Full Code of PacktPublishing/Learning-Data-Mining-with-Python for AI

Repository: PacktPublishing/Learning-Data-Mining-with-Python
Branch: master
Commit: 9fdae2873df3
Files: 32
Total size: 14.3 MB

Directory structure:
gitextract_qbrouktj/

├── Chapter 1/
│   ├── affinity_dataset.txt
│   ├── ch1_affinity.ipynb
│   ├── ch1_affinity_create.ipynb
│   └── ch1_oner_application.ipynb
├── Chapter 10/
│   └── Chapter 10 Clusterer.ipynb
├── Chapter 11/
│   ├── Chapter 11 (CIFAR).ipynb
│   └── Chapter 11 (Theano and Lasagne).ipynb
├── Chapter 12/
│   ├── CH12 MapReduce Basics.ipynb
│   ├── Chapter 12 (NB Predict).ipynb
│   ├── Chapter 12 (Test load).ipynb
│   ├── extract_posts.py
│   ├── nb_predict.py
│   └── nb_train.py
├── Chapter 2/
│   └── Ionosphere Nearest Neighbour.ipynb
├── Chapter 3/
│   └── Basketball Results.ipynb
├── Chapter 4/
│   └── ch4 Affinity Analysis.ipynb
├── Chapter 5/
│   ├── adult_tests.py
│   ├── ch5_adult.ipynb
│   └── ch5_advertisements.ipynb
├── Chapter 6/
│   ├── ch6_classify_twitter.ipynb
│   ├── ch6_get_twitter.ipynb
│   ├── ch6_label_twitter.ipynb
│   └── replicable_dataset.json
├── Chapter 7/
│   ├── CH7 From Load.ipynb
│   ├── ch7_collect_twitter_data.ipynb
│   └── ch7_part2_twitter.ipynb
├── Chapter 8/
│   ├── CH8 Rewrite.ipynb
│   └── Sigmoid.ipynb
├── Chapter 9/
│   ├── Chapter 9 Authorship Analysis.ipynb
│   └── getdata.py
├── LICENSE
└── README.md

================================================
FILE CONTENTS
================================================

================================================
FILE: Chapter 1/affinity_dataset.txt
================================================
0 0 1 1 1
1 1 0 1 0
1 0 1 1 0
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================================================
FILE: Chapter 1/ch1_affinity.ipynb
================================================
{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "This dataset has 100 samples and 5 features\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "dataset_filename = \"affinity_dataset.txt\"\n",
    "X = np.loadtxt(dataset_filename)\n",
    "n_samples, n_features = X.shape\n",
    "print(\"This dataset has {0} samples and {1} features\".format(n_samples, n_features))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 0.  0.  1.  1.  1.]\n",
      " [ 1.  1.  0.  1.  0.]\n",
      " [ 1.  0.  1.  1.  0.]\n",
      " [ 0.  0.  1.  1.  1.]\n",
      " [ 0.  1.  0.  0.  1.]]\n"
     ]
    }
   ],
   "source": [
    "print(X[:5])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# The names of the features, for your reference.\n",
    "features = [\"bread\", \"milk\", \"cheese\", \"apples\", \"bananas\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In our first example, we will compute the Support and Confidence of the rule \"If a person buys Apples, they also buy Bananas\"."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "36 people bought Apples\n"
     ]
    }
   ],
   "source": [
    "# First, how many rows contain our premise: that a person is buying apples\n",
    "num_apple_purchases = 0\n",
    "for sample in X:\n",
    "    if sample[3] == 1:  # This person bought Apples\n",
    "        num_apple_purchases += 1\n",
    "print(\"{0} people bought Apples\".format(num_apple_purchases))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "21 cases of the rule being valid were discovered\n",
      "15 cases of the rule being invalid were discovered\n"
     ]
    }
   ],
   "source": [
    "# How many of the cases that a person bought Apples involved the people purchasing Bananas too?\n",
    "# Record both cases where the rule is valid and is invalid.\n",
    "rule_valid = 0\n",
    "rule_invalid = 0\n",
    "for sample in X:\n",
    "    if sample[3] == 1:  # This person bought Apples\n",
    "        if sample[4] == 1:\n",
    "            # This person bought both Apples and Bananas\n",
    "            rule_valid += 1\n",
    "        else:\n",
    "            # This person bought Apples, but not Bananas\n",
    "            rule_invalid += 1\n",
    "print(\"{0} cases of the rule being valid were discovered\".format(rule_valid))\n",
    "print(\"{0} cases of the rule being invalid were discovered\".format(rule_invalid))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The support is 21 and the confidence is 0.583.\n",
      "As a percentage, that is 58.3%.\n"
     ]
    }
   ],
   "source": [
    "# Now we have all the information needed to compute Support and Confidence\n",
    "support = rule_valid  # The Support is the number of times the rule is discovered.\n",
    "confidence = rule_valid / num_apple_purchases\n",
    "print(\"The support is {0} and the confidence is {1:.3f}.\".format(support, confidence))\n",
    "# Confidence can be thought of as a percentage using the following:\n",
    "print(\"As a percentage, that is {0:.1f}%.\".format(100 * confidence))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from collections import defaultdict\n",
    "# Now compute for all possible rules\n",
    "valid_rules = defaultdict(int)\n",
    "invalid_rules = defaultdict(int)\n",
    "num_occurences = defaultdict(int)\n",
    "\n",
    "for sample in X:\n",
    "    for premise in range(n_features):\n",
    "        if sample[premise] == 0: continue\n",
    "        # Record that the premise was bought in another transaction\n",
    "        num_occurences[premise] += 1\n",
    "        for conclusion in range(n_features):\n",
    "            if premise == conclusion:  # It makes little sense to measure if X -> X.\n",
    "                continue\n",
    "            if sample[conclusion] == 1:\n",
    "                # This person also bought the conclusion item\n",
    "                valid_rules[(premise, conclusion)] += 1\n",
    "            else:\n",
    "                # This person bought the premise, but not the conclusion\n",
    "                invalid_rules[(premise, conclusion)] += 1\n",
    "support = valid_rules\n",
    "confidence = defaultdict(float)\n",
    "for premise, conclusion in valid_rules.keys():\n",
    "    confidence[(premise, conclusion)] = valid_rules[(premise, conclusion)] / num_occurences[premise]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Rule: If a person buys bread they will also buy milk\n",
      " - Confidence: 0.519\n",
      " - Support: 14\n",
      "\n",
      "Rule: If a person buys milk they will also buy cheese\n",
      " - Confidence: 0.152\n",
      " - Support: 7\n",
      "\n",
      "Rule: If a person buys apples they will also buy cheese\n",
      " - Confidence: 0.694\n",
      " - Support: 25\n",
      "\n",
      "Rule: If a person buys milk they will also buy apples\n",
      " - Confidence: 0.196\n",
      " - Support: 9\n",
      "\n",
      "Rule: If a person buys bread they will also buy apples\n",
      " - Confidence: 0.185\n",
      " - Support: 5\n",
      "\n",
      "Rule: If a person buys apples they will also buy bread\n",
      " - Confidence: 0.139\n",
      " - Support: 5\n",
      "\n",
      "Rule: If a person buys apples they will also buy bananas\n",
      " - Confidence: 0.583\n",
      " - Support: 21\n",
      "\n",
      "Rule: If a person buys apples they will also buy milk\n",
      " - Confidence: 0.250\n",
      " - Support: 9\n",
      "\n",
      "Rule: If a person buys milk they will also buy bananas\n",
      " - Confidence: 0.413\n",
      " - Support: 19\n",
      "\n",
      "Rule: If a person buys cheese they will also buy bananas\n",
      " - Confidence: 0.659\n",
      " - Support: 27\n",
