pyNM/spiking-regressor-model.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4ff40b60",
   "metadata": {},
   "outputs": [],
   "source": [
    "\"\"\"\n",
    "@author: Ajay\n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "4add4a55",
   "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.spiking_regressor import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "7ad46c4d",
   "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",
    "    spiking_model_regressor = SpikingNeuralNetwork(device, X_train.shape[1], hidden_dim_l1=16, hidden_dim_l2=32, hidden_dim_l3=16, n_time_steps=64, begin_eval=0)\n",
    "    \n",
    "    print(\"Model created\")\n",
    "    print(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",
    "    \n",
    "    loss_func = nn.MSELoss()\n",
    "    optimizer = optim.SGD(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",
    "        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 = 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",
    "            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 = 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",
    "        spiking_model_regressor.eval()\n",
    "        for X_batch, _ in test_loader:\n",
    "\n",
    "            X_batch = X_batch.to(device)\n",
    "            y_test_pred = 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 regressormetricgraphplot 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))"
   ]
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   "id": "00428838",
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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",
      "SpikingNeuralNetwork(\n",
      "  (input_conversion): InputDataToSpikingPerceptronLayer()\n",
      "  (layer1): SpikingNeuronLayer(\n",
      "    (fc): Linear(in_features=3, out_features=100, bias=True)\n",
      "  )\n",
      "  (layer2): SpikingNeuronLayer(\n",
      "    (fc): Linear(in_features=100, out_features=1, bias=True)\n",
      "  )\n",
      "  (output_conversion): OutputDataToSpikingPerceptronLayer(\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",
      ")\n",
      "Optimizer done\n",
      "Begin Training...\n"
     ]
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     "text": [
      "Epoch 001: | Train Loss: 1566.53967 |            Val Loss:  1756.31530\n",
      "Epoch 002: | Train Loss: 1540.15491 |            Val Loss:  1713.95180\n",
      "Epoch 003: | Train Loss: 1458.51871 |            Val Loss:  1640.74032\n",
      "Epoch 004: | Train Loss: 1379.09207 |            Val Loss:  1414.05775\n",
      "Epoch 005: | Train Loss: 921.87095 |            Val Loss:  236.05281\n",
      "Epoch 006: | Train Loss: 154.59461 |            Val Loss:  130.85639\n",
      "Epoch 007: | Train Loss: 136.09735 |            Val Loss:  176.78030\n",
      "Epoch 008: | Train Loss: 149.27492 |            Val Loss:  195.53264\n",
      "Epoch 009: | Train Loss: 120.66407 |            Val Loss:  94.58384\n",
      "Epoch 010: | Train Loss: 103.13349 |            Val Loss:  94.34525\n",
      "Epoch 011: | Train Loss: 94.54906 |            Val Loss:  85.58343\n",
      "Epoch 012: | Train Loss: 94.68282 |            Val Loss:  103.05614\n",
      "Epoch 013: | Train Loss: 93.97477 |            Val Loss:  88.06934\n",
      "Epoch 014: | Train Loss: 87.72086 |            Val Loss:  80.30201\n",
