Active Learning Regression.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Active Learning on Logistic Regression\n",
    "## Configuration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Config\n",
    "\"\"\"\n",
    "Set to a number to generate same split\n",
    "Number of Iteration: number of iteraton of active learning\n",
    "Number of Query: number of queries for to extract samples near to decision boundary\n",
    "\"\"\"\n",
    "\n",
    "random_state=0\n",
    "number_of_iteration=7\n",
    "number_of_query = 20\n",
    "\n",
    "N_THRESHOLD = 1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Active Learning Helper\n",
    "### least_confident\n",
    "\n",
    "1. Get probability of test data by calling `predict_proba`, return 2d vectors [0_probability, 1_probability]\n",
    "2. For each vector, get max value of 2 probability values and negate the number to generate score. Higher score means higher uncertainty \n",
    "3. Get top N data.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Active Learning Helper\n",
    "# From https://github.com/davefernig/alp/\n",
    "from __future__ import unicode_literals, division\n",
    "\n",
    "from scipy.sparse import csc_matrix, vstack\n",
    "from scipy.stats import entropy\n",
    "from collections import Counter\n",
    "import numpy as np\n",
    "\n",
    "class ActiveLearner(object):\n",
    "    _uncertainty_sampling_frameworks = [\n",
    "        'entropy',\n",
    "        'max_margin',\n",
    "        'least_confident',\n",
    "    ]\n",
    "\n",
    "    _query_by_committee_frameworks = [\n",
    "        'vote_entropy',\n",
    "        'average_kl_divergence',\n",
    "    ]\n",
    "\n",
    "    def __init__(self, strategy='least_confident'):\n",
    "        self.strategy = strategy\n",
    "\n",
    "    def rank(self, clf, X_unlabeled, num_queries=None):\n",
    "        if num_queries == None:\n",
    "            num_queries = X_unlabeled.shape[0]\n",
    "\n",
    "        elif type(num_queries) == float:\n",
    "            num_queries = int(num_queries * X_unlabeled.shape[0])\n",
    "\n",
    "        if self.strategy in self._uncertainty_sampling_frameworks:\n",
    "            scores = self.__uncertainty_sampling(clf, X_unlabeled)\n",
    "\n",
    "        elif self.strategy in self._query_by_committee_frameworks:\n",
    "            scores = self.__query_by_committee(clf, X_unlabeled)\n",
    "\n",
    "        else: \n",
    "            raise ValueError(\n",
    "                \"No input strategy\"\n",
    "            )\n",
    "        rankings = np.argsort(-scores)[:num_queries]\n",
    "        return rankings\n",
    "\n",
    "    def __uncertainty_sampling(self, clf, X_unlabeled):\n",
    "        probs = clf.predict_proba(X_unlabeled)\n",
    "\n",
    "        if self.strategy == 'least_confident':\n",
    "            return 1 - np.amax(probs, axis=1)\n",
    "\n",
    "        elif self.strategy == 'max_margin':\n",
    "            margin = np.partition(-probs, 1, axis=1)\n",
    "            return -np.abs(margin[:,0] - margin[:, 1])\n",
    "\n",
    "        elif self.strategy == 'entropy':\n",
    "            return np.apply_along_axis(entropy, 1, probs)\n",
    "\n",
    "    def __query_by_committee(self, clf, X_unlabeled):\n",
    "        num_classes = len(clf[0].classes_)\n",
    "        C = len(clf)\n",
    "        preds = []\n",
    "\n",
    "        if self.strategy == 'vote_entropy':\n",
    "            for model in clf:\n",
    "                y_out = map(int, model.predict(X_unlabeled))\n",
    "                preds.append(np.eye(num_classes)[y_out])\n",
    "\n",
    "            votes = np.apply_along_axis(np.sum, 0, np.stack(preds)) / C\n",
    "            return np.apply_along_axis(entropy, 1, votes)\n",
    "\n",
    "        elif self.strategy == 'average_kl_divergence':\n",
    "            for model in clf:\n",
    "                preds.append(model.predict_proba(X_unlabeled))\n",
    "\n",
    "            consensus = np.mean(np.stack(preds), axis=0)\n",
    "            divergence = []\n",
    "            for y_out in preds:\n",
    "                divergence.append(entropy(consensus.T, y_out.T))\n",
    "            \n",
    "            return np.apply_along_axis(np.mean, 0, np.stack(divergence))\n",
    "\n",
    "class RandomLearner(object):\n",
    "    \"\"\"\n",
    "    Random return idx\n",
    "    for control group\n",
    "    \"\"\"\n",
    "\n",
    "    def __init__(self, strategy=''):\n",
