sap-hana-ml

name	sap-hana-ml
description	SAP HANA Machine Learning Python Client (hana-ml) development skill. Use when: Building ML solutions with SAP HANA's in-database machine learning using 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
license	GPL-3.0
metadata	{"version":"1.1.0","last_verified":"2025-11-27T00:00:00.000Z","package_version":"2.22.241011"}

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

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 (12K-19K lines each)

• references/DATAFRAME_REFERENCE.md (10,356 lines)

  • ConnectionContext API, DataFrame operations, SQL generation

• references/PAL_ALGORITHMS.md (18,647 lines)

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

• references/APL_ALGORITHMS.md (12,271 lines)

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

• references/VISUALIZERS.md (13,535 lines)

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

• references/SUPPORTING_MODULES.md (13,692 lines)

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

Quick Reference Guides

• Algorithm Categories - PAL vs APL comparison
• Connection Patterns - Different authentication methods
• Model Deployment - Export, save, load patterns
• Performance Tuning - Optimization techniques

---

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/