pyNM/nonspiking-regressor-model.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1cf747ec",
   "metadata": {},
   "outputs": [],
   "source": [
    "\"\"\"\n",
    "@author: Ajay\n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "00bbd673",
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "import torch.nn.functional as F\n",
    "import torch.nn as nn\n",
    "from torch.autograd import Variable\n",
    "from torch.utils.data import Dataset, DataLoader\n",
    "import torch.optim as optim\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.decomposition import PCA\n",
    "from sklearn.preprocessing import LabelEncoder, OneHotEncoder\n",
    "from sklearn.preprocessing import StandardScaler, MinMaxScaler    \n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import accuracy_score, precision_score, recall_score\n",
    "from sklearn.metrics import mean_squared_error, r2_score\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from xgboost import XGBRegressor\n",
    "from tqdm.notebook import tqdm\n",
    "from sklearn.datasets import load_iris\n",
    "from sklearn.metrics import roc_curve, auc\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from pyNM.cf_matrix import make_confusion_matrix\n",
    "from plot_metric.functions import MultiClassClassification\n",
    "import seaborn as sns\n",
    "from random import *\n",
    "get_ipython().run_line_magic('matplotlib', 'inline')\n",
    "from pyNM.nonspiking_regressor import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "a53942c0",
   "metadata": {},
   "outputs": [],
   "source": [
    "def run_regressor():\n",
    "    torch.multiprocessing.freeze_support()\n",
    "    device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
    "    print(device)\n",
    "\n",
    "    # read house price dataset\n",
    "\n",
    "    df_house = pd.read_csv('../data/house_price_data.csv')\n",
    "    print(df_house.shape)\n",
    "    print(df_house.columns)\n",
    "\n",
    "    # peek into the data\n",
    "    print(df_house.head())\n",
    "\n",
    "    # final dataset\n",
    "\n",
    "    X = df_house.iloc[:,1:4] # independent attributes\n",
    "    y = df_house.iloc[:,0] # house price - target\n",
    "        \n",
    "    #scaler = StandardScaler()\n",
    "    #scaler = MinMaxScaler()\n",
    "    #X_scaled = scaler.fit_transform(X)\n",
    "\n",
    "    # Create Train — Validation — Test split\n",
    "\n",
    "    # Train - Test\n",
    "    X_trainval, X_test, y_trainval, y_test = train_test_split(X, y, test_size=0.3, random_state=1) \n",
    "\n",
    "    # Split train into train-val\n",
    "    X_train, X_val, y_train, y_val = train_test_split(X_trainval, y_trainval, test_size=0.2, random_state=2)\n",
    "    \n",
    "    print(X_train.shape)\n",
    "    print(y_train.shape)\n",
    "    print(X_val.shape)\n",
    "    print(y_val.shape)\n",
    "    print(X_test.shape)\n",
    "    print(y_test.shape)\n",
    "\n",
    "    # Scale data to have mean '0' and variance '1' \n",
    "    # which is importance for convergence of the neural network\n",
    "    scaler = MinMaxScaler()\n",
    "    X_train = scaler.fit_transform(X_train)\n",
    "    X_val = scaler.transform(X_val)\n",
    "    X_test = scaler.transform(X_test)\n",
    "\n",
    "    X_train, y_train = np.array(X_train), np.array(y_train)\n",
    "\n",
    "    X_val, y_val = np.array(X_val), np.array(y_val)\n",
    "\n",
    "    X_test, y_test = np.array(X_test), np.array(y_test)   \n",
    "\n",
    "    # Create & Initialize dataset\n",
    "\n",
    "    class CreateRegressionDataset(Dataset):\n",
    "        def __init__(self, X_data, y_data):\n",
    "            self.X_data = X_data\n",
    "            self.y_data = y_data\n",
    "\n",
    "        def __getitem__(self, index):\n",
    "            return self.X_data[index], self.y_data[index]\n",
    "\n",
    "        def __len__(self):\n",
    "            return len(self.X_data)\n",
    "\n",
    "    # create train, val, test dataset\n",
    "\n",
    "    train_dataset = CreateRegressionDataset(torch.from_numpy(X_train).float(),\n",
    "                        torch.from_numpy(y_train).float())\n",
    "\n",
    "\n",
    "    val_dataset = CreateRegressionDataset(torch.from_numpy(X_val).float(),\n",
    "                            torch.from_numpy(y_val).float())\n",
    "\n",
