementation\u001b[0m\n" + "\u001b[32mINFO:lightwood-2394:XGBoost mixer does not have a `partial_fit` implementation\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:lightwood-2205: `adjust` runtime: 0.06 seconds\u001b[0m\n" + "\u001b[37mDEBUG:lightwood-2394: `adjust` runtime: 0.07 seconds\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:lightwood-2205: `learn` runtime: 5.36 seconds\u001b[0m\n" + "\u001b[37mDEBUG:lightwood-2394: `learn` runtime: 5.64 seconds\u001b[0m\n" ] } ], @@ -5187,10 +5194,10 @@ "execution_count": 10, "metadata": { "execution": { - "iopub.execute_input": "2024-03-18T10:31:06.604127Z", - "iopub.status.busy": "2024-03-18T10:31:06.603680Z", - "iopub.status.idle": "2024-03-18T10:31:07.405807Z", - "shell.execute_reply": "2024-03-18T10:31:07.405111Z" + "iopub.execute_input": "2024-03-18T12:12:38.737511Z", + "iopub.status.busy": "2024-03-18T12:12:38.737048Z", + "iopub.status.idle": "2024-03-18T12:12:39.637965Z", + "shell.execute_reply": "2024-03-18T12:12:39.637072Z" } }, "outputs": [ diff --git a/tutorials/custom_mixer/custom_mixer.html b/tutorials/custom_mixer/custom_mixer.html index 87f4e294b..10b316d77 100644 --- a/tutorials/custom_mixer/custom_mixer.html +++ b/tutorials/custom_mixer/custom_mixer.html @@ -210,7 +210,7 @@

Step 3: Using our mixer 
-INFO:lightwood-2520:No torchvision detected, image helpers not supported.
+INFO:lightwood-2694:No torchvision detected, image helpers not supported.
@@ -218,7 +218,7 @@

Step 3: Using our mixer 
-INFO:lightwood-2520:No torchvision/pillow detected, image encoder not supported
+INFO:lightwood-2694:No torchvision/pillow detected, image encoder not supported

@@ -226,7 +226,7 @@

Step 3: Using our mixer 
-INFO:type_infer-2520:Analyzing a sample of 298
+INFO:type_infer-2694:Analyzing a sample of 298

@@ -234,7 +234,7 @@

Step 3: Using our mixer 
-INFO:type_infer-2520:from a total population of 303, this is equivalent to 98.3% of your data.
+INFO:type_infer-2694:from a total population of 303, this is equivalent to 98.3% of your data.

@@ -242,7 +242,7 @@

Step 3: Using our mixer 
-INFO:type_infer-2520:Infering type for: age
+INFO:type_infer-2694:Infering type for: age

@@ -250,7 +250,7 @@

Step 3: Using our mixer 
-INFO:type_infer-2520:Column age has data type integer
+INFO:type_infer-2694:Column age has data type integer

@@ -258,7 +258,7 @@

Step 3: Using our mixer 
-INFO:type_infer-2520:Infering type for: sex
+INFO:type_infer-2694:Infering type for: sex

@@ -266,7 +266,7 @@

Step 3: Using our mixer 
-INFO:type_infer-2520:Column sex has data type binary
+INFO:type_infer-2694:Column sex has data type binary

@@ -274,7 +274,7 @@

Step 3: Using our mixer 
-INFO:type_infer-2520:Infering type for: cp
+INFO:type_infer-2694:Infering type for: cp

@@ -282,7 +282,7 @@

Step 3: Using our mixer 
-INFO:type_infer-2520:Column cp has data type categorical
+INFO:type_infer-2694:Column cp has data type categorical

@@ -290,7 +290,7 @@

Step 3: Using our mixer 
-INFO:type_infer-2520:Infering type for: trestbps
+INFO:type_infer-2694:Infering type for: trestbps

@@ -298,7 +298,7 @@

Step 3: Using our mixer 
-INFO:type_infer-2520:Column trestbps has data type integer
+INFO:type_infer-2694:Column trestbps has data type integer

@@ -306,7 +306,7 @@

Step 3: Using our mixer 
-INFO:type_infer-2520:Infering type for: chol
+INFO:type_infer-2694:Infering type for: chol

@@ -314,7 +314,7 @@

Step 3: Using our mixer 
-INFO:type_infer-2520:Column chol has data type integer
+INFO:type_infer-2694:Column chol has data type integer

@@ -322,7 +322,7 @@

Step 3: Using our mixer 
-INFO:type_infer-2520:Infering type for: fbs
+INFO:type_infer-2694:Infering type for: fbs

@@ -330,7 +330,7 @@

Step 3: Using our mixer 
-INFO:type_infer-2520:Column fbs has data type binary
+INFO:type_infer-2694:Column fbs has data type binary

@@ -338,7 +338,7 @@

Step 3: Using our mixer 
-INFO:type_infer-2520:Infering type for: restecg
+INFO:type_infer-2694:Infering type for: restecg

@@ -346,7 +346,7 @@

Step 3: Using our mixer 
-INFO:type_infer-2520:Column restecg has data type categorical
+INFO:type_infer-2694:Column restecg has data type categorical

@@ -354,7 +354,7 @@

Step 3: Using our mixer 
-INFO:type_infer-2520:Infering type for: thalach
+INFO:type_infer-2694:Infering type for: thalach

@@ -362,7 +362,7 @@

Step 3: Using our mixer 
-INFO:type_infer-2520:Column thalach has data type integer
+INFO:type_infer-2694:Column thalach has data type integer

@@ -370,7 +370,7 @@

Step 3: Using our mixer 
-INFO:type_infer-2520:Infering type for: exang
+INFO:type_infer-2694:Infering type for: exang

@@ -378,7 +378,7 @@

Step 3: Using our mixer 
-INFO:type_infer-2520:Column exang has data type binary
+INFO:type_infer-2694:Column exang has data type binary

@@ -386,7 +386,7 @@

Step 3: Using our mixer 
-INFO:type_infer-2520:Infering type for: oldpeak
+INFO:type_infer-2694:Infering type for: oldpeak

@@ -394,7 +394,7 @@

Step 3: Using our mixer 
-INFO:type_infer-2520:Column oldpeak has data type float
+INFO:type_infer-2694:Column oldpeak has data type float

@@ -402,7 +402,7 @@

Step 3: Using our mixer 
-INFO:type_infer-2520:Infering type for: slope
+INFO:type_infer-2694:Infering type for: slope

@@ -410,7 +410,7 @@

Step 3: Using our mixer 
-INFO:type_infer-2520:Column slope has data type categorical
+INFO:type_infer-2694:Column slope has data type categorical

@@ -418,7 +418,7 @@

Step 3: Using our mixer 
-INFO:type_infer-2520:Infering type for: ca
+INFO:type_infer-2694:Infering type for: ca

@@ -426,7 +426,7 @@

Step 3: Using our mixer 
-INFO:type_infer-2520:Column ca has data type categorical
+INFO:type_infer-2694:Column ca has data type categorical

@@ -434,7 +434,7 @@

Step 3: Using our mixer 
-INFO:type_infer-2520:Infering type for: thal
+INFO:type_infer-2694:Infering type for: thal

@@ -442,7 +442,7 @@

Step 3: Using our mixer 
-INFO:type_infer-2520:Column thal has data type categorical
+INFO:type_infer-2694:Column thal has data type categorical

@@ -450,7 +450,7 @@

Step 3: Using our mixer 
-INFO:type_infer-2520:Infering type for: target
+INFO:type_infer-2694:Infering type for: target

@@ -458,7 +458,7 @@

Step 3: Using our mixer 
-INFO:type_infer-2520:Column target has data type binary
+INFO:type_infer-2694:Column target has data type binary

@@ -466,7 +466,7 @@

Step 3: Using our mixer 
-INFO:dataprep_ml-2520:Starting statistical analysis
+INFO:dataprep_ml-2694:Starting statistical analysis

@@ -474,7 +474,7 @@

Step 3: Using our mixer 
-INFO:dataprep_ml-2520:Finished statistical analysis
+INFO:dataprep_ml-2694:Finished statistical analysis

@@ -602,7 +602,7 @@

Step 3: Using our mixerStep 3: Using our mixer 
-INFO:dataprep_ml-2520:[Learn phase 1/8] - Statistical analysis
+INFO:dataprep_ml-2694:[Learn phase 1/8] - Statistical analysis

@@ -687,7 +687,7 @@

Step 3: Using our mixer 
-INFO:dataprep_ml-2520:Starting statistical analysis
+INFO:dataprep_ml-2694:Starting statistical analysis

@@ -695,7 +695,7 @@

Step 3: Using our mixer 
-INFO:dataprep_ml-2520:Finished statistical analysis
+INFO:dataprep_ml-2694:Finished statistical analysis

@@ -703,7 +703,7 @@

Step 3: Using our mixer 
-DEBUG:lightwood-2520: `analyze_data` runtime: 0.03 seconds
+DEBUG:lightwood-2694: `analyze_data` runtime: 0.03 seconds

@@ -711,7 +711,7 @@

Step 3: Using our mixer 
-INFO:dataprep_ml-2520:[Learn phase 2/8] - Data preprocessing
+INFO:dataprep_ml-2694:[Learn phase 2/8] - Data preprocessing

@@ -719,7 +719,7 @@

Step 3: Using our mixer 
-INFO:dataprep_ml-2520:Cleaning the data
+INFO:dataprep_ml-2694:Cleaning the data

@@ -727,7 +727,7 @@

Step 3: Using our mixer 
-DEBUG:lightwood-2520: `preprocess` runtime: 0.01 seconds
+DEBUG:lightwood-2694: `preprocess` runtime: 0.01 seconds

@@ -735,7 +735,7 @@

Step 3: Using our mixer 
-INFO:dataprep_ml-2520:[Learn phase 3/8] - Data splitting
+INFO:dataprep_ml-2694:[Learn phase 3/8] - Data splitting

@@ -743,7 +743,7 @@

Step 3: Using our mixer 
-INFO:dataprep_ml-2520:Splitting the data into train/test
+INFO:dataprep_ml-2694:Splitting the data into train/test

@@ -751,7 +751,7 @@

Step 3: Using our mixer 
-DEBUG:lightwood-2520: `split` runtime: 0.01 seconds
+DEBUG:lightwood-2694: `split` runtime: 0.01 seconds

@@ -759,7 +759,7 @@

Step 3: Using our mixer 
-INFO:dataprep_ml-2520:[Learn phase 4/8] - Preparing encoders
+INFO:dataprep_ml-2694:[Learn phase 4/8] - Preparing encoders

@@ -767,7 +767,7 @@

Step 3: Using our mixer 
-DEBUG:dataprep_ml-2520:Preparing sequentially...
+DEBUG:dataprep_ml-2694:Preparing sequentially...

@@ -775,7 +775,7 @@

Step 3: Using our mixer 
-DEBUG:dataprep_ml-2520:Preparing encoder for age...
+DEBUG:dataprep_ml-2694:Preparing encoder for age...

@@ -783,7 +783,7 @@

Step 3: Using our mixer 
-DEBUG:dataprep_ml-2520:Preparing encoder for sex...
+DEBUG:dataprep_ml-2694:Preparing encoder for sex...

@@ -791,7 +791,7 @@

Step 3: Using our mixer 
-DEBUG:dataprep_ml-2520:Preparing encoder for cp...
+DEBUG:dataprep_ml-2694:Preparing encoder for cp...

@@ -799,7 +799,7 @@

Step 3: Using our mixer 
-DEBUG:lightwood-2520:Encoding UNKNOWN categories as index 0
+DEBUG:lightwood-2694:Encoding UNKNOWN categories as index 0

@@ -807,7 +807,7 @@

Step 3: Using our mixer 
-DEBUG:dataprep_ml-2520:Preparing encoder for trestbps...
+DEBUG:dataprep_ml-2694:Preparing encoder for trestbps...

@@ -815,7 +815,7 @@

Step 3: Using our mixer 
-DEBUG:dataprep_ml-2520:Preparing encoder for chol...
+DEBUG:dataprep_ml-2694:Preparing encoder for chol...

@@ -823,7 +823,7 @@

Step 3: Using our mixer 
-DEBUG:dataprep_ml-2520:Preparing encoder for fbs...
+DEBUG:dataprep_ml-2694:Preparing encoder for fbs...

@@ -831,7 +831,7 @@

Step 3: Using our mixer 
-DEBUG:dataprep_ml-2520:Preparing encoder for restecg...
+DEBUG:dataprep_ml-2694:Preparing encoder for restecg...

@@ -839,7 +839,7 @@

Step 3: Using our mixer 
-DEBUG:lightwood-2520:Encoding UNKNOWN categories as index 0
+DEBUG:lightwood-2694:Encoding UNKNOWN categories as index 0

@@ -847,7 +847,7 @@

Step 3: Using our mixer 
-DEBUG:dataprep_ml-2520:Preparing encoder for thalach...
+DEBUG:dataprep_ml-2694:Preparing encoder for thalach...

@@ -855,7 +855,7 @@

Step 3: Using our mixer 
-DEBUG:dataprep_ml-2520:Preparing encoder for exang...
+DEBUG:dataprep_ml-2694:Preparing encoder for exang...

@@ -863,7 +863,7 @@

Step 3: Using our mixer 
-DEBUG:dataprep_ml-2520:Preparing encoder for oldpeak...
+DEBUG:dataprep_ml-2694:Preparing encoder for oldpeak...

@@ -871,7 +871,7 @@

Step 3: Using our mixer 
-DEBUG:dataprep_ml-2520:Preparing encoder for slope...
+DEBUG:dataprep_ml-2694:Preparing encoder for slope...

@@ -879,7 +879,7 @@

Step 3: Using our mixer 
-DEBUG:lightwood-2520:Encoding UNKNOWN categories as index 0
+DEBUG:lightwood-2694:Encoding UNKNOWN categories as index 0

@@ -887,7 +887,7 @@

Step 3: Using our mixer 
-DEBUG:dataprep_ml-2520:Preparing encoder for ca...
+DEBUG:dataprep_ml-2694:Preparing encoder for ca...

@@ -895,7 +895,7 @@

Step 3: Using our mixer 
-DEBUG:lightwood-2520:Encoding UNKNOWN categories as index 0
+DEBUG:lightwood-2694:Encoding UNKNOWN categories as index 0

@@ -903,7 +903,7 @@

Step 3: Using our mixer 
-DEBUG:dataprep_ml-2520:Preparing encoder for thal...
+DEBUG:dataprep_ml-2694:Preparing encoder for thal...

@@ -911,7 +911,7 @@

Step 3: Using our mixer 
-DEBUG:lightwood-2520:Encoding UNKNOWN categories as index 0
+DEBUG:lightwood-2694:Encoding UNKNOWN categories as index 0

@@ -919,7 +919,7 @@

Step 3: Using our mixer 
-DEBUG:lightwood-2520: `prepare` runtime: 0.02 seconds
+DEBUG:lightwood-2694: `prepare` runtime: 0.02 seconds

@@ -927,7 +927,7 @@

Step 3: Using our mixer 
-INFO:dataprep_ml-2520:[Learn phase 5/8] - Feature generation
+INFO:dataprep_ml-2694:[Learn phase 5/8] - Feature generation

@@ -935,7 +935,7 @@

Step 3: Using our mixer 
-INFO:dataprep_ml-2520:Featurizing the data
+INFO:dataprep_ml-2694:Featurizing the data

@@ -943,7 +943,7 @@

Step 3: Using our mixer 
-DEBUG:lightwood-2520: `featurize` runtime: 0.09 seconds
+DEBUG:lightwood-2694: `featurize` runtime: 0.09 seconds

@@ -951,7 +951,7 @@

Step 3: Using our mixer 
-INFO:dataprep_ml-2520:[Learn phase 6/8] - Mixer training
+INFO:dataprep_ml-2694:[Learn phase 6/8] - Mixer training

@@ -959,7 +959,7 @@

Step 3: Using our mixer 
-INFO:dataprep_ml-2520:Training the mixers
+INFO:dataprep_ml-2694:Training the mixers

@@ -967,7 +967,7 @@

Step 3: Using our mixer 
-DEBUG:lightwood-2520: `fit_mixer` runtime: 0.12 seconds
+DEBUG:lightwood-2694: `fit_mixer` runtime: 0.12 seconds

@@ -975,7 +975,7 @@

Step 3: Using our mixer 
-INFO:dataprep_ml-2520:Ensembling the mixer
+INFO:dataprep_ml-2694:Ensembling the mixer

@@ -983,7 +983,7 @@

Step 3: Using our mixer 
-INFO:lightwood-2520:Mixer: RandomForestMixer got accuracy: 0.798
+INFO:lightwood-2694:Mixer: RandomForestMixer got accuracy: 0.798

@@ -991,7 +991,7 @@

Step 3: Using our mixer 
-INFO:lightwood-2520:Picked best mixer: RandomForestMixer
+INFO:lightwood-2694:Picked best mixer: RandomForestMixer

@@ -999,7 +999,7 @@

Step 3: Using our mixer 
-DEBUG:lightwood-2520: `fit` runtime: 0.13 seconds
+DEBUG:lightwood-2694: `fit` runtime: 0.13 seconds

@@ -1007,7 +1007,7 @@

Step 3: Using our mixer 
-INFO:dataprep_ml-2520:[Learn phase 7/8] - Ensemble analysis
+INFO:dataprep_ml-2694:[Learn phase 7/8] - Ensemble analysis

@@ -1015,7 +1015,7 @@

Step 3: Using our mixer 
-INFO:dataprep_ml-2520:Analyzing the ensemble of mixers
+INFO:dataprep_ml-2694:Analyzing the ensemble of mixers

@@ -1023,7 +1023,7 @@

Step 3: Using our mixer 
-INFO:lightwood-2520:The block ICP is now running its analyze() method
+INFO:lightwood-2694:The block ICP is now running its analyze() method

@@ -1033,7 +1033,7 @@

Step 3: Using our mixer
 /opt/hostedtoolcache/Python/3.9.18/x64/lib/python3.9/site-packages/sklearn/preprocessing/_encoders.py:975: FutureWarning: `sparse` was renamed to `sparse_output` in version 1.2 and will be removed in 1.4. `sparse_output` is ignored unless you leave `sparse` to its default value.
   warnings.warn(
-INFO:lightwood-2520:The block ConfStats is now running its analyze() method
+INFO:lightwood-2694:The block ConfStats is now running its analyze() method

@@ -1041,7 +1041,7 @@

Step 3: Using our mixer 
-INFO:lightwood-2520:The block AccStats is now running its analyze() method
+INFO:lightwood-2694:The block AccStats is now running its analyze() method

@@ -1049,7 +1049,7 @@

Step 3: Using our mixer 
-INFO:lightwood-2520:The block PermutationFeatureImportance is now running its analyze() method
+INFO:lightwood-2694:The block PermutationFeatureImportance is now running its analyze() method

@@ -1057,7 +1057,7 @@

Step 3: Using our mixer 
-INFO:lightwood-2520:[PFI] Using a random sample (1000 rows out of 31).
+INFO:lightwood-2694:[PFI] Using a random sample (1000 rows out of 31).

@@ -1065,7 +1065,7 @@

Step 3: Using our mixer 
-INFO:lightwood-2520:[PFI] Set to consider first 10 columns out of 10: ['age', 'sex', 'cp', 'trestbps', 'chol', 'fbs', 'restecg', 'thalach', 'exang', 'oldpeak'].
+INFO:lightwood-2694:[PFI] Set to consider first 10 columns out of 10: ['age', 'sex', 'cp', 'trestbps', 'chol', 'fbs', 'restecg', 'thalach', 'exang', 'oldpeak'].

@@ -1084,6 +1084,7 @@

Step 3: Using our mixer
 /opt/hostedtoolcache/Python/3.9.18/x64/lib/python3.9/site-packages/sklearn/metrics/_classification.py:2399: UserWarning: y_pred contains classes not in y_true
   warnings.warn("y_pred contains classes not in y_true")
+DEBUG:lightwood-2694: `analyze_ensemble` runtime: 0.28 seconds

@@ -1091,7 +1092,7 @@

Step 3: Using our mixer 
-DEBUG:lightwood-2520: `analyze_ensemble` runtime: 0.27 seconds
+INFO:dataprep_ml-2694:[Learn phase 8/8] - Adjustment on validation requested

@@ -1099,7 +1100,7 @@

Step 3: Using our mixer 
-INFO:dataprep_ml-2520:[Learn phase 8/8] - Adjustment on validation requested
+INFO:dataprep_ml-2694:Updating the mixers

@@ -1107,15 +1108,7 @@

Step 3: Using our mixer 
-INFO:dataprep_ml-2520:Updating the mixers
-
-

-
-
-
-
-
-DEBUG:lightwood-2520: `adjust` runtime: 0.04 seconds
+DEBUG:lightwood-2694: `adjust` runtime: 0.04 seconds
@@ -1123,7 +1116,7 @@

Step 3: Using our mixer 
-DEBUG:lightwood-2520: `learn` runtime: 0.62 seconds
+DEBUG:lightwood-2694: `learn` runtime: 0.64 seconds

Finally, we can use the trained predictor to make some predictions, or save it to a pickle for later use

@@ -1147,7 +1140,7 @@

Step 3: Using our mixer 
-INFO:dataprep_ml-2520:[Predict phase 1/4] - Data preprocessing
+INFO:dataprep_ml-2694:[Predict phase 1/4] - Data preprocessing
@@ -1155,7 +1148,7 @@

Step 3: Using our mixer 
-INFO:dataprep_ml-2520:Cleaning the data
+INFO:dataprep_ml-2694:Cleaning the data

@@ -1163,7 +1156,7 @@

Step 3: Using our mixer 
-DEBUG:lightwood-2520: `preprocess` runtime: 0.01 seconds
+DEBUG:lightwood-2694: `preprocess` runtime: 0.01 seconds

@@ -1171,7 +1164,7 @@

Step 3: Using our mixer 
-INFO:dataprep_ml-2520:[Predict phase 2/4] - Feature generation
+INFO:dataprep_ml-2694:[Predict phase 2/4] - Feature generation

@@ -1179,7 +1172,7 @@

Step 3: Using our mixer 
-INFO:dataprep_ml-2520:Featurizing the data
+INFO:dataprep_ml-2694:Featurizing the data

@@ -1203,7 +1196,7 @@

Step 3: Using our mixerDEBUG:lightwood-2520: `featurize` runtime: 0.02 seconds +DEBUG:lightwood-2694: `featurize` runtime: 0.02 seconds

@@ -1211,7 +1204,7 @@

Step 3: Using our mixer 
-INFO:dataprep_ml-2520:[Predict phase 3/4] - Calling ensemble
+INFO:dataprep_ml-2694:[Predict phase 3/4] - Calling ensemble

@@ -1219,7 +1212,7 @@

Step 3: Using our mixer 
-DEBUG:lightwood-2520: `_timed_call` runtime: 0.01 seconds
+DEBUG:lightwood-2694: `_timed_call` runtime: 0.01 seconds

@@ -1227,7 +1220,7 @@

Step 3: Using our mixer 
-INFO:dataprep_ml-2520:[Predict phase 4/4] - Analyzing output
+INFO:dataprep_ml-2694:[Predict phase 4/4] - Analyzing output

@@ -1235,7 +1228,7 @@

Step 3: Using our mixer 
-INFO:lightwood-2520:The block ICP is now running its explain() method
+INFO:lightwood-2694:The block ICP is now running its explain() method

@@ -1243,7 +1236,7 @@

Step 3: Using our mixer 
-INFO:lightwood-2520:The block ConfStats is now running its explain() method
+INFO:lightwood-2694:The block ConfStats is now running its explain() method

@@ -1251,7 +1244,7 @@

Step 3: Using our mixer 
-INFO:lightwood-2520:ConfStats.explain() has not been implemented, no modifications will be done to the data insights.
+INFO:lightwood-2694:ConfStats.explain() has not been implemented, no modifications will be done to the data insights.

@@ -1259,7 +1252,7 @@

Step 3: Using our mixer 
-INFO:lightwood-2520:The block AccStats is now running its explain() method
+INFO:lightwood-2694:The block AccStats is now running its explain() method

@@ -1267,7 +1260,7 @@

Step 3: Using our mixer 
-INFO:lightwood-2520:AccStats.explain() has not been implemented, no modifications will be done to the data insights.
+INFO:lightwood-2694:AccStats.explain() has not been implemented, no modifications will be done to the data insights.

@@ -1275,7 +1268,7 @@

Step 3: Using our mixer 
-INFO:lightwood-2520:The block PermutationFeatureImportance is now running its explain() method
+INFO:lightwood-2694:The block PermutationFeatureImportance is now running its explain() method

@@ -1283,7 +1276,7 @@

Step 3: Using our mixer 
-INFO:lightwood-2520:PermutationFeatureImportance.explain() has not been implemented, no modifications will be done to the data insights.
+INFO:lightwood-2694:PermutationFeatureImportance.explain() has not been implemented, no modifications will be done to the data insights.

