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Add README and Formatted
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meng-ustc
@@ -232,6 +232,7 @@ Here is a list of models built on `Qlib`.
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- [SFM based on pytorch (Liheng Zhang, et al. 2017)](qlib/contrib/model/pytorch_sfm.py)
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- [TFT based on tensorflow (Bryan Lim, et al. 2019)](examples/benchmarks/TFT/tft.py)
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- [TabNet based on pytorch (Sercan O. Arik, et al. 2019)](qlib/contrib/model/pytorch_tabnet.py)
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- [DoubleEnsemble based on LightGBM (Chuheng Zhang, et al. 2020)](qlib/contrib/model/double_ensemble.py)
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Your PR of new Quant models is highly welcomed.
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4
examples/benchmarks/DoubleEnsemble/README.md
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4
examples/benchmarks/DoubleEnsemble/README.md
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@@ -0,0 +1,4 @@
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# DoubleEnsemble
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* DoubleEnsemble is an ensemble framework leveraging learning trajectory based sample reweighting and shuffling based feature selection, to solve both the low signal-to-noise ratio and increasing number of features problems. They identify the key samples based on the training dynamics on each sample and elicit key features based on the ablation impact of each feature via shuffling. The model is applicable to a wide range of base models, capable of extracting complex patterns, while mitigating the overfitting and instability issues for financial market prediction.
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* This code used in Qlib is implemented by ourselves.
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* Paper: DoubleEnsemble: A New Ensemble Method Based on Sample Reweighting and Feature Selection for Financial Data Analysis [https://arxiv.org/pdf/2010.01265.pdf](https://arxiv.org/pdf/2010.01265.pdf).
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@@ -15,21 +15,22 @@ class DEnsembleModel(Model):
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"""Double Ensemble Model"""
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def __init__(
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self,
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base="gbm",
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loss="mse",
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k=6,
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enable_sr=True,
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enable_fs=True,
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alpha1=1.,
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alpha2=1.,
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bins_sr=10,
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bins_fs=5,
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decay=None,
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sample_ratios=None,
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sub_weights=None,
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epochs=100,
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**kwargs):
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self,
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base="gbm",
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loss="mse",
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k=6,
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enable_sr=True,
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enable_fs=True,
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alpha1=1.0,
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alpha2=1.0,
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bins_sr=10,
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bins_fs=5,
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decay=None,
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sample_ratios=None,
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sub_weights=None,
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epochs=100,
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**kwargs
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):
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self.base = base # "gbm" or "mlp", specifically, we use lgbm for "gbm"
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self.k = k
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self.enable_sr = enable_sr
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@@ -54,10 +55,7 @@ class DEnsembleModel(Model):
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self.params.update(kwargs)
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self.loss = loss
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def fit(
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self,
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dataset: DatasetH
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):
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def fit(self, dataset: DatasetH):
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df_train, df_valid = dataset.prepare(
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["train", "valid"], col_set=["feature", "label"], data_key=DataHandlerLP.DK_L
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)
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@@ -71,7 +69,7 @@ class DEnsembleModel(Model):
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# train k sub-models
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for i_k in range(self.k):
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self.sub_features.append(features)
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self.logger.info("Training sub-model: ({}/{})".format(i_k+1, self.k))
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self.logger.info("Training sub-model: ({}/{})".format(i_k + 1, self.k))
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model_k = self.train_submodel(df_train, df_valid, weights, features)
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self.ensemble.append(model_k)
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# no further sample re-weight and feature selection needed for the last sub-model
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@@ -82,12 +80,12 @@ class DEnsembleModel(Model):
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loss_curve = self.retrieve_loss_curve(model_k, df_train, features)
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pred_k = self.predict_sub(model_k, df_train, features)
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pred_sub.iloc[:, i_k] = pred_k
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pred_ensemble = pred_sub.iloc[:, :i_k+1].mean(axis=1)
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pred_ensemble = pred_sub.iloc[:, : i_k + 1].mean(axis=1)
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loss_values = pd.Series(self.get_loss(y_train.values.squeeze(), pred_ensemble.values))
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if self.enable_sr:
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self.logger.info("Sample re-weighting...")
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weights = self.sample_reweight(loss_curve, loss_values, i_k+1)
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weights = self.sample_reweight(loss_curve, loss_values, i_k + 1)
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if self.enable_fs:
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self.logger.info("Feature selection...")
