Finding similar texts or images is a very common problem in machine learning used extensively for search and recommendation. Although the problem is very common and has high business value to some organisations, but still this has remained one of the most challenging problems when the database size is very large such as >50GB and we do not want to lose on precision and recall much by retrieving only 'approximately' close results.

In this post we are going to look how to find similar items using deep learning techniques along with standard engineering best practices. But most importantly we are going to tackle this for a dataset that is larger than the available CPU memory of a 8GB or even a 16GB machine.

A generic approach to solve this kind of problems is to:

- For text data.
- Compute some kind of representation for each item using either PCA, LDA or TF-IDF weighted word vectors etc.
- Either do a hierarchical tree clustering or create a fast nearest neighbour lookup index such as a KD-Tree with the representations.
- For a new incoming item, compute its representation and then search the clusters or the KD-Tree for the most similar items within certain distance threshold.
- The run-time complexity for lookup in both approaches would be O(D*logN) where N is the number of items and D is the dimension of the representations.

- For image data.

The above approach is generally suitable when we are in a purely unsupervised setting i.e. we do not know which items are similar and which are not. For e.g. in question-answer mining, we cannot know without looking at all the questions themselves, which of them are similar.

But assuming that we have supervised data or some high-level grouping abstraction which places similar items under one group. For e.g. in e-commerce data, 'product type' such as 'Laptops', 'T-Shirts', 'Mobile Phones' etc. is a high-level grouping abstraction because items under 'Laptops' would be more similar to each other as compared to an item from 'Laptop' and an item from 'T-Shirt'.

The level of grouping abstraction can go to any depth, for e.g. different color and size variants for the same t-shirt or different t-shirts from the same brand and so on. It depends on the kind of problem and business requirement till what level of grouping abstraction we want.

Unlike in unsupervised similarity models where each feature of an item has equal weightage or best we can do is to give TF-IDF weighting, if we train a supervised model with pairs of items and learn which items are similar and which are not, then the model will learn to give appropriate weightage to each feature of the item as to how important that feature is when finding the similar items.

For e.g. if two t-shirts have the following titles:

"Katso Men's Cotton T-Shirt"

"Maniac Men's Cotton T-Shirt"

Then although 3/4-th of the words are same but these items are not similar because they belong to different brands "Katso" and "Maniac" respectively. Thus one word makes all the difference here. Only a supervised model can learn that the weightage of the words "Katso" and "Maniac" matters.

Thus we train a model using pairs of items by giving a label 1 to pair in which the two items are similar and label 0 to a pair where the two items are dissimilar. Train a Siamese Network.

- For text data we use an Embedding+BiLSTM based Siamese network.
- For image data we use a CNN+MaxPool based Siamese network.
- Merge the shared layers of the Siamese network using weighted L2 distance.
- Output is the sigmoid function of the weighted L2 distance.
- Model is trained using "Adam" optimizer and 'binary_crossentropy" loss function.
- Batch size for text is kept as 32 whereas for image it is kept at 256.

BiLSTM based Text Siamese Model:

def init_model(self): input1 = Input(shape=(cnt.MAX_WORDS, cnt.WV_EMBEDDING_SIZE)) input2 = Input(shape=(cnt.MAX_WORDS, cnt.WV_EMBEDDING_SIZE)) nlayer = TimeDistributed(Dense(cnt.SIAMESE_EMBEDDING_SIZE, activation="relu")) nlayer1 = nlayer(input1) nlayer2 = nlayer(input2) nlayer = Flatten() nlayer1 = nlayer(nlayer1) nlayer2 = nlayer(nlayer2) nlayer = Dense(cnt.SIAMESE_EMBEDDING_SIZE, activation="linear") nlayer1 = nlayer(nlayer1) nlayer2 = nlayer(nlayer2) nlayer = Lambda(lambda x: K.l2_normalize(x, axis=1)) nlayer1 = nlayer(nlayer1) nlayer2 = nlayer(nlayer2) merge = Lambda(lambda x: K.sqrt(K.maximum(K.sum(K.square(x[0]-x[1]), axis=1, keepdims=True), K.epsilon())))([nlayer1, nlayer2]) out = Dense(1, activation="sigmoid")(merge) self.model = Model([input1, input2], out) adam = optimizers.Adam(lr=0.001) self.model.compile(optimizer=adam, loss='binary_crossentropy', metrics=['accuracy'])

CNN base Image Siamese Model:

def get_shared_model(image_shape): input = Input(shape=image_shape) n_layer = input n_layer = Conv2D(filters=64, kernel_size=(3, 3), activation='relu')(n_layer) n_layer = BatchNormalization()(n_layer) n_layer = MaxPooling2D(pool_size=(2, 2))(n_layer) n_layer = Conv2D(filters=64, kernel_size=(3, 3), activation='relu')(n_layer) n_layer = BatchNormalization()(n_layer) n_layer = MaxPooling2D(pool_size=(2, 2))(n_layer) n_layer = Conv2D(filters=128, kernel_size=(3, 3), activation='relu')(n_layer) n_layer = BatchNormalization()(n_layer) n_layer = MaxPooling2D(pool_size=(2, 2))(n_layer) n_layer = Flatten()(n_layer) n_layer = Dense(4096, activation='linear')(n_layer) n_layer = BatchNormalization()(n_layer) model = Model(inputs=[input], outputs=[n_layer]) return model def init_model(self): image_shape = (IMAGE_HEIGHT, IMAGE_WIDTH, 3) input_a, input_b = Input(shape=image_shape), Input(shape=image_shape) shared_model = get_shared_model(image_shape) shared_model_a, shared_model_b = shared_model(input_a), shared_model(input_b) n_layer = Lambda(lambda x: K.sqrt(K.maximum(K.sum(K.square(x[0]-x[1]), axis=1, keepdims=True), K.epsilon())))([shared_model_a, shared_model_b]) n_layer = BatchNormalization()(n_layer) out = Dense(1, activation="sigmoid")(n_layer) self.model = Model(inputs=[input_a, input_b], outputs=[out]) adam = optimizers.Adam(lr=0.001) self.model.compile(optimizer=adam, loss="binary_crossentropy", metrics=['accuracy', km.precision(label=0), km.recall(label=0)])

Most importantly we need to select the positive and the negative pairs in such a way that the model effectively learns which words or patches of image are more important to an item for finding other similar items.

- Find unsupervised representation for each item such as word embeddings for text and perceptual hashes for images.
- Select positive pair of items such that the two items belong to the same true abstract group such as same brand or product type but they have a high a distance in the unsupervised representation space.
- Find pairwise euclidean distances between unsupervised item representations.
- Select the pairs from same abstract group with the highest distances.

- Select negative pair of items such that the two items have minimum 'distance' in the unsupervised representation space but belong to different true abstract groups.
- Index the unsupervised item representations in a KD-Tree.
- Index only one item from each abstract group.
- For each pair, find the other pair which is nearest to the item in the KD-Tree.
- Since there are one item from each abstract group, fetching 2 items will ensure that at-least one of them belongs to a different group.

- Train the pairs using the Siamese network.
- Finally, we can obtain the item representations from the layer before the L2 merge layer in the Siamese network.
- Once we obtain the item representations, the indexing part and the lookup part remains the same as the unsupervised model.

Obtain item embeddings from BiLSTM Siamese Network:

def get_embeddings(self, X): embeddings = K.function([self.model.layers[0].input, self.model.layers[1].input], [self.model.layers[5].get_output_at(0)]) return embeddings([X, X])[0]

Note that we could have also used the Siamese model at the time of lookup for the nearest matching items. For e.g. Pass the query item against all other items in our training corpus through the Siamese network and let the network predict 1 or 0. If the prediction is 1 then an item is similar to the query item else it is not similar.

But there are obvious drawbacks to this:

- We need to pair up the query item against all other items. So if there are 30 million items, then for each query, we need to make 30 million comparisons to find the similar items.
- Instead of embeddings we need to store the items themselves which involves storing all kinds of metadata along with it, which are obviously more heavy in size.

But how can we ensure that two items which are predicted to be similar by the Siamese network are also similar when the euclidean distance between their representations are within certain threshold and vice versa ?

Recall that while training the Siamese network we had used L2 distance to merge the two inputs i.e. the outputs of the last layer before the merge layer. These outputs are also the item representations. After L2 merge, these output is passed through a weighted sigmoid layer with weights say W and bias B.

Thus if the inputs to the L2 distance layer were U_{1} and U_{2} and the output from the merge layer is X, i.e.

X = ||U_{1}-U_{2}||^{2}

Then the input to the sigmoid layer is = W*X+B

Thus the sigmoid layer output is (1.0 + exp(-(W*X+B)))^{-1}

We know that the model predicts an output of 1 when the sigmoid layer output is greater than 0.5, thus the euclidean distance X must satisfy:

(1.0 + exp(-(W*X+B)))^{-1} > 0.5

or X < -B/W

One of W or B has to be negative else the model will not learn any positive examples. Thus for X < |B|/|W|, the Siamese network will predict two items to be similar. But if we want to apply a different threshold Y other than 0.5, then the condition becomes:

(1.0 + exp(-(W*X+B)))^{-1} > Y

or X < |(B+ln((1.0/Y)-1))|/|W|

Thus we can simulate the output of the Siamese network just by using the item representations, and the weight and the bias of the output sigmoid layer. Moreover we can index the item representations into a KD-Tree which reduces our lookup time from O(D*N) to O(D*logN).

