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Merge branch 'main' of github.com:you-n-g/qlib into main

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This commit is contained in:
Hong Zhang
2020-11-26 13:55:35 +08:00
parent f42661f2d4 2a17062421
commit 85f9d5594e
7 changed files with 151 additions and 72 deletions
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8
examples/benchmarks/TFT/README.md
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@@ -5,8 +5,10 @@
**GitHub**: https://github.com/google-research/google-research/tree/master/tft
## Run the Workflow
Users can follow the ``workflow_by_code_tft.py`` to run the benchmark. Please be **aware** that this script can only support Python 3.5 - 3.8.
Users can follow the ``workflow_by_code_tft.py`` to run the benchmark.
### Notes
1. The model must run in GPU, or an error will be raised.
2. New datasets should be registered in ``data_formatters``, for detail please visit the source.
1. Please be **aware** that this script can only support `Python 3.5 - 3.8`.
2. If the CUDA version on your machine is not 10.0, please remember to run the following commands `conda install anaconda cudatoolkit=10.0` and `conda install cudnn` on your machine.
3. The model must run in GPU, or an error will be raised.
4. New datasets should be registered in ``data_formatters``, for detail please visit the source.

53
examples/run_all_model.py
View File

@@ -10,6 +10,7 @@ import shutil
import tempfile
import statistics
from pathlib import Path
from operator import xor
from subprocess import Popen, PIPE
from threading import Thread
from pprint import pprint
@@ -174,11 +175,22 @@ def cal_mean_std(results) -> dict:
# function to get all the folders benchmark folder
def get_all_folders() -> dict:
def get_all_folders(models, exclude) -> dict:
folders = dict()
if isinstance(models, str):
model_list = models.split(",")
models = [m.lower().strip("[ ]") for m in model_list]
elif isinstance(models, list):
models = [m.lower() for m in models]
elif models is None:
models = [f.name.lower() for f in os.scandir("benchmarks")]
else:
raise ValueError("Input models type is not supported. Please provide str or list without space.")
for f in os.scandir("benchmarks"):
path = Path("benchmarks") / f.name
folders[f.name] = str(path.resolve())
add = xor(bool(f.name.lower() in models), bool(exclude))
if add:
path = Path("benchmarks") / f.name
folders[f.name] = str(path.resolve())
return folders
@@ -225,13 +237,44 @@ def gen_and_save_md_table(metrics):
# function to run the all the models
def run(times=1):
def run(times=1, models=None, exclude=False):
"""
Please be aware that this function can only work under Linux. MacOS and Windows will be supported in the future.
Any PR to enhance this method is highly welcomed.
Parameters:
-----------
times : int
determines how many times the model should be running.
models : str or list
determines the specific model or list of models to run or exclude.
exclude : boolean
determines whether the model being used is excluded or included.
Usage:
-------
Here are some use cases of the function in the bash:
.. code-block:: bash
# Case 1 - run all models multiple times
python run_all_model.py 3
# Case 2 - run specific models multiple times
python run_all_model.py 3 dnn
# Case 3 - run other models except those are given as arguments for multiple times
python run_all_model.py 3 [dnn,tft,lstm] True
# Case 4 - run specific models for one time
python run_all_model.py --models=[dnn,lightgbm]
# Case 5 - run other models except those are given as aruments for one time
python run_all_model.py --models=[dnn,tft,sfm] --exclude=True
"""
# get all folders
folders = get_all_folders()
folders = get_all_folders(models, exclude)
# set up
compatible = True
if sys.version_info < (3, 3):

84
qlib/contrib/data/handler.py
View File

@@ -10,6 +10,28 @@ from inspect import getfullargspec
import copy
def check_transform_proc(proc_l, fit_start_time, fit_end_time):
new_l = []
for p in proc_l:
if not isinstance(p, Processor):
klass, pkwargs = get_cls_kwargs(p, processor_module)
args = getfullargspec(klass).args
if "fit_start_time" in args and "fit_end_time" in args:
assert (
fit_start_time is not None and fit_end_time is not None
), "Make sure `fit_start_time` and `fit_end_time` are not None."
pkwargs.update(
{
"fit_start_time": fit_start_time,
"fit_end_time": fit_end_time,
}
)
new_l.append({"class": klass.__name__, "kwargs": pkwargs})
else:
new_l.append(p)
return new_l
class ALPHA360_Denoise(DataHandlerLP):
def __init__(self, instruments="csi500", start_time=None, end_time=None, fit_start_time=None, fit_end_time=None):
data_loader = {
@@ -83,8 +105,31 @@ class ALPHA360_Denoise(DataHandlerLP):
return fields, names
_DEFAULT_LEARN_PROCESSORS = [
{"class": "DropnaLabel"},
{"class": "CSZScoreNorm", "kwargs": {"fields_group": "label"}},
]
_DEFAULT_INFER_PROCESSORS = [
{"class": "ProcessInf", "kwargs": {}},
{"class": "ZScoreNorm", "kwargs": {}},
{"class": "Fillna", "kwargs": {}},
]
class ALPHA360(DataHandlerLP):
def __init__(self, instruments="csi500", start_time=None, end_time=None, fit_start_time=None, fit_end_time=None):
def __init__(
self,
instruments="csi500",
start_time=None,
end_time=None,
infer_processors=_DEFAULT_INFER_PROCESSORS,
learn_processors=_DEFAULT_LEARN_PROCESSORS,
fit_start_time=None,
fit_end_time=None,
):
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)
data_loader = {
"class": "QlibDataLoader",
"kwargs": {
@@ -95,16 +140,6 @@ class ALPHA360(DataHandlerLP):
},
}
learn_processors = [
{"class": "DropnaLabel", "kwargs": {"fields_group": "label"}},
{"class": "CSZScoreNorm", "kwargs": {"fields_group": "label"}},
]
infer_processors = [
{"class": "ProcessInf", "kwargs": {}},
{"class": "ZscoreNorm", "kwargs": {"fit_start_time": fit_start_time, "fit_end_time": fit_end_time}},
{"class": "Fillna", "kwargs": {}},
]
super().__init__(
instruments,
start_time,
@@ -168,33 +203,12 @@ class Alpha158(DataHandlerLP):
start_time=None,
end_time=None,
infer_processors=[],
learn_processors=["DropnaLabel", {"class": "CSZScoreNorm", "kwargs": {"fields_group": "label"}}],
learn_processors=_DEFAULT_LEARN_PROCESSORS,
fit_start_time=None,
fit_end_time=None,
):
def check_transform_proc(proc_l):
new_l = []
for p in proc_l:
if not isinstance(p, Processor):
klass, pkwargs = get_cls_kwargs(p, processor_module)
args = getfullargspec(klass).args
if "fit_start_time" in args and "fit_end_time" in args:
assert (
fit_start_time is not None and fit_end_time is not None
), "Make sure `fit_start_time` and `fit_end_time` are not None."
pkwargs.update(
{
"fit_start_time": fit_start_time,
"fit_end_time": fit_end_time,
}
)
new_l.append({"class": klass.__name__, "kwargs": pkwargs})
else:
new_l.append(p)
return new_l
infer_processors = check_transform_proc(infer_processors)
learn_processors = check_transform_proc(learn_processors)
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)
data_loader = {
"class": "QlibDataLoader",

