repo_id stringclasses 409
values | prefix large_stringlengths 34 36.3k | target large_stringlengths 1 498 | assertion_type stringclasses 31
values | difficulty stringclasses 8
values | test_file stringlengths 10 121 | test_function stringlengths 1 104 | test_class stringlengths 0 51 | lineno int32 2 11.3k | commit_idx int32 |
|---|---|---|---|---|---|---|---|---|---|
massquantity/LibRecommender | import sys
import numpy as np
import pytest
from numpy.testing import assert_allclose, assert_array_equal
from libreco.layers import (
conv_nn,
dense_nn,
layer_normalization,
max_pool,
multi_head_attention,
rms_norm,
shared_dense,
tf_dense,
tf_rnn,
)
from libreco.layers.activation ... | (10, 10) | assert | collection | tests/test_tf_layers.py | test_positional_encoding | 193 | null | |
massquantity/LibRecommender | import numpy as np
import pytest
from libreco.utils.constants import FeatModels, SequenceModels
def recommend_in_former_consumed(data_info, reco, user):
user_id = data_info.user2id[user]
user_consumed = data_info.user_consumed[user_id]
user_consumed_id = [data_info.id2item[i] for i in user_consumed]
r... | len(cold_reco2) | assert | func_call | tests/utils_reco.py | ptest_recommends | 33 | null | |
massquantity/LibRecommender | from io import StringIO
import numpy as np
import pandas as pd
import pytest
import torch
from libreco.algorithms import DIN, LightGCN, PinSageDGL, RNN4Rec
from libreco.batch.batch_data import BatchData
from libreco.batch.batch_unit import (
PairFeats,
PairwiseBatch,
PointwiseBatch,
PointwiseSepFeatBa... | (3,) | assert | collection | tests/test_collators.py | test_normal_collator | 111 | null | |
massquantity/LibRecommender | from io import StringIO
import numpy as np
import pandas as pd
import pytest
import torch
from libreco.algorithms import DIN, LightGCN, PinSageDGL, RNN4Rec
from libreco.batch.batch_data import BatchData
from libreco.batch.batch_unit import (
PairFeats,
PairwiseBatch,
PointwiseBatch,
PointwiseSepFeatBa... | negatives[0][:4] | assert | complex_expr | tests/test_collators.py | test_negatives_exceed_sampling_tolerance | 412 | null | |
massquantity/LibRecommender | import sys
import pytest
from libreco.data.consumed import _fill_empty, _merge_dedup, interaction_consumed
def test_merge_remove_duplicates():
num = 3
old_consumed = {0: [1, 2, 3], 1: [4, 5]}
new_consumed = {0: [2, 1], 2: [7, 8]}
consumed = _merge_dedup(new_consumed, num, old_consumed)
assert con... | [7, 8] | assert | collection | tests/test_consumed.py | test_merge_remove_duplicates | 55 | null | |
massquantity/LibRecommender | import functools
from io import StringIO
import numpy as np
import pandas as pd
import pytest
from scipy.sparse import csr_matrix
from libreco.data import DatasetPure
from libreco.utils.similarities import (
_choose_blocks,
cosine_sim,
jaccard_sim,
pearson_sim,
)
raw_data = """
user,item,label
1,8,2
... | invert_sim.shape | assert | complex_expr | tests/test_similarities.py | test_similarities | 63 | null | |
massquantity/LibRecommender | import sys
import pytest
import tensorflow as tf
from libreco.algorithms import FM
from tests.models.utils_tf import ptest_tf_variables
from tests.utils_data import SAVE_PATH, remove_path, set_ranking_labels
from tests.utils_metrics import get_metrics
from tests.utils_multi_sparse_models import fit_multi_sparse
from ... | AssertionError) | pytest.raises | variable | tests/models/test_fm.py | test_fm | 62 | null | |
massquantity/LibRecommender | import json
import os
import numpy as np
import pytest
from tensorflow.core.protobuf.meta_graph_pb2 import MetaGraphDef
from libreco.bases import CfBase, TfBase
from libreco.tfops import tf
from libserving.serialization import (
embed2redis,
knn2redis,
online2redis,
save_embed,
save_knn,
save... | model.n_users - 1 | assert | complex_expr | tests/serving/test_serialization.py | check_user_consumed | 144 | null | |
massquantity/LibRecommender | import sys
from pathlib import Path
import numpy as np
import pandas as pd
import pytest
from libreco.algorithms import Swing
from libreco.data import DataInfo, DatasetPure, split_by_ratio_chrono
from libreco.evaluation import evaluate
from tests.utils_data import SAVE_PATH, remove_path
from tests.utils_reco import p... | eval_result["roc_auc"] | assert | complex_expr | tests/retrain/test_rs_swing_retrain.py | test_rs_cf_retrain | 119 | null | |
massquantity/LibRecommender | import sys
import numpy as np
import pytest
from numpy.testing import assert_allclose, assert_array_equal
from libreco.layers import (
conv_nn,
dense_nn,
layer_normalization,
max_pool,
multi_head_attention,
rms_norm,
shared_dense,
tf_dense,
tf_rnn,
)
from libreco.layers.activation ... | (100, 16) | assert | collection | tests/test_tf_layers.py | test_rnn_layer | 141 | null | |
massquantity/LibRecommender | import sys
import pytest
import tensorflow as tf
from libreco.algorithms import PinSageDGL
from tests.utils_data import remove_path, set_ranking_labels
from tests.utils_metrics import get_metrics
from tests.utils_pred import ptest_preds
from tests.utils_reco import ptest_recommends
from tests.utils_save_load import s... | ValueError) | pytest.raises | variable | tests/models/test_pinsage_dgl.py | test_pinsage_dgl | 167 | null | |
massquantity/LibRecommender | import sys
import numpy as np
import pytest
import tensorflow as tf
from numpy.testing import assert_array_equal
from libreco.algorithms import SIM
from tests.models.utils_tf import ptest_tf_variables
from tests.utils_data import set_ranking_labels
from tests.utils_metrics import get_metrics
