def generate_counterfactuals(self, query_instance, total_CFs, desired_class="opposite", proximity_weight=0.5,
diversity_weight=1.0, categorical_penalty=0.1, algorithm="DiverseCF",
features_to_vary="all", permitted_range=None, yloss_type="log_loss",
diversity_loss_type="dpp_style:inverse_dist", feature_weights="inverse_mad", stopping_threshold=0.5, posthoc_sparsity_param=0.1, posthoc_sparsity_algorithm="linear", verbose=True):
"""Generates diverse counterfactual explanations
:param query_instance: A dictionary of feature names and values. Test point of interest.
:param total_CFs: Total number of counterfactuals required.
:param desired_class: Desired counterfactual class - can take 0 or 1. Default value is "opposite" to the outcome class of query_instance for binary classification.
:param proximity_weight: A positive float. Larger this weight, more close the counterfactuals are to the query_instance.
:param diversity_weight: A positive float. Larger this weight, more diverse the counterfactuals are.
:param categorical_penalty: A positive float. A weight to ensure that all levels of a categorical variable sums to 1.
:param algorithm: Counterfactual generation algorithm. Either "DiverseCF" or "RandomInitCF".
:param features_to_vary: Either a string "all" or a list of feature names to vary.
:param permitted_range: Dictionary with continuous feature names as keys and permitted min-max range in list as values. Defaults to the range inferred from training data. If None, uses the parameters initialized in data_interface.
:param yloss_type: Metric for y-loss of the optimization function. Takes "l2_loss" or "log_loss" or "hinge_loss".
:param diversity_loss_type: Metric for diversity loss of the optimization function. Takes "avg_dist" or "dpp_style:inverse_dist".
:param feature_weights: Either "inverse_mad" or a dictionary with feature names as keys and corresponding weights as values. Default option is "inverse_mad" where the weight for a continuous feature is the inverse of the Median Absolute Devidation (MAD) of the feature's values in the training set; the weight for a categorical feature is equal to 1 by default.
:param stopping_threshold: Minimum threshold for counterfactuals target class probability.
:param posthoc_sparsity_param: Parameter for the post-hoc operation on continuous features to enhance sparsity.
:param posthoc_sparsity_algorithm: Perform either linear or binary search. Takes "linear" or "binary". Prefer binary search when a feature range is large (for instance, income varying from 10k to 1000k) and only if the features share a monotonic relationship with predicted outcome in the model.
:param verbose: Parameter to determine whether to print 'Diverse Counterfactuals found!'
:return: A CounterfactualExamples object to store and visualize the resulting counterfactual explanations (see diverse_counterfactuals.py).
"""
self.check_mad_validity(feature_weights)
self.check_permitted_range(permitted_range)
self.do_param_initializations(total_CFs, algorithm, features_to_vary, yloss_type, diversity_loss_type, feature_weights, proximity_weight, diversity_weight, categorical_penalty)
query_instance, test_pred = self.find_counterfactuals(query_instance, desired_class, stopping_threshold, posthoc_sparsity_param, posthoc_sparsity_algorithm, verbose)
return exp.CounterfactualExamples(self.data_interface, query_instance, test_pred, self.final_cfs, self.cfs_preds, self.final_cfs_sparse, self.cfs_preds_sparse, posthoc_sparsity_param, desired_class, encoding='label')
def generate_counterfactuals(self,
query_instance,
total_CFs,
desired_class="opposite",
feature_weights="inverse_mad"):
"""Generates diverse counterfactual explanations
:param query_instance: A dictionary of feature names and values. Test point of interest.
:param total_CFs: Total number of counterfactuals required.
:param desired_class: Desired counterfactual class - can take 0 or 1. Default value is "opposite" to the outcome class of query_instance for binary classification.
:return: A CounterfactualExamples object to store and visualize the resulting counterfactual explanations (see diverse_counterfactuals.py).
"""
# check feature MAD validity and throw warnings
if feature_weights == "inverse_mad":
self.data_interface.get_valid_mads(display_warnings=True,
return_mads=False)
query_instance, test_pred, final_cfs, cfs_preds = self.find_counterfactuals(
query_instance, desired_class, total_CFs)
return exp.CounterfactualExamples(self.data_interface,
query_instance,
test_pred,
final_cfs,
cfs_preds,
desired_class=desired_class)
def generate_counterfactuals(self, query_instance, total_CFs, desired_class="opposite", proximity_weight=0.5, diversity_weight=1.0, categorical_penalty=0.1, algorithm="DiverseCF", features_to_vary="all", yloss_type="hinge_loss", diversity_loss_type="dpp_style:inverse_dist", feature_weights="inverse_mad", optimizer="tensorflow:adam", learning_rate=0.05, min_iter=500, max_iter=5000, project_iter=0, loss_diff_thres=1e-5, loss_converge_maxiter=1, verbose=False, init_near_query_instance=True, tie_random=False, stopping_threshold=0.5, posthoc_sparsity_param=0.1):
"""Generates diverse counterfactual explanations
:param query_instance: A dictionary of feature names and values. Test point of interest.
:param total_CFs: Total number of counterfactuals required.
:param desired_class: Desired counterfactual class - can take 0 or 1. Default value is "opposite" to the outcome class of query_instance for binary classification.
:param proximity_weight: A positive float. Larger this weight, more close the counterfactuals are to the query_instance.
:param diversity_weight: A positive float. Larger this weight, more diverse the counterfactuals are.
:param categorical_penalty: A positive float. A weight to ensure that all levels of a categorical variable sums to 1.
:param algorithm: Counterfactual generation algorithm. Either "DiverseCF" or "RandomInitCF".
:param features_to_vary: Either a string "all" or a list of feature names to vary.
:param yloss_type: Metric for y-loss of the optimization function. Takes "l2_loss" or "log_loss" or "hinge_loss".
:param diversity_loss_type: Metric for diversity loss of the optimization function. Takes "avg_dist" or "dpp_style:inverse_dist".
