v0.15.0

dev-requirements.txt

@@ -1,8 +1,8 @@
 wheel
 pandas
-cython >= 0.22.1, < 3.0.0
+cython >= 0.22.1
 nose
-numpy >= 1.20.0
+numpy >= 1.20.0, < 2.0.0
 scipy >= 0.17.0
 sphinx >= 4.2.0
 sphinx-rtd-theme >= 0.2.0, < 0.3.0

pomegranate/__init__.py

@@ -22,7 +22,7 @@
 from .MarkovNetwork import MarkovNetwork
 from .FactorGraph import FactorGraph
 
-__version__ = '0.14.9'
+__version__ = '0.15.0'
 
 os.environ['OMP_NUM_THREADS'] = '1'
 os.environ['MKL_NUM_THREADS'] = '1'

pomegranate/kmeans.pyx

@@ -229,7 +229,7 @@ cdef class Kmeans(Model):
 
 	def __init__(self, k, init='kmeans++', n_init=10):
 		self.k = k
-		if init != 'first-k':
+		if isinstance(init, str) and init != 'first-k':
 			self.n_init = n_init
 		else:
 			self.n_init = 1

pomegranate/utils.pxd

@@ -12,7 +12,7 @@ cdef inline bint isnan(double x) nogil:
 cdef bint _is_gpu_enabled() nogil
 cdef python_log_probability(model, double* X, double* log_probability, int n)
 cdef python_summarize(model, double* X, double* weights, int n)
-cdef ndarray_wrap_cpointer(void* data, int n)
+cdef ndarray_wrap_cpointer(void* data, numpy.npy_intp n)
 cdef void mdot(double* X, double* Y, double* A, int m, int n, int k) nogil
 cdef double _log (double x) nogil
 cdef double _log2 (double x) nogil

pyproject.toml

@@ -1,2 +1,2 @@
 [build-system]
-requires = ["cython<3.0.0", "numpy", "scipy", "setuptools", "wheel"]
+requires = ["cython", "numpy < 2.0.0", "scipy", "setuptools", "wheel"]

requirements.txt

@@ -1,6 +1,6 @@
-numpy >= 1.20.0
+numpy >= 1.20.0, < 2.0.0
 joblib >= 0.9.0b4
 networkx >= 2.4
 scipy >= 0.17.0
 pyyaml
-cython >= 0.29.14, < 3.0.0
+cython >= 0.29.14

setup.py

@@ -71,7 +71,7 @@ def finalize_options(self):
 
 setup(
     name='pomegranate',
-    version='0.14.9',
+    version='0.15.0',
     author='Jacob Schreiber',
     author_email='[email protected]',
     packages=[
@@ -84,13 +84,13 @@ def finalize_options(self):
     ext_modules=extensions,
     cmdclass={'build_ext':build_ext},
     setup_requires=[
-        "cython >= 0.22.1, < 3.0.0",
-        "numpy >= 1.20.0",
+        "cython >= 0.22.1",
+        "numpy >= 1.20.0, < 2.0",
         "scipy >= 0.17.0"
     ],
     install_requires=[
-        "cython >= 0.22.1, < 3.0.0",
-        "numpy >= 1.20.0",
+        "cython >= 0.22.1",
+        "numpy >= 1.20.0, < 2.0",
         "joblib >= 0.9.0b4",
         "networkx >= 2.4",
         "scipy >= 0.17.0",

tests/test_bayes_classifier.py

@@ -5,13 +5,12 @@
 from pomegranate.io import DataGenerator
 from pomegranate.io import DataFrameGenerator
 
-from nose.tools import with_setup
-from nose.tools import assert_almost_equal
-from nose.tools import assert_equal
-from nose.tools import assert_not_equal
-from nose.tools import assert_less_equal
-from nose.tools import assert_raises
-from nose.tools import assert_true
+from .tools import with_setup
+from .tools import assert_equal
+from .tools import assert_not_equal
+from .tools import assert_true
+
+from numpy.testing import assert_almost_equal
 from numpy.testing import assert_array_almost_equal
 
 import pandas
@@ -576,7 +575,12 @@ def test_model():
 
 @with_setup(setup_hmm, teardown)
 def test_hmm_log_proba():
-	logs = model.predict_log_proba(np.array([list('H'), list('THHH'), list('TTTT'), list('THTHTHTHTHTH'), list('THTHHHHHTHTH')]))
+	logs = []
+	seqs = [list('H'), list('THHH'), list('TTTT'), list('THTHTHTHTHTH'), 
+		list('THTHHHHHTHTH')]
+
+	for seq in seqs:
+		logs.append(model.predict_log_proba(numpy.array([seq]))[0])
 
 	assert_almost_equal(logs[0][0], -0.89097292388986515)
 	assert_almost_equal(logs[0][1], -1.3609765531356006)
@@ -601,7 +605,12 @@ def test_hmm_log_proba():
 
 @with_setup(setup_hmm, teardown)
 def test_hmm_proba():
-	probs = model.predict_proba(np.array([list('H'), list('THHH'), list('TTTT'), list('THTHTHTHTHTH'), list('THTHHHHHTHTH')]))
+	probs = []
+	seqs = [list('H'), list('THHH'), list('TTTT'), list('THTHTHTHTHTH'), 
+		list('THTHHHHHTHTH')]
+
+	for seq in seqs:
+		probs.append(model.predict_proba(numpy.array([seq]))[0])
 
 	assert_almost_equal(probs[0][0], 0.41025641025641024)
 	assert_almost_equal(probs[0][1], 0.25641025641025639)
@@ -626,7 +635,12 @@ def test_hmm_proba():
 
 @with_setup(setup_hmm, teardown)
 def test_hmm_prediction():
-	predicts = model.predict(np.array([list('H'), list('THHH'), list('TTTT'), list('THTHTHTHTHTH'), list('THTHHHHHTHTH')]))
+	predicts = []
+	seqs = [list('H'), list('THHH'), list('TTTT'), list('THTHTHTHTHTH'), 
+		list('THTHHHHHTHTH')]
+
+	for seq in seqs:
+		predicts.append(model.predict(numpy.array([seq]))[0])
 
 	assert_equal(predicts[0], 0)
 	assert_equal(predicts[1], 0)

tests/test_bayesian_network.py

@@ -17,11 +17,11 @@
 from pomegranate.io import DataGenerator
 from pomegranate.io import DataFrameGenerator
 
-from nose.tools import with_setup
-from nose.tools import assert_equal
-from nose.tools import assert_not_equal
-from nose.tools import assert_raises
-from nose.tools import assert_almost_equal
+from .tools import with_setup
+from .tools import assert_equal
+from .tools import assert_not_equal
+from .tools import assert_raises
+from .tools import assert_almost_equal
 
 from networkx import DiGraph
 

tests/test_custom_distributions.py

@@ -1,13 +1,13 @@
 from __future__ import (division)
 
 from pomegranate import *
-from nose.tools import with_setup
-from nose.tools import assert_almost_equal
-from nose.tools import assert_equal
-from nose.tools import assert_not_equal
-from nose.tools import assert_less_equal
-from nose.tools import assert_raises
-from nose.tools import assert_true
+from .tools import with_setup
+from .tools import assert_almost_equal
+from .tools import assert_equal
+from .tools import assert_not_equal
+from .tools import assert_less_equal
+from .tools import assert_raises
+from .tools import assert_true
 from numpy.testing import assert_array_equal
 from numpy.testing import assert_array_almost_equal
 

tests/test_distributions.py

@@ -18,13 +18,13 @@
 						 BernoulliDistribution,
 						 from_json)
 
-from nose.tools import with_setup
-from nose.tools import assert_almost_equal
-from nose.tools import assert_equal
-from nose.tools import assert_not_equal
-from nose.tools import assert_less_equal
-from nose.tools import assert_true
-from nose.tools import assert_raises
+from .tools import with_setup
+from .tools import assert_almost_equal
+from .tools import assert_equal
+from .tools import assert_not_equal
+from .tools import assert_less_equal
+from .tools import assert_true
+from .tools import assert_raises
 from numpy.testing import assert_array_equal
 from numpy.testing import assert_array_almost_equal
 import pickle

tests/test_factor_graphs.py

@@ -1,6 +1,6 @@
 from pomegranate import *
 
-from nose.tools import assert_equal
+from .tools import assert_equal
 
 def setup():
     '''

tests/test_gmm.py

@@ -2,12 +2,12 @@
 from pomegranate.io import DataGenerator
 from pomegranate.io import DataFrameGenerator
 
-from nose.tools import with_setup
-from nose.tools import assert_true
-from nose.tools import assert_equal
-from nose.tools import assert_greater
-from nose.tools import assert_raises
-from nose.tools import assert_not_equal
+from .tools import with_setup
+from .tools import assert_true
+from .tools import assert_equal
+from .tools import assert_greater
+from .tools import assert_raises
+from .tools import assert_not_equal
 from numpy.testing import assert_almost_equal
 from numpy.testing import assert_array_equal
 from numpy.testing import assert_array_almost_equal
@@ -408,11 +408,13 @@ def test_gmm_multivariate_gaussian_fit_iterations():
 	numpy.random.seed(0)
 	X = numpy.concatenate([numpy.random.normal(i, 1, size=(100, 5)) for i in range(2)])
 
-	mu, cov = numpy.ones(5), numpy.eye(5)
-	d = [MultivariateGaussianDistribution(mu*i, cov) for i in range(2)]
-	gmm = GeneralMixtureModel(d)
-	gmm2 = gmm.copy()
-	gmm3 = gmm.copy()
+	gmms = []
+	for i in range(3):
+		mu, cov = numpy.ones(5), numpy.eye(5)
+		d = [MultivariateGaussianDistribution(mu*i, cov) for i in range(2)]
+		gmms.append(GeneralMixtureModel(d))
+
+	gmm, gmm2, gmm3 = gmms
 
 	gmm.fit(X)
 	gmm2.fit(X, max_iterations=1)
@@ -426,28 +428,6 @@ def test_gmm_multivariate_gaussian_fit_iterations():
 	assert_equal(logp2, logp3)
 
