getml.pipeline¶
Contains handlers for all steps involved in a data science project after data preparation:
automated feature learning
automated feature selection
training and evaluation of machine learning (ML) algorithms
deployment of the fitted models
Example:
We assume that you have already set up your data model using
Placeholder
, your feature learners (refer tofeature_learning
) as well as your feature selectors and predictors (refer topredictors
, which can be used for prediction and feature selection).pipe = getml.pipeline.Pipeline( tags=["multirel", "relboost", "31 features"], population=population_placeholder, peripheral=[order_placeholder, trans_placeholder], feature_learners=[feature_learner_1, feature_learner_2], feature_selectors=feature_selector, predictors=predictor, share_selected_features=0.5 ) # "order" and "trans" refer to the names of the # placeholders. pipe.check( population_table=population_training, peripheral_tables={"order": order, "trans": trans} ) pipe.fit( population_table=population_training, peripheral_tables={"order": order, "trans": trans} ) pipe.score( population_table=population_testing, peripheral_tables={"order": order, "trans": trans} )
Classes¶
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Custom class for handling the columns inserted into the pipeline. |
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Custom class for handling the features generated by the pipeline. |
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Custom class for handling the metrics generated by the pipeline. |
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A Pipeline is the main class for feature learning and prediction. |
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Custom class for handling the SQL code of the features generated by the pipeline. |