From 3ed04d803ca0ec162dc7c714dc0141c11b761a98 Mon Sep 17 00:00:00 2001 From: Trevor Bedford Date: Tue, 20 Jun 2023 17:28:13 -0700 Subject: [PATCH] Update MLR estimates Run via command line script run-mlr-model.py rather than Jupyter notebook --- .gitignore | 3 + mlr-fitness/estimates/README.md | 19 +- mlr-fitness/estimates/evofr_mlr.ipynb | 388 - mlr-fitness/estimates/growth_advantages.tsv | 538 +- mlr-fitness/estimates/mlr-config.yaml | 23 + mlr-fitness/estimates/plotting.nb | 37863 ------------------ mlr-fitness/estimates/prune-mlr-results.py | 29 + mlr-fitness/estimates/run-mlr-model.py | 300 + 8 files changed, 727 insertions(+), 38436 deletions(-) delete mode 100644 mlr-fitness/estimates/evofr_mlr.ipynb create mode 100644 mlr-fitness/estimates/mlr-config.yaml delete mode 100644 mlr-fitness/estimates/plotting.nb create mode 100644 mlr-fitness/estimates/prune-mlr-results.py create mode 100644 mlr-fitness/estimates/run-mlr-model.py diff --git a/.gitignore b/.gitignore index b64d411b1..de7a39177 100644 --- a/.gitignore +++ b/.gitignore @@ -16,6 +16,9 @@ metadata.tsv.gz predictors/nextclade_sars-cov-2_21L.json predictors/nextclade_sars-cov-2_21L.json.gz +mlr-fitness/estimates/models/ +mlr-fitness/estimates/pango_results.json + # Compiled LaTeX output ncov-escape.pdf diff --git a/mlr-fitness/estimates/README.md b/mlr-fitness/estimates/README.md index e59007382..58f12989d 100644 --- a/mlr-fitness/estimates/README.md +++ b/mlr-fitness/estimates/README.md @@ -1,9 +1,20 @@ # MLR fitness estimates using evofr -1. Clone [evofr](https://github.com/blab/evofr) repo elsewhere in the file system and install via +1. Install latest version of [evofr](https://github.com/blab/evofr) via pip: ``` -poetry build -pip install +pip install evofr ``` -2. Run the notebook `evofr_mlr.ipynb` via `jupyter notebook`. This will export a file called `growth_advantages.tsv` that contains the median growth advantage and 80% credible interval per Pango lineage. +2. Run script `run-mlr-model.py` via: +``` +python -u run-mlr-model.py --config mlr-config.yaml --export-path . --data-name "pango" +``` + +This produces the file `pango_results.json`. + +3. This file includes unnecessary information of full frequencies trajectories. The script `prune-mlr-results.py` walks through this JSON and extracts just the useful `site` of `ga`: +``` +python prune-mlr-results.py --results pango_results.json > growth_advantages.tsv +``` + +And exports the TSV file called `growth_advantages.tsv` that contains the median growth advantage and 80% credible interval per Pango lineage. diff --git a/mlr-fitness/estimates/evofr_mlr.ipynb b/mlr-fitness/estimates/evofr_mlr.ipynb deleted file mode 100644 index e607a8149..000000000 --- a/mlr-fitness/estimates/evofr_mlr.ipynb +++ /dev/null @@ -1,388 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "id": "2762e536", - "metadata": {}, - "outputs": [], - "source": [ - "import pandas as pd\n", - "import numpy as np\n", - "from jax import vmap\n", - "import jax.numpy as jnp\n", - "from jax.nn import softmax\n", - "import evofr as ef" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "4d621e95", - "metadata": {}, - "outputs": [], - "source": [ - "# Getting data\n", - "raw_seq = pd.read_csv(\"../data/pango_location-variant-sequence-counts.tsv\", sep=\"\\t\")\n", - "raw_seq = raw_seq[raw_seq.location == \"USA\"]\n", - "variant_frequencies = ef.VariantFrequencies(raw_seq)" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "8f9ac2ec", - "metadata": {}, - "outputs": [], - "source": [ - "# Defining model\n", - "mlr = ef.MultinomialLogisticRegression(tau=4.2)" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "b78e8853", - "metadata": {}, - "outputs": [], - "source": [ - "# Defining inference method\n", - "inference_method = ef.InferFullRank(iters=50_000, lr=4e-3, num_samples=100)" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "760d058a", - "metadata": {}, - "outputs": [], - "source": [ - "# Fitting model\n", - "posterior = inference_method.fit(mlr, variant_frequencies)\n", - "samples = posterior.samples" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "88de95a3", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Text(0, 0.5, 'loss')" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "import matplotlib.pyplot as plt\n", - "\n", - "plt.plot(posterior.samples[\"losses\"])\n", - "plt.yscale(\"log\")\n", - "plt.xlabel(\"iterations\")\n", - "plt.ylabel(\"loss\")" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "faac7458", - "metadata": {}, - "outputs": [], - "source": [ - "def forecast_frequencies(samples, mlr, forecast_L):\n", - " \"\"\"\n", - " Use posterior beta to forecast posterior frequenicies.\n", - " \"\"\"\n", - " \n", - " # Making feature matrix for forecasting\n", - " last_T = samples[\"freq\"].shape[1]\n", - " X = mlr.make_ols_feature(start=last_T, stop=last_T + forecast_L)\n", - " \n", - " # Posterior beta\n", - " beta = jnp.array(samples[\"beta\"])\n", - " \n", - " # Matrix multiplication by sample\n", - " dot_by_sample = vmap(jnp.dot, in_axes=(None, 0), out_axes=0)\n", - " logits = dot_by_sample(X, beta) # Logit frequencies by variant\n", - " return softmax(logits, axis=-1)\n", - "\n", - "forecast_L = 30\n", - "samples[\"freq_forecast\"] = forecast_frequencies(samples, mlr, forecast_L)" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "statewide-harris", - "metadata": {}, - "outputs": [], - "source": [ - "ga = samples[\"ga\"]" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "standing-burning", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(100, 180)" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "ga.shape" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "expected-basis", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "jaxlib.xla_extension.DeviceArray" - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "type(ga)" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "fa2e4ed5", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "['B.1.1.529',\n", - " 'BA.1',\n", - " 'BA.1.1',\n", - " 'BA.1.1.1',\n", - " 'BA.1.1.10',\n", - " 'BA.1.1.14',\n", - " 'BA.1.1.16',\n", - " 'BA.1.1.18',\n", - " 'BA.1.1.2',\n", - " 'BA.1.15']" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "posterior.data.var_names[:10]" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "id": "2389eaf8", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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locationvariantmedian_gaga_upper_80ga_lower_80
0USAB.1.1.5291.61978861.63141621.6109486
1USABA.10.64915730.65555590.64386785
