transform
cod_to_nat_X(cod_X, cod_type, min_X=None, max_X=None, mean_X=None, std_X=None)
¶
Compute natural X-values from coded units based on the
setting of the cod_type
attribute. If cod_type
is “norm”, the values are
de-normalized from [0,1]. If cod_type
is “std”, the values are de-standardized.
Otherwise, the values are not modified.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
cod_X |
array
|
The coded X-values. |
required |
cod_type |
str
|
The type of coding (“norm”, “std”, or other). |
required |
min_X |
array
|
The minimum values of X. Defaults to None. |
None
|
max_X |
array
|
The maximum values of X. Defaults to None. |
None
|
mean_X |
array
|
The mean values of X. Defaults to None. |
None
|
std_X |
array
|
The standard deviation of X. Defaults to None. |
None
|
Returns:
Name | Type | Description |
---|---|---|
X |
array
|
The natural (physical or real world) X-values. |
Source code in spotpython/utils/transform.py
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|
cod_to_nat_y(cod_y, cod_type, min_y=None, max_y=None, mean_y=None, std_y=None)
¶
Compute natural y-values from coded units based on the
setting of the cod_type
attribute. If cod_type
is “norm”, the values are
de-normalized from [0,1]. If cod_type
is “std”, the values are de-standardized.
Otherwise, the values are not modified.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
cod_y |
array
|
The coded y-values. |
required |
cod_type |
str
|
The type of coding (“norm”, “std”, or other). |
required |
min_y |
array
|
The minimum values of y. Defaults to None. |
None
|
max_y |
array
|
The maximum values of y. Defaults to None. |
None
|
mean_y |
array
|
The mean values of y. Defaults to None. |
None
|
std_y |
array
|
The standard deviation of y. Defaults to None. |
None
|
Returns:
Name | Type | Description |
---|---|---|
y |
array
|
The natural (physical or real world) y-values. |
Source code in spotpython/utils/transform.py
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|
nat_to_cod_X(X, cod_type)
¶
Compute coded X-values from natural (physical or real world) units based on the
setting of the cod_type
attribute. If cod_type
is “norm”, the values are
normalized to [0,1]. If cod_type
is “std”, the values are standardized.
Otherwise, the values are not modified.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
X |
array
|
The input array. |
required |
cod_type |
str
|
The type of coding (“norm”, “std”, or other). |
required |
Returns:
Name | Type | Description |
---|---|---|
cod_X |
array
|
The coded X-values. |
min_X |
array
|
The minimum values of X. |
max_X |
array
|
The maximum values of X. |
mean_X |
array
|
The mean values of X. |
std_X |
array
|
The standard deviation of X. |
Source code in spotpython/utils/transform.py
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|
nat_to_cod_y(y, cod_type)
¶
Compute coded y-values from natural (physical or real world) units based on the
setting of the cod_type
attribute. If cod_type
is “norm”, the values are
normalized to [0,1]. If cod_type
is “std”, the values are standardized.
Otherwise, the values are not modified.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
y |
array
|
The input array. |
required |
cod_type |
str
|
The type of coding (“norm”, “std”, or other). |
required |
Returns:
Name | Type | Description |
---|---|---|
cod_y |
array
|
The coded y-values. |
min_y |
array
|
The minimum values of y. |
max_y |
array
|
The maximum values of y. |
mean_y |
array
|
The mean values of y. |
std_y |
array
|
The standard deviation of y. |
Source code in spotpython/utils/transform.py
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|
scale(X, lower, upper)
¶
Sample scaling from unit hypercube to different bounds. Converts a sample from [0, 1)
to [a, b)
.
The following transformation is used:
(b - a) * X + a
Note
equal lower and upper bounds are feasible.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
X |
array
|
Sample to scale. |
required |
lower |
array
|
lower bound of transformed data. |
required |
upper |
array
|
upper bounds of transformed data. |
required |
Returns:
Type | Description |
---|---|
array
|
Scaled sample. |
Examples:
Transform three samples in the unit hypercube to (lower, upper) bounds:
>>> import numpy as np
>>> from scipy.stats import qmc
>>> from spotpython.utils.transform import scale
>>> lower = np.array([6, 0])
>>> upper = np.array([6, 5])
>>> sample = np.array([[0.5 , 0.75],
>>> [0.5 , 0.5],
>>> [0.75, 0.25]])
>>> scale(sample, lower, upper)
Source code in spotpython/utils/transform.py
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|
transform_hyper_parameter_values(fun_control, hyper_parameter_values)
¶
Transform the values of the hyperparameters according to the transform function specified in fun_control if the hyperparameter is of type “int”, or “float” or “num”. Let fun_control = {“core_model_hyper_dict”:{ “leaf_prediction”: { “levels”: [“mean”, “model”, “adaptive”], “type”: “factor”, “default”: “mean”, “core_model_parameter_type”: “str”}, “max_depth”: { “type”: “int”, “default”: 20, “transform”: “transform_power_2”, “lower”: 2, “upper”: 20}}} and v = {‘max_depth’: 20,’leaf_prediction’: ‘mean’} and def transform_power_2(x): return 2**x. The function takes fun_control and v as input and returns a dictionary with the same structure as v. The function transforms the values of the hyperparameters according to the transform function specified in fun_control if the hyperparameter is of type “int”, or “float” or “num”. For example, transform_hyper_parameter_values(fun_control, v) returns {‘max_depth’: 1048576, ‘leaf_prediction’: ‘mean’}.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
fun_control |
dict
|
A dictionary containing the information about the core model and the hyperparameters. |
required |
hyper_parameter_values |
dict
|
A dictionary containing the values of the hyperparameters. |
required |
Returns:
Type | Description |
---|---|
dict
|
A dictionary containing the values of the hyperparameters. |
Examples:
>>> import copy
from spotpython.utils.prepare import transform_hyper_parameter_values
fun_control = {
"core_model_hyper_dict": {
"leaf_prediction": {
"levels": ["mean", "model", "adaptive"],
"type": "factor",
"default": "mean",
"core_model_parameter_type": "str"},
"max_depth": {"type": "int",
"default": 20
"transform": "transform_power_2",
"lower": 2,
"upper": 20}}}
hyper_parameter_values = {'max_depth': 20,
'leaf_prediction': 'mean'}
transform_hyper_parameter_values(fun_control, hyper_parameter_values)
{'max_depth': 1048576,
'leaf_prediction': 'mean'}
Source code in spotpython/utils/transform.py
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|
transform_multby2_int(x)
¶
Transformations for hyperparameters of type int.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
x |
int
|
input, will be multiplied by 2 |
required |
Returns:
Type | Description |
---|---|
int
|
The result of multiplying x by 2. |
Examples:
>>> from spotpython.utils.transform import transform_multby2_int
>>> transform_multby2_int(3)
6
Source code in spotpython/utils/transform.py
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|
transform_none_to_None(x)
¶
Transformations for hyperparameters of type None.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
x |
str
|
The string to transform. |
required |
Returns:
Type | Description |
---|---|
str
|
The transformed string. |
Examples:
>>> from spotpython.utils.transform import transform_none_to_None
>>> transform_none_to_None("none")
None
Note
Needed for sklearn.linear_model.LogisticRegression
Source code in spotpython/utils/transform.py
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|
transform_power(base, x, as_int=False)
¶
Raises a given base to the power of x.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
base |
int
|
The base to raise to the power of x. |
required |
x |
int
|
The exponent. |
required |
as_int |
bool
|
If True, returns the result as an integer. |
False
|
Returns:
Type | Description |
---|---|
float
|
The result of raising the base to the power of x. |
Examples:
>>> from spotpython.utils.transform import transform_power
>>> transform_power(2, 3)
8
Source code in spotpython/utils/transform.py
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|
transform_power_10(x)
¶
Transformations for hyperparameters of type float.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
x |
float
|
The exponent. |
required |
Returns:
Type | Description |
---|---|
float
|
The result of raising 10 to the power of x. |
Examples:
>>> from spotpython.utils.transform import transform_power_10
>>> transform_power_10(3)
1000
Source code in spotpython/utils/transform.py
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|
transform_power_10_int(x)
¶
Transformations for hyperparameters of type int. Args: x (int): The exponent.
Returns:
Type | Description |
---|---|
int
|
The result of raising 10 to the power of x. |
Examples:
>>> from spotpython.utils.transform import transform_power_10_int
>>> transform_power_10_int(3)
1000
Source code in spotpython/utils/transform.py
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|
transform_power_2(x)
¶
Transformations for hyperparameters of type float.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
x |
float
|
The exponent. |
required |
Returns:
Type | Description |
---|---|
float
|
The result of raising 2 to the power of x. |
Examples:
>>> from spotpython.utils.transform import transform_power_2
>>> transform_power_2(3)
8
Source code in spotpython/utils/transform.py
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|
transform_power_2_int(x)
¶
Transformations for hyperparameters of type int.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
x |
int
|
The exponent. |
required |
Returns:
Type | Description |
---|---|
int
|
The result of raising 2 to the power of x. |
Examples:
>>> from spotpython.utils.transform import transform_power_2_int
>>> transform_power_2_int(3)
8
Source code in spotpython/utils/transform.py
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|