Refine DDG-DA (#1472)

* Run ddg-da successfully

* Support include valid; More parameters

* Support L2 reg & visualization

* Blackformat

* Enable fill_method

* Support specify handler & optim dataset

* Fix Pylint

qlib/contrib/model/linear.py

@@ -4,6 +4,7 @@
 import numpy as np
 import pandas as pd
 from typing import Text, Union
+from qlib.log import get_module_logger
 from qlib.data.dataset.weight import Reweighter
 from scipy.optimize import nnls
 from sklearn.linear_model import LinearRegression, Ridge, Lasso
@@ -29,7 +30,7 @@ class LinearModel(Model):
     RIDGE = "ridge"
     LASSO = "lasso"
 
-    def __init__(self, estimator="ols", alpha=0.0, fit_intercept=False):
+    def __init__(self, estimator="ols", alpha=0.0, fit_intercept=False, include_valid: bool = False):
         """
         Parameters
         ----------
@@ -39,6 +40,9 @@ class LinearModel(Model):
             l1 or l2 regularization parameter
         fit_intercept : bool
             whether fit intercept
+        include_valid: bool
+            Should the validation data be included for training?
+            The validation data should be included
         """
         assert estimator in [self.OLS, self.NNLS, self.RIDGE, self.LASSO], f"unsupported estimator `{estimator}`"
         self.estimator = estimator
@@ -49,9 +53,16 @@ class LinearModel(Model):
         self.fit_intercept = fit_intercept
 
         self.coef_ = None
+        self.include_valid = include_valid
 
     def fit(self, dataset: DatasetH, reweighter: Reweighter = None):
         df_train = dataset.prepare("train", col_set=["feature", "label"], data_key=DataHandlerLP.DK_L)
+        if self.include_valid:
+            try:
+                df_valid = dataset.prepare("valid", col_set=["feature", "label"], data_key=DataHandlerLP.DK_L)
+                df_train = pd.concat([df_train, df_valid])
+            except KeyError:
+                get_module_logger("LinearModel").info("include_valid=True, but valid does not exist")
         if df_train.empty:
             raise ValueError("Empty data from dataset, please check your dataset config.")
         if reweighter is not None: