From b26b0cf4b3296efdb401e04e32b5860958cf6164 Mon Sep 17 00:00:00 2001
From: jaberkow <jackberkowitz88@gmail.com>
Date: Mon, 14 Dec 2020 18:16:33 -0800
Subject: [PATCH] added documentation to detection_utils

---
 LICENSE               |  2 +-
 detection_utils.py    | 34 ++++++++++++++++++++++----
 hypothesis_test.ipynb | 55 ++++++++++++++++++++-----------------------
 3 files changed, 57 insertions(+), 34 deletions(-)

diff --git a/LICENSE b/LICENSE
index 261eeb9..8d789bb 100644
--- a/LICENSE
+++ b/LICENSE
@@ -186,7 +186,7 @@
       same "printed page" as the copyright notice for easier
       identification within third-party archives.
 
-   Copyright [yyyy] [name of copyright owner]
+   Copyright 2020 IQT Labs LLC, All Rights Reserved.
 
    Licensed under the Apache License, Version 2.0 (the "License");
    you may not use this file except in compliance with the License.
diff --git a/detection_utils.py b/detection_utils.py
index 2ba2801..f69d1d1 100644
--- a/detection_utils.py
+++ b/detection_utils.py
@@ -5,7 +5,21 @@
 def MMD_test(source_data,target_data,p_val=0.05,preprocess_kwargs={},chunk_size=100,
     n_permutations=20):
     """
-    Functional wrapper around alibi_detect MMDDrift class, uses gaussian kernel
+    Functional wrapper around alibi_detect MMDDrift class that uses uses gaussian kernel
+    (https://docs.seldon.io/projects/alibi-detect/en/stable/api/alibi_detect.cd.mmd.html)
+    
+
+    Inputs:
+        source_data - numpy.ndarray of shape (number of source samples,embedding dimension),
+            samples from the source distribution
+        target_data - numpy.ndarray of shape (number of target samples,embedding dimension),
+            samples from the target distribution
+        p_val - p-value used for the significance of the permutation test.
+        preprocess_kwargs - Kwargs for a preprocessing function, pass callables under "model" key
+        chunk_size - Chunk size if dask is used to parallelise the computation.
+        n_permutations - Number of permutations used in the permutation test.
+    Outputs:
+        p - float, empirical p-value determined using the permutation test
     """
     source_size,source_dim = np.shape(source_data)
     target_size,target_dim = np.shape(target_data)
@@ -24,8 +38,20 @@ def MMD_test(source_data,target_data,p_val=0.05,preprocess_kwargs={},chunk_size=
 def repeated_MMD_test(source_data,target_data,p_val=0.05,preprocess_kwargs={},chunk_size=100,
     n_permutations=20,n_samples=100,n_splits=5):
     """
-    Repeatedly carry out the MMD test, subsampling the data each time.  Returns mean and standard
-    deviation of the p_values
+    Repeatedly carry out the MMD test, subsampling the data each time.  Returns an array of p-values
+    Inputs:
+        source_data - numpy.ndarray of shape (number of source samples,embedding dimension),
+            samples from the source distribution
+        target_data - numpy.ndarray of shape (number of target samples,embedding dimension),
+            samples from the target distribution
+        p_val - p-value used for the significance of the permutation test.
+        preprocess_kwargs - Kwargs for a preprocessing function, pass callables under "model" key
+        chunk_size - Chunk size if dask is used to parallelise the computation.
+        n_permutations - Number of permutations used in the permutation test.
+        n_samples - number of samples to use from the source and target data in each subsampling
+        n_splits - number of different subsamplings to carry out
+    Outputs:
+        p_array - np.ndarray of shape (n_splits,), the set of p-values computed
     """
     source_size,source_dim = np.shape(source_data)
     target_size,target_dim = np.shape(target_data)
@@ -38,7 +64,7 @@ def repeated_MMD_test(source_data,target_data,p_val=0.05,preprocess_kwargs={},ch
         preprocess_kwargs=preprocess_kwargs,n_permutations=n_permutations)
         p_list.append(p_temp)
     p_array = np.array(p_list)
-    return np.mean(p_array),np.std(p_array)
+    return p_array
     
