Chapter2/Ex_2_8_reg.ipynb

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      "             precision    recall  f1-score   support\n",
      "\n",
      "        2.0       0.96      0.96      0.96       198\n",
      "        3.0       0.96      0.95      0.95       166\n",
      "\n",
      "avg / total       0.96      0.96      0.96       364\n",
      "\n",
      "[[191   7]\n",
      " [  8 158]]\n",
      "error_rate = 4.12087912088 %\n"
     ]
    }
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   "source": [
    "import rpy2.robjects as robjects\n",
    "import pandas.rpy.common as com\n",
    "import pandas as pd\n",
    "from rpy2.robjects import pandas2ri\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.metrics import classification_report\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn.metrics import accuracy_score\n",
    "pandas2ri.activate()\n",
    "\n",
    "# load zip train data\n",
    "robj    = robjects.r.load(\"/Users/lasiadhi/Dropbox-2/Dropbox/Statistical_Learning/data/zip.train.RData\")\n",
    "\n",
    "# load zip test data\n",
    "robj_test    = robjects.r.load(\"/Users/lasiadhi/Dropbox-2/Dropbox/Statistical_Learning/data/zip.test.RData\")\n",
    "\n",
    "for sets in robj:\n",
    "    myRData = com.load_data(sets)\n",
    "for sets in robj_test:\n",
    "    myRData_test = com.load_data(sets)\n",
    "    \n",
    "Zipdata = pd.DataFrame(myRData)\n",
    "Zipdata_test = pd.DataFrame(myRData_test)\n",
    "\n",
    "# read observations (rows) with 2's and 3's for this exercise\n",
    "Zip23 = Zipdata[(Zipdata[0] == 2) | (Zipdata[0] == 3)]  #print(Zip23.shape) #(1389, 257)\n",
    "Zip23_test = Zipdata_test[(Zipdata_test[0] == 2) | (Zipdata_test[0] == 3)]  # print(Zip23_test.shape) (364, 257)\n",
    "\n",
    "\n",
    "# Linear regression\n",
    "model = LinearRegression(normalize=False)\n",
    "model.fit(Zip23.iloc[:,1:],Zip23[0])  # pass 256 features and response vector\n",
    "\n",
    "## Perform prediction on the test data set\n",
    "#row_index = 0\n",
    "#print(Zip23_test[row_index:row_index+1])\n",
    "#num_pred = model.predict(Zip23_test.iloc[row_index:row_index+1,1:])\n",
    "#print(int(num_pred))\n",
    "num_pred = model.predict(Zip23_test.iloc[:,1:])   \n",
    "\n",
    "# convert predictions to 2s and 3s\n",
    "num_pred[num_pred>=2.5]=3\n",
    "num_pred[num_pred<2.5]=2\n",
    "\n",
    "print(classification_report(Zip23_test.iloc[:,:1], num_pred))\n",
    "print(confusion_matrix(Zip23_test.iloc[:,:1],num_pred))\n",
    "error_rate = 1-accuracy_score(Zip23_test.iloc[:,:1],num_pred)\n",
    "print('error_rate =',error_rate * 100,'%')"
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