From b3a67382812763a7da8eb16c7fac66bea1a22fd4 Mon Sep 17 00:00:00 2001 From: Szubie Date: Wed, 26 Jun 2019 16:42:19 +0100 Subject: [PATCH 01/13] Add supervised-softmax option to ivis. Used when labels are provided to the fit function. --- ivis/data/triplet_generators.py | 117 +++++++++++++++++++++++++++++--- ivis/ivis.py | 46 +++++++++---- ivis/nn/network.py | 4 +- tests/nn/test_network.py | 6 +- 4 files changed, 144 insertions(+), 29 deletions(-) diff --git a/ivis/data/triplet_generators.py b/ivis/data/triplet_generators.py index edbc9b7..57e83cf 100644 --- a/ivis/data/triplet_generators.py +++ b/ivis/data/triplet_generators.py @@ -20,22 +20,34 @@ from scipy.sparse import issparse -def generator_from_index(X, index_path, k, batch_size, search_k=-1, precompute=True, verbose=1): +def generator_from_index(X, Y, index_path, k, batch_size, search_k=-1, precompute=True, verbose=1): if k >= X.shape[0] - 1: raise Exception('k value greater than or equal to (num_rows - 1) (k={}, rows={}). Lower k to a smaller value.'.format(k, X.shape[0])) if batch_size > X.shape[0]: raise Exception('batch_size value larger than num_rows in dataset (batch_size={}, rows={}). Lower batch_size to a smaller value.'.format(batch_size, X.shape[0])) - if precompute: - if verbose > 0: - print('Extracting KNN from index') - - neighbour_matrix = extract_knn(X, index_path, k=k, search_k=search_k, verbose=verbose) - return KnnTripletGenerator(X, neighbour_matrix, batch_size=batch_size) + if Y is None: + if precompute: + if verbose > 0: + print('Extracting KNN from index') + + neighbour_matrix = extract_knn(X, index_path, k=k, search_k=search_k, verbose=verbose) + return KnnTripletGenerator(X, neighbour_matrix, batch_size=batch_size) + else: + index = AnnoyIndex(X.shape[1]) + index.load(index_path) + return AnnoyTripletGenerator(X, index, k=k, batch_size=batch_size, search_k=search_k) else: - index = AnnoyIndex(X.shape[1]) - index.load(index_path) - return AnnoyTripletGenerator(X, index, k=k, batch_size=batch_size, search_k=search_k) + if precompute: + if verbose > 0: + print('Extracting KNN from index') + + neighbour_matrix = extract_knn(X, index_path, k=k, search_k=search_k, verbose=verbose) + return LabeledKnnTripletGenerator(X, Y, neighbour_matrix, batch_size=batch_size) + else: + index = AnnoyIndex(X.shape[1]) + index.load(index_path) + return LabeledAnnoyTripletGenerator(X, Y, index, k=k, batch_size=batch_size, search_k=search_k) class AnnoyTripletGenerator(Sequence): @@ -124,3 +136,88 @@ def knn_triplet_from_neighbour_list(self, row_index, neighbour_list): triplets += [self.X[row_index], self.X[neighbour_ind], self.X[negative_ind]] return triplets + +class LabeledAnnoyTripletGenerator(Sequence): + + def __init__(self, X, Y, annoy_index, k=150, batch_size=32, search_k=-1): + self.X, self.Y = np.array(X), np.array(Y) + self.annoy_index = annoy_index + self.k = k + self.batch_size = batch_size + self.search_k = search_k + + def __len__(self): + return int(np.ceil(len(self.X) / float(self.batch_size))) + + def __getitem__(self, idx): + batch_indices = range(idx * self.batch_size, min((idx + 1) * self.batch_size, self.X.shape[0])) + + label_batch = self.Y[batch_indices] + triplet_batch = [ self.knn_triplet_from_annoy_index(row_index) + for row_index in batch_indices ] + + if (issparse(self.X)): + triplet_batch = [[e.toarray()[0] for e in t] for t in triplet_batch] + + triplet_batch = np.array(triplet_batch) + + return ([triplet_batch[:,0], triplet_batch[:,1], triplet_batch[:,2]], [np.array(label_batch), np.array(label_batch)]) + + + def knn_triplet_from_annoy_index(self, row_index): + """ A random (unweighted) positive example chosen. """ + triplet = [] + neighbour_list = np.array(self.annoy_index.get_nns_by_item(row_index, self.k + 1, + search_k=self.search_k, include_distances=False), dtype=np.uint32) + + # Take a random neighbour as positive + neighbour_ind = np.random.choice(neighbour_list) + + # Take a random non-neighbour as negative + negative_ind = np.random.randint(0, self.X.shape[0]) # Pick a random index until one fits constraint. An optimization. + while negative_ind in neighbour_list: + negative_ind = np.random.randint(0, self.X.shape[0]) + + triplet += [self.X[row_index], self.X[neighbour_ind], self.X[negative_ind]] + return triplet + +class LabeledKnnTripletGenerator(Sequence): + + def __init__(self, X, Y, neighbour_matrix, batch_size=32): + self.X, self.Y = np.array(X), np.array(Y) + self.neighbour_matrix = neighbour_matrix + self.batch_size = batch_size + + def __len__(self): + return int(np.ceil(len(self.X) / float(self.batch_size))) + + def __getitem__(self, idx): + batch_indices = range(idx * self.batch_size, min((idx + 1) * self.batch_size, self.X.shape[0])) + + label_batch = self.Y[batch_indices] + triplet_batch = [ self.knn_triplet_from_neighbour_list(row_index, self.neighbour_matrix[row_index]) + for row_index in batch_indices ] + + if (issparse(self.X)): + triplet_batch = [[e.toarray()[0] for e in t] for t in triplet_batch] + + triplet_batch = np.array(triplet_batch) + + return ([triplet_batch[:,0], triplet_batch[:,1], triplet_batch[:,2]], [np.array(label_batch), np.array(label_batch)]) + + + def knn_triplet_from_neighbour_list(self, row_index, neighbour_list): + """ A random (unweighted) positive example chosen. """ + triplets = [] + + # Take a random neighbour as positive + neighbour_ind = np.random.choice(neighbour_list) + + # Take a random non-neighbour as negative + + negative_ind = np.random.randint(0, self.X.shape[0]) # Pick a random index until one fits constraint. An optimization. + while negative_ind in neighbour_list: + negative_ind = np.random.randint(0, self.X.shape[0]) + + triplets += [self.X[row_index], self.X[neighbour_ind], self.X[negative_ind]] + return triplets diff --git a/ivis/ivis.py b/ivis/ivis.py index bcb4600..90ab244 100644 --- a/ivis/ivis.py +++ b/ivis/ivis.py @@ -1,11 +1,13 @@ """ scikit-learn wrapper class for the Ivis algorithm. """ from .data.triplet_generators import generator_from_index -from .nn.network import build_network, base_network +from .nn.network import triplet_network, base_network from .nn.losses import triplet_loss from .data.knn import build_annoy_index from keras.callbacks import EarlyStopping -from keras.models import model_from_json, load_model +from keras.models import model_from_json, load_model, Model +from keras.layers import Dense +import numpy as np from sklearn.base import BaseEstimator @@ -65,7 +67,7 @@ def __getstate__(self): del state['encoder'] return state - def _fit(self, X, shuffle_mode=True): + def _fit(self, X, Y=None, shuffle_mode=True): if self.annoy_index_path is None: self.annoy_index_path = 'annoy.index' @@ -73,7 +75,7 @@ def _fit(self, X, shuffle_mode=True): print('Building KNN index') build_annoy_index(X, self.annoy_index_path, ntrees=self.ntrees, verbose=self.verbose) - datagen = generator_from_index(X, + datagen = generator_from_index(X, Y, index_path=self.annoy_index_path, k=self.k, batch_size=self.batch_size, @@ -82,17 +84,33 @@ def _fit(self, X, shuffle_mode=True): verbose=self.verbose) loss_monitor = 'loss' + try: + triplet_loss_func = triplet_loss(distance=self.distance, margin=self.margin) + except KeyError: + raise ValueError('Loss function `{}` not implemented.'