Keep non-feature columns when encoding feature matrix #111
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@@ Coverage Diff @@
## master #111 +/- ##
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+ Coverage 88.24% 88.3% +0.05%
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Files 73 73
Lines 7412 7447 +35
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+ Hits 6541 6576 +35
Misses 871 871
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kmax12
reviewed
Mar 14, 2018
| @@ -65,6 +65,8 @@ def encode_features(feature_matrix, features, top_n=10, include_unknown=True, | |||
| X = feature_matrix.copy() | |||
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| encoded = [] | |||
| feature_names = [f.get_name() for f in features] | |||
| extra_columns = [col for col in X.columns if col not in feature_names] | |||
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to make this safer, can we add in a check that all the features you pass in are columns in the feature matrix? with this change, we might silence issues where the feature matrix and features don't match
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looks good to me. merging in. |
Merged
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#78 added the option of including non-feature columns with the calculated feature matrix, but
encode_featureswas not including them when creating an encoded feature matrix. This PR fixesencode_featuresso it includes non-feature columns in the encoded feature matrix.