8000 Fix numpy depreciation errors · scikit-optimize/scikit-optimize@863575f · GitHub
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Commit 863575f

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xmatthiasglouppe
xmatthias
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glouppe
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Fix numpy depreciation errors
1 parent 273db8f commit 863575f

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3 files changed

+7
-7
lines changed

3 files changed

+7
-7
lines changed

‎skopt/learning/gaussian_process/kernels.py

Lines changed: 1 addition & 1 deletion
Original file line numberDiff line numberDiff line change
@@ -378,7 +378,7 @@ def __call__(self, X, Y=None, eval_gradient=False):
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379379
if np.iterable(length_scale):
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if len(length_scale) > 1:
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length_scale = np.asarray(length_scale, dtype=np.float)
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length_scale = np.asarray(length_scale, dtype=float)
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else:
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length_scale = float(length_scale[0])
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else:

‎skopt/space/space.py

Lines changed: 4 additions & 4 deletions
Original file line numberDiff line numberDiff line change
@@ -244,13 +244,13 @@ class Real(Dimension):
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name : str or None
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Name associated with the dimension, e.g., "learning rate".
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dtype : str or dtype, default=np.float
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dtype : str or dtype, default=np.float64
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float type which will be used in inverse_transform,
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can be float.
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"""
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def __init__(self, low, high, prior="uniform", base=10, transform=None,
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name=None, dtype=np.float):
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name=None, dtype=float):
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if high <= low:
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raise ValueError("the lower bound {} has to be less than the"
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" upper bound {}".format(low, high))
@@ -273,8 +273,8 @@ def __init__(self, low, high, prior="uniform", base=10, transform=None,
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"or 'float64'"
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" got {}".format(self.dtype))
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elif isinstance(self.dtype, type) and self.dtype\
276-
not in [float, np.float, np.float16, np.float32, np.float64]:
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raise ValueError("dtype must be float, np.float"
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not in [float, np.float16, np.float32, np.float64]:
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raise ValueError("dtype must be float, np.float64"
278278
" got {}".format(self.dtype))
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if transform is None:

‎skopt/space/transformers.py

Lines changed: 2 additions & 2 deletions
Original file line numberDiff line numberDiff line change
@@ -89,10 +89,10 @@ def __init__(self, base):
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self._base = base
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def transform(self, X):
92-
return np.log10(np.asarray(X, dtype=np.float)) / np.log10(self._base)
92+
return np.log10(np.asarray(X, dtype=float)) / np.log10(self._base)
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def inverse_transform(self, Xt):
95-
return self._base ** np.asarray(Xt, dtype=np.float)
95+
return self._base ** np.asarray(Xt, dtype=float)
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class CategoricalEncoder(Transformer):

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