1.4.1. Models and feature importance .ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Predicting Prescriber induced Overdose"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Author : Kiros Gebremariam**\n",
    "\n",
    "Cohorts of the Data Science Immersive, General Assembly @ Washington DC campus"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#  Modeling and feature importance visual\n",
    "\n",
    "## Feature Selection\n",
    "\n",
    "In this section, I will select specific features for model training. The original dataset contains 265 features, most of which are categorical attributes, dominantly the list of drug names that are approved as pain medications that need special AED licensed prescriber.Here,my main target is to predict the overdoses using the  the list of prescriber specialists with NPI information and the FDA approved drug lists. The data set has only total opiod cliams and total claim counts and i took the standard prescription for opiod drugs is 84 days which is 12 weeks. Therefore, an additional feature was created in the data cleaning, pre processing that shows avg_op_supply which i limit to the standard to be 84 and any ones who prescribes the category of medications under the controlled substance act(CSA). The CSA is used for organizing drugs based on the risk of abuse or harm to users in five and my capestone focuses on the approved by [FDA](https://www.fda.gov/drugs/drugsafety/informationbydrugclass/ucm251735.htm) and [DEA](https://www.deadiversion.usdoj.gov/schedules/) to be prescribed by licensed and certified health professional.  These drug lists that are considered controlled substances under the Controlled Substances Act (CSA) are divided into five schedules.  An updated and complete list of the schedules is published annually in [Title 21 Code of Federal Regulations (C.F.R.) §§ 1308.11 through 1308.15](https://www.deadiversion.usdoj.gov/21cfr/cfr/2108cfrt.htm).\n",
    "\n",
    "Therfore, for analysis and visualization  i only took those physicians who have greater opood cliams from the center for medical services dataset and created a feature average opiod supply  by dividing the total cliam by the opiod cliam and reviewed those specialists who prescribe more than the set limit of 12 weeks and that also become a new feature that measures the length of opiod prescription supply more than 84 days. As been visualized in notebook 1.3, significant amount of prescribers are family physicians, followed by internal medicine,nurse practitioners, Physicain Assistant and physical medicine and rehabilitation. When i group based on speciality and the vsuals can be accessed   [here top 20](https://plot.ly/~kiros/112) and the lists are as follows:-\n",
    "  > $'Cardiology', 'Nephrology', 'Endocrinology', 'Neurology'  'General Practice', 'Internal Medicine', 'Urology'$,   > $'Pulmonary Disease', 'Gastroenterology', 'Family Practice', 'Infectious Disease','Psychiatry'$ \n",
    "  >$'Nurse Practitioner', 'Ophthalmology','Allergy/Immunology', 'Otolaryngology', 'Psychiatry & Neurology'$ , $'Rheumatology', 'Dermatology', 'Obstetrics/Gynecology'$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "# Import Libraries \n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import re\n",
    "\n",
    "#Importing Machine learning libraries\n",
    "from sklearn.ensemble import BaggingClassifier, RandomForestClassifier,ExtraTreesClassifier, BaggingRegressor, RandomForestRegressor,  AdaBoostClassifier, AdaBoostRegressor\n",
    "from sklearn.pipeline import Pipeline, FeatureUnion\n",
    "from sklearn.tree import DecisionTreeClassifier, DecisionTreeRegressor\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.neighbors import KNeighborsRegressor, KNeighborsClassifier\n",
    "from sklearn.model_selection import train_test_split, GridSearchCV, cross_val_score,StratifiedKFold\n",
    "from sklearn.linear_model import LogisticRegression, LinearRegression\n",
    "# importing NN and Keras library\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense\n",
    "from sklearn import preprocessing\n",
    "from keras.callbacks import EarlyStopping\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "from keras.wrappers.scikit_learn import KerasClassifier\n",
    "from sklearn import metrics\n",
    "from sklearn import svm\n",
    "np.random.seed(2018)\n",
    "# Configure visual settings:\n",
    "plt.style.use('fivethirtyeight')\n",
    "sns.set(font_scale=2)\n",
    "cmap = sns.diverging_palette(220, 10, as_cmap=True) # one of the many color mappings\n",
    "sns.set_style('whitegrid')\n",
    "plt.rcParams['figure.figsize'] = (10.0, 8.0) \n",
    "%config InlineBackend.figure_format = 'retina'\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "prescriber_df = pd.read_csv('./inputs/prescribers_clean832.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(24237, 355)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prescriber_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
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       "   Unnamed: 0  ABILIFY  ACETAMINOPHEN.CODEINE  ACYCLOVIR  ADVAIR.DISKUS  \\\n",
