print("Best model found so far:")
pprint(tuning_job_result['BestTrainingJob'])
else:
print("No training jobs have reported results yet.")
# ### Fetch all results as DataFrame
#
# You can list hyperparameters and objective metrics of all training jobs and pick up the training job with the best objective metric.
# In[ ]:
import pandas as pd
tuner = sagemaker.HyperparameterTuningJobAnalytics(job_name)
full_df = tuner.dataframe()
if len(full_df) > 0:
df = full_df[full_df['FinalObjectiveValue'] > -float('inf')]
if len(df) > 0:
df = df.sort_values('FinalObjectiveValue', ascending=is_minimize)
print("Number of training jobs with valid objective: %d" % len(df))
print({
"lowest": min(df['FinalObjectiveValue']),
"highest": max(df['FinalObjectiveValue'])
})
pd.set_option('display.max_colwidth',
-1) # Don't truncate TrainingJobName
else:
print("No training jobs have reported valid results yet.")
