(name: str, path: str)
| 74 | |
| 75 | |
| 76 | def run_scikit_model_check(name: str, path: str) -> None: |
| 77 | if name.find("reg") != -1: |
| 78 | reg = xgboost.XGBRegressor() |
| 79 | reg.load_model(path) |
| 80 | config = json.loads(reg.get_booster().save_config()) |
| 81 | assert ( |
| 82 | config["learner"]["learner_train_param"]["objective"] == "reg:squarederror" |
| 83 | ) |
| 84 | assert len(reg.get_booster().get_dump()) == get_n_rounds(name) * gm.kForests |
| 85 | run_model_param_check(name, config) |
| 86 | elif name.find("cls") != -1: |
| 87 | cls = xgboost.XGBClassifier() |
| 88 | cls.load_model(path) |
| 89 | n_rounds = get_n_rounds(name) |
| 90 | assert ( |
| 91 | len(cls.get_booster().get_dump()) == n_rounds * gm.kForests * gm.kClasses |
| 92 | ), path |
| 93 | config = json.loads(cls.get_booster().save_config()) |
| 94 | assert ( |
| 95 | config["learner"]["learner_train_param"]["objective"] == "multi:softprob" |
| 96 | ), path |
| 97 | run_model_param_check(name, config) |
| 98 | elif name.find("ltr") != -1: |
| 99 | ltr = xgboost.XGBRanker() |
| 100 | ltr.load_model(path) |
| 101 | assert len(ltr.get_booster().get_dump()) == get_n_rounds(name) * gm.kForests |
| 102 | config = json.loads(ltr.get_booster().save_config()) |
| 103 | assert config["learner"]["learner_train_param"]["objective"] == "rank:ndcg" |
| 104 | run_model_param_check(name, config) |
| 105 | elif name.find("logitraw") != -1: |
| 106 | logit = xgboost.XGBClassifier() |
| 107 | logit.load_model(path) |
| 108 | assert len(logit.get_booster().get_dump()) == get_n_rounds(name) * gm.kForests |
| 109 | config = json.loads(logit.get_booster().save_config()) |
| 110 | assert ( |
| 111 | config["learner"]["learner_train_param"]["objective"] == "binary:logitraw" |
| 112 | ) |
| 113 | run_model_param_check(name, config) |
| 114 | elif name.find("logit") != -1: |
| 115 | logit = xgboost.XGBClassifier() |
| 116 | logit.load_model(path) |
| 117 | assert len(logit.get_booster().get_dump()) == get_n_rounds(name) * gm.kForests |
| 118 | config = json.loads(logit.get_booster().save_config()) |
| 119 | assert ( |
| 120 | config["learner"]["learner_train_param"]["objective"] == "binary:logistic" |
| 121 | ) |
| 122 | run_model_param_check(name, config) |
| 123 | else: |
| 124 | assert False |
| 125 | |
| 126 | |
| 127 | def download(path: str) -> None: |
no test coverage detected