pytest failures and Lint Errors

zhenyuz0500 commented 2 years ago

Describe the bug Recent builds create pytest failures and lint errors that need diagnosis.

Lint errors occurred in PR #492 and #496

To Reproduce pytest -vs tests/ --cov causalml/

Screenshots Error logs:

_% pytest -vs tests/ --cov causalml/ ==================================================================================================== test session starts ===================================================================================================== platform darwin -- Python 3.8.8, pytest-6.2.3, py-1.10.0, pluggy-0.13.1 -- /Users/zhenyuzhao-zz/opt/anaconda3/bin/python cachedir: .pytest_cache rootdir: /Users/zhenyuzhao-zz/Documents/Programming/git_repo/causalml plugins: anyio-2.2.0, cov-3.0.0 collected 64 items

tests/test_cevae.py::test_CEVAE PASSED tests/test_counterfactual_unit_selection.py::test_counterfactual_unit_selection PASSED tests/test_datasets.py::test_get_synthetic_preds[simulate_nuisance_and_easy_treatment] PASSED tests/test_datasets.py::test_get_synthetic_preds[simulate_hidden_confounder] PASSED tests/test_datasets.py::test_get_synthetic_preds[simulate_randomized_trial] PASSED tests/test_datasets.py::test_get_synthetic_summary Abs % Error of ATE MSE KL Divergence Actuals 0.000000 0.000000 0.000000 S Learner (LR) 0.581879 0.125334 3.828739 T Learner (XGB) 0.323199 1.186263 1.424861 PASSED tests/test_datasets.py::test_get_synthetic_preds_holdout PASSED tests/test_datasets.py::test_get_synthetic_summary_holdout ( Abs % Error of ATE MSE KL Divergence Actuals 0.000000 0.000000 0.000000 S Learner (LR) 0.359446 0.072330 4.033648 S Learner (XGB) 0.041486 0.319411 0.824989 T Learner (LR) 0.358963 0.037750 0.440597 T Learner (XGB) 0.085843 1.257743 1.500363 X Learner (LR) 0.358963 0.037750 0.440597 X Learner (XGB) 0.081450 0.504336 1.116033 R Learner (LR) 0.327808 0.044548 0.408275 R Learner (XGB) 0.112043 4.740827 2.079625, Abs % Error of ATE MSE KL Divergence Actuals 0.000000 0.000000 0.000000 S Learner (LR) 0.401601 0.080840 3.944126 S Learner (XGB) 0.073994 0.283661 0.948965 T Learner (LR) 0.353086 0.033973 0.695373 T Learner (XGB) 0.090676 0.652876 1.350948 X Learner (LR) 0.353086 0.033973 0.695373 X Learner (XGB) 0.020359 0.332149 1.097056 R Learner (LR) 0.299843 0.037296 0.616230 R Learner (XGB) 0.164259 1.830500 1.492768) PASSED tests/test_datasets.py::test_get_synthetic_auuc Learner cum_gain_auuc 0 Actuals 3082.158899 2 T Learner (XGB) 2630.595869 3 Random 2490.139546 1 S Learner (LR) 2463.126242 PASSED tests/test_features.py::test_load_data PASSED tests/test_features.py::test_LabelEncoder PASSED tests/test_features.py::test_OneHotEncoder PASSED 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 10/10 [00:08<00:00, 1.19it/s] PASSED tests/test_match.py::test_nearest_neighbor_match_by_group PASSED tests/test_match.py::test_match_optimizer PASSED tests/test_meta_learners.py::test_synthetic_data PASSED tests/test_meta_learners.py::test_BaseSLearner PASSED 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 10/10 [00:02<00:00, 3.61it/s] PASSED tests/test_meta_learners.py::test_LRSRegressor PASSED 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 10/10 [00:03<00:00, 2.82it/s] PASSED 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 10/10 [00:04<00:00, 2.22it/s] PASSED 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 10/10 [00:25<00:00, 2.57s/it] PASSED 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 10/10 [00:03<00:00, 2.65it/s] PASSED 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 10/10 [00:08<00:00, 1.15it/s] PASSED 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 10/10 [00:08<00:00, 1.23it/s] PASSED 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 10/10 [00:08<00:00, 1.14it/s] PASSED 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 10/10 [00:08<00:00, 1.19it/s] PASSED 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 10/10 [00:23<00:00, 2.37s/it] PASSED 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 10/10 [00:24<00:00, 2.44s/it] PASSED 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 10/10 [00:24<00:00, 2.45s/it] PASSED 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 10/10 [00:24<00:00, 2.41s/it] PASSED tests/test_meta_learners.py::test_TMLELearner PASSED tests/test_meta_learners.py::test_BaseSClassifier [01:42:02] WARNING: /opt/concourse/worker/volumes/live/7a2b9f41-3287-451b-6691-43e9a6c0910f/volume/xgboost-split_1619728204606/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior. PASSED tests/test_meta_learners.py::test_BaseTClassifier PASSED tests/test_meta_learners.py::test_BaseXClassifier [01:42:03] WARNING: /opt/concourse/worker/volumes/live/7a2b9f41-3287-451b-6691-43e9a6c0910f/volume/xgboost-split_1619728204606/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior. [01:42:04] WARNING: /opt/concourse/worker/volumes/live/7a2b9f41-3287-451b-6691-43e9a6c0910f/volume/xgboost-split_1619728204606/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior. [01:42:05] WARNING: /opt/concourse/worker/volumes/live/7a2b9f41-3287-451b-6691-43e9a6c0910f/volume/xgboost-split_1619728204606/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior. [01:42:05] WARNING: /opt/concourse/worker/volumes/live/7a2b9f41-3287-451b-6691-43e9a6c0910f/volume/xgboost-split_1619728204606/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior. PASSED tests/test_meta_learners.py::test_BaseRClassifier /Users/zhenyuzhao-zz/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1]. warnings.warn(label_encoder_deprecation_msg, UserWarning) /Users/zhenyuzhao-zz/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1]. warnings.warn(label_encoder_deprecation_msg, UserWarning) /Users/zhenyuzhao-zz/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1]. warnings.warn(label_encoder_deprecation_msg, UserWarning) /Users/zhenyuzhao-zz/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1]. warnings.warn(label_encoder_deprecation_msg, UserWarning) /Users/zhenyuzhao-zz/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1]. warnings.warn(label_encoder_deprecation_msg, UserWarning) [01:42:07] WARNING: /opt/concourse/worker/volumes/live/7a2b9f41-3287-451b-6691-43e9a6c0910f/volume/xgboost-split_1619728204606/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior. [01:42:07] WARNING: /opt/concourse/worker/volumes/live/7a2b9f41-3287-451b-6691-43e9a6c0910f/volume/xgboost-split_1619728204606/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior. [01:42:07] WARNING: /opt/concourse/worker/volumes/live/7a2b9f41-3287-451b-6691-43e9a6c0910f/volume/xgboost-split_1619728204606/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior. [01:42:07] WARNING: /opt/concourse/worker/volumes/live/7a2b9f41-3287-451b-6691-43e9a6c0910f/volume/xgboost-split_1619728204606/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior. [01:42:07] WARNING: /opt/concourse/worker/volumes/live/7a2b9f41-3287-451b-6691-43e9a6c0910f/volume/xgboost-split_1619728204606/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior. PASSED tests/test_meta_learners.py::test_BaseRClassifier_with_sample_weights /Users/zhenyuzhao-zz/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1]. warnings.warn(label_encoder_deprecation_msg, UserWarning) /Users/zhenyuzhao-zz/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1]. warnings.warn(label_encoder_deprecation_msg, UserWarning) /Users/zhenyuzhao-zz/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1]. warnings.warn(label_encoder_deprecation_msg, UserWarning) /Users/zhenyuzhao-zz/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1]. warnings.warn(label_encoder_deprecation_msg, UserWarning) /Users/zhenyuzhao-zz/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1]. warnings.warn(label_encoder_deprecation_msg, UserWarning) [01:42:10] WARNING: /opt/concourse/worker/volumes/live/7a2b9f41-3287-451b-6691-43e9a6c0910f/volume/xgboost-split_1619728204606/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior. [01:42:10] WARNING: /opt/concourse/worker/volumes/live/7a2b9f41-3287-451b-6691-43e9a6c0910f/volume/xgboost-split_1619728204606/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior. [01:42:10] WARNING: /opt/concourse/worker/volumes/live/7a2b9f41-3287-451b-6691-43e9a6c0910f/volume/xgboost-split_1619728204606/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior. [01:42:10] WARNING: /opt/concourse/worker/volumes/live/7a2b9f41-3287-451b-6691-43e9a6c0910f/volume/xgboost-split_1619728204606/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior. [01:42:10] WARNING: /opt/concourse/worker/volumes/live/7a2b9f41-3287-451b-6691-43e9a6c0910f/volume/xgboost-split_1619728204606/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior. [0] validation_0-auc:2.75329 [1] validation_0-auc:-4.78198 [2] validation_0-auc:-3.52732 [3] validation_0-auc:-5.61398 [4] validation_0-auc:-4.07529 [5] validation_0-auc:-4.58928 [6] validation_0-auc:-3.94142 [7] validation_0-auc:-3.24399 [8] validation_0-auc:-6.07570 [9] validation_0-auc:-8.59653 [10] validation_0-auc:-7.84374 [11] validation_0-auc:-6.41278 [12] validation_0-auc:-4.45992 [13] validation_0-auc:-5.09416 [14] validation_0-auc:-5.12351 [15] validation_0-auc:-3.64573 [16] validation_0-auc:-3.01243 [17] validation_0-auc:-2.80259 [18] validation_0-auc:-1.67346 [19] validation_0-auc:-2.18767 [20] validation_0-auc:-2.77537 [21] validation_0-auc:-2.62919 [22] validation_0-auc:-2.49174 [23] validation_0-auc:-1.50853 [24] validation_0-auc:-0.98859 [25] validation_0-auc:-0.79339 [26] validation_0-auc:-0.52949 [27] validation_0-auc:-0.60107 [28] validation_0-auc:0.03568 [29] validation_0-auc:0.38625 PASSED tests/test_meta_learners.py::test_pandas_input PASSED 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 10/10 [00:11<00:00, 1.10s/it] PASSED tests/test_propensity.py::test_logistic_regression_propensity_model PASSED tests/test_propensity.py::test_logistic_regression_propensity_model_model_kwargs PASSED tests/test_propensity.py::test_elasticnet_propensity_model PASSED tests/test_propensity.py::test_gradientboosted_propensity_model [01:42:28] WARNING: /opt/concourse/worker/volumes/live/7a2b9f41-3287-451b-6691-43e9a6c0910f/volume/xgboost-split_1619728204606/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior. PASSED tests/test_propensity.py::test_gradientboosted_propensity_model_earlystopping [01:42:28] WARNING: /opt/concourse/worker/volumes/live/7a2b9f41-3287-451b-6691-43e9a6c0910f/volume/xgboost-split_1619728204606/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior. [0] validation_0-logloss:0.68365 [1] validation_0-logloss:0.67243 [2] validation_0-logloss:0.64779 [3] validation_0-logloss:0.62546 [4] validation_0-logloss:0.60664 [5] validation_0-logloss:0.58847 [6] validation_0-logloss:0.57660 [7] validation_0-logloss:0.57160 [8] validation_0-logloss:0.56192 [9] validation_0-logloss:0.55328 [10] validation_0-logloss:0.54668 [11] validation_0-logloss:0.54318 [12] validation_0-logloss:0.54001 [13] validation_0-logloss:0.53335 [14] validation_0-logloss:0.53029 [15] validation_0-logloss:0.52541 [16] validation_0-logloss:0.52656 [17] validation_0-logloss:0.52321 [18] validation_0-logloss:0.52327 [19] validation_0-logloss:0.52239 [20] validation_0-logloss:0.52392 [21] validation_0-logloss:0.52404 [22] validation_0-logloss:0.52376 [23] validation_0-logloss:0.52374 [24] validation_0-logloss:0.52289 [25] validation_0-logloss:0.52229 [26] validation_0-logloss:0.52028 [27] validation_0-logloss:0.52110 [28] validation_0-logloss:0.52270 [29] validation_0-logloss:0.52229 [30] validation_0-logloss:0.52084 [31] validation_0-logloss:0.52308 [32] validation_0-logloss:0.52387 [33] validation_0-logloss:0.52583 [34] validation_0-logloss:0.52824 [35] validation_0-logloss:0.53039 [36] validation_0-logloss:0.52990 PASSED tests/test_sensitivity.py::test_Sensitivity Method ATE New ATE New ATE LB New ATE UB 0 Placebo Treatment 0.680042 -0.009359 -0.022652 0.003934 0 Random Cause 0.680042 0.680049 0.667253 0.692846 0 Subset Data(sample size @0.5) 0.680042 0.682025 0.663973 0.700077 0 Random Replace 0.680042 0.678623 0.665698 0.691548 0 Selection Bias (alpha@-0.80626, with r-sqaure:... 0.680042 1.353547 1.34094 1.366155 0 Selection Bias (alpha@-0.645, with r-sqaure:0.... 0.680042 1.218846 1.206217 1.231475 0 Selection Bias (alpha@-0.48375, with r-sqaure:... 0.680042 1.084145 1.071487 1.096803 0 Selection Bias (alpha@-0.3225, with r-sqaure:0... 0.680042 0.949444 0.936748 0.96214 0 Selection Bias (alpha@-0.16125, with r-sqaure:... 0.680042 0.814743 0.802001 0.827485 0 Selection Bias (alpha@0.0, with r-sqaure:0.0 0.680042 0.680042 0.667245 0.692838 0 Selection Bias (alpha@0.16125, with r-sqaure:0... 0.680042 0.545341 0.532482 0.558199 0 Selection Bias (alpha@0.3225, with r-sqaure:0.... 0.680042 0.41064 0.397711 0.423568 0 Selection Bias (alpha@0.48375, with r-sqaure:0... 0.680042 0.275939 0.262933 0.288944 0 Selection Bias (alpha@0.645, with r-sqaure:0.0... 0.680042 0.141237 0.128146 0.154329 0 Selection Bias (alpha@0.80626, with r-sqaure:0... 0.680042 0.006536 -0.006648 0.01972 PASSED tests/test_sensitivity.py::test_SensitivityPlaceboTreatment Method ATE New ATE New ATE LB New ATE UB 0 Random Cause 0.678358 -0.004009 -0.017335 0.009316 PASSED tests/test_sensitivity.py::test_SensitivityRandomCause Method ATE New ATE New ATE LB New ATE UB 0 Random Cause 0.674445 0.674436 0.661632 0.68724 PASSED tests/test_sensitivity.py::test_SensitivityRandomReplace Method ATE New ATE New ATE LB New ATE UB 0 Random Replace 0.68259 0.809523 0.796646 0.822401 PASSED tests/test_sensitivity.py::test_SensitivitySelectionBias alpha rsqs New ATE New ATE LB New ATE UB 0 -0.800120 0.107832 0.660142 0.647300 0.672984 0 -0.640096 0.072265 0.662217 0.649428 0.675006 0 -0.480072 0.042282 0.664292 0.651535 0.677050 0 -0.320048 0.019399 0.666367 0.653619 0.679115 0 -0.160024 0.004965 0.668442 0.655683 0.681202 0 0.000000 0.000000 0.670517 0.657724 0.683311 0 0.160024 0.005063 0.672592 0.659744 0.685441 0 0.320048 0.020160 0.674667 0.661743 0.687592 0 0.480072 0.044736 0.676742 0.663720 0.689765 0 0.640096 0.077731 0.678818 0.665678 0.691957 0 0.800120 0.117713 0.680893 0.667615 0.694170 feature partial_rsqs 0 feature_0 -0.065992 1 feature_1 -0.067405 2 feature_2 -0.000011 3 feature_3 -0.000623 4 feature_4 -0.000643 5 feature_5 -0.000026 PASSED tests/test_sensitivity.py::test_one_sided PASSED tests/test_sensitivity.py::test_alignment PASSED tests/test_sensitivity.py::test_one_sided_att PASSED tests/test_sensitivity.py::test_alignment_att PASSED tests/test_uplift_trees.py::test_make_uplift_classification PASSED tests/test_uplift_trees.py::test_UpliftRandomForestClassifier[threads-loky] FAILED tests/test_uplift_trees.py::test_UpliftRandomForestClassifier[threads-threading] FAILED tests/test_uplift_trees.py::test_UpliftRandomForestClassifier[threads-multiprocessing] FAILED tests/test_uplift_trees.py::test_UpliftRandomForestClassifier[processes-loky] FAILED tests/test_uplift_trees.py::test_UpliftRandomForestClassifier[processes-threading] FAILED tests/test_uplift_trees.py::test_UpliftRandomForestClassifier[processes-multiprocessing] FAILED tests/test_uplift_trees.py::test_UpliftTreeClassifier PASSED tests/test_uplift_trees.py::test_UpliftTreeClassifier_feature_importance PASSED tests/test_utils.py::test_weighted_variance PASSED tests/test_value_optimization.py::test_counterfactual_value_optimization PASSED

