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"source": [
"# Perform an Automatic Hyperparameter Search\n",
"\n",
"In this tutorial, we use AutoNLU to detect all categories and the corresponding opinions of laptop reviews, similar to tutorial 04. But in this tutorial, we will show how AutoNLU can automatically search for the best hyperparameters (called automatic HPO) to produce the highest performance models."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"%load_ext tensorboard\n",
"\n",
"!pip install xmltodict -q"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"import autonlu\n",
"from autonlu import AutoMl\n",
"from autonlu.automl import Categorical, FloatRange, IntRange\n",
"import pandas as pd\n",
"import numpy as np\n",
"\n",
"import requests\n",
"import xmltodict"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"User name/Email: admin\n",
"Password: ········\n"
]
}
],
"source": [
"autonlu.login()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Download data and prepare a dataset\n",
"We start by downloading the SemEval laptop dataset and convert the XML file into lists as needed by AutoNLU"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"def download_data():\n",
" url = \"https://raw.githubusercontent.com/davidsbatista/Aspect-Based-Sentiment-Analysis/master/datasets/ABSA-SemEval2015/ABSA-15_Laptops_Train_Data.xml\"\n",
" response = requests.get(url)\n",
" data = xmltodict.parse(response.content)\n",
" return data"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"def parse_data(data):\n",
" X, Y = [], []\n",
"\n",
" for review in data[\"Reviews\"][\"Review\"]:\n",
" sentences = review[\"sentences\"][\"sentence\"]\n",
" sentences = [sentences] if \"text\" in sentences else sentences\n",
"\n",
" for entry in sentences:\n",
" text = entry[\"text\"]\n",
" \n",
" aspects = []\n",
" if \"Opinions\" in entry:\n",
" aspect_list = entry[\"Opinions\"][\"Opinion\"]\n",
" aspect_list = [aspect_list] if \"@category\" in aspect_list else aspect_list\n",
" for aspect in aspect_list:\n",
" category = aspect[\"@category\"]\n",
" sentiment = aspect[\"@polarity\"]\n",
" aspects.append([category, sentiment])\n",
" \n",
" X.append(text)\n",
" Y.append(aspects)\n",
" return X, Y"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"This computer is really fast and I'm shocked as to how easy it is to get used to...\n",
"[['LAPTOP#OPERATION_PERFORMANCE', 'positive'], ['LAPTOP#USABILITY', 'positive']]\n"
]
}
],
"source": [
"data = download_data()\n",
"X, Y = parse_data(data)\n",
"\n",
"print(X[5])\n",
"print(Y[5])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Searching for the best model\n",
"\n",
"Now we want to find the best model over a certain set of options.\n",
"\n",
"We specify that the model to be used should be `roberta-base`, `bert-base-uncased` and `albert-base-v2` in their OMI variant. In addition, we specify that we want to search for a good learning rate in a range of 1e-6 to 1e-3.\n",
"\n",
"We use the `model_arguments` to set the `standard_label` to `\"NONE\"` (something we would usually specify in the constructor of the `Model` class).\n",
"\n",
"Since we want to speed up the search for this tutorial, we will see how far we can get by using only 1000 optimization steps. Therefore, we deactivate early stopping and set `nb_opti_steps` to 1000 (both options that we would usually give as arguments to the `.train` method of `Model`)\n",
"\n",
"Then we load the dataset and start a hyperparameter optimization for 10 minutes"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
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"name": "stderr",
"output_type": "stream",
"text": [
"\u001b[32m[I 2022-02-14 14:08:46,424]\u001b[0m A new study created in memory with name: tutorial_09\u001b[0m\n",
"/home/paethon/git/py39env/lib/python3.9/site-packages/optuna/progress_bar.py:47: ExperimentalWarning: Progress bar is experimental (supported from v1.2.0). The interface can change in the future.\n",
" self._init_valid()\n"
]
},
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"0it [00:00, ?it/s]"
]
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"/home/paethon/git/py39env/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1248: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
" _warn_prf(average, modifier, msg_start, len(result))\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\u001b[32m[I 2022-02-14 14:10:20,007]\u001b[0m Trial 0 finished with value: 0.9897828863346104 and parameters: {'model_folder': 'bert-base-uncased#omi', 'learning_rate': 6.251373574521755e-05}. Best is trial 0 with value: 0.9897828863346104.\u001b[0m\n",
