xai
check_for_nans(data, layer_index)
¶
Checks for NaN values in the tensor data.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
data |
Tensor
|
The tensor to check for NaN values. |
required |
layer_index |
int
|
The index of the layer for logging purposes. |
required |
Returns:
Name | Type | Description |
---|---|---|
bool |
bool
|
True if NaNs are found, False otherwise. |
Source code in spotpython/plot/xai.py
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|
get_activations(net, fun_control, batch_size, device='cpu', normalize=False)
¶
Computes the activations for each layer of the network, the mean activations, and the sizes of the activations for each layer.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
net |
Module
|
The neural network model. |
required |
fun_control |
dict
|
A dictionary containing the dataset. |
required |
batch_size |
int
|
The batch size for the data loader. |
required |
device |
str
|
The device to run the model on. Defaults to “cpu”. |
'cpu'
|
normalize |
bool
|
Whether to normalize the input data. Defaults to False. |
False
|
Returns:
Name | Type | Description |
---|---|---|
tuple |
tuple
|
A tuple containing the activations, mean activations, and layer sizes for each layer. |
Examples:
>>> from spotpython.plot.xai import get_activations
import torch
import numpy as np
import torch.nn as nn
from spotpython.utils.init import fun_control_init
from spotpython.data.diabetes import Diabetes
from spotpython.light.regression.nn_linear_regressor import NNLinearRegressor
from spotpython.hyperdict.light_hyper_dict import LightHyperDict
from spotpython.hyperparameters.values import (
get_default_hyperparameters_as_array, get_one_config_from_X)
from spotpython.hyperdict.light_hyper_dict import LightHyperDict
from spotpython.data.lightdatamodule import LightDataModule
from spotpython.plot.xai import get_gradients
fun_control = fun_control_init(
_L_in=10, # 10: diabetes
_L_out=1,
_torchmetric="mean_squared_error",
data_set=Diabetes(),
core_model=NNLinearRegressor,
hyperdict=LightHyperDict)
X = get_default_hyperparameters_as_array(fun_control)
config = get_one_config_from_X(X, fun_control)
_L_in = fun_control["_L_in"]
_L_out = fun_control["_L_out"]
_torchmetric = fun_control["_torchmetric"]
batch_size = 16
model = fun_control["core_model"](**config, _L_in=_L_in, _L_out=_L_out, _torchmetric=_torchmetric)
activations, mean_activations, layer_sizes = get_activations(net=model, fun_control=fun_control, batch_size=batch_size, device = "cpu")
plot_nn_values_scatter(nn_values=activations, layer_sizes=layer_sizes, nn_values_names="Activations")
Source code in spotpython/plot/xai.py
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|
get_all_layers_conductance(spot_tuner, fun_control, device='cpu', remove_spot_attributes=False)
¶
Get the conductance of all layers.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
spot_tuner |
object
|
The spot tuner object. |
required |
fun_control |
dict
|
A dictionary with the function control. |
required |
device |
str
|
The device to use. Defaults to “cpu”. |
'cpu'
|
remove_spot_attributes |
bool
|
Whether to remove the spot attributes. Defaults to False. |
False
|
Source code in spotpython/plot/xai.py
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|
get_attributions(spot_tuner, fun_control, attr_method='IntegratedGradients', baseline=None, abs_attr=True, n_rel=5, device='cpu', normalize=True, remove_spot_attributes=False)
¶
Get the attributions of a neural network.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
spot_tuner |
object
|
The spot tuner object. |
required |
fun_control |
dict
|
A dictionary with the function control. |
required |
attr_method |
str
|
The attribution method. Defaults to “IntegratedGradients”. |
'IntegratedGradients'
|
baseline |
Tensor
|
The baseline for the attribution methods. Defaults to None. |
None
|
abs_attr |
bool
|
Whether the method should sort by the absolute attribution values. Defaults to True. |
True
|
n_rel |
int
|
The number of relevant features. Defaults to 5. |
5
|
device |
str
|
The device to use. Defaults to “cpu”. |
'cpu'
|
normalize |
bool
|
Whether to normalize the input data. Defaults to True. |
True
|
remove_spot_attributes |
bool
|
Whether to remove the spot attributes.
