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")
-