multilayer to multiway

.github/workflows/validate.yml

@@ -1,4 +1,4 @@
-name: multilayer-alignment
+name: multiway-alignment
 
 on:
   push:

README.md

@@ -1,15 +1,15 @@
-[![multilayer-alignment](https://github.com/letiziaia/multilayer-alignment/actions/workflows/validate.yml/badge.svg)](https://github.com/letiziaia/multilayer-alignment/actions/workflows/validate.yml)
+[![multiway-alignment](https://github.com/letiziaia/multiway-alignment/actions/workflows/validate.yml/badge.svg)](https://github.com/letiziaia/multiway-alignment/actions/workflows/validate.yml)
 [![Python 3.10+](https://img.shields.io/badge/python-3.10+-blue.svg)](https://www.python.org/downloads/release/python-3100/)
-[![License](https://img.shields.io/badge/License-Apache_2.0-blue.svg)](https://github.com/letiziaia/multilayer-alignment/blob/main/LICENSE)
-[![codecov](https://codecov.io/gh/letiziaia/multilayer-alignment/graph/badge.svg?token=KSXP8K5A8S)](https://codecov.io/gh/letiziaia/multilayer-alignment)
+[![License](https://img.shields.io/badge/License-Apache_2.0-blue.svg)](https://github.com/letiziaia/multiway-alignment/blob/main/LICENSE)
+[![codecov](https://codecov.io/gh/letiziaia/multiway-alignment/graph/badge.svg?token=KSXP8K5A8S)](https://codecov.io/gh/letiziaia/multiway-alignment)
 
-# multilayer-alignment
+# multiway-alignment
 
-This repository implements an algorithm for quantifying multilayer alignment or higher-order alignment, that is, the alignment across n different dimensions. You can refer to the [slide deck](https://docs.google.com/presentation/d/1HMEE5kOwwJPLBmAgycKIMSWRx0eCxd3RtSxVR1Jdczw/) for the original idea.
+This repository implements an algorithm for quantifying multiway alignment or higher-order alignment, that is, the alignment across n different dimensions. You can refer to the [slide deck](https://docs.google.com/presentation/d/1HMEE5kOwwJPLBmAgycKIMSWRx0eCxd3RtSxVR1Jdczw/) for the original idea.
 
 ## Structure of the repo
 
-- `\multilayer_alignment\`: source code
+- `\multiway_alignment\`: source code
 - `\tests\`: tests for the source code
 
 ## Installing the package
@@ -19,16 +19,16 @@ This repository implements an algorithm for quantifying multilayer alignment or
 This package can be installed directly from the Python Package Index (PyPI) using `pip` from the command-line interface by executing the following command:
 
 ```shell
-$ pip install multilayer-alignment
+$ pip install multiway-alignment
 ```
 
 ### Build from source
 
 Alternatively, the package can be installed by first cloning the repository containing the source code and then installing the package locally in a chosen directory:
 
 ```shell
-$ git clone git@github.com:letiziaia/multilayer-alignment.git
-$ cd multilayer-alignment
+$ git clone git@github.com:letiziaia/multiway-alignment.git
+$ cd multiway-alignment
 $ pip install .
 ```
 

RUNBOOK.md

@@ -32,7 +32,7 @@ $ python -m unittest discover -v
 ```python
 # import needed libraries
 >>> import pandas as pd
->>> import multilayer_alignment.consensus as mac
+>>> import multiway_alignment.consensus as mac
 
 # load the opinion labels to a pandas DataFrame
 >>> df = pd.DataFrame(
@@ -73,8 +73,8 @@ $ python -m unittest discover -v
 ```python
 # import needed libraries
 >>> import pandas as pd
->>> import multilayer_alignment.consensus as mac
->>> import multilayer_alignment.score as mas
+>>> import multiway_alignment.consensus as mac
+>>> import multiway_alignment.score as mas
 
 # load the partitions labels to a pandas DataFrame
 >>> df = pd.DataFrame(
@@ -92,21 +92,21 @@ $ python -m unittest discover -v
 
