Merge pull request #298 from rsagroup/rdms-to-pandas

RDMs to Pandas DataFrame

src/rsatoolbox/io/pandas.py

@@ -0,0 +1,42 @@
+"""Conversions from rsatoolbox classes to pandas table objects
+"""
+from __future__ import annotations
+from typing import TYPE_CHECKING
+from pandas import DataFrame
+import numpy
+from numpy import asarray
+if TYPE_CHECKING:
+    from rsatoolbox.rdm.rdms import RDMs
+
+
+def rdms_to_df(rdms: RDMs) -> DataFrame:
+    """Create DataFrame representation of the RDMs object
+
+    A column for:
+    - dissimilarity
+    - each rdm descriptor
+    - two for each pattern descriptor, suffixed by _1 and _2 respectively
+
+    Multiple RDMs are stacked row-wise.
+    See also the `RDMs.to_df()` method which calls this function
+
+    Args:
+        rdms (RDMs): the object to convert
+
+    Returns:
+        DataFrame: long-form pandas DataFrame with
+            dissimilarities and descriptors.
+    """
+    n_rdms, n_pairs = rdms.dissimilarities.shape
+    cols = dict(dissimilarity=rdms.dissimilarities.ravel())
+    for dname, dvals in rdms.rdm_descriptors.items():
+        # rename the default index desc as that has special meaning in df
+        cname = 'rdm_index' if dname == 'index' else dname
+        cols[cname] = numpy.repeat(dvals, n_pairs)
+    for dname, dvals in rdms.pattern_descriptors.items():
+        ix = numpy.triu_indices(len(dvals), 1)
+        # rename the default index desc as that has special meaning in df
+        cname = 'pattern_index' if dname == 'index' else dname
+        for p in (0, 1):
+            cols[f'{cname}_{p+1}'] = numpy.tile(asarray(dvals)[ix[p]], n_rdms)
+    return DataFrame(cols)

src/rsatoolbox/rdm/rdms.py

@@ -10,6 +10,7 @@
 from copy import deepcopy
 from collections.abc import Iterable
 import numpy as np
+from rsatoolbox.io.pandas import rdms_to_df
 from rsatoolbox.rdm.combine import _mean
 from rsatoolbox.util.rdm_utils import batch_to_vectors
 from rsatoolbox.util.rdm_utils import batch_to_matrices
@@ -400,6 +401,16 @@ def to_dict(self):
         rdm_dict['dissimilarity_measure'] = self.dissimilarity_measure
         return rdm_dict
 
+    def to_df(self):
+        """Return a new long-form pandas DataFrame representing this RDM
+
+        See `rsatoolbox.io.pandas.rdms_to_df` for details
+
+        Returns:
+            pandas.DataFrame: The DataFrame for this RDMs object
+        """
+        return rdms_to_df(self)
+
     def reorder(self, new_order):
         """Reorder the patterns according to the index in new_order
 

src/rsatoolbox/rdm/transform.py

@@ -2,14 +2,14 @@
 # -*- coding: utf-8 -*-
 """ transforms, which can be applied to RDMs
 """
-
+from __future__ import annotations
 from copy import deepcopy
 import numpy as np
 from scipy.stats import rankdata
 from .rdms import RDMs
 
 
-def rank_transform(rdms, method='average'):
+def rank_transform(rdms: RDMs, method='average'):
     """ applies a rank_transform and generates a new RDMs object
     This assigns a rank to each dissimilarity estimate in the RDM,
     deals with rank ties and saves ranks as new dissimilarity estimates.
@@ -30,9 +30,9 @@ def rank_transform(rdms, method='average'):
     dissimilarities = rdms.get_vectors()
     dissimilarities = np.array([rankdata(dissimilarities[i], method=method)
                                 for i in range(rdms.n_rdm)])
-    measure = rdms.dissimilarity_measure
-    if not measure[-7:] == '(ranks)':
-        measure = measure + ' (ranks)'
+    measure = rdms.dissimilarity_measure or ''
+    if '(ranks)' not in measure:
+        measure = (measure + ' (ranks)').strip()
     rdms_new = RDMs(dissimilarities,
                     dissimilarity_measure=measure,
                     descriptors=deepcopy(rdms.descriptors),
@@ -103,8 +103,9 @@ def transform(rdms, fun):
     """
     dissimilarities = rdms.get_vectors()
     dissimilarities = fun(dissimilarities)
+    meas = 'transformed ' + rdms.dissimilarity_measure
     rdms_new = RDMs(dissimilarities,
-                    dissimilarity_measure='transformed ' + rdms.dissimilarity_measure,
+                    dissimilarity_measure=meas,
                     descriptors=deepcopy(rdms.descriptors),
                     rdm_descriptors=deepcopy(rdms.rdm_descriptors),
                     pattern_descriptors=deepcopy(rdms.pattern_descriptors))

