values
add_core_model_to_fun_control(fun_control, core_model, hyper_dict=None, filename=None)
¶
Add the core model to the function control dictionary. It updates the keys “core_model”, “core_model_hyper_dict”, “var_type”, “var_name” in the fun_control dictionary.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
fun_control |
dict
|
The fun_control dictionary. |
required |
core_model |
class
|
The core model. |
required |
hyper_dict |
dict
|
The hyper parameter dictionary. Optional. Default is None. If no hyper_dict is provided, the function will try to load the hyper_dict from the file specified by filename. |
None
|
filename |
str
|
The name of the json file that contains the hyper parameter dictionary. Optional. Default is None. If no filename is provided, the function will try to load the hyper_dict from the hyper_dict dictionary. |
None
|
Returns:
Type | Description |
---|---|
dict
|
The updated fun_control dictionary. |
Notes
The function adds the following keys to the fun_control dictionary: “core_model”: The core model. “core_model_hyper_dict”: The hyper parameter dictionary for the core model. “core_model_hyper_dict_default”: The hyper parameter dictionary for the core model. “var_type”: A list of variable types. “var_name”: A list of variable names. The original hyperparameters of the core model are stored in the “core_model_hyper_dict_default” key. These remain unmodified, while the “core_model_hyper_dict” key is modified during the tuning process.
Examples:
>>> from spotpython.light.regression.netlightregression import NetLightRegression
from spotpython.hyperdict.light_hyper_dict import LightHyperDict
from spotpython.hyperparameters.values import add_core_model_to_fun_control
add_core_model_to_fun_control(fun_control=fun_control,
core_model=NetLightRegression,
hyper_dict=LightHyperDict)
# or, if a user wants to use a custom hyper_dict:
>>> from spotpython.light.regression.netlightregression import NetLightRegression
from spotpython.hyperparameters.values import add_core_model_to_fun_control
add_core_model_to_fun_control(fun_control=fun_control,
core_model=NetLightRegression,
filename="./hyperdict/user_hyper_dict.json")
Source code in spotpython/hyperparameters/values.py
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|
assign_values(X, var_list)
¶
This function takes an np.array X and a list of variable names as input arguments and returns a dictionary where the keys are the variable names and the values are assigned from X.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
X |
array
|
A 2D numpy array where each column represents a variable. |
required |
var_list |
list
|
A list of strings representing variable names. |
required |
Returns:
Name | Type | Description |
---|---|---|
dict |
dict
|
A dictionary where keys are variable names and values are assigned from X. |
Examples:
>>> import numpy as np
>>> from spotpython.hyperparameters.values import assign_values
>>> X = np.array([[1, 2], [3, 4], [5, 6]])
>>> var_list = ['a', 'b']
>>> result = assign_values(X, var_list)
>>> print(result)
{'a': array([1, 3, 5]), 'b': array([2, 4, 6])}
Source code in spotpython/hyperparameters/values.py
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|
convert_keys(d, var_type)
¶
Convert values in a dictionary to integers based on a list of variable types.
This function takes a dictionary d
and a list of variable types var_type
as arguments.
For each key in the dictionary,
if the corresponding entry in var_type
is not equal to "num"
,
the value associated with that key is converted to an integer.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
d |
dict
|
The input dictionary. |
required |
var_type |
list
|
A list of variable types. If the entry is not |
required |
Returns:
Name | Type | Description |
---|---|---|
dict |
Dict[str, Union[int, float]]
|
The modified dictionary with values converted to integers based on |
Examples:
>>> from spotpython.hyperparameters.values import convert_keys
>>> d = {'a': '1.1', 'b': '2', 'c': '3.1'}
>>> var_type = ["int", "num", "int"]
>>> convert_keys(d, var_type)
{'a': 1, 'b': '2', 'c': 3}
Source code in spotpython/hyperparameters/values.py
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|
create_model(config, fun_control, **kwargs)
¶
Creates a model for the given configuration and control parameters.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
config |
dict
|
dictionary containing the configuration for the hyperparameter tuning. |
required |
fun_control |
dict
|
dictionary containing control parameters for the hyperparameter tuning. |
required |
**kwargs |
Any
|
additional keyword arguments. |
{}
|
Returns:
Type | Description |
---|---|
object
|
model object. |
Source code in spotpython/hyperparameters/values.py
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|
generate_one_config_from_var_dict(var_dict, fun_control, default=False)
¶
Generate one configuration from a dictionary of variables (as a generator).
