Create blackbox tests for some functions migrated from R

Issue:
https://github.com/genenetwork/gn-gemtext-threads/blob/main/topics/gn1-migration-to-gn2/partial-correlations.gmi

* gn3/computations/partial_correlations.py: new stub
  functions (partial_correlation_matrix, partial_correlation_recursive)
*
  tests/unit/computations/partial_correlations_test_data/pcor_mat_blackbox_test.csv:
  blackbox sample data and results for variance-covariance matrix method
*
  tests/unit/computations/partial_correlations_test_data/pcor_rec_blackbox_test.csv:
  blackbox sample data and results for recursive method
* tests/unit/computations/test_partial_correlations.py: Tests for new function

  Provide some blackbox testing sample data for checking the operation of the
  functions migrated from R.

4 files changed, 292 insertions, 1 deletions

diff --git a/gn3/computations/partial_correlations.py b/gn3/computations/partial_correlations.py
index fb372a9..07dc16d 100644
--- a/gn3/computations/partial_correlations.py
+++ b/gn3/computations/partial_correlations.py
@@ -257,3 +257,33 @@ def compute_partial_correlations_fast(# pylint: disable=[R0913, R0914]
             (fetched_correlations[corr[0]],) if correlation_type == "literature"
             else fetched_correlations[corr[0]][0:2])
         for idx, corr in enumerate(all_correlations))
+
+def partial_correlation_matrix(
+        xdata: Tuple[float, ...], ydata: Tuple[float, ...],
+        zdata: Tuple[float, ...], method: str = "pearsons",
+        omit_nones: bool = True) -> float:
+    """
+    Computes the partial correlation coefficient using the
+    'variance-covariance matrix' method
+
+    This is a partial migration of the
+    `web.webqtl.correlation.correlationFunction.determinPartialsByR` function in
+    GeneNetwork1, specifically the `pcor.mat` function written in the R
+    programming language.
+    """
+    return 0
+
+def partial_correlation_recursive(
+        xdata: Tuple[float, ...], ydata: Tuple[float, ...],
+        zdata: Tuple[float, ...], method: str = "pearsons",
+        omit_nones: bool = True) -> float:
+    """
+    Computes the partial correlation coefficient using the 'recursive formula'
+    method
+
+    This is a partial migration of the
+    `web.webqtl.correlation.correlationFunction.determinPartialsByR` function in
+    GeneNetwork1, specifically the `pcor.rec` function written in the R
+    programming language.
+    """
+    return 0
diff --git a/tests/unit/computations/partial_correlations_test_data/pcor_mat_blackbox_test.csv b/tests/unit/computations/partial_correlations_test_data/pcor_mat_blackbox_test.csv
new file mode 100644
index 0000000..a1558a0
--- /dev/null
+++ b/tests/unit/computations/partial_correlations_test_data/pcor_mat_blackbox_test.csv
@@ -0,0 +1,101 @@
+"function_name","count","x","y","z","method","rm","result"
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diff --git a/tests/unit/computations/partial_correlations_test_data/pcor_rec_blackbox_test.csv b/tests/unit/computations/partial_correlations_test_data/pcor_rec_blackbox_test.csv
new file mode 100644
index 0000000..a1558a0
--- /dev/null
+++ b/tests/unit/computations/partial_correlations_test_data/pcor_rec_blackbox_test.csv
@@ -0,0 +1,101 @@
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+"pcor.mat",8,"18.1551295798272, -8.32464881241322, -99.8132115229964, 18.6201244592667, -53.1589215621352, 70.2180631458759, -36.3900125026703, 92.1965328045189","6.02451944723725, -68.5814322903752, 70.58563423343, 1.00183109752834, 16.1975951399654, 64.5838780794293, -84.6291496884078, -54.131712205708","80.0181794445962, -12.9483319818974, -3.88606782071292, -48.0255630798638, -3.62709653563797, 31.62224679254, 57.1325340308249, -93.3892488945276","p",TRUE,-0.141482085540883