    "        self.strategy = strategy\n",
    "\n",
    "    def rank(self, clf, X_unlabeled, num_queries=None):\n",
    "        if num_queries == None:\n",
    "            num_queries = X_unlabeled.shape[0]\n",
    "\n",
    "        elif type(num_queries) == float:\n",
    "            num_queries = int(num_queries * X_unlabeled.shape[0])\n",
    "\n",
    "        idx = np.arange(len(X_unlabeled))\n",
    "        np.random.shuffle(idx)\n",
    "        return idx[:num_queries]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def process(X, y, learner, model, config):\n",
    "    # Initialization\n",
    "    precisions = []\n",
    "    recalls = []\n",
    "    predict_proba = []\n",
    "    \n",
    "    parameter = [\n",
    "        \"test_size\", # test/(train + test)\n",
    "        \"random_state\",\n",
    "        \"init_size\", # \n",
    "        \"query_each_iteration\",\n",
    "    ]\n",
    "    \n",
    "    default_config = {\n",
    "        \"test_size\": 0.4,\n",
    "        \"random_state\": 0,\n",
    "        \"init_size\": 0.5,\n",
    "        \"query_each_iteration\": 5,\n",
    "    }\n",
    "    \n",
    "    cfg = {}\n",
    "\n",
    "    # load config\n",
    "    for k in parameter:\n",
    "        cfg[k] = config[k] if config.get(k) else default_config[k]\n",
    "\n",
    "    # Split\n",
    "    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=cfg[\"test_size\"], random_state=cfg[\"random_state\"])\n",
    "\n",
    "    init_size = int(np.ceil(X_train.shape[0] * cfg[\"init_size\"]))\n",
    "    pool_size = X_train.shape[0] - init_size\n",
    "    X_init = X_train[:init_size] \n",
    "    y_init = y_train[:init_size]\n",
    "    X_pool = X_train[init_size:]\n",
    "    y_pool = y_train[init_size:]\n",
    "    X_train = X_init\n",
    "    y_train = y_init\n",
    "    number_of_iteration = int(np.ceil(pool_size/cfg[\"query_each_iteration\"]))\n",
    "    query_each_iteration = cfg[\"query_each_iteration\"]\n",
    "    \n",
    "    print(\"Initial training set size:\", init_size, \"(\", cfg[\"init_size\"], \")\")\n",
    "    print(\"Query each iteration:\", cfg[\"query_each_iteration\"])\n",
    "\n",
    "    # init state\n",
    "    model.fit(X_train, y_train)\n",
    "    y_predict = model.predict(X_test)\n",
    "    precision, recall, thresholds = precision_recall_curve(y_test, y_predict)\n",
    "    precisions.append(precision[N_THRESHOLD])\n",
    "    recalls.append(recall[N_THRESHOLD])\n",
    "    print(\"init\")\n",
    "    print(\"@P:\", precision)\n",
    "    print(\"@R:\", recall)\n",
    "    print(thresholds)\n",
    "    print()\n",
    "    \n",
    "\n",
    "    # Active Learning\n",
    "    for i in range(number_of_iteration):\n",
    "        idx = learner.rank(model, X_pool, query_each_iteration)\n",
    "        t = model.predict_proba(X_pool[idx[0]:])\n",
    "        min_certain = np.amax(model.predict_proba(X_pool[idx[0:1]]))\n",
    "        max_certain = np.amax(model.predict_proba(X_pool[idx[-1:]]))\n",
    "        predict_proba.append([min_certain, max_certain])\n",
    "        X_train = np.concatenate([X_train, X_pool[idx, :]])\n",
    "        y_train = np.concatenate([y_train, y_pool[idx]])\n",
    "        X_pool = np.delete(X_pool, idx, axis=0)\n",
    "        y_pool = np.delete(y_pool, idx, axis=0)\n",
    "        model.fit(X_train, y_train)\n",
    "        # metrics\n",
    "        y_predict = model.predict(X_test)\n",
    "        precision, recall, thresholds = precision_recall_curve(y_test, y_predict)\n",
    "        precisions.append(precision[N_THRESHOLD])\n",
    "        recalls.append(recall[N_THRESHOLD])\n",
    "        print(\"Round: \", i+1)\n",
    "        print(\"Proba:\", min_certain, max_certain)\n",
    "        print(\"Train: \", len(X_train))\n",
    "        print(\"Test: \", len(X_test))\n",
    "        print(\"@P:\", precision)\n",
    "        print(\"@R:\", recall)\n",
    "        print(thresholds)\n",
    "    \n",
    "    debug = {\n",
    "        \"X_train\": X_train\n",
    "    }\n",
    "    \n",
    "    info = {\n",
    "        \"predict_proba\": [predict_proba[0]] + predict_proba\n",
    "    }\n",
    "    \n",
    "    return precisions, recalls, debug, info"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import sklearn\n",
    "from sklearn import datasets\n",
    "from sklearn import linear_model\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn import preprocessing\n",
    "# metrics\n",
    "from sklearn.metrics import precision_recall_curve\n",
