{ "cells": [ { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "tTccJ1reRz-e" }, "source": [ "# Hyperparameter Optimization for Transfer Learning\n", "> optimising Hyperparameters using optuna and fastai2\n", "\n", "- toc: true \n", "- badges: true\n", "- comments: true\n", "- categories: [Hyperparameter optimisation, optimisation, optuna, fastai2, transfer learning]\n", "- search_exclude: false\n", "- image: images/optuna.png" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 399 }, "colab_type": "code", "id": "fXq7LxURFa0-", "outputId": "716f3184-448a-4537-a8f7-29565547ca41" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\u001b[K |████████████████████████████████| 194kB 3.5MB/s \n", "\u001b[K |████████████████████████████████| 204kB 8.2MB/s \n", "\u001b[K |████████████████████████████████| 1.1MB 10.9MB/s \n", "\u001b[?25h Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n", " Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n", " Preparing wheel metadata ... \u001b[?25l\u001b[?25hdone\n", "\u001b[K |████████████████████████████████| 81kB 7.6MB/s \n", "\u001b[K |████████████████████████████████| 3.6MB 16.8MB/s \n", "\u001b[K |████████████████████████████████| 450kB 45.4MB/s \n", "\u001b[K |████████████████████████████████| 81kB 7.4MB/s \n", "\u001b[K |████████████████████████████████| 112kB 53.0MB/s \n", "\u001b[K |████████████████████████████████| 51kB 6.5MB/s \n", "\u001b[K |████████████████████████████████| 61kB 8.0MB/s \n", "\u001b[K |████████████████████████████████| 645kB 54.2MB/s \n", "\u001b[K |████████████████████████████████| 102kB 11.6MB/s \n", "\u001b[?25h Building wheel for alembic (PEP 517) ... \u001b[?25l\u001b[?25hdone\n", " Building wheel for optuna (setup.py) ... \u001b[?25l\u001b[?25hdone\n", " Building wheel for psutil (setup.py) ... \u001b[?25l\u001b[?25hdone\n", " Building wheel for pyperclip (setup.py) ... \u001b[?25l\u001b[?25hdone\n", " Building wheel for locket (setup.py) ... \u001b[?25l\u001b[?25hdone\n", " Building wheel for contextvars (setup.py) ... \u001b[?25l\u001b[?25hdone\n", "\u001b[31mERROR: distributed 2.18.0 has requirement tornado>=5; python_version < \"3.8\", but you'll have tornado 4.5.3 which is incompatible.\u001b[0m\n" ] } ], "source": [ "#hide\n", "!pip -q install fastai2 optuna swifter toolz " ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 124 }, "colab_type": "code", "id": "3iLNklL-Hr-L", "outputId": "944d8bb8-c968-4dbe-bb9d-a9be0895e151" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Go to this URL in a browser: https://accounts.google.com/o/oauth2/auth?client_id=947318989803-6bn6qk8qdgf4n4g3pfee6491hc0brc4i.apps.googleusercontent.com&redirect_uri=urn%3aietf%3awg%3aoauth%3a2.0%3aoob&response_type=code&scope=email%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdocs.test%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive.photos.readonly%20https%3a%2f%2fwww.googleapis.com%2fauth%2fpeopleapi.readonly\n", "\n", "Enter your authorization code:\n", "··········\n", "Mounted at /content/drive\n" ] } ], "source": [ "#hide\n", "from google.colab import drive\n", "drive.mount('/content/drive')" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "colab": {}, "colab_type": "code", "id": "XOlcU2rLFXh2" }, "outputs": [], "source": [ "#hide\n", "from pathlib import Path\n", "from fastai2.text.all import *\n", "from fastai2.vision.all import *\n", "from fastai2.vision.all import *\n", "import pandas as pd\n", "import optuna\n", "import pprint" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": {}, "colab_type": "code", "id": "dVvB_NH3ID-2" }, "outputs": [], "source": [ "#hide\n", "Path('data').mkdir(exist_ok=True)\n", "out_path = Path('/content/drive/My Drive/Models/')\n", "path = Path('data/')" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "oy7UUk_KgAlF" }, "source": [ "## tl;dr\n", "This post covers:\n", "\n", "- the motivations for 'pragmatic hyperparameters optimization'\n", "- how to do this using Optuna (with an example applied to the fastai2 library)\n", "\n", "## Optimizing hyperparameters? \n", "\n", "Deep learning models have a range of Hyperparameters. These include the basic building blocks of a model like the number of layers used or the size of embedding layers, and the parameters for the training of models such as learning rate. Changing *some* of these parameters will improve the performance of a model. There is therefore a potential win from finding the right values for these parameters. \n", "\n", "### Auto ML vs pragmatic hyperparameters optimization\n", "\n", "As a way of framing 'pragmatic search', it is useful to contrast it to Auto ML. If you haven't come across it before:\n", "\n", "> The term AutoML has traditionally been used to describe automated methods for model selection and/or hyperparameter optimization. - {% fn 1 %}.\n", "\n", "\n", "In particular what is termed Auto ML often includes a search across model and Hyperparameters but can also refer to 'Neural Architecture Search' in which the objective is to piece together a new model type for a specific problem or dataset. An underlying assumption of some of this Auto ML approach is that each problem or dataset requires a unique model architecture. \n", "\n", "In contrast a more 'pragmatic' approach uses an existing model architectures which have been shown to work across a range of datasets and tasks, and utilise transfer learning and other 'tricks' like cyclical learning rates and data augmentation. In a heritage context, it is likely that there are going to be bigger issues with imbalanced classes, noisy labels etc, and focusing on designing a custom architecture is probably going to lead to modest improvements in the performance of the model. \n", "\n", "### So what remains to be optimized?\n", "\n", "In contrast to Auto ML which can involve looking at huge range of potential architectures and parameters we could instead limit our focus to smaller set of things which may have a large impact on the performance of your model.\n", "\n", "As an example use case for hyperparameters optimization I'll use two datasets which contain transcripts of trials from the [Old Bailey online](https://www.oldbaileyonline.org/static/Data.jsp) and which are classified into various categories (theft, deception, etc). One of the datasets is drawn the decade 1830 the other one 1730. \n", "\n", "The approach taken to classifying these trials will be to follow the \"Universal Language Model Fine-tuning for Text Classification\" approach. {% fn 2 %}. \n", "\n", "I won't give an in depth summary of the approach here but idea is that:\n", "\n", "- A language model - in this case a [LSTM](https://arxiv.org/abs/1708.02182) based model - is trained on a Wikipedia text. This provides a \"general\" language model that learns to \"understand\" general features of a language, in this case English \n", "- this language model is then fine-tuned on a target dataset, in the orginal paper this is IMDB movie reviews. \n", "- one this language model has been fine-tuned on the target dataset this fine-tuned language model is used as input for a classifier \n", "\n", "The intuition here is that by utilising a pre-trained language model the Wikipedia part, and the fine-tuning part we get the benefits of a massive training set (Wikipedia) whilst also being able to 'focus' the language model on a target corpus which will use language differently. This makes a lot of intuitive sense, but a question in this use case is how much to fine-tune the language model on our target datasets. A reasonable assumption might be that since language will be more different in 1730 compared to 1830 we may want to fine tune the language model trained on Wikipedia more on the 1730 dataset. \n", "\n", "We could of course test through some trial and error experiments, but this is a question which may benefit from some more systematic searching for appropriate hyperparameters. Before we get into this example in more depth I'll discuss the library I'm working with for doing this hyperparameter searching. \n", "\n", "## Optuna: A hyperparameter optimization framework\n", "\n", "In this post I will be using Optuna [\"an automatic hyperparameter optimization software framework, particularly designed for machine learning\"](https://optuna.readthedocs.io/en/latest/). {% fn 3 %}.\n", "\n", "There are some really nice features in Optuna which I'll cover in this post as I explore the question of language model fine-tuning, so hopefully even if you don't care about the specific use case it might still provide a useful overview of Optuna. \n", "\n", "In this blog post my examples will use version two of the fastai library but there really isn't anything that won't translate to other frameworks. Optuna has integrations for a number of libraries (including version 1 of fastai) but for this blog I won't use this integration. " ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "leoBjmVThRdL" }, "source": [ "## A simple optimization example \n", "\n", "To show the approach used in Optuna I'll use a simple image classification example. In this case using a toy example of classifying people vs cats in images taken from [19th Century books](https://doi.org/10.5281/zenodo.3689444). \n", "\n", "Optuna has two main concepts to understand: study and trial. A study is the overarching process of optimization based on some objective function. A trial is a single test/execution of the objective function. We'll return to this in more detail. For now lets look at a simple example. \n", "\n", "For our first example we'll just use Optuna to test whether to use a pre-trained model or not. If the option is ```True``` then the ResNet18 model we use will use weights from pre-training on ImageNet, if ```False``` the model will start with random weights. \n", "\n", "Looking at the high level steps of using Optuna (I'll go into more detail later). We create an objective function: " ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": {}, "colab_type": "code", "id": "jz2vOr51l_o1" }, "outputs": [], "source": [ "#collapse-hide\n", "!wget -q https://zenodo.org/record/3689444/files/humancats.zip?download=1\n", "!unzip -q *humancats.zip* -d data/" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": {}, "colab_type": "code", "id": "K7AIfuRom8Wz" }, "outputs": [], "source": [ "def objective(trial):\n", " is_pretrained = trial.suggest_categorical('pre_trained', [True, False])\n", " dls = ImageDataLoaders.from_folder('data/human_vs_cats/', valid_pct=0.4, item_tfms=Resize(64))\n", " learn = cnn_learner(dls, resnet18, pretrained=is_pretrained, metrics=[accuracy])\n", " learn.fit(1)\n", " acc = learn.recorder.values[-1][-1]\n", " return acc" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "SLA9AuBqeG7Z" }, "source": [ "Most of this will look familiar if you are have used fastai before. Once we have this we create a study:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": {}, "colab_type": "code", "id": "b8wVdLFInR8q" }, "outputs": [], "source": [ "study = optuna.create_study(direction='maximize')" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "gVjekQIxfpwW" }, "source": [ "and then optimize this study:" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 199 }, "colab_type": "code", "id": "dMFjnfGqft40", "outputId": "4de7c65a-5070-443f-e379-e56560ebed23" }, "outputs": [ { "data": { "text/html": [ "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
epochtrain_lossvalid_lossaccuracytime
01.5030350.7109540.55555600:06
" ], "text/plain": [ "" ] }, "metadata": { "tags": [] }, "output_type": "display_data" }, { "name": "stderr", "output_type": "stream", "text": [ "\u001b[32m[I 2020-06-04 16:58:49,862]\u001b[0m Finished trial#0 with value: 0.5555555820465088 with parameters: {'pre_trained': False}. Best is trial#0 with value: 0.5555555820465088.\u001b[0m\n" ] }, { "data": { "text/html": [ "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
epochtrain_lossvalid_lossaccuracytime
01.6911651.2184400.55555600:05
" ], "text/plain": [ "" ] }, "metadata": { "tags": [] }, "output_type": "display_data" }, { "name": "stderr", "output_type": "stream", "text": [ "\u001b[32m[I 2020-06-04 16:58:56,272]\u001b[0m Finished trial#1 with value: 0.5555555820465088 with parameters: {'pre_trained': False}. Best is trial#0 with value: 0.5555555820465088.\u001b[0m\n" ] } ], "source": [ "study.optimize(objective, n_trials=2)" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "XBKqSsLkfutt" }, "source": [ "Once we've run some trials we can inspect the study object for the best value we're optimizing for. In this case this is the accuracy but it will be whatever is returned by our function. We can also see the parameters which led to this value. " ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 34 }, "colab_type": "code", "id": "9ISxvy2BqaA_", "outputId": "50cfe815-9af7-41c6-d102-a7d40afa8844" }, "outputs": [ { "data": { "text/plain": [ "(0.5555555820465088, {'pre_trained': False})" ] }, "execution_count": 11, "metadata": { "tags": [] }, "output_type": "execute_result" } ], "source": [ "study.best_value, study.best_params" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "KHsCYqYdqdxs" }, "source": [ "This toy example wasn't particularly useful (it just confirmed we probably want to use a pre-trained model) but going through the steps provides an overview of the main things required by Optuna. Starting with defining a function ```objective```" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "RK7j8Dh-m8dH" }, "source": [ "```python\n", "def objective(trial):\n", "```" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "wCSSsi2Em8YE" }, "source": [ "this is the function we want to optimize. We could call it something else but following the convention in the Optuna docs the function we'll call it objective. This function takes 'trial' as an argument. " ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "bGZ9SjTeskuG" }, "source": [ "```python\n", "is_pretrained = trial.suggest_categorical('pre_trained', [True, False])\n", "```" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "MwlCGpmdsrG3" }, "source": [ "here we use trial to \"suggest\" a categorical in this case one of two options (whether pre trained is set to true or false). We do this using ```trial.suggest_categorical``` and pass it the potential options (in this case ```True``` or ```False```).\n", "\n", "```trial.suggest_blah``` defines the paramater \"search space\" for Optuna. We'll look at all of the options for this later on. The final step in defining our objective function i.e. the thing we want to optimize: " ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "PFzuuqedt5am" }, "source": [ "```python\n", "return acc\n", "```" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "2EREeTRzt5w5" }, "source": [ "This return value is objective value that Optuna will optimize. Because this is just the return value of a function there is a lot of flexibility in what this can be. In this example it is accuracy but it could be training or validation loss, or another training metrics. Later on we'll look at this in more detail. \n", "\n", "Now let's look at the study part:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": {}, "colab_type": "code", "id": "sEtwbePzu--5" }, "outputs": [], "source": [ "study = optuna.create_study(direction='maximize')" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "QhPFa9edvCHP" }, "source": [ "This is the most simple way of creating a study. This creates a ```study``` object, again, we'll look at more options as we go along. The one option we pass here is the ```direction```. This refers to to whether Optuna should try to increase the return value of our optimization function or decrease it. This depends on what you a tracking i.e. you'd want to minimize error or validation loss but increase accuracy or F1 score. " ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "-qkNpv-_iWQa" }, "source": [ "Looking at the overview provided in the Optuna docs we have three main building blocks:\n", "\n", "- Trial: A single call of the objective function\n", "\n", "- Study: An optimization session, which is a set of trials\n", "\n", "- Parameter: A variable whose value is to be optimized\n", "\n", "## Parameter search space\n", "\n", "Borrowing once more from the docs:\n", "\n", "> [The difficulty of optimization increases roughly exponentially with regard to the number of parameters. That is, the number of necessary trials increases exponentially when you increase the number of parameters, so it is recommended to not add unimportant parameters](https://optuna.readthedocs.io/en/latest/tutorial/configurations.html)\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This is a crucial point. Particularly if we want to use optimization in a pragmatic way. When we have existing knowledge or evidence about what works well for a particular problem, we should use that rather than asking Optuna to find this out for us. There are some extra tricks to make our search for the best parameters more efficient which will be explored below but for now let's get back to the example use case." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Fine-tuning a language model" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": {}, "colab_type": "code", "id": "2iMpC5yMFXi4" }, "outputs": [], "source": [ "#collapse-hide\n", "df_1830 = pd.read_csv('https://gist.githubusercontent.com/davanstrien/4bc85d8a4127a2791732280ffaa43293/raw/cd1a3cc53674b64c8f130edbcb34e835afa665fb/1830trial.csv')\n", "df_1730 = pd.read_csv('https://gist.githubusercontent.com/davanstrien/4bc85d8a4127a2791732280ffaa43293/raw/cd1a3cc53674b64c8f130edbcb34e835afa665fb/1730trial.csv')" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "7GOD70EFfGNt" }, "source": [ "For the sake of brevity I won't cover the steps to generate this dataset the instructions for doing so for the 1830s trials can be found [here](https://programminghistorian.org/en/lessons/naive-bayesian#warning---technical-issues-with-old-bailey-online-website) (and can be easily adapted for the 1730s trial). " ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 303 }, "colab_type": "code", "id": "O4bIuAwBFXi9", "outputId": "544eadfa-8c09-4e02-d426-91164add52d5" }, "outputs": [ { "data": { "text/html": [ "
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014463.0t18361128-57atheft-housebreakingt18361128-57a.txtthefthousebreaking\\n\\n\\n\\n\\n57. \\n\\n\\n\\n\\nJOHN BYE\\n the younger and \\n\\n\\n\\n\\nFREDERICK BYE\\n were indicted for\\n\\n feloniously breaking and entering the dwelling-house of \\n\\n\\n\\nJohn Bye, on the \\n21st of November, at \\nSt. Giles-in-the-Fields, and stealing therein 12 apples, value 9d.; 1 box, value 1d.; 24 pence, and 1 twopenny-piece; the goods and monies of \\n\\n\\n\\nMary Byrne.\\n\\n\\n\\n\\n\\n\\nMARY BYRNE\\n. I sell fruit; I live in Titchbourne-court, Holborn. On the 21st of November I went out at one o'clock, and locked my door?I left 2s. worth of penny-pieces in my drawer, and two dozen large apples?I came...
119021.0t18380917-2214theft-pocketpickingt18380917-2214.txttheftpocketpicking\\n\\n\\n\\n2214. \\n\\n\\n\\n\\nMARY SMITH\\n was indicted\\n\\n for stealing, on the \\n16th of September, 1 purse, value 2d.; 3 half-crowns, and twopence; the goods and monies of \\n\\n\\n\\nGeorge Sainsbury, from his person.\\n\\n\\n\\n\\n\\n\\nGEORGE SAINSBURY\\n. Between twelve and one o'clock, on the 16th of September, I went to sleep in the fields, at Barnsbury-park, Islington, I had three half-crowns, and twopence, in my pocket?I was awoke, and missed my money?I went to the prisoner, and charged her with it?she said she had not got it?I followed her, and saw her drop ray purse down, it had two penny piece...