from tests.utils_multi_sp... | RuntimeError) | pytest.raises | variable | tests/models/test_sim.py | test_sim_multi_sparse | 131 | null | |
massquantity/LibRecommender | import subprocess
import pytest
from libreco.bases import TfBase
from libserving.serialization import save_tf, tf2redis
from tests.utils_data import SAVE_PATH
@pytest.mark.parametrize(
"tf_model", ["pure", "feat-all", "feat-user", "feat-item"], indirect=True
)
def test_tf_serving(tf_model, session, close_server)... | 1 | assert | numeric_literal | tests/serving/test_tf_serving.py | test_tf_serving | 29 | null | |
massquantity/LibRecommender | import itertools
import numpy as np
import pytest
from libreco.utils.initializers import (
he_init,
truncated_normal,
variance_scaling,
xavier_init,
)
def test_initializers():
np_rng = np.random.default_rng(42)
mean, std, fan_in, fan_out, scale = 0.1, 0.01, 4, 2, 2.5
variables = truncated... | (3, 2) | assert | collection | tests/test_initializers.py | test_initializers | 18 | null | |
massquantity/LibRecommender | import sys
import pytest
import tensorflow as tf
from numpy.testing import assert_array_equal
from libreco.algorithms import Caser
from tests.models.utils_tf import ptest_tf_variables
from tests.utils_data import set_ranking_labels
from tests.utils_metrics import get_metrics
from tests.utils_pred import ptest_preds
f... | ValueError) | pytest.raises | variable | tests/models/test_caser.py | test_caser | 69 | null | |
massquantity/LibRecommender | from io import StringIO
import numpy as np
import pandas as pd
import pytest
import torch
from libreco.algorithms import DIN, LightGCN, PinSageDGL, RNN4Rec
from libreco.batch.batch_data import BatchData
from libreco.batch.batch_unit import (
PairFeats,
PairwiseBatch,
PointwiseBatch,
PointwiseSepFeatBa... | (9, 10) | assert | collection | tests/test_collators.py | test_pointwise_collator | 248 | null | |
massquantity/LibRecommender | import sys
import pytest
import tensorflow as tf
from libreco.algorithms import NGCF
from tests.utils_data import remove_path, set_ranking_labels
from tests.utils_metrics import get_metrics
from tests.utils_pred import ptest_preds
from tests.utils_reco import ptest_recommends
from tests.utils_save_load import save_lo... | AssertionError) | pytest.raises | variable | tests/models/test_ngcf.py | test_ngcf | 81 | null | |
massquantity/LibRecommender | from pathlib import Path
import pandas as pd
import tensorflow as tf
from libreco.algorithms import PinSageDGL
from libreco.data import DataInfo, DatasetFeat, split_by_ratio_chrono
from libreco.evaluation import evaluate
from tests.utils_data import SAVE_PATH, remove_path
from tests.utils_pred import ptest_preds
from... | eval_result["roc_auc"] | assert | complex_expr | tests/retrain/test_thmodel_retrain_feat_dgl.py | test_torchmodel_retrain_feat_dgl | 177 | null | |
massquantity/LibRecommender | import pytest
import tensorflow as tf
from libreco.algorithms import Item2Vec
from tests.utils_data import remove_path, set_ranking_labels
from tests.utils_metrics import get_metrics
from tests.utils_pred import ptest_preds
from tests.utils_reco import ptest_recommends
from tests.utils_save_load import save_load_model... | RuntimeError) | pytest.raises | variable | tests/models/test_item2vec.py | test_item2vec | 50 | null | |
massquantity/LibRecommender | import json
import os
import numpy as np
import pytest
from tensorflow.core.protobuf.meta_graph_pb2 import MetaGraphDef
from libreco.bases import CfBase, TfBase
from libreco.tfops import tf
from libserving.serialization import (
embed2redis,
knn2redis,
online2redis,
save_embed,
save_knn,
save... | 0 | assert | numeric_literal | tests/serving/test_serialization.py | test_knn_serialization | 41 | null | |
massquantity/LibRecommender | import sys
import pytest
import tensorflow as tf
from libreco.algorithms import NGCF
from tests.utils_data import remove_path, set_ranking_labels
from tests.utils_metrics import get_metrics
from tests.utils_pred import ptest_preds
from tests.utils_reco import ptest_recommends
from tests.utils_save_load import save_lo... | ValueError) | pytest.raises | variable | tests/models/test_ngcf.py | test_ngcf | 75 | null | |
massquantity/LibRecommender | import sys
import numpy as np
import pytest
import tensorflow as tf
from numpy.testing import assert_array_equal
from libreco.algorithms import SIM
from tests.models.utils_tf import ptest_tf_variables
from tests.utils_data import set_ranking_labels
from tests.utils_metrics import get_metrics
from tests.utils_multi_sp... | ValueError) | pytest.raises | variable | tests/models/test_sim.py | test_sim | 70 | null | |
massquantity/LibRecommender | import os
import pandas as pd
import pytest
from libreco.data import split_multi_value
def test_multi_sparse_processing():
data_path = os.path.join(
os.path.dirname(os.path.realpath(__file__)),
"sample_data",
"sample_movielens_genre.csv",
)
data = pd.read_csv(data_path, sep=",", h... | all_columns | assert | variable | tests/test_multi_sparse_processing.py | test_multi_sparse_processing | 49 | null | |
massquantity/LibRecommender | import pytest
import tensorflow as tf
from libreco.algorithms import Transformer
from tests.models.utils_tf import ptest_tf_variables
from tests.utils_metrics import get_metrics
from tests.utils_multi_sparse_models import fit_multi_sparse
from tests.utils_pred import ptest_preds
from tests.utils_reco import ptest_dyn_... | RuntimeError) | pytest.raises | variable | tests/models/test_transformer.py | test_transformer_multi_sparse | 103 | null | |
massquantity/LibRecommender | import os.path
from io import StringIO
