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Setup.py, custom model, and import hotfixes; minor refactoring #9

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Setup.py, custom model, and import hotfixes; minor refactoring #9

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6837323
hotfix: install requirements via pip install
ZaxR Dec 31, 2020
75c2c97
import readme to setup.py as long_description
ZaxR Dec 31, 2020
4694d75
hotfix custom model training; closes PR #8
ZaxR Dec 31, 2020
0ed7a26
hotfix namespace collisions w/ keras/tensorflow
ZaxR Dec 31, 2020
9a5005f
refactor code for simplicity and to keep it dry
ZaxR Dec 31, 2020
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2 changes: 1 addition & 1 deletion chars2vec/__init__.py
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Original file line number Diff line number Diff line change
@@ -1 +1 @@
from .model import *
from .model import Chars2Vec, load_model, save_model, train_model
210 changes: 101 additions & 109 deletions chars2vec/model.py
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Original file line number Diff line number Diff line change
@@ -1,18 +1,17 @@
import numpy as np
import os
import pickle

import keras
import os
import numpy as np


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 All @@ -25,131 +24,121 @@ def __init__(self, emb_dim, char_to_ix):
self.vocab_size = len(self.char_to_ix)
self.dim = emb_dim
self.cache = {}
self.embedding_model = self._get_embedding_model()
self.model = self._get_model()
self.model.compile(optimizer="adam", loss="mae")

lstm_input = keras.layers.Input(shape=(None, self.vocab_size))
def _get_embedding_model(self):
inputs = keras.layers.Input(shape=(None, self.vocab_size))

x = keras.layers.LSTM(emb_dim, return_sequences=True)(lstm_input)
x = keras.layers.LSTM(emb_dim)(x)
outputs = keras.layers.LSTM(self.dim, return_sequences=True)(inputs)
outputs = keras.layers.LSTM(self.dim)(outputs)

self.embedding_model = keras.models.Model(inputs=[lstm_input], outputs=x)
return keras.models.Model(inputs=[inputs], outputs=outputs)

def _get_model(self):
model_input_1 = keras.layers.Input(shape=(None, self.vocab_size))
model_input_2 = keras.layers.Input(shape=(None, self.vocab_size))

embedding_1 = self.embedding_model(model_input_1)
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')


def fit(self, word_pairs, targets,
max_epochs, patience, validation_split, batch_size):
'''
model_output = keras.layers.Dense(1, activation="sigmoid")(x)

return keras.models.Model(
inputs=[model_input_1, model_input_2], outputs=model_output
)

def _create_word_embedding(self, word):
word_embedding = []
for char in word.lower():
char_embedding = np.zeros(self.vocab_size)
if char in self.char_to_ix:
char_embedding[self.char_to_ix[char]] = 1
word_embedding.append(char_embedding)
return word_embedding

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.
:param targets: list or numpy.ndarray of targets.
:param max_epochs: parameter 'epochs' of keras model.
: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):
if not isinstance(word_pairs, (list, np.ndarray)):
raise TypeError("parameters 'word_pairs' must be a list or numpy.ndarray")

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

x_1, x_2 = [], []
for word_pair in word_pairs:
if len(word_pair) != 2:
raise ValueError(
"`word_pairs` contains a 'pair' with more than two words."
)

for pair_words in word_pairs:
emb_list_1 = []
emb_list_2 = []

if not isinstance(pair_words[0], str) or not isinstance(pair_words[1], str):
raise TypeError("word must be a string")

first_word = pair_words[0].lower()
second_word = pair_words[1].lower()

for t in range(len(first_word)):

if first_word[t] in self.char_to_ix:
x = np.zeros(self.vocab_size)
x[self.char_to_ix[first_word[t]]] = 1
emb_list_1.append(x)

else:
emb_list_1.append(np.zeros(self.vocab_size))

x_1.append(np.array(emb_list_1))
if not all(isinstance(word, str) for word in word_pair):
raise TypeError("Both words must be strings.")

for t in range(len(second_word)):
first_word, second_word = word_pair

if second_word[t] in self.char_to_ix:
x = np.zeros(self.vocab_size)
x[self.char_to_ix[second_word[t]]] = 1
emb_list_2.append(x)
first_word_embedding = self._create_word_embedding(word=first_word.lower())
x_1.append(np.array(first_word_embedding))

else:
emb_list_2.append(np.zeros(self.vocab_size))

x_2.append(np.array(emb_list_2))
second_word_embedding = self._create_word_embedding(
word=second_word.lower()
)
x_2.append(np.array(second_word_embedding))

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

words = [w.lower() for w in words]
unique_words = np.unique(words)
new_words = [w for w in unique_words if w not in self.cache]

if len(new_words) > 0:

if new_words:
list_of_embeddings = []

for current_word in new_words:

if not isinstance(current_word, str):
for word in new_words:
if not isinstance(word, str):
raise TypeError("word must be a string")

current_embedding = []

for t in range(len(current_word)):

if current_word[t] in self.char_to_ix:
x = np.zeros(self.vocab_size)
x[self.char_to_ix[current_word[t]]] = 1
current_embedding.append(x)

else:
current_embedding.append(np.zeros(self.vocab_size))
word_embedding = self._create_word_embedding(word=word.lower())
list_of_embeddings.append(np.array(word_embedding))

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 @@ -161,54 +150,60 @@ 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.
:param X_train: list or numpy.ndarray of word pairs.
:param y_train: list or numpy.ndarray of target values that describe the proximity of words.
Expand All @@ -217,23 +212,20 @@ def train_model(emb_dim, X_train, y_train, model_chars,
: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.

: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")\

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

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

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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