      "\n",
      "Rule: If a person buys cheese they will also buy bread\n",
      " - Confidence: 0.098\n",
      " - Support: 4\n",
      "\n",
      "Rule: If a person buys cheese they will also buy apples\n",
      " - Confidence: 0.610\n",
      " - Support: 25\n",
      "\n",
      "Rule: If a person buys cheese they will also buy milk\n",
      " - Confidence: 0.171\n",
      " - Support: 7\n",
      "\n",
      "Rule: If a person buys bananas they will also buy apples\n",
      " - Confidence: 0.356\n",
      " - Support: 21\n",
      "\n",
      "Rule: If a person buys bread they will also buy bananas\n",
      " - Confidence: 0.630\n",
      " - Support: 17\n",
      "\n",
      "Rule: If a person buys bananas they will also buy cheese\n",
      " - Confidence: 0.458\n",
      " - Support: 27\n",
      "\n",
      "Rule: If a person buys milk they will also buy bread\n",
      " - Confidence: 0.304\n",
      " - Support: 14\n",
      "\n",
      "Rule: If a person buys bananas they will also buy milk\n",
      " - Confidence: 0.322\n",
      " - Support: 19\n",
      "\n",
      "Rule: If a person buys bread they will also buy cheese\n",
      " - Confidence: 0.148\n",
      " - Support: 4\n",
      "\n",
      "Rule: If a person buys bananas they will also buy bread\n",
      " - Confidence: 0.288\n",
      " - Support: 17\n",
      "\n"
     ]
    }
   ],
   "source": [
    "for premise, conclusion in confidence:\n",
    "    premise_name = features[premise]\n",
    "    conclusion_name = features[conclusion]\n",
    "    print(\"Rule: If a person buys {0} they will also buy {1}\".format(premise_name, conclusion_name))\n",
    "    print(\" - Confidence: {0:.3f}\".format(confidence[(premise, conclusion)]))\n",
    "    print(\" - Support: {0}\".format(support[(premise, conclusion)]))\n",
    "    print(\"\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def print_rule(premise, conclusion, support, confidence, features):\n",
    "    premise_name = features[premise]\n",
    "    conclusion_name = features[conclusion]\n",
    "    print(\"Rule: If a person buys {0} they will also buy {1}\".format(premise_name, conclusion_name))\n",
    "    print(\" - Confidence: {0:.3f}\".format(confidence[(premise, conclusion)]))\n",
    "    print(\" - Support: {0}\".format(support[(premise, conclusion)]))\n",
    "    print(\"\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Rule: If a person buys milk they will also buy apples\n",
      " - Confidence: 0.196\n",
      " - Support: 9\n",
      "\n"
     ]
    }
   ],
   "source": [
    "premise = 1\n",
    "conclusion = 3\n",
    "print_rule(premise, conclusion, support, confidence, features)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[((0, 1), 14),\n",
      " ((1, 2), 7),\n",
      " ((3, 2), 25),\n",
      " ((1, 3), 9),\n",
      " ((0, 2), 4),\n",
      " ((3, 0), 5),\n",
      " ((4, 1), 19),\n",
      " ((3, 1), 9),\n",
      " ((1, 4), 19),\n",
      " ((2, 4), 27),\n",
      " ((2, 0), 4),\n",
      " ((2, 3), 25),\n",
      " ((2, 1), 7),\n",
      " ((4, 3), 21),\n",
      " ((0, 4), 17),\n",
      " ((4, 2), 27),\n",
      " ((1, 0), 14),\n",
      " ((3, 4), 21),\n",
      " ((0, 3), 5),\n",
      " ((4, 0), 17)]\n"
     ]
    }
   ],
   "source": [
    "# Sort by support\n",
    "from pprint import pprint\n",
    "pprint(list(support.items()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from operator import itemgetter\n",
    "sorted_support = sorted(support.items(), key=itemgetter(1), reverse=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Rule #1\n",
      "Rule: If a person buys cheese they will also buy bananas\n",
      " - Confidence: 0.659\n",
      " - Support: 27\n",
      "\n",
      "Rule #2\n",
      "Rule: If a person buys bananas they will also buy cheese\n",
      " - Confidence: 0.458\n",
      " - Support: 27\n",
      "\n",
      "Rule #3\n",
      "Rule: If a person buys apples they will also buy cheese\n",
      " - Confidence: 0.694\n",
      " - Support: 25\n",
      "\n",
      "Rule #4\n",
      "Rule: If a person buys cheese they will also buy apples\n",
      " - Confidence: 0.610\n",
      " - Support: 25\n",
      "\n",
      "Rule #5\n",
      "Rule: If a person buys bananas they will also buy apples\n",
      " - Confidence: 0.356\n",
      " - Support: 21\n",
      "\n"
     ]
    }
   ],
   "source": [
    "for index in range(5):\n",
    "    print(\"Rule #{0}\".format(index + 1))\n",
    "    (premise, conclusion) = sorted_support[index][0]\n",
    "    print_rule(premise, conclusion, support, confidence, features)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "sorted_confidence = sorted(confidence.items(), key=itemgetter(1), reverse=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Rule #1\n",
      "Rule: If a person buys apples they will also buy cheese\n",
      " - Confidence: 0.694\n",
      " - Support: 25\n",
      "\n",
      "Rule #2\n",
      "Rule: If a person buys cheese they will also buy bananas\n",
      " - Confidence: 0.659\n",
      " - Support: 27\n",
      "\n",
      "Rule #3\n",
      "Rule: If a person buys bread they will also buy bananas\n",
      " - Confidence: 0.630\n",
      " - Support: 17\n",
      "\n",
      "Rule #4\n",
      "Rule: If a person buys cheese they will also buy apples\n",
      " - Confidence: 0.610\n",
      " - Support: 25\n",
      "\n",
      "Rule #5\n",
      "Rule: If a person buys apples they will also buy bananas\n",
      " - Confidence: 0.583\n",
      " - Support: 21\n",
      "\n"
     ]
    }
   ],
   "source": [
    "for index in range(5):\n",
    "    print(\"Rule #{0}\".format(index + 1))\n",
    "    (premise, conclusion) = sorted_confidence[index][0]\n",
    "    print_rule(premise, conclusion, support, confidence, features)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
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   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
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   "pygments_lexer": "ipython3",
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 },
 "nbformat": 4,
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}


================================================
FILE: Chapter 1/ch1_affinity_create.ipynb
================================================
{
 "metadata": {
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 },
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 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import numpy as np"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "X = np.zeros((100, 5), dtype='bool')\n",
      "features = [\"bread\", \"milk\", \"cheese\", \"apples\", \"bananas\"]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 2
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "for i in range(X.shape[0]):\n",
      "    if np.random.random() < 0.3:\n",
      "        # A bread winner\n",
      "        X[i][0] = 1\n",
      "        if np.random.random() < 0.5:\n",
      "            # Who likes milk\n",
      "            X[i][1] = 1\n",
      "        if np.random.random() < 0.2:\n",
      "            # Who likes cheese\n",
      "            X[i][2] = 1\n",
      "        if np.random.random() < 0.25:\n",
      "            # Who likes apples\n",
      "            X[i][3] = 1\n",
      "        if np.random.random() < 0.5:\n",
      "            # Who likes bananas\n",
      "            X[i][4] = 1\n",
      "    else:\n",
      "        # Not a bread winner\n",
      "        if np.random.random() < 0.5:\n",
      "            # Who likes milk\n",
      "            X[i][1] = 1\n",
      "            if np.random.random() < 0.2:\n",
      "                # Who likes cheese\n",
      "                X[i][2] = 1\n",
      "            if np.random.random() < 0.25:\n",
      "                # Who likes apples\n",
      "                X[i][3] = 1\n",
      "            if np.random.random() < 0.5:\n",
      "                # Who likes bananas\n",
      "                X[i][4] = 1\n",
      "        else:\n",
      "            if np.random.random() < 0.8:\n",
      "                # Who likes cheese\n",
      "                X[i][2] = 1\n",
      "            if np.random.random() < 0.6:\n",
      "                # Who likes apples\n",
      "                X[i][3] = 1\n",
      "            if np.random.random() < 0.7:\n",
      "                # Who likes bananas\n",
      "                X[i][4] = 1\n",
      "    if X[i].sum() == 0:\n",
      "        X[i][4] = 1  # Must buy something, so gets bananas\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 3
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print(X[:5])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "[[False False  True  True  True]\n",
        " [ True  True False  True False]\n",
        " [ True False  True  True False]\n",
        " [False False  True  True  True]\n",
        " [False  True False False  True]]\n"
       ]
      }
     ],
     "prompt_number": 5
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "np.savetxt(\"affinity_dataset.txt\", X, fmt='%d')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 7
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    }
   ],
   "metadata": {}
  }
 ]
}

================================================
FILE: Chapter 1/ch1_oner_application.ipynb
================================================
{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The OneR algorithm is quite simple but can be quite effective, showing the power of using even basic statistics in many applications.\n",
    "The algorithm is:\n",
    "\n",
    "* For each variable\n",
    "    * For each value of the variable\n",
    "        * The prediction based on this variable goes the most frequent class\n",
    "        * Compute the error of this prediction\n",
    "    * Sum the prediction errors for all values of the variable\n",
    "* Use the variable with the lowest error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iris Plants Database\n",
      "\n",
      "Notes\n",
      "-----\n",
      "Data Set Characteristics:\n",
      "    :Number of Instances: 150 (50 in each of three classes)\n",
      "    :Number of Attributes: 4 numeric, predictive attributes and the class\n",
      "    :Attribute Information:\n",
      "        - sepal length in cm\n",
      "        - sepal width in cm\n",
      "        - petal length in cm\n",
      "        - petal width in cm\n",
      "        - class:\n",
      "                - Iris-Setosa\n",
      "                - Iris-Versicolour\n",
      "                - Iris-Virginica\n",
      "    :Summary Statistics:\n",
      "    ============== ==== ==== ======= ===== ====================\n",
      "                    Min  Max   Mean    SD   Class Correlation\n",
      "    ============== ==== ==== ======= ===== ====================\n",
      "    sepal length:   4.3  7.9   5.84   0.83    0.7826\n",
      "    sepal width:    2.0  4.4   3.05   0.43   -0.4194\n",
      "    petal length:   1.0  6.9   3.76   1.76    0.9490  (high!)\n",
      "    petal width:    0.1  2.5   1.20  0.76     0.9565  (high!)\n",
      "    ============== ==== ==== ======= ===== ====================\n",
      "    :Missing Attribute Values: None\n",
      "    :Class Distribution: 33.3% for each of 3 classes.\n",
      "    :Creator: R.A. Fisher\n",
      "    :Donor: Michael Marshall (MARSHALL%PLU@io.arc.nasa.gov)\n",
      "    :Date: July, 1988\n",
      "\n",
      "This is a copy of UCI ML iris datasets.\n",
      "http://archive.ics.uci.edu/ml/datasets/Iris\n",
      "\n",
      "The famous Iris database, first used by Sir R.A Fisher\n",
      "\n",
      "This is perhaps the best known database to be found in the\n",
      "pattern recognition literature.  Fisher's paper is a classic in the field and\n",
      "is referenced frequently to this day.  (See Duda & Hart, for example.)  The\n",
      "data set contains 3 classes of 50 instances each, where each class refers to a\n",
      "type of iris plant.  One class is linearly separable from the other 2; the\n",
      "latter are NOT linearly separable from each other.\n",
      "\n",
      "References\n",
      "----------\n",
      "   - Fisher,R.A. \"The use of multiple measurements in taxonomic problems\"\n",
      "     Annual Eugenics, 7, Part II, 179-188 (1936); also in \"Contributions to\n",
      "     Mathematical Statistics\" (John Wiley, NY, 1950).\n",
      "   - Duda,R.O., & Hart,P.E. (1973) Pattern Classification and Scene Analysis.\n",
      "     (Q327.D83) John Wiley & Sons.  ISBN 0-471-22361-1.  See page 218.\n",
      "   - Dasarathy, B.V. (1980) \"Nosing Around the Neighborhood: A New System\n",
      "     Structure and Classification Rule for Recognition in Partially Exposed\n",
      "     Environments\".  IEEE Transactions on Pattern Analysis and Machine\n",
      "     Intelligence, Vol. PAMI-2, No. 1, 67-71.\n",
      "   - Gates, G.W. (1972) \"The Reduced Nearest Neighbor Rule\".  IEEE Transactions\n",
      "     on Information Theory, May 1972, 431-433.\n",
      "   - See also: 1988 MLC Proceedings, 54-64.  Cheeseman et al\"s AUTOCLASS II\n",
      "     conceptual clustering system finds 3 classes in the data.\n",
      "   - Many, many more ...\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Load our dataset\n",
    "from sklearn.datasets import load_iris\n",
    "#X, y = np.loadtxt(\"X_classification.txt\"), np.loadtxt(\"y_classification.txt\")\n",
    "dataset = load_iris()\n",
    "X = dataset.data\n",
    "y = dataset.target\n",
    "print(dataset.DESCR)\n",
    "n_samples, n_features = X.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Our attributes are continuous, while we want categorical features to use OneR. We will perform a *preprocessing* step called discretisation. At this stage, we will perform a simple procedure: compute the mean and determine whether a value is above or below the mean."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Compute the mean for each attribute\n",
    "attribute_means = X.mean(axis=0)\n",
    "assert attribute_means.shape == (n_features,)\n",
    "X_d = np.array(X >= attribute_means, dtype='int')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are (112,) training samples\n",
      "There are (38,) testing samples\n"
     ]
    }
   ],
   "source": [
    "# Now, we split into a training and test set\n",
    "from sklearn.cross_validation import train_test_split\n",
    "\n",
    "# Set the random state to the same number to get the same results as in the book\n",
    "random_state = 14\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X_d, y, random_state=random_state)\n",
    "print(\"There are {} training samples\".format(y_train.shape))\n",
    "print(\"There are {} testing samples\".format(y_test.shape))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from collections import defaultdict\n",
    "from operator import itemgetter\n",
    "\n",
    "\n",
    "def train(X, y_true, feature):\n",
    "    \"\"\"Computes the predictors and error for a given feature using the OneR algorithm\n",
    "    \n",
    "    Parameters\n",
    "    ----------\n",
    "    X: array [n_samples, n_features]\n",
    "        The two dimensional array that holds the dataset. Each row is a sample, each column\n",
    "        is a feature.\n",
    "    \n",
    "    y_true: array [n_samples,]\n",
    "        The one dimensional array that holds the class values. Corresponds to X, such that\n",
    "        y_true[i] is the class value for sample X[i].\n",
    "    \n",
    "    feature: int\n",
    "        An integer corresponding to the index of the variable we wish to test.\n",
    "        0 <= variable < n_features\n",
    "        \n",
    "    Returns\n",
    "    -------\n",
    "    predictors: dictionary of tuples: (value, prediction)\n",
    "        For each item in the array, if the variable has a given value, make the given prediction.\n",
    "    \n",
    "    error: float\n",
    "        The ratio of training data that this rule incorrectly predicts.\n",
    "    \"\"\"\n",
    "    # Check that variable is a valid number\n",
    "    n_samples, n_features = X.shape\n",
    "    assert 0 <= feature < n_features\n",
    "    # Get all of the unique values that this variable has\n",
    "    values = set(X[:,feature])\n",
    "    # Stores the predictors array that is returned\n",
    "    predictors = dict()\n",
    "    errors = []\n",
    "    for current_value in values:\n",
    "        most_frequent_class, error = train_feature_value(X, y_true, feature, current_value)\n",
    "        predictors[current_value] = most_frequent_class\n",
    "        errors.append(error)\n",
    "    # Compute the total error of using this feature to classify on\n",
    "    total_error = sum(errors)\n",
    "    return predictors, total_error\n",
    "\n",
    "# Compute what our predictors say each sample is based on its value\n",
    "#y_predicted = np.array([predictors[sample[feature]] for sample in X])\n",
    "    \n",
    "\n",
    "def train_feature_value(X, y_true, feature, value):\n",
    "    # Create a simple dictionary to count how frequency they give certain predictions\n",
    "    class_counts = defaultdict(int)\n",
    "    # Iterate through each sample and count the frequency of each class/value pair\n",
    "    for sample, y in zip(X, y_true):\n",
    "        if sample[feature] == value:\n",
    "            class_counts[y] += 1\n",
    "    # Now get the best one by sorting (highest first) and choosing the first item\n",
    "    sorted_class_counts = sorted(class_counts.items(), key=itemgetter(1), reverse=True)\n",
    "    most_frequent_class = sorted_class_counts[0][0]\n",
    "    # The error is the number of samples that do not classify as the most frequent class\n",
    "    # *and* have the feature value.\n",
    "    n_samples = X.shape[1]\n",
    "    error = sum([class_count for class_value, class_count in class_counts.items()\n",
    "                 if class_value != most_frequent_class])\n",
    "    return most_frequent_class, error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The best model is based on variable 2 and has error 37.00\n",
      "{'variable': 2, 'predictor': {0: 0, 1: 2}}\n"
     ]
    }
   ],
   "source": [
    "# Compute all of the predictors\n",
    "all_predictors = {variable: train(X_train, y_train, variable) for variable in range(X_train.shape[1])}\n",
    "errors = {variable: error for variable, (mapping, error) in all_predictors.items()}\n",
    "# Now choose the best and save that as \"model\"\n",
    "# Sort by error\n",
    "best_variable, best_error = sorted(errors.items(), key=itemgetter(1))[0]\n",
    "print(\"The best model is based on variable {0} and has error {1:.2f}\".format(best_variable, best_error))\n",
    "\n",
    "# Choose the bset model\n",
    "model = {'variable': best_variable,\n",
    "         'predictor': all_predictors[best_variable][0]}\n",
    "print(model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def predict(X_test, model):\n",
    "    variable = model['variable']\n",
    "    predictor = model['predictor']\n",
    "    y_predicted = np.array([predictor[int(sample[variable])] for sample in X_test])\n",
    "    return y_predicted"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0 0 0 2 2 2 0 2 0 2 2 0 2 2 0 2 0 2 2 2 0 0 0 2 0 2 0 2 2 0 0 0 2 0 2 0 2\n",
      " 2]\n"
     ]
    }
   ],
   "source": [
    "y_predicted = predict(X_test, model)\n",
    "print(y_predicted)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The test accuracy is 65.8%\n"
     ]
    }
   ],
   "source": [
    "# Compute the accuracy by taking the mean of the amounts that y_predicted is equal to y_test\n",
    "accuracy = np.mean(y_predicted == y_test) * 100\n",
    "print(\"The test accuracy is {:.1f}%\".format(accuracy))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from sklearn.metrics import classification_report"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             precision    recall  f1-score   support\n",
      "\n",
      "          0       0.94      1.00      0.97        17\n",
      "          1       0.00      0.00      0.00        13\n",
      "          2       0.40      1.00      0.57         8\n",
      "\n",
      "avg / total       0.51      0.66      0.55        38\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.4/dist-packages/sklearn/metrics/metrics.py:1771: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test, y_predicted))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  }
 ],
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   "display_name": "Python 3",
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    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
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   "nbconvert_exporter": "python",
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  }
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================================================
FILE: Chapter 10/Chapter 10 Clusterer.ipynb
================================================
{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "490"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import os\n",
    "import numpy as np\n",
    "data_folder = os.path.join(os.path.expanduser(\"~\"), \"Data\", \"websites\", \"textonly\")\n",
    "\n",
    "documents = [open(os.path.join(data_folder, filename)).read() for filename in os.listdir(data_folder)]\n",
    "len(documents)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pretty printing has been turned OFF\n"
     ]
    }
   ],
   "source": [
    "pprint([document[:100] for document in documents[:5]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from sklearn.cluster import KMeans\n",
    "from sklearn.feature_extraction.text import CountVectorizer, TfidfVectorizer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from sklearn.pipeline import Pipeline\n",
    "\n",
    "n_clusters = 10\n",
    "\n",
    "pipeline = Pipeline([('feature_extraction', TfidfVectorizer(max_df=0.4)),\n",
    "                     ('clusterer', KMeans(n_clusters=n_clusters))\n",
    "                     ])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pipeline(steps=[('feature_extraction', TfidfVectorizer(analyzer='word', binary=False, charset=None,\n",
       "        charset_error=None, decode_error='strict',\n",
       "        dtype=<class 'numpy.int64'>, encoding='utf-8', input='content',\n",
       "        lowercase=True, max_df=0.4, max_features=None, min_df=1,\n",
       "        ngram_range=(... n_init=10,\n",
       "    n_jobs=1, precompute_distances=True, random_state=None, tol=0.0001,\n",
       "    verbose=0))])"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pipeline.fit(documents)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "labels = pipeline.predict(documents)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cluster 0 contains 1 samples\n",
      "Cluster 1 contains 2 samples\n",
      "Cluster 2 contains 439 samples\n",
      "Cluster 3 contains 1 samples\n",
      "Cluster 4 contains 2 samples\n",
      "Cluster 5 contains 3 samples\n",
      "Cluster 6 contains 27 samples\n",
      "Cluster 7 contains 2 samples\n",
      "Cluster 8 contains 12 samples\n",
      "Cluster 9 contains 1 samples\n"
     ]
    }
   ],
   "source": [
    "from collections import Counter\n",
    "c = Counter(labels)\n",
    "for cluster_number in range(n_clusters):\n",
    "    print(\"Cluster {} contains {} samples\".format(cluster_number, c[cluster_number]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "381.97317261989065"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pipeline.named_steps['clusterer'].inertia_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "inertia_scores = []\n",
    "n_cluster_values = list(range(2, 20))\n",
    "for n_clusters in n_cluster_values:\n",
    "    cur_inertia_scores = []\n",
    "    X = TfidfVectorizer(max_df=0.4).fit_transform(documents)\n",
    "    for i in range(30):\n",
    "        km = KMeans(n_clusters=n_clusters).fit(X)\n",
    "        cur_inertia_scores.append(km.inertia_)\n",
    "    inertia_scores.append(cur_inertia_scores)\n",
    "inertia_scores = np.array(inertia_scores)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
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p3xpTAQAAAFiHCAcAAADgYE3vqp67uWq6cXVc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\nQHOoTa+rHtD0iOqHWp6CdF71hOr5TR9bdR8AAACHjAgHAAA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      "text/plain": [
       "<matplotlib.figure.Figure object at 0x7f0bc5ae9240>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "from matplotlib import pyplot as plt\n",
    "\n",
    "inertia_means = np.mean(inertia_scores, axis=1)\n",
    "inertia_stderr = np.std(inertia_scores, axis=1)\n",
    "\n",
    "fig = plt.figure(figsize=(40,20))\n",
    "plt.errorbar(n_cluster_values, inertia_means, inertia_stderr, color='green')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pipeline(steps=[('feature_extraction', TfidfVectorizer(analyzer='word', binary=False, charset=None,\n",
       "        charset_error=None, decode_error='strict',\n",
       "        dtype=<class 'numpy.int64'>, encoding='utf-8', input='content',\n",
       "        lowercase=True, max_df=0.4, max_features=None, min_df=1,\n",
       "        ngram_range=(... n_init=10,\n",
       "    n_jobs=1, precompute_distances=True, random_state=None, tol=0.0001,\n",
       "    verbose=0))])"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "n_clusters = 6\n",
    "\n",
    "pipeline = Pipeline([('feature_extraction', TfidfVectorizer(max_df=0.4)),\n",
    "                     ('clusterer', KMeans(n_clusters=n_clusters))\n",
    "                     ])\n",
    "pipeline.fit(documents)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "labels = pipeline.predict(documents)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cluster 0 contains 21 samples\n",
      "  Most important terms\n",
      "  1) korea (score: 0.1591)\n",
      "  2) korean (score: 0.1573)\n",
      "  3) south (score: 0.1336)\n",
      "  4) kim (score: 0.1088)\n",
      "  5) north (score: 0.1077)\n",
      "\n",
      "Cluster 1 contains 33 samples\n",
      "  Most important terms\n",
      "  1) iran (score: 0.1047)\n",
      "  2) netanyahu (score: 0.0869)\n",
      "  3) nuclear (score: 0.0660)\n",
      "  4) he (score: 0.0615)\n",
      "  5) his (score: 0.0550)\n",
      "\n",
      "Cluster 2 contains 156 samples\n",
      "  Most important terms\n",
      "  1) palestinian (score: 0.0196)\n",
      "  2) israel (score: 0.0138)\n",
      "  3) security (score: 0.0118)\n",
      "  4) bank (score: 0.0115)\n",
      "  5) its (score: 0.0103)\n",
      "\n",
      "Cluster 3 contains 31 samples\n",
      "  Most important terms\n",
      "  1) browser (score: 0.2786)\n",
      "  2) able (score: 0.2429)\n",
      "  3) you (score: 0.2018)\n",
      "  4) css (score: 0.1925)\n",
      "  5) sheets (score: 0.1925)\n",
      "\n",
      "Cluster 4 contains 48 samples\n",
      "  Most important terms\n",
      "  1) al (score: 0.0862)\n",
      "  2) isis (score: 0.0723)\n",
      "  3) islamic (score: 0.0664)\n",
      "  4) iraq (score: 0.0657)\n",
      "  5) state (score: 0.0580)\n",
      "\n",
      "Cluster 5 contains 201 samples\n",
      "  Most important terms\n",
      "  1) he (score: 0.0448)\n",
      "  2) we (score: 0.0306)\n",
      "  3) they (score: 0.0294)\n",
      "  4) his (score: 0.0254)\n",
      "  5) who (score: 0.0253)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "c = Counter(labels)\n",
    "\n",
    "terms = pipeline.named_steps['feature_extraction'].get_feature_names()\n",
    "\n",
    "for cluster_number in range(n_clusters):\n",
    "    print(\"Cluster {} contains {} samples\".format(cluster_number, c[cluster_number]))\n",
    "    print(\"  Most important terms\")\n",
    "    centroid = pipeline.named_steps['clusterer'].cluster_centers_[cluster_number]\n",
    "    most_important = centroid.argsort()\n",
    "    for i in range(5):\n",
    "        term_index = most_important[-(i+1)]\n",
    "        print(\"  {0}) {1} (score: {2:.4f})\".format(i+1, terms[term_index], centroid[term_index]))\n",
    "    print()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.031820420989154712"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import silhouette_score\n",
    "X = pipeline.named_steps['feature_extraction'].transform(documents)\n",
    "silhouette_score(X, labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "14327"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(terms)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "Y = pipeline.transform(documents) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "km = KMeans(n_clusters=n_clusters)\n",
    "labels = km.fit_predict(Y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cluster 0 contains 89 samples\n",
      "Cluster 1 contains 65 samples\n",
      "Cluster 2 contains 7 samples\n",
      "Cluster 3 contains 24 samples\n",
      "Cluster 4 contains 290 samples\n",
      "Cluster 5 contains 15 samples\n"
     ]
    }
   ],
   "source": [
    "c = Counter(labels)\n",
    "for cluster_number in range(n_clusters):\n",
    "    print(\"Cluster {} contains {} samples\".format(cluster_number, c[cluster_number]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.56564461613141714"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "silhouette_score(Y, labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(490, 6)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Evidence Accumulation Clustering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from scipy.sparse import csr_matrix\n",
    "\n",
    "\n",
    "def create_coassociation_matrix(labels):\n",
    "    rows = []\n",
    "    cols = []\n",
    "    unique_labels = set(labels)\n",
    "    for label in unique_labels:\n",
    "        indices = np.where(labels == label)[0]\n",
    "        for index1 in indices:\n",
    "            for index2 in indices:\n",
    "                rows.append(index1)\n",
    "                cols.append(index2)\n",
