AutomaticRegression

class hana_ml.algorithms.pal.auto_ml.AutomaticRegression(scorings=None, generations=None, population_size=None, offspring_size=None, elite_number=None, min_layer=None, max_layer=None, mutation_rate=None, crossover_rate=None, random_seed=None, config_dict=None, progress_indicator_id=None, fold_num=None, resampling_method=None, max_eval_time_mins=None, early_stop=None, successive_halving=None, min_budget=None, max_budget=None, min_individuals=None)

AutomaticRegression offers an intelligent search amongst machine learning pipelines for supervised regression tasks. Each machine learning pipeline contains several operators such as preprocessors, supervised regression models and transformer that follows API of hana-ml algorithms.

For AutomaticRegression parameter mappings of hana_ml and HANA PAL, please refer to the doc page: Parameter Mappings

Parameters

scoringsdict, optional

AutomaticRegression supports multi-objective optimization with specified weights of each target. The goal is to maximize the target. Therefore, if you want to minimize the target, the weight of target needs to be negative.

The target options are below:

R2 : R-squared. The bigger, the better. Should use a positive weight.
RMSE : Root Mean Squared Error. The smaller, the better. Should use a negative weight.
MAE : Mean Absolute Error. The smaller, the better. Should use a negative weight.
WMAPE : Weighted Mean Absolute Percentage Error. The smaller, the better. Should use a negative weight.
MSLE : Mean Squared Logarithmic Error. The smaller, the better. Should use a negative weight.
MAX_ERROR : The max absolute difference between the observed value and the expected value. The smaller, the better. Should use a negative weight.
EVAR : Explained Variance measures the proportion to which a mathematical model accounts for the variation (dispersion) of a given data set. The bigger, the better. Should use a positive weight.
LAYERS : The number of operators. The smaller, the better. Should use a negative weight.

Defaults to {MAE":-1.0, "EVAR":1.0}.

generationsint, optional

The number of iterations of the pipeline optimization.

Defaults to 5.

population_sizeint, optional

The number of individuals in each generation in genetic programming algorithm.

Defaults to 20.

offspring_sizeint, optional

The number of offsprings to produce in each generation.

Defaults to the number of population_size.

elite_numberint, optional

The number of elite to output into result table.

Defaults to 1/4 of population_size.

min_layerint, optional

The minimum number of operators in a pipeline.

Defaults to 1.

max_layerint, optional

The maximum number of operators in a pipeline.

Defaults to 5.

mutation_ratefloat, optional

The mutation rate for the genetic programming algorithm.

Defaults to 0.9.

crossover_ratefloat, optional

The crossover rate for the genetic programming algorithm.

Defaults to 0.1.

random_seedint, optional

Specifies the seed for random number generator. Use system time if not provided.

No default value.

config_dictstr or dict, optional

The customized configuration for the searching space. If it is None, the default config_dict will be used.

Defaults to None.

progress_indicator_idstr, optional

Set the ID used to output monitoring information of the optimization progress.

No default value.

fold_numint, optional

The number of fold in the cross validation process.

Defaults to 5.

resampling_method{'cv'}, optional

Specifies the resampling method for pipeline evaluation.

Defaults to 'cv'.

max_eval_time_minsfloat, optional

Time limit to evaluate a single pipeline. The unit is minute.

Defaults to 0.0 (there is no time limit).

early_stopint, optional

Stop optimization progress when best pipeline is not updated for the give consecutive generations. 0 means there is no early stop.

Defaults to 5.

successive_halvingbool, optional

Specifies whether uses successive_halving in the evaluation phase.

Defaults to True.

min_budgetint, optional

Specifies the minimum budget (the mininum evaluation dataset size) when successive halving has been applied.

Defaults to (dataset size)/5.

max_budgetint, optional

Specifies the maximum budget (the maximum evaluation dataset size) when successive halving has been applied.

Defaults to the whole dataset size.

min_individualsint, optional

Specifies the minimum individuals in the evaluation phase when successive halving has been applied.

Defaults to 3.

References

Under the given config_dict and scoring, AutomaticRegression uses genetic programming to to search for the best valid pipeline. Please see Genetic Optimization in AutoML for more details.

