Reimplement correlations2.compute_correlation using pearsonr.

correlations2.compute_correlation computes the Pearson correlation
coefficient. Outsource this computation to scipy.stats.pearsonr. When the
inputs are constant, the Pearson correlation coefficient does not exist and is
represented by NaN. Update the tests to reflect this.

* gn3/computations/correlations2.py: Remove import of sqrt from math.
(compute_correlation): Reimplement using scipy.stats.pearsonr.
* tests/unit/computations/test_correlation.py: Import math.
(TestCorrelation.test_compute_correlation): When inputs are constant, set
expected correlation coefficient to NaN.

1 files changed, 4 insertions, 17 deletions

diff --git a/gn3/computations/correlations2.py b/gn3/computations/correlations2.py
index 69921b1..d0222ae 100644
--- a/gn3/computations/correlations2.py
+++ b/gn3/computations/correlations2.py
@@ -6,7 +6,7 @@ FUNCTIONS:
 compute_correlation:
     TODO: Describe what the function does..."""
 
-from math import sqrt
+from scipy import stats
 ## From GN1: mostly for clustering and heatmap generation
 
 def __items_with_values(dbdata, userdata):
@@ -16,24 +16,11 @@ def __items_with_values(dbdata, userdata):
     return tuple(zip(*filtered)) if filtered else ([], [])
 
 def compute_correlation(dbdata, userdata):
-    """Compute some form of correlation.
+    """Compute the Pearson correlation coefficient.
 
     This is extracted from
     https://github.com/genenetwork/genenetwork1/blob/master/web/webqtl/utility/webqtlUtil.py#L622-L647
     """
     x_items, y_items = __items_with_values(dbdata, userdata)
-    if len(x_items) < 6:
-        return (0.0, len(x_items))
-    meanx = sum(x_items)/len(x_items)
-    meany = sum(y_items)/len(y_items)
-    def cal_corr_vals(acc, item):
-        xitem, yitem = item
-        return [
-            acc[0] + ((xitem - meanx) * (yitem - meany)),
-            acc[1] + ((xitem - meanx) * (xitem - meanx)),
-            acc[2] + ((yitem - meany) * (yitem - meany))]
-    xyd, sxd, syd = reduce(cal_corr_vals, zip(x_items, y_items), [0.0, 0.0, 0.0])
-    try:
-        return ((xyd/(sqrt(sxd)*sqrt(syd))), len(x_items))
-    except ZeroDivisionError:
-        return(0, len(x_items))
+    correlation = stats.pearsonr(x_items, y_items)[0] if len(x_items) >= 6 else 0
+    return (correlation, len(x_items))