slurm_logs now in custom dir

docs/build/html/_modules/domainlab/algos/builder_dann.html

@@ -322,6 +322,7 @@ <h1 id="modules-domainlab-algos-builder-dann--page-root">Source code for domainl
 from domainlab.compos.zoo_nn import FeatExtractNNBuilderChainNodeGetter
 from domainlab.models.model_dann import mk_dann
 from domainlab.utils.utils_cuda import get_device
+from domainlab.utils.hyperparameter_retrieval import get_gamma_reg
 
 
 <div class="viewcode-block" id="NodeAlgoBuilderDANN"><a class="viewcode-back" href="../../../domainlab.algos.html#domainlab.algos.builder_dann.NodeAlgoBuilderDANN">[docs]</a>class NodeAlgoBuilderDANN(NodeAlgoBuilder):
@@ -364,7 +365,7 @@ <h1 id="modules-domainlab-algos-builder-dann--page-root">Source code for domainl
         model = mk_dann(list_str_y=task.list_str_y,
                         net_classifier=net_classifier)(
             list_d_tr=task.list_domain_tr,
-            alpha=args.gamma_reg,
+            alpha=get_gamma_reg(args, 'dann'),
             net_encoder=net_encoder,
             net_discriminator=net_discriminator,
             builder=self)

docs/build/html/_modules/domainlab/algos/builder_jigen1.html

@@ -324,6 +324,7 @@ <h1 id="modules-domainlab-algos-builder-jigen1--page-root">Source code for domai
 from domainlab.dsets.utils_wrapdset_patches import WrapDsetPatches
 from domainlab.models.model_jigen import mk_jigen
 from domainlab.utils.utils_cuda import get_device
+from domainlab.utils.hyperparameter_retrieval import get_gamma_reg
 
 
 <div class="viewcode-block" id="NodeAlgoBuilderJiGen"><a class="viewcode-back" href="../../../domainlab.algos.html#domainlab.algos.builder_jigen1.NodeAlgoBuilderJiGen">[docs]</a>class NodeAlgoBuilderJiGen(NodeAlgoBuilder):
@@ -365,7 +366,7 @@ <h1 id="modules-domainlab-algos-builder-jigen1--page-root">Source code for domai
         model = mk_jigen(
             list_str_y=task.list_str_y,
             net_classifier=net_classifier)(
-            coeff_reg=args.gamma_reg,
+            coeff_reg=get_gamma_reg(args, 'jigen'),
             net_encoder=net_encoder,
             net_classifier_permutation=net_classifier_perm,
             n_perm=args.nperm,

docs/build/html/_modules/domainlab/algos/trainers/a_trainer.html

@@ -566,7 +566,15 @@ <h1 id="modules-domainlab-algos-trainers-a-trainer--page-root">Source code for d
         """
         if self._decoratee is not None:
             return self._decoratee.dset_decoration_args_algo(args, ddset)
-        return ddset</div></div>
+        return ddset</div>
+
+<div class="viewcode-block" id="AbstractTrainer.print_parameters"><a class="viewcode-back" href="../../../../domainlab.algos.trainers.html#domainlab.algos.trainers.a_trainer.AbstractTrainer.print_parameters">[docs]</a>    def print_parameters(self):
+        """
+        Function to print all parameters of the object. 
+        Can be used to print the parameters of any child class
+        """
+        params = vars(self)
+        print(f"Parameters of {type(self).__name__}: {params}")</div></div>
 </pre></div>
 
           </article>

docs/build/html/_modules/domainlab/algos/trainers/train_dial.html

@@ -314,6 +314,7 @@ <h1 id="modules-domainlab-algos-trainers-train-dial--page-root">Source code for
 from torch.autograd import Variable
 
 from domainlab.algos.trainers.train_basic import TrainerBasic
+from domainlab.utils.hyperparameter_retrieval import get_gamma_reg
 
