QSAR Regression Modeling.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "stable-movie",
   "metadata": {},
   "outputs": [],
   "source": [
    "## Get training and test sets for the regression model. \n",
    "import pandas as pd\n",
    "from sklearn.metrics import mean_squared_error, mean_absolute_error\n",
    "regression_data = pd.read_csv('regression_model_data.csv')\n",
    "regression_data.drop(columns='Unnamed: 0', inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "egyptian-quantum",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = regression_data.drop(columns='pIC50')\n",
    "y = regression_data.pIC50"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "vocational-disney",
   "metadata": {},
   "source": [
    "# Multiple Linear Regression with Polynomial terms "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "offshore-product",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train,X_test,y_train,y_test = train_test_split(X,y,test_size=0.25, random_state=44)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "related-rainbow",
   "metadata": {},
   "outputs": [],
   "source": [
    "### Now, run models again for regression type data.\n",
    "## First model will be a multiple linear regression model. \n",
    "from sklearn.linear_model import LinearRegression\n",
    "MLR = LinearRegression().fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "powered-bridges",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.33281408189168216"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r_sq = MLR.score(X_train, y_train)  ## Calculate the R^2 value to determine model fit \n",
    "r_sq"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "suited-classics",
   "metadata": {},
   "outputs": [],
   "source": [
    "predictions = MLR.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "egyptian-magnet",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9856798535475064\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "print(np.sqrt(mean_squared_error(y_test, predictions)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "occasional-eleven",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.7805618137797526\n"
     ]
    }
   ],
   "source": [
    "print(mean_absolute_error(y_test, predictions))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "personal-postage",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Predicted Values')"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "plt.figure(figsize=(10,10))\n",
    "plt.scatter(y_test, predictions)\n",
    "plt.xlabel('Actual Values')\n",
    "plt.ylabel('Predicted Values')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "shared-arbor",
   "metadata": {},
   "outputs": [],
   "source": [
    "## The results from the multiple linear regression is quite poor, with lots of unexplained variance in our response having \n",
    "## no connection to our selected features.The model shows a poor RMSE which is anouhter sign of underfitting our data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "martial-ribbon",
   "metadata": {},
   "outputs": [],
   "source": [
    "## By looking at the results of the regression and the support vector classification that a linear relationship is not \n",
    "## present in the model. To imporve the perfomance of the linear model, we should include higher order polynomials to help\n",
    "## capture the nonlinear relationship. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "acquired-purpose",
   "metadata": {},
   "outputs": [],
   "source": [
    "## Now calculate the polynmial features for the set. we will compute up to the third order polynomial.\n",
    "from sklearn.preprocessing import PolynomialFeatures\n",
    "poly_data = PolynomialFeatures(2)\n",
    "X_poly = poly_data.fit_transform(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "religious-plant",
   "metadata": {},
   "outputs": [],
   "source": [
    "## Before we calculate the poynomials, we should rescale the data using StandardScaler.\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "scaler = StandardScaler()\n",