 
 
diff --git a/hypothesis_test.ipynb b/hypothesis_test.ipynb
index ebfbd4e..8414d57 100644
--- a/hypothesis_test.ipynb
+++ b/hypothesis_test.ipynb
@@ -36,7 +36,6 @@
    "source": [
     "# Change this component to the root of the VOiCES dataset\n",
     "DATASET_ROOT = '/Users/jberkowitz/Datasets/VOiCES_devkit'\n",
-    "#DATASET_ROOT = '/mnt/fs03/shared/datasets/VOiCES_devkit'\n",
     "# Convenience function to add root to data path\n",
     "add_root = lambda x: os.path.join(DATASET_ROOT,x)"
    ]
@@ -3967,7 +3966,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 86,
+   "execution_count": 96,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -3988,42 +3987,40 @@
     "            temp_model = w2v_uae\n",
     "        else:\n",
     "            temp_model = trill_uae\n",
-    "        p_mean,p_std= detection_utils.repeated_MMD_test(source_data,target_data,preprocess_kwargs={'model':temp_model,'batch_size':128},n_permutations=100,n_samples=50,n_splits=10)\n",
-    "        res = {'distractor':distractor_type,'embedding':embedding_type,'p value':p_mean,'p error':p_std}\n",
-    "        res_list.append(res)"
+    "        p_array = detection_utils.repeated_MMD_test(source_data,target_data,preprocess_kwargs={'model':temp_model,'batch_size':128},n_permutations=100,n_samples=50,n_splits=10)\n",
+    "        for p in p_array:\n",
+    "            res = {'distractor':distractor_type,'embedding':embedding_type,'p value':p}\n",
+    "            res_list.append(res)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 88,
+   "execution_count": 97,
    "metadata": {},
    "outputs": [
     {
      "output_type": "execute_result",
      "data": {
       "text/plain": [
-       "   distractor   embedding  p value   p error\n",
-       "0        none    w2v_mean    0.518  0.302185\n",
-       "1        none     w2v_pca    0.612  0.252460\n",
-       "2        none  trill_mean    0.419  0.287453\n",
-       "3        none   trill_pca    0.452  0.281950\n",
-       "4        musi    w2v_mean    0.001  0.003000\n",
-       "5        musi     w2v_pca    0.139  0.152476\n",
-       "6        musi  trill_mean    0.000  0.000000\n",
-       "7        musi   trill_pca    0.184  0.221142\n",
-       "8        tele    w2v_mean    0.001  0.003000\n",
-       "9        tele     w2v_pca    0.319  0.211587\n",
-       "10       tele  trill_mean    0.157  0.253576\n",
-       "11       tele   trill_pca    0.246  0.277712\n",
-       "12       babb    w2v_mean    0.000  0.000000\n",
-       "13       babb     w2v_pca    0.070  0.115672\n",
-       "14       babb  trill_mean    0.000  0.000000\n",
-       "15       babb   trill_pca    0.021  0.046787"
+       "    distractor  embedding  p value\n",
+       "0         none   w2v_mean     0.41\n",
+       "1         none   w2v_mean     0.31\n",
+       "2         none   w2v_mean     0.38\n",
+       "3         none   w2v_mean     0.53\n",
+       "4         none   w2v_mean     0.88\n",
+       "..         ...        ...      ...\n",
+       "155       babb  trill_pca     0.00\n",
+       "156       babb  trill_pca     0.05\n",
+       "157       babb  trill_pca     0.00\n",
+       "158       babb  trill_pca     0.07\n",
+       "159       babb  trill_pca     0.01\n",
+       "\n",
+       "[160 rows x 3 columns]"
       ],
