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test_crossfold.py
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test_crossfold.py
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import itertools as it
import functools as ft
import pytest
import math
import numpy as np
import lenskit.util.test as lktu
import lenskit.crossfold as xf
def test_partition_rows():
ratings = lktu.ml_test.ratings
splits = xf.partition_rows(ratings, 5)
splits = list(splits)
assert len(splits) == 5
for s in splits:
assert len(s.test) + len(s.train) == len(ratings)
assert all(s.test.index.union(s.train.index) == ratings.index)
test_idx = s.test.set_index(['user', 'item']).index
train_idx = s.train.set_index(['user', 'item']).index
assert len(test_idx.intersection(train_idx)) == 0
# we should partition!
for s1, s2 in it.product(splits, splits):
if s1 is s2:
continue
i1 = s1.test.set_index(['user', 'item']).index
i2 = s2.test.set_index(['user', 'item']).index
inter = i1.intersection(i2)
assert len(inter) == 0
union = ft.reduce(lambda i1, i2: i1.union(i2), (s.test.index for s in splits))
assert len(union.unique()) == len(ratings)
def test_sample_rows():
ratings = lktu.ml_test.ratings
splits = xf.sample_rows(ratings, partitions=5, size=1000)
splits = list(splits)
assert len(splits) == 5
for s in splits:
assert len(s.test) == 1000
assert len(s.test) + len(s.train) == len(ratings)
test_idx = s.test.set_index(['user', 'item']).index
train_idx = s.train.set_index(['user', 'item']).index
assert len(test_idx.intersection(train_idx)) == 0
for s1, s2 in it.product(splits, splits):
if s1 is s2:
continue
i1 = s1.test.set_index(['user', 'item']).index
i2 = s2.test.set_index(['user', 'item']).index
inter = i1.intersection(i2)
assert len(inter) == 0
def test_sample_rows_more_smaller_parts():
ratings = lktu.ml_test.ratings
splits = xf.sample_rows(ratings, partitions=10, size=500)
splits = list(splits)
assert len(splits) == 10
for s in splits:
assert len(s.test) == 500
assert len(s.test) + len(s.train) == len(ratings)
test_idx = s.test.set_index(['user', 'item']).index
train_idx = s.train.set_index(['user', 'item']).index
assert len(test_idx.intersection(train_idx)) == 0
for s1, s2 in it.product(splits, splits):
if s1 is s2:
continue
i1 = s1.test.set_index(['user', 'item']).index
i2 = s2.test.set_index(['user', 'item']).index
inter = i1.intersection(i2)
assert len(inter) == 0
def test_sample_non_disjoint():
ratings = lktu.ml_test.ratings
splits = xf.sample_rows(ratings, partitions=10, size=1000, disjoint=False)
splits = list(splits)
assert len(splits) == 10
for s in splits:
assert len(s.test) == 1000
assert len(s.test) + len(s.train) == len(ratings)
test_idx = s.test.set_index(['user', 'item']).index
train_idx = s.train.set_index(['user', 'item']).index
assert len(test_idx.intersection(train_idx)) == 0
# There are enough splits & items we should pick at least one duplicate
ipairs = ((s1.test.set_index('user', 'item').index, s2.test.set_index('user', 'item').index)
for (s1, s2) in it.product(splits, splits))
isizes = [len(i1.intersection(i2)) for (i1, i2) in ipairs]
assert any(n > 0 for n in isizes)
@pytest.mark.slow
def test_sample_oversize():
ratings = lktu.ml_test.ratings
splits = xf.sample_rows(ratings, 150, 1000)
splits = list(splits)
