[WIP] Add a script to check discrepancies between function parameter list and function docstring parameters #7793

neerajgangwar · 2016-10-30T15:38:21Z

Reference Issue

What does this implement/fix? Explain your changes.

Add a script to check if there is a discrepancy between function parameter list and parameter list specified in function docstring

Any other comments?

Requires numpydoc package

jnothman · 2016-10-31T11:21:32Z

If we are to consider including this, we would have to do one of:

move numpydoc from doc/sphinxext/numpy_ext to sklearn/externals
run these checks and log errors at documentation compile time, rather than at main testing time
run these checks and fail on errors as a separate process that includes a numpydoc dependency

Please choose one. Thanks!

jnothman · 2016-10-31T11:25:49Z

Feature request: list args present in docstring but not in signature separately from the reverse case.

jnothman · 2016-10-31T11:27:33Z

(And what I'd really like to see in this vein is a list of attributes ending _ added by fit but undocumented. Not in scope for this PR.)

… the opposite case

neerajgangwar · 2016-11-01T21:15:53Z

@jnothman Made changes to list args present in docstring but not in signature from the opposite case.

Regarding including the script, how do we go about running this as a separate process that includes numpydoc dependency?

jnothman · 2016-11-01T22:55:48Z

Sorry for the unclarity, I meant as a separate Travis run, for instance. We first should see whether we can / should fix these.

jnothman · 2016-11-01T22:57:17Z

FWIW, here are the current failures where numpydoc is available:


sklearn.__check_build.raise_build_error => params present in signature but not in docstring: e
sklearn.base.fit_predict => params present in signature but not in docstring: y
sklearn.base.get_indices => params present in signature but not in docstring: i
sklearn.base.get_shape => params present in signature but not in docstring: i
sklearn.base.get_submatrix => params present in signature but not in docstring: data, i
sklearn.cluster.affinity_propagation_.fit => params present in signature but not in docstring: y
sklearn.cluster.birch.fit => params present in signature but not in docstring: y
sklearn.cluster.birch.partial_fit => params present in signature but not in docstring: y
sklearn.cluster.birch.transform => params present in signature but not in docstring: y
sklearn.cluster.dbscan_.fit => params present in signature but not in docstring: y
sklearn.cluster.dbscan_.fit_predict => params present in signature but not in docstring: y
sklearn.cluster.hierarchical.fit => params present in signature but not in docstring: y
sklearn.cluster.hierarchical.linkage_tree => params present in signature but not in docstring: n_components
sklearn.cluster.k_means_.fit => params present in signature but not in docstring: y
sklearn.cluster.k_means_.fit_predict => params present in signature but not in docstring: X, y
sklearn.cluster.k_means_.fit_transform => params present in signature but not in docstring: X, y
sklearn.cluster.k_means_.partial_fit => params present in signature but not in docstring: y
sklearn.cluster.k_means_.score => params present in signature but not in docstring: y
sklearn.cluster.k_means_.transform => params present in signature but not in docstring: y
sklearn.cluster.mean_shift_.fit => params present in signature but not in docstring: y
sklearn.cluster.spectral.fit => params present in signature but not in docstring: y
sklearn.covariance.graph_lasso_.fit => params present in signature but not in docstring: y
sklearn.covariance.outlier_detection.__init__ => params present in signature but not in docstring: random_state
sklearn.cross_decomposition.pls_.fit => params present in signature but not in docstring: X, Y
sklearn.cross_decomposition.pls_.fit_transform => params present in docstring but not in signature: Y
sklearn.cross_decomposition.pls_.fit_transform => params present in docstring but not in signature: Y, copy
sklearn.cross_decomposition.pls_.fit_transform => params present in signature but not in docstring: y
sklearn.cross_decomposition.pls_.transform => params present in signature but not in docstring: X, Y
sklearn.datasets.base.clear_data_home => params present in signature but not in docstring: data_home
sklearn.datasets.base.get_data_home => params present in signature but not in docstring: data_home
sklearn.datasets.mlcomp.load_mlcomp => params present in docstring but not in signature: Read more in the
sklearn.datasets.mldata.mldata_filename => params present in signature but not in docstring: dataname
sklearn.datasets.samples_generator.make_circles => params present in signature but not in docstring: random_state
sklearn.datasets.samples_generator.make_moons => params present in signature but not in docstring: random_state
sklearn.decomposition.base.inverse_transform => params present in signature but not in docstring: y
sklearn.decomposition.base.transform => params present in signature but not in docstring: y
sklearn.decomposition.dict_learning.fit => params present in signature but not in docstring: X, y
sklearn.decomposition.dict_learning.fit => params present in signature but not in docstring: y
sklearn.decomposition.dict_learning.partial_fit => params present in signature but not in docstring: y
sklearn.decomposition.dict_learning.transform => params present in signature but not in docstring: y
sklearn.decomposition.factor_analysis.fit => params present in signature but not in docstring: y
sklearn.decomposition.factor_analysis.score => params present in signature but not in docstring: y
sklearn.decomposition.fastica_.fit => params present in signature but not in docstring: y
sklearn.decomposition.fastica_.fit_transform => params present in signature but not in docstring: y
sklearn.decomposition.fastica_.transform => params present in signature but not in docstring: y
sklearn.decomposition.incremental_pca.partial_fit => params present in signature but not in docstring: check_input, y
sklearn.decomposition.kernel_pca.fit => params present in signature but not in docstring: y
sklearn.decomposition.kernel_pca.fit_transform => params present in signature but not in docstring: y
sklearn.decomposition.nmf.__init__ => params present in signature but not in docstring: verbose
sklearn.decomposition.nmf.fit => params present in signature but not in docstring: y
sklearn.decomposition.nmf.fit_transform => params present in signature but not in docstring: y
sklearn.decomposition.online_lda.fit => params present in signature but not in docstring: y
sklearn.decomposition.online_lda.partial_fit => params present in signature but not in docstring: y
sklearn.decomposition.online_lda.perplexity => params present in signature but not in docstring: sub_sampling
sklearn.decomposition.online_lda.score => params present in signature but not in docstring: y
sklearn.decomposition.pca.fit => params present in signature but not in docstring: y
sklearn.decomposition.pca.fit_transform => params present in signature but not in docstring: y
sklearn.decomposition.pca.inverse_transform => params present in signature but not in docstring: y
sklearn.decomposition.pca.score => params present in signature but not in docstring: y
sklearn.decomposition.pca.transform => params present in signature but not in docstring: y
sklearn.decomposition.sparse_pca.fit => params present in signature but not in docstring: y
sklearn.decomposition.truncated_svd.fit => params present in signature but not in docstring: y
sklearn.decomposition.truncated_svd.fit_transform => params present in signature but not in docstring: y
sklearn.ensemble.bagging.decision_function => params present in docstring but not in signature: X
sklearn.ensemble.forest.fit => params present in signature but not in docstring: sample_weight, y
sklearn.ensemble.forest.fit_transform => params present in signature but not in docstring: sample_weight, y
sklearn.ensemble.iforest.fit => params present in signature but not in docstring: sample_weight, y
sklearn.ensemble.voting_classifier.get_params => params present in signature but not in docstring: deep
sklearn.externals.joblib.hashing.hash => params present in signature but not in docstring: obj
sklearn.externals.joblib.logger.__call__ => params present in signature but not in docstring: msg, total
sklearn.externals.joblib.logger.__init__ => params present in signature but not in docstring: depth
sklearn.externals.joblib.logger.__init__ => params present in signature but not in docstring: logdir, logfile
sklearn.externals.joblib.logger.debug => params present in signature but not in docstring: msg
sklearn.externals.joblib.logger.format => params present in signature but not in docstring: indent, obj
sklearn.externals.joblib.logger.warn => params present in signature but not in docstring: msg
sklearn.externals.joblib.memory.__init__ => params present in signature but not in docstring: argument_hash, cachedir, func, metadata, mmap_mode, timestamp, verbose
sklearn.externals.joblib.memory.__init__ => params present in signature but not in docstring: cachedir, compress, mmap_mode, verbose
sklearn.externals.joblib.memory.clear => params present in signature but not in docstring: warn
sklearn.externals.joblib.memory.eval => params present in signature but not in docstring: func
sklearn.externals.joblib.parallel.__call__ => params present in signature but not in docstring: iterable
sklearn.externals.joblib.parallel.__init__ => params present in docstring but not in signature: max_nbytes int, str, or None, optional, 1M by default
sklearn.externals.joblib.parallel.__init__ => params present in signature but not in docstring: max_nbytes
sklearn.externals.joblib.parallel.delayed => params present in signature but not in docstring: check_pickle, function
sklearn.externals.joblib.parallel.dispatch_one_batch => params present in signature but not in docstring: iterator
