Commit 78d8baef authored by lucas_miranda's avatar lucas_miranda
Browse files

Prototyped KNN_purity callback

parent a13aac38
......@@ -186,10 +186,7 @@ class one_cycle_scheduler(tf.keras.callbacks.Callback):
self.iteration += 1
K.set_value(self.model.optimizer.lr, rate)
def on_batch_end(self, epoch, logs=None):
"""Add current learning rate as a metric, to check whether scheduling is working properly"""
return self.last_rate
logs["learning_rate"] = self.last_rate
class knn_cluster_purity(tf.keras.callbacks.Callback):
......@@ -208,51 +205,57 @@ class knn_cluster_purity(tf.keras.callbacks.Callback):
def on_epoch_end(self, batch: int, logs):
""" Passes samples through the encoder and computes cluster purity on the latent embedding """
# Get encoer and grouper from full model
cluster_means = [
layer for layer in self.model.layers if layer.name == "cluster_means"
][0]
cluster_assignment = [
layer for layer in self.model.layers if layer.name == "cluster_assignment"
][0]
if self.validation_data is not None:
encoder = tf.keras.models.Model(
self.model.layers[0].input, cluster_means.output
)
grouper = tf.keras.models.Model(
self.model.layers[0].input, cluster_assignment.output
)
# Get encoer and grouper from full model
cluster_means = [
layer for layer in self.model.layers if layer.name == "cluster_means"
][0]
cluster_assignment = [
layer
for layer in self.model.layers
if layer.name == "cluster_assignment"
][0]
# Use encoder and grouper to predict on validation data
encoding = encoder.predict(self.validation_data)
groups = grouper.predict(self.validation_data)
encoder = tf.keras.models.Model(
self.model.layers[0].input, cluster_means.output
)
grouper = tf.keras.models.Model(
self.model.layers[0].input, cluster_assignment.output
)
# Multiply encodings by groups, to get a weighted version of the matrix
encoding = (
encoding
* tf.tile(groups, [1, encoding.shape[1] // groups.shape[1]]).numpy()
)
hard_groups = groups.argmax(axis=1)
print(self.validation_data)
# Fit KNN model
knn = NearestNeighbors().fit(encoding)
# Use encoder and grouper to predict on validation data
encoding = encoder.predict(self.validation_data)
groups = grouper.predict(self.validation_data)
# Iterate over samples and compute purity over k neighbours
random_idxs = np.random.choice(
range(encoding.shape[0]), self.samples, replace=False
)
purity_vector = np.zeros(self.samples)
for i, sample in enumerate(random_idxs):
indexes = knn.kneighbors(
encoding[sample][np.newaxis, :], self.k, return_distance=False
# Multiply encodings by groups, to get a weighted version of the matrix
encoding = (
encoding
* tf.tile(groups, [1, encoding.shape[1] // groups.shape[1]]).numpy()
)
purity_vector[i] = (
np.sum(hard_groups[indexes] == hard_groups[sample])
/ self.k
* np.max(groups[sample])
hard_groups = groups.argmax(axis=1)
# Fit KNN model
knn = NearestNeighbors().fit(encoding)
# Iterate over samples and compute purity over k neighbours
random_idxs = np.random.choice(
range(encoding.shape[0]), self.samples, replace=False
)
purity_vector = np.zeros(self.samples)
for i, sample in enumerate(random_idxs):
indexes = knn.kneighbors(
encoding[sample][np.newaxis, :], self.k, return_distance=False
)
purity_vector[i] = (
np.sum(hard_groups[indexes] == hard_groups[sample])
/ self.k
* np.max(groups[sample])
)
return purity_vector.mean()
logs["knn_cluster_purity"] = purity_vector.mean()
class uncorrelated_features_constraint(Constraint):
......
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