    "X_scaled_poly = scaler.fit_transform(X_poly)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "adjacent-bachelor",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The old dataset had the shape: (8189, 33); the new dataset has the shape: (8189, 595)\n"
     ]
    }
   ],
   "source": [
    "print(\"The old dataset had the shape: {}; the new dataset has the shape: {}\".format(X.shape,X_scaled_poly.shape))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "annoying-object",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train,X_test,y_train,y_test = train_test_split(X_scaled_poly,y,test_size=0.25, random_state=44)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "global-reach",
   "metadata": {},
   "outputs": [],
   "source": [
    "MLR_2 = LinearRegression().fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "involved-beaver",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5965943949613285"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r_sq = MLR_2.score(X_train, y_train)  ## Calculate the R^2 value to determine model fit \n",
    "r_sq"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "perfect-harbor",
   "metadata": {},
   "outputs": [],
   "source": [
    "## we see a much improved R^2 value, but lets look at other metrics to see if we avoided overfitting. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "wrong-reading",
   "metadata": {},
   "outputs": [],
   "source": [
    "predictions = MLR_2.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "genetic-timeline",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "62.27637609158733\n"
     ]
    }
   ],
   "source": [
    "print(mean_squared_error(y_test, predictions))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "baking-merchant",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Predicted Values')"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,10))\n",
    "plt.scatter(y_test, predictions)\n",
    "plt.xlabel('Actual Values')\n",
    "plt.xlim(0,10)\n",
    "plt.ylim(0,10)\n",
    "plt.ylabel('Predicted Values')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "thirty-gardening",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9265378654578427\n"
     ]
    }
   ],
   "source": [
    "print(mean_absolute_error(y_test, predictions))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "isolated-living",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([7.46916951, 6.58621388, 6.5912927 , ..., 5.97718561, 5.83458083,\n",
       "       6.7612483 ])"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "angry-terrorist",
   "metadata": {},
   "outputs": [],
   "source": [
    "## the results show that despite the impressive model fit, the model seems to make the poor decision of predicting 0 for \n",
    "## most , if not all, model instances. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "silver-pharmacy",
   "metadata": {},
   "source": [
    "# Support Vector Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "located-upper",
   "metadata": {},
   "outputs": [],
   "source": [
    "## Utilizing the non-linear kernels in support vector machines may allow for better modeling on the non-linear data\n",
    "from sklearn.svm import SVR\n",
    "from sklearn.pipeline import make_pipeline\n",
    "X_train,X_test,y_train,y_test = train_test_split(X,y,test_size=0.25, random_state=44)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "suitable-cambridge",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pipeline(steps=[('standardscaler', StandardScaler()), ('svr', SVR(C=0.6))])"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "SVR_pipeline = make_pipeline(StandardScaler(), SVR(kernel='rbf',C=0.6))\n",
    "SVR_pipeline.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "rising-fields",
   "metadata": {},
   "outputs": [],
   "source": [
    "predictions = SVR_pipeline.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "retained-horizon",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The mean absolute error: 0.6080520387818553\n"
     ]
    }
   ],
   "source": [
    "print(\"The mean absolute error: {}\".format(mean_absolute_error(y_test, predictions)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "civilian-irrigation",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The root mean squared error: 0.8213411966869648\n"
