Learning from imbalanced data.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Whats the big deal with imbalanced data?\n",
    "\n",
    "In some cases, it can be very hard to learn from.\n",
    "\n",
    "This notebook is an example implementation for a [blog post I wrote](jeremyjordan.me/imbalanced-data/) on learning from imbalanced data. I recommend you start there, and then come explore this notebook after familiarizing yourself with the various techniques. \n",
    "\n",
    "**Note**: I didn't set a `random_state` when training the models in this notebook, so you may observe slightly difference performance than you see here or on my blog. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from imblearn.datasets import fetch_datasets\n",
    "\n",
    "datasets = fetch_datasets()\n",
    "\n",
    "# Wine quality dataset contains 12 features, descriptions found here: \n",
    "# https://archive.ics.uci.edu/ml/datasets/wine+quality\n",
    "# Target class derived as target: <=4 (score between 1 and 10)\n",
    "data = datasets['wine_quality']\n",
    "X, y = data.data, data.target"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Check out a curated list of possible datasets here:\n",
    "http://contrib.scikit-learn.org/imbalanced-learn/stable/datasets/index.html"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# US crime dataset contains 100 features, descriptions found here: \n",
    "# http://archive.ics.uci.edu/ml/datasets/communities+and+crime\n",
    "# Target class derived as target: >0.65\n",
    "\n",
    "# Uncomment the lines below to try a different dataset\n",
    "# data = datasets['us_crime']\n",
    "# X, y = data.data, data.target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = (y==1).astype(int)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Visualize dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x120d61a58>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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J38bGtQ+buda13u3II9uyatVKIDZdcneuAWjR4lB++GEtAGvWfA1AUlISJSUl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\n+ipvAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x120be6e48>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "from sklearn.decomposition import PCA\n",
    "import seaborn as sns\n",
    "\n",
    "# Reduce dataset to 2 feature dimensions in order to visualize the data\n",
    "pca = PCA(n_components=2)\n",
    "pca.fit(X)\n",
    "X_reduced = pca.transform(X)\n",
    "\n",
    "fig, ax = plt.subplots(1, 2, figsize= (15,5))\n",
    "\n",
    "ax[0].scatter(X_reduced[y == 0, 0], X_reduced[y == 0, 1], label=\"low quality wine\", alpha=0.2)\n",
    "ax[0].scatter(X_reduced[y == 1, 0], X_reduced[y == 1, 1], label=\"high quality wine\", alpha=0.2)\n",
    "ax[0].set_title('PCA of original dataset')\n",
    "ax[0].legend()\n",
    "\n",
    "ax[1] = sns.countplot(y)\n",
    "ax[1].set_title('Number of observations per class')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Train test split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, stratify=y, test_size=0.2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Build a pipeline for trying various sampling methods"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import metrics \n",
    "from collections import Counter\n",
    "\n",
    "from imblearn.over_sampling import RandomOverSampler, SMOTE, ADASYN\n",
    "from imblearn.under_sampling import RandomUnderSampler, NearMiss, TomekLinks, EditedNearestNeighbours\n",
    "\n",
    "def model_resampling_pipeline(X_train, X_test, y_train, y_test, model):\n",
    "    results = {'ordinary': {},\n",
    "               'class_weight': {},\n",
    "               'oversample': {},\n",
    "               'undersample': {}}\n",
    "    \n",
    "    # ------ No balancing ------\n",
    "    model.fit(X_train, y_train)\n",
    "    predictions = model.predict(X_test)\n",
    "    accuracy = metrics.accuracy_score(y_test, predictions)\n",
    "    precision, recall, fscore, support = metrics.precision_recall_fscore_support(y_test, predictions)\n",
    "    tn, fp, fn, tp = metrics.confusion_matrix(y_test, predictions).ravel()\n",
    "    fpr, tpr, thresholds = metrics.roc_curve(y_test, predictions, pos_label=1)\n",
    "    auc = metrics.auc(fpr, tpr)\n",
    "    \n",
    "    results['ordinary'] = {'accuracy': accuracy, 'precision': precision, 'recall': recall, \n",
    "                          'fscore': fscore, 'n_occurences': support,\n",
    "                          'predictions_count': Counter(predictions),\n",
