Model#
Model composition is the process of defining a probabilistic model as a collection of model components, which are ultimate fitted to a dataset via a non-linear search.
This cookbook provides an overview of basic model composition tools.
Contents:
Models:
If first describes how to use the af.Model object to define models with a single model component from single
Python classes, with the following sections:
Python Class Template: The template of a model component written as a Python class.
Model Composition (Model): Creating a model via
af.Model().Priors (Model): How the default priors of a model are set and how to customize them.
Instances (Model): Creating an instance of a model via input parameters.
Model Customization (Model): Customizing a model (e.g. fixing parameters or linking them to one another).
Tuple Parameters (Model): Defining model components with parameters that are tuples.
Json Output (Model): Output a model in human readable text via a .json file and loading it back again.
Collections:
It then describes how to use the af.Collection object to define models with many model components from multiple
Python classes, with the following sections:
Model Composition (Collection): Creating a model via
af.Collection().Priors (Collection): How the default priors of a collection are set and how to customize them.
Instances (Collection): Create an instance of a collection via input parameters.
Model Customization (Collection): Customize a collection (e.g. fixing parameters or linking them to one another).
Json Output (Collection): Output a collection in human readable text via a .json file and loading it back again.
Extensible Models (Collection): Using collections to extend models with new model components, including the use of Python dictionaries and lists.
Arrays:
The cookbook next describes using NumPy arrays via tbe af.Array object to compose models, where each entry of the
array is a free parameters, therefore offering maximum flexibility with the number of free parameter. This has
the following sections:
Model Composition (af.Array): Composing models using NumPy arrays and af.Array`().
Prior Customization (af.Array): How to customize the priors of a numpy array model.
Instances (af.Array): Create an instance of a numpy array model via input parameters.
Model Customization (af.Array): Customize a numpy array model (e.g. fixing parameters or linking them to one another).
Json Output (af.Array): Output a numpy array model in human readable text via a .json file and loading it back again.
Extensible Models (af.Array): Using numpy arrays to compose models with a flexible number of parameters.
Python Class Template#
A model component is written as a Python class using the following format:
The name of the class is the name of the model component, in this case, “Gaussian”.
The input arguments of the constructor are the parameters of the mode (here
centre,normalizationandsigma).The default values of the input arguments tell PyAutoFit whether a parameter is a single-valued float or a multi-valued tuple.
We define a 1D Gaussian model component to illustrate model composition in PyAutoFit.
class Gaussian:
def __init__(
self,
centre : float = 30.0, # <- **PyAutoFit** recognises these constructor arguments
normalization : float = 1.0, # <- are the Gaussian``s model parameters.
sigma : float = 5.0,
):
self.centre = centre
self.normalization = normalization
self.sigma = sigma
Model Composition (Model)#
We can instantiate a Python class as a model component using af.Model().
model = af.Model(Gaussian)
The model has 3 free parameters, corresponding to the 3 parameters defined above (centre, normalization
and sigma).
Each parameter has a prior associated with it, meaning they are fitted for if the model is passed to a non-linear search.
print(f"Model Total Free Parameters = {model.total_free_parameters}")
If we print the info attribute of the model we get information on all of the parameters and their priors.
print(model.info)
This gives the following output:
Total Free Parameters = 3
model Gaussian (N=3)
centre UniformPrior [1], lower_limit = 0.0, upper_limit = 100.0
normalization LogUniformPrior [2], lower_limit = 1e-06, upper_limit = 1000000.0
sigma UniformPrior [3], lower_limit = 0.0, upper_limit = 25.0
Priors (Model)#
The model has a set of default priors, which have been loaded from a config file in the PyAutoFit workspace.
The config cookbook describes how to setup config files in order to produce custom priors, which means that you do not need to manually specify priors in your Python code every time you compose a model.
If you do not setup config files, all priors must be manually specified before you fit the model, as shown below.
model = af.Model(Gaussian)
model.centre = af.UniformPrior(lower_limit=0.0, upper_limit=100.0)
model.normalization = af.LogUniformPrior(lower_limit=1e-4, upper_limit=1e4)
model.sigma = af.GaussianPrior(mean=0.0, sigma=1.0, lower_limit=0.0, upper_limit=1e5)
Instances (Model)#
Instances of the model components above (created via af.Model) can be created, where an input vector of
parameters is mapped to create an instance of the Python class of the model.
We first need to know the order of parameters in the model, so we know how to define the input vector. This
information is contained in the models paths attribute:
print(model.paths)
The paths appear as follows:
[('centre',), ('normalization',), ('sigma',)]
We create an instance of the Gaussian class via the model where centre=30.0, normalization=2.0 and sigma=3.0.
instance = model.instance_from_vector(vector=[30.0, 2.0, 3.0])
print("Model Instance: \n")
print(instance)
print("Instance Parameters \n")
print("centre = ", instance.centre)
print("normalization = ", instance.normalization)
print("sigma = ", instance.sigma)
This gives the following output:
Model Instance:
<__main__.Gaussian object at 0x7f6f11d437c0>
Instance Parameters
centre = 30.0
normalization = 2.0
sigma = 3.0
We can create an instance by inputting unit values (e.g. between 0.0 and 1.0) which are mapped to the input values
via the priors.
The inputs of 0.5 below are mapped as follows:
centre: goes to 0.5 because this is the midpoint of aUniformPriorwithlower_limit=0.0andupper_limit=1.0.normalizationgoes to 1.0 because this is the midpoint of theLogUniformPrior’ withlower_limit=1e-4andupper_limit=1e4corresponding to log10 space.sigma: goes to 0.0 because this is themeanof theGaussianPrior.
instance = model.instance_from_unit_vector(unit_vector=[0.5, 0.5, 0.5])
print("Model Instance:\n")
print(instance)
print("\nInstance Parameters \n")
print("centre = ", instance.centre)
print("normalization = ", instance.normalization)
print("sigma = ", instance.sigma)
This gives the following output:
Model Instance:
<__main__.Gaussian object at 0x7f6f11d43f70>
Instance Parameters
centre = 50.0
normalization = 1.0
sigma = 0.0
We can create instances of the Gaussian using the median value of the prior of every parameter.
instance = model.instance_from_prior_medians()
print("Instance Parameters \n")
print("centre = ", instance.centre)
print("normalization = ", instance.normalization)
print("sigma = ", instance.sigma)
This gives the following output:
Instance Parameters
centre = 50.0
normalization = 1.0
sigma = 0.0
We can create a random instance, where the random values are unit values drawn between 0.0 and 1.0.
This means the parameter values of this instance are randomly drawn from the priors.
model = af.Model(Gaussian)
instance = model.random_instance()
Model Customization (Model)#
We can fix a free parameter to a specific value (reducing the dimensionality of parameter space by 1):
model = af.Model(Gaussian)
model.centre = 0.0
We can link two parameters together such they always assume the same value (reducing the dimensionality of parameter space by 1):
model.centre = model.normalization
Offsets between linked parameters or with certain values are possible:
model.centre = model.normalization + model.sigma
Assertions remove regions of parameter space (but do not reduce the dimensionality of parameter space):
model.add_assertion(model.sigma > 5.0)
model.add_assertion(model.centre > model.normalization)
The customized model can be inspected by printing its info attribute.
print(model.info)
This gives the following output:
Total Free Parameters = 2
model Gaussian (N=2)
centre SumPrior (N=2)
centre
self LogUniformPrior [14], lower_limit = 1e-06, upper_limit = 1000000.0
other UniformPrior [15], lower_limit = 0.0, upper_limit = 25.0
normalization LogUniformPrior [14], lower_limit = 1e-06, upper_limit = 1000000.0
sigma UniformPrior [15], lower_limit = 0.0, upper_limit = 25.0
The overwriting of priors shown above can be achieved via the following alternative API:
model = af.Model(
Gaussian,
centre=af.UniformPrior(lower_limit=0.0, upper_limit=1.0),
normalization=af.LogUniformPrior(lower_limit=1e-4, upper_limit=1e4),
sigma=af.GaussianPrior(mean=0.0, sigma=1.0),
)
This API can also be used for fixing a parameter to a certain value:
model = af.Model(Gaussian, centre=0.0)
Tuple Parameters (Model)#
The Gaussian model component above only has parameters that are single-valued floats.
Parameters can also be tuples, which is useful for defining model components where certain parameters are naturally grouped together.
For example, we can define a 2D Gaussian with a center that has two coordinates and therefore free parameters, (x, y), using a tuple.
class Gaussian2D:
def __init__(
self,
centre: Tuple[float, float] = (0.0, 0.0), # <- **PyAutoFit** recognises these constructor arguments
normalization: float = 0.1, # <- are the Gaussian``s model parameters.
sigma: float = 1.0,
):
self.centre = centre
self.normalization = normalization
self.sigma = sigma
The model’s total_free_parameters attribute now includes 4 free parameters, as the tuple centre parameter accounts
for 2 free parameters.
model = af.Model(Gaussian2D)
print(f"Model Total Free Parameters = {model.total_free_parameters}")
This information is again displayed in the info attribute:
print(model.info)
This gives the following output:
Total Free Parameters = 4
model Gaussian2D (N=4)
centre
centre_0 UniformPrior [3], lower_limit = 0.0, upper_limit = 100.0
centre_1 UniformPrior [4], lower_limit = 0.0, upper_limit = 100.0
normalization LogUniformPrior [5], lower_limit = 1e-06, upper_limit = 1000000.0
sigma UniformPrior [6], lower_limit = 0.0, upper_limit = 25.0
Here are examples of how model customization can be applied to a model with tuple parameters:
model = af.Model(Gaussian2D)
model.centre = (0.0, 0.0)
model.centre_0 = model.normalization
model.centre_1 = model.normalization + model.sigma
model.add_assertion(model.centre_0 > model.normalization)
Json Outputs (Model)#
A model has a dict attribute, which expresses all information about the model as a Python dictionary.
By printing this dictionary we can therefore get a concise summary of the model.
model = af.Model(Gaussian)
print(model.dict())
This gives the following output:
{
'class_path': '__main__.Gaussian', 'type': 'model',
'centre': {'lower_limit': 0.0, 'upper_limit': 100.0, 'type': 'Uniform'},
'normalization': {'lower_limit': 1e-06, 'upper_limit': 1000000.0, 'type': 'LogUniform'},
'sigma': {'lower_limit': 0.0, 'upper_limit': 25.0, 'type': 'Uniform'}
}
The dictionary representation printed above can be saved to hard disk as a .json file.
This means we can save any PyAutoFit model to hard-disk in a human readable format.
Checkout the file autofit_workspace/*/cookbooks/jsons/model.json to see the model written as a .json.
model_path = path.join("scripts", "cookbooks", "jsons")
os.makedirs(model_path, exist_ok=True)
model_file = path.join(model_path, "model.json")
with open(model_file, "w+") as f:
json.dump(model.dict(), f, indent=4)
We can load the model from its .json file, meaning that one can easily save a model to hard disk and load it
elsewhere.
model = af.Model.from_json(file=model_file)
Model Composition (Collection)#
To illustrate Collection objects we define a second model component, representing a Exponential profile.
class Exponential:
def __init__(
self,
centre=0.0, # <- PyAutoFit recognises these constructor arguments are the model
normalization=0.1, # <- parameters of the Exponential.
rate=0.01,
):
self.centre = centre
self.normalization = normalization
self.rate = rate
To instantiate multiple Python classes into a combined model component we combine the af.Collection() and af.Model()
objects.
By passing the key word arguments gaussian and exponential below, these are used as the names of the attributes of
instances created using this model (which is illustrated clearly below).
model = af.Collection(gaussian=af.Model(Gaussian), exponential=af.Model(Exponential))
We can check the model has a total_free_parameters of 6, meaning the 3 parameters defined
above (centre, normalization, sigma and rate) for both the Gaussian and Exponential classes all have
priors associated with them .
This also means each parameter is fitted for if we fitted the model to data via a non-linear search.
print(f"Model Total Free Parameters = {model.total_free_parameters}")
Printing the info attribute of the model gives us information on all of the parameters.
print(model.info)
This gives the following output:
Total Free Parameters = 6
model Collection (N=6)
gaussian Gaussian (N=3)
exponential Exponential (N=3)
gaussian
centre UniformPrior [39], lower_limit = 0.0, upper_limit = 100.0
normalization LogUniformPrior [40], lower_limit = 1e-06, upper_limit = 1000000.0
sigma UniformPrior [41], lower_limit = 0.0, upper_limit = 25.0
exponential
centre UniformPrior [42], lower_limit = 0.0, upper_limit = 100.0
normalization LogUniformPrior [43], lower_limit = 1e-06, upper_limit = 1000000.0
rate UniformPrior [44], lower_limit = 0.0, upper_limit = 1.0
Priors (Collection)#
The model has a set of default priors, which have been loaded from a config file in the PyAutoFit workspace.
The configs cookbook describes how to setup config files in order to produce custom priors, which means that you do not need to manually specify priors in your Python code every time you compose a model.
If you do not setup config files, all priors must be manually specified before you fit the model, as shown below.
model.gaussian.centre = af.UniformPrior(lower_limit=0.0, upper_limit=100.0)
model.gaussian.normalization = af.UniformPrior(lower_limit=0.0, upper_limit=1e2)
model.gaussian.sigma = af.UniformPrior(lower_limit=0.0, upper_limit=30.0)
model.exponential.centre = af.UniformPrior(lower_limit=0.0, upper_limit=100.0)
model.exponential.normalization = af.UniformPrior(lower_limit=0.0, upper_limit=1e2)
model.exponential.rate = af.UniformPrior(lower_limit=0.0, upper_limit=10.0)
When creating a model via a Collection, there is no need to actually pass the python classes as an af.Model()
because PyAutoFit implicitly assumes they are to be created as a Model().
This enables more concise code, whereby the following code:
model = af.Collection(gaussian=af.Model(Gaussian), exponential=af.Model(Exponential))
Can instead be written as:
model = af.Collection(gaussian=Gaussian, exponential=Exponential)
Instances (Collection)#
We can create an instance of collection containing both the Gaussian and Exponential classes using this model.
We create an instance where:
The
Gaussianclass hascentre=30.0,normalization=2.0andsigma=3.0.The
Exponentialclass hascentre=60.0,normalization=4.0andrate=1.0``.
instance = model.instance_from_vector(vector=[30.0, 2.0, 3.0, 60.0, 4.0, 1.0])
Because we passed the key word arguments gaussian and exponential above, these are the names of the attributes of
instances created using this model (e.g. this is why we write instance.gaussian):
print("Model Instance: \n")
print(instance)
print("Instance Parameters \n")
print("centre (Gaussian) = ", instance.gaussian.centre)
print("normalization (Gaussian) = ", instance.gaussian.normalization)
print("sigma (Gaussian) = ", instance.gaussian.sigma)
print("centre (Exponential) = ", instance.exponential.centre)
print("normalization (Exponential) = ", instance.exponential.normalization)
print("rate (Exponential) = ", instance.exponential.rate)
This gives the following output:
Model Instance:
<autofit.mapper.model.ModelInstance object at 0x7f6f11b73580>
Instance Parameters
centre (Gaussian) = 30.0
normalization (Gaussian) = 2.0
sigma (Gaussian) = 3.0
centre (Exponential) = 60.0
normalization (Exponential) = 4.0
rate (Exponential) = 1.0
Alternatively, the instance’s variables can also be accessed as a list, whereby instead of using attribute names
(e.g. gaussian_0) we input the list index.
Note that the order of the instance model components is determined from the order the components are input into the
Collection.
For example, for the line af.Collection(gaussian=gaussian, exponential=exponential), the first entry in the list
is the gaussian because it is the first input to the Collection.
print("centre (Gaussian) = ", instance[0].centre)
print("normalization (Gaussian) = ", instance[0].normalization)
print("sigma (Gaussian) = ", instance[0].sigma)
print("centre (Gaussian) = ", instance[1].centre)
print("normalization (Gaussian) = ", instance[1].normalization)
print("rate (Exponential) = ", instance[1].rate)
This gives the following output:
centre (Gaussian) = 30.0
normalization (Gaussian) = 2.0
sigma (Gaussian) = 3.0
centre (Exponential) = 60.0
normalization (Exponential) = 4.0
rate (Exponential) = 1.0
Model Customization (Collection)#
By setting up each Model first the model can be customized using either of the API’s shown above:
gaussian = af.Model(Gaussian)
gaussian.normalization = 1.0
gaussian.sigma = af.GaussianPrior(mean=0.0, sigma=1.0)
exponential = af.Model(Exponential)
exponential.centre = 50.0
exponential.add_assertion(exponential.rate > 5.0)
model = af.Collection(gaussian=gaussian, exponential=exponential)
print(model.info)
This gives the following output:
Total Free Parameters = 4
- model Collection (N=4)
gaussian Gaussian (N=2) exponential Exponential (N=2)
- gaussian
centre UniformPrior [71], lower_limit = 0.0, upper_limit = 100.0 normalization 1.0 sigma GaussianPrior [70], mean = 0.0, sigma = 1.0
- exponential
centre 50.0 normalization LogUniformPrior [72], lower_limit = 1e-06, upper_limit = 1000000.0 rate UniformPrior [73], lower_limit = 0.0, upper_limit = 1.0
Below is an alternative API that can be used to create the same model as above.
