classifier-visualizations-OLD.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "a5b1e907",
   "metadata": {},
   "source": [
    "# Importing the classifier pickle\n",
    "NOTE: pickle must contain the trained __classifier__, not just the model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "e08590c0",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import math\n",
    "import json\n",
    "import pickle\n",
    "import numpy as np\n",
    "import statistics as stats\n",
    "\n",
    "from sklearn import tree\n",
    "from pandas import DataFrame\n",
    "from matplotlib import pyplot as plt\n",
    "# ------ Fill this in to specify which pickle should be opened ------\n",
    "# --- NOTE: should include full path and filename but NO extension ---\n",
    "file = \"/Users/tylerskluzacek/Dropbox/THESIS/e_etc-head-512-cdiac-ml-best.csv-2022-04-13-21.05.16\"\n",
    "# file = \"/Users/tylerskluzacek/e_etc-head-16-cdiac-ml-best.csv-2022-04-13-20.33.07\"\n",
    "# --------------------------------------------------------------\n",
    "\n",
    "byte_count = int(file.split(\"/\")[-1].split(\"-\")[2])\n",
    "\n",
    "classifier_path = f\"{file}.pkl\"\n",
    "# Syler uncommented 1 of 2\n",
    "#info_metrics_path = f\"{file}-info-{byte_count}Bytes.json\"\n",
    "# info_metrics = json.load(open(info_metrics_path))\n",
    "\n",
    "os.chdir('/Users/tylerskluzacek/XtractPredictor/')\n",
    "classifier = pickle.load(open(classifier_path, \"rb\"))\n",
    "os.chdir('/Users/tylerskluzacek/Desktop/xtract-research-notebooks/')\n",
    "\n",
    "# Syler uncommented 2 of 2\n",
    "# for key in info_metrics.keys():\n",
    "#     print(key)\n",
    "#     print(f'\\t{info_metrics[key]}')\n",
    "\n",
    "# precision = info_metrics['Model precision']\n",
    "# recall = info_metrics['Model recall']\n",
    "# print(f'F1-Score\\n\\t{2*precision*recall/(precision+recall)}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "eccba871",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Now let's see if we can access all of the predictions\n",
    "# import time \n",
    "# for item in classifier.Y_test: \n",
    "#     print(item)\n",
    "#     time.sleep(1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "165cfbce",
   "metadata": {},
   "source": [
    "# Decision tree visualization\n",
    "Acceptable classifiers:\n",
    "- Decision tree classifier (dtc)\n",
    "- Single-tree extra tree classifier (t_etc)\n",
    "\n",
    "NOTE: If decision tree has too high of a maximum depth, this visualization will be illegible. I recommend setting max_depth to 3."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "49770e61",
   "metadata": {},
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "'ExtraTreesClassifier' object has no attribute 'tree_'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-5-33b14ef2b50f>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mfig\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfigsize\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m25\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m25\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m _ = tree.plot_tree(classifier.model,\n\u001b[0m\u001b[1;32m      3\u001b[0m                    filled=True)\n\u001b[1;32m      4\u001b[0m \u001b[0mprops\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mboxstyle\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'round'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfacecolor\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'wheat'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0malpha\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0.5\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0mlabel_legend\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/envs/jupyter_primary/lib/python3.8/site-packages/sklearn/tree/_export.py\u001b[0m in \u001b[0;36mplot_tree\u001b[0;34m(decision_tree, max_depth, feature_names, class_names, label, filled, impurity, node_ids, proportion, rounded, precision, ax, fontsize)\u001b[0m\n\u001b[1;32m    193\u001b[0m         \u001b[0mfontsize\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfontsize\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    194\u001b[0m     )\n\u001b[0;32m--> 195\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0mexporter\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexport\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdecision_tree\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0max\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0max\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    196\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    197\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/envs/jupyter_primary/lib/python3.8/site-packages/sklearn/tree/_export.py\u001b[0m in \u001b[0;36mexport\u001b[0;34m(self, decision_tree, ax)\u001b[0m\n\u001b[1;32m    654\u001b[0m         \u001b[0max\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclear\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    655\u001b[0m         \u001b[0max\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_axis_off\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 656\u001b[0;31m         \u001b[0mmy_tree\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_make_tree\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdecision_tree\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtree_\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdecision_tree\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcriterion\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    657\u001b[0m         \u001b[0mdraw_tree\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mbuchheim\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmy_tree\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    658\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mAttributeError\u001b[0m: 'ExtraTreesClassifier' object has no attribute 'tree_'"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1800x1800 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(25, 25))\n",
    "_ = tree.plot_tree(classifier.model,\n",
    "                   filled=True)\n",
    "props = dict(boxstyle='round', facecolor='wheat', alpha=0.5)\n",
    "label_legend = \"\"\n",
    "for label in classifier.unique_labels:\n",
    "    label_legend += (label + \"\\n\")\n",
    "fig.text(0.13, 0.7, label_legend[:-1], fontsize=25, bbox=props)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f7f89b35",
   "metadata": {},
   "source": [
    "# HeadByte heatmap visualization\n",
    "*Acceptable classifiers: any*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "479dcc10",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The labels and their binarized lists:\n",
      "unknown\n",
      "[0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
      "executable\n",
      "[0. 0. 0. 0. 0. 0. 0. 0. 0. 1.]\n",
      "jsonxml\n",
      "[0. 0. 0. 0. 0. 0. 0. 0. 1. 0.]\n",
      "tabular\n",
      "[0. 0. 0. 0. 0. 0. 0. 1. 0. 0.]\n",
      "images\n",
      "[0. 0. 0. 0. 0. 0. 1. 0. 0. 0.]\n",
      "compressed\n",
      "[0. 0. 0. 0. 0. 1. 0. 0. 0. 0.]\n",
      "netcdf\n",
      "[0. 0. 0. 1. 0. 0. 0. 0. 0. 0.]\n",
      "empty\n",
      "[0. 0. 1. 0. 0. 0. 0. 0. 0. 0.]\n",
      "keyword\n",
      "[0. 1. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
      "keyword, jsonxml\n",
      "[0. 1. 0. 0. 0. 0. 0. 0. 1. 0.]\n",
      "keyword, tabular\n",
      "[0. 1. 0. 0. 0. 0. 0. 1. 0. 0.]\n",
      "keyword, python, tabular\n",
      "[0. 1. 0. 0. 1. 0. 0. 1. 0. 0.]\n",
      "keyword, netcdf\n",
      "[0. 1. 0. 1. 0. 0. 0. 0. 0. 0.]\n",
      "c-code, keyword\n",
      "[1. 1. 0. 0. 0. 0. 0. 0. 0. 0.]\n"
     ]
    }
   ],
   "source": [
    "y_pred = classifier.model.predict(classifier.X_test)\n",
    "\n",
    "# Building a dictionary of the predicted (multi)labels and their corresponding binary lists\n",
    "\n",
    "unique_rows = np.unique(y_pred, axis=0)\n",
    "unique_dict = {}\n",
    "for unique_row in unique_rows:\n",
    "    labels = []\n",
    "    for pair in zip(unique_row, classifier.unique_labels):\n",
    "        if pair[0]:\n",
    "            labels.append(pair[1])\n",
    "    if len(labels) == 0:\n",
    "        labels.append(\"unknown\")\n",
    "    unique_dict[\", \".join(labels)] = unique_row\n",
    "    \n",
    "# Building a dictionary of all the feature info for each predicted label\n",
    "\n",
    "large_dict = {}\n",
    "for key in unique_dict.keys():\n",
    "    large_dict[key] = []\n",
    "\n",
    "for i in range(len(classifier.X_test)):\n",
    "    for key in unique_dict.keys():\n",
    "        if np.all(y_pred[i]==unique_dict[key]):\n",
    "            large_dict[key].append(classifier.X_test[i].tolist())\n",
    "            break\n",
    "\n",
    "sample_counts = \"Predicted sample counts\\n\\n\"\n",
    "for key in large_dict.keys():\n",
    "    sample_counts += key + \" : \" + str(len(large_dict[key])) + \"\\n\"\n",
    "\n",
    "print(\"The labels and their binarized lists:\")\n",
    "for key in unique_dict.keys():\n",
    "    print(key)\n",
    "    print(unique_dict[key])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bc902821",
   "metadata": {},
   "source": [
    "## Average value heatmap"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "e0e5aafb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Averaging the features for each bit\n",
    "\n",
    "avgs = []\n",
    "for key in large_dict.keys():\n",
    "    lst = large_dict[key]\n",
    "    lst = [list(x) for x in zip(*lst)]\n",
    "    lst = [stats.mean(x) for x in lst]\n",
    "    avgs.append(lst)\n",
    "    \n",
    "# UNCOMMENT to fill in empty labels in case you run on a limited range of samples\n",
    "\n",
    "# for i in range(len(avgs)):\n",
    "#     if (len(avgs[i])==0):\n",
    "#         avgs[i] = [0.0] * byte_count\n",
    "\n",
    "# Creating the plot\n",
    "\n",
    "fig, ax1 = plt.subplots()\n",
    "\n",
    "# Label y axis side with the class name\n",
    "\n",
    "y1_axis_labels = large_dict.keys()\n",
    "ax1.set_yticks(np.arange(len(y1_axis_labels)), labels=y1_axis_labels)\n",
    "\n",
    "# Plot the heatmap set good x-axis ticks\n",
    "# fig.set_size_inches((2*byte_count)/10, 10.5) # UNCOMMENT FOR LARGER BYTECOUNTS\n",
    "plt.imshow(np.array(avgs))\n",
    "plt.xticks(ticks=[i for i in range(byte_count)], labels=[i+1 for i in range(byte_count)])\n",
    "\n",
    "# Adding title and sample count legend\n",
    "\n",
    "ax1.set_title(\"Average scalar value of n-th head byte for each predicted multilabel\")\n",
    "plt.tight_layout()\n",
    "\n",
    "props = dict(boxstyle='round', facecolor='wheat', alpha=0.5)\n",
    "plt.text(byte_count, -0.25, sample_counts[:-1], fontsize=10,\n",
    "         verticalalignment='top', bbox=props)\n",
    "\n",
    "plt.savefig('/Users/tylerskluzacek/thing.png', bbox_inches='tight') "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "be416262",
   "metadata": {},
   "source": [
    "## Standard deviation heatmap"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "9ea20a2a",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Finding the standard deviation for each bit\n",
    "\n",
    "stdevs = []\n",
    "y_axis_labels = []\n",
    "for key in large_dict.keys():\n",
    "    lst = large_dict[key]\n",
    "    lst = [list(x) for x in zip(*lst)]\n",
    "    if (len(lst[0])>1):\n",
    "        lst = [stats.stdev(x) for x in lst]\n",
    "        stdevs.append(lst)\n",
    "        y_axis_labels.append(key)\n",
    "    \n",
    "# UNCOMMENT to fill in empty labels in case you run on a limited range of samples\n",
    "\n",
    "# for i in range(len(stdevs)):\n",
    "#     if (len(stdevs[i])==0):\n",
    "#         stdevs[i] = [0.0] * byte_count\n",
    "\n",
    "# Creating the plot\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "im = ax.imshow(np.array(stdevs))\n",
    "\n",
    "# Show all ticks and label them with the respective list entries\n",
    "\n",
    "ax.set_yticks(np.arange(len(y_axis_labels)), labels=y_axis_labels)\n",
    "\n",
    "# Plot the heatmap set good x-axis ticks\n",
    "\n",
    "#  fig.set_size_inches(18.5, 10.5) # UNCOMMENT FOR LARGER BYTECOUNTS\n",
    "plt.imshow(np.array(stdevs))\n",
    "plt.xticks(ticks=[i for i in range(byte_count)], labels=[i+1 for i in range(byte_count)])\n",
    "\n",
    "# Adding title and sample count legend\n",
    "\n",
    "ax1.set_title(\"Standard deviation of scalar values of n-th head byte\")\n",
    "plt.title(\"Standard deviation of scalar values of n-th head byte\")\n",
    "plt.tight_layout()\n",
    "\n",
    "props = dict(boxstyle='round', facecolor='wheat', alpha=0.5)\n",
    "plt.text(byte_count, -0.25, sample_counts[:-1], fontsize=10,\n",
    "         verticalalignment='top', bbox=props)\n",
    "\n",
    "plt.savefig('/Users/tylerskluzacek/thing2.png', bbox_inches='tight') "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1071c819",
   "metadata": {},
   "source": [
    "# Multilabel Confusion Matrices\n",
    "*Acceptable classifiers: any*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0f802575",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.metrics import multilabel_confusion_matrix\n",
    "\n",
    "def print_confusion_matrix(confusion_matrix, axes, class_label, class_names, fontsize=16):\n",
    "\n",
    "    df_cm = pd.DataFrame(\n",
    "        confusion_matrix, index=class_names, columns=class_names,\n",
    "    )\n",
    "\n",
    "    try:\n",
    "        heatmap = sns.heatmap(df_cm, annot=True, fmt=\"d\", cbar=False, ax=axes, annot_kws={\"fontsize\":fontsize})\n",
    "    except ValueError:\n",
    "        raise ValueError(\"Confusion matrix values must be integers.\")\n",
    "    heatmap.yaxis.set_ticklabels(heatmap.yaxis.get_ticklabels(), rotation=0, ha='right', fontsize=fontsize)\n",
    "    heatmap.xaxis.set_ticklabels(heatmap.xaxis.get_ticklabels(), rotation=45, ha='right', fontsize=fontsize)\n",
    "    axes.set_ylabel('True label', fontsize=fontsize)\n",
    "    axes.set_xlabel('Predicted label', fontsize=fontsize)\n",
    "    axes.set_title(\"Class - \" + class_label, fontsize=fontsize)\n",
    "\n",
    "y_pred = classifier.model.predict(classifier.X_test)\n",
    "    \n",
    "# Some customizable values here, first two values should multiply to at least label count\n",
    "fig, ax = plt.subplots(2, 5, figsize=(20, 7))\n",
    "    \n",
    "multilabel_confusion_matrix = multilabel_confusion_matrix(y_pred, classifier.Y_test)\n",
    "multilabel_labels = classifier.unique_labels\n",
    "\n",
    "for axes, cfs_matrix, label in zip(ax.flatten(), multilabel_confusion_matrix, multilabel_labels):\n",
    "    print_confusion_matrix(cfs_matrix, axes, label, [\"N\", \"Y\"])\n",
    "    \n",
    "fig.tight_layout()\n",
    "#plt.show()\n",
    "plt.savefig('/Users/tylerskluzacek/Desktop/ml-confusion-matrices.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "54795907",
   "metadata": {},
   "source": [
    "# Precision-Recall Curve"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f8cb3983",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import precision_recall_curve\n",
    "from sklearn.metrics import average_precision_score\n",
    "\n",
    "# For each class\n",
    "precision = dict()\n",
    "recall = dict()\n",
    "average_precision = dict()\n",
    "n_classes = len(classifier.Y_test[0])\n",
    "for i in range(n_classes):\n",
    "    precision[i], recall[i], _ = precision_recall_curve(classifier.Y_test[:, i], y_score[:, i])\n",
    "    average_precision[i] = average_precision_score(classifier.Y_test[:, i], y_score[:, i])\n",
    "\n",
    "# A \"micro-average\": quantifying score on all classes jointly\n",
    "precision[\"micro\"], recall[\"micro\"], _ = precision_recall_curve(\n",
    "    Y_test.ravel(), y_score.ravel()\n",
    ")\n",
    "average_precision[\"micro\"] = average_precision_score(Y_test, y_score, average=\"micro\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "64bb2065",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import multilabel_confusion_matrix\n",
    "\n",
    "y_pred = classifier.model.predict(classifier.X_test)\n",
    "    \n",
    "multilabel_confusion_matrix = multilabel_confusion_matrix(y_pred, classifier.Y_test)\n",
    "multilabel_labels = classifier.unique_labels\n",
    "\n",
    "# for axes, cfs_matrix, label in zip(ax.flatten(), multilabel_confusion_matrix, multilabel_labels):\n",
    "#     print_confusion_matrix(cfs_matrix, axes, label, [\"N\", \"Y\"])\n",
    "tps = []\n",
    "fps = []\n",
    "fns = []\n",
    "for cfs_matrix, label in zip(multilabel_confusion_matrix, multilabel_labels):\n",
    "    print(label)\n",
