update run_all_model and black format

examples/run_all_model.py

@@ -23,7 +23,6 @@ from qlib.config import REG_CN
 from qlib.workflow import R
 from qlib.tests.data import GetData
 
-
 # init qlib
 provider_uri = "~/.qlib/qlib_data/cn_data"
 exp_folder_name = "run_all_model_records"
@@ -40,6 +39,7 @@ exp_manager = {
 GetData().qlib_data(target_dir=provider_uri, region=REG_CN, exists_skip=True)
 qlib.init(provider_uri=provider_uri, region=REG_CN, exp_manager=exp_manager)
 
+
 # decorator to check the arguments
 def only_allow_defined_args(function_to_decorate):
     @functools.wraps(function_to_decorate)
@@ -92,7 +92,8 @@ def create_env():
 
 
 # function to execute the cmd
-def execute(cmd):
+def execute(cmd, wait_when_err=False):
+    print("Running CMD:", cmd)
     with subprocess.Popen(cmd, stdout=subprocess.PIPE, bufsize=1, universal_newlines=True, shell=True) as p:
         for line in p.stdout:
             sys.stdout.write(line.split("\b")[0])
@@ -102,6 +103,8 @@ def execute(cmd):
                 sys.stdout.write("\b" * 10 + "\b".join(line.split("\b")[1:-1]))
 
     if p.returncode != 0:
+        if wait_when_err:
+            input("Press Enter to Continue")
         return p.stderr
     else:
         return None
@@ -184,7 +187,15 @@ def gen_and_save_md_table(metrics, dataset):
 
 # function to run the all the models
 @only_allow_defined_args
-def run(times=1, models=None, dataset="Alpha360", exclude=False):
+def run(
+    times=1,
+    models=None,
+    dataset="Alpha360",
+    exclude=False,
+    qlib_uri: str = "git+https://github.com/microsoft/qlib#egg=pyqlib",
+    wait_before_rm_env: bool = False,
+    wait_when_err: bool = 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. Besides, this script doesn't support parrallel running the same model
@@ -200,6 +211,13 @@ def run(times=1, models=None, dataset="Alpha360", exclude=False):
         determines whether the model being used is excluded or included.
     dataset : str
         determines the dataset to be used for each model.
+    qlib_uri : str
+        the uri to install qlib with pip
+        it could be url on the we or local path
+    wait_before_rm_env : bool
+        wait before remove environment.
+    wait_when_err : bool
+        wait when errors raised when executing commands
 
     Usage:
     -------
@@ -240,32 +258,36 @@ def run(times=1, models=None, dataset="Alpha360", exclude=False):
         sys.stderr.write("\n")
         # install requirements.txt
         sys.stderr.write("Installing requirements.txt...\n")
-        execute(f"{python_path} -m pip install -r {req_path}")
+        execute(f"{python_path} -m pip install -r {req_path}", wait_when_err=wait_when_err)
         sys.stderr.write("\n")
         # setup gpu for tft
         if fn == "TFT":
             execute(
-                f"conda install -y --prefix {env_path} anaconda cudatoolkit=10.0 && conda install -y --prefix {env_path} cudnn"
+                f"conda install -y --prefix {env_path} anaconda cudatoolkit=10.0 && conda install -y --prefix {env_path} cudnn",
+                wait_when_err=wait_when_err,
             )
             sys.stderr.write("\n")
         # install qlib
         sys.stderr.write("Installing qlib...\n")
-        execute(f"{python_path} -m pip install --upgrade pip")  # TODO: FIX ME!
-        execute(f"{python_path} -m pip install --upgrade cython")  # TODO: FIX ME!
+        execute(f"{python_path} -m pip install --upgrade pip", wait_when_err=wait_when_err)  # TODO: FIX ME!
+        execute(f"{python_path} -m pip install --upgrade cython", wait_when_err=wait_when_err)  # TODO: FIX ME!
         if fn == "TFT":
             execute(
-                f"cd {env_path} && {python_path} -m pip install --upgrade --force-reinstall --ignore-installed PyYAML -e git+https://github.com/microsoft/qlib#egg=pyqlib"
+                f"cd {env_path} && {python_path} -m pip install --upgrade --force-reinstall --ignore-installed PyYAML -e {qlib_uri}",
+                wait_when_err=wait_when_err,
             )  # TODO: FIX ME!
         else:
             execute(
-                f"cd {env_path} && {python_path} -m pip install --upgrade --force-reinstall -e git+https://github.com/microsoft/qlib#egg=pyqlib"
+                f"cd {env_path} && {python_path} -m pip install --upgrade --force-reinstall -e {qlib_uri}",
+                wait_when_err=wait_when_err,
             )  # TODO: FIX ME!
         sys.stderr.write("\n")
         # run workflow_by_config for multiple times
         for i in range(times):
             sys.stderr.write(f"Running the model: {fn} for iteration {i+1}...\n")
             errs = execute(
-                f"{python_path} {env_path / 'src/pyqlib/qlib/workflow/cli.py'} {yaml_path} {fn} {exp_folder_name}"
+                f"{python_path} {env_path / 'bin' / 'qrun'} {yaml_path} {fn} {exp_folder_name}",
+                wait_when_err=wait_when_err,
             )
             if errs is not None:
                 _errs = errors.get(fn, {})
@@ -274,6 +296,8 @@ def run(times=1, models=None, dataset="Alpha360", exclude=False):
             sys.stderr.write("\n")
         # remove env
         sys.stderr.write(f"Deleting the environment: {env_path}...\n")
+        if wait_before_rm_env:
+            input("Press Enter to Continue")
         shutil.rmtree(env_path)
     # getting all results
     sys.stderr.write(f"Retrieving results...\n")

qlib/contrib/model/pytorch_localformer.py

@@ -24,6 +24,7 @@ from ...model.base import Model
 from ...data.dataset import DatasetH, TSDatasetH
 from ...data.dataset.handler import DataHandlerLP
 from torch.nn.modules.container import ModuleList
+
 # qrun examples/benchmarks/Localformer/workflow_config_localformer_Alpha360.yaml ”
 
 
@@ -150,8 +151,8 @@ class LocalformerModel(Model):
             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)
+            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)
 
             with torch.no_grad():
                 pred = self.model(feature)

qlib/contrib/model/pytorch_localformer_ts.py

@@ -154,6 +154,7 @@ class LocalformerModel(Model):
         dl_train = dataset.prepare("train", col_set=["feature", "label"], data_key=DataHandlerLP.DK_L)
         dl_valid = dataset.prepare("valid", col_set=["feature", "label"], data_key=DataHandlerLP.DK_L)
         import pdb
+
         pdb.set_trace()
 
         dl_train.config(fillna_type="ffill+bfill")  # process nan brought by dataloader

qlib/contrib/model/pytorch_transformer.py

@@ -23,6 +23,7 @@ from .pytorch_utils import count_parameters
 from ...model.base import Model
 from ...data.dataset import DatasetH, TSDatasetH
 from ...data.dataset.handler import DataHandlerLP
+
 # qrun examples/benchmarks/Transformer/workflow_config_transformer_Alpha360.yaml ”
 
 
@@ -149,8 +150,8 @@ class TransformerModel(Model):
             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)
+            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)
 
             with torch.no_grad():
                 pred = self.model(feature)