# Create Neptune Callback
import neptunecontrib.monitoring.skopt as skopt_utils
neptune_callback = skopt_utils.NeptuneCallback()
# Run the skopt minimize function with the Neptune Callback
results = skopt.forest_minimize(objective,
space,
n_calls=25,
n_random_starts=10,
callback=[neptune_callback])
## Step 4: Log best parameter configuration, best score and diagnostic plots
skopt_utils.log_results(results)
## Step 5: Stop logging and Explore results in the Neptune UI
# tests
exp = neptune.get_experiment()
neptune.stop()
# tests
all_logs = exp.get_logs()
## check logs
correct_logs = [
# Create Neptune Callback
import neptunecontrib.monitoring.skopt as skopt_utils
neptune_callback = skopt_utils.NeptuneCallback()
# Run the skopt minimize function with the Neptune Callback
results = skopt.forest_minimize(objective,
space,
n_calls=25,
n_random_starts=10,
callback=[neptune_callback])
## Step 3: Log best parameter configuration, best score and diagnostic plots
skopt_utils.log_results(results)
## Step 4: Stop logging and Explore results in the Neptune UI
neptune.stop()
# Logging BayesSearchCV
## Prepare the data and initialize BayesSearchCV optimizer
from skopt import BayesSearchCV
from skopt.space import Real, Categorical, Integer
from sklearn.datasets import load_iris
from sklearn.svm import SVC
from sklearn.model_selection import train_test_split