trust score imported
This commit is contained in:
Lorenzo Volpi
parent
2235fd35c0
commit
d6b1f6e796
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# Copyright 2018 Google LLC
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# https://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import numpy as np
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from sklearn.neighbors import KDTree, KNeighborsClassifier
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class TrustScore:
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"""
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Trust Score: a measure of classifier uncertainty based on nearest neighbors.
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"""
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def __init__(self, k=10, alpha=0.0, filtering="none", min_dist=1e-12):
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"""
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k and alpha are the tuning parameters for the filtering,
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filtering: method of filtering. option are "none", "density",
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"uncertainty"
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min_dist: some small number to mitigate possible division by 0.
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"""
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self.k = k
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self.filtering = filtering
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self.alpha = alpha
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self.min_dist = min_dist
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def filter_by_density(self, X: np.array):
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"""Filter out points with low kNN density.
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Args:
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X: an array of sample points.
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Returns:
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A subset of the array without points in the bottom alpha-fraction of
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original points of kNN density.
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"""
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kdtree = KDTree(X)
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knn_radii = kdtree.query(X, k=self.k)[0][:, -1]
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eps = np.percentile(knn_radii, (1 - self.alpha) * 100)
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return X[np.where(knn_radii <= eps)[0], :]
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def filter_by_uncertainty(self, X: np.array, y: np.array):
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"""Filter out points with high label disagreement amongst its kNN neighbors.
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Args:
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X: an array of sample points.
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Returns:
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A subset of the array without points in the bottom alpha-fraction of
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samples with highest disagreement amongst its k nearest neighbors.
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"""
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neigh = KNeighborsClassifier(n_neighbors=self.k)
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neigh.fit(X, y)
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confidence = neigh.predict_proba(X)
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cutoff = np.percentile(confidence, self.alpha * 100)
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unfiltered_idxs = np.where(confidence >= cutoff)[0]
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return X[unfiltered_idxs, :], y[unfiltered_idxs]
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def fit(self, X: np.array, y: np.array):
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"""Initialize trust score precomputations with training data.
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WARNING: assumes that the labels are 0-indexed (i.e.
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0, 1,..., n_labels-1).
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Args:
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X: an array of sample points.
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y: corresponding labels.
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"""
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self.n_labels = np.max(y) + 1
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self.kdtrees = [None] * self.n_labels
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if self.filtering == "uncertainty":
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X_filtered, y_filtered = self.filter_by_uncertainty(X, y)
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for label in range(self.n_labels):
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if self.filtering == "none":
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X_to_use = X[np.where(y == label)[0]]
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self.kdtrees[label] = KDTree(X_to_use)
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elif self.filtering == "density":
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X_to_use = self.filter_by_density(X[np.where(y == label)[0]])
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self.kdtrees[label] = KDTree(X_to_use)
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elif self.filtering == "uncertainty":
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X_to_use = X_filtered[np.where(y_filtered == label)[0]]
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self.kdtrees[label] = KDTree(X_to_use)
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if len(X_to_use) == 0:
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print(
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"Filtered too much or missing examples from a label! Please lower "
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"alpha or check data."
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)
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def get_score(self, X: np.array, y_pred: np.array):
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"""Compute the trust scores.
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Given a set of points, determines the distance to each class.
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Args:
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X: an array of sample points.
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y_pred: The predicted labels for these points.
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Returns:
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The trust score, which is ratio of distance to closest class that was not
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the predicted class to the distance to the predicted class.
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"""
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d = np.tile(None, (X.shape[0], self.n_labels))
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for label_idx in range(self.n_labels):
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d[:, label_idx] = self.kdtrees[label_idx].query(X, k=2)[0][:, -1]
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sorted_d = np.sort(d, axis=1)
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d_to_pred = d[range(d.shape[0]), y_pred]
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d_to_closest_not_pred = np.where(
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sorted_d[:, 0] != d_to_pred, sorted_d[:, 0], sorted_d[:, 1]
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)
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return d_to_closest_not_pred / (d_to_pred + self.min_dist)
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class KNNConfidence:
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"""Baseline which uses disagreement to kNN classifier.
