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Model_Classification.py
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Model_Classification.py
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from sklearn.model_selection import train_test_split
from DecisionTree import DecisionTree
from MultiLayerPerceptron import MultiLayerPerceptron
from LogisticRegression import LogisticReg
from XGBoost import XGBoost
class ModelClassification:
def __init__(self, model_name, data_matrix, labels):
self.x_train = None
self.y_train = None
self.x_test = None
self.y_test = None
self.data = data_matrix
self.labels = labels
self.model_name = model_name
self.model = None
self.split_test_train()
""" Splitting the data, 80% to train and 20% to test the accyracy of trained model"""
def split_test_train(self):
self.x_train, self.x_test, self.y_train, self.y_test = train_test_split(self.data, self.labels, test_size=0.2,
shuffle=True, random_state=42)
""" Model to call corresponding models """
def get_trained_model(self):
if self.model_name == 'MLP':
self.model = MultiLayerPerceptron(self.x_train, self.y_train, self.x_test, self.y_test)
self.model.train_model()
elif self.model_name == 'LR':
self.model = LogisticReg(self.x_train, self.y_train, self.x_test, self.y_test)
self.model.train_model()
elif self.model_name == 'DT':
self.model = DecisionTree(self.x_train, self.y_train, self.x_test, self.y_test)
self.model.train_model()
elif self.model_name == 'XGB':
self.model = XGBoost(self.x_train, self.y_train, self.x_test, self.y_test)
self.model.train_model()
return self.model.get_model()
def get_model(self):
return self.model.get_model()