MassiveAutomaticClassification

class hana_ml.algorithms.pal.massive_auto_ml.MassiveAutomaticClassification(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, 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, connections=None, alpha=None, delta=None, top_k_connections=None, top_k_pipelines=None, search_method=None, fine_tune_pipeline=None, fine_tune_resource=None, special_group_id='PAL_MASSIVE_PROCESSING_SPECIAL_GROUP_ID2', progress_indicator_id=None)

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

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

Parameters:

scoringsdict, optional

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

The available target options are as follows:

ACCURACY : Represents the percentage of correctly classified samples. Higher values indicate better performance. It is recommended to assign a positive weight to this metric.
AUC: Stands for Area Under Curve. Higher values indicate better performance. It is recommended to assign a positive weight to this metric.
KAPPA : Cohen's kappa coefficient measures the agreement between predicted and actual classifications. Higher values indicate better performance. It is recommended to assign a positive weight to this metric.
MCC: Matthews Correlation Coefficient measures the quality of binary classifications. Higher values indicate better performance. It is recommended to assign a positive weight to this metric.
RECALL_<CLASS> : Recall represents the ability of a model to identify instances of a specific class. Higher values indicate better performance. It is recommended to assign a positive weight to this metric.
PRECISION_<CLASS> : Precision represents the ability of a model to accurately classify instances for a specific class. Higher values indicate better performance. It is recommended to assign a positive weight to this metric.
F1_SCORE_<CLASS> : The F1 score measures the balance between precision and recall for a specific class. Higher values indicate better performance. It is recommended to assign a positive weight to this metric.
SUPPORT_<CLASS> : The support metric represents the number of instances of a specific class. Higher values indicate better performance. It is recommended to assign a positive weight to this metric.
LAYERS: Represents the number of operators used. Lower values indicate better performance. It is recommended to assign a negative weight to this metric.
TIME: Represents the computational time in seconds used. Lower values indicate better performance. It is recommended to assign a negative weight to this metric.

Defaults to {"ACCURACY": 1.0, "AUC": 1.0} (maximize ACCURACY and AUC).

generationsint, optional

The number of iterations of the pipeline optimization.

Defaults to 5.

population_sizeint, optional

When search_method takes the value of 'GA', population_size is the number of individuals in each generation in genetic programming algorithm. Having too few individuals can limit the possibilities of crossover and exploration of the search space to only a small portion. Conversely, if there are too many individuals, the performance of the genetic algorithm may slow down.
When search_method takes the value of 'random', population_size is the number of pipelines randomly generated and evaluated in random search.

Defaults to 20.

offspring_sizeint, optional

The number of offsprings to produce in each generation.

It controls the number of new individuals generated in each iteration by genetic operations, from population.

Defaults to the size of population_size.

elite_numberint, optional

The number of elite to produce in each generation.

Defaults to 1/4 of population_size.

min_layerint, optional

The minimum number of operators in the 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.

Represents the random search ability. A suitable value can prevent the GA from falling into a local optimum.

The sum of mutation_rate and crossover_rate cannot be greater than 1.0. When the sum is less than 1.0, the remaining probability will be used to regenerate.

Defaults to 0.9.

crossover_ratefloat, optional

The crossover rate for the genetic programming algorithm.

Represents the local search ability. A larger crossover rate will cause GA to converge to a local optimum faster.

The sum of mutation_rate and crossover_rate cannot be greater than 1.0. When the sum is less than 1.0, the remaining probability will be used to regenerate.

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{'cv', 'stratified_cv'}, optional

Specifies the resampling method for pipeline evaluation.

Defaults to 'stratified_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 minimum evaluation dataset size) when successive halving has been applied.

Defaults to 1/5 of dataset size.

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.

connectionsstr or dict, optional

Specifies the connections in the Connection Constrained Optimization. The options are:

'default'
customized connections json string or a dict.

Defaults to None. If connections is not provided, connection constrained optimization is not applied.

alphafloat, optional

Adjusts rejection probability in connection optimization.

Valid only when connections is set.

