diff --git a/.gitignore b/.gitignore
index cc53efad33c4618a2d89ea837e94cab2822d8fb0..41ddd6927fde1dca00ab937e2195b442a9c78b57 100644
--- a/.gitignore
+++ b/.gitignore
@@ -87,3 +87,16 @@ scikit_gilearn.egg-info/
scikit_splearn.egg-info/*
.idea/
*.tar.gz
+.pytest_cache/
+examples/simple_example-2.json.gv
+examples/simple_example-2.json.gv.pdf
+examples/simple_example-2.yaml.gv
+examples/simple_example-2.yaml.gv.pdf
+examples/simple_example.json
+examples/simple_example.json.gv
+examples/simple_example.json.gv.pdf
+examples/simple_example.yaml
+examples/simple_example.yaml.gv
+examples/simple_example.yaml.gv.pdf
+examples/simple_example_hankel.json
+examples/simple_example_hankel.yaml
diff --git a/docker/Dockerfile b/docker/Dockerfile
index d2d52e587289dfe7a805d7351960fa8717410b84..ff71df4696bd9e93f9705b1de1862f8054f99776 100644
--- a/docker/Dockerfile
+++ b/docker/Dockerfile
@@ -7,11 +7,11 @@ RUN apt-get update && apt-get install -y \
python3-scipy \
graphviz-dev
RUN pip3 install --upgrade pip
-RUN pip3 install pyyaml nose coverage sphinx sphinxcontrib-bibtex
+RUN pip3 install pyyaml nose coverage pytest pytest-coverage pytest-html sphinx sphinxcontrib-bibtex
# Copy the scikit-splearn sdist in the docker directory and uncomment the following line
# if you want to include grackelpy sources in the docker image :
-# ADD scikit-splearn-1.1.0.tar.gz /
+ADD scikit-splearn-1.1.0.tar.gz /
# cleanup
diff --git a/setup.cfg b/setup.cfg
index e44d1f85e5179d7d99b21a3acb00e7fc579609aa..413aca51d9e970ffc9e70e75359088c957f23380 100644
--- a/setup.cfg
+++ b/setup.cfg
@@ -5,18 +5,17 @@ with-coverage=1
cover-package=splearn
cover-erase=1
cover-html=1
-cover-html-dir=../htmlcov
+cover-html-dir=../build/htmlcov
# Options for py.test command
-[pytest]
+[tool:pytest]
# Specifies a minimal pytest version required for running tests.
-minversion = 2.6
+minversion = 3.0
# Specifies the options
-addopts = --resultlog=pytests_results.txt -k "not _old" --cov-report term-missing --cov=sksplearn
+addopts = --cov-config .coveragerc --html=build/pytest_report.html -k "not _old" --cov-report html:build/htmlcov --cov=splearn
# Set the directory basename patterns to avoid when recursing for test discovery.
-norecursedirs = .git sandboxes .settings .cache htmlcov doc references
-
+norecursedirs = .git sandboxes .settings .cache htmlcov doc references build
[coverage:run]
source=./splearn
diff --git a/setup.py b/setup.py
index 37c9e14db8b58bda38dcba6bc8274dce9dcbd863..190a341882156a4084b5a1859ea905cd4d83e117 100755
--- a/setup.py
+++ b/setup.py
@@ -148,7 +148,7 @@ def setup_package():
read('HISTORY.rst') + '\n\n' +
read('AUTHORS.rst')),
packages=["splearn", "splearn.datasets", "splearn.tests", "splearn.tests.datasets"],
- package_data={'splearn.tests.datasets': ['*.*']},
+ package_data={'splearn.tests.datasets': ['*']},
url="https://gitlab.lif.univ-mrs.fr/dominique.benielli/scikit-splearn.git",
license='new BSD',
author='François Denis and Rémi Eyraud and Denis Arrivault and Dominique Benielli',
diff --git a/splearn/hankel.py b/splearn/hankel.py
index 1758ac98cbd61c82c66b34f70a91946385e9aa1d..faff8619253acd72e824c4efcdfb18e9e3d5d06e 100644
--- a/splearn/hankel.py
+++ b/splearn/hankel.py
@@ -51,20 +51,14 @@ class Hankel(object):
>>> pT = load_data_sample(adr=train_file)
>>> sp = Spectral()
>>> sp.fit(X=pT.data)
- >>> lhankel = Hankel( sample=pT.sample, pref=pT.pref,
- >>> suff=pT.suff, fact=pT.fact,
+ >>> lhankel = Hankel( sample_instance=pT.sample,
>>> nbL=pT.nbL, nbEx=pT.nbEx,
>>> lrows=6, lcolumns=6, version="classic",
>>> partial=True, sparse=True, mode_quiet=True).lhankel
- Input:
- :param dict sample_instance: sample dictionary
- :param dict pref: prefix dictionary
- :param dict suff: suffix dictionary
- :param dict fact: factor dictionary
- :param int nbL: the number of letters
- :param int nbS: the number of states
+ :param Splearn_array sample_instance: instance of Splearn_array
:param lrows: number or list of rows,
