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How to Do Hyperparameter Tuning on Any Python Script in 3 Easy Steps

You wrote a Python script that trains and evaluates your machine learning model. Now, you would like to automatically tune hyperparameters to improve its performance?

I got you!

In this article, I will show you how to convert your script into an objective function that can be optimized with any hyperparameter optimization library.  

hyperparameter optimization

It will take just 3 steps and you will be tuning model parameters like there is no tomorrow.

Ready? 

Let’s go!

I suppose your main.py script looks something like this one:

import pandas as pd
import lightgbm as lgb
from sklearn.model_selection import train_test_split

data = pd.read_csv('data/train.csv', nrows=10000)
X = data.drop(['ID_code', 'target'], axis=1)
y = data['target']
(X_train, X_valid, 
y_train, y_valid )= train_test_split(X, y, test_size=0.2, random_state=1234)

train_data = lgb.Dataset(X_train, label=y_train)
valid_data = lgb.Dataset(X_valid, label=y_valid, reference=train_data)

params = {'objective': 'binary',
          'metric': 'auc',
          'learning_rate': 0.4,
          'max_depth': 15,
          'num_leaves': 20,
          'feature_fraction': 0.8,
          'subsample': 0.2}

model = lgb.train(params, train_data,
                  num_boost_round=300,
                  early_stopping_rounds=30,
                  valid_sets=[valid_data],
                  valid_names=['valid'])

score = model.best_score['valid']['auc']
print('validation AUC:', score)

Step 1: Decouple search parameters from code

Take the parameters that you want to tune and put them in a dictionary at the top of your script. By doing that you effectively decouple search parameters from the rest of the code.

import pandas as pd
import lightgbm as lgb
from sklearn.model_selection import train_test_split

SEARCH_PARAMS = {'learning_rate': 0.4,
                 'max_depth': 15,
                 'num_leaves': 20,
                 'feature_fraction': 0.8,
                 'subsample': 0.2}

data = pd.read_csv('../data/train.csv', nrows=10000)
X = data.drop(['ID_code', 'target'], axis=1)
y = data['target']
X_train, X_valid, y_train, y_valid = train_test_split(X, y, test_size=0.2, random_state=1234)

train_data = lgb.Dataset(X_train, label=y_train)
valid_data = lgb.Dataset(X_valid, label=y_valid, reference=train_data)

params = {'objective': 'binary',
          'metric': 'auc',
          **SEARCH_PARAMS}

model = lgb.train(params, train_data,
                  num_boost_round=300,
                  early_stopping_rounds=30,
                  valid_sets=[valid_data],
                  valid_names=['valid'])

score = model.best_score['valid']['auc']
print('validation AUC:', score)

Step 2: Wrap training and evaluation into a function

Now, you can put the entire training and evaluation logic inside of a train_evaluate function. This function takes parameters as input and outputs the validation score. 

import pandas as pd
import lightgbm as lgb
from sklearn.model_selection import train_test_split

SEARCH_PARAMS = {'learning_rate': 0.4,
                 'max_depth': 15,
                 'num_leaves': 20,
                 'feature_fraction': 0.8,
                 'subsample': 0.2}


def train_evaluate(search_params):
    data = pd.read_csv('../data/train.csv', nrows=10000)
    X = data.drop(['ID_code', 'target'], axis=1)
    y = data['target']
    X_train, X_valid, y_train, y_valid = train_test_split(X, y, test_size=0.2, random_state=1234)

    train_data = lgb.Dataset(X_train, label=y_train)
    valid_data = lgb.Dataset(X_valid, label=y_valid, reference=train_data)

    params = {'objective': 'binary',
              'metric': 'auc',
              **search_params}

    model = lgb.train(params, train_data,
                      num_boost_round=300,
                      early_stopping_rounds=30,
                      valid_sets=[valid_data],
                      valid_names=['valid'])

    score = model.best_score['valid']['auc']
    return score


if __name__ == '__main__':
    score = train_evaluate(SEARCH_PARAMS)
    print('validation AUC:', score)

Step 3: Run hypeparameter tuning script

We are almost there.

All you need to do now is to use this train_evaluate function as an objective for the black-box optimization library of your choice. 

I will use Scikit Optimize which I have described in great detail in another article but you can use any hyperparameter optimization library out there.