    "                          'tp': tp, 'tn': tn, 'fp': fp, 'fn': fn,\n",
    "                          'auc': auc}\n",
    "    \n",
    "    \n",
    "    # ------ Class weight ------\n",
    "    if 'class_weight' in model.get_params().keys():\n",
    "        model.set_params(class_weight='balanced')\n",
    "        model.fit(X_train, y_train)\n",
    "        predictions = model.predict(X_test)\n",
    "        accuracy = metrics.accuracy_score(y_test, predictions)\n",
    "        precision, recall, fscore, support = metrics.precision_recall_fscore_support(y_test, predictions)\n",
    "        tn, fp, fn, tp = metrics.confusion_matrix(y_test, predictions).ravel()\n",
    "        fpr, tpr, thresholds = metrics.roc_curve(y_test, predictions, pos_label=1)\n",
    "        auc = metrics.auc(fpr, tpr)\n",
    "\n",
    "        results['class_weight'] = {'accuracy': accuracy, 'precision': precision, 'recall': recall, \n",
    "                                  'fscore': fscore, 'n_occurences': support,\n",
    "                                  'predictions_count': Counter(predictions),\n",
    "                                  'tp': tp, 'tn': tn, 'fp': fp, 'fn': fn,\n",
    "                                  'auc': auc}\n",
    "\n",
    "    \n",
    "    # ------------ OVERSAMPLING TECHNIQUES ------------\n",
    "    print('------ Oversampling methods ------')\n",
    "    techniques = [RandomOverSampler(),\n",
    "                  SMOTE(),\n",
    "                  ADASYN()]\n",
    "    \n",
    "    for sampler in techniques:\n",
    "        technique = sampler.__class__.__name__\n",
    "        print(f'Technique: {technique}')\n",
    "        print(f'Before resampling: {sorted(Counter(y_train).items())}')\n",
    "        X_resampled, y_resampled = sampler.fit_sample(X_train, y_train)\n",
    "        print(f'After resampling: {sorted(Counter(y_resampled).items())}')\n",
    "\n",
    "        model.fit(X_resampled, y_resampled)\n",
    "        predictions = model.predict(X_test)\n",
    "        accuracy = metrics.accuracy_score(y_test, predictions)\n",
    "        precision, recall, fscore, support = metrics.precision_recall_fscore_support(y_test, predictions)\n",
    "        tn, fp, fn, tp = metrics.confusion_matrix(y_test, predictions).ravel()\n",
    "        fpr, tpr, thresholds = metrics.roc_curve(y_test, predictions, pos_label=1)\n",
    "        auc = metrics.auc(fpr, tpr)\n",
    "\n",
    "        results['oversample'][technique] = {'accuracy': accuracy, \n",
    "                                            'precision': precision, \n",
    "                                            'recall': recall,\n",
    "                                            'fscore': fscore, \n",
    "                                            'n_occurences': support,\n",
    "                                            'predictions_count': Counter(predictions),\n",
    "                                            'tp': tp, 'tn': tn, 'fp': fp, 'fn': fn,\n",
    "                                            'auc': auc}\n",
    "\n",
    "    \n",
    "    # ------------ UNDERSAMPLING TECHNIQUES ------------\n",
    "    print('------ Undersampling methods ------')\n",
    "    techniques = [RandomUnderSampler(),\n",
    "                  NearMiss(version=1),\n",
    "                  NearMiss(version=2),\n",
    "                  TomekLinks(),\n",
    "                  EditedNearestNeighbours()]\n",
    "    \n",
    "    for sampler in techniques:\n",
    "        technique = sampler.__class__.__name__\n",
    "        if technique == 'NearMiss': technique+=str(sampler.version)\n",
    "        print(f'Technique: {technique}')\n",
    "        print(f'Before resampling: {sorted(Counter(y_train).items())}')\n",
    "        X_resampled, y_resampled = sampler.fit_sample(X_train, y_train)\n",
    "        print(f'After resampling: {sorted(Counter(y_resampled).items())}')\n",
    "\n",
    "        model.fit(X_resampled, y_resampled)\n",
    "        predictions = model.predict(X_test)\n",
    "        accuracy = metrics.accuracy_score(y_test, predictions)\n",
    "        precision, recall, fscore, support = metrics.precision_recall_fscore_support(y_test, predictions)\n",
    "        tn, fp, fn, tp = metrics.confusion_matrix(y_test, predictions).ravel()\n",
    "        fpr, tpr, thresholds = metrics.roc_curve(y_test, predictions, pos_label=1)\n",
    "        auc = metrics.auc(fpr, tpr)\n",
    "\n",
    "        results['undersample'][technique] = {'accuracy': accuracy, \n",
    "                                            'precision': precision, \n",
    "                                            'recall': recall,\n",
    "                                            'fscore': fscore, \n",
    "                                            'n_occurences': support,\n",
    "                                            'predictions_count': Counter(predictions),\n",
    "                                            'tp': tp, 'tn': tn, 'fp': fp, 'fn': fn,\n",
    "                                            'auc': auc}\n",
    "        \n",
    "\n",
    "    return results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Build tool to visualize results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def evaluate_method(results, method, metrics = ['precision', 'recall', 'fscore']):\n",
    "    fig, ax = plt.subplots(1, 7, sharey=True, figsize=(16, 6))\n",
    "    \n",
    "    for i, metric in enumerate(metrics):\n",
    "        ax[i*2].axhline(results['ordinary'][metric][0], label='No Resampling')\n",
    "        ax[i*2+1].axhline(results['ordinary'][metric][1], label='No Resampling')\n",
    "        \n",
    "        if results['class_weight']:\n",
    "            ax[i*2].bar(0, results['class_weight'][metric][0], label='Adjust Class Weight')\n",
    "            ax[i*2+1].bar(0, results['class_weight'][metric][1], label='Adjust Class Weight')\n",
    "            \n",
    "        ax[0].legend(loc='upper center', bbox_to_anchor=(9, 1.01),\n",
    "                     ncol=1, fancybox=True, shadow=True)\n",
    "        \n",
    "        for j, (technique, result) in enumerate(results[method].items()):\n",
    "            ax[i*2].bar(j+1, result[metric][0], label=technique)\n",
    "            \n",
    "            ax[i*2+1].bar(j+1, result[metric][1], label=technique)\n",
    "        \n",
    "        \n",
    "        ax[i*2].set_title(f'Low quality wine: \\n{metric}')\n",
    "        ax[i*2+1].set_title(f'High quality wine: \\n{metric}')\n",
    "    \n",
    "    # AUC vis\n",
    "    ax[6].set_title(f'Area under curve')\n",
    "    ax[6].axhline(results['ordinary']['auc'], label='No Resampling')\n",
    "    if results['class_weight']:\n",
    "        ax[6].bar(0, results['class_weight']['auc'], label='Adjust Class Weight')\n",
    "    for j, (technique, result) in enumerate(results[method].items()):\n",
    "        ax[6].bar(j+1, result['auc'], label=technique)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Try some models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.ensemble import AdaBoostClassifier\n",
    "from sklearn.neural_network import MLPClassifier"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Decision tree"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Visualize a decision tree trained on imbalanced data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
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Tfv36mQwmlun9/PxMRpdbt25J1apVzQ/4tczV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\nIIAAAgikWCAt53spvngyB4T6fC9+9wPN0VavXm01atSIs2vHjh3t7bf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YlIHriSeesDp1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2e7oDX1z9cUuPfFF1/Y008/bTNmzPCtTtFPjUUPFUeNGmUHDhxIcKxKFakpSM6/KRhPTf2lIYAA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\nAQQQyFQCl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5X+IoAAAggggAACCCCAAAIIIICABPzLQB4/ftwrA/npp596ZSDbtWtnCgBr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LLabnmmsuNrRoSBzyAMMu8uzFEyGTDGUut9xyeuTIkSbKuV5xxRWs0Kq70Zch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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = DecisionTreeClassifier(max_depth=4)\n",
    "model.fit(X_train, y_train)\n",
    "\n",
    "from IPython.display import Image  \n",
    "from sklearn.externals.six import StringIO  \n",
    "import pydot  \n",
    "from sklearn import tree\n",
    "\n",
    "dot_data = StringIO()  \n",
    "tree.export_graphviz(model, out_file=dot_data)\n",
    "graph = pydot.graph_from_dot_data(dot_data.getvalue())  \n",
    "Image(graph[0].create_png())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "------ Oversampling methods ------\n",
      "Technique: RandomOverSampler\n",
      "Before resampling: [(0, 3772), (1, 146)]\n",
      "After resampling: [(0, 3772), (1, 3772)]\n",
      "Technique: SMOTE\n",
      "Before resampling: [(0, 3772), (1, 146)]\n",
      "After resampling: [(0, 3772), (1, 3772)]\n",
      "Technique: ADASYN\n",
      "Before resampling: [(0, 3772), (1, 146)]\n",
      "After resampling: [(0, 3772), (1, 3810)]\n",
      "------ Undersampling methods ------\n",
      "Technique: RandomUnderSampler\n",
      "Before resampling: [(0, 3772), (1, 146)]\n",
      "After resampling: [(0, 146), (1, 146)]\n",
      "Technique: NearMiss1\n",
      "Before resampling: [(0, 3772), (1, 146)]\n",
      "After resampling: [(0, 146), (1, 146)]\n",
      "Technique: NearMiss2\n",
      "Before resampling: [(0, 3772), (1, 146)]\n",
      "After resampling: [(0, 146), (1, 146)]\n",
      "Technique: TomekLinks\n",
      "Before resampling: [(0, 3772), (1, 146)]\n",
      "After resampling: [(0, 3717), (1, 146)]\n",
      "Technique: EditedNearestNeighbours\n",
      "Before resampling: [(0, 3772), (1, 146)]\n",
      "After resampling: [(0, 3490), (1, 146)]\n"
     ]
    }
   ],
   "source": [
    "model = DecisionTreeClassifier()\n",
    "results = model_resampling_pipeline(X_train, X_test, y_train, y_test, model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1262eb4a8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "evaluate_method(results, 'oversample')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Yy5cvN5o3b24MGTLE6NSpk3m/gIAAw8/Pz/xotenTpxujR482DMMwvLy8jK+//towDMO4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\nuIrQVGIt1JUAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1277480f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "evaluate_method(results, 'undersample')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Random forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "------ Oversampling methods ------\n",
      "Technique: RandomOverSampler\n",
      "Before resampling: [(0, 3772), (1, 146)]\n",
      "After resampling: [(0, 3772), (1, 3772)]\n",
      "Technique: SMOTE\n",
      "Before resampling: [(0, 3772), (1, 146)]\n",
      "After resampling: [(0, 3772), (1, 3772)]\n",
      "Technique: ADASYN\n",
      "Before resampling: [(0, 3772), (1, 146)]\n",
      "After resampling: [(0, 3772), (1, 3810)]\n",
      "------ Undersampling methods ------\n",
      "Technique: RandomUnderSampler\n",
      "Before resampling: [(0, 3772), (1, 146)]\n",
      "After resampling: [(0, 146), (1, 146)]\n",
      "Technique: NearMiss1\n",
      "Before resampling: [(0, 3772), (1, 146)]\n",
      "After resampling: [(0, 146), (1, 146)]\n",
      "Technique: NearMiss2\n",
      "Before resampling: [(0, 3772), (1, 146)]\n",
      "After resampling: [(0, 146), (1, 146)]\n",
      "Technique: TomekLinks\n",
      "Before resampling: [(0, 3772), (1, 146)]\n",
      "After resampling: [(0, 3717), (1, 146)]\n",
      "Technique: EditedNearestNeighbours\n",
      "Before resampling: [(0, 3772), (1, 146)]\n",
      "After resampling: [(0, 3490), (1, 146)]\n"
     ]
    }
   ],
   "source": [
    "model = RandomForestClassifier()\n",
    "results = model_resampling_pipeline(X_train, X_test, y_train, y_test, model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1262d7550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "evaluate_method(results, 'oversample')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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v3zTvMZKXPHdpzOuvv46/v3+q7WFhYbz++usULVoUS0tLateuzaFDh7IkSBER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\n3wpQKSWLkDlz5sTWrVvjrLPOit7e3mhraytuO+W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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1282e79e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "evaluate_method(results, 'undersample')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Logistic regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/jeremyjordan/anaconda/envs/py36/lib/python3.6/site-packages/sklearn/metrics/classification.py:1135: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "------ Oversampling methods ------\n",
      "Technique: RandomOverSampler\n",
      "Before resampling: [(0, 3772), (1, 146)]\n",
      "After resampling: [(0, 3772), (1, 3772)]\n",
      "Technique: SMOTE\n",
      "Before resampling: [(0, 3772), (1, 146)]\n",
      "After resampling: [(0, 3772), (1, 3772)]\n",
      "Technique: ADASYN\n",
      "Before resampling: [(0, 3772), (1, 146)]\n",
      "After resampling: [(0, 3772), (1, 3810)]\n",
      "------ Undersampling methods ------\n",
      "Technique: RandomUnderSampler\n",
      "Before resampling: [(0, 3772), (1, 146)]\n",
      "After resampling: [(0, 146), (1, 146)]\n",
      "Technique: NearMiss1\n",
      "Before resampling: [(0, 3772), (1, 146)]\n",
      "After resampling: [(0, 146), (1, 146)]\n",
      "Technique: NearMiss2\n",
