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Jactus
2020-10-29 13:22:49 +08:00
parent 490dbd908b
commit da9d1c8ac6
20 changed files with 290 additions and 251 deletions
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51
qlib/contrib/model/gbdt.py
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@@ -12,26 +12,29 @@ from ...data.dataset.handler import DataHandlerLP
class LGBModel(Model):
"""LightGBM Model"""
def __init__(self, loss="mse", **kwargs):
if loss not in {"mse", "binary"}:
raise NotImplementedError
self._params = {'objective': loss}
self._params = {"objective": loss}
self._params.update(kwargs)
self.model = None
def fit(self,
dataset: DatasetH,
num_boost_round=1000,
early_stopping_rounds=50,
verbose_eval=20,
evals_result=dict(),
**kwargs):
def fit(
self,
dataset: DatasetH,
num_boost_round=1000,
early_stopping_rounds=50,
verbose_eval=20,
evals_result=dict(),
**kwargs
):
df_train, df_valid = dataset.prepare(['train', 'valid'],
col_set=['feature', 'label'],
data_key=DataHandlerLP.DK_L)
x_train, y_train = df_train['feature'], df_train['label']
x_valid, y_valid = df_valid['feature'], df_valid['label']
df_train, df_valid = dataset.prepare(
["train", "valid"], col_set=["feature", "label"], data_key=DataHandlerLP.DK_L
)
x_train, y_train = df_train["feature"], df_train["label"]
x_valid, y_valid = df_valid["feature"], df_valid["label"]
# Lightgbm need 1D array as its label
if y_train.values.ndim == 2 and y_train.values.shape[1] == 1:
@@ -41,20 +44,22 @@ class LGBModel(Model):
dtrain = lgb.Dataset(x_train.values, label=y_train_1d)
dvalid = lgb.Dataset(x_valid.values, label=y_valid_1d)
self.model = lgb.train(self._params,
dtrain,
num_boost_round=num_boost_round,
valid_sets=[dtrain, dvalid],
valid_names=["train", "valid"],
early_stopping_rounds=early_stopping_rounds,
verbose_eval=verbose_eval,
evals_result=evals_result,
**kwargs)
self.model = lgb.train(
self._params,
dtrain,
num_boost_round=num_boost_round,
valid_sets=[dtrain, dvalid],
valid_names=["train", "valid"],
early_stopping_rounds=early_stopping_rounds,
verbose_eval=verbose_eval,
evals_result=evals_result,
**kwargs
)
evals_result["train"] = list(evals_result["train"].values())[0]
evals_result["valid"] = list(evals_result["valid"].values())[0]
def predict(self, dataset):
if self.model is None:
raise ValueError("model is not fitted yet!")
x_test = dataset.prepare('test', col_set='feature')
x_test = dataset.prepare("test", col_set="feature")
return pd.Series(self.model.predict(np.squeeze(x_test.values)), index=x_test.index)