+"pcor.mat",17,"90.1813358999789, -3.33601352758706, -70.5867488868535, -40.8890372607857, 58.0897845327854, 8.4102772641927, 31.2019024044275, 67.8843731526285, -5.85478777065873, 93.743189284578, -21.4788604527712, 48.0386326089501, -42.5454632379115, -78.037649858743, 5.827397108078, -59.5929707866162, 22.9059528559446","46.3524929713458, -42.5642458721995, -69.567626202479, 91.2107714917511, 80.4405058268458, -83.3648740779608, -5.59279890730977, 67.8364232182503, 9.23628495074809, -45.9923652466387, -2.29323050007224, 92.1013004146516, 34.326039114967, -10.8744602650404, 89.988622488454, -23.287376947701, 72.6627949625254","48.8092821557075, -84.7605162765831, -68.7700709793717, -67.7357694599777, 25.1485624350607, 61.709990631789, 29.2394532822073, 47.8424397762865, 37.0008739177138, -47.5637663155794, -14.6964757237583, -69.9305152054876, -54.4029568322003, 80.0897337496281, 9.39622772857547, -1.27441040240228, 74.3666623719037","s",TRUE,0.270551782409472
+"pcor.mat",13,"-68.030820786953, 48.6679933033884, 51.8114904407412, 78.5725246183574, 18.9902665093541, 25.0479029957205, 56.8353782407939, -4.79650879278779, -87.2707166243345, -64.176924712956, -56.511168833822, 41.7948929592967, 79.8323729075491","87.0083963032812, 12.2851114254445, -48.7783022690564, 2.98262075521052, 61.5149905905128, 72.0769232138991, -33.8758388534188, 19.778781151399, -12.315073935315, -95.3089885413647, -40.8825332764536, -50.1593578606844, -29.2145641520619","82.5196304824203, 86.4081613719463, -63.7102510314435, 82.9122426919639, 86.7011873517185, 1.18320896290243, -7.11194663308561, 31.899410719052, -69.8009483516216, -57.3877718299627, 2.83222463913262, -17.2023222781718, -38.1276280619204","p",FALSE,-0.215982700387935
+"pcor.mat",12,"-36.3307877443731, 74.7712590266019, -79.8354192636907, 93.8916243612766, -50.0289557501674, -54.7662570606917, -38.7290718965232, -84.6648510545492, 71.2978508323431, 88.4917345829308, 32.1320930030197, 76.4375317841768","2.62319510802627, -97.154287295416, 21.7567156534642, 95.8672656677663, -0.763413123786449, -9.49476859532297, 83.9058271143585, -38.3374994620681, -16.9302582275122, -85.5358060449362, -83.2731044851243, -18.7622617464513","-76.2503104750067, 36.5515212062746, 48.5381714068353, 72.03823258169, 36.652302602306, 29.980877507478, 21.0754222236574, -96.8017131090164, -66.5918422862887, -10.5546385981143, 91.4459123276174, -84.6182245295495","k",TRUE,-0.161695275103975
+"pcor.mat",13,"65.4542396776378, 2.08085202611983, -88.9037624932826, 52.2526708897203, -38.4136536624283, -13.373964605853, -48.226101975888, 93.4839099645615, 39.563751546666, 69.0497747156769, -94.8003361001611, 24.9876524321735, 25.2306204754859","82.8150092158467, -9.80691406875849, -84.2437265440822, -48.5185658093542, 93.8153511844575, 76.507264515385, 96.1720596533269, 91.2560781463981, -86.2542728893459, -47.9744947515428, 25.5869822110981, -15.71259717457, 99.3623040616512","64.0070253051817, -90.157330268994, 78.4984151832759, 36.6091389209032, -85.7790939509869, -43.4162854682654, -81.61596711725, -7.2117717936635, 69.8510519228876, 87.7512107603252, 60.0242395885289, -77.6109307538718, 33.9131449349225","s",FALSE,0.0846468464958684
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diff --git a/tests/unit/computations/test_partial_correlations.py b/tests/unit/computations/test_partial_correlations.py
index f7217a9..c5c35d1 100644
--- a/tests/unit/computations/test_partial_correlations.py
+++ b/tests/unit/computations/test_partial_correlations.py
@@ -1,5 +1,6 @@
 """Module contains tests for gn3.partial_correlations"""
 