\n", "
" ], "text/plain": [ " Unnamed: 0 ... text\n", "0 14463.0 ... \\n\\n\\n\\n\\n57. \\n\\n\\n\\n\\nJOHN BYE\\n the younger and \\n\\n\\n\\n\\nFREDERICK BYE\\n were indicted for\\n\\n feloniously breaking and entering the dwelling-house of \\n\\n\\n\\nJohn Bye, on the \\n21st of November, at \\nSt. Giles-in-the-Fields, and stealing therein 12 apples, value 9d.; 1 box, value 1d.; 24 pence, and 1 twopenny-piece; the goods and monies of \\n\\n\\n\\nMary Byrne.\\n\\n\\n\\n\\n\\n\\nMARY BYRNE\\n. I sell fruit; I live in Titchbourne-court, Holborn. On the 21st of November I went out at one o'clock, and locked my door?I left 2s. worth of penny-pieces in my drawer, and two dozen large apples?I came...\n", "1 19021.0 ... \\n\\n\\n\\n2214. \\n\\n\\n\\n\\nMARY SMITH\\n was indicted\\n\\n for stealing, on the \\n16th of September, 1 purse, value 2d.; 3 half-crowns, and twopence; the goods and monies of \\n\\n\\n\\nGeorge Sainsbury, from his person.\\n\\n\\n\\n\\n\\n\\nGEORGE SAINSBURY\\n. Between twelve and one o'clock, on the 16th of September, I went to sleep in the fields, at Barnsbury-park, Islington, I had three half-crowns, and twopence, in my pocket?I was awoke, and missed my money?I went to the prisoner, and charged her with it?she said she had not got it?I followed her, and saw her drop ray purse down, it had two penny piece...\n", "\n", "[2 rows x 7 columns]" ] }, "execution_count": 17, "metadata": { "tags": [] }, "output_type": "execute_result" } ], "source": [ "#hide_input \n", "df_1830.head(2)" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "iJvM04WpTlKX" }, "source": [ "We load the data using fastai2 ```TextDataLoaders```" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": {}, "colab_type": "code", "id": "etAaTC28FXje" }, "outputs": [], "source": [ "# collapse-show\n", "def load_lm_data(df):\n", " data_lm = TextDataLoaders.from_df(\n", " df.sample(frac=0.5), text_col=\"text\", is_lm=True, bs=128\n", " )\n", " return data_lm\n", "\n", "\n", "# Classification data\n", "def load_class_data(df, data_lm):\n", " data_class = TextDataLoaders.from_df(\n", " df.sample(frac=0.5),\n", " text_col=\"text\",\n", " label_col=\"broad\",\n", " valid_pct=0.3,\n", " bs=128,\n", " text_vocab=data_lm.vocab,\n", " )\n", " return data_class" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 17 }, "colab_type": "code", "id": "jzcwweQQ9Mqi", "outputId": "774fb1ee-b9d8-4291-d5c9-94cb93609664" }, "outputs": [ { "data": { "text/html": [], "text/plain": [ "" ] }, "metadata": { "tags": [] }, "output_type": "display_data" }, { "data": { "text/html": [], "text/plain": [ "" ] }, "metadata": { "tags": [] }, "output_type": "display_data" } ], "source": [ "data_lm = load_lm_data(df_1830)\n", "data_class = load_class_data(df_1830, data_lm)" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "0uXyrGODSRtD" }, "source": [ "Create the language model learner and classifier learner:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": {}, "colab_type": "code", "id": "UWTo-3RLFXjk" }, "outputs": [], "source": [ "# collapse-show\n", "def create_lm():\n", " return language_model_learner(data_lm, AWD_LSTM, pretrained=True).to_fp16()\n", "\n", "\n", "def create_class_learn():\n", " return text_classifier_learner(\n", " data_class, AWD_LSTM, metrics=[accuracy, F1Score(average=\"weighted\")]\n", " ).to_fp16()\n" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "X7TX3UsHgRon" }, "source": [ "## Optuna trial suggest \n", "\n", "In the example above ```trial.suggest_categorical``` was used to define the potential parameter. Optuna has five kinds of parameters which can be optimized. These all work through the ```trial.suggest``` method.\n", "\n", "### Categorical \n", "\n", "This can be used for models, optimizers, and for True/False flags. \n", "\n", "```python \n", "optimizer = trial.suggest_categorical('optimizer', ['MomentumSGD', 'Adam'])\n", "```\n", "\n", "### Integer\n", "\n", "```python\n", "n_epochs = trial.suggest_int('num_epochs', 1, 3)\n", "```\n", "\n", "### Uniform\n", "```python\n", "max_zoom = trial.suggest_uniform('max_zoom', 0.0, 1.0)\n", "```\n", "\n", "\n", "### Loguniform \n", "```python\n", "learning_rate = trial.suggest_loguniform('learning_rate', 1e-4, 1e-2)\n", "```\n", "\n", "### Discrete-uniform \n", "```python\n", "drop_path_rate = trial.suggest_discrete_uniform('drop_path_rate', 0.0, 1.0)\n", "```\n", "\n", "The string value provides a key for the parameters which is used to access these parameters later, it's therefore important to give them a sensible name. \n", "\n", "### Limiting parameters? \n", "\n", "Adding additional ```trial.suggest``` to your optimization function increases the search space for Optuna to optimize over so you should avoid adding additional parameters if they are not necessary. \n", "\n", "The other way in which the search space can be constrained is to limit the range of the search i.e. for learning rate \n", "\n", "```python\n", "learning_rate = trial.suggest_loguniform('learning_rate', 1e-4, 1e-2)\n", "```\n", "\n", "is preferable over\n", "\n", "```python\n", "learning_rate = trial.suggest_loguniform('learning_rate', 1e-10, 1e-1)\n", "```\n", "if it's not likely the optimal learning rate will sit outside of this range. \n", "\n", "How many parameters you include will also depend on the type of model you are trying to train. In the use case of fine-tuning a language model we will want to limit the options more since language models are generally quite slow to train. If, on the other hand, we were trying to improve an image classification model which only takes minutes to train then searching through a larger parameter space would become more feasible. " ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "FOQATK_DnEiU" }, "source": [ "## Objective function for fine-tuning a language model \n", "\n", "The objective function below has two stages; train a language model, use the encoder from this language model for a classifier. \n", "\n", "The parameters we're trying to optimize in this case are:\n", "\n", "- learning rate for the frozen language model \n", "- number of epochs to train only the final layers of the language model \n", "- learning rate for the unfrozen language model \n", "- number of epochs for training the whole language model \n", "\n", "We use lm_learn.no_bar() as a context manager to reduce the amount of logging. " ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": {}, "colab_type": "code", "id": "VWLCDePAgHU_" }, "outputs": [], "source": [ "def objective(trial):\n", " lm_learn = create_lm()\n", " lr_frozen = trial.suggest_loguniform(\"learning_rate_frozen\", 1e-4, 1e-1)\n", " head_epochs = trial.suggest_int(\"head_epochs\", 1, 5)\n", " with lm_learn.no_bar():\n", " lm_learn.fit_one_cycle(head_epochs, lr_max=lr_frozen)\n", " # Unfrozen Language model\n", " lr_unfreeze = trial.suggest_loguniform(\"learning_rate_unfrozen\", 1e-7, 1e-1)\n", " body_epochs = trial.suggest_int(\"lm_body_epochs\", 1, 5)\n", " lm_learn.unfreeze()\n", " with lm_learn.no_bar():\n", " lm_learn.fit_one_cycle(body_epochs, lr_unfreeze)\n", " lm_learn.save_encoder(\"finetuned\")\n", " # Classification\n", " cl_learn = create_class_learn()\n", " cl_learn.load_encoder(\"finetuned\")\n", " cl_learn.fit_one_cycle(3)\n", " f1 = cl_learn.recorder.values[-1][-1]\n", " return f1" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "Tj8AgJaJ7ywA" }, "source": [ "We can give our study a name and also store it in a database. This allows for resuming previous trials later and accessing the history of previous trials. There are various options for database backends outlined in the [documentation](https://optuna.readthedocs.io/en/latest/tutorial/rdb.html)." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Creating the study " ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 34 }, "colab_type": "code", "id": "gT8QSK72hgaX", "outputId": "aaae9054-b148-479f-fc30-9383e2d82787" }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "\u001b[32m[I 2020-06-05 15:09:05,470]\u001b[0m A new study created with name: tunelm1830\u001b[0m\n" ] } ], "source": [ "study_name = \"tunelm1830\"\n", "study = optuna.create_study(\n", " study_name=study_name,\n", " direction=\"maximize\",\n", " storage=f\"sqlite:///{out_path}/optuma/example.db\",\n", ")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Optimize" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "f9CESeInh8Mb" }, "source": [ "Now we'll run 3 trials and use ```show_progress_bar=True``` to give an ETA on when the trials will finish. " ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 969, "referenced_widgets": [ "45933ce059b445888732ee52b9b96b07", "fccf2d8fbe22449e88fcd7c6864dc03c", "87db5dfaee1240258cd92d87c705249f", "67b71f2587ce43c8a5c933d927adee9e", "a5b783a86dbc4693832f857438d861d9", "ac71c831f01a4f76a014942e6b19207c", "532bfcf26cff492ea110ef51a4c19973", "db078ea1e1d141e79a3f9dcb69bb7e1a" ] }, "colab_type": "code", "id": "8bs5kJiShgNW", "outputId": "01f5adf7-07a7-4b70-bba3-3259c03b8934" }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/usr/local/lib/python3.6/dist-packages/optuna/_experimental.py:61: ExperimentalWarning:\n", "\n", "Progress bar is experimental (supported from v1.2.0). The interface can change in the future.\n", "\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "45933ce059b445888732ee52b9b96b07", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(FloatProgress(value=0.0, max=3.0), HTML(value='')))" ] }, "metadata": { "tags": [] }, "output_type": "display_data" }, { "data": { "text/html": [], "text/plain": [ "" ] }, "metadata": { "tags": [] }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "(#4) [0,5.382655620574951,4.875850200653076,'00:24']\n", "(#4) [1,5.292355537414551,4.737764835357666,'00:24']\n", "(#4) [2,5.183778285980225,4.647550106048584,'00:24']\n", "(#4) [3,5.11093282699585,4.608272552490234,'00:24']\n", "(#4) [4,5.072442054748535,4.601930618286133,'00:24']\n", "(#4) [0,4.7495622634887695,4.241390228271484,'00:27']\n" ] }, { "data": { "text/html": [ "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
