def fit(self, data_instance):
# local
data = self._select_columns(data_instance)
n, normed = self._standardized(data)
self.local_corr = table_dot(normed, normed)
self.local_corr /= n
if self.model_param.cross_parties:
with SPDZ("pearson",
local_party=self.local_party,
all_parties=self.parties,
use_mix_rand=self.model_param.use_mix_rand) as spdz:
source = [normed, self.other_party]
if self.local_party.role == "guest":
x, y = FixedPointTensor.from_source(
"x", source[0]), FixedPointTensor.from_source(
"y", source[1])
else:
y, x = FixedPointTensor.from_source(
"y", source[0]), FixedPointTensor.from_source(
"x", source[1])
m1 = len(x.value.first()[1])
m2 = len(y.value.first()[1])
self.shapes.append(m1)
self.shapes.append(m2)
self.corr = spdz.dot(x, y, "corr").get() / n
else:
self.shapes.append(self.local_corr.shape[0])
self.parties = [self.local_party]
self._callback()
def fit(self, data_instance):
# local
data = self._select_columns(data_instance)
n, normed = self._standardized(data)
self.local_corr = table_dot(normed, normed)
self.local_corr /= n
self._summary["local_corr"] = self.local_corr.tolist()
self._summary["num_local_features"] = n
self.shapes.append(self.local_corr.shape[0])
return self._summary
def fit(self, data_instance):
# local
data = self._select_columns(data_instance)
n, normed = self._standardized(data)
self.local_corr = table_dot(normed, normed)
self.local_corr /= n
if self.model_param.calc_local_vif:
self.local_vif = self._vif_from_pearson_matrix(self.local_corr)
self._summary["local_corr"] = self.local_corr.tolist()
self._summary["num_local_features"] = n
if self.model_param.cross_parties:
with SPDZ(
"pearson",
local_party=self.local_party,
all_parties=self.parties,
use_mix_rand=self.model_param.use_mix_rand,
) as spdz:
source = [normed, self.other_party]
if self.local_party.role == "guest":
x, y = (
FixedPointTensor.from_source("x", source[0]),
FixedPointTensor.from_source("y", source[1]),
)
else:
y, x = (
FixedPointTensor.from_source("y", source[0]),
FixedPointTensor.from_source("x", source[1]),
)
m1 = len(x.value.first()[1])
m2 = len(y.value.first()[1])
self.shapes.append(m1)
self.shapes.append(m2)
self.corr = spdz.dot(x, y, "corr").get() / n
self._summary["corr"] = self.corr.tolist()
self._summary["num_remote_features"] = (
m2 if self.local_party.role == "guest" else m1)
else:
self.shapes.append(self.local_corr.shape[0])
self.parties = [self.local_party]
self._callback()
self.set_summary(self._summary)
def fit(self, data_instance):
data = self._select_columns(data_instance)
n, normed = self._standardized(data)
self.local_corr = table_dot(normed, normed)
with SPDZ("pearson") as spdz:
source = [normed, self._other_party]
if self._local_party.role == "guest":
x, y = FixedPointTensor.from_source(
"x",
source[0]), FixedPointTensor.from_source("y", source[1])
else:
y, x = FixedPointTensor.from_source(
"y",
source[0]), FixedPointTensor.from_source("x", source[1])
m1 = len(x.value.first()[1])
m2 = len(y.value.first()[1])
self.shapes.append(m1)
self.shapes.append(m2)
self.corr = spdz.dot(x, y, "corr").get() / n
self.local_corr /= n
self._callback()
