concepts: introduce the Craft method

Signed-off-by: Frederic Boisnard <[email protected]>

tests/concepts/test_craft_tf.py

@@ -0,0 +1,261 @@
+import numpy as np
+import tensorflow as tf
+import pytest
+import tensorflow as tf
+from tensorflow.keras.models import Sequential, Model
+from tensorflow.keras.layers import Dense, Conv1D, Conv2D, Activation, GlobalAveragePooling1D, Dropout, Flatten, MaxPooling2D, Input
+from tensorflow.keras.utils import to_categorical
+
+from xplique.concepts import CraftTf as Craft
+from ..utils import generate_data, generate_model, generate_txt_images_data
+
+def test_shape():
+    """Ensure the output shape is correct"""
+
+    input_shapes = [(32, 32, 3), (32, 32, 1), (64, 64, 3)]
+    nb_labels = 3
+    nb_samples = 100
+
+    for input_shape in input_shapes:
+        # Generate a fake dataset
+        x, y = generate_data(input_shape, nb_labels, nb_samples)
+        model = generate_model(input_shape, nb_labels)
+        model.compile(loss='categorical_crossentropy', optimizer=tf.keras.optimizers.Adam(0.1))
+
+        # cut the model in two parts (as explained in the paper)
+        # first part is g(.) our 'input_to_latent' model, second part is h(.) our 'latent_to_logit' model
+        g = tf.keras.Model(model.input, model.layers[0].output)
+        h = tf.keras.Model(model.layers[1].input, model.layers[-1].output)
+
+        # The activations must be positives
+        assert np.all(g(x) >= 0.0)
+
+        # Initialize Craft
+        number_of_concepts = 10
+        patch_size = 15
+        craft = Craft(input_to_latent = g,
+                    latent_to_logit = h,
+                    number_of_concepts = number_of_concepts,
+                    patch_size = patch_size,
+                    batch_size = 64)
+
+        # Now we can fit the concept using our images
+        # Focus on class id 0
+        class_id = 0
+        images_preprocessed = x[y.argmax(1)==class_id] # select only images of class 'class_id'
+        crops, crops_u, w = craft.fit(images_preprocessed, class_id)
+
+        print('Checking shape of crops, crops_u, w:', crops.shape, crops_u.shape, w.shape)
+        assert(crops.shape[1] == crops.shape[2] == patch_size) # Check patch sizes
+        assert(crops.shape[0] == crops_u.shape[0]) # Check numbers of patches
+        assert(crops_u.shape[1] == w.shape[0])
+
+        # Importance estimation
+        importances = craft.estimate_importance(nb_most_important_concepts=5)
+        print('Checking shape of importances:', importances.shape)
+        assert(len(importances) == number_of_concepts)
+
+        images_u = craft.transform(images_preprocessed)
+        print('Checking shape of images_u:', images_u.shape)
+        assert(images_u.shape[1:] == (input_shape[0]-1, input_shape[1]-1, number_of_concepts))     
+
+
+def test_shape2():
+    """Ensure the output shape is correct when activation.shape == 2"""
+
+    input_shapes = [(32, 32, 3), (32, 32, 1), (64, 64, 3)]
+    nb_labels = 3
+    nb_samples = 100
+
+    for input_shape in input_shapes:
+        # Generate a fake dataset
+        x, y = generate_data(input_shape, nb_labels, nb_samples)
+        model = generate_model(input_shape, nb_labels)
+        model.compile(loss='categorical_crossentropy', optimizer=tf.keras.optimizers.Adam(0.1))
+
+        # cut the model in two parts (as explained in the paper)
+        # first part is g(.) our 'input_to_latent' model, second part is h(.) our 'latent_to_logit' model
+        g = tf.keras.Model(model.input, model.layers[-1].output)