      "Epoch 015: | Train Loss: 102.88232 |            Val Loss:  77.40944\n",
      "Epoch 016: | Train Loss: 92.53366 |            Val Loss:  76.22484\n",
      "Epoch 017: | Train Loss: 84.34086 |            Val Loss:  79.47236\n",
      "Epoch 018: | Train Loss: 89.53094 |            Val Loss:  77.09332\n",
      "Epoch 019: | Train Loss: 80.68892 |            Val Loss:  105.76058\n",
      "Epoch 020: | Train Loss: 89.34258 |            Val Loss:  79.15528\n",
      "Epoch 021: | Train Loss: 76.08395 |            Val Loss:  97.13862\n",
      "Epoch 022: | Train Loss: 81.96147 |            Val Loss:  76.88859\n",
      "Epoch 023: | Train Loss: 75.29848 |            Val Loss:  92.06501\n",
      "Epoch 024: | Train Loss: 78.33701 |            Val Loss:  88.53187\n",
      "Epoch 025: | Train Loss: 83.85546 |            Val Loss:  79.98767\n",
      "Epoch 026: | Train Loss: 71.30328 |            Val Loss:  80.66753\n",
      "Epoch 027: | Train Loss: 72.29020 |            Val Loss:  81.04031\n",
      "Epoch 028: | Train Loss: 76.44425 |            Val Loss:  77.39146\n",
      "Epoch 029: | Train Loss: 74.04227 |            Val Loss:  76.14180\n",
      "Epoch 030: | Train Loss: 79.68431 |            Val Loss:  79.70824\n",
      "Epoch 031: | Train Loss: 74.75617 |            Val Loss:  77.56862\n",
      "Epoch 032: | Train Loss: 111.90288 |            Val Loss:  81.82654\n",
      "Epoch 033: | Train Loss: 112.62268 |            Val Loss:  108.78566\n",
      "Epoch 034: | Train Loss: 133.52260 |            Val Loss:  80.13879\n",
      "Epoch 035: | Train Loss: 75.01431 |            Val Loss:  81.96329\n",
      "Epoch 036: | Train Loss: 89.93995 |            Val Loss:  76.17046\n",
      "Epoch 037: | Train Loss: 69.83051 |            Val Loss:  89.14864\n",
      "Epoch 038: | Train Loss: 72.67999 |            Val Loss:  125.36679\n",
      "Epoch 039: | Train Loss: 82.79312 |            Val Loss:  117.98603\n",
      "Epoch 040: | Train Loss: 100.95465 |            Val Loss:  173.85570\n",
      "Epoch 041: | Train Loss: 86.89648 |            Val Loss:  143.54960\n",
      "Epoch 042: | Train Loss: 76.10587 |            Val Loss:  90.12469\n",
      "Epoch 043: | Train Loss: 66.98783 |            Val Loss:  75.46376\n",
      "Epoch 044: | Train Loss: 65.84495 |            Val Loss:  78.65690\n",
      "Epoch 045: | Train Loss: 76.01176 |            Val Loss:  136.09858\n",
      "Epoch 046: | Train Loss: 83.61880 |            Val Loss:  168.40089\n",
      "Epoch 047: | Train Loss: 88.20929 |            Val Loss:  81.41222\n",
      "Epoch 048: | Train Loss: 72.76045 |            Val Loss:  93.28674\n",
      "Epoch 049: | Train Loss: 86.56948 |            Val Loss:  88.52743\n",
      "Epoch 050: | Train Loss: 77.85165 |            Val Loss:  76.10051\n",
      "Epoch 051: | Train Loss: 71.20413 |            Val Loss:  95.95706\n",
      "Epoch 052: | Train Loss: 73.75219 |            Val Loss:  78.49618\n",
      "Epoch 053: | Train Loss: 63.31945 |            Val Loss:  73.93807\n",
      "Epoch 054: | Train Loss: 68.78605 |            Val Loss:  101.98815\n",
      "Epoch 055: | Train Loss: 88.28148 |            Val Loss:  117.50741\n",
      "Epoch 056: | Train Loss: 82.86083 |            Val Loss:  90.37602\n",
      "Epoch 057: | Train Loss: 75.32815 |            Val Loss:  115.11392\n",
