add base methods for model.base_model

.idea/deployment.xml

@@ -0,0 +1,4 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="PublishConfigData" persistUploadOnCheckin="false" />
+</project>

.idea/fastNLP.iml

@@ -0,0 +1,11 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<module type="PYTHON_MODULE" version="4">
+  <component name="NewModuleRootManager">
+    <content url="file://$MODULE_DIR$" />
+    <orderEntry type="inheritedJdk" />
+    <orderEntry type="sourceFolder" forTests="false" />
+  </component>
+  <component name="TestRunnerService">
+    <option name="PROJECT_TEST_RUNNER" value="Unittests" />
+  </component>
+</module>

.idea/misc.xml

@@ -0,0 +1,4 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="ProjectRootManager" version="2" project-jdk-name="Python 3.5 (PCA_emb)" project-jdk-type="Python SDK" />
+</project>

.idea/modules.xml

@@ -0,0 +1,8 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="ProjectModuleManager">
+    <modules>
+      <module fileurl="file://$PROJECT_DIR$/.idea/fastNLP.iml" filepath="$PROJECT_DIR$/.idea/fastNLP.iml" />
+    </modules>
+  </component>
+</project>

.idea/vcs.xml

@@ -0,0 +1,6 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="VcsDirectoryMappings">
+    <mapping directory="$PROJECT_DIR$" vcs="Git" />
+  </component>
+</project>

README.md

@@ -1,2 +1,2 @@
 # FastNLP
-FastNLP
+FastNLP

model/empty.txt → action/README.md

@@ -1,4 +1,3 @@
-Some useful reference:
 SpaCy "Doc"
 https://github.com/explosion/spaCy/blob/75d2a05c2938f412f0fae44748374e4de19cc2be/spacy/tokens/doc.pyx#L80
 

action/action.py

@@ -8,10 +8,10 @@ class Action(object):
         self.logger = None
 
     def load_config(self, args):
-        pass
+        raise NotImplementedError
 
     def load_dataset(self, args):
-        pass
+        raise NotImplementedError
 
     def log(self, args):
         self.logger.log(args)
@@ -22,7 +22,7 @@ class Action(object):
 
     def batchify(self, X, Y=None):
         # a generator
-        pass
+        raise NotImplementedError
 
     def make_log(self, *args):
-        pass
+        raise NotImplementedError

action/tester.py

@@ -29,7 +29,7 @@ class Tester(Action):
         for step in range(iterations):
             batch_x, batch_y = test_batch_generator.__next__()
 
-            # forward pass from test input to predicted output
+            # forward pass from tests input to predicted output
             prediction = network.data_forward(batch_x)
 
             # get the loss

action/trainer.py

@@ -11,4 +11,4 @@ class Trainer(Action):
         self.arg = arg
 
     def train(self, args):
-        pass
+        raise NotImplementedError

loader/config_loader.py

@@ -10,5 +10,4 @@ class ConfigLoader(BaseLoader):
 
     @staticmethod
     def parse(string):
-        # To do
-        return string
+        raise NotImplementedError

model/base_model.py

@@ -0,0 +1,20 @@
+class BaseModel(object):
+    """base model for all models"""
+
+    def __init__(self):
+        pass
+
+    def prepare_input(self, data):
+        raise NotImplementedError
+
+    def mode(self, test=False):
+        raise NotImplementedError
+
+    def data_forward(self, x):
+        raise NotImplementedError
+
+    def grad_backward(self):
+        raise NotImplementedError
+
+    def loss(self, pred, truth):
+        raise NotImplementedError

reproduction/CNN-sentence_classification/train.py

@@ -1,17 +1,12 @@
 import os
-import torch 
+
+import
+import torch
 import torch.nn as nn
-import torchvision.datasets as dsets
-import torchvision.transforms as transforms
-import dataset as dst
-from model import CNN_text
+.dataset as dst
+from .model import CNN_text
 from torch.autograd import Variable
 
-from sklearn import cross_validation
-from sklearn import datasets
-
-
-
 # Hyper Parameters
 batch_size = 50
 learning_rate = 0.0001
@@ -51,8 +46,7 @@ if cuda:
 criterion = nn.CrossEntropyLoss()
 optimizer = torch.optim.Adam(cnn.parameters(), lr=learning_rate)
 