 # compute 3-way alignment score using AMI (adjusted mutual info score)
 # and adjust with the null model
->>> mas.multilayer_alignment_score(
+>>> mas.multiway_alignment_score(
 ...     df, partition_labels, which_score="ami", adjusted=True,
 ... )
 6.40685300762983e-16
 
 # compute 3-way alignment score using NMI (normalized mutual info score)
 # and adjust with the null model
->>> mas.multilayer_alignment_score(
+>>> mas.multiway_alignment_score(
 ...     df, partition_labels, which_score="nmi", adjusted=True,
 ... )
 0.0
 
 # if we use NMI (normalized mutual info score) without adjusting it
 # with a null model, the resulting score is inflated
->>> mas.multilayer_alignment_score(
+>>> mas.multiway_alignment_score(
 ...     df, partition_labels, which_score="nmi", adjusted=False,
 ... )
 0.6666666666666666

multilayer_alignment/score.py → multiway_alignment/score.py

@@ -12,9 +12,9 @@
 from multiprocessing.pool import Pool
 from tqdm import tqdm
 
-from multilayer_alignment.consensus import get_consensus_labels
+from multiway_alignment.consensus import get_consensus_labels
 
-from multilayer_alignment.utils.logging import logger
+from multiway_alignment.utils.logging import logger
 
 
 def _compute_layer_expectation(
@@ -37,7 +37,7 @@ def _compute_layer_expectation(
     return np.array(_all_scores).mean()
 
 
-def multilayer_alignment_score(
+def multiway_alignment_score(
     opinions: typing.Union[pd.DataFrame, pd.Series],
     mutual_clusters_labels: typing.List,
     which_score: str = "nmi",
@@ -92,10 +92,10 @@ def maximal_alignment_curve(
         a tuple of two dictionaries, the first one including all the scores for all the combinations,
         and the second one being the maximal alignment curve.
         In the first dictionary, the key is the size of the combination (int) and
-        the list of layers and the value is a tuple, where the first element is the multilayer alignment score,
+        the list of layers and the value is a tuple, where the first element is the multiway alignment score,
         and the second element is the dictionary of mutual communities for that combination;
         In the second dictionary, the key is the size of the combination (int) and
-        the value is a list, where the first element is the highest multilayer alignment score for that size,
+        the value is a list, where the first element is the highest multiway alignment score for that size,
         the second element is the list of layers that gives the highest alignment score,
         and the last element is the dictionary of mutual communities for that combination
     """
@@ -125,7 +125,7 @@ def maximal_alignment_curve(
             labels_list = get_consensus_labels(opinions=l_comb_df)
 
             # CRITERIA
-            nmi = multilayer_alignment_score(
+            nmi = multiway_alignment_score(
                 l_comb_df, labels_list, which_score=which_score, adjusted=adjusted
             )
 

multilayer_alignment/visualizations.py → multiway_alignment/visualizations.py

@@ -8,9 +8,9 @@
 import seaborn as sns  # type: ignore
 import typing
 
-from multilayer_alignment.score import maximal_alignment_curve  # type: ignore
-from multilayer_alignment.null_models import expected_curve_equal_sized_clusters
-from multilayer_alignment.utils.logging import logger
+from multiway_alignment.score import maximal_alignment_curve  # type: ignore
+from multiway_alignment.null_models import expected_curve_equal_sized_clusters
+from multiway_alignment.utils.logging import logger
 
 
 def plot_maximal_alignment_curve(

setup.py

@@ -1,15 +1,15 @@
 from setuptools import setup, find_packages  # type: ignore
 
 setup(
-    name="multilayer_alignment",
+    name="multiway_alignment",
     version="0.1.0",
     packages=find_packages(),
     author="Letizia Iannucci",
     author_email="letizia.iannucci@aalto.fi",
     description="Quantifying multiway (higher-order) alignment with mutual information",
     long_description=open("README.md").read(),
     long_description_content_type="text/markdown",
-    url="https://github.com/letiziaia/multilayer-alignment",
+    url="https://github.com/letiziaia/multiway-alignment",
     license="Apache 2.0",
     classifiers=[
         "Development Status :: 3 - Alpha",