.format(self.distance)) if self.model_ is None: if type(self.model_def) is str: input_size = (X.shape[-1],) - self.model_ = build_network(base_network(self.model_def, input_size), embedding_dims=self.embedding_dims) + self.model_, anchor_embedding, _, _ = triplet_network(base_network(self.model_def, input_size), embedding_dims=self.embedding_dims) else: - self.model_ = build_network(self.model_def, embedding_dims=self.embedding_dims) - try: - self.model_.compile(optimizer='adam', loss=triplet_loss(distance=self.distance, margin=self.margin)) - except KeyError: - raise Exception('Loss function not implemented.') + self.model_, anchor_embedding, _, _ = triplet_network(self.model_def, embedding_dims=self.embedding_dims) + + if Y is None: + self.model_.compile(optimizer='adam', loss=triplet_loss_func) + else: + n_classes = len(np.unique(Y, axis=0)) + + classification_output = Dense(n_classes, activation='softmax', name='classification_out')(anchor_embedding) + final_network = Model(inputs=self.model_.inputs, outputs=[self.model_.output, classification_output]) + self.model_ = final_network + self.model_.compile(optimizer='adam', + loss={'lambda_1': triplet_loss_func, + 'classification_out' : 'sparse_categorical_crossentropy' + }, + loss_weights={'lambda_1': 0.5, + 'classification_out': 0.5}) + self.encoder = self.model_.layers[3] if self.verbose > 0: @@ -107,7 +125,7 @@ def _fit(self, X, shuffle_mode=True): verbose=self.verbose) self.loss_history_ += hist.history['loss'] - def fit(self, X, shuffle_mode=True): + def fit(self, X, Y=None, shuffle_mode=True): """Fit an ivis model. Parameters @@ -120,10 +138,10 @@ def fit(self, X, shuffle_mode=True): returns an instance of self """ - self._fit(X, shuffle_mode) + self._fit(X, Y, shuffle_mode) return self - def fit_transform(self, X, shuffle_mode=True): + def fit_transform(self, X, Y=None, shuffle_mode=True): """Fit to data then transform Parameters @@ -137,7 +155,7 @@ def fit_transform(self, X, shuffle_mode=True): Embedding of the new data in low-dimensional space. """ - self.fit(X, shuffle_mode) + self.fit(X, Y, shuffle_mode) return self.transform(X) def transform(self, X): diff --git a/ivis/nn/network.py b/ivis/nn/network.py index 3242097..8d027c2 100644 --- a/ivis/nn/network.py +++ b/ivis/nn/network.py @@ -7,7 +7,7 @@ from keras import regularizers -def build_network(base_network, embedding_dims=2, embedding_l2=0.0): +def triplet_network(base_network, embedding_dims=2, embedding_l2=0.0): def output_shape(shapes): shape1, shape2, shape3 = shapes return (3, shape1[0],) @@ -30,7 +30,7 @@ def output_shape(shapes): processed_n]) model = Model([input_a, input_p, input_n], triplet) - return model + return model, processed_a, processed_p, processed_n def base_network(model_name, input_shape): diff --git a/tests/nn/test_network.py b/tests/nn/test_network.py index 4e9eec7..937c601 100644 --- a/tests/nn/test_network.py +++ b/tests/nn/test_network.py @@ -1,4 +1,4 @@ -from ivis.nn.network import get_base_networks, base_network, build_network +from ivis.nn.network import get_base_networks, base_network, triplet_network import keras from keras.models import Model, Sequential @@ -13,7 +13,7 @@ def test_base_networks(): model = base_network(name, input_shape) assert isinstance(model, Model) -def test_build_network(): +def test_triplet_network(): X = np.zeros(shape=(10, 5)) embedding_dims = 3 @@ -21,7 +21,7 @@ def test_build_network(): base_model = Sequential() base_model.add(Dense(8, input_shape=(X.shape[-1],))) - model = build_network(base_model, embedding_dims=embedding_dims, embedding_l2=0.1) + model, _, _, _ = triplet_network(base_model, embedding_dims=embedding_dims, embedding_l2=0.1) encoder = model.layers[3] assert model.layers[3].output_shape == (None, 3) From fcf92c8a5b736c26399c83d85e24fd495e6e43c1 Mon Sep 17 00:00:00 2001 From: idroz Date: Wed, 26 Jun 2019 19:00:46 +0100 Subject: [PATCH 02/13] explicitly name the Lambda layer to enable multiple runs in a single session --- ivis/ivis.py | 66 ++++++++++++++++++++++++---------------------- ivis/nn/network.py | 5 ++-- 2 files changed, 37 insertions(+), 34 deletions(-) diff --git a/ivis/ivis.py b/ivis/ivis.py index 90ab244..2bafb3f 100644 --- a/ivis/ivis.py +++ b/ivis/ivis.py @@ -17,10 +17,10 @@ import tensorflow as tf class Ivis(BaseEstimator): - """Ivis is a technique that uses an artificial neural network for dimensionality reduction, often useful for the purposes of visualization. - The network trains on triplets of data-points at a time and pulls positive points together, while pushing more distant points away from each other. + """Ivis is a technique that uses an artificial neural network for dimensionality reduction, often useful for the purposes of visualization. + The network trains on triplets of data-points at a time and pulls positive points together, while pushing more distant points away from each other. Triplets are sampled from the original data using KNN aproximation using the Annoy library. - + :param int embedding_dims: Number of dimensions in the embedding space :param int k: The number of neighbours to retrieve for each point. Must be less than one minus the number of rows in the dataset. :param str distance: The loss function used to train the neural network. One of "pn", "euclidean", "manhattan_pn", "manhattan", "chebyshev", "chebyshev_pn", "softmax_ratio_pn", "softmax_ratio". @@ -36,11 +36,12 @@ class Ivis(BaseEstimator): :param int verbose: Controls the volume of logging output the model produces when training. When set to 0, silences outputs, when above 0 will print outputs. """ - + def __init__(self, embedding_dims=2, k=150, distance='pn', batch_size=128, - epochs=1000, n_epochs_without_progress=50, - margin=1, ntrees=50, search_k=-1, - precompute=True, model='default', annoy_index_path=None, verbose=1): + epochs=1000, n_epochs_without_progress=50, + margin=1, ntrees=50, search_k=-1, + precompute=True, model='default', + annoy_index_path=None, verbose=1): self.embedding_dims = embedding_dims self.k = k @@ -68,20 +69,21 @@ def __getstate__(self): return state def _fit(self, X, Y=None, shuffle_mode=True): - + if self.annoy_index_path is None: self.annoy_index_path = 'annoy.index' if self.verbose > 0: print('Building KNN index') - build_annoy_index(X, self.annoy_index_path, ntrees=self.ntrees, verbose=self.verbose) + build_annoy_index(X, self.annoy_index_path, + ntrees=self.ntrees, verbose=self.verbose) datagen = generator_from_index(X, Y, - index_path=self.annoy_index_path, - k=self.k, - batch_size=self.batch_size, - search_k=self.search_k, - precompute=self.precompute, - verbose=self.verbose) + index_path=self.annoy_index_path, + k=self.k, + batch_size=self.batch_size, + search_k=self.search_k, + precompute=self.precompute, + verbose=self.verbose) loss_monitor = 'loss' try: @@ -89,7 +91,7 @@ def _fit(self, X, Y=None, shuffle_mode=True): except KeyError: raise ValueError('Loss function `{}` not implemented.'