Impetus Day 7 PCA.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.datasets import load_iris"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "iris = load_iris()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.decomposition import PCA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "trainX,testX, trainY,testY = train_test_split(iris.data, iris.target)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "lr = LogisticRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
       "          intercept_scaling=1, max_iter=100, multi_class='ovr', n_jobs=1,\n",
       "          penalty='l2', random_state=None, solver='liblinear', tol=0.0001,\n",
       "          verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr.fit(trainX,trainY)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.94736842105263153"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr.score(testX,testY)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "pca = PCA(n_components=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "iris_data = pca.fit_transform(iris.data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "lr = LogisticRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
       "          intercept_scaling=1, max_iter=100, multi_class='ovr', n_jobs=1,\n",
       "          penalty='l2', random_state=None, solver='liblinear', tol=0.0001,\n",
       "          verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trainX,testX, trainY,testY = train_test_split(iris_data, iris.target)\n",
    "lr.fit(trainX,trainY)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.89473684210526316"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr.score(testX,testY)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.datasets import load_digits"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "digits = load_digits()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x2b9dab3aef0>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAPgAAAD8CAYAAABaQGkdAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nI3AgMQIHEvsv5ZKXEPXbXisAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2b9d95ec780>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "plt.imshow(digits.data[66].reshape(8,8), cmap='gray')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1797, 64)"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "digits.data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1797,)"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "digits.target.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "digits.target[66]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "trainX,testX, trainY, testY = train_test_split(digits.data, digits.target)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "rf = RandomForestClassifier(n_estimators=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\n",
       "            max_depth=None, max_features='auto', max_leaf_nodes=None,\n",
       "            min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "            min_samples_leaf=1, min_samples_split=2,\n",
       "            min_weight_fraction_leaf=0.0, n_estimators=10, n_jobs=1,\n",
       "            oob_score=False, random_state=None, verbose=0,\n",
       "            warm_start=False)"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf.fit(trainX,trainY)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.93777777777777782"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf.score(testX,testY)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "pca = PCA(n_components=32)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X = pca.fit_transform(digits.data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1797, 32)"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "rf = RandomForestClassifier(n_estimators=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "trainX,testX, trainY, testY = train_test_split(X, digits.target)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\n",
       "            max_depth=None, max_features='auto', max_leaf_nodes=None,\n",
       "            min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "            min_samples_leaf=1, min_samples_split=2,\n",
       "            min_weight_fraction_leaf=0.0, n_estimators=10, n_jobs=1,\n",
       "            oob_score=False, random_state=None, verbose=0,\n",
       "            warm_start=False)"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf.fit(trainX,trainY)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.92222222222222228"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf.score(testX,testY)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
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