VCC-Note.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "#### TODO\n",
    "* Precision-Recall http://scikit-learn.org/stable/auto_examples/model_selection/plot_precision_recall.html\n",
    "* Use Git meta data as feature"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Columns\n",
    "0. id\n",
    "1. repository_id\n",
    "2. blamed_commit_id\n",
    "3. type\n",
    "4. sha\n",
    "5. url\n",
    "6. author_email\n",
    "7. author_name\n",
    "8. author_when\n",
    "9. committer_email\n",
    "10. committer_name\n",
    "11. committer_when\n",
    "12. additions\n",
    "13. deletions\n",
    "14. total_changes\n",
    "15. past_changes\n",
    "16. future_changes\n",
    "17. past_different_authors\n",
    "18. future_different_authors\n",
    "19. author_contributions_percent\n",
    "20. message\n",
    "21. patch\n",
    "22. hunk_count\n",
    "23. cve\n",
    "24. files_changed\n",
    "25. patch_keywords"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('blamed_commit', 714L), ('fixing_commit', 1137L), ('other_commit', 349558L)]"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import psycopg2\n",
    "conn = psycopg2.connect(\"dbname=postgres host=localhost port=55432 user=postgres\")\n",
    "cur = conn.cursor()\n",
    "cur.execute(\"SELECT type, COUNT(id) FROM export.commits GROUP BY type\")\n",
    "cur.fetchall()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.95 (+/- 0.00)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.svm import LinearSVC\n",
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "from sklearn import cross_validation\n",
    "import numpy as np\n",
    "import scipy as sp\n",
    "import psycopg2\n",
    "import enum\n",
    "\n",
    "class Colum(enum.IntEnum):\n",
    "    id = 1\n",
    "    type = 3\n",
    "    patch = 21\n",
    "    \n",
    "class Data:\n",
    "    def __init__(self, config):\n",
    "        self.cur = psycopg2.connect(config).cursor()\n",
    "    def fetchAll(self, sql):\n",
    "        self.cur.execute(sql)\n",
    "        return np.array(self.cur.fetchall())\n",
    "\n",
    "def tfidf(text):\n",
    "    # @TODO preprocessor: log number\n",
    "    vectorizer = TfidfVectorizer(min_df=1)\n",
    "    vectorized = vectorizer.fit_transform(text)\n",
    "#     print vectorizer.get_feature_names()\n",
    "    return vectorized.toarray()\n",
    "\n",
    "# Fetch original data\n",
    "data = Data(\"dbname=postgres host=localhost port=55432 user=postgres\")\n",
    "vcc = data.fetchAll(\"SELECT * FROM export.commits WHERE type  = 'blamed_commit' ORDER BY RANDOM() LIMIT 50\")\n",
    "ucc = data.fetchAll(\"SELECT * FROM export.commits WHERE type != 'blamed_commit' ORDER BY RANDOM() LIMIT 1000\")\n",
    "\n",
    "# Concat onece and shuffle\n",
    "sample = np.concatenate([vcc, ucc])\n",
    "np.random.shuffle(sample)\n",
    "patches = sample[:, Colum.patch]\n",
    "labels = sample[:, Colum.type]\n",
    "\n",
    "x = tfidf(patches)\n",
    "y = is_vcc = (labels == 'blamed_commit')\n",
    "vcc_paches = patches[is_vcc]\n",
    "ucc_paches = patches[~is_vcc]\n",
    "\n",
    "clf = LinearSVC(C=1.0)\n",
    "scores = cross_validation.cross_val_score(clf, x, y, cv=5)\n",
    "print(\"Accuracy: %0.2f (+/- %0.2f)\" % (scores.mean(), scores.std() * 2))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Precision-Recall by TfidfVectorizer\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.metrics import precision_recall_curve\n",
