Pipeline_brainhack.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Calling libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import scipy.io as sio\n",
    "import glob\n",
    "from sklearn import preprocessing\n",
    "from sklearn.model_selection import StratifiedShuffleSplit, cross_val_score, train_test_split\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.datasets import make_moons, make_circles, make_classification\n",
    "\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.ensemble import ExtraTreesClassifier\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.discriminant_analysis import LinearDiscriminantAnalysis\n",
    "\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn.metrics import classification_report\n",
    "\n",
    "import seaborn\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Load data\n",
    "## a) from file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "x=sio.loadmat('/home/rocio/Documents/Anaconda/Python_scripts/name_file.mat')['x']\n",
    "s_y=sio.loadmat('/home/rocio/Documents/Anaconda/Python_scripts/name_file.mat')['y']\n",
    "y=np.ravel(s_y)\n",
    "        \n",
    "print(x.shape, y.shape) #size of matrix\n",
    "print(\"\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## b) fake data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(100, 5)\n",
      "(100,)\n"
     ]
    }
   ],
   "source": [
    "x, y = make_classification(n_features=5, n_redundant=0, n_informative=2,\n",
    "                           random_state=1, n_clusters_per_class=1)\n",
    "\n",
    "print(x.shape)\n",
    "print(y.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.2, random_state=1)         \n",
    "\n",
    "pipe=Pipeline([('clf', RandomForestClassifier())])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Optimizing clasifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best score:0.989\n",
      "{'clf__n_estimators': 1, 'clf__max_features': 5}\n",
      "Test accuracy: 1.000\n",
      "             precision    recall  f1-score   support\n",
      "\n",
      "          0       1.00      1.00      1.00         5\n",
      "          1       1.00      1.00      1.00         5\n",
      "\n",
      "avg / total       1.00      1.00      1.00        10\n",
      "\n"
     ]
    }
   ],
   "source": [
    "param_range_estimators=[1, 10, 12] #number of trees: 100, 120, 150\n",
    "param_range_features=[2, 5] #number of features: 2, 3, 5\n",
    "param_grid=[{'clf__n_estimators':param_range_estimators,\n",
    "             'clf__max_features':param_range_features}]\n",
    "\n",
    "\n",
    "gs=GridSearchCV(estimator=pipe,\n",
    "                param_grid=param_grid,\n",
    "                scoring='accuracy',\n",
    "                cv=2, #cv=10\n",
    "                n_jobs=-1)\n",
    "\n",
    "\n",
    "gs.fit(x_train, y_train)\n",
    "\n",
    "y_pred=gs.predict(x_test)\n",
    "cm=confusion_matrix(y_test, y_pred)\n",
    "\n",
    "print('Best score:%.3f' % gs.best_score_)\n",
    "print(gs.best_params_)\n",
    "\n",
    "print('Test accuracy: %.3f' % gs.score(x_test, y_test))\n",
    "print(classification_report(y_test, y_pred))\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import itertools\n",
    "\n",
    "#http://matplotlib.org/users/colormaps.html ---elegir colores para cmap\n",
    "def plot_confusion_matrix(cm, title='Confusion matrix', cmap=plt.cm.viridis):\n",
    "    plt.imshow(cm, interpolation='nearest', cmap=cmap)  #Display an image on the axes\n",
    "    plt.title(title)\n",
    "    plt.colorbar() #Es la barra vertical que aparece a la derecha\n",
    "    tick_marks = np.arange(len(np.unique(y))) #Tiene la longitud del # de clases. Es una matriz\n",
    "                                                   \n",
    "    plt.xticks(tick_marks, rotation=90) #los nombres girados\n",
    "    ax = plt.gca()\n",
    "    ax.set_xticklabels(['s'+lab for lab in (ax.get_xticks()+1).astype(str)])\n",
    "    plt.yticks(tick_marks)\n",
    "    ax.set_yticklabels(['s'+lab for lab in (ax.get_yticks()+1).astype(str)])\n",
    "            \n",
    "    plt.tight_layout()  #No veo ninguna diferencia\n",
    "    plt.ylabel('True label')\n",
    "    plt.xlabel('Predicted label')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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qYBlARBwE3JCZtwNk5iTww4jYtzr3YCAHdebtYJKk4jT5WM/MvDwiroyItcAkcGJEHA9s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\nktQi/x8mG+0GtEz5YQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xa8c97aec>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cm_normalized = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n",
    "\n",
    "plt.figure(figsize=(8,6))\n",
    "plot_confusion_matrix(cm_normalized)"
   ]
  },
  {
   "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": 0
}