:param feature_weights: Either "inverse_mad" or a dictionary with feature names as keys and corresponding weights as values. Default option is "inverse_mad" where the weight for a continuous feature is the inverse of the Median Absolute Devidation (MAD) of the feature's values in the training set; the weight for a categorical feature is equal to 1 by default.
:param optimizer: Tensorflow optimization algorithm. Currently tested only with "tensorflow:adam".
:param learning_rate: Learning rate for optimizer.
:param min_iter: Min iterations to run gradient descent for.
:param max_iter: Max iterations to run gradient descent for.
:param project_iter: Project the gradients at an interval of these many iterations.
:param loss_diff_thres: Minimum difference between successive loss values to check convergence.
:param loss_converge_maxiter: Maximum number of iterations for loss_diff_thres to hold to declare convergence. Defaults to 1, but we assigned a more conservative value of 2 in the paper.
:param verbose: Print intermediate loss value.
:param init_near_query_instance: Boolean to indicate if counterfactuals are to be initialized near query_instance.
:param tie_random: Used in rounding off CFs and intermediate projection.
:param stopping_threshold: Minimum threshold for counterfactuals target class probability.
:param posthoc_sparsity_param: Parameter for the post-hoc operation on continuous features to enhance sparsity.
:return: A CounterfactualExamples object to store and visualize the resulting counterfactual explanations (see diverse_counterfactuals.py).
"""
# check feature MAD validity and throw warnings
if feature_weights == "inverse_mad":
self.data_interface.get_valid_mads(display_warnings=True, return_mads=False)
if([total_CFs, algorithm, features_to_vary, yloss_type, diversity_loss_type, feature_weights, optimizer] != (self.cf_init_weights + self.loss_weights + self.optimizer_weights)):
self.do_cf_initializations(total_CFs, algorithm, features_to_vary)
self.do_loss_initializations(yloss_type, diversity_loss_type, feature_weights)
self.do_optimizer_initializations(optimizer)
"""
Future Support: We have three main components in our tensorflow graph: (1) initialization of tf.variables (2) defining ops for loss function initializations, and (3) defining ops for optimizer initializations. Need to define methods to delete some nodes from a tensorflow graphs or update variables/ops in a tensorflow graph dynamically, so that only those components corresponding to the variables that are updated change.
"""
# check if hyperparameters are to be updated
if not collections.Counter([proximity_weight, diversity_weight, categorical_penalty]) == collections.Counter(self.hyperparameters):
self.update_hyperparameters(proximity_weight, diversity_weight, categorical_penalty)
query_instance, test_pred = self.find_counterfactuals(query_instance, desired_class, learning_rate, min_iter, max_iter, project_iter, loss_diff_thres, loss_converge_maxiter, verbose, init_near_query_instance, tie_random, stopping_threshold, posthoc_sparsity_param)
return exp.CounterfactualExamples(self.data_interface, query_instance,
test_pred, self.final_cfs, self.cfs_preds, self.final_cfs_sparse, self.cfs_preds_sparse, posthoc_sparsity_param)
def generate_countefactuals(self,
query_instance,
total_CFs,
desired_class="opposite"):
# Converting query_instance into numpy array
query_instance_org = query_instance
query_instance = self.data_interface.prepare_query_instance(
query_instance=query_instance, encode=True)
query_instance = np.array([query_instance.iloc[0].values])
print(query_instance.shape[0])
if query_instance.shape[0] > self.batch_size:
test_dataset = np.array_split(query_instance,
query_instance.shape[0] //
self.batch_size,
axis=0)
else:
test_dataset = [query_instance]
final_gen_cf = []
final_cf_pred = []
final_test_pred = []
for i in range(len(query_instance)):
train_x = test_dataset[i]
train_x = torch.tensor(train_x).float()
train_y = torch.argmax(self.pred_model(train_x), dim=1)
train_size += train_x.shape[0]
curr_gen_cf = []
curr_cf_pred = []
curr_test_pred = []
for cf_count in range(total_CFs):
recon_err, kl_err, x_true, x_pred, cf_label = model.compute_elbo(
train_x, 1.0 - train_y, pred_model)
curr_gen_cf.append(x_pred.numpy())
curr_cf_pred.append(cf_label.numpy())
curr_test_pred.append(train_y.numpy())
# Code for converting tensor countefactuals into pandas dataframe
# x_pred= d.de_normalize_data( d.get_decoded_data(x_pred.detach().cpu().numpy()) )
# x_true= d.de_normalize_data( d.get_decoded_data(x_true.detach().cpu().numpy()) )
final_gen_cf.append(curr_gen_cf)
final_cf_pred.append(curr_cf_pred)
final_test_pred.append(curr_test_pred)
#CF Gen out
result = {}
result['CF'] = final_gen_cf[0]
result['CF-Pred'] = final_cf_pred[0]
result['test-pred'] = torch.argmax()
return exp.CounterfactualExamples(self.data_interface,
query_instance_org, test_pred,
final_gen_cf[0], final_cf_pred[0])
def generate_counterfactuals(self,
query_instance,
total_CFs,
desired_class="opposite",
features_to_vary="all",
permitted_range=None,
training_points_only=True,
feature_weights="inverse_mad",
stopping_threshold=0.5,
posthoc_sparsity_param=0.1,
posthoc_sparsity_algorithm="linear",
verbose=True):
"""Generates diverse counterfactual explanations
:param query_instance: A dictionary of feature names and values. Test point of interest.
:param total_CFs: Total number of counterfactuals required.
:param desired_class: Desired counterfactual class - can take 0 or 1. Default value is "opposite" to the outcome class of query_instance for binary classification.
:param features_to_vary: Either a string "all" or a list of feature names to vary.
:param permitted_range: Dictionary with continuous feature names as keys and permitted min-max range in list as values. Defaults to the range inferred from training data. If None, uses the parameters initialized in data_interface.
:param training_points_only: Parameter to determine if the returned counterfactuals should be a subset of the training data points
:param feature_weights: Either "inverse_mad" or a dictionary with feature names as keys and corresponding weights as values. Default option is "inverse_mad" where the weight for a continuous feature is the inverse of the Median Absolute Devidation (MAD) of the feature's values in the training set; the weight for a categorical feature is equal to 1 by default.
:param stopping_threshold: Minimum threshold for counterfactuals target class probability.
:param posthoc_sparsity_param: Parameter for the post-hoc operation on continuous features to enhance sparsity.
:param posthoc_sparsity_algorithm: Perform either linear or binary search. Takes "linear" or "binary". Prefer binary search when a feature range is large (for instance, income varying from 10k to 1000k) and only if the features share a monotonic relationship with predicted outcome in the model.
:param verbose: Parameter to determine whether to print 'Diverse Counterfactuals found!'
:return: A CounterfactualExamples object to store and visualize the resulting counterfactual explanations (see diverse_counterfactuals.py).
"""
# check feature MAD validity and throw warnings
if feature_weights == "inverse_mad":
self.data_interface.get_valid_mads(display_warnings=True,
return_mads=False)
if features_to_vary == 'all':
features_to_vary = self.data_interface.feature_names
query_instance, test_pred, final_cfs, cfs_preds = self.find_counterfactuals(
query_instance, desired_class, total_CFs, features_to_vary,
permitted_range, training_points_only, stopping_threshold,
posthoc_sparsity_param, posthoc_sparsity_algorithm, verbose)
return exp.CounterfactualExamples(self.data_interface, query_instance,
test_pred, final_cfs, cfs_preds,
self.final_cfs_sparse,
self.cfs_preds_sparse,
posthoc_sparsity_param,
desired_class)
def generate_counterfactuals(self,
query_instance,
total_CFs,
desired_class="opposite"):
# Loading the latest trained CFVAE model
self.cf_vae.load_state_dict(torch.load(self.save_path))
self.cf_vae.eval()
query_instance = self.data_interface.get_ohe_min_max_normalized_data(
query_instance)
query_instance = np.array([query_instance.iloc[0].values])
if query_instance.shape[0] > self.batch_size:
test_dataset = np.array_split(query_instance,
query_instance.shape[0] //
self.batch_size,
axis=0)
else:
test_dataset = [query_instance]
final_gen_cf = []
final_cf_pred = []
final_test_pred = []
for i in range(len(query_instance)):
train_x = test_dataset[i]
train_x = torch.tensor(train_x).float()
train_y = torch.argmax(self.pred_model(train_x), dim=1)
curr_gen_cf = []
curr_cf_pred = []
curr_test_pred = train_y.numpy()
for _ in range(total_CFs):
recon_err, kl_err, x_true, x_pred, cf_label = \
self.cf_vae.compute_elbo(train_x, 1.0-train_y, self.pred_model)
while (cf_label == train_y):
print(cf_label, train_y)
recon_err, kl_err, x_true, x_pred, cf_label = \
self.cf_vae.compute_elbo(train_x, 1.0-train_y, self.pred_model)
x_pred = x_pred.detach().numpy()
# Converting mixed scores into one hot feature representations
for v in self.cf_vae.encoded_categorical_feature_indexes:
curr_max = x_pred[:, v[0]]
curr_max_idx = v[0]
for idx in v:
if curr_max < x_pred[:, idx]:
curr_max = x_pred[:, idx]
curr_max_idx = idx
for idx in v:
if idx == curr_max_idx:
x_pred[:, idx] = 1
else:
x_pred[:, idx] = 0
cf_label = cf_label.detach().numpy()
cf_label = np.reshape(cf_label, (cf_label.shape[0], 1))
curr_gen_cf.append(x_pred)
curr_cf_pred.append(cf_label)
final_gen_cf.append(curr_gen_cf)
final_cf_pred.append(curr_cf_pred)
final_test_pred.append(curr_test_pred)
# CF Gen out
result = {}
result['query-instance'] = query_instance
result['test-pred'] = final_test_pred[0][0]
result['CF'] = final_gen_cf[0]
result['CF-Pred'] = final_cf_pred[0]
# do inverse transform of CFs to original user-fed format
cfs = np.array([cfval[0] for cfval in result['CF']])
final_cfs_df = self.data_interface.get_inverse_ohe_min_max_normalized_data(
cfs)
cfs_preds = [
np.round(preds.flatten().tolist(), 3)
for preds in result['CF-Pred']
]
cfs_preds = [item for sublist in cfs_preds for item in sublist]
final_cfs_df[self.data_interface.outcome_name] = np.array(cfs_preds)
test_instance_df = self.data_interface.get_inverse_ohe_min_max_normalized_data(
result['query-instance'])
test_instance_df[self.data_interface.outcome_name] = np.array(
np.round(result['test-pred'], 3))
# Adding empty list for sparse cf gen and pred; adding 'NA' for the posthoc sparsity cofficient
return exp.CounterfactualExamples(data_interface=self.data_interface,
final_cfs_df=final_cfs_df,
test_instance_df=test_instance_df,
final_cfs_df_sparse=None,
posthoc_sparsity_param=None,
desired_class=desired_class)
def generate_counterfactuals(self,
query_instance,
total_CFs,
desired_class="opposite",
proximity_weight=0.5,
diversity_weight=1.0,
categorical_penalty=0.1,
algorithm="DiverseCF",
features_to_vary="all",
permitted_range=None,
yloss_type="hinge_loss",
diversity_loss_type="dpp_style:inverse_dist",
feature_weights="inverse_mad",
optimizer="tensorflow:adam",
learning_rate=0.05,
min_iter=500,
max_iter=5000,
project_iter=0,
loss_diff_thres=1e-5,
loss_converge_maxiter=1,
verbose=False,
init_near_query_instance=True,
tie_random=False,
stopping_threshold=0.5,
posthoc_sparsity_param=0.1,
posthoc_sparsity_algorithm="linear"):
"""Generates diverse counterfactual explanations
:param query_instance: Test point of interest. A dictionary of feature names and values or a single row dataframe
:param total_CFs: Total number of counterfactuals required.