 
 <div class="viewcode-block" id="TrainerDIAL"><a class="viewcode-back" href="../../../../domainlab.algos.trainers.html#domainlab.algos.trainers.train_dial.TrainerDIAL">[docs]</a>class TrainerDIAL(TrainerBasic):
@@ -358,7 +359,7 @@ <h1 id="modules-domainlab-algos-trainers-train-dial--page-root">Source code for
         tensor_x_adv = self.gen_adversarial(self.device, tensor_x, tensor_y)
         tensor_x_batch_adv_no_grad = Variable(tensor_x_adv, requires_grad=False)
         loss_dial = self.model.cal_task_loss(tensor_x_batch_adv_no_grad, tensor_y)
-        return [loss_dial], [self.aconf.gamma_reg]</div>
+        return [loss_dial], [get_gamma_reg(self.aconf, self.name)]</div>
 </pre></div>
 
           </article>

docs/build/html/_modules/domainlab/algos/trainers/train_matchdg.html

@@ -322,6 +322,7 @@ <h1 id="modules-domainlab-algos-trainers-train-matchdg--page-root">Source code f
 )
 from domainlab.tasks.utils_task_dset import DsetIndDecorator4XYD
 from domainlab.utils.logger import Logger
+from domainlab.utils.hyperparameter_retrieval import get_gamma_reg
 
 
 <div class="viewcode-block" id="TrainerMatchDG"><a class="viewcode-back" href="../../../../domainlab.algos.trainers.html#domainlab.algos.trainers.train_matchdg.TrainerMatchDG">[docs]</a>class TrainerMatchDG(AbstractTrainer):
@@ -345,7 +346,7 @@ <h1 id="modules-domainlab-algos-trainers-train-matchdg--page-root">Source code f
         self.base_domain_size = get_base_domain_size4match_dg(self.task)
         self.epo_loss_tr = 0
         self.flag_erm = flag_erm
-        self.lambda_ctr = self.aconf.gamma_reg
+        self.lambda_ctr = get_gamma_reg(aconf, self.name)
         self.mk_match_tensor(epoch=0)
         self.flag_match_tensor_sweep_over = False
         self.tuple_tensor_ref_domain2each_y = None

docs/build/html/_modules/domainlab/algos/trainers/train_mldg.html

@@ -319,6 +319,7 @@ <h1 id="modules-domainlab-algos-trainers-train-mldg--page-root">Source code for
 from domainlab.algos.trainers.train_basic import TrainerBasic
 from domainlab.tasks.utils_task import mk_loader
 from domainlab.tasks.utils_task_dset import DsetZip
+from domainlab.utils.hyperparameter_retrieval import get_gamma_reg
 
 
 <div class="viewcode-block" id="TrainerMLDG"><a class="viewcode-back" href="../../../../domainlab.algos.trainers.html#domainlab.algos.trainers.train_mldg.TrainerMLDG">[docs]</a>class TrainerMLDG(AbstractTrainer):
@@ -417,7 +418,7 @@ <h1 id="modules-domainlab-algos-trainers-train-mldg--page-root">Source code for
             loss = (
                 loss_source_task.sum()
                 + source_reg_tr.sum()
-                + self.aconf.gamma_reg * loss_look_forward.sum()
+                + get_gamma_reg(self.aconf, self.name) * loss_look_forward.sum()
             )
             #
             loss.backward()

docs/build/html/_modules/domainlab/algos/trainers/zoo_trainer.html

@@ -316,6 +316,7 @@ <h1 id="modules-domainlab-algos-trainers-zoo-trainer--page-root">Source code for
 from domainlab.algos.trainers.train_matchdg import TrainerMatchDG
 from domainlab.algos.trainers.train_mldg import TrainerMLDG
 from domainlab.algos.trainers.train_fishr import TrainerFishr
+from domainlab.algos.trainers.train_irm import TrainerIRM
 