2USABA.1.10.674856540.67626730.67309487
3USABA.1.1.10.660553460.68107380.64071125
4USABA.1.1.100.7570430.77216090.74437594
5USABA.1.1.140.69066160.7053450.6756013
6USABA.1.1.160.72988750.740419450.7181965
7USABA.1.1.180.64282070.64774740.63685846
8USABA.1.1.20.63164770.65089440.6157101
9USABA.1.150.626457330.6326790.62161255
\n", - "
" - ], - "text/plain": [ - " location variant median_ga ga_upper_80 ga_lower_80\n", - "0 USA B.1.1.529 1.6197886 1.6314162 1.6109486\n", - "1 USA BA.1 0.6491573 0.6555559 0.64386785\n", - "2 USA BA.1.1 0.67485654 0.6762673 0.67309487\n", - "3 USA BA.1.1.1 0.66055346 0.6810738 0.64071125\n", - "4 USA BA.1.1.10 0.757043 0.7721609 0.74437594\n", - "5 USA BA.1.1.14 0.6906616 0.705345 0.6756013\n", - "6 USA BA.1.1.16 0.7298875 0.74041945 0.7181965\n", - "7 USA BA.1.1.18 0.6428207 0.6477474 0.63685846\n", - "8 USA BA.1.1.2 0.6316477 0.6508944 0.6157101\n", - "9 USA BA.1.15 0.62645733 0.632679 0.62161255" - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "ga_df = pd.DataFrame(\n", - " ef.posterior.get_growth_advantage(samples, posterior.data, ps=[0.8], name=\"USA\", rel_to=\"BA.2\")\n", - ")\n", - "ga_df.head(10)" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "id": "retained-caution", - "metadata": {}, - "outputs": [], - "source": [ - "ga_df.to_csv('growth_advantages.tsv', sep=\"\\t\")" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.13" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/mlr-fitness/estimates/growth_advantages.tsv b/mlr-fitness/estimates/growth_advantages.tsv index 70122d8c5..7992b4865 100644 --- a/mlr-fitness/estimates/growth_advantages.tsv +++ b/mlr-fitness/estimates/growth_advantages.tsv @@ -1,181 +1,357 @@ - location variant median_ga ga_upper_80 ga_lower_80 -0 USA B.1.1.529 1.596814 1.6061649 1.5858728 -1 USA BA.1 0.6476118 0.64970005 0.64570475 -2 USA BA.1.1 0.6747711 0.6764148 0.6728002 -3 USA BA.1.1.1 0.67475915 0.69391716 0.6575288 -4 USA BA.1.1.10 0.7552886 0.77209896 0.7432441 -5 USA BA.1.1.14 0.69627714 0.70854044 0.68115306 -6 USA BA.1.1.16 0.7183915 0.7318605 0.7085033 -7 USA BA.1.1.18 0.6399752 0.6462689 0.6341029 -8 USA BA.1.1.2 0.61189365 0.63070726 0.5957508 -9 USA BA.1.15 0.6321796 0.6372749 0.6275044 -10 USA BA.1.15.2 0.67857814 0.6907085 0.6628231 -11 USA BA.1.17.2 0.6902538 0.70559883 0.6784877 -12 USA BA.1.18 0.6896565 0.70248044 0.6760555 -13 USA BA.1.20 0.67960215 0.69075733 0.6708567 -14 USA BA.2.1 1.0327835 1.0386494 1.0267848 -15 USA BA.2.10 0.88535273 0.88852733 0.88177353 -16 USA BA.2.10.1 0.901754 0.9077684 0.89644253 -17 USA BA.2.12 1.2108467 1.2110872 1.2107126 -18 USA BA.2.12.1 1.2185476 1.2189102 1.2181531 -19 USA BA.2.13 1.2231681 1.2277751 1.2187061 -20 USA BA.2.13.1 1.3531756 1.3606133 1.3449613 -21 USA BA.2.16 0.97809386 0.99534965 0.9628434 -22 USA BA.2.18 1.2064061 1.2096506 1.2022842 -23 USA BA.2.20 1.0405374 1.05068 1.0333182 -24 USA BA.2.21 0.99093175 0.9961177 0.9851903 -25 USA BA.2.22 1.056726 1.0700883 1.0464723 -26 USA BA.2.23 1.0956361 1.1012086 1.0902957 -27 USA BA.2.23.1 1.0440332 1.0514994 1.0353996 -28 USA BA.2.26 0.93043077 0.93592876 0.9237186 -29 USA BA.2.3 0.9900829 0.9911914 0.9891147 -30 USA BA.2.3.10 0.98818916 1.0005215 0.97698516 -31 USA BA.2.3.14 1.005672 1.0216498 0.99223953 -32 USA BA.2.3.17 0.96523213 0.9693169 0.9603641 -33 USA BA.2.3.2 1.1263285 1.1377664 1.1136315 -34 USA BA.2.3.20 2.3698282 2.3804657 2.3588047 -35 USA BA.2.3.4 0.975934 0.98421365 0.9661379 -36 USA BA.2.3.6 1.0688226 1.077039 1.0608062 -37 USA BA.2.31 1.0437891 1.050697 1.0360903 -38 USA BA.2.32 0.97946984 0.9879059 0.9713355 -39 USA BA.2.36 1.1654052 1.1735816 1.1591089 -40 USA BA.2.37 0.99950314 1.0053803 0.9933215 -41 USA BA.2.38 1.3552823 1.3628885 1.3465866 -42 USA BA.2.40.1 1.184423 1.1967744 1.1727005 -43 USA BA.2.41 1.1769874 1.1861951 1.1659132 -44 USA BA.2.47 1.1129682 1.1272352 1.1025943 -45 USA BA.2.48 1.1696138 1.1736791 1.1646849 -46 USA BA.2.5 0.98260665 1.0021067 0.9684848 -47 USA BA.2.52 1.2649446 1.2785321 1.2509863 -48 USA BA.2.56 1.2942932 1.3060864 1.2856587 -49 USA BA.2.59 1.1106879 1.1208873 1.1012665 -50 USA BA.2.6 1.0081967 1.0184318 0.99864596 -51 USA BA.2.65 1.0812864 1.0855095 1.0769155 -52 USA BA.2.7 0.9257295 0.9285567 0.92252773 -53 USA BA.2.72 1.1247696 1.1343266 1.1158046 -54 USA BA.2.73 1.0207716 1.028494 1.01457 -55 USA BA.2.75 2.0305068 2.0336096 2.0280795 -56 USA BA.2.75.1 2.0320463 2.0399418 2.0226908 -57 USA BA.2.75.2 2.194076 2.2012608 2.1873152 -58 USA BA.2.75.5 2.1874456 2.1939886 2.1804457 -59 USA BA.2.76 1.5622388 1.569945 1.5540913 -60 USA BA.2.8 1.0271719 1.0388632 1.0134085 -61 USA BA.2.9 1.0114617 1.012749 1.0100131 -62 USA BA.2.9.2 0.82063484 0.8279738 0.8135531 -63 USA BA.2.9.3 1.2143209 1.2286471 1.203359 -64 USA BA.4 1.5803298 1.5804201 1.5802459 -65 USA BA.4.1 1.558344 1.5587467 1.557918 -66 USA BA.4.1.1 1.5402964 1.5455116 1.5356673 -67 USA BA.4.1.6 1.5307941 1.5359704 1.5259501 -68 USA BA.4.1.8 1.8849787 1.8893052 1.8804281 -69 USA BA.4.1.9 1.9803939 1.989987 1.9676278 -70 USA BA.4.2 1.5688636 1.5715612 1.5670501 -71 USA BA.4.3 1.5784302 1.5844454 1.570974 -72 USA BA.4.4 1.5731817 1.5751642 1.5709891 -73 USA BA.4.6 1.923332 1.9237167 1.9229417 -74 USA BA.5 1.7857393 1.7858596 1.7856568 -75 USA BA.5.1 1.7965524 1.7967954 1.7963529 -76 USA BA.5.1.1 1.7249472 1.7263188 1.7234596 -77 USA BA.5.1.10 1.8430201 1.8445321 1.8415067 -78 USA BA.5.1.12 2.0361135 2.043586 2.0284126 -79 USA BA.5.1.18 2.0704565 2.0765254 2.0656438 -80 USA BA.5.1.2 1.8773434 1.8800964 1.87449 -81 USA BA.5.1.21 1.8678515 1.8766521 1.8596866 -82 USA BA.5.1.22 1.8375369 1.8396851 1.8355309 -83 USA BA.5.1.23 1.8237848 1.8257197 1.8221959 -84 USA BA.5.1.24 1.7975003 1.8027831 1.792596 -85 USA BA.5.1.25 1.7894092 1.7916664 1.7873801 -86 USA BA.5.1.27 2.2029328 2.210886 2.1936235 -87 USA BA.5.1.3 1.8013465 1.8044555 1.7983745 -88 USA BA.5.1.30 1.8380342 1.8394738 1.8364617 -89 USA BA.5.1.5 2.0578594 2.061645 2.053427 -90 USA BA.5.1.6 1.862623 1.8658708 1.8600107 -91 USA BA.5.1.7 1.9184113 1.9249274 1.9105486 -92 USA BA.5.1.8 1.742799 1.7508935 1.7371861 -93 USA BA.5.10.1 2.0224638 2.0306306 2.014527 -94 USA BA.5.2 1.8799001 1.880099 1.8797511 -95 USA BA.5.2.1 1.8085591 1.8088504 1.8083194 -96 USA BA.5.2.16 1.9469271 1.9531529 1.9406201 -97 USA BA.5.2.18 2.164917 2.1754436 2.1539245 -98 USA BA.5.2.19 1.9625454 1.969273 1.9552876 -99 USA BA.5.2.2 1.7427623 1.7496371 1.73696 -100 USA BA.5.2.20 1.8918931 1.8938082 1.889924 -101 USA BA.5.2.21 1.8735079 1.8747157 1.8720636 -102 USA BA.5.2.22 1.9589759 