unsupervised_dimensionality_reduction.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\wishw\\Anaconda3\\lib\\site-packages\\sklearn\\cross_validation.py:41: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. Also note that the interface of the new CV iterators are different from that of this module. This module will be removed in 0.20.\n",
      "  \"This module will be removed in 0.20.\", DeprecationWarning)\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "from sklearn.cross_validation import train_test_split\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "df_wine = pd.read_csv('https://archive.ics.uci.edu/ml/machine-learning-databases/wine/wine.data', header=None)\n",
    "\n",
    "X,y = df_wine.iloc[:,1:].values, df_wine.iloc[:,0].values                \n",
    "\n",
    "X_train,X_test,y_train,y_test = train_test_split(X,y,test_size=0.3,random_state=0)                     \n",
    "\n",
    "sc = StandardScaler()\n",
    "\n",
    "X_train_std = sc.fit_transform(X_train)\n",
    "X_test_std = sc.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "cov_mat = np.cov(X_train_std.T)\n",
    "eigen_values,eigen_vec = np.linalg.eig(cov_mat)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([4.8923083 , 2.46635032, 1.42809973, 1.01233462, 0.84906459,\n",
       "       0.60181514, 0.52251546, 0.08414846, 0.33051429, 0.29595018,\n",
       "       0.16831254, 0.21432212, 0.2399553 ])"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "eigen_values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "tot = sum(eigen_values)\n",
    "var_exp = [i/tot for i in sorted(eigen_values,reverse=True)]\n",
    "cum_var_exp = np.cumsum(var_exp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1e60b59f160>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "plt.bar(range(1,14), var_exp, alpha=0.5, align= 'center',label='individual explained variable',color='blue')\n",
    "plt.step(range(1,14), cum_var_exp, where='mid', label='cummulative explained variance')\n",
    "plt.xlabel('principle component')\n",
    "plt.ylabel('explained variance ratio')\n",
    "plt.legend(loc='best')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "eigen_pairs =[(np.abs(eigen_values[i]),eigen_vec[:,i]) for i in range(len(eigen_values))]\n",
    "eigen_pairs.sort(reverse=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.14669811,  0.50417079],\n",
       "       [-0.24224554,  0.24216889],\n",
       "       [-0.02993442,  0.28698484],\n",
       "       [-0.25519002, -0.06468718],\n",
       "       [ 0.12079772,  0.22995385],\n",
       "       [ 0.38934455,  0.09363991],\n",
       "       [ 0.42326486,  0.01088622],\n",
       "       [-0.30634956,  0.01870216],\n",
       "       [ 0.30572219,  0.03040352],\n",
       "       [-0.09869191,  0.54527081],\n",
       "       [ 0.30032535, -0.27924322],\n",
       "       [ 0.36821154, -0.174365  ],\n",
       "       [ 0.29259713,  0.36315461]])"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "w= np.hstack((eigen_pairs[0][1][:, np.newaxis],eigen_pairs[1][1][:, np.newaxis]))\n",
    "w"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train_pca = X_train_std.dot(w)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1e60b8e0518>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "colors = ['r', 'b', 'g']\n",
    "markers = ['s', 'x', 'o']\n",
    "for l, c, m in zip(np.unique(y_train), colors, markers):\n",
    "    plt.scatter(X_train_pca[y_train==l, 0],X_train_pca[y_train==l, 1],c=c, label=l, marker=m)\n",
    "plt.xlabel('PC 1')\n",
    "plt.ylabel('PC 2')\n",
    "plt.legend(loc='lower left')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "from matplotlib.colors import ListedColormap\n",
    "def plot_decision_regions(X, y, classifier, resolution=0.02):\n",
    "    # setup marker generator and color map\n",
    "    markers = ('s', 'x', 'o', '^', 'v')\n",
    "    colors = ('red', 'blue', 'lightgreen', 'gray', 'cyan')\n",
    "    cmap = ListedColormap(colors[:len(np.unique(y))])\n",
    "    # plot the decision surface\n",
    "    x1_min, x1_max = X[:, 0].min() - 1, X[:, 0].max() + 1\n",
    "    x2_min, x2_max = X[:, 1].min() - 1, X[:, 1].max() + 1\n",
    "    xx1, xx2 = np.meshgrid(np.arange(x1_min, x1_max, resolution),\n",
    "    np.arange(x2_min, x2_max, resolution))\n",
    "    Z = classifier.predict(np.array([xx1.ravel(), xx2.ravel()]).T)\n",
    "    Z = Z.reshape(xx1.shape)\n",
    "    plt.contourf(xx1, xx2, Z, alpha=0.4, cmap=cmap)\n",
    "    plt.xlim(xx1.min(), xx1.max())\n",
    "    plt.ylim(xx2.min(), xx2.max())\n",
    "    # plot class samples\n",
    "    for idx, cl in enumerate(np.unique(y)):\n",
    "        plt.scatter(x=X[y == cl, 0], y=X[y == cl, 1],alpha=0.8, c=cmap(idx),marker=markers[idx], label=cl)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1e60b82a940>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.decomposition import PCA\n",
    "pca = PCA(n_components=2)\n",
    "lr = LogisticRegression()\n",
    "X_train_pca = pca.fit_transform(X_train_std)\n",
    "X_test_pca = pca.transform(X_test_std)\n",
    "lr.fit(X_train_pca, y_train)\n",
    "plot_decision_regions(X_train_pca, y_train, classifier=lr)\n",
    "plt.xlabel('PC1')\n",
    "plt.ylabel('PC2')\n",
    "plt.legend(loc='lower left')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "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": "3.6.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}