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test_knn_user_user.py
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test_knn_user_user.py
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import lenskit.algorithms.user_knn as knn
from pathlib import Path
import logging
import pickle
import pandas as pd
import numpy as np
from scipy.sparse import linalg as spla
from pytest import approx, mark
import lenskit.util.test as lktu
_log = logging.getLogger(__name__)
ml_ratings = lktu.ml_test.ratings
def test_uu_train():
algo = knn.UserUser(30)
ret = algo.fit(ml_ratings)
assert ret is algo
# it should have computed correct means
umeans = ml_ratings.groupby('user').rating.mean()
mlmeans = pd.Series(algo.user_means_, index=algo.user_index_, name='mean')
umeans, mlmeans = umeans.align(mlmeans)
assert mlmeans.values == approx(umeans.values)
# we should be able to reconstruct rating values
uir = ml_ratings.set_index(['user', 'item']).rating
r_items = algo.transpose_matrix_.rowinds()
ui_rbdf = pd.DataFrame({
'user': algo.user_index_[algo.transpose_matrix_.colinds],
'item': algo.item_index_[r_items],
'nrating': algo.transpose_matrix_.values
}).set_index(['user', 'item'])
ui_rbdf = ui_rbdf.join(mlmeans)
ui_rbdf['rating'] = ui_rbdf['nrating'] + ui_rbdf['mean']
ui_rbdf['orig_rating'] = uir
assert ui_rbdf.rating.values == approx(ui_rbdf.orig_rating.values)
def test_uu_predict_one():
algo = knn.UserUser(30)
algo.fit(ml_ratings)
preds = algo.predict_for_user(4, [1016])
assert len(preds) == 1
assert preds.index == [1016]
assert preds.values == approx([3.62221550680778])
def test_uu_predict_too_few():
algo = knn.UserUser(30, min_nbrs=2)
algo.fit(ml_ratings)
preds = algo.predict_for_user(4, [2091])
assert len(preds) == 1
assert preds.index == [2091]
assert all(preds.isna())
def test_uu_predict_too_few_blended():
algo = knn.UserUser(30, min_nbrs=2)
algo.fit(ml_ratings)
preds = algo.predict_for_user(4, [1016, 2091])
assert len(preds) == 2
assert np.isnan(preds.loc[2091])
assert preds.loc[1016] == approx(3.62221550680778)
def test_uu_predict_live_ratings():
algo = knn.UserUser(30, min_nbrs=2)
no4 = ml_ratings[ml_ratings.user != 4]
algo.fit(no4)
ratings = ml_ratings[ml_ratings.user == 4].set_index('item').rating
preds = algo.predict_for_user(20381, [1016, 2091], ratings)
assert len(preds) == 2
assert np.isnan(preds.loc[2091])
assert preds.loc[1016] == approx(3.62221550680778)
def test_uu_save_load(tmp_path):
orig = knn.UserUser(30)
_log.info('training model')
orig.fit(ml_ratings)
fn = tmp_path / 'uu.model'
_log.info('saving to %s', fn)
with fn.open('wb') as f:
pickle.dump(orig, f)
_log.info('reloading model')
with fn.open('rb') as f:
algo = pickle.load(f)
_log.info('checking model')
# it should have computed correct means
umeans = ml_ratings.groupby('user').rating.mean()
mlmeans = pd.Series(algo.user_means_, index=algo.user_index_, name='mean')
umeans, mlmeans = umeans.align(mlmeans)
assert mlmeans.values == approx(umeans.values)
# we should be able to reconstruct rating values
uir = ml_ratings.set_index(['user', 'item']).rating
r_items = algo.transpose_matrix_.rowinds()
ui_rbdf = pd.DataFrame({
'user': algo.user_index_[algo.transpose_matrix_.colinds],
'item': algo.item_index_[r_items],
'nrating': algo.transpose_matrix_.values
}).set_index(['user', 'item'])
ui_rbdf = ui_rbdf.join(mlmeans)
ui_rbdf['rating'] = ui_rbdf['nrating'] + ui_rbdf['mean']
ui_rbdf['orig_rating'] = uir
assert ui_rbdf.rating.values == approx(ui_rbdf.orig_rating.values)
# running the predictor should work
preds = algo.predict_for_user(4, [1016])
assert len(preds) == 1
assert preds.index == [1016]
assert preds.values == approx([3.62221550680778])
def test_uu_predict_unknown_empty():
algo = knn.UserUser(30, min_nbrs=2)
algo.fit(ml_ratings)
preds = algo.predict_for_user(-28018, [1016, 2091])
assert len(preds) == 2
assert all(preds.isna())
def test_uu_implicit():
"Train and use user-user on an implicit data set."
