Hyperparameter Search for Rasa NLU

This repo provides a setup for doing hyperparameter search for the best configuration of the pipeline components. This can either be done locally or on a cluster. It uses hyperopt to do the actual work. This is based on a template here.

For local development, you can run this without docker or mongodb for fast debugging.

This repo also includes a Github action for running nlu-hyperopt in a workflow.

Installation

Development

pip install -r requirements.txt

On a brand new ubuntu 16 VPS (e.g. on google cloud)

clone this repo
sudo bash install/install.sh

This will install Docker and docker-compose.

Quickstart

To run a quick test whether everything works, run docker-compose up. This will run a default experiment with the provided sample configuration and data.

How to Use

Step 1: Write a Template Configuration

Here is an example. Replace the parameters you want to search over with variable names:

language: en
pipeline:
- name: "intent_featurizer_count_vectors"
  analyzer: char_wb
  max_df: {max_df}
  min_ngrams: 2
  max_ngrams: {max_ngrams}
- name: "intent_classifier_tensorflow_embedding"
  epochs: {epochs}

Save this at data/template_config.yml

Step 2: Define a Search Space

You need to define a search space in the nlu_hyperopt/space.py file.

from hyperopt import hp
from hyperopt.pyll.base import scope

search_space = {
    'epochs': hp.qloguniform('epochs', 0, 4, 2),
    'max_df': hp.uniform('max_df', 1, 2),
    'max_ngrams': scope.int(hp.quniform('max_ngram', 3, 9, 1))
    }
}

Check the hyperopt docs for details on how to define a space.

Step 3: Provide Your Training and Test data

Put your training and test data in data/{train, validation}.md You can do a train-test split in Rasa NLU with:

from rasa_nlu.training_data import load_data
data = load_data('all_my_data.md')
train, test = data.train_test_split(train_frac=0.7)

and you can write markdown by writing the output of train.as_markdown() to a file.

Step 4: Configure your experiment

This table lists all the options you can configure through environment variables:

Environment Variable	Description
INPUT_MAX_EVALS	Maximum number of evaluations which are run during the hyperparameter search
INPUT_DATA_DIRECTORY	Directory which contains the files `train.md`,`validation.md`, and `template_config.yml` (default: `./data`)
INPUT_MODEL_DIRECTORY	Directory which contains the trained models (default: `./models`)
INPUT_TARGET_METRIC	Target metric for the evaluation. You can choose between `f1_score`, `accuracy`, `precision`, and `threshold_loss`.
INPUT_THRESHOLD	Only used by `threshold_loss`. Sets the threshold which the confidence of the correct intent has to be above or wrong predictions have to be below (default: 0.8).
INPUT_ABOVE_BELOW_WEIGHT	Only used by `threshold_loss` (default: 0.5). This loss function penalizes incorrect predictions above the given threshold and correct predictions below a certain threshold. With the `ABOVE_BELOW_WEIGHT` you can configure the balance between these penalties. A larger value means that incorrect predictions above the threshold are penalized more heavily than correct predictions below the threshold.

Step 5: Start your experiment

Run it locally

To quickly test on your local machine without docker or mongodb:

python -m nlu_hyperopt.app

Running on a Server

Set the experiment name and max evaluations in your .env file

Here is an example:

INPUT_EXPERIMENT_KEY=default-experiment
INPUT_MAX_EVALS=100
INPUT_MONGO_URL=mongodb:27017/nlu-hyperopt

To run:

docker-compose up -d --scale hyperopt-worker=4

It's up to you how many workers you want to run. A good first guess is to set it to the numer of CPUs your machine has.

Experiment Results

The best configuration is printed by the hyperopt-master at the end of the the hyperparameter search. All evaluation results are stored in the mongodb immediately after they run. To see the results while the optimization is running, open a mongo shell session in the mongo container:

Run this command to see the experiment with the lowest value of the loss so far

use nlu-hyperopt
db.jobs.find({"exp_key" : "default-experiment", "result.loss":{$exists: 1}}).sort({"result.loss": 1}).limit(1).pretty()

replacing the value of the exp_key with your experiment name.

Loss Functions

f1_score

This loss is defined as 1 - f, where f is the f1 score of the intent evaluation on your test data.

accuracy

This loss is defined as 1 - f, where f is the accuracy score of the intent evaluation on your test data.

precision

This loss is defined as 1 - f, where f is the precision score of the intent evaluation on your test data.

threshold_loss

This loss is defined as

l * incorrect_above + (1-l) * correct_below

where incorrect_above describes the fraction of incorrect predictions above a certain threshold and correct_below describes the fraction of correct predictions below a certain threshold. Threshold and l can be configured through environment variables.

Github Action

Take note of Github Action's usage limit of 360 minutes per job. Keep this in mind when choosing max_evals.

Inputs

search_space: Required Path to your search space definition (space.py)
data_directory: Required see INPUT_DATA_DIRECTORY
max_evals: see INPUT_MAX_EVALS
target_metric: see INPUT_TARGET_METRIC
threshold: see INPUT_THRESHOLD
above_below_weight: see INPUT_ABOVE_BELOW_WEIGHT

Example usage

jobs:
  nlu-hyperopt:
    name: NLU hyperparameter optimization
    runs-on: ubuntu-latest
    steps:
    - uses: actions/checkout@v2
    - uses: RasaHQ/nlu-hyperopt@v1
      name: Run NLU Hyperoptimization
      with:
        max_evals: 50
        target_metric: f1_score
        data_directory: ${{ github.workspace }}/data
        search_space: ${{ github.workspace }}/nlu_hyperopt/space.py

JulianGerhard21/nlu-hyperopt