PR: inverse_transform is implemented for scikit-learn utility

[sshojiro]

Hello,

I have found that there is no inverse_transform, which of PCA is found in scikit-learn.
A new method inverse_transform in this PR maps responsibilities onto the original data space.

The short sample code is here:

from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from ugtm.ugtm_sklearn import eGTM
X,y = load_iris(return_X_y=True)
Xtrain, Xtest = train_test_split(X, test_size=0.20,
                random_state=42)
model = eGTM(model='responsibilities')
_ = model.fit_transform( Xtrain )
matR = model.transform(Xtest)
Xhat = model.inverse_transform(matR)
print("original data", Xtest.shape)
print("projected data", Xhat.shape)
#original data (30, 4)
#projected data (30, 4)

This may help analyses with GTM.
Thank you in advance for your consideration.

[sshojiro]

The commit 3d1086e (formerly e8f6459 ) supports sklearn.pipeline.Pipeline integration.
Now the class eGTM switches the output format by eGTM.set_params and parameter eGTM.model.
eGTM.model takes either of 'means', 'modes', 'responsibilities' or 'complete'.

It is confirmed that the new feature works properly as follows:

from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from ugtm.ugtm_sklearn import eGTM
from sklearn.pipeline import make_pipeline
from sklearn.preprocessing import StandardScaler 
X,y = load_iris(return_X_y=True)
model = make_pipeline(StandardScaler(), eGTM())
Xtrain, Xtest = train_test_split(X, test_size=0.20,
                random_state=42)

model.set_params(**{"egtm__model": "responsibilities"})
model.fit_transform(Xtrain) # pass 
model.fit(Xtrain)           # pass 
for mtype in ['means', 'modes', 'responsibilities']:
    model.set_params(**{"egtm__model": mtype})
    Xttest = model.transform(Xtest)
    print(mtype, Xttest.shape)
# means (30, 2)
# modes (30, 2)
# responsibilities (30, 256)