This function takes a dictionary of variables as input arguments and returns a generator that yields dictionaries with the values from the arrays in the input dictionary.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
var_dict |
dict
|
A dictionary where keys are variable names and values are numpy arrays. |
required |
fun_control |
dict
|
A dictionary which (at least) has an entry with the following key: “var_type” (list): A list of variable types. If the entry is not “num” the corresponding value will be converted to the type “int”. |
required |
default |
bool
|
A boolean value indicating whether to use the default values from fun_control. |
False
|
Returns:
Type | Description |
---|---|
Generator[Dict[str, Union[int, float]], None, None]
|
Generator[dict]: A generator that yields dictionaries with the values from the arrays in the input dictionary. |
Examples:
>>> import numpy as np
>>> from spotpython.hyperparameters.values import generate_one_config_from_var_dict
>>> var_dict = {'a': np.array([1, 3, 5]), 'b': np.array([2, 4, 6])}
>>> fun_control = {"var_type": ["int", "num"]}
>>> list(generate_one_config_from_var_dict(var_dict, fun_control))
[{'a': 1, 'b': 2}, {'a': 3, 'b': 4}, {'a': 5, 'b': 6}]
Source code in spotpython/hyperparameters/values.py
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|
get_bound_values(fun_control, bound, as_list=False)
¶
Generate a list or array from a dictionary. This function takes the values from the keys “bound” in the fun_control[“core_model_hyper_dict”] dictionary and returns a list or array of the values in the same order as the keys in the dictionary.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
fun_control |
dict
|
A dictionary containing a key “core_model_hyper_dict” which is a dictionary with keys that have either an “upper” or “lower” value. |
required |
bound |
str
|
Either “upper” or “lower”, indicating which value to extract from the inner dictionary. |
required |
as_list |
bool
|
If True, return a list. If False, return a numpy array. Default is False. |
False
|
Returns:
Type | Description |
---|---|
Union[List, ndarray]
|
list or np.ndarray: A list or array of the extracted values. |
Raises:
Type | Description |
---|---|
ValueError
|
If bound is not “upper” or “lower”. |
Examples:
>>> from spotpython.hyperparameters.values import get_bound_values
>>> fun_control = {"core_model_hyper_dict": {"a": {"upper": 1}, "b": {"upper": 2}}}
>>> get_bound_values(fun_control, "upper", as_list=True)
[1, 2]
Source code in spotpython/hyperparameters/values.py
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|
get_control_key_value(control_dict=None, key=None)
¶
This function gets the key value pair from the control_dict dictionary. If the key does not exist, return None. If the control_dict dictionary is None, return None.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
control_dict |
dict
|
control_dict dictionary |
None
|
key |
str
|
key |
None
|
Returns:
Name | Type | Description |
---|---|---|
value |
Any
|
value |
Examples:
>>> from spotpython.utils.init import fun_control_init
from spotpython.hyperparameters.values import get_control_key_value
control_dict = fun_control_init()
get_control_key_value(control_dict=control_dict,
key="key")
"value"
Source code in spotpython/hyperparameters/values.py
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|
get_core_model_from_name(core_model_name)
¶
Returns the sklearn or spotpython lightning core model name and instance from a core model name.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
core_model_name |
str
|
The full name of the core model in the format ‘module.Model’. |
required |
Returns:
Type | Description |
---|---|
(str, object)
|
A tuple containing the core model name and an instance of the core model. |
Examples:
>>> model_name, model_instance = get_core_model_from_name("light.regression.NNLinearRegressor")
print(f"Model Name: {model_name}, Model Instance: {model_instance}")
Model Name:
NNLinearRegressor,
Model Instance:
<class 'spotpython.light.regression.nn_linear_regressor.NNLinearRegressor'>
Source code in spotpython/hyperparameters/values.py
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|
get_core_model_parameter_type_from_var_name(fun_control, var_name)
¶
Extracts the core_model_parameter_type value from a dictionary for a specified key.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
fun_control |
dict
|
The dictionary containing the information. |
required |
var_name |
str
|
The key for which to extract the core_model_parameter_type value. |
required |
Returns:
Type | Description |
---|---|
str
|
The core_model_parameter_type value if available, else None. |
Source code in spotpython/hyperparameters/values.py
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|
get_default_hyperparameters_as_array(fun_control)
¶
Get the default hyper parameters as array.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
fun_control |
dict
|
The function control dictionary. |
required |
Returns:
Type | Description |
---|---|
array
|
The default hyper parameters as array. |
Examples:
>>> from river.tree import HoeffdingAdaptiveTreeRegressor
from spotriver.data.river_hyper_dict import RiverHyperDict
from spotpython.hyperparameters.values import (
get_default_hyperparameters_as_array,
add_core_model_to_fun_control)
fun_control = {}
add_core_model_to_fun_control(core_model=HoeffdingAdaptiveTreeRegressor,
fun_control=func_control,
hyper_dict=RiverHyperDict,
filename=None)
get_default_hyperparameters_as_array(fun_control)
array([0, 0, 0, 0, 0])
Source code in spotpython/hyperparameters/values.py
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|
get_default_values(fun_control)
¶
Get the values from the “default” keys from the dictionary fun_control as a dict. If the key of the value has as “type” the value “int” or “float”, convert the value to the corresponding type.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
fun_control |
dict
|
dictionary with levels and types |
required |
Returns:
Name | Type | Description |
---|---|---|
new_dict |
dict
|
dictionary with default values |
Examples:
>>> from spotpython.hyperparameters.values import get_default_values
d = {"core_model_hyper_dict":{
"leaf_prediction": {
"levels": ["mean", "model", "adaptive"],
"type": "factor",
"default": "mean",
"core_model_parameter_type": "str"},
"leaf_model": {
"levels": ["linear_model.LinearRegression", "linear_model.PARegressor", "linear_model.Perceptron"],
"type": "factor",
"default": "LinearRegression",
"core_model_parameter_type": "instance"},
"splitter": {
"levels": ["EBSTSplitter", "TEBSTSplitter", "QOSplitter"],
"type": "factor",
"default": "EBSTSplitter",
"core_model_parameter_type": "instance()"},
"binary_split": {
"levels": [0, 1],
"type": "factor",
"default": 0,
"core_model_parameter_type": "bool"},
"stop_mem_management": {
"levels": [0, 1],
"type": "factor",
"default": 0,
"core_model_parameter_type": "bool"}}}
get_default_values(d)
{'leaf_prediction': 'mean',
'leaf_model': 'linear_model.LinearRegression',
'splitter': 'EBSTSplitter',
'binary_split': 0,
'stop_mem_management': 0}
Source code in spotpython/hyperparameters/values.py
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|
get_dict_with_levels_and_types(fun_control, v, default=False)
¶
Get dictionary with levels and types. The function maps the numerical output of the hyperparameter optimization to the corresponding levels of the hyperparameter needed by the core model, i.e., the tuned algorithm. The function takes the dictionaries fun_control and v and returns a new dictionary with the same keys as v but with the values of the levels of the keys from fun_control. If the key value in the dictionary is 0, it takes the first value from the list, if it is 1, it takes the second and so on. If a key is not in fun_control, it takes the key from v. If the core_model_parameter_type value is instance, it returns the class of the value from the module via getattr(“class”, value).
