sap-hana-ml

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SAP HANA Machine Learning Python Client (hana-ml) development skill.Use when: Building ML solutions with SAP HANA's in-database machine learningusing Python hana-ml library for PAL/APL algorithms, DataFrame operations,AutoML, model persistence, and visualization.Keywords: hana-ml, SAP HANA, machine learning, PAL, APL, predictive analytics,HANA DataFrame, ConnectionContext, classification, regression, clustering,time series, ARIMA, gradient boosting, AutoML, SHAP, model storage

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[SQL and Databases]

When & Why to Use This Skill

The SAP HANA Machine Learning (hana-ml) skill enables developers to build, train, and deploy advanced machine learning models directly within the SAP HANA environment using Python. By leveraging in-database processing, it eliminates the need for costly data movement, ensuring high performance for large-scale enterprise analytics. This skill provides comprehensive support for the Predictive Analysis Library (PAL), Automated Predictive Library (APL), and HANA DataFrames, covering the entire data science lifecycle from exploratory data analysis and feature engineering to AutoML and model persistence.

Use Cases

  • In-Database Predictive Analytics: Implementing classification, regression, and clustering models using PAL algorithms directly on SAP HANA data to maximize performance.
  • Automated Business Forecasting: Leveraging APL's AutoML capabilities for rapid feature engineering and model selection in scenarios like lead scoring or churn prediction.
  • Enterprise Time Series Analysis: Utilizing ARIMA and other time-series algorithms for demand planning and supply chain optimization within the database.
  • Model Lifecycle Management: Using ModelStorage to save, version, and deploy machine learning models for seamless integration into SAP business applications.
  • Explainable AI (XAI): Generating SHAP-based explanations and visual insights to provide transparency for complex enterprise model predictions.
namesap-hana-ml
description|
Use whenBuilding ML solutions with SAP HANA's in-database machine learning
Keywordshana-ml, SAP HANA, machine learning, PAL, APL, predictive analytics,
licenseGPL-3.0
version1.1.0
last_verified2025-11-27
package_version2.22.241011

SAP HANA ML Python Client (hana-ml)

Package Version: 2.22.241011
Last Verified: 2025-11-27

Table of Contents

Installation & Setup

pip install hana-ml

Requirements: Python 3.8+, SAP HANA 2.0 SPS03+ or SAP HANA Cloud

Quick Start

Connection & DataFrame

from hana_ml import ConnectionContext

# Connect
conn = ConnectionContext(
    address='<hostname>',
    port=443,
    user='<username>',
    password='<password>',
    encrypt=True
)

# Create DataFrame
df = conn.table('MY_TABLE', schema='MY_SCHEMA')
print(f"Shape: {df.shape}")
df.head(10).collect()

PAL Classification

from hana_ml.algorithms.pal.unified_classification import UnifiedClassification

# Train model
clf = UnifiedClassification(func='RandomDecisionTree')
clf.fit(train_df, features=['F1', 'F2', 'F3'], label='TARGET')

# Predict & evaluate
predictions = clf.predict(test_df, features=['F1', 'F2', 'F3'])
score = clf.score(test_df, features=['F1', 'F2', 'F3'], label='TARGET')

APL AutoML

from hana_ml.algorithms.apl.classification import AutoClassifier

# Automated classification
auto_clf = AutoClassifier()
auto_clf.fit(train_df, label='TARGET')
predictions = auto_clf.predict(test_df)

Model Persistence

from hana_ml.model_storage import ModelStorage

ms = ModelStorage(conn)
clf.name = 'MY_CLASSIFIER'
ms.save_model(model=clf, if_exists='replace')

Core Libraries

PAL (Predictive Analysis Library)

  • 100+ algorithms executed in-database
  • Categories: Classification, Regression, Clustering, Time Series, Preprocessing
  • Key classes: UnifiedClassification, UnifiedRegression, KMeans, ARIMA
  • See: references/PAL_ALGORITHMS.md for complete list

APL (Automated Predictive Library)

  • AutoML capabilities with automatic feature engineering
  • Key classes: AutoClassifier, AutoRegressor, GradientBoostingClassifier
  • See: references/APL_ALGORITHMS.md for details

DataFrames

  • Lazy evaluation - builds SQL until collect() called
  • In-database processing for optimal performance
  • See: references/DATAFRAME_REFERENCE.md for complete API

Visualizers

  • EDA plots, model explanations, metrics
  • SHAP integration for model interpretability
  • See: references/VISUALIZERS.md for 14 visualization modules

Common Patterns

Train-Test Split

from hana_ml.algorithms.pal.partition import train_test_val_split

train, test, val = train_test_val_split(
    data=df,
    training_percentage=0.7,
    testing_percentage=0.2,
    validation_percentage=0.1
)

Feature Importance

# APL models
importance = auto_clf.get_feature_importances()

# PAL models
from hana_ml.algorithms.pal.preprocessing import FeatureSelection
fs = FeatureSelection()
fs.fit(train_df, features=features, label='TARGET')

Pipeline

from hana_ml.algorithms.pal.pipeline import Pipeline
from hana_ml.algorithms.pal.preprocessing import Imputer, FeatureNormalizer

pipeline = Pipeline([
    ('imputer', Imputer(strategy='mean')),
    ('normalizer', FeatureNormalizer()),
    ('classifier', UnifiedClassification(func='RandomDecisionTree'))
])

Best Practices

  1. Use lazy evaluation - Operations build SQL without execution until collect()
  2. Leverage in-database processing - Keep data in HANA for performance
  3. Use Unified interfaces - Consistent APIs across algorithms
  4. Save models - Use ModelStorage for persistence
  5. Explain predictions - Use SHAP explainers for interpretability
  6. Monitor AutoML - Use PipelineProgressStatusMonitor for long-running jobs

Bundled Resources

Reference Files

  • references/DATAFRAME_REFERENCE.md (479 lines)

    • ConnectionContext API, DataFrame operations, SQL generation

  • references/PAL_ALGORITHMS.md (869 lines)

    • Complete PAL algorithm reference (100+ algorithms)
    • Classification, Regression, Clustering, Time Series, Preprocessing

  • references/APL_ALGORITHMS.md (534 lines)

    • AutoML capabilities, automated feature engineering
    • AutoClassifier, AutoRegressor, GradientBoosting classes

  • references/VISUALIZERS.md (704 lines)

    • 14 visualization modules (EDA, SHAP, metrics, time series)
    • Plot types, configuration, export options

  • references/SUPPORTING_MODULES.md (626 lines)

    • Model storage, spatial analytics, graph algorithms
    • Text mining, statistics, error handling

Error Handling

from hana_ml.ml_exceptions import Error

try:
    clf.fit(train_df, features=features, label='TARGET')
except Error as e:
    print(f"HANA ML Error: {e}")

Documentation