Agent skill

sap-hana-ml

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

View SKILL.md on GitHub Repository
Stars 204
Forks 51

Install this agent skill to your Project

npx add-skill https://github.com/secondsky/sap-skills/tree/main/plugins/sap-hana-ml/skills/sap-hana-ml

Metadata

Additional technical details for this skill

version
1.1.0
last verified
1764201600
package version
2.22.241011

SKILL.md

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

bash
pip install hana-ml

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

Quick Start

Connection & DataFrame

python
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

python
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

python
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

python
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

python
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

python
# 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

python
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

python
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

Recommended Agent Skills

Expand your agent's capabilities with these related and highly-rated skills.

secondsky/sap-skills

sap-cap-capire

SAP Cloud Application Programming Model (CAP) development skill using Capire documentation. Use when: building CAP applications, defining CDS models, implementing services, working with SAP HANA/SQLite/PostgreSQL databases, deploying to SAP BTP Cloud Foundry or Kyma, implementing Fiori UIs, handling authorization, multitenancy, or messaging. Covers CDL/CQL/CSN syntax, Node.js and Java runtimes, event handlers, OData services, and CAP plugins.

204 51
Explore
secondsky/sap-skills

sap-btp-cloud-platform

204 51
Explore
secondsky/sap-skills

sap-btp-service-manager

This skill provides comprehensive knowledge for SAP Service Manager on SAP Business Technology Platform (BTP). It should be used when managing service instances, bindings, brokers, and platforms across Cloud Foundry, Kyma, Kubernetes, and other environments. Use when provisioning services via SMCTL CLI, BTP CLI, or REST APIs, configuring OAuth2 authentication, working with the SAP BTP Service Operator in Kubernetes, troubleshooting service consumption issues, or implementing cross-environment service management. Keywords: SAP Service Manager, BTP, service instances, service bindings, SMCTL, service broker, OSBAPI, Cloud Foundry, Kyma, Kubernetes, service-manager, service-operator-access, subaccount-admin, OAuth2, X.509, service marketplace, service plans, rate limiting, cf create-service, btp create services/instance, ServiceInstance CRD, ServiceBinding CRD

204 51
Explore
secondsky/sap-skills

sap-btp-business-application-studio

This skill provides comprehensive guidance for SAP Business Application Studio (BAS), the cloud-based IDE on SAP BTP built on Code-OSS. Use when setting up BAS subscriptions, creating dev spaces, connecting to external systems, deploying MTA applications, troubleshooting connectivity issues, managing Git repositories, configuring runtime versions, or using the layout editor. Keywords: SAP Business Application Studio, BAS, SAP BTP, dev space, Cloud Foundry, MTA, multitarget application, SAP Fiori, CAP, HANA, destination, WebIDEEnabled, Cloud Connector, Service Center, Storyboard, Layout Editor, ABAP, OData, subscription, entitlements, role collection, Business_Application_Studio_Developer, Git, clone, push, pull, Gerrit, PAT, OAuth, asdf, runtime, Node.js, Java, Python, Task Explorer, CI/CD, Yeoman, generator, template wizard, mbt, mtar, debugging, breakpoint

204 51
Explore
secondsky/sap-skills

sap-btp-cias

SAP BTP Cloud Integration Automation Service (CIAS) skill for guided integration workflows. Use when: setting up CIAS subscriptions, configuring destinations, assigning roles (CIASIntegrationAdministrator, CIASIntegrationExpert, CIASIntegrationMonitor), planning integration scenarios, working with My Inbox tasks, monitoring scenario execution, troubleshooting CIAS errors, creating OAuth2 instances, configuring identity providers for CIAS, understanding CIAS security architecture, or integrating SAP products (S/4HANA, SuccessFactors, BTP services, SAP Build, IBP).

204 51
Explore
secondsky/sap-skills

sap-ai-core

Guides development with SAP AI Core and SAP AI Launchpad for enterprise AI/ML workloads on SAP BTP. Use when: deploying generative AI models (GPT, Llama, Gemini, Mistral), building orchestration workflows with templating/filtering/grounding, implementing RAG with vector databases, managing ML training pipelines with Argo Workflows, configuring content filtering and data masking for PII protection, using the Generative AI Hub for prompt experimentation, or integrating AI capabilities into SAP applications. Covers service plans (Free/Standard/Extended), model providers (Azure OpenAI, AWS Bedrock, GCP Vertex AI, Mistral, IBM), orchestration modules, embeddings, tool calling, and structured outputs.

204 51
Explore

Didn't find tool you were looking for?

Be as detailed as possible for better results