Source code for CytOpT.main

# 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)