nn.py

import numpy as np
import pandas as pd
from sklearn.neural_network import MLPClassifier
from sklearn.model_selection import train_test_split
from sklearn import metrics
import time

data = pd.read_csv("./data/data.csv")
data.drop(['Reference1', 'Dataset1', 'Reference2', 'Dataset2'], axis=1, inplace=True)
data = data.sample(frac=1).reset_index(drop=True)
y = data.Same.copy()
features = data.drop(['Same'], axis=1)

start = time.time()
features_train, features_test, y_train, y_test = train_test_split(features, y, test_size=0.20, random_state=31)
model = MLPClassifier(hidden_layer_sizes=(150,100,50),
                        max_iter = 300,activation = 'relu',
                        solver = 'adam')
model.fit(features_train, y_train)
y_pred = pd.Series(model.predict(features_test))
y_test = y_test.reset_index(drop=True)

end = time.time()
print(end - start)
print("Accuracy:", metrics.accuracy_score(y_test, y_pred))
print("Precision:", metrics.precision_score(y_test, y_pred))
print("Recall:", metrics.recall_score(y_test, y_pred))
print("F1:", metrics.f1_score(y_test, y_pred))