Fix incompatible types in assignment #344

daikikatsuragawa · 2022-11-16T15:03:11Z

Signed-off-by: Daiki Katsuragawa [email protected]

This pull request is one of the steps of #331 . Most of the "Incompatible types in assignment" errors detected by mypy are fixed. Basically, the policy is to modify the process without major changes. Therefore, there are also temporary responses (e.g., defining type hints Any). These are excluded from the modifications in this pull request and will be revised later.

In conclusion, this pull request will have the following effects.

Deletion of "Incompatible types in assignment" processes
Prevention of unexpected exceptions
Improved readability with type hints

Signed-off-by: Daiki Katsuragawa <[email protected]>

daikikatsuragawa · 2022-11-16T15:05:48Z

dice_ml/explainer_interfaces/dice_genetic.py

@@ -343,12 +344,16 @@ def _predict_fn_custom(self, input_instance, desired_class):

    def compute_yloss(self, cfs, desired_range, desired_class):
        """Computes the first part (y-loss) of the loss function."""
-        yloss = 0.0
+        yloss: Any = 0.0


Currently, multiple types of values are stored.

daikikatsuragawa · 2022-11-16T15:05:54Z

dice_ml/explainer_interfaces/dice_pytorch.py

        if opt_method == "adam":
            self.optimizer = torch.optim.Adam(self.cfs, lr=learning_rate)
        elif opt_method == "rmsprop":
            self.optimizer = torch.optim.RMSprop(self.cfs, lr=learning_rate)

    def compute_yloss(self):
        """Computes the first part (y-loss) of the loss function."""
-        yloss = 0.0
+        yloss: Any = 0.0


Currently, multiple types of values are stored.

daikikatsuragawa · 2022-11-16T15:06:04Z

dice_ml/explainer_interfaces/dice_pytorch.py

@@ -307,7 +310,7 @@ def compute_diversity_loss(self):
    def compute_regularization_loss(self):
        """Adds a linear equality constraints to the loss functions -
           to ensure all levels of a categorical variable sums to one"""
-        regularization_loss = 0.0
+        regularization_loss: Any = 0.0


Currently, multiple types of values are stored.

daikikatsuragawa · 2022-11-16T15:06:14Z

dice_ml/explainer_interfaces/dice_pytorch.py

@@ -425,7 +428,7 @@ def find_counterfactuals(self, query_instance, desired_class, optimizer, learnin
        test_pred = self.predict_fn(torch.tensor(query_instance).float())[0]
        if desired_class == "opposite":
            desired_class = 1.0 - np.round(test_pred)
-        self.target_cf_class = torch.tensor(desired_class).float()
+        self.target_cf_class: Any = torch.tensor(desired_class).float()


Currently, multiple types of values are stored.

daikikatsuragawa · 2022-11-16T15:07:46Z

dice_ml/explainer_interfaces/dice_tensorflow2.py

@@ -341,8 +341,7 @@ def initialize_CFs(self, query_instance, init_near_query_instance=False):
                        one_init.append(np.random.uniform(self.minx[0][i], self.maxx[0][i]))
                else:
                    one_init.append(query_instance[0][i])
-            one_init = np.array([one_init], dtype=np.float32)
-            self.cfs[n].assign(one_init)
+            self.cfs[n].assign(np.array([one_init], dtype=np.float32))


Substitute directly. This prevents one_init from being of more than one type.

Signed-off-by: Daiki Katsuragawa <[email protected]>

daikikatsuragawa · 2022-11-16T15:18:05Z

dice_ml/explainer_interfaces/feasible_base_vae.py

-            train_dataset = torch.tensor(self.vae_train_feat).float()
-            train_dataset = torch.utils.data.DataLoader(train_dataset, batch_size=self.batch_size, shuffle=True)
+            train_dataset = torch.utils.data.DataLoader(
+                torch.tensor(self.vae_train_feat).float(), # type: ignore


Set type: ignore because the error depends on the implementation of torch.utils.data.DataLoader.

daikikatsuragawa · 2022-11-16T15:18:11Z

dice_ml/explainer_interfaces/feasible_model_approx.py

-            train_dataset = torch.tensor(self.vae_train_feat).float()
-            train_dataset = torch.utils.data.DataLoader(train_dataset, batch_size=self.batch_size, shuffle=True)
+            train_dataset = torch.utils.data.DataLoader(
+                torch.tensor(self.vae_train_feat).float(), # type: ignore


Set type: ignore because the error depends on the implementation of torch.utils.data.DataLoader.

codecov-commenter · 2022-11-16T15:20:55Z

Codecov Report

Base: 72.04% // Head: 72.12% // Increases project coverage by +0.07% 🎉

Coverage data is based on head (61b7d79) compared to base (8f17cfd).
Patch coverage: 78.12% of modified lines in pull request are covered.

