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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 1,
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"id": "b8e97ab8-4f81-4e8b-9b71-7076a11e9280",
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"metadata": {
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"tags": []
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},
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"outputs": [
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"/home/vlpr/.local/lib/python3.10/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
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" from .autonotebook import tqdm as notebook_tqdm\n"
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]
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}
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],
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"source": [
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"from scipy.io import loadmat\n",
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"import pandas as pd\n",
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"import numpy as np\n",
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"import matplotlib.pyplot as plt\n",
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"from matplotlib import cm\n",
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"import seaborn as sns\n",
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"plt.rcParams['figure.figsize'] = [9, 5]\n",
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"from cycler import cycler\n",
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"from IPython.display import display, clear_output\n",
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"import optuna\n",
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"import time \n",
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"import ray\n",
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"import torch\n",
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"\n",
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"# import joblib\n",
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"# from ray.util.joblib import register_ray\n",
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"# register_ray()"
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]
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},
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{
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"cell_type": "markdown",
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"id": "95472450-29ba-4f4a-8641-a623b6d6d76f",
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"metadata": {},
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"source": [
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"В этой книге подбираются гиперпараметры синтетической модели движения головы в фмр томографе с помощью библиотеки optuna. \n",
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"В качестве базовой модели взят классификатор из 3х свёрточных и двух полносвязных слоёв. \n",
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"Решается задача максимизации точности классификатора, расчитанной для данных записанных с реального томографа. \n",
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"Классификатор обучен на синтетических данных для варьируемых гипераметров. "
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"id": "2b5d4395-2896-4254-9dd2-a00d5b599e5e",
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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"window_size = 3"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "137610a1-fd50-465c-a366-8727f8172fbe",
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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"from synthetic_dataset_utils import gen_Xy \n",
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"\n",
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"X_train, y_train, X_val, y_val = gen_Xy(sample_num=20, timesteps=340, channels=6, window_size=window_size)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"id": "9413e010-4084-4970-ad6f-abbaba2c7ff8",
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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"# Use one of \"Prepare\" in generate_real_data.ipynb\n",
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"\n",
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"X_test = np.load(f\"../assets/X_test_{window_size}.npy\")\n",
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"y_test = np.load(f\"../assets/y_test_{window_size}.npy\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"id": "7dfd59a0-4b38-4172-977d-0bc9a15575a6",
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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"import torch\n",
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"import torch.nn as nn\n",
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"from torch_train_utils import print_results, train\n",
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"\n",
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"class Model1D(nn.Module):\n",
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" def __init__(self, input_shape):\n",
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" super(Model1D, self).__init__()\n",
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" self.instance_norm = nn.InstanceNorm1d(input_shape[0])\n",
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" self.conv1 = nn.Sequential(nn.Conv1d(input_shape[0], 128, kernel_size=3, padding='same'), nn.ELU())\n",
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" self.dense1 = nn.Sequential(nn.Flatten(), nn.Linear(128*input_shape[1], 128), nn.ELU())\n",
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" self.dense2 = nn.Sequential(nn.Linear(128, 128), nn.ELU())\n",
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" self.classifier = nn.Linear(128, 1)\n",
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" self.sigmoid = nn.Sigmoid()\n",
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"\n",
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" def forward(self, x):\n",
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" x = self.instance_norm(x)\n",
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" x = self.conv1(x)\n",
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" x = self.dense1(x)\n",
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" x = self.dense2(x)\n",
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" x = self.sigmoid(self.classifier(x))\n",
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" return x.squeeze(1)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "220e60bf-2d43-4bfd-8026-a12e0f448c61",
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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"# check that model trains\n",
