Metadata-Version: 2.1
Name: torch_explain
Version: 0.6.1
Summary: PyTorch Explain: Logic Explained Networks in Python.
Home-page: https://github.com/pietrobarbiero/pytorch_explain
Maintainer: P. Barbiero
Maintainer-email: barbiero@tutanota.com
License: Apache 2.0
Download-URL: https://github.com/pietrobarbiero/pytorch_explain
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        `PyTorch, Explain!` is an extension library for PyTorch to develop
        explainable deep learning models called Logic Explained Networks (LENs).
        
        It consists of various methods for explainability from a variety of published papers, including the APIs
        required to get first-order logic explanations from deep neural networks.
        
        Quick start
        -----------
        
        You can install ``torch_explain`` along with all its dependencies from
        `PyPI <https://pypi.org/project/pytorch_explain/>`__:
        
        .. code:: bash
        
            pip install -r requirements.txt torch-explain
        
        
        Example
        -----------
        
        For this simple experiment, let's solve the XOR problem
        (augmented with 100 dummy features):
        
        .. code:: python
        
            import torch
            import torch_explain as te
        
            x0 = torch.zeros((4, 100))
            x_train = torch.tensor([
                [0, 0],
                [0, 1],
                [1, 0],
                [1, 1],
            ], dtype=torch.float)
            x_train = torch.cat([x_train, x0], dim=1)
            y_train = torch.tensor([0, 1, 1, 0], dtype=torch.long)
        
        We can instantiate a simple feed-forward neural network
        with 3 layers using the ``EntropyLayer`` as the first one:
        
        .. code:: python
        
            layers = [
                te.nn.EntropyLinear(x_train.shape[1], 10, n_classes=2),
                torch.nn.LeakyReLU(),
                torch.nn.Linear(10, 4),
                torch.nn.LeakyReLU(),
                torch.nn.Linear(4, 1),
            ]
            model = torch.nn.Sequential(*layers)
        
        We can now train the network by optimizing the cross entropy loss and the
        ``entropy_logic_loss`` loss function incorporating the human prior towards
        simple explanations:
        
        .. code:: python
        
            optimizer = torch.optim.AdamW(model.parameters(), lr=0.01)
            loss_form = torch.nn.CrossEntropyLoss()
            model.train()
            for epoch in range(1001):
                optimizer.zero_grad()
                y_pred = model(x_train).squeeze(-1)
                loss = loss_form(y_pred, y_train) + 0.00001 * te.nn.functional.entropy_logic_loss(model)
                loss.backward()
                optimizer.step()
        
        Once trained we can extract first-order logic formulas describing
        how the network composed the input features to obtain the predictions:
        
        .. code:: python
        
            from torch_explain.logic.nn import entropy
            from torch.nn.functional import one_hot
        
            y1h = one_hot(y_train)
            explanation, _ = entropy.explain_class(model, x_train, y1h, x_train, y1h, target_class=1)
        
        Explanations will be logic formulas in disjunctive normal form.
        In this case, the explanation will be ``y=1 IFF (f1 AND ~f2) OR (f2  AND ~f1)``
        corresponding to ``y=1 IFF f1 XOR f2``.
        
        The quality of the logic explanation can **quantitatively** assessed in terms
        of classification accuracy and rule complexity as follows:
        
        .. code:: python
        
            from torch_explain.logic.metrics import test_explanation, complexity
        
            accuracy, preds = test_explanation(explanation, x_train, y1h, target_class=1)
            explanation_complexity = complexity(explanation)
        
        In this case the accuracy is 100% and the complexity is 4.
        
        
        Experiments
        ------------
        
        Training
        ~~~~~~~~~~
        
        To train the model(s) in the paper, run the scripts and notebooks inside the folder `experiments`.
        
        Results
        ~~~~~~~~~~
        
        Results on test set and logic formulas will be saved in the folder `experiments/results`.
        
        Data
        ~~~~~~~~~~
        
        The original datasets can be downloaded from the links provided in the supplementary material of the paper.
        
        
        Theory
        --------
        Theoretical foundations can be found in the following papers.
        
        Entropy-based LENs::
        
            @article{barbiero2021entropy,
              title={Entropy-based Logic Explanations of Neural Networks},
              author={Barbiero, Pietro and Ciravegna, Gabriele and Giannini, Francesco and Li{\'o}, Pietro and Gori, Marco and Melacci, Stefano},
              journal={arXiv preprint arXiv:2106.06804},
              year={2021}
            }
        
        Psi network ("learning of constraints")::
        
            @inproceedings{ciravegna2020constraint,
              title={A Constraint-Based Approach to Learning and Explanation.},
              author={Ciravegna, Gabriele and Giannini, Francesco and Melacci, Stefano and Maggini, Marco and Gori, Marco},
              booktitle={AAAI},
              pages={3658--3665},
              year={2020}
            }
        
        Learning with constraints::
        
            @inproceedings{marra2019lyrics,
              title={LYRICS: A General Interface Layer to Integrate Logic Inference and Deep Learning},
              author={Marra, Giuseppe and Giannini, Francesco and Diligenti, Michelangelo and Gori, Marco},
              booktitle={Joint European Conference on Machine Learning and Knowledge Discovery in Databases},
              pages={283--298},
              year={2019},
              organization={Springer}
            }
        
        Constraints theory in machine learning::
        
            @book{gori2017machine,
              title={Machine Learning: A constraint-based approach},
              author={Gori, Marco},
              year={2017},
              publisher={Morgan Kaufmann}
            }
        
        
        Authors
        -------
        
        * `Pietro Barbiero <http://www.pietrobarbiero.eu/>`__, University of Cambridge, UK.
        * Francesco Giannini, University of Florence, IT.
        * Gabriele Ciravegna, University of Florence, IT.
        * Dobrik Georgiev, University of Cambridge, UK.
        
        
        Licence
        -------
        
        Copyright 2020 Pietro Barbiero, Francesco Giannini, Gabriele Ciravegna, and Dobrik Georgiev.
        
        Licensed under the Apache License, Version 2.0 (the "License"); you may
        not use this file except in compliance with the License. You may obtain
        a copy of the License at: http://www.apache.org/licenses/LICENSE-2.0.
        
        Unless required by applicable law or agreed to in writing, software
        distributed under the License is distributed on an "AS IS" BASIS,
        WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
        
        See the License for the specific language governing permissions and
        limitations under the License.
        
Platform: UNKNOWN
Classifier: Intended Audience :: Science/Research
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved
Classifier: Programming Language :: Python
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Classifier: Programming Language :: Python :: 2.7
Classifier: Programming Language :: Python :: 3.5
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
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