210 lines
8.8 KiB
Ruby
Generated
210 lines
8.8 KiB
Ruby
Generated
# typed: true
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# DO NOT EDIT MANUALLY
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# This is an autogenerated file for types exported from the `rumale-pipeline` gem.
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# Please instead update this file by running `bin/tapioca gem rumale-pipeline`.
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# Rumale is a machine learning library in Ruby.
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#
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# source://rumale-pipeline//lib/rumale/pipeline/feature_union.rb#5
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module Rumale; end
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# Module implements utilities of pipeline that cosists of a chain of transfomers and estimators.
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#
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# source://rumale-pipeline//lib/rumale/pipeline/feature_union.rb#6
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module Rumale::Pipeline; end
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# FeatureUnion is a class that implements the function concatenating the multi-transformer results.
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#
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# @example
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# require 'rumale/kernel_approximation/rbf'
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# require 'rumale/decomposition/pca'
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# require 'rumale/pipeline/feature_union'
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#
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# fu = Rumale::Pipeline::FeatureUnion.new(
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# transformers: {
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# 'rbf': Rumale::KernelApproximation::RBF.new(gamma: 1.0, n_components: 96, random_seed: 1),
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# 'pca': Rumale::Decomposition::PCA.new(n_components: 32)
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# }
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# )
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# fu.fit(training_samples, traininig_labels)
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# results = fu.predict(testing_samples)
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#
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# # > p results.shape[1]
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# # > 128
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#
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# source://rumale-pipeline//lib/rumale/pipeline/feature_union.rb#26
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class Rumale::Pipeline::FeatureUnion < ::Rumale::Base::Estimator
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# Create a new feature union.
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#
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# @param transformers [Hash] List of transformers. The order of transforms follows the insertion order of hash keys.
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# @return [FeatureUnion] a new instance of FeatureUnion
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#
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# source://rumale-pipeline//lib/rumale/pipeline/feature_union.rb#34
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def initialize(transformers:); end
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# Fit the model with given training data.
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#
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# @param x [Numo::DFloat] (shape: [n_samples, n_features]) The training data to be used for fitting the transformers.
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# @param y [Numo::NArray/Nil] (shape: [n_samples, n_outputs]) The target values or labels to be used for fitting the transformers.
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# @return [FeatureUnion] The learned feature union itself.
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#
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# source://rumale-pipeline//lib/rumale/pipeline/feature_union.rb#45
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def fit(x, y = T.unsafe(nil)); end
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# Fit the model with training data, and then transform them with the learned model.
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#
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# @param x [Numo::DFloat] (shape: [n_samples, n_features]) The training data to be used for fitting the transformers.
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# @param y [Numo::NArray/Nil] (shape: [n_samples, n_outputs]) The target values or labels to be used for fitting the transformers.
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# @return [Numo::DFloat] (shape: [n_samples, sum_n_components]) The transformed and concatenated data.
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#
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# source://rumale-pipeline//lib/rumale/pipeline/feature_union.rb#55
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def fit_transform(x, y = T.unsafe(nil)); end
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# Transform the given data with the learned model.
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#
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# @param x [Numo::DFloat] (shape: [n_samples, n_features]) The data to be transformed with the learned transformers.
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# @return [Numo::DFloat] (shape: [n_samples, sum_n_components]) The transformed and concatenated data.
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#
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# source://rumale-pipeline//lib/rumale/pipeline/feature_union.rb#63
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def transform(x); end
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# Return the transformers
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#
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# @return [Hash]
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#
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# source://rumale-pipeline//lib/rumale/pipeline/feature_union.rb#29
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def transformers; end
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end
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# Pipeline is a class that implements the function to perform the transformers and estimators sequencially.
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#
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# @example
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# require 'rumale/kernel_approximation/rbf'
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# require 'rumale/linear_model/svc'
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# require 'rumale/pipeline/pipeline'
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#
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# rbf = Rumale::KernelApproximation::RBF.new(gamma: 1.0, n_components: 128, random_seed: 1)
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# svc = Rumale::LinearModel::SVC.new(reg_param: 1.0, fit_bias: true, max_iter: 5000)
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# pipeline = Rumale::Pipeline::Pipeline.new(steps: { trs: rbf, est: svc })
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# pipeline.fit(training_samples, traininig_labels)
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# results = pipeline.predict(testing_samples)
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#
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# source://rumale-pipeline//lib/rumale/pipeline/pipeline.rb#21
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class Rumale::Pipeline::Pipeline < ::Rumale::Base::Estimator
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# Create a new pipeline.
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#
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# @param steps [Hash] List of transformers and estimators. The order of transforms follows the insertion order of hash keys.
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# The last entry is considered an estimator.
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# @return [Pipeline] a new instance of Pipeline
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#
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# source://rumale-pipeline//lib/rumale/pipeline/pipeline.rb#30
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def initialize(steps:); end
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# Call the decision_function method of last estimator after applying all transforms.
