Merge pull request #56 from genenetwork/partial-correlations

Partial correlations

8 files changed, 1155 insertions, 94 deletions

diff --git a/gn3/computations/partial_correlations.py b/gn3/computations/partial_correlations.py
index 07dc16d..4f45159 100644
--- a/gn3/computations/partial_correlations.py
+++ b/gn3/computations/partial_correlations.py
@@ -5,10 +5,14 @@ It is an attempt to migrate over the partial correlations feature from
 GeneNetwork1.
 """
 
+import math
 from functools import reduce
-from typing import Any, Tuple, Sequence
+from typing import Any, Tuple, Union, Sequence
 from scipy.stats import pearsonr, spearmanr
 
+import pandas
+import pingouin
+
 from gn3.settings import TEXTDIR
 from gn3.data_helpers import parse_csv_line
 
@@ -152,7 +156,7 @@ def tissue_correlation(
         "Method must be one of: {}".format(",".join(method_fns.keys())))
 
     corr, pvalue = method_fns[method](primary_trait_values, target_trait_values)
-    return (round(corr, 10), round(pvalue, 10))
+    return (corr, pvalue)
 
 def batch_computed_tissue_correlation(
         primary_trait_values: Tuple[float, ...], target_traits_dict: dict,
@@ -245,7 +249,7 @@ def compute_partial_correlations_fast(# pylint: disable=[R0913, R0914]
     all_target_trait_names: Tuple[str, ...] = processed_trait_names_values[0]
     all_target_trait_values: Tuple[float, ...] = processed_trait_names_values[1]
 
-    all_correlations = determine_partials(
+    all_correlations = compute_partial(
         primary_vals, control_vals, all_target_trait_names,
         all_target_trait_values, method)
     ## Line 772 to 779 in GN1 are the cause of the weird complexity in the
@@ -258,32 +262,71 @@ def compute_partial_correlations_fast(# pylint: disable=[R0913, R0914]
             else fetched_correlations[corr[0]][0:2])
         for idx, corr in enumerate(all_correlations))
 
-def partial_correlation_matrix(
+def build_data_frame(
         xdata: Tuple[float, ...], ydata: Tuple[float, ...],
-        zdata: Tuple[float, ...], method: str = "pearsons",
-        omit_nones: bool = True) -> float:
+        zdata: Union[
+            Tuple[float, ...],
+            Tuple[Tuple[float, ...], ...]]) -> pandas.DataFrame:
     """
-    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.
+    Build a pandas DataFrame object from xdata, ydata and zdata
     """
-    return 0
-
-def partial_correlation_recursive(
-        xdata: Tuple[float, ...], ydata: Tuple[float, ...],
-        zdata: Tuple[float, ...], method: str = "pearsons",
-        omit_nones: bool = True) -> float:
+    x_y_df = pandas.DataFrame({"x": xdata, "y": ydata})
+    if isinstance(zdata[0], float):
+        return x_y_df.join(pandas.DataFrame({"z": zdata}))
+    interm_df = x_y_df.join(pandas.DataFrame(
+        {"z{}".format(i): val for i, val in enumerate(zdata)}))
+    if interm_df.shape[1] == 3:
+        return interm_df.rename(columns={"z0": "z"})
+    return interm_df
+
+def compute_partial(
+        primary_vals, control_vals, target_vals, target_names,
+        method: str) -> Tuple[
+            Union[
+                Tuple[str, int, float, float, float, float], None],
+            ...]:
     """
-    Computes the partial correlation coefficient using the 'recursive formula'
-    method
+    Compute the partial correlations.
 
-    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.
+    This is a re-implementation of the
+    `web.webqtl.correlation.correlationFunction.determinePartialsByR` function
+    in GeneNetwork1.
+
+    This implementation reworks the child function `compute_partial` which will
+    then be used in the place of `determinPartialsByR`.
+
+    TODO: moving forward, we might need to use the multiprocessing library to
+    speed up the computations, in case they are found to be slow.
     """
-    return 0
+    # replace the R code with `pingouin.partial_corr`
+    def __compute_trait_info__(target):
+        primary = [
+            prim for targ, prim in zip(target, primary_vals)
+            if targ is not None]
+        datafrm = build_data_frame(
+            primary,
+            [targ for targ in target if targ is not None],
+            [cont for i, cont in enumerate(control_vals)
+             if target[i] is not None])
+        covariates = "z" if datafrm.shape[1] == 3 else [
+            col for col in datafrm.columns if col not in ("x", "y")]
+        ppc = pingouin.partial_corr(
+            data=datafrm, x="x", y="y", covar=covariates, method=method)
+        pc_coeff = ppc["r"]
+
+        zero_order_corr = pingouin.corr(
+            datafrm["x"], datafrm["y"], method=method)
+
+        if math.isnan(pc_coeff):
+            return (
+                target[1], len(primary), pc_coeff, 1, zero_order_corr["r"],
+                zero_order_corr["p-val"])
+        return (
+            target[1], len(primary), pc_coeff,
+            (ppc["p-val"] if not math.isnan(ppc["p-val"]) else (
+                0 if (abs(pc_coeff - 1) < 0.0000001) else 1)),
+            zero_order_corr["r"], zero_order_corr["p-val"])
+
+    return tuple(
+        __compute_trait_info__(target)
+        for target in zip(target_vals, target_names))
diff --git a/gn3/data_helpers.py b/gn3/data_helpers.py
index d3f942b..b72fbc5 100644
--- a/gn3/data_helpers.py
+++ b/gn3/data_helpers.py
@@ -24,6 +24,21 @@ def partition_all(num: int, items: Sequence[Any]) -> Tuple[Tuple[Any, ...], ...]
         in reduce(
             __compute_start_stop__, iterations, tuple())])
 
