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Chris Proctor
2026-06-06 21:36:59 -04:00
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.gitignore vendored Normal file
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**/__pycache__/*

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classifiers/__init__.py Normal file
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classifiers/feature_classifier.py Normal file
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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
from cleaning.transformers import LowercaseTransformer
class FeatureExtractor(BaseEstimator, TransformerMixin):
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]
class FeatureClassifier(BaseEstimator, ClassifierMixin):
def __init__(self, C=1.0):
self.C = C
def fit(self, X, y):
self._pipeline = Pipeline([
("lowercase", LowercaseTransformer()),
("features", FeatureExtractor(self.extract_features)),
("vec", DictVectorizer()),
("clf", LogisticRegression(C=self.C, max_iter=1000)),
])
y_binary = (np.array(y) == "spam").astype(int)
self._pipeline.fit(X, y_binary)
return self
def predict(self, X):
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),
"num_exclamations": message.count("!"),
"length": len(message),
}
def feature_weights(self, top_n=10):
vec = self._pipeline.named_steps["vec"]
clf = self._pipeline.named_steps["clf"]
names = vec.get_feature_names_out()
weights = clf.coef_[0]
pairs = sorted(zip(names, weights), key=lambda x: abs(x[1]), reverse=True)
return pairs[:top_n]

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classifiers/manual.py Normal file
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import numpy as np
from sklearn.base import BaseEstimator, ClassifierMixin
class ManualClassifier(BaseEstimator, ClassifierMixin):
def fit(self, X, y):
return self
def predict(self, X):
return np.array([self.predict_one(msg) for msg in X])
def predict_one(self, message):
return "ham"

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classifiers/manual_cleaning.py Normal file
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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])

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cleaning/__init__.py Normal file
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cleaning/transformers.py Normal file
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import re
import numpy as np
from sklearn.base import BaseEstimator, TransformerMixin
STOPWORDS = {
"a", "an", "the", "is", "it", "in", "on", "at", "to", "for",
"of", "and", "or", "but", "not", "with", "as", "by", "from",
"this", "that", "was", "are", "be", "been", "have", "has",
"had", "do", "did", "will", "would", "could", "should",
"i", "me", "my", "you", "your", "he", "she", "we", "they",
"his", "her", "our", "their", "its", "what", "which",
}
class LowercaseTransformer(BaseEstimator, TransformerMixin):
def fit(self, X, y=None):
self.fitted_ = True
return self
def transform(self, X):
return np.array([msg.lower() for msg in X])
class StopwordRemover(BaseEstimator, TransformerMixin):
def fit(self, X, y=None):
self.fitted_ = True
return self
def transform(self, X):
return np.array([self._remove(msg) for msg in X])
def _remove(self, message):
words = message.split()
return " ".join(w for w in words if w.lower() not in STOPWORDS)
class PunctuationRemover(BaseEstimator, TransformerMixin):
def fit(self, X, y=None):
self.fitted_ = True
return self
def transform(self, X):
return np.array([re.sub(r"[^\w\s]", " ", msg) for msg in X])

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cli/__init__.py Normal file
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cli/data.py Normal file
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import io
import os
import zipfile
import urllib.request
import pandas as pd
from sklearn.datasets import get_data_home
URL = (
"https://archive.ics.uci.edu/ml/machine-learning-databases/"
"00228/smsspamcollection.zip"
)
def load_spam():
path = os.path.join(get_data_home(), "spam", "SMSSpamCollection")
if not os.path.exists(path):
_fetch(path)
return pd.read_csv(path, sep="\t", header=None, names=["label", "message"])
def _fetch(dest):
os.makedirs(os.path.dirname(dest), exist_ok=True)
print("Downloading SMS Spam Collection...")
with urllib.request.urlopen(URL) as response:
data = response.read()
with zipfile.ZipFile(io.BytesIO(data)) as zf:
with zf.open("SMSSpamCollection") as f:
content = f.read()
with open(dest, "wb") as f:
f.write(content)

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cli/main.py Normal file
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"""Evaluate a spam classifier.
Usage:
spam -e
spam classifiers.manual.ManualClassifier
spam classifiers.feature_classifier.FeatureClassifier
spam classifiers.manual.ManualClassifier -t 0.2
spam classifiers.manual.ManualClassifier -a
spam classifiers.manual.ManualClassifier -a 5
"""
import argparse
import importlib
from sklearn.model_selection import train_test_split
import cli.output as out
from cli.data import load_spam
def load_classifier(class_path):
module_path, class_name = class_path.rsplit(".", 1)
module = importlib.import_module(module_path)
return getattr(module, class_name)()
def main():
parser = argparse.ArgumentParser(
description="Train and evaluate a spam classifier."
)
parser.add_argument(
"classifier",
nargs="?",
help="Fully-qualified class, e.g. classifiers.manual.ManualClassifier",
)
parser.add_argument(
"-e", "--explore",
action="store_true",
help="Drop into an interactive shell with the dataset loaded as `df`",
)
parser.add_argument(
"-t", "--test-ratio",
type=float,
default=0.3,
help="Fraction held out for testing (default: 0.3)",
)
parser.add_argument(
"-a", "--error-analysis",
type=int,
nargs="?",
const=10,
default=None,
metavar="N",
help="Show up to N misclassified examples (default: 10)",
)
args = parser.parse_args()
if not args.classifier and not args.explore:
parser.print_help()
return
df = load_spam()
if args.explore:
out.explore(df)
if not args.classifier:
return
X = df["message"].values
y = df["label"].values
X_train, X_test, y_train, y_test = train_test_split(
X, y, test_size=args.test_ratio, random_state=42
)
out.dataset_summary(df, len(X_train), len(X_test))
clf = load_classifier(args.classifier)
clf.fit(X_train, y_train)
y_pred = clf.predict(X_test)
out.evaluation(y_test, y_pred, type(clf).__name__)
out.feature_weights(clf)
if args.error_analysis is not None:
out.error_analysis(X_test, y_test, y_pred, args.error_analysis)
if __name__ == "__main__":
main()

