Merge branch 'JDRomano2-nn'

docs/index.html

@@ -213,5 +213,5 @@
 
 <!--
 MkDocs version : 1.0
-Build Date UTC : 2020-05-29 15:57:34
+Build Date UTC : 2020-06-01 22:06:11
 -->

docs/installing/index.html

@@ -174,9 +174,6 @@
 <li>
 <p><a href="https://joblib.readthedocs.io/en/latest/">joblib</a></p>
 </li>
-<li>
-<p><a href="https://pytorch.org/">PyTorch</a></p>
-</li>
 </ul>
 <p>Most of the necessary Python packages can be installed via the <a href="https://www.continuum.io/downloads">Anaconda Python distribution</a>, which we strongly recommend that you use. We also strongly recommend that you use of Python 3 over Python 2 if you're given the choice.</p>
 <p>You can install TPOT using <code>pip</code> or <code>conda-forge</code>.</p>
@@ -202,6 +199,8 @@ <h2 id="pip">pip</h2>
 <pre><code class="Shell">pip install scikit-mdr skrebate
 </code></pre>
 
+<p>To enable support for <a href="https://pytorch.org/">PyTorch</a>-based neural networks (TPOT-NN), you will need to install PyTorch. TPOT-NN will work with either CPU or GPU PyTorch, but we strongly recommend using a GPU version, if possible, as CPU PyTorch models tend to train very slowly.</p>
+<p>We recommend following <a href="https://pytorch.org/get-started/locally/">PyTorch's installation instructions</a> customized for your operating system and Python distribution.</p>
 <p>Finally to install TPOT itself, run the following command:</p>
 <pre><code class="Shell">pip install tpot
 </code></pre>
@@ -212,9 +211,10 @@ <h2 id="conda-forge">conda-forge</h2>
 </code></pre>
 
 <p>To install additional dependencies you can use:</p>
-<pre><code class="Shell">conda install -c conda-forge tpot xgboost dask dask-ml scikit-mdr skrebate pytorch
+<pre><code class="Shell">conda install -c conda-forge tpot xgboost dask dask-ml scikit-mdr skrebate
 </code></pre>
 
+<p>As mentioned above, we recommend following <a href="https://pytorch.org/get-started/locally/">PyTorch's installation instructions</a> for installing it to enable support for <a href="https://pytorch.org/">PyTorch</a>-based neural networks (TPOT-NN).</p>
 <h2 id="installation-problems">Installation problems</h2>
 <p>Please <a href="https://github.com/EpistasisLab/tpot/issues/new">file a new issue</a> if you run into installation problems.</p>
 

docs/sitemap.xml

@@ -2,52 +2,52 @@
 <urlset xmlns="http://www.sitemaps.org/schemas/sitemap/0.9">
     <url>
      <loc>http://epistasislab.github.io/tpot/</loc>
-     <lastmod>2020-05-29</lastmod>
+     <lastmod>2020-06-01</lastmod>
      <changefreq>daily</changefreq>
     </url>
     <url>
      <loc>http://epistasislab.github.io/tpot/installing/</loc>
-     <lastmod>2020-05-29</lastmod>
+     <lastmod>2020-06-01</lastmod>
      <changefreq>daily</changefreq>
     </url>
     <url>
      <loc>http://epistasislab.github.io/tpot/using/</loc>
-     <lastmod>2020-05-29</lastmod>
+     <lastmod>2020-06-01</lastmod>
      <changefreq>daily</changefreq>
     </url>
     <url>
      <loc>http://epistasislab.github.io/tpot/api/</loc>
-     <lastmod>2020-05-29</lastmod>
+     <lastmod>2020-06-01</lastmod>
      <changefreq>daily</changefreq>
     </url>
     <url>
      <loc>http://epistasislab.github.io/tpot/examples/</loc>
-     <lastmod>2020-05-29</lastmod>
+     <lastmod>2020-06-01</lastmod>
      <changefreq>daily</changefreq>
     </url>
     <url>
      <loc>http://epistasislab.github.io/tpot/contributing/</loc>
-     <lastmod>2020-05-29</lastmod>
+     <lastmod>2020-06-01</lastmod>
      <changefreq>daily</changefreq>
     </url>
     <url>
      <loc>http://epistasislab.github.io/tpot/releases/</loc>
-     <lastmod>2020-05-29</lastmod>
+     <lastmod>2020-06-01</lastmod>
      <changefreq>daily</changefreq>
     </url>
     <url>
      <loc>http://epistasislab.github.io/tpot/citing/</loc>
-     <lastmod>2020-05-29</lastmod>
+     <lastmod>2020-06-01</lastmod>
      <changefreq>daily</changefreq>
     </url>
     <url>
      <loc>http://epistasislab.github.io/tpot/support/</loc>
-     <lastmod>2020-05-29</lastmod>
+     <lastmod>2020-06-01</lastmod>
      <changefreq>daily</changefreq>
     </url>
     <url>
      <loc>http://epistasislab.github.io/tpot/related/</loc>
-     <lastmod>2020-05-29</lastmod>
+     <lastmod>2020-06-01</lastmod>
      <changefreq>daily</changefreq>
     </url>
 </urlset>

docs_sources/installing.md

@@ -18,8 +18,6 @@ TPOT is built on top of several existing Python libraries, including:
 
 * [joblib](https://joblib.readthedocs.io/en/latest/)
 
-* [PyTorch](https://pytorch.org/)
-
 Most of the necessary Python packages can be installed via the [Anaconda Python distribution](https://www.continuum.io/downloads), which we strongly recommend that you use. We also strongly recommend that you use of Python 3 over Python 2 if you're given the choice.
 
 You can install TPOT using `pip` or `conda-forge`.
@@ -58,6 +56,10 @@ If you plan to use the [TPOT-MDR configuration](https://arxiv.org/abs/1702.01780
 pip install scikit-mdr skrebate
 ```
 
+To enable support for [PyTorch](https://pytorch.org/)-based neural networks (TPOT-NN), you will need to install PyTorch. TPOT-NN will work with either CPU or GPU PyTorch, but we strongly recommend using a GPU version, if possible, as CPU PyTorch models tend to train very slowly.
+
+We recommend following [PyTorch's installation instructions](https://pytorch.org/get-started/locally/) customized for your operating system and Python distribution.
+
 Finally to install TPOT itself, run the following command:
 
 ```Shell
@@ -75,9 +77,11 @@ conda install -c conda-forge tpot
 To install additional dependencies you can use:
 
 ```Shell
-conda install -c conda-forge tpot xgboost dask dask-ml scikit-mdr skrebate pytorch
+conda install -c conda-forge tpot xgboost dask dask-ml scikit-mdr skrebate
 ```
 
+As mentioned above, we recommend following [PyTorch's installation instructions](https://pytorch.org/get-started/locally/) for installing it to enable support for [PyTorch](https://pytorch.org/)-based neural networks (TPOT-NN).
+
 ## Installation problems
 
 Please [file a new issue](https://github.com/EpistasisLab/tpot/issues/new) if you run into installation problems.

optional-requirements.txt

@@ -1,3 +1,4 @@
 xgboost==0.90
 scikit-mdr==0.4.4
 skrebate==0.3.4
+torch==1.3.1

requirements.txt

@@ -8,4 +8,3 @@ update-checker>=0.16
 stopit>=1.1.2
 pandas>=0.24.2
 joblib>=0.13.2
-torch==1.3.1

setup.py

@@ -51,6 +51,7 @@ def calculate_version():
         'dask': ['dask>=0.18.2',
                  'distributed>=1.22.1',
                  'dask-ml>=1.0.0'],
+        'torch': ['torch==1.3.1'],
     },
     classifiers=[
         'Intended Audience :: Science/Research',

tpot/_version.py

@@ -23,4 +23,4 @@
 
 """
 
-__version__ = '0.11.4'
+__version__ = '0.11.5'

tpot/builtins/__init__.py

@@ -29,4 +29,8 @@
 from .one_hot_encoder import OneHotEncoder, auto_select_categorical_features, _transform_selected
 from .feature_transformers import CategoricalSelector, ContinuousSelector
 from .feature_set_selector import FeatureSetSelector
-from .nn import PytorchLRClassifier, PytorchMLPClassifier
+try:
+    from .nn import PytorchLRClassifier, PytorchMLPClassifier
+except (ModuleNotFoundError, ImportError):
+    import warnings
+    warnings.warn("Warning: optional dependency `torch` is not available. - skipping import of NN models.")

tpot/builtins/nn.py

@@ -39,18 +39,21 @@
 from sklearn.utils.validation import check_X_y, assert_all_finite, check_array, check_is_fitted
 from sklearn.utils.multiclass import type_of_target
 
-import torch
-from torch import nn
-from torch.autograd import Variable
-from torch.optim import Adam
-from torch.utils.data import TensorDataset, DataLoader
+try:
+    import torch
+    from torch import nn
+    from torch.autograd import Variable
+    from torch.optim import Adam
+    from torch.utils.data import TensorDataset, DataLoader
+except ModuleNotFoundError:
+    raise
 
 def _pytorch_model_is_fully_initialized(clf: BaseEstimator):
     if all([
         hasattr(clf, 'network'),
         hasattr(clf, 'loss_function'),
         hasattr(clf, 'optimizer'),
-        hasattr(clf, 'data_loader'), 
+        hasattr(clf, 'data_loader'),
         hasattr(clf, 'train_dset_len'),
         hasattr(clf, 'device')
     ]):
@@ -100,15 +103,15 @@ def _init_model(self, X, y): # pragma: no cover
     def fit(self, X, y):
         """Generalizable method for fitting a PyTorch estimator to a training
         set.
-        
+
         Parameters
         ----------
         X : array-like of shape (n_samples, n_features)
             Training vector, where n_samples is the number of samples and
             n_features is the number of features.
         y : array-like of shape (n_samples,)
             Target vector relative to X.
-        
+
         Returns
         -------
         self
@@ -243,7 +246,7 @@ def __init__(
         self.learning_rate = learning_rate
         self.weight_decay = weight_decay
         self.verbose = verbose
-        
+
         self.input_size = None
         self.num_classes = None
         self.network = None