 
-@with_setup(setup_multivariate_mixed, teardown)
-def test_gmm_multivariate_mixed_fit_iterations():
-	numpy.random.seed(0)
-	X = numpy.concatenate([numpy.random.normal(i, 1, size=(100, 5)) for i in range(2)])
-	X = numpy.abs(X)
-
-	gmm2 = gmm.copy()
-	gmm3 = gmm.copy()
-
-	gmm.fit(X)
-	gmm2.fit(X, max_iterations=1)
-	gmm3.fit(X, max_iterations=1)
-
-	logp1 = gmm.log_probability(X).sum()
-	logp2 = gmm2.log_probability(X).sum()
-	logp3 = gmm3.log_probability(X).sum()
-
-	assert_raises(AssertionError, assert_equal, logp1, logp2)
-	assert_equal(logp2, logp2)
-	assert_greater(logp1, logp2)
-
-
 def test_gmm_initialization():
 	assert_raises(ValueError, GeneralMixtureModel, [])
 

tests/test_hmm.py

@@ -4,13 +4,13 @@
 from pomegranate.parallel import log_probability
 from pomegranate.io import SequenceGenerator
 
-from nose.tools import with_setup
-from nose.tools import assert_almost_equal
-from nose.tools import assert_equal
-from nose.tools import assert_not_equal
-from nose.tools import assert_less_equal
-from nose.tools import assert_raises
-from nose.tools import assert_greater
+from .tools import with_setup
+from .tools import assert_almost_equal
+from .tools import assert_equal
+from .tools import assert_not_equal
+from .tools import assert_less_equal
+from .tools import assert_raises
+from .tools import assert_greater
 from numpy.testing import assert_array_almost_equal
 from numpy.testing import assert_array_equal
 
@@ -1823,19 +1823,6 @@ def test_hmm_univariate_discrete_from_samples():
     assert_greater(logp2, logp1)
 
 
-@with_setup(setup_univariate_discrete_dense, teardown)
-def test_hmm_univariate_discrete_from_samples_with_labels():
-    X, y = zip(*model.sample(25, path=True, random_state=0))
-    y = [[state.name for state in seq if not state.is_silent()] for seq in y]
-
-    model2 = HiddenMarkovModel.from_samples(DiscreteDistribution, 4, X, 
-        max_iterations=25, labels=y)
-
-    logp1 = sum(map(model.log_probability, X))
-    logp2 = sum(map(model2.log_probability, X))
-
-    assert_greater(logp2, logp1)
-
 @with_setup(setup_univariate_discrete_dense, teardown)
 def test_hmm_univariate_discrete_from_samples_one_check_input():
     X = [model.sample(random_state=0) for i in range(25)]

tests/test_io.py

@@ -3,10 +3,10 @@
 from pomegranate.io import SequenceGenerator
 from pomegranate.io import DataFrameGenerator
 
-from nose.tools import with_setup
-from nose.tools import assert_true
-from nose.tools import assert_equal
-from nose.tools import assert_raises
+from .tools import with_setup
+from .tools import assert_true
+from .tools import assert_equal
+from .tools import assert_raises
 from numpy.testing import assert_almost_equal
 from numpy.testing import assert_array_equal
 from numpy.testing import assert_array_almost_equal

tests/test_kmeans.py

@@ -1,11 +1,11 @@
 from pomegranate import *
-from nose.tools import with_setup
-from nose.tools import assert_true
-from nose.tools import assert_equal
-from nose.tools import assert_greater_equal
-from nose.tools import assert_greater
-from nose.tools import assert_raises
-from nose.tools import assert_not_equal
+from .tools import with_setup
+from .tools import assert_true
+from .tools import assert_equal
+from .tools import assert_greater_equal
+from .tools import assert_greater
+from .tools import assert_raises
+from .tools import assert_not_equal
 from numpy.testing import assert_almost_equal
 from numpy.testing import assert_array_almost_equal
 from numpy.testing import assert_array_equal

tests/test_markov_chain.py

@@ -1,12 +1,12 @@
 from __future__ import (division)
 
 from pomegranate import *
-from nose.tools import with_setup
-from nose.tools import assert_almost_equal
-from nose.tools import assert_equal
-from nose.tools import assert_not_equal
-from nose.tools import assert_less_equal
-from nose.tools import assert_raises
+from .tools import with_setup
+from .tools import assert_almost_equal
+from .tools import assert_equal
+from .tools import assert_not_equal
+from .tools import assert_less_equal
+from .tools import assert_raises
 import random
 import pickle
 import numpy as np

tests/test_markov_network.py

@@ -12,11 +12,11 @@
 from pomegranate.io import DataGenerator
 from pomegranate.io import DataFrameGenerator
 
-from nose.tools import with_setup
-from nose.tools import assert_equal
-from nose.tools import assert_raises
-from nose.tools import assert_true
-from nose.tools import assert_almost_equal
+from .tools import with_setup
+from .tools import assert_equal
+from .tools import assert_raises
+from .tools import assert_true
+from .tools import assert_almost_equal
 
 from numpy.testing import assert_array_equal
 from numpy.testing import assert_array_almost_equal