demo_reproducibility.ipynb

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    "## Demonstration of reproducibility and deployability of the computational workflow of the article \"Profiling the baseline performance and limits of machine learning models for adaptive immune receptor repertoire classification\"\n",
    "\n",
    "> In the original article (https://doi.org/10.1101/2021.05.23.445346), the findings were based on reading in ~ 2 TB of data and writing to disk ~ 10 TB of data. Note that the main purpose of this document is only to demonstrate the ease of re-usability and reproducibility of the containerized computational workflow of this article. Therefore, we feed a very small toy dataset to each of the analysis scripts tested in this document and reduced the number of cross-validation loops, iterations to reduce the running time for this demonstration. **Thus, the findings in this document will not make any sense - but should reflect the fact that the containerized computational workflow of this manuscript is well-functioning and hence re-usable, deployable across computational environments.**"
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    "### Code and data availability from original manuscript\n",
    "\n",
    "- Code from the original manuscript with permanent DOI: https://doi.org/10.11582/2021.00038\n",
    "- Containerized computational environment (Docker image on DockerHub): `kanduric/immuneml-v1:latest`\n",
    "- Input data (~ 2.1 TB) with permanent DOI: https://doi.org/10.11582/2021.00064\n",
    "\n",
    "> **Note:** To reproduce the findings of the manuscript, the code, input data, and the docker image provided above have to be used in a similar fashion as demonstrated below. The files in this github repository are modified versions of the code used in the manuscript (fed with a small dataset and reduced iterations in ML training only for the sake of demonstration) and cannot be used for replication of findings."
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    "### Organization of the analyses in this document for the demonstration of re-usability, reproducibility, and deployability\n",
    "\n",
    "- In the original manuscript (https://doi.org/10.1101/2021.05.23.445346), the findings were organized into 10 heatmaps. In each heatmap, one particular variable property of AIRR-ML model training setup was explored (y-axis of heatmaps) at different witness rates (x-axis). These analyses were specified using a configurable YAML specification files, where for each experiment only the properties that are of interest are varied while other parameters remained the same. **Therefore, it would be sufficient to test the re-usability, reproducibility and deployability of the computational environment by re-running and producing output from one cell of each heatmap.** In the original manuscript, to gauge uncertainty, we repeated each analysis 3 times on separate datasets. However, here we re-run each cell only one time.\n",
    "\n",
    "- Below, under each sub-heading (matching the descriptions of the manuscript), we re-run one cell of each heatmap on toy data (the output of which will make no sense as explained above). To check the reproducibility on your machine, clone this github repository and make sure that the **analyses** directory is empty before re-running the code snippets below either in a terminal or from this jupyter notebook. Note that jupyter notebook uses \"!\" symbol for running command-line commands. If you instead run these commands directly in a terminal, the \"!\" symbol has to be removed from the code snippets below.\n",
    "\n",
    "- The stdout and stderr of each analysis snippet are printed below each snippet whereas the output of each excution is redirected to respective analysis directories as indicated under each snippet. "
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    "#### Model sparsity (varying regularization rate)\n",
    "\n",
    "The stdout printed to screen when running the simulation and ML model training instructions are shown below indicating successful execution, and the resulting output of each instruction is stored under `analyses/olga/varying_regularization_rate/n_kmers_1_64/` in this github repository."
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     "text": [
      "Deleted Containers:\r\n",
      "a10ac3e2819004eca444a6d4d2ebc4ee2cd3b77c41a42b703348b436cde15178\r\n",
      "2d2ed2ce8454a85d0663ea3edd827d33ee2731010aecdb83a2b4b35acf2abf3a\r\n",
      "\r\n",
      "Total reclaimed space: 47.09kB\r\n",
      "2021-07-16 10:22:57.268717: Setting temporary cache path to wd/analyses/olga/varying_regularization_rate/n_kmers_1_64/simulated_data/repertoire_implanting_rate__0.002_-_motifs__list_length_64/cache/\r\n",
      "2021-07-16 10:22:57.268864: ImmuneML: parsing the specification...\r\n",
      "\r\n",
      "2021-07-16 10:23:02.596013: Full specification is available at wd/analyses/olga/varying_regularization_rate/n_kmers_1_64/simulated_data/repertoire_implanting_rate__0.002_-_motifs__list_length_64/full_repertoire_implanting_rate__0.yaml.\r\n",
      "\r\n",
      "2021-07-16 10:23:02.596103: ImmuneML: starting the analysis...\r\n",
      "\r\n",
      "2021-07-16 10:23:02.596155: Instruction 1/1 has started.\r\n",
      "2021-07-16 10:23:08.155653: Instruction 1/1 has finished.\r\n",
      "2021-07-16 10:23:08.161668: Generating HTML reports...\r\n",
      "2021-07-16 10:23:08.259841: HTML reports are generated.\r\n",
      "2021-07-16 10:23:08.261290: ImmuneML: finished analysis.\r\n",
      "\r\n",
      "2021-07-16 10:23:10.577429: Setting temporary cache path to wd/analyses/olga/varying_regularization_rate/n_kmers_1_64/ML_output/repertoire_implanting_rate__0.002_-_motifs__list_length_64_-_C__0.05/cache/\r\n",
      "2021-07-16 10:23:10.577502: ImmuneML: parsing the specification...\r\n",
      "\r\n",
      "2021-07-16 10:23:13.269279: Full specification is available at wd/analyses/olga/varying_regularization_rate/n_kmers_1_64/ML_output/repertoire_implanting_rate__0.002_-_motifs__list_length_64_-_C__0.05/full_repertoire_implanting_rate__0.yaml.\r\n",
      "\r\n",
      "2021-07-16 10:23:13.269461: ImmuneML: starting the analysis...\r\n",
      "\r\n",
      "2021-07-16 10:23:13.269590: Instruction 1/2 has started.\r\n",
      "2021-07-16 10:23:13.411202: Training ML model: running outer CV loop: started split 1/5.\r\n",
      "\r\n",
      "2021-07-16 10:23:13.454997: Hyperparameter optimization: running the inner loop of nested CV: selection for label signal_cancer (label 1 / 1).\r\n",
      "\r\n",
      "2021-07-16 10:23:13.460578: Evaluating hyperparameter setting: feature_size_4_lasso_rate...\r\n",
      "2021-07-16 10:23:13.471926: Encoding started...\r\n",
      "2021-07-16 10:23:17.490247: Encoding finished.\r\n",
      "2021-07-16 10:23:17.490562: ML model training started...\r\n",
      "2021-07-16 10:23:26.986851: ML model training finished.\r\n",
      "2021-07-16 10:23:26.986970: Completed hyperparameter setting feature_size_4_lasso_rate.\r\n",
      "\r\n",
      "2021-07-16 10:23:26.991255: Hyperparameter optimization: running the inner loop of nested CV: completed selection for label signal_cancer (label 1 / 1).\r\n",
      "\r\n",
      "2021-07-16 10:23:26.991387: Training ML model: running the inner loop of nested CV: retrain models for label signal_cancer (label 1 / 1).\r\n",
      "\r\n",
      "2021-07-16 10:23:26.998225: Evaluating hyperparameter setting: feature_size_4_lasso_rate...\r\n",
      "2021-07-16 10:23:27.009062: Encoding started...\r\n",
      "2021-07-16 10:23:29.700703: Encoding finished.\r\n",
      "2021-07-16 10:23:29.701005: ML model training started...\r\n",
      "2021-07-16 10:23:38.593952: ML model training finished.\r\n",
      "2021-07-16 10:23:38.596460: Encoding started...\r\n",
      "2021-07-16 10:23:40.068939: Encoding finished.\r\n",
      "2021-07-16 10:24:08.211463: Completed hyperparameter setting feature_size_4_lasso_rate.\r\n",
      "\r\n",
      "2021-07-16 10:24:08.215071: Training ML model: running the inner loop of nested CV: completed retraining models for label signal_cancer (label 1 / 1).\r\n",
      "\r\n",
      "2021-07-16 10:24:08.215162: Training ML model: running outer CV loop: finished split 1/5.\r\n",
      "\r\n",
      "2021-07-16 10:24:08.215202: Training ML model: running outer CV loop: started split 2/5.\r\n",
      "\r\n",
      "2021-07-16 10:24:08.257313: Hyperparameter optimization: running the inner loop of nested CV: selection for label signal_cancer (label 1 / 1).\r\n",
      "\r\n",
      "2021-07-16 10:24:08.262526: Evaluating hyperparameter setting: feature_size_4_lasso_rate...\r\n",
      "2021-07-16 10:24:08.274757: Encoding started...\r\n",
      "2021-07-16 10:24:11.279456: Encoding finished.\r\n",
      "2021-07-16 10:24:11.279890: ML model training started...\r\n",
      "2021-07-16 10:24:20.927994: ML model training finished.\r\n",
      "2021-07-16 10:24:20.928168: Completed hyperparameter setting feature_size_4_lasso_rate.\r\n",
      "\r\n",
      "2021-07-16 10:24:20.932678: Hyperparameter optimization: running the inner loop of nested CV: completed selection for label signal_cancer (label 1 / 1).\r\n",
      "\r\n",
      "2021-07-16 10:24:20.932768: Training ML model: running the inner loop of nested CV: retrain models for label signal_cancer (label 1 / 1).\r\n",
      "\r\n",
      "2021-07-16 10:24:20.940792: Evaluating hyperparameter setting: feature_size_4_lasso_rate...\r\n",
      "2021-07-16 10:24:20.952437: Encoding started...\r\n",
      "2021-07-16 10:24:23.904564: Encoding finished.\r\n",
      "2021-07-16 10:24:23.905068: ML model training started...\r\n",
      "2021-07-16 10:24:33.842602: ML model training finished.\r\n",
      "2021-07-16 10:24:33.846983: Encoding started...\r\n",
      "2021-07-16 10:24:35.677005: Encoding finished.\r\n",
      "2021-07-16 10:25:07.836291: Completed hyperparameter setting feature_size_4_lasso_rate.\r\n",
      "\r\n",
      "2021-07-16 10:25:07.839680: Training ML model: running the inner loop of nested CV: completed retraining models for label signal_cancer (label 1 / 1).\r\n",
      "\r\n",
      "2021-07-16 10:25:07.839773: Training ML model: running outer CV loop: finished split 2/5.\r\n",
      "\r\n",
      "2021-07-16 10:25:07.839815: Training ML model: running outer CV loop: started split 3/5.\r\n",
      "\r\n",
      "2021-07-16 10:25:07.885320: Hyperparameter optimization: running the inner loop of nested CV: selection for label signal_cancer (label 1 / 1).\r\n",
      "\r\n",
      "2021-07-16 10:25:07.892764: Evaluating hyperparameter setting: feature_size_4_lasso_rate...\r\n",
      "2021-07-16 10:25:07.902764: Encoding started...\r\n",
      "2021-07-16 10:25:10.597496: Encoding finished.\r\n",
      "2021-07-16 10:25:10.597938: ML model training started...\r\n",
      "2021-07-16 10:25:20.153114: ML model training finished.\r\n",
      "2021-07-16 10:25:20.153282: Completed hyperparameter setting feature_size_4_lasso_rate.\r\n",
      "\r\n",
      "2021-07-16 10:25:20.157035: Hyperparameter optimization: running the inner loop of nested CV: completed selection for label signal_cancer (label 1 / 1).\r\n",
      "\r\n",
      "2021-07-16 10:25:20.157151: Training ML model: running the inner loop of nested CV: retrain models for label signal_cancer (label 1 / 1).\r\n",
      "\r\n",
      "2021-07-16 10:25:20.163547: Evaluating hyperparameter setting: feature_size_4_lasso_rate...\r\n",
      "2021-07-16 10:25:20.176195: Encoding started...\r\n",
      "2021-07-16 10:25:25.957618: Encoding finished.\r\n",
      "2021-07-16 10:25:25.958656: ML model training started...\r\n",
      "2021-07-16 10:25:39.906233: ML model training finished.\r\n",
      "2021-07-16 10:25:39.910780: Encoding started...\r\n",
      "2021-07-16 10:25:42.536987: Encoding finished.\r\n",
      "2021-07-16 10:26:16.104868: Completed hyperparameter setting feature_size_4_lasso_rate.\r\n",
      "\r\n",
      "2021-07-16 10:26:16.108355: Training ML model: running the inner loop of nested CV: completed retraining models for label signal_cancer (label 1 / 1).\r\n",
      "\r\n",
      "2021-07-16 10:26:16.108448: Training ML model: running outer CV loop: finished split 3/5.\r\n",
      "\r\n",
      "2021-07-16 10:26:16.108487: Training ML model: running outer CV loop: started split 4/5.\r\n",
      "\r\n",
      "2021-07-16 10:26:16.150788: Hyperparameter optimization: running the inner loop of nested CV: selection for label signal_cancer (label 1 / 1).\r\n",
      "\r\n",
      "2021-07-16 10:26:16.155914: Evaluating hyperparameter setting: feature_size_4_lasso_rate...\r\n",
      "2021-07-16 10:26:16.165144: Encoding started...\r\n",
      "2021-07-16 10:26:18.841410: Encoding finished.\r\n",
      "2021-07-16 10:26:18.841913: ML model training started...\r\n",
      "2021-07-16 10:26:27.882402: ML model training finished.\r\n",
      "2021-07-16 10:26:27.882690: Completed hyperparameter setting feature_size_4_lasso_rate.\r\n",
      "\r\n",
      "2021-07-16 10:26:27.887448: Hyperparameter optimization: running the inner loop of nested CV: completed selection for label signal_cancer (label 1 / 1).\r\n",
      "\r\n",
      "2021-07-16 10:26:27.888083: Training ML model: running the inner loop of nested CV: retrain models for label signal_cancer (label 1 / 1).\r\n",
      "\r\n",
      "2021-07-16 10:26:27.893834: Evaluating hyperparameter setting: feature_size_4_lasso_rate...\r\n",
      "2021-07-16 10:26:27.903879: Encoding started...\r\n",
      "2021-07-16 10:26:30.696984: Encoding finished.\r\n",
      "2021-07-16 10:26:30.697348: ML model training started...\r\n",
      "2021-07-16 10:26:40.551204: ML model training finished.\r\n",
      "2021-07-16 10:26:40.554099: Encoding started...\r\n",
      "2021-07-16 10:26:42.139086: Encoding finished.\r\n",
      "2021-07-16 10:27:12.323233: Completed hyperparameter setting feature_size_4_lasso_rate.\r\n",
      "\r\n",
      "2021-07-16 10:27:12.334611: Training ML model: running the inner loop of nested CV: completed retraining models for label signal_cancer (label 1 / 1).\r\n",
      "\r\n",
      "2021-07-16 10:27:12.334724: Training ML model: running outer CV loop: finished split 4/5.\r\n",
      "\r\n",
      "2021-07-16 10:27:12.334742: Training ML model: running outer CV loop: started split 5/5.\r\n",
      "\r\n",
      "2021-07-16 10:27:12.383421: Hyperparameter optimization: running the inner loop of nested CV: selection for label signal_cancer (label 1 / 1).\r\n",
      "\r\n",
      "2021-07-16 10:27:12.390495: Evaluating hyperparameter setting: feature_size_4_lasso_rate...\r\n",
      "2021-07-16 10:27:12.401014: Encoding started...\r\n",
      "2021-07-16 10:27:15.257916: Encoding finished.\r\n",
      "2021-07-16 10:27:15.258868: ML model training started...\r\n",
      "2021-07-16 10:27:25.482438: ML model training finished.\r\n",
      "2021-07-16 10:27:25.482603: Completed hyperparameter setting feature_size_4_lasso_rate.\r\n",
      "\r\n",
      "2021-07-16 10:27:25.487253: Hyperparameter optimization: running the inner loop of nested CV: completed selection for label signal_cancer (label 1 / 1).\r\n",
      "\r\n",
      "2021-07-16 10:27:25.487350: Training ML model: running the inner loop of nested CV: retrain models for label signal_cancer (label 1 / 1).\r\n",
      "\r\n",
      "2021-07-16 10:27:25.496018: Evaluating hyperparameter setting: feature_size_4_lasso_rate...\r\n",
      "2021-07-16 10:27:25.510529: Encoding started...\r\n",
      "2021-07-16 10:27:28.934840: Encoding finished.\r\n",
      "2021-07-16 10:27:28.935218: ML model training started...\r\n",
      "2021-07-16 10:27:41.812662: ML model training finished.\r\n",
      "2021-07-16 10:27:41.816674: Encoding started...\r\n",
      "2021-07-16 10:27:44.053196: Encoding finished.\r\n",
      "2021-07-16 10:28:17.992512: Completed hyperparameter setting feature_size_4_lasso_rate.\r\n",
      "\r\n",
      "2021-07-16 10:28:17.996968: Training ML model: running the inner loop of nested CV: completed retraining models for label signal_cancer (label 1 / 1).\r\n",
      "\r\n",
      "2021-07-16 10:28:17.997076: Training ML model: running outer CV loop: finished split 5/5.\r\n",
      "\r\n",
      "Performances (accuracy) -----------------------------------------------\r\n",
      "\r\n",
      "\r\n",
      "Label: signal_cancer\r\n",
      "Performance (accuracy) per assessment split:\r\n",
      "Split 1: 0.35\r\n",
      "Split 2: 0.35\r\n",
      "Split 3: 0.45\r\n",
      "Split 4: 0.4\r\n",
      "Split 5: 0.3\r\n",
      "Average performance (accuracy): 0.37\r\n",
      "------------------------------\r\n",
      "2021-07-16 10:28:21.346726: Instruction 1/2 has finished.\r\n",
      "2021-07-16 10:28:21.346810: Instruction 2/2 has started.\r\n",
      "2021-07-16 10:28:21.346852: Started analysis signal_freq (1/1).\r\n",
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      "2021-07-16 10:28:22.974185: Encoding repertoire 61/200\r\n",
      "2021-07-16 10:28:22.997907: Encoding repertoire 62/200\r\n",
      "2021-07-16 10:28:23.024325: Encoding repertoire 63/200\r\n",
      "2021-07-16 10:28:23.055701: Encoding repertoire 64/200\r\n",
      "2021-07-16 10:28:23.084419: Encoding repertoire 65/200\r\n",
      "2021-07-16 10:28:23.117941: Encoding repertoire 66/200\r\n",
      "2021-07-16 10:28:23.148738: Encoding repertoire 67/200\r\n",
      "2021-07-16 10:28:23.183773: Encoding repertoire 68/200\r\n",
      "2021-07-16 10:28:23.217611: Encoding repertoire 69/200\r\n",
      "2021-07-16 10:28:23.245766: Encoding repertoire 70/200\r\n",
      "2021-07-16 10:28:23.278694: Encoding repertoire 71/200\r\n",
      "2021-07-16 10:28:23.311536: Encoding repertoire 72/200\r\n",
      "2021-07-16 10:28:23.341924: Encoding repertoire 73/200\r\n",
      "2021-07-16 10:28:23.373571: Encoding repertoire 74/200\r\n",
      "2021-07-16 10:28:23.402716: Encoding repertoire 75/200\r\n",
      "2021-07-16 10:28:23.433237: Encoding repertoire 76/200\r\n",
      "2021-07-16 10:28:23.469368: Encoding repertoire 77/200\r\n",
      "2021-07-16 10:28:23.501497: Encoding repertoire 78/200\r\n",
      "2021-07-16 10:28:23.534186: Encoding repertoire 79/200\r\n",
      "2021-07-16 10:28:23.564722: Encoding repertoire 80/200\r\n",
      "2021-07-16 10:28:23.599049: Encoding repertoire 81/200\r\n",
      "2021-07-16 10:28:23.631648: Encoding repertoire 82/200\r\n",
      "2021-07-16 10:28:23.665143: Encoding repertoire 83/200\r\n",
      "2021-07-16 10:28:23.701316: Encoding repertoire 84/200\r\n",
      "2021-07-16 10:28:23.734260: Encoding repertoire 85/200\r\n",
      "2021-07-16 10:28:23.766734: Encoding repertoire 86/200\r\n",
      "2021-07-16 10:28:23.800451: Encoding repertoire 87/200\r\n",
      "2021-07-16 10:28:23.839470: Encoding repertoire 88/200\r\n",
      "2021-07-16 10:28:23.873721: Encoding repertoire 89/200\r\n",
      "2021-07-16 10:28:23.913349: Encoding repertoire 90/200\r\n",
      "2021-07-16 10:28:23.951681: Encoding repertoire 91/200\r\n",
      "2021-07-16 10:28:23.983929: Encoding repertoire 92/200\r\n",
      "2021-07-16 10:28:24.010356: Encoding repertoire 93/200\r\n",
      "2021-07-16 10:28:24.037425: Encoding repertoire 94/200\r\n",
      "2021-07-16 10:28:24.065287: Encoding repertoire 95/200\r\n",
      "2021-07-16 10:28:24.091768: Encoding repertoire 96/200\r\n",
      "2021-07-16 10:28:24.122860: Encoding repertoire 97/200\r\n",
      "2021-07-16 10:28:24.149393: Encoding repertoire 98/200\r\n",
      "2021-07-16 10:28:24.172907: Encoding repertoire 99/200\r\n",
      "2021-07-16 10:28:24.200095: Encoding repertoire 100/200\r\n",
      "2021-07-16 10:28:24.226415: Encoding repertoire 101/200\r\n",
      "2021-07-16 10:28:24.250727: Encoding repertoire 102/200\r\n",
      "2021-07-16 10:28:24.276390: Encoding repertoire 103/200\r\n",
      "2021-07-16 10:28:24.307092: Encoding repertoire 104/200\r\n",
      "2021-07-16 10:28:24.343927: Encoding repertoire 105/200\r\n",
      "2021-07-16 10:28:24.372644: Encoding repertoire 106/200\r\n",
      "2021-07-16 10:28:24.398745: Encoding repertoire 107/200\r\n",
      "2021-07-16 10:28:24.423360: Encoding repertoire 108/200\r\n",
      "2021-07-16 10:28:24.451431: Encoding repertoire 109/200\r\n",
      "2021-07-16 10:28:24.476275: Encoding repertoire 110/200\r\n",
      "2021-07-16 10:28:24.503574: Encoding repertoire 111/200\r\n",
      "2021-07-16 10:28:24.532632: Encoding repertoire 112/200\r\n",
      "2021-07-16 10:28:24.558730: Encoding repertoire 113/200\r\n",
      "2021-07-16 10:28:24.583729: Encoding repertoire 114/200\r\n",
      "2021-07-16 10:28:24.610060: Encoding repertoire 115/200\r\n",
      "2021-07-16 10:28:24.634570: Encoding repertoire 116/200\r\n",
      "2021-07-16 10:28:24.659618: Encoding repertoire 117/200\r\n",
      "2021-07-16 10:28:24.687371: Encoding repertoire 118/200\r\n",
      "2021-07-16 10:28:24.717302: Encoding repertoire 119/200\r\n",
      "2021-07-16 10:28:24.747764: Encoding repertoire 120/200\r\n",
      "2021-07-16 10:28:24.772680: Encoding repertoire 121/200\r\n",
      "2021-07-16 10:28:24.803412: Encoding repertoire 122/200\r\n",
      "2021-07-16 10:28:24.831346: Encoding repertoire 123/200\r\n",
      "2021-07-16 10:28:24.857926: Encoding repertoire 124/200\r\n",
      "2021-07-16 10:28:24.883940: Encoding repertoire 125/200\r\n",
      "2021-07-16 10:28:24.908758: Encoding repertoire 126/200\r\n",
      "2021-07-16 10:28:24.934477: Encoding repertoire 127/200\r\n",
      "2021-07-16 10:28:24.963161: Encoding repertoire 128/200\r\n",
      "2021-07-16 10:28:24.988365: Encoding repertoire 129/200\r\n",
      "2021-07-16 10:28:25.012869: Encoding repertoire 130/200\r\n",
      "2021-07-16 10:28:25.042198: Encoding repertoire 131/200\r\n",
      "2021-07-16 10:28:25.068593: Encoding repertoire 132/200\r\n",
      "2021-07-16 10:28:25.093442: Encoding repertoire 133/200\r\n",
      "2021-07-16 10:28:25.120757: Encoding repertoire 134/200\r\n",
      "2021-07-16 10:28:25.160024: Encoding repertoire 135/200\r\n",
      "2021-07-16 10:28:25.210422: Encoding repertoire 136/200\r\n",
      "2021-07-16 10:28:25.244441: Encoding repertoire 137/200\r\n",
      "2021-07-16 10:28:25.276887: Encoding repertoire 138/200\r\n",
      "2021-07-16 10:28:25.312489: Encoding repertoire 139/200\r\n",
      "2021-07-16 10:28:25.353665: Encoding repertoire 140/200\r\n",
      "2021-07-16 10:28:25.387554: Encoding repertoire 141/200\r\n",
      "2021-07-16 10:28:25.420296: Encoding repertoire 142/200\r\n",
      "2021-07-16 10:28:25.454349: Encoding repertoire 143/200\r\n",
      "2021-07-16 10:28:25.481442: Encoding repertoire 144/200\r\n",
      "2021-07-16 10:28:25.506566: Encoding repertoire 145/200\r\n",
      "2021-07-16 10:28:25.532502: Encoding repertoire 146/200\r\n",
      "2021-07-16 10:28:25.559948: Encoding repertoire 147/200\r\n",
      "2021-07-16 10:28:25.585815: Encoding repertoire 148/200\r\n",
      "2021-07-16 10:28:25.615278: Encoding repertoire 149/200\r\n",
      "2021-07-16 10:28:25.639975: Encoding repertoire 150/200\r\n",
      "2021-07-16 10:28:25.665712: Encoding repertoire 151/200\r\n",
      "2021-07-16 10:28:25.690844: Encoding repertoire 152/200\r\n",
      "2021-07-16 10:28:25.718029: Encoding repertoire 153/200\r\n",
      "2021-07-16 10:28:25.743136: Encoding repertoire 154/200\r\n",
      "2021-07-16 10:28:25.771620: Encoding repertoire 155/200\r\n",
      "2021-07-16 10:28:25.799377: Encoding repertoire 156/200\r\n",
      "2021-07-16 10:28:25.825438: Encoding repertoire 157/200\r\n",
      "2021-07-16 10:28:25.850956: Encoding repertoire 158/200\r\n",
      "2021-07-16 10:28:25.879424: Encoding repertoire 159/200\r\n",
      "2021-07-16 10:28:25.904495: Encoding repertoire 160/200\r\n",
      "2021-07-16 10:28:25.928877: Encoding repertoire 161/200\r\n",
      "2021-07-16 10:28:25.955251: Encoding repertoire 162/200\r\n",
      "2021-07-16 10:28:25.983650: Encoding repertoire 163/200\r\n",
      "2021-07-16 10:28:26.009064: Encoding repertoire 164/200\r\n",
      "2021-07-16 10:28:26.036406: Encoding repertoire 165/200\r\n",
      "2021-07-16 10:28:26.063025: Encoding repertoire 166/200\r\n",
      "2021-07-16 10:28:26.088510: Encoding repertoire 167/200\r\n",
      "2021-07-16 10:28:26.113042: Encoding repertoire 168/200\r\n",
      "2021-07-16 10:28:26.137891: Encoding repertoire 169/200\r\n",
      "2021-07-16 10:28:26.162535: Encoding repertoire 170/200\r\n",
      "2021-07-16 10:28:26.192999: Encoding repertoire 171/200\r\n",
      "2021-07-16 10:28:26.223610: Encoding repertoire 172/200\r\n",
      "2021-07-16 10:28:26.249556: Encoding repertoire 173/200\r\n",
      "2021-07-16 10:28:26.276341: Encoding repertoire 174/200\r\n",
      "2021-07-16 10:28:26.301246: Encoding repertoire 175/200\r\n",
      "2021-07-16 10:28:26.327993: Encoding repertoire 176/200\r\n",
      "2021-07-16 10:28:26.354404: Encoding repertoire 177/200\r\n",
      "2021-07-16 10:28:26.383587: Encoding repertoire 178/200\r\n",
      "2021-07-16 10:28:26.409558: Encoding repertoire 179/200\r\n",
      "2021-07-16 10:28:26.435676: Encoding repertoire 180/200\r\n",
      "2021-07-16 10:28:26.462861: Encoding repertoire 181/200\r\n",
      "2021-07-16 10:28:26.487344: Encoding repertoire 182/200\r\n",
      "2021-07-16 10:28:26.512629: Encoding repertoire 183/200\r\n",
      "2021-07-16 10:28:26.537399: Encoding repertoire 184/200\r\n",
      "2021-07-16 10:28:26.563022: Encoding repertoire 185/200\r\n",
      "2021-07-16 10:28:26.650659: Encoding repertoire 186/200\r\n",
      "2021-07-16 10:28:26.756155: Encoding repertoire 187/200\r\n",
      "2021-07-16 10:28:26.853896: Encoding repertoire 188/200\r\n",
      "2021-07-16 10:28:26.889046: Encoding repertoire 189/200\r\n",
      "2021-07-16 10:28:26.946549: Encoding repertoire 190/200\r\n",
      "2021-07-16 10:28:26.973074: Encoding repertoire 191/200\r\n",
      "2021-07-16 10:28:26.998411: Encoding repertoire 192/200\r\n",
      "2021-07-16 10:28:27.028548: Encoding repertoire 193/200\r\n",
      "2021-07-16 10:28:27.056368: Encoding repertoire 194/200\r\n",
      "2021-07-16 10:28:27.086384: Encoding repertoire 195/200\r\n",
      "2021-07-16 10:28:27.112871: Encoding repertoire 196/200\r\n",
      "2021-07-16 10:28:27.149095: Encoding repertoire 197/200\r\n",
      "2021-07-16 10:28:27.179652: Encoding repertoire 198/200\r\n",
      "2021-07-16 10:28:27.206466: Encoding repertoire 199/200\r\n",
      "2021-07-16 10:28:27.237305: Encoding repertoire 200/200\r\n",
      "2021-07-16 10:28:27.331825: Saving encoded dataset to disk.\r\n",
      "2021-07-16 10:28:31.133512: Encoding finished.\r\n",
      "2021-07-16 10:28:33.260037: Finished analysis signal_freq (1/1).\r\n",
      "\r\n",
      "2021-07-16 10:28:33.260139: Instruction 2/2 has finished.\r\n",
      "2021-07-16 10:28:33.270734: Generating HTML reports...\r\n",
      "2021-07-16 10:28:35.782422: HTML reports are generated.\r\n",
      "2021-07-16 10:28:37.026057: ImmuneML: finished analysis.\r\n",
      "\r\n"
     ]
    }
   ],
   "source": [
    "!mkdir -p analyses/olga/varying_regularization_rate/n_kmers_1_64/{simulated_data,ML_output}\n",
    "!docker container prune -f\n",
    "!docker run -it -v $(pwd):/wd --name simulation_container kanduric/immuneml-v1:latest immune-ml /wd/scripts/olga/run_1/varying_regularization_rate/n_kmers_1_64/generated_yaml_files/simulation_instructions/repertoire_implanting_rate__0.002_-_motifs__list_length_64.yaml /wd/analyses/olga/varying_regularization_rate/n_kmers_1_64/simulated_data/repertoire_implanting_rate__0.002_-_motifs__list_length_64\n",
    "!docker run -it -v $(pwd):/wd --name ml_container kanduric/immuneml-v1:latest immune-ml /wd/scripts/olga/run_1/varying_regularization_rate/n_kmers_1_64/generated_yaml_files/ml_instructions/repertoire_implanting_rate__0.002_-_motifs__list_length_64_-_C__0.05.yaml /wd/analyses/olga/varying_regularization_rate/n_kmers_1_64/ML_output/repertoire_implanting_rate__0.002_-_motifs__list_length_64_-_C__0.05\n"
   ]
  },
  {
   "cell_type": "markdown",
   "source": [
    "#### Sample size\n",
    "\n",
    "The stdout printed to screen when running the simulation and ML model training instructions are shown below indicating successful execution, and the resulting output of each instruction is stored under `analyses/olga/varying_sample_size/sample_size_100/` in this github repository."
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Deleted Containers:\r\n",
      "10ba16f12577bb59041ea8f45a339bb7f9bf69c5a34f623d50fd16c1c020b111\r\n",
      "0beca4600c61f14a7a8db0f8468a1453c362b8b52b6e7f12be54dc5747a78c1e\r\n",
      "\r\n",
      "Total reclaimed space: 0B\r\n",
      "2021-07-16 09:25:39.956716: Setting temporary cache path to wd/analyses/olga/varying_sample_size/sample_size_100/simulated_data/repertoire_implanting_rate__0.002/cache/\r\n",
      "2021-07-16 09:25:39.956902: ImmuneML: parsing the specification...\r\n",
      "\r\n",
      "2021-07-16 09:25:44.839742: Full specification is available at wd/analyses/olga/varying_sample_size/sample_size_100/simulated_data/repertoire_implanting_rate__0.002/full_repertoire_implanting_rate__0.yaml.\r\n",
      "\r\n",
      "2021-07-16 09:25:44.839936: ImmuneML: starting the analysis...\r\n",
      "\r\n",
      "2021-07-16 09:25:44.840263: Instruction 1/1 has started.\r\n",
      "2021-07-16 09:25:51.693237: Instruction 1/1 has finished.\r\n",
      "2021-07-16 09:25:51.699871: Generating HTML reports...\r\n",
      "2021-07-16 09:25:51.793676: HTML reports are generated.\r\n",
      "2021-07-16 09:25:51.795248: ImmuneML: finished analysis.\r\n",
      "\r\n",
      "2021-07-16 09:25:53.913762: Setting temporary cache path to wd/analyses/olga/varying_sample_size/sample_size_100/ML_output/repertoire_implanting_rate__0.002/cache/\r\n",
      "2021-07-16 09:25:53.913997: ImmuneML: parsing the specification...\r\n",
      "\r\n",
      "2021-07-16 09:25:56.605898: Full specification is available at wd/analyses/olga/varying_sample_size/sample_size_100/ML_output/repertoire_implanting_rate__0.002/full_repertoire_implanting_rate__0.yaml.\r\n",
      "\r\n",
      "2021-07-16 09:25:56.605996: ImmuneML: starting the analysis...\r\n",
      "\r\n",
      "2021-07-16 09:25:56.606122: Instruction 1/2 has started.\r\n",
      "2021-07-16 09:25:56.759926: Training ML model: running outer CV loop: started split 1/5.\r\n",
      "\r\n",
      "2021-07-16 09:25:56.802094: Hyperparameter optimization: running the inner loop of nested CV: selection for label signal_cancer (label 1 / 1).\r\n",
      "\r\n",
      "2021-07-16 09:25:56.808247: Evaluating hyperparameter setting: feature_size_4_logistic_regression...\r\n",
      "2021-07-16 09:25:56.818523: Encoding started...\r\n",
      "2021-07-16 09:26:01.233906: Encoding finished.\r\n",
      "2021-07-16 09:26:01.234379: ML model training started...\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "2021-07-16 09:26:33.044091: ML model training finished.\r\n",
      "2021-07-16 09:26:33.044559: Completed hyperparameter setting feature_size_4_logistic_regression.\r\n",
      "\r\n",
      "2021-07-16 09:26:33.050015: Hyperparameter optimization: running the inner loop of nested CV: completed selection for label signal_cancer (label 1 / 1).\r\n",
      "\r\n",
      "2021-07-16 09:26:33.050196: Training ML model: running the inner loop of nested CV: retrain models for label signal_cancer (label 1 / 1).\r\n",
      "\r\n",
      "2021-07-16 09:26:33.056630: Evaluating hyperparameter setting: feature_size_4_logistic_regression...\r\n",
      "2021-07-16 09:26:33.068571: Encoding started...\r\n",
      "2021-07-16 09:26:35.822887: Encoding finished.\r\n",
      "2021-07-16 09:26:35.823186: ML model training started...\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "2021-07-16 09:27:04.748527: ML model training finished.\r\n",
      "2021-07-16 09:27:04.751829: Encoding started...\r\n",
      "2021-07-16 09:27:06.557696: Encoding finished.\r\n",
      "2021-07-16 09:27:09.095842: Completed hyperparameter setting feature_size_4_logistic_regression.\r\n",
      "\r\n",
      "2021-07-16 09:27:09.099308: Training ML model: running the inner loop of nested CV: completed retraining models for label signal_cancer (label 1 / 1).\r\n",
      "\r\n",
      "2021-07-16 09:27:09.099394: Training ML model: running outer CV loop: finished split 1/5.\r\n",
      "\r\n",
      "2021-07-16 09:27:09.099490: Training ML model: running outer CV loop: started split 2/5.\r\n",
      "\r\n",
      "2021-07-16 09:27:09.145020: Hyperparameter optimization: running the inner loop of nested CV: selection for label signal_cancer (label 1 / 1).\r\n",
      "\r\n",
      "2021-07-16 09:27:09.149783: Evaluating hyperparameter setting: feature_size_4_logistic_regression...\r\n",
      "2021-07-16 09:27:09.158609: Encoding started...\r\n",
      "2021-07-16 09:27:12.521597: Encoding finished.\r\n",
      "2021-07-16 09:27:12.522083: ML model training started...\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "2021-07-16 09:27:46.883065: ML model training finished.\r\n",
      "2021-07-16 09:27:46.883323: Completed hyperparameter setting feature_size_4_logistic_regression.\r\n",
      "\r\n",
      "2021-07-16 09:27:46.887487: Hyperparameter optimization: running the inner loop of nested CV: completed selection for label signal_cancer (label 1 / 1).\r\n",
      "\r\n",
      "2021-07-16 09:27:46.887619: Training ML model: running the inner loop of nested CV: retrain models for label signal_cancer (label 1 / 1).\r\n",
      "\r\n",
      "2021-07-16 09:27:46.894880: Evaluating hyperparameter setting: feature_size_4_logistic_regression...\r\n",
      "2021-07-16 09:27:46.904906: Encoding started...\r\n",
      "2021-07-16 09:27:49.796600: Encoding finished.\r\n",
      "2021-07-16 09:27:49.796967: ML model training started...\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "2021-07-16 09:28:12.900441: ML model training finished.\r\n",
      "2021-07-16 09:28:12.906733: Encoding started...\r\n",
      "2021-07-16 09:28:14.581787: Encoding finished.\r\n",
      "2021-07-16 09:28:16.520133: Completed hyperparameter setting feature_size_4_logistic_regression.\r\n",
      "\r\n",
      "2021-07-16 09:28:16.524070: Training ML model: running the inner loop of nested CV: completed retraining models for label signal_cancer (label 1 / 1).\r\n",
      "\r\n",
      "2021-07-16 09:28:16.524212: Training ML model: running outer CV loop: finished split 2/5.\r\n",
      "\r\n",
      "2021-07-16 09:28:16.524310: Training ML model: running outer CV loop: started split 3/5.\r\n",
      "\r\n",
      "2021-07-16 09:28:16.573854: Hyperparameter optimization: running the inner loop of nested CV: selection for label signal_cancer (label 1 / 1).\r\n",
      "\r\n",
      "2021-07-16 09:28:16.581123: Evaluating hyperparameter setting: feature_size_4_logistic_regression...\r\n",
      "2021-07-16 09:28:16.596675: Encoding started...\r\n",
      "2021-07-16 09:28:19.611618: Encoding finished.\r\n",
      "2021-07-16 09:28:19.611989: ML model training started...\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "2021-07-16 09:28:52.394526: ML model training finished.\r\n",
      "2021-07-16 09:28:52.394761: Completed hyperparameter setting feature_size_4_logistic_regression.\r\n",
      "\r\n",
      "2021-07-16 09:28:52.400159: Hyperparameter optimization: running the inner loop of nested CV: completed selection for label signal_cancer (label 1 / 1).\r\n",
      "\r\n",
      "2021-07-16 09:28:52.400304: Training ML model: running the inner loop of nested CV: retrain models for label signal_cancer (label 1 / 1).\r\n",
      "\r\n",
      "2021-07-16 09:28:52.406942: Evaluating hyperparameter setting: feature_size_4_logistic_regression...\r\n",
      "2021-07-16 09:28:52.422522: Encoding started...\r\n",
      "2021-07-16 09:28:55.667617: Encoding finished.\r\n",
      "2021-07-16 09:28:55.667963: ML model training started...\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "2021-07-16 09:29:31.171024: ML model training finished.\r\n",
      "2021-07-16 09:29:31.174406: Encoding started...\r\n",
      "2021-07-16 09:29:33.124292: Encoding finished.\r\n",
      "2021-07-16 09:29:35.575324: Completed hyperparameter setting feature_size_4_logistic_regression.\r\n",
      "\r\n",
      "2021-07-16 09:29:35.580312: Training ML model: running the inner loop of nested CV: completed retraining models for label signal_cancer (label 1 / 1).\r\n",
      "\r\n",
      "2021-07-16 09:29:35.580425: Training ML model: running outer CV loop: finished split 3/5.\r\n",
      "\r\n",
      "2021-07-16 09:29:35.580472: Training ML model: running outer CV loop: started split 4/5.\r\n",
      "\r\n",
      "2021-07-16 09:29:35.639754: Hyperparameter optimization: running the inner loop of nested CV: selection for label signal_cancer (label 1 / 1).\r\n",
      "\r\n",
      "2021-07-16 09:29:35.647989: Evaluating hyperparameter setting: feature_size_4_logistic_regression...\r\n",
      "2021-07-16 09:29:35.660702: Encoding started...\r\n",
      "2021-07-16 09:29:38.875548: Encoding finished.\r\n",
      "2021-07-16 09:29:38.876145: ML model training started...\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "2021-07-16 09:30:14.665742: ML model training finished.\r\n",
      "2021-07-16 09:30:14.666584: Completed hyperparameter setting feature_size_4_logistic_regression.\r\n",
      "\r\n",
      "2021-07-16 09:30:14.672112: Hyperparameter optimization: running the inner loop of nested CV: completed selection for label signal_cancer (label 1 / 1).\r\n",
      "\r\n",
      "2021-07-16 09:30:14.672495: Training ML model: running the inner loop of nested CV: retrain models for label signal_cancer (label 1 / 1).\r\n",
      "\r\n",
      "2021-07-16 09:30:14.682564: Evaluating hyperparameter setting: feature_size_4_logistic_regression...\r\n",
      "2021-07-16 09:30:14.696818: Encoding started...\r\n",
      "2021-07-16 09:30:18.501457: Encoding finished.\r\n",
      "2021-07-16 09:30:18.501838: ML model training started...\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "2021-07-16 09:30:52.975161: ML model training finished.\r\n",
      "2021-07-16 09:30:52.979985: Encoding started...\r\n",
      "2021-07-16 09:30:54.914133: Encoding finished.\r\n",
      "2021-07-16 09:30:57.120583: Completed hyperparameter setting feature_size_4_logistic_regression.\r\n",
      "\r\n",
      "2021-07-16 09:30:57.124827: Training ML model: running the inner loop of nested CV: completed retraining models for label signal_cancer (label 1 / 1).\r\n",
      "\r\n",
      "2021-07-16 09:30:57.124970: Training ML model: running outer CV loop: finished split 4/5.\r\n",
      "\r\n",
      "2021-07-16 09:30:57.125011: Training ML model: running outer CV loop: started split 5/5.\r\n",
      "\r\n",
      "2021-07-16 09:30:57.177293: Hyperparameter optimization: running the inner loop of nested CV: selection for label signal_cancer (label 1 / 1).\r\n",
      "\r\n",
      "2021-07-16 09:30:57.184889: Evaluating hyperparameter setting: feature_size_4_logistic_regression...\r\n",
      "2021-07-16 09:30:57.199683: Encoding started...\r\n",
      "2021-07-16 09:31:00.375353: Encoding finished.\r\n",
      "2021-07-16 09:31:00.376109: ML model training started...\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "2021-07-16 09:31:32.393985: ML model training finished.\r\n",
      "2021-07-16 09:31:32.394135: Completed hyperparameter setting feature_size_4_logistic_regression.\r\n",
      "\r\n",
      "2021-07-16 09:31:32.398552: Hyperparameter optimization: running the inner loop of nested CV: completed selection for label signal_cancer (label 1 / 1).\r\n",
      "\r\n",
      "2021-07-16 09:31:32.398639: Training ML model: running the inner loop of nested CV: retrain models for label signal_cancer (label 1 / 1).\r\n",
      "\r\n",
      "2021-07-16 09:31:32.404735: Evaluating hyperparameter setting: feature_size_4_logistic_regression...\r\n",
      "2021-07-16 09:31:32.415203: Encoding started...\r\n",
      "2021-07-16 09:31:35.367028: Encoding finished.\r\n",
      "2021-07-16 09:31:35.367404: ML model training started...\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "2021-07-16 09:32:01.957153: ML model training finished.\r\n",
      "2021-07-16 09:32:01.965572: Encoding started...\r\n",
      "2021-07-16 09:32:03.879197: Encoding finished.\r\n",
      "2021-07-16 09:32:05.809996: Completed hyperparameter setting feature_size_4_logistic_regression.\r\n",
      "\r\n",
      "2021-07-16 09:32:05.817213: Training ML model: running the inner loop of nested CV: completed retraining models for label signal_cancer (label 1 / 1).\r\n",
      "\r\n",
      "2021-07-16 09:32:05.817346: Training ML model: running outer CV loop: finished split 5/5.\r\n",
      "\r\n",
      "Performances (accuracy) -----------------------------------------------\r\n",
      "\r\n",
      "\r\n",
      "Label: signal_cancer\r\n",
      "Performance (accuracy) per assessment split:\r\n",
      "Split 1: 0.55\r\n",
      "Split 2: 0.475\r\n",
      "Split 3: 0.525\r\n",
      "Split 4: 0.6\r\n",
      "Split 5: 0.45\r\n",
      "Average performance (accuracy): 0.52\r\n",
      "------------------------------\r\n",
      "2021-07-16 09:32:09.688650: Instruction 1/2 has finished.\r\n",
      "2021-07-16 09:32:09.688741: Instruction 2/2 has started.\r\n",
      "2021-07-16 09:32:09.688834: Started analysis signal_freq (1/1).\r\n",
      "2021-07-16 09:32:09.695920: Encoding started...\r\n",
      "2021-07-16 09:32:09.707574: Encoding repertoire 1/200\r\n",
      "2021-07-16 09:32:09.792495: Encoding repertoire 2/200\r\n",
      "2021-07-16 09:32:09.799633: Encoding repertoire 3/200\r\n",
      "2021-07-16 09:32:09.806096: Encoding repertoire 4/200\r\n",
      "2021-07-16 09:32:09.812504: Encoding repertoire 5/200\r\n",
      "2021-07-16 09:32:09.817936: Encoding repertoire 6/200\r\n",
      "2021-07-16 09:32:09.823297: Encoding repertoire 7/200\r\n",
      "2021-07-16 09:32:09.828707: Encoding repertoire 8/200\r\n",
      "2021-07-16 09:32:09.834408: Encoding repertoire 9/200\r\n",
      "2021-07-16 09:32:09.840758: Encoding repertoire 10/200\r\n",
      "2021-07-16 09:32:09.846171: Encoding repertoire 11/200\r\n",
      "2021-07-16 09:32:09.851692: Encoding repertoire 12/200\r\n",
      "2021-07-16 09:32:09.856788: Encoding repertoire 13/200\r\n",
      "2021-07-16 09:32:09.862422: Encoding repertoire 14/200\r\n",
      "2021-07-16 09:32:09.867547: Encoding repertoire 15/200\r\n",
      "2021-07-16 09:32:09.873516: Encoding repertoire 16/200\r\n",
      "2021-07-16 09:32:09.879455: Encoding repertoire 17/200\r\n",
      "2021-07-16 09:32:09.885221: Encoding repertoire 18/200\r\n",
      "2021-07-16 09:32:09.891164: Encoding repertoire 19/200\r\n",
      "2021-07-16 09:32:09.896213: Encoding repertoire 20/200\r\n",
      "2021-07-16 09:32:09.902038: Encoding repertoire 21/200\r\n",
      "2021-07-16 09:32:09.907587: Encoding repertoire 22/200\r\n",
      "2021-07-16 09:32:09.912751: Encoding repertoire 23/200\r\n",
      "2021-07-16 09:32:09.918149: Encoding repertoire 24/200\r\n",
      "2021-07-16 09:32:09.924541: Encoding repertoire 25/200\r\n",
      "2021-07-16 09:32:09.930162: Encoding repertoire 26/200\r\n",
      "2021-07-16 09:32:09.935895: Encoding repertoire 27/200\r\n",
      "2021-07-16 09:32:09.942181: Encoding repertoire 28/200\r\n",
      "2021-07-16 09:32:09.947808: Encoding repertoire 29/200\r\n",
      "2021-07-16 09:32:09.952922: Encoding repertoire 30/200\r\n",
      "2021-07-16 09:32:09.958557: Encoding repertoire 31/200\r\n",
      "2021-07-16 09:32:09.964037: Encoding repertoire 32/200\r\n",
      "2021-07-16 09:32:09.969327: Encoding repertoire 33/200\r\n",
      "2021-07-16 09:32:09.974946: Encoding repertoire 34/200\r\n",
      "2021-07-16 09:32:09.980595: Encoding repertoire 35/200\r\n",
      "2021-07-16 09:32:09.986518: Encoding repertoire 36/200\r\n",
      "2021-07-16 09:32:09.992807: Encoding repertoire 37/200\r\n",
      "2021-07-16 09:32:09.998508: Encoding repertoire 38/200\r\n",
      "2021-07-16 09:32:10.003763: Encoding repertoire 39/200\r\n",
      "2021-07-16 09:32:10.009737: Encoding repertoire 40/200\r\n",
      "2021-07-16 09:32:10.015773: Encoding repertoire 41/200\r\n",
      "2021-07-16 09:32:10.021266: Encoding repertoire 42/200\r\n",
      "2021-07-16 09:32:10.027423: Encoding repertoire 43/200\r\n",
      "2021-07-16 09:32:10.035179: Encoding repertoire 44/200\r\n",
      "2021-07-16 09:32:10.040946: Encoding repertoire 45/200\r\n",
      "2021-07-16 09:32:10.046025: Encoding repertoire 46/200\r\n",
      "2021-07-16 09:32:10.051470: Encoding repertoire 47/200\r\n",
      "2021-07-16 09:32:10.056499: Encoding repertoire 48/200\r\n",
      "2021-07-16 09:32:10.062071: Encoding repertoire 49/200\r\n",
      "2021-07-16 09:32:10.067093: Encoding repertoire 50/200\r\n",
      "2021-07-16 09:32:10.072573: Encoding repertoire 51/200\r\n",
      "2021-07-16 09:32:10.078271: Encoding repertoire 52/200\r\n",
      "2021-07-16 09:32:10.083664: Encoding repertoire 53/200\r\n",
      "2021-07-16 09:32:10.089170: Encoding repertoire 54/200\r\n",
      "2021-07-16 09:32:10.094867: Encoding repertoire 55/200\r\n",
      "2021-07-16 09:32:10.102694: Encoding repertoire 56/200\r\n",
      "2021-07-16 09:32:10.108448: Encoding repertoire 57/200\r\n",
      "2021-07-16 09:32:10.113778: Encoding repertoire 58/200\r\n",
      "2021-07-16 09:32:10.119078: Encoding repertoire 59/200\r\n",
      "2021-07-16 09:32:10.126424: Encoding repertoire 60/200\r\n",
      "2021-07-16 09:32:10.132086: Encoding repertoire 61/200\r\n",
      "2021-07-16 09:32:10.137507: Encoding repertoire 62/200\r\n",
      "2021-07-16 09:32:10.143136: Encoding repertoire 63/200\r\n",
      "2021-07-16 09:32:10.148475: Encoding repertoire 64/200\r\n",
      "2021-07-16 09:32:10.153928: Encoding repertoire 65/200\r\n",
      "2021-07-16 09:32:10.161696: Encoding repertoire 66/200\r\n",
      "2021-07-16 09:32:10.167748: Encoding repertoire 67/200\r\n",
      "2021-07-16 09:32:10.173504: Encoding repertoire 68/200\r\n",
      "2021-07-16 09:32:10.179059: Encoding repertoire 69/200\r\n",
      "2021-07-16 09:32:10.184438: Encoding repertoire 70/200\r\n",
      "2021-07-16 09:32:10.189898: Encoding repertoire 71/200\r\n",
      "2021-07-16 09:32:10.197593: Encoding repertoire 72/200\r\n",
      "2021-07-16 09:32:10.203656: Encoding repertoire 73/200\r\n",
      "2021-07-16 09:32:10.209596: Encoding repertoire 74/200\r\n",
      "2021-07-16 09:32:10.216245: Encoding repertoire 75/200\r\n",
      "2021-07-16 09:32:10.221311: Encoding repertoire 76/200\r\n",
      "2021-07-16 09:32:10.227102: Encoding repertoire 77/200\r\n",
      "2021-07-16 09:32:10.234879: Encoding repertoire 78/200\r\n",
      "2021-07-16 09:32:10.240677: Encoding repertoire 79/200\r\n",
      "2021-07-16 09:32:10.245551: Encoding repertoire 80/200\r\n",
      "2021-07-16 09:32:10.251156: Encoding repertoire 81/200\r\n",
      "2021-07-16 09:32:10.257274: Encoding repertoire 82/200\r\n",
      "2021-07-16 09:32:10.262932: Encoding repertoire 83/200\r\n",
      "2021-07-16 09:32:10.268268: Encoding repertoire 84/200\r\n",
      "2021-07-16 09:32:10.274199: Encoding repertoire 85/200\r\n",
      "2021-07-16 09:32:10.282045: Encoding repertoire 86/200\r\n",
      "2021-07-16 09:32:10.287204: Encoding repertoire 87/200\r\n",
      "2021-07-16 09:32:10.293130: Encoding repertoire 88/200\r\n",
      "2021-07-16 09:32:10.298846: Encoding repertoire 89/200\r\n",
      "2021-07-16 09:32:10.304602: Encoding repertoire 90/200\r\n",
      "2021-07-16 09:32:10.310013: Encoding repertoire 91/200\r\n",
      "2021-07-16 09:32:10.315544: Encoding repertoire 92/200\r\n",
      "2021-07-16 09:32:10.320572: Encoding repertoire 93/200\r\n",
      "2021-07-16 09:32:10.326339: Encoding repertoire 94/200\r\n",
      "2021-07-16 09:32:10.331822: Encoding repertoire 95/200\r\n",
      "2021-07-16 09:32:10.337572: Encoding repertoire 96/200\r\n",
      "2021-07-16 09:32:10.342952: Encoding repertoire 97/200\r\n",
      "2021-07-16 09:32:10.348829: Encoding repertoire 98/200\r\n",
      "2021-07-16 09:32:10.353592: Encoding repertoire 99/200\r\n",
      "2021-07-16 09:32:10.359053: Encoding repertoire 100/200\r\n",
      "2021-07-16 09:32:10.363995: Encoding repertoire 101/200\r\n",
      "2021-07-16 09:32:10.371544: Encoding repertoire 102/200\r\n",
      "2021-07-16 09:32:10.379339: Encoding repertoire 103/200\r\n",
      "2021-07-16 09:32:10.386981: Encoding repertoire 104/200\r\n",
      "2021-07-16 09:32:10.393822: Encoding repertoire 105/200\r\n",
      "2021-07-16 09:32:10.399079: Encoding repertoire 106/200\r\n",
      "2021-07-16 09:32:10.403954: Encoding repertoire 107/200\r\n",
      "2021-07-16 09:32:10.409124: Encoding repertoire 108/200\r\n",
      "2021-07-16 09:32:10.414726: Encoding repertoire 109/200\r\n",
      "2021-07-16 09:32:10.420180: Encoding repertoire 110/200\r\n",
      "2021-07-16 09:32:10.425677: Encoding repertoire 111/200\r\n",
      "2021-07-16 09:32:10.431026: Encoding repertoire 112/200\r\n",
      "2021-07-16 09:32:10.436224: Encoding repertoire 113/200\r\n",
      "2021-07-16 09:32:10.441794: Encoding repertoire 114/200\r\n",
      "2021-07-16 09:32:10.447319: Encoding repertoire 115/200\r\n",
      "2021-07-16 09:32:10.452964: Encoding repertoire 116/200\r\n",
      "2021-07-16 09:32:10.458315: Encoding repertoire 117/200\r\n",
      "2021-07-16 09:32:10.463689: Encoding repertoire 118/200\r\n",
      "2021-07-16 09:32:10.468858: Encoding repertoire 119/200\r\n",
      "2021-07-16 09:32:10.473744: Encoding repertoire 120/200\r\n",
      "2021-07-16 09:32:10.478522: Encoding repertoire 121/200\r\n",
      "2021-07-16 09:32:10.483812: Encoding repertoire 122/200\r\n",
      "2021-07-16 09:32:10.489047: Encoding repertoire 123/200\r\n",
      "2021-07-16 09:32:10.494728: Encoding repertoire 124/200\r\n",
      "2021-07-16 09:32:10.500300: Encoding repertoire 125/200\r\n",
      "2021-07-16 09:32:10.505952: Encoding repertoire 126/200\r\n",
      "2021-07-16 09:32:10.511645: Encoding repertoire 127/200\r\n",
      "2021-07-16 09:32:10.516992: Encoding repertoire 128/200\r\n",
      "2021-07-16 09:32:10.524898: Encoding repertoire 129/200\r\n",
      "2021-07-16 09:32:10.530587: Encoding repertoire 130/200\r\n",
      "2021-07-16 09:32:10.536391: Encoding repertoire 131/200\r\n",
      "2021-07-16 09:32:10.542043: Encoding repertoire 132/200\r\n",
      "2021-07-16 09:32:10.547393: Encoding repertoire 133/200\r\n",
      "2021-07-16 09:32:10.553108: Encoding repertoire 134/200\r\n",
      "2021-07-16 09:32:10.558101: Encoding repertoire 135/200\r\n",
      "2021-07-16 09:32:10.563025: Encoding repertoire 136/200\r\n",
      "2021-07-16 09:32:10.568493: Encoding repertoire 137/200\r\n",
      "2021-07-16 09:32:10.573427: Encoding repertoire 138/200\r\n",
      "2021-07-16 09:32:10.579148: Encoding repertoire 139/200\r\n",
      "2021-07-16 09:32:10.584148: Encoding repertoire 140/200\r\n",
      "2021-07-16 09:32:10.589215: Encoding repertoire 141/200\r\n",
      "2021-07-16 09:32:10.593858: Encoding repertoire 142/200\r\n",
      "2021-07-16 09:32:10.599144: Encoding repertoire 143/200\r\n",
      "2021-07-16 09:32:10.604898: Encoding repertoire 144/200\r\n",
      "2021-07-16 09:32:10.612572: Encoding repertoire 145/200\r\n",
      "2021-07-16 09:32:10.618112: Encoding repertoire 146/200\r\n",
      "2021-07-16 09:32:10.623084: Encoding repertoire 147/200\r\n",
      "2021-07-16 09:32:10.628717: Encoding repertoire 148/200\r\n",
      "2021-07-16 09:32:10.633514: Encoding repertoire 149/200\r\n",
      "2021-07-16 09:32:10.638747: Encoding repertoire 150/200\r\n",
      "2021-07-16 09:32:10.643299: Encoding repertoire 151/200\r\n",
      "2021-07-16 09:32:10.648797: Encoding repertoire 152/200\r\n",
      "2021-07-16 09:32:10.654296: Encoding repertoire 153/200\r\n",
      "2021-07-16 09:32:10.659944: Encoding repertoire 154/200\r\n",
      "2021-07-16 09:32:10.664743: Encoding repertoire 155/200\r\n",
      "2021-07-16 09:32:10.670856: Encoding repertoire 156/200\r\n",
      "2021-07-16 09:32:10.676209: Encoding repertoire 157/200\r\n",
      "2021-07-16 09:32:10.681825: Encoding repertoire 158/200\r\n",
      "2021-07-16 09:32:10.686915: Encoding repertoire 159/200\r\n",
      "2021-07-16 09:32:10.691616: Encoding repertoire 160/200\r\n",
      "2021-07-16 09:32:10.697149: Encoding repertoire 161/200\r\n",
      "2021-07-16 09:32:10.703136: Encoding repertoire 162/200\r\n",
      "2021-07-16 09:32:10.709179: Encoding repertoire 163/200\r\n",
      "2021-07-16 09:32:10.715011: Encoding repertoire 164/200\r\n",
      "2021-07-16 09:32:10.722666: Encoding repertoire 165/200\r\n",
      "2021-07-16 09:32:10.727730: Encoding repertoire 166/200\r\n",
      "2021-07-16 09:32:10.733082: Encoding repertoire 167/200\r\n",
      "2021-07-16 09:32:10.737818: Encoding repertoire 168/200\r\n",
      "2021-07-16 09:32:10.743242: Encoding repertoire 169/200\r\n",
      "2021-07-16 09:32:10.748194: Encoding repertoire 170/200\r\n",
      "2021-07-16 09:32:10.753789: Encoding repertoire 171/200\r\n",
      "2021-07-16 09:32:10.759204: Encoding repertoire 172/200\r\n",
      "2021-07-16 09:32:10.764502: Encoding repertoire 173/200\r\n",
      "2021-07-16 09:32:10.771337: Encoding repertoire 174/200\r\n",
      "2021-07-16 09:32:10.776785: Encoding repertoire 175/200\r\n",
      "2021-07-16 09:32:10.781566: Encoding repertoire 176/200\r\n",
      "2021-07-16 09:32:10.786607: Encoding repertoire 177/200\r\n",
      "2021-07-16 09:32:10.791927: Encoding repertoire 178/200\r\n",
      "2021-07-16 09:32:10.797240: Encoding repertoire 179/200\r\n",
      "2021-07-16 09:32:10.802832: Encoding repertoire 180/200\r\n",
      "2021-07-16 09:32:10.808851: Encoding repertoire 181/200\r\n",
      "2021-07-16 09:32:10.814475: Encoding repertoire 182/200\r\n",
      "2021-07-16 09:32:10.819797: Encoding repertoire 183/200\r\n",
      "2021-07-16 09:32:10.825712: Encoding repertoire 184/200\r\n",
      "2021-07-16 09:32:10.831334: Encoding repertoire 185/200\r\n",
      "2021-07-16 09:32:10.837532: Encoding repertoire 186/200\r\n",
      "2021-07-16 09:32:10.843393: Encoding repertoire 187/200\r\n",
      "2021-07-16 09:32:10.848977: Encoding repertoire 188/200\r\n",
      "2021-07-16 09:32:10.854025: Encoding repertoire 189/200\r\n",
      "2021-07-16 09:32:10.859213: Encoding repertoire 190/200\r\n",
      "2021-07-16 09:32:10.864369: Encoding repertoire 191/200\r\n",
      "2021-07-16 09:32:10.869780: Encoding repertoire 192/200\r\n",
      "2021-07-16 09:32:10.874903: Encoding repertoire 193/200\r\n",
      "2021-07-16 09:32:10.880245: Encoding repertoire 194/200\r\n",
      "2021-07-16 09:32:10.885875: Encoding repertoire 195/200\r\n",
      "2021-07-16 09:32:10.891736: Encoding repertoire 196/200\r\n",
      "2021-07-16 09:32:10.896857: Encoding repertoire 197/200\r\n",
      "2021-07-16 09:32:10.902181: Encoding repertoire 198/200\r\n",
      "2021-07-16 09:32:10.907634: Encoding repertoire 199/200\r\n",
      "2021-07-16 09:32:10.912967: Encoding repertoire 200/200\r\n",
      "2021-07-16 09:32:10.963200: Saving encoded dataset to disk.\r\n",
      "2021-07-16 09:32:13.582807: Encoding finished.\r\n",
      "2021-07-16 09:32:15.269544: Finished analysis signal_freq (1/1).\r\n",
      "\r\n",
      "2021-07-16 09:32:15.269599: Instruction 2/2 has finished.\r\n",
      "2021-07-16 09:32:15.280513: Generating HTML reports...\r\n",
      "2021-07-16 09:32:17.016719: HTML reports are generated.\r\n",
      "2021-07-16 09:32:17.924155: ImmuneML: finished analysis.\r\n",
      "\r\n"
     ]
    }
   ],
   "source": [
    "!mkdir -p analyses/olga/varying_sample_size/sample_size_100/{simulated_data,ML_output}\n",
    "!docker container prune -f\n",
    "!docker run -it -v $(pwd):/wd --name simulation_container kanduric/immuneml-v1:latest immune-ml /wd/scripts/olga/run_1/varying_sample_size/sample_size_100/generated_yaml_files/simulation_instructions/repertoire_implanting_rate__0.002.yaml /wd/analyses/olga/varying_sample_size/sample_size_100/simulated_data/repertoire_implanting_rate__0.002\n",
    "!docker run -it -v $(pwd):/wd --name ml_container kanduric/immuneml-v1:latest immune-ml /wd/scripts/olga/run_1/varying_sample_size/sample_size_100/generated_yaml_files/ml_instructions/repertoire_implanting_rate__0.002_-_ml_instruction.yaml /wd/analyses/olga/varying_sample_size/sample_size_100/ML_output/repertoire_implanting_rate__0.002\n"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "#### Repertoire size\n",
    "\n",
    "The stdout printed to screen when running the simulation and ML model training instructions are shown below indicating successful execution, and the resulting output of each instruction is stored under `analyses/olga/varying_rep_size/seqcount_100000/` in this github repository."
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Deleted Containers:\r\n",
      "e729a02b960261dbfc7bff10d11cf50b9c196e49e941d395b0a0187bd2b6aa5c\r\n",
      "dd93670a9f7e1896e18b02f7980a9d91c47dda19a3c143d0dd554a45f79724d0\r\n",
      "\r\n",
      "Total reclaimed space: 47.09kB\r\n",
      "2021-07-16 08:37:46.797731: Setting temporary cache path to wd/analyses/olga/varying_rep_size/seqcount_100000/simulated_data/repertoire_implanting_rate__0.002/cache/\r\n",
      "2021-07-16 08:37:46.797817: ImmuneML: parsing the specification...\r\n",
      "\r\n",
      "2021-07-16 08:37:51.307002: Full specification is available at wd/analyses/olga/varying_rep_size/seqcount_100000/simulated_data/repertoire_implanting_rate__0.002/full_repertoire_implanting_rate__0.yaml.\r\n",
      "\r\n",
      "2021-07-16 08:37:51.307112: ImmuneML: starting the analysis...\r\n",
      "\r\n",
      "2021-07-16 08:37:51.307198: Instruction 1/1 has started.\r\n",
      "2021-07-16 08:37:58.492471: Instruction 1/1 has finished.\r\n",
      "2021-07-16 08:37:58.499234: Generating HTML reports...\r\n",
      "2021-07-16 08:37:58.595385: HTML reports are generated.\r\n",
      "2021-07-16 08:37:58.596730: ImmuneML: finished analysis.\r\n",
      "\r\n",
      "2021-07-16 08:38:00.913630: Setting temporary cache path to wd/analyses/olga/varying_rep_size/seqcount_100000/ML_output/repertoire_implanting_rate__0.002/cache/\r\n",
      "2021-07-16 08:38:00.913733: ImmuneML: parsing the specification...\r\n",
      "\r\n",
      "2021-07-16 08:38:03.867519: Full specification is available at wd/analyses/olga/varying_rep_size/seqcount_100000/ML_output/repertoire_implanting_rate__0.002/full_repertoire_implanting_rate__0.yaml.\r\n",
      "\r\n",
      "2021-07-16 08:38:03.867795: ImmuneML: starting the analysis...\r\n",
      "\r\n",
      "2021-07-16 08:38:03.867901: Instruction 1/2 has started.\r\n",
      "2021-07-16 08:38:04.027876: Training ML model: running outer CV loop: started split 1/5.\r\n",
      "\r\n",
      "2021-07-16 08:38:04.074250: Hyperparameter optimization: running the inner loop of nested CV: selection for label signal_cancer (label 1 / 1).\r\n",
      "\r\n",
      "2021-07-16 08:38:04.081449: Evaluating hyperparameter setting: feature_size_4_logistic_regression...\r\n",
      "2021-07-16 08:38:04.092599: Encoding started...\r\n",
      "2021-07-16 08:38:08.391402: Encoding finished.\r\n",
      "2021-07-16 08:38:08.391805: ML model training started...\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "2021-07-16 08:38:32.702736: ML model training finished.\r\n",
      "2021-07-16 08:38:32.703030: Completed hyperparameter setting feature_size_4_logistic_regression.\r\n",
      "\r\n",
      "2021-07-16 08:38:32.706962: Hyperparameter optimization: running the inner loop of nested CV: completed selection for label signal_cancer (label 1 / 1).\r\n",
      "\r\n",
      "2021-07-16 08:38:32.707075: Training ML model: running the inner loop of nested CV: retrain models for label signal_cancer (label 1 / 1).\r\n",
      "\r\n",
      "2021-07-16 08:38:32.714319: Evaluating hyperparameter setting: feature_size_4_logistic_regression...\r\n",
      "2021-07-16 08:38:32.726107: Encoding started...\r\n",
      "2021-07-16 08:38:35.570128: Encoding finished.\r\n",
      "2021-07-16 08:38:35.570564: ML model training started...\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "2021-07-16 08:38:58.690912: ML model training finished.\r\n",
      "2021-07-16 08:38:58.694213: Encoding started...\r\n",
      "2021-07-16 08:39:00.398125: Encoding finished.\r\n",
      "2021-07-16 08:39:02.696950: Completed hyperparameter setting feature_size_4_logistic_regression.\r\n",
      "\r\n",
      "2021-07-16 08:39:02.700563: Training ML model: running the inner loop of nested CV: completed retraining models for label signal_cancer (label 1 / 1).\r\n",
      "\r\n",
      "2021-07-16 08:39:02.700663: Training ML model: running outer CV loop: finished split 1/5.\r\n",
      "\r\n",
      "2021-07-16 08:39:02.700703: Training ML model: running outer CV loop: started split 2/5.\r\n",
      "\r\n",
      "2021-07-16 08:39:02.751684: Hyperparameter optimization: running the inner loop of nested CV: selection for label signal_cancer (label 1 / 1).\r\n",
      "\r\n",
      "2021-07-16 08:39:02.758163: Evaluating hyperparameter setting: feature_size_4_logistic_regression...\r\n",
      "2021-07-16 08:39:02.768838: Encoding started...\r\n",
      "2021-07-16 08:39:06.092586: Encoding finished.\r\n",
      "2021-07-16 08:39:06.092813: ML model training started...\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "2021-07-16 08:39:27.873235: ML model training finished.\r\n",
      "2021-07-16 08:39:27.873561: Completed hyperparameter setting feature_size_4_logistic_regression.\r\n",
      "\r\n",
      "2021-07-16 08:39:27.877876: Hyperparameter optimization: running the inner loop of nested CV: completed selection for label signal_cancer (label 1 / 1).\r\n",
      "\r\n",
      "2021-07-16 08:39:27.878058: Training ML model: running the inner loop of nested CV: retrain models for label signal_cancer (label 1 / 1).\r\n",
      "\r\n",
      "2021-07-16 08:39:27.883459: Evaluating hyperparameter setting: feature_size_4_logistic_regression...\r\n",
      "2021-07-16 08:39:27.896588: Encoding started...\r\n",
      "2021-07-16 08:39:31.149403: Encoding finished.\r\n",
      "2021-07-16 08:39:31.149962: ML model training started...\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "2021-07-16 08:40:02.918261: ML model training finished.\r\n",
      "2021-07-16 08:40:02.939403: Encoding started...\r\n",
      "2021-07-16 08:40:05.803992: Encoding finished.\r\n",
      "2021-07-16 08:40:09.544165: Completed hyperparameter setting feature_size_4_logistic_regression.\r\n",
      "\r\n",
      "2021-07-16 08:40:09.552030: Training ML model: running the inner loop of nested CV: completed retraining models for label signal_cancer (label 1 / 1).\r\n",
      "\r\n",
      "2021-07-16 08:40:09.553989: Training ML model: running outer CV loop: finished split 2/5.\r\n",
      "\r\n",
      "2021-07-16 08:40:09.554113: Training ML model: running outer CV loop: started split 3/5.\r\n",
      "\r\n",
      "2021-07-16 08:40:09.652828: Hyperparameter optimization: running the inner loop of nested CV: selection for label signal_cancer (label 1 / 1).\r\n",
      "\r\n",
      "2021-07-16 08:40:09.667632: Evaluating hyperparameter setting: feature_size_4_logistic_regression...\r\n",
      "2021-07-16 08:40:09.694582: Encoding started...\r\n",
      "2021-07-16 08:40:14.500797: Encoding finished.\r\n",
      "2021-07-16 08:40:14.501453: ML model training started...\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "2021-07-16 08:40:45.895839: ML model training finished.\r\n",
      "2021-07-16 08:40:45.896391: Completed hyperparameter setting feature_size_4_logistic_regression.\r\n",
      "\r\n",
      "2021-07-16 08:40:45.900034: Hyperparameter optimization: running the inner loop of nested CV: completed selection for label signal_cancer (label 1 / 1).\r\n",
      "\r\n",
      "2021-07-16 08:40:45.900333: Training ML model: running the inner loop of nested CV: retrain models for label signal_cancer (label 1 / 1).\r\n",
      "\r\n",
      "2021-07-16 08:40:45.906300: Evaluating hyperparameter setting: feature_size_4_logistic_regression...\r\n",
      "2021-07-16 08:40:45.916421: Encoding started...\r\n",
      "2021-07-16 08:40:48.624143: Encoding finished.\r\n",
      "2021-07-16 08:40:48.624825: ML model training started...\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "2021-07-16 08:41:12.486987: ML model training finished.\r\n",
      "2021-07-16 08:41:12.489589: Encoding started...\r\n",
      "2021-07-16 08:41:14.085470: Encoding finished.\r\n",
      "2021-07-16 08:41:16.038916: Completed hyperparameter setting feature_size_4_logistic_regression.\r\n",
      "\r\n",
      "2021-07-16 08:41:16.042902: Training ML model: running the inner loop of nested CV: completed retraining models for label signal_cancer (label 1 / 1).\r\n",
      "\r\n",
      "2021-07-16 08:41:16.043024: Training ML model: running outer CV loop: finished split 3/5.\r\n",
      "\r\n",
      "2021-07-16 08:41:16.043049: Training ML model: running outer CV loop: started split 4/5.\r\n",
      "\r\n",
      "2021-07-16 08:41:16.091000: Hyperparameter optimization: running the inner loop of nested CV: selection for label signal_cancer (label 1 / 1).\r\n",
      "\r\n",
      "2021-07-16 08:41:16.096939: Evaluating hyperparameter setting: feature_size_4_logistic_regression...\r\n",
      "2021-07-16 08:41:16.107038: Encoding started...\r\n",
      "2021-07-16 08:41:18.916442: Encoding finished.\r\n",
      "2021-07-16 08:41:18.916862: ML model training started...\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "2021-07-16 08:41:45.206020: ML model training finished.\r\n",
      "2021-07-16 08:41:45.206240: Completed hyperparameter setting feature_size_4_logistic_regression.\r\n",
      "\r\n",
      "2021-07-16 08:41:45.210836: Hyperparameter optimization: running the inner loop of nested CV: completed selection for label signal_cancer (label 1 / 1).\r\n",
      "\r\n",
      "2021-07-16 08:41:45.211043: Training ML model: running the inner loop of nested CV: retrain models for label signal_cancer (label 1 / 1).\r\n",
      "\r\n",
      "2021-07-16 08:41:45.218600: Evaluating hyperparameter setting: feature_size_4_logistic_regression...\r\n",
      "2021-07-16 08:41:45.230796: Encoding started...\r\n",
      "2021-07-16 08:41:48.388485: Encoding finished.\r\n",
      "2021-07-16 08:41:48.388768: ML model training started...\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "2021-07-16 08:42:13.784530: ML model training finished.\r\n",
      "2021-07-16 08:42:13.787892: Encoding started...\r\n",
      "2021-07-16 08:42:15.643091: Encoding finished.\r\n",
      "2021-07-16 08:42:17.528686: Completed hyperparameter setting feature_size_4_logistic_regression.\r\n",
      "\r\n",
      "2021-07-16 08:42:17.533809: Training ML model: running the inner loop of nested CV: completed retraining models for label signal_cancer (label 1 / 1).\r\n",
      "\r\n",
      "2021-07-16 08:42:17.533913: Training ML model: running outer CV loop: finished split 4/5.\r\n",
      "\r\n",
      "2021-07-16 08:42:17.533957: Training ML model: running outer CV loop: started split 5/5.\r\n",
      "\r\n",
      "2021-07-16 08:42:17.583638: Hyperparameter optimization: running the inner loop of nested CV: selection for label signal_cancer (label 1 / 1).\r\n",
      "\r\n",
      "2021-07-16 08:42:17.589912: Evaluating hyperparameter setting: feature_size_4_logistic_regression...\r\n",
      "2021-07-16 08:42:17.601369: Encoding started...\r\n",
      "2021-07-16 08:42:20.680969: Encoding finished.\r\n",
      "2021-07-16 08:42:20.681910: ML model training started...\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "2021-07-16 08:42:57.179922: ML model training finished.\r\n",
      "2021-07-16 08:42:57.180285: Completed hyperparameter setting feature_size_4_logistic_regression.\r\n",
      "\r\n",
      "2021-07-16 08:42:57.186386: Hyperparameter optimization: running the inner loop of nested CV: completed selection for label signal_cancer (label 1 / 1).\r\n",
      "\r\n",
      "2021-07-16 08:42:57.186603: Training ML model: running the inner loop of nested CV: retrain models for label signal_cancer (label 1 / 1).\r\n",
      "\r\n",
      "2021-07-16 08:42:57.195908: Evaluating hyperparameter setting: feature_size_4_logistic_regression...\r\n",
      "2021-07-16 08:42:57.210761: Encoding started...\r\n",
      "2021-07-16 08:43:02.379836: Encoding finished.\r\n",
      "2021-07-16 08:43:02.380091: ML model training started...\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "2021-07-16 08:43:29.583126: ML model training finished.\r\n",
      "2021-07-16 08:43:29.586327: Encoding started...\r\n",
      "2021-07-16 08:43:31.417592: Encoding finished.\r\n",
      "2021-07-16 08:43:33.257297: Completed hyperparameter setting feature_size_4_logistic_regression.\r\n",
      "\r\n",
      "2021-07-16 08:43:33.261286: Training ML model: running the inner loop of nested CV: completed retraining models for label signal_cancer (label 1 / 1).\r\n",
      "\r\n",
      "2021-07-16 08:43:33.261388: Training ML model: running outer CV loop: finished split 5/5.\r\n",
      "\r\n",
      "Performances (accuracy) -----------------------------------------------\r\n",
      "\r\n",
      "\r\n",
      "Label: signal_cancer\r\n",
      "Performance (accuracy) per assessment split:\r\n",
      "Split 1: 0.375\r\n",
      "Split 2: 0.4\r\n",
      "Split 3: 0.525\r\n",
      "Split 4: 0.475\r\n",
      "Split 5: 0.45\r\n",
      "Average performance (accuracy): 0.445\r\n",
      "------------------------------\r\n",
      "2021-07-16 08:43:36.165064: Instruction 1/2 has finished.\r\n",
      "2021-07-16 08:43:36.165141: Instruction 2/2 has started.\r\n",
      "2021-07-16 08:43:36.165269: Started analysis signal_freq (1/1).\r\n",
      "2021-07-16 08:43:36.170224: Encoding started...\r\n",
      "2021-07-16 08:43:36.185455: Encoding repertoire 1/200\r\n",
      "2021-07-16 08:43:36.197211: Encoding repertoire 2/200\r\n",
      "2021-07-16 08:43:36.205646: Encoding repertoire 3/200\r\n",
      "2021-07-16 08:43:36.213376: Encoding repertoire 4/200\r\n",
      "2021-07-16 08:43:36.223106: Encoding repertoire 5/200\r\n",
      "2021-07-16 08:43:36.229961: Encoding repertoire 6/200\r\n",
      "2021-07-16 08:43:36.236947: Encoding repertoire 7/200\r\n",
      "2021-07-16 08:43:36.242059: Encoding repertoire 8/200\r\n",
      "2021-07-16 08:43:36.247268: Encoding repertoire 9/200\r\n",
      "2021-07-16 08:43:36.252684: Encoding repertoire 10/200\r\n",
      "2021-07-16 08:43:36.258441: Encoding repertoire 11/200\r\n",
      "2021-07-16 08:43:36.264162: Encoding repertoire 12/200\r\n",
      "2021-07-16 08:43:36.269736: Encoding repertoire 13/200\r\n",
      "2021-07-16 08:43:36.276513: Encoding repertoire 14/200\r\n",
      "2021-07-16 08:43:36.282126: Encoding repertoire 15/200\r\n",
      "2021-07-16 08:43:36.288766: Encoding repertoire 16/200\r\n",
      "2021-07-16 08:43:36.295445: Encoding repertoire 17/200\r\n",
      "2021-07-16 08:43:36.301237: Encoding repertoire 18/200\r\n",
      "2021-07-16 08:43:36.308209: Encoding repertoire 19/200\r\n",
      "2021-07-16 08:43:36.316871: Encoding repertoire 20/200\r\n",
      "2021-07-16 08:43:36.323741: Encoding repertoire 21/200\r\n",
      "2021-07-16 08:43:36.330482: Encoding repertoire 22/200\r\n",
      "2021-07-16 08:43:36.337294: Encoding repertoire 23/200\r\n",
      "2021-07-16 08:43:36.344163: Encoding repertoire 24/200\r\n",
      "2021-07-16 08:43:36.350008: Encoding repertoire 25/200\r\n",
      "2021-07-16 08:43:36.355576: Encoding repertoire 26/200\r\n",
      "2021-07-16 08:43:36.362287: Encoding repertoire 27/200\r\n",
      "2021-07-16 08:43:36.369597: Encoding repertoire 28/200\r\n",
      "2021-07-16 08:43:36.376016: Encoding repertoire 29/200\r\n",
      "2021-07-16 08:43:36.381698: Encoding repertoire 30/200\r\n",
      "2021-07-16 08:43:36.389585: Encoding repertoire 31/200\r\n",
      "2021-07-16 08:43:36.394875: Encoding repertoire 32/200\r\n",
      "2021-07-16 08:43:36.402168: Encoding repertoire 33/200\r\n",
      "2021-07-16 08:43:36.408272: Encoding repertoire 34/200\r\n",
      "2021-07-16 08:43:36.413505: Encoding repertoire 35/200\r\n",
      "2021-07-16 08:43:36.421443: Encoding repertoire 36/200\r\n",
      "2021-07-16 08:43:36.428169: Encoding repertoire 37/200\r\n",
      "2021-07-16 08:43:36.434359: Encoding repertoire 38/200\r\n",
      "2021-07-16 08:43:36.439844: Encoding repertoire 39/200\r\n",
      "2021-07-16 08:43:36.447129: Encoding repertoire 40/200\r\n",
      "2021-07-16 08:43:36.452767: Encoding repertoire 41/200\r\n",
      "2021-07-16 08:43:36.458079: Encoding repertoire 42/200\r\n",
      "2021-07-16 08:43:36.466771: Encoding repertoire 43/200\r\n",
      "2021-07-16 08:43:36.472914: Encoding repertoire 44/200\r\n",
      "2021-07-16 08:43:36.480004: Encoding repertoire 45/200\r\n",
      "2021-07-16 08:43:36.486449: Encoding repertoire 46/200\r\n",
      "2021-07-16 08:43:36.494669: Encoding repertoire 47/200\r\n",
      "2021-07-16 08:43:36.502575: Encoding repertoire 48/200\r\n",
      "2021-07-16 08:43:36.509482: Encoding repertoire 49/200\r\n",
      "2021-07-16 08:43:36.515477: Encoding repertoire 50/200\r\n",
      "2021-07-16 08:43:36.520894: Encoding repertoire 51/200\r\n",
      "2021-07-16 08:43:36.530231: Encoding repertoire 52/200\r\n",
      "2021-07-16 08:43:36.536822: Encoding repertoire 53/200\r\n",
      "2021-07-16 08:43:36.543704: Encoding repertoire 54/200\r\n",
      "2021-07-16 08:43:36.554116: Encoding repertoire 55/200\r\n",
      "2021-07-16 08:43:36.561289: Encoding repertoire 56/200\r\n",
      "2021-07-16 08:43:36.567758: Encoding repertoire 57/200\r\n",
      "2021-07-16 08:43:36.576003: Encoding repertoire 58/200\r\n",
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      "2021-07-16 08:43:36.588595: Encoding repertoire 60/200\r\n",
      "2021-07-16 08:43:36.595191: Encoding repertoire 61/200\r\n",
      "2021-07-16 08:43:36.600960: Encoding repertoire 62/200\r\n",
      "2021-07-16 08:43:36.609358: Encoding repertoire 63/200\r\n",
      "2021-07-16 08:43:36.614583: Encoding repertoire 64/200\r\n",
      "2021-07-16 08:43:36.620019: Encoding repertoire 65/200\r\n",
      "2021-07-16 08:43:36.625391: Encoding repertoire 66/200\r\n",
      "2021-07-16 08:43:36.631128: Encoding repertoire 67/200\r\n",
      "2021-07-16 08:43:36.637083: Encoding repertoire 68/200\r\n",
      "2021-07-16 08:43:36.643888: Encoding repertoire 69/200\r\n",
      "2021-07-16 08:43:36.650073: Encoding repertoire 70/200\r\n",
      "2021-07-16 08:43:36.655316: Encoding repertoire 71/200\r\n",
      "2021-07-16 08:43:36.661765: Encoding repertoire 72/200\r\n",
      "2021-07-16 08:43:36.667355: Encoding repertoire 73/200\r\n",
      "2021-07-16 08:43:36.673273: Encoding repertoire 74/200\r\n",
      "2021-07-16 08:43:36.680930: Encoding repertoire 75/200\r\n",
      "2021-07-16 08:43:36.690811: Encoding repertoire 76/200\r\n",
      "2021-07-16 08:43:36.696339: Encoding repertoire 77/200\r\n",
      "2021-07-16 08:43:36.707775: Encoding repertoire 78/200\r\n",
      "2021-07-16 08:43:36.716892: Encoding repertoire 79/200\r\n",
      "2021-07-16 08:43:36.728104: Encoding repertoire 80/200\r\n",
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      "2021-07-16 08:43:36.760147: Encoding repertoire 83/200\r\n",
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      "2021-07-16 08:43:36.775297: Encoding repertoire 85/200\r\n",
      "2021-07-16 08:43:36.782837: Encoding repertoire 86/200\r\n",
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      "2021-07-16 08:43:36.797814: Encoding repertoire 88/200\r\n",
      "2021-07-16 08:43:36.806908: Encoding repertoire 89/200\r\n",
      "2021-07-16 08:43:36.814075: Encoding repertoire 90/200\r\n",
      "2021-07-16 08:43:36.820838: Encoding repertoire 91/200\r\n",
      "2021-07-16 08:43:36.829478: Encoding repertoire 92/200\r\n",
      "2021-07-16 08:43:36.839291: Encoding repertoire 93/200\r\n",
      "2021-07-16 08:43:36.846427: Encoding repertoire 94/200\r\n",
      "2021-07-16 08:43:36.856823: Encoding repertoire 95/200\r\n",
      "2021-07-16 08:43:36.863848: Encoding repertoire 96/200\r\n",
      "2021-07-16 08:43:36.873607: Encoding repertoire 97/200\r\n",
      "2021-07-16 08:43:36.885110: Encoding repertoire 98/200\r\n",
      "2021-07-16 08:43:36.893502: Encoding repertoire 99/200\r\n",
      "2021-07-16 08:43:36.903550: Encoding repertoire 100/200\r\n",
      "2021-07-16 08:43:36.910711: Encoding repertoire 101/200\r\n",
      "2021-07-16 08:43:36.916430: Encoding repertoire 102/200\r\n",
      "2021-07-16 08:43:36.925721: Encoding repertoire 103/200\r\n",
      "2021-07-16 08:43:36.933095: Encoding repertoire 104/200\r\n",
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      "2021-07-16 08:43:36.946744: Encoding repertoire 106/200\r\n",
      "2021-07-16 08:43:36.955451: Encoding repertoire 107/200\r\n",
      "2021-07-16 08:43:36.964283: Encoding repertoire 108/200\r\n",
      "2021-07-16 08:43:36.971519: Encoding repertoire 109/200\r\n",
      "2021-07-16 08:43:36.977996: Encoding repertoire 110/200\r\n",
      "2021-07-16 08:43:36.987451: Encoding repertoire 111/200\r\n",
      "2021-07-16 08:43:36.995290: Encoding repertoire 112/200\r\n",
      "2021-07-16 08:43:37.005913: Encoding repertoire 113/200\r\n",
      "2021-07-16 08:43:37.013338: Encoding repertoire 114/200\r\n",
      "2021-07-16 08:43:37.020392: Encoding repertoire 115/200\r\n",
      "2021-07-16 08:43:37.029918: Encoding repertoire 116/200\r\n",
      "2021-07-16 08:43:37.038969: Encoding repertoire 117/200\r\n",
      "2021-07-16 08:43:37.044895: Encoding repertoire 118/200\r\n",
      "2021-07-16 08:43:37.055171: Encoding repertoire 119/200\r\n",
      "2021-07-16 08:43:37.062802: Encoding repertoire 120/200\r\n",
      "2021-07-16 08:43:37.072342: Encoding repertoire 121/200\r\n",
      "2021-07-16 08:43:37.080057: Encoding repertoire 122/200\r\n",
      "2021-07-16 08:43:37.090447: Encoding repertoire 123/200\r\n",
      "2021-07-16 08:43:37.098809: Encoding repertoire 124/200\r\n",
      "2021-07-16 08:43:37.105511: Encoding repertoire 125/200\r\n",
      "2021-07-16 08:43:37.115073: Encoding repertoire 126/200\r\n",
      "2021-07-16 08:43:37.122612: Encoding repertoire 127/200\r\n",
      "2021-07-16 08:43:37.128274: Encoding repertoire 128/200\r\n",
      "2021-07-16 08:43:37.139422: Encoding repertoire 129/200\r\n",
      "2021-07-16 08:43:37.145107: Encoding repertoire 130/200\r\n",
      "2021-07-16 08:43:37.156487: Encoding repertoire 131/200\r\n",
      "2021-07-16 08:43:37.164106: Encoding repertoire 132/200\r\n",
      "2021-07-16 08:43:37.172365: Encoding repertoire 133/200\r\n",
      "2021-07-16 08:43:37.180792: Encoding repertoire 134/200\r\n",
      "2021-07-16 08:43:37.190051: Encoding repertoire 135/200\r\n",
      "2021-07-16 08:43:37.196879: Encoding repertoire 136/200\r\n",
      "2021-07-16 08:43:37.209145: Encoding repertoire 137/200\r\n",
      "2021-07-16 08:43:37.214736: Encoding repertoire 138/200\r\n",
      "2021-07-16 08:43:37.227149: Encoding repertoire 139/200\r\n",
      "2021-07-16 08:43:37.234881: Encoding repertoire 140/200\r\n",
      "2021-07-16 08:43:37.242333: Encoding repertoire 141/200\r\n",
      "2021-07-16 08:43:37.249742: Encoding repertoire 142/200\r\n",
      "2021-07-16 08:43:37.257706: Encoding repertoire 143/200\r\n",
      "2021-07-16 08:43:37.264764: Encoding repertoire 144/200\r\n",
      "2021-07-16 08:43:37.271998: Encoding repertoire 145/200\r\n",
      "2021-07-16 08:43:37.277878: Encoding repertoire 146/200\r\n",
      "2021-07-16 08:43:37.284766: Encoding repertoire 147/200\r\n",
      "2021-07-16 08:43:37.293264: Encoding repertoire 148/200\r\n",
      "2021-07-16 08:43:37.300677: Encoding repertoire 149/200\r\n",
      "2021-07-16 08:43:37.309902: Encoding repertoire 150/200\r\n",
      "2021-07-16 08:43:37.316222: Encoding repertoire 151/200\r\n",
      "2021-07-16 08:43:37.322151: Encoding repertoire 152/200\r\n",
      "2021-07-16 08:43:37.329861: Encoding repertoire 153/200\r\n",
      "2021-07-16 08:43:37.340516: Encoding repertoire 154/200\r\n",
      "2021-07-16 08:43:37.349250: Encoding repertoire 155/200\r\n",
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      "2021-07-16 08:43:37.434038: Encoding repertoire 167/200\r\n",
      "2021-07-16 08:43:37.443573: Encoding repertoire 168/200\r\n",
      "2021-07-16 08:43:37.451878: Encoding repertoire 169/200\r\n",
      "2021-07-16 08:43:37.461621: Encoding repertoire 170/200\r\n",
      "2021-07-16 08:43:37.470360: Encoding repertoire 171/200\r\n",
      "2021-07-16 08:43:37.479439: Encoding repertoire 172/200\r\n",
      "2021-07-16 08:43:37.488874: Encoding repertoire 173/200\r\n",
      "2021-07-16 08:43:37.496725: Encoding repertoire 174/200\r\n",
      "2021-07-16 08:43:37.507003: Encoding repertoire 175/200\r\n",
      "2021-07-16 08:43:37.514107: Encoding repertoire 176/200\r\n",
      "2021-07-16 08:43:37.502065: Encoding repertoire 177/200\r\n",
      "2021-07-16 08:43:37.509440: Encoding repertoire 178/200\r\n",
      "2021-07-16 08:43:37.519978: Encoding repertoire 179/200\r\n",
      "2021-07-16 08:43:37.528168: Encoding repertoire 180/200\r\n",
      "2021-07-16 08:43:37.536873: Encoding repertoire 181/200\r\n",
      "2021-07-16 08:43:37.544489: Encoding repertoire 182/200\r\n",
      "2021-07-16 08:43:37.554152: Encoding repertoire 183/200\r\n",
      "2021-07-16 08:43:37.560052: Encoding repertoire 184/200\r\n",
      "2021-07-16 08:43:37.570139: Encoding repertoire 185/200\r\n",
      "2021-07-16 08:43:37.578377: Encoding repertoire 186/200\r\n",
      "2021-07-16 08:43:37.588091: Encoding repertoire 187/200\r\n",
      "2021-07-16 08:43:37.594468: Encoding repertoire 188/200\r\n",
      "2021-07-16 08:43:37.603632: Encoding repertoire 189/200\r\n",
      "2021-07-16 08:43:37.610110: Encoding repertoire 190/200\r\n",
      "2021-07-16 08:43:37.619293: Encoding repertoire 191/200\r\n",
      "2021-07-16 08:43:37.626865: Encoding repertoire 192/200\r\n",
      "2021-07-16 08:43:37.636335: Encoding repertoire 193/200\r\n",
      "2021-07-16 08:43:37.641838: Encoding repertoire 194/200\r\n",
      "2021-07-16 08:43:37.649374: Encoding repertoire 195/200\r\n",
      "2021-07-16 08:43:37.656325: Encoding repertoire 196/200\r\n",
      "2021-07-16 08:43:37.666226: Encoding repertoire 197/200\r\n",
      "2021-07-16 08:43:37.673763: Encoding repertoire 198/200\r\n",
      "2021-07-16 08:43:37.684724: Encoding repertoire 199/200\r\n",
      "2021-07-16 08:43:37.691407: Encoding repertoire 200/200\r\n",
      "2021-07-16 08:43:37.765098: Saving encoded dataset to disk.\r\n",
      "2021-07-16 08:43:41.022546: Encoding finished.\r\n",
      "2021-07-16 08:43:42.928205: Finished analysis signal_freq (1/1).\r\n",
      "\r\n",
      "2021-07-16 08:43:42.928666: Instruction 2/2 has finished.\r\n",
      "2021-07-16 08:43:42.940775: Generating HTML reports...\r\n",
      "2021-07-16 08:43:44.971320: HTML reports are generated.\r\n",
      "2021-07-16 08:43:46.093825: ImmuneML: finished analysis.\r\n",
      "\r\n"
     ]
    }
   ],
   "source": [
    "!mkdir -p analyses/olga/varying_rep_size/seqcount_100000/{simulated_data,ML_output}\n",
    "!docker container prune -f\n",
    "!docker run -it -v $(pwd):/wd --name simulation_container kanduric/immuneml-v1:latest immune-ml /wd/scripts/olga/run_1/varying_rep_size/seqcount_100000/generated_yaml_files/simulation_instructions/repertoire_implanting_rate__0.002.yaml /wd/analyses/olga/varying_rep_size/seqcount_100000/simulated_data/repertoire_implanting_rate__0.002\n",
    "!docker run -it -v $(pwd):/wd --name ml_container kanduric/immuneml-v1:latest immune-ml /wd/scripts/olga/run_1/varying_rep_size/seqcount_100000/generated_yaml_files/ml_instructions/repertoire_implanting_rate__0.002_-_ml_instruction.yaml /wd/analyses/olga/varying_rep_size/seqcount_100000/ML_output/repertoire_implanting_rate__0.002\n"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "#### Number of motifs increased steadily with repertoire size\n",
    "\n",
    "The stdout printed to screen when running the simulation and ML model training instructions are shown below indicating successful execution, and the resulting output of each instruction is stored under `analyses/olga/varying_n_kmers/seqcount_266k/` in this github repository."
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Deleted Containers:\r\n",
      "1a227f8164064e061331f9f4a01a712f85507a3318de790419f2f8c4528f76f5\r\n",
      "\r\n",
      "Total reclaimed space: 0B\r\n",
      "2021-07-16 09:35:10.033604: Setting temporary cache path to wd/analyses/olga/varying_n_kmers/seqcount_266k/simulated_data/repertoire_implanting_rate__0.002/cache/\r\n",
      "2021-07-16 09:35:10.033757: ImmuneML: parsing the specification...\r\n",
      "\r\n",
      "2021-07-16 09:35:14.525969: Full specification is available at wd/analyses/olga/varying_n_kmers/seqcount_266k/simulated_data/repertoire_implanting_rate__0.002/full_repertoire_implanting_rate__0.yaml.\r\n",
      "\r\n",
      "2021-07-16 09:35:14.526194: ImmuneML: starting the analysis...\r\n",
      "\r\n",
      "2021-07-16 09:35:14.526631: Instruction 1/1 has started.\r\n",
      "2021-07-16 09:35:20.293167: Instruction 1/1 has finished.\r\n",
      "2021-07-16 09:35:20.299080: Generating HTML reports...\r\n",
      "2021-07-16 09:35:20.382491: HTML reports are generated.\r\n",
      "2021-07-16 09:35:20.383710: ImmuneML: finished analysis.\r\n",
      "\r\n",
      "2021-07-16 09:35:22.428921: Setting temporary cache path to wd/analyses/olga/varying_n_kmers/seqcount_266k/ML_output/repertoire_implanting_rate__0.002/cache/\r\n",
      "2021-07-16 09:35:22.429018: ImmuneML: parsing the specification...\r\n",
      "\r\n",
      "2021-07-16 09:35:25.156247: Full specification is available at wd/analyses/olga/varying_n_kmers/seqcount_266k/ML_output/repertoire_implanting_rate__0.002/full_repertoire_implanting_rate__0.yaml.\r\n",
      "\r\n",
      "2021-07-16 09:35:25.156335: ImmuneML: starting the analysis...\r\n",
      "\r\n",
      "2021-07-16 09:35:25.156554: Instruction 1/2 has started.\r\n",
      "2021-07-16 09:35:25.296107: Training ML model: running outer CV loop: started split 1/5.\r\n",
      "\r\n",
      "2021-07-16 09:35:25.337960: Hyperparameter optimization: running the inner loop of nested CV: selection for label signal_cancer (label 1 / 1).\r\n",
      "\r\n",
      "2021-07-16 09:35:25.343756: Evaluating hyperparameter setting: feature_size_4_logistic_regression...\r\n",
      "2021-07-16 09:35:25.353418: Encoding started...\r\n",
      "2021-07-16 09:35:29.068399: Encoding finished.\r\n",
      "2021-07-16 09:35:29.068827: ML model training started...\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "2021-07-16 09:35:52.852719: ML model training finished.\r\n",
      "2021-07-16 09:35:52.853600: Completed hyperparameter setting feature_size_4_logistic_regression.\r\n",
      "\r\n",
      "2021-07-16 09:35:52.857452: Hyperparameter optimization: running the inner loop of nested CV: completed selection for label signal_cancer (label 1 / 1).\r\n",
      "\r\n",
      "2021-07-16 09:35:52.857853: Training ML model: running the inner loop of nested CV: retrain models for label signal_cancer (label 1 / 1).\r\n",
      "\r\n",
      "2021-07-16 09:35:52.864309: Evaluating hyperparameter setting: feature_size_4_logistic_regression...\r\n",
      "2021-07-16 09:35:52.876355: Encoding started...\r\n",
      "2021-07-16 09:35:55.659798: Encoding finished.\r\n",
      "2021-07-16 09:35:55.660409: ML model training started...\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "2021-07-16 09:36:19.712446: ML model training finished.\r\n",
      "2021-07-16 09:36:19.716656: Encoding started...\r\n",
      "2021-07-16 09:36:21.482405: Encoding finished.\r\n",
      "2021-07-16 09:36:27.263644: Completed hyperparameter setting feature_size_4_logistic_regression.\r\n",
      "\r\n",
      "2021-07-16 09:36:27.269459: Training ML model: running the inner loop of nested CV: completed retraining models for label signal_cancer (label 1 / 1).\r\n",
      "\r\n",
      "2021-07-16 09:36:27.269584: Training ML model: running outer CV loop: finished split 1/5.\r\n",
      "\r\n",
      "2021-07-16 09:36:27.269767: Training ML model: running outer CV loop: started split 2/5.\r\n",
      "\r\n",
      "2021-07-16 09:36:27.335229: Hyperparameter optimization: running the inner loop of nested CV: selection for label signal_cancer (label 1 / 1).\r\n",
      "\r\n",
      "2021-07-16 09:36:27.342296: Evaluating hyperparameter setting: feature_size_4_logistic_regression...\r\n",
      "2021-07-16 09:36:27.356871: Encoding started...\r\n",
      "2021-07-16 09:36:31.344974: Encoding finished.\r\n",
      "2021-07-16 09:36:31.345225: ML model training started...\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "2021-07-16 09:37:02.940204: ML model training finished.\r\n",
      "2021-07-16 09:37:02.940743: Completed hyperparameter setting feature_size_4_logistic_regression.\r\n",
      "\r\n",
      "2021-07-16 09:37:02.944471: Hyperparameter optimization: running the inner loop of nested CV: completed selection for label signal_cancer (label 1 / 1).\r\n",
      "\r\n",
      "2021-07-16 09:37:02.944662: Training ML model: running the inner loop of nested CV: retrain models for label signal_cancer (label 1 / 1).\r\n",
      "\r\n",
      "2021-07-16 09:37:02.951242: Evaluating hyperparameter setting: feature_size_4_logistic_regression...\r\n",
      "2021-07-16 09:37:02.960891: Encoding started...\r\n",
      "2021-07-16 09:37:05.691940: Encoding finished.\r\n",
      "2021-07-16 09:37:05.692327: ML model training started...\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "2021-07-16 09:37:40.660975: ML model training finished.\r\n",
      "2021-07-16 09:37:40.664332: Encoding started...\r\n",
      "2021-07-16 09:37:42.336566: Encoding finished.\r\n",
      "2021-07-16 09:37:47.193793: Completed hyperparameter setting feature_size_4_logistic_regression.\r\n",
      "\r\n",
      "2021-07-16 09:37:47.197897: Training ML model: running the inner loop of nested CV: completed retraining models for label signal_cancer (label 1 / 1).\r\n",
      "\r\n",
      "2021-07-16 09:37:47.198032: Training ML model: running outer CV loop: finished split 2/5.\r\n",
      "\r\n",
      "2021-07-16 09:37:47.198080: Training ML model: running outer CV loop: started split 3/5.\r\n",
      "\r\n",
      "2021-07-16 09:37:47.251006: Hyperparameter optimization: running the inner loop of nested CV: selection for label signal_cancer (label 1 / 1).\r\n",
      "\r\n",
      "2021-07-16 09:37:47.257482: Evaluating hyperparameter setting: feature_size_4_logistic_regression...\r\n",
      "2021-07-16 09:37:47.268312: Encoding started...\r\n",
      "2021-07-16 09:37:50.355730: Encoding finished.\r\n",
      "2021-07-16 09:37:50.356421: ML model training started...\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "2021-07-16 09:38:22.628914: ML model training finished.\r\n",
      "2021-07-16 09:38:22.629070: Completed hyperparameter setting feature_size_4_logistic_regression.\r\n",
      "\r\n",
      "2021-07-16 09:38:22.632426: Hyperparameter optimization: running the inner loop of nested CV: completed selection for label signal_cancer (label 1 / 1).\r\n",
      "\r\n",
      "2021-07-16 09:38:22.632583: Training ML model: running the inner loop of nested CV: retrain models for label signal_cancer (label 1 / 1).\r\n",
      "\r\n",
      "2021-07-16 09:38:22.638591: Evaluating hyperparameter setting: feature_size_4_logistic_regression...\r\n",
      "2021-07-16 09:38:22.651454: Encoding started...\r\n",
      "2021-07-16 09:38:25.875874: Encoding finished.\r\n",
      "2021-07-16 09:38:25.876437: ML model training started...\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "2021-07-16 09:38:54.985209: ML model training finished.\r\n",
      "2021-07-16 09:38:54.989215: Encoding started...\r\n",
      "2021-07-16 09:38:56.669364: Encoding finished.\r\n",
      "2021-07-16 09:39:01.335322: Completed hyperparameter setting feature_size_4_logistic_regression.\r\n",
      "\r\n",
      "2021-07-16 09:39:01.340367: Training ML model: running the inner loop of nested CV: completed retraining models for label signal_cancer (label 1 / 1).\r\n",
      "\r\n",
      "2021-07-16 09:39:01.340582: Training ML model: running outer CV loop: finished split 3/5.\r\n",
      "\r\n",
      "2021-07-16 09:39:01.340893: Training ML model: running outer CV loop: started split 4/5.\r\n",
      "\r\n",
      "2021-07-16 09:39:01.402056: Hyperparameter optimization: running the inner loop of nested CV: selection for label signal_cancer (label 1 / 1).\r\n",
      "\r\n",
      "2021-07-16 09:39:01.408681: Evaluating hyperparameter setting: feature_size_4_logistic_regression...\r\n",
      "2021-07-16 09:39:01.419557: Encoding started...\r\n",
      "2021-07-16 09:39:04.811355: Encoding finished.\r\n",
      "2021-07-16 09:39:04.812090: ML model training started...\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "2021-07-16 09:39:38.417899: ML model training finished.\r\n",
      "2021-07-16 09:39:38.418813: Completed hyperparameter setting feature_size_4_logistic_regression.\r\n",
      "\r\n",
      "2021-07-16 09:39:38.423084: Hyperparameter optimization: running the inner loop of nested CV: completed selection for label signal_cancer (label 1 / 1).\r\n",
      "\r\n",
      "2021-07-16 09:39:38.423559: Training ML model: running the inner loop of nested CV: retrain models for label signal_cancer (label 1 / 1).\r\n",
      "\r\n",
      "2021-07-16 09:39:38.430196: Evaluating hyperparameter setting: feature_size_4_logistic_regression...\r\n",
      "2021-07-16 09:39:38.440941: Encoding started...\r\n",
      "2021-07-16 09:39:41.613254: Encoding finished.\r\n",
      "2021-07-16 09:39:41.614427: ML model training started...\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "2021-07-16 09:40:13.210641: ML model training finished.\r\n",
      "2021-07-16 09:40:13.214773: Encoding started...\r\n",
      "2021-07-16 09:40:14.880156: Encoding finished.\r\n",
      "2021-07-16 09:40:19.267348: Completed hyperparameter setting feature_size_4_logistic_regression.\r\n",
      "\r\n",
      "2021-07-16 09:40:19.274063: Training ML model: running the inner loop of nested CV: completed retraining models for label signal_cancer (label 1 / 1).\r\n",
      "\r\n",
      "2021-07-16 09:40:19.274188: Training ML model: running outer CV loop: finished split 4/5.\r\n",
      "\r\n",
      "2021-07-16 09:40:19.274213: Training ML model: running outer CV loop: started split 5/5.\r\n",
      "\r\n",
      "2021-07-16 09:40:19.322264: Hyperparameter optimization: running the inner loop of nested CV: selection for label signal_cancer (label 1 / 1).\r\n",
      "\r\n",
      "2021-07-16 09:40:19.328478: Evaluating hyperparameter setting: feature_size_4_logistic_regression...\r\n",
      "2021-07-16 09:40:19.338873: Encoding started...\r\n",
      "2021-07-16 09:40:22.580959: Encoding finished.\r\n",
      "2021-07-16 09:40:22.581182: ML model training started...\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "2021-07-16 09:40:48.918754: ML model training finished.\r\n",
      "2021-07-16 09:40:48.919501: Completed hyperparameter setting feature_size_4_logistic_regression.\r\n",
      "\r\n",
      "2021-07-16 09:40:48.925689: Hyperparameter optimization: running the inner loop of nested CV: completed selection for label signal_cancer (label 1 / 1).\r\n",
      "\r\n",
      "2021-07-16 09:40:48.926423: Training ML model: running the inner loop of nested CV: retrain models for label signal_cancer (label 1 / 1).\r\n",
      "\r\n",
      "2021-07-16 09:40:48.933499: Evaluating hyperparameter setting: feature_size_4_logistic_regression...\r\n",
      "2021-07-16 09:40:48.944839: Encoding started...\r\n",
      "2021-07-16 09:40:51.867489: Encoding finished.\r\n",
      "2021-07-16 09:40:51.867781: ML model training started...\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "2021-07-16 09:41:23.801788: ML model training finished.\r\n",
      "2021-07-16 09:41:23.804646: Encoding started...\r\n",
      "2021-07-16 09:41:25.420215: Encoding finished.\r\n",
      "2021-07-16 09:41:29.848590: Completed hyperparameter setting feature_size_4_logistic_regression.\r\n",
      "\r\n",
      "2021-07-16 09:41:29.855798: Training ML model: running the inner loop of nested CV: completed retraining models for label signal_cancer (label 1 / 1).\r\n",
      "\r\n",
      "2021-07-16 09:41:29.855963: Training ML model: running outer CV loop: finished split 5/5.\r\n",
      "\r\n",
      "Performances (accuracy) -----------------------------------------------\r\n",
      "\r\n",
      "\r\n",
      "Label: signal_cancer\r\n",
      "Performance (accuracy) per assessment split:\r\n",
      "Split 1: 0.65\r\n",
      "Split 2: 0.55\r\n",
      "Split 3: 0.575\r\n",
      "Split 4: 0.5\r\n",
      "Split 5: 0.45\r\n",
      "Average performance (accuracy): 0.5450000000000002\r\n",
      "------------------------------\r\n",
      "2021-07-16 09:41:32.521497: Instruction 1/2 has finished.\r\n",
      "2021-07-16 09:41:32.521658: Instruction 2/2 has started.\r\n",
      "2021-07-16 09:41:32.521721: Started analysis signal_freq (1/1).\r\n",
      "2021-07-16 09:41:32.527240: Encoding started...\r\n",
      "2021-07-16 09:41:32.536356: Encoding repertoire 1/200\r\n",
      "2021-07-16 09:41:32.551197: Encoding repertoire 2/200\r\n",
      "2021-07-16 09:41:32.557895: Encoding repertoire 3/200\r\n",
      "2021-07-16 09:41:32.564987: Encoding repertoire 4/200\r\n",
      "2021-07-16 09:41:32.571796: Encoding repertoire 5/200\r\n",
      "2021-07-16 09:41:32.578563: Encoding repertoire 6/200\r\n",
      "2021-07-16 09:41:32.585519: Encoding repertoire 7/200\r\n",
      "2021-07-16 09:41:32.592603: Encoding repertoire 8/200\r\n",
      "2021-07-16 09:41:32.599999: Encoding repertoire 9/200\r\n",
      "2021-07-16 09:41:32.609386: Encoding repertoire 10/200\r\n",
      "2021-07-16 09:41:32.616257: Encoding repertoire 11/200\r\n",
      "2021-07-16 09:41:32.623032: Encoding repertoire 12/200\r\n",
      "2021-07-16 09:41:32.629311: Encoding repertoire 13/200\r\n",
      "2021-07-16 09:41:32.636081: Encoding repertoire 14/200\r\n",
      "2021-07-16 09:41:32.643208: Encoding repertoire 15/200\r\n",
      "2021-07-16 09:41:32.649700: Encoding repertoire 16/200\r\n",
      "2021-07-16 09:41:32.656609: Encoding repertoire 17/200\r\n",
      "2021-07-16 09:41:32.664221: Encoding repertoire 18/200\r\n",
      "2021-07-16 09:41:32.671508: Encoding repertoire 19/200\r\n",
      "2021-07-16 09:41:32.678320: Encoding repertoire 20/200\r\n",
      "2021-07-16 09:41:32.685835: Encoding repertoire 21/200\r\n",
      "2021-07-16 09:41:32.693832: Encoding repertoire 22/200\r\n",
      "2021-07-16 09:41:32.703369: Encoding repertoire 23/200\r\n",
      "2021-07-16 09:41:32.711179: Encoding repertoire 24/200\r\n",
      "2021-07-16 09:41:32.718578: Encoding repertoire 25/200\r\n",
      "2021-07-16 09:41:32.727277: Encoding repertoire 26/200\r\n",
      "2021-07-16 09:41:32.734794: Encoding repertoire 27/200\r\n",
      "2021-07-16 09:41:32.742983: Encoding repertoire 28/200\r\n",
      "2021-07-16 09:41:32.751932: Encoding repertoire 29/200\r\n",
      "2021-07-16 09:41:32.759677: Encoding repertoire 30/200\r\n",
      "2021-07-16 09:41:32.766332: Encoding repertoire 31/200\r\n",
      "2021-07-16 09:41:32.772992: Encoding repertoire 32/200\r\n",
      "2021-07-16 09:41:32.782313: Encoding repertoire 33/200\r\n",
      "2021-07-16 09:41:32.791314: Encoding repertoire 34/200\r\n",
      "2021-07-16 09:41:32.798793: Encoding repertoire 35/200\r\n",
      "2021-07-16 09:41:32.807815: Encoding repertoire 36/200\r\n",
      "2021-07-16 09:41:32.816529: Encoding repertoire 37/200\r\n",
      "2021-07-16 09:41:32.823253: Encoding repertoire 38/200\r\n",
      "2021-07-16 09:41:32.830356: Encoding repertoire 39/200\r\n",
      "2021-07-16 09:41:32.837856: Encoding repertoire 40/200\r\n",
      "2021-07-16 09:41:32.845050: Encoding repertoire 41/200\r\n",
      "2021-07-16 09:41:32.851316: Encoding repertoire 42/200\r\n",
      "2021-07-16 09:41:32.858706: Encoding repertoire 43/200\r\n",
      "2021-07-16 09:41:32.865809: Encoding repertoire 44/200\r\n",
      "2021-07-16 09:41:32.872108: Encoding repertoire 45/200\r\n",
      "2021-07-16 09:41:32.879015: Encoding repertoire 46/200\r\n",
      "2021-07-16 09:41:32.885250: Encoding repertoire 47/200\r\n",
      "2021-07-16 09:41:32.892567: Encoding repertoire 48/200\r\n",
      "2021-07-16 09:41:32.899422: Encoding repertoire 49/200\r\n",
      "2021-07-16 09:41:32.906907: Encoding repertoire 50/200\r\n",
      "2021-07-16 09:41:32.913993: Encoding repertoire 51/200\r\n",
      "2021-07-16 09:41:32.920702: Encoding repertoire 52/200\r\n",
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      "2021-07-16 09:41:32.941618: Encoding repertoire 55/200\r\n",
      "2021-07-16 09:41:32.948008: Encoding repertoire 56/200\r\n",
      "2021-07-16 09:41:32.955356: Encoding repertoire 57/200\r\n",
      "2021-07-16 09:41:32.962078: Encoding repertoire 58/200\r\n",
      "2021-07-16 09:41:32.969069: Encoding repertoire 59/200\r\n",
      "2021-07-16 09:41:32.976517: Encoding repertoire 60/200\r\n",
      "2021-07-16 09:41:32.983476: Encoding repertoire 61/200\r\n",
      "2021-07-16 09:41:32.990349: Encoding repertoire 62/200\r\n",
      "2021-07-16 09:41:32.996594: Encoding repertoire 63/200\r\n",
      "2021-07-16 09:41:33.003343: Encoding repertoire 64/200\r\n",
      "2021-07-16 09:41:33.010921: Encoding repertoire 65/200\r\n",
      "2021-07-16 09:41:33.018165: Encoding repertoire 66/200\r\n",
      "2021-07-16 09:41:33.026224: Encoding repertoire 67/200\r\n",
      "2021-07-16 09:41:33.032544: Encoding repertoire 68/200\r\n",
      "2021-07-16 09:41:33.040297: Encoding repertoire 69/200\r\n",
      "2021-07-16 09:41:33.048710: Encoding repertoire 70/200\r\n",
      "2021-07-16 09:41:33.056172: Encoding repertoire 71/200\r\n",
      "2021-07-16 09:41:33.063295: Encoding repertoire 72/200\r\n",
      "2021-07-16 09:41:33.070094: Encoding repertoire 73/200\r\n",
      "2021-07-16 09:41:33.076860: Encoding repertoire 74/200\r\n",
      "2021-07-16 09:41:33.083339: Encoding repertoire 75/200\r\n",
      "2021-07-16 09:41:33.089371: Encoding repertoire 76/200\r\n",
      "2021-07-16 09:41:33.096029: Encoding repertoire 77/200\r\n",
      "2021-07-16 09:41:33.103243: Encoding repertoire 78/200\r\n",
      "2021-07-16 09:41:33.110115: Encoding repertoire 79/200\r\n",
      "2021-07-16 09:41:33.116365: Encoding repertoire 80/200\r\n",
      "2021-07-16 09:41:33.123610: Encoding repertoire 81/200\r\n",
      "2021-07-16 09:41:33.130160: Encoding repertoire 82/200\r\n",
      "2021-07-16 09:41:33.137057: Encoding repertoire 83/200\r\n",
      "2021-07-16 09:41:33.143477: Encoding repertoire 84/200\r\n",
      "2021-07-16 09:41:33.149974: Encoding repertoire 85/200\r\n",
      "2021-07-16 09:41:33.156561: Encoding repertoire 86/200\r\n",
      "2021-07-16 09:41:33.163654: Encoding repertoire 87/200\r\n",
      "2021-07-16 09:41:33.170778: Encoding repertoire 88/200\r\n",
      "2021-07-16 09:41:33.177591: Encoding repertoire 89/200\r\n",
      "2021-07-16 09:41:33.184317: Encoding repertoire 90/200\r\n",
      "2021-07-16 09:41:33.190674: Encoding repertoire 91/200\r\n",
      "2021-07-16 09:41:33.197739: Encoding repertoire 92/200\r\n",
      "2021-07-16 09:41:33.204924: Encoding repertoire 93/200\r\n",
      "2021-07-16 09:41:33.211457: Encoding repertoire 94/200\r\n",
      "2021-07-16 09:41:33.217875: Encoding repertoire 95/200\r\n",
      "2021-07-16 09:41:33.224949: Encoding repertoire 96/200\r\n",
      "2021-07-16 09:41:33.232147: Encoding repertoire 97/200\r\n",
      "2021-07-16 09:41:33.240197: Encoding repertoire 98/200\r\n",
      "2021-07-16 09:41:33.247151: Encoding repertoire 99/200\r\n",
      "2021-07-16 09:41:33.254569: Encoding repertoire 100/200\r\n",
      "2021-07-16 09:41:33.260720: Encoding repertoire 101/200\r\n",
      "2021-07-16 09:41:33.267301: Encoding repertoire 102/200\r\n",
      "2021-07-16 09:41:33.273552: Encoding repertoire 103/200\r\n",
      "2021-07-16 09:41:33.280577: Encoding repertoire 104/200\r\n",
      "2021-07-16 09:41:33.287352: Encoding repertoire 105/200\r\n",
      "2021-07-16 09:41:33.294001: Encoding repertoire 106/200\r\n",
      "2021-07-16 09:41:33.300016: Encoding repertoire 107/200\r\n",
      "2021-07-16 09:41:33.307058: Encoding repertoire 108/200\r\n",
      "2021-07-16 09:41:33.313858: Encoding repertoire 109/200\r\n",
      "2021-07-16 09:41:33.320265: Encoding repertoire 110/200\r\n",
      "2021-07-16 09:41:33.326802: Encoding repertoire 111/200\r\n",
      "2021-07-16 09:41:33.333460: Encoding repertoire 112/200\r\n",
      "2021-07-16 09:41:33.340004: Encoding repertoire 113/200\r\n",
      "2021-07-16 09:41:33.346537: Encoding repertoire 114/200\r\n",
      "2021-07-16 09:41:33.353820: Encoding repertoire 115/200\r\n",
      "2021-07-16 09:41:33.360308: Encoding repertoire 116/200\r\n",
      "2021-07-16 09:41:33.366179: Encoding repertoire 117/200\r\n",
      "2021-07-16 09:41:33.373127: Encoding repertoire 118/200\r\n",
      "2021-07-16 09:41:33.379996: Encoding repertoire 119/200\r\n",
      "2021-07-16 09:41:33.385963: Encoding repertoire 120/200\r\n",
      "2021-07-16 09:41:33.393002: Encoding repertoire 121/200\r\n",
      "2021-07-16 09:41:33.399299: Encoding repertoire 122/200\r\n",
      "2021-07-16 09:41:33.405790: Encoding repertoire 123/200\r\n",
      "2021-07-16 09:41:33.412615: Encoding repertoire 124/200\r\n",
      "2021-07-16 09:41:33.419218: Encoding repertoire 125/200\r\n",
      "2021-07-16 09:41:33.425211: Encoding repertoire 126/200\r\n",
      "2021-07-16 09:41:33.432049: Encoding repertoire 127/200\r\n",
      "2021-07-16 09:41:33.438684: Encoding repertoire 128/200\r\n",
      "2021-07-16 09:41:33.445337: Encoding repertoire 129/200\r\n",
      "2021-07-16 09:41:33.454145: Encoding repertoire 130/200\r\n",
      "2021-07-16 09:41:33.461024: Encoding repertoire 131/200\r\n",
      "2021-07-16 09:41:33.466896: Encoding repertoire 132/200\r\n",
      "2021-07-16 09:41:33.473541: Encoding repertoire 133/200\r\n",
      "2021-07-16 09:41:33.479515: Encoding repertoire 134/200\r\n",
      "2021-07-16 09:41:33.486350: Encoding repertoire 135/200\r\n",
      "2021-07-16 09:41:33.492707: Encoding repertoire 136/200\r\n",
      "2021-07-16 09:41:33.499366: Encoding repertoire 137/200\r\n",
      "2021-07-16 09:41:33.505642: Encoding repertoire 138/200\r\n",
      "2021-07-16 09:41:33.512626: Encoding repertoire 139/200\r\n",
      "2021-07-16 09:41:33.519575: Encoding repertoire 140/200\r\n",
      "2021-07-16 09:41:33.525816: Encoding repertoire 141/200\r\n",
      "2021-07-16 09:41:33.532350: Encoding repertoire 142/200\r\n",
      "2021-07-16 09:41:33.538877: Encoding repertoire 143/200\r\n",
      "2021-07-16 09:41:33.545119: Encoding repertoire 144/200\r\n",
      "2021-07-16 09:41:33.551439: Encoding repertoire 145/200\r\n",
      "2021-07-16 09:41:33.557871: Encoding repertoire 146/200\r\n",
      "2021-07-16 09:41:33.563753: Encoding repertoire 147/200\r\n",
      "2021-07-16 09:41:33.569703: Encoding repertoire 148/200\r\n",
      "2021-07-16 09:41:33.575804: Encoding repertoire 149/200\r\n",
      "2021-07-16 09:41:33.582449: Encoding repertoire 150/200\r\n",
      "2021-07-16 09:41:33.589309: Encoding repertoire 151/200\r\n",
      "2021-07-16 09:41:33.596016: Encoding repertoire 152/200\r\n",
      "2021-07-16 09:41:33.602793: Encoding repertoire 153/200\r\n",
      "2021-07-16 09:41:33.609464: Encoding repertoire 154/200\r\n",
      "2021-07-16 09:41:33.616325: Encoding repertoire 155/200\r\n",
      "2021-07-16 09:41:33.622302: Encoding repertoire 156/200\r\n",
      "2021-07-16 09:41:33.628464: Encoding repertoire 157/200\r\n",
      "2021-07-16 09:41:33.635503: Encoding repertoire 158/200\r\n",
      "2021-07-16 09:41:33.643115: Encoding repertoire 159/200\r\n",
      "2021-07-16 09:41:33.649099: Encoding repertoire 160/200\r\n",
      "2021-07-16 09:41:33.658506: Encoding repertoire 161/200\r\n",
      "2021-07-16 09:41:33.666891: Encoding repertoire 162/200\r\n",
      "2021-07-16 09:41:33.673456: Encoding repertoire 163/200\r\n",
      "2021-07-16 09:41:33.679232: Encoding repertoire 164/200\r\n",
      "2021-07-16 09:41:33.685759: Encoding repertoire 165/200\r\n",
      "2021-07-16 09:41:33.692521: Encoding repertoire 166/200\r\n",
      "2021-07-16 09:41:33.699578: Encoding repertoire 167/200\r\n",
      "2021-07-16 09:41:33.706215: Encoding repertoire 168/200\r\n",
      "2021-07-16 09:41:33.712939: Encoding repertoire 169/200\r\n",
      "2021-07-16 09:41:33.719565: Encoding repertoire 170/200\r\n",
      "2021-07-16 09:41:33.726681: Encoding repertoire 171/200\r\n",
      "2021-07-16 09:41:33.732745: Encoding repertoire 172/200\r\n",
      "2021-07-16 09:41:33.740416: Encoding repertoire 173/200\r\n",
      "2021-07-16 09:41:33.749117: Encoding repertoire 174/200\r\n",
      "2021-07-16 09:41:33.756903: Encoding repertoire 175/200\r\n",
      "2021-07-16 09:41:33.763037: Encoding repertoire 176/200\r\n",
      "2021-07-16 09:41:33.769045: Encoding repertoire 177/200\r\n",
      "2021-07-16 09:41:33.777143: Encoding repertoire 178/200\r\n",
      "2021-07-16 09:41:33.783346: Encoding repertoire 179/200\r\n",
      "2021-07-16 09:41:33.792670: Encoding repertoire 180/200\r\n",
      "2021-07-16 09:41:33.803383: Encoding repertoire 181/200\r\n",
      "2021-07-16 09:41:33.814485: Encoding repertoire 182/200\r\n",
      "2021-07-16 09:41:33.822020: Encoding repertoire 183/200\r\n",
      "2021-07-16 09:41:33.828647: Encoding repertoire 184/200\r\n",
      "2021-07-16 09:41:33.834601: Encoding repertoire 185/200\r\n",
      "2021-07-16 09:41:33.841318: Encoding repertoire 186/200\r\n",
      "2021-07-16 09:41:33.847785: Encoding repertoire 187/200\r\n",
      "2021-07-16 09:41:33.857883: Encoding repertoire 188/200\r\n",
      "2021-07-16 09:41:33.865391: Encoding repertoire 189/200\r\n",
      "2021-07-16 09:41:33.872034: Encoding repertoire 190/200\r\n",
      "2021-07-16 09:41:33.878512: Encoding repertoire 191/200\r\n",
      "2021-07-16 09:41:33.885061: Encoding repertoire 192/200\r\n",
      "2021-07-16 09:41:33.892708: Encoding repertoire 193/200\r\n",
      "2021-07-16 09:41:33.900959: Encoding repertoire 194/200\r\n",
      "2021-07-16 09:41:33.907970: Encoding repertoire 195/200\r\n",
      "2021-07-16 09:41:33.914367: Encoding repertoire 196/200\r\n",
      "2021-07-16 09:41:33.920380: Encoding repertoire 197/200\r\n",
      "2021-07-16 09:41:33.926368: Encoding repertoire 198/200\r\n",
      "2021-07-16 09:41:33.932822: Encoding repertoire 199/200\r\n",
      "2021-07-16 09:41:33.939405: Encoding repertoire 200/200\r\n",
      "2021-07-16 09:41:33.993079: Saving encoded dataset to disk.\r\n",
      "2021-07-16 09:41:36.770615: Encoding finished.\r\n",
      "2021-07-16 09:41:38.467579: Finished analysis signal_freq (1/1).\r\n",
      "\r\n",
      "2021-07-16 09:41:38.467675: Instruction 2/2 has finished.\r\n",
      "2021-07-16 09:41:38.475342: Generating HTML reports...\r\n",
      "2021-07-16 09:41:40.266413: HTML reports are generated.\r\n",
      "2021-07-16 09:41:41.240961: ImmuneML: finished analysis.\r\n",
      "\r\n"
     ]
    }
   ],
   "source": [
    "!mkdir -p analyses/olga/varying_n_kmers/seqcount_266k/{simulated_data,ML_output}\n",
    "!docker container prune -f\n",
    "!docker run -it -v $(pwd):/wd --name simulation_container kanduric/immuneml-v1:latest immune-ml /wd/scripts/olga/run_1/varying_n_kmers/seqcount_266k/generated_yaml_files/simulation_instructions/repertoire_implanting_rate__0.002.yaml /wd/analyses/olga/varying_n_kmers/seqcount_266k/simulated_data/repertoire_implanting_rate__0.002\n",
    "!docker run -it -v $(pwd):/wd --name ml_container kanduric/immuneml-v1:latest immune-ml /wd/scripts/olga/run_1/varying_n_kmers/seqcount_266k/generated_yaml_files/ml_instructions/repertoire_implanting_rate__0.002_-_ml_instruction.yaml /wd/analyses/olga/varying_n_kmers/seqcount_266k/ML_output/repertoire_implanting_rate__0.002\n"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "#### Class balance\n",
    "\n",
    "The stdout printed to screen when running the simulation and ML model training instructions are shown below indicating successful execution, and the resulting output of each instruction is stored under `analyses/olga/dataset_implanting_rate/` in this github repository."
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Deleted Containers:\r\n",
      "ded674508a97e0a70f21a2a04ec43fd7966efcb2fc7fcbe32bfcc87f23ae97ae\r\n",
      "d2d03befd0422ea378a06a2ea089657cbb45f99bdb36278355967d297f923ba2\r\n",
      "\r\n",
      "Total reclaimed space: 47.09kB\r\n",
      "2021-07-16 10:14:52.273121: Setting temporary cache path to wd/analyses/olga/dataset_implanting_rate/simulated_data/repertoire_implanting_rate__1e-05_-_dataset_implanting_rate__0.6/cache/\r\n",
      "2021-07-16 10:14:52.273174: ImmuneML: parsing the specification...\r\n",
      "\r\n",
      "2021-07-16 10:14:57.175949: Full specification is available at wd/analyses/olga/dataset_implanting_rate/simulated_data/repertoire_implanting_rate__1e-05_-_dataset_implanting_rate__0.6/full_repertoire_implanting_rate__1e-05_-_dataset_implanting_rate__0.yaml.\r\n",
      "\r\n",
      "2021-07-16 10:14:57.176086: ImmuneML: starting the analysis...\r\n",
      "\r\n",
      "2021-07-16 10:14:57.176167: Instruction 1/1 has started.\r\n",
      "2021-07-16 10:15:04.232690: Instruction 1/1 has finished.\r\n",
      "2021-07-16 10:15:04.239148: Generating HTML reports...\r\n",
      "2021-07-16 10:15:04.331540: HTML reports are generated.\r\n",
      "2021-07-16 10:15:04.333070: ImmuneML: finished analysis.\r\n",
      "\r\n",
      "2021-07-16 10:15:06.849390: Setting temporary cache path to wd/analyses/olga/dataset_implanting_rate/ML_output/repertoire_implanting_rate__1e-05_-_dataset_implanting_rate__0.6/cache/\r\n",
      "2021-07-16 10:15:06.849461: ImmuneML: parsing the specification...\r\n",
      "\r\n",
      "2021-07-16 10:15:09.592053: Full specification is available at wd/analyses/olga/dataset_implanting_rate/ML_output/repertoire_implanting_rate__1e-05_-_dataset_implanting_rate__0.6/full_repertoire_implanting_rate__1e-05_-_dataset_implanting_rate__0.yaml.\r\n",
      "\r\n",
      "2021-07-16 10:15:09.592148: ImmuneML: starting the analysis...\r\n",
      "\r\n",
      "2021-07-16 10:15:09.592620: Instruction 1/2 has started.\r\n",
      "2021-07-16 10:15:09.741106: Training ML model: running outer CV loop: started split 1/5.\r\n",
      "\r\n",
      "2021-07-16 10:15:09.790221: Hyperparameter optimization: running the inner loop of nested CV: selection for label signal_cancer (label 1 / 1).\r\n",
      "\r\n",
      "2021-07-16 10:15:09.796275: Evaluating hyperparameter setting: feature_size_4_logistic_regression...\r\n",
      "2021-07-16 10:15:09.805772: Encoding started...\r\n",
      "2021-07-16 10:15:13.945493: Encoding finished.\r\n",
      "2021-07-16 10:15:13.946090: ML model training started...\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "2021-07-16 10:15:37.243289: ML model training finished.\r\n",
      "2021-07-16 10:15:37.243539: Completed hyperparameter setting feature_size_4_logistic_regression.\r\n",
      "\r\n",
      "2021-07-16 10:15:37.247076: Hyperparameter optimization: running the inner loop of nested CV: completed selection for label signal_cancer (label 1 / 1).\r\n",
      "\r\n",
      "2021-07-16 10:15:37.247198: Training ML model: running the inner loop of nested CV: retrain models for label signal_cancer (label 1 / 1).\r\n",
      "\r\n",
      "2021-07-16 10:15:37.255205: Evaluating hyperparameter setting: feature_size_4_logistic_regression...\r\n",
      "2021-07-16 10:15:37.266173: Encoding started...\r\n",
      "2021-07-16 10:15:39.928682: Encoding finished.\r\n",
      "2021-07-16 10:15:39.929032: ML model training started...\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "2021-07-16 10:16:04.290858: ML model training finished.\r\n",
      "2021-07-16 10:16:04.295192: Encoding started...\r\n",
      "2021-07-16 10:16:06.432118: Encoding finished.\r\n",
      "2021-07-16 10:16:08.661012: Completed hyperparameter setting feature_size_4_logistic_regression.\r\n",
      "\r\n",
      "2021-07-16 10:16:08.664425: Training ML model: running the inner loop of nested CV: completed retraining models for label signal_cancer (label 1 / 1).\r\n",
      "\r\n",
      "2021-07-16 10:16:08.664520: Training ML model: running outer CV loop: finished split 1/5.\r\n",
      "\r\n",
      "2021-07-16 10:16:08.664564: Training ML model: running outer CV loop: started split 2/5.\r\n",
      "\r\n",
      "2021-07-16 10:16:08.713069: Hyperparameter optimization: running the inner loop of nested CV: selection for label signal_cancer (label 1 / 1).\r\n",
      "\r\n",
      "2021-07-16 10:16:08.719031: Evaluating hyperparameter setting: feature_size_4_logistic_regression...\r\n",
      "2021-07-16 10:16:08.729985: Encoding started...\r\n",
      "2021-07-16 10:16:12.530585: Encoding finished.\r\n",
      "2021-07-16 10:16:12.531033: ML model training started...\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "2021-07-16 10:16:42.812601: ML model training finished.\r\n",
      "2021-07-16 10:16:42.813447: Completed hyperparameter setting feature_size_4_logistic_regression.\r\n",
      "\r\n",
      "2021-07-16 10:16:42.819848: Hyperparameter optimization: running the inner loop of nested CV: completed selection for label signal_cancer (label 1 / 1).\r\n",
      "\r\n",
      "2021-07-16 10:16:42.820903: Training ML model: running the inner loop of nested CV: retrain models for label signal_cancer (label 1 / 1).\r\n",
      "\r\n",
      "2021-07-16 10:16:42.830420: Evaluating hyperparameter setting: feature_size_4_logistic_regression...\r\n",
      "2021-07-16 10:16:42.844931: Encoding started...\r\n",
      "2021-07-16 10:16:46.405038: Encoding finished.\r\n",
      "2021-07-16 10:16:46.405795: ML model training started...\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "2021-07-16 10:17:14.943993: ML model training finished.\r\n",
      "2021-07-16 10:17:14.946820: Encoding started...\r\n",
      "2021-07-16 10:17:16.828012: Encoding finished.\r\n",
      "2021-07-16 10:17:18.808227: Completed hyperparameter setting feature_size_4_logistic_regression.\r\n",
      "\r\n",
      "2021-07-16 10:17:18.812640: Training ML model: running the inner loop of nested CV: completed retraining models for label signal_cancer (label 1 / 1).\r\n",
      "\r\n",
      "2021-07-16 10:17:18.812763: Training ML model: running outer CV loop: finished split 2/5.\r\n",
      "\r\n",
      "2021-07-16 10:17:18.812788: Training ML model: running outer CV loop: started split 3/5.\r\n",
      "\r\n",
      "2021-07-16 10:17:18.865434: Hyperparameter optimization: running the inner loop of nested CV: selection for label signal_cancer (label 1 / 1).\r\n",
      "\r\n",
      "2021-07-16 10:17:18.871522: Evaluating hyperparameter setting: feature_size_4_logistic_regression...\r\n",
      "2021-07-16 10:17:18.882557: Encoding started...\r\n",
      "2021-07-16 10:17:21.995729: Encoding finished.\r\n",
      "2021-07-16 10:17:21.996453: ML model training started...\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "2021-07-16 10:17:48.125962: ML model training finished.\r\n",
      "2021-07-16 10:17:48.126263: Completed hyperparameter setting feature_size_4_logistic_regression.\r\n",
      "\r\n",
      "2021-07-16 10:17:48.130632: Hyperparameter optimization: running the inner loop of nested CV: completed selection for label signal_cancer (label 1 / 1).\r\n",
      "\r\n",
      "2021-07-16 10:17:48.130790: Training ML model: running the inner loop of nested CV: retrain models for label signal_cancer (label 1 / 1).\r\n",
      "\r\n",
      "2021-07-16 10:17:48.136567: Evaluating hyperparameter setting: feature_size_4_logistic_regression...\r\n",
      "2021-07-16 10:17:48.148158: Encoding started...\r\n",
      "2021-07-16 10:17:51.297962: Encoding finished.\r\n",
      "2021-07-16 10:17:51.298295: ML model training started...\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "2021-07-16 10:18:17.661422: ML model training finished.\r\n",
      "2021-07-16 10:18:17.665155: Encoding started...\r\n",
      "2021-07-16 10:18:19.464829: Encoding finished.\r\n",
      "2021-07-16 10:18:21.842006: Completed hyperparameter setting feature_size_4_logistic_regression.\r\n",
      "\r\n",
      "2021-07-16 10:18:21.847360: Training ML model: running the inner loop of nested CV: completed retraining models for label signal_cancer (label 1 / 1).\r\n",
      "\r\n",
      "2021-07-16 10:18:21.847515: Training ML model: running outer CV loop: finished split 3/5.\r\n",
      "\r\n",
      "2021-07-16 10:18:21.847584: Training ML model: running outer CV loop: started split 4/5.\r\n",
      "\r\n",
      "2021-07-16 10:18:21.926269: Hyperparameter optimization: running the inner loop of nested CV: selection for label signal_cancer (label 1 / 1).\r\n",
      "\r\n",
      "2021-07-16 10:18:21.937950: Evaluating hyperparameter setting: feature_size_4_logistic_regression...\r\n",
      "2021-07-16 10:18:21.956298: Encoding started...\r\n",
      "2021-07-16 10:18:25.939727: Encoding finished.\r\n",
      "2021-07-16 10:18:25.940378: ML model training started...\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "2021-07-16 10:18:54.069175: ML model training finished.\r\n",
      "2021-07-16 10:18:54.070091: Completed hyperparameter setting feature_size_4_logistic_regression.\r\n",
      "\r\n",
      "2021-07-16 10:18:54.075595: Hyperparameter optimization: running the inner loop of nested CV: completed selection for label signal_cancer (label 1 / 1).\r\n",
      "\r\n",
      "2021-07-16 10:18:54.075831: Training ML model: running the inner loop of nested CV: retrain models for label signal_cancer (label 1 / 1).\r\n",
      "\r\n",
      "2021-07-16 10:18:54.084150: Evaluating hyperparameter setting: feature_size_4_logistic_regression...\r\n",
      "2021-07-16 10:18:54.098177: Encoding started...\r\n",
      "2021-07-16 10:18:57.816706: Encoding finished.\r\n",
      "2021-07-16 10:18:57.817541: ML model training started...\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "2021-07-16 10:19:26.435606: ML model training finished.\r\n",
      "2021-07-16 10:19:26.441005: Encoding started...\r\n",
      "2021-07-16 10:19:28.760878: Encoding finished.\r\n",
      "2021-07-16 10:19:31.183491: Completed hyperparameter setting feature_size_4_logistic_regression.\r\n",
      "\r\n",
      "2021-07-16 10:19:31.188647: Training ML model: running the inner loop of nested CV: completed retraining models for label signal_cancer (label 1 / 1).\r\n",
      "\r\n",
      "2021-07-16 10:19:31.188758: Training ML model: running outer CV loop: finished split 4/5.\r\n",
      "\r\n",
      "2021-07-16 10:19:31.188808: Training ML model: running outer CV loop: started split 5/5.\r\n",
      "\r\n",
      "2021-07-16 10:19:31.249994: Hyperparameter optimization: running the inner loop of nested CV: selection for label signal_cancer (label 1 / 1).\r\n",
      "\r\n",
      "2021-07-16 10:19:31.256143: Evaluating hyperparameter setting: feature_size_4_logistic_regression...\r\n",
      "2021-07-16 10:19:31.268288: Encoding started...\r\n",
      "2021-07-16 10:19:35.816824: Encoding finished.\r\n",
      "2021-07-16 10:19:35.817399: ML model training started...\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "2021-07-16 10:20:04.597272: ML model training finished.\r\n",
      "2021-07-16 10:20:04.597636: Completed hyperparameter setting feature_size_4_logistic_regression.\r\n",
      "\r\n",
      "2021-07-16 10:20:04.603262: Hyperparameter optimization: running the inner loop of nested CV: completed selection for label signal_cancer (label 1 / 1).\r\n",
      "\r\n",
      "2021-07-16 10:20:04.603599: Training ML model: running the inner loop of nested CV: retrain models for label signal_cancer (label 1 / 1).\r\n",
      "\r\n",
      "2021-07-16 10:20:04.610678: Evaluating hyperparameter setting: feature_size_4_logistic_regression...\r\n",
      "2021-07-16 10:20:04.621967: Encoding started...\r\n",
      "2021-07-16 10:20:07.735071: Encoding finished.\r\n",
      "2021-07-16 10:20:07.735587: ML model training started...\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "2021-07-16 10:20:37.639507: ML model training finished.\r\n",
      "2021-07-16 10:20:37.646431: Encoding started...\r\n",
      "2021-07-16 10:20:40.288417: Encoding finished.\r\n",
      "2021-07-16 10:20:42.886782: Completed hyperparameter setting feature_size_4_logistic_regression.\r\n",
      "\r\n",
      "2021-07-16 10:20:42.892727: Training ML model: running the inner loop of nested CV: completed retraining models for label signal_cancer (label 1 / 1).\r\n",
      "\r\n",
      "2021-07-16 10:20:42.893053: Training ML model: running outer CV loop: finished split 5/5.\r\n",
      "\r\n",
      "Performances (accuracy) -----------------------------------------------\r\n",
      "\r\n",
      "\r\n",
      "Label: signal_cancer\r\n",
      "Performance (accuracy) per assessment split:\r\n",
      "Split 1: 0.8\r\n",
      "Split 2: 0.475\r\n",
      "Split 3: 0.575\r\n",
      "Split 4: 0.475\r\n",
      "Split 5: 0.425\r\n",
      "Average performance (accuracy): 0.5499999999999999\r\n",
      "------------------------------\r\n",
      "2021-07-16 10:20:46.643295: Instruction 1/2 has finished.\r\n",
      "2021-07-16 10:20:46.643528: Instruction 2/2 has started.\r\n",
      "2021-07-16 10:20:46.643933: Started analysis signal_freq (1/1).\r\n",
      "2021-07-16 10:20:46.651537: Encoding started...\r\n",
      "2021-07-16 10:20:46.662197: Encoding repertoire 1/200\r\n",
      "2021-07-16 10:20:46.674244: Encoding repertoire 2/200\r\n",
      "2021-07-16 10:20:46.680937: Encoding repertoire 3/200\r\n",
      "2021-07-16 10:20:46.687709: Encoding repertoire 4/200\r\n",
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      "2021-07-16 10:20:47.903397: Encoding repertoire 185/200\r\n",
      "2021-07-16 10:20:47.910556: Encoding repertoire 186/200\r\n",
      "2021-07-16 10:20:47.917696: Encoding repertoire 187/200\r\n",
      "2021-07-16 10:20:47.923399: Encoding repertoire 188/200\r\n",
      "2021-07-16 10:20:47.932449: Encoding repertoire 189/200\r\n",
      "2021-07-16 10:20:47.938437: Encoding repertoire 190/200\r\n",
      "2021-07-16 10:20:47.944995: Encoding repertoire 191/200\r\n",
      "2021-07-16 10:20:47.952698: Encoding repertoire 192/200\r\n",
      "2021-07-16 10:20:47.958906: Encoding repertoire 193/200\r\n",
      "2021-07-16 10:20:47.965013: Encoding repertoire 194/200\r\n",
      "2021-07-16 10:20:47.971588: Encoding repertoire 195/200\r\n",
      "2021-07-16 10:20:47.978095: Encoding repertoire 196/200\r\n",
      "2021-07-16 10:20:47.985283: Encoding repertoire 197/200\r\n",
      "2021-07-16 10:20:47.990880: Encoding repertoire 198/200\r\n",
      "2021-07-16 10:20:48.000546: Encoding repertoire 199/200\r\n",
      "2021-07-16 10:20:48.010694: Encoding repertoire 200/200\r\n",
      "2021-07-16 10:20:48.086679: Saving encoded dataset to disk.\r\n",
      "2021-07-16 10:20:51.863027: Encoding finished.\r\n",
      "2021-07-16 10:20:54.472989: Finished analysis signal_freq (1/1).\r\n",
      "\r\n",
      "2021-07-16 10:20:54.473098: Instruction 2/2 has finished.\r\n",
      "2021-07-16 10:20:54.483110: Generating HTML reports...\r\n",
      "2021-07-16 10:20:56.881329: HTML reports are generated.\r\n",
      "2021-07-16 10:20:58.370362: ImmuneML: finished analysis.\r\n",
      "\r\n"
     ]
    }
   ],
   "source": [
    "!mkdir -p analyses/olga/dataset_implanting_rate/{simulated_data,ML_output}\n",
    "!docker container prune -f\n",
    "!docker run -it -v $(pwd):/wd --name simulation_container kanduric/immuneml-v1:latest immune-ml /wd/scripts/olga/run_1/dataset_implanting_rate/generated_yaml_files/simulation_instructions/repertoire_implanting_rate__1e-05_-_dataset_implanting_rate__0.6.yaml /wd/analyses/olga/dataset_implanting_rate/simulated_data/repertoire_implanting_rate__1e-05_-_dataset_implanting_rate__0.6\n",
    "!docker run -it -v $(pwd):/wd --name ml_container kanduric/immuneml-v1:latest immune-ml /wd/scripts/olga/run_1/dataset_implanting_rate/generated_yaml_files/ml_instructions/repertoire_implanting_rate__1e-05_-_dataset_implanting_rate__0.6_-_ml_instruction.yaml /wd/analyses/olga/dataset_implanting_rate/ML_output/repertoire_implanting_rate__1e-05_-_dataset_implanting_rate__0.6"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "#### Distributional shift\n",
    "\n",
    "The stdout printed to screen when running the simulation and ML model training instructions are shown below indicating successful execution, and the resulting output of each instruction is stored under `analyses/olga/trained_model_on_unseen_testdata` in this github repository."
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Deleted Containers:\r\n",
      "9cafe60af6b3ba24639073a6d2f2bde2bb11e39100401ffc810c521624ac7da3\r\n",
      "ffe4e1bee415903e5b17f8ef5fd520e6c5404e168a194c68f9211e996f3343f0\r\n",
      "\r\n",
      "Total reclaimed space: 47.09kB\r\n",
      "2021-07-16 09:34:06.977534: Setting temporary cache path to wd/analyses/olga/trained_model_on_unseen_testdata/ML_output/repertoire_implanting_rate__0.001_with_repertoire_implanting_rate__0.001/cache/\r\n",
      "2021-07-16 09:34:06.977622: ImmuneML: parsing the specification...\r\n",
      "\r\n",
      "2021-07-16 09:34:07.432582: Full specification is available at wd/analyses/olga/trained_model_on_unseen_testdata/ML_output/repertoire_implanting_rate__0.001_with_repertoire_implanting_rate__0.001/full_repertoire_implanting_rate__0.yaml.\r\n",
      "\r\n",
      "2021-07-16 09:34:07.432629: ImmuneML: starting the analysis...\r\n",
      "\r\n",
      "2021-07-16 09:34:07.432699: Instruction 1/1 has started.\r\n",
      "2021-07-16 09:34:07.443301: Encoding started...\r\n",
      "2021-07-16 09:34:11.938973: Encoding finished.\r\n",
      "2021-07-16 09:34:11.961882: Instruction 1/1 has finished.\r\n",
      "2021-07-16 09:34:11.967962: Generating HTML reports...\r\n",
      "2021-07-16 09:34:12.004185: HTML reports are generated.\r\n",
      "2021-07-16 09:34:12.207763: ImmuneML: finished analysis.\r\n",
      "\r\n"
     ]
    }
   ],
   "source": [
    "!mkdir -p analyses/olga/trained_model_on_unseen_testdata\n",
    "!docker container prune -f\n",
    "!docker run -it -v $(pwd):/wd --name ml_container kanduric/immuneml-v1:latest immune-ml /wd/scripts/olga/run_1/trained_model_on_unseen_testdata/run_1_model_on_run_2_data/dataset_path_filled_files/repertoire_implanting_rate__0.001_with_repertoire_implanting_rate__0.001_-_ml_instruction_config.yaml /wd/analyses/olga/trained_model_on_unseen_testdata/ML_output/repertoire_implanting_rate__0.001_with_repertoire_implanting_rate__0.001"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "#### Size of subsequences (k-mer size)\n",
    "\n",
    "The stdout printed to screen when running the simulation and ML model training instructions are shown below indicating successful execution, and the resulting output of each instruction is stored under `analyses/olga/varying_kmer_size/` in this github repository."
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Deleted Containers:\r\n",
      "e20b970831868b7629543446a3e06b9329d34447e8c696502df5f6bb5d84230a\r\n",
      "ba9639c13d342b252e19105e825c750f5588980a8d692ff200f38bbde3eaf94c\r\n",
      "\r\n",
      "Total reclaimed space: 47.09kB\r\n",
      "2021-07-16 09:51:44.202458: Setting temporary cache path to wd/analyses/olga/varying_kmer_size/simulated_data/repertoire_implanting_rate__0.002_-_motifs__kmer1_size_4_kmer2_size_4_kmer3_size_4/cache/\r\n",
      "2021-07-16 09:51:44.202554: ImmuneML: parsing the specification...\r\n",
      "\r\n",
      "2021-07-16 09:51:48.701393: Full specification is available at wd/analyses/olga/varying_kmer_size/simulated_data/repertoire_implanting_rate__0.002_-_motifs__kmer1_size_4_kmer2_size_4_kmer3_size_4/full_repertoire_implanting_rate__0.yaml.\r\n",
      "\r\n",
      "2021-07-16 09:51:48.701503: ImmuneML: starting the analysis...\r\n",
      "\r\n",
      "2021-07-16 09:51:48.701711: Instruction 1/1 has started.\r\n",
      "2021-07-16 09:51:54.823709: Instruction 1/1 has finished.\r\n",
      "2021-07-16 09:51:54.829916: Generating HTML reports...\r\n",
      "2021-07-16 09:51:54.913145: HTML reports are generated.\r\n",
      "2021-07-16 09:51:54.914422: ImmuneML: finished analysis.\r\n",
      "\r\n",
      "2021-07-16 09:51:57.226735: Setting temporary cache path to wd/analyses/olga/varying_kmer_size/ML_output/repertoire_implanting_rate__0.002_-_motifs__kmer1_size_4_kmer2_size_4_kmer3_size_4/cache/\r\n",
      "2021-07-16 09:51:57.226807: ImmuneML: parsing the specification...\r\n",
      "\r\n",
      "2021-07-16 09:51:59.888125: Full specification is available at wd/analyses/olga/varying_kmer_size/ML_output/repertoire_implanting_rate__0.002_-_motifs__kmer1_size_4_kmer2_size_4_kmer3_size_4/full_repertoire_implanting_rate__0.yaml.\r\n",
      "\r\n",
      "2021-07-16 09:51:59.888264: ImmuneML: starting the analysis...\r\n",
      "\r\n",
      "2021-07-16 09:51:59.888491: Instruction 1/2 has started.\r\n",
      "2021-07-16 09:52:00.022189: Training ML model: running outer CV loop: started split 1/5.\r\n",
      "\r\n",
      "2021-07-16 09:52:00.065374: Hyperparameter optimization: running the inner loop of nested CV: selection for label signal_cancer (label 1 / 1).\r\n",
      "\r\n",
      "2021-07-16 09:52:00.070608: Evaluating hyperparameter setting: feature_size_4_logistic_regression...\r\n",
      "2021-07-16 09:52:00.081823: Encoding started...\r\n",
      "2021-07-16 09:52:04.222712: Encoding finished.\r\n",
      "2021-07-16 09:52:04.223190: ML model training started...\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "2021-07-16 09:52:33.619436: ML model training finished.\r\n",
      "2021-07-16 09:52:33.619892: Completed hyperparameter setting feature_size_4_logistic_regression.\r\n",
      "\r\n",
      "2021-07-16 09:52:33.623287: Hyperparameter optimization: running the inner loop of nested CV: completed selection for label signal_cancer (label 1 / 1).\r\n",
      "\r\n",
      "2021-07-16 09:52:33.623428: Training ML model: running the inner loop of nested CV: retrain models for label signal_cancer (label 1 / 1).\r\n",
      "\r\n",
      "2021-07-16 09:52:33.629829: Evaluating hyperparameter setting: feature_size_4_logistic_regression...\r\n",
      "2021-07-16 09:52:33.640178: Encoding started...\r\n",
      "2021-07-16 09:52:36.205981: Encoding finished.\r\n",
      "2021-07-16 09:52:36.206236: ML model training started...\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "2021-07-16 09:53:03.790739: ML model training finished.\r\n",
      "2021-07-16 09:53:03.793807: Encoding started...\r\n",
      "2021-07-16 09:53:05.333715: Encoding finished.\r\n",
      "2021-07-16 09:53:05.846839: Completed hyperparameter setting feature_size_4_logistic_regression.\r\n",
      "\r\n",
      "2021-07-16 09:53:05.850321: Training ML model: running the inner loop of nested CV: completed retraining models for label signal_cancer (label 1 / 1).\r\n",
      "\r\n",
      "2021-07-16 09:53:05.850497: Training ML model: running outer CV loop: finished split 1/5.\r\n",
      "\r\n",
      "2021-07-16 09:53:05.850567: Training ML model: running outer CV loop: started split 2/5.\r\n",
      "\r\n",
      "2021-07-16 09:53:05.892363: Hyperparameter optimization: running the inner loop of nested CV: selection for label signal_cancer (label 1 / 1).\r\n",
      "\r\n",
      "2021-07-16 09:53:05.899709: Evaluating hyperparameter setting: feature_size_4_logistic_regression...\r\n",
      "2021-07-16 09:53:05.908684: Encoding started...\r\n",
      "2021-07-16 09:53:08.890426: Encoding finished.\r\n",
      "2021-07-16 09:53:08.890771: ML model training started...\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "2021-07-16 09:53:29.587084: ML model training finished.\r\n",
      "2021-07-16 09:53:29.587934: Completed hyperparameter setting feature_size_4_logistic_regression.\r\n",
      "\r\n",
      "2021-07-16 09:53:29.592475: Hyperparameter optimization: running the inner loop of nested CV: completed selection for label signal_cancer (label 1 / 1).\r\n",
      "\r\n",
      "2021-07-16 09:53:29.593344: Training ML model: running the inner loop of nested CV: retrain models for label signal_cancer (label 1 / 1).\r\n",
      "\r\n",
      "2021-07-16 09:53:29.600160: Evaluating hyperparameter setting: feature_size_4_logistic_regression...\r\n",
      "2021-07-16 09:53:29.613805: Encoding started...\r\n",
      "2021-07-16 09:53:32.305564: Encoding finished.\r\n",
      "2021-07-16 09:53:32.305812: ML model training started...\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "2021-07-16 09:54:03.587260: ML model training finished.\r\n",
      "2021-07-16 09:54:03.594851: Encoding started...\r\n",
      "2021-07-16 09:54:05.470468: Encoding finished.\r\n",
      "2021-07-16 09:54:05.861849: Completed hyperparameter setting feature_size_4_logistic_regression.\r\n",
      "\r\n",
      "2021-07-16 09:54:05.866469: Training ML model: running the inner loop of nested CV: completed retraining models for label signal_cancer (label 1 / 1).\r\n",
      "\r\n",
      "2021-07-16 09:54:05.866610: Training ML model: running outer CV loop: finished split 2/5.\r\n",
      "\r\n",
      "2021-07-16 09:54:05.866650: Training ML model: running outer CV loop: started split 3/5.\r\n",
      "\r\n",
      "2021-07-16 09:54:05.926278: Hyperparameter optimization: running the inner loop of nested CV: selection for label signal_cancer (label 1 / 1).\r\n",
      "\r\n",
      "2021-07-16 09:54:05.933904: Evaluating hyperparameter setting: feature_size_4_logistic_regression...\r\n",
      "2021-07-16 09:54:05.946830: Encoding started...\r\n",
      "2021-07-16 09:54:09.274691: Encoding finished.\r\n",
      "2021-07-16 09:54:09.275028: ML model training started...\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "2021-07-16 09:54:34.962511: ML model training finished.\r\n",
      "2021-07-16 09:54:34.962895: Completed hyperparameter setting feature_size_4_logistic_regression.\r\n",
      "\r\n",
      "2021-07-16 09:54:34.966536: Hyperparameter optimization: running the inner loop of nested CV: completed selection for label signal_cancer (label 1 / 1).\r\n",
      "\r\n",
      "2021-07-16 09:54:34.966660: Training ML model: running the inner loop of nested CV: retrain models for label signal_cancer (label 1 / 1).\r\n",
      "\r\n",
      "2021-07-16 09:54:34.972123: Evaluating hyperparameter setting: feature_size_4_logistic_regression...\r\n",
      "2021-07-16 09:54:34.981542: Encoding started...\r\n",
      "2021-07-16 09:54:37.534939: Encoding finished.\r\n",
      "2021-07-16 09:54:37.535258: ML model training started...\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "2021-07-16 09:55:08.006620: ML model training finished.\r\n",
      "2021-07-16 09:55:08.010174: Encoding started...\r\n",
      "2021-07-16 09:55:09.801106: Encoding finished.\r\n",
      "2021-07-16 09:55:10.148892: Completed hyperparameter setting feature_size_4_logistic_regression.\r\n",
      "\r\n",
      "2021-07-16 09:55:10.153152: Training ML model: running the inner loop of nested CV: completed retraining models for label signal_cancer (label 1 / 1).\r\n",
      "\r\n",
      "2021-07-16 09:55:10.153351: Training ML model: running outer CV loop: finished split 3/5.\r\n",
      "\r\n",
      "2021-07-16 09:55:10.153409: Training ML model: running outer CV loop: started split 4/5.\r\n",
      "\r\n",
      "2021-07-16 09:55:10.207128: Hyperparameter optimization: running the inner loop of nested CV: selection for label signal_cancer (label 1 / 1).\r\n",
      "\r\n",
      "2021-07-16 09:55:10.213747: Evaluating hyperparameter setting: feature_size_4_logistic_regression...\r\n",
      "2021-07-16 09:55:10.225919: Encoding started...\r\n",
      "2021-07-16 09:55:13.420384: Encoding finished.\r\n",
      "2021-07-16 09:55:13.420983: ML model training started...\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "2021-07-16 09:55:44.576946: ML model training finished.\r\n",
      "2021-07-16 09:55:44.577328: Completed hyperparameter setting feature_size_4_logistic_regression.\r\n",
      "\r\n",
      "2021-07-16 09:55:44.581564: Hyperparameter optimization: running the inner loop of nested CV: completed selection for label signal_cancer (label 1 / 1).\r\n",
      "\r\n",
      "2021-07-16 09:55:44.581759: Training ML model: running the inner loop of nested CV: retrain models for label signal_cancer (label 1 / 1).\r\n",
      "\r\n",
      "2021-07-16 09:55:44.587356: Evaluating hyperparameter setting: feature_size_4_logistic_regression...\r\n",
      "2021-07-16 09:55:44.598922: Encoding started...\r\n",
      "2021-07-16 09:55:47.705196: Encoding finished.\r\n",
      "2021-07-16 09:55:47.705670: ML model training started...\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "2021-07-16 09:56:15.083450: ML model training finished.\r\n",
      "2021-07-16 09:56:15.087070: Encoding started...\r\n",
      "2021-07-16 09:56:16.982332: Encoding finished.\r\n",
      "2021-07-16 09:56:17.272231: Completed hyperparameter setting feature_size_4_logistic_regression.\r\n",
      "\r\n",
      "2021-07-16 09:56:17.278140: Training ML model: running the inner loop of nested CV: completed retraining models for label signal_cancer (label 1 / 1).\r\n",
      "\r\n",
      "2021-07-16 09:56:17.278304: Training ML model: running outer CV loop: finished split 4/5.\r\n",
      "\r\n",
      "2021-07-16 09:56:17.278354: Training ML model: running outer CV loop: started split 5/5.\r\n",
      "\r\n",
      "2021-07-16 09:56:17.353435: Hyperparameter optimization: running the inner loop of nested CV: selection for label signal_cancer (label 1 / 1).\r\n",
      "\r\n",
      "2021-07-16 09:56:17.363271: Evaluating hyperparameter setting: feature_size_4_logistic_regression...\r\n",
      "2021-07-16 09:56:17.379813: Encoding started...\r\n",
      "2021-07-16 09:56:20.790401: Encoding finished.\r\n",
      "2021-07-16 09:56:20.790949: ML model training started...\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "2021-07-16 09:56:56.035276: ML model training finished.\r\n",
      "2021-07-16 09:56:56.035465: Completed hyperparameter setting feature_size_4_logistic_regression.\r\n",
      "\r\n",
      "2021-07-16 09:56:56.039076: Hyperparameter optimization: running the inner loop of nested CV: completed selection for label signal_cancer (label 1 / 1).\r\n",
      "\r\n",
      "2021-07-16 09:56:56.039192: Training ML model: running the inner loop of nested CV: retrain models for label signal_cancer (label 1 / 1).\r\n",
      "\r\n",
      "2021-07-16 09:56:56.045085: Evaluating hyperparameter setting: feature_size_4_logistic_regression...\r\n",
      "2021-07-16 09:56:56.057244: Encoding started...\r\n",
      "2021-07-16 09:56:58.984616: Encoding finished.\r\n",
      "2021-07-16 09:56:58.985078: ML model training started...\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "2021-07-16 09:57:33.545973: ML model training finished.\r\n",
      "2021-07-16 09:57:33.548617: Encoding started...\r\n",
      "2021-07-16 09:57:35.235868: Encoding finished.\r\n",
      "2021-07-16 09:57:35.608383: Completed hyperparameter setting feature_size_4_logistic_regression.\r\n",
      "\r\n",
      "2021-07-16 09:57:35.612329: Training ML model: running the inner loop of nested CV: completed retraining models for label signal_cancer (label 1 / 1).\r\n",
      "\r\n",
      "2021-07-16 09:57:35.612433: Training ML model: running outer CV loop: finished split 5/5.\r\n",
      "\r\n",
      "Performances (accuracy) -----------------------------------------------\r\n",
      "\r\n",
      "\r\n",
      "Label: signal_cancer\r\n",
      "Performance (accuracy) per assessment split:\r\n",
      "Split 1: 0.5\r\n",
      "Split 2: 0.525\r\n",
      "Split 3: 0.575\r\n",
      "Split 4: 0.65\r\n",
      "Split 5: 0.55\r\n",
      "Average performance (accuracy): 0.5599999999999999\r\n",
      "------------------------------\r\n",
      "2021-07-16 09:57:39.436389: Instruction 1/2 has finished.\r\n",
      "2021-07-16 09:57:39.436485: Instruction 2/2 has started.\r\n",
      "2021-07-16 09:57:39.436522: Started analysis signal_freq (1/1).\r\n",
      "2021-07-16 09:57:39.442985: Encoding started...\r\n",
      "2021-07-16 09:57:39.455725: Encoding repertoire 1/200\r\n",
      "2021-07-16 09:57:39.476421: Encoding repertoire 2/200\r\n",
      "2021-07-16 09:57:39.486440: Encoding repertoire 3/200\r\n",
      "2021-07-16 09:57:39.496608: Encoding repertoire 4/200\r\n",
      "2021-07-16 09:57:39.506946: Encoding repertoire 5/200\r\n",
      "2021-07-16 09:57:39.518825: Encoding repertoire 6/200\r\n",
      "2021-07-16 09:57:39.530319: Encoding repertoire 7/200\r\n",
      "2021-07-16 09:57:39.540947: Encoding repertoire 8/200\r\n",
      "2021-07-16 09:57:39.552953: Encoding repertoire 9/200\r\n",
      "2021-07-16 09:57:39.563259: Encoding repertoire 10/200\r\n",
      "2021-07-16 09:57:39.573310: Encoding repertoire 11/200\r\n",
      "2021-07-16 09:57:39.584643: Encoding repertoire 12/200\r\n",
      "2021-07-16 09:57:39.596647: Encoding repertoire 13/200\r\n",
      "2021-07-16 09:57:39.608347: Encoding repertoire 14/200\r\n",
      "2021-07-16 09:57:39.618855: Encoding repertoire 15/200\r\n",
      "2021-07-16 09:57:39.631751: Encoding repertoire 16/200\r\n",
      "2021-07-16 09:57:39.641303: Encoding repertoire 17/200\r\n",
      "2021-07-16 09:57:39.653696: Encoding repertoire 18/200\r\n",
      "2021-07-16 09:57:39.663488: Encoding repertoire 19/200\r\n",
      "2021-07-16 09:57:39.674037: Encoding repertoire 20/200\r\n",
      "2021-07-16 09:57:39.684826: Encoding repertoire 21/200\r\n",
      "2021-07-16 09:57:39.694762: Encoding repertoire 22/200\r\n",
      "2021-07-16 09:57:39.705413: Encoding repertoire 23/200\r\n",
      "2021-07-16 09:57:39.716173: Encoding repertoire 24/200\r\n",
      "2021-07-16 09:57:39.725939: Encoding repertoire 25/200\r\n",
      "2021-07-16 09:57:39.737161: Encoding repertoire 26/200\r\n",
      "2021-07-16 09:57:39.746759: Encoding repertoire 27/200\r\n",
      "2021-07-16 09:57:39.756479: Encoding repertoire 28/200\r\n",
      "2021-07-16 09:57:39.766792: Encoding repertoire 29/200\r\n",
      "2021-07-16 09:57:39.778285: Encoding repertoire 30/200\r\n",
      "2021-07-16 09:57:39.789056: Encoding repertoire 31/200\r\n",
      "2021-07-16 09:57:39.800045: Encoding repertoire 32/200\r\n",
      "2021-07-16 09:57:39.810031: Encoding repertoire 33/200\r\n",
      "2021-07-16 09:57:39.820197: Encoding repertoire 34/200\r\n",
      "2021-07-16 09:57:39.830414: Encoding repertoire 35/200\r\n",
      "2021-07-16 09:57:39.839776: Encoding repertoire 36/200\r\n",
      "2021-07-16 09:57:39.850255: Encoding repertoire 37/200\r\n",
      "2021-07-16 09:57:39.860047: Encoding repertoire 38/200\r\n",
      "2021-07-16 09:57:39.870762: Encoding repertoire 39/200\r\n",
      "2021-07-16 09:57:39.881199: Encoding repertoire 40/200\r\n",
      "2021-07-16 09:57:39.890787: Encoding repertoire 41/200\r\n",
      "2021-07-16 09:57:39.900264: Encoding repertoire 42/200\r\n",
      "2021-07-16 09:57:39.910955: Encoding repertoire 43/200\r\n",
      "2021-07-16 09:57:39.920548: Encoding repertoire 44/200\r\n",
      "2021-07-16 09:57:39.931049: Encoding repertoire 45/200\r\n",
      "2021-07-16 09:57:39.941280: Encoding repertoire 46/200\r\n",
      "2021-07-16 09:57:39.951514: Encoding repertoire 47/200\r\n",
      "2021-07-16 09:57:39.960947: Encoding repertoire 48/200\r\n",
      "2021-07-16 09:57:39.970913: Encoding repertoire 49/200\r\n",
      "2021-07-16 09:57:39.980823: Encoding repertoire 50/200\r\n",
      "2021-07-16 09:57:39.990521: Encoding repertoire 51/200\r\n",
      "2021-07-16 09:57:40.000510: Encoding repertoire 52/200\r\n",
      "2021-07-16 09:57:40.011747: Encoding repertoire 53/200\r\n",
      "2021-07-16 09:57:40.022472: Encoding repertoire 54/200\r\n",
      "2021-07-16 09:57:40.033473: Encoding repertoire 55/200\r\n",
      "2021-07-16 09:57:40.044329: Encoding repertoire 56/200\r\n",
      "2021-07-16 09:57:40.054902: Encoding repertoire 57/200\r\n",
      "2021-07-16 09:57:40.064748: Encoding repertoire 58/200\r\n",
      "2021-07-16 09:57:40.075327: Encoding repertoire 59/200\r\n",
      "2021-07-16 09:57:40.085487: Encoding repertoire 60/200\r\n",
      "2021-07-16 09:57:40.095577: Encoding repertoire 61/200\r\n",
      "2021-07-16 09:57:40.105043: Encoding repertoire 62/200\r\n",
      "2021-07-16 09:57:40.114428: Encoding repertoire 63/200\r\n",
      "2021-07-16 09:57:40.124468: Encoding repertoire 64/200\r\n",
      "2021-07-16 09:57:40.134520: Encoding repertoire 65/200\r\n",
      "2021-07-16 09:57:40.144446: Encoding repertoire 66/200\r\n",
      "2021-07-16 09:57:40.153946: Encoding repertoire 67/200\r\n",
      "2021-07-16 09:57:40.163819: Encoding repertoire 68/200\r\n",
      "2021-07-16 09:57:40.173657: Encoding repertoire 69/200\r\n",
      "2021-07-16 09:57:40.183855: Encoding repertoire 70/200\r\n",
      "2021-07-16 09:57:40.194293: Encoding repertoire 71/200\r\n",
      "2021-07-16 09:57:40.205015: Encoding repertoire 72/200\r\n",
      "2021-07-16 09:57:40.214815: Encoding repertoire 73/200\r\n",
      "2021-07-16 09:57:40.224999: Encoding repertoire 74/200\r\n",
      "2021-07-16 09:57:40.235415: Encoding repertoire 75/200\r\n",
      "2021-07-16 09:57:40.245961: Encoding repertoire 76/200\r\n",
      "2021-07-16 09:57:40.256313: Encoding repertoire 77/200\r\n",
      "2021-07-16 09:57:40.266297: Encoding repertoire 78/200\r\n",
      "2021-07-16 09:57:40.277932: Encoding repertoire 79/200\r\n",
      "2021-07-16 09:57:40.287780: Encoding repertoire 80/200\r\n",
      "2021-07-16 09:57:40.298405: Encoding repertoire 81/200\r\n",
      "2021-07-16 09:57:40.308001: Encoding repertoire 82/200\r\n",
      "2021-07-16 09:57:40.317696: Encoding repertoire 83/200\r\n",
      "2021-07-16 09:57:40.328397: Encoding repertoire 84/200\r\n",
      "2021-07-16 09:57:40.338564: Encoding repertoire 85/200\r\n",
      "2021-07-16 09:57:40.348559: Encoding repertoire 86/200\r\n",
      "2021-07-16 09:57:40.357766: Encoding repertoire 87/200\r\n",
      "2021-07-16 09:57:40.368033: Encoding repertoire 88/200\r\n",
      "2021-07-16 09:57:40.378533: Encoding repertoire 89/200\r\n",
      "2021-07-16 09:57:40.389030: Encoding repertoire 90/200\r\n",
      "2021-07-16 09:57:40.399202: Encoding repertoire 91/200\r\n",
      "2021-07-16 09:57:40.409554: Encoding repertoire 92/200\r\n",
      "2021-07-16 09:57:40.419510: Encoding repertoire 93/200\r\n",
      "2021-07-16 09:57:40.428805: Encoding repertoire 94/200\r\n",
      "2021-07-16 09:57:40.439116: Encoding repertoire 95/200\r\n",
      "2021-07-16 09:57:40.449524: Encoding repertoire 96/200\r\n",
      "2021-07-16 09:57:40.459243: Encoding repertoire 97/200\r\n",
      "2021-07-16 09:57:40.468531: Encoding repertoire 98/200\r\n",
      "2021-07-16 09:57:40.478032: Encoding repertoire 99/200\r\n",
      "2021-07-16 09:57:40.488353: Encoding repertoire 100/200\r\n",
      "2021-07-16 09:57:40.498316: Encoding repertoire 101/200\r\n",
      "2021-07-16 09:57:40.507530: Encoding repertoire 102/200\r\n",
      "2021-07-16 09:57:40.517023: Encoding repertoire 103/200\r\n",
      "2021-07-16 09:57:40.527038: Encoding repertoire 104/200\r\n",
      "2021-07-16 09:57:40.536966: Encoding repertoire 105/200\r\n",
      "2021-07-16 09:57:40.546773: Encoding repertoire 106/200\r\n",
      "2021-07-16 09:57:40.555979: Encoding repertoire 107/200\r\n",
      "2021-07-16 09:57:40.566312: Encoding repertoire 108/200\r\n",
      "2021-07-16 09:57:40.575916: Encoding repertoire 109/200\r\n",
      "2021-07-16 09:57:40.588813: Encoding repertoire 110/200\r\n",
      "2021-07-16 09:57:40.598711: Encoding repertoire 111/200\r\n",
      "2021-07-16 09:57:40.608049: Encoding repertoire 112/200\r\n",
      "2021-07-16 09:57:40.617845: Encoding repertoire 113/200\r\n",
      "2021-07-16 09:57:40.627860: Encoding repertoire 114/200\r\n",
      "2021-07-16 09:57:40.637362: Encoding repertoire 115/200\r\n",
      "2021-07-16 09:57:40.647509: Encoding repertoire 116/200\r\n",
      "2021-07-16 09:57:40.657584: Encoding repertoire 117/200\r\n",
      "2021-07-16 09:57:40.667692: Encoding repertoire 118/200\r\n",
      "2021-07-16 09:57:40.677620: Encoding repertoire 119/200\r\n",
      "2021-07-16 09:57:40.687928: Encoding repertoire 120/200\r\n",
      "2021-07-16 09:57:40.698356: Encoding repertoire 121/200\r\n",
      "2021-07-16 09:57:40.708301: Encoding repertoire 122/200\r\n",
      "2021-07-16 09:57:40.717459: Encoding repertoire 123/200\r\n",
      "2021-07-16 09:57:40.726783: Encoding repertoire 124/200\r\n",
      "2021-07-16 09:57:40.736114: Encoding repertoire 125/200\r\n",
      "2021-07-16 09:57:40.746816: Encoding repertoire 126/200\r\n",
      "2021-07-16 09:57:40.756140: Encoding repertoire 127/200\r\n",
      "2021-07-16 09:57:40.765623: Encoding repertoire 128/200\r\n",
      "2021-07-16 09:57:40.775387: Encoding repertoire 129/200\r\n",
      "2021-07-16 09:57:40.785784: Encoding repertoire 130/200\r\n",
      "2021-07-16 09:57:40.795471: Encoding repertoire 131/200\r\n",
      "2021-07-16 09:57:40.805772: Encoding repertoire 132/200\r\n",
      "2021-07-16 09:57:40.815649: Encoding repertoire 133/200\r\n",
      "2021-07-16 09:57:40.826006: Encoding repertoire 134/200\r\n",
      "2021-07-16 09:57:40.835733: Encoding repertoire 135/200\r\n",
      "2021-07-16 09:57:40.846624: Encoding repertoire 136/200\r\n",
      "2021-07-16 09:57:40.857125: Encoding repertoire 137/200\r\n",
      "2021-07-16 09:57:40.868248: Encoding repertoire 138/200\r\n",
      "2021-07-16 09:57:40.878408: Encoding repertoire 139/200\r\n",
      "2021-07-16 09:57:40.888635: Encoding repertoire 140/200\r\n",
      "2021-07-16 09:57:40.899361: Encoding repertoire 141/200\r\n",
      "2021-07-16 09:57:40.910167: Encoding repertoire 142/200\r\n",
      "2021-07-16 09:57:40.920385: Encoding repertoire 143/200\r\n",
      "2021-07-16 09:57:40.931815: Encoding repertoire 144/200\r\n",
      "2021-07-16 09:57:40.941536: Encoding repertoire 145/200\r\n",
      "2021-07-16 09:57:40.951867: Encoding repertoire 146/200\r\n",
      "2021-07-16 09:57:40.961650: Encoding repertoire 147/200\r\n",
      "2021-07-16 09:57:40.972231: Encoding repertoire 148/200\r\n",
      "2021-07-16 09:57:40.982376: Encoding repertoire 149/200\r\n",
      "2021-07-16 09:57:40.992568: Encoding repertoire 150/200\r\n",
      "2021-07-16 09:57:41.001907: Encoding repertoire 151/200\r\n",
      "2021-07-16 09:57:41.013031: Encoding repertoire 152/200\r\n",
      "2021-07-16 09:57:41.023275: Encoding repertoire 153/200\r\n",
      "2021-07-16 09:57:41.032738: Encoding repertoire 154/200\r\n",
      "2021-07-16 09:57:41.042776: Encoding repertoire 155/200\r\n",
      "2021-07-16 09:57:41.053484: Encoding repertoire 156/200\r\n",
      "2021-07-16 09:57:41.062618: Encoding repertoire 157/200\r\n",
      "2021-07-16 09:57:41.072259: Encoding repertoire 158/200\r\n",
      "2021-07-16 09:57:41.082087: Encoding repertoire 159/200\r\n",
      "2021-07-16 09:57:41.091467: Encoding repertoire 160/200\r\n",
      "2021-07-16 09:57:41.102001: Encoding repertoire 161/200\r\n",
      "2021-07-16 09:57:41.111701: Encoding repertoire 162/200\r\n",
      "2021-07-16 09:57:41.121240: Encoding repertoire 163/200\r\n",
      "2021-07-16 09:57:41.131672: Encoding repertoire 164/200\r\n",
      "2021-07-16 09:57:41.142315: Encoding repertoire 165/200\r\n",
      "2021-07-16 09:57:41.152623: Encoding repertoire 166/200\r\n",
      "2021-07-16 09:57:41.161994: Encoding repertoire 167/200\r\n",
      "2021-07-16 09:57:41.171636: Encoding repertoire 168/200\r\n",
      "2021-07-16 09:57:41.181380: Encoding repertoire 169/200\r\n",
      "2021-07-16 09:57:41.191230: Encoding repertoire 170/200\r\n",
      "2021-07-16 09:57:41.203285: Encoding repertoire 171/200\r\n",
      "2021-07-16 09:57:41.213295: Encoding repertoire 172/200\r\n",
      "2021-07-16 09:57:41.222861: Encoding repertoire 173/200\r\n",
      "2021-07-16 09:57:41.232240: Encoding repertoire 174/200\r\n",
      "2021-07-16 09:57:41.243036: Encoding repertoire 175/200\r\n",
      "2021-07-16 09:57:41.253466: Encoding repertoire 176/200\r\n",
      "2021-07-16 09:57:41.263620: Encoding repertoire 177/200\r\n",
      "2021-07-16 09:57:41.277381: Encoding repertoire 178/200\r\n",
      "2021-07-16 09:57:41.288312: Encoding repertoire 179/200\r\n",
      "2021-07-16 09:57:41.297890: Encoding repertoire 180/200\r\n",
      "2021-07-16 09:57:41.378075: Encoding repertoire 181/200\r\n",
      "2021-07-16 09:57:41.389187: Encoding repertoire 182/200\r\n",
      "2021-07-16 09:57:41.399938: Encoding repertoire 183/200\r\n",
      "2021-07-16 09:57:41.410626: Encoding repertoire 184/200\r\n",
      "2021-07-16 09:57:41.420771: Encoding repertoire 185/200\r\n",
      "2021-07-16 09:57:41.431671: Encoding repertoire 186/200\r\n",
      "2021-07-16 09:57:41.441505: Encoding repertoire 187/200\r\n",
      "2021-07-16 09:57:41.452131: Encoding repertoire 188/200\r\n",
      "2021-07-16 09:57:41.461780: Encoding repertoire 189/200\r\n",
      "2021-07-16 09:57:41.471738: Encoding repertoire 190/200\r\n",
      "2021-07-16 09:57:41.481769: Encoding repertoire 191/200\r\n",
      "2021-07-16 09:57:41.491278: Encoding repertoire 192/200\r\n",
      "2021-07-16 09:57:41.500783: Encoding repertoire 193/200\r\n",
      "2021-07-16 09:57:41.510647: Encoding repertoire 194/200\r\n",
      "2021-07-16 09:57:41.520464: Encoding repertoire 195/200\r\n",
      "2021-07-16 09:57:41.530625: Encoding repertoire 196/200\r\n",
      "2021-07-16 09:57:41.540797: Encoding repertoire 197/200\r\n",
      "2021-07-16 09:57:41.550293: Encoding repertoire 198/200\r\n",
      "2021-07-16 09:57:41.560116: Encoding repertoire 199/200\r\n",
      "2021-07-16 09:57:41.570460: Encoding repertoire 200/200\r\n",
      "2021-07-16 09:57:41.629677: Saving encoded dataset to disk.\r\n",
      "2021-07-16 09:57:44.222186: Encoding finished.\r\n",
      "2021-07-16 09:57:45.918707: Finished analysis signal_freq (1/1).\r\n",
      "\r\n",
      "2021-07-16 09:57:45.919385: Instruction 2/2 has finished.\r\n",
      "2021-07-16 09:57:45.930760: Generating HTML reports...\r\n",
      "2021-07-16 09:57:47.107880: HTML reports are generated.\r\n",
      "2021-07-16 09:57:48.274621: ImmuneML: finished analysis.\r\n",
      "\r\n"
     ]
    }
   ],
   "source": [
    "!mkdir -p analyses/olga/varying_kmer_size/{simulated_data,ML_output}\n",
    "!docker container prune -f\n",
    "!docker run -it -v $(pwd):/wd --name simulation_container kanduric/immuneml-v1:latest immune-ml /wd/scripts/olga/run_1/varying_kmer_size/generated_yaml_files/simulation_instructions/repertoire_implanting_rate__0.002_-_motifs__kmer1_size_4_kmer2_size_4_kmer3_size_4.yaml /wd/analyses/olga/varying_kmer_size/simulated_data/repertoire_implanting_rate__0.002_-_motifs__kmer1_size_4_kmer2_size_4_kmer3_size_4\n",
    "!docker run -it -v $(pwd):/wd --name ml_container kanduric/immuneml-v1:latest immune-ml /wd/scripts/olga/run_1/varying_kmer_size/generated_yaml_files/ml_instructions/repertoire_implanting_rate__0.002_-_motifs__kmer1_size_4_kmer2_size_4_kmer3_size_4_-_ml_instruction.yaml /wd/analyses/olga/varying_kmer_size/ML_output/repertoire_implanting_rate__0.002_-_motifs__kmer1_size_4_kmer2_size_4_kmer3_size_4"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "#### Size of subsequences with gaps (complex k-mer size)\n",
    "\n",
    "The stdout printed to screen when running the simulation and ML model training instructions are shown below indicating successful execution, and the resulting output of each instruction is stored under `analyses/olga/varying_complex_kmer_size` in this github repository."
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Deleted Containers:\r\n",
      "375b5ed85b5f268a62a5c63305117dc0802acd89099bdd3ca77b1b74acd0f3f6\r\n",
      "53d720cb3f9a23bb7d167ee23195ed976898fafe940e0c29f447952df66960df\r\n",
      "\r\n",
      "Total reclaimed space: 47.09kB\r\n",
      "2021-07-16 10:32:03.878172: Setting temporary cache path to wd/analyses/olga/varying_complex_kmer_size/simulated_data/repertoire_implanting_rate__0.0005_-_motifs__kmer1_size_3_kmer2_size_3_kmer3_size_3/cache/\r\n",
      "2021-07-16 10:32:03.878247: ImmuneML: parsing the specification...\r\n",
      "\r\n",
      "2021-07-16 10:32:07.941174: Full specification is available at wd/analyses/olga/varying_complex_kmer_size/simulated_data/repertoire_implanting_rate__0.0005_-_motifs__kmer1_size_3_kmer2_size_3_kmer3_size_3/full_repertoire_implanting_rate__0.yaml.\r\n",
      "\r\n",
      "2021-07-16 10:32:07.941281: ImmuneML: starting the analysis...\r\n",
      "\r\n",
      "2021-07-16 10:32:07.941338: Instruction 1/1 has started.\r\n",
      "2021-07-16 10:32:13.572297: Instruction 1/1 has finished.\r\n",
      "2021-07-16 10:32:13.578680: Generating HTML reports...\r\n",
      "2021-07-16 10:32:13.665298: HTML reports are generated.\r\n",
      "2021-07-16 10:32:13.666436: ImmuneML: finished analysis.\r\n",
      "\r\n",
      "2021-07-16 10:32:15.976132: Setting temporary cache path to wd/analyses/olga/varying_complex_kmer_size/ML_output/repertoire_implanting_rate__0.0005_-_motifs__kmer1_size_3_kmer2_size_3_kmer3_size_3/cache/\r\n",
      "2021-07-16 10:32:15.976208: ImmuneML: parsing the specification...\r\n",
      "\r\n",
      "2021-07-16 10:32:18.529721: Full specification is available at wd/analyses/olga/varying_complex_kmer_size/ML_output/repertoire_implanting_rate__0.0005_-_motifs__kmer1_size_3_kmer2_size_3_kmer3_size_3/full_repertoire_implanting_rate__0.yaml.\r\n",
      "\r\n",
      "2021-07-16 10:32:18.529847: ImmuneML: starting the analysis...\r\n",
      "\r\n",
      "2021-07-16 10:32:18.529900: Instruction 1/2 has started.\r\n",
      "2021-07-16 10:32:18.669065: Training ML model: running outer CV loop: started split 1/5.\r\n",
      "\r\n",
      "2021-07-16 10:32:18.715069: Hyperparameter optimization: running the inner loop of nested CV: selection for label signal_cancer (label 1 / 1).\r\n",
      "\r\n",
      "2021-07-16 10:32:18.720657: Evaluating hyperparameter setting: feature_size_4_logistic_regression...\r\n",
      "2021-07-16 10:32:18.730382: Encoding started...\r\n",
      "2021-07-16 10:32:21.394338: Encoding finished.\r\n",
      "2021-07-16 10:32:21.394660: ML model training started...\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "2021-07-16 10:32:27.706906: ML model training finished.\r\n",
      "2021-07-16 10:32:27.707246: Completed hyperparameter setting feature_size_4_logistic_regression.\r\n",
      "\r\n",
      "2021-07-16 10:32:27.711302: Hyperparameter optimization: running the inner loop of nested CV: completed selection for label signal_cancer (label 1 / 1).\r\n",
      "\r\n",
      "2021-07-16 10:32:27.711410: Training ML model: running the inner loop of nested CV: retrain models for label signal_cancer (label 1 / 1).\r\n",
      "\r\n",
      "2021-07-16 10:32:27.718615: Evaluating hyperparameter setting: feature_size_4_logistic_regression...\r\n",
      "2021-07-16 10:32:27.730328: Encoding started...\r\n",
      "2021-07-16 10:32:29.128621: Encoding finished.\r\n",
      "2021-07-16 10:32:29.129008: ML model training started...\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "2021-07-16 10:32:35.250441: ML model training finished.\r\n",
      "2021-07-16 10:32:35.253551: Encoding started...\r\n",
      "2021-07-16 10:32:36.346549: Encoding finished.\r\n",
      "2021-07-16 10:32:36.803882: Completed hyperparameter setting feature_size_4_logistic_regression.\r\n",
      "\r\n",
      "2021-07-16 10:32:36.804851: Training ML model: running the inner loop of nested CV: completed retraining models for label signal_cancer (label 1 / 1).\r\n",
      "\r\n",
      "2021-07-16 10:32:36.805011: Training ML model: running outer CV loop: finished split 1/5.\r\n",
      "\r\n",
      "2021-07-16 10:32:36.805154: Training ML model: running outer CV loop: started split 2/5.\r\n",
      "\r\n",
      "2021-07-16 10:32:36.853234: Hyperparameter optimization: running the inner loop of nested CV: selection for label signal_cancer (label 1 / 1).\r\n",
      "\r\n",
      "2021-07-16 10:32:36.858965: Evaluating hyperparameter setting: feature_size_4_logistic_regression...\r\n",
      "2021-07-16 10:32:36.868653: Encoding started...\r\n",
      "2021-07-16 10:32:38.646319: Encoding finished.\r\n",
      "2021-07-16 10:32:38.646923: ML model training started...\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "2021-07-16 10:32:42.464341: ML model training finished.\r\n",
      "2021-07-16 10:32:42.465086: Completed hyperparameter setting feature_size_4_logistic_regression.\r\n",
      "\r\n",
      "2021-07-16 10:32:42.469770: Hyperparameter optimization: running the inner loop of nested CV: completed selection for label signal_cancer (label 1 / 1).\r\n",
      "\r\n",
      "2021-07-16 10:32:42.470196: Training ML model: running the inner loop of nested CV: retrain models for label signal_cancer (label 1 / 1).\r\n",
      "\r\n",
      "2021-07-16 10:32:42.476547: Evaluating hyperparameter setting: feature_size_4_logistic_regression...\r\n",
      "2021-07-16 10:32:42.487586: Encoding started...\r\n",
      "2021-07-16 10:32:43.915810: Encoding finished.\r\n",
      "2021-07-16 10:32:43.916100: ML model training started...\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "2021-07-16 10:32:47.852992: ML model training finished.\r\n",
      "2021-07-16 10:32:47.857377: Encoding started...\r\n",
      "2021-07-16 10:32:48.848743: Encoding finished.\r\n",
      "2021-07-16 10:32:48.984243: Completed hyperparameter setting feature_size_4_logistic_regression.\r\n",
      "\r\n",
      "2021-07-16 10:32:48.988124: Training ML model: running the inner loop of nested CV: completed retraining models for label signal_cancer (label 1 / 1).\r\n",
      "\r\n",
      "2021-07-16 10:32:48.988596: Training ML model: running outer CV loop: finished split 2/5.\r\n",
      "\r\n",
      "2021-07-16 10:32:48.988844: Training ML model: running outer CV loop: started split 3/5.\r\n",
      "\r\n",
      "2021-07-16 10:32:49.056434: Hyperparameter optimization: running the inner loop of nested CV: selection for label signal_cancer (label 1 / 1).\r\n",
      "\r\n",
      "2021-07-16 10:32:49.064693: Evaluating hyperparameter setting: feature_size_4_logistic_regression...\r\n",
      "2021-07-16 10:32:49.078141: Encoding started...\r\n",
      "2021-07-16 10:32:50.473652: Encoding finished.\r\n",
      "2021-07-16 10:32:50.473997: ML model training started...\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "2021-07-16 10:32:55.097191: ML model training finished.\r\n",
      "2021-07-16 10:32:55.097413: Completed hyperparameter setting feature_size_4_logistic_regression.\r\n",
      "\r\n",
      "2021-07-16 10:32:55.101454: Hyperparameter optimization: running the inner loop of nested CV: completed selection for label signal_cancer (label 1 / 1).\r\n",
      "\r\n",
      "2021-07-16 10:32:55.101574: Training ML model: running the inner loop of nested CV: retrain models for label signal_cancer (label 1 / 1).\r\n",
      "\r\n",
      "2021-07-16 10:32:55.106694: Evaluating hyperparameter setting: feature_size_4_logistic_regression...\r\n",
      "2021-07-16 10:32:55.116345: Encoding started...\r\n",
      "2021-07-16 10:32:56.486906: Encoding finished.\r\n",
      "2021-07-16 10:32:56.487792: ML model training started...\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "2021-07-16 10:33:00.357161: ML model training finished.\r\n",
      "2021-07-16 10:33:00.360883: Encoding started...\r\n",
      "2021-07-16 10:33:01.271255: Encoding finished.\r\n",
      "2021-07-16 10:33:01.409805: Completed hyperparameter setting feature_size_4_logistic_regression.\r\n",
      "\r\n",
      "2021-07-16 10:33:01.413838: Training ML model: running the inner loop of nested CV: completed retraining models for label signal_cancer (label 1 / 1).\r\n",
      "\r\n",
      "2021-07-16 10:33:01.413989: Training ML model: running outer CV loop: finished split 3/5.\r\n",
      "\r\n",
      "2021-07-16 10:33:01.414060: Training ML model: running outer CV loop: started split 4/5.\r\n",
      "\r\n",
      "2021-07-16 10:33:01.507837: Hyperparameter optimization: running the inner loop of nested CV: selection for label signal_cancer (label 1 / 1).\r\n",
      "\r\n",
      "2021-07-16 10:33:01.521994: Evaluating hyperparameter setting: feature_size_4_logistic_regression...\r\n",
      "2021-07-16 10:33:01.545452: Encoding started...\r\n",
      "2021-07-16 10:33:03.091869: Encoding finished.\r\n",
      "2021-07-16 10:33:03.092450: ML model training started...\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "2021-07-16 10:33:06.770457: ML model training finished.\r\n",
      "2021-07-16 10:33:06.770695: Completed hyperparameter setting feature_size_4_logistic_regression.\r\n",
      "\r\n",
      "2021-07-16 10:33:06.774677: Hyperparameter optimization: running the inner loop of nested CV: completed selection for label signal_cancer (label 1 / 1).\r\n",
      "\r\n",
      "2021-07-16 10:33:06.774794: Training ML model: running the inner loop of nested CV: retrain models for label signal_cancer (label 1 / 1).\r\n",
      "\r\n",
      "2021-07-16 10:33:06.780737: Evaluating hyperparameter setting: feature_size_4_logistic_regression...\r\n",
      "2021-07-16 10:33:06.791569: Encoding started...\r\n",
      "2021-07-16 10:33:08.168669: Encoding finished.\r\n",
      "2021-07-16 10:33:08.168968: ML model training started...\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "2021-07-16 10:33:12.023462: ML model training finished.\r\n",
      "2021-07-16 10:33:12.026515: Encoding started...\r\n",
      "2021-07-16 10:33:12.964627: Encoding finished.\r\n",
      "2021-07-16 10:33:13.102678: Completed hyperparameter setting feature_size_4_logistic_regression.\r\n",
      "\r\n",
      "2021-07-16 10:33:13.108973: Training ML model: running the inner loop of nested CV: completed retraining models for label signal_cancer (label 1 / 1).\r\n",
      "\r\n",
      "2021-07-16 10:33:13.109760: Training ML model: running outer CV loop: finished split 4/5.\r\n",
      "\r\n",
      "2021-07-16 10:33:13.110169: Training ML model: running outer CV loop: started split 5/5.\r\n",
      "\r\n",
      "2021-07-16 10:33:13.176915: Hyperparameter optimization: running the inner loop of nested CV: selection for label signal_cancer (label 1 / 1).\r\n",
      "\r\n",
      "2021-07-16 10:33:13.183788: Evaluating hyperparameter setting: feature_size_4_logistic_regression...\r\n",
      "2021-07-16 10:33:13.195328: Encoding started...\r\n",
      "2021-07-16 10:33:14.714020: Encoding finished.\r\n",
      "2021-07-16 10:33:14.714745: ML model training started...\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "2021-07-16 10:33:20.717994: ML model training finished.\r\n",
      "2021-07-16 10:33:20.718490: Completed hyperparameter setting feature_size_4_logistic_regression.\r\n",
      "\r\n",
      "2021-07-16 10:33:20.722929: Hyperparameter optimization: running the inner loop of nested CV: completed selection for label signal_cancer (label 1 / 1).\r\n",
      "\r\n",
      "2021-07-16 10:33:20.723187: Training ML model: running the inner loop of nested CV: retrain models for label signal_cancer (label 1 / 1).\r\n",
      "\r\n",
      "2021-07-16 10:33:20.730165: Evaluating hyperparameter setting: feature_size_4_logistic_regression...\r\n",
      "2021-07-16 10:33:20.742350: Encoding started...\r\n",
      "2021-07-16 10:33:22.360230: Encoding finished.\r\n",
      "2021-07-16 10:33:22.360627: ML model training started...\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "2021-07-16 10:33:26.475877: ML model training finished.\r\n",
      "2021-07-16 10:33:26.480844: Encoding started...\r\n",
      "2021-07-16 10:33:27.558347: Encoding finished.\r\n",
      "2021-07-16 10:33:27.699958: Completed hyperparameter setting feature_size_4_logistic_regression.\r\n",
      "\r\n",
      "2021-07-16 10:33:27.704635: Training ML model: running the inner loop of nested CV: completed retraining models for label signal_cancer (label 1 / 1).\r\n",
      "\r\n",
      "2021-07-16 10:33:27.704753: Training ML model: running outer CV loop: finished split 5/5.\r\n",
      "\r\n",
      "Performances (accuracy) -----------------------------------------------\r\n",
      "\r\n",
      "\r\n",
      "Label: signal_cancer\r\n",
      "Performance (accuracy) per assessment split:\r\n",
      "Split 1: 0.4\r\n",
      "Split 2: 0.475\r\n",
      "Split 3: 0.35\r\n",
      "Split 4: 0.45\r\n",
      "Split 5: 0.475\r\n",
      "Average performance (accuracy): 0.43\r\n",
      "------------------------------\r\n",
      "2021-07-16 10:33:28.665184: Instruction 1/2 has finished.\r\n",
      "2021-07-16 10:33:28.665260: Instruction 2/2 has started.\r\n",
      "2021-07-16 10:33:28.665286: Started analysis signal_freq (1/1).\r\n",
      "2021-07-16 10:33:28.669981: Encoding started...\r\n",
      "2021-07-16 10:33:28.677078: Encoding repertoire 1/200\r\n",
      "2021-07-16 10:33:28.690004: Encoding repertoire 2/200\r\n",
      "2021-07-16 10:33:28.697451: Encoding repertoire 3/200\r\n",
      "2021-07-16 10:33:28.706420: Encoding repertoire 4/200\r\n",
      "2021-07-16 10:33:28.714636: Encoding repertoire 5/200\r\n",
      "2021-07-16 10:33:28.721986: Encoding repertoire 6/200\r\n",
      "2021-07-16 10:33:28.729724: Encoding repertoire 7/200\r\n",
      "2021-07-16 10:33:28.737343: Encoding repertoire 8/200\r\n",
      "2021-07-16 10:33:28.745088: Encoding repertoire 9/200\r\n",
      "2021-07-16 10:33:28.752509: Encoding repertoire 10/200\r\n",
      "2021-07-16 10:33:28.760521: Encoding repertoire 11/200\r\n",
      "2021-07-16 10:33:28.768913: Encoding repertoire 12/200\r\n",
      "2021-07-16 10:33:28.777266: Encoding repertoire 13/200\r\n",
      "2021-07-16 10:33:28.785175: Encoding repertoire 14/200\r\n",
      "2021-07-16 10:33:28.794335: Encoding repertoire 15/200\r\n",
      "2021-07-16 10:33:28.802179: Encoding repertoire 16/200\r\n",
      "2021-07-16 10:33:28.809833: Encoding repertoire 17/200\r\n",
      "2021-07-16 10:33:28.820803: Encoding repertoire 18/200\r\n",
      "2021-07-16 10:33:28.828392: Encoding repertoire 19/200\r\n",
      "2021-07-16 10:33:28.835583: Encoding repertoire 20/200\r\n",
      "2021-07-16 10:33:28.842574: Encoding repertoire 21/200\r\n",
      "2021-07-16 10:33:28.852398: Encoding repertoire 22/200\r\n",
      "2021-07-16 10:33:28.859559: Encoding repertoire 23/200\r\n",
      "2021-07-16 10:33:28.867997: Encoding repertoire 24/200\r\n",
      "2021-07-16 10:33:28.877817: Encoding repertoire 25/200\r\n",
      "2021-07-16 10:33:28.885782: Encoding repertoire 26/200\r\n",
      "2021-07-16 10:33:28.893122: Encoding repertoire 27/200\r\n",
      "2021-07-16 10:33:28.902284: Encoding repertoire 28/200\r\n",
      "2021-07-16 10:33:28.911346: Encoding repertoire 29/200\r\n",
      "2021-07-16 10:33:28.919604: Encoding repertoire 30/200\r\n",
      "2021-07-16 10:33:28.926819: Encoding repertoire 31/200\r\n",
      "2021-07-16 10:33:28.934122: Encoding repertoire 32/200\r\n",
      "2021-07-16 10:33:28.941578: Encoding repertoire 33/200\r\n",
      "2021-07-16 10:33:28.951135: Encoding repertoire 34/200\r\n",
      "2021-07-16 10:33:28.959605: Encoding repertoire 35/200\r\n",
      "2021-07-16 10:33:28.967442: Encoding repertoire 36/200\r\n",
      "2021-07-16 10:33:28.974573: Encoding repertoire 37/200\r\n",
      "2021-07-16 10:33:28.981495: Encoding repertoire 38/200\r\n",
      "2021-07-16 10:33:28.988789: Encoding repertoire 39/200\r\n",
      "2021-07-16 10:33:28.995824: Encoding repertoire 40/200\r\n",
      "2021-07-16 10:33:29.003189: Encoding repertoire 41/200\r\n",
      "2021-07-16 10:33:29.010297: Encoding repertoire 42/200\r\n",
      "2021-07-16 10:33:29.018038: Encoding repertoire 43/200\r\n",
      "2021-07-16 10:33:29.025887: Encoding repertoire 44/200\r\n",
      "2021-07-16 10:33:29.033798: Encoding repertoire 45/200\r\n",
      "2021-07-16 10:33:29.040917: Encoding repertoire 46/200\r\n",
      "2021-07-16 10:33:29.049149: Encoding repertoire 47/200\r\n",
      "2021-07-16 10:33:29.056362: Encoding repertoire 48/200\r\n",
      "2021-07-16 10:33:29.064349: Encoding repertoire 49/200\r\n",
      "2021-07-16 10:33:29.071804: Encoding repertoire 50/200\r\n",
      "2021-07-16 10:33:29.079135: Encoding repertoire 51/200\r\n",
      "2021-07-16 10:33:29.086161: Encoding repertoire 52/200\r\n",
      "2021-07-16 10:33:29.093546: Encoding repertoire 53/200\r\n",
      "2021-07-16 10:33:29.100650: Encoding repertoire 54/200\r\n",
      "2021-07-16 10:33:29.108244: Encoding repertoire 55/200\r\n",
      "2021-07-16 10:33:29.115349: Encoding repertoire 56/200\r\n",
      "2021-07-16 10:33:29.125296: Encoding repertoire 57/200\r\n",
      "2021-07-16 10:33:29.133657: Encoding repertoire 58/200\r\n",
      "2021-07-16 10:33:29.141879: Encoding repertoire 59/200\r\n",
      "2021-07-16 10:33:29.150083: Encoding repertoire 60/200\r\n",
      "2021-07-16 10:33:29.157285: Encoding repertoire 61/200\r\n",
      "2021-07-16 10:33:29.165168: Encoding repertoire 62/200\r\n",
      "2021-07-16 10:33:29.173891: Encoding repertoire 63/200\r\n",
      "2021-07-16 10:33:29.182029: Encoding repertoire 64/200\r\n",
      "2021-07-16 10:33:29.189676: Encoding repertoire 65/200\r\n",
      "2021-07-16 10:33:29.196927: Encoding repertoire 66/200\r\n",
      "2021-07-16 10:33:29.204584: Encoding repertoire 67/200\r\n",
      "2021-07-16 10:33:29.212393: Encoding repertoire 68/200\r\n",
      "2021-07-16 10:33:29.220839: Encoding repertoire 69/200\r\n",
      "2021-07-16 10:33:29.229139: Encoding repertoire 70/200\r\n",
      "2021-07-16 10:33:29.236546: Encoding repertoire 71/200\r\n",
      "2021-07-16 10:33:29.243860: Encoding repertoire 72/200\r\n",
      "2021-07-16 10:33:29.251166: Encoding repertoire 73/200\r\n",
      "2021-07-16 10:33:29.258246: Encoding repertoire 74/200\r\n",
      "2021-07-16 10:33:29.266328: Encoding repertoire 75/200\r\n",
      "2021-07-16 10:33:29.274459: Encoding repertoire 76/200\r\n",
      "2021-07-16 10:33:29.282265: Encoding repertoire 77/200\r\n",
      "2021-07-16 10:33:29.291319: Encoding repertoire 78/200\r\n",
      "2021-07-16 10:33:29.299217: Encoding repertoire 79/200\r\n",
      "2021-07-16 10:33:29.306393: Encoding repertoire 80/200\r\n",
      "2021-07-16 10:33:29.314202: Encoding repertoire 81/200\r\n",
      "2021-07-16 10:33:29.321511: Encoding repertoire 82/200\r\n",
      "2021-07-16 10:33:29.329909: Encoding repertoire 83/200\r\n",
      "2021-07-16 10:33:29.337403: Encoding repertoire 84/200\r\n",
      "2021-07-16 10:33:29.345527: Encoding repertoire 85/200\r\n",
      "2021-07-16 10:33:29.352810: Encoding repertoire 86/200\r\n",
      "2021-07-16 10:33:29.360165: Encoding repertoire 87/200\r\n",
      "2021-07-16 10:33:29.368087: Encoding repertoire 88/200\r\n",
      "2021-07-16 10:33:29.376148: Encoding repertoire 89/200\r\n",
      "2021-07-16 10:33:29.383355: Encoding repertoire 90/200\r\n",
      "2021-07-16 10:33:29.390660: Encoding repertoire 91/200\r\n",
      "2021-07-16 10:33:29.398800: Encoding repertoire 92/200\r\n",
      "2021-07-16 10:33:29.406194: Encoding repertoire 93/200\r\n",
      "2021-07-16 10:33:29.413506: Encoding repertoire 94/200\r\n",
      "2021-07-16 10:33:29.420706: Encoding repertoire 95/200\r\n",
      "2021-07-16 10:33:29.428509: Encoding repertoire 96/200\r\n",
      "2021-07-16 10:33:29.435630: Encoding repertoire 97/200\r\n",
      "2021-07-16 10:33:29.442731: Encoding repertoire 98/200\r\n",
      "2021-07-16 10:33:29.449998: Encoding repertoire 99/200\r\n",
      "2021-07-16 10:33:29.457251: Encoding repertoire 100/200\r\n",
      "2021-07-16 10:33:29.464218: Encoding repertoire 101/200\r\n",
      "2021-07-16 10:33:29.470801: Encoding repertoire 102/200\r\n",
      "2021-07-16 10:33:29.478018: Encoding repertoire 103/200\r\n",
      "2021-07-16 10:33:29.484940: Encoding repertoire 104/200\r\n",
      "2021-07-16 10:33:29.491859: Encoding repertoire 105/200\r\n",
      "2021-07-16 10:33:29.498521: Encoding repertoire 106/200\r\n",
      "2021-07-16 10:33:29.505382: Encoding repertoire 107/200\r\n",
      "2021-07-16 10:33:29.512074: Encoding repertoire 108/200\r\n",
      "2021-07-16 10:33:29.518928: Encoding repertoire 109/200\r\n",
      "2021-07-16 10:33:29.526890: Encoding repertoire 110/200\r\n",
      "2021-07-16 10:33:29.534241: Encoding repertoire 111/200\r\n",
      "2021-07-16 10:33:29.541933: Encoding repertoire 112/200\r\n",
      "2021-07-16 10:33:29.549816: Encoding repertoire 113/200\r\n",
      "2021-07-16 10:33:29.557419: Encoding repertoire 114/200\r\n",
      "2021-07-16 10:33:29.564703: Encoding repertoire 115/200\r\n",
      "2021-07-16 10:33:29.571599: Encoding repertoire 116/200\r\n",
      "2021-07-16 10:33:29.578633: Encoding repertoire 117/200\r\n",
      "2021-07-16 10:33:29.585755: Encoding repertoire 118/200\r\n",
      "2021-07-16 10:33:29.592985: Encoding repertoire 119/200\r\n",
      "2021-07-16 10:33:29.599677: Encoding repertoire 120/200\r\n",
      "2021-07-16 10:33:29.606748: Encoding repertoire 121/200\r\n",
      "2021-07-16 10:33:29.614112: Encoding repertoire 122/200\r\n",
      "2021-07-16 10:33:29.620973: Encoding repertoire 123/200\r\n",
      "2021-07-16 10:33:29.627783: Encoding repertoire 124/200\r\n",
      "2021-07-16 10:33:29.634470: Encoding repertoire 125/200\r\n",
      "2021-07-16 10:33:29.641141: Encoding repertoire 126/200\r\n",
      "2021-07-16 10:33:29.647820: Encoding repertoire 127/200\r\n",
      "2021-07-16 10:33:29.654587: Encoding repertoire 128/200\r\n",
      "2021-07-16 10:33:29.661302: Encoding repertoire 129/200\r\n",
      "2021-07-16 10:33:29.667989: Encoding repertoire 130/200\r\n",
      "2021-07-16 10:33:29.674739: Encoding repertoire 131/200\r\n",
      "2021-07-16 10:33:29.682186: Encoding repertoire 132/200\r\n",
      "2021-07-16 10:33:29.689242: Encoding repertoire 133/200\r\n",
      "2021-07-16 10:33:29.696092: Encoding repertoire 134/200\r\n",
      "2021-07-16 10:33:29.703022: Encoding repertoire 135/200\r\n",
      "2021-07-16 10:33:29.709890: Encoding repertoire 136/200\r\n",
      "2021-07-16 10:33:29.716632: Encoding repertoire 137/200\r\n",
      "2021-07-16 10:33:29.723475: Encoding repertoire 138/200\r\n",
      "2021-07-16 10:33:29.732152: Encoding repertoire 139/200\r\n",
      "2021-07-16 10:33:29.739304: Encoding repertoire 140/200\r\n",
      "2021-07-16 10:33:29.746349: Encoding repertoire 141/200\r\n",
      "2021-07-16 10:33:29.753658: Encoding repertoire 142/200\r\n",
      "2021-07-16 10:33:29.760735: Encoding repertoire 143/200\r\n",
      "2021-07-16 10:33:29.768688: Encoding repertoire 144/200\r\n",
      "2021-07-16 10:33:29.775695: Encoding repertoire 145/200\r\n",
      "2021-07-16 10:33:29.783578: Encoding repertoire 146/200\r\n",
      "2021-07-16 10:33:29.792476: Encoding repertoire 147/200\r\n",
      "2021-07-16 10:33:29.800026: Encoding repertoire 148/200\r\n",
      "2021-07-16 10:33:29.806721: Encoding repertoire 149/200\r\n",
      "2021-07-16 10:33:29.813932: Encoding repertoire 150/200\r\n",
      "2021-07-16 10:33:29.820695: Encoding repertoire 151/200\r\n",
      "2021-07-16 10:33:29.827433: Encoding repertoire 152/200\r\n",
      "2021-07-16 10:33:29.834528: Encoding repertoire 153/200\r\n",
      "2021-07-16 10:33:29.841037: Encoding repertoire 154/200\r\n",
      "2021-07-16 10:33:29.847641: Encoding repertoire 155/200\r\n",
      "2021-07-16 10:33:29.854217: Encoding repertoire 156/200\r\n",
      "2021-07-16 10:33:29.860751: Encoding repertoire 157/200\r\n",
      "2021-07-16 10:33:29.867338: Encoding repertoire 158/200\r\n",
      "2021-07-16 10:33:29.873816: Encoding repertoire 159/200\r\n",
      "2021-07-16 10:33:29.880553: Encoding repertoire 160/200\r\n",
      "2021-07-16 10:33:29.887358: Encoding repertoire 161/200\r\n",
      "2021-07-16 10:33:29.894390: Encoding repertoire 162/200\r\n",
      "2021-07-16 10:33:29.902764: Encoding repertoire 163/200\r\n",
      "2021-07-16 10:33:29.911351: Encoding repertoire 164/200\r\n",
      "2021-07-16 10:33:29.918325: Encoding repertoire 165/200\r\n",
      "2021-07-16 10:33:29.925577: Encoding repertoire 166/200\r\n",
      "2021-07-16 10:33:29.933261: Encoding repertoire 167/200\r\n",
      "2021-07-16 10:33:29.940375: Encoding repertoire 168/200\r\n",
      "2021-07-16 10:33:29.947541: Encoding repertoire 169/200\r\n",
      "2021-07-16 10:33:29.956425: Encoding repertoire 170/200\r\n",
      "2021-07-16 10:33:29.964311: Encoding repertoire 171/200\r\n",
      "2021-07-16 10:33:29.971453: Encoding repertoire 172/200\r\n",
      "2021-07-16 10:33:29.978783: Encoding repertoire 173/200\r\n",
      "2021-07-16 10:33:29.985940: Encoding repertoire 174/200\r\n",
      "2021-07-16 10:33:29.993232: Encoding repertoire 175/200\r\n",
      "2021-07-16 10:33:30.000389: Encoding repertoire 176/200\r\n",
      "2021-07-16 10:33:30.007389: Encoding repertoire 177/200\r\n",
      "2021-07-16 10:33:30.013969: Encoding repertoire 178/200\r\n",
      "2021-07-16 10:33:30.021199: Encoding repertoire 179/200\r\n",
      "2021-07-16 10:33:30.028089: Encoding repertoire 180/200\r\n",
      "2021-07-16 10:33:30.036244: Encoding repertoire 181/200\r\n",
      "2021-07-16 10:33:30.043983: Encoding repertoire 182/200\r\n",
      "2021-07-16 10:33:30.053361: Encoding repertoire 183/200\r\n",
      "2021-07-16 10:33:30.062351: Encoding repertoire 184/200\r\n",
      "2021-07-16 10:33:30.069757: Encoding repertoire 185/200\r\n",
      "2021-07-16 10:33:30.076653: Encoding repertoire 186/200\r\n",
      "2021-07-16 10:33:30.083176: Encoding repertoire 187/200\r\n",
      "2021-07-16 10:33:30.090008: Encoding repertoire 188/200\r\n",
      "2021-07-16 10:33:30.096668: Encoding repertoire 189/200\r\n",
      "2021-07-16 10:33:30.103303: Encoding repertoire 190/200\r\n",
      "2021-07-16 10:33:30.110592: Encoding repertoire 191/200\r\n",
      "2021-07-16 10:33:30.118302: Encoding repertoire 192/200\r\n",
      "2021-07-16 10:33:30.125142: Encoding repertoire 193/200\r\n",
      "2021-07-16 10:33:30.131759: Encoding repertoire 194/200\r\n",
      "2021-07-16 10:33:30.138710: Encoding repertoire 195/200\r\n",
      "2021-07-16 10:33:30.145795: Encoding repertoire 196/200\r\n",
      "2021-07-16 10:33:30.152921: Encoding repertoire 197/200\r\n",
      "2021-07-16 10:33:30.161073: Encoding repertoire 198/200\r\n",
      "2021-07-16 10:33:30.168956: Encoding repertoire 199/200\r\n",
      "2021-07-16 10:33:30.176219: Encoding repertoire 200/200\r\n",
      "2021-07-16 10:33:30.232069: Saving encoded dataset to disk.\r\n",
      "2021-07-16 10:33:32.912700: Encoding finished.\r\n",
      "2021-07-16 10:33:34.507374: Finished analysis signal_freq (1/1).\r\n",
      "\r\n",
      "2021-07-16 10:33:34.507483: Instruction 2/2 has finished.\r\n",
      "2021-07-16 10:33:34.514807: Generating HTML reports...\r\n",
      "2021-07-16 10:33:35.506472: HTML reports are generated.\r\n",
      "2021-07-16 10:33:36.019598: ImmuneML: finished analysis.\r\n",
      "\r\n"
     ]
    }
   ],
   "source": [
    "!mkdir -p analyses/olga/varying_complex_kmer_size/{simulated_data,ML_output}\n",
    "!docker container prune -f\n",
    "!docker run -it -v $(pwd):/wd --name simulation_container kanduric/immuneml-v1:latest immune-ml /wd/scripts/olga/run_1/varying_complex_kmer_size/generated_yaml_files/simulation_instructions/repertoire_implanting_rate__0.0005_-_motifs__kmer1_size_3_kmer2_size_3_kmer3_size_3.yaml /wd/analyses/olga/varying_complex_kmer_size/simulated_data/repertoire_implanting_rate__0.0005_-_motifs__kmer1_size_3_kmer2_size_3_kmer3_size_3\n",
    "!docker run -it -v $(pwd):/wd --name ml_container kanduric/immuneml-v1:latest immune-ml /wd/scripts/olga/run_1/varying_complex_kmer_size/generated_yaml_files/ml_instructions/repertoire_implanting_rate__0.0005_-_motifs__kmer1_size_3_kmer2_size_3_kmer3_size_3_-_ml_instruction.yaml /wd/analyses/olga/varying_complex_kmer_size/ML_output/repertoire_implanting_rate__0.0005_-_motifs__kmer1_size_3_kmer2_size_3_kmer3_size_3"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "#### Feature size assumed by the model\n",
    "\n",
    "The stdout printed to screen when running the simulation and ML model training instructions are shown below indicating successful execution, and the resulting output of each instruction is stored under `analyses/olga/varying_feature_size/` in this github repository."
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Deleted Containers:\r\n",
      "30e9f1cfb16495dc446c0296e13535e2724f3da6592c758330062d4afd2a48d5\r\n",
      "5a36e432cc97ad3a066b1490aad49d0f47cc3f89ba733370a986728731bba22c\r\n",
      "\r\n",
      "Total reclaimed space: 47.09kB\r\n",
      "2021-07-16 10:35:58.412276: Setting temporary cache path to wd/analyses/olga/varying_feature_size/simulated_data/repertoire_implanting_rate__0.002/cache/\r\n",
      "2021-07-16 10:35:58.412365: ImmuneML: parsing the specification...\r\n",
      "\r\n",
      "2021-07-16 10:36:03.238325: Full specification is available at wd/analyses/olga/varying_feature_size/simulated_data/repertoire_implanting_rate__0.002/full_repertoire_implanting_rate__0.yaml.\r\n",
      "\r\n",
      "2021-07-16 10:36:03.238468: ImmuneML: starting the analysis...\r\n",
      "\r\n",
      "2021-07-16 10:36:03.238657: Instruction 1/1 has started.\r\n",
      "2021-07-16 10:36:08.892258: Instruction 1/1 has finished.\r\n",
      "2021-07-16 10:36:08.898362: Generating HTML reports...\r\n",
      "2021-07-16 10:36:08.975157: HTML reports are generated.\r\n",
      "2021-07-16 10:36:08.976642: ImmuneML: finished analysis.\r\n",
      "\r\n",
      "2021-07-16 10:36:11.327535: Setting temporary cache path to wd/analyses/olga/varying_feature_size/ML_output/repertoire_implanting_rate__0.002_-_k__3/cache/\r\n",
      "2021-07-16 10:36:11.327648: ImmuneML: parsing the specification...\r\n",
      "\r\n",
      "2021-07-16 10:36:13.907109: Full specification is available at wd/analyses/olga/varying_feature_size/ML_output/repertoire_implanting_rate__0.002_-_k__3/full_repertoire_implanting_rate__0.yaml.\r\n",
      "\r\n",
      "2021-07-16 10:36:13.907227: ImmuneML: starting the analysis...\r\n",
      "\r\n",
      "2021-07-16 10:36:13.907339: Instruction 1/2 has started.\r\n",
      "2021-07-16 10:36:14.052770: Training ML model: running outer CV loop: started split 1/5.\r\n",
      "\r\n",
      "2021-07-16 10:36:14.099742: Hyperparameter optimization: running the inner loop of nested CV: selection for label signal_cancer (label 1 / 1).\r\n",
      "\r\n",
      "2021-07-16 10:36:14.108046: Evaluating hyperparameter setting: feature_size_k_logistic_regression...\r\n",
      "2021-07-16 10:36:14.122146: Encoding started...\r\n",
      "2021-07-16 10:36:16.867517: Encoding finished.\r\n",
      "2021-07-16 10:36:16.868007: ML model training started...\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "2021-07-16 10:36:22.434716: ML model training finished.\r\n",
      "2021-07-16 10:36:22.435019: Completed hyperparameter setting feature_size_k_logistic_regression.\r\n",
      "\r\n",
      "2021-07-16 10:36:22.438378: Hyperparameter optimization: running the inner loop of nested CV: completed selection for label signal_cancer (label 1 / 1).\r\n",
      "\r\n",
      "2021-07-16 10:36:22.438476: Training ML model: running the inner loop of nested CV: retrain models for label signal_cancer (label 1 / 1).\r\n",
      "\r\n",
      "2021-07-16 10:36:22.445915: Evaluating hyperparameter setting: feature_size_k_logistic_regression...\r\n",
      "2021-07-16 10:36:22.458489: Encoding started...\r\n",
      "2021-07-16 10:36:23.851871: Encoding finished.\r\n",
      "2021-07-16 10:36:23.852155: ML model training started...\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "2021-07-16 10:36:30.539540: ML model training finished.\r\n",
      "2021-07-16 10:36:30.543634: Encoding started...\r\n",
      "2021-07-16 10:36:31.484433: Encoding finished.\r\n",
      "2021-07-16 10:36:32.244525: Completed hyperparameter setting feature_size_k_logistic_regression.\r\n",
      "\r\n",
      "2021-07-16 10:36:32.245060: Training ML model: running the inner loop of nested CV: completed retraining models for label signal_cancer (label 1 / 1).\r\n",
      "\r\n",
      "2021-07-16 10:36:32.245157: Training ML model: running outer CV loop: finished split 1/5.\r\n",
      "\r\n",
      "2021-07-16 10:36:32.245180: Training ML model: running outer CV loop: started split 2/5.\r\n",
      "\r\n",
      "2021-07-16 10:36:32.311012: Hyperparameter optimization: running the inner loop of nested CV: selection for label signal_cancer (label 1 / 1).\r\n",
      "\r\n",
      "2021-07-16 10:36:32.317972: Evaluating hyperparameter setting: feature_size_k_logistic_regression...\r\n",
      "2021-07-16 10:36:32.328468: Encoding started...\r\n",
      "2021-07-16 10:36:34.202030: Encoding finished.\r\n",
      "2021-07-16 10:36:34.202797: ML model training started...\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "2021-07-16 10:36:38.182021: ML model training finished.\r\n",
      "2021-07-16 10:36:38.182224: Completed hyperparameter setting feature_size_k_logistic_regression.\r\n",
      "\r\n",
      "2021-07-16 10:36:38.187340: Hyperparameter optimization: running the inner loop of nested CV: completed selection for label signal_cancer (label 1 / 1).\r\n",
      "\r\n",
      "2021-07-16 10:36:38.187561: Training ML model: running the inner loop of nested CV: retrain models for label signal_cancer (label 1 / 1).\r\n",
      "\r\n",
      "2021-07-16 10:36:38.194760: Evaluating hyperparameter setting: feature_size_k_logistic_regression...\r\n",
      "2021-07-16 10:36:38.206807: Encoding started...\r\n",
      "2021-07-16 10:36:39.620736: Encoding finished.\r\n",
      "2021-07-16 10:36:39.621110: ML model training started...\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "2021-07-16 10:36:44.971324: ML model training finished.\r\n",
      "2021-07-16 10:36:44.974248: Encoding started...\r\n",
      "2021-07-16 10:36:45.893738: Encoding finished.\r\n",
      "2021-07-16 10:36:46.368735: Completed hyperparameter setting feature_size_k_logistic_regression.\r\n",
      "\r\n",
      "2021-07-16 10:36:46.372740: Training ML model: running the inner loop of nested CV: completed retraining models for label signal_cancer (label 1 / 1).\r\n",
      "\r\n",
      "2021-07-16 10:36:46.372859: Training ML model: running outer CV loop: finished split 2/5.\r\n",
      "\r\n",
      "2021-07-16 10:36:46.373068: Training ML model: running outer CV loop: started split 3/5.\r\n",
      "\r\n",
      "2021-07-16 10:36:46.419286: Hyperparameter optimization: running the inner loop of nested CV: selection for label signal_cancer (label 1 / 1).\r\n",
      "\r\n",
      "2021-07-16 10:36:46.424558: Evaluating hyperparameter setting: feature_size_k_logistic_regression...\r\n",
      "2021-07-16 10:36:46.434294: Encoding started...\r\n",
      "2021-07-16 10:36:47.813368: Encoding finished.\r\n",
      "2021-07-16 10:36:47.813678: ML model training started...\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "2021-07-16 10:36:52.698700: ML model training finished.\r\n",
      "2021-07-16 10:36:52.698854: Completed hyperparameter setting feature_size_k_logistic_regression.\r\n",
      "\r\n",
      "2021-07-16 10:36:52.702563: Hyperparameter optimization: running the inner loop of nested CV: completed selection for label signal_cancer (label 1 / 1).\r\n",
      "\r\n",
      "2021-07-16 10:36:52.702668: Training ML model: running the inner loop of nested CV: retrain models for label signal_cancer (label 1 / 1).\r\n",
      "\r\n",
      "2021-07-16 10:36:52.709007: Evaluating hyperparameter setting: feature_size_k_logistic_regression...\r\n",
      "2021-07-16 10:36:52.720413: Encoding started...\r\n",
      "2021-07-16 10:36:54.065396: Encoding finished.\r\n",
      "2021-07-16 10:36:54.065688: ML model training started...\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "2021-07-16 10:36:57.766457: ML model training finished.\r\n",
      "2021-07-16 10:36:57.770687: Encoding started...\r\n",
      "2021-07-16 10:36:58.678477: Encoding finished.\r\n",
      "2021-07-16 10:36:59.135044: Completed hyperparameter setting feature_size_k_logistic_regression.\r\n",
      "\r\n",
      "2021-07-16 10:36:59.139411: Training ML model: running the inner loop of nested CV: completed retraining models for label signal_cancer (label 1 / 1).\r\n",
      "\r\n",
      "2021-07-16 10:36:59.139579: Training ML model: running outer CV loop: finished split 3/5.\r\n",
      "\r\n",
      "2021-07-16 10:36:59.139619: Training ML model: running outer CV loop: started split 4/5.\r\n",
      "\r\n",
      "2021-07-16 10:36:59.183317: Hyperparameter optimization: running the inner loop of nested CV: selection for label signal_cancer (label 1 / 1).\r\n",
      "\r\n",
      "2021-07-16 10:36:59.188205: Evaluating hyperparameter setting: feature_size_k_logistic_regression...\r\n",
      "2021-07-16 10:36:59.197541: Encoding started...\r\n",
      "2021-07-16 10:37:00.710096: Encoding finished.\r\n",
      "2021-07-16 10:37:00.710431: ML model training started...\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "2021-07-16 10:37:06.331111: ML model training finished.\r\n",
      "2021-07-16 10:37:06.331377: Completed hyperparameter setting feature_size_k_logistic_regression.\r\n",
      "\r\n",
      "2021-07-16 10:37:06.335276: Hyperparameter optimization: running the inner loop of nested CV: completed selection for label signal_cancer (label 1 / 1).\r\n",
      "\r\n",
      "2021-07-16 10:37:06.335602: Training ML model: running the inner loop of nested CV: retrain models for label signal_cancer (label 1 / 1).\r\n",
      "\r\n",
      "2021-07-16 10:37:06.341028: Evaluating hyperparameter setting: feature_size_k_logistic_regression...\r\n",
      "2021-07-16 10:37:06.351839: Encoding started...\r\n",
      "2021-07-16 10:37:07.891758: Encoding finished.\r\n",
      "2021-07-16 10:37:07.892286: ML model training started...\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "2021-07-16 10:37:11.841474: ML model training finished.\r\n",
      "2021-07-16 10:37:11.843970: Encoding started...\r\n",
      "2021-07-16 10:37:12.924199: Encoding finished.\r\n",
      "2021-07-16 10:37:13.404667: Completed hyperparameter setting feature_size_k_logistic_regression.\r\n",
      "\r\n",
      "2021-07-16 10:37:13.408589: Training ML model: running the inner loop of nested CV: completed retraining models for label signal_cancer (label 1 / 1).\r\n",
      "\r\n",
      "2021-07-16 10:37:13.408694: Training ML model: running outer CV loop: finished split 4/5.\r\n",
      "\r\n",
      "2021-07-16 10:37:13.408737: Training ML model: running outer CV loop: started split 5/5.\r\n",
      "\r\n",
      "2021-07-16 10:37:13.451453: Hyperparameter optimization: running the inner loop of nested CV: selection for label signal_cancer (label 1 / 1).\r\n",
      "\r\n",
      "2021-07-16 10:37:13.456816: Evaluating hyperparameter setting: feature_size_k_logistic_regression...\r\n",
      "2021-07-16 10:37:13.466316: Encoding started...\r\n",
      "2021-07-16 10:37:14.829184: Encoding finished.\r\n",
      "2021-07-16 10:37:14.829628: ML model training started...\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "2021-07-16 10:37:18.573144: ML model training finished.\r\n",
      "2021-07-16 10:37:18.573552: Completed hyperparameter setting feature_size_k_logistic_regression.\r\n",
      "\r\n",
      "2021-07-16 10:37:18.577517: Hyperparameter optimization: running the inner loop of nested CV: completed selection for label signal_cancer (label 1 / 1).\r\n",
      "\r\n",
      "2021-07-16 10:37:18.577962: Training ML model: running the inner loop of nested CV: retrain models for label signal_cancer (label 1 / 1).\r\n",
      "\r\n",
      "2021-07-16 10:37:18.584754: Evaluating hyperparameter setting: feature_size_k_logistic_regression...\r\n",
      "2021-07-16 10:37:18.596076: Encoding started...\r\n",
      "2021-07-16 10:37:20.010719: Encoding finished.\r\n",
      "2021-07-16 10:37:20.011201: ML model training started...\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "2021-07-16 10:37:23.975692: ML model training finished.\r\n",
      "2021-07-16 10:37:23.978748: Encoding started...\r\n",
      "2021-07-16 10:37:24.916819: Encoding finished.\r\n",
      "2021-07-16 10:37:25.385981: Completed hyperparameter setting feature_size_k_logistic_regression.\r\n",
      "\r\n",
      "2021-07-16 10:37:25.390031: Training ML model: running the inner loop of nested CV: completed retraining models for label signal_cancer (label 1 / 1).\r\n",
      "\r\n",
      "2021-07-16 10:37:25.390181: Training ML model: running outer CV loop: finished split 5/5.\r\n",
      "\r\n",
      "Performances (accuracy) -----------------------------------------------\r\n",
      "\r\n",
      "\r\n",
      "Label: signal_cancer\r\n",
      "Performance (accuracy) per assessment split:\r\n",
      "Split 1: 0.35\r\n",
      "Split 2: 0.375\r\n",
      "Split 3: 0.45\r\n",
      "Split 4: 0.5\r\n",
      "Split 5: 0.375\r\n",
      "Average performance (accuracy): 0.41\r\n",
      "------------------------------\r\n",
      "2021-07-16 10:37:26.114400: Instruction 1/2 has finished.\r\n",
      "2021-07-16 10:37:26.114468: Instruction 2/2 has started.\r\n",
      "2021-07-16 10:37:26.114821: Started analysis signal_freq (1/1).\r\n",
      "2021-07-16 10:37:26.119181: Encoding started...\r\n",
      "2021-07-16 10:37:26.126461: Encoding repertoire 1/200\r\n",
      "2021-07-16 10:37:26.135131: Encoding repertoire 2/200\r\n",
      "2021-07-16 10:37:26.139582: Encoding repertoire 3/200\r\n",
      "2021-07-16 10:37:26.144044: Encoding repertoire 4/200\r\n",
      "2021-07-16 10:37:26.149099: Encoding repertoire 5/200\r\n",
      "2021-07-16 10:37:26.155714: Encoding repertoire 6/200\r\n",
      "2021-07-16 10:37:26.160782: Encoding repertoire 7/200\r\n",
      "2021-07-16 10:37:26.165825: Encoding repertoire 8/200\r\n",
      "2021-07-16 10:37:26.170741: Encoding repertoire 9/200\r\n",
      "2021-07-16 10:37:26.175455: Encoding repertoire 10/200\r\n",
      "2021-07-16 10:37:26.180013: Encoding repertoire 11/200\r\n",
      "2021-07-16 10:37:26.184936: Encoding repertoire 12/200\r\n",
      "2021-07-16 10:37:26.189422: Encoding repertoire 13/200\r\n",
      "2021-07-16 10:37:26.193687: Encoding repertoire 14/200\r\n",
      "2021-07-16 10:37:26.198317: Encoding repertoire 15/200\r\n",
      "2021-07-16 10:37:26.203605: Encoding repertoire 16/200\r\n",
      "2021-07-16 10:37:26.209259: Encoding repertoire 17/200\r\n",
      "2021-07-16 10:37:26.214919: Encoding repertoire 18/200\r\n",
      "2021-07-16 10:37:26.220819: Encoding repertoire 19/200\r\n",
      "2021-07-16 10:37:26.226836: Encoding repertoire 20/200\r\n",
      "2021-07-16 10:37:26.232629: Encoding repertoire 21/200\r\n",
      "2021-07-16 10:37:26.238333: Encoding repertoire 22/200\r\n",
      "2021-07-16 10:37:26.243757: Encoding repertoire 23/200\r\n",
      "2021-07-16 10:37:26.249327: Encoding repertoire 24/200\r\n",
      "2021-07-16 10:37:26.254175: Encoding repertoire 25/200\r\n",
      "2021-07-16 10:37:26.259209: Encoding repertoire 26/200\r\n",
      "2021-07-16 10:37:26.265063: Encoding repertoire 27/200\r\n",
      "2021-07-16 10:37:26.270021: Encoding repertoire 28/200\r\n",
      "2021-07-16 10:37:26.274911: Encoding repertoire 29/200\r\n",
      "2021-07-16 10:37:26.279902: Encoding repertoire 30/200\r\n",
      "2021-07-16 10:37:26.284714: Encoding repertoire 31/200\r\n",
      "2021-07-16 10:37:26.289097: Encoding repertoire 32/200\r\n",
      "2021-07-16 10:37:26.293615: Encoding repertoire 33/200\r\n",
      "2021-07-16 10:37:26.299043: Encoding repertoire 34/200\r\n",
      "2021-07-16 10:37:26.303843: Encoding repertoire 35/200\r\n",
      "2021-07-16 10:37:26.308982: Encoding repertoire 36/200\r\n",
      "2021-07-16 10:37:26.314116: Encoding repertoire 37/200\r\n",
      "2021-07-16 10:37:26.318851: Encoding repertoire 38/200\r\n",
      "2021-07-16 10:37:26.323615: Encoding repertoire 39/200\r\n",
      "2021-07-16 10:37:26.327816: Encoding repertoire 40/200\r\n",
      "2021-07-16 10:37:26.332179: Encoding repertoire 41/200\r\n",
      "2021-07-16 10:37:26.337242: Encoding repertoire 42/200\r\n",
      "2021-07-16 10:37:26.341667: Encoding repertoire 43/200\r\n",
      "2021-07-16 10:37:26.345906: Encoding repertoire 44/200\r\n",
      "2021-07-16 10:37:26.351242: Encoding repertoire 45/200\r\n",
      "2021-07-16 10:37:26.355626: Encoding repertoire 46/200\r\n",
      "2021-07-16 10:37:26.361708: Encoding repertoire 47/200\r\n",
      "2021-07-16 10:37:26.367241: Encoding repertoire 48/200\r\n",
      "2021-07-16 10:37:26.372207: Encoding repertoire 49/200\r\n",
      "2021-07-16 10:37:26.377671: Encoding repertoire 50/200\r\n",
      "2021-07-16 10:37:26.382720: Encoding repertoire 51/200\r\n",
      "2021-07-16 10:37:26.387577: Encoding repertoire 52/200\r\n",
      "2021-07-16 10:37:26.392833: Encoding repertoire 53/200\r\n",
      "2021-07-16 10:37:26.398607: Encoding repertoire 54/200\r\n",
      "2021-07-16 10:37:26.403179: Encoding repertoire 55/200\r\n",
      "2021-07-16 10:37:26.407437: Encoding repertoire 56/200\r\n",
      "2021-07-16 10:37:26.412170: Encoding repertoire 57/200\r\n",
      "2021-07-16 10:37:26.416383: Encoding repertoire 58/200\r\n",
      "2021-07-16 10:37:26.420627: Encoding repertoire 59/200\r\n",
      "2021-07-16 10:37:26.424770: Encoding repertoire 60/200\r\n",
      "2021-07-16 10:37:26.428852: Encoding repertoire 61/200\r\n",
      "2021-07-16 10:37:26.433027: Encoding repertoire 62/200\r\n",
      "2021-07-16 10:37:26.437311: Encoding repertoire 63/200\r\n",
      "2021-07-16 10:37:26.441473: Encoding repertoire 64/200\r\n",
      "2021-07-16 10:37:26.445842: Encoding repertoire 65/200\r\n",
      "2021-07-16 10:37:26.450201: Encoding repertoire 66/200\r\n",
      "2021-07-16 10:37:26.454470: Encoding repertoire 67/200\r\n",
      "2021-07-16 10:37:26.459103: Encoding repertoire 68/200\r\n",
      "2021-07-16 10:37:26.463782: Encoding repertoire 69/200\r\n",
      "2021-07-16 10:37:26.468309: Encoding repertoire 70/200\r\n",
      "2021-07-16 10:37:26.472611: Encoding repertoire 71/200\r\n",
      "2021-07-16 10:37:26.477182: Encoding repertoire 72/200\r\n",
      "2021-07-16 10:37:26.481457: Encoding repertoire 73/200\r\n",
      "2021-07-16 10:37:26.485606: Encoding repertoire 74/200\r\n",
      "2021-07-16 10:37:26.490163: Encoding repertoire 75/200\r\n",
      "2021-07-16 10:37:26.494562: Encoding repertoire 76/200\r\n",
      "2021-07-16 10:37:26.498764: Encoding repertoire 77/200\r\n",
      "2021-07-16 10:37:26.503172: Encoding repertoire 78/200\r\n",
      "2021-07-16 10:37:26.507488: Encoding repertoire 79/200\r\n",
      "2021-07-16 10:37:26.512263: Encoding repertoire 80/200\r\n",
      "2021-07-16 10:37:26.516873: Encoding repertoire 81/200\r\n",
      "2021-07-16 10:37:26.521633: Encoding repertoire 82/200\r\n",
      "2021-07-16 10:37:26.525907: Encoding repertoire 83/200\r\n",
      "2021-07-16 10:37:26.530207: Encoding repertoire 84/200\r\n",
      "2021-07-16 10:37:26.534485: Encoding repertoire 85/200\r\n",
      "2021-07-16 10:37:26.538711: Encoding repertoire 86/200\r\n",
      "2021-07-16 10:37:26.543224: Encoding repertoire 87/200\r\n",
      "2021-07-16 10:37:26.547545: Encoding repertoire 88/200\r\n",
      "2021-07-16 10:37:26.554051: Encoding repertoire 89/200\r\n",
      "2021-07-16 10:37:26.558565: Encoding repertoire 90/200\r\n",
      "2021-07-16 10:37:26.563373: Encoding repertoire 91/200\r\n",
      "2021-07-16 10:37:26.567955: Encoding repertoire 92/200\r\n",
      "2021-07-16 10:37:26.572594: Encoding repertoire 93/200\r\n",
      "2021-07-16 10:37:26.576969: Encoding repertoire 94/200\r\n",
      "2021-07-16 10:37:26.582021: Encoding repertoire 95/200\r\n",
      "2021-07-16 10:37:26.586805: Encoding repertoire 96/200\r\n",
      "2021-07-16 10:37:26.591354: Encoding repertoire 97/200\r\n",
      "2021-07-16 10:37:26.595987: Encoding repertoire 98/200\r\n",
      "2021-07-16 10:37:26.600728: Encoding repertoire 99/200\r\n",
      "2021-07-16 10:37:26.605425: Encoding repertoire 100/200\r\n",
      "2021-07-16 10:37:26.609611: Encoding repertoire 101/200\r\n",
      "2021-07-16 10:37:26.614494: Encoding repertoire 102/200\r\n",
      "2021-07-16 10:37:26.618732: Encoding repertoire 103/200\r\n",
      "2021-07-16 10:37:26.623019: Encoding repertoire 104/200\r\n",
      "2021-07-16 10:37:26.627097: Encoding repertoire 105/200\r\n",
      "2021-07-16 10:37:26.631329: Encoding repertoire 106/200\r\n",
      "2021-07-16 10:37:26.635565: Encoding repertoire 107/200\r\n",
      "2021-07-16 10:37:26.639840: Encoding repertoire 108/200\r\n",
      "2021-07-16 10:37:26.643973: Encoding repertoire 109/200\r\n",
      "2021-07-16 10:37:26.648120: Encoding repertoire 110/200\r\n",
      "2021-07-16 10:37:26.652287: Encoding repertoire 111/200\r\n",
      "2021-07-16 10:37:26.659254: Encoding repertoire 112/200\r\n",
      "2021-07-16 10:37:26.664352: Encoding repertoire 113/200\r\n",
      "2021-07-16 10:37:26.668859: Encoding repertoire 114/200\r\n",
      "2021-07-16 10:37:26.673229: Encoding repertoire 115/200\r\n",
      "2021-07-16 10:37:26.678839: Encoding repertoire 116/200\r\n",
      "2021-07-16 10:37:26.683279: Encoding repertoire 117/200\r\n",
      "2021-07-16 10:37:26.687501: Encoding repertoire 118/200\r\n",
      "2021-07-16 10:37:26.691842: Encoding repertoire 119/200\r\n",
      "2021-07-16 10:37:26.697951: Encoding repertoire 120/200\r\n",
      "2021-07-16 10:37:26.702963: Encoding repertoire 121/200\r\n",
      "2021-07-16 10:37:26.707377: Encoding repertoire 122/200\r\n",
      "2021-07-16 10:37:26.712023: Encoding repertoire 123/200\r\n",
      "2021-07-16 10:37:26.717306: Encoding repertoire 124/200\r\n",
      "2021-07-16 10:37:26.722208: Encoding repertoire 125/200\r\n",
      "2021-07-16 10:37:26.727216: Encoding repertoire 126/200\r\n",
      "2021-07-16 10:37:26.731492: Encoding repertoire 127/200\r\n",
      "2021-07-16 10:37:26.735757: Encoding repertoire 128/200\r\n",
      "2021-07-16 10:37:26.740393: Encoding repertoire 129/200\r\n",
      "2021-07-16 10:37:26.745324: Encoding repertoire 130/200\r\n",
      "2021-07-16 10:37:26.752061: Encoding repertoire 131/200\r\n",
      "2021-07-16 10:37:26.756967: Encoding repertoire 132/200\r\n",
      "2021-07-16 10:37:26.762322: Encoding repertoire 133/200\r\n",
      "2021-07-16 10:37:26.767958: Encoding repertoire 134/200\r\n",
      "2021-07-16 10:37:26.772674: Encoding repertoire 135/200\r\n",
      "2021-07-16 10:37:26.779155: Encoding repertoire 136/200\r\n",
      "2021-07-16 10:37:26.783635: Encoding repertoire 137/200\r\n",
      "2021-07-16 10:37:26.787841: Encoding repertoire 138/200\r\n",
      "2021-07-16 10:37:26.792022: Encoding repertoire 139/200\r\n",
      "2021-07-16 10:37:26.796106: Encoding repertoire 140/200\r\n",
      "2021-07-16 10:37:26.800322: Encoding repertoire 141/200\r\n",
      "2021-07-16 10:37:26.804631: Encoding repertoire 142/200\r\n",
      "2021-07-16 10:37:26.808663: Encoding repertoire 143/200\r\n",
      "2021-07-16 10:37:26.812721: Encoding repertoire 144/200\r\n",
      "2021-07-16 10:37:26.817814: Encoding repertoire 145/200\r\n",
      "2021-07-16 10:37:26.822397: Encoding repertoire 146/200\r\n",
      "2021-07-16 10:37:26.826876: Encoding repertoire 147/200\r\n",
      "2021-07-16 10:37:26.831744: Encoding repertoire 148/200\r\n",
      "2021-07-16 10:37:26.836749: Encoding repertoire 149/200\r\n",
      "2021-07-16 10:37:26.841314: Encoding repertoire 150/200\r\n",
      "2021-07-16 10:37:26.845764: Encoding repertoire 151/200\r\n",
      "2021-07-16 10:37:26.851285: Encoding repertoire 152/200\r\n",
      "2021-07-16 10:37:26.856508: Encoding repertoire 153/200\r\n",
      "2021-07-16 10:37:26.861551: Encoding repertoire 154/200\r\n",
      "2021-07-16 10:37:26.866874: Encoding repertoire 155/200\r\n",
      "2021-07-16 10:37:26.871886: Encoding repertoire 156/200\r\n",
      "2021-07-16 10:37:26.876620: Encoding repertoire 157/200\r\n",
      "2021-07-16 10:37:26.880762: Encoding repertoire 158/200\r\n",
      "2021-07-16 10:37:26.884852: Encoding repertoire 159/200\r\n",
      "2021-07-16 10:37:26.889088: Encoding repertoire 160/200\r\n",
      "2021-07-16 10:37:26.894363: Encoding repertoire 161/200\r\n",
      "2021-07-16 10:37:26.898721: Encoding repertoire 162/200\r\n",
      "2021-07-16 10:37:26.903337: Encoding repertoire 163/200\r\n",
      "2021-07-16 10:37:26.907876: Encoding repertoire 164/200\r\n",
      "2021-07-16 10:37:26.912217: Encoding repertoire 165/200\r\n",
      "2021-07-16 10:37:26.916420: Encoding repertoire 166/200\r\n",
      "2021-07-16 10:37:26.920811: Encoding repertoire 167/200\r\n",
      "2021-07-16 10:37:26.924901: Encoding repertoire 168/200\r\n",
      "2021-07-16 10:37:26.928935: Encoding repertoire 169/200\r\n",
      "2021-07-16 10:37:26.932792: Encoding repertoire 170/200\r\n",
      "2021-07-16 10:37:26.936868: Encoding repertoire 171/200\r\n",
      "2021-07-16 10:37:26.940966: Encoding repertoire 172/200\r\n",
      "2021-07-16 10:37:26.944850: Encoding repertoire 173/200\r\n",
      "2021-07-16 10:37:26.948804: Encoding repertoire 174/200\r\n",
      "2021-07-16 10:37:26.952952: Encoding repertoire 175/200\r\n",
      "2021-07-16 10:37:26.957051: Encoding repertoire 176/200\r\n",
      "2021-07-16 10:37:26.961706: Encoding repertoire 177/200\r\n",
      "2021-07-16 10:37:26.966132: Encoding repertoire 178/200\r\n",
      "2021-07-16 10:37:26.970577: Encoding repertoire 179/200\r\n",
      "2021-07-16 10:37:26.975041: Encoding repertoire 180/200\r\n",
      "2021-07-16 10:37:26.978926: Encoding repertoire 181/200\r\n",
      "2021-07-16 10:37:26.982880: Encoding repertoire 182/200\r\n",
      "2021-07-16 10:37:26.987158: Encoding repertoire 183/200\r\n",
      "2021-07-16 10:37:26.991166: Encoding repertoire 184/200\r\n",
      "2021-07-16 10:37:26.995198: Encoding repertoire 185/200\r\n",
      "2021-07-16 10:37:26.999511: Encoding repertoire 186/200\r\n",
      "2021-07-16 10:37:27.003675: Encoding repertoire 187/200\r\n",
      "2021-07-16 10:37:27.008019: Encoding repertoire 188/200\r\n",
      "2021-07-16 10:37:27.012622: Encoding repertoire 189/200\r\n",
      "2021-07-16 10:37:27.016678: Encoding repertoire 190/200\r\n",
      "2021-07-16 10:37:27.022953: Encoding repertoire 191/200\r\n",
      "2021-07-16 10:37:27.027830: Encoding repertoire 192/200\r\n",
      "2021-07-16 10:37:27.033191: Encoding repertoire 193/200\r\n",
      "2021-07-16 10:37:27.039025: Encoding repertoire 194/200\r\n",
      "2021-07-16 10:37:27.044048: Encoding repertoire 195/200\r\n",
      "2021-07-16 10:37:27.048756: Encoding repertoire 196/200\r\n",
      "2021-07-16 10:37:27.053860: Encoding repertoire 197/200\r\n",
      "2021-07-16 10:37:27.058181: Encoding repertoire 198/200\r\n",
      "2021-07-16 10:37:27.062262: Encoding repertoire 199/200\r\n",
      "2021-07-16 10:37:27.066681: Encoding repertoire 200/200\r\n",
      "2021-07-16 10:37:27.112581: Saving encoded dataset to disk.\r\n",
      "2021-07-16 10:37:29.846589: Encoding finished.\r\n",
      "2021-07-16 10:37:31.293839: Finished analysis signal_freq (1/1).\r\n",
      "\r\n",
      "2021-07-16 10:37:31.293930: Instruction 2/2 has finished.\r\n",
      "2021-07-16 10:37:31.299925: Generating HTML reports...\r\n",
      "2021-07-16 10:37:32.598505: HTML reports are generated.\r\n",
      "2021-07-16 10:37:33.175481: ImmuneML: finished analysis.\r\n",
      "\r\n"
     ]
    }
   ],
   "source": [
    "!mkdir -p analyses/olga/varying_feature_size/{simulated_data,ML_output}\n",
    "!docker container prune -f\n",
    "!docker run -it -v $(pwd):/wd --name simulation_container kanduric/immuneml-v1:latest immune-ml /wd/scripts/olga/run_1/varying_feature_size/generated_yaml_files/simulation_instructions/repertoire_implanting_rate__0.002.yaml /wd/analyses/olga/varying_feature_size/simulated_data/repertoire_implanting_rate__0.002\n",
    "!docker run -it -v $(pwd):/wd --name ml_container kanduric/immuneml-v1:latest immune-ml /wd/scripts/olga/run_1/varying_feature_size/generated_yaml_files/ml_instructions/repertoire_implanting_rate__0.002_-_k__3.yaml /wd/analyses/olga/varying_feature_size/ML_output/repertoire_implanting_rate__0.002_-_k__3\n"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "#### Noise level in negative class\n",
    "\n",
    "The stdout printed to screen when running the simulation and ML model training instructions are shown below indicating successful execution, and the resulting output of each instruction is stored under `analyses/olga/varying_noise/` in this github repository."
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Deleted Containers:\r\n",
      "ce0338dbc6e54760a43427e441e8116e0704622e68442dbc49c818251040e684\r\n",
      "ab05f976a250bc8f80aa2304c87d4217f60dec6ca18274ef6e50baa42c666acd\r\n",
      "\r\n",
      "Total reclaimed space: 47.09kB\r\n",
      "2021-07-16 11:04:21.215463: Setting temporary cache path to wd/analyses/olga/varying_noise/simulated_data/repertoire_implanting_rate__0.0001/cache/\r\n",
      "2021-07-16 11:04:21.215591: ImmuneML: parsing the specification...\r\n",
      "\r\n",
      "2021-07-16 11:04:25.679407: Full specification is available at wd/analyses/olga/varying_noise/simulated_data/repertoire_implanting_rate__0.0001/full_repertoire_implanting_rate__0.yaml.\r\n",
      "\r\n",
      "2021-07-16 11:04:25.679532: ImmuneML: starting the analysis...\r\n",
      "\r\n",
      "2021-07-16 11:04:25.679604: Instruction 1/1 has started.\r\n",
      "2021-07-16 11:04:32.327213: Instruction 1/1 has finished.\r\n",
      "2021-07-16 11:04:32.333538: Generating HTML reports...\r\n",
      "2021-07-16 11:04:32.412183: HTML reports are generated.\r\n",
      "2021-07-16 11:04:32.413119: ImmuneML: finished analysis.\r\n",
      "\r\n",
      "2021-07-16 11:04:34.855733: Setting temporary cache path to wd/analyses/olga/varying_noise/ML_output/repertoire_implanting_rate__0.0001/cache/\r\n",
      "2021-07-16 11:04:34.855853: ImmuneML: parsing the specification...\r\n",
      "\r\n",
      "2021-07-16 11:04:37.428624: Full specification is available at wd/analyses/olga/varying_noise/ML_output/repertoire_implanting_rate__0.0001/full_repertoire_implanting_rate__0.yaml.\r\n",
      "\r\n",
      "2021-07-16 11:04:37.428716: ImmuneML: starting the analysis...\r\n",
      "\r\n",
      "2021-07-16 11:04:37.429076: Instruction 1/2 has started.\r\n",
      "2021-07-16 11:04:37.574981: Training ML model: running outer CV loop: started split 1/5.\r\n",
      "\r\n",
      "2021-07-16 11:04:37.618864: Hyperparameter optimization: running the inner loop of nested CV: selection for label signal_cancer (label 1 / 1).\r\n",
      "\r\n",
      "2021-07-16 11:04:37.625252: Evaluating hyperparameter setting: feature_size_4_logistic_regression...\r\n",
      "2021-07-16 11:04:37.634796: Encoding started...\r\n",
      "2021-07-16 11:04:41.598194: Encoding finished.\r\n",
      "2021-07-16 11:04:41.598576: ML model training started...\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "2021-07-16 11:05:03.953251: ML model training finished.\r\n",
      "2021-07-16 11:05:03.953630: Completed hyperparameter setting feature_size_4_logistic_regression.\r\n",
      "\r\n",
      "2021-07-16 11:05:03.958159: Hyperparameter optimization: running the inner loop of nested CV: completed selection for label signal_cancer (label 1 / 1).\r\n",
      "\r\n",
      "2021-07-16 11:05:03.958254: Training ML model: running the inner loop of nested CV: retrain models for label signal_cancer (label 1 / 1).\r\n",
      "\r\n",
      "2021-07-16 11:05:03.965288: Evaluating hyperparameter setting: feature_size_4_logistic_regression...\r\n",
      "2021-07-16 11:05:03.977108: Encoding started...\r\n",
      "2021-07-16 11:05:06.804733: Encoding finished.\r\n",
      "2021-07-16 11:05:06.805156: ML model training started...\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "2021-07-16 11:05:31.658144: ML model training finished.\r\n",
      "2021-07-16 11:05:31.661745: Encoding started...\r\n",
      "2021-07-16 11:05:33.287014: Encoding finished.\r\n",
      "2021-07-16 11:05:35.448565: Completed hyperparameter setting feature_size_4_logistic_regression.\r\n",
      "\r\n",
      "2021-07-16 11:05:35.452407: Training ML model: running the inner loop of nested CV: completed retraining models for label signal_cancer (label 1 / 1).\r\n",
      "\r\n",
      "2021-07-16 11:05:35.452543: Training ML model: running outer CV loop: finished split 1/5.\r\n",
      "\r\n",
      "2021-07-16 11:05:35.452630: Training ML model: running outer CV loop: started split 2/5.\r\n",
      "\r\n",
      "2021-07-16 11:05:35.502140: Hyperparameter optimization: running the inner loop of nested CV: selection for label signal_cancer (label 1 / 1).\r\n",
      "\r\n",
      "2021-07-16 11:05:35.508029: Evaluating hyperparameter setting: feature_size_4_logistic_regression...\r\n",
      "2021-07-16 11:05:35.518267: Encoding started...\r\n",
      "2021-07-16 11:05:38.742401: Encoding finished.\r\n",
      "2021-07-16 11:05:38.742906: ML model training started...\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "2021-07-16 11:06:12.715558: ML model training finished.\r\n",
      "2021-07-16 11:06:12.715688: Completed hyperparameter setting feature_size_4_logistic_regression.\r\n",
      "\r\n",
      "2021-07-16 11:06:12.719797: Hyperparameter optimization: running the inner loop of nested CV: completed selection for label signal_cancer (label 1 / 1).\r\n",
      "\r\n",
      "2021-07-16 11:06:12.719916: Training ML model: running the inner loop of nested CV: retrain models for label signal_cancer (label 1 / 1).\r\n",
      "\r\n",
      "2021-07-16 11:06:12.728785: Evaluating hyperparameter setting: feature_size_4_logistic_regression...\r\n",
      "2021-07-16 11:06:12.744413: Encoding started...\r\n",
      "2021-07-16 11:06:15.989197: Encoding finished.\r\n",
      "2021-07-16 11:06:15.989483: ML model training started...\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "2021-07-16 11:06:50.670390: ML model training finished.\r\n",
      "2021-07-16 11:06:50.674725: Encoding started...\r\n",
      "2021-07-16 11:06:52.581486: Encoding finished.\r\n",
      "2021-07-16 11:06:54.807101: Completed hyperparameter setting feature_size_4_logistic_regression.\r\n",
      "\r\n",
      "2021-07-16 11:06:54.811342: Training ML model: running the inner loop of nested CV: completed retraining models for label signal_cancer (label 1 / 1).\r\n",
      "\r\n",
      "2021-07-16 11:06:54.811450: Training ML model: running outer CV loop: finished split 2/5.\r\n",
      "\r\n",
      "2021-07-16 11:06:54.811498: Training ML model: running outer CV loop: started split 3/5.\r\n",
      "\r\n",
      "2021-07-16 11:06:54.870208: Hyperparameter optimization: running the inner loop of nested CV: selection for label signal_cancer (label 1 / 1).\r\n",
      "\r\n",
      "2021-07-16 11:06:54.876314: Evaluating hyperparameter setting: feature_size_4_logistic_regression...\r\n",
      "2021-07-16 11:06:54.887588: Encoding started...\r\n",
      "2021-07-16 11:06:58.117428: Encoding finished.\r\n",
      "2021-07-16 11:06:58.117864: ML model training started...\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "2021-07-16 11:07:32.071261: ML model training finished.\r\n",
      "2021-07-16 11:07:32.071537: Completed hyperparameter setting feature_size_4_logistic_regression.\r\n",
      "\r\n",
      "2021-07-16 11:07:32.075349: Hyperparameter optimization: running the inner loop of nested CV: completed selection for label signal_cancer (label 1 / 1).\r\n",
      "\r\n",
      "2021-07-16 11:07:32.075465: Training ML model: running the inner loop of nested CV: retrain models for label signal_cancer (label 1 / 1).\r\n",
      "\r\n",
      "2021-07-16 11:07:32.080573: Evaluating hyperparameter setting: feature_size_4_logistic_regression...\r\n",
      "2021-07-16 11:07:32.090286: Encoding started...\r\n",
      "2021-07-16 11:07:34.794455: Encoding finished.\r\n",
      "2021-07-16 11:07:34.794750: ML model training started...\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "2021-07-16 11:08:05.689022: ML model training finished.\r\n",
      "2021-07-16 11:08:05.691418: Encoding started...\r\n",
      "2021-07-16 11:08:07.307526: Encoding finished.\r\n",
      "2021-07-16 11:08:09.301385: Completed hyperparameter setting feature_size_4_logistic_regression.\r\n",
      "\r\n",
      "2021-07-16 11:08:09.305344: Training ML model: running the inner loop of nested CV: completed retraining models for label signal_cancer (label 1 / 1).\r\n",
      "\r\n",
      "2021-07-16 11:08:09.305440: Training ML model: running outer CV loop: finished split 3/5.\r\n",
      "\r\n",
      "2021-07-16 11:08:09.305483: Training ML model: running outer CV loop: started split 4/5.\r\n",
      "\r\n",
      "2021-07-16 11:08:09.354032: Hyperparameter optimization: running the inner loop of nested CV: selection for label signal_cancer (label 1 / 1).\r\n",
      "\r\n",
      "2021-07-16 11:08:09.358936: Evaluating hyperparameter setting: feature_size_4_logistic_regression...\r\n",
      "2021-07-16 11:08:09.368377: Encoding started...\r\n",
      "2021-07-16 11:08:12.167690: Encoding finished.\r\n",
      "2021-07-16 11:08:12.168192: ML model training started...\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "2021-07-16 11:08:38.456249: ML model training finished.\r\n",
      "2021-07-16 11:08:38.456558: Completed hyperparameter setting feature_size_4_logistic_regression.\r\n",
      "\r\n",
      "2021-07-16 11:08:38.460841: Hyperparameter optimization: running the inner loop of nested CV: completed selection for label signal_cancer (label 1 / 1).\r\n",
      "\r\n",
      "2021-07-16 11:08:38.460966: Training ML model: running the inner loop of nested CV: retrain models for label signal_cancer (label 1 / 1).\r\n",
      "\r\n",
      "2021-07-16 11:08:38.467998: Evaluating hyperparameter setting: feature_size_4_logistic_regression...\r\n",
      "2021-07-16 11:08:38.479621: Encoding started...\r\n",
      "2021-07-16 11:08:41.454084: Encoding finished.\r\n",
      "2021-07-16 11:08:41.459417: ML model training started...\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "2021-07-16 11:09:14.915208: ML model training finished.\r\n",
      "2021-07-16 11:09:14.919105: Encoding started...\r\n",
      "2021-07-16 11:09:17.548572: Encoding finished.\r\n",
      "2021-07-16 11:09:19.983971: Completed hyperparameter setting feature_size_4_logistic_regression.\r\n",
      "\r\n",
      "2021-07-16 11:09:19.988884: Training ML model: running the inner loop of nested CV: completed retraining models for label signal_cancer (label 1 / 1).\r\n",
      "\r\n",
      "2021-07-16 11:09:19.989014: Training ML model: running outer CV loop: finished split 4/5.\r\n",
      "\r\n",
      "2021-07-16 11:09:19.989043: Training ML model: running outer CV loop: started split 5/5.\r\n",
      "\r\n",
      "2021-07-16 11:09:20.048286: Hyperparameter optimization: running the inner loop of nested CV: selection for label signal_cancer (label 1 / 1).\r\n",
      "\r\n",
      "2021-07-16 11:09:20.055665: Evaluating hyperparameter setting: feature_size_4_logistic_regression...\r\n",
      "2021-07-16 11:09:20.070739: Encoding started...\r\n",
      "2021-07-16 11:09:23.548224: Encoding finished.\r\n",
      "2021-07-16 11:09:23.548500: ML model training started...\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "2021-07-16 11:09:49.129697: ML model training finished.\r\n",
      "2021-07-16 11:09:49.130077: Completed hyperparameter setting feature_size_4_logistic_regression.\r\n",
      "\r\n",
      "2021-07-16 11:09:49.133843: Hyperparameter optimization: running the inner loop of nested CV: completed selection for label signal_cancer (label 1 / 1).\r\n",
      "\r\n",
      "2021-07-16 11:09:49.134235: Training ML model: running the inner loop of nested CV: retrain models for label signal_cancer (label 1 / 1).\r\n",
      "\r\n",
      "2021-07-16 11:09:49.139822: Evaluating hyperparameter setting: feature_size_4_logistic_regression...\r\n",
      "2021-07-16 11:09:49.150297: Encoding started...\r\n",
      "2021-07-16 11:09:52.052319: Encoding finished.\r\n",
      "2021-07-16 11:09:52.052676: ML model training started...\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "/usr/local/lib64/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\r\n",
      "  warnings.warn(\"The max_iter was reached which means \"\r\n",
      "2021-07-16 11:10:25.169027: ML model training finished.\r\n",
      "2021-07-16 11:10:25.171849: Encoding started...\r\n",
      "2021-07-16 11:10:27.006951: Encoding finished.\r\n",
      "2021-07-16 11:10:29.328184: Completed hyperparameter setting feature_size_4_logistic_regression.\r\n",
      "\r\n",
      "2021-07-16 11:10:29.332132: Training ML model: running the inner loop of nested CV: completed retraining models for label signal_cancer (label 1 / 1).\r\n",
      "\r\n",
      "2021-07-16 11:10:29.332241: Training ML model: running outer CV loop: finished split 5/5.\r\n",
      "\r\n",
      "Performances (accuracy) -----------------------------------------------\r\n",
      "\r\n",
      "\r\n",
      "Label: signal_cancer\r\n",
      "Performance (accuracy) per assessment split:\r\n",
      "Split 1: 0.55\r\n",
      "Split 2: 0.575\r\n",
      "Split 3: 0.45\r\n",
      "Split 4: 0.55\r\n",
      "Split 5: 0.55\r\n",
      "Average performance (accuracy): 0.5349999999999999\r\n",
      "------------------------------\r\n",
      "2021-07-16 11:10:32.444008: Instruction 1/2 has finished.\r\n",
      "2021-07-16 11:10:32.444049: Instruction 2/2 has started.\r\n",
      "2021-07-16 11:10:32.444068: Started analysis signal_freq (1/1).\r\n",
      "2021-07-16 11:10:32.450617: Encoding started...\r\n",
      "2021-07-16 11:10:32.462940: Encoding repertoire 1/200\r\n",
      "2021-07-16 11:10:32.473924: Encoding repertoire 2/200\r\n",
      "2021-07-16 11:10:32.480631: Encoding repertoire 3/200\r\n",
      "2021-07-16 11:10:32.488395: Encoding repertoire 4/200\r\n",
      "2021-07-16 11:10:32.495871: Encoding repertoire 5/200\r\n",
      "2021-07-16 11:10:32.501762: Encoding repertoire 6/200\r\n",
      "2021-07-16 11:10:32.509554: Encoding repertoire 7/200\r\n",
      "2021-07-16 11:10:32.515935: Encoding repertoire 8/200\r\n",
      "2021-07-16 11:10:32.521492: Encoding repertoire 9/200\r\n",
      "2021-07-16 11:10:32.528868: Encoding repertoire 10/200\r\n",
      "2021-07-16 11:10:32.534985: Encoding repertoire 11/200\r\n",
      "2021-07-16 11:10:32.544664: Encoding repertoire 12/200\r\n",
      "2021-07-16 11:10:32.555013: Encoding repertoire 13/200\r\n",
      "2021-07-16 11:10:32.564763: Encoding repertoire 14/200\r\n",
      "2021-07-16 11:10:32.573198: Encoding repertoire 15/200\r\n",
      "2021-07-16 11:10:32.582042: Encoding repertoire 16/200\r\n",
      "2021-07-16 11:10:32.590679: Encoding repertoire 17/200\r\n",
      "2021-07-16 11:10:32.597261: Encoding repertoire 18/200\r\n",
      "2021-07-16 11:10:32.602612: Encoding repertoire 19/200\r\n",
      "2021-07-16 11:10:32.611462: Encoding repertoire 20/200\r\n",
      "2021-07-16 11:10:32.617727: Encoding repertoire 21/200\r\n",
      "2021-07-16 11:10:32.622978: Encoding repertoire 22/200\r\n",
      "2021-07-16 11:10:32.634183: Encoding repertoire 23/200\r\n",
      "2021-07-16 11:10:32.643962: Encoding repertoire 24/200\r\n",
      "2021-07-16 11:10:32.650395: Encoding repertoire 25/200\r\n",
      "2021-07-16 11:10:32.656181: Encoding repertoire 26/200\r\n",
      "2021-07-16 11:10:32.663156: Encoding repertoire 27/200\r\n",
      "2021-07-16 11:10:32.668987: Encoding repertoire 28/200\r\n",
      "2021-07-16 11:10:32.675523: Encoding repertoire 29/200\r\n",
      "2021-07-16 11:10:32.685371: Encoding repertoire 30/200\r\n",
      "2021-07-16 11:10:32.698322: Encoding repertoire 31/200\r\n",
      "2021-07-16 11:10:32.715980: Encoding repertoire 32/200\r\n",
      "2021-07-16 11:10:32.729585: Encoding repertoire 33/200\r\n",
      "2021-07-16 11:10:32.736266: Encoding repertoire 34/200\r\n",
      "2021-07-16 11:10:32.742366: Encoding repertoire 35/200\r\n",
      "2021-07-16 11:10:32.750356: Encoding repertoire 36/200\r\n",
      "2021-07-16 11:10:32.757284: Encoding repertoire 37/200\r\n",
      "2021-07-16 11:10:32.767138: Encoding repertoire 38/200\r\n",
      "2021-07-16 11:10:32.773794: Encoding repertoire 39/200\r\n",
      "2021-07-16 11:10:32.781515: Encoding repertoire 40/200\r\n",
      "2021-07-16 11:10:32.787682: Encoding repertoire 41/200\r\n",
      "2021-07-16 11:10:32.795686: Encoding repertoire 42/200\r\n",
      "2021-07-16 11:10:32.804535: Encoding repertoire 43/200\r\n",
      "2021-07-16 11:10:32.810612: Encoding repertoire 44/200\r\n",
      "2021-07-16 11:10:32.817799: Encoding repertoire 45/200\r\n",
      "2021-07-16 11:10:32.824420: Encoding repertoire 46/200\r\n",
      "2021-07-16 11:10:32.831653: Encoding repertoire 47/200\r\n",
      "2021-07-16 11:10:32.837272: Encoding repertoire 48/200\r\n",
      "2021-07-16 11:10:32.842953: Encoding repertoire 49/200\r\n",
      "2021-07-16 11:10:32.849768: Encoding repertoire 50/200\r\n",
      "2021-07-16 11:10:32.857301: Encoding repertoire 51/200\r\n",
      "2021-07-16 11:10:32.864530: Encoding repertoire 52/200\r\n",
      "2021-07-16 11:10:32.873465: Encoding repertoire 53/200\r\n",
      "2021-07-16 11:10:32.879665: Encoding repertoire 54/200\r\n",
      "2021-07-16 11:10:32.885731: Encoding repertoire 55/200\r\n",
      "2021-07-16 11:10:32.891760: Encoding repertoire 56/200\r\n",
      "2021-07-16 11:10:32.898567: Encoding repertoire 57/200\r\n",
      "2021-07-16 11:10:32.905003: Encoding repertoire 58/200\r\n",
      "2021-07-16 11:10:32.911398: Encoding repertoire 59/200\r\n",
      "2021-07-16 11:10:32.919783: Encoding repertoire 60/200\r\n",
      "2021-07-16 11:10:32.926421: Encoding repertoire 61/200\r\n",
      "2021-07-16 11:10:32.932183: Encoding repertoire 62/200\r\n",
      "2021-07-16 11:10:32.938690: Encoding repertoire 63/200\r\n",
      "2021-07-16 11:10:32.922978: Encoding repertoire 64/200\r\n",
      "2021-07-16 11:10:32.928339: Encoding repertoire 65/200\r\n",
      "2021-07-16 11:10:32.934762: Encoding repertoire 66/200\r\n",
      "2021-07-16 11:10:32.941263: Encoding repertoire 67/200\r\n",
      "2021-07-16 11:10:32.946911: Encoding repertoire 68/200\r\n",
      "2021-07-16 11:10:32.953390: Encoding repertoire 69/200\r\n",
      "2021-07-16 11:10:32.959474: Encoding repertoire 70/200\r\n",
      "2021-07-16 11:10:32.964935: Encoding repertoire 71/200\r\n",
      "2021-07-16 11:10:32.970510: Encoding repertoire 72/200\r\n",
      "2021-07-16 11:10:32.976192: Encoding repertoire 73/200\r\n",
      "2021-07-16 11:10:32.981851: Encoding repertoire 74/200\r\n",
      "2021-07-16 11:10:32.987225: Encoding repertoire 75/200\r\n",
      "2021-07-16 11:10:32.993124: Encoding repertoire 76/200\r\n",
      "2021-07-16 11:10:32.998584: Encoding repertoire 77/200\r\n",
      "2021-07-16 11:10:33.005440: Encoding repertoire 78/200\r\n",
      "2021-07-16 11:10:33.012253: Encoding repertoire 79/200\r\n",
      "2021-07-16 11:10:33.018730: Encoding repertoire 80/200\r\n",
      "2021-07-16 11:10:33.025494: Encoding repertoire 81/200\r\n",
      "2021-07-16 11:10:33.031708: Encoding repertoire 82/200\r\n",
      "2021-07-16 11:10:33.037307: Encoding repertoire 83/200\r\n",
      "2021-07-16 11:10:33.043207: Encoding repertoire 84/200\r\n",
      "2021-07-16 11:10:33.049723: Encoding repertoire 85/200\r\n",
      "2021-07-16 11:10:33.056338: Encoding repertoire 86/200\r\n",
      "2021-07-16 11:10:33.062712: Encoding repertoire 87/200\r\n",
      "2021-07-16 11:10:33.068005: Encoding repertoire 88/200\r\n",
      "2021-07-16 11:10:33.073745: Encoding repertoire 89/200\r\n",
      "2021-07-16 11:10:33.080104: Encoding repertoire 90/200\r\n",
      "2021-07-16 11:10:33.086365: Encoding repertoire 91/200\r\n",
      "2021-07-16 11:10:33.092323: Encoding repertoire 92/200\r\n",
      "2021-07-16 11:10:33.098620: Encoding repertoire 93/200\r\n",
      "2021-07-16 11:10:33.104919: Encoding repertoire 94/200\r\n",
      "2021-07-16 11:10:33.111491: Encoding repertoire 95/200\r\n",
      "2021-07-16 11:10:33.117139: Encoding repertoire 96/200\r\n",
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      "2021-07-16 11:10:33.128805: Encoding repertoire 98/200\r\n",
      "2021-07-16 11:10:33.134814: Encoding repertoire 99/200\r\n",
      "2021-07-16 11:10:33.140801: Encoding repertoire 100/200\r\n",
      "2021-07-16 11:10:33.146853: Encoding repertoire 101/200\r\n",
      "2021-07-16 11:10:33.152567: Encoding repertoire 102/200\r\n",
      "2021-07-16 11:10:33.158603: Encoding repertoire 103/200\r\n",
      "2021-07-16 11:10:33.164151: Encoding repertoire 104/200\r\n",
      "2021-07-16 11:10:33.169550: Encoding repertoire 105/200\r\n",
      "2021-07-16 11:10:33.175245: Encoding repertoire 106/200\r\n",
      "2021-07-16 11:10:33.180783: Encoding repertoire 107/200\r\n",
      "2021-07-16 11:10:33.187234: Encoding repertoire 108/200\r\n",
      "2021-07-16 11:10:33.193383: Encoding repertoire 109/200\r\n",
      "2021-07-16 11:10:33.198892: Encoding repertoire 110/200\r\n",
      "2021-07-16 11:10:33.205422: Encoding repertoire 111/200\r\n",
      "2021-07-16 11:10:33.212860: Encoding repertoire 112/200\r\n",
      "2021-07-16 11:10:33.218506: Encoding repertoire 113/200\r\n",
      "2021-07-16 11:10:33.224339: Encoding repertoire 114/200\r\n",
      "2021-07-16 11:10:33.231198: Encoding repertoire 115/200\r\n",
      "2021-07-16 11:10:33.237830: Encoding repertoire 116/200\r\n",
      "2021-07-16 11:10:33.245508: Encoding repertoire 117/200\r\n",
      "2021-07-16 11:10:33.251252: Encoding repertoire 118/200\r\n",
      "2021-07-16 11:10:33.257780: Encoding repertoire 119/200\r\n",
      "2021-07-16 11:10:33.264269: Encoding repertoire 120/200\r\n",
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      "2021-07-16 11:10:33.300289: Encoding repertoire 126/200\r\n",
      "2021-07-16 11:10:33.305818: Encoding repertoire 127/200\r\n",
      "2021-07-16 11:10:33.312439: Encoding repertoire 128/200\r\n",
      "2021-07-16 11:10:33.318073: Encoding repertoire 129/200\r\n",
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      "2021-07-16 11:10:33.329341: Encoding repertoire 131/200\r\n",
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      "2021-07-16 11:10:33.346518: Encoding repertoire 134/200\r\n",
      "2021-07-16 11:10:33.352527: Encoding repertoire 135/200\r\n",
      "2021-07-16 11:10:33.358348: Encoding repertoire 136/200\r\n",
      "2021-07-16 11:10:33.367460: Encoding repertoire 137/200\r\n",
      "2021-07-16 11:10:33.375338: Encoding repertoire 138/200\r\n",
      "2021-07-16 11:10:33.383497: Encoding repertoire 139/200\r\n",
      "2021-07-16 11:10:33.390267: Encoding repertoire 140/200\r\n",
      "2021-07-16 11:10:33.397053: Encoding repertoire 141/200\r\n",
      "2021-07-16 11:10:33.402759: Encoding repertoire 142/200\r\n",
      "2021-07-16 11:10:33.409922: Encoding repertoire 143/200\r\n",
      "2021-07-16 11:10:33.416070: Encoding repertoire 144/200\r\n",
      "2021-07-16 11:10:33.421393: Encoding repertoire 145/200\r\n",
      "2021-07-16 11:10:33.427407: Encoding repertoire 146/200\r\n",
      "2021-07-16 11:10:33.433042: Encoding repertoire 147/200\r\n",
      "2021-07-16 11:10:33.438249: Encoding repertoire 148/200\r\n",
      "2021-07-16 11:10:33.443868: Encoding repertoire 149/200\r\n",
      "2021-07-16 11:10:33.450092: Encoding repertoire 150/200\r\n",
      "2021-07-16 11:10:33.456172: Encoding repertoire 151/200\r\n",
      "2021-07-16 11:10:33.461811: Encoding repertoire 152/200\r\n",
      "2021-07-16 11:10:33.467950: Encoding repertoire 153/200\r\n",
      "2021-07-16 11:10:33.473624: Encoding repertoire 154/200\r\n",
      "2021-07-16 11:10:33.479891: Encoding repertoire 155/200\r\n",
      "2021-07-16 11:10:33.486157: Encoding repertoire 156/200\r\n",
      "2021-07-16 11:10:33.492067: Encoding repertoire 157/200\r\n",
      "2021-07-16 11:10:33.497941: Encoding repertoire 158/200\r\n",
      "2021-07-16 11:10:33.504305: Encoding repertoire 159/200\r\n",
      "2021-07-16 11:10:33.510363: Encoding repertoire 160/200\r\n",
      "2021-07-16 11:10:33.516847: Encoding repertoire 161/200\r\n",
      "2021-07-16 11:10:33.523085: Encoding repertoire 162/200\r\n",
      "2021-07-16 11:10:33.528731: Encoding repertoire 163/200\r\n",
      "2021-07-16 11:10:33.534782: Encoding repertoire 164/200\r\n",
      "2021-07-16 11:10:33.540188: Encoding repertoire 165/200\r\n",
      "2021-07-16 11:10:33.545623: Encoding repertoire 166/200\r\n",
      "2021-07-16 11:10:33.550974: Encoding repertoire 167/200\r\n",
      "2021-07-16 11:10:33.560406: Encoding repertoire 168/200\r\n",
      "2021-07-16 11:10:33.567276: Encoding repertoire 169/200\r\n",
      "2021-07-16 11:10:33.573921: Encoding repertoire 170/200\r\n",
      "2021-07-16 11:10:33.580872: Encoding repertoire 171/200\r\n",
      "2021-07-16 11:10:33.587843: Encoding repertoire 172/200\r\n",
      "2021-07-16 11:10:33.594070: Encoding repertoire 173/200\r\n",
      "2021-07-16 11:10:33.600497: Encoding repertoire 174/200\r\n",
      "2021-07-16 11:10:33.606021: Encoding repertoire 175/200\r\n",
      "2021-07-16 11:10:33.611428: Encoding repertoire 176/200\r\n",
      "2021-07-16 11:10:33.617204: Encoding repertoire 177/200\r\n",
      "2021-07-16 11:10:33.622618: Encoding repertoire 178/200\r\n",
      "2021-07-16 11:10:33.628809: Encoding repertoire 179/200\r\n",
      "2021-07-16 11:10:33.634795: Encoding repertoire 180/200\r\n",
      "2021-07-16 11:10:33.640908: Encoding repertoire 181/200\r\n",
      "2021-07-16 11:10:33.647615: Encoding repertoire 182/200\r\n",
      "2021-07-16 11:10:33.653940: Encoding repertoire 183/200\r\n",
      "2021-07-16 11:10:33.659943: Encoding repertoire 184/200\r\n",
      "2021-07-16 11:10:33.667627: Encoding repertoire 185/200\r\n",
      "2021-07-16 11:10:33.674726: Encoding repertoire 186/200\r\n",
      "2021-07-16 11:10:33.683928: Encoding repertoire 187/200\r\n",
      "2021-07-16 11:10:33.693983: Encoding repertoire 188/200\r\n",
      "2021-07-16 11:10:33.703477: Encoding repertoire 189/200\r\n",
      "2021-07-16 11:10:33.715352: Encoding repertoire 190/200\r\n",
      "2021-07-16 11:10:33.729618: Encoding repertoire 191/200\r\n",
      "2021-07-16 11:10:33.749529: Encoding repertoire 192/200\r\n",
      "2021-07-16 11:10:33.759634: Encoding repertoire 193/200\r\n",
      "2021-07-16 11:10:33.766584: Encoding repertoire 194/200\r\n",
      "2021-07-16 11:10:33.773973: Encoding repertoire 195/200\r\n",
      "2021-07-16 11:10:33.780802: Encoding repertoire 196/200\r\n",
      "2021-07-16 11:10:33.788998: Encoding repertoire 197/200\r\n",
      "2021-07-16 11:10:33.796354: Encoding repertoire 198/200\r\n",
      "2021-07-16 11:10:33.804235: Encoding repertoire 199/200\r\n",
      "2021-07-16 11:10:33.815621: Encoding repertoire 200/200\r\n",
      "2021-07-16 11:10:33.894860: Saving encoded dataset to disk.\r\n",
      "2021-07-16 11:10:37.156625: Encoding finished.\r\n",
      "2021-07-16 11:10:38.957484: Finished analysis signal_freq (1/1).\r\n",
      "\r\n",
      "2021-07-16 11:10:38.957647: Instruction 2/2 has finished.\r\n",
      "2021-07-16 11:10:38.967342: Generating HTML reports...\r\n",
      "2021-07-16 11:10:40.835243: HTML reports are generated.\r\n",
      "2021-07-16 11:10:42.070658: ImmuneML: finished analysis.\r\n",
      "\r\n"
     ]
    }
   ],
   "source": [
    "!mkdir -p analyses/olga/varying_noise/{simulated_data,ML_output}\n",
    "!docker container prune -f\n",
    "!docker run -it -v $(pwd):/wd --name simulation_container kanduric/immuneml-v1:latest immune-ml /wd/scripts/olga/run_1/varying_noise/generated_yaml_files/simulation_instructions/repertoire_implanting_rate__0.0001.yaml /wd/analyses/olga/varying_noise/simulated_data/repertoire_implanting_rate__0.0001\n",
    "!docker run -it -v $(pwd):/wd --name ml_container kanduric/immuneml-v1:latest immune-ml /wd/scripts/olga/run_1/varying_noise/generated_yaml_files/ml_instructions/repertoire_implanting_rate__0.0001_-_ml_instruction.yaml /wd/analyses/olga/varying_noise/ML_output/repertoire_implanting_rate__0.0001"
   ],
   "metadata": {
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    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  }
 ],
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