Myujth

I have a directory structure on my laptop as shown.

/Directory
1. file1.model
2. file2.model
3. file3.model

/Directory2
1. features1.csv
2. features2.csv
3. features3.csv

`def LoadModelFiles(self):
 model1 = Directory/file1.model
 model2 = Directory/file2.model
 model3 = Directory/file3.model
 return model1,model2, model3

 def LoadFeaturesets(self):
 feature_set1 = Directory/features1.csv
 feature_set2 = Directory/features2.csv
 feature_set3 = Directory/feature3.csv
 return feature_set1,feature_set2,feature_set3

 def Classifier(self):
 model1,model2,model3 = self.LoadModelFiles()
 set1, set2, set3 = self.LoadFeaturesets()

 pred1 = model1.predict(feature1)
 pred2 = model2.predict(feature2)
 pred3 = model3.predict(feature3)

 return pred1, pred2, pred3`

what if I have multiple file. The above code is bad as I am new to python.

I want to load them into different variable, so I wrote the code as shown. I can use those model variables to pass different set of feature stored in those csv files.

So even in Classifier method I have to write 3 prediction statements, what if I have N number of models with relative feature set.

Can we write a pythonic or few lines of code to eliminate duplicated lines and load the files.

Added thing is I am writing a web service where 1st model predictions(based on its predictions) invokes the next models.

Currently I have 15 model files. Where 1st model file invokes the next 14 files.

Thanks in advance.

def load_model(model_path):
 pass

def load_feature(feature_path):
 pass

def predict(idx):
 model = load_model('Directory/file.model'.format(idx))
 features = load_feature('Directory/feature.csv'.format(idx))
 return model.predict(features)

predictions = [predict(i) for i in range(n)]

Hope this would help