+def partition_by(partition_fn, items):
+    """
+    Given a sequence `items`, return a tuple of tuples, each of which contain
+    the values in `items` partitioned such that the first item in each internal
+    tuple, when passed to `partition_function` returns True.
+
+    This is an approximation of Clojure's `partition-by` function.
+    """
+    def __partitioner__(accumulator, item):
+        if partition_fn(item):
+            return accumulator + ((item,),)
+        return accumulator[:-1] + (accumulator[-1] + (item,),)
+
+    return reduce(__partitioner__, items, tuple())
+
 def parse_csv_line(
         line: str, delimiter: str = ",",
         quoting: Optional[str] = '"') -> Tuple[str, ...]:
diff --git a/gn3/settings.py b/gn3/settings.py
index 0a6d03b..0ac6698 100644
--- a/gn3/settings.py
+++ b/gn3/settings.py
@@ -52,3 +52,5 @@ CORS_HEADERS = [
 
 GNSHARE = os.environ.get("GNSHARE", "/gnshare/gn/")
 TEXTDIR = f"{GNSHARE}/web/ProbeSetFreeze_DataMatrix"
+
+ROUND_TO = 10
diff --git a/requirements.txt b/requirements.txt
index 54c04a6..e268674 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -35,4 +35,4 @@ Werkzeug==1.0.1
 wrapt==1.12.1
 plotly==4.14.3
 flask-cors==3.0.9
-pingouin==0.3.12
+pingouin
diff --git a/tests/unit/computations/partial_correlations_test_data/pcor_rec_blackbox_test.txt b/tests/unit/computations/partial_correlations_test_data/pcor_rec_blackbox_test.txt
new file mode 100644
index 0000000..6254902
--- /dev/null
+++ b/tests/unit/computations/partial_correlations_test_data/pcor_rec_blackbox_test.txt
@@ -0,0 +1,1009 @@
+------------------------------------------------------------
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+------------------------------------------------------------
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diff --git a/tests/unit/computations/test_partial_correlations.py b/tests/unit/computations/test_partial_correlations.py
index 83cb9d9..3e1b6e1 100644
--- a/tests/unit/computations/test_partial_correlations.py
+++ b/tests/unit/computations/test_partial_correlations.py
@@ -1,16 +1,18 @@
 """Module contains tests for gn3.partial_correlations"""
 
-import csv
-from unittest import TestCase, skip
+from unittest import TestCase
+
+import pandas
+from numpy.testing import assert_allclose
+
 from gn3.computations.partial_correlations import (
     fix_samples,
     control_samples,
+    build_data_frame,
     dictify_by_samples,
     tissue_correlation,
     find_identical_traits,
-    partial_correlation_matrix,
-    good_dataset_samples_indexes,
-    partial_correlation_recursive)
+    good_dataset_samples_indexes)
 
 sampleslist = ["B6cC3-1", "BXD1", "BXD12", "BXD16", "BXD19", "BXD2"]
 control_traits = (
@@ -93,29 +95,6 @@ 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"""
 
@@ -272,7 +251,7 @@ class TestPartialCorrelations(TestCase):
                 with self.assertRaises(error, msg=error_msg):
                     tissue_correlation(primary, target, method)
 
