Changelog

Version 2.16.230601

Bug Fixes

• Fixed model load issue for pipeline module in model storage.

• Fixed parameter missing in HGBT.

Version 2.16.230526

Bug Fixes

• Fixed pipeline missing evaluation function.

• Fixed tips and chart width for model report.

• Fixed built-in operation missing in pipeline module.

• Fixed fixed wordcloud issues to disable stopwords.

Version 2.16.230519

Bug Fixes

• Fixed auto-ml time series config_dict template.

• Fixed progress logging in auto-ml.

• Fixed the progress monitor issue when early_stop is enabled.

• Fixed KNN NaN issue due to the pandas new changes.

• Fixed describe function to support SMALLINT.

Version 2.16.230508

Bug Fixes

• Fixed pipeline module for model storage.

• Fixed stuck in progress monitor when progress table is not empty.

• Fixed quote issue in serializing pipeline object.

• Fixed parameter missing in FACCM.

• Fixed dependency issue for pydotplus.

• Fixed tail function with default rel_col.

• Fixed NaN in KNN optimal parameter collect.

Version 2.16.230413

Bug Fixes

• Fixed unsupported issue of precomputed distance matrix in predict() of UnifiedClustering

• Fixed wrong error message when PAL functions are executed in HANA SPS07.

• Fixed no timestamp support in plot_time_series_outlier.

• Fixed timestamp error in progress monitor in AutomaticClassification(), AutomaticRegression(), AutomaticTimeSeries.

• Fixed polynomial feature generation category undefined issue.

Version 2.16.230323

Bug Fixes

• Fixed wrong error message when "hint" has been used.

• Fixed load_model issue for APL model.

Version 2.16.230316

New Functions

• Added model report items to time series report.

• Added time series report to unified report.

• Added TimeSeriesClassification for time series classification.

• Added TUDF code generation function.

• Added AMDP generator for pipeline.

• Added import_csv_from() function for importing csv file from the cloud storage locations like Azure, Amazon(AWS) Google Cloud, SAP HANA Cloud, Data Lake Files(HDLFS).

• Added set_scale_out() to enable APL functions execution in scaling out environment.

Enhancements

• Enhanced model report with new framework.

• Enhanced MLR with prediction/confidence interval.

• Enhanced accuracy_measure() with SPEC measure.

• Enhanced UnifiedExponentialSmoothing with reason code control parameter.

• Enhanced precalculated distances matrix input for KMEDOIDS in UnifiedClustering.

• Enhanced AutomaticClassification() and AutomaticRegression() with successive halving.

API Changes

• Added a parameter called 'decom_state' in UnifiedExponentialSmoothing for the control of reason code display.

• Added a parameter called 'lang' in WordCloud for language selection.

Version 2.15.230217

Bug Fixes

• Fixed cmap issues in eda visualizer.

• Fixed FFM label bug.

• Fixed missing WordCloud module issue.

Version 2.15.230111

Bug Fixes

• Fixed the blank chart issue in DatasetReportBuilder.

• Fixed dataset report crash due to empty column.

Version 2.15.221223

Bug Fixes

• Fixed detected season change-points missing error of BCPD.

• Fixed Changepoints Chart in TimeSeriesReport.

Version 2.15.221216

New Functions

• Added nullif function in dataframe.

• Added long-term time series forecast algorithm LTSF.

• Added plot_change_points() to plot change points.

• Added plot_psd() to plot power spectral density.

• Added periodogram() to perform power spectral density estimate of the input signal.

• Added change_point detection item to TimeSeriesReport.

Enhancements

• Enhanced successive halving/hyperband in FRM and ALS.

• Enhanced OutlierDetectionTS with IsolationForest and DBSCAN.

• Enhanced Data Type Timestamp support in BCPD.

• Enhanced UnifiedClustering with massive mode.

• Enhanced UnifiedReport with scoring report.

• Enhanced ModelStorage with model export/import function.

• Enhanced MLFlow integration with pipeline module and model export.