Parameters:
Name | Type | Description | Default |
---|---|---|---|
fun_control |
Dict[str, Any]
|
A dictionary containing information about the core model hyperparameters. |
required |
v |
Dict[str, Any]
|
A dictionary containing the numerical output of the hyperparameter optimization. |
required |
default |
bool
|
A boolean value indicating whether to use the default values from fun_control. |
False
|
Returns:
Type | Description |
---|---|
Dict[str, Any]
|
Dict[str, Any]: A new dictionary with the same keys as v but with the values of the levels of the keys from fun_control. |
Examples:
>>> fun_control = {
... "core_model_hyper_dict": {
... "leaf_prediction": {
... "levels": ["mean", "model", "adaptive"],
... "type": "factor",
... "default": "mean",
... "core_model_parameter_type": "str"
... },
... "leaf_model": {
... "levels": [
... "linear_model.LinearRegression",
... "linear_model.PARegressor",
... "linear_model.Perceptron"
... ],
... "type": "factor",
... "default": "LinearRegression",
... "core_model_parameter_type": "instance"
... },
... "splitter": {
... "levels": ["EBSTSplitter", "TEBSTSplitter", "QOSplitter"],
... "type": "factor",
... "default": "EBSTSplitter",
... "core_model_parameter_type": "instance()"
... },
... "binary_split": {
... "levels": [0, 1],
... "type": "factor",
... "default": 0,
... "core_model_parameter_type": "bool"
... },
... "stop_mem_management": {
... "levels": [0, 1],
... "type": "factor",
... "default": 0,
... "core_model_parameter_type": "bool"
... }
... }
... }
>>> v = {
... 'grace_period': 200,
... 'max_depth': 10,
... 'delta': 1e-07,
... 'tau': 0.05,
... 'leaf_prediction': 0,
... 'leaf_model': 0,
... 'model_selector_decay': 0.95,
... 'splitter': 1,
... 'min_samples_split': 9,
... 'binary_split': 0,
... 'max_size': 500.0
... }
>>> get_dict_with_levels_and_types(fun_control, v)
{
'grace_period': 200,
'max_depth': 10,
'delta': 1e-07,
'tau': 0.05,
'leaf_prediction': 'mean',
'leaf_model': linear_model.LinearRegression,
'model_selector_decay': 0.95,
'splitter': TEBSTSplitter,
'min_samples_split': 9,
'binary_split': False,
'max_size': 500.0
}
Source code in spotpython/hyperparameters/values.py
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|
get_ith_hyperparameter_name_from_fun_control(fun_control, key, i)
¶
Get the ith hyperparameter name from the fun_control dictionary.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
fun_control |
dict
|
fun_control dictionary |
required |
key |
str
|
key |
required |
i |
int
|
index |
required |
Returns:
Type | Description |
---|---|
str
|
hyperparameter name |
Examples:
>>> from spotpython.utils.device import getDevice
from spotpython.utils.init import fun_control_init
from spotpython.utils.file import get_experiment_name
import numpy as np
from spotpython.data.diabetes import Diabetes
from spotpython.light.regression.netlightregression import NetLightRegression
from spotpython.hyperdict.light_hyper_dict import LightHyperDict
from spotpython.hyperparameters.values import add_core_model_to_fun_control
from spotpython.hyperparameters.values import get_ith_hyperparameter_name_from_fun_control
from spotpython.hyperparameters.values import set_control_key_value
from spotpython.hyperparameters.values import set_control_hyperparameter_value
experiment_name = get_experiment_name(prefix="000")
fun_control = fun_control_init(
_L_in=10,
_L_out=1,
TENSORBOARD_CLEAN=True,
device=getDevice(),
enable_progress_bar=False,
fun_evals=15,
log_level=10,
max_time=1,
num_workers=0,
show_progress=True,
tolerance_x=np.sqrt(np.spacing(1)),
)
dataset = Diabetes()
set_control_key_value(control_dict=fun_control,
key="data_set",
value=dataset,
replace=True)
add_core_model_to_fun_control(core_model=NetLightRegression,
fun_control=fun_control,
hyper_dict=LightHyperDict)
set_control_hyperparameter_value(fun_control, "l1", [3,8])
set_control_hyperparameter_value(fun_control, "optimizer", ["Adam", "AdamW", "Adamax", "NAdam"])
get_ith_hyperparameter_name_from_fun_control(fun_control, key="optimizer", i=0)
Adam
Source code in spotpython/hyperparameters/values.py
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|
get_metric_sklearn(metric_name)
¶
Returns the sklearn metric from the metric name.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
metric_name |
str
|
The name of the metric. |
required |
Returns:
Type | Description |
---|---|
object
|
sklearn.metrics (object): The sklearn metric. |
Source code in spotpython/hyperparameters/values.py
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|
get_one_config_from_X(X, fun_control=None)
¶
Get one config from X.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
X |
array
|
The array with the hyper parameter values. |
required |
fun_control |
dict
|
The function control dictionary. |
None
|
Returns:
Type | Description |
---|---|
dict
|
The config dictionary. |
Examples:
>>> from river.tree import HoeffdingAdaptiveTreeRegressor
from spotriver.data.river_hyper_dict import RiverHyperDict
fun_control = {}
add_core_model_to_fun_control(core_model=HoeffdingAdaptiveTreeRegressor,
fun_control=func_control,
hyper_dict=RiverHyperDict,
filename=None)
X = np.array([0, 0, 0, 0, 0])
get_one_config_from_X(X, fun_control)
{'leaf_prediction': 'mean',
'leaf_model': 'NBAdaptive',
'splitter': 'HoeffdingAdaptiveTreeSplitter',
'binary_split': 'info_gain',
'stop_mem_management': False}
Source code in spotpython/hyperparameters/values.py
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|
get_one_core_model_from_X(X, fun_control=None, default=False)
¶
Get one core model from X.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
X |
array
|
The array with the hyper parameter values. |
required |
fun_control |
dict
|
The function control dictionary. |
None
|
default |
bool
|
A boolean value indicating whether to use the default values from fun_control. |
False
|
Returns:
Type | Description |
---|---|
class
|
The core model. |
Examples:
>>> from river.tree import HoeffdingAdaptiveTreeRegressor
from spotriver.data.river_hyper_dict import RiverHyperDict
fun_control = {}
add_core_model_to_fun_control(core_model=HoeffdingAdaptiveTreeRegressor,
fun_control=fun_control,
hyper_dict=RiverHyperDict,
filename=None)
X = np.array([0, 0, 0, 0, 0])