sklearn.externals.joblib.parallel.effective_n_jobs => params present in signature but not in docstring: n_jobs
sklearn.externals.joblib.parallel.register_parallel_backend => params present in signature but not in docstring: factory, make_default, name
sklearn.feature_extraction.hashing.fit => params present in signature but not in docstring: X, y
sklearn.feature_selection.rfe.predict => params present in docstring but not in signature: X
sklearn.feature_selection.rfe.score => params present in docstring but not in signature: X, y
sklearn.feature_selection.univariate_selection.f_oneway => params present in docstring but not in signature: sample1, sample2, ...
sklearn.linear_model.coordinate_descent.enet_path => params present in docstring but not in signature: params
sklearn.linear_model.coordinate_descent.lasso_path => params present in docstring but not in signature: params
sklearn.linear_model.least_angle.lars_path => params present in signature but not in docstring: Xy
sklearn.linear_model.omp.__init__ => params present in docstring but not in signature: Read more in the
sklearn.linear_model.randomized_l1.get_support => params present in signature but not in docstring: indices
sklearn.linear_model.randomized_l1.inverse_transform => params present in signature but not in docstring: X
sklearn.linear_model.randomized_l1.transform => params present in signature but not in docstring: X
sklearn.manifold.isomap.fit => params present in signature but not in docstring: y
sklearn.manifold.isomap.fit_transform => params present in signature but not in docstring: y
sklearn.manifold.locally_linear.fit => params present in signature but not in docstring: y
sklearn.manifold.locally_linear.fit_transform => params present in signature but not in docstring: y
sklearn.manifold.mds.fit => params present in signature but not in docstring: y
sklearn.manifold.mds.fit_transform => params present in signature but not in docstring: y
sklearn.manifold.spectral_embedding_.fit => params present in signature but not in docstring: y
sklearn.manifold.spectral_embedding_.fit_transform => params present in signature but not in docstring: y
sklearn.manifold.t_sne.fit => params present in signature but not in docstring: y
sklearn.manifold.t_sne.fit_transform => params present in signature but not in docstring: y
sklearn.metrics.cluster.supervised.entropy => params present in signature but not in docstring: labels
sklearn.metrics.cluster.supervised.fowlkes_mallows_score => params present in signature but not in docstring: sparse
sklearn.metrics.pairwise.pairwise_distances_argmin_min => params present in docstring but not in signature: X, Y
sklearn.metrics.pairwise.pairwise_distances_argmin_min => params present in signature but not in docstring: X, Y
sklearn.metrics.scorer.get_scorer => params present in signature but not in docstring: scoring
sklearn.mixture.base.fit => params present in signature but not in docstring: y
sklearn.mixture.base.predict => params present in signature but not in docstring: y
sklearn.mixture.base.score => params present in signature but not in docstring: y
sklearn.mixture.dpgmm.lower_bound => params present in signature but not in docstring: X, z
sklearn.mixture.gmm.fit => params present in signature but not in docstring: y
sklearn.mixture.gmm.fit_predict => params present in signature but not in docstring: y
sklearn.mixture.gmm.sample => params present in signature but not in docstring: random_state
sklearn.mixture.gmm.score => params present in signature but not in docstring: y
sklearn.model_selection._search.decision_function => params present in docstring but not in signature: X
sklearn.model_selection._search.inverse_transform => params present in docstring but not in signature: Xt
sklearn.model_selection._search.predict => params present in docstring but not in signature: X
sklearn.model_selection._search.predict_log_proba => params present in docstring but not in signature: X
sklearn.model_selection._search.predict_proba => params present in docstring but not in signature: X
sklearn.model_selection._search.transform => params present in docstring but not in signature: X
sklearn.model_selection._split.__init__ => params present in signature but not in docstring: test_fold
sklearn.model_selection._split.get_n_splits => params present in signature but not in docstring: X, groups, y
sklearn.model_selection._split.train_test_split => params present in docstring but not in signature: random_state, stratify, test_size, train_size
sklearn.model_selection._validation.learning_curve => params present in docstring but not in signature: -------, test_scores, train_scores, train_sizes_abs
sklearn.neighbors.approximate.fit => params present in signature but not in docstring: y
sklearn.neighbors.approximate.partial_fit => params present in signature but not in docstring: y
sklearn.neighbors.base.fit => params present in signature but not in docstring: y
sklearn.neighbors.kde.fit => params present in signature but not in docstring: y
sklearn.neighbors.kde.score => params present in signature but not in docstring: y
sklearn.neighbors.lof.fit => params present in signature but not in docstring: y
sklearn.neighbors.lof.fit_predict => params present in signature but not in docstring: y
sklearn.neural_network.rbm.fit => params present in signature but not in docstring: y
sklearn.neural_network.rbm.partial_fit => params present in signature but not in docstring: y
sklearn.preprocessing._function_transformer.fit => params present in signature but not in docstring: X, y
sklearn.preprocessing._function_transformer.inverse_transform => params present in signature but not in docstring: X, y
sklearn.preprocessing._function_transformer.transform => params present in signature but not in docstring: X, y
sklearn.preprocessing.data.fit => params present in signature but not in docstring: X, y
sklearn.preprocessing.data.fit => params present in signature but not in docstring: y
sklearn.preprocessing.data.fit_transform => params present in signature but not in docstring: X, y
sklearn.preprocessing.data.inverse_transform => params present in signature but not in docstring: copy
sklearn.preprocessing.data.maxabs_scale => params present in signature but not in docstring: X
sklearn.preprocessing.data.minmax_scale => params present in signature but not in docstring: X
sklearn.preprocessing.data.transform => params present in signature but not in docstring: copy, y
sklearn.preprocessing.data.transform => params present in signature but not in docstring: y
sklearn.preprocessing.imputation.fit => params present in signature but not in docstring: y
sklearn.semi_supervised.label_propagation.__init__ => params present in signature but not in docstring: n_jobs
sklearn.svm.classes.fit => params present in signature but not in docstring: y
sklearn.tree.tree.__init__ => params present in signature but not in docstring: class_weight, criterion, max_depth, max_features, max_leaf_nodes, min_impurity_split, min_samples_leaf, min_samples_split, min_weight_fraction_leaf, random_state, splitter
sklearn.tree.tree.__init__ => params present in signature but not in docstring: criterion, max_depth, max_features, max_leaf_nodes, min_impurity_split, min_samples_leaf, min_samples_split, min_weight_fraction_leaf, random_state, splitter
sklearn.tree.tree.predict_proba => params present in signature but not in docstring: check_input
sklearn.utils.axis0_safe_slice => params present in signature but not in docstring: X, len_mask, mask
sklearn.utils.gen_batches => params present in signature but not in docstring: batch_size, n
sklearn.utils.gen_even_slices => params present in signature but not in docstring: n, n_packs, n_samples
sklearn.utils.resample => params present in docstring but not in signature: n_samples, random_state, replace
sklearn.utils.shuffle => params present in docstring but not in signature: n_samples, random_state
sklearn.utils.tosequence => params present in signature but not in docstring: x
sklearn.utils.validation.assert_all_finite => params present in signature but not in docstring: X
sklearn.utils.validation.check_random_state => params present in signature but not in docstring: seed