    "from sklearn.metrics import average_precision_score\n",
    "from sklearn.cross_validation import train_test_split\n",
    "from sklearn.svm import LinearSVC\n",
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "from sklearn.preprocessing import label_binarize\n",
    "from sklearn import cross_validation\n",
    "import numpy as np\n",
    "import scipy as sp\n",
    "import psycopg2\n",
    "import enum\n",
    "\n",
    "class Colum(enum.IntEnum):\n",
    "    id = 1\n",
    "    type = 3\n",
    "    patch = 21\n",
    "    \n",
    "class Data:\n",
    "    def __init__(self, config):\n",
    "        self.cur = psycopg2.connect(config).cursor()\n",
    "    def fetchAll(self, sql):\n",
    "        self.cur.execute(sql)\n",
    "        return np.array(self.cur.fetchall())\n",
    "\n",
    "def tfidf(text):\n",
    "    vectorizer = TfidfVectorizer(min_df=1)\n",
    "    vectorized = vectorizer.fit_transform(text)\n",
    "    return vectorized.toarray()\n",
    "\n",
    "precision = dict()\n",
    "recall = dict()\n",
    "average_precision = dict()\n",
    "\n",
    "# Fetch original data\n",
    "data = Data(\"dbname=postgres host=localhost port=55432 user=postgres\")\n",
    "vcc = data.fetchAll(\"SELECT * FROM export.commits WHERE type  = 'blamed_commit' ORDER BY RANDOM() LIMIT 5\")\n",
    "ucc = data.fetchAll(\"SELECT * FROM export.commits WHERE type != 'blamed_commit' ORDER BY RANDOM() LIMIT 100\")\n",
    "\n",
    "# Concat onece and shuffle\n",
    "sample = np.concatenate([vcc, ucc])\n",
    "np.random.shuffle(sample)\n",
    "patches = sample[:, Colum.patch]\n",
    "labels = sample[:, Colum.type]\n",
    "\n",
    "X = tfidf(patches)\n",
    "y = is_vcc = (labels == 'blamed_commit')\n",
    "\n",
    "# Split into training and test\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=.5, random_state=0)\n",
    "\n",
    "# Run classifier\n",
    "classifier = LinearSVC(C=1.0)\n",
    "y_score = classifier.fit(X_train, y_train).decision_function(X_test)\n",
    "\n",
    "# Compute Precision-Recall and plot curve\n",
    "precision[0], recall[0], _ = precision_recall_curve(y_test, y_score)\n",
    "average_precision[0] = average_precision_score(y_test, y_score)\n",
    "\n",
    "print precision, recall, average_precision\n",
    "\n",
    "# Plot Precision-Recall curve\n",
    "plt.clf()\n",
    "plt.plot(recall[0], precision[0], label='Precision-Recall curve')\n",
    "plt.xlabel('Recall')\n",
    "plt.ylabel('Precision')\n",
    "plt.ylim([0.0, 1.05])\n",
    "plt.xlim([0.0, 1.0])\n",
    "plt.title('Precision-Recall example: AUC={0:0.2f}'.format(average_precision[0]))\n",
    "plt.legend(loc=\"lower left\")\n",
    "plt.show()\n",
    "\n",
    "# # Plot Precision-Recall curve for each class\n",
    "# plt.clf()\n",
    "# plt.plot(recall[0], precision[0], label='Precision-recall curve of class {0} (area = {1:0.2f})'\n",
    "#          ''.format(i, average_precision[0]))\n",
    "\n",
    "# plt.xlim([0.0, 1.0])\n",
    "# plt.ylim([0.0, 1.05])\n",
    "# plt.xlabel('Recall')\n",
    "# plt.ylabel('Precision')\n",
    "# plt.title('Extension of Precision-Recall curve to multi-class')\n",
    "# plt.legend(loc=\"lower right\")\n",
    "# plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{0: array([ 0.05882353,  0.04      ,  0.04081633,  0.04166667,  0.04255319,\n",
      "        0.04347826,  0.04444444,  0.04545455,  0.04651163,  0.02380952,\n",
      "        0.02439024,  0.        ,  0.        ,  0.        ,  0.        ,\n",
