hana-ml
2.18.230927
  • Python Machine Learning Client for SAP HANA
    • Prerequisites
    • SAP HANA DataFrame
    • Machine Learning API
    • End-to-End Example: Using SAP HANA Predictive Analysis Library (PAL) Module
    • End-to-End Example: Using SAP HANA Automated Predictive Library (APL) Module
    • Visualizers Module
    • Spatial and Graph Features
    • Summary
  • Installation Guide
  • hana-ml Tutorials
  • Changelog
  • hana_ml.dataframe
    • quotename()
    • ConnectionContext
      • ConnectionContext.enable_abap_sql()
      • ConnectionContext.disable_abap_sql()
      • ConnectionContext.close()
      • ConnectionContext.add_primary_key()
      • ConnectionContext.copy()
      • ConnectionContext.create_schema()
      • ConnectionContext.create_table()
      • ConnectionContext.create_virtual_table()
      • ConnectionContext.drop_view()
      • 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_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()
    • DataFrame
      • DataFrame.columns
      • DataFrame.shape
      • DataFrame.name
      • DataFrame.quoted_name
      • DataFrame.declare_lttab_usage()
      • DataFrame.disable_validate_columns()
      • DataFrame.enable_validate_columns()
      • DataFrame.add_id()
      • DataFrame.add_constant()
      • DataFrame.alias()
      • DataFrame.count()
      • DataFrame.diff()
      • DataFrame.drop()
      • DataFrame.distinct()
      • DataFrame.drop_duplicates()
      • DataFrame.dropna()
      • DataFrame.deselect()
      • 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.save()
      • DataFrame.save_nativedisktable()
      • DataFrame.split_column()
      • DataFrame.concat_columns()
      • DataFrame.nullif()
      • DataFrame.replace()
      • DataFrame.sort()
      • DataFrame.sort_values()
      • DataFrame.sort_index()
      • DataFrame.select()
      • DataFrame.union()
      • DataFrame.collect()
      • DataFrame.geometries
      • DataFrame.srids
      • DataFrame.rename_columns()
      • DataFrame.cast()
      • DataFrame.tail()
      • DataFrame.to_head()
      • DataFrame.to_tail()
      • DataFrame.stats
      • DataFrame.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()
    • read_pickle()
    • create_dataframe_from_pandas()
    • create_dataframe_from_spark()
    • melt()
    • create_dataframe_from_shapefile()
    • import_csv_from()
  • hana_ml.algorithms.apl package
    • hana_ml.algorithms.apl.gradient_boosting_classification
      • GradientBoostingClassifier
        • GradientBoostingClassifier.set_params()
        • GradientBoostingClassifier.fit()
        • GradientBoostingClassifier.score()
        • GradientBoostingClassifier.get_metrics_per_class()
        • GradientBoostingClassifier.build_report()
        • GradientBoostingClassifier.set_metric_samplings()
        • GradientBoostingClassifier.disable_hana_execution()
        • GradientBoostingClassifier.enable_hana_execution()
        • GradientBoostingClassifier.export_apply_code()
        • GradientBoostingClassifier.generate_html_report()
        • GradientBoostingClassifier.generate_notebook_iframe_report()
        • GradientBoostingClassifier.get_apl_version()
        • GradientBoostingClassifier.get_artifacts_recorder()
        • GradientBoostingClassifier.get_best_iteration()
        • GradientBoostingClassifier.get_debrief_report()
        • GradientBoostingClassifier.get_evalmetrics()
        • GradientBoostingClassifier.get_feature_importances()
        • GradientBoostingClassifier.get_fit_operation_log()
        • GradientBoostingClassifier.get_indicators()
        • GradientBoostingClassifier.get_model_info()
        • GradientBoostingClassifier.get_params()
        • GradientBoostingClassifier.get_performance_metrics()
        • GradientBoostingClassifier.get_predict_operation_log()
        • GradientBoostingClassifier.get_summary()
        • GradientBoostingClassifier.is_fitted()
        • GradientBoostingClassifier.load_model()
        • GradientBoostingClassifier.predict()
        • GradientBoostingClassifier.save_artifact()
        • GradientBoostingClassifier.save_model()