    "# plot\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "config = {\n",
    "    \"test_size\":0.4, \n",
    "    \"random_state\": random_state,\n",
    "    \"init_size\": 0.3,\n",
    "    \"query_each_iteration\": 5\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Initial training set size: 102 ( 0.3 )\n",
      "Query each iteration: 5\n",
      "init\n",
      "@P: [ 0.34801762  0.77941176  1.        ]\n",
      "@R: [ 1.          0.67088608  0.        ]\n",
      "[ 0.  1.]\n",
      "\n",
      "Round:  1\n",
      "Proba: 0.524174094404 0.554203980677\n",
      "Train:  107\n",
      "Test:  227\n",
      "@P: [ 0.34801762  0.82089552  1.        ]\n",
      "@R: [ 1.          0.69620253  0.        ]\n",
      "[ 0.  1.]\n",
      "Round:  2\n",
      "Proba: 0.503867103055 0.530038556387\n",
      "Train:  112\n",
      "Test:  227\n",
      "@P: [ 0.34801762  0.80882353  1.        ]\n",
      "@R: [ 1.          0.69620253  0.        ]\n",
      "[ 0.  1.]\n",
      "Round:  3\n",
      "Proba: 0.513052517667 0.557879575369\n",
      "Train:  117\n",
      "Test:  227\n",
      "@P: [ 0.34801762  0.84375     1.        ]\n",
      "@R: [ 1.         0.6835443  0.       ]\n",
      "[ 0.  1.]\n",
      "Round:  4\n",
      "Proba: 0.502498541846 0.525823551139\n",
      "Train:  122\n",
      "Test:  227\n",
      "@P: [ 0.34801762  0.86885246  1.        ]\n",
      "@R: [ 1.          0.67088608  0.        ]\n",
      "[ 0.  1.]\n",
      "Round:  5\n",
      "Proba: 0.547683846135 0.57559999079\n",
      "Train:  127\n",
      "Test:  227\n",
      "@P: [ 0.34801762  0.9         1.        ]\n",
      "@R: [ 1.         0.6835443  0.       ]\n",
      "[ 0.  1.]\n",
      "Round:  6\n",
      "Proba: 0.50572809525 0.533877800417\n",
      "Train:  132\n",
      "Test:  227\n",
      "@P: [ 0.34801762  0.87096774  1.        ]\n",
      "@R: [ 1.         0.6835443  0.       ]\n",
      "[ 0.  1.]\n",
      "Round:  7\n",
      "Proba: 0.543398616531 0.597210608542\n",
      "Train:  137\n",
      "Test:  227\n",
      "@P: [ 0.34801762  0.83823529  1.        ]\n",
      "@R: [ 1.          0.72151899  0.        ]\n",
      "[ 0.  1.]\n",
      "Round:  8\n",
      "Proba: 0.574075124838 0.63413091546\n",
      "Train:  142\n",
      "Test:  227\n",
      "@P: [ 0.34801762  0.84615385  1.        ]\n",
      "@R: [ 1.          0.69620253  0.        ]\n",
      "[ 0.  1.]\n",
      "Round:  9\n",
      "Proba: 0.549081583016 0.644292286889\n",
      "Train:  147\n",
      "Test:  227\n",
      "@P: [ 0.34801762  0.86666667  1.        ]\n",
      "@R: [ 1.          0.65822785  0.        ]\n",
      "[ 0.  1.]\n",
      "Round:  10\n",
      "Proba: 0.540750641543 0.573695797546\n",
      "Train:  152\n",
      "Test:  227\n",
      "@P: [ 0.34801762  0.88135593  1.        ]\n",
      "@R: [ 1.          0.65822785  0.        ]\n",
      "[ 0.  1.]\n",
      "Round:  11\n",
      "Proba: 0.554391664353 0.61604718807\n",
      "Train:  157\n",
      "Test:  227\n",
      "@P: [ 0.34801762  0.85483871  1.        ]\n",
      "@R: [ 1.          0.67088608  0.        ]\n",
      "[ 0.  1.]\n",
      "Round:  12\n",
      "Proba: 0.619474475912 0.632591441564\n",
      "Train:  162\n",
      "Test:  227\n",
      "@P: [ 0.34801762  0.85714286  1.        ]\n",
      "@R: [ 1.         0.6835443  0.       ]\n",
      "[ 0.  1.]\n",
      "Round:  13\n",
      "Proba: 0.634452976696 0.654252243659\n",
      "Train:  167\n",
      "Test:  227\n",
      "@P: [ 0.34801762  0.84126984  1.        ]\n",
      "@R: [ 1.          0.67088608  0.        ]\n",
      "[ 0.  1.]\n",
      "Round:  14\n",
      "Proba: 0.621034904439 0.668180651335\n",
      "Train:  172\n",
      "Test:  227\n",
      "@P: [ 0.34801762  0.83333333  1.        ]\n",
      "@R: [ 1.          0.75949367  0.        ]\n",
      "[ 0.  1.]\n",
      "Round:  15\n",
      "Proba: 0.664630076427 0.783108628773\n",
      "Train:  177\n",
      "Test:  227\n",
      "@P: [ 0.34801762  0.84285714  1.        ]\n",
      "@R: [ 1.          0.74683544  0.        ]\n",
      "[ 0.  1.]\n",
      "Round:  16\n",
      "Proba: 0.773865260941 0.797489969112\n",
      "Train:  182\n",
      "Test:  227\n",
      "@P: [ 0.34801762  0.84507042  1.        ]\n",
      "@R: [ 1.          0.75949367  0.        ]\n",
      "[ 0.  1.]\n",
      "Round:  17\n",
      "Proba: 0.806686403791 0.812498648012\n",
      "Train:  187\n",
      "Test:  227\n",
      "@P: [ 0.34801762  0.83333333  1.        ]\n",
      "@R: [ 1.          0.75949367  0.        ]\n",
      "[ 0.  1.]\n",
      "Round:  18\n",
      "Proba: 0.81342858916 0.864545053154\n",
      "Train:  192\n",
      "Test:  227\n",
      "@P: [ 0.34801762  0.84931507  1.        ]\n",
      "@R: [ 1.          0.78481013  0.        ]\n",
      "[ 0.  1.]\n",
      "Round:  19\n",
      "Proba: 0.853388016951 0.869162705326\n",
      "Train:  197\n",
      "Test:  227\n",
      "@P: [ 0.34801762  0.85135135  1.        ]\n",
      "@R: [ 1.          0.79746835  0.        ]\n",
      "[ 0.  1.]\n",
      "Round:  20\n",
      "Proba: 0.882082864247 0.896986461653\n",
      "Train:  202\n",
      "Test:  227\n",
      "@P: [ 0.34801762  0.84722222  1.        ]\n",
      "@R: [ 1.         0.7721519  0.       ]\n",
      "[ 0.  1.]\n",
      "Round:  21\n",
      "Proba: 0.894648816048 0.906822795186\n",
      "Train:  207\n",
      "Test:  227\n",
      "@P: [ 0.34801762  0.84722222  1.        ]\n",
      "@R: [ 1.         0.7721519  0.       ]\n",
      "[ 0.  1.]\n",
      "Round:  22\n",
      "Proba: 0.910683036604 0.918734914859\n",
      "Train:  212\n",
      "Test:  227\n",
      "@P: [ 0.34801762  0.84722222  1.        ]\n",
      "@R: [ 1.         0.7721519  0.       ]\n",
      "[ 0.  1.]\n",
      "Round:  23\n",
      "Proba: 0.919150217813 0.926942491392\n",
      "Train:  217\n",
      "Test:  227\n",
      "@P: [ 0.34801762  0.84722222  1.        ]\n",
      "@R: [ 1.         0.7721519  0.       ]\n",
      "[ 0.  1.]\n",
      "Round:  24\n",
      "Proba: 0.929353304493 0.932239686585\n",
      "Train:  222\n",
      "Test:  227\n",
      "@P: [ 0.34801762  0.85915493  1.        ]\n",
      "@R: [ 1.         0.7721519  0.       ]\n",
      "[ 0.  1.]\n",
      "Round:  25\n",
      "Proba: 0.929905507834 0.936628551004\n",
      "Train:  227\n",
      "Test:  227\n",
      "@P: [ 0.34801762  0.8630137   1.        ]\n",
      "@R: [ 1.          0.79746835  0.        ]\n",
      "[ 0.  1.]\n",
      "Round:  26\n",
      "Proba: 0.926745643617 0.944428227032\n",
      "Train:  232\n",
      "Test:  227\n",
      "@P: [ 0.34801762  0.89041096  1.        ]\n",
      "@R: [ 1.          0.82278481  0.        ]\n",
      "[ 0.  1.]\n",
      "Round:  27\n",
      "Proba: 0.881181695105 0.937960112602\n",
      "Train:  237\n",
      "Test:  227\n",
      "@P: [ 0.34801762  0.8630137   1.        ]\n",
      "@R: [ 1.          0.79746835  0.        ]\n",
      "[ 0.  1.]\n",
      "Round:  28\n",
      "Proba: 0.915517263287 0.933273034782\n",
      "Train:  242\n",
      "Test:  227\n",
      "@P: [ 0.34801762  0.86486486  1.        ]\n",
      "@R: [ 1.          0.81012658  0.        ]\n",
      "[ 0.  1.]\n",
      "Round:  29\n",
      "Proba: 0.937777725492 0.943390522529\n",
      "Train:  247\n",
      "Test:  227\n",
      "@P: [ 0.34801762  0.85333333  1.        ]\n",
      "@R: [ 1.          0.81012658  0.        ]\n",
      "[ 0.  1.]\n",
      "Round:  30\n",
      "Proba: 0.946150699921 0.952746221666\n",
      "Train:  252\n",
      "Test:  227\n",
      "@P: [ 0.34801762  0.84931507  1.        ]\n",
      "@R: [ 1.          0.78481013  0.        ]\n",
      "[ 0.  1.]\n",
      "Round:  31\n",
      "Proba: 0.959263916719 0.963059735737\n",
      "Train:  257\n",
      "Test:  227\n",
      "@P: [ 0.34801762  0.8630137   1.        ]\n",
      "@R: [ 1.          0.79746835  0.        ]\n",
      "[ 0.  1.]\n",
      "Round:  32\n",
      "Proba: 0.964620783736 0.967113483969\n",
      "Train:  262\n",
      "Test:  227\n",
      "@P: [ 0.34801762  0.84931507  1.        ]\n",
      "@R: [ 1.          0.78481013  0.        ]\n",
      "[ 0.  1.]\n",
      "Round:  33\n",
      "Proba: 0.944382241918 0.965151643897\n",
      "Train:  267\n",
      "Test:  227\n",
      "@P: [ 0.34801762  0.85333333  1.        ]\n",
      "@R: [ 1.          0.81012658  0.        ]\n",
      "[ 0.  1.]\n",
      "Round:  34\n",
      "Proba: 0.960667143662 0.967057526324\n",
      "Train:  272\n",
      "Test:  227\n",
      "@P: [ 0.34801762  0.85333333  1.        ]\n",
      "@R: [ 1.          0.81012658  0.        ]\n",
      "[ 0.  1.]\n",
      "Round:  35\n",
      "Proba: 0.967093222961 0.970254910903\n",
      "Train:  277\n",
      "Test:  227\n",
      "@P: [ 0.34801762  0.8852459   1.        ]\n",
      "@R: [ 1.         0.6835443  0.       ]\n",
      "[ 0.  1.]\n",
      "Round:  36\n",
      "Proba: 0.820397294302 0.834188943689\n",
      "Train:  282\n",
      "Test:  227\n",
      "@P: [ 0.34801762  0.88709677  1.        ]\n",
