add finetune example & fix serial bug

examples/workflow_by_code_finetune.py

@@ -0,0 +1,131 @@
+#  Copyright (c) Microsoft Corporation.
+#  Licensed under the MIT License.
+
+import sys
+from pathlib import Path
+
+import qlib
+import pandas as pd
+from qlib.config import REG_CN
+from qlib.contrib.model.gbdt import LGBModel
+from qlib.contrib.data.handler import Alpha158
+from qlib.contrib.strategy.strategy import TopkDropoutStrategy
+from qlib.contrib.evaluate import (
+    backtest as normal_backtest,
+    risk_analysis,
+)
+from qlib.utils import exists_qlib_data, init_instance_by_config
+from qlib.workflow import R
+from qlib.workflow.record_temp import SignalRecord, PortAnaRecord
+
+
+if __name__ == "__main__":
+
+    # use default data
+    provider_uri = "~/.qlib/qlib_data/cn_data"  # target_dir
+    if not exists_qlib_data(provider_uri):
+        print(f"Qlib data is not found in {provider_uri}")
+        sys.path.append(str(Path(__file__).resolve().parent.parent.joinpath("scripts")))
+        from get_data import GetData
+
+        GetData().qlib_data_cn(target_dir=provider_uri)
+
+    qlib.init(provider_uri=provider_uri, region=REG_CN)
+
+    MARKET = "csi300"
+    BENCHMARK = "SH000300"
+
+    ###################################
+    # train model
+    ###################################
+    DATA_HANDLER_CONFIG = {
+        "start_time": "2008-01-01",
+        "end_time": "2020-08-01",
+        "fit_start_time": "2008-01-01",
+        "fit_end_time": "2014-12-31",
+        "instruments": MARKET,
+    }
+
+    task = {
+        "model": {
+            "class": "LGBModel",
+            "module_path": "qlib.contrib.model.gbdt",
+            "kwargs": {
+                "loss": "mse",
+                "colsample_bytree": 0.8879,
+                "learning_rate": 0.0421,
+                "subsample": 0.8789,
+                "lambda_l1": 205.6999,
+                "lambda_l2": 580.9768,
+                "max_depth": 8,
+                "num_leaves": 210,
+                "num_threads": 20,
+            },
+        },
+        "dataset": {
+            "class": "DatasetH",
+            "module_path": "qlib.data.dataset",
+            "kwargs": {
+                "handler": {
+                    "class": "Alpha158",
+                    "module_path": "qlib.contrib.data.handler",
+                    "kwargs": DATA_HANDLER_CONFIG,
+                },
+                "segments": {
+                    "train": ("2008-01-01", "2014-12-31"),
+                    "valid": ("2015-01-01", "2016-12-31"),
+                    "test": ("2017-01-01", "2020-08-01"),
+                },
+            },
+        },
+        # You shoud record the data in specific sequence
+        "record": ["SignalRecord", "PortAnaRecord"],
+    }
+
+    port_analysis_config = {
+        "strategy": {
+            "class": "TopkDropoutStrategy",
+            "module_path": "qlib.contrib.strategy.strategy",
+            "kwargs": {
+                "topk": 50,
+                "n_drop": 5,
+            }
+        },
+        "backtest": {
+            "verbose": False,
+            "limit_threshold": 0.095,
+            "account": 100000000,
+            "benchmark": BENCHMARK,
+            "deal_price": "close",
+            "open_cost": 0.0005,
+            "close_cost": 0.0015,
+            "min_cost": 5,
+        },
+    }
+
+    # model initiaiton
+    model = init_instance_by_config(task["model"])
+    dataset = init_instance_by_config(task["dataset"])
+
+    # start exp to train init model
+    with R.start(experiment_name="init models"):
+        model.fit(dataset)
+        R.save_objects(init_model=model)
+        rid = R.get_recorder().id
+
+
+    # Finetune model based on previous trained model
+    with R.start(experiment_name="finetune model"):
+        recorder = R.get_recorder(rid, experiment_name="init models")
+        model = recorder.load_object("init_model")
+        model.finetune(dataset, num_boost_round=10)
+        R.save_objects(model=model)
+
+        # prediction
+        recorder = R.get_recorder()
+        sr = SignalRecord(model, dataset, recorder)
+        sr.generate()
+
+        # backtest
+        par = PortAnaRecord(recorder, port_analysis_config)
+        par.generate()

qlib/contrib/model/gbdt.py

@@ -5,56 +5,54 @@ import numpy as np
 import pandas as pd
 import lightgbm as lgb
 
-from ...model.base import Model
+from ...model.base import ModelFT
 from ...data.dataset import DatasetH
 from ...data.dataset.handler import DataHandlerLP
 
 
-class LGBModel(Model):
+class LGBModel(ModelFT):
     """LightGBM Model"""
-
     def __init__(self, loss="mse", **kwargs):
         if loss not in {"mse", "binary"}:
             raise NotImplementedError
-        self._params = {"objective": loss}
-        self._params.update(kwargs)
+        self.params = {"objective": loss}
+        self.params.update(kwargs)
         self.model = None
 
-    def fit(
-        self,
-        dataset: DatasetH,
-        num_boost_round=1000,
-        early_stopping_rounds=50,
-        verbose_eval=20,
-        evals_result=dict(),
-        **kwargs
-    ):
-
-        df_train, df_valid = dataset.prepare(
-            ["train", "valid"], col_set=["feature", "label"], data_key=DataHandlerLP.DK_L
-        )
+    def _prepare_data(self, dataset: DatasetH):
+        df_train, df_valid = dataset.prepare(["train", "valid"],
+                                             col_set=["feature", "label"],
+                                             data_key=DataHandlerLP.DK_L)
         x_train, y_train = df_train["feature"], df_train["label"]
         x_valid, y_valid = df_valid["feature"], df_valid["label"]
 