• Enhanced UnifiedReport with model debriefing and pipeline report.

• Enhanced force_plot in ShapleyExplainer to handle missing features.

• Enhanced HybridGradientBoostingClassifier and HybridGradientBoostingRegressor with early_stop option.

• Enhanced pipeline and auto-ml module (AutomaticClassification, AutomaticRegression and AutomaticTimeSeries) with predefined output tabels.

• Enhanced PAL functions to generate SQL without HANA execution.

API Changes

• Change the "JSON" column to NCLOB table type in ModelStorage.

Version 2.14.221208

Bug Fixes

• Fixed dependency issues in dataset report.

• Fixed documentation link in PyPI portal.

• Fixed replace function to support NULL replacement.

Version 2.14.221201

Bug Fixes

• Fixed dataset report display issue:

  • wrong binning method for distribution plot.

  • NA handling issue in scatter matrix.

• Fixed SQL generation issue in pipeline module.

• Fixed model state creation in KNN.

• Fixed missing parameters in AutomaticTimeSeries.

• Fixed wrong type of split_method in AutomaticTimeSeries.

Version 2.14.221028

Bug Fixes

• Fixed pipeline monitor when password contains ','.

• Fixed message not defined error in auto-ml.

• Fixed pipeline error for PCA, DT and FN when HANA execution is disabled.

• Fixed json pipeline generation for HGBT and RDT.

• Fixed parameter name typos for DT and RDT in unified classification.

• Fixed execute_statement parser when parameters contain special characters.

Version 2.14.221014

Bug Fixes

• Fixed legend issues in forecast_line_plot.

• Fixed duplicated outputs issue in artifact generator.

• Fixed best pipeline report that points exceed the chart.

• Fixed progress bar counter issue for auto-ml time series.

• Fixed predefined partition in unified API.

Version 2.14.220923

Bug Fixes

• Fixed degree_values issue in unified regression.

• Fixed legend order in seasonal_plot.

• Fixed cross validation parameters in automatic time series forecast.

Version 2.14.220918

New Functions

• Added replace function in dataframe.

• Added the time series outlier detection algorithm called ts_outlier_detection().

• Added AutoML Time Series.

• Added make_future_dataframe

• Added force_plot for SHAP explainer.

• Added time series imputer.

• Added time series data report.

• Added KS test.

• Added create_dataframe_from_spark.

• Added set_model_state function.

• Added outlier profiling.

• Added outlier plot in EDA.

Enhancements

• Enhanced model storage

  • support pipeline in auto-ml.

  • support model report.

• MLFlow integration for auto-ml.

• Pipeline module enhancement with PAL_PIPELINE_FIT and PAL_PIPELINE_PREDICT.

• Enhanced dataframe function with enable_abap_sql.

• Successive halving for HGBT, KNN, SVM and MLR.

• Enhanced auto-ml with lightweight config dict option.

• Enhanced JSON model support in Multi-class LogisticRegression and LinearRegression.

• Enhanced the support of French and Russian in tf_analysis, text_classification, get_related_doc, get_related_term, get_relevant_doc, get_relevant_term, get_suggested_term functions.

• Enhanced the support of pre-defined period setting in seasonal_decompose with a new parameter 'periods'.

API Changes

• Added 'handling_missing' and 'json_export' in LinearRegression.

• Added 'json_export', 'precompute_lms_sketch', 'stable_sketch_alg', 'sparse_sketch_alg' in Multi-class LinearRegression.

• Added 'periods' in seasonal_decompose.

• Added parameters for APL segmented modeling, segmented forecast and parallel apply: 'max_tasks' and 'segment_column_name' (see APL 2209 and APL 2211 release notes).

Version 2.13.220722

Bug Fixes

• Fixed early_stop in auto-ml.

• Fixed display issue in unified report for APL.

Version 2.13.220715

Bug Fixes

• Fixed class_map0, class_map1 issue in UnifiedClassification.

• Fixed early_stop parameter missing in AutomaticClassification and AutomaticRegression.