:param desired_class: Desired counterfactual class - can take 0 or 1. Default value is "opposite" to the outcome class of query_instance for binary classification.
:param proximity_weight: A positive float. Larger this weight, more close the counterfactuals are to the query_instance.
:param diversity_weight: A positive float. Larger this weight, more diverse the counterfactuals are.
:param categorical_penalty: A positive float. A weight to ensure that all levels of a categorical variable sums to 1.
:param algorithm: Counterfactual generation algorithm. Either "DiverseCF" or "RandomInitCF".
:param features_to_vary: Either a string "all" or a list of feature names to vary.
param permitted_range: Dictionary with continuous feature names as keys and permitted min-max range in list as values. Defaults to the range inferred from training data. If None, uses the parameters initialized in data_interface.
:param yloss_type: Metric for y-loss of the optimization function. Takes "l2_loss" or "log_loss" or "hinge_loss".
:param diversity_loss_type: Metric for diversity loss of the optimization function. Takes "avg_dist" or "dpp_style:inverse_dist".
:param feature_weights: Either "inverse_mad" or a dictionary with feature names as keys and corresponding weights as values. Default option is "inverse_mad" where the weight for a continuous feature is the inverse of the Median Absolute Devidation (MAD) of the feature's values in the training set; the weight for a categorical feature is equal to 1 by default.
:param optimizer: Tensorflow optimization algorithm. Currently tested only with "tensorflow:adam".
:param learning_rate: Learning rate for optimizer.
:param min_iter: Min iterations to run gradient descent for.
:param max_iter: Max iterations to run gradient descent for.
:param project_iter: Project the gradients at an interval of these many iterations.
:param loss_diff_thres: Minimum difference between successive loss values to check convergence.
:param loss_converge_maxiter: Maximum number of iterations for loss_diff_thres to hold to declare convergence. Defaults to 1, but we assigned a more conservative value of 2 in the paper.
:param verbose: Print intermediate loss value.
:param init_near_query_instance: Boolean to indicate if counterfactuals are to be initialized near query_instance.
:param tie_random: Used in rounding off CFs and intermediate projection.
:param stopping_threshold: Minimum threshold for counterfactuals target class probability.
:param posthoc_sparsity_param: Parameter for the post-hoc operation on continuous features to enhance sparsity.
:param posthoc_sparsity_algorithm: Perform either linear or binary search. Takes "linear" or "binary". Prefer binary search when a feature range is large (for instance, income varying from 10k to 1000k) and only if the features share a monotonic relationship with predicted outcome in the model.
:return: A CounterfactualExamples object to store and visualize the resulting counterfactual explanations (see diverse_counterfactuals.py).
"""
# check feature MAD validity and throw warnings
if feature_weights == "inverse_mad":
self.data_interface.get_valid_mads(display_warnings=True,
return_mads=False)
# check permitted range for continuous features
if permitted_range is not None:
# if not self.data_interface.check_features_range(permitted_range):
# raise ValueError(
# "permitted range of features should be within their original range")
# else:
self.data_interface.permitted_range = permitted_range
self.minx, self.maxx = self.data_interface.get_minx_maxx(
normalized=True)
self.cont_minx = []
self.cont_maxx = []
for feature in self.data_interface.continuous_feature_names:
self.cont_minx.append(
self.data_interface.permitted_range[feature][0])
self.cont_maxx.append(
self.data_interface.permitted_range[feature][1])
#if([total_CFs, algorithm, features_to_vary] != self.cf_init_weights):
self.do_cf_initializations(total_CFs, algorithm, features_to_vary)
if ([yloss_type, diversity_loss_type, feature_weights] !=
self.loss_weights):
self.do_loss_initializations(yloss_type, diversity_loss_type,
feature_weights)
if ([proximity_weight, diversity_weight, categorical_penalty] !=
self.hyperparameters):
self.update_hyperparameters(proximity_weight, diversity_weight,
categorical_penalty)
final_cfs_df, test_instance_df, final_cfs_df_sparse = self.find_counterfactuals(
query_instance, desired_class, optimizer, learning_rate, min_iter,
max_iter, project_iter, loss_diff_thres, loss_converge_maxiter,
verbose, init_near_query_instance, tie_random, stopping_threshold,
posthoc_sparsity_param, posthoc_sparsity_algorithm)
return exp.CounterfactualExamples(
data_interface=self.data_interface,
final_cfs_df=final_cfs_df,
test_instance_df=test_instance_df,
final_cfs_df_sparse=final_cfs_df_sparse,
posthoc_sparsity_param=posthoc_sparsity_param,
desired_class=desired_class)
def generate_counterfactuals(self,
query_instance,
total_CFs,
desired_class="opposite",
permitted_range=None,
features_to_vary="all",
stopping_threshold=0.5,
posthoc_sparsity_param=0.1,
posthoc_sparsity_algorithm="linear",
sample_size=1000,
random_seed=17):
"""Generate counterfactuals by randomly sampling features.
:param query_instance: A dictionary of feature names and values. Test point of interest.
:param total_CFs: Total number of counterfactuals required.
:param desired_class: Desired counterfactual class - can take 0 or 1. Default value is "opposite" to the outcome class of query_instance for binary classification.
:param permitted_range: Dictionary with feature names as keys and permitted range in list as values. Defaults to the range inferred from training data. If None, uses the parameters initialized in data_interface.
:param features_to_vary: Either a string "all" or a list of feature names to vary.
:param stopping_threshold: Minimum threshold for counterfactuals target class probability.
:param posthoc_sparsity_param: Parameter for the post-hoc operation on continuous features to enhance sparsity.
:param posthoc_sparsity_algorithm: Perform either linear or binary search. Takes "linear" or "binary". Prefer binary search when a feature range is large (for instance, income varying from 10k to 1000k) and only if the features share a monotonic relationship with predicted outcome in the model.