    "    data = np.ones((len(rows),))\n",
    "    return csr_matrix((data, (rows, cols)), dtype='float')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "C = create_coassociation_matrix(labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<490x490 sparse matrix of type '<class 'numpy.float64'>'\n",
       "\twith 97096 stored elements in Compressed Sparse Row format>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "C"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((490, 490), 240100)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "C.shape, C.shape[0] * C.shape[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.4043981674302374"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(C.nonzero()[0]) / (C.shape[0] * C.shape[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from scipy.sparse.csgraph import minimum_spanning_tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "mst = minimum_spanning_tree(C)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<490x490 sparse matrix of type '<class 'numpy.float64'>'\n",
       "\twith 484 stored elements in Compressed Sparse Row format>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mst"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "pipeline = Pipeline([('feature_extraction', TfidfVectorizer(max_df=0.4)),\n",
    "                     ('clusterer', KMeans(n_clusters=3))\n",
    "                     ])\n",
    "pipeline.fit(documents)\n",
    "labels2 = pipeline.predict(documents)\n",
    "C2 = create_coassociation_matrix(labels2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "matrix([[ 1. ,  0.5,  0.5, ...,  0.5,  0.5,  0.5],\n",
       "        [ 0.5,  1. ,  1. , ...,  1. ,  1. ,  1. ],\n",
       "        [ 0.5,  1. ,  1. , ...,  1. ,  1. ,  1. ],\n",
       "        ..., \n",
       "        [ 0.5,  1. ,  1. , ...,  1. ,  1. ,  1. ],\n",
       "        [ 0.5,  1. ,  1. , ...,  1. ,  1. ,  1. ],\n",
       "        [ 0.5,  1. ,  1. , ...,  1. ,  1. ,  1. ]])"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "C_sum = (C + C2) / 2\n",
    "#C_sum.data = C_sum.data\n",
    "C_sum.todense()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<490x490 sparse matrix of type '<class 'numpy.float64'>'\n",
       "\twith 489 stored elements in Compressed Sparse Row format>"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mst = minimum_spanning_tree(-C_sum)\n",
    "mst"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<490x490 sparse matrix of type '<class 'numpy.float64'>'\n",
       "\twith 481 stored elements in Compressed Sparse Row format>"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#mst.data[mst.data < 1] = 0\n",
    "mst.data[mst.data > -1] = 0\n",
    "mst.eliminate_zeros()\n",
    "mst"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from scipy.sparse.csgraph import connected_components\n",
    "number_of_clusters, labels = connected_components(mst)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "\n",
    "from sklearn.base import BaseEstimator, ClusterMixin\n",
    "\n",
    "class EAC(BaseEstimator, ClusterMixin):\n",
    "    def __init__(self, n_clusterings=10, cut_threshold=0.5, n_clusters_range=(3, 10)):\n",
    "        self.n_clusterings = n_clusterings\n",
    "        self.cut_threshold = cut_threshold\n",
    "        self.n_clusters_range = n_clusters_range\n",
    "    \n",
    "    def fit(self, X, y=None):\n",
    "        C = sum((create_coassociation_matrix(self._single_clustering(X))\n",
    "                 for i in range(self.n_clusterings)))\n",
    "        mst = minimum_spanning_tree(-C)\n",
    "        mst.data[mst.data > -self.cut_threshold] = 0\n",
    "        mst.eliminate_zeros()\n",
    "        self.n_components, self.labels_ = connected_components(mst)\n",
    "        return self\n",
    "    \n",
    "    def _single_clustering(self, X):\n",
    "        n_clusters = np.random.randint(*self.n_clusters_range)\n",
    "        km = KMeans(n_clusters=n_clusters)\n",
    "        return km.fit_predict(X)\n",
    "    \n",
    "    def fit_predict(self, X):\n",
    "        self.fit(X)\n",
    "        return self.labels_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "pipeline = Pipeline([('feature_extraction', TfidfVectorizer(max_df=0.4)),\n",
    "                     ('clusterer', EAC())\n",
    "                     ])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pipeline(steps=[('feature_extraction', TfidfVectorizer(analyzer='word', binary=False, charset=None,\n",
       "        charset_error=None, decode_error='strict',\n",
       "        dtype=<class 'numpy.int64'>, encoding='utf-8', input='content',\n",
       "        lowercase=True, max_df=0.4, max_features=None, min_df=1,\n",
       "        ngram_range=(...ocabulary=None)), ('clusterer', EAC(cut_threshold=0.5, n_clusterings=10, n_clusters_range=(3, 10)))])"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pipeline.fit(documents)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "labels = pipeline.named_steps['clusterer'].labels_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "c = Counter(labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Counter({0: 490})"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Online Learning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from sklearn.cluster import MiniBatchKMeans"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "vec = TfidfVectorizer(max_df=0.4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "X = vec.fit_transform(documents)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "mbkm = MiniBatchKMeans(random_state=14, n_clusters=3)\n",
    "batch_size = 500\n",
    "\n",
    "indices = np.arange(0, X.shape[0])\n",
    "for iteration in range(100):\n",
    "    sample = np.random.choice(indices, size=batch_size, replace=True)\n",
    "    mbkm.partial_fit(X[sample[:batch_size]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "mbkm = MiniBatchKMeans(random_state=14, n_clusters=3)\n",
    "batch_size = 10\n",
    "\n",
    "for iteration in range(int(X.shape[0] / batch_size)):\n",
    "    start = batch_size * iteration\n",
    "    end = batch_size * (iteration + 1)\n",
    "    mbkm.partial_fit(X[start:end])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "401.6913550000382"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "labels_mbkm = mbkm.predict(X)\n",
    "mbkm.inertia_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "392.6561949236311"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "km = KMeans(random_state=14, n_clusters=3)\n",
    "labels_km = km.fit_predict(X)\n",
    "km.inertia_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from sklearn.metrics import adjusted_mutual_info_score, homogeneity_score\n",
    "from sklearn.metrics import mutual_info_score, v_measure_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.39531764243316064"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "v_measure_score(labels_mbkm, labels_km)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(490, 14327)"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2, 2, 0, 2, 1, 2, 2, 0, 2, 2, 2, 2, 0, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
       "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 0, 0, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
       "       2, 2, 2, 0, 2, 2, 2, 2, 2, 0, 0, 1, 2, 0, 2, 2, 2, 1, 2, 2, 2, 2, 2,\n",
       "       2, 2, 1, 1, 1, 0, 2, 2, 2, 2, 2, 2, 2, 2, 0, 2, 2, 2, 2, 2, 2, 2, 0,\n",
       "       2, 0, 0, 2, 2, 2, 2, 2, 2, 2, 0, 0, 2, 2, 1, 0, 0, 2, 2, 2, 2, 2, 2,\n",
