From 6a305c73ae51254c9c8d7629b968720bc099ac6f Mon Sep 17 00:00:00 2001
From: Charles Young <cy@unitive.tech>
Date: Mon, 8 Mar 2021 19:08:55 +0800
Subject: [PATCH] Resolve
 https://github.com/microsoft/qlib/pull/280\#discussion_r589166529

---
 qlib/model/riskmodel/base.py       | 14 +++++++++-----
 qlib/model/riskmodel/poet.py       |  2 +-
 qlib/model/riskmodel/shrink.py     |  3 +--
 qlib/model/riskmodel/structured.py | 16 ++++++----------
 4 files changed, 17 insertions(+), 18 deletions(-)

diff --git a/qlib/model/riskmodel/base.py b/qlib/model/riskmodel/base.py
index 89df80e8f..bb067e3d5 100644
--- a/qlib/model/riskmodel/base.py
+++ b/qlib/model/riskmodel/base.py
@@ -38,8 +38,11 @@ class RiskModel(BaseModel):
         self.scale_return = scale_return
 
     def predict(
-            self, X: Union[pd.Series, pd.DataFrame, np.ndarray], return_corr: bool = False, is_price: bool = True,
-            return_decomposed_components=False,
+        self,
+        X: Union[pd.Series, pd.DataFrame, np.ndarray],
+        return_corr: bool = False,
+        is_price: bool = True,
+        return_decomposed_components=False,
     ) -> Union[pd.DataFrame, np.ndarray, tuple]:
         """
         Args:
@@ -53,7 +56,7 @@ class RiskModel(BaseModel):
             pd.DataFrame or np.ndarray: estimated covariance (or correlation).
         """
         assert (
-                not return_corr or not return_decomposed_components
+            not return_corr or not return_decomposed_components
         ), "Can only return either correlation matrix or decomposed components."
 
         # transform input into 2D array
@@ -84,8 +87,9 @@ class RiskModel(BaseModel):
 
         # return decomposed components if needed
         if return_decomposed_components:
-            assert 'return_decomposed_components' in inspect.getfullargspec(self._predict).args, \
-                'This risk model does not support return decomposed components of the covariance matrix '
+            assert (
+                "return_decomposed_components" in inspect.getfullargspec(self._predict).args
+            ), "This risk model does not support return decomposed components of the covariance matrix "
 
             F, cov_b, var_u = self._predict(X, return_decomposed_components=True)
             return F, cov_b, var_u
diff --git a/qlib/model/riskmodel/poet.py b/qlib/model/riskmodel/poet.py
index 8dbe89036..840384555 100644
--- a/qlib/model/riskmodel/poet.py
+++ b/qlib/model/riskmodel/poet.py
@@ -50,7 +50,7 @@ class POETCovEstimator(RiskModel):
         if self.num_factors > 0:
             Dd, V = np.linalg.eig(Y.T.dot(Y))
             V = V[:, np.argsort(Dd)]
-            F = V[:, -self.num_factors:][:, ::-1] * np.sqrt(n)
+            F = V[:, -self.num_factors :][:, ::-1] * np.sqrt(n)
             LamPCA = Y.dot(F) / n
             uhat = np.asarray(Y - LamPCA.dot(F.T))
             Lowrank = np.asarray(LamPCA.dot(LamPCA.T))
diff --git a/qlib/model/riskmodel/shrink.py b/qlib/model/riskmodel/shrink.py
index 1298891fb..3cb2620d1 100644
--- a/qlib/model/riskmodel/shrink.py
+++ b/qlib/model/riskmodel/shrink.py
@@ -248,8 +248,7 @@ class ShrinkCovEstimator(RiskModel):
         roff1 = np.sum(v1 * cov_mkt[:, None].T) / var_mkt - np.sum(np.diag(v1) * cov_mkt) / var_mkt
         v3 = z.T.dot(z) / t - var_mkt * S
         roff3 = (
-                np.sum(v3 * np.outer(cov_mkt, cov_mkt)) / var_mkt ** 2 - np.sum(
-            np.diag(v3) * cov_mkt ** 2) / var_mkt ** 2
+            np.sum(v3 * np.outer(cov_mkt, cov_mkt)) / var_mkt ** 2 - np.sum(np.diag(v3) * cov_mkt ** 2) / var_mkt ** 2
         )
         roff = 2 * roff1 - roff3
         rho = rdiag + roff
diff --git a/qlib/model/riskmodel/structured.py b/qlib/model/riskmodel/structured.py
index 39ff0166e..878503401 100644
--- a/qlib/model/riskmodel/structured.py
+++ b/qlib/model/riskmodel/structured.py
@@ -32,23 +32,19 @@ class StructuredCovEstimator(RiskModel):
     FACTOR_MODEL_FA = "fa"
     DEFAULT_NAN_OPTION = "fill"
 
-    def __init__(
-            self,
-            factor_model: str = "pca",
-            num_factors: int = 10,
-            **kwargs
-    ):
+    def __init__(self, factor_model: str = "pca", num_factors: int = 10, **kwargs):
         """
         Args:
             factor_model (str): the latent factor models used to estimate the structured covariance (`pca`/`fa`).
             num_factors (int): number of components to keep.
             kwargs: see `RiskModel` for more information
         """
-        if 'nan_option' in kwargs.keys():
-            assert kwargs['nan_option'] in [self.DEFAULT_NAN_OPTION], \
-                "nan_option={} is not supported".format(kwargs['nan_option'])
+        if "nan_option" in kwargs.keys():
+            assert kwargs["nan_option"] in [self.DEFAULT_NAN_OPTION], "nan_option={} is not supported".format(
+                kwargs["nan_option"]
+            )
         else:
-            kwargs['nan_option'] = self.DEFAULT_NAN_OPTION
+            kwargs["nan_option"] = self.DEFAULT_NAN_OPTION
 
         super().__init__(**kwargs)