I’m trying to use Canonical Correlation Analysis (CCA)
in scikit-learn. Still, I’m getting a TypeError
, which asserts inverse_transform() takes 2 positional arguments but 3 were given
.
Here is my code:
from sklearn.ensemble import RandomForestRegressor from sklearn.cross_decomposition import CCA from sklearn.model_selection import RandomizedSearchCV mycca = CCA(n_components=4) mycca.fit(x, y) x, y = mycca.transform(x, y) x_test = mycca.transform(x_test) parameters = { 'n_estimators': [50 , 100, 200, 300], 'max_depth': [3, 5, 6 ,8 , 9 ,10] } modell = RandomForestRegressor(max_leaf_nodes=2) modell = RandomizedSearchCV(modell, param_distributions=parameters, cv=5, n_iter=10, n_jobs=-1 ) modell.fit(x , y) predicted = modell.predict(x_test).flatten() mycca.inverse_transform(x_test, predicted)
And the last line throws a TypeError
:
--------------------------------------------------------------------------- TypeError Traceback (most recent call last) <ipython-input-34-7d035a65b067> in <module> ----> 1 mycca.inverse_transform(x_test, predicted) TypeError: inverse_transform() takes 2 positional arguments but 3 were given
It’s ridiculous since I passed two arguments exactly, named x_test
and predicted
. If you’re curious about shape of x_test
and predicted
:
>>>x_test.shape, predicted.shape ((1, 4), (4,)) >>>x_test, predicted (array([[-0.36689807, -0.03637745, 0.1099326 , -0.22595879]]), array([-0.02223153, -0.11932753, 0.08806947, -0.0044004 ]))
How to fix this?
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Answer
You are not counting the “self” argument that is given to the method when calling it. This means the third argument is the instance of CCA class which is “mycca”. try:
mycca.inverse_transform(X=x_test, Y=predicted)
If your are using version 1.0.2 then you will get an error as this is not supported in that version.This is for version 1.0.2: