Start prototype

lib/trade_models/__init__.py

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+from .quant_transformer import QuantTransformer

lib/trade_models/quant_transformer.py

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+# Copyright (c) Microsoft Corporation.
+# Licensed under the MIT License.
+
+
+from __future__ import division
+from __future__ import print_function
+
+import os
+import numpy as np
+import pandas as pd
+import copy
+from functools import partial
+from sklearn.metrics import roc_auc_score, mean_squared_error
+from typing import Optional
+import logging
+
+from qlib.utils import (
+  unpack_archive_with_buffer,
+  save_multiple_parts_file,
+  create_save_path,
+  drop_nan_by_y_index,
+)
+from qlib.log import get_module_logger, TimeInspector
+
+import torch
+import torch.nn as nn
+import torch.optim as optim
+
+from layers import DropPath, trunc_normal_
+
+from qlib.model.base import Model
+from qlib.data.dataset import DatasetH
+from qlib.data.dataset.handler import DataHandlerLP
+
+
+class QuantTransformer(Model):
+  """Transformer-based Quant Model
+
+  """
+
+  def __init__(
+    self,
+    d_feat=6,
+    hidden_size=64,
+    num_layers=2,
+    dropout=0.0,
+    n_epochs=200,
+    lr=0.001,
+    metric="",
+    batch_size=2000,
+    early_stop=20,
+    loss="mse",
+    optimizer="adam",
+    GPU=0,
+    seed=None,
+    **kwargs
+  ):
+    # Set logger.
+    self.logger = get_module_logger("QuantTransformer")
+    self.logger.info("QuantTransformer pytorch version...")
+
+    # set hyper-parameters.
+    self.d_feat = d_feat
+    self.hidden_size = hidden_size
+    self.num_layers = num_layers
+    self.dropout = dropout
+    self.n_epochs = n_epochs
+    self.lr = lr
+    self.metric = metric
+    self.batch_size = batch_size
+    self.early_stop = early_stop
+    self.optimizer = optimizer.lower()
+    self.loss = loss
+    self.device = torch.device("cuda:{:}".format(GPU) if torch.cuda.is_available() else "cpu")
+    self.use_gpu = torch.cuda.is_available()
+    self.seed = seed
+
+    self.logger.info(
+      "GRU parameters setting:"
+      "\nd_feat : {}"
+      "\nhidden_size : {}"
+      "\nnum_layers : {}"
+      "\ndropout : {}"
+      "\nn_epochs : {}"
+      "\nlr : {}"
+      "\nmetric : {}"
+      "\nbatch_size : {}"
+      "\nearly_stop : {}"
+      "\noptimizer : {}"
+      "\nloss_type : {}"
+      "\nvisible_GPU : {}"
+      "\nuse_GPU : {}"
+      "\nseed : {}".format(
+        d_feat,
+        hidden_size,
+        num_layers,
+        dropout,
+        n_epochs,
+        lr,
+        metric,
+        batch_size,
+        early_stop,
+        optimizer.lower(),
+        loss,
+        GPU,
+        self.use_gpu,
+        seed,
+      )
+    )
+
+    if self.seed is not None:
+      np.random.seed(self.seed)
+      torch.manual_seed(self.seed)
+
+    self.model = TransformerModel(d_feat=self.d_feat)
+    if optimizer.lower() == "adam":
+      self.train_optimizer = optim.Adam(self.model.parameters(), lr=self.lr)
+    elif optimizer.lower() == "gd":
+      self.train_optimizer = optim.SGD(self.model.parameters(), lr=self.lr)
+    else:
+      raise NotImplementedError("optimizer {:} is not supported!".format(optimizer))
+
+    self.fitted = False
+    self.model.to(self.device)
+
+  def mse(self, pred, label):
+    loss = (pred - label) ** 2
+    return torch.mean(loss)
+
+  def loss_fn(self, pred, label):
+    mask = ~torch.isnan(label)
+
+    if self.loss == "mse":
+      return self.mse(pred[mask], label[mask])
+
+    raise ValueError("unknown loss `%s`" % self.loss)
+
+  def metric_fn(self, pred, label):
+
+    mask = torch.isfinite(label)
+
+    if self.metric == "" or self.metric == "loss":
+      return -self.loss_fn(pred[mask], label[mask])
+
+    raise ValueError("unknown metric `%s`" % self.metric)
+
+  def train_epoch(self, x_train, y_train):
+
+    x_train_values = x_train.values
+    y_train_values = np.squeeze(y_train.values)
+
+    self.model.train()
+
+    indices = np.arange(len(x_train_values))
+    np.random.shuffle(indices)
+
+    for i in range(len(indices))[:: self.batch_size]:
+
+      if len(indices) - i < self.batch_size:
+        break
+
+      feature = torch.from_numpy(x_train_values[indices[i : i + self.batch_size]]).float().to(self.device)
+      label = torch.from_numpy(y_train_values[indices[i : i + self.batch_size]]).float().to(self.device)
+
+      pred = self.model(feature)
+      loss = self.loss_fn(pred, label)
+
+      self.train_optimizer.zero_grad()
+      loss.backward()
+      torch.nn.utils.clip_grad_value_(self.model.parameters(), 3.0)
+      self.train_optimizer.step()
+
+  def test_epoch(self, data_x, data_y):
+
+    # prepare training data
+    x_values = data_x.values
+    y_values = np.squeeze(data_y.values)
+
+    self.model.eval()
+
+    scores = []
+    losses = []
+
+    indices = np.arange(len(x_values))
+    import pdb; pdb.set_trace()
+
+    for i in range(len(indices))[:: self.batch_size]:
+
+      if len(indices) - i < self.batch_size:
+        break
+
+      feature = torch.from_numpy(x_values[indices[i : i + self.batch_size]]).float().to(self.device)
+      label = torch.from_numpy(y_values[indices[i : i + self.batch_size]]).float().to(self.device)
+
+      pred = self.model(feature)
+      loss = self.loss_fn(pred, label)
+      losses.append(loss.item())
+
+      score = self.metric_fn(pred, label)
+      scores.append(score.item())
+
+    return np.mean(losses), np.mean(scores)
+
+  def fit(
+    self,
+    dataset: DatasetH,
+    evals_result=dict(),
+    verbose=True,
+    save_path=None,
+  ):
+
+    df_train, df_valid, df_test = dataset.prepare(
+      ["train", "valid", "test"],
+      col_set=["feature", "label"],
+      data_key=DataHandlerLP.DK_L,
+    )
+
+    x_train, y_train = df_train["feature"], df_train["label"]
+    x_valid, y_valid = df_valid["feature"], df_valid["label"]
+
+    if save_path == None:
+      save_path = create_save_path(save_path)
+    stop_steps = 0
+    train_loss = 0
+    best_score = -np.inf
+    best_epoch = 0
+    evals_result["train"] = []
+    evals_result["valid"] = []
+
+    # train
+    self.logger.info("training...")
+    self.fitted = True
+
+    for step in range(self.n_epochs):
+      self.logger.info("Epoch%d:", step)
+      self.logger.info("training...")
+      self.train_epoch(x_train, y_train)
+      self.logger.info("evaluating...")
+      train_loss, train_score = self.test_epoch(x_train, y_train)
+      val_loss, val_score = self.test_epoch(x_valid, y_valid)
+      self.logger.info("train %.6f, valid %.6f" % (train_score, val_score))
+      evals_result["train"].append(train_score)
+      evals_result["valid"].append(val_score)
+
+      if val_score > best_score:
+        best_score = val_score
+        stop_steps = 0
+        best_epoch = step
+        best_param = copy.deepcopy(self.model.state_dict())
+      else:
+        stop_steps += 1
+        if stop_steps >= self.early_stop:
+          self.logger.info("early stop")
+          break
+
+    self.logger.info("best score: %.6lf @ %d" % (best_score, best_epoch))
+    self.model.load_state_dict(best_param)
+    torch.save(best_param, save_path)
+
+    if self.use_gpu:
+      torch.cuda.empty_cache()
+
+  def predict(self, dataset):
+    if not self.fitted:
+      raise ValueError("model is not fitted yet!")