         # Lightgbm need 1D array as its label
         if y_train.values.ndim == 2 and y_train.values.shape[1] == 1:
-            y_train_1d, y_valid_1d = np.squeeze(y_train.values), np.squeeze(y_valid.values)
+            y_train, y_valid = np.squeeze(y_train.values), np.squeeze(y_valid.values)
         else:
             raise ValueError("LightGBM doesn't support multi-label training")
 
-        dtrain = lgb.Dataset(x_train.values, label=y_train_1d)
-        dvalid = lgb.Dataset(x_valid.values, label=y_valid_1d)
-        self.model = lgb.train(
-            self._params,
-            dtrain,
-            num_boost_round=num_boost_round,
-            valid_sets=[dtrain, dvalid],
-            valid_names=["train", "valid"],
-            early_stopping_rounds=early_stopping_rounds,
-            verbose_eval=verbose_eval,
-            evals_result=evals_result,
-            **kwargs
-        )
+        dtrain = lgb.Dataset(x_train.values, label=y_train)
+        dvalid = lgb.Dataset(x_valid.values, label=y_valid)
+        return dtrain, dvalid
+
+    def fit(self,
+            dataset: DatasetH,
+            num_boost_round=1000,
+            early_stopping_rounds=50,
+            verbose_eval=20,
+            evals_result=dict(),
+            **kwargs):
+        dtrain, dvalid = self._prepare_data(dataset)
+        self.model = lgb.train(self.params,
+                               dtrain,
+                               num_boost_round=num_boost_round,
+                               valid_sets=[dtrain, dvalid],
+                               valid_names=["train", "valid"],
+                               early_stopping_rounds=early_stopping_rounds,
+                               verbose_eval=verbose_eval,
+                               evals_result=evals_result,
+                               **kwargs)
         evals_result["train"] = list(evals_result["train"].values())[0]
         evals_result["valid"] = list(evals_result["valid"].values())[0]
 
@@ -63,3 +61,25 @@ class LGBModel(Model):
             raise ValueError("model is not fitted yet!")
         x_test = dataset.prepare("test", col_set="feature")
         return pd.Series(self.model.predict(np.squeeze(x_test.values)), index=x_test.index)
+
+    def finetune(self, dataset: DatasetH, num_boost_round=10, verbose_eval=20):
+        """
+        finetune model
+
+        Parameters
+        ----------
+        dataset : DatasetH
+            dataset for finetuning
+        num_boost_round : int
+            number of round to finetune model
+        verbose_eval : int
+            verbose level
+        """
+        dtrain, _ = self._prepare_data(dataset)
+        self.model = lgb.train(self.params,
+                               dtrain,
+                               num_boost_round=num_boost_round,
+                               init_model=self.model,
+                               valid_sets=[dtrain],
+                               valid_names=["train"],
+                               verbose_eval=verbose_eval)

qlib/model/base.py

@@ -45,3 +45,18 @@ class Model(BaseModel):
             dataset will generate the processed dataset from model training
         """
         raise NotImplementedError()
+
+
+class ModelFT(Model):
+    '''Model (F)ine(t)unable'''
+
+    @abc.abstractmethod
+    def finetune(self, dataset: Dataset):
+        """finetune model based given dataset
+
+        Parameters
+        ----------
+        dataset : Dataset
+            dataset will generate the processed dataset from model training
+        """
+        raise NotImplementedError()

qlib/utils/serial.py

@@ -8,11 +8,11 @@ import pickle
 class Serializable:
     """
     Serializable behaves like pickle.
-    But it only save the state whose name starts with `_`
+    But it only saves the state whose name **does not** start with `_`
     """
 
     def __getstate__(self) -> dict:
-        return {k: v for k, v in self.__dict__.items() if k.startswith("_")}
+        return {k: v for k, v in self.__dict__.items() if not k.startswith("_")}
 
     def __setstate__(self, state: dict):
         self.__dict__.update(state)

qlib/workflow/exp.py

@@ -226,7 +226,7 @@ class MLflowExperiment(Experiment):
                     return self.active_recorder
                 else:
                     raise Exception(
-                        "Something went wrong when retrieving recorders. Please check if QlibRecorder is running or the name/id of the recorder is correct."
+                        "Something went wrong when retrieving recorders. Please check if QlibRecorder is running."
                     )
         else:
             if recorder_id is not None:
@@ -235,7 +235,7 @@ class MLflowExperiment(Experiment):
                 else:
                     # mlflow does not support create a run with given id
                     raise Exception(
-                        "Something went wrong when retrieving recorders. Please check if QlibRecorder is running or the name/id of the recorder is correct."
+                        "Something went wrong when retrieving recorders. Please check if id of the recorder is correct."
                     )
             else:
                 for rid in recorders:
@@ -250,7 +250,7 @@ class MLflowExperiment(Experiment):
                     return recorder
                 else:
                     raise Exception(
-                        "Something went wrong when retrieving experiments. Please check if QlibRecorder is running or the name/id of the experiment is correct."
+                        "Something went wrong when retrieving experiments. Please check if the name of the experiment is correct."
                     )
 
     def list_recorders(self):