immutable feature changes when using

[Berlyli866]

Hi team,
First of all, thanks to the team for working on building such a good package for us to use.

I follow the example Counterfactual with Reinforcement Learning (CFRL) on Adult Census to build my own CFL.

I have a data set that is a mix of numerical, binary, and category features.
I trained a random forest classification model as the predictor model and ran counterfactualtabluer to generate the counterfactual for features that I am interested in. Below is part of the code on how i specify the candidate features and immutable feature

ranges = {'num_image': [1, 16], 
         'num_alternative_image': [0,6],
          'num_market_bullets':[5,19]
         }

from alibi.explainers import CounterfactualRLTabular
explainer = CounterfactualRLTabular(predictor=predictor,
                                    encoder=heae.encoder,
                                    decoder=heae.decoder,
                                    latent_dim=LATENT_DIM,
                                    encoder_preprocessor=heae_preprocessor,
                                    decoder_inv_preprocessor=heae_inv_preprocessor,
                                    coeff_sparsity=COEFF_SPARSITY,
                                    coeff_consistency=COEFF_CONSISTENCY,
                                    category_map=cate_map,
                                    feature_names=model_attr,
                                    #ranges=ranges,
                                    immutable_features=immutable_features,
                                    train_steps=TRAIN_STEPS,
                                    batch_size=BATCH_SIZE,
                                    backend="tensorflow")


explainer = explainer.fit(X=X_train.to_numpy())

X_positive = X_test[np.argmax(predictor(X_test), axis=1) == 1]
X = X_positive[:1000]
Y_t = np.array([0])
#index 20 num_image, 21 num_alternative_image, 22 num_market_bullets. if i put feature name i will get error somehow. 
C = [{20: [1, 10],21:[0,6], 22: [5, 10]}]
explanation = explainer.explain(X, Y_t, C)

after I get the counterfactual df I compared it with original df and got the difference columns below. The avg_delivery_days is immutable but also changes though very tiny change, for 'num_image', 'num_alternative_image' , 'num_market_bullets' the change is also minimal. Can I see the changed features play an important role in predicting the label (>0.4 or <=0.4) since a small change and flip the label ? Did i use the right counterfactual function for my use case? :

For tabluar data , do i always need encoder and decoder? if its already binary should i put binary feature in category_map in below function ?

heae_preprocessor, heae_inv_preprocessor = get_he_preprocessor(X=X_train, feature_names=model_attr, category_map=cate_map, feature_types=feature_types)

Another question I have is what function I can use for the environment models, such as boost regression or a regression type of black box model?
If I tried to use

explainer = CounterfactualRLTabular(predictor=predictor,
                                    encoder=heae.encoder,
                                    decoder=heae.decoder,
                                    latent_dim=LATENT_DIM,
                                    encoder_preprocessor=heae_preprocessor,
                                    decoder_inv_preprocessor=heae_inv_preprocessor,
                                    coeff_sparsity=COEFF_SPARSITY,
                                    coeff_consistency=COEFF_CONSISTENCY,
                                    category_map=cate_map,
                                    feature_names=model_attr,
                                    #ranges=ranges,
                                    immutable_features=immutable_features,
                                    train_steps=TRAIN_STEPS,
                                    batch_size=BATCH_SIZE,
                                    backend="tensorflow")

but replace predictor as the boost regression model. What other changes do I need to make since the regression model, the prediction is continuous, how can i customize the reward function?

sorry for all these questions, as i am a starter in RL and is still learning everthing so forgive me if my questions sounds dump.

thanks for your time and help

[RobertSamoilescu]

Hi @Berlyli866,

From what I can see in the code snippet above, you are not really following the conventions presented in the documentation. Please also have a look at the docs here, besides the Adult Census example we provide.

Some issues that I can see in the code above are:

• ranges - are incorrectly specified.
  An example of correctly specified feature in your case would be:

ranges = {
   'num_image': [0.0, 1.0], 
   'num_alternative_image': [-1.0, 0.0],
   'num_market_bullets': [-1.0, 1.0]
}

This means that when generating the counterfactual for a given instance num_image can only increase, num_alternative_image can only decrease, num_market_bulltets can increase or decrease (you can actually omit specifying the feature which can increase or decrease).

• feature_names - should be a list of strings containing the column names of your data frame. The order of the features within feature_names is important. They should match exactly the order of your columns.

• immutable_features - should be a list of strings and contain column names that are present in the feature_names list. I can't tell exactly what you passed, but I think it is worth mentioning.

• C - should be a list of dictionaries with strings as keys, where each key is a name of a column. From what I see, you are providing column indices as keys. Note that for numerical features, the conditioning represents a delta change from the original value and 0 must be included in the interval you specified. For example C=[{"num_image": [0, 5]}] means that num_image value will be changed by a maximum of 5 units.

You don't necessarily need to train an autoencoder. You can see an example here how to do that. Please read the final paragraph in this comment.

You should provide a complete description of category_map. So, yes, binary features should be specified as well.

Although we didn't test explicitly for regression models, I think the implementation still supports it for a single target. It is really up to you how to design the reward function. You can give a sparse or continuous reward based on the distance from your target. The reward function can be specified through the parameter reward_func (see docs here)

[Berlyli866]

Hi Robert,
Thanks for your response and help here !

For C if i use the string as keys, i will have error
``

but if i use the indices key, it will run through. In my code the C is

index map : 
26 num_image
27 num_alternative_image
28 num_market_bullets
2 salient_bullet
12 image_life_style
39 return_policy_90-Day

C = [{26: [4, 17],27:[0,6], 28: [5, 22]}]

which means I want the num_image changes from 4 to 17 , num_alternative_image changes from 0 to 6, and num_market_bullets change from 5 to 22, sailent_bullets can only be 1 , image_life_style can only be 1, return_policy_90-Day can only be 1

I actually have other features in C but the output after i ran explanation = explainer.explain(X, Y_t, C) i did not see any change for the feature I specify in C and not in the immutable_features list. Do you know what the reason may be?
below are my change features and their range :

change_features=['num_image',
 'num_alternative_image',
 'num_market_bullets','image_life_style','return_policy_90-Day','salient_bullet']

ranges = {'image_life_style'::[0.0, 1.0]
          'return_policy_90-Day':[0.0, 1.0]
          'salient_bullet':[0.0, 1.0]
         }

image_life_style, return_policy_90-Day,salient_bullet are all binary features. I want them to change only from 0 to 1. If it's 1, don't change it. Do I specify the range right? For other changeable features, they can increase or decrease, so I omit them here.

the category_map=cate_map in my code is all binary features index and their value [0,1] so something like :

{0: [0, 1],
 2: [1, 0],
 6: [1, 0],
 7: [0, 1]}

0,2,6,7 is the feature index. Now that I know if they are binary, we also need to specify. thanks.