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cli/output.py Normal file
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import code
import pandas as pd
from sklearn.metrics import confusion_matrix, f1_score, precision_score, recall_score
def explore(df):
print("The dataset is available as `df`. Press Ctrl-D or type exit() to quit.\n")
code.interact(local={"df": df, "pd": pd}, banner="")
def dataset_summary(df, n_train, n_test):
print("=" * 60)
print("DATASET")
print("=" * 60)
counts = df["label"].value_counts()
total = n_train + n_test
print(f"\n Total messages: {len(df)}")
for label, n in counts.items():
print(f" {label:4s}: {n:5d} ({100 * n / len(df):.1f}%)")
print(f"\n train: {n_train} ({100 * n_train / total:.0f}%) test: {n_test} ({100 * n_test / total:.0f}%)")
print()
def evaluation(y_true, y_pred, clf_name):
print("=" * 60)
print(f"RESULTS: {clf_name}")
print("=" * 60)
print()
print(f"{'':12s} {'precision':>10} {'recall':>10} {'f1':>10}")
for label in ["ham", "spam"]:
p = precision_score(y_true, y_pred, pos_label=label, zero_division=0)
r = recall_score(y_true, y_pred, pos_label=label, zero_division=0)
f = f1_score(y_true, y_pred, pos_label=label, zero_division=0)
print(f" {label:<10} {p:>10.3f} {r:>10.3f} {f:>10.3f}")
avg_f1 = f1_score(y_true, y_pred, average="macro", zero_division=0)
print(f"\n {'average f1':10} {'':>10} {'':>10} {avg_f1:>10.3f}")
print()
cm = confusion_matrix(y_true, y_pred, labels=["ham", "spam"])
print("Confusion matrix:")
print(f"{'':18s} {'pred ham':>10} {'pred spam':>10}")
print(f"{'actual ham':18s} {cm[0][0]:>10} {cm[0][1]:>10}")
print(f"{'actual spam':18s} {cm[1][0]:>10} {cm[1][1]:>10}")
print()
def feature_weights(clf, top_n=10):
if not hasattr(clf, "feature_weights"):
return
weights = clf.feature_weights(top_n=top_n)
if not weights:
return
print("=" * 60)
print(f"TOP {len(weights)} FEATURES BY WEIGHT")
print("=" * 60)
for name, w in weights:
direction = "spam" if w > 0 else "ham "
bar_len = min(int(abs(w) * 5), 25)
bar = ("+" if w > 0 else "-") * bar_len
print(f" {name:<28} {w:+.3f} → {direction} {bar}")
print()
def error_analysis(X, y_true, y_pred, n):
errors = [
(x, t, p)
for x, t, p in zip(X, y_true, y_pred)
if t != p
]
shown = errors[:n]
print("=" * 60)
print(f"ERROR ANALYSIS ({len(shown)} of {len(errors)} misclassified)")
print("=" * 60)
for msg, true_label, pred_label in shown:
display = msg if len(msg) <= 80 else msg[:77] + "..."
print(f"\n true={true_label:<4} pred={pred_label}")
print(f" {display}")
print()

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pyproject.toml Normal file
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[project]
name = "classification-features"
version = "0.1.0"
description = "Classification: Features lab"
requires-python = ">=3.11"
dependencies = [
"pandas>=2.0",
"scikit-learn>=1.3",
]
[project.scripts]
spam = "cli.main:main"
[build-system]
requires = ["hatchling"]
build-backend = "hatchling.build"
[tool.hatch.build.targets.wheel]
packages = ["cli", "classifiers", "cleaning"]

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questions.md Normal file
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# Spam Classifier: Questions
---
## Checkpoint 1: Exploring the Dataset
**1. How many messages are in the dataset? How many are ham, and how many are spam?**
*Your answer:*
**2. Look at at least ten spam messages (`df[df.label == "spam"]`). List three patterns you notice.**
1.
2.
3.
**3. Look at at least ten ham messages (`df[df.label == "ham"]`). How do they differ from spam?**
*Your answer:*
---
## Checkpoint 2: Manual Classifier
**4. What rules did you write? List each rule and the pattern it targets.**
*Your answer:*
**5. Record your best results:**
| Metric | Value |
|--------|-------|
| Spam precision | |
| Spam recall | |
| Spam F1 | |
**6. Does your classifier make more false positives (ham flagged as spam) or false negatives (spam missed)?**
*Your answer:*
**7. Describe one rule you tried that did not help and explain why.**
*Your answer:*
---
## Checkpoint 3: Data Cleaning
**8. Which transformers did you add to the pipeline, and in what order?**
*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 |
|-------------|-----------------|-----------|
| | | |
| | | |
| | | |
**12. Record your best results:**
| Metric | Value |
|--------|-------|
| Spam precision | |
| Spam recall | |
| Spam F1 | |
**13. Which features received the largest positive weights (most predictive of spam)?**
*Your answer:*
**14. Which features received near-zero weights? Why might the model have ignored them?**
*Your answer:*
---
## Final Questions
**15. 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.**
Problem I chose:
| Feature name | What it measures | Why it might help |
|-------------|-----------------|------------------|
| | | |
| | | |
| | | |
| | | |
| | | |
**16. 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?**
*Your answer:*

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