DDG-DA paper code (#743)

* Merge data selection to main * Update trainer for reweighter * Typos fixed. * update data selection interface * successfully run exp after refactor some interface * data selection share handler & trainer * fix meta model time series bug * fix online workflow set_uri bug * fix set_uri bug * updawte ds docs and delay trainer bug * docs * resume reweighter * add reweighting result * fix qlib model import * make recorder more friendly * fix experiment workflow bug * commit for merging master incase of conflictions * Successful run DDG-DA with a single command * remove unused code * asdd more docs * Update README.md * Update & fix some bugs. * Update configuration & remove debug functions * Update README.md * Modfify horizon from code rather than yaml * Update performance in README.md * fix part comments * Remove unfinished TCTS. * Fix some details. * Update meta docs * Update README.md of the benchmarks_dynamic * Update README.md files * Add README.md to the rolling_benchmark baseline. * Refine the docs and link * Rename README.md in benchmarks_dynamic. * Remove comments. * auto download data Co-authored-by: wendili-cs <wendili.academic@qq.com> Co-authored-by: demon143 <785696300@qq.com>
2026-07-12 23:36:54 +08:00 · 2022-01-10 16:52:37 +08:00
parent 184ce34a34
commit cf35562e84
52 changed files with 2441 additions and 456 deletions
--- a/qlib/contrib/meta/data_selection/net.py
+++ b/qlib/contrib/meta/data_selection/net.py
@@ -0,0 +1,68 @@
+# Copyright (c) Microsoft Corporation.
+# Licensed under the MIT License.
+
+import pandas as pd
+import numpy as np
+import torch
+from torch import nn
+
+from .utils import preds_to_weight_with_clamp, SingleMetaBase
+
+
+class TimeWeightMeta(SingleMetaBase):
+    def __init__(self, hist_step_n, clip_weight=None, clip_method="clamp"):
+        # clip_method includes "tanh" or "clamp"
+        super().__init__(hist_step_n, clip_weight, clip_method)
+        self.linear = nn.Linear(hist_step_n, 1)
+        self.k = nn.Parameter(torch.Tensor([8.0]))
+
+    def forward(self, time_perf, time_belong=None, return_preds=False):
+        hist_step_n = self.linear.in_features
+        # NOTE: the reshape order is very important
+        time_perf = time_perf.reshape(hist_step_n, time_perf.shape[0] // hist_step_n, *time_perf.shape[1:])
+        time_perf = torch.mean(time_perf, dim=1, keepdim=False)
+
+        preds = []
+        for i in range(time_perf.shape[1]):
+            preds.append(self.linear(time_perf[:, i]))
+        preds = torch.cat(preds)
+        preds = preds - torch.mean(preds)  # avoid using future information
+        preds = preds * self.k
+        if return_preds:
+            if time_belong is None:
+                return preds
+            else:
+                return time_belong @ preds
+        else:
+            weights = preds_to_weight_with_clamp(preds, self.clip_weight, self.clip_method)
+            if time_belong is None:
+                return weights
+            else:
+                return time_belong @ weights
+
+
+class PredNet(nn.Module):
+    def __init__(self, step, hist_step_n, clip_weight=None, clip_method="tanh"):
+        super().__init__()
+        self.step = step
+        self.twm = TimeWeightMeta(hist_step_n=hist_step_n, clip_weight=clip_weight, clip_method=clip_method)
+        self.init_paramters(hist_step_n)
+
+    def get_sample_weights(self, X, time_perf, time_belong, ignore_weight=False):
+        weights = torch.from_numpy(np.ones(X.shape[0])).float().to(X.device)
+        if not ignore_weight:
+            if time_perf is not None:
+                weights_t = self.twm(time_perf, time_belong)
+                weights = weights * weights_t
+        return weights
+
+    def forward(self, X, y, time_perf, time_belong, X_test, ignore_weight=False):
+        """Please refer to the docs of MetaTaskDS for the description of the variables"""
+        weights = self.get_sample_weights(X, time_perf, time_belong, ignore_weight=ignore_weight)
+        X_w = X.T * weights.view(1, -1)
+        theta = torch.inverse(X_w @ X) @ X_w @ y
+        return X_test @ theta, weights
+
+    def init_paramters(self, hist_step_n):
+        self.twm.linear.weight.data = 1.0 / hist_step_n + self.twm.linear.weight.data * 0.01
+        self.twm.linear.bias.data.fill_(0.0)