def fit(self, X, y):
self.model = XGBModel(n_estimators=self.n_best, **self.params)
self.model.fit(X=X[self.features],
y=y,
eval_metric="mae",
verbose=False)
return self
def objective(hyperparams):
model = XGBModel(n_estimators=self.n_est, **self.params, **hyperparams)
model.fit(X=X_trn, y=y_trn,
eval_set=[(X_val, y_val)],
eval_metric=self.metric,
early_stopping_rounds=self.n_stop,
verbose=False)
score = model.evals_result()['validation_0'][self.metric][model.best_iteration] * self.loss_sign
return {'loss': score, 'status': STATUS_OK, 'model': model}
def objective(hyperparams):
model = XGBModel(n_estimators=self.n_est,
**self.params,
**hyperparams)
model.fit(
X=X_trn,
y=y_trn,
eval_set=[(X_val, y_val)],
eval_metric=self.metric,
early_stopping_rounds=self.n_stop,
verbose=False,
)
score = (model.evals_result()["validation_0"][self.metric][
model.best_iteration] * self.loss_sign)
return {"loss": score, "status": STATUS_OK, "model": model}
def _train_test(
x: pd.DataFrame,
y: pd.Series,
model: xgb.XGBModel,
scorer: Callable,
test_samples: Optional[int] = None,
test_ratio: Optional[float] = None,
time_series: bool = False,
random_state: Optional[int] = None,
):
datasets = split(x, y, test_samples, test_ratio, time_series,
random_state)
x_train, x_test, y_train, y_test = datasets
model.fit(x_train, y_train)
return scorer(model, x_test, y_test)
def create_name_for_xgbmodel(run_cfg: Dict[str, Any],
outer_split_num: int,
model: XGBModel,
inner_split_num: int,
prefix_location='logs/',
suffix='.pkl') -> str:
if run_cfg['analysis_type'] == AnalysisType.FLATTEN_CORRS:
model_str_representation = run_cfg['analysis_type'].value
for key in [
'colsample_bylevel', 'colsample_bynode', 'colsample_bytree',
'gamma', 'learning_rate', 'max_depth', 'min_child_weight',
'n_estimators', 'subsample'
]:
model_str_representation += key[-3:] + '_' + str(
model.get_params()[key])
return prefix_location + '_'.join([
run_cfg['target_var'], run_cfg['dataset_type'].value,
str(outer_split_num),
str(inner_split_num), model_str_representation,
str(run_cfg['num_nodes']), run_cfg['param_conn_type'].value
]) + suffix
class AutoXGB(BaseAutoML):
params = {"random_state": RANDOM_SEED, "n_jobs": -1}
default_xgb_space = {
"learning_rate": hp.loguniform("learning_rate", np.log(0.01),
np.log(0.3)),
"max_depth": hp.choice("num_leaves", [6, 8, 10]),
"colsample_bytree": hp.quniform("colsample_bytree", 0.5, 0.9, 0.1),
"subsample": hp.quniform("subsample", 0.5, 0.9, 0.1),
"min_child_weight": hp.choice("min_child_weight", [10, 25, 100]),
}
def __init__(
self,
objective="reg:squarederror",
metric="rmse",
boosting="gbtree",
params=params,
space=default_xgb_space,
n_est=500,
n_stop=10,
sample_size=SAMPLE_SIZE,
feature_selection=True,
n_fs=10,
fs_th=1e-5,
fs_pct=0.1,
hyperparam_opt=True,
n_hpopt=100,
n_random_col=10,
random_state=RANDOM_SEED,
shuffle=True,
):
self.metric, minimize = self._get_metric_alias_minimize(metric)
self.params.update(params)
self.params.update({"objective": objective, "booster": boosting})
super(AutoXGB, self).__init__(
params=self.params,
space=space,
n_est=n_est,
n_stop=n_stop,
sample_size=sample_size,
feature_selection=feature_selection,
n_fs=n_fs,
fs_th=fs_th,
fs_pct=fs_pct,
hyperparam_opt=hyperparam_opt,
n_hpopt=n_hpopt,
minimize=minimize,
n_random_col=n_random_col,
random_state=random_state,
shuffle=shuffle,
)
@staticmethod
def _get_metric_alias_minimize(metric):
"""Get XGBoost metric alias.
As defined at https://xgboost.readthedocs.io/en/latest/parameter.html#learning-task-parameters
Args:
metric (str): a metric name
Returns:
(tuple):
- (str): the standard metric name for LightGBM
- (bool): a flag whether to minimize or maximize the metric
"""
assert metric in [
"rmse",
"rmsle",
"mae",
"logloss",
"error",
"merror",
"mlogloss",
"auc",
"aucpr",
"ndcg",
"map",
"poisson-nloglik",
"gamma-nloglik",
"cox-nloglik",
"gamma-deviance",
"tweedie-nloglik",
], "Invalid metric: {}".format(metric)
if metric in ["auc", "aucpr", "ndcg", "map"]:
minimize = False
else:
minimize = True
return metric, minimize
@staticmethod
def get_feature_importance(model):
return model.feature_importances_
def feature_importance(self):
return self.model.feature_importances_
def optimize_hyperparam(self, X, y, test_size=0.2, n_eval=100):
X_trn, X_val, y_trn, y_val = train_test_split(X,
y,
test_size=test_size,
shuffle=self.shuffle)
def objective(hyperparams):
model = XGBModel(n_estimators=self.n_est,
**self.params,
**hyperparams)
model.fit(
X=X_trn,
y=y_trn,
eval_set=[(X_val, y_val)],
eval_metric=self.metric,
early_stopping_rounds=self.n_stop,
verbose=False,
)
score = (model.evals_result()["validation_0"][self.metric][
model.best_iteration] * self.loss_sign)
return {"loss": score, "status": STATUS_OK, "model": model}
trials = Trials()
best = hyperopt.fmin(
fn=objective,
space=self.space,
trials=trials,
algo=tpe.suggest,
max_evals=n_eval,
verbose=1,
)
hyperparams = space_eval(self.space, best)
return hyperparams, trials
def fit(self, X, y):
self.model = XGBModel(n_estimators=self.n_best, **self.params)
self.model.fit(X=X[self.features],
y=y,
eval_metric="mae",
verbose=False)
return self
def predict(self, X):
return self.model.predict(X[self.features])
class AutoXGB(BaseAutoML):
params = {'random_state': RANDOM_SEED, 'n_jobs': -1}
space = {
"learning_rate": hp.loguniform("learning_rate", np.log(0.01),
np.log(0.3)),
"max_depth": hp.choice("num_leaves", [6, 8, 10]),
"colsample_bytree": hp.quniform("colsample_bytree", .5, .9, 0.1),
"subsample": hp.quniform("subsample", .5, .9, 0.1),
"min_child_weight": hp.choice('min_child_weight', [10, 25, 100]),
}
def __init__(self,
objective='reg:linear',
metric='rmse',
boosting='gbtree',
params=params,
space=space,
n_est=500,
n_stop=10,
sample_size=SAMPLE_SIZE,
feature_selection=True,
n_fs=10,
fs_th=1e-5,
hyperparam_opt=True,
n_hpopt=100,
n_random_col=10,
random_state=RANDOM_SEED,
shuffle=True):
self.metric, minimize = self._get_metric_alias_minimize(metric)
self.params.update(params)
self.params.update({'objective': objective, 'booster': boosting})
super(AutoXGB, self).__init__(params=self.params,
space=space,
n_est=n_est,
n_stop=n_stop,
sample_size=sample_size,
feature_selection=feature_selection,
n_fs=n_fs,
fs_th=1e-5,
hyperparam_opt=hyperparam_opt,
n_hpopt=n_hpopt,
minimize=minimize,
n_random_col=n_random_col,
random_state=random_state,
shuffle=shuffle)
@staticmethod
def _get_metric_alias_minimize(metric):
"""Get XGBoost metric alias.
As defined at https://xgboost.readthedocs.io/en/latest/parameter.html#learning-task-parameters
Args:
metric (str): a metric name
Returns:
(tuple):
- (str): the standard metric name for LightGBM
- (bool): a flag whether to minimize or maximize the metric
"""
assert metric in [
'rmse', 'rmsle', 'mae', 'logloss', 'error', 'merror', 'mlogloss',
'auc', 'aucpr', 'ndcg', 'map', 'poisson-nloglik', 'gamma-nloglik',
'cox-nloglik', 'gamma-deviance', 'tweedie-nloglik'
], 'Invalid metric: {}'.format(metric)
if metric in ['auc', 'aucpr', 'ndcg', 'map']:
minimize = False
else:
minimize = True
return metric, minimize
@staticmethod
def get_feature_importance(model):
return model.feature_importances_
def feature_importance(self):
return self.model.feature_importances_
def optimize_hyperparam(self, X, y, test_size=.2, n_eval=100):
X_trn, X_val, y_trn, y_val = train_test_split(X,
y,
test_size=test_size,
shuffle=self.shuffle)
def objective(hyperparams):
model = XGBModel(n_estimators=self.n_est,
**self.params,
**hyperparams)
model.fit(X=X_trn,
y=y_trn,
eval_set=[(X_val, y_val)],
eval_metric=self.metric,
early_stopping_rounds=self.n_stop,
verbose=False)
score = model.evals_result()['validation_0'][self.metric][
model.best_iteration] * self.loss_sign
return {'loss': score, 'status': STATUS_OK, 'model': model}
trials = Trials()
best = hyperopt.fmin(fn=objective,
space=self.space,
trials=trials,
algo=tpe.suggest,
max_evals=n_eval,
verbose=1,
rstate=self.random_state)
hyperparams = space_eval(self.space, best)
return hyperparams, trials
def fit(self, X, y):
self.model = XGBModel(n_estimators=self.n_best, **self.params)
self.model.fit(X=X[self.features],
y=y,
eval_metric='mae',
verbose=False)
return self
def predict(self, X):
return self.model.predict(X[self.features])
def build_model(self):
model = XGBModel(**self.model_params)
return model
def fit_xgb_model(out_fold_num: int, in_fold_num: int, run_cfg: Dict[str, Any],
model: XGBModel, X_train_in: FlattenCorrsDataset,
X_val_in: FlattenCorrsDataset) -> Dict:
model_saving_path = create_name_for_xgbmodel(model=model,
outer_split_num=out_fold_num,
inner_split_num=in_fold_num,
run_cfg=run_cfg)
train_arr = np.array([data.x.numpy() for data in X_train_in])
val_arr = np.array([data.x.numpy() for data in X_val_in])
if run_cfg['target_var'] == 'gender':
y_train = [int(data.sex.item()) for data in X_train_in]
y_val = [int(data.sex.item()) for data in X_val_in]
elif run_cfg['target_var'] == 'age':
# np.array() because of printing calls in the regressor_metrics function
y_train = np.array([float(data.age.item()) for data in X_train_in])
y_val = np.array([float(data.age.item()) for data in X_val_in])
model.fit(train_arr, y_train, callbacks=[wandb.xgboost.wandb_callback()])
pickle.dump(model, open(model_saving_path, "wb"))
if run_cfg['target_var'] == 'gender':
train_metrics = return_classifier_metrics(
y_train,
pred_prob=model.predict_proba(train_arr)[:, 1],
pred_binary=model.predict(train_arr),
flatten_approach=True)
val_metrics = return_classifier_metrics(
y_val,
pred_prob=model.predict_proba(val_arr)[:, 1],
pred_binary=model.predict(val_arr),
flatten_approach=True)
print(
'{:1d}-{:1d}: Auc: {:.4f} / {:.4f}, Acc: {:.4f} / {:.4f}, F1: {:.4f} /'
' {:.4f} '.format(out_fold_num, in_fold_num, train_metrics['auc'],
val_metrics['auc'], train_metrics['acc'],
val_metrics['acc'], train_metrics['f1'],
val_metrics['f1']))
wandb.log({
f'train_auc{in_fold_num}': train_metrics['auc'],
f'val_auc{in_fold_num}': val_metrics['auc'],
f'train_acc{in_fold_num}': train_metrics['acc'],
f'val_acc{in_fold_num}': val_metrics['acc'],
f'train_sens{in_fold_num}': train_metrics['sensitivity'],
f'val_sens{in_fold_num}': val_metrics['sensitivity'],
f'train_spec{in_fold_num}': train_metrics['specificity'],
f'val_spec{in_fold_num}': val_metrics['specificity'],
f'train_f1{in_fold_num}': train_metrics['f1'],
f'val_f1{in_fold_num}': val_metrics['f1']
})
else:
train_metrics = return_regressor_metrics(
y_train, pred_prob=model.predict(train_arr))
val_metrics = return_regressor_metrics(
y_val, pred_prob=model.predict(val_arr))
print('{:1d}-{:1d}: R2: {:.4f} / {:.4f}, R: {:.4f} / {:.4f}'.format(
out_fold_num, in_fold_num, train_metrics['r2'], val_metrics['r2'],
train_metrics['r'], val_metrics['r']))
wandb.log({
f'train_r2{in_fold_num}': train_metrics['r2'],
f'val_r2{in_fold_num}': val_metrics['r2'],
f'train_r{in_fold_num}': train_metrics['r'],
f'val_r{in_fold_num}': val_metrics['r']
})
return val_metrics
def to_mls(xgboost_model: xgboost.XGBModel, **kwargs):
params = xgboost_model.get_params()
def standardize_types(v):
if isinstance(v, np.ndarray):
return [normalize_float(x) for x in v.tolist()]
elif isinstance(v, float):
return normalize_float(v)
elif callable(v):
return str(v) # TODO
return v
def deep_get_params(params):
if isinstance(params, (list, tuple)):
return [deep_get_params(x) for x in params]
elif isinstance(params, dict):
return {k: deep_get_params(v) for k, v in params.items()}
else:
v = standardize_types(params)
try:
p = v.get_params()
t = type(v).__module__ + "." + type(v).__name__
return {"@value": {"type": t, "params": deep_get_params(p)}}
except AttributeError:
try:
json.dumps(v)
return v
except TypeError as e:
raise NotImplementedError(
"can't convert sklearn model of type {} to mls: {}".format(
type(xgboost_model), e
)
)
params = deep_get_params(params)
model_hash = xgboost_model.__hash__()
model_class = "{}.{}".format(
type(xgboost_model).__module__, type(xgboost_model).__name__
)
algo = Algorithm(_id=model_class)
implementation = Implementation(
_id=generate_unique_id("http://www.w3.org/ns/mls#Implementation"),
parameters=[
HyperParameter(key, model_hash=model_hash) for key in params.keys()
],
implements=algo,
version=xgboost.__version__,
)
input_values = [
HyperParameterSetting(
value=val,
specified_by=HyperParameter(key, model_hash=model_hash),
model_hash=model_hash,
)
for key, val in params.items()
if val is not None
]
output_values = []
if EVALUATION_MEASURE_KEY in kwargs:
eval_measure = kwargs[EVALUATION_MEASURE_KEY]
output_values.append(evaluation_measure(eval_measure[0], eval_measure[1]))
model = Run(model_hash, implementation, input_values, output_values, algo)
return RunSchema().dumps(model)