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: And the last line throws a TypeError: 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: How to
Tag: scikit-learn
Strange results when scaling data using scikit learn
I have an input dataset that has 4 time series with 288 values for 80 days. So the actual shape is (80,4,288). I would like to cluster differnt days. I have 80 days and all of them have 4 time series: outside temperature, solar radiation, electrical demand, electricity prices. What I want is to group similar days with regard to
create an array or dataframe using different variables from nested for loop in python
How do I create an array or dataframe to store seedN, clf.score(X_test, y_test),n_neighbors? Answer Create a temporary empty list to store the results : For each fit, add a new list with the desired values : Finally, create the dataframe with this temporary list :
Is preprocessing repeated in a Pipeline each time a new ML model is loaded?
I have created a pipeline using sklearn so that multiple models will go through it. Since there is vectorization before fitting the model, I wonder if this vectorization is performed always before the model fitting process? If yes, maybe I should take this preprocessing out of the pipeline. Answer When you are running a GridSearchCV, pipeline steps will be recomputed
sklearn2pmml not giving the same prediction output as sklearn pickle after loading it with pypmml
When testing a PMML object previously converted from a Pickle file (dumped from a sklearn fitted object), I am unable to reproduce the same results as with the pickle model. In the sklearn we see I obtain [0 1 0] as classes for the input given in X. However in PMML I would approaximate the probabilities to [1 1 1].
Custom Transformer Class Inheritance [closed]
Closed. This question needs debugging details. It is not currently accepting answers. Edit the question to include desired behavior, a specific problem or error, and the shortest code necessary to reproduce the problem. This will help others answer the question. Closed 6 months ago. Improve this question I’m attempting to put together a Custom Transformer for sklearn which returns either
cant print sfs features selected in pipeline
I am selecting best features and then doing grid search. When finished, I want to print the best features that have been selected. When trying to print with I get the following error Ive also tried but have gotten an error. Answer The grid search clones its estimator before fitting, so your pipe itself remains unfitted. You can access the
why i can’t predict my x value in linear regression model using reg.predict ( )
Answer You provide a scalar value to .predict method. You need to provide a 2-dimensional array:
Sklearn – Best estimator from GridSearchCV with refit = True
I’m trying to finds the best estimator using GridSearchCV and I’m using refit = True as per default. Given that the documentation states: Should I do .fit on the training data afterwards as such: Or should I do it like this instead: Answer You should do it like your first verison. You need to always call classifier.fit otherwise it doesn’t
Python OOP using sklearn API
I want to learn more advanced OOP methods and create a class using sklearn APIs, my idea is to integrate feature selection methods. I want to be able to call a feature selection method by name, and next fit and transform data. I am not sure, what I am doing wrong but currently, I have the following error that I