From 679e1d4a6ab6c5a093e298cf4aa08d7f0736734f Mon Sep 17 00:00:00 2001 From: Eduardo Carvalho Date: Tue, 12 Jan 2021 19:16:49 +0100 Subject: [PATCH] chore: release v3.1.0 --- CHANGELOG.md | 14 +++++ .../pytorch_tabnet/abstract_model.html | 21 +++++-- docs/_modules/pytorch_tabnet/callbacks.html | 6 +- docs/_modules/pytorch_tabnet/metrics.html | 8 +-- .../pytorch_tabnet/multiclass_utils.html | 13 +++- docs/_modules/pytorch_tabnet/multitask.html | 4 +- docs/_modules/pytorch_tabnet/pretraining.html | 2 +- .../pytorch_tabnet/pretraining_utils.html | 2 +- docs/_modules/pytorch_tabnet/sparsemax.html | 2 +- docs/_modules/pytorch_tabnet/tab_model.html | 7 ++- docs/_modules/pytorch_tabnet/tab_network.html | 38 ++++++------ docs/_modules/pytorch_tabnet/utils.html | 11 +++- docs/_sources/generated_docs/README.md.txt | 6 +- docs/generated_docs/README.html | 5 +- docs/generated_docs/pytorch_tabnet.html | 61 +++++++++++++++---- docs/genindex.html | 10 ++- docs/searchindex.js | 2 +- pyproject.toml | 2 +- 18 files changed, 155 insertions(+), 59 deletions(-) diff --git a/CHANGELOG.md b/CHANGELOG.md index 6702ccce..08c0de67 100755 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -1,4 +1,18 @@ +# [3.1.0](https://github.com/dreamquark-ai/tabnet/compare/v3.0.0...v3.1.0) (2021-01-12) + + +### Bug Fixes + +* n_a not being used ([7ae20c9](https://github.com/dreamquark-ai/tabnet/commit/7ae20c98a601da95040b9ecf79eac19f1d3e4a7b)) + + +### Features + +* save and load preds_mapper ([cab643b](https://github.com/dreamquark-ai/tabnet/commit/cab643b156fdecfded51d70d29072fc43f397bbb)) + + + # [3.0.0](https://github.com/dreamquark-ai/tabnet/compare/v2.0.1...v3.0.0) (2020-12-15) diff --git a/docs/_modules/pytorch_tabnet/abstract_model.html b/docs/_modules/pytorch_tabnet/abstract_model.html index 03e82c1e..21b10933 100644 --- a/docs/_modules/pytorch_tabnet/abstract_model.html +++ b/docs/_modules/pytorch_tabnet/abstract_model.html @@ -170,6 +170,7 @@

Source code for pytorch_tabnet.abstract_model

validate_eval_set, create_dataloaders, define_device, + ComplexEncoder, ) from pytorch_tabnet.callbacks import ( CallbackContainer, @@ -491,6 +492,10 @@

Source code for pytorch_tabnet.abstract_model

self.network.load_state_dict(update_state_dict)
+
[docs] def load_class_attrs(self, class_attrs): + for attr_name, attr_value in class_attrs.items(): + setattr(self, attr_name, attr_value)
+
[docs] def save_model(self, path): """Saving TabNet model in two distinct files. @@ -506,19 +511,26 @@

Source code for pytorch_tabnet.abstract_model

""" saved_params = {} + init_params = {} for key, val in self.get_params().items(): if isinstance(val, type): # Don't save torch specific params continue else: - saved_params[key] = val + init_params[key] = val + saved_params["init_params"] = init_params + + class_attrs = { + "preds_mapper": self.preds_mapper + } + saved_params["class_attrs"] = class_attrs # Create folder Path(path).mkdir(parents=True, exist_ok=True) # Save models params with open(Path(path).joinpath("model_params.json"), "w", encoding="utf8") as f: - json.dump(saved_params, f) + json.dump(saved_params, f, cls=ComplexEncoder) # Save state_dict torch.save(self.network.state_dict(), Path(path).joinpath("network.pt")) @@ -539,7 +551,7 @@

Source code for pytorch_tabnet.abstract_model

with zipfile.ZipFile(filepath) as z: with z.open("model_params.json") as f: loaded_params = json.load(f) - loaded_params["device_name"] = self.device_name + loaded_params["init_params"]["device_name"] = self.device_name with z.open("network.pt") as f: try: saved_state_dict = torch.load(f, map_location=self.device) @@ -554,11 +566,12 @@