+
+    x_test = dataset.prepare("test", col_set="feature")
+    index = x_test.index
+    self.model.eval()
+    x_values = x_test.values
+    sample_num = x_values.shape[0]
+    preds = []
+
+    for begin in range(sample_num)[:: self.batch_size]:
+
+      if sample_num - begin < self.batch_size:
+        end = sample_num
+      else:
+        end = begin + self.batch_size
+
+      x_batch = torch.from_numpy(x_values[begin:end]).float().to(self.device)
+
+      with torch.no_grad():
+        if self.use_gpu:
+          pred = self.model(x_batch).detach().cpu().numpy()
+        else:
+          pred = self.model(x_batch).detach().numpy()
+
+      preds.append(pred)
+
+    return pd.Series(np.concatenate(preds), index=index)
+
+
+# Real Model
+
+
+class Mlp(nn.Module):
+  def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, drop=0.):
+    super().__init__()
+    out_features = out_features or in_features
+    hidden_features = hidden_features or in_features
+    self.fc1 = nn.Linear(in_features, hidden_features)
+    self.act = act_layer()
+    self.fc2 = nn.Linear(hidden_features, out_features)
+    self.drop = nn.Dropout(drop)
+
+  def forward(self, x):
+    x = self.fc1(x)
+    x = self.act(x)
+    x = self.drop(x)
+    x = self.fc2(x)
+    x = self.drop(x)
+    return x
+
+
+class Attention(nn.Module):
+  def __init__(self, dim, num_heads=8, qkv_bias=False, qk_scale=None, attn_drop=0., proj_drop=0.):
+    super().__init__()
+    self.num_heads = num_heads
+    head_dim = dim // num_heads
+    # NOTE scale factor was wrong in my original version, can set manually to be compat with prev weights
+    self.scale = qk_scale or head_dim ** -0.5
+
+    self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
+    self.attn_drop = nn.Dropout(attn_drop)
+    self.proj = nn.Linear(dim, dim)
+    self.proj_drop = nn.Dropout(proj_drop)
+
+  def forward(self, x):
+    B, N, C = x.shape
+    qkv = self.qkv(x).reshape(B, N, 3, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4)
+    q, k, v = qkv[0], qkv[1], qkv[2]   # make torchscript happy (cannot use tensor as tuple)
+
+    attn = (q @ k.transpose(-2, -1)) * self.scale
+    attn = attn.softmax(dim=-1)
+    attn = self.attn_drop(attn)
+
+    x = (attn @ v).transpose(1, 2).reshape(B, N, C)
+    x = self.proj(x)
+    x = self.proj_drop(x)
+    return x
+
+
+class Block(nn.Module):
+
+  def __init__(self, dim, num_heads, mlp_ratio=4., qkv_bias=False, qk_scale=None, drop=0., attn_drop=0.,
+         drop_path=0., act_layer=nn.GELU, norm_layer=nn.LayerNorm):
+    super().__init__()
+    self.norm1 = norm_layer(dim)
+    self.attn = Attention(
+      dim, num_heads=num_heads, qkv_bias=qkv_bias, qk_scale=qk_scale, attn_drop=attn_drop, proj_drop=drop)
+    # NOTE: drop path for stochastic depth, we shall see if this is better than dropout here
+    self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()
+    self.norm2 = norm_layer(dim)
+    mlp_hidden_dim = int(dim * mlp_ratio)
+    self.mlp = Mlp(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop)
+
+  def forward(self, x):
+    x = x + self.drop_path(self.attn(self.norm1(x)))
+    x = x + self.drop_path(self.mlp(self.norm2(x)))
+    return x
+
+
+class SimpleEmbed(nn.Module):
+
+  def __init__(self, d_feat, embed_dim):
+    super(SimpleEmbed, self).__init__()
+    self.proj = nn.Linear(d_feat, embed_dim)
+
+  def forward(self, x):
+    import pdb; pdb.set_trace()
+    B, C, H, W = x.shape
+    # FIXME look at relaxing size constraints
+    assert H == self.img_size[0] and W == self.img_size[1], \
+      f"Input image size ({H}*{W}) doesn't match model ({self.img_size[0]}*{self.img_size[1]})."
