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Rebuilt the docs
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‎_images/tutorials_custom_explainer_custom_explainer_20_0.png

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‎_sources/index.rst.txt

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@@ -19,7 +19,7 @@ Lightwood works with a variety of data types such as numbers, dates, categories,
1919

2020
Our JSON-AI syntax allows users to change any and all parts of the models Lightwood automatically generates. The syntax outlines the specifics details in each step of the modeling pipeline. Users may override default values (for example, changing the type of a column) or alternatively, entirely replace steps with their own methods (ex: use a random forest model for a predictor). Lightwood creates a "JSON-AI" object from this syntax which can then be used to automatically generate python code to represent your pipeline.
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For details as to how Lightwood works, check out the `Lightwood Philosophy <https://lightwood.io/lightwood_philosophy.html>`_ .
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For details as to how Lightwood works, check out the `Lightwood Philosophy <https://mindsdb.github.io/lightwood/lightwood_philosophy.html>`_ .
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Quick Guide
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=======================
@@ -124,7 +124,7 @@ BYOM: Bring your own models
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Lightwood supports user architectures/approaches so long as you follow the abstractions provided within each step.
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Our `tutorials <https://lightwood.io/tutorials.html>`_ provide specific use cases for how to introduce customization into your pipeline. Check out "custom cleaner", "custom splitter", "custom explainer", and "custom mixer". Stay tuned for further updates.
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Our `tutorials <https://mindsdb.github.io/lightwood/tutorials.html>`_ provide specific use cases for how to introduce customization into your pipeline. Check out "custom cleaner", "custom splitter", "custom explainer", and "custom mixer". Stay tuned for further updates.
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Contribute to Lightwood

‎_sources/tutorials/custom_cleaner/custom_cleaner.ipynb.txt

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"id": "happy-wheat",
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"metadata": {
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"execution": {
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"iopub.execute_input": "2024-02-29T11:02:15.503091Z",
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"shell.execute_reply": "2024-02-29T11:02:18.295457Z"
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}
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},
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"outputs": [
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"\u001b[32mINFO:lightwood-2886:No torchvision detected, image helpers not supported.\u001b[0m\n"
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"\u001b[32mINFO:lightwood-2554:No torchvision detected, image helpers not supported.\u001b[0m\n"
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"\u001b[32mINFO:lightwood-2886:No torchvision/pillow detected, image encoder not supported\u001b[0m\n"
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"\u001b[32mINFO:lightwood-2554:No torchvision/pillow detected, image encoder not supported\u001b[0m\n"
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],
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"id": "recognized-parish",
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"metadata": {
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"execution": {
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"shell.execute_reply": "2024-02-29T11:02:19.241028Z"
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"id": "chicken-truth",
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"metadata": {
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"execution": {
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"iopub.execute_input": "2024-01-23T19:58:32.429859Z",
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{
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"text": [
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"\u001b[32mINFO:lightwood-2886:Dropping features: ['url_legal', 'license', 'standard_error']\u001b[0m\n"
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"\u001b[32mINFO:lightwood-2554:Dropping features: ['url_legal', 'license', 'standard_error']\u001b[0m\n"
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"\u001b[32mINFO:type_infer-2886:Analyzing a sample of 2478\u001b[0m\n"
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"\u001b[32mINFO:type_infer-2554:Analyzing a sample of 2478\u001b[0m\n"
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"text": [
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"\u001b[32mINFO:type_infer-2886:from a total population of 2834, this is equivalent to 87.4% of your data.\u001b[0m\n"
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"\u001b[32mINFO:type_infer-2554:from a total population of 2834, this is equivalent to 87.4% of your data.\u001b[0m\n"
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"\u001b[32mINFO:type_infer-2886:Infering type for: id\u001b[0m\n"
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"\u001b[32mINFO:type_infer-2886:Doing text detection for column: id\u001b[0m\n"
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"\u001b[32mINFO:type_infer-2886:Column id has data type categorical\u001b[0m\n"
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"\u001b[32mINFO:type_infer-2554:Column id has data type categorical\u001b[0m\n"
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"\u001b[32mINFO:type_infer-2886:Column target has data type float\u001b[0m\n"
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"\u001b[32mINFO:type_infer-2554:Column target has data type float\u001b[0m\n"
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"\u001b[33mWARNING:type_infer-2886:Column id is an identifier of type \"Hash-like identifier\"\u001b[0m\n"
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"\u001b[33mWARNING:type_infer-2554:Column id is an identifier of type \"Hash-like identifier\"\u001b[0m\n"
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"\u001b[32mINFO:dataprep_ml-2886:Starting statistical analysis\u001b[0m\n"
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"\u001b[32mINFO:dataprep_ml-2554:Starting statistical analysis\u001b[0m\n"
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"\u001b[32mINFO:dataprep_ml-2886:Dropping features: ['id']\u001b[0m\n"
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"\u001b[32mINFO:dataprep_ml-2554:Dropping features: ['id']\u001b[0m\n"
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"id": "designed-condition",
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"execution": {
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" \"unbias_target\": true,\n",
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" \"expected_additional_time\": 22.386889696121216,\n",
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" \"expected_additional_time\": 22.358832359313965,\n",
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"metadata": {
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"id": "f030f8ca",
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"execution": {
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"id": "floating-patent",
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" \"expected_additional_time\": 22.358832359313965,\n",
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"id": "violent-guard",
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"\u001b[37mDEBUG:lightwood-2886: `preprocess` runtime: 0.07 seconds\u001b[0m\n"
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"\u001b[37mDEBUG:lightwood-2554: `preprocess` runtime: 0.07 seconds\u001b[0m\n"
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]
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},
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{
@@ -1577,10 +1577,10 @@
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"id": "major-stake",
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"metadata": {
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"execution": {
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"iopub.execute_input": "2024-01-23T19:58:55.225827Z",
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"iopub.status.busy": "2024-01-23T19:58:55.225454Z",
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"iopub.status.idle": "2024-01-23T19:58:55.230030Z",
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"shell.execute_reply": "2024-01-23T19:58:55.229387Z"
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"iopub.execute_input": "2024-02-29T11:02:42.014139Z",
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"iopub.status.busy": "2024-02-29T11:02:42.013942Z",
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"iopub.status.idle": "2024-02-29T11:02:42.019144Z",
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"shell.execute_reply": "2024-02-29T11:02:42.018488Z"
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}
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},
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"outputs": [

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