Not define a reducer and cleaner method

[Osmar]

Hi,

I would like to create a MatPipe without the necessity to define a reducer and cleaner method. Is there a way to do so?

Thanks!

[Alex Dunn]

Hey Osmar,

Currently, no. This is a good issue I had not considered: The matpipe is just a way to link together multiple modular data frame transformers. I’ll add it as a issue todo to take of this.

However (in the meantime), each part of the automatminer pipeline can be used with essentially the same syntax as MatPipe (fit/transform/predict operations). You can just use the underlying parts by themselves in whatever order you want (not recommended but if you know what you're doing go for it). They accept a df as input and return a df as output, just like MatPipe. For example, initialize an AutoFeaturizer and TPOTAdaptor and run the fit/transform actions manually. Just make sure you are using fit/transform/predict correctly and things should be fine.

For example

af = AutoFeaturizer()

tpot = TPOTAdaptor()

training_df = af.fit_transform(training_df, "my_target_col")

tpot.fit(training_df, "my_target_col")

prediction_df = af.transform(prediction_df, "my_target_col")

tpot.predict(prediction_df, "my_target_col")

Check out the fit/transform methods of MatPipe for more tips on how to use the underlying AutoFeaturizer/MLAdaptor pieces. 

Thanks,

Alex

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