    "    test_dataset = CreateRegressionDataset(torch.from_numpy(X_test).float(),\n",
    "                            torch.from_numpy(y_test).float())\n",
    "\n",
    "    # define model parameters\n",
    "\n",
    "    EPOCHS = 150\n",
    "    BATCH_SIZE = 64\n",
    "    LEARNING_RATE = 0.001\n",
    "\n",
    "    NUM_OF_FEATURES = len(X.columns)\n",
    "\n",
    "    # load the training, validation, and test sets\n",
    "\n",
    "    print(\"Loading training, validation, and test data\")\n",
    "\n",
    "    train_loader = DataLoader(dataset=train_dataset,\n",
    "        batch_size=BATCH_SIZE, shuffle=True)\n",
    "\n",
    "    val_loader = DataLoader(dataset=val_dataset,\n",
    "        batch_size=1)\n",
    "\n",
    "    test_loader = DataLoader(dataset=test_dataset,\n",
    "        batch_size=1)\n",
    "\n",
    "\n",
    "    print(\"Data loaded...\")\n",
    "    \n",
    "    # create spiking neural network architecture\n",
    "    '''\n",
    "    We will create a feed forward SNN with 3 hidden layers \n",
    "    '''\n",
    "    non_spiking_model_regressor = NonSpikingNeuralNetwork(input_dim=X_train.shape[1], hidden_dim_l1=16,\n",
    "                                                      hidden_dim_l2=32, hidden_dim_l3=16)\n",
    "    \n",
    "    print(\"Model created\")\n",
    "    print(non_spiking_model_regressor)\n",
    "    \n",
    "    # Initialize the model, optimizer, and loss function\n",
    "    '''\n",
    "    Transfer the model to GPU/CPU. \n",
    "    Here, we use Mean Squared Error (MSE) as loss function\n",
    "    '''\n",
    "    loss_func = nn.MSELoss()\n",
    "    optimizer = optim.SGD(non_spiking_model_regressor.parameters(), lr = LEARNING_RATE)\n",
    "    print(\"Optimizer done\")\n",
    "\n",
    "    # dictionary to store loss each epoch for both train & val sets\n",
    "\n",
    "    loss_stats = {'train': [],\n",
    "                   'val': []\n",
    "                    }\n",
    "\n",
    "    # Train Model\n",
    "\n",
    "    print(\"Begin Training...\")\n",
    "\n",
    "    for e in tqdm(range(1, EPOCHS+1)):\n",
    "        # TRAINING\n",
    "        train_epoch_loss = 0\n",
    "        non_spiking_model_regressor.train()\n",
    "        for X_train_batch, y_train_batch in train_loader:\n",
    "            X_train_batch, y_train_batch = X_train_batch.to(device), y_train_batch.to(device)\n",
    "            #feed forward\n",
    "            y_train_pred = non_spiking_model_regressor(X_train_batch.requires_grad_())\n",
    "            #calculate the loss\n",
    "            train_loss = loss_func(y_train_pred, y_train_batch.unsqueeze(1))\n",
    "            #backward propagation: calculate gradients\n",
    "            train_loss.backward()\n",
    "            #update the weights\n",
    "            optimizer.step()\n",
    "            #clear out the gradients from the last step loss.backward()\n",
    "            optimizer.zero_grad()\n",
    "\n",
    "            train_epoch_loss += train_loss.item()\n",
    "\n",
    "        # VALIDATION\n",
    "        with torch.no_grad():\n",
    "            val_epoch_loss = 0\n",
    "            non_spiking_model_regressor.eval()\n",
    "            for X_val_batch, y_val_batch in val_loader:\n",
    "                X_val_batch, y_val_batch = X_val_batch.to(device), y_val_batch.to(device)\n",
    "                y_val_pred = non_spiking_model_regressor(X_val_batch)\n",
    "                val_loss = loss_func(y_val_pred, y_val_batch.unsqueeze(1))\n",
    "                val_epoch_loss += val_loss.item()\n",
    "\n",
    "        loss_stats['train'].append(train_epoch_loss/len(train_loader))\n",
    "        loss_stats['val'].append(val_epoch_loss/len(val_loader))\n",
    "\n",
    "        print(f'Epoch {e+0:03}: | Train Loss: {train_epoch_loss/len(train_loader):.5f} | \\\n",
    "           Val Loss: {val_epoch_loss/len(val_loader): .5f}')\n",
    "\n",
    "\n",
    "    # Visualize Loss and Accuracy\n",
    "\n",
    "    #print(loss_stats)\n",
    "    \n",
    "    \n",
    "    #To plot the loss line plots, we will create dataframe\n",
    "    #from the loss_stats dictionary\n",
    "\n",
    "    train_val_loss_df = pd.DataFrame.from_dict(loss_stats).reset_index().melt(id_vars=['index']).rename(columns={\"index\":\"epochs\"})\n",
    "    plt.figure(figsize=(15,8))\n",