 
 <div class="viewcode-block" id="TrainerChainNodeGetter"><a class="viewcode-back" href="../../../../domainlab.algos.trainers.html#domainlab.algos.trainers.zoo_trainer.TrainerChainNodeGetter">[docs]</a>class TrainerChainNodeGetter(object):
@@ -358,6 +359,7 @@ <h1 id="modules-domainlab-algos-trainers-zoo-trainer--page-root">Source code for
         chain = TrainerMatchDG(chain)
         chain = TrainerMLDG(chain)
         chain = TrainerFishr(chain)
+        chain = TrainerIRM(chain)
         chain = TrainerHyperScheduler(chain)
         node = chain.handle(self.request)
         head = node

docs/build/html/_modules/domainlab/arg_parser.html

@@ -321,6 +321,37 @@ <h1 id="modules-domainlab-arg-parser--page-root">Source code for domainlab.arg_p
 from domainlab.models.args_vae import add_args2parser_vae
 from domainlab.utils.logger import Logger
 
+<div class="viewcode-block" id="ParseValuesOrKeyValuePairs"><a class="viewcode-back" href="../../domainlab.html#domainlab.arg_parser.ParseValuesOrKeyValuePairs">[docs]</a>class ParseValuesOrKeyValuePairs(argparse.Action):
+    """Class used for arg parsing where values are provided in a key value format"""
+
+    def __call__(self, parser: argparse.ArgumentParser,
+    namespace: argparse.Namespace, values: str, option_string: str = None):
+        """
+            Handle parsing of key value pairs, or a single value instead
+
+            Args:
+                parser (argparse.ArgumentParser): The ArgumentParser object.
+                namespace (argparse.Namespace): The namespace object to store parsed values.
+                values (str): The string containing key=value pairs or a single float value.
+                option_string (str, optional): The option string that triggered this action (unused).
+
+            Raises:
+                ValueError: If the values cannot be parsed to float.
+        """
+        if "=" in values:
+            my_dict = {}
+            for kv in values.split(","):
+                k, v = kv.split("=")
+                try:
+                    my_dict[k.strip()] = float(v.strip())
+                except ValueError:
+                    raise ValueError(f"Invalid value in key-value pair: '{kv}', must be float")
+            setattr(namespace, self.dest, my_dict)
+        else:
+            try:
+                setattr(namespace, self.dest, float(values))
+            except ValueError:
+                raise ValueError(f"Invalid value for {self.dest}: '{values}', must be float")</div>
 
 <div class="viewcode-block" id="mk_parser_main"><a class="viewcode-back" href="../../domainlab.html#domainlab.arg_parser.mk_parser_main">[docs]</a>def mk_parser_main():
     """
@@ -340,7 +371,13 @@ <h1 id="modules-domainlab-arg-parser--page-root">Source code for domainlab.arg_p
     parser.add_argument("--lr", type=float, default=1e-4, help="learning rate")
 
     parser.add_argument(
-        "--gamma_reg", type=float, default=0.1, help="weight of regularization loss"
+        "--gamma_reg",
+        default=0.1,
+        help="weight of regularization loss in the form of $$\ell(\cdot) + \mu \times R(\cdot)$$ \
+        can specify per model as 'default=3.0, dann=1.0,jigen=2.0', where default refer to gamma for trainer \
+        note diva is implemented $$\ell(\cdot) + \mu \times R(\cdot)$$ \
+        so diva does not have gamma_reg",
+        action=ParseValuesOrKeyValuePairs
     )
 
     parser.add_argument("--es", type=int, default=1, help="early stop steps")

docs/build/html/_modules/domainlab/exp_protocol/aggregate_results.html

@@ -352,6 +352,7 @@ <h1 id="modules-domainlab-exp-protocol-aggregate-results--page-root">Source code
 