+import csv
 from unittest import TestCase
 from gn3.computations.partial_correlations import (
     fix_samples,
@@ -7,7 +8,9 @@ from gn3.computations.partial_correlations import (
     dictify_by_samples,
     tissue_correlation,
     find_identical_traits,
-    good_dataset_samples_indexes)
+    partial_correlation_matrix,
+    good_dataset_samples_indexes,
+    partial_correlation_recursive)
 
 sampleslist = ["B6cC3-1", "BXD1", "BXD12", "BXD16", "BXD19", "BXD2"]
 control_traits = (
@@ -90,6 +93,28 @@ dictified_control_samples = (
      "BXD1": {"sample_name": "BXD1", "value": 7.77141, "variance": None},
      "BXD2": {"sample_name": "BXD2", "value":  7.80944, "variance": None}})
 
+def parse_test_data_csv(filename):
+    """
+    Parse test data csv files for R -> Python conversion of some functions.
+    """
+    def __str__to_tuple(line, field):
+        return tuple(float(s.strip()) for s in line[field].split(","))
+
+    with open(filename, newline="\n") as csvfile:
+        reader = csv.DictReader(csvfile, delimiter=",", quotechar='"')
+        lines = tuple(row for row in reader)
+
+    methods = {"p": "pearson", "s": "spearman", "k": "kendall"}
+    return tuple({
+        **line,
+        "x": __str__to_tuple(line, "x"),
+        "y": __str__to_tuple(line, "y"),
+        "z": __str__to_tuple(line, "z"),
+        "method": methods[line["method"]],
+        "rm": line["rm"] == "TRUE",
+        "result": float(line["result"])
+    } for line in lines)
+
 class TestPartialCorrelations(TestCase):
     """Class for testing partial correlations computation functions"""
 
@@ -271,3 +296,37 @@ class TestPartialCorrelations(TestCase):
                 ("a", "e", "i", "k"),
                 ("a", "b", "c", "d", "e", "f", "g", "h", "i", "j", "k", "l")),
             (0, 4, 8, 10))
+
+    def test_partial_correlation_matrix(self):
+        """
+        Test that `partial_correlation_matrix` computes the appropriate
+        correlation value.
+        """
+        for sample in parse_test_data_csv(
+                ("tests/unit/computations/partial_correlations_test_data/"
+                 "pcor_mat_blackbox_test.csv")):
+            with self.subTest(
+                    xdata=sample["x"], ydata=sample["y"], zdata=sample["z"],
+                    method=sample["method"], omit_nones=sample["rm"]):
+                self.assertEqual(
+                    partial_correlation_matrix(
+                        sample["x"], sample["y"], sample["z"],
+                        method=sample["method"], omit_nones=sample["rm"]),
+                    sample["result"])
+
+    def test_partial_correlation_recursive(self):
+        """
+        Test that `partial_correlation_recursive` computes the appropriate
+        correlation value.
+        """
+        for sample in parse_test_data_csv(
+                ("tests/unit/computations/partial_correlations_test_data/"
+                 "pcor_rec_blackbox_test.csv")):
+            with self.subTest(
+                    xdata=sample["x"], ydata=sample["y"], zdata=sample["z"],
+                    method=sample["method"], omit_nones=sample["rm"]):
+                self.assertEqual(
+                    partial_correlation_recursive(
+                        sample["x"], sample["y"], sample["z"],
+                        method=sample["method"], omit_nones=sample["rm"]),
+                    sample["result"])