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Fix pylint (#888)

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* add_pylint_to_workflow

* fix-pylint

* fix_pylinterror

* fix-issue
This commit is contained in:
SunsetWolf
2022-01-26 19:27:24 +08:00
committed by GitHub
parent 635632e4ed
commit 144e1e2459
103 changed files with 318 additions and 387 deletions
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36
qlib/contrib/model/pytorch_adarnn.py
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@@ -1,12 +1,10 @@
# Copyright (c) Microsoft Corporation.
import os
from pdb import set_trace
from torch.utils.data import Dataset, DataLoader
import copy
from typing import Text, Union
import math
import numpy as np
import pandas as pd
import torch
@@ -182,11 +180,11 @@ class ADARNN(Model):
continue
total_loss = torch.zeros(1).cuda()
for i in range(len(index)):
feature_s = list_feat[index[i][0]]
feature_t = list_feat[index[i][1]]
label_reg_s = list_label[index[i][0]]
label_reg_t = list_label[index[i][1]]
for i, n in enumerate(index):
feature_s = list_feat[n[0]]
feature_t = list_feat[n[1]]
label_reg_s = list_label[n[0]]
label_reg_t = list_label[n[1]]
feature_all = torch.cat((feature_s, feature_t), 0)
if epoch < self.pre_epoch:
@@ -410,7 +408,7 @@ class AdaRNN(nn.Module):
in_size = hidden
self.features = nn.Sequential(*features)
if use_bottleneck == True: # finance
if use_bottleneck is True: # finance
self.bottleneck = nn.Sequential(
nn.Linear(n_hiddens[-1], bottleneck_width),
nn.Linear(bottleneck_width, bottleneck_width),
@@ -449,7 +447,7 @@ class AdaRNN(nn.Module):
def forward_pre_train(self, x, len_win=0):
out = self.gru_features(x)
fea = out[0] # [2N,L,H]
if self.use_bottleneck == True:
if self.use_bottleneck is True:
fea_bottleneck = self.bottleneck(fea[:, -1, :])
fc_out = self.fc(fea_bottleneck).squeeze()
else:
@@ -458,8 +456,8 @@ class AdaRNN(nn.Module):
out_list_all, out_weight_list = out[1], out[2]
out_list_s, out_list_t = self.get_features(out_list_all)
loss_transfer = torch.zeros((1,)).cuda()
for i in range(len(out_list_s)):
criterion_transder = TransferLoss(loss_type=self.trans_loss, input_dim=out_list_s[i].shape[2])
for i, n in enumerate(out_list_s):
criterion_transder = TransferLoss(loss_type=self.trans_loss, input_dim=n.shape[2])
h_start = 0
for j in range(h_start, self.len_seq, 1):
i_start = j - len_win if j - len_win >= 0 else 0
@@ -471,7 +469,7 @@ class AdaRNN(nn.Module):
else 1 / (self.len_seq - h_start) * (2 * len_win + 1)
)
loss_transfer = loss_transfer + weight * criterion_transder.compute(
out_list_s[i][:, j, :], out_list_t[i][:, k, :]
n[:, j, :], out_list_t[i][:, k, :]
)
return fc_out, loss_transfer, out_weight_list
@@ -484,7 +482,7 @@ class AdaRNN(nn.Module):
out, _ = self.features[i](x_input.float())
x_input = out
out_lis.append(out)
if self.model_type == "AdaRNN" and predict == False:
if self.model_type == "AdaRNN" and predict is False:
out_gate = self.process_gate_weight(x_input, i)
out_weight_list.append(out_gate)
return out, out_lis, out_weight_list
@@ -524,10 +522,10 @@ class AdaRNN(nn.Module):
else:
weight = weight_mat
dist_mat = torch.zeros(self.num_layers, self.len_seq).cuda()
for i in range(len(out_list_s)):
criterion_transder = TransferLoss(loss_type=self.trans_loss, input_dim=out_list_s[i].shape[2])
for i, n in enumerate(out_list_s):
criterion_transder = TransferLoss(loss_type=self.trans_loss, input_dim=n.shape[2])
for j in range(self.len_seq):
loss_trans = criterion_transder.compute(out_list_s[i][:, j, :], out_list_t[i][:, j, :])
loss_trans = criterion_transder.compute(n[:, j, :], out_list_t[i][:, j, :])
loss_transfer = loss_transfer + weight[i, j] * loss_trans
dist_mat[i, j] = loss_trans
return fc_out, loss_transfer, dist_mat, weight
@@ -546,7 +544,7 @@ class AdaRNN(nn.Module):
def predict(self, x):
out = self.gru_features(x, predict=True)
fea = out[0]
if self.use_bottleneck == True:
if self.use_bottleneck is True:
fea_bottleneck = self.bottleneck(fea[:, -1, :])
fc_out = self.fc(fea_bottleneck).squeeze()
else:
@@ -572,12 +570,12 @@ class TransferLoss:
Returns:
[tensor] -- transfer loss
"""
if self.loss_type == "mmd_lin" or self.loss_type == "mmd":
if self.loss_type in ("mmd_lin", "mmd"):
mmdloss = MMD_loss(kernel_type="linear")
loss = mmdloss(X, Y)
elif self.loss_type == "coral":
loss = CORAL(X, Y)
elif self.loss_type == "cosine" or self.loss_type == "cos":
elif self.loss_type in ("cosine", "cos"):
loss = 1 - cosine(X, Y)
elif self.loss_type == "kl":
loss = kl_div(X, Y)