      "Epoch 058: | Train Loss: 72.80935 |            Val Loss:  77.45752\n",
      "Epoch 059: | Train Loss: 81.06507 |            Val Loss:  91.45412\n",
      "Epoch 060: | Train Loss: 80.51895 |            Val Loss:  77.33853\n",
      "Epoch 061: | Train Loss: 68.12753 |            Val Loss:  73.16317\n",
      "Epoch 062: | Train Loss: 69.70877 |            Val Loss:  121.18410\n",
      "Epoch 063: | Train Loss: 82.19016 |            Val Loss:  104.92014\n",
      "Epoch 064: | Train Loss: 68.10391 |            Val Loss:  71.79901\n",
      "Epoch 065: | Train Loss: 64.79690 |            Val Loss:  72.35349\n",
      "Epoch 066: | Train Loss: 72.34424 |            Val Loss:  171.58410\n",
      "Epoch 067: | Train Loss: 84.94571 |            Val Loss:  75.76227\n",
      "Epoch 068: | Train Loss: 74.05400 |            Val Loss:  121.78050\n",
      "Epoch 069: | Train Loss: 69.23656 |            Val Loss:  71.33755\n",
      "Epoch 070: | Train Loss: 71.12640 |            Val Loss:  81.54479\n",
      "Epoch 071: | Train Loss: 67.30503 |            Val Loss:  72.91575\n",
      "Epoch 072: | Train Loss: 82.18803 |            Val Loss:  162.21618\n",
      "Epoch 073: | Train Loss: 77.69966 |            Val Loss:  78.42656\n",
      "Epoch 074: | Train Loss: 61.29395 |            Val Loss:  72.05555\n",
      "Epoch 075: | Train Loss: 62.77113 |            Val Loss:  74.25337\n",
      "Epoch 076: | Train Loss: 57.10708 |            Val Loss:  86.06742\n",
      "Epoch 077: | Train Loss: 67.70807 |            Val Loss:  73.57253\n",
      "Epoch 078: | Train Loss: 55.98743 |            Val Loss:  80.08029\n",
      "Epoch 079: | Train Loss: 58.07998 |            Val Loss:  87.38825\n",
      "Epoch 080: | Train Loss: 59.53257 |            Val Loss:  72.71431\n",
      "Epoch 081: | Train Loss: 60.53514 |            Val Loss:  75.56340\n",
      "Epoch 082: | Train Loss: 72.90873 |            Val Loss:  80.30644\n",
      "Epoch 083: | Train Loss: 71.15129 |            Val Loss:  77.25261\n",
      "Epoch 084: | Train Loss: 77.99817 |            Val Loss:  71.74577\n",
      "Epoch 085: | Train Loss: 63.63316 |            Val Loss:  74.94161\n",
      "Epoch 086: | Train Loss: 86.30248 |            Val Loss:  75.89654\n",
      "Epoch 087: | Train Loss: 61.26263 |            Val Loss:  72.73915\n",
      "Epoch 088: | Train Loss: 57.67533 |            Val Loss:  109.07941\n",
      "Epoch 089: | Train Loss: 103.04692 |            Val Loss:  93.32760\n",
      "Epoch 090: | Train Loss: 83.60635 |            Val Loss:  115.23972\n",
      "Epoch 091: | Train Loss: 116.92093 |            Val Loss:  71.13011\n",
      "Epoch 092: | Train Loss: 61.97638 |            Val Loss:  71.60395\n",
      "Epoch 093: | Train Loss: 61.35181 |            Val Loss:  78.60350\n",
      "Epoch 094: | Train Loss: 84.88080 |            Val Loss:  77.85528\n",
      "Epoch 095: | Train Loss: 57.09777 |            Val Loss:  84.17474\n",
      "Epoch 096: | Train Loss: 57.51144 |            Val Loss:  75.03792\n",
      "Epoch 097: | Train Loss: 59.64773 |            Val Loss:  72.36652\n",
      "Epoch 098: | Train Loss: 56.38335 |            Val Loss:  83.11440\n",
      "Epoch 099: | Train Loss: 69.11080 |            Val Loss:  140.25868\n",
      "Epoch 100: | Train Loss: 72.91027 |            Val Loss:  75.72834\n",
      "Epoch 101: | Train Loss: 56.74298 |            Val Loss:  77.27458\n",
      "Epoch 102: | Train Loss: 59.24394 |            Val Loss:  106.14667\n",
      "Epoch 103: | Train Loss: 60.97258 |            Val Loss:  73.93168\n",
      "Epoch 104: | Train Loss: 82.64351 |            Val Loss:  84.52500\n",
      "Epoch 105: | Train Loss: 71.25526 |            Val Loss:  72.73400\n",
      "Epoch 106: | Train Loss: 58.96239 |            Val Loss:  73.93541\n",
      "Epoch 107: | Train Loss: 55.11819 |            Val Loss:  77.04557\n",
      "Epoch 108: | Train Loss: 55.97430 |            Val Loss:  78.93355\n",
      "Epoch 109: | Train Loss: 79.70107 |            Val Loss:  150.79354\n",
      "Epoch 110: | Train Loss: 112.33813 |            Val Loss:  76.43553\n",
      "Epoch 111: | Train Loss: 73.38776 |            Val Loss:  140.38479\n",
      "Epoch 112: | Train Loss: 80.03303 |            Val Loss:  114.33445\n",
      "Epoch 113: | Train Loss: 70.12602 |            Val Loss:  90.06017\n",
      "Epoch 114: | Train Loss: 56.64649 |            Val Loss:  76.47951\n",
      "Epoch 115: | Train Loss: 58.39949 |            Val Loss:  80.33796\n",
      "Epoch 116: | Train Loss: 55.37264 |            Val Loss:  73.68105\n",
      "Epoch 117: | Train Loss: 55.62857 |            Val Loss:  75.28272\n",
      "Epoch 118: | Train Loss: 57.67372 |            Val Loss:  73.00293\n",
      "Epoch 119: | Train Loss: 61.11554 |            Val Loss:  73.28050\n",
      "Epoch 120: | Train Loss: 59.39424 |            Val Loss:  80.67162\n",
      "Epoch 121: | Train Loss: 61.09570 |            Val Loss:  92.56274\n",
      "Epoch 122: | Train Loss: 82.31698 |            Val Loss:  179.68423\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 123: | Train Loss: 98.61300 |            Val Loss:  99.89246\n",
      "Epoch 124: | Train Loss: 64.25112 |            Val Loss:  73.02085\n",
      "Epoch 125: | Train Loss: 61.96314 |            Val Loss:  106.91275\n",
      "Epoch 126: | Train Loss: 59.90038 |            Val Loss:  92.27699\n",
      "Epoch 127: | Train Loss: 56.68209 |            Val Loss:  91.29436\n",
      "Epoch 128: | Train Loss: 69.19487 |            Val Loss:  115.43612\n",
      "Epoch 129: | Train Loss: 77.64169 |            Val Loss:  79.76655\n",
      "Epoch 130: | Train Loss: 64.50627 |            Val Loss:  83.77339\n",
      "Epoch 131: | Train Loss: 57.90193 |            Val Loss:  89.44370\n",
      "Epoch 132: | Train Loss: 57.89750 |            Val Loss:  76.44499\n",
      "Epoch 133: | Train Loss: 55.54608 |            Val Loss:  73.92433\n",
      "Epoch 134: | Train Loss: 57.74343 |            Val Loss:  76.71786\n",
      "Epoch 135: | Train Loss: 55.32250 |            Val Loss:  74.65528\n",
      "Epoch 136: | Train Loss: 69.31677 |            Val Loss:  110.05834\n",
      "Epoch 137: | Train Loss: 80.03327 |            Val Loss:  76.36286\n",
      "Epoch 138: | Train Loss: 76.61634 |            Val Loss:  76.84780\n",
      "Epoch 139: | Train Loss: 57.73526 |            Val Loss:  79.19443\n",
      "Epoch 140: | Train Loss: 54.11670 |            Val Loss:  72.96114\n",
      "Epoch 141: | Train Loss: 59.60852 |            Val Loss:  75.19897\n",
      "Epoch 142: | Train Loss: 59.71652 |            Val Loss:  86.47859\n",
      "Epoch 143: | Train Loss: 57.24666 |            Val Loss:  74.40260\n",
      "Epoch 144: | Train Loss: 57.05689 |            Val Loss:  91.19044\n",
