enhancementhelp wanted
Repository metrics
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- (17,706 stars)
- PR merge metrics
- (平均マージ 6d 16h) (30d で 10 merged PRs)
説明
Description
conda activate reco_full
pip install pytest-cov
pytest tests/unit/ --cov=reco_utils/ --disable-warnings
---------- coverage: platform linux, python 3.6.10-final-0 -----------
Name Stmts Miss Cover
-----------------------------------------------------------------------------------------------
reco_utils/__init__.py 10 0 100%
reco_utils/azureml/__init__.py 0 0 100%
reco_utils/azureml/aks_utils.py 17 0 100%
reco_utils/azureml/azureml_utils.py 21 21 0%
reco_utils/azureml/svd_training.py 81 81 0%
reco_utils/azureml/wide_deep_training.py 73 73 0%
reco_utils/common/__init__.py 0 0 100%
reco_utils/common/constants.py 10 0 100%
reco_utils/common/general_utils.py 13 4 69%
reco_utils/common/gpu_utils.py 67 19 72%
reco_utils/common/notebook_memory_management.py 50 50 0%
reco_utils/common/notebook_utils.py 16 4 75%
reco_utils/common/plot.py 34 1 97%
reco_utils/common/python_utils.py 40 2 95%
reco_utils/common/spark_utils.py 25 6 76%
reco_utils/common/tf_utils.py 128 10 92%
reco_utils/common/timer.py 28 0 100%
reco_utils/dataset/__init__.py 0 0 100%
reco_utils/dataset/amazon_reviews.py 354 309 13%
reco_utils/dataset/blob_utils.py 6 3 50%
reco_utils/dataset/cosmos_cli.py 31 31 0%
reco_utils/dataset/covid_utils.py 47 21 55%
reco_utils/dataset/criteo.py 53 25 53%
reco_utils/dataset/download_utils.py 44 5 89%
reco_utils/dataset/mind.py 190 190 0%
reco_utils/dataset/movielens.py 178 36 80%
reco_utils/dataset/pandas_df_utils.py 134 17 87%
reco_utils/dataset/python_splitters.py 57 5 91%
reco_utils/dataset/spark_splitters.py 72 6 92%
reco_utils/dataset/sparse.py 60 5 92%
reco_utils/dataset/split_utils.py 48 7 85%
reco_utils/dataset/wikidata.py 71 11 85%
reco_utils/evaluation/__init__.py 0 0 100%
reco_utils/evaluation/python_evaluation.py 99 6 94%
reco_utils/evaluation/spark_evaluation.py 113 24 79%
reco_utils/recommender/__init__.py 0 0 100%
reco_utils/recommender/cornac/__init__.py 0 0 100%
reco_utils/recommender/cornac/cornac_utils.py 23 0 100%
reco_utils/recommender/deeprec/DataModel/ImplicitCF.py 87 16 82%
reco_utils/recommender/deeprec/__init__.py 0 0 100%
reco_utils/recommender/deeprec/deeprec_utils.py 169 49 71%
reco_utils/recommender/deeprec/io/__init__.py 0 0 100%
reco_utils/recommender/deeprec/io/dkn_iterator.py 153 42 73%
reco_utils/recommender/deeprec/io/iterator.py 105 7 93%
reco_utils/recommender/deeprec/io/nextitnet_iterator.py 123 110 11%
reco_utils/recommender/deeprec/io/sequential_iterator.py 227 210 7%
reco_utils/recommender/deeprec/models/__init__.py 0 0 100%
reco_utils/recommender/deeprec/models/base_model.py 313 109 65%
reco_utils/recommender/deeprec/models/dkn.py 177 20 89%
reco_utils/recommender/deeprec/models/graphrec/lightgcn.py 163 77 53%
reco_utils/recommender/deeprec/models/sequential/nextitnet.py 61 51 16%
reco_utils/recommender/deeprec/models/sequential/rnn_cell_implement.py 282 254 10%
reco_utils/recommender/deeprec/models/sequential/sequential_base_model.py 153 134 12%
reco_utils/recommender/deeprec/models/sequential/sli_rec.py 49 41 16%
reco_utils/recommender/deeprec/models/xDeepFM.py 208 79 62%
reco_utils/recommender/fastai/__init__.py 0 0 100%
reco_utils/recommender/fastai/fastai_utils.py 31 5 84%
reco_utils/recommender/geoimc/__init__.py 0 0 100%
reco_utils/recommender/geoimc/geoimc_algorithm.py 74 10 86%
reco_utils/recommender/geoimc/geoimc_data.py 97 56 42%
reco_utils/recommender/geoimc/geoimc_predict.py 43 3 93%
reco_utils/recommender/geoimc/geoimc_utils.py 15 0 100%
reco_utils/recommender/lightfm/__init__.py 0 0 100%
reco_utils/recommender/lightfm/lightfm_utils.py 74 27 64%
reco_utils/recommender/lightgbm/__init__.py 0 0 100%
reco_utils/recommender/lightgbm/lightgbm_utils.py 133 0 100%
reco_utils/recommender/ncf/__init__.py 0 0 100%
reco_utils/recommender/ncf/dataset.py 113 9 92%
reco_utils/recommender/ncf/ncf_singlenode.py 131 2 98%
reco_utils/recommender/newsrec/__init__.py 0 0 100%
reco_utils/recommender/newsrec/io/__init__.py 0 0 100%
reco_utils/recommender/newsrec/io/mind_all_iterator.py 262 83 68%
reco_utils/recommender/newsrec/io/mind_iterator.py 185 60 68%
reco_utils/recommender/newsrec/models/__init__.py 0 0 100%
reco_utils/recommender/newsrec/models/base_model.py 163 114 30%
reco_utils/recommender/newsrec/models/layers.py 118 55 53%
reco_utils/recommender/newsrec/models/lstur.py 72 9 88%
reco_utils/recommender/newsrec/models/naml.py 120 9 92%
reco_utils/recommender/newsrec/models/npa.py 70 3 96%
reco_utils/recommender/newsrec/models/nrms.py 61 5 92%
reco_utils/recommender/newsrec/newsrec_utils.py 81 18 78%
reco_utils/recommender/rbm/__init__.py 0 0 100%
reco_utils/recommender/rbm/rbm.py 222 59 73%
reco_utils/recommender/rlrmc/RLRMCalgorithm.py 127 42 67%
reco_utils/recommender/rlrmc/RLRMCdataset.py 57 7 88%
reco_utils/recommender/rlrmc/__init__.py 0 0 100%
reco_utils/recommender/rlrmc/conjugate_gradient_ms.py 118 36 69%
reco_utils/recommender/sar/__init__.py 1 0 100%
reco_utils/recommender/sar/sar_singlenode.py 160 5 97%
reco_utils/recommender/surprise/__init__.py 0 0 100%
reco_utils/recommender/surprise/surprise_utils.py 28 6 79%
reco_utils/recommender/tfidf/__init__.py 0 0 100%
reco_utils/recommender/tfidf/tfidf_utils.py 142 34 76%
reco_utils/recommender/vowpal_wabbit/__init__.py 0 0 100%
reco_utils/recommender/vowpal_wabbit/vw.py 67 3 96%
reco_utils/recommender/wide_deep/__init__.py 0 0 100%
reco_utils/recommender/wide_deep/wide_deep_utils.py 31 2 94%
reco_utils/tuning/__init__.py 0 0 100%
reco_utils/tuning/nni/nni_utils.py 72 11 85%
reco_utils/tuning/parameter_sweep.py 16 0 100%
-----------------------------------------------------------------------------------------------
TOTAL 7347 2844 61%
Expected behavior with the suggested feature
We should target for coverage>80%