{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
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   },
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   "source": [
    "%matplotlib inline\n",
    "\n",
    "import matplotlib as plt\n",
    "import numpy as np\n",
    "\n",
    "from autotagger.helpers.preprocess import load_dataset\n",
    "from sklearn import cross_validation,linear_model\n",
    "from sklearn.datasets import make_multilabel_classification\n",
    "from sklearn.metrics import f1_score, precision_score, recall_score\n",
    "from sklearn.multiclass import OneVsRestClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "X,Y = load_dataset(\"stackoverflow\", max_features=100)\n",
    "X_train, X_test, Y_train, Y_test = cross_validation.train_test_split(X,Y,test_size=0.80, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "clf = linear_model.LogisticRegression()\n",
    "meta_clf = OneVsRestClassifier(clf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "OneVsRestClassifier(estimator=LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
       "          intercept_scaling=1, max_iter=100, multi_class='ovr', n_jobs=1,\n",
       "          penalty='l2', random_state=None, solver='liblinear', tol=0.0001,\n",
       "          verbose=0, warm_start=False),\n",
       "          n_jobs=1)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "meta_clf.fit(X_train,Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "Y_pred = meta_clf.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/felipe/auto-tagger/venv3/lib/python3.4/site-packages/sklearn/metrics/classification.py:1074: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.037940736071986787"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# macro average refers to the average f1_score for each label\n",
    "f1_score(Y_test,Y_pred, average='macro')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/felipe/auto-tagger/venv3/lib/python3.4/site-packages/sklearn/metrics/classification.py:1074: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.10539093353329665"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# if we just consider the labels that have had at least one instance predicted,\n",
    "# our score goes up:\n",
    "\n",
    "label_scores = f1_score(Y_test,Y_pred,average=None)\n",
    "valid_label_indices = np.nonzero(label_scores)[0]\n",
    "f1_score(Y_test,Y_pred,average='macro',labels=valid_label_indices)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.18395809178395894"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# micro average refers to the average f1_score for each instance\n",
    "f1_score(Y_test,Y_pred,average='micro')"
   ]
  }
 ],
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