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client2.py
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import numpy as np
from fedps.channel import ClientChannel
from fedps.preprocessing import *
channel = ClientChannel(
local_ip="127.0.0.1",
local_port=5557,
remote_ip="127.0.0.1",
remote_port=5555,
)
""" KBinsDiscretizer """
# X = [[0, 3, -2, 0.5],
# [1, 4, -1, 2]]
# est = KBinsDiscretizer(
# FL_type="H",
# role="client",
# n_bins=3,
# encode="ordinal",
# strategy="uniform",
# channel=channel
# )
# Xt = est.fit_transform(X)
# print(Xt)
""" LabelBinarizer """
# y = [4, 2]
# est = LabelBinarizer(
# FL_type="H",
# role="client",
# channel=channel,
# )
# yt = est.fit_transform(y)
# print(yt)
""" LabelEncoder """
# y = [2, 6]
# est = LabelEncoder(
# FL_type="H",
# role="client",
# channel=channel,
# )
# yt = est.fit_transform(y)
# print(yt)
""" MultiLabelBinarizer """
# y = [(3,)]
# est = MultiLabelBinarizer(
# FL_type="H",
# role="client",
# channel=channel,
# )
# yt = est.fit_transform(y)
# print(yt)
""" OneHotEncoder """
# X = [["Female", 2]]
# est = OneHotEncoder(
# FL_type="H",
# role="client",
# sparse_output=False,
# channel=channel,
# )
# Xt = est.fit_transform(X)
# print(Xt)
""" OrdinalEncoder """
# X = [["Female", 2]]
# est = OrdinalEncoder(
# FL_type="H",
# role="client",
# channel=channel,
# )
# Xt = est.fit_transform(X)
# print(Xt)
""" TargetEncoder """
# X = np.array([["snake"] * 38], dtype=object).T
# y = [21.2] * 8 + [49] * 30
# est = TargetEncoder(
# FL_type="H",
# role="client",
# channel=channel,
# )
# Xt = est.fit_transform(X, y)
# print(Xt)
""" MaxAbsScaler """
# X = [[0., 1., -1.]]
# est = MaxAbsScaler(
# FL_type="H",
# role="client",
# channel=channel,
# )
# Xt = est.fit_transform(X)
# print(Xt)
""" MinMaxScaler """
X = [[0, 10], [1, 18]]
est = MinMaxScaler(
FL_type="H",
role="client",
channel=channel,
)
Xt = est.fit_transform(X)
print(Xt)
""" RobustScaler """
# X = [[4., 1., -2.]]
# est = RobustScaler(
# FL_type="H",
# role="client",
# channel=channel,
# )
# Xt = est.fit_transform(X)
# print(Xt)
""" StandardScaler """
# X = [[1, 1], [1, 1]]
# est = StandardScaler(
# FL_type="H",
# role="client",
# channel=channel,
# )
# Xt = est.fit_transform(X)
# print(Xt)
""" Normalizer """
# X = [[2, 2], [9, 3], [5, 1]]
# est = Normalizer(
# FL_type="V",
# role="client",
# channel=channel,
# )
# Xt = est.fit_transform(X)
# print(Xt)
""" PowerTransformer """
# X = [[4, 5]]
# est = PowerTransformer(
# FL_type="H",
# role="client",
# channel=channel,
# )
# Xt = est.fit_transform(X)
# print(Xt)
""" QuantileTransformer """
# rng = np.random.RandomState(1)
# X = rng.normal(loc=0.5, scale=0.25, size=(25, 1))
# est = QuantileTransformer(
# FL_type="H",
# role="client",
# n_quantiles=10,
# channel=channel,
# )
# Xt = est.fit_transform(X)
# print(Xt)
""" SplineTransformer """
# X = [[3], [4], [5]]
# est = SplineTransformer(
# FL_type="H",
# role="client",
# channel=channel,
# )
# Xt = est.fit_transform(X)
# print(Xt)
""" IterativeImputer (H) """
# X = [[np.nan, 6, 5], [8, 8, 7]]
# est = IterativeImputer(
# FL_type="H",
# role="client",
# channel=channel,
# )
# Xt = est.fit_transform(X)
# print(Xt)
""" IterativeImputer (V) """
# X = [[np.nan], [3], [5], [7]]
# est = IterativeImputer(
# FL_type="V",
# role="client",
# channel=channel,
# )
# Xt = est.fit_transform(X)
# print(Xt)
""" KNNImputer (H) """
# X = [[np.nan, 6, 5], [8, 8, 7]]
# est = KNNImputer(
# FL_type="H",
# role="client",
# n_neighbors=2,
# channel=channel,
# )
# Xt = est.fit_transform(X)
# print(Xt)
""" KNNImputer (V) """
# X = [[np.nan], [3], [5], [7]]
# est = KNNImputer(
# FL_type="V",
# role="client",
# n_neighbors=2,
# channel=channel,
# )
# Xt = est.fit_transform(X)
# print(Xt)
""" SimpleImputer """
# X = [[10, 5, np.nan]]
# est = SimpleImputer(
# FL_type="H",
# role="client",
# missing_values=np.nan,
# strategy="mean",
# channel=channel,
# )
# Xt = est.fit_transform(X)
# print(Xt)