    "    sns.lineplot(data=train_val_loss_df, x = \"epochs\", y=\"value\", hue=\"variable\").set_title('Train-Val Loss/Epoch')\n",
    "\n",
    "\n",
    "    # Test the performance of the model\n",
    "\n",
    "    y_pred_list = list()\n",
    "\n",
    "    with torch.no_grad():\n",
    "        non_spiking_model_regressor.eval()\n",
    "        for X_batch, _ in test_loader:\n",
    "\n",
    "            X_batch = X_batch.to(device)\n",
    "            y_test_pred = non_spiking_model_regressor(X_batch)\n",
    "            y_pred_list.append(y_test_pred.cpu().numpy()) \n",
    "\n",
    "    y_pred_list = [b.squeeze().tolist() for a in y_pred_list for b in a]\n",
    "    \n",
    "    #print(y_pred_list)\n",
    "    #print(y_test)\n",
    "\n",
    "    #print(len(y_pred_list))\n",
    "\n",
    "    #print(len(y_test))\n",
    "\n",
    "\n",
    "    # Lets check widely used regression metrics like MSE, R-squared, RMSE, RMSRE, \n",
    "\n",
    "    mse = mean_squared_error(y_test, y_pred_list) # MSE\n",
    "    r_square = r2_score(y_test, y_pred_list) # R^2\n",
    "    r = np.corrcoef(y_test, y_pred_list)[0, 1], # r\n",
    "    rmse = np.sqrt(mean_squared_error(y_test, y_pred_list)), # RMSE\n",
    "    rmsre = np.sqrt(np.mean(((y_test-y_pred_list)/y_test)**2)), # RMSRE\n",
    "    mape = np.mean(np.abs((y_test-y_pred_list) / y_test)) * 100 # MAPE\n",
    "\n",
    "    print(\"Mean Squared Error:\", mse)\n",
    "    print(\"R^2 :\",r_square)\n",
    "    print(\"pcc coefficient\", r)\n",
    "    print(\"Root Mean Squared Error:\", rmse)\n",
    "    print(\"Root Mean Squared Relative Error\", rmsre)\n",
    "    print(\"Mean Absolute Percentage Error\", mape)\n",
    "\n",
    "\n",
    "    # dictionary to store all the metrics\n",
    "\n",
    "    eval_metrics = {'Mean Squared Error': mse,\n",
    "                       'R^2': r_square,\n",
    "                       'pcc coefficient': r,\n",
    "                       'Root Mean Squared Error':rmse,\n",
    "                       'Root Mean Squared Relative Error': rmsre,\n",
    "                       'Mean Absolute Percentage Error': mape\n",
    "                    }\n",
    "\n",
    "    #print(eval_metrics)\n",
    "\n",
    "    # Visualize Metrics as bar plot with sns\n",
    "\n",
    "    fig, axes = plt.subplots(nrows=1, ncols=1, figsize=(25,7))\n",
    "\n",
    "    sns.barplot(data = pd.DataFrame.from_dict(eval_metrics).melt(), x = \"variable\", y=\"value\", \n",
    "            hue=\"variable\").set_title('Model Peformance on Test set') \n",
    "\n",
    "\n",
    "    #eval_metric_df = pd.DataFrame()\n",
    "    '''\n",
    "    eval_metric_df = pd.DataFrame((eval_metrics_list), \\\n",
    "                                      columns=['MSE', 'R-Squared','PearsonCorrelationCoefficient',\\\n",
    "                                               'RMSE', 'RMSRE', 'MAPE'])\n",
    "\n",
    "    print(eval_metric_df)\n",
    "    '''\n",
    "\n",
    "    # plot\n",
    "\n",
    "    #plt.figure(figsize=(6,3))\n",
    "    #sns.barplot(data=eval_metric_df, x=\"variable\", y=\"value\", hue=\"variable\").set_title('Model Peformance on Test set')\n",
    "    '''\n",
    "    plt.plot(log['epoch'], log['loss'], label='Training')\n",
    "    plt.plot(log['epoch'], log['val_loss'], label='Validation')\n",
    "    plt.set_title('Loss Plot', fontsize=35)\n",
    "    plt.xlabel('Epoch')\n",
    "    plt.ylabel('Loss')\n",
    "    plt.legend()\n",
    "    '''\n",
    "    # Install regressormetricgraphplot package from terminal or notebook\n",
    "    '''\n",
    "    Terminal: \n",
    "    $ pip install regressormetricgraphplot\n",
    "        OR\n",
    "    $ git clone https://github.com/ajayarunachalam/RegressorMetricGraphPlot\n",
    "    $ cd RegressorMetricGraphPlot\n",
    "    $ python setup.py install\n",
    "    Notebook:\n",
    "    !git clone https://github.com/ajayarunachalam/RegressorMetricGraphPlot.git\n",
    "    cd RegressorMetricGraphPlot/\n",
    "    Just replace the line 'from CompareModels import *' with 'from regressormetricgraphplot import CompareModels' \n",
    "    '''\n",
    "    # Now, let us check how machine learning algorithms perform on this dataset in comparison to the build neural network\n",
    "    from regressioncomparemetricplot import CompareModels\n",
    "\n",
    "    # Linear Regression \n",
    "\n",
    "    # Fitting training set to linear regression model\n",
    "    lr = LinearRegression(n_jobs=-1)\n",
    "    lr.fit(X_train, y_train)\n",
    "\n",
    "    # Predicting the house price\n",
    "    y_pred = lr.predict(X_test)\n",
    "\n",
    "    # Metrics\n",
    "    print(f'R2_nd_RMSE LR MODEL: {CompareModels.R2AndRMSE(y_test=y_test, y_pred=y_pred)}')\n",
    "\n",
    "    plot = CompareModels()\n",
    "\n",
    "    plot.add(model_name='Linear Regression', y_test=y_test, y_pred=y_pred)\n",
    "\n",
    "    plot.show(figsize=(10, 5))\n",
    "\n",
    "    # Fitting Random Forest model to the dataset\n",
    "    rfr = RandomForestRegressor(n_estimators=10, random_state=10, n_jobs=-1)\n",
    "    rfr.fit(X_train, y_train)\n",
    "\n",
    "    # Predicting the house price\n",
    "    y_pred = rfr.predict(X_test)\n",
    "\n",
    "    print(f'R2_nd_RMSE RF MODEL: {CompareModels.R2AndRMSE(y_test=y_test, y_pred=y_pred)}')\n",
    "\n",
    "    plot.add('Random Forest', y_test, y_pred)\n",
    "\n",
    "    plot.show(figsize=(10, 5))\n",
    "\n",
    "    xgb = XGBRegressor(n_jobs=4, silent=False, objective='reg:linear',\n",
    "                    max_depth=3, random_state=10, n_estimators=100,\n",
    "                    learning_rate=0.3, verbose=True)\n",
    "\n",
    "    xgb.fit(X_train, y_train)\n",
    "\n",
    "    # Predicting the house price\n",
    "    y_pred = xgb.predict(X_test)\n",
    "\n",
    "    print(f'R2_nd_RMSE XGB MODEL: {CompareModels.R2AndRMSE(y_test=y_test, y_pred=y_pred)}')\n",
    "\n",
    "    plot.add('XGBoost', y_test, y_pred)\n",
    "\n",
    "    plot.show(figsize=(10, 5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "23e9e777",
   "metadata": {},
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     "name": "stdout",
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     "text": [
      "cpu\n",
      "(414, 4)\n",
      "Index(['house_price', 'house_age', 'distance_to_nearest_MRT station',\n",
      "       'no_of_convenience_stores'],\n",
      "      dtype='object')\n",
      "   house_price  house_age  distance_to_nearest_MRT station  \\\n",
      "0         37.9       32.0                         84.87882   \n",
      "1         42.2       19.5                        306.59470   \n",
      "2         47.3       13.3                        561.98450   \n",
      "3         54.8       13.3                        561.98450   \n",
      "4         43.1        5.0                        390.56840   \n",
      "\n",
      "   no_of_convenience_stores  \n",
      "0                        10  \n",
      "1                         9  \n",
      "2                         5  \n",
      "3                         5  \n",
      "4                         5  \n",
      "(231, 3)\n",
      "(231,)\n",
      "(58, 3)\n",
      "(58,)\n",
      "(125, 3)\n",
      "(125,)\n",
      "Loading training, validation, and test data\n",
      "Data loaded...\n",
      "Model created\n",
      "NonSpikingNeuralNetwork(\n",
      "  (layer1): Linear(in_features=3, out_features=16, bias=True)\n",
      "  (layer2): Linear(in_features=16, out_features=32, bias=True)\n",
      "  (layer3): Linear(in_features=32, out_features=16, bias=True)\n",
      "  (layerout): Linear(in_features=16, out_features=1, bias=True)\n",
      "  (relu): ReLU()\n",
      ")\n",
      "Optimizer done\n",
      "Begin Training...\n"
     ]
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     "text": [
      "Epoch 001: | Train Loss: 1539.08911 |            Val Loss:  1730.85363\n",
      "Epoch 002: | Train Loss: 1497.79498 |            Val Loss:  1681.87518\n",
      "Epoch 003: | Train Loss: 1446.08704 |            Val Loss:  1574.07699\n",
      "Epoch 004: | Train Loss: 1252.40686 |            Val Loss:  1101.42051\n",
      "Epoch 005: | Train Loss: 505.94064 |            Val Loss:  144.63452\n",
      "Epoch 006: | Train Loss: 171.20954 |            Val Loss:  124.84012\n",
      "Epoch 007: | Train Loss: 155.10128 |            Val Loss:  136.47384\n",
      "Epoch 008: | Train Loss: 137.41861 |            Val Loss:  105.52999\n",
      "Epoch 009: | Train Loss: 118.49502 |            Val Loss:  97.40223\n",
      "Epoch 010: | Train Loss: 116.03207 |            Val Loss:  117.91476\n",
      "Epoch 011: | Train Loss: 111.75282 |            Val Loss:  91.14872\n",
      "Epoch 012: | Train Loss: 105.55559 |            Val Loss:  81.70143\n",
