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engine.py
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'''import tensorflow as tf
from tensorflow import feature_column
from tensorflow.keras import layers'''
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression
import numpy as np
import pandas as pd
import seaborn as sns
from keras import Sequential
from keras.layers import Dense
import pickle
#splitting data between train and test
def data_split(data, ratio):
np.random.seed(42)
shuffled = np.random.permutation(len(data))
test_set_size = int(len(data) * ratio)
test_indices = shuffled[:test_set_size]
train_indices = shuffled[test_set_size:]
return data.iloc[train_indices], data.iloc[test_indices]
if __name__=="__main__":
#reading the data from the csv file
dataset = pd.read_csv('data1.csv')
x= dataset.iloc[:,0:5]
y= dataset.iloc[:,5]
#Train test splitting
X_train, X_test, y_train, y_test = train_test_split(x, y, test_size=0.3)
#Starting our model
classifier = Sequential()
#First Hidden Layer
classifier.add(Dense(4, activation='relu', kernel_initializer='random_normal', input_dim=5))
#Second Hidden Layer
classifier.add(Dense(4, activation='relu', kernel_initializer='random_normal'))
#Output Layer
classifier.add(Dense(1, activation='sigmoid', kernel_initializer='random_normal'))
#Compiling the neural network
classifier.compile(optimizer ='adam',loss='binary_crossentropy', metrics =['accuracy'])
classifier.fit(X_train,y_train, batch_size=10, epochs=100)
#Dumping model in a file
file1 = open('model_neural.pkl', 'wb')
pickle.dump(classifier, file1)
'''df=pd.read_csv('data.csv')
#getting splitted data from function
train,test = data_split(df, 0.3)
# splitting features and output for prediction
x_train = train[['fever','bodypain','age','runnynose','diffbreath']].to_numpy()
x_test = test[['fever','bodypain','age','runnynose','diffbreath']].to_numpy()
y_train = train[['infected']].to_numpy().reshape(train.shape[0],)
y_test = test[['infected']].to_numpy().reshape(test.shape[0],)
clf=LogisticRegression();
clf.fit(x_train, y_train);
file1 = open('model.pkl', 'wb')
pickle.dump(clf, file1)
'''
'''train, test = train_test_split(df, test_size=0.2)
train, val = train_test_split(train, test_size=0.2)
print(len(train), 'train examples')
print(len(val), 'validation examples')
print(len(test), 'test examples')
batch_size = 5 # A small batch sized is used for demonstration purposes
train_ds = df_to_dataset(train, batch_size=batch_size)
val_ds = df_to_dataset(val, shuffle=False, batch_size=batch_size)
test_ds = df_to_dataset(test, shuffle=False, batch_size=batch_size)
for feature_batch, label_batch in train_ds.take(1):
print('Every feature:', list(feature_batch.keys()))
print('A batch of ages:', feature_batch['age'])
print('A batch of targets:', label_batch )'''
'''
#example_batch = next(iter(train_ds))[0]
#def demo(feature_column):
# feature_layer = layers.DenseFeatures(feature_column)
# print(feature_layer(example_batch).numpy())
age = feature_column.numeric_column("age")
diffb = feature_column.numeric_column("diffbreath")
#demo(age)
feature_columns = []
for header in ['fever', 'bodypain', 'runnynose']:
feature_columns.append(feature_column.numeric_column(header))
age_buckets = feature_column.bucketized_column(age, boundaries=[12, 20, 30, 40, 50, 60, 70, 82])
feature_columns.append(age_buckets)
diffb_buckets = feature_column.bucketized_column(diffb, boundaries=[-1,0,1])
feature_columns.append(diffb_buckets)
feature_layer = tf.keras.layers.DenseFeatures(feature_columns)
batch_size = 1999
train_ds = df_to_dataset(train, shuffle=True)
val_ds = df_to_dataset(val, shuffle=True)
test_ds = df_to_dataset(test, shuffle=True)
model = tf.keras.Sequential();
model.add(feature_layer);
model.add(tf.keras.layers.Dense(128, activation='relu'))
model.add(tf.keras.layers.Dense(128, activation='relu'))
model.add(tf.keras.layers.Dense(1))
model.compile(optimizer='adam', loss=tf.keras.losses.BinaryCrossentropy(from_logits=True), metrics=['accuracy'])
model.fit(train_ds,validation_data=val_ds, epochs=100)
#model.save('model');
print(test_ds);
pred = model.predict(test_ds)
#print(pred)
#file1 = open('model.pkl', 'wb')
# dump information to that file
#pickle.dump(model, file1)'''