================================================================================================================================== FAILURES ================================================================================================================================== __ test_UpliftRandomForestClassifier[threads-loky] ___ joblib.externals.loky.process_executor._RemoteTraceback: """ Traceback (most recent call last): File "/Users/zhenyuzhao-zz/opt/anaconda3/lib/python3.8/site-packages/joblib/externals/loky/backend/queues.py", line 153, in feed obj = dumps(obj, reducers=reducers) File "/Users/zhenyuzhao-zz/opt/anaconda3/lib/python3.8/site-packages/joblib/externals/loky/backend/reduction.py", line 271, in dumps dump(obj, buf, reducers=reducers, protocol=protocol) File "/Users/zhenyuzhao-zz/opt/anaconda3/lib/python3.8/site-packages/joblib/externals/loky/backend/reduction.py", line 264, in dump _LokyPickler(file, reducers=reducers, protocol=protocol).dump(obj) File "/Users/zhenyuzhao-zz/opt/anaconda3/lib/python3.8/site-packages/joblib/externals/cloudpickle/cloudpickle_fast.py", line 563, in dump return Pickler.dump(self, obj) _pickle.PicklingError: Can't pickle <cyfunction UpliftRandomForestClassifier.bootstrap at 0x7fd5fff8b5f0>: attribute lookup bootstrap on causalml.inference.tree.uplift failed """

The above exception was the direct cause of the following exception:

generate_classification_data = <function generate_classification_data.._generate_data at 0x7fd5d1eab430>, backend = 'loky', joblib_prefer = 'threads'

@pytest.mark.parametrize("backend", ["loky", "threading", "multiprocessing"])
@pytest.mark.parametrize("joblib_prefer", ["threads", "processes"])
def test_UpliftRandomForestClassifier(
    generate_classification_data, backend, joblib_prefer
):
    df, x_names = generate_classification_data()
    df_train, df_test = train_test_split(df, test_size=0.2, random_state=RANDOM_SEED)

    with parallel_backend(backend):
        # Train the UpLift Random Forest classifier
        uplift_model = UpliftRandomForestClassifier(
            min_samples_leaf=50,
            control_name=TREATMENT_NAMES[0],
            random_state=RANDOM_SEED,
            joblib_prefer=joblib_prefer,
        )

      uplift_model.fit(
df_train[x_names].values, treatment=df_train["treatment_group_key"].values, y=df_train[CONVERSION].values, )

tests/test_uplift_trees.py:37:

causalml/inference/tree/uplift.pyx:1331: in causalml.inference.tree.uplift.UpliftRandomForestClassifier.fit (delayed(self.bootstrap)(X, treatment, y, tree) for tree in self.uplift_forest) ../../../../opt/anaconda3/lib/python3.8/site-packages/joblib/parallel.py:1054: in call self.retrieve() ../../../../opt/anaconda3/lib/python3.8/site-packages/joblib/parallel.py:933: in retrieve self._output.extend(job.get(timeout=self.timeout)) ../../../../opt/anaconda3/lib/python3.8/site-packages/joblib/_parallel_backends.py:542: in wrap_future_result return future.result(timeout=timeout) ../../../../opt/anaconda3/lib/python3.8/concurrent/futures/_base.py:439: in result return self.__get_result()

self = <Future at 0x7fd5d180afa0 state=finished raised PicklingError>

def __get_result(self):
    if self._exception:

      raise self._exception
E _pickle.PicklingError: Could not pickle the task to send it to the workers.

../../../../opt/anaconda3/lib/python3.8/concurrent/futures/_base.py:388: PicklingError ____ test_UpliftRandomForestClassifier[threads-threading] ____ joblib.externals.loky.process_executor._RemoteTraceback: """ Traceback (most recent call last): File "/Users/zhenyuzhao-zz/opt/anaconda3/lib/python3.8/site-packages/joblib/externals/loky/backend/queues.py", line 153, in feed obj = dumps(obj, reducers=reducers) File "/Users/zhenyuzhao-zz/opt/anaconda3/lib/python3.8/site-packages/joblib/externals/loky/backend/reduction.py", line 271, in dumps dump(obj, buf, reducers=reducers, protocol=protocol) File "/Users/zhenyuzhao-zz/opt/anaconda3/lib/python3.8/site-packages/joblib/externals/loky/backend/reduction.py", line 264, in dump _LokyPickler(file, reducers=reducers, protocol=protocol).dump(obj) File "/Users/zhenyuzhao-zz/opt/anaconda3/lib/python3.8/site-packages/joblib/externals/cloudpickle/cloudpickle_fast.py", line 563, in dump return Pickler.dump(self, obj) _pickle.PicklingError: Can't pickle <cyfunction UpliftTreeClassifier.evaluate_KL at 0x7fd5fff7aba0>: attribute lookup evaluate_KL on causalml.inference.tree.uplift failed """

The above exception was the direct cause of the following exception:

generate_classification_data = <function generate_classification_data.._generate_data at 0x7fd5d1eab430>, backend = 'threading', joblib_prefer = 'threads'

@pytest.mark.parametrize("backend", ["loky", "threading", "multiprocessing"])
@pytest.mark.parametrize("joblib_prefer", ["threads", "processes"])
def test_UpliftRandomForestClassifier(
    generate_classification_data, backend, joblib_prefer
):
    df, x_names = generate_classification_data()
    df_train, df_test = train_test_split(df, test_size=0.2, random_state=RANDOM_SEED)

    with parallel_backend(backend):
        # Train the UpLift Random Forest classifier
        uplift_model = UpliftRandomForestClassifier(
            min_samples_leaf=50,
            control_name=TREATMENT_NAMES[0],
            random_state=RANDOM_SEED,
            joblib_prefer=joblib_prefer,
        )

        uplift_model.fit(
            df_train[x_names].values,
            treatment=df_train["treatment_group_key"].values,
            y=df_train[CONVERSION].values,
        )

        predictions = {}
        predictions["single"] = uplift_model.predict(df_test[x_names].values)
        with parallel_backend("loky", n_jobs=2):

          predictions["loky_2"] = uplift_model.predict(df_test[x_names].values)

tests/test_uplift_trees.py:46:

../../../../opt/anaconda3/lib/python3.8/site-packages/sklearn/utils/_testing.py:308: in wrapper return fn(*args, **kwargs) causalml/inference/tree/uplift.pyx:1379: in causalml.inference.tree.uplift.UpliftRandomForestClassifier.predict (delayed(tree.predict)(X=X) for tree in self.uplift_forest) ../../../../opt/anaconda3/lib/python3.8/site-packages/joblib/parallel.py:1054: in call self.retrieve() ../../../../opt/anaconda3/lib/python3.8/site-packages/joblib/parallel.py:933: in retrieve self._output.extend(job.get(timeout=self.timeout)) ../../../../opt/anaconda3/lib/python3.8/site-packages/joblib/_parallel_backends.py:542: in wrap_future_result return future.result(timeout=timeout) ../../../../opt/anaconda3/lib/python3.8/concurrent/futures/_base.py:439: in result return self.__get_result()

self = <Future at 0x7fd5d18ab7c0 state=finished raised PicklingError>

def __get_result(self):
    if self._exception:

      raise self._exception
E _pickle.PicklingError: Could not pickle the task to send it to the workers.

../../../../opt/anaconda3/lib/python3.8/concurrent/futures/base.py:388: PicklingError ____ testUpliftRandomForestClassifier[threads-multiprocessing] ____

generate_classification_data = <function generate_classification_data.._generate_data at 0x7fd5d1eab430>, backend = 'multiprocessing', joblib_prefer = 'threads'

@pytest.mark.parametrize("backend", ["loky", "threading", "multiprocessing"])
@pytest.mark.parametrize("joblib_prefer", ["threads", "processes"])
def test_UpliftRandomForestClassifier(
    generate_classification_data, backend, joblib_prefer
):
    df, x_names = generate_classification_data()
    df_train, df_test = train_test_split(df, test_size=0.2, random_state=RANDOM_SEED)

    with parallel_backend(backend):
        # Train the UpLift Random Forest classifier
        uplift_model = UpliftRandomForestClassifier(
            min_samples_leaf=50,
            control_name=TREATMENT_NAMES[0],
            random_state=RANDOM_SEED,
            joblib_prefer=joblib_prefer,
        )

      uplift_model.fit(
df_train[x_names].values, treatment=df_train["treatment_group_key"].values, y=df_train[CONVERSION].values, )

tests/test_uplift_trees.py:37:

causalml/inference/tree/uplift.pyx:1331: in causalml.inference.tree.uplift.UpliftRandomForestClassifier.fit (delayed(self.bootstrap)(X, treatment, y, tree) for tree in self.uplift_forest) ../../../../opt/anaconda3/lib/python3.8/site-packages/joblib/parallel.py:1054: in call self.retrieve() ../../../../opt/anaconda3/lib/python3.8/site-packages/joblib/parallel.py:933: in retrieve self._output.extend(job.get(timeout=self.timeout)) ../../../../opt/anaconda3/lib/python3.8/multiprocessing/pool.py:771: in get raise self._value ../../../../opt/anaconda3/lib/python3.8/multiprocessing/pool.py:537: in _handle_tasks put(task)

obj = (140, 0, <joblib._parallel_backends.SafeFunction object at 0x7fd5d196bbb0>, (), {})

def send(obj):
    buffer = BytesIO()

  CustomizablePickler(buffer, self._reducers).dump(obj)
E _pickle.PicklingError: Can't pickle <cyfunction UpliftRandomForestClassifier.bootstrap at 0x7fd5fff8b5f0>: attribute lookup bootstrap on causalml.inference.tree.uplift failed

../../../../opt/anaconda3/lib/python3.8/site-packages/joblib/pool.py:156: PicklingError _ test_UpliftRandomForestClassifier[processes-loky] __ joblib.externals.loky.process_executor._RemoteTraceback: """ Traceback (most recent call last): File "/Users/zhenyuzhao-zz/opt/anaconda3/lib/python3.8/site-packages/joblib/externals/loky/backend/queues.py", line 153, in feed obj = dumps(obj, reducers=reducers) File "/Users/zhenyuzhao-zz/opt/anaconda3/lib/python3.8/site-packages/joblib/externals/loky/backend/reduction.py", line 271, in dumps dump(obj, buf, reducers=reducers, protocol=protocol) File "/Users/zhenyuzhao-zz/opt/anaconda3/lib/python3.8/site-packages/joblib/externals/loky/backend/reduction.py", line 264, in dump _LokyPickler(file, reducers=reducers, protocol=protocol).dump(obj) File "/Users/zhenyuzhao-zz/opt/anaconda3/lib/python3.8/site-packages/joblib/externals/cloudpickle/cloudpickle_fast.py", line 563, in dump return Pickler.dump(self, obj) _pickle.PicklingError: Can't pickle <cyfunction UpliftRandomForestClassifier.bootstrap at 0x7fd5fff8b5f0>: attribute lookup bootstrap on causalml.inference.tree.uplift failed """

The above exception was the direct cause of the following exception:

generate_classification_data = <function generate_classification_data.._generate_data at 0x7fd5d1eab430>, backend = 'loky', joblib_prefer = 'processes'

@pytest.mark.parametrize("backend", ["loky", "threading", "multiprocessing"])
@pytest.mark.parametrize("joblib_prefer", ["threads", "processes"])
def test_UpliftRandomForestClassifier(
    generate_classification_data, backend, joblib_prefer
):
    df, x_names = generate_classification_data()
    df_train, df_test = train_test_split(df, test_size=0.2, random_state=RANDOM_SEED)

    with parallel_backend(backend):
        # Train the UpLift Random Forest classifier
        uplift_model = UpliftRandomForestClassifier(
            min_samples_leaf=50,
            control_name=TREATMENT_NAMES[0],
            random_state=RANDOM_SEED,
            joblib_prefer=joblib_prefer,
        )

      uplift_model.fit(
df_train[x_names].values, treatment=df_train["treatment_group_key"].values, y=df_train[CONVERSION].values, )

tests/test_uplift_trees.py:37:

causalml/inference/tree/uplift.pyx:1331: in causalml.inference.tree.uplift.UpliftRandomForestClassifier.fit (delayed(self.bootstrap)(X, treatment, y, tree) for tree in self.uplift_forest) ../../../../opt/anaconda3/lib/python3.8/site-packages/joblib/parallel.py:1054: in call self.retrieve() ../../../../opt/anaconda3/lib/python3.8/site-packages/joblib/parallel.py:933: in retrieve self._output.extend(job.get(timeout=self.timeout)) ../../../../opt/anaconda3/lib/python3.8/site-packages/joblib/_parallel_backends.py:542: in wrap_future_result return future.result(timeout=timeout) ../../../../opt/anaconda3/lib/python3.8/concurrent/futures/_base.py:439: in result return self.__get_result()

self = <Future at 0x7fd5d1898a00 state=finished raised PicklingError>

def __get_result(self):
    if self._exception:

      raise self._exception
E _pickle.PicklingError: Could not pickle the task to send it to the workers.