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     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prescriber_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "#prescriber_df = pd.get_dummies(prescriber_df, columns=['Credentials', 'Specialty','State', 'Abbrev'], drop_first=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Setting up for Classification "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Setting up for train test split\n",
    "\n",
    "X = prescriber_df.drop(['Opioid.Prescriber'], axis = 1).values.astype(float)\n",
    "y = prescriber_df['Opioid.Prescriber'].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# train and test split\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9268426842684269\n",
      "0.9257294429708223\n"
     ]
    }
   ],
   "source": [
    "# Logistic Regression \n",
    "\n",
    "# Set up pipeline \n",
    "ss = StandardScaler()\n",
    "logreg = LogisticRegression()\n",
    "pipes = Pipeline([('ss', ss), ('logreg', logreg)])\n",
    "\n",
    "# Fit pipeline \n",
    "pipes.fit(X_train, y_train)\n",
    "print(pipes.score(X_test, y_test))\n",
    "print(pipes.score(X_train, y_train))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.7376237623762376\n",
      "0.832124963159446\n"
     ]
    }
   ],
   "source": [
    "# KNN \n",
    "\n",
    "knn = KNeighborsClassifier()\n",
    "knn.fit(X_train, y_train)\n",
    "print(knn.score(X_test, y_test)) \n",
    "print(knn.score(X_train, y_train))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.903052805280528\n",
      "1.0\n"
     ]
    }
   ],
   "source": [
    "# Decision Tree \n",
    "decisiontree = DecisionTreeClassifier()\n",
    "model = decisiontree.fit(X_train, y_train)\n",
    "print(model.score(X_test, y_test))\n",
    "print(model.score(X_train, y_train))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9276677667766776\n",
      "0.9908045977011494\n"
     ]
    }
   ],
   "source": [
    "# Bagged Decision\n",
    "bagging = BaggingClassifier()\n",
    "bagging.fit(X_train, y_train)\n",
    "print(bagging.score(X_test, y_test))\n",
    "print(bagging.score(X_train, y_train))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9253300330033003\n",
      "0.9930445033893309\n"
     ]
    }
   ],
   "source": [
    "# Random Forest\n",
    "rf = RandomForestClassifier()\n",
    "rf.fit(X_train, y_train)\n",
    "print(rf.score(X_test, y_test))\n",
    "print(rf.score(X_train, y_train))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9335808580858086\n",
      "0.928676687297377\n"
     ]
    }
   ],
   "source": [
    "# Adaboost\n",
    "\n",
    "abr = AdaBoostClassifier()\n",
    "abr.fit(X_train, y_train)\n",
    "print(abr.score(X_test, y_test))\n",
    "print(abr.score(X_train, y_train))\n",
    "\n",
    "# This model is overfitting, I think "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### EVALUATING MODELS \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define f1-score function\n",
    "def f1(model, X, y):\n",
    "    y_pred = model.predict(X)\n",
    "    f1 = metrics.f1_score(y, y_pred) \n",
    "    return f1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.935919055649241, 0.9340106839844978)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Logistic Regression Evaluation\n",
    "\n",
    "f1(pipes, X_test, y_test), f1(pipes, X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.7611417125688533, 0.848025613660619)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# KNN Evaluation \n",
    "\n",
    "f1(knn, X_test, y_test), f1(knn, X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.9181279758448495, 1.0)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Decision Tree Evaluation\n",
    "f1(decisiontree, X_test, y_test), f1(decisiontree, X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.9354416835096897, 0.9940584088620343)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Random Forest Evaluation \n",
    "\n",
    "f1(rf, X_test, y_test), f1(rf, X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.9418142392482833, 0.9366691091803622)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# ADA Evaluation\n",
    "\n",
    "f1(abr, X_test, y_test), f1(abr, X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Creating/Evaluating Model with Cross Validation "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Encoding class values as integers \n",
    "encoder = LabelEncoder()\n",
    "encoder.fit(y)\n",
    "encoded_y = encoder.transform(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "def create_baseline():  # defining a function, which as my baseline\n",
    "    # Build model \n",
    "    model = Sequential()    #first layer defines the shape of the input which is X_train.shape:354\n",
    "    model.add(Dense(60, input_dim = 354, kernel_initializer= 'normal', activation = 'relu'))\n",
    "    ##then add a layer defining the activation function - relu is on of the most popular\n",
    "    #activation - should the neuron be fired or not - relu is quite efficient.\n",
    "    #A dense layer is a classic fully connected neural network layer(60) : each input node is connected to each output node.\n",
    "    \n",
    "    model.add(Dense(1, kernel_initializer = 'normal', activation = 'sigmoid'))\n",
    "    #for this layer use the sigmoid activation function\n",
    "    #sigmoid will assign a propability to each tag (total output = 1) since its a classification problem\n",
    "    # Compile Model \n",
    "    model.compile(loss = 'binary_crossentropy', optimizer = 'adam', metrics = ['accuracy'])\n",
    "    # optimizer is the function used to search for the right weights\n",
    "    #here we are using Adaptive Moment Estimation (Adam) that also uses adaptive learning rates\n",
    "    #The loss function, also called the objective function is the evaluation of the model used by the optimizer \n",
    "    # to navigate the weight space; only accuracy is supported at the moment for metrics.\n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/1\n",
      "21812/21812 [==============================] - 10s 457us/step - loss: 3.1461 - acc: 0.7196\n",
      "2425/2425 [==============================] - 0s 178us/step\n",
      "Epoch 1/1\n",
      "21813/21813 [==============================] - 10s 453us/step - loss: 6.4176 - acc: 0.5855\n",
      "2424/2424 [==============================] - 0s 187us/step\n",
      "Epoch 1/1\n",
      "21813/21813 [==============================] - 10s 464us/step - loss: 6.2699 - acc: 0.5974\n",
      "2424/2424 [==============================] - 0s 194us/step\n",
      "Epoch 1/1\n",
      "21813/21813 [==============================] - 10s 457us/step - loss: 5.3807 - acc: 0.6323\n",
      "2424/2424 [==============================] - 0s 187us/step\n",
      "Epoch 1/1\n",
      "21813/21813 [==============================] - 10s 472us/step - loss: 1.6460 - acc: 0.76521s - loss - ETA: 0s - loss: 1\n",
      "2424/2424 [==============================] - 1s 360us/step\n",
      "Epoch 1/1\n",
      "21813/21813 [==============================] - 13s 594us/step - loss: 6.6290 - acc: 0.5798\n",
      "2424/2424 [==============================] - 1s 245us/step\n",
      "Epoch 1/1\n",
      "21814/21814 [==============================] - 12s 551us/step - loss: 5.7322 - acc: 0.6195\n",
      "2423/2423 [==============================] - 1s 277us/step\n",
      "Epoch 1/1\n",
      "21814/21814 [==============================] - 12s 554us/step - loss: 6.3298 - acc: 0.58730s - loss: 6.3095 -\n",
      "2423/2423 [==============================] - 1s 399us/step\n",
      "Epoch 1/1\n",
      "21814/21814 [==============================] - 12s 550us/step - loss: 6.0110 - acc: 0.6066\n",
      "2423/2423 [==============================] - 1s 277us/step\n",
      "Epoch 1/1\n",
      "21814/21814 [==============================] - 12s 544us/step - loss: 2.6170 - acc: 0.7344\n",
      "2423/2423 [==============================] - 1s 321us/step\n",
      "Results: 69.53% (11.49%)\n"
     ]
    }
   ],
   "source": [
    "# Evaluating model after standardizing the standardized dataset \n",
    "# early_stop = EarlyStopping(monitor='val_loss', min_delta=0) \n",
    "# callbacks=[early_stop])\n",
    "estimator = KerasClassifier(build_fn = create_baseline, nb_epoch = 100, batch_size = 5, verbose = 1)\n",
    "kfold = StratifiedKFold(n_splits = 10, shuffle = True, random_state = 42)\n",
    "results = cross_val_score(estimator, X, encoded_y, cv = kfold)\n",
    "print(\"Results: %.2f%% (%.2f%%)\" % (results.mean()*100, results.std()*100))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/20\n",
      " - 11s - loss: 0.3706 - acc: 0.8286\n",
      "Epoch 2/20\n",
      " - 10s - loss: 0.2939 - acc: 0.8691\n",
      "Epoch 3/20\n",
      " - 11s - loss: 0.2575 - acc: 0.8882\n",
      "Epoch 4/20\n",
      " - 12s - loss: 0.2324 - acc: 0.9007\n",
      "Epoch 5/20\n",
      " - 12s - loss: 0.2151 - acc: 0.9101\n",
      "Epoch 6/20\n",
      " - 12s - loss: 0.2017 - acc: 0.9167\n",
      "Epoch 7/20\n",
      " - 12s - loss: 0.1897 - acc: 0.9209\n",
      "Epoch 8/20\n",
      " - 11s - loss: 0.1796 - acc: 0.9244\n",
      "Epoch 9/20\n",
      " - 10s - loss: 0.1726 - acc: 0.9301\n",
      "Epoch 10/20\n",
      " - 10s - loss: 0.1636 - acc: 0.9344\n",
      "Epoch 11/20\n",
      " - 10s - loss: 0.1635 - acc: 0.9354\n",
      "Epoch 12/20\n",
      " - 11s - loss: 0.1532 - acc: 0.9404\n",
      "Epoch 13/20\n",
      " - 11s - loss: 0.1479 - acc: 0.9413\n",
      "Epoch 14/20\n",
      " - 11s - loss: 0.1418 - acc: 0.9450\n",
      "Epoch 15/20\n",
      " - 11s - loss: 0.1398 - acc: 0.9442\n",
      "Epoch 16/20\n",
      " - 11s - loss: 0.1349 - acc: 0.9459\n",
      "Epoch 17/20\n",
      " - 11s - loss: 0.1289 - acc: 0.9482\n",
      "Epoch 18/20\n",
      " - 11s - loss: 0.1276 - acc: 0.9501\n",
      "Epoch 19/20\n",
      " - 11s - loss: 0.1215 - acc: 0.9524\n",
      "Epoch 20/20\n",
      " - 12s - loss: 0.1211 - acc: 0.9531\n",
      "Epoch 1/20\n",
      " - 13s - loss: 0.3702 - acc: 0.8290\n",
      "Epoch 2/20\n",
      " - 11s - loss: 0.2932 - acc: 0.8678\n",
      "Epoch 3/20\n",
      " - 11s - loss: 0.2576 - acc: 0.8866\n",
      "Epoch 4/20\n",
