{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import torch\n", "import pandas as pd" ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Cell line: A375\n", " Split: 1\n", " Passed test\n", " Split: 2\n", " Passed test\n", " Split: 3\n", " Passed test\n", " Split: 4\n", " Passed test\n", " Split: 5\n", " Passed test\n", "Cell line: A549\n", " Split: 1\n", " Passed test\n", " Split: 2\n", " Passed test\n", " Split: 3\n", " Passed test\n", " Split: 4\n", " Passed test\n", " Split: 5\n", " Passed test\n", "Cell line: BT20\n", " Split: 1\n", " Passed test\n", " Split: 2\n", " Passed test\n", " Split: 3\n", " Passed test\n", " Split: 4\n", " Passed test\n", " Split: 5\n", " Passed test\n", "Cell line: HA1E\n", " Split: 1\n", " Passed test\n", " Split: 2\n", " Passed test\n", " Split: 3\n", " Passed test\n", " Split: 4\n", " Passed test\n", " Split: 5\n", " Passed test\n", "Cell line: HELA\n", " Split: 1\n", " Passed test\n", " Split: 2\n", " Passed test\n", " Split: 3\n", " Passed test\n", " Split: 4\n", " Passed test\n", " Split: 5\n", " Passed test\n", "Cell line: HT29\n", " Split: 1\n", " Passed test\n", " Split: 2\n", " Passed test\n", " Split: 3\n", " Passed test\n", " Split: 4\n", " Passed test\n", " Split: 5\n", " Passed test\n", "Cell line: MCF7\n", " Split: 1\n", " Passed test\n", " Split: 2\n", " Passed test\n", " Split: 3\n", " Passed test\n", " Split: 4\n", " Passed test\n", " Split: 5\n", " Passed test\n", "Cell line: MDAMB231\n", " Split: 1\n", " Passed test\n", " Split: 2\n", " Passed test\n", " Split: 3\n", " Passed test\n", " Split: 4\n", " Passed test\n", " Split: 5\n", " Passed test\n", "Cell line: PC3\n", " Split: 1\n", " Passed test\n", " Split: 2\n", " Passed test\n", " Split: 3\n", " Passed test\n", " Split: 4\n", " Passed test\n", " Split: 5\n", " Passed test\n", "Cell line: VCAP\n", " Split: 1\n", " Passed test\n", " Split: 2\n", " Passed test\n", " Split: 3\n", " Passed test\n", " Split: 4\n", " Passed test\n", " Split: 5\n", " Passed test\n" ] } ], "source": [ "##Chemical perturbations\n", "cell_line = ['A375', 'A549', 'BT20', 'HA1E', 'HELA', 'HT29', 'MCF7', 'MDAMB231', 'PC3', 'VCAP']\n", "\n", "for cl in cell_line:\n", " split = torch.load('processed/splits/chemical/{}/random/5fold/splits.pt'.format(cl))\n", "\n", " print('Cell line: {}'.format(cl))\n", "\n", " #Loads data\n", " b_data = torch.load('processed/torch_data/chemical/real_lognorm/data_backward_{}.pt'.format(cl))\n", " f_data = torch.load('processed/torch_data/chemical/real_lognorm/data_forward_{}.pt'.format(cl))\n", "\n", " for i in range(1, 6):\n", " print(' Split: {}'.format(i))\n", "\n", " error = False\n", " #Backward data\n", " d = b_data\n", " if len(d) != len(split[i]['train_index_backward']) + len(split[i]['val_index_backward']) + len(split[i]['test_index_backward']):\n", " print('length is not the same for splits: {}'.format(cl))\n", " error = True\n", " \n", " #Forward data\n", " d = f_data\n", " if len(d) == 0:\n", " if split[i]['train_index_forward'] is not None or split[i]['val_index_forward'] is not None or split[i]['test_index_forward'] is not None:\n", " print('split is not None when it should be (no forward data)')\n", " error = True\n", " else:\n", " if len(d) != len(split[i]['train_index_forward']) + len(split[i]['val_index_forward']) + len(split[i]['test_index_forward']):\n", " print('length is not the same for splits: {}'.format(cl))\n", " error = True\n", " \n", " if not error:\n", " print(' Passed test')\n", " else:\n", " print(' Failed test')\n", " \n", "\n", "\n", "\n" ] }, { "cell_type": "code", "execution_count": 27, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Cell line: A375\n", " Split: 1\n", " Passed test\n", " Split: 2\n", " Passed test\n", " Split: 3\n", " Passed test\n", " Split: 4\n", " Passed test\n", " Split: 5\n", " Passed test\n", "Cell line: A549\n", " Split: 1\n", " Passed test\n", " Split: 2\n", " Passed test\n", " Split: 3\n", " Passed test\n", " Split: 4\n", " Passed test\n", " Split: 5\n", " Passed test\n", "Cell line: AGS\n", " Split: 1\n", " Passed test\n", " Split: 2\n", " Passed test\n", " Split: 3\n", " Passed test\n", " Split: 4\n", " Passed test\n", " Split: 5\n", " Passed test\n", "Cell line: BICR6\n", " Split: 1\n", " Passed test\n", " Split: 2\n", " Passed test\n", " Split: 3\n", " Passed test\n", " Split: 4\n", " Passed test\n", " Split: 5\n", " Passed test\n", "Cell line: ES2\n", " Split: 1\n", " Passed test\n", " Split: 2\n", " Passed test\n", " Split: 3\n", " Passed test\n", " Split: 4\n", " Passed test\n", " Split: 5\n", " Passed test\n", "Cell line: HT29\n", " Split: 1\n", " Passed test\n", " Split: 2\n", " Passed test\n", " Split: 3\n", " Passed test\n", " Split: 4\n", " Passed test\n", " Split: 5\n", " Passed test\n", "Cell line: MCF7\n", " Split: 1\n", " Passed test\n", " Split: 2\n", " Passed test\n", " Split: 3\n", " Passed test\n", " Split: 4\n", " Passed test\n", " Split: 5\n", " Passed test\n", "Cell line: PC3\n", " Split: 1\n", " Passed test\n", " Split: 2\n", " Passed test\n", " Split: 3\n", " Passed test\n", " Split: 4\n", " Passed test\n", " Split: 5\n", " Passed test\n", "Cell line: U251MG\n", " Split: 1\n", " Passed test\n", " Split: 2\n", " Passed test\n", " Split: 3\n", " Passed test\n", " Split: 4\n", " Passed test\n", " Split: 5\n", " Passed test\n", "Cell line: YAPC\n", " Split: 1\n", " Passed test\n", " Split: 2\n", " Passed test\n", " Split: 3\n", " Passed test\n", " Split: 4\n", " Passed test\n", " Split: 5\n", " Passed test\n" ] } ], "source": [ "##Genetic perturbations\n", "cell_line = ['A375', 'A549', 'AGS', 'BICR6', 'ES2', 'HT29', 'MCF7', 'PC3', 'U251MG', 'YAPC']\n", "\n", "for cl in cell_line:\n", " split = torch.load('processed/splits/genetic/{}/random/5fold/splits.pt'.format(cl))\n", "\n", " print('Cell line: {}'.format(cl))\n", "\n", " #Loads data\n", " b_data = torch.load('processed/torch_data/real_lognorm/data_backward_{}.pt'.format(cl))\n", " f_data = torch.load('processed/torch_data/real_lognorm/data_forward_{}.pt'.format(cl))\n", "\n", " for i in range(1, 6):\n", " print(' Split: {}'.format(i))\n", "\n", " error = False\n", " #Backward data\n", " d = b_data\n", " if len(d) != len(split[i]['train_index_backward']) + len(split[i]['val_index_backward']) + len(split[i]['test_index_backward']):\n", " print('length is not the same for splits: {}'.format(cl))\n", " error = True\n", " \n", " #Forward data\n", " d = f_data\n", " if len(d) == 0:\n", " if split[i]['train_index_forward'] is not None or split[i]['val_index_forward'] is not None or split[i]['test_index_forward'] is not None:\n", " print('split is not None when it should be (no forward data)')\n", " error = True\n", " else:\n", " if len(d) != len(split[i]['train_index_forward']) + len(split[i]['val_index_forward']) + len(split[i]['test_index_forward']):\n", " print('length is not the same for splits: {}'.format(cl))\n", " error = True\n", " \n", " if not error:\n", " print(' Passed test')\n", " else:\n", " print(' Failed test')\n", " \n", "\n", "\n", "\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "myenv", "language": "python", "name": "myenv" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.8.12" } }, "nbformat": 4, "nbformat_minor": 2 }