Defaults to 0.1.

deltafloat, optional

Controls the increase rate of connection weights.

Valid only when connections is set.

Defaults to 1.0.

top_k_connectionsint, optional

The number of top connections used to generate optimal connections.

Valid only when connections is set.

Defaults to 1/2 of (connection size in connections).

top_k_pipelinesint, optional

The number of pipelines used to update connections in each iteration.

Valid only when connections is set.

Defaults to 1/2 of offspring_size.

search_methodstr, optional

Optimization algorithm used in AutoML.

'GA': Genetic Algorithm
'random': Random Search

Defaults to 'GA'.

fine_tune_pipelineint, optional

Specifies whether or not to fine-tune the pipelines generated by the genetic algorithm.

Valid only when search_method takes the value of 'GA'.

Defaults to False.

fine_tune_resourceint, optional

Specifies the resource limit to use for fine-tuning the pipelines generated by the genetic algorithm.

Valid only when fine_tune_pipeline is set as True.

Defaults to the value of population_size.

special_group_id{'PAL_MASSIVE_PROCESSING_SPECIAL_GROUP_ID', 'PAL_MASSIVE_PROCESSING_SPECIAL_GROUP_ID2'}, optional

The special group id used in the pipeline processing.

Defaults to 'PAL_MASSIVE_PROCESSING_SPECIAL_GROUP_ID2'.

Examples

Create an MassiveAutomaticClassification instance:

>>> auto_c = MassiveAutomaticClassification(generations=2,
                                     population_size=5,
                                     offspring_size=5)
>>> auto_c.enable_workload_class("MY_WORKLOAD_CLASS")
>>> auto_c.fit(data=df_train, group_key="GROUP_ID", label="LABEL")
>>> pipelines = auto_c.get_best_pipelines() # get the best pipelines
>>> auto_c.fit(data=df_train, group_key="GROUP_ID", pipeline=pipelines) # refit with the best pipelines
>>> res = auto_c.predict(data=df_test, group_key="GROUP_ID")

Attributes:

best_pipeline_: DataFrame

Best pipelines selected, structured as follows:

1st column: GROUP_ID, type INTEGER or NVARCHAR, pipeline GROUP IDs.
2nd column: ID, type INTEGER, pipeline IDs.
3rd column: PIPELINE, type NVARCHAR, pipeline contents.
4th column: SCORES, type NVARCHAR, scoring metrics for pipeline.

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

model_DataFrame or a list of DataFrames

If pipeline is not None, structured as follows

1st column: GROUP_ID.
2nd column: ROW_INDEX.
3rd column: MODEL_CONTENT.

If auto-ml is enabled, structured as follows

1st DataFrame:

1st column: GROUP_ID.

2nd column: ROW_INDEX.

3rd column: MODEL_CONTENT.

2nd DataFrame: best_pipeline_

info_DataFrame

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

1st column: GROUP_ID.
2nd column: STAT_NAME.
3rd column: STAT_VALUE.

error_: DataFrame

Error information for the pipeline fitting process.

Methods

`cleanup_progress_log`(connection_context)	Clean up the progress log.
`delete_config_dict`([operator_name, ...])	Deletes the content of the config dict.
`disable_auto_sql_content`([disable])	Disable auto SQL content logging.
`disable_log_cleanup`([disable])	Disable the log clean up.
`disable_mlflow_autologging`()	Disable the mlflow autologging.
`disable_workload_class_check`()	Disable the workload class check.
`display_config_dict`([operator_name, category])	Displays the config dict.
`display_progress_table`(connection_context)	Return the progress table.
`enable_mlflow_autologging`([schema, meta, ...])	Enable the mlflow autologging.
`evaluate`(data[, pipeline, key, features, ...])	Evaluates a pipeline.
`fit`(data, group_key[, key, features, label, ...])	Fit function of MassiveAutomaticClassification.
`get_best_pipeline`()	Return the best pipeline.
`get_best_pipelines`()	Return the best pipeline.
`get_config_dict`()	Return the config_dict.
`get_optimal_config_dict`()	Return the optimal config_dict.
`get_optimal_connections`()	Return the optimal connections.
`get_workload_classes`(connection_context)	Return the available workload classes information.
`make_future_dataframe`([data, key, ...])	Create a new dataframe for time series prediction.
`persist_progress_log`()	Persist the progress log.
`pipeline_plot`([name, iframe_height])	Pipeline plot.
`predict`(data, group_key[, key, features, ...])	Predict function for AutomaticClassification.
`score`(data, group_key[, key, features, ...])	Pipeline model score function, with final estimator being a classifier.
`set_progress_log_level`(log_level)	Set progress log level to output scorings.
`update_category_map`(connection_context)	Updates the list of operators.
`update_config_dict`(operator_name[, ...])	Updates the config dict.