jnothman · 2016-11-01T23:20:29Z

The error messages are currently missing class names where method docstrings are the problem.

jnothman · 2016-11-01T23:26:31Z

What would be really cool: If instead of just listing the errors, this could (have a mode to) actually write an initial diff file that patches the errors (adding a blank entry in approximately the right place for undocumented params; dropping the entire param description where the docstring has excess). This would make inspecting and fixing the issues so much easier.

Btw, we should not be checking anything in externals (although it looks like Joblib needs this kind of quality assurance).

GaelVaroquaux · 2016-11-02T07:10:19Z

• move numpydoc from doc/sphinxext/numpy_ext to sklearn/externals

That's probably the right solution.

neerajgangwar · 2016-11-03T04:01:01Z

@jnothman If I run the script from doc/sphinxext/numpy_ext, the class names are present in errors. How are you running the script?

sklearn.__check_build.raise_build_error => params present in signature but not in docstring: e
sklearn.base.AffinityPropagation.fit_predict => params present in signature but not in docstring: y
sklearn.base.AgglomerativeClustering.fit_predict => params present in signature but not in docstring: y
sklearn.base.Birch.fit_predict => params present in signature but not in docstring: y
sklearn.base.MeanShift.fit_predict => params present in signature but not in docstring: y
sklearn.base.SpectralBiclustering.get_indices => params present in signature but not in docstring: i
sklearn.base.SpectralBiclustering.get_shape => params present in signature but not in docstring: i
sklearn.base.SpectralBiclustering.get_submatrix => params present in signature but not in docstring: data, i
sklearn.base.SpectralClustering.fit_predict => params present in signature but not in docstring: y
sklearn.base.SpectralCoclustering.get_indices => params present in signature but not in docstring: i
sklearn.base.SpectralCoclustering.get_shape => params present in signature but not in docstring: i
sklearn.base.SpectralCoclustering.get_submatrix => params present in signature but not in docstring: data, i
sklearn.cluster.affinity_propagation_.AffinityPropagation.fit => params present in signature but not in docstring: y
sklearn.cluster.birch.Birch.fit => params present in signature but not in docstring: y
sklearn.cluster.birch.Birch.partial_fit => params present in signature but not in docstring: y
sklearn.cluster.birch.Birch.transform => params present in signature but not in docstring: y
sklearn.cluster.dbscan_.DBSCAN.__init__ => params present in signature but not in docstring: p
sklearn.cluster.dbscan_.DBSCAN.fit => params present in signature but not in docstring: y
sklearn.cluster.dbscan_.DBSCAN.fit_predict => params present in signature but not in docstring: y
sklearn.cluster.hierarchical.AgglomerativeClustering.fit => params present in signature but not in docstring: y
sklearn.cluster.hierarchical.FeatureAgglomeration.fit => params present in signature but not in docstring: y
sklearn.cluster.k_means_.KMeans.fit => params present in signature but not in docstring: y
sklearn.cluster.k_means_.KMeans.fit_predict => params present in signature but not in docstring: X, y
sklearn.cluster.k_means_.KMeans.fit_transform => params present in signature but not in docstring: X, y
sklearn.cluster.k_means_.KMeans.score => params present in signature but not in docstring: y
sklearn.cluster.k_means_.KMeans.transform => params present in signature but not in docstring: y
sklearn.cluster.k_means_.MiniBatchKMeans.fit => params present in signature but not in docstring: y
sklearn.cluster.k_means_.MiniBatchKMeans.fit_predict => params present in signature but not in docstring: X, y
sklearn.cluster.k_means_.MiniBatchKMeans.fit_transform => params present in signature but not in docstring: X, y
sklearn.cluster.k_means_.MiniBatchKMeans.partial_fit => params present in signature but not in docstring: y
sklearn.cluster.k_means_.MiniBatchKMeans.score => params present in signature but not in docstring: y
sklearn.cluster.k_means_.MiniBatchKMeans.transform => params present in signature but not in docstring: y
sklearn.cluster.mean_shift_.MeanShift.fit => params present in signature but not in docstring: y
sklearn.cluster.mean_shift_.mean_shift => params present in signature but not in docstring: max_iterations
sklearn.cluster.spectral.SpectralClustering.fit => params present in signature but not in docstring: y
sklearn.covariance.graph_lasso_.GraphLasso.fit => params present in signature but not in docstring: X, y
sklearn.covariance.graph_lasso_.GraphLassoCV.fit => params present in signature but not in docstring: y
sklearn.covariance.outlier_detection.EllipticEnvelope.__init__ => params present in signature but not in docstring: random_state
sklearn.covariance.outlier_detection.EllipticEnvelope.fit => params present in signature but not in docstring: X, y
sklearn.cross_decomposition.pls_.CCA.fit_transform => params present in docstring but not in signature: Y, copy
sklearn.cross_decomposition.pls_.CCA.fit_transform => params present in signature but not in docstring: y
sklearn.cross_decomposition.pls_.PLSCanonical.fit_transform => params present in docstring but not in signature: Y, copy
sklearn.cross_decomposition.pls_.PLSCanonical.fit_transform => params present in signature but not in docstring: y
sklearn.cross_decomposition.pls_.PLSRegression.fit_transform => params present in docstring but not in signature: Y, copy
sklearn.cross_decomposition.pls_.PLSRegression.fit_transform => params present in signature but not in docstring: y
sklearn.cross_decomposition.pls_.PLSSVD.fit => params present in signature but not in docstring: X, Y
sklearn.cross_decomposition.pls_.PLSSVD.fit_transform => params present in docstring but not in signature: Y
sklearn.cross_decomposition.pls_.PLSSVD.fit_transform => params present in signature but not in docstring: y
sklearn.cross_decomposition.pls_.PLSSVD.transform => params present in signature but not in docstring: X, Y
sklearn.datasets.base.clear_data_home => params present in signature but not in docstring: data_home
sklearn.datasets.base.get_data_home => params present in signature but not in docstring: data_home
sklearn.datasets.mlcomp.load_mlcomp => params present in docstring but not in signature: Read more in the
sklearn.datasets.mldata.mldata_filename => params present in signature but not in docstring: dataname
sklearn.datasets.samples_generator.make_circles => params present in signature but not in docstring: random_state
sklearn.datasets.samples_generator.make_moons => params present in docstring but not in signature: Read more in the
sklearn.datasets.samples_generator.make_moons => params present in signature but not in docstring: random_state
sklearn.decomposition.base.IncrementalPCA.inverse_transform => params present in signature but not in docstring: y
sklearn.decomposition.base.IncrementalPCA.transform => params present in signature but not in docstring: y
sklearn.decomposition.dict_learning.DictionaryLearning.fit => params present in signature but not in docstring: y
sklearn.decomposition.dict_learning.DictionaryLearning.transform => params present in signature but not in docstring: y