+        h = tf.keras.Model(model.layers[-1].input, model.layers[-1].output)
+
+        # The activations must be positives
+        assert np.all(g(x) >= 0.0)
+
+        # Initialize Craft
+        number_of_concepts = 10
+        patch_size = 15
+        craft = Craft(input_to_latent = g,
+                    latent_to_logit = h,
+                    number_of_concepts = number_of_concepts,
+                    patch_size = patch_size,
+                    batch_size = 64)
+
+        # Now we can fit the concept using our images
+        # Focus on class id 0
+        class_id = 0
+        images_preprocessed = x[y.argmax(1)==class_id] # select only images of class 'class_id'
+        crops, crops_u, w = craft.fit(images_preprocessed, class_id)
+
+        print('Checking shape of crops, crops_u, w:', crops.shape, crops_u.shape, w.shape)
+        assert(crops.shape[1] == crops.shape[2] == patch_size) # Check patch sizes
+        assert(crops.shape[0] == crops_u.shape[0]) # Check numbers of patches
+        assert(crops_u.shape[1] == w.shape[0] == number_of_concepts)
+
+        # Importance estimation
+        importances = craft.estimate_importance(nb_most_important_concepts=5)
+        print('Checking shape of importances:', importances.shape)
+        assert(len(importances) == number_of_concepts)
+
+        images_u = craft.transform(images_preprocessed)
+        print('Checking shape of images_u:', images_u.shape)
+        assert(input_shape[1], number_of_concepts)
+
+def test_wrong_layers():
+    """Ensure that Craft complains when the input models are incompatible"""
+
+    input_shapes = [(32, 32, 3)]
+    nb_labels = 3
+
+    for input_shape in input_shapes:
+        # Generate a fake dataset
+        model = generate_model(input_shape, nb_labels)
+        model.compile(loss='categorical_crossentropy', optimizer=tf.keras.optimizers.Adam(0.1))
+
+        g = tf.keras.Model(model.input, model.layers[0].output)
+        h = lambda x: 2*x
+
+        # Initialize Craft
+        number_of_concepts = 10
+        patch_size = 15
+        with pytest.raises(TypeError):
+            craft = Craft(input_to_latent = g,
+                        latent_to_logit = h,
+                        number_of_concepts = number_of_concepts,
+                        patch_size = patch_size,
+                        batch_size = 64)
+
+def test_classifier():
+    """ Check the Craft results on a small fake dataset """
+
+    input_shape = (64, 64, 3)
+    nb_labels = 3
+    nb_samples = 200
+
+    # Create a dataset of 'ABC', 'BCD', 'CDE' images
+    x, y, nb_samples, labels_str = generate_txt_images_data(input_shape, nb_labels, nb_samples)
+    print(f'Dataset: {nb_samples} samples generated, {nb_labels} classes: {labels_str}')
+
+    # train a small classifier on the dataset
+    def generate_model(input_shape=(64, 64, 3), output_shape=10):
+        model = Sequential()
+        model.add(Input(shape=input_shape))
+        model.add(Conv2D(6, kernel_size=(2, 2)))
+        model.add(Activation('relu'))
+        model.add(Conv2D(6, kernel_size=(2, 2)))
+        model.add(Activation('relu'))
+        model.add(Conv2D(6, kernel_size=(2, 2)))
+        model.add(Activation('relu'))
+        model.add(Flatten())
+        model.add(Dense(output_shape))
+        model.add(Activation('softmax'))
+        model.compile(loss='categorical_crossentropy', optimizer='sgd', metrics=['accuracy'])
+
+        return model
+
+    model = generate_model(input_shape, nb_labels)