cleanup_progress_log(connection_context)

Clean up 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)

Deletes 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_auto_sql_content(disable=True): Disable auto SQL content logging. Use AFL's default progress logging.

disable_log_cleanup(disable=True): Disable the log clean up.

disable_mlflow_autologging(): Disable the mlflow autologging.

disable_workload_class_check(): Disable 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)

Displays 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)

Enable 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=None, key=None, features=None, label=None, categorical_variable=None, resampling_method=None, fold_num=None, random_state=None)

Evaluates 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.

featuresa list 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 a list of str, optional

Specifies which INTEGER columns should be treated as categorical, with all other INTEGER columns treated as continuous.

No default value.

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. If the value is 0, the function will automatically determine the fold number.

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: Scores.

get_best_pipeline(): Return the best pipeline.

get_best_pipelines(): Return the best pipeline.

get_config_dict(): Return the config_dict.

get_optimal_config_dict(): Return the optimal config_dict. Only available when connections is used.

get_optimal_connections(): Return the optimal connections. Only available when connections is used.

get_workload_classes(connection_context)

Return the available workload classes information.

Parameters:

connection_contextstr, optional: The connection to a SAP HANA instance.

make_future_dataframe(data=None, key=None, group_key=None, periods=1, increment_type='seconds')

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 specified key in fit() or the value in data.index or the PAL's default key column position.

group_keystr, optional

Specify the group id column.

This parameter is only valid when massive is True.

Defaults to the specified group_key in fit() or the first column of the dataframe.

periodsint, optional

The number of rows created in the predict dataframe.

Defaults to 1.

increment_type{'seconds', 'days', 'months', 'years'}, optional

The increment type of the time series.

Defaults to 'seconds'.

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.

set_progress_log_level(log_level)

Set progress log level to output scorings.

Parameters:

log_level: {'full', 'full_best', 'specified'}: 'full' prints all scores. 'full_best' prints all scores only for the 'current_best' of each generation; other pipelines print only the scores specified by SCORINGS. 'Specified' means all pipelines print only the specified scores.

update_category_map(connection_context)

Updates the list of operators.

Parameters:

connection_contextstr, optional: The connection to a SAP HANA instance.

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

Updates 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.

fit(data, group_key, key=None, features=None, label=None, group_pipelines=None, categorical_variable=None, background_size=None, background_sampling_seed=None, model_table_name=None, use_explain=None, explain_method=None, group_params=None)

Fit function of MassiveAutomaticClassification.

Parameters:

dataDataFrame

The input data.

group_keystr

Name of the group column.

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.

featuresa list 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.

group_pipelinesdict of str or nested dict, optional

Directly uses the group of pipelines to fit. Keys are group IDs and values are pipelines.

Defaults to None.

categorical_variablestr or a list of str, optional

Specifies which INTEGER columns should be treated as categorical, with all other INTEGER columns treated as continuous.

No default value.

background_sizeint, optional

If set, the reason code procedure will be enabled. Only valid when pipeline is provided and: explain_method is 'kernelshap'. It should not be larger than the row size of train data.

Defaults to None.

background_sampling_seedint, optional

Specifies the seed for random number generator in the background sampling. Only valid when pipeline is provided and explain_method is 'kernelshap'.

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.

use_explainbool, optional

Specifies whether to store information for pipeline explanation.

