feature-engineering

Describe the issue:
During computing Channel Dependencies reshape_break_channel_dependency does following code to ensure that the number of input channels equals the number of output channels:

in_shape = op_node.auxiliary['in_shape']
out_shape = op_node.auxiliary['out_shape']
in_channel = in_shape[1]
out_channel = out_shape[1]
return in_channel != out_channel

This is correct

• As a user, I wish featuretools dfs would take a string as cutoff_time aswell as a datetime object

Code Example

fm, features = ft.dfs(entityset=es,
                      target_dataframe_name='customers',
                      cutoff_time="2014-1-1 05:00",
                      instance_ids=[1],
                      cutoff_time_in_index=True)

as well as

Is your feature request related to a problem? Please describe.
The current Feast online store for GCP implementation requires Firestore in Datastore mode. Firestore can only be in one mode at a time per GCP account. You cannot use native mode for some applications and Datastore mode for others within the same account. Adding a feature store to an existing GCP account that uses native mode wou

I trained models on Windows, then I tried to use them on Linux, however, I could not load them due to an incorrect path joining. During model loading, I got learner_path in the following format experiments_dir/model_1/100_LightGBM\\learner_fold_0.lightgbm. The last two slashes were incorrectly concatenated with the rest part of the path. In this regard, I would suggest adding something like `l

Delete INVALID_TID from tablet_client

We can use the inv_boxcox functionality from scipy.special: https://docs.scipy.org/doc/scipy-1.8.0/html-scipyorg/reference/generated/scipy.special.inv_boxcox.html

This is reported by Manjunath from Slack channel.
For work_dir: "dbfs:/feathr_getting_started"
The user was running jupyter notebook from windows.
without forward slash('dbfs:/feathr_getting_started') the job configs were coming as '--join-config', 'dbfs:/feathr_getting_started\feature_join.conf'

To fix this, the user need to add foward slash: `work

Current version of bucketize uses fixed boundaries. If the user doesn't know these boundaries they need to calculate them using cudf.

We should support splitting continuous variables into buckets based on quantile and uniform splits of the data.

For uniform splits the statistics gathering phase needs to compute the min and max of the column and figure out the boundaries to create N buckets.

Currently if no value for y is passed to the TimeSeriesImputer, the resulting output returns a pd.Series object with one None value due to this.

The behaviour should match our other components and y should be returned as None if nothing has been passed in.

Is your feature request related to a problem? Please describe.
The friction to getting the examples up and running is installing the dependencies. A docker container with them already provided would reduce friction for people to get started with Hamilton.

Describe the solution you'd like

1. A docker container, that has different python virtual environments, that has the dependencies t