rename

.travis.yml

@@ -0,0 +1,10 @@
+language: python
+python:
+  - "3.6"
+# command to install dependencies
+install:
+  - pip install -r requirements.txt
+  - pip install -r test-requirements.txt
+# command to run tests
+script:
+  - pytest tests/

README.md

@@ -1,6 +1,6 @@
-# medallion
+# spacecutter
 
-`medallion` is a library for implementing ordinal regression models in PyTorch. The library consists of models and loss functions. It is recommended to use [skorch](http://skorch.readthedocs.io/) to wrap the models to make them compatible with scikit-learn.
+`spacecutter` is a library for implementing ordinal regression models in PyTorch. The library consists of models and loss functions. It is recommended to use [skorch](http://skorch.readthedocs.io/) to wrap the models to make them compatible with scikit-learn.
 
 ## Installation
 
@@ -14,14 +14,14 @@ pip install -e .
 
 ### Models
 
-Define any PyTorch model you want that generates a single, scalar prediction value. This will be our `predictor` model. This model can then be wrapped with `medallion.models.OrdinalLogisticModel` which will convert the output of the `predictor` from a single number to an array of ordinal class probabilities. The following example shows how to do this for a two layer neural network `predictor` for a problem with three ordinal classes.
+Define any PyTorch model you want that generates a single, scalar prediction value. This will be our `predictor` model. This model can then be wrapped with `spacecutter.models.OrdinalLogisticModel` which will convert the output of the `predictor` from a single number to an array of ordinal class probabilities. The following example shows how to do this for a two layer neural network `predictor` for a problem with three ordinal classes.
 
 ```python
 import numpy as np
 import torch
 from torch import nn
 
-from medallion.models import OrdinalLogisticModel
+from spacecutter.models import OrdinalLogisticModel
 
 
 X = np.array([[0.5, 0.1, -0.1],
@@ -54,13 +54,13 @@ print(y_pred)
 
 ### Training
 
-It is recommended to use [skorch](http://skorch.readthedocs.io/) to train `medallion` models. The following shows how to train the model from the previous section using cumulative link loss with `skorch`:
+It is recommended to use [skorch](http://skorch.readthedocs.io/) to train `spacecutter` models. The following shows how to train the model from the previous section using cumulative link loss with `skorch`:
 
 ```python
 from skorch import NeuralNet
 
-from medallion.callbacks import AscensionCallback
-from medallion.losses import CumulativeLinkLoss
+from spacecutter.callbacks import AscensionCallback
+from spacecutter.losses import CumulativeLinkLoss
 
 skorch_model = NeuralNet(
     module=OrdinalLogisticModel,
@@ -77,4 +77,4 @@ skorch_model.fit(X, y)
 
 ```
 
-Note that we must add the `AscensionCallback`. This ensures that the ordinal cutpoints stay in ascending order. While ideally this constraint would be factored directly into the model optimization, `medallion` currently hacks an SGD-compatible solution by utilizing a post-backwards-pass callback to clip the cutpoint values.
+Note that we must add the `AscensionCallback`. This ensures that the ordinal cutpoints stay in ascending order. While ideally this constraint would be factored directly into the model optimization, `spacecutter` currently hacks an SGD-compatible solution by utilizing a post-backwards-pass callback to clip the cutpoint values.

setup.py

@@ -1,7 +1,7 @@
 import os
 from setuptools import setup, find_packages
 
-from medallion import __version__
+from spacecutter import __version__
 
 
 here = os.path.abspath(os.path.dirname(__file__))
@@ -17,12 +17,12 @@
 
 
 setup(
-    name='medallion',
+    name='spacecutter',
     version=__version__,
     description='Ordinal regression models in PyTorch',
     long_description=long_description,
     long_description_content_type='text/markdown',
-    url='https://github.com/EthanRosenthal/medallion',
+    url='https://github.com/EthanRosenthal/spacecutter',
     author='Ethan Rosenthal',
     author_email='ethanrosenthal@gmail.com',
     classifiers=[

medallion/callbacks.py → spacecutter/callbacks.py

@@ -1,7 +1,7 @@
 from skorch.callbacks import Callback
 from torch.nn import Module
 
-from medallion.models import LogisticCumulativeLink
+from spacecutter.models import LogisticCumulativeLink
 
 
 class AscensionCallback(Callback):

tests/test_callbacks.py

@@ -1,8 +1,8 @@
 import torch
 from torch import nn
 
-from medallion import callbacks
-from medallion.models import OrdinalLogisticModel
+from spacecutter import callbacks
+from spacecutter.models import OrdinalLogisticModel
 
 
 def test_clip_ensures_sorted_cutpoints():

tests/test_losses.py

@@ -2,7 +2,7 @@
 import pytest
 import torch
 
-from medallion import losses
+from spacecutter import losses
 
 
 class Test_reduction:

tests/test_models.py

@@ -3,9 +3,9 @@
 import torch
 from torch import nn
 
-from medallion.callbacks import AscensionCallback
-from medallion.losses import CumulativeLinkLoss
-from medallion.models import OrdinalLogisticModel
+from spacecutter.callbacks import AscensionCallback
+from spacecutter.losses import CumulativeLinkLoss
+from spacecutter.models import OrdinalLogisticModel
 
 
 SEED = 666