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The easy guide for building python collaborative filtering recommendation system in 2017
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import zipfile | |
from surprise import Reader, Dataset, SVD, evaluate | |
# Unzip ml-100k.zip | |
zipfile = zipfile.ZipFile('ml-100k.zip', 'r') | |
zipfile.extractall() | |
zipfile.close() | |
# Read data into an array of strings | |
with open('./ml-100k/u.data') as f: | |
all_lines = f.readlines() | |
# Prepare the data to be used in Surprise | |
reader = Reader(line_format='user item rating timestamp', sep='\t') | |
data = Dataset.load_from_file('./ml-100k/u.data', reader=reader) | |
# Split the dataset into 5 folds and choose the algorithm | |
data.split(n_folds=5) | |
algo = SVD() | |
# Train and test reporting the RMSE and MAE scores | |
evaluate(algo, data, measures=['RMSE', 'MAE']) | |
# Retrieve the trainset. | |
trainset = data.build_full_trainset() | |
algo.train(trainset) | |
# Predict a certain item | |
userid = str(196) | |
itemid = str(302) | |
actual_rating = 4 | |
print(algo.predict(userid, itemid, actual_rating)) |
How to get the latent feature (or embedding) for each user and item from the trained model?
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Hi. Now i want to change back the data type back to DataFrame (pandas) after applying SVD. Could you suggest the way to do it. Thanks