def test_remove_low_information_features(test_es, feature_matrix):
features = [Feature(v) for v in test_es['test'].variables]
feature_matrix, features = remove_low_information_features(
feature_matrix, features)
assert feature_matrix.shape == (3, 5)
assert len(features) == 5
for f in features:
assert f.get_name() in feature_matrix.columns
assert 'one_value' not in feature_matrix.columns
assert 'all_null' not in feature_matrix.columns
def test_remove_low_information_features(test_es, feature_matrix):
features = [
Feature(test_es["test"].ww[col]) for col in test_es["test"].columns
]
feature_matrix, features = ft.selection.remove_low_information_features(
feature_matrix, features)
assert feature_matrix.shape == (3, 5)
assert len(features) == 5
for f in features:
assert f.get_name() in feature_matrix.columns
assert "one_value" not in feature_matrix.columns
assert "all_null" not in feature_matrix.columns
plays_df = pd.read_csv("game_plays.csv")
plays_df = plays_df.drop(
['secondaryType', 'periodType', 'dateTime', 'rink_side'], axis=1).fillna(0)
import featuretools as ft
from featuretools import Feature
es = ft.EntitySet(id="plays")
es = es.entity_from_dataframe(entity_id="plays",
dataframe=plays_df,
index="play_id",
variable_types={
"event": ft.variable_types.Categorical,
"description": ft.variable_types.Categorical
})
f1 = Feature(es["plays"]["event"])
f2 = Feature(es["plays"]["description"])
encoded, defs = ft.encode_features(plays_df, [f1, f2], top_n=10)
encoded.reset_index(inplace=True)
encoded.head()
es = ft.EntitySet(id="plays")
es = es.entity_from_dataframe(entity_id="plays",
dataframe=encoded,
index="play_id")
es = es.normalize_entity(base_entity_id="plays",
new_entity_id="games",
index="game_id")
features, transform = ft.dfs(entityset=es, target_entity="games", max_depth=2)
features.reset_index(inplace=True)
features.head()