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update python version (#1868)

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* update python version

* fix: Correct selector handling and add time filtering in storage.py

* fix: convert index and columns to list in repr methods

* feat: Add Makefile for managing project prerequisites

* feat: Add Cython extensions for rolling and expanding operations

* resolve install error

* fix lint error

* fix lint error

* fix lint error

* fix lint error

* fix lint error

* update build package

* update makefile

* update ci yaml

* fix docs build error

* fix ubuntu install error

* fix docs build error

* fix install error

* fix install error

* fix install error

* fix install error

* fix pylint error

* fix pylint error

* fix pylint error

* fix pylint error

* fix pylint error E1123

* fix pylint error R0917

* fix pytest error

* fix pytest error

* fix pytest error

* update code

* update code

* fix ci error

* fix pylint error

* fix black error

* fix pytest error

* fix CI error

* fix CI error

* add python version to CI

* add python version to CI

* add python version to CI

* fix pylint error

* fix pytest general nn error

* fix CI error

* optimize code

* add coments

* Extended macos version

* remove build package

---------

Co-authored-by: Young <afe.young@gmail.com>
This commit is contained in:
Linlang
2024-12-17 11:30:06 +08:00
committed by GitHub
parent 7acb4f3484
commit a0cef033cb
63 changed files with 460 additions and 426 deletions
Download Patch File Download Diff File Expand all files Collapse all files

8
qlib/contrib/data/handler.py
View File

@@ -58,7 +58,7 @@ class Alpha360(DataHandlerLP):
fit_end_time=None,
filter_pipe=None,
inst_processors=None,
**kwargs
**kwargs,
):
infer_processors = check_transform_proc(infer_processors, fit_start_time, fit_end_time)
learn_processors = check_transform_proc(learn_processors, fit_start_time, fit_end_time)
@@ -83,7 +83,7 @@ class Alpha360(DataHandlerLP):
data_loader=data_loader,
learn_processors=learn_processors,
infer_processors=infer_processors,
**kwargs
**kwargs,
)
def get_label_config(self):
@@ -109,7 +109,7 @@ class Alpha158(DataHandlerLP):
process_type=DataHandlerLP.PTYPE_A,
filter_pipe=None,
inst_processors=None,
**kwargs
**kwargs,
):
infer_processors = check_transform_proc(infer_processors, fit_start_time, fit_end_time)
learn_processors = check_transform_proc(learn_processors, fit_start_time, fit_end_time)
@@ -134,7 +134,7 @@ class Alpha158(DataHandlerLP):
infer_processors=infer_processors,
learn_processors=learn_processors,
process_type=process_type,
**kwargs
**kwargs,
)
def get_feature_config(self):

2
qlib/contrib/model/catboost_model.py
View File

@@ -33,7 +33,7 @@ class CatBoostModel(Model, FeatureInt):
verbose_eval=20,
evals_result=dict(),
reweighter=None,
**kwargs
**kwargs,
):
df_train, df_valid = dataset.prepare(
["train", "valid"],

2
qlib/contrib/model/double_ensemble.py
View File

@@ -31,7 +31,7 @@ class DEnsembleModel(Model, FeatureInt):
sub_weights=None,
epochs=100,
early_stopping_rounds=None,
**kwargs
**kwargs,
):
self.base_model = base_model # "gbm" or "mlp", specifically, we use lgbm for "gbm"
self.num_models = num_models # the number of sub-models

8
qlib/contrib/model/pytorch_adarnn.py
View File

@@ -56,7 +56,7 @@ class ADARNN(Model):
n_splits=2,
GPU=0,
seed=None,
**_
**_,
):
# Set logger.
self.logger = get_module_logger("ADARNN")
@@ -154,10 +154,7 @@ class ADARNN(Model):
self.model.train()
criterion = nn.MSELoss()
dist_mat = torch.zeros(self.num_layers, self.len_seq).to(self.device)
len_loader = np.inf
for loader in train_loader_list:
if len(loader) < len_loader:
len_loader = len(loader)
out_weight_list = None
for data_all in zip(*train_loader_list):
# for data_all in zip(*train_loader_list):
self.train_optimizer.zero_grad()
@@ -571,6 +568,7 @@ class TransferLoss:
Returns:
[tensor] -- transfer loss
"""
loss = None
if self.loss_type in ("mmd_lin", "mmd"):
mmdloss = MMD_loss(kernel_type="linear")
loss = mmdloss(X, Y)

2
qlib/contrib/model/pytorch_add.py
View File

@@ -63,7 +63,7 @@ class ADD(Model):
mu=0.05,
GPU=0,
seed=None,
**kwargs
**kwargs,
):
# Set logger.
self.logger = get_module_logger("ADD")

2
qlib/contrib/model/pytorch_alstm.py
View File

@@ -52,7 +52,7 @@ class ALSTM(Model):
optimizer="adam",
GPU=0,
seed=None,
**kwargs
**kwargs,
):
# Set logger.
self.logger = get_module_logger("ALSTM")

