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hotfix custom model training; closes PR IntuitionEngineeringTeam#8
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@ZaxR
ZaxR committed Dec 31, 2020
1 parent 75c2c97 commit 4694d75
Showing 1 changed file with 54 additions and 36 deletions.
90 changes: 54 additions & 36 deletions chars2vec/model.py
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Original file line number Diff line number Diff line change
Expand Up @@ -5,14 +5,13 @@


class Chars2Vec:

def __init__(self, emb_dim, char_to_ix):
'''
"""
Creates chars2vec model.
:param emb_dim: int, dimension of embeddings.
:param char_to_ix: dict, keys are characters, values are sequence numbers of characters.
'''
"""

if not isinstance(emb_dim, int) or emb_dim < 1:
raise TypeError("parameter 'emb_dim' must be a positive integer")
Expand Down Expand Up @@ -40,15 +39,17 @@ def __init__(self, emb_dim, char_to_ix):
embedding_2 = self.embedding_model(model_input_2)
x = keras.layers.Subtract()([embedding_1, embedding_2])
x = keras.layers.Dot(1)([x, x])
model_output = keras.layers.Dense(1, activation='sigmoid')(x)

self.model = keras.models.Model(inputs=[model_input_1, model_input_2], outputs=model_output)
self.model.compile(optimizer='adam', loss='mae')
model_output = keras.layers.Dense(1, activation="sigmoid")(x)

self.model = keras.models.Model(
inputs=[model_input_1, model_input_2], outputs=model_output
)
self.model.compile(optimizer="adam", loss="mae")

def fit(self, word_pairs, targets,
max_epochs, patience, validation_split, batch_size):
'''
def fit(
self, word_pairs, targets, max_epochs, patience, validation_split, batch_size
):
"""
Fits model.
:param word_pairs: list or numpy.ndarray of word pairs.
Expand All @@ -57,7 +58,7 @@ def fit(self, word_pairs, targets,
:param patience: parameter 'patience' of callback in keras model.
:param validation_split: parameter 'validation_split' of keras model.
:param batch_size: parameter 'batch_size' of keras model.
'''
"""

if not isinstance(word_pairs, list) and not isinstance(word_pairs, np.ndarray):
raise TypeError("parameters 'word_pairs' must be a list or numpy.ndarray")
Expand Down Expand Up @@ -104,20 +105,26 @@ def fit(self, word_pairs, targets,
x_1_pad_seq = keras.preprocessing.sequence.pad_sequences(x_1)
x_2_pad_seq = keras.preprocessing.sequence.pad_sequences(x_2)

self.model.fit([x_1_pad_seq, x_2_pad_seq], targets,
batch_size=batch_size, epochs=max_epochs,
validation_split=validation_split,
callbacks=[keras.callbacks.EarlyStopping(monitor='val_loss', patience=patience)])
self.model.fit(
[x_1_pad_seq, x_2_pad_seq],
targets,
batch_size=batch_size,
epochs=max_epochs,
validation_split=validation_split,
callbacks=[
keras.callbacks.EarlyStopping(monitor="val_loss", patience=patience)
],
)

def vectorize_words(self, words, maxlen_padseq=None):
'''
"""
Returns embeddings for list of words. Uses cache of word embeddings to vectorization speed up.
:param words: list or numpy.ndarray of strings.
:param maxlen_padseq: parameter 'maxlen' for keras pad_sequences transform.
:return word_vectors: numpy.ndarray, word embeddings.
'''
"""

if not isinstance(words, list) and not isinstance(words, np.ndarray):
raise TypeError("parameter 'words' must be a list or numpy.ndarray")
Expand Down Expand Up @@ -149,7 +156,9 @@ def vectorize_words(self, words, maxlen_padseq=None):

list_of_embeddings.append(np.array(current_embedding))