        • GradientBoostingClassifier.schedule_fit()
        • GradientBoostingClassifier.schedule_predict()
        • GradientBoostingClassifier.set_framework_version()
        • GradientBoostingClassifier.set_scale_out()
        • GradientBoostingClassifier.set_shapley_explainer_of_predict_phase()
        • GradientBoostingClassifier.set_shapley_explainer_of_score_phase()
      • GradientBoostingBinaryClassifier
        • GradientBoostingBinaryClassifier.set_params()
        • GradientBoostingBinaryClassifier.score()
        • GradientBoostingBinaryClassifier.build_report()
        • GradientBoostingBinaryClassifier.disable_hana_execution()
        • GradientBoostingBinaryClassifier.enable_hana_execution()
        • GradientBoostingBinaryClassifier.export_apply_code()
        • GradientBoostingBinaryClassifier.fit()
        • GradientBoostingBinaryClassifier.generate_html_report()
        • GradientBoostingBinaryClassifier.generate_notebook_iframe_report()
        • GradientBoostingBinaryClassifier.get_apl_version()
        • GradientBoostingBinaryClassifier.get_artifacts_recorder()
        • GradientBoostingBinaryClassifier.get_best_iteration()
        • GradientBoostingBinaryClassifier.get_debrief_report()
        • GradientBoostingBinaryClassifier.get_evalmetrics()
        • GradientBoostingBinaryClassifier.get_feature_importances()
        • GradientBoostingBinaryClassifier.get_fit_operation_log()
        • GradientBoostingBinaryClassifier.get_indicators()
        • GradientBoostingBinaryClassifier.get_model_info()
        • GradientBoostingBinaryClassifier.get_params()
        • GradientBoostingBinaryClassifier.get_performance_metrics()
        • GradientBoostingBinaryClassifier.get_predict_operation_log()
        • GradientBoostingBinaryClassifier.get_summary()
        • GradientBoostingBinaryClassifier.is_fitted()
        • GradientBoostingBinaryClassifier.load_model()
        • GradientBoostingBinaryClassifier.predict()
        • GradientBoostingBinaryClassifier.save_artifact()
        • GradientBoostingBinaryClassifier.save_model()
        • GradientBoostingBinaryClassifier.schedule_fit()
        • GradientBoostingBinaryClassifier.schedule_predict()
        • GradientBoostingBinaryClassifier.set_framework_version()
        • GradientBoostingBinaryClassifier.set_metric_samplings()
        • GradientBoostingBinaryClassifier.set_scale_out()
        • GradientBoostingBinaryClassifier.set_shapley_explainer_of_predict_phase()
        • GradientBoostingBinaryClassifier.set_shapley_explainer_of_score_phase()
    • hana_ml.algorithms.apl.gradient_boosting_regression
      • GradientBoostingRegressor
        • GradientBoostingRegressor.set_params()
        • GradientBoostingRegressor.predict()
        • GradientBoostingRegressor.score()
        • GradientBoostingRegressor.build_report()
        • GradientBoostingRegressor.disable_hana_execution()
        • GradientBoostingRegressor.enable_hana_execution()
        • GradientBoostingRegressor.export_apply_code()
        • GradientBoostingRegressor.fit()
        • GradientBoostingRegressor.generate_html_report()
        • GradientBoostingRegressor.generate_notebook_iframe_report()
        • GradientBoostingRegressor.get_apl_version()
        • GradientBoostingRegressor.get_artifacts_recorder()
        • GradientBoostingRegressor.get_best_iteration()
        • GradientBoostingRegressor.get_debrief_report()
        • GradientBoostingRegressor.get_evalmetrics()
        • GradientBoostingRegressor.get_feature_importances()
        • GradientBoostingRegressor.get_fit_operation_log()
        • GradientBoostingRegressor.get_indicators()
        • GradientBoostingRegressor.get_model_info()
        • GradientBoostingRegressor.get_params()
        • GradientBoostingRegressor.get_performance_metrics()
        • GradientBoostingRegressor.get_predict_operation_log()
        • GradientBoostingRegressor.get_summary()
        • GradientBoostingRegressor.is_fitted()
        • GradientBoostingRegressor.load_model()
        • GradientBoostingRegressor.save_artifact()
        • GradientBoostingRegressor.save_model()
        • GradientBoostingRegressor.schedule_fit()
        • GradientBoostingRegressor.schedule_predict()
        • GradientBoostingRegressor.set_framework_version()
        • GradientBoostingRegressor.set_scale_out()
        • GradientBoostingRegressor.set_shapley_explainer_of_predict_phase()
        • GradientBoostingRegressor.set_shapley_explainer_of_score_phase()
    • hana_ml.algorithms.apl.time_series
      • AutoTimeSeries
        • AutoTimeSeries.set_params()
        • AutoTimeSeries.fit()
        • AutoTimeSeries.predict()
        • AutoTimeSeries.fit_predict()
        • AutoTimeSeries.forecast()
        • AutoTimeSeries.get_model_components()
        • AutoTimeSeries.get_performance_metrics()
        • AutoTimeSeries.get_horizon_wide_metric()
        • AutoTimeSeries.load_model()
        • AutoTimeSeries.export_apply_code()
        • AutoTimeSeries.build_report()
        • AutoTimeSeries.generate_html_report()
        • AutoTimeSeries.generate_notebook_iframe_report()
        • AutoTimeSeries.disable_hana_execution()
        • AutoTimeSeries.enable_hana_execution()
        • AutoTimeSeries.get_apl_version()
        • AutoTimeSeries.get_artifacts_recorder()
        • AutoTimeSeries.get_debrief_report()
        • AutoTimeSeries.get_fit_operation_log()
        • AutoTimeSeries.get_indicators()
        • AutoTimeSeries.get_model_info()
        • AutoTimeSeries.get_params()
        • AutoTimeSeries.get_predict_operation_log()
        • AutoTimeSeries.get_summary()
        • AutoTimeSeries.is_fitted()
        • AutoTimeSeries.save_artifact()
        • AutoTimeSeries.save_model()
        • AutoTimeSeries.schedule_fit()
        • AutoTimeSeries.schedule_predict()
        • AutoTimeSeries.set_scale_out()
    • hana_ml.algorithms.apl.classification
      • AutoClassifier
        • AutoClassifier.fit()
        • AutoClassifier.predict()
        • AutoClassifier.score()
        • AutoClassifier.disable_hana_execution()
        • AutoClassifier.enable_hana_execution()
        • AutoClassifier.export_apply_code()
        • AutoClassifier.get_apl_version()
        • AutoClassifier.get_artifacts_recorder()
        • AutoClassifier.get_debrief_report()
        • AutoClassifier.get_feature_importances()
        • AutoClassifier.get_fit_operation_log()
        • AutoClassifier.get_indicators()
        • AutoClassifier.get_model_info()
        • AutoClassifier.get_params()
        • AutoClassifier.get_performance_metrics()
        • AutoClassifier.get_predict_operation_log()
        • AutoClassifier.get_summary()
        • AutoClassifier.is_fitted()
        • AutoClassifier.load_model()
        • AutoClassifier.save_artifact()
        • AutoClassifier.save_model()
        • AutoClassifier.schedule_fit()
        • AutoClassifier.schedule_predict()
        • AutoClassifier.set_params()
        • AutoClassifier.set_scale_out()
    • hana_ml.algorithms.apl.regression
      • AutoRegressor
        • AutoRegressor.fit()
        • AutoRegressor.predict()
        • AutoRegressor.score()
        • AutoRegressor.disable_hana_execution()
        • AutoRegressor.enable_hana_execution()
        • AutoRegressor.export_apply_code()
        • AutoRegressor.get_apl_version()
        • AutoRegressor.get_artifacts_recorder()
        • AutoRegressor.get_debrief_report()
        • AutoRegressor.get_feature_importances()
        • AutoRegressor.get_fit_operation_log()
        • AutoRegressor.get_indicators()
        • AutoRegressor.get_model_info()
        • AutoRegressor.get_params()
        • AutoRegressor.get_performance_metrics()
        • AutoRegressor.get_predict_operation_log()
        • AutoRegressor.get_summary()
        • AutoRegressor.is_fitted()
        • AutoRegressor.load_model()
        • AutoRegressor.save_artifact()
        • AutoRegressor.save_model()
        • AutoRegressor.schedule_fit()
        • AutoRegressor.schedule_predict()
        • AutoRegressor.set_params()
        • AutoRegressor.set_scale_out()
    • hana_ml.algorithms.apl.clustering
      • AutoUnsupervisedClustering
        • AutoUnsupervisedClustering.fit()
        • AutoUnsupervisedClustering.fit_predict()
        • AutoUnsupervisedClustering.get_metrics()
        • AutoUnsupervisedClustering.disable_hana_execution()
        • AutoUnsupervisedClustering.enable_hana_execution()
        • AutoUnsupervisedClustering.export_apply_code()
        • AutoUnsupervisedClustering.get_apl_version()
        • AutoUnsupervisedClustering.get_artifacts_recorder()
        • AutoUnsupervisedClustering.get_debrief_report()
        • AutoUnsupervisedClustering.get_fit_operation_log()
        • AutoUnsupervisedClustering.get_indicators()
        • AutoUnsupervisedClustering.get_model_info()
        • AutoUnsupervisedClustering.get_params()
        • AutoUnsupervisedClustering.get_predict_operation_log()
        • AutoUnsupervisedClustering.get_summary()
        • AutoUnsupervisedClustering.is_fitted()
        • AutoUnsupervisedClustering.load_model()
        • AutoUnsupervisedClustering.predict()
        • AutoUnsupervisedClustering.save_artifact()
        • AutoUnsupervisedClustering.save_model()
        • AutoUnsupervisedClustering.schedule_fit()
        • AutoUnsupervisedClustering.schedule_predict()
        • AutoUnsupervisedClustering.set_params()
        • AutoUnsupervisedClustering.set_scale_out()
      • AutoSupervisedClustering
        • AutoSupervisedClustering.set_params()
        • AutoSupervisedClustering.fit()
        • AutoSupervisedClustering.predict()
        • AutoSupervisedClustering.fit_predict()
        • AutoSupervisedClustering.get_metrics()
        • AutoSupervisedClustering.load_model()
        • AutoSupervisedClustering.disable_hana_execution()
        • AutoSupervisedClustering.enable_hana_execution()
        • AutoSupervisedClustering.export_apply_code()
        • AutoSupervisedClustering.get_apl_version()
        • AutoSupervisedClustering.get_artifacts_recorder()
        • AutoSupervisedClustering.get_debrief_report()
        • AutoSupervisedClustering.get_fit_operation_log()
        • AutoSupervisedClustering.get_indicators()
        • AutoSupervisedClustering.get_model_info()
        • AutoSupervisedClustering.get_params()
        • AutoSupervisedClustering.get_predict_operation_log()
        • AutoSupervisedClustering.get_summary()
        • AutoSupervisedClustering.is_fitted()
        • AutoSupervisedClustering.save_artifact()
        • AutoSupervisedClustering.save_model()
        • AutoSupervisedClustering.schedule_fit()
        • AutoSupervisedClustering.schedule_predict()
        • AutoSupervisedClustering.set_scale_out()
  • hana_ml.algorithms.pal package
    • Algorithms
      • PAL Base
        • PALBase
      • Auto ML
        • AutomaticClassification
        • AutomaticRegression
        • AutomaticTimeSeries
        • Preprocessing
      • Unified Interface
        • UnifiedClassification
        • UnifiedRegression
        • UnifiedClustering
        • UnifiedExponentialSmoothing
      • Clustering
        • AffinityPropagation
        • AgglomerateHierarchicalClustering
        • DBSCAN
        • GeometryDBSCAN
        • KMeans
        • KMedians
        • KMedoids
        • SpectralClustering
        • KMeansOutlier
        • GaussianMixture
        • SOM
        • SlightSilhouette
        • outlier_detection_kmeans
      • Classification
        • LinearDiscriminantAnalysis
        • LogisticRegression
        • OnlineMultiLogisticRegression
        • NaiveBayes
        • KNNClassifier
        • MLPClassifier
        • SVC
        • OneClassSVM
        • DecisionTreeClassifier
        • RDTClassifier
        • HybridGradientBoostingClassifier
      • Regression
        • LinearRegression
        • OnlineLinearRegression
        • KNNRegressor
        • MLPRegressor
        • PolynomialRegression
        • GLM
        • ExponentialRegression
        • BiVariateGeometricRegression
        • BiVariateNaturalLogarithmicRegression
        • CoxProportionalHazardModel
        • SVR
        • DecisionTreeRegressor
        • RDTRegressor
        • HybridGradientBoostingRegressor
      • Preprocessing
        • FeatureNormalizer
        • FeatureSelection
        • IsolationForest
        • KBinsDiscretizer
        • Imputer
