From 4867580637f214441d2f7afcfb629aa30448e3fb Mon Sep 17 00:00:00 2001 From: Hu Chuan-Peng Date: Sun, 14 Apr 2024 21:32:44 +0800 Subject: [PATCH] added docker info in readme added the dockerHDDM's url and adjusted the order of different version --- README.rst | 100 +++++++++++++++++++++++++++-------------------------- 1 file changed, 51 insertions(+), 49 deletions(-) diff --git a/README.rst b/README.rst index 368902c4f..ae5442f79 100755 --- a/README.rst +++ b/README.rst @@ -51,38 +51,26 @@ Features model (RLDDM), including a module for estimating the impact of continuous regressors onto RLDDM parameters, and a reinforcement learning (RL) model. See tutorial for the RLDDM and RL modules here: https://nbviewer.jupyter.org/github/hddm-devs/hddm/blob/master/hddm/examples/demo_RLHDDMtutorial.ipynb and in the paper here: https://rdcu.be/b4q6Z -* HDDM 0.9.0 brings a host of new features. - HDDM includes `likelihood approximation networks`_ via the **HDDMnn**, **HDDMnnRegressor** and **HDDMnnStimCoding** classes. - This allows fitting of a number of variants of sequential sampling models. You can now easily use custom likelihoods - for model fitting. We included a range of new **simulators**, which allow data generation for a host of variants of sequential sampling models. - There are some new out of the box **plots**, in the **hddm.plotting** module. Fast posterior predictives for regression based models. - Some sampler settings are now exposed to the user via a customizable **model_config dictionary**. Lastly you are now able to save and load **HDDMRegression** models with - custom link functions. Please see the **documentation** (under **LAN Extension**) for illustrations on how to use the new features. - -* HDDM 0.9.1 improved documentation for LAN models. - Comprehensive tutorial using LAN included. Bugfixes for ``simulator_h_c()`` function. - -* HDDM 0.9.2 major overhaul of the plotting functions under hddm.plotting. - Old capabilities are preserved under ``hddm.plotting_old``, but will be deprecated. - The new plotting functions replicate the existing functionality, but improve on various aspects of the plot and provide a more abstracted and extensible interface. - Fixes an error with posterior predictive sampling using hierarchical regression models based on LANs with ``HDDMnnRegressor()``. ``HDDMnnRegressor()`` now issues a - single warning for boundary condition violations instead of flagging all occurences. +* HDDM 0.9.8 adds a **breaking change**. + To accommodate some user requests, **all parameters** that should be estimated for a given model should be made explicitly + in the *include* argument of the calling class (HDDM, HDDMnn, etc.). The remaining parameters can now be set to arbitrary, user defined, defaults. + Check the documentation for a **new tutorial on parameter defaults**. + Moreover, **model plot** is made much more flexible (new tutorial included to showcase some of the options). + Two **tutorials** are added to showcase the capabilities for simulation based inference via **custom likelihoods**. + The legacy models with "vanilla" in their name are globally renamed to instead include "hddm_base". + The simulator backend is now completely outsourced to the `ssms`_ package (severe code simplifications). -* HDDM 0.9.3 Lot's of minor improvements. - Model plots now working for race and lca models with n > 2 choices (use **_plot_func_model_n** as **plot_func** argument in **hddm.plotting.plot_posterior_predictive**). - **model_config** files are simplified and class construction is a bit more robust to lack of specification, improving ease of use with custom models. - Various plots received a bit more styling features. - Better defaults for **simulator_h_c** function in **hddm.simulators.hddm_dataset_generators**. - Posterior predictives now properly take into account the *p_outlier* parameter when generating data from the implicit mixture model. *p_outlier* percent of the data, - now explicitly come from random choices with uniform reaction times. - The documentation is updated to reflect / illustrate the improvements. +* HDDM 0.9.7 adds the **HDDMnnRLRegressor** class, the equivalent to the **HDDMrlRegressor** with support for many more *SSMs* via *LANs*. + Please check the documentation for usage examples. -* HDDM 0.9.4 Bug fixes and one major new functionality. - **HDDMnnRegressor** now allows you to define **indirect regressors**, latent parameters that are driven by their own regression and link to model parameters. - See the documentation for more information on this. **Note** this functionality is experimental for now. Model fitting will work, but extraenous functionality may not, - including posterior predictives for models that include such indirect regressors. Including indirect regressors might demand you to think carefully about the supplied - **model_config**. E.g. in the **race_no_bias_3** model, the usual lower bounds on the **v0, v1 ,v2, v3** parameters are 0. If we allow these parameters to be driven by an - indirect regressor **v**, which is added to the regressions of **v0, v1, v2, v3**, then **v0, v1, v2, v3** +* HDDM 0.9.6 brings a host of new features. + HDDM now includes use of `likelihood approximation networks`_ in conjunction with reinforcement learning models via the **HDDMnnRL** class. + This allows researchers to study not only the across-trial dynamics of learning but the within-trial dynamics of choice processes, using a single model. + This module greatly extends the previous functionality for fitting RL+DDM models (via HDDMrl class) by allowing fitting of a number of variants of sequential sampling models in conjuction with a learning process (RL+SSM models). + We have included a new **simulator**, which allows data generation for a host of variants of sequential sampling models in conjunction with the Rescorla-Wagner update rule on a 2-armed bandit task environment. + There are some new, out-of-the-box **plots** and **utility function** in the **hddm.plotting** and **hddm.utils** modules, respectively, to facilitate posterior visualization and posterior predictive checks. + Lastly you can also save and load **HDDMnnRL** models. + Please see the **documentation** (under **HDDMnnRL Extension**) for illustrations on how to use the new features. * HDDM 0.9.5 Bug fixes and another new