def test_split_data(self):
features = [
"flujo", "NU_COORD_UTM ESTE", "NU_COORD_UTM NORTE", "°API",
"antiguedad"
]
label = "BBPD"
data_engineering = DataEngineering()
data_engineering.load_data(self.csv_path)
data = data_engineering.get_data()
max_date = data["año"].max()
age = max_date - data["año"]
data_engineering.add_column("antiguedad", age)
flow_data = data["E_FLUJO"].copy().astype("category").cat.codes
data_engineering.add_column("flujo", flow_data)
data_engineering.set_label(label)
data_engineering.set_features(features)
data_engineering.split_data()
assert data_engineering.x_train is not None
assert data_engineering.x_test is not None
assert data_engineering.y_train is not None
assert data_engineering.y_test is not None
def test_set_get_label(self):
label = "BNPD"
data_engineering = DataEngineering()
assert data_engineering.get_label() is None
data_engineering.load_data(self.csv_path)
data_engineering.set_label(label)
assert data_engineering.get_label() == label
data_e.clean_data()
# Create new features
# "age" feature
max_date = data_e.get_data()["año"].max()
age = max_date - data_e.get_data()["año"]
data_e.add_column("age", age)
# "flow" feature
flow_data = data_e.get_data()["E_FLUJO"].copy().astype("category").cat.codes
data_e.add_column("flow", flow_data)
# Set features and label
features = ["flow", "NU_COORD_UTM ESTE", "NU_COORD_UTM NORTE", "°API", "age"]
label = "BBPD"
data_e.set_features(features)
data_e.set_label(label)
# Split Train-Test data
data_e.split_data()
# Create a Model
model = Regression(data_e)
# Train and test the model
model.train()
print(f"------------------------------\nMean score: {model.score()}")
# Make a prediction
model.predict(data_e.x_test.iloc[0], data_e.y_test.iloc[0])