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Update parameter names: 'k' and 'base'

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This commit is contained in:
meng-ustc
2021-03-02 16:14:56 +09:00
parent ee4692a355
commit 1de4def444
3 changed files with 19 additions and 19 deletions
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4
examples/benchmarks/DoubleEnsemble/workflow_config_doubleensemble_Alpha158.yaml
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@@ -30,9 +30,9 @@ task:
class: DEnsembleModel class: DEnsembleModel
module_path: qlib.contrib.model.double_ensemble module_path: qlib.contrib.model.double_ensemble
kwargs: kwargs:
base: "gbm" base_model: "gbm"
loss: mse loss: mse
k: 6 num_models: 6
enable_sr: True enable_sr: True
enable_fs: True enable_fs: True
alpha1: 1 alpha1: 1

4
examples/benchmarks/DoubleEnsemble/workflow_config_doubleensemble_Alpha360.yaml
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@@ -37,9 +37,9 @@ task:
class: DEnsembleModel class: DEnsembleModel
module_path: qlib.contrib.model.double_ensemble module_path: qlib.contrib.model.double_ensemble
kwargs: kwargs:
base: "gbm" base_model: "gbm"
loss: mse loss: mse
k: 6 num_models: 6
enable_sr: True enable_sr: True
enable_fs: True enable_fs: True
alpha1: 1 alpha1: 1

30
qlib/contrib/model/double_ensemble.py
View File

@@ -16,9 +16,9 @@ class DEnsembleModel(Model):
def __init__( def __init__(
self, self,
base="gbm", base_model="gbm",
loss="mse", loss="mse",
k=6, num_models=6,
enable_sr=True, enable_sr=True,
enable_fs=True, enable_fs=True,
alpha1=1.0, alpha1=1.0,
@@ -31,8 +31,8 @@ class DEnsembleModel(Model):
epochs=100, epochs=100,
**kwargs **kwargs
): ):
self.base = base # "gbm" or "mlp", specifically, we use lgbm for "gbm" self.base_model = base_model # "gbm" or "mlp", specifically, we use lgbm for "gbm"
self.k = k self.num_models = num_models # the number of sub-models
self.enable_sr = enable_sr self.enable_sr = enable_sr
self.enable_fs = enable_fs self.enable_fs = enable_fs
self.alpha1 = alpha1 self.alpha1 = alpha1
@@ -43,8 +43,8 @@ class DEnsembleModel(Model):
if not len(sample_ratios) == bins_fs: if not len(sample_ratios) == bins_fs:
raise ValueError("The length of sample_ratios should be equal to bins_fs.") raise ValueError("The length of sample_ratios should be equal to bins_fs.")
self.sample_ratios = sample_ratios self.sample_ratios = sample_ratios
if not len(sub_weights) == k: if not len(sub_weights) == num_models:
raise ValueError("The length of sub_weights should be equal to k.") raise ValueError("The length of sub_weights should be equal to num_models.")
self.sub_weights = sub_weights self.sub_weights = sub_weights
self.epochs = epochs self.epochs = epochs
self.logger = get_module_logger("DEnsembleModel") self.logger = get_module_logger("DEnsembleModel")
@@ -65,27 +65,27 @@ class DEnsembleModel(Model):
weights = pd.Series(np.ones(N, dtype=float)) weights = pd.Series(np.ones(N, dtype=float))
# initialize the features # initialize the features
features = x_train.columns features = x_train.columns
pred_sub = pd.DataFrame(np.zeros((N, self.k), dtype=float), index=x_train.index) pred_sub = pd.DataFrame(np.zeros((N, self.num_models), dtype=float), index=x_train.index)
# train k sub-models # train sub-models
for i_k in range(self.k): for k in range(self.num_models):
self.sub_features.append(features) self.sub_features.append(features)
self.logger.info("Training sub-model: ({}/{})".format(i_k + 1, self.k)) self.logger.info("Training sub-model: ({}/{})".format(k + 1, self.num_models))
model_k = self.train_submodel(df_train, df_valid, weights, features) model_k = self.train_submodel(df_train, df_valid, weights, features)
self.ensemble.append(model_k) self.ensemble.append(model_k)
# no further sample re-weight and feature selection needed for the last sub-model # no further sample re-weight and feature selection needed for the last sub-model
if i_k + 1 == self.k: if k + 1 == self.num_models:
break break
self.logger.info("Retrieving loss curve and loss values...") self.logger.info("Retrieving loss curve and loss values...")
loss_curve = self.retrieve_loss_curve(model_k, df_train, features) loss_curve = self.retrieve_loss_curve(model_k, df_train, features)
pred_k = self.predict_sub(model_k, df_train, features) pred_k = self.predict_sub(model_k, df_train, features)
pred_sub.iloc[:, i_k] = pred_k pred_sub.iloc[:, k] = pred_k
pred_ensemble = pred_sub.iloc[:, : i_k + 1].mean(axis=1) pred_ensemble = pred_sub.iloc[:, : k + 1].mean(axis=1)
loss_values = pd.Series(self.get_loss(y_train.values.squeeze(), pred_ensemble.values)) loss_values = pd.Series(self.get_loss(y_train.values.squeeze(), pred_ensemble.values))
if self.enable_sr: if self.enable_sr:
self.logger.info("Sample re-weighting...") self.logger.info("Sample re-weighting...")
weights = self.sample_reweight(loss_curve, loss_values, i_k + 1) weights = self.sample_reweight(loss_curve, loss_values, k + 1)
if self.enable_fs: if self.enable_fs:
self.logger.info("Feature selection...") self.logger.info("Feature selection...")
@@ -209,7 +209,7 @@ class DEnsembleModel(Model):
raise ValueError("not implemented yet") raise ValueError("not implemented yet")
def retrieve_loss_curve(self, model, df_train, features): def retrieve_loss_curve(self, model, df_train, features):
if self.base == "gbm": if self.base_model == "gbm":
num_trees = model.num_trees() num_trees = model.num_trees()
x_train, y_train = df_train["feature"].loc[:, features], df_train["label"] x_train, y_train = df_train["feature"].loc[:, features], df_train["label"]
# Lightgbm need 1D array as its label # Lightgbm need 1D array as its label