sklearn.decomposition.dict_learning.MiniBatchDictionaryLearning.fit => params present in signature but not in docstring: y
sklearn.decomposition.dict_learning.MiniBatchDictionaryLearning.partial_fit => params present in signature but not in docstring: y
sklearn.decomposition.dict_learning.MiniBatchDictionaryLearning.transform => params present in signature but not in docstring: y
sklearn.decomposition.dict_learning.SparseCoder.fit => params present in signature but not in docstring: X, y
sklearn.decomposition.dict_learning.SparseCoder.transform => params present in signature but not in docstring: y
sklearn.decomposition.factor_analysis.FactorAnalysis.fit => params present in signature but not in docstring: y
sklearn.decomposition.factor_analysis.FactorAnalysis.score => params present in signature but not in docstring: y
sklearn.decomposition.fastica_.FastICA.fit => params present in signature but not in docstring: y
sklearn.decomposition.fastica_.FastICA.fit_transform => params present in signature but not in docstring: y
sklearn.decomposition.fastica_.FastICA.transform => params present in signature but not in docstring: y
sklearn.decomposition.incremental_pca.IncrementalPCA.partial_fit => params present in signature but not in docstring: check_input, y
sklearn.decomposition.kernel_pca.KernelPCA.fit => params present in signature but not in docstring: y
sklearn.decomposition.kernel_pca.KernelPCA.fit_transform => params present in signature but not in docstring: y
sklearn.decomposition.nmf.NMF.__init__ => params present in signature but not in docstring: verbose
sklearn.decomposition.nmf.NMF.fit => params present in signature but not in docstring: y
sklearn.decomposition.nmf.NMF.fit_transform => params present in signature but not in docstring: y
sklearn.decomposition.nmf.ProjectedGradientNMF.fit => params present in signature but not in docstring: y
sklearn.decomposition.nmf.ProjectedGradientNMF.fit_transform => params present in signature but not in docstring: y
sklearn.decomposition.online_lda.LatentDirichletAllocation.fit => params present in signature but not in docstring: y
sklearn.decomposition.online_lda.LatentDirichletAllocation.partial_fit => params present in signature but not in docstring: y
sklearn.decomposition.online_lda.LatentDirichletAllocation.perplexity => params present in signature but not in docstring: sub_sampling
sklearn.decomposition.online_lda.LatentDirichletAllocation.score => params present in signature but not in docstring: y
sklearn.decomposition.pca.PCA.fit => params present in signature but not in docstring: y
sklearn.decomposition.pca.PCA.fit_transform => params present in signature but not in docstring: y
sklearn.decomposition.pca.PCA.score => params present in signature but not in docstring: y
sklearn.decomposition.pca.RandomizedPCA.fit => params present in signature but not in docstring: y
sklearn.decomposition.pca.RandomizedPCA.fit_transform => params present in signature but not in docstring: y
sklearn.decomposition.pca.RandomizedPCA.inverse_transform => params present in signature but not in docstring: y
sklearn.decomposition.pca.RandomizedPCA.transform => params present in signature but not in docstring: y
sklearn.decomposition.sparse_pca.MiniBatchSparsePCA.fit => params present in signature but not in docstring: y
sklearn.decomposition.sparse_pca.SparsePCA.fit => params present in signature but not in docstring: y
sklearn.decomposition.truncated_svd.TruncatedSVD.fit => params present in signature but not in docstring: y
sklearn.decomposition.truncated_svd.TruncatedSVD.fit_transform => params present in signature but not in docstring: y
sklearn.ensemble.bagging.decision_function => params present in docstring but not in signature: X
sklearn.ensemble.forest.RandomTreesEmbedding.fit => params present in signature but not in docstring: sample_weight, y
sklearn.ensemble.forest.RandomTreesEmbedding.fit_transform => params present in signature but not in docstring: sample_weight, y
sklearn.ensemble.voting_classifier.VotingClassifier.get_params => params present in signature but not in docstring: deep
sklearn.externals.joblib.hashing.hash => params present in signature but not in docstring: obj
sklearn.externals.joblib.logger.Logger.__init__ => params present in signature but not in docstring: depth
sklearn.externals.joblib.logger.Logger.debug => params present in signature but not in docstring: msg
sklearn.externals.joblib.logger.Logger.format => params present in signature but not in docstring: indent, obj
sklearn.externals.joblib.logger.Logger.warn => params present in signature but not in docstring: msg
sklearn.externals.joblib.logger.MemorizedResult.debug => params present in signature but not in docstring: msg
sklearn.externals.joblib.logger.MemorizedResult.format => params present in signature but not in docstring: indent, obj
sklearn.externals.joblib.logger.MemorizedResult.warn => params present in signature but not in docstring: msg
sklearn.externals.joblib.logger.Memory.debug => params present in signature but not in docstring: msg
sklearn.externals.joblib.logger.Memory.format => params present in signature but not in docstring: indent, obj
sklearn.externals.joblib.logger.Memory.warn => params present in signature but not in docstring: msg
sklearn.externals.joblib.logger.Parallel.debug => params present in signature but not in docstring: msg
sklearn.externals.joblib.logger.Parallel.format => params present in signature but not in docstring: indent, obj
sklearn.externals.joblib.logger.Parallel.warn => params present in signature but not in docstring: msg
sklearn.externals.joblib.logger.PrintTime.__call__ => params present in signature but not in docstring: msg, total
sklearn.externals.joblib.logger.PrintTime.__init__ => params present in signature but not in docstring: logdir, logfile
sklearn.externals.joblib.memory.MemorizedResult.__init__ => params present in signature but not in docstring: argument_hash, cachedir, func, metadata, mmap_mode, timestamp, verbose
sklearn.externals.joblib.memory.Memory.__init__ => params present in signature but not in docstring: cachedir, compress, mmap_mode, verbose
sklearn.externals.joblib.memory.Memory.clear => params present in signature but not in docstring: warn
sklearn.externals.joblib.memory.Memory.eval => params present in signature but not in docstring: func
sklearn.externals.joblib.parallel.Parallel.__call__ => params present in signature but not in docstring: iterable
sklearn.externals.joblib.parallel.Parallel.__init__ => params present in docstring but not in signature: max_nbytes int, str, or None, optional, 1M by default
sklearn.externals.joblib.parallel.Parallel.__init__ => params present in signature but not in docstring: max_nbytes, mmap_mode
sklearn.externals.joblib.parallel.Parallel.dispatch_one_batch => params present in signature but not in docstring: iterator
sklearn.externals.joblib.parallel.delayed => params present in signature but not in docstring: check_pickle, function
sklearn.feature_extraction.hashing.FeatureHasher.fit => params present in signature but not in docstring: X, y
sklearn.feature_selection.rfe.predict => params present in docstring but not in signature: X