      "Epoch 013: | Train Loss: 96.59934 |            Val Loss:  98.18393\n",
      "Epoch 014: | Train Loss: 97.04573 |            Val Loss:  78.15544\n",
      "Epoch 015: | Train Loss: 90.54581 |            Val Loss:  75.55381\n",
      "Epoch 016: | Train Loss: 92.70965 |            Val Loss:  95.24945\n",
      "Epoch 017: | Train Loss: 101.95110 |            Val Loss:  124.59202\n",
      "Epoch 018: | Train Loss: 102.89342 |            Val Loss:  89.30631\n",
      "Epoch 019: | Train Loss: 83.73923 |            Val Loss:  89.70344\n",
      "Epoch 020: | Train Loss: 83.04234 |            Val Loss:  75.05556\n",
      "Epoch 021: | Train Loss: 89.26797 |            Val Loss:  99.85270\n",
      "Epoch 022: | Train Loss: 83.80077 |            Val Loss:  75.14260\n",
      "Epoch 023: | Train Loss: 88.39467 |            Val Loss:  78.71446\n",
      "Epoch 024: | Train Loss: 81.30667 |            Val Loss:  77.62029\n",
      "Epoch 025: | Train Loss: 77.64853 |            Val Loss:  119.02858\n",
      "Epoch 026: | Train Loss: 94.61068 |            Val Loss:  83.20125\n",
      "Epoch 027: | Train Loss: 75.32415 |            Val Loss:  89.97835\n",
      "Epoch 028: | Train Loss: 78.81546 |            Val Loss:  81.66679\n",
      "Epoch 029: | Train Loss: 76.89938 |            Val Loss:  75.42564\n",
      "Epoch 030: | Train Loss: 77.39138 |            Val Loss:  75.67498\n",
      "Epoch 031: | Train Loss: 77.31393 |            Val Loss:  90.74499\n",
      "Epoch 032: | Train Loss: 75.65655 |            Val Loss:  86.78881\n",
      "Epoch 033: | Train Loss: 95.63464 |            Val Loss:  76.58406\n",
      "Epoch 034: | Train Loss: 80.22257 |            Val Loss:  79.43216\n",
      "Epoch 035: | Train Loss: 77.27429 |            Val Loss:  115.92152\n",
      "Epoch 036: | Train Loss: 82.32143 |            Val Loss:  129.35836\n",
      "Epoch 037: | Train Loss: 128.49378 |            Val Loss:  98.51058\n",
      "Epoch 038: | Train Loss: 78.45907 |            Val Loss:  81.34463\n",
      "Epoch 039: | Train Loss: 70.99305 |            Val Loss:  88.35258\n",
      "Epoch 040: | Train Loss: 74.56717 |            Val Loss:  105.42885\n",
      "Epoch 041: | Train Loss: 67.55091 |            Val Loss:  76.93263\n",
      "Epoch 042: | Train Loss: 73.43685 |            Val Loss:  77.33226\n",
      "Epoch 043: | Train Loss: 73.80496 |            Val Loss:  127.13417\n",
      "Epoch 044: | Train Loss: 69.61675 |            Val Loss:  93.97385\n",
      "Epoch 045: | Train Loss: 70.47309 |            Val Loss:  76.00417\n",
      "Epoch 046: | Train Loss: 73.25637 |            Val Loss:  122.01721\n",
      "Epoch 047: | Train Loss: 66.96604 |            Val Loss:  86.12411\n",
      "Epoch 048: | Train Loss: 80.13910 |            Val Loss:  79.18760\n",
      "Epoch 049: | Train Loss: 63.56744 |            Val Loss:  77.16403\n",
      "Epoch 050: | Train Loss: 64.41233 |            Val Loss:  85.64671\n",
      "Epoch 051: | Train Loss: 66.39370 |            Val Loss:  82.76160\n",
      "Epoch 052: | Train Loss: 70.78230 |            Val Loss:  89.19095\n",
      "Epoch 053: | Train Loss: 66.49314 |            Val Loss:  75.03990\n",
      "Epoch 054: | Train Loss: 70.26698 |            Val Loss:  74.85650\n",
      "Epoch 055: | Train Loss: 85.91443 |            Val Loss:  87.19673\n",
      "Epoch 056: | Train Loss: 78.57949 |            Val Loss:  80.30427\n",
      "Epoch 057: | Train Loss: 79.12382 |            Val Loss:  105.04844\n",
      "Epoch 058: | Train Loss: 88.16183 |            Val Loss:  77.88177\n",
      "Epoch 059: | Train Loss: 62.45691 |            Val Loss:  95.65529\n",
      "Epoch 060: | Train Loss: 71.80470 |            Val Loss:  86.59804\n",
      "Epoch 061: | Train Loss: 97.24023 |            Val Loss:  74.89808\n",
      "Epoch 062: | Train Loss: 64.28691 |            Val Loss:  76.31891\n",
      "Epoch 063: | Train Loss: 67.14213 |            Val Loss:  79.72053\n",
      "Epoch 064: | Train Loss: 78.21184 |            Val Loss:  75.52766\n",
      "Epoch 065: | Train Loss: 85.46758 |            Val Loss:  81.80258\n",
      "Epoch 066: | Train Loss: 67.17653 |            Val Loss:  73.75139\n",
      "Epoch 067: | Train Loss: 65.04493 |            Val Loss:  75.34765\n",
      "Epoch 068: | Train Loss: 99.55625 |            Val Loss:  77.99689\n",