17
qlib/contrib/model/pytorch_gru.py
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@@ -28,14 +28,10 @@ class GRU(Model):
Parameters
----------
input_dim : int
input dimension
output_dim : int
output dimension
layers : tuple
layer sizes
lr : float
learning rate
d_feat : int
input dimension for each time step
metric: str
the evaluate metric used in early stop
optimizer : str
optimizer name
GPU : str
@@ -112,10 +108,6 @@ class GRU(Model):
)
)
if loss not in {"mse", "binary"}:
raise NotImplementedError("loss {} is not supported!".format(loss))
self._scorer = mean_squared_error if loss == "mse" else roc_auc_score
self.gru_model = GRUModel(
d_feat=self.d_feat, hidden_size=self.hidden_size, num_layers=self.num_layers, dropout=self.dropout
)
@@ -251,7 +243,6 @@ class GRU(Model):
# train
self.logger.info("training...")
self._fitted = True
# return
for step in range(self.n_epochs):
self.logger.info("Epoch%d:", step)

17
qlib/contrib/model/pytorch_lstm.py
View File

@@ -28,14 +28,10 @@ class LSTM(Model):
Parameters
----------
input_dim : int
input dimension
output_dim : int
output dimension
layers : tuple
layer sizes
lr : float
learning rate
d_feat : int
input dimension for each time step
metric: str
the evaluate metric used in early stop
optimizer : str
optimizer name
GPU : str
@@ -112,10 +108,6 @@ class LSTM(Model):
)
)
if loss not in {"mse", "binary"}:
raise NotImplementedError("loss {} is not supported!".format(loss))
self._scorer = mean_squared_error if loss == "mse" else roc_auc_score
self.lstm_model = LSTMModel(
d_feat=self.d_feat, hidden_size=self.hidden_size, num_layers=self.num_layers, dropout=self.dropout
)
@@ -251,7 +243,6 @@ class LSTM(Model):
# train
self.logger.info("training...")
self._fitted = True
# return
for step in range(self.n_epochs):
self.logger.info("Epoch%d:", step)

40
qlib/data/dataset/processor.py
View File

@@ -166,7 +166,9 @@ class MinMaxNorm(Processor):
return df
class ZscoreNorm(Processor):
class ZScoreNorm(Processor):
"""ZScore Normalization"""
def __init__(self, fit_start_time, fit_end_time, fields_group=None):
self.fit_start_time = fit_start_time
self.fit_end_time = fit_end_time
@@ -193,6 +195,42 @@ class ZscoreNorm(Processor):
return df
class RobustZScoreNorm(Processor):
"""Robust ZScore Normalization
Use robust statistics for Z-Score normalization:
mean(x) = median(x)
std(x) = MAD(x) * 1.4826
Reference:
https://en.wikipedia.org/wiki/Median_absolute_deviation.
"""
def __init__(self, fit_start_time, fit_end_time, fields_group=None, clip_outlier=True):
self.fit_start_time = fit_start_time
self.fit_end_time = fit_end_time
self.fields_group = fields_group
self.clip_outlier = clip_outlier
def fit(self, df):
df = fetch_df_by_index(df, slice(self.fit_start_time, self.fit_end_time), level="datetime")
self.cols = get_group_columns(df, self.fields_group)
X = df[self.cols].values
self.mean_train = np.nanmedian(X, axis=0)
self.std_train = np.nanmedian(np.abs(X - self.mean_train), axis=0)
self.std_train += EPS
self.std_train *= 1.4826
def __call__(self, df):
X = df[self.cols]
X -= self.mean_train
X /= self.std_train
df[self.cols] = X
if self.clip_outlier:
df.clip(-3, 3, inplace=True)
return df
class CSZScoreNorm(Processor):
"""Cross Sectional ZScore Normalization"""

4
qlib/workflow/cli.py
View File

@@ -27,9 +27,9 @@ def sys_config(config, config_path):
Parameters
----------
config : dict
configuration of the workflow
configuration of the workflow.
config_path : str
configuration of the path
configuration of the path.
"""
sys_config = config.get("sys", {})