      "Epoch 145: | Train Loss: 61.36061 |            Val Loss:  74.79558\n",
      "Epoch 146: | Train Loss: 60.43075 |            Val Loss:  82.07570\n",
      "Epoch 147: | Train Loss: 63.46329 |            Val Loss:  105.96234\n",
      "Epoch 148: | Train Loss: 66.60610 |            Val Loss:  83.93360\n",
      "Epoch 149: | Train Loss: 60.73163 |            Val Loss:  75.24391\n",
      "Epoch 150: | Train Loss: 54.96332 |            Val Loss:  78.89063\n",
      "{'train': [1566.5396728515625, 1540.1549072265625, 1458.5187072753906, 1379.0920715332031, 921.8709487915039, 154.5946102142334, 136.09734535217285, 149.2749195098877, 120.6640739440918, 103.13348579406738, 94.54905986785889, 94.68281936645508, 93.97476768493652, 87.72085952758789, 102.88232421875, 92.53366279602051, 84.34086036682129, 89.53094291687012, 80.68892097473145, 89.34257507324219, 76.08395385742188, 81.96147060394287, 75.2984848022461, 78.33700942993164, 83.8554573059082, 71.30327892303467, 72.29020404815674, 76.44425201416016, 74.0422716140747, 79.68430709838867, 74.75617027282715, 111.90288352966309, 112.62268447875977, 133.52260208129883, 75.01430606842041, 89.93995094299316, 69.83050918579102, 72.67999267578125, 82.79311752319336, 100.95465469360352, 86.896484375, 76.10586643218994, 66.98782920837402, 65.84494972229004, 76.0117597579956, 83.61879825592041, 88.20928764343262, 72.76044654846191, 86.56948471069336, 77.8516492843628, 71.20413112640381, 73.75219345092773, 63.31944751739502, 68.78604888916016, 88.28148460388184, 82.86082935333252, 75.32814884185791, 72.80934715270996, 81.06507110595703, 80.51895427703857, 68.12753486633301, 69.70876502990723, 82.19015884399414, 68.10390758514404, 64.79690456390381, 72.34424209594727, 84.94570827484131, 74.05399703979492, 69.23655700683594, 71.12640380859375, 67.30502700805664, 82.18803215026855, 77.69966125488281, 61.29394817352295, 62.77113437652588, 57.10708236694336, 67.70806884765625, 55.98742723464966, 58.07998275756836, 59.53256893157959, 60.5351448059082, 72.90873432159424, 71.15129470825195, 77.99816608428955, 63.633161544799805, 86.30248069763184, 61.26263427734375, 57.67532825469971, 103.04692268371582, 83.60634708404541, 116.92092895507812, 61.976378440856934, 61.351810455322266, 84.88079833984375, 57.09776782989502, 57.51143836975098, 59.64773178100586, 56.3833532333374, 69.1107988357544, 72.91027355194092, 56.742981910705566, 59.24394416809082, 60.97258186340332, 82.64351081848145, 71.25525665283203, 58.96238994598389, 55.118191719055176, 55.974300384521484, 79.70106506347656, 112.338125705719, 73.38776016235352, 80.0330286026001, 70.12601518630981, 56.64649486541748, 58.39949417114258, 55.37264347076416, 55.62856864929199, 57.67371845245361, 61.1155424118042, 59.39424419403076, 61.095699310302734, 82.31698417663574, 98.61299514770508, 64.25112056732178, 61.963144302368164, 59.90038299560547, 56.682090759277344, 69.19487380981445, 77.64169406890869, 64.50626564025879, 57.90192699432373, 57.897504806518555, 55.54608345031738, 57.74343299865723, 55.322495460510254, 69.31676864624023, 80.03327083587646, 76.61634254455566, 57.735262870788574, 54.11670398712158, 59.60851860046387, 59.716522216796875, 57.24666118621826, 57.056891441345215, 61.36060810089111, 60.43074607849121, 63.46329116821289, 66.60609912872314, 60.73163414001465, 54.963322162628174], 'val': [1756.3153030132426, 1713.9517991953883, 1640.7403150098078, 1414.0577499784272, 236.05280988791893, 130.85638746054008, 176.78030326280037, 195.5326438116207, 94.58384219675871, 94.34525163754307, 85.58343490238848, 103.05613758401336, 88.06934148959559, 80.30201251149691, 77.40943750941419, 76.22484432045242, 79.47236340971176, 77.09331713721221, 105.7605773101336, 79.1552767420415, 97.13862373066488, 76.88858673734397, 92.06501163002746, 88.53186522835287, 79.98767401856081, 80.66752530520397, 81.04030600094045, 77.39146183914272, 76.14179743580713, 79.70824179486853, 77.56861766719997, 81.82653991043054, 108.78565929341933, 80.13879212801312, 81.96329038186352, 76.17045926302671, 89.14864475048822, 125.3667923887742, 117.98602952164872, 173.8557007822112, 143.5495958784274, 90.12469249051706, 75.463756858596, 78.65690394927715, 136.09858045598557, 168.40088757060082, 81.41221601602717, 93.28673650109177, 88.52743017692761, 76.10051055255387, 95.95706276903893, 78.49618204419725, 73.93807417342182, 101.98814624702108, 117.50740965108933, 90.37601605275293, 115.11392466929452, 77.45751684409534, 91.45411536559976, 77.33852977967211, 73.16316959833533, 121.18410007417972, 104.9201436017094, 71.7990130016516, 72.35348818207095, 171.58410381419776, 75.76227287896748, 121.78050030209124, 71.33755165711045, 81.54479187932508, 72.91574576355774, 162.216177746772, 78.42656403516644, 72.05554991652225, 74.2533717068105, 86.06742069822062, 73.57253434303907, 80.08028701146068, 87.38825120247002, 72.71430717485732, 75.5633983755392, 80.30643733018826, 77.2526083255382, 71.74576822423064, 74.94160513229822, 75.89654387231788, 72.73915384967152, 109.07941359803402, 93.3276045784868, 115.23971956300123, 71.13010777149285, 71.60395226192436, 78.60350474085386, 77.85528014576191, 84.17473806093993, 75.03792071072706, 72.36652459033989, 83.11440065426045, 140.25868282059272, 75.72833961013961, 77.27458413587561, 106.14666773319438, 73.9316834582032, 84.5250022033679, 72.73400180443122, 73.93540945186697, 77.04557255432331, 78.93355099445586, 150.7935423727693, 76.43553292673636, 140.38478875924545, 114.33445433424464, 90.06016876196674, 76.4795101714355, 80.33795758336782, 73.68105395523638, 75.2827155785314, 73.00293112481977, 73.28050459336875, 80.67162140715739, 92.5627437624438, 179.68422821331126, 99.89245967339757, 73.02084960056276, 106.91275463330335, 92.2769858454292, 91.29436354920799, 115.43611533374622, 79.76655153043825, 83.77339007377881, 89.44369559081528, 76.44499357173154, 73.92433042905759, 76.71786359674297, 74.65528180990529, 110.05834394035026, 76.36285798083429, 76.84780099464516, 79.19442839504637, 72.96113981820386, 75.19897288795468, 86.47858924866686, 74.40259895971495, 91.19044157848212, 74.79558065032651, 82.07570235277045, 105.96233888124597, 83.93359699896698, 75.2439133073685, 78.89063308542144]}\n",
      "Mean Squared Error: 98.83022659677768\n",
      "R^2 : 0.5710958283478592\n",
      "pcc coefficient (0.7664820980140832,)\n",
      "Root Mean Squared Error: (9.941339275810764,)\n",
      "Root Mean Squared Relative Error (0.23113338189741017,)\n",
      "Mean Absolute Percentage Error 15.028998477127937\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-5-3d047b454172>\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-4-78579e150848>\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": "4cb8a621",
   "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
}