Revert "Bottlechest, bottleneck"

Orange/__init__.py

@@ -1,9 +1,5 @@
 from .misc.lazy_module import _LazyModule
 from .misc.datasets import _DatasetInfo
-
-# bottlechest patches bottleneck
-from .misc import bottlechest  # pylint: disable=unused-import
-
 from .version import \
     short_version as __version__, git_revision as __git_version__
 

Orange/base.py

@@ -2,12 +2,12 @@
 
 import numpy as np
 import scipy
+import bottlechest as bn
 
 from Orange.data import Table, Storage, Instance, Value
 from Orange.preprocess import (RemoveNaNClasses, Continuize,
                                RemoveNaNColumns, SklImpute)
 from Orange.misc.wrapper_meta import WrapperMeta
-from Orange.util import one_hot
 
 __all__ = ["Learner", "Model", "SklLearner", "SklModel"]
 
@@ -157,9 +157,11 @@ def __call__(self, data, ret=Value):
                                for c in self.domain.class_vars)
                 probs = np.zeros(value.shape + (max_card,), float)
                 for i, cvar in enumerate(self.domain.class_vars):
-                    probs[:, i, :] = one_hot(value[:, i])
+                    probs[:, i, :], _ = bn.bincount(np.atleast_2d(value[:, i]),
+                                                    max_card - 1)
             else:
-                probs = one_hot(value)
+                probs, _ = bn.bincount(np.atleast_2d(value),
+                                       len(self.domain.class_var.values) - 1)
             if ret == Model.ValueProbs:
                 return value, probs
             else:

Orange/data/filter.py

@@ -5,7 +5,7 @@
 
 from ..misc.enum import Enum
 import numpy as np
-import bottleneck as bn
+import bottlechest as bn
 from Orange.data import Instance, Storage, Variable
 
 

Orange/data/io.py

@@ -18,7 +18,7 @@
 from urllib.parse import urlparse, unquote as urlunquote
 from urllib.request import urlopen
 
-import bottleneck as bn
+import bottlechest as bn
 import numpy as np
 from chardet.universaldetector import UniversalDetector
 

Orange/data/table.py

@@ -13,7 +13,7 @@
 from urllib.request import urlopen
 from urllib.error import URLError
 
-import bottleneck as bn
+import bottlechest as bn
 from scipy import sparse as sp
 
 from .instance import *
@@ -935,7 +935,12 @@ def __determine_density(data):
         if data is None:
             return Storage.Missing
         if data is not None and sp.issparse(data):
-            return Storage.SPARSE_BOOL if (data.data == 1).all() else Storage.SPARSE
+            try:
+                if bn.bincount(data.data, 1)[0][0] == 0:
+                    return Storage.SPARSE_BOOL
+            except ValueError as e:
+                pass
+            return Storage.SPARSE
         else:
             return Storage.DENSE
 

Orange/preprocess/preprocess.py

@@ -5,7 +5,7 @@
 """
 import numpy as np
 import sklearn.preprocessing as skl_preprocessing
-import bottleneck as bn
+import bottlechest
 
 import Orange.data
 from Orange.data import Table
@@ -198,8 +198,8 @@ def __call__(self, data):
         data : an input data set
         """
 
-        oks = bn.nanmin(data.X, axis=0) != \
-              bn.nanmax(data.X, axis=0)
+        oks = bottlechest.nanmin(data.X, axis=0) != \
+              bottlechest.nanmax(data.X, axis=0)
         atts = [data.domain.attributes[i] for i, ok in enumerate(oks) if ok]
         domain = Orange.data.Domain(atts, data.domain.class_vars,
                                     data.domain.metas)

Orange/tests/test_contingency.py

@@ -163,7 +163,7 @@ def _construct_sparse():
     def test_sparse(self):
         d = self._construct_sparse()
         cont = contingency.Discrete(d, 5)
-        np.testing.assert_almost_equal(cont[0], [2, 0, 0])
+        np.testing.assert_almost_equal(cont[0], [1, 0, 0])
         np.testing.assert_almost_equal(cont["b"], [0, 1, 1])
         np.testing.assert_almost_equal(cont[2], [1, 0, 0])
 
@@ -193,7 +193,7 @@ def test_get_contingency(self):
         d = self._construct_sparse()
         cont = contingency.get_contingency(d, 5)
         self.assertIsInstance(cont, contingency.Discrete)
-        np.testing.assert_almost_equal(cont[0], [2, 0, 0])
+        np.testing.assert_almost_equal(cont[0], [1, 0, 0])
         np.testing.assert_almost_equal(cont["b"], [0, 1, 1])
         np.testing.assert_almost_equal(cont[2], [1, 0, 0])
 
