use black(1) for uniform code formatting

1 files changed, 24 insertions, 14 deletions

diff --git a/bin/gptest.py b/bin/gptest.py
index 869328e..82b4575 100755
--- a/bin/gptest.py
+++ b/bin/gptest.py
@@ -2,10 +2,16 @@
 
 import sys
 import numpy as np
-from dfatool.dfatool import PTAModel, RawData, regression_measures, pta_trace_to_aggregate
+from dfatool.dfatool import (
+    PTAModel,
+    RawData,
+    regression_measures,
+    pta_trace_to_aggregate,
+)
 from gplearn.genetic import SymbolicRegressor
 from multiprocessing import Pool
 
+
 def splitidx_srs(length):
     shuffled = np.random.permutation(np.arange(length))
     border = int(length * float(2) / 3)
@@ -13,16 +19,17 @@ def splitidx_srs(length):
     validation = shuffled[border:]
     return (training, validation)
 
+
 def _gp_fit(arg):
     param = arg[0]
     X = arg[1]
     Y = arg[2]
     est_gp = SymbolicRegressor(
-        population_size = param[0],
-        generations = 450,
-        parsimony_coefficient = param[1],
-        function_set = param[2].split(' '),
-        const_range = (-param[3], param[3])
+        population_size=param[0],
+        generations=450,
+        parsimony_coefficient=param[1],
+        function_set=param[2].split(" "),
+        const_range=(-param[3], param[3]),
     )
 
     training, validation = splitidx_srs(len(Y))
@@ -33,22 +40,27 @@ def _gp_fit(arg):
 
     try:
         est_gp.fit(X_train, Y_train)
-        return (param, str(est_gp._program), est_gp._program.raw_fitness_, regression_measures(est_gp.predict(X_validation), Y_validation))
+        return (
+            param,
+            str(est_gp._program),
+            est_gp._program.raw_fitness_,
+            regression_measures(est_gp.predict(X_validation), Y_validation),
+        )
     except Exception as e:
-        return (param, 'Exception: {}'.format(str(e)), 999999999)
+        return (param, "Exception: {}".format(str(e)), 999999999)
 
 
-if __name__ == '__main__':
+if __name__ == "__main__":
     population_size = [100, 500, 1000, 2000, 5000, 10000]
     parsimony_coefficient = [0.1, 0.5, 0.1, 1]
-    function_set = ['add mul', 'add mul sub div', 'add mul sub div sqrt log inv']
+    function_set = ["add mul", "add mul sub div", "add mul sub div sqrt log inv"]
     const_lim = [100000, 50000, 10000, 1000, 500, 10, 1]
     filenames = sys.argv[4:]
     raw_data = RawData(filenames)
 
     preprocessed_data = raw_data.get_preprocessed_data()
     by_name, parameters, arg_count = pta_trace_to_aggregate(preprocessed_data)
-    model = PTAModel(by_name, parameters, arg_count, traces = preprocessed_data)
+    model = PTAModel(by_name, parameters, arg_count, traces=preprocessed_data)
 
     by_param = model.by_param
 
@@ -61,14 +73,12 @@ if __name__ == '__main__':
     X = [[] for i in range(dimension)]
     Y = []
 
-
     for key, val in by_param.items():
         if key[0] == state_or_tran and len(key[1]) == dimension:
             Y.extend(val[model_attribute])
             for i in range(dimension):
                 X[i].extend([float(key[1][i])] * len(val[model_attribute]))
 
-
     X = np.array(X)
     Y = np.array(Y)
 
@@ -85,4 +95,4 @@ if __name__ == '__main__':
         results = pool.map(_gp_fit, paramqueue)
 
     for res in sorted(results, key=lambda r: r[2]):
-        print('{} {:.0f} ({:.0f})\n{}'.format(res[0], res[3]['mae'], res[2], res[1]))
+        print("{} {:.0f} ({:.0f})\n{}".format(res[0], res[3]["mae"], res[2], res[1]))