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Implement pooled_cov and scaling as per matchit R package #63

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Implement pooled_cov and scaling as per matchit R package #63

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49 changes: 39 additions & 10 deletions methods/matching/find_pairs.py
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Original file line number Diff line number Diff line change
Expand Up @@ -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,
Expand Down Expand Up @@ -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.
Expand Down Expand Up @@ -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()

Expand Down