Python method to get distance threshold for similarity for BiLSTM Siamese model:

def get_distance_threshold(self, thres): weight, bias = self.model.model.layers[7].get_weights() return abs(float(bias[0]+math.log((1.0/thres)-1.0)))/abs(weight[0][0])

Now let's checkout some of the challenges in the implementation discussed so far:

- Keras 'model.fit' with all data loaded into memory together will cause memory error.
- The CPU limitation is 16 GB but the data size is 50 GB.
- Use 'model.fit_generator' instead of 'model.fit' in Keras. Also the training and validation data needs to be supplied through a Python generator to 'fit_generator' method.
- Using a batch size of 256 will drastically reduce the memory requirements.

Using generators for reading data and training deep learning models:

def fit(self): self.init_model() callbacks = [ EarlyStopping(monitor='val_loss', patience=3), ModelCheckpoint(filepath=self.model_file_path, monitor='val_loss', save_best_only=True), ] if self.use_generator: train_num_batches = int(math.ceil(float(self.training_samples)/self.batch_size)) valid_num_batches = int(math.ceil(float(self.validation_samples)/self.batch_size)) self.model.fit_generator(dg.get_data_as_generator(self.training_samples, 'train', batch_size=self.batch_size), callbacks=callbacks, steps_per_epoch=train_num_batches, validation_data=dg.get_data_as_generator(self.validation_samples, 'validation', batch_size=self.batch_size), validation_steps=valid_num_batches, epochs=3, verbose=1, use_multiprocessing=True) else: X_train, y_train = dg.get_data_as_vanilla(self.training_samples, 'train') X_valid, y_valid = dg.get_data_as_vanilla(self.validation_samples, 'validation') self.model.fit(X_train, y_train, batch_size=self.batch_size, validation_data=(X_valid, y_valid), callbacks=callbacks, epochs=3, verbose=1, shuffle=True)

- Reading and saving data in in-memory data structures will cause memory error.
- Generally we will be reading the data and pre-process them in-memory and also store them in in-memory data structures like a Numpy array.
- Assuming a numpy array of size 256 is used and number of items is 30 million, thus the total memory requirement would be around 57 GB.
- Use disk based database storage such as MongoDB or Pytables.
- Pytables is much convenient as its syntax is similar to numpy array but persists data in disk and reads from disk.

def insert_embeddings_pytables(self, batch_size=25000): try: embeds_file = tables.open_file('data/embeddings.h5', mode='w') atom = tables.Float32Atom() embeds_arr = embeds_file.create_earray(embeds_file.root, 'data', atom, (0, 128)) sents_arr_file = tables.open_file('data/sent_arrays.h5', mode='r') sents_arr = sents_arr_file.root.data n = len(sents_arr) num_batches = int(math.ceil(float(n)/batch_size)) for m in range(num_batches): start, end = m*batch_size, min((m+1)*batch_size, n) embeds = self.model.get_embeddings(sents_arr[start:end,:]) embeds_arr.append(embeds) finally: sents_arr_file.close() embeds_file.close() def fetch_embeddings_pytables(self, item_indexes=None): try: embeds_file = tables.open_file('data/embeddings.h5', mode='r') embeds_arr = embeds_file.root.data if item_indexes is not None: output = np.array([embeds_arr[i] for i in item_indexes]) else: output = np.array([embeds_arr[i] for i in range(len(embeds_arr))]) return output finally: embeds_file.close()

- High KD-Tree overhead.
- Although KD-Tree is an efficient indexing data structure but it suffers from few drawbacks.
- KD-Tree in its original implementation cannot be distributed across machines. When data size becomes too large KD-Tree will not fit in single machine and needs to be stored in distributed manner.
- Currently looking at few distributed KD-Tree approaches and alternative spatial indexing data structures such as R-Trees.

- No incremental updates possible or merging two KD-Trees efficiently without re-building the tree.

In the next few posts I will be exploring alternative Fast Spatial Indexing data structures and discuss their implementations.

The full codes are available in my Github repository:

- https://github.com/funktor/stokastik/tree/master/Product%20Type%20Variant
- https://github.com/funktor/stokastik/tree/master/Placeholder%20Detection

Categories: AI, DESIGN, MACHINE LEARNING

Tags: BiLSTM, CNN, Deep Learning, E-commerce, KD Tree, LSTM, MongoDB, Pymongo, Pytables, Siamese Network, Similarity Models

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