+    x = self.proj(x).flatten(2).transpose(1, 2)
+    return x
+
+
+class TransformerModel(nn.Module):
+  def __init__(self,
+         d_feat: int,
+         embed_dim: int = 64,
+         depth: int = 4,
+         num_heads: int = 4,
+         mlp_ratio: float = 4.,
+         qkv_bias: bool = True,
+         qk_scale: Optional[float] = None,
+         drop_rate=0., attn_drop_rate=0., drop_path_rate=0., norm_layer=None):
+    """
+    Args:
+      d_feat (int, tuple): input image size
+      embed_dim (int): embedding dimension
+      depth (int): depth of transformer
+      num_heads (int): number of attention heads
+      mlp_ratio (int): ratio of mlp hidden dim to embedding dim
+      qkv_bias (bool): enable bias for qkv if True
+      qk_scale (float): override default qk scale of head_dim ** -0.5 if set
+      drop_rate (float): dropout rate
+      attn_drop_rate (float): attention dropout rate
+      drop_path_rate (float): stochastic depth rate
+      norm_layer: (nn.Module): normalization layer
+    """
+    super(TransformerModel, self).__init__()
+    self.embed_dim = embed_dim
+    self.num_features = embed_dim
+    norm_layer = norm_layer or partial(nn.LayerNorm, eps=1e-6)
+
+    self.input_embed = SimpleEmbed(d_feat, embed_dim=embed_dim)
+
+    """
+    self.cls_token = nn.Parameter(torch.zeros(1, 1, embed_dim))
+    self.pos_embed = nn.Parameter(torch.zeros(1, num_patches + 1, embed_dim))
+    self.pos_drop = nn.Dropout(p=drop_rate)
+    """
+
+    dpr = [x.item() for x in torch.linspace(0, drop_path_rate, depth)]  # stochastic depth decay rule
+    self.blocks = nn.ModuleList([
+      Block(
+        dim=embed_dim, num_heads=num_heads, mlp_ratio=mlp_ratio, qkv_bias=qkv_bias, qk_scale=qk_scale,
+        drop=drop_rate, attn_drop=attn_drop_rate, drop_path=dpr[i], norm_layer=norm_layer)
+      for i in range(depth)])
+    self.norm = norm_layer(embed_dim)
+
+    # regression head
+    self.head = nn.Linear(self.num_features, 1)
+
+    """
+    trunc_normal_(self.pos_embed, std=.02)
+    trunc_normal_(self.cls_token, std=.02)
+    """
+    self.apply(self._init_weights)
+
+  def _init_weights(self, m):
+    if isinstance(m, nn.Linear):
+      trunc_normal_(m.weight, std=.02)
+      if isinstance(m, nn.Linear) and m.bias is not None:
+        nn.init.constant_(m.bias, 0)
+    elif isinstance(m, nn.LayerNorm):
+      nn.init.constant_(m.bias, 0)
+      nn.init.constant_(m.weight, 1.0)
+
+  def forward_features(self, x):
+    B = x.shape[0]
+    x = self.input_embed(x)
+
+    cls_tokens = self.cls_token.expand(B, -1, -1)  # stole cls_tokens impl from Phil Wang, thanks
+    x = torch.cat((cls_tokens, x), dim=1)
+    x = x + self.pos_embed
+    x = self.pos_drop(x)
+
+    for blk in self.blocks:
+      x = blk(x)
+
+    x = self.norm(x)[:, 0]
+    return x
+
+  def forward(self, x):
+    x = self.forward_features(x)
+    x = self.head(x)
+    return x