def testKNNbasic():
for i in range(1,10):
print 'k=',i*10
algo=KNNBasic(k=i*10)
#algo=SVD()
evaluate(algo,data)
from recsys import AlgoBase
from recsys import Dataset
from recsys import evaluate
class MyOwnAlgorithm(AlgoBase):
def __init__(self):
# Always call base method before doing anything.
AlgoBase.__init__(self)
def train(self, trainset):
# Here again: call base method before doing anything.
AlgoBase.train(self, trainset)
# Compute the average rating. We might as well use the
# trainset.global_mean attribute ;)
self.the_mean = np.mean(
[r for (_, _, r) in self.trainset.all_ratings()])
def estimate(self, u, i):
return self.the_mean
data = Dataset.load_builtin('ml-100k')
algo = MyOwnAlgorithm()
evaluate(algo, data)
"""
This module descibes the most basic usage of RecSys: you define a prediction
algorithm, (down)load a dataset and evaluate the performances of the algorithm.
"""
from __future__ import (absolute_import, division, print_function,
unicode_literals)
from recsys import NormalPredictor
from recsys import Dataset
from recsys import evaluate
# Load the movielens-100k dataset and split it into 3 folds for
# cross-validation.
data = Dataset.load_builtin('ml-100k')
data.split(n_folds=3)
# This algorithm predicts a random rating sampled from a normal distribution.
algo = NormalPredictor()
# Evaluate performances of our algorithm on the dataset.
perf = evaluate(algo, data, measures=['RMSE', 'MAE'])
print(perf['RMSE'])
print(perf['MAE'])