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"""
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def __init__(self, k=10):
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self.k = k
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def fit(self, X, y):
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self.kdtree = KDTree(X)
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self.y = y
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def get_score(self, X, y_pred):
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knn_idxs = self.kdtree.query(X, k=self.k)[1]
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knn_outputs = self.y[knn_idxs]
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return np.mean(
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knn_outputs == np.transpose(np.tile(y_pred, (self.k, 1))), axis=1
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)
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@ -0,0 +1,286 @@
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# Copyright 2018 Google LLC
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# https://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import numpy as np
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from sklearn.model_selection import StratifiedShuffleSplit
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import matplotlib.pyplot as plt
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from sklearn.decomposition import PCA
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import matplotlib.cm as cm
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from sklearn.metrics import precision_recall_curve
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import tensorflow as tf
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from sklearn.linear_model import LogisticRegression
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from sklearn.svm import LinearSVC
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from sklearn.ensemble import RandomForestClassifier
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def run_logistic(X_train, y_train, X_test, y_test, get_training=False):
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model = LogisticRegression()
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model.fit(X_train, y_train)
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y_pred = model.predict(X_test)
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all_confidence = model.predict_proba(X_test)
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confidences = all_confidence[range(len(y_pred)), y_pred]
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if not get_training:
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return y_pred, confidences
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y_pred_training = model.predict(X_train)
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all_confidence_training = model.predict_proba(X_train)
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confidence_training = all_confidence_training[range(len(y_pred_training)),
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y_pred_training]
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return y_pred, confidences, y_pred_training, confidence_training
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def run_linear_svc(X_train, y_train, X_test, y_test, get_training=False):
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model = LinearSVC()
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model.fit(X_train, y_train)
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y_pred = model.predict(X_test)
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all_confidence = model.decision_function(X_test)
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confidences = all_confidence[range(len(y_pred)), y_pred]
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if not get_training:
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return y_pred, confidences
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y_pred_training = model.predict(X_train)
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all_confidence_training = model.decision_function(X_train)
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confidence_training = all_confidence_training[range(len(y_pred_training)),
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y_pred_training]
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return y_pred, confidences, y_pred_training, confidence_training
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def run_random_forest(X_train, y_train, X_test, y_test, get_training=False):
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model = RandomForestClassifier()
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model.fit(X_train, y_train)
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y_pred = model.predict(X_test)
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all_confidence = model.predict_proba(X_test)
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confidences = all_confidence[range(len(y_pred)), y_pred]
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if not get_training:
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return y_pred, confidences
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y_pred_training = model.predict(X_train)
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all_confidence_training = model.predict_proba(X_train)
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confidence_training = all_confidence_training[range(len(y_pred_training)),
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y_pred_training]
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return y_pred, confidences, y_pred_training, confidence_training
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def run_simple_NN(X,
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y,
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X_test,
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y_test,
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num_iter=10000,
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hidden_units=100,
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learning_rate=0.05,
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batch_size=100,
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display_steps=1000,
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n_layers=1,
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get_training=False):
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"""Run a NN with a single layer on some data.
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Returns the predicted values as well as the confidences.
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"""
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n_labels = np.max(y) + 1
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n_features = X.shape[1]
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x = tf.placeholder(tf.float32, [None, n_features])
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y_ = tf.placeholder(tf.float32, [None, n_labels])
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def simple_NN(input_placeholder, n_layers):
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W_in = weight_variable([n_features, hidden_units])
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b_in = bias_variable([hidden_units])
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W_mid = [
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weight_variable([hidden_units, hidden_units])
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for i in range(n_layers - 1)
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]
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b_mid = [bias_variable([hidden_units]) for i in range(n_layers - 1)]
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W_out = weight_variable([hidden_units, n_labels])
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b_out = bias_variable([n_labels])
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layers = [tf.nn.relu(tf.matmul(input_placeholder, W_in) + b_in)]
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for i in range(n_layers - 1):
