sklearn/F1-P-R+calculation+for+multi-class+output+by+sklearn.ipynb

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 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# F1/P/R calculation for multi-class output"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "y_true=[1,2,3,4,5]\n",
    "y_pred=[1,2,3,1,5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.733333333333 0.7 0.8\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/root/anaconda3/envs/envtf/lib/python3.5/site-packages/sklearn/metrics/classification.py:1113: 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",
      "/root/anaconda3/envs/envtf/lib/python3.5/site-packages/sklearn/metrics/classification.py:1113: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import f1_score, precision_score, recall_score\n",
    "f1 = f1_score( y_true, y_pred, average='macro' )\n",
    "p = precision_score(y_true, y_pred, average='macro')\n",
    "r = recall_score(y_true, y_pred, average='macro')\n",
    "\n",
    "print(f1, p, r)"
   ]
  }
 ],
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