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توضیحات

Classy Probabilistic Programming

ویژگی	مقدار
سیستم عامل	-
نام فایل	autofit-2023.3.27.1
نام	autofit
نسخه کتابخانه	2023.3.27.1
نگهدارنده	[]
ایمیل نگهدارنده	[]
نویسنده	James Nightingale and Richard Hayes
ایمیل نویسنده	richard@rghsoftware.co.uk
آدرس صفحه اصلی	https://github.com/rhayes777/PyAutoFit
آدرس اینترنتی	https://pypi.org/project/autofit/
مجوز	MIT License

PyAutoFit: Classy Probabilistic Programming =========================================== .. |binder| image:: https://mybinder.org/badge_logo.svg :target: https://mybinder.org/v2/gh/Jammy2211/autofit_workspace/HEAD .. |RTD| image:: https://readthedocs.org/projects/pyautofit/badge/?version=latest :target: https://pyautofit.readthedocs.io/en/latest/?badge=latest :alt: Documentation Status .. |Tests| image:: https://github.com/rhayes777/PyAutoFit/actions/workflows/main.yml/badge.svg :target: https://github.com/rhayes777/PyAutoFit/actions .. |Build| image:: https://github.com/rhayes777/PyAutoBuild/actions/workflows/release.yml/badge.svg :target: https://github.com/rhayes777/PyAutoBuild/actions .. |JOSS| image:: https://joss.theoj.org/papers/10.21105/joss.02550/status.svg :target: https://doi.org/10.21105/joss.02550 |binder| |Tests| |Build| |RTD| |JOSS| `Installation Guide <https://pyautofit.readthedocs.io/en/latest/installation/overview.html>`_ | `readthedocs <https://pyautofit.readthedocs.io/en/latest/index.html>`_ | `Introduction on Binder <https://mybinder.org/v2/gh/Jammy2211/autofit_workspace/release?filepath=introduction.ipynb>`_ | `HowToFit <https://pyautofit.readthedocs.io/en/latest/howtofit/howtofit.html>`_ .. _ One day make these BOLD with a colon like my fellowsahip proposa,s where the first is Model Composition & Fitting: Tools for composing a complex model and fitting it with dynesty... PyAutoFit is a Python based probabilistic programming language for the fully Bayesian analysis of extremely large datasets which: - Makes it simple to compose and fit multi-level models using a range of Bayesian inference libraries, such as `emcee <https://github.com/dfm/emcee>`_ and `dynesty <https://github.com/joshspeagle/dynesty>`_. - Handles the 'heavy lifting' that comes with model-fitting, including model composition & customization, outputting results, model-specific visualization and posterior analysis. - Is built for *big-data* analysis, whereby results are output as a sqlite database which can be queried after model-fitting is complete. **PyAutoFit** supports advanced statistical methods such as `graphical and hierarchical models <https://pyautofit.readthedocs.io/en/latest/features/graphical.html>`_, `model-fit chaining <https://pyautofit.readthedocs.io/en/latest/features/search_chaining.html>`_, `sensitivity mapping <https://pyautofit.readthedocs.io/en/latest/features/sensitivity_mapping.html>`_ and `massively parallel model-fits <https://pyautofit.readthedocs.io/en/latest/features/search_grid_search.html>`_ . Getting Started --------------- The following links are useful for new starters: - `The introduction Jupyter Notebook on Binder <https://mybinder.org/v2/gh/Jammy2211/autofit_workspace/release?filepath=introduction.ipynb>`_, where you can try **PyAutoFit** in a web browser (without installation). - `The PyAutoFit readthedocs <https://pyautofit.readthedocs.io/en/latest>`_, which includes an `installation guide <https://pyautofit.readthedocs.io/en/latest/installation/overview.html>`_ and an overview of **PyAutoFit**'s core features. - `The autofit_workspace GitHub repository <https://github.com/Jammy2211/autofit_workspace>`_, which includes example scripts and the `HowToFit Jupyter notebook tutorials <https://github.com/Jammy2211/autofit_workspace/tree/master/notebooks/howtofit>`_ which give new users a step-by-step introduction to **PyAutoFit**. Why PyAutoFit? -------------- **PyAutoFit** began as an Astronomy project for fitting large imaging datasets of galaxies after the developers found that existing PPLs (e.g., `PyMC3 <https://github.com/pymc-devs/pymc3>`_, `Pyro <https://github.com/pyro-ppl/pyro>`_, `STAN <https://github.com/stan-dev/stan>`_) were not suited to the model fitting problems many Astronomers faced. This includes: - Efficiently analysing large and homogenous