:param sample_size: Sampling size
:param random_seed: Random seed for reproducibility
"""
# permitted range for continuous features
if permitted_range is not None:
if not self.data_interface.check_features_range():
raise ValueError(
"permitted range of features should be within their original range"
)
else:
self.data_interface.permitted_range = permitted_range
self.minx, self.maxx = self.data_interface.get_minx_maxx(
normalized=True)
self.cont_minx = []
self.cont_maxx = []
for feature in self.data_interface.continuous_feature_names:
self.cont_minx.append(
self.data_interface.permitted_range[feature][0])
self.cont_maxx.append(
self.data_interface.permitted_range[feature][1])
# fixing features that are to be fixed
self.total_CFs = total_CFs
if features_to_vary == "all":
self.fixed_features_values = {}
else:
self.fixed_features_values = {}
for feature in self.data_interface.feature_names:
if feature not in features_to_vary:
self.fixed_features_values[feature] = query_instance[
feature]
# number of output nodes of ML model
temp_input = np.random.rand(
1, len(self.data_interface.encoded_feature_names))
self.num_ouput_nodes = len(self.model.get_output(temp_input))
# Prepares user defined query_instance for DiCE.
query_instance = self.data_interface.prepare_query_instance(
query_instance=query_instance, encode=True)
query_instance = np.array([query_instance.iloc[0].values],
dtype=np.float32)
# find the predicted value of query_instance
test_pred = self.predict_fn(query_instance)[0]
if desired_class == "opposite":
desired_class = 1.0 - round(test_pred)
self.target_cf_class = desired_class
self.stopping_threshold = stopping_threshold
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:
self.stopping_threshold = 0.75
# get random samples for each feature independently
start_time = timeit.default_timer()
samples = get_samples(self,
self.fixed_features_values,
sampling_random_seed=random_seed,
sampling_size=sample_size)
cfs = self.data_interface.prepare_query_instance(
query_instance=samples, encode=True).values
cf_preds = self.predict_fn(cfs)
cfs_df = pd.DataFrame(
np.append(cfs, np.array([cf_preds]).T, axis=1),
columns=self.data_interface.encoded_feature_names +
[self.data_interface.outcome_name])
# check validity of CFs
cfs_df['validity'] = cfs_df[self.data_interface.outcome_name].apply(
lambda pred: 1 if
((self.target_cf_class == 0 and pred <= self.stopping_threshold) or
(self.target_cf_class == 1 and pred >= self.stopping_threshold
)) else 0)
self.total_CFs_found = cfs_df[cfs_df['validity'] == 1].shape[0]
if self.total_CFs_found >= self.total_CFs:
cfs_df = cfs_df[cfs_df['validity'] == 1].sample(
n=self.total_CFs, random_state=random_seed)
self.valid_cfs_found = True
else:
temp_df = cfs_df[cfs_df['validity'] == 0].sample(
n=self.total_CFs - self.total_CFs_found,
random_state=random_seed)
cfs_df = pd.concat([cfs_df[cfs_df['validity'] == 1], temp_df],
ignore_index=True)
self.valid_cfs_found = False
# convert to the format that is consistent with dice_tensorflow
temp = cfs_df[self.data_interface.encoded_feature_names].values
self.final_cfs = [np.array([arr]) for arr in temp]
temp = cfs_df[[self.data_interface.outcome_name]].values
self.cfs_preds = [np.array([arr]) for arr in temp]
# post-hoc operation on continuous features to enhance sparsity - only for public data
if posthoc_sparsity_param != None and posthoc_sparsity_param > 0 and 'data_df' in self.data_interface.__dict__:
final_cfs_sparse = copy.deepcopy(self.final_cfs)
cfs_preds_sparse = copy.deepcopy(self.cfs_preds)
self.final_cfs_sparse, self.cfs_preds_sparse = self.do_posthoc_sparsity_enhancement(
final_cfs_sparse, cfs_preds_sparse, query_instance,
posthoc_sparsity_param, posthoc_sparsity_algorithm)
else:
self.final_cfs_sparse = None
self.cfs_preds_sparse = None
self.elapsed = timeit.default_timer() - start_time
m, s = divmod(self.elapsed, 60)
if self.valid_cfs_found:
print('Diverse Counterfactuals found! total time taken: %02d' % m,
'min %02d' % s, 'sec')
else:
print(
'Only %d (required %d) Diverse Counterfactuals found for the given configuation, perhaps try with different values of proximity (or diversity) weights or learning rate...'
% (self.total_CFs_found, self.total_CFs),
'; total time taken: %02d' % m, 'min %02d' % s, 'sec')
return exp.CounterfactualExamples(
self.data_interface, query_instance, test_pred, self.final_cfs,
self.cfs_preds, self.final_cfs_sparse, self.cfs_preds_sparse,
posthoc_sparsity_param, desired_class)
def _generate_counterfactuals(self, query_instance, total_CFs, initialization="kdtree",
desired_range=None, desired_class="opposite", proximity_weight=0.2,
sparsity_weight=0.2, diversity_weight=5.0, categorical_penalty=0.1,
algorithm="DiverseCF", features_to_vary="all", permitted_range=None,
yloss_type="hinge_loss", diversity_loss_type="dpp_style:inverse_dist",
feature_weights="inverse_mad", stopping_threshold=0.5, posthoc_sparsity_param=0.1,
posthoc_sparsity_algorithm="binary", maxiterations=500, thresh=1e-2, verbose=False):
"""Generates diverse counterfactual explanations
:param query_instance: A dictionary of feature names and values. Test point of interest.
:param total_CFs: Total number of counterfactuals required.
:param initialization: Method to use to initialize the population of the genetic algorithm
:param desired_range: For regression problems. Contains the outcome range to generate counterfactuals in.
:param desired_class: For classification problems. Desired counterfactual class - can take 0 or 1.
Default value is "opposite" to the outcome class of query_instance for binary classification.
:param proximity_weight: A positive float. Larger this weight, more close the counterfactuals are to the
query_instance.