2
qlib/contrib/model/pytorch_alstm_ts.py
View File

@@ -56,7 +56,7 @@ class ALSTM(Model):
n_jobs=10,
GPU=0,
seed=None,
**kwargs
**kwargs,
):
# Set logger.
self.logger = get_module_logger("ALSTM")

2
qlib/contrib/model/pytorch_gats.py
View File

@@ -56,7 +56,7 @@ class GATs(Model):
optimizer="adam",
GPU=0,
seed=None,
**kwargs
**kwargs,
):
# Set logger.
self.logger = get_module_logger("GATs")

2
qlib/contrib/model/pytorch_gats_ts.py
View File

@@ -73,7 +73,7 @@ class GATs(Model):
GPU=0,
n_jobs=10,
seed=None,
**kwargs
**kwargs,
):
# Set logger.
self.logger = get_module_logger("GATs")

7
qlib/contrib/model/pytorch_general_nn.py
View File

@@ -319,7 +319,12 @@ class GeneralPTNN(Model):
if self.use_gpu:
torch.cuda.empty_cache()
def predict(self, dataset: Union[DatasetH, TSDatasetH]):
def predict(
self,
dataset: Union[DatasetH, TSDatasetH],
batch_size=None,
n_jobs=None,
):
if not self.fitted:
raise ValueError("model is not fitted yet!")

2
qlib/contrib/model/pytorch_gru.py
View File

@@ -52,7 +52,7 @@ class GRU(Model):
optimizer="adam",
GPU=0,
seed=None,
**kwargs
**kwargs,
):
# Set logger.
self.logger = get_module_logger("GRU")

2
qlib/contrib/model/pytorch_gru_ts.py
View File

@@ -54,7 +54,7 @@ class GRU(Model):
n_jobs=10,
GPU=0,
seed=None,
**kwargs
**kwargs,
):
# Set logger.
self.logger = get_module_logger("GRU")

2
qlib/contrib/model/pytorch_hist.py
View File

@@ -59,7 +59,7 @@ class HIST(Model):
optimizer="adam",
GPU=0,
seed=None,
**kwargs
**kwargs,
):
# Set logger.
self.logger = get_module_logger("HIST")

2
qlib/contrib/model/pytorch_igmtf.py
View File

@@ -55,7 +55,7 @@ class IGMTF(Model):
optimizer="adam",
GPU=0,
seed=None,
**kwargs
**kwargs,
):
# Set logger.
self.logger = get_module_logger("IGMTF")

2
qlib/contrib/model/pytorch_krnn.py
View File

@@ -255,7 +255,7 @@ class KRNN(Model):
optimizer="adam",
GPU=0,
seed=None,
**kwargs
**kwargs,
):
# Set logger.
self.logger = get_module_logger("KRNN")

2
qlib/contrib/model/pytorch_localformer.py
View File

@@ -44,7 +44,7 @@ class LocalformerModel(Model):
n_jobs=10,
GPU=0,
seed=None,
**kwargs
**kwargs,
):
# set hyper-parameters.
self.d_model = d_model

2
qlib/contrib/model/pytorch_localformer_ts.py
View File

@@ -42,7 +42,7 @@ class LocalformerModel(Model):
n_jobs=10,
GPU=0,
seed=None,
**kwargs
**kwargs,
):
# set hyper-parameters.
self.d_model = d_model

2
qlib/contrib/model/pytorch_lstm.py
View File

@@ -51,7 +51,7 @@ class LSTM(Model):
optimizer="adam",
GPU=0,
seed=None,
**kwargs
**kwargs,
):
# Set logger.
self.logger = get_module_logger("LSTM")

2
qlib/contrib/model/pytorch_lstm_ts.py
View File

@@ -53,7 +53,7 @@ class LSTM(Model):
n_jobs=10,
GPU=0,
seed=None,
**kwargs
**kwargs,
):
# Set logger.
self.logger = get_module_logger("LSTM")

4
qlib/contrib/model/pytorch_sandwich.py
View File

@@ -35,7 +35,7 @@ class SandwichModel(nn.Module):
rnn_layers,
dropout,
device,
**params
**params,
):
"""Build a Sandwich model
@@ -129,7 +129,7 @@ class Sandwich(Model):
optimizer="adam",
GPU=0,
seed=None,
**kwargs
**kwargs,
):
# Set logger.
self.logger = get_module_logger("Sandwich")

2
qlib/contrib/model/pytorch_sfm.py
View File

@@ -212,7 +212,7 @@ class SFM(Model):
optimizer="gd",
GPU=0,
seed=None,
**kwargs
**kwargs,
):
# Set logger.
self.logger = get_module_logger("SFM")

2
qlib/contrib/model/pytorch_tcn.py
View File

@@ -56,7 +56,7 @@ class TCN(Model):
optimizer="adam",
GPU=0,
seed=None,
**kwargs
**kwargs,
):
# Set logger.
self.logger = get_module_logger("TCN")

2
qlib/contrib/model/pytorch_tcn_ts.py
View File

@@ -54,7 +54,7 @@ class TCN(Model):
n_jobs=10,
GPU=0,
seed=None,
**kwargs
**kwargs,
):
# Set logger.
self.logger = get_module_logger("TCN")