     ]
    }
   ],
   "source": [
    "print(\"The root mean squared error: {}\".format(np.sqrt(mean_squared_error(y_test, predictions))))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "breeding-nothing",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Predicted Values')"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,10))\n",
    "best = plt.scatter(y_test, predictions)\n",
    "plt.xlim(0,10)\n",
    "plt.ylim(0,10)\n",
    "plt.xlabel('Actual Values')\n",
    "plt.ylabel('Predicted Values')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "latest-treasure",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5963523645014688"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "SVR_pipeline.score(X_train, y_train)  ## R^2 value "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "regulation-tours",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(8189, 33)"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "surrounded-edition",
   "metadata": {},
   "source": [
    "# Feed Forward Neural Network "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "demanding-clause",
   "metadata": {},
   "outputs": [],
   "source": [
    "## we will be using a deep neural network to predict the pIC50 value, while utilizing K-fold cross validation. \n",
    "## First define the nearual network architecture.\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense\n",
    "from sklearn.model_selection import KFold, cross_val_score \n",
    "def deep_learning_model():\n",
    "    model = Sequential()\n",
    "    model.add(Dense(120, input_dim=33, activation='relu'))\n",
    "    model.add(Dense(60, activation='relu'))\n",
    "    model.add(Dense(1, activation='linear'))\n",
    "    model.compile(optimizer='adam', loss='mean_squared_error')\n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "smart-march",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "dense (Dense)                (None, 120)               4080      \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 60)                7260      \n",
      "_________________________________________________________________\n",
      "dense_2 (Dense)              (None, 1)                 61        \n",
      "=================================================================\n",
      "Total params: 11,401\n",
      "Trainable params: 11,401\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "deep_model = deep_learning_model()\n",
    "deep_model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "accepting-treasurer",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/150\n",
      "192/192 [==============================] - 1s 3ms/step - loss: 347196217.0425\n",
      "Epoch 2/150\n",
      "192/192 [==============================] - 0s 1ms/step - loss: 132724836.0150\n",
      "Epoch 3/150\n",
      "192/192 [==============================] - 0s 2ms/step - loss: 1837151055.0102\n",
      "Epoch 4/150\n",
      "192/192 [==============================] - 0s 2ms/step - loss: 1249636.1870\n",
      "Epoch 5/150\n",
      "192/192 [==============================] - 0s 2ms/step - loss: 22625.6911\n",
      "Epoch 6/150\n",
      "192/192 [==============================] - 0s 2ms/step - loss: 13.9134\n",
      "Epoch 7/150\n",
      "192/192 [==============================] - 0s 2ms/step - loss: 3.5809\n",
      "Epoch 8/150\n",
      "192/192 [==============================] - 0s 1ms/step - loss: 3.0410\n",
      "Epoch 9/150\n",
      "192/192 [==============================] - 0s 1ms/step - loss: 13.7028\n",
      "Epoch 10/150\n",
      "192/192 [==============================] - 0s 2ms/step - loss: 21.3028\n",
      "Epoch 11/150\n",
      "192/192 [==============================] - 0s 2ms/step - loss: 14.5755\n",
      "Epoch 12/150\n",
      "192/192 [==============================] - 0s 2ms/step - loss: 7.2879\n",
      "Epoch 13/150\n",
      "192/192 [==============================] - 0s 2ms/step - loss: 35310.2154\n",
      "Epoch 14/150\n",
      "192/192 [==============================] - 0s 3ms/step - loss: 442658.1053\n",
      "Epoch 15/150\n",
