AutomaticTimeSeries

class hana_ml.algorithms.pal.auto_ml.AutomaticTimeSeries(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, percentage=None, gap_num=None)

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

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

Parameters

scoringsdict, optional

AutomaticTimeSeries 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 available target options are as follows:

• EVAR: Explained Variance. Higher values indicate better performance. It is recommended to assign a positive weight to this metric.

• MAE: Mean Absolute Error. Lower values indicate better performance. It is recommended to assign a negative weight to this metric.

• MAPE: Mean Absolute Percentage Error. Lower values indicate better performance. It is recommended to assign a negative weight to this metric.

• MAX_ERROR: The maximum absolute difference between the observed value and the expected value. Lower values indicate better performance. It is recommended to assign a negative weight to this metric.

• MSE: Mean Squared Error. Lower values indicate better performance. It is recommended to assign a negative weight to this metric.

• R2: R-squared. Higher values indicate better performance. It is recommended to assign a positive weight to this metric.

• RMSE: Root Mean Squared Error. Lower values indicate better performance. It is recommended to assign a negative weight to this metric.

• WMAPE: Weighted Mean Absolute Percentage Error. Lower values indicate better performance. It is recommended to assign a negative weight to this metric.

• LAYERS: The number of operators. Lower values indicate better performance. It is recommended to assign a negative weight to this metric.

Defaults to {MAE":-1.0, "EVAR":1.0} (minimize MAE and maximize EVAR).

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. - {'light', 'default'}: use provided config_dict templates. - JSON format config_dict. It could be JSON string or dict. 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{'rocv', 'block'}, optional

Specifies the resampling method for pipeline evaluation.

Defaults to 'rocv'.

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.

percentagefloat, optional

Percentage between training data and test data. Only applicable when resampling_method is 'block'.

Defaults to 0.7.

gap_numint, optional

Number of samples to exclude from the end of each train set before the test set.

Defaults to 0.

References

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

Examples

Create an AutomaticTimeSeries instance:

>>> progress_id = "automl_{}".format(uuid.uuid1())
>>> auto_ts = AutomaticTimeSeries(generations=2,
                                  population_size=5,
                                  offspring_size=5,
                                  progress_indicator_id=progress_id)
>>> auto_ts.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_ts)
>>> progress_status_monitor.start()
>>> auto_ts.fit(data=df_ts, key='ID', endog="SERIES")

Output:

Show the best pipeline:

>>> print(auto_ts.best_pipeline_.collect())
   ID                                           PIPELINE      0   0  {"SingleExpSm":{"args":{"ALPHA":0.6,"PHI":0.3}...

Plot the best pipeline:

>>> BestPipelineReport(auto_ts).generate_notebook_iframe()

Make prediction:

>>> res = auto_ts.predict(data=df_predict)

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

>>> pipeline = auto_ts.best_pipeline_.collect().iat[0, 1]
>>> auto_ts.fit(df_ts, pipeline=pipeline)
>>> res = auto_ts.predict(df_predict)

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 DataFrames

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 AutomaticTimeSeries pipeline fitting, structured as follows:

• 1st column: STAT_NAME.

• 2nd column: STAT_VALUE.

Methods

build_report()	Generate time series report.
cleanup_progress_log(connection_context)	Cleanup the progress log.
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.
display_progress_table(connection_context)	Return the progress table.
enable_mlflow_autologging([schema, meta, ...])	Enables the mlflow autologging.
evaluate(data[, pipeline, key, endog, exog, ...])	This function is to evaluate the pipeline.
fit(data[, key, endog, exog, pipeline, ...])	The fit function for AutomaticTimeSeries.
generate_html_report([filename])	Display function.
generate_notebook_iframe_report()	Display function.
get_best_pipeline()	Return the best pipeline.
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.
persist_progress_log()	Persist the progress log.
pipeline_plot([name, iframe_height])	Pipeline plot.
predict(data[, key, exog, model, show_explainer])	Predict function for 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.

fit(data, key=None, endog=None, exog=None, pipeline=None, categorical_variable=None, background_size=None, background_sampling_seed=None, model_table_name=None)

The fit function for AutomaticTimeSeries.