      "Epoch 069: | Train Loss: 74.91925 |            Val Loss:  83.34904\n",
      "Epoch 070: | Train Loss: 74.28870 |            Val Loss:  98.04654\n",
      "Epoch 071: | Train Loss: 92.11538 |            Val Loss:  78.85297\n",
      "Epoch 072: | Train Loss: 81.42361 |            Val Loss:  72.71133\n",
      "Epoch 073: | Train Loss: 68.62227 |            Val Loss:  77.12601\n",
      "Epoch 074: | Train Loss: 62.91780 |            Val Loss:  80.76389\n",
      "Epoch 075: | Train Loss: 60.02374 |            Val Loss:  85.86711\n",
      "Epoch 076: | Train Loss: 60.14027 |            Val Loss:  74.64239\n",
      "Epoch 077: | Train Loss: 66.62233 |            Val Loss:  73.59462\n",
      "Epoch 078: | Train Loss: 71.54373 |            Val Loss:  75.01288\n",
      "Epoch 079: | Train Loss: 63.00699 |            Val Loss:  96.30788\n",
      "Epoch 080: | Train Loss: 83.07855 |            Val Loss:  214.91241\n",
      "Epoch 081: | Train Loss: 90.08270 |            Val Loss:  109.42848\n",
      "Epoch 082: | Train Loss: 72.86507 |            Val Loss:  151.52733\n",
      "Epoch 083: | Train Loss: 98.20479 |            Val Loss:  73.16692\n",
      "Epoch 084: | Train Loss: 62.71117 |            Val Loss:  83.92991\n",
      "Epoch 085: | Train Loss: 67.45475 |            Val Loss:  123.20150\n",
      "Epoch 086: | Train Loss: 68.41520 |            Val Loss:  97.21388\n",
      "Epoch 087: | Train Loss: 69.56462 |            Val Loss:  84.26720\n",
      "Epoch 088: | Train Loss: 66.59554 |            Val Loss:  135.54219\n",
      "Epoch 089: | Train Loss: 113.99552 |            Val Loss:  242.88259\n",
      "Epoch 090: | Train Loss: 122.71147 |            Val Loss:  134.46652\n",
      "Epoch 091: | Train Loss: 71.95809 |            Val Loss:  72.67636\n",
      "Epoch 092: | Train Loss: 62.50190 |            Val Loss:  73.06171\n",
      "Epoch 093: | Train Loss: 62.03483 |            Val Loss:  76.19169\n",
      "Epoch 094: | Train Loss: 59.35433 |            Val Loss:  76.12897\n",
      "Epoch 095: | Train Loss: 61.02354 |            Val Loss:  73.51771\n",
      "Epoch 096: | Train Loss: 57.33862 |            Val Loss:  74.67289\n",
      "Epoch 097: | Train Loss: 64.61366 |            Val Loss:  75.37592\n",
      "Epoch 098: | Train Loss: 60.73813 |            Val Loss:  82.01560\n",
      "Epoch 099: | Train Loss: 59.36760 |            Val Loss:  81.09123\n",
      "Epoch 100: | Train Loss: 66.76188 |            Val Loss:  73.42906\n",
      "Epoch 101: | Train Loss: 59.47097 |            Val Loss:  73.81769\n",
      "Epoch 102: | Train Loss: 63.18867 |            Val Loss:  73.64383\n",
      "Epoch 103: | Train Loss: 104.81305 |            Val Loss:  90.38734\n",
      "Epoch 104: | Train Loss: 102.83750 |            Val Loss:  72.74486\n",
      "Epoch 105: | Train Loss: 65.40114 |            Val Loss:  94.08299\n",
      "Epoch 106: | Train Loss: 96.70402 |            Val Loss:  74.82340\n",
      "Epoch 107: | Train Loss: 55.67766 |            Val Loss:  80.96590\n",
      "Epoch 108: | Train Loss: 63.66949 |            Val Loss:  75.11349\n",
      "Epoch 109: | Train Loss: 54.86055 |            Val Loss:  76.49003\n",
      "Epoch 110: | Train Loss: 66.64062 |            Val Loss:  86.16580\n",
      "Epoch 111: | Train Loss: 72.60343 |            Val Loss:  93.76232\n",
      "Epoch 112: | Train Loss: 64.33092 |            Val Loss:  73.03908\n",
      "Epoch 113: | Train Loss: 58.86089 |            Val Loss:  74.86607\n",
      "Epoch 114: | Train Loss: 83.49887 |            Val Loss:  94.96813\n",
      "Epoch 115: | Train Loss: 59.75483 |            Val Loss:  73.71664\n",
      "Epoch 116: | Train Loss: 63.58625 |            Val Loss:  73.95631\n",
      "Epoch 117: | Train Loss: 60.01031 |            Val Loss:  77.77890\n",
      "Epoch 118: | Train Loss: 82.16146 |            Val Loss:  72.40564\n",
      "Epoch 119: | Train Loss: 57.53833 |            Val Loss:  99.16692\n",
      "Epoch 120: | Train Loss: 58.09349 |            Val Loss:  72.96615\n",
      "Epoch 121: | Train Loss: 58.02258 |            Val Loss:  73.56522\n",
      "Epoch 122: | Train Loss: 62.09311 |            Val Loss:  82.97186\n",
      "Epoch 123: | Train Loss: 86.45575 |            Val Loss:  99.30637\n",
      "Epoch 124: | Train Loss: 105.60238 |            Val Loss:  69.72184\n",
