diff --git a/.dockerignore b/.dockerignore index 672592c..f4bde6c 100644 --- a/.dockerignore +++ b/.dockerignore @@ -8,3 +8,4 @@ static/ notebooks/ .tox/ .git/ +primitives/ diff --git a/.gitignore b/.gitignore index 53777ae..f6c3557 100644 --- a/.gitignore +++ b/.gitignore @@ -105,8 +105,8 @@ ENV/ # vim .*.swp -input* +input output/ -static/ notebooks/ -templates.bak/ +primitives/ +static diff --git a/Dockerfile b/Dockerfile index ff6b923..6e6992c 100644 --- a/Dockerfile +++ b/Dockerfile @@ -1,5 +1,4 @@ -FROM registry.gitlab.com/datadrivendiscovery/images/primitives:ubuntu-bionic-python36-v2019.11.10-20191127-050901 -# FROM registry.gitlab.com/datadrivendiscovery/images/primitives:ubuntu-bionic-python36-v2019.11.10 +FROM registry.gitlab.com/datadrivendiscovery/images/primitives:ubuntu-bionic-python36-v2020.1.9-20200212-063959 ARG UID=1000 ARG D3MPORT=45042 @@ -10,10 +9,6 @@ EXPOSE $D3MPORT RUN mkdir -p /user_dev -# RUN mkdir -p /user_dev && \ -# mkdir -p /user_dev/output && \ -# mkdir -p /user_dev/input && \ -# mkdir -p /user_dev/static && \ RUN ln -s /output /user_dev/output && \ ln -s /input /user_dev/input && \ ln -s /static /user_dev/static @@ -24,11 +19,9 @@ RUN pip3 install -r /user_dev/requirements.txt # Copy code COPY setup.py MANIFEST.in /user_dev/ -COPY ta2 /user_dev/ta2 -RUN chown -R $UID:$UID /user_dev +RUN pip3 install -e /user_dev ipdb -# Install project -RUN pip3 install /user_dev -RUN pip3 install ipdb +COPY ta2 /user_dev/ta2 +# RUN chown -R $UID:$UID /user_dev CMD ["python3", "/user_dev/ta2/ta3/server.py", "-v"] diff --git a/Makefile b/Makefile index 127e26c..c8aefbb 100644 --- a/Makefile +++ b/Makefile @@ -231,8 +231,8 @@ build: ## build the mit-d3m-ta2 docker image .PHONY: submit submit: login build ## push to TA2 submission registry - docker tag mit-d3m-ta2:latest registry.datadrivendiscovery.org/ta2-submissions/ta2-mit/december2019 - docker push registry.datadrivendiscovery.org/ta2-submissions/ta2-mit/december2019 + docker tag mit-d3m-ta2:latest registry.datadrivendiscovery.org/ta2-submissions/ta2-mit/winter2020evaluation + docker push registry.datadrivendiscovery.org/ta2-submissions/ta2-mit/winter2020evaluation .PHONY: submit-ci submit-ci: login build ## push to TA2 submission registry diff --git a/requirements.txt b/requirements.txt index e0d79a5..630b116 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1 +1,2 @@ --e git+https://gitlab.com/datadrivendiscovery/ta3ta2-api.git@0494c7088542c79c4d8eed0059d512bc08414e42#egg=ta3ta2-api +-e git+https://gitlab.com/datadrivendiscovery/ta3ta2-api.git@1214abaac7cccd3f578e9589509b279bd820a758#egg=ta3ta2-api +-e git+https://github.com/HDI-Project/BTB.git@31c6349932accd6b168ad2d00af6b4110e8c4a66#egg=baytune diff --git a/run_docker.sh b/run_docker.sh index 6cf5eb9..65f5c54 100755 --- a/run_docker.sh +++ b/run_docker.sh @@ -10,7 +10,6 @@ rm -r output mkdir -p output chown $USER output - function echodo() { echo $* $* @@ -22,6 +21,7 @@ echodo docker run -i -t --rm \ -e D3MINPUTDIR=/input \ -e D3MOUTPUTDIR=/output \ -e D3MSTATICDIR=/static \ + -v $(pwd)/ta2:/user_dev/ta2 \ -v $(pwd)/input:/input \ -v $(pwd)/output:/output \ -v $(pwd)/static:/static \ diff --git a/scripts/evaluate_templates.sh b/scripts/evaluate_templates.sh new file mode 100755 index 0000000..84775cd --- /dev/null +++ b/scripts/evaluate_templates.sh @@ -0,0 +1,9 @@ +#!/bin/bash + +docker build --build-arg UID=$UID -t mit-d3m-ta2 . + +COMMANDS=${*:-/bin/bash} +DATASETS=/home/pythia/Projects/d3m/datasets/seed_datasets_current/ + +docker run -i -t --rm -v $DATASETS:/input -v $(pwd):/home/user -w /home/user -u $UID mit-d3m-ta2 \ + python3 run_templates.py templates /input/LL1_terra_canopy_height_long_form_s4_90_MIN_METADATA diff --git a/scripts/generate_templates.py b/scripts/generate_templates.py new file mode 100644 index 0000000..35a2460 --- /dev/null +++ b/scripts/generate_templates.py @@ -0,0 +1,306 @@ +import argparse +import glob +import json +import logging +import os +import sys +import traceback +from datetime import datetime, timezone + +import numpy as np +import pandas as pd +from d3m.metadata.pipeline import Pipeline +from d3m.metadata.problem import Problem +from d3m.utils import yaml_load_all + +LOGGER = logging.getLogger(__name__) +TUNING_PARAMETER = 'https://metadata.datadrivendiscovery.org/types/TuningParameter' + + +def load_pipeline(pipeline): + with open(pipeline) as _pipeline: + if pipeline.endswith('.json'): + pipeline = Pipeline.from_json(_pipeline) + else: + pipeline = Pipeline.from_yaml(_pipeline) + + return pipeline + + +def get_default_step_hyperparams(step): + default_tunable_hyperparams = {} + for name, hp in step.get_all_hyperparams().items(): + if TUNING_PARAMETER not in hp.semantic_types: + continue + + default_tunable_hyperparams[name] = hp.get_default() + + return default_tunable_hyperparams + + +def clean_hyperparams(pipeline): + for step in pipeline.steps: + default_tunable_hyperparams = get_default_step_hyperparams(step) + + for name, value in step.hyperparams.items(): + if name in default_tunable_hyperparams.keys(): + value['data'] = default_tunable_hyperparams[name] + + return pipeline + + +def pipeline_to_template(pipeline_path): + pipeline = load_pipeline(pipeline_path) + template = clean_hyperparams(pipeline) + + template.id = '' + template.schema = 'https://metadata.datadrivendiscovery.org/schemas/v0/pipeline.json' + template.created = datetime(2016, 11, 11, 12, 30, tzinfo=timezone.utc) + + return