medium3.py

class TFIDFPredictor:
    def __init__(self):
        self.vectorizer = TfidfVectorizer()
    def train(self, data):
        self.vectorizer.fit(np.append(data.Context.values,data.Utterance.values))
    def predict(self, context, utterances):
        # Convert context and utterances into tfidf vector
        vector_context = self.vectorizer.transform([context])
        vector_doc = self.vectorizer.transform(utterances)
        # The dot product measures the similarity of the resulting vectors
        result = np.dot(vector_doc, vector_context.T).todense()
        result = np.asarray(result).flatten()
        # Sort by top results and return the indices in descending order
        return np.argsort(result, axis=0)[::-1]
    # Evaluate TFIDF predictor
pred = TFIDFPredictor()
pred.train(train_df)
y = [pred.predict(test_df.Context[x], test_df.iloc[x,1:].values) for x in range(len(test_df))]
for n in [1, 2, 5, 10]:
print("Recall @ ({}, 10): {:g}".format(n, evaluate_recall(y, y_test, n)))