T = basic.get_X_from_dataframe(test_set) Id = basic.get_Id_from_test_dataframe(test_set) #Get a classifier. Later, it will be a pipeline pipeline = bayes.GaussianNB() #TODO tune pipeline metaparameters. #The most time consuming part! We have to be really creative here! #Once the metaparameters were found, set them and fit ("learn") the training set pipeline.fit(X,Y) #Sometimes a little time consuming but not very much predicted_proba = pipeline.predict_proba(T) #Build nicely the pandas so we can write a nice csv output = basic.build_pandas_output(Id,predicted_proba,class_labels) output_file_name = fh.get_out_path("cv_Prediction_Attempt_01.08.2015.csv") output.to_csv(output_file_name,index=False) #Write the file, ready to be submitted print "Done"
def test_how_to_write_a_file(): file_name = fh.get_out_path("sample_writable_file.txt") with open(file_name,"w") as file: file.write("Some string in the file\n")