 <div class="viewcode-block" id="agg_main"><a class="viewcode-back" href="../../../domainlab.exp_protocol.html#domainlab.exp_protocol.aggregate_results.agg_main">[docs]</a>def agg_main(bm_dir: str, skip_plotting: bool = False):
     """Aggregates partial results and generate plots."""
+    bm_dir.rstrip("/")
     agg_output = f"{bm_dir}/results.csv"
     agg_input = f"{bm_dir}/rule_results"
     agg_from_directory(agg_input, agg_output)

docs/build/html/_modules/domainlab/models/a_model.html

@@ -486,7 +486,39 @@ <h1 id="modules-domainlab-models-a-model--page-root">Source code for domainlab.m
         """
         if self._decoratee is not None:
             return self._decoratee.dset_decoration_args_algo(args, ddset)
-        return ddset</div></div>
+        return ddset</div>
+
+    @property
+    def p_na_prefix(self):
+        """
+        common prefix for Models
+        """
+        return "Model"
+
+    @property
+    def name(self):
+        """
+        get the name of the algorithm
+        """
+        na_prefix = self.p_na_prefix
+        len_prefix = len(na_prefix)
+        na_class = type(self).__name__
+        if na_class[:len_prefix] != na_prefix:
+            raise RuntimeError(
+                "Model builder node class must start with ",
+                na_prefix,
+                "the current class is named: ",
+                na_class,
+            )
+        return type(self).__name__[len_prefix:].lower()
+
+<div class="viewcode-block" id="AModel.print_parameters"><a class="viewcode-back" href="../../../domainlab.models.html#domainlab.models.a_model.AModel.print_parameters">[docs]</a>    def print_parameters(self):
+        """
+        Function to print all parameters of the object. 
+        Can be used to print the parameters of every child class.
+        """
+        params = vars(self)
+        print(f"Parameters of {type(self).__name__}: {params}")</div></div>
 </pre></div>
 
           </article>

docs/build/html/_modules/domainlab/models/model_dann.html

@@ -394,13 +394,15 @@ <h1 id="modules-domainlab-models-model-dann--page-root">Source code for domainla
             dict_rst = fun_scheduler(
                 epoch
             )  # the __call__ method of hyperparameter scheduler
-            self.alpha = dict_rst["alpha"]
+            self.alpha = dict_rst[self.name + "_alpha"]
 
         def hyper_init(self, functor_scheduler):
             """hyper_init.
             :param functor_scheduler:
             """
-            return functor_scheduler(trainer=None, alpha=self.alpha)
+            parameters = {}
+            parameters[self.name + "_alpha"] = self.alpha
+            return functor_scheduler(trainer=None, **parameters)
 
         def _cal_reg_loss(self, tensor_x, tensor_y, tensor_d, others):
             _ = others

docs/build/html/_modules/domainlab/models/model_diva.html

@@ -407,18 +407,22 @@ <h1 id="modules-domainlab-models-model-diva--page-root">Source code for domainla
             :param fun_scheduler:
             """
             dict_rst = fun_scheduler(epoch)
-            self.beta_d = dict_rst["beta_d"]
-            self.beta_y = dict_rst["beta_y"]
-            self.beta_x = dict_rst["beta_x"]
+            self.beta_d = dict_rst[self.name + "_beta_d"]
+            self.beta_y = dict_rst[self.name + "_beta_x"]
+            self.beta_x = dict_rst[self.name + "_beta_y"]
 
         def hyper_init(self, functor_scheduler):
             """
             initiate a scheduler object via class name and things inside this model
 
