Merge pull request #206 from delira-dev/remove_trixi2

Remove trixi
delira-dev · Sep 11, 2019 · 4801709 · 4801709
2 parents 488bc88 + 1d3d272
commit 4801709
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Showing 27 changed files with 1,930 additions and 717 deletions.
diff --git a/delira/training/__init__.py b/delira/training/__init__.py
@@ -1,5 +1,4 @@
 
-from delira.training.parameters import Parameters
 from delira.training.base_experiment import BaseExperiment
 from delira.training.base_trainer import BaseNetworkTrainer
 from delira.training.predictor import Predictor

diff --git a/delira/training/backends/chainer/experiment.py b/delira/training/backends/chainer/experiment.py
@@ -4,7 +4,7 @@
 from delira.models.backends.chainer import AbstractChainerNetwork
 from delira.data_loading import BaseDataManager
 from delira.training.base_experiment import BaseExperiment
-from delira.training.parameters import Parameters
+from delira.utils import DeliraConfig
 
 from delira.training.backends.chainer.utils import create_optims_default
 from delira.training.backends.chainer.utils import convert_to_numpy
@@ -13,7 +13,7 @@
 
 class ChainerExperiment(BaseExperiment):
     def __init__(self,
-                 params: typing.Union[str, Parameters],
+                 config: typing.Union[str, DeliraConfig],
                  model_cls: AbstractChainerNetwork,
                  n_epochs=None,
                  name=None,
@@ -28,10 +28,10 @@ def __init__(self,
 
         Parameters
         ----------
-        params : :class:`Parameters` or str
-            the training parameters, if string is passed,
-            it is treated as a path to a pickle file, where the
-            parameters are loaded from
+        config : :class:`DeliraConfig` or str
+            the training config, if string is passed,
+            it is treated as a path to a file, where the
+            config is loaded from
         model_cls : Subclass of :class:`AbstractChainerNetwork`
             the class implementing the model to train
         n_epochs : int or None
@@ -67,7 +67,7 @@ def __init__(self,
 
         if key_mapping is None:
             key_mapping = {"x": "data"}
-        super().__init__(params=params, model_cls=model_cls,
+        super().__init__(config=config, model_cls=model_cls,
                          n_epochs=n_epochs, name=name, save_path=save_path,
                          key_mapping=key_mapping,
                          val_score_key=val_score_key,

diff --git a/delira/training/backends/sklearn/experiment.py b/delira/training/backends/sklearn/experiment.py
@@ -7,14 +7,14 @@
 from delira.models.backends.sklearn import SklearnEstimator
 
 from delira.training.base_experiment import BaseExperiment
-from delira.training.parameters import Parameters
+from delira.utils import DeliraConfig
 
 from delira.training.backends.sklearn.trainer import SklearnEstimatorTrainer
 
 
 class SklearnExperiment(BaseExperiment):
     def __init__(self,
-                 params: typing.Union[str, Parameters],
+                 config: typing.Union[str, DeliraConfig],
                  model_cls: BaseEstimator,
                  n_epochs=None,
                  name=None,
@@ -29,10 +29,10 @@ def __init__(self,
 
         Parameters
         ----------
-        params : :class:`Parameters` or str
-            the training parameters, if string is passed,
-            it is treated as a path to a pickle file, where the
-            parameters are loaded from
+        config : :class:`DeliraConfig` or str
+            the training config, if string is passed,
+            it is treated as a path to a file, where the
+            config is loaded from
         model_cls : Subclass of :class:`sklearn.base.BaseEstimator`
             the class implementing the model to train (will be wrapped by
             :class:`SkLearnEstimator`)
@@ -65,7 +65,7 @@ class wrapping the actual sklearn model to provide delira
         if key_mapping is None:
             key_mapping = {"X": "X"}
 
