Analysis¶
pairplot(samples, points=None, limits=None, subset=None, offdiag='hist', diag='hist', figsize=(10, 10), labels=None, ticks=None, upper=None, fig=None, axes=None, **kwargs)
¶
Plot samples in a 2D grid showing marginals and pairwise marginals.
Each of the diagonal plots can be interpreted as a 1D-marginal of the distribution that the samples were drawn from. Each upper-diagonal plot can be interpreted as a 2D-marginal of the distribution.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
samples |
Union[List[ndarray], List[Tensor], ndarray, Tensor]
|
Samples used to build the histogram. |
required |
points |
Optional[Union[List[ndarray], List[Tensor], ndarray, Tensor]]
|
List of additional points to scatter. |
None
|
limits |
Optional[Union[List, Tensor]]
|
Array containing the plot xlim for each parameter dimension. If None, just use the min and max of the passed samples |
None
|
subset |
Optional[List[int]]
|
List containing the dimensions to plot. E.g. subset=[1,3] will plot plot only the 1st and 3rd dimension but will discard the 0th and 2nd (and, if they exist, the 4th, 5th and so on). |
None
|
offdiag |
Optional[Union[List[str], str]]
|
Plotting style for upper diagonal, {hist, scatter, contour, cond, None}. |
'hist'
|
upper |
Optional[str]
|
deprecated, use offdiag instead. |
None
|
diag |
Optional[Union[List[str], str]]
|
Plotting style for diagonal, {hist, cond, None}. |
'hist'
|
figsize |
Tuple
|
Size of the entire figure. |
(10, 10)
|
labels |
Optional[List[str]]
|
List of strings specifying the names of the parameters. |
None
|
ticks |
Optional[Union[List, Tensor]]
|
Position of the ticks. |
None
|
fig |
matplotlib figure to plot on. |
None
|
|
axes |
matplotlib axes corresponding to fig. |
None
|
|
**kwargs |
Additional arguments to adjust the plot, e.g., |
{}
|
Returns: figure and axis of posterior distribution plot
Source code in sbi/analysis/plot.py
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|
marginal_plot(samples, points=None, limits=None, subset=None, diag='hist', figsize=(10, 10), labels=None, ticks=None, fig=None, axes=None, **kwargs)
¶
Plot samples in a row showing 1D marginals of selected dimensions.
Each of the plots can be interpreted as a 1D-marginal of the distribution that the samples were drawn from.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
samples |
Union[List[ndarray], List[Tensor], ndarray, Tensor]
|
Samples used to build the histogram. |
required |
points |
Optional[Union[List[ndarray], List[Tensor], ndarray, Tensor]]
|
List of additional points to scatter. |
None
|
limits |
Optional[Union[List, Tensor]]
|
Array containing the plot xlim for each parameter dimension. If None, just use the min and max of the passed samples |
None
|
subset |
Optional[List[int]]
|
List containing the dimensions to plot. E.g. subset=[1,3] will plot plot only the 1st and 3rd dimension but will discard the 0th and 2nd (and, if they exist, the 4th, 5th and so on). |
None
|
diag |
Optional[str]
|
Plotting style for 1D marginals, {hist, kde cond, None}. |
'hist'
|
figsize |
Tuple
|
Size of the entire figure. |
(10, 10)
|
labels |
Optional[List[str]]
|
List of strings specifying the names of the parameters. |
None
|
ticks |
Optional[Union[List, Tensor]]
|
Position of the ticks. |
None
|
points_colors |
Colors of the |
required | |
fig |
matplotlib figure to plot on. |
None
|
|
axes |
matplotlib axes corresponding to fig. |
None
|
|
**kwargs |
Additional arguments to adjust the plot, e.g., |
{}
|
Returns: figure and axis of posterior distribution plot
Source code in sbi/analysis/plot.py
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conditional_pairplot(density, condition, limits, points=None, subset=None, resolution=50, figsize=(10, 10), labels=None, ticks=None, fig=None, axes=None, **kwargs)
¶
Plot conditional distribution given all other parameters.
The conditionals can be interpreted as slices through the density
at a location
given by condition
.
For example:
Say we have a 3D density with parameters \(\theta_0\), \(\theta_1\), \(\theta_2\) and
a condition \(c\) passed by the user in the condition
argument.
For the plot of \(\theta_0\) on the diagonal, this will plot the conditional
\(p(\theta_0 | \theta_1=c[1], \theta_2=c[2])\). For the upper
diagonal of \(\theta_1\) and \(\theta_2\), it will plot
\(p(\theta_1, \theta_2 | \theta_0=c[0])\). All other diagonals and upper-diagonals
are built in the corresponding way.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
density |
Any
|
Probability density with a |
required |
condition |
Tensor
|
Condition that all but the one/two regarded parameters are fixed to. The condition should be of shape (1, dim_theta), i.e. it could e.g. be a sample from the posterior distribution. |
required |
limits |
Union[List, Tensor]
|
Limits in between which each parameter will be evaluated. |
required |
points |
Optional[Union[List[ndarray], List[Tensor], ndarray, Tensor]]
|
Additional points to scatter. |
None
|
subset |
Optional[List[int]]
|
List containing the dimensions to plot. E.g. subset=[1,3] will plot plot only the 1st and 3rd dimension but will discard the 0th and 2nd (and, if they exist, the 4th, 5th and so on) |
None
|
resolution |
int
|
Resolution of the grid at which we evaluate the |
50
|
figsize |
Tuple
|
Size of the entire figure. |
(10, 10)
|
labels |
Optional[List[str]]
|
List of strings specifying the names of the parameters. |
None
|
ticks |
Optional[Union[List, Tensor]]
|
Position of the ticks. |
None
|
points_colors |
Colors of the |
required | |
fig |
matplotlib figure to plot on. |
None
|
|
axes |
matplotlib axes corresponding to fig. |
None
|
|
**kwargs |
Additional arguments to adjust the plot, e.g., |
{}
|
Returns: figure and axis of posterior distribution plot
Source code in sbi/analysis/plot.py
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|
conditional_corrcoeff(density, limits, condition, subset=None, resolution=50)
¶
Returns the conditional correlation matrix of a distribution.
To compute the conditional distribution, we condition all but two parameters to
values from condition
, and then compute the Pearson correlation
coefficient \(\rho\) between the remaining two parameters under the distribution
density
. We do so for any pair of parameters specified in subset
, thus
creating a matrix containing conditional correlations between any pair of
parameters.
If condition
is a batch of conditions, this function computes the conditional
correlation matrix for each one of them and returns the mean.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
density |
Any
|
Probability density function with |
required |
limits |
Tensor
|
Limits within which to evaluate the |
required |
condition |
Tensor
|
Values to condition the |
required |
subset |
Optional[List[int]]
|
Evaluate the conditional distribution only on a subset of dimensions.
If |
None
|
resolution |
int
|
Number of grid points on which the conditional distribution is evaluated. A higher value increases the accuracy of the estimated correlation but also increases the computational cost. |
50
|
Returns: Average conditional correlation matrix of shape either (num_dim, num_dim)
or (len(subset), len(subset))
if subset
was specified.
Source code in sbi/analysis/conditional_density.py
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|