If True, a torch model is created via |
False
|
Returns:
Type | Description |
---|---|
DataFrame
|
pd.DataFrame (object): A DataFrame with the attributions. |
Source code in spotpython/plot/xai.py
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|
get_gradients(net, fun_control, batch_size, device='cpu', normalize=False)
¶
Get the gradients of a neural network and the size of each layer.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
net |
object
|
A neural network. |
required |
fun_control |
dict
|
A dictionary with the function control. |
required |
batch_size |
int
|
The batch size. |
required |
device |
str
|
The device to use. Defaults to “cpu”. |
'cpu'
|
normalize |
bool
|
Whether to normalize the input data. Defaults to False. |
False
|
Returns:
Name | Type | Description |
---|---|---|
tuple |
tuple
|
A tuple containing: - grads: A dictionary with the gradients of the neural network. - layer_sizes: A dictionary with layer names as keys and their sizes as entries in NumPy array format. |
Examples:
>>> from spotpython.plot.xai import get_gradients
import torch
import numpy as np
import torch.nn as nn
from spotpython.utils.init import fun_control_init
from spotpython.data.diabetes import Diabetes
from spotpython.light.regression.nn_linear_regressor import NNLinearRegressor
from spotpython.hyperdict.light_hyper_dict import LightHyperDict
from spotpython.hyperparameters.values import (
get_default_hyperparameters_as_array, get_one_config_from_X)
from spotpython.hyperdict.light_hyper_dict import LightHyperDict
from spotpython.data.lightdatamodule import LightDataModule
# from spotpython.plot.xai import get_gradients
fun_control = fun_control_init(
_L_in=10, # 10: diabetes
_L_out=1,
_torchmetric="mean_squared_error",
data_set=Diabetes(),
core_model=NNLinearRegressor,
hyperdict=LightHyperDict)
X = get_default_hyperparameters_as_array(fun_control)
config = get_one_config_from_X(X, fun_control)
_L_in = fun_control["_L_in"]
_L_out = fun_control["_L_out"]
_torchmetric = fun_control["_torchmetric"]
batch_size = 16
model = fun_control["core_model"](**config, _L_in=_L_in, _L_out=_L_out, _torchmetric=_torchmetric)
gradients, layer_sizes = get_gradients(net=model)
gradients, layer_sizes
Source code in spotpython/plot/xai.py
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|
get_layer_conductance(spot_tuner, fun_control, layer_idx, device='cpu', normalize=True, remove_spot_attributes=False)
¶
Compute the average layer conductance attributions for a specified layer in the model.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
spot_tuner |
Spot
|
The spot tuner object containing the trained model. |
required |
fun_control |
dict
|
The fun_control dictionary containing the hyperparameters used to train the model. |
required |
layer_idx |
int
|
Index of the layer for which to compute layer conductance attributions. |
required |
device |
str
|
The device to use. Defaults to “cpu”. |
'cpu'
|
normalize |
bool
|
Whether to normalize the input data. Defaults to True. |
True
|
remove_spot_attributes |
bool
|
Whether to remove the spot attributes. Defaults to False. |
False
|
Returns:
Type | Description |
---|---|
ndarray
|
numpy.ndarray: An array containing the average layer conductance attributions for the specified layer. The shape of the array corresponds to the shape of the attributions. |
Source code in spotpython/plot/xai.py
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|
get_weights(net, return_index=False)
¶
Get the weights of a neural network and the size of each layer.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
net |
object
|
A neural network. |
required |
return_index |
bool
|
Whether to return the index. Defaults to False. |
False
|
Returns:
Name | Type | Description |
---|---|---|
tuple |
tuple
|
A tuple containing: - weights: A dictionary with the weights of the neural network. - index: The layer index list (only if return_index is True). - layer_sizes: A dictionary with layer names as keys and their sizes as entries in NumPy array format. |
Examples:
>>> from spotpython.plot.xai import get_weights
import torch
import numpy as np
import torch.nn as nn
from spotpython.utils.init import fun_control_init
from spotpython.data.diabetes import Diabetes
from spotpython.light.regression.nn_linear_regressor import NNLinearRegressor
