Operators that scaling input distributions in the phenotype simulation.
Classes:
-
Clip: A node that clips the input to be greater than or equal to some minimum value and/or less than or equal to some maximum value.
-
MinMaxScaler: A node that scales the input to be between 0 and 1 using the minimum and maximum values of the input.
-
StandardScaler: A node that scales the input to have mean 0 and standard deviation 1 using the mean and standard deviation of the input.
-
RobustScaler: A node that scales the input to have median 0 and interquartile range 1 using the median and interquartile range of the input.
Operator node that clips the input based on a min and/or max value(s).
If a minimum value is provided, then all values less than the minimum value are set to the minimum value. If a maximum value is provided, then all values greater than the maximum value are set to the maximum value.
Example:
>>> vals = np.array([
[1, 2, 3],
[4, 5, 6],
[7, 8, 9]
])
>>> clip = Clip("clip", "vals", min_val=2, max_val=8)
>>> clip(vals)
array([[2, 2, 3],
[4, 5, 6],
[7, 8, 8]])
>>> clip = Clip("clip", "vals", min_val=4)
>>> clip(vals)
array([[4, 4, 4],
[4, 5, 6],
[7, 8, 9]])
>>> clip = Clip("clip", "vals", max_val=6)
>>> clip(vals)
array([[1, 2, 3],
[4, 5, 6],
[6, 6, 6]])__init__(
alias: str,
input_alias: str,
min_val: float = None,
max_val: float = None
)Initialize Clip node.
Args:
alias: The alias of the node.input_alias: The alias of the input node.min_val(float, default None): The minimum value to clip to.max_val(float, default None): The maximum value to clip to.
run(input_vals)Return the input clipped to the min and/or max value(s).
Operator node that scales the input to be between 0 and 1.
The scaling is based on the minimum and maximum values of the input, such that the minimum value of the input is mapped to 0 and the maximum value of the input is mapped to 1. All other values are scaled linearly between 0 and 1.
Scaling is done either by feature or among all features based on the 'by_feat' argument.
Example:
>>> vals = np.array([
[1, 2, 3],
[4, 5, 6],
[7, 8, 9]
])
>>> mms_by_feat = MinMaxScaler("mms", "vals", by_feat=True)
>>> mms_all = MinMaxScaler("mms", "vals", by_feat=False)
>>> print(mms_by_feat(vals))
array([[0. , 0.5, 1. ],
[0. , 0.5, 1. ],
[0. , 0.5, 1. ]])
>>> print(mms_all(vals))
array([[0. , 0.125, 0.25 ],
[0.375, 0.5 , 0.625],
[0.75 , 0.875, 1. ]])__init__(alias: str, input_alias: str, by_feat: bool = True)Initialize MinMaxScaler node.
Args:
alias: The alias of the node.input_alias: The alias of the input node.by_feat(bool, default True): Whether to scale by feature or among all features.
run(input_vals)Return the input scaled to be between 0 and 1.
Args:
input_vals: The input values to scale.
Returns: The input scaled to be between 0 and 1.
Operator that scales input to have mean 0 and standard deviation 1.
Scaling is either done by feature or among all features based on the 'by_feat' argument.
Example:
>>> vals = np.array([
[1, 2, 3],
[4, 5, 6],
[7, 8, 9]
])
>>> std_scaler_by_feat = StandardScaler(
"std_scaler", "vals", by_feat=True
)
>>> std_scaler_all = StandardScaler(
"std_scaler", "vals", by_feat=False
)
>>> by_feat_out = std_scaler_by_feat(vals)
>>> all_out = std_scaler_all(vals)
>>> by_feat_out
array([[-1.225, 0. , 1.225],
[-1.225, 0. , 1.225],
[-1.225, 0. , 1.225]])
>>> all_out
array([[-1.549, -1.162, -0.775],
[-0.387, 0. , 0.387],
[ 0.775, 1.162, 1.549]])
>>> by_feat_out.mean(1)
array([0., 0., 0.])
>>> by_feat_out.std(1)
array([1., 1., 1.])
>>> all_out.mean(1)
array([-1.162, 0. , 1.162])
>>> all_out.mean()
0.0
>>> all_out.std()
1.0
<a href="../../pheno_sim/func_nodes/scaling.py#L206"><img align="right" style="float:right;" src="https://img.shields.io/badge/-source-cccccc?style=flat-square"></a>
### <kbd>method</kbd> `__init__`
```python
__init__(alias: str, input_alias: str, by_feat: bool = True)Initialize StandardScaler node.
Args:
alias: The alias of the node.input_alias: The alias of the input node.by_feat(bool, default True): Whether to scale by feature or among all features.
run(input_vals)Scale the input to have mean 0 and standard deviation 1.
Operator that scales input to have median 0 and interquartile range 1.
Output interquartile range can be changed by using the 'out_iqr' argument. Output median can be changed by using the 'out_median' argument.
Scaling is either done by feature or among all features based on the 'by_feat' argument.
Example:
>>> vals = np.array([
[1, 2, 3],
[4, 5, 6],
[7, 8, 9]
])
>>> robust_by_feat = RobustScaler("robust", "vals", by_feat=True)
>>> robust_all = RobustScaler("robust", "vals", by_feat=False)
>>> robust_by_feat(vals)
array([[-1., 0., 1.],
[-1., 0., 1.],
[-1., 0., 1.]])
>>> robust_all(vals)
array([[-1. , -0.75, -0.5 ],
[-0.25, 0. , 0.25],
[ 0.5 , 0.75, 1. ]])
>>> extreme_vals = np.array(
np.random.normal(0, 10, 1000).tolist() + [-1000000000000]
)
>>> robust_by_feat(extreme_vals)
array([[-7.454e-01, -6.908e-01, ..., -1.848e+00, -7.676e+10]])
>>> vals = np.random.uniform(0, 10, size=(1000, 5))
>>> robust_scaled = RobustScaler(
"robust", "vals", out_iqr=.5, out_median=1
)
>>> scaled_vals = robust_scaled(vals)
>>> np.median(scaled_vals, axis=0)
array([1., 1., 1., 1., 1.])
>>> iqr(scaled_vals, axis=0)
array([0.608, 0.546, 0.528, 0.483, 0.571])__init__(
alias: str,
input_alias: str,
by_feat: bool = True,
out_iqr: float = 1.0,
out_median: float = 0.0
)Initialize RobustScaler node.
Args:
alias: The alias of the node.input_alias: The alias of the input node.by_feat(bool, default True): Whether to scale by feature or among all features.out_iqr(float, default 1.0): The interquartile range of the output.out_median(float, default 0.0): The median of the output.
run(input_vals)Scale the input to have median 0 and interquartile range 1.
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