Added notebook on how to use custom components. Version will be still beta...

Added notebook on how to use custom components. Version will be still beta because I prefer to wait for V1 until I have tried it with more datasets

VERSION

-1.0.0
\ No newline at end of file
+0.4.8
\ No newline at end of file

docs/conf.py

@@ -26,7 +26,7 @@ sys.path.insert(0, PACKAGEDIR)
 # -- Project information -----------------------------------------------------
 
 project = "pytorch-widedeep"
-copyright = "2020, Javier Rodriguez Zaurin"
+copyright = "2021, Javier Rodriguez Zaurin"
 author = "Javier Rodriguez Zaurin"
 
 # # The full version, including alpha/beta/rc tags

docs/examples.rst

@@ -12,3 +12,4 @@ them to address different problems
 * `Binary Classification with varying parameters <https://github.com/jrzaurin/pytorch-widedeep/blob/master/examples/04_Binary_Classification_Varying_Parameters.ipynb>`__
 * `Regression with Images and Text <https://github.com/jrzaurin/pytorch-widedeep/blob/master/examples/05_Regression_with_Images_and_Text.ipynb>`__
 * `FineTune routines <https://github.com/jrzaurin/pytorch-widedeep/blob/master/examples/06_FineTune_and_WarmUp_Model_Components.ipynb>`__
+* `Custom Components <https://github.com/jrzaurin/pytorch-widedeep/blob/master/examples/07_Custom_Components.ipynb>`__

docs/index.rst

@@ -53,7 +53,7 @@ within the faded-pink rectangle are concatenated.
 Note that it is not possible to illustrate the number of possible
 architectures and components available in ``pytorch-widedeep`` in one Figure.
 Therefore, for more details on possible architectures (and more) please, read
-this documentation, or seethe Examples folders in the repo.
+this documentation, or see the Examples folders in the repo.
 
 In math terms, and following the notation in the `paper
 <https://arxiv.org/abs/1606.07792>`_, the expression for the architecture

docs/installation.rst

@@ -40,3 +40,4 @@ Dependencies
 * torch
 * torchvision
 * einops
+* wrapt
\ No newline at end of file

docs/utils/index.rst

@@ -2,10 +2,10 @@ The ``utils`` module
 ====================
 
 These are a series utilities that might be useful for a number of
-preprocessing tasks. All the classes and functions discussed here are
-available directly from the ``utils`` module. For example, the
-``LabelEncoder`` within the ``deeptabular_utils`` submodule can be imported
-as:
+preprocessing tasks, even not directly related to ``pytorch-widedeep``. All
+the classes and functions discussed here are available directly from the
+``utils`` module. For example, the ``LabelEncoder`` within the
+``deeptabular_utils`` submodule can be imported as:
 
 .. code-block:: python
 

pytorch_widedeep/callbacks.py

@@ -121,10 +121,8 @@ class Callback(object):
 class History(Callback):
     r"""Callback that records metrics to a ``history`` attribute.
 
-    This callback runs by default within :obj:`Trainer`. Callbacks are passed
-    as input parameters to the ``Trainer`` class See
-    :class:`pytorch_widedeep.trainer.Trainer`. Documentation is included here
-    for completion.
+    This callback runs by default within :obj:`Trainer`, therefore, should not
+    be passed to the ``Trainer``. Is included here just for completion.
     """
 
     def on_train_begin(self, logs: Optional[Dict] = None):

pytorch_widedeep/training/trainer.py

@@ -93,7 +93,7 @@ class Trainer:
             - ``root_mean_squared_error``, aliases:  ``rmse``
 
             - ``root_mean_squared_log_error``, aliases: ``rmsle``
-        custom_loss: ``nn.Module``, Optional, default = None
+        custom_loss_function: ``nn.Module``, Optional, default = None
             object of class ``nn.Module``. If none of the loss functions
             available suits the user, it is possible to pass a custom loss
             function. See for example
@@ -101,6 +101,11 @@ class Trainer:
             structure of the object or the `Examples
             <https://github.com/jrzaurin/pytorch-widedeep/tree/master/examples>`_
             folder in the repo.
+
+            .. note:: If ``custom_loss_function`` is not None, ``objective`` must be
+                'binary', 'multiclass' or 'regression', consistent with the loss
+                function
+
         optimizers: ``Optimzer`` or Dict, Optional, default= ``AdamW``
             - An instance of Pytorch's ``Optimizer`` object (e.g. :obj:`torch.optim.Adam()`) or
             - a dictionary where there keys are the model components (i.e.
@@ -222,7 +227,7 @@ class Trainer:
             "regression",
         ]:
             raise ValueError(
-                "If 'custom_loss_function' is not None, 'objective' might be 'binary' "
+                "If 'custom_loss_function' is not None, 'objective' must be 'binary' "
                 "'multiclass' or 'regression', consistent with the loss function"
             )
 

pytorch_widedeep/version.py

-__version__ = "1.0.0"
+__version__ = "0.4.8"