From 5cc18cc7479cba8b913e44b6c7097dd3ebda4466 Mon Sep 17 00:00:00 2001 From: niklases <60261497+niklases@users.noreply.github.com> Date: Mon, 26 Jun 2023 13:22:26 +0200 Subject: [PATCH] finalization v0.3 added .ipynb for sequence encoding and model testing --- README.md | 2 +- workflow/api_encoding_train_test.ipynb | 574 +++++++++++++++++++++++++ workflow/run_cli_tests_win.ps1 | 2 +- 3 files changed, 576 insertions(+), 2 deletions(-) create mode 100644 workflow/api_encoding_train_test.ipynb diff --git a/README.md b/README.md index 5d77c5c..22bf0f5 100644 --- a/README.md +++ b/README.md @@ -36,7 +36,7 @@ Preprint available at bioRxiv: https://doi.org/10.1101/2022.06.07.495081. # PyPEF: Pythonic Protein Engineering Framework [![PyPI version](https://img.shields.io/pypi/v/PyPEF?color=blue)](https://pypi.org/project/pypef/) -[![Python version](https://img.shields.io/pypi/pyversions/PyPEF)](https://pypi.org/project/pypef/) +[![Python version](https://img.shields.io/pypi/pyversions/PyPEF)](https://www.python.org/downloads/) a framework written in Python 3 for performing sequence-based machine learning-assisted protein engineering to predict a protein's fitness from its sequence using different forms of sequence encoding: - One-hot encoding diff --git a/workflow/api_encoding_train_test.ipynb b/workflow/api_encoding_train_test.ipynb new file mode 100644 index 0000000..d50d536 --- /dev/null +++ b/workflow/api_encoding_train_test.ipynb @@ -0,0 +1,574 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "755d3522", + "metadata": {}, + "outputs": [], + "source": [ + "from IPython.display import Image, display\n", + "\n", + "import os\n", + "import pandas as pd\n", + "import numpy as np\n", + "from scipy.stats import spearmanr\n", + "from tqdm import tqdm\n", + "import matplotlib.pyplot as plt\n", + "import matplotlib.image as mpimg\n", + "from sklearn.model_selection import KFold, train_test_split\n", + "\n", + "# pypef imports\n", + "from pypef.utils.variant_data import remove_nan_encoded_positions, get_seqs_from_var_name\n", + "from pypef.ml.regression import (\n", + " OneHotEncoding, AAIndexEncoding, get_regressor_performances,\n", + " path_aaindex_dir, full_aaidx_txt_path\n", + ")\n", + "from pypef.dca.hybrid_model import DCAHybridModel, remove_gap_pos, get_delta_e_statistical_model\n", + "from pypef.dca.plmc_encoding import PLMC\n", + "from pypef.dca.gremlin_inference import GREMLIN" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "e9073dfb", + "metadata": {}, + "outputs": [], + "source": [ + "n_aaindices_to_test = 10 # all would be 566 AAindex indices, only testing 10 here for shorter run time\n", + "# avGFP wild type sequence\n", + "wt_sequence = 'MSKGEELFTGVVPILVELDGDVNGHKFSVSGEGEGDATYGKLTLKFICTTGKLPVPWPTL' \\\n", + " 'VTTLSYGVQCFSRYPDHMKQHDFFKSAMPEGYVQERTIFFKDDGNYKTRAEVKFEGDTLV' \\\n", + " 'NRIELKGIDFKEDGNILGHKLEYNYNSHNVYIMADKQKNGIKVNFKIRHNIEDGSVQLAD' \\\n", + " 'HYQQNTPIGDGPVLLPDNHYLSTQSALSKDPNEKRDHMVLLEFVTAAGITHGMDELYK'" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "bf24fe48", + "metadata": {}, + "outputs": [], + "source": [ + "variant_fitness_data = pd.read_csv('./test_dataset_avgfp/avGFP.csv', sep=';') # loading the avGFP dataset\n", + "\n", + "variants = variant_fitness_data.iloc[:2000, 0].values # \"just\" using 2000 variants for faster processing\n", + "fitnesses = variant_fitness_data.iloc[:2000, 1].values\n", + "variants_split = []\n", + "for variant in variants:\n", + " variants_split.append(variant.split('/'))\n", + "variants, fitnesses, sequences = get_seqs_from_var_name(wt_sequence, variants_split, fitnesses)\n", + "\n", + "# Splitting in sets for training (fitting and hyperparameter validation) and testing\n", + "# Change number of applied splits for training and testing here, default: n_splits = 5\n", + "n_splits = 5\n", + "kf = KFold(n_splits=n_splits, random_state=42, shuffle=True)\n", + "train_val_splits_indices, test_splits_indices = [], []" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "7854d189", + "metadata": {}, + "outputs": [], + "source": [ + "dca_encoder = GREMLIN(\n", + " alignment='./test_dataset_avgfp/uref100_avgfp_jhmmer_119.a2m',\n", + " wt_seq=wt_sequence,\n", + " opt_iter=100,\n", + " optimize=True\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "2a21926c", + "metadata": {}, + "outputs": [], + "source": [ + "dca_encoder.plot_correlation_matrix(matrix_type='apc')" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "81f3eb0c", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": { + "image/png": { + "width": 500 + } + }, + "output_type": "display_data" + } + ], + "source": [ + "display(Image(filename='apc.png', width=500))" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "6112bb71", + "metadata": {}, + "outputs": [], + "source": [ + "gaps = dca_encoder.gaps\n", + "variants, sequences, fitnesses = remove_gap_pos(gaps, variants, sequences, fitnesses)\n", + "x_dca = dca_encoder.collect_encoded_sequences(sequences)\n", + "x_wt = dca_encoder.x_wt" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "0ee5ed22", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Statistical DCA model performance on all (2000) datapoints: 0.697\n", + "N Variants remaining after excluding non-DCA-encodable positions: 1981\n" + ] + } + ], + "source": [ + "# Statistical model performance\n", + "y_pred = get_delta_e_statistical_model(x_dca, x_wt)\n", + "print(f'Statistical DCA model performance on all (2000) datapoints: {spearmanr(fitnesses, y_pred)[0]:.3f}')\n", + "# Split double and higher substituted variants to multiple single substitutions separated by '/'\n", + "\n", + "print(f'N Variants remaining after excluding non-DCA-encodable positions: {len(x_dca)}')\n", + "assert len(x_dca) == len(fitnesses) == len(variants) == len(sequences)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "f0318e79", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Total number of variant-fitness data for training and validation-based hyperparameter tuning: 1585\n", + "Variants for testing: 396\n", + "\n" + ] + } + ], + "source": [ + "for train_val_indices, test_indices in kf.split(variants): # several variants are not included in the data anymore\n", + " train_val_splits_indices.append(train_val_indices)\n", + " test_splits_indices.append(test_indices)\n", + "print(f'Total number of variant-fitness data for training and '\n", + " f'validation-based hyperparameter tuning: {len(train_val_indices)}' # indices only vary by +- 1 for the splits\n", + " f'\\nVariants for testing: {len(test_indices)}\\n')" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "e9f8f301", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Split 1/5:\n", + "Spearmans rho (ML) = 0.787\n", + "Spearmans rho (Hybrid) = 0.785\n", + "Split 2/5:\n", + "Spearmans rho (ML) = 0.805\n", + "Spearmans rho (Hybrid) = 0.803\n", + "Split 3/5:\n", + "Spearmans rho (ML) = 0.835\n", + "Spearmans rho (Hybrid) = 0.823\n", + "Split 4/5:\n", + "Spearmans rho (ML) = 0.819\n", + "Spearmans rho (Hybrid) = 0.822\n", + "Split 5/5:\n", + "Spearmans rho (ML) = 0.801\n", + "Spearmans rho (Hybrid) = 0.803\n", + "--------------------------------------------------------------------------------\n", + "5-fold mean Spearmans rho (ML) = 0.809 +- 0.018\n", + "5-fold mean Spearmans rho (Hybrid) = 0.807 +- 0.018\n" + ] + } + ], + "source": [ + "ten_split_performance_ml, ten_split_performance_hybrid = [], []\n", + "for i, indices in enumerate(train_val_splits_indices):\n", + " x_train_val = np.array(x_dca)[indices]\n", + " y_train_val = np.array(fitnesses)[indices]\n", + " x_test = np.array(x_dca)[test_splits_indices[i]]\n", + " y_test = np.array(fitnesses)[test_splits_indices[i]]\n", + " # get_regressor_performances() already splits train_val data in data for fitting (training) and validation\n", + " # and after each CV-round shifts to the next hyperparameter of the regressor hyperparameter grid\n", + " performances = get_regressor_performances(x_train_val, x_test, y_train_val, y_test, regressor='ridge')\n", + " # performances[4] is Spearmans correlation between y_true and y_pred\n", + " ten_split_performance_ml.append(performances[4])\n", + " print(f'Split {i + 1}/{len(train_val_splits_indices)}:\\nSpearmans rho (ML) = {performances[4]:.3f}')\n", + " # B. Hybrid modeling\n", + " # -------------------------------------------------------------------------------\n", + " hybrid_model = DCAHybridModel(x_train=x_train_val, y_train=y_train_val, x_wt=x_wt)\n", + " beta_1, beta_2, regressor = hybrid_model.settings(x_train=x_train_val, y_train=y_train_val)\n", + " y_test_pred = hybrid_model.hybrid_prediction(x=x_test, reg=regressor, beta_1=beta_1, beta_2=beta_2)\n", + " ten_split_performance_hybrid.append(spearmanr(y_test, y_test_pred)[0])\n", + " print(f'Spearmans rho (Hybrid) = {spearmanr(y_test, y_test_pred)[0]:.3f}')\n", + "print('-'*80 + f'\\n{n_splits}-fold mean Spearmans rho (ML) = {np.mean(ten_split_performance_ml):.3f} '\n", + " f'+- {np.std(ten_split_performance_ml, ddof=1):.3f}\\n'\n", + " f'{n_splits}-fold mean Spearmans rho (Hybrid) = {np.mean(ten_split_performance_hybrid):.3f} '\n", + " f'+- {np.std(ten_split_performance_ml, ddof=1):.3f}')" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "a72d27ee", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + "(2/4) Testing AAindex-based sequence encoding (using 10 indices)...\n", + "===========================================================================\n", + "1/10: AAindex ANDN920101.txt, 5-fold mean Spearmans rho = 0.707 +- 0.015\n", + "2/10: AAindex ARGP820101.txt, 5-fold mean Spearmans rho = 0.551 +- 0.080\n", + "3/10: AAindex ARGP820102.txt, 5-fold mean Spearmans rho = 0.666 +- 0.035\n", + "4/10: AAindex ARGP820103.txt, 5-fold mean Spearmans rho = 0.673 +- 0.035\n", + "5/10: AAindex AURR980101.txt, 5-fold mean Spearmans rho = 0.668 +- 0.031\n", + "6/10: AAindex AURR980102.txt, 5-fold mean Spearmans rho = 0.670 +- 0.025\n", + "7/10: AAindex AURR980103.txt, 5-fold mean Spearmans rho = 0.621 +- 0.025\n", + "8/10: AAindex AURR980104.txt, 5-fold mean Spearmans rho = 0.573 +- 0.026\n", + "9/10: AAindex AURR980105.txt, 5-fold mean Spearmans rho = 0.568 +- 0.031\n", + "10/10: AAindex AURR980106.txt, 5-fold mean Spearmans rho = 0.718 +- 0.019\n", + "--------------------------------------------------------------------------------\n", + "Best 5-fold mean Spearmans rho = 0.718 +- 0.019, AAindex descriptor set = AURR980106.txt\n", + "\n" + ] + } + ], + "source": [ + "# 2nd example: AAindex encoding over all 566 amino acid descriptor sets\n", + "# -------------------------------------------------------------------------------\n", + "print(f'\\n\\n(2/4) Testing AAindex-based sequence encoding (using {n_aaindices_to_test} indices)...\\n' + \"=\" * 75)\n", + "spearmans_rhos_aaidx, aa_index = [], []\n", + "# e.g., looping over the (up to 566) AAindex entries, encode with each AAindex and test performance\n", + "# which can be seen as a AAindex hyperparameter search on the test set, i.e.,\n", + "# not totally fair comparison to onehot- and DCA-based encoding techniques.\n", + "# Limiting indices for testing to N = n_aaindices_to_test...