import numpy as np
import pandas as pd
import pytest
from numpy.testing import assert_array_equal
from scipy.sparse import csr_matrix
from libreco.data import (
DataInfo,
DatasetFeat,
DatasetPure,
TransformedEvalSet,
TransformedSet,
process_data,
)
from l... | [3] | assert | collection | tests/test_data.py | test_transformed_evalset | 198 | null | |
massquantity/LibRecommender | from io import StringIO
import numpy as np
import pandas as pd
import pytest
import torch
from libreco.algorithms import DIN, LightGCN, PinSageDGL, RNN4Rec
from libreco.batch.batch_data import BatchData
from libreco.batch.batch_unit import (
PairFeats,
PairwiseBatch,
PointwiseBatch,
PointwiseSepFeatBa... | (6, 10) | assert | collection | tests/test_collators.py | test_pairwise_collator | 333 | null | |
massquantity/LibRecommender | import sys
import pytest
from libreco.data.consumed import _fill_empty, _merge_dedup, interaction_consumed
@pytest.mark.skipif(
sys.version_info[:2] >= (3, 7),
reason="Specific python 3.6 implementation",
)
def test_remove_duplicates():
user_indices = [1, 1, 1, 2, 2, 1, 2, 3, 2, 3]
item_indices = [11... | [11, 999] | assert | collection | tests/test_consumed.py | test_remove_duplicates | 40 | null | |
massquantity/LibRecommender | import numpy as np
import pandas as pd
import pytest
from libreco.algorithms import ItemCF
from libreco.data import DatasetPure
from tests.utils_data import remove_path, set_ranking_labels
from tests.utils_metrics import get_metrics
from tests.utils_pred import ptest_preds
from tests.utils_reco import ptest_recommends... | TypeError) | pytest.raises | variable | tests/models/test_item_cf.py | test_item_cf | 56 | null | |
massquantity/LibRecommender | import numpy as np
import pytest
from libreco.utils.constants import FeatModels, SequenceModels
def recommend_in_former_consumed(data_info, reco, user):
user_id = data_info.user2id[user]
user_consumed = data_info.user_consumed[user_id]
user_consumed_id = [data_info.id2item[i] for i in user_consumed]
r... | ValueError) | pytest.raises | variable | tests/utils_reco.py | ptest_recommends | 16 | null | |
massquantity/LibRecommender | from pathlib import Path
import pandas as pd
from libreco.algorithms import DeepWalk, Item2Vec
from libreco.data import DataInfo, DatasetPure, split_by_ratio_chrono
from libreco.evaluation import evaluate
from tests.utils_data import SAVE_PATH, remove_path
from tests.utils_pred import ptest_preds
from tests.utils_rec... | eval_result["roc_auc"] | assert | complex_expr | tests/retrain/test_gensim_model_retrain.py | retrain | 148 | null | |
massquantity/LibRecommender | import pytest
from libreco.bases import Base
def test_base(prepare_pure_data):
_, train_data, _, data_info = prepare_pure_data
with pytest.raises(ValueError):
_ = NCF(task="unknown", data_info=data_info)
with pytest.raises( | AssertionError) | pytest.raises | variable | tests/models/test_base.py | test_base | 31 | null | |
massquantity/LibRecommender | import sys
import pytest
from libreco.data.consumed import _fill_empty, _merge_dedup, interaction_consumed
@pytest.mark.skipif(
sys.version_info[:2] < (3, 7),
reason="Rust implementation only supports Python >= 3.7.",
)
def test_remove_consecutive_duplicates():
user_indices = [1, 1, 1, 2, 2, 1, 2, 3, 2, ... | [2, 3] | assert | collection | tests/test_consumed.py | test_remove_consecutive_duplicates | 25 | null | |
massquantity/LibRecommender | import numpy as np
import pandas as pd
import pytest
from libreco.algorithms import ItemCF
from libreco.data import DatasetPure
from tests.utils_data import remove_path, set_ranking_labels
from tests.utils_metrics import get_metrics
from tests.utils_pred import ptest_preds
from tests.utils_reco import ptest_recommends... | ValueError) | pytest.raises | variable | tests/models/test_item_cf.py | test_item_cf | 36 | null | |
massquantity/LibRecommender | import sys
import pytest
from libreco.algorithms import ALS, LightGCN, RNN4Rec
from tests.utils_data import set_ranking_labels
def ptest_knn(model, pd_data):
assert model.get_user_embedding().shape[0] == model.n_users
assert model.get_user_embedding().shape[1] == model.embed_size
assert model.get_item_em... | model.embed_size | assert | complex_expr | tests/test_knn_embed.py | test_get_embeddings | 92 | null | |
massquantity/LibRecommender | import sys
import pytest
import tensorflow as tf
from libreco.algorithms import NCF
from tests.models.utils_tf import ptest_tf_variables
from tests.utils_data import set_ranking_labels
from tests.utils_metrics import get_metrics
from tests.utils_pred import ptest_preds
from tests.utils_reco import ptest_recommends
fr... | ValueError) | pytest.raises | variable | tests/models/test_ncf.py | test_ncf | 64 | null | |
massquantity/LibRecommender | import sys
import pytest
import tensorflow as tf
from libreco.algorithms import PinSage
from tests.utils_data import remove_path, set_ranking_labels
from tests.utils_metrics import get_metrics
from tests.utils_pred import ptest_preds
from tests.utils_reco import ptest_recommends
from tests.utils_save_load import save... | ValueError) | pytest.raises | variable | tests/models/test_pinsage.py | test_pinsage | 139 | null | |
massquantity/LibRecommender | from io import StringIO
import pandas as pd
from libreco.data import (
random_split,
split_by_num,
split_by_num_chrono,
split_by_ratio,
split_by_ratio_chrono,
)
raw_data = StringIO(
"""
user,item,label,time
4617,296,2,964138229
4617,296,2,964138221
4617,296,2,964138222
1298,208,4,974849526
45... | 2 | assert | numeric_literal | tests/test_split_data.py | test_split_by_ratio | 70 | null | |
massquantity/LibRecommender | import sys
import pytest
import tensorflow as tf
from libreco.algorithms import LightGCN
from tests.utils_data import remove_path, set_ranking_labels
from tests.utils_metrics import get_metrics
from tests.utils_pred import ptest_preds
from tests.utils_reco import ptest_recommends
from tests.utils_save_load import sav... | RuntimeError) | pytest.raises | variable | tests/models/test_lightgcn.py | test_lightgcn | 117 | null | |
massquantity/LibRecommender | from io import StringIO
import numpy as np
import pandas as pd
import pytest
import torch
from libreco.algorithms import DIN, LightGCN, PinSageDGL, RNN4Rec
from libreco.batch.batch_data import BatchData
from libreco.batch.batch_unit import (
PairFeats,
PairwiseBatch,
PointwiseBatch,
PointwiseSepFeatBa... | user_dense_len | assert | variable | tests/test_collators.py | test_pairwise_collator | 327 | null | |
massquantity/LibRecommender | import json
import os
import numpy as np
import pytest
from tensorflow.core.protobuf.meta_graph_pb2 import MetaGraphDef
from libreco.bases import CfBase, TfBase
from libreco.tfops import tf
from libserving.serialization import (
embed2redis,
knn2redis,
online2redis,
save_embed,
save_knn,
save... | model_num - 1 | assert | complex_expr | tests/serving/test_serialization.py | test_knn_serialization | 42 | null | |
massquantity/LibRecommender | import sys
import numpy as np
import pytest
import tensorflow as tf
from numpy.testing import assert_array_equal
from libreco.algorithms import SIM
from tests.models.utils_tf import ptest_tf_variables
from tests.utils_data import set_ranking_labels
from tests.utils_metrics import get_metrics
from tests.utils_multi_sp... | len(reco2) | assert | func_call | tests/models/test_sim.py | long_short_seq_ptest | 148 | null | |
massquantity/LibRecommender | import os
from dataclasses import astuple
from pathlib import Path
import numpy as np
import pandas as pd
import pytest
from numpy.testing import assert_array_equal
from libreco.data import DatasetFeat
from libreco.data.data_info import EmptyFeature, Feature, store_old_info
from libreco.feature.multi_sparse import (
... | ["age"] | assert | collection | tests/test_feature.py | test_data_info_features | 117 | null | |
massquantity/LibRecommender | import numpy as np
from libreco.prediction import predict_data_with_feats
def ptest_preds(model, task, pd_data, with_feats):
user = pd_data.user.iloc[0]
item = pd_data.item.iloc[0]
pred = model.predict(user=user, item=item)
# prediction in range
if task == "rating":
assert 1 <= pred <= 5
... | 5 | assert | numeric_literal | tests/utils_pred.py | ptest_preds | 26 | null | |
massquantity/LibRecommender | import functools
from io import StringIO
import numpy as np
import pandas as pd
import pytest
from scipy.sparse import csr_matrix
from libreco.data import DatasetPure
from libreco.utils.similarities import (
_choose_blocks,
cosine_sim,
jaccard_sim,
pearson_sim,
)
raw_data = """
user,item,label
1,8,2
... | invert_sim.toarray()) | assert_* | func_call | tests/test_similarities.py | test_similarities | 64 | null | |
massquantity/LibRecommender | import numpy as np
import pytest
from libreco.utils.constants import FeatModels, SequenceModels
def recommend_in_former_consumed(data_info, reco, user):
user_id = data_info.user2id[user]
user_consumed = data_info.user_consumed[user_id]
user_consumed_id = [data_info.id2item[i] for i in user_consumed]
r... | len(reco_take_two) | assert | func_call | tests/utils_reco.py | ptest_recommends | 23 | null | |
massquantity/LibRecommender | import sys
import numpy as np
import pytest
from numpy.testing import assert_allclose, assert_array_equal
from libreco.layers import (
conv_nn,
dense_nn,
layer_normalization,
max_pool,
multi_head_attention,
rms_norm,
shared_dense,
tf_dense,
tf_rnn,
)
from libreco.layers.activation ... | ValueError) | pytest.raises | variable | tests/test_tf_layers.py | test_config | 252 | null | |
massquantity/LibRecommender | import sys
import pytest
from libreco.algorithms import ALS, LightGCN, RNN4Rec
from tests.utils_data import set_ranking_labels
def ptest_knn(model, pd_data):
assert model.get_user_embedding().shape[0] == model.n_users
assert model.get_user_embedding().shape[1] == model.embed_size
assert model.get_item_em... | 1 | assert | numeric_literal | tests/test_knn_embed.py | ptest_knn | 66 | null | |
massquantity/LibRecommender | import sys
import numpy as np
import pytest
from numpy.testing import assert_allclose, assert_array_equal
from libreco.layers import (
conv_nn,
dense_nn,
layer_normalization,
max_pool,
multi_head_attention,
rms_norm,
shared_dense,
tf_dense,
tf_rnn,
)
from libreco.layers.activation ... | (3, 3) | assert | collection | tests/test_tf_layers.py | test_positional_encoding | 187 | null | |
massquantity/LibRecommender | import pytest
from libreco.algorithms import ALS
from libreco.algorithms.als import least_squares, least_squares_cg
from libreco.evaluation import evaluate
from tests.utils_data import SAVE_PATH, remove_path, set_ranking_labels
from tests.utils_metrics import get_metrics
from tests.utils_pred import ptest_preds
from t... | ValueError) | pytest.raises | variable | tests/models/test_als.py | test_als | 35 | null | |
massquantity/LibRecommender | import subprocess
import pytest
from libserving.serialization import online2redis, save_online
from tests.utils_data import SAVE_PATH
@pytest.mark.parametrize(
"online_model",
["pure", "user_feat", "separate", "multi_sparse", "item_feat", "all"],
indirect=True,
)