Which API is used is up to the user and which they find most intuitive.
gaussian = af.Model(
Gaussian, normalization=1.0, sigma=af.GaussianPrior(mean=0.0, sigma=1.0)
)
exponential = af.Model(Exponential, centre=50.0)
exponential.add_assertion(exponential.rate > 5.0)
model = af.Collection(gaussian=gaussian, exponential=exponential)
print(model.info)
This gives the following output:
Total Free Parameters = 4
model Collection (N=4)
gaussian Gaussian (N=2)
exponential Exponential (N=2)
gaussian
centre UniformPrior [63], lower_limit = 0.0, upper_limit = 100.0
normalization 1.0
sigma GaussianPrior [66], mean = 0.0, sigma = 1.0
exponential
centre 50.0
normalization LogUniformPrior [68], lower_limit = 1e-06, upper_limit = 1000000.0
rate UniformPrior [69], lower_limit = 0.0, upper_limit = 1.0
After creating the model as a Collection we can customize it afterwards:
model = af.Collection(gaussian=Gaussian, exponential=Exponential)
model.gaussian.normalization = 1.0
model.gaussian.sigma = af.GaussianPrior(mean=0.0, sigma=1.0)
model.exponential.centre = 50.0
model.exponential.add_assertion(exponential.rate > 5.0)
print(model.info)
This gives the following output:
Total Free Parameters = 4
model Collection (N=4)
gaussian Gaussian (N=2)
exponential Exponential (N=2)
gaussian
centre UniformPrior [71], lower_limit = 0.0, upper_limit = 100.0
normalization 1.0
sigma GaussianPrior [70], mean = 0.0, sigma = 1.0
exponential
centre 50.0
normalization LogUniformPrior [72], lower_limit = 1e-06, upper_limit = 1000000.0
rate UniformPrior [73], lower_limit = 0.0, upper_limit = 1.0
JSon Outputs (Collection)#
A Collection has a dict attribute, which express all information about the model as a Python dictionary.
By printing this dictionary we can therefore get a concise summary of the model.
model = af.Model(Gaussian)
print(model.dict())
This gives the following output:
{
'type': 'collection',
'gaussian': {
'class_path': '__main__.Gaussian', 'type': 'model',
'centre': {'lower_limit': 0.0, 'upper_limit': 100.0, 'type': 'Uniform'},
'normalization': 1.0, 'sigma': {'lower_limit': -inf, 'upper_limit': inf, 'type': 'Gaussian', 'mean': 0.0, 'sigma': 1.0}},
'exponential': {
'class_path': '__main__.Exponential', 'type': 'model',
'centre': 50.0,
'normalization': {'lower_limit': 1e-06, 'upper_limit': 1000000.0, 'type': 'LogUniform'},
'rate': {'lower_limit': 0.0, 'upper_limit': 1.0, 'type': 'Uniform'}}
}
Python dictionaries can easily be saved to hard disk as a .json file.
This means we can save any PyAutoFit model to hard-disk.
Checkout the file autofit_workspace/*/model/jsons/collection.json to see the model written as a .json.
model_path = path.join("scripts", "model", "jsons")
os.makedirs(model_path, exist_ok=True)
model_file = path.join(model_path, "collection.json")
with open(model_file, "w+") as f:
json.dump(model.dict(), f, indent=4)
We can load the model from its .json file, meaning that one can easily save a model to hard disk and load it
elsewhere.
model = af.Model.from_json(file=model_file)
print(f"\n Model via Json Prior Count = {model.prior_count}")
Extensible Models (Collection)#
There is no limit to the number of components we can use to set up a model via a Collection.
