Tests custom objective and metric functions.
( # pylint: disable=too-many-locals
tree_method: str,
device: Device,
dtrain: DMatrix,
dtest: DMatrix,
)
| 12 | |
| 13 | |
| 14 | def run_custom_objective( # pylint: disable=too-many-locals |
| 15 | tree_method: str, |
| 16 | device: Device, |
| 17 | dtrain: DMatrix, |
| 18 | dtest: DMatrix, |
| 19 | ) -> None: |
| 20 | """Tests custom objective and metric functions.""" |
| 21 | param = { |
| 22 | "max_depth": 2, |
| 23 | "eta": 1, |
| 24 | "objective": "reg:logistic", |
| 25 | "tree_method": tree_method, |
| 26 | "device": device, |
| 27 | } |
| 28 | watchlist = [(dtest, "eval"), (dtrain, "train")] |
| 29 | num_round = 10 |
| 30 | |
| 31 | def evalerror(preds: np.ndarray, dtrain: DMatrix) -> Tuple[str, np.float64]: |
| 32 | return tm.eval_error_metric(preds, dtrain, rev_link=True) |
| 33 | |
| 34 | # test custom_objective in training |
| 35 | bst = train( |
| 36 | param, |
| 37 | dtrain, |
| 38 | num_round, |
| 39 | evals=watchlist, |
| 40 | obj=tm.logregobj, |
| 41 | custom_metric=evalerror, |
| 42 | ) |
| 43 | assert isinstance(bst, Booster) |
| 44 | preds = bst.predict(dtest) |
| 45 | labels = dtest.get_label() |
| 46 | err = sum(1 for i in range(len(preds)) if int(preds[i] > 0.5) != labels[i]) / float( |
| 47 | len(preds) |
| 48 | ) |
| 49 | assert err < 0.1 |
| 50 | |
| 51 | # test custom_objective in cross-validation |
| 52 | cv( |
| 53 | param, |
| 54 | dtrain, |
| 55 | num_round, |
| 56 | nfold=5, |
| 57 | seed=0, |
| 58 | obj=tm.logregobj, |
| 59 | custom_metric=evalerror, |
| 60 | ) |
| 61 | |
| 62 | # test maximize parameter |
| 63 | def neg_evalerror(preds: np.ndarray, dtrain: DMatrix) -> Tuple[str, float]: |
| 64 | labels = dtrain.get_label() |
| 65 | preds = 1.0 / (1.0 + np.exp(-preds)) |
| 66 | return "error", float(sum(labels == (preds > 0.0))) / len(labels) |
| 67 | |
| 68 | bst2 = train( |
| 69 | param, |
| 70 | dtrain, |
| 71 | num_round, |