template + + +def write_template(templates_path, template): + template_id = template.get_digest()[:12] + template_path = os.path.join(templates_path, template_id + '.json') + + with open(template_path, 'w') as template_file: + print("Creating template {}".format(template_path)) + template.to_json(template_file) + + +def generate_templates(pipelines_path, templates_path): + for pipeline in os.listdir(pipelines_path): + pipeline_path = os.path.join(pipelines_path, pipeline) + try: + template = pipeline_to_template(pipeline_path) + write_template(templates_path, template) + except Exception as ex: + print(ex) + + +def read_pipeline_run(pipeline_run_path): + data = open(pipeline_run_path) + docs = yaml_load_all(stream=data) + res = [] + for doc in docs: + res.append(doc) + + data.close() + + return res + + +def load_problem(root_path, phase): + path = os.path.join(root_path, phase, 'problem_' + phase, 'problemDoc.json') + return Problem.load(problem_uri=path) + + +def detect_data_modality(dataset_doc): + with open(dataset_doc) as f: + dataset_doc = json.load(f) + + resources = list() + for resource in dataset_doc['dataResources']: + resources.append(resource['resType']) + + if len(resources) == 1: + return 'single_table' + else: + for resource in resources: + if resource == 'edgeList': + return 'graph' + elif resource not in ('table', 'raw'): + return resource + + return 'multi_table' + + +def get_dataset_info(dataset_name, datasets_path): + + dataset_root = os.path.join(datasets_path, dataset_name) + + if not os.path.exists(dataset_root): + dataset_root += '_MIN_METADATA' + + dataset_doc = os.path.join(dataset_root, 'TRAIN', 'dataset_TRAIN', 'datasetDoc.json') + dataset_root = 'file://' + os.path.abspath(dataset_root) + problem = load_problem(dataset_root, 'TRAIN') + + # Dataset Meta + data_modality = detect_data_modality(dataset_doc) + task_type = problem['problem']['task_keywords'][0].name.lower() + task_subtype = problem['problem']['task_keywords'][1].name.lower() + + return data_modality, task_type, task_subtype + + +def get_template_id(pipeline_id, pipelines_path, templates_path): + + pipeline_path = os.path.join(pipelines_path, '{}.json'.format(pipeline_id)) + if not os.path.isfile(pipeline_path): + raise ValueError('Can not find: {}'.format(pipeline_path)) + + template = pipeline_to_template(pipeline_path) + write_template(templates_path, template) + return template.get_digest()[:12] + + +def produce_phase(pipeline_run): + """Produce result with Produce phase data.""" + scores = pipeline_run['run']['results']['scores'] + + if len(scores) > 1: + raise ValueError('This run has more than one score!') + + scores = scores[0] + + return { + 'metric': scores['metric']['metric'], + 'context': pipeline_run['context'], + 'normalized_score': scores['normalized'] + } + + +def extract_pipeline_run(pipeline_run, pipelines_path, templates_path, datasets_path): + dataset_id = pipeline_run['datasets'][0]['id'] + phase = pipeline_run['run']['phase'] + succeed = pipeline_run.get('status').get('state') + pipeline_id = pipeline_run['pipeline']['id'] + + if dataset_id.endswith('TRAIN'): + dataset_name = dataset_id.replace('_dataset_TRAIN', '') + else: + dataset_name = dataset_id.replace('_dataset_SCORE', '') + + # TODO: Lazy Loader + data_modality, task_type, task_subtype = get_dataset_info(dataset_name, datasets_path) + + template_id = get_template_id(pipeline_id, pipelines_path, templates_path) + + result = { + 'dataset': dataset_name, + 'pipeline_id': pipeline_id, + 'template_id': template_id, + 'modality': data_modality, + 'type': task_type, + 'subtype': task_subtype, + 'phase': phase, + 'succeed': succeed, + } + + if phase == 'PRODUCE' and succeed != 'FAILURE': + try: + score = produce_phase(pipeline_run) + result.update(score) + except: + # Timeout + result['phase'] = 'TIMEOUT' + + return result, succeed + + +def extract_meta_information(pipeline_runs, pipelines_path, templates_path, datasets_path): + pipeline_runs_path = os.path.join(pipeline_runs, '*') + + results = [] + errored = [] + discarded = [] + + for pipeline_run_path in glob.glob(pipeline_runs_path): + pipeline_runs = load_pipeline_run(pipeline_run_path) + + data_extracted = [] + + failed = False + + for pipeline_run in pipeline_runs: + try: + run_data, run_status = extract_pipeline_run( + pipeline_run, pipelines_path, templates_path, datasets_path) + + failed = run_status == 'FAILURE' + + data_extracted.append(run_data) + + except Exception as e: + LOGGER.warning('Failed %s with: %s', pipeline_run_path, e) + continue + + if not failed: + results.extend(data_extracted) + + else: + LOGGER.warning('Pipeline run %s discarded.', pipeline_run_path) + discarded.append(data_extracted) + + return results, discarded + + +def apply_mean_score(df): + mean_score = df.groupby(['pipeline_id', 'context'])['normalized_score'].mean() + mean_score = mean_score.reset_index() + mean_score.rename(columns={'normalized_score': 'mean_score'}, inplace=True) + return df.merge(mean_score, on=['pipeline_id', 'context'], how='left') + + +def z_score(x): + if len(x) == 1 or x.std() == 0: + return pd.Series(np.zeros(len(x)), index=x.index) + + return (x - x.mean()) / x.std() + + +def apply_z_score(df): + z_scores = df.groupby('dataset').normalized_score.apply(z_score) + df['z_score'] = z_scores + templates_z_score = df.groupby('template_id').z_score.mean() + del df['z_score'] + + return df.merge(templates_z_score, how='left', left_on='template_id', right_index=True) + + +def