WIP: Cross-validation support for multiple covariate matching #64
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| def stratified_crossvalidate( | ||
| covariate_df, | ||
| pipeline_estimator=None, | ||
| cv_splitter=StratifiedShuffleSplit(n_splits=1, test_size=0.25), |
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Might set this to None, and initialize below. Just to be on the safe side.
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| # Check covariate_df is a dataframe | ||
| if not isinstance(covariate_df, pd.DataFrame): | ||
| raise TypeError |
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| raise TypeError | |
| raise TypeError("`covariate_df` input needs to be a DataFrame object") |
Is that crucial, by the way? Why not take arrays?
| if not isinstance(covariate_df, pd.DataFrame): | ||
| raise TypeError | ||
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| X = covariate_df.to_numpy() |
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I guess you can check if it's a dataframe, and if not, skip this line
| if pipeline_estimator is None: | ||
| pipeline_estimator = make_pipeline( | ||
| KNeighborsTransformer(n_neighbors=10, mode="distance"), | ||
| Isomap(n_components=5, neighbors_algorithm="auto"), |
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I might expose n_neighbors and n_components as key-word arguments for the whole function, set to these values as default.
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Maybe not. If they want to customize, just pass your own pipeline_estimator
| ) | ||
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| X_reduced = pipeline_estimator.fit_transform(X) | ||
| cluster_stratify = KMeans(n_clusters=5).fit_predict(X_reduced) |
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Same here : expose n_clusters
| for foldno, (train_index, test_index) in enumerate( | ||
| cv_splitter.split(X, cluster_stratify) | ||
| ): | ||
| print("Fold No: {}".format(foldno)) |
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Maybe use logging instead of print?
| A scikit-learn object for splitting the dataset into | ||
| train/test set such that splits are stratified | ||
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| Yields |
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I think it Returns these, not Yields
| print(train_clusters) | ||
| print(train_freq) | ||
| print(test_clusters) | ||
| print(test_freq) |
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@mnarayan : any progress on this one? Looks like the build failures are just some minor linting. Would you mind rebasing and adding tests? |
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I believe this was superseded by #107 |
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