Examples

Create an AutomaticRegression instance:

>>> progress_id = "automl_{}".format(uuid.uuid1())
>>> auto_r = AutomaticRegression(generations=5,
                                 population_size=5,
                                 offspring_size=5,
                                 scorings={'MSE':-1.0, 'RMSE':-1.0},
                                 progress_indicator_id=progress_id)
>>> auto_r.enable_workload_class("MY_WORKLOAD_CLASS")

Invoke a PipelineProgressStatusMonitor instance:

>>> progress_status_monitor = PipelineProgressStatusMonitor(connection_context=dataframe.ConnectionContext(url, port, user, pwd),
                                                            automatic_obj=auto_r)
>>> progress_status_monitor.start()
>>> auto_r.fit(data=df_train)

Output:

Show the best pipeline:

>>> print(auto_r.best_pipeline_.collect())

Plot the best pipeline:

../../_images/best_pipeline_regression.png

>>> BestPipelineReport(auto_r).generate_notebook_iframe()

Make prediction:

>>> res = auto_r.predict(df_test)

If you want to use an existing pipeline to fit and predict:

>>> pipeline = auto_r.best_pipeline_.collect().iat[0, 1]
>>> auto_r.fit(df_train, pipeline=pipeline)
>>> res = auto_r.predict(df_test)

Attributes

best_pipeline_: DataFrame

Best pipelines selected, structured as follows:

1st column: ID, type INTEGER, pipeline IDs.

2nd column: PIPELINE, type NVARCHAR, pipeline contents.

3rd column: SCORES, type NVARCHAR, scoring metrics for pipeline.

Available only when the pipeline parameter is not specified during the fitting process.

model_DataFrame or list of DataFrame

If pipeline is not None, structured as follows

1st column: ROW_INDEX.

2nd column: MODEL_CONTENT.

If auto-ml is enabled, structured as follows

1st DataFrame:

1st column: ROW_INDEX.

2nd column: MODEL_CONTENT.

2nd DataFrame: best_pipeline_

info_DataFrame

Related info/statistics for AutomaticRegression pipeline fitting, structured as follows:

1st column: STAT_NAME.

2nd column: STAT_VALUE.

Methods

`delete_config_dict`([operator_name, ...])	Delete the content of the config dict.
`disable_mlflow_autologging`()	Disables the mlflow autologging.
`disable_workload_class_check`()	Disables the workload class check.
`display_config_dict`([operator_name, category])	Display the config dict.
`enable_mlflow_autologging`([schema, meta, ...])	Enables the mlflow autologging.
`evaluate`(data, pipeline[, key, features, ...])	This function is to evaluate a pipeline.
`fit`(data[, key, features, label, pipeline, ...])	Fit function of AutomaticClassification/AutomaticRegression/AutomaticTimeSeries.
`get_workload_classes`(connection_context)	Returns the available workload classes information.
`make_future_dataframe`([data, key, periods])	Create a new dataframe for time series prediction.
`pipeline_plot`([name, iframe_height])	Pipeline plot.
`predict`(data[, key, features, model])	Predict function for AutomaticClassification/AutomaticRegression/AutomaticTimeSeries.
`reset_config_dict`([connection_context, ...])	Reset config dict.
`update_category_map`(connection_context)	Update the list of operators.
`update_config_dict`(operator_name[, ...])	Update the config dict.

reset_config_dict(connection_context=None, template_type='default')

Reset config dict.

Parameters

connection_contextConnectionContext, optional

If it is set, the default config dict will use the one stored in HANA DB.

Defaults to None.

template_typestr, optional

HANA config dict type.

Defaults to 'default'.

delete_config_dict(operator_name=None, category=None, param_name=None)

Delete the content of the config dict.

Parameters

operator_namestr, optional

Deletes the operator based on the given name in the config dict.

Defaults to None.

categorystr, optional

Deletes the whole given category in the config dict.

Defaults to None.

param_namestr, optional

Deletes the parameter based on the given name once the operator name is provided.