get_one_core_model_from_X(X, fun_control)
HoeffdingAdaptiveTreeRegressor()
Source code in spotpython/hyperparameters/values.py
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|
get_one_river_model_from_X(X, fun_control=None)
¶
Get one river model from X.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
X |
array
|
The array with the hyper parameter values. |
required |
fun_control |
dict
|
The function control dictionary. |
None
|
Returns:
Type | Description |
---|---|
class
|
The river model. |
Examples:
>>> from river.tree import HoeffdingAdaptiveTreeRegressor
from spotriver.data.river_hyper_dict import RiverHyperDict
fun_control = {}
add_core_model_to_fun_control(core_model=HoeffdingAdaptiveTreeRegressor,
fun_control=func_control,
hyper_dict=RiverHyperDict,
filename=None)
X = np.array([0, 0, 0, 0, 0])
get_one_river_model_from_X(X, fun_control)
HoeffdingAdaptiveTreeRegressor()
Source code in spotpython/hyperparameters/values.py
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|
get_one_sklearn_model_from_X(X, fun_control=None)
¶
Get one sklearn model from X.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
X |
array
|
The array with the hyper parameter values. |
required |
fun_control |
dict
|
The function control dictionary. |
None
|
Returns:
Type | Description |
---|---|
class
|
The sklearn model. |
Examples:
>>> from sklearn.linear_model import LinearRegression
from spotriver.data.sklearn_hyper_dict import SklearnHyperDict
fun_control = {}
add_core_model_to_fun_control(core_model=LinearRegression,
fun_control=func_control,
hyper_dict=SklearnHyperDict,
filename=None)
X = np.array([0, 0, 0, 0, 0])
get_one_sklearn_model_from_X(X, fun_control)
LinearRegression()
Source code in spotpython/hyperparameters/values.py
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|
get_prep_model(prepmodel_name)
¶
Get the sklearn preprocessing model from the name.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
prepmodel_name |
str
|
The name of the preprocessing model. |
required |
Returns:
Type | Description |
---|---|
object
|
river.preprocessing (object): The river preprocessing model. |
Source code in spotpython/hyperparameters/values.py
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|
get_river_core_model_from_name(core_model_name)
¶
Returns the river core model name and instance from a core model name.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
core_model_name |
str
|
The full name of the core model in the format ‘module.Model’. |
required |
Returns:
Type | Description |
---|---|
(str, object)
|
A tuple containing the core model name and an instance of the core model. |
Examples:
>>> from spotpython.hyperparameters.values import get_core_model_from_name
model_name, model_instance = get_core_model_from_name('tree.HoeffdingTreeRegressor')
print(f"Model Name: {model_name}, Model Instance: {model_instance}")
Model Name:
HoeffdingTreeRegressor,
Model Instance:
<class 'river.tree.hoeffding_tree_regressor.HoeffdingTreeRegressor'>
Source code in spotpython/hyperparameters/values.py
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|
get_river_prep_model(prepmodel_name)
¶
Get the river preprocessing model from the name.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
prepmodel_name |
str
|
The name of the preprocessing model. |
required |
Returns:
Type | Description |
---|---|
object
|
river.preprocessing (object): The river preprocessing model. |
Source code in spotpython/hyperparameters/values.py
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|
get_sklearn_scaler(scaler_name)
¶
Get the sklearn scaler model from the name.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
scaler_name |
str
|
The name of the preprocessing model. |
required |
Returns:
Type | Description |
---|---|
object
|
sklearn.preprocessing (object): The sklearn scaler. |
Source code in spotpython/hyperparameters/values.py
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|
get_transform(fun_control)
¶
Get the transformations of the values from the dictionary fun_control as a list.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
fun_control |
dict
|
dictionary with levels and types |
required |
Returns:
Type | Description |
---|---|
list
|
list with transformations |
Examples:
>>> from spotpython.hyperparameters.values import get_transform
d = {"core_model_hyper_dict":{
"leaf_prediction": {
"levels": ["mean", "model", "adaptive"],
"type": "factor",
"default": "mean",
"transform": "None",
"core_model_parameter_type": "str"},
"leaf_model": {
"levels": ["linear_model.LinearRegression", "linear_model.PARegressor", "linear_model.Perceptron"],
"type": "factor",
"default": "LinearRegression",
"transform": "None",
"core_model_parameter_type": "instance"},
"splitter": {
"levels": ["EBSTSplitter", "TEBSTSplitter", "QOSplitter"],
"type": "factor",
"default": "EBSTSplitter",
"transform": "None",
"core_model_parameter_type": "instance()"},
"binary_split": {
"levels": [0, 1],
"type": "factor",
"default": 0,
"transform": "None",
"core_model_parameter_type": "bool"},
"stop_mem_management": { "levels": [0, 1],
"type": "factor",
"default": 0,
"transform": "None",
"core_model_parameter_type": "bool"}}}
get_transform(d)
['None', 'None', 'None', 'None', 'None']
Source code in spotpython/hyperparameters/values.py
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|
get_tuned_architecture(spot_tuner, fun_control, force_minX=False)
¶
Returns the tuned architecture. If the spot tuner has noise, it returns the architecture with the lowest mean (.min_mean_X), otherwise it returns the architecture with the lowest value (.min_X).
Parameters:
Name | Type | Description | Default |
---|---|---|---|
spot_tuner |
object
|
spot tuner object. |
required |
fun_control |
dict
|
dictionary containing control parameters for the hyperparameter tuning. |
required |
force_minX |
bool
|
If True, return the architecture with the lowest value (.min_X). |
False
|
Returns:
Type | Description |
---|---|
dict
|
dictionary containing the tuned architecture. |
Source code in spotpython/hyperparameters/values.py
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|
get_tuned_hyperparameters(spot_tuner, fun_control=None)
¶
Get the tuned hyperparameters from the spot tuner.
This is just a wrapper function for the spot get_tuned_hyperparameters
method.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
spot_tuner |
object
|
spot tuner object. |
required |
fun_control |
dict
|
dictionary containing control parameters for the hyperparameter tuning. Optional. Default is None. |
None
|
Returns:
Type | Description |
---|---|
dict
|
dictionary containing the tuned hyperparameters. |
Examples:
>>> from spotpython.utils.device import getDevice
from math import inf
from spotpython.utils.init import fun_control_init
import numpy as np
from spotpython.hyperparameters.values import set_control_key_value
from spotpython.data.diabetes import Diabetes
from spotpython.hyperparameters.values import get_tuned_hyperparameters
MAX_TIME = 1
FUN_EVALS = 10
INIT_SIZE = 5
WORKERS = 0
PREFIX="037"
DEVICE = getDevice()
DEVICES = 1
TEST_SIZE = 0.4
TORCH_METRIC = "mean_squared_error"
dataset = Diabetes()
fun_control = fun_control_init(
_L_in=10,
_L_out=1,
_torchmetric=TORCH_METRIC,
PREFIX=PREFIX,
TENSORBOARD_CLEAN=True,
data_set=dataset,
device=DEVICE,
enable_progress_bar=False,
fun_evals=FUN_EVALS,
log_level=50,
max_time=MAX_TIME,
num_workers=WORKERS,
show_progress=True,
test_size=TEST_SIZE,
tolerance_x=np.sqrt(np.spacing(1)),
)
from spotpython.light.regression.netlightregression import NetLightRegression
from spotpython.hyperdict.light_hyper_dict import LightHyperDict
from spotpython.hyperparameters.values import add_core_model_to_fun_control
add_core_model_to_fun_control(fun_control=fun_control,
core_model=NetLightRegression,
hyper_dict=LightHyperDict)
from spotpython.hyperparameters.values import set_control_hyperparameter_value
set_control_hyperparameter_value(fun_control, "l1", [7, 8])
set_control_hyperparameter_value(fun_control, "epochs", [3, 5])
set_control_hyperparameter_value(fun_control, "batch_size", [4, 5])
set_control_hyperparameter_value(fun_control, "optimizer", [
"Adam",
"RAdam",
])
set_control_hyperparameter_value(fun_control, "dropout_prob", [0.01, 0.1])
set_control_hyperparameter_value(fun_control, "lr_mult", [0.5, 5.0])
set_control_hyperparameter_value(fun_control, "patience", [2, 3])
set_control_hyperparameter_value(fun_control, "act_fn",[
"ReLU",
"LeakyReLU"
] )
from spotpython.utils.init import design_control_init, surrogate_control_init
design_control = design_control_init(init_size=INIT_SIZE)
surrogate_control = surrogate_control_init(noise=True,
n_theta=2)
from spotpython.fun.hyperlight import HyperLight
fun = HyperLight(log_level=50).fun
from spotpython.spot import spot
spot_tuner = spot.Spot(fun=fun,
fun_control=fun_control,
design_control=design_control,
surrogate_control=surrogate_control)
spot_tuner.run()
get_tuned_hyperparameters(spot_tuner)
{'l1': 7.0,
'epochs': 5.0,
'batch_size': 4.0,
'act_fn': 0.0,
'optimizer': 0.0,
'dropout_prob': 0.01,
'lr_mult': 5.0,
'patience': 3.0,
'initialization': 1.0}
Source code in spotpython/hyperparameters/values.py
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|
get_values_from_dict(dictionary)
¶
Get the values from a dictionary as an array. Generate an np.array that contains the values of the keys of a dictionary in the same order as the keys of the dictionary.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
dictionary |
dict
|
dictionary with values |
required |
Returns:
Type | Description |
---|---|
array
|
array with values |
Examples:
>>> from spotpython.hyperparameters.values import get_values_from_dict
>>> d = {"a": 1, "b": 2, "c": 3}
>>> get_values_from_dict(d)
array([1, 2, 3])
Source code in spotpython/hyperparameters/values.py
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|
get_var_name(fun_control)
¶
Get the names of the values from the dictionary fun_control as a list. If no “core_model_hyper_dict” key exists in fun_control, return None.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
fun_control |
dict
|
dictionary with names |
required |
Returns:
Type | Description |
---|---|
list
|
ist with names |
Examples:
>>> from spotpython.hyperparameters.values import get_var_name
fun_control = {"core_model_hyper_dict":{
"leaf_prediction": {
"levels": ["mean", "model", "adaptive"],
"type": "factor",
"default": "mean",
"core_model_parameter_type": "str"},
"leaf_model": {
"levels": ["linear_model.LinearRegression",
"linear_model.PARegressor",
"linear_model.Perceptron"],
"type": "factor",
"default": "LinearRegression",
"core_model_parameter_type": "instance"},
"splitter": {
"levels": ["EBSTSplitter", "TEBSTSplitter", "QOSplitter"],
"type": "factor",
"default": "EBSTSplitter",
"core_model_parameter_type": "instance()"},
"binary_split": {
"levels": [0, 1],
"type": "factor",
"default": 0,
"core_model_parameter_type": "bool"},
"stop_mem_management": {
"levels": [0, 1],
"type": "factor",
"default": 0,
"core_model_parameter_type": "bool"}}}
get_var_name(fun_control)
['leaf_prediction',
'leaf_model',
'splitter',
'binary_split',
'stop_mem_management']
Source code in spotpython/hyperparameters/values.py
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|
get_var_type(fun_control)
¶
Get the types of the values from the dictionary fun_control as a list. If no “core_model_hyper_dict” key exists in fun_control, return None.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
fun_control |
dict
|
dictionary with levels and types |
required |
Returns:
Type | Description |
---|---|
list
|
list with types |
Examples:
>>> from spotpython.hyperparameters.values import get_var_type
d = {"core_model_hyper_dict":{
"leaf_prediction": {
"levels": ["mean", "model", "adaptive"],
"type": "factor",
"default": "mean",
"core_model_parameter_type": "str"},
"leaf_model": {
"levels": ["linear_model.LinearRegression", "linear_model.PARegressor", "linear_model.Perceptron"],
"type": "factor",
"default": "LinearRegression",
"core_model_parameter_type": "instance"},
"splitter": {
"levels": ["EBSTSplitter", "TEBSTSplitter", "QOSplitter"],
"type": "factor",
"default": "EBSTSplitter",
"core_model_parameter_type": "instance()"},
"binary_split": {
"levels": [0, 1],
"type": "factor",
"default": 0,
"core_model_parameter_type": "bool"},
"stop_mem_management": { "levels": [0, 1],
"type": "factor",
"default": 0,
"core_model_parameter_type": "bool"}}}
get_var_type(d)
['factor', 'factor', 'factor', 'factor', 'factor']
Source code in spotpython/hyperparameters/values.py
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|
get_var_type_from_var_name(fun_control, var_name)
¶
This function gets the variable type from the variable name.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
