Merge pull request #22 from Yoctol/seq2vec-0.4.0

Seq2vec 0.4.0

Author: stegben

.gitignore

@@ -87,3 +87,11 @@ ENV/
 
 # Rope project settings
 .ropeproject
+
+# model files
+*.h5
+*.model
+*_best
+
+#dict
+*.dict

README.md

@@ -1,6 +1,7 @@
-# seq2vec
+# seq2vec 0.4.0
 Turn sequence of words into a fix-length representation vector
 
+This is a version to refactor all the seq2vec structures and use customed layers in yklz.
 
 ## Install
 ```
@@ -17,9 +18,9 @@ python setup.py install
 
 Simple hash:
 ```python
-from seq2vec import HashSeq2Vec
+from seq2vec import Seq2VecHash
 
-transformer = HashSeq2Vec(vector_length=100)
+transformer = Seq2VecHash(vector_length=100)
 seqs = [
     ['我', '有', '一個', '蘋果'],
     ['我', '有', 'pineapple'],
@@ -46,36 +47,229 @@ array([[ 0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,
 '''
 ```
 
-TFIDF:
-```python
-
-```
-
 Sequence-to-sequence auto-encoder:
-```python
 
-```
+* LSTM to LSTM auto-encoder with word embedding (RNN to RNN architecture)
 
-Seq2Seq
-```python
-from seq2vec import Seq2SeqAutoEncoderUseWordHash
+  ```python
+  from seq2vec.word2vec import GensimWord2vec
+  from seq2vec import Seq2VecR2RWord
+  
+  # load Gensim word2vec from word2vec_model_path
+  word2vec = GensimWord2vec(word2vec_model_path)
+  
+  transformer = Seq2VecR2RWord(
+        word2vec_model=word2vec,
+        max_length=20,
+        latent_size=300,
+        encoding_size=300,
+        learning_rate=0.05
+  )
+  
+  train_seq = [
+    ['我', '有', '一個', '蘋果'],
+    ['我', '有', '筆'],
+    ['一個', '鳳梨'],
+  ]
+  test_seq = [
+    ['我', '愛', '吃', '鳳梨'],
+  ]
+  transformer.fit(train_seq)
+  result = transformer.transform(test_seq)
+  ```
+  
+* CNN to LSTM auto-encoder with word embedding (CNN to RNN architecture)
 
-transformer = Seq2SeqAutoEncoderUseWordHash(
-    max_index=1000,
-    max_length=10,
-    latent_size=20,
-)
+  ```python
+  from seq2vec.word2vec import GensimWord2vec
+  from seq2vec import Seq2VecC2RWord
+  
+  # load Gensim word2vec from word2vec_model_path
+  word2vec = GensimWord2vec(word2vec_model_path)
+  
+  transformer = Seq2VecC2RWord(
+        word2vec_model=word2vec,
+        max_length=20,
+        latent_size=300,
+        conv_size=5,
+        channel_size=10,
+        learning_rate=0.05,
+  )
+  
+  train_seq = [
+    ['我', '有', '一個', '蘋果'],
+    ['我', '有', '筆'],
+    ['一個', '鳳梨'],
+  ]
+  test_seq = [
+    ['我', '愛', '吃', '鳳梨'],
+  ]
+  transformer.fit(train_seq)
+  result = transformer.transform(test_seq)
+  ```
 
-train_seq = [
+* CNN to LSTM auto-encoder with char embedding (CNN to RNN architecture)
+
+  ```python
+  from seq2vec.word2vec import GensimWord2vec
+  from seq2vec import Seq2VecC2RChar
+  
+  # load Gensim word2vec from word2vec_model_path
+  word2vec = GensimWord2vec(word2vec_model_path)
+  
+  transformer = Seq2VecC2RChar(
+        word2vec_model=word2vec,
+        max_index=1000,
+        max_length=20,
+        embedding_size=200,
+        latent_size=200,
+        learning_rate=0.05,
+        channel_size=10,
+        conv_size=5
+  )
+  
+  train_seq = [
     ['我', '有', '一個', '蘋果'],
     ['我', '有', '筆'],
     ['一個', '鳳梨'],
-]
-test_seq = [
+  ]
+  test_seq = [
     ['我', '愛', '吃', '鳳梨'],
-]
-transformer.fit(train_seq)
-result = transformer.transform(test_seq)
+  ]
+  transformer.fit(train_seq)
+  result = transformer.transform(test_seq)
+  ```
+  
+* LSTM to LSTM auto-encoder with hash word embedding (RNN to RNN architecture)
+
+ ```python
+ from seq2vec import Seq2VecR2RHash
+
+ transformer = Seq2VecR2RHash(
+     max_index=1000,
+     max_length=10,
+     latent_size=20,
+     embedding_size=200,
+     encoding_size=300,
+     learning_rate=0.05
+ )
+
+ train_seq = [
+     ['我', '有', '一個', '蘋果'],
+     ['我', '有', '筆'],
+     ['一個', '鳳梨'], 
+ ]
+ test_seq = [
+     ['我', '愛', '吃', '鳳梨'],