Source code for pytorch_tabnet.abstract_model

except KeyError: raise KeyError("Your zip file is missing at least one component") - self.__init__(**loaded_params) + self.__init__(**loaded_params["init_params"]) self._set_network() self.network.load_state_dict(saved_state_dict) self.network.eval() + self.load_class_attrs(loaded_params["class_attrs"]) return
diff --git a/docs/_modules/pytorch_tabnet/callbacks.html b/docs/_modules/pytorch_tabnet/callbacks.html index 2ccd6e86..a59b2516 100644 --- a/docs/_modules/pytorch_tabnet/callbacks.html +++ b/docs/_modules/pytorch_tabnet/callbacks.html @@ -265,8 +265,8 @@

Source code for pytorch_tabnet.callbacks

         minimum change in monitored value to qualify as improvement.
         This number should be positive.
     patience : integer
-        number of epochs to wait for improvment before terminating.
-        the counter be reset after each improvment
+        number of epochs to wait for improvement before terminating.
+        the counter be reset after each improvement
 
     """
 
@@ -312,7 +312,7 @@ 
Source code for pytorch_tabnet.callbacks
             self.trainer.network.load_state_dict(self.best_weights)
 
         if self.stopped_epoch > 0:
-            msg = f"\nEarly stopping occured at epoch {self.stopped_epoch}"
+            msg = f"\nEarly stopping occurred at epoch {self.stopped_epoch}"
             msg += (
                 f" with best_epoch = {self.best_epoch} and "
                 + f"best_{self.early_stopping_metric} = {round(self.best_loss, 5)}"
diff --git a/docs/_modules/pytorch_tabnet/metrics.html b/docs/_modules/pytorch_tabnet/metrics.html
index e0a0db78..d807616e 100644
--- a/docs/_modules/pytorch_tabnet/metrics.html
+++ b/docs/_modules/pytorch_tabnet/metrics.html
@@ -182,7 +182,7 @@ 
Source code for pytorch_tabnet.metrics
     y_pred : torch.Tensor or np.array
         Reconstructed prediction (with embeddings)
     embedded_x : torch.Tensor
-        Orginal input embedded by network
+        Original input embedded by network
     obf_vars : torch.Tensor
         Binary mask for obfuscated variables.
         1 means the variable was obfuscated so reconstruction is based on this.
@@ -217,7 +217,7 @@ 
Source code for pytorch_tabnet.metrics
     y_pred : torch.Tensor or np.array
         Reconstructed prediction (with embeddings)
     embedded_x : torch.Tensor
-        Orginal input embedded by network
+        Original input embedded by network
     obf_vars : torch.Tensor
         Binary mask for obfuscated variables.
         1 means the variables was obfuscated so reconstruction is based on this.
@@ -509,7 +509,7 @@ 
Source code for pytorch_tabnet.metrics
 
[docs]class RMSLE(Metric):
     """
     Mean squared logarithmic error regression loss.
-    Scikit-imeplementation:
+    Scikit-implementation:
     https://scikit-learn.org/stable/modules/generated/sklearn.metrics.mean_squared_log_error.html
     Note: In order to avoid error, negative predictions are clipped to 0.
     This means that you should clip negative predictions manually after calling predict.
@@ -557,7 +557,7 @@ 
Source code for pytorch_tabnet.metrics
         y_pred : torch.Tensor or np.array
             Reconstructed prediction (with embeddings)
         embedded_x : torch.Tensor
-            Orginal input embedded by network
+            Original input embedded by network
         obf_vars : torch.Tensor
             Binary mask for obfuscated variables.
             1 means the variables was obfuscated so reconstruction is based on this.
diff --git a/docs/_modules/pytorch_tabnet/multiclass_utils.html b/docs/_modules/pytorch_tabnet/multiclass_utils.html
index 6b7501f5..e579c068 100644
--- a/docs/_modules/pytorch_tabnet/multiclass_utils.html
+++ b/docs/_modules/pytorch_tabnet/multiclass_utils.html
@@ -174,6 +174,7 @@ 
Source code for pytorch_tabnet.multiclass_utils
<
 import scipy.sparse as sp
 
 import numpy as np
+import pandas as pd
 
 
 def _assert_all_finite(X, allow_nan=False):
@@ -502,6 +503,14 @@ 
Source code for pytorch_tabnet.multiclass_utils
<
         return "binary"  # [1, 2] or [["a"], ["b"]]