        • Discretize
        • MDS
        • SMOTE
        • SMOTETomek
        • TomekLinks
        • Sampling
        • ImputeTS
        • PowerTransform
        • PCA
        • CATPCA
        • train_test_val_split
        • variance_test
      • Time Series
        • AdditiveModelForecast
        • ARIMA
        • AutoARIMA
        • CPD
        • BCPD
        • OnlineBCPD
        • BSTS
        • TimeSeriesClassification
        • SingleExponentialSmoothing
        • DoubleExponentialSmoothing
        • TripleExponentialSmoothing
        • AutoExponentialSmoothing
        • BrownExponentialSmoothing
        • Croston
        • CrostonTSB
        • GARCH
        • Hierarchical_Forecast
        • LR_seasonal_adjust
        • LSTM
        • LTSF
        • OnlineARIMA
        • OutlierDetectionTS
        • GRUAttention
        • ROCKET
        • VectorARIMA
        • DWT
        • accuracy_measure
        • correlation
        • fft
        • dtw
        • fast_dtw
        • intermittent_forecast
        • periodogram
        • stationarity_test
        • seasonal_decompose
        • trend_test
        • wavedec
        • waverec
        • wpdec
        • wprec
        • white_noise_test
      • Statistics
        • bernoulli
        • beta
        • binomial
        • cauchy
        • chi_squared
        • exponential
        • gumbel
        • f
        • gamma
        • geometric
        • lognormal
        • negative_binomial
        • normal
        • pert
        • poisson
        • student_t
        • uniform
        • weibull
        • multinomial
        • mcmc
        • chi_squared_goodness_of_fit
        • chi_squared_independence
        • ttest_1samp
        • ttest_ind
        • ttest_paired
        • f_oneway
        • f_oneway_repeated
        • univariate_analysis
        • covariance_matrix
        • pearsonr_matrix
        • iqr
        • wilcoxon
        • median_test_1samp
        • grubbs_test
        • entropy
        • condition_index
        • cdf
        • ftest_equal_var
        • factor_analysis
        • kaplan_meier_survival_analysis
        • quantile
        • distribution_fit
        • ks_test
        • interval_quality
        • KDE
      • Association
        • Apriori
        • AprioriLite
        • FPGrowth
        • KORD
        • SPM
      • Recommender System
        • ALS
        • FRM
        • FFMClassifier
        • FFMRegressor
        • FFMRanker
      • Social Network Analysis
        • LinkPrediction
        • PageRank
      • Ranking
        • SVRanking
      • Miscellaneous
        • abc_analysis
        • weighted_score_table
        • TSNE
      • Metrics
        • accuracy_score
        • auc
        • confusion_matrix
        • multiclass_auc
        • r2_score
      • Model and Pipeline
        • ParamSearchCV
        • GridSearchCV
        • RandomSearchCV
        • Pipeline
      • Text Processing
        • CRF
        • LatentDirichletAllocation
    • Topics
      • Model Evaluation and Parameter Selection
        • Resampling Methods
        • Search Strategies
      • Successive Halving and Hyperband for Parameter Selection
        • Key Relevant Parameters
      • Biased Linear Models
      • Model State for Real-Time Scoring
      • Local Interpretability of Models
        • SHAP
        • Surrogate
        • Direct Explanation
        • Models/Algorithms in hana_ml.algorithms.pal Packages that Support Local Interpretability
        • Key Relevant Parameters in hana-ml.algorithms.pal Package
      • Explaining the Forecasts of ARIMA
      • Methods for Residual Extraction in Time-Series Outlier Detection
        • 1. Residual from Median Filter
        • 2. Residual from Seasonal Decomposition
        • 3. Residual Extraction from Median Filter and Seasonal Decomposition
        • 4. Meaningless Parameter Combination to be Avoided
      • Methods of Outlier Detection from Residual
        • 1. Z1 Score
        • 2. Z2 Score
        • 3. IQR Score
        • 4. MAD Score
        • 5. Isolation Forest Score
        • 6. DBSCAN
      • Genetic Optimization in AutoML
        • Individual Representation
