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
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({
'beef': 5,
'pb': 5,
'mayo': 1,
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['beef']))],
'rating': [1, 1, 4, 0, 4, 8, 1, 0, 5, 0, 9, 0, 0, 0, 0, 0]
})
df = df.apply('rating', lambda x: 0.1 if x == 0 else x)
regressor = LogisticRegressor(df, prediction_column='rating', max_value=10)
# print("\n Testing multipliers")
# assert regressor.multipliers == {
# 'beef': -0.03900793,
# 'pb': -0.02047944,
# 'mayo': 1.74825378,
# 'jelly': -0.39777219,
# 'beef_pb': 0.14970983,
# 'beef_mayo': -0.74854916,
# 'beef_jelly': 0.46821312,
# 'pb_mayo': 0.32958369,
# 'pb_jelly': -0.5288267,
data_dict = {
'percentile': [95, 95, 92, 85, 80, 85, 95, 87, 99, 95],
'ACT': [33, 34, 35, 30, 36, 29, 36, 31, 36, 32],
'extracurricular': [1, 0, 1, 1, 1, 1, 1, 1, 0, 0],
'acceptance':
[0.999, 0.001, 0.999, 0.001, 0.999, 0.001, 0.999, 0.001, 0.999, 0.001]
}
df = DataFrame(data_dict,
column_order=['percentile', 'ACT', 'extracurricular'])
# print(df.ordered_dict)
df = df.append_pairwise_interactions()
# print(df.ordered_dict)
df = df.append_columns({
'constant': [1 for _ in range(len(data_dict['percentile']))],
'acceptance':
[0.999, 0.001, 0.999, 0.001, 0.999, 0.001, 0.999, 0.001, 0.999, 0.001]
})
# print(df.ordered_dict)
df = df.apply('acceptance', lambda x: 0.1 if x == 0 else x)
# print(df.ordered_dict)
regressor = LogisticRegressor(df, prediction_column='acceptance', max_value=1)
print(regressor.coefficients)
print(
"Martha: " +
str(regressor.predict({
'percentile': 95,
'ACT': 33,
'extracurricular': 1
})))
