coding-assignments/3.NN-Regression.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Load the Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from sklearn import datasets\n",
    "#from sklearn import linear_model\n",
    "from sklearn.neural_network import MLPClassifier\n",
    "from sklearn.metrics import mean_squared_error, r2_score\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Load the Diabetes Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>sex</th>\n",
       "      <th>bmi</th>\n",
       "      <th>bp</th>\n",
       "      <th>s1</th>\n",
       "      <th>s2</th>\n",
       "      <th>s3</th>\n",
       "      <th>s4</th>\n",
       "      <th>s5</th>\n",
       "      <th>s6</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.038076</td>\n",
       "      <td>0.050680</td>\n",
       "      <td>0.061696</td>\n",
       "      <td>0.021872</td>\n",
       "      <td>-0.044223</td>\n",
       "      <td>-0.034821</td>\n",
       "      <td>-0.043401</td>\n",
       "      <td>-0.002592</td>\n",
       "      <td>0.019908</td>\n",
       "      <td>-0.017646</td>\n",
       "      <td>151.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.001882</td>\n",
       "      <td>-0.044642</td>\n",
       "      <td>-0.051474</td>\n",
       "      <td>-0.026328</td>\n",
       "      <td>-0.008449</td>\n",
       "      <td>-0.019163</td>\n",
       "      <td>0.074412</td>\n",
       "      <td>-0.039493</td>\n",
       "      <td>-0.068330</td>\n",
       "      <td>-0.092204</td>\n",
       "      <td>75.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.085299</td>\n",
       "      <td>0.050680</td>\n",
       "      <td>0.044451</td>\n",
       "      <td>-0.005671</td>\n",
       "      <td>-0.045599</td>\n",
       "      <td>-0.034194</td>\n",
       "      <td>-0.032356</td>\n",
       "      <td>-0.002592</td>\n",
       "      <td>0.002864</td>\n",
       "      <td>-0.025930</td>\n",
       "      <td>141.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.089063</td>\n",
       "      <td>-0.044642</td>\n",
       "      <td>-0.011595</td>\n",
       "      <td>-0.036656</td>\n",
       "      <td>0.012191</td>\n",
       "      <td>0.024991</td>\n",
       "      <td>-0.036038</td>\n",
       "      <td>0.034309</td>\n",
       "      <td>0.022692</td>\n",
       "      <td>-0.009362</td>\n",
       "      <td>206.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.005383</td>\n",
       "      <td>-0.044642</td>\n",
       "      <td>-0.036385</td>\n",
       "      <td>0.021872</td>\n",
       "      <td>0.003935</td>\n",
       "      <td>0.015596</td>\n",
       "      <td>0.008142</td>\n",
       "      <td>-0.002592</td>\n",
       "      <td>-0.031991</td>\n",
       "      <td>-0.046641</td>\n",
       "      <td>135.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>-0.092695</td>\n",
       "      <td>-0.044642</td>\n",
       "      <td>-0.040696</td>\n",
       "      <td>-0.019442</td>\n",
       "      <td>-0.068991</td>\n",
       "      <td>-0.079288</td>\n",
       "      <td>0.041277</td>\n",
       "      <td>-0.076395</td>\n",
       "      <td>-0.041180</td>\n",
       "      <td>-0.096346</td>\n",
       "      <td>97.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>-0.045472</td>\n",
       "      <td>0.050680</td>\n",
       "      <td>-0.047163</td>\n",
       "      <td>-0.015999</td>\n",
       "      <td>-0.040096</td>\n",
       "      <td>-0.024800</td>\n",
       "      <td>0.000779</td>\n",
       "      <td>-0.039493</td>\n",
       "      <td>-0.062913</td>\n",
       "      <td>-0.038357</td>\n",
       "      <td>138.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.063504</td>\n",
       "      <td>0.050680</td>\n",