algo = knn.UserUser(20, center=False, aggregate='sum')
data = ml_ratings.loc[:, ['user', 'item']]
algo.fit(data)
assert algo.user_means_ is None
mat = algo.rating_matrix_.to_scipy()
norms = spla.norm(mat, 2, 1)
assert norms == approx(1.0)
preds = algo.predict_for_user(50, [1, 2, 42])
assert all(preds[preds.notna()] > 0)
@mark.slow
def test_uu_save_load_implicit(tmp_path):
"Save and load user-user on an implicit data set."
orig = knn.UserUser(20, center=False, aggregate='sum')
data = ml_ratings.loc[:, ['user', 'item']]
orig.fit(data)
ser = pickle.dumps(orig)
algo = pickle.loads(ser)
assert algo.user_means_ is None
assert all(algo.user_index_ == orig.user_index_)
assert all(algo.item_index_ == orig.item_index_)
assert all(algo.rating_matrix_.rowptrs == orig.rating_matrix_.rowptrs)
assert all(algo.rating_matrix_.colinds == orig.rating_matrix_.colinds)
assert all(algo.rating_matrix_.values == orig.rating_matrix_.values)
assert all(algo.transpose_matrix_.rowptrs == orig.transpose_matrix_.rowptrs)
assert all(algo.transpose_matrix_.colinds == orig.transpose_matrix_.colinds)
assert algo.transpose_matrix_.values is None
@mark.slow
def test_uu_known_preds():
from lenskit import batch
algo = knn.UserUser(30, min_sim=1.0e-6)
_log.info('training %s on ml data', algo)
algo.fit(lktu.ml_test.ratings)
dir = Path(__file__).parent
pred_file = dir / 'user-user-preds.csv'
_log.info('reading known predictions from %s', pred_file)
known_preds = pd.read_csv(str(pred_file))
pairs = known_preds.loc[:, ['user', 'item']]
_log.info('generating %d known predictions', len(pairs))
preds = batch.predict(algo, pairs)
merged = pd.merge(known_preds.rename(columns={'prediction': 'expected'}), preds)
assert len(merged) == len(preds)
merged['error'] = merged.expected - merged.prediction
try:
assert not any(merged.prediction.isna() & merged.expected.notna())
except AssertionError as e:
bad = merged[merged.prediction.isna() & merged.expected.notna()]
_log.error('%d missing predictions:\n%s', len(bad), bad)
raise e
err = merged.error
err = err[err.notna()]
try:
assert all(err.abs() < 0.01)
except AssertionError as e:
bad = merged[merged.error.notna() & (merged.error.abs() >= 0.01)]
_log.error('%d erroneous predictions:\n%s', len(bad), bad)
raise e
def __batch_eval(job):
from lenskit import batch
algo, train, test = job
_log.info('running training')
algo.fit(train)
_log.info('testing %d users', test.user.nunique())
return batch.predict(algo, test)
@mark.slow
@mark.eval
@mark.skipif(not lktu.ml100k.available, reason='ML100K data not present')
def test_uu_batch_accuracy():
from lenskit.algorithms import basic
import lenskit.crossfold as xf
import lenskit.metrics.predict as pm
ratings = lktu.ml100k.ratings
uu_algo = knn.UserUser(30)
algo = basic.Fallback(uu_algo, basic.Bias())
folds = xf.partition_users(ratings, 5, xf.SampleFrac(0.2))
preds = [__batch_eval((algo, train, test)) for (train, test) in folds]
preds = pd.concat(preds)
mae = pm.mae(preds.prediction, preds.rating)
assert mae == approx(0.71, abs=0.028)
user_rmse = preds.groupby('user').apply(lambda df: pm.rmse(df.prediction, df.rating))
assert user_rmse.mean() == approx(0.91, abs=0.055)
@mark.slow
@mark.eval
@mark.skipif(not lktu.ml100k.available, reason='ML100K data not present')
def test_uu_implicit_batch_accuracy():
from lenskit import batch, topn
import lenskit.crossfold as xf
ratings = lktu.ml100k.ratings
algo = knn.UserUser(30, center=False, aggregate='sum')
folds = list(xf.partition_users(ratings, 5, xf.SampleFrac(0.2)))
all_test = pd.concat(f.test for f in folds)
rec_lists = []
for train, test in folds:
_log.info('running training')
algo.fit(train.loc[:, ['user', 'item']])
cands = topn.UnratedCandidates(train)
_log.info('testing %d users', test.user.nunique())
recs = batch.recommend(algo, test.user.unique(), 100, cands, n_jobs=2)
rec_lists.append(recs)
recs = pd.concat(rec_lists)
rla = topn.RecListAnalysis()
rla.add_metric(topn.ndcg)
results = rla.compute(recs, all_test)
user_dcg = results.ndcg
dcg = user_dcg.mean()
assert dcg >= 0.03