../../../../opt/anaconda3/lib/python3.8/concurrent/futures/_base.py:388: PicklingError ___ test_UpliftRandomForestClassifier[processes-threading] ___ joblib.externals.loky.process_executor._RemoteTraceback: """ Traceback (most recent call last): File "/Users/zhenyuzhao-zz/opt/anaconda3/lib/python3.8/site-packages/joblib/externals/loky/backend/queues.py", line 153, in feed obj = dumps(obj, reducers=reducers) File "/Users/zhenyuzhao-zz/opt/anaconda3/lib/python3.8/site-packages/joblib/externals/loky/backend/reduction.py", line 271, in dumps dump(obj, buf, reducers=reducers, protocol=protocol) File "/Users/zhenyuzhao-zz/opt/anaconda3/lib/python3.8/site-packages/joblib/externals/loky/backend/reduction.py", line 264, in dump _LokyPickler(file, reducers=reducers, protocol=protocol).dump(obj) File "/Users/zhenyuzhao-zz/opt/anaconda3/lib/python3.8/site-packages/joblib/externals/cloudpickle/cloudpickle_fast.py", line 563, in dump return Pickler.dump(self, obj) _pickle.PicklingError: Can't pickle <cyfunction UpliftTreeClassifier.evaluate_KL at 0x7fd5fff7aba0>: attribute lookup evaluate_KL on causalml.inference.tree.uplift failed """

The above exception was the direct cause of the following exception:

generate_classification_data = <function generate_classification_data.._generate_data at 0x7fd5d1eab430>, backend = 'threading', joblib_prefer = 'processes'

@pytest.mark.parametrize("backend", ["loky", "threading", "multiprocessing"])
@pytest.mark.parametrize("joblib_prefer", ["threads", "processes"])
def test_UpliftRandomForestClassifier(
    generate_classification_data, backend, joblib_prefer
):
    df, x_names = generate_classification_data()
    df_train, df_test = train_test_split(df, test_size=0.2, random_state=RANDOM_SEED)

    with parallel_backend(backend):
        # Train the UpLift Random Forest classifier
        uplift_model = UpliftRandomForestClassifier(
            min_samples_leaf=50,
            control_name=TREATMENT_NAMES[0],
            random_state=RANDOM_SEED,
            joblib_prefer=joblib_prefer,
        )

        uplift_model.fit(
            df_train[x_names].values,
            treatment=df_train["treatment_group_key"].values,
            y=df_train[CONVERSION].values,
        )

        predictions = {}
        predictions["single"] = uplift_model.predict(df_test[x_names].values)
        with parallel_backend("loky", n_jobs=2):

          predictions["loky_2"] = uplift_model.predict(df_test[x_names].values)

tests/test_uplift_trees.py:46:

../../../../opt/anaconda3/lib/python3.8/site-packages/sklearn/utils/_testing.py:308: in wrapper return fn(*args, **kwargs) causalml/inference/tree/uplift.pyx:1379: in causalml.inference.tree.uplift.UpliftRandomForestClassifier.predict (delayed(tree.predict)(X=X) for tree in self.uplift_forest) ../../../../opt/anaconda3/lib/python3.8/site-packages/joblib/parallel.py:1054: in call self.retrieve() ../../../../opt/anaconda3/lib/python3.8/site-packages/joblib/parallel.py:933: in retrieve self._output.extend(job.get(timeout=self.timeout)) ../../../../opt/anaconda3/lib/python3.8/site-packages/joblib/_parallel_backends.py:542: in wrap_future_result return future.result(timeout=timeout) ../../../../opt/anaconda3/lib/python3.8/concurrent/futures/_base.py:439: in result return self.__get_result()

self = <Future at 0x7fd5d1e8d6d0 state=finished raised PicklingError>

def __get_result(self):
    if self._exception:

      raise self._exception
E _pickle.PicklingError: Could not pickle the task to send it to the workers.

../../../../opt/anaconda3/lib/python3.8/concurrent/futures/_base.py:388: PicklingError ____ test_UpliftRandomForestClassifier[processes-multiprocessing] ____

generate_classification_data = <function generate_classification_data.._generate_data at 0x7fd5d1eab430>, backend = 'multiprocessing', joblib_prefer = 'processes'

@pytest.mark.parametrize("backend", ["loky", "threading", "multiprocessing"])
@pytest.mark.parametrize("joblib_prefer", ["threads", "processes"])
def test_UpliftRandomForestClassifier(
    generate_classification_data, backend, joblib_prefer
):
    df, x_names = generate_classification_data()
    df_train, df_test = train_test_split(df, test_size=0.2, random_state=RANDOM_SEED)

    with parallel_backend(backend):
        # Train the UpLift Random Forest classifier
        uplift_model = UpliftRandomForestClassifier(
            min_samples_leaf=50,
            control_name=TREATMENT_NAMES[0],
            random_state=RANDOM_SEED,
            joblib_prefer=joblib_prefer,
        )

      uplift_model.fit(
df_train[x_names].values, treatment=df_train["treatment_group_key"].values, y=df_train[CONVERSION].values, )

tests/test_uplift_trees.py:37:

causalml/inference/tree/uplift.pyx:1331: in causalml.inference.tree.uplift.UpliftRandomForestClassifier.fit (delayed(self.bootstrap)(X, treatment, y, tree) for tree in self.uplift_forest) ../../../../opt/anaconda3/lib/python3.8/site-packages/joblib/parallel.py:1054: in call self.retrieve() ../../../../opt/anaconda3/lib/python3.8/site-packages/joblib/parallel.py:933: in retrieve self._output.extend(job.get(timeout=self.timeout)) ../../../../opt/anaconda3/lib/python3.8/multiprocessing/pool.py:771: in get raise self._value ../../../../opt/anaconda3/lib/python3.8/multiprocessing/pool.py:537: in _handle_tasks put(task)

obj = (170, 0, <joblib._parallel_backends.SafeFunction object at 0x7fd5d1955d30>, (), {})

def send(obj):
    buffer = BytesIO()

  CustomizablePickler(buffer, self._reducers).dump(obj)
E _pickle.PicklingError: Can't pickle <cyfunction UpliftRandomForestClassifier.bootstrap at 0x7fd5fff8b5f0>: attribute lookup bootstrap on causalml.inference.tree.uplift failed

../../../../opt/anaconda3/lib/python3.8/site-packages/joblib/pool.py:156: PicklingError ============================================================================================================================== warnings summary ============================================================================================================================== ../../../../opt/anaconda3/lib/python3.8/site-packages/scipy/fft/init.py:97 The module numpy.dual is deprecated. Instead of using dual, use the functions directly from numpy or scipy.

../../../../opt/anaconda3/lib/python3.8/site-packages/scipy/special/orthogonal.py:81: 2 warnings tests/test_datasets.py: 16 warnings tests/test_ivlearner.py: 8 warnings tests/test_meta_learners.py: 48 warnings np.int is a deprecated alias for the builtin int. To silence this warning, use int by itself. Doing this will not modify any behavior and is safe. When replacing np.int, you may wish to use e.g. np.int64 or np.int32 to specify the precision. If you wish to review your current use, check the release note link for additional information. Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations

../../../../opt/anaconda3/lib/python3.8/site-packages/scipy/io/matlab/mio5.py:98 np.bool is a deprecated alias for the builtin bool. To silence this warning, use bool by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use np.bool_ here. Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations

../../../../opt/anaconda3/lib/python3.8/site-packages/patsy/constraint.py:13 Using or importing the ABCs from 'collections' instead of from 'collections.abc' is deprecated since Python 3.3, and in 3.9 it will stop working

tests/test_cevae.py::test_CEVAE tests/test_cevae.py::test_CEVAE tests/test_cevae.py::test_CEVAE To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requiresgrad(True), rather than torch.tensor(sourceTensor).

tests/test_counterfactual_unit_selection.py: 1032 warnings tests/test_meta_learners.py: 209 warnings tests/test_value_optimization.py: 305 warnings tostring() is deprecated. Use tobytes() instead.

tests/test_datasets.py::test_get_synthetic_preds[simulate_hidden_confounder] tests/test_datasets.py::test_get_synthetic_preds[simulate_hidden_confounder] tests/test_datasets.py::test_get_synthetic_preds[simulate_hidden_confounder] tests/test_datasets.py::test_get_synthetic_preds[simulate_hidden_confounder] invalid value encountered in true_divide

tests/test_features.py::test_load_data tests/test_features.py::test_load_data tests/test_features.py::test_load_data np.object is a deprecated alias for the builtin object. To silence this warning, use object by itself. Doing this will not modify any behavior and is safe. Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations

tests/test_features.py::test_LabelEncoder tests/test_features.py::test_LabelEncoder

A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

tests/test_meta_learners.py::test_BaseSClassifier tests/test_meta_learners.py::test_BaseXClassifier tests/test_meta_learners.py::test_BaseXClassifier tests/test_propensity.py::test_gradientboosted_propensity_model tests/test_propensity.py::test_gradientboosted_propensity_model_earlystopping The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].

tests/test_meta_learners.py::test_BaseRClassifier tests/test_meta_learners.py::test_BaseRClassifier_with_sample_weights inspect.getargspec() is deprecated since Python 3.0, use inspect.signature() or inspect.getfullargspec()