       "       2, 2, 2, 2, 2, 2, 2, 2, 0, 2, 2, 2, 2, 2, 0, 0, 2, 1, 0, 0, 2, 2, 1,\n",
       "       2, 0, 2, 0, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 0,\n",
       "       0, 2, 2, 2, 2, 2, 2, 2, 0, 2, 2, 2, 1, 2, 2, 0, 0, 2, 0, 2, 2, 0, 2,\n",
       "       2, 2, 2, 2, 0, 2, 0, 2, 2, 2, 0, 2, 2, 2, 2, 0, 0, 2, 2, 2, 0, 2, 2,\n",
       "       2, 1, 2, 0, 2, 2, 2, 2, 0, 2, 2, 2, 1, 2, 2, 2, 2, 2, 2, 2, 1, 2, 0,\n",
       "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 1, 2, 2, 2, 2, 0, 2, 0, 2, 0, 2, 2,\n",
       "       2, 2, 2, 0, 2, 2, 0, 2, 2, 2, 2, 2, 0, 2, 2, 0, 1, 2, 2, 2, 2, 2, 2,\n",
       "       0, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 0, 2, 2, 2, 2, 2, 0, 2, 2, 2,\n",
       "       2, 2, 2, 2, 1, 2, 2, 2, 2, 2, 1, 0, 0, 1, 2, 2, 2, 2, 0, 2, 2, 2, 2,\n",
       "       2, 2, 0, 2, 1, 2, 2, 2, 2, 1, 2, 0, 2, 2, 2, 2, 2, 2, 0, 2, 2, 2, 2,\n",
       "       0, 2, 0, 2, 2, 0, 2, 2, 2, 1, 2, 2, 0, 2, 2, 1, 2, 2, 0, 0, 2, 2, 0,\n",
       "       2, 2, 2, 1, 2, 1, 2, 1, 2, 2, 0, 0, 2, 0, 0, 2, 2, 2, 2, 2, 2, 2, 0,\n",
       "       0, 2, 2, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 2, 2, 2, 2, 2,\n",
       "       2, 2, 2, 2, 2, 2, 0, 0, 1, 2, 0, 1, 2, 2, 2, 2, 2, 2, 0, 0, 2, 0, 2,\n",
       "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 2, 0, 2, 2, 2, 0, 0, 1, 2, 2, 2,\n",
       "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 0, 2, 2, 2, 2, 0, 0, 2, 2, 2, 2, 0, 2,\n",
       "       2, 0, 0, 0, 2, 2, 0], dtype=int32)"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "labels_mbkm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from sklearn.feature_extraction.text import HashingVectorizer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "class PartialFitPipeline(Pipeline):\n",
    "    def partial_fit(self, X, y=None):\n",
    "        Xt = X\n",
    "        for name, transform in self.steps[:-1]:\n",
    "            Xt = transform.transform(Xt)\n",
    "        return self.steps[-1][1].partial_fit(Xt, y=y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "pipeline = PartialFitPipeline([('feature_extraction', HashingVectorizer()),\n",
    "                             ('clusterer', MiniBatchKMeans(random_state=14, n_clusters=3))\n",
    "                             ])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "batch_size = 10\n",
    "\n",
    "for iteration in range(int(len(documents) / batch_size)):\n",
    "    start = batch_size * iteration\n",
    "    end = batch_size * (iteration + 1)\n",
    "    pipeline.partial_fit(documents[start:end])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 2, 2, 0, 1, 0, 2, 2, 2, 2, 1, 2, 2, 0, 2, 2, 0, 2, 0, 2, 2, 1, 2,\n",
       "       2, 2, 2, 2, 2, 2, 2, 0, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 0, 0, 2, 2,\n",
       "       2, 2, 2, 2, 2, 2, 2, 2, 1, 2, 2, 1, 2, 2, 2, 0, 2, 1, 2, 2, 2, 1, 2,\n",
       "       2, 2, 2, 1, 1, 1, 2, 2, 2, 0, 2, 2, 2, 2, 2, 2, 0, 2, 2, 1, 0, 0, 2,\n",
       "       1, 2, 2, 2, 1, 0, 0, 2, 2, 2, 2, 2, 0, 2, 2, 2, 1, 2, 2, 0, 2, 2, 0,\n",
       "       0, 2, 0, 2, 2, 0, 0, 2, 2, 2, 1, 0, 0, 2, 2, 2, 2, 1, 2, 2, 2, 2, 1,\n",
       "       2, 2, 1, 2, 2, 2, 2, 2, 1, 2, 2, 0, 2, 2, 2, 0, 2, 0, 2, 2, 2, 2, 2,\n",
       "       2, 2, 2, 2, 0, 2, 2, 2, 2, 0, 2, 0, 1, 2, 2, 1, 2, 2, 2, 2, 2, 2, 2,\n",
       "       2, 2, 2, 2, 2, 0, 2, 0, 2, 2, 2, 2, 2, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
       "       2, 2, 2, 1, 2, 2, 2, 2, 2, 2, 1, 2, 1, 1, 2, 2, 2, 0, 0, 0, 1, 0, 2,\n",
       "       1, 2, 0, 2, 2, 2, 2, 2, 2, 2, 2, 1, 2, 1, 2, 2, 2, 0, 2, 0, 2, 2, 2,\n",
       "       0, 2, 2, 2, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 0, 2, 2, 2, 2, 0, 2, 2, 0,\n",
       "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 2, 2, 0, 2, 2, 2, 2,\n",
       "       2, 2, 2, 2, 1, 2, 2, 2, 2, 2, 2, 2, 2, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
       "       2, 2, 2, 1, 2, 2, 2, 2, 2, 2, 1, 2, 0, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
       "       2, 1, 2, 0, 2, 2, 2, 2, 2, 2, 1, 2, 2, 2, 1, 1, 2, 2, 2, 2, 2, 2, 2,\n",
       "       0, 2, 2, 1, 0, 1, 0, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 2, 2, 2,\n",
       "       2, 2, 2, 1, 2, 2, 2, 0, 0, 0, 2, 0, 2, 2, 0, 0, 2, 1, 2, 2, 2, 2, 2,\n",
       "       0, 2, 0, 2, 0, 2, 2, 1, 2, 2, 2, 1, 2, 2, 2, 2, 0, 2, 2, 2, 2, 2, 2,\n",
       "       0, 2, 0, 2, 2, 2, 2, 1, 0, 2, 1, 2, 2, 2, 2, 2, 2, 2, 2, 1, 2, 2, 2,\n",
       "       2, 2, 2, 2, 0, 2, 2, 2, 0, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
       "       0, 2, 2, 2, 2, 2, 2], dtype=int32)"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "labels = pipeline.predict(documents)\n",
    "labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "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": "3.4.0"
  }
 },
 "nbformat": 4,
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}


================================================
FILE: Chapter 11/Chapter 11 (CIFAR).ipynb
================================================
{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import os\n",
    "data_folder = os.path.join(os.path.expanduser(\"~\"), \"Data\", \"cifar-10-batches-py\")\n",
    "batch1_filename = os.path.join(data_folder, \"data_batch_1\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pickle\n",
    "# Bigfix thanks to: http://stackoverflow.com/questions/11305790/pickle-incompatability-of-numpy-arrays-between-python-2-and-3\n",
    "def unpickle(filename):\n",
    "    with open(filename, 'rb') as fo:\n",
    "        return pickle.load(fo, encoding='latin1')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "batch1 = unpickle(batch1_filename)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "image_index = 100\n",
    "image = batch1['data'][image_index]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "image = image.reshape((32,32, 3), order='F')\n",
    "import numpy as np\n",
    "image = np.rot90(image, -1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "from matplotlib import pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7fe57222a2e8>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Co04fmcRJFp6Hi8s80izuc13kXn3vOmfG5NDAreLWg890dA/KOGbOyPLhVNIn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\n21xa+5MA/GkAvwXATyR0ndMnTATr7H7fjHd2hT4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VyfC03uL34K/1Ha98Kw74RqoL4Hf3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\nTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe58c2f6278>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(image)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##Application"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "batches = []\n",
    "for i in range(1, 6):\n",
    "    batch_filename = os.path.join(data_folder, \"data_batch_{}\".format(i))\n",
    "    batches.append(unpickle(batch1_filename))\n",
    "    break    #IMPORTANT -- see chapter for explanation of this line"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X = np.vstack([batch['data'] for batch in batches])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X = np.array(X) / X.max()\n",
    "X = X.astype(np.float32)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import OneHotEncoder\n",
    "y = np.hstack(batch['labels'] for batch in batches).flatten()\n",
    "y = OneHotEncoder().fit_transform(y.reshape(y.shape[0],1)).todense()\n",
    "y = y.astype(np.float32)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from sklearn.cross_validation import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "X_train = X_train.reshape(-1, 3, 32, 32)\n",