-
-#train and test
+# train and tests
 best_acc = None
 
 for epoch in range(num_epochs):

reproduction/Char-aware_NLM/test.py

@@ -1,12 +1,12 @@
 import os
+from collections import namedtuple
+
+import numpy as np
 import torch
-from torch.autograd import Variable
 import torch.nn as nn
-import torch.nn.functional as F
-import numpy as np
-from model import charLM
+from torch.autograd import Variable
 from utilities import *
-from collections import namedtuple
+
 
 def to_var(x):
     if torch.cuda.is_available():
@@ -76,18 +76,18 @@ if __name__ == "__main__":
 
 
     if os.path.exists("cache/data_sets.pt") is False:
-        
-        test_text  = read_data("./test.txt")
+
+        test_text = read_data("./tests.txt")
         test_set  = np.array(text2vec(test_text,  char_dict, max_word_len))
 
         # Labels are next-word index in word_dict with the same length as inputs
         test_label  = np.array([word_dict[w] for w in test_text[1:]] + [word_dict[test_text[-1]]])
 
-        category = {"test": test_set, "tlabel":test_label}
+        category = {"tests": test_set, "tlabel": test_label}
         torch.save(category, "cache/data_sets.pt") 
     else:
         data_sets = torch.load("cache/data_sets.pt")
-        test_set  = data_sets["test"]
+        test_set = data_sets["tests"]
         test_label = data_sets["tlabel"]
         train_set = data_sets["tdata"]
         train_label = data_sets["trlabel"]

reproduction/Char-aware_NLM/train.py

@@ -13,8 +13,7 @@ from .utilities import *
 
 
 def preprocess():
-    
-    word_dict, char_dict = create_word_char_dict("valid.txt", "train.txt", "test.txt")
+    word_dict, char_dict = create_word_char_dict("valid.txt", "train.txt", "tests.txt")
     num_words = len(word_dict)
     num_char  = len(char_dict)
     char_dict["BOW"] = num_char+1
@@ -195,7 +194,7 @@ if __name__=="__main__":
     if os.path.exists("cache/data_sets.pt") is False:
         train_text = read_data("./train.txt")
         valid_text = read_data("./valid.txt")
-        test_text  = read_data("./test.txt")
+        test_text = read_data("./tests.txt")
 
         train_set = np.array(text2vec(train_text, char_dict, max_word_len))
         valid_set = np.array(text2vec(valid_text, char_dict, max_word_len))
@@ -206,14 +205,14 @@ if __name__=="__main__":
         valid_label = np.array([word_dict[w] for w in valid_text[1:]] + [word_dict[valid_text[-1]]])
         test_label  = np.array([word_dict[w] for w in test_text[1:]] + [word_dict[test_text[-1]]])
 
-        category = {"tdata":train_set, "vdata":valid_set, "test": test_set, 
+        category = {"tdata": train_set, "vdata": valid_set, "tests": test_set,
                     "trlabel":train_label, "vlabel":valid_label, "tlabel":test_label}
         torch.save(category, "cache/data_sets.pt") 
     else:
         data_sets = torch.load("cache/data_sets.pt")
         train_set = data_sets["tdata"]
         valid_set = data_sets["vdata"]
-        test_set  = data_sets["test"]
+        test_set = data_sets["tests"]
         train_label = data_sets["trlabel"]
         valid_label = data_sets["vlabel"]
         test_label = data_sets["tlabel"]

saver/base_saver.py

@@ -5,10 +5,10 @@ class BaseSaver(object):
         self.save_path = save_path
 
     def save_bytes(self):
-        pass
+        raise NotImplementedError
 
     def save_str(self):
-        pass
+        raise NotImplementedError
 
     def compress(self):
-        pass
+        raise NotImplementedError

saver/logger.py

@@ -8,4 +8,4 @@ class Logger(BaseSaver):
         super(Logger, self).__init__(save_path)
 
     def log(self, string):
-        pass
+        raise NotImplementedError