embeddings_pad_seq = keras.preprocessing.sequence.pad_sequences(list_of_embeddings, maxlen=maxlen_padseq)
embeddings_pad_seq = keras.preprocessing.sequence.pad_sequences(
list_of_embeddings, maxlen=maxlen_padseq
)
new_words_vectors = self.embedding_model.predict([embeddings_pad_seq])

for i in range(len(new_words)):
Expand All @@ -161,52 +170,62 @@ def vectorize_words(self, words, maxlen_padseq=None):


def save_model(c2v_model, path_to_model):
'''
"""
Saves trained model to directory.
:param c2v_model: Chars2Vec object, trained model.
:param path_to_model: str, path to save model.
'''
"""

if not os.path.exists(path_to_model):
os.makedirs(path_to_model)

c2v_model.embedding_model.save_weights(path_to_model + '/weights.h5')
c2v_model.embedding_model.save_weights(path_to_model + "/weights.h5")

with open(path_to_model + '/model.pkl', 'wb') as f:
with open(path_to_model + "/model.pkl", "wb") as f:
pickle.dump([c2v_model.dim, c2v_model.char_to_ix], f, protocol=2)


def load_model(path):
'''
"""
Loads trained model.
:param path: str, if it is 'eng_50', 'eng_100', 'eng_150', 'eng_200' or 'eng_300' then loads one of default models,
else loads model from `path`.
:return c2v_model: Chars2Vec object, trained model.
'''
"""

if path in ['eng_50', 'eng_100', 'eng_150', 'eng_200', 'eng_300']:
path_to_model = os.path.dirname(os.path.abspath(__file__)) + '/trained_models/' + path
if path in ["eng_50", "eng_100", "eng_150", "eng_200", "eng_300"]:
path_to_model = (
os.path.dirname(os.path.abspath(__file__)) + "/trained_models/" + path
)

else:
path_to_model = path

with open(path_to_model + '/model.pkl', 'rb') as f:
with open(path_to_model + "/model.pkl", "rb") as f:
structure = pickle.load(f)
emb_dim, char_to_ix = structure[0], structure[1]

c2v_model = Chars2Vec(emb_dim, char_to_ix)
c2v_model.embedding_model.load_weights(path_to_model + '/weights.h5')
c2v_model.embedding_model.compile(optimizer='adam', loss='mae')
c2v_model.embedding_model.load_weights(path_to_model + "/weights.h5")
c2v_model.embedding_model.compile(optimizer="adam", loss="mae")

return c2v_model


def train_model(emb_dim, X_train, y_train, model_chars,
max_epochs=200, patience=10, validation_split=0.05, batch_size=64):
'''
def train_model(
emb_dim,
X_train,
y_train,
model_chars,
max_epochs=200,
patience=10,
validation_split=0.05,
batch_size=64,
):
"""
Creates and trains chars2vec model using given training data.
:param emb_dim: int, dimension of embeddings.
Expand All @@ -219,11 +238,10 @@ def train_model(emb_dim, X_train, y_train, model_chars,
:param batch_size: parameter 'batch_size' of keras model.
:return c2v_model: Chars2Vec object, trained model.
'''
"""

if not isinstance(X_train, list) and not isinstance(X_train, np.ndarray):
raise TypeError("parameter 'X_train' must be a list or numpy.ndarray")\

raise TypeError("parameter 'X_train' must be a list or numpy.ndarray")
if not isinstance(y_train, list) and not isinstance(y_train, np.ndarray):
raise TypeError("parameter 'y_train' must be a list or numpy.ndarray")

Expand All @@ -233,7 +251,7 @@ def train_model(emb_dim, X_train, y_train, model_chars,
char_to_ix = {ch: i for i, ch in enumerate(model_chars)}
c2v_model = Chars2Vec(emb_dim, char_to_ix)

targets = [float(el) for el in y_train]
targets = np.array(y_train)
c2v_model.fit(X_train, targets, max_epochs, patience, validation_split, batch_size)

return c2v_model

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