tutorials.examples.train_conditional
====================================

.. py:module:: tutorials.examples.train_conditional

.. autoapi-nested-parse::

   Conditional GFlowNet training on the HyperGrid environment.

   This script demonstrates how to train conditional GFlowNets that learn different
   distributions based on a continuous condition variable on the HyperGrid environment.
   The condition interpolates between two extremes:

   - Condition = 0: Uniform distribution (all states get reward R0+R1+R2)
   - Condition = 1: Original HyperGrid multi-modal distribution
   - Condition ∈ (0,1): Linear interpolation between uniform and original

   During training:
   - Condition values are sampled uniformly from [0, 1] for each batch
   - The GFlowNet learns to generate different distributions based on the condition
   - LogZ is modeled as a function of condition only (not states)

   During validation:
   - Fresh trajectories are sampled for multiple condition values [0, 0.25, 0.5, 0.75, 1]
   - L1 distance is computed between empirical and true distributions
   - Mode discovery is tracked for condition=1

   Example usage:
   python train_conditional.py --ndim 2 --height 8 --epsilon 0.1



Attributes
----------

.. autoapisummary::

   tutorials.examples.train_conditional.DEFAULT_SEED
   tutorials.examples.train_conditional.GFN_FNS
   tutorials.examples.train_conditional.parser


Functions
---------

.. autoapisummary::

   tutorials.examples.train_conditional.build_conditional_logF_scalar_estimator
   tutorials.examples.train_conditional.build_conditional_pf_pb
   tutorials.examples.train_conditional.build_db_gflownet
   tutorials.examples.train_conditional.build_db_mod_gflownet
   tutorials.examples.train_conditional.build_fm_gflownet
   tutorials.examples.train_conditional.build_subTB_gflownet
   tutorials.examples.train_conditional.build_tb_gflownet
   tutorials.examples.train_conditional.evaluate_conditional_sampling
   tutorials.examples.train_conditional.main
   tutorials.examples.train_conditional.train


Module Contents
---------------

.. py:data:: DEFAULT_SEED
   :type:  int
   :value: 4444


.. py:data:: GFN_FNS

.. py:function:: build_conditional_logF_scalar_estimator(env)

   Build conditional log flow estimator.

   :param env: The ConditionalHyperGrid environment

   :returns: A conditional scalar estimator for log flow


.. py:function:: build_conditional_pf_pb(env)

   Build conditional policy forward and backward estimators.

   :param env: The ConditionalHyperGrid environment

   :returns: A tuple of (forward policy estimator, backward policy estimator)


.. py:function:: build_db_gflownet(env)

.. py:function:: build_db_mod_gflownet(env)

.. py:function:: build_fm_gflownet(env)

.. py:function:: build_subTB_gflownet(env)

.. py:function:: build_tb_gflownet(env)

   Build a Trajectory Balance GFlowNet.

   :param env: The ConditionalHyperGrid environment

   :returns: A TBGFlowNet instance


.. py:function:: evaluate_conditional_sampling(env, gflownet, device, n_eval_samples=10000)

   Evaluate the conditional sampling distributions with detailed metrics.


.. py:function:: main(args)

.. py:data:: parser

.. py:function:: train(env, gflownet, seed, device, n_iterations=10, batch_size=1000, validation_interval=100, validation_samples=20000, lr=0.001, lr_logz=0.01, epsilon=0.0)