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 to feature_learning
)
as well as your feature selectors and predictors
(refer to predictors
, 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}
)
Functions¶
|
If a pipeline named ‘name’ exists, it is deleted. |
|
Returns true if a pipeline named ‘name’ exists. |
Lists all pipelines present in the engine. |
|
|
Loads a pipeline from the getML engine into Python. |
Classes¶
|
Custom class for handling the columns inserted into the pipeline. |
|
Custom class for handling the features generated by the pipeline. |
|
Custom class for handling the metrics generated by the pipeline. |
|
A Pipeline is the main class for feature learning and prediction. |
|
Custom class for handling the SQL code of the features generated by the pipeline. |