Random Search in Scikit Learn

Hyperparameter Tuning in Python

Alex Scriven

Data Scientist

Comparing to GridSearchCV

We don't need to reinvent the wheel. Let's recall the steps for a Grid Search:

Decide an algorithm/estimator
Defining which hyperparameters we will tune
Defining a range of values for each hyperparameter
Setting a cross-validation scheme; and
Define a score function
Include extra useful information or functions

Comparing to Grid Search

There is only one difference:

Step 7 = Decide how many samples to take (then sample)

That's it! (mostly)

Comparing Scikit Learn Modules

The modules are similar too:

GridSearchCV:

sklearn.model_selection.GridSearchCV(estimator, param_grid, 
        scoring=None, fit_params=None, 
        n_jobs=None,
        refit=True, cv='warn', verbose=0, 
        pre_dispatch='2*n_jobs', 
        error_score='raise-deprecating',
        return_train_score='warn')

RandomizedSearchCV:

sklearn.model_selection.RandomizedSearchCV(estimator, 
        param_distributions, n_iter=10, 
        scoring=None, fit_params=None, 
        n_jobs=None, refit=True, 
        cv='warn', verbose=0, 
        pre_dispatch='2*n_jobs',
        random_state=None, 
        error_score='raise-deprecating', 
        return_train_score='warn')

Key differences

Two key differences:

n_iter which is the number of samples for the random search to take from your grid. In the previous example you did 300.
param_distributions is slightly different from param_grid, allowing optional ability to set a distribution for sampling.
- The default is all combinations have equal chance to be chosen.

Build a RandomizedSearchCV Object

Now we can build a random search object just like the grid search, but with our small change:

# Set up the sample space
learn_rate_list = np.linspace(0.001,2,150)
min_samples_leaf_list = list(range(1,51))

# Create the grid
parameter_grid = {
    'learning_rate' : learn_rate_list,
    'min_samples_leaf' : min_samples_leaf_list}


# Define how many samples
number_models = 10

Build a RandomizedSearchCV Object

Now we can build the object

# Create a random search object
random_GBM_class = RandomizedSearchCV(
    estimator = GradientBoostingClassifier(),
    param_distributions = parameter_grid,
    n_iter = number_models,
    scoring='accuracy',
    n_jobs=4, 
    cv = 10,
    refit=True, 
    return_train_score = True)
# Fit the object to our data
random_GBM_class.fit(X_train, y_train)

Analyze the output

The output is exactly the same!

How do we see what hyperparameter values were chosen?

The cv_results_ dictionary (in the relevant param_ columns)!

Extract the lists:

rand_x = list(random_GBM_class.cv_results_['param_learning_rate'])
rand_y = list(random_GBM_class.cv_results_['param_min_samples_leaf'])

Analyze the output

Build our visualization:

# Make sure we set the limits of Y and X appriately
x_lims = [np.min(learn_rate_list), np.max(learn_rate_list)]
y_lims = [np.min(min_samples_leaf_list), np.max(min_samples_leaf_list)]

# Plot grid results    
plt.scatter(rand_y, rand_x, c=['blue']*10)
plt.gca().set(xlabel='learn_rate', ylabel='min_samples_leaf',
                title='Random Search Hyperparameters')
plt.show()

Analyze the output

A similar graph to before:

random graph coverage

Let's practice!

Hyperparameter Tuning in Python