from spotpython.hyperdict.light_hyper_dict import LightHyperDict
from spotpython.hyperparameters.values import (
get_default_hyperparameters_as_array, get_one_config_from_X)
from spotpython.hyperdict.light_hyper_dict import LightHyperDict
from spotpython.data.lightdatamodule import LightDataModule
from spotpython.plot.xai import get_gradients
fun_control = fun_control_init(
_L_in=10, # 10: diabetes
_L_out=1,
_torchmetric="mean_squared_error",
data_set=Diabetes(),
core_model=NNLinearRegressor,
hyperdict=LightHyperDict)
X = get_default_hyperparameters_as_array(fun_control)
config = get_one_config_from_X(X, fun_control)
_L_in = fun_control["_L_in"]
_L_out = fun_control["_L_out"]
_torchmetric = fun_control["_torchmetric"]
batch_size = 16
model = fun_control["core_model"](**config, _L_in=_L_in, _L_out=_L_out, _torchmetric=_torchmetric)
weights, layer_sizes = get_weights(net=model)
weights, layer_sizes
Source code in spotpython/plot/xai.py
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|
get_weights_conductance_last_layer(spot_tuner, fun_control, device='cpu', remove_spot_attributes=False)
¶
Get the weights and the conductance of the last layer.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
spot_tuner |
object
|
The spot tuner object. |
required |
fun_control |
dict
|
A dictionary with the function control. |
required |
device |
str
|
The device to use. Defaults to “cpu”. |
'cpu'
|
remove_spot_attributes |
bool
|
Whether to remove the spot attributes. Defaults to False. |
False
|
Source code in spotpython/plot/xai.py
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|
is_square(n)
¶
Check if a number is a square number.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
n |
int
|
The number to check. |
required |
Returns:
Name | Type | Description |
---|---|---|
bool |
bool
|
True if the number is a square number, False otherwise. |
Examples:
>>> is_square(4)
True
>>> is_square(5)
False
Source code in spotpython/plot/xai.py
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|
plot_attributions(df, attr_method='IntegratedGradients')
¶
Plot the attributions of a neural network.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
df |
DataFrame
|
A DataFrame with the attributions. |
required |
attr_method |
str
|
The attribution method. Defaults to “IntegratedGradients”. |
'IntegratedGradients'
|
Returns:
Type | Description |
---|---|
None
|
None |
Source code in spotpython/plot/xai.py
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|
plot_conductance_last_layer(weights_last, layer_conductance_last, figsize=(12, 6), show=True)
¶
Plot the conductance of the last layer.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
weights_last |
ndarray
|
The weights of the last layer. |
required |
layer_conductance_last |
ndarray
|
The conductance of the last layer. |
required |
figsize |
tuple
|
The figure size. Defaults to (12, 6). |
(12, 6)
|
show |
bool
|
Whether to show the plot. Defaults |
True
|
Examples:
>>> import numpy as np
from spotpython.plot.xai import plot_conductance_last_layer
weights_last = np.random.rand(10)
layer_conductance_last = np.random.rand(10)
plot_conductance_last_layer(weights_last, layer_conductance_last, show=True)
Source code in spotpython/plot/xai.py
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|
plot_nn_values_hist(nn_values, net, nn_values_names='', color='C0', columns=2)
¶
Plot the values of a neural network. Can be used to plot the weights, gradients, or activations of a neural network.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
nn_values |
dict
|
A dictionary with the values of the neural network. For example, the weights, gradients, or activations. |
required |
net |
object
|
A neural network. |
required |
color |
str
|
The color to use. Defaults to “C0”. |
'C0'
|
columns |
int
|
The number of columns. Defaults to 2. |
2
|
Source code in spotpython/plot/xai.py
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|
plot_nn_values_scatter(nn_values, layer_sizes, nn_values_names='', absolute=True, cmap='gray', figsize=(6, 6), return_reshaped=False, show=True)
¶
Plot the values of a neural network including a marker for padding values. For simplicity, this example will annotate ‘P’ directly on the plot for padding values using a unique marker value approach.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