-- Docs: https://docs.pytest.org/en/stable/warnings.html

---------- coverage: platform darwin, python 3.8.8-final-0 ----------- Name Stmts Miss Cover

causalml/init.py 3 0 100% causalml/dataset/init.py 14 0 100% causalml/dataset/classification.py 73 20 73% causalml/dataset/regression.py 54 0 100% causalml/dataset/synthetic.py 244 103 58% causalml/feature_selection/init.py 1 1 0% causalml/feature_selection/filters.py 153 153 0% causalml/features.py 85 10 88% causalml/inference/init.py 0 0 100% causalml/inference/iv/init.py 2 0 100% causalml/inference/iv/drivlearner.py 258 58 78% causalml/inference/iv/iv_regression.py 17 8 53% causalml/inference/meta/init.py 6 0 100% causalml/inference/meta/base.py 71 17 76% causalml/inference/meta/drlearner.py 195 41 79% causalml/inference/meta/explainer.py 101 80 21% causalml/inference/meta/rlearner.py 251 35 86% causalml/inference/meta/slearner.py 178 27 85% causalml/inference/meta/tlearner.py 170 28 84% causalml/inference/meta/tmle.py 98 22 78% causalml/inference/meta/utils.py 49 10 80% causalml/inference/meta/xlearner.py 252 44 83% causalml/inference/nn/init.py 1 0 100% causalml/inference/nn/cevae.py 29 3 90% causalml/inference/tf/init.py 1 1 0% causalml/inference/tf/dragonnet.py 64 64 0% causalml/inference/tf/utils.py 52 52 0% causalml/inference/tree/init.py 4 0 100% causalml/inference/tree/plot.py 100 95 5% causalml/inference/tree/utils.py 43 36 16% causalml/match.py 185 40 78% causalml/metrics/init.py 7 0 100% causalml/metrics/classification.py 11 4 64% causalml/metrics/const.py 1 0 100% causalml/metrics/regression.py 41 3 93% causalml/metrics/sensitivity.py 257 50 81% causalml/metrics/visualize.py 299 237 21% causalml/optimize/init.py 5 0 100% causalml/optimize/pns.py 23 20 13% causalml/optimize/policylearner.py 54 39 28% causalml/optimize/unit_selection.py 90 45 50% causalml/optimize/utils.py 26 4 85% causalml/optimize/value_optimization.py 30 3 90% causalml/propensity.py 74 4 95%

TOTAL 3672 1357 63%

========================================================================================================================== short test summary info =========================================================================================================================== FAILED tests/test_uplift_trees.py::test_UpliftRandomForestClassifier[threads-loky] - _pickle.PicklingError: Could not pickle the task to send it to the workers. FAILED tests/test_uplift_trees.py::test_UpliftRandomForestClassifier[threads-threading] - _pickle.PicklingError: Could not pickle the task to send it to the workers. FAILED tests/test_uplift_trees.py::test_UpliftRandomForestClassifier[threads-multiprocessing] - _pickle.PicklingError: Can't pickle <cyfunction UpliftRandomForestClassifier.bootstrap at 0x7fd5fff8b5f0>: attribute lookup bootstrap on causalml.inference.tree.uplift failed FAILED tests/test_uplift_trees.py::test_UpliftRandomForestClassifier[processes-loky] - _pickle.PicklingError: Could not pickle the task to send it to the workers. FAILED tests/test_uplift_trees.py::test_UpliftRandomForestClassifier[processes-threading] - _pickle.PicklingError: Could not pickle the task to send it to the workers. FAILED tests/test_uplift_trees.py::test_UpliftRandomForestClassifier[processes-multiprocessing] - _pickle.PicklingError: Can't pickle <cyfunction UpliftRandomForestClassifier.bootstrap at 0x7fd5fff8b5f0>: attribute lookup bootstrap on causalml.inference.tree.uplift f... ========================================================================================================== 6 failed, 58 passed, 1642 warnings in 571.89s (0:09:31) ===========================================================================================================` Environment (please complete the following information):

OS: [e.g. macOS, Windows, Ubuntu]
Python Version: [e.g. 3.6, 3.7]
Versions of Major Dependencies (pandas, scikit-learn, cython): [e.g. pandas==0.25, scikit-learn==0.22, cython==0.28]_

jeongyoonlee commented 2 years ago

Hi @zhenyuz0500, this error message is from UpliftRandomForestClassifier which requires Cython code to be compiled. Please build with the build_ext --inplace option and run the test again.

python setup.py build_ext --inplace
pytest -sv tests --cov causalml

paullo0106 commented 2 years ago

noticed the recent lint errors too, we'll need to take a look. Take the latest master for example https://github.com/uber/causalml/runs/6236927941?check_suite_focus=true

zhenyuz0500 commented 2 years ago

It seems the errors are the same:

(base) zhenyuzhao-zz@MB0 causalml % pip install Cython

Requirement already satisfied: Cython in /Users/zhenyuzhao-zz/opt/anaconda3/lib/python3.8/site-packages (0.29.23)

(base) zhenyuzhao-zz@MB0 causalml % python setup.py build_ext --inplace running build_ext copying build/lib.macosx-10.9-x86_64-3.8/causalml/inference/tree/causaltree.cpython-38-darwin.so -> causalml/inference/tree copying build/lib.macosx-10.9-x86_64-3.8/causalml/inference/tree/uplift.cpython-38-darwin.so -> causalml/inference/tree

(base) zhenyuzhao-zz@MB0 causalml % pytest -sv tests --cov causalml ============================================================================================================================ test session starts ============================================================================================================================= platform darwin -- Python 3.8.8, pytest-6.2.3, py-1.10.0, pluggy-0.13.1 -- /Users/zhenyuzhao-zz/opt/anaconda3/bin/python cachedir: .pytest_cache rootdir: /Users/zhenyuzhao-zz/Documents/Programming/git_repo/causalml plugins: anyio-2.2.0, cov-3.0.0 collected 64 items

tests/test_cevae.py::test_CEVAE PASSED tests/test_counterfactual_unit_selection.py::test_counterfactual_unit_selection PASSED tests/test_datasets.py::test_get_synthetic_preds[simulate_nuisance_and_easy_treatment] PASSED tests/test_datasets.py::test_get_synthetic_preds[simulate_hidden_confounder] PASSED tests/test_datasets.py::test_get_synthetic_preds[simulate_randomized_trial] PASSED tests/test_datasets.py::test_get_synthetic_summary Abs % Error of ATE MSE KL Divergence Actuals 0.000000 0.000000 0.000000 S Learner (LR) 0.581879 0.125334 3.828739 T Learner (XGB) 0.323199 1.186263 1.424861 PASSED tests/test_datasets.py::test_get_synthetic_preds_holdout PASSED tests/test_datasets.py::test_get_synthetic_summary_holdout ( Abs % Error of ATE MSE KL Divergence Actuals 0.000000 0.000000 0.000000 S Learner (LR) 0.359446 0.072330 4.033648 S Learner (XGB) 0.041486 0.319411 0.824989 T Learner (LR) 0.358963 0.037750 0.440597 T Learner (XGB) 0.085843 1.257743 1.500363 X Learner (LR) 0.358963 0.037750 0.440597 X Learner (XGB) 0.081450 0.504336 1.116033 R Learner (LR) 0.327808 0.044548 0.408275 R Learner (XGB) 0.112043 4.740827 2.079625, Abs % Error of ATE MSE KL Divergence Actuals 0.000000 0.000000 0.000000 S Learner (LR) 0.401601 0.080840 3.944126 S Learner (XGB) 0.073994 0.283661 0.948965 T Learner (LR) 0.353086 0.033973 0.695373 T Learner (XGB) 0.090676 0.652876 1.350948 X Learner (LR) 0.353086 0.033973 0.695373 X Learner (XGB) 0.020359 0.332149 1.097056 R Learner (LR) 0.299843 0.037296 0.616230 R Learner (XGB) 0.164259 1.830500 1.492768) PASSED tests/test_datasets.py::test_get_synthetic_auuc Learner cum_gain_auuc 0 Actuals 3082.158899 2 T Learner (XGB) 2630.595869 3 Random 2490.139546 1 S Learner (LR) 2463.126242 PASSED tests/test_features.py::test_load_data PASSED tests/test_features.py::test_LabelEncoder PASSED tests/test_features.py::test_OneHotEncoder PASSED 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 10/10 [00:18<00:00, 1.86s/it] PASSED tests/test_match.py::test_nearest_neighbor_match_by_group PASSED tests/test_match.py::test_match_optimizer PASSED tests/test_meta_learners.py::test_synthetic_data PASSED tests/test_meta_learners.py::test_BaseSLearner PASSED 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 10/10 [00:06<00:00, 1.65it/s] PASSED tests/test_meta_learners.py::test_LRSRegressor PASSED 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 10/10 [00:09<00:00, 1.10it/s] PASSED 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 10/10 [00:08<00:00, 1.16it/s] PASSED 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 10/10 [00:35<00:00, 3.56s/it] PASSED 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/opt/concourse/worker/volumes/live/7a2b9f41-3287-451b-6691-43e9a6c0910f/volume/xgboost-split_1619728204606/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior. PASSED tests/test_meta_learners.py::test_BaseTClassifier PASSED tests/test_meta_learners.py::test_BaseXClassifier [10:59:30] WARNING: /opt/concourse/worker/volumes/live/7a2b9f41-3287-451b-6691-43e9a6c0910f/volume/xgboost-split_1619728204606/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior. [10:59:31] WARNING: /opt/concourse/worker/volumes/live/7a2b9f41-3287-451b-6691-43e9a6c0910f/volume/xgboost-split_1619728204606/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior. [10:59:34] WARNING: /opt/concourse/worker/volumes/live/7a2b9f41-3287-451b-6691-43e9a6c0910f/volume/xgboost-split_1619728204606/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior. [10:59:34] WARNING: /opt/concourse/worker/volumes/live/7a2b9f41-3287-451b-6691-43e9a6c0910f/volume/xgboost-split_1619728204606/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior. PASSED tests/test_meta_learners.py::test_BaseRClassifier /Users/zhenyuzhao-zz/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1]. warnings.warn(label_encoder_deprecation_msg, UserWarning) /Users/zhenyuzhao-zz/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1]. warnings.warn(label_encoder_deprecation_msg, UserWarning) /Users/zhenyuzhao-zz/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1]. warnings.warn(label_encoder_deprecation_msg, UserWarning) /Users/zhenyuzhao-zz/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1]. warnings.warn(label_encoder_deprecation_msg, UserWarning) /Users/zhenyuzhao-zz/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1]. warnings.warn(label_encoder_deprecation_msg, UserWarning) [10:59:37] WARNING: /opt/concourse/worker/volumes/live/7a2b9f41-3287-451b-6691-43e9a6c0910f/volume/xgboost-split_1619728204606/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior. [10:59:37] WARNING: /opt/concourse/worker/volumes/live/7a2b9f41-3287-451b-6691-43e9a6c0910f/volume/xgboost-split_1619728204606/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior. [10:59:37] WARNING: /opt/concourse/worker/volumes/live/7a2b9f41-3287-451b-6691-43e9a6c0910f/volume/xgboost-split_1619728204606/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior. [10:59:37] WARNING: /opt/concourse/worker/volumes/live/7a2b9f41-3287-451b-6691-43e9a6c0910f/volume/xgboost-split_1619728204606/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior. [10:59:37] WARNING: /opt/concourse/worker/volumes/live/7a2b9f41-3287-451b-6691-43e9a6c0910f/volume/xgboost-split_1619728204606/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior. PASSED tests/test_meta_learners.py::test_BaseRClassifier_with_sample_weights /Users/zhenyuzhao-zz/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1]. warnings.warn(label_encoder_deprecation_msg, UserWarning) /Users/zhenyuzhao-zz/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1]. warnings.warn(label_encoder_deprecation_msg, UserWarning) /Users/zhenyuzhao-zz/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1]. warnings.warn(label_encoder_deprecation_msg, UserWarning) /Users/zhenyuzhao-zz/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1]. warnings.warn(label_encoder_deprecation_msg, UserWarning) /Users/zhenyuzhao-zz/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1]. warnings.warn(label_encoder_deprecation_msg, UserWarning) [10:59:42] WARNING: /opt/concourse/worker/volumes/live/7a2b9f41-3287-451b-6691-43e9a6c0910f/volume/xgboost-split_1619728204606/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior. [10:59:42] WARNING: /opt/concourse/worker/volumes/live/7a2b9f41-3287-451b-6691-43e9a6c0910f/volume/xgboost-split_1619728204606/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior. [10:59:42] WARNING: /opt/concourse/worker/volumes/live/7a2b9f41-3287-451b-6691-43e9a6c0910f/volume/xgboost-split_1619728204606/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior. [10:59:42] WARNING: /opt/concourse/worker/volumes/live/7a2b9f41-3287-451b-6691-43e9a6c0910f/volume/xgboost-split_1619728204606/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior. [10:59:42] WARNING: /opt/concourse/worker/volumes/live/7a2b9f41-3287-451b-6691-43e9a6c0910f/volume/xgboost-split_1619728204606/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior. [0] validation_0-auc:2.75329 [1] validation_0-auc:-4.78198 [2] validation_0-auc:-3.52732 [3] validation_0-auc:-5.61398 [4] validation_0-auc:-4.07529 [5] validation_0-auc:-4.58928 [6] validation_0-auc:-3.94142 [7] validation_0-auc:-3.24399 [8] validation_0-auc:-6.07570 [9] validation_0-auc:-8.59653 [10] validation_0-auc:-7.84374 [11] validation_0-auc:-6.41278 [12] validation_0-auc:-4.45992 [13] validation_0-auc:-5.09416 [14] validation_0-auc:-5.12351 [15] validation_0-auc:-3.64573 [16] validation_0-auc:-3.01243 [17] validation_0-auc:-2.80259 [18] validation_0-auc:-1.67346 [19] validation_0-auc:-2.18767 [20] validation_0-auc:-2.77537 [21] validation_0-auc:-2.62919 [22] validation_0-auc:-2.49174 [23] validation_0-auc:-1.50853 [24] validation_0-auc:-0.98859 [25] validation_0-auc:-0.79339 [26] validation_0-auc:-0.52949 [27] validation_0-auc:-0.60107 [28] validation_0-auc:0.03568 [29] validation_0-auc:0.38625 [30] validation_0-auc:-0.41416 PASSED tests/test_meta_learners.py::test_pandas_input PASSED 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 10/10 [00:24<00:00, 2.43s/it] PASSED tests/test_propensity.py::test_logistic_regression_propensity_model PASSED tests/test_propensity.py::test_logistic_regression_propensity_model_model_kwargs PASSED tests/test_propensity.py::test_elasticnet_propensity_model PASSED tests/test_propensity.py::test_gradientboosted_propensity_model [11:00:19] WARNING: /opt/concourse/worker/volumes/live/7a2b9f41-3287-451b-6691-43e9a6c0910f/volume/xgboost-split_1619728204606/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior. PASSED tests/test_propensity.py::test_gradientboosted_propensity_model_earlystopping [11:00:20] WARNING: /opt/concourse/worker/volumes/live/7a2b9f41-3287-451b-6691-43e9a6c0910f/volume/xgboost-split_1619728204606/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior. [0] validation_0-logloss:0.68365 [1] validation_0-logloss:0.67243 [2] validation_0-logloss:0.64779 [3] validation_0-logloss:0.62546 [4] validation_0-logloss:0.60664 [5] validation_0-logloss:0.58847 [6] validation_0-logloss:0.57660 [7] validation_0-logloss:0.57160 [8] validation_0-logloss:0.56192 [9] validation_0-logloss:0.55328 [10] validation_0-logloss:0.54668 [11] validation_0-logloss:0.54318 [12] validation_0-logloss:0.54001 [13] validation_0-logloss:0.53335 [14] validation_0-logloss:0.53029 [15] validation_0-logloss:0.52541 [16] validation_0-logloss:0.52656 [17] validation_0-logloss:0.52321 [18] validation_0-logloss:0.52327 [19] validation_0-logloss:0.52239 [20] validation_0-logloss:0.52392 [21] validation_0-logloss:0.52404 [22] validation_0-logloss:0.52376 [23] validation_0-logloss:0.52374 [24] validation_0-logloss:0.52289 [25] validation_0-logloss:0.52229 [26] validation_0-logloss:0.52028 [27] validation_0-logloss:0.52110 [28] validation_0-logloss:0.52270 [29] validation_0-logloss:0.52229 [30] validation_0-logloss:0.52084 [31] validation_0-logloss:0.52308 [32] validation_0-logloss:0.52387 [33] validation_0-logloss:0.52583 [34] validation_0-logloss:0.52824 [35] validation_0-logloss:0.53039 PASSED tests/test_sensitivity.py::test_Sensitivity Method ATE New ATE New ATE LB New ATE UB 0 Placebo Treatment 0.680042 -0.009359 -0.022652 0.003934 0 Random Cause 0.680042 0.680049 0.667253 0.692846 0 Subset Data(sample size @0.5) 0.680042 0.682025 0.663973 0.700077 0 Random Replace 0.680042 0.678623 0.665698 0.691548 0 Selection Bias (alpha@-0.80626, with r-sqaure:... 0.680042 1.353547 1.34094 1.366155 0 Selection Bias (alpha@-0.645, with r-sqaure:0.... 0.680042 1.218846 1.206217 1.231475 0 Selection Bias (alpha@-0.48375, with r-sqaure:... 0.680042 1.084145 1.071487 1.096803 0 Selection Bias (alpha@-0.3225, with r-sqaure:0... 0.680042 0.949444 0.936748 0.96214 0 Selection Bias (alpha@-0.16125, with r-sqaure:... 0.680042 0.814743 0.802001 0.827485 0 Selection Bias (alpha@0.0, with r-sqaure:0.0 0.680042 0.680042 0.667245 0.692838 0 Selection Bias (alpha@0.16125, with r-sqaure:0... 0.680042 0.545341 0.532482 0.558199 0 Selection Bias (alpha@0.3225, with r-sqaure:0.... 0.680042 0.41064 0.397711 0.423568 0 Selection Bias (alpha@0.48375, with r-sqaure:0... 0.680042 0.275939 0.262933 0.288944 0 Selection Bias (alpha@0.645, with r-sqaure:0.0... 0.680042 0.141237 0.128146 0.154329 0 Selection Bias (alpha@0.80626, with r-sqaure:0... 0.680042 0.006536 -0.006648 0.01972 PASSED tests/test_sensitivity.py::test_SensitivityPlaceboTreatment Method ATE New ATE New ATE LB New ATE UB 0 Random Cause 0.678358 -0.004009 -0.017335 0.009316 PASSED tests/test_sensitivity.py::test_SensitivityRandomCause Method ATE New ATE New ATE LB New ATE UB 0 Random Cause 0.674445 0.674436 0.661632 0.68724 PASSED tests/test_sensitivity.py::test_SensitivityRandomReplace Method ATE New ATE New ATE LB New ATE UB 0 Random Replace 0.68259 0.809523 0.796646 0.822401 PASSED tests/test_sensitivity.py::test_SensitivitySelectionBias alpha rsqs New ATE New ATE LB New ATE UB 0 -0.800120 0.107832 0.660142 0.647300 0.672984 0 -0.640096 0.072265 0.662217 0.649428 0.675006 0 -0.480072 0.042282 0.664292 0.651535 0.677050 0 -0.320048 0.019399 0.666367 0.653619 0.679115 0 -0.160024 0.004965 0.668442 0.655683 0.681202 0 0.000000 0.000000 0.670517 0.657724 0.683311 0 0.160024 0.005063 0.672592 0.659744 0.685441 0 0.320048 0.020160 0.674667 0.661743 0.687592 0 0.480072 0.044736 0.676742 0.663720 0.689765 0 0.640096 0.077731 0.678818 0.665678 0.691957 0 0.800120 0.117713 0.680893 0.667615 0.694170 feature partial_rsqs 0 feature_0 -0.065992 1 feature_1 -0.067405 2 feature_2 -0.000011 3 feature_3 -0.000623 4 feature_4 -0.000643 5 feature_5 -0.000026 PASSED tests/test_sensitivity.py::test_one_sided PASSED tests/test_sensitivity.py::test_alignment PASSED tests/test_sensitivity.py::test_one_sided_att PASSED tests/test_sensitivity.py::test_alignment_att PASSED tests/test_uplift_trees.py::test_make_uplift_classification PASSED tests/test_uplift_trees.py::test_UpliftRandomForestClassifier[threads-loky] FAILED tests/test_uplift_trees.py::test_UpliftRandomForestClassifier[threads-threading] FAILED tests/test_uplift_trees.py::test_UpliftRandomForestClassifier[threads-multiprocessing] FAILED tests/test_uplift_trees.py::test_UpliftRandomForestClassifier[processes-loky] FAILED tests/test_uplift_trees.py::test_UpliftRandomForestClassifier[processes-threading] FAILED tests/test_uplift_trees.py::test_UpliftRandomForestClassifier[processes-multiprocessing] FAILED tests/test_uplift_trees.py::test_UpliftTreeClassifier PASSED tests/test_uplift_trees.py::test_UpliftTreeClassifier_feature_importance PASSED tests/test_utils.py::test_weighted_variance PASSED tests/test_value_optimization.py::test_counterfactual_value_optimization PASSEDCouldn't parse Python file '/Users/zhenyuzhao-zz/Documents/Programming/git_repo/causalml/causalml/feature_selection/filters.py' (couldnt-parse)