      "        0.        ,  0.        ,  0.        ,  0.        ,  0.        ,\n",
      "        0.        ,  0.        ,  0.        ,  0.        ,  0.        ,\n",
      "        0.        ,  0.        ,  0.        ,  0.        ,  0.        ,\n",
      "        0.        ,  0.        ,  0.        ,  0.        ,  0.        ,\n",
      "        0.        ,  0.        ,  0.        ,  0.        ,  0.        ,\n",
      "        0.        ,  0.        ,  0.        ,  0.        ,  0.        ,\n",
      "        0.        ,  0.        ,  0.        ,  0.        ,  0.        ,\n",
      "        0.        ,  1.        ])} {0: array([ 1.        ,  0.66666667,  0.66666667,  0.66666667,  0.66666667,\n",
      "        0.66666667,  0.66666667,  0.66666667,  0.66666667,  0.33333333,\n",
      "        0.33333333,  0.        ,  0.        ,  0.        ,  0.        ,\n",
      "        0.        ,  0.        ,  0.        ,  0.        ,  0.        ,\n",
      "        0.        ,  0.        ,  0.        ,  0.        ,  0.        ,\n",
      "        0.        ,  0.        ,  0.        ,  0.        ,  0.        ,\n",
      "        0.        ,  0.        ,  0.        ,  0.        ,  0.        ,\n",
      "        0.        ,  0.        ,  0.        ,  0.        ,  0.        ,\n",
      "        0.        ,  0.        ,  0.        ,  0.        ,  0.        ,\n",
      "        0.        ,  0.        ,  0.        ,  0.        ,  0.        ,\n",
      "        0.        ,  0.        ])} {0: 0.032255820838450716}\n"
     ]
    }
   ],
   "source": [
    "# Precision-Recall by CountVectorizer\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.metrics import precision_recall_curve\n",
    "from sklearn.metrics import average_precision_score\n",
    "from sklearn.cross_validation import train_test_split\n",
    "from sklearn.svm import LinearSVC\n",
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "from sklearn.preprocessing import label_binarize\n",
    "from sklearn import cross_validation\n",
    "import numpy as np\n",
    "import scipy as sp\n",
    "import psycopg2\n",
    "import enum\n",
    "\n",
    "class Colum(enum.IntEnum):\n",
    "    id = 1\n",
    "    type = 3\n",
    "    patch = 21\n",
    "    \n",
    "class Data:\n",
    "    def __init__(self, config):\n",
    "        self.cur = psycopg2.connect(config).cursor()\n",
    "    def fetchAll(self, sql):\n",
    "        self.cur.execute(sql)\n",
    "        return np.array(self.cur.fetchall())\n",
    "\n",
    "def vectorize(text):\n",
    "    vectorizer = CountVectorizer(min_df=1)\n",
    "    vectorized = vectorizer.fit_transform(text)\n",
    "    return vectorized.toarray()\n",
    "\n",
    "precision = dict()\n",
    "recall = dict()\n",
    "average_precision = dict()\n",
    "\n",
    "# Fetch original data\n",
    "data = Data(\"dbname=postgres host=localhost port=55432 user=postgres\")\n",
    "vcc = data.fetchAll(\"SELECT * FROM export.commits WHERE type  = 'blamed_commit' ORDER BY RANDOM() LIMIT 5\")\n",
    "ucc = data.fetchAll(\"SELECT * FROM export.commits WHERE type != 'blamed_commit' ORDER BY RANDOM() LIMIT 100\")\n",
    "\n",
    "# Concat onece and shuffle\n",
    "sample = np.concatenate([vcc, ucc])\n",
    "np.random.shuffle(sample)\n",
    "patches = sample[:, Colum.patch]\n",
    "labels = sample[:, Colum.type]\n",
    "\n",
    "X = vectorize(patches)\n",
    "y = is_vcc = (labels == 'blamed_commit')\n",
    "\n",
    "# Split into training and test\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=.5, random_state=0)\n",
