Week12 - Multilayer Perceptron.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Multilayer Perceptron"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "#import libraries\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "from sklearn.datasets import fetch_openml\n",
    "from sklearn.neural_network import MLPClassifier\n",
    "\n",
    "from sklearn.metrics import accuracy_score, confusion_matrix, ConfusionMatrixDisplay, plot_confusion_matrix, classification_report\n",
    "from sklearn.model_selection import cross_val_score, train_test_split, GridSearchCV, StratifiedShuffleSplit\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Classification: MNIST"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "cv = StratifiedShuffleSplit(n_splits=5, test_size=0.2, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load dataset\n",
    "X, y = fetch_openml('mnist_784', version=1, return_X_y=True)\n",
    "X = X.to_numpy()\n",
    "y = y.to_numpy()\n",
    "\n",
    "X_train, X_test, y_train, y_test = X[:60000], X[60000:], y[:60000], y[60000:]\n",
    "X_train = X_train.reshape(X_train.shape[0], 28*28)\n",
    "X_test = X_test.reshape(X_test.shape[0], 28*28)\n",
    "\n",
    "X_train /= 255\n",
    "X_test /= 255"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of training data (60000, 784)\n",
      "Shape of test data (10000, 784)\n",
      "Shape of training labels (60000,)\n",
      "Shape of test labels (10000,)\n"
     ]
    }
   ],
   "source": [
    "print(\"Shape of training data\", X_train.shape)\n",
    "print(\"Shape of test data\", X_test.shape)\n",
    "\n",
    "print(\"Shape of training labels\", y_train.shape)\n",
    "print(\"Shape of test labels\", y_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>MLPClassifier(hidden_layer_sizes=(128,))</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">MLPClassifier</label><div class=\"sk-toggleable__content\"><pre>MLPClassifier(hidden_layer_sizes=(128,))</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "MLPClassifier(hidden_layer_sizes=(128,))"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mlpc = MLPClassifier(hidden_layer_sizes=(128,))\n",
    "mlpc.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Accuracy: 97.72 %\n"
     ]
    }
   ],
   "source": [
    "cv_score = cross_val_score(mlpc, X_train, y_train.ravel(), cv =cv)\n",
    "print(\"Training Accuracy: {:.2f} %\".format(cv_score.mean()*100))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1.67540336e-13, 1.20206455e-15, 3.16822686e-11, 6.38436255e-09,\n",
       "        3.42376688e-20, 1.40613154e-14, 2.07057259e-26, 9.99999994e-01,\n",
       "        9.89849954e-13, 1.71120797e-11],\n",
       "       [9.36342748e-15, 9.95914896e-12, 9.99999999e-01, 1.12205446e-09,\n",
       "        4.78802277e-33, 1.29866014e-12, 4.70057402e-14, 1.77436755e-24,\n",
       "        8.60719240e-12, 4.35325146e-22],\n",
       "       [2.99954614e-10, 9.99976933e-01, 5.02186461e-06, 7.44412542e-09,\n",
       "        1.53334311e-08, 6.50067371e-08, 5.67822591e-07, 1.80276817e-06,\n",
       "        1.55861802e-05, 7.73781887e-10],\n",
       "       [1.00000000e+00, 3.55098874e-17, 1.91774670e-10, 3.66240264e-16,\n",
       "        1.56163177e-16, 1.56899474e-15, 2.20691949e-13, 4.76529723e-12,\n",
       "        7.21672630e-20, 2.92081584e-14],\n",
       "       [1.31436473e-12, 1.07630912e-15, 2.74923832e-11, 5.49491193e-15,\n",
       "        9.99958693e-01, 1.22044885e-14, 1.12554216e-11, 4.05233462e-10,\n",
       "        2.69083086e-12, 4.13070008e-05]])"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mlpc.predict_proba(X_test[:5])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = mlpc.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train_accuracy: 100.00 %\n",
      "Train_accuracy: 97.98 %\n"
     ]
    }
   ],
   "source": [
    "print(\"Train_accuracy: {:.2f} %\".format(accuracy_score(y_train, mlpc.predict(X_train))*100))\n",
    "print(\"Train_accuracy: {:.2f} %\".format(accuracy_score(y_test, y_pred)*100))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "cm = confusion_matrix(y_test, y_pred)\n",
    "disp = ConfusionMatrixDisplay(confusion_matrix=cm)\n",
    "disp.plot()\n",
    "plt.title('Confusion Matrix')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Classification report \n",
      "\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.99      0.99      0.99       980\n",
      "           1       0.99      0.99      0.99      1135\n",
      "           2       0.97      0.98      0.98      1032\n",
      "           3       0.98      0.98      0.98      1010\n",
      "           4       0.98      0.98      0.98       982\n",
      "           5       0.98      0.98      0.98       892\n",
      "           6       0.98      0.98      0.98       958\n",
      "           7       0.98      0.97      0.98      1028\n",
      "           8       0.98      0.97      0.97       974\n",
      "           9       0.98      0.98      0.98      1009\n",
      "\n",