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layer = tf.nn.relu(tf.matmul(layers[-1], W_mid[i]) + b_mid[i])
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layers.append(layer)
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logits = tf.matmul(layers[-1], W_out) + b_out
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return logits
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NN_logits = simple_NN(x, n_layers)
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cross_entropy = tf.reduce_mean(
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tf.nn.softmax_cross_entropy_with_logits(labels=y_, logits=NN_logits))
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train_step = tf.train.AdamOptimizer(learning_rate).minimize(cross_entropy)
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correct_prediction = tf.equal(tf.argmax(NN_logits, 1), tf.argmax(y_, 1))
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accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
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def one_hot(ns):
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return np.eye(n_labels)[ns]
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y_onehot = one_hot(y)
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y_test_onehot = one_hot(y_test)
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with tf.Session() as sess:
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sess.run(tf.global_variables_initializer())
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for i in range(num_iter):
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ns = np.random.randint(0, len(X), size=batch_size)
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if (i + 1) % display_steps == 0:
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train_accuracy = accuracy.eval(feed_dict={x: X, y_: y_onehot})
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test_accuracy = accuracy.eval(feed_dict={x: X_test, y_: y_test_onehot})
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print("step %d, training accuracy %g, test accuracy %g" %
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(i + 1, train_accuracy, test_accuracy))
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train_step.run(feed_dict={x: X[ns, :], y_: y_onehot[ns, :]})
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testing_logits = NN_logits.eval(feed_dict={x: X_test})
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testing_prediction = tf.argmax(NN_logits, 1).eval(feed_dict={x: X_test})
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NN_softmax = tf.nn.softmax(NN_logits).eval(feed_dict={x: X_test})
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testing_confidence_raw = tf.reduce_max(NN_softmax,
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1).eval(feed_dict={x: X_test})
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if not get_training:
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return testing_prediction, testing_confidence_raw
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training_prediction = tf.argmax(NN_logits, 1).eval(feed_dict={x: X})
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NN_softmax = tf.nn.softmax(NN_logits).eval(feed_dict={x: X})
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training_confidence_raw = tf.reduce_max(NN_softmax,
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1).eval(feed_dict={x: X})
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return testing_prediction, testing_confidence_raw, training_prediction, training_confidence_raw
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def plot_precision_curve(
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extra_plot_title,
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percentile_levels,
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signal_names,
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final_TPs,
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final_stderrs,
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final_misclassification,
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model_name="Model",
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colors=["blue", "darkorange", "brown", "red", "purple"],
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legend_loc=None,
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figure_size=None,
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ylim=None):
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if figure_size is not None:
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plt.figure(figsize=figure_size)
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title = "Precision Curve" if extra_plot_title == "" else extra_plot_title
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plt.title(title, fontsize=20)
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colors = colors + list(cm.rainbow(np.linspace(0, 1, len(final_TPs))))
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plt.xlabel("Percentile level", fontsize=18)
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plt.ylabel("Precision", fontsize=18)
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for i, signal_name in enumerate(signal_names):
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ls = "--" if ("Model" in signal_name) else "-"
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plt.plot(
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percentile_levels, final_TPs[i], ls, c=colors[i], label=signal_name)
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plt.fill_between(
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percentile_levels,
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final_TPs[i] - final_stderrs[i],
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final_TPs[i] + final_stderrs[i],
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color=colors[i],
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alpha=0.1)
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if legend_loc is None:
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if 0. in percentile_levels:
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plt.legend(loc="lower right", fontsize=14)
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else:
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plt.legend(loc="upper left", fontsize=14)
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else:
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if legend_loc == "outside":
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plt.legend(bbox_to_anchor=(1.04, 1), loc="upper left", fontsize=14)
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else:
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plt.legend(loc=legend_loc, fontsize=14)
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if ylim is not None:
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plt.ylim(*ylim)
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model_acc = 100 * (1 - final_misclassification)
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plt.axvline(x=model_acc, linestyle="dotted", color="black")
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plt.show()
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def run_precision_recall_experiment_general(X,
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y,
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n_repeats,
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percentile_levels,
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trainer,
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test_size=0.5,
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extra_plot_title="",
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signals=[],
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signal_names=[],
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predict_when_correct=False,
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skip_print=False):
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def get_stderr(L):
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return np.std(L) / np.sqrt(len(L))
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all_signal_names = ["Model Confidence"] + signal_names
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all_TPs = [[[] for p in percentile_levels] for signal in all_signal_names]
