Merge branch 'release/0.1.0'

Author: nwtgck

.gitignore

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+/.idea
+*.egg-info/
+__pycache__/

.travis.yml

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+# (from: https://qiita.com/masashi127/items/5bfcba5cad8e82958844)
+
+language: python
+
+python:
+  - 3.4
+  - 3.5
+  - 3.6
+
+addons:
+  apt:
+    packages:
+    # (from: https://github.com/dnouri/nolearn/blob/master/.travis.yml)
+    - libblas-dev
+    - liblapack-dev
+    - gfortran
+
+
+before_install:
+  - pip install -U pip setuptools wheel # (from: https://github.com/dnouri/nolearn/blob/master/.travis.yml)
+
+install:
+  - travis_wait travis_retry pip install -r requirements.txt # (from: https://github.com/dnouri/nolearn/blob/master/.travis.yml)
+  - pip install coveralls
+
+script:
+  - coverage run --source=nwtgck_hello_test setup.py test
+
+after_success:
+  - coveralls
+
+cache:
+  - apt
+  - directories:
+    - $HOME/.cache/pip

README.md

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+# multi_svr [![Build Status](https://travis-ci.org/nwtgck/multi-svr-python.svg?branch=develop)](https://travis-ci.org/nwtgck/multi-svr-python) [![Coverage Status](https://coveralls.io/repos/github/nwtgck/multi-svr-python/badge.svg?branch=develop)](https://coveralls.io/github/nwtgck/multi-svr-python?branch=develop)
+
+Support Vector Regression (SVR) for multidimensional labels
+ 
+ 
+ ## Installation
+ 
+ ```bash
+ pip3 install git+https://github.com/nwtgck/multi-svr-python
+ ```
+ 
+ 
+ ## Usage
+ 
+ ```python
+X = [
+  [0, 0],
+  [0, 10],
+  [1, 10],
+  [1, 20],
+  [1, 30],
+  [1, 40]
+]
+
+y = [
+  [0, 0],
+  [0, 10],
+  [2, 10],
+  [2, 20],
+  [2, 30],
+  [2, 40]
+]
+
+#  Create SVR
+regressor = multi_svr.MutilSVR(kernel='linear')
+# Fit
+regressor.fit(X, y)
+# Predict
+pred_y = regressor.predict(X)
+# Calc errors
+errs = metrics.mean_squared_error(y, pred_y)
+ ```
+ 
+ ## How to test
+ 
+ ```bash
+ cd <this repo
+ python setup.py test
+ ```

requirements.txt

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+certifi==2017.11.5
+chardet==3.0.4
+docopt==0.6.2
+idna==2.6
+numpy==1.13.3
+requests==2.18.4
+scikit-learn==0.19.1
+scipy==1.0.0
+urllib3==1.22

setup.py

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+# (from: https://github.com/masaponto/Python-MLP/blob/master/setup.py)
+# (from: https://qiita.com/masashi127/items/5bfcba5cad8e82958844)
+# (from: https://qiita.com/hotoku/items/4789533f5e497f3dc6e0)
+
+from setuptools import setup, find_packages
+import sys
+
+sys.path.append('./src')
+sys.path.append('./tests')
+
+setup(
+    name='multi_svr',
+    version='0.1.0',
+    description='SVR for multidimensional label',
+    author='Ryo Ota',
+    author_email='[email protected]',
+    install_requires=['scikit-learn', 'numpy', 'SciPy'],
+    py_modules=["multi_svr"],
+    packages=find_packages(),
+    test_suite='tests'
+)

src/__init__.py

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+import numpy as np
+import sklearn
+from sklearn import svm
+
+
+class MutilSVR(sklearn.base.BaseEstimator, sklearn.base.RegressorMixin):
+  def __init__(self, **kwargs):
+    self.__init_kwargs = kwargs
+
+  def fit(self, X, y, **kwargs):
+    X = np.array(X)
+    y = np.array(y)
+
+    # Get dimension of y
+    y_dim = np.ndim(y)
+    if(y_dim == 2):
+      # Feature dimension
+      feature_dim = len(y[0])
+      # Create SVRs
+      self.svrs   = [svm.SVR(**self.__init_kwargs) for _ in range(feature_dim)]
+
+      # For each SVR
+      for curr_feature_dim, svr in enumerate(self.svrs): # (curr=Current)
+        # Select y
+        selected_y = y[:,curr_feature_dim]
+        # Fit
+        svr.fit(X, selected_y, **kwargs)
+    else:
+      raise Exception("Dimension of y must be 2, but found %d" % y_dim)
+
+
+  def predict(self, X):
+    # Init predict list
+    preds = []
+    # For each SVR
+    for curr_feature_dim, svr in enumerate(self.svrs):  # (curr=Current)
+      # Predict
+      pred = svr.predict(X)
+      # Append to preds
+      preds.append(pred)
+
+    pred = np.column_stack(tuple(preds))
+    return pred
+
+
+
+
+if __name__ == '__main__':
+  from sklearn import metrics
+  X = [
+    [0,  0],
+    [0, 10],
+    [1, 10],
+    [1, 20],
+    [1, 30],
+    [1, 40]
+  ]
+
+  y = [
+    [0,  0],
+    [0, 10],
+    [2, 10],
+    [2, 20],
+    [2, 30],
+    [2, 40]
+  ]
+
+  regressor = MutilSVR(kernel='linear')
+
+  regressor.fit(X, y)
+
+  pred_y = regressor.predict(X)
+  errs   = metrics.mean_squared_error(y, pred_y, multioutput='raw_values')
+
+  print('pred_y:', pred_y)
+  print('errs:', errs)

src/multi_svr.py

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+import unittest
+from sklearn import metrics
+
+import multi_svr
+
+class MultiSVRTest(unittest.TestCase):
+
+  def test_prediction(self):
+    X = [
+      [0, 0],
+      [0, 10],
+      [1, 10],
+      [1, 20],
+      [1, 30],
+      [1, 40]
+    ]
+
+    y = [
+      [0, 0],
+      [0, 10],
+      [2, 10],
+      [2, 20],
+      [2, 30],
+      [2, 40]
+    ]
+
+    #  Create SVR
+    regressor = multi_svr.MutilSVR(kernel='linear')
+    # Fit
+    regressor.fit(X, y)
+    # Predict
+    pred_y = regressor.predict(X)
+    # Calc errors
+    errs = metrics.mean_squared_error(y, pred_y, multioutput='raw_values')
+
+    # Errors should be small
+    assert(errs[0] < 0.05)
+    assert(errs[1] < 0.05)
+
+
+def suite():
+  suite = unittest.TestSuite()
+  suite.addTest(unittest.makeSuite(MultiSVRTest))
+  return suite