a list of strings if partial=True;
otherwise, based on self.pref if version="classic" or
diff --git a/splearn/serializer.py b/splearn/serializer.py
index eae59a7994f997e534a1eadfda322d06d877eec0..f027c9c58fa329bad58a6ed7cee55500422efccd 100644
--- a/splearn/serializer.py
+++ b/splearn/serializer.py
@@ -119,6 +119,10 @@ class Serializer(object):
@staticmethod
def __restore_yaml(data_str):
+ if data_str is None or isinstance(data_str, (bool, int, float, str)):
+ return data_str
+ if isinstance(data_str, list):
+ return [Serializer.__restore_yaml(k) for k in data_str]
if "dict" in data_str:
return dict(data_str["dict"])
if "tuple" in data_str:
diff --git a/splearn/tests/test_serializer.py b/splearn/tests/test_serializer.py
index bf9cdb30b1085fae1a24a99066edc366ff56c9a4..3a510140b2627983e9881ad138d74957a90394a1 100644
--- a/splearn/tests/test_serializer.py
+++ b/splearn/tests/test_serializer.py
@@ -35,12 +35,14 @@
# ######### COPYRIGHT #########
import unittest
import numpy as np
-import filecmp
import os
from splearn.automaton import Automaton
from splearn.hankel import Hankel
+from splearn.serializer import Serializer
+from splearn.spectral import Spectral
from splearn.tests.datasets.get_dataset_path import get_dataset_path
+from splearn.datasets.base import load_data_sample
class UnitaryTest(unittest.TestCase):
@@ -62,13 +64,14 @@ class UnitaryTest(unittest.TestCase):
def testWriteAutomata(self):
for f in self.formats:
Automaton.write(self.A, get_dataset_path(self.input_file + '_2.' + f), format=f)
- self.assertTrue(filecmp.cmp(get_dataset_path(self.input_file + '_2.' + f),
- get_dataset_path(self.input_file + '.' + f)))
+ B = Automaton.read(get_dataset_path(self.input_file + '_2.' + f), format=f)
+ for w in self.words:
+ np.testing.assert_almost_equal(self.A.val(w), B.val(w))
for f in self.formats:
- os.remove(get_dataset_path(self.input_file + '_2.' + f))
+ os.remove(get_dataset_path(self.input_file + '_2.' + f))
def testReadHankel(self):
- for f in self.formats:
+ for f in self.formats:
H = self.A.to_hankel([(), (0,), (1,)], [(), (0,), (1,)])
Hankel.write(H, get_dataset_path(self.input_file + "_hankel" + "." + f), format=f)
Hb = Hankel.read(get_dataset_path(self.input_file + "_hankel" + "." + f), format = f)
@@ -76,8 +79,40 @@ class UnitaryTest(unittest.TestCase):
for w in self.words:
np.testing.assert_almost_equal(self.A.val(w), B.val(w))
for f in self.formats:
- os.remove(get_dataset_path(self.input_file + "_hankel" + "." + f))
+ os.remove(get_dataset_path(self.input_file + "_hankel" + "." + f))
+
+# def testReadWriteRealHankel(self):
+# adr = get_dataset_path("3.pautomac.train")
+# data = load_data_sample(adr=adr)
+# X = data.data
+# sp = Spectral()
+# sp = sp.fit(X)
+# H = Hankel( sample_instance=X.sample,
+# lrows=6, lcolumns=6, version="classic",
+# partial=True, sparse=True, mode_quiet=True)
+# for f in self.formats:
+# Hankel.write(H, get_dataset_path("3.pautomac.train" + "_hankel" + "." + f), format=f)
+# Hb = Hankel.read(get_dataset_path("3.pautomac.train" + "_hankel" + "." + f), format = f)
+# self.assertEqual(H, Hb)
+# for f in self.formats:
+# os.remove(get_dataset_path("3.pautomac.train" + "_hankel" + "." + f))
+
+ def testOthersSerializationTypes(self):
+ data = [{'a' : 10, 40 : 'gu'}, {'toto', 5, 2.5, 'b'}, ('gh', 25, 'ko', 1.0)]
+ data_json_str = Serializer.data_to_json(data)
+ data_yaml_str = Serializer.data_to_yaml(data)
+ data_json = Serializer.json_to_data(data_json_str)
+ data_yaml = Serializer.yaml_to_data(data_yaml_str)
+ self.assertEqual(data, data_json)
+ self.assertEqual(data, data_yaml)
+ data = [1, 2, 3.0]
+ data_json_str = Serializer.data_to_json(data)
+ data_yaml_str = Serializer.data_to_yaml(data)
+ data_json = Serializer.json_to_data(data_json_str)
+ data_yaml = Serializer.yaml_to_data(data_yaml_str)
+ self.assertEqual(data, data_json)
+ self.assertEqual(data, data_yaml)
if __name__ == "__main__":
#import sys;sys.argv = ['', 'Test.testName']