generate_metadata_report(pipeline_runs, pipelines_path, templates_path, datasets_path, report): + + results, discarded = extract_meta_information( + pipeline_runs, pipelines_path, templates_path, datasets_path) + + if report is None: + report = os.path.join(templates_path, 'templates.csv') + + df = pd.DataFrame(results) + df = apply_mean_score(df) + df = apply_z_score(df) + df.to_csv(report, index=False) + + if errored: + with open('errors.txt', 'w') as f: + for error in errored: + f.write('{}\n'.format(error)) + + +def get_parser(): + parser = argparse.ArgumentParser( + description='Generate new templates from pipeline runs and the metadata reffered to them.') + parser.add_argument('pipeline_runs_path', help='Path to the pipeline runs folder') + parser.add_argument('pipelines_path', help='Path to the pipelines folder') + parser.add_argument('templates_path', help='Path to the templates folder') + parser.add_argument('datasets_path', help='Path where the datasets are located') + parser.add_argument('-r', '--report', help='Path to the CSV file where scores will be dumped.') + + return parser.parse_args() + + +def main(): + args = parse_args() + generate_metadata_report( + args.pipeline_runs_path, + args.pipelines_scored_path, + args.templates_path, + args.datasets_path, + args.report, + ) + + +if __name__ == '__main__': + main() diff --git a/scripts/run_on_d3m.sh b/scripts/run_on_d3m.sh new file mode 100755 index 0000000..dc76218 --- /dev/null +++ b/scripts/run_on_d3m.sh @@ -0,0 +1,13 @@ +#!/bin/bash + +function echodo() { + echo $* + $* +} + +docker build --build-arg UID=$UID -t mit-d3m-ta2 . + +COMMANDS=${*:-/bin/bash} +DATASETS=/home/pythia/Projects/d3m/datasets/seed_datasets_current/ + +echodo docker run -i -t --rm -v $DATASETS:/input -v $(pwd):/home/user -w /home/user -u $UID mit-d3m-ta2 $COMMANDS diff --git a/scripts/run_templates.py b/scripts/run_templates.py new file mode 100644 index 0000000..066ffab --- /dev/null +++ b/scripts/run_templates.py @@ -0,0 +1,106 @@ +import argparse +import io +import json +import os +import sys +import tarfile +import urllib +from multiprocessing import Manager, Process + +import pandas as pd + +from d3m import index +from d3m.container.dataset import Dataset +from d3m.metadata.base import Context +from d3m.metadata.pipeline import Pipeline +from d3m.metadata.problem import parse_problem_description +from d3m.runtime import DEFAULT_SCORING_PIPELINE_PATH, Runtime, score + + +from ta2.__main__ import load_problem, score_pipeline + + +def detect_data_modality(doc_path): + with open(doc_path) as f: + dataset_doc = json.load(f) + + resources = list() + for resource in dataset_doc['dataResources']: + resources.append(resource['resType']) + + if len(resources) == 1: + return 'single_table' + else: + for res in resources: + if res == 'edgeList': + return 'graph' + elif res not in ('table', 'raw'): + return res + + return 'multi_table' + + +def run_pipeline(output, dataset_root, problem, pipeline_path, static=None): + score = score_pipeline(dataset_root, problem, pipeline_path, static) + output.append(score) + + +def test_pipelines(pipelines, dataset_root): + datasets_path, dataset_name = dataset_root.rsplit('/', 1) + # train_dataset = load_dataset(dataset_root, 'TRAIN') + # test_dataset = load_dataset(dataset_root, 'TEST') + problem = load_problem(dataset_root, 'TRAIN') + evaluated_pipelines = list() + + task_type = problem['problem']['task_keywords'][0].name.lower() + task_subtype = problem['problem']['task_keywords'][1].name.lower() + + path = os.path.join(dataset_root, 'TRAIN', 'dataset_' + 'TRAIN', 'datasetDoc.json') + data_modality = detect_data_modality(path) + + for pipeline in os.listdir(pipelines): + print(pipeline.rsplit('.', 1)[0] + ':', end=' ') + + pipeline_path = os.path.join(pipelines, pipeline) + try: + with Manager() as manager: + output = manager.list() + process = Process( + target=run_pipeline, + args=(output, dataset_root, problem, pipeline_path) + ) + process.daemon = True + process.start() + process.join(10) + process.terminate() + + if output: + print("success -", output[0]) + evaluated_pipelines.append({ + 'dataset_name': dataset_name, + 'template_id': pipeline, + 'problem_type': '{}_{}'.format(data_modality, task_type), + 'task_type': task_type, + 'task_subtype': task_subtype, + }) + + elif process.exitcode == -15: + print("timeout") + else: + print("error") + + except Exception as ex: + print("error") + + df = pd.DataFrame(evaluated_pipelines) + df.to_csv('{}_{}.csv'.format(data_modality, task_type), index=False) + + +if __name__ == '__main__': + + # Make all the d3m traces quiet + null = open(os.devnull, 'w') + sys.stderr = null + + pipelines, dataset = sys.argv[1:3] + test_pipelines(pipelines, dataset) diff --git a/setup.py b/setup.py index f8ebe77..526849a 100644 --- a/setup.py +++ b/setup.py @@ -5,14 +5,15 @@ install_requires = [ - 'baytune==0.3.5', - 'd3m==2019.11.10', + 'baytune>=0.3.6.dev0', 'tabulate>=0.8.3,<0.9', - 'numpy==1.17.3', - 'scikit-learn[alldeps]==0.21.3', - 'Cython==0.29.7', + 'd3m==2020.1.9', 'datamart-rest==0.2.3', - 'PyYAML==5.1.2', + 'gitdb2>=2,<3.0.2', + # 'numpy==1.17.3', + # 'scikit-learn[alldeps]==0.21.3', + # 'Cython==0.29.7', + # 'PyYAML==5.1.2', ] diff --git a/ta2/__main__.py b/ta2/__main__.py index 89d0c51..93a4cd5 100644 --- a/ta2/__main__.py +++ b/ta2/__main__.py @@ -2,12 +2,12 @@ import gc import logging import os -import shutil import socket import