sklearn.feature_selection.rfe.score => params present in docstring but not in signature: X, y
sklearn.feature_selection.univariate_selection.f_oneway => params present in docstring but not in signature: sample1, sample2, ...
sklearn.linear_model.coordinate_descent.ElasticNet.fit => params present in signature but not in docstring: check_input
sklearn.linear_model.coordinate_descent.Lasso.fit => params present in signature but not in docstring: check_input
sklearn.linear_model.coordinate_descent.enet_path => params present in docstring but not in signature: params
sklearn.linear_model.coordinate_descent.lasso_path => params present in docstring but not in signature: params
sklearn.linear_model.least_angle.lars_path => params present in signature but not in docstring: Xy
sklearn.linear_model.omp.OrthogonalMatchingPursuit.__init__ => params present in docstring but not in signature: Read more in the
sklearn.linear_model.omp.OrthogonalMatchingPursuitCV.__init__ => params present in docstring but not in signature: Read more in the
sklearn.linear_model.randomized_l1.RandomizedLasso.get_support => params present in signature but not in docstring: indices
sklearn.linear_model.randomized_l1.RandomizedLasso.inverse_transform => params present in signature but not in docstring: X
sklearn.linear_model.randomized_l1.RandomizedLasso.transform => params present in signature but not in docstring: X
sklearn.linear_model.randomized_l1.RandomizedLogisticRegression.get_support => params present in signature but not in docstring: indices
sklearn.linear_model.randomized_l1.RandomizedLogisticRegression.inverse_transform => params present in signature but not in docstring: X
sklearn.linear_model.randomized_l1.RandomizedLogisticRegression.transform => params present in signature but not in docstring: X
sklearn.manifold.isomap.Isomap.fit => params present in signature but not in docstring: y
sklearn.manifold.isomap.Isomap.fit_transform => params present in signature but not in docstring: y
sklearn.manifold.locally_linear.LocallyLinearEmbedding.fit => params present in signature but not in docstring: y
sklearn.manifold.locally_linear.LocallyLinearEmbedding.fit_transform => params present in signature but not in docstring: y
sklearn.manifold.mds.MDS.fit => params present in signature but not in docstring: y
sklearn.manifold.mds.MDS.fit_transform => params present in signature but not in docstring: y
sklearn.manifold.spectral_embedding_.SpectralEmbedding.fit => params present in signature but not in docstring: y
sklearn.manifold.spectral_embedding_.SpectralEmbedding.fit_transform => params present in signature but not in docstring: y
sklearn.manifold.t_sne.TSNE.fit => params present in signature but not in docstring: y
sklearn.manifold.t_sne.TSNE.fit_transform => params present in signature but not in docstring: y
sklearn.metrics.cluster.supervised.entropy => params present in signature but not in docstring: labels
sklearn.metrics.pairwise.pairwise_distances_argmin_min => params present in docstring but not in signature: X, Y
sklearn.metrics.pairwise.pairwise_distances_argmin_min => params present in signature but not in docstring: X, Y
sklearn.metrics.scorer.get_scorer => params present in signature but not in docstring: scoring
sklearn.mixture.dpgmm.DPGMM.__init__ => params present in signature but not in docstring: min_covar, random_state, thresh
sklearn.mixture.dpgmm.DPGMM.lower_bound => params present in signature but not in docstring: X, z
sklearn.mixture.dpgmm.VBGMM.__init__ => params present in signature but not in docstring: min_covar, random_state, thresh
sklearn.mixture.dpgmm.VBGMM.lower_bound => params present in signature but not in docstring: X, z
sklearn.mixture.gmm.DPGMM.fit => params present in signature but not in docstring: y
sklearn.mixture.gmm.DPGMM.fit_predict => params present in signature but not in docstring: y
sklearn.mixture.gmm.DPGMM.sample => params present in signature but not in docstring: random_state
sklearn.mixture.gmm.DPGMM.score => params present in signature but not in docstring: y
sklearn.mixture.gmm.GMM.__init__ => params present in signature but not in docstring: thresh
sklearn.mixture.gmm.GMM.fit => params present in signature but not in docstring: y
sklearn.mixture.gmm.GMM.fit_predict => params present in signature but not in docstring: y
sklearn.mixture.gmm.GMM.sample => params present in signature but not in docstring: random_state
sklearn.mixture.gmm.GMM.score => params present in signature but not in docstring: y
sklearn.mixture.gmm.VBGMM.fit => params present in signature but not in docstring: y
sklearn.mixture.gmm.VBGMM.fit_predict => params present in signature but not in docstring: y
sklearn.mixture.gmm.VBGMM.sample => params present in signature but not in docstring: random_state
sklearn.mixture.gmm.VBGMM.score => params present in signature but not in docstring: y
sklearn.mixture.gmm.distribute_covar_matrix_to_match_covariance_type => params present in signature but not in docstring: covariance_type, n_components, tied_cv
sklearn.mixture.gmm.sample_gaussian => params present in signature but not in docstring: random_state
sklearn.neighbors.approximate.LSHForest.fit => params present in signature but not in docstring: y
sklearn.neighbors.approximate.LSHForest.partial_fit => params present in signature but not in docstring: y
sklearn.neighbors.base.NearestNeighbors.fit => params present in signature but not in docstring: y
sklearn.neighbors.kde.KernelDensity.fit => params present in signature but not in docstring: y
sklearn.neighbors.kde.KernelDensity.score => params present in signature but not in docstring: y
sklearn.neural_network.rbm.BernoulliRBM.fit => params present in signature but not in docstring: y
sklearn.neural_network.rbm.BernoulliRBM.partial_fit => params present in signature but not in docstring: y
sklearn.preprocessing._function_transformer.FunctionTransformer.fit => params present in signature but not in docstring: X, y
sklearn.preprocessing._function_transformer.FunctionTransformer.transform => params present in signature but not in docstring: X, y
sklearn.preprocessing.data.Binarizer.fit => params present in signature but not in docstring: X, y
sklearn.preprocessing.data.Binarizer.transform => params present in signature but not in docstring: copy, y
sklearn.preprocessing.data.KernelCenterer.fit => params present in signature but not in docstring: y
sklearn.preprocessing.data.KernelCenterer.transform => params present in signature but not in docstring: y
sklearn.preprocessing.data.MaxAbsScaler.fit => params present in signature but not in docstring: y
sklearn.preprocessing.data.MaxAbsScaler.transform => params present in signature but not in docstring: y
sklearn.preprocessing.data.MinMaxScaler.fit => params present in signature but not in docstring: y
sklearn.preprocessing.data.Normalizer.fit => params present in signature but not in docstring: X, y
sklearn.preprocessing.data.Normalizer.transform => params present in signature but not in docstring: copy, y
sklearn.preprocessing.data.OneHotEncoder.fit => params present in signature but not in docstring: y
sklearn.preprocessing.data.OneHotEncoder.fit_transform => params present in signature but not in docstring: X, y