      "192/192 [==============================] - 0s 2ms/step - loss: 898587981.8012\n",
      "Epoch 16/150\n",
      "192/192 [==============================] - 0s 2ms/step - loss: 21924486.7095\n",
      "Epoch 17/150\n",
      "192/192 [==============================] - 0s 2ms/step - loss: 1680.6373\n",
      "Epoch 18/150\n",
      "192/192 [==============================] - 0s 2ms/step - loss: 63596.1111\n",
      "Epoch 19/150\n",
      "192/192 [==============================] - 0s 2ms/step - loss: 139154.3280\n",
      "Epoch 20/150\n",
      "192/192 [==============================] - 0s 1ms/step - loss: 242020199.9362\n",
      "Epoch 21/150\n",
      "192/192 [==============================] - 0s 2ms/step - loss: 15964.4730\n",
      "Epoch 22/150\n",
      "192/192 [==============================] - 0s 2ms/step - loss: 38.7640\n",
      "Epoch 23/150\n",
      "192/192 [==============================] - 1s 3ms/step - loss: 1.6625\n",
      "Epoch 24/150\n",
      "192/192 [==============================] - 0s 2ms/step - loss: 1.6307\n",
      "Epoch 25/150\n",
      "192/192 [==============================] - 0s 2ms/step - loss: 1.5384\n",
      "Epoch 26/150\n",
      "192/192 [==============================] - 0s 2ms/step - loss: 1.8536\n",
      "Epoch 27/150\n",
      "192/192 [==============================] - 0s 2ms/step - loss: 1.5828\n",
      "Epoch 28/150\n",
      "192/192 [==============================] - 0s 2ms/step - loss: 1.5855\n",
      "Epoch 29/150\n",
      "192/192 [==============================] - 0s 2ms/step - loss: 1.7779\n",
      "Epoch 30/150\n",
      "192/192 [==============================] - 0s 2ms/step - loss: 290.8589\n",
      "Epoch 31/150\n",
      "192/192 [==============================] - 0s 2ms/step - loss: 20.8768\n",
      "Epoch 32/150\n",
      "192/192 [==============================] - 0s 2ms/step - loss: 13815.7032\n",
      "Epoch 33/150\n",
      "192/192 [==============================] - 0s 2ms/step - loss: 315539143.1941\n",
      "Epoch 34/150\n",
      "192/192 [==============================] - 1s 3ms/step - loss: 1447795.8021- loss: 33\n",
      "Epoch 35/150\n",
      "192/192 [==============================] - 0s 2ms/step - loss: 20361.7132\n",
      "Epoch 36/150\n",
      "192/192 [==============================] - 1s 3ms/step - loss: 557690.3867: 0s - loss:\n",
      "Epoch 37/150\n",
      "192/192 [==============================] - 0s 2ms/step - loss: 4.4339\n",
      "Epoch 38/150\n",
      "192/192 [==============================] - 0s 2ms/step - loss: 3.2601\n",
      "Epoch 39/150\n",
      "192/192 [==============================] - 0s 2ms/step - loss: 2.8687\n",
      "Epoch 40/150\n",
      "192/192 [==============================] - 0s 2ms/step - loss: 2.8545\n",
      "Epoch 41/150\n",
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      "Epoch 42/150\n",
      "192/192 [==============================] - 0s 2ms/step - loss: 2.4871\n",
      "Epoch 43/150\n",
      "192/192 [==============================] - 0s 2ms/step - loss: 2.4437\n",
      "Epoch 44/150\n",
      "192/192 [==============================] - 0s 2ms/step - loss: 2.4129\n",
      "Epoch 45/150\n",
      "192/192 [==============================] - 0s 2ms/step - loss: 2.3181\n",
      "Epoch 46/150\n",
      "192/192 [==============================] - 0s 2ms/step - loss: 20.1444\n",
      "Epoch 47/150\n",
      "192/192 [==============================] - 0s 2ms/step - loss: 2.8304\n",
      "Epoch 48/150\n",
      "192/192 [==============================] - 0s 2ms/step - loss: 2.1442\n",
      "Epoch 49/150\n",
      "192/192 [==============================] - 0s 2ms/step - loss: 57.7871\n",
      "Epoch 50/150\n",
      "192/192 [==============================] - 0s 2ms/step - loss: 316.9342\n",
      "Epoch 51/150\n",
      "192/192 [==============================] - 0s 2ms/step - loss: 193019.4517\n",
      "Epoch 52/150\n",
      "192/192 [==============================] - 0s 2ms/step - loss: 256.5762\n",
      "Epoch 53/150\n",
      "192/192 [==============================] - 0s 2ms/step - loss: 9.5100\n",
      "Epoch 54/150\n",
      "192/192 [==============================] - 0s 2ms/step - loss: 7.9226\n",
