from sklearn.feature_extraction import DictVectorizer
from sklearn.linear_model import LogisticRegression
from sklearn.pipeline import Pipeline


class FeatureExtractor:
    def fit(self, X, y=None):
        return self

    def transform(self, X):
        return [self.extract_features(pixels) for pixels in X]

    def extract_features(self, pixels):
        img = pixels.reshape(28, 28)
        return {
            "mean_brightness": float(pixels.mean()),
            "top_half_brightness": float(img[:14, :].mean()),
        }


class FeatureClassifier:
    def fit(self, X, y):
        self._pipeline = Pipeline([
            ("features", FeatureExtractor()),
            ("vectorizer", DictVectorizer()),
            ("classifier", LogisticRegression(max_iter=1000)),
        ])
        self._pipeline.fit(X, y)
        return self

    def predict(self, X):
        return self._pipeline.predict(X)

    def predict_proba(self, X):
        return self._pipeline.predict_proba(X)