-      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>distractor</th>\n      <th>embedding</th>\n      <th>p value</th>\n      <th>p error</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>none</td>\n      <td>w2v_mean</td>\n      <td>0.518</td>\n      <td>0.302185</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>none</td>\n      <td>w2v_pca</td>\n      <td>0.612</td>\n      <td>0.252460</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>none</td>\n      <td>trill_mean</td>\n      <td>0.419</td>\n      <td>0.287453</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>none</td>\n      <td>trill_pca</td>\n      <td>0.452</td>\n      <td>0.281950</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>musi</td>\n      <td>w2v_mean</td>\n      <td>0.001</td>\n      <td>0.003000</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>musi</td>\n      <td>w2v_pca</td>\n      <td>0.139</td>\n      <td>0.152476</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>musi</td>\n      <td>trill_mean</td>\n      <td>0.000</td>\n      <td>0.000000</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>musi</td>\n      <td>trill_pca</td>\n      <td>0.184</td>\n      <td>0.221142</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>tele</td>\n      <td>w2v_mean</td>\n      <td>0.001</td>\n      <td>0.003000</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>tele</td>\n      <td>w2v_pca</td>\n      <td>0.319</td>\n      <td>0.211587</td>\n    </tr>\n    <tr>\n      <th>10</th>\n      <td>tele</td>\n      <td>trill_mean</td>\n      <td>0.157</td>\n      <td>0.253576</td>\n    </tr>\n    <tr>\n      <th>11</th>\n      <td>tele</td>\n      <td>trill_pca</td>\n      <td>0.246</td>\n      <td>0.277712</td>\n    </tr>\n    <tr>\n      <th>12</th>\n      <td>babb</td>\n      <td>w2v_mean</td>\n      <td>0.000</td>\n      <td>0.000000</td>\n    </tr>\n    <tr>\n      <th>13</th>\n      <td>babb</td>\n      <td>w2v_pca</td>\n      <td>0.070</td>\n      <td>0.115672</td>\n    </tr>\n    <tr>\n      <th>14</th>\n      <td>babb</td>\n      <td>trill_mean</td>\n      <td>0.000</td>\n      <td>0.000000</td>\n    </tr>\n    <tr>\n      <th>15</th>\n      <td>babb</td>\n      <td>trill_pca</td>\n      <td>0.021</td>\n      <td>0.046787</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
+      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>distractor</th>\n      <th>embedding</th>\n      <th>p value</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>none</td>\n      <td>w2v_mean</td>\n      <td>0.41</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>none</td>\n      <td>w2v_mean</td>\n      <td>0.31</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>none</td>\n      <td>w2v_mean</td>\n      <td>0.38</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>none</td>\n      <td>w2v_mean</td>\n      <td>0.53</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>none</td>\n      <td>w2v_mean</td>\n      <td>0.88</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>155</th>\n      <td>babb</td>\n      <td>trill_pca</td>\n      <td>0.00</td>\n    </tr>\n    <tr>\n      <th>156</th>\n      <td>babb</td>\n      <td>trill_pca</td>\n      <td>0.05</td>\n    </tr>\n    <tr>\n      <th>157</th>\n      <td>babb</td>\n      <td>trill_pca</td>\n      <td>0.00</td>\n    </tr>\n    <tr>\n      <th>158</th>\n      <td>babb</td>\n      <td>trill_pca</td>\n      <td>0.07</td>\n    </tr>\n    <tr>\n      <th>159</th>\n      <td>babb</td>\n      <td>trill_pca</td>\n      <td>0.01</td>\n    </tr>\n  </tbody>\n</table>\n<p>160 rows × 3 columns</p>\n</div>"
      },
      "metadata": {},
-     "execution_count": 88
+     "execution_count": 97
     }
    ],
    "source": [
@@ -4033,15 +4030,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 93,
+   "execution_count": 101,
    "metadata": {},
    "outputs": [
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      "output_type": "display_data",
      "data": {
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\n"
      },
      "metadata": {}
     }
@@ -4051,7 +4048,7 @@
     "g = sns.catplot(\n",
     "    data=res_df, kind=\"bar\",\n",
     "    x=\"distractor\", y=\"p value\", hue=\"embedding\",\n",
-    "    ci=\"p error\", palette=\"dark\", alpha=.6, height=6\n",
+    "    palette=\"dark\", alpha=.6, height=6\n",
     ")\n",
     "g.despine(left=True)\n",
     "g.set_axis_labels(\"\", \"p value\",fontsize=15)\n",