+    model.fit(x, y, batch_size=32, epochs=70)
+    print('acc:', np.sum(np.argmax(model(x), axis=1) == np.argmax(y, axis=1)) / nb_samples)
+
+    # cut the model in two parts (as explained in the paper)
+    # first part is g(.) our 'input_to_latent' model, second part is h(.) our 'latent_to_logit' model
+    g = tf.keras.Model(model.input, model.layers[-4].output)
+    h = tf.keras.Model(model.layers[-3].input, model.layers[-1].output)
+    print(model.layers[-4])
+    print(model.layers[-3])
+
+    assert np.all(g(x) >= 0.0)
+
+    # Init Craft on the full dataset
+    craft = Craft(input_to_latent = g,
+                latent_to_logit = h,
+                number_of_concepts = 3,
+                patch_size = 12,
+                batch_size = 32)
+
+    # Expected best crop for class 0 (ABC) is AB
+    AB_str = """
+        0 0 0 0 0 0 0 0 0 0 0 0
+        0 0 0 0 0 0 0 0 0 0 0 0
+        0 0 0 0 1 1 1 1 1 1 1 1
+        0 0 0 0 0 0 1 0 0 0 1 1
+        1 0 0 0 0 0 1 0 0 0 0 1
+        1 0 0 0 0 0 1 0 0 0 1 1
+        1 0 0 0 0 0 1 1 1 1 1 1
+        1 1 0 0 0 0 1 0 0 0 0 1
+        0 1 0 0 0 0 1 0 0 0 0 0
+        0 1 1 0 0 0 1 0 0 0 0 1
+        1 1 1 1 1 1 1 1 1 1 1 1
+        0 0 0 0 0 0 0 0 0 0 0 0
+    """
+    AB = np.genfromtxt(AB_str.splitlines())
+
+    # Expected best crop for class 1 (BCD) is BC
+    BC_str = """
+        0 0 0 0 0 0 0 0 0 0 0 0
+        0 0 0 0 0 0 0 0 0 0 0 0
+        0 0 0 0 0 0 0 0 0 0 0 0
+        1 1 1 1 1 1 0 0 0 0 1 1
+        1 0 0 0 1 1 0 0 0 1 1 0
+        1 0 0 0 0 1 0 0 0 1 0 0
+        1 0 0 0 1 1 0 0 0 1 0 0
+        1 1 1 1 1 1 0 0 0 1 0 0
+        1 0 0 0 0 1 1 0 0 1 0 0
+        1 0 0 0 0 0 1 0 0 1 0 0
+        1 0 0 0 0 1 1 0 0 1 1 0
+        1 1 1 1 1 1 0 0 0 0 1 1
+    """
+    BC = np.genfromtxt(BC_str.splitlines())
+
+    # Expected best crop for class 2 (CDE) is DE
+    DE_str = """
+        0 0 0 0 0 0 0 0 0 0 0 0
+        1 0 0 0 1 1 1 1 1 1 1 1
+        1 1 0 0 0 0 1 0 0 0 0 1
+        0 1 0 0 0 0 1 0 0 0 0 1
+        0 1 1 0 0 0 1 0 0 1 0 0
+        0 1 1 0 0 0 1 1 1 1 0 0
+        0 1 1 0 0 0 1 0 0 1 0 0
+        0 1 0 0 0 0 1 0 0 0 0 1
+        1 1 0 0 0 0 1 0 0 0 0 1
+        1 0 0 0 1 1 1 1 1 1 1 1
+        0 0 0 0 0 0 0 0 0 0 0 0
+        0 0 0 0 0 0 0 0 0 0 0 0
+        """
+    DE = np.genfromtxt(DE_str.splitlines())
+
+    expected_best_crops = [AB, BC, DE]
+
+    # Run 3 Craft studies on each class, and in each case check if the best crop is the expected one
+    for class_id in range(3):
+        # Focus on class class_id
+        print(f'Selecting subset for class {class_id} : {labels_str[class_id]}')
+        x_subset = x[np.argmax(y, axis=1)==class_id,:,:,:]
+
+        print('fit craft on the selected class')
+        crops, crops_u, w = craft.fit(x_subset, class_id)
+
+        print('compute importances')
+        importances = craft.estimate_importance(nb_most_important_concepts=3)
+        assert(importances[0] > 0.7)
+
+        print('find the best crop and compare it to the expected best crop')
+        most_important_concepts = np.argsort(importances)[::-1]
+
+        # Find the best crop for the most important concept
+        c_id = most_important_concepts[0]
+        best_crops_ids = np.argsort(crops_u[:, c_id])[::-1]
+        best_crop = np.array(crops)[best_crops_ids[0]]
+
+        # Compare this best crop to the expectation
+        bin_best_crop = np.where(best_crop.sum(axis=2) > 0.25, 1, 0)
+        assert(np.all(bin_best_crop == expected_best_crops[class_id]))