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PassiveRFModel.py
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PassiveRFModel.py
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from sklearn.metrics import accuracy_score, f1_score, precision_score, recall_score
class PassiveRFModel:
def __init__(self, model, type, name, encoder, data, labels):
self.model = model
self.model_type = type
self.model_name = name
self.encoder = encoder
self.data = data
self.labels = labels
def get_model(self):
return self.model
def get_model_type(self):
return self.model_type
def get_model_name(self):
return self.model_name
def get_encoder(self):
return self.encoder
def get_data(self):
return self.data
def get_labels(self):
return self.labels
def print(self):
print("Model: ", self.model)
print("Model Name: ", self.model_name)
print("Encoder: ", self.encoder)
print("Data: ", self.data)
print("Labels: ", self.labels)
def fit(self, feature_columns, weight = "No_Weight", timeout = None, penalty = None):
if weight == "Weighted":
diff = (self.data[self.model_name].replace(timeout, timeout * penalty)[self.model_name[0]] - self.data[self.model_name].replace(timeout, timeout * penalty)[self.model_name[1]]).abs()
self.model.fit(self.data[feature_columns], self.labels, sample_weight=diff.values)
else:
self.model.fit(self.data[feature_columns], self.labels)
def predict(self, X):
return self.model.predict(X)
def predict_proba(self, X):
return self.model.predict_proba(X)
def score(self, X, y):
print("Accuracy: ", round(accuracy_score(y, self.model.predict(X)), 2))
print("F1 Score: ", round(f1_score(y, self.model.predict(X)) , 2))
print("Precision: ", round(precision_score(y, self.model.predict(X)) , 2))
print("Recall: ", round(recall_score(y, self.model.predict(X)), 2))