================================================================================================================================== FAILURES ================================================================================================================================== __ test_UpliftRandomForestClassifier[threads-loky] ___ joblib.externals.loky.process_executor._RemoteTraceback: """ Traceback (most recent call last): File "/Users/zhenyuzhao-zz/opt/anaconda3/lib/python3.8/site-packages/joblib/externals/loky/backend/queues.py", line 153, in feed obj = dumps(obj, reducers=reducers) File "/Users/zhenyuzhao-zz/opt/anaconda3/lib/python3.8/site-packages/joblib/externals/loky/backend/reduction.py", line 271, in dumps dump(obj, buf, reducers=reducers, protocol=protocol) File "/Users/zhenyuzhao-zz/opt/anaconda3/lib/python3.8/site-packages/joblib/externals/loky/backend/reduction.py", line 264, in dump _LokyPickler(file, reducers=reducers, protocol=protocol).dump(obj) File "/Users/zhenyuzhao-zz/opt/anaconda3/lib/python3.8/site-packages/joblib/externals/cloudpickle/cloudpickle_fast.py", line 563, in dump return Pickler.dump(self, obj) _pickle.PicklingError: Can't pickle <cyfunction UpliftRandomForestClassifier.bootstrap at 0x7fa378fb05f0>: attribute lookup bootstrap on causalml.inference.tree.uplift failed """

The above exception was the direct cause of the following exception:

generate_classification_data = <function generate_classification_data.._generate_data at 0x7fa34a1ba430>, backend = 'loky', joblib_prefer = 'threads'

@pytest.mark.parametrize("backend", ["loky", "threading", "multiprocessing"])
@pytest.mark.parametrize("joblib_prefer", ["threads", "processes"])
def test_UpliftRandomForestClassifier(
    generate_classification_data, backend, joblib_prefer
):
    df, x_names = generate_classification_data()
    df_train, df_test = train_test_split(df, test_size=0.2, random_state=RANDOM_SEED)

    with parallel_backend(backend):
        # Train the UpLift Random Forest classifier
        uplift_model = UpliftRandomForestClassifier(
            min_samples_leaf=50,
            control_name=TREATMENT_NAMES[0],
            random_state=RANDOM_SEED,
            joblib_prefer=joblib_prefer,
        )

      uplift_model.fit(
df_train[x_names].values, treatment=df_train["treatment_group_key"].values, y=df_train[CONVERSION].values, )

tests/test_uplift_trees.py:37:

causalml/inference/tree/uplift.pyx:1331: in causalml.inference.tree.uplift.UpliftRandomForestClassifier.fit (delayed(self.bootstrap)(X, treatment, y, tree) for tree in self.uplift_forest) ../../../../opt/anaconda3/lib/python3.8/site-packages/joblib/parallel.py:1054: in call self.retrieve() ../../../../opt/anaconda3/lib/python3.8/site-packages/joblib/parallel.py:933: in retrieve self._output.extend(job.get(timeout=self.timeout)) ../../../../opt/anaconda3/lib/python3.8/site-packages/joblib/_parallel_backends.py:542: in wrap_future_result return future.result(timeout=timeout) ../../../../opt/anaconda3/lib/python3.8/concurrent/futures/_base.py:439: in result return self.__get_result()

self = <Future at 0x7fa34a12a040 state=finished raised PicklingError>

def __get_result(self):
    if self._exception:

      raise self._exception
E _pickle.PicklingError: Could not pickle the task to send it to the workers.

../../../../opt/anaconda3/lib/python3.8/concurrent/futures/_base.py:388: PicklingError ____ test_UpliftRandomForestClassifier[threads-threading] ____ joblib.externals.loky.process_executor._RemoteTraceback: """ Traceback (most recent call last): File "/Users/zhenyuzhao-zz/opt/anaconda3/lib/python3.8/site-packages/joblib/externals/loky/backend/queues.py", line 153, in feed obj = dumps(obj, reducers=reducers) File "/Users/zhenyuzhao-zz/opt/anaconda3/lib/python3.8/site-packages/joblib/externals/loky/backend/reduction.py", line 271, in dumps dump(obj, buf, reducers=reducers, protocol=protocol) File "/Users/zhenyuzhao-zz/opt/anaconda3/lib/python3.8/site-packages/joblib/externals/loky/backend/reduction.py", line 264, in dump _LokyPickler(file, reducers=reducers, protocol=protocol).dump(obj) File "/Users/zhenyuzhao-zz/opt/anaconda3/lib/python3.8/site-packages/joblib/externals/cloudpickle/cloudpickle_fast.py", line 563, in dump return Pickler.dump(self, obj) _pickle.PicklingError: Can't pickle <cyfunction UpliftTreeClassifier.evaluate_KL at 0x7fa378fa0ba0>: attribute lookup evaluate_KL on causalml.inference.tree.uplift failed """

The above exception was the direct cause of the following exception:

generate_classification_data = <function generate_classification_data.._generate_data at 0x7fa34a1ba430>, backend = 'threading', joblib_prefer = 'threads'

@pytest.mark.parametrize("backend", ["loky", "threading", "multiprocessing"])
@pytest.mark.parametrize("joblib_prefer", ["threads", "processes"])
def test_UpliftRandomForestClassifier(
    generate_classification_data, backend, joblib_prefer
):
    df, x_names = generate_classification_data()
    df_train, df_test = train_test_split(df, test_size=0.2, random_state=RANDOM_SEED)

    with parallel_backend(backend):
        # Train the UpLift Random Forest classifier
        uplift_model = UpliftRandomForestClassifier(
            min_samples_leaf=50,
            control_name=TREATMENT_NAMES[0],
            random_state=RANDOM_SEED,
            joblib_prefer=joblib_prefer,
        )

        uplift_model.fit(
            df_train[x_names].values,
            treatment=df_train["treatment_group_key"].values,
            y=df_train[CONVERSION].values,
        )

        predictions = {}
        predictions["single"] = uplift_model.predict(df_test[x_names].values)
        with parallel_backend("loky", n_jobs=2):

          predictions["loky_2"] = uplift_model.predict(df_test[x_names].values)

tests/test_uplift_trees.py:46:

../../../../opt/anaconda3/lib/python3.8/site-packages/sklearn/utils/_testing.py:308: in wrapper return fn(*args, **kwargs) causalml/inference/tree/uplift.pyx:1379: in causalml.inference.tree.uplift.UpliftRandomForestClassifier.predict (delayed(tree.predict)(X=X) for tree in self.uplift_forest) ../../../../opt/anaconda3/lib/python3.8/site-packages/joblib/parallel.py:1054: in call self.retrieve() ../../../../opt/anaconda3/lib/python3.8/site-packages/joblib/parallel.py:933: in retrieve self._output.extend(job.get(timeout=self.timeout)) ../../../../opt/anaconda3/lib/python3.8/site-packages/joblib/_parallel_backends.py:542: in wrap_future_result return future.result(timeout=timeout) ../../../../opt/anaconda3/lib/python3.8/concurrent/futures/_base.py:439: in result return self.__get_result()

self = <Future at 0x7fa34c09beb0 state=finished raised PicklingError>

def __get_result(self):
    if self._exception:

      raise self._exception
E _pickle.PicklingError: Could not pickle the task to send it to the workers.

../../../../opt/anaconda3/lib/python3.8/concurrent/futures/base.py:388: PicklingError ____ testUpliftRandomForestClassifier[threads-multiprocessing] ____

generate_classification_data = <function generate_classification_data.._generate_data at 0x7fa34a1ba430>, backend = 'multiprocessing', joblib_prefer = 'threads'

@pytest.mark.parametrize("backend", ["loky", "threading", "multiprocessing"])
@pytest.mark.parametrize("joblib_prefer", ["threads", "processes"])
def test_UpliftRandomForestClassifier(
    generate_classification_data, backend, joblib_prefer
):
    df, x_names = generate_classification_data()
    df_train, df_test = train_test_split(df, test_size=0.2, random_state=RANDOM_SEED)

    with parallel_backend(backend):
        # Train the UpLift Random Forest classifier
        uplift_model = UpliftRandomForestClassifier(
            min_samples_leaf=50,
            control_name=TREATMENT_NAMES[0],
            random_state=RANDOM_SEED,
            joblib_prefer=joblib_prefer,
        )

      uplift_model.fit(
df_train[x_names].values, treatment=df_train["treatment_group_key"].values, y=df_train[CONVERSION].values, )

tests/test_uplift_trees.py:37:

causalml/inference/tree/uplift.pyx:1331: in causalml.inference.tree.uplift.UpliftRandomForestClassifier.fit (delayed(self.bootstrap)(X, treatment, y, tree) for tree in self.uplift_forest) ../../../../opt/anaconda3/lib/python3.8/site-packages/joblib/parallel.py:1054: in call self.retrieve() ../../../../opt/anaconda3/lib/python3.8/site-packages/joblib/parallel.py:933: in retrieve self._output.extend(job.get(timeout=self.timeout)) ../../../../opt/anaconda3/lib/python3.8/multiprocessing/pool.py:771: in get raise self._value ../../../../opt/anaconda3/lib/python3.8/multiprocessing/pool.py:537: in _handle_tasks put(task)

obj = (140, 0, <joblib._parallel_backends.SafeFunction object at 0x7fa34debefd0>, (), {})

def send(obj):
    buffer = BytesIO()

