def group_mean(data: pyspark.sql.DataFrame, groups, response, features):
means = scipy.zeros((len(groups), len(features)))
for i, target in enumerate(groups):
df_t = data.filter("{} == {}".format(response, target))
X_t = df_t.select(features).rdd.map(numpy.array)
means[i, :] = column_means(X_t)
return means
def within_group_scatter(data: pyspark.sql.DataFrame, features, response,
targets):
p = len(features)
sw = numpy.zeros((p, p))
for target in targets:
df_t = data.filter("{} == '{}'".format(response, target))
X_t = RowMatrix(df_t.select(features).rdd.map(numpy.array))
sw += X_t.computeCovariance().toArray() * (df_t.count() - 1)
return sw
