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6b4156ab9fb5dbe3062dbe4bc347fc6ddcb8cf86
qlib/qlib/contrib/model/pytorch_hats.py
Dong Zhou 6b4156ab9f black format
2020-11-27 08:58:44 +08:00

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Python
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# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from __future__ import division
from __future__ import print_function
import os
import numpy as np
import pandas as pd
import copy
from ...utils import create_save_path
from ...log import get_module_logger
import torch
import torch.nn as nn
import torch.optim as optim
from ...model.base import Model
from ...data.dataset import DatasetH
from ...data.dataset.handler import DataHandlerLP
class HATS(Model):
"""HATS Model
Parameters
----------
d_feat : int
input dimension for each time step
metric: str
the evaluate metric used in early stop
optimizer : str
optimizer name
GPU : str
the GPU ID(s) used for training
"""
def __init__(
self,
d_feat=6,
hidden_size=64,
num_layers=2,
dropout=0.5,
n_epochs=200,
lr=0.0001,
metric="loss",
early_stop=20,
loss="mse",
base_model="LSTM",
with_pretrain=True,
optimizer="adam",
GPU="0",
seed=0,
**kwargs
):
# Set logger.
self.logger = get_module_logger("HATS")
self.logger.info("HATS 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.early_stop = early_stop
self.optimizer = optimizer.lower()
self.loss = loss
self.base_model = base_model
self.with_pretrain = with_pretrain
self.visible_GPU = GPU
self.use_gpu = torch.cuda.is_available()
self.seed = seed
self.logger.info(
"HATS parameters setting:"
"\nd_feat : {}"
"\nhidden_size : {}"
"\nnum_layers : {}"
"\ndropout : {}"
"\nn_epochs : {}"
"\nlr : {}"
"\nmetric : {}"
"\nearly_stop : {}"
"\noptimizer : {}"
"\nloss_type : {}"
"\nbase_model : {}"
"\nwith_pretrain : {}"
"\nvisible_GPU : {}"
"\nuse_GPU : {}"
"\nseed : {}".format(
d_feat,
hidden_size,
num_layers,
dropout,
n_epochs,
lr,
metric,
early_stop,
optimizer.lower(),
loss,
base_model,
with_pretrain,
GPU,
self.use_gpu,
seed,
)
)
self.HATS_model = HATSModel(
d_feat=self.d_feat,
hidden_size=self.hidden_size,
num_layers=self.num_layers,
dropout=self.dropout,
base_model=self.base_model,
)
if optimizer.lower() == "adam":
self.train_optimizer = optim.Adam(self.HATS_model.parameters(), lr=self.lr)
elif optimizer.lower() == "gd":
self.train_optimizer = optim.SGD(self.HATS_model.parameters(), lr=self.lr)
else:
raise NotImplementedError("optimizer {} is not supported!".format(optimizer))
self._fitted = False
if self.use_gpu:
self.HATS_model.cuda()
# set the visible GPU
if self.visible_GPU:
os.environ["CUDA_VISIBLE_DEVICES"] = self.visible_GPU
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 == "IC":
return self.cal_ic(pred[mask], label[mask])
if self.metric == "" or self.metric == "loss": # use loss
return -self.loss_fn(pred[mask], label[mask])
raise ValueError("unknown metric `%s`" % self.metric)
def cal_ic(self, pred, label):
return torch.mean(pred * label)
def get_daily_inter(self, df, shuffle=False):
# organize the train data into daily inter as daily batches
daily_count = df.groupby(level=0).size().values
daily_index = np.roll(np.cumsum(daily_count), 1)
daily_index[0] = 0
if shuffle:
# shuffle the daily inter data
daily_shuffle = list(zip(daily_index, daily_count))
np.random.shuffle(daily_shuffle)
daily_index, daily_count = zip(*daily_shuffle)
return daily_index, daily_count
def train_epoch(self, x_train, y_train):
x_train_values = x_train.values
y_train_values = np.squeeze(y_train.values)
self.HATS_model.train()
# organize the train data into daily inter as daily batches
daily_index, daily_count = self.get_daily_inter(x_train, shuffle=True)
for idx, count in zip(daily_index, daily_count):
batch = slice(idx, idx + count)
feature = torch.from_numpy(x_train_values[batch]).float()
label = torch.from_numpy(y_train_values[batch]).float()
if self.use_gpu:
feature = feature.cuda()
label = label.cuda()
pred = self.HATS_model(feature)
loss = self.loss_fn(pred, label)
self.train_optimizer.zero_grad()
loss.backward()
torch.nn.utils.clip_grad_value_(self.HATS_model.parameters(), 3.0)
self.train_optimizer.step()
def test_epoch(self, data_x, data_y):
# prepare testing data
x_values = data_x.values
y_values = np.squeeze(data_y.values)
self.HATS_model.eval()
scores = []
losses = []
# organize the test data into daily inter as daily batches
daily_index, daily_count = self.get_daily_inter(data_x, shuffle=False)
for idx, count in zip(daily_index, daily_count):
batch = slice(idx, idx + count)
feature = torch.from_numpy(x_values[batch]).float()
label = torch.from_numpy(y_values[batch]).float()
if self.use_gpu:
feature = feature.cuda()
label = label.cuda()
pred = self.HATS_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
best_score = -np.inf
best_epoch = 0
evals_result["train"] = []
evals_result["valid"] = []
# load pretrained base_model
if self.with_pretrain:
self.logger.info("Loading pretrained model...")
if self.base_model == "LSTM":
from ...contrib.model.pytorch_lstm import LSTMModel
pretrained_model = LSTMModel()
pretrained_model.load_state_dict(torch.load("benchmarks/LSTM/model_lstm_csi300.pkl"))
elif self.base_model == "GRU":
from ...contrib.model.pytorch_gru import GRUModel
pretrained_model = GRUModel()
pretrained_model.load_state_dict(torch.load("benchmarks/GRU/model_gru_csi300.pkl"))
model_dict = self.HATS_model.state_dict()
pretrained_dict = {k: v for k, v in pretrained_model.state_dict().items() if k in model_dict}
model_dict.update(pretrained_dict)
self.HATS_model.load_state_dict(model_dict)
self.logger.info("Loading pretrained model Done...")
# 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.HATS_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.HATS_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.HATS_model.eval()
x_values = x_test.values
sample_num = x_values.shape[0]
preds = []
# organize the data into daily inter as daily batches
daily_index, daily_count = self.get_daily_inter(x_test, shuffle=False)
for idx, count in zip(daily_index, daily_count):
batch = slice(idx, idx + count)
x_batch = torch.from_numpy(x_values[batch]).float()
if self.use_gpu:
x_batch = x_batch.cuda()
with torch.no_grad():
if self.use_gpu:
pred = self.HATS_model(x_batch).detach().cpu().numpy()
else:
pred = self.HATS_model(x_batch).detach().numpy()
preds.append(pred)
return pd.Series(np.concatenate(preds), index=index)
class HATSModel(nn.Module):
def __init__(self, d_feat=6, hidden_size=64, num_layers=2, dropout=0.0, base_model="GRU"):
super().__init__()
if base_model == "GRU":
self.model = nn.GRU(
input_size=d_feat,
hidden_size=hidden_size,
num_layers=num_layers,
batch_first=True,
dropout=dropout,
)
elif base_model == "LSTM":
self.model = nn.LSTM(
input_size=d_feat,
hidden_size=hidden_size,
num_layers=num_layers,
batch_first=True,
dropout=dropout,
)
else:
raise ValueError("unknown base model name `%s`" % base_model)
self.hidden_size = hidden_size
self.bn1 = nn.BatchNorm1d(num_features=hidden_size, track_running_stats=False)
self.fc = nn.Linear(hidden_size, hidden_size)
self.bn2 = nn.BatchNorm1d(num_features=hidden_size, track_running_stats=False)
self.fc_out = nn.Linear(hidden_size, 1)
self.leaky_relu = nn.LeakyReLU()
self.softmax = nn.Softmax(dim=1)
self.d_feat = d_feat
num_head_att = [1] * num_layers
hidden_dim = [hidden_size] * num_layers
dims = [d_feat] + [d * nh for (d, nh) in zip(hidden_dim, num_head_att[:-1])] + [num_head_att[-1]]
in_dims = dims[:-1]
out_dims = [d // nh for (d, nh) in zip(dims[1:], num_head_att)]
self.attn = nn.ModuleList(
[GraphAttention(i, o, nh, dropout) for (i, o, nh) in zip(in_dims, out_dims, num_head_att)]
)
self.bns = nn.ModuleList([nn.BatchNorm1d(dim) for dim in dims[1:-1]])
self.dropout = nn.Dropout(dropout)
self.elu = nn.ELU()
def forward(self, x):
x = x.reshape(len(x), self.d_feat, -1) # [N, F, T]
x = x.permute(0, 2, 1) # [N, T, F]
out, _ = self.model(x)
hidden = out[:, -1, :]
hidden = self.bn1(hidden)
attention = GraphAttention.cal_attention(hidden, hidden)
output = attention.mm(hidden)
output = self.fc(output)
output = self.bn2(output)
output = self.leaky_relu(output)
return self.fc_out(output).squeeze()
class GraphAttention(nn.Module):
def __init__(self, input_dim, output_dim, num_heads, dropout=0.5):
super().__init__()
"""
Parameters
----------
input_dim : int
Dimension of input node features.