      "    accuracy                           0.98     10000\n",
      "   macro avg       0.98      0.98      0.98     10000\n",
      "weighted avg       0.98      0.98      0.98     10000\n",
      "\n"
     ]
    }
   ],
   "source": [
    "CR = classification_report(y_test, y_pred)\n",
    "print('Classification report \\n')\n",
    "print(CR)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Loss curve')"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(mlpc.loss_curve_, 'b-')\n",
    "plt.xlabel('epoch')\n",
    "plt.ylabel('loss')\n",
    "plt.title('Loss curve')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Regression: California housing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.datasets import fetch_california_housing\n",
    "\n",
    "from sklearn.neural_network import MLPRegressor\n",
    "\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.metrics import mean_absolute_error, mean_absolute_percentage_error\n",
    "from sklearn.model_selection import cross_validate, ShuffleSplit\n",
    "\n",
    "np.random.seed(306)\n",
    "cv = ShuffleSplit(n_splits=5, test_size=0.2, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "X, y = fetch_california_housing(as_frame=True, return_X_y=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of training data (16512, 8)\n",
      "Shape of test data (4128, 8)\n",
      "Shape of training labels (16512,)\n",
      "Shape of test labels (4128,)\n"
     ]
    }
   ],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=1)\n",
    "\n",
    "print(\"Shape of training data\", X_train.shape)\n",
    "print(\"Shape of test data\", X_test.shape)\n",
    "\n",
    "print(\"Shape of training labels\", y_train.shape)\n",
    "print(\"Shape of test labels\", y_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean absolute error of linear regression model on the train set:\n",
      "0.214 +/- 0.004\n",
      "Mean absolute error of linear regression model on the test set:\n",
      "0.215 +/- 0.005\n"
     ]
    }
   ],
   "source": [
    "pipe = Pipeline([('scaler', StandardScaler()),\n",
    "                    ('regressor', MLPRegressor(hidden_layer_sizes=(32)))])\n",
    "\n",
    "cv_results = cross_validate(pipe, X_train, y_train, cv = cv,\n",
    "                            scoring = \"neg_mean_absolute_percentage_error\",\n",
    "                            return_train_score=True,\n",
    "                            return_estimator=True,\n",
    "                            n_jobs=2)\n",
    "\n",
    "mlp_train_error = -1 * cv_results['train_score']\n",
    "mlp_test_error = -1 * cv_results['test_score']\n",
    "\n",
    "print(f\"Mean absolute error of linear regression model on the train set:\\n\"\n",
    "        f\"{mlp_train_error.mean():.3f} +/- {mlp_train_error.std():.3f}\")\n",
    "\n",
    "print(f\"Mean absolute error of linear regression model on the test set:\\n\"\n",
    "        f\"{mlp_test_error.mean():.3f} +/- {mlp_test_error.std():.3f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Pipeline(steps=[(&#x27;scaler&#x27;, StandardScaler()),\n",
       "                (&#x27;regressor&#x27;, MLPRegressor(hidden_layer_sizes=32))])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" ><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">Pipeline</label><div class=\"sk-toggleable__content\"><pre>Pipeline(steps=[(&#x27;scaler&#x27;, StandardScaler()),\n",
       "                (&#x27;regressor&#x27;, MLPRegressor(hidden_layer_sizes=32))])</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">StandardScaler</label><div class=\"sk-toggleable__content\"><pre>StandardScaler()</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" ><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">MLPRegressor</label><div class=\"sk-toggleable__content\"><pre>MLPRegressor(hidden_layer_sizes=32)</pre></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "Pipeline(steps=[('scaler', StandardScaler()),\n",
       "                ('regressor', MLPRegressor(hidden_layer_sizes=32))])"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pipe.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.20517616916407153"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mean_absolute_percentage_error(y_train, pipe.predict(X_train))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.216801641868833"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mean_absolute_percentage_error(y_test, pipe.predict(X_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'predicted')"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(y_test, pipe.predict(X_test), 'b*')\n",
    "plt.plot(y_test, y_test, 'g-')\n",
    "plt.xlabel('actual')\n",
    "plt.ylabel('predicted')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3.10.2 64-bit",
   "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": "3.10.2"
  },
  "orig_nbformat": 4,
  "vscode": {
   "interpreter": {
    "hash": "369f2c481f4da34e4445cda3fffd2e751bd1c4d706f27375911949ba6bb62e1c"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}