def test_ii_implicit_save_load(tmp_path):
"Save and load a model"
original = knn.ItemItem(30, save_nbrs=500, center=False, aggregate='sum')
_log.info('building model')
original.fit(lktu.ml_sample().loc[:, ['user', 'item']])
fn = tmp_path / 'ii.mod'
_log.info('saving model to %s', fn)
with fn.open('wb') as modf:
pickle.dump(original, modf)
_log.info('reloading model')
with fn.open('rb') as modf:
algo = pickle.load(modf)
_log.info('checking model')
assert all(np.logical_not(np.isnan(algo.sim_matrix_.values)))
assert all(algo.sim_matrix_.values > 0)
# a little tolerance
assert all(algo.sim_matrix_.values < 1 + 1.0e-6)
assert all(algo.item_counts_ == original.item_counts_)
assert algo.item_counts_.sum() == algo.sim_matrix_.nnz
assert algo.sim_matrix_.nnz == original.sim_matrix_.nnz
assert all(algo.sim_matrix_.rowptrs == original.sim_matrix_.rowptrs)
assert algo.sim_matrix_.values == approx(original.sim_matrix_.values)
assert algo.rating_matrix_.values is None
r_mat = algo.sim_matrix_
o_mat = original.sim_matrix_
assert all(r_mat.rowptrs == o_mat.rowptrs)
for i in range(len(algo.item_index_)):
sp = r_mat.rowptrs[i]
ep = r_mat.rowptrs[i + 1]
# everything is in decreasing order
assert all(np.diff(r_mat.values[sp:ep]) <= 0)
assert all(r_mat.values[sp:ep] == o_mat.values[sp:ep])
assert algo.item_means_ is None
matrix = algo.sim_matrix_.to_scipy()
items = pd.Series(algo.item_index_)
items = items[algo.item_counts_ > 0]
for i in items.sample(50):
ipos = algo.item_index_.get_loc(i)
_log.debug('checking item %d at position %d', i, ipos)
row = matrix.getrow(ipos)
# it should be sorted !
# check this by diffing the row values, and make sure they're negative
assert all(np.diff(row.data) < 1.0e-6)
def test_ii_save_load(tmp_path):
"Save and load a model"
tmp_path = lktu.norm_path(tmp_path)
original = knn.ItemItem(30, save_nbrs=500)
_log.info('building model')
original.fit(lktu.ml_sample())
fn = tmp_path / 'ii.mod'
_log.info('saving model to %s', fn)
original.save(fn)
_log.info('reloading model')
algo = knn.ItemItem(30)
algo.load(fn)
_log.info('checking model')
assert all(np.logical_not(np.isnan(algo.sim_matrix_.values)))
assert all(algo.sim_matrix_.values > 0)
# a little tolerance
assert all(algo.sim_matrix_.values < 1 + 1.0e-6)
assert all(algo.item_counts_ == original.item_counts_)
assert algo.item_counts_.sum() == algo.sim_matrix_.nnz
assert algo.sim_matrix_.nnz == original.sim_matrix_.nnz
assert all(algo.sim_matrix_.rowptrs == original.sim_matrix_.rowptrs)
assert algo.sim_matrix_.values == approx(original.sim_matrix_.values)
r_mat = algo.sim_matrix_
o_mat = original.sim_matrix_
assert all(r_mat.rowptrs == o_mat.rowptrs)
for i in range(len(algo.item_index_)):
sp = r_mat.rowptrs[i]
ep = r_mat.rowptrs[i + 1]
# everything is in decreasing order
assert all(np.diff(r_mat.values[sp:ep]) <= 0)
assert all(r_mat.values[sp:ep] == o_mat.values[sp:ep])
means = ml_ratings.groupby('item').rating.mean()
assert means[algo.item_index_].values == approx(original.item_means_)
matrix = lm.csr_to_scipy(algo.sim_matrix_)
items = pd.Series(algo.item_index_)
items = items[algo.item_counts_ > 0]
for i in items.sample(50):
ipos = algo.item_index_.get_loc(i)
_log.debug('checking item %d at position %d', i, ipos)
row = matrix.getrow(ipos)
# it should be sorted !
# check this by diffing the row values, and make sure they're negative
assert all(np.diff(row.data) < 1.0e-6)