      "Epoch 55/150\n",
      "192/192 [==============================] - 0s 2ms/step - loss: 65.4860\n",
      "Epoch 56/150\n",
      "192/192 [==============================] - 0s 2ms/step - loss: 780.1229\n",
      "Epoch 57/150\n",
      "192/192 [==============================] - 1s 3ms/step - loss: 4682738.8655\n",
      "Epoch 58/150\n",
      "192/192 [==============================] - 0s 2ms/step - loss: 13748387.5069\n",
      "Epoch 59/150\n",
      "192/192 [==============================] - 0s 2ms/step - loss: 1843.2410\n",
      "Epoch 60/150\n",
      "192/192 [==============================] - 0s 2ms/step - loss: 2.5342\n",
      "Epoch 61/150\n",
      "192/192 [==============================] - 0s 2ms/step - loss: 1.7513\n",
      "Epoch 62/150\n",
      "192/192 [==============================] - 0s 2ms/step - loss: 3.8020\n",
      "Epoch 63/150\n",
      "192/192 [==============================] - 0s 2ms/step - loss: 2.7515\n",
      "Epoch 64/150\n",
      "192/192 [==============================] - 0s 2ms/step - loss: 6471.7076\n",
      "Epoch 65/150\n",
      "192/192 [==============================] - 0s 2ms/step - loss: 1724725.5897\n",
      "Epoch 66/150\n",
      "192/192 [==============================] - 0s 2ms/step - loss: 44655254.0759: 0s - loss: 5036721\n",
      "Epoch 67/150\n",
      "192/192 [==============================] - 0s 2ms/step - loss: 1189.2735\n",
      "Epoch 68/150\n",
      "192/192 [==============================] - 1s 3ms/step - loss: 1.7663\n",
      "Epoch 69/150\n",
      "192/192 [==============================] - 0s 2ms/step - loss: 1.6041\n",
      "Epoch 70/150\n",
      "192/192 [==============================] - 0s 2ms/step - loss: 1.4932\n",
      "Epoch 71/150\n",
      "192/192 [==============================] - 0s 2ms/step - loss: 1.4531\n",
      "Epoch 72/150\n",
      "192/192 [==============================] - 0s 2ms/step - loss: 1.3710\n",
      "Epoch 73/150\n",
      "192/192 [==============================] - 0s 2ms/step - loss: 1.4236\n",
      "Epoch 74/150\n",
      "192/192 [==============================] - 0s 2ms/step - loss: 1.4327\n",
      "Epoch 75/150\n",
      "192/192 [==============================] - 0s 2ms/step - loss: 1.4015\n",
      "Epoch 76/150\n",
      "192/192 [==============================] - 0s 2ms/step - loss: 1.3864\n",
      "Epoch 77/150\n",
      "192/192 [==============================] - 0s 2ms/step - loss: 1.3753\n",
      "Epoch 78/150\n",
      "192/192 [==============================] - 0s 2ms/step - loss: 1.3328\n",
      "Epoch 79/150\n",
      "192/192 [==============================] - 0s 2ms/step - loss: 1.3982\n",
      "Epoch 80/150\n",
      "192/192 [==============================] - 0s 2ms/step - loss: 1.3079\n",
      "Epoch 81/150\n",
      "192/192 [==============================] - 0s 2ms/step - loss: 1.8524\n",
      "Epoch 82/150\n",
      "192/192 [==============================] - 0s 1ms/step - loss: 1.5271\n",
      "Epoch 83/150\n",
      "192/192 [==============================] - 0s 2ms/step - loss: 1.3129\n",
      "Epoch 84/150\n",
      "192/192 [==============================] - 0s 2ms/step - loss: 2.3906\n",
      "Epoch 85/150\n",
      "192/192 [==============================] - 0s 2ms/step - loss: 1.7766\n",
      "Epoch 86/150\n",
      "192/192 [==============================] - 0s 2ms/step - loss: 2.6840\n",
      "Epoch 87/150\n",
      "192/192 [==============================] - 0s 2ms/step - loss: 39.7378\n",
      "Epoch 88/150\n",
      "192/192 [==============================] - 0s 2ms/step - loss: 6691.6432\n",
      "Epoch 89/150\n",
      "192/192 [==============================] - 0s 2ms/step - loss: 385093.7884\n",
      "Epoch 90/150\n",
      "192/192 [==============================] - 0s 2ms/step - loss: 902.5724\n",
      "Epoch 91/150\n",
      "192/192 [==============================] - 0s 2ms/step - loss: 2468.2831\n",
      "Epoch 92/150\n",
      "192/192 [==============================] - 0s 2ms/step - loss: 213432.4049\n",
      "Epoch 93/150\n",
      "192/192 [==============================] - 0s 2ms/step - loss: 1645.5623\n",