@@ -218,7 +218,7 @@ def test_get_contingencies(self):
 
         cont = conts[5]
         self.assertIsInstance(cont, contingency.Discrete)
-        np.testing.assert_almost_equal(cont[0], [2, 0, 0])
+        np.testing.assert_almost_equal(cont[0], [1, 0, 0])
         np.testing.assert_almost_equal(cont["b"], [0, 1, 1])
         np.testing.assert_almost_equal(cont[2], [1, 0, 0])
 
@@ -240,7 +240,7 @@ def test_get_contingencies(self):
         self.assertEqual(len(conts), 10)
         cont = conts[5]
         self.assertIsInstance(cont, contingency.Discrete)
-        np.testing.assert_almost_equal(cont[0], [2, 0, 0])
+        np.testing.assert_almost_equal(cont[0], [1, 0, 0])
         np.testing.assert_almost_equal(cont["b"], [0, 1, 1])
         np.testing.assert_almost_equal(cont[2], [1, 0, 0])
 

Orange/util.py

@@ -90,22 +90,6 @@ def scale(values, min=0, max=1):
     return (-np.nanmin(values) + values) / ptp * (max - min) + min
 
 
-def one_hot(values, dtype=float):
-    """Return a one-hot transform of values
-
-    Parameters
-    ----------
-    values : 1d array
-        Integer values (hopefully 0-max).
-
-    Returns
-    -------
-    result
-        2d array with ones in respective indicator columns.
-    """
-    return np.eye(np.max(values) + 1, dtype=dtype)[np.asanyarray(values, dtype=int)]
-
-
 class Registry(type):
     """Metaclass that registers subtypes."""
     def __new__(cls, name, bases, attrs):

Orange/widgets/classify/tests/test_owlogisticregression.py

@@ -1,6 +1,6 @@
 import unittest
 
-import bottleneck as bn
+import bottlechest as bn
 
 from Orange.data import Table
 from Orange.classification import LogisticRegressionLearner

codecov.yml

@@ -4,7 +4,7 @@ coverage:
   status:
     patch:
       default:
-        target: '90'
+        target: '95'
     project:
       default:
         target: auto

requirements-core.txt

@@ -1,7 +1,7 @@
 numpy>=1.9.0
 scipy>=0.11.0
 scikit-learn>=0.17
-bottleneck>=1.0.0
+bottlechest>=0.7.1
 # Reading Excel files
 xlrd>=0.9.2
 # Encoding detection

scripts/build-osx-app.sh

@@ -113,6 +113,12 @@ echo "Installing/updating setuptools and pip"
 echo "======================================"
 "$PIP" install 'setuptools==18.*' 'pip==7.*'
 
+echo "Installing Bottlechest"
+echo "======================"
+"$PIP" install --find-links http://orange.biolab.si/download/files/wheelhouse/ \
+               --use-wheel --trusted-host orange.biolab.si \
+               Bottlechest
+
 echo "Installing orangeqt"
 echo "==================="
 FDIR=$TEMPLATE/Contents/Frameworks

scripts/windows/build-win-application.sh

@@ -120,6 +120,10 @@ touch "$BUILDBASE"/requirements.txt
 echo "
 #:wheel: scikit-learn https://pypi.python.org/packages/b8/9a/02d5d76be66c57aaa9f917c87007b9b0bf486992cc7701512464d1ce11e9/scikit_learn-0.17.1-cp34-cp34m-win32.whl#md5=ab00daed7cdac4cb16ad0613b91be07e
 scikit-learn==0.17.1
+
+#:wheel: Bottlecheset https://dl.dropboxusercontent.com/u/100248799/Bottlechest-0.7.1-cp34-none-win32.whl#md5=629ba2a148dfa784d0e6817497d42e97
+--find-links https://dl.dropboxusercontent.com/u/100248799/Bottlechest-0.7.1-cp34-none-win32.whl
+Bottlechest==0.7.1
 " > "$BUILDBASE"/requirements.txt
 
 function __download_url {
@@ -287,7 +291,7 @@ function prepare_orange {
         bdist_wheel -d "$BUILDBASE/wheelhouse"
 
     # Ensure all install dependencies are available in the wheelhouse
-    prepare_req --only-binary numpy,scipy,scikit-learn .
+    prepare_req --only-binary numpy,scipy,scikit-learn,bottlechest .
 
     echo "# Orange " >> "$BUILDBASE/requirements.txt"
     echo "$name==$version" >> "$BUILDBASE/requirements.txt"