import keras
from keras_aquarium import dmf
from scipy.sparse import csr_matrix, coo_matrix

# suppose you have a sparse matrix as a user item rating matrix
rating_matrix = coo_matrix( shape=[n_user, n_item] )
users = rating_matrix.row
items = rating_matrix.col
ratings = rating_matrix.data

# you also have a sparse feature matrix for item, such as location of item, price of item, etc.
item_features = csr_matrix( shape=[n_item, n_item_feature] )
# and sadly, you dont have any feature for user
user_features = None

# then you want to apply a Matrix Factorization model to predict ratings
model = dmf.DualMatrixFactorization(
    # we choose user as row, item as col
    n_row=n_user, n_col=n_item, # specified matrix shape
    # or we can choose item as row, user as col, n_row=n_item, n_col=n_user, just transpose the matrix

    n_row_feature=None, # we dont have user feature
    n_col_feature=n_item_feature, # we do have item feature
    row_dim=30, col_dim=20, # specifed embedding dim, each user and item is then first embedded as a vector
    row_feature_dim=None, # no user feature
    col_feature_dim=None, # specifed item feature embedding dim, each item feature will be encoded as a dense vector

    # row_layers and col_layers are for encoding layers for user and item,
    # they work separately, so they can have different number of hidden layers
    # just make sure the finla hidden layer are the same dim
    row_layers=[(50, "relu"), (30, keras.losses.tanh)], # use two hidden layers, first one is a dense layer with 50 unit and relu activation, second one is a dense layer with 30 unit and tanh activation
    col_layers=[ (lambda x: Dense(30, regularizer=l2(0.001))(x) ), ], # can use a callable to create a hidden layer
)

# use it as a keras model
model.compile(loss="mse", optimizer="adam", )

inputs = [users] + [items, item_features[items], ] # note that there is no user_features
model.fit(inputs, ratings, )

import keras
from keras_aquarium import sae
from scipy.sparse import csr_matrix
import numpy as np

# suppose you have a sparse matrix, which represents bag-of-words documents
bow_docs = csr_matrix([n_docs, n_words])

model = sae.SoftmaxAutoEncoder(
    input_dim, # dim of input sample
    latent_dim=50, # latent dim of latent vector
    encoder=None, # if not None, then will be used as latent_vector = encoder(input_layer),
    decoder=None, # if not None, then will be used as generated_input = decoder(latent_vector)
    activation=None, # default is "tanh" when use_binary_activation is False, otherwise variant sigmoid
    loss=None, # default is kullback_leibler_divergence
    use_tied_layer=True, # whether to use tied layer or not, used only when encoder and decoder is None
    use_binary_activation=True, # if True, using variant sigmoid 1/(1+exp(alpha*-x))
    alpha=50, # alpha in variant sigmoid
)

def generate_dataset(batch_size):
    # memory friendly
    indices = np.arange(len(bow_docs))
    while True:
        np.random.shuffle(indices)
        for i in xrange(0, len(indices), batch_size):
            inds = indices[i:i+batch_size]
            yield bow_docs[inds], bow_docs[inds].toarray()

batch_size = 32
model.fit_generator( generate_dataset(batch_size),  len(bow_docs)/batch_size, )

# after training model, we can run kmeans to get topic distribution
from sklearn.cluster import KMeans
km = KMeans(n_clsuters=20)
y_pred = km.fit_predict(sae.get_latent_code(bow_docs))

# scoring our topic by comparing topic and true document labels
from sklearn.metrics import homogeneity_completeness_v_measure
homogeneity, completeness, v_measure = homogeneity_completeness_v_measure(y_true, y_pred)

import keras
from keras_aquarium import hatt_rnn
from scipy.sparse import csr_matrix
import numpy as np

# suppose you have a 3D matrix (n_docs * n_sentences_in_doc * n_words_in_sentence), represents documents,
sequence_docs = np.zeros([n_docs, n_sentences_in_doc, n_words_in_sentence]) # padding zeros
word_embeddings = load_glove_word_embeddings()
vocabulary = load_vocabulary()

model = hatt_rnn.HierarchicalAttentionRNN(
    max_sents,
    max_sent_length,
    n_classes,

    # if use word_embeddings to initialize word embeddings layer
    embeddings=word_embeddings,
    # else
    n_words=len(vocabulary),
    word_dim=50,

    # units in words and sentences gru layer
    word_hidden_dim=100,
    sent_hidden_dim=100,
)

model.fit(sequence_docs, labels)