functionality. **HDDMnnRegressor** now allows you to also define **indirect betas**, latent parameters that can be used in regression models. @@ -105,26 +93,40 @@ Features Note also that the usage of **indirect betas** as well as **indirect regressors** may affect the speed of sampling in general. Both translate into more computational work at the stage of regression likelihood evaluation. -* HDDM 0.9.6 brings a host of new features. - HDDM now includes use of `likelihood approximation networks`_ in conjunction with reinforcement learning models via the **HDDMnnRL** class. - This allows researchers to study not only the across-trial dynamics of learning but the within-trial dynamics of choice processes, using a single model. - This module greatly extends the previous functionality for fitting RL+DDM models (via HDDMrl class) by allowing fitting of a number of variants of sequential sampling models in conjuction with a learning process (RL+SSM models). - We have included a new **simulator**, which allows data generation for a host of variants of sequential sampling models in conjunction with the Rescorla-Wagner update rule on a 2-armed bandit task environment. - There are some new, out-of-the-box **plots** and **utility function** in the **hddm.plotting** and **hddm.utils** modules, respectively, to facilitate posterior visualization and posterior predictive checks. - Lastly you can also save and load **HDDMnnRL** models. - Please see the **documentation** (under **HDDMnnRL Extension**) for illustrations on how to use the new features. +* HDDM 0.9.4 Bug fixes and one major new functionality. + **HDDMnnRegressor** now allows you to define **indirect regressors**, latent parameters that are driven by their own regression and link to model parameters. + See the documentation for more information on this. **Note** this functionality is experimental for now. Model fitting will work, but extraenous functionality may not, + including posterior predictives for models that include such indirect regressors. Including indirect regressors might demand you to think carefully about the supplied + **model_config**. E.g. in the **race_no_bias_3** model, the usual lower bounds on the **v0, v1 ,v2, v3** parameters are 0. If we allow these parameters to be driven by an + indirect regressor **v**, which is added to the regressions of **v0, v1, v2, v3**, then **v0, v1, v2, v3** -* HDDM 0.9.7 adds the **HDDMnnRLRegressor** class, the equivalent to the **HDDMrlRegressor** with support for many more *SSMs* via *LANs*. - Please check the documentation for usage examples. +* HDDM 0.9.3 Lot's of minor improvements. + Model plots now working for race and lca models with n > 2 choices (use **_plot_func_model_n** as **plot_func** argument in **hddm.plotting.plot_posterior_predictive**). + **model_config** files are simplified and class construction is a bit more robust to lack of specification, improving ease of use with custom models. + Various plots received a bit more styling features. + Better defaults for **simulator_h_c** function in **hddm.simulators.hddm_dataset_generators**. + Posterior predictives now properly take into account the *p_outlier* parameter when generating data from the implicit mixture model. *p_outlier* percent of the data, + now explicitly come from random choices with uniform reaction times. + The documentation is updated to reflect / illustrate the improvements. + +* HDDM 0.9.2 major overhaul of the plotting functions under hddm.plotting. + Old capabilities are preserved under ``hddm.plotting_old``, but will be deprecated. + The new plotting functions replicate the existing functionality, but improve on various aspects of the plot and provide a more abstracted and extensible interface. + Fixes an error with posterior predictive sampling using hierarchical regression models based on LANs with ``HDDMnnRegressor()``. ``HDDMnnRegressor()`` now issues a + single warning for boundary condition violations instead of flagging all occurences. + + +* HDDM 0.9.1 improved documentation for LAN models. + Comprehensive tutorial using LAN included. Bugfixes for ``simulator_h_c()`` function. + +* HDDM 0.9.0 brings a host of new features. + HDDM includes `likelihood approximation networks`_ via the **HDDMnn**, **HDDMnnRegressor** and **HDDMnnStimCoding** classes. + This allows fitting of a number of variants of sequential sampling models. You can now easily use custom likelihoods + for model fitting. We included a range of new **simulators**, which allow data generation for a host of variants of sequential sampling models. + There are some new out of the box **plots**, in the **hddm.plotting** module. Fast posterior predictives for regression based models. + Some sampler settings are now exposed to the user via a customizable **model_config dictionary**. Lastly you are now able to save and load **HDDMRegression** models with + custom link functions. Please see the **documentation** (under **LAN Extension**) for illustrations on how to use the new features. -* HDDM 0.9.8 adds a **breaking change**. - To accommodate some user requests, **all parameters** that should be estimated for a given model should be made explicitly - in the *include* argument of the calling class (HDDM, HDDMnn, etc.). The remaining parameters can now be set to arbitrary, user defined, defaults. - Check the documentation for a **new tutorial on parameter defaults**. - Moreover, **model plot** is made much more flexible (new tutorial included to showcase some of the options). - Two **tutorials** are added to showcase the capabilities for simulation based inference via **custom likelihoods**. - The legacy models with "vanilla" in their name are globally renamed to instead include "hddm_base". - The simulator backend is now completely outsourced to the `ssms`_ package (severe code simplifications). Comparison to other packages @@ -168,7 +170,7 @@ Installation ============ For **HDDM >= 0.9.0**, currently in beta release, the most convenient way to install HDDM, is to directly -install via **github**. In a fresh environment (we recommend to use **python 3.7**) type. +install via **github**. In a fresh environment (we recommend to use **python 3.7**) type. **Alternatively**, you can also use docker and pull the latest docker image from `here `_. We recommend you to open a **conda** environment first.