Formatting

Author: franflame

foqus_lib/framework/surrogate/keras_nn.py

@@ -57,11 +57,11 @@
     BaseScaler,
     LinearScaler,
     LogScaler,
-    LogScaler2, 
-    PowerScaler, 
+    LogScaler2,
+    PowerScaler,
     PowerScaler2,
     map_name_to_scaler,
-    scale_dataframe
+    scale_dataframe,
 )
 
 # mapping between the human-readable name for the scaling variant

foqus_lib/framework/surrogate/pytorch_nn.py

@@ -54,11 +54,11 @@
     BaseScaler,
     LinearScaler,
     LogScaler,
-    LogScaler2, 
-    PowerScaler, 
+    LogScaler2,
+    PowerScaler,
     PowerScaler2,
     map_name_to_scaler,
-    scale_dataframe
+    scale_dataframe,
 )
 
 # custom class to define Keras NN layers

foqus_lib/framework/surrogate/scaling.py

@@ -19,7 +19,8 @@ def validate_for_scaling(array_in, lo, hi) -> None:
     if lo == hi:
         raise ValueError("Array must contain non-identical values")
     if not check_under_or_overflow(array_in):
-        raise ValueError("Array contains under/overflow values for dtype")        
+        raise ValueError("Array contains under/overflow values for dtype")
+
 
 def check_under_or_overflow(arr):
     if np.issubdtype(arr.dtype, np.integer):
@@ -28,7 +29,7 @@ def check_under_or_overflow(arr):
         info = np.finfo(arr.dtype)
     else:
         raise ValueError("Unsupported data type")
-    max_value = info.max 
+    max_value = info.max
     min_value = info.min
     return np.all(arr < max_value) & np.all(arr > min_value)
 
@@ -45,19 +46,20 @@ def scale_linear(array_in, lo=None, hi=None):
         result = (array_in - lo) / (hi - lo)
     return result
 
+
 def scale_log(array_in, lo=None, hi=None):
-# need to account for log domain 
+    # need to account for log domain
     if np.any(array_in <= 0):
         raise ValueError("All values must be > 0 to use scale_log")
     if lo is None:
         lo = np.min(array_in)
     if hi is None:
         hi = np.max(array_in)
     validate_for_scaling(array_in, lo, hi)
-    result = ((np.log10(array_in) - np.log10(lo))
-                 / (np.log10(hi) - np.log10(lo)))
+    result = (np.log10(array_in) - np.log10(lo)) / (np.log10(hi) - np.log10(lo))
     return result
 
+
 def scale_log2(array_in, lo=None, hi=None):
     if lo is None:
         lo = np.min(array_in)
@@ -66,71 +68,76 @@ def scale_log2(array_in, lo=None, hi=None):
     validate_for_scaling(array_in, lo, hi)
     result = np.log10(9 * (array_in - lo) / (hi - lo) + 1)
     return result
+
+
 # fix expected values in test
 
+
 def scale_power(array_in, lo=None, hi=None):
     if lo is None:
         lo = np.min(array_in)
     if hi is None:
         hi = np.max(array_in)
     validate_for_scaling(array_in, lo, hi)
-    result = (np.power(10, array_in) - np.power(10, lo)) / (np.power(10, hi) - np.power(10, lo))
+    result = (np.power(10, array_in) - np.power(10, lo)) / (
+        np.power(10, hi) - np.power(10, lo)
+    )
     return result
 
+
 def scale_power2(array_in, lo=None, hi=None):
     if lo is None:
         lo = np.min(array_in)
     if hi is None:
         hi = np.max(array_in)
     validate_for_scaling(array_in, lo, hi)
-    result = (1/9 *
-                (np.power(10, (array_in - lo) / (hi - lo)) - 1)
-                )
+    result = 1 / 9 * (np.power(10, (array_in - lo) / (hi - lo)) - 1)
     return result
 
+
 def unscale_linear(array_in, lo, hi):
     result = array_in * (hi - lo) / 1.0 + lo
     return result
 
+
 def unscale_log(array_in, lo, hi):
     result = lo * np.power(hi / lo, array_in)
 
     # result = ((np.log10(array_in) - np.log10(lo))
     #              / (np.log10(hi) - np.log10(lo)))
-# out = math.pow(lo * (hi / lo), (array_in / 10.0))
-#                 out = (
-#                     10
-#                     * (math.log10(array_in) - math.log10(lo))
-#                     / (math.log10(hi) - math.log10(lo))
-#                 )
+    # out = math.pow(lo * (hi / lo), (array_in / 10.0))
+    #                 out = (
+    #                     10
+    #                     * (math.log10(array_in) - math.log10(lo))
+    #                     / (math.log10(hi) - math.log10(lo))
+    #                 )
     return result
 
+
 def unscale_log2(array_in, lo=None, hi=None):
-    result = (np.power(10, array_in / 1.0) - 1) * (
-                        hi - lo
-                    ) / 9.0 + lo
-                # out = (math.pow(10, array_in / 10.0) - 1) * (
-                #     hi - lo
-                # ) / 9.0 + lo
-                
+    result = (np.power(10, array_in / 1.0) - 1) * (hi - lo) / 9.0 + lo
+    # out = (math.pow(10, array_in / 10.0) - 1) * (
+    #     hi - lo
+    # ) / 9.0 + lo
+
     return result
 
+
 def unscale_power(array_in, lo, hi):
-    # check if lo and hi were provided 
+    # check if lo and hi were provided
     # result = np.log10((array_in / 10.0) * (np.power(10, hi) - np.power(10, lo))
     #                 + np.power(10, lo))
     result = np.log10(
-                    (array_in / 1.0) * (np.power(10, hi) - np.power(10, lo))
-                    + np.power(10, lo)
-                )
+        (array_in / 1.0) * (np.power(10, hi) - np.power(10, lo)) + np.power(10, lo)
+    )
     return result
 
+
 def unscale_power2(array_in, lo, hi):
-    result = (
-                    np.log10(9.0 * array_in / 1.0 + 1) * (hi - lo) + lo
-                )
+    result = np.log10(9.0 * array_in / 1.0 + 1) * (hi - lo) + lo
     return result
 
+
 class BaseScaler:
     # def __init__(self, data_array: np.ndarray):
     #     self.data = data_array
@@ -143,11 +150,7 @@ def fit(self, X: np.ndarray):
         return self
 
     def fit_transform(self, X: np.ndarray) -> np.ndarray:
-        return (
-            self
-            .fit(X)
-            .transform(X)
-        )
+        return self.fit(X).transform(X)
 
     def transform(self, X: np.ndarray) -> np.ndarray:
         raise NotImplementedError
@@ -162,44 +165,50 @@ def transform(self, X: np.ndarray) -> np.ndarray:
 
     def inverse_transform(self, X: np.ndarray) -> np.ndarray:
         return unscale_linear(X, self.lo_, self.hi_)
-    
+
+
 class LogScaler(BaseScaler):
     def transform(self, X: np.ndarray) -> np.ndarray:
         return scale_log(X, self.lo_, self.hi_)
 
     def inverse_transform(self, X: np.ndarray) -> np.ndarray:
         return unscale_log(X, self.lo_, self.hi_)
 
+
 class LogScaler2(BaseScaler):
     def transform(self, X: np.ndarray) -> np.ndarray:
         return scale_log2(X, self.lo_, self.hi_)
 
     def inverse_transform(self, X: np.ndarray) -> np.ndarray:
         return unscale_log2(X, self.lo_, self.hi_)
 
+
 class PowerScaler(BaseScaler):
     def transform(self, X: np.ndarray) -> np.ndarray:
         return scale_power(X, self.lo_, self.hi_)
 
     def inverse_transform(self, X: np.ndarray) -> np.ndarray:
         return unscale_power(X, self.lo_, self.hi_)
 
+
 class PowerScaler2(BaseScaler):
     def transform(self, X: np.ndarray) -> np.ndarray:
         return scale_power2(X, self.lo_, self.hi_)
 
     def inverse_transform(self, X: np.ndarray) -> np.ndarray:
         return unscale_power2(X, self.lo_, self.hi_)
 
+
 map_name_to_scaler = {
     "Linear": LinearScaler(),
     "Log": LogScaler(),
     "Log2": LogScaler2(),
     "Power": PowerScaler(),
     "Power2": PowerScaler2(),
-    #...
+    # ...
 }
 
+
 def scale_dataframe(df: pd.DataFrame, scaler: BaseScaler) -> Tuple[pd.DataFrame, dict]:
     scaled_df = pd.DataFrame(np.nan, columns=df.columns, index=df.index)
     bounds = {}

foqus_lib/framework/surrogate/scikit_nn.py

@@ -56,13 +56,14 @@
     BaseScaler,
     LinearScaler,
     LogScaler,
-    LogScaler2, 
-    PowerScaler, 
+    LogScaler2,
+    PowerScaler,
     PowerScaler2,
     map_name_to_scaler,
-    scale_dataframe
+    scale_dataframe,
 )
 
+
 def validate_training_data(xdata: np.ndarray, zdata: np.ndarray):
     number_columns_in_xdata = xdata.shape[1]
     number_columns_in_zdata = zdata.shape[1]