fun_control |
dict
|
fun_control dictionary |
required |
var_name |
str
|
variable name |
required |
Returns:
Type | Description |
---|---|
str
|
variable type |
Examples:
>>> from spotpython.utils.init import fun_control_init
from spotpython.hyperparameters.values import get_var_type_from_var_name
control_dict = fun_control_init()
get_var_type_from_var_name(var_name="max_depth",
fun_control=control_dict)
"int"
Source code in spotpython/hyperparameters/values.py
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|
iterate_dict_values(var_dict)
¶
Iterate over the values of a dictionary of variables. This function takes a dictionary of variables as input arguments and returns a generator that yields dictionaries with the values from the arrays in the input dictionary.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
var_dict |
dict
|
A dictionary where keys are variable names and values are numpy arrays. |
required |
Returns:
Type | Description |
---|---|
Generator[Dict[str, Union[int, float]], None, None]
|
Generator[dict]: A generator that yields dictionaries with the values from the arrays in the input dictionary. |
Examples:
>>> import numpy as np
>>> from spotpython.hyperparameters.values import iterate_dict_values
>>> var_dict = {'a': np.array([1, 3, 5]), 'b': np.array([2, 4, 6])}
>>> list(iterate_dict_values(var_dict))
[{'a': 1, 'b': 2}, {'a': 3, 'b': 4}, {'a': 5, 'b': 6}]
Source code in spotpython/hyperparameters/values.py
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|
modify_boolean_hyper_parameter_levels(fun_control, hyperparameter, levels)
¶
This function modifies the levels of a boolean hyperparameter in the fun_control dictionary. It also sets the lower and upper bounds of the hyperparameter to 0 and len(levels) - 1, respectively.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
fun_control |
dict
|
fun_control dictionary |
required |
hyperparameter |
str
|
hyperparameter name |
required |
levels |
list
|
list of levels |
required |
Returns:
Type | Description |
---|---|
None
|
None. |
Source code in spotpython/hyperparameters/values.py
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|
modify_hyper_parameter_bounds(fun_control, hyperparameter, bounds)
¶
Modify the bounds of a hyperparameter in the fun_control dictionary.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
fun_control |
dict
|
fun_control dictionary |
required |
hyperparameter |
str
|
hyperparameter name |
required |
bounds |
list
|
list of two bound values. The first value represents the lower bound and the second value represents the upper bound. |
required |
Returns:
Type | Description |
---|---|
None
|
None. |
Examples:
>>> from spotpython.hyperparameters.values import modify_hyper_parameter_levels
fun_control = {}
core_model = HoeffdingTreeRegressor
fun_control.update({"core_model": core_model})
fun_control.update({"core_model_hyper_dict": river_hyper_dict[core_model.__name__]})
bounds = [3, 11]
fun_control = modify_hyper_parameter_levels(fun_control, "min_samples_split", bounds)
Source code in spotpython/hyperparameters/values.py
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|
modify_hyper_parameter_levels(fun_control, hyperparameter, levels)
¶
This function modifies the levels of a hyperparameter in the fun_control dictionary. It also sets the lower and upper bounds of the hyperparameter to 0 and len(levels) - 1, respectively.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
fun_control |
dict
|
fun_control dictionary |
required |
hyperparameter |
str
|
hyperparameter name |
required |
levels |
list
|
list of levels |
required |
Returns:
Type | Description |
---|---|
None
|
None. |
Examples:
>>> fun_control = {}
from spotpython.hyperparameters.values import modify_hyper_parameter_levels
core_model = HoeffdingTreeRegressor
fun_control.update({"core_model": core_model})
fun_control.update({"core_model_hyper_dict": river_hyper_dict[core_model.__name__]})
levels = ["mean", "model"]
fun_control = modify_hyper_parameter_levels(fun_control, "leaf_prediction", levels)
Source code in spotpython/hyperparameters/values.py
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|
replace_levels_with_positions(hyper_dict, hyper_dict_values)
¶
Replace the levels with the position in the levels list. The function that takes two dictionaries. The first contains as hyperparameters as keys. If the hyperparameter has the key “levels”, then the value of the corresponding hyperparameter in the second dictionary is replaced by the position of the value in the list of levels. The function returns a dictionary with the same keys as the second dictionary. For example, if the second dictionary is {“a”: 1, “b”: “model1”, “c”: 3} and the first dictionary is { “a”: {“type”: “int”}, “b”: {“levels”: [“model4”, “model5”, “model1”]}, “d”: {“type”: “float”}}, then the function should return {“a”: 1, “b”: 2, “c”: 3}.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
hyper_dict |
dict
|
dictionary with levels |
required |
hyper_dict_values |
dict
|
dictionary with values |
required |
Returns:
Type | Description |
---|---|
dict
|
dictionary with values |
Examples:
>>> from spotpython.hyperparameters.values import replace_levels_with_positions
hyper_dict = {"leaf_prediction": {
"levels": ["mean", "model", "adaptive"],
"type": "factor",
"default": "mean",
"core_model_parameter_type": "str"},
"leaf_model": {
"levels": ["linear_model.LinearRegression", "linear_model.PARegressor", "linear_model.Perceptron"],
"type": "factor",
"default": "LinearRegression",
"core_model_parameter_type": "instance"},
"splitter": {
"levels": ["EBSTSplitter", "TEBSTSplitter", "QOSplitter"],
"type": "factor",
"default": "EBSTSplitter",
"core_model_parameter_type": "instance()"},
"binary_split": {
"levels": [0, 1],
"type": "factor",
"default": 0,
"core_model_parameter_type": "bool"},
"stop_mem_management": {
"levels": [0, 1],
"type": "factor",
"default": 0,
"core_model_parameter_type": "bool"}}
hyper_dict_values = {"leaf_prediction": "mean",
"leaf_model": "linear_model.LinearRegression",
"splitter": "EBSTSplitter",
"binary_split": 0,
"stop_mem_management": 0}
replace_levels_with_position(hyper_dict, hyper_dict_values)
{'leaf_prediction': 0,
'leaf_model': 0,
'splitter': 0,
'binary_split': 0,
'stop_mem_management': 0}
Source code in spotpython/hyperparameters/values.py
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|
return_conf_list_from_var_dict(var_dict, fun_control, default=False)
¶
Return a list of configurations from a dictionary of variables.
