├── tests
    ├── __init__.py
    └── multi_svr_test.py
├── .gitignore
├── Pipfile
├── examples
    └── example1.py
├── .travis.yml
├── setup.py
├── multi_svr
    └── __init__.py
├── README.md
└── Pipfile.lock


/tests/__init__.py:
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1 | 


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/.gitignore:
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1 | /.idea
2 | *.egg-info/
3 | __pycache__/


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/Pipfile:
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 1 | [[source]]
 2 | verify_ssl = true
 3 | name = "pypi"
 4 | url = "https://pypi.org/simple"
 5 | 
 6 | [dev-packages]
 7 | toml = "*"
 8 | coveralls = "*"
 9 | 
10 | [packages]
11 | certifi = "==2017.11.5"
12 | chardet = "==3.0.4"
13 | docopt = "==0.6.2"
14 | idna = "==2.6"
15 | numpy = "==1.13.3"
16 | requests = "==2.20.1"
17 | scikit-learn = "==0.19.1"
18 | scipy = "==1.0.0"
19 | "urllib3" = "==1.22"
20 | 
21 | [requires]
22 | python_version = "3.5"
23 | 


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/examples/example1.py:
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 1 | import multi_svr
 2 | from sklearn import metrics
 3 | 
 4 | X = [
 5 |   [0, 0],
 6 |   [0, 10],
 7 |   [1, 10],
 8 |   [1, 20],
 9 |   [1, 30],
10 |   [1, 40]
11 | ]
12 | 
13 | y = [
14 |   [0, 0],
15 |   [0, 10],
16 |   [2, 10],
17 |   [2, 20],
18 |   [2, 30],
19 |   [2, 40]
20 | ]
21 | 
22 | #  Create SVR
23 | regressor = multi_svr.MultiSVR(kernel='linear')
24 | # Fit
25 | regressor.fit(X, y)
26 | # Predict
27 | pred_y = regressor.predict(X)
28 | # Calc errors
29 | errs = metrics.mean_squared_error(y, pred_y)
30 | 
31 | print('pred_y:', pred_y)
32 | print('errs:', errs)


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/.travis.yml:
--------------------------------------------------------------------------------
 1 | # (from: https://qiita.com/masashi127/items/5bfcba5cad8e82958844)
 2 | 
 3 | language: python
 4 | 
 5 | python:
 6 |   - 3.4
 7 |   - 3.5
 8 |   - 3.6
 9 | 
10 | addons:
11 |   apt:
12 |     packages:
13 |     # (from: https://github.com/dnouri/nolearn/blob/master/.travis.yml)
14 |     - libblas-dev
15 |     - liblapack-dev
16 |     - gfortran
17 | 
18 | 
19 | before_install:
20 |   - pip install -U pip setuptools wheel # (from: https://github.com/dnouri/nolearn/blob/master/.travis.yml)
21 | 
22 | install:
23 |   - pip install pipenv
24 | 
25 | script:
26 |   - pipenv install
27 |   - pipenv install --dev
28 |   - pipenv run coverage run --source=multi_svr setup.py test
29 | 
30 |   - pip3 install --upgrade git+https://github.com/nwtgck/multi-svr-python.git#$TRAVIS_COMMIT
31 |   - python examples/example1.py
32 | 
33 | after_success:
34 |   - coveralls
35 | 
36 | cache:
37 |   - apt
38 |   - directories:
39 |     - $HOME/.cache/pip


