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Use average weights in DoubleEnsemble. (#1205)

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* Use average weights in DoubleEnsemble.

* Use average weights in DoubleEnsemble.

Co-authored-by: lwwang1995 <lewenwang@msrawsa02.corp.microsoft.com>
This commit is contained in:
Lewen Wang
2022-07-17 23:02:46 +08:00
committed by GitHub
parent 6fddae9965
commit d149c2b177
4 changed files with 15 additions and 19 deletions
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11
qlib/contrib/model/double_ensemble.py
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@@ -44,7 +44,7 @@ class DEnsembleModel(Model, FeatureInt):
if sample_ratios is None: # the default values for sample_ratios
sample_ratios = [0.8, 0.7, 0.6, 0.5, 0.4]
if sub_weights is None: # the default values for sub_weights
sub_weights = [1.0, 0.2, 0.2, 0.2, 0.2, 0.2]
sub_weights = [1] * self.num_models
if not len(sample_ratios) == bins_fs:
raise ValueError("The length of sample_ratios should be equal to bins_fs.")
self.sample_ratios = sample_ratios
@@ -87,7 +87,9 @@ class DEnsembleModel(Model, FeatureInt):
loss_curve = self.retrieve_loss_curve(model_k, df_train, features)
pred_k = self.predict_sub(model_k, df_train, features)
pred_sub.iloc[:, k] = pred_k
pred_ensemble = pred_sub.iloc[:, : k + 1].mean(axis=1)
pred_ensemble = (pred_sub.iloc[:, : k + 1] * self.sub_weights[0 : k + 1]).sum(axis=1) / np.sum(
self.sub_weights[0 : k + 1]
)
loss_values = pd.Series(self.get_loss(y_train.values.squeeze(), pred_ensemble.values))
if self.enable_sr:
@@ -159,8 +161,8 @@ class DEnsembleModel(Model, FeatureInt):
h["bins"] = pd.cut(h["h_value"], self.bins_sr)
h_avg = h.groupby("bins")["h_value"].mean()
weights = pd.Series(np.zeros(N, dtype=float))
for i_b, b in enumerate(h_avg.index):
weights[h["bins"] == b] = 1.0 / (self.decay**k_th * h_avg[i_b] + 0.1)
for b in h_avg.index:
weights[h["bins"] == b] = 1.0 / (self.decay**k_th * h_avg[b] + 0.1)
return weights
def feature_selection(self, df_train, loss_values):
@@ -246,6 +248,7 @@ class DEnsembleModel(Model, FeatureInt):
pd.Series(submodel.predict(x_test.loc[:, feat_sub].values), index=x_test.index)
* self.sub_weights[i_sub]
)
pred = pred / np.sum(self.sub_weights)
return pred
def predict_sub(self, submodel, df_data, features):