1.9622217 1.9551277 -103 USA BA.5.2.23 2.2682 2.2769983 2.260662 -104 USA BA.5.2.24 2.145071 2.1541014 2.13455 -105 USA BA.5.2.26 2.0193882 2.032238 2.00945 -106 USA BA.5.2.27 1.8767712 1.8807268 1.8711973 -107 USA BA.5.2.28 1.902312 1.9065167 1.8971637 -108 USA BA.5.2.3 1.8713746 1.8754077 1.8680965 -109 USA BA.5.2.31 1.9312266 1.9349598 1.9281971 -110 USA BA.5.2.33 1.904211 1.9123317 1.8976157 -111 USA BA.5.2.34 2.3490112 2.3560266 2.3422198 -112 USA BA.5.2.6 2.1901295 2.1951246 2.1859663 -113 USA BA.5.2.8 1.867162 1.8759247 1.8594657 -114 USA BA.5.2.9 1.8720534 1.8729367 1.8710586 -115 USA BA.5.3 1.6134024 1.6135607 1.6132314 -116 USA BA.5.3.1 1.7321234 1.7323846 1.7318715 -117 USA BA.5.5 1.6374959 1.6379468 1.6371235 -118 USA BA.5.5.1 1.9357479 1.9394948 1.9326998 -119 USA BA.5.5.2 1.734108 1.7384202 1.7293807 -120 USA BA.5.5.3 1.7536292 1.758042 1.747053 -121 USA BA.5.6 1.7309287 1.7313557 1.7305052 -122 USA BA.5.6.1 1.9171768 1.9276458 1.9071481 -123 USA BA.5.6.2 1.8856034 1.8899665 1.8801849 -124 USA BA.5.8 1.7058182 1.7084893 1.7037022 -125 USA BA.5.9 2.049085 2.0552871 2.043408 -126 USA BE.1 1.7419165 1.7423155 1.7415268 -127 USA BE.1.1 1.8159677 1.8165379 1.8154647 -128 USA BE.1.1.1 2.0352793 2.036248 2.034325 -129 USA BE.1.1.2 1.9479831 1.9553303 1.9414531 -130 USA BE.1.2 2.0466905 2.0507793 2.0418704 -131 USA BE.1.4 1.7924864 1.7944525 1.7903355 -132 USA BE.1.4.1 1.7754235 1.7814298 1.769303 -133 USA BE.2 1.8036575 1.8067394 1.8003808 -134 USA BE.3 1.7049772 1.7056688 1.7041653 -135 USA BE.4 1.9204456 1.9238764 1.9155554 -136 USA BE.5 1.6228139 1.628783 1.6168711 -137 USA BF.1 1.6740909 1.6770624 1.6719385 -138 USA BF.1.1 1.3847493 1.3918899 1.3783443 -139 USA BF.10 1.8236167 1.824323 1.8230277 -140 USA BF.11 2.1374552 2.139928 2.13455 -141 USA BF.13 1.965148 1.9687508 1.9620633 -142 USA BF.14 2.0230489 2.0290258 2.0152035 -143 USA BF.16 1.9018723 1.9069475 1.8954651 -144 USA BF.21 1.7469282 1.7483021 1.7458432 -145 USA BF.24 1.7635826 1.7654346 1.7620071 -146 USA BF.25 2.0279944 2.0353541 2.0231466 -147 USA BF.26 1.8749452 1.8760047 1.873993 -148 USA BF.27 1.7619336 1.7633787 1.7609915 -149 USA BF.28 1.6992681 1.7015089 1.6974422 -150 USA BF.3 1.8639374 1.8695842 1.8597888 -151 USA BF.4 1.7736502 1.7771983 1.7706214 -152 USA BF.5 1.8982155 1.899315 1.8973373 -153 USA BF.6 1.845345 1.8564572 1.8369436 -154 USA BF.7 2.224102 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1.047 +USA XZ 1.121 1.137 1.105 +USA other 1.747 1.755 1.74 +USA BA.2 1 1 1 diff --git a/mlr-fitness/estimates/mlr-config.yaml b/mlr-fitness/estimates/mlr-config.yaml new file mode 100644 index 000000000..f2b5aff10 --- /dev/null +++ b/mlr-fitness/estimates/mlr-config.yaml @@ -0,0 +1,23 @@ +# These data options are over-ridden when calling run-mlr-model.py but are expected by MLRConfig +data: + name: "pango" +# case_path: "data/test/prepared_cases.tsv" + seq_path: "../data/pango_seq_counts.tsv" + +settings: + fit: true # Fit the model? + save: true # Save model state? + load: false # Load old model? + export_json: true # Export model results as json + ps: [0.8] # HPDI intervals to be exported + +model: + generation_time: 4.8 + pivot: "BA.2" + +inference: + method: "NUTS" + iters: 50000 # Number of iterations for SVI + lr: 4e-4 # Learning rate for the model + num_warmup: 1000 + num_samples: 200 diff --git a/mlr-fitness/estimates/plotting.nb b/mlr-fitness/estimates/plotting.nb deleted 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b/mlr-fitness/estimates/prune-mlr-results.py @@ -0,0 +1,29 @@ +import os, sys +import json +import argparse + +def collect_args(): + parser = argparse.ArgumentParser(description = "Extract relevant results from evofr JSON") + parser.add_argument('--results', required=True, type=str, help="input JSON file") + return parser.parse_args() + +if __name__=="__main__": + params = collect_args() + + json_fh = open(params.results, "r") + json_dict = json.load(json_fh) + + locations = json_dict["metadata"]["location"] + variants = json_dict["metadata"]["variants"] + json_ga = [d for d in json_dict["data"] if d["site"]=="ga"] + + print("location" + "\t" + "variant" + "\t" + "median_ga" + "\t" + "ga_upper_80" + "\t" + "ga_lower_80") + + for location in locations: + for variant in variants: + + # default to 1 if absent, this is the pivot variant + median = next( (d["value"] for d in json_ga if d["location"]==location and d["variant"]==variant and d["ps"]=="median"), 1) + upper = next( (d["value"] for d in json_ga if d["location"]==location and d["variant"]==variant and d["ps"]=="HDI_80_upper"), 1) + lower = next( (d["value"] for d in json_ga if d["location"]==location and d["variant"]==variant and d["ps"]=="HDI_80_lower"), 1) + print(location + "\t" + variant + "\t" + str(median) + "\t" + str(upper) + "\t" + str(lower)) diff --git a/mlr-fitness/estimates/run-mlr-model.py b/mlr-fitness/estimates/run-mlr-model.py new file mode 100644 index 000000000..915cb4e22 --- /dev/null +++ b/mlr-fitness/estimates/run-mlr-model.py @@ -0,0 +1,300 @@ +#!/usr/bin/env python +# coding: utf-8 + +import argparse +import pandas as pd +import os +import yaml +import json +import evofr as ef + + +def parse_with_default(cf, var, dflt): + if var in cf: + return cf[var] + else: + print(f"Using default value for {var}") + return dflt + + +def parse_generation_time(cf_m): + tau = parse_with_default(cf_m, "generation_time", 4.8) + return tau + + +class NUTS_from_MAP: + def __init__(self, num_warmup, num_samples, iters, lr): + self.num_warmup = num_warmup + self.num_samples = num_samples + self.iters = iters + self.lr = lr + + def fit(self, model, data, name=None): + init_strat, _ = ef.init_to_MAP(model, data, iters=30_000, lr=self.lr) + inference_method = ef.InferNUTS( + num_warmup=self.num_warmup, + num_samples=self.num_samples, + init_strategy=init_strat + ) + return inference_method.fit(model, data, name=name) + + +def parse_inference_method(method_name, lr, iters, num_warmup, num_samples): + if method_name == "FullRank": + method = ef.InferFullRank(lr=lr, iters=iters, num_samples=num_samples) + elif method_name == "MAP": + method = ef.InferMAP(lr=lr, iters=iters) + elif method_name == "NUTS": + method = NUTS_from_MAP( + num_warmup=num_warmup, num_samples=num_samples, iters=iters, lr=lr + ) + else: # Default is full rank + method = ef.InferFullRank(lr=lr, iters=iters, num_samples=num_samples) + return method + + +class MLRConfig: + def __init__(self, path): + self.path = path + self.config = self.read_config(path) + + def read_config(self, path): + with