/sap-hana-ml

SAP HANA ML Skill

Claude Code skill for SAP HANA Machine Learning Python Client (hana-ml) development.

Overview

This skill provides comprehensive guidance for building machine learning solutions using SAP HANA's in-database ML capabilities with Python. It covers the hana-ml library including PAL (Predictive Analysis Library), APL (Automated Predictive Library), DataFrames, visualizations, and model management.

Version

• Skill Version: 1.1.0
• hana-ml Version: 2.22.241011
• Last Verified: 2025-11-27

Capability Index

Auto-Trigger Keywords

This skill activates when working with:

Library & Connection

• hana-ml, hana_ml, hana ml
• SAP HANA machine learning, HANA ML
• ConnectionContext, HANA connection
• hdbcli, SAP HANA Python driver

DataFrame Operations

• HANA DataFrame, hana_ml.dataframe
• create_dataframe_from_pandas
• collect(), filter(), select()
• HANA table operations

PAL Algorithms

• PAL, Predictive Analysis Library
• UnifiedClassification, UnifiedRegression, UnifiedClustering
• KMeans, DBSCAN, clustering HANA
• LogisticRegression HANA, DecisionTree HANA
• ARIMA HANA, AutoARIMA, time series HANA
• LSTM HANA, GRUAttention
• HybridGradientBoostingClassifier, HybridGradientBoostingRegressor
• FeatureNormalizer, PCA HANA, Imputer HANA
• SMOTE HANA, train_test_val_split
• GridSearchCV HANA, RandomSearchCV HANA

APL Algorithms

• APL, Automated Predictive Library
• AutoClassifier, AutoRegressor
• GradientBoostingClassifier APL
• AutoTimeSeries, HANA forecasting
• AutoML HANA, automated machine learning HANA

Visualizations

• EDAVisualizer, HANA visualization
• ShapleyExplainer, SHAP HANA
• TreeModelDebriefing
• MetricsVisualizer, confusion matrix HANA
• plot_acf, plot_pacf, seasonal_plot

Model Management

• ModelStorage, save_model HANA
• load_model HANA, model persistence
• export_apply_code

Advanced Features

• GeometryDBSCAN, spatial clustering HANA
• LatentDirichletAllocation, topic modeling HANA
• Pipeline HANA ML
• feature_importances HANA

Statistics & Testing

• ttest HANA, chi_squared HANA
• f_oneway, ANOVA HANA
• distribution_fit, KDE HANA
• kaplan_meier HANA, survival analysis

Spatial & Graph

• hana_ml.spatial, spatial analytics
• hana_ml.graph, graph algorithms
• PageRank HANA, LinkPrediction
• create_dataframe_from_shapefile

Scheduling & Artifacts

• schedule_fit, schedule_predict
• hana_ml.artifacts, model artifacts
• get_artifacts_recorder

Error Keywords

• hana_ml.ml_exceptions
• ConnectionContext error
• PAL algorithm error
• HANA ML fit error

Contents

sap-hana-ml/
├── SKILL.md                    # Main skill file
├── README.md                   # This file
└── references/
    ├── DATAFRAME_REFERENCE.md  # Complete DataFrame API
    ├── PAL_ALGORITHMS.md       # All PAL algorithms (100+)
    ├── APL_ALGORITHMS.md       # All APL algorithms (AutoML)
    ├── VISUALIZERS.md          # Visualization API (14 submodules)
    └── SUPPORTING_MODULES.md   # Model storage, spatial, graph, stats

Quick Start

from hana_ml import ConnectionContext
from hana_ml.algorithms.pal.unified_classification import UnifiedClassification

# Connect to HANA
conn = ConnectionContext(address='host', port=443, user='user', password='pwd', encrypt=True)

# Load data
df = conn.table('TRAINING_DATA')

# Train model
clf = UnifiedClassification(func='RandomDecisionTree')
clf.fit(df, features=['F1', 'F2'], label='TARGET')

# Predict
predictions = clf.predict(conn.table('TEST_DATA'), features=['F1', 'F2'])

Use Cases

• Building classification models with PAL or APL
• Creating regression models for prediction
• Clustering analysis with KMeans, DBSCAN
• Time series forecasting with ARIMA, LSTM
• AutoML with APL AutoClassifier/AutoRegressor
• Model explainability with SHAP
• Feature engineering and preprocessing
• Hyperparameter tuning with GridSearchCV
• Model persistence and deployment

Documentation Links

• Main Documentation
• Installation Guide
• DataFrame API
• PAL Algorithms
• APL Algorithms
• Visualizers
• PyPI Package

License

GPL-3.0