normal_equation_test.py

from dataset import *
from normal_equation import *

features = [
    'bias',
    'timedelta',
    'n_tokens_title',
    'n_tokens_content',
    'n_unique_tokens',
    'n_non_stop_words',
    'n_non_stop_unique_tokens',
    'num_hrefs',
    'num_self_hrefs',
    'num_imgs',
    'num_videos',
    'average_token_length',
    'num_keywords',
    'data_channel_is_lifestyle',
    'data_channel_is_entertainment',
    'data_channel_is_bus',
    'data_channel_is_socmed',
    'data_channel_is_tech',
    'data_channel_is_world',
    'kw_min_min',
    'kw_max_min',
    'kw_avg_min',
    'kw_min_max',
    'kw_max_max',
    'kw_avg_max',
    'kw_min_avg',
    'kw_max_avg',
    'kw_avg_avg',
    'self_reference_min_shares',
    'self_reference_max_shares',
    'self_reference_avg_sharess',
    'weekday_is_monday',
    'weekday_is_tuesday',
    'weekday_is_wednesday',
    'weekday_is_thursday',
    'weekday_is_friday',
    'weekday_is_saturday',
    'weekday_is_sunday',
    'is_weekend',
    'LDA_00',
    'LDA_01',
    'LDA_02',
    'LDA_03',
    'LDA_04',
    'global_subjectivity',
    'global_sentiment_polarity',
    'global_rate_positive_words',
    'global_rate_negative_words',
    'rate_positive_words',
    'rate_negative_words',
    'avg_positive_polarity',
    'min_positive_polarity',
    'max_positive_polarity',
    'avg_negative_polarity',
    'min_negative_polarity',
    'max_negative_polarity',
    'title_subjectivity',
    'title_sentiment_polarity',
    'abs_title_subjectivity',
    'abs_title_sentiment_polarity'
]
target = 'shares'

ds = Dataset('train_start.csv', features, target)
ds.load()

ne = NormalEquation(ds)
ne.calculate_optimal()
np.set_printoptions(suppress = True)
print(ne.theta)