tests/test_compute_maximal_alignment_curve.py

@@ -2,7 +2,7 @@
 
 import pandas as pd
 
-from multilayer_alignment.score import maximal_alignment_curve
+from multiway_alignment.score import maximal_alignment_curve
 
 
 class TestComputeMaximalAlignmentCurve(unittest.TestCase):

tests/test_compute_multilayer_alignment_score.py

@@ -2,44 +2,44 @@
 
 import pandas as pd
 
-from multilayer_alignment.score import multilayer_alignment_score
+from multiway_alignment.score import multiway_alignment_score
 
 
-class TestComputeMultilayerAlignmentScore(unittest.TestCase):
+class TestComputeMultiwayAlignmentScore(unittest.TestCase):
     """
-    Test functionality of mutual_clusters.compute_multilayer_alignment_score()
+    Test functionality of mutual_clusters.compute_multiway_alignment_score()
     ------------
     Example
     ------------
-    >>> python3 -m unittest -v tests.test_compute_multilayer_alignment_score
+    >>> python3 -m unittest -v tests.test_compute_multiway_alignment_score
     """
 
     def test_on_empty(self):
         """
-        compute_multilayer_alignment_score returns a float
+        compute_multiway_alignment_score returns a float
         """
         _a = pd.DataFrame({"A": [0, 1, 2]})
         _labels = ["a", "b", "c"]
-        _res0 = multilayer_alignment_score(_a, _labels)
+        _res0 = multiway_alignment_score(_a, _labels)
         self.assertIsInstance(
             _res0,
             float,
-            f"""compute_multilayer_alignment_score should return a float, but returned {type(_res0)}""",
+            f"""compute_multiway_alignment_score should return a float, but returned {type(_res0)}""",
         )
         self.assertGreaterEqual(
             _res0,
             0.0,
-            """compute_multilayer_alignment_score should return the correct value of avg NMI""",
+            """compute_multiway_alignment_score should return the correct value of avg NMI""",
         )
         self.assertLessEqual(
             _res0,
             1.0,
-            """compute_multilayer_alignment_score should return the correct value of avg NMI""",
+            """compute_multiway_alignment_score should return the correct value of avg NMI""",
         )
         self.assertEqual(
             _res0,
             1.0,
-            """compute_multilayer_alignment_score should return the correct value of avg NMI""",
+            """compute_multiway_alignment_score should return the correct value of avg NMI""",
         )
 
 

tests/test_get_consensus_labels.py

@@ -2,7 +2,7 @@
 
 import pandas as pd
 
-from multilayer_alignment.consensus import get_consensus_labels
+from multiway_alignment.consensus import get_consensus_labels
 
 
 class TestGetConsensusLabels(unittest.TestCase):

tests/test_get_consensus_labels_df.py

@@ -2,7 +2,7 @@
 
 import pandas as pd
 
-from multilayer_alignment.consensus import _get_consensus_labels_df
+from multiway_alignment.consensus import _get_consensus_labels_df
 
 
 class TestGetConsensusLabels(unittest.TestCase):

tests/test_get_consensus_partition.py

@@ -2,7 +2,7 @@
 
 import pandas as pd
 
-from multilayer_alignment.consensus import get_consensus_partition
+from multiway_alignment.consensus import get_consensus_partition
 
 
 class TestGetConsensusPartition(unittest.TestCase):

tests/test_get_consensus_partition_recursive.py

@@ -2,7 +2,7 @@
 
 import pandas as pd
 
-from multilayer_alignment.consensus import get_consensus_partition_recursive
+from multiway_alignment.consensus import get_consensus_partition_recursive
 
 
 class TestGetConsensusPartitionRecursive(unittest.TestCase):