Update Featuretools Branch Name #1038
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rwedge
reviewed
Jun 29, 2020
release.md
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| @@ -28,12 +28,12 @@ Branches on the conda-forge featuretools repo are automatically built and the pa | |||
| ```bash | |||
| git remote add upstream https://github.com/conda-forge/featuretools-feedstock.git | |||
| ``` | |||
| 4. If you made the fork previously and its master branch is missing commits, update it with any changes from upstream | |||
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Unsure if we will be able to rename this branch since it is part of conda-forge, worth looking into
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It seems that conda-forge defaults to master, and all the packages use master.
Codecov Report
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## main #1038 +/- ##
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- Coverage 98.35% 98.33% -0.02%
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Files 126 126
Lines 13052 13052
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- Hits 12837 12835 -2
- Misses 215 217 +2
Continue to review full report at Codecov.
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gsheni
commented
Jun 30, 2020
| @@ -88,7 +88,7 @@ Featuretools contains many [different types of built-in primitives](https://prim | |||
| ## Demos | |||
| **Predict Next Purchase** | |||
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| [Repository](https://github.com/Featuretools/predict_next_purchase/) | [Notebook](https://github.com/Featuretools/predict_next_purchase/blob/master/Tutorial.ipynb) | |||
| [Repository](https://github.com/Featuretools/predict_next_purchase/) | [Notebook](https://github.com/Featuretools/predict_next_purchase/blob/main/Tutorial.ipynb) | |||
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Main Branch Added:
https://github.com/Featuretools/predict-next-purchase/tree/main
gsheni
commented
Jun 30, 2020
| @@ -113,9 +113,9 @@ When an entire dataset is not required to calculate the features for a given set | |||
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| An example of this approach can be seen in the `Predict Next Purchase demo notebook <https://github.com/featuretools/predict_next_purchase>`_. In this example, we partition data by customer and only load a fixed number of customers into memory at any given time. We implement this easily using `Dask <https://dask.pydata.org/>`_, which could also be used to scale the computation to a cluster of computers. A framework like `Spark <https://spark.apache.org/>`_ could be used similarly. | |||
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| An additional example of partitioning data to distribute on multiple cores or a cluster using Dask can be seen in the `Featuretools on Dask notebook <https://github.com/Featuretools/Automated-Manual-Comparison/blob/master/Loan%20Repayment/notebooks/Featuretools%20on%20Dask.ipynb>`_. This approach is detailed in the `Parallelizing Feature Engineering with Dask article <https://medium.com/feature-labs-engineering/scaling-featuretools-with-dask-ce46f9774c7d>`_ on the Feature Labs engineering blog. Dask allows for simple scaling to multiple cores on a single computer or multiple machines on a cluster. | |||
| An additional example of partitioning data to distribute on multiple cores or a cluster using Dask can be seen in the `Featuretools on Dask notebook <https://github.com/Featuretools/Automated-Manual-Comparison/blob/main/Loan%20Repayment/notebooks/Featuretools%20on%20Dask.ipynb>`_. This approach is detailed in the `Parallelizing Feature Engineering with Dask article <https://medium.com/feature-labs-engineering/scaling-featuretools-with-dask-ce46f9774c7d>`_ on the Feature Labs engineering blog. Dask allows for simple scaling to multiple cores on a single computer or multiple machines on a cluster. | |||
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Main Branch Added:
https://github.com/Featuretools/Automated-Manual-Comparison/tree/main
gsheni
commented
Jun 30, 2020
docs/source/guides/performance.rst
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| For a similar partition and distribute implementation using Apache Spark with PySpark, refer to the `Feature Engineering on Spark notebook <https://github.com/Featuretools/predicting-customer-churn/blob/master/churn/4.%20Feature%20Engineering%20on%20Spark.ipynb>`_. This implementation shows how to carry out feature engineering on a cluster of EC2 instances using Spark as the distributed framework. A write-up of this approach is described in the `Featuretools on Spark article <https://blog.featurelabs.com/featuretools-on-spark-2/>`_ on the Feature Labs engineering blog. | ||
| For a similar partition and distribute implementation using Apache Spark with PySpark, refer to the `Feature Engineering on Spark notebook <https://github.com/Featuretools/predicting-customer-churn/blob/main/churn/4.%20Feature%20Engineering%20on%20Spark.ipynb>`_. This implementation shows how to carry out feature engineering on a cluster of EC2 instances using Spark as the distributed framework. A write-up of this approach is described in the `Featuretools on Spark article <https://blog.featurelabs.com/featuretools-on-spark-2/>`_ on the Feature Labs engineering blog. |
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Main Branch Added:
https://github.com/Featuretools/predict-customer-churn/tree/main/churn
gsheni
commented
Jun 30, 2020
.github/workflows/release.yml
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| - name: PyPI Upload | ||
| uses: FeatureLabs/gh-action-pypi-upload@master | ||
| uses: FeatureLabs/gh-action-pypi-upload@main |
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Main Branch added:
https://github.com/FeatureLabs/gh-action-pypi-upload/tree/main
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Perhaps we should switch to using tagged releases of this action so that the branch name isn't even a factor
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Pull Request Description
mastertomain