+ ]
+ transformer.fit(train_seq)
+ result = transformer.transform(test_seq)
+ ```
+
+### Training with generator on file
+
+We provide an example with LSTM to LSTM auto-encoder (word embedding).
+
+Use the following training method while lack of memory is an issue for you.
+
+The file should be a tokenized txt file splitted by whitespace with a sequence
+per line.
+
+```python
+from seq2vec.word2vec import GensimWord2vec
+
+from seq2vec.model import Seq2VecR2RWord
+from seq2vec.transformer import WordEmbeddingTransformer
+from seq2vec.util import DataGenterator
+
+word2vec = GensimWord2vec(word2vec_model_path)
+max_length = 20
+
+transformer = Seq2VecR2RWord(
+    word2vec_model=word2vec,
+    max_length=max_length,
+    latent_size=200,
+    encoding_size=300,
+    learning_rate=0.05
+)
+
+train_data = DataGenterator(
+    corpus_for_training_path, 
+    transformer.input_transformer,
+    transformer.output_transformer, 
+    batch_size=128
+)
+test_data = DataGenterator(
+    corpus_for_validation_path, 
+    transformer.input_transformer,
+    transformer.output_transformer, 
+    batch_size=128
+)
+
+transformer.fit_generator(
+    train_data,
+    test_data,
+    epochs=10,
+    batch_number=1250 # The number of batch per epoch
+)
+
+transformer.save_model(model_path) # save your model
+
+# You can reload your model and retrain it.
+transformer.load_model(model_path)
+transformer.fit_generator(
+    train_data,
+    test_data,
+    epochs=10,
+    batch_number=1250 # The number of batch per epoch
+)
+```
+
+### Customized your seq2vec model with our auto-encoder framework
+
+You can customize your seq2vec model easily with our framework.
+
+```python
+import keras
+from seq2vec.model import TrainableSeq2VecBase
+
+class YourSeq2Vec(TrainableSeq2VecBase):
+
+   def __init__(self
+      max_length,
+      latent_size,
+      learning_rate
+   ):
+      # initialize your setting and set input_transformer
+      # and output_transformer
+      # Input and output transformers transform data from 
+      # raw sequence into Keras Layer input format
+      # See seq2vec.transformer for more detail
+
+      self.input_transformer = YourInputTransformer()
+      self.output_transformer = YourOutputTransformer()
+
+      # add your customized layer
+      self.custom_objects = {}
+      self.custom_objects[customized_class_name] = customized_class
+
+      super(YourSeq2Vec, self).__init__(
+         max_length,
+         latent_size,
+         learning_rate
+      )
+
+   def create_model(self):
+      # create and compile your model in this function
+      # You should return your model and encoder here
+      # encoder is the one encoded input sequences
+
+      model.compile(loss)
+      return model, encoder
+
+   def load_model(self, file_path):
+      # load your seq2vec model here and set its attribute values
+      self.model = self.load_customed_model(file_path)
 ```
 
 ## Lint
@@ -86,6 +280,6 @@ pylint --rcfile=./yoctol-pylintrc/.pylintrc seq2vec
 
 ## Test
 ```
-python setup.py test
+python -m unittest
 ```
 

circle.yml

@@ -17,4 +17,4 @@ dependencies:
 
 test:
   override:
-    - python setup.py test
+    - python -m unittest

requirements.txt

@@ -5,3 +5,4 @@ keras
 yoctol_utils
 gensim
 h5py
+yoctol_keras_layer_zoo

seq2vec/__init__.py

@@ -1,5 +1,7 @@
-from .hash_text import HashSeq2Vec
-from .seq2seq_auto_encoder import Seq2SeqAutoEncoderUseWordHash
-from .seq2seq_word2vec import Seq2SeqWord2Vec
-from .seq2seq_cnn3D import Seq2SeqCNN
-from .seq2seq_char2vec import Seq2SeqChar2vec
+from .model import Seq2VecC2RChar
+from .model import Seq2VecR2RHash
+from .model import Seq2VecC2RWord
+from .model import Seq2VecR2RWord
+from .model import Seq2VecBase
+from .model import Seq2VecHash
+from .model import TrainableSeq2VecBase