-        super().__init__(params=params,
+        super().__init__(config=config,
                          model_cls=model_cls,
                          n_epochs=n_epochs,
                          name=name,
@@ -77,14 +77,14 @@ class wrapping the actual sklearn model to provide delira
                          **kwargs)
         self._model_wrapper_cls = model_wrapper_cls
 
-    def _setup_training(self, params, **kwargs):
+    def _setup_training(self, config, **kwargs):
         """
             Handles the setup for training case
 
             Parameters
             ----------
-            params : :class:`Parameters`
-                the parameters containing the model and training kwargs
+            config : :class:`DeliraConfig`
+                the config containing the model and training kwargs
             **kwargs :
                 additional keyword arguments
 
@@ -93,14 +93,13 @@ def _setup_training(self, params, **kwargs):
             :class:`BaseNetworkTrainer`
                 the created trainer
         """
-        model_params = params.permute_training_on_top().model
-
-        model_kwargs = {**model_params.fixed, **model_params.variable}
+        model_kwargs = config.model_params
+        model_kwargs = {**model_kwargs["variable"], **model_kwargs["fixed"]}
 
         _model = self.model_cls(**model_kwargs)
         model = self._model_wrapper_cls(_model)
 
-        training_params = params.permute_training_on_top().training
+        training_params = config.training_params
         train_metrics = training_params.nested_get("train_metrics", {})
         val_metrics = training_params.nested_get("val_metrics", {})
 
@@ -120,14 +119,14 @@ def _setup_training(self, params, **kwargs):
             **kwargs
         )
 
-    def _setup_test(self, params, model, convert_batch_to_npy_fn,
+    def _setup_test(self, config, model, convert_batch_to_npy_fn,
                     prepare_batch_fn, **kwargs):
         """
 
         Parameters
         ----------
-        params : :class:`Parameters`
-            the parameters containing the model and training kwargs
+        config : :class:`DeliraConfig`
+            the config containing the model and training kwargs
             (ignored here, just passed for subclassing and unified API)
         model : :class:`sklearn.base.BaseEstimator`
             the model to test
@@ -155,5 +154,5 @@ def _setup_test(self, params, model, convert_batch_to_npy_fn,
                                        input_device="cpu",
                                        output_device="cpu")
 
-        return super()._setup_test(params, model, convert_batch_to_npy_fn,
+        return super()._setup_test(config, model, convert_batch_to_npy_fn,
                                    prepare_batch_fn, **kwargs)
diff --git a/delira/training/backends/tf_eager/experiment.py b/delira/training/backends/tf_eager/experiment.py
@@ -7,7 +7,7 @@
 from delira.models.backends.tf_eager import AbstractTfEagerNetwork
 
 from delira.training.base_experiment import BaseExperiment
-from delira.training.parameters import Parameters
+from delira.utils import DeliraConfig
 
 from delira.training.backends.tf_eager.trainer import TfEagerNetworkTrainer
 from delira.training.backends.tf_eager.utils import create_optims_default
@@ -16,7 +16,7 @@
 
 class TfEagerExperiment(BaseExperiment):
     def __init__(self,
-                 params: typing.Union[str, Parameters],
+                 config: typing.Union[str, DeliraConfig],
                  model_cls: AbstractTfEagerNetwork,
                  n_epochs=None,
                  name=None,
@@ -31,10 +31,10 @@ def __init__(self,
 
         Parameters
         ----------
-        params : :class:`Parameters` or str
-            the training parameters, if string is passed,
-            it is treated as a path to a pickle file, where the
-            parameters are loaded from
+        config : :class:`DeliraConfig` or str
+            the training config, if string is passed,
+            it is treated as a path to a file, where the
+            config is loaded from
         model_cls : Subclass of :class:`AbstractTfEagerNetwork`
             the class implementing the model to train
         n_epochs : int or None
@@ -71,7 +71,7 @@ def __init__(self,
 