def test_online_serving(online_model, ses... | 3 | assert | numeric_literal | tests/serving/test_online_serving.py | test_online_serving | 38 | null | |
massquantity/LibRecommender | import numpy as np
import pytest
from libreco.recommendation import rank_recommendations
def test_rank_random():
user_ids = [1, 2]
# fmt: off
preds = np.array([-0.1, -1e8, 0, 0.1, 0.01, 1e8, -0.01, 1e7, 0.1, 0.01]) # inf probs
n_rec = 2
n_items = 5
consumed = {1: [3, 4], 2: [4]}
rec_item... | rec_items[0] | assert | complex_expr | tests/test_rank_reco.py | test_rank_random | 110 | null | |
massquantity/LibRecommender | import numpy as np
import pandas as pd
import pytest
from libreco.algorithms import ItemCF
from libreco.data import DatasetPure
from tests.utils_data import remove_path, set_ranking_labels
from tests.utils_metrics import get_metrics
from tests.utils_pred import ptest_preds
from tests.utils_reco import ptest_recommends... | indptr[out_inner_id + 1] | assert | complex_expr | tests/models/test_item_cf.py | test_no_sim_recommend | 103 | null | |
massquantity/LibRecommender | import json
import os
import numpy as np
import pytest
from tensorflow.core.protobuf.meta_graph_pb2 import MetaGraphDef
from libreco.bases import CfBase, TfBase
from libreco.tfops import tf
from libserving.serialization import (
embed2redis,
knn2redis,
online2redis,
save_embed,
save_knn,
save... | user_consumed | assert | variable | tests/serving/test_serialization.py | check_user_consumed | 149 | null | |
massquantity/LibRecommender | import subprocess
from pathlib import Path
from libserving.serialization import embed2redis, save_embed, save_faiss_index
from tests.utils_data import SAVE_PATH, remove_path
def test_embed_serving(embed_model, session, close_server):
save_embed(SAVE_PATH, embed_model)
embed2redis(SAVE_PATH)
faiss_path = s... | 3 | assert | numeric_literal | tests/serving/test_embed_serving.py | test_embed_serving | 32 | null | |
massquantity/LibRecommender | import os
from dataclasses import astuple
from pathlib import Path
import numpy as np
import pandas as pd
import pytest
from numpy.testing import assert_array_equal
from libreco.data import DatasetFeat
from libreco.data.data_info import EmptyFeature, Feature, store_old_info
from libreco.feature.multi_sparse import (
... | [2, 3]) | assert_* | collection | tests/test_feature.py | test_multi_sparse_indices | 298 | null | |
massquantity/LibRecommender | import numpy as np
import pytest
from libreco.recommendation import rank_recommendations
def test_rank_reco():
user_ids = [1, 2]
preds = np.array([-0.1, -0.01, 0, 0.1, 0.01, 1, -2, 4, 5, 6])
n_rec = 2
n_items = 5
consumed = {1: [3, 4], 2: [4]}
with pytest.raises(ValueError):
_ = rank_... | (2, 2) | assert | collection | tests/test_rank_reco.py | test_rank_reco | 38 | null | |
massquantity/LibRecommender | import numpy as np
import pandas as pd
import pytest
from libreco.algorithms import UserCF
from libreco.data import DatasetPure
from tests.utils_data import remove_path, set_ranking_labels
from tests.utils_metrics import get_metrics
from tests.utils_pred import ptest_preds
from tests.utils_reco import ptest_recommends... | indptr[out_inner_id + 1] | assert | complex_expr | tests/models/test_user_cf.py | test_no_sim_recommend | 105 | null | |
massquantity/LibRecommender | import os.path
from io import StringIO
import numpy as np
import pandas as pd
import pytest
from numpy.testing import assert_array_equal
from scipy.sparse import csr_matrix
from libreco.data import (
DataInfo,
DatasetFeat,
DatasetPure,
TransformedEvalSet,
TransformedSet,
process_data,
)
from l... | AssertionError) | pytest.raises | variable | tests/test_data.py | test_dataset_pure | 92 | null | |
massquantity/LibRecommender | import sys
import numpy as np
import pytest
from libreco.algorithms import RsUserCF
from tests.utils_data import remove_path, set_ranking_labels
from tests.utils_metrics import get_metrics
from tests.utils_pred import ptest_preds
from tests.utils_reco import ptest_recommends
from tests.utils_save_load import save_loa... | ValueError) | pytest.raises | variable | tests/models/test_user_cf_rs.py | test_user_cf_rs | 30 | null | |
massquantity/LibRecommender | import subprocess
import pytest
from libreco.bases import CfBase
from libserving.serialization import knn2redis, save_knn
from tests.utils_data import SAVE_PATH
@pytest.mark.parametrize("knn_model", ["UserCF", "ItemCF"], indirect=True)
def test_knn_serving(knn_model, session, close_server):
assert isinstance(knn... | 1 | assert | numeric_literal | tests/serving/test_knn_serving.py | test_knn_serving | 26 | null | |
massquantity/LibRecommender | import sys
import pytest
from libreco.algorithms import ALS, LightGCN, RNN4Rec
from tests.utils_data import set_ranking_labels
def ptest_knn(model, pd_data):
assert model.get_user_embedding().shape[0] == model.n_users
assert model.get_user_embedding().shape[1] == model.embed_size
assert model.get_item_em... | model.n_users | assert | complex_expr | tests/test_knn_embed.py | test_get_embeddings | 91 | null | |
massquantity/LibRecommender | from io import StringIO
import numpy as np
import pandas as pd
import pytest
import torch
from libreco.algorithms import DIN, LightGCN, PinSageDGL, RNN4Rec
from libreco.batch.batch_data import BatchData
from libreco.batch.batch_unit import (
PairFeats,
PairwiseBatch,
PointwiseBatch,
PointwiseSepFeatBa... | 3 | assert | numeric_literal | tests/test_collators.py | test_normal_collator | 100 | null | |
massquantity/LibRecommender | import subprocess
import pytest
from libreco.bases import TfBase
from libserving.serialization import save_tf, tf2redis
from tests.utils_data import SAVE_PATH
@pytest.mark.parametrize(
"tf_model", ["pure", "feat-all", "feat-user", "feat-item"], indirect=True
)
def test_tf_serving(tf_model, session, close_server)... | 3 | assert | numeric_literal | tests/serving/test_tf_serving.py | test_tf_serving | 33 | null | |
massquantity/LibRecommender | import sys
import pytest
from libreco.algorithms import ALS, LightGCN, RNN4Rec
from tests.utils_data import set_ranking_labels
def ptest_knn(model, pd_data):
assert model.get_user_embedding().shape[0] == model.n_users
assert model.get_user_embedding().shape[1] == model.embed_size
assert model.get_item_em... | dyn_embed.shape | assert | complex_expr | tests/test_knn_embed.py | test_get_embeddings | 107 | null | |
massquantity/LibRecommender | import pytest
import tensorflow as tf
from libreco.algorithms import DeepWalk
from tests.utils_data import remove_path, set_ranking_labels
from tests.utils_metrics import get_metrics
from tests.utils_pred import ptest_preds
from tests.utils_reco import ptest_recommends
from tests.utils_save_load import save_load_model... | RuntimeError) | pytest.raises | variable | tests/models/test_deepwalk.py | test_deepwalk | 57 | null | |
massquantity/LibRecommender | import os
from dataclasses import astuple
from pathlib import Path
import numpy as np
import pandas as pd
import pytest
from numpy.testing import assert_array_equal
from libreco.data import DatasetFeat
from libreco.data.data_info import EmptyFeature, Feature, store_old_info
from libreco.feature.multi_sparse import (
... | j) | assert_* | variable | tests/test_feature.py | test_multi_sparse_indices | 292 | null | |
massquantity/LibRecommender | import subprocess
import pytest
from libserving.serialization import online2redis, save_online
from tests.utils_data import SAVE_PATH
@pytest.mark.parametrize(
"online_model",
["pure", "user_feat", "separate", "multi_sparse", "item_feat", "all"],
indirect=True,
)
def test_online_serving(online_model, ses... | response.text | assert | complex_expr | tests/serving/test_online_serving.py | test_online_serving | 77 | null | |
massquantity/LibRecommender | import numpy as np
import pytest
from libreco.recommendation import rank_recommendations
def test_rank_reco():
user_ids = [1, 2]
preds = np.array([-0.1, -0.01, 0, 0.1, 0.01, 1, -2, 4, 5, 6])
n_rec = 2
n_items = 5
consumed = {1: [3, 4], 2: [4]}
with pytest.raises(ValueError):
_ = rank_... | (2, 4) | assert | collection | tests/test_rank_reco.py | test_rank_reco | 53 | null | |
massquantity/LibRecommender | import sys
import pytest
from libreco.data.consumed import _fill_empty, _merge_dedup, interaction_consumed
def test_no_merge():
num = 4
old_consumed = {0: [1, 2, 3], 1: [4, 5], 2: [0], 3: [99]}
new_consumed = {0: [2, 1], 2: [7, 8]}
consumed = _fill_empty(new_consumed, num, old_consumed)
assert c... | [2, 1] | assert | collection | tests/test_consumed.py | test_no_merge | 63 | null | |
massquantity/LibRecommender | import os
from dataclasses import astuple
from pathlib import Path
import numpy as np
import pandas as pd
import pytest
from numpy.testing import assert_array_equal
from libreco.data import DatasetFeat
from libreco.data.data_info import EmptyFeature, Feature, store_old_info
from libreco.feature.multi_sparse import (
... | KeyError) | pytest.raises | variable | tests/test_feature.py | test_sparse_indices | 210 | null | |
massquantity/LibRecommender | import os
from dataclasses import astuple
from pathlib import Path
import numpy as np
import pandas as pd
import pytest
from numpy.testing import assert_array_equal
from libreco.data import DatasetFeat
from libreco.data.data_info import EmptyFeature, Feature, store_old_info
from libreco.feature.multi_sparse import (
... | 3 | assert | numeric_literal | tests/test_feature.py | test_assign_features | 542 | null | |
massquantity/LibRecommender | import sys
import numpy as np
import pytest
from libreco.algorithms import RsItemCF
from tests.utils_data import remove_path, set_ranking_labels
from tests.utils_metrics import get_metrics
from tests.utils_pred import ptest_preds
from tests.utils_reco import ptest_recommends
from tests.utils_save_load import save_loa... | AssertionError) | pytest.raises | variable | tests/models/test_item_cf_rs.py | test_item_cf_rs | 27 | null | |
massquantity/LibRecommender | from io import StringIO
import numpy as np
import pandas as pd
import pytest
import torch
from libreco.algorithms import DIN, LightGCN, PinSageDGL, RNN4Rec
from libreco.batch.batch_data import BatchData
from libreco.batch.batch_unit import (
PairFeats,
PairwiseBatch,
PointwiseBatch,
PointwiseSepFeatBa... | 6 | assert | numeric_literal | tests/test_collators.py | test_pairwise_collator | 309 | null | |
massquantity/LibRecommender | import os
import pytest
from libreco.algorithms import BPR
from libserving.serialization import save_faiss_index
from tests.utils_data import SAVE_PATH
def test_faiss_index(embed_model):
import faiss
save_faiss_index(SAVE_PATH, embed_model, 80, 10)
index = faiss.read_index(os.path.join(SAVE_PATH, "faiss... | embed_model.n_items | assert | complex_expr | tests/serving/test_faiss_index.py | test_faiss_index | 17 | null | |
massquantity/LibRecommender | import sys
import numpy as np
import pytest
from libreco.algorithms import RsItemCF
from tests.utils_data import remove_path, set_ranking_labels
from tests.utils_metrics import get_metrics
from tests.utils_pred import ptest_preds
from tests.utils_reco import ptest_recommends
