model.py

import pandas as pd
from keras.models import Sequential
from keras.layers import Dense
from keras.models import load_model
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.feature_extraction.text import TfidfVectorizer


class Allisto:

    def __init__(self):
        self.df = pd.read_csv('cry.csv')
        self.X = self.df.iloc[:, 0:19].values
        self.y = self.df.iloc[:, 20]
        self.X_train, self.X_test, self.y_train, self.y_test = train_test_split(
            self.X, self.y, test_size=0.2, random_state=0)
        self.sc = StandardScaler()
        self.X_train = self.sc.fit_transform(self.X_train)
        self.X_test = self.sc.transform(self.X_test)
        self.classifier = Sequential()
        self.vectorizer = TfidfVectorizer()

    def vectorizer_fit(self, X):
        self.vectorizer.fit(X)

    def vectorizer_transform(self, X):
        X_transformed = self.vectorizer.transform(X)
        return X_transformed

    def build_model(self):
        self.classifier.add(Dense(output_dim=6, init='uniform',
                                  activation='relu', input_dim=19))
        self.classifier.add(
            Dense(output_dim=6, init='uniform', activation='relu'))
        self.classifier.add(
            Dense(output_dim=1, init='uniform', activation='sigmoid'))
        self.classifier.compile(
            optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])

    def train(self):
        self.classifier.fit(self.X_train, self.y_train,
                            batch_size=10, nb_epoch=100)

    def predict(self, input_params):
        return self.classifier.predict(input_params) >= 0

    def save(self):
        self.classifier.save('model.h5')

    def load(self):
        self.classifier = load_model('model.h5')


class AllistoPredict:

    def __init__(self):
        self.model = load_model('model.h5')

    def predict(self, input_params):
        return self.model.predict(input_params) >= 0.5

"""
Testing of the Classes Created
model = Allisto()
model.build_model()
model.train()
model.save()

model.load()

obj = AllistoPredict()
obj.predict(np.array([[0.13201746241462,0.0846523059846263,0.130812215724496,0.0480311890838207,0.210136452241715,0.162105263157895,1.31334024389288,4.99252406943563,0.972450815429729,0.787036038235428,0.0282001299545159,0.13201746241462,0.178684467460517,0.015984015984016,0.275862068965517,0.751302083333333,0.0078125,6.1953125,6.1875]]))
"""


# import numpy as np
# value = clf.predict(np.array([[0.13201746241462,0.0846523059846263,0.130812215724496,0.0480311890838207,0.210136452241715,0.162105263157895,1.31334024389288,4.99252406943563,0.972450815429729,0.787036038235428,0.0282001299545159,0.13201746241462,0.178684467460517,0.015984015984016,0.275862068965517,0.751302083333333,0.0078125,6.1953125,6.1875]]))