model = af.Collection(
gaussian_0=Gaussian,
gaussian_1=Gaussian,
exponential_0=Exponential,
exponential_1=Exponential,
exponential_2=Exponential,
)
print(model.info)
This gives the following output:
Total Free Parameters = 15
model Collection (N=15)
gaussian_0 Gaussian (N=3)
gaussian_1 Gaussian (N=3)
exponential_0 Exponential (N=3)
exponential_1 Exponential (N=3)
exponential_2 Exponential (N=3)
gaussian_0
centre UniformPrior [91], lower_limit = 0.0, upper_limit = 100.0
normalization LogUniformPrior [92], lower_limit = 1e-06, upper_limit = 1000000.0
sigma UniformPrior [93], lower_limit = 0.0, upper_limit = 25.0
gaussian_1
centre UniformPrior [94], lower_limit = 0.0, upper_limit = 100.0
normalization LogUniformPrior [95], lower_limit = 1e-06, upper_limit = 1000000.0
sigma UniformPrior [96], lower_limit = 0.0, upper_limit = 25.0
exponential_0
centre UniformPrior [97], lower_limit = 0.0, upper_limit = 100.0
normalization LogUniformPrior [98], lower_limit = 1e-06, upper_limit = 1000000.0
rate UniformPrior [99], lower_limit = 0.0, upper_limit = 1.0
exponential_1
centre UniformPrior [100], lower_limit = 0.0, upper_limit = 100.0
normalization LogUniformPrior [101], lower_limit = 1e-06, upper_limit = 1000000.0
rate UniformPrior [102], lower_limit = 0.0, upper_limit = 1.0
exponential_2
centre UniformPrior [103], lower_limit = 0.0, upper_limit = 100.0
normalization LogUniformPrior [104], lower_limit = 1e-06, upper_limit = 1000000.0
rate UniformPrior [105], lower_limit = 0.0, upper_limit = 1.0
Total Free Parameters = 6
model Collection (N=6)
gaussian_0 Gaussian (N=3)
gaussian_1 Gaussian (N=3)
gaussian_0
centre UniformPrior [106], lower_limit = 0.0, upper_limit = 100.0
normalization LogUniformPrior [107], lower_limit = 1e-06, upper_limit = 1000000.0
sigma UniformPrior [108], lower_limit = 0.0, upper_limit = 25.0
gaussian_1
centre UniformPrior [109], lower_limit = 0.0, upper_limit = 100.0
normalization LogUniformPrior [110], lower_limit = 1e-06, upper_limit = 1000000.0
sigma UniformPrior [111], lower_limit = 0.0, upper_limit = 25.0
Total Free Parameters = 6
model Collection (N=6)
gaussian_0 Gaussian (N=3)
gaussian_1 Gaussian (N=3)
gaussian_0
centre UniformPrior [112], lower_limit = 0.0, upper_limit = 100.0
normalization LogUniformPrior [113], lower_limit = 1e-06, upper_limit = 1000000.0
sigma UniformPrior [114], lower_limit = 0.0, upper_limit = 25.0
gaussian_1
centre UniformPrior [115], lower_limit = 0.0, upper_limit = 100.0
normalization LogUniformPrior [116], lower_limit = 1e-06, upper_limit = 1000000.0
sigma UniformPrior [117], lower_limit = 0.0, upper_limit = 25.0
A model can be created via af.Collection() where a dictionary of af.Model() objects are passed to it.
The two models created below are identical- one uses the API detailed above whereas the second uses a dictionary.
model = af.Collection(gaussian_0=Gaussian, gaussian_1=Gaussian)
model_dict = {"gaussian_0": Gaussian, "gaussian_1": Gaussian}
model = af.Collection(**model_dict)
The keys of the dictionary passed to the model (e.g. gaussian_0 and gaussian_1 above) are used to create the
names of the attributes of instances of the model.
instance = model.instance_from_vector(vector=[1.0, 2.0, 3.0, 4.0, 5.0, 6.0])
print("Model Instance: \n")
print(instance)
print("Instance Parameters \n")
print("centre (Gaussian) = ", instance.gaussian_0.centre)
print("normalization (Gaussian) = ", instance.gaussian_0.normalization)
print("sigma (Gaussian) = ", instance.gaussian_0.sigma)
print("centre (Gaussian) = ", instance.gaussian_1.centre)
print("normalization (Gaussian) = ", instance.gaussian_1.normalization)
print("sigma (Gaussian) = ", instance.gaussian_1.sigma)
This gives the following output:
Model Instance:
<autofit.mapper.model.ModelInstance object at 0x7f10a40f3a60>
Instance Parameters:
centre (Gaussian) = 1.0
normalization (Gaussian) = 2.0
sigma (Gaussian) = 3.0
centre (Gaussian) = 4.0
normalization (Gaussian) = 5.0
sigma (Gaussian) = 6.0
A list of model components can also be passed to an af.Collection to create a model:
model = af.Collection([Gaussian, Gaussian])
print(model.info)
When a list is used, there is no string with which to name the model components (e.g. we do not input gaussian_0
and gaussian_1 anywhere.
The instance therefore can only be accessed via list indexing.
instance = model.instance_from_vector(vector=[1.0, 2.0, 3.0, 4.0, 5.0, 6.0])
print("Model Instance: \n")
print(instance)
print("Instance Parameters \n")
print("centre (Gaussian) = ", instance[0].centre)
print("normalization (Gaussian) = ", instance[0].normalization)
print("sigma (Gaussian) = ", instance[0].sigma)
print("centre (Gaussian) = ", instance[1].centre)
print("normalization (Gaussian) = ", instance[1].normalization)
print("sigma (Gaussian) = ", instance[1].sigma)
This gives the following output:
Model Instance:
<autofit.mapper.model.ModelInstance object at 0x7f10a40f3a60>
Instance Parameters:
centre (Gaussian) = 1.0
normalization (Gaussian) = 2.0
sigma (Gaussian) = 3.0
centre (Gaussian) = 4.0
normalization (Gaussian) = 5.0
sigma (Gaussian) = 6.0
Model Composition (af.Array)#
Models can be composed using NumPy arrays, where each element of the array is a free parameter.
This offers a lot more flexibility than using Model and Collection objects, as the number of parameters in the
model is chosen on initialization via the input of the shape attribute.
For many use cases, this flexibility is key to ensuring model composition is as easy as possible, for example when a part of the model being fitted is a matrix of parameters which may change shape depending on the dataset being fitted.
To compose models using NumPy arrays, we use the af.Array object.
model = af.Array(
shape=(2, 2),
prior=af.GaussianPrior(mean=0.0, sigma=1.0),
)
Each element of the array is a free parameter, which for shape=(2,2) means the model has 4 free parameters.
print(f"Model Total Free Parameters = {model.total_free_parameters}")
The info attribute of the model gives information on all of the parameters and their priors.
print(model.info)
This gives the following output:
Total Free Parameters = 4
model Array (N=4)
indices list (N=0)
shape (2, 2)
indices
0 (0, 0)
1 (0, 1)
2 (1, 0)
3 (1, 1)
prior_0_0 GaussianPrior [124], mean = 0.0, sigma = 1.0
prior_0_1 GaussianPrior [125], mean = 0.0, sigma = 1.0
prior_1_0 GaussianPrior [126], mean = 0.0, sigma = 1.0
prior_1_1 GaussianPrior [127], mean = 0.0, sigma = 1.0
Prior Customization (af.Array)#
The prior of every parameter in the array is set via the prior input above.
NumPy array models do not currently support default priors via config files, so all priors must be manually specified.
The prior of every parameter in the array can be customized by normal NumPy array indexing:
model = af.Array(shape=(2, 2), prior=af.GaussianPrior(mean=0.0, sigma=1.0))
model.array[0, 0] = af.UniformPrior(lower_limit=0.0, upper_limit=1.0)
model.array[0, 1] = af.LogUniformPrior(lower_limit=1e-4, upper_limit=1e4)
model.array[1, 0] = af.GaussianPrior(mean=0.0, sigma=2.0)
The info attribute shows the customized priors.
print(model.info)
The output is as follows:
Total Free Parameters = 4
model Array (N=4)
indices list (N=0)
shape (2, 2)
indices
0 (0, 0)
1 (0, 1)
2 (1, 0)
3 (1, 1)
prior_0_0 UniformPrior [133], lower_limit = 0.0, upper_limit = 1.0
prior_0_1 LogUniformPrior [134], lower_limit = 0.0001, upper_limit = 10000.0
prior_1_0 GaussianPrior [135], mean = 0.0, sigma = 2.0
prior_1_1 GaussianPrior [132], mean = 0.0, sigma = 1.0
Instances (af.Array)#
Instances of numpy array model components can be created, where an input vector of parameters is mapped to create
an instance of the Python class of the model.
If the priors of the numpy array are not customized, ordering of parameters goes from element [0,0] to [0,1] to [1,0],
as shown by the paths attribute.
model = af.Array(
shape=(2, 2),
prior=af.GaussianPrior(mean=0.0, sigma=1.0),
)
print(model.paths)
The output is as follows:
['prior_0_0', 'prior_0_1', 'prior_1_0', 'prior_1_1']
An instance can then be created by passing a vector of parameters to the model via the instance_from_vector method.
The instance created is a NumPy array, where each element is the value passed in the vector.
instance = model.instance_from_vector(vector=[0.0, 1.0, 2.0, 3.0])
print("\nModel Instance:")
print(instance)
The output is as follows:
Model Instance:
[[0. 1.]
[2. 3.]]
