test.py

from lightfm import LightFM
from lightfm.datasets import fetch_movielens
from lightfm.evaluation import precision_at_k
import pandas as pd
import numpy as np
# Load the MovieLens 100k dataset. Only five
# star ratings are treated as positive.
data = fetch_movielens(min_rating=5.0)

# Instantiate and train the model
model = LightFM(loss='warp')
model.fit(data['train'], epochs=30, num_threads=2,)

# Evaluate the trained model
test_precision = precision_at_k(model, data['test'], k=5).mean()

def sample_recommendation(model, data, user_ids):

    n_users, n_items = data['train'].shape

    for user_id in user_ids:
        known_positives = data['item_labels'][data['train'].tocsr()[user_id].indices]
        scores = model.predict(user_id, np.arange(n_items))
        print(data['item_labels'])
        top_items = data['item_labels'][np.argsort(-scores)]

        print("User %s" % user_id)
        print("     Known positives:")

        for x in known_positives[:3]:
            print("        %s" % x)

        print("     Recommended:")

        for x in top_items[:3]:
            print("        %s" % x)

sample_recommendation(model, data, [3, 25, 450])