sklearn.preprocessing.data.PolynomialFeatures.fit => params present in signature but not in docstring: X, y
sklearn.preprocessing.data.PolynomialFeatures.transform => params present in signature but not in docstring: y
sklearn.preprocessing.data.RobustScaler.fit => params present in signature but not in docstring: y
sklearn.preprocessing.data.RobustScaler.transform => params present in signature but not in docstring: y
sklearn.preprocessing.data.StandardScaler.inverse_transform => params present in signature but not in docstring: copy
sklearn.preprocessing.data.StandardScaler.transform => params present in signature but not in docstring: copy, y
sklearn.preprocessing.data.maxabs_scale => params present in signature but not in docstring: X
sklearn.preprocessing.data.minmax_scale => params present in signature but not in docstring: X
sklearn.preprocessing.imputation.Imputer.fit => params present in signature but not in docstring: y
sklearn.svm.classes.OneClassSVM.fit => params present in signature but not in docstring: y
sklearn.tree.tree.DecisionTreeClassifier.predict_proba => params present in signature but not in docstring: check_input
sklearn.tree.tree.ExtraTreeClassifier.__init__ => params present in signature but not in docstring: class_weight, criterion, max_depth, max_features, max_leaf_nodes, min_samples_leaf, min_samples_split, min_weight_fraction_leaf, random_state, splitter
sklearn.tree.tree.ExtraTreeClassifier.predict_proba => params present in signature but not in docstring: check_input
sklearn.tree.tree.ExtraTreeRegressor.__init__ => params present in signature but not in docstring: criterion, max_depth, max_features, max_leaf_nodes, min_samples_leaf, min_samples_split, min_weight_fraction_leaf, random_state, splitter
sklearn.utils.ARDRegression.decision_function => params present in docstring but not in signature: X
sklearn.utils.BayesianRidge.decision_function => params present in docstring but not in signature: X
sklearn.utils.DecisionTreeClassifier.transform => params present in docstring but not in signature: X
sklearn.utils.DecisionTreeRegressor.transform => params present in docstring but not in signature: X
sklearn.utils.ElasticNet.decision_function => params present in docstring but not in signature: X
sklearn.utils.ElasticNetCV.decision_function => params present in docstring but not in signature: X
sklearn.utils.ExtraTreeClassifier.transform => params present in docstring but not in signature: X
sklearn.utils.ExtraTreeRegressor.transform => params present in docstring but not in signature: X
sklearn.utils.ExtraTreesClassifier.transform => params present in docstring but not in signature: X
sklearn.utils.ExtraTreesRegressor.transform => params present in docstring but not in signature: X
sklearn.utils.GradientBoostingClassifier.transform => params present in docstring but not in signature: X
sklearn.utils.GradientBoostingRegressor.decision_function => params present in docstring but not in signature: X
sklearn.utils.GradientBoostingRegressor.staged_decision_function => params present in docstring but not in signature: X
sklearn.utils.GradientBoostingRegressor.transform => params present in docstring but not in signature: X
sklearn.utils.Lars.decision_function => params present in docstring but not in signature: X
sklearn.utils.LarsCV.decision_function => params present in docstring but not in signature: X
sklearn.utils.Lasso.decision_function => params present in docstring but not in signature: X
sklearn.utils.LassoCV.decision_function => params present in docstring but not in signature: X
sklearn.utils.LassoLars.decision_function => params present in docstring but not in signature: X
sklearn.utils.LassoLarsCV.decision_function => params present in docstring but not in signature: X
sklearn.utils.LassoLarsIC.decision_function => params present in docstring but not in signature: X
sklearn.utils.LinearRegression.decision_function => params present in docstring but not in signature: X
sklearn.utils.LinearSVC.transform => params present in docstring but not in signature: X
sklearn.utils.LinearSVR.decision_function => params present in docstring but not in signature: X
sklearn.utils.LogisticRegression.transform => params present in docstring but not in signature: X
sklearn.utils.LogisticRegressionCV.transform => params present in docstring but not in signature: X
sklearn.utils.MultiTaskElasticNet.decision_function => params present in docstring but not in signature: X
sklearn.utils.MultiTaskElasticNetCV.decision_function => params present in docstring but not in signature: X
sklearn.utils.MultiTaskLasso.decision_function => params present in docstring but not in signature: X
sklearn.utils.MultiTaskLassoCV.decision_function => params present in docstring but not in signature: X
sklearn.utils.NuSVR.decision_function => params present in docstring but not in signature: X
sklearn.utils.OrthogonalMatchingPursuit.decision_function => params present in docstring but not in signature: X
sklearn.utils.OrthogonalMatchingPursuitCV.decision_function => params present in docstring but not in signature: X
sklearn.utils.PassiveAggressiveRegressor.decision_function => params present in docstring but not in signature: X
sklearn.utils.Perceptron.transform => params present in docstring but not in signature: X
sklearn.utils.ProjectedGradientNMF.__init__ => params present in docstring but not in signature: alpha, beta, eta, init, l1_ratio, max_iter, n_components, nls_max_iter, random_state, shuffle, solver, sparseness, tol
sklearn.utils.RandomForestClassifier.transform => params present in docstring but not in signature: X
sklearn.utils.RandomForestRegressor.transform => params present in docstring but not in signature: X
sklearn.utils.Ridge.decision_function => params present in docstring but not in signature: X
sklearn.utils.RidgeCV.decision_function => params present in docstring but not in signature: X
sklearn.utils.SGDClassifier.transform => params present in docstring but not in signature: X
sklearn.utils.SGDRegressor.decision_function => params present in docstring but not in signature: X
sklearn.utils.SGDRegressor.transform => params present in docstring but not in signature: X
sklearn.utils.SVR.decision_function => params present in docstring but not in signature: X
sklearn.utils.TheilSenRegressor.decision_function => params present in docstring but not in signature: X
sklearn.utils.deprecated.__call__ => params present in signature but not in docstring: obj
sklearn.utils.deprecated.__init__ => params present in signature but not in docstring: extra
sklearn.utils.gen_batches => params present in signature but not in docstring: batch_size, n
sklearn.utils.gen_even_slices => params present in signature but not in docstring: n, n_packs, n_samples
sklearn.utils.resample => params present in docstring but not in signature: n_samples, random_state, replace
sklearn.utils.shuffle => params present in docstring but not in signature: n_samples, random_state
sklearn.utils.tosequence => params present in signature but not in docstring: x
sklearn.utils.validation.assert_all_finite => params present in signature but not in docstring: X
sklearn.utils.validation.check_random_state => params present in signature but not in docstring: seed

jnothman · 2016-11-03T04:48:13Z

$ nosetests sklearn/tests/test_docstring_parameters.py

neerajgangwar · 2016-11-03T10:07:38Z

It's giving an error. Not able to import docscrape.

lesteve · 2016-11-04T12:22:08Z

You probably need to exclude sklearn.externals from these checks because that's the responsibility of packages included there to fix these problems.

lesteve · 2016-11-04T12:24:50Z

It's giving an error. Not able to import docscrape.

numpydoc needs to be installed. If we go ahead with this PR, we will want to move numpydoc to sklearn.externals as pointed out by @jnothman. pip install numpydoc is a temporary solution for now.

neerajgangwar · 2016-11-09T02:15:23Z

@lesteve I had numpydoc installed earlier but I wanted to run it with doc/sphinxext/numpy_ext/numpydoc.

What all needs to be done to move it to sklearn/externals?

amueller · 2016-11-17T22:44:53Z

Hm so we want to add the optional and ignored y to the unsupervised model fit method docstrings?

jnothman · 2016-11-20T02:02:33Z

I think y : ignored, optional wouldn't hurt

jnothman · 2016-11-20T02:02:48Z

and makes it clearer

…check

neerajgangwar · 2016-11-20T09:41:47Z

I moved numpydoc to sklearn/externals. Status:

Two travis-ci jobs are failing because docscrape uses deprecated methods of inspect module. This is happening with Python 3.5 and has been fixed in latest numpydoc code. So I think we need to update numpydoc.
Two travis-ci jobs are failing because of the error raised by this script. So either we need to fix all the discrepancies (:P) or find some other way as @jnothman suggested.

Same with AppVeyor.

jnothman · 2016-11-20T11:05:10Z

See #7355 regarding the numpydoc upgrade...

Complies with numpydoc to improve rendering and automatic quality assurance such as scikit-learn#7793. Affects listings of Parameters Attributes, Returns. Performed with the help of: grep -nE '^( )+[a-zA-Z][a-zA-Z0-9_]*: ' sklearn -R | grep -v -e externals -e tests | grep -v -e default: -e else: -e Warning: -e Note: -e TRAIN: -e Default: -e True: -e False: -e DOI: -e In: | gsed 's|$[^:]*$:$[0-9]*$:$[^:]*$:$.*$|--- a/\1\n+++ b/\1\n@@ -\2,1 +\2,1 @@\n-\3:\4\n+\3 :\4|' | git apply --unidiff-zero -

* DOC insert spaces before colons in parameter lists Complies with numpydoc to improve rendering and automatic quality assurance such as #7793. Affects listings of Parameters Attributes, Returns. Performed with the help of: grep -nE '^( )+[a-zA-Z][a-zA-Z0-9_]*: ' sklearn -R | grep -v -e externals -e tests | grep -v -e default: -e else: -e Warning: -e Note: -e TRAIN: -e Default: -e True: -e False: -e DOI: -e In: | gsed 's|$[^:]*$:$[0-9]*$:$[^:]*$:$.*$|--- a/\1\n+++ b/\1\n@@ -\2,1 +\2,1 @@\n-\3:\4\n+\3 :\4|' | git apply --unidiff-zero - * DOC fix numpydoc format for param

…arn#7920) * DOC insert spaces before colons in parameter lists Complies with numpydoc to improve rendering and automatic quality assurance such as scikit-learn#7793. Affects listings of Parameters Attributes, Returns. Performed with the help of: grep -nE '^( )+[a-zA-Z][a-zA-Z0-9_]*: ' sklearn -R | grep -v -e externals -e tests | grep -v -e default: -e else: -e Warning: -e Note: -e TRAIN: -e Default: -e True: -e False: -e DOI: -e In: | gsed 's|$[^:]*$:$[0-9]*$:$[^:]*$:$.*$|--- a/\1\n+++ b/\1\n@@ -\2,1 +\2,1 @@\n-\3:\4\n+\3 :\4|' | git apply --unidiff-zero - * DOC fix numpydoc format for param

neerajgangwar · 2017-06-26T14:54:10Z

Closing this as I am not able to contribute to this for the time being.

…arn#7920) * DOC insert spaces before colons in parameter lists Complies with numpydoc to improve rendering and automatic quality assurance such as scikit-learn#7793. Affects listings of Parameters Attributes, Returns. Performed with the help of: grep -nE '^( )+[a-zA-Z][a-zA-Z0-9_]*: ' sklearn -R | grep -v -e externals -e tests | grep -v -e default: -e else: -e Warning: -e Note: -e TRAIN: -e Default: -e True: -e False: -e DOI: -e In: | gsed 's|$[^:]*$:$[0-9]*$:$[^:]*$:$.*$|--- a/\1\n+++ b/\1\n@@ -\2,1 +\2,1 @@\n-\3:\4\n+\3 :\4|' | git apply --unidiff-zero - * DOC fix numpydoc format for param

neerajgangwar added 2 commits October 28, 2016 19:44

Add test to check for function parameter description

17dbd2b

Add function descriptions

c29551d

raghavrv added Documentation Build / CI and removed Build / CI labels Oct 30, 2016

List params present in docstring but not in signature separately from…

e452291

… the opposite case

neerajgangwar added 4 commits November 20, 2016 10:51

Move numpydoc to sklearn/externals and ignore externals directory in …

8bae9f4

…check

Fix pep8 warnings

6733032

Fix sklearn module import

5e1a488

Fix value of level in import

e8346fd

neerajgangwar mentioned this pull request Nov 20, 2016

[MRG] update numpydoc to upstream, include fix for line numbers #7355

Merged

jnothman mentioned this pull request Nov 21, 2016

[MRG+1] DOC insert spaces before colons in parameter lists #7920

Merged

maflcko mentioned this pull request Feb 12, 2017

[MRG] Add default values to doc of RandomizedLasso #8340

Closed

maflcko mentioned this pull request Apr 17, 2017

doc: Some parameters denoted optional, whereas they are required scipy/scipy#7307

Open

jnothman mentioned this pull request Jun 7, 2017

[WIP/RFC] Test docstring parameters (with order) #9023

Closed

raghavrv mentioned this pull request Jun 23, 2017

[MRG + 1 (rv) + 1 (alex) + 1] Add a check to test the docstring params and their order #9206

Merged

neerajgangwar closed this Jun 26, 2017

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[WIP] Add a script to check discrepancies between function parameter list and function docstring parameters #7793

[WIP] Add a script to check discrepancies between function parameter list and function docstring parameters #7793

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[WIP] Add a script to check discrepancies between function parameter list and function docstring parameters #7793

[WIP] Add a script to check discrepancies between function parameter list and function docstring parameters #7793

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Reference Issue

What does this implement/fix? Explain your changes.

Any other comments?

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