      " - 12s - loss: 0.2330 - acc: 0.8997\n",
      "Epoch 5/20\n",
      " - 12s - loss: 0.2156 - acc: 0.9065\n",
      "Epoch 6/20\n",
      " - 12s - loss: 0.2020 - acc: 0.9141\n",
      "Epoch 7/20\n",
      " - 13s - loss: 0.1928 - acc: 0.9191\n",
      "Epoch 8/20\n",
      " - 13s - loss: 0.1786 - acc: 0.9266\n",
      "Epoch 9/20\n",
      " - 12s - loss: 0.1710 - acc: 0.9309\n",
      "Epoch 10/20\n",
      " - 11s - loss: 0.1637 - acc: 0.9337\n",
      "Epoch 11/20\n",
      " - 12s - loss: 0.1587 - acc: 0.9343\n",
      "Epoch 12/20\n",
      " - 11s - loss: 0.1529 - acc: 0.9381\n",
      "Epoch 13/20\n",
      " - 12s - loss: 0.1458 - acc: 0.9408\n",
      "Epoch 14/20\n",
      " - 11s - loss: 0.1422 - acc: 0.9429\n",
      "Epoch 15/20\n",
      " - 11s - loss: 0.1377 - acc: 0.9451\n",
      "Epoch 16/20\n",
      " - 11s - loss: 0.1329 - acc: 0.9461\n",
      "Epoch 17/20\n",
      " - 11s - loss: 0.1298 - acc: 0.9480\n",
      "Epoch 18/20\n",
      " - 11s - loss: 0.1270 - acc: 0.9510\n",
      "Epoch 19/20\n",
      " - 11s - loss: 0.1259 - acc: 0.9509\n",
      "Epoch 20/20\n",
      " - 11s - loss: 0.1229 - acc: 0.9524\n",
      "Epoch 1/20\n",
      " - 12s - loss: 0.3685 - acc: 0.8296\n",
      "Epoch 2/20\n",
      " - 12s - loss: 0.2907 - acc: 0.8715\n",
      "Epoch 3/20\n",
      " - 10s - loss: 0.2558 - acc: 0.8903\n",
      "Epoch 4/20\n",
      " - 10s - loss: 0.2323 - acc: 0.9012\n",
      "Epoch 5/20\n",
      " - 10s - loss: 0.2145 - acc: 0.9091\n",
      "Epoch 6/20\n",
      " - 10s - loss: 0.2039 - acc: 0.9144\n",
      "Epoch 7/20\n",
      " - 9s - loss: 0.1911 - acc: 0.9221\n",
      "Epoch 8/20\n",
      " - 10s - loss: 0.1801 - acc: 0.9257\n",
      "Epoch 9/20\n",
      " - 10s - loss: 0.1741 - acc: 0.9290\n",
      "Epoch 10/20\n",
      " - 10s - loss: 0.1645 - acc: 0.9341\n",
      "Epoch 11/20\n",
      " - 10s - loss: 0.1606 - acc: 0.9357\n",
      "Epoch 12/20\n",
      " - 10s - loss: 0.1544 - acc: 0.9398\n",
      "Epoch 13/20\n",
      " - 10s - loss: 0.1460 - acc: 0.9414\n",
      "Epoch 14/20\n",
      " - 11s - loss: 0.1419 - acc: 0.9432\n",
      "Epoch 15/20\n",
      " - 10s - loss: 0.1396 - acc: 0.9444\n",
      "Epoch 16/20\n",
      " - 10s - loss: 0.1385 - acc: 0.9455\n",
      "Epoch 17/20\n",
      " - 10s - loss: 0.1321 - acc: 0.9466\n",
      "Epoch 18/20\n",
      " - 10s - loss: 0.1269 - acc: 0.9491\n",
      "Epoch 19/20\n",
      " - 10s - loss: 0.1239 - acc: 0.9505\n",
      "Epoch 20/20\n",
      " - 10s - loss: 0.1217 - acc: 0.9525\n",
      "Epoch 1/20\n",
      " - 10s - loss: 0.3633 - acc: 0.8314\n",
      "Epoch 2/20\n",
      " - 10s - loss: 0.2893 - acc: 0.8713\n",
      "Epoch 3/20\n",
      " - 10s - loss: 0.2540 - acc: 0.8883\n",
      "Epoch 4/20\n",
      " - 11s - loss: 0.2275 - acc: 0.9046\n",
      "Epoch 5/20\n",
      " - 11s - loss: 0.2110 - acc: 0.9093\n",
      "Epoch 6/20\n",
      " - 10s - loss: 0.2001 - acc: 0.9178\n",
      "Epoch 7/20\n",
      " - 11s - loss: 0.1895 - acc: 0.9227\n",
      "Epoch 8/20\n",
      " - 10s - loss: 0.1830 - acc: 0.9258\n",
      "Epoch 9/20\n",
      " - 10s - loss: 0.1724 - acc: 0.9304\n",
      "Epoch 10/20\n",
      " - 10s - loss: 0.1644 - acc: 0.9341\n",
      "Epoch 11/20\n",
      " - 10s - loss: 0.1609 - acc: 0.9360\n",
      "Epoch 12/20\n",
      " - 10s - loss: 0.1515 - acc: 0.9410\n",
      "Epoch 13/20\n",
      " - 10s - loss: 0.1482 - acc: 0.9429\n",
      "Epoch 14/20\n",
      " - 10s - loss: 0.1432 - acc: 0.9435\n",
      "Epoch 15/20\n",
      " - 9s - loss: 0.1399 - acc: 0.9445\n",
      "Epoch 16/20\n",
      " - 9s - loss: 0.1363 - acc: 0.9469\n",
      "Epoch 17/20\n",
      " - 10s - loss: 0.1327 - acc: 0.9469\n",
      "Epoch 18/20\n",
      " - 10s - loss: 0.1279 - acc: 0.9479\n",
      "Epoch 19/20\n",
      " - 9s - loss: 0.1235 - acc: 0.9516\n",
      "Epoch 20/20\n",
      " - 9s - loss: 0.1235 - acc: 0.9518\n",
      "Epoch 1/20\n",
      " - 10s - loss: 0.3712 - acc: 0.8249\n",
      "Epoch 2/20\n",
      " - 9s - loss: 0.2922 - acc: 0.8708\n",
      "Epoch 3/20\n",
      " - 9s - loss: 0.2561 - acc: 0.8884\n",
      "Epoch 4/20\n",
      " - 9s - loss: 0.2341 - acc: 0.8988\n",
      "Epoch 5/20\n",
      " - 10s - loss: 0.2135 - acc: 0.9073\n",
      "Epoch 6/20\n",
      " - 9s - loss: 0.2022 - acc: 0.9150\n",
      "Epoch 7/20\n",
      " - 9s - loss: 0.1918 - acc: 0.9212\n",
      "Epoch 8/20\n",
      " - 10s - loss: 0.1819 - acc: 0.9258\n",
      "Epoch 9/20\n",
      " - 10s - loss: 0.1747 - acc: 0.9278\n",
      "Epoch 10/20\n",
      " - 10s - loss: 0.1673 - acc: 0.9318\n",
      "Epoch 11/20\n",
      " - 9s - loss: 0.1607 - acc: 0.9374\n",
      "Epoch 12/20\n",
      " - 9s - loss: 0.1513 - acc: 0.9380\n",
      "Epoch 13/20\n",
      " - 10s - loss: 0.1453 - acc: 0.9406\n",
      "Epoch 14/20\n",
      " - 9s - loss: 0.1427 - acc: 0.9429\n",
      "Epoch 15/20\n",
      " - 9s - loss: 0.1371 - acc: 0.9459\n",
      "Epoch 16/20\n",
      " - 9s - loss: 0.1340 - acc: 0.9457\n",
      "Epoch 17/20\n",
      " - 9s - loss: 0.1318 - acc: 0.9473\n",
      "Epoch 18/20\n",
      " - 9s - loss: 0.1292 - acc: 0.9480\n",
      "Epoch 19/20\n",
      " - 9s - loss: 0.1232 - acc: 0.9513\n",
      "Epoch 20/20\n",
      " - 9s - loss: 0.1226 - acc: 0.9531\n",
      "Epoch 1/20\n",
      " - 10s - loss: 0.3672 - acc: 0.8283\n",
      "Epoch 2/20\n",
      " - 10s - loss: 0.2897 - acc: 0.8716\n",
      "Epoch 3/20\n",
      " - 10s - loss: 0.2543 - acc: 0.8905\n",
      "Epoch 4/20\n",
      " - 9s - loss: 0.2275 - acc: 0.9036\n",
      "Epoch 5/20\n",
      " - 9s - loss: 0.2121 - acc: 0.9110\n",
      "Epoch 6/20\n",
      " - 10s - loss: 0.1979 - acc: 0.9170\n",
      "Epoch 7/20\n",
      " - 9s - loss: 0.1870 - acc: 0.9223\n",
      "Epoch 8/20\n",
      " - 10s - loss: 0.1784 - acc: 0.9276\n",
      "Epoch 9/20\n",
      " - 9s - loss: 0.1678 - acc: 0.9323\n",
      "Epoch 10/20\n",
      " - 10s - loss: 0.1637 - acc: 0.9339\n",
      "Epoch 11/20\n",
      " - 10s - loss: 0.1573 - acc: 0.9362\n",
      "Epoch 12/20\n",
      " - 10s - loss: 0.1509 - acc: 0.9387\n",
      "Epoch 13/20\n",
      " - 9s - loss: 0.1464 - acc: 0.9428\n",
      "Epoch 14/20\n",
      " - 10s - loss: 0.1412 - acc: 0.9430\n",
      "Epoch 15/20\n",
      " - 9s - loss: 0.1369 - acc: 0.9458\n",
      "Epoch 16/20\n",
      " - 9s - loss: 0.1309 - acc: 0.9483\n",
      "Epoch 17/20\n",
      " - 10s - loss: 0.1312 - acc: 0.9480\n",
      "Epoch 18/20\n",
      " - 10s - loss: 0.1253 - acc: 0.9510\n",
      "Epoch 19/20\n",
      " - 10s - loss: 0.1217 - acc: 0.9532\n",
      "Epoch 20/20\n",
      " - 10s - loss: 0.1220 - acc: 0.9529\n",
      "Epoch 1/20\n",
      " - 10s - loss: 0.3786 - acc: 0.8230\n",
      "Epoch 2/20\n",
      " - 10s - loss: 0.2976 - acc: 0.8665\n",
      "Epoch 3/20\n",
      " - 10s - loss: 0.2591 - acc: 0.8855\n",
      "Epoch 4/20\n",
      " - 10s - loss: 0.2342 - acc: 0.9012\n",
      "Epoch 5/20\n",
      " - 10s - loss: 0.2173 - acc: 0.9105\n",
      "Epoch 6/20\n",
      " - 10s - loss: 0.2044 - acc: 0.9136\n",
      "Epoch 7/20\n",
      " - 10s - loss: 0.1949 - acc: 0.9199\n",
      "Epoch 8/20\n",
      " - 10s - loss: 0.1844 - acc: 0.9244\n",
      "Epoch 9/20\n",
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      "Epoch 10/20\n",
      " - 9s - loss: 0.1687 - acc: 0.9308\n",
      "Epoch 11/20\n",
      " - 9s - loss: 0.1633 - acc: 0.9342\n",
      "Epoch 12/20\n",
      " - 9s - loss: 0.1546 - acc: 0.9386\n",
      "Epoch 13/20\n",
      " - 9s - loss: 0.1489 - acc: 0.9405\n",
      "Epoch 14/20\n",
      " - 9s - loss: 0.1459 - acc: 0.9425\n",
      "Epoch 15/20\n",
      " - 9s - loss: 0.1436 - acc: 0.9432\n",
      "Epoch 16/20\n",
      " - 9s - loss: 0.1350 - acc: 0.9465\n",
      "Epoch 17/20\n",
      " - 9s - loss: 0.1317 - acc: 0.9472\n",
      "Epoch 18/20\n",
      " - 9s - loss: 0.1283 - acc: 0.9487\n",
      "Epoch 19/20\n",
      " - 9s - loss: 0.1261 - acc: 0.9499\n",
      "Epoch 20/20\n",
      " - 9s - loss: 0.1228 - acc: 0.9514\n",
      "Epoch 1/20\n",
      " - 10s - loss: 0.3727 - acc: 0.8279\n",
      "Epoch 2/20\n",
      " - 10s - loss: 0.2957 - acc: 0.8657\n",
      "Epoch 3/20\n",
      " - 10s - loss: 0.2598 - acc: 0.8858\n",
      "Epoch 4/20\n",
      " - 10s - loss: 0.2336 - acc: 0.8995\n",
      "Epoch 5/20\n",
      " - 10s - loss: 0.2163 - acc: 0.9094\n",
      "Epoch 6/20\n",
      " - 10s - loss: 0.2035 - acc: 0.9158\n",
      "Epoch 7/20\n",
      " - 10s - loss: 0.1909 - acc: 0.9213\n",
      "Epoch 8/20\n",
      " - 10s - loss: 0.1813 - acc: 0.9255\n",
      "Epoch 9/20\n",
      " - 10s - loss: 0.1721 - acc: 0.9298\n",
      "Epoch 10/20\n",
      " - 10s - loss: 0.1664 - acc: 0.9320\n",
      "Epoch 11/20\n",
      " - 10s - loss: 0.1583 - acc: 0.9369\n",
      "Epoch 12/20\n",
      " - 10s - loss: 0.1542 - acc: 0.9377\n",
      "Epoch 13/20\n",
      " - 10s - loss: 0.1510 - acc: 0.9417\n",
      "Epoch 14/20\n",
      " - 10s - loss: 0.1429 - acc: 0.9433\n",
      "Epoch 15/20\n",
      " - 10s - loss: 0.1377 - acc: 0.9462\n",
      "Epoch 16/20\n",
      " - 10s - loss: 0.1344 - acc: 0.9472\n",
      "Epoch 17/20\n",
      " - 10s - loss: 0.1295 - acc: 0.9480\n",
      "Epoch 18/20\n",
      " - 10s - loss: 0.1297 - acc: 0.9494\n",
      "Epoch 19/20\n",
      " - 10s - loss: 0.1232 - acc: 0.9527\n",
      "Epoch 20/20\n",
      " - 10s - loss: 0.1194 - acc: 0.9538\n",
      "Epoch 1/20\n",
      " - 10s - loss: 0.3675 - acc: 0.8305\n",
      "Epoch 2/20\n",
      " - 10s - loss: 0.2935 - acc: 0.8674\n",
      "Epoch 3/20\n",
      " - 10s - loss: 0.2559 - acc: 0.8887\n",
      "Epoch 4/20\n",
      " - 10s - loss: 0.2334 - acc: 0.8988\n",
      "Epoch 5/20\n",
      " - 10s - loss: 0.2144 - acc: 0.9075\n",
      "Epoch 6/20\n",
      " - 10s - loss: 0.2036 - acc: 0.9136\n",
      "Epoch 7/20\n",
      " - 10s - loss: 0.1888 - acc: 0.9225\n",
      "Epoch 8/20\n",
      " - 9s - loss: 0.1797 - acc: 0.9263\n",
      "Epoch 9/20\n",
      " - 10s - loss: 0.1718 - acc: 0.9305\n",
      "Epoch 10/20\n",
      " - 9s - loss: 0.1625 - acc: 0.9348\n",
      "Epoch 11/20\n",
      " - 10s - loss: 0.1579 - acc: 0.9362\n",
      "Epoch 12/20\n",
      " - 10s - loss: 0.1539 - acc: 0.9381\n",