This function takes a dictionary of variables and a dictionary of function control as input arguments. It performs similar steps as generate_one_config_from_var_dict() but returns a list of dictionaries of hyper parameter values.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
var_dict |
dict
|
A dictionary where keys are variable names and values are numpy arrays. |
required |
fun_control |
dict
|
A dictionary which (at least) has an entry with the following key: “var_type” (list): A list of variable types. If the entry is not “num” the corresponding value will be converted to the type “int”. |
required |
Returns:
Name | Type | Description |
---|---|---|
list |
List[Dict[str, Union[int, float]]]
|
A list of dictionaries of hyper parameter values. Transformations are applied to the values. |
Examples:
>>> import numpy as np
>>> from spotpython.hyperparameters.values import return_conf_list_from_var_dict
>>> var_dict = {'a': np.array([1, 3, 5]), 'b': np.array([2, 4, 6])}
>>> fun_control = {'var_type': ['int', 'int']}
>>> return_conf_list_from_var_dict(var_dict, fun_control)
[{'a': 1, 'b': 2}, {'a': 3, 'b': 4}, {'a': 5, 'b': 6}]
Source code in spotpython/hyperparameters/values.py
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|
set_boolean_hyperparameter_values(fun_control, key, lower, upper)
¶
Set the boolean hyperparameter values in the fun_control dictionary.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
fun_control |
dict
|
The fun_control dictionary. |
required |
key |
str
|
The key of the hyperparameter. |
required |
lower |
bool
|
The lower bound of the hyperparameter. |
required |
upper |
bool
|
The upper bound of the hyperparameter. |
required |
Examples:
>>> from spotriver.hyperdict.river_hyper_dict import RiverHyperDict
from spotpython.utils.init import fun_control_init
from spotpython.hyperparameters.values import set_boolean_hyperparameter_values
from spotpython.utils.eda import gen_design_table
fun_control = fun_control_init(
core_model_name="forest.AMFRegressor",
hyperdict=RiverHyperDict,
)
print("Before modification:")
print(gen_design_table(fun_control))
set_boolean_hyperparameter_values(fun_control, "use_aggregation", 0, 0)
print("After modification:")
print(gen_design_table(fun_control))
Seed set to 123
Before modification:
| name | type | default | lower | upper | transform |
|-----------------|--------|-----------|---------|---------|-------------|
| n_estimators | int | 10 | 2 | 1000 | None |
| step | float | 1 | 0.1 | 10 | None |
| use_aggregation | factor | 1 | 0 | 1 | None |
Setting hyperparameter use_aggregation to value [0, 0].
Variable type is factor.
Core type is bool.
Calling modify_boolean_hyper_parameter_levels().
After modification:
| name | type | default | lower | upper | transform |
|-----------------|--------|-----------|---------|---------|-------------|
| n_estimators | int | 10 | 2 | 1000 | None |
| step | float | 1 | 0.1 | 10 | None |
| use_aggregation | factor | 1 | 0 | 0 | None |
Source code in spotpython/hyperparameters/values.py
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|
set_control_hyperparameter_value(control_dict, hyperparameter, value)
¶
This function sets the hyperparameter values depending on the var_type via modify_hyperameter_levels or modify_hyperparameter_bounds in the control_dict dictionary. If the hyperparameter is a factor, it calls modify_hyper_parameter_levels. Otherwise, it calls modify_hyper_parameter_bounds.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
control_dict |
dict
|
control_dict dictionary |
required |
hyperparameter |
str
|
key |
required |
value |
Any
|
value |
required |
Returns:
Type | Description |
---|---|
None
|
None. |
Source code in spotpython/hyperparameters/values.py
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|
set_control_key_value(control_dict, key, value, replace=False)
¶
This function sets the key value pair in the control_dict dictionary.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
control_dict |
dict
|
control_dict dictionary |
required |
key |
str
|
key |
required |
value |
Any
|
value |
required |
replace |
bool
|
replace value if key already exists. Default is False. |
False
|
Returns:
Type | Description |
---|---|
None
|
None. |
Attributes:
Name | Type | Description |
---|---|---|
key |
str
|
key |
value |
Any
|
value |
Examples:
>>> from spotpython.utils.init import fun_control_init
from spotpython.hyperparameters.values import set_control_key_value
control_dict = fun_control_init()
set_control_key_value(control_dict=control_dict,
key="key",
value="value")
control_dict["key"]
Source code in spotpython/hyperparameters/values.py
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|
set_factor_hyperparameter_values(fun_control, key, levels)
¶
Set the factor hyperparameter values in the fun_control dictionary.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
fun_control |
dict
|
The fun_control dictionary. |
required |
key |
str
|
The key of the hyperparameter. |
required |
levels |
list
|
The levels of the hyperparameter. |
required |
Examples:
>>> from spotriver.hyperdict.river_hyper_dict import RiverHyperDict
from spotpython.utils.init import fun_control_init
from spotpython.hyperparameters.values import set_factor_hyperparameter_values
from spotpython.utils.eda import gen_design_table
fun_control = fun_control_init(
core_model_name="tree.HoeffdingTreeRegressor",
hyperdict=RiverHyperDict,
)
print("Before modification:")
print(gen_design_table(fun_control))
set_factor_hyperparameter_values(fun_control, "leaf_model", ['LinearRegression',
'Perceptron'])
print("After modification:")
print(gen_design_table(fun_control))
Seed set to 123
Before modification:
| name | type | default | lower | upper | transform |
|------------------------|--------|------------------|---------|----------|------------------------|
| grace_period | int | 200 | 10 | 1000 | None |
| max_depth | int | 20 | 2 | 20 | transform_power_2_int |
| delta | float | 1e-07 | 1e-08 | 1e-06 | None |
| tau | float | 0.05 | 0.01 | 0.1 | None |
| leaf_prediction | factor | mean | 0 | 2 | None |
| leaf_model | factor | LinearRegression | 0 | 2 | None |
| model_selector_decay | float | 0.95 | 0.9 | 0.99 | None |
| splitter | factor | EBSTSplitter | 0 | 2 | None |
| min_samples_split | int | 5 | 2 | 10 | None |
| binary_split | factor | 0 | 0 | 1 | None |
| max_size | float | 500.0 | 100 | 1000 | None |
| memory_estimate_period | int | 6 | 3 | 8 | transform_power_10_int |
| stop_mem_management | factor | 0 | 0 | 1 | None |
