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.
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
Check how you can visualize the runs as they are running, log the parameters tried at every run, and more, with the Scikit-Optimize + Neptune integration.
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.
Recommended for you
️ The Best Tools to Visualize Metrics and Hyperparameters of Machine Learning Experiments
️ Hyperparameter Tuning in Python: a Complete Guide
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!