         if key_mapping is None:
             key_mapping = {"x": "data"}
-        super().__init__(params=params, model_cls=model_cls,
+        super().__init__(config=config, model_cls=model_cls,
                          n_epochs=n_epochs, name=name, save_path=save_path,
                          key_mapping=key_mapping,
                          val_score_key=val_score_key,
@@ -84,7 +84,7 @@ def kfold(self, data: BaseDataManager, metrics: dict, num_epochs=None,
               num_splits=None, shuffle=False, random_seed=None,
               split_type="random", val_split=0.2, label_key="label",
               train_kwargs: dict = None, test_kwargs: dict = None,
-              metric_keys: dict = None, params=None, verbose=False,
+              metric_keys: dict = None, config=None, verbose=False,
               **kwargs):
         """
         Performs a k-Fold cross-validation
@@ -98,7 +98,7 @@ def kfold(self, data: BaseDataManager, metrics: dict, num_epochs=None,
             dictionary containing the metrics to evaluate during k-fold
         num_epochs : int or None
             number of epochs to train (if not given, will either be
-            extracted from ``params``, ``self.parms`` or ``self.n_epochs``)
+            extracted from ``config``, ``self.config`` or ``self.n_epochs``)
         num_splits : int or None
             the number of splits to extract from ``data``.
             If None: uses a default of 10
@@ -133,9 +133,9 @@ def kfold(self, data: BaseDataManager, metrics: dict, num_epochs=None,
             kwargs to update the behavior of the :class:`BaseDataManager`
             containing the test and validation data.
             If None: empty dict will be passed
-        params : :class:`Parameters`or None
+        config : :class:`DeliraConfig` or None
             the training and model parameters
-            (will be merged with ``self.params``)
+            (will be merged with ``self.config``)
         verbose : bool
             verbosity
         **kwargs :
@@ -196,7 +196,7 @@ def kfold(self, data: BaseDataManager, metrics: dict, num_epochs=None,
             train_kwargs=train_kwargs,
             test_kwargs=test_kwargs,
             metric_keys=metric_keys,
-            params=params,
+            config=config,
             verbose=verbose,
             **kwargs)
 
@@ -261,14 +261,14 @@ def test(self, network, test_data: BaseDataManager,
                             verbose=verbose, prepare_batch=prepare_batch,
                             convert_fn=convert_fn, **kwargs)
 
-    def setup(self, params, training=True, **kwargs):
+    def setup(self, config, training=True, **kwargs):
         """
         Defines the setup behavior (model, trainer etc.) for training and
         testing case
 
         Parameters
         ----------
-        params : :class:`Parameters`
+        config : :class:`DeliraConfig`
             the parameters to use for setup
         training : bool
             whether to setup for training case or for testing case
@@ -291,5 +291,5 @@ def setup(self, params, training=True, **kwargs):
 
         """
         tf.reset_default_graph()
-        return super().setup(params=params, training=training,
+        return super().setup(config=config, training=training,
                              **kwargs)
diff --git a/delira/training/backends/tf_graph/experiment.py b/delira/training/backends/tf_graph/experiment.py
@@ -6,7 +6,7 @@
 from delira.models.backends.tf_graph import AbstractTfGraphNetwork
 from delira.data_loading import BaseDataManager
 
-from delira.training.parameters import Parameters
+from delira.utils import DeliraConfig
 from delira.training.backends.tf_eager.experiment import TfEagerExperiment
 from delira.training.backends.tf_eager.utils import create_optims_default
 
@@ -16,7 +16,7 @@
 
 class TfGraphExperiment(TfEagerExperiment):
     def __init__(self,
-                 params: typing.Union[str, Parameters],
+                 config: typing.Union[str, DeliraConfig],
                  model_cls: AbstractTfGraphNetwork,
                  n_epochs=None,
                  name=None,
@@ -31,10 +31,10 @@ def __init__(self,
 