datasets with an identical model fitting procedure, with tools for processing the large libraries of results output. - Problems where likelihood evaluations are expensive (e.g. run times of days per model-fit), necessitating highly customizable model-fitting pipelines with support for massively parallel computing. - Fitting many different models to the same dataset with tools that streamline model comparison. If these challenges sound familiar, then **PyAutoFit** may be the right software for your model-fitting needs! API Overview ------------ To illustrate the **PyAutoFit** API, we'll use an illustrative toy model of fitting a one-dimensional Gaussian to noisy 1D data. Here's the ``data`` (black) and the model (red) we'll fit: .. image:: https://raw.githubusercontent.com/rhayes777/PyAutoFit/master/files/toy_model_fit.png :width: 400 We define our model, a 1D Gaussian by writing a Python class using the format below: .. code-block:: python class Gaussian: def __init__( self, centre=0.0, # <- PyAutoFit recognises these normalization=0.1, # <- constructor arguments are sigma=0.01, # <- the Gaussian's parameters. ): self.centre = centre self.normalization = normalization self.sigma = sigma """ An instance of the Gaussian class will be available during model fitting. This method will be used to fit the model to data and compute a likelihood. """ def model_data_1d_via_xvalues_from(self, xvalues): transformed_xvalues = xvalues - self.centre return (self.normalization / (self.sigma * (2.0 * np.pi) ** 0.5)) * \ np.exp(-0.5 * (transformed_xvalues / self.sigma) ** 2.0) **PyAutoFit** recognises that this Gaussian may be treated as a model component whose parameters can be fitted for via a non-linear search like `emcee <https://github.com/dfm/emcee>`_. To fit this Gaussian to the ``data`` we create an Analysis object, which gives **PyAutoFit** the ``data`` and a ``log_likelihood_function`` describing how to fit the ``data`` with the model: .. code-block:: python class Analysis(af.Analysis): def __init__(self, data, noise_map): self.data = data self.noise_map = noise_map def log_likelihood_function(self, instance): """ The 'instance' that comes into this method is an instance of the Gaussian class above, with the parameters set to values chosen by the non-linear search. """ print("Gaussian Instance:") print("Centre = ", instance.centre) print("normalization = ", instance.normalization) print("Sigma = ", instance.sigma) """ We fit the ``data`` with the Gaussian instance, using its "model_data_1d_via_xvalues_from" function to create the model data. """ xvalues = np.arange(self.data.shape[0]) model_data = instance.model_data_1d_via_xvalues_from(xvalues=xvalues) residual_map = self.data - model_data chi_squared_map = (residual_map / self.noise_map) ** 2.0 log_likelihood = -0.5 * sum(chi_squared_map) return log_likelihood We can now fit our model to the ``data`` using a non-linear search: .. code-block:: python model = af.Model(Gaussian) analysis = Analysis(data=data, noise_map=noise_map) emcee = af.Emcee(nwalkers=50, nsteps=2000) result = emcee.fit(model=model, analysis=analysis) The ``result`` contains information on the model-fit, for example the parameter samples, maximum log likelihood model and marginalized probability density functions. Support ------- Support for installation issues, help with Fit modeling and using **PyAutoFit** is available by `raising an issue on the GitHub issues page <https://github.com/rhayes777/PyAutoFit/issues>`_. We also offer support on the **PyAutoFit** `Slack channel <https://pyautoFit.slack.com/>`_, where we also provide the latest updates on **PyAutoFit**. Slack is invitation-only, so if you'd like to join send an `email <https://github.com/Jammy2211>`_ requesting an invite.

نیازمندی

مقدار	نام
==2.2.1	corner
>=4.2.1	decorator
>=0.3.1.1	dill
==2.1.0	dynesty
>=0.4.0	typing-inspect
>=3.1.3	emcee
-	matplotlib
>=1.0.0	numpydoc
==0.2.0	pyprojroot
==1.3.0	pyswarms
>=2.10.0	h5py
==1.3.20	SQLAlchemy
<=1.8.1,>=1.5.4	scipy
==1.6.3	astunparse
==3.0.0	xxhash
==2023.3.27.1	autoconf

زبان مورد نیاز

مقدار	نام
>=3.7	Python

نحوه نصب

نصب پکیج whl autofit-2023.3.27.1:

pip install autofit-2023.3.27.1.whl

نصب پکیج tar.gz autofit-2023.3.27.1:

pip install autofit-2023.3.27.1.tar.gz