assert len(splits) == 150
for s in splits:
assert len(s.test) + len(s.train) == len(ratings)
assert all(s.test.index.union(s.train.index) == ratings.index)
test_idx = s.test.set_index(['user', 'item']).index
train_idx = s.train.set_index(['user', 'item']).index
assert len(test_idx.intersection(train_idx)) == 0
def test_sample_n():
ratings = lktu.ml_test.ratings
users = np.random.choice(ratings.user.unique(), 5, replace=False)
s5 = xf.SampleN(5)
for u in users:
udf = ratings[ratings.user == u]
tst = s5(udf)
trn = udf.loc[udf.index.difference(tst.index), :]
assert len(tst) == 5
assert len(tst) + len(trn) == len(udf)
s10 = xf.SampleN(10)
for u in users:
udf = ratings[ratings.user == u]
tst = s10(udf)
trn = udf.loc[udf.index.difference(tst.index), :]
assert len(tst) == 10
assert len(tst) + len(trn) == len(udf)
def test_sample_frac():
ratings = lktu.ml_test.ratings
users = np.random.choice(ratings.user.unique(), 5, replace=False)
samp = xf.SampleFrac(0.2)
for u in users:
udf = ratings[ratings.user == u]
tst = samp(udf)
trn = udf.loc[udf.index.difference(tst.index), :]
assert len(tst) + len(trn) == len(udf)
assert len(tst) >= math.floor(len(udf) * 0.2)
assert len(tst) <= math.ceil(len(udf) * 0.2)
samp = xf.SampleFrac(0.5)
for u in users:
udf = ratings[ratings.user == u]
tst = samp(udf)
trn = udf.loc[udf.index.difference(tst.index), :]
assert len(tst) + len(trn) == len(udf)
assert len(tst) >= math.floor(len(udf) * 0.5)
assert len(tst) <= math.ceil(len(udf) * 0.5)
def test_last_n():
ratings = lktu.ml_test.ratings
users = np.random.choice(ratings.user.unique(), 5, replace=False)
samp = xf.LastN(5)
for u in users:
udf = ratings[ratings.user == u]
tst = samp(udf)
trn = udf.loc[udf.index.difference(tst.index), :]
assert len(tst) == 5
assert len(tst) + len(trn) == len(udf)
assert tst.timestamp.min() >= trn.timestamp.max()
samp = xf.LastN(7)
for u in users:
udf = ratings[ratings.user == u]
tst = samp(udf)
trn = udf.loc[udf.index.difference(tst.index), :]
assert len(tst) == 7
assert len(tst) + len(trn) == len(udf)
assert tst.timestamp.min() >= trn.timestamp.max()
def test_last_frac():
ratings = lktu.ml_test.ratings
users = np.random.choice(ratings.user.unique(), 5, replace=False)
samp = xf.LastFrac(0.2, 'timestamp')
for u in users:
udf = ratings[ratings.user == u]
tst = samp(udf)
trn = udf.loc[udf.index.difference(tst.index), :]
assert len(tst) + len(trn) == len(udf)
assert len(tst) >= math.floor(len(udf) * 0.2)
assert len(tst) <= math.ceil(len(udf) * 0.2)
assert tst.timestamp.min() >= trn.timestamp.max()
samp = xf.LastFrac(0.5, 'timestamp')
for u in users:
udf = ratings[ratings.user == u]
tst = samp(udf)
trn = udf.loc[udf.index.difference(tst.index), :]
assert len(tst) + len(trn) == len(udf)
assert len(tst) >= math.floor(len(udf) * 0.5)
assert len(tst) <= math.ceil(len(udf) * 0.5)
assert tst.timestamp.min() >= trn.timestamp.max()
def test_partition_users():
ratings = lktu.ml_test.ratings
splits = xf.partition_users(ratings, 5, xf.SampleN(5))
splits = list(splits)
assert len(splits) == 5
for s in splits:
ucounts = s.test.groupby('user').agg('count')
assert all(ucounts == 5)
assert all(s.test.index.union(s.train.index) == ratings.index)
assert len(s.test) + len(s.train) == len(ratings)
users = ft.reduce(lambda us1, us2: us1 | us2,