| remove_poor_attrs | factor | 0 | 0 | 1 | None |
| merit_preprune | factor | 1 | 0 | 1 | None |
After modification:
| name | type | default | lower | upper | transform |
|------------------------|--------|------------------|---------|----------|------------------------|
| grace_period | int | 200 | 10 | 1000 | None |
| max_depth | int | 20 | 2 | 20 | transform_power_2_int |
| delta | float | 1e-07 | 1e-08 | 1e-06 | None |
| tau | float | 0.05 | 0.01 | 0.1 | None |
| leaf_prediction | factor | mean | 0 | 2 | None |
| leaf_model | factor | LinearRegression | 0 | 1 | None |
| model_selector_decay | float | 0.95 | 0.9 | 0.99 | None |
| splitter | factor | EBSTSplitter | 0 | 2 | None |
| min_samples_split | int | 5 | 2 | 10 | None |
| binary_split | factor | 0 | 0 | 1 | None |
| max_size | float | 500.0 | 100 | 1000 | None |
| memory_estimate_period | int | 6 | 3 | 8 | transform_power_10_int |
| stop_mem_management | factor | 0 | 0 | 1 | None |
| remove_poor_attrs | factor | 0 | 0 | 1 | None |
| merit_preprune | factor | 1 | 0 | 1 | None |
Source code in spotpython/hyperparameters/values.py
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|
set_float_hyperparameter_values(fun_control, key, lower, upper)
¶
Set the float hyperparameter values in the fun_control dictionary.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
fun_control |
dict
|
The fun_control dictionary. |
required |
key |
str
|
The key of the hyperparameter. |
required |
lower |
float
|
The lower bound of the hyperparameter. |
required |
upper |
float
|
The upper bound of the hyperparameter. |
required |
Examples:
>>> from spotriver.hyperdict.river_hyper_dict import RiverHyperDict
from spotpython.utils.init import fun_control_init
from spotpython.hyperparameters.values import set_float_hyperparameter_values
from spotpython.utils.eda import gen_design_table
fun_control = fun_control_init(
core_model_name="forest.AMFRegressor",
hyperdict=RiverHyperDict,
)
print("Before modification:")
print(gen_design_table(fun_control))
set_float_hyperparameter_values(fun_control, "step", 0.2, 5)
print("After modification:")
print(gen_design_table(fun_control))
Seed set to 123
Source code in spotpython/hyperparameters/values.py
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|
set_hyperparameter(fun_control, key, values)
¶
Set hyperparameter values in the fun_control dictionary based on the type of the values argument.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
fun_control |
dict
|
The fun_control dictionary. |
required |
key |
str
|
The key of the hyperparameter. |
required |
values |
Union[int, float, bool, list]
|
The values of the hyperparameter. This can be: - For int and float: a list containing lower and upper bounds. - For bool: a list containing two boolean values. - For factor: a list of strings representing levels. |
required |
Examples:
>>> from spotpython.hyperparameters.values import set_hyperparameter
>>> fun_control = {
"core_model_hyper_dict": {
"n_estimators": {"type": "int", "default": 10, "lower": 2, "upper": 1000},
"step": {"type": "float", "default": 1.0, "lower": 0.1, "upper": 10.0},
"use_aggregation": {"type": "factor", "default": 1, "lower": 0, "upper": 1, "levels": [0, 1]},
"leaf_model": {"type": "factor", "default": "LinearRegression", "upper": 2}
}
}
>>> set_hyperparameter(fun_control, "n_estimators", [2, 5])
>>> set_hyperparameter(fun_control, "step", [0.2, 5.0])
>>> set_hyperparameter(fun_control, "use_aggregation", [False, True])
>>> set_hyperparameter(fun_control, "leaf_model", ["LinearRegression", "Perceptron"])
>>> set_hyperparameter(fun_control, "leaf_model", "LinearRegression")
Source code in spotpython/hyperparameters/values.py
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|
set_int_hyperparameter_values(fun_control, key, lower, upper)
¶
Set (modify) the integer hyperparameter values in the fun_control dictionary.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
fun_control |
dict
|
The fun_control dictionary. |
required |
key |
str
|
The key of the hyperparameter. |
required |
lower |
int
|
The lower bound of the hyperparameter. |
required |
upper |
int
|
The upper bound of the hyperparameter. |
required |
Examples:
>>> from spotriver.hyperdict.river_hyper_dict import RiverHyperDict
from spotpython.utils.init import fun_control_init
from spotpython.hyperparameters.values import set_int_hyperparameter_values
from spotpython.utils.eda import gen_design_table
fun_control = fun_control_init(
core_model_name="forest.AMFRegressor",
hyperdict=RiverHyperDict,
)
print("Before modification:")
print(gen_design_table(fun_control))
set_int_hyperparameter_values(fun_control, "n_estimators", 2, 5)
print("After modification:")
print(gen_design_table(fun_control))
Seed set to 123
Before modification:
| name | type | default | lower | upper | transform |
|-----------------|--------|-----------|---------|---------|-------------|
| n_estimators | int | 10 | 2 | 1000 | None |
| step | float | 1 | 0.1 | 10 | None |
| use_aggregation | factor | 1 | 0 | 1 | None |
Setting hyperparameter n_estimators to value [2, 5].
Variable type is int.
Core type is None.
Calling modify_hyper_parameter_bounds().
After modification:
| name | type | default | lower | upper | transform |
|-----------------|--------|-----------|---------|---------|-------------|
| n_estimators | int | 10 | 2 | 5 | None |
| step | float | 1 | 0.1 | 10 | None |
| use_aggregation | factor | 1 | 0 | 1 | None |
Source code in spotpython/hyperparameters/values.py
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|
update_fun_control_with_hyper_num_cat_dicts(fun_control, num_dict, cat_dict, dict)
¶
Update an existing fun_control dictionary with new hyperparameter values. All values from the hyperparameter dict (dict) are updated in the fun_control dictionary using the num_dict and cat_dict dictionaries.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
fun_control |
dict
|
The fun_control dictionary. This dictionary is updated with the new hyperparameter values. |
required |
num_dict |
dict
|
The dictionary containing the numerical hyperparameter values, which are used to update the fun_control dictionary. |
required |
cat_dict |
dict
|
The dictionary containing the categorical hyperparameter values, which are used to update the fun_control dictionary. |
required |
dict |
dict
|
The dictionary containing the “old” hyperparameter values. |
required |
Source code in spotpython/hyperparameters/values.py
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|