nn_values |
dict
|
A dictionary with the values of the neural network. For example, the weights, gradients, or activations. |
required |
layer_sizes |
dict
|
A dictionary with layer names as keys and their sizes as entries in NumPy array format. |
required |
nn_values_names |
str
|
The name of the values. Defaults to “”. |
''
|
absolute |
bool
|
Whether to use the absolute values. Defaults to True. |
True
|
cmap |
str
|
The colormap to use. Defaults to “gray”. |
'gray'
|
figsize |
tuple
|
The figure size. Defaults to (6, 6). |
(6, 6)
|
return_reshaped |
bool
|
Whether to return the reshaped values. Defaults to False. |
False
|
show |
bool
|
Whether to show the plot. Defaults to True. |
True
|
Returns:
Name | Type | Description |
---|---|---|
dict |
dict
|
A dictionary with the reshaped values. |
Examples:
>>> from spotpython.utils.init import fun_control_init
from spotpython.data.diabetes import Diabetes
from spotpython.light.regression.nn_linear_regressor import NNLinearRegressor
from spotpython.hyperdict.light_hyper_dict import LightHyperDict
from spotpython.hyperparameters.values import (
get_default_hyperparameters_as_array, get_one_config_from_X)
from spotpython.hyperdict.light_hyper_dict import LightHyperDict
# from spotpython.plot.xai import get_gradients
fun_control = fun_control_init(
_L_in=10, # 10: diabetes
_L_out=1,
_torchmetric="mean_squared_error",
data_set=Diabetes(),
core_model=NNLinearRegressor,
hyperdict=LightHyperDict)
X = get_default_hyperparameters_as_array(fun_control)
config = get_one_config_from_X(X, fun_control)
_L_in = fun_control["_L_in"]
_L_out = fun_control["_L_out"]
_torchmetric = fun_control["_torchmetric"]
batch_size = 16
model = fun_control["core_model"](**config, _L_in=_L_in, _L_out=_L_out, _torchmetric=_torchmetric)
gradients, layer_sizes = get_gradients(model, fun_control=fun_control, batch_size=batch_size, device = "cpu")
plot_nn_values_scatter(nn_values=gradients, layer_sizes=layer_sizes, nn_values_names="Weights")
Source code in spotpython/plot/xai.py
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|
sort_layers(data_dict)
¶
Sorts a dictionary with keys in the format “Layer X” based on the numerical value X.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
data_dict |
dict
|
A dictionary with keys in the format “Layer X”. |
required |
Returns:
Name | Type | Description |
---|---|---|
dict |
dict
|
A dictionary with the keys sorted based on the numerical value X. |
Examples:
>>> data_dict = {
... "Layer 1": [1, 2, 3],
... "Layer 3": [4, 5, 6],
... "Layer 2": [7, 8, 9]
... }
>>> sort_layers(data_dict)
{'Layer 1': [1, 2, 3], 'Layer 2': [7, 8, 9], 'Layer 3': [4,
Source code in spotpython/plot/xai.py
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|
visualize_activations_distributions(activations, net, color='C0', columns=4, bins=50, show=True)
¶
Plots the distribution of activations for each layer that were determined via the get_activations function.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
activations |
dict
|
A dictionary containing activations for each layer. |
required |
net |
Module
|
The neural network model. |
required |
color |
str
|
The color for the plot histogram. Defaults to “C0”. |
'C0'
|
columns |
int
|
The number of columns for the subplots. Defaults to 4. |
4
|
bins |
int
|
The number of bins for the histogram. Defaults to 50. |
50
|
show |
bool
|
Whether to show the plot. Defaults to True. |
True
|
Returns:
Type | Description |
---|---|
None
|
None |
Source code in spotpython/plot/xai.py
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|
visualize_gradient_distributions(net, fun_control, batch_size, device='cpu', color='C0', xlabel=None, stat='count', use_kde=True, columns=2, normalize=True)
¶
Plot the gradients distributions of a neural network.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
net |
object
|
A neural network. |
required |
fun_control |
dict
|
A dictionary with the function control. |
required |
batch_size |
int
|
The batch size. |
required |
device |
str
|
The device to use. Defaults to “cpu”. |
'cpu'
|
color |
str
|
The color to use. Defaults to “C0”. |
'C0'
|
xlabel |
str
|
The x label. Defaults to None. |
None
|
stat |
str
|
The stat. Defaults to “count”. |
'count'
|
use_kde |
bool
|
Whether to use kde. Defaults to True. |
True
|
columns |
int