from tests.utils_save_load import save_loa... | ValueError) | pytest.raises | variable | tests/models/test_item_cf_rs.py | test_item_cf_rs | 30 | null | |
massquantity/LibRecommender | import importlib
import sys
import pytest
from libreco.graph import check_dgl
def test_dgl(prepare_feat_data, monkeypatch):
*_, data_info = prepare_feat_data
with monkeypatch.context() as m:
m.setitem(sys.modules, "dgl", None)
with pytest.raises(ModuleNotFoundError):
from libreco... | "dgl" | assert | string_literal | tests/test_dgl.py | test_dgl | 29 | null | |
massquantity/LibRecommender | import subprocess
import pytest
from libserving.serialization import online2redis, save_online
from tests.utils_data import SAVE_PATH
@pytest.mark.parametrize(
"online_model",
["pure", "user_feat", "separate", "multi_sparse", "item_feat", "all"],
indirect=True,
)
def test_online_serving(online_model, ses... | 1 | assert | numeric_literal | tests/serving/test_online_serving.py | test_online_serving | 31 | null | |
massquantity/LibRecommender | import os.path
from io import StringIO
import numpy as np
import pandas as pd
import pytest
from numpy.testing import assert_array_equal
from scipy.sparse import csr_matrix
from libreco.data import (
DataInfo,
DatasetFeat,
DatasetPure,
TransformedEvalSet,
TransformedSet,
process_data,
)
from l... | IndexError) | pytest.raises | variable | tests/test_data.py | test_dataset_pure | 89 | null | |
massquantity/LibRecommender | import os
from dataclasses import astuple
from pathlib import Path
import numpy as np
import pandas as pd
import pytest
from numpy.testing import assert_array_equal
from libreco.data import DatasetFeat
from libreco.data.data_info import EmptyFeature, Feature, store_old_info
from libreco.feature.multi_sparse import (
... | ["a", "c"]) | assert_* | collection | tests/test_feature.py | test_update_features | 435 | null | |
massquantity/LibRecommender | import os
from dataclasses import astuple
from pathlib import Path
import numpy as np
import pandas as pd
import pytest
from numpy.testing import assert_array_equal
from libreco.data import DatasetFeat
from libreco.data.data_info import EmptyFeature, Feature, store_old_info
from libreco.feature.multi_sparse import (
... | None | assert | none_literal | tests/test_feature.py | test_sparse_indices | 179 | null | |
massquantity/LibRecommender | import sys
import pytest
import tensorflow as tf
from libreco.algorithms import BPR
from tests.models.utils_tf import ptest_tf_variables
from tests.utils_data import remove_path
from tests.utils_metrics import get_metrics
from tests.utils_pred import ptest_preds
from tests.utils_reco import ptest_recommends
from test... | ValueError) | pytest.raises | variable | tests/models/test_bpr.py | test_bpr | 62 | null | |
massquantity/LibRecommender | import os.path
from io import StringIO
import numpy as np
import pandas as pd
import pytest
from numpy.testing import assert_array_equal
from scipy.sparse import csr_matrix
from libreco.data import (
DataInfo,
DatasetFeat,
DatasetPure,
TransformedEvalSet,
TransformedSet,
process_data,
)
from l... | 5 | assert | numeric_literal | tests/test_data.py | test_dataset_feat | 110 | null | |
massquantity/LibRecommender | import numpy as np
import pandas as pd
import pytest
from libreco.algorithms import UserCF
from libreco.data import DatasetPure
from tests.utils_data import remove_path, set_ranking_labels
from tests.utils_metrics import get_metrics
from tests.utils_pred import ptest_preds
from tests.utils_reco import ptest_recommends... | ValueError) | pytest.raises | variable | tests/models/test_user_cf.py | test_user_cf | 36 | null | |
massquantity/LibRecommender | import sys
import pytest
import tensorflow as tf
from libreco.algorithms import DeepFM
from tests.models.utils_tf import ptest_tf_variables
from tests.utils_data import set_ranking_labels
from tests.utils_metrics import get_metrics
from tests.utils_multi_sparse_models import fit_multi_sparse
from tests.utils_pred imp... | AssertionError) | pytest.raises | variable | tests/models/test_deepfm.py | test_deepfm | 67 | null | |
massquantity/LibRecommender | import os
import sys
from pathlib import Path
import pandas as pd
import pytest
import tensorflow as tf
from numpy.testing import assert_array_equal
from libreco.algorithms import DIN
from libreco.data import DatasetFeat, split_by_ratio_chrono
from tests.models.utils_tf import ptest_tf_variables
from tests.utils_data... | AssertionError) | pytest.raises | variable | tests/models/test_din.py | test_din | 70 | null | |
massquantity/LibRecommender | import json
import os
import numpy as np
import pytest
from tensorflow.core.protobuf.meta_graph_pb2 import MetaGraphDef
from libreco.bases import CfBase, TfBase
from libreco.tfops import tf
from libserving.serialization import (
embed2redis,
knn2redis,
online2redis,
save_embed,
save_knn,
save... | model.max_seq_len | assert | complex_expr | tests/serving/test_serialization.py | check_features | 164 | null | |
massquantity/LibRecommender | import time
import pytest
from libreco.utils.misc import colorize, time_block, time_func
def long_work():
time.sleep(0.1)
print(colorize("done!", color="red", bold=True, highlight=True))
def test_misc():
long_work()
with time_block("long work2", verbose=0):
time.sleep(0.1)
with pytest.r... | RuntimeError) | pytest.raises | variable | tests/test_misc.py | test_misc | 18 | null | |
massquantity/LibRecommender | import os
import pytest
from libreco.algorithms import BPR
from libserving.serialization import save_faiss_index
from tests.utils_data import SAVE_PATH
def test_faiss_index(embed_model):
import faiss
save_faiss_index(SAVE_PATH, embed_model, 80, 10)
