Workshop 4 - Part1 Train:Test Split and Cross Validation.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from sklearn import datasets, linear_model\n",
    "from sklearn.model_selection import train_test_split\n",
    "from matplotlib import pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load the Diabetes Housing dataset\n",
    "columns = \"age sex bmi map tc ldl hdl tch ltg glu\".split(\" \")\n",
    "diabetes = datasets.load_diabetes()\n",
    "df = pd.DataFrame(diabetes.data, columns=columns)\n",
    "y = diabetes.target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(353, 10) (353,)\n",
      "(89, 10) (89,)\n"
     ]
    }
   ],
   "source": [
    "# create training and testing vars\n",
    "X_train, X_test, y_train, y_test = train_test_split(df, y, test_size=0.2)\n",
    "print(X_train.shape, y_train.shape)\n",
    "print(X_test.shape, y_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/Elizabeth.Ke.Zhang/anaconda/lib/python3.6/site-packages/scipy/linalg/basic.py:1226: RuntimeWarning: internal gelsd driver lwork query error, required iwork dimension not returned. This is likely the result of LAPACK bug 0038, fixed in LAPACK 3.2.2 (released July 21, 2010). Falling back to 'gelss' driver.\n",
      "  warnings.warn(mesg, RuntimeWarning)\n"
     ]
    }
   ],
   "source": [
    "lm = linear_model.LinearRegression()\n",
    "\n",
    "model = lm.fit(X_train, y_train)\n",
    "predictions = lm.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([175.63569207, 158.00265803,  89.06852001, 201.40275209,\n",
       "       211.56406869])"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predictions[0:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
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ebf8M8K7s8buBz2SPTwe+VPc5FLm5BjFYpwFXZ4+vBt5UY1k6iogfAI9O29yp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\ncjlAmJlZLgcIMzPL5QBhZma5/j/A2r1ne7SyCgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10adfe198>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## The line / model\n",
    "plt.scatter(y_test, predictions)\n",
    "plt.xlabel(\"True Values\")\n",
    "plt.ylabel(\"Predictions\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Score:  0.6085557659576022\n"
     ]
    }
   ],
   "source": [
    "print(\"Score: \", model.score(X_test, y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "KFold(n_splits=2, random_state=None, shuffle=False)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import KFold # import KFold\n",
    "import numpy as np\n",
    "X = np.array([[1, 2], [3, 4], [1, 2], [3, 4]]) # create an array\n",
    "y = np.array([1, 2, 3, 4]) # Create another array\n",
    "kf = KFold(n_splits=2) # Define the split - into 2 folds \n",
    "kf.get_n_splits(X) # returns the number of splitting iterations in the cross-validator\n",
    "print(kf) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TRAIN: [2 3] TEST: [0 1]\n",
      "TRAIN: [0 1] TEST: [2 3]\n"
     ]
    }
   ],
   "source": [
    "for train_index, test_index in kf.split(X):\n",
    "    print(\"TRAIN:\", train_index, \"TEST:\", test_index)\n",
    "    X_train, X_test = X[train_index], X[test_index]\n",
    "    y_train, y_test = y[train_index], y[test_index]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TRAIN: [1] TEST: [0]\n",
      "[[3 4]] [[1 2]] [2] [1]\n",
      "TRAIN: [0] TEST: [1]\n",
      "[[1 2]] [[3 4]] [1] [2]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import LeaveOneOut \n",
    "X = np.array([[1, 2], [3, 4]])\n",
    "y = np.array([1, 2])\n",
    "loo = LeaveOneOut()\n",
    "loo.get_n_splits(X)\n",
    "\n",
    "for train_index, test_index in loo.split(X):\n",
    "    print(\"TRAIN:\", train_index, \"TEST:\", test_index)\n",
    "    X_train, X_test = X[train_index], X[test_index]\n",
    "    y_train, y_test = y[train_index], y[test_index]\n",
    "    print(X_train, X_test, y_train, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/Elizabeth.Ke.Zhang/anaconda/lib/python3.6/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": [
    "from sklearn.cross_validation import cross_val_score, cross_val_predict\n",
    "from sklearn import metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "Found input variables with inconsistent numbers of samples: [442, 2]",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-19-a3fca929e01d>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m# Perform 6-fold cross validation\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mscores\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcross_val_score\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcv\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m6\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      3\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Cross-validated scores:\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mscores\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda/lib/python3.6/site-packages/sklearn/cross_validation.py\u001b[0m in \u001b[0;36mcross_val_score\u001b[0;34m(estimator, X, y, scoring, cv, n_jobs, verbose, fit_params, pre_dispatch)\u001b[0m\n\u001b[1;32m   1568\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1569\u001b[0m     \"\"\"\n\u001b[0;32m-> 1570\u001b[0;31m     \u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mindexable\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1571\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1572\u001b[0m     \u001b[0mcv\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcheck_cv\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcv\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mclassifier\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mis_classifier\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mestimator\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda/lib/python3.6/site-packages/sklearn/utils/validation.py\u001b[0m in \u001b[0;36mindexable\u001b[0;34m(*iterables)\u001b[0m\n\u001b[1;32m    196\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    197\u001b[0m             \u001b[0mresult\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 198\u001b[0;31m     \u001b[0mcheck_consistent_length\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mresult\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    199\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    200\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda/lib/python3.6/site-packages/sklearn/utils/validation.py\u001b[0m in \u001b[0;36mcheck_consistent_length\u001b[0;34m(*arrays)\u001b[0m\n\u001b[1;32m    171\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0muniques\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    172\u001b[0m         raise ValueError(\"Found input variables with inconsistent numbers of\"\n\u001b[0;32m--> 173\u001b[0;31m                          \" samples: %r\" % [int(l) for l in lengths])\n\u001b[0m\u001b[1;32m    174\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    175\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mValueError\u001b[0m: Found input variables with inconsistent numbers of samples: [442, 2]"
     ]
    }
   ],
   "source": [
    "# Perform 6-fold cross validation\n",
    "scores = cross_val_score(model, df, y, cv=6)\n",
    "print(\"Cross-validated scores:\", scores)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "Found input variables with inconsistent numbers of samples: [442, 2]",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-20-6db6dac17433>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m# Make cross validated predictions\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mpredictions\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcross_val_predict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcv\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m6\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      3\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mscatter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpredictions\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda/lib/python3.6/site-packages/sklearn/cross_validation.py\u001b[0m in \u001b[0;36mcross_val_predict\u001b[0;34m(estimator, X, y, cv, n_jobs, verbose, fit_params, pre_dispatch)\u001b[0m\n\u001b[1;32m   1372\u001b[0m     \u001b[0;34m>>\u001b[0m\u001b[0;34m>\u001b[0m \u001b[0my_pred\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcross_val_predict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlasso\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1373\u001b[0m     \"\"\"\n\u001b[0;32m-> 1374\u001b[0;31m     \u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mindexable\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1375\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1376\u001b[0m     \u001b[0mcv\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcheck_cv\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcv\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mclassifier\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mis_classifier\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mestimator\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda/lib/python3.6/site-packages/sklearn/utils/validation.py\u001b[0m in \u001b[0;36mindexable\u001b[0;34m(*iterables)\u001b[0m\n\u001b[1;32m    196\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    197\u001b[0m             \u001b[0mresult\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 198\u001b[0;31m     \u001b[0mcheck_consistent_length\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mresult\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    199\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    200\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda/lib/python3.6/site-packages/sklearn/utils/validation.py\u001b[0m in \u001b[0;36mcheck_consistent_length\u001b[0;34m(*arrays)\u001b[0m\n\u001b[1;32m    171\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0muniques\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    172\u001b[0m         raise ValueError(\"Found input variables with inconsistent numbers of\"\n\u001b[0;32m--> 173\u001b[0;31m                          \" samples: %r\" % [int(l) for l in lengths])\n\u001b[0m\u001b[1;32m    174\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    175\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mValueError\u001b[0m: Found input variables with inconsistent numbers of samples: [442, 2]"
     ]
    }
   ],
   "source": [
    "# Make cross validated predictions\n",
    "predictions = cross_val_predict(model, df, y, cv=6)\n",
    "plt.scatter(y, predictions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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