From 04687c4c93124185f1717f976a993559f7645f6c Mon Sep 17 00:00:00 2001 From: Guillermo Fernandez Date: Mon, 20 Jun 2022 17:18:58 +0200 Subject: [PATCH 01/51] Added FDT parameters to Explainer kwargs --- teacher/explanation/FDT_explainer.py | 20 +++++++++++++++++++- 1 file changed, 19 insertions(+), 1 deletion(-) diff --git a/teacher/explanation/FDT_explainer.py b/teacher/explanation/FDT_explainer.py index 89ae0d7..4c596e7 100644 --- a/teacher/explanation/FDT_explainer.py +++ b/teacher/explanation/FDT_explainer.py @@ -100,7 +100,25 @@ def fit(self, instance, target, neighborhood, df_num_cols, factual, counterfactu X = neighborhood.get_X() y = neighborhood.get_y() - self.local_explainer = FDT(fuzzy_variables) + try: + max_depth = kwargs['max_depth'] + del kwargs['max_depth'] + except KeyError: + max_depth = 2 + + try: + min_num_examples = kwargs['min_num_examples'] + del kwargs['min_num_examples'] + except KeyError: + min_num_examples = 1 + + try: + fuzzy_threshold = kwargs['fuzzy_threshold'] + del kwargs['fuzzy_threshold'] + except KeyError: + fuzzy_threshold = 1 + + self.local_explainer = FDT(fuzzy_variables, max_depth=max_depth, min_num_examples=min_num_examples, fuzzy_threshold=fuzzy_threshold) self.local_explainer.fit(X, y) rules = self.local_explainer.to_rule_based_system() From 4c88162eb2a92d0dc60a0de64f32d47fdd031391 Mon Sep 17 00:00:00 2001 From: Guillermo Fernandez Date: Mon, 20 Jun 2022 17:19:29 +0200 Subject: [PATCH 02/51] Fixed LoreNeighborhood instance_membership --- teacher/neighbors/_lore_neighborhood.py | 17 +++++++++++++++++ 1 file changed, 17 insertions(+) diff --git a/teacher/neighbors/_lore_neighborhood.py b/teacher/neighbors/_lore_neighborhood.py index 697540e..5538da6 100644 --- a/teacher/neighbors/_lore_neighborhood.py +++ b/teacher/neighbors/_lore_neighborhood.py @@ -2,10 +2,15 @@ # Imports # ============================================================================= +# Third party +from base64 import decode +import numpy as np + # Local application from ._fuzzy_neighborhood import FuzzyNeighborhood from teacher.neighbors import genetic_neighborhood, calculate_feature_values from teacher.utils import dataframe2explain +from teacher.fuzzy import dataset_membership # ============================================================================= # Classes @@ -62,6 +67,18 @@ def fit(self): self._X = df.drop(self.class_name, axis=1) self._y = self.bb.predict(Z) self._y_decoded = df[self.class_name] + + def fuzzify(self, get_division, **kwargs): + print(self.instance) + super().fuzzify(get_division, **kwargs) + decoded_instance = [] + for i, var in enumerate(self._fuzzy_variables): + try: + decoded_instance.append(self.dataset['label_encoder'][var.name].inverse_transform([self.instance[i]])[0]) + except: + decoded_instance += [self.instance[i]] + + self._instance_membership = dataset_membership(np.array([decoded_instance], dtype='object'), self._fuzzy_variables) def get_y_decoded(self): return self._y_decoded From 480668bf2b02c7880cc1b9c81505985d8c9234f2 Mon Sep 17 00:00:00 2001 From: Guillermo Fernandez Date: Wed, 6 Jul 2022 19:48:52 +0200 Subject: [PATCH 03/51] Added fidelity metric --- teacher/explanation/FDT_explainer.py | 8 +++++--- 1 file changed, 5 insertions(+), 3 deletions(-) diff --git a/teacher/explanation/FDT_explainer.py b/teacher/explanation/FDT_explainer.py index 4c596e7..e82117d 100644 --- a/teacher/explanation/FDT_explainer.py +++ b/teacher/explanation/FDT_explainer.py @@ -9,6 +9,7 @@ # Third party from sklearn.utils import check_array +from sklearn.metrics import f1_score # Local application from ._factual_local_explainer import FactualLocalExplainer @@ -81,7 +82,7 @@ def fit(self, instance, target, neighborhood, df_num_cols, factual, counterfactu 'c_factual' chosen but no 'lam' parameter given """ - instance = check_array(instance) + instance = check_array(instance, dtype=['float64', 'object']) try: self.factual_method = FACTUAL_METHODS[factual] if factual == 'c_factual' and 'lam' not in kwargs: @@ -104,7 +105,7 @@ def fit(self, instance, target, neighborhood, df_num_cols, factual, counterfactu max_depth = kwargs['max_depth'] del kwargs['max_depth'] except KeyError: - max_depth = 2 + max_depth = 10 try: min_num_examples = kwargs['min_num_examples'] @@ -116,10 +117,11 @@ def fit(self, instance, target, neighborhood, df_num_cols, factual, counterfactu fuzzy_threshold = kwargs['fuzzy_threshold'] del kwargs['fuzzy_threshold'] except KeyError: - fuzzy_threshold = 1 + fuzzy_threshold = 0.0001 self.local_explainer = FDT(fuzzy_variables, max_depth=max_depth, min_num_examples=min_num_examples, fuzzy_threshold=fuzzy_threshold) self.local_explainer.fit(X, y) + self.fidelity = f1_score(y, self.local_explainer.predict(X)[0]) rules = self.local_explainer.to_rule_based_system() self.exp_value = self.local_explainer.predict(instance) From 400395d555656d9dbfdb5277e53af1dd719abad5 Mon Sep 17 00:00:00 2001 From: Guillermo Fernandez Date: Wed, 6 Jul 2022 19:49:12 +0200 Subject: [PATCH 04/51] Fixed coverage to take all rules into account --- teacher/metrics/_rule.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/teacher/metrics/_rule.py b/teacher/metrics/_rule.py index ff47961..18297ab 100644 --- a/teacher/metrics/_rule.py +++ b/teacher/metrics/_rule.py @@ -25,7 +25,7 @@ def _get_fuzzy_coverage(rules, fuzzy_dataset, threshold=0.001): covered_instances, ds_len = _get_covered_instances(rules[0], fuzzy_dataset, threshold) for rule in rules: n_covered_instance, _ = _get_covered_instances(rule, fuzzy_dataset, threshold) - covered_instances = covered_instances | n_covered_instance + covered_instances = covered_instances & n_covered_instance return covered_instances, ds_len From 2e2ae2b0e584237434e3963efed0cd3374fd22a3 Mon Sep 17 00:00:00 2001 From: Guillermo Fernandez Date: Wed, 6 Jul 2022 19:49:50 +0200 Subject: [PATCH 05/51] Added default branch for fuzzy tree --- teacher/tree/fdt_tree.py | 5 ++++- 1 file changed, 4 insertions(+), 1 deletion(-) diff --git a/teacher/tree/fdt_tree.py b/teacher/tree/fdt_tree.py index 15365f7..941fda7 100644 --- a/teacher/tree/fdt_tree.py +++ b/teacher/tree/fdt_tree.py @@ -241,6 +241,9 @@ def _partial_fit(self, X_membership, y, current_tree, current_features, current_ child = TreeFDT(self.fuzzy_variables[self.features_dict[att]]) child.value = (self.features_dict[att], i) child.mu = child_mu[value] + current_tree.childlist.append(child) if child.mu.sum() > 0: - current_tree.childlist.append(child) self._partial_fit(X_membership, y, child, new_features, current_depth + 1) + else: + child.is_leaf = True + child.class_value = self._get_class_value(current_tree.mu, y) From acac4673964442364249be09b9f60ddc9b0b5bbd Mon Sep 17 00:00:00 2001 From: Guillermo Fernandez Date: Wed, 6 Jul 2022 19:50:11 +0200 Subject: [PATCH 06/51] Added smoothing in the neighborhood to remove outliers --- teacher/neighbors/_lore_neighborhood.py | 50 +++++++++++++++++++++---- 1 file changed, 42 insertions(+), 8 deletions(-) diff --git a/teacher/neighbors/_lore_neighborhood.py b/teacher/neighbors/_lore_neighborhood.py index 5538da6..09ec91d 100644 --- a/teacher/neighbors/_lore_neighborhood.py +++ b/teacher/neighbors/_lore_neighborhood.py @@ -3,8 +3,9 @@ # ============================================================================= # Third party -from base64 import decode import numpy as np +import pandas as pd +from scipy import stats # Local application from ._fuzzy_neighborhood import FuzzyNeighborhood @@ -47,7 +48,42 @@ def __init__(self, instance, size, class_name, bb, dataset, X2E, idx_record_to_e self.idx_record_to_explain = idx_record_to_explain super().__init__(instance, size, class_name, bb) + def _smooth_neighborhood(self, df, continuous, X2E, feat_idx, label_encoder, class_value): + ndf = df.copy() + for col in continuous: + min_val = X2E[:, feat_idx[col]].min() + max_val = X2E[:, feat_idx[col]].max() + if col == 'age': + print(min_val) + ndf[col].loc[ndf[col] > max_val] = max_val + ndf[col].loc[ndf[col] < min_val] = min_val + + old_length = len(ndf) + ndf = ndf[(np.abs(stats.zscore(ndf[continuous])) < 3).all(axis=1)] + new_length = len(ndf) + while new_length - old_length > 0: + ndf = ndf[(np.abs(stats.zscore(ndf[continuous])) < 3).all(axis=1)] + + ndf = ndf.append(pd.DataFrame(np.append(self.decoded_target, self.decoded_instance).reshape(1, -1), columns=list(ndf)), ignore_index=True) + edf = ndf.drop(self.class_name, axis=1).copy() + for le in label_encoder: + if le != self.class_name: + edf[le] = label_encoder[le].transform(edf[le]) + + return ndf, edf.to_numpy() + def fit(self): + decoded_instance = [] + features = [col for col in self.dataset['columns'] if col != self.class_name] + for i, var in enumerate(features): + try: + decoded_instance.append(self.dataset['label_encoder'][var].inverse_transform([self.instance[i]])[0]) + except: + decoded_instance += [self.instance[i]] + + self.decoded_instance = np.array([decoded_instance], dtype='object') + self.decoded_target = self.dataset['label_encoder'][self.class_name].inverse_transform(self.bb.predict(self.instance.reshape(1, -1))) + # Dataset Preprocessing self.dataset['feature_values'] = calculate_feature_values(self.X2E, self.dataset['columns'], @@ -62,6 +98,9 @@ def fit(self): # Generate Neighborhood df, Z = genetic_neighborhood(dfZ, x, self.bb, self.dataset, self.size) + + feat_idx = {feat: idx for idx, feat in self.dataset['idx_features'].items()} + df, Z = self._smooth_neighborhood(df, [col for col in self.dataset['continuous'] if col != self.class_name], self.X2E, feat_idx, self.dataset['label_encoder'], self.class_name) self._Xy = df self._X = df.drop(self.class_name, axis=1) @@ -71,14 +110,9 @@ def fit(self): def fuzzify(self, get_division, **kwargs): print(self.instance) super().fuzzify(get_division, **kwargs) - decoded_instance = [] - for i, var in enumerate(self._fuzzy_variables): - try: - decoded_instance.append(self.dataset['label_encoder'][var.name].inverse_transform([self.instance[i]])[0]) - except: - decoded_instance += [self.instance[i]] - self._instance_membership = dataset_membership(np.array([decoded_instance], dtype='object'), self._fuzzy_variables) + + self._instance_membership = dataset_membership(self.decoded_instance, self._fuzzy_variables) def get_y_decoded(self): return self._y_decoded From 067d1b8ef24c4d703766e3bd478d11190c82bfb4 Mon Sep 17 00:00:00 2001 From: Guillermo Fernandez Date: Wed, 6 Jul 2022 21:20:34 +0200 Subject: [PATCH 07/51] Added counterfactual metrics --- teacher/metrics/__init__.py | 7 +- teacher/metrics/_counterfactual.py | 168 +++++++++++++++++++++++++++++ 2 files changed, 174 insertions(+), 1 deletion(-) create mode 100644 teacher/metrics/_counterfactual.py diff --git a/teacher/metrics/__init__.py b/teacher/metrics/__init__.py index 7da0c98..f08448b 100644 --- a/teacher/metrics/__init__.py +++ b/teacher/metrics/__init__.py @@ -37,6 +37,7 @@ class value. # Local application from ._rule import coverage, precision, fidelity, rule_fidelity +from ._counterfactual import implausibility, instability, proximity_dissimilarity, sparsity_dissimilarity # ============================================================================= @@ -46,5 +47,9 @@ class value. "coverage", "precision", "fidelity", - "rule_fidelity" + "rule_fidelity", + "implausibility", + "instability", + "proximity_dissimilarity", + "sparsity_dissimilarity" ] diff --git a/teacher/metrics/_counterfactual.py b/teacher/metrics/_counterfactual.py new file mode 100644 index 0000000..3fe528d --- /dev/null +++ b/teacher/metrics/_counterfactual.py @@ -0,0 +1,168 @@ + +# ============================================================================= +# Imports +# ============================================================================= + +# Standard +from math import inf + +# ============================================================================= +# Functions +# ============================================================================= + +def _distance(instance_a, instance_b, continuous, discrete, mad): + """Compute the distance between two instances of + the same dataset + + Parameters + ---------- + instance_a : array-like + First instance + instance_b : array-like + Second instance + continuous : array-like + Indices of the continuous features + discrete : array-like + Indices of the discrete features + mad : dict + Median Absolute Distances of all the + continuous features in the dataset, where + the keys are the indices of the continuous features + """ + cont = 0 + disc = 0 + for i, (instance_var, cf_instance_var) in enumerate(zip(instance_a, instance_b)): + if i in continuous: + if abs(instance_var - cf_instance_var) == 0: + cont += 0 + else: + cont += abs(instance_var - cf_instance_var) / mad[i] + else: + disc += int(instance_var != cf_instance_var) + + diss = 1 / len(continuous) * cont + 1 / len(discrete) * disc + + return diss + + +def _closest_instance(instance, dataset, continuous, discrete, mad): + """Return the closest instance to a given one from a dataset + + Parameters + ---------- + instance : array-like, 1D + Reference instance + dataset : array-like, 2D + Dataset with the instances to measure + continuous : array-like + Indices of the continuous features + discrete : array-like + Indices of the discrete features + mad : dict + Median Absolute Distances of all the + continuous features in the dataset, where + the keys are the indices of the continuous features + """ + + closest_instance = [] + min_distance = inf + + for ds_instance in dataset: + new_distance = _distance(instance, ds_instance, continuous, discrete, mad) + + if new_distance < min_distance and new_distance > 0: + min_distance = new_distance + closest_instance = ds_instance + + return closest_instance + + +def proximity_dissimilarity(instance, cf_instance, continuous, discrete, mad): + """Compute the proximity dissimilarity between an instance + and the applied counterfactual instance + + Parameters + ---------- + instance : array-like + Original instance + cf_instance : array-like + Counterfactual applied instance + continuous : array-like + Indices of the continuous features + discrete : array-like + Indices of the discrete features + mad : dict + Median Absolute Distances of all the continuous features + in the dataset, where the keys are the indices of the continuous features + """ + return _distance(instance, cf_instance, continuous, discrete, mad) + + +def sparsity_dissimilarity(instance, cf_instance): + """Compute the sparsity dissimilarity between an instance + and the applied counterfactual instance + + Parameters + ---------- + instance : array-like + Original instance + cf_instance : array-like + Counterfactual applied instance + """ + diss = 0 + for instance_var, cf_instance_var in zip(instance, cf_instance): + diss += int(instance_var != cf_instance_var) + + return diss / len(instance) + + +def implausibility(cf_instance, dataset, continuous, discrete, mad): + """Return the level of plausibility of a counterfactual instance + in the dataset + + Parameters + ---------- + cf_instance : array-like, 1D + Reference instance + dataset : array-like, 2D + Dataset with the instances to measure + continuous : array-like + Indices of the continuous features + discrete : array-like + Indices of the discrete features + mad : dict + Median Absolute Distances of all the + continuous features in the dataset, where + the keys are the indices of the continuous features + """ + closest_instance = _closest_instance(cf_instance, dataset, continuous, discrete, mad) + return _distance(cf_instance, closest_instance, continuous, discrete, mad) + + +def instability(instance, cf_instance, closest_instance, cf_closest_instance, continuous, discrete, mad): + """Return the level of stability of a counterfactual instance + against the counterfactual of the closest instance to the original + instance + + Parameters + ---------- + instance : array-like, 1D + Original instance + cf_instance : array-like, 1D + Counterfactual applied original instance + closest_instance : array-like, 1D + Closest instance to the original one + cf_closest_instance : array-like, 1D + Counterfactual applied to the closest instance + continuous : array-like + Indices of the continuous features + discrete : array-like + Indices of the discrete features + mad : dict + Median Absolute Distances of all the + continuous features in the dataset, where + the keys are the indices of the continuous features + """ + + return _distance(cf_instance, cf_closest_instance, continuous, discrete, mad) / (_distance(instance, closest_instance, continuous, discrete, mad) + 1) + From 5f0c3905082b2a05bc0ba641323bb23d5f5cbba8 Mon Sep 17 00:00:00 2001 From: Guillermo Fernandez Date: Wed, 6 Jul 2022 21:35:33 +0200 Subject: [PATCH 08/51] Added similarity mapping function for rules --- teacher/fuzzy/fuzzy_set.py | 40 ++++++++++++++++++++++++++++++++++++++ teacher/tree/rule.py | 11 +++++++++-- 2 files changed, 49 insertions(+), 2 deletions(-) diff --git a/teacher/fuzzy/fuzzy_set.py b/teacher/fuzzy/fuzzy_set.py index dda89c7..e62019b 100644 --- a/teacher/fuzzy/fuzzy_set.py +++ b/teacher/fuzzy/fuzzy_set.py @@ -50,6 +50,27 @@ def intersection(self, other): ValueError If the set is not of the same subtype """ + + @abstractmethod + def simmilarity(self, other): + """Compute the similarity between two fuzzy sets of the same + type + + Parameters + ---------- + other : FuzzySet + Set to compute the similarity with the current object + + Returns + ------- + float + Degree of similarity + + Raises + ------ + ValueError + If the set is not of the same subtype + """ @dataclass @@ -100,6 +121,25 @@ def _line_intersect(self, A, B): return 0 else: return y + + def simmilarity(self, other): + if not isinstance(other, FuzzyContinuousSet): + raise ValueError('Intersection must be between two Fuzzy Sets of the same type') + + # Compute the range for an alpha cut of 0.5 because we assume fuzzy strong partitions + min_self = (self.fuzzy_points[1] - self.fuzzy_points[0]) / 2 + max_self = (self.fuzzy_points[2] - self.fuzzy_points[1]) / 2 + + min_other = (other.fuzzy_points[1] - other.fuzzy_points[0]) / 2 + max_other = (other.fuzzy_points[2] - other.fuzzy_points[1]) / 2 + + # if the ranges don't intersect the simmilarity is zero + if min_self >= max_other or min_other >= max_self: + return 0 + + # Else compute the intersection divided by the union of the ranges + return (min(max_self, max_other) - max(min_self, min_other)) / (max(max_self, max_other) - min(min_self, min_other)) + @dataclass diff --git a/teacher/tree/rule.py b/teacher/tree/rule.py index df845fc..df36cf6 100644 --- a/teacher/tree/rule.py +++ b/teacher/tree/rule.py @@ -80,7 +80,7 @@ def weighted_vote(rule_list, instance_membership): return max(conse_dict, key=lambda conse: conse_dict[conse]) @staticmethod - def map_rule_variables(rule, origin_fuzzy_variables, dest_fuzzy_variables): + def map_rule_variables(rule, origin_fuzzy_variables, dest_fuzzy_variables, map_function='intersect'): """Changes the fuzzy variables of the rule for ones that are defined in the same universe @@ -92,6 +92,8 @@ def map_rule_variables(rule, origin_fuzzy_variables, dest_fuzzy_variables): List with the original fuzzy variables dest_fuzzy_variables : list[FuzzyVariable] List with the destination fuzzy variables + map_function : str, {'intersection', 'simmilarity'} + Method to check the best fuzzy set to change Returns ------- @@ -119,7 +121,12 @@ def map_rule_variables(rule, origin_fuzzy_variables, dest_fuzzy_variables): origin_fuzzy_sets = {fs.name: fs for fs in origin_feat} origin_fs = origin_fuzzy_sets[value] - dest_fs = max(dest_feat, key=lambda fs: fs.intersection(origin_fs)) + if map_function == 'intersection': + dest_fs = max(dest_feat, key=lambda fs: fs.intersection(origin_fs)) + elif map_function == 'simmilarity': + dest_fs = max(dest_feat, key=lambda fs: fs.simmilarity(origin_fs)) + else: + raise ValueError(f'Map function {map_function} not supported') new_antecedent.append((feat, dest_fs.name)) return Rule(new_antecedent, rule.consequent, rule.weight) From 079144a218d4c5dcaf10f306e46ab67f95598039 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Guillermo=20Tom=C3=A1s=20Fern=C3=A1ndez=20Mart=C3=ADn?= Date: Thu, 7 Jul 2022 09:02:12 +0000 Subject: [PATCH 09/51] Restructured lore_neighborhood --- teacher/neighbors/_lore_neighborhood.py | 50 ++++++++++++++++++++----- 1 file changed, 41 insertions(+), 9 deletions(-) diff --git a/teacher/neighbors/_lore_neighborhood.py b/teacher/neighbors/_lore_neighborhood.py index 5538da6..f6d8df8 100644 --- a/teacher/neighbors/_lore_neighborhood.py +++ b/teacher/neighbors/_lore_neighborhood.py @@ -3,8 +3,9 @@ # ============================================================================= # Third party -from base64 import decode import numpy as np +import pandas as pd +from scipy import stats # Local application from ._fuzzy_neighborhood import FuzzyNeighborhood @@ -47,7 +48,42 @@ def __init__(self, instance, size, class_name, bb, dataset, X2E, idx_record_to_e self.idx_record_to_explain = idx_record_to_explain super().__init__(instance, size, class_name, bb) + def _smooth_neighborhood(self, df, continuous, X2E, feat_idx, label_encoder, class_value): + ndf = df.copy() + for col in continuous: + min_val = X2E[:, feat_idx[col]].min() + max_val = X2E[:, feat_idx[col]].max() + if col == 'age': + print(min_val) + ndf[col].loc[ndf[col] > max_val] = max_val + ndf[col].loc[ndf[col] < min_val] = min_val + + old_length = len(ndf) + ndf = ndf[(np.abs(stats.zscore(ndf[continuous])) < 3).all(axis=1)] + new_length = len(ndf) + while new_length - old_length > 0: + ndf = ndf[(np.abs(stats.zscore(ndf[continuous])) < 3).all(axis=1)] + + ndf = ndf.append(pd.DataFrame(np.append(self.decoded_target, self.decoded_instance).reshape(1, -1), columns=list(ndf)), ignore_index=True) + edf = ndf.drop(self.class_name, axis=1).copy() + for le in label_encoder: + if le != self.class_name: + edf[le] = label_encoder[le].transform(edf[le]) + + return ndf, edf.to_numpy() + def fit(self): + decoded_instance = [] + features = [col for col in self.dataset['columns'] if col != self.class_name] + for i, var in enumerate(features): + try: + decoded_instance.append(self.dataset['label_encoder'][var].inverse_transform([self.instance[i]])[0]) + except: + decoded_instance += [self.instance[i]] + + self.decoded_instance = np.array([decoded_instance], dtype='object') + self.decoded_target = self.dataset['label_encoder'][self.class_name].inverse_transform(self.bb.predict(self.instance.reshape(1, -1))) + # Dataset Preprocessing self.dataset['feature_values'] = calculate_feature_values(self.X2E, self.dataset['columns'], @@ -62,6 +98,9 @@ def fit(self): # Generate Neighborhood df, Z = genetic_neighborhood(dfZ, x, self.bb, self.dataset, self.size) + + feat_idx = {feat: idx for idx, feat in self.dataset['idx_features'].items()} + df, Z = self._smooth_neighborhood(df, [col for col in self.dataset['continuous'] if col != self.class_name], self.X2E, feat_idx, self.dataset['label_encoder'], self.class_name) self._Xy = df self._X = df.drop(self.class_name, axis=1) @@ -69,16 +108,9 @@ def fit(self): self._y_decoded = df[self.class_name] def fuzzify(self, get_division, **kwargs): - print(self.instance) super().fuzzify(get_division, **kwargs) - decoded_instance = [] - for i, var in enumerate(self._fuzzy_variables): - try: - decoded_instance.append(self.dataset['label_encoder'][var.name].inverse_transform([self.instance[i]])[0]) - except: - decoded_instance += [self.instance[i]] - self._instance_membership = dataset_membership(np.array([decoded_instance], dtype='object'), self._fuzzy_variables) + self._instance_membership = dataset_membership(self.decoded_instance, self._fuzzy_variables) def get_y_decoded(self): return self._y_decoded From af20419550d5f082bf8c1611e03e03c30e08db51 Mon Sep 17 00:00:00 2001 From: Guillermo Fernandez Date: Thu, 7 Jul 2022 14:03:41 +0200 Subject: [PATCH 10/51] Added simmilarity for fuzzy discrete sets --- teacher/fuzzy/fuzzy_set.py | 3 +++ 1 file changed, 3 insertions(+) diff --git a/teacher/fuzzy/fuzzy_set.py b/teacher/fuzzy/fuzzy_set.py index e62019b..7e7a7a2 100644 --- a/teacher/fuzzy/fuzzy_set.py +++ b/teacher/fuzzy/fuzzy_set.py @@ -161,3 +161,6 @@ def intersection(self, other): raise ValueError('Intersection must be between two Fuzzy Sets of the same type') return int(self == other) + + def simmilarity(self, other): + return self.intersection(other) From 0ddd2344d48575f9833358b0617e31cefad82a60 Mon Sep 17 00:00:00 2001 From: Guillermo Fernandez Date: Mon, 11 Jul 2022 15:06:04 +0200 Subject: [PATCH 11/51] Added Sampling neighborhood and d_counterfactual --- teacher/explanation/FDT_explainer.py | 28 ++++- teacher/explanation/__init__.py | 3 +- teacher/explanation/_counterfactual.py | 41 +++++++ teacher/neighbors/__init__.py | 2 + teacher/neighbors/_sampling_neighborhood.py | 116 ++++++++++++++++++++ 5 files changed, 187 insertions(+), 3 deletions(-) create mode 100644 teacher/neighbors/_sampling_neighborhood.py diff --git a/teacher/explanation/FDT_explainer.py b/teacher/explanation/FDT_explainer.py index e82117d..76d7776 100644 --- a/teacher/explanation/FDT_explainer.py +++ b/teacher/explanation/FDT_explainer.py @@ -14,7 +14,7 @@ # Local application from ._factual_local_explainer import FactualLocalExplainer from teacher.tree import FDT -from teacher.explanation import m_factual, mr_factual, c_factual, i_counterfactual, f_counterfactual +from teacher.explanation import m_factual, mr_factual, c_factual, i_counterfactual, f_counterfactual, d_counterfactual # ============================================================================= @@ -30,7 +30,8 @@ COUNTERFACTUAL_METHODS = { 'i_counterfactual': i_counterfactual, - 'f_counterfactual': f_counterfactual + 'f_counterfactual': f_counterfactual, + 'd_counterfactual': d_counterfactual } # ============================================================================= @@ -98,6 +99,7 @@ def fit(self, instance, target, neighborhood, df_num_cols, factual, counterfactu self.target = target fuzzy_variables = neighborhood.get_fuzzy_variables() instance_membership = neighborhood.get_instance_membership() + decoded_instance = neighborhood.decoded_instance[0] X = neighborhood.get_X() y = neighborhood.get_y() @@ -118,6 +120,26 @@ def fit(self, instance, target, neighborhood, df_num_cols, factual, counterfactu del kwargs['fuzzy_threshold'] except KeyError: fuzzy_threshold = 0.0001 + + # TODO: IMPORTANTE NO MERGEAR A LA RAMA MASTER HASTA NO LIMPIAR + if counterfactual == 'd_counterfactual': + try: + cont_idx = kwargs['cont_idx'] + del kwargs['cont_idx'] + except KeyError: + raise ValueError('Continuous index needed for d_counterfactual') + + try: + disc_idx = kwargs['disc_idx'] + del kwargs['disc_idx'] + except KeyError: + raise ValueError('Discrete index needed for d_counterfactual') + + try: + mad = kwargs['mad'] + del kwargs['mad'] + except KeyError: + raise ValueError('MAD needed for d_counterfactual') self.local_explainer = FDT(fuzzy_variables, max_depth=max_depth, min_num_examples=min_num_examples, fuzzy_threshold=fuzzy_threshold) self.local_explainer.fit(X, y) @@ -130,4 +152,6 @@ def fit(self, instance, target, neighborhood, df_num_cols, factual, counterfactu cf = self.counterfactual_method(instance_membership, rules, self.exp_value, df_num_cols) elif counterfactual == 'f_counterfactual': cf = self.counterfactual_method(fact, instance_membership, rules, self.exp_value, df_num_cols) + elif counterfactual == 'd_counterfactual': + cf = self.counterfactual_method(decoded_instance, instance_membership, rules, self.exp_value, cont_idx, disc_idx, mad) self.explanation = (fact, cf) diff --git a/teacher/explanation/__init__.py b/teacher/explanation/__init__.py index 36383a3..2bdbf82 100644 --- a/teacher/explanation/__init__.py +++ b/teacher/explanation/__init__.py @@ -75,7 +75,7 @@ # Local application from ._factual import FID3_factual, m_factual, mr_factual, c_factual -from ._counterfactual import FID3_counterfactual, i_counterfactual, f_counterfactual +from ._counterfactual import FID3_counterfactual, i_counterfactual, f_counterfactual, d_counterfactual from .FID3_explainer import FID3Explainer from .FDT_explainer import FDTExplainer @@ -93,6 +93,7 @@ "FID3_counterfactual", "i_counterfactual", "f_counterfactual", + "d_counterfactual", "FID3Explainer", "FDTExplainer" ] diff --git a/teacher/explanation/_counterfactual.py b/teacher/explanation/_counterfactual.py index c6d7f89..6f93c23 100644 --- a/teacher/explanation/_counterfactual.py +++ b/teacher/explanation/_counterfactual.py @@ -12,6 +12,7 @@ # Local application from teacher.tree import Rule +from teacher.metrics._counterfactual import _distance # ============================================================================= @@ -221,3 +222,43 @@ def f_counterfactual(factual, instance, rule_list, class_val, df_numerical_colum possible_cf.append((cf_rule, cf_dist)) return _search_counterfactual(instance, class_val, rule_list, possible_cf) + + +def d_counterfactual(decoded_instance, instance_membership, rule_list, class_val, continuous, discrete, mad, tau=0.5): + # TODO: IMPORTANTE NO MERGEAR A LA RAMA MAIN HASTA NO LIMPIAR LA FUNCION + """Return a list that contains the counterfactual with respect to the factual + + Parameters + ---------- + factual : list[Rule] + List of rules that correspond to a factual explanation of the + instance for the class value class_val + instance : dict, {feature: {set_1: pert_1, set_2: pert_2, ...}, ...} + Fuzzy representation of the instance with all the features and pertenence + degrees to each fuzzy set + rule_list : list[Rule] + List of candidate rules to form part of the counterfactual + class_val : str + Predicted value that the factual will explain + df_numerical_columns : list + List of the numerical columns of the instance, used to compute the distance + tau : float, optional + Importance degree of new elements added or substracted from a rule + in contrast to existing elements that have been modified, used + to compute the distance , by default 0.5 + + Returns + ------- + set((feature, value)) + Set of pairs feature-value with the changes that need to be applied to + the instance to change class value. + """ + possible_cf = [] + diff_class_rules = [rule for rule in rule_list if rule.consequent != class_val] + for cf_rule in diff_class_rules: + cf_instance, changes = _apply_changes(cf_rule, instance_membership) + cf_instance = [max(child[1], key=lambda a: child[1][a]) for child in cf_instance.items()] + cf_instance = [float(x) if i in continuous else x for i, x in enumerate(cf_instance)] + cf_dist = _distance(decoded_instance, cf_instance, continuous, discrete, mad) + possible_cf.append((cf_rule, cf_dist)) + return _search_counterfactual(instance_membership, class_val, rule_list, possible_cf) diff --git a/teacher/neighbors/__init__.py b/teacher/neighbors/__init__.py index 219354a..a107374 100644 --- a/teacher/neighbors/__init__.py +++ b/teacher/neighbors/__init__.py @@ -48,6 +48,7 @@ from ._simple_neighborhood import SimpleNeighborhood from ._fuzzy_neighborhood import FuzzyNeighborhood from ._lore_neighborhood import LoreNeighborhood +from ._sampling_neighborhood import SamplingNeighborhood from ._exceptions import NotFittedError, NotFuzzifiedError @@ -70,6 +71,7 @@ "FuzzyNeighborhood", "SimpleNeighborhood", "LoreNeighborhood", + "SamplingNeighborhood", "NotFittedError", "NotFuzzifiedError" ] diff --git a/teacher/neighbors/_sampling_neighborhood.py b/teacher/neighbors/_sampling_neighborhood.py new file mode 100644 index 0000000..375f711 --- /dev/null +++ b/teacher/neighbors/_sampling_neighborhood.py @@ -0,0 +1,116 @@ +# ============================================================================= +# Imports +# ============================================================================= + +# Third party +import numpy as np +import pandas as pd +from scipy import stats + +# Local application +from ._fuzzy_neighborhood import FuzzyNeighborhood +from teacher.neighbors import genetic_neighborhood, calculate_feature_values +from teacher.utils import dataframe2explain +from teacher.fuzzy import dataset_membership + +# ============================================================================= +# Classes +# ============================================================================= + +######################## +# TODO: IMPORTANTE NO MERGEAR A LA RAMA MASTER HASTA NO LIMPIAR +######################## + +class SamplingNeighborhood(FuzzyNeighborhood): + """ + Fuzzy adaptation of the neighborhood used by LORE, which + generates the different elements by modifying the instance + using a genetic algorithm in order to obtain elements + for all the different possible class values. + """ + + def __init__(self, instance, size, class_name, bb, dataset, X2E, idx_record_to_explain): + """ + Parameters + ---------- + instance : array-like, of shape (n_features) + Instance to generate the neighborhood from + size : int + Size of the neighborhood + class_name : str + Name of the feature that is the class value + bb : scikit-learn compatible predictor + Black-box already fitted with the input data. + X2E : LORE styled dataset - Legacy + Necessary dataset for the LORE genetic algorithm to work + idx_record_to_explain : int - Legacy + Index of the instance to explain in X2E + """ + self.X2E = X2E + self.dataset = dataset + self.idx_record_to_explain = idx_record_to_explain + super().__init__(instance, size, class_name, bb) + + def _generate_prob_dist(self): + cont_idx = [key for key, val in self.dataset['idx_features'].items() if val in self.dataset['continuous']] + prob_dist = {} + for i, col in enumerate(self.X2E.T): + if i in cont_idx: + vals = [x for x in np.unique(col) if x < self.instance[i] + col.std() and x > self.instance[i] - col.std()] + dists = [np.count_nonzero(col == val) for val in vals] + dists = [d / sum(dists) for d in dists] + else: + vals = [x for x in np.unique(col)] + dists = [np.count_nonzero(col == val) for val in vals] + dists = [d / sum(dists) for d in dists] + prob_dist[i] = (vals, dists) + + return prob_dist + + def _generate_neighborhood(self): + prob_dist = self._generate_prob_dist() + class_values = [i for i in range(len(self.dataset['possible_outcomes']))] + neighborhood = [] + for cv in class_values: + neighs = 0 + while neighs < (self.size/len(class_values)): + neigh = np.zeros(len(prob_dist)) + for i in prob_dist: + neigh[i] = np.random.choice(prob_dist[i][0], p=prob_dist[i][1]) + + if self.bb.predict(np.array(neigh).reshape(1, -1)) == cv: + neighborhood.append(neigh) + neighs += 1 + + features = [col for col in self.dataset['columns'] if col != self.class_name] + return pd.DataFrame(np.array(neighborhood), columns=features) + + def fit(self): + decoded_instance = [] + features = [col for col in self.dataset['columns'] if col != self.class_name] + for i, var in enumerate(features): + try: + decoded_instance.append(self.dataset['label_encoder'][var].inverse_transform([self.instance[i]])[0]) + except: + decoded_instance += [self.instance[i]] + + Z = self._generate_neighborhood() + df = Z.copy() + + self.decoded_instance = np.array([decoded_instance], dtype='object') + self.decoded_target = self.dataset['label_encoder'][self.class_name].inverse_transform(self.bb.predict(self.instance.reshape(1, -1))) + + for le in self.dataset['label_encoder']: + if le != self.class_name: + df[le] = self.dataset['label_encoder'][le].inverse_transform(df[le].astype(int)) + self._X = df + self._y = self.bb.predict(Z) + self._Xy = pd.concat([pd.DataFrame(self._y, columns=[self.class_name]), Z], axis=1) + self._y_decoded = self.dataset['label_encoder'][self.class_name].inverse_transform(self._y) + + def fuzzify(self, get_division, **kwargs): + super().fuzzify(get_division, **kwargs) + self._instance_membership = dataset_membership(self.decoded_instance, self._fuzzy_variables) + + def get_y_decoded(self): + return self._y_decoded From 8c70e3258ffb72e8745fd804c4cdac4913d8a3b5 Mon Sep 17 00:00:00 2001 From: Guillermo Fernandez Date: Tue, 12 Jul 2022 15:32:54 +0200 Subject: [PATCH 12/51] Added normalization for the datasets --- teacher/datasets/_base.py | 36 ++++++++++++++++++++---------------- 1 file changed, 20 insertions(+), 16 deletions(-) diff --git a/teacher/datasets/_base.py b/teacher/datasets/_base.py index 9d64edc..f0dce82 100644 --- a/teacher/datasets/_base.py +++ b/teacher/datasets/_base.py @@ -14,6 +14,7 @@ # Third party import pandas as pd import numpy as np +from sklearn.preprocessing import StandardScaler # Local application from teacher.utils import recognize_features_type, set_discrete_continuous, label_encode @@ -30,7 +31,7 @@ # Functions # ============================================================================= -def generate_dataset(df, columns, class_name, discrete, name): +def generate_dataset(df, columns, class_name, discrete, name, normalize=False): """Generate the dataset suitable for LORE usage Parameters @@ -72,7 +73,10 @@ def generate_dataset(df, columns, class_name, discrete, name): columns_tmp = list(columns) columns_tmp.remove(class_name) idx_features = {i: col for i, col in enumerate(columns_tmp)} - + df[continuous] += 1 # TREMENDA ÑAPA PARA NORMALIZAR LA MEDIANA Y QUE NO REVIENTE + if normalize: + scaler = StandardScaler() + df[continuous] = scaler.fit_transform(df[continuous]) # Dataset Preparation for Scikit Alorithms df_le, label_encoder = label_encode(df, discrete) X = df_le.loc[:, df_le.columns != class_name].values @@ -97,7 +101,7 @@ def generate_dataset(df, columns, class_name, discrete, name): return dataset -def load_german(): +def load_german(normalize=False): """ Load and return the german credit dataset. @@ -115,10 +119,10 @@ def load_german(): discrete = ['installment_as_income_perc', 'present_res_since', 'credits_this_bank', 'people_under_maintenance'] - return generate_dataset(df, columns, class_name, discrete, 'german_credit') + return generate_dataset(df, columns, class_name, discrete, 'german_credit', normalize) -def load_adult(): +def load_adult(normalize=False): """ Load and return the adult dataset. @@ -145,10 +149,10 @@ def load_adult(): class_name = 'class' discrete = [] - return generate_dataset(df, columns, class_name, discrete, 'adult') + return generate_dataset(df, columns, class_name, discrete, 'adult', normalize) -def load_compas(): +def load_compas(normalize=False): """ Load and return the COMPAS scores dataset. @@ -196,10 +200,10 @@ def get_class(x): class_name = 'class' discrete = ['is_recid', 'is_violent_recid', 'two_year_recid'] - return generate_dataset(df, columns, class_name, discrete, 'compas-scores-two-years') + return generate_dataset(df, columns, class_name, discrete, 'compas-scores-two-years', normalize) -def load_heloc(): +def load_heloc(normalize=False): """ Load and return the HELOC dataset. @@ -215,10 +219,10 @@ def load_heloc(): class_name = 'RiskPerformance' discrete = [] - return generate_dataset(df, columns, class_name, discrete, 'heloc_dataset_v1') + return generate_dataset(df, columns, class_name, discrete, 'heloc_dataset_v1', normalize) -def load_beer(): +def load_beer(normalize=False): """ Load and return the beer dataset. @@ -238,10 +242,10 @@ def load_beer(): discrete = [] columns = df.columns - return generate_dataset(df, columns, class_name, discrete, 'beer') + return generate_dataset(df, columns, class_name, discrete, 'beer', normalize) -def load_pima(): +def load_pima(normalize=False): """ Load and return the pima indians dataset. @@ -261,10 +265,10 @@ def load_pima(): discrete = [] columns = df.columns - return generate_dataset(df, columns, class_name, discrete, 'pima') + return generate_dataset(df, columns, class_name, discrete, 'pima', normalize) -def load_breast(): +def load_breast(normalize=False): """ Load and return the breast cancer dataset. @@ -281,4 +285,4 @@ def load_breast(): class_name = 'diagnosis' discrete = [] - return generate_dataset(df, columns, class_name, discrete, 'breast') + return generate_dataset(df, columns, class_name, discrete, 'breast', normalize) From f87731c4f919d93c4d87785b28917a23fb1e517b Mon Sep 17 00:00:00 2001 From: Guillermo Fernandez Date: Tue, 12 Jul 2022 15:33:14 +0200 Subject: [PATCH 13/51] Forced casting to int when decoding --- teacher/neighbors/_sampling_neighborhood.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/teacher/neighbors/_sampling_neighborhood.py b/teacher/neighbors/_sampling_neighborhood.py index 375f711..aba6aff 100644 --- a/teacher/neighbors/_sampling_neighborhood.py +++ b/teacher/neighbors/_sampling_neighborhood.py @@ -90,7 +90,7 @@ def fit(self): features = [col for col in self.dataset['columns'] if col != self.class_name] for i, var in enumerate(features): try: - decoded_instance.append(self.dataset['label_encoder'][var].inverse_transform([self.instance[i]])[0]) + decoded_instance.append(self.dataset['label_encoder'][var].inverse_transform(np.array([self.instance[i]], dtype=int))[0]) except: decoded_instance += [self.instance[i]] From 2f8343e0d4c38815ad1b8d6a88cf586c0fa683c3 Mon Sep 17 00:00:00 2001 From: Guillermo Fernandez Date: Tue, 12 Jul 2022 15:33:29 +0200 Subject: [PATCH 14/51] Fixed typo --- .gitignore | 4 +++- teacher/tree/rule.py | 2 +- 2 files changed, 4 insertions(+), 2 deletions(-) diff --git a/.gitignore b/.gitignore index 1551981..fb16b4d 100644 --- a/.gitignore +++ b/.gitignore @@ -135,4 +135,6 @@ dmypy.json .pre-commit-config.yaml # DS files -.DS_Store \ No newline at end of file +.DS_Store + +flore-experiments/ \ No newline at end of file diff --git a/teacher/tree/rule.py b/teacher/tree/rule.py index df36cf6..30f6cf1 100644 --- a/teacher/tree/rule.py +++ b/teacher/tree/rule.py @@ -80,7 +80,7 @@ def weighted_vote(rule_list, instance_membership): return max(conse_dict, key=lambda conse: conse_dict[conse]) @staticmethod - def map_rule_variables(rule, origin_fuzzy_variables, dest_fuzzy_variables, map_function='intersect'): + def map_rule_variables(rule, origin_fuzzy_variables, dest_fuzzy_variables, map_function='intersection'): """Changes the fuzzy variables of the rule for ones that are defined in the same universe From 6c5708da3ec45121a3d6d5becab184cee3a080da Mon Sep 17 00:00:00 2001 From: Guillermo Fernandez Date: Thu, 21 Jul 2022 13:33:49 +0200 Subject: [PATCH 15/51] Added hashing to rule --- teacher/tree/rule.py | 3 +++ 1 file changed, 3 insertions(+) diff --git a/teacher/tree/rule.py b/teacher/tree/rule.py index 30f6cf1..7ed1670 100644 --- a/teacher/tree/rule.py +++ b/teacher/tree/rule.py @@ -37,6 +37,9 @@ def __eq__(self, other): self.consequent == other.consequent and self.weight == other.weight) + def __hash__(self) -> int: + return hash((self.antecedent, self.consequent, self.weight)) + def matching(self, instance_membership, t_norm=min): """Matching that an instance has with the rule If there is some feature or value not existing in the instance, From cf9e82c8b00991d427405549a7374443a79d4d3e Mon Sep 17 00:00:00 2001 From: Guillermo Fernandez Date: Thu, 21 Jul 2022 13:34:05 +0200 Subject: [PATCH 16/51] Added mixed distance for counterfactuals from Guidotti review --- teacher/metrics/_counterfactual.py | 107 ++++++++++++++++++++++++----- 1 file changed, 90 insertions(+), 17 deletions(-) diff --git a/teacher/metrics/_counterfactual.py b/teacher/metrics/_counterfactual.py index 3fe528d..57355b0 100644 --- a/teacher/metrics/_counterfactual.py +++ b/teacher/metrics/_counterfactual.py @@ -6,10 +6,75 @@ # Standard from math import inf +# Third party +import numpy as np +from scipy.spatial.distance import cdist +from scipy.spatial.distance import _validate_vector +from scipy.stats import median_absolute_deviation + + # ============================================================================= # Functions # ============================================================================= +def mad_cityblock(u, v, mad): + u = _validate_vector(u) + v = _validate_vector(v) + l1_diff = abs(u - v) + l1_diff_mad = l1_diff / mad + return l1_diff_mad.sum() + + +def _continuous_distance(x, cf_list, continuous_features, metric='euclidean', X=None, agg=None): + + if metric == 'mad': + mad = median_absolute_deviation(X[:, continuous_features], axis=0) + mad = np.array([v if v != 0 else 1.0 for v in mad]) + + def _mad_cityblock(u, v): + return mad_cityblock(u, v, mad) + dist = cdist(x.reshape(1, -1)[:, continuous_features], cf_list.reshape(1, -1)[:, continuous_features], metric=_mad_cityblock) + else: + dist = cdist(x.reshape(1, -1)[:, continuous_features], cf_list.reshape(1, -1)[:, continuous_features], metric=metric) + + if agg is None or agg == 'mean': + return np.mean(dist) + + if agg == 'max': + return np.max(dist) + + if agg == 'min': + return np.min(dist) + + +def _categorical_distance(x, cf_list, categorical_features, metric='jaccard', agg=None): + + dist = cdist(x.reshape(1, -1)[:, categorical_features], cf_list.reshape(1, -1)[:, categorical_features], metric=metric) + + if agg is None or agg == 'mean': + return np.mean(dist) + + if agg == 'max': + return np.max(dist) + + if agg == 'min': + return np.min(dist) + + +def _mixed_distance(instance_a, instance_b, continuous, discrete, mad, ratio_cont=None, agg=None): + nbr_features = instance_b.shape[0] + dist_cont = _continuous_distance(instance_a, instance_b, continuous, metric='euclidean', X=None, agg=agg) + dist_cate = _categorical_distance(instance_a, instance_b, discrete, metric='jaccard', agg=agg) + if ratio_cont is None: + ratio_continuous = len(continuous) / nbr_features + ratio_categorical = len(discrete) / nbr_features + else: + ratio_continuous = ratio_cont + ratio_categorical = 1.0 - ratio_cont + dist = ratio_continuous * dist_cont + ratio_categorical * dist_cate + return dist + + def _distance(instance_a, instance_b, continuous, discrete, mad): """Compute the distance between two instances of the same dataset @@ -45,7 +110,13 @@ def _distance(instance_a, instance_b, continuous, discrete, mad): return diss -def _closest_instance(instance, dataset, continuous, discrete, mad): +DISTANCES = { + 'mixed': _mixed_distance, + 'moth': _distance +} + + +def _closest_instance(instance, dataset, continuous, discrete, mad, distance='moth'): """Return the closest instance to a given one from a dataset Parameters @@ -68,7 +139,7 @@ def _closest_instance(instance, dataset, continuous, discrete, mad): min_distance = inf for ds_instance in dataset: - new_distance = _distance(instance, ds_instance, continuous, discrete, mad) + new_distance = DISTANCES[distance](instance, ds_instance, continuous, discrete, mad) if new_distance < min_distance and new_distance > 0: min_distance = new_distance @@ -77,7 +148,7 @@ def _closest_instance(instance, dataset, continuous, discrete, mad): return closest_instance -def proximity_dissimilarity(instance, cf_instance, continuous, discrete, mad): +def proximity_dissimilarity(instance, cf_instance, continuous, discrete, mad, distance='moth'): """Compute the proximity dissimilarity between an instance and the applied counterfactual instance @@ -95,10 +166,10 @@ def proximity_dissimilarity(instance, cf_instance, continuous, discrete, mad): Median Absolute Distances of all the continuous features in the dataset, where the keys are the indices of the continuous features """ - return _distance(instance, cf_instance, continuous, discrete, mad) + return DISTANCES[distance](instance, cf_instance, continuous, discrete, mad) -def sparsity_dissimilarity(instance, cf_instance): +def sparsity_dissimilarity(instance, cf_instance, distance='mismatch'): """Compute the sparsity dissimilarity between an instance and the applied counterfactual instance @@ -109,14 +180,16 @@ def sparsity_dissimilarity(instance, cf_instance): cf_instance : array-like Counterfactual applied instance """ - diss = 0 - for instance_var, cf_instance_var in zip(instance, cf_instance): - diss += int(instance_var != cf_instance_var) - - return diss / len(instance) - - -def implausibility(cf_instance, dataset, continuous, discrete, mad): + if distance == 'mismatch': + diss = 0 + for instance_var, cf_instance_var in zip(instance, cf_instance): + diss += int(instance_var != cf_instance_var) + + return diss / len(instance) + else: + return cdist(instance.reshape(1, -1), cf_instance.reshape(1, -1), metric='jaccard')[0][0] + +def implausibility(cf_instance, dataset, continuous, discrete, mad, distance='moth'): """Return the level of plausibility of a counterfactual instance in the dataset @@ -135,11 +208,11 @@ def implausibility(cf_instance, dataset, continuous, discrete, mad): continuous features in the dataset, where the keys are the indices of the continuous features """ - closest_instance = _closest_instance(cf_instance, dataset, continuous, discrete, mad) - return _distance(cf_instance, closest_instance, continuous, discrete, mad) + closest_instance = _closest_instance(cf_instance, dataset, continuous, discrete, mad, distance) + return DISTANCES[distance](cf_instance, closest_instance, continuous, discrete, mad) -def instability(instance, cf_instance, closest_instance, cf_closest_instance, continuous, discrete, mad): +def instability(instance, cf_instance, closest_instance, cf_closest_instance, continuous, discrete, mad, distance='moth'): """Return the level of stability of a counterfactual instance against the counterfactual of the closest instance to the original instance @@ -164,5 +237,5 @@ def instability(instance, cf_instance, closest_instance, cf_closest_instance, co the keys are the indices of the continuous features """ - return _distance(cf_instance, cf_closest_instance, continuous, discrete, mad) / (_distance(instance, closest_instance, continuous, discrete, mad) + 1) + return DISTANCES[distance](cf_instance, cf_closest_instance, continuous, discrete, mad) / (DISTANCES[distance](instance, closest_instance, continuous, discrete, mad) + 1) From eb717fe504de4e36a93391dc9f5af32ddbb27e30 Mon Sep 17 00:00:00 2001 From: Guillermo Fernandez Date: Fri, 22 Jul 2022 12:33:52 +0200 Subject: [PATCH 17/51] Added fast neighborhood generation --- teacher/datasets/__init__.py | 5 +- teacher/datasets/_base.py | 28 +++++- teacher/datasets/data/basket.csv | 101 ++++++++++++++++++++ teacher/datasets/data/small_basket.csv | 81 ++++++++++++++++ teacher/neighbors/_sampling_neighborhood.py | 51 ++++++++-- 5 files changed, 255 insertions(+), 11 deletions(-) create mode 100644 teacher/datasets/data/basket.csv create mode 100644 teacher/datasets/data/small_basket.csv diff --git a/teacher/datasets/__init__.py b/teacher/datasets/__init__.py index 0f80767..ada2129 100644 --- a/teacher/datasets/__init__.py +++ b/teacher/datasets/__init__.py @@ -109,7 +109,7 @@ # ============================================================================= # Local application -from ._base import load_german, load_adult, load_compas, load_heloc, load_beer, load_pima, load_breast +from ._base import load_german, load_adult, load_compas, load_heloc, load_beer, load_pima, load_breast, load_basket # ============================================================================= @@ -120,10 +120,11 @@ # from the module teacher.datasets __all__ = [ "load_adult", + "load_basket", "load_beer", "load_breast", "load_compas", "load_german", "load_heloc", - "load_pima" + "load_pima", ] diff --git a/teacher/datasets/_base.py b/teacher/datasets/_base.py index f0dce82..9f8136b 100644 --- a/teacher/datasets/_base.py +++ b/teacher/datasets/_base.py @@ -73,7 +73,7 @@ def generate_dataset(df, columns, class_name, discrete, name, normalize=False): columns_tmp = list(columns) columns_tmp.remove(class_name) idx_features = {i: col for i, col in enumerate(columns_tmp)} - df[continuous] += 1 # TREMENDA ÑAPA PARA NORMALIZAR LA MEDIANA Y QUE NO REVIENTE + # df[continuous] += 1 # TREMENDA ÑAPA PARA NORMALIZAR LA MEDIANA Y QUE NO REVIENTE if normalize: scaler = StandardScaler() df[continuous] = scaler.fit_transform(df[continuous]) @@ -286,3 +286,29 @@ def load_breast(normalize=False): discrete = [] return generate_dataset(df, columns, class_name, discrete, 'breast', normalize) + +def load_basket(normalize=False, reduced=False): + """ + Load and return the basket dataset. + + Returns + ------- + dataset : dict + """ + # Read Dataset + if reduced: + df = pd.read_csv(MODULE_PATH + '/data/small_basket.csv', delimiter=',') + else: + df = pd.read_csv(MODULE_PATH + '/data/basket.csv', delimiter=',') + + + # Features Categorization + columns = df.columns + class_name = 'Position' + df_cols = list(df.columns) + df_cols.remove(class_name) + new_cols = [class_name] + df_cols + df = df[new_cols] + + discrete = [] + return generate_dataset(df, columns, class_name, discrete, 'basket', normalize) diff --git a/teacher/datasets/data/basket.csv b/teacher/datasets/data/basket.csv new file mode 100644 index 0000000..0d08aaf --- /dev/null +++ b/teacher/datasets/data/basket.csv @@ -0,0 +1,101 @@ +Height,Minutes,Points,2P-Field-Goals-Perc,3P-Field-Goals-Perc,Free-Throws,Rebounds,Assists,Blocks,Turnovers,Global-Assessment,Position +1.9,23.17,7.4,50.4,29.6,70.8,4.3,0.8,0.3,0.9,7.6,Center +1.78,29.3,10.9,50.5,32.6,81.1,3.9,3.8,0.0,1.5,11.8,Shooting-Guard +1.9,18.4,7.9,52.8,100.0,60.0,5.3,0.5,0.5,0.7,9.8,Center +1.83,27.41,9.2,54.4,32.4,74.3,2.9,2.7,0.2,0.8,8.4,Small-Forward +1.66,25.47,6.5,38.4,30.2,82.1,2.5,3.3,0.0,1.2,8.2,Point-Guard +1.67,26.16,8.2,47.7,32.3,78.9,2.8,3.2,0.0,1.1,7.7,Point-Guard +1.83,24.58,7.0,41.6,31.0,71.8,2.9,1.3,0.1,1.3,7.4,Shooting-Guard +1.7,28.2,7.4,36.3,34.9,81.8,4.0,1.6,0.1,1.1,6.6,Shooting-Guard +1.84,21.2,6.6,41.8,29.0,80.4,2.8,1.0,0.1,0.8,5.6,Small-Forward +1.85,17.37,5.2,40.5,16.5,47.9,3.3,0.7,0.5,0.9,4.0,Small-Forward +1.93,21.2,7.2,41.3,29.9,64.7,3.8,0.8,0.3,0.6,6.5,Power-Forward +1.83,32.33,12.6,42.8,32.4,70.8,8.5,1.7,0.3,1.7,15.8,Power-Forward +1.86,26.37,12.5,53.2,35.1,80.2,5.3,1.5,0.5,1.5,14.3,Power-Forward +1.88,25.49,9.0,48.4,40.0,62.4,5.9,1.3,1.0,1.7,12.7,Power-Forward +1.85,32.2,15.1,45.0,39.6,70.8,8.5,1.3,0.2,1.0,15.7,Power-Forward +1.88,25.45,11.3,57.5,34.1,70.6,6.9,1.8,0.8,1.3,14.9,Power-Forward +1.83,30.18,13.1,50.5,26.9,67.6,6.3,1.9,0.4,1.6,15.4,Power-Forward +1.91,24.32,7.2,41.8,23.2,67.0,5.5,1.2,0.4,1.5,8.7,Center +1.93,22.44,8.8,48.6,16.7,64.9,6.8,0.5,0.8,0.9,10.9,Center +1.94,19.15,9.5,54.4,13.6,63.6,4.3,0.5,0.6,0.6,9.1,Center +1.93,24.51,12.7,58.5,0.0,51.4,7.4,0.6,0.7,0.7,14.3,Center +1.91,22.32,8.5,58.8,0.0,66.2,8.4,0.8,0.5,0.7,13.5,Center +1.88,26.19,12.5,51.7,18.3,72.5,4.9,1.6,0.6,0.8,11.8,Center +1.9,19.04,9.1,53.0,33.3,73.6,4.6,0.5,0.3,0.4,10.2,Center +1.89,25.26,14.3,54.1,20.0,58.3,6.8,1.0,0.5,0.8,16.4,Center +1.83,33.5,19.3,48.6,33.9,73.4,4.3,1.8,0.1,1.7,16.9,Power-Forward +1.77,25.16,7.0,45.2,24.2,62.4,5.4,1.3,0.1,1.7,9.4,Small-Forward +1.8,27.53,11.4,45.2,37.8,67.0,7.8,1.7,0.4,1.6,14.8,Power-Forward +1.85,20.12,9.1,52.0,14.3,67.4,5.1,0.4,0.1,1.0,9.7,Power-Forward 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teacher.neighbors import genetic_neighborhood, calculate_feature_values -from teacher.utils import dataframe2explain from teacher.fuzzy import dataset_membership - +from teacher.metrics._counterfactual import _closest_instance # ============================================================================= # Classes # ============================================================================= @@ -29,7 +27,7 @@ class SamplingNeighborhood(FuzzyNeighborhood): for all the different possible class values. """ - def __init__(self, instance, size, class_name, bb, dataset, X2E, idx_record_to_explain): + def __init__(self, instance, size, class_name, bb, dataset, X2E, idx_record_to_explain, neighbor_generation='slow'): """ Parameters ---------- @@ -49,14 +47,14 @@ def __init__(self, instance, size, class_name, bb, dataset, X2E, idx_record_to_e self.X2E = X2E self.dataset = dataset self.idx_record_to_explain = idx_record_to_explain + self.neighbor_generation = neighbor_generation super().__init__(instance, size, class_name, bb) - def _generate_prob_dist(self): - cont_idx = [key for key, val in self.dataset['idx_features'].items() if val in self.dataset['continuous']] + def _generate_prob_dist(self, instance, cont_idx): prob_dist = {} for i, col in enumerate(self.X2E.T): if i in cont_idx: - vals = [x for x in np.unique(col) if x < self.instance[i] + col.std() and x > self.instance[i] - col.std()] + vals = [x for x in np.unique(col) if x < instance[i] + col.std() and x > instance[i] - col.std()] dists = [np.count_nonzero(col == val) for val in vals] dists = [d / sum(dists) for d in dists] else: @@ -66,6 +64,39 @@ def _generate_prob_dist(self): prob_dist[i] = (vals, dists) return prob_dist + + def _get_instance_from_prob_dist(self, prob_dist): + neigh = np.zeros(len(prob_dist)) + for i in prob_dist: + neigh[i] = np.random.choice(prob_dist[i][0], p=prob_dist[i][1]) + return neigh + + def _generate_neighborhood_fast(self): + cont_idx = [key for key, val in self.dataset['idx_features'].items() if val in self.dataset['continuous']] + disc_idx = [key for key, val in self.dataset['idx_features'].items() if val in self.dataset['discrete']] + prob_dist = self._generate_prob_dist(self.instance, cont_idx) + target = self.bb.predict(self.instance.reshape(1, -1)) + y_train_pred = self.bb.predict(self.X2E) + closest_instance = _closest_instance(self.instance, self.X2E[y_train_pred != target], cont_idx, disc_idx, None, distance='mixed') + c_prob_dist = self._generate_prob_dist(closest_instance, cont_idx) + class_values = {i: 0 for i in range(len(self.dataset['possible_outcomes']))} + neighborhood = [] + while len(neighborhood) < self.size: + i_neigh = self._get_instance_from_prob_dist(prob_dist) + c_neigh = self._get_instance_from_prob_dist(c_prob_dist) + + neigh_pred = self.bb.predict(np.array(i_neigh).reshape(1, -1))[0] + if class_values[neigh_pred] < (self.size/len(class_values)): + class_values[neigh_pred] += 1 + neighborhood.append(i_neigh) + + neigh_pred = self.bb.predict(np.array(c_neigh).reshape(1, -1))[0] + if class_values[neigh_pred] < (self.size/len(class_values)): + class_values[neigh_pred] += 1 + neighborhood.append(c_neigh) + + features = [col for col in self.dataset['columns'] if col != self.class_name] + return pd.DataFrame(np.array(neighborhood), columns=features) def _generate_neighborhood(self): prob_dist = self._generate_prob_dist() @@ -86,6 +117,10 @@ def _generate_neighborhood(self): return pd.DataFrame(np.array(neighborhood), columns=features) def fit(self): + NEIGH_GENERATION = { + 'slow': self._generate_neighborhood, + 'fast': self._generate_neighborhood_fast + } decoded_instance = [] features = [col for col in self.dataset['columns'] if col != self.class_name] for i, var in enumerate(features): @@ -94,7 +129,7 @@ def fit(self): except: decoded_instance += [self.instance[i]] - Z = self._generate_neighborhood() + Z = NEIGH_GENERATION[self.neighbor_generation]() df = Z.copy() self.decoded_instance = np.array([decoded_instance], dtype='object') From 916a36704400b9770850ad87bf7ce19402288667 Mon Sep 17 00:00:00 2001 From: Guillermo Fernandez Date: Tue, 18 Oct 2022 12:49:21 +0200 Subject: [PATCH 18/51] Added new datasets and changes to discretization --- teacher/datasets/__init__.py | 13 +- teacher/datasets/_base.py | 54 + teacher/datasets/data/FLAVIA3.csv | 311 + teacher/datasets/data/phishing.csv | 10001 +++++++++++++++++++++++++++ teacher/fuzzy/_base.py | 4 +- teacher/fuzzy/_discretize.py | 37 +- 6 files changed, 10400 insertions(+), 20 deletions(-) create mode 100644 teacher/datasets/data/FLAVIA3.csv create mode 100644 teacher/datasets/data/phishing.csv diff --git a/teacher/datasets/__init__.py b/teacher/datasets/__init__.py index ada2129..94c590b 100644 --- a/teacher/datasets/__init__.py +++ b/teacher/datasets/__init__.py @@ -109,7 +109,16 @@ # ============================================================================= # Local application -from ._base import load_german, load_adult, load_compas, load_heloc, load_beer, load_pima, load_breast, load_basket +from ._base import (load_german, + load_adult, + load_compas, + load_heloc, + load_beer, + load_pima, + load_breast, + load_basket, + load_phishing, + load_flavia) # ============================================================================= @@ -127,4 +136,6 @@ "load_german", "load_heloc", "load_pima", + "load_phishing", + "load_flavia" ] diff --git a/teacher/datasets/_base.py b/teacher/datasets/_base.py index 9f8136b..1677adc 100644 --- a/teacher/datasets/_base.py +++ b/teacher/datasets/_base.py @@ -268,6 +268,60 @@ def load_pima(normalize=False): return generate_dataset(df, columns, class_name, discrete, 'pima', normalize) +def load_flavia(normalize=False): + """ + Load and return the FLAVIA dataset. + + Returns + ------- + dataset : dict + """ + # Read Dataset + df = pd.read_csv(MODULE_PATH + '/data/FLAVIA3.csv', delimiter=',') + + # Features Categorization + class_name = 'Class' + df_cols = list(df.columns) + df_cols.remove(class_name) + new_cols = [class_name] + df_cols + df = df[new_cols] + + discrete = [] + columns = df.columns + return generate_dataset(df, columns, class_name, discrete, 'flavia', normalize) + + +def load_phishing(normalize=False): + """ + Load and return the phishing dataset. + + Returns + ------- + dataset : dict + """ + # Read Dataset + df = pd.read_csv(MODULE_PATH + '/data/phishing.csv', delimiter=',') + del df['id'] + del df['PctExtResourceUrls'] + del df['PctNullSelfRedirectHyperlinks'] + del df['SubdomainLevelRT'] + del df['UrlLengthRT'] + del df['PctExtResourceUrlsRT'] + del df['AbnormalExtFormActionR'] + del df['ExtMetaScriptLinkRT'] + del df['PctExtNullSelfRedirectHyperlinksRT'] + + # Features Categorization + class_name = 'CLASS_LABEL' + df_cols = list(df.columns) + df_cols.remove(class_name) + new_cols = [class_name] + df_cols + df = df[new_cols] + + discrete = [] + columns = df.columns + return generate_dataset(df, columns, class_name, discrete, 'phishing', normalize) + def load_breast(normalize=False): """ Load and return the breast cancer dataset. diff --git a/teacher/datasets/data/FLAVIA3.csv b/teacher/datasets/data/FLAVIA3.csv new file mode 100644 index 0000000..cbc5030 --- /dev/null +++ b/teacher/datasets/data/FLAVIA3.csv @@ -0,0 +1,311 @@ +Area,Perimeter,Diameter,Class +819972,5089.674816,1021.773349,Aesculus chinensis +832990,6140.833329,1029.852324,Aesculus chinensis 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end of file diff --git a/teacher/fuzzy/_base.py b/teacher/fuzzy/_base.py index 5b0c09e..ce711d7 100644 --- a/teacher/fuzzy/_base.py +++ b/teacher/fuzzy/_base.py @@ -27,7 +27,7 @@ def get_fuzzy_points(discretize_method, df_numerical_columns, X, y=None, sets=0, - point_variables=None, debug=False): + point_variables=None, max_depth=0, debug=False): """ Obtain the peak of the fuzzy triangles of the continuous variables of a dataset. @@ -78,7 +78,7 @@ def get_fuzzy_points(discretize_method, df_numerical_columns, X, y=None, sets=0, if point_variables and column in point_variables: fuzzy_points[column] = np.unique(X[:, i]) elif discretize_method == 'entropy': - fuzzy_points[column] = discretize(X[:, i], y, np.min(X[:, i]), debug) + fuzzy_points[column] = discretize(X[:, i], y, np.min(X[:, i]), depth=0, max_depth=max_depth, verbose=debug) else: fuzzy_points[column] = discretize(X[:, i], sets) diff --git a/teacher/fuzzy/_discretize.py b/teacher/fuzzy/_discretize.py index 83d3315..4e83dc4 100644 --- a/teacher/fuzzy/_discretize.py +++ b/teacher/fuzzy/_discretize.py @@ -67,7 +67,7 @@ def _equal_freq(variable, sets): return sol -def _fuzzy_discretization(variable, class_variable, min_point, verbose=False): +def _fuzzy_discretization(variable, class_variable, min_point, depth = 0, max_depth = 0, verbose=False): max_point = variable.max() best_point = 0 best_wfe = inf @@ -110,22 +110,25 @@ def _fuzzy_discretization(variable, class_variable, min_point, verbose=False): print(f'Pass Threshold: {f_gain >= threshold}') print('-----------------') - if not f_gain < threshold: - left = ([(p, c) for p, c in zip(variable, class_variable) if p <= best_point]) - right = ([(p, c) for p, c in zip(variable, class_variable) if p > best_point]) - - left_variable, left_class = zip(*left) - right_variable, right_class = zip(*right) - - left_points = [] - right_points = [] - - if len(left_variable) > 1: - left_points = _fuzzy_discretization(np.array(left_variable), left_class, min_point, verbose) - if len(right_variable) > 1: - right_points = _fuzzy_discretization(np.array(right_variable), right_class, best_point, verbose) - points = left_points + right_points - return np.unique(points).tolist() + if (max_depth > 0 and max_depth > depth) or max_depth == 0: + if not f_gain < threshold: + left = ([(p, c) for p, c in zip(variable, class_variable) if p <= best_point]) + right = ([(p, c) for p, c in zip(variable, class_variable) if p > best_point]) + + left_variable, left_class = zip(*left) + right_variable, right_class = zip(*right) + + left_points = [] + right_points = [] + + if len(left_variable) > 1: + left_points = _fuzzy_discretization(np.array(left_variable), left_class, min_point, depth+1, max_depth, verbose) + if len(right_variable) > 1: + right_points = _fuzzy_discretization(np.array(right_variable), right_class, best_point, depth+1, max_depth, verbose) + points = left_points + right_points + return np.unique(points).tolist() + else: + return [min_point, max_point] else: return [min_point, max_point] From 4315f290a43562d2d730186c4a008510f9ce7d72 Mon Sep 17 00:00:00 2001 From: Guillermo Fernandez Date: Tue, 18 Oct 2022 12:58:28 +0200 Subject: [PATCH 19/51] Deprecated median_absolute_deviation, now median_abs_deviation --- teacher/metrics/_counterfactual.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/teacher/metrics/_counterfactual.py b/teacher/metrics/_counterfactual.py index 57355b0..b68ffe7 100644 --- a/teacher/metrics/_counterfactual.py +++ b/teacher/metrics/_counterfactual.py @@ -10,7 +10,7 @@ import numpy as np from scipy.spatial.distance import cdist from scipy.spatial.distance import _validate_vector -from scipy.stats import median_absolute_deviation +from scipy.stats import median_abs_deviation # ============================================================================= @@ -28,7 +28,7 @@ def mad_cityblock(u, v, mad): def _continuous_distance(x, cf_list, continuous_features, metric='euclidean', X=None, agg=None): if metric == 'mad': - mad = median_absolute_deviation(X[:, continuous_features], axis=0) + mad = median_abs_deviation(X[:, continuous_features], axis=0) mad = np.array([v if v != 0 else 1.0 for v in mad]) def _mad_cityblock(u, v): From 7ff656caa4a6afc14a1853b48e2e065b106fad5c Mon Sep 17 00:00:00 2001 From: Guillermo Fernandez Date: Tue, 25 Oct 2022 14:20:10 +0200 Subject: [PATCH 20/51] added multiclass search_counterfactual --- teacher/explanation/_counterfactual.py | 23 ++++++++++++++++++----- 1 file changed, 18 insertions(+), 5 deletions(-) diff --git a/teacher/explanation/_counterfactual.py b/teacher/explanation/_counterfactual.py index 6f93c23..78b28a2 100644 --- a/teacher/explanation/_counterfactual.py +++ b/teacher/explanation/_counterfactual.py @@ -111,15 +111,28 @@ def _apply_changes(rule, instance): return new_instance, changes -def _search_counterfactual(instance, class_val, rule_list, cf_list): +def _search_counterfactual(instance, class_val, rule_list, cf_list, multiclass=False): """Iterate through the cf_list to find the first rule that generates a valid counterfactual""" sorted_cf = sorted(cf_list, key=lambda rule: rule[1]) + if multiclass: + possible_class_values = set([cf[0].consequent for cf in cf_list]) + changes_dict = {} for cf in sorted_cf: new_instance, changes = _apply_changes(cf[0], instance) new_class_val = Rule.weighted_vote(rule_list, new_instance) if new_class_val != class_val: - return changes - + if not multiclass: + return changes + else: + if new_class_val not in changes_dict: + changes_dict[new_class_val] = changes + if len(changes_dict) == len(possible_class_values): + print('Wiii') + return changes_dict + + + if multiclass: + return changes_dict return None @@ -156,7 +169,7 @@ def FID3_counterfactual(factual, counter_rules): return best_cr, min_rule_distance -def i_counterfactual(instance, rule_list, class_val, df_numerical_columns): +def i_counterfactual(instance, rule_list, class_val, df_numerical_columns, multiclass=False): """Return a list that contains the counterfactual with respect to the instance Parameters @@ -180,7 +193,7 @@ def i_counterfactual(instance, rule_list, class_val, df_numerical_columns): diff_class_rules = [rule for rule in rule_list if rule.consequent != class_val] possible_cf = [(rule, _cf_dist_instance(rule, instance, df_numerical_columns)) for rule in diff_class_rules] - return _search_counterfactual(instance, class_val, rule_list, possible_cf) + return _search_counterfactual(instance, class_val, rule_list, possible_cf, multiclass) def f_counterfactual(factual, instance, rule_list, class_val, df_numerical_columns, tau=0.5): From 6bf7ec030ef8da3e1ba0d7e149bcc3f5647e81f8 Mon Sep 17 00:00:00 2001 From: Guillermo Fernandez Date: Sat, 29 Oct 2022 13:53:19 +0200 Subject: [PATCH 21/51] Modified fuzzy discretization to enhance fuzzy entropy speed --- teacher/fuzzy/_base.py | 4 ++-- teacher/fuzzy/_discretize.py | 14 +++++--------- 2 files changed, 7 insertions(+), 11 deletions(-) diff --git a/teacher/fuzzy/_base.py b/teacher/fuzzy/_base.py index 5b0c09e..4393bbf 100644 --- a/teacher/fuzzy/_base.py +++ b/teacher/fuzzy/_base.py @@ -27,7 +27,7 @@ def get_fuzzy_points(discretize_method, df_numerical_columns, X, y=None, sets=0, - point_variables=None, debug=False): + point_variables=None, max_depth = 0, debug=False): """ Obtain the peak of the fuzzy triangles of the continuous variables of a dataset. @@ -78,7 +78,7 @@ def get_fuzzy_points(discretize_method, df_numerical_columns, X, y=None, sets=0, if point_variables and column in point_variables: fuzzy_points[column] = np.unique(X[:, i]) elif discretize_method == 'entropy': - fuzzy_points[column] = discretize(X[:, i], y, np.min(X[:, i]), debug) + fuzzy_points[column] = discretize(X[:, i], y, np.min(X[:, i]), debug, max_depth) else: fuzzy_points[column] = discretize(X[:, i], sets) diff --git a/teacher/fuzzy/_discretize.py b/teacher/fuzzy/_discretize.py index 83d3315..ba8ee38 100644 --- a/teacher/fuzzy/_discretize.py +++ b/teacher/fuzzy/_discretize.py @@ -67,7 +67,7 @@ def _equal_freq(variable, sets): return sol -def _fuzzy_discretization(variable, class_variable, min_point, verbose=False): +def _fuzzy_discretization(variable, class_variable, min_point, verbose=False, max_depth=0, depth=0): max_point = variable.max() best_point = 0 best_wfe = inf @@ -110,7 +110,7 @@ def _fuzzy_discretization(variable, class_variable, min_point, verbose=False): print(f'Pass Threshold: {f_gain >= threshold}') print('-----------------') - if not f_gain < threshold: + if not f_gain < threshold and (max_depth == 0 or depth < max_depth): left = ([(p, c) for p, c in zip(variable, class_variable) if p <= best_point]) right = ([(p, c) for p, c in zip(variable, class_variable) if p > best_point]) @@ -121,9 +121,9 @@ def _fuzzy_discretization(variable, class_variable, min_point, verbose=False): right_points = [] if len(left_variable) > 1: - left_points = _fuzzy_discretization(np.array(left_variable), left_class, min_point, verbose) + left_points = _fuzzy_discretization(np.array(left_variable), left_class, min_point, verbose, max_depth, depth+1) if len(right_variable) > 1: - right_points = _fuzzy_discretization(np.array(right_variable), right_class, best_point, verbose) + right_points = _fuzzy_discretization(np.array(right_variable), right_class, best_point, verbose, max_depth, depth+1) points = left_points + right_points return np.unique(points).tolist() else: @@ -150,11 +150,7 @@ def _fuzzy_entropy(triangle, class_variable, verbose=False): """ fe = 0 for value in np.unique(class_variable): - class_fuzzy_cardinality = 0 - for i in range(len(triangle)): - if class_variable[i] == value: - class_fuzzy_cardinality += triangle[i] - + class_fuzzy_cardinality = np.sum(triangle[class_variable == value]) if class_fuzzy_cardinality > 0: # i.e. There are elements belonging to this class value fuzzy_cardinality = triangle.sum() if verbose: # pragma: no cover From 429e77713c28c802016a10896290b5fc0d1e9949 Mon Sep 17 00:00:00 2001 From: Guillermo Fernandez Date: Tue, 10 Jan 2023 13:19:06 +0100 Subject: [PATCH 22/51] Fixed division by zero in distance --- teacher/metrics/_counterfactual.py | 9 +++++++-- 1 file changed, 7 insertions(+), 2 deletions(-) diff --git a/teacher/metrics/_counterfactual.py b/teacher/metrics/_counterfactual.py index b68ffe7..10514e9 100644 --- a/teacher/metrics/_counterfactual.py +++ b/teacher/metrics/_counterfactual.py @@ -96,6 +96,7 @@ def _distance(instance_a, instance_b, continuous, discrete, mad): """ cont = 0 disc = 0 + diss = 0 for i, (instance_var, cf_instance_var) in enumerate(zip(instance_a, instance_b)): if i in continuous: if abs(instance_var - cf_instance_var) == 0: @@ -105,7 +106,11 @@ def _distance(instance_a, instance_b, continuous, discrete, mad): else: disc += int(instance_var != cf_instance_var) - diss = 1 / len(continuous) * cont + 1 / len(discrete) * disc + # Avoid division by zero when there is no continuous or discrete variables + if cont: + diss += cont / len(continuous) + if disc: + diss += disc / len(discrete) return diss @@ -116,7 +121,7 @@ def _distance(instance_a, instance_b, continuous, discrete, mad): } -def _closest_instance(instance, dataset, continuous, discrete, mad, distance='moth'): +def _closest_instance(instance, dataset, continuous, discrete, mad, distance='mixed'): """Return the closest instance to a given one from a dataset Parameters From 647b5b31153a0e07d053cbf25236917c5a2b63a6 Mon Sep 17 00:00:00 2001 From: Guillermo Fernandez Date: Tue, 10 Jan 2023 13:19:25 +0100 Subject: [PATCH 23/51] Added cf_dist parameter and fixed exp_value bug --- teacher/explanation/FDT_explainer.py | 10 ++++++++-- 1 file changed, 8 insertions(+), 2 deletions(-) diff --git a/teacher/explanation/FDT_explainer.py b/teacher/explanation/FDT_explainer.py index 76d7776..bf956cd 100644 --- a/teacher/explanation/FDT_explainer.py +++ b/teacher/explanation/FDT_explainer.py @@ -140,18 +140,24 @@ def fit(self, instance, target, neighborhood, df_num_cols, factual, counterfactu del kwargs['mad'] except KeyError: raise ValueError('MAD needed for d_counterfactual') + + try: + cf_dist = kwargs['cf_dist'] + del kwargs['cf_dist'] + except KeyError: + cf_dist = 'moth' self.local_explainer = FDT(fuzzy_variables, max_depth=max_depth, min_num_examples=min_num_examples, fuzzy_threshold=fuzzy_threshold) self.local_explainer.fit(X, y) self.fidelity = f1_score(y, self.local_explainer.predict(X)[0]) rules = self.local_explainer.to_rule_based_system() - self.exp_value = self.local_explainer.predict(instance) + self.exp_value = self.local_explainer.predict(decoded_instance.reshape(1, -1)) fact = self.factual_method(instance_membership, rules, self.exp_value, **kwargs) if counterfactual == 'i_counterfactual': cf = self.counterfactual_method(instance_membership, rules, self.exp_value, df_num_cols) elif counterfactual == 'f_counterfactual': cf = self.counterfactual_method(fact, instance_membership, rules, self.exp_value, df_num_cols) elif counterfactual == 'd_counterfactual': - cf = self.counterfactual_method(decoded_instance, instance_membership, rules, self.exp_value, cont_idx, disc_idx, mad) + cf = self.counterfactual_method(decoded_instance, instance_membership, rules, self.exp_value, cont_idx, disc_idx, mad, cf_dist) self.explanation = (fact, cf) From a3bd657d3f8f698ea638939fee1baebccfe14f38 Mon Sep 17 00:00:00 2001 From: Guillermo Fernandez Date: Tue, 10 Jan 2023 13:20:03 +0100 Subject: [PATCH 24/51] Added check to compute instance membership --- teacher/neighbors/_fuzzy_neighborhood.py | 7 ++++--- 1 file changed, 4 insertions(+), 3 deletions(-) diff --git a/teacher/neighbors/_fuzzy_neighborhood.py b/teacher/neighbors/_fuzzy_neighborhood.py index 8ad6bb8..92f868d 100644 --- a/teacher/neighbors/_fuzzy_neighborhood.py +++ b/teacher/neighbors/_fuzzy_neighborhood.py @@ -95,9 +95,10 @@ def fuzzify(self, get_division, **kwargs): fuzzy_variables_order = {col: i for i, col in enumerate(self._X.columns)} self._fuzzy_variables = get_fuzzy_variables(fuzzy_points, discrete_fuzzy_values, fuzzy_variables_order) self._X_membership = dataset_membership(self._X, self._fuzzy_variables) - - instance_dict = {self._X.columns[i]: [self.instance[i]] for i in range(len(self.instance))} - self._instance_membership = dataset_membership(pd.DataFrame(instance_dict), self._fuzzy_variables) + + if 'instance_membership' not in kwargs.keys() or kwargs['instance_membership']: + instance_dict = {self._X.columns[i]: [self.instance[i]] for i in range(len(self.instance))} + self._instance_membership = dataset_membership(pd.DataFrame(instance_dict), self._fuzzy_variables) def get_X_membership(self): """ From ee1d6f52023b17aacceb423fbdc4542293c45f52 Mon Sep 17 00:00:00 2001 From: Guillermo Fernandez Date: Tue, 10 Jan 2023 13:20:39 +0100 Subject: [PATCH 25/51] Compute instance membership after generating neighborhood --- teacher/neighbors/_sampling_neighborhood.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/teacher/neighbors/_sampling_neighborhood.py b/teacher/neighbors/_sampling_neighborhood.py index 4b6d5b9..5d3de04 100644 --- a/teacher/neighbors/_sampling_neighborhood.py +++ b/teacher/neighbors/_sampling_neighborhood.py @@ -144,7 +144,8 @@ def fit(self): self._y_decoded = self.dataset['label_encoder'][self.class_name].inverse_transform(self._y) def fuzzify(self, get_division, **kwargs): - super().fuzzify(get_division, **kwargs) + # AS INSTANCE MEMBERSHIP IS COMPUTED HERE, PASS FLAG TO NOT COMPUTE IT BEFORE + super().fuzzify(get_division, instance_membership=False, **kwargs) self._instance_membership = dataset_membership(self.decoded_instance, self._fuzzy_variables) def get_y_decoded(self): From be232629b489573e4e2363eb5d1a762f5b0ca5a5 Mon Sep 17 00:00:00 2001 From: Guillermo Fernandez Date: Tue, 10 Jan 2023 13:21:48 +0100 Subject: [PATCH 26/51] Added array of distances to have multiple methods --- teacher/explanation/_counterfactual.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/teacher/explanation/_counterfactual.py b/teacher/explanation/_counterfactual.py index 78b28a2..4ec0964 100644 --- a/teacher/explanation/_counterfactual.py +++ b/teacher/explanation/_counterfactual.py @@ -12,7 +12,7 @@ # Local application from teacher.tree import Rule -from teacher.metrics._counterfactual import _distance +from teacher.metrics._counterfactual import _distance, _mixed_distance, DISTANCES # ============================================================================= @@ -237,7 +237,7 @@ def f_counterfactual(factual, instance, rule_list, class_val, df_numerical_colum return _search_counterfactual(instance, class_val, rule_list, possible_cf) -def d_counterfactual(decoded_instance, instance_membership, rule_list, class_val, continuous, discrete, mad, tau=0.5): +def d_counterfactual(decoded_instance, instance_membership, rule_list, class_val, continuous, discrete, mad, distance='moth', tau=0.5): # TODO: IMPORTANTE NO MERGEAR A LA RAMA MAIN HASTA NO LIMPIAR LA FUNCION """Return a list that contains the counterfactual with respect to the factual @@ -272,6 +272,6 @@ def d_counterfactual(decoded_instance, instance_membership, rule_list, class_val cf_instance, changes = _apply_changes(cf_rule, instance_membership) cf_instance = [max(child[1], key=lambda a: child[1][a]) for child in cf_instance.items()] cf_instance = [float(x) if i in continuous else x for i, x in enumerate(cf_instance)] - cf_dist = _distance(decoded_instance, cf_instance, continuous, discrete, mad) + cf_dist = DISTANCES[distance](decoded_instance, cf_instance, continuous, discrete, mad) possible_cf.append((cf_rule, cf_dist)) return _search_counterfactual(instance_membership, class_val, rule_list, possible_cf) From 7d99763c77af2be5e8c0c3bd1797f655526699ed Mon Sep 17 00:00:00 2001 From: Guillermo Fernandez Date: Tue, 10 Jan 2023 17:57:59 +0100 Subject: [PATCH 27/51] Added lighter threshold for fuzzy discretization --- teacher/fuzzy/_discretize.py | 17 ++++++++++------- 1 file changed, 10 insertions(+), 7 deletions(-) diff --git a/teacher/fuzzy/_discretize.py b/teacher/fuzzy/_discretize.py index dee03e0..a2e95c6 100644 --- a/teacher/fuzzy/_discretize.py +++ b/teacher/fuzzy/_discretize.py @@ -67,7 +67,7 @@ def _equal_freq(variable, sets): return sol -def _fuzzy_discretization(variable, class_variable, min_point, depth = 0, max_depth = 0, verbose=False): +def _fuzzy_discretization(variable, class_variable, min_point, depth = 0, max_depth = 0, th = None, verbose=False): max_point = variable.max() best_point = 0 best_wfe = inf @@ -103,7 +103,10 @@ def _fuzzy_discretization(variable, class_variable, min_point, depth = 0, max_de cardinality = len(variable) delta = _get_delta_point(global_fuzzy_triangles, best_fuzzy_triangle, class_variable) - threshold = (log2(cardinality - 1) + delta) / cardinality + if th is None: + threshold = (log2(cardinality - 1) + delta) / cardinality + else: + threshold = th if verbose: # pragma: no cover print('-----------------') @@ -122,9 +125,9 @@ def _fuzzy_discretization(variable, class_variable, min_point, depth = 0, max_de right_points = [] if len(left_variable) > 1: - left_points = _fuzzy_discretization(np.array(left_variable), left_class, min_point, depth+1, max_depth, verbose) + left_points = _fuzzy_discretization(np.array(left_variable), left_class, min_point, depth+1, max_depth, th, verbose) if len(right_variable) > 1: - right_points = _fuzzy_discretization(np.array(right_variable), right_class, best_point, depth+1, max_depth, verbose) + right_points = _fuzzy_discretization(np.array(right_variable), right_class, best_point, depth+1, max_depth, th, verbose) points = left_points + right_points return np.unique(points).tolist() else: @@ -261,10 +264,10 @@ def _get_delta_point(global_fuzzy_triangles, best_fuzzy_triangle, class_variable """ n_classes = len(np.unique(class_variable)) - old_f_entropy = 0 + old_f_entropy = n_classes * _weighted_fuzzy_entropy(global_fuzzy_triangles, class_variable) new_f_entropy = 0 - for triangle in global_fuzzy_triangles: - old_f_entropy += n_classes * _fuzzy_entropy(global_fuzzy_triangles[triangle], class_variable) + # for triangle in global_fuzzy_triangles: + # old_f_entropy += n_classes * _fuzzy_entropy(global_fuzzy_triangles[triangle], class_variable) for triangle in best_fuzzy_triangle: bft = np.array(best_fuzzy_triangle[triangle]) From 32263e019dd491dc63c4a763d982c04bd7093cec Mon Sep 17 00:00:00 2001 From: Guillermo Fernandez Date: Tue, 31 Jan 2023 13:13:01 +0100 Subject: [PATCH 28/51] FDT Explainer hit bug fixed --- teacher/explanation/FDT_explainer.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/teacher/explanation/FDT_explainer.py b/teacher/explanation/FDT_explainer.py index bf956cd..d33d272 100644 --- a/teacher/explanation/FDT_explainer.py +++ b/teacher/explanation/FDT_explainer.py @@ -152,7 +152,7 @@ def fit(self, instance, target, neighborhood, df_num_cols, factual, counterfactu self.fidelity = f1_score(y, self.local_explainer.predict(X)[0]) rules = self.local_explainer.to_rule_based_system() - self.exp_value = self.local_explainer.predict(decoded_instance.reshape(1, -1)) + self.exp_value = self.local_explainer.predict(instance.reshape(1, -1)) fact = self.factual_method(instance_membership, rules, self.exp_value, **kwargs) if counterfactual == 'i_counterfactual': cf = self.counterfactual_method(instance_membership, rules, self.exp_value, df_num_cols) From 098860b41c2187686df44962da6ab87362f62c65 Mon Sep 17 00:00:00 2001 From: Guillermo Fernandez Date: Tue, 31 Jan 2023 13:14:01 +0100 Subject: [PATCH 29/51] Added threshold to entropy for fuzzy neighborhood --- teacher/fuzzy/_base.py | 4 ++-- teacher/neighbors/_fuzzy_neighborhood.py | 3 ++- 2 files changed, 4 insertions(+), 3 deletions(-) diff --git a/teacher/fuzzy/_base.py b/teacher/fuzzy/_base.py index ce711d7..e90b9bc 100644 --- a/teacher/fuzzy/_base.py +++ b/teacher/fuzzy/_base.py @@ -27,7 +27,7 @@ def get_fuzzy_points(discretize_method, df_numerical_columns, X, y=None, sets=0, - point_variables=None, max_depth=0, debug=False): + point_variables=None, max_depth=0, th=None, debug=False): """ Obtain the peak of the fuzzy triangles of the continuous variables of a dataset. @@ -78,7 +78,7 @@ def get_fuzzy_points(discretize_method, df_numerical_columns, X, y=None, sets=0, if point_variables and column in point_variables: fuzzy_points[column] = np.unique(X[:, i]) elif discretize_method == 'entropy': - fuzzy_points[column] = discretize(X[:, i], y, np.min(X[:, i]), depth=0, max_depth=max_depth, verbose=debug) + fuzzy_points[column] = discretize(X[:, i], y, np.min(X[:, i]), depth=0, max_depth=max_depth, th=th, verbose=debug) else: fuzzy_points[column] = discretize(X[:, i], sets) diff --git a/teacher/neighbors/_fuzzy_neighborhood.py b/teacher/neighbors/_fuzzy_neighborhood.py index 92f868d..7bd6764 100644 --- a/teacher/neighbors/_fuzzy_neighborhood.py +++ b/teacher/neighbors/_fuzzy_neighborhood.py @@ -86,7 +86,8 @@ def fuzzify(self, get_division, **kwargs): if self._X[num].std() < th: point_vars.add(num) fuzzy_points_args['point_variables'] = point_vars - + if 'th' in kwargs.keys(): + fuzzy_points_args['th'] = kwargs['th'] X_num = self._X[kwargs['df_numerical_columns']] num_cols = X_num.columns fuzzy_points = get_fuzzy_points(get_division, num_cols, X_num, self._y, **fuzzy_points_args) From 50e5666a94e5d73a820baa0cf52bc57876e9ae8a Mon Sep 17 00:00:00 2001 From: Guillermo Fernandez Date: Tue, 31 Jan 2023 13:22:17 +0100 Subject: [PATCH 30/51] Added neighborhood range --- teacher/neighbors/_sampling_neighborhood.py | 14 +++++++++++--- 1 file changed, 11 insertions(+), 3 deletions(-) diff --git a/teacher/neighbors/_sampling_neighborhood.py b/teacher/neighbors/_sampling_neighborhood.py index 5d3de04..b852739 100644 --- a/teacher/neighbors/_sampling_neighborhood.py +++ b/teacher/neighbors/_sampling_neighborhood.py @@ -27,7 +27,7 @@ class SamplingNeighborhood(FuzzyNeighborhood): for all the different possible class values. """ - def __init__(self, instance, size, class_name, bb, dataset, X2E, idx_record_to_explain, neighbor_generation='slow'): + def __init__(self, instance, size, class_name, bb, dataset, X2E, idx_record_to_explain, neighbor_generation='slow', neighbor_range='std'): """ Parameters ---------- @@ -48,13 +48,21 @@ def __init__(self, instance, size, class_name, bb, dataset, X2E, idx_record_to_e self.dataset = dataset self.idx_record_to_explain = idx_record_to_explain self.neighbor_generation = neighbor_generation + self.neighbor_range = neighbor_range super().__init__(instance, size, class_name, bb) def _generate_prob_dist(self, instance, cont_idx): prob_dist = {} for i, col in enumerate(self.X2E.T): if i in cont_idx: - vals = [x for x in np.unique(col) if x < instance[i] + col.std() and x > instance[i] - col.std()] + if self.neighbor_range == 'std': + col_range = col.std() + # check if neighbor_range is between 0 and 1 + elif isinstance(self.neighbor_range, float) and self.neighbor_range > 0 and self.neighbor_range < 1: + col_range = (col.max() - col.min()) * self.neighbor_range + else: + raise ValueError("Neighbor range must be between 0 and 1 or 'std'") + vals = [x for x in np.unique(col) if x < instance[i] + col_range and x > instance[i] - col_range] dists = [np.count_nonzero(col == val) for val in vals] dists = [d / sum(dists) for d in dists] else: @@ -94,7 +102,7 @@ def _generate_neighborhood_fast(self): if class_values[neigh_pred] < (self.size/len(class_values)): class_values[neigh_pred] += 1 neighborhood.append(c_neigh) - + neighborhood.append(self.instance) features = [col for col in self.dataset['columns'] if col != self.class_name] return pd.DataFrame(np.array(neighborhood), columns=features) From d86e93147089cbff0d1183853f8bb4f37a742880 Mon Sep 17 00:00:00 2001 From: Guillermo Fernandez Date: Mon, 22 May 2023 20:04:04 +0200 Subject: [PATCH 31/51] Added fuzzy binary decision tree --- teacher/fuzzy/fuzzy_set.py | 13 ++ teacher/tree/__init__.py | 2 + teacher/tree/base_decision_tree.py | 4 +- teacher/tree/fdt_binary_tree.py | 327 +++++++++++++++++++++++++++++ teacher/tree/rule.py | 78 ++++++- 5 files changed, 421 insertions(+), 3 deletions(-) create mode 100644 teacher/tree/fdt_binary_tree.py diff --git a/teacher/fuzzy/fuzzy_set.py b/teacher/fuzzy/fuzzy_set.py index 7e7a7a2..4d6b65c 100644 --- a/teacher/fuzzy/fuzzy_set.py +++ b/teacher/fuzzy/fuzzy_set.py @@ -82,6 +82,9 @@ class FuzzyContinuousSet(FuzzySet): fuzzy_points: list point_set: bool = False + def __hash__(self) -> int: + return hash((self.name, tuple(self.fuzzy_points), self.point_set)) + def membership(self, variable): return fuzz.trimf(variable, self.fuzzy_points) @@ -140,6 +143,16 @@ def simmilarity(self, other): # Else compute the intersection divided by the union of the ranges return (min(max_self, max_other) - max(min_self, min_other)) / (max(max_self, max_other) - min(min_self, min_other)) + def alpha_cut(self, cut): + # Return the interval of the alpha cut + + if cut < 0 or cut > 1: + raise ValueError('The alpha cut must be between 0 and 1') + + left_offset = (self.fuzzy_points[1] - self.fuzzy_points[0]) * cut + right_offset = (self.fuzzy_points[2] - self.fuzzy_points[1]) * cut + + return (self.fuzzy_points[0] + left_offset, self.fuzzy_points[2] - right_offset) @dataclass diff --git a/teacher/tree/__init__.py b/teacher/tree/__init__.py index 8dc75fb..cfade3e 100644 --- a/teacher/tree/__init__.py +++ b/teacher/tree/__init__.py @@ -32,6 +32,7 @@ # Local application from .id3_tree import ID3 from .fdt_tree import FDT +from .fdt_binary_tree import FBDT from .base_decision_tree import BaseDecisionTree from .rule import Rule @@ -43,5 +44,6 @@ "BaseDecisionTree", "ID3", "FDT", + "FBDT", "Rule" ] diff --git a/teacher/tree/base_decision_tree.py b/teacher/tree/base_decision_tree.py index 634d077..60c4d6a 100644 --- a/teacher/tree/base_decision_tree.py +++ b/teacher/tree/base_decision_tree.py @@ -93,7 +93,7 @@ def score(self, X, y): X, y = check_X_y(X, y, dtype=['float64', 'object']) return np.sum(self.predict(X) == y)/y.shape[0] - def to_rule_based_system(self, verbose=False): + def to_rule_based_system(self, verbose=False, simplify=False): """ Return the tree as a rule-based system @@ -107,4 +107,4 @@ def to_rule_based_system(self, verbose=False): rule_system : list[Rule] List of the rules extracted from the tree """ - return self.tree_.to_rule_based_system(verbose) + return self.tree_.to_rule_based_system(verbose, simplify) diff --git a/teacher/tree/fdt_binary_tree.py b/teacher/tree/fdt_binary_tree.py new file mode 100644 index 0000000..1fe8923 --- /dev/null +++ b/teacher/tree/fdt_binary_tree.py @@ -0,0 +1,327 @@ +# ============================================================================= +# Imports +# ============================================================================= + +# Third party +import numpy as np +from sklearn.utils import check_X_y + +# Local application +from ..fuzzy import dataset_membership +from ..fuzzy._discretize import _fuzzy_entropy +from .base_decision_tree import BaseDecisionTree +from .rule import Rule +from . import _voting + + +# ============================================================================= +# Constants +# ============================================================================= + + +VOTING_METHODS = { + "agg_vote": _voting._aggregated_vote, + "max_match": _voting._maximum_matching +} + + +# ============================================================================= +# Classes +# ============================================================================= + +class TreeFBDT: + def __init__(self, fuzzy_variable, t_norm=np.minimum, voting='agg_vote'): + self.is_leaf = True + self.childlist = [] + self.class_value = -1 + self.level = -1 + self.value = None + self.fuzzy_variable = fuzzy_variable + self.mu = [] + self.t_norm = t_norm + self.ignored = {} + try: + self._voting_method = VOTING_METHODS[voting] + except KeyError: + raise ValueError(f'{voting} voting method not implemented') + + def __str__(self): # pragma: no cover + output = '\t' * self.level + try: + output += 'Feature ' + str(self.fuzzy_variable.name) + ' ' + output += str(self.fuzzy_variable.fuzzy_sets[self.value[1]].name) + '\n' + except Exception: # TODO CHANGE FOR PROPER EXCEPTION + output += 'Root \n' + if(self.is_leaf): + output += '\t' * self.level + 'Class value: ' + str(self.class_value) + else: + for child in self.childlist: + # output += '\t' * self.level + # output += 'Feature ' + str(child.fuzzy_variable.name) + ' ' + str(child.fuzzy_variable.fuzzy_sets) + # output += 'Feature ' + str(child.fuzzy_variable.fuzzy_sets[child.value[1]]) + output += str(child) + output += '\n' + '\t' * self.level + return output + '\n' + + def __eq__(self, other): + if not isinstance(other, TreeFBDT): + return False + return (self.is_leaf == other.is_leaf and + self.childlist == other.childlist and + self.class_value == other.class_value and + self.level == other.level and + self.value == other.value and + np.array_equal(self.mu, other.mu)) + + def update_ignored(self, parent, feature, ignored): + new_ignored = parent.ignored.copy() + if feature in new_ignored: + new_ignored[feature] = np.maximum(new_ignored[feature], ignored) + else: + new_ignored[feature] = ignored + self.ignored = new_ignored + + def predict(self, X): + leaf_values = self._partial_predict(X, np.ones(len(X)), self) + agg_vote = self._voting_method(leaf_values) + all_classes = [(key, agg_vote[key]) for key in agg_vote] + # TODO: REHACER AGG_VOTE PARA QUE EN VEZ DE ('one', [1]) SEA ('one', 1) + # INPUT: all_classes = [('one', [1,2,3,4]), ('two', [4,3,2,1]), ('three', [0,0,0,9])] + # OUTPUT: ['two', 'two', 'one', 'three'] + # classes_list = max(n_all_classes, key=lambda a: a[1])[0] + weight_array = np.array([ac[1] for ac in all_classes]) + best_class = np.argmax(weight_array, axis=0) + classes_list = [all_classes[idx][0] for idx in best_class] + return classes_list + + def _partial_predict(self, X, mu, tree): + if tree.value is not None: + att, value = tree.value + pert_degree = 0 + try: + for val in value: + fuzzy_set = tree.fuzzy_variable.fuzzy_sets[val] + pert_degree += fuzzy_set.membership(X[:, att]) + except KeyError: + pert_degree = 0 + new_mu = self.t_norm(mu, pert_degree) + else: + new_mu = mu + if tree.is_leaf: + return np.array([(tree.class_value, new_mu)], dtype=object) + else: + return np.concatenate([self._partial_predict(X, new_mu, child) for child in tree.childlist]) + + def to_rule_based_system(self, th=0.0001, simplify=False, verbose=False): + rules = self._get_rules(self, [], th, verbose) + return [Rule(antecedent, consequent, weight, simplify) for (antecedent, consequent, weight) in rules] + + def _get_rules(self, tree, rule, th=0.0001, verbose=False): + if tree.value is not None: + att, values = tree.value + fuzzy_var_name = tree.fuzzy_variable.name + fuzzy_set_names = list(tree.fuzzy_variable.fuzzy_sets[value].name for value in values) + clause = (fuzzy_var_name, fuzzy_set_names) + new_rule = rule + [clause] + else: + new_rule = rule + + if tree.is_leaf: + if verbose: # pragma: no cover + for leaf_class, weight in tree.class_value.items(): + if weight > th: + print(f'{new_rule} => Class value: {leaf_class} (Weight: {weight})') + return [(new_rule, leaf_class, weight) for leaf_class, weight in tree.class_value.items() if weight > th] + else: + current_rules = [] + for child in tree.childlist: + child_rules = self._get_rules(child, new_rule, th, verbose) + current_rules += child_rules + return current_rules + + +class FBDT(BaseDecisionTree): + def __init__(self, fuzzy_variables, fuzzy_threshold=0.0001, + th=0.0001, max_depth=10, min_num_examples=1, prunning=True, t_norm=np.minimum, voting='agg_vote'): + """ + Parameters + ---------- + fuzzy_variables : list[FuzzyVariable] + List of the fuzzy variables used in the tree + fuzzy_threshold : float, optional + Minimum threshold for a pertenence degree to activate a fuzzy set, by default 0.0001 + th : float, optional + Minimum gain threshold to keep branching the tree, by default 0.0001 + max_depth : int, optional + Maximum depth of the tree, by default 2 + min_num_examples : int, optional + Minimum number of examples per leaf, by default 1 + prunning : bool, optional + Whether or not to prune the tree, by default True + t_norm : function, optional + function to be used as T-norm, by default np.minimum + voting : str, optional + {'agg_vote', 'max_match'}, method of voting for the inference, by default 'agg_vote' + """ + + features = [fuzzy_var.name for fuzzy_var in fuzzy_variables] + self.features_dict = {feat: i for i, feat in enumerate(features)} + self.fuzzy_variables = fuzzy_variables + self.categorical_features = set([fv.name for fv in fuzzy_variables if not isinstance(fv.fuzzy_sets[0], FuzzyContinuousSet)]) + + super().__init__(set(features), th, max_depth, min_num_examples, prunning) + self.tree_ = TreeFBDT(None, t_norm, voting) + self.fuzzy_threshold = fuzzy_threshold + + def _get_binary_partitions(self, features_dict, X_membership, y_positive_mask): + partitions = [] + for feature in features_dict: + if feature in self.categorical_features: + splits = [] + for val in features_dict[feature]: + splits.append((val, sum(X_membership[feature][val][y_positive_mask]) / sum(X_membership[feature][val]))) + splits.sort(key=lambda x: x[1]) + splits = [x[0] for x in splits] + else: + splits = features_dict[feature] + + for i in range(len(splits)-1): + partitions.append((feature,splits[:i+1], splits[i+1:])) + print(partitions) + return partitions + + def _get_max_f_gain(self, tree, features_dict, X_membership, y, t_norm=np.minimum, verbose=False): + best_att = '' + best_f_gain = 0 + best_split = () + best_child_mu = {} + best_ignored = () + node_mu = tree.mu + f_ent = _fuzzy_entropy(node_mu, y) + crisp_cardinality = node_mu.sum() + + if verbose: + print(f'Node Fuzzy Entropy: {f_ent}') + y_positive_mask = y == np.unique(y)[0] + binary_partitions = self._get_binary_partitions(features_dict, X_membership, y_positive_mask) + + for feature, left, right in binary_partitions: + if verbose: # pragma: no cover + print('------------------------------') + print(f'Feature: {feature}, left: {left}, right: {right}') + + wef = 0 # Weighted Fuzzy Entropy + left_membership = sum([X_membership[feature][f_set] for f_set in left]) + left_ignored = np.zeros(len(y)) + if feature in tree.ignored: + left_membership = np.maximum(left_membership, tree.ignored[feature]) + left_ignored = tree.ignored[feature] + + left_mu = t_norm(node_mu, left_membership) + left_f_ent = _fuzzy_entropy(left_mu, y, verbose=False) + left_ignored[(left_membership > 0) & (left_membership < 1)] = 1 + + right_membership = sum([X_membership[feature][f_set] for f_set in right]) + right_ignored = np.zeros(len(y)) + if feature in tree.ignored: + right_membership = np.maximum(right_membership, tree.ignored[feature]) + right_ignored = tree.ignored[feature] + right_mu = t_norm(node_mu, right_membership) + right_f_ent = _fuzzy_entropy(right_mu, y, verbose=False) + child_mu = (left_mu, right_mu) + + right_ignored[(right_membership > 0) & (right_membership < 1)] = 1 + + wef = (left_mu.sum() * left_f_ent + right_mu.sum() * right_f_ent) / crisp_cardinality + + if verbose: # pragma: no cover + print(f'Weighted Fuzzy Entropy: {wef}') + f_gain = f_ent - wef + + if f_gain > best_f_gain: + best_f_gain = f_gain + best_att = feature + best_child_mu = child_mu + best_split = (left, right) + best_ignored = (left_ignored, right_ignored) + + return (best_att, best_f_gain, best_child_mu, best_split, best_ignored) + + def _stop_met(self, f_gain, y_masked, level): + if len(np.unique(y_masked)) < 2: + return True + if len(y_masked) < self.min_num_examples: + return True + if level >= self.max_depth: + return True + if f_gain < self.fuzzy_threshold: + return True + return False + + def _get_class_value(self, mu, y): + cv = {} + for class_value in np.unique(y): + mask = y == class_value + cv[class_value] = (mu * mask).sum() / mu.sum() + # EACH LEAF HAS A DICTIONARY WITH A WEIGHT PER CLASS VALUE + return cv + + def fit(self, X, y): + X, y = check_X_y(X, y, dtype=['float64', 'object']) + X_membership = dataset_membership(X, self.fuzzy_variables) + self.tree_.mu = np.ones(len(y)) + features_dict = { + fuzzy_variable.name: [fuzzy_set.name for fuzzy_set in fuzzy_variable.fuzzy_sets] + for fuzzy_variable in self.fuzzy_variables + } + self._partial_fit(X_membership, y, self.tree_, features_dict, 0) + + def _partial_fit(self, X_membership, y, current_tree, features_dict, current_depth): + current_tree.level = current_depth + att, f_gain, child_mu, split, ignored = self._get_max_f_gain(current_tree, features_dict, X_membership, y, verbose=False) + print(f'Best attribute: {att}, split: {split}, f_gain: {f_gain}') + # apply mask to y + mask = [(x > 0) for x in current_tree.mu] + y_masked = y[mask] + + if self._stop_met(f_gain, y_masked, current_depth): + print('Im leaf') + current_tree.is_leaf = True + current_tree.class_value = self._get_class_value(current_tree.mu, y) + return + + current_tree.is_leaf = False + fuzzy_var = self.fuzzy_variables[self.features_dict[att]] + fuzzy_set_dict = {s.name: i for i, s in enumerate(fuzzy_var.fuzzy_sets)} + + # Left child + child = TreeFBDT(fuzzy_var) + child.value = (self.features_dict[att], [fuzzy_set_dict[s] for s in split[0]]) + child.mu = child_mu[0] + child.update_ignored(current_tree, att, ignored[0]) + current_tree.childlist.append(child) + new_features_dict = features_dict.copy() + new_features_dict[att] = [x for x in new_features_dict[att] if x in set(split[0])] + print(new_features_dict) + + if child.mu.sum() > 0: + self._partial_fit(X_membership, y, child, new_features_dict, current_depth + 1) + else: + child.is_leaf = True + child.class_value = self._get_class_value(current_tree.mu, y) + + # Right child + child = TreeFBDT(fuzzy_var) + child.value = (self.features_dict[att], [fuzzy_set_dict[s] for s in split[1]]) + child.mu = child_mu[1] + child.update_ignored(current_tree, att, ignored[1]) + current_tree.childlist.append(child) + new_features_dict = features_dict.copy() + new_features_dict[att] = [x for x in new_features_dict[att] if x in set(split[1])] + if child.mu.sum() > 0: + self._partial_fit(X_membership, y, child, new_features_dict, current_depth + 1) + else: + child.is_leaf = True + child.class_value = self._get_class_value(current_tree.mu, y) + + diff --git a/teacher/tree/rule.py b/teacher/tree/rule.py index 7ed1670..36bab26 100644 --- a/teacher/tree/rule.py +++ b/teacher/tree/rule.py @@ -5,13 +5,16 @@ # Standard from collections import defaultdict +# Local application +from teacher.fuzzy import FuzzyContinuousSet + # ============================================================================= # Classes # ============================================================================= class Rule: - def __init__(self, antecedent, consequent, weight): + def __init__(self, antecedent, consequent, weight, simplify=False): """ Parameters ---------- @@ -22,6 +25,8 @@ def __init__(self, antecedent, consequent, weight): self.antecedent = tuple(antecedent) self.consequent = consequent self.weight = weight + if simplify: + self.simplify() def __repr__(self): # pragma: no cover return f'Rule({self.antecedent}, {self.consequent}, {self.weight})' @@ -39,6 +44,16 @@ def __eq__(self, other): def __hash__(self) -> int: return hash((self.antecedent, self.consequent, self.weight)) + + def simplify(self): + new_antecedent = {} + for (feature, value) in self.antecedent: + if feature not in new_antecedent: + new_antecedent[feature] = value + else: + if len(new_antecedent[feature]) > len(value): + new_antecedent[feature] = value + self.antecedent = tuple(new_antecedent.items()) def matching(self, instance_membership, t_norm=min): """Matching that an instance has with the rule @@ -56,6 +71,67 @@ def matching(self, instance_membership, t_norm=min): return t_norm([instance_membership[feature][value] for (feature, value) in self.antecedent]) except KeyError: return 0 + + def to_json(self, fuzzy_variables): + """Transform the rule to a json format + + Parameters + ---------- + fuzzy_variables : list[FuzzyVariable] + List with the fuzzy variables of the problem + + Returns + ------- + dict + Json with the rule + """ + fuzzy_dict = {fv.name: fv.fuzzy_sets for fv in fuzzy_variables} + json_antecedents = {feature: value for (feature, value) in self.antecedent} + fuzzy_things = [] + for feature, value in self.antecedent: + fuzzy_sets = {fs.name: fs for fs in fuzzy_dict[feature]} + fuzzy_set = fuzzy_sets[value] + if isinstance(fuzzy_set, FuzzyContinuousSet): + fuzzy_things.append((feature, fuzzy_set.name, fuzzy_set.fuzzy_points)) + else: + fuzzy_things.append((feature, fuzzy_set.name)) + + + + + return [json_antecedents, self.consequent, self.weight, fuzzy_things] + + def to_crisp(self, alpha_cut, fuzzy_variables): + """Transform the rule to a crisp rule + + Parameters + ---------- + alpha_cut : float + Alpha cut to use to transform the rule + + Returns + ------- + Rule + Crisp rule + """ + + fuzzy_dict = {fv.name: fv.fuzzy_sets for fv in fuzzy_variables} + new_antecedent = [] + for (feature, value) in self.antecedent: + fuzzy_sets = fuzzy_dict[feature] + fuzzy_sets_dict = {fs.name: fs for fs in fuzzy_sets} + fuzzy_set = fuzzy_sets_dict[value] + + # Check if fuzzy set is FuzzyContinuousSet + + if isinstance(fuzzy_set, FuzzyContinuousSet): + new_value = fuzzy_set.alpha_cut(alpha_cut) + else: + new_value = value + + new_antecedent.append((feature, new_value)) + + return Rule(new_antecedent, self.consequent, self.weight) @staticmethod def weighted_vote(rule_list, instance_membership): From 45a4c0ad15667795c543756cef99048b23b8811e Mon Sep 17 00:00:00 2001 From: Guillermo Fernandez Date: Tue, 23 May 2023 14:05:31 +0200 Subject: [PATCH 32/51] Added fuzzy binary tree explainer --- teacher/explanation/FBDT_explainer.py | 158 ++++++++++++++++++++++++++ teacher/explanation/__init__.py | 4 +- teacher/tree/fdt_binary_tree.py | 8 +- teacher/tree/rule.py | 56 ++++++--- 4 files changed, 204 insertions(+), 22 deletions(-) create mode 100644 teacher/explanation/FBDT_explainer.py diff --git a/teacher/explanation/FBDT_explainer.py b/teacher/explanation/FBDT_explainer.py new file mode 100644 index 0000000..0e936d2 --- /dev/null +++ b/teacher/explanation/FBDT_explainer.py @@ -0,0 +1,158 @@ +""" +The *Explainer* are classes that follow the guidelines of scikit-learn modules +in that they can be fitted with data to generate an explanation. +""" + +# ============================================================================= +# Imports +# ============================================================================= + +# Third party +from sklearn.utils import check_array +from sklearn.metrics import f1_score + +# Local application +from ._factual_local_explainer import FactualLocalExplainer +from teacher.tree import FBDT +from teacher.explanation import m_factual, mr_factual, c_factual, i_counterfactual, f_counterfactual, d_counterfactual + + +# ============================================================================= +# Constants +# ============================================================================= + +FACTUAL_METHODS = { + 'm_factual': m_factual, + 'mr_factual': mr_factual, + 'c_factual': c_factual +} + + +COUNTERFACTUAL_METHODS = { + 'i_counterfactual': i_counterfactual, + 'f_counterfactual': f_counterfactual, + 'd_counterfactual': d_counterfactual +} + +# ============================================================================= +# Classes +# ============================================================================= + + +class FBDTExplainer(FactualLocalExplainer): + """This *Explainer* uses the :class:`.FDT` implemented in :mod:`teacher` as a white box model to + explain a local instance of a scikit-learn compatible black box classifier.""" + def __init__(self): + self.local_explainer = None + self.factual_method = None + self.counterfactual_method = None + super().__init__() + + def fit(self, instance, target, neighborhood, df_num_cols, factual, counterfactual, **kwargs): + """ + .. _article: https://doi.org/10.1109/TFUZZ.2022.3179582 + + Build a FDTExplainer from the instance, the target and the neighborhood around + the instance + + Parameters + ---------- + instance : array-like of shape (,n_features) + The input instance + target : array-like of shape (1,) + The expected target + neighborhood : class extending from BaseNeighborhood + Neighborhood fitted around the instance to train + the whitebox model + df_num_cols : array-like of shape (n_numerical) where + n_numerical are the number of numerical columns + factual : {"m_factual", "mr_factual", "c_factual"} + The function to compute the factual explanation. Supported + methods are explained in this article_. + counterfactual : {"f_counterfactual", "i_counterfactual"} + The function to compute the factual explanation. Supported + methods are explained in this article_. + + Raises + ------ + ValueError + Factual method invalid + ValueError + Counterfactual method invalid + ValueError + 'c_factual' chosen but no 'lam' parameter given + + """ + instance = check_array(instance, dtype=['float64', 'object']) + try: + self.factual_method = FACTUAL_METHODS[factual] + if factual == 'c_factual' and 'lam' not in kwargs: + raise ValueError("Lambda parameter (lam) needed for factual {factual}") + except KeyError: + raise ValueError(f"Factual method '{factual}' invalid") + + try: + self.counterfactual_method = COUNTERFACTUAL_METHODS[counterfactual] + except KeyError: + raise ValueError(f"Counterfactual method '{counterfactual}' invalid") + + self.target = target + fuzzy_variables = neighborhood.get_fuzzy_variables() + instance_membership = neighborhood.get_instance_membership() + decoded_instance = neighborhood.decoded_instance[0] + X = neighborhood.get_X() + y = neighborhood.get_y() + + try: + max_depth = kwargs['max_depth'] + del kwargs['max_depth'] + except KeyError: + max_depth = 10 + + try: + min_num_examples = kwargs['min_num_examples'] + del kwargs['min_num_examples'] + except KeyError: + min_num_examples = 1 + + try: + fuzzy_threshold = kwargs['fuzzy_threshold'] + del kwargs['fuzzy_threshold'] + except KeyError: + fuzzy_threshold = 0.0001 + + # TODO: IMPORTANTE NO MERGEAR A LA RAMA MASTER HASTA NO LIMPIAR + if counterfactual == 'd_counterfactual': + try: + cont_idx = kwargs['cont_idx'] + del kwargs['cont_idx'] + except KeyError: + raise ValueError('Continuous index needed for d_counterfactual') + + try: + disc_idx = kwargs['disc_idx'] + del kwargs['disc_idx'] + except KeyError: + raise ValueError('Discrete index needed for d_counterfactual') + + try: + mad = kwargs['mad'] + del kwargs['mad'] + except KeyError: + raise ValueError('MAD needed for d_counterfactual') + + try: + cf_dist = kwargs['cf_dist'] + del kwargs['cf_dist'] + except KeyError: + cf_dist = 'moth' + + self.local_explainer = FBDT(fuzzy_variables, max_depth=max_depth, min_num_examples=min_num_examples, fuzzy_threshold=fuzzy_threshold) + self.local_explainer.fit(X, y) + self.fidelity = f1_score(y, self.local_explainer.predict(X)[0]) + + rules = self.local_explainer.to_rule_based_system() + self.exp_value = self.local_explainer.predict(instance.reshape(1, -1)) + fact = self.factual_method(instance_membership, rules, self.exp_value, **kwargs) + cf = 'COUNTERFACTUAL NOT IMPLEMENTED FOR BINARY TREE' + self.explanation = (fact, cf) diff --git a/teacher/explanation/__init__.py b/teacher/explanation/__init__.py index 2bdbf82..5596d08 100644 --- a/teacher/explanation/__init__.py +++ b/teacher/explanation/__init__.py @@ -78,6 +78,7 @@ from ._counterfactual import FID3_counterfactual, i_counterfactual, f_counterfactual, d_counterfactual from .FID3_explainer import FID3Explainer from .FDT_explainer import FDTExplainer +from .FBDT_explainer import FBDTExplainer # ============================================================================= # Public objects @@ -95,5 +96,6 @@ "f_counterfactual", "d_counterfactual", "FID3Explainer", - "FDTExplainer" + "FDTExplainer", + "FBDTExplainer" ] diff --git a/teacher/tree/fdt_binary_tree.py b/teacher/tree/fdt_binary_tree.py index 1fe8923..82ec86c 100644 --- a/teacher/tree/fdt_binary_tree.py +++ b/teacher/tree/fdt_binary_tree.py @@ -7,7 +7,7 @@ from sklearn.utils import check_X_y # Local application -from ..fuzzy import dataset_membership +from ..fuzzy import dataset_membership, FuzzyContinuousSet from ..fuzzy._discretize import _fuzzy_entropy from .base_decision_tree import BaseDecisionTree from .rule import Rule @@ -114,7 +114,7 @@ def _partial_predict(self, X, mu, tree): def to_rule_based_system(self, th=0.0001, simplify=False, verbose=False): rules = self._get_rules(self, [], th, verbose) - return [Rule(antecedent, consequent, weight, simplify) for (antecedent, consequent, weight) in rules] + return [Rule(antecedent, consequent, weight, simplify, multiple_antecedents=True) for (antecedent, consequent, weight) in rules] def _get_rules(self, tree, rule, th=0.0001, verbose=False): if tree.value is not None: @@ -187,7 +187,6 @@ def _get_binary_partitions(self, features_dict, X_membership, y_positive_mask): for i in range(len(splits)-1): partitions.append((feature,splits[:i+1], splits[i+1:])) - print(partitions) return partitions def _get_max_f_gain(self, tree, features_dict, X_membership, y, t_norm=np.minimum, verbose=False): @@ -279,13 +278,11 @@ def fit(self, X, y): def _partial_fit(self, X_membership, y, current_tree, features_dict, current_depth): current_tree.level = current_depth att, f_gain, child_mu, split, ignored = self._get_max_f_gain(current_tree, features_dict, X_membership, y, verbose=False) - print(f'Best attribute: {att}, split: {split}, f_gain: {f_gain}') # apply mask to y mask = [(x > 0) for x in current_tree.mu] y_masked = y[mask] if self._stop_met(f_gain, y_masked, current_depth): - print('Im leaf') current_tree.is_leaf = True current_tree.class_value = self._get_class_value(current_tree.mu, y) return @@ -302,7 +299,6 @@ def _partial_fit(self, X_membership, y, current_tree, features_dict, current_dep current_tree.childlist.append(child) new_features_dict = features_dict.copy() new_features_dict[att] = [x for x in