2
qlib/contrib/model/pytorch_tcts.py
View File

@@ -58,7 +58,7 @@ class TCTS(Model):
mode="soft",
seed=None,
lowest_valid_performance=0.993,
**kwargs
**kwargs,
):
# Set logger.
self.logger = get_module_logger("TCTS")

2
qlib/contrib/model/pytorch_transformer.py
View File

@@ -43,7 +43,7 @@ class TransformerModel(Model):
n_jobs=10,
GPU=0,
seed=None,
**kwargs
**kwargs,
):
# set hyper-parameters.
self.d_model = d_model

2
qlib/contrib/model/pytorch_transformer_ts.py
View File

@@ -41,7 +41,7 @@ class TransformerModel(Model):
n_jobs=10,
GPU=0,
seed=None,
**kwargs
**kwargs,
):
# set hyper-parameters.
self.d_model = d_model

4
qlib/contrib/model/xgboost.py
View File

@@ -28,7 +28,7 @@ class XGBModel(Model, FeatureInt):
verbose_eval=20,
evals_result=dict(),
reweighter=None,
**kwargs
**kwargs,
):
df_train, df_valid = dataset.prepare(
["train", "valid"],
@@ -63,7 +63,7 @@ class XGBModel(Model, FeatureInt):
early_stopping_rounds=early_stopping_rounds,
verbose_eval=verbose_eval,
evals_result=evals_result,
**kwargs
**kwargs,
)
evals_result["train"] = list(evals_result["train"].values())[0]
evals_result["valid"] = list(evals_result["valid"].values())[0]

5
qlib/contrib/online/manager.py
View File

@@ -4,10 +4,10 @@
# pylint: skip-file
# flake8: noqa
import yaml
import pathlib
import pandas as pd
import shutil
from ruamel.yaml import YAML
from ...backtest.account import Account
from .user import User
from .utils import load_instance, save_instance
@@ -110,7 +110,8 @@ class UserManager:
raise ValueError("User data for {} already exists".format(user_id))
with config_file.open("r") as fp:
config = yaml.safe_load(fp)
yaml = YAML(typ="safe", pure=True)
config = yaml.load(fp)
# load model
model = init_instance_by_config(config["model"])

5
qlib/contrib/online/utils.py
View File

@@ -6,8 +6,8 @@
import pathlib
import pickle
import yaml
import pandas as pd
from ruamel.yaml import YAML
from ...data import D
from ...config import C
from ...log import get_module_logger
@@ -91,7 +91,8 @@ def prepare(um, today, user_id, exchange_config=None):
dates.append(get_next_trading_date(dates[-1], future=True))
if exchange_config:
with pathlib.Path(exchange_config).open("r") as fp:
exchange_paras = yaml.safe_load(fp)
yaml = YAML(typ="safe", pure=True)
exchange_paras = yaml.load(fp)
else:
exchange_paras = {}
trade_exchange = Exchange(trade_dates=dates, **exchange_paras)

6
qlib/contrib/report/graph.py
View File

@@ -176,7 +176,7 @@ class HeatmapGraph(BaseGraph):
x=self._df.columns,
y=self._df.index,
z=self._df.values.tolist(),
**self._graph_kwargs
**self._graph_kwargs,
)
]
return _data
@@ -213,7 +213,7 @@ class SubplotsGraph:
sub_graph_layout: dict = None,
sub_graph_data: list = None,
subplots_kwargs: dict = None,
**kwargs
**kwargs,
):
"""
@@ -355,7 +355,7 @@ class SubplotsGraph:
df=self._df.loc[:, [column_name]],
name_dict={column_name: temp_name},
graph_kwargs=_graph_kwargs,
)
),
)
else:
raise TypeError()

5
qlib/contrib/rolling/base.py
View File

@@ -2,11 +2,11 @@
# Licensed under the MIT License.
from copy import deepcopy
from pathlib import Path
from ruamel.yaml import YAML
from typing import List, Optional, Union
import fire
import pandas as pd
import yaml
from qlib import auto_init
from qlib.log import get_module_logger
@@ -117,7 +117,8 @@ class Rolling:
def _raw_conf(self) -> dict:
with self.conf_path.open("r") as f:
return yaml.safe_load(f)
yaml = YAML(typ="safe", pure=True)
return yaml.load(f)
def _replace_handler_with_cache(self, task: dict):
"""

5
qlib/contrib/tuner/config.py
View File

@@ -4,9 +4,9 @@
# pylint: skip-file
# flake8: noqa
import yaml
import copy
import os
from ruamel.yaml import YAML
class TunerConfigManager:
@@ -16,7 +16,8 @@ class TunerConfigManager:
self.config_path = config_path
with open(config_path) as fp:
config = yaml.safe_load(fp)
yaml = YAML(typ="safe", pure=True)
config = yaml.load(fp)
self.config = copy.deepcopy(config)
self.pipeline_ex_config = PipelineExperimentConfig(config.get("experiment", dict()), self)