Generate additional meta-features derived from pairwise differences and ratios of most important features (helpful for tree-based models).
This utility package imports numpy, pandas, matplotlib and a helper kg module into the root namespace.
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from pygoose import *
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import itertools
Automatically discover the paths to various data folders and compose the project structure.
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project = kg.Project.discover()
Identifier for storing these features on disk and referring to them later.
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feature_list_id = 'meta_pairwise_interactions'
Feature lists containing the most important features.
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feature_lists = [
# Place feature lists here.
]
Names of the features to use for pairwise interactions.
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most_important_features = [
# Place features here.
]
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df_train, df_test, _ = project.load_feature_lists(feature_lists)
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feature_names = []
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for a, b in itertools.combinations(most_important_features, 2):
diff_feature_name = f'diff_{a}_{b}'
df_train[diff_feature_name] = df_train[a] - df_train[b]
df_test[diff_feature_name] = df_test[a] - df_test[b]
feature_names.append(diff_feature_name)
ratio_feature_name = f'ratio_{a}_{b}'
df_train[ratio_feature_name] = df_train[a] / df_train[b]
df_test[ratio_feature_name] = df_test[a] / df_test[b]
feature_names.append(ratio_feature_name)
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features_to_keep = feature_names
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X_train = df_train[features_to_keep].values
X_test = df_test[features_to_keep].values
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project.save_features(X_train, X_test, feature_names, feature_list_id)