    "    print(cfs_matrix)\n",
    "    tps.append(cfs_matrix[1][1])\n",
    "    fps.append(cfs_matrix[0][1])\n",
    "    fn = cfs_matrix[1][0]\n",
    "\n",
    "micro_prec_avg = sum(tps) / (sum(tps)+sum(fps))\n",
    "micro_recall_avg = sum\n",
    "\n",
    "print(classifier.unique_labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "8c9debad",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/tylerskluzacek/opt/anaconda3/envs/jupyter_primary/lib/python3.8/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "/Users/tylerskluzacek/opt/anaconda3/envs/jupyter_primary/lib/python3.8/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "/Users/tylerskluzacek/opt/anaconda3/envs/jupyter_primary/lib/python3.8/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "/Users/tylerskluzacek/opt/anaconda3/envs/jupyter_primary/lib/python3.8/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "/Users/tylerskluzacek/opt/anaconda3/envs/jupyter_primary/lib/python3.8/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "/Users/tylerskluzacek/opt/anaconda3/envs/jupyter_primary/lib/python3.8/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "/Users/tylerskluzacek/opt/anaconda3/envs/jupyter_primary/lib/python3.8/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "/Users/tylerskluzacek/opt/anaconda3/envs/jupyter_primary/lib/python3.8/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "/Users/tylerskluzacek/opt/anaconda3/envs/jupyter_primary/lib/python3.8/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "/Users/tylerskluzacek/opt/anaconda3/envs/jupyter_primary/lib/python3.8/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.preprocessing import label_binarize\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.multiclass import OneVsRestClassifier\n",
    "from sklearn.pipeline import make_pipeline\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.svm import LinearSVC\n",
    "from sklearn.metrics import precision_recall_curve\n",
    "from sklearn.metrics import average_precision_score\n",
    "from sklearn.metrics import PrecisionRecallDisplay\n",
    "\n",
    "random_state = np.random.RandomState(0)\n",
    "\n",
    "y = []\n",
    "y.extend(classifier.Y_train)\n",
    "y.extend(classifier.Y_test)\n",
    "\n",
    "X = []\n",
    "X.extend(classifier.X_train)\n",
    "X.extend(classifier.X_test)\n",
    "\n",
    "# Use label_binarize to be multi-label like settings\n",
    "Y = label_binarize(y, classes=[0, 1, 2, 3, 4, 5, 6, 7, 8, 9])\n",
    "n_classes = Y.shape[1]\n",
    "\n",
    "# Split into training and test\n",
    "X_train, X_test, Y_train, Y_test = train_test_split(\n",
    "    X, Y, test_size=0.33, random_state=random_state\n",
    ")\n",
    "\n",
    "ovr_classifier = OneVsRestClassifier(\n",
    "    make_pipeline(StandardScaler(), LinearSVC(random_state=random_state))\n",
    ")\n",
    "ovr_classifier.fit(X_train, Y_train)\n",
    "y_score = ovr_classifier.decision_function(X_test)\n",
    "\n",
    "# For each class\n",
    "precision = dict()\n",
    "recall = dict()\n",
    "average_precision = dict()\n",
    "for i in range(n_classes):\n",
    "    precision[i], recall[i], _ = precision_recall_curve(Y_test[:, i], y_score[:, i])\n",
    "    average_precision[i] = average_precision_score(Y_test[:, i], y_score[:, i])\n",
    "\n",
    "# A \"micro-average\": quantifying score on all classes jointly\n",
    "precision[\"micro\"], recall[\"micro\"], _ = precision_recall_curve(\n",
    "    Y_test.ravel(), y_score.ravel()\n",
    ")\n",
    "average_precision[\"micro\"] = average_precision_score(Y_test, y_score, average=\"micro\")\n",
    "\n",
    "display = PrecisionRecallDisplay(\n",
    "    recall=recall[\"micro\"],\n",
    "    precision=precision[\"micro\"],\n",
    "    average_precision=average_precision[\"micro\"],\n",
    ")\n",
    "display.plot()\n",
    "_ = display.ax_.set_title(\"Micro-averaged over all classes\")\n",
    "\n",
    "plt.savefig(\"all_classes_cdiac_512.png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "595e5a46",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "jupyter_primary",
   "language": "python",
   "name": "jupyter_primary"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}