{"id":9637,"library":"databricks-automl-runtime","title":"Databricks AutoML Runtime","description":"The Databricks AutoML Runtime package provides utilities and custom transformers designed to integrate with Databricks AutoML, particularly for time series and other specialized machine learning tasks. It offers custom scikit-learn compatible transformers, hyperparameter tuning wrappers for models like Prophet and pmdarima, and MLflow logging integrations. The current version is 0.2.21, and it has a moderate release cadence, often addressing compatibility or bug fixes.","status":"active","version":"0.2.21","language":"en","source_language":"en","source_url":"https://github.com/databricks/automl","tags":["databricks","automl","mlflow","machine-learning","time-series","sklearn-compatible"],"install":[{"cmd":"pip install databricks-automl-runtime","lang":"bash","label":"Install core package"},{"cmd":"pip install databricks-automl-runtime[prophet]","lang":"bash","label":"Install with Prophet support"},{"cmd":"pip install databricks-automl-runtime[pmdarima]","lang":"bash","label":"Install with pmdarima support"}],"dependencies":[{"reason":"Core dependency for MLflow integration and experiment tracking.","package":"mlflow"},{"reason":"Provides base classes and utilities for sklearn-compatible transformers.","package":"scikit-learn","version":">=0.23.2,<=1.0"},{"reason":"Data manipulation for input/output data structures.","package":"pandas"},{"reason":"Used for configuration and serialization.","package":"pyyaml"},{"reason":"Scientific computing dependency.","package":"scipy"},{"reason":"Numerical computing dependency.","package":"numpy"},{"reason":"Required for hyperparameter tuning functionalities, specifically HyperoptEstimators.","package":"hyperopt","version":">=0.2.7"},{"reason":"Optional: Required for Prophet-based time series models and hyperparameter tuning.","package":"prophet","version":">=1.0.1,<1.1","optional":true},{"reason":"Optional: Required for pmdarima-based ARIMA time series models and hyperparameter tuning.","package":"pmdarima","version":">=2.0.0,<2.1","optional":true}],"imports":[{"symbol":"DateTransformer","correct":"from automl_runtime.sklearn.date_time_transformers import DateTransformer"},{"note":"Requires 'prophet' extra to be installed.","symbol":"ProphetHyperoptEstimator","correct":"from automl_runtime.hyperopt.prophet_hyperopt_estimator import ProphetHyperoptEstimator"},{"note":"Requires 'pmdarima' extra to be installed.","symbol":"ArimaHyperoptEstimator","correct":"from automl_runtime.hyperopt.arima_hyperopt_estimator import ArimaHyperoptEstimator"}],"quickstart":{"code":"import pandas as pd\nfrom automl_runtime.sklearn.date_time_transformers import DateTransformer\n\n# Create a sample DataFrame with a datetime column\ndf = pd.DataFrame({\n    'timestamp_col': pd.to_datetime(['2023-01-01', '2023-01-02', '2023-01-03']),\n    'value': [10, 12, 15]\n})\n\n# Instantiate the DateTransformer\n# This transformer extracts date-related features like year, month, day, day_of_week, etc.\ndate_transformer = DateTransformer(\n    timestamp_col='timestamp_col', \n    output_timestamp_col_name='datetime_features'\n)\n\n# Fit and transform the DataFrame\nX_transformed = date_transformer.fit_transform(df)\n\nprint(\"Original DataFrame:\\n\", df)\nprint(\"\\nTransformed DataFrame (first 5 columns):\\n\", X_transformed.iloc[:, :5])","lang":"python","description":"This quickstart demonstrates how to use the `DateTransformer` to extract various date and time features from a datetime column in a Pandas DataFrame. This is a common preprocessing step for time series models."},"warnings":[{"fix":"Ensure `hyperopt` is updated: `pip install -U \"hyperopt>=0.2.7\"`","message":"The library explicitly requires `hyperopt>=0.2.7`. Using older versions of hyperopt can lead to unexpected errors or crashes during hyperparameter tuning tasks, particularly when using `HyperoptEstimator` classes.","severity":"breaking","affected_versions":"<0.2.4"},{"fix":"Install with extras: `pip install databricks-automl-runtime[prophet]` or `pip install databricks-automl-runtime[pmdarima]`.","message":"Prophet and pmdarima are optional dependencies. If you intend to use `ProphetHyperoptEstimator` or `ArimaHyperoptEstimator`, you must install the respective packages separately using the extra syntax (e.g., `pip install databricks-automl-runtime[prophet]`). A `ModuleNotFoundError` will occur otherwise.","severity":"gotcha","affected_versions":"All versions"},{"fix":"Upgrade `databricks-automl-runtime` to version `0.2.4.1` or newer: `pip install -U databricks-automl-runtime`.","message":"Versions of the library before `0.2.4.1` (e.g., `0.2.3.1`, `0.2.4`) had known issues with Prophet hyper-parameter enumeration. This could lead to incorrect tuning outcomes or runtime errors when defining certain hyperparameter search spaces for Prophet models.","severity":"gotcha","affected_versions":"<0.2.4.1"}],"env_vars":null,"last_verified":"2026-04-17T00:00:00.000Z","next_check":"2026-07-16T00:00:00.000Z","problems":[{"fix":"Install `databricks-automl-runtime` with the `prophet` extra: `pip install databricks-automl-runtime[prophet]`","cause":"The Prophet library is an optional dependency and needs to be installed explicitly if you wish to use Prophet-related functionality.","error":"ModuleNotFoundError: No module named 'prophet'"},{"fix":"Upgrade `hyperopt` to the required version: `pip install -U \"hyperopt>=0.2.7\"`","cause":"This error or similar `hyperopt` related issues often indicate an incompatibility with the installed `hyperopt` version. The library explicitly requires `hyperopt>=0.2.7`.","error":"hyperopt.exceptions.UnsupportedSpace: The search space is not supported"},{"fix":"Upgrade `databricks-automl-runtime` to the latest version (`0.2.4.1` or newer) to get bug fixes for Prophet hyper-parameter handling: `pip install -U databricks-automl-runtime`","cause":"Older versions of `databricks-automl-runtime` had bugs related to Prophet's hyperparameter space, especially for enumeration types, causing issues during tuning.","error":"TypeError: '...' object is not iterable (related to hyper-parameters) or ValueError during Prophet tuning."}]}