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"model = Model1D(X_train.shape[1:])\n",
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"train(X_train, y_train, X_test, y_test, model, epochs=100, lr=1e-1, verbose=True);"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 8,
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"id": "69aadbd5-30d5-42d2-8089-f0c75fbeca1f",
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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"def objective(trial):\n",
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" clear_output(wait=True)\n",
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" noise_std_min = trial.suggest_float('noise_std_min', 0.000001, 0.0001)\n",
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" noise_std_max = trial.suggest_float('noise_std_max', 0.0001, 0.1)\n",
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" noise_std_stp = 6.643190903827471e-05\n",
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" harm_ampl_min = trial.suggest_float('harm_ampl_min', 0.001, 0.005)\n",
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" harm_ampl_max = trial.suggest_float('harm_ampl_max', 0.005, 0.1)\n",
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" harm_ampl_step = 0.0006277917832562148\n",
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" probability_steps = 0.01\n",
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" step_ampl_min = trial.suggest_float('step_ampl_min', 0.00001, 0.0001)\n",
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" step_ampl_max = trial.suggest_float('step_ampl_max', 0.0001, 0.8)\n",
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" \n",
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" X_train, y_train, X_val, y_val = gen_Xy(\n",
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" sample_num=100, \n",
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" timesteps=340, \n",
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" channels=6, \n",
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" window_size=window_size,\n",
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" noise_std_min=noise_std_min,\n",
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" noise_std_max=noise_std_max,\n",
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" noise_std_stp=noise_std_stp,\n",
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" harm_ampl_min=harm_ampl_min,\n",
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" harm_ampl_max=harm_ampl_max,\n",
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" harm_ampl_step=harm_ampl_step,\n",
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" probability_steps=probability_steps,\n",
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" step_ampl_min=step_ampl_min,\n",
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" step_ampl_max=step_ampl_max\n",
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" )\n",
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" model = Model1D(X_train.shape[1:])\n",
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" results, history = train(X_train, y_train, X_test, y_test, model, epochs=100, lr=1e-1)\n",
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" \n",
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" return results['val_accuracy']\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 9,
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"id": "df871ce2-01ed-470a-b7fc-2f371af2cbd7",
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"metadata": {
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"tags": []
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},
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"outputs": [
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"\u001b[32m[I 2023-04-19 15:01:21,126]\u001b[0m A new study created in RDB with name: ray\u001b[0m\n"
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]
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}
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],
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"source": [
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"study = optuna.create_study(direction=\"maximize\", storage=f'sqlite:///../results/hyperparameter_study_{window_size}.db', study_name='ray')#, sampler=optuna.samplers.CmaEsSampler())"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 10,
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"id": "4bbe0684-702d-4cb3-92bd-86123505c0f0",
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"metadata": {
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"tags": []
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},
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"outputs": [
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"2023-04-19 15:01:23,341\tWARNING services.py:1780 -- WARNING: The object store is using /tmp instead of /dev/shm because /dev/shm has only 67084288 bytes available. This will harm performance! You may be able to free up space by deleting files in /dev/shm. If you are inside a Docker container, you can increase /dev/shm size by passing '--shm-size=10.24gb' to 'docker run' (or add it to the run_options list in a Ray cluster config). Make sure to set this to more than 30% of available RAM.\n",
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"2023-04-19 15:01:24,578\tINFO worker.py:1553 -- Started a local Ray instance.\n"
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]
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},
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{
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" <h3 style=\"color: var(--jp-ui-font-color0)\">Ray</h3>\n",
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" C71.6,134.6,71.7,134.6,71.8,134.6z\"/>\n",
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" </g>\n",
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" </svg>\n",
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" <table>\n",
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" <tr>\n",
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" <td style=\"text-align: left\"><b>Python version:</b></td>\n",
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" <td style=\"text-align: left\"><b>3.10.6</b></td>\n",
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" </tr>\n",
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" <tr>\n",
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" <td style=\"text-align: left\"><b>Ray version:</b></td>\n",
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" <td style=\"text-align: left\"><b> 2.3.1</b></td>\n",
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" </tr>\n",
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" \n",
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" </table>\n",
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" </div>\n",
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"</div>\n"
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],
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"text/plain": [
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"RayContext(dashboard_url='', python_version='3.10.6', ray_version='2.3.1', ray_commit='5f14cee8dfc6d61ec4fd3bc2c440f9944e92b33a', address_info={'node_ip_address': '172.17.0.3', 'raylet_ip_address': '172.17.0.3', 'redis_address': None, 'object_store_address': '/tmp/ray/session_2023-04-19_15-01-21_178597_339730/sockets/plasma_store', 'raylet_socket_name': '/tmp/ray/session_2023-04-19_15-01-21_178597_339730/sockets/raylet', 'webui_url': '', 'session_dir': '/tmp/ray/session_2023-04-19_15-01-21_178597_339730', 'metrics_export_port': 54619, 'gcs_address': '172.17.0.3:41322', 'address': '172.17.0.3:41322', 'dashboard_agent_listen_port': 52365, 'node_id': '57bf2181b750b2a14b11cae95b0e7be64561e3a69a817a7c7645f4b1'})"
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]
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},
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"execution_count": 10,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"ray.init(num_gpus=1, \n",
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" ignore_reinit_error=True,\n",
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" resources={\"cpu\": 32, \"gpu\": 32} )"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "21977c0d-29ce-42f6-80c0-942654d5df51",
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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"@ray.remote(num_gpus=0.01)\n",
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"def optimize(n_trials):\n",
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" study = optuna.load_study(study_name='ray', storage=f'sqlite:///../results/hyperparameter_study_{window_size}.db')\n",
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" study.optimize(objective, n_trials=n_trials)\n",
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" \n",
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"results = []\n",
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"for i in range(12):\n",
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" time.sleep(1)\n",
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" results.append(optimize.remote(1000)) \n",
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"\n",
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"\n",
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"for i in range(10000):\n",
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" time.sleep(2)\n",
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" clear_output(wait=True)\n",
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" \n",
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" \n",
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"# [ray.get(x) for x in results] \n",
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"# study.best_trial, study.best_params "
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]
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},
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{
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"cell_type": "code",
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"execution_count": 13,
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"id": "35d60e56-1028-4428-a69d-d4e217efe11c",
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"metadata": {
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"tags": []
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},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"(FrozenTrial(number=8316, state=TrialState.COMPLETE, values=[0.7329734219269103], datetime_start=datetime.datetime(2023, 4, 19, 17, 5, 3, 109691), datetime_complete=datetime.datetime(2023, 4, 19, 17, 5, 14, 306688), params={'harm_ampl_max': 0.05141408658277873, 'harm_ampl_min': 0.004216894373183093, 'noise_std_max': 0.04576914309029287, 'noise_std_min': 4.005219535133996e-05, 'step_ampl_max': 0.2241382196779908, 'step_ampl_min': 8.371016275499698e-05}, user_attrs={}, system_attrs={}, intermediate_values={}, distributions={'harm_ampl_max': FloatDistribution(high=0.1, log=False, low=0.005, step=None), 'harm_ampl_min': FloatDistribution(high=0.005, log=False, low=0.001, step=None), 'noise_std_max': FloatDistribution(high=0.1, log=False, low=0.0001, step=None), 'noise_std_min': FloatDistribution(high=0.0001, log=False, low=1e-06, step=None), 'step_ampl_max': FloatDistribution(high=0.8, log=False, low=0.0001, step=None), 'step_ampl_min': FloatDistribution(high=0.0001, log=False, low=1e-05, step=None)}, trial_id=8317, value=None),\n",
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" {'harm_ampl_max': 0.05141408658277873,\n",
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" 'harm_ampl_min': 0.004216894373183093,\n",
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" 'noise_std_max': 0.04576914309029287,\n",
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" 'noise_std_min': 4.005219535133996e-05,\n",
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" 'step_ampl_max': 0.2241382196779908,\n",
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" 'step_ampl_min': 8.371016275499698e-05})"
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]
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},
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"execution_count": 13,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"study.best_trial, study.best_params "
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"cell_type": "code",
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"execution_count": 14,
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"id": "a04877b4-defa-4730-b3ce-fa275e709ae7",
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"metadata": {
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"outputs": [
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"data": {
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"<div>\n",
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"<style scoped>\n",
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" .dataframe tbody tr th:only-of-type {\n",
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" vertical-align: top;\n",
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"\n",
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" }\n",
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"</style>\n",
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"<table border=\"1\" class=\"dataframe\">\n",
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" <thead>\n",
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" <tr style=\"text-align: right;\">\n",
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" <th></th>\n",
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" <th>harm_ampl_max</th>\n",