       "      <td>-0.001895</td>\n",
       "      <td>0.066630</td>\n",
       "      <td>0.090620</td>\n",
       "      <td>0.108914</td>\n",
       "      <td>0.022869</td>\n",
       "      <td>0.017703</td>\n",
       "      <td>-0.035817</td>\n",
       "      <td>0.003064</td>\n",
       "      <td>63.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.041708</td>\n",
       "      <td>0.050680</td>\n",
       "      <td>0.061696</td>\n",
       "      <td>-0.040099</td>\n",
       "      <td>-0.013953</td>\n",
       "      <td>0.006202</td>\n",
       "      <td>-0.028674</td>\n",
       "      <td>-0.002592</td>\n",
       "      <td>-0.014956</td>\n",
       "      <td>0.011349</td>\n",
       "      <td>110.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>-0.070900</td>\n",
       "      <td>-0.044642</td>\n",
       "      <td>0.039062</td>\n",
       "      <td>-0.033214</td>\n",
       "      <td>-0.012577</td>\n",
       "      <td>-0.034508</td>\n",
       "      <td>-0.024993</td>\n",
       "      <td>-0.002592</td>\n",
       "      <td>0.067736</td>\n",
       "      <td>-0.013504</td>\n",
       "      <td>310.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>-0.096328</td>\n",
       "      <td>-0.044642</td>\n",
       "      <td>-0.083808</td>\n",
       "      <td>0.008101</td>\n",
       "      <td>-0.103389</td>\n",
       "      <td>-0.090561</td>\n",
       "      <td>-0.013948</td>\n",
       "      <td>-0.076395</td>\n",
       "      <td>-0.062913</td>\n",
       "      <td>-0.034215</td>\n",
       "      <td>101.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0.027178</td>\n",
       "      <td>0.050680</td>\n",
       "      <td>0.017506</td>\n",
       "      <td>-0.033214</td>\n",
       "      <td>-0.007073</td>\n",
       "      <td>0.045972</td>\n",
       "      <td>-0.065491</td>\n",
       "      <td>0.071210</td>\n",
       "      <td>-0.096433</td>\n",
       "      <td>-0.059067</td>\n",
       "      <td>69.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0.016281</td>\n",
       "      <td>-0.044642</td>\n",
       "      <td>-0.028840</td>\n",
       "      <td>-0.009113</td>\n",
       "      <td>-0.004321</td>\n",
       "      <td>-0.009769</td>\n",
       "      <td>0.044958</td>\n",
       "      <td>-0.039493</td>\n",
       "      <td>-0.030751</td>\n",
       "      <td>-0.042499</td>\n",
       "      <td>179.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>0.005383</td>\n",
       "      <td>0.050680</td>\n",
       "      <td>-0.001895</td>\n",
       "      <td>0.008101</td>\n",
       "      <td>-0.004321</td>\n",
       "      <td>-0.015719</td>\n",
       "      <td>-0.002903</td>\n",
       "      <td>-0.002592</td>\n",
       "      <td>0.038393</td>\n",
       "      <td>-0.013504</td>\n",
       "      <td>185.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>0.045341</td>\n",
       "      <td>-0.044642</td>\n",
       "      <td>-0.025607</td>\n",
       "      <td>-0.012556</td>\n",
       "      <td>0.017694</td>\n",
       "      <td>-0.000061</td>\n",
       "      <td>0.081775</td>\n",
       "      <td>-0.039493</td>\n",
       "      <td>-0.031991</td>\n",
       "      <td>-0.075636</td>\n",
       "      <td>118.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>-0.052738</td>\n",
       "      <td>0.050680</td>\n",
       "      <td>-0.018062</td>\n",
       "      <td>0.080401</td>\n",
       "      <td>0.089244</td>\n",
       "      <td>0.107662</td>\n",
       "      <td>-0.039719</td>\n",
       "      <td>0.108111</td>\n",
       "      <td>0.036056</td>\n",
       "      <td>-0.042499</td>\n",
       "      <td>171.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>-0.005515</td>\n",
       "      <td>-0.044642</td>\n",
       "      <td>0.042296</td>\n",
       "      <td>0.049415</td>\n",
       "      <td>0.024574</td>\n",
       "      <td>-0.023861</td>\n",
       "      <td>0.074412</td>\n",
       "      <td>-0.039493</td>\n",
       "      <td>0.052280</td>\n",
       "      <td>0.027917</td>\n",
       "      <td>166.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>0.070769</td>\n",
       "      <td>0.050680</td>\n",
       "      <td>0.012117</td>\n",
       "      <td>0.056301</td>\n",
       "      <td>0.034206</td>\n",
       "      <td>0.049416</td>\n",
       "      <td>-0.039719</td>\n",
       "      <td>0.034309</td>\n",
       "      <td>0.027368</td>\n",
       "      <td>-0.001078</td>\n",
       "      <td>144.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>-0.038207</td>\n",
       "      <td>-0.044642</td>\n",
       "      <td>-0.010517</td>\n",
       "      <td>-0.036656</td>\n",
       "      <td>-0.037344</td>\n",
       "      <td>-0.019476</td>\n",
       "      <td>-0.028674</td>\n",
       "      <td>-0.002592</td>\n",
       "      <td>-0.018118</td>\n",
       "      <td>-0.017646</td>\n",
       "      <td>97.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>-0.027310</td>\n",
       "      <td>-0.044642</td>\n",
       "      <td>-0.018062</td>\n",
       "      <td>-0.040099</td>\n",
       "      <td>-0.002945</td>\n",
       "      <td>-0.011335</td>\n",
       "      <td>0.037595</td>\n",
       "      <td>-0.039493</td>\n",
       "      <td>-0.008944</td>\n",
       "      <td>-0.054925</td>\n",
       "      <td>168.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         age       sex       bmi        bp        s1        s2        s3  \\\n",
       "0   0.038076  0.050680  0.061696  0.021872 -0.044223 -0.034821 -0.043401   \n",
       "1  -0.001882 -0.044642 -0.051474 -0.026328 -0.008449 -0.019163  0.074412   \n",
       "2   0.085299  0.050680  0.044451 -0.005671 -0.045599 -0.034194 -0.032356   \n",
       "3  -0.089063 -0.044642 -0.011595 -0.036656  0.012191  0.024991 -0.036038   \n",
       "4   0.005383 -0.044642 -0.036385  0.021872  0.003935  0.015596  0.008142   \n",
       "5  -0.092695 -0.044642 -0.040696 -0.019442 -0.068991 -0.079288  0.041277   \n",
       "6  -0.045472  0.050680 -0.047163 -0.015999 -0.040096 -0.024800  0.000779   \n",
       "7   0.063504  0.050680 -0.001895  0.066630  0.090620  0.108914  0.022869   \n",
       "8   0.041708  0.050680  0.061696 -0.040099 -0.013953  0.006202 -0.028674   \n",
       "9  -0.070900 -0.044642  0.039062 -0.033214 -0.012577 -0.034508 -0.024993   \n",
       "10 -0.096328 -0.044642 -0.083808  0.008101 -0.103389 -0.090561 -0.013948   \n",
       "11  0.027178  0.050680  0.017506 -0.033214 -0.007073  0.045972 -0.065491   \n",
       "12  0.016281 -0.044642 -0.028840 -0.009113 -0.004321 -0.009769  0.044958   \n",
       "13  0.005383  0.050680 -0.001895  0.008101 -0.004321 -0.015719 -0.002903   \n",
       "14  0.045341 -0.044642 -0.025607 -0.012556  0.017694 -0.000061  0.081775   \n",
       "15 -0.052738  0.050680 -0.018062  0.080401  0.089244  0.107662 -0.039719   \n",
       "16 -0.005515 -0.044642  0.042296  0.049415  0.024574 -0.023861  0.074412   \n",
       "17  0.070769  0.050680  0.012117  0.056301  0.034206  0.049416 -0.039719   \n",
       "18 -0.038207 -0.044642 -0.010517 -0.036656 -0.037344 -0.019476 -0.028674   \n",
       "19 -0.027310 -0.044642 -0.018062 -0.040099 -0.002945 -0.011335  0.037595   \n",
       "\n",
       "          s4        s5        s6  target  \n",
       "0  -0.002592  0.019908 -0.017646   151.0  \n",
       "1  -0.039493 -0.068330 -0.092204    75.0  \n",
       "2  -0.002592  0.002864 -0.025930   141.0  \n",
       "3   0.034309  0.022692 -0.009362   206.0  \n",
       "4  -0.002592 -0.031991 -0.046641   135.0  \n",
       "5  -0.076395 -0.041180 -0.096346    97.0  \n",
       "6  -0.039493 -0.062913 -0.038357   138.0  \n",
       "7   0.017703 -0.035817  0.003064    63.0  \n",
       "8  -0.002592 -0.014956  0.011349   110.0  \n",
       "9  -0.002592  0.067736 -0.013504   310.0  \n",
       "10 -0.076395 -0.062913 -0.034215   101.0  \n",
       "11  0.071210 -0.096433 -0.059067    69.0  \n",
       "12 -0.039493 -0.030751 -0.042499   179.0  \n",
       "13 -0.002592  0.038393 -0.013504   185.0  \n",
       "14 -0.039493 -0.031991 -0.075636   118.0  \n",
       "15  0.108111  0.036056 -0.042499   171.0  \n",
       "16 -0.039493  0.052280  0.027917   166.0  \n",
       "17  0.034309  0.027368 -0.001078   144.0  \n",
       "18 -0.002592 -0.018118 -0.017646    97.0  \n",
       "19 -0.039493 -0.008944 -0.054925   168.0  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Load the diabetes dataset\n",
    "diabetes = datasets.load_diabetes()\n",
    "\n",
    "df = pd.DataFrame(data= np.c_[diabetes['data'], diabetes['target']],\n",
    "                     columns= diabetes['feature_names'] + ['target'])\n",
    "\n",
    "df.head(20)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Process the Dataset - Split into Train and Test Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Use only one feature\n",
    "diabetes_X = diabetes.data[:, np.newaxis, 2]\n",
    "\n",
    "# Split the data into training/testing sets\n",
    "diabetes_X_train = diabetes_X[:-20]\n",
    "diabetes_X_test = diabetes_X[-20:]\n",
    "\n",
    "# Split the targets into training/testing sets\n",
    "diabetes_y_train = diabetes.target[:-20]\n",
    "diabetes_y_test = diabetes.target[-20:]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Train a Neural Network Regression Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MLPClassifier(activation='relu', alpha=1, batch_size='auto', beta_1=0.9,\n",
       "       beta_2=0.999, early_stopping=False, epsilon=1e-08,\n",
       "       hidden_layer_sizes=(100,), learning_rate='constant',\n",
       "       learning_rate_init=0.001, max_iter=1000, momentum=0.9,\n",
       "       n_iter_no_change=10, nesterovs_momentum=True, power_t=0.5,\n",
       "       random_state=None, shuffle=True, solver='adam', tol=0.0001,\n",
       "       validation_fraction=0.1, verbose=False, warm_start=False)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create linear regression object\n",
    "regr = MLPClassifier(alpha=1, max_iter=1000)\n",
    "\n",
    "# Train the model using the training sets\n",
    "regr.fit(diabetes_X_train, diabetes_y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Test (Predict) the Neural Network Regression Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Make predictions using the testing set\n",
    "diabetes_y_pred = regr.predict(diabetes_X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean squared error: 10277.60\n",
      "Variance score: -1.13\n"
     ]
    }
   ],
   "source": [
    "# The mean squared error\n",
    "print(\"Mean squared error: %.2f\"\n",
    "      % mean_squared_error(diabetes_y_test, diabetes_y_pred))\n",
    "# Explained variance score: 1 is perfect prediction\n",
    "print('Variance score: %.2f' % r2_score(diabetes_y_test, diabetes_y_pred))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Plot the Output"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot outputs\n",
    "plt.scatter(diabetes_X_test, diabetes_y_test,  color='black')\n",
    "plt.plot(diabetes_X_test, diabetes_y_pred, color='blue', linewidth=3)\n",
    "\n",
    "plt.xticks(())\n",
    "plt.yticks(())\n",
    "\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": "3.6.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}