Merge pull request #328 from D-X-Y/fshare

Move get_path to get_or_create_path, use the best model of SFM / TabNet

qlib/contrib/model/pytorch_alstm.py

@@ -14,7 +14,7 @@ import logging
 from ...utils import (
     unpack_archive_with_buffer,
     save_multiple_parts_file,
-    create_save_path,
+    get_or_create_path,
     drop_nan_by_y_index,
 )
 from ...log import get_module_logger, TimeInspector
@@ -230,8 +230,7 @@ class ALSTM(Model):
         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 = get_or_create_path(save_path)
-            save_path = create_save_path(save_path)
         stop_steps = 0
         train_loss = 0
         best_score = -np.inf

qlib/contrib/model/pytorch_alstm_ts.py

@@ -14,7 +14,7 @@ import logging
 from ...utils import (
     unpack_archive_with_buffer,
     save_multiple_parts_file,
-    create_save_path,
+    get_or_create_path,
     drop_nan_by_y_index,
 )
 from ...log import get_module_logger, TimeInspector
@@ -220,8 +220,7 @@ class ALSTM(Model):
             dl_valid, batch_size=self.batch_size, shuffle=False, num_workers=self.n_jobs, drop_last=True
         )
 
-        if save_path == None:
+        save_path = get_or_create_path(save_path)
-            save_path = create_save_path(save_path)
 
         stop_steps = 0
         train_loss = 0

qlib/contrib/model/pytorch_gats.py

@@ -14,7 +14,7 @@ import logging
 from ...utils import (
     unpack_archive_with_buffer,
     save_multiple_parts_file,
-    create_save_path,
+    get_or_create_path,
     drop_nan_by_y_index,
 )
 from ...log import get_module_logger, TimeInspector
@@ -248,8 +248,7 @@ class GATs(Model):
         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 = get_or_create_path(save_path)
-            save_path = create_save_path(save_path)
         stop_steps = 0
         best_score = -np.inf
         best_epoch = 0

qlib/contrib/model/pytorch_gats_ts.py

@@ -14,7 +14,7 @@ import logging
 from ...utils import (
     unpack_archive_with_buffer,
     save_multiple_parts_file,
-    create_save_path,
+    get_or_create_path,
     drop_nan_by_y_index,
 )
 from ...log import get_module_logger, TimeInspector
@@ -264,8 +264,7 @@ class GATs(Model):
         train_loader = DataLoader(dl_train, sampler=sampler_train, num_workers=self.n_jobs, drop_last=True)
         valid_loader = DataLoader(dl_valid, sampler=sampler_valid, num_workers=self.n_jobs, drop_last=True)
 
-        if save_path == None:
+        save_path = get_or_create_path(save_path)
-            save_path = create_save_path(save_path)
 
         stop_steps = 0
         train_loss = 0

qlib/contrib/model/pytorch_gru.py

@@ -14,7 +14,7 @@ import logging
 from ...utils import (
     unpack_archive_with_buffer,
     save_multiple_parts_file,
-    create_save_path,
+    get_or_create_path,
     drop_nan_by_y_index,
 )
 from ...log import get_module_logger, TimeInspector
@@ -230,8 +230,7 @@ class GRU(Model):
         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 = get_or_create_path(save_path)
-            save_path = create_save_path(save_path)
         stop_steps = 0
         train_loss = 0
         best_score = -np.inf

qlib/contrib/model/pytorch_gru_ts.py

@@ -14,7 +14,7 @@ import logging
 from ...utils import (
     unpack_archive_with_buffer,
     save_multiple_parts_file,
-    create_save_path,
+    get_or_create_path,
     drop_nan_by_y_index,
 )
 from ...log import get_module_logger, TimeInspector
@@ -220,8 +220,7 @@ class GRU(Model):
             dl_valid, batch_size=self.batch_size, shuffle=False, num_workers=self.n_jobs, drop_last=True
         )
 
-        if save_path == None:
+        save_path = get_or_create_path(save_path)
-            save_path = create_save_path(save_path)
 
         stop_steps = 0
         train_loss = 0

qlib/contrib/model/pytorch_lstm.py

@@ -14,7 +14,7 @@ import logging
 from ...utils import (
     unpack_archive_with_buffer,
     save_multiple_parts_file,
-    create_save_path,
+    get_or_create_path,
     drop_nan_by_y_index,
 )
 from ...log import get_module_logger, TimeInspector
@@ -226,8 +226,7 @@ class LSTM(Model):
         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 = get_or_create_path(save_path)
-            save_path = create_save_path(save_path)
         stop_steps = 0
         train_loss = 0
         best_score = -np.inf

qlib/contrib/model/pytorch_lstm_ts.py

@@ -14,7 +14,7 @@ import logging
 from ...utils import (
     unpack_archive_with_buffer,
     save_multiple_parts_file,
-    create_save_path,
+    get_or_create_path,
     drop_nan_by_y_index,
 )
 from ...log import get_module_logger, TimeInspector
@@ -216,8 +216,7 @@ class LSTM(Model):
             dl_valid, batch_size=self.batch_size, shuffle=False, num_workers=self.n_jobs, drop_last=True
         )
 