      "Epoch 94/150\n",
      "192/192 [==============================] - 0s 2ms/step - loss: 1.6346\n",
      "Epoch 95/150\n",
      "192/192 [==============================] - 0s 1ms/step - loss: 1.2016\n",
      "Epoch 96/150\n",
      "192/192 [==============================] - 0s 2ms/step - loss: 1.2099\n",
      "Epoch 97/150\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "192/192 [==============================] - 0s 2ms/step - loss: 13.6881\n",
      "Epoch 98/150\n",
      "192/192 [==============================] - 0s 2ms/step - loss: 1.4731\n",
      "Epoch 99/150\n",
      "192/192 [==============================] - 0s 2ms/step - loss: 788.4721\n",
      "Epoch 100/150\n",
      "192/192 [==============================] - 0s 2ms/step - loss: 3122828.5569\n",
      "Epoch 101/150\n",
      "192/192 [==============================] - 0s 2ms/step - loss: 53.8424\n",
      "Epoch 102/150\n",
      "192/192 [==============================] - 0s 2ms/step - loss: 49.9625\n",
      "Epoch 103/150\n",
      "192/192 [==============================] - 0s 3ms/step - loss: 1.2516A: 0s - loss: 1.\n",
      "Epoch 104/150\n",
      "192/192 [==============================] - 0s 2ms/step - loss: 1.1568\n",
      "Epoch 105/150\n",
      "192/192 [==============================] - 0s 2ms/step - loss: 1.3947\n",
      "Epoch 106/150\n",
      "192/192 [==============================] - 0s 1ms/step - loss: 1.3773\n",
      "Epoch 107/150\n",
      "192/192 [==============================] - 0s 2ms/step - loss: 233.4788\n",
      "Epoch 108/150\n",
      "192/192 [==============================] - 0s 2ms/step - loss: 274.3815\n",
      "Epoch 109/150\n",
      "192/192 [==============================] - 0s 1ms/step - loss: 8.8163\n",
      "Epoch 110/150\n",
      "192/192 [==============================] - 0s 1ms/step - loss: 1.3664\n",
      "Epoch 111/150\n",
      "192/192 [==============================] - 0s 2ms/step - loss: 2.1098\n",
      "Epoch 112/150\n",
      "192/192 [==============================] - 0s 2ms/step - loss: 1.2582\n",
      "Epoch 113/150\n",
      "192/192 [==============================] - 0s 1ms/step - loss: 14.2121\n",
      "Epoch 114/150\n",
      "192/192 [==============================] - 0s 2ms/step - loss: 302.9562\n",
      "Epoch 115/150\n",
      "192/192 [==============================] - 0s 2ms/step - loss: 57.8954\n",
      "Epoch 116/150\n",
      "192/192 [==============================] - 0s 1ms/step - loss: 544.1766\n",
      "Epoch 117/150\n",
      "192/192 [==============================] - 0s 2ms/step - loss: 50.1885\n",
      "Epoch 118/150\n",
      "192/192 [==============================] - 0s 2ms/step - loss: 2.4609\n",
      "Epoch 119/150\n",
      "192/192 [==============================] - 0s 2ms/step - loss: 2.7745\n",
      "Epoch 120/150\n",
      "192/192 [==============================] - 0s 2ms/step - loss: 1.0862\n",
      "Epoch 121/150\n",
      "192/192 [==============================] - 0s 2ms/step - loss: 3.0648\n",
      "Epoch 122/150\n",
      "192/192 [==============================] - 0s 2ms/step - loss: 103.4563\n",
      "Epoch 123/150\n",
      "192/192 [==============================] - 0s 2ms/step - loss: 72.8968\n",
      "Epoch 124/150\n",
      "192/192 [==============================] - 0s 2ms/step - loss: 1.7707\n",
      "Epoch 125/150\n",
      "192/192 [==============================] - 0s 2ms/step - loss: 47.1658\n",
      "Epoch 126/150\n",
      "192/192 [==============================] - 0s 2ms/step - loss: 6835.6907\n",
      "Epoch 127/150\n",
      "192/192 [==============================] - 0s 2ms/step - loss: 11.4529\n",
      "Epoch 128/150\n",
      "192/192 [==============================] - 0s 2ms/step - loss: 57.4002\n",
      "Epoch 129/150\n",
      "192/192 [==============================] - 0s 2ms/step - loss: 208.8389\n",
      "Epoch 130/150\n",
      "192/192 [==============================] - 0s 2ms/step - loss: 2.7684\n",
      "Epoch 131/150\n",
      "192/192 [==============================] - 0s 1ms/step - loss: 1564.2129\n",
      "Epoch 132/150\n",
      "192/192 [==============================] - 0s 2ms/step - loss: 12.6341\n",