    "\n",
    "# Run classifier\n",
    "classifier = LinearSVC(C=1.0)\n",
    "y_score = classifier.fit(X_train, y_train).decision_function(X_test)\n",
    "\n",
    "# Compute Precision-Recall and plot curve\n",
    "precision[0], recall[0], _ = precision_recall_curve(y_test, y_score)\n",
    "average_precision[0] = average_precision_score(y_test, y_score)\n",
    "\n",
    "#print precision, recall, average_precision\n",
    "\n",
    "# Plot Precision-Recall curve\n",
    "plt.clf()\n",
    "plt.plot(recall[0], precision[0], label='Precision-Recall curve')\n",
    "plt.xlabel('Recall')\n",
    "plt.ylabel('Precision')\n",
    "plt.ylim([0.0, 1.05])\n",
    "plt.xlim([0.0, 1.0])\n",
    "plt.title('Precision-Recall example: AUC={0:0.2f}'.format(average_precision[0]))\n",
    "plt.legend(loc=\"lower left\")\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{0: array([ 0.06      ,  0.04081633,  0.04166667,  0.04255319,  0.04347826,\n",
      "        0.02222222,  0.02272727,  0.02325581,  0.02380952,  0.        ,\n",
      "        0.        ,  0.        ,  0.        ,  0.        ,  0.        ,\n",
      "        0.        ,  0.        ,  0.        ,  0.        ,  0.        ,\n",
      "        0.        ,  0.        ,  0.        ,  0.        ,  0.        ,\n",
      "        0.        ,  0.        ,  0.        ,  0.        ,  0.        ,\n",
      "        0.        ,  0.        ,  0.        ,  0.        ,  0.        ,\n",
      "        0.        ,  0.        ,  0.        ,  0.        ,  0.        ,\n",
      "        0.        ,  0.        ,  0.        ,  0.        ,  0.        ,\n",
      "        0.        ,  0.        ,  1.        ])} {0: array([ 1.        ,  0.66666667,  0.66666667,  0.66666667,  0.66666667,\n",
      "        0.33333333,  0.33333333,  0.33333333,  0.33333333,  0.        ,\n",
      "        0.        ,  0.        ,  0.        ,  0.        ,  0.        ,\n",
      "        0.        ,  0.        ,  0.        ,  0.        ,  0.        ,\n",
      "        0.        ,  0.        ,  0.        ,  0.        ,  0.        ,\n",
      "        0.        ,  0.        ,  0.        ,  0.        ,  0.        ,\n",
      "        0.        ,  0.        ,  0.        ,  0.        ,  0.        ,\n",
      "        0.        ,  0.        ,  0.        ,  0.        ,  0.        ,\n",
      "        0.        ,  0.        ,  0.        ,  0.        ,  0.        ,\n",
      "        0.        ,  0.        ,  0.        ])} {0: 0.031721055571987247}\n"
     ]
    }
   ],
   "source": [
    "# Precision-Recall\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.metrics import precision_recall_curve\n",
    "from sklearn.metrics import average_precision_score\n",
    "from sklearn.cross_validation import train_test_split\n",
    "from sklearn.svm import LinearSVC\n",
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "from sklearn.preprocessing import label_binarize\n",
    "from sklearn import cross_validation\n",
    "import numpy as np\n",
    "import scipy as sp\n",
    "import psycopg2\n",
    "import enum\n",
    "from datetime import datetime\n",
    "from diff_extractor import extract_lines, extract_added_lines, extract_removed_lines\n",
    "\n",
    "import re\n",
    "from unidiff import PatchSet\n",
    "\n",
    "\n",
    "def _normalize(str):\n",
    "    return re.sub(r'[ \\t]+', ' ', str.value.strip())\n",
    "\n",
    "def is_added_or_removed(line):\n",
