Hierarchical clustering implementation started

Author: cosmic-cortex

examples/keras_integration.py

@@ -7,7 +7,7 @@
 import numpy as np
 from keras.datasets import mnist
 from keras.models import Sequential
-from keras.layers import Dense, Dropout
+from keras.layers import Conv2D, MaxPooling2D, Dropout, Flatten, Dense
 from keras.wrappers.scikit_learn import KerasClassifier
 from modAL.models import ActiveLearner
 
@@ -19,11 +19,14 @@ def create_keras_model():
     Should be passed to KerasClassifier in the Keras scikit-learn API.
     """
     model = Sequential()
-    model.add(Dense(512, activation='relu', input_shape=(784, )))
-    model.add(Dropout(0.2))
-    model.add(Dense(512, activation='relu'))
-    model.add(Dropout(0.2))
-    model.add(Dense(10, activation='sigmoid'))
+    model.add(Conv2D(32, kernel_size=(3, 3), activation='relu', input_shape=(28, 28, 1)))
+    model.add(MaxPooling2D(pool_size=(2, 2)))
+    model.add(Dropout(0.25))
+    model.add(Flatten())
+    model.add(Dense(128, activation='relu'))
+    model.add(Dropout(0.5))
+    model.add(Dense(10, activation='softmax'))
+
     model.compile(loss='categorical_crossentropy', optimizer='adadelta', metrics=['accuracy'])
 
     return model
@@ -41,8 +44,8 @@ def create_keras_model():
 
 # read training data
 (X_train, y_train), (X_test, y_test) = mnist.load_data()
-X_train = X_train.reshape(60000, 784).astype('float32') / 255
-X_test = X_test.reshape(10000, 784).astype('float32') / 255
+X_train = X_train.reshape(60000, 28, 28, 1).astype('float32') / 255
+X_test = X_test.reshape(10000, 28, 28, 1).astype('float32') / 255
 y_train = keras.utils.to_categorical(y_train, 10)
 y_test = keras.utils.to_categorical(y_test, 10)
 

modAL/cluster.py

@@ -0,0 +1,21 @@
+"""
+========================================
+Cluster-based active learning algorithms
+========================================
+"""
+
+import numpy as np
+from sklearn.cluster.hierarchical import AgglomerativeClustering, _hc_cut
+
+
+class HierarchicalClustering:
+    def __init__(self, X, labels):
+        self.labels = labels
+        self.cluster = AgglomerativeClustering(n_clusters=2, compute_full_tree=True)
+        self.cluster.fit(X)
+
+    def __call__(self, *args, **kwargs):
+        pass
+
+    def compute_errors(self):
+        pass

modAL/models.py

@@ -1,5 +1,7 @@
 """
-Core models for active learning algorithms.
+==========================================
+Core models for active learning algorithms
+==========================================
 """
 
 import abc