b1de080823e41b921bec2949db2b6c3cb1f1d5ef,examples/plot_feature_rep.py,,,#,30
Before Change
y = data["y"]
// create a feature representation pipeline
feed = Pipeline([("segment", Segment()),
("features", SegFeatures(features = base_features()))])
est = Pipeline([("scaler", StandardScaler()),
("rf", RandomForestClassifier())])
pipe = SegPipe(feed, est)
After Change
print("CV Scores: ", pd.DataFrame(cv_scores))
// lets see what feature we used
print("Features: ", pipe .est.steps[0][1].f_labels)
img = mpimg.imread("feet.jpg")
plt.imshow(img)In pattern: SUPERPATTERN
Frequency: 3
Non-data size: 3
Instances Project Name: dmbee/seglearn
Commit Name: b1de080823e41b921bec2949db2b6c3cb1f1d5ef
Time: 2018-03-11
Author: david.mo.burns@gmail.com
File Name: examples/plot_feature_rep.py
Class Name:
Method Name:
Project Name: scikit-learn/scikit-learn
Commit Name: a49752375d5775b1f0e6393a811c937332dccb18
Time: 2020-05-17
Author: jliu176@gmail.com
File Name: examples/compose/plot_column_transformer.py
Class Name:
Method Name:
Project Name: stanfordnlp/stanza
Commit Name: 04382986f977de4f7bc84bf70ef62915ed0ef2cf
Time: 2019-01-23
Author: jebolton@stanford.edu
File Name: stanfordnlp/run_pipeline.py
Class Name:
Method Name:
