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# Group Members                     #

# Bhoopalsinh Musale    002269332   #

# Syed Malik Muzaffar   002269955   #

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# Imports

import pandas as pd

import numpy as np

from sklearn.base import BaseEstimator, TransformerMixin

from sklearn.utils.validation import check_X_y, check_array, check_is_fitted

from sklearn.utils.multiclass import unique_labels

import scipy.stats as st

from sklearn import tree

from sklearn import naive_bayes

from sklearn.neural_network import MLPClassifier

from sklearn.neighbors import KNeighborsClassifier

from sklearn.ensemble import AdaBoostClassifier

from sklearn.pipeline import Pipeline

from sklearn.model_selection import GridSearchCV

from sklearn.model_selection import cross_val_score

from sklearn.model_selection import KFold

from tempfile import mkdtemp

from TScoreSelection import TScoreSelection

import os

import warnings

warnings.filterwarnings("ignore")

def load_data():

    '''

        Load data from CSV file

        returns X,Y and random seeds

    '''

    dataFrame = pd.read_csv('pp5i_train.gr.csv')

    dataFrame.set_index('SNO', inplace=True)

    dataFrame = dataFrame.transpose()

    dataFrame.reset_index(drop=True, inplace=True)

    y = pd.read_csv('pp5i_train_class.txt')

    dataFrame = pd.concat([dataFrame, y], axis=1)

    myRndSeeds = 72

    dataFrame = dataFrame.sample(

        frac=1, random_state=myRndSeeds).reset_index(drop=True)

    print(dataFrame.shape)

    print(dataFrame.head())

    X = dataFrame.drop('Class', axis=1)

    y = dataFrame['Class']

    return X, y, myRndSeeds

def clean_data(X):

    '''

        Thresholding both train and test data 

        to a minimum value of 20, maximum of 16,000.

    '''

    X.clip(upper=16000, lower=20, inplace=True)

    print(X.shape)

    X = X.loc[:, X.max() - X.min() > 2]

    print(X.shape)

    return X

if __name__ == "__main__":

    # Loading Dataset

    X, y, myRndSeeds = load_data()

    # Cleaning Dataset

    X = clean_data(X)

    # Feature selection using Ttest

    cachedir = mkdtemp()

    pipe = Pipeline([('featureSelection', TScoreSelection(w=10)),

                     ('classify', KNeighborsClassifier(n_neighbors=1))],

                    memory=cachedir)

    # Top Gene Selection

    N_GENES = [2, 4, 6, 8, 10, 12, 15, 20, 25, 30]

    N_LAYERS = [(32,), (64,), (128,)]

    # Hyperparameter Optimization

    tuned_parameters = [

        # KNN Classifier(2,3,4)

        {'featureSelection__w': N_GENES,

         'classify': [KNeighborsClassifier()],

         'classify__n_neighbors': [2, 3, 4]

         },

        # Decision Tree Classifier(J48 algorithm)

        {'featureSelection__w': N_GENES,

         'classify': [tree.DecisionTreeClassifier()],

         'classify__criterion':['gini', 'entropy'],

         'classify__min_samples_leaf': [1, 3, 5],

         'classify__max_depth': [3, 6, 9],

         'classify__presort': [True]

         },

        # Neural Network Multi-label Classifier

        {'featureSelection__w': N_GENES,

         'classify': [MLPClassifier()],

         'classify__hidden_layer_sizes': N_LAYERS,

         'classify__activation': ['logistic'],

         'classify__alpha':[0.05, 0.01, 0.005, 0.001],

         'classify__max_iter':[1000],

         'classify__solver': ['lbfgs'],

         'classify__verbose': [True]

         },

        # Naïve Bayes Classifier

        {'featureSelection__w': N_GENES,

         'classify': [naive_bayes.GaussianNB()]

         },

        # AdaBoost Classifier

        {'featureSelection__w': N_GENES,

         'classify': [AdaBoostClassifier()]

         }

    ]

    # Model Selection using Pipeline and Cross validation

    kfolds = KFold(n_splits=5, shuffle=True, random_state=myRndSeeds)

    model = GridSearchCV(pipe, tuned_parameters, cv=kfolds,

                         return_train_score=True)

    model.fit(X, y)

    results = pd.DataFrame(model.cv_results_)

    print(results.sort_values(by='mean_test_score', ascending=False).head())

    # Best Model

    best_estimator_ = model.best_estimator_

    print(best_estimator_)

    # Running best model on Test dataset

    testDataFrame = pd.read_csv('pp5i_test.gr.csv')

    testDataFrame.set_index('SNO', inplace=True)

    X_test = testDataFrame.transpose()

    X_test.reset_index(drop=True, inplace=True)

    # Generating output Y for given Test Dataset

    Y = pd.DataFrame()

    Y['predicted'] = model.predict(X_test)

    finalResult = Y

    # Final Output

    print(finalResult)