Python Client API for machine learning algorithmsΒΆ
Contents:
- Python Machine Learning Client for SAP HANA
- Installation Guide
- hana-ml Tutorials
- Changelog
- hana_ml.dataframe
quotename()ConnectionContextConnectionContext.enable_abap_sql()ConnectionContext.disable_abap_sql()ConnectionContext.close()ConnectionContext.add_primary_key()ConnectionContext.drop_primary_key()ConnectionContext.add_auto_incremented_key()ConnectionContext.copy()ConnectionContext.create_pse()ConnectionContext.create_certificate()ConnectionContext.add_certificate_to_pse()ConnectionContext.create_schema()ConnectionContext.create_table()ConnectionContext.create_remote_source()ConnectionContext.create_virtual_table()ConnectionContext.drop_procedure()ConnectionContext.drop_view()ConnectionContext.truncate_table()ConnectionContext.drop_table()ConnectionContext.copy_to_data_lake()ConnectionContext.explain_plan_statement()ConnectionContext.has_schema()ConnectionContext.has_table()ConnectionContext.hana_version()ConnectionContext.get_current_schema()ConnectionContext.get_tables()ConnectionContext.get_schemas()ConnectionContext.get_procedures()ConnectionContext.get_temporary_tables()ConnectionContext.get_connection_id()ConnectionContext.cancel_session_process()ConnectionContext.restart_session()ConnectionContext.clean_up_temporary_tables()ConnectionContext.hana_major_version()ConnectionContext.is_cloud_version()ConnectionContext.sql()ConnectionContext.execute_sql()ConnectionContext.table()ConnectionContext.upsert_streams_data()ConnectionContext.update_streams_data()ConnectionContext.to_sqlalchemy()ConnectionContext.create_vector_index()ConnectionContext.drop_vector_index()ConnectionContext.embed_query()
DataFrameDataFrame.columnsDataFrame.shapeDataFrame.nameDataFrame.quoted_nameDataFrame.descriptionDataFrame.description_extDataFrame.declare_lttab_usage()DataFrame.disable_validate_columns()DataFrame.enable_validate_columns()DataFrame.add_vector()DataFrame.add_id()DataFrame.add_constant()DataFrame.alias()DataFrame.count()DataFrame.diff()DataFrame.drop()DataFrame.distinct()DataFrame.drop_duplicates()DataFrame.dropna()DataFrame.deselect()DataFrame.has_constant_columns()DataFrame.drop_constant_columns()DataFrame.dtypes()DataFrame.empty()DataFrame.filter()DataFrame.has()DataFrame.head()DataFrame.hasna()DataFrame.fillna()DataFrame.get_table_structure()DataFrame.join()DataFrame.set_name()DataFrame.set_index()DataFrame.smart_save()DataFrame.save()DataFrame.save_nativedisktable()DataFrame.split_column()DataFrame.concat_columns()DataFrame.nullif()DataFrame.replace()DataFrame.sort()DataFrame.sort_values()DataFrame.sort_index()DataFrame.sort_by_similarity()DataFrame.select()DataFrame.set_operations()DataFrame.union()DataFrame.collect()DataFrame.geometriesDataFrame.sridsDataFrame.rename_columns()DataFrame.auto_cast()DataFrame.cast()DataFrame.tail()DataFrame.to_head()DataFrame.to_tail()DataFrame.summary()DataFrame.info()DataFrame.statsDataFrame.describe()DataFrame.bin()DataFrame.agg()DataFrame.is_numeric()DataFrame.corr()DataFrame.min()DataFrame.max()DataFrame.sum()DataFrame.median()DataFrame.mean()DataFrame.stddev()DataFrame.value_counts()DataFrame.pivot_table()DataFrame.generate_table_type()DataFrame.rearrange()DataFrame.set_source_table()DataFrame.to_pickle()DataFrame.to_datetime()DataFrame.generate_feature()DataFrame.mutate()
read_pickle()create_dataframe_from_pandas()create_dataframe_from_spark()melt()create_dataframe_from_shapefile()import_csv_from()
- hana_ml.algorithms.apl package
- Overview
- Python API to APL SQL API Mapping
- Available Modules
- hana_ml.algorithms.apl.gradient_boosting_classification
- hana_ml.algorithms.apl.gradient_boosting_regression
- hana_ml.algorithms.apl.time_series
- hana_ml.algorithms.apl.classification
- hana_ml.algorithms.apl.regression
- hana_ml.algorithms.apl.clustering
- hana_ml.algorithms.apl.drift_detector
- hana_ml.algorithms.pal package
- Algorithms
- Topics
- Model Evaluation and Parameter Selection
- Successive Halving and Hyperband for Parameter Selection
- Biased Linear Models
- Model State for Real-Time Scoring
- Local Interpretability of Models
- Explaining the Forecasts of ARIMA
- Methods for Residual Extraction in Time-Series Outlier Detection
- Methods of Outlier Detection from Residual
- Genetic Optimization in AutoML
- Probability Density Functions for MCMC Sampling
- Miscellaneous Topics
- Precomputed Distance Matrix as input data in UnifiedClustering
- Parameters for Missing Value Handling in HANA DataFrame
- Permutation Feature Importance
- Permutation Feature Importance for Time Series
- Parameter Mappings
- hana_ml.visualizers package
- hana_ml.visualizers.eda
- hana_ml.visualizers.metrics
- hana_ml.visualizers.m4_sampling
- hana_ml.visualizers.model_debriefing
- hana_ml.visualizers.dataset_report
- hana_ml.visualizers.shap
- hana_ml.visualizers.unified_report
- hana_ml.visualizers.visualizer_base
- hana_ml.visualizers.digraph
- hana_ml.visualizers.word_cloud
- hana_ml.visualizers.automl_progress
- hana_ml.visualizers.automl_report
- hana_ml.visualizers.time_series_report
- hana_ml.visualizers.automl_config
- hana_ml.visualizers.tracking
- hana_ml.text package
- hana_ml.hana_scheduler
HANASchedulerHANAScheduler.check_scheduler_job_exist()HANAScheduler.list_schedules()HANAScheduler.get_job_names()HANAScheduler.display_schedule_status()HANAScheduler.set_schedule()HANAScheduler.delete_schedule()HANAScheduler.delete_schedules()HANAScheduler.drop_procedure()HANAScheduler.clean_up_schedules()HANAScheduler.create_training_schedule()HANAScheduler.create_applying_schedule()HANAScheduler.create_scoring_schedule()
- hana_ml.ml_exceptions
- hana_ml.model_storage
ModelStorageErrorModelStorageModelStorage.export_model()ModelStorage.load_model_from_files()ModelStorage.import_model()ModelStorage.list_models()ModelStorage.model_already_exists()ModelStorage.change_storage_type()ModelStorage.save_model()ModelStorage.save_model_to_files()ModelStorage.delete_model()ModelStorage.delete_models()ModelStorage.load_mlflow_model()ModelStorage.clean_up()ModelStorage.load_model()ModelStorage.get_model_card()ModelStorage.display_model_report()ModelStorage.enable_persistent_memory()ModelStorage.disable_persistent_memory()ModelStorage.load_into_memory()ModelStorage.unload_from_memory()ModelStorage.set_data_lake_container()ModelStorage.set_schedule()ModelStorage.display_hana_schedule()ModelStorage.start_schedule()ModelStorage.terminate_schedule()ModelStorage.set_logfile()ModelStorage.upgrade_meta()
- hana_ml.artifacts package
- AMDP Examples
- hana_ml.artifacts.deployers.amdp
- hana_ml.artifacts.generators.abap
- hana_ml.artifacts.generators.hana
- hana_ml.artifacts.tracking.tracking
MLExperimentsget_tracking_log()get_tracking_metadata()add_tag_by_run()modify_tag_by_run()remove_tag_by_run()get_tags_by_run()get_tracking_log_by_run()get_tracking_log_header_by_run()get_tracking_metadata_by_run()delete_run_log()delete_tracking_log_by_run()delete_tracking_log()delete_experiment_log()
- hana_ml.docstore package
- hana_ml.spatial package
- hana_ml.graph package
- hana_ml.graph.algorithms package
ShortestPathNeighborsNeighborsSubgraphKShortestPathsTopologicalSortShortestPathsOneToAllStronglyConnectedComponentsWeaklyConnectedComponentsAlgorithmBaseAlgorithmBase.signature_from_cols()AlgorithmBase.projection_expr_from_cols()AlgorithmBase._default_vertex_cols()AlgorithmBase._default_edge_cols()AlgorithmBase._default_vertex_select()AlgorithmBase._default_edge_select()AlgorithmBase._process_parameters()AlgorithmBase._validate_parameters()AlgorithmBase.execute()
- FAQs