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Add README and Formatted

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meng-ustc
2021-02-24 16:59:31 +09:00
parent 1a990fdd25
commit ce60097722
3 changed files with 45 additions and 37 deletions
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77
qlib/contrib/model/double_ensemble.py
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@@ -15,21 +15,22 @@ class DEnsembleModel(Model):
"""Double Ensemble Model"""
def __init__(
self,
base="gbm",
loss="mse",
k=6,
enable_sr=True,
enable_fs=True,
alpha1=1.,
alpha2=1.,
bins_sr=10,
bins_fs=5,
decay=None,
sample_ratios=None,
sub_weights=None,
epochs=100,
**kwargs):
self,
base="gbm",
loss="mse",
k=6,
enable_sr=True,
enable_fs=True,
alpha1=1.0,
alpha2=1.0,
bins_sr=10,
bins_fs=5,
decay=None,
sample_ratios=None,
sub_weights=None,
epochs=100,
**kwargs
):
self.base = base # "gbm" or "mlp", specifically, we use lgbm for "gbm"
self.k = k
self.enable_sr = enable_sr
@@ -54,10 +55,7 @@ class DEnsembleModel(Model):
self.params.update(kwargs)
self.loss = loss
def fit(
self,
dataset: DatasetH
):
def fit(self, dataset: DatasetH):
df_train, df_valid = dataset.prepare(
["train", "valid"], col_set=["feature", "label"], data_key=DataHandlerLP.DK_L
)
@@ -71,7 +69,7 @@ class DEnsembleModel(Model):
# train k sub-models
for i_k in range(self.k):
self.sub_features.append(features)
self.logger.info("Training sub-model: ({}/{})".format(i_k+1, self.k))
self.logger.info("Training sub-model: ({}/{})".format(i_k + 1, self.k))
model_k = self.train_submodel(df_train, df_valid, weights, features)
self.ensemble.append(model_k)
# no further sample re-weight and feature selection needed for the last sub-model
@@ -82,12 +80,12 @@ class DEnsembleModel(Model):
loss_curve = self.retrieve_loss_curve(model_k, df_train, features)
pred_k = self.predict_sub(model_k, df_train, features)
pred_sub.iloc[:, i_k] = pred_k
pred_ensemble = pred_sub.iloc[:, :i_k+1].mean(axis=1)
pred_ensemble = pred_sub.iloc[:, : i_k + 1].mean(axis=1)
loss_values = pd.Series(self.get_loss(y_train.values.squeeze(), pred_ensemble.values))
if self.enable_sr:
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, i_k + 1)
if self.enable_fs:
self.logger.info("Feature selection...")
@@ -148,14 +146,14 @@ class DEnsembleModel(Model):
# calculate h-value for each sample
h1 = loss_values_norm
h2 = (l_end / l_start).rank(pct=True)
h = pd.DataFrame({'h_value': self.alpha1 * h1 + self.alpha2 * h2})
h = pd.DataFrame({"h_value": self.alpha1 * h1 + self.alpha2 * h2})
# calculate weights
h['bins'] = pd.cut(h['h_value'], self.bins_sr)
h_avg = h.groupby('bins')['h_value'].mean()
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. / (self.decay ** k_th * h_avg[i_b] + 0.1)
weights[h["bins"] == b] = 1.0 / (self.decay ** k_th * h_avg[i_b] + 0.1)
return weights
def feature_selection(self, df_train, loss_values):
@@ -170,7 +168,7 @@ class DEnsembleModel(Model):
x_train, y_train = df_train["feature"], df_train["label"]
features = x_train.columns
N, F = x_train.shape
g = pd.DataFrame({'g_value': np.zeros(F, dtype=float)})
g = pd.DataFrame({"g_value": np.zeros(F, dtype=float)})
M = len(self.ensemble)
# shuffle specific columns and calculate g-value for each feature
@@ -179,23 +177,27 @@ class DEnsembleModel(Model):
x_train_tmp.loc[:, feat] = np.random.permutation(x_train_tmp.loc[:, feat].values)
pred = pd.Series(np.zeros(N), index=x_train_tmp.index)
for i_s, submodel in enumerate(self.ensemble):
pred += pd.Series(submodel.predict(x_train_tmp.loc[:, self.sub_features[i_s]].values),
index=x_train_tmp.index) / M
pred += (
pd.Series(
submodel.predict(x_train_tmp.loc[:, self.sub_features[i_s]].values), index=x_train_tmp.index
)
/ M
)
loss_feat = self.get_loss(y_train.values.squeeze(), pred.values)
g.loc[i_f, 'g_value'] = np.mean(loss_feat - loss_values) / np.std(loss_feat - loss_values)
g.loc[i_f, "g_value"] = np.mean(loss_feat - loss_values) / np.std(loss_feat - loss_values)
x_train_tmp.loc[:, feat] = x_train.loc[:, feat].copy()
# one column in train features is all-nan # if g['g_value'].isna().any()
g['g_value'].replace(np.nan, 0, inplace=True)
g["g_value"].replace(np.nan, 0, inplace=True)
# divide features into bins_fs bins
g['bins'] = pd.cut(g['g_value'], self.bins_fs)
g["bins"] = pd.cut(g["g_value"], self.bins_fs)
# randomly sample features from bins to construct the new features
res_feat = []
sorted_bins = sorted(g['bins'].unique(), reverse=True)
sorted_bins = sorted(g["bins"].unique(), reverse=True)
for i_b, b in enumerate(sorted_bins):
b_feat = features[g['bins'] == b]
b_feat = features[g["bins"] == b]
num_feat = int(np.ceil(self.sample_ratios[i_b] * len(b_feat)))
res_feat = res_feat + np.random.choice(b_feat, size=num_feat).tolist()
return pd.Index(res_feat)
@@ -233,12 +235,13 @@ class DEnsembleModel(Model):
pred = pd.Series(np.zeros(x_test.shape[0]), index=x_test.index)
for i_sub, submodel in enumerate(self.ensemble):
feat_sub = self.sub_features[i_sub]
pred += pd.Series(submodel.predict(x_test.loc[:, feat_sub].values), index=x_test.index) * self.sub_weights[i_sub]
pred += (
pd.Series(submodel.predict(x_test.loc[:, feat_sub].values), index=x_test.index)
* self.sub_weights[i_sub]
)
return pred
def predict_sub(self, submodel, df_data, features):
x_data, y_data = df_data["feature"].loc[:, features], df_data["label"]
pred_sub = pd.Series(submodel.predict(x_data.values), index=x_data.index)
return pred_sub