hypersklearn
HyperSklearn
¶
Hyperparameter Tuning for Sklearn.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
seed |
int
|
seed. See Numpy Random Sampling |
126
|
log_level |
int
|
log level for logger. Default is 50. |
50
|
Attributes:
Name | Type | Description |
---|---|---|
seed |
int
|
seed for random number generator. |
rng |
Generator
|
random number generator. |
fun_control |
dict
|
dictionary containing control parameters for the function. |
log_level |
int
|
log level for logger. |
Examples:
>>> from spotpython.fun.hypersklearn import HyperSklearn
>>> hyper_sklearn = HyperSklearn(seed=126, log_level=50)
>>> print(hyper_sklearn.seed)
126
Source code in spotpython/fun/hypersklearn.py
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|
check_X_shape(X)
¶
Check the shape of the input array X.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
X |
ndarray
|
input array. |
required |
Raises:
Type | Description |
---|---|
Exception
|
if the second dimension of X does not match the length of var_name in fun_control. |
Examples:
>>> from spotpython.fun.hypersklearn import HyperSklearn
>>> hyper_sklearn = HyperSklearn(seed=126, log_level=50)
>>> hyper_sklearn.fun_control["var_name"] = ["a", "b", "c"]
>>> hyper_sklearn.check_X_shape(X=np.array([[1, 2, 3]]))
>>> hyper_sklearn.check_X_shape(X=np.array([[1, 2]]))
Traceback (most recent call last):
...
Exception
Source code in spotpython/fun/hypersklearn.py
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|
fun_sklearn(X, fun_control=None)
¶
Evaluate a sklearn model using hyperparameters specified in X.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
X |
ndarray
|
input array containing hyperparameters. |
required |
fun_control |
dict
|
dictionary containing control parameters for the function. Default is None. |
None
|
Returns:
Type | Description |
---|---|
ndarray
|
array containing evaluation results. |
Raises:
Type | Description |
---|---|
Exception
|
if call to evaluate_model fails. |
Source code in spotpython/fun/hypersklearn.py
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|
get_sklearn_df_eval_preds(model)
¶
Get evaluation and prediction dataframes for a given model. Args: model (sklearn model): sklearn model.
Returns:
Type | Description |
---|---|
tuple
|
tuple containing evaluation and prediction dataframes. |
Raises:
Type | Description |
---|---|
Exception
|
if call to evaluate_model fails. |
Source code in spotpython/fun/hypersklearn.py
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|