epochtrain_lossvalid_lossaccuracyf1_scoretime
02.3260322.0704120.0200000.01703400:10
12.3022302.1368640.0233330.00359000:10
22.2690612.1806630.0166670.00440800:10
" ], "text/plain": [ "" ] }, "metadata": { "tags": [] }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\u001b[32m[I 2020-06-05 15:12:20,128]\u001b[0m Finished trial#0 with value: 0.00440805109922757 with parameters: {'learning_rate_frozen': 0.00014124685078723662, 'head_epochs': 5, 'learning_rate_unfrozen': 0.00010276862511970148, 'lm_body_epochs': 1}. Best is trial#0 with value: 0.00440805109922757.\u001b[0m\n", "(#4) [0,4.713407516479492,3.7350399494171143,'00:24']\n", "(#4) [1,3.998744249343872,3.3055806159973145,'00:24']\n", "(#4) [2,3.6486754417419434,3.192685842514038,'00:24']\n", "(#4) [3,3.4996860027313232,3.1756556034088135,'00:24']\n", "(#4) [0,3.4227023124694824,3.163315534591675,'00:27']\n", "(#4) [1,3.3954737186431885,3.140226364135742,'00:27']\n", "(#4) [2,3.3778774738311768,3.125929117202759,'00:27']\n", "(#4) [3,3.357388973236084,3.119621753692627,'00:27']\n", "(#4) [4,3.3542206287384033,3.1186859607696533,'00:27']\n" ] }, { "data": { "text/html": [ "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
epochtrain_lossvalid_lossaccuracyf1_scoretime
02.3689842.1213070.0133330.00075900:11
12.3350332.0228530.2500000.36865200:10
22.2966301.9487860.3133330.45236500:10
" ], "text/plain": [ "" ] }, "metadata": { "tags": [] }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\u001b[32m[I 2020-06-05 15:16:49,562]\u001b[0m Finished trial#1 with value: 0.45236502121696065 with parameters: {'learning_rate_frozen': 0.0060643425219262335, 'head_epochs': 4, 'learning_rate_unfrozen': 2.734844423029637e-05, 'lm_body_epochs': 5}. Best is trial#1 with value: 0.45236502121696065.\u001b[0m\n", "(#4) [0,5.3748459815979,4.851675987243652,'00:24']\n", "(#4) [1,5.247058868408203,4.672318935394287,'00:24']\n", "(#4) [2,5.111597061157227,4.559732437133789,'00:24']\n", "(#4) [3,5.026832103729248,4.512131690979004,'00:24']\n", "(#4) [4,4.982809066772461,4.5044732093811035,'00:24']\n", "(#4) [0,4.915407657623291,4.423311233520508,'00:27']\n", "(#4) [1,4.857243061065674,4.394893646240234,'00:27']\n" ] }, { "data": { "text/html": [ "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
epochtrain_lossvalid_lossaccuracyf1_scoretime
02.3684392.0367060.2400000.36035500:10
12.3597902.0931030.0333330.04587800:09
22.3319452.1401940.0166670.01358900:10
" ], "text/plain": [ "" ] }, "metadata": { "tags": [] }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\u001b[32m[I 2020-06-05 15:20:20,119]\u001b[0m Finished trial#2 with value: 0.013588651008106425 with parameters: {'learning_rate_frozen': 0.0001971120155925954, 'head_epochs': 5, 'learning_rate_unfrozen': 1.0649951798153689e-05, 'lm_body_epochs': 2}. Best is trial#1 with value: 0.45236502121696065.\u001b[0m\n", "\n" ] } ], "source": [ "study.optimize(objective, n_trials=3, show_progress_bar=True)" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "Z8AzOo-78yDC" }, "source": [ "## Results\n", "\n", "You can see how trials are peforming in the logs with the last part of the log reporting the best trial so far. We can now access the best value and best_params. " ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 104 }, "colab_type": "code", "id": "9yIapmNgupr0", "outputId": "ddbafb27-29b7-43ac-ad10-54179b2ad40d" }, "outputs": [ { "data": { "text/plain": [ "(0.45236502121696065,\n", " {'head_epochs': 4,\n", " 'learning_rate_frozen': 0.0060643425219262335,\n", " 'learning_rate_unfrozen': 2.734844423029637e-05,\n", " 'lm_body_epochs': 5})" ] }, "execution_count": 24, "metadata": { "tags": [] }, "output_type": "execute_result" } ], "source": [ "study.best_value, study.best_params" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "wewJb4shGKO6" }, "source": [ "# Where to start the search?\n", "\n", "As I mentioned at the start I think it's worth trying to think pragmatically about how to use hyper-parameter optimizations. I already mentioned limiting the number of parameters and limiting the potential options in these parameters. However we can also intervene more directly in how Optuna runs a trial. \n", "\n", "\n", "## Suggesting a learning rate\n", "\n", "One of the yummiest features in fastai which has also made it into other deep-learning libraries is the learning rate finer ```lr_find()```. As a reminder:\n", "\n", ">the LR Finder trains the model with exp\n", "onentially growing learning rates from start_lr to end_lr for num_it and stops in case of divergence (unless stop_div=False) then plots the losses vs the learning rates with a log scale. \n", "\n", "Since the Learning rate finder often gives a good learning rate we should see if we can use this as a starting point for our trials." ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "8HJFXNtkFmY9" }, "source": [ "### Enqueue trial\n", "\n", "Using ```enqueue_trial``` you can queue up trials with specied paramters. This can be for all of the parameters or just a subset. We can use lr_find to suggest a learning rate for the language model and then que a trial with this learning rate. " ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": {}, "colab_type": "code", "id": "muwZagakMHT_" }, "outputs": [], "source": [ "lm_learn = create_lm()\n", "lm_learn.unfreeze()" ] }, { "cell_type": "code", "execution_count": 26, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 283 }, "colab_type": "code", "id": "D6gTVRj3POhr", "outputId": "e0b7b549-2947-4e37-cb34-6d2f850d4dde" }, "outputs": [ { "data": { "text/html": [], "text/plain": [ "" ] }, "metadata": { "tags": [] }, "output_type": "display_data" }, { "data": { "image/png": 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" ] }, "metadata": { "needs_background": "light", "tags": [] }, "output_type": "display_data" } ], "source": [ "lr_min,lr_steep = lm_learn.lr_find(suggestions=True)" ] }, { "cell_type": "code", "execution_count": 27, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 34 }, "colab_type": "code", "id": "Xe4R7ol3Qkvf", "outputId": "1dc0f12f-a66d-4da7-f3ee-fc5660bcc866" }, "outputs": [ { "data": { "text/plain": [ "(0.014454397559165954, 0.033113110810518265)" ] }, "execution_count": 27, "metadata": { "tags": [] }, "output_type": "execute_result" } ], "source": [ "lr_min, lr_steep" ] }, { "cell_type": "code", "execution_count": 28, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 389 }, "colab_type": "code", "id": "m190YSUvQpvk", "outputId": "4c37725d-4df3-4142-b1f3-236b3cc02bd7" }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/usr/local/lib/python3.6/dist-packages/optuna/_experimental.py:61: ExperimentalWarning:\n", "\n", "enqueue_trial is experimental (supported from v1.2.0). The interface can change in the future.\n", "\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "(#4) [0,5.322241306304932,4.736147403717041,'00:24']\n", "(#4) [1,5.095097541809082,4.474568843841553,'00:24']\n", "(#4) [2,4.91882848739624,4.365659713745117,'00:24']\n", "(#4) [3,4.820737838745117,4.348053455352783,'00:24']\n", "(#4) [0,3.5270116329193115,3.0885186195373535,'00:27']\n", "(#4) [1,3.1028788089752197,2.8053553104400635,'00:27']\n", "(#4) [2,2.7882776260375977,2.611638069152832,'00:27']\n", "(#4) [3,2.49800705909729,2.539992094039917,'00:27']\n" ] }, { "data": { "text/html": [ "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
epochtrain_lossvalid_lossaccuracyf1_scoretime
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" ], "text/plain": [ "" ] }, "metadata": { "tags": [] }, "output_type": "display_data" }, { "name": "stderr", "output_type": "stream", "text": [ "\u001b[32m[I 2020-06-05 15:28:58,707]\u001b[0m Finished trial#3 with value: 0.6866621960133127 with parameters: {'head_epochs': 4, 'learning_rate_frozen': 0.0003841551576945897, 'learning_rate_unfrozen': 0.033113110810518265, 'lm_body_epochs': 4}. Best is trial#3 with value: 0.6866621960133127.\u001b[0m\n" ] } ], "source": [ "study.enqueue_trial({'learning_rate_unfrozen': lr_steep})\n", "study.optimize(objective, n_trials=1)" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "ajfENpbI2tbU" }, "source": [ "Using the learning rate from the LR_finder gives us our best trial so far. This is likely to be because learning rate is a particularly important hyper-parameter. The suggested learning rate from lr_find may not always be the best but using either the suggested one or picking one based on the plot as a starting point for the trial may help Optuna to start from sensible starting point while still giving the freedom for optuna to diverge away from this in later trials if helps the objective function. \n", "\n", "## Pruning trials\n", "The next feature of Optuna which helps make parameter searching more efficient is pruning. Pruning is a process for stopping bad trials early. \n", "\n", "For example if we have the following three trials:\n", "- Trial 1 - epoch 1: 87% accuracy \n", "- Trial 2 - epoch 1: 85% accuracy \n", "- Trial 3 - epoch 1: 60% accuracy \n", "\n", "probably it's not worth continuing with trial 3. Pruning trials helps focus computational resources on trials which are *likely* to improve on previous trials. The *likely* here is important. It is possible that some trials may be pruned early which actually would have done better in the end. Optuna offers a number of different pruning algorithms, I won't cover these here but the documentation gives a good overview and includes links to the papers which propose the implemented pruning algorithms. \n", "\n", "### How to do pruning in Optuna?