Implement pooled_cov and scaling as per matchit R package

Author: robinmessage

methods/matching/find_pairs.py

@@ -15,6 +15,15 @@
 
 logging.basicConfig(level=logging.INFO, format="%(asctime)s [%(levelname)s] %(message)s")
 
+# Implement similar logic to the R matchit package
+# https://github.com/kosukeimai/MatchIt/blob/7b7b48af995ad1d2ab83a72bad9a689bbb9e8151/R/aux_functions.R#L550
+def pooled_scaled_cov(treatment, control):
+    treatment -= np.mean(treatment, axis=0)
+    control -= np.mean(control, axis=0)
+    combined_scaled = np.concatenate([treatment, control])
+    size = len(combined_scaled)
+    return np.cov(combined_scaled, rowvar=False) * (size-1) / (size-2)
+
 def find_match_iteration(
     k_parquet_filename: str,
     s_parquet_filename: str,
@@ -52,10 +61,36 @@ def find_match_iteration(
     # As well as all the LUC columns for later use
     luc_columns = [x for x in s_set.columns if x.startswith('luc')]
 
-    s_subset_for_cov = s_subset[['elevation', 'slope', 'access', \
-        'cpc0_u', 'cpc0_d', 'cpc5_u', 'cpc5_d', 'cpc10_u', 'cpc10_d']]
-    covarience = np.cov(s_subset_for_cov, rowvar=False)
-    invconv = np.linalg.inv(covarience)
+    distance_columns = [
+        "elevation", "slope", "access",
+        "cpc0_u", "cpc0_d",
+        "cpc5_u", "cpc5_d",
+        "cpc10_u", "cpc10_d"
+    ]
+
+    s_subset_for_cov = s_subset[distance_columns]
+    k_subset_for_cov = k_subset[distance_columns]
+
+    logging.info("Scaling the columns...")
+
+    # Scale the columns as per matchit
+    # https://github.com/kosukeimai/MatchIt/blob/7b7b48af995ad1d2ab83a72bad9a689bbb9e8151/R/dist_functions.R#L240
+    combined = np.concatenate([s_subset_for_cov, k_subset_for_cov])
+    means = np.mean(combined, axis=0)
+    stds = np.std(combined - means, axis=0)
+    s_subset_for_cov -= means
+    s_subset_for_cov /= stds
+    k_subset_for_cov -= means
+    k_subset_for_cov /= stds
+
+    s_subset[distance_columns] = s_subset_for_cov
+    k_subset[distance_columns] = k_subset_for_cov
+
+    logging.info("Calculating pooled covariance...")
+    covariance = pooled_scaled_cov(k_subset_for_cov, s_subset_for_cov)
+
+    logging.info("Calculating inverse covariance...")
+    invconv = np.linalg.inv(covariance).astype(np.float32)
 
     for _, k_row in k_subset.iterrows():
         # Methodology 6.5.7: find the matches.
@@ -85,12 +120,6 @@ def find_match_iteration(
         #  * slope
         #  * accessibility
         #  * coarsened proportional coverage
-        distance_columns = [
-            "elevation", "slope", "access",
-            "cpc0_u", "cpc0_d",
-            "cpc5_u", "cpc5_d",
-            "cpc10_u", "cpc10_d"
-        ]
         k_soft =  np.array(k_row[distance_columns].to_list())
         just_cols = filtered_s[distance_columns].to_numpy()