@@ -1291,7 +1284,7 @@

Step 3: Using our mixer 
-DEBUG:lightwood-2520: `explain` runtime: 0.01 seconds
+DEBUG:lightwood-2694: `explain` runtime: 0.01 seconds

@@ -1299,7 +1292,7 @@

Step 3: Using our mixer 
-DEBUG:lightwood-2520: `predict` runtime: 0.05 seconds
+DEBUG:lightwood-2694: `predict` runtime: 0.06 seconds

diff --git a/tutorials/custom_mixer/custom_mixer.ipynb b/tutorials/custom_mixer/custom_mixer.ipynb index 288ab230b..718ea6fc5 100644 --- a/tutorials/custom_mixer/custom_mixer.ipynb +++ b/tutorials/custom_mixer/custom_mixer.ipynb @@ -46,10 +46,10 @@ "execution_count": 1, "metadata": { "execution": { - "iopub.execute_input": "2024-03-18T10:31:39.165227Z", - "iopub.status.busy": "2024-03-18T10:31:39.165033Z", - "iopub.status.idle": "2024-03-18T10:31:39.173257Z", - "shell.execute_reply": "2024-03-18T10:31:39.172655Z" + "iopub.execute_input": "2024-03-18T12:13:14.798659Z", + "iopub.status.busy": "2024-03-18T12:13:14.798214Z", + "iopub.status.idle": "2024-03-18T12:13:14.807631Z", + "shell.execute_reply": "2024-03-18T12:13:14.806928Z" } }, "outputs": [ @@ -133,10 +133,10 @@ "execution_count": 2, "metadata": { "execution": { - "iopub.execute_input": "2024-03-18T10:31:39.209970Z", - "iopub.status.busy": "2024-03-18T10:31:39.209477Z", - "iopub.status.idle": "2024-03-18T10:31:41.899985Z", - "shell.execute_reply": "2024-03-18T10:31:41.899281Z" + "iopub.execute_input": "2024-03-18T12:13:14.846502Z", + "iopub.status.busy": "2024-03-18T12:13:14.846021Z", + "iopub.status.idle": "2024-03-18T12:13:17.928216Z", + "shell.execute_reply": "2024-03-18T12:13:17.927465Z" } }, "outputs": [ @@ -144,238 +144,238 @@ "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2520:No torchvision detected, image helpers not supported.\u001b[0m\n" + "\u001b[32mINFO:lightwood-2694:No torchvision detected, image helpers not supported.\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2520:No torchvision/pillow detected, image encoder not supported\u001b[0m\n" + "\u001b[32mINFO:lightwood-2694:No torchvision/pillow detected, image encoder not supported\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2520:Analyzing a sample of 298\u001b[0m\n" + "\u001b[32mINFO:type_infer-2694:Analyzing a sample of 298\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2520:from a total population of 303, this is equivalent to 98.3% of your data.\u001b[0m\n" + "\u001b[32mINFO:type_infer-2694:from a total population of 303, this is equivalent to 98.3% of your data.\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2520:Infering type for: age\u001b[0m\n" + "\u001b[32mINFO:type_infer-2694:Infering type for: age\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2520:Column age has data type integer\u001b[0m\n" + "\u001b[32mINFO:type_infer-2694:Column age has data type integer\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2520:Infering type for: sex\u001b[0m\n" + "\u001b[32mINFO:type_infer-2694:Infering type for: sex\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2520:Column sex has data type binary\u001b[0m\n" + "\u001b[32mINFO:type_infer-2694:Column sex has data type binary\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2520:Infering type for: cp\u001b[0m\n" + "\u001b[32mINFO:type_infer-2694:Infering type for: cp\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2520:Column cp has data type categorical\u001b[0m\n" + "\u001b[32mINFO:type_infer-2694:Column cp has data type categorical\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2520:Infering type for: trestbps\u001b[0m\n" + "\u001b[32mINFO:type_infer-2694:Infering type for: trestbps\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2520:Column trestbps has data type integer\u001b[0m\n" + "\u001b[32mINFO:type_infer-2694:Column trestbps has data type integer\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2520:Infering type for: chol\u001b[0m\n" + "\u001b[32mINFO:type_infer-2694:Infering type for: chol\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2520:Column chol has data type integer\u001b[0m\n" + "\u001b[32mINFO:type_infer-2694:Column chol has data type integer\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2520:Infering type for: fbs\u001b[0m\n" + "\u001b[32mINFO:type_infer-2694:Infering type for: fbs\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2520:Column fbs has data type binary\u001b[0m\n" + "\u001b[32mINFO:type_infer-2694:Column fbs has data type binary\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2520:Infering type for: restecg\u001b[0m\n" + "\u001b[32mINFO:type_infer-2694:Infering type for: restecg\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2520:Column restecg has data type categorical\u001b[0m\n" + "\u001b[32mINFO:type_infer-2694:Column restecg has data type categorical\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2520:Infering type for: thalach\u001b[0m\n" + "\u001b[32mINFO:type_infer-2694:Infering type for: thalach\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2520:Column thalach has data type integer\u001b[0m\n" + "\u001b[32mINFO:type_infer-2694:Column thalach has data type integer\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2520:Infering type for: exang\u001b[0m\n" + "\u001b[32mINFO:type_infer-2694:Infering type for: exang\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2520:Column exang has data type binary\u001b[0m\n" + "\u001b[32mINFO:type_infer-2694:Column exang has data type binary\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2520:Infering type for: oldpeak\u001b[0m\n" + "\u001b[32mINFO:type_infer-2694:Infering type for: oldpeak\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2520:Column oldpeak has data type float\u001b[0m\n" + "\u001b[32mINFO:type_infer-2694:Column oldpeak has data type float\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2520:Infering type for: slope\u001b[0m\n" + "\u001b[32mINFO:type_infer-2694:Infering type for: slope\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2520:Column slope has data type categorical\u001b[0m\n" + "\u001b[32mINFO:type_infer-2694:Column slope has data type categorical\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2520:Infering type for: ca\u001b[0m\n" + "\u001b[32mINFO:type_infer-2694:Infering type for: ca\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2520:Column ca has data type categorical\u001b[0m\n" + "\u001b[32mINFO:type_infer-2694:Column ca has data type categorical\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2520:Infering type for: thal\u001b[0m\n" + "\u001b[32mINFO:type_infer-2694:Infering type for: thal\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2520:Column thal has data type categorical\u001b[0m\n" + "\u001b[32mINFO:type_infer-2694:Column thal has data type categorical\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2520:Infering type for: target\u001b[0m\n" + "\u001b[32mINFO:type_infer-2694:Infering type for: target\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2520:Column target has data type binary\u001b[0m\n" + "\u001b[32mINFO:type_infer-2694:Column target has data type binary\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2520:Starting statistical analysis\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2694:Starting statistical analysis\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2520:Finished statistical analysis\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2694:Finished statistical analysis\u001b[0m\n" ] }, { @@ -502,7 +502,7 @@ " \"unbias_target\": true,\n", " \"seconds_per_mixer\": 42768.0,\n", " \"seconds_per_encoder\": null,\n", - " \"expected_additional_time\": 0.06839251518249512,\n", + " \"expected_additional_time\": 0.07393026351928711,\n", " \"time_aim\": 259200,\n", " \"target_weights\": null,\n", " \"positive_domain\": false,\n", @@ -571,10 +571,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2024-03-18T10:31:41.902933Z", - "iopub.status.busy": "2024-03-18T10:31:41.902517Z", - "iopub.status.idle": "2024-03-18T10:31:41.905841Z", - "shell.execute_reply": "2024-03-18T10:31:41.905210Z" + "iopub.execute_input": "2024-03-18T12:13:17.931147Z", + "iopub.status.busy": "2024-03-18T12:13:17.930695Z", + "iopub.status.idle": "2024-03-18T12:13:17.934544Z", + "shell.execute_reply": "2024-03-18T12:13:17.933897Z" } }, "outputs": [], @@ -603,10 +603,10 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2024-03-18T10:31:41.908451Z", - "iopub.status.busy": "2024-03-18T10:31:41.908026Z", - "iopub.status.idle": "2024-03-18T10:31:42.240157Z", - "shell.execute_reply": "2024-03-18T10:31:42.239529Z" + "iopub.execute_input": "2024-03-18T12:13:17.937482Z", + "iopub.status.busy": "2024-03-18T12:13:17.937008Z", + "iopub.status.idle": "2024-03-18T12:13:18.301601Z", + "shell.execute_reply": "2024-03-18T12:13:18.300927Z" } }, "outputs": [], @@ -622,10 +622,10 @@ "execution_count": 5, "metadata": { "execution": { - "iopub.execute_input": "2024-03-18T10:31:42.243248Z", - "iopub.status.busy": "2024-03-18T10:31:42.242700Z", - "iopub.status.idle": "2024-03-18T10:31:42.863413Z", - "shell.execute_reply": "2024-03-18T10:31:42.862885Z" + "iopub.execute_input": "2024-03-18T12:13:18.304835Z", + "iopub.status.busy": "2024-03-18T12:13:18.304383Z", + "iopub.status.idle": "2024-03-18T12:13:18.945519Z", + "shell.execute_reply": "2024-03-18T12:13:18.944829Z" } }, "outputs": [ @@ -633,308 +633,308 @@ "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2520:[Learn phase 1/8] - Statistical analysis\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2694:[Learn phase 1/8] - Statistical analysis\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2520:Starting statistical analysis\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2694:Starting statistical analysis\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2520:Finished statistical analysis\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2694:Finished statistical analysis\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - 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"\u001b[32mINFO:lightwood-2520:Mixer: RandomForestMixer got accuracy: 0.798\u001b[0m\n" + "\u001b[32mINFO:lightwood-2694:Mixer: RandomForestMixer got accuracy: 0.798\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2520:Picked best mixer: RandomForestMixer\u001b[0m\n" + "\u001b[32mINFO:lightwood-2694:Picked best mixer: RandomForestMixer\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:lightwood-2520: `fit` runtime: 0.13 seconds\u001b[0m\n" + "\u001b[37mDEBUG:lightwood-2694: `fit` runtime: 0.13 seconds\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2520:[Learn phase 7/8] - Ensemble analysis\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2694:[Learn phase 7/8] - Ensemble analysis\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2520:Analyzing the ensemble of mixers\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2694:Analyzing the ensemble of mixers\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2520:The block ICP is now running its analyze() method\u001b[0m\n" + "\u001b[32mINFO:lightwood-2694:The block ICP is now running its analyze() method\u001b[0m\n" ] }, { @@ -943,35 +943,35 @@ "text": [ "/opt/hostedtoolcache/Python/3.9.18/x64/lib/python3.9/site-packages/sklearn/preprocessing/_encoders.py:975: FutureWarning: `sparse` was renamed to `sparse_output` in version 1.2 and will be removed in 1.4. `sparse_output` is ignored unless you leave `sparse` to its default value.\n", " warnings.warn(\n", - "\u001b[32mINFO:lightwood-2520:The block ConfStats is now running its analyze() method\u001b[0m\n" + "\u001b[32mINFO:lightwood-2694:The block ConfStats is now running its analyze() method\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2520:The block AccStats is now running its analyze() method\u001b[0m\n" + "\u001b[32mINFO:lightwood-2694:The block AccStats is now running its analyze() method\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2520:The block PermutationFeatureImportance is now running its analyze() method\u001b[0m\n" + "\u001b[32mINFO:lightwood-2694:The block PermutationFeatureImportance is now running its analyze() method\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2520:[PFI] Using a random sample (1000 rows out of 31).\u001b[0m\n" + "\u001b[32mINFO:lightwood-2694:[PFI] Using a random sample (1000 rows out of 31).\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2520:[PFI] Set to consider first 10 columns out of 10: ['age', 'sex', 'cp', 'trestbps', 'chol', 'fbs', 'restecg', 'thalach', 'exang', 'oldpeak'].\u001b[0m\n" + "\u001b[32mINFO:lightwood-2694:[PFI] Set to consider first 10 columns out of 10: ['age', 'sex', 'cp', 'trestbps', 'chol', 'fbs', 'restecg', 'thalach', 'exang', 'oldpeak'].\u001b[0m\n" ] }, { @@ -987,42 +987,36 @@ "output_type": "stream", "text": [ "/opt/hostedtoolcache/Python/3.9.18/x64/lib/python3.9/site-packages/sklearn/metrics/_classification.py:2399: UserWarning: y_pred contains classes not in y_true\n", - " warnings.warn(\"y_pred contains classes not in y_true\")\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\u001b[37mDEBUG:lightwood-2520: `analyze_ensemble` runtime: 0.27 seconds\u001b[0m\n" + " warnings.warn(\"y_pred contains classes not in y_true\")\n", + "\u001b[37mDEBUG:lightwood-2694: `analyze_ensemble` runtime: 0.28 seconds\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2520:[Learn phase 8/8] - Adjustment on validation requested\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2694:[Learn phase 8/8] - Adjustment on validation requested\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2520:Updating the mixers\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2694:Updating the mixers\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:lightwood-2520: `adjust` runtime: 0.04 seconds\u001b[0m\n" + "\u001b[37mDEBUG:lightwood-2694: `adjust` runtime: 0.04 seconds\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:lightwood-2520: `learn` runtime: 0.62 seconds\u001b[0m\n" + "\u001b[37mDEBUG:lightwood-2694: `learn` runtime: 0.64 seconds\u001b[0m\n" ] } ], @@ -1042,10 +1036,10 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2024-03-18T10:31:42.866072Z", - "iopub.status.busy": "2024-03-18T10:31:42.865679Z", - "iopub.status.idle": "2024-03-18T10:31:42.985005Z", - "shell.execute_reply": "2024-03-18T10:31:42.984402Z" + "iopub.execute_input": "2024-03-18T12:13:18.948374Z", + "iopub.status.busy": "2024-03-18T12:13:18.948128Z", + "iopub.status.idle": "2024-03-18T12:13:19.076130Z", + "shell.execute_reply": "2024-03-18T12:13:19.075363Z" } }, "outputs": [ @@ -1053,35 +1047,35 @@ "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2520:[Predict phase 1/4] - Data preprocessing\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2694:[Predict phase 1/4] - Data preprocessing\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2520:Cleaning the data\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2694:Cleaning the data\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:lightwood-2520: `preprocess` runtime: 0.01 seconds\u001b[0m\n" + "\u001b[37mDEBUG:lightwood-2694: `preprocess` runtime: 0.01 seconds\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2520:[Predict phase 2/4] - Feature generation\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2694:[Predict phase 2/4] - Feature generation\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2520:Featurizing the data\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2694:Featurizing the data\u001b[0m\n" ] }, { @@ -1104,91 +1098,91 @@ " outputs = ufunc(*inputs)\n", "/opt/hostedtoolcache/Python/3.9.18/x64/lib/python3.9/site-packages/numpy/lib/function_base.py:2455: RuntimeWarning: invalid value encountered in _none_fn (vectorized)\n", " outputs = ufunc(*inputs)\n", - "\u001b[37mDEBUG:lightwood-2520: `featurize` runtime: 0.02 seconds\u001b[0m\n" + "\u001b[37mDEBUG:lightwood-2694: `featurize` runtime: 0.02 seconds\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2520:[Predict phase 3/4] - Calling ensemble\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2694:[Predict phase 3/4] - Calling ensemble\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:lightwood-2520: `_timed_call` runtime: 0.01 seconds\u001b[0m\n" + "\u001b[37mDEBUG:lightwood-2694: `_timed_call` runtime: 0.01 seconds\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2520:[Predict phase 4/4] - Analyzing output\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2694:[Predict phase 4/4] - Analyzing output\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2520:The block ICP is now running its explain() method\u001b[0m\n" + "\u001b[32mINFO:lightwood-2694:The block ICP is now running its explain() method\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2520:The block ConfStats is now running its explain() method\u001b[0m\n" + "\u001b[32mINFO:lightwood-2694:The block ConfStats is now running its explain() method\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2520:ConfStats.explain() has not been implemented, no modifications will be done to the data insights.\u001b[0m\n" + "\u001b[32mINFO:lightwood-2694:ConfStats.explain() has not been implemented, no modifications will be done to the data insights.\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2520:The block AccStats is now running its explain() method\u001b[0m\n" + "\u001b[32mINFO:lightwood-2694:The block AccStats is now running its explain() method\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2520:AccStats.explain() has not been implemented, no modifications will be done to the data insights.\u001b[0m\n" + "\u001b[32mINFO:lightwood-2694:AccStats.explain() has not been implemented, no modifications will be done to the data insights.\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2520:The block PermutationFeatureImportance is now running its explain() method\u001b[0m\n" + "\u001b[32mINFO:lightwood-2694:The block PermutationFeatureImportance is now running its explain() method\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2520:PermutationFeatureImportance.explain() has not been implemented, no modifications will be done to the data insights.\u001b[0m\n" + "\u001b[32mINFO:lightwood-2694:PermutationFeatureImportance.explain() has not been implemented, no modifications will be done to the data insights.\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:lightwood-2520: `explain` runtime: 0.01 seconds\u001b[0m\n" + "\u001b[37mDEBUG:lightwood-2694: `explain` runtime: 0.01 seconds\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:lightwood-2520: `predict` runtime: 0.05 seconds\u001b[0m\n" + "\u001b[37mDEBUG:lightwood-2694: `predict` runtime: 0.06 seconds\u001b[0m\n" ] }, { diff --git a/tutorials/custom_splitter/custom_splitter.html b/tutorials/custom_splitter/custom_splitter.html index 0094fdcd8..e66ffe61c 100644 --- a/tutorials/custom_splitter/custom_splitter.html +++ b/tutorials/custom_splitter/custom_splitter.html @@ -129,7 +129,7 @@

Date: 2021.10.07 
-INFO:lightwood-2670:No torchvision detected, image helpers not supported.
+INFO:lightwood-2840:No torchvision detected, image helpers not supported.

@@ -137,7 +137,7 @@

Date: 2021.10.07 
-INFO:lightwood-2670:No torchvision/pillow detected, image encoder not supported
+INFO:lightwood-2840:No torchvision/pillow detected, image encoder not supported

@@ -383,7 +383,7 @@

2) Create a JSON-AI default object 
-INFO:type_infer-2670:Analyzing a sample of 18424
+INFO:type_infer-2840:Analyzing a sample of 18424
@@ -391,7 +391,7 @@

2) Create a JSON-AI default object 
-INFO:type_infer-2670:from a total population of 284807, this is equivalent to 6.5% of your data.
+INFO:type_infer-2840:from a total population of 284807, this is equivalent to 6.5% of your data.

@@ -399,7 +399,7 @@

2) Create a JSON-AI default object 
-INFO:type_infer-2670:Using 3 processes to deduct types.
+INFO:type_infer-2840:Using 3 processes to deduct types.

@@ -407,7 +407,7 @@

2) Create a JSON-AI default object 
-INFO:type_infer-2670:Infering type for: Time
+INFO:type_infer-2840:Infering type for: Time

@@ -415,7 +415,7 @@

2) Create a JSON-AI default object 
-INFO:type_infer-2670:Infering type for: V3
+INFO:type_infer-2840:Infering type for: V3

@@ -423,7 +423,7 @@

2) Create a JSON-AI default object 
-INFO:type_infer-2670:Infering type for: V6
+INFO:type_infer-2840:Infering type for: V6

@@ -431,7 +431,7 @@

2) Create a JSON-AI default object 
-INFO:type_infer-2670:Column Time has data type integer
+INFO:type_infer-2840:Column Time has data type integer

@@ -439,7 +439,7 @@

2) Create a JSON-AI default object 
-INFO:type_infer-2670:Infering type for: V1
+INFO:type_infer-2840:Infering type for: V1

@@ -447,7 +447,7 @@

2) Create a JSON-AI default object 
-INFO:type_infer-2670:Column V1 has data type float
+INFO:type_infer-2840:Column V3 has data type float

@@ -455,7 +455,7 @@

2) Create a JSON-AI default object 
-INFO:type_infer-2670:Infering type for: V2
+INFO:type_infer-2840:Infering type for: V4

@@ -463,7 +463,7 @@

2) Create a JSON-AI default object 
-INFO:type_infer-2670:Column V3 has data type float
+INFO:type_infer-2840:Column V1 has data type float

@@ -471,7 +471,7 @@

2) Create a JSON-AI default object 
-INFO:type_infer-2670:Infering type for: V4
+INFO:type_infer-2840:Infering type for: V2

@@ -479,7 +479,7 @@

2) Create a JSON-AI default object 
-INFO:type_infer-2670:Column V6 has data type float
+INFO:type_infer-2840:Column V6 has data type float

@@ -487,7 +487,7 @@

2) Create a JSON-AI default object 
-INFO:type_infer-2670:Infering type for: V7
+INFO:type_infer-2840:Infering type for: V7

@@ -495,7 +495,7 @@

2) Create a JSON-AI default object 
-INFO:type_infer-2670:Column V2 has data type float
+INFO:type_infer-2840:Column V2 has data type float

@@ -503,7 +503,7 @@

2) Create a JSON-AI default object 
-INFO:type_infer-2670:Infering type for: V9
+INFO:type_infer-2840:Infering type for: V9

@@ -511,7 +511,7 @@

2) Create a JSON-AI default object 
-INFO:type_infer-2670:Column V4 has data type float
+INFO:type_infer-2840:Column V4 has data type float

@@ -519,7 +519,7 @@

2) Create a JSON-AI default object 
-INFO:type_infer-2670:Infering type for: V5
+INFO:type_infer-2840:Infering type for: V5

@@ -527,7 +527,7 @@

2) Create a JSON-AI default object 
-INFO:type_infer-2670:Column V9 has data type float
+INFO:type_infer-2840:Column V7 has data type float

@@ -535,7 +535,7 @@

2) Create a JSON-AI default object 
-INFO:type_infer-2670:Infering type for: V10
+INFO:type_infer-2840:Infering type for: V8

@@ -543,7 +543,7 @@

2) Create a JSON-AI default object 
-INFO:type_infer-2670:Column V7 has data type float
+INFO:type_infer-2840:Column V9 has data type float

@@ -551,7 +551,7 @@

2) Create a JSON-AI default object 
-INFO:type_infer-2670:Infering type for: V8
+INFO:type_infer-2840:Infering type for: V10

@@ -559,7 +559,7 @@

2) Create a JSON-AI default object 
-INFO:type_infer-2670:Column V5 has data type float
+INFO:type_infer-2840:Column V5 has data type float

@@ -567,7 +567,7 @@

2) Create a JSON-AI default object 
-INFO:type_infer-2670:Infering type for: V12
+INFO:type_infer-2840:Infering type for: V12

@@ -575,7 +575,7 @@

2) Create a JSON-AI default object 
-INFO:type_infer-2670:Column V10 has data type float
+INFO:type_infer-2840:Column V8 has data type float

@@ -583,7 +583,7 @@

2) Create a JSON-AI default object 
-INFO:type_infer-2670:Infering type for: V11
+INFO:type_infer-2840:Column V10 has data type float

@@ -591,7 +591,7 @@

2) Create a JSON-AI default object 
-INFO:type_infer-2670:Column V8 has data type float
+INFO:type_infer-2840:Infering type for: V11

@@ -599,7 +599,7 @@

2) Create a JSON-AI default object 
-INFO:type_infer-2670:Infering type for: V15
+INFO:type_infer-2840:Infering type for: V15

@@ -607,7 +607,7 @@

2) Create a JSON-AI default object 
-INFO:type_infer-2670:Column V12 has data type float
+INFO:type_infer-2840:Column V11 has data type float

@@ -615,7 +615,7 @@

2) Create a JSON-AI default object 
-INFO:type_infer-2670:Infering type for: V13
+INFO:type_infer-2840:Infering type for: V18

@@ -623,7 +623,7 @@

2) Create a JSON-AI default object 
-INFO:type_infer-2670:Column V13 has data type float
+INFO:type_infer-2840:Column V12 has data type float

@@ -631,7 +631,7 @@

2) Create a JSON-AI default object 
-INFO:type_infer-2670:Infering type for: V14
+INFO:type_infer-2840:Infering type for: V13

@@ -639,7 +639,7 @@

2) Create a JSON-AI default object 
-INFO:type_infer-2670:Column V11 has data type float
+INFO:type_infer-2840:Column V15 has data type float

@@ -647,7 +647,7 @@

2) Create a JSON-AI default object 
-INFO:type_infer-2670:Infering type for: V18
+INFO:type_infer-2840:Infering type for: V16

@@ -655,7 +655,7 @@

2) Create a JSON-AI default object 
-INFO:type_infer-2670:Column V15 has data type float
+INFO:type_infer-2840:Column V18 has data type float

@@ -663,7 +663,7 @@

2) Create a JSON-AI default object 
-INFO:type_infer-2670:Infering type for: V16
+INFO:type_infer-2840:Infering type for: V19

@@ -671,7 +671,7 @@

2) Create a JSON-AI default object 
-INFO:type_infer-2670:Column V14 has data type float
+INFO:type_infer-2840:Column V13 has data type float

@@ -679,7 +679,7 @@

2) Create a JSON-AI default object 
-INFO:type_infer-2670:Infering type for: V21
+INFO:type_infer-2840:Infering type for: V14

@@ -687,7 +687,7 @@

2) Create a JSON-AI default object 
-INFO:type_infer-2670:Column V18 has data type float
+INFO:type_infer-2840:Column V16 has data type float

@@ -695,7 +695,7 @@

2) Create a JSON-AI default object 
-INFO:type_infer-2670:Column V16 has data type float
+INFO:type_infer-2840:Column V19 has data type float

@@ -703,7 +703,7 @@

2) Create a JSON-AI default object 
-INFO:type_infer-2670:Infering type for: V19
+INFO:type_infer-2840:Infering type for: V20

@@ -711,7 +711,7 @@

2) Create a JSON-AI default object 
-INFO:type_infer-2670:Infering type for: V17
+INFO:type_infer-2840:Infering type for: V17

@@ -719,7 +719,7 @@

2) Create a JSON-AI default object 
-INFO:type_infer-2670:Column V21 has data type float
+INFO:type_infer-2840:Column V20 has data type float

@@ -727,7 +727,7 @@

2) Create a JSON-AI default object 
-INFO:type_infer-2670:Infering type for: V22
+INFO:type_infer-2840:Infering type for: V21

@@ -735,7 +735,7 @@

2) Create a JSON-AI default object 
-INFO:type_infer-2670:Column V19 has data type float
+INFO:type_infer-2840:Column V14 has data type float

@@ -743,7 +743,7 @@

2) Create a JSON-AI default object 
-INFO:type_infer-2670:Infering type for: V20
+INFO:type_infer-2840:Column V17 has data type float

@@ -751,7 +751,7 @@

2) Create a JSON-AI default object 
-INFO:type_infer-2670:Column V17 has data type float
+INFO:type_infer-2840:Infering type for: V24

@@ -759,7 +759,7 @@

2) Create a JSON-AI default object 
-INFO:type_infer-2670:Infering type for: V24
+INFO:type_infer-2840:Infering type for: V27

@@ -767,7 +767,7 @@

2) Create a JSON-AI default object 
-INFO:type_infer-2670:Column V22 has data type float
+INFO:type_infer-2840:Column V21 has data type float

@@ -775,7 +775,7 @@

2) Create a JSON-AI default object 
-INFO:type_infer-2670:Infering type for: V23
+INFO:type_infer-2840:Infering type for: V22

@@ -783,7 +783,7 @@

2) Create a JSON-AI default object 
-INFO:type_infer-2670:Column V20 has data type float
+INFO:type_infer-2840:Column V24 has data type float

@@ -791,7 +791,7 @@

2) Create a JSON-AI default object 
-INFO:type_infer-2670:Infering type for: V27
+INFO:type_infer-2840:Infering type for: V25

@@ -799,7 +799,7 @@

2) Create a JSON-AI default object 
-INFO:type_infer-2670:Column V23 has data type float
+INFO:type_infer-2840:Column V27 has data type float

@@ -807,7 +807,7 @@

2) Create a JSON-AI default object 
-INFO:type_infer-2670:Infering type for: Class
+INFO:type_infer-2840:Infering type for: V28

@@ -815,7 +815,7 @@

2) Create a JSON-AI default object 
-INFO:type_infer-2670:Column V24 has data type float
+INFO:type_infer-2840:Column V25 has data type float

@@ -823,7 +823,7 @@

2) Create a JSON-AI default object 
-INFO:type_infer-2670:Infering type for: V25
+INFO:type_infer-2840:Infering type for: V26

@@ -831,7 +831,7 @@

2) Create a JSON-AI default object 
-INFO:type_infer-2670:Column Class has data type binary
+INFO:type_infer-2840:Column V22 has data type float

@@ -839,7 +839,7 @@

2) Create a JSON-AI default object 
-INFO:type_infer-2670:Column V27 has data type float
+INFO:type_infer-2840:Infering type for: V23

@@ -847,7 +847,7 @@

2) Create a JSON-AI default object 
-INFO:type_infer-2670:Infering type for: V28
+INFO:type_infer-2840:Column V26 has data type float

@@ -855,7 +855,7 @@

2) Create a JSON-AI default object 
-INFO:type_infer-2670:Column V25 has data type float
+INFO:type_infer-2840:Infering type for: Class

@@ -863,7 +863,7 @@

2) Create a JSON-AI default object 
-INFO:type_infer-2670:Infering type for: V26
+INFO:type_infer-2840:Column Class has data type binary

@@ -871,7 +871,7 @@

2) Create a JSON-AI default object 
-INFO:type_infer-2670:Column V28 has data type float
+INFO:type_infer-2840:Column V28 has data type float

@@ -879,7 +879,7 @@

2) Create a JSON-AI default object 
-INFO:type_infer-2670:Infering type for: Amount
+INFO:type_infer-2840:Infering type for: Amount

@@ -887,7 +887,7 @@

2) Create a JSON-AI default object 
-INFO:type_infer-2670:Column V26 has data type float
+INFO:type_infer-2840:Column V23 has data type float

@@ -895,7 +895,7 @@

2) Create a JSON-AI default object 
-INFO:type_infer-2670:Column Amount has data type float
+INFO:type_infer-2840:Column Amount has data type float

@@ -903,7 +903,7 @@

2) Create a JSON-AI default object 
-INFO:dataprep_ml-2670:Starting statistical analysis
+INFO:dataprep_ml-2840:Starting statistical analysis

@@ -911,7 +911,7 @@

2) Create a JSON-AI default object 
-INFO:dataprep_ml-2670:Finished statistical analysis
+INFO:dataprep_ml-2840:Finished statistical analysis

Lightwood looks at each of the many columns and indicates they are mostly float, with exception of “Class” which is binary.