• Fixed binary_classification_debriefing: divided by zero issue.

Version 2.13.220701

Bug Fixes

• Fixed table name too long in model storage save function.

• Fixed mlflow autologging with additional fit parameters.

• Fixed no mlflow model info display issue.

• Fixed metric sampling for model report.

• Fixed wrong schedule template in model storage.

Version 2.13.220608

Bug Fixes

• fixed identifier length too long issue for function outputs.

Version 2.13.220511

New Functions

• Added upsert/update streams data in dataframe function.

• Added stationarity_test function.

• Added CrostonTSB function.

• Added get_temporary_tables and clean_up_temporary_tables functions.

• Added Pipeline class json outputs for auto-ml pipeline_fit.

• Added EDA for time series data.

  • Added plot_pacf, plot_acf

  • Added plot_moving_average

  • Added plot_rolling_stddev

  • Added seasonal_plot

  • Added timeseries_box_plot

  • Added plot_seasonal_decompose

  • Added quarter_plot

• Added rolling window in generate_feature function.

• Added get_connection_id, restart_session and cancel_session_operation in dataframe function.

Enhancements

• Added support of the following collection of new parameters for HGBT in UnifiedClassification and UnifiedRegression: 'replacemissing', 'default_missing_direction', 'feature_grouping', 'tol_rate', 'compression', 'max_bin_num'.

• Improved the performance of box_plot.

• Enhanced the massive mode support of UninfiedClassification, UnifiedRegression, ARIMA, AutoARIMA, AdditiveModelForecast.

• Enhanced MLFlow autologging for unified classification and regression.

API Changes

• Added 'interpret' in predict() of KNNClassifier and KNNRegressor for enabling procedure PAL_KNN_INTERPRET.

• Added 'sample_size', 'top_k_attributions', 'random_state' in predict() of KNNClassifier and KNNRegressor for generating local interpretation result.

• Enabled missing value handling for input data by adding imputation related parameters in fit(), predict() and score() functions of both UnifiedClassification and UnifiedRegression.

• Added 'model_type' in GARCH initialization for allowing variant GARCH models.

Bug Fixes

• Fixed key error bug for parameter 'param_values' in DecisionTreeClassifier/Regressor.

• Fixed the encoding error of imputation strategy of NONE type in Imputer.

• Fixed the key error bug when enabling AFL states for clustering algorithms.

Version 2.12.220428

Bug Fixes

• Adapted the auto-ml logging according to the PAL function changes.

Version 2.12.220425

Bug Fixes

• Fixed the display issue for the pipeline report.

• Fixed the missing ptype issue in automl evaluate function.

• Fixed the transform issue in pipeline fit_predict function.

Version 2.12.220408

Bug Fixes

• Fixed cancellation button in auto_ml.

• Fixed pivot_table for handling NULL values.

• Fixed tree debriefing dot visualizer for decision trees.

• Fixed the display issue for dataset report with NULL values.

Version 2.12.220325

New Functions

• Added IsolationForest.

• Added auto_ml including AutomaticClassification, AutomaticRegression and Preprocessing.

• Added progress monitor called PipelineProgressStatusMonitor for AutomaticClassification and AutomaticRegression.

• Added best pipeline report called BestPipelineReport for AutomaticClassification and AutomaticRegression.

• Added to_datetime(), to_tail() in hanaml.dataframe.

Enhancements

• Added validation procedure for n_components in CATPCA.

• Improved display name in pivot_table.

• Added compression and thresholding in wavelet transform.

• Moved generate_feature to dataframe function.

• Enhanced create_dataframe_from_pandas() with upsert option.

• Added ignore_scatter_matrix option in dataset report.

• Expose APL variable selection parameters.

• Enhanced text mining with German support.

• Support more loss functions in HybridGradientBoostingClassifier and HybridGradientBoostingRegressor.

• Enhanced white_noise_test() with an option: the degree of freedom, model_df.

• Enhanced Attention with local interpretability of model.

