Preprocessor fit_transform adapts mutable object (train_data) #139

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1. sandervh14 commented on Nov 30, 2022

   

   sandervh14

   on Nov 30, 2022

   Contributor

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   I'm copying the problem and its solution here from within an e-mail trace, for later reference.

   To recap the minimal code to reproduce the problem:

   from sklearn import datasets
   r"""# For debugging Cobra:   
   import sys   
   LOCAL_COBRA_PATH =   r"C:\Users\sander.vandenhautte\PycharmProjects\cobra"   
   sys.path.insert(0, LOCAL_COBRA_PATH)   
   %load_ext autoreload   
   %autoreload 2   
   """       
   from cobra.preprocessing import PreProcessor       
   data =   datasets.load_iris(as_frame=True)["data"].reset_index()   
   data.head()       
   
   id_col = "index"   
   target_col = "petal width (cm)"   
   model_type = "regression"       
   col_dtypes = data.dtypes       
   discrete_vars = [col for col in col_dtypes[col_dtypes==object].index.tolist() if col not in [id_col, target_col]]
   print(discrete_vars)       
   continuous_vars = list(set(data.columns) - set(discrete_vars) - set([id_col, target_col]))   
   print(continuous_vars)       
   
   weights = [0,.8,50]   
   preprocessed = {}   
   for w in weights:       
       print(w)      
       print("START OF ITERATION-----------------------------")       
       print("data", data)       
       print("preprocessed", preprocessed)       
       preprocessor = PreProcessor.from_params(model_type=model_type, n_bins=10, weight= w)
       result = preprocessor.fit_transform(data, #   <===================================== SOLUTION TO BE PUT HERE, SEE BELOW             
                                                                continuous_vars=continuous_vars,           
                                                                discrete_vars=discrete_vars,             
                                                                target_column_name=target_col)       
       preprocessed[w] = result       
       print("AFTER   ITERATION-----------------------------")       
       print("data", data)       
       print("result", result)       
       print("preprocessed", preprocessed)
   ```
   
   
   As you mentioned, the variable preprocessed should contain different
   target encoded columns for the three configured parameters <b>w</b> in weights.
   
   With my added print statements above, you’ll see that result shows that
   the target encoding in fact HAS calculated the target encodings differently in
   every iteration.
   
   However, at every iteration step, result is written not only to
   preprocessed[w], but also to preprocessed[all_former_w_values].
   
   The cause is that Cobra, when it writes “result” to preprocessed[w],
   does not just return a result dataframe, but actually it is the modified
   dataframe it got at the input (the variable data) with added columns.
   
   See it as if Cobra is not writing away a value (a dataframe) to a
   certain location in memory, but just refers to the original dataframe data, and
   that dataframe has had some extra columns by now.
   
   Then, in the next iteration, Cobra adds new columns to the dataframe
   presented in the variable data, which python refers to as the dataframe in
   memory that we have used before.
   
   When Cobra adds the preprocessed columns in that dataframe, it is
   essentially overwriting the dataframe.
   
   And since this dataframe is referenced by both preprocessed[0] and
   preprocessed[0.8] also on the writing of result (=the modified dataframe
   again), both items in preprocessed have been overwritten after the second
   iteration.
   
   **The solution is to call preprocessor.fit_transform(data.copy()).**
   Then, Cobra is forced to work on a new copy of the dataframe everytime,
   and it cannot overwrite the same dataframe in every new iteration.
   
   This is python stuff you don’t normally encounter everyday.
   
   **The better-for-the-long-term fix would be to perform the copy() within
   fit_transform() – at the start of that function – of the preprocessor.**
   
   You will notice that many methods in Cobra already perform such a copy()
   at the start to avoid such nasty problems, but this
   preprocessor.fit_transform() does not.<o:p></o:p></span></p>
   
2. sandervh14 commented on Nov 30, 2022

   

   sandervh14

   on Nov 30, 2022

   Contributor

   More actions

   This issue seems to not auto-close now the pull request #140 successfully merged. I'm closing it manually.
3. sandervh14

   closed this as completedon Nov 30, 2022
4. sandervh14

   added

   bugSomething isn't workingSomething isn't working

   on Apr 7, 2023
5. sandervh14

   added this to the 2023-03 milestone on Apr 7, 2023