@@ -1141,7 +1141,7 @@

5) Generate Python code representing your ML pipeline6) Call python to run your code and see your preprocessed outputs 
-INFO:dataprep_ml-2670:Cleaning the data
+INFO:dataprep_ml-2840:Cleaning the data
@@ -1890,7 +1890,7 @@

6) Call python to run your code and see your preprocessed outputs 
-DEBUG:lightwood-2670: `preprocess` runtime: 18.89 seconds
+DEBUG:lightwood-2840: `preprocess` runtime: 18.63 seconds

@@ -1898,7 +1898,7 @@

6) Call python to run your code and see your preprocessed outputs 
-INFO:dataprep_ml-2670:Splitting the data into train/test
+INFO:dataprep_ml-2840:Splitting the data into train/test

@@ -1906,7 +1906,7 @@

6) Call python to run your code and see your preprocessed outputs 
-DEBUG:lightwood-2670: `split` runtime: 1.63 seconds
+DEBUG:lightwood-2840: `split` runtime: 2.12 seconds

diff --git a/tutorials/custom_splitter/custom_splitter.ipynb b/tutorials/custom_splitter/custom_splitter.ipynb index 1775b9344..78b93a84d 100644 --- a/tutorials/custom_splitter/custom_splitter.ipynb +++ b/tutorials/custom_splitter/custom_splitter.ipynb @@ -28,10 +28,10 @@ "id": "interim-discussion", "metadata": { "execution": { - "iopub.execute_input": "2024-03-18T10:32:26.642823Z", - "iopub.status.busy": "2024-03-18T10:32:26.642629Z", - "iopub.status.idle": "2024-03-18T10:32:29.510740Z", - "shell.execute_reply": "2024-03-18T10:32:29.509930Z" + "iopub.execute_input": "2024-03-18T12:14:06.775523Z", + "iopub.status.busy": "2024-03-18T12:14:06.775322Z", + "iopub.status.idle": "2024-03-18T12:14:09.903896Z", + "shell.execute_reply": "2024-03-18T12:14:09.903162Z" } }, "outputs": [ @@ -39,14 +39,14 @@ "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2670:No torchvision detected, image helpers not supported.\u001b[0m\n" + "\u001b[32mINFO:lightwood-2840:No torchvision detected, image helpers not supported.\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2670:No torchvision/pillow detected, image encoder not supported\u001b[0m\n" + "\u001b[32mINFO:lightwood-2840:No torchvision/pillow detected, image encoder not supported\u001b[0m\n" ] } ], @@ -87,10 +87,10 @@ "id": "foreign-orchestra", "metadata": { "execution": { - "iopub.execute_input": "2024-03-18T10:32:29.514196Z", - "iopub.status.busy": "2024-03-18T10:32:29.513818Z", - "iopub.status.idle": "2024-03-18T10:32:34.219711Z", - "shell.execute_reply": "2024-03-18T10:32:34.219020Z" + "iopub.execute_input": "2024-03-18T12:14:09.907258Z", + "iopub.status.busy": "2024-03-18T12:14:09.906900Z", + "iopub.status.idle": "2024-03-18T12:14:16.385696Z", + "shell.execute_reply": "2024-03-18T12:14:16.384954Z" } }, "outputs": [ @@ -316,10 +316,10 @@ "id": "cathedral-mills", "metadata": { "execution": { - "iopub.execute_input": "2024-03-18T10:32:34.222771Z", - "iopub.status.busy": "2024-03-18T10:32:34.222257Z", - "iopub.status.idle": "2024-03-18T10:32:34.583402Z", - "shell.execute_reply": "2024-03-18T10:32:34.582727Z" + "iopub.execute_input": "2024-03-18T12:14:16.388631Z", + "iopub.status.busy": "2024-03-18T12:14:16.388201Z", + "iopub.status.idle": "2024-03-18T12:14:16.758868Z", + "shell.execute_reply": "2024-03-18T12:14:16.758208Z" } }, "outputs": [ @@ -374,10 +374,10 @@ "id": "medieval-zambia", "metadata": { "execution": { - "iopub.execute_input": "2024-03-18T10:32:34.586342Z", - "iopub.status.busy": "2024-03-18T10:32:34.585854Z", - "iopub.status.idle": "2024-03-18T10:33:44.164777Z", - "shell.execute_reply": "2024-03-18T10:33:44.164066Z" + "iopub.execute_input": "2024-03-18T12:14:16.761890Z", + "iopub.status.busy": "2024-03-18T12:14:16.761276Z", + "iopub.status.idle": "2024-03-18T12:15:26.682541Z", + "shell.execute_reply": "2024-03-18T12:15:26.681823Z" } }, "outputs": [ @@ -385,469 +385,469 @@ "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2670:Analyzing a sample of 18424\u001b[0m\n" + "\u001b[32mINFO:type_infer-2840:Analyzing a sample of 18424\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2670:from a total population of 284807, this is equivalent to 6.5% of your data.\u001b[0m\n" + "\u001b[32mINFO:type_infer-2840:from a total population of 284807, this is equivalent to 6.5% of your data.\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2670:Using 3 processes to deduct types.\u001b[0m\n" + "\u001b[32mINFO:type_infer-2840:Using 3 processes to deduct types.\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2670:Infering type for: Time\u001b[0m\n" + "\u001b[32mINFO:type_infer-2840:Infering type for: Time\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2670:Infering type for: V3\u001b[0m\n" + "\u001b[32mINFO:type_infer-2840:Infering type for: V3\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2670:Infering type for: V6\u001b[0m\n" + "\u001b[32mINFO:type_infer-2840:Infering type for: V6\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2670:Column Time has data type integer\u001b[0m\n" + "\u001b[32mINFO:type_infer-2840:Column Time has data type integer\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2670:Infering type for: V1\u001b[0m\n" + "\u001b[32mINFO:type_infer-2840:Infering type for: V1\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2670:Column V1 has data type float\u001b[0m\n" + "\u001b[32mINFO:type_infer-2840:Column V3 has data type float\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2670:Infering type for: V2\u001b[0m\n" + "\u001b[32mINFO:type_infer-2840:Infering type for: V4\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2670:Column V3 has data type float\u001b[0m\n" + "\u001b[32mINFO:type_infer-2840:Column V1 has data type float\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2670:Infering type for: V4\u001b[0m\n" + "\u001b[32mINFO:type_infer-2840:Infering type for: V2\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2670:Column V6 has data type float\u001b[0m\n" + "\u001b[32mINFO:type_infer-2840:Column V6 has data type float\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2670:Infering type for: V7\u001b[0m\n" + "\u001b[32mINFO:type_infer-2840:Infering type for: V7\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2670:Column V2 has data type float\u001b[0m\n" + "\u001b[32mINFO:type_infer-2840:Column V2 has data type float\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2670:Infering type for: V9\u001b[0m\n" + "\u001b[32mINFO:type_infer-2840:Infering type for: V9\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2670:Column V4 has data type float\u001b[0m\n" + "\u001b[32mINFO:type_infer-2840:Column V4 has data type float\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2670:Infering type for: V5\u001b[0m\n" + "\u001b[32mINFO:type_infer-2840:Infering type for: V5\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2670:Column V9 has data type float\u001b[0m\n" + "\u001b[32mINFO:type_infer-2840:Column V7 has data type float\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2670:Infering type for: V10\u001b[0m\n" + "\u001b[32mINFO:type_infer-2840:Infering type for: V8\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2670:Column V7 has data type float\u001b[0m\n" + "\u001b[32mINFO:type_infer-2840:Column V9 has data type float\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2670:Infering type for: V8\u001b[0m\n" + "\u001b[32mINFO:type_infer-2840:Infering type for: V10\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2670:Column V5 has data type float\u001b[0m\n" + "\u001b[32mINFO:type_infer-2840:Column V5 has data type float\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2670:Infering type for: V12\u001b[0m\n" + "\u001b[32mINFO:type_infer-2840:Infering type for: V12\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2670:Column V10 has data type float\u001b[0m\n" + "\u001b[32mINFO:type_infer-2840:Column V8 has data type float\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2670:Infering type for: V11\u001b[0m\n" + "\u001b[32mINFO:type_infer-2840:Column V10 has data type float\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2670:Column V8 has data type float\u001b[0m\n" + "\u001b[32mINFO:type_infer-2840:Infering type for: V11\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2670:Infering type for: V15\u001b[0m\n" + "\u001b[32mINFO:type_infer-2840:Infering type for: V15\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2670:Column V12 has data type float\u001b[0m\n" + "\u001b[32mINFO:type_infer-2840:Column V11 has data type float\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2670:Infering type for: V13\u001b[0m\n" + "\u001b[32mINFO:type_infer-2840:Infering type for: V18\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2670:Column V13 has data type float\u001b[0m\n" + "\u001b[32mINFO:type_infer-2840:Column V12 has data type float\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2670:Infering type for: V14\u001b[0m\n" + "\u001b[32mINFO:type_infer-2840:Infering type for: V13\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2670:Column V11 has data type float\u001b[0m\n" + "\u001b[32mINFO:type_infer-2840:Column V15 has data type float\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2670:Infering type for: V18\u001b[0m\n" + "\u001b[32mINFO:type_infer-2840:Infering type for: V16\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2670:Column V15 has data type float\u001b[0m\n" + "\u001b[32mINFO:type_infer-2840:Column V18 has data type float\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2670:Infering type for: V16\u001b[0m\n" + "\u001b[32mINFO:type_infer-2840:Infering type for: V19\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2670:Column V14 has data type float\u001b[0m\n" + "\u001b[32mINFO:type_infer-2840:Column V13 has data type float\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2670:Infering type for: V21\u001b[0m\n" + "\u001b[32mINFO:type_infer-2840:Infering type for: V14\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2670:Column V18 has data type float\u001b[0m\n" + "\u001b[32mINFO:type_infer-2840:Column V16 has data type float\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2670:Column V16 has data type float\u001b[0m\n" + "\u001b[32mINFO:type_infer-2840:Column V19 has data type float\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2670:Infering type for: V19\u001b[0m\n" + "\u001b[32mINFO:type_infer-2840:Infering type for: V20\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2670:Infering type for: V17\u001b[0m\n" + "\u001b[32mINFO:type_infer-2840:Infering type for: V17\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2670:Column V21 has data type float\u001b[0m\n" + "\u001b[32mINFO:type_infer-2840:Column V20 has data type float\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2670:Infering type for: V22\u001b[0m\n" + "\u001b[32mINFO:type_infer-2840:Infering type for: V21\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2670:Column V19 has data type float\u001b[0m\n" + "\u001b[32mINFO:type_infer-2840:Column V14 has data type float\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2670:Infering type for: V20\u001b[0m\n" + "\u001b[32mINFO:type_infer-2840:Column V17 has data type float\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2670:Column V17 has data type float\u001b[0m\n" + "\u001b[32mINFO:type_infer-2840:Infering type for: V24\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2670:Infering type for: V24\u001b[0m\n" + "\u001b[32mINFO:type_infer-2840:Infering type for: V27\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2670:Column V22 has data type float\u001b[0m\n" + "\u001b[32mINFO:type_infer-2840:Column V21 has data type float\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2670:Infering type for: V23\u001b[0m\n" + "\u001b[32mINFO:type_infer-2840:Infering type for: V22\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2670:Column V20 has data type float\u001b[0m\n" + "\u001b[32mINFO:type_infer-2840:Column V24 has data type float\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2670:Infering type for: V27\u001b[0m\n" + "\u001b[32mINFO:type_infer-2840:Infering type for: V25\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2670:Column V23 has data type float\u001b[0m\n" + "\u001b[32mINFO:type_infer-2840:Column V27 has data type float\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2670:Infering type for: Class\u001b[0m\n" + "\u001b[32mINFO:type_infer-2840:Infering type for: V28\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2670:Column V24 has data type float\u001b[0m\n" + "\u001b[32mINFO:type_infer-2840:Column V25 has data type float\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2670:Infering type for: V25\u001b[0m\n" + "\u001b[32mINFO:type_infer-2840:Infering type for: V26\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2670:Column Class has data type binary\u001b[0m\n" + "\u001b[32mINFO:type_infer-2840:Column V22 has data type float\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2670:Column V27 has data type float\u001b[0m\n" + "\u001b[32mINFO:type_infer-2840:Infering type for: V23\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2670:Infering type for: V28\u001b[0m\n" + "\u001b[32mINFO:type_infer-2840:Column V26 has data type float\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2670:Column V25 has data type float\u001b[0m\n" + "\u001b[32mINFO:type_infer-2840:Infering type for: Class\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2670:Infering type for: V26\u001b[0m\n" + "\u001b[32mINFO:type_infer-2840:Column Class has data type binary\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2670:Column V28 has data type float\u001b[0m\n" + "\u001b[32mINFO:type_infer-2840:Column V28 has data type float\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2670:Infering type for: Amount\u001b[0m\n" + "\u001b[32mINFO:type_infer-2840:Infering type for: Amount\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2670:Column V26 has data type float\u001b[0m\n" + "\u001b[32mINFO:type_infer-2840:Column V23 has data type float\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2670:Column Amount has data type float\u001b[0m\n" + "\u001b[32mINFO:type_infer-2840:Column Amount has data type float\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2670:Starting statistical analysis\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2840:Starting statistical analysis\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2670:Finished statistical analysis\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2840:Finished statistical analysis\u001b[0m\n" ] } ], @@ -901,10 +901,10 @@ "id": "4411ee53", "metadata": { "execution": { - "iopub.execute_input": "2024-03-18T10:33:44.167938Z", - "iopub.status.busy": "2024-03-18T10:33:44.167724Z", - "iopub.status.idle": "2024-03-18T10:33:44.173216Z", - "shell.execute_reply": "2024-03-18T10:33:44.172671Z" + "iopub.execute_input": "2024-03-18T12:15:26.685990Z", + "iopub.status.busy": "2024-03-18T12:15:26.685721Z", + "iopub.status.idle": "2024-03-18T12:15:26.691571Z", + "shell.execute_reply": "2024-03-18T12:15:26.690891Z" } }, "outputs": [ @@ -996,10 +996,10 @@ "id": "34092d12", "metadata": { "execution": { - "iopub.execute_input": "2024-03-18T10:33:44.175771Z", - "iopub.status.busy": "2024-03-18T10:33:44.175404Z", - "iopub.status.idle": "2024-03-18T10:33:44.178602Z", - "shell.execute_reply": "2024-03-18T10:33:44.178067Z" + "iopub.execute_input": "2024-03-18T12:15:26.694541Z", + "iopub.status.busy": "2024-03-18T12:15:26.694114Z", + "iopub.status.idle": "2024-03-18T12:15:26.697712Z", + "shell.execute_reply": "2024-03-18T12:15:26.697149Z" } }, "outputs": [], @@ -1055,10 +1055,10 @@ "id": "alleged-concentrate", "metadata": { "execution": { - "iopub.execute_input": "2024-03-18T10:33:44.181235Z", - "iopub.status.busy": "2024-03-18T10:33:44.180788Z", - "iopub.status.idle": "2024-03-18T10:33:44.552667Z", - "shell.execute_reply": "2024-03-18T10:33:44.551983Z" + "iopub.execute_input": "2024-03-18T12:15:26.700467Z", + "iopub.status.busy": "2024-03-18T12:15:26.700050Z", + "iopub.status.idle": "2024-03-18T12:15:27.095902Z", + "shell.execute_reply": "2024-03-18T12:15:27.095206Z" } }, "outputs": [ @@ -1139,7 +1139,7 @@ " \"unbias_target\": True,\n", " \"seconds_per_mixer\": 42768.0,\n", " \"seconds_per_encoder\": None,\n", - " \"expected_additional_time\": 69.56466770172119,\n", + " \"expected_additional_time\": 69.90523362159729,\n", " \"time_aim\": 259200,\n", " \"target_weights\": None,\n", " \"positive_domain\": False,\n", @@ -1902,10 +1902,10 @@ "id": "organic-london", "metadata": { "execution": { - "iopub.execute_input": "2024-03-18T10:33:44.555398Z", - "iopub.status.busy": "2024-03-18T10:33:44.555057Z", - "iopub.status.idle": "2024-03-18T10:33:44.563230Z", - "shell.execute_reply": "2024-03-18T10:33:44.562720Z" + "iopub.execute_input": "2024-03-18T12:15:27.098742Z", + "iopub.status.busy": "2024-03-18T12:15:27.098311Z", + "iopub.status.idle": "2024-03-18T12:15:27.107127Z", + "shell.execute_reply": "2024-03-18T12:15:27.106588Z" } }, "outputs": [], @@ -1920,10 +1920,10 @@ "id": "fabulous-prime", "metadata": { "execution": { - "iopub.execute_input": "2024-03-18T10:33:44.565670Z", - "iopub.status.busy": "2024-03-18T10:33:44.565232Z", - "iopub.status.idle": "2024-03-18T10:34:05.084019Z", - "shell.execute_reply": "2024-03-18T10:34:05.083387Z" + "iopub.execute_input": "2024-03-18T12:15:27.109742Z", + "iopub.status.busy": "2024-03-18T12:15:27.109328Z", + "iopub.status.idle": "2024-03-18T12:15:47.870683Z", + "shell.execute_reply": "2024-03-18T12:15:47.870045Z" } }, "outputs": [ @@ -1931,28 +1931,28 @@ "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2670:Cleaning the data\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2840:Cleaning the data\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:lightwood-2670: `preprocess` runtime: 18.89 seconds\u001b[0m\n" + "\u001b[37mDEBUG:lightwood-2840: `preprocess` runtime: 18.63 seconds\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2670:Splitting the data into train/test\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2840:Splitting the data into train/test\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:lightwood-2670: `split` runtime: 1.63 seconds\u001b[0m\n" + "\u001b[37mDEBUG:lightwood-2840: `split` runtime: 2.12 seconds\u001b[0m\n" ] } ], @@ -1968,10 +1968,10 @@ "id": "suspended-biography", "metadata": { "execution": { - "iopub.execute_input": "2024-03-18T10:34:05.086748Z", - "iopub.status.busy": "2024-03-18T10:34:05.086529Z", - "iopub.status.idle": "2024-03-18T10:34:06.382420Z", - "shell.execute_reply": "2024-03-18T10:34:06.381657Z" + "iopub.execute_input": "2024-03-18T12:15:47.873560Z", + "iopub.status.busy": "2024-03-18T12:15:47.873132Z", + "iopub.status.idle": "2024-03-18T12:15:49.329385Z", + "shell.execute_reply": "2024-03-18T12:15:49.328650Z" } }, "outputs": [ diff --git a/tutorials/tutorial_data_analysis/tutorial_data_analysis.html b/tutorials/tutorial_data_analysis/tutorial_data_analysis.html index 3c5048f76..33aa2063c 100644 --- a/tutorials/tutorial_data_analysis/tutorial_data_analysis.html +++ b/tutorials/tutorial_data_analysis/tutorial_data_analysis.html @@ -222,7 +222,7 @@

Step 1: load the dataset and define the predictive task 
-INFO:lightwood-2482:No torchvision detected, image helpers not supported.
+INFO:lightwood-2657:No torchvision detected, image helpers not supported.

@@ -230,7 +230,7 @@

Step 1: load the dataset and define the predictive task 
-INFO:lightwood-2482:No torchvision/pillow detected, image encoder not supported
+INFO:lightwood-2657:No torchvision/pillow detected, image encoder not supported

Let’s see how this object has been populated. ProblemDefinition is a Python dataclass, so it comes with some convenient tools to achieve this:

@@ -290,7 +290,7 @@

Step 1: load the dataset and define the predictive task 
-INFO:type_infer-2482:Analyzing a sample of 222
+INFO:type_infer-2657:Analyzing a sample of 222
@@ -298,7 +298,7 @@

Step 1: load the dataset and define the predictive task 
-INFO:type_infer-2482:from a total population of 225, this is equivalent to 98.7% of your data.
+INFO:type_infer-2657:from a total population of 225, this is equivalent to 98.7% of your data.

@@ -306,7 +306,7 @@

Step 1: load the dataset and define the predictive task 
-INFO:type_infer-2482:Infering type for: Population
+INFO:type_infer-2657:Infering type for: Population

@@ -314,7 +314,7 @@

Step 1: load the dataset and define the predictive task 
-INFO:type_infer-2482:Column Population has data type integer
+INFO:type_infer-2657:Column Population has data type integer

@@ -322,7 +322,7 @@

Step 1: load the dataset and define the predictive task 
-INFO:type_infer-2482:Infering type for: Area (sq. mi.)
+INFO:type_infer-2657:Infering type for: Area (sq. mi.)

@@ -330,7 +330,7 @@

Step 1: load the dataset and define the predictive task 
-INFO:type_infer-2482:Column Area (sq. mi.) has data type integer
+INFO:type_infer-2657:Column Area (sq. mi.) has data type integer

@@ -338,7 +338,7 @@

Step 1: load the dataset and define the predictive task 
-INFO:type_infer-2482:Infering type for: Pop. Density 
+INFO:type_infer-2657:Infering type for: Pop. Density

@@ -346,7 +346,7 @@

Step 1: load the dataset and define the predictive task 
-INFO:type_infer-2482:Column Pop. Density  has data type float
+INFO:type_infer-2657:Column Pop. Density  has data type float

@@ -354,7 +354,7 @@

Step 1: load the dataset and define the predictive task 
-INFO:type_infer-2482:Infering type for: GDP ($ per capita)
+INFO:type_infer-2657:Infering type for: GDP ($ per capita)

@@ -362,7 +362,7 @@

Step 1: load the dataset and define the predictive task 
-INFO:type_infer-2482:Column GDP ($ per capita) has data type integer
+INFO:type_infer-2657:Column GDP ($ per capita) has data type integer

@@ -370,7 +370,7 @@

Step 1: load the dataset and define the predictive task 
-INFO:type_infer-2482:Infering type for: Literacy (%)
+INFO:type_infer-2657:Infering type for: Literacy (%)

@@ -378,7 +378,7 @@

Step 1: load the dataset and define the predictive task 
-INFO:type_infer-2482:Column Literacy (%) has data type float
+INFO:type_infer-2657:Column Literacy (%) has data type float

@@ -386,7 +386,7 @@

Step 1: load the dataset and define the predictive task 
-INFO:type_infer-2482:Infering type for: Infant mortality 
+INFO:type_infer-2657:Infering type for: Infant mortality

@@ -394,7 +394,7 @@

Step 1: load the dataset and define the predictive task 
-INFO:type_infer-2482:Column Infant mortality  has data type float
+INFO:type_infer-2657:Column Infant mortality  has data type float

@@ -402,7 +402,7 @@

Step 1: load the dataset and define the predictive task 
-INFO:type_infer-2482:Infering type for: Development Index
+INFO:type_infer-2657:Infering type for: Development Index

@@ -410,7 +410,7 @@

Step 1: load the dataset and define the predictive task 
-INFO:type_infer-2482:Column Development Index has data type categorical
+INFO:type_infer-2657:Column Development Index has data type categorical

@@ -472,7 +472,7 @@

Step 2: Run the statistical analysis 
-INFO:dataprep_ml-2482:Starting statistical analysis
+INFO:dataprep_ml-2657:Starting statistical analysis

@@ -480,7 +480,7 @@

Step 2: Run the statistical analysis 
-INFO:dataprep_ml-2482:Finished statistical analysis
+INFO:dataprep_ml-2657:Finished statistical analysis

diff --git a/tutorials/tutorial_data_analysis/tutorial_data_analysis.ipynb b/tutorials/tutorial_data_analysis/tutorial_data_analysis.ipynb index 5d12c6f82..161b40864 100644 --- a/tutorials/tutorial_data_analysis/tutorial_data_analysis.ipynb +++ b/tutorials/tutorial_data_analysis/tutorial_data_analysis.ipynb @@ -35,10 +35,10 @@ "execution_count": 1, "metadata": { "execution": { - "iopub.execute_input": "2024-03-18T10:31:30.957410Z", - "iopub.status.busy": "2024-03-18T10:31:30.956859Z", - "iopub.status.idle": "2024-03-18T10:31:31.284834Z", - "shell.execute_reply": "2024-03-18T10:31:31.284162Z" + "iopub.execute_input": "2024-03-18T12:13:05.545255Z", + "iopub.status.busy": "2024-03-18T12:13:05.545047Z", + "iopub.status.idle": "2024-03-18T12:13:05.926141Z", + "shell.execute_reply": "2024-03-18T12:13:05.925446Z" } }, "outputs": [ @@ -175,10 +175,10 @@ "execution_count": 2, "metadata": { "execution": { - "iopub.execute_input": "2024-03-18T10:31:31.321665Z", - "iopub.status.busy": "2024-03-18T10:31:31.321247Z", - "iopub.status.idle": "2024-03-18T10:31:33.536309Z", - "shell.execute_reply": "2024-03-18T10:31:33.535656Z" + "iopub.execute_input": "2024-03-18T12:13:05.964928Z", + "iopub.status.busy": "2024-03-18T12:13:05.964337Z", + "iopub.status.idle": "2024-03-18T12:13:08.437191Z", + "shell.execute_reply": "2024-03-18T12:13:08.436484Z" } }, "outputs": [ @@ -186,14 +186,14 @@ "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2482:No torchvision detected, image helpers not supported.\u001b[0m\n" + "\u001b[32mINFO:lightwood-2657:No torchvision detected, image helpers not supported.\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2482:No torchvision/pillow detected, image encoder not supported\u001b[0m\n" + "\u001b[32mINFO:lightwood-2657:No torchvision/pillow detected, image encoder not supported\u001b[0m\n" ] } ], @@ -215,10 +215,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2024-03-18T10:31:33.539401Z", - "iopub.status.busy": "2024-03-18T10:31:33.539136Z", - "iopub.status.idle": "2024-03-18T10:31:33.544458Z", - "shell.execute_reply": "2024-03-18T10:31:33.543810Z" + "iopub.execute_input": "2024-03-18T12:13:08.440807Z", + "iopub.status.busy": "2024-03-18T12:13:08.440196Z", + "iopub.status.idle": "2024-03-18T12:13:08.445917Z", + "shell.execute_reply": "2024-03-18T12:13:08.445190Z" } }, "outputs": [ @@ -270,10 +270,10 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2024-03-18T10:31:33.547310Z", - "iopub.status.busy": "2024-03-18T10:31:33.546748Z", - "iopub.status.idle": "2024-03-18T10:31:33.573011Z", - "shell.execute_reply": "2024-03-18T10:31:33.572417Z" + "iopub.execute_input": "2024-03-18T12:13:08.449281Z", + "iopub.status.busy": "2024-03-18T12:13:08.448775Z", + "iopub.status.idle": "2024-03-18T12:13:08.477079Z", + "shell.execute_reply": "2024-03-18T12:13:08.476441Z" } }, "outputs": [ @@ -281,112 +281,112 @@ "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2482:Analyzing a sample of 222\u001b[0m\n" + "\u001b[32mINFO:type_infer-2657:Analyzing a sample of 222\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2482:from a total population of 225, this is equivalent to 98.7% of your data.\u001b[0m\n" + "\u001b[32mINFO:type_infer-2657:from a total population of 225, this is equivalent to 98.7% of your data.\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2482:Infering type for: Population\u001b[0m\n" + "\u001b[32mINFO:type_infer-2657:Infering type for: Population\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2482:Column Population has data type integer\u001b[0m\n" + "\u001b[32mINFO:type_infer-2657:Column Population has data type integer\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2482:Infering type for: Area (sq. mi.)\u001b[0m\n" + "\u001b[32mINFO:type_infer-2657:Infering type for: Area (sq. mi.)\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2482:Column Area (sq. mi.) has data type integer\u001b[0m\n" + "\u001b[32mINFO:type_infer-2657:Column Area (sq. mi.) has data type integer\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2482:Infering type for: Pop. Density \u001b[0m\n" + "\u001b[32mINFO:type_infer-2657:Infering type for: Pop. Density \u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2482:Column Pop. Density has data type float\u001b[0m\n" + "\u001b[32mINFO:type_infer-2657:Column Pop. Density has data type float\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2482:Infering type for: GDP ($ per capita)\u001b[0m\n" + "\u001b[32mINFO:type_infer-2657:Infering type for: GDP ($ per capita)\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2482:Column GDP ($ per capita) has data type integer\u001b[0m\n" + "\u001b[32mINFO:type_infer-2657:Column GDP ($ per capita) has data type integer\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2482:Infering type for: Literacy (%)\u001b[0m\n" + "\u001b[32mINFO:type_infer-2657:Infering type for: Literacy (%)\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2482:Column Literacy (%) has data type float\u001b[0m\n" + "\u001b[32mINFO:type_infer-2657:Column Literacy (%) has data type float\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2482:Infering type for: Infant mortality \u001b[0m\n" + "\u001b[32mINFO:type_infer-2657:Infering type for: Infant mortality \u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2482:Column Infant mortality has data type float\u001b[0m\n" + "\u001b[32mINFO:type_infer-2657:Column Infant mortality has data type float\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2482:Infering type for: Development Index\u001b[0m\n" + "\u001b[32mINFO:type_infer-2657:Infering type for: Development Index\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2482:Column Development Index has data type categorical\u001b[0m\n" + "\u001b[32mINFO:type_infer-2657:Column Development Index has data type categorical\u001b[0m\n" ] }, { @@ -421,10 +421,10 @@ "execution_count": 5, "metadata": { "execution": { - "iopub.execute_input": "2024-03-18T10:31:33.575615Z", - "iopub.status.busy": "2024-03-18T10:31:33.575224Z", - "iopub.status.idle": "2024-03-18T10:31:33.579328Z", - "shell.execute_reply": "2024-03-18T10:31:33.578714Z" + "iopub.execute_input": "2024-03-18T12:13:08.480056Z", + "iopub.status.busy": "2024-03-18T12:13:08.479564Z", + "iopub.status.idle": "2024-03-18T12:13:08.484517Z", + "shell.execute_reply": "2024-03-18T12:13:08.483762Z" } }, "outputs": [ @@ -474,10 +474,10 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2024-03-18T10:31:33.581881Z", - "iopub.status.busy": "2024-03-18T10:31:33.581527Z", - "iopub.status.idle": "2024-03-18T10:31:33.608327Z", - "shell.execute_reply": "2024-03-18T10:31:33.607793Z" + "iopub.execute_input": "2024-03-18T12:13:08.487525Z", + "iopub.status.busy": "2024-03-18T12:13:08.487081Z", + "iopub.status.idle": "2024-03-18T12:13:08.516522Z", + "shell.execute_reply": "2024-03-18T12:13:08.515803Z" } }, "outputs": [ @@ -485,14 +485,14 @@ "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2482:Starting statistical analysis\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2657:Starting statistical analysis\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2482:Finished statistical analysis\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2657:Finished statistical analysis\u001b[0m\n" ] } ], @@ -520,10 +520,10 @@ "execution_count": 7, "metadata": { "execution": { - "iopub.execute_input": "2024-03-18T10:31:33.610868Z", - "iopub.status.busy": "2024-03-18T10:31:33.610441Z", - "iopub.status.idle": "2024-03-18T10:31:33.615023Z", - "shell.execute_reply": "2024-03-18T10:31:33.614403Z" + "iopub.execute_input": "2024-03-18T12:13:08.519606Z", + "iopub.status.busy": "2024-03-18T12:13:08.519165Z", + "iopub.status.idle": "2024-03-18T12:13:08.524187Z", + "shell.execute_reply": "2024-03-18T12:13:08.523481Z" } }, "outputs": [ @@ -569,10 +569,10 @@ "execution_count": 8, "metadata": { "execution": { - "iopub.execute_input": "2024-03-18T10:31:33.617561Z", - "iopub.status.busy": "2024-03-18T10:31:33.617181Z", - "iopub.status.idle": "2024-03-18T10:31:33.621308Z", - "shell.execute_reply": "2024-03-18T10:31:33.620693Z" + "iopub.execute_input": "2024-03-18T12:13:08.526971Z", + "iopub.status.busy": "2024-03-18T12:13:08.526551Z", + "iopub.status.idle": "2024-03-18T12:13:08.531002Z", + "shell.execute_reply": "2024-03-18T12:13:08.530306Z" } }, "outputs": [ @@ -612,10 +612,10 @@ "execution_count": 9, "metadata": { "execution": { - "iopub.execute_input": "2024-03-18T10:31:33.623812Z", - "iopub.status.busy": "2024-03-18T10:31:33.623493Z", - "iopub.status.idle": "2024-03-18T10:31:33.627977Z", - "shell.execute_reply": "2024-03-18T10:31:33.627346Z" + "iopub.execute_input": "2024-03-18T12:13:08.534224Z", + "iopub.status.busy": "2024-03-18T12:13:08.533746Z", + "iopub.status.idle": "2024-03-18T12:13:08.538987Z", + "shell.execute_reply": "2024-03-18T12:13:08.538298Z" }, "scrolled": false }, @@ -673,10 +673,10 @@ "execution_count": 10, "metadata": { "execution": { - "iopub.execute_input": "2024-03-18T10:31:33.630489Z", - "iopub.status.busy": "2024-03-18T10:31:33.630170Z", - "iopub.status.idle": "2024-03-18T10:31:33.634186Z", - "shell.execute_reply": "2024-03-18T10:31:33.633604Z" + "iopub.execute_input": "2024-03-18T12:13:08.542241Z", + "iopub.status.busy": "2024-03-18T12:13:08.541677Z", + "iopub.status.idle": "2024-03-18T12:13:08.546474Z", + "shell.execute_reply": "2024-03-18T12:13:08.545783Z" } }, "outputs": [ @@ -700,10 +700,10 @@ "execution_count": 11, "metadata": { "execution": { - "iopub.execute_input": "2024-03-18T10:31:33.636639Z", - "iopub.status.busy": "2024-03-18T10:31:33.636196Z", - "iopub.status.idle": "2024-03-18T10:31:33.640738Z", - "shell.execute_reply": "2024-03-18T10:31:33.640098Z" + "iopub.execute_input": "2024-03-18T12:13:08.549300Z", + "iopub.status.busy": "2024-03-18T12:13:08.548787Z", + "iopub.status.idle": "2024-03-18T12:13:08.553922Z", + "shell.execute_reply": "2024-03-18T12:13:08.553224Z" }, "scrolled": false }, @@ -786,10 +786,10 @@ "execution_count": 12, "metadata": { "execution": { - "iopub.execute_input": "2024-03-18T10:31:33.643295Z", - "iopub.status.busy": "2024-03-18T10:31:33.642925Z", - "iopub.status.idle": "2024-03-18T10:31:33.646473Z", - "shell.execute_reply": "2024-03-18T10:31:33.645803Z" + "iopub.execute_input": "2024-03-18T12:13:08.556686Z", + "iopub.status.busy": "2024-03-18T12:13:08.556451Z", + "iopub.status.idle": "2024-03-18T12:13:08.560876Z", + "shell.execute_reply": "2024-03-18T12:13:08.560184Z" } }, "outputs": [ @@ -841,10 +841,10 @@ "execution_count": 13, "metadata": { "execution": { - "iopub.execute_input": "2024-03-18T10:31:33.649181Z", - "iopub.status.busy": "2024-03-18T10:31:33.648813Z", - "iopub.status.idle": "2024-03-18T10:31:36.233826Z", - "shell.execute_reply": "2024-03-18T10:31:36.233128Z" + "iopub.execute_input": "2024-03-18T12:13:08.564014Z", + "iopub.status.busy": "2024-03-18T12:13:08.563369Z", + "iopub.status.idle": "2024-03-18T12:13:11.366294Z", + "shell.execute_reply": "2024-03-18T12:13:11.365542Z" }, "scrolled": false }, diff --git a/tutorials/tutorial_time_series/tutorial_time_series.html b/tutorials/tutorial_time_series/tutorial_time_series.html index c4fa7f916..2732169f2 100644 --- a/tutorials/tutorial_time_series/tutorial_time_series.html +++ b/tutorials/tutorial_time_series/tutorial_time_series.html @@ -216,7 +216,7 @@

Define the predictive task 
-INFO:lightwood-2349:No torchvision detected, image helpers not supported.
+INFO:lightwood-2526:No torchvision detected, image helpers not supported.
@@ -224,7 +224,7 @@

Define the predictive task 
-INFO:lightwood-2349:No torchvision/pillow detected, image encoder not supported
+INFO:lightwood-2526:No torchvision/pillow detected, image encoder not supported

@@ -292,7 +292,7 @@

Generate the predictor object 
-INFO:type_infer-2349:Analyzing a sample of 2467
+INFO:type_infer-2526:Analyzing a sample of 2467

@@ -300,7 +300,7 @@

Generate the predictor object 
-INFO:type_infer-2349:from a total population of 2820, this is equivalent to 87.5% of your data.
+INFO:type_infer-2526:from a total population of 2820, this is equivalent to 87.5% of your data.

@@ -308,7 +308,7 @@

Generate the predictor object 
-INFO:type_infer-2349:Infering type for: Month
+INFO:type_infer-2526:Infering type for: Month

@@ -316,7 +316,7 @@

Generate the predictor object 
-INFO:type_infer-2349:Column Month has data type date
+INFO:type_infer-2526:Column Month has data type date

@@ -324,7 +324,7 @@

Generate the predictor object 
-INFO:type_infer-2349:Infering type for: Sunspots
+INFO:type_infer-2526:Infering type for: Sunspots

@@ -332,7 +332,7 @@

Generate the predictor object 
-INFO:type_infer-2349:Column Sunspots has data type float
+INFO:type_infer-2526:Column Sunspots has data type float

@@ -340,7 +340,7 @@

Generate the predictor object 
-INFO:dataprep_ml-2349:Starting statistical analysis
+INFO:dataprep_ml-2526:Starting statistical analysis

@@ -350,7 +350,7 @@

Generate the predictor object
 /opt/hostedtoolcache/Python/3.9.18/x64/lib/python3.9/site-packages/dataprep_ml/cleaners.py:163: UserWarning: The argument 'infer_datetime_format' is deprecated and will be removed in a future version. A strict version of it is now the default, see https://pandas.pydata.org/pdeps/0004-consistent-to-datetime-parsing.html. You can safely remove this argument.
   result = pd.to_datetime(element,
-INFO:dataprep_ml-2349:Finished statistical analysis
+INFO:dataprep_ml-2526:Finished statistical analysis

@@ -372,7 +372,7 @@

Train 
-INFO:dataprep_ml-2349:[Learn phase 1/8] - Statistical analysis
+INFO:dataprep_ml-2526:[Learn phase 1/8] - Statistical analysis
@@ -380,7 +380,7 @@

Train 

-INFO:dataprep_ml-2349:Starting statistical analysis
+INFO:dataprep_ml-2526:Starting statistical analysis
@@ -390,7 +390,7 @@

Train 
 /opt/hostedtoolcache/Python/3.9.18/x64/lib/python3.9/site-packages/dataprep_ml/cleaners.py:163: UserWarning: The argument 'infer_datetime_format' is deprecated and will be removed in a future version. A strict version of it is now the default, see https://pandas.pydata.org/pdeps/0004-consistent-to-datetime-parsing.html. You can safely remove this argument.
   result = pd.to_datetime(element,
-INFO:dataprep_ml-2349:Finished statistical analysis
+INFO:dataprep_ml-2526:Finished statistical analysis

@@ -398,7 +398,7 @@

Train 

-DEBUG:lightwood-2349: `analyze_data` runtime: 0.07 seconds
+DEBUG:lightwood-2526: `analyze_data` runtime: 0.06 seconds
@@ -406,7 +406,7 @@

Train 

-INFO:dataprep_ml-2349:[Learn phase 2/8] - Data preprocessing
+INFO:dataprep_ml-2526:[Learn phase 2/8] - Data preprocessing
@@ -414,7 +414,7 @@

Train 

-INFO:dataprep_ml-2349:Cleaning the data
+INFO:dataprep_ml-2526:Cleaning the data
@@ -424,7 +424,7 @@

Train 
 /opt/hostedtoolcache/Python/3.9.18/x64/lib/python3.9/site-packages/dataprep_ml/cleaners.py:163: UserWarning: The argument 'infer_datetime_format' is deprecated and will be removed in a future version. A strict version of it is now the default, see https://pandas.pydata.org/pdeps/0004-consistent-to-datetime-parsing.html. You can safely remove this argument.
   result = pd.to_datetime(element,
-INFO:dataprep_ml-2349:Transforming timeseries data
+INFO:dataprep_ml-2526:Transforming timeseries data

@@ -432,7 +432,7 @@

Train 

-DEBUG:lightwood-2349: `preprocess` runtime: 0.1 seconds
+DEBUG:lightwood-2526: `preprocess` runtime: 0.09 seconds
@@ -440,7 +440,7 @@

Train 

-INFO:dataprep_ml-2349:[Learn phase 3/8] - Data splitting
+INFO:dataprep_ml-2526:[Learn phase 3/8] - Data splitting
@@ -448,7 +448,7 @@

Train 

-INFO:dataprep_ml-2349:Splitting the data into train/test
+INFO:dataprep_ml-2526:Splitting the data into train/test
@@ -456,7 +456,7 @@

Train 

-DEBUG:lightwood-2349: `split` runtime: 0.0 seconds
+DEBUG:lightwood-2526: `split` runtime: 0.0 seconds
@@ -464,7 +464,7 @@

Train 

-INFO:dataprep_ml-2349:[Learn phase 4/8] - Preparing encoders
+INFO:dataprep_ml-2526:[Learn phase 4/8] - Preparing encoders
@@ -472,7 +472,7 @@

Train 

-DEBUG:dataprep_ml-2349:Preparing sequentially...
+DEBUG:dataprep_ml-2526:Preparing sequentially...
@@ -480,7 +480,7 @@

Train 

-DEBUG:lightwood-2349: `prepare` runtime: 0.05 seconds
+DEBUG:lightwood-2526: `prepare` runtime: 0.05 seconds
@@ -488,7 +488,7 @@

Train 

-INFO:dataprep_ml-2349:[Learn phase 5/8] - Feature generation
+INFO:dataprep_ml-2526:[Learn phase 5/8] - Feature generation
@@ -496,7 +496,7 @@

Train 

-INFO:dataprep_ml-2349:Featurizing the data
+INFO:dataprep_ml-2526:Featurizing the data
@@ -504,7 +504,7 @@

Train 

-DEBUG:lightwood-2349: `featurize` runtime: 0.05 seconds
+DEBUG:lightwood-2526: `featurize` runtime: 0.05 seconds
@@ -512,7 +512,7 @@

Train 

-INFO:dataprep_ml-2349:[Learn phase 6/8] - Mixer training
+INFO:dataprep_ml-2526:[Learn phase 6/8] - Mixer training
@@ -520,7 +520,7 @@

Train 

-INFO:dataprep_ml-2349:Training the mixers
+INFO:dataprep_ml-2526:Training the mixers
@@ -528,7 +528,7 @@

Train 

-WARNING:lightwood-2349:XGBoost running on CPU
+WARNING:lightwood-2526:XGBoost running on CPU
@@ -536,7 +536,7 @@

Train 

-WARNING:lightwood-2349:XGBoost running on CPU
+WARNING:lightwood-2526:XGBoost running on CPU
@@ -544,7 +544,7 @@

Train 

-WARNING:lightwood-2349:XGBoost running on CPU
+WARNING:lightwood-2526:XGBoost running on CPU
@@ -552,7 +552,7 @@

Train 

-WARNING:lightwood-2349:XGBoost running on CPU
+WARNING:lightwood-2526:XGBoost running on CPU
@@ -560,7 +560,7 @@

Train 

-WARNING:lightwood-2349:XGBoost running on CPU
+WARNING:lightwood-2526:XGBoost running on CPU
@@ -568,7 +568,7 @@

Train 

-WARNING:lightwood-2349:XGBoost running on CPU
+WARNING:lightwood-2526:XGBoost running on CPU
@@ -585,12 +585,12 @@

Train 

-[10:31:17] WARNING: ../src/learner.cc:339: No visible GPU is found, setting `gpu_id` to -1
-[10:31:17] WARNING: ../src/learner.cc:339: No visible GPU is found, setting `gpu_id` to -1
-[10:31:17] WARNING: ../src/learner.cc:339: No visible GPU is found, setting `gpu_id` to -1
-[10:31:17] WARNING: ../src/learner.cc:339: No visible GPU is found, setting `gpu_id` to -1
-[10:31:17] WARNING: ../src/learner.cc:339: No visible GPU is found, setting `gpu_id` to -1
-[10:31:17] WARNING: ../src/learner.cc:339: No visible GPU is found, setting `gpu_id` to -1
+[12:12:50] WARNING: ../src/learner.cc:339: No visible GPU is found, setting `gpu_id` to -1
+[12:12:50] WARNING: ../src/learner.cc:339: No visible GPU is found, setting `gpu_id` to -1
+[12:12:50] WARNING: ../src/learner.cc:339: No visible GPU is found, setting `gpu_id` to -1
+[12:12:50] WARNING: ../src/learner.cc:339: No visible GPU is found, setting `gpu_id` to -1
+[12:12:50] WARNING: ../src/learner.cc:339: No visible GPU is found, setting `gpu_id` to -1
+[12:12:50] WARNING: ../src/learner.cc:339: No visible GPU is found, setting `gpu_id` to -1
@@ -603,7 +603,7 @@

Train Consider using one of the following signatures instead: addcmul_(Tensor tensor1, Tensor tensor2, *, Number value) (Triggered internally at ../torch/csrc/utils/python_arg_parser.cpp:1630.) exp_avg_sq.mul_(beta2).addcmul_(1 - beta2, grad, grad) -INFO:lightwood-2349:Loss of 9.051180630922318 with learning rate 0.0001 +INFO:lightwood-2526:Loss of 9.051180630922318 with learning rate 0.0001

@@ -611,7 +611,7 @@

Train 

-INFO:lightwood-2349:Loss of 9.014871209859848 with learning rate 0.0005
+INFO:lightwood-2526:Loss of 9.014871209859848 with learning rate 0.0005
@@ -619,7 +619,7 @@

Train 

-INFO:lightwood-2349:Loss of 8.969509482383728 with learning rate 0.001
+INFO:lightwood-2526:Loss of 8.969509482383728 with learning rate 0.001
@@ -627,7 +627,7 @@

Train 

-INFO:lightwood-2349:Loss of 8.879052013158798 with learning rate 0.002
+INFO:lightwood-2526:Loss of 8.879052013158798 with learning rate 0.002
@@ -635,7 +635,7 @@

Train 

-INFO:lightwood-2349:Loss of 8.788950502872467 with learning rate 0.003
+INFO:lightwood-2526:Loss of 8.788950502872467 with learning rate 0.003
@@ -643,7 +643,7 @@

Train 

-INFO:lightwood-2349:Loss of 8.611965209245682 with learning rate 0.005
+INFO:lightwood-2526:Loss of 8.611965209245682 with learning rate 0.005
@@ -651,7 +651,7 @@

Train 

-INFO:lightwood-2349:Loss of 8.195775926113129 with learning rate 0.01
+INFO:lightwood-2526:Loss of 8.195775926113129 with learning rate 0.01
@@ -659,7 +659,7 @@

Train 

-INFO:lightwood-2349:Loss of 6.255893141031265 with learning rate 0.05
+INFO:lightwood-2526:Loss of 6.255893141031265 with learning rate 0.05
@@ -667,7 +667,7 @@

Train 

-INFO:lightwood-2349:Found learning rate of: 0.05
+INFO:lightwood-2526:Found learning rate of: 0.05
@@ -677,7 +677,7 @@

Train 
 /opt/hostedtoolcache/Python/3.9.18/x64/lib/python3.9/site-packages/torch/cuda/amp/grad_scaler.py:126: UserWarning: torch.cuda.amp.GradScaler is enabled, but CUDA is not available.  Disabling.
   warnings.warn(
-INFO:lightwood-2349:Loss @ epoch 1: 0.5818348675966263
+INFO:lightwood-2526:Loss @ epoch 1: 0.5818348675966263

@@ -685,7 +685,7 @@

Train 

-INFO:lightwood-2349:Loss @ epoch 2: 0.4797109067440033
+INFO:lightwood-2526:Loss @ epoch 2: 0.4797109067440033
@@ -693,7 +693,7 @@

Train 

-INFO:lightwood-2349:Loss @ epoch 3: 0.48386093974113464
+INFO:lightwood-2526:Loss @ epoch 3: 0.48386093974113464
@@ -701,7 +701,7 @@

Train 

-INFO:lightwood-2349:Loss @ epoch 4: 0.49511992931365967
+INFO:lightwood-2526:Loss @ epoch 4: 0.49511992931365967
@@ -709,7 +709,7 @@

Train 

-INFO:lightwood-2349:Loss @ epoch 5: 0.39475560188293457
+INFO:lightwood-2526:Loss @ epoch 5: 0.39475560188293457
@@ -717,7 +717,7 @@

Train 

-INFO:lightwood-2349:Loss @ epoch 6: 0.39592696726322174
+INFO:lightwood-2526:Loss @ epoch 6: 0.39592696726322174
@@ -725,7 +725,7 @@

Train 

-INFO:lightwood-2349:Loss @ epoch 7: 0.3622782379388809
+INFO:lightwood-2526:Loss @ epoch 7: 0.3622782379388809
@@ -733,7 +733,7 @@

Train 

-INFO:lightwood-2349:Loss @ epoch 8: 0.38170479238033295
+INFO:lightwood-2526:Loss @ epoch 8: 0.38170479238033295
@@ -741,7 +741,7 @@

Train 

-INFO:lightwood-2349:Loss @ epoch 9: 0.5138543993234634
+INFO:lightwood-2526:Loss @ epoch 9: 0.5138543993234634
@@ -749,7 +749,7 @@

Train 

-INFO:lightwood-2349:Loss @ epoch 10: 0.6360723078250885
+INFO:lightwood-2526:Loss @ epoch 10: 0.6360723078250885
@@ -757,7 +757,7 @@

Train 

-INFO:lightwood-2349:Loss @ epoch 1: 0.29868809472430835
+INFO:lightwood-2526:Loss @ epoch 1: 0.29868809472430835
@@ -765,7 +765,7 @@

Train 

-INFO:lightwood-2349:Loss @ epoch 2: 0.30318967591632495
+INFO:lightwood-2526:Loss @ epoch 2: 0.30318967591632495
@@ -773,7 +773,7 @@

Train 

-DEBUG:lightwood-2349: `fit_mixer` runtime: 0.93 seconds
+DEBUG:lightwood-2526: `fit_mixer` runtime: 1.0 seconds
@@ -781,7 +781,7 @@

Train 

-INFO:lightwood-2349:Started fitting LGBM models for array prediction
+INFO:lightwood-2526:Started fitting LGBM models for array prediction
@@ -789,7 +789,7 @@

Train 

-INFO:lightwood-2349:Started fitting XGBoost model
+INFO:lightwood-2526:Started fitting XGBoost model
@@ -805,7 +805,7 @@

Train 

-INFO:lightwood-2349:A single GBM iteration takes 0.1 seconds
+INFO:lightwood-2526:A single GBM iteration takes 0.1 seconds
@@ -813,7 +813,7 @@

Train 

-INFO:lightwood-2349:Training XGBoost with 57023 iterations given 7127.98668384552 seconds constraint
+INFO:lightwood-2526:Training XGBoost with 57023 iterations given 7127.98410153389 seconds constraint
@@ -941,7 +941,7 @@

Train 

-INFO:lightwood-2349:Started fitting XGBoost model
+INFO:lightwood-2526:Started fitting XGBoost model
@@ -957,7 +957,7 @@

Train 

-INFO:lightwood-2349:A single GBM iteration takes 0.1 seconds
+INFO:lightwood-2526:A single GBM iteration takes 0.1 seconds
@@ -965,7 +965,7 @@

Train 

-INFO:lightwood-2349:Training XGBoost with 57023 iterations given 7127.9880249500275 seconds constraint
+INFO:lightwood-2526:Training XGBoost with 57023 iterations given 7127.986747741699 seconds constraint
@@ -1085,7 +1085,7 @@

Train 

-INFO:lightwood-2349:Started fitting XGBoost model
+INFO:lightwood-2526:Started fitting XGBoost model
@@ -1101,7 +1101,7 @@

