from lasso import LassoRegressor
from knn import KnnRegressor
from sklearn import preprocessing, metrics
from util import Util
if __name__ == '__main__':
# min/max norm'ed
normalized_data_path = "cleaner/data/cleaned.json"
# no normalization
data_path = "cleaner/data/cleaned_no_normal.json"
# min/max X,y
X_mm, y_mm = Util.load_json_to_numpy_array(normalized_data_path)
# X, y
X, y = Util.load_json_to_numpy_array(data_path)
# scaled X, y
X_s = preprocessing.scale(X)
# Run experiments on scaled X,y
print("-------------Evaluate with Unnormalized Data-------------")
knn = KnnRegressor(X, y)
l = LassoRegressor(X, y)
knn.evaluate()
l.evaluate()
print("-------------DONE-------------")
# Run experiments on scaled X,y
print("-------------Evaluate with Min/Max Normalized Data-------------")
knn_mm = KnnRegressor(X_mm, y)