      "Epoch 125: | Train Loss: 68.05566 |            Val Loss:  85.32046\n",
      "Epoch 126: | Train Loss: 71.00401 |            Val Loss:  79.95059\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 127: | Train Loss: 62.31488 |            Val Loss:  134.51526\n",
      "Epoch 128: | Train Loss: 70.42626 |            Val Loss:  94.09497\n",
      "Epoch 129: | Train Loss: 64.40216 |            Val Loss:  77.92920\n",
      "Epoch 130: | Train Loss: 66.54365 |            Val Loss:  83.06314\n",
      "Epoch 131: | Train Loss: 58.71367 |            Val Loss:  71.88260\n",
      "Epoch 132: | Train Loss: 57.21257 |            Val Loss:  72.55699\n",
      "Epoch 133: | Train Loss: 58.32669 |            Val Loss:  82.34049\n",
      "Epoch 134: | Train Loss: 60.13916 |            Val Loss:  74.53589\n",
      "Epoch 135: | Train Loss: 61.83564 |            Val Loss:  77.78301\n",
      "Epoch 136: | Train Loss: 60.24130 |            Val Loss:  72.92389\n",
      "Epoch 137: | Train Loss: 58.40701 |            Val Loss:  95.64810\n",
      "Epoch 138: | Train Loss: 60.34191 |            Val Loss:  78.50175\n",
      "Epoch 139: | Train Loss: 77.37963 |            Val Loss:  71.60853\n",
      "Epoch 140: | Train Loss: 59.58102 |            Val Loss:  83.65891\n",
      "Epoch 141: | Train Loss: 60.13532 |            Val Loss:  87.62832\n",
      "Epoch 142: | Train Loss: 64.92050 |            Val Loss:  78.11753\n",
      "Epoch 143: | Train Loss: 69.09130 |            Val Loss:  72.34503\n",
      "Epoch 144: | Train Loss: 78.66072 |            Val Loss:  74.81139\n",
      "Epoch 145: | Train Loss: 68.39913 |            Val Loss:  118.52488\n",
      "Epoch 146: | Train Loss: 65.60293 |            Val Loss:  72.96954\n",
      "Epoch 147: | Train Loss: 59.48317 |            Val Loss:  72.65363\n",
      "Epoch 148: | Train Loss: 56.86925 |            Val Loss:  72.96507\n",
      "Epoch 149: | Train Loss: 56.38840 |            Val Loss:  73.17531\n",
      "Epoch 150: | Train Loss: 61.50422 |            Val Loss:  116.27744\n",
      "{'train': [1539.089111328125, 1497.7949829101562, 1446.0870361328125, 1252.4068603515625, 505.9406394958496, 171.20954132080078, 155.1012840270996, 137.4186134338379, 118.49502372741699, 116.03207015991211, 111.75281524658203, 105.55558967590332, 96.59933853149414, 97.04572868347168, 90.54580879211426, 92.70965003967285, 101.95109939575195, 102.8934154510498, 83.73922538757324, 83.04233837127686, 89.26797199249268, 83.80077362060547, 88.39466857910156, 81.30666923522949, 77.64853477478027, 94.61067581176758, 75.32415008544922, 78.81546211242676, 76.89938354492188, 77.39138412475586, 77.31392860412598, 75.65655326843262, 95.63464164733887, 80.22257423400879, 77.27429103851318, 82.32142639160156, 128.49378108978271, 78.45906734466553, 70.99305057525635, 74.56716918945312, 67.55090999603271, 73.43684959411621, 73.80496025085449, 69.61674690246582, 70.4730920791626, 73.25637149810791, 66.96603870391846, 80.13909530639648, 63.567444801330566, 64.41232872009277, 66.39369678497314, 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102.83749961853027, 65.40114116668701, 96.7040205001831, 55.67766094207764, 63.6694917678833, 54.860548973083496, 66.64061641693115, 72.60342597961426, 64.33091640472412, 58.86088848114014, 83.49887466430664, 59.75482749938965, 63.58625030517578, 60.010313987731934, 82.16146183013916, 57.538330078125, 58.09349250793457, 58.022579193115234, 62.09311485290527, 86.45574569702148, 105.60237693786621, 68.05565547943115, 71.00400829315186, 62.3148832321167, 70.4262580871582, 64.40216064453125, 66.54365158081055, 58.71366786956787, 57.212571144104004, 58.326687812805176, 60.13915538787842, 61.83563709259033, 60.24130153656006, 58.407010078430176, 60.34191036224365, 77.3796272277832, 59.58101844787598, 60.1353235244751, 64.92050361633301, 69.09130096435547, 78.66071891784668, 68.39912986755371, 65.60293102264404, 59.483174324035645, 56.86924743652344, 56.38840198516846, 61.50421714782715], 'val': [1730.8536334859914, 1681.8751844208816, 1574.0769876940496, 1101.4205122322871, 144.63452123516592, 124.84012225886872, 136.47383811215644, 105.52999335283349, 97.40222896584149, 117.91476340524467, 91.1487212711624, 81.70143056712274, 98.18393055465401, 78.15544379207081, 75.553805780308, 95.24945472778174, 124.59202303853014, 89.30631428873488, 89.7034358123783, 75.05556394323979, 99.8526955120715, 75.14259840474175, 