Train 

-INFO:lightwood-2349:A single GBM iteration takes 0.1 seconds
+INFO:lightwood-2526:A single GBM iteration takes 0.1 seconds
@@ -1109,7 +1109,7 @@

Train 

-INFO:lightwood-2349:Training XGBoost with 57023 iterations given 7127.988857269287 seconds constraint
+INFO:lightwood-2526:Training XGBoost with 57023 iterations given 7127.986978292465 seconds constraint
@@ -1174,6 +1174,14 @@

Train 
 [7]     validation_0-rmse:19.00714
+
+

+
+
+
+
+
+
@@ -1190,6 +1198,14 @@

Train 
 [9]     validation_0-rmse:19.12589
+
+

+
+
+
+
+
+
@@ -1198,6 +1214,14 @@

Train 
 [10]    validation_0-rmse:19.34977
+
+

+
+
+
+
+
+
@@ -1211,9 +1235,9 @@

Train
-
+
-INFO:lightwood-2349:Started fitting XGBoost model
+
@@ -1221,7 +1245,7 @@

Train 

-[0]     validation_0-rmse:44.19079
+[12]    validation_0-rmse:19.48230
@@ -1237,7 +1261,23 @@

Train 

-INFO:lightwood-2349:A single GBM iteration takes 0.1 seconds
+INFO:lightwood-2526:Started fitting XGBoost model
+
+

+
+
+
+
+
+[0]     validation_0-rmse:44.19079
+
+
+
+
+
+
+
+INFO:lightwood-2526:A single GBM iteration takes 0.1 seconds
@@ -1245,7 +1285,7 @@

Train 

-INFO:lightwood-2349:Training XGBoost with 57023 iterations given 7127.9874176979065 seconds constraint
+INFO:lightwood-2526:Training XGBoost with 57023 iterations given 7127.987501621246 seconds constraint
@@ -1355,9 +1395,17 @@

Train
+
+
+[13]    validation_0-rmse:20.77980
+
+
+
+
+
-INFO:lightwood-2349:Started fitting XGBoost model
+INFO:lightwood-2526:Started fitting XGBoost model
@@ -1373,7 +1421,7 @@

Train 

-INFO:lightwood-2349:A single GBM iteration takes 0.1 seconds
+INFO:lightwood-2526:A single GBM iteration takes 0.1 seconds

@@ -1381,7 +1429,7 @@

Train 

-INFO:lightwood-2349:Training XGBoost with 57023 iterations given 7127.987009763718 seconds constraint
+INFO:lightwood-2526:Training XGBoost with 57023 iterations given 7127.986826181412 seconds constraint
@@ -1454,6 +1502,14 @@

Train 
 [8]     validation_0-rmse:22.21348
+
+

+
+
+
+
+
+
@@ -1469,7 +1525,7 @@

Train 

-[10]    validation_0-rmse:22.20352
+
@@ -1477,7 +1533,7 @@

Train 

-[11]    validation_0-rmse:22.25761
+[10]    validation_0-rmse:22.20352
@@ -1485,7 +1541,7 @@

Train 

-[12]    validation_0-rmse:22.25308
+[11]    validation_0-rmse:22.25761
@@ -1493,7 +1549,7 @@

Train 

-[13]    validation_0-rmse:22.31415
+[12]    validation_0-rmse:22.25308
@@ -1501,7 +1557,7 @@

Train 

-[14]    validation_0-rmse:22.31000
+[13]    validation_0-rmse:22.31415
@@ -1509,7 +1565,7 @@

Train 

-INFO:lightwood-2349:Started fitting XGBoost model
+INFO:lightwood-2526:Started fitting XGBoost model
@@ -1525,7 +1581,7 @@

Train 

-INFO:lightwood-2349:A single GBM iteration takes 0.1 seconds
+INFO:lightwood-2526:A single GBM iteration takes 0.1 seconds
@@ -1533,7 +1589,7 @@

Train 

-INFO:lightwood-2349:Training XGBoost with 57023 iterations given 7127.988016605377 seconds constraint
+INFO:lightwood-2526:Training XGBoost with 57023 iterations given 7127.986331701279 seconds constraint
@@ -1574,6 +1630,14 @@

Train 
 [4]     validation_0-rmse:23.09943
+
+

+
+
+
+
+
+
@@ -1651,9 +1715,17 @@

Train
+
+
+[14]    validation_0-rmse:21.70025
+
+
+
+
+
-DEBUG:lightwood-2349: `fit_mixer` runtime: 0.5 seconds
+DEBUG:lightwood-2526: `fit_mixer` runtime: 0.54 seconds
@@ -1661,7 +1733,7 @@

Train 

-INFO:dataprep_ml-2349:Ensembling the mixer
+INFO:dataprep_ml-2526:Ensembling the mixer

@@ -1669,7 +1741,7 @@

Train 

-INFO:lightwood-2349:Mixer: NeuralTs got accuracy: 0.875
+INFO:lightwood-2526:Mixer: NeuralTs got accuracy: 0.875
@@ -1677,7 +1749,7 @@

Train 

-WARNING:lightwood-2349:This model does not output probability estimates
+WARNING:lightwood-2526:This model does not output probability estimates
@@ -1685,7 +1757,7 @@

Train 

-INFO:lightwood-2349:Mixer: XGBoostArrayMixer got accuracy: 0.869
+INFO:lightwood-2526:Mixer: XGBoostArrayMixer got accuracy: 0.869
@@ -1693,7 +1765,7 @@

Train 

-INFO:lightwood-2349:Picked best mixer: NeuralTs
+INFO:lightwood-2526:Picked best mixer: NeuralTs
@@ -1701,7 +1773,7 @@

Train 

-DEBUG:lightwood-2349: `fit` runtime: 1.48 seconds
+DEBUG:lightwood-2526: `fit` runtime: 1.59 seconds
@@ -1709,7 +1781,7 @@

Train 

-INFO:dataprep_ml-2349:[Learn phase 7/8] - Ensemble analysis
+INFO:dataprep_ml-2526:[Learn phase 7/8] - Ensemble analysis
@@ -1717,7 +1789,7 @@

Train 

-INFO:dataprep_ml-2349:Analyzing the ensemble of mixers
+INFO:dataprep_ml-2526:Analyzing the ensemble of mixers
@@ -1725,7 +1797,7 @@

Train 

-INFO:lightwood-2349:The block ICP is now running its analyze() method
+INFO:lightwood-2526:The block ICP is now running its analyze() method
@@ -1733,7 +1805,7 @@

Train 

-INFO:lightwood-2349:The block ConfStats is now running its analyze() method
+INFO:lightwood-2526:The block ConfStats is now running its analyze() method
@@ -1741,7 +1813,7 @@

Train 

-INFO:lightwood-2349:The block AccStats is now running its analyze() method
+INFO:lightwood-2526:The block AccStats is now running its analyze() method
@@ -1749,7 +1821,7 @@

Train 

-INFO:lightwood-2349:The block PermutationFeatureImportance is now running its analyze() method
+INFO:lightwood-2526:The block PermutationFeatureImportance is now running its analyze() method
@@ -1757,7 +1829,7 @@

Train 

-WARNING:lightwood-2349:Block 'PermutationFeatureImportance' does not support time series nor text encoding, skipping...
+WARNING:lightwood-2526:Block 'PermutationFeatureImportance' does not support time series nor text encoding, skipping...
@@ -1765,7 +1837,7 @@

Train 

-DEBUG:lightwood-2349: `analyze_ensemble` runtime: 0.17 seconds
+DEBUG:lightwood-2526: `analyze_ensemble` runtime: 0.18 seconds
@@ -1773,7 +1845,7 @@

Train 

-INFO:dataprep_ml-2349:[Learn phase 8/8] - Adjustment on validation requested
+INFO:dataprep_ml-2526:[Learn phase 8/8] - Adjustment on validation requested
@@ -1781,7 +1853,7 @@

Train 

-INFO:dataprep_ml-2349:Updating the mixers
+INFO:dataprep_ml-2526:Updating the mixers
@@ -1791,7 +1863,7 @@

Train 
 /opt/hostedtoolcache/Python/3.9.18/x64/lib/python3.9/site-packages/torch/cuda/amp/grad_scaler.py:126: UserWarning: torch.cuda.amp.GradScaler is enabled, but CUDA is not available.  Disabling.
   warnings.warn(
-INFO:lightwood-2349:Loss @ epoch 1: 0.29626286526521045
+INFO:lightwood-2526:Loss @ epoch 1: 0.29626286526521045

@@ -1799,7 +1871,7 @@

Train 

-INFO:lightwood-2349:Loss @ epoch 2: 0.2954987535874049
+INFO:lightwood-2526:Loss @ epoch 2: 0.2954987535874049
@@ -1807,7 +1879,7 @@

Train 

-INFO:lightwood-2349:Updating array of LGBM models...
+INFO:lightwood-2526:Updating array of LGBM models...
@@ -1815,7 +1887,7 @@

Train 

-INFO:lightwood-2349:XGBoost mixer does not have a `partial_fit` implementation
+INFO:lightwood-2526:XGBoost mixer does not have a `partial_fit` implementation
@@ -1823,7 +1895,7 @@

Train 

-INFO:lightwood-2349:XGBoost mixer does not have a `partial_fit` implementation
+INFO:lightwood-2526:XGBoost mixer does not have a `partial_fit` implementation
@@ -1831,7 +1903,7 @@

Train 

-INFO:lightwood-2349:XGBoost mixer does not have a `partial_fit` implementation
+INFO:lightwood-2526:XGBoost mixer does not have a `partial_fit` implementation
@@ -1839,7 +1911,7 @@

Train 

-INFO:lightwood-2349:XGBoost mixer does not have a `partial_fit` implementation
+INFO:lightwood-2526:XGBoost mixer does not have a `partial_fit` implementation
@@ -1847,7 +1919,7 @@

Train 

-INFO:lightwood-2349:XGBoost mixer does not have a `partial_fit` implementation
+INFO:lightwood-2526:XGBoost mixer does not have a `partial_fit` implementation
@@ -1855,7 +1927,7 @@

Train 

-INFO:lightwood-2349:XGBoost mixer does not have a `partial_fit` implementation
+INFO:lightwood-2526:XGBoost mixer does not have a `partial_fit` implementation
@@ -1863,7 +1935,7 @@

Train 

-DEBUG:lightwood-2349: `adjust` runtime: 0.09 seconds
+DEBUG:lightwood-2526: `adjust` runtime: 0.09 seconds
@@ -1871,7 +1943,7 @@

Train 

-DEBUG:lightwood-2349: `learn` runtime: 2.01 seconds
+DEBUG:lightwood-2526: `learn` runtime: 2.13 seconds
@@ -1891,7 +1963,7 @@

Predict 
-INFO:dataprep_ml-2349:[Predict phase 1/4] - Data preprocessing
+INFO:dataprep_ml-2526:[Predict phase 1/4] - Data preprocessing
@@ -1899,13 +1971,13 @@

Predict 
-/tmp/1f926d4632fedc27db202c5ff831e365e4fa0b9b785a349717107578774839697.py:584: SettingWithCopyWarning:
+/tmp/3b68360fcd1f78fb3b4ddb152a98e8448a8364ced0f8a5001710763970581605.py:584: SettingWithCopyWarning:
 A value is trying to be set on a copy of a slice from a DataFrame.
 Try using .loc[row_indexer,col_indexer] = value instead
 
 See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
   data[col] = [None] * len(data)
-INFO:dataprep_ml-2349:Cleaning the data
+INFO:dataprep_ml-2526:Cleaning the data

@@ -1915,7 +1987,6 @@

Predict
 /opt/hostedtoolcache/Python/3.9.18/x64/lib/python3.9/site-packages/dataprep_ml/cleaners.py:163: UserWarning: The argument 'infer_datetime_format' is deprecated and will be removed in a future version. A strict version of it is now the default, see https://pandas.pydata.org/pdeps/0004-consistent-to-datetime-parsing.html. You can safely remove this argument.
   result = pd.to_datetime(element,
-INFO:dataprep_ml-2349:Transforming timeseries data

@@ -1923,7 +1994,15 @@

Predict 
-DEBUG:lightwood-2349: `preprocess` runtime: 0.02 seconds
+INFO:dataprep_ml-2526:Transforming timeseries data
+

+ +
+
+
+
+
+DEBUG:lightwood-2526: `preprocess` runtime: 0.03 seconds
@@ -1931,7 +2010,7 @@

Predict 
-INFO:dataprep_ml-2349:[Predict phase 2/4] - Feature generation
+INFO:dataprep_ml-2526:[Predict phase 2/4] - Feature generation

@@ -1939,7 +2018,7 @@

Predict 
-INFO:dataprep_ml-2349:Featurizing the data
+INFO:dataprep_ml-2526:Featurizing the data

@@ -1947,7 +2026,7 @@

Predict 
-DEBUG:lightwood-2349: `featurize` runtime: 0.01 seconds
+DEBUG:lightwood-2526: `featurize` runtime: 0.01 seconds

@@ -1955,7 +2034,7 @@

Predict 
-INFO:dataprep_ml-2349:[Predict phase 3/4] - Calling ensemble
+INFO:dataprep_ml-2526:[Predict phase 3/4] - Calling ensemble

@@ -1963,7 +2042,7 @@

Predict 
-DEBUG:lightwood-2349: `_timed_call` runtime: 0.09 seconds
+DEBUG:lightwood-2526: `_timed_call` runtime: 0.09 seconds

@@ -1971,7 +2050,7 @@

Predict 
-INFO:dataprep_ml-2349:[Predict phase 4/4] - Analyzing output
+INFO:dataprep_ml-2526:[Predict phase 4/4] - Analyzing output

@@ -1979,7 +2058,7 @@

Predict 
-INFO:lightwood-2349:The block ICP is now running its explain() method
+INFO:lightwood-2526:The block ICP is now running its explain() method

@@ -1987,7 +2066,7 @@

Predict 
-INFO:lightwood-2349:The block ConfStats is now running its explain() method
+INFO:lightwood-2526:The block ConfStats is now running its explain() method

@@ -1995,7 +2074,7 @@

Predict 
-INFO:lightwood-2349:ConfStats.explain() has not been implemented, no modifications will be done to the data insights.
+INFO:lightwood-2526:ConfStats.explain() has not been implemented, no modifications will be done to the data insights.

@@ -2003,7 +2082,7 @@

Predict 
-INFO:lightwood-2349:The block AccStats is now running its explain() method
+INFO:lightwood-2526:The block AccStats is now running its explain() method

@@ -2011,7 +2090,7 @@

Predict 
-INFO:lightwood-2349:AccStats.explain() has not been implemented, no modifications will be done to the data insights.
+INFO:lightwood-2526:AccStats.explain() has not been implemented, no modifications will be done to the data insights.

@@ -2019,7 +2098,7 @@

Predict 
-INFO:lightwood-2349:The block PermutationFeatureImportance is now running its explain() method
+INFO:lightwood-2526:The block PermutationFeatureImportance is now running its explain() method

@@ -2027,7 +2106,7 @@

Predict 
-INFO:lightwood-2349:PermutationFeatureImportance.explain() has not been implemented, no modifications will be done to the data insights.
+INFO:lightwood-2526:PermutationFeatureImportance.explain() has not been implemented, no modifications will be done to the data insights.

@@ -2035,7 +2114,7 @@

Predict 
-DEBUG:lightwood-2349: `explain` runtime: 0.1 seconds
+DEBUG:lightwood-2526: `explain` runtime: 0.09 seconds

@@ -2043,7 +2122,7 @@

Predict 
-DEBUG:lightwood-2349: `predict` runtime: 0.23 seconds
+DEBUG:lightwood-2526: `predict` runtime: 0.23 seconds

Let’s check how a single row might look:

diff --git a/tutorials/tutorial_time_series/tutorial_time_series.ipynb b/tutorials/tutorial_time_series/tutorial_time_series.ipynb index 666208a50..03ddc61b1 100644 --- a/tutorials/tutorial_time_series/tutorial_time_series.ipynb +++ b/tutorials/tutorial_time_series/tutorial_time_series.ipynb @@ -24,10 +24,10 @@ "execution_count": 1, "metadata": { "execution": { - "iopub.execute_input": "2024-03-18T10:31:10.675092Z", - "iopub.status.busy": "2024-03-18T10:31:10.674541Z", - "iopub.status.idle": "2024-03-18T10:31:11.090125Z", - "shell.execute_reply": "2024-03-18T10:31:11.089404Z" + "iopub.execute_input": "2024-03-18T12:12:43.212199Z", + "iopub.status.busy": "2024-03-18T12:12:43.211949Z", + "iopub.status.idle": "2024-03-18T12:12:43.771553Z", + "shell.execute_reply": "2024-03-18T12:12:43.770829Z" } }, "outputs": [ @@ -162,10 +162,10 @@ "execution_count": 2, "metadata": { "execution": { - "iopub.execute_input": "2024-03-18T10:31:11.127376Z", - "iopub.status.busy": "2024-03-18T10:31:11.126896Z", - "iopub.status.idle": "2024-03-18T10:31:13.412768Z", - "shell.execute_reply": "2024-03-18T10:31:13.412026Z" + "iopub.execute_input": "2024-03-18T12:12:43.810740Z", + "iopub.status.busy": "2024-03-18T12:12:43.810255Z", + "iopub.status.idle": "2024-03-18T12:12:46.271817Z", + "shell.execute_reply": "2024-03-18T12:12:46.271020Z" } }, "outputs": [ @@ -173,14 +173,14 @@ "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2349:No torchvision detected, image helpers not supported.\u001b[0m\n" + "\u001b[32mINFO:lightwood-2526:No torchvision detected, image helpers not supported.\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2349:No torchvision/pillow detected, image encoder not supported\u001b[0m\n" + "\u001b[32mINFO:lightwood-2526:No torchvision/pillow detected, image encoder not supported\u001b[0m\n" ] } ], @@ -193,10 +193,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2024-03-18T10:31:13.416144Z", - "iopub.status.busy": "2024-03-18T10:31:13.415631Z", - "iopub.status.idle": "2024-03-18T10:31:13.419412Z", - "shell.execute_reply": "2024-03-18T10:31:13.418797Z" + "iopub.execute_input": "2024-03-18T12:12:46.275639Z", + "iopub.status.busy": "2024-03-18T12:12:46.274895Z", + "iopub.status.idle": "2024-03-18T12:12:46.279199Z", + "shell.execute_reply": "2024-03-18T12:12:46.278569Z" } }, "outputs": [], @@ -223,10 +223,10 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2024-03-18T10:31:13.421879Z", - "iopub.status.busy": "2024-03-18T10:31:13.421525Z", - "iopub.status.idle": "2024-03-18T10:31:13.425477Z", - "shell.execute_reply": "2024-03-18T10:31:13.424875Z" + "iopub.execute_input": "2024-03-18T12:12:46.281788Z", + "iopub.status.busy": "2024-03-18T12:12:46.281581Z", + "iopub.status.idle": "2024-03-18T12:12:46.286226Z", + "shell.execute_reply": "2024-03-18T12:12:46.285517Z" } }, "outputs": [ @@ -261,10 +261,10 @@ "execution_count": 5, "metadata": { "execution": { - "iopub.execute_input": "2024-03-18T10:31:13.428172Z", - "iopub.status.busy": "2024-03-18T10:31:13.427791Z", - "iopub.status.idle": "2024-03-18T10:31:17.491379Z", - "shell.execute_reply": "2024-03-18T10:31:17.490539Z" + "iopub.execute_input": "2024-03-18T12:12:46.289099Z", + "iopub.status.busy": "2024-03-18T12:12:46.288679Z", + "iopub.status.idle": "2024-03-18T12:12:50.587952Z", + "shell.execute_reply": "2024-03-18T12:12:50.587366Z" } }, "outputs": [ @@ -272,49 +272,49 @@ "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2349:Analyzing a sample of 2467\u001b[0m\n" + "\u001b[32mINFO:type_infer-2526:Analyzing a sample of 2467\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2349:from a total population of 2820, this is equivalent to 87.5% of your data.\u001b[0m\n" + "\u001b[32mINFO:type_infer-2526:from a total population of 2820, this is equivalent to 87.5% of your data.\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2349:Infering type for: Month\u001b[0m\n" + "\u001b[32mINFO:type_infer-2526:Infering type for: Month\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2349:Column Month has data type date\u001b[0m\n" + "\u001b[32mINFO:type_infer-2526:Column Month has data type date\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2349:Infering type for: Sunspots\u001b[0m\n" + "\u001b[32mINFO:type_infer-2526:Infering type for: Sunspots\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2349:Column Sunspots has data type float\u001b[0m\n" + "\u001b[32mINFO:type_infer-2526:Column Sunspots has data type float\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2349:Starting statistical analysis\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2526:Starting statistical analysis\u001b[0m\n" ] }, { @@ -323,7 +323,7 @@ "text": [ "/opt/hostedtoolcache/Python/3.9.18/x64/lib/python3.9/site-packages/dataprep_ml/cleaners.py:163: UserWarning: The argument 'infer_datetime_format' is deprecated and will be removed in a future version. A strict version of it is now the default, see https://pandas.pydata.org/pdeps/0004-consistent-to-datetime-parsing.html. You can safely remove this argument.\n", " result = pd.to_datetime(element,\n", - "\u001b[32mINFO:dataprep_ml-2349:Finished statistical analysis\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2526:Finished statistical analysis\u001b[0m\n" ] } ], @@ -360,10 +360,10 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2024-03-18T10:31:17.494768Z", - "iopub.status.busy": "2024-03-18T10:31:17.494475Z", - "iopub.status.idle": "2024-03-18T10:31:19.509975Z", - "shell.execute_reply": "2024-03-18T10:31:19.509394Z" + "iopub.execute_input": "2024-03-18T12:12:50.591170Z", + "iopub.status.busy": "2024-03-18T12:12:50.590744Z", + "iopub.status.idle": "2024-03-18T12:12:52.722053Z", + "shell.execute_reply": "2024-03-18T12:12:52.721367Z" } }, "outputs": [ @@ -371,14 +371,14 @@ "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2349:[Learn phase 1/8] - Statistical analysis\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2526:[Learn phase 1/8] - Statistical analysis\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2349:Starting statistical analysis\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2526:Starting statistical analysis\u001b[0m\n" ] }, { @@ -387,28 +387,28 @@ "text": [ "/opt/hostedtoolcache/Python/3.9.18/x64/lib/python3.9/site-packages/dataprep_ml/cleaners.py:163: UserWarning: The argument 'infer_datetime_format' is deprecated and will be removed in a future version. A strict version of it is now the default, see https://pandas.pydata.org/pdeps/0004-consistent-to-datetime-parsing.html. You can safely remove this argument.\n", " result = pd.to_datetime(element,\n", - "\u001b[32mINFO:dataprep_ml-2349:Finished statistical analysis\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2526:Finished statistical analysis\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:lightwood-2349: `analyze_data` runtime: 0.07 seconds\u001b[0m\n" + "\u001b[37mDEBUG:lightwood-2526: `analyze_data` runtime: 0.06 seconds\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2349:[Learn phase 2/8] - Data preprocessing\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2526:[Learn phase 2/8] - Data preprocessing\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2349:Cleaning the data\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2526:Cleaning the data\u001b[0m\n" ] }, { @@ -417,133 +417,133 @@ "text": [ "/opt/hostedtoolcache/Python/3.9.18/x64/lib/python3.9/site-packages/dataprep_ml/cleaners.py:163: UserWarning: The argument 'infer_datetime_format' is deprecated and will be removed in a future version. A strict version of it is now the default, see https://pandas.pydata.org/pdeps/0004-consistent-to-datetime-parsing.html. You can safely remove this argument.\n", " result = pd.to_datetime(element,\n", - "\u001b[32mINFO:dataprep_ml-2349:Transforming timeseries data\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2526:Transforming timeseries data\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:lightwood-2349: `preprocess` runtime: 0.1 seconds\u001b[0m\n" + "\u001b[37mDEBUG:lightwood-2526: `preprocess` runtime: 0.09 seconds\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2349:[Learn phase 3/8] - Data splitting\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2526:[Learn phase 3/8] - Data splitting\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2349:Splitting the data into train/test\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2526:Splitting the data into train/test\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:lightwood-2349: `split` runtime: 0.0 seconds\u001b[0m\n" + "\u001b[37mDEBUG:lightwood-2526: `split` runtime: 0.0 seconds\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2349:[Learn phase 4/8] - Preparing encoders\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2526:[Learn phase 4/8] - Preparing encoders\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:dataprep_ml-2349:Preparing sequentially...\u001b[0m\n" + "\u001b[37mDEBUG:dataprep_ml-2526:Preparing sequentially...\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:lightwood-2349: `prepare` runtime: 0.05 seconds\u001b[0m\n" + "\u001b[37mDEBUG:lightwood-2526: `prepare` runtime: 0.05 seconds\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2349:[Learn phase 5/8] - Feature generation\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2526:[Learn phase 5/8] - Feature generation\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2349:Featurizing the data\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2526:Featurizing the data\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:lightwood-2349: `featurize` runtime: 0.05 seconds\u001b[0m\n" + "\u001b[37mDEBUG:lightwood-2526: `featurize` runtime: 0.05 seconds\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2349:[Learn phase 6/8] - Mixer training\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2526:[Learn phase 6/8] - Mixer training\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2349:Training the mixers\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2526:Training the mixers\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[33mWARNING:lightwood-2349:XGBoost running on CPU\u001b[0m\n" + "\u001b[33mWARNING:lightwood-2526:XGBoost running on CPU\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[33mWARNING:lightwood-2349:XGBoost running on CPU\u001b[0m\n" + "\u001b[33mWARNING:lightwood-2526:XGBoost running on CPU\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[33mWARNING:lightwood-2349:XGBoost running on CPU\u001b[0m\n" + "\u001b[33mWARNING:lightwood-2526:XGBoost running on CPU\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[33mWARNING:lightwood-2349:XGBoost running on CPU\u001b[0m\n" + "\u001b[33mWARNING:lightwood-2526:XGBoost running on CPU\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[33mWARNING:lightwood-2349:XGBoost running on CPU\u001b[0m\n" + "\u001b[33mWARNING:lightwood-2526:XGBoost running on CPU\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[33mWARNING:lightwood-2349:XGBoost running on CPU\u001b[0m\n" + "\u001b[33mWARNING:lightwood-2526:XGBoost running on CPU\u001b[0m\n" ] }, { @@ -558,12 +558,12 @@ "name": "stdout", "output_type": "stream", "text": [ - "[10:31:17] WARNING: ../src/learner.cc:339: No visible GPU is found, setting `gpu_id` to -1\n", - "[10:31:17] WARNING: ../src/learner.cc:339: No visible GPU is found, setting `gpu_id` to -1\n", - "[10:31:17] WARNING: ../src/learner.cc:339: No visible GPU is found, setting `gpu_id` to -1\n", - "[10:31:17] WARNING: ../src/learner.cc:339: No visible GPU is found, setting `gpu_id` to -1\n", - "[10:31:17] WARNING: ../src/learner.cc:339: No visible GPU is found, setting `gpu_id` to -1\n", - "[10:31:17] WARNING: ../src/learner.cc:339: No visible GPU is found, setting `gpu_id` to -1\n" + "[12:12:50] WARNING: ../src/learner.cc:339: No visible GPU is found, setting `gpu_id` to -1\n", + "[12:12:50] WARNING: ../src/learner.cc:339: No visible GPU is found, setting `gpu_id` to -1\n", + "[12:12:50] WARNING: ../src/learner.cc:339: No visible GPU is found, setting `gpu_id` to -1\n", + "[12:12:50] WARNING: ../src/learner.cc:339: No visible GPU is found, setting `gpu_id` to -1\n", + "[12:12:50] WARNING: ../src/learner.cc:339: No visible GPU is found, setting `gpu_id` to -1\n", + "[12:12:50] WARNING: ../src/learner.cc:339: No visible GPU is found, setting `gpu_id` to -1\n" ] }, { @@ -575,63 +575,63 @@ "Consider using one of the following signatures instead:\n", "\taddcmul_(Tensor tensor1, Tensor tensor2, *, Number value) (Triggered internally at ../torch/csrc/utils/python_arg_parser.cpp:1630.)\n", " exp_avg_sq.mul_(beta2).addcmul_(1 - beta2, grad, grad)\n", - "\u001b[32mINFO:lightwood-2349:Loss of 9.051180630922318 with learning rate 0.0001\u001b[0m\n" + "\u001b[32mINFO:lightwood-2526:Loss of 9.051180630922318 with learning rate 0.0001\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2349:Loss of 9.014871209859848 with learning rate 0.0005\u001b[0m\n" + "\u001b[32mINFO:lightwood-2526:Loss of 9.014871209859848 with learning rate 0.0005\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2349:Loss of 8.969509482383728 with learning rate 0.001\u001b[0m\n" + "\u001b[32mINFO:lightwood-2526:Loss of 8.969509482383728 with learning rate 0.001\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2349:Loss of 8.879052013158798 with learning rate 0.002\u001b[0m\n" + "\u001b[32mINFO:lightwood-2526:Loss of 8.879052013158798 with learning rate 0.002\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2349:Loss of 8.788950502872467 with learning rate 0.003\u001b[0m\n" + "\u001b[32mINFO:lightwood-2526:Loss of 8.788950502872467 with learning rate 0.003\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2349:Loss of 8.611965209245682 with learning rate 0.005\u001b[0m\n" + "\u001b[32mINFO:lightwood-2526:Loss of 8.611965209245682 with learning rate 0.005\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2349:Loss of 8.195775926113129 with learning rate 0.01\u001b[0m\n" + "\u001b[32mINFO:lightwood-2526:Loss of 8.195775926113129 with learning rate 0.01\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2349:Loss of 6.255893141031265 with learning rate 0.05\u001b[0m\n" + "\u001b[32mINFO:lightwood-2526:Loss of 6.255893141031265 with learning rate 0.05\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2349:Found learning rate of: 0.05\u001b[0m\n" + "\u001b[32mINFO:lightwood-2526:Found learning rate of: 0.05\u001b[0m\n" ] }, { @@ -640,105 +640,105 @@ "text": [ "/opt/hostedtoolcache/Python/3.9.18/x64/lib/python3.9/site-packages/torch/cuda/amp/grad_scaler.py:126: UserWarning: torch.cuda.amp.GradScaler is enabled, but CUDA is not available. Disabling.\n", " warnings.warn(\n", - "\u001b[32mINFO:lightwood-2349:Loss @ epoch 1: 0.5818348675966263\u001b[0m\n" + "\u001b[32mINFO:lightwood-2526:Loss @ epoch 1: 0.5818348675966263\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2349:Loss @ epoch 2: 0.4797109067440033\u001b[0m\n" + "\u001b[32mINFO:lightwood-2526:Loss @ epoch 2: 0.4797109067440033\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2349:Loss @ epoch 3: 0.48386093974113464\u001b[0m\n" + "\u001b[32mINFO:lightwood-2526:Loss @ epoch 3: 0.48386093974113464\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2349:Loss @ epoch 4: 0.49511992931365967\u001b[0m\n" + "\u001b[32mINFO:lightwood-2526:Loss @ epoch 4: 0.49511992931365967\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2349:Loss @ epoch 5: 0.39475560188293457\u001b[0m\n" + "\u001b[32mINFO:lightwood-2526:Loss @ epoch 5: 0.39475560188293457\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2349:Loss @ epoch 6: 0.39592696726322174\u001b[0m\n" + "\u001b[32mINFO:lightwood-2526:Loss @ epoch 6: 0.39592696726322174\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2349:Loss @ epoch 7: 0.3622782379388809\u001b[0m\n" + "\u001b[32mINFO:lightwood-2526:Loss @ epoch 7: 0.3622782379388809\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2349:Loss @ epoch 8: 0.38170479238033295\u001b[0m\n" + "\u001b[32mINFO:lightwood-2526:Loss @ epoch 8: 0.38170479238033295\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2349:Loss @ epoch 9: 0.5138543993234634\u001b[0m\n" + "\u001b[32mINFO:lightwood-2526:Loss @ epoch 9: 0.5138543993234634\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2349:Loss @ epoch 10: 0.6360723078250885\u001b[0m\n" + "\u001b[32mINFO:lightwood-2526:Loss @ epoch 10: 0.6360723078250885\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2349:Loss @ epoch 1: 0.29868809472430835\u001b[0m\n" + "\u001b[32mINFO:lightwood-2526:Loss @ epoch 1: 0.29868809472430835\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2349:Loss @ epoch 2: 0.30318967591632495\u001b[0m\n" + "\u001b[32mINFO:lightwood-2526:Loss @ epoch 2: 0.30318967591632495\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:lightwood-2349: `fit_mixer` runtime: 0.93 seconds\u001b[0m\n" + "\u001b[37mDEBUG:lightwood-2526: `fit_mixer` runtime: 1.0 seconds\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2349:Started fitting LGBM models for array prediction\u001b[0m\n" + "\u001b[32mINFO:lightwood-2526:Started fitting LGBM models for array prediction\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2349:Started fitting XGBoost model\u001b[0m\n" + "\u001b[32mINFO:lightwood-2526:Started fitting XGBoost model\u001b[0m\n" ] }, { @@ -752,14 +752,14 @@ "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2349:A single GBM iteration takes 0.1 seconds\u001b[0m\n" + "\u001b[32mINFO:lightwood-2526:A single GBM iteration takes 0.1 seconds\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2349:Training XGBoost with 57023 iterations given 7127.98668384552 seconds constraint\u001b[0m\n" + "\u001b[32mINFO:lightwood-2526:Training XGBoost with 57023 iterations given 7127.98410153389 seconds constraint\u001b[0m\n" ] }, { @@ -871,7 +871,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2349:Started fitting XGBoost model\u001b[0m\n" + "\u001b[32mINFO:lightwood-2526:Started fitting XGBoost model\u001b[0m\n" ] }, { @@ -885,14 +885,14 @@ "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2349:A single GBM iteration takes 0.1 seconds\u001b[0m\n" + "\u001b[32mINFO:lightwood-2526:A single GBM iteration takes 0.1 seconds\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2349:Training XGBoost with 57023 iterations given 7127.9880249500275 seconds constraint\u001b[0m\n" + "\u001b[32mINFO:lightwood-2526:Training XGBoost with 57023 iterations given 7127.986747741699 seconds constraint\u001b[0m\n" ] }, { @@ -997,7 +997,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2349:Started fitting XGBoost model\u001b[0m\n" + "\u001b[32mINFO:lightwood-2526:Started fitting XGBoost model\u001b[0m\n" ] }, { @@ -1011,14 +1011,14 @@ "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2349:A single GBM iteration takes 0.1 seconds\u001b[0m\n" + "\u001b[32mINFO:lightwood-2526:A single GBM iteration takes 0.1 seconds\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2349:Training XGBoost with 57023 iterations given 7127.988857269287 seconds constraint\u001b[0m\n" + "\u001b[32mINFO:lightwood-2526:Training XGBoost with 57023 iterations given 7127.986978292465 seconds constraint\u001b[0m\n" ] }, { @@ -1074,7 +1074,14 @@ "name": "stdout", "output_type": "stream", "text": [ - "[7]\tvalidation_0-rmse:19.00714\n" + "[7]\tvalidation_0-rmse:19.00714" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n" ] }, { @@ -1088,35 +1095,49 @@ "name": "stdout", "output_type": "stream", "text": [ - "[9]\tvalidation_0-rmse:19.12589\n" + "[9]\tvalidation_0-rmse:19.12589" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "[10]\tvalidation_0-rmse:19.34977\n" + "\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "[11]\tvalidation_0-rmse:19.43217\n" + "[10]\tvalidation_0-rmse:19.34977" ] }, { - "name": "stderr", + "name": "stdout", + "output_type": "stream", + "text": [ + "\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[11]\tvalidation_0-rmse:19.43217" + ] + }, + { + "name": "stdout", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2349:Started fitting XGBoost model\u001b[0m\n" + "\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "[0]\tvalidation_0-rmse:44.19079" + "[12]\tvalidation_0-rmse:19.48230" ] }, { @@ -1130,14 +1151,28 @@ "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2349:A single GBM iteration takes 0.1 seconds\u001b[0m\n" + "\u001b[32mINFO:lightwood-2526:Started fitting XGBoost model\u001b[0m\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[0]\tvalidation_0-rmse:44.19079\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[32mINFO:lightwood-2526:A single GBM iteration takes 0.1 seconds\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2349:Training XGBoost with 57023 iterations given 7127.9874176979065 seconds constraint\u001b[0m\n" + "\u001b[32mINFO:lightwood-2526:Training XGBoost with 57023 iterations given 7127.987501621246 seconds constraint\u001b[0m\n" ] }, { @@ -1231,11 +1266,18 @@ "[12]\tvalidation_0-rmse:20.83998\n" ] }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[13]\tvalidation_0-rmse:20.77980\n" + ] + }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2349:Started fitting XGBoost model\u001b[0m\n" + "\u001b[32mINFO:lightwood-2526:Started fitting XGBoost model\u001b[0m\n" ] }, { @@ -1249,14 +1291,14 @@ "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2349:A single GBM iteration takes 0.1 seconds\u001b[0m\n" + "\u001b[32mINFO:lightwood-2526:A single GBM iteration takes 0.1 seconds\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2349:Training XGBoost with 57023 iterations given 7127.987009763718 seconds constraint\u001b[0m\n" + "\u001b[32mINFO:lightwood-2526:Training XGBoost with 57023 iterations given 7127.986826181412 seconds constraint\u001b[0m\n" ] }, { @@ -1319,56 +1361,63 @@ "name": "stdout", "output_type": "stream", "text": [ - "[8]\tvalidation_0-rmse:22.21348\n" + "[8]\tvalidation_0-rmse:22.21348" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "[9]\tvalidation_0-rmse:22.10747\n" + "\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "[10]\tvalidation_0-rmse:22.20352\n" + "[9]\tvalidation_0-rmse:22.10747" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "[11]\tvalidation_0-rmse:22.25761\n" + "\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "[12]\tvalidation_0-rmse:22.25308\n" + "[10]\tvalidation_0-rmse:22.20352\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "[13]\tvalidation_0-rmse:22.31415\n" + "[11]\tvalidation_0-rmse:22.25761\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[12]\tvalidation_0-rmse:22.25308\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "[14]\tvalidation_0-rmse:22.31000\n" + "[13]\tvalidation_0-rmse:22.31415\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2349:Started fitting XGBoost model\u001b[0m\n" + "\u001b[32mINFO:lightwood-2526:Started fitting XGBoost model\u001b[0m\n" ] }, { @@ -1382,14 +1431,14 @@ "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2349:A single GBM iteration takes 0.1 seconds\u001b[0m\n" + "\u001b[32mINFO:lightwood-2526:A single GBM iteration takes 0.1 seconds\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2349:Training XGBoost with 57023 iterations given 7127.988016605377 seconds constraint\u001b[0m\n" + "\u001b[32mINFO:lightwood-2526:Training XGBoost with 57023 iterations given 7127.986331701279 seconds constraint\u001b[0m\n" ] }, { @@ -1424,7 +1473,14 @@ "name": "stdout", "output_type": "stream", "text": [ - "[4]\tvalidation_0-rmse:23.09943\n" + "[4]\tvalidation_0-rmse:23.09943" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n" ] }, { @@ -1490,123 +1546,130 @@ "[13]\tvalidation_0-rmse:21.68890\n" ] }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[14]\tvalidation_0-rmse:21.70025\n" + ] + }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:lightwood-2349: `fit_mixer` runtime: 0.5 seconds\u001b[0m\n" + "\u001b[37mDEBUG:lightwood-2526: `fit_mixer` runtime: 0.54 seconds\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2349:Ensembling the mixer\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2526:Ensembling the mixer\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2349:Mixer: NeuralTs got accuracy: 0.875\u001b[0m\n" + "\u001b[32mINFO:lightwood-2526:Mixer: NeuralTs got accuracy: 0.875\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[33mWARNING:lightwood-2349:This model does not output probability estimates\u001b[0m\n" + "\u001b[33mWARNING:lightwood-2526:This model does not output probability estimates\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2349:Mixer: XGBoostArrayMixer got accuracy: 0.869\u001b[0m\n" + "\u001b[32mINFO:lightwood-2526:Mixer: XGBoostArrayMixer got accuracy: 0.869\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2349:Picked best mixer: NeuralTs\u001b[0m\n" + "\u001b[32mINFO:lightwood-2526:Picked best mixer: NeuralTs\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:lightwood-2349: `fit` runtime: 1.48 seconds\u001b[0m\n" + "\u001b[37mDEBUG:lightwood-2526: `fit` runtime: 1.59 seconds\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2349:[Learn phase 7/8] - Ensemble analysis\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2526:[Learn phase 7/8] - Ensemble analysis\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2349:Analyzing the ensemble of mixers\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2526:Analyzing the ensemble of mixers\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2349:The block ICP is now running its analyze() method\u001b[0m\n" + "\u001b[32mINFO:lightwood-2526:The block ICP is now running its analyze() method\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2349:The block ConfStats is now running its analyze() method\u001b[0m\n" + "\u001b[32mINFO:lightwood-2526:The block ConfStats is now running its analyze() method\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2349:The block AccStats is now running its analyze() method\u001b[0m\n" + "\u001b[32mINFO:lightwood-2526:The block AccStats is now running its analyze() method\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2349:The block PermutationFeatureImportance is now running its analyze() method\u001b[0m\n" + "\u001b[32mINFO:lightwood-2526:The block PermutationFeatureImportance is now running its analyze() method\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[33mWARNING:lightwood-2349:Block 'PermutationFeatureImportance' does not support time series nor text encoding, skipping...\u001b[0m\n" + "\u001b[33mWARNING:lightwood-2526:Block 'PermutationFeatureImportance' does not support time series nor text encoding, skipping...\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:lightwood-2349: `analyze_ensemble` runtime: 0.17 seconds\u001b[0m\n" + "\u001b[37mDEBUG:lightwood-2526: `analyze_ensemble` runtime: 0.18 seconds\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2349:[Learn phase 8/8] - Adjustment on validation requested\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2526:[Learn phase 8/8] - Adjustment on validation requested\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2349:Updating the mixers\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2526:Updating the mixers\u001b[0m\n" ] }, { @@ -1615,77 +1678,77 @@ "text": [ "/opt/hostedtoolcache/Python/3.9.18/x64/lib/python3.9/site-packages/torch/cuda/amp/grad_scaler.py:126: UserWarning: torch.cuda.amp.GradScaler is enabled, but CUDA is not available. Disabling.\n", " warnings.warn(\n", - "\u001b[32mINFO:lightwood-2349:Loss @ epoch 1: 0.29626286526521045\u001b[0m\n" + "\u001b[32mINFO:lightwood-2526:Loss @ epoch 1: 0.29626286526521045\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2349:Loss @ epoch 2: 0.2954987535874049\u001b[0m\n" + "\u001b[32mINFO:lightwood-2526:Loss @ epoch 2: 0.2954987535874049\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2349:Updating array of LGBM models...\u001b[0m\n" + "\u001b[32mINFO:lightwood-2526:Updating array of LGBM models...\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2349:XGBoost mixer does not have a `partial_fit` implementation\u001b[0m\n" + "\u001b[32mINFO:lightwood-2526:XGBoost mixer does not have a `partial_fit` implementation\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2349:XGBoost mixer does not have a `partial_fit` implementation\u001b[0m\n" + "\u001b[32mINFO:lightwood-2526:XGBoost mixer does not have a `partial_fit` implementation\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2349:XGBoost mixer does not have a `partial_fit` implementation\u001b[0m\n" + "\u001b[32mINFO:lightwood-2526:XGBoost mixer does not have a `partial_fit` implementation\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2349:XGBoost mixer does not have a `partial_fit` implementation\u001b[0m\n" + "\u001b[32mINFO:lightwood-2526:XGBoost mixer does not have a `partial_fit` implementation\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2349:XGBoost mixer does not have a `partial_fit` implementation\u001b[0m\n" + "\u001b[32mINFO:lightwood-2526:XGBoost mixer does not have a `partial_fit` implementation\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2349:XGBoost mixer does not have a `partial_fit` implementation\u001b[0m\n" + "\u001b[32mINFO:lightwood-2526:XGBoost mixer does not have a `partial_fit` implementation\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:lightwood-2349: `adjust` runtime: 0.09 seconds\u001b[0m\n" + "\u001b[37mDEBUG:lightwood-2526: `adjust` runtime: 0.09 seconds\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:lightwood-2349: `learn` runtime: 2.01 seconds\u001b[0m\n" + "\u001b[37mDEBUG:lightwood-2526: `learn` runtime: 2.13 seconds\u001b[0m\n" ] } ], @@ -1707,10 +1770,10 @@ "execution_count": 7, "metadata": { "execution": { - "iopub.execute_input": "2024-03-18T10:31:19.512963Z", - "iopub.status.busy": "2024-03-18T10:31:19.512366Z", - "iopub.status.idle": "2024-03-18T10:31:19.744570Z", - "shell.execute_reply": "2024-03-18T10:31:19.743883Z" + "iopub.execute_input": "2024-03-18T12:12:52.725282Z", + "iopub.status.busy": "2024-03-18T12:12:52.724752Z", + "iopub.status.idle": "2024-03-18T12:12:52.958333Z", + "shell.execute_reply": "2024-03-18T12:12:52.957658Z" } }, "outputs": [ @@ -1718,20 +1781,20 @@ "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2349:[Predict phase 1/4] - Data preprocessing\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2526:[Predict phase 1/4] - Data preprocessing\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "/tmp/1f926d4632fedc27db202c5ff831e365e4fa0b9b785a349717107578774839697.py:584: SettingWithCopyWarning: \n", + "/tmp/3b68360fcd1f78fb3b4ddb152a98e8448a8364ced0f8a5001710763970581605.py:584: SettingWithCopyWarning: \n", "A value is trying to be set on a copy of a slice from a DataFrame.\n", "Try using .loc[row_indexer,col_indexer] = value instead\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", " data[col] = [None] * len(data)\n", - "\u001b[32mINFO:dataprep_ml-2349:Cleaning the data\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2526:Cleaning the data\u001b[0m\n" ] }, { @@ -1739,120 +1802,126 @@ "output_type": "stream", "text": [ "/opt/hostedtoolcache/Python/3.9.18/x64/lib/python3.9/site-packages/dataprep_ml/cleaners.py:163: UserWarning: The argument 'infer_datetime_format' is deprecated and will be removed in a future version. A strict version of it is now the default, see https://pandas.pydata.org/pdeps/0004-consistent-to-datetime-parsing.html. You can safely remove this argument.\n", - " result = pd.to_datetime(element,\n", - "\u001b[32mINFO:dataprep_ml-2349:Transforming timeseries data\u001b[0m\n" + " result = pd.to_datetime(element,\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[32mINFO:dataprep_ml-2526:Transforming timeseries data\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:lightwood-2349: `preprocess` runtime: 0.02 seconds\u001b[0m\n" + "\u001b[37mDEBUG:lightwood-2526: `preprocess` runtime: 0.03 seconds\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2349:[Predict phase 2/4] - Feature generation\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2526:[Predict phase 2/4] - Feature generation\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2349:Featurizing the data\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2526:Featurizing the data\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:lightwood-2349: `featurize` runtime: 0.01 seconds\u001b[0m\n" + "\u001b[37mDEBUG:lightwood-2526: `featurize` runtime: 0.01 seconds\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2349:[Predict phase 3/4] - Calling ensemble\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2526:[Predict phase 3/4] - Calling ensemble\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:lightwood-2349: `_timed_call` runtime: 0.09 seconds\u001b[0m\n" + "\u001b[37mDEBUG:lightwood-2526: `_timed_call` runtime: 0.09 seconds\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2349:[Predict phase 4/4] - Analyzing output\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2526:[Predict phase 4/4] - Analyzing output\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2349:The block ICP is now running its explain() method\u001b[0m\n" + "\u001b[32mINFO:lightwood-2526:The block ICP is now running its explain() method\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2349:The block ConfStats is now running its explain() method\u001b[0m\n" + "\u001b[32mINFO:lightwood-2526:The block ConfStats is now running its explain() method\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2349:ConfStats.explain() has not been implemented, no modifications will be done to the data insights.\u001b[0m\n" + "\u001b[32mINFO:lightwood-2526:ConfStats.explain() has not been implemented, no modifications will be done to the data insights.\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2349:The block AccStats is now running its explain() method\u001b[0m\n" + "\u001b[32mINFO:lightwood-2526:The block AccStats is now running its explain() method\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2349:AccStats.explain() has not been implemented, no modifications will be done to the data insights.\u001b[0m\n" + "\u001b[32mINFO:lightwood-2526:AccStats.explain() has not been implemented, no modifications will be done to the data insights.\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2349:The block PermutationFeatureImportance is now running its explain() method\u001b[0m\n" + "\u001b[32mINFO:lightwood-2526:The block PermutationFeatureImportance is now running its explain() method\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2349:PermutationFeatureImportance.explain() has not been implemented, no modifications will be done to the data insights.\u001b[0m\n" + "\u001b[32mINFO:lightwood-2526:PermutationFeatureImportance.explain() has not been implemented, no modifications will be done to the data insights.\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:lightwood-2349: `explain` runtime: 0.1 seconds\u001b[0m\n" + "\u001b[37mDEBUG:lightwood-2526: `explain` runtime: 0.09 seconds\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:lightwood-2349: `predict` runtime: 0.23 seconds\u001b[0m\n" + "\u001b[37mDEBUG:lightwood-2526: `predict` runtime: 0.23 seconds\u001b[0m\n" ] } ], @@ -1872,10 +1941,10 @@ "execution_count": 8, "metadata": { "execution": { - "iopub.execute_input": "2024-03-18T10:31:19.747589Z", - "iopub.status.busy": "2024-03-18T10:31:19.747091Z", - "iopub.status.idle": "2024-03-18T10:31:19.758976Z", - "shell.execute_reply": "2024-03-18T10:31:19.758329Z" + "iopub.execute_input": "2024-03-18T12:12:52.961444Z", + "iopub.status.busy": "2024-03-18T12:12:52.961022Z", + "iopub.status.idle": "2024-03-18T12:12:52.973201Z", + "shell.execute_reply": "2024-03-18T12:12:52.972469Z" } }, "outputs": [ @@ -1980,10 +2049,10 @@ "execution_count": 9, "metadata": { "execution": { - "iopub.execute_input": "2024-03-18T10:31:19.761609Z", - "iopub.status.busy": "2024-03-18T10:31:19.761234Z", - "iopub.status.idle": "2024-03-18T10:31:20.163813Z", - "shell.execute_reply": "2024-03-18T10:31:20.163154Z" + "iopub.execute_input": "2024-03-18T12:12:52.976262Z", + "iopub.status.busy": "2024-03-18T12:12:52.975865Z", + "iopub.status.idle": "2024-03-18T12:12:53.399503Z", + "shell.execute_reply": "2024-03-18T12:12:53.398790Z" } }, "outputs": [], @@ -1996,10 +2065,10 @@ "execution_count": 10, "metadata": { "execution": { - "iopub.execute_input": "2024-03-18T10:31:20.166951Z", - "iopub.status.busy": "2024-03-18T10:31:20.166343Z", - "iopub.status.idle": "2024-03-18T10:31:20.357226Z", - "shell.execute_reply": "2024-03-18T10:31:20.356518Z" + "iopub.execute_input": "2024-03-18T12:12:53.402849Z", + "iopub.status.busy": "2024-03-18T12:12:53.402310Z", + "iopub.status.idle": "2024-03-18T12:12:53.602066Z", + "shell.execute_reply": "2024-03-18T12:12:53.601246Z" } }, "outputs": [ diff --git a/tutorials/tutorial_update_models/tutorial_update_models.html b/tutorials/tutorial_update_models/tutorial_update_models.html index 5b238b141..fb5daa502 100644 --- a/tutorials/tutorial_update_models/tutorial_update_models.html +++ b/tutorials/tutorial_update_models/tutorial_update_models.html @@ -110,7 +110,7 @@

Initial model training 
-INFO:lightwood-2398:No torchvision detected, image helpers not supported.
+INFO:lightwood-2576:No torchvision detected, image helpers not supported.
@@ -118,7 +118,7 @@

Initial model training 
-INFO:lightwood-2398:No torchvision/pillow detected, image encoder not supported
+INFO:lightwood-2576:No torchvision/pillow detected, image encoder not supported

@@ -188,7 +188,7 @@

Initial model training 
-INFO:type_infer-2398:Analyzing a sample of 979
+INFO:type_infer-2576:Analyzing a sample of 979

@@ -196,7 +196,7 @@

Initial model training 
-INFO:type_infer-2398:from a total population of 1030, this is equivalent to 95.0% of your data.
+INFO:type_infer-2576:from a total population of 1030, this is equivalent to 95.0% of your data.

@@ -204,7 +204,7 @@

Initial model training 
-INFO:type_infer-2398:Using 3 processes to deduct types.
+INFO:type_infer-2576:Using 3 processes to deduct types.