output_dim : int
Dimension of output node features.
num_heads : list of ints
Number of attention heads in each hidden layer and output layer. Must be non empty. Note that len(num_heads) = len(hidden_dims)+1.
dropout : float
Dropout rate. Default: 0.5.
"""
self.input_dim = input_dim
self.output_dim = output_dim
self.num_heads = num_heads
self.fcs = nn.ModuleList([nn.Linear(input_dim, output_dim) for _ in range(num_heads)])
self.a = nn.ModuleList([nn.Linear(2 * output_dim, 1) for _ in range(num_heads)])
self.dropout = nn.Dropout(dropout)
self.softmax = nn.Softmax(dim=0)
self.leakyrelu = nn.LeakyReLU()
def forward(self, features, nodes, mappings, rows):
"""
Parameters
----------
features : torch.Tensor
An (n' x input_dim) tensor of input node features.
nodes : list of numpy array
nodes[i] is an array of the nodes in the ith layer of the
computation graph.
mappings : list of dictionary
mappings[i] is a dictionary mappings node v (labelled 0 to |V|-1)
in nodes[i] to its position in nodes[i]. For example,
if nodes[i] = [2,5], then mappings[i][2] = 0 and
mappings[i][5] = 1.
rows : numpy array
rows[i] is an array of neighbors of node i.
Returns
-------
out : torch.Tensor
An (len(node_layers[-1]) x output_dim) tensor of output node features.
"""
nprime = features.shape[0]
rows = [np.array([mappings[v] for v in row], dtype=np.int64) for row in rows]
sum_degs = np.hstack(([0], np.cumsum([len(row) for row in rows])))
mapped_nodes = [mappings[v] for v in nodes]
indices = torch.LongTensor([[v, c] for (v, row) in zip(mapped_nodes, rows) for c in row]).t()
out = []
for k in range(self.num_heads):
h = self.fcs[k](features)
nbr_h = torch.cat(tuple([h[row] for row in rows]), dim=0)
self_h = torch.cat(
tuple([h[mappings[nodes[i]]].repeat(len(row), 1) for (i, row) in enumerate(rows)]), dim=0
)
cat_h = torch.cat((self_h, nbr_h), dim=1)
e = self.leakyrelu(self.a[k](cat_h))
alpha = [self.softmax(e[lo:hi]) for (lo, hi) in zip(sum_degs, sum_degs[1:])]
alpha = torch.cat(tuple(alpha), dim=0)
alpha = alpha.squeeze(1)
alpha = self.dropout(alpha)
adj = torch.sparse.FloatTensor(indices, alpha, torch.Size([nprime, nprime]))
out.append(torch.sparse.mm(adj, h)[mapped_nodes])
return out
@staticmethod
def cal_attention(x, y):
att_x = torch.mean(x, dim=1).reshape(-1, 1)
att_y = torch.mean(y, dim=1).reshape(-1, 1)
att = att_x.mm(torch.t(att_y))
return (
torch.mean(
x.reshape(x.shape[0], 1, x.shape[1]).repeat(1, y.shape[0], 1)
* y.reshape(1, y.shape[0], y.shape[1]).repeat(x.shape[0], 1, 1),
dim=2,
)
- att
)
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