:param sparsity_weight: A positive float. Larger this weight, less features are changed from the query_instance.
:param diversity_weight: A positive float. Larger this weight, more diverse the counterfactuals are.
:param categorical_penalty: A positive float. A weight to ensure that all levels of a categorical variable sums to 1.
:param algorithm: Counterfactual generation algorithm. Either "DiverseCF" or "RandomInitCF".
:param features_to_vary: Either a string "all" or a list of feature names to vary.
:param permitted_range: Dictionary with continuous feature names as keys and permitted min-max range in list as values.
Defaults to the range inferred from training data. If None, uses the parameters initialized
in data_interface.
:param yloss_type: Metric for y-loss of the optimization function. Takes "l2_loss" or "log_loss" or "hinge_loss".
:param diversity_loss_type: Metric for diversity loss of the optimization function.
Takes "avg_dist" or "dpp_style:inverse_dist".
:param feature_weights: Either "inverse_mad" or a dictionary with feature names as keys and
corresponding weights as values. Default option is "inverse_mad" where the
weight for a continuous feature is the inverse of the Median Absolute Devidation (MAD)
of the feature's values in the training set; the weight for a categorical feature is
equal to 1 by default.
:param stopping_threshold: Minimum threshold for counterfactuals target class probability.
:param posthoc_sparsity_param: Parameter for the post-hoc operation on continuous features to enhance sparsity.
:param posthoc_sparsity_algorithm: Perform either linear or binary search. Takes "linear" or "binary".
Prefer binary search when a feature range is large
(for instance, income varying from 10k to 1000k) and only if the features
share a monotonic relationship with predicted outcome in the model.
:param maxiterations: Maximum iterations to run the genetic algorithm for.
:param thresh: The genetic algorithm stops when the difference between the previous best loss and current
best loss is less than thresh
:param verbose: Parameter to determine whether to print 'Diverse Counterfactuals found!'
:return: A CounterfactualExamples object to store and visualize the resulting counterfactual explanations
(see diverse_counterfactuals.py).
"""
self.population_size = 10 * total_CFs
self.start_time = timeit.default_timer()
features_to_vary = self.setup(features_to_vary, permitted_range, query_instance, feature_weights)
# Prepares user defined query_instance for DiCE.
query_instance_orig = query_instance
query_instance = self.data_interface.prepare_query_instance(query_instance=query_instance)
query_instance = self.label_encode(query_instance)
query_instance = np.array(query_instance.values[0])
self.x1 = query_instance
# find the predicted value of query_instance
test_pred = self.predict_fn(query_instance)
self.test_pred = test_pred
desired_class = self.misc_init(stopping_threshold, desired_class, desired_range, test_pred)
query_instance_df_dummies = pd.get_dummies(query_instance_orig)
for col in pd.get_dummies(self.data_interface.data_df[self.data_interface.feature_names]).columns:
if col not in query_instance_df_dummies.columns:
query_instance_df_dummies[col] = 0
self.do_param_initializations(total_CFs, initialization, desired_range, desired_class, query_instance,
query_instance_df_dummies, algorithm, features_to_vary, permitted_range,
yloss_type, diversity_loss_type, feature_weights, proximity_weight,
sparsity_weight, diversity_weight, categorical_penalty, verbose)
query_instance_df = self.find_counterfactuals(query_instance, desired_range, desired_class, features_to_vary,
maxiterations, thresh, verbose)
return exp.CounterfactualExamples(data_interface=self.data_interface,
test_instance_df=query_instance_df,
final_cfs_df=self.final_cfs_df,
final_cfs_df_sparse=self.final_cfs_df_sparse,
posthoc_sparsity_param=posthoc_sparsity_param,
desired_range=desired_range,
desired_class=desired_class,
model_type=self.model.model_type)
def generate_counterfactuals(
self,
query_instance,
total_CFs,
desired_class="opposite",
):
## Loading the latest trained CFVAE model
self.cf_vae.load_state_dict(torch.load(self.save_path))
self.cf_vae.eval()
# Converting query_instance into numpy array
query_instance_org = query_instance
query_instance = self.data_interface.prepare_query_instance(
query_instance=query_instance, encode=True)
query_instance = np.array([query_instance.iloc[0].values])
print(query_instance.shape[0])
if query_instance.shape[0] > self.batch_size:
test_dataset = np.array_split(query_instance,
query_instance.shape[0] //
self.batch_size,
axis=0)
else:
test_dataset = [query_instance]
final_gen_cf = []
final_cf_pred = []
final_test_pred = []
for i in range(len(query_instance)):
train_x = test_dataset[i]
train_x = torch.tensor(train_x).float()
train_y = torch.argmax(self.pred_model(train_x), dim=1)
curr_gen_cf = []
curr_cf_pred = []
curr_test_pred = train_y.numpy()
for cf_count in range(total_CFs):
recon_err, kl_err, x_true, x_pred, cf_label = self.cf_vae.compute_elbo(
train_x, 1.0 - train_y, self.pred_model)
while (cf_label == train_y):
print(cf_label, train_y)
recon_err, kl_err, x_true, x_pred, cf_label = self.cf_vae.compute_elbo(
train_x, 1.0 - train_y, self.pred_model)
x_pred = x_pred.detach().numpy()
#Converting mixed scores into one hot feature representations
for v in self.cf_vae.encoded_categorical_feature_indexes:
curr_max = x_pred[:, v[0]]
curr_max_idx = v[0]
for idx in v:
if curr_max < x_pred[:, idx]:
curr_max = x_pred[:, idx]
curr_max_idx = idx
for idx in v:
if idx == curr_max_idx:
x_pred[:, idx] = 1
else:
x_pred[:, idx] = 0
cf_label = cf_label.detach().numpy()
cf_label = np.reshape(cf_label, (cf_label.shape[0], 1))
curr_gen_cf.append(x_pred)
curr_cf_pred.append(cf_label)
final_gen_cf.append(curr_gen_cf)
final_cf_pred.append(curr_cf_pred)
final_test_pred.append(curr_test_pred)
#CF Gen out
result = {}
result['query-instance'] = query_instance[0]
result['test-pred'] = final_test_pred[0][0]
result['CF'] = final_gen_cf[0]
result['CF-Pred'] = final_cf_pred[0]
# Adding empty list for sparse cf gen and pred; adding 0 for the sparsity coffecient
return exp.CounterfactualExamples(self.data_interface,
result['query-instance'],
result['test-pred'], result['CF'],
result['CF-Pred'], None, None, 0)
def _generate_counterfactuals(self, query_instance, total_CFs, desired_range=None, desired_class="opposite",
features_to_vary="all",
permitted_range=None, sparsity_weight=1,
feature_weights="inverse_mad", stopping_threshold=0.5, posthoc_sparsity_param=0.1,
posthoc_sparsity_algorithm="linear", verbose=False):
"""Generates diverse counterfactual explanations
:param query_instance: A dictionary of feature names and values. Test point of interest.
