Streamline code

classifiers/bag_of_words.py

@@ -1,41 +1,41 @@
 from collections import Counter
 
 import numpy as np
-from sklearn.base import BaseEstimator, ClassifierMixin
 from sklearn.feature_extraction import DictVectorizer
 from sklearn.linear_model import LogisticRegression
 from sklearn.pipeline import Pipeline
 
-from classifiers.feature_classifier import FeatureExtractor
 from cleaning.transformers import LowercaseTransformer, PunctuationRemover
 
 
-class BagOfWordsClassifier(BaseEstimator, ClassifierMixin):
+class FeatureExtractor:
-    def __init__(self):
+    def fit(self, X, y=None):
-        self.cleaning = Pipeline([
+        return self
+
+    def transform(self, X):
+        return [self.extract_features(msg) for msg in X]
+
+    def extract_features(self, message):
+        return dict(Counter(message.split()))
+
+
+class BagOfWordsClassifier:
+    def fit(self, X, y):
+        self._pipeline = Pipeline([
             ("lowercase", LowercaseTransformer()),
             ("punctuation", PunctuationRemover()),
-        ])
+            ("features", FeatureExtractor()),
-
-    def fit(self, X, y):
-        X_clean = self.cleaning.fit_transform(X)
-        self._pipeline = Pipeline([
-            ("features", FeatureExtractor(self.extract_features)),
             ("vectorizer", DictVectorizer()),
             ("classifier", LogisticRegression(max_iter=1000)),
         ])
         y_binary = (np.array(y) == "spam").astype(int)
-        self._pipeline.fit(X_clean, y_binary)
+        self._pipeline.fit(X, y_binary)
         return self
 
     def predict(self, X):
-        X_clean = self.cleaning.transform(X)
+        y_binary = self._pipeline.predict(X)
-        y_binary = self._pipeline.predict(X_clean)
         return np.where(y_binary == 1, "spam", "ham")
 
-    def extract_features(self, message):
-        return dict(Counter(message.split()))
-
     def feature_weights(self, top_n=10):
         vectorizer = self._pipeline.named_steps["vectorizer"]
         classifier = self._pipeline.named_steps["classifier"]

classifiers/feature_classifier.py

@@ -1,30 +1,30 @@
 import numpy as np
-from sklearn.base import BaseEstimator, ClassifierMixin, TransformerMixin
 from sklearn.feature_extraction import DictVectorizer
 from sklearn.linear_model import LogisticRegression
 from sklearn.pipeline import Pipeline
 
 
-class FeatureExtractor(BaseEstimator, TransformerMixin):
+class FeatureExtractor:
-    def __init__(self, extract_fn):
-        self.extract_fn = extract_fn
-
     def fit(self, X, y=None):
         return self
 
     def transform(self, X):
-        return [self.extract_fn(msg) for msg in X]
+        return [self.extract_features(msg) for msg in X]
+
+    def extract_features(self, message):
+        return {
+            "contains_free": int("free" in message.lower()),
+            "num_exclamations": message.count("!"),
+            "length": len(message),
+        }
 
 
-class FeatureClassifier(BaseEstimator, ClassifierMixin):
+class FeatureClassifier:
-    def __init__(self, C=1.0):
-        self.C = C
-
     def fit(self, X, y):
         self._pipeline = Pipeline([
-            ("features", FeatureExtractor(self.extract_features)),
+            ("features", FeatureExtractor()),
             ("vectorizer", DictVectorizer()),
-            ("classifier", LogisticRegression(C=self.C, max_iter=1000)),
+            ("classifier", LogisticRegression(max_iter=1000)),
         ])
         y_binary = (np.array(y) == "spam").astype(int)
         self._pipeline.fit(X, y_binary)
@@ -34,13 +34,6 @@ class FeatureClassifier(BaseEstimator, ClassifierMixin):
         y_binary = self._pipeline.predict(X)
         return np.where(y_binary == 1, "spam", "ham")
 
-    def extract_features(self, message):
-        return {
-            "contains_free": int("free" in message.lower()),
-            "num_exclamations": message.count("!"),
-            "length": len(message),
-        }
-
     def feature_weights(self, top_n=10):
         vectorizer = self._pipeline.named_steps["vectorizer"]
         classifier = self._pipeline.named_steps["classifier"]

classifiers/manual.py

@@ -1,8 +1,7 @@
 import numpy as np
-from sklearn.base import BaseEstimator, ClassifierMixin
 
 
-class ManualClassifier(BaseEstimator, ClassifierMixin):
+class ManualClassifier:
     def fit(self, X, y):
         return self