open(path, "r") as file: + config = yaml.safe_load(file) + return config + + def load_data(self, override_seq_path=None): + data_cf = self.config["data"] + + # Load sequence count data + seq_path = override_seq_path or data_cf["seq_path"] + if seq_path.endswith(".tsv"): + raw_seq = pd.read_csv(seq_path, sep="\t") + else: + raw_seq = pd.read_csv(seq_path) + + # Load locations + if "locations" in data_cf: + locations = data_cf["locations"] + else: + # Check if raw_seq has location column + locations = pd.unique(raw_seq["location"]) + + return raw_seq, locations + + def load_model(self): + model_cf = self.config["model"] + + # Processing generation time + tau = parse_generation_time(model_cf) + forecast_L = parse_with_default(model_cf, "forecast_L", dflt=30) + + # Processing likelihoods + model = ef.MultinomialLogisticRegression(tau=tau) + model.forecast_L = forecast_L + return model + + def load_optim(self): + infer_cf = self.config["inference"] + lr = float(parse_with_default(infer_cf, "lr", dflt=1e-2)) + iters = int(parse_with_default(infer_cf, "iters", dflt=50000)) + num_warmup = int(parse_with_default(infer_cf, "num_warmup", dflt=500)) + num_samples = int( + parse_with_default(infer_cf, "num_samples", dflt=1500) + ) + + method_name = parse_with_default(infer_cf, "method", dflt="FullRank") + inference_method = parse_inference_method( + method_name, lr, iters, num_warmup, num_samples + ) + return inference_method + + def load_settings(self, override_export_path=None): + settings_cf = self.config["settings"] + fit = parse_with_default(settings_cf, "fit", dflt=False) + save = parse_with_default(settings_cf, "save", dflt=False) + load = parse_with_default(settings_cf, "load", dflt=False) + export_json = parse_with_default( + settings_cf, "export_json", dflt=False + ) + export_path = override_export_path or parse_with_default( + settings_cf, "export_path", dflt=None + ) + return fit, save, load, export_json, export_path + + +def fit_models(rs, locations, model, inference_method, path, save, pivot=None): + multi_posterior = ef.MultiPosterior() + + for location in locations: + # Subset to data of interest + raw_seq = rs[rs.location == location].copy() + + # Check to see if location available + if len(raw_seq) == 0: + print(f"Location {location} not in data") + continue + + data = ef.VariantFrequencies(raw_seq=raw_seq, pivot=pivot) + + # Fit model + posterior = inference_method.fit(model, data, name=location) + + # Forecast frequencies + model.forecast_frequencies(posterior.samples, forecast_L=model.forecast_L) + + # Add posterior to group + multi_posterior.add_posterior(posterior=posterior) + + # if save, save + if save: + posterior.save_posterior(f"{path}/models/{location}.json") + + return multi_posterior + + +def load_models(rs, locations, model, path=None): + multi_posterior = ef.MultiPosterior() + + for location in locations: + # Subset to data of interest + raw_seq = rs[rs.location == location].copy() + data = ef.VariantFrequencies(raw_seq=raw_seq) + + # Load samples + posterior = ef.PosteriorHandler(data=data, name=location) + posterior.load_posterior(f"{path}/models/{location}.json") + + # Add posterior to group + multi_posterior.add_posterior(posterior=posterior) + + return multi_posterior + + +def