• Enhanced integer index support for TimeSeriesExplainer.explain_arima_model() for ARIMA and AutoARIMA.

• Added procomputed affinity for AgglomerateHierarchicalClustering.

• Added model compression related parameters for HybridGradientBoostingClassifier and HybridGradientBoostingRegressor.

Bug Fixes

• Fixed M4 sampling with lowercase column name.

• Fixed inconsistent IDs assigned to solvers between LOGR and M_LOGR.

• Fixed a parameter naming error in fft(): flattop_model -> flattop_mode.

• Fixed a validation error for endog parameter in Attention predict().

API Changes

• Added 'model_df' in the white_noise_test() for selecting the degree of freedom.

• Added 'explain_mode' in predict() of GRUAttention for selecting the mechanism for generating the reason code for inference results.

Version 2.11.220209

Bug Fixes

• Fixed wrong arg check for 'histogram' in HGBT split method.

• Fixed bug in deploy_class with transport_request.

Version 2.11.220107

Bug Fixes

• Fixed box plot with lower case column name.

• Fixed add_id when the rel_col input is list type.

• Fixed shortest_path and shortest_path_one_to_all type cast error.

• Fixed fast DTW alignment error.

• Position correction for random search times in LOGR.

• Fixed HANA hint script generation for resource restriction.

Version 2.11.211211

New Functions

• Added FeatureSelection.

• Added BSTS.

• Added Word Cloud.

• Added hdbprocedure generation in pal_base and applied to all functions.

• Added GARCH.

• APL classification, regression, clustering: a new method, 'export_apply_code', generates code which can be used to apply a trained model outside APL.

Enhancements

• Enhanced Preprocessing with FeatureSelection.

• Enhanced the model storage with fit parameters in json format.

• Enhanced PCA categorical support.

• Enhanced model storage with fit parameters info.

• Enhanced UnifiedExponentialSmoothing with massive mode.

• Enhanced AMDP generation as a function in unified_classification.

• Enhanced ARIMA with a explainer in the predict function.

• Enhanced additive_model_forecast with a explainer in the predict function.

• Enhanced HybridGradientBoostingClassifier with continue training of a trained HybridGradientBoostingClassifier model.

• Enhanced APL AutoTimeSeries with advanced predict outputs: the 'APL/ApplyExtraMode' parameter can be set in 'extra_applyout_settings'.

• Enhanced the stored procedure information retrieval.

• Enhanced fillna to support non-numeric columns.

• Enhanced dataset report to convert PAL unsupported type.

API Changes

• Added 'background_size' in the init() and 'thread_ratio', 'top_k_attributions', 'trend_mod', 'trend_width', 'seasonal_width' in the predict() function of ARIMA() and AutoARIMA().

• Added 'show_explainer', 'decompose_seasonality', 'decompose_holiday' in the predict() function of additive_model_forecast().

• Added 'warm_start' in the fit() function of HybridGradientBoostingClassifier() and HybridGradientBoostingRegressor() for continuing training with existing model.

Bug Fixes

• Fixed index creation bug in on-premise text_classification api.

• Fixed multi-class logistic regression init check bug.

• Fix has_table with local temporary tables.

Version 2.10.210918

New Functions

• Added dtw() for generic dynamic time warping with predefined and custom defined step pattern.

• Added wavedec() for multi-level discrete wavelet transformation, and waverec() for the corresponding inverse transformation.

• Added wpdec() and wprec() for multi-level (discrete) wavelet packet transformation and inverse.

• Added OnlineMultiLogisticRegression() which is the online version of Multi-Class Logistic Regression.

• Added spectral clustering.

• Added LSTM with attention.

• Added OneHotEncoding.

• Added unified preprocessor.

• Added Pipeline plot.

• Added UnifiedExponentialSmoothing().

Enhancements

• Enhanced the model storage support for OnlineLinearRegression().

• Enhanced multi-threading in tm functions.

• Enhanced HDL container option.