      "@R: [ 1.          0.69620253  0.        ]\n",
      "[ 0.  1.]\n",
      "Round:  37\n",
      "Proba: 0.853200484542 0.861709039166\n",
      "Train:  287\n",
      "Test:  227\n",
      "@P: [ 0.34801762  0.90163934  1.        ]\n",
      "@R: [ 1.          0.69620253  0.        ]\n",
      "[ 0.  1.]\n",
      "Round:  38\n",
      "Proba: 0.866265227139 0.876767202952\n",
      "Train:  292\n",
      "Test:  227\n",
      "@P: [ 0.34801762  0.90163934  1.        ]\n",
      "@R: [ 1.          0.69620253  0.        ]\n",
      "[ 0.  1.]\n",
      "Round:  39\n",
      "Proba: 0.881898850404 0.888466544627\n",
      "Train:  297\n",
      "Test:  227\n",
      "@P: [ 0.34801762  0.90163934  1.        ]\n",
      "@R: [ 1.          0.69620253  0.        ]\n",
      "[ 0.  1.]\n",
      "Round:  40\n",
      "Proba: 0.890597364647 0.896867335193\n",
      "Train:  302\n",
      "Test:  227\n",
      "@P: [ 0.34801762  0.9         1.        ]\n",
      "@R: [ 1.         0.6835443  0.       ]\n",
      "[ 0.  1.]\n",
      "Round:  41\n",
      "Proba: 0.899370404766 0.905974712037\n",
      "Train:  307\n",
      "Test:  227\n",
      "@P: [ 0.34801762  0.90163934  1.        ]\n",
      "@R: [ 1.          0.69620253  0.        ]\n",
      "[ 0.  1.]\n",
      "Round:  42\n",
      "Proba: 0.909186738982 0.926209007925\n",
      "Train:  312\n",
      "Test:  227\n",
      "@P: [ 0.34801762  0.90163934  1.        ]\n",
      "@R: [ 1.          0.69620253  0.        ]\n",
      "[ 0.  1.]\n",
      "Round:  43\n",
      "Proba: 0.927930682858 0.932718474619\n",
      "Train:  317\n",
      "Test:  227\n",
      "@P: [ 0.34801762  0.9         1.        ]\n",
      "@R: [ 1.         0.6835443  0.       ]\n",
      "[ 0.  1.]\n",
      "Round:  44\n",
      "Proba: 0.932718184532 0.944388044218\n",
      "Train:  322\n",
      "Test:  227\n",
      "@P: [ 0.34801762  0.9         1.        ]\n",
      "@R: [ 1.         0.6835443  0.       ]\n",
      "[ 0.  1.]\n",
      "Round:  45\n",
      "Proba: 0.948760292765 0.958717716837\n",
      "Train:  327\n",
      "Test:  227\n",
      "@P: [ 0.34801762  0.9137931   1.        ]\n",
      "@R: [ 1.          0.67088608  0.        ]\n",
      "[ 0.  1.]\n",
      "Round:  46\n",
      "Proba: 0.954356730015 0.966603563546\n",
      "Train:  332\n",
      "Test:  227\n",
      "@P: [ 0.34801762  0.91525424  1.        ]\n",
      "@R: [ 1.         0.6835443  0.       ]\n",
      "[ 0.  1.]\n",
      "Round:  47\n",
      "Proba: 0.969037622687 0.977210922517\n",
      "Train:  337\n",
      "Test:  227\n",
      "@P: [ 0.34801762  0.91525424  1.        ]\n",
      "@R: [ 1.         0.6835443  0.       ]\n",
      "[ 0.  1.]\n",
      "Round:  48\n",
      "Proba: 0.980573524689 0.991767034754\n",
      "Train:  339\n",
      "Test:  227\n",
      "@P: [ 0.34801762  0.91525424  1.        ]\n",
      "@R: [ 1.         0.6835443  0.       ]\n",
      "[ 0.  1.]\n"
     ]
    }
   ],
   "source": [
    "# Active Learning\n",
    "\n",
    "#Load Data\n",
    "path = \"data/\"\n",
    "y = np.array([])\n",
    "T1 = np.loadtxt(path+\"hurricane.vec\")\n",
    "T = T1\n",
    "y = np.append(y, np.ones(len(T)))\n",
    "F1 = np.loadtxt(path+\"lasvegas.vec\")\n",
    "F2 = np.loadtxt(path+\"false.vec\")\n",
    "F = np.append(F1, F2, axis=0)\n",
    "y = np.append(y, np.zeros(len(F)))\n",
    "embedding = np.append(T,F,axis=0)\n",
    "X = sklearn.preprocessing.normalize(embedding)\n",
    "\n",
    "model = sklearn.linear_model.LogisticRegressionCV()\n",
    "active_learner = ActiveLearner(\"least_confident\")\n",
    "\n",
    "active_learning_precision, active_learning_recall, debug1, info1 = process(X, y, active_learner, model, config)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Initial training set size: 102 ( 0.3 )\n",
      "Query each iteration: 5\n",
      "init\n",
      "@P: [ 0.34801762  0.77941176  1.        ]\n",
      "@R: [ 1.          0.67088608  0.        ]\n",
      "[ 0.  1.]\n",
      "\n",
      "Round:  1\n",
      "Proba: 0.961406701689 0.999058831472\n",
      "Train:  107\n",
      "Test:  227\n",
      "@P: [ 0.34801762  0.82089552  1.        ]\n",
      "@R: [ 1.          0.69620253  0.        ]\n",
      "[ 0.  1.]\n",
      "Round:  2\n",
      "Proba: 0.909182164552 0.941741534992\n",
      "Train:  112\n",
      "Test:  227\n",
      "@P: [ 0.34801762  0.80882353  1.        ]\n",
      "@R: [ 1.          0.69620253  0.        ]\n",
      "[ 0.  1.]\n",
      "Round:  3\n",
      "Proba: 0.997804415561 0.94146552959\n",
      "Train:  117\n",
      "Test:  227\n",
      "@P: [ 0.34801762  0.828125    1.        ]\n",
      "@R: [ 1.          0.67088608  0.        ]\n",
      "[ 0.  1.]\n",
      "Round:  4\n",
      "Proba: 0.990802201386 0.714756248637\n",
      "Train:  122\n",
      "Test:  227\n",
      "@P: [ 0.34801762  0.87272727  1.        ]\n",
      "@R: [ 1.          0.60759494  0.        ]\n",
      "[ 0.  1.]\n",
      "Round:  5\n",
      "Proba: 0.773933996972 0.972455961053\n",
      "Train:  127\n",
      "Test:  227\n",
      "@P: [ 0.34801762  0.88888889  1.        ]\n",
      "@R: [ 1.          0.60759494  0.        ]\n",
      "[ 0.  1.]\n",
      "Round:  6\n",
      "Proba: 0.904051478999 0.869157546233\n",
      "Train:  132\n",
      "Test:  227\n",
      "@P: [ 0.34801762  0.89285714  1.        ]\n",
      "@R: [ 1.          0.63291139  0.        ]\n",
      "[ 0.  1.]\n",
      "Round:  7\n",
      "Proba: 0.898842762892 0.877101996146\n",
      "Train:  137\n",
      "Test:  227\n",
      "@P: [ 0.34801762  0.85483871  1.        ]\n",
      "@R: [ 1.          0.67088608  0.        ]\n",
      "[ 0.  1.]\n",
      "Round:  8\n",
      "Proba: 0.99610726416 0.980760838075\n",
      "Train:  142\n",
      "Test:  227\n",
      "@P: [ 0.34801762  0.86206897  1.        ]\n",
      "@R: [ 1.          0.63291139  0.        ]\n",
      "[ 0.  1.]\n",
      "Round:  9\n",
      "Proba: 0.980423558387 0.887254223948\n",
      "Train:  147\n",
      "Test:  227\n",
      "@P: [ 0.34801762  0.8028169   1.        ]\n",
      "@R: [ 1.          0.72151899  0.        ]\n",
      "[ 0.  1.]\n",
      "Round:  10\n",
      "Proba: 0.508489603109 0.941112090679\n",
      "Train:  152\n",
      "Test:  227\n",
      "@P: [ 0.34801762  0.91666667  1.        ]\n",
      "@R: [ 1.          0.55696203  0.        ]\n",
      "[ 0.  1.]\n",
      "Round:  11\n",
      "Proba: 0.516300163503 0.860931886216\n",
      "Train:  157\n",
      "Test:  227\n",
      "@P: [ 0.34801762  0.9         1.        ]\n",
      "@R: [ 1.         0.6835443  0.       ]\n",
      "[ 0.  1.]\n",
      "Round:  12\n",
      "Proba: 0.887699277206 0.948058039879\n",
      "Train:  162\n",
      "Test:  227\n",
      "@P: [ 0.34801762  0.80555556  1.        ]\n",
      "@R: [ 1.          0.73417722  0.        ]\n",
      "[ 0.  1.]\n",
      "Round:  13\n",
      "Proba: 0.99997467534 0.998296238196\n",
      "Train:  167\n",
      "Test:  227\n",
      "@P: [ 0.34801762  0.8852459   1.        ]\n",
      "@R: [ 1.         0.6835443  0.       ]\n",
      "[ 0.  1.]\n",
      "Round:  14\n",
      "Proba: 0.626912305368 0.756377277681\n",
      "Train:  172\n",
      "Test:  227\n",
      "@P: [ 0.34801762  0.87096774  1.        ]\n",
      "@R: [ 1.         0.6835443  0.       ]\n",
      "[ 0.  1.]\n",
      "Round:  15\n",
      "Proba: 0.802506368377 0.887459086802\n",
      "Train:  177\n",
      "Test:  227\n",
      "@P: [ 0.34801762  0.79452055  1.        ]\n",
      "@R: [ 1.          0.73417722  0.        ]\n",
      "[ 0.  1.]\n",
      "Round:  16\n",
      "Proba: 0.99999998027 0.999564804153\n",
      "Train:  182\n",
      "Test:  227\n",
      "@P: [ 0.34801762  0.82191781  1.        ]\n",
      "@R: [ 1.          0.75949367  0.        ]\n",
      "[ 0.  1.]\n",
      "Round:  17\n",
      "Proba: 0.926036477647 0.905083071318\n",
      "Train:  187\n",
      "Test:  227\n",
      "@P: [ 0.34801762  0.8852459   1.        ]\n",
      "@R: [ 1.         0.6835443  0.       ]\n",
      "[ 0.  1.]\n",
      "Round:  18\n",
      "Proba: 0.972723651308 0.888421851482\n",
      "Train:  192\n",
      "Test:  227\n",
      "@P: [ 0.34801762  0.81944444  1.        ]\n",
      "@R: [ 1.          0.74683544  0.        ]\n",
      "[ 0.  1.]\n",
      "Round:  19\n",
      "Proba: 0.992394440531 0.969970600679\n",
      "Train:  197\n",
      "Test:  227\n",
      "@P: [ 0.34801762  0.89830508  1.        ]\n",
      "@R: [ 1.          0.67088608  0.        ]\n",
      "[ 0.  1.]\n",
      "Round:  20\n",
      "Proba: 0.991078295866 0.507003431058\n",
      "Train:  202\n",
      "Test:  227\n",
      "@P: [ 0.34801762  0.85245902  1.        ]\n",
      "@R: [ 1.          0.65822785  0.        ]\n",
      "[ 0.  1.]\n",
      "Round:  21\n",
      "Proba: 0.758808535619 0.972534885697\n",
      "Train:  207\n",
      "Test:  227\n",
      "@P: [ 0.34801762  0.92307692  1.        ]\n",
      "@R: [ 1.          0.60759494  0.        ]\n",
      "[ 0.  1.]\n",
      "Round:  22\n",
