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Update hyperopt.py

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Italo
2022-02-06 00:17:48 +00:00
parent 992eac9efa
commit 6a4cae1f8c
1 changed files with 3 additions and 3 deletions
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6
freqtrade/optimize/hyperopt.py
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@@ -540,7 +540,7 @@ class Hyperopt:
if not hasattr(self, 'mse_list'): if not hasattr(self, 'mse_list'):
self.mse_list = [] self.mse_list = []
model = clone(res.models[-1]) # model = clone(res.models[-1])
# i_subset = random.sample(range(len(res.x_iters)), 100) if len(res.x_iters) > 100 else range(len(res.x_iters)) # i_subset = random.sample(range(len(res.x_iters)), 100) if len(res.x_iters) > 100 else range(len(res.x_iters))
# i_train = random.sample(i_subset, round(.8*len(i_subset))) # get 80% random indices # i_train = random.sample(i_subset, round(.8*len(i_subset))) # get 80% random indices
@@ -550,11 +550,11 @@ class Hyperopt:
# i_test = [i for i in i_subset if i not in i_train] # get 20% random indices # i_test = [i for i in i_subset if i not in i_train] # get 20% random indices
# x_test = [x for i, x in enumerate(res.x_iters) if i in i_test] # x_test = [x for i, x in enumerate(res.x_iters) if i in i_test]
# y_test = [y for i, y in enumerate(res.func_vals) if i in i_test] # y_test = [y for i, y in enumerate(res.func_vals) if i in i_test]
model.fit(res.x_iters, res.func_vals) # model.fit(res.x_iters, res.func_vals)
# Perform a cross-validation estimate of the coefficient of determination using # Perform a cross-validation estimate of the coefficient of determination using
# the cross_validation module using all CPUs available on the machine # the cross_validation module using all CPUs available on the machine
# K = 5 # folds # K = 5 # folds
R2 = cross_val_score(model, X=res.x_iters, y=res.func_vals, cv=5, n_jobs=jobs).mean() R2 = cross_val_score(res.models[-1], X=res.x_iters, y=res.func_vals, cv=5, n_jobs=jobs).mean()
print(f'R2: {R2}') print(f'R2: {R2}')
R2 = R2 if R2 > -5 else -5 R2 = R2 if R2 > -5 else -5
self.mse_list.append(R2) self.mse_list.append(R2)