Defaults to None.

disable_mlflow_autologging(): Disables the mlflow autologging.

disable_workload_class_check(): Disables the workload class check. Please note that the AutomaticClassification/AutomaticRegression/AutomaticTimeSeries may cause large resource. Without setting workload class, there's no resource restriction on the training process.

display_config_dict(operator_name=None, category=None)

Display the config dict.

Parameters

operator_namestr, optional

Only displays the information on the given operator name.

Defaults to None.

categorystr, optional

Only displays the information on the given category.

Defaults to None.

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

Enables the mlflow autologging.

Parameters

schemastr, optional

Defines the model storage schema for mlflow autologging.

Defaults to the current schema.

metastr, optional

Defines the model storage meta table for mlflow autologging.

Defaults to 'HANAML_MLFLOW_MODEL_STORAGE'.

is_exportedbool, optional

Determines whether export a HANA model to mlflow.

Defaults to False.

registered_model_namestr, optional

MLFlow registered_model_name.

Defaults to None.

evaluate(data, pipeline, key=None, features=None, label=None, categorical_variable=None, resampling_method=None, fold_num=None, random_state=None)

This function is to evaluate a pipeline.

Parameters

dataDataFrame

Data for pipeline evaluation.

pipelinejson str or dict

Pipeline to be evaluated.

keystr, optional

Name of the ID column.

Mandatory if data is not indexed, or the index of data contains multiple columns.

Defaults to the single index column of data if not provided.

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.

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.

Defaults to None.

resampling_methodcharacter, optional

The resampling method for pipeline model evaluation. For different pipeline, the options are different.

regressor: {'cv', 'stratified_cv'}
classifier: {'cv'}
timeseries: {'rocv', 'block'}

Defaults to 'stratified_cv' if the estimator in pipeline is a classifier, and defaults to(and can only be) 'cv' if the estimator in pipeline is a regressor, and defaults to 'rocv' if if the estimator in pipeline is a timeseries.

fold_numint, optional

The fold number for cross validation.

Defaults to 5.

random_stateint, optional

Specifies the seed for random number generator.

0: Uses the current time (in seconds) as the seed.
Others: Uses the specified value as the seed.

Returns

DataFrame

DataFrame of scores:

Score Name.

Score Value.

fit(data, key=None, features=None, label=None, pipeline=None, categorical_variable=None, model_table_name=None)

Fit function of AutomaticClassification/AutomaticRegression/AutomaticTimeSeries.

Parameters

dataDataFrame

The training data.

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.

pipelinestr or dict, optional

Directly uses the input pipeline to fit.

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.

model_table_namestr, optional

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

Defaults to None.

Returns

A fitted object.

property fit_hdbprocedure: Returns the generated hdbprocedure for fit.

get_workload_classes(connection_context)

Returns the available workload classes information.

Parameters

connection_contextstr, optional: The connection to SAP HANA.

make_future_dataframe(data=None, key=None, periods=1)

Create a new dataframe for time series prediction.

Parameters

dataDataFrame, optional

The training data contains the index.

Defaults to the data used in the fit().

keystr, optional

The index defined in the training data.

Defaults to the data.index or the first column of the data.

periodsint, optional

The number of rows created in the predict dataframe.

Defaults to 1.

Returns

DataFrame

pipeline_plot(name='my_pipeline', iframe_height=450)

Pipeline plot.

Parameters

namestr, optional

The name of the pipeline plot.

Defaults to 'my_pipeline'.

iframe_heightint, optional

The display height.

Defaults to 450.

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

Predict function for AutomaticClassification/AutomaticRegression/AutomaticTimeSeries.

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).

property predict_hdbprocedure: Returns the generated hdbprocedure for predict.

update_category_map(connection_context)

Update the list of operators.

Parameters

connection_contextstr, optional: The connection to SAP HANA.

update_config_dict(operator_name, param_name=None, param_config=None)

Update the config dict.

Parameters

operator_namestr

The name of operator.

param_namestr, optional

The parameter name to be updated. If the parameter name doesn't exist in the config dict, it will create a new one.

Defaults to None.

param_configany, optional

The parameter config value.

Defaults to None.

Inherited Methods from PALBase

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