renaming models

models/classifier_builder.py → models/classifier_model.py

@@ -5,16 +5,16 @@
 from sklearn.linear_model import LogisticRegression
 from sklearn.metrics import accuracy_score, f1_score
 from sklearn.externals import joblib
-from .model_builder import ModelBuilder
-from .doc2vec_builder import doc2VecBuilder
+from .model import Model
+from .doc2vec_model import doc2VecModel
 
 logging.basicConfig(format='%(asctime)s : %(levelname)s : %(message)s', level=logging.INFO)
 base_file_path = inspect.getframeinfo(inspect.currentframe()).filename
 base_path = os.path.dirname(os.path.abspath(base_file_path))
 project_dir_path = os.path.dirname(os.path.abspath(base_path))
 classifiers_path = os.path.join(project_dir_path, 'classifiers')
 
-class classifierBuilder(ModelBuilder):
+class classifierModel(Model):
     def __init__(self):
         super().__init__()
 
@@ -23,7 +23,7 @@ def initialize_model(self):
 
     def train_model(self, d2v, training_vectors, training_labels):
         logging.info("Classifier training")
-        train_vectors = doc2VecBuilder.get_vectors(d2v, len(training_vectors), 300, 'Train')
+        train_vectors = doc2VecModel.get_vectors(d2v, len(training_vectors), 300, 'Train')
         self.model.fit(train_vectors, np.array(training_labels))
         training_predictions = self.model.predict(train_vectors)
         logging.info('Training predicted classes: {}'.format(np.unique(training_predictions)))
@@ -47,7 +47,7 @@ def load_model(self, filename):
 
     def test_model(self, d2v, testing_vectors, testing_labels):
         logging.info("Classifier testing")
-        test_vectors = doc2VecBuilder.get_vectors(d2v, len(testing_vectors), 300, 'Test')
+        test_vectors = doc2VecModel.get_vectors(d2v, len(testing_vectors), 300, 'Test')
         testing_predictions = self.model.predict(test_vectors)
         logging.info('Testing predicted classes: {}'.format(np.unique(testing_predictions)))
         logging.info('Testing accuracy: {}'.format(accuracy_score(testing_labels, testing_predictions)))

models/doc2vec_builder.py → models/doc2vec_model.py

@@ -6,15 +6,15 @@
 from gensim.models import doc2vec
 from gensim.models.doc2vec import Doc2Vec
 
-from .model_builder import ModelBuilder
+from .model import Model
 
 logging.basicConfig(format='%(asctime)s : %(levelname)s : %(message)s', level=logging.INFO)
 base_file_path = inspect.getframeinfo(inspect.currentframe()).filename
 base_path = os.path.dirname(os.path.abspath(base_file_path))
 project_dir_path = os.path.dirname(os.path.abspath(base_path))
 classifiers_path = os.path.join(project_dir_path, 'classifiers')
 
-class doc2VecBuilder(ModelBuilder):
+class doc2VecModel(Model):
 
     def __init__(self):
         super().__init__()

models/model_builder.py → models/model.py

@@ -1,7 +1,7 @@
 from abc import ABC, abstractmethod
 
 
-class ModelBuilder(ABC):
+class Model(ABC):
 
     def __init__(self):
         self.model = None

text_classifier.py

@@ -3,8 +3,8 @@
 import sys, getopt
 import os, inspect
 from sklearn.model_selection import train_test_split
-from models.doc2vec_builder import doc2VecBuilder
-from models.classifier_builder import classifierBuilder
+from models.doc2vec_model import doc2VecModel
+from models.classifier_model import classifierModel
 
 logging.basicConfig(format='%(asctime)s : %(levelname)s : %(message)s', level=logging.INFO)
 base_file_path = inspect.getframeinfo(inspect.currentframe()).filename
@@ -15,8 +15,8 @@ class TextClassifier():
 
     def __init__(self):
         super().__init__()
-        self.d2v = doc2VecBuilder()
-        self.classifier = classifierBuilder()
+        self.d2v = doc2VecModel()
+        self.classifier = classifierModel()
         self.dataset = None
 
     def read_data(self, filename):
@@ -26,8 +26,8 @@ def read_data(self, filename):
     def prepare_all_data(self):
         x_train, x_test, y_train, y_test = train_test_split(self.dataset.review, self.dataset.sentiment, random_state=0,
                                                             test_size=0.1)
-        x_train = doc2VecBuilder.label_sentences(x_train, 'Train')
-        x_test = doc2VecBuilder.label_sentences(x_test, 'Test')
+        x_train = doc2VecModel.label_sentences(x_train, 'Train')
+        x_test = doc2VecModel.label_sentences(x_test, 'Test')
         all_data = x_train + x_test
         return x_train, x_test, y_train, y_test, all_data