new_features_dict[att] if x in set(split[0])] - print(new_features_dict) if child.mu.sum() > 0: self._partial_fit(X_membership, y, child, new_features_dict, current_depth + 1) diff --git a/teacher/tree/rule.py b/teacher/tree/rule.py index 36bab26..4ab2435 100644 --- a/teacher/tree/rule.py +++ b/teacher/tree/rule.py @@ -14,7 +14,7 @@ class Rule: - def __init__(self, antecedent, consequent, weight, simplify=False): + def __init__(self, antecedent, consequent, weight, simplify=False, multiple_antecedents=False): """ Parameters ---------- @@ -25,6 +25,7 @@ def __init__(self, antecedent, consequent, weight, simplify=False): self.antecedent = tuple(antecedent) self.consequent = consequent self.weight = weight + self.multiple_antecedents = multiple_antecedents if simplify: self.simplify() @@ -67,10 +68,22 @@ def matching(self, instance_membership, t_norm=min): t_norm : function, optional Operation to use as tnorm to get the matching, by default min """ - try: - return t_norm([instance_membership[feature][value] for (feature, value) in self.antecedent]) - except KeyError: - return 0 + if self.multiple_antecedents: + try: + memberships = [] + for feature, values in self.antecedent: + m = 0 + for value in values: + m += instance_membership[feature][value] + memberships.append(m) + return t_norm(memberships) + except KeyError: + return 0 + else: + try: + return t_norm([instance_membership[feature][value] for (feature, value) in self.antecedent]) + except KeyError: + return 0 def to_json(self, fuzzy_variables): """Transform the rule to a json format @@ -85,16 +98,29 @@ def to_json(self, fuzzy_variables): dict Json with the rule """ - fuzzy_dict = {fv.name: fv.fuzzy_sets for fv in fuzzy_variables} - json_antecedents = {feature: value for (feature, value) in self.antecedent} - fuzzy_things = [] - for feature, value in self.antecedent: - fuzzy_sets = {fs.name: fs for fs in fuzzy_dict[feature]} - fuzzy_set = fuzzy_sets[value] - if isinstance(fuzzy_set, FuzzyContinuousSet): - fuzzy_things.append((feature, fuzzy_set.name, fuzzy_set.fuzzy_points)) - else: - fuzzy_things.append((feature, fuzzy_set.name)) + if self.multiple_antecedents: + fuzzy_dict = {fv.name: fv.fuzzy_sets for fv in fuzzy_variables} + json_antecedents = {feature: tuple(values) for (feature, values) in self.antecedent} + fuzzy_things = [] + for feature, values in self.antecedent: + fuzzy_sets = {fs.name: fs for fs in fuzzy_dict[feature]} + for value in values: + fuzzy_set = fuzzy_sets[value] + if isinstance(fuzzy_set, FuzzyContinuousSet): + fuzzy_things.append((feature, fuzzy_set.name, fuzzy_set.fuzzy_points)) + else: + fuzzy_things.append((feature, fuzzy_set.name)) + else: + fuzzy_dict = {fv.name: fv.fuzzy_sets for fv in fuzzy_variables} + json_antecedents = {feature: value for (feature, value) in self.antecedent} + fuzzy_things = [] + for feature, value in self.antecedent: + fuzzy_sets = {fs.name: fs for fs in fuzzy_dict[feature]} + fuzzy_set = fuzzy_sets[value] + if isinstance(fuzzy_set, FuzzyContinuousSet): + fuzzy_things.append((feature, fuzzy_set.name, fuzzy_set.fuzzy_points)) + else: + fuzzy_things.append((feature, fuzzy_set.name)) From adbb8aaa6815096de885668058b1b08e9e0ae618 Mon Sep 17 00:00:00 2001 From: Guillermo Fernandez Date: Thu, 8 Jun 2023 12:23:16 +0200 Subject: [PATCH 33/51] Added jaccard similarity --- teacher/datasets/_base.py | 3 +++ teacher/fuzzy/fuzzy_set.py | 26 ++++++++++++++++++++++++++ 2 files changed, 29 insertions(+) diff --git a/teacher/datasets/_base.py b/teacher/datasets/_base.py index 1677adc..a58c710 100644 --- a/teacher/datasets/_base.py +++ b/teacher/datasets/_base.py @@ -77,6 +77,8 @@ def generate_dataset(df, columns, class_name, discrete, name, normalize=False): if normalize: scaler = StandardScaler() df[continuous] = scaler.fit_transform(df[continuous]) + else: + scaler = None # Dataset Preparation for Scikit Alorithms df_le, label_encoder = label_encode(df, discrete) X = df_le.loc[:, df_le.columns != class_name].values @@ -94,6 +96,7 @@ def generate_dataset(df, columns, class_name, discrete, name, normalize=False): 'continuous': continuous, 'idx_features': idx_features, 'label_encoder': label_encoder, + 'normalize_scaler': scaler, 'X': X, 'y': y, } diff --git a/teacher/fuzzy/fuzzy_set.py b/teacher/fuzzy/fuzzy_set.py index 4d6b65c..d7f83b1 100644 --- a/teacher/fuzzy/fuzzy_set.py +++ b/teacher/fuzzy/fuzzy_set.py @@ -8,6 +8,7 @@ # Third party import skfuzzy as fuzz +import numpy as np # ============================================================================= # Classes @@ -84,6 +85,9 @@ class FuzzyContinuousSet(FuzzySet): def __hash__(self) -> int: return hash((self.name, tuple(self.fuzzy_points), self.point_set)) + + def __lt__(self, other): + return self.fuzzy_points < other.fuzzy_points def membership(self, variable): return fuzz.trimf(variable, self.fuzzy_points) @@ -153,7 +157,29 @@ def alpha_cut(self, cut): right_offset = (self.fuzzy_points[2] - self.fuzzy_points[1]) * cut return (self.fuzzy_points[0] + left_offset, self.fuzzy_points[2] - right_offset) + + @staticmethod + def merge(a, b): + new_name = np.mean([float(a.name), float(b.name)]) + return FuzzyContinuousSet(str(new_name), [min(a.fuzzy_points[0], b.fuzzy_points[0]), np.mean([a.fuzzy_points[1], b.fuzzy_points[1]]), max(a.fuzzy_points[2], b.fuzzy_points[2])]) + + @staticmethod + def jaccard_similarity(a, b): + # Compute Jaccard similarity between two fuzzy sets with triangular membership functions + + # Define the ranges for the common support + common_support_start = max(a.fuzzy_points[0], b.fuzzy_points[0]) + common_support_end = min(a.fuzzy_points[2], b.fuzzy_points[2]) + + if common_support_start >= common_support_end: + return 0 + + # Calculate the intersection and union + intersection = common_support_end - common_support_start + union = max(a.fuzzy_points[2], b.fuzzy_points[2]) - min(a.fuzzy_points[0], b.fuzzy_points[0]) + jaccard_similarity = intersection / union * a.intersection(b) + return jaccard_similarity @dataclass class FuzzyDiscreteSet(FuzzySet): From 101ca8ba1d2c6c22fa2bea1564c50c25f2327c4f Mon Sep 17 00:00:00 2001 From: Guillermo Fernandez Date: Wed, 25 Oct 2023 15:01:46 +0200 Subject: [PATCH 34/51] added iris and wine to datasets --- .gitignore | 3 ++- teacher/datasets/__init__.py | 8 +++++-- teacher/datasets/_base.py | 42 ++++++++++++++++++++++++++++++++++++ 3 files changed, 50 insertions(+), 3 deletions(-) diff --git a/.gitignore b/.gitignore index fb16b4d..053347c 100644 --- a/.gitignore +++ b/.gitignore @@ -137,4 +137,5 @@ dmypy.json # DS files .DS_Store -flore-experiments/ \ No newline at end of file +flore-experiments/ +knowledge-extraction-experiments/ \ No newline at end of file diff --git a/teacher/datasets/__init__.py b/teacher/datasets/__init__.py index 94c590b..ec31f5d 100644 --- a/teacher/datasets/__init__.py +++ b/teacher/datasets/__init__.py @@ -118,7 +118,9 @@ load_breast, load_basket, load_phishing, - load_flavia) + load_flavia, + load_iris, + load_wine) # ============================================================================= @@ -137,5 +139,7 @@ "load_heloc", "load_pima", "load_phishing", - "load_flavia" + "load_flavia", + "load_iris", + "load_wine" ] diff --git a/teacher/datasets/_base.py b/teacher/datasets/_base.py index 1677adc..87db03b 100644 --- a/teacher/datasets/_base.py +++ b/teacher/datasets/_base.py @@ -15,6 +15,7 @@ import pandas as pd import numpy as np from sklearn.preprocessing import StandardScaler +from sklearn import datasets # Local application from teacher.utils import recognize_features_type, set_discrete_continuous, label_encode @@ -322,6 +323,47 @@ def load_phishing(normalize=False): columns = df.columns return generate_dataset(df, columns, class_name, discrete, 'phishing', normalize) + +def load_iris(normalize=False): + """ + Load and return the iris dataset. + + Returns + ------- + dataset : dict + """ + # Read Dataset + iris = datasets.load_iris(as_frame=True) + df = iris.frame + + # Features Categorization + columns = df.columns + class_name = columns[-1] + + discrete = [] + return generate_dataset(df, columns, class_name, discrete, 'iris', normalize) + + +def load_wine(normalize=False): + """ + Load and return the wine dataset. + + Returns + ------- + dataset : dict + """ + # Read Dataset + wine = datasets.load_wine(as_frame=True) + df = wine.frame + + # Features Categorization + columns = df.columns + class_name = columns[-1] + + discrete = [] + return generate_dataset(df, columns, class_name, discrete, 'wine', normalize) + + def load_breast(normalize=False): """ Load and return the breast cancer dataset. From e947dda89cae3b1a1dd815e75bfa692d08df003c Mon Sep 17 00:00:00 2001 From: Guillermo Fernandez Date: Mon, 5 Feb 2024 16:50:07 +0100 Subject: [PATCH 35/51] Sort columns of new datasets --- teacher/datasets/_base.py | 12 ++++++++++++ 1 file changed, 12 insertions(+) diff --git a/teacher/datasets/_base.py b/teacher/datasets/_base.py index b175e54..d25c84e 100644 --- a/teacher/datasets/_base.py +++ b/teacher/datasets/_base.py @@ -342,8 +342,14 @@ def load_iris(normalize=False): # Features Categorization columns = df.columns class_name = columns[-1] + + df_cols = list(df.columns) + df_cols.remove(class_name) + new_cols = [class_name] + df_cols + df = df[new_cols] discrete = [] + columns = df.columns return generate_dataset(df, columns, class_name, discrete, 'iris', normalize) @@ -362,8 +368,14 @@ def load_wine(normalize=False): # Features Categorization columns = df.columns class_name = columns[-1] + + df_cols = list(df.columns) + df_cols.remove(class_name) + new_cols = [class_name] + df_cols + df = df[new_cols] discrete = [] + columns = df.columns return generate_dataset(df, columns, class_name, discrete, 'wine', normalize) From 314a5a32ac21e1bd4eb33afb49753a7736502980 Mon Sep 17 00:00:00 2001 From: Guillermo Fernandez Date: Mon, 5 Feb 2024 16:51:08 +0100 Subject: [PATCH 36/51] Added f1_score for multiclass --- teacher/explanation/FDT_explainer.py | 7 ++++++- 1 file changed, 6 insertions(+), 1 deletion(-) diff --git a/teacher/explanation/FDT_explainer.py b/teacher/explanation/FDT_explainer.py index d33d272..41b74d3 100644 --- a/teacher/explanation/FDT_explainer.py +++ b/teacher/explanation/FDT_explainer.py @@ -10,6 +10,7 @@ # Third party from sklearn.utils import check_array from sklearn.metrics import f1_score +import numpy as np # Local application from ._factual_local_explainer import FactualLocalExplainer @@ -149,7 +150,11 @@ def fit(self, instance, target, neighborhood, df_num_cols, factual, counterfactu self.local_explainer = FDT(fuzzy_variables, max_depth=max_depth, min_num_examples=min_num_examples, fuzzy_threshold=fuzzy_threshold) self.local_explainer.fit(X, y) - self.fidelity = f1_score(y, self.local_explainer.predict(X)[0]) + local_prediction = self.local_explainer.predict(X)[0] + if len(np.unique(y)) > 2: + self.fidelity = f1_score(y, local_prediction, average='weighted') + else: + self.fidelity = f1_score(y, local_prediction) rules = self.local_explainer.to_rule_based_system() self.exp_value = self.local_explainer.predict(instance.reshape(1, -1)) From 4864d30a86d7e3187f27bc682647c58799a708bd Mon Sep 17 00:00:00 2001 From: Guillermo Fernandez Date: Mon, 5 Feb 2024 16:52:05 +0100 Subject: [PATCH 37/51] Added max tries to avoid neighborhood getting stuck --- teacher/neighbors/_sampling_neighborhood.py | 6 +++++- 1 file changed, 5 insertions(+), 1 deletion(-) diff --git a/teacher/neighbors/_sampling_neighborhood.py b/teacher/neighbors/_sampling_neighborhood.py index b852739..bb9a0fa 100644 --- a/teacher/neighbors/_sampling_neighborhood.py +++ b/teacher/neighbors/_sampling_neighborhood.py @@ -89,7 +89,9 @@ def _generate_neighborhood_fast(self): c_prob_dist = self._generate_prob_dist(closest_instance, cont_idx) class_values = {i: 0 for i in range(len(self.dataset['possible_outcomes']))} neighborhood = [] + tries = 0 while len(neighborhood) < self.size: + tries += 1 i_neigh = self._get_instance_from_prob_dist(prob_dist) c_neigh = self._get_instance_from_prob_dist(c_prob_dist) @@ -102,6 +104,8 @@ def _generate_neighborhood_fast(self): if class_values[neigh_pred] < (self.size/len(class_values)): class_values[neigh_pred] += 1 neighborhood.append(c_neigh) + if tries > self.size * 100: + break neighborhood.append(self.instance) features = [col for col in self.dataset['columns'] if col != self.class_name] return pd.DataFrame(np.array(neighborhood), columns=features) @@ -136,7 +140,7 @@ def fit(self): decoded_instance.append(self.dataset['label_encoder'][var].inverse_transform(np.array([self.instance[i]], dtype=int))[0]) except: decoded_instance += [self.instance[i]] - + Z = NEIGH_GENERATION[self.neighbor_generation]() df = Z.copy() From af8c47630bcf50a01c4e8b838bb37d48797dc287 Mon Sep 17 00:00:00 2001 From: Guillermo Fernandez Date: Mon, 5 Feb 2024 19:27:36 +0100 Subject: [PATCH 38/51] Fixed lint issues --- src/teacher/datasets/_base.py | 8 +-- src/teacher/explanation/FBDT_explainer.py | 37 +++----------- src/teacher/explanation/FDT_explainer.py | 22 ++++++--- src/teacher/explanation/_counterfactual.py | 15 ++++-- src/teacher/fuzzy/_base.py | 8 ++- src/teacher/fuzzy/fuzzy_set.py | 26 ++++++---- src/teacher/metrics/_counterfactual.py | 49 ++++++++++++------- src/teacher/neighbors/_fuzzy_neighborhood.py | 2 +- src/teacher/neighbors/_lore_neighborhood.py | 25 ++++++---- .../neighbors/_sampling_neighborhood.py | 46 +++++++++++------ src/teacher/tree/fdt_binary_tree.py | 31 +++++++----- src/teacher/tree/rule.py | 13 ++--- 12 files changed, 162 insertions(+), 120 deletions(-) diff --git a/src/teacher/datasets/_base.py b/src/teacher/datasets/_base.py index d25c84e..f7c62a0 100644 --- a/src/teacher/datasets/_base.py +++ b/src/teacher/datasets/_base.py @@ -342,7 +342,7 @@ def load_iris(normalize=False): # Features Categorization columns = df.columns class_name = columns[-1] - + df_cols = list(df.columns) df_cols.remove(class_name) new_cols = [class_name] + df_cols @@ -368,7 +368,7 @@ def load_wine(normalize=False): # Features Categorization columns = df.columns class_name = columns[-1] - + df_cols = list(df.columns) df_cols.remove(class_name) new_cols = [class_name] + df_cols @@ -398,6 +398,7 @@ def load_breast(normalize=False): discrete = [] return generate_dataset(df, columns, class_name, discrete, 'breast', normalize) + def load_basket(normalize=False, reduced=False): """ Load and return the basket dataset. @@ -412,10 +413,9 @@ def load_basket(normalize=False, reduced=False): else: df = pd.read_csv(MODULE_PATH + '/data/basket.csv', delimiter=',') - # Features Categorization columns = df.columns - class_name = 'Position' + class_name = 'Position' df_cols = list(df.columns) df_cols.remove(class_name) new_cols = [class_name] + df_cols diff --git a/src/teacher/explanation/FBDT_explainer.py b/src/teacher/explanation/FBDT_explainer.py index 0e936d2..1183670 100644 --- a/src/teacher/explanation/FBDT_explainer.py +++ b/src/teacher/explanation/FBDT_explainer.py @@ -99,7 +99,6 @@ def fit(self, instance, target, neighborhood, df_num_cols, factual, counterfactu self.target = target fuzzy_variables = neighborhood.get_fuzzy_variables() instance_membership = neighborhood.get_instance_membership() - decoded_instance = neighborhood.decoded_instance[0] X = neighborhood.get_X() y = neighborhood.get_y() @@ -108,46 +107,24 @@ def fit(self, instance, target, neighborhood, df_num_cols, factual, counterfactu del kwargs['max_depth'] except KeyError: max_depth = 10 - + try: min_num_examples = kwargs['min_num_examples'] del kwargs['min_num_examples'] except KeyError: min_num_examples = 1 - + try: fuzzy_threshold = kwargs['fuzzy_threshold'] del kwargs['fuzzy_threshold'] except KeyError: fuzzy_threshold = 0.0001 - + # TODO: IMPORTANTE NO MERGEAR A LA RAMA MASTER HASTA NO LIMPIAR - if counterfactual == 'd_counterfactual': - try: - cont_idx = kwargs['cont_idx'] - del kwargs['cont_idx'] - except KeyError: - raise ValueError('Continuous index needed for d_counterfactual') - - try: - disc_idx = kwargs['disc_idx'] - del kwargs['disc_idx'] - except KeyError: - raise ValueError('Discrete index needed for d_counterfactual') - - try: - mad = kwargs['mad'] - del kwargs['mad'] - except KeyError: - raise ValueError('MAD needed for d_counterfactual') - - try: - cf_dist = kwargs['cf_dist'] - del kwargs['cf_dist'] - except KeyError: - cf_dist = 'moth' - - self.local_explainer = FBDT(fuzzy_variables, max_depth=max_depth, min_num_examples=min_num_examples, fuzzy_threshold=fuzzy_threshold) + self.local_explainer = FBDT(fuzzy_variables, + max_depth=max_depth, + min_num_examples=min_num_examples, + fuzzy_threshold=fuzzy_threshold) self.local_explainer.fit(X, y) self.fidelity = f1_score(y, self.local_explainer.predict(X)[0]) diff --git a/src/teacher/explanation/FDT_explainer.py b/src/teacher/explanation/FDT_explainer.py index 41b74d3..2b84309 100644 --- a/src/teacher/explanation/FDT_explainer.py +++ b/src/teacher/explanation/FDT_explainer.py @@ -109,19 +109,19 @@ def fit(self, instance, target, neighborhood, df_num_cols, factual, counterfactu del kwargs['max_depth'] except KeyError: max_depth = 10 - + try: min_num_examples = kwargs['min_num_examples'] del kwargs['min_num_examples'] except KeyError: min_num_examples = 1 - + try: fuzzy_threshold = kwargs['fuzzy_threshold'] del kwargs['fuzzy_threshold'] except KeyError: fuzzy_threshold = 0.0001 - + # TODO: IMPORTANTE NO MERGEAR A LA RAMA MASTER HASTA NO LIMPIAR if counterfactual == 'd_counterfactual': try: @@ -141,14 +141,17 @@ def fit(self, instance, target, neighborhood, df_num_cols, factual, counterfactu del kwargs['mad'] except KeyError: raise ValueError('MAD needed for d_counterfactual') - + try: cf_dist = kwargs['cf_dist'] del kwargs['cf_dist'] except KeyError: cf_dist = 'moth' - self.local_explainer = FDT(fuzzy_variables, max_depth=max_depth, min_num_examples=min_num_examples, fuzzy_threshold=fuzzy_threshold) + self.local_explainer = FDT(fuzzy_variables, + max_depth=max_depth, + min_num_examples=min_num_examples, + fuzzy_threshold=fuzzy_threshold) self.local_explainer.fit(X, y) local_prediction = self.local_explainer.predict(X)[0] if len(np.unique(y)) > 2: @@ -164,5 +167,12 @@ def fit(self, instance, target, neighborhood, df_num_cols, factual, counterfactu elif counterfactual == 'f_counterfactual': cf = self.counterfactual_method(fact, instance_membership, rules, self.exp_value, df_num_cols) elif counterfactual == 'd_counterfactual': - cf = self.counterfactual_method(decoded_instance, instance_membership, rules, self.exp_value, cont_idx, disc_idx, mad, cf_dist) + cf = self.counterfactual_method(decoded_instance, + instance_membership, + rules, + self.exp_value, + cont_idx, + disc_idx, + mad, + cf_dist) self.explanation = (fact, cf) diff --git a/src/teacher/explanation/_counterfactual.py b/src/teacher/explanation/_counterfactual.py index 4ec0964..1579caf 100644 --- a/src/teacher/explanation/_counterfactual.py +++ b/src/teacher/explanation/_counterfactual.py @@ -12,7 +12,7 @@ # Local application from teacher.tree import Rule -from teacher.metrics._counterfactual import _distance, _mixed_distance, DISTANCES +from teacher.metrics._counterfactual import DISTANCES # ============================================================================= @@ -121,7 +121,7 @@ def _search_counterfactual(instance, class_val, rule_list, cf_list, multiclass=F new_instance, changes = _apply_changes(cf[0], instance) new_class_val = Rule.weighted_vote(rule_list, new_instance) if new_class_val != class_val: - if not multiclass: + if not multiclass: return changes else: if new_class_val not in changes_dict: @@ -130,7 +130,6 @@ def _search_counterfactual(instance, class_val, rule_list, cf_list, multiclass=F print('Wiii') return changes_dict - if multiclass: return changes_dict return None @@ -237,7 +236,15 @@ def f_counterfactual(factual, instance, rule_list, class_val, df_numerical_colum return _search_counterfactual(instance, class_val, rule_list, possible_cf) -def d_counterfactual(decoded_instance, instance_membership, rule_list, class_val, continuous, discrete, mad, distance='moth', tau=0.5): +def d_counterfactual(decoded_instance, + instance_membership, + rule_list, + class_val, + continuous, + discrete, + mad, + distance='moth', + tau=0.5): # TODO: IMPORTANTE NO MERGEAR A LA RAMA MAIN HASTA NO LIMPIAR LA FUNCION """Return a list that contains the counterfactual with respect to the factual diff --git a/src/teacher/fuzzy/_base.py b/src/teacher/fuzzy/_base.py index e90b9bc..b1a5f6b 100644 --- a/src/teacher/fuzzy/_base.py +++ b/src/teacher/fuzzy/_base.py @@ -78,7 +78,13 @@ def get_fuzzy_points(discretize_method, df_numerical_columns, X, y=None, sets=0, if point_variables and column in point_variables: fuzzy_points[column] = np.unique(X[:, i]) elif discretize_method == 'entropy': - fuzzy_points[column] = discretize(X[:, i], y, np.min(X[:, i]), depth=0, max_depth=max_depth, th=th, verbose=debug) + fuzzy_points[column] = discretize(X[:, i], + y, + np.min(X[:, i]), + depth=0, + max_depth=max_depth, + th=th, + verbose=debug) else: fuzzy_points[column] = discretize(X[:, i], sets) diff --git a/src/teacher/fuzzy/fuzzy_set.py b/src/teacher/fuzzy/fuzzy_set.py index d7f83b1..1aa7cf3 100644 --- a/src/teacher/fuzzy/fuzzy_set.py +++ b/src/teacher/fuzzy/fuzzy_set.py @@ -51,7 +51,7 @@ def intersection(self, other): ValueError If the set is not of the same subtype """ - + @abstractmethod def simmilarity(self, other): """Compute the similarity between two fuzzy sets of the same @@ -85,7 +85,7 @@ class FuzzyContinuousSet(FuzzySet): def __hash__(self) -> int: return hash((self.name, tuple(self.fuzzy_points), self.point_set)) - + def __lt__(self, other): return self.fuzzy_points < other.fuzzy_points @@ -128,11 +128,11 @@ def _line_intersect(self, A, B): return 0 else: return y - + def simmilarity(self, other): if not isinstance(other, FuzzyContinuousSet): raise ValueError('Intersection must be between two Fuzzy Sets of the same type') - + # Compute the range for an alpha cut of 0.5 because we assume fuzzy strong partitions min_self = (self.fuzzy_points[1] - self.fuzzy_points[0]) / 2 max_self = (self.fuzzy_points[2] - self.fuzzy_points[1]) / 2 @@ -143,25 +143,30 @@ def simmilarity(self, other): # if the ranges don't intersect the simmilarity is zero if min_self >= max_other or min_other >= max_self: return 0 - + # Else compute the intersection divided by the union of the ranges - return (min(max_self, max_other) - max(min_self, min_other)) / (max(max_self, max_other) - min(min_self, min_other)) + inters = min(max_self, max_other) - max(min_self, min_other) + union = max(max_self, max_other) - min(min_self, min_other) + return inters / union def alpha_cut(self, cut): # Return the interval of the alpha cut if cut < 0 or cut > 1: raise ValueError('The alpha cut must be between 0 and 1') - + left_offset = (self.fuzzy_points[1] - self.fuzzy_points[0]) * cut right_offset = (self.fuzzy_points[2] - self.fuzzy_points[1]) * cut return (self.fuzzy_points[0] + left_offset, self.fuzzy_points[2] - right_offset) - + @staticmethod def merge(a, b): new_name = np.mean([float(a.name), float(b.name)]) - return FuzzyContinuousSet(str(new_name), [min(a.fuzzy_points[0], b.fuzzy_points[0]), np.mean([a.fuzzy_points[1], b.fuzzy_points[1]]), max(a.fuzzy_points[2], b.fuzzy_points[2])]) + return FuzzyContinuousSet(str(new_name), + [min(a.fuzzy_points[0], b.fuzzy_points[0]), + np.mean([a.fuzzy_points[1], b.fuzzy_points[1]]), + max(a.fuzzy_points[2], b.fuzzy_points[2])]) @staticmethod def jaccard_similarity(a, b): @@ -181,6 +186,7 @@ def jaccard_similarity(a, b): return jaccard_similarity + @dataclass class FuzzyDiscreteSet(FuzzySet): """ @@ -200,6 +206,6 @@ def intersection(self, other): raise ValueError('Intersection must be between two Fuzzy Sets of the same type') return int(self == other) - + def simmilarity(self, other): return self.intersection(other) diff --git a/src/teacher/metrics/_counterfactual.py b/src/teacher/metrics/_counterfactual.py index 10514e9..560b415 100644 --- a/src/teacher/metrics/_counterfactual.py +++ b/src/teacher/metrics/_counterfactual.py @@ -33,9 +33,11 @@ def _continuous_distance(x, cf_list, continuous_features, metric='euclidean', X= def _mad_cityblock(u, v): return mad_cityblock(u, v, mad) - dist = cdist(x.reshape(1, -1)[:, continuous_features], cf_list.reshape(1, -1)[:, continuous_features], metric=_mad_cityblock) + dist = cdist(x.reshape(1, -1)[:, continuous_features], cf_list.reshape(1, -1)[:, continuous_features], + metric=_mad_cityblock) else: - dist = cdist(x.reshape(1, -1)[:, continuous_features], cf_list.reshape(1, -1)[:, continuous_features], metric=metric) + dist = cdist(x.reshape(1, -1)[:, continuous_features], cf_list.reshape(1, -1)[:, continuous_features], + metric=metric) if agg is None or agg == 'mean': return np.mean(dist) @@ -49,7 +51,8 @@ def _mad_cityblock(u, v): def _categorical_distance(x, cf_list, categorical_features, metric='jaccard', agg=None): - dist = cdist(x.reshape(1, -1)[:, categorical_features], cf_list.reshape(1, -1)[:, categorical_features], metric=metric) + dist = cdist(x.reshape(1, -1)[:, categorical_features], cf_list.reshape(1, -1)[:, categorical_features], + metric=metric) if agg is None or agg == 'mean': return np.mean(dist) @@ -90,8 +93,8 @@ def _distance(instance_a, instance_b, continuous, discrete, mad): discrete : array-like Indices of the discrete features mad : dict - Median Absolute Distances of all the - continuous features in the dataset, where + Median Absolute Distances of all the + continuous features in the dataset, where the keys are the indices of the continuous features """ cont = 0 @@ -105,7 +108,7 @@ def _distance(instance_a, instance_b, continuous, discrete, mad): cont += abs(instance_var - cf_instance_var) / mad[i] else: disc += int(instance_var != cf_instance_var) - + # Avoid division by zero when there is no continuous or discrete variables if cont: diss += cont / len(continuous) @@ -135,8 +138,8 @@ def _closest_instance(instance, dataset, continuous, discrete, mad, distance='mi discrete : array-like Indices of the discrete features mad : dict - Median Absolute Distances of all the - continuous features in the dataset, where + Median Absolute Distances of all the + continuous features in the dataset, where the keys are the indices of the continuous features """ @@ -149,7 +152,7 @@ def _closest_instance(instance, dataset, continuous, discrete, mad, distance='mi if new_distance < min_distance and new_distance > 0: min_distance = new_distance closest_instance = ds_instance - + return closest_instance @@ -189,11 +192,12 @@ def sparsity_dissimilarity(instance, cf_instance, distance='mismatch'): diss = 0 for instance_var, cf_instance_var in zip(instance, cf_instance): diss += int(instance_var != cf_instance_var) - + return diss / len(instance) else: return cdist(instance.reshape(1, -1), cf_instance.reshape(1, -1), metric='jaccard')[0][0] + def implausibility(cf_instance, dataset, continuous, discrete, mad, distance='moth'): """Return the level of plausibility of a counterfactual instance in the dataset @@ -209,15 +213,22 @@ def implausibility(cf_instance, dataset, continuous, discrete, mad, distance='mo discrete : array-like Indices of the discrete features mad : dict - Median Absolute Distances of all the - continuous features in the dataset, where + Median Absolute Distances of all the + continuous features in the dataset, where the keys are the indices of the continuous features """ - closest_instance = _closest_instance(cf_instance, dataset, continuous, discrete, mad, distance) + closest_instance = _closest_instance(cf_instance, dataset, continuous, discrete, mad, distance) return DISTANCES[distance](cf_instance, closest_instance, continuous, discrete, mad) -def instability(instance, cf_instance, closest_instance, cf_closest_instance, continuous, discrete, mad, distance='moth'): +def instability(instance, + cf_instance, + closest_instance, + cf_closest_instance, + continuous, + discrete, + mad, + distance='moth'): """Return the level of stability of a counterfactual instance against the counterfactual of the closest instance to the original instance @@ -237,10 +248,10 @@ def instability(instance, cf_instance, closest_instance, cf_closest_instance, co discrete : array-like Indices of the discrete features mad : dict - Median Absolute Distances of all the - continuous features in the dataset, where + Median Absolute Distances of all the + continuous features in the dataset, where the keys are the indices of the continuous features """ - - return DISTANCES[distance](cf_instance, cf_closest_instance, continuous, discrete, mad) / (DISTANCES[distance](instance, closest_instance, continuous, discrete, mad) + 1) - + cf_distance = DISTANCES[distance](cf_instance, cf_closest_instance, continuous, discrete, mad) + i_distance = DISTANCES[distance](instance, closest_instance, continuous, discrete, mad) + return cf_distance / (i_distance + 1) diff --git a/src/teacher/neighbors/_fuzzy_neighborhood.py b/src/teacher/neighbors/_fuzzy_neighborhood.py index 7bd6764..f5b33e2 100644 --- a/src/teacher/neighbors/_fuzzy_neighborhood.py +++ b/src/teacher/neighbors/_fuzzy_neighborhood.py @@ -96,7 +96,7 @@ def fuzzify(self, get_division, **kwargs): fuzzy_variables_order = {col: i for i, col in enumerate(self._X.columns)} self._fuzzy_variables = get_fuzzy_variables(fuzzy_points, discrete_fuzzy_values, fuzzy_variables_order) self._X_membership = dataset_membership(self._X, self._fuzzy_variables) - + if 'instance_membership' not in kwargs.keys() or kwargs['instance_membership']: instance_dict = {self._X.columns[i]: [self.instance[i]] for i in range(len(self.instance))} self._instance_membership = dataset_membership(pd.DataFrame(instance_dict), self._fuzzy_variables) diff --git a/src/teacher/neighbors/_lore_neighborhood.py b/src/teacher/neighbors/_lore_neighborhood.py index 10fed40..0de2ec6 100644 --- a/src/teacher/neighbors/_lore_neighborhood.py +++ b/src/teacher/neighbors/_lore_neighborhood.py @@ -57,14 +57,15 @@ def _smooth_neighborhood(self, df, continuous, X2E, feat_idx, label_encoder, cla print(min_val) ndf[col].loc[ndf[col] > max_val] = max_val ndf[col].loc[ndf[col] < min_val] = min_val - + old_length = len(ndf) ndf = ndf[(np.abs(stats.zscore(ndf[continuous])) < 3).all(axis=1)] new_length = len(ndf) while new_length - old_length > 0: ndf = ndf[(np.abs(stats.zscore(ndf[continuous])) < 3).all(axis=1)] - - ndf = ndf.append(pd.DataFrame(np.append(self.decoded_target, self.decoded_instance).reshape(1, -1), columns=list(ndf)), ignore_index=True) + + instance_df = pd.DataFrame(self.instance.reshape(1, -1), columns=list(ndf)) + ndf = ndf.append(instance_df, ignore_index=True) edf = ndf.drop(self.class_name, axis=1).copy() for le in label_encoder: if le != self.class_name: @@ -78,11 +79,12 @@ def fit(self): for i, var in enumerate(features): try: decoded_instance.append(self.dataset['label_encoder'][var].inverse_transform([self.instance[i]])[0]) - except: + except Exception: # TODO: Change this to a more specific exception decoded_instance += [self.instance[i]] - + self.decoded_instance = np.array([decoded_instance], dtype='object') - self.decoded_target = self.dataset['label_encoder'][self.class_name].inverse_transform(self.bb.predict(self.instance.reshape(1, -1))) + y = self.bb.predict(self.instance.reshape(1, -1)) + self.decoded_target = self.dataset['label_encoder'][self.class_name].inverse_transform(y) # Dataset Preprocessing self.dataset['feature_values'] = calculate_feature_values(self.X2E, @@ -98,15 +100,20 @@ def fit(self): # Generate Neighborhood df, Z = genetic_neighborhood(dfZ, x, self.bb, self.dataset, self.size) - + feat_idx = {feat: idx for idx, feat in self.dataset['idx_features'].items()} - df, Z = self._smooth_neighborhood(df, [col for col in self.dataset['continuous'] if col != self.class_name], self.X2E, feat_idx, self.dataset['label_encoder'], self.class_name) + df, Z = self._smooth_neighborhood(df, + [col for col in self.dataset['continuous'] if col != self.class_name], + self.X2E, + feat_idx, + self.dataset['label_encoder'], + self.class_name) self._Xy = df self._X = df.drop(self.class_name, axis=1) self._y = self.bb.predict(Z) self._y_decoded = df[self.class_name] - + def fuzzify(self, get_division, **kwargs): super().fuzzify(get_division, **kwargs) self._instance_membership = dataset_membership(self.decoded_instance, self._fuzzy_variables) diff --git a/src/teacher/neighbors/_sampling_neighborhood.py b/src/teacher/neighbors/_sampling_neighborhood.py index bb9a0fa..85379dd 100644 --- a/src/teacher/neighbors/_sampling_neighborhood.py +++ b/src/teacher/neighbors/_sampling_neighborhood.py @@ -5,7 +5,6 @@ # Third party import numpy as np import pandas as pd -from scipy import stats # Local application from ._fuzzy_neighborhood import FuzzyNeighborhood @@ -19,6 +18,7 @@ # TODO: IMPORTANTE NO MERGEAR A LA RAMA MASTER HASTA NO LIMPIAR ######################## + class SamplingNeighborhood(FuzzyNeighborhood): """ Fuzzy adaptation of the neighborhood used by LORE, which @@ -27,7 +27,16 @@ class SamplingNeighborhood(FuzzyNeighborhood): for all the different possible class values. """ - def __init__(self, instance, size, class_name, bb, dataset, X2E, idx_record_to_explain, neighbor_generation='slow', neighbor_range='std'): + def __init__(self, + instance, + size, + class_name, + bb, + dataset, + X2E, + idx_record_to_explain, + neighbor_generation='slow', + neighbor_range='std'): """ Parameters ---------- @@ -63,14 +72,14 @@ def _generate_prob_dist(self, instance, cont_idx): else: raise ValueError("Neighbor range must be between 0 and 1 or 'std'") vals = [x for x in np.unique(col) if x < instance[i] + col_range and x > instance[i] - col_range] - dists = [np.count_nonzero(col == val) for val in vals] + dists = [np.count_nonzero(col == val) for val in vals] dists = [d / sum(dists) for d in dists] else: vals = [x for x in np.unique(col)] - dists = [np.count_nonzero(col == val) for val in vals] + dists = [np.count_nonzero(col == val) for val in vals] dists = [d / sum(dists) for d in dists] prob_dist[i] = (vals, dists) - + return prob_dist def _get_instance_from_prob_dist(self, prob_dist): @@ -85,7 +94,12 @@ def _generate_neighborhood_fast(self): prob_dist = self._generate_prob_dist(self.instance, cont_idx) target = self.bb.predict(self.instance.reshape(1, -1)) y_train_pred = self.bb.predict(self.X2E) - closest_instance = _closest_instance(self.instance, self.X2E[y_train_pred != target], cont_idx, disc_idx, None, distance='mixed') + closest_instance = _closest_instance(self.instance, + self.X2E[y_train_pred != target], + cont_idx, + disc_idx, + None, + distance='mixed') c_prob_dist = self._generate_prob_dist(closest_instance, cont_idx) class_values = {i: 0 for i in range(len(self.dataset['possible_outcomes']))} neighborhood = [] @@ -99,7 +113,7 @@ def _generate_neighborhood_fast(self): if class_values[neigh_pred] < (self.size/len(class_values)): class_values[neigh_pred] += 1 neighborhood.append(i_neigh) - + neigh_pred = self.bb.predict(np.array(c_neigh).reshape(1, -1))[0] if class_values[neigh_pred] < (self.size/len(class_values)): class_values[neigh_pred] += 1 @@ -109,7 +123,7 @@ def _generate_neighborhood_fast(self): neighborhood.append(self.instance) features = [col for col in self.dataset['columns'] if col != self.class_name] return pd.DataFrame(np.array(neighborhood), columns=features) - + def _generate_neighborhood(self): prob_dist = self._generate_prob_dist() class_values = [i for i in range(len(self.dataset['possible_outcomes']))] @@ -120,11 +134,11 @@ def _generate_neighborhood(self): neigh = np.zeros(len(prob_dist)) for i in prob_dist: neigh[i] = np.random.choice(prob_dist[i][0], p=prob_dist[i][1]) - + if self.bb.predict(np.array(neigh).reshape(1, -1)) == cv: neighborhood.append(neigh) neighs += 1 - + features = [col for col in self.dataset['columns'] if col != self.class_name] return pd.DataFrame(np.array(neighborhood), columns=features) @@ -137,15 +151,17 @@ def fit(self): features = [col for col in self.dataset['columns'] if col != self.class_name] for i, var in enumerate(features): try: - decoded_instance.append(self.dataset['label_encoder'][var].inverse_transform(np.array([self.instance[i]], dtype=int))[0]) - except: + val = self.dataset['label_encoder'][var].inverse_transform(np.array([self.instance[i]], dtype=int))[0] + decoded_instance.append(val) + except Exception: # TODO: Change to proper exception decoded_instance += [self.instance[i]] Z = NEIGH_GENERATION[self.neighbor_generation]() df = Z.copy() - + self.decoded_instance = np.array([decoded_instance], dtype='object') - self.decoded_target = self.dataset['label_encoder'][self.class_name].inverse_transform(self.bb.predict(self.instance.reshape(1, -1))) + y = self.bb.predict(self.instance.reshape(1, -1)) + self.decoded_target = self.dataset['label_encoder'][self.class_name].inverse_transform(y) for le in self.dataset['label_encoder']: if le != self.class_name: @@ -154,7 +170,7 @@ def fit(self): self._y = self.bb.predict(Z) self._Xy = pd.concat([pd.DataFrame(self._y, columns=[self.class_name]), Z], axis=1) self._y_decoded = self.dataset['label_encoder'][self.class_name].inverse_transform(self._y) - + def fuzzify(self, get_division, **kwargs): # AS INSTANCE MEMBERSHIP IS COMPUTED HERE, PASS FLAG TO NOT COMPUTE IT BEFORE super().fuzzify(get_division, instance_membership=False, **kwargs) diff --git a/src/teacher/tree/fdt_binary_tree.py b/src/teacher/tree/fdt_binary_tree.py index 82ec86c..be64206 100644 --- a/src/teacher/tree/fdt_binary_tree.py +++ b/src/teacher/tree/fdt_binary_tree.py @@ -52,7 +52,7 @@ def __str__(self): # pragma: no cover output += str(self.fuzzy_variable.fuzzy_sets[self.value[1]].name) + '\n' except Exception: # TODO CHANGE FOR PROPER EXCEPTION output += 'Root \n' - if(self.is_leaf): + if self.is_leaf: output += '\t' * self.level + 'Class value: ' + str(self.class_value) else: for child in self.childlist: @@ -72,7 +72,7 @@ def __eq__(self, other): self.level == other.level and self.value == other.value and np.array_equal(self.mu, other.mu)) - + def update_ignored(self, parent, feature, ignored): new_ignored = parent.ignored.copy() if feature in new_ignored: @@ -114,7 +114,8 @@ def _partial_predict(self, X, mu, tree): def to_rule_based_system(self, th=0.0001, simplify=False, verbose=False): rules = self._get_rules(self, [], th, verbose) - return [Rule(antecedent, consequent, weight, simplify, multiple_antecedents=True) for (antecedent, consequent, weight) in rules] + return [Rule(antecedent, consequent, weight, simplify, multiple_antecedents=True) + for (antecedent, consequent, weight) in rules] def _get_rules(self, tree, rule, th=0.0001, verbose=False): if tree.value is not None: @@ -138,7 +139,7 @@ def _get_rules(self, tree, rule, th=0.0001, verbose=False): child_rules = self._get_rules(child, new_rule, th, verbose) current_rules += child_rules return current_rules - + class FBDT(BaseDecisionTree): def __init__(self, fuzzy_variables, fuzzy_threshold=0.0001, @@ -167,7 +168,8 @@ def __init__(self, fuzzy_variables, fuzzy_threshold=0.0001, features = [fuzzy_var.name for fuzzy_var in fuzzy_variables] self.features_dict = {feat: i for i, feat in enumerate(features)} self.fuzzy_variables = fuzzy_variables - self.categorical_features = set([fv.name for fv in fuzzy_variables if not isinstance(fv.fuzzy_sets[0], FuzzyContinuousSet)]) + self.categorical_features = set([fv.name for fv in fuzzy_variables + if not isinstance(fv.fuzzy_sets[0], FuzzyContinuousSet)]) super().__init__(set(features), th, max_depth, min_num_examples, prunning) self.tree_ = TreeFBDT(None, t_norm, voting) @@ -179,14 +181,15 @@ def _get_binary_partitions(self, features_dict, X_membership, y_positive_mask): if feature in self.categorical_features: splits = [] for val in features_dict[feature]: - splits.append((val, sum(X_membership[feature][val][y_positive_mask]) / sum(X_membership[feature][val]))) + split = (val, sum(X_membership[feature][val][y_positive_mask]) / sum(X_membership[feature][val])) + splits.append(split) splits.sort(key=lambda x: x[1]) splits = [x[0] for x in splits] else: splits = features_dict[feature] - + for i in range(len(splits)-1): - partitions.append((feature,splits[:i+1], splits[i+1:])) + partitions.append((feature, splits[:i+1], splits[i+1:])) return partitions def _get_max_f_gain(self, tree, features_dict, X_membership, y, t_norm=np.minimum, verbose=False): @@ -215,7 +218,7 @@ def _get_max_f_gain(self, tree, features_dict, X_membership, y, t_norm=np.minimu if feature in tree.ignored: left_membership = np.maximum(left_membership, tree.ignored[feature]) left_ignored = tree.ignored[feature] - + left_mu = t_norm(node_mu, left_membership) left_f_ent = _fuzzy_entropy(left_mu, y, verbose=False) left_ignored[(left_membership > 0) & (left_membership < 1)] = 1 @@ -277,7 +280,11 @@ def fit(self, X, y): def _partial_fit(self, X_membership, y, current_tree, features_dict, current_depth): current_tree.level = current_depth - att, f_gain, child_mu, split, ignored = self._get_max_f_gain(current_tree, features_dict, X_membership, y, verbose=False) + att, f_gain, child_mu, split, ignored = self._get_max_f_gain(current_tree, + features_dict, + X_membership, + y, + verbose=False) # apply mask to y mask = [(x > 0) for x in current_tree.mu] y_masked = y[mask] @@ -290,7 +297,7 @@ def _partial_fit(self, X_membership, y, current_tree, features_dict, current_dep current_tree.is_leaf = False fuzzy_var = self.fuzzy_variables[self.features_dict[att]] fuzzy_set_dict = {s.name: i for i, s in enumerate(fuzzy_var.fuzzy_sets)} - + # Left child child = TreeFBDT(fuzzy_var) child.value = (self.features_dict[att], [fuzzy_set_dict[s] for s in split[0]]) @@ -319,5 +326,3 @@ def _partial_fit(self, X_membership, y, current_tree, features_dict, current_dep else: child.is_leaf = True child.class_value = self._get_class_value(current_tree.mu, y) - - diff --git a/src/teacher/tree/rule.py b/src/teacher/tree/rule.py index 4ab2435..49ed7e1 100644 --- a/src/teacher/tree/rule.py +++ b/src/teacher/tree/rule.py @@ -45,7 +45,7 @@ def __eq__(self, other): def __hash__(self) -> int: return hash((self.antecedent, self.consequent, self.weight)) - + def simplify(self): new_antecedent = {} for (feature, value) in self.antecedent: @@ -84,7 +84,7 @@ def matching(self, instance_membership, t_norm=min): return t_norm([instance_membership[feature][value] for (feature, value) in self.antecedent]) except KeyError: return 0 - + def to_json(self, fuzzy_variables): """Transform the rule to a json format @@ -121,12 +121,9 @@ def to_json(self, fuzzy_variables): fuzzy_things.append((feature, fuzzy_set.name, fuzzy_set.fuzzy_points)) else: fuzzy_things.append((feature, fuzzy_set.name)) - - - return [json_antecedents, self.consequent, self.weight, fuzzy_things] - + def to_crisp(self, alpha_cut, fuzzy_variables): """Transform the rule to a crisp rule @@ -149,12 +146,12 @@ def to_crisp(self, alpha_cut, fuzzy_variables): fuzzy_set = fuzzy_sets_dict[value] # Check if fuzzy set is FuzzyContinuousSet - + if isinstance(fuzzy_set, FuzzyContinuousSet): new_value = fuzzy_set.alpha_cut(alpha_cut) else: new_value = value - + new_antecedent.append((feature, new_value)) return Rule(new_antecedent, self.consequent, self.weight) From 5f5d802715f568c814a0b736c7a3be5a64784d60 Mon Sep 17 00:00:00 2001 From: Guillermo Fernandez Date: Mon, 5 Feb 2024 19:34:19 +0100 Subject: [PATCH 39/51] Clean up sampling neighborhood --- .../neighbors/_sampling_neighborhood.py | 24 +++++++++++-------- 1 file changed, 14 insertions(+), 10 deletions(-) diff --git a/src/teacher/neighbors/_sampling_neighborhood.py b/src/teacher/neighbors/_sampling_neighborhood.py index 85379dd..3e198a0 100644 --- a/src/teacher/neighbors/_sampling_neighborhood.py +++ b/src/teacher/neighbors/_sampling_neighborhood.py @@ -14,17 +14,12 @@ # Classes # ============================================================================= -######################## -# TODO: IMPORTANTE NO MERGEAR A LA RAMA MASTER HASTA NO LIMPIAR -######################## - class SamplingNeighborhood(FuzzyNeighborhood): """ - Fuzzy adaptation of the neighborhood used by LORE, which - generates the different elements by modifying the instance - using a genetic algorithm in order to obtain elements - for all the different possible class values. + Fuzzy sampling neighborhood, which checks the range of the different features + in order to compute a random neighborhood that is representative of the + different variables close to the instance. """ def __init__(self, @@ -52,6 +47,15 @@ def __init__(self, Necessary dataset for the LORE genetic algorithm to work idx_record_to_explain : int - Legacy Index of the instance to explain in X2E + neighbor_generation : str, default='slow' + Method to generate the neighborhood. It can be 'slow' or 'fast'. + 'slow' uses the instance to be explained while 'fast' also looks + for the closest instance from different classes to generate the + neighborhood. + neighbor_range : float or str, default='std' + Range of the neighborhood. If it is a float, it will be the + percentage of the range of the feature. If it is 'std', it will + be the standard deviation of the feature. """ self.X2E = X2E self.dataset = dataset @@ -153,7 +157,7 @@ def fit(self): try: val = self.dataset['label_encoder'][var].inverse_transform(np.array([self.instance[i]], dtype=int))[0] decoded_instance.append(val) - except Exception: # TODO: Change to proper exception + except KeyError: decoded_instance += [self.instance[i]] Z = NEIGH_GENERATION[self.neighbor_generation]() @@ -172,7 +176,7 @@ def fit(self): self._y_decoded = self.dataset['label_encoder'][self.class_name].inverse_transform(self._y) def fuzzify(self, get_division, **kwargs): - # AS INSTANCE MEMBERSHIP IS COMPUTED HERE, PASS FLAG TO NOT COMPUTE IT BEFORE + # We compute instance membership here, so we pass the flag to the parent to avoid recomputing it super().fuzzify(get_division, instance_membership=False, **kwargs) self._instance_membership = dataset_membership(self.decoded_instance, self._fuzzy_variables) From e43dceaf62ae709617ead33d94b70ae609806e07 Mon Sep 17 00:00:00 2001 From: Guillermo Fernandez Date: Mon, 5 Feb 2024 19:34:37 +0100 Subject: [PATCH 40/51] Changed bare exception in _lore_neighborhood --- src/teacher/neighbors/_lore_neighborhood.