    "X_test = X_test.reshape(-1, 3, 32, 32)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from lasagne import layers\n",
    "layers=[\n",
    "        ('input', layers.InputLayer),\n",
    "        ('conv1', layers.Conv2DLayer),\n",
    "        ('pool1', layers.MaxPool2DLayer),\n",
    "        ('conv2', layers.Conv2DLayer),\n",
    "        ('pool2', layers.MaxPool2DLayer),\n",
    "        ('conv3', layers.Conv2DLayer),\n",
    "        ('pool3', layers.MaxPool2DLayer),\n",
    "        ('hidden4', layers.DenseLayer),\n",
    "        ('hidden5', layers.DenseLayer),\n",
    "        ('output', layers.DenseLayer),\n",
    "        ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from nolearn.lasagne import NeuralNet\n",
    "from lasagne.nonlinearities import sigmoid, softmax\n",
    "nnet = NeuralNet(layers=layers,\n",
    "                 input_shape=(None, 3, 32, 32),\n",
    "                 conv1_num_filters=32,\n",
    "                 conv1_filter_size=(3, 3),\n",
    "                 conv2_num_filters=64,\n",
    "                 conv2_filter_size=(2, 2),\n",
    "                 conv3_num_filters=128,\n",
    "                 conv3_filter_size=(2, 2),\n",
    "                 pool1_ds=(2,2),\n",
    "                 pool2_ds=(2,2),\n",
    "                 pool3_ds=(2,2),\n",
    "                 hidden4_num_units=500,\n",
    "                 hidden5_num_units=500,\n",
    "                 output_num_units=10,\n",
    "                 output_nonlinearity=softmax,\n",
    "                 update_learning_rate=0.01,\n",
    "                 update_momentum=0.9,\n",
    "                 regression=True,\n",
    "                 max_epochs=3,\n",
    "                 verbose=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  input             \t(None, 3, 32, 32)   \tproduces    3072 outputs\n",
      "  conv1             \t(None, 32, 30, 30)  \tproduces   28800 outputs\n",
      "  pool1             \t(None, 32, 15, 15)  \tproduces    7200 outputs\n",
      "  conv2             \t(None, 64, 14, 14)  \tproduces   12544 outputs\n",
      "  pool2             \t(None, 64, 7, 7)    \tproduces    3136 outputs\n",
      "  conv3             \t(None, 128, 6, 6)   \tproduces    4608 outputs\n",
      "  pool3             \t(None, 128, 3, 3)   \tproduces    1152 outputs\n",
      "  hidden4           \t(None, 500)         \tproduces     500 outputs\n",
      "  hidden5           \t(None, 500)         \tproduces     500 outputs\n",
      "  output            \t(None, 10)          \tproduces      10 outputs\n",
      "\n",
      " Epoch  |  Train loss  |  Valid loss  |  Train / Val  |  Valid acc  |  Dur\n",
      "--------|--------------|--------------|---------------|-------------|-------\n",
      "     1  |  \u001b[94m  0.090018\u001b[0m  |  \u001b[32m  0.090022\u001b[0m  |     0.999963  |             |  103.9s\n",
      "     2  |  \u001b[94m  0.089988\u001b[0m  |  \u001b[32m  0.090000\u001b[0m  |     0.999858  |             |  103.8s\n",
      "     3  |  \u001b[94m  0.089961\u001b[0m  |  \u001b[32m  0.089982\u001b[0m  |     0.999759  |             |  104.2s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.4/dist-packages/Lasagne-0.1dev-py3.4.egg/lasagne/init.py:30: UserWarning: The uniform initializer no longer uses Glorot et al.'s approach to determine the bounds, but defaults to the range (-0.01, 0.01) instead. Please use the new GlorotUniform initializer to get the old behavior. GlorotUniform is now the default for all layers.\n",
      "  warnings.warn(\"The uniform initializer no longer uses Glorot et al.'s \"\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "NeuralNet(X_tensor_type=<function tensor4 at 0x7fe55b73a0d0>,\n",
       "     batch_iterator_test=<nolearn.lasagne.BatchIterator object at 0x7fe559e70710>,\n",
       "     batch_iterator_train=<nolearn.lasagne.BatchIterator object at 0x7fe559e706d8>,\n",
       "     conv1_filter_size=(3, 3), conv1_num_filters=32,\n",
       "     conv2_filter_size=(2, 2), conv2_num_filters=64,\n",
       "     conv3_filter_size=(2, 2), conv3_num_filters=128, eval_size=0.2,\n",
       "     hidden4_num_units=500, hidden5_num_units=500,\n",
       "     input_shape=(None, 3, 32, 32),\n",
       "     layers=[('input', <class 'lasagne.layers.input.InputLayer'>), ('conv1', <class 'lasagne.layers.conv.Conv2DLayer'>), ('pool1', <class 'lasagne.layers.pool.MaxPool2DLayer'>), ('conv2', <class 'lasagne.layers.conv.Conv2DLayer'>), ('pool2', <class 'lasagne.layers.pool.MaxPool2DLayer'>), ('conv3', <class..., <class 'lasagne.layers.dense.DenseLayer'>), ('output', <class 'lasagne.layers.dense.DenseLayer'>)],\n",
       "     loss=None, max_epochs=3, more_params={},\n",
       "     objective=<class 'lasagne.objectives.Objective'>,\n",
       "     objective_loss_function=<function mse at 0x7fe559e622f0>,\n",
       "     on_epoch_finished=(), on_training_finished=(),\n",
       "     output_nonlinearity=<theano.tensor.nnet.nnet.Softmax object at 0x7fe57cfddba8>,\n",
       "     output_num_units=10, pool1_ds=(2, 2), pool2_ds=(2, 2),\n",
       "     pool3_ds=(2, 2), regression=True,\n",
       "     update=<function nesterov_momentum at 0x7fe559e628c8>,\n",
       "     update_learning_rate=0.01, update_momentum=0.9,\n",
       "     use_label_encoder=False, verbose=1,\n",
       "     y_tensor_type=TensorType(float32, matrix))"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nnet.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.0364246254873\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.4/dist-packages/sklearn/metrics/metrics.py:1771: 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"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import f1_score\n",
    "y_pred = nnet.predict(X_test)\n",
    "print(f1_score(y_test.argmax(axis=1), y_pred.argmax(axis=1)))"
   ]
  },
  {
   "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": "3.4.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}


================================================
FILE: Chapter 11/Chapter 11 (Theano and Lasagne).ipynb
================================================
{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##Cifar Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import os\n",
    "data_folder = os.path.join(os.path.expanduser(\"~\"), \"Data\", \"cifar-10-batches-py\")\n",
    "batch1_filename = os.path.join(data_folder, \"data_batch_1\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pickle\n",
    "# Bigfix thanks to: http://stackoverflow.com/questions/11305790/pickle-incompatability-of-numpy-arrays-between-python-2-and-3\n",
    "def unpickle(filename):\n",
    "    with open(filename, 'rb') as fo:\n",
    "        return pickle.load(fo, encoding='latin1')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "batch1 = unpickle(batch1_filename)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "image_index = 100\n",
    "image = batch1['data'][image_index]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "image = image.reshape((32,32, 3), order='F')\n",
    "import numpy as np\n",
    "image = np.rot90(image, -1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "from matplotlib import pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f12f47efac8>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Co04fmcRJFp6Hi8s80izuc13kXn3vOmfG5NDAreLWg890dA/KOGbOyPLhVNIn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\n21xa+5MA/GkAvwXATyR0ndMnTATr7H7fjHd2hT4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