Apply the featured engineering to new data to make predictions?
chitown88 opened this issue · 9 comments
I'm using the auto feature engineering feature to create the new_features then to train my model. Now that I have new data that I want to predict on, how would I go about to apply that process to my new data so that the trained model doesn't mismatch in its core dimensions with the new data?
Hi, yes this an issue which will be resolved in next updates. for now, you have to make a seprate object of GML everytime you do training.
Hi,
When I doing "from GML.Ghalat_Machine_Learning import Ghalat_Machine_Learning"
There is a error AttributeError: module 'keras.backend.tensorflow_backend' has no attribute '_is_tf_1'
I have tried pip install --upgrade keras , but it's still not working.
Could you tell me which version of keras fit GML ,please ?
hi @jason022085, any latest version of keras should work.
have you tried upgrading GML?
It’s an issue with the latest version of Keras and tensorflow. Uninstall your Kera’s and then reinstall a previous version of Keras.
@jason022085 I had the same issue. You need to downgrade your Keras version.
@jason022085 I had the same issue. You need to downgrade your Keras version.
Thanks a lots.
I use Keras==2.1.0 works
And I offer an information that keras==2.0.0 doesn't work with lastest GML.
@jason022085 so did that work? I’ll have to check my versions.
@chitown88 thank you for your contribution :) I will resolve this issue in next update :)
The classification demo document works in the following environment :
category_encoders==2.1.0
tensorflow==2.1.0
keras==2.3.1
GML==2.0.4
I think the core problem is not about the version of GML, but the specific version of keras corresponding to the lastest tensorflow.