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)
print(preditions)
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))