      "Proba: 0.771202729844 0.83792655688\n",
      "Train:  212\n",
      "Test:  227\n",
      "@P: [ 0.34801762  0.85483871  1.        ]\n",
      "@R: [ 1.          0.67088608  0.        ]\n",
      "[ 0.  1.]\n",
      "Round:  23\n",
      "Proba: 0.692265569856 0.969030576881\n",
      "Train:  217\n",
      "Test:  227\n",
      "@P: [ 0.34801762  0.84848485  1.        ]\n",
      "@R: [ 1.          0.70886076  0.        ]\n",
      "[ 0.  1.]\n",
      "Round:  24\n",
      "Proba: 0.848259723666 0.663857344151\n",
      "Train:  222\n",
      "Test:  227\n",
      "@P: [ 0.34801762  0.87096774  1.        ]\n",
      "@R: [ 1.         0.6835443  0.       ]\n",
      "[ 0.  1.]\n",
      "Round:  25\n",
      "Proba: 0.909999712372 0.985955466335\n",
      "Train:  227\n",
      "Test:  227\n",
      "@P: [ 0.34801762  0.87096774  1.        ]\n",
      "@R: [ 1.         0.6835443  0.       ]\n",
      "[ 0.  1.]\n",
      "Round:  26\n",
      "Proba: 0.950113498005 0.997695713541\n",
      "Train:  232\n",
      "Test:  227\n",
      "@P: [ 0.34801762  0.87096774  1.        ]\n",
      "@R: [ 1.         0.6835443  0.       ]\n",
      "[ 0.  1.]\n",
      "Round:  27\n",
      "Proba: 0.587453323202 0.771197708226\n",
      "Train:  237\n",
      "Test:  227\n",
      "@P: [ 0.34801762  0.859375    1.        ]\n",
      "@R: [ 1.          0.69620253  0.        ]\n",
      "[ 0.  1.]\n",
      "Round:  28\n",
      "Proba: 0.93037703476 0.990279075703\n",
      "Train:  242\n",
      "Test:  227\n",
      "@P: [ 0.34801762  0.87301587  1.        ]\n",
      "@R: [ 1.          0.69620253  0.        ]\n",
      "[ 0.  1.]\n",
      "Round:  29\n",
      "Proba: 0.736860513227 0.992514109204\n",
      "Train:  247\n",
      "Test:  227\n",
      "@P: [ 0.34801762  0.84615385  1.        ]\n",
      "@R: [ 1.          0.69620253  0.        ]\n",
      "[ 0.  1.]\n",
      "Round:  30\n",
      "Proba: 0.790527844978 0.687582623383\n",
      "Train:  252\n",
      "Test:  227\n",
      "@P: [ 0.34801762  0.84375     1.        ]\n",
      "@R: [ 1.         0.6835443  0.       ]\n",
      "[ 0.  1.]\n",
      "Round:  31\n",
      "Proba: 0.877687833229 0.987678290059\n",
      "Train:  257\n",
      "Test:  227\n",
      "@P: [ 0.34801762  0.90740741  1.        ]\n",
      "@R: [ 1.          0.62025316  0.        ]\n",
      "[ 0.  1.]\n",
      "Round:  32\n",
      "Proba: 0.57371935434 0.801328814282\n",
      "Train:  262\n",
      "Test:  227\n",
      "@P: [ 0.34801762  0.88888889  1.        ]\n",
      "@R: [ 1.          0.70886076  0.        ]\n",
      "[ 0.  1.]\n",
      "Round:  33\n",
      "Proba: 0.997809586642 0.967720740408\n",
      "Train:  267\n",
      "Test:  227\n",
      "@P: [ 0.34801762  0.92307692  1.        ]\n",
      "@R: [ 1.          0.60759494  0.        ]\n",
      "[ 0.  1.]\n",
      "Round:  34\n",
      "Proba: 0.705278695375 0.832389300937\n",
      "Train:  272\n",
      "Test:  227\n",
      "@P: [ 0.34801762  0.85714286  1.        ]\n",
      "@R: [ 1.          0.75949367  0.        ]\n",
      "[ 0.  1.]\n",
      "Round:  35\n",
      "Proba: 0.999986919402 0.996006092265\n",
      "Train:  277\n",
      "Test:  227\n",
      "@P: [ 0.34801762  0.9245283   1.        ]\n",
      "@R: [ 1.          0.62025316  0.        ]\n",
      "[ 0.  1.]\n",
      "Round:  36\n",
      "Proba: 0.669668302025 0.681635998773\n",
      "Train:  282\n",
      "Test:  227\n",
      "@P: [ 0.34801762  0.90740741  1.        ]\n",
      "@R: [ 1.          0.62025316  0.        ]\n",
      "[ 0.  1.]\n",
      "Round:  37\n",
      "Proba: 0.87137010128 0.740589809105\n",
      "Train:  287\n",
      "Test:  227\n",
      "@P: [ 0.34801762  0.84848485  1.        ]\n",
      "@R: [ 1.          0.70886076  0.        ]\n",
      "[ 0.  1.]\n",
      "Round:  38\n",
      "Proba: 0.606007565289 0.968692791824\n",
      "Train:  292\n",
      "Test:  227\n",
      "@P: [ 0.34801762  0.83823529  1.        ]\n",
      "@R: [ 1.          0.72151899  0.        ]\n",
      "[ 0.  1.]\n",
      "Round:  39\n",
      "Proba: 0.88026398122 0.924013360521\n",
      "Train:  297\n",
      "Test:  227\n",
      "@P: [ 0.34801762  0.85074627  1.        ]\n",
      "@R: [ 1.          0.72151899  0.        ]\n",
      "[ 0.  1.]\n",
      "Round:  40\n",
      "Proba: 0.997308220413 0.881357747532\n",
      "Train:  302\n",
      "Test:  227\n",
      "@P: [ 0.34801762  0.82857143  1.        ]\n",
      "@R: [ 1.          0.73417722  0.        ]\n",
      "[ 0.  1.]\n",
