baseModels.py

import csv
import matplotlib.pyplot as plt
from itertools import cycle
from sklearn.preprocessing import StandardScaler
from sklearn.model_selection import train_test_split
from sklearn.neural_network import MLPClassifier
from sklearn.neighbors import KNeighborsClassifier
from sklearn.tree import DecisionTreeClassifier
from sklearn.ensemble import  RandomForestClassifier, AdaBoostClassifier
from sklearn.naive_bayes import GaussianNB
from sklearn.metrics import classification_report,confusion_matrix
from sklearn.preprocessing import label_binarize
from sklearn import metrics
from sklearn.metrics import roc_curve, auc
from sklearn.multiclass import OneVsRestClassifier
import numpy as np
import pandas as pd
from scipy import interp
from openpyxl.workbook import Workbook
from sklearn.preprocessing import StandardScaler, label_binarize

# import matplotlib.pyplot as #plt
import pickle
import multiclassROC
import preprocess

def construct_models(X_train, X_test, y_train, y_test, y_test_bin):
    confusion_matrices={}
    classifiers = [
        AdaBoostClassifier(n_estimators=1000,learning_rate=1.0,algorithm='SAMME'),
        GaussianNB(),
        KNeighborsClassifier(n_neighbors=8,leaf_size=1,weights='distance'),
        DecisionTreeClassifier(max_depth=15,min_samples_leaf=8,min_samples_split=3),
        RandomForestClassifier(n_estimators=1000,max_depth=80),
        MLPClassifier(learning_rate='adaptive',alpha=0.01)

        ]
    names = ["AdaBoostClassifierModel","GaussianNBModel","KNeighborsClassifierModel","DecisionTreeClassifierModel","RandomForestClassifierModel","MLPClassifierModel"]
    for clf,name in  zip(classifiers,names):
        model = clf.fit(X_train, y_train)
        print("classifier", model)
        print ("Accuracy on Train Set")
        print (model.score(X_train, y_train))
        print (name)
        print ("Accuracy on Test Set")
        print (model.score(X_test, y_test))
        filename = 'pickled_models/' + name + '.pkl'
        pickle.dump(model, open(filename, 'wb'))
        print ("Report")
        print (classification_report(y_test,model.predict(X_test)))


def generate_roc(X_train, X_test, y_train, y_test, y_test_bin,n_classes):
    confusion_matrices={}
    classifiers = [
        AdaBoostClassifier(),
        GaussianNB(),
        KNeighborsClassifier(),
        DecisionTreeClassifier(),
        RandomForestClassifier(),
        MLPClassifier()
        ]
    names = ["AdaBoostClassifierModel","GaussianNBModel","KNeighborsClassifierModel","DecisionTreeClassifierModel","RandomForestClassifierModel","MLPClassifierModel"]
    overall = []
    for clf,name in  zip(classifiers,names):
        filename = 'pickled_models/' + name + '.pkl'
        model = pickle.load(open(filename, 'rb'))
        y_score = model.predict_proba(X_test)
        multiclassROC.calculate_roc(y_test_bin, y_score, name,n_classes)

if __name__ == '__main__':

    Data_train = []
    Output_train = []
    Data_test = []
    Output_test = []
    bin_output = []

    #Exporting the CSV paths
    #Path = 'datasets/NN/16_riboswitches.csv'
    # Path = 'datasets/NN/24_riboswitches.csv'
    Path = 'processed_datasets/final_32train.csv'

    #Call function to Load Dataset
    Data_train, Output_train = preprocess.Load_Data_baseModel(Path, Data_train, Output_train)

    #Converting the train data into Float
    Data_train, Output_train = preprocess.Convert_to_Float(Data_train, Output_train)

    Path = 'processed_datasets/final_32test.csv'

    #Call function to Load Dataset
    Data_test, Output_test = preprocess.Load_Data_baseModel(Path, Data_test, Output_test)

    #Converting the train data into Float
    Data_test, Output_test = preprocess.Convert_to_Float(Data_test, Output_test)

    bin_output = preprocess.binarize(Output_test)



    scaler = StandardScaler()
    scaler.fit(Data_train)
    Data_train = scaler.transform(Data_train)
    Data_test = scaler.transform(Data_test)


    construct_models(Data_train, Data_test, Output_train, Output_test, bin_output)
    total_class=preprocess.get_totalclass('processed_datasets/final_32test.csv')
    generate_roc(Data_train, Data_test, Output_train, Output_test, bin_output,total_class)