\n", + "aa_indices = [file for file in os.listdir(path_aaindex_dir()) if file.endswith('.txt')][:n_aaindices_to_test]\n", + "mean_performances, ten_split_performance_std_dev, aa_indices_collected = [], [], []\n", + "for index, aaindex in enumerate(aa_indices):\n", + " aaidx_encoder = AAIndexEncoding(full_aaidx_txt_path(aaindex), sequences)\n", + " # two encoding options possible, FFT-ed and 'raw' encoded sequences (here we keep using 'raw' encoded sequences)\n", + " x_aaidx_fft, x_aaidx_no_fft = aaidx_encoder.collect_encoded_sequences()\n", + " ten_split_performance = []\n", + " if x_aaidx_no_fft == 'skip':\n", + " print(f'Skipped AAindex {aaindex}')\n", + " continue\n", + " for i, indices in enumerate(train_val_splits_indices):\n", + " print(f'\\rSplit {i+1}/{len(train_val_splits_indices)}', end='')\n", + " x_train_val = np.array(x_aaidx_no_fft)[indices]\n", + " y_train_val = np.array(fitnesses)[indices]\n", + " x_test = np.array(x_aaidx_no_fft)[test_splits_indices[i]]\n", + " y_test = np.array(fitnesses)[test_splits_indices[i]]\n", + " performances = get_regressor_performances(x_train_val, x_test, y_train_val, y_test, regressor='ridge')\n", + " ten_split_performance.append(performances[4])\n", + " print(f'\\r{index + 1}/{len(aa_indices)}: AAindex {aaindex}, '\n", + " f'{n_splits}-fold mean Spearmans rho = {np.mean(ten_split_performance):.3f} '\n", + " f'+- {np.std(ten_split_performance, ddof=1):.3f}')\n", + " mean_performances.append(np.mean(ten_split_performance))\n", + "\n", + " ten_split_performance_std_dev.append(np.std(ten_split_performance, ddof=1))\n", + " aa_indices_collected.append(aaindex)\n", + "max_value = max(mean_performances)\n", + "max_idx = mean_performances.index(max_value)\n", + "max_value_std = ten_split_performance_std_dev[max_idx]\n", + "max_value_aaidx = aa_indices_collected[max_idx]\n", + "print('-'*80 + f'\\nBest {n_splits}-fold mean Spearmans rho = {max_value:.3f} '\n", + " f'+- {max_value_std:.3f}, AAindex descriptor set = {max_value_aaidx}\\n')" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "a44155db", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "(3/4) Testing OneHot sequence encoding...\n", + "==================================================\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1981/1981 [00:05<00:00, 364.12it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Split 1/5: Spearmans rho = 0.779\n", + "Split 2/5: Spearmans rho = 0.824\n", + "Split 3/5: Spearmans rho = 0.842\n", + "Split 4/5: Spearmans rho = 0.816\n", + "Split 5/5: Spearmans rho = 0.820\n", + "--------------------------------------------------------------------------------\n", + "5-fold mean Spearmans rho = 0.816 +- 0.023\n", + "\n" + ] + } + ], + "source": [ + "# 3rd example: OneHot encoding\n", + "# -------------------------------------------------------------------------------\n", + "print('\\n(3/4) Testing OneHot sequence encoding...\\n' + \"=\" * 50)\n", + "onehot_encoder = OneHotEncoding(sequences)\n", + "x_onehot = onehot_encoder.collect_encoded_sequences()\n", + "ten_split_performance = []\n", + "for i, indices in enumerate(train_val_splits_indices):\n", + " x_train_val = np.array(x_onehot)[indices]\n", + " y_train_val = np.array(fitnesses)[indices]\n", + " x_test = np.array(x_onehot)[test_splits_indices[i]]\n", + " y_test = np.array(fitnesses)[test_splits_indices[i]]\n", + " performances = get_regressor_performances(x_train_val, x_test, y_train_val, y_test, regressor='ridge')\n", + " ten_split_performance.append(performances[4])\n", + " print(f'Split {i + 1}/{len(train_val_splits_indices)}: Spearmans rho = {performances[4]:.3f}')\n", + "print('-'*80 + f'\\n{n_splits}-fold mean Spearmans rho = {np.mean(ten_split_performance):.3f} '\n", + " f'+- {np.std(ten_split_performance, ddof=1):.3f}\\n')" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "33693a90", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Lastly, encoding all variants and performing \"low-N\" protein engineering task.\n", + "This could require some time... < 1 h for GREMLIN DCA-based encoding left...\n", + "N Total variants = 51714.\n", + "Encoding sequences...\n", + "N Variants remaining after excluding non-DCA-encodable positions = 51552\n", + "AAIndex-based encoding of the 51552 variants (using index AURR980106.txt)...\n", + "One-hot encoding of the 51552 variants...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████| 51552/51552 [02:21<00:00, 364.81it/s]\n" + ] + } + ], + "source": [ + "# 4th example: Low-N and plotting using all encoding techniques and all data\n", + "# -------------------------------------------------------------------------------\n", + "print('Lastly, encoding all variants and performing \"low-N\" protein engineering task.\\n'\n", + " 'This could require some time... < 1 h for GREMLIN DCA-based encoding left...')\n", + "\n", + "variants = variant_fitness_data.iloc[:, 0]\n", + "fitnesses = variant_fitness_data.iloc[:, 1].tolist()\n", + "variants_split = []\n", + "for variant in variants:\n", + " variants_split.append(variant.split('/'))\n", + "variants, fitnesses, sequences = get_seqs_from_var_name(wt_sequence, variants_split, fitnesses)\n", + "print(f'N Total variants = {len(variants)}.\\nEncoding sequences...')\n", + "\n", + "variants, sequences, fitnesses = remove_gap_pos(dca_encoder.gaps, variants, sequences, fitnesses)\n", + "x_dca = dca_encoder.get_score(sequences, encode=True)\n", + "\n", + "print(f'N Variants remaining after excluding non-DCA-encodable positions = {len(x_dca)}')\n", + "\n", + "# using the best identified index, i.e., AURR980106 for 10 tested indices or QIAN880130 for all 566 indices\n", + "print(f'AAIndex-based encoding of the {len(x_dca)} variants (using index {max_value_aaidx})...')\n", + "x_aaindex_fft, x_aaindex_no_fft = AAIndexEncoding(\n", + " full_aaidx_txt_path(max_value_aaidx), sequences).collect_encoded_sequences()\n", + "print(f'One-hot encoding of the {len(x_dca)} variants...')\n", + "x_onehot = OneHotEncoding(sequences).collect_encoded_sequences()\n", + "\n", + "assert len(x_dca) == len(x_aaindex_no_fft) == len(x_onehot) == len(fitnesses) == len(variants) == len(sequences)" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "d9e26b1d", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "(4/4) Testing low N performance (with N_train = [50, 100, 150, 200, 250, 300, 350, 400, 450, 500, 550, 600, 650, 700, 750, 800, 850, 900, 950, 1000])...\n", + "==================================================\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [36:27<00:00, 109.38s/it]\n" + ] + } + ], + "source": [ + "all_mean_performances_dca, all_mean_performances_hybrid, \\\n", + " all_mean_performances_aaidx, all_mean_performances_onehot = [], [], [], []\n", + "all_stddevs_dca, all_stddevs_hybrid, all_stddevs_aaidx, all_stddevs_onehot = [], [], [], []\n", + "low_n_train = np.arange(50, 1001, 50)\n", + "print(f'\\n(4/4) Testing low N performance (with N_train = {list(low_n_train)})...\\n' + \"=\" * 50)\n", + "for n_train in tqdm(low_n_train):\n", + " performances_dca, performances_hybrid, performances_aaidx, performances_onehot = [], [], [], []\n", + " for rnd_state in [42, 213, 573, 917, 823]:\n", + " x_dca_train, x_dca_test, y_train, y_test = train_test_split(\n", + " x_dca, fitnesses, train_size=n_train, random_state=rnd_state)\n", + " performances_dca.append(get_regressor_performances(\n", + " x_dca_train, x_dca_test, y_train, y_test, regressor='ridge')[4]) # [4] defines spearmanr correlation\n", + "\n", + " hybrid_model = DCAHybridModel(x_train=x_dca_train, y_train=y_train, x_wt=x_wt)\n", + " beta_1, beta_2, hybrid_regressor = hybrid_model.settings(x_train=x_dca_train, y_train=y_train)\n", + " y_hybrid_pred = hybrid_model.hybrid_prediction(x=x_dca_test, reg=hybrid_regressor, beta_1=beta_1, beta_2=beta_2)\n", + " performances_hybrid.append(spearmanr(y_test, y_hybrid_pred)[0])\n", + "\n", + " x_aaidx_train, x_aaidx_test, y_train, y_test = train_test_split(\n", + " x_aaindex_no_fft, fitnesses, train_size=n_train, random_state=rnd_state)\n", + " performances_aaidx.append(get_regressor_performances(\n", + " x_aaidx_train, x_aaidx_test, y_train, y_test, regressor='ridge')[4])\n", + "\n", + "\n", + " x_onehot_train, x_onehot_test, y_train, y_test = train_test_split(\n", + " x_onehot, fitnesses, train_size=n_train, random_state=rnd_state)\n", + " performances_onehot.append(get_regressor_performances(\n", + " x_onehot_train, x_onehot_test, y_train, y_test, regressor='ridge')[4])\n", + "\n", + " all_mean_performances_dca.append(np.mean(performances_dca))\n", + " all_stddevs_dca.append(np.std(performances_dca, ddof=1))\n", + " all_mean_performances_hybrid.append(np.mean(performances_hybrid))\n", + " all_stddevs_hybrid.append(np.std(performances_hybrid, ddof=1))\n", + " all_mean_performances_aaidx.append(np.mean(performances_aaidx))\n", + " all_stddevs_aaidx.append(np.std(performances_aaidx, ddof=1))\n", + " all_mean_performances_onehot.append(np.mean(performances_onehot))\n", + " all_stddevs_onehot.append(np.std(performances_onehot, ddof=1))" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "a8c7b371", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Done! Created figure 'low_N_avGFP_extrapolation.png'.\n" + ] + } + ], + "source": [ + "# Plotting all the achieved \"low-N\" performances\n", + "plt.plot(low_n_train, all_mean_performances_dca, 'o--', color='tab:orange', label='DCA encoding (pure ML)')\n", + "plt.fill_between(low_n_train,\n", + " np.array(all_mean_performances_dca) - np.array(all_stddevs_dca),\n", + " np.array(all_mean_performances_dca) + np.array(all_stddevs_dca),\n", + " color='tab:orange', alpha=0.2)\n", + "plt.plot(low_n_train, all_mean_performances_hybrid, 'o--', color='tab:blue', label='Hybrid DCA model (MERGE)')\n", + "plt.fill_between(low_n_train,\n", + " np.array(all_mean_performances_hybrid) - np.array(all_stddevs_hybrid),\n", + " np.array(all_mean_performances_hybrid) + np.array(all_stddevs_hybrid),\n", + " color='tab:blue', alpha=0.2)\n", + "plt.plot(low_n_train, all_mean_performances_aaidx, 'o--', color='tab:green',\n", + " label=f\"AAindex ({max_value_aaidx.split('.')[0]})\")\n", + "plt.fill_between(low_n_train,\n", + " np.array(all_mean_performances_aaidx) - np.array(all_stddevs_aaidx),\n", + " np.array(all_mean_performances_aaidx) + np.array(all_stddevs_aaidx),\n", + " color='tab:green', alpha=0.2)\n", + "plt.plot(low_n_train, all_mean_performances_onehot, 'o--', color='tab:grey', label='One-hot')\n", + "plt.fill_between(low_n_train,\n", + " np.array(all_mean_performances_onehot) - np.array(all_stddevs_onehot),\n", + " np.array(all_mean_performances_onehot) + np.array(all_stddevs_onehot),\n", + " color='tab:grey', alpha=0.2)\n", + "plt.legend()\n", + "plt.xlabel(r'$N_\\mathrm{Train}$')\n", + "plt.ylabel(r'$\\rho$')\n", + "plt.savefig('low_N_avGFP_extrapolation.png', dpi=500)\n", + "%matplotlib inline\n", + "plt.show()\n", + "print('\\nDone! Created figure \\'low_N_avGFP_extrapolation.png\\'.')" + ] + }, + { + "cell_type": "markdown", + "id": "b74cd0cf", + "metadata": {}, + "source": [ + "Done! Notebook end." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "pypef", + "language": "python", + "name": "pypef" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.11" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/workflow/run_cli_tests_win.ps1 b/workflow/run_cli_tests_win.ps1 index 388a682..3ff7453 100644 --- a/workflow/run_cli_tests_win.ps1 +++ b/workflow/run_cli_tests_win.ps1 @@ -24,7 +24,7 @@ $env:PYTHONPATH="C:\path\to\pypef-main" function pypef { python C:\path\to\pypef-main\pypef\main.py @args } # ########################################################################################################################## ### else just use pip-installed pypef version (uncomment): # -#function pypef { pypef @args } # +#pypef = pypef # ########################################################################################################################## $threads = 16 # ##########################################################################################################################