featuretools
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alteryx
An open source python library for automated feature engineering
- The featuretools_initialize entry point allows users to registry functions to featuretools, and for those functions to be run when featuretools is imported. - This issue tracks removing the `featuretools_initialize`...
Koalas DataFrames cannot be deepcopied, so currently `EntitySet.__deepcopy__` will not work for EntitySets with koalas dataframes. The tests for EntitySet.concat xfail for koalas because "Koalas deepcopy fails", so this is...
Currently `EntitySet.__eq__` uses Woodwork's equality check `df.ww.__eq__(df2.ww)` which only looks at dataframe equality for pandas DataFrames and uses `df.equals(df2)` to do that equality check. Before Woodwork integration, the dataframes in...
Cannot create/control the application of ```where_primitives``` independent of the ```agg_primitives``` **Use case**: Say, I have a column for ```spend```. I create a ```seed_feature``` to create buckets on the ```spend``` column...
I am using official prediction of customer churn example from [here](https://github.com/Featuretools/predict-customer-churn/blob/main/churn/3.%20Feature%20Engineering.ipynb) For quick experimentation, I have added a cell between ```cel 19``` and ```cell 20``` to subset the cutoff_times to...
There are two tests in `test_calculate_feature_matrix.py` that use the `int_es` test fixture. Previously we did not have a Dask and Koalas version of this fixture, but this has been created...
Hey guys! When executing `import featuretools as ft` I get a lot of warnings: ``` 2024-05-21 16:26:19,117 featuretools - WARNING While loading primitives via "nlp_primitives" entry point, ignored primitive "DiversityScore"...
#### Code Sample, a copy-pastable example to reproduce your bug. ```python feature_matrix, feature_defs = ft.dfs( entityset=es, target_dataframe_name="acc", agg_primitives=["count", "sum"], trans_primitives=[ "MultiplyNumericBoolean"], cutoff_time=cutoff_times, cutoff_time_in_index=True, training_window="24 hour", max_depth=2, verbose=True, n_jobs = 36...