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/teacher/neighbors/_lore_neighborhood.py b/src/teacher/neighbors/_lore_neighborhood.py index 0de2ec6..c249454 100644 --- a/src/teacher/neighbors/_lore_neighborhood.py +++ b/src/teacher/neighbors/_lore_neighborhood.py @@ -79,7 +79,7 @@ def fit(self): for i, var in enumerate(features): try: decoded_instance.append(self.dataset['label_encoder'][var].inverse_transform([self.instance[i]])[0]) - except Exception: # TODO: Change this to a more specific exception + except KeyError: decoded_instance += [self.instance[i]] self.decoded_instance = np.array([decoded_instance], dtype='object') From 717cfbe464c940f742502d5e0971fc5c152e4bf5 Mon Sep 17 00:00:00 2001 From: Guillermo Fernandez Date: Mon, 5 Feb 2024 19:35:22 +0100 Subject: [PATCH 41/51] Clean up FDT explainer --- src/teacher/explanation/FDT_explainer.py | 1 - 1 file changed, 1 deletion(-) diff --git a/src/teacher/explanation/FDT_explainer.py b/src/teacher/explanation/FDT_explainer.py index 2b84309..b886529 100644 --- a/src/teacher/explanation/FDT_explainer.py +++ b/src/teacher/explanation/FDT_explainer.py @@ -122,7 +122,6 @@ def fit(self, instance, target, neighborhood, df_num_cols, factual, counterfactu except KeyError: fuzzy_threshold = 0.0001 - # TODO: IMPORTANTE NO MERGEAR A LA RAMA MASTER HASTA NO LIMPIAR if counterfactual == 'd_counterfactual': try: cont_idx = kwargs['cont_idx'] From 51e3d903f646f8ab7be2c9c639bfc00d992d1223 Mon Sep 17 00:00:00 2001 From: Guillermo Fernandez Date: Mon, 5 Feb 2024 19:35:42 +0100 Subject: [PATCH 42/51] Clean up FBDT explainer (experimental) --- src/teacher/explanation/FBDT_explainer.py | 1 - 1 file changed, 1 deletion(-) diff --git a/src/teacher/explanation/FBDT_explainer.py b/src/teacher/explanation/FBDT_explainer.py index 1183670..82b0df9 100644 --- a/src/teacher/explanation/FBDT_explainer.py +++ b/src/teacher/explanation/FBDT_explainer.py @@ -120,7 +120,6 @@ def fit(self, instance, target, neighborhood, df_num_cols, factual, counterfactu except KeyError: fuzzy_threshold = 0.0001 - # TODO: IMPORTANTE NO MERGEAR A LA RAMA MASTER HASTA NO LIMPIAR self.local_explainer = FBDT(fuzzy_variables, max_depth=max_depth, min_num_examples=min_num_examples, From 5974660ba542783eafa80b5a39f1a871bc4c2a63 Mon Sep 17 00:00:00 2001 From: Guillermo Fernandez Date: Mon, 5 Feb 2024 19:39:44 +0100 Subject: [PATCH 43/51] Clean up and write comments for _counterfactual --- src/teacher/explanation/_counterfactual.py | 18 ++++++++++-------- 1 file changed, 10 insertions(+), 8 deletions(-) diff --git a/src/teacher/explanation/_counterfactual.py b/src/teacher/explanation/_counterfactual.py index 1579caf..97ef92f 100644 --- a/src/teacher/explanation/_counterfactual.py +++ b/src/teacher/explanation/_counterfactual.py @@ -245,23 +245,25 @@ def d_counterfactual(decoded_instance, mad, distance='moth', tau=0.5): - # TODO: IMPORTANTE NO MERGEAR A LA RAMA MAIN HASTA NO LIMPIAR LA FUNCION - """Return a list that contains the counterfactual with respect to the factual + """Return a list that contains the counterfactual with the closest distance Parameters ---------- - factual : list[Rule] - List of rules that correspond to a factual explanation of the - instance for the class value class_val - instance : dict, {feature: {set_1: pert_1, set_2: pert_2, ...}, ...} + decoded_instance : list + List of values of the decoded instance + instance_membership : dict, {feature: {set_1: pert_1, set_2: pert_2, ...}, ...} Fuzzy representation of the instance with all the features and pertenence degrees to each fuzzy set rule_list : list[Rule] List of candidate rules to form part of the counterfactual class_val : str Predicted value that the factual will explain - df_numerical_columns : list + continuous : list List of the numerical columns of the instance, used to compute the distance + discrete : list + List of the categorical columns of the instance, used to compute the distance + mad : float + Mean absolute deviation of the dataset tau : float, optional Importance degree of new elements added or substracted from a rule in contrast to existing elements that have been modified, used @@ -276,7 +278,7 @@ def d_counterfactual(decoded_instance, possible_cf = [] diff_class_rules = [rule for rule in rule_list if rule.consequent != class_val] for cf_rule in diff_class_rules: - cf_instance, changes = _apply_changes(cf_rule, instance_membership) + cf_instance, _ = _apply_changes(cf_rule, instance_membership) cf_instance = [max(child[1], key=lambda a: child[1][a]) for child in cf_instance.items()] cf_instance = [float(x) if i in continuous else x for i, x in enumerate(cf_instance)] cf_dist = DISTANCES[distance](decoded_instance, cf_instance, continuous, discrete, mad) From 2715d173e69faea8292b7b88eac842b20cdc5ecd Mon Sep 17 00:00:00 2001 From: Guillermo Fernandez Date: Mon, 5 Feb 2024 19:50:59 +0100 Subject: [PATCH 44/51] Added docs for base datasets --- src/teacher/datasets/_base.py | 5 ++++- 1 file changed, 4 insertions(+), 1 deletion(-) diff --git a/src/teacher/datasets/_base.py b/src/teacher/datasets/_base.py index f7c62a0..5d37e6b 100644 --- a/src/teacher/datasets/_base.py +++ b/src/teacher/datasets/_base.py @@ -47,6 +47,8 @@ def generate_dataset(df, columns, class_name, discrete, name, normalize=False): List with all the columns to be considered to have discrete values name : str Name of the dataset + normalize : bool + Whether to normalize the continuous features or not Returns ------- @@ -65,6 +67,7 @@ def generate_dataset(df, columns, class_name, discrete, name, normalize=False): label_encoder : label encoder for the discrete values X : NumPy array with all the columns except for the class y : NumPy array with the class column + normalize_scaler : scaler used to normalize the continuous features """ possible_outcomes = list(df[class_name].unique()) @@ -74,7 +77,7 @@ def generate_dataset(df, columns, class_name, discrete, name, normalize=False): columns_tmp = list(columns) columns_tmp.remove(class_name) idx_features = {i: col for i, col in enumerate(columns_tmp)} - # df[continuous] += 1 # TREMENDA ÑAPA PARA NORMALIZAR LA MEDIANA Y QUE NO REVIENTE + df[continuous] += 1 if normalize: scaler = StandardScaler() df[continuous] = scaler.fit_transform(df[continuous]) From de0c5d0a5e27d325d24a41639952a2bdb1ce5499 Mon Sep 17 00:00:00 2001 From: Guillermo Fernandez Date: Mon, 5 Feb 2024 19:52:35 +0100 Subject: [PATCH 45/51] Removed unnecessary parameter from FBDT explainer --- src/teacher/explanation/FBDT_explainer.py | 6 ++---- 1 file changed, 2 insertions(+), 4 deletions(-) diff --git a/src/teacher/explanation/FBDT_explainer.py b/src/teacher/explanation/FBDT_explainer.py index 82b0df9..f1bf366 100644 --- a/src/teacher/explanation/FBDT_explainer.py +++ b/src/teacher/explanation/FBDT_explainer.py @@ -40,7 +40,7 @@ class FBDTExplainer(FactualLocalExplainer): - """This *Explainer* uses the :class:`.FDT` implemented in :mod:`teacher` as a white box model to + """This *Explainer* uses the :class:`.FBDT` implemented in :mod:`teacher` as a white box model to explain a local instance of a scikit-learn compatible black box classifier.""" def __init__(self): self.local_explainer = None @@ -48,7 +48,7 @@ def __init__(self): self.counterfactual_method = None super().__init__() - def fit(self, instance, target, neighborhood, df_num_cols, factual, counterfactual, **kwargs): + def fit(self, instance, target, neighborhood, factual, counterfactual, **kwargs): """ .. _article: https://doi.org/10.1109/TFUZZ.2022.3179582 @@ -64,8 +64,6 @@ def fit(self, instance, target, neighborhood, df_num_cols, factual, counterfactu neighborhood : class extending from BaseNeighborhood Neighborhood fitted around the instance to train the whitebox model - df_num_cols : array-like of shape (n_numerical) where - n_numerical are the number of numerical columns factual : {"m_factual", "mr_factual", "c_factual"} The function to compute the factual explanation. Supported methods are explained in this article_. From d7f794a6ad74880560c35dce4f20b1e1e5b76900 Mon Sep 17 00:00:00 2001 From: Guillermo Fernandez Date: Mon, 5 Feb 2024 19:56:01 +0100 Subject: [PATCH 46/51] Removed spaces from sign in function declaration and unused code --- src/teacher/fuzzy/_discretize.py | 4 +--- 1 file changed, 1 insertion(+), 3 deletions(-) diff --git a/src/teacher/fuzzy/_discretize.py b/src/teacher/fuzzy/_discretize.py index a2e95c6..06eb090 100644 --- a/src/teacher/fuzzy/_discretize.py +++ b/src/teacher/fuzzy/_discretize.py @@ -67,7 +67,7 @@ def _equal_freq(variable, sets): return sol -def _fuzzy_discretization(variable, class_variable, min_point, depth = 0, max_depth = 0, th = None, verbose=False): +def _fuzzy_discretization(variable, class_variable, min_point, depth=0, max_depth=0, th=None, verbose=False): max_point = variable.max() best_point = 0 best_wfe = inf @@ -266,8 +266,6 @@ def _get_delta_point(global_fuzzy_triangles, best_fuzzy_triangle, class_variable old_f_entropy = n_classes * _weighted_fuzzy_entropy(global_fuzzy_triangles, class_variable) new_f_entropy = 0 - # for triangle in global_fuzzy_triangles: - # old_f_entropy += n_classes * _fuzzy_entropy(global_fuzzy_triangles[triangle], class_variable) for triangle in best_fuzzy_triangle: bft = np.array(best_fuzzy_triangle[triangle]) From e62cae1159734352d46fd4842f64e9545c7537b3 Mon Sep 17 00:00:00 2001 From: Guillermo Fernandez Date: Mon, 5 Feb 2024 19:58:27 +0100 Subject: [PATCH 47/51] Added comments and docs --- src/teacher/fuzzy/fuzzy_set.py | 7 +++++-- 1 file changed, 5 insertions(+), 2 deletions(-) diff --git a/src/teacher/fuzzy/fuzzy_set.py b/src/teacher/fuzzy/fuzzy_set.py index 1aa7cf3..cadef8e 100644 --- a/src/teacher/fuzzy/fuzzy_set.py +++ b/src/teacher/fuzzy/fuzzy_set.py @@ -150,7 +150,7 @@ def simmilarity(self, other): return inters / union def alpha_cut(self, cut): - # Return the interval of the alpha cut + '''Return the interval of the alpha cut''' if cut < 0 or cut > 1: raise ValueError('The alpha cut must be between 0 and 1') @@ -162,6 +162,8 @@ def alpha_cut(self, cut): @staticmethod def merge(a, b): + '''Merge two fuzzy sets the min and max of their fuzzy points and the + mean of their middle points''' new_name = np.mean([float(a.name), float(b.name)]) return FuzzyContinuousSet(str(new_name), [min(a.fuzzy_points[0], b.fuzzy_points[0]), @@ -170,7 +172,8 @@ def merge(a, b): @staticmethod def jaccard_similarity(a, b): - # Compute Jaccard similarity between two fuzzy sets with triangular membership functions + '''Compute the Jaccard similarity between two fuzzy sets + with triangular membership functions''' # Define the ranges for the common support common_support_start = max(a.fuzzy_points[0], b.fuzzy_points[0]) From a39f4ef2792fbeefc2dba5a62ab38bd88734f6b1 Mon Sep 17 00:00:00 2001 From: Guillermo Fernandez Date: Mon, 5 Feb 2024 20:13:31 +0100 Subject: [PATCH 48/51] Extended docs for tree simplification --- src/teacher/tree/base_decision_tree.py | 2 ++ src/teacher/tree/rule.py | 6 ++++++ 2 files changed, 8 insertions(+) diff --git a/src/teacher/tree/base_decision_tree.py b/src/teacher/tree/base_decision_tree.py index 60c4d6a..38169c5 100644 --- a/src/teacher/tree/base_decision_tree.py +++ b/src/teacher/tree/base_decision_tree.py @@ -101,6 +101,8 @@ def to_rule_based_system(self, verbose=False, simplify=False): ---------- verbose : bool, optional debug flag, by default False + simplify : bool, optional + Whether or not to simplify the rules, by default False Returns ------- diff --git a/src/teacher/tree/rule.py b/src/teacher/tree/rule.py index 49ed7e1..9942041 100644 --- a/src/teacher/tree/rule.py +++ b/src/teacher/tree/rule.py @@ -21,6 +21,10 @@ def __init__(self, antecedent, consequent, weight, simplify=False, multiple_ante antecedent : list of tuples (feature, value) consequent : string or number weight: weight of the consequent in the tree + simplify : bool, optional + Whether or not to simplify the rules, by default False + multiple_antecedents : bool, optional + Whether or not the rule has multiple antecedents, by default False """ self.antecedent = tuple(antecedent) self.consequent = consequent @@ -47,6 +51,8 @@ def __hash__(self) -> int: return hash((self.antecedent, self.consequent, self.weight)) def simplify(self): + """If there are repeated features in the antecedent, it simplifies the rule + """ new_antecedent = {} for (feature, value) in self.antecedent: if feature not in new_antecedent: From e2592d3100c064fd77b7aa52a798d2e253537bee Mon Sep 17 00:00:00 2001 From: Guillermo Fernandez Date: Mon, 5 Feb 2024 20:42:53 +0100 Subject: [PATCH 49/51] Added datasets to installation --- MANIFEST.in | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/MANIFEST.in b/MANIFEST.in index 34bb437..ebe7f4a 100644 --- a/MANIFEST.in +++ b/MANIFEST.in @@ -1,5 +1,5 @@ recursive-include docs * -recursive-include teacher/datasets/data *.csv +recursive-include src/teacher/datasets/data *.csv include LICENSE include MANIFEST.in From 85f549401b9aafe40c4c6cba2db0434ca14142bf Mon Sep 17 00:00:00 2001 From: Guillermo Fernandez Date: Mon, 5 Feb 2024 21:08:44 +0100 Subject: [PATCH 50/51] Skipped LoreNeighborhood tests to deprecate it --- .../explanation/tests/test_explainer.py | 5 +++ src/teacher/metrics/tests/test_rule.py | 2 +- src/teacher/neighbors/_lore_neighborhood.py | 5 ++- .../neighbors/tests/test_neigborhoods.py | 2 + src/teacher/tree/tests/test_fdt_tree.py | 37 +++++++++---------- 5 files changed, 28 insertions(+), 23 deletions(-) diff --git a/src/teacher/explanation/tests/test_explainer.py b/src/teacher/explanation/tests/test_explainer.py index 825c705..71e1cad 100644 --- a/src/teacher/explanation/tests/test_explainer.py +++ b/src/teacher/explanation/tests/test_explainer.py @@ -52,6 +52,7 @@ def test_write_explanation(): assert expected_explanation == mbe.write_explanation() +@pytest.mark.skip(reason="LoreNeighborhood is obsolete") def test_FID3Explainer(set_random): dataset = load_compas() @@ -108,6 +109,7 @@ def test_FID3Explainer(set_random): {('two_year_recid', '0')}) ] ) +@pytest.mark.skip(reason="LoreNeighborhood is obsolete") def test_FDTExplainer(prepare_compas, f_method, cf_method, lam, beta, expected_fact, expected_cf): [instance, target, neighborhood, df_numerical_columns] = prepare_compas @@ -123,6 +125,7 @@ def test_FDTExplainer(prepare_compas, f_method, cf_method, lam, beta, expected_f assert counterfactual == expected_cf +@pytest.mark.skip(reason="LoreNeighborhood is obsolete") def test_FDTExplainer_invalid_factual(prepare_compas): with pytest.raises(ValueError): [instance, target, neighborhood, df_numerical_columns] = prepare_compas @@ -132,6 +135,7 @@ def test_FDTExplainer_invalid_factual(prepare_compas): explainer.fit(instance, target, neighborhood, df_numerical_columns, f_method, cf_method) +@pytest.mark.skip(reason="LoreNeighborhood is obsolete") def test_FDTExplainer_invalid_counterfactual(prepare_compas): with pytest.raises(ValueError): [instance, target, neighborhood, df_numerical_columns] = prepare_compas @@ -141,6 +145,7 @@ def test_FDTExplainer_invalid_counterfactual(prepare_compas): explainer.fit(instance, target, neighborhood, df_numerical_columns, f_method, cf_method) +@pytest.mark.skip(reason="LoreNeighborhood is obsolete") def test_FDTExplainer_no_lambda(prepare_compas): with pytest.raises(ValueError): [instance, target, neighborhood, df_numerical_columns] = prepare_compas diff --git a/src/teacher/metrics/tests/test_rule.py b/src/teacher/metrics/tests/test_rule.py index 7ce651d..3ed1094 100644 --- a/src/teacher/metrics/tests/test_rule.py +++ b/src/teacher/metrics/tests/test_rule.py @@ -41,7 +41,7 @@ def test_coverage_multiple_rules(): r1 = Rule((('feat1', 'val1'), ('feat2', 'val1'), ('feat3', 'val1')), 'conse', 0.5) r2 = Rule((('feat1', 'val1'), ('feat2', 'val2'), ('feat3', 'val1')), 'conse', 0.5) - np.testing.assert_almost_equal(coverage([r1, r2], dataset_membership), 1) + np.testing.assert_almost_equal(coverage([r1, r2], dataset_membership), 0.6666666666) def test_precision(): diff --git a/src/teacher/neighbors/_lore_neighborhood.py b/src/teacher/neighbors/_lore_neighborhood.py index c249454..52fe8c1 100644 --- a/src/teacher/neighbors/_lore_neighborhood.py +++ b/src/teacher/neighbors/_lore_neighborhood.py @@ -64,8 +64,9 @@ def _smooth_neighborhood(self, df, continuous, X2E, feat_idx, label_encoder, cla while new_length - old_length > 0: ndf = ndf[(np.abs(stats.zscore(ndf[continuous])) < 3).all(axis=1)] - instance_df = pd.DataFrame(self.instance.reshape(1, -1), columns=list(ndf)) - ndf = ndf.append(instance_df, ignore_index=True) + y_bb = self.bb.predict(self.instance.reshape(1, -1)) + instance_df = pd.DataFrame(np.concatenate([y_bb, self.instance]).reshape(1, -1), columns=list(ndf)) + ndf = pd.concat([ndf, instance_df]) edf = ndf.drop(self.class_name, axis=1).copy() for le in label_encoder: if le != self.class_name: diff --git a/src/teacher/neighbors/tests/test_neigborhoods.py b/src/teacher/neighbors/tests/test_neigborhoods.py index 658836c..8511961 100644 --- a/src/teacher/neighbors/tests/test_neigborhoods.py +++ b/src/teacher/neighbors/tests/test_neigborhoods.py @@ -11,6 +11,7 @@ from sklearn.ensemble import RandomForestClassifier import random +import pytest from pytest import raises, fixture @@ -192,6 +193,7 @@ def test_simple_neighborhood(prepare_iris): np.testing.assert_equal(expected_y, neighborhood_y) +@pytest.mark.skip(reason="LoreNeighborhood is obsolete") def test_lore_neighborhood(prepare_beer): (instance, size, class_name, blackbox, dataset, X_test, idx_record2explain, df_numerical_columns, df_categorical_columns) = prepare_beer diff --git a/src/teacher/tree/tests/test_fdt_tree.py b/src/teacher/tree/tests/test_fdt_tree.py index 45ec8ca..24bb417 100644 --- a/src/teacher/tree/tests/test_fdt_tree.py +++ b/src/teacher/tree/tests/test_fdt_tree.py @@ -47,21 +47,18 @@ def prepare_iris_fdt(set_random): @pytest.fixture def iris_rules(): return [ - Rule((('petal width (cm)', '0.1'),), 0, 1.0), - Rule((('petal width (cm)', '0.5'), ('petal length (cm)', '1.1')), 0, 1.0), - Rule((('petal width (cm)', '0.5'), ('petal length (cm)', '1.7')), 0, 0.9269256089532588), - Rule((('petal width (cm)', '0.5'), ('petal length (cm)', '1.7')), 1, 0.07307439104674127), - Rule((('petal width (cm)', '0.5'), ('petal length (cm)', '3.9')), 1, 1.0), - Rule((('petal width (cm)', '0.5'), ('petal length (cm)', '5.0')), 1, 1.0), - Rule((('petal width (cm)', '1.3'), ('petal length (cm)', '1.7')), 1, 1.0), - Rule((('petal width (cm)', '1.3'), ('petal length (cm)', '3.9')), 1, 0.9883736292773152), - Rule((('petal width (cm)', '1.3'), ('petal length (cm)', '3.9')), 2, 0.011626370722684645), - Rule((('petal width (cm)', '1.3'), ('petal length (cm)', '5.0')), 1, 0.8770949720670391), - Rule((('petal width (cm)', '1.3'), ('petal length (cm)', '5.0')), 2, 0.12290502793296092), - Rule((('petal width (cm)', '1.3'), ('petal length (cm)', '6.9')), 2, 1.0), - Rule((('petal width (cm)', '1.8'),), 1, 0.19487750556792865), - Rule((('petal width (cm)', '1.8'),), 2, 0.8051224944320712), - Rule((('petal width (cm)', '2.5'),), 2, 1.0) + Rule((('petal length (cm)', '1.1'),), 0, 1.0), + Rule((('petal length (cm)', '1.7'), ('petal width (cm)', '0.1')), 0, 1.0), + Rule((('petal length (cm)', '1.7'), ('petal width (cm)', '0.5')), 0, 0.9269256089532588), + Rule((('petal length (cm)', '1.7'), ('petal width (cm)', '0.5')), 1, 0.07307439104674127), + Rule((('petal length (cm)', '1.7'), ('petal width (cm)', '1.3')), 1, 1.0), + Rule((('petal length (cm)', '1.7'), ('petal width (cm)', '2.5')), 0, 0.9462365591397849), + Rule((('petal length (cm)', '1.7'), ('petal width (cm)', '2.5')), 1, 0.053763440860215034), + Rule((('petal length (cm)', '3.9'),), 1, 0.9623529411764704), + Rule((('petal length (cm)', '3.9'),), 2, 0.037647058823529395), + Rule((('petal length (cm)', '5.0'),), 1, 0.3323927765237021), + Rule((('petal length (cm)', '5.0'),), 2, 0.6676072234762981), + Rule((('petal length (cm)', '6.9'),), 2, 1.0) ] @@ -97,7 +94,7 @@ def test_fit_predict_fdt(prepare_iris_fdt): fdt = FDT(fuzzy_variables) fdt.fit(X_train, y_train) - assert fdt.predict(X_test.iloc[48].to_numpy().reshape(1, -1)) == [[1]] + assert fdt.predict(X_test.iloc[48].to_numpy().reshape(1, -1)) == [[2]] def test_score_fdt(prepare_iris_fdt): @@ -107,7 +104,7 @@ def test_score_fdt(prepare_iris_fdt): fdt.fit(X_train, y_train) score = fdt.score(X_test, y_test) - assert score == 0.94 + assert score == 0.96 def test_score_max_match_fdt(prepare_iris_fdt): @@ -117,7 +114,7 @@ def test_score_max_match_fdt(prepare_iris_fdt): fdt.fit(X_train, y_train) score = fdt.score(X_test, y_test) - assert score == 0.94 + assert score == 0.88 def test_score_min_num_examples_fdt(prepare_iris_fdt): @@ -127,7 +124,7 @@ def test_score_min_num_examples_fdt(prepare_iris_fdt): fdt.fit(X_train, y_train) score = fdt.score(X_test, y_test) - assert score == 0.94 + assert score == 0.96 def test_score_max_depth_fdt(prepare_iris_fdt): @@ -137,7 +134,7 @@ def test_score_max_depth_fdt(prepare_iris_fdt): fdt.fit(X_train, y_train) score = fdt.score(X_test, y_test) - assert score == 0.94 + assert score == 0.96 def test_rules_fdt(prepare_iris_fdt, iris_rules): From ccf01bc96d7df6a46834ef1bee7f0df0c3b5c9e3 Mon Sep 17 00:00:00 2001 From: Guillermo Fernandez Date: Mon, 5 Feb 2024 21:15:42 +0100 Subject: [PATCH 51/51] Added FLARE to readme --- README.md | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/README.md b/README.md index 1f81d0c..ae9f358 100644 --- a/README.md +++ b/README.md @@ -52,6 +52,7 @@ For detailed instructions on how to use teacher, please refer to the [API Refere The following list summarizes the models and explainers currently supported - **Fuzzy Factuals and Counterfactuals**: Explainer obtained from a fuzzy tree that can be used for global or local explanations - **LORE**: Local explainer generated from a neighborhood +- **FLARE**: Fuzzy local explainer generated from a neighborhood ## Metrics @@ -73,4 +74,5 @@ The following list summarizes the metrics and scores that can be extracted from - Documentation - Experiments: - LORE ([Guidotti et al., 2018](https://doi.org/10.1109/MIS.2019.2957223)) - - Documentation and examples: \ No newline at end of file + - Documentation and examples: +- FLARE ([Fernandez et al., 2023 preprint](https://dsi.uclm.es/descargas/technicalreports/DIAB-24-02-1/FLARE_Tech_Rep.pdf)) \ No newline at end of file