      "Round:  41\n",
      "Proba: 0.948659978859 0.941218326115\n",
      "Train:  307\n",
      "Test:  227\n",
      "@P: [ 0.34801762  0.82608696  1.        ]\n",
      "@R: [ 1.          0.72151899  0.        ]\n",
      "[ 0.  1.]\n",
      "Round:  42\n",
      "Proba: 0.990846738619 0.938048989143\n",
      "Train:  312\n",
      "Test:  227\n",
      "@P: [ 0.34801762  0.88333333  1.        ]\n",
      "@R: [ 1.          0.67088608  0.        ]\n",
      "[ 0.  1.]\n",
      "Round:  43\n",
      "Proba: 0.897185751445 0.785582839948\n",
      "Train:  317\n",
      "Test:  227\n",
      "@P: [ 0.34801762  0.89830508  1.        ]\n",
      "@R: [ 1.          0.67088608  0.        ]\n",
      "[ 0.  1.]\n",
      "Round:  44\n",
      "Proba: 0.654686947923 0.820849906978\n",
      "Train:  322\n",
      "Test:  227\n",
      "@P: [ 0.34801762  0.88333333  1.        ]\n",
      "@R: [ 1.          0.67088608  0.        ]\n",
      "[ 0.  1.]\n",
      "Round:  45\n",
      "Proba: 0.546825062859 0.971993426478\n",
      "Train:  327\n",
      "Test:  227\n",
      "@P: [ 0.34801762  0.84931507  1.        ]\n",
      "@R: [ 1.          0.78481013  0.        ]\n",
      "[ 0.  1.]\n",
      "Round:  46\n",
      "Proba: 0.842475142236 0.955915738365\n",
      "Train:  332\n",
      "Test:  227\n",
      "@P: [ 0.34801762  0.9         1.        ]\n",
      "@R: [ 1.         0.6835443  0.       ]\n",
      "[ 0.  1.]\n",
      "Round:  47\n",
      "Proba: 0.796611842222 0.901825864433\n",
      "Train:  337\n",
      "Test:  227\n",
      "@P: [ 0.34801762  0.9         1.        ]\n",
      "@R: [ 1.         0.6835443  0.       ]\n",
      "[ 0.  1.]\n",
      "Round:  48\n",
      "Proba: 0.820152048503 0.88321692431\n",
      "Train:  339\n",
      "Test:  227\n",
      "@P: [ 0.34801762  0.91525424  1.        ]\n",
      "@R: [ 1.         0.6835443  0.       ]\n",
      "[ 0.  1.]\n"
     ]
    }
   ],
   "source": [
    "# Control Group\n",
    "\n",
    "#Load Data\n",
    "path = \"data/\"\n",
    "y = np.array([])\n",
    "T1 = np.loadtxt(path+\"hurricane.vec\")\n",
    "T = T1\n",
    "y = np.append(y, np.ones(len(T)))\n",
    "F1 = np.loadtxt(path+\"lasvegas.vec\")\n",
    "F2 = np.loadtxt(path+\"false.vec\")\n",
    "F = np.append(F1, F2, axis=0)\n",
    "y = np.append(y, np.zeros(len(F)))\n",
    "embedding = np.append(T,F,axis=0)\n",
    "X = sklearn.preprocessing.normalize(embedding)\n",
    "\n",
    "model = sklearn.linear_model.LogisticRegressionCV()\n",
    "random_learner = RandomLearner()\n",
    "\n",
    "control_group_precision, control_group_recall, debug2, info2 = process(X, y, random_learner, model, config)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f913fe0fc18>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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M7CgiIiIiIiLZXsiFEF5f/joL2i3Ay80LB8fsUyiASgURERERERGRVDl98zSBcwNxdHDE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7TzqaPh1YsUIy9FFRnvf1J6lgxljJp5+u/jWbTe78cQIEEVlZq1bSqNGV3r2Bxx4DnnvO2JiI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z5uZ+H7PvjAntkcjz0m3X135ceprPvn3a/UrFS9f/37qhZUzdFYK/euVMsXW57eP+FMlza+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\n14e/rvt63lfpsVqGt1RknUgt2bOk0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PGP+45cJoVODzO95d7vJ5nMOe89y9PbJ9PT333P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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f9141f2b4e0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = range(len(active_learning_precision))\n",
    "plt.figure(figsize=(18, 18))\n",
    "plt.xlabel(\"number of iteration\")\n",
    "proba, = plt.plot(x, [n[0] for n in info1[\"predict_proba\"]],\"g--\" , label=\"proba\")\n",
    "p1, = plt.plot(x, active_learning_precision, \"r-\", label=\"AL @P\")\n",
    "r1, = plt.plot(x, active_learning_recall, \"r--\", label=\"AL @R\")\n",
    "p2, = plt.plot(x, control_group_precision, \"b-\", label=\"Control @P\")\n",
    "r2, = plt.plot(x, control_group_recall, \"b--\", label=\"Control @R\")\n",
    "plt.legend(handles=[p1, r1, p2, r2, proba])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Debug Utils\n",
    "def compare_X_train():\n",
    "    xt1 = debug1[\"X_train\"]\n",
    "    xt2 = debug2[\"X_train\"]\n",
    "    a = set([tuple(l) for l in xt1])\n",
    "    b = set([tuple(l) for l in xt2])\n",
    "    if a - b:\n",
    "        print(\"invalid X train: should be same X train\")\n",
    "compare_X_train()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}