Prior customization changes the order of the parameters, therefore if you customize the priors of the numpy
array you must check the ordering of the parameters in the paths attribute before passing a vector to
the instance_from_vector
model[0, 0] = af.UniformPrior(lower_limit=0.0, upper_limit=1.0)
model[0, 1] = af.LogUniformPrior(lower_limit=1e-4, upper_limit=1e4)
model[1, 0] = af.GaussianPrior(mean=0.0, sigma=2.0)
print(model.paths)
The output is as follows:
[('prior_1_1',), ('prior_0_0',), ('prior_0_1',), ('prior_1_0',)]
If we create a vector and print its values from this customized model:
instance = model.instance_from_vector(vector=[0.0, 1.0, 2.0, 3.0])
print("\nModel Instance:")
print(instance)
The output is as follows:
Model Instance:
[[1. 2.]
[3. 0.]]
Model Customization (af.Array)#
The model customization API for numpy array models is the same as for af.Model and af.Collection objects.
model = af.Array(
shape=(2, 2),
prior=af.GaussianPrior(mean=0.0, sigma=1.0),
)
model[0,0] = 50.0
model[0,1] = model[1,0]
model.add_assertion(model[1,1] > 0.0)
print(model.info)
The output is as follows:
model Array (N=2)
indices list (N=0)
shape (2, 2)
indices
0 (0, 0)
1 (0, 1)
2 (1, 0)
3 (1, 1)
prior_0_0 50.0
prior_0_1 - prior_1_0 GaussianPrior [147], mean = 0.0, sigma = 1.0
prior_1_1 GaussianPrior [148], mean = 0.0, sigma = 1.0
JSon Outputs (af.Array)#
An Array has a dict attribute, which express all information about the model as a Python dictionary.
By printing this dictionary we can therefore get a concise summary of the model.
model = af.Array(
shape=(2, 2),
prior=af.GaussianPrior(mean=0.0, sigma=1.0),
)
print(model.dict())
Python dictionaries can easily be saved to hard disk as a .json file.
This means we can save any PyAutoFit model to hard-disk.
Checkout the file autofit_workspace/*/model/jsons/array.json to see the model written as a .json.
model_path = path.join("scripts", "model", "jsons")
os.makedirs(model_path, exist_ok=True)
model_file = path.join(model_path, "array.json")
with open(model_file, "w+") as f:
json.dump(model.dict(), f, indent=4)
We can load the model from its .json file, meaning that one can easily save a model to hard disk and load it
elsewhere.
model = af.Array.from_json(file=model_file)
print(f"\n Model via Json Prior Count = {model.prior_count}")
Extensible Models (af.Array)#
For Model objects, the number of parameters is fixed to those listed in the input Python class when the model is
created.
For Collection objects, the use of dictionaries and lists allows for the number of parameters to be extended, but it
was still tied to the input Python classes when the model was created.
For Array objects, the number of parameters is fully customizable, you choose the shape of the array and therefore
the number of parameters in the model when you create it.
This makes Array objects the most extensible and flexible way to compose models.
You can also combine Array objects with Collection objects to create models with a mix of fixed and extensible
parameters.
model = af.Collection(
gaussian=Gaussian,
array=af.Array(shape=(3, 2), prior=af.GaussianPrior(mean=0.0, sigma=1.0))
)
model.gaussian.sigma = 2.0
model.array[0, 0] = 1.0
print(model.info)
The output is as follows:
Total Free Parameters = 7
model Collection (N=7)
gaussian Gaussian (N=2)
array Array (N=5)
indices list (N=0)
gaussian
centre UniformPrior [165], lower_limit = 0.0, upper_limit = 100.0
normalization LogUniformPrior [166], lower_limit = 1e-06, upper_limit = 1000000.0
sigma 2.0
array
shape (3, 2)
indices
0 (0, 0)
1 (0, 1)
2 (1, 0)
3 (1, 1)
4 (2, 0)
5 (2, 1)
prior_0_0 1.0
prior_0_1 GaussianPrior [160], mean = 0.0, sigma = 1.0
prior_1_0 GaussianPrior [161], mean = 0.0, sigma = 1.0
prior_1_1 GaussianPrior [162], mean = 0.0, sigma = 1.0
prior_2_0 GaussianPrior [163], mean = 0.0, sigma = 1.0
prior_2_1 GaussianPrior [164], mean = 0.0, sigma = 1.0
Wrap Up#
This cookbook shows how to compose models consisting of multiple components using the af.Model()
and af.Collection() object.
Advanced model composition uses multi-level models, which compose models from hierarchies of Python classes. This is described in the multi-level model cookbook.