78.71446158134796, 77.62028945755246, 119.02857523642737, 83.20124686474045, 89.9783510802121, 81.66678557647714, 75.4256405792488, 75.67498009299844, 90.74498861154606, 86.78880503362623, 76.58405555998314, 79.43216439848766, 115.9215160505525, 129.35836346651396, 98.51057834781963, 81.344625224825, 88.35257742613763, 105.42885404515305, 76.93263433710095, 77.33225832595329, 127.13416803294214, 93.97384875665965, 76.00416655594418, 122.017209852307, 86.12410798411945, 79.18759594162412, 77.16403048815319, 85.64671027448414, 82.76159561309835, 89.19095396195891, 75.03989882914928, 74.85650281338343, 87.1967269031909, 80.30427270960328, 105.048441045757, 77.8817741639013, 95.65528804320714, 86.59803831885601, 74.89807773335006, 76.31891401638758, 79.72052735429567, 75.52766446028997, 81.80257815645696, 73.75138872168188, 75.34764929326825, 77.99689035394614, 83.34904319818678, 98.04654257718056, 78.8529708487331, 72.71133239573703, 77.12600832728765, 80.76388662453371, 85.86711423894378, 74.64238761098059, 73.59462092058926, 75.01288296481401, 96.30787580216239, 214.91240779515195, 109.42848269073924, 151.52732586616585, 73.16691905484888, 83.92991370656367, 123.20149719952767, 97.21388084072106, 84.26719601092667, 135.5421850888464, 242.8825928615085, 134.46651865689662, 72.67636319142285, 73.06170632818649, 76.19168703670509, 76.12896534350925, 73.51771067442566, 74.67289485914054, 75.37592444645948, 82.01559541790328, 81.09123421704462, 73.42906394632381, 73.81768734799698, 73.64382626912717, 90.38734364766499, 72.74486311953058, 94.08299296657587, 74.82339641053615, 80.96589657626163, 75.11349468710752, 76.49003052955558, 86.16579545431209, 93.7623239362227, 73.03907904799642, 74.86606983622086, 94.9681297500682, 73.71663857433255, 73.95630962805068, 77.77889965686562, 72.40564077720046, 99.16691753358263, 72.96615074959519, 73.56522262004076, 82.97185901167065, 99.30637125954748, 69.72184481452909, 85.32046336449827, 79.95058848459178, 134.51526402001238, 94.09497141703193, 77.92920141906382, 83.06314499567038, 71.88259649695026, 72.55699229177795, 82.34049212855511, 74.53588934769404, 77.78300788383609, 72.92388909817512, 95.64810467232019, 78.50175333301264, 71.60853341486153, 83.65891220619709, 87.62831636261323, 78.11752669939145, 72.34502937859085, 74.811386666919, 118.52488284994816, 72.96954163604899, 72.6536311377161, 72.96507151761286, 73.17531007001425, 116.27743566100453]}\n",
      "Mean Squared Error: 132.28355226825656\n",
      "R^2 : 0.4259148302846334\n",
      "pcc coefficient (0.7607823612115979,)\n",
      "Root Mean Squared Error: (11.50145870175851,)\n",
      "Root Mean Squared Relative Error (0.22820136868847674,)\n",
      "Mean Absolute Percentage Error 17.570206427530284\n"
     ]
    },
    {
     "ename": "ModuleNotFoundError",
     "evalue": "No module named 'regressioncomparemetricplot'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-3-3d047b454172>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[1;32mif\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0m__name__\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;34m'__main__'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m     \u001b[0mrun_regressor\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32m<ipython-input-2-b1e29463a349>\u001b[0m in \u001b[0;36mrun_regressor\u001b[1;34m()\u001b[0m\n\u001b[0;32m    270\u001b[0m     '''\n\u001b[0;32m    271\u001b[0m     \u001b[1;31m# Now, let us check how machine learning algorithms perform on this dataset in comparison to the build neural network\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 272\u001b[1;33m     \u001b[1;32mfrom\u001b[0m \u001b[0mregressioncomparemetricplot\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mCompareModels\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    273\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    274\u001b[0m     \u001b[1;31m# Linear Regression\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mModuleNotFoundError\u001b[0m: No module named 'regressioncomparemetricplot'"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1800x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "if (__name__ == '__main__'):\n",
    "    run_regressor()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4409c1dd",
   "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": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}