sys import traceback from datetime import datetime +import numpy as np import pandas as pd import tabulate from d3m.container.dataset import Dataset @@ -31,12 +31,12 @@ def load_dataset(root_path, phase, inner_phase=None): return Dataset.load(dataset_uri='file://' + os.path.abspath(path)) else: path = os.path.join(root_path, phase, 'dataset_' + phase, 'datasetDoc.json') - return Dataset.load(dataset_uri='file://' + os.path.abspath(path)) + return Dataset.load(dataset_uri=path) def load_problem(root_path, phase): path = os.path.join(root_path, phase, 'problem_' + phase, 'problemDoc.json') - return Problem.load(problem_uri='file://' + os.path.abspath(path)) + return Problem.load(problem_uri=path) def load_pipeline(pipeline_path): @@ -47,47 +47,41 @@ def load_pipeline(pipeline_path): return Pipeline.from_yaml(pipeline_file) -def search(dataset_root, problem, args): - pps = PipelineSearcher( - args.input, - args.output, - args.static, - dump=True, - hard_timeout=args.hard, - ignore_errors=args.ignore_errors, - ) - dataset_root = os.path.abspath(dataset_root) - dataset_path = 'file://{}/TRAIN/dataset_TRAIN/datasetDoc.json'.format(dataset_root) - - return pps.search(dataset_path, problem, args.timeout, args.budget, args.template) +def _to_yaml_run(pipeline_run, output_path): + run_dir = os.path.join(output_path, 'pipeline_runs') + run_dir = os.path.join(run_dir, '{}.yml'.format(pipeline_run.get_id())) + with open(run_dir, 'w') as output_file: + pipeline_run.to_yaml(file=output_file) -def score_pipeline(dataset_root, problem, pipeline_path, static=None): - train_dataset = load_dataset(dataset_root, 'TRAIN') - test_dataset = load_dataset(dataset_root, 'SCORE', 'TEST') +def score_pipeline(dataset, problem, pipeline_path, static=None, output_path=None): pipeline = load_pipeline(pipeline_path) # Creating an instance on runtime with pipeline description and problem description. runtime = Runtime( pipeline=pipeline, problem_description=problem, - context=Context.TESTING, + context=Context.EVALUATION, volumes_dir=static, ) - LOGGER.info("Fitting the pipeline") - fit_results = runtime.fit(inputs=[train_dataset]) + LOGGER.info("Fitting pipeline %s", pipeline_path) + fit_results = runtime.fit(inputs=[dataset]) fit_results.check_success() + dataset_doc_path = dataset.metadata.query(())['location_uris'][0] + dataset_root = dataset_doc_path[:-len('/TRAIN/dataset_TRAIN/datasetDoc.json')] + test_dataset = load_dataset(dataset_root, 'SCORE', 'TEST') + # Producing results using the fitted pipeline. - LOGGER.info("Producing predictions") + LOGGER.info("Producing predictions for pipeline %s", pipeline_path) produce_results = runtime.produce(inputs=[test_dataset]) produce_results.check_success() predictions = produce_results.values['outputs.0'] metrics = problem['problem']['performance_metrics'] - LOGGER.info("Computing the score") + LOGGER.info("Computing the score for pipeline %s", pipeline_path) scoring_pipeline = load_pipeline(DEFAULT_SCORING_PIPELINE_PATH) scores, scoring_pipeline_run = score( scoring_pipeline=scoring_pipeline, @@ -95,80 +89,142 @@ def score_pipeline(dataset_root, problem, pipeline_path, static=None): predictions=predictions, score_inputs=[test_dataset], metrics=metrics, - context=Context.TESTING, + context=Context.EVALUATION, random_seed=0, ) + + evaluated_pipeline_run = produce_results.pipeline_run + evaluated_pipeline_run.is_standard_pipeline = True + evaluated_pipeline_run.set_scores(scores, metrics) + evaluated_pipeline_run.set_scoring_pipeline_run(scoring_pipeline_run.pipeline_run, [dataset]) + + _to_yaml_run(evaluated_pipeline_run, output_path) + return scores.iloc[0].value def box_print(message, strong=False): char = '#' if strong else '*' - print(char * len(message)) - print(message) - print(char * len(message)) - LOGGER.info(message) + line = char * max(len(line) for line in message.split('\n')) + LOGGER.warn('\n'.join(('', line, message, line))) + + +def get_datasets(input_dir, datasets=None, data_modality=None, task_type=None, task_subtype=None): + if not datasets: + datasets = os.listdir(input_dir) + for dataset_name in datasets: + dataset_root = os.path.join(input_dir, dataset_name) + if not os.path.exists(dataset_root): + dataset_root += '_MIN_METADATA' -def get_datasets(args): - for dataset_name in args.dataset: - dataset_root = os.path.join(args.input, dataset_name) - dataset_path = os.path.join(dataset_root, 'TRAIN', 'dataset_TRAIN', 'datasetDoc.json') + dataset_root = 'file://' + os.path.abspath(dataset_root) try: + dataset = load_dataset(dataset_root, 'TRAIN') problem = load_problem(dataset_root, 'TRAIN') - data_modality, task_type, task_subtype = get_dataset_details(dataset_path, problem) + data_modality, task_type, task_subtype = get_dataset_details(dataset, problem) except Exception: continue - if args.data_modality and not args.data_modality == data_modality: + if data_modality and not data_modality == data_modality: continue - if args.task_type and not args.task_type == task_type: + if task_type and not task_type == task_type: continue - if args.task_subtype and not args.task_subtype == task_subtype: + if task_subtype and not task_subtype == task_subtype: continue - yield dataset_name, dataset_root, problem + yield dataset_name, dataset, problem + + +def _append_report(result, path): + report = pd.DataFrame( + [result], + columns=REPORT_COLUMNS + ) + report['dataset'] = report['dataset'].str.replace('_MIN_METADATA', '') + report['template'] = report['template'].str[0:12] + report['host'] = socket.gethostname() + report['timestamp'] = datetime.utcnow() + report.to_csv(path, mode='a', header=False, index=False) -def process_dataset(dataset_name, dataset_root, problem, args): - start_ts = datetime.utcnow() + return report + + +def _select_candidates(summary): + summary = summary[summary.status == 'SCORED'] + summary = summary[['template', 'pipeline', 'score', 'normalized']] + candidates = summary.sort_values('normalized', ascending=False).head(20) + candidates['pipeline'] += '.json' + return candidates + + +def process_dataset(dataset_name, dataset, problem, args): box_print("Processing dataset {}".format(dataset_name), True) + output_path = os.path.join(args.output, dataset_name) + os.makedirs(output_path, exist_ok=True) + LOGGER.info("Searching Pipeline for dataset {}".format(dataset_name)) try: - result = search(dataset_root, problem, args) - result['elapsed_time'] = datetime.utcnow() - start_ts + start_ts = datetime.utcnow() + pps = PipelineSearcher( + args.input, + output_path, + args.static, + dump=True, + hard_timeout=args.hard, + ignore_errors=args.ignore_errors, + cv_folds=args.folds, + subprocess_timeout=args.subprocess_timeout, + max_errors=args.max_errors, + store_summary=True + ) + result = pps.search(dataset, problem, args.timeout, args.budget, args.templates_csv) + + result['elapsed'] = datetime.utcnow() - start_ts result['dataset'] = dataset_name - pipeline_id = result['pipeline'] - cv_score = result['cv_score'] - if cv_score is not None: - box_print("Best Pipeline: {} - CV Score: {}".format(pipeline_id, cv_score)) - - pipeline_path = os.path.join(args.output, 'pipelines_ranked', pipeline_id + '.json') - test_score = score_pipeline(dataset_root, problem, pipeline_path, args.static) - box_print("Test Score for pipeline {}: {}".format(pipeline_id, test_score)) - - result['test_score'] = test_score - - return result - except Exception: - return { + except Exception as ex: + result = { 'dataset': dataset_name, - 'pipeline': None, - 'cv_score': None, - 'template': None, - 'data_modality': None, - 'task_type': None, - 'task_subtype': None, - 'tuning_iterations': None, - 'error': 'error from main', - 'killed_by_timeout': None, - 'pipelines_scheduled': None, - 'found_by_name': None, - 'test_score': None, - 'elapsed_time': None + 'error': '{}: {}'.format(type(ex).__name__, ex), } + else: + try: + summary = result.pop('summary') + candidates = _select_candidates(summary) + if candidates.empty: + box_print('No valid pipelines found for dataset {}'.format(dataset_name)) + else: + ranked_path = os.path.join(output_path, 'pipelines_ranked') + test_scores = list() + for _, candidate in candidates.iterrows(): + try: + pipeline = candidate.pipeline + pipeline_path = os.path.join(ranked_path, pipeline) + test_score = score_pipeline(dataset, problem, pipeline_path, + args.static, output_path) + test_scores.append(test_score) + except Exception: + test_scores.append(None) + + candidates['test_score'] = test_scores + candidates = candidates.sort_values('test_score', ascending=False) + + best = candidates.iloc[0] + result['test_score'] = best.test_score + result['template'] = best.template + result['cv_score'] = best.score + box_print('Best pipelines for dataset {}:\n{}'.format( + dataset_name, candidates.to_string())) + + except Exception as ex: + LOGGER.exception('Error while testing the winner pipeline') + result['error'] = 'TEST Error: {}'.format(ex) + + return result REPORT_COLUMNS = [ @@ -176,27 +232,25 @@ def process_dataset(dataset_name, dataset_root, problem, args): 'template', 'cv_score', 'test_score', - 'elapsed_time', - 'tuning_iterations', - 'data_modality', - 'task_type', - 'error', - 'killed_by_timeout', + 'metric', + 'elapsed', + 'templates', + 'iterations', + 'scored', + 'errored', + 'invalid', + 'timedout', + 'killed', + 'modality', + 'type', + 'subtype', + 'host', + 'timestamp' ] def _ta2_test(args): - - # Cleanup output dir - shutil.rmtree(os.path.join(args.output, 'pipelines_ranked'), ignore_errors=True) - shutil.rmtree(os.path.join(args.output, 'pipelines_scored'), ignore_errors=True) - shutil.rmtree(os.path.join(args.output, 'pipelines_searched'), ignore_errors=True) - shutil.rmtree(os.path.join(args.output, 'predictions'), ignore_errors=True) - - results = list() - if args.all: - args.dataset = os.listdir(args.input) - elif not args.dataset: + if not args.all and not args.dataset: print('ERROR: provide at least one dataset name or set --all') sys.exit(1) @@ -205,32 +259,32 @@ def _ta2_test(args): else: report_name = os.path.join(args.output, 'results.csv') - if os.path.exists(report_name): - os.remove(report_name) + report = pd.DataFrame(columns=REPORT_COLUMNS) + if not os.path.exists(report_name): + report.to_csv(report_name, index=False) - report = None - for dataset_name, dataset_root, problem in get_datasets(args): + datasets = get_datasets(args.input, args.dataset, args.data_modality, + args.task_type, args.task_subtype) + for dataset_name, dataset, problem in datasets: try: - results.append(process_dataset(dataset_name, dataset_root, problem, args)) + result = process_dataset(dataset_name, dataset, problem, args) gc.collect() except Exception as ex: box_print("Error processing dataset {}".format(dataset_name), True) traceback.print_exc() - results.append({ + result = { 'dataset': dataset_name, 'error': '{}: {}'.format(type(ex).