tests/test_rdm.py

@@ -246,6 +246,12 @@ def test_rank_transform(self):
         self.assertEqual(rank_rdm.n_cond, rdms.n_cond)
         self.assertEqual(rank_rdm.dissimilarity_measure, 'Euclidean (ranks)')
 
+    def test_rank_transform_unknown_measure(self):
+        from rsatoolbox.rdm import rank_transform
+        rdms = rsr.RDMs(dissimilarities=np.zeros((8, 10)))
+        rank_rdm = rank_transform(rdms)
+        self.assertEqual(rank_rdm.dissimilarity_measure, '(ranks)')
+
     def test_sqrt_transform(self):
         from rsatoolbox.rdm import sqrt_transform
         dis = np.zeros((8, 10))
@@ -463,17 +469,19 @@ def test_copy(self):
             )
         )
         copy = orig.copy()
-        ## We don't want a reference:
+        # We don't want a reference:
         self.assertIsNot(copy, orig)
         self.assertIsNot(copy.dissimilarities, orig.dissimilarities)
         self.assertIsNot(
             copy.pattern_descriptors.get('order'),
             orig.pattern_descriptors.get('order')
         )
-        ## But check that attributes are equal
+        # But check that attributes are equal
         assert_array_equal(copy.dissimilarities, orig.dissimilarities)
-        self.assertEqual(copy.dissimilarity_measure,
-            orig.dissimilarity_measure)
+        self.assertEqual(
+            copy.dissimilarity_measure,
+            orig.dissimilarity_measure
+        )
         self.assertEqual(copy.descriptors, orig.descriptors)
         self.assertEqual(copy.rdm_descriptors, orig.rdm_descriptors)
         assert_array_equal(

tests/test_rdms_pandas.py

@@ -0,0 +1,40 @@
+from __future__ import annotations
+from unittest import TestCase
+from typing import TYPE_CHECKING, Union, List
+from numpy.testing import assert_array_equal
+import numpy
+from pandas import Series, DataFrame
+if TYPE_CHECKING:
+    from numpy.typing import NDArray
+
+
+class RdmsToPandasTests(TestCase):
+
+    def assertValuesEqual(self,
+                          actual: Series,
+                          expected: Union[NDArray, List]):
+        assert_array_equal(numpy.asarray(actual.values), expected)
+
+    def test_to_df(self):
+        """Convert an RDMs object to a pandas DataFrame
+
+        Default is long form; multiple rdms are stacked row-wise.
+        """
+        from rsatoolbox.rdm.rdms import RDMs
+        dissimilarities = numpy.random.rand(2, 6)
+        rdms = RDMs(
+            dissimilarities,
+            rdm_descriptors=dict(xy=[c for c in 'xy']),
+            pattern_descriptors=dict(abcd=numpy.asarray([c for c in 'abcd']))
+        )
+        df = rdms.to_df()
+        self.assertIsInstance(df, DataFrame)
+        self.assertEqual(len(df.columns), 7)
+        self.assertValuesEqual(df.dissimilarity, dissimilarities.ravel())
+        self.assertValuesEqual(df['rdm_index'], ([0]*6) + ([1]*6))
+        self.assertValuesEqual(df['xy'], (['x']*6) + (['y']*6))
+        self.assertValuesEqual(df['pattern_index_1'],
+                               ([0]*3 + [1]*2 + [2]*1)*2)
+        self.assertValuesEqual(df['pattern_index_2'], [1, 2, 3, 2, 3, 3]*2)
+        self.assertValuesEqual(df['abcd_1'], [c for c in 'aaabbc']*2)
+        self.assertValuesEqual(df['abcd_2'], [c for c in 'bcdcdd']*2)