:param total_CFs: Total number of counterfactuals required.
:param desired_range: For regression problems. Contains the outcome range to generate counterfactuals in.
:param desired_class: Desired counterfactual class - can take 0 or 1. Default value is "opposite" to the outcome class of query_instance for binary classification.
:param features_to_vary: Either a string "all" or a list of feature names to vary.
:param permitted_range: Dictionary with continuous feature names as keys and permitted min-max range in list as values. Defaults to the range inferred from training data. If None, uses the parameters initialized in data_interface.
:param sparsity_weight: Parameter to determine how much importance to give to sparsity
:param feature_weights: Either "inverse_mad" or a dictionary with feature names as keys and corresponding weights as values. Default option is "inverse_mad" where the weight for a continuous feature is the inverse of the Median Absolute Devidation (MAD) of the feature's values in the training set; the weight for a categorical feature is equal to 1 by default.
:param stopping_threshold: Minimum threshold for counterfactuals target class probability.
:param posthoc_sparsity_param: Parameter for the post-hoc operation on continuous features to enhance sparsity.
:param posthoc_sparsity_algorithm: Perform either linear or binary search. Takes "linear" or "binary". Prefer binary search when a feature range is large (for instance, income varying from 10k to 1000k) and only if the features share a monotonic relationship with predicted outcome in the model.
:param verbose: Parameter to determine whether to print 'Diverse Counterfactuals found!'
:return: A CounterfactualExamples object to store and visualize the resulting counterfactual explanations (see diverse_counterfactuals.py).
"""
data_df_copy = self.data_interface.data_df.copy()
features_to_vary = self.setup(features_to_vary, permitted_range, query_instance, feature_weights)
# Prepares user defined query_instance for DiCE.
query_instance_orig = query_instance.copy()
query_instance = self.data_interface.prepare_query_instance(query_instance=query_instance)
# find the predicted value of query_instance
test_pred = self.predict_fn(query_instance)[0]
query_instance[self.data_interface.outcome_name] = test_pred
desired_class = self.misc_init(stopping_threshold, desired_class, desired_range, test_pred)
if desired_range != None:
if desired_range[0] > desired_range[1]:
raise ValueError("Invalid Range!")
if desired_class == "opposite" and self.model.model_type == 'classifier':
if self.num_output_nodes == 2:
desired_class = 1.0 - test_pred
elif self.num_output_nodes > 2:
raise ValueError("Desired class can't be opposite if the number of classes is more than 2.")
if isinstance(desired_class, int) and desired_class > self.num_output_nodes - 1:
raise ValueError("Desired class should be within 0 and num_classes-1.")
# Partitioned dataset and KD Tree for each class (binary) of the dataset
self.dataset_with_predictions, self.KD_tree, self.predictions = self.build_KD_tree(data_df_copy, desired_range,
desired_class, self.predicted_outcome_name)
query_instance, cfs_preds = self.find_counterfactuals(data_df_copy,
query_instance, query_instance_orig,
desired_range,
desired_class,
total_CFs, features_to_vary,
permitted_range,
sparsity_weight,
stopping_threshold,
posthoc_sparsity_param,
posthoc_sparsity_algorithm, verbose)
self.cfs_preds = cfs_preds
return exp.CounterfactualExamples(data_interface=self.data_interface,
final_cfs_df=self.final_cfs_df,
test_instance_df=query_instance,
final_cfs_df_sparse=self.final_cfs_df_sparse,
posthoc_sparsity_param=posthoc_sparsity_param,
desired_range=desired_range,
desired_class=desired_class,
model_type=self.model.model_type)
def _generate_counterfactuals(self,
query_instance,
total_CFs,
desired_range,
desired_class,
permitted_range,
features_to_vary,
stopping_threshold=0.5,
posthoc_sparsity_param=0.1,
posthoc_sparsity_algorithm="linear",
sample_size=1000,
random_seed=None,
verbose=False):
"""Generate counterfactuals by randomly sampling features.
:param query_instance: Test point of interest. A dictionary of feature names and values or a single row dataframe.
:param total_CFs: Total number of counterfactuals required.
:param desired_range: For regression problems. Contains the outcome range to generate counterfactuals in.
:param desired_class: Desired counterfactual class - can take 0 or 1. Default value is "opposite" to the outcome
class of query_instance for binary classification.
:param permitted_range: Dictionary with feature names as keys and permitted range in list as values.
Defaults to the range inferred from training data. If None, uses the parameters
initialized in data_interface.
:param features_to_vary: Either a string "all" or a list of feature names to vary.
:param stopping_threshold: Minimum threshold for counterfactuals target class probability.
:param posthoc_sparsity_param: Parameter for the post-hoc operation on continuous features to enhance sparsity.
:param posthoc_sparsity_algorithm: Perform either linear or binary search. Takes "linear" or "binary".
Prefer binary search when a feature range is large
(for instance, income varying from 10k to 1000k) and only if the features
share a monotonic relationship with predicted outcome in the model.