__name__, ex) - }) - - report = pd.DataFrame( - results, - columns=REPORT_COLUMNS - ).sort_values('dataset') - report['host'] = socket.gethostname() + } - report.to_csv(report_name, index=False) + report = report.append( + _append_report(result, report_name), + sort=False, + ignore_index=True + ) - if report is None: - print("No matiching datasets found") + if report.empty: + print("No matching datasets found") sys.exit(1) # print to stdout @@ -264,7 +318,7 @@ def _ta3_test_dataset(client, dataset, timeout): request_id = score_response.request_id print('### {} => client.GetScoreSolutionsResults("{}")'.format(dataset, request_id)) - client.get_score_solution_results(request_id) + score_solution_results = client.get_score_solution_results(request_id) print('### {} => client.FitSolution("{}")'.format(dataset, solution_id)) fit_response = client.fit_solution(solution_id, dataset) @@ -287,6 +341,11 @@ def _ta3_test_dataset(client, dataset, timeout): print('### {} => client.EndSearchSolutions("{}")'.format(dataset, search_id)) client.end_search_solutions(search_id) + return np.mean([ + score.value.raw.double + for score in score_solution_results.scores + ]) + def _ta3_test(args): local_input = args.input @@ -299,11 +358,28 @@ def _ta3_test(args): if args.all: args.dataset = os.listdir(args.input) + results = list() for dataset in args.dataset: try: - _ta3_test_dataset(client, dataset, args.timeout / 60) - except Exception as e: - print('An error occurred trying to process the dataset {}, produced by {}'.format(dataset, e)) + score = _ta3_test_dataset(client, dataset, args.timeout / 60) + results.append({ + 'dataset': dataset, + 'score': score + }) + except Exception as ex: + results.append({ + 'dataset': dataset, + 'score': 'ERROR' + }) + print('TA3 Error on dataset {}: {}'.format(dataset, ex)) + + results = pd.DataFrame(results) + print(tabulate.tabulate( + results[['dataset', 'score']], + showindex=False, + tablefmt='github', + headers=['dataset', 'score'] + )) def _server(args): @@ -388,8 +464,16 @@ def parse_args(): '-I', '--ignore-errors', action='store_true', help='Ignore errors when counting tuning iterations.') ta2_parser.add_argument( - '-e', '--template', action='append', - help='Name of the template to Use.') + '-e', '--templates-csv', help='Path to the templates csv file to use.') + ta2_parser.add_argument( + '-f', '--folds', type=int, default=5, + help='Number of folds to use for cross validation') + ta2_parser.add_argument( + '-p', '--subprocess-timeout', type=int, + help='Maximum time allowed per pipeline execution, in seconds') + ta2_parser.add_argument( + '-m', '--max-errors', type=int, default=5, + help='Maximum amount of errors per template.') # TA3 Mode ta3_parents = [logging_args, io_args, search_args, ta3_args, dataset_args] @@ -422,10 +506,12 @@ def parse_args(): if not os.path.exists(args.output): os.makedirs(args.output, exist_ok=True) - if args.mode is _ta2_test and not args.logfile: - args.logfile = os.path.join(args.output, 'ta2.log') - if os.path.exists(args.logfile): - os.remove(args.logfile) + if args.logfile: + if not os.path.isabs(args.logfile): + args.logfile = os.path.join(args.output, args.logfile) + + logdir = os.path.dirname(args.logfile) + os.makedirs(logdir, exist_ok=True) logging_setup(args.verbose, args.logfile, stdout=args.stdout) logging.getLogger("d3m").setLevel(logging.ERROR) diff --git a/ta2/curated.csv b/ta2/curated.csv new file mode 100644 index 0000000..8310021 --- /dev/null +++ b/ta2/curated.csv @@ -0,0 +1,27 @@ +template,data_modality,task_type +d781ed63c51e.json,single_table,classification +9bb9ffcac9e7.json,single_table,classification +114d6c40ae9f.json,single_table,regression +816ea7a09986.json,single_table,regression +03e8e75698d6.json,single_table,regression +d781ed63c51e.json,single_table,graph_matching +d781ed63c51e.json,single_table,forecasting +d781ed63c51e.json,single_table,link_prediction +d781ed63c51e.json,single_table,community_detection +d781ed63c51e.json,single_table,vertex_classification +d781ed63c51e.json,single_table,object_detection +dbd359409d05.json,multi_table,classification +05f103916567.json,multi_table,regression +d781ed63c51e.json,multi_table,graph_matching +d781ed63c51e.json,multi_table,forecasting +d781ed63c51e.json,multi_table,link_prediction +d781ed63c51e.json,multi_table,community_detection +d781ed63c51e.json,multi_table,vertex_classification +d781ed63c51e.json,multi_table,object_detection +d781ed63c51e.json,graph,classification +d781ed63c51e.json,graph,regression +d781ed63c51e.json,graph,graph_matching +d781ed63c51e.json,graph,forecasting +d781ed63c51e.json,graph,link_prediction +d781ed63c51e.json,graph,vertex_classification +d781ed63c51e.json,graph,object_detection diff --git a/ta2/curated_templates/2e1ad943e48d.json b/ta2/curated_templates/2e1ad943e48d.json new file mode 100644 index 0000000..7ca6310 --- /dev/null +++ b/ta2/curated_templates/2e1ad943e48d.json @@ -0,0 +1 @@ +{"id": "", "schema": "https://metadata.datadrivendiscovery.org/schemas/v0/pipeline.json", "created": "2016-11-11T12:30:00Z", "inputs": [{"name": "inputs"}], "outputs": [{"data": "steps.4.produce", "name": "output predictions"}], "steps": [{"type": "PRIMITIVE", "primitive": {"id": "f31f8c1f-d1c5-43e5-a4b2-2ae4a761ef2e", "version": "0.2.0", "python_path": "d3m.primitives.data_transformation.denormalize.Common", "name": "Denormalize datasets", "digest": 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file diff --git a/ta2/loader.py b/ta2/loader.py index 3bbdc72..f3901b2 100644 --- a/ta2/loader.py +++ b/ta2/loader.py @@ -7,6 +7,7 @@ from btb.tuning.hyperparams.boolean import BooleanHyperParam from btb.tuning.hyperparams.categorical import CategoricalHyperParam from btb.tuning.hyperparams.numerical import FloatHyperParam, IntHyperParam +from btb.tuning.tunable import Tunable from d3m.metadata.hyperparams import Bounded, Enumeration, Uniform, UniformBool, UniformInt from d3m.metadata.pipeline import Pipeline @@ -131,3 +132,23 @@ def load_pipeline(path, tunables=True, defaults=True): return pipeline, tunable_hyperparameters return pipeline + + +class LazyLoader(dict): + def __init__(self, keys, templates_dir): + super().