• Enhanced timestamp support for ARIMA(), AutoARIMA(), VectorARIMA(), OnlineARIMA(), SingleExponentialSmoothing(), DoubleExponentialSmoothing(), TripleExponentialSmoothing(), AutoExponentialSmoothing(), BrownExponentialSmoothing(), Croston(), LR_seasonal_adjust().

• Enhanced new distributions for MCMC sampling.

• Support multiple accuracy_measure methods in Single/Double/Triple ExponentialSmoothing, BrownExponentialSmoothing, Croston and LR_seasonal_adjust.

• Added plotly support.

API Changes

• Added 'key', 'endog', 'exog', 'categorical_variable' in the fit() function of AdditiveModelForecast().

• Added 'prediction_confidence_1' and 'prediction_confidence_2' in BrownExponentialSmoothing().

Version 2.9.210726

Bug Fixes

• Fixed load_model initialized error in model storage service.

• Fixed bad link in pypi portal.

Version 2.9.210709

Bug Fixes

• Fixed missing WeaklyConnectedComponents in hana_ml.graph.algorithms.

• Fixed missing statistics in hana_ml.graph.Graph.describe.

• Fixed a bug, where the Graph object creation and discover_graph_workspace() and Graph.describe() do not work on an on-premise system

Version 2.9.210630

Bug Fixes

• Fixed accuracy_measure issue in Single/Double/Triple/Auto Exponential Smoothing().

• Fixed empty input table error in Croston()

• Fixed class_map error for multiclass logisticregreesion in UnifiedClassification().

Version 2.9.210619

Enhancements

• Constants for directions used in graph functions can be found in hana_ml.graph.constants.DIRECTION_*

• Following functions and objects are now available in hana_ml.graph for import

  • Graph object

  • create_graph_from_dataframes and create_graph_from_hana_dataframes factory methods

  • discover_graph_workspaces

  • discover_graph_workspace

• The geometries do not need to be to be specified when creating a DataFrame instance anymore. The geometries are analyzed automatically.

• Support list of targets and trans_param in feature_tool.

• Enhanced unified report for unified_regression to view feature importance.

• Enhanced join() to support list of DataFrame.

• Enhanced union() to support list of DataFrame.

• Streamlined the create_dataframe_from_pandas geo parameters. Now there is only one list of geo_cols, which supports column references as well as (lon, lat) tuples, and one SRID parameter for all columns

• When you 'create_dataframe_from_pandas' and pass a GeoPandas DataFrame, the geometry column will be detected automatically and processed as a geometry. You don't need to add it manually to geo_cols

• The Graph constructor is simplified. You can instantiate a graph simply by the workspace name.

• Enhanced ModelStorage for APL to support HANA Data Lake.

New Functions

• Introduced hana_ml.graph.algorithms which contains all graph algorithms in the future. The package provides a AlgorithmBase class which can be used to build additional algorithms for a graph.

• Add hana_ml.graph.algorithms.ShortestPath, which replaces Graph.shortest_path

• Add hana_ml.graph.algorithms.Neighbors, which replaces Graph.neighbors

• Add hana_ml.graph.algorithms.NeighborsSubgraph, which replaces Graph.neighbors_with_edges

• Add hana_ml.graph.algorithms.KShortestPaths

• Add hana_ml.graph.algorithms.ShortestPathsOneToAll

• Add hana_ml.graph.discovery.discover_graph_workspace, which reads the metadata of a graph

• Add hana_ml.graph.create_graph_from_edges_dataframe

• Add hana_ml.graph.Graph.has_vertices, to check if a list of vertices exist in a graph

• Add hana_ml.graph.Graph.subgraph, to create a vertices or edges induced subgraph

• Add hana_ml.graph.Graph.describe, to get some statistics

• Add hana_ml.graph.Graph.degree_distribution

• Add hana_ml.DataFrame.srids, which returns the SRS of each geometry column

• Add hana_ml.DataFrame.geometries, which returns the geometry columns if there are any

• Add hana_ml.spatial package, that contains

  • create_predefined_srs

  • is_srs_created

  • get_created_srses

• Add hana_ml.docstore package, that contains

  • create_collection_from_elements

• Added BCPD() for Bayesian change point detection.

