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fix: introduce explicit conversion to scalar to avoid implicit conversion issues after numpy 2.0.0 #475

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fix: introduce explicit conversion to scalar to avoid implicit conversion issues after numpy 2.0.0 #475

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6 changes: 4 additions & 2 deletions dice_ml/explainer_interfaces/dice_genetic.py
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Original file line number Diff line number Diff line change
Expand Up @@ -458,7 +458,8 @@ def find_counterfactuals(self, query_instance, desired_range, desired_class,
population_fitness = population_fitness[population_fitness[:, 1].argsort()]

current_best_loss = population_fitness[0][1]
to_pred = np.array([population[int(tup[0])] for tup in population_fitness[:self.total_CFs]])
to_pred = np.array([population[int(tup[0].item()) if hasattr(tup[0], 'item') else int(tup[0])]
for tup in population_fitness[:self.total_CFs]])

if self.total_CFs > 0:
if self.model.model_type == ModelTypes.Classifier:
Expand All @@ -468,7 +469,8 @@ def find_counterfactuals(self, query_instance, desired_range, desired_class,

# self.total_CFS of the next generation obtained from the fittest members of current generation
top_members = self.total_CFs
new_generation_1 = np.array([population[int(tup[0])] for tup in population_fitness[:top_members]])
new_generation_1 = np.array([population[int(tup[0].item()) if hasattr(tup[0], 'item') else int(tup[0])]
for tup in population_fitness[:top_members]])

# rest of the next generation obtained from top 50% of fittest members of current generation
rest_members = self.population_size - top_members
Expand Down
11 changes: 10 additions & 1 deletion dice_ml/explainer_interfaces/explainer_base.py
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Original file line number Diff line number Diff line change
Expand Up @@ -669,7 +669,10 @@ def misc_init(self, stopping_threshold, desired_class, desired_range, test_pred)
self.target_cf_class = np.array(
[[self.infer_target_cfs_class(desired_class, test_pred, self.num_output_nodes)]],
dtype=np.float32)
desired_class = int(self.target_cf_class[0][0])
target_cf_class_scalar = self.target_cf_class[0][0]
if hasattr(target_cf_class_scalar, "item"):
target_cf_class_scalar = target_cf_class_scalar.item()
desired_class = int(target_cf_class_scalar)
if self.target_cf_class == 0 and self.stopping_threshold > 0.5:
self.stopping_threshold = 0.25
elif self.target_cf_class == 1 and self.stopping_threshold < 0.5:
Expand All @@ -692,12 +695,16 @@ def infer_target_cfs_class(self, desired_class_input, original_pred, num_output_
# already contains the class.
if hasattr(original_pred, "__len__") and len(original_pred) > 1:
original_pred_1 = np.argmax(original_pred)
if hasattr(original_pred_1, "item"):
original_pred_1 = original_pred_1.item()
else:
original_pred_1 = original_pred
target_class = int(1 - original_pred_1)
return target_class
elif num_output_nodes == 1: # only for pytorch DL model
original_pred_1 = np.round(original_pred)
if hasattr(original_pred_1, "item"):
original_pred_1 = original_pred_1.item()
target_class = int(1-original_pred_1)
return target_class
elif num_output_nodes > 2:
Expand Down Expand Up @@ -768,6 +775,8 @@ def is_cf_valid(self, model_score):
target_cf_class = self.target_cf_class[0]
elif len(self.target_cf_class.shape) == 2:
target_cf_class = self.target_cf_class[0][0]
if hasattr(target_cf_class, "item"):
target_cf_class = target_cf_class.item()
target_cf_class = int(target_cf_class)

if len(model_score) == 1: # for tensorflow/pytorch models
Expand Down