@@ -212,7 +212,7 @@

Initial model training 
-INFO:type_infer-2398:Infering type for: slag
+INFO:type_infer-2576:Infering type for: cement

@@ -220,7 +220,7 @@

Initial model training 
-INFO:type_infer-2398:Infering type for: cement
+INFO:type_infer-2576:Infering type for: slag

@@ -228,7 +228,7 @@

Initial model training 
-INFO:type_infer-2398:Column cement has data type float
+INFO:type_infer-2576:Column slag has data type float

@@ -236,7 +236,7 @@

Initial model training 
-INFO:type_infer-2398:Column slag has data type float
+INFO:type_infer-2576:Column cement has data type float

@@ -244,7 +244,7 @@

Initial model training 
-INFO:type_infer-2398:Infering type for: flyAsh
+INFO:type_infer-2576:Infering type for: flyAsh

@@ -252,7 +252,7 @@

Initial model training 
-INFO:type_infer-2398:Infering type for: water
+INFO:type_infer-2576:Infering type for: water

@@ -260,7 +260,7 @@

Initial model training 
-INFO:type_infer-2398:Column flyAsh has data type float
+INFO:type_infer-2576:Column flyAsh has data type float

@@ -268,7 +268,7 @@

Initial model training 
-INFO:type_infer-2398:Infering type for: superPlasticizer
+INFO:type_infer-2576:Column water has data type float

@@ -276,7 +276,7 @@

Initial model training 
-INFO:type_infer-2398:Column water has data type float
+INFO:type_infer-2576:Infering type for: id

@@ -284,7 +284,7 @@

Initial model training 
-INFO:type_infer-2398:Infering type for: coarseAggregate
+INFO:type_infer-2576:Infering type for: coarseAggregate

@@ -292,7 +292,7 @@

Initial model training 
-INFO:type_infer-2398:Column superPlasticizer has data type float
+INFO:type_infer-2576:Infering type for: superPlasticizer

@@ -300,7 +300,7 @@

Initial model training 
-INFO:type_infer-2398:Infering type for: fineAggregate
+INFO:type_infer-2576:Column id has data type integer

@@ -308,7 +308,7 @@

Initial model training 
-INFO:type_infer-2398:Column coarseAggregate has data type float
+INFO:type_infer-2576:Column coarseAggregate has data type float

@@ -316,7 +316,7 @@

Initial model training 
-INFO:type_infer-2398:Column fineAggregate has data type float
+INFO:type_infer-2576:Infering type for: age

@@ -324,7 +324,7 @@

Initial model training 
-INFO:type_infer-2398:Infering type for: age
+INFO:type_infer-2576:Infering type for: fineAggregate

@@ -332,7 +332,7 @@

Initial model training 
-INFO:type_infer-2398:Infering type for: concrete_strength
+INFO:type_infer-2576:Column superPlasticizer has data type float

@@ -340,7 +340,7 @@

Initial model training 
-INFO:type_infer-2398:Infering type for: id
+INFO:type_infer-2576:Infering type for: concrete_strength

@@ -348,7 +348,7 @@

Initial model training 
-INFO:type_infer-2398:Column age has data type integer
+INFO:type_infer-2576:Column age has data type integer

@@ -356,7 +356,7 @@

Initial model training 
-INFO:type_infer-2398:Column concrete_strength has data type float
+INFO:type_infer-2576:Column fineAggregate has data type float

@@ -364,7 +364,7 @@

Initial model training 
-INFO:type_infer-2398:Column id has data type integer
+INFO:type_infer-2576:Column concrete_strength has data type float

@@ -372,7 +372,7 @@

Initial model training 
-INFO:dataprep_ml-2398:Starting statistical analysis
+INFO:dataprep_ml-2576:Starting statistical analysis

@@ -380,7 +380,7 @@

Initial model training 
-INFO:dataprep_ml-2398:Finished statistical analysis
+INFO:dataprep_ml-2576:Finished statistical analysis

@@ -388,7 +388,7 @@

Initial model training 
-INFO:dataprep_ml-2398:[Learn phase 1/8] - Statistical analysis
+INFO:dataprep_ml-2576:[Learn phase 1/8] - Statistical analysis

@@ -396,7 +396,7 @@

Initial model training 
-INFO:dataprep_ml-2398:Starting statistical analysis
+INFO:dataprep_ml-2576:Starting statistical analysis

@@ -404,7 +404,7 @@

Initial model training 
-INFO:dataprep_ml-2398:Finished statistical analysis
+INFO:dataprep_ml-2576:Finished statistical analysis

@@ -412,7 +412,7 @@

Initial model training 
-DEBUG:lightwood-2398: `analyze_data` runtime: 0.02 seconds
+DEBUG:lightwood-2576: `analyze_data` runtime: 0.03 seconds

@@ -420,7 +420,7 @@

Initial model training 
-INFO:dataprep_ml-2398:[Learn phase 2/8] - Data preprocessing
+INFO:dataprep_ml-2576:[Learn phase 2/8] - Data preprocessing

@@ -428,7 +428,7 @@

Initial model training 
-INFO:dataprep_ml-2398:Cleaning the data
+INFO:dataprep_ml-2576:Cleaning the data

@@ -436,7 +436,7 @@

Initial model training 
-DEBUG:lightwood-2398: `preprocess` runtime: 0.01 seconds
+DEBUG:lightwood-2576: `preprocess` runtime: 0.01 seconds

@@ -444,7 +444,7 @@

Initial model training 
-INFO:dataprep_ml-2398:[Learn phase 3/8] - Data splitting
+INFO:dataprep_ml-2576:[Learn phase 3/8] - Data splitting

@@ -452,7 +452,7 @@

Initial model training 
-INFO:dataprep_ml-2398:Splitting the data into train/test
+INFO:dataprep_ml-2576:Splitting the data into train/test

@@ -460,7 +460,7 @@

Initial model training 
-DEBUG:lightwood-2398: `split` runtime: 0.0 seconds
+DEBUG:lightwood-2576: `split` runtime: 0.0 seconds

@@ -468,7 +468,7 @@

Initial model training 
-INFO:dataprep_ml-2398:[Learn phase 4/8] - Preparing encoders
+INFO:dataprep_ml-2576:[Learn phase 4/8] - Preparing encoders

@@ -476,7 +476,7 @@

Initial model training 
-DEBUG:dataprep_ml-2398:Preparing sequentially...
+DEBUG:dataprep_ml-2576:Preparing sequentially...

@@ -484,7 +484,7 @@

Initial model training 
-DEBUG:dataprep_ml-2398:Preparing encoder for id...
+DEBUG:dataprep_ml-2576:Preparing encoder for id...

@@ -492,7 +492,7 @@

Initial model training 
-DEBUG:dataprep_ml-2398:Preparing encoder for cement...
+DEBUG:dataprep_ml-2576:Preparing encoder for cement...

@@ -500,7 +500,7 @@

Initial model training 
-DEBUG:dataprep_ml-2398:Preparing encoder for slag...
+DEBUG:dataprep_ml-2576:Preparing encoder for slag...

@@ -508,7 +508,7 @@

Initial model training 
-DEBUG:dataprep_ml-2398:Preparing encoder for flyAsh...
+DEBUG:dataprep_ml-2576:Preparing encoder for flyAsh...

@@ -516,7 +516,7 @@

Initial model training 
-DEBUG:dataprep_ml-2398:Preparing encoder for water...
+DEBUG:dataprep_ml-2576:Preparing encoder for water...

@@ -524,7 +524,7 @@

Initial model training 
-DEBUG:dataprep_ml-2398:Preparing encoder for superPlasticizer...
+DEBUG:dataprep_ml-2576:Preparing encoder for superPlasticizer...

@@ -532,7 +532,7 @@

Initial model training 
-DEBUG:dataprep_ml-2398:Preparing encoder for coarseAggregate...
+DEBUG:dataprep_ml-2576:Preparing encoder for coarseAggregate...

@@ -540,7 +540,7 @@

Initial model training 
-DEBUG:dataprep_ml-2398:Preparing encoder for fineAggregate...
+DEBUG:dataprep_ml-2576:Preparing encoder for fineAggregate...

@@ -548,7 +548,7 @@

Initial model training 
-DEBUG:dataprep_ml-2398:Preparing encoder for age...
+DEBUG:dataprep_ml-2576:Preparing encoder for age...

@@ -556,7 +556,7 @@

Initial model training 
-DEBUG:lightwood-2398: `prepare` runtime: 0.01 seconds
+DEBUG:lightwood-2576: `prepare` runtime: 0.01 seconds

@@ -564,7 +564,7 @@

Initial model training 
-INFO:dataprep_ml-2398:[Learn phase 5/8] - Feature generation
+INFO:dataprep_ml-2576:[Learn phase 5/8] - Feature generation

@@ -572,7 +572,7 @@

Initial model training 
-INFO:dataprep_ml-2398:Featurizing the data
+INFO:dataprep_ml-2576:Featurizing the data

@@ -580,7 +580,7 @@

Initial model training 
-DEBUG:lightwood-2398: `featurize` runtime: 0.06 seconds
+DEBUG:lightwood-2576: `featurize` runtime: 0.06 seconds

@@ -588,7 +588,7 @@

Initial model training 
-INFO:dataprep_ml-2398:[Learn phase 6/8] - Mixer training
+INFO:dataprep_ml-2576:[Learn phase 6/8] - Mixer training

@@ -596,7 +596,7 @@

Initial model training 
-INFO:dataprep_ml-2398:Training the mixers
+INFO:dataprep_ml-2576:Training the mixers

@@ -618,7 +618,7 @@

Initial model trainingINFO:lightwood-2398:Loss of 39.99637508392334 with learning rate 0.0001 +INFO:lightwood-2576:Loss of 39.99637508392334 with learning rate 0.0001

@@ -626,7 +626,7 @@

Initial model training 
-INFO:lightwood-2398:Loss of 21.826460361480713 with learning rate 0.0005
+INFO:lightwood-2576:Loss of 21.826460361480713 with learning rate 0.0005

@@ -634,7 +634,7 @@

Initial model training 
-INFO:lightwood-2398:Loss of 15.12899512052536 with learning rate 0.001
+INFO:lightwood-2576:Loss of 15.12899512052536 with learning rate 0.001

@@ -642,7 +642,7 @@

Initial model training 
-INFO:lightwood-2398:Loss of 15.062753021717072 with learning rate 0.002
+INFO:lightwood-2576:Loss of 15.062753021717072 with learning rate 0.002

@@ -650,7 +650,7 @@

Initial model training 
-INFO:lightwood-2398:Loss of 26.490495562553406 with learning rate 0.003
+INFO:lightwood-2576:Loss of 26.490495562553406 with learning rate 0.003

@@ -658,7 +658,7 @@

Initial model training 
-INFO:lightwood-2398:Loss of 33.6572003364563 with learning rate 0.005
+INFO:lightwood-2576:Loss of 33.6572003364563 with learning rate 0.005

@@ -666,7 +666,7 @@

Initial model training 
-INFO:lightwood-2398:Loss of 303.60721158981323 with learning rate 0.01
+INFO:lightwood-2576:Loss of 303.60721158981323 with learning rate 0.01

@@ -674,7 +674,7 @@

Initial model training 
-INFO:lightwood-2398:Loss of nan with learning rate 0.05
+INFO:lightwood-2576:Loss of nan with learning rate 0.05

@@ -682,7 +682,7 @@

Initial model training 
-INFO:lightwood-2398:Found learning rate of: 0.002
+INFO:lightwood-2576:Found learning rate of: 0.002

@@ -692,7 +692,7 @@

Initial model training
 /opt/hostedtoolcache/Python/3.9.18/x64/lib/python3.9/site-packages/torch/cuda/amp/grad_scaler.py:126: UserWarning: torch.cuda.amp.GradScaler is enabled, but CUDA is not available.  Disabling.
   warnings.warn(
-INFO:lightwood-2398:Loss @ epoch 1: 0.11838734149932861
+INFO:lightwood-2576:Loss @ epoch 1: 0.11838734149932861

@@ -700,7 +700,7 @@

Initial model training 
-INFO:lightwood-2398:Loss @ epoch 2: 0.4641949534416199
+INFO:lightwood-2576:Loss @ epoch 2: 0.4641949534416199

@@ -708,7 +708,7 @@

Initial model training 
-INFO:lightwood-2398:Loss @ epoch 3: 0.3976145386695862
+INFO:lightwood-2576:Loss @ epoch 3: 0.3976145386695862

@@ -716,7 +716,7 @@

Initial model training 
-INFO:lightwood-2398:Loss @ epoch 4: 0.3706841468811035
+INFO:lightwood-2576:Loss @ epoch 4: 0.3706841468811035

@@ -724,7 +724,7 @@

Initial model training 
-INFO:lightwood-2398:Loss @ epoch 5: 0.2367912232875824
+INFO:lightwood-2576:Loss @ epoch 5: 0.2367912232875824

@@ -732,7 +732,7 @@

Initial model training 
-INFO:lightwood-2398:Loss @ epoch 6: 0.22560915350914001
+INFO:lightwood-2576:Loss @ epoch 6: 0.22560915350914001

@@ -740,7 +740,7 @@

Initial model training 
-INFO:lightwood-2398:Loss @ epoch 7: 0.12089195847511292
+INFO:lightwood-2576:Loss @ epoch 7: 0.12089195847511292

@@ -748,7 +748,7 @@

Initial model training 
-DEBUG:lightwood-2398: `fit_mixer` runtime: 0.58 seconds
+DEBUG:lightwood-2576: `fit_mixer` runtime: 0.64 seconds

@@ -756,7 +756,7 @@

Initial model training 
-INFO:dataprep_ml-2398:Ensembling the mixer
+INFO:dataprep_ml-2576:Ensembling the mixer

@@ -764,7 +764,7 @@

Initial model training 
-INFO:lightwood-2398:Mixer: Neural got accuracy: 0.238
+INFO:lightwood-2576:Mixer: Neural got accuracy: 0.238

@@ -772,7 +772,7 @@

Initial model training 
-INFO:lightwood-2398:Picked best mixer: Neural
+INFO:lightwood-2576:Picked best mixer: Neural

@@ -780,7 +780,7 @@

Initial model training 
-DEBUG:lightwood-2398: `fit` runtime: 0.58 seconds
+DEBUG:lightwood-2576: `fit` runtime: 0.65 seconds

@@ -788,7 +788,7 @@

Initial model training 
-INFO:dataprep_ml-2398:[Learn phase 7/8] - Ensemble analysis
+INFO:dataprep_ml-2576:[Learn phase 7/8] - Ensemble analysis

@@ -796,7 +796,7 @@

Initial model training 
-INFO:dataprep_ml-2398:Analyzing the ensemble of mixers
+INFO:dataprep_ml-2576:Analyzing the ensemble of mixers

@@ -804,7 +804,7 @@

Initial model training 
-INFO:lightwood-2398:The block ICP is now running its analyze() method
+INFO:lightwood-2576:The block ICP is now running its analyze() method

@@ -812,7 +812,7 @@

Initial model training 
-INFO:lightwood-2398:The block ConfStats is now running its analyze() method
+INFO:lightwood-2576:The block ConfStats is now running its analyze() method

@@ -820,7 +820,7 @@

Initial model training 
-INFO:lightwood-2398:The block AccStats is now running its analyze() method
+INFO:lightwood-2576:The block AccStats is now running its analyze() method

@@ -828,7 +828,7 @@

Initial model training 
-INFO:lightwood-2398:The block PermutationFeatureImportance is now running its analyze() method
+INFO:lightwood-2576:The block PermutationFeatureImportance is now running its analyze() method

@@ -836,7 +836,7 @@

Initial model training 
-INFO:lightwood-2398:[PFI] Using a random sample (1000 rows out of 10).
+INFO:lightwood-2576:[PFI] Using a random sample (1000 rows out of 10).

@@ -844,7 +844,7 @@

Initial model training 
-INFO:lightwood-2398:[PFI] Set to consider first 10 columns out of 9: ['id', 'cement', 'slag', 'flyAsh', 'water', 'superPlasticizer', 'coarseAggregate', 'fineAggregate', 'age'].
+INFO:lightwood-2576:[PFI] Set to consider first 10 columns out of 9: ['id', 'cement', 'slag', 'flyAsh', 'water', 'superPlasticizer', 'coarseAggregate', 'fineAggregate', 'age'].

@@ -852,7 +852,7 @@

Initial model training 
-DEBUG:lightwood-2398: `analyze_ensemble` runtime: 0.15 seconds
+DEBUG:lightwood-2576: `analyze_ensemble` runtime: 0.16 seconds

@@ -860,7 +860,7 @@

Initial model training 
-INFO:dataprep_ml-2398:[Learn phase 8/8] - Adjustment on validation requested
+INFO:dataprep_ml-2576:[Learn phase 8/8] - Adjustment on validation requested

@@ -868,7 +868,7 @@

Initial model training 
-INFO:dataprep_ml-2398:Updating the mixers
+INFO:dataprep_ml-2576:Updating the mixers

@@ -878,6 +878,7 @@

Initial model training
 /opt/hostedtoolcache/Python/3.9.18/x64/lib/python3.9/site-packages/torch/cuda/amp/grad_scaler.py:126: UserWarning: torch.cuda.amp.GradScaler is enabled, but CUDA is not available.  Disabling.
   warnings.warn(
+INFO:lightwood-2576:Loss @ epoch 1: 0.1678172747294108

@@ -885,15 +886,7 @@

Initial model training 
-INFO:lightwood-2398:Loss @ epoch 1: 0.1678172747294108
-
-

-
-
-
-
-
-DEBUG:lightwood-2398: `adjust` runtime: 0.03 seconds
+DEBUG:lightwood-2576: `adjust` runtime: 0.03 seconds
@@ -901,7 +894,7 @@

Initial model training 
-DEBUG:lightwood-2398: `learn` runtime: 0.86 seconds
+DEBUG:lightwood-2576: `learn` runtime: 0.95 seconds

@@ -919,7 +912,7 @@

Initial model training 
-INFO:dataprep_ml-2398:[Predict phase 1/4] - Data preprocessing
+INFO:dataprep_ml-2576:[Predict phase 1/4] - Data preprocessing

@@ -927,7 +920,7 @@

Initial model training 
-INFO:dataprep_ml-2398:Cleaning the data
+INFO:dataprep_ml-2576:Cleaning the data

@@ -935,7 +928,7 @@

Initial model training 
-DEBUG:lightwood-2398: `preprocess` runtime: 0.01 seconds
+DEBUG:lightwood-2576: `preprocess` runtime: 0.01 seconds

@@ -943,7 +936,7 @@

Initial model training 
-INFO:dataprep_ml-2398:[Predict phase 2/4] - Feature generation
+INFO:dataprep_ml-2576:[Predict phase 2/4] - Feature generation

@@ -951,7 +944,7 @@

Initial model training 
-INFO:dataprep_ml-2398:Featurizing the data
+INFO:dataprep_ml-2576:Featurizing the data

@@ -959,7 +952,7 @@

Initial model training 
-DEBUG:lightwood-2398: `featurize` runtime: 0.03 seconds
+DEBUG:lightwood-2576: `featurize` runtime: 0.03 seconds

@@ -967,7 +960,7 @@

Initial model training 
-INFO:dataprep_ml-2398:[Predict phase 3/4] - Calling ensemble
+INFO:dataprep_ml-2576:[Predict phase 3/4] - Calling ensemble

@@ -975,7 +968,7 @@

Initial model training 
-DEBUG:lightwood-2398: `_timed_call` runtime: 0.03 seconds
+DEBUG:lightwood-2576: `_timed_call` runtime: 0.03 seconds

@@ -983,7 +976,7 @@

Initial model training 
-INFO:dataprep_ml-2398:[Predict phase 4/4] - Analyzing output
+INFO:dataprep_ml-2576:[Predict phase 4/4] - Analyzing output

@@ -991,7 +984,7 @@

Initial model training 
-INFO:lightwood-2398:The block ICP is now running its explain() method
+INFO:lightwood-2576:The block ICP is now running its explain() method

@@ -999,7 +992,7 @@

Initial model training 
-INFO:lightwood-2398:The block ConfStats is now running its explain() method
+INFO:lightwood-2576:The block ConfStats is now running its explain() method

@@ -1007,7 +1000,7 @@

Initial model training 
-INFO:lightwood-2398:ConfStats.explain() has not been implemented, no modifications will be done to the data insights.
+INFO:lightwood-2576:ConfStats.explain() has not been implemented, no modifications will be done to the data insights.

@@ -1015,7 +1008,7 @@

Initial model training 
-INFO:lightwood-2398:The block AccStats is now running its explain() method
+INFO:lightwood-2576:The block AccStats is now running its explain() method

@@ -1023,7 +1016,7 @@

Initial model training 
-INFO:lightwood-2398:AccStats.explain() has not been implemented, no modifications will be done to the data insights.
+INFO:lightwood-2576:AccStats.explain() has not been implemented, no modifications will be done to the data insights.

@@ -1031,7 +1024,7 @@

Initial model training 
-INFO:lightwood-2398:The block PermutationFeatureImportance is now running its explain() method
+INFO:lightwood-2576:The block PermutationFeatureImportance is now running its explain() method

@@ -1039,7 +1032,7 @@

Initial model training 
-INFO:lightwood-2398:PermutationFeatureImportance.explain() has not been implemented, no modifications will be done to the data insights.
+INFO:lightwood-2576:PermutationFeatureImportance.explain() has not been implemented, no modifications will be done to the data insights.

@@ -1047,7 +1040,7 @@

Initial model training 
-DEBUG:lightwood-2398: `explain` runtime: 0.05 seconds
+DEBUG:lightwood-2576: `explain` runtime: 0.05 seconds

@@ -1055,7 +1048,7 @@

Initial model training 
-DEBUG:lightwood-2398: `predict` runtime: 0.13 seconds
+DEBUG:lightwood-2576: `predict` runtime: 0.13 seconds

@@ -1217,7 +1210,7 @@

PredictorInterf 

-INFO:dataprep_ml-2398:Cleaning the data
+INFO:dataprep_ml-2576:Cleaning the data
@@ -1225,7 +1218,7 @@

PredictorInterf 

-DEBUG:lightwood-2398: `preprocess` runtime: 0.02 seconds
+DEBUG:lightwood-2576: `preprocess` runtime: 0.02 seconds
@@ -1233,7 +1226,7 @@

PredictorInterf 

-INFO:dataprep_ml-2398:Cleaning the data
+INFO:dataprep_ml-2576:Cleaning the data
@@ -1241,7 +1234,7 @@

PredictorInterf 

-DEBUG:lightwood-2398: `preprocess` runtime: 0.01 seconds
+DEBUG:lightwood-2576: `preprocess` runtime: 0.01 seconds
@@ -1249,7 +1242,7 @@

PredictorInterf 

-INFO:dataprep_ml-2398:Updating the mixers
+INFO:dataprep_ml-2576:Updating the mixers
@@ -1259,7 +1252,14 @@

PredictorInterf 
 /opt/hostedtoolcache/Python/3.9.18/x64/lib/python3.9/site-packages/torch/cuda/amp/grad_scaler.py:126: UserWarning: torch.cuda.amp.GradScaler is enabled, but CUDA is not available.  Disabling.
   warnings.warn(
-INFO:lightwood-2398:Loss @ epoch 1: 0.10915952424208324
+

+ +
+
+
+
+
+INFO:lightwood-2576:Loss @ epoch 1: 0.10915952424208324
@@ -1267,7 +1267,7 @@

PredictorInterf 

-DEBUG:lightwood-2398: `adjust` runtime: 0.1 seconds
+DEBUG:lightwood-2576: `adjust` runtime: 0.12 seconds
@@ -1284,7 +1284,7 @@

PredictorInterf 

-INFO:dataprep_ml-2398:[Predict phase 1/4] - Data preprocessing
+INFO:dataprep_ml-2576:[Predict phase 1/4] - Data preprocessing
@@ -1292,7 +1292,7 @@

PredictorInterf 

-INFO:dataprep_ml-2398:Cleaning the data
+INFO:dataprep_ml-2576:Cleaning the data
@@ -1300,7 +1300,7 @@

PredictorInterf 

-DEBUG:lightwood-2398: `preprocess` runtime: 0.01 seconds
+DEBUG:lightwood-2576: `preprocess` runtime: 0.01 seconds
@@ -1308,7 +1308,7 @@

PredictorInterf 

-INFO:dataprep_ml-2398:[Predict phase 2/4] - Feature generation
+INFO:dataprep_ml-2576:[Predict phase 2/4] - Feature generation
@@ -1316,7 +1316,7 @@

PredictorInterf 

-INFO:dataprep_ml-2398:Featurizing the data
+INFO:dataprep_ml-2576:Featurizing the data
@@ -1324,7 +1324,7 @@

PredictorInterf 

-DEBUG:lightwood-2398: `featurize` runtime: 0.03 seconds
+DEBUG:lightwood-2576: `featurize` runtime: 0.04 seconds
@@ -1332,7 +1332,7 @@

PredictorInterf 

-INFO:dataprep_ml-2398:[Predict phase 3/4] - Calling ensemble
+INFO:dataprep_ml-2576:[Predict phase 3/4] - Calling ensemble
@@ -1340,7 +1340,7 @@

PredictorInterf 

-DEBUG:lightwood-2398: `_timed_call` runtime: 0.03 seconds
+DEBUG:lightwood-2576: `_timed_call` runtime: 0.03 seconds
@@ -1348,7 +1348,7 @@

PredictorInterf 

-INFO:dataprep_ml-2398:[Predict phase 4/4] - Analyzing output
+INFO:dataprep_ml-2576:[Predict phase 4/4] - Analyzing output
@@ -1356,7 +1356,7 @@

PredictorInterf 

-INFO:lightwood-2398:The block ICP is now running its explain() method
+INFO:lightwood-2576:The block ICP is now running its explain() method
@@ -1364,7 +1364,7 @@

PredictorInterf 

-INFO:lightwood-2398:The block ConfStats is now running its explain() method
+INFO:lightwood-2576:The block ConfStats is now running its explain() method
@@ -1372,7 +1372,7 @@

PredictorInterf 

-INFO:lightwood-2398:ConfStats.explain() has not been implemented, no modifications will be done to the data insights.
+INFO:lightwood-2576:ConfStats.explain() has not been implemented, no modifications will be done to the data insights.
@@ -1380,7 +1380,7 @@

PredictorInterf 

-INFO:lightwood-2398:The block AccStats is now running its explain() method
+INFO:lightwood-2576:The block AccStats is now running its explain() method
@@ -1388,7 +1388,7 @@

PredictorInterf 

-INFO:lightwood-2398:AccStats.explain() has not been implemented, no modifications will be done to the data insights.
+INFO:lightwood-2576:AccStats.explain() has not been implemented, no modifications will be done to the data insights.
@@ -1396,7 +1396,7 @@

PredictorInterf 

-INFO:lightwood-2398:The block PermutationFeatureImportance is now running its explain() method
+INFO:lightwood-2576:The block PermutationFeatureImportance is now running its explain() method
@@ -1404,7 +1404,7 @@

PredictorInterf 

-INFO:lightwood-2398:PermutationFeatureImportance.explain() has not been implemented, no modifications will be done to the data insights.
+INFO:lightwood-2576:PermutationFeatureImportance.explain() has not been implemented, no modifications will be done to the data insights.
@@ -1412,7 +1412,7 @@

PredictorInterf 

-DEBUG:lightwood-2398: `explain` runtime: 0.05 seconds
+DEBUG:lightwood-2576: `explain` runtime: 0.05 seconds
@@ -1420,7 +1420,7 @@