        • Selection
        • Crossover
        • Mutation
        • Evolutional Iteration Step
        • Control Parameters
      • Probability Density Functions for MCMC Sampling
      • Miscellaneous Topics
        • Early Stop in HGBT
        • Feature Grouping in HGBT
        • Histogram Splitting in HGBT
        • Model Compression for Random Decision Trees
        • Model Compression for Support Vector Machine
        • Seasonalities in Additive Model Forecast
      • Precomputed Distance Matrix as input data in UnifiedClustering
      • Parameters for Missing Value Handling in HANA DataFrame
      • Permutation Feature Importance
    • Parameter Mappings
  • hana_ml.visualizers package
    • hana_ml.visualizers.eda
      • quarter_plot()
      • seasonal_plot()
      • timeseries_box_plot()
      • plot_acf()
      • plot_pacf()
      • plot_time_series_outlier()
      • plot_change_points()
      • plot_moving_average()
      • plot_rolling_stddev()
      • plot_seasonal_decompose()
      • kdeplot()
      • hist()
      • plot_psd()
      • EDAVisualizer
        • EDAVisualizer.distribution_plot()
        • EDAVisualizer.pie_plot()
        • EDAVisualizer.correlation_plot()
        • EDAVisualizer.scatter_plot()
        • EDAVisualizer.bar_plot()
        • EDAVisualizer.box_plot()
        • EDAVisualizer.ax
        • EDAVisualizer.cmap
        • EDAVisualizer.reset()
        • EDAVisualizer.set_ax()
        • EDAVisualizer.set_cmap()
        • EDAVisualizer.set_size()
        • EDAVisualizer.size
      • Profiler
        • Profiler.description()
        • Profiler.set_size()
    • hana_ml.visualizers.metrics
      • MetricsVisualizer
        • MetricsVisualizer.plot_confusion_matrix()
        • MetricsVisualizer.ax
        • MetricsVisualizer.cmap
        • MetricsVisualizer.reset()
        • MetricsVisualizer.set_ax()
        • MetricsVisualizer.set_cmap()
        • MetricsVisualizer.set_size()
        • MetricsVisualizer.size
    • hana_ml.visualizers.m4_sampling
      • get_min_index()
      • get_max_index()
      • m4_sampling()
    • hana_ml.visualizers.model_debriefing
      • TreeModelDebriefing
        • TreeModelDebriefing.tree_debrief()
        • TreeModelDebriefing.tree_export()
        • TreeModelDebriefing.tree_parse()
        • TreeModelDebriefing.tree_debrief_with_dot()
        • TreeModelDebriefing.tree_export_with_dot()
        • TreeModelDebriefing.shapley_explainer()
    • hana_ml.visualizers.dataset_report
      • DatasetReportBuilder
        • DatasetReportBuilder.build()
        • DatasetReportBuilder.generate_html_report()
        • DatasetReportBuilder.generate_notebook_iframe_report()
        • DatasetReportBuilder.get_report_html()
        • DatasetReportBuilder.get_iframe_report_html()
    • hana_ml.visualizers.shap
      • ShapleyExplainer
        • ShapleyExplainer.get_feature_value_and_effect()
        • ShapleyExplainer.get_force_plot_item()
        • ShapleyExplainer.get_beeswarm_plot_item()
        • ShapleyExplainer.get_bar_plot_item()
        • ShapleyExplainer.get_dependence_plot_items()
        • ShapleyExplainer.get_enhanced_dependence_plot_items()
        • ShapleyExplainer.force_plot()
        • ShapleyExplainer.summary_plot()
      • TimeSeriesExplainer
        • TimeSeriesExplainer.explain_arima_model()
        • TimeSeriesExplainer.explain_additive_model()
    • hana_ml.visualizers.unified_report
      • UnifiedReport
        • UnifiedReport.set_model_report_style()
        • UnifiedReport.build()
        • UnifiedReport.set_metric_samplings()
        • UnifiedReport.tree_debrief()
        • UnifiedReport.display()
        • UnifiedReport.get_iframe_report()
    • hana_ml.visualizers.visualizer_base
      • forecast_line_plot()
    • hana_ml.visualizers.digraph
      • Node
      • InPort
      • OutPort
      • Edge
      • DigraphConfig
        • DigraphConfig.set_text_layout()
        • DigraphConfig.set_digraph_layout()
        • DigraphConfig.set_node_sep()
        • DigraphConfig.set_rank_sep()
      • Digraph
        • Digraph.to_json()
        • Digraph.build()
        • Digraph.generate_html()
        • Digraph.generate_notebook_iframe()
        • Digraph.add_edge()
        • Digraph.add_model_node()
        • Digraph.add_python_node()
      • MultiDigraph
        • MultiDigraph.ChildDigraph
        • MultiDigraph.add_child_digraph()
        • MultiDigraph.to_json()
        • MultiDigraph.build()
        • MultiDigraph.generate_html()
        • MultiDigraph.generate_notebook_iframe()
    • hana_ml.visualizers.word_cloud
      • WordCloud
        • WordCloud.build()
        • WordCloud.fit_words()
        • WordCloud.generate()
        • WordCloud.generate_from_frequencies()
        • WordCloud.generate_from_text()
        • WordCloud.process_text()
        • WordCloud.recolor()
        • WordCloud.to_array()
        • WordCloud.to_file()
        • WordCloud.to_svg()
    • hana_ml.visualizers.automl_progress
      • TaskManager
        • TaskManager.run()
        • TaskManager.daemon
        • TaskManager.getName()
        • TaskManager.ident
        • TaskManager.isDaemon()
        • TaskManager.is_alive()
        • TaskManager.join()
        • TaskManager.name
        • TaskManager.native_id
        • TaskManager.setDaemon()
        • TaskManager.setName()
        • TaskManager.start()
      • FetchProgressStatusFromSystemTableTask
        • FetchProgressStatusFromSystemTableTask.run()
        • FetchProgressStatusFromSystemTableTask.daemon
        • FetchProgressStatusFromSystemTableTask.getName()
        • FetchProgressStatusFromSystemTableTask.ident
        • FetchProgressStatusFromSystemTableTask.isDaemon()
        • FetchProgressStatusFromSystemTableTask.is_alive()
        • FetchProgressStatusFromSystemTableTask.join()
        • FetchProgressStatusFromSystemTableTask.name
        • FetchProgressStatusFromSystemTableTask.native_id
        • FetchProgressStatusFromSystemTableTask.setDaemon()
        • FetchProgressStatusFromSystemTableTask.setName()
        • FetchProgressStatusFromSystemTableTask.start()
      • PipelineProgressStatusMonitor
        • PipelineProgressStatusMonitor.start()
    • hana_ml.visualizers.automl_report
      • BestPipelineReport
        • BestPipelineReport.generate_notebook_iframe()
        • BestPipelineReport.generate_html()
    • hana_ml.visualizers.time_series_report
      • TimeSeriesReport
        • TimeSeriesReport.addPage()
        • TimeSeriesReport.addPages()
        • TimeSeriesReport.build()
        • TimeSeriesReport.generate_html()
        • TimeSeriesReport.generate_notebook_iframe()
        • TimeSeriesReport.to_json()
      • DatasetAnalysis
        • DatasetAnalysis.pacf_item()
        • DatasetAnalysis.moving_average_item()
        • DatasetAnalysis.rolling_stddev_item()
        • DatasetAnalysis.seasonal_item()
        • DatasetAnalysis.timeseries_box_item()
        • DatasetAnalysis.seasonal_decompose_items()
        • DatasetAnalysis.quarter_item()
        • DatasetAnalysis.outlier_item()
        • DatasetAnalysis.stationarity_item()
        • DatasetAnalysis.real_item()
        • DatasetAnalysis.change_points_item()
  • hana_ml.ml_exceptions
    • Error
    • FitIncompleteError
    • BadSQLError
    • PALUnusableError
    • ModelExistingError
  • hana_ml.model_storage
    • ModelStorageError
    • ModelStorage
      • ModelStorage.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.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.generators.abap
      • AMDPGenerator
        • AMDPGenerator.generate()
    • hana_ml.artifacts.generators.hana
      • HANAGeneratorForCAP
        • HANAGeneratorForCAP.materialize_ds_data()
        • HANAGeneratorForCAP.generate_artifacts()
      • HanaGenerator
        • HanaGenerator.generate_artifacts()
  • hana_ml.docstore package
    • create_collection_from_elements()
  • hana_ml.spatial package
    • create_predefined_srs()
    • is_srs_created()
    • get_created_srses()
  • hana_ml.graph package
    • Graph
      • Graph.describe()
      • Graph.degree_distribution()
      • Graph.drop()
      • Graph.has_vertices()
      • Graph.vertices()
      • Graph.edges()
      • Graph.in_edges()
      • Graph.out_edges()
