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I have really simple code model = bc.train(df=df, target='score', features=['brand', 'category', 'source']) Fails with following error
model = bc.train(df=df, target='score', features=['brand', 'category', 'source'])
[/usr/local/lib/python3.7/dist-packages/blobcity/main/driver.py](https://localhost:8080/#) in train(file, df, target, features, model_types, accuracy_criteria, disable_colinearity, epochs, max_neural_search) 76 77 accuracy_criteria= accuracy_criteria if accuracy_criteria<=1.0 else (accuracy_criteria/100) ---> 78 modelClass = model_search(dataframe=CleanedDF,target=target,DictClass=dict_class,disable_colinearity=disable_colinearity,model_types=model_types,accuracy_criteria=accuracy_criteria,epochs=epochs,max_neural_search=max_neural_search) 79 modelClass.yamldata=dict_class.getdict() 80 modelClass.feature_importance_=dict_class.feature_importance if(features==None) else calculate_feature_importance(CleanedDF.drop(target,axis=1),CleanedDF[target],dict_class) [/usr/local/lib/python3.7/dist-packages/blobcity/main/modelSelection.py](https://localhost:8080/#) in model_search(dataframe, target, DictClass, disable_colinearity, model_types, accuracy_criteria, epochs, max_neural_search) 289 290 elif model_types=='all': --> 291 modelResult=classic_model(ptype,dataframe,target,X,Y,DictClass,modelsList,accuracy_criteria,4) 292 if modelResult[2]<accuracy_criteria: 293 gpu_num=tf.config.list_physical_devices('GPU') [/usr/local/lib/python3.7/dist-packages/blobcity/main/modelSelection.py](https://localhost:8080/#) in classic_model(ptype, dataframe, target, X, Y, DictClass, modelsList, accuracy_criteria, stages) 206 print("Quick Search(Stage 1 of {}) is skipped".format(stages)) 207 best=train_on_full_data(X,Y,modelsList,modelsList,DictClass,stages) --> 208 modelResult = Tuner.tune_model(dataframe,target,best,modelsList,ptype,accuracy_criteria,DictClass,stages) 209 return modelResult 210 [/usr/local/lib/python3.7/dist-packages/blobcity/config/tuner.py](https://localhost:8080/#) in tune_model(dataframe, target, modelkey, modelList, ptype, accuracy, DictionaryClass, stages) 203 prog=Progress() 204 X,Y=dataframe.drop(target,axis=1),dataframe[target] --> 205 cv=modelSelection.getKFold(X) 206 get_param_list(modelkey,modelList) 207 EarlyStopper.criterion=accuracy AttributeError: module 'blobcity.main.modelSelection' has no attribute 'getKFold'
I use Google Colab, python3.7.13, latest version of all libs installed with :
!pip install git+https://github.com/keras-team/keras-tuner.git !pip install autokeras !pip install blobcity
My df consists of 3 categorical features (source, brand, category) used to predict float score
score
The text was updated successfully, but these errors were encountered:
On my personal CPU, with python3.10 and same libs config, it does not fail at this step.
Sorry, something went wrong.
@NicolasMICAUX we tried to reproduce the issue on Colab for multiple similar datasets but didn't encounter the same issue.
for the installation procedure on colab, we did only
!pip install autokeras !pip install blobcity
can you try only installing these two dependencies?
if the issue still exists, if possible do share your Colab notebook over here.
No branches or pull requests
I have really simple code
model = bc.train(df=df, target='score', features=['brand', 'category', 'source'])
Fails with following error
I use Google Colab, python3.7.13, latest version of all libs installed with :
My df consists of 3 categorical features (source, brand, category) used to predict float
score
The text was updated successfully, but these errors were encountered: