Merge pull request #146 from Vishu26/dev

Implementation of Cost Effective Active Learning

Author: cosmic-cortex

examples/cost_effective_active_learning.py

@@ -0,0 +1,102 @@
+"""
+This is a modified implementation of the algorithm Cost Effective Active Learning
+(Pl. refer - https://arxiv.org/abs/1701.03551). This version not only picks up the 
+top K uncertain samples but also picks up the top N highly confident samples that
+may represent information and diversity. It is different than the original implementation
+as it does not involve tuning the confidence threshold parameter for every dataset.
+"""
+
+from keras.datasets import mnist
+import numpy as np
+from modAL.models import ActiveLearner
+from sklearn.ensemble import RandomForestClassifier
+from scipy.special import entr
+
+
+(X_train, y_train), (X_test, y_test) = mnist.load_data()
+
+X_train = X_train / 255
+X_test = X_test / 255
+y_train = y_train.astype(np.uint8)
+y_test = y_test.astype(np.uint8)
+
+X_train = X_train.reshape(-1, 784)
+X_test = X_test.reshape(-1, 784)
+
+model = RandomForestClassifier(n_estimators=100)
+
+INITIAL_SET_SIZE = 32
+
+U_x = np.copy(X_train)
+U_y = np.copy(y_train)
+
+ind = np.random.choice(range(len(U_x)), size=INITIAL_SET_SIZE)
+
+X_initial = U_x[ind]
+y_initial = U_y[ind]
+
+U_x = np.delete(U_x, ind, axis=0)
+U_y = np.delete(U_y, ind, axis=0)
+
+
+def assign_pseudo_labels(active_learner, X, confidence_idx):
+    conf_samples = X[confidence_idx]
+    labels = active_learner.predict(conf_samples)
+    return labels
+
+
+def max_entropy(active_learner, X, K=16, N=16):
+
+    class_prob = active_learner.predict_proba(X)
+    entropy = entr(class_prob).sum(axis=1)
+    uncertain_idx = np.argpartition(entropy, -K)[-K:]
+
+    """
+    Original Implementation -- Pick most confident samples with
+    entropy less than a threshold. Threshold is decayed in every
+    iteration.
+
+    Different than original -- Pick top n most confident samples.
+    """
+ 
+    confidence_idx = np.argpartition(entropy, N)[:N]
+
+    return np.concatenate((uncertain_idx, confidence_idx), axis=0)
+
+
+active_learner = ActiveLearner(
+    estimator=model,
+    X_training=X_initial,
+    y_training=y_initial,
+    query_strategy=max_entropy
+)
+
+N_QUERIES = 20
+
+K_MAX_ENTROPY = 16
+N_MIN_ENTROPY = 16
+
+scores = [active_learner.score(X_test, y_test)]
+
+for index in range(N_QUERIES):
+
+    query_idx, query_instance = active_learner.query(U_x, K_MAX_ENTROPY, N_MIN_ENTROPY)
+
+    uncertain_idx = query_idx[:K_MAX_ENTROPY]
+    confidence_idx = query_idx[K_MAX_ENTROPY:]
+
+    conf_labels = assign_pseudo_labels(active_learner, U_x, confidence_idx)
+
+    L_x = U_x[query_idx]
+    L_y = np.concatenate((U_y[uncertain_idx], conf_labels), axis=0)
+
+    active_learner.teach(L_x, L_y)
+
+    U_x = np.delete(U_x, query_idx, axis=0)
+    U_y = np.delete(U_y, query_idx, axis=0)
+    
+    acc = active_learner.score(X_test, y_test)
+
+    print(F'Query {index+1}: Test Accuracy: {acc}')
+    
+    scores.append(acc)