Parameters

dataDataFrame

The input time-series data for training.

keystr, optional

Specifies the column that represents the ordering of time-series data.

If data is indexed by a single column, then key defaults to that index column; otherwise key must be specified(i.e. is mandatory).

endogstr, optional

Specifies the endogenous variable for time-series data.

Defaults to the 1st non-key column of data

exogstr, optional

Specifies the exogenous variables for time-series data.

Defaults to all non-key, non-endog columns in data.

pipelinestr or dict, optional

Directly use 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.

background_sizeint, optional

If set, the reason code procedure will be enabled.

Defaults to None.

background_sampling_seedint, optional

Specifies the seed for random number generator in the background sampling. - 0: Uses the current time (in second) as seed - Others: Uses the specified value as seed

Defaults to 0.

model_table_namestr, optional

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

Defaults to None.

Returns

A fitted instance of class AutomaticTimeSeries.

predict(data, key=None, exog=None, model=None, show_explainer=False)

Predict function for AutomaticTimeSeries.

Parameters

dataDataFrame

The input time-series data to be predicted.

keystr, optional

Specifies the column that represents the ordering of the input time-series data.

If data is indexed by a single column, then key defaults to that index column; otherwise key must be specified(i.e. is mandatory).

exogstr or list of str, optional

Names of the exogenous variables in data.

Defaults to all non-key columns if not provided.

modelDataFrame, optional

The model to be used for prediction.

Defaults to the fitted model(i.e. self.model_).

show_explainerbool, optional

If True, the reason code will be returned. Only valid when background_size is provided during the fit process.

Defaults to False

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.

evaluate(data, pipeline=None, key=None, endog=None, exog=None, categorical_variable=None, resampling_method=None, fold_num=None, random_state=None, percentage=None, gap_num=None)

This function is to evaluate the pipeline.

Parameters

dataDataFrame

Data for pipeline evaluation.

pipelinejson str or dict

Pipeline to be evaluated.

keystr, optional

Specifies the column that represents the ordering of the input time-series data.

If data is indexed by a single column, then key defaults to that index column; otherwise key must be specified(i.e. is mandatory).

endogstr, optional

Specifies the endogenous variable for time-series data.

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

exogstr, optional

Specifies the exogenous variables for time-series data.

Defaults to all non-key, non-endog columns in data.

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_method{'rocv', 'block'}, optional

The resampling method for pipeline model evaluation.

Defaults to 'rocv'.

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.

percentagefloat, optional

Percentage between training data and test data. Only applicable when resampling_method is 'block'.

Defaults to 0.7.

gap_numint, optional

Number of samples to exclude from the end of each train set before the test set.

Defaults to 0.

Returns

DataFrame

DataFrame of scores:

• Score Name.

• Score Value.

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 SAP HANA DB.

Defaults to None.

template_type{'default', 'light'}, optional

HANA config dict type.

Defaults to 'default'.

build_report()

Generate time series report.

generate_html_report(filename=None)

Display function.

generate_notebook_iframe_report()

Display function.

cleanup_progress_log(connection_context)

Cleanup the progress log.

Parameters

connection_contextConnectionContext

The connection object to a SAP HANA database.

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.

display_progress_table(connection_context)

Return the progress table.

Parameters

connection_contextConnectionContext

The connection object to a SAP HANA database.

Returns

DataFrame

Progress table.

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.

property fit_hdbprocedure

Returns the generated hdbprocedure for fit.

get_best_pipeline()

Return the best pipeline.

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

persist_progress_log()

Persist the progress log.

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.

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 AutomaticTimeSeries class also inherits methods from PALBase class, please refer to PAL Base for more details.