 
 
+
[docs]def check_unique_type(y):
+    target_types = pd.Series(y).map(type).unique()
+    if len(target_types) != 1:
+        raise TypeError(
+            f"Values on the target must have the same type. Target has types {target_types}"
+        )

+
+
 
[docs]def infer_output_dim(y_train):
     """
     Infer output_dim from targets
@@ -518,6 +527,7 @@ 
Source code for pytorch_tabnet.multiclass_utils
<
     train_labels : list
         Sorted list of initial classes
     """
+    check_unique_type(y_train)
     train_labels = unique_labels(y_train)
     output_dim = len(train_labels)
 
@@ -526,6 +536,7 @@ 
Source code for pytorch_tabnet.multiclass_utils
<
 
 
[docs]def check_output_dim(labels, y):
     if y is not None:
+        check_unique_type(y)
         valid_labels = unique_labels(y)
         if not set(valid_labels).issubset(set(labels)):
             raise ValueError(
@@ -556,7 +567,7 @@ 
Source code for pytorch_tabnet.multiclass_utils
<
 
     if len(y_train.shape) < 2:
         raise ValueError(
-            "y_train shoud be of shape (n_examples, n_tasks)"
+            "y_train should be of shape (n_examples, n_tasks)"
             + f"but got {y_train.shape}"
         )
     nb_tasks = y_train.shape[1]
diff --git a/docs/_modules/pytorch_tabnet/multitask.html b/docs/_modules/pytorch_tabnet/multitask.html
index 8279b9df..067feb01 100644
--- a/docs/_modules/pytorch_tabnet/multitask.html
+++ b/docs/_modules/pytorch_tabnet/multitask.html
@@ -233,7 +233,7 @@ 
Source code for pytorch_tabnet.multitask
             for classes in self.classes_
         ]
         self.preds_mapper = [
-            {index: class_label for index, class_label in enumerate(classes)}
+            {str(index): str(class_label) for index, class_label in enumerate(classes)}
             for classes in self.classes_
         ]
         self.updated_weights = weights
@@ -279,7 +279,7 @@ 
Source code for pytorch_tabnet.multitask
         results = [np.hstack(task_res) for task_res in results.values()]
         # map all task individually
         results = [
-            np.vectorize(self.preds_mapper[task_idx].get)(task_res)
+            np.vectorize(self.preds_mapper[task_idx].get)(task_res.astype(str))
             for task_idx, task_res in enumerate(results)
         ]
         return results

diff --git a/docs/_modules/pytorch_tabnet/pretraining.html b/docs/_modules/pytorch_tabnet/pretraining.html
index ccb1f8e8..c15c3eed 100644
--- a/docs/_modules/pytorch_tabnet/pretraining.html
+++ b/docs/_modules/pytorch_tabnet/pretraining.html
@@ -236,7 +236,7 @@ 
Source code for pytorch_tabnet.pretraining
             a PyTorch loss function
             should be left to None for self supervised and non experts
         pretraining_ratio : float
-            Between 0 and 1, percentage of featue to mask for reconstruction
+            Between 0 and 1, percentage of feature to mask for reconstruction
         weights : np.array
             Sampling weights for each example.
         max_epochs : int
diff --git a/docs/_modules/pytorch_tabnet/pretraining_utils.html b/docs/_modules/pytorch_tabnet/pretraining_utils.html
index 276bbcda..43c1af09 100644
--- a/docs/_modules/pytorch_tabnet/pretraining_utils.html
+++ b/docs/_modules/pytorch_tabnet/pretraining_utils.html
@@ -168,7 +168,7 @@ 
Source code for pytorch_tabnet.pretraining_utils

     X_train, eval_set, weights, batch_size, num_workers, drop_last, pin_memory
 ):
     """
-    Create dataloaders with or wihtout subsampling depending on weights and balanced.
+    Create dataloaders with or without subsampling depending on weights and balanced.
 
     Parameters
     ----------
diff --git a/docs/_modules/pytorch_tabnet/sparsemax.html b/docs/_modules/pytorch_tabnet/sparsemax.html
index 177f420c..0595e900 100644
--- a/docs/_modules/pytorch_tabnet/sparsemax.html
+++ b/docs/_modules/pytorch_tabnet/sparsemax.html
@@ -319,7 +319,7 @@ 
Source code for pytorch_tabnet.sparsemax
 
 
 
[docs]class Entmoid15(Function):
-    """ A highly optimized equivalent of labda x: Entmax15([x, 0]) """
+    """ A highly optimized equivalent of lambda x: Entmax15([x, 0]) """
 
 
[docs]    @staticmethod
     def forward(ctx, input):
diff --git a/docs/_modules/pytorch_tabnet/tab_model.html b/docs/_modules/pytorch_tabnet/tab_model.html
index da4bfd54..4c926370 100644
--- a/docs/_modules/pytorch_tabnet/tab_model.html
+++ b/docs/_modules/pytorch_tabnet/tab_model.html
@@ -174,7 +174,7 @@ 
Source code for pytorch_tabnet.tab_model
 
 
[docs]    def weight_updater(self, weights):
         """
-        Updates weights dictionnary according to target_mapper.
+        Updates weights dictionary according to target_mapper.
 
         Parameters
         ----------
@@ -217,7 +217,7 @@ 
Source code for pytorch_tabnet.tab_model
             class_label: index for index, class_label in enumerate(self.classes_)
         }
         self.preds_mapper = {
-            index: class_label for index, class_label in enumerate(self.classes_)
+            str(index): class_label for index, class_label in enumerate(self.classes_)
         }
         self.updated_weights = self.weight_updater(weights)

 
@@ -229,7 +229,7 @@ 
Source code for pytorch_tabnet.tab_model
 
 
[docs]    def predict_func(self, outputs):
         outputs = np.argmax(outputs, axis=1)
-        return np.vectorize(self.preds_mapper.get)(outputs)

+        return np.vectorize(self.preds_mapper.get)(outputs.astype(str))

 
 
[docs]    def predict_proba(self, X):
         """
@@ -290,6 +290,7 @@ 
Source code for pytorch_tabnet.tab_model
                   "Use reshape(-1, 1) for single regression."
             raise ValueError(msg)
         self.output_dim = y_train.shape[1]
+        self.preds_mapper = None
 
         self.updated_weights = weights
         filter_weights(self.updated_weights)

diff --git a/docs/_modules/pytorch_tabnet/tab_network.html b/docs/_modules/pytorch_tabnet/tab_network.html
index 4c3064e7..81f9d77e 100644
--- a/docs/_modules/pytorch_tabnet/tab_network.html
+++ b/docs/_modules/pytorch_tabnet/tab_network.html
@@ -227,9 +227,9 @@ 
Source code for pytorch_tabnet.tab_network
         n_a : int
             Dimension of the attention  layer (usually between 4 and 64)
         n_steps : int
-            Number of sucessive steps in the newtork (usually betwenn 3 and 10)
+            Number of successive steps in the network (usually between 3 and 10)
         gamma : float
-            Float above 1, scaling factor for attention updates (usually betwenn 1.0 to 2.0)
+            Float above 1, scaling factor for attention updates (usually between 1.0 to 2.0)
         n_independent : int
             Number of independent GLU layer in each GLU block (default 2)
         n_shared : int
@@ -382,9 +382,9 @@ 
Source code for pytorch_tabnet.tab_network
         n_d : int
             Dimension of the prediction  layer (usually between 4 and 64)
         n_steps : int
-            Number of sucessive steps in the newtork (usually betwenn 3 and 10)
+            Number of successive steps in the network (usually between 3 and 10)
         gamma : float
-            Float above 1, scaling factor for attention updates (usually betwenn 1.0 to 2.0)
+            Float above 1, scaling factor for attention updates (usually between 1.0 to 2.0)
         n_independent : int
             Number of independent GLU layer in each GLU block (default 2)
         n_shared : int
@@ -478,7 +478,7 @@ 
Source code for pytorch_tabnet.tab_network
         if self.n_steps <= 0:
             raise ValueError("n_steps should be a positive integer.")
         if self.n_independent == 0 and self.n_shared == 0:
-            raise ValueError("n_shared and n_independant can't be both zero.")
+            raise ValueError("n_shared and n_independent can't be both zero.")
 
