Docs are coming soon. In the meantime
, Imagine a first step containing a TrainTestSplit class with a similar
behaviour to train_test_split but capable of producing results by using fit
and predict (this is a goodie). The inputs will be X, y, z, ... , and the
outputs the same names + _train and _test.
A second step could be a MinMaxScaler taking only X_train.
A third step a linear model using the output from MinMaxScaler as X.
This would be written:
connections['split'] = {'A': 'X', 'B': 'y'}
Meaning that the 'split' step will use the X and y from the fit or predict
call calling them A and B internally.
If you use, for instance,
my_pipegraph.fit(X=myX, y=myY)
This step will produce A_train with a piece of myX
You can use this later:
connections['scaler'] = { 'X': ('split', 'A_train')}
Expressing that the output A_train from the split step will be use as input
X for the scaler. The output from this step is called 'predict'
Finally, for the third step:
connections['linear_model'] ={'X': ('scaler', 'predict'), 'y': ('split',
'B_train')}
Notice, that if we are talking about an external input variable we don't
use a tuple.
So the syntax is something like connection[step_label] =
{internal_variable: (input_step, variable_there)}
Docs are coming anyway. Travis CI, Circle CI and Appveyor have been
successfully activated at GitHub.com/mcasl/PipeGraph
Sorry if you found mistypos, I use my smartphone for replying.
Best
Manuel
El 7 feb. 2018 11:32 p. m., "Andreas Mueller" <t3k...@gmail.com> escribió:
> Thanks Manuel, that looks pretty cool.
> Do you have a write-up about it? I don't entirely understand the
> connections setup.
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