      "Before resampling: [(0, 3772), (1, 146)]\n",
      "After resampling: [(0, 146), (1, 146)]\n",
      "Technique: TomekLinks\n",
      "Before resampling: [(0, 3772), (1, 146)]\n",
      "After resampling: [(0, 3717), (1, 146)]\n",
      "Technique: EditedNearestNeighbours\n",
      "Before resampling: [(0, 3772), (1, 146)]\n",
      "After resampling: [(0, 3490), (1, 146)]\n"
     ]
    }
   ],
   "source": [
    "model = LogisticRegression()\n",
    "results = model_resampling_pipeline(X_train, X_test, y_train, y_test, model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x12783bcc0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "evaluate_method(results, 'oversample')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Z9xjJSV44I+S1117D39+fIUOGJFl+7tw5XnvtNQoUKABA9erVOXDgAM2bN8+Y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\nAAAAKIYgBAAAACjG/wMrp4iAzBIKzwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x125b55e80>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "evaluate_method(results, 'undersample')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## SVM Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "------ Oversampling methods ------\n",
      "Technique: RandomOverSampler\n",
      "Before resampling: [(0, 3772), (1, 146)]\n",
      "After resampling: [(0, 3772), (1, 3772)]\n",
      "Technique: SMOTE\n",
      "Before resampling: [(0, 3772), (1, 146)]\n",
      "After resampling: [(0, 3772), (1, 3772)]\n",
      "Technique: ADASYN\n",
      "Before resampling: [(0, 3772), (1, 146)]\n",
      "After resampling: [(0, 3772), (1, 3810)]\n",
      "------ Undersampling methods ------\n",
      "Technique: RandomUnderSampler\n",
      "Before resampling: [(0, 3772), (1, 146)]\n",
      "After resampling: [(0, 146), (1, 146)]\n",
      "Technique: NearMiss1\n",
      "Before resampling: [(0, 3772), (1, 146)]\n",
      "After resampling: [(0, 146), (1, 146)]\n",
      "Technique: NearMiss2\n",
      "Before resampling: [(0, 3772), (1, 146)]\n",
      "After resampling: [(0, 146), (1, 146)]\n",
      "Technique: TomekLinks\n",
      "Before resampling: [(0, 3772), (1, 146)]\n",
      "After resampling: [(0, 3717), (1, 146)]\n",
      "Technique: EditedNearestNeighbours\n",
      "Before resampling: [(0, 3772), (1, 146)]\n",
      "After resampling: [(0, 3490), (1, 146)]\n"
     ]
    }
   ],
   "source": [
    "model = SVC()\n",
    "results = model_resampling_pipeline(X_train, X_test, y_train, y_test, model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x127f2b9e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "evaluate_method(results, 'oversample')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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kJ3nmjJBXXnmFoKAgBg0alGJ9REQEr7zyCgUKFACgatWq7N27l6ZNm2ZOpJLj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\nRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x127f2bf60>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "evaluate_method(results, 'undersample')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## KNN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "------ Oversampling methods ------\n",
      "Technique: RandomOverSampler\n",
      "Before resampling: [(0, 3772), (1, 146)]\n",
      "After resampling: [(0, 3772), (1, 3772)]\n",
      "Technique: SMOTE\n",
      "Before resampling: [(0, 3772), (1, 146)]\n",
      "After resampling: [(0, 3772), (1, 3772)]\n",
      "Technique: ADASYN\n",
      "Before resampling: [(0, 3772), (1, 146)]\n",
      "After resampling: [(0, 3772), (1, 3810)]\n",
      "------ Undersampling methods ------\n",
      "Technique: RandomUnderSampler\n",
      "Before resampling: [(0, 3772), (1, 146)]\n",
      "After resampling: [(0, 146), (1, 146)]\n",
      "Technique: NearMiss1\n",
      "Before resampling: [(0, 3772), (1, 146)]\n",
      "After resampling: [(0, 146), (1, 146)]\n",
      "Technique: NearMiss2\n",
      "Before resampling: [(0, 3772), (1, 146)]\n",
      "After resampling: [(0, 146), (1, 146)]\n",
      "Technique: TomekLinks\n",
      "Before resampling: [(0, 3772), (1, 146)]\n",
      "After resampling: [(0, 3717), (1, 146)]\n",
      "Technique: EditedNearestNeighbours\n",
      "Before resampling: [(0, 3772), (1, 146)]\n",
      "After resampling: [(0, 3490), (1, 146)]\n"
     ]
    }
   ],
   "source": [
    "model = KNeighborsClassifier()\n",
    "results = model_resampling_pipeline(X_train, X_test, y_train, y_test, model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x127f9abe0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "evaluate_method(results, 'oversample')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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s3br1iY7ds2cPMTEx1KxZk9OnT9O1a9dHrjHyPEl1RkjDhg05f/58su3R0dG4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\nAAAAgGQIQgAAAIBkCEIAAACAZPwX3HRisA6rrOgAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x127f9a6d8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "evaluate_method(results, 'undersample')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## AdaBoost Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "------ Oversampling methods ------\n",
      "Technique: RandomOverSampler\n",
      "Before resampling: [(0, 3772), (1, 146)]\n",
      "After resampling: [(0, 3772), (1, 3772)]\n",
      "Technique: SMOTE\n",
      "Before resampling: [(0, 3772), (1, 146)]\n",
      "After resampling: [(0, 3772), (1, 3772)]\n",
      "Technique: ADASYN\n",
      "Before resampling: [(0, 3772), (1, 146)]\n",
      "After resampling: [(0, 3772), (1, 3810)]\n",
      "------ Undersampling methods ------\n",
      "Technique: RandomUnderSampler\n",
      "Before resampling: [(0, 3772), (1, 146)]\n",
      "After resampling: [(0, 146), (1, 146)]\n",
      "Technique: NearMiss1\n",
      "Before resampling: [(0, 3772), (1, 146)]\n",
      "After resampling: [(0, 146), (1, 146)]\n",
      "Technique: NearMiss2\n",
      "Before resampling: [(0, 3772), (1, 146)]\n",
      "After resampling: [(0, 146), (1, 146)]\n",
      "Technique: TomekLinks\n",
      "Before resampling: [(0, 3772), (1, 146)]\n",
      "After resampling: [(0, 3717), (1, 146)]\n",
      "Technique: EditedNearestNeighbours\n",
      "Before resampling: [(0, 3772), (1, 146)]\n",
      "After resampling: [(0, 3490), (1, 146)]\n"
     ]
    }
   ],
   "source": [
    "model = AdaBoostClassifier()\n",
    "results = model_resampling_pipeline(X_train, X_test, y_train, y_test, model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x127cfe588>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "evaluate_method(results, 'oversample')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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5njzxZKlubm7cvn3bsnz79m3c3d1TPO7q1egnPWWa8vR05+LFmxkdxiNZS4ye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\nREQsWbIkTp48ObNtwYIFsXfv3rjpppsiImJ6ejp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      "text/plain": [
       "<matplotlib.figure.Figure at 0x127f1ea90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "evaluate_method(results, 'undersample')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Simple neural network"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "------ Oversampling methods ------\n",
      "Technique: RandomOverSampler\n",
      "Before resampling: [(0, 3772), (1, 146)]\n",
      "After resampling: [(0, 3772), (1, 3772)]\n",
      "Technique: SMOTE\n",
      "Before resampling: [(0, 3772), (1, 146)]\n",
      "After resampling: [(0, 3772), (1, 3772)]\n",
      "Technique: ADASYN\n",
      "Before resampling: [(0, 3772), (1, 146)]\n",
      "After resampling: [(0, 3772), (1, 3810)]\n",
      "------ Undersampling methods ------\n",
      "Technique: RandomUnderSampler\n",
      "Before resampling: [(0, 3772), (1, 146)]\n",
      "After resampling: [(0, 146), (1, 146)]\n",
      "Technique: NearMiss1\n",
      "Before resampling: [(0, 3772), (1, 146)]\n",
      "After resampling: [(0, 146), (1, 146)]\n",
      "Technique: NearMiss2\n",
      "Before resampling: [(0, 3772), (1, 146)]\n",
      "After resampling: [(0, 146), (1, 146)]\n",
      "Technique: TomekLinks\n",
      "Before resampling: [(0, 3772), (1, 146)]\n",
      "After resampling: [(0, 3717), (1, 146)]\n",
      "Technique: EditedNearestNeighbours\n",
      "Before resampling: [(0, 3772), (1, 146)]\n",
      "After resampling: [(0, 3490), (1, 146)]\n"
     ]
    }
   ],
   "source": [
    "model = MLPClassifier(hidden_layer_sizes=(50, 50), activation='relu', solver='lbfgs')\n",
    "results = model_resampling_pipeline(X_train, X_test, y_train, y_test, model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x126302d68>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "evaluate_method(results, 'oversample')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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GYRiLFy82mjVrZgwYMMBo3769eb/g4GAjKCjI/Gi1yZMnG8OHDzcMwzCaNm1qfPPNN4Zh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\nAAAASIYgBAAAAEiGIAQAAABIxn8BP+9gR1KaaRIAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x127e9b8d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "evaluate_method(results, 'undersample')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Techniques\n",
    "\n",
    "A reminder of the techniques discussed in my post."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Class weight\n",
    "\n",
    "One of the simplest ways to address the class imbalance is to simply provide a weight for each class which places more emphasis on the minority classes such that the end result is a classifier which can learn equally from all classes. \n",
    "\n",
    "To calculate the proper weights for each class, you can use the sklearn utility function shown in the example below.\n",
    "```\n",
    "from sklearn.utils.class_weight import compute_class_weight\n",
    "weights = compute_class_weight('balanced', classes, y)\n",
    "```\n",
    "\n",
    "In a tree-based model where you're determining the optimal split according to some measure such as decreased entropy, you can simply scale the entropy component of each class by the corresponding weight such that you place more emphasis on the minority classes. As a reminder, the entropy of a node can be calculated as\n",
    "\n",
    "$$ \\mathop - \\sum \\limits_i^{}  {p_i}{\\log}\\left( {{p_i}} \\right) $$ \n",
    "\n",
    "where $p_i$ is the fraction of data points within class $i$.\n",
    "\n",
    "In a gradient-based model, you can scale the calculated loss for each observation by the appropriate class weight such that you place more significance on the losses associated with minority classes. As a reminder, a common loss function for classification is the categorical cross entropy (which is very similar to the above equation, albeit with slight differences). This may be calculated as \n",
    "\n",
    " $$ -\\sum_i y_i \\log \\hat{y}_ i $$\n",
    " \n",
    " where $y_i$ represents the true class (typically a one-hot encoded vector) and $\\hat{y}_ i$ represents the predicted class distribution. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Oversampling\n",
    "Another approach towards dealing with a class imbalance is to simply alter the dataset to remove such an imbalance. In this section, I'll discuss common techniques for *oversampling* the minority classes to increase the number of minority observations until we've reached a balanced dataset. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Random oversampling\n",
    "The most naive method of oversampling is to randomly sample the minority classes and simply duplicate the sampled observations. With this technique, it's important to note that you're artificially reducing the variance of the dataset. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Generating new data points (oversampling)\n",
    "\n",
    "There are two approaches supported for generating new data points, `SMOTE` (Synthetic Minority Over-sampling Technique) and `ADASYN` (Adaptive Synthetic Sampling). Both techniques use interpolation to generate new datapoints. \n",
    "\n",
    "For a given observation $x_i$, a new (synthetic) observation is generated by interpolating between one of the k-nearest neighbors, $x_{zi}$.\n",
    "\n",
    "$$x_{new} = x_i + \\lambda (x_{zi} - x_i)$$\n",
    "\n",
    "where $\\lambda$ is a random number in the range $\\left[ {0,1} \\right]$. This interpolation will create a sample on the line between $x_{i}$ and $x_{zi}$.\n",
    "\n",
    "<img src=\"http://contrib.scikit-learn.org/imbalanced-learn/stable/_images/sphx_glr_plot_illustration_generation_sample_0011.png\" alt=\"Drawing\" style=\"width: 400px;\"/>\n",
    "\n",
    "<small>[Image credit](http://contrib.scikit-learn.org/imbalanced-learn/stable/_images/sphx_glr_plot_illustration_generation_sample_0011.png)</small>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### SMOTE\n",
    "Synthetic Minority Over-sampling Technique (SMOTE) is a technique that generates new observations by interpolating between observations in the original dataset.\n",
    "\n",
    "This algorithm has three options for selecting which observations, $x_i$, to use in generating new data points.\n",
    "\n",
    "1. `regular`: No selection rules, randomly sample all possible $x_i$.\n",
    "2. `borderline`: Separates all possible $x_i$ into three classes using the k nearest neighbors of each point.\n",
    "    - *noise*: all nearest-neighbors are from a different class than $x_i$\n",
    "    - *in danger*: at least half of the nearest neighbors are of the same class as $x_i$\n",
    "    - *safe*: all nearest neighbors are from the same class as $x_i$\n",
    "3. `svm`: Uses an SVM classifier to identify the support vectors (samples close to the decision boundary) and samples $x_i$ from these points."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### ADASYN\n",
    "Adaptive Synthetic (ADASYN) sampling works in a similar manner as SMOTE, however, the number of samples generated for a given $x_i$ is proportional to the number of nearby samples which **do not** belong to the same class as $x_i$. Thus, ADASYN tends to focus solely on outliers when generating new synthetic training examples. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Undersampling\n",
    "Rather than oversampling the minority classes, it's also possible to achieve class balance by *undersampling* the majority class - essentially throwing away data to make it easier to learn characteristics about the minority classes.  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Random undersampling\n",
    "As with oversampling, a naive implementation would be to simply sample the majority class at random until reaching a similar number of observations as the minority classes. For example, if your majority class has 1,000 observations and you have a minority class with 20 observations, you would collect your training data for the majority class by randomly sampling 20 observations from the original 1,000. As you might expect, this could potentially result in removing key characteristics of the majority class."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Near miss\n",
    "The general idea behind near miss is to only the sample the points from the majority class necessary to distinguish between other classes.\n",
    "\n",
    "**NearMiss-1** select samples from the majority class for which the average distance of the N *closest* samples of a minority class is smallest. \n",
    "\n",
    "<img src=\"http://contrib.scikit-learn.org/imbalanced-learn/stable/_images/sphx_glr_plot_illustration_nearmiss_0011.png\" alt=\"Drawing\" style=\"width: 400px;\"/>\n",
    "\n",
    "\n",
    "<small>[Image credit](http://contrib.scikit-learn.org/imbalanced-learn/stable/under_sampling.html)</small>\n",
    "\n",
    "**NearMiss-2** select samples from the majority class for which the average distance of the N *farthest* samples of a minority class is smallest. \n",
    "\n",
    "<img src=\"http://contrib.scikit-learn.org/imbalanced-learn/stable/_images/sphx_glr_plot_illustration_nearmiss_0021.png\" alt=\"Drawing\" style=\"width: 400px;\"/>\n",
    "\n",
    "<small>[Image credit](http://contrib.scikit-learn.org/imbalanced-learn/stable/under_sampling.html)</small>\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Tomeks links\n",
    "A Tomek’s link is defined as two observations of different classes ($x$ and $y$) such that there is no example $z$ for which:\n",
    "\n",
    "$$d(x, z) < d(x, y) \\text{ or } d(y, z) < d(x, y)$$\n",
    "\n",
    "where $d()$ is the distance between the two samples. **In other words, a Tomek’s link exists if two observations of different classes are the nearest neighbors of each other.** In the figure below, a Tomek’s link is illustrated by highlighting the samples of interest in green.\n",
    "\n",
    "<img src=\"http://contrib.scikit-learn.org/imbalanced-learn/stable/_images/sphx_glr_plot_illustration_tomek_links_0011.png\" alt=\"Drawing\" style=\"width: 400px;\"/>\n",
    "\n",
    "<small>[Image credit](http://contrib.scikit-learn.org/imbalanced-learn/stable/under_sampling.html)</small>\n",
    "\n",
    "\n",
    "For this undersampling strategy, we'll remove any observations *from the majority class* for which a Tomek's link is identified. Depending on the dataset, this technique won't actually achieve a balance among the classes - it will simply \"clean\" the dataset by removing some noisy observations, which may result in an easier classification problem. "
   ]
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    "#### Edited nearest neighbors\n",
    "EditedNearestNeighbours applies a nearest-neighbors algorithm and “edit” the dataset by removing samples which do not agree “enough” with their neighboorhood. For each sample in the class to be under-sampled, the nearest-neighbours are computed and if the selection criterion is not fulfilled, the sample is removed.\n",
    "\n",
    "This is a similar approach as Tomek's links in the respect that we're not necessarily focused on actually achieving a class balance, we're simply looking to remove noisy observations in an attempt to make for an easier classification problem. "
   ]
  }
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