(set(s.test.user) for s in splits))
assert len(users) == ratings.user.nunique()
assert users == set(ratings.user)
def test_partition_users_frac():
ratings = lktu.ml_test.ratings
splits = xf.partition_users(ratings, 5, xf.SampleFrac(0.2))
splits = list(splits)
assert len(splits) == 5
ucounts = ratings.groupby('user').item.count()
uss = ucounts * 0.2
for s in splits:
tucs = s.test.groupby('user').item.count()
assert all(tucs >= uss.loc[tucs.index] - 1)
assert all(tucs <= uss.loc[tucs.index] + 1)
assert all(s.test.index.union(s.train.index) == ratings.index)
assert len(s.test) + len(s.train) == len(ratings)
# we have all users
users = ft.reduce(lambda us1, us2: us1 | us2,
(set(s.test.user) for s in splits))
assert len(users) == ratings.user.nunique()
assert users == set(ratings.user)
def test_sample_users():
ratings = lktu.ml_test.ratings
splits = xf.sample_users(ratings, 5, 100, xf.SampleN(5))
splits = list(splits)
assert len(splits) == 5
for s in splits:
ucounts = s.test.groupby('user').agg('count')
assert len(s.test) == 5 * 100
assert len(ucounts) == 100
assert all(ucounts == 5)
assert all(s.test.index.union(s.train.index) == ratings.index)
assert len(s.test) + len(s.train) == len(ratings)
# no overlapping users
for s1, s2 in it.product(splits, splits):
if s1 is s2:
continue
us1 = s1.test.user.unique()
us2 = s2.test.user.unique()
assert len(np.intersect1d(us1, us2)) == 0
def test_sample_users_frac():
ratings = lktu.ml_test.ratings
splits = xf.sample_users(ratings, 5, 100, xf.SampleFrac(0.2))
splits = list(splits)
assert len(splits) == 5
ucounts = ratings.groupby('user').item.count()
uss = ucounts * 0.2
for s in splits:
tucs = s.test.groupby('user').item.count()
assert len(tucs) == 100
assert all(tucs >= uss.loc[tucs.index] - 1)
assert all(tucs <= uss.loc[tucs.index] + 1)
assert all(s.test.index.union(s.train.index) == ratings.index)
assert len(s.test) + len(s.train) == len(ratings)
# no overlapping users
for s1, s2 in it.product(splits, splits):
if s1 is s2:
continue
us1 = s1.test.user.unique()
us2 = s2.test.user.unique()
assert len(np.intersect1d(us1, us2)) == 0
@pytest.mark.slow
def test_sample_users_frac_oversize():
ratings = lktu.ml_test.ratings
splits = xf.sample_users(ratings, 20, 100, xf.SampleN(5))
splits = list(splits)
assert len(splits) == 20
for s in splits:
ucounts = s.test.groupby('user').agg('count')
assert len(ucounts) < 100
assert all(ucounts == 5)
assert all(s.test.index.union(s.train.index) == ratings.index)
assert len(s.test) + len(s.train) == len(ratings)
users = ft.reduce(lambda us1, us2: us1 | us2,
(set(s.test.user) for s in splits))
assert len(users) == ratings.user.nunique()
assert users == set(ratings.user)
for s1, s2 in it.product(splits, splits):
if s1 is s2:
continue
us1 = s1.test.user.unique()
us2 = s2.test.user.unique()
assert len(np.intersect1d(us1, us2)) == 0
def test_sample_users_frac_oversize_ndj():
ratings = lktu.ml_test.ratings
splits = xf.sample_users(ratings, 20, 100, xf.SampleN(5), disjoint=False)
splits = list(splits)
assert len(splits) == 20
for s in splits:
ucounts = s.test.groupby('user').agg('count')
assert len(ucounts) == 100
assert len(s.test) == 5 * 100
assert all(ucounts == 5)
assert all(s.test.index.union(s.train.index) == ratings.index)
assert len(s.test) + len(s.train) == len(ratings)