PredictorInterf 

-DEBUG:lightwood-2398: `predict` runtime: 0.12 seconds
+DEBUG:lightwood-2576: `predict` runtime: 0.13 seconds
diff --git a/tutorials/tutorial_update_models/tutorial_update_models.ipynb b/tutorials/tutorial_update_models/tutorial_update_models.ipynb index b74cfc2b1..6927fe7f8 100644 --- a/tutorials/tutorial_update_models/tutorial_update_models.ipynb +++ b/tutorials/tutorial_update_models/tutorial_update_models.ipynb @@ -21,10 +21,10 @@ "execution_count": 1, "metadata": { "execution": { - "iopub.execute_input": "2024-03-18T10:31:23.547591Z", - "iopub.status.busy": "2024-03-18T10:31:23.546962Z", - "iopub.status.idle": "2024-03-18T10:31:26.071211Z", - "shell.execute_reply": "2024-03-18T10:31:26.070500Z" + "iopub.execute_input": "2024-03-18T12:12:57.043269Z", + "iopub.status.busy": "2024-03-18T12:12:57.043006Z", + "iopub.status.idle": "2024-03-18T12:12:59.864287Z", + "shell.execute_reply": "2024-03-18T12:12:59.863434Z" } }, "outputs": [ @@ -32,14 +32,14 @@ "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2398:No torchvision detected, image helpers not supported.\u001b[0m\n" + "\u001b[32mINFO:lightwood-2576:No torchvision detected, image helpers not supported.\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2398:No torchvision/pillow detected, image encoder not supported\u001b[0m\n" + "\u001b[32mINFO:lightwood-2576:No torchvision/pillow detected, image encoder not supported\u001b[0m\n" ] } ], @@ -53,10 +53,10 @@ "execution_count": 2, "metadata": { "execution": { - "iopub.execute_input": "2024-03-18T10:31:26.074489Z", - "iopub.status.busy": "2024-03-18T10:31:26.074012Z", - "iopub.status.idle": "2024-03-18T10:31:26.190366Z", - "shell.execute_reply": "2024-03-18T10:31:26.189797Z" + "iopub.execute_input": "2024-03-18T12:12:59.867639Z", + "iopub.status.busy": "2024-03-18T12:12:59.867254Z", + "iopub.status.idle": "2024-03-18T12:13:00.088594Z", + "shell.execute_reply": "2024-03-18T12:13:00.087866Z" } }, "outputs": [ @@ -98,10 +98,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2024-03-18T10:31:26.192914Z", - "iopub.status.busy": "2024-03-18T10:31:26.192528Z", - "iopub.status.idle": "2024-03-18T10:31:27.603098Z", - "shell.execute_reply": "2024-03-18T10:31:27.602461Z" + "iopub.execute_input": "2024-03-18T12:13:00.091440Z", + "iopub.status.busy": "2024-03-18T12:13:00.091021Z", + "iopub.status.idle": "2024-03-18T12:13:01.674571Z", + "shell.execute_reply": "2024-03-18T12:13:01.673937Z" }, "scrolled": true }, @@ -110,364 +110,364 @@ "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2398:Analyzing a sample of 979\u001b[0m\n" + "\u001b[32mINFO:type_infer-2576:Analyzing a sample of 979\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2398:from a total population of 1030, this is equivalent to 95.0% of your data.\u001b[0m\n" + "\u001b[32mINFO:type_infer-2576:from a total population of 1030, this is equivalent to 95.0% of your data.\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2398:Using 3 processes to deduct types.\u001b[0m\n" + "\u001b[32mINFO:type_infer-2576:Using 3 processes to deduct types.\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2398:Infering type for: slag\u001b[0m\n" + "\u001b[32mINFO:type_infer-2576:Infering type for: cement\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2398:Infering type for: cement\u001b[0m\n" + "\u001b[32mINFO:type_infer-2576:Infering type for: slag\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2398:Column cement has data type float\u001b[0m\n" + "\u001b[32mINFO:type_infer-2576:Column slag has data type float\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2398:Column slag has data type float\u001b[0m\n" + "\u001b[32mINFO:type_infer-2576:Column cement has data type float\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2398:Infering type for: flyAsh\u001b[0m\n" + "\u001b[32mINFO:type_infer-2576:Infering type for: flyAsh\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2398:Infering type for: water\u001b[0m\n" + "\u001b[32mINFO:type_infer-2576:Infering type for: water\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2398:Column flyAsh has data type float\u001b[0m\n" + "\u001b[32mINFO:type_infer-2576:Column flyAsh has data type float\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2398:Infering type for: superPlasticizer\u001b[0m\n" + "\u001b[32mINFO:type_infer-2576:Column water has data type float\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2398:Column water has data type float\u001b[0m\n" + "\u001b[32mINFO:type_infer-2576:Infering type for: id\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2398:Infering type for: coarseAggregate\u001b[0m\n" + "\u001b[32mINFO:type_infer-2576:Infering type for: coarseAggregate\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2398:Column superPlasticizer has data type float\u001b[0m\n" + "\u001b[32mINFO:type_infer-2576:Infering type for: superPlasticizer\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2398:Infering type for: fineAggregate\u001b[0m\n" + "\u001b[32mINFO:type_infer-2576:Column id has data type integer\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2398:Column coarseAggregate has data type float\u001b[0m\n" + "\u001b[32mINFO:type_infer-2576:Column coarseAggregate has data type float\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2398:Column fineAggregate has data type float\u001b[0m\n" + "\u001b[32mINFO:type_infer-2576:Infering type for: age\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2398:Infering type for: age\u001b[0m\n" + "\u001b[32mINFO:type_infer-2576:Infering type for: fineAggregate\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2398:Infering type for: concrete_strength\u001b[0m\n" + "\u001b[32mINFO:type_infer-2576:Column superPlasticizer has data type float\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2398:Infering type for: id\u001b[0m\n" + "\u001b[32mINFO:type_infer-2576:Infering type for: concrete_strength\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2398:Column age has data type integer\u001b[0m\n" + "\u001b[32mINFO:type_infer-2576:Column age has data type integer\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2398:Column concrete_strength has data type float\u001b[0m\n" + "\u001b[32mINFO:type_infer-2576:Column fineAggregate has data type float\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2398:Column id has data type integer\u001b[0m\n" + "\u001b[32mINFO:type_infer-2576:Column concrete_strength has data type float\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2398:Starting statistical analysis\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2576:Starting statistical analysis\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2398:Finished statistical analysis\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2576:Finished statistical analysis\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2398:[Learn phase 1/8] - Statistical analysis\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2576:[Learn phase 1/8] - Statistical analysis\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2398:Starting statistical analysis\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2576:Starting statistical analysis\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2398:Finished statistical analysis\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2576:Finished statistical analysis\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:lightwood-2398: `analyze_data` runtime: 0.02 seconds\u001b[0m\n" + "\u001b[37mDEBUG:lightwood-2576: `analyze_data` runtime: 0.03 seconds\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2398:[Learn phase 2/8] - Data preprocessing\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2576:[Learn phase 2/8] - Data preprocessing\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2398:Cleaning the data\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2576:Cleaning the data\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:lightwood-2398: `preprocess` runtime: 0.01 seconds\u001b[0m\n" + "\u001b[37mDEBUG:lightwood-2576: `preprocess` runtime: 0.01 seconds\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2398:[Learn phase 3/8] - Data splitting\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2576:[Learn phase 3/8] - Data splitting\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2398:Splitting the data into train/test\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2576:Splitting the data into train/test\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:lightwood-2398: `split` runtime: 0.0 seconds\u001b[0m\n" + "\u001b[37mDEBUG:lightwood-2576: `split` runtime: 0.0 seconds\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2398:[Learn phase 4/8] - Preparing encoders\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2576:[Learn phase 4/8] - Preparing encoders\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:dataprep_ml-2398:Preparing sequentially...\u001b[0m\n" + "\u001b[37mDEBUG:dataprep_ml-2576:Preparing sequentially...\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:dataprep_ml-2398:Preparing encoder for id...\u001b[0m\n" + "\u001b[37mDEBUG:dataprep_ml-2576:Preparing encoder for id...\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:dataprep_ml-2398:Preparing encoder for cement...\u001b[0m\n" + "\u001b[37mDEBUG:dataprep_ml-2576:Preparing encoder for cement...\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:dataprep_ml-2398:Preparing encoder for slag...\u001b[0m\n" + "\u001b[37mDEBUG:dataprep_ml-2576:Preparing encoder for slag...\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:dataprep_ml-2398:Preparing encoder for flyAsh...\u001b[0m\n" + "\u001b[37mDEBUG:dataprep_ml-2576:Preparing encoder for flyAsh...\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:dataprep_ml-2398:Preparing encoder for water...\u001b[0m\n" + "\u001b[37mDEBUG:dataprep_ml-2576:Preparing encoder for water...\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:dataprep_ml-2398:Preparing encoder for superPlasticizer...\u001b[0m\n" + "\u001b[37mDEBUG:dataprep_ml-2576:Preparing encoder for superPlasticizer...\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:dataprep_ml-2398:Preparing encoder for coarseAggregate...\u001b[0m\n" + "\u001b[37mDEBUG:dataprep_ml-2576:Preparing encoder for coarseAggregate...\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:dataprep_ml-2398:Preparing encoder for fineAggregate...\u001b[0m\n" + "\u001b[37mDEBUG:dataprep_ml-2576:Preparing encoder for fineAggregate...\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:dataprep_ml-2398:Preparing encoder for age...\u001b[0m\n" + "\u001b[37mDEBUG:dataprep_ml-2576:Preparing encoder for age...\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:lightwood-2398: `prepare` runtime: 0.01 seconds\u001b[0m\n" + "\u001b[37mDEBUG:lightwood-2576: `prepare` runtime: 0.01 seconds\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2398:[Learn phase 5/8] - Feature generation\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2576:[Learn phase 5/8] - Feature generation\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2398:Featurizing the data\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2576:Featurizing the data\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:lightwood-2398: `featurize` runtime: 0.06 seconds\u001b[0m\n" + "\u001b[37mDEBUG:lightwood-2576: `featurize` runtime: 0.06 seconds\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2398:[Learn phase 6/8] - Mixer training\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2576:[Learn phase 6/8] - Mixer training\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2398:Training the mixers\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2576:Training the mixers\u001b[0m\n" ] }, { @@ -487,63 +487,63 @@ "Consider using one of the following signatures instead:\n", "\taddcmul_(Tensor tensor1, Tensor tensor2, *, Number value) (Triggered internally at ../torch/csrc/utils/python_arg_parser.cpp:1630.)\n", " exp_avg_sq.mul_(beta2).addcmul_(1 - beta2, grad, grad)\n", - "\u001b[32mINFO:lightwood-2398:Loss of 39.99637508392334 with learning rate 0.0001\u001b[0m\n" + "\u001b[32mINFO:lightwood-2576:Loss of 39.99637508392334 with learning rate 0.0001\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2398:Loss of 21.826460361480713 with learning rate 0.0005\u001b[0m\n" + "\u001b[32mINFO:lightwood-2576:Loss of 21.826460361480713 with learning rate 0.0005\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2398:Loss of 15.12899512052536 with learning rate 0.001\u001b[0m\n" + "\u001b[32mINFO:lightwood-2576:Loss of 15.12899512052536 with learning rate 0.001\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2398:Loss of 15.062753021717072 with learning rate 0.002\u001b[0m\n" + "\u001b[32mINFO:lightwood-2576:Loss of 15.062753021717072 with learning rate 0.002\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2398:Loss of 26.490495562553406 with learning rate 0.003\u001b[0m\n" + "\u001b[32mINFO:lightwood-2576:Loss of 26.490495562553406 with learning rate 0.003\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2398:Loss of 33.6572003364563 with learning rate 0.005\u001b[0m\n" + "\u001b[32mINFO:lightwood-2576:Loss of 33.6572003364563 with learning rate 0.005\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2398:Loss of 303.60721158981323 with learning rate 0.01\u001b[0m\n" + "\u001b[32mINFO:lightwood-2576:Loss of 303.60721158981323 with learning rate 0.01\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2398:Loss of nan with learning rate 0.05\u001b[0m\n" + "\u001b[32mINFO:lightwood-2576:Loss of nan with learning rate 0.05\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2398:Found learning rate of: 0.002\u001b[0m\n" + "\u001b[32mINFO:lightwood-2576:Found learning rate of: 0.002\u001b[0m\n" ] }, { @@ -552,161 +552,161 @@ "text": [ "/opt/hostedtoolcache/Python/3.9.18/x64/lib/python3.9/site-packages/torch/cuda/amp/grad_scaler.py:126: UserWarning: torch.cuda.amp.GradScaler is enabled, but CUDA is not available. Disabling.\n", " warnings.warn(\n", - "\u001b[32mINFO:lightwood-2398:Loss @ epoch 1: 0.11838734149932861\u001b[0m\n" + "\u001b[32mINFO:lightwood-2576:Loss @ epoch 1: 0.11838734149932861\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2398:Loss @ epoch 2: 0.4641949534416199\u001b[0m\n" + "\u001b[32mINFO:lightwood-2576:Loss @ epoch 2: 0.4641949534416199\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2398:Loss @ epoch 3: 0.3976145386695862\u001b[0m\n" + "\u001b[32mINFO:lightwood-2576:Loss @ epoch 3: 0.3976145386695862\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2398:Loss @ epoch 4: 0.3706841468811035\u001b[0m\n" + "\u001b[32mINFO:lightwood-2576:Loss @ epoch 4: 0.3706841468811035\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2398:Loss @ epoch 5: 0.2367912232875824\u001b[0m\n" + "\u001b[32mINFO:lightwood-2576:Loss @ epoch 5: 0.2367912232875824\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2398:Loss @ epoch 6: 0.22560915350914001\u001b[0m\n" + "\u001b[32mINFO:lightwood-2576:Loss @ epoch 6: 0.22560915350914001\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2398:Loss @ epoch 7: 0.12089195847511292\u001b[0m\n" + "\u001b[32mINFO:lightwood-2576:Loss @ epoch 7: 0.12089195847511292\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:lightwood-2398: `fit_mixer` runtime: 0.58 seconds\u001b[0m\n" + "\u001b[37mDEBUG:lightwood-2576: `fit_mixer` runtime: 0.64 seconds\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2398:Ensembling the mixer\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2576:Ensembling the mixer\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2398:Mixer: Neural got accuracy: 0.238\u001b[0m\n" + "\u001b[32mINFO:lightwood-2576:Mixer: Neural got accuracy: 0.238\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2398:Picked best mixer: Neural\u001b[0m\n" + "\u001b[32mINFO:lightwood-2576:Picked best mixer: Neural\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:lightwood-2398: `fit` runtime: 0.58 seconds\u001b[0m\n" + "\u001b[37mDEBUG:lightwood-2576: `fit` runtime: 0.65 seconds\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2398:[Learn phase 7/8] - Ensemble analysis\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2576:[Learn phase 7/8] - Ensemble analysis\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2398:Analyzing the ensemble of mixers\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2576:Analyzing the ensemble of mixers\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2398:The block ICP is now running its analyze() method\u001b[0m\n" + "\u001b[32mINFO:lightwood-2576:The block ICP is now running its analyze() method\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2398:The block ConfStats is now running its analyze() method\u001b[0m\n" + "\u001b[32mINFO:lightwood-2576:The block ConfStats is now running its analyze() method\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2398:The block AccStats is now running its analyze() method\u001b[0m\n" + "\u001b[32mINFO:lightwood-2576:The block AccStats is now running its analyze() method\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2398:The block PermutationFeatureImportance is now running its analyze() method\u001b[0m\n" + "\u001b[32mINFO:lightwood-2576:The block PermutationFeatureImportance is now running its analyze() method\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2398:[PFI] Using a random sample (1000 rows out of 10).\u001b[0m\n" + "\u001b[32mINFO:lightwood-2576:[PFI] Using a random sample (1000 rows out of 10).\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2398:[PFI] Set to consider first 10 columns out of 9: ['id', 'cement', 'slag', 'flyAsh', 'water', 'superPlasticizer', 'coarseAggregate', 'fineAggregate', 'age'].\u001b[0m\n" + "\u001b[32mINFO:lightwood-2576:[PFI] Set to consider first 10 columns out of 9: ['id', 'cement', 'slag', 'flyAsh', 'water', 'superPlasticizer', 'coarseAggregate', 'fineAggregate', 'age'].\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:lightwood-2398: `analyze_ensemble` runtime: 0.15 seconds\u001b[0m\n" + "\u001b[37mDEBUG:lightwood-2576: `analyze_ensemble` runtime: 0.16 seconds\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2398:[Learn phase 8/8] - Adjustment on validation requested\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2576:[Learn phase 8/8] - Adjustment on validation requested\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2398:Updating the mixers\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2576:Updating the mixers\u001b[0m\n" ] }, { @@ -714,28 +714,22 @@ "output_type": "stream", "text": [ "/opt/hostedtoolcache/Python/3.9.18/x64/lib/python3.9/site-packages/torch/cuda/amp/grad_scaler.py:126: UserWarning: torch.cuda.amp.GradScaler is enabled, but CUDA is not available. Disabling.\n", - " warnings.warn(\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\u001b[32mINFO:lightwood-2398:Loss @ epoch 1: 0.1678172747294108\u001b[0m\n" + " warnings.warn(\n", + "\u001b[32mINFO:lightwood-2576:Loss @ epoch 1: 0.1678172747294108\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:lightwood-2398: `adjust` runtime: 0.03 seconds\u001b[0m\n" + "\u001b[37mDEBUG:lightwood-2576: `adjust` runtime: 0.03 seconds\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:lightwood-2398: `learn` runtime: 0.86 seconds\u001b[0m\n" + "\u001b[37mDEBUG:lightwood-2576: `learn` runtime: 0.95 seconds\u001b[0m\n" ] } ], @@ -772,10 +766,10 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2024-03-18T10:31:27.606418Z", - "iopub.status.busy": "2024-03-18T10:31:27.605734Z", - "iopub.status.idle": "2024-03-18T10:31:27.746155Z", - "shell.execute_reply": "2024-03-18T10:31:27.745504Z" + "iopub.execute_input": "2024-03-18T12:13:01.677744Z", + "iopub.status.busy": "2024-03-18T12:13:01.677327Z", + "iopub.status.idle": "2024-03-18T12:13:01.826253Z", + "shell.execute_reply": "2024-03-18T12:13:01.825506Z" } }, "outputs": [ @@ -783,126 +777,126 @@ "name": "stderr", "output_type": "stream", "text": [ - 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"\u001b[37mDEBUG:lightwood-2398: `featurize` runtime: 0.03 seconds\u001b[0m\n" + "\u001b[37mDEBUG:lightwood-2576: `featurize` runtime: 0.03 seconds\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2398:[Predict phase 3/4] - Calling ensemble\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2576:[Predict phase 3/4] - Calling ensemble\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:lightwood-2398: `_timed_call` runtime: 0.03 seconds\u001b[0m\n" + "\u001b[37mDEBUG:lightwood-2576: `_timed_call` runtime: 0.03 seconds\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2398:[Predict phase 4/4] - Analyzing output\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2576:[Predict phase 4/4] - Analyzing output\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2398:The block ICP is now running its explain() method\u001b[0m\n" + "\u001b[32mINFO:lightwood-2576:The block ICP is now running its explain() method\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2398:The block ConfStats is now running its explain() method\u001b[0m\n" + "\u001b[32mINFO:lightwood-2576:The block ConfStats is now running its explain() method\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2398:ConfStats.explain() has not been implemented, no modifications will be done to the data insights.\u001b[0m\n" + "\u001b[32mINFO:lightwood-2576:ConfStats.explain() has not been implemented, no modifications will be done to the data insights.\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2398:The block AccStats is now running its explain() method\u001b[0m\n" + "\u001b[32mINFO:lightwood-2576:The block AccStats is now running its explain() method\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2398:AccStats.explain() has not been implemented, no modifications will be done to the data insights.\u001b[0m\n" + "\u001b[32mINFO:lightwood-2576:AccStats.explain() has not been implemented, no modifications will be done to the data insights.\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2398:The block PermutationFeatureImportance is now running its explain() method\u001b[0m\n" + "\u001b[32mINFO:lightwood-2576:The block PermutationFeatureImportance is now running its explain() method\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2398:PermutationFeatureImportance.explain() has not been implemented, no modifications will be done to the data insights.\u001b[0m\n" + "\u001b[32mINFO:lightwood-2576:PermutationFeatureImportance.explain() has not been implemented, no modifications will be done to the data insights.\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:lightwood-2398: `explain` runtime: 0.05 seconds\u001b[0m\n" + "\u001b[37mDEBUG:lightwood-2576: `explain` runtime: 0.05 seconds\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:lightwood-2398: `predict` runtime: 0.13 seconds\u001b[0m\n" + "\u001b[37mDEBUG:lightwood-2576: `predict` runtime: 0.13 seconds\u001b[0m\n" ] }, { @@ -1096,10 +1090,10 @@ "execution_count": 5, "metadata": { "execution": { - "iopub.execute_input": "2024-03-18T10:31:27.748907Z", - "iopub.status.busy": "2024-03-18T10:31:27.748459Z", - "iopub.status.idle": "2024-03-18T10:31:27.854192Z", - "shell.execute_reply": "2024-03-18T10:31:27.853569Z" + "iopub.execute_input": "2024-03-18T12:13:01.829389Z", + "iopub.status.busy": "2024-03-18T12:13:01.828926Z", + "iopub.status.idle": "2024-03-18T12:13:01.949500Z", + "shell.execute_reply": "2024-03-18T12:13:01.948852Z" } }, "outputs": [ @@ -1107,35 +1101,35 @@ "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2398:Cleaning the data\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2576:Cleaning the data\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:lightwood-2398: `preprocess` runtime: 0.02 seconds\u001b[0m\n" + "\u001b[37mDEBUG:lightwood-2576: `preprocess` runtime: 0.02 seconds\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2398:Cleaning the data\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2576:Cleaning the data\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:lightwood-2398: `preprocess` runtime: 0.01 seconds\u001b[0m\n" + "\u001b[37mDEBUG:lightwood-2576: `preprocess` runtime: 0.01 seconds\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2398:Updating the mixers\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2576:Updating the mixers\u001b[0m\n" ] }, { @@ -1143,15 +1137,21 @@ "output_type": "stream", "text": [ "/opt/hostedtoolcache/Python/3.9.18/x64/lib/python3.9/site-packages/torch/cuda/amp/grad_scaler.py:126: UserWarning: torch.cuda.amp.GradScaler is enabled, but CUDA is not available. Disabling.\n", - " warnings.warn(\n", - "\u001b[32mINFO:lightwood-2398:Loss @ epoch 1: 0.10915952424208324\u001b[0m\n" + " warnings.warn(\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[32mINFO:lightwood-2576:Loss @ epoch 1: 0.10915952424208324\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:lightwood-2398: `adjust` runtime: 0.1 seconds\u001b[0m\n" + "\u001b[37mDEBUG:lightwood-2576: `adjust` runtime: 0.12 seconds\u001b[0m\n" ] } ], @@ -1164,10 +1164,10 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2024-03-18T10:31:27.856839Z", - "iopub.status.busy": "2024-03-18T10:31:27.856418Z", - "iopub.status.idle": "2024-03-18T10:31:27.992119Z", - "shell.execute_reply": "2024-03-18T10:31:27.991478Z" + "iopub.execute_input": "2024-03-18T12:13:01.952937Z", + "iopub.status.busy": "2024-03-18T12:13:01.952459Z", + "iopub.status.idle": "2024-03-18T12:13:02.101103Z", + "shell.execute_reply": "2024-03-18T12:13:02.100367Z" } }, "outputs": [ @@ -1175,126 +1175,126 @@ "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2398:[Predict phase 1/4] - Data preprocessing\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2576:[Predict phase 1/4] - Data preprocessing\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2398:Cleaning the data\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2576:Cleaning the data\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:lightwood-2398: `preprocess` runtime: 0.01 seconds\u001b[0m\n" + "\u001b[37mDEBUG:lightwood-2576: `preprocess` runtime: 0.01 seconds\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2398:[Predict phase 2/4] - Feature generation\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2576:[Predict phase 2/4] - Feature generation\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2398:Featurizing the data\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2576:Featurizing the data\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:lightwood-2398: `featurize` runtime: 0.03 seconds\u001b[0m\n" + "\u001b[37mDEBUG:lightwood-2576: `featurize` runtime: 0.04 seconds\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2398:[Predict phase 3/4] - Calling ensemble\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2576:[Predict phase 3/4] - Calling ensemble\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:lightwood-2398: `_timed_call` runtime: 0.03 seconds\u001b[0m\n" + "\u001b[37mDEBUG:lightwood-2576: `_timed_call` runtime: 0.03 seconds\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2398:[Predict phase 4/4] - Analyzing output\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2576:[Predict phase 4/4] - Analyzing output\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2398:The block ICP is now running its explain() method\u001b[0m\n" + "\u001b[32mINFO:lightwood-2576:The block ICP is now running its explain() method\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2398:The block ConfStats is now running its explain() method\u001b[0m\n" + "\u001b[32mINFO:lightwood-2576:The block ConfStats is now running its explain() method\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2398:ConfStats.explain() has not been implemented, no modifications will be done to the data insights.\u001b[0m\n" + "\u001b[32mINFO:lightwood-2576:ConfStats.explain() has not been implemented, no modifications will be done to the data insights.\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2398:The block AccStats is now running its explain() method\u001b[0m\n" + "\u001b[32mINFO:lightwood-2576:The block AccStats is now running its explain() method\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2398:AccStats.explain() has not been implemented, no modifications will be done to the data insights.\u001b[0m\n" + "\u001b[32mINFO:lightwood-2576:AccStats.explain() has not been implemented, no modifications will be done to the data insights.\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2398:The block PermutationFeatureImportance is now running its explain() method\u001b[0m\n" + "\u001b[32mINFO:lightwood-2576:The block PermutationFeatureImportance is now running its explain() method\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2398:PermutationFeatureImportance.explain() has not been implemented, no modifications will be done to the data insights.\u001b[0m\n" + "\u001b[32mINFO:lightwood-2576:PermutationFeatureImportance.explain() has not been implemented, no modifications will be done to the data insights.\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:lightwood-2398: `explain` runtime: 0.05 seconds\u001b[0m\n" + "\u001b[37mDEBUG:lightwood-2576: `explain` runtime: 0.05 seconds\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:lightwood-2398: `predict` runtime: 0.12 seconds\u001b[0m\n" + "\u001b[37mDEBUG:lightwood-2576: `predict` runtime: 0.13 seconds\u001b[0m\n" ] }, { @@ -1458,10 +1458,10 @@ "execution_count": 7, "metadata": { "execution": { - "iopub.execute_input": "2024-03-18T10:31:27.994662Z", - "iopub.status.busy": "2024-03-18T10:31:27.994461Z", - "iopub.status.idle": "2024-03-18T10:31:28.000217Z", - "shell.execute_reply": "2024-03-18T10:31:27.999577Z" + "iopub.execute_input": "2024-03-18T12:13:02.104430Z", + "iopub.status.busy": "2024-03-18T12:13:02.103755Z", + "iopub.status.idle": "2024-03-18T12:13:02.110215Z", + "shell.execute_reply": "2024-03-18T12:13:02.109494Z" } }, "outputs": [