-    def test_tissue_correlation(self):
+    def test_tissue_correlation(self): # pylint: disable=R0201
         """
         Test that the correct correlation values are computed for the given:
         - primary trait
@@ -281,11 +260,11 @@ class TestPartialCorrelations(TestCase):
         """
         for primary, target, method, expected in (
                 ((12.34, 18.36, 42.51), (37.25, 46.25, 46.56), "pearson",
-                 (0.6761779253, 0.5272701134)),
+                 (0.6761779252651052, 0.5272701133657985)),
                 ((1, 2, 3, 4, 5), (5, 6, 7, 8, 7), "spearman",
-                 (0.8207826817, 0.0885870053))):
+                 (0.8207826816681233, 0.08858700531354381))):
             with self.subTest(primary=primary, target=target, method=method):
-                self.assertEqual(
+                assert_allclose(
                     tissue_correlation(primary, target, method), expected)
 
     def test_good_dataset_samples_indexes(self):
@@ -298,38 +277,21 @@ class TestPartialCorrelations(TestCase):
                 ("a", "b", "c", "d", "e", "f", "g", "h", "i", "j", "k", "l")),
             (0, 4, 8, 10))
 
-    @skip
-    def test_partial_correlation_matrix(self):
+    def test_build_data_frame(self):
         """
-        Test that `partial_correlation_matrix` computes the appropriate
-        correlation value.
+        Check that the function builds the correct data frame.
         """
-        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"])
-
-    @skip
-    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"])
+        for xdata, ydata, zdata, expected in (
+                ((0.1, 1.1, 2.1), (2.1, 3.1, 4.1), (5.1, 6.1, 7.1),
+                 pandas.DataFrame({
+                     "x": (0.1, 1.1, 2.1), "y": (2.1, 3.1, 4.1),
+                     "z": (5.1, 6.1, 7.1)})),
+                ((0.1, 1.1, 2.1), (2.1, 3.1, 4.1),
+                 ((5.1, 6.1, 7.1), (5.2, 6.2, 7.2), (5.3, 6.3, 7.3)),
+                 pandas.DataFrame({
+                     "x": (0.1, 1.1, 2.1), "y": (2.1, 3.1, 4.1),
+                     "z0": (5.1, 6.1, 7.1), "z1": (5.2, 6.2, 7.2),
+                     "z2": (5.3, 6.3, 7.3)}))):
+            with self.subTest(xdata=xdata, ydata=ydata, zdata=zdata):
+                self.assertTrue(
+                    build_data_frame(xdata, ydata, zdata).equals(expected))
diff --git a/tests/unit/test_data_helpers.py b/tests/unit/test_data_helpers.py
index 39aea45..88ea469 100644
--- a/tests/unit/test_data_helpers.py
+++ b/tests/unit/test_data_helpers.py
@@ -4,7 +4,7 @@ Test functions in gn3.data_helpers
 
 from unittest import TestCase
 
-from gn3.data_helpers import partition_all, parse_csv_line
+from gn3.data_helpers import partition_by, partition_all, parse_csv_line
 
 class TestDataHelpers(TestCase):
     """
@@ -59,3 +59,31 @@ class TestDataHelpers(TestCase):
                     parse_csv_line(
                         line=line, delimiter=delimiter, quoting=quoting),
                     expected)
+
+    def test_partition_by(self):
+        """
+        Test that `partition_by` groups the data using the given predicate
+
+        Given:
+          - `part_fn`: a predicate funtion that return boolean True/False
+          - `items`: a sequence of items
+        When:
+          - the partitioning predicate function and the sequence of items are
+            passed to the `partition_by` function
+        Then:
+          - the result is a tuple, with sub-tuples containing the data in the
+            original sequence. Each sub-tuple is a partition, ending as soon as
+            the next value in the sequence, when passed to `part_fn`, returns
+            boolean `True`.
+        """
+        for part_fn, items, expected in (
+                (lambda s: s.startswith("----"),
+                 ("------", "a", "b", "-----", "c", "----", "d", "e", "---",
+                  "f"),
+                 (("------", "a", "b"), ("-----", "c"),
+                  ("----", "d", "e", "---", "f"))),
+                (lambda x: (x % 2) == 0,
+                 (0, 1, 3, 2, 4, 5, 7, 6, 9, 1),
+                 ((0, 1, 3), (2,), (4, 5, 7), (6, 9, 1))),):
+            with self.subTest(items=items):
+                self.assertEqual(partition_by(part_fn, items), expected)
diff --git a/tests/unit/test_heatmaps.py b/tests/unit/test_heatmaps.py
index e4c929d..a88341b 100644
--- a/tests/unit/test_heatmaps.py
+++ b/tests/unit/test_heatmaps.py
@@ -1,6 +1,8 @@
 """Module contains tests for gn3.heatmaps.heatmaps"""
 from unittest import TestCase
+
 from numpy.testing import assert_allclose
+
 from gn3.heatmaps import (
     cluster_traits,
     get_loci_names,
@@ -25,7 +27,7 @@ slinked = (
 class TestHeatmap(TestCase):
     """Class for testing heatmap computation functions"""
 
-    def test_cluster_traits(self):
+    def test_cluster_traits(self): # pylint: disable=R0201
         """
         Test that the clustering is working as expected.
         """