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"