      "Epoch 133/150\n",
      "192/192 [==============================] - 0s 2ms/step - loss: 7.1314\n",
      "Epoch 134/150\n",
      "192/192 [==============================] - 0s 2ms/step - loss: 1.0018\n",
      "Epoch 135/150\n",
      "192/192 [==============================] - 0s 1ms/step - loss: 4.7979\n",
      "Epoch 136/150\n",
      "192/192 [==============================] - 1s 3ms/step - loss: 48.9494\n",
      "Epoch 137/150\n",
      "192/192 [==============================] - 1s 3ms/step - loss: 180.5691\n",
      "Epoch 138/150\n",
      "192/192 [==============================] - 0s 2ms/step - loss: 1731.3550\n",
      "Epoch 139/150\n",
      "192/192 [==============================] - 0s 2ms/step - loss: 1.0383\n",
      "Epoch 140/150\n",
      "192/192 [==============================] - 0s 2ms/step - loss: 1.0494\n",
      "Epoch 141/150\n",
      "192/192 [==============================] - 0s 2ms/step - loss: 1.2001\n",
      "Epoch 142/150\n",
      "192/192 [==============================] - 0s 2ms/step - loss: 1.1242\n",
      "Epoch 143/150\n",
      "192/192 [==============================] - 0s 2ms/step - loss: 0.9700\n",
      "Epoch 144/150\n",
      "192/192 [==============================] - 0s 2ms/step - loss: 1.5184\n",
      "Epoch 145/150\n",
      "192/192 [==============================] - 0s 2ms/step - loss: 350.0295\n",
      "Epoch 146/150\n",
      "192/192 [==============================] - 0s 2ms/step - loss: 0.9975\n",
      "Epoch 147/150\n",
      "192/192 [==============================] - 0s 2ms/step - loss: 1.1050\n",
      "Epoch 148/150\n",
      "192/192 [==============================] - 0s 3ms/step - loss: 5589.2270\n",
      "Epoch 149/150\n",
      "192/192 [==============================] - 0s 2ms/step - loss: 1.9997\n",
      "Epoch 150/150\n",
      "192/192 [==============================] - 0s 2ms/step - loss: 1.6265\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<tensorflow.python.keras.callbacks.History at 0x23291119518>"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "deep_model.fit(X_train, y_train, epochs=150, shuffle=True, verbose=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "attached-tennis",
   "metadata": {},
   "outputs": [],
   "source": [
    "predictions = deep_model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "outstanding-restriction",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The mean absolute error: 0.8449907699903157\n"
     ]
    }
   ],
   "source": [
    "print(\"The mean absolute error: {}\".format(mean_absolute_error(y_test, predictions)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "confident-inclusion",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The root mean squared error: 1.2253226008540798\n"
     ]
    }
   ],
   "source": [
    "print(\"The root mean squared error: {}\". format(np.sqrt(mean_squared_error(y_test, predictions))))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "dependent-candidate",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Predicted Values')"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,10))\n",
    "plt.scatter(y_test, predictions)\n",
    "plt.xlim(0,10)\n",
    "plt.ylim(0,10)\n",
    "plt.xlabel('Actual Values')\n",
    "plt.ylabel('Predicted Values')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "solved-silly",
   "metadata": {},
   "outputs": [],
   "source": [
    "## Despite using a neural network and achieving good measurements for the training data, we get poor predictions of desired\n",
    "## dependent variable, pIC50. Based on the results of the three regression models, this current dataset would be a poor \n",
    "## application for reression type model. The solution would be to make adjustments to the way features were selected, \n",
    "## perhaps using more domain expertise to select features. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "piano-modeling",
   "metadata": {},
   "outputs": [],
   "source": [
    "## make x and y axis the same values \n"
   ]
  }
 ],
 "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": "3.6.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}