.format(self.distance)) - if self.model_ is None: + if self.model_ is None: if type(self.model_def) is str: input_size = (X.shape[-1],) self.model_, anchor_embedding, _, _ = triplet_network(base_network(self.model_def, input_size), embedding_dims=self.embedding_dims) @@ -110,16 +112,16 @@ def _fit(self, X, Y=None, shuffle_mode=True): }, loss_weights={'lambda_1': 0.5, 'classification_out': 0.5}) - + self.encoder = self.model_.layers[3] if self.verbose > 0: print('Training neural network') - hist = self.model_.fit_generator(datagen, - steps_per_epoch=int(X.shape[0] / self.batch_size), - epochs=self.epochs, - callbacks=[EarlyStopping(monitor=loss_monitor, patience=self.n_epochs_without_progress)], + hist = self.model_.fit_generator(datagen, + steps_per_epoch=int(X.shape[0] / self.batch_size), + epochs=self.epochs, + callbacks=[EarlyStopping(monitor=loss_monitor, patience=self.n_epochs_without_progress)], shuffle=shuffle_mode, workers=multiprocessing.cpu_count(), verbose=self.verbose) @@ -127,12 +129,12 @@ def _fit(self, X, Y=None, shuffle_mode=True): def fit(self, X, Y=None, shuffle_mode=True): """Fit an ivis model. - + Parameters ---------- X : array, shape (n_samples, n_features) Data to be embedded. - + Returns ------- returns an instance of self @@ -148,7 +150,7 @@ def fit_transform(self, X, Y=None, shuffle_mode=True): ---------- X : array, shape (n_samples, n_features) Data to be embedded. - + Returns ------- X_new : transformed array, shape (n_samples, embedding_dims) @@ -157,16 +159,16 @@ def fit_transform(self, X, Y=None, shuffle_mode=True): self.fit(X, Y, shuffle_mode) return self.transform(X) - + def transform(self, X): """Transform X into the existing embedded space and return that transformed output. - + Parameters ---------- X : array, shape (n_samples, n_features) New data to be transformed. - + Returns ------- X_new : array, shape (n_samples, embedding_dims) @@ -184,12 +186,12 @@ def save_model(self, folder_path): folder_path : string Path to serialised model files and metadata """ - os.makedirs(folder_path) + os.makedirs(folder_path) # serialize weights to HDF5 self.model_.save(os.path.join(folder_path, 'ivis_model.h5')) json.dump(self.__getstate__(), open(os.path.join(folder_path, 'ivis_params.json'), 'w')) - + def load_model(self, folder_path): """Load ivis model @@ -201,16 +203,16 @@ def load_model(self, folder_path): Returns ------- returns an ivis instance - """ + """ ivis_config = json.load(open(os.path.join(folder_path, 'ivis_params.json'), 'r')) self.__dict__ = ivis_config - + loss_function = triplet_loss(self.distance, self.margin) self.model_ = load_model(os.path.join(folder_path, 'ivis_model.h5'), custom_objects={'tf':tf, loss_function.__name__ : loss_function }) self.encoder = self.model_.layers[3] self.encoder._make_predict_function() return self - + diff --git a/ivis/nn/network.py b/ivis/nn/network.py index 8d027c2..765a918 100644 --- a/ivis/nn/network.py +++ b/ivis/nn/network.py @@ -25,9 +25,10 @@ def output_shape(shapes): processed_n = network(input_n) triplet = Lambda(K.stack, - output_shape=output_shape)([processed_a, + output_shape=output_shape, + name='lambda_1')([processed_a, processed_p, - processed_n]) + processed_n],) model = Model([input_a, input_p, input_n], triplet) return model, processed_a, processed_p, processed_n From 5aa71dffd82cab9d29cd757f41d7e80ade7917bf Mon Sep 17 00:00:00 2001 From: idroz Date: Wed, 26 Jun 2019 19:19:31 +0100 Subject: [PATCH 03/13] add docstrings for supervised ivis --- ivis/ivis.py | 111 +++++++++++++++++++++++++++++++++++++-------------- 1 file changed, 80 insertions(+), 31 deletions(-) diff --git a/ivis/ivis.py b/ivis/ivis.py index 2bafb3f..fd9e9ea 100644 --- a/ivis/ivis.py +++ b/ivis/ivis.py @@ -5,7 +5,7 @@ from .data.knn import build_annoy_index from keras.callbacks import EarlyStopping -from keras.models import model_from_json, load_model, Model +from keras.models import load_model, Model from keras.layers import Dense import numpy as np @@ -16,24 +16,56 @@ import multiprocessing import tensorflow as tf + class Ivis(BaseEstimator): - """Ivis is a technique that uses an artificial neural network for dimensionality reduction, often useful for the purposes of visualization. - The network trains on triplets of data-points at a time and pulls positive points together, while pushing more distant points away from each other. - Triplets are sampled from the original data using KNN aproximation using the Annoy library. + """Ivis is a technique that uses an artificial neural network for + dimensionality reduction, often useful for the purposes of visualization. + The network trains on triplets of data-points at a time and pulls positive + points together, while pushing more distant points away from each other. + Triplets are sampled from the original data using KNN aproximation using + the Annoy library. :param int embedding_dims: Number of dimensions in the embedding space - :param int k: The number of neighbours to retrieve for each point. Must be less than one minus the number of rows in the dataset. - :param str distance: The loss function used to train the neural network. One of "pn", "euclidean", "manhattan_pn", "manhattan", "chebyshev", "chebyshev_pn", "softmax_ratio_pn", "softmax_ratio". - :param int batch_size: The size of mini-batches used during gradient descent while training the neural network. Must be less than the num_rows in the dataset. - :param int epochs: The maximum number of epochs to train the model for. Each epoch the network will see a triplet based on each data-point once. - :param int n_epochs_without_progress: After n number of epochs without an improvement to the loss, terminate training early. - :param float margin: The distance that is enforced between points by the triplet loss functions. - :param int ntrees: The number of random projections trees built by Annoy to approximate KNN. The more trees the higher the memory usage, but the better the accuracy of results. - :param int search_k: The maximum number of nodes inspected during a nearest neighbour query by Annoy. The higher, the more computation time required, but the higher the accuracy. The default is n_trees * k, where k is the number of neighbours to retrieve. If this is set too low, a variable number of neighbours may be retrieved per data-point. - :param bool precompute: Whether to pre-compute the nearest neighbours. Pre-computing is significantly faster, but requires more memory. If memory is limited, try setting this to False. - :param str model: str or keras.models.Model. The keras model to train using triplet loss. If a model object is provided, an embedding layer of size 'embedding_dims' will be appended to the end of the network. If a string, a pre-defined network by that name will be used. Possible options are: 'default', 'hinton', 'maaten'. By default, a selu network composed of 3 dense layers of 128 neurons each will be created, followed by an embedding layer of size 'embedding_dims'. - :param str annoy_index_path: The filepath of a pre-trained annoy index file saved on disk. If provided, the annoy index file will be used. Otherwise, a new index will be generated and saved to disk in the current directory as 'annoy.index'. - :param int verbose: Controls the volume of logging output the model produces when training. When set to 0, silences outputs, when above 0 will print outputs. + :param int k: The number of neighbours to retrieve for each point. + Must be less than one minus the number of rows in the dataset. + :param str distance: The loss function used to train the neural network. + One of "pn", "euclidean", "manhattan_pn", "manhattan", "chebyshev", + "chebyshev_pn", "softmax_ratio_pn", "softmax_ratio". + :param int batch_size: The size of mini-batches used during gradient + descent while training the neural network. Must be less than the + num_rows in the dataset. + :param int epochs: The maximum number of epochs to train the model for. + Each epoch the network will see a triplet based on each data-point + once. + :param int n_epochs_without_progress: After n number of epochs without an + improvement to the loss, terminate training early. + :param float margin: The distance that is enforced between points by the + triplet loss functions. + :param int ntrees: The number of random projections trees built by Annoy to + approximate KNN. The more trees the higher the memory usage, but the + better the accuracy of results. + :param int search_k: The maximum number of nodes inspected during a nearest + neighbour query by Annoy. The higher, the more computation time + required, but the higher the accuracy. The default is n_trees * k, + where k is the number of neighbours to retrieve. If this is set too + low, a variable number of neighbours may be retrieved per data-point. + :param bool precompute: Whether to pre-compute the nearest neighbours. + Pre-computing is a little faster, but requires more memory. If memory + is limited, try setting this to False. + :param str model: str or keras.models.Model. The keras model to train using + triplet loss. If a model object is provided, an embedding layer of size + 'embedding_dims' will be appended to the end of the network. + If a string, a pre-defined network by that name will be used. Possible + options are: 'default', 'hinton', 'maaten'. By default, a selu network + composed of 3 dense layers of 128 neurons each will be created, + followed by an embedding layer of size 'embedding_dims'. + :param str annoy_index_path: The filepath of a pre-trained annoy index file + saved on disk. If provided, the annoy index file will be used. + Otherwise, a new index will be generated and saved to disk in the + current directory as 'annoy.index'. + :param int verbose: Controls the volume of logging output the model + produces when training. When set to 0, silences outputs, when above 0 + will print outputs. """ @@ -87,31 +119,39 @@ def _fit(self, X, Y=None, shuffle_mode=True): loss_monitor = 'loss' try: - triplet_loss_func = triplet_loss(distance=self.distance, margin=self.margin) + triplet_loss_func = triplet_loss(distance=self.distance, + margin=self.margin) except KeyError: raise ValueError('Loss function `{}` not implemented.'.format(self.distance)) if self.model_ is None: if type(self.model_def) is str: input_size = (X.shape[-1],) - self.model_, anchor_embedding, _, _ = triplet_network(base_network(self.model_def, input_size), embedding_dims=self.embedding_dims) + self.model_, anchor_embedding, _, _ = \ + triplet_network(base_network(self.model_def, input_size), + embedding_dims=self.embedding_dims) else: - self.model_, anchor_embedding, _, _ = triplet_network(self.model_def, embedding_dims=self.embedding_dims) + self.model_, anchor_embedding, _, _ = \ + triplet_network(self.model_def, + embedding_dims=self.embedding_dims) if Y is None: self.model_.compile(optimizer='adam', loss=triplet_loss_func) else: n_classes = len(np.unique(Y, axis=0)) - classification_output = Dense(n_classes, activation='softmax', name='classification_out')(anchor_embedding) - final_network = Model(inputs=self.model_.inputs, outputs=[self.model_.output, classification_output]) + classification_output = Dense(n_classes, activation='softmax', + name='classification_out')(anchor_embedding) + final_network = Model(inputs=self.model_.inputs, + outputs=[self.model_.output, + classification_output]) self.model_ = final_network self.model_.compile(optimizer='adam', - loss={'lambda_1': triplet_loss_func, - 'classification_out' : 'sparse_categorical_crossentropy' - }, - loss_weights={'lambda_1': 0.5, - 'classification_out': 0.5}) + loss={'lambda_1': triplet_loss_func, + 'classification_out': 'sparse_categorical_crossentropy' + }, + loss_weights={'lambda_1': 0.5, + 'classification_out': 0.5}) self.encoder = self.model_.layers[3] @@ -121,7 +161,8 @@ def _fit(self, X, Y=None, shuffle_mode=True): hist = self.model_.fit_generator(datagen, steps_per_epoch=int(X.shape[0] / self.batch_size), epochs=self.epochs, - callbacks=[EarlyStopping(monitor=loss_monitor, patience=self.n_epochs_without_progress)], + callbacks=[EarlyStopping(monitor=loss_monitor, + patience=self.n_epochs_without_progress)], shuffle=shuffle_mode, workers=multiprocessing.cpu_count(), verbose=self.verbose) @@ -134,6 +175,9 @@ def fit(self, X, Y=None, shuffle_mode=True): ---------- X : array, shape (n_samples, n_features) Data to be embedded. + Y : array, shape (n_samples) + Optional array for supervised dimentionality reduction. Currently + only classification is supported. Returns ------- @@ -150,6 +194,10 @@ def fit_transform(self, X, Y=None, shuffle_mode=True): ---------- X : array, shape (n_samples, n_features) Data to be embedded. + Y : array, shape (n_samples) + Optional array for supervised dimentionality reduction. Currently + only classification is supported. + Returns ------- @@ -190,7 +238,8 @@ def save_model(self, folder_path): # serialize weights to HDF5 self.model_.save(os.path.join(folder_path, 'ivis_model.h5')) - json.dump(self.__getstate__(), open(os.path.join(folder_path, 'ivis_params.json'), 'w')) + json.dump(self.__getstate__(), + open(os.path.join(folder_path, 'ivis_params.json'), 'w')) def load_model(self, folder_path): """Load ivis model @@ -206,13 +255,13 @@ def load_model(self, folder_path): """ ivis_config = json.load(open(os.path.join(folder_path, - 'ivis_params.json'), 'r')) + 'ivis_params.json'), 'r')) self.__dict__ = ivis_config loss_function = triplet_loss(self.distance, self.margin) self.model_ = load_model(os.path.join(folder_path, 'ivis_model.h5'), - custom_objects={'tf':tf, loss_function.__name__ : loss_function }) + custom_objects={'tf': tf, + loss_function.__name__ : loss_function }) self.encoder = self.model_.layers[3] self.encoder._make_predict_function() return self - From 22b182284485a17ee7aff62b6a64e8a6c1d03487 Mon Sep 17 00:00:00 2001 From: idroz Date: Wed, 26 Jun 2019 19:20:12 +0100 Subject: [PATCH 04/13] feature version bump --- setup.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/setup.py b/setup.py index 356a964..f3151a3 100644 --- a/setup.py +++ b/setup.py @@ -5,7 +5,7 @@ long_description = f.read() setup(name='ivis', - version='1.1.5', + version='1.2.0', description='Artificial neural network-driven visualization of high-dimensional data using triplets.', long_description=long_description, long_description_content_type='text/markdown', From 93d62691e0f8914d202627122f3e49dc2362ba2a Mon Sep 17 00:00:00 2001 From: Szubie Date: Fri, 28 Jun 2019 18:18:59 +0100 Subject: [PATCH 05/13] Add classification_weight parameter to allow users to tune balance between classification vs. triplet loss. --- ivis/ivis.py | 34 ++++++++++++++++++++++------------ ivis/nn/network.py | 12 +++++++----- 2 files changed, 29 insertions(+), 17 deletions(-) diff --git a/ivis/ivis.py b/ivis/ivis.py index fd9e9ea..ac4dee9 100644 --- a/ivis/ivis.py +++ b/ivis/ivis.py @@ -73,7 +73,8 @@ def __init__(self, embedding_dims=2, k=150, distance='pn', batch_size=128, epochs=1000, n_epochs_without_progress=50, margin=1, ntrees=50, search_k=-1, precompute=True, model='default', - annoy_index_path=None, verbose=1): + classification_weight=0.5, annoy_index_path=None, + callbacks=[], verbose=1): self.embedding_dims = embedding_dims self.k = k @@ -88,8 +89,10 @@ def __init__(self, embedding_dims=2, k=150, distance='pn', batch_size=128, self.model_def = model self.model_ = None self.encoder = None + self.classification_weight = classification_weight self.loss_history_ = [] self.annoy_index_path = annoy_index_path + self.callbacks = callbacks self.verbose = verbose def __getstate__(self): @@ -98,6 +101,7 @@ def __getstate__(self): state = dict(self.__dict__) del state['model_'] del state['encoder'] + del state['callbacks'] return state def _fit(self, X, Y=None, shuffle_mode=True): @@ -146,23 +150,29 @@ def _fit(self, X, Y=None, shuffle_mode=True): outputs=[self.model_.output, classification_output]) self.model_ = final_network - self.model_.compile(optimizer='adam', - loss={'lambda_1': triplet_loss_func, - 'classification_out': 'sparse_categorical_crossentropy' - }, - loss_weights={'lambda_1': 0.5, - 'classification_out': 0.5}) + self.model_.compile( + optimizer='adam', + loss={ + 'stacked_triplets': triplet_loss_func, + 'classification_out': 'sparse_categorical_crossentropy' + }, + loss_weights={ + 'stacked_triplets': 1 - self.classification_weight, + 'classification_out': self.classification_weight}) self.encoder = self.model_.layers[3] if self.verbose > 0: print('Training neural network') - hist = self.model_.fit_generator(datagen, - steps_per_epoch=int(X.shape[0] / self.batch_size), + hist = self.model_.fit_generator( + datagen, + steps_per_epoch=X.shape[0] // self.batch_size, epochs=self.epochs, - callbacks=[EarlyStopping(monitor=loss_monitor, - patience=self.n_epochs_without_progress)], + callbacks=[ + *self.callbacks, + EarlyStopping(monitor=loss_monitor, + patience=self.n_epochs_without_progress)], shuffle=shuffle_mode, workers=multiprocessing.cpu_count(), verbose=self.verbose) @@ -261,7 +271,7 @@ def load_model(self, folder_path): loss_function = triplet_loss(self.distance, self.margin) self.model_ = load_model(os.path.join(folder_path, 'ivis_model.h5'), custom_objects={'tf': tf, - loss_function.__name__ : loss_function }) + loss_function.__name__: loss_function }) self.encoder = self.model_.layers[3] self.encoder._make_predict_function() return self diff --git a/ivis/nn/network.py b/ivis/nn/network.py index 765a918..de81c98 100644 --- a/ivis/nn/network.py +++ b/ivis/nn/network.py @@ -4,7 +4,7 @@ from keras.layers import Input, Dense, AlphaDropout, Lambda from keras import backend as K -from keras import regularizers +from keras.regularizers import l2 def triplet_network(base_network, embedding_dims=2, embedding_l2=0.0): @@ -17,7 +17,7 @@ def output_shape(shapes): input_n = Input(shape=base_network.input_shape[1:]) embeddings = Dense(embedding_dims, - kernel_regularizer=regularizers.l2(embedding_l2))(base_network.output) + kernel_regularizer=l2(embedding_l2))(base_network.output) network = Model(base_network.input, embeddings) processed_a = network(input_a) @@ -26,9 +26,9 @@ def output_shape(shapes): triplet = Lambda(K.stack, output_shape=output_shape, - name='lambda_1')([processed_a, - processed_p, - processed_n],) + name='stacked_triplets')([processed_a, + processed_p, + processed_n],) model = Model([input_a, input_p, input_n], triplet) return model, processed_a, processed_p, processed_n @@ -47,9 +47,11 @@ def base_network(model_name, input_shape): raise NotImplementedError( 'Base network {} is not implemented'.format(model_name)) + def get_base_networks(): return ['default', 'hinton', 'maaten'] + def default_base_network(input_shape): '''Base network to be shared (eq. to feature extraction). ''' From f6ca7200aa54d0eb208cf84922335b235ec073b7 Mon Sep 17 00:00:00 2001 From: idroz Date: Sun, 30 Jun 2019 16:47:03 +0100 Subject: [PATCH 06/13] make version importable as per sklearn template --- ivis/__init__.py | 4 +++- setup.py | 6 +++++- 2 files changed, 8 insertions(+), 2 deletions(-) diff --git a/ivis/__init__.py b/ivis/__init__.py index f12f52a..4d81502 100644 --- a/ivis/__init__.py +++ b/ivis/__init__.py @@ -1,3 +1,5 @@ from . import data from . import nn -from .ivis import Ivis \ No newline at end of file +from .ivis import Ivis + +__version__ = '1.2.0' diff --git a/setup.py b/setup.py index f3151a3..e47d8b6 100644 --- a/setup.py +++ b/setup.py @@ -1,11 +1,15 @@ +import ivis + from setuptools import setup from setuptools import find_packages +VERSION = ivis.__version__ + with open('README.md', encoding='utf-8') as f: long_description = f.read() setup(name='ivis', - version='1.2.0', + version=VERSION, description='Artificial neural network-driven visualization of high-dimensional data using triplets.', long_description=long_description, long_description_content_type='text/markdown', From 68dec6933e9339385ed4adc88364ea1a5bcc8b9a Mon Sep 17 00:00:00 2001 From: Szubie Date: Mon, 1 Jul 2019 14:43:59 +0100 Subject: [PATCH 07/13] Document new Ivis parameters --- ivis/ivis.py | 7 +++++++ 1 file changed, 7 insertions(+) diff --git a/ivis/ivis.py b/ivis/ivis.py index ac4dee9..0c68873 100644 --- a/ivis/ivis.py +++ b/ivis/ivis.py @@ -59,10 +59,17 @@ class Ivis(BaseEstimator): options are: 'default', 'hinton', 'maaten'. By default, a selu network composed of 3 dense layers of 128 neurons each will be created, followed by an embedding layer of size 'embedding_dims'. + :param float classification_weight: Float between 0 and 1 denoting the + weighting to give to classification vs triplet loss when training + in supervised mode. The higher the weight, the more classification + influences training. Ignored if using Ivis in unsupervised mode. :param str annoy_index_path: The filepath of a pre-trained annoy index file saved on disk. If provided, the annoy index file will be used. Otherwise, a new index will be generated and saved to disk in the current directory as 'annoy.index'. + :param list[keras.callbacks.Callback] callbacks: List of keras Callbacks to + pass model during training, such as the TensorBoard callback. A set of + ivis-specific callbacks are provided in the ivis.nn.callbacks module. :param int verbose: Controls the volume of logging output the model produces when training. When set to 0, silences outputs, when above 0 will print outputs. From 4e152bf9a6d87f5a5dfb826de64ead82680a40e8 Mon Sep 17 00:00:00 2001 From: Szubie Date: Mon, 1 Jul 2019 14:45:31 +0100 Subject: [PATCH 08/13] Add Ivis callbacks module for ivis-specific callbacks such as checkpointing during training. Ivis object code changed to deal with provided callbacks. --- ivis/ivis.py | 10 ++++- ivis/nn/callbacks.py | 87 ++++++++++++++++++++++++++++++++++++++++++++ 2 files changed, 96 insertions(+), 1 deletion(-) create mode 100644 ivis/nn/callbacks.py diff --git a/ivis/ivis.py b/ivis/ivis.py index 0c68873..d7da1b5 100644 --- a/ivis/ivis.py +++ b/ivis/ivis.py @@ -1,6 +1,7 @@ """ scikit-learn wrapper class for the Ivis algorithm. """ from .data.triplet_generators import generator_from_index from .nn.network import triplet_network, base_network +from .nn.callbacks import ModelCheckpoint from .nn.losses import triplet_loss from .data.knn import build_annoy_index @@ -13,6 +14,7 @@ import json import os +import shutil import multiprocessing import tensorflow as tf @@ -100,6 +102,9 @@ def __init__(self, embedding_dims=2, k=150, distance='pn', batch_size=128, self.loss_history_ = [] self.annoy_index_path = annoy_index_path self.callbacks = callbacks + for callback in self.callbacks: + if isinstance(callback, ModelCheckpoint): + callback = callback.register_ivis_model(self) self.verbose = verbose def __getstate__(self): @@ -243,7 +248,7 @@ def transform(self, X): embedding = self.encoder.predict(X, verbose=self.verbose) return embedding - def save_model(self, folder_path): + def save_model(self, folder_path, overwrite=False): """Save an ivis model Parameters @@ -251,6 +256,9 @@ def save_model(self, folder_path): folder_path : string Path to serialised model files and metadata """ + if overwrite: + if os.path.exists(folder_path): + shutil.rmtree(folder_path) os.makedirs(folder_path) # serialize weights to HDF5 self.model_.save(os.path.join(folder_path, 'ivis_model.h5')) diff --git a/ivis/nn/callbacks.py b/ivis/nn/callbacks.py new file mode 100644 index 0000000..53301af --- /dev/null +++ b/ivis/nn/callbacks.py @@ -0,0 +1,87 @@ +from keras.callbacks import Callback +from matplotlib import pyplot as plt +import seaborn as sns +from sklearn.preprocessing import MinMaxScaler +import numpy as np +import io +import tensorflow as tf +import os + + +class ModelCheckpoint(Callback): + def __init__(self, log_dir='./model_checkpoints', + filename='model-checkpoint_{}.ivis', epoch_interval=1): + super(ModelCheckpoint, self).__init__() + self.log_dir = log_dir + os.makedirs(log_dir, exist_ok=True) + self.filename = filename + self.epoch_interval = epoch_interval + self.epochs_since_last_save = 0 + + def on_epoch_end(self, epoch, logs=None): + self.epochs_since_last_save += 1 + if self.epochs_since_last_save >= self.epoch_interval: + self.epochs_since_last_save = 0 + filename = self.filename.format(epoch + 1) + self.ivis_model.save_model( + os.path.join(self.log_dir, filename), overwrite=True) + + def register_ivis_model(self, ivis_model): + self.ivis_model = ivis_model + + +class EmbeddingsLogging(Callback): + def __init__(self, batch, log_dir='./embeddings_logs', + filename='{}_embeddings.npy', epoch_interval=1): + super(EmbeddingsLogging, self).__init__() + self.batch = batch + self.log_dir = log_dir + os.makedirs(log_dir, exist_ok=True) + self.filename = filename + self.epoch_interval = epoch_interval + self.epochs_since_last_save = 0 + + def on_epoch_end(self, epoch, logs=None): + self.epochs_since_last_save += 1 + if self.epochs_since_last_save >= self.epoch_interval: + self.epochs_since_last_save = 0 + self.embeddings = self.model.layers[3].predict(self.batch) + filename = self.filename.format(epoch + 1) + np.save(os.path.join(self.log_dir, filename), self.embeddings) + + +class EmbeddingsImage(Callback): + def __init__(self, batch, batch_labels=None, log_dir='./logs', + filename='{}_embeddings.png', epoch_interval=1): + super(EmbeddingsImage, self).__init__() + self.batch = batch + if batch_labels is not None: + self.batch_labels = batch_labels + else: + self.batch_labels = np.zeros(len(batch)) + self.n_classes = len(np.unique(self.batch_labels, axis=0)) + self.log_dir = log_dir + os.makedirs(log_dir, exist_ok=True) + self.filename = filename + self.epoch_interval = epoch_interval + self.epochs_since_last_save = 0 + + def on_epoch_end(self, epoch, logs=None): + self.epochs_since_last_save += 1 + if self.epochs_since_last_save >= self.epoch_interval: + self.epochs_since_last_save = 0 + self.embeddings = self.model.layers[3].predict(self.batch) + filename = self.filename.format(epoch + 1) + self.plot_embeddings(filename) + + def plot_embeddings(self, filename): + embeddings = MinMaxScaler((0, 1)).fit_transform(self.embeddings) + + fig = plt.figure() + sns.scatterplot(x=embeddings[:, 0], y=embeddings[:, 1], s=1, + hue=self.batch_labels, + palette=sns.color_palette("hls", self.n_classes), + linewidth=0) + + plt.savefig(os.path.join(self.log_dir, filename), dpi=300) + plt.close(fig) From cf6a5ed8ff98a2c6cb106387a359029dd2475449 Mon Sep 17 00:00:00 2001 From: Szubie Date: Mon, 1 Jul 2019 15:05:38 +0100 Subject: [PATCH 09/13] Expand .gitignore, restructure some code for clarity and readability, following pep8. --- .gitignore | 1 + ivis/data/knn.py | 43 ++++++++++++++-------- ivis/data/triplet_generators.py | 63 ++++++++++++++++++++------------- 3 files changed, 68 insertions(+), 39 deletions(-) diff --git a/.gitignore b/.gitignore index 3381df8..858a026 100644 --- a/.gitignore +++ b/.gitignore @@ -7,3 +7,4 @@ __pycache__/ *.swp .ipynb_checkpoints _build +*.pyc diff --git a/ivis/data/knn.py b/ivis/data/knn.py index 230f0b7..2b91366 100644 --- a/ivis/data/knn.py +++ b/ivis/data/knn.py @@ -7,17 +7,18 @@ from collections import namedtuple from operator import attrgetter from tqdm import tqdm -import time +import time from scipy.sparse import issparse + def build_annoy_index(X, path, ntrees=50, verbose=1): - + index = AnnoyIndex(X.shape[1]) index.on_disk_build(path) if issparse(X): for i in tqdm(range(X.shape[0]), disable=verbose < 1): - v = X[i].toarray()[0] + v = X[i].toarray()[0] index.add_item(i, v) else: for i in tqdm(range(X.shape[0]), disable=verbose < 1): @@ -28,8 +29,10 @@ def build_annoy_index(X, path, ntrees=50, verbose=1): index.build(ntrees) return index + def extract_knn(X, index_filepath, k=150, search_k=-1, verbose=1): - """ Starts multiple processes to retrieve nearest neighbours using an Annoy Index in parallel """ + """ Starts multiple processes to retrieve nearest neighbours using + an Annoy Index in parallel """ n_dims = X.shape[1] @@ -41,15 +44,17 @@ def extract_knn(X, index_filepath, k=150, search_k=-1, verbose=1): # Split up the indices and assign processes for each chunk i = 0 while (i + chunk_size) <= X.shape[0]: - process_pool.append(KNN_Worker(index_filepath, k, search_k, n_dims, (i, i+chunk_size), results_queue)) + process_pool.append(KNN_Worker(index_filepath, k, search_k, n_dims, + (i, i+chunk_size), results_queue)) i += chunk_size if remainder: - process_pool.append(KNN_Worker(index_filepath, k, search_k, n_dims, (i, X.shape[0]), results_queue)) + process_pool.append(KNN_Worker(index_filepath, k, search_k, n_dims, + (i, X.shape[0]), results_queue)) for process in process_pool: process.start() - # Read from results queue as processes write to it to prevent queue becoming full + # Read from queue constantly to prevent it from becoming full with tqdm(total=X.shape[0], disable=verbose < 1) as pbar: neighbour_list = [] neighbour_list_length = len(neighbour_list) @@ -71,15 +76,19 @@ def extract_knn(X, index_filepath, k=150, search_k=-1, verbose=1): IndexNeighbours = namedtuple('IndexNeighbours', 'row_index neighbour_list') + class KNN_Worker(Process): """ - Upon construction, this worker process loads an annoy index from disk. Upon being started, the neighbours of the - data-points specified by 'data_indices' variable will be retrieved from the index according to - the provided parameters and stored in the 'results_queue'. + Upon construction, this worker process loads an annoy index from disk. + When started, the neighbours of the data-points specified by `data_indices` + will be retrieved from the index according to the provided parameters + and stored in the 'results_queue'. - data_indices is a tuple of integers denoting the start and end range of indices to retrieve + `data_indices` is a tuple of integers denoting the start and end range of + indices to retrieve. """ - def __init__(self, index_filepath, k, search_k, n_dims, data_indices, results_queue): + def __init__(self, index_filepath, k, search_k, n_dims, + data_indices, results_queue): self.index = AnnoyIndex(n_dims) self.index.load(index_filepath) self.k = k @@ -91,9 +100,13 @@ def __init__(self, index_filepath, k, search_k, n_dims, data_indices, results_qu def run(self): try: for i in range(self.data_indices[0], self.data_indices[1]): - neighbour_indexes = self.index.get_nns_by_item(i, self.k, search_k=self.search_k, include_distances=False) - neighbour_indexes = np.array(neighbour_indexes, dtype=np.uint32) - self.results_queue.put(IndexNeighbours(row_index=i, neighbour_list=neighbour_indexes)) + neighbour_indexes = self.index.get_nns_by_item( + i, self.k, search_k=self.search_k, include_distances=False) + neighbour_indexes = np.array(neighbour_indexes, + dtype=np.uint32) + self.results_queue.put( + IndexNeighbours(row_index=i, + neighbour_list=neighbour_indexes)) except Exception as e: self.exception = e finally: diff --git a/ivis/data/triplet_generators.py b/ivis/data/triplet_generators.py index 57e83cf..5e152c8 100644 --- a/ivis/data/triplet_generators.py +++ b/ivis/data/triplet_generators.py @@ -3,12 +3,12 @@ Functions for creating generators that will yield batches of triplets. -Triplets will be created using KNNs, which can either be precomputed or +Triplets will be created using KNNs, which can either be precomputed or dynamically generated. - generate_knn_triplets_from_neighbour_list will precompute KNNs - generate_knn_triplets_from_annoy_index will dynamically generate KNNs -Where possible, precomputed KNNs are advised for speed, but where memory is +Where possible, precomputed KNNs are advised for speed, but where memory is a concern, dynamically generated triplets can be useful. """ @@ -20,34 +20,47 @@ from scipy.sparse import issparse -def generator_from_index(X, Y, index_path, k, batch_size, search_k=-1, precompute=True, verbose=1): +def generator_from_index(X, Y, index_path, k, batch_size, search_k=-1, + precompute=True, verbose=1): if k >= X.shape[0] - 1: - raise Exception('k value greater than or equal to (num_rows - 1) (k={}, rows={}). Lower k to a smaller value.'.format(k, X.shape[0])) + raise Exception('''k value greater than or equal to (num_rows - 1) + (k={}, rows={}). Lower k to a smaller + value.'''.format(k, X.shape[0])) if batch_size > X.shape[0]: - raise Exception('batch_size value larger than num_rows in dataset (batch_size={}, rows={}). Lower batch_size to a smaller value.'.format(batch_size, X.shape[0])) + raise Exception('''batch_size value larger than num_rows in dataset + (batch_size={}, rows={}). Lower batch_size to a + smaller value.'''.format(batch_size, X.shape[0])) if Y is None: if precompute: if verbose > 0: print('Extracting KNN from index') - neighbour_matrix = extract_knn(X, index_path, k=k, search_k=search_k, verbose=verbose) - return KnnTripletGenerator(X, neighbour_matrix, batch_size=batch_size) + neighbour_matrix = extract_knn(X, index_path, k=k, + search_k=search_k, verbose=verbose) + return KnnTripletGenerator(X, neighbour_matrix, + batch_size=batch_size) else: index = AnnoyIndex(X.shape[1]) index.load(index_path) - return AnnoyTripletGenerator(X, index, k=k, batch_size=batch_size, search_k=search_k) + return AnnoyTripletGenerator(X, index, k=k, + batch_size=batch_size, + search_k=search_k) else: if precompute: if verbose > 0: print('Extracting KNN from index') - - neighbour_matrix = extract_knn(X, index_path, k=k, search_k=search_k, verbose=verbose) - return LabeledKnnTripletGenerator(X, Y, neighbour_matrix, batch_size=batch_size) + + neighbour_matrix = extract_knn(X, index_path, k=k, + search_k=search_k, verbose=verbose) + return LabeledKnnTripletGenerator(X, Y, neighbour_matrix, + batch_size=batch_size) else: index = AnnoyIndex(X.shape[1]) index.load(index_path) - return LabeledAnnoyTripletGenerator(X, Y, index, k=k, batch_size=batch_size, search_k=search_k) + return LabeledAnnoyTripletGenerator(X, Y, index, + k=k, batch_size=batch_size, + search_k=search_k) class AnnoyTripletGenerator(Sequence): @@ -64,7 +77,8 @@ def __len__(self): return int(np.ceil(len(self.X) / float(self.batch_size))) def __getitem__(self, idx): - batch_indices = range(idx * self.batch_size, min((idx + 1) * self.batch_size, self.X.shape[0])) + batch_indices = range(idx * self.batch_size, + min((idx + 1) * self.batch_size, self.X.shape[0])) placeholder_labels = self.placeholder_labels[:len(batch_indices)] triplet_batch = [self.knn_triplet_from_annoy_index(row_index) @@ -88,7 +102,7 @@ def knn_triplet_from_annoy_index(self, row_index): neighbour_ind = np.random.choice(neighbour_list) # Take a random non-neighbour as negative - negative_ind = np.random.randint(0, self.X.shape[0]) # Pick a random index until one fits constraint. An optimization. + negative_ind = np.random.randint(0, self.X.shape[0]) # Pick a random index until one fits constraint. An optimization. while negative_ind in neighbour_list: negative_ind = np.random.randint(0, self.X.shape[0]) @@ -137,33 +151,34 @@ def knn_triplet_from_neighbour_list(self, row_index, neighbour_list): triplets += [self.X[row_index], self.X[neighbour_ind], self.X[negative_ind]] return triplets + class LabeledAnnoyTripletGenerator(Sequence): - + def __init__(self, X, Y, annoy_index, k=150, batch_size=32, search_k=-1): self.X, self.Y = np.array(X), np.array(Y) self.annoy_index = annoy_index self.k = k self.batch_size = batch_size self.search_k = search_k - + def __len__(self): return int(np.ceil(len(self.X) / float(self.batch_size))) - + def __getitem__(self, idx): batch_indices = range(idx * self.batch_size, min((idx + 1) * self.batch_size, self.X.shape[0])) - + label_batch = self.Y[batch_indices] triplet_batch = [ self.knn_triplet_from_annoy_index(row_index) for row_index in batch_indices ] - + if (issparse(self.X)): triplet_batch = [[e.toarray()[0] for e in t] for t in triplet_batch] - + triplet_batch = np.array(triplet_batch) - - return ([triplet_batch[:,0], triplet_batch[:,1], triplet_batch[:,2]], [np.array(label_batch), np.array(label_batch)]) - - + + return ([triplet_batch[:, 0], triplet_batch[:, 1], triplet_batch[:, 2]], + [np.array(label_batch), np.array(label_batch)]) + def knn_triplet_from_annoy_index(self, row_index): """ A random (unweighted) positive example chosen. """ triplet = [] From d1c7496e097ab76d09a72ece564c31cb32b77846 Mon Sep 17 00:00:00 2001 From: Szubie Date: Mon, 1 Jul 2019 15:36:49 +0100 Subject: [PATCH 10/13] Add TensorBoard scatterplot callback --- ivis/nn/callbacks.py | 43 +++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 43 insertions(+) diff --git a/ivis/nn/callbacks.py b/ivis/nn/callbacks.py index 53301af..0853489 100644 --- a/ivis/nn/callbacks.py +++ b/ivis/nn/callbacks.py @@ -85,3 +85,46 @@ def plot_embeddings(self, filename): plt.savefig(os.path.join(self.log_dir, filename), dpi=300) plt.close(fig) + + +class TensorBoardEmbeddingsImage(Callback): + def __init__(self, batch, batch_labels=None, + log_dir='./logs', epoch_interval=1): + super(TensorBoardEmbeddingsImage, self).__init__() + self.batch = batch + self.log_dir = log_dir + if batch_labels is not None: + self.batch_labels = batch_labels + else: + self.batch_labels = np.zeros(len(batch)) + self.n_classes = len(np.unique(self.batch_labels, axis=0)) + self.epochs_since_last_save = 0 + + def on_epoch_end(self, epoch, logs=None): + self.epochs_since_last_save += 1 + if self.epochs_since_last_save >= self.epoch_interval: + self.embeddings = self.model.layers[3].predict(self.batch) + image = self.plot_embeddings(self.embeddings) + summary = tf.summary.Summary( + value=[tf.Summary.Value(tag='Embeddings', image=image)]) + + writer = tf.summary.FileWriter(self.log_dir + '/embeddings') + writer.add_summary(summary, epoch) + writer.close() + + def plot_embeddings(self, embeddings): + embeddings = MinMaxScaler((0, 1)).fit_transform(self.embeddings) + + fig = plt.figure() + buf = io.BytesIO() + sns.scatterplot(x=embeddings[:, 0], y=embeddings[:, 1], s=1, + hue=self.batch_labels, + palette=sns.color_palette("hls", self.n_classes), + linewidth=0) + + plt.savefig(buf, format='png', dpi=300) + plt.close(fig) + buf.seek(0) + + image = tf.Summary.Image(encoded_image_string=buf.getvalue()) + return image From 4d4ff97a887220311f82e954b0291e7f0046a69e Mon Sep 17 00:00:00 2001 From: Szubie Date: Mon, 1 Jul 2019 16:34:32 +0100 Subject: [PATCH 11/13] Fixed sparse matrix support with new data generators - replacing len() function call with .shape[0] --- ivis/data/triplet_generators.py | 20 +++++++++++--------- 1 file changed, 11 insertions(+), 9 deletions(-) diff --git a/ivis/data/triplet_generators.py b/ivis/data/triplet_generators.py index 5e152c8..6d86746 100644 --- a/ivis/data/triplet_generators.py +++ b/ivis/data/triplet_generators.py @@ -74,7 +74,7 @@ def __init__(self, X, annoy_index, k=150, batch_size=32, search_k=-1): self.placeholder_labels = np.empty(batch_size, dtype=np.uint8) def __len__(self): - return int(np.ceil(len(self.X) / float(self.batch_size))) + return int(np.ceil(self.X.shape[0] / float(self.batch_size))) def __getitem__(self, idx): batch_indices = range(idx * self.batch_size, @@ -119,7 +119,7 @@ def __init__(self, X, neighbour_matrix, batch_size=32): self.placeholder_labels = np.empty(batch_size, dtype=np.uint8) def __len__(self): - return int(np.ceil(len(self.X) / float(self.batch_size))) + return int(np.ceil(self.X.shape[0] / float(self.batch_size))) def __getitem__(self, idx): batch_indices = range(idx * self.batch_size, min((idx + 1) * self.batch_size, self.X.shape[0])) @@ -162,14 +162,14 @@ def __init__(self, X, Y, annoy_index, k=150, batch_size=32, search_k=-1): self.search_k = search_k def __len__(self): - return int(np.ceil(len(self.X) / float(self.batch_size))) + return int(np.ceil(self.X.shape[0] / float(self.batch_size))) def __getitem__(self, idx): batch_indices = range(idx * self.batch_size, min((idx + 1) * self.batch_size, self.X.shape[0])) label_batch = self.Y[batch_indices] - triplet_batch = [ self.knn_triplet_from_annoy_index(row_index) - for row_index in batch_indices ] + triplet_batch = [self.knn_triplet_from_annoy_index(row_index) + for row_index in batch_indices] if (issparse(self.X)): triplet_batch = [[e.toarray()[0] for e in t] for t in triplet_batch] @@ -196,6 +196,7 @@ def knn_triplet_from_annoy_index(self, row_index): triplet += [self.X[row_index], self.X[neighbour_ind], self.X[negative_ind]] return triplet + class LabeledKnnTripletGenerator(Sequence): def __init__(self, X, Y, neighbour_matrix, batch_size=32): @@ -204,21 +205,22 @@ def __init__(self, X, Y, neighbour_matrix, batch_size=32): self.batch_size = batch_size def __len__(self): - return int(np.ceil(len(self.X) / float(self.batch_size))) + return int(np.ceil(self.X.shape[0] / float(self.batch_size))) def __getitem__(self, idx): batch_indices = range(idx * self.batch_size, min((idx + 1) * self.batch_size, self.X.shape[0])) label_batch = self.Y[batch_indices] - triplet_batch = [ self.knn_triplet_from_neighbour_list(row_index, self.neighbour_matrix[row_index]) - for row_index in batch_indices ] + triplet_batch = [self.knn_triplet_from_neighbour_list(row_index, self.neighbour_matrix[row_index]) + for row_index in batch_indices] if (issparse(self.X)): triplet_batch = [[e.toarray()[0] for e in t] for t in triplet_batch] triplet_batch = np.array(triplet_batch) - return ([triplet_batch[:,0], triplet_batch[:,1], triplet_batch[:,2]], [np.array(label_batch), np.array(label_batch)]) + return ([triplet_batch[:, 0], triplet_batch[:, 1], triplet_batch[:, 2]], + [np.array(label_batch), np.array(label_batch)]) def knn_triplet_from_neighbour_list(self, row_index, neighbour_list): From 439c74391c7b1473e48081aab76c17b1bee278bd Mon Sep 17 00:00:00 2001 From: Szubie Date: Mon, 1 Jul 2019 17:23:25 +0100 Subject: [PATCH 12/13] Add docs for supervised ivis --- .../classification-weight-impact-mnist.png | Bin 0 -> 1046743 bytes .../mnist-embedding-comparison_titled.png | Bin 0 -> 1080849 bytes docs/index.rst | 1 + docs/supervised.rst | 62 ++++++++++++++++++ 4 files changed, 63 insertions(+) create mode 100644 docs/_static/classification-weight-impact-mnist.png create mode 100644 docs/_static/mnist-embedding-comparison_titled.png create mode 100644 docs/supervised.rst diff --git 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