    "    return line.is_added or line.is_removed\n",
    "    \n",
    "class Colum(enum.IntEnum):\n",
    "    id = 1\n",
    "    type = 3\n",
    "    patch = 21\n",
    "    \n",
    "class Data:\n",
    "    def __init__(self, config):\n",
    "        self.cur = psycopg2.connect(config).cursor()\n",
    "    def fetchAll(self, sql):\n",
    "        self.cur.execute(sql)\n",
    "        return np.array(self.cur.fetchall())\n",
    "\n",
    "def vectorize(text):\n",
    "    vectorizer = CountVectorizer(min_df=1)\n",
    "    vectorized = vectorizer.fit_transform(text)\n",
    "    return vectorized.toarray()\n",
    "\n",
    "precision = dict()\n",
    "recall = dict()\n",
    "average_precision = dict()\n",
    "\n",
    "# Fetch original data\n",
    "data = Data(\"dbname=postgres host=localhost port=55432 user=postgres\")\n",
    "vcc = data.fetchAll(\"SELECT * FROM export.commits WHERE type  = 'blamed_commit' LIMIT 5\")\n",
    "ucc = data.fetchAll(\"SELECT * FROM export.commits WHERE type != 'blamed_commit' LIMIT 100\")\n",
    "\n",
    "# Concat onece and shuffle\n",
    "sample = np.concatenate([vcc, ucc])\n",
    "np.random.shuffle(sample)\n",
    "patches = sample[:, Colum.patch]\n",
    "labels = sample[:, Colum.type]\n",
    "\n",
    "X = vectorize([\" \".join(extract_lines(patch.decode('utf8', 'ignore').encode('utf8', 'ignore').splitlines(), is_added_or_removed)) for patch in patches])\n",
    "\n",
    "y = is_vcc = (labels == 'blamed_commit')\n",
    "\n",
    "# Split into training and test\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=.5, random_state=0)\n",
    "\n",
    "# Run classifier\n",
    "classifier = LinearSVC(C=1.0)\n",
    "y_score = classifier.fit(X_train, y_train).decision_function(X_test)\n",
    "\n",
    "# Compute Precision-Recall and plot curve\n",
    "precision[0], recall[0], _ = precision_recall_curve(y_test, y_score)\n",
    "average_precision[0] = average_precision_score(y_test, y_score)\n",
    "\n",
    "print precision, recall, average_precision\n",
    "\n",
    "# Plot Precision-Recall curve\n",
    "plt.clf()\n",
    "plt.plot(recall[0], precision[0], label='Precision-Recall curve')\n",
    "plt.xlabel('Recall')\n",
    "plt.ylabel('Precision')\n",
    "plt.ylim([0.0, 1.05])\n",
    "plt.xlim([0.0, 1.0])\n",
    "plt.title('Precision-Recall example: AUC={0:0.2f}'.format(average_precision[0]))\n",
    "plt.legend(loc=\"lower left\")\n",
    "# plt.show()\n",
    "plt.savefig(\"figure_%s\" % datetime.now().strftime('%s'))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# TODO According to VCC-Finder paper, combination with Git metrics improve Precision-Recall score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['arr_0']\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "npz = np.load(\"var/vcc_data.npz\")\n",
    "print npz.files\n",
    "# npz['arr_0']\n",
    "npz.close()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{0: array([ 0.03381643,  0.031477  ,  0.0315534 ,  0.03163017,  0.03170732,\n",
      "        0.03178484,  0.03186275,  0.03194103,  0.0320197 ,  0.03209877,\n",
      "        0.03217822,  0.03225806,  0.03233831,  0.03241895,  0.0325    ,\n",
      "        0.03258145,  0.03266332,  0.03274559,  0.03282828,  0.03291139,\n",
      "        0.03299492,  0.03307888,  0.03316327,  0.03324808,  0.03333333,\n",
      "        0.03341902,  0.03350515,  0.03359173,  0.03367876,  0.03376623,\n",
      "        0.03385417,  0.03394256,  0.03403141,  0.03412073,  0.03421053,\n",
      "        0.03430079,  0.03439153,  0.03183024,  0.03191489,  0.032     ,\n",
      "        0.03208556,  0.03217158,  0.03225806,  0.03234501,  0.03243243,\n",
      "        0.03252033,  0.0298913 ,  0.02997275,  0.03005464,  0.03013699,\n",
      "        0.03021978,  0.03030303,  0.03038674,  0.03047091,  0.03055556,\n",
      "        0.02785515,  0.02793296,  0.0280112 ,  0.02808989,  0.02816901,\n",
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      "        0.07142857,  0.07142857,  0.        ])} {0: 0.19201792765190365}\n"
     ]
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from sklearn.metrics import precision_recall_curve\n",
    "from sklearn.metrics import average_precision_score\n",
    "from sklearn.cross_validation import train_test_split\n",
    "from sklearn.svm import LinearSVC\n",
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "from sklearn.preprocessing import label_binarize\n",
    "from sklearn import cross_validation\n",
    "import numpy as np\n",
    "import scipy as sp\n",
    "import enum\n",
    "from datetime import datetime\n",
    "from diff_extractor import extract_lines, extract_added_lines, extract_removed_lines\n",
    "\n",
    "class Colum(enum.IntEnum):\n",
    "    id = 1\n",
    "    type = 3\n",
    "    patch = 21\n",
    "    \n",
    "def fetch(filename, key = None):\n",
    "    npz = np.load(filename)\n",
    "    key = npz.files[0] if key is None else key\n",
    "    data = npz[key]\n",
    "    npz.close()\n",
    "    return data\n",
    "\n",
    "def vectorize(text):\n",
    "    vectorizer = CountVectorizer(min_df=1)\n",
    "    vectorized = vectorizer.fit_transform(text)\n",
    "    return vectorized.toarray()\n",
    "\n",
    "data = fetch('var/vcc_sample_40x800.npz')\n",
    "\n",
    "patches = data[:, Colum.patch]\n",
    "labels = data[:, Colum.type]\n",
    "\n",
    "# Note: Be sure that unicode(patch, 'utf-8')\n",
    "X = vectorize([\" \".join(extract_lines(patch.splitlines())) for patch in patches])\n",
    "y = is_vcc = (labels == 'blamed_commit')\n",
    "\n",
    "# Split into training and test\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=.5, random_state=0)\n",
    "\n",
    "# Run classifier\n",
    "classifier = LinearSVC(C=1.0)\n",
    "y_score = classifier.fit(X_train, y_train).decision_function(X_test)\n",
    "\n",
    "# Compute Precision-Recall and plot curve\n",
    "precision = dict()\n",
    "recall = dict()\n",
    "average_precision = dict()\n",
    "precision[0], recall[0], _ = precision_recall_curve(y_test, y_score)\n",
    "average_precision[0] = average_precision_score(y_test, y_score)\n",
    "\n",
    "print precision, recall, average_precision\n",
    "\n",
    "# Plot Precision-Recall curve\n",
    "plt.clf()\n",
    "plt.plot(recall[0], precision[0], label='Precision-Recall curve')\n",
    "plt.xlabel('Recall')\n",
    "plt.ylabel('Precision')\n",
    "plt.ylim([0.0, 1.05])\n",
    "plt.xlim([0.0, 1.0])\n",
    "plt.title('Precision-Recall example: AUC={0:0.2f}'.format(average_precision[0]))\n",
    "plt.legend(loc=\"lower left\")\n",
    "plt.show()\n",
    "plt.savefig(\"figure_%s\" % datetime.now().strftime('%s'))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ascii\n"
     ]
    }
   ],
   "source": [
    "import sys\n",
    "print sys.getdefaultencoding()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.11"
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 },
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}