      "Epoch 13/20\n",
      " - 10s - loss: 0.1464 - acc: 0.9418\n",
      "Epoch 14/20\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " - 9s - loss: 0.1451 - acc: 0.9426\n",
      "Epoch 15/20\n",
      " - 10s - loss: 0.1378 - acc: 0.9442\n",
      "Epoch 16/20\n",
      " - 10s - loss: 0.1345 - acc: 0.9470\n",
      "Epoch 17/20\n",
      " - 9s - loss: 0.1312 - acc: 0.9472\n",
      "Epoch 18/20\n",
      " - 10s - loss: 0.1278 - acc: 0.9492\n",
      "Epoch 19/20\n",
      " - 10s - loss: 0.1266 - acc: 0.9505\n",
      "Epoch 20/20\n",
      " - 10s - loss: 0.1209 - acc: 0.9533\n",
      "Epoch 1/20\n",
      " - 11s - loss: 0.3728 - acc: 0.8276\n",
      "Epoch 2/20\n",
      " - 10s - loss: 0.2941 - acc: 0.8686\n",
      "Epoch 3/20\n",
      " - 10s - loss: 0.2585 - acc: 0.8864\n",
      "Epoch 4/20\n",
      " - 10s - loss: 0.2359 - acc: 0.8984\n",
      "Epoch 5/20\n",
      " - 10s - loss: 0.2182 - acc: 0.9090\n",
      "Epoch 6/20\n",
      " - 10s - loss: 0.2042 - acc: 0.9151\n",
      "Epoch 7/20\n",
      " - 10s - loss: 0.1944 - acc: 0.9203\n",
      "Epoch 8/20\n",
      " - 10s - loss: 0.1826 - acc: 0.9261\n",
      "Epoch 9/20\n",
      " - 10s - loss: 0.1758 - acc: 0.9303\n",
      "Epoch 10/20\n",
      " - 10s - loss: 0.1708 - acc: 0.9321\n",
      "Epoch 11/20\n",
      " - 10s - loss: 0.1631 - acc: 0.9355\n",
      "Epoch 12/20\n",
      " - 10s - loss: 0.1556 - acc: 0.9375\n",
      "Epoch 13/20\n",
      " - 10s - loss: 0.1493 - acc: 0.9396\n",
      "Epoch 14/20\n",
      " - 10s - loss: 0.1438 - acc: 0.9427\n",
      "Epoch 15/20\n",
      " - 10s - loss: 0.1418 - acc: 0.9443\n",
      "Epoch 16/20\n",
      " - 10s - loss: 0.1380 - acc: 0.9460\n",
      "Epoch 17/20\n",
      " - 10s - loss: 0.1337 - acc: 0.9462\n",
      "Epoch 18/20\n",
      " - 10s - loss: 0.1326 - acc: 0.9483\n",
      "Epoch 19/20\n",
      " - 10s - loss: 0.1279 - acc: 0.9480\n",
      "Epoch 20/20\n",
      " - 10s - loss: 0.1250 - acc: 0.9502\n",
      "Standardized: 88.77% (1.02%)\n"
     ]
    }
   ],
   "source": [
    "#Evaluatig  baseline Neural network model with standardized dataset \n",
    "# if you have patience to wait  35-45 minutes then re-run this, it needs ver very longer time to process.\n",
    "estimators = []\n",
    "estimators.append(('standardize', StandardScaler()))\n",
    "estimators.append(('mlp', KerasClassifier(build_fn=create_baseline, epochs = 20, batch_size = 5, verbose = 2)))\n",
    "pipeline = Pipeline(estimators)\n",
    "kfold = StratifiedKFold(n_splits = 10, shuffle = True, random_state = 42)\n",
    "results = cross_val_score(pipeline, X, y, cv = kfold)\n",
    "print(\"Standardized: %.2f%% (%.2f%%)\" % (results.mean()*100, results.std()*100))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "On cross validation model gives accuracy of: 88.77% with a std of (1.02%)\n"
     ]
    }
   ],
   "source": [
    "print(\"On cross validation model gives accuracy of: %.2f%% with a std of (%.2f%%)\" % (results.mean()*100, results.std()*100))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Conclusive Models "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "def create_baseline():  # defining a function, which as my baseline\n",
    "    # Build model \n",
    "    model = Sequential()    #first layer defines the shape of the input which is X_train.shape:354\n",
    "    model.add(Dense(60, input_dim = 354, kernel_initializer= 'normal', activation = 'relu'))\n",
    "    ##then add a layer defining the activation function - relu is on of the most popular\n",
    "    #activation - should the neuron be fired or not - relu is quite efficient.\n",
    "    #A dense layer is a classic fully connected neural network layer(60) : each input node is connected to each output node.\n",
    "    \n",
    "    model.add(Dense(1, kernel_initializer = 'normal', activation = 'sigmoid'))\n",
    "    #for this layer use the sigmoid activation function\n",
    "    #sigmoid will assign a propability to each tag (total output = 1) since its a classification problem\n",
    "    # Compile Model \n",
    "    model.compile(loss = 'binary_crossentropy', optimizer = 'adam', metrics = ['accuracy'])\n",
    "    # optimizer is the function used to search for the right weights\n",
    "    #here we are using Adaptive Moment Estimation (Adam) that also uses adaptive learning rates\n",
    "    #The loss function, also called the objective function is the evaluation of the model used by the optimizer \n",
    "    # to navigate the weight space; only accuracy is supported at the moment for metrics.\n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = Sequential()    #first layer defines the shape of the input which is X_train.shape:354\n",
    "##then add a layer defining the activation function - relu is on of the most popular\n",
    "#activation - should the neuron be fired or not - relu is quite efficient.\n",
    "#A dense layer is a classic fully connected neural network layer(60) : each input node is connected to each output node.\n",
    "    \n",
    "model.add(Dense(60, input_dim = 354, kernel_initializer = 'normal', activation = 'relu'))\n",
    "#for this layer use the sigmoid activation function\n",
    "#sigmoid will assign a propability to each tag (total output = 1) since its a classification problem\n",
    "model.add(Dense(1, kernel_initializer = 'normal', activation = 'sigmoid'))\n",
    "\n",
    "# Compiling Model \n",
    "\n",
    "earlystop = EarlyStopping(monitor = 'val_loss', min_delta = 0, patience = 2, verbose = 0, mode = 'auto')\n",
    "callbacks_list = [earlystop]\n",
    "model.compile(loss = 'binary_crossentropy', optimizer = 'adam', metrics = ['accuracy'])\n",
    "# optimizer is the function used to search for the right weights\n",
    "#here we are using Adaptive Moment Estimation (Adam) that also uses adaptive learning rates\n",
    " #The loss function, also called the objective function is the evaluation of the model used by the optimizer \n",
    "# to navigate the weight space; only accuracy is supported at the moment for metrics."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 21813 samples, validate on 2424 samples\n",
      "Epoch 1/20\n",
      " - 11s - loss: 4.0963 - acc: 0.6854 - val_loss: 6.4082 - val_acc: 0.5957\n",
      "Epoch 2/20\n",
      " - 11s - loss: 2.2342 - acc: 0.7976 - val_loss: 1.3709 - val_acc: 0.8317\n",
      "Epoch 3/20\n",
      " - 11s - loss: 0.5032 - acc: 0.8770 - val_loss: 0.3846 - val_acc: 0.7240\n",
      "Epoch 4/20\n",
      " - 11s - loss: 0.2883 - acc: 0.8963 - val_loss: 0.3006 - val_acc: 0.8894\n",
      "Epoch 5/20\n",
      " - 11s - loss: 0.2634 - acc: 0.9008 - val_loss: 0.2714 - val_acc: 0.8944\n",
      "Epoch 6/20\n",
      " - 11s - loss: 0.2453 - acc: 0.9070 - val_loss: 0.2389 - val_acc: 0.9097\n",
      "Epoch 7/20\n",
      " - 11s - loss: 0.2342 - acc: 0.9116 - val_loss: 0.3404 - val_acc: 0.7574\n",
      "Epoch 8/20\n",
      " - 11s - loss: 0.2333 - acc: 0.9144 - val_loss: 0.2147 - val_acc: 0.9175\n",
      "Epoch 9/20\n",
      " - 11s - loss: 0.2293 - acc: 0.9165 - val_loss: 0.2273 - val_acc: 0.9130\n",
      "Epoch 10/20\n",
      " - 11s - loss: 0.2305 - acc: 0.9168 - val_loss: 0.2919 - val_acc: 0.8833\n"
     ]
    }
   ],
   "source": [
    "history = model.fit(X, y, validation_split = 0.1, epochs = 20, batch_size = 5, verbose = 2, callbacks = callbacks_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "24237/24237 [==============================] - 1s 38us/step\n"
     ]
    }
   ],
   "source": [
    "loss, accuracy = model.evaluate(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9148409456665103"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.23092762610008993"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loss"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Standardize variables or  Features of the dataset "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "min_max_scaler = preprocessing.MinMaxScaler()\n",
    "x_minmax = min_max_scaler.fit_transform(X)\n",
    "X_test_minmax = min_max_scaler.transform(X_test)\n",
    "\n",
    "scaler = StandardScaler()\n",
    "scaler.fit(X)\n",
    "X_scaled = scaler.transform(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 18177 samples, validate on 6060 samples\n",
      "Epoch 1/20\n",
      " - 10s - loss: 0.3745 - acc: 0.8300 - val_loss: 0.3668 - val_acc: 0.8252\n",
      "Epoch 2/20\n",
      " - 10s - loss: 0.3045 - acc: 0.8633 - val_loss: 0.3197 - val_acc: 0.8655\n",
      "Epoch 3/20\n",
      " - 10s - loss: 0.2674 - acc: 0.8833 - val_loss: 0.3026 - val_acc: 0.8762\n",
      "Epoch 4/20\n",
      " - 10s - loss: 0.2380 - acc: 0.8968 - val_loss: 0.3037 - val_acc: 0.8746\n",
      "Epoch 5/20\n",
      " - 10s - loss: 0.2163 - acc: 0.9093 - val_loss: 0.3061 - val_acc: 0.8805\n"
     ]
    }
   ],
   "source": [
    "history = model.fit(X_scaled, y, validation_split = 0.25, epochs = 20, batch_size = 5, verbose = 2, callbacks = callbacks_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "24237/24237 [==============================] - 1s 40us/step\n"
     ]
    }
   ],
   "source": [
    "loss, accuracy = model.evaluate(X_scaled, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.914057020260742"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.21761873970682405"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dict_keys(['val_loss', 'val_acc', 'loss', 'acc'])\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 304,
       "width": 448
      }
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 304,
       "width": 449
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# formula adapted from Justin Lecture notes and python machin learning packtbooks online\n",
    "print(history.history.keys())\n",
    "# summarize history for accuracy\n",
    "plt.plot(history.history['acc'])\n",
    "plt.plot(history.history['val_acc'])\n",
    "plt.title('model accuracy')\n",
    "plt.ylabel('accuracy')\n",
    "plt.xlabel('epoch')\n",
    "plt.ylim(0.7,1)\n",
    "plt.legend(['train', 'test'], loc='upper right')\n",
    "plt.show()\n",
    "# summarize history for loss\n",
    "plt.plot(history.history['loss'])\n",
    "plt.plot(history.history['val_loss'])\n",
    "plt.title('model loss')\n",
    "plt.ylabel('loss')\n",
    "plt.xlabel('epoch')\n",
    "plt.ylim(0,0.7)\n",
    "plt.legend(['train', 'test'], loc='upper right')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 16965 samples, validate on 7272 samples\n",
      "Epoch 1/30\n",
      " - 16s - loss: 6.5879 - acc: 0.5861 - val_loss: 6.5024 - val_acc: 0.5921\n",
      "Epoch 2/30\n",
      " - 14s - loss: 6.6163 - acc: 0.5849 - val_loss: 6.4980 - val_acc: 0.5923\n",
      "Epoch 3/30\n",
      " - 14s - loss: 6.5284 - acc: 0.5905 - val_loss: 6.4801 - val_acc: 0.5935\n",
      "Epoch 4/30\n",
      " - 14s - loss: 7.9655 - acc: 0.5038 - val_loss: 8.8759 - val_acc: 0.4491\n",
      "Epoch 5/30\n",
      " - 14s - loss: 7.7084 - acc: 0.5195 - val_loss: 6.4774 - val_acc: 0.5935\n",
      "Epoch 6/30\n",
      " - 14s - loss: 6.5002 - acc: 0.5922 - val_loss: 6.4747 - val_acc: 0.5938\n",
      "Epoch 7/30\n",
      " - 14s - loss: 8.3948 - acc: 0.4785 - val_loss: 9.0143 - val_acc: 0.4405\n",
      "Epoch 8/30\n",
      " - 14s - loss: 7.0827 - acc: 0.5576 - val_loss: 8.0303 - val_acc: 0.5017\n"
     ]
    }
   ],
   "source": [
    "from keras import regularizers\n",
    "import numpy as np\n",
    "from keras.layers import Dropout\n",
    "from keras.callbacks import EarlyStopping\n",
    "np.random.seed(2018)\n",
    "\n",
    "model= Sequential()\n",
    "n_input = X_train.shape[1]\n",
    "n_hidden = n_input\n",
    "n_output = 1\n",
    "#first layer defines the shape of the input which is X_train.shape\n",
    "#then add a layer defining the activation function - relu is on of the most popular\n",
    "#activation - should the neuron be fired or not - relu is quite efficient.\n",
    "model.add(Dense(n_hidden, input_dim=n_input, activation='relu'))\n",
    "#A dense layer is a classic fully connected neural network layer : each input node is connected to each output node.\n",
    "#for this layer use the sigmoid activation function\n",
    "#sigmoid will assign a propability to each tag (total output = 1) since its a classification problem\n",
    "model.add(Dense(n_output, activation='sigmoid'))\n",
    "\n",
    "#now we compile the model\n",
    "#optimizer is the function used to search for the right weights\n",
    "#here we are using Adaptive Moment Estimation (Adam) that also uses adaptive learning rates\n",
    "#The loss function, also called the objective function is the evaluation of the model used by the optimizer \n",
    "# to navigate the weight space; only accuracy is supported at the moment for metrics.\n",
    "\n",
    "earlystop = EarlyStopping(monitor = 'val_loss', min_delta = 0, patience = 2, verbose = 0, mode = 'auto')\n",
    "callbacks_list = [earlystop]\n",
    "model.compile(loss='binary_crossentropy', optimizer='adam', \n",
    "                metrics=['acc'])\n",
    "\n",
    "#early_stop = EarlyStopping(monitor='val_loss', min_delta=0) \n",
    "\n",
    "history = model.fit(X_train, y_train, validation_data=(X_test, y_test),\n",
    "                      epochs=30, batch_size= 5,verbose =2,callbacks = callbacks_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1a3a63a828>"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 256,
       "width": 408
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "train_loss = history.history['loss']\n",
    "test_loss = history.history['val_loss']\n",
    "plt.plot(train_loss, label='Training loss')\n",
    "plt.plot(test_loss, label='Testing loss')\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.5576186388941806, 0.5016501766718654)"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "history.history['acc'][-1], history.history['val_acc'][-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "24237/24237 [==============================] - 1s 52us/step\n"
     ]
    }
   ],
   "source": [
    "loss, accuracy = model.evaluate(X_scaled, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5752362091054753"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6.493203932712607"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dict_keys(['val_loss', 'val_acc', 'loss', 'acc'])\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 304,
       "width": 448
      }
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 304,
       "width": 449
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# formula adapted from Justin Lecture notes and python machin learning packtbooks online\n",
    "print(history.history.keys())\n",
    "# summarize history for accuracy\n",
    "plt.plot(history.history['acc'])\n",
    "plt.plot(history.history['val_acc'])\n",
    "plt.title('model accuracy')\n",
    "plt.ylabel('accuracy')\n",
    "plt.xlabel('epoch')\n",
    "plt.ylim(0.7,1)\n",
    "plt.legend(['train', 'test'], loc='upper right')\n",
    "plt.show()\n",
    "# summarize history for loss\n",
    "plt.plot(history.history['loss'])\n",
    "plt.plot(history.history['val_loss'])\n",
    "plt.title('model loss')\n",
    "plt.ylabel('loss')\n",
    "plt.xlabel('epoch')\n",
    "plt.ylim(0,0.7)\n",
    "plt.legend(['train', 'test'], loc='upper right')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}