from checknotebookoutput import checkNotebookOutput
from checkexperimentresult import checkExperimentResult
from checkexperimentresult import check_experiment_model_explanation_of_best_run
from download_run_files import download_run_files
# Download files for the remote run.
download_run_files(experiment_names=[
'automl-bikeshareforecasting', 'automl-bikeshareforecasting_test'
],
download_all_runs=True)
checkExperimentResult(experiment_name='automl-bikeshareforecasting',
expected_num_iteration='1000',
expected_minimum_score=0.01,
expected_maximum_score=0.3,
metric_name='normalized_root_mean_squared_error',
absolute_minimum_score=0.0,
absolute_maximum_score=1.0)
check_experiment_model_explanation_of_best_run(
experiment_name='automl-bikeshareforecasting')
# Check the output cells of the notebook.
checkNotebookOutput(
"auto-ml-forecasting-bike-share.nbconvert.ipynb"
if len(sys.argv) < 2 else sys.argv[1],
"warning[except]retrying[except]UserWarning: Matplotlib is building the font cache"
"[except]warning: a newer version of conda exists"
"[except]UserWarning: Starting from version 2.2.1, "
"the library file in distribution wheels for macOS is built by the Apple Clang"
from checknotebookoutput import checkNotebookOutput
from checkexperimentresult import checkExperimentResult
checkExperimentResult(experiment_name='automl-classification-deployment',
expected_num_iteration='10',
expected_minimum_score=0.5,
metric_name='AUC_weighted')
# Check the output cells of the notebook.
checkNotebookOutput('auto-ml-classification-with-deployment.nbconvert.ipynb',
'warning', 'nan')
from checknotebookoutput import checkNotebookOutput
from checkexperimentresult import checkExperimentResult
from checkcelloutput import checkCellOutput
checkExperimentResult(experiment_name='automl-local-regression',
expected_num_iteration='10',
expected_minimum_score=0.45,
metric_name='spearman_correlation',
absolute_minimum_score=-1.0)
# Check the output cells of the notebook.
checkNotebookOutput('auto-ml-regression.nbconvert.ipynb', 'warning', 'nan')
# Check expected cell output contents.
expected_cells = [
[], ["Found the config file in: "], [], [],
[
"Running on local machine\n", "Parent Run ID: ",
"Current status: DatasetCrossValidationSplit. Generating individually featurized CV splits.\n",
"Current status: ModelSelection. Beginning model selection.\n", "\n",
"***********************************************************************"
+ "*****************************\n",
"ITERATION: The iteration being evaluated.\n",
"PIPELINE: A summary description of the pipeline being evaluated.\n",
"DURATION: Time taken for the current iteration.\n",
"METRIC: The result of computing score on the fitted pipeline.\n",
"BEST: The best observed score thus far.\n",
"***********************************************************************"
+ "*****************************\n", "\n",
" ITERATION PIPELINE DURATION"
+ " METRIC BEST\n", " 0 ", " 1 ",
from checknotebookoutput import checkNotebookOutput
from checkexperimentresult import checkExperimentResult
checkExperimentResult(experiment_name='non_sample_weight_experiment',
expected_num_iteration='10',
expected_minimum_score=0.3,
metric_name='AUC_weighted')
checkExperimentResult(experiment_name='sample_weight_experiment',
expected_num_iteration='10',
expected_minimum_score=0.3,
metric_name='AUC_weighted')
# Check the output cells of the notebook.
checkNotebookOutput('auto-ml-sample-weight.nbconvert.ipynb', 'warning', 'nan')
from checknotebookoutput import checkNotebookOutput
from checkexperimentresult import checkExperimentResult
checkExperimentResult(experiment_name='automl-dataprep-local',
expected_num_iteration='2',
expected_minimum_score=0.5,
metric_name='AUC_weighted')
# Check the output cells of the notebook.
checkNotebookOutput('auto-ml-dataprep.nbconvert.ipynb', 'warning', 'nan')