\n", "\n", "Optuna has intergrations with various machine learning libraries. These intergrations can help with the pruning but setting up pruning manually is also pretty straight forward to do. \n", "\n", "The two things we need to do is report the value and the stage in the training porcess:\n", "```python\n", "trial.report(metric, step)\n", "``` \n", "\n", "then we call:\n", "\n", "```python\n", "if trial.should_prune():\n", " raise optuna.exceptions.TrialPruned()\n", "```\n", "\n", "Depending on your objective function this will be put in different places. In the example of fine-tuning the language model, because we're trying to optimize the classification part it, it means the pruning step can only be called quite late in the traing loop. Ideally it would be called earlier but we still save a little bit of time on unpromising trials. \n", "\n", "The new objective function with pruning:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": {}, "colab_type": "code", "id": "iGxCbXmnFXjq" }, "outputs": [], "source": [ "def objective(trial):\n", " lm_learn = create_lm()\n", " lr_frozen = trial.suggest_loguniform(\"learning_rate_frozen\", 1e-4, 1e-1)\n", " head_epochs = trial.suggest_int(\"head_epochs\", 1, 5)\n", " with lm_learn.no_bar():\n", " lm_learn.fit_one_cycle(head_epochs, lr_max=lr_frozen)\n", " # Unfrozen Language model\n", " lr_unfreeze = trial.suggest_loguniform(\"learning_rate_unfrozen\", 1e-7, 1e-1)\n", " body_epochs = trial.suggest_int(\"lm_body_epochs\", 1, 5)\n", " lm_learn.unfreeze()\n", " with lm_learn.no_bar():\n", " lm_learn.fit_one_cycle(body_epochs, lr_unfreeze)\n", " lm_learn.save_encoder(\"finetuned\")\n", " # Classification\n", " cl_learn = create_class_learn()\n", " cl_learn.load_encoder(\"finetuned\")\n", " for step in range(3):\n", " cl_learn.fit(1)\n", " # Pruning\n", " intermediate_f1 = cl_learn.recorder.values[-1][\n", " -1\n", " ] # get f1 score for current step\n", " trial.report(intermediate_f1, step) # report f1\n", " if trial.should_prune(): # let optuna decide whether to prune\n", " raise optuna.exceptions.TrialPruned()\n", " f1 = cl_learn.recorder.values[-1][-1]\n", " return f1\n" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "ihGy4WB90DHZ" }, "source": [ "We can load the same study as before using the ```python load_if_exists``` flag." ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 34 }, "colab_type": "code", "id": "HH7mLy1X51hV", "outputId": "59a053da-6714-40bd-d077-b11f1ead53c5" }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "\u001b[32m[I 2020-06-06 14:30:47,724]\u001b[0m Using an existing study with name 'tunelm1830' instead of creating a new one.\u001b[0m\n" ] } ], "source": [ "study_name = \"tunelm1830\"\n", "study = optuna.create_study(\n", " study_name=study_name,\n", " direction=\"maximize\",\n", " storage=f\"sqlite:///{out_path}/optuma/example.db\",\n", " load_if_exists=True,\n", " pruner=optuna.pruners.SuccessiveHalvingPruner(),\n", ")" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "2cOgk5tl6npw" }, "source": [ "We can now run some more trials. Instead of specifying the number of trials we can also specify how long optuma should search for." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": {}, "colab_type": "code", "id": "SUQ0Bd9g6MN2" }, "outputs": [], "source": [ "study.enqueue_trial({'learning_rate_unfrozen': lr_steep})\n", "study.optimize(objective, timeout=60*60*0.5)" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "XKSAvkUKB40n" }, "source": [ "and get the best trial:" ] }, { "cell_type": "code", "execution_count": 32, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 54 }, "colab_type": "code", "id": "qjuxqggoOgH5", "outputId": "10815690-57de-4a58-dc50-981151433407" }, "outputs": [ { "data": { "text/plain": [ "FrozenTrial(number=13, value=0.8657462002717475, datetime_start=datetime.datetime(2020, 6, 5, 15, 59, 26, 230967), datetime_complete=datetime.datetime(2020, 6, 5, 16, 3, 26, 392390), params={'head_epochs': 4, 'learning_rate_frozen': 0.0012866609022148768, 'learning_rate_unfrozen': 1.3302852136460371e-06, 'lm_body_epochs': 4}, distributions={'head_epochs': IntUniformDistribution(high=5, low=1, step=1), 'learning_rate_frozen': LogUniformDistribution(high=0.1, low=0.0001), 'learning_rate_unfrozen': LogUniformDistribution(high=0.1, low=1e-07), 'lm_body_epochs': IntUniformDistribution(high=5, low=1, step=1)}, user_attrs={}, system_attrs={'completed_rung_0': 0.8156506309537317}, intermediate_values={0: 0.251088767516275, 1: 0.8156506309537317, 2: 0.8657462002717475}, trial_id=14, state=TrialState.COMPLETE)" ] }, "execution_count": 32, "metadata": { "tags": [] }, "output_type": "execute_result" } ], "source": [ "study.best_trial" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "erUHd3UyB9-6" }, "source": [ "and best value and pararms:" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 104 }, "colab_type": "code", "id": "uxLytWYaO7NN", "outputId": "45aeb834-50e3-45db-c95e-3956456f788a" }, "outputs": [ { "data": { "text/plain": [ "(0.8657462002717475,\n", " {'head_epochs': 4,\n", " 'learning_rate_frozen': 0.0012866609022148768,\n", " 'learning_rate_unfrozen': 1.3302852136460371e-06,\n", " 'lm_body_epochs': 4})" ] }, "execution_count": 7, "metadata": { "tags": [] }, "output_type": "execute_result" } ], "source": [ "study.best_value, study.best_params" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "VBOAOjul51aG" }, "source": [ "## Paramters for the 1730s trials data\n", "\n", "We can do the same process with the 1730s trials, starting with a suggested learning rate.\n", "\n", "\n" ] }, { "cell_type": "code", "execution_count": 35, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 283 }, "colab_type": "code", "id": "8BY2EicVeRng", "outputId": "3d55cb58-0de2-454f-9e6d-cd0999c02e1e" }, "outputs": [ { "data": { "text/html": [], "text/plain": [ "" ] }, 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\n", 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" ] }, "metadata": { "needs_background": "light", "tags": [] }, "output_type": "display_data" } ], "source": [ "#collapse-show\n", "data_lm = load_lm_data(df_1730)\n", "data_class = load_class_data(df_1730, data_lm)\n", "lm_learn = create_lm()\n", "lm_learn.unfreeze()\n", "lr_min,lr_steep = lm_learn.lr_find(suggestions=True)\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": {}, "colab_type": "code", "id": "xx3jKMXHeRhw" }, "outputs": [], "source": [ "# collapse-hide\n", "def objective(trial):\n", " lm_learn = create_lm()\n", " lr_frozen = trial.suggest_loguniform(\"learning_rate_frozen\", 1e-4, 1e-1)\n", " head_epochs = trial.suggest_int(\"head_epochs\", 1, 5)\n", " with lm_learn.no_bar():\n", " lm_learn.fit_one_cycle(head_epochs, lr_max=lr_frozen)\n", " # Unfrozen Language model\n", " lr_unfreeze = trial.suggest_loguniform(\"learning_rate_unfrozen\", 1e-7, 1e-1)\n", " body_epochs = trial.suggest_int(\"lm_body_epochs\", 1, 5)\n", " lm_learn.unfreeze()\n", " with lm_learn.no_bar():\n", " lm_learn.fit_one_cycle(body_epochs, lr_unfreeze)\n", " lm_learn.save_encoder(\"finetuned\")\n", " # Classification\n", " cl_learn = create_class_learn()\n", " cl_learn.load_encoder(\"finetuned\")\n", " for step in range(3):\n", " cl_learn.fit(1)\n", " intermediate_f1 = cl_learn.recorder.values[-1][-1]\n", " trial.report(intermediate_f1, step)\n", " if trial.should_prune():\n", " raise optuna.exceptions.TrialPruned()\n", " f1 = cl_learn.recorder.values[-1][-1]\n", " return f1" ] }, { "cell_type": "code", "execution_count": 58, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 34 }, "colab_type": "code", "id": "47XAHgheeRcU", "outputId": "cb7708b2-84f6-4605-b475-654f53cf55f9" }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "\u001b[32m[I 2020-06-08 15:06:54,474]\u001b[0m Using an existing study with name 'tunelm1730' instead of creating a new one.\u001b[0m\n" ] } ], "source": [ "# collapse-hide\n", "study_name = \"tunelm1730\"\n", "study = optuna.create_study(\n", " study_name=study_name,\n", " direction=\"maximize\",\n", " storage=f\"sqlite:///{out_path}/optuma/example.db\",\n", " load_if_exists=True,\n", " pruner=optuna.pruners.SuccessiveHalvingPruner(),\n", ")" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": {}, "colab_type": "code", "id": "dF8RYS_xeRYl" }, "outputs": [], "source": [ "study.enqueue_trial({'learning_rate_unfrozen': lr_steep})\n", "study.optimize(objective, timeout=60*60*0.5)" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "8eTOZe-QDWcV" }, "source": [ "Trials can be accssed as part of the study object. Running trials for 30 mins with early pruning results in 20 trials" ] }, { "cell_type": "code", "execution_count": 65, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 34 }, "colab_type": "code", "id": "7xOv8umTPhH3", "outputId": "0760571b-f783-4d3e-d17c-5b289ba15451" }, "outputs": [ { "data": { "text/plain": [ "20" ] }, "execution_count": 65, "metadata": { "tags": [] }, "output_type": "execute_result" } ], "source": [ "len(study.trials)" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "5fvFx-iGDfBt" }, "source": [ "We can also see which was the best trial." ] }, { "cell_type": "code", "execution_count": 64, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 34 }, "colab_type": "code", "id": "GC1IE2HQD1bb", "outputId": "65b7da98-f5cb-44e3-d763-62e2f9cee096" }, "outputs": [ { "data": { "text/plain": [ "2" ] }, "execution_count": 64, "metadata": { "tags": [] }, "output_type": "execute_result" } ], "source": [ "study.best_trial.number" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "-vnjfH1XD4Jm" }, "source": [ "The number of trials run depends mainly on how long your model takes to train, the size of the paramter search space and your patience. If trials are failing to improve better scores for a long time it's probably better to actively think about how to improve your approach to the problem (better data, more data, chaning model design etc.) rather than hoping hyperaparmet tuning will fix the problem. " ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "KyH7q9o7eRJ_" }, "source": [ "## Comparing language model parameters\n", "Previous trials can be loaded using ```load_study```" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": {}, "colab_type": "code", "id": "OW-Fvnk3wpyM" }, "outputs": [], "source": [ "study1830 = optuna.load_study('tunelm1830', storage=f'sqlite:///{out_path}/optuma/example.db')\n", "study1730 = optuna.load_study('tunelm1730', storage=f'sqlite:///{out_path}/optuma/example.db')" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "u1J8Z-1vE3kD" }, "source": [ "First comparing the best f1 values for both