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/setup.py:
--------------------------------------------------------------------------------
 1 | # (from: https://github.com/masaponto/Python-MLP/blob/master/setup.py)
 2 | # (from: https://qiita.com/masashi127/items/5bfcba5cad8e82958844)
 3 | # (from: https://qiita.com/hotoku/items/4789533f5e497f3dc6e0)
 4 | 
 5 | from setuptools import setup, find_packages
 6 | import sys
 7 | import toml
 8 | 
 9 | def name_ver_join(name, ver):
10 |     if ver == "*":
11 |         return name
12 |     else:
13 |         return name + ver
14 | with open("Pipfile") as f:
15 |   pipfile_dict = toml.load(f)
16 | install_requires = [name_ver_join(name, ver) for name, ver in pipfile_dict['packages'].items()]
17 | 
18 | 
19 | sys.path.append('./tests')
20 | 
21 | setup(
22 |     name='multi_svr',
23 |     version='0.1.2',
24 |     description='SVR for multidimensional label',
25 |     author='Ryo Ota',
26 |     author_email='nwtgck@nwtgck.org',
27 |     install_requires=install_requires,
28 |     py_modules=["multi_svr"],
29 |     packages=find_packages(),
30 |     test_suite='tests'
31 | )
32 | 


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/tests/multi_svr_test.py:
--------------------------------------------------------------------------------
 1 | import unittest
 2 | from sklearn import metrics
 3 | 
 4 | import multi_svr
 5 | 
 6 | class MultiSVRTest(unittest.TestCase):
 7 | 
 8 |   def test_prediction(self):
 9 |     X = [
10 |       [0, 0],
11 |       [0, 10],
12 |       [1, 10],
13 |       [1, 20],
14 |       [1, 30],
15 |       [1, 40]
16 |     ]
17 | 
18 |     y = [
19 |       [0, 0],
20 |       [0, 10],
21 |       [2, 10],
22 |       [2, 20],
23 |       [2, 30],
24 |       [2, 40]
25 |     ]
26 | 
27 |     #  Create SVR
28 |     regressor = multi_svr.MultiSVR(kernel='linear')
29 |     # Fit
30 |     regressor.fit(X, y)
31 |     # Predict
32 |     pred_y = regressor.predict(X)
33 |     # Calc errors
34 |     errs = metrics.mean_squared_error(y, pred_y, multioutput='raw_values')
35 | 
36 |     # Errors should be small
37 |     assert(errs[0] < 0.05)
38 |     assert(errs[1] < 0.05)
39 | 
40 | 
41 | def suite():
42 |   suite = unittest.TestSuite()
43 |   suite.addTest(unittest.makeSuite(MultiSVRTest))
44 |   return suite


--------------------------------------------------------------------------------
/multi_svr/__init__.py:
--------------------------------------------------------------------------------
 1 | import numpy as np
 2 | import sklearn
 3 | from sklearn import svm
 4 | 
 5 | 
 6 | class MultiSVR(sklearn.base.BaseEstimator, sklearn.base.RegressorMixin):
 7 |   def __init__(self, **kwargs):
 8 |     self.__init_kwargs = kwargs
 9 | 
10 |   def fit(self, X, y, **kwargs):
11 |     X = np.array(X)
12 |     y = np.array(y)
13 | 
14 |     # Get dimension of y
15 |     y_dim = np.ndim(y)
16 |     if(y_dim == 2):
17 |       # Feature dimension
18 |       feature_dim = len(y[0])
19 |       # Create SVRs
20 |       self.svrs   = [svm.SVR(**self.__init_kwargs) for _ in range(feature_dim)]
21 | 
22 |       # For each SVR
23 |       for curr_feature_dim, svr in enumerate(self.svrs): # (curr=Current)
24 |         # Select y
25 |         selected_y = y[:,curr_feature_dim]
26 |         # Fit
27 |         svr.fit(X, selected_y, **kwargs)
28 |     else:
29 |       raise Exception("Dimension of y must be 2, but found %d" % y_dim)
30 | 
31 | 
32 |   def predict(self, X):
33 |     # Init predict list
34 |     preds = []
35 |     # For each SVR
36 |     for curr_feature_dim, svr in enumerate(self.svrs):  # (curr=Current)
37 |       # Predict
38 |       pred = svr.predict(X)
39 |       # Append to preds
40 |       preds.append(pred)
41 | 
42 |     pred = np.column_stack(tuple(preds))
43 |     return pred
44 | 


--------------------------------------------------------------------------------
/README.md:
--------------------------------------------------------------------------------
 1 | # multi_svr
 2 | [![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)
 3 | 
 4 | Support Vector Regression (SVR) for multidimensional labels
 5 |  
 6 |  
 7 | ## Install with pip
 8 |  
 9 | ```bash
10 | pip3 install git+https://github.com/nwtgck/multi-svr-python
11 | ```
12 |  
13 | ## Install with Pipenv
14 |  
15 | ```bash
16 | pipenv install --dev toml
17 | pipenv install git+https://github.com/nwtgck/multi-svr-python.git@v0.1.2#egg=multi_svr
18 | ```
19 |  
20 |  ## Usage
21 |  
22 | ```python
23 | import multi_svr
24 | from sklearn import metrics
25 | 
26 | X = [
27 | [0, 0],
28 | [0, 10],
29 | [1, 10],
30 | [1, 20],
31 | [1, 30],
32 | [1, 40]
33 | ]
34 | 
35 | y = [
36 | [0, 0],
37 | [0, 10],
38 | [2, 10],
39 | [2, 20],
40 | [2, 30],
41 | [2, 40]
42 | ]
43 | 
44 | #  Create SVR
45 | regressor = multi_svr.MultiSVR(kernel='linear')
46 | # Fit
47 | regressor.fit(X, y)
48 | # Predict
49 | pred_y = regressor.predict(X)
50 | # Calc errors
51 | errs = metrics.mean_squared_error(y, pred_y)
52 | ```
53 | 
54 | ## How to test
55 | 
56 | ```bash
57 | cd <this repo>
58 | python setup.py test
59 | ```


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170 |         },
171 |         "coverage": {
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197 |                 "sha256:bab8e6d510d2ea0f1d14f12642e3f35cefa47a9b2e4c7cea1852b52bc9c49647",
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203 |                 "sha256:ee5b8abc35b549012e03a7b1e86c09491457dba6c94112a2482b18589cc2bdb9"
204 |             ],
205 |             "version": "==4.5.2"
206 |         },
207 |         "coveralls": {
208 |             "hashes": [
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210 |                 "sha256:b2388747e2529fa4c669fb1e3e2756e4e07b6ee56c7d9fce05f35ccccc913aa0"
211 |             ],
212 |             "index": "pypi",
213 |             "version": "==1.5.1"
214 |         },
215 |         "docopt": {
216 |             "hashes": [
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218 |             ],
219 |             "index": "pypi",
220 |             "version": "==0.6.2"
221 |         },
222 |         "idna": {
223 |             "hashes": [
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226 |             ],
227 |             "index": "pypi",
228 |             "version": "==2.6"
229 |         },
230 |         "requests": {
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233 |                 "sha256:ea881206e59f41dbd0bd445437d792e43906703fff75ca8ff43ccdb11f33f263"
234 |             ],
235 |             "index": "pypi",
236 |             "version": "==2.20.1"
237 |         },
238 |         "toml": {
239 |             "hashes": [
240 |                 "sha256:8e86bd6ce8cc11b9620cb637466453d94f5d57ad86f17e98a98d1f73e3baab2d"
241 |             ],
242 |             "index": "pypi",
243 |             "version": "==0.9.4"
244 |         },
245 |         "urllib3": {
246 |             "hashes": [
247 |                 "sha256:06330f386d6e4b195fbfc736b297f58c5a892e4440e54d294d7004e3a9bbea1b",
248 |                 "sha256:cc44da8e1145637334317feebd728bd869a35285b93cbb4cca2577da7e62db4f"
249 |             ],
250 |             "index": "pypi",
251 |             "version": "==1.22"
252 |         }
253 |     }
254 | }
255 | 


--------------------------------------------------------------------------------