"\u001b[32m[I 2022-02-14 14:11:56,406]\u001b[0m Trial 1 finished with value: 0.9928338299985809 and parameters: {'model_folder': 'roberta-base#omi', 'learning_rate': 0.0003967605077052988}. Best is trial 1 with value: 0.9928338299985809.\u001b[0m\n",
"\u001b[32m[I 2022-02-14 14:13:31,678]\u001b[0m Trial 2 finished with value: 0.9908471690080886 and parameters: {'model_folder': 'bert-base-uncased#omi', 'learning_rate': 0.0008123245085588687}. Best is trial 1 with value: 0.9928338299985809.\u001b[0m\n",
"\u001b[32m[I 2022-02-14 14:13:49,994]\u001b[0m Trial 3 pruned. \u001b[0m\n",
"\u001b[32m[I 2022-02-14 14:14:08,367]\u001b[0m Trial 4 pruned. \u001b[0m\n",
"\u001b[32m[I 2022-02-14 14:14:26,246]\u001b[0m Trial 5 pruned. \u001b[0m\n"
]
},
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"name": "stderr",
"output_type": "stream",
"text": [
"/home/paethon/git/py39env/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1248: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
" _warn_prf(average, modifier, msg_start, len(result))\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\u001b[32m[I 2022-02-14 14:16:00,167]\u001b[0m Trial 6 finished with value: 0.9891443167305236 and parameters: {'model_folder': 'bert-base-uncased#omi', 'learning_rate': 3.489018845491386e-05}. Best is trial 1 with value: 0.9928338299985809.\u001b[0m\n",
"\u001b[32m[I 2022-02-14 14:16:18,216]\u001b[0m Trial 7 pruned. \u001b[0m\n",
"\u001b[32m[I 2022-02-14 14:17:47,187]\u001b[0m Trial 8 finished with value: 0.9907762168298567 and parameters: {'model_folder': 'albert-base-v2#omi', 'learning_rate': 0.0002661901888489054}. Best is trial 1 with value: 0.9928338299985809.\u001b[0m\n",
"\u001b[32m[I 2022-02-14 14:18:01,725]\u001b[0m Trial 9 pruned. \u001b[0m\n",
"\u001b[32m[I 2022-02-14 14:18:16,152]\u001b[0m Trial 10 pruned. \u001b[0m\n",
"\u001b[32m[I 2022-02-14 14:18:35,808]\u001b[0m Trial 11 pruned. \u001b[0m\n",
"\u001b[32m[I 2022-02-14 14:18:51,469]\u001b[0m Trial 12 pruned. \u001b[0m\n",
"Study statistics: \n",
" Number of finished trials: 13\n",
" Number of pruned trials: 8\n",
" Number of complete trials: 5\n",
"Best trial:\n",
" Value: 0.9928338299985809\n",
" Params: \n",
" model_folder: roberta-base#omi\n",
" learning_rate: 0.0003967605077052988\n"
]
}
],
"source": [
"automl = AutoMl(\"tutorial_09\",\n",
" hyperparameters=[\n",
" Categorical(\"model_folder\", choices=[\"roberta-base#omi\", \"bert-base-uncased#omi\", \"albert-base-v2#omi\"]),\n",
" FloatRange(\"learning_rate\", low=1e-6, high=1e-3, log=True)\n",
" ],\n",
" model_arguments={\"standard_label\": \"NONE\"},\n",
" train_arguments={\n",
" \"do_early_stopping\": False,\n",
" \"nb_opti_steps\": 1000\n",
" })\n",
"automl.load_dataset(X, Y)\n",
"model = automl.create(timeout=10*60, verbose=True)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"`model` now contains the best model we could find, and you can also see the hyperparameters that were used to train this model. So let's test the model qualitatively with a few samples:"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
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"name": "stdout",
"output_type": "stream",
"text": [
"The device is great! - [['LAPTOP#GENERAL', 'positive']]\n",
"\n",
"The battery live is bad, but they wont replace it! - [['BATTERY#OPERATION_PERFORMANCE', 'negative'], ['BATTERY#QUALITY', 'negative'], ['SUPPORT#QUALITY', 'negative']]\n",
"\n",
"Wow, this laptop has an incredible long battery life and blazing speed. - [['BATTERY#OPERATION_PERFORMANCE', 'positive'], ['LAPTOP#OPERATION_PERFORMANCE', 'positive']]\n",
"\n"
]
}
],
"source": [
"questions = [\n",
" \"The device is great!\",\n",
" \"The battery live is bad, but they wont replace it!\",\n",
" \"Wow, this laptop has an incredible long battery life and blazing speed.\"\n",
"]\n",
"\n",
"tags = model.predict(questions)\n",
"\n",
"for i, q in enumerate(questions):\n",
" print(f\"{q} - {tags[i]}\\n\")\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Seems like even with only 1000 optimization steps the trained model is doing really well!"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Optuna\n",
"\n",
"Internally, AutoNLU uses https://optuna.org/ with custom designed pruners. The attribute `.study` contains an Optuna study and can for example be used to visualize the hyperparameter search [See here for a tutorial](https://optuna.readthedocs.io/en/stable/tutorial/10_key_features/005_visualization.html#sphx-glr-tutorial-10-key-features-005-visualization-py)"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [],
"source": [
"import optuna"
]
},
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