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@@ -148,14 +146,14 @@ class DEnsembleModel(Model):
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# calculate h-value for each sample
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h1 = loss_values_norm
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h2 = (l_end / l_start).rank(pct=True)
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h = pd.DataFrame({'h_value': self.alpha1 * h1 + self.alpha2 * h2})
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h = pd.DataFrame({"h_value": self.alpha1 * h1 + self.alpha2 * h2})
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# calculate weights
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h['bins'] = pd.cut(h['h_value'], self.bins_sr)
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h_avg = h.groupby('bins')['h_value'].mean()
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h["bins"] = pd.cut(h["h_value"], self.bins_sr)
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h_avg = h.groupby("bins")["h_value"].mean()
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weights = pd.Series(np.zeros(N, dtype=float))
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for i_b, b in enumerate(h_avg.index):
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weights[h['bins'] == b] = 1. / (self.decay ** k_th * h_avg[i_b] + 0.1)
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weights[h["bins"] == b] = 1.0 / (self.decay ** k_th * h_avg[i_b] + 0.1)
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return weights
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def feature_selection(self, df_train, loss_values):
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@@ -170,7 +168,7 @@ class DEnsembleModel(Model):
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x_train, y_train = df_train["feature"], df_train["label"]
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features = x_train.columns
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N, F = x_train.shape
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g = pd.DataFrame({'g_value': np.zeros(F, dtype=float)})
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g = pd.DataFrame({"g_value": np.zeros(F, dtype=float)})
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M = len(self.ensemble)
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# shuffle specific columns and calculate g-value for each feature
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@@ -179,23 +177,27 @@ class DEnsembleModel(Model):
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x_train_tmp.loc[:, feat] = np.random.permutation(x_train_tmp.loc[:, feat].values)
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pred = pd.Series(np.zeros(N), index=x_train_tmp.index)
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for i_s, submodel in enumerate(self.ensemble):
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pred += pd.Series(submodel.predict(x_train_tmp.loc[:, self.sub_features[i_s]].values),
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index=x_train_tmp.index) / M
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pred += (
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pd.Series(
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submodel.predict(x_train_tmp.loc[:, self.sub_features[i_s]].values), index=x_train_tmp.index
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)
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/ M
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)
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loss_feat = self.get_loss(y_train.values.squeeze(), pred.values)
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g.loc[i_f, 'g_value'] = np.mean(loss_feat - loss_values) / np.std(loss_feat - loss_values)
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g.loc[i_f, "g_value"] = np.mean(loss_feat - loss_values) / np.std(loss_feat - loss_values)
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x_train_tmp.loc[:, feat] = x_train.loc[:, feat].copy()
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# one column in train features is all-nan # if g['g_value'].isna().any()
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g['g_value'].replace(np.nan, 0, inplace=True)
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g["g_value"].replace(np.nan, 0, inplace=True)
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# divide features into bins_fs bins
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g['bins'] = pd.cut(g['g_value'], self.bins_fs)
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g["bins"] = pd.cut(g["g_value"], self.bins_fs)
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# randomly sample features from bins to construct the new features
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res_feat = []
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sorted_bins = sorted(g['bins'].unique(), reverse=True)
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sorted_bins = sorted(g["bins"].unique(), reverse=True)
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for i_b, b in enumerate(sorted_bins):
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b_feat = features[g['bins'] == b]
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b_feat = features[g["bins"] == b]
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num_feat = int(np.ceil(self.sample_ratios[i_b] * len(b_feat)))
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res_feat = res_feat + np.random.choice(b_feat, size=num_feat).tolist()
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return pd.Index(res_feat)
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@@ -233,12 +235,13 @@ class DEnsembleModel(Model):
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pred = pd.Series(np.zeros(x_test.shape[0]), index=x_test.index)
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for i_sub, submodel in enumerate(self.ensemble):
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feat_sub = self.sub_features[i_sub]
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pred += pd.Series(submodel.predict(x_test.loc[:, feat_sub].values), index=x_test.index) * self.sub_weights[i_sub]
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pred += (
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pd.Series(submodel.predict(x_test.loc[:, feat_sub].values), index=x_test.index)
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* self.sub_weights[i_sub]
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)
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return pred
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def predict_sub(self, submodel, df_data, features):
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x_data, y_data = df_data["feature"].loc[:, features], df_data["label"]
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pred_sub = pd.Series(submodel.predict(x_data.values), index=x_data.index)
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return pred_sub
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