             :param functor_scheduler: the class name of the scheduler
             """
+            parameters = {}
+            parameters[self.name + "_beta_d"] = self.beta_d
+            parameters[self.name + "_beta_y"] = self.beta_y
+            parameters[self.name + "_beta_x"] = self.beta_x
             return functor_scheduler(
-                trainer=None, beta_d=self.beta_d, beta_y=self.beta_y, beta_x=self.beta_x
+                trainer=None, **parameters
             )
 
         def _cal_reg_loss(self, tensor_x, tensor_y, tensor_d, others=None):

docs/build/html/_modules/domainlab/models/model_hduva.html

@@ -375,10 +375,10 @@ <h1 id="modules-domainlab-models-model-hduva--page-root">Source code for domainl
             dict_rst = fun_scheduler(
                 epoch
             )  # the __call__ function of hyper-para-scheduler object
-            self.beta_d = dict_rst["beta_d"]
-            self.beta_y = dict_rst["beta_y"]
-            self.beta_x = dict_rst["beta_x"]
-            self.beta_t = dict_rst["beta_t"]
+            self.beta_d = dict_rst[self.name + "_beta_d"]
+            self.beta_y = dict_rst[self.name + "_beta_y"]
+            self.beta_x = dict_rst[self.name + "_beta_x"]
+            self.beta_t = dict_rst[self.name + "_beta_t"]
 
         def hyper_init(self, functor_scheduler):
             """hyper_init.
@@ -387,12 +387,13 @@ <h1 id="modules-domainlab-models-model-hduva--page-root">Source code for domainl
             # calling the constructor of the hyper-parameter-scheduler class, so that this scheduler
             # class build a dictionary {"beta_d":self.beta_d, "beta_y":self.beta_y}
             # constructor signature is def __init__(self, **kwargs):
+            parameters = {}
+            parameters[self.name + "_beta_d"] = self.beta_d
+            parameters[self.name + "_beta_y"] = self.beta_y
+            parameters[self.name + "_beta_x"] = self.beta_x
+            parameters[self.name + "_beta_t"] = self.beta_t
             return functor_scheduler(
-                trainer=None,
-                beta_d=self.beta_d,
-                beta_y=self.beta_y,
-                beta_x=self.beta_x,
-                beta_t=self.beta_t,
+                trainer=None, **parameters
             )
 
         @store_args

docs/build/html/_modules/domainlab/utils/generate_benchmark_plots.html

@@ -399,7 +399,8 @@ <h1 id="modules-domainlab-utils-generate-benchmark-plots--page-root">Source code
     ['param_index','task',' algo',' epos',' te_d',' seed',' params',' acc','precision',...]
     """
     os.makedirs(output_dir, exist_ok=True)
-    obj = dataframe.columns[G_DF_PLOT_COL_METRIC_START:G_DF_PLOT_COL_METRIC_END]
+    pos_numeric_end = min(G_DF_PLOT_COL_METRIC_END, dataframe.shape[1])
+    obj = dataframe.columns[G_DF_PLOT_COL_METRIC_START:pos_numeric_end]
     # boxplots
     for objective in obj:
         boxplot(
@@ -576,7 +577,8 @@ <h1 id="modules-domainlab-utils-generate-benchmark-plots--page-root">Source code
         but also between the parameter setups
     """
     dataframe = dataframe_in.copy()
-    index = list(range(G_DF_PLOT_COL_METRIC_START, G_DF_PLOT_COL_METRIC_END))
+    pos_numeric_end = min(G_DF_PLOT_COL_METRIC_END, dataframe.shape[1])
+    index = list(range(G_DF_PLOT_COL_METRIC_START, pos_numeric_end))
     if distinguish_param_setups:
         dataframe_ = dataframe.iloc[:, index]
         dataframe_.insert(
@@ -589,7 +591,8 @@ <h1 id="modules-domainlab-utils-generate-benchmark-plots--page-root">Source code
 
         g_p = sns.pairplot(data=dataframe_, hue="label", corner=True, kind=kind)
     else:
-        index_ = list(range(G_DF_PLOT_COL_METRIC_START, G_DF_PLOT_COL_METRIC_END))
+        pos_numeric_end = min(G_DF_PLOT_COL_METRIC_END, dataframe.shape[1])
+        index_ = list(range(G_DF_PLOT_COL_METRIC_START, pos_numeric_end))
         index_.insert(0, G_DF_TASK_COL)
         dataframe_ = dataframe.iloc[:, index_]
 