__init__({key: None for key in keys}) + self._templates_dir = templates_dir + + def __getitem__(self, key): + value = super().__getitem__(key) + if value is not None: + return value + + path = os.path.join(self._templates_dir, key) + if not path.endswith('.json'): + path += '.json' + + template, tunable_hp = load_pipeline(path) + self[key] = template + + return Tunable(tunable_hp) diff --git a/ta2/new_templates/01717d30-57ea-43e5-9d4d-d457b124be43.json b/ta2/new_templates/01717d30-57ea-43e5-9d4d-d457b124be43.json deleted file mode 100644 index 214ebb1..0000000 --- a/ta2/new_templates/01717d30-57ea-43e5-9d4d-d457b124be43.json +++ /dev/null @@ -1 +0,0 @@ -{"id": "01717d30-57ea-43e5-9d4d-d457b124be43", "schema": "https://metadata.datadrivendiscovery.org/schemas/v0/pipeline.json", "created": "2019-11-24T18:06:40.404171Z", "inputs": [{"name": "inputs"}], "outputs": [{"data": "steps.3.produce", "name": "output predictions"}], "steps": [{"type": "PRIMITIVE", "primitive": {"id": "4b42ce1e-9b98-4a25-b68e-fad13311eb65", "version": "0.3.0", "python_path": "d3m.primitives.data_transformation.dataset_to_dataframe.Common", "name": "Extract a DataFrame from a Dataset", "digest": "e796f76607089407dcd9b646eaccb244f06520e759112fd21846993f88fcd208"}, "arguments": 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-185_baseball,8b925a5a-ab90-4430-b6e3-58993fd1c5e3,F1_MACRO,0.5916643898561883,0.5916643898561883,single_table_classification,0.3336339401314893 -27_wordLevels,356835b4-5de8-4189-a23b-edd74e9331c0,F1_MACRO,0.2863424048943403,0.2863424048943403,single_table_classification,-0.22059108372564326 -LL0_186_braziltourism,b54af5c6-1900-4674-8d76-be29a884c5b9,F1_MACRO,0.40389576883384937,0.40389576883384937,single_table_classification,1.0861539785101593 -1491_one_hundred_plants_margin,b73b2670-5b50-4a69-a82d-6c9c9604775f,F1_MACRO,0.6255873015873016,0.6255873015873016,single_table_classification,0.11547876807053425 -SEMI_1053_jm1,6c84a133-f297-4167-b116-0b07dc6cebb4,F1,0.3830787309048179,0.3830787309048179,single_table_classification,0.5325022307659951 -38_sick,6531b053-402c-4a64-b326-0ef0edf49f33,F1,0.6075949367088608,0.6075949367088608,single_table_classification,-1.4059578155454184 -26_radon_seed,82660bf2-f584-4dba-bef1-da2f19c55c7f,ROOT_MEAN_SQUARED_ERROR,0.9999911727417203,0.017654516559962193,single_table_regression,0.19611613513818407 -LL0_acled_reduced,06888384-cca4-46d2-bdb5-f5ae6d668786,ACCURACY,0.7958616858769615,0.7958616858769615,single_table_classification,0.04652569467478522 -57_hypothyroid,d8683bf2-567b-4b3b-9995-100cb29331fd,F1_MACRO,1.0,1.0,single_table_classification,1.188006931022193 -196_autoMpg,7d54f770-f0c1-4b85-b683-76d6bf3d7fb1,MEAN_SQUARED_ERROR,0.9915125038150704,16.9754,single_table_regression,-0.0016004838019275213 -26_radon_seed,01717d30-57ea-43e5-9d4d-d457b124be43,ROOT_MEAN_SQUARED_ERROR,0.9996671296893225,0.6657406459436747,single_table_regression,0.19611613513818407 -185_baseball,f0d94e6a-c43b-4d9b-bed0-e92c2db17754,F1_MACRO,0.31696136214800263,0.31696136214800263,single_table_classification,-1.156920925082984 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-26_radon_seed,082a7979-b5a2-4c6d-ab92-84c24d55c019,ROOT_MEAN_SQUARED_ERROR,0.9999562289391931,0.08754212166951983,single_table_regression,0.19611613513818407 -LL0_acled_reduced,6029289e-5a16-485b-9c51-3e657a1b278d,ACCURACY,0.8036383835578392,0.8036383835578392,single_table_classification,0.13353478601464452 -uu_101_object_categories,1d6b19b5-dc6b-41cd-b6cf-0d063f3a5ed4,ACCURACY,0.9412442396313364,0.9412442396313364,image_classification,0.0 -1491_one_hundred_plants_margin,7bf9411b-fb90-410a-85c0-5db57d3d9f09,F1_MACRO,0.6206697191697191,0.6206697191697191,single_table_classification,-0.13424332623749358 -LL0_1100_popularkids,c3d8a5be-bf82-4864-9423-a536f9764c43,F1_MACRO,0.3560187705789324,0.3560187705789324,single_table_classification,-1.401125716627364 -SEMI_1459_artificial_characters,36c5ead1-d02b-44eb-afe0-92404ccba0a1,F1_MACRO,0.6729830215829791,0.6729830215829791,single_table_classification,-0.7071067811865475 -196_autoMpg,83067afe-2785-473f-a3ee-78b089405864,MEAN_SQUARED_ERROR,0.993438387408285,13.123413527398125,single_table_regression,0.29412040724825395 -LL0_1100_popularkids,956f3f73-73b0-499b-8a16-d0332c9cd04e,F1_MACRO,0.511131559207869,0.511131559207869,single_table_classification,1.082105399688966 -185_baseball,5d04a564-d573-4c34-8a12-67f1d61ae17a,F1_MACRO,0.6686705107757739,0.6686705107757739,single_table_classification,0.751473703841389 -38_sick,29c29398-4acd-4906-8792-cb5ff396a88b,F1,0.9010989010989011,0.9010989010989011,single_table_classification,0.6655013716494104 -185_baseball,36e435cb-2d8a-4b32-9d09-f470843d3bed,F1_MACRO,0.31696136214800263,0.31696136214800263,single_table_classification,-1.156920925082984 -LL0_186_braziltourism,793b4864-4d1d-4f39-a3b0-a219c008e5d1,F1_MACRO,0.2744319950202303,0.2744319950202303,single_table_classification,-0.28327404807294243 -LL1_ArrowHead,1131eda4-d87e-472d-adb8-e6a50fda2e70,F1_MACRO,0.4599296893486124,0.4599296893486124,timeseries_classification,0.22261735954935402 -LL1_FordA,0be7c7e7-cb41-4fc1-a6c1-9ef9ce3aaf73,F1,0.7128065395095369,0.7128065395095369,timeseries_classification,0.08410162947557574 -LL0_acled_reduced,b73b2670-5b50-4a69-a82d-6c9c9604775f,ACCURACY,0.7954450770726288,0.7954450770726288,single_table_classification,0.11547876807053425 -1491_one_hundred_plants_margin,ef706d8d-156c-4d82-939f-971afc0777d0,F1_MACRO,0.6767582972582973,0.6767582972582973,single_table_classification,0.1302220702915288 -185_baseball,7bbbc17d-50f9-4d79-8e6d-e55530bc47df,F1_MACRO,0.31696136214800263,0.31696136214800263,single_table_classification,-1.156920925082984 -LL0_acled_reduced,9a7a9525-01e0-4890-af23-959236cf96ee,ACCURACY,0.5596444938203028,0.5596444938203028,single_table_classification,-2.5963754547734106 -38_sick,3645257f-3623-4cfc-81e3-108e7e0c2258,F1,0.9052631578947369,0.9052631578947369,single_table_classification,0.6948913937416135 -56_sunspots_monthly,1eaba585-d621-4bfe-9ecc-8d06ebc60684,ROOT_MEAN_SQUARED_ERROR,0.9456562086603649,108.7947664505723,single_table_forecasting,-0.0011074126986062138 -196_autoMpg,83067afe-2785-473f-a3ee-78b089405864,MEAN_SQUARED_ERROR,0.993438387408285,13.123413527398125,single_table_regression,0.29412040724825395 -LL1_TXT_CLS_airline_opinion,818dcf9f-ba75-44e2-86a1-4befa0a94e19,ACCURACY,0.719603825136612,0.719603825136612,text_classification,-0.7071067811865444 -196_autoMpg,83067afe-2785-473f-a3ee-78b089405864,MEAN_SQUARED_ERROR,0.993438387408285,13.123413527398125,single_table_regression,0.29412040724825395 -185_baseball,e7d73d01-cf76-48f2-9155-3652c4aa884c,F1_MACRO,0.6800633627517537,0.6800633627517537,single_table_classification,0.8132919920602744 -185_baseball,7a2af564-4358-4892-b018-b5c5073f29d3,F1_MACRO,0.743915343915344,0.743915343915344,single_table_classification,1.1597566159525006 -LL1_terra_canopy_height_long_form_s4_100,c946b500-7645-4de2-b2b6-1a4023376914,MEAN_ABSOLUTE_ERROR,0.98398153926436,32.039662022686024,single_table_forecasting,-0.1500939591079336 -LL1_terra_canopy_height_long_form_s4_80,994a9845-f2ef-4388-879f-72de13c30467,MEAN_ABSOLUTE_ERROR,0.0,2844600644910.581,single_table_forecasting,-0.14939552212048252 -38_sick,3645257f-3623-4cfc-81e3-108e7e0c2258,F1,0.9052631578947369,0.9052631578947369,single_table_classification,0.6948913937416135 -534_cps_85_wages,0431cde4-cc0a-4fc7-811a-b7a8fc1af5cf,MEAN_SQUARED_ERROR,0.9907771738439068,18.44617533781979,single_table_regression,0.9186022057131922 -loan_status,af9ca8f0-62a5-4b20-bb2c-734dc979156d,F1_MACRO,0.38848540822680744,0.38848540822680744,multi_table_classification,0.7071067811865469 -30_personae,66b85a4d-793d-4cf3-8f79-984084c49899,F1,0.625,0.625,text_classification,0.37934501887406963 -26_radon_seed,1981ef83-f458-4fc1-9c0a-58c1cf4f895e,ROOT_MEAN_SQUARED_ERROR,0.9999826020576421,0.034795884719306815,single_table_regression,0.19611613513818407 -185_baseball,e2ef1994-ec91-4fb0-950f-3d578e98ed6c,F1_MACRO,0.6686705107757739,0.6686705107757739,single_table_classification,0.751473703841389 -38_sick,6531b053-402c-4a64-b326-0ef0edf49f33,F1,0.6075949367088608,0.6075949367088608,single_table_classification,-1.4059578155454184 -LL0_acled_reduced,30a98dae-5a02-4333-9707-527362a43fc6,ACCURACY,0.6456047771142897,0.6456047771142897,single_table_classification,-0.6172616881812866 -LL0_1100_popularkids,66b85a4d-793d-4cf3-8f79-984084c49899,F1_MACRO,0.4248804801139254,0.4248804801139254,single_table_classification,0.37934501887406963 -LL1_Cricket_Y,0be7c7e7-cb41-4fc1-a6c1-9ef9ce3aaf73,F1_MACRO,0.5070581834585989,0.5070581834585989,timeseries_classification,0.08410162947557574 -185_baseball,b64d6a22-c49d-4341-8871-af7f298f9653,F1_MACRO,0.3118138278595992,0.3118138278595992,single_table_classification,-1.1848517503871328 -26_radon_seed,693b7765-5708-433d-8b0c-0cc64ae33c46,ROOT_MEAN_SQUARED_ERROR,0.9992986742947989,1.4026516403705995,single_table_regression,0.19611613513818407 -LL0_1100_popularkids,b73b2670-5b50-4a69-a82d-6c9c9604775f,F1_MACRO,0.36334329101319396,0.36334329101319396,single_table_classification,0.11547876807053425 -LL1_net_nomination_seed,784d3b0b-bfe3-4346-8740-136dd23fb7dc,ACCURACY,0.8,0.8,graph_vertex_classification,0.0 diff --git a/ta2/search.py b/ta2/search.py index 626facc..f3eda0e 100644 --- a/ta2/search.py +++ b/ta2/search.py @@ -1,19 +1,17 @@ -import glob import json import logging import os import random import signal import warnings -from collections import defaultdict from datetime import datetime, timedelta from multiprocessing import Manager, Process import numpy as np import pandas as pd -from btb.session import BTBSession -from btb.tuning.tunable import Tunable -from d3m.container.dataset import Dataset +import yaml +from btb import BTBSession +from btb.tuning import StopTuning from d3m.metadata.base import ArgumentType, Context from d3m.metadata.pipeline import Pipeline, PrimitiveStep from d3m.runtime import DEFAULT_SCORING_PIPELINE_PATH @@ -21,77 +19,27 @@ from datamart import DatamartQuery from datamart_rest import RESTDatamart -from ta2.loader import load_pipeline -from ta2.utils import dump_pipeline +from ta2.loader import LazyLoader +from ta2.utils import dump_pipeline, get_dataset_details, to_dicts BASE_DIR = os.path.abspath(os.path.dirname(__