Pipeline

class hana_ml.algorithms.pal.pipeline.Pipeline(steps)

Pipeline construction to run transformers and estimators sequentially.

Parameters

steplist: List of (name, transform) tuples that are chained. The last object should be an estimator.

Attributes

fit_hdbprocedure: Returns the generated hdbprocedure for fit.
predict_hdbprocedure: Returns the generated hdbprocedure for predict.

Methods

`abap_class_mapping`(value)	Mapping the abap class.
`add_amdp_item`(template_key, value)	Add item.
`add_amdp_name`(amdp_name)	Add AMDP name.
`add_amdp_template`(template_name)	Add AMDP template
`build_amdp_class`()	After add_item, generate amdp file from template.
`create_amdp_class`(amdp_name, ...)	Create AMDP class file.
`disable_mlflow_autologging`()	It will disable mlflow autologging.
`enable_mlflow_autologging`([schema, meta, ...])	It will enable mlflow autologging.
`fit`(data[, key, features, label, ...])	Fit all the transforms one after the other and transform the data, then fit the transformed data using the final estimator.
`fit_predict`(data[, apply_data, fit_params, ...])	Fit all the transforms one after the other and transform the data, then fit_predict the transformed data using the last estimator.
`fit_transform`(data[, fit_params])	Fit all the transforms one after the other and transform the data, then fit the transformed data using the final estimator.
`generate_json_pipeline`()	Generate the json formatted pipeline for auto-ml's pipeline_fit function.
`get_amdp_notfillin_key`()	Get AMDP not fillin keys.
`load_abap_class_mapping`()	Load ABAP class mapping.
`load_amdp_template`(template_name)	Load AMDP template
`plot`([name, iframe_height])	Plot pipeline.
`predict`(data[, key, features, model])	Predict function for AutoML.
`write_amdp_file`([filepath, version, outdir])	Write template to file.

enable_mlflow_autologging(schema=None, meta=None, is_exported=False, registered_model_name=None)

It will enable mlflow autologging. Only works for fit function.

Parameters

schemastr, optional

Define the model storage schema for mlflow autologging.

Defaults to the current schema.

metastr, optional

Define the model storage meta table for mlflow autologging.

Defaults to 'HANAML_MLFLOW_MODEL_STORAGE'.

is_exportedbool, optional

Determine whether export the HANA model to mlflow.

Defaults to False.

registered_model_namestr, optional

MLFlow registered_model_name.

disable_mlflow_autologging(): It will disable mlflow autologging.

fit_transform(data, fit_params=None)

Fit all the transforms one after the other and transform the data, then fit the transformed data using the final estimator.

Parameters

dataDataFrame: SAP HANA DataFrame to be transformed in the pipeline.
fit_paramsdict: The parameters corresponding to the transformers/estimator name where each parameter name is prefixed such that parameter p for step s has key s__p.

Returns

DataFrame: Transformed SAP HANA DataFrame.

Examples

>>> my_pipeline = Pipeline([
        ('pca', PCA(scaling=True, scores=True)),
        ('imputer', Imputer(strategy='mean'))
        ])
>>> fit_params = {'pca__key': 'ID', 'pca__label': 'CLASS'}
>>> my_pipeline.fit_transform(data=train_data, fit_params=fit_params)

fit(data, key=None, features=None, label=None, fit_params=None, categorical_variable=None, generate_json_pipeline=False, use_pal_pipeline_fit=True, endog=None, exog=None, model_table_name=None)

Fit all the transforms one after the other and transform the data, then fit the transformed data using the final estimator.

Parameters

dataDataFrame

SAP HANA DataFrame to be transformed in the pipeline.

keystr, optional

Name of the ID column.

If key is not provided, then:

if data is indexed by a single column, then key defaults to that index column;

otherwise, it is assumed that data contains no ID column.

featureslist of str, optional

Names of the feature columns.

If features is not provided, it defaults to all non-ID, non-label columns.

labelstr, optional

Name of the dependent variable.

Defaults to the name of the last non-ID column.

fit_paramsdict, optional

Parameters corresponding to the transformers/estimator name where each parameter name is prefixed such that parameter p for step s has key s__p.

categorical_variablestr or list of str, optional

Specify INTEGER column(s) that should be be treated as categorical data. Other INTEGER columns will be treated as continuous.

generate_json_pipelinebool, optional

Help generate json formatted pipeline.

Defaults to False.

use_pal_pipeline_fitbool, optional

Use PAL's pipeline fit function instead of the original chain execution.

Defaults to True.

endogstr, optional

Specifies the endogenous variable in time-series data. Please use endog instead of label if data is time-series data.

Defaults to the name of 1st non-key column in data.

exogListOfStrings or str, optional

Specifies the exogenous variables in time-series data. Please use exog instead of features if data is time-series data.

Defaults to

the list of names of all non-key, non-endog columns in data if final estimator is not ExponentialSmoothing based

[] otherwise.

model_table_namestr, optional

Specifies the HANA model table name instead of the generated temporary table.

Defaults to None.

Returns

DataFrame: Transformed SAP HANA DataFrame.

Examples

>>> my_pipeline = Pipeline([
    ('pca', PCA(scaling=True, scores=True)),
    ('imputer', Imputer(strategy='mean')),
    ('hgbt', HybridGradientBoostingClassifier(
    n_estimators=4, split_threshold=0, learning_rate=0.5, fold_num=5,
    max_depth=6, cross_validation_range=cv_range))
    ])
>>> fit_params = {'pca__key': 'ID',
                  'pca__label': 'CLASS',
                  'hgbt__key': 'ID',
                  'hgbt__label': 'CLASS',
                  'hgbt__categorical_variable': 'CLASS'}
>>> hgbt_model = my_pipeline.fit(data=train_data, fit_params=fit_params)

predict(data, key=None, features=None, model=None)

Predict function for AutoML.

Parameters

dataDataFrame

Data to be predicted.

keystr, optional

Name of the ID column.

Mandatory if data is not indexed, or is indexed by multiple columns.

Defaults to the index of data if data is indexed by a single column.

featureslist of str, optional

Names of the feature columns.

If features is not provided, it defaults to all non-ID columns.

modelDataFrame, optional

The model to be used for prediction.

Defaults to the fitted model (model_).

Returns

DataFrame

Predicted result, structured as follows:

1st column: Data type and name same as the 1st column of data.

2nd column: SCORE, predicted values(for regression) or class labels(for classification).

fit_predict(data, apply_data=None, fit_params=None, predict_params=None)

Fit all the transforms one after the other and transform the data, then fit_predict the transformed data using the last estimator.

Parameters

dataDataFrame: SAP HANA DataFrame to be transformed in the pipeline.
apply_dataDataFrame: SAP HANA DataFrame to be predicted in the pipeline.
fit_paramsdict, optional: Parameters corresponding to the transformers/estimator name where each parameter name is prefixed such that parameter p for step s has key s__p.
predict_paramsdict, optional: Parameters corresponding to the predictor name where each parameter name is prefixed such that parameter p for step s has key s__p.

Returns

DataFrame: Transformed SAP HANA DataFrame.

Examples

>>> my_pipeline = Pipeline([
    ('pca', PCA(scaling=True, scores=True)),
    ('imputer', Imputer(strategy='mean')),
    ('hgbt', HybridGradientBoostingClassifier(
    n_estimators=4, split_threshold=0, learning_rate=0.5, fold_num=5,
    max_depth=6, cross_validation_range=cv_range))
    ])
>>> fit_params = {'pca__key': 'ID',
                  'pca__label': 'CLASS',
                  'hgbt__key': 'ID',
                  'hgbt__label': 'CLASS',
                  'hgbt__categorical_variable': 'CLASS'}
>>> hgbt_model = my_pipeline.fit_predict(data=train_data, apply_data=test_data, fit_params=fit_params)

plot(name='my_pipeline', iframe_height=450): Plot pipeline.

generate_json_pipeline(): Generate the json formatted pipeline for auto-ml's pipeline_fit function.

create_amdp_class(amdp_name, training_dataset, apply_dataset)

Create AMDP class file. Then build_amdp_class can be called to generate amdp class.

Parameters

training_datasetstr: Name of training dataset.
apply_datasetstr: Name of apply dataset.

abap_class_mapping(value): Mapping the abap class.

add_amdp_item(template_key, value): Add item.

add_amdp_name(amdp_name): Add AMDP name.

add_amdp_template(template_name): Add AMDP template

build_amdp_class(): After add_item, generate amdp file from template.

property fit_hdbprocedure: Returns the generated hdbprocedure for fit.

get_amdp_notfillin_key(): Get AMDP not fillin keys.

load_abap_class_mapping(): Load ABAP class mapping.

load_amdp_template(template_name): Load AMDP template

property predict_hdbprocedure: Returns the generated hdbprocedure for predict.

write_amdp_file(filepath=None, version=1, outdir='out'): Write template to file.

Inherited Methods from PALBase

Besides those methods mentioned above, the Pipeline class also inherits methods from PALBase class, please refer to PAL Base for more details.