# Copyright (C) 2022, Kalidou BA <kalidou.ia.mlds@gmail.com>=
#
# License: MIT (see COPYING file)
# !/usr/bin/env python
# coding: utf-8
import sys
from os import path
import numpy as np
import pandas as pd
from CytOpT.CytOpt import CytOpT
from tests.TwoClasses_TwoDimensions import TwoClassesTwoDimension
sys.path.append(path.dirname(path.dirname(path.abspath(__file__))))
data = {"X_source": np.asarray(pd.read_csv('../tests/data/W2_1_values.csv',
usecols=np.arange(1, 8))[['CD4', 'CD8']]),
'X_target': np.asarray(pd.read_csv('../tests/data/W2_7_values.csv',
usecols=np.arange(1, 8))[['CD4', 'CD8']]),
'X_sou_display': np.asarray(pd.read_csv('../tests/data/W2_1_values.csv',
usecols=np.arange(1, 8))[['CD4', 'CD8']]),
'Lab_source': np.asarray(pd.read_csv('../tests/data/W2_1_clust.csv',
usecols=[1])['x'] >= 6, dtype=int),
'Lab_target': np.asarray(pd.read_csv('../tests/data/W2_7_clust.csv',
usecols=[1])['x'] >= 6, dtype=int),
'X_tar_display': np.asarray(pd.read_csv('../tests/data/W2_7_values.csv',
usecols=np.arange(1, 8))[['CD4', 'CD8']]),
'names_pop': ['CD8', 'CD4']}
if __name__ == '__main__':
test1 = TwoClassesTwoDimension(data)
print(test1.__dict__)
print(test1.optimal_reweighted())
[docs]def main():
Stanford1A_values = pd.read_csv('../tests/data/W2_1_values.csv',
usecols=np.arange(1, 8))
Stanford1A_clust = pd.read_csv('../tests/data/W2_1_clust.csv',
usecols=[1])
# Target Data
Stanford3A_values = pd.read_csv('../tests/data/W2_7_values.csv',
usecols=np.arange(1, 8))
Stanford3A_clust = pd.read_csv('../tests/data/W2_7_clust.csv',
usecols=[1])
X_source = np.asarray(Stanford1A_values)
X_target = np.asarray(Stanford3A_values)
Lab_source = np.asarray(Stanford1A_clust['x'])
Lab_target = np.asarray(Stanford3A_clust['x'])
theta_true = np.zeros(10)
for k in range(10):
theta_true[k] = np.sum(Lab_target == k + 1) / len(Lab_target)
CytOpT(X_source, X_target, Lab_source, Lab_target=None, cell_type=None,
method="comparison_opt", theta_true=theta_true)