import pandas as pd import numpy as np from preprocessors import Pipeline import config pipeline = Pipeline(target=config.TARGET, categorical_encode=config.CATEGORICAL_ENCODE, random_state=42) if __name__ == '__main__': # load data set data = pd.read_csv(config.PATH_TO_DATASET) pipeline.fit(data) print('model performance') pipeline.evaluate_model() print() print('Some predictions:') preditions = pipeline.predict(data) print(preditions)
import pandas as pd import numpy as np from preprocessors import Pipeline import config pipeline = Pipeline(target = config.TARGET, categorical_to_impute = config.CATEGORICAL_TO_IMPUTE, year_variable = config.YEAR_VARIABLE, numerical_to_impute = config.NUMERICAL_TO_IMPUTE, numerical_log = config.NUMERICAL_LOG, categorical_encode = config.CATEGORICAL_ENCODE, features = config.FEATURES ) if __name__ == '__main__': # load data set data = pd.read_csv(config.PATH_TO_DATASET) pipeline.fit(data) print('model performance') pipeline.evaluate_model() print() print('Some predictions:') preditions = pipeline.predict(data)
import pandas as pd import numpy as np from preprocessors import Pipeline import config pipeline = Pipeline(variables=config.VARIABLES, regional_eps=config.REGIONAL_EPS, regional_min_samples=config.REGIONAL_MIN_SAMPLES, regional_metric=config.REGIONAL_METRIC, local_eps=config.LOCAL_EPS, local_min_samples=config.LOCAL_MIN_SAMPLES, local_metric=config.LOCAL_METRIC) if __name__ == '__main__': # load data set data = pd.read_csv(config.PATH_TO_DATASET, sep=';') pipeline.fit(data) home_list, work_list = pipeline.predict() print('Likely home locations : {}'.format(home_list)) print('Likely work locations : {}'.format(work_list))