Defaults to False.

explain_methodstr, optional

Specifies the explanation method. Only valid when use_explain is True.

Options are:

'kernelshap' : To make explanation by Kernel SHAP, background_size should be larger than 0.
'globalsurrogate'

Defaults to 'globalsurrogate'.

Returns:

A fitted object of class "MassiveAutomaticClassification"

predict(data, group_key, key=None, features=None, model=None, show_explainer=False, top_k_attributions=None, random_state=None, sample_size=None, verbose_output=None, predict_args=None, group_params=None)

Predict function for AutomaticClassification.

Parameters:

dataDataFrame

Data to be predicted.

group_keystr

Name of the group column.

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.

featuresa list 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_).

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

top_k_attributionsint, optional

Display the top k attributions in reason code.

Effective only when model contains background data from the training phase.

Defaults to PAL's default value.

random_stateDataFrame, optional

Specifies the random seed.

Defaults to 0(system time).

sample_sizeint, optional

Specifies the number of sampled combinations of features.

It is better to use a number that is greater than the number of features in data.

If set as 0, it is determined by algorithm heuristically.

Defaults to 0.

verbose_outputbool, optional

True: Outputs the probability of all label categories.
False: Outputs the category of the highest probability only.

Defaults to True.

predict_argsdict, optional

Specifies estimator-specific parameters passed to the predict method.

If not None, it must be specified as a dict with one of the following format

key for estimator name, and value for estimator-specific parameter setting in a dict. For example {'RDT_Classifier':{'block_size': 5}, 'NB_Classifier':{'laplace':1.0}}.

Defaults to None(i.e. no estimator-specific predict parameter provided).

group_paramsdict, optional

Specifies the group parameters for the prediction.

Defaults to None.

Returns:

DataFrame

Predicted result, structured as follows:

1st column: GROUP_ID, group IDs.
2nd column: Data type and name same as the 1st column of data.
3rd column: SCORE, class labels.
4th column: CONFIDENCE, confidence of a class(available only if show_explainer is True).
5th column: REASON CODE, attributions of features(available only if show_explainer is True).
6th & 7th columns: placeholder columns for future implementations(available only if show_explainer is True).

score(data, group_key, key=None, features=None, label=None, model=None, random_state=None, top_k_attributions=None, sample_size=None, verbose_output=None, predict_args=None, group_params=None)

Pipeline model score function, with final estimator being a classifier.

Parameters:

dataDataFrame

Data for pipeline model scoring.

group_keystr

Name of the group column.

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.

featuresa list of str, optional

Names of the feature columns. Should be same as those provided in the training data.

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.

modelDataFrame, optional

DataFrame that contains the pipeline model for scoring.

Defaults to the fitted pipeline model of the current class instance.

random_stateDataFrame, optional

Specifies the random seed.

Defaults to -1(system time).

top_k_attributionsint, optional

Display the top k attributions in reason code.

Effective only when model contains background data from the training phase.

Defaults to PAL's default value.

sample_sizeint, optional

Specifies the number of sampled combinations of features.

It is better to use a number that is greater than the number of features in data.

If set as 0, it is determined by algorithm heuristically.

Defaults to 0.

verbose_outputbool, optional

True: Outputs the probability of all label categories.
False: Outputs the category of the highest probability only.

Defaults to True.

predict_argsdict, optional

Specifies estimator-specific parameters passed to the predict phase of the score method.

If not None, it must be specified as a dict with one of the following format

key for estimator name, and value for estimator-specific parameter setting in a dict. For example {'RDT_Classifier':{'block_size': 5}, 'NB_Classifier':{'laplace':1.0}}.

group_paramsdict, optional

Specifies the group parameters for the prediction.

Defaults to None.

Returns:

DataFrames

DataFrame 1 : Prediction result for the input data, structured as follows:

1st column, GROUP_ID, group IDs.
2nd column, ID of input data.
3rd column, SCORE, class assignment.
4th column, REASON CODE, attribution of features.
5th & 6th column, placeholder columns for future implementations.

DataFrame 2 : Statistics.

Inherited Methods from PALBase

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