         self.virtual_batch_size = virtual_batch_size
         self.embedder = EmbeddingGenerator(input_dim, cat_dims, cat_idxs, cat_emb_dim)
@@ -489,7 +489,7 @@ 
Source code for pytorch_tabnet.tab_network
             input_dim=self.post_embed_dim,
             output_dim=self.post_embed_dim,
             n_d=n_d,
-            n_a=n_d,
+            n_a=n_a,
             n_steps=n_steps,
             gamma=gamma,
             n_independent=n_independent,
@@ -565,9 +565,9 @@ 
Source code for pytorch_tabnet.tab_network
         n_a : int
             Dimension of the attention  layer (usually between 4 and 64)
         n_steps : int
-            Number of sucessive steps in the newtork (usually betwenn 3 and 10)
+            Number of successive steps in the network (usually between 3 and 10)
         gamma : float
-            Float above 1, scaling factor for attention updates (usually betwenn 1.0 to 2.0)
+            Float above 1, scaling factor for attention updates (usually between 1.0 to 2.0)
         n_independent : int
             Number of independent GLU layer in each GLU block (default 2)
         n_shared : int
@@ -600,7 +600,7 @@ 
Source code for pytorch_tabnet.tab_network
             input_dim=input_dim,
             output_dim=output_dim,
             n_d=n_d,
-            n_a=n_d,
+            n_a=n_a,
             n_steps=n_steps,
             gamma=gamma,
             n_independent=n_independent,
@@ -673,9 +673,9 @@ 
Source code for pytorch_tabnet.tab_network
         n_a : int
             Dimension of the attention  layer (usually between 4 and 64)
         n_steps : int
-            Number of sucessive steps in the newtork (usually betwenn 3 and 10)
+            Number of successive steps in the network (usually between 3 and 10)
         gamma : float
-            Float above 1, scaling factor for attention updates (usually betwenn 1.0 to 2.0)
+            Float above 1, scaling factor for attention updates (usually between 1.0 to 2.0)
         cat_idxs : list of int
             Index of each categorical column in the dataset
         cat_dims : list of int
@@ -716,7 +716,7 @@ 
Source code for pytorch_tabnet.tab_network
         if self.n_steps <= 0:
             raise ValueError("n_steps should be a positive integer.")
         if self.n_independent == 0 and self.n_shared == 0:
-            raise ValueError("n_shared and n_independant can't be both zero.")
+            raise ValueError("n_shared and n_independent can't be both zero.")
 
         self.virtual_batch_size = virtual_batch_size
         self.embedder = EmbeddingGenerator(input_dim, cat_dims, cat_idxs, cat_emb_dim)
@@ -762,7 +762,7 @@ 
Source code for pytorch_tabnet.tab_network
         input_dim : int
             Input size
         output_dim : int
-            Outpu_size
+            Output_size
         virtual_batch_size : int
             Batch size for Ghost Batch Normalization
         momentum : float
@@ -815,10 +815,10 @@ 
Source code for pytorch_tabnet.tab_network
         input_dim : int
             Input size
         output_dim : int
-            Outpu_size
+            Output_size
         shared_layers : torch.nn.ModuleList
             The shared block that should be common to every step
-        n_glu_independant : int
+        n_glu_independent : int
             Number of independent GLU layers
         virtual_batch_size : int
             Batch size for Ghost Batch Normalization within GLU block(s)
@@ -865,7 +865,7 @@ 
Source code for pytorch_tabnet.tab_network
 
 
[docs]class GLU_Block(torch.nn.Module):
     """
-    Independant GLU block, specific to each step
+    Independent GLU block, specific to each step
     """
 
     def __init__(
@@ -936,7 +936,7 @@ 
Source code for pytorch_tabnet.tab_network
     """
 
     def __init__(self, input_dim, cat_dims, cat_idxs, cat_emb_dim):
-        """This is an embedding module for an entier set of features
+        """This is an embedding module for an entire set of features
 