  CustomizablePickler(buffer, self._reducers).dump(obj)
E _pickle.PicklingError: Can't pickle <cyfunction UpliftRandomForestClassifier.bootstrap at 0x7fa378fb05f0>: attribute lookup bootstrap on causalml.inference.tree.uplift failed

../../../../opt/anaconda3/lib/python3.8/site-packages/joblib/pool.py:156: PicklingError _ test_UpliftRandomForestClassifier[processes-loky] __ joblib.externals.loky.process_executor._RemoteTraceback: """ Traceback (most recent call last): File "/Users/zhenyuzhao-zz/opt/anaconda3/lib/python3.8/site-packages/joblib/externals/loky/backend/queues.py", line 153, in feed obj = dumps(obj, reducers=reducers) File "/Users/zhenyuzhao-zz/opt/anaconda3/lib/python3.8/site-packages/joblib/externals/loky/backend/reduction.py", line 271, in dumps dump(obj, buf, reducers=reducers, protocol=protocol) File "/Users/zhenyuzhao-zz/opt/anaconda3/lib/python3.8/site-packages/joblib/externals/loky/backend/reduction.py", line 264, in dump _LokyPickler(file, reducers=reducers, protocol=protocol).dump(obj) File "/Users/zhenyuzhao-zz/opt/anaconda3/lib/python3.8/site-packages/joblib/externals/cloudpickle/cloudpickle_fast.py", line 563, in dump return Pickler.dump(self, obj) _pickle.PicklingError: Can't pickle <cyfunction UpliftRandomForestClassifier.bootstrap at 0x7fa378fb05f0>: attribute lookup bootstrap on causalml.inference.tree.uplift failed """

The above exception was the direct cause of the following exception:

generate_classification_data = <function generate_classification_data.._generate_data at 0x7fa34a1ba430>, backend = 'loky', joblib_prefer = 'processes'

@pytest.mark.parametrize("backend", ["loky", "threading", "multiprocessing"])
@pytest.mark.parametrize("joblib_prefer", ["threads", "processes"])
def test_UpliftRandomForestClassifier(
    generate_classification_data, backend, joblib_prefer
):
    df, x_names = generate_classification_data()
    df_train, df_test = train_test_split(df, test_size=0.2, random_state=RANDOM_SEED)

    with parallel_backend(backend):
        # Train the UpLift Random Forest classifier
        uplift_model = UpliftRandomForestClassifier(
            min_samples_leaf=50,
            control_name=TREATMENT_NAMES[0],
            random_state=RANDOM_SEED,
            joblib_prefer=joblib_prefer,
        )

      uplift_model.fit(
df_train[x_names].values, treatment=df_train["treatment_group_key"].values, y=df_train[CONVERSION].values, )

tests/test_uplift_trees.py:37:

causalml/inference/tree/uplift.pyx:1331: in causalml.inference.tree.uplift.UpliftRandomForestClassifier.fit (delayed(self.bootstrap)(X, treatment, y, tree) for tree in self.uplift_forest) ../../../../opt/anaconda3/lib/python3.8/site-packages/joblib/parallel.py:1054: in call self.retrieve() ../../../../opt/anaconda3/lib/python3.8/site-packages/joblib/parallel.py:933: in retrieve self._output.extend(job.get(timeout=self.timeout)) ../../../../opt/anaconda3/lib/python3.8/site-packages/joblib/_parallel_backends.py:542: in wrap_future_result return future.result(timeout=timeout) ../../../../opt/anaconda3/lib/python3.8/concurrent/futures/_base.py:432: in result return self.__get_result()

self = <Future at 0x7fa34a0ed3a0 state=finished raised PicklingError>

def __get_result(self):
    if self._exception:

      raise self._exception
E _pickle.PicklingError: Could not pickle the task to send it to the workers.

../../../../opt/anaconda3/lib/python3.8/concurrent/futures/_base.py:388: PicklingError ___ test_UpliftRandomForestClassifier[processes-threading] ___ joblib.externals.loky.process_executor._RemoteTraceback: """ Traceback (most recent call last): File "/Users/zhenyuzhao-zz/opt/anaconda3/lib/python3.8/site-packages/joblib/externals/loky/backend/queues.py", line 153, in feed obj = dumps(obj, reducers=reducers) File "/Users/zhenyuzhao-zz/opt/anaconda3/lib/python3.8/site-packages/joblib/externals/loky/backend/reduction.py", line 271, in dumps dump(obj, buf, reducers=reducers, protocol=protocol) File "/Users/zhenyuzhao-zz/opt/anaconda3/lib/python3.8/site-packages/joblib/externals/loky/backend/reduction.py", line 264, in dump _LokyPickler(file, reducers=reducers, protocol=protocol).dump(obj) File "/Users/zhenyuzhao-zz/opt/anaconda3/lib/python3.8/site-packages/joblib/externals/cloudpickle/cloudpickle_fast.py", line 563, in dump return Pickler.dump(self, obj) _pickle.PicklingError: Can't pickle <cyfunction UpliftTreeClassifier.evaluate_KL at 0x7fa378fa0ba0>: attribute lookup evaluate_KL on causalml.inference.tree.uplift failed """

The above exception was the direct cause of the following exception:

generate_classification_data = <function generate_classification_data.._generate_data at 0x7fa34a1ba430>, backend = 'threading', joblib_prefer = 'processes'

@pytest.mark.parametrize("backend", ["loky", "threading", "multiprocessing"])
@pytest.mark.parametrize("joblib_prefer", ["threads", "processes"])
def test_UpliftRandomForestClassifier(
    generate_classification_data, backend, joblib_prefer
):
    df, x_names = generate_classification_data()
    df_train, df_test = train_test_split(df, test_size=0.2, random_state=RANDOM_SEED)

    with parallel_backend(backend):
        # Train the UpLift Random Forest classifier
        uplift_model = UpliftRandomForestClassifier(
            min_samples_leaf=50,
            control_name=TREATMENT_NAMES[0],
            random_state=RANDOM_SEED,
            joblib_prefer=joblib_prefer,
        )

        uplift_model.fit(
            df_train[x_names].values,
            treatment=df_train["treatment_group_key"].values,
            y=df_train[CONVERSION].values,
        )

        predictions = {}
        predictions["single"] = uplift_model.predict(df_test[x_names].values)
        with parallel_backend("loky", n_jobs=2):

          predictions["loky_2"] = uplift_model.predict(df_test[x_names].values)

tests/test_uplift_trees.py:46:

../../../../opt/anaconda3/lib/python3.8/site-packages/sklearn/utils/_testing.py:308: in wrapper return fn(*args, **kwargs) causalml/inference/tree/uplift.pyx:1379: in causalml.inference.tree.uplift.UpliftRandomForestClassifier.predict (delayed(tree.predict)(X=X) for tree in self.uplift_forest) ../../../../opt/anaconda3/lib/python3.8/site-packages/joblib/parallel.py:1054: in call self.retrieve() ../../../../opt/anaconda3/lib/python3.8/site-packages/joblib/parallel.py:933: in retrieve self._output.extend(job.get(timeout=self.timeout)) ../../../../opt/anaconda3/lib/python3.8/site-packages/joblib/_parallel_backends.py:542: in wrap_future_result return future.result(timeout=timeout) ../../../../opt/anaconda3/lib/python3.8/concurrent/futures/_base.py:439: in result return self.__get_result()

self = <Future at 0x7fa34a091b50 state=finished raised PicklingError>

def __get_result(self):
    if self._exception:

      raise self._exception
E _pickle.PicklingError: Could not pickle the task to send it to the workers.

../../../../opt/anaconda3/lib/python3.8/concurrent/futures/_base.py:388: PicklingError ____ test_UpliftRandomForestClassifier[processes-multiprocessing] ____

generate_classification_data = <function generate_classification_data.._generate_data at 0x7fa34a1ba430>, backend = 'multiprocessing', joblib_prefer = 'processes'

@pytest.mark.parametrize("backend", ["loky", "threading", "multiprocessing"])
@pytest.mark.parametrize("joblib_prefer", ["threads", "processes"])
def test_UpliftRandomForestClassifier(
    generate_classification_data, backend, joblib_prefer
):
    df, x_names = generate_classification_data()
    df_train, df_test = train_test_split(df, test_size=0.2, random_state=RANDOM_SEED)

    with parallel_backend(backend):
        # Train the UpLift Random Forest classifier
        uplift_model = UpliftRandomForestClassifier(
            min_samples_leaf=50,
            control_name=TREATMENT_NAMES[0],
            random_state=RANDOM_SEED,
            joblib_prefer=joblib_prefer,
        )

      uplift_model.fit(
df_train[x_names].values, treatment=df_train["treatment_group_key"].values, y=df_train[CONVERSION].values, )

tests/test_uplift_trees.py:37:

causalml/inference/tree/uplift.pyx:1331: in causalml.inference.tree.uplift.UpliftRandomForestClassifier.fit (delayed(self.bootstrap)(X, treatment, y, tree) for tree in self.uplift_forest) ../../../../opt/anaconda3/lib/python3.8/site-packages/joblib/parallel.py:1054: in call self.retrieve() ../../../../opt/anaconda3/lib/python3.8/site-packages/joblib/parallel.py:933: in retrieve self._output.extend(job.get(timeout=self.timeout)) ../../../../opt/anaconda3/lib/python3.8/multiprocessing/pool.py:771: in get raise self._value ../../../../opt/anaconda3/lib/python3.8/multiprocessing/pool.py:537: in _handle_tasks put(task)

obj = (170, 0, <joblib._parallel_backends.SafeFunction object at 0x7fa34f7ad7f0>, (), {})

def send(obj):
    buffer = BytesIO()