|
The number of columns. Defaults to 2. |
2
|
normalize |
bool
|
Whether to normalize the input data. Defaults to True. |
True
|
Returns:
Type | Description |
---|---|
None
|
None |
Source code in spotpython/plot/xai.py
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|
visualize_gradients(net, fun_control, batch_size, absolute=True, cmap='gray', figsize=(6, 6), device='cpu', normalize=True)
¶
Scatter plots the gradients of a neural network.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
net |
object
|
A neural network. |
required |
fun_control |
dict
|
A dictionary with the function control. |
required |
batch_size |
int
|
The batch size. |
required |
absolute |
bool
|
Whether to use the absolute values. Defaults to True. |
True
|
cmap |
str
|
The colormap to use. Defaults to “gray”. |
'gray'
|
figsize |
tuple
|
The figure size. Defaults to (6, 6). |
(6, 6)
|
device |
str
|
The device to use. Defaults to “cpu”. |
'cpu'
|
normalize |
bool
|
Whether to normalize the input data. Defaults to True. |
True
|
Returns:
Type | Description |
---|---|
None
|
None |
Source code in spotpython/plot/xai.py
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|
visualize_mean_activations(mean_activations, layer_sizes, absolute=True, cmap='gray', figsize=(6, 6))
¶
Scatter plots the mean activations of a neural network for each layer. means_activations is a dictionary with the mean activations of the neural network computed via the get_activations function.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
mean_activations |
dict
|
A dictionary with the mean activations of the neural network. |
required |
layer_sizes |
dict
|
A dictionary with layer names as keys and their sizes as entries in NumPy array format. |
required |
absolute |
bool
|
Whether to use the absolute values. Defaults to True. |
True
|
cmap |
str
|
The colormap to use. Defaults to “gray”. |
'gray'
|
figsize |
tuple
|
The figure size. Defaults to (6, 6). |
(6, 6)
|
Returns:
Type | Description |
---|---|
None
|
None |
Examples:
>>> from spotpython.plot.xai import get_activations
activations, mean_activations, layer_sizes = get_activations(net, fun_control)
visualize_mean_activations(mean_activations, layer_sizes)
Source code in spotpython/plot/xai.py
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visualize_weights(net, absolute=True, cmap='gray', figsize=(6, 6))
¶
Scatter plots the weights of a neural network.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
net |
object
|
A neural network. |
required |
absolute |
bool
|
Whether to use the absolute values. Defaults to True. |
True
|
cmap |
str
|
The colormap to use. Defaults to “gray”. |
'gray'
|
figsize |
tuple
|
The figure size. Defaults to (6, 6). |
(6, 6)
|
Returns:
Type | Description |
---|---|
None
|
None |
Examples:
>>> from spotpython.utils.init import fun_control_init
from spotpython.data.diabetes import Diabetes
from spotpython.light.regression.nn_linear_regressor import NNLinearRegressor
from spotpython.hyperdict.light_hyper_dict import LightHyperDict
from spotpython.hyperparameters.values import (
get_default_hyperparameters_as_array, get_one_config_from_X)
from spotpython.plot.xai import visualize_weights
fun_control = fun_control_init(
_L_in=10, # 10: diabetes
_L_out=1,
_torchmetric="mean_squared_error",
data_set=Diabetes(),
core_model=NNLinearRegressor,
hyperdict=LightHyperDict)
X = get_default_hyperparameters_as_array(fun_control)
config = get_one_config_from_X(X, fun_control)
_L_in = fun_control["_L_in"]
_L_out = fun_control["_L_out"]
_torchmetric = fun_control["_torchmetric"]
batch_size = 16
model = fun_control["core_model"](**config, _L_in=_L_in, _L_out=_L_out, _torchmetric=_torchmetric)
visualize_weights(net=model, absolute=True, cmap="gray", figsize=(6, 6))
Source code in spotpython/plot/xai.py
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visualize_weights_distributions(net, color='C0', columns=2)
¶
Plot the weights distributions of a neural network.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
net |
object
|
A neural network. |
required |
color |
str
|
The color to use. Defaults to “C0”. |
'C0'
|
columns |
int
|
The number of columns. Defaults to 2. |
2
|
Returns:
Type | Description |
---|---|
None
|
None |
Source code in spotpython/plot/xai.py
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