"""
Perceptron Feature Extractor - ML-based importance scoring for course content.
"""
import pickle
from pathlib import Path
import numpy as np
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.neural_network import MLPClassifier
class PerceptronFeatureExtractor:
"""Extracts features and predicts importance using MLP classifier."""
def __init__(self, hidden_layers: tuple[int, ...] = (100, 50)):
self.vectorizer = TfidfVectorizer(
max_features=1000, stop_words="english", max_df=0.95, min_df=1
)
self.perceptron = MLPClassifier(
hidden_layer_sizes=hidden_layers,
activation="relu",
solver="adam",
max_iter=500,
random_state=42,
)
self.is_fitted = False
def _collect_texts(self, content: dict) -> list[str]:
"""Extract text from course content for vectorization."""
texts = []
for m in content.get("modules", []):
texts.append(m.get("name", ""))
for item in m.get("items", []):
texts.append(item.get("title", ""))
return [t for t in texts if t]
def extract_features(self, content: dict) -> np.ndarray:
"""Extract feature vector from course content."""
texts = self._collect_texts(content)
if not texts:
return np.zeros(self.vectorizer.max_features)
if not self.is_fitted:
features = self.vectorizer.fit_transform(texts)
self.is_fitted = True
else:
features = self.vectorizer.transform(texts)
return np.mean(features.toarray(), axis=0)
def learn_patterns(
self,
course_contents: list[dict],
labels: np.ndarray | None = None,
) -> "PerceptronFeatureExtractor":
"""Fit the perceptron on course contents."""
if labels is None:
labels = np.array([i % 3 for i in range(len(course_contents))])
features = np.array([self.extract_features(c) for c in course_contents])
self.perceptron.fit(features, labels)
return self
def predict_importance(self, content: dict) -> float:
"""Predict importance score for content (higher = more important)."""
features = self.extract_features(content).reshape(1, -1)
if not self.is_fitted or features.shape[1] == 0:
return 0.0
proba = self.perceptron.predict_proba(features)[0]
return float(np.max(proba))
def save(self, path: Path) -> None:
"""Save model to disk."""
path.parent.mkdir(parents=True, exist_ok=True)
with open(path, "wb") as f:
pickle.dump(
{
"vectorizer": self.vectorizer,
"perceptron": self.perceptron,
"is_fitted": self.is_fitted,
},
f,
)
def load(self, path: Path) -> "PerceptronFeatureExtractor":
"""Load model from disk."""
with open(path, "rb") as f:
data = pickle.load(f)
self.vectorizer = data["vectorizer"]
self.perceptron = data["perceptron"]
self.is_fitted = data.get("is_fitted", True)
return self