index = faiss.read_index(os.path.join(SAVE_PATH, "faiss... | (1, 10) | assert | collection | tests/serving/test_faiss_index.py | test_faiss_index | 16 | null | |
massquantity/LibRecommender | import sys
import pytest
from libreco.data.consumed import _fill_empty, _merge_dedup, interaction_consumed
def test_no_merge():
num = 4
old_consumed = {0: [1, 2, 3], 1: [4, 5], 2: [0], 3: [99]}
new_consumed = {0: [2, 1], 2: [7, 8]}
consumed = _fill_empty(new_consumed, num, old_consumed)
assert co... | [99] | assert | collection | tests/test_consumed.py | test_no_merge | 66 | null | |
massquantity/LibRecommender | import sys
import pytest
import tensorflow as tf
from libreco.algorithms import SVDpp
from libreco.data import DatasetPure
from tests.models.utils_tf import ptest_tf_variables
from tests.utils_data import SAVE_PATH, set_ranking_labels
from tests.utils_metrics import get_metrics
from tests.utils_pred import ptest_pred... | RuntimeError) | pytest.raises | variable | tests/models/test_svdpp.py | test_svdpp | 104 | null | |
massquantity/LibRecommender | from io import StringIO
import numpy as np
import pandas as pd
import pytest
import torch
from libreco.algorithms import DIN, LightGCN, PinSageDGL, RNN4Rec
from libreco.batch.batch_data import BatchData
from libreco.batch.batch_unit import (
PairFeats,
PairwiseBatch,
PointwiseBatch,
PointwiseSepFeatBa... | dense_len | assert | variable | tests/test_collators.py | test_pointwise_collator | 265 | null | |
gitlabform/gitlabform | import pytest
from gitlabform.gitlab import AccessLevel
from tests.acceptance import (
allowed_codes,
run_gitlabform,
)
def one_owner(root_user, group, groups, subgroup, users):
group.members.create({"user_id": users[0].id, "access_level": AccessLevel.OWNER.value})
group.members.delete(root_user.id)
... | 2 | assert | numeric_literal | tests/acceptance/standard/test_group_members_groups.py | test__add_group | TestGroupMembersGroups | 61 | null |
gitlabform/gitlabform | from tests.acceptance import run_gitlabform
class TestGroupLabels:
def test__can_add_a_label_to_group(self, gl, group_for_function):
group = gl.groups.get(group_for_function.id)
labels = group.labels.list()
assert len(labels) == | 0 | assert | numeric_literal | tests/acceptance/standard/test_group_labels.py | test__can_add_a_label_to_group | TestGroupLabels | 8 | null |
gitlabform/gitlabform | import logging
import pytest
from typing import TYPE_CHECKING
from gitlab.v4.objects import GroupHook
from tests.acceptance import run_gitlabform, get_random_name
def urls():
first_name = get_random_name("hook")
second_name = get_random_name("hook")
third_name = get_random_name("hook")
first_url = f"h... | 2 | assert | numeric_literal | tests/acceptance/standard/test_group_hooks.py | test_hooks_delete | TestGroupHooksProcessor | 158 | null |
gitlabform/gitlabform | import os
import pytest
import time
from gitlab import GitlabGetError
from gitlabform.gitlab import AccessLevel
from tests.acceptance import (
get_only_branch_access_levels,
run_gitlabform,
DEFAULT_README,
)
def no_access_branch(project):
branch = project.branches.create({"branch": "no_access_branch"... | "foobar" | assert | string_literal | tests/acceptance/standard/test_files.py | test__set_file_single_protected_branch | TestFiles | 260 | null |
gitlabform/gitlabform | import pytest
from gitlabform.gitlab import AccessLevel
from tests.acceptance import (
run_gitlabform,
)
def tags(project):
tag_names = [
"tag1",
"tag2",
"tag3",
]
tags = []
for tag_name in tag_names:
tag = project.tags.create({"tag_name": tag_name, "ref": "main"})... | "tag1" | assert | string_literal | tests/acceptance/standard/test_tags.py | test__protect_single_tag | TestTags | 54 | null |
gitlabform/gitlabform | import pytest
import time
import gitlab
from gitlabform.gitlab import AccessLevel
from tests.acceptance import get_only_branch_access_levels, run_gitlabform
pytestmark = pytest.mark.requires_license
class TestBranches:
def test__allow_user_ids(self, project, branch, make_user):
user_allowed_to_push = m... | [AccessLevel.NO_ACCESS.value] | assert | collection | tests/acceptance/premium/test_branches.py | test__allow_user_ids | TestBranches | 74 | null |
gitlabform/gitlabform | import pytest
from gitlabform.gitlab import AccessLevel
from tests.acceptance import run_gitlabform, get_only_environment_access_levels
pytestmark = pytest.mark.requires_license
class TestProtectedEnvironments:
def test__add_user_to_protected_environment(self, project, make_user) -> str:
config = self.t... | 4 | assert | numeric_literal | tests/acceptance/premium/test_protected_environments.py | test__add_user_to_protected_environment | TestProtectedEnvironments | 48 | null |
gitlabform/gitlabform | from tests.acceptance import (
run_gitlabform,
)
class TestGroupBadges:
def test__badges_update_choose_the_right_one(self, group):
config = f"""
projects_and_groups:
{group.full_path}/*:
group_badges:
pipeline-status:
name: "Group Badge"
... | 2 | assert | numeric_literal | tests/acceptance/standard/test_group_badges.py | test__badges_update_choose_the_right_one | TestGroupBadges | 100 | null |
gitlabform/gitlabform | from tests.acceptance import run_gitlabform
class TestGroupVariables:
def test__single_variable(self, group_for_function):
config_single_variable = f"""
projects_and_groups:
{group_for_function.full_path}/*:
group_variables:
foo:
key: FOO
... | 1 | assert | numeric_literal | tests/acceptance/standard/test_group_variables.py | test__single_variable | TestGroupVariables | 18 | null |
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