Rewrite lab with BoW

classifiers/bag_of_words.py

@@ -0,0 +1,46 @@
+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):
+    def __init__(self):
+        self.cleaning = Pipeline([
+            ("lowercase", LowercaseTransformer()),
+            ("punctuation", PunctuationRemover()),
+        ])
+
+    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)
+        return self
+
+    def predict(self, X):
+        X_clean = self.cleaning.transform(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"]
+        names = vectorizer.get_feature_names_out()
+        weights = classifier.coef_[0]
+        pairs = sorted(zip(names, weights), key=lambda x: x[1])
+        half = top_n // 2
+        return pairs[-half:][::-1] + pairs[:half]

classifiers/feature_classifier.py

@@ -4,8 +4,6 @@ from sklearn.feature_extraction import DictVectorizer
 from sklearn.linear_model import LogisticRegression
 from sklearn.pipeline import Pipeline
 
-from cleaning.transformers import LowercaseTransformer
-
 
 class FeatureExtractor(BaseEstimator, TransformerMixin):
     def __init__(self, extract_fn):
@@ -24,10 +22,9 @@ class FeatureClassifier(BaseEstimator, ClassifierMixin):
 
     def fit(self, X, y):
         self._pipeline = Pipeline([
-            ("lowercase", LowercaseTransformer()),
             ("features", FeatureExtractor(self.extract_features)),
-            ("vec", DictVectorizer()),
+            ("vectorizer", DictVectorizer()),
-            ("clf", LogisticRegression(C=self.C, max_iter=1000)),
+            ("classifier", LogisticRegression(C=self.C, max_iter=1000)),
         ])
         y_binary = (np.array(y) == "spam").astype(int)
         self._pipeline.fit(X, y_binary)
@@ -39,15 +36,15 @@ class FeatureClassifier(BaseEstimator, ClassifierMixin):
 
     def extract_features(self, message):
         return {
-            "contains_free": int("free" in message),
+            "contains_free": int("free" in message.lower()),
             "num_exclamations": message.count("!"),
             "length": len(message),
         }
 
     def feature_weights(self, top_n=10):
-        vec = self._pipeline.named_steps["vec"]
+        vectorizer = self._pipeline.named_steps["vectorizer"]
-        clf = self._pipeline.named_steps["clf"]
+        classifier = self._pipeline.named_steps["classifier"]
-        names = vec.get_feature_names_out()
+        names = vectorizer.get_feature_names_out()
-        weights = clf.coef_[0]
+        weights = classifier.coef_[0]
         pairs = sorted(zip(names, weights), key=lambda x: abs(x[1]), reverse=True)
         return pairs[:top_n]

classifiers/manual_cleaning.py

@@ -1,20 +0,0 @@
-import numpy as np
-from sklearn.pipeline import Pipeline
-
-from classifiers.manual import ManualClassifier
-from cleaning.transformers import LowercaseTransformer
-
-
-class ManualCleaningClassifier(ManualClassifier):
-    def __init__(self):
-        self.cleaning = Pipeline([
-            ("lowercase", LowercaseTransformer()),
-        ])
-
-    def fit(self, X, y):
-        self.cleaning.fit(X)
-        return self
-
-    def predict(self, X):
-        X_clean = self.cleaning.transform(X)
-        return np.array([self.predict_one(msg) for msg in X_clean])

questions.md

@@ -44,25 +44,9 @@
 
 ---
 
-## Checkpoint 3: Data Cleaning
+## Checkpoint 3: Designing Features by Hand
 
-**8. Which transformers did you add to the pipeline, and in what order?**
+**8. List all the features you implemented and the reasoning behind each:**
-
-*Your answer:*
-
-**9. Did any transformer hurt performance? Why might cleaning sometimes make things worse?**
-
-*Your answer:*
-
-**10. If you removed the lowercasing from your `predict_one` method now that the pipeline handles it, would your results change?**
-
-*Your answer:*
-
----
-
-## Checkpoint 4: Feature Engineering
-
-**11. List all the features you implemented and the reasoning behind each:**
 
 | Feature name | What it measures | Reasoning |
 |-------------|-----------------|-----------|
@@ -70,7 +54,7 @@
 | | | |
 | | | |
 
-**12. Record your best results:**
+**9. Record your best results:**
 
 | Metric | Value |
 |--------|-------|
@@ -78,11 +62,35 @@
 | Spam recall | |
 | Spam F1 | |
 
-**13. Which features received the largest positive weights (most predictive of spam)?**
+**10. Which features received the largest positive weights (most predictive of spam)? The largest negative weights (predictive of ham)? Does this match your expectations?**
 
 *Your answer:*
 
-**14. Which features received near-zero weights? Why might the model have ignored them?**
+**11. Did any feature you thought would help receive a near-zero weight? Why might the model have decided it was unimportant?**
+
+*Your answer:*
+
+---
+
+## Checkpoint 4: Bag of Words
+
+**12. Which transformers did you include in your cleaning pipeline, and in what order? Explain how each one changes the vocabulary.**
+
+*Your answer:*
+
+**13. Record your best results:**
+
+| Metric | Value |
+|--------|-------|
+| Spam precision | |
+| Spam recall | |
+| Spam F1 | |
+
+**14. How did the bag-of-words classifier's performance compare to your best hand-designed-features classifier? What do you think accounts for the difference?**
+
+*Your answer:*
+
+**15. Look at the words with the strongest weights (in either direction). Do any surprise you? What do they suggest about how the model is making its decisions?**
 
 *Your answer:*
 
@@ -90,7 +98,7 @@
 
 ## Final Questions
 
-**15. Pick a different classification problem (for example: positive vs. negative movie reviews,
+**16. Pick a different classification problem (for example: positive vs. negative movie reviews,
 news articles vs. opinion pieces, or medical vs. general-audience text).
 Propose five features you would extract to classify it, and explain your reasoning.**
 
@@ -104,17 +112,11 @@ Problem I chose:
 | | | |
 | | | |
 
-**16. Could adding more features ever *hurt* the performance of a classifier? Explain
+**17. Could adding more features ever *hurt* the performance of a classifier? Explain
 when and why this might happen.**
 
 *Your answer:*
 
-**17. It would be possible to make every word in the vocabulary a feature, where each
-feature indicates whether that word appears in the message. Explain how this could
-be implemented. Do you think it would work well? What are the trade-offs?**
-
-*Your answer:*
-
 **18. In this lab you split the data into 70% training and 30% testing. What would happen
 if you used 99% for training and 1% for testing? What about 1% for training and 99%
 for testing?**