datasets:" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 52 }, "colab_type": "code", "id": "hnAbGEkvwvto", "outputId": "85705f82-0bc4-4c17-e73c-30a7620692e7" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Best 1830 value was: 0.866\n", "Best 1730 value was: 0.781\n" ] } ], "source": [ "print(f'Best 1830 value was: {study1830.best_value:.3}')\n", "print(f'Best 1730 value was: {study1730.best_value:.3}')" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "AuEjeS2rGOcY" }, "source": [ "The paramters used to get the best value: \n", "\n", "#### 1830 parameters" ] }, { "cell_type": "code", "execution_count": 33, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 86 }, "colab_type": "code", "id": "i1h0QH0TrRJs", "outputId": "4e65f074-4e10-4ddb-a0e0-437a273a402f" }, "outputs": [ { "data": { "text/plain": [ "{'head_epochs': 4,\n", " 'learning_rate_frozen': 0.0012866609022148768,\n", " 'learning_rate_unfrozen': 1.3302852136460371e-06,\n", " 'lm_body_epochs': 4}" ] }, "execution_count": 33, "metadata": { "tags": [] }, "output_type": "execute_result" } ], "source": [ "#hide_input\n", "study1830.best_params" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "dGyzHTcOG7dE" }, "source": [ "#### 1730 parameters" ] }, { "cell_type": "code", "execution_count": 34, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 86 }, "colab_type": "code", "id": "Mk5LjMr2G_WY", "outputId": "dda38473-7818-472f-a00f-261181eec4e0" }, "outputs": [ { "data": { "text/plain": [ "{'head_epochs': 3,\n", " 'learning_rate_frozen': 0.002145480897071231,\n", " 'learning_rate_unfrozen': 9.889236991663078e-06,\n", " 'lm_body_epochs': 1}" ] }, "execution_count": 34, "metadata": { "tags": [] }, "output_type": "execute_result" } ], "source": [ "#hide_input\n", "study1730.best_params" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "Mv1OgXkYG-nv" }, "source": [ "Specific parameters can also be accessed " ] }, { "cell_type": "code", "execution_count": 46, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 34 }, "colab_type": "code", "id": "ifmzjjyps8k8", "outputId": "71be4d56-3418-4860-e41e-49df63daf23a" }, "outputs": [ { "data": { "text/plain": [ "(1.3302852136460371e-06, 9.889236991663078e-06)" ] }, "execution_count": 46, "metadata": { "tags": [] }, "output_type": "execute_result" } ], "source": [ "study1830.best_params['learning_rate_unfrozen'], study1730.best_params['learning_rate_unfrozen']" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "M5JfOKBMImdl" }, "source": [ "## Visualizations\n", "\n", "Optuna has a variety of visulizations, I will only briefly show a few of these here. \n" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "JulYRTFHwZJf" }, "source": [ "```plot_intermediate_values``` shows the intermediate values. This can be useful for getting a sense of how trials progress and also help give a sense of whether some trials are being pruned prematurely " ] }, { "cell_type": "code", "execution_count": 48, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 542 }, "colab_type": "code", "id": "qeF5qcqpwvX4", "outputId": "35788958-9c3a-4f0a-b9cf-73c6c772a9f1" }, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "
\n", " \n", " \n", " \n", "
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\n", "\n", "" ] }, "metadata": { "tags": [] }, "output_type": "display_data" } ], "source": [ "optuna.visualization.plot_intermediate_values(study1830)" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "cZeqIEX7wqCm" }, "source": [ "```plot_parallel_coordinate``` plots parameters choices in relation to values. It can be hard to read these plots but they can also be helpful for giving a sense of which choices for parameters work best. " ] }, { "cell_type": "code", "execution_count": 37, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 542 }, "colab_type": "code", "id": "aGM3AhLOwvKm", "outputId": "44b9f3b7-9b31-4a00-f7e5-b5be7bfd2ca7" }, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "
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\n", "\n", "" ] }, "metadata": { "tags": [] }, "output_type": "display_data" } ], "source": [ "optuna.visualization.plot_parallel_coordinate(study1830)" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "0DKZr_BlIwha" }, "source": [ "### Parameter importance\n", "\n", "Optuna has experimental support for getting parameter importance. " ] }, { "cell_type": "code", "execution_count": 35, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 225 }, "colab_type": "code", "id": "vf54tawZHQ7o", "outputId": "5ce9e077-3b5c-47eb-dd6d-cbff1225cc62" }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/usr/local/lib/python3.6/dist-packages/optuna/_experimental.py:61: ExperimentalWarning:\n", "\n", "get_param_importances is experimental (supported from v1.3.0). The interface can change in the future.\n", "\n", "/usr/local/lib/python3.6/dist-packages/optuna/_experimental.py:83: ExperimentalWarning:\n", "\n", "MeanDecreaseImpurityImportanceEvaluator is experimental (supported from v1.5.0). The interface can change in the future.\n", "\n" ] }, { "data": { "text/plain": [ "OrderedDict([('learning_rate_frozen', 0.43423246892923717),\n", " ('learning_rate_unfrozen', 0.2904735896601219),\n", " ('head_epochs', 0.2433021650269149),\n", " ('lm_body_epochs', 0.031991776383726155)])" ] }, "execution_count": 35, "metadata": { "tags": [] }, "output_type": "execute_result" } ], "source": [ "optuna.importance.get_param_importances(study1730)" ] }, { "cell_type": "code", "execution_count": 36, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 225 }, "colab_type": "code", "id": "6aF9A9HbHfyO", "outputId": "54b44b9b-824b-44e9-efc0-35ecace349f4" }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/usr/local/lib/python3.6/dist-packages/optuna/_experimental.py:61: ExperimentalWarning:\n", "\n", "get_param_importances is experimental (supported from v1.3.0). The interface can change in the future.\n", "\n", "/usr/local/lib/python3.6/dist-packages/optuna/_experimental.py:83: ExperimentalWarning:\n", "\n", "MeanDecreaseImpurityImportanceEvaluator is experimental (supported from v1.5.0). The interface can change in the future.\n", "\n" ] }, { "data": { "text/plain": [ "OrderedDict([('learning_rate_unfrozen', 0.35548906967729954),\n", " ('learning_rate_frozen', 0.33998779901100146),\n", " ('head_epochs', 0.21196438930810765),\n", " ('lm_body_epochs', 0.09255874200359132)])" ] }, "execution_count": 36, "metadata": { "tags": [] }, "output_type": "execute_result" } ], "source": [ "optuna.importance.get_param_importances(study1830)" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "9n3AZmMfyUzT" }, "source": [ "These are broadly similar although learning rate frozen/unfrozen are in different places for the 1730 and 1830 trials. " ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "Di26l77YoyMU" }, "source": [ "# Multi objective \n", "\n", "Optuna has experimental support for multi-objective optimization. This might be useful if you don't want to optimize for only one metrics. \n", "\n", "An alternative to using this approach is to report other things you care about during the trial but don't directly want to optimize for. As an example, you might mostly care about the accuracy of a model but also care a bit about how long it takes to do inference. \n", "\n", "One approach is to use a multi-objective trial. An alternative is to instead log inference time as part of the trial and continue to optimize for other metrics. You can then later on balance the accuracy of different trials with the inference time. It may turn out later that a slightly slower inference time can be dealt with by scaling vertically. Not prematurely optimizing for multi-objectives can therefore give you more flexibility. To show this in practice I'll use an image classification dataset. " ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": {}, "colab_type": "code", "id": "HAnucUWo6UzJ" }, "outputs": [], "source": [ "#hide\n", "!unzip -q '{out_path}/optuma/1905_maps.zip' -d data/" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## The data\n", "\n", "The data is images of maps and other things from historic newspapers. The aim is to classify whether the image is a map or something else." ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 536 }, "colab_type": "code", "id": "6YrQTy67SASf", "outputId": "07c89202-44d0-49f0-8e89-4cbb754f87a7" }, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light", "tags": [] }, "output_type": "display_data" } ], "source": [ "dls = ImageDataLoaders.from_folder(\n", " \"data/1905_maps/\", valid_pct=0.3, item_tfms=Resize(256)\n", ")\n", "dls.show_batch()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": {}, "colab_type": "code", "id": "p1rM8zFnSSqG" }, "outputs": [], "source": [ "#hide\n", "learn = cnn_learner(dls, resnet50, metrics=[F1Score(average='weighted')])" ] }, { "cell_type": "code", "execution_count": 34, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 283 }, "colab_type": "code", "id": "QhqFAAtGI6-a", "outputId": "da533fd1-825e-4d54-8e48-7263322775c5" }, "outputs": [ { "data": { "text/html": [], "text/plain": [ "" ] }, "metadata": { "tags": [] }, "output_type": "display_data" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light", "tags": [] }, "output_type": "display_data" } ], "source": [ "learn.unfreeze()\n", "lr_min,unfrozen_lr_steep = learn.lr_find(suggestions=True)" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "mmzsox1NWC17" }, "source": [ "### Excessive model parameter search\n", "Since the time to train the model is more reasonable we can add a more parameters to the search space. In practice this is pretty overkill but is useful as an example of working with the outputs of trials with many parameters." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": {}, "colab_type": "code", "id": "jaX4xSuuuQgY" }, "outputs": [], "source": [ "def objective(trial):\n", " apply_tfms = trial.suggest_categorical(\"apply_tfms\", [True, False])\n", " if apply_tfms:\n", " aug_tfms = aug_transforms(\n", " mult=trial.suggest_uniform(\"mult\", 0.0, 1.0),\n", " do_flip=trial.suggest_categorical(\"do_flip\", [True, False]),\n", " flip_vert=trial.suggest_categorical(\"flip_vert\", [True, False]),\n", " max_rotate=trial.suggest_uniform(\"max_rotate\", 0, 180),\n", " max_zoom=trial.suggest_uniform(\"max_zoom\", 0, 3.0),\n", " max_lighting=trial.suggest_uniform(\"max_lighting\", 0.0, 1.0),\n", " )\n", " else:\n", " aug_tfms = None\n", " dls = ImageDataLoaders.from_folder(\n", " \"data/1905_maps/\", valid_pct=0.3, item_tfms=Resize(256), aug_transforms=aug_tfms\n", " )\n", " model = trial.suggest_categorical(\n", " \"model\", [\"resnet18\", \"resnet50\", \"xresnet50\", \"squeezenet1_0\", \"densenet121\"]\n", " )\n", " learn = cnn_learner(\n", " dls, arch=eval(model), pretrained=True, metrics=[F1Score(average=\"weighted\")]\n", " ).to_fp16()\n", " epochs = trial.suggest_int(\"epochs\", 1, 10)\n", " for step in range(epochs):\n", " with learn.no_bar():\n", " learn.fit_one_cycle(\n", " 1, base_lr=trial.suggest_loguniform(\"learning_rate\", 1e-5, 1e-1)\n", " )\n", " unfrozen_epochs = trial.suggest_int(\"unfrozen_epochs\", 1, 10)\n", " unfrozen_lr = trial.suggest_loguniform(\"unfrozen_learning_rate\", 1e-10, 1e-1)\n", " learn.unfreeze()\n", " for step in range(unfrozen_epochs):\n", " with learn.no_bar():\n", " learn.fit_one_cycle(1, lr_max=unfrozen_lr)\n", " int_f1 = learn.recorder.values[-1][-1]\n", " trial.report(int_f1, step)\n", " if trial.should_prune():\n", " raise optuna.exceptions.TrialPruned()\n", " t0 = time.time()\n", " learn.validate()\n", " t1 = time.time()\n", " execute_time = t1 - t0\n", " trial.set_user_attr(\"execute_time\", execute_time)\n", " f1 = learn.recorder.values[-1][-1]\n", " return f1" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Create the study" ] }, { "cell_type": "code", "execution_count": 36, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 34 }, "colab_type": "code", "id": "HbLxDEq_yS1u", "outputId": "fde00e81-e030-4882-a8de-4b6bea07d0b9" }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "\u001b[32m[I 2020-06-07 15:03:24,138]\u001b[0m Using an existing study with name 'mapsmegastudyXL' instead of creating a new one.\u001b[0m\n" ] } ], "source": [ "study_name = \"mapsmegastudyXL\" # Unique identifier of the study.\n", "study = optuna.create_study(\n", " direction=\"maximize\",\n", " load_if_exists=True,\n", " study_name=study_name,\n", " storage=f\"sqlite:///{out_path}/optuma/blog.db\",\n", " pruner=optuna.pruners.SuccessiveHalvingPruner(min_resource=2),\n", ")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Queue up with some parameters " ] }, { "cell_type": "code", "execution_count": 37, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 364, "referenced_widgets": [ "9c80a7a1df00409c9aa864bbbf98c3d9", "dfd4c79e357c42c694e91c11edccba42", "b8fbb96630d54ecb887bc8e977ac2850", "feb71d99309f49b3a2e4c9d06c008d4e", "b27e28e3b1104ef9b2fc1d8bf709b5fa", "7ccafe214cb245479fc601d4e7c79dc9", "4d679a674b18436186424bda6dee6bf6", "e3eed74576c94ed4af3dc738dcc6b41a" ] }, "colab_type": "code", "id": "JHCxMouA9Y82", "outputId": "f8ecfc51-5c98-418e-957b-4a285706cbdd" }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/usr/local/lib/python3.6/dist-packages/optuna/_experimental.py:61: ExperimentalWarning:\n", "\n", "enqueue_trial is experimental (supported from v1.2.0). The interface can change in the future.\n", "\n", "/usr/local/lib/python3.6/dist-packages/optuna/_experimental.py:61: ExperimentalWarning:\n", "\n", "Progress bar is experimental (supported from v1.2.0). The interface can change in the future.\n", "\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "9c80a7a1df00409c9aa864bbbf98c3d9", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(FloatProgress(value=0.0, max=1.0), HTML(value='')))" ] }, "metadata": { "tags": [] }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "(#5) [0,1.162647008895874,1.0643662214279175,0.38594077225581136,'00:04']\n", "(#5) [0,1.1730060577392578,0.8583190441131592,0.45458674870439575,'00:02']\n", "(#5) [0,0.7940309047698975,0.40110471844673157,0.8101934029975151,'00:02']\n", "(#5) [0,0.3774714767932892,0.3251221776008606,0.8738329238329239,'00:02']\n", "(#5) [0,0.20592834055423737,0.304998517036438,0.8914149443561209,'00:02']\n", "(#5) [0,0.14400754868984222,0.3399428725242615,0.9003332765709003,'00:03']\n", "(#5) [0,0.11649172753095627,0.3571062982082367,0.8729641116526362,'00:03']\n" ] }, { "data": { "text/html": [], "text/plain": [ "" ] }, "metadata": { "tags": [] }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\u001b[32m[I 2020-06-07 15:03:57,405]\u001b[0m Finished trial#0 with value: 0.8729641116526362 with parameters: {'apply_tfms': True, 'do_flip': False, 'epochs': 5, 'flip_vert': True, 'learning_rate': 0.00015848931798245758, 'max_lighting': 0.5155363265412508, 'max_rotate': 93.50185801538605, 'max_zoom': 2.5014402368129147, 'model': 'resnet50', 'mult': 0.7973732804273224, 'unfrozen_epochs': 2, 'unfrozen_learning_rate': 1.4454397387453355e-05}. Best is trial#0 with value: 0.8729641116526362.\u001b[0m\n", "\n" ] } ], "source": [ "study.enqueue_trial(\n", " {\n", " \"pre_trained\": True,\n", " \"apply_tfms\": True,\n", " \"epochs\": 5,\n", " \"learning_rate\": lr_steep,\n", " \"model\": \"resnet50\",\n", " \"unfrozen_learning_rate\": unfrozen_lr_steep,\n", " }\n", ")\n", "study.optimize(objective, n_trials=1, show_progress_bar=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "queue up with some less sensible defaults " ] }, { "cell_type": "code", "execution_count": 38, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 277, "referenced_widgets": [ "2e42c12e18e9436baf5825b244963077", "1022f3be5ae049a4a46da5f1be01e9b0", "cfe3ec625d3241a0a4bc063c60fa1eb8", "082e7c985bb2441ea7f0e3a64d5ff19b", "d902b890fbdd40f89a90ad754f66382c", "6e8e34b5eb6e49319930128d0f9c7f62", "14ab899ab2694d6f911f20a40763b475", "93821af899bd4f0593088e01a073bb13" ] }, "colab_type": "code", "id": "XVDBbYryqsci", "outputId": "bdf27d5f-68e9-43fe-a029-ff18ffd62802" }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/usr/local/lib/python3.6/dist-packages/optuna/_experimental.py:61: ExperimentalWarning:\n", "\n", "enqueue_trial is experimental (supported from v1.2.0). The interface can change in the future.\n", "\n", "/usr/local/lib/python3.6/dist-packages/optuna/_experimental.py:61: ExperimentalWarning:\n", "\n", "Progress bar is experimental (supported from v1.2.0). The interface can change in the future.\n", "\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "2e42c12e18e9436baf5825b244963077", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(FloatProgress(value=0.0, max=1.0), HTML(value='')))" ] }, "metadata": { "tags": [] }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "(#5) [0,1.1455873250961304,1.7333940267562866,0.3123010228273386,'00:01']\n", "(#5) [0,1.0485259294509888,1.4432364702224731,0.4545249081834448,'00:01']\n" ] }, { "data": { "text/html": [], "text/plain": [ "" ] }, "metadata": { "tags": [] }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\u001b[32m[I 2020-06-07 15:04:18,823]\u001b[0m Finished trial#1 with value: 0.4545249081834448 with parameters: {'apply_tfms': False, 'epochs': 1, 'learning_rate': 1.4039901997074766e-05, 'model': 'resnet18', 'unfrozen_epochs': 1, 'unfrozen_learning_rate': 4.041607859100835e-07}. Best is trial#0 with value: 0.8729641116526362.\u001b[0m\n", "\n" ] } ], "source": [ "study.enqueue_trial(\n", " {\"pre_trained\": False, \"apply_tfms\": False, \"epochs\": 1, \"unfrozen_epochs\": 1}\n", ")\n", "study.optimize(objective, n_trials=1, show_progress_bar=True)\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now optimize for 500 trials " ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": {}, "colab_type": "code", "id": "f5VE-P7ntFHw" }, "outputs": [], "source": [ "study.optimize(objective, n_trials=500,show_progress_bar=True)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": {}, "colab_type": "code", "id": "0as7PgKa4zid" }, "outputs": [], "source": [ "study = optuna.load_study('mapsmegastudyXL', storage=f'sqlite:///{out_path}/optuma/blog.db')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The best finishing values and parameters:" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 243 }, "colab_type": "code", "id": "GZ8QLjjnc6--", "outputId": "a0d84702-d9e4-40fe-b96b-cc672a88cbe6" }, "outputs": [ { "data": { "text/plain": [ "(0.963975663975664,\n", " {'apply_tfms': True,\n", " 'do_flip': True,\n", " 'epochs': 10,\n", " 'flip_vert': False,\n", " 'learning_rate': 0.0785689562916925,\n", " 'max_lighting': 0.5064203068969654,\n", " 'max_rotate': 168.972217754609,\n", " 'max_zoom': 1.6141746329756919,\n", " 'model': 'densenet121',\n", " 'mult': 0.6087267126078458,\n", " 'unfrozen_epochs': 4,\n", " 'unfrozen_learning_rate': 7.6080876225791396e-06})" ] }, "execution_count": 8, "metadata": { "tags": [] }, "output_type": "execute_result" } ], "source": [ "study.best_value, study.best_params" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Visualization\n", "\n", "Taking a look at parallel_coordinate in this case gives some sense of which options work best." ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 542 }, "colab_type": "code", "id": "o6gLpdj5ns57", "outputId": "072e2b18-998e-4d70-fc9c-e4ef970a1349" }, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "
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\n", " \n", "
\n", "\n", "" ] }, "metadata": { "tags": [] }, "output_type": "display_data" } ], "source": [ "optuna.visualization.plot_parallel_coordinate(study)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Importance " ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 260 }, "colab_type": "code", "id": "La4U9NiPnzu7", "outputId": "a81054cb-a296-4ee9-d650-06eda4031cee" }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/usr/local/lib/python3.6/dist-packages/optuna/_experimental.py:61: ExperimentalWarning:\n", "\n", "get_param_importances is experimental (supported from v1.3.0). The interface can change in the future.\n", "\n", "/usr/local/lib/python3.6/dist-packages/optuna/_experimental.py:83: ExperimentalWarning:\n", "\n", "MeanDecreaseImpurityImportanceEvaluator is experimental (supported from v1.5.0). The interface can change in the future.\n", "\n" ] }, { "data": { "text/plain": [ "OrderedDict([('unfrozen_learning_rate', 0.6945560978629778),\n", " ('epochs', 0.13207296757949719),\n", " ('model', 0.07996254760084977),\n", " ('unfrozen_epochs', 0.04455237119259635),\n", " ('learning_rate', 0.04014544684326522),\n", " ('apply_tfms', 0.008710568920813712)])" ] }, "execution_count": 11, "metadata": { "tags": [] }, "output_type": "execute_result" } ], "source": [ "optuna.importance.get_param_importances(study)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Learning rate is by far the most important learning rate, again this suggests that using learning rate finder makes a lot of sense as a starting point. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Working with Optuna trial data\n", "\n", "There are now ~500 trials which are stored in the study. Each of these trials contains the parameters used, metadata about the trial, the value of the thing being optimized, and importantly for this example the user attribute which stores the validation time. Optuna makes it very easy to export this information to a dataframe. " ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 302 }, "colab_type": "code", "id": "r-zRuvTMhwZS", "outputId": "23668f57-7009-42f3-eb45-02a11bc3e83e" }, "outputs": [ { "data": { "text/html": [ "
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" ], "text/plain": [ " number ... state\n", "0 0 ... COMPLETE\n", "1 1 ... COMPLETE\n", "2 2 ... RUNNING\n", "\n", "[3 rows x 22 columns]" ] }, "execution_count": 13, "metadata": { "tags": [] }, "output_type": "execute_result" } ], "source": [ "df = study.trials_dataframe()\n", "df.head(3)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": {}, "colab_type": "code", "id": "EpnnlZ5HAm4O" }, "outputs": [], "source": [ "#hide\n", "df.to_csv('optuna_study.csv')" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "colab": {}, "colab_type": "code", "id": "k3VUWOMEEAfl" }, "outputs": [], "source": [ "#hide\n", "df = pd.read_csv('https://gist.githubusercontent.com/davanstrien/0c9670d02cdf8a9a866b8a467664b690/raw/cb3222f1baf8ae894923e2b8898beaa22ebeadd8/optuna_trials.csv')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can now easily work with the trial data using pandas. Lets start by getting the best two values" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 184 }, "colab_type": "code", "id": "FPPa0k-8kYfQ", "outputId": "e6899d8e-f273-418b-8c99-96a4d1a07e5a" }, "outputs": [ { "data": { "text/html": [ "
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" ], "text/plain": [ " Unnamed: 0 number value datetime_start \\\n", "177 177 177 0.963976 2020-06-07 16:48:36.232551 \n", "302 302 302 0.955064 2020-06-07 18:11:00.667449 \n", "\n", " datetime_complete duration params_apply_tfms \\\n", "177 2020-06-07 16:49:21.393454 0 days 00:00:45.160903000 True \n", "302 2020-06-07 18:11:45.658241 0 days 00:00:44.990792000 True \n", "\n", " params_do_flip params_epochs params_flip_vert ... params_max_zoom \\\n", "177 True 10.0 False ... 1.614175 \n", "302 True 10.0 False ... 0.921689 \n", "\n", " params_model params_mult params_unfrozen_epochs \\\n", "177 densenet121 0.608727 4.0 \n", "302 densenet121 0.115708 4.0 \n", "\n", " params_unfrozen_learning_rate user_attrs_execute_time \\\n", "177 7.608088e-06 0.880459 \n", "302 6.210737e-10 0.878865 \n", "\n", " system_attrs_completed_rung_0 system_attrs_completed_rung_1 \\\n", "177 0.954955 NaN \n", "302 0.945946 NaN \n", "\n", " system_attrs_fixed_params state \n", "177 NaN COMPLETE \n", "302 NaN COMPLETE \n", "\n", "[2 rows x 23 columns]" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.sort_values(['value'], ascending=False).head(2)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can see how often transforms were applied in the trials" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "True 360\n", "False 142\n", "Name: params_apply_tfms, dtype: int64" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df['params_apply_tfms'].value_counts()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Viewing the number of trials for each model which had a value over 90 " ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 86 }, "colab_type": "code", "id": "RcoLy_V4mHHQ", "outputId": "db505940-c2a0-431b-9363-ea4d19b67983" }, "outputs": [ { "data": { "text/plain": [ "densenet121 181\n", "resnet50 9\n", "squeezenet1_0 2\n", "Name: params_model, dtype: int64" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df['params_model'][df['value'] >= 0.90].value_counts()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Filtering a bit more aggressively (value above 94) " ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "colab": {}, "colab_type": "code", "id": "tK_DCh8X8dvs" }, "outputs": [], "source": [ "df94 = df[df['value'] >= 0.94]" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "13" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ "len(df94)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "How often were transforms applied for these trials" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 69 }, "colab_type": "code", "id": "F6mpdOhmmNcj", "outputId": "f005ae61-71e7-4725-b5ed-491f738e03b1" }, "outputs": [ { "data": { "text/plain": [ "True 11\n", "False 2\n", "Name: params_apply_tfms, dtype: int64" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df94['params_apply_tfms'].value_counts()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The number of unfrozen epochs" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "4.0 6\n", "2.0 3\n", "3.0 2\n", "6.0 1\n", "5.0 1\n", "Name: params_unfrozen_epochs, dtype: int64" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df94['params_unfrozen_epochs'].value_counts()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Getting back to the validation time we can get the max, min and mean values " ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 34 }, "colab_type": "code", "id": "Ip2fSlF69Fie", "outputId": "1dd997f0-972b-413b-b104-ea7d43145fae" }, "outputs": [ { "data": { "text/plain": [ "(0.9760787487030028, 0.6313643455505371, 0.8461264789613903)" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df['user_attrs_execute_time'].max(), df['user_attrs_execute_time'].min(), df['user_attrs_execute_time'].mean()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "If we did care about reducing the execution time we could use these values to find the trial with the shortest execution time: " ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 260 }, "colab_type": "code", "id": "wni1fETjdNGl", "outputId": "11f33c8e-0dc8-4881-b003-f8b588487125" }, "outputs": [ { "data": { "text/plain": [ "96 0.837618\n", "426 0.848863\n", "394 0.849243\n", "395 0.851704\n", "438 0.852672\n", "500 0.863168\n", "344 0.875520\n", "432 0.877422\n", "302 0.878865\n", "177 0.880459\n", "473 0.884703\n", "372 0.906770\n", "294 0.907221\n", "Name: user_attrs_execute_time, dtype: float64" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df94['user_attrs_execute_time'].sort_values()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "If we were happy with slightly lower performance we could pick the study with the shortest execution time which is still achieves a f1 above 94% " ] }, { "cell_type": "code", "execution_count": 45, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 416 }, "colab_type": "code", "id": "mVCRkoR49egz", "outputId": "0a2ad20b-a291-4680-fdce-62285ffbe80d" }, "outputs": [ { "data": { "text/plain": [ "number 96\n", "value 0.945755\n", "datetime_start 2020-06-07 15:57:20.382634\n", "datetime_complete 2020-06-07 15:57:54.848296\n", "duration 0 days 00:00:34.465662\n", "params_apply_tfms False\n", "params_do_flip NaN\n", "params_epochs 9\n", "params_flip_vert NaN\n", "params_learning_rate 8.47479e-05\n", "params_max_lighting NaN\n", "params_max_rotate NaN\n", "params_max_zoom NaN\n", "params_model densenet121\n", "params_mult NaN\n", "params_unfrozen_epochs 2\n", "params_unfrozen_learning_rate 4.31178e-07\n", "user_attrs_execute_time 0.837618\n", "system_attrs_completed_rung_0 NaN\n", "system_attrs_completed_rung_1 NaN\n", "system_attrs_fixed_params NaN\n", "state COMPLETE\n", "Name: 96, dtype: object" ] }, "execution_count": 45, "metadata": { "tags": [] }, "output_type": "execute_result" } ], "source": [ "df94.loc[96]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This is a slightly artificial example but hopefully shows the possibility of logging user attributes which can then be accessed easily later without prematurely optimizing for something which may not be important. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Further reading\n", "\n", "Hopefully this post has been a helpful overview of Optuna with a somewhat realistic use case. I would recommend reading the Optuna [docs](https://optuna.readthedocs.io/en/latest/0) which covers things in much more detail. \n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## References\n", "\n", "{{'Auto ml [[auto-ml, fastai blog(https://www.fast.ai/2018/07/16/auto-ml2/#auto-ml]()' | fndetail: 1 }}\n", "\n", "{{'introducting ulmfit [nlp.fast.ai/classification/2018/05/15/introducting-ulmfit.html]()' | fndetail: 2}}\n", "\n", "{{'Takuya Akiba, Shotaro Sano, Toshihiko Yanase, Takeru Ohta,and Masanori Koyama. 2019.\n", "Optuna: A Next-generation Hyperparameter Optimization Framework. In KDD.' | fndetail: 3 }}\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "accelerator": "GPU", "colab": { "collapsed_sections": [], "machine_shape": "hm", "name": "optuna_blog.ipynb", "provenance": [] }, "kernelspec": { "display_name": "Python [conda env:fastai2]", "language": "python", "name": "conda-env-fastai2-py" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.7" }, "widgets": { "application/vnd.jupyter.widget-state+json": { "082e7c985bb2441ea7f0e3a64d5ff19b": { "model_module": "@jupyter-widgets/controls", "model_name": "HTMLModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", 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