Updates

notebooks/Q/workflow-demo.py

@@ -1,75 +0,0 @@
-import os
-import sys
-import ruamel.yaml as yaml
-import pprint
-import numpy as np
-import pandas as pd
-from pathlib import Path
-
-import qlib
-from qlib import config as qconfig
-from qlib.utils import init_instance_by_config
-from qlib.workflow import R
-from qlib.data.dataset import DatasetH
-from qlib.data.dataset.handler import DataHandlerLP
-
-qlib.init(provider_uri="~/.qlib/qlib_data/cn_data", region=qconfig.REG_CN)
-
-dataset_config = {
-    "class": "DatasetH",
-    "module_path": "qlib.data.dataset",
-    "kwargs": {
-        "handler": {
-            "class": "Alpha360",
-            "module_path": "qlib.contrib.data.handler",
-            "kwargs": {
-                "start_time": "2008-01-01",
-                "end_time": "2020-08-01",
-                "fit_start_time": "2008-01-01",
-                "fit_end_time": "2014-12-31",
-                "instruments": "csi300",
-                "infer_processors": [
-                    {"class": "RobustZScoreNorm", "kwargs": {"fields_group": "feature", "clip_outlier": True}},
-                    {"class": "Fillna", "kwargs": {"fields_group": "feature"}},
-                ],
-                "learn_processors": [
-                    {"class": "DropnaLabel"},
-                    {"class": "CSRankNorm", "kwargs": {"fields_group": "label"}},
-                ],
-               "label": ["Ref($close, -2) / Ref($close, -1) - 1"]
-            },
-        },
-        "segments": {
-            "train": ("2008-01-01", "2014-12-31"),
-            "valid": ("2015-01-01", "2016-12-31"),
-            "test": ("2017-01-01", "2020-08-01"),
-        },
-    },
-}
-
-if __name__ == "__main__":
-
-    qlib_root_dir = (Path(__file__).parent / '..' / '..' / '.latent-data' / 'qlib').resolve()
-    demo_yaml_path = qlib_root_dir / 'examples' / 'benchmarks' / 'GRU' / 'workflow_config_gru_Alpha360.yaml'
-    print('Demo-workflow-yaml: {:}'.format(demo_yaml_path))
-    with open(demo_yaml_path, 'r') as fp:
-      config = yaml.safe_load(fp)
-    pprint.pprint(config['task']['dataset'])
-    
-    dataset = init_instance_by_config(dataset_config)
-    pprint.pprint(dataset_config)
-    pprint.pprint(dataset)
-
-    df_train, df_valid, df_test = dataset.prepare(
-        ["train", "valid", "test"],
-        col_set=["feature", "label"],
-        data_key=DataHandlerLP.DK_L,
-    )
-
-    x_train, y_train = df_train["feature"], df_train["label"]
-
-    import pdb
-
-    pdb.set_trace()
-    print("Complete")
-

notebooks/spaces/test.py

@@ -0,0 +1,75 @@
+import os
+import sys
+import qlib
+import pprint
+import numpy as np
+import pandas as pd
+
+from pathlib import Path
+import torch
+
+__file__ = os.path.dirname(os.path.realpath("__file__"))
+
+lib_dir = (Path(__file__).parent / ".." / "lib").resolve()
+print("library path: {:}".format(lib_dir))
+assert lib_dir.exists(), "{:} does not exist".format(lib_dir)
+if str(lib_dir) not in sys.path:
+    sys.path.insert(0, str(lib_dir))
+
+from trade_models import get_transformer
+
+from qlib import config as qconfig
+from qlib.utils import init_instance_by_config
+from qlib.model.base import Model
+from qlib.data.dataset import DatasetH
+from qlib.data.dataset.handler import DataHandlerLP
+
+qlib.init(provider_uri='~/.qlib/qlib_data/cn_data', region=qconfig.REG_CN)
+
+dataset_config = {
+            "class": "DatasetH",
+            "module_path": "qlib.data.dataset",
+            "kwargs": {
+                "handler": {
+                    "class": "Alpha360",
+                    "module_path": "qlib.contrib.data.handler",
+                    "kwargs": {
+                        "start_time": "2008-01-01",
+                        "end_time": "2020-08-01",
+                        "fit_start_time": "2008-01-01",
+                        "fit_end_time": "2014-12-31",
+                        "instruments": "csi100",
+                    },
+                },
+                "segments": {
+                    "train": ("2008-01-01", "2014-12-31"),
+                    "valid": ("2015-01-01", "2016-12-31"),
+                    "test": ("2017-01-01", "2020-08-01"),
+                },
+            },
+        }
+pprint.pprint(dataset_config)
+dataset = init_instance_by_config(dataset_config)
+
+df_train, df_valid, df_test = dataset.prepare(
+            ["train", "valid", "test"],
+            col_set=["feature", "label"],
+            data_key=DataHandlerLP.DK_L,
+        )
+model = get_transformer(None)
+print(model)
+
+features = torch.from_numpy(df_train["feature"].values).float()
+labels = torch.from_numpy(df_train["label"].values).squeeze().float()
+
+batch = list(range(2000))
+predicts = model(features[batch])
+mask = ~torch.isnan(labels[batch])
+
+pred = predicts[mask]
+label = labels[batch][mask]
+
+loss = torch.nn.functional.mse_loss(pred, label)
+
+from sklearn.metrics import mean_squared_error
+mse_loss = mean_squared_error(pred.numpy(), label.numpy())