         Parameters
         ----------
@@ -949,7 +949,7 @@ 
Source code for pytorch_tabnet.tab_network
             Positional index for each categorical features in inputs
         cat_emb_dim : int or list of int
             Embedding dimension for each categorical features
-            If int, the same embdeding dimension will be used for all categorical features
+            If int, the same embedding dimension will be used for all categorical features
         """
         super(EmbeddingGenerator, self).__init__()
         if cat_dims == [] or cat_idxs == []:
@@ -988,7 +988,7 @@ 
Source code for pytorch_tabnet.tab_network
 
 
[docs]    def forward(self, x):
         """
-        Apply embdeddings to inputs
+        Apply embeddings to inputs
         Inputs should be (batch_size, input_dim)
         Outputs will be of size (batch_size, self.post_embed_dim)
         """
diff --git a/docs/_modules/pytorch_tabnet/utils.html b/docs/_modules/pytorch_tabnet/utils.html
index 8d5c29af..a9c18e4f 100644
--- a/docs/_modules/pytorch_tabnet/utils.html
+++ b/docs/_modules/pytorch_tabnet/utils.html
@@ -161,6 +161,7 @@ 
Source code for pytorch_tabnet.utils
 import torch
 import numpy as np
 import scipy
+import json
 from sklearn.utils import check_array
 
 
@@ -262,7 +263,7 @@ 
Source code for pytorch_tabnet.utils
     X_train, y_train, eval_set, weights, batch_size, num_workers, drop_last, pin_memory
 ):
     """
-    Create dataloaders with or wihtout subsampling depending on weights and balanced.
+    Create dataloaders with or without subsampling depending on weights and balanced.
 
     Parameters
     ----------
@@ -486,6 +487,14 @@ 
Source code for pytorch_tabnet.utils
         return "cpu"
     else:
         return device_name

+
+
+
[docs]class ComplexEncoder(json.JSONEncoder):
+
[docs]    def default(self, obj):
+        if isinstance(obj, np.int64):
+            return int(obj)
+        # Let the base class default method raise the TypeError
+        return json.JSONEncoder.default(self, obj)

 

 
            

diff --git a/docs/_sources/generated_docs/README.md.txt b/docs/_sources/generated_docs/README.md.txt
index f5c7ef1c..049c9c49 100644
--- a/docs/_sources/generated_docs/README.md.txt
+++ b/docs/_sources/generated_docs/README.md.txt
@@ -70,6 +70,8 @@ clf.fit(
 preds = clf.predict(X_test)
 ```
 
+The targets on `y_train/y_valid` should contain a unique type (i.e. they must all be strings or integers).
+
 ### Default eval_metric
 
 A few classical evaluation metrics are implemented (see bellow section for custom ones):
@@ -146,7 +148,7 @@ clf.fit(
 )
 ```
 
-The loss function has been normalized to be independant of `pretraining_ratio`, `batch_size` and number of features in the problem.
+The loss function has been normalized to be independent of `pretraining_ratio`, `batch_size` and number of features in the problem.
 A self supervised loss greater than 1 means that your model is reconstructing worse than predicting the mean for each feature, a loss bellow 1 means that the model is doing better than predicting the mean.
 
 A complete example can be found within the notebook `pretraining_example.ipynb`.
@@ -302,7 +304,7 @@ A complete example can be found within the notebook `pretraining_example.ipynb`.
     /!\ Only for TabNetClassifier
     Sampling parameter
     0 : no sampling
-    1 : automated sampling with inverse class occurences
+    1 : automated sampling with inverse class occurrences
     dict : keys are classes, values are weights for each class
 
 - `loss_fn` : torch.loss or list of torch.loss
diff --git a/docs/generated_docs/README.html b/docs/generated_docs/README.html
index 5e787d5d..329f3a77 100644
--- a/docs/generated_docs/README.html
+++ b/docs/generated_docs/README.html
@@ -251,6 +251,7 @@ 
How to use it?preds = clf.predict(X_test)
 

 

+
The targets on y_train/y_valid should contain a unique type (i.e. they must all be strings or integers).

 

 
Default eval_metric

 
A few classical evaluation metrics are implemented (see bellow section for custom ones):

@@ -322,7 +323,7 @@ 
Semi-supervised pre-training)
 

 

-
The loss function has been normalized to be independant of pretraining_ratio, batch_size and number of features in the problem.
+
The loss function has been normalized to be independent of pretraining_ratio, batch_size and number of features in the problem.
 A self supervised loss greater than 1 means that your model is reconstructing worse than predicting the mean for each feature, a loss bellow 1 means that the model is doing better than predicting the mean.