Additional details and impacted files

@@            Coverage Diff             @@
##             main     #344      +/-   ##
==========================================
+ Coverage   72.04%   72.12%   +0.07%     
==========================================
  Files          26       26              
  Lines        3595     3598       +3     
==========================================
+ Hits         2590     2595       +5     
+ Misses       1005     1003       -2

Flag	Coverage Δ
unittests	`72.12% <78.12%> (+0.07%)`	⬆️

Flags with carried forward coverage won't be shown. Click here to find out more.

Impacted Files	Coverage Δ
dice_ml/explainer_interfaces/feasible_base_vae.py	`0.00% <0.00%> (ø)`
...e_ml/explainer_interfaces/feasible_model_approx.py	`0.00% <0.00%> (ø)`
dice_ml/explainer_interfaces/explainer_base.py	`89.46% <75.00%> (-0.21%)`	⬇️
dice_ml/explainer_interfaces/dice_genetic.py	`94.89% <87.50%> (-0.25%)`	⬇️
dice_ml/explainer_interfaces/dice_KD.py	`91.72% <100.00%> (ø)`
dice_ml/explainer_interfaces/dice_pytorch.py	`85.84% <100.00%> (+0.12%)`	⬆️
dice_ml/explainer_interfaces/dice_random.py	`93.84% <100.00%> (+0.66%)`	⬆️
dice_ml/explainer_interfaces/dice_tensorflow2.py	`86.23% <100.00%> (-0.05%)`	⬇️

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Signed-off-by: Daiki Katsuragawa <[email protected]>

amit-sharma · 2022-11-18T06:54:18Z

@daikikatsuragawa thanks for this contribution. Can you share a few lines on the motivation for this PR (e.g., why change cfs from [] to pd.DataFrame()?) and are there any bugs that this PR fixes?

daikikatsuragawa · 2022-11-18T10:03:52Z

@amit-sharma

Comment on each. After completion, send a message with a Mention.

daikikatsuragawa · 2022-11-18T10:05:03Z

Operational error for closed (and reopened). My apologies.

gaugup

Should we enable mypy in dice-ml linting build and fix all the mypy errors?

daikikatsuragawa · 2023-02-19T04:12:31Z

@amit-sharma

Should we enable mypy in dice-ml linting build and fix all the mypy errors?

Yes. However, some mypy errors remain at present. It would be good to add them to Lint after they have been resolved.

daikikatsuragawa · 2023-02-19T04:24:12Z

@amit-sharma

@daikikatsuragawa thanks for this contribution. Can you share a few lines on the motivation for this PR (e.g., why change cfs from [] to pd.DataFrame()?)

During the function, the process immediately following was checked and it was determined that the type of cfs is pd.DataFrame. If this is not appropriate, please let me know.

DiCE/dice_ml/explainer_interfaces/dice_KD.py

Lines 173 to 176 in 490b7ab

    
           cfs = self.dataset_with_predictions.iloc[indices].copy() 
        
           cfs['distance'] = distances 
        
           cfs = self.do_sparsity_check(cfs, query_instance, sparsity_weight) 
        
           cfs = cfs.drop(self.data_interface.outcome_name, axis=1)

and are there any bugs that this PR fixes?

Probably this case (cfs = pd.DataFrame()) only. The others are fixes related to type hinting and refactoring and are not expected to affect behaviour. Unit tests also passed.

Fix incompatible types in assignment

490b7ab

Signed-off-by: Daiki Katsuragawa <[email protected]>

daikikatsuragawa requested review from gaugup and amit-sharma as code owners November 16, 2022 15:03

daikikatsuragawa commented Nov 16, 2022

View reviewed changes

Delete unnecessary differences

904c425

Signed-off-by: Daiki Katsuragawa <[email protected]>

daikikatsuragawa commented Nov 16, 2022

View reviewed changes

Fix errors checked by flake8

61b7d79

Signed-off-by: Daiki Katsuragawa <[email protected]>

daikikatsuragawa closed this Nov 18, 2022

daikikatsuragawa reopened this Nov 18, 2022

gaugup requested changes Nov 29, 2022

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Fix incompatible types in assignment #344

Fix incompatible types in assignment #344

daikikatsuragawa commented Nov 16, 2022

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daikikatsuragawa commented Feb 19, 2023

Fix incompatible types in assignment #344

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Fix incompatible types in assignment #344

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