@@ -726,7 +729,8 @@ <h1 id="modules-domainlab-utils-generate-benchmark-plots--page-root">Source code
     else:
         dataframe.insert(0, "label", dataframe[COLNAME_METHOD])
     # we need "G_DF_PLOT_COL_METRIC_START + 1" as we did insert the columns 'label' at index 0
-    index = list(range(G_DF_PLOT_COL_METRIC_START + 1, G_DF_PLOT_COL_METRIC_END))
+    pos_numeric_end = min(G_DF_PLOT_COL_METRIC_END, dataframe.shape[1])
+    index = list(range(G_DF_PLOT_COL_METRIC_START + 1, pos_numeric_end))
     num_lines = len(dataframe["label"].unique())
     _, axis = plt.subplots(
         figsize=(9, 9 + (0.28 * num_lines)), subplot_kw=dict(polar=True)

docs/build/html/_modules/index.html

@@ -332,6 +332,7 @@ <h1 id="modules-index--page-root">All modules for which code is available</h1>
 <li><a href="domainlab/algos/trainers/train_dial.html">domainlab.algos.trainers.train_dial</a></li>
 <li><a href="domainlab/algos/trainers/train_fishr.html">domainlab.algos.trainers.train_fishr</a></li>
 <li><a href="domainlab/algos/trainers/train_hyper_scheduler.html">domainlab.algos.trainers.train_hyper_scheduler</a></li>
+<li><a href="domainlab/algos/trainers/train_irm.html">domainlab.algos.trainers.train_irm</a></li>
 <li><a href="domainlab/algos/trainers/train_matchdg.html">domainlab.algos.trainers.train_matchdg</a></li>
 <li><a href="domainlab/algos/trainers/train_mldg.html">domainlab.algos.trainers.train_mldg</a></li>
 <li><a href="domainlab/algos/trainers/zoo_trainer.html">domainlab.algos.trainers.zoo_trainer</a></li>
@@ -417,6 +418,7 @@ <h1 id="modules-index--page-root">All modules for which code is available</h1>
 <li><a href="domainlab/utils/generate_benchmark_plots.html">domainlab.utils.generate_benchmark_plots</a></li>
 <li><a href="domainlab/utils/get_git_tag.html">domainlab.utils.get_git_tag</a></li>
 <li><a href="domainlab/utils/hyperparameter_gridsearch.html">domainlab.utils.hyperparameter_gridsearch</a></li>
+<li><a href="domainlab/utils/hyperparameter_retrieval.html">domainlab.utils.hyperparameter_retrieval</a></li>
 <li><a href="domainlab/utils/hyperparameter_sampling.html">domainlab.utils.hyperparameter_sampling</a></li>
 <li><a href="domainlab/utils/logger.html">domainlab.utils.logger</a></li>
 <li><a href="domainlab/utils/override_interface.html">domainlab.utils.override_interface</a></li>

docs/build/html/_sources/doc_benchmark.md.txt

@@ -74,10 +74,10 @@ hyperparameter sampling and pytorch.
 The following script will help to find out which job has failed and the error message, so that you could direct to the
 specific log file
 ```cluster
-bash ./sh_list_error.sh ./zoutput/slurm_logs
+bash ./sh_list_error.sh ./zoutput/benchmarks/[output folder of the sepcifed benchmark in the yaml file]/slurm_logs
 ```
 #### Map between slurm job id and sampled hyperparameter index
-suppose the slurm job id is 14144163, one could the corresponding log file in `./zoutput/slurm_logs` folder via
+suppose the slurm job id is 14144163, one could the corresponding log file in `./zoutput/[output folder of the sepcifed benchmark in the yaml file]/slurm_logs` folder via
 `find . | grep -i "14144163"`
 
 the results can be