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misclassifications = []
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sign = 1 if predict_when_correct else -1
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sss = StratifiedShuffleSplit(
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n_splits=n_repeats, test_size=test_size, random_state=0)
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for train_idx, test_idx in sss.split(X, y):
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X_train = X[train_idx, :]
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y_train = y[train_idx]
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X_test = X[test_idx, :]
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y_test = y[test_idx]
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testing_prediction, testing_confidence_raw = trainer(
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X_train, y_train, X_test, y_test)
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target_points = np.where(
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testing_prediction == y_test)[0] if predict_when_correct else np.where(
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testing_prediction != y_test)[0]
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final_signals = [testing_confidence_raw]
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for signal in signals:
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signal.fit(X_train, y_train)
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final_signals.append(signal.get_score(X_test, testing_prediction))
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for p, percentile_level in enumerate(percentile_levels):
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all_high_confidence_points = [
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np.where(sign * signal >= np.percentile(sign *
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signal, percentile_level))[0]
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for signal in final_signals
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]
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if 0 in map(len, all_high_confidence_points):
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continue
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TP = [
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len(np.intersect1d(high_confidence_points, target_points)) /
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(1. * len(high_confidence_points))
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for high_confidence_points in all_high_confidence_points
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]
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for i in range(len(all_signal_names)):
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all_TPs[i][p].append(TP[i])
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misclassifications.append(len(target_points) / (1. * len(X_test)))
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final_TPs = [[] for signal in all_signal_names]
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final_stderrs = [[] for signal in all_signal_names]
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for p, percentile_level in enumerate(percentile_levels):
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for i in range(len(all_signal_names)):
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final_TPs[i].append(np.mean(all_TPs[i][p]))
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final_stderrs[i].append(get_stderr(all_TPs[i][p]))
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if not skip_print:
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print("Precision at percentile", percentile_level)
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ss = ""
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for i, signal_name in enumerate(all_signal_names):
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ss += (signal_name + (": %.4f " % final_TPs[i][p]))
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print(ss)
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print()
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final_misclassification = np.mean(misclassifications)
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if not skip_print:
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print("Misclassification rate mean/std", np.mean(misclassifications),
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get_stderr(misclassifications))
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for i in range(len(all_signal_names)):
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final_TPs[i] = np.array(final_TPs[i])
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final_stderrs[i] = np.array(final_stderrs[i])
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plot_precision_curve(extra_plot_title, percentile_levels, all_signal_names,
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final_TPs, final_stderrs, final_misclassification)
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return (all_signal_names, final_TPs, final_stderrs, final_misclassification)
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@ -23,7 +23,7 @@ pytest-mock = "^3.11.1"
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pytest-cov = "^4.1.0"
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||||
|
||||
[tool.pytest.ini_options]
|
||||
addopts = "--cov=quacc"
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||||
addopts = "--cov=quacc --capture=tee-sys"
|
||||
|
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[build-system]
|
||||
requires = ["poetry-core"]
|
||||
|
|
|
|||
|
|
@ -10,7 +10,7 @@ from garg22_ATC.ATC_helper import (
|
|||
get_max_conf,
|
||||
)
|
||||
import numpy as np
|
||||
|
||||
from jiang18_trustscore.trustscore import TrustScore
|
||||
|
||||
|
||||
def kfcv(c_model: BaseEstimator, validation: LabelledCollection) -> Dict:
|
||||
|
|
@ -43,10 +43,11 @@ def ATC_MC(
|
|||
ATC_accuracy = get_ATC_acc(ATC_thres, test_scores)
|
||||
|
||||
return {
|
||||
"true_acc": 100*np.mean(np.argmax(test_probs, axis=-1) == test.y),
|
||||
"pred_acc": ATC_accuracy
|
||||
"true_acc": 100 * np.mean(np.argmax(test_probs, axis=-1) == test.y),
|
||||
"pred_acc": ATC_accuracy,
|
||||
}
|
||||
|
||||
|
||||
def ATC_NE(
|
||||
c_model: BaseEstimator,
|
||||
validation: LabelledCollection,
|
||||
|
|
@ -71,7 +72,23 @@ def ATC_NE(
|
|||
ATC_accuracy = get_ATC_acc(ATC_thres, test_scores)
|
||||
|
||||
return {
|
||||
"true_acc": 100*np.mean(np.argmax(test_probs, axis=-1) == test.y),
|
||||
"pred_acc": ATC_accuracy
|
||||
"true_acc": 100 * np.mean(np.argmax(test_probs, axis=-1) == test.y),
|
||||
"pred_acc": ATC_accuracy,
|
||||
}
|
||||
|
||||
|
||||
def trust_score(
|
||||
c_model: BaseEstimator,
|
||||
validation: LabelledCollection,
|
||||
test: LabelledCollection,
|
||||
predict_method="predict",
|
||||
):
|
||||
c_model_predict = getattr(c_model, predict_method)
|
||||
|
||||
test_pred = c_model_predict(test.X)
|
||||
|
||||
trust_model = TrustScore()
|
||||
trust_model.fit(validation.X, validation.y)
|
||||
|
||||
return trust_model.get_score(test.X, test_pred)
|
||||
|
||||
|
|
|
|||
|
|
@ -99,4 +99,4 @@ def estimate_binary():
|
|||
|
||||
|
||||
if __name__ == "__main__":
|
||||
estimate_multiclass()
|
||||
estimate_binary()
|
||||
|
|
|
|||
|
|
@ -1,12 +1,20 @@
|
|||
|
||||
from sklearn.linear_model import LogisticRegression
|
||||
from quacc.baseline import kfcv
|
||||
from quacc.baseline import kfcv, trust_score
|
||||
from quacc.dataset import get_spambase
|
||||
|
||||
|
||||
class TestBaseline:
|
||||
|
||||
def test_kfcv(self):
|
||||
train, _, _ = get_spambase()
|
||||
train, validation, _ = get_spambase()
|
||||
c_model = LogisticRegression()
|
||||
assert "f1_score" in kfcv(c_model, train)
|
||||
c_model.fit(train.X, train.y)
|
||||
assert "f1_score" in kfcv(c_model, validation)
|
||||
|
||||
def test_trust_score(self):
|
||||
train, validation, test = get_spambase()
|
||||
c_model = LogisticRegression()
|
||||
c_model.fit(train.X, train.y)
|
||||
trustscore = trust_score(c_model, train, test)
|
||||
assert len(trustscore) == len(test.y)
|
||||
Loading…
Reference in New Issue