def gather_all_inputs(self, input_dict):
col_order = [key for key in input_dict.keys()]
for key, data in input_dict.items():
input_dict.update({key: [data]})
data = DataFrame(input_dict, col_order)
data.append_pairwise_interactions()
data.append_columns({'constant': [1]})
self.current_input = data
return data.data_dict
from logistic_regressor import LogisticRegressor
from dataframe import DataFrame
data_dict = {
'beef': [0, 0, 0, 0, 5, 5, 5, 5, 0, 0, 0, 0, 5, 5, 5, 5],
'pb': [0, 0, 0, 0, 0, 0, 0, 0, 5, 5, 5, 5, 5, 5, 5, 5],
'condiments': [[], ['mayo'], ['jelly'], ['mayo', 'jelly'], [], ['mayo'],
['jelly'], ['mayo', 'jelly'], [], ['mayo'], ['jelly'],
['mayo', 'jelly'], [], ['mayo'], ['jelly'],
['mayo', 'jelly']],
'rating': [1, 1, 4, 0, 4, 8, 1, 0, 5, 0, 9, 0, 0, 0, 0, 0]
}
df = DataFrame(data_dict, column_order=['beef', 'pb', 'condiments'])
df = df.create_dummy_variables()
df = df.append_pairwise_interactions()
df = df.append_columns({
'constant': [1 for _ in range(len(data_dict['rating']))],
'rating': data_dict['rating']
})
df = df.apply('rating', lambda x: 0.1 if x == 0 else x)
regressor = LogisticRegressor(df, prediction_column='rating', max_val=10)
assert regressor.multipliers == [
-0.039, -0.0205, 1.7483, -0.3978, 0.1497, -0.7485, 0.4682, 0.3296, -0.5288,
2.6441, 1.0125
], 'Wong multipliers'
assert regressor.predict({