      • Graph.source()
      • Graph.target()
      • Graph.subgraph()
    • create_graph_from_dataframes()
    • create_graph_from_edges_dataframe()
    • create_graph_from_hana_dataframes()
    • discover_graph_workspace()
    • discover_graph_workspaces()
  • hana_ml.graph.algorithms package
    • ShortestPath
      • ShortestPath.execute()
      • ShortestPath.vertices
      • ShortestPath.edges
      • ShortestPath.weight
    • Neighbors
      • Neighbors.execute()
      • Neighbors.vertices
    • NeighborsSubgraph
      • NeighborsSubgraph.execute()
      • NeighborsSubgraph.vertices
      • NeighborsSubgraph.edges
    • KShortestPaths
      • KShortestPaths.execute()
      • KShortestPaths.paths
    • TopologicalSort
      • TopologicalSort.execute()
      • TopologicalSort.vertices
      • TopologicalSort.is_sortable
    • ShortestPathsOneToAll
      • ShortestPathsOneToAll.execute()
      • ShortestPathsOneToAll.vertices
      • ShortestPathsOneToAll.edges
    • StronglyConnectedComponents
      • StronglyConnectedComponents.execute()
      • StronglyConnectedComponents.vertices
      • StronglyConnectedComponents.components
      • StronglyConnectedComponents.components_count
    • WeaklyConnectedComponents
      • WeaklyConnectedComponents.execute()
      • WeaklyConnectedComponents.vertices
      • WeaklyConnectedComponents.components
      • WeaklyConnectedComponents.components_count
    • AlgorithmBase
      • AlgorithmBase.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()
  • hana_ml.text.tm package
    • hana_ml.text.tm
      • tf_analysis()
      • text_classification()
      • get_related_doc()
      • get_related_term()
      • get_relevant_doc()
      • get_relevant_term()
      • get_suggested_term()
      • TFIDF
        • TFIDF.text_collector()
        • TFIDF.text_tfidf()
        • TFIDF.add_attribute()
        • TFIDF.apply_with_hint()
        • TFIDF.consume_fit_hdbprocedure()
        • TFIDF.consume_predict_hdbprocedure()
        • TFIDF.create_apply_func()
        • TFIDF.disable_arg_check()
        • TFIDF.disable_convert_bigint()
        • TFIDF.disable_hana_execution()
        • TFIDF.disable_with_hint()
        • TFIDF.enable_arg_check()
        • TFIDF.enable_convert_bigint()
        • TFIDF.enable_hana_execution()
        • TFIDF.enable_no_inline()
        • TFIDF.enable_parallel_by_parameter_partitions()
        • TFIDF.enable_workload_class()
        • TFIDF.fit_hdbprocedure
        • TFIDF.get_fit_execute_statement()
        • TFIDF.get_fit_output_table_names()
        • TFIDF.get_fit_parameters()
        • TFIDF.get_pal_function()
        • TFIDF.get_parameters()
        • TFIDF.get_predict_execute_statement()
        • TFIDF.get_predict_output_table_names()
        • TFIDF.get_predict_parameters()
        • TFIDF.get_score_execute_statement()
        • TFIDF.get_score_output_table_names()
        • TFIDF.get_score_parameters()
        • TFIDF.is_fitted()
        • TFIDF.load_model()
        • TFIDF.predict_hdbprocedure
        • TFIDF.set_scale_out()
  • hana_ml.hana_scheduler
    • HANAScheduler
      • HANAScheduler.check_scheduler_job_exist()
      • HANAScheduler.list_schedules()
      • HANAScheduler.display_schedule_status()
      • HANAScheduler.set_schedule()
      • HANAScheduler.delete_schedule()
      • HANAScheduler.create_training_schedule()
      • HANAScheduler.create_applying_schedule()
      • HANAScheduler.create_scoring_schedule()
  • FAQs
hana-ml
  • hana-ml Tutorials
  • View page source
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hana-ml Tutorials

We provide a series of Jupyter notebooks to illustrate the usage of hana-ml.

  • DataFrame

PAL package

  • Auto ML

  • Classification

  • Regression

  • Clustering

APL package

  • Classification (part 1)

  • Classification (part 2)

  • Regression

  • Time Series Forecasting

End-to-End Scenarios

  • Scenario 1: Analyze the Cash Flow of an Investment on a New Product

  • Scenario 2: Predict Segmentation of New Customers for a Supermarket

  • Scenario 3: Survival Analysis


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