-        if save_path == None:
+        save_path = get_or_create_path(save_path)
-            save_path = create_save_path(save_path)
 
         stop_steps = 0
         train_loss = 0

qlib/contrib/model/pytorch_nn.py

@@ -19,7 +19,7 @@ from .pytorch_utils import count_parameters
 from ...model.base import Model
 from ...data.dataset import DatasetH
 from ...data.dataset.handler import DataHandlerLP
-from ...utils import unpack_archive_with_buffer, save_multiple_parts_file, create_save_path, drop_nan_by_y_index
+from ...utils import unpack_archive_with_buffer, save_multiple_parts_file, get_or_create_path, drop_nan_by_y_index
 from ...log import get_module_logger, TimeInspector
 from ...workflow import R
 
@@ -176,7 +176,7 @@ class DNNModelPytorch(Model):
             w_train = pd.DataFrame(np.ones_like(y_train.values), index=y_train.index)
             w_valid = pd.DataFrame(np.ones_like(y_valid.values), index=y_valid.index)
 
-        save_path = create_save_path(save_path)
+        save_path = get_or_create_path(save_path)
         stop_steps = 0
         train_loss = 0
         best_loss = np.inf

qlib/contrib/model/pytorch_sfm.py

@@ -13,7 +13,7 @@ import logging
 from ...utils import (
     unpack_archive_with_buffer,
     save_multiple_parts_file,
-    create_save_path,
+    get_or_create_path,
     drop_nan_by_y_index,
 )
 from ...log import get_module_logger, TimeInspector
@@ -380,6 +380,7 @@ class SFM(Model):
         x_train, y_train = df_train["feature"], df_train["label"]
         x_valid, y_valid = df_valid["feature"], df_valid["label"]
 
+        save_path = get_or_create_path(save_path)
         stop_steps = 0
         train_loss = 0
         best_score = -np.inf
@@ -412,7 +413,10 @@ class SFM(Model):
                 if stop_steps >= self.early_stop:
                     self.logger.info("early stop")
                     break
+
         self.logger.info("best score: %.6lf @ %d" % (best_score, best_epoch))
+        self.sfm_model.load_state_dict(best_param)
+        torch.save(best_param, save_path)
         if self.device != "cpu":
             torch.cuda.empty_cache()
 

qlib/contrib/model/pytorch_tabnet.py

@@ -12,7 +12,7 @@ import logging
 from ...utils import (
     unpack_archive_with_buffer,
     save_multiple_parts_file,
-    create_save_path,
+    get_or_create_path,
     drop_nan_by_y_index,
 )
 from ...log import get_module_logger, TimeInspector
@@ -117,10 +117,7 @@ class TabnetModel(Model):
             raise NotImplementedError("optimizer {} is not supported!".format(optimizer))
 
     def pretrain_fn(self, dataset=DatasetH, pretrain_file="./pretrain/best.model"):
-        # make a directory if pretrian director does not exist
+        get_or_create_path(pretrain_file)
-        if pretrain_file.startswith("./pretrain") and not os.path.exists("pretrain"):
-            self.logger.info("make folder to store model...")
-            os.makedirs("pretrain")
 
         [df_train, df_valid] = dataset.prepare(
             ["pretrain", "pretrain_validation"],
@@ -181,6 +178,7 @@ class TabnetModel(Model):
         df_train.fillna(df_train.mean(), inplace=True)
         x_train, y_train = df_train["feature"], df_train["label"]
         x_valid, y_valid = df_valid["feature"], df_valid["label"]
+        save_path = get_or_create_path(save_path)
 
         stop_steps = 0
         train_loss = 0
@@ -207,12 +205,16 @@ class TabnetModel(Model):
                 best_score = val_score
                 stop_steps = 0
                 best_epoch = epoch_idx
+                best_param = copy.deepcopy(self.tabnet_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.tabnet_model.load_state_dict(best_param)
+        torch.save(best_param, save_path)
 
     def predict(self, dataset):
         if not self.fitted:

qlib/utils/__init__.py

@@ -24,7 +24,7 @@ import collections
 import numpy as np
 import pandas as pd
 from pathlib import Path
-from typing import Union, Tuple
+from typing import Union, Tuple, Text, Optional
 
 from ..config import C
 from ..log import get_module_logger, set_log_with_config
@@ -276,23 +276,31 @@ def compare_dict_value(src_data: dict, dst_data: dict):
     return changes
 
 
-def create_save_path(save_path=None):
+def get_or_create_path(path: Optional[Text] = None, return_dir: bool = False):
-    """Create save path
+    """Create or get a file or directory given the path and return_dir.
 
     Parameters
     ----------
-    save_path: str
+    path: a string indicates the path or None indicates creating a temporary path.
+    return_dir: if True, create and return a directory; otherwise c&r a file.
 
     """
-    if save_path:
+    if path:
-        if not os.path.exists(save_path):
+        if return_dir and not os.path.exists(path):
-            os.makedirs(save_path)
+            os.makedirs(path)
+        elif not return_dir:  # return a file, thus we need to create its parent directory
+            xpath = os.path.abspath(os.path.join(path, ".."))
+            if not os.path.exists(xpath):
+                os.makedirs(xpath)
     else:
         temp_dir = os.path.expanduser("~/tmp")
         if not os.path.exists(temp_dir):
             os.makedirs(temp_dir)
-        _, save_path = tempfile.mkstemp(dir=temp_dir)
+        if return_dir:
-    return save_path
+            _, path = tempfile.mkdtemp(dir=temp_dir)
+        else:
+            _, path = tempfile.mkstemp(dir=temp_dir)
+    return path
 
 
 @contextlib.contextmanager