LEARN MORE
Explore our integration with Scikit-Optimize


In a nutshell I:

  • define the search SPACE,
  • create the objective function that will be minimized,
  • run the optimization via skopt.forest_minimize function.

In this example, I will try 100 different configurations starting with 10 randomly chosen parameter sets.

import skopt

from script_step2 import train_evaluate

SPACE = [
    skopt.space.Real(0.01, 0.5, name='learning_rate', prior='log-uniform'),
    skopt.space.Integer(1, 30, name='max_depth'),
    skopt.space.Integer(2, 100, name='num_leaves'),
    skopt.space.Real(0.1, 1.0, name='feature_fraction', prior='uniform'),
    skopt.space.Real(0.1, 1.0, name='subsample', prior='uniform')]


@skopt.utils.use_named_args(SPACE)
def objective(**params):
    return -1.0 * train_evaluate(params)


results = skopt.forest_minimize(objective, SPACE, n_calls=30, n_random_starts=10)
best_auc = -1.0 * results.fun
best_params = results.x

print('best result: ', best_auc)
print('best parameters: ', best_params)

This is it.

The results object contains information about the best score and parameters that produced it.

Note:

If you want to visualize your training and save diagnostic charts after it finishes you can add one callback and one function call to log every hyperparameter search to Neptune. Just use this helper function from neptune-contrib library.

import neptune
import neptunecontrib.monitoring.skopt as sk_utils
import skopt

from script_step2 import train_evaluate

neptune.init('jakub-czakon/blog-hpo')
neptune.create_experiment('hpo-on-any-script', upload_source_files=['*.py'])

SPACE = [
    skopt.space.Real(0.01, 0.5, name='learning_rate', prior='log-uniform'),
    skopt.space.Integer(1, 30, name='max_depth'),
    skopt.space.Integer(2, 100, name='num_leaves'),
    skopt.space.Real(0.1, 1.0, name='feature_fraction', prior='uniform'),
    skopt.space.Real(0.1, 1.0, name='subsample', prior='uniform')]


@skopt.utils.use_named_args(SPACE)
def objective(**params):
    return -1.0 * train_evaluate(params)


monitor = sk_utils.NeptuneMonitor()
results = skopt.forest_minimize(objective, SPACE, n_calls=100, n_random_starts=10, callback=[monitor])
sk_utils.log_results(results)

neptune.stop()

Now, when you run your parameter sweep you will see the following:

optuna monitoring

Check out the skopt hyperparameter sweep experiment with all the code, charts and results.


SEE ALSO
➡️ The Best Tools to Visualize Metrics and Hyperparameters of Machine Learning Experiments
➡️ Hyperparameter Tuning in Python: a Complete Guide 2020


Final thoughts

In this article, you’ve learned how to optimize hyperparameters of pretty much any Python script in just 3 steps. 

Hopefully, with this knowledge, you will build better machine learning models with less effort.

Happy training!

Jakub Czakon Senior Data Scientist

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ML Experiment Tracking: What It Is, Why It Matters, and How to Implement It

Jakub Czakon | Posted November 26, 2020

Let me share a story that I’ve heard too many times.

”… We were developing an ML model with my team, we ran a lot of experiments and got promising results…

…unfortunately, we couldn’t tell exactly what performed best because we forgot to save some model parameters and dataset versions…

…after a few weeks, we weren’t even sure what we have actually tried and we needed to re-run pretty much everything”

– unfortunate ML researcher.

And the truth is, when you develop ML models you will run a lot of experiments.

Those experiments may:

  • use different models and model hyperparameters
  • use different training or evaluation data, 
  • run different code (including this small change that you wanted to test quickly)
  • run the same code in a different environment (not knowing which PyTorch or Tensorflow version was installed)

And as a result, they can produce completely different evaluation metrics. 

Keeping track of all that information can very quickly become really hard. Especially if you want to organize and compare those experiments and feel confident that you know which setup produced the best result.  

This is where ML experiment tracking comes in. 

Continue reading ->

How to Track Hyperparameters of Machine Learning Models?

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HyperBand and BOHB: Understanding State of the Art Hyperparameter Optimization Algorithms

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Optuna Hyperopt

Optuna vs Hyperopt: Which Hyperparameter Optimization Library Should You Choose?

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Hyperparameter Tuning in Python: a Complete Guide 2021

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