         Parameters
         ----------
-        params : :class:`Parameters` or str
-            the training parameters, if string is passed,
-            it is treated as a path to a pickle file, where the
-            parameters are loaded from
+        config : :class:`DeliraConfig` or str
+            the training config, if string is passed,
+            it is treated as a path to a file, where the
+            config is loaded from
         model_cls : Subclass of :class:`AbstractTfEagerNetwork`
             the class implementing the model to train
         n_epochs : int or None
@@ -73,7 +73,7 @@ def __init__(self,
             key_mapping = {"data": "data"}
 
         super().__init__(
-            params=params,
+            config=config,
             model_cls=model_cls,
             n_epochs=n_epochs,
             name=name,

diff --git a/delira/training/backends/torch/experiment.py b/delira/training/backends/torch/experiment.py
@@ -7,7 +7,7 @@
 from delira.data_loading import BaseDataManager
 
 from delira.training.base_experiment import BaseExperiment
-from delira.training.parameters import Parameters
+from delira.utils import DeliraConfig
 
 from delira.training.backends.torch.trainer import PyTorchNetworkTrainer
 from delira.training.backends.torch.utils import create_optims_default
@@ -16,7 +16,7 @@
 
 class PyTorchExperiment(BaseExperiment):
     def __init__(self,
-                 params: typing.Union[str, Parameters],
+                 config: typing.Union[str, DeliraConfig],
                  model_cls: AbstractPyTorchNetwork,
                  n_epochs=None,
                  name=None,
@@ -31,10 +31,10 @@ def __init__(self,
 
         Parameters
         ----------
-        params : :class:`Parameters` or str
-            the training parameters, if string is passed,
-            it is treated as a path to a pickle file, where the
-            parameters are loaded from
+        config : :class:`DeliraConfig` or str
+            the training config, if string is passed,
+            it is treated as a path to a file, where the
+            config is loaded from
         model_cls : Subclass of :class:`AbstractPyTorchNetwork`
             the class implementing the model to train
         n_epochs : int or None
@@ -70,7 +70,7 @@ def __init__(self,
 
         if key_mapping is None:
             key_mapping = {"x": "data"}
-        super().__init__(params=params, model_cls=model_cls,
+        super().__init__(config=config, model_cls=model_cls,
                          n_epochs=n_epochs, name=name, save_path=save_path,
                          key_mapping=key_mapping,
                          val_score_key=val_score_key,
@@ -83,7 +83,7 @@ def kfold(self, data: BaseDataManager, metrics: dict, num_epochs=None,
               num_splits=None, shuffle=False, random_seed=None,
               split_type="random", val_split=0.2, label_key="label",
               train_kwargs: dict = None, test_kwargs: dict = None,
-              metric_keys: dict = None, params=None, verbose=False,
+              metric_keys: dict = None, config=None, verbose=False,
               **kwargs):
         """
         Performs a k-Fold cross-validation
@@ -97,7 +97,7 @@ def kfold(self, data: BaseDataManager, metrics: dict, num_epochs=None,
             dictionary containing the metrics to evaluate during k-fold
         num_epochs : int or None
             number of epochs to train (if not given, will either be
-            extracted from ``params``, ``self.parms`` or ``self.n_epochs``)
+            extracted from ``config``, ``self.config`` or ``self.n_epochs``)
         num_splits : int or None
             the number of splits to extract from ``data``.
             If None: uses a default of 10
@@ -132,9 +132,9 @@ def kfold(self, data: BaseDataManager, metrics: dict, num_epochs=None,
             kwargs to update the behavior of the :class:`BaseDataManager`
             containing the test and validation data.
             If None: empty dict will be passed
-        params : :class:`Parameters`or None
+        config : :class:`Parameters`or None
             the training and model parameters
-            (will be merged with ``self.params``)
+            (will be merged with ``self.config``)
         verbose : bool
             verbosity
         **kwargs :
@@ -195,7 +195,7 @@ def kfold(self, data: BaseDataManager, metrics: dict, num_epochs=None,
             train_kwargs=train_kwargs,
             test_kwargs=test_kwargs,
             metric_keys=metric_keys,
-            params=params,
+            config=config,
             verbose=verbose,
             **kwargs)