I’m trying to build a Voting Ensemble model, with a data transformation pipeline. I still need to put the transformation of the response variable into the pipeline. I’m trying to use GridSearchCV to evaluate the best parameters for each algorithm, but when I try to run the last code block, I get an error. But when I run this last
Tag: pipeline
Make available .best_params_ after pipeline
How to go about making available the clf.best_params_ after carrying a pipeline? For the code I have below, I get an: AttributeError: ‘GridSearchCV’ object has no attribute ‘best_params_’ Here is my code: Answer Your clf is never fitted. You probably meant clf.fit(X_train,y_train). Also, np.linspace(10,50,11) yields floats, while max_depth expects ints, so this may fail and you should probably add a
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
Pipline with SMOTE and Imputer Errors
i am trying to create a pipeline that first impute missing data , do oversampling with the SMOTE and the the model my code worked perfectly before i try smote not i cant find any solution here is the code without smote And here’s the code after adding smote Note: I tired importing make pipeline from imlearn when i import
Extracting feature names from sklearn column transformer
I’m using sklearn.pipeline to transform my features and fit a model, so my general flow looks like this: column transformer –> general pipeline –> model. I would like to be able to extract feature names from the column transformer (since the following step, general pipeline applies the same transformation to all columns, e.g. nan_to_zero) and use them for model explainability
ModuleNotFoundError: No module named in AWS Build
I can run the project on my local MAC, but when I use the pipeline to build it. I got this error: Command “python setup.py egg_info” failed with error code 1 in /tmp/pip-build-axjgd0da/MarkupSafe/ This project is working well, and I did not update any new lib in it. Even I redeployed to the old branch, it has the same error.
Is it possible to optimize hyperparameters for optional sklearn pipeline steps?
I tried to construct a pipeline that has some optional steps. However, I would like to optimize hyperparameters for those steps as I want to get the best option between not using them and using them with different configurations (in my case SelectFromModel – sfm). The error that I get is ‘string’ object has no attribute ‘set_params’ which is understandable.
Scrapy can’t find items
I am currently still learning Scrapy and trying to work with pipelines and ItemLoader. However, I currently have the problem that the spider shows that Item.py does not exist. What exactly am I doing wrong and why am I not getting any data from the spider into my pipeline? Running the Spider without importing the items works fine. The Pipeline
ModuleNotFoundError in Dataflow job
I am trying to execute a apache beam pipeline as a dataflow job in Google Cloud Platform. My project structure is as follows: Here’s my setup.py Here’s my pipeline code: Functionality of pipeline is Query against BigQuery Table. Count the total records fetched from querying. Print using the custom Log module present in utils folder. I am running the job
NoSuchElementException: Failed to find a default value for layers in MultiLayerPerceptronClassifier
I am having a problem running a prediction using a saved MultiLayerPerceptronClassifier model. It throws error: The original mlpc in the pipeline had layers defined: My attempts to solve it: If I run the pipeline model and do predictions without first saving the model. I works with no error. But saving and re-using the model throws this error. Any help