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" <th>harm_ampl_min</th>\n",
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" <th>noise_std_max</th>\n",
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" <th>noise_std_min</th>\n",
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" <th>step_ampl_max</th>\n",
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" <th>step_ampl_min</th>\n",
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" </tr>\n",
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" </thead>\n",
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" <tbody>\n",
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" <tr>\n",
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" <th>0</th>\n",
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" <td>0.051414</td>\n",
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" <td>0.004217</td>\n",
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" <td>0.045769</td>\n",
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" <td>0.00004</td>\n",
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" <td>0.224138</td>\n",
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" <td>0.000084</td>\n",
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" </tr>\n",
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"</table>\n",
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"text/plain": [
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" harm_ampl_max harm_ampl_min noise_std_max noise_std_min step_ampl_max \\\n",
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"0 0.051414 0.004217 0.045769 0.00004 0.224138 \n",
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"\n",
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" step_ampl_min \n",
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"0 0.000084 "
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]
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},
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"execution_count": 14,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"pd.DataFrame(data={k:[v] for k,v in study.best_params.items()})"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 15,
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"id": "44564237-c95b-4e58-9646-b76059482cc9",
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"metadata": {},
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"outputs": [],
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"source": [
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"data = {k:[v] for k,v in study.best_params.items()}\n",
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"data['best_acc'] = study.best_trial.values\n",
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"data['total_trials'] = 12*1000\n",
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"pd.DataFrame(data=data).to_csv(f\"../results/{window_size}_synth_model_best_params.csv\", index=False)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "4042fb3d-7cda-4fa4-b333-fa31122e1661",
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"metadata": {},
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"outputs": [],
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"source": [
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"# 'noise_std_min': 9.502179883627595e-05,\n",
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"# 'noise_std_max': 0.0018858019888194724,\n",
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"# 'noise_std_stp': 6.643190903827471e-05,\n",
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"# 'harm_ampl_min': 0.0038549419185346817,\n",
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"# 'harm_ampl_max': 0.005978867746555492,\n",
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"# 'harm_ampl_step': 0.0006277917832562148,\n",
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"# 'probability_steps': 0.035397264960945196,\n",
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"# 'step_ampl_min': 0.004726791192229224,\n",
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"# 'step_ampl_max': 0.17299860153013677}"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 12,
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"id": "d1e416f5-6724-4b33-bc07-7d3dfa1c32da",
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"metadata": {
|
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"tags": []
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},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"[None, None, None, None, None, None, None, None, None, None, None, None]"
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]
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},
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"execution_count": 12,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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|
"[ray.cancel(a) for a in results]"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "04220a72-0917-4306-9ab8-b2783dbf44ad",
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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
|
|
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"kernelspec": {
|
|
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"display_name": "Python 3 (ipykernel)",
|
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|
"language": "python",
|
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|
"name": "python3"
|
|
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|
|
},
|
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|
|
"language_info": {
|
|
|
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|
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|
|
"codemirror_mode": {
|
|
|
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|
|
"name": "ipython",
|
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"version": 3
|
|
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|
|
},
|
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|
"file_extension": ".py",
|
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|
|
"mimetype": "text/x-python",
|
|
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|
"name": "python",
|
|
|
|
|
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|
|
"nbconvert_exporter": "python",
|
|
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|
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|
|
"pygments_lexer": "ipython3",
|
|
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|
"version": "3.10.6"
|
|
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|
|
}
|
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},
|
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"nbformat": 4,
|
|
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"nbformat_minor": 5
|
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|
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}
|
protsenkovi
2 years ago