 
A complete example can be found within the notebook pretraining_example.ipynb.

 
/!\ : current implementation is trying to reconstruct the original inputs, but Batch Normalization applies a random transformation that can’t be deduced by a single line, making the reconstruction harder. Lowering the batch_size might make the pretraining easier.

@@ -443,7 +444,7 @@ 
Fit parametersloss_fn : torch.loss or list of torch.loss

diff --git a/docs/generated_docs/pytorch_tabnet.html b/docs/generated_docs/pytorch_tabnet.html
index 5379b129..5583b3b0 100644
--- a/docs/generated_docs/pytorch_tabnet.html
+++ b/docs/generated_docs/pytorch_tabnet.html
@@ -178,6 +178,33 @@
 
pytorch_tabnet package

 

 
pytorch_tabnet.utils module

+

+

+class pytorch_tabnet.utils.ComplexEncoder(*, skipkeys=False, ensure_ascii=True, check_circular=True, allow_nan=True, sort_keys=False, indent=None, separators=None, default=None)[source]

+
Bases: json.encoder.JSONEncoder

+

+

+default(obj)[source]

+
Implement this method in a subclass such that it returns
+a serializable object for o, or calls the base implementation
+(to raise a TypeError).

+
For example, to support arbitrary iterators, you could
+implement default like this:

+
def default(self, o):
+    try:
+        iterable = iter(o)
+    except TypeError:
+        pass
+    else:
+        return list(iterable)
+    # Let the base class default method raise the TypeError
+    return JSONEncoder.default(self, o)
+

+

+

+
+

+
 

 

 class pytorch_tabnet.utils.PredictDataset(x)[source]

@@ -208,7 +235,7 @@ 
pytorch_tabnet package
 

 pytorch_tabnet.utils.create_dataloaders(X_train, y_train, eval_set, weights, batch_size, num_workers, drop_last, pin_memory)[source]

-
Create dataloaders with or wihtout subsampling depending on weights and balanced.

+
Create dataloaders with or without subsampling depending on weights and balanced.

 

 
Parameters

 
    
@@ -353,7 +380,7 @@ 
    pytorch_tabnet package
 
    
 pytorch_tabnet.pretraining_utils.create_dataloaders(X_train, eval_set, weights, batch_size, num_workers, drop_last, pin_memory)[source]
    
-
    Create dataloaders with or wihtout subsampling depending on weights and balanced.
    
+
    Create dataloaders with or without subsampling depending on weights and balanced.
    
 
    
 
    Parameters
    
 
      
@@ -437,7 +464,7 @@ 
      pytorch_tabnet package
 
      
 forward(x)[source]
      
-
      Apply embdeddings to inputs
+
      Apply embeddings to inputs
 Inputs should be (batch_size, input_dim)
 Outputs will be of size (batch_size, self.post_embed_dim)
      
 
    
@@ -490,7 +517,7 @@ 
    pytorch_tabnet package
 class pytorch_tabnet.tab_network.GLU_Block(input_dim, output_dim, n_glu=2, first=False, shared_layers=None, virtual_batch_size=128, momentum=0.02)[source]
 
    Bases: torch.nn.modules.module.Module
    
-
    Independant GLU block, specific to each step
    
+
    Independent GLU block, specific to each step
    
 
    
 
    
 forward(x)[source]
    
@@ -837,6 +864,11 @@ 
    Multi-class / multi-label utility functionpytorch_tabnet.multiclass_utils.check_output_dim(labels, y)[source]
 
    
 
+
    
+
    
+pytorch_tabnet.multiclass_utils.check_unique_type(y)[source]
    
+
    
+
 
    
 
    
 pytorch_tabnet.multiclass_utils.infer_multitask_output(y_train)[source]
    
@@ -1129,7 +1161,7 @@ 
    Multi-class / multi-label utility function
 
    
 weight_updater(weights)[source]
    
-
    Updates weights dictionnary according to target_mapper.
    
+
    Updates weights dictionary according to target_mapper.
    
 
    
 
    Parameters
    
 
    weights (bool or dict) – Given weights for balancing training.
    
@@ -1360,6 +1392,11 @@ 
    Multi-class / multi-label utility functionlambda_sparse: float = 0.001
 
    
 
+
    
+
    
+load_class_attrs(class_attrs)[source]
    
+
    
+
 
    
 
    
 load_model(filepath)[source]
    
@@ -1638,8 +1675,8 @@ 
    Multi-class / multi-label utility function
 class pytorch_tabnet.sparsemax.Entmoid15[source]
 
    Bases: torch.autograd.function.Function
    
-
    A highly optimized equivalent of labda x: Entmax15([x, 0])
    
+
    A highly optimized equivalent of lambda x: Entmax15([x, 0])
    
 
    
 
    
 static backward(ctx, grad_output)[source]
    
@@ -2200,7 +2237,7 @@ 
    Multi-class / multi-label utility functionclass pytorch_tabnet.metrics.RMSLE[source]
 
    Bases: pytorch_tabnet.metrics.Metric
    
 
    Mean squared logarithmic error regression loss.
-Scikit-imeplementation:
+Scikit-implementation:
 https://scikit-learn.org/stable/modules/generated/sklearn.metrics.mean_squared_log_error.html
 Note: In order to avoid error, negative predictions are clipped to 0.
 This means that you should clip negative predictions manually after calling predict.
    
@@ -2215,7 +2252,7 @@ 
    Multi-class / multi-label utility functionParameters
 
      
 
    • y_pred (torch.Tensor or np.array) – Reconstructed prediction (with embeddings)
      • 
-
    • embedded_x (torch.Tensor) – Orginal input embedded by network
      • 
+
    • embedded_x (torch.Tensor) – Original input embedded by network
      • 
 
    • obf_vars (torch.Tensor) – Binary mask for obfuscated variables.
 1 means the variables was obfuscated so reconstruction is based on this.
      • 
 
    
@@ -2243,7 +2280,7 @@ 
    Multi-class / multi-label utility functionParameters
 
      
 
    • y_pred (torch.Tensor or np.array) – Reconstructed prediction (with embeddings)
      • 
-
    • embedded_x (torch.Tensor) – Orginal input embedded by network
      • 
+
    • embedded_x (torch.Tensor) – Original input embedded by network
      • 
 
    • obf_vars (torch.Tensor) – Binary mask for obfuscated variables.
 1 means the variable was obfuscated so reconstruction is based on this.
      • 
 
    • eps (float) – A small floating point to avoid ZeroDivisionError
diff --git a/docs/genindex.html b/docs/genindex.html
index 1d21afba..c8dd5366 100644
--- a/docs/genindex.html
+++ b/docs/genindex.html
@@ -260,8 +260,12 @@ 
      C
      
       
    • check_metrics() (in module pytorch_tabnet.metrics)
 
      • 
       
    • check_output_dim() (in module pytorch_tabnet.multiclass_utils)
+
      • 
+      
    • check_unique_type() (in module pytorch_tabnet.multiclass_utils)
 
      • 
       
    • clip_value (pytorch_tabnet.abstract_model.TabModel attribute)
+
      • 
+      
    • ComplexEncoder (class in pytorch_tabnet.utils)
 
      • 
       
    • compute_loss() (pytorch_tabnet.abstract_model.TabModel method)
 
@@ -291,10 +295,12 @@ 
      C
      
 
      D
      
 
@@ -450,6 +456,8 @@ 
      L
      
   
   
 
      
   
        
       
      • lambda_sparse (pytorch_tabnet.abstract_model.TabModel attribute)
+
        • 
+      
      • load_class_attrs() (pytorch_tabnet.abstract_model.TabModel method)
 
        • 
       
      • load_model() (pytorch_tabnet.abstract_model.TabModel method)
 
        • 
diff --git a/docs/searchindex.js b/docs/searchindex.js
index e0a0a50c..4d732e3f 100644
--- a/docs/searchindex.js
+++ b/docs/searchindex.js
@@ -1 +1 @@
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\ No newline at end of file
diff --git a/pyproject.toml b/pyproject.toml
index 19e28921..b9950554 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "pytorch_tabnet"
-version = "3.0.0"
+version = "3.1.0"
 description = "PyTorch implementation of TabNet"
 homepage = "https://github.com/dreamquark-ai/tabnet"
 repository = "https://github.com/dreamquark-ai/tabnet"