• Added shape in dataframe.

• Added sort_values, sort_index in dataframe.

• Added scheduler for model renew in model_storage.

• Added min, max, mean, median, sum, value_counts in dataframe.

• Added SHAP support for unified regression.

• Added data lake support in model_storage.

• Added data lake support in dataframe functions.

• Added line plot for time series forecast.

• Added split_column().

• Added concat_columns().

• Added outlier_detection_kmeans(), which detects outliers in datasets based on the result of k-means clustering.

• Added intermittent_forecast() for forecasting intermittent demand data(time-series).

• Added OnlineLinearRegression() which is an online version of the Linear Regression.

API Changes

• Removed geo_cols from dataframe.create_dataframe_from_shapefile

• Removed geo_cols from ConnectionContext.sql()

• Removed geo_cols from ConnectionContext.table()

• Removed Graph.neighbors and Graph.neighbors_with_edges

• Removed Graph.shortest_path

• Removed hana_ml.graph.Path. This is not used anymore

• Removed hana_ml.graph.create_hana_graph_from_existing_workspace. This is replaced by a simplified Graph object constructor.

• Renamed hana_ml.graph.create_hana_graph_from_vertex_and_edge_frames to create_graph_from_dataframes

• Changed the type of geo_cols in create_dataframe_from_pandas to list, which supports direct column references or (lon, lat) tuples for generating POINT geometries

Bug Fixes

• Fixed inflexible default locations of selected columns of input data, e.g. key, features and endog.

• Fixed model report's feature importance when it has 0 importance.

Version 2.8.210421

Version 2.8.210421 supports SAP HANA SPS05 and SAP HANA Cloud

Bug Fixes

• Fixed model report's feature importance when it has 0 importance.

• Fixed pivot_table with multiple index issue.

• Fixed the verbose missing for RDT regressor.

• Fixed the shap display for categorical columns.

Version 2.8.210321

Version 2.8.210321 supports SAP HANA SPS05 and SAP HANA Cloud

Enhancements

• Enhanced sql() to enable multiline execution.

• Enhanced save() to add append option.

• Enhanced diff() to enable negative input.

• Enhanced model report functionality of UnifiedClassification with added model and data visualization.

• Enhanced dataset_report module with a optimized process of report generation and better user experience.

• Enhanced UnifiedClustering to support 'distance_level' in AgglomerateHierarchicalClustering and DBSCAN functions. Please refer to documentation for details.

• Enhanced model storage to support unified report.

New Functions

• Added generate_html_report() and generate_notebook_iframe_report() functions for UnifiedRegression which could display the output, e.g. statistic and model.

• APL Gradient Boosting: the other_params parameter is now supported.

• APL all models: a new method, get_model_info, is created, allowing users to retrieve the summary and the performance metrics of a saved model.

• APL all models: users can now specify the weight of explanatory variables via the weight parameter.

• Added LSTM.

• Added Text Mining functions support for both SAP HANA on-premise and cloud version.

  • tf_analysis

  • text_classification

  • get_related_doc

  • get_related_term

  • get_relevant_doc

  • get_relevant_term

  • get_suggested_term

• Added unified report.

New dependency:

• Added new dependency 'htmlmin' for generating dataset and model report.

API Changes

• KMeans with two added parameters 'use_fast_library' and 'use_float'.

• UnifiedRegression with one added parameter 'build_report'.

• Added a parameter 'distance_level' in UnifiedClustering when 'func' is AgglomerateHierarchicalClustering and DBSCAN. Please refer to documentation for details.

• Renamed 'batch_size' with 'chunk_size' in create_dataframe_from_pandas.

• OnlineARIMA has two added parameters 'random_state', 'random_initialization' and its partial_fit() function supports two parameters 'learning_rate' and 'epsilon' for updating the values in the input model.

Bug Fixes

• Fixed onlineARIMA model storage support.

• Fixed inflexible default locations of selected columns of input data, e.g. key, features and endog.

• Fixed accuracy_measure issue in AutoExponentialSmoothing.

Version 2.6.210126

Version 2.6.210126 supports SAP HANA SPS05 and SAP HANA Cloud

Bug Fixes

• Fixed uuid issue for Python 3.8.

• Fixed wrong legend for unified classification model report.

• Fixed dataset report to handle the dataset with missing value.

Version 2.6.210113

Version 2.6.210113 supports SAP HANA SPS05 and SAP HANA Cloud

Bug Fixes

• Fixed load_model issue for KMeans clustering.

• Removed pypi installation of Shapely for windows user.

• Fixed duplicate rows bug in save() function.

• Fixed loading issue in model report.

• Replaced the option 'batch_size' with 'chunk_size' in create_dataframe_from_pandas.

Version 2.6.201209

Version 2.6.201209 supports SAP HANA SPS05 and SAP HANA Cloud

Bug Fixes

• Remove shap from installation.

• Fixed bugs in dataframe functions when autocommit=False.

• Fixed font properties bugs in eda functions.

• APL Documentation: other_train_apl_aliases is now documented.

• APL Gradient Boosting Classification: the target variable won't be displayed in prediction if it is not given in input.

• APL Gradient Boosting: the default parameter values are now set in the APL backend level. They won't be set in the Python API level.

• Fixed handling of geometry columns in the context of Dataframe.collect calls.

• Fixed shapely not being a required dependency.

• Fixed the displacement of parameter 'dispersion' in CPD.

Version 2.6.201106

Version 2.6.201116 supports SAP HANA SPS05 and SAP HANA Cloud

Enhancements

• Enhanced collect() performance for large datasets.

• Enhanced create_dataframe_from_pandas performance for large datasets.

New Functions

• Added kdeplot() for 1D and 2D kde plotting.

• Added SHAPLEY visualization.

Bug Fixes

• Fixed incompatibility issue with matplotlib>=3.3.0.

Version 2.6.201016(2.6.200928)

Version 2.6.201016 supports SAP HANA SPS05 and SAP HANA Cloud

API Changes

• HybridGradientBoostingClassifier, HybridGradientBoostingRegressor: added a parameter 'adopt_prior' to indicate whether to adopt the prior distribution as the initial point.

• SVC, SVR, OneClassSVM, SVRanking: added parameters 'compression', 'max_bits', 'max_quantization_iter' for model compression.

• RDTClassifier: added parameters 'compression', 'max_bits', 'quantize_rate' for model compression.

• RDTRegressor: added parameters 'compression', 'max_bits', 'quantize_rate', 'fittings_quantization' for model compression.

• In prediction function ARIMA and AutoARIMA, new value 'truncation_algorithm' of forecast_method is introduced to improve the prediction performance.

• New parameters 'string_variable', 'variable_weight' are added in KNNClassifier, KNNRegressor and DBSCAN to enable distance calculation based on String distance.

• New parameters 'extrapolation', 'smooth_width', 'auxiliary_normalitytest' are added in seasonal_decompose function.

New functions:

• Added dataset manager.

• Added graph and spatial modules.

• Added dataset report.

• Added clustering function: SlightSilhouette.

• Added native storage support in model storage service and dataset manager.

• Added vector ARIMA.

• Added unified regression.

• Added unified clustering.

Bug Fixes

• Fixed ROC curve display in model report with disordered points.

• Fixed load_model for unified_classification in model storage service.

• Fixed model_selection for unified_classification.

Version 2.5.200626

Version 2.5.200626 supports SAP HANA SPS05 and SAP HANA Cloud

API Changes

• Removed parameter ConnectionContext in PAL functions.

• Updated parameter algorithm from mandatory to optional in DecisionTreeClassifier/Regressor(), with default value 'cart'.

• Added parameter key in fit() function of tsa.ARIMA() and tsa.AutoARIMA().

• Added parameter decompose_type in tsa.seasonal_decompose().

• Added parameter save_alignment and a new output statistic table in tsa.fast_dtw().

• Added parameter table_structure in create_dataframe_from_pandas().

• Added parameter resampling_method and param_search_strategy in HybridGradientBoostingClassifier/Regressor().

New Functions

• Added functions in dataframe.py: melt(), read_pickle().

• Added unified classification function. Especially, generate_html_report() and generate_notebook_iframe_report() are provided to visualize the output, e.g. confusion matrix and ROC curve.

• Added mcmc function.

• Added model selection services.

• Added visualizers (model Debriefing).

Enhancements

• Enhanced smart sampling for visualizers.

• Enhanced import function to SAP HANA.

• Enhanced bytes, TIMESTAMP and BIGINT support in create_dataframe_from_pandas() in dataframe.py.

• Enhanced TIMESTAMP and DATE support in describe() in dataframe.py.

• Predictions made with APL gradient boosting can now be complemented with the reasons that led to these predictions: number of top or bottom explanatory variables, strength values, etc.

• Supported more data types, SMALLINT, DECIMAL, TINYINT, BIGINT, CLOB and BLOB in DataFrame.dtypes(), generate_table_type() and is_numeric().

• Enhanced the missing value handling in hana_ml.visualizers.eda bar/box/pie plot in the groupby column by creating a new class for missing values.

• Predictions made with APL gradient boosting can now be complemented with the reasons that led to these predictions: number of top or bottom explanatory variables, strength values, etc.

• APL gradient boosting can provide metrics about feature interactions strength.

• The connection parameter is no longer required for APL model creation.

Bug Fixes

• Fixed wrong ID issue in fit function by adding key option in tsa.ARIMA() and tsa.AutoARIMA().

• Fixed CLOB type issue in create_dataframe_from_pandas() by adding table_structure and drop_exit_tab options.

• Fixed pivot_table() index naming bug.

• Fixed temporary view from temporary table issue in APL time series function by adding sort_data and get_horizon_wide_metric.

• Fixed bugs in create_dataframe_from_pandas() if the table is temporary.

• Fixed bugs for data type of init centers in GMM().

• Fixed bugs when some data types, e.g. SMALLINT, DECIMAL or TINYINT are not supported in DataFrame.dtypes(), generate_table_type() and is_numeric().

• Fixed bugs when data types, e.g. DATE and TIMESTAMP, are not supported in DataFrame.describe().

• Fixed the table overwrite bug in DataFrame.save() if the table name is duplicate.

• Fixed missing quotation mark in column name bugs in hana_ml.visualizers.eda.

• Users can set 'Cutting Strategy' in APL Gradient Boosting.

• APL models are saved correctly.

Deprecated Functions:

• GradientBoostingClassifier.

• GradientBoostingRegressor.

Version 1.0.8

Version 1.0.8 supports SAP HANA SP04 (100% coverage for SAP HANA SPS04 PAL algorithms)

New Functions in the PAL package:

• preprocessing : Multidimensional Scaling(MDS), Synthetic Minority Over-Sampling Technique(SMOTE, only supported in SAP HANA SPS05), Sampling, Variance Test.

• statistics : condition index, Cumulative Distribution Function(cdf), Distribution fitting, Distribution Quantile, Entropy, Equal Variance Test, Factor Analysis, Grubbs' Test, Kaplan-Meier Survival Analysis, Kernel Density, One-Sample Median Test, Wilcox Signed Rank Test.

• time series : Linear Regression with Damped Trend and Seasonal Adjust, Additive Model Forecast, Hierarchical Forecast, Correlation Function, online algorithms and dynamic time warping(fast DTW).

• miscellaneous : ABC Analysis, T-distributed Stochastic Neighbour Embedding(TSNE), Weighted Score Table.

• Added functions in dataframe.py: data_manipulation().

• Added cross-validation options to SAP HANA PAL functions.

• Added visualizers (EDA profiler).

• Added model storage services.