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chore(Linter): reformatted code with black (#211)

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* chore(Linter): reformatted code with black

* Create black.yaml
This commit is contained in:
Mark Aron Szulyovszky
2022-02-17 19:22:17 +01:00
committed by GitHub
parent f3fee4a4e1
commit 8dd2d88740
101 changed files with 2595 additions and 2319 deletions
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tests/test_walk_forward.py
+14 -16
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@@ -6,10 +6,10 @@ from sklearn.base import BaseEstimator, ClassifierMixin
no_of_rows = 100
def __generate_incremental_test_data(no_of_rows) -> tuple[pd.DataFrame, pd.Series]:
''' Test data, where X[n][any_column] == y[n]+1
'''
"""Test data, where X[n][any_column] == y[n]+1"""
no_columns = 6
X = [[row] * no_columns for row in range(no_of_rows)]
assert X[1][0] == 1
@@ -18,7 +18,7 @@ def __generate_incremental_test_data(no_of_rows) -> tuple[pd.DataFrame, pd.Serie
assert X[4][0] == 4
X = pd.DataFrame(X)
y = [row+1 for row in range(no_of_rows)]
y = [row + 1 for row in range(no_of_rows)]
assert y[1] == 2
assert y[2] == 3
assert y[3] == 4
@@ -27,17 +27,15 @@ def __generate_incremental_test_data(no_of_rows) -> tuple[pd.DataFrame, pd.Serie
return X, y
class IncrementingStubModel(Model, BaseEstimator, ClassifierMixin):
'''
"""
A deteministic model that can predict the future with 100% accuracy
It verifies that the X[n][any_column]+1 == y[n]
'''
"""
data_transformation = "original"
only_column = None
predict_window_size = 'single_timestamp'
predict_window_size = "single_timestamp"
def __init__(self, window_length) -> None:
super().__init__()
@@ -53,14 +51,15 @@ class IncrementingStubModel(Model, BaseEstimator, ClassifierMixin):
def predict_proba(self, X):
return np.array([[row[0] + 1, 0] for row in X])
def test_walk_forward_train_test():
X, y = __generate_incremental_test_data(no_of_rows)
window_length = 10
retrain_every = 10
model = IncrementingStubModel(window_length = window_length)
model = IncrementingStubModel(window_length=window_length)
model_over_time = walk_forward_train(
model=model,
@@ -74,7 +73,7 @@ def test_walk_forward_train_test():
transformations_over_time=[],
)
predictions, _ = walk_forward_inference(
model_name='test',
model_name="test",
model_over_time=model_over_time,
transformations_over_time=[],
X=X,
@@ -83,8 +82,7 @@ def test_walk_forward_train_test():
retrain_every=retrain_every,
from_index=None,
)
# verify if predictions are the same as y
for i in range(window_length+2, no_of_rows):
assert predictions[i] == y[i]
# verify if predictions are the same as y
for i in range(window_length + 2, no_of_rows):
assert predictions[i] == y[i]