make_path_if_absent(path): + dirname = os.getcwd() + file_path = os.path.join(dirname, path) + if not os.path.exists(file_path): + os.makedirs(file_path) + print(f"{path} created.") + return None + + +def make_model_directories(path): + make_path_if_absent(path) + make_path_if_absent(path + "/models") + + +def export_results(multi_posterior, ps, path, data_name): + EXPORT_SITES = ["freq", "ga", "freq_forecast"] + EXPORT_DATED = [True, False, True] + EXPORT_FORECASTS = [False, False, True] + EXPORT_ATTRS = ["pivot"] + # Make directories + make_model_directories(path) + + # Combine jsons from multiple model runs + results = [] + for location, posterior in multi_posterior.locator.items(): + results.append( + ef.posterior.get_sites_variants_tidy( + posterior.samples, + posterior.data, + EXPORT_SITES, + EXPORT_DATED, + EXPORT_FORECASTS, + ps, + location, + ) + ) + results = ef.posterior.combine_sites_tidy(results) + ef.save_json(results, path=f"{path}/{data_name}_results.json") + + +if __name__ == "__main__": + + parser = argparse.ArgumentParser( + description="Estimating variant growth rates." + ) + parser.add_argument("--config", help="path to config file") + parser.add_argument( + "--seq-path", + help="File path to sequence data. Overrides data.seq_path in config.", + ) + parser.add_argument( + "--export-path", + help="Path to export directory. Overrides settings.export_path in config.", + ) + parser.add_argument( + "--data-name", + help="Name of the data set to include in the results filename as _results.json. " + + "Overrides data.name in config.", + ) + parser.add_argument( + "--pivot", + help="Variant to use as pivot. Overrides model.pivot in config.", + ) + args = parser.parse_args() + + # Load configuration, data, and create model + config = MLRConfig(args.config) + print(f"Config loaded: {config.path}") + + raw_seq, locations = config.load_data(args.seq_path) + print("Data loaded sucessfuly") + + mlr_model = config.load_model() + print("Model created.") + + inference_method = config.load_optim() + print("Inference method defined.") + + fit, save, load, export_json, export_path = config.load_settings( + args.export_path + ) + print("Settings loaded") + + # Find export path + if export_path: + make_model_directories(export_path) + + # Find pivot + # Use mlr config pivot unless a dataset-specific pivot is specified + if config.config["model"]["pivot"]: + pivot = config.config["model"]["pivot"] + if args.pivot and args.pivot != "None": + pivot = args.pivot + print("pivot", pivot) + + # Fit or load model results + if fit: + print("Fitting model") + multi_posterior = fit_models( + raw_seq, + locations, + mlr_model, + inference_method, + export_path, + save, + pivot=pivot + ) + elif load: + print("Loading results") + multi_posterior = load_models( + raw_seq, + locations, + mlr_model, + export_path, + ) + else: + print("No models fit or results loaded.") + multi_posterior = ef.MultiPosterior() + + # Export results + if export_json and (fit or load): + print(f"Exporting results as .json at {export_path}") + ps = parse_with_default( + config.config["settings"], "ps", dflt=[0.5, 0.8, 0.95] + ) + data_name = args.data_name or config.config["data"]["name"] + export_results(multi_posterior, ps, export_path, data_name)