SAP HANA ML Python Client (hana-ml)

Package Version: 2.22.241011

Last Verified: 2025-11-27

Table of Contents

• Installation & Setup

• Quick Start

• Core Libraries

• Common Patterns

• Best Practices

• Bundled Resources

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

• Use lazy evaluation - Operations build SQL without execution until collect()

• Leverage in-database processing - Keep data in HANA for performance

• Use Unified interfaces - Consistent APIs across algorithms

• Save models - Use ModelStorage for persistence

• Explain predictions - Use SHAP explainers for interpretability

• 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

• Official Docs: https://help.sap.com/doc/1d0ebfe5e8dd44d09606814d83308d4b/2.0.07/en-US/hana_ml.html

• PyPI Package: https://pypi.org/project/hana-ml/