:param sample_size: Sampling size
:param random_seed: Random seed for reproducibility
:returns: A CounterfactualExamples object that contains the dataframe of generated counterfactuals as an attribute.
"""
if permitted_range is None:
# use the precomputed default
self.feature_range = self.data_interface.permitted_range
else:
# compute the new ranges based on user input
self.feature_range, feature_ranges_orig = self.data_interface.get_features_range(
permitted_range)
# Do predictions once on the query_instance and reuse across to reduce the number
# inferences.
model_predictions = self.predict_fn(query_instance)
# number of output nodes of ML model
self.num_output_nodes = None
if self.model.model_type == ModelTypes.Classifier:
self.num_output_nodes = model_predictions.shape[1]
# query_instance need no transformation for generating CFs using random sampling.
# find the predicted value of query_instance
test_pred = model_predictions[0]
if self.model.model_type == ModelTypes.Classifier:
self.target_cf_class = self.infer_target_cfs_class(
desired_class, test_pred, self.num_output_nodes)
elif self.model.model_type == ModelTypes.Regressor:
self.target_cf_range = self.infer_target_cfs_range(desired_range)
# fixing features that are to be fixed
self.total_CFs = total_CFs
self.features_to_vary = features_to_vary
if features_to_vary == "all":
self.features_to_vary = self.data_interface.feature_names
self.fixed_features_values = {}
else:
self.fixed_features_values = {}
for feature in self.data_interface.feature_names:
if feature not in features_to_vary:
self.fixed_features_values[feature] = query_instance[
feature].iat[0]
self.stopping_threshold = stopping_threshold
if self.model.model_type == ModelTypes.Classifier:
# TODO Generalize this for multi-class
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:
self.stopping_threshold = 0.75
# get random samples for each feature independently
start_time = timeit.default_timer()
random_instances = self.get_samples(self.fixed_features_values,
self.feature_range,
sampling_random_seed=random_seed,
sampling_size=sample_size)
# Generate copies of the query instance that will be changed one feature
# at a time to encourage sparsity.
cfs_df = None
candidate_cfs = pd.DataFrame(np.repeat(query_instance.values,
sample_size,
axis=0),
columns=query_instance.columns)
# Loop to change one feature at a time, then two features, and so on.
for num_features_to_vary in range(1, len(self.features_to_vary) + 1):
selected_features = np.random.choice(self.features_to_vary,
(sample_size, 1),
replace=True)
for k in range(sample_size):
candidate_cfs.at[
k, selected_features[k][0]] = random_instances.at[
k, selected_features[k][0]]
scores = self.predict_fn(candidate_cfs)
validity = self.decide_cf_validity(scores)
if sum(validity) > 0:
rows_to_add = candidate_cfs[validity == 1]
if cfs_df is None:
cfs_df = rows_to_add.copy()
else:
cfs_df = cfs_df.append(rows_to_add)
cfs_df.drop_duplicates(inplace=True)
# Always change at least 2 features before stopping
if num_features_to_vary >= 2 and len(cfs_df) >= total_CFs:
break
self.total_cfs_found = 0
self.valid_cfs_found = False
if cfs_df is not None and len(cfs_df) > 0:
if len(cfs_df) > total_CFs:
cfs_df = cfs_df.sample(total_CFs)
cfs_df.reset_index(inplace=True, drop=True)
self.cfs_pred_scores = self.predict_fn(cfs_df)
cfs_df[self.data_interface.
outcome_name] = self.get_model_output_from_scores(
self.cfs_pred_scores)
self.total_cfs_found = len(cfs_df)
self.valid_cfs_found = True if self.total_cfs_found >= self.total_CFs else False
final_cfs_df = cfs_df[self.data_interface.feature_names +
[self.data_interface.outcome_name]]
final_cfs_df[self.data_interface.outcome_name] = \
final_cfs_df[self.data_interface.outcome_name].round(self.outcome_precision)
self.cfs_preds = final_cfs_df[[self.data_interface.outcome_name
]].values
self.final_cfs = final_cfs_df[
self.data_interface.feature_names].values
else:
final_cfs_df = None
self.cfs_preds = None
self.cfs_pred_scores = None
self.final_cfs = None
test_instance_df = self.data_interface.prepare_query_instance(
query_instance)
test_instance_df[self.data_interface.outcome_name] = \
np.array(np.round(self.get_model_output_from_scores((test_pred,)), self.outcome_precision))
# post-hoc operation on continuous features to enhance sparsity - only for public data
if posthoc_sparsity_param is not None and posthoc_sparsity_param > 0 and \
self.final_cfs is not None and 'data_df' in self.data_interface.__dict__:
final_cfs_df_sparse = final_cfs_df.copy()
final_cfs_df_sparse = self.do_posthoc_sparsity_enhancement(
final_cfs_df_sparse, test_instance_df, posthoc_sparsity_param,
posthoc_sparsity_algorithm)
else:
final_cfs_df_sparse = None
self.elapsed = timeit.default_timer() - start_time
m, s = divmod(self.elapsed, 60)
if self.valid_cfs_found:
if verbose:
print(
'Diverse Counterfactuals found! total time taken: %02d' %
m, 'min %02d' % s, 'sec')
else:
if self.total_cfs_found == 0:
print(
'No Counterfactuals found for the given configuration, perhaps try with different parameters...',
'; total time taken: %02d' % m, 'min %02d' % s, 'sec')
else:
print(
'Only %d (required %d) ' %
(self.total_cfs_found, self.total_CFs),
'Diverse Counterfactuals found for the given configuration, perhaps try with different parameters...',
'; total time taken: %02d' % m, 'min %02d' % s, 'sec')
return exp.CounterfactualExamples(
data_interface=self.data_interface,
final_cfs_df=final_cfs_df,
test_instance_df=test_instance_df,
final_cfs_df_sparse=final_cfs_df_sparse,
posthoc_sparsity_param=posthoc_sparsity_param,
desired_class=desired_class,
desired_range=desired_range,
model_type=self.model.model_type)