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#
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# @param x [Numo::DFloat] (shape: [n_samples, n_features]) The samples to compute the scores.
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# @return [Numo::DFloat] (shape: [n_samples]) Confidence score per sample.
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#
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# source://rumale-pipeline//lib/rumale/pipeline/pipeline.rb#72
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def decision_function(x); end
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# Fit the model with given training data.
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#
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# @param x [Numo::DFloat] (shape: [n_samples, n_features]) The training data to be transformed and used for fitting the model.
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# @param y [Numo::NArray] (shape: [n_samples, n_outputs]) The target values or labels to be used for fitting the model.
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# @return [Pipeline] The learned pipeline itself.
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#
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# source://rumale-pipeline//lib/rumale/pipeline/pipeline.rb#42
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def fit(x, y); end
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# Call the fit_predict method of last estimator after applying all transforms.
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#
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# @param x [Numo::DFloat] (shape: [n_samples, n_features]) The training data to be transformed and used for fitting the model.
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# @param y [Numo::NArray] (shape: [n_samples, n_outputs], default: nil) The target values or labels to be used for fitting the model.
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# @return [Numo::NArray] The predicted results by last estimator.
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#
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# source://rumale-pipeline//lib/rumale/pipeline/pipeline.rb#53
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def fit_predict(x, y = T.unsafe(nil)); end
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# Call the fit_transform method of last estimator after applying all transforms.
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#
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# @param x [Numo::DFloat] (shape: [n_samples, n_features]) The training data to be transformed and used for fitting the model.
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# @param y [Numo::NArray] (shape: [n_samples, n_outputs], default: nil) The target values or labels to be used for fitting the model.
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# @return [Numo::NArray] The predicted results by last estimator.
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#
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# source://rumale-pipeline//lib/rumale/pipeline/pipeline.rb#63
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def fit_transform(x, y = T.unsafe(nil)); end
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# Call the inverse_transform method in reverse order.
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#
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# @param z [Numo::DFloat] (shape: [n_samples, n_components]) The transformed samples to be restored into original space.
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# @return [Numo::DFloat] (shape: [n_samples, n_featuress]) The restored samples.
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#
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# source://rumale-pipeline//lib/rumale/pipeline/pipeline.rb#117
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def inverse_transform(z); end
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# Call the predict method of last estimator after applying all transforms.
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#
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# @param x [Numo::DFloat] (shape: [n_samples, n_features]) The samples to obtain prediction result.
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# @return [Numo::NArray] The predicted results by last estimator.
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#
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# source://rumale-pipeline//lib/rumale/pipeline/pipeline.rb#81
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def predict(x); end
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# Call the predict_log_proba method of last estimator after applying all transforms.
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#
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# @param x [Numo::DFloat] (shape: [n_samples, n_features]) The samples to predict the log-probailities.
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# @return [Numo::DFloat] (shape: [n_samples, n_classes]) Predicted log-probability of each class per sample.
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#
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# source://rumale-pipeline//lib/rumale/pipeline/pipeline.rb#90
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def predict_log_proba(x); end
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# Call the predict_proba method of last estimator after applying all transforms.
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#
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# @param x [Numo::DFloat] (shape: [n_samples, n_features]) The samples to predict the probailities.
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# @return [Numo::DFloat] (shape: [n_samples, n_classes]) Predicted probability of each class per sample.
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#
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# source://rumale-pipeline//lib/rumale/pipeline/pipeline.rb#99
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def predict_proba(x); end
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# Call the score method of last estimator after applying all transforms.
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#
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# @param x [Numo::DFloat] (shape: [n_samples, n_features]) Testing data.
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# @param y [Numo::NArray] (shape: [n_samples, n_outputs]) True target values or labels for testing data.
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# @return [Float] The score of last estimator
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#
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# source://rumale-pipeline//lib/rumale/pipeline/pipeline.rb#133
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def score(x, y); end
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# Return the steps.
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#
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# @return [Hash]
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#
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# source://rumale-pipeline//lib/rumale/pipeline/pipeline.rb#24
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def steps; end
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# Call the transform method of last estimator after applying all transforms.
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#
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# @param x [Numo::DFloat] (shape: [n_samples, n_features]) The samples to be transformed.
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# @return [Numo::DFloat] (shape: [n_samples, n_components]) The transformed samples.
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#
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# source://rumale-pipeline//lib/rumale/pipeline/pipeline.rb#108
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def transform(x); end
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private
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# source://rumale-pipeline//lib/rumale/pipeline/pipeline.rb#158
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def apply_transforms(x, y = T.unsafe(nil), fit: T.unsafe(nil)); end
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# source://rumale-pipeline//lib/rumale/pipeline/pipeline.rb#170
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def last_estimator; end
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# source://rumale-pipeline//lib/rumale/pipeline/pipeline.rb#140
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def validate_steps(steps); end
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end
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# source://rumale-pipeline//lib/rumale/pipeline/version.rb#8
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Rumale::Pipeline::VERSION = T.let(T.unsafe(nil), String)
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