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Install & Compatibility
Where this runs
tested against v0.2.21 · pip install
no network on importno background threads
Install × environment matrix
Each cell = how many times install + import succeeded across repeated harness runs. Partial = flaky.
glibc = Debian/Ubuntu slim · musl = Alpine Linux
muslpy 3.10–3.930 runs
installs and imports cleanly · install 0.0s · import 0.000s · 18.2MB
glibcpy 3.10–3.930 runs
installs and imports cleanly · install 1.6s · import 0.000s · 19MB
16MB installed
● package 16MB
Code
Verified usage
Verified import paths — ran on the pinned version, not inferred.
DateTransformer
✓ from databricks.automl_runtime.sklearn.date_time_transformers import DateTransformer
✗ from databricks.automl_runtime.sklearn.date_time_transformers import DateTransformer
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.
import pandas as pd
from automl_runtime.sklearn.date_time_transformers import DateTransformer
# Create a sample DataFrame with a datetime column
df = pd.DataFrame({
'timestamp_col': pd.to_datetime(['2023-01-01', '2023-01-02', '2023-01-03']),
'value': [10, 12, 15]
})
# Instantiate the DateTransformer
# This transformer extracts date-related features like year, month, day, day_of_week, etc.
date_transformer = DateTransformer(
timestamp_col='timestamp_col',
output_timestamp_col_name='datetime_features'
)
# Fit and transform the DataFrame
X_transformed = date_transformer.fit_transform(df)
print("Original DataFrame:\n", df)
print("\nTransformed DataFrame (first 5 columns):\n", X_transformed.iloc[:, :5])
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Version history
0.2.21latest on PyPI · released Feb 21, 2024
Audit
Dependencies
mlflowrequiredCore dependency for MLflow integration and experiment tracking.
scikit-learnrequiredProvides base classes and utilities for sklearn-compatible transformers.
pandasrequiredData manipulation for input/output data structures.
pyyamlrequiredUsed for configuration and serialization.
scipyrequiredScientific computing dependency.
numpyrequiredNumerical computing dependency.
hyperoptrequiredRequired for hyperparameter tuning functionalities, specifically HyperoptEstimators.
prophetoptionalOptional: Required for Prophet-based time series models and hyperparameter tuning.
pmdarimaoptionalOptional: Required for pmdarima-based ARIMA time series models and hyperparameter tuning.