def _test_confidence_intervals_all(dataset, group_compose=False):
pipeline = VarPipeline(group_compose=group_compose,
confidence_intervals=True)
pipeline.write_pipeline()
pipeline.fit_produce_all(dataset)
pipeline.delete_pipeline()
def _test_serialize(dataset, group_compose=False):
pipeline = VarPipeline(group_compose=group_compose)
pipeline.write_pipeline()
pipeline.fit_serialize(dataset)
pipeline.deserialize_score(dataset)
pipeline.delete_pipeline()
pipeline.delete_serialized_pipeline()
def generate_pipelines(gpu=False):
gpu_prims = [
"d3m.primitives.classification.inceptionV3_image_feature.Gator",
"d3m.primitives.object_detection.retina_net.ObjectDetectionRN",
"d3m.primitives.time_series_classification.convolutional_neural_net.LSTM_FCN",
"d3m.primitives.feature_extraction.nk_sent2vec.Sent2Vec",
"d3m.primitives.remote_sensing.mlp.MlpClassifier"
]
prims_to_pipelines = {
"d3m.primitives.data_cleaning.column_type_profiler.Simon":
[(SimonPipeline(), ('185_baseball_MIN_METADATA', ))],
"d3m.primitives.data_cleaning.geocoding.Goat_forward":
[(GoatForwardPipeline(), ('LL0_acled_reduced_MIN_METADATA', ))],
"d3m.primitives.data_cleaning.geocoding.Goat_reverse":
[(GoatReversePipeline(), ('LL0_acled_reduced_MIN_METADATA', ))],
"d3m.primitives.feature_extraction.nk_sent2vec.Sent2Vec":
[(Sent2VecPipeline(),
('LL1_TXT_CLS_apple_products_sentiment_MIN_METADATA', ))],
"d3m.primitives.clustering.k_means.Sloth": [
(StorcPipeline(), ('66_chlorineConcentration_MIN_METADATA', ))
],
"d3m.primitives.clustering.hdbscan.Hdbscan": [
(HdbscanPipeline(), ('SEMI_1044_eye_movements_MIN_METADATA', ))
],
"d3m.primitives.clustering.spectral_graph.SpectralClustering": [
(SpectralClusteringPipeline(),
('SEMI_1044_eye_movements_MIN_METADATA', ))
],
"d3m.primitives.dimensionality_reduction.t_distributed_stochastic_neighbor_embedding.Tsne":
[(TsnePipeline(), ('SEMI_1044_eye_movements_MIN_METADATA', ))],
"d3m.primitives.time_series_classification.k_neighbors.Kanine": [
(KaninePipeline(), ('66_chlorineConcentration_MIN_METADATA', ))
],
"d3m.primitives.time_series_classification.convolutional_neural_net.LSTM_FCN":
[(LstmFcnPipeline(), (
'66_chlorineConcentration_MIN_METADATA',
"LL1_Adiac_MIN_METADATA",
"LL1_ArrowHead_MIN_METADATA",
"LL1_Cricket_Y_MIN_METADATA",
"LL1_ECG200_MIN_METADATA",
"LL1_ElectricDevices_MIN_METADATA",
"LL1_FISH_MIN_METADATA",
"LL1_FaceFour_MIN_METADATA",
"LL1_HandOutlines_MIN_METADATA",
"LL1_Haptics_MIN_METADATA",
"LL1_ItalyPowerDemand_MIN_METADATA",
"LL1_Meat_MIN_METADATA",
"LL1_OSULeaf_MIN_METADATA",
)),
(LstmFcnPipeline(attention_lstm=True), (
'66_chlorineConcentration_MIN_METADATA',
"LL1_Adiac_MIN_METADATA",
"LL1_ArrowHead_MIN_METADATA",
"LL1_Cricket_Y_MIN_METADATA",
"LL1_ECG200_MIN_METADATA",
"LL1_ElectricDevices_MIN_METADATA",
"LL1_FISH_MIN_METADATA",
"LL1_FaceFour_MIN_METADATA",
"LL1_HandOutlines_MIN_METADATA",
"LL1_Haptics_MIN_METADATA",
"LL1_ItalyPowerDemand_MIN_METADATA",
"LL1_Meat_MIN_METADATA",
"LL1_OSULeaf_MIN_METADATA",
))],
"d3m.primitives.time_series_forecasting.vector_autoregression.VAR": [
(VarPipeline(), (
'56_sunspots_MIN_METADATA',
'56_sunspots_monthly_MIN_METADATA',
'LL1_736_population_spawn_MIN_METADATA',
'LL1_736_stock_market_MIN_METADATA',
'LL1_terra_canopy_height_long_form_s4_100_MIN_METADATA',
"LL1_terra_canopy_height_long_form_s4_90_MIN_METADATA",
"LL1_terra_canopy_height_long_form_s4_80_MIN_METADATA",
"LL1_terra_canopy_height_long_form_s4_70_MIN_METADATA",
'LL1_terra_leaf_angle_mean_long_form_s4_MIN_METADATA',
'LL1_PHEM_Monthly_Malnutrition_MIN_METADATA',
'LL1_PHEM_weeklyData_malnutrition_MIN_METADATA',
))
],
"d3m.primitives.time_series_forecasting.lstm.DeepAR": [
(DeepARPipeline(prediction_length=21, context_length=21),
('56_sunspots_MIN_METADATA', )),
(DeepARPipeline(prediction_length=38, context_length=38),
('56_sunspots_monthly_MIN_METADATA', )),
(DeepARPipeline(prediction_length=60, context_length=30),
('LL1_736_population_spawn_MIN_METADATA', )),
(DeepARPipeline(prediction_length=34, context_length=17),
('LL1_736_stock_market_MIN_METADATA', )),
],
"d3m.primitives.time_series_forecasting.feed_forward_neural_net.NBEATS":
[
(NBEATSPipeline(prediction_length=21),
('56_sunspots_MIN_METADATA', )),
(NBEATSPipeline(prediction_length=38),
('56_sunspots_monthly_MIN_METADATA', )),
(NBEATSPipeline(prediction_length=60),
('LL1_736_population_spawn_MIN_METADATA', )),
(NBEATSPipeline(prediction_length=34),
('LL1_736_stock_market_MIN_METADATA', )),
],
"d3m.primitives.object_detection.retina_net.ObjectDetectionRN": [
(ObjectDetectionRNPipeline(),
('LL1_tidy_terra_panicle_detection_MIN_METADATA',
'LL1_penn_fudan_pedestrian_MIN_METADATA'))
],
"d3m.primitives.data_cleaning.data_cleaning.Datacleaning": [
(DataCleaningPipeline(), ('185_baseball_MIN_METADATA', ))
],
"d3m.primitives.data_cleaning.text_summarization.Duke": [
(DukePipeline(), ('185_baseball_MIN_METADATA', ))
],
"d3m.primitives.feature_selection.pca_features.Pcafeatures": [
(PcaFeaturesPipeline(), ('185_baseball_MIN_METADATA', ))
],
"d3m.primitives.feature_selection.rffeatures.Rffeatures": [
(RfFeaturesPipeline(), ('185_baseball_MIN_METADATA', ))
],
"d3m.primitives.classification.inceptionV3_image_feature.Gator": [
(GatorPipeline(), (
"124_174_cifar10_MIN_METADATA",
"124_188_usps_MIN_METADATA",
"124_214_coil20_MIN_METADATA",
"uu_101_object_categories_MIN_METADATA",
))
],
"d3m.primitives.remote_sensing.mlp.MlpClassifier": [
(MlpClassifierPipeline(), ('LL1_bigearth_landuse_detection', ))
]
}
for primitive, pipelines in prims_to_pipelines.items():
if gpu:
if primitive not in gpu_prims:
continue
else:
if primitive in gpu_prims:
continue
os.chdir(f'/annotations/{primitive}')
os.chdir(os.listdir('.')[0])
if not os.path.isdir('pipelines'):
os.mkdir('pipelines')
else:
[
os.remove(f'pipelines/{pipeline}')
for pipeline in os.listdir('pipelines')
]
if not os.path.isdir('pipeline_runs'):
os.mkdir('pipeline_runs')
else:
[
os.remove(f'pipeline_runs/{pipeline_run}')
for pipeline_run in os.listdir('pipeline_runs')
]
if not os.path.isdir(f'/pipeline_scores/{primitive.split(".")[-1]}'):
os.mkdir(f'/pipeline_scores/{primitive.split(".")[-1]}')
for pipeline, datasets in pipelines:
pipeline.write_pipeline(output_dir='./pipelines')
for dataset in datasets:
print(
f'Generating pipeline for {primitive.split(".")[-1]} on {dataset}'
)
if primitive.split(".")[-1] in ['Duke', 'Sloth']:
pipeline.fit_produce(dataset,
output_yml_dir='./pipeline_runs',
submission=True)
else:
if primitive.split(".")[-1] == 'NBEATS':
shutil.rmtree(f'/scratch_dir/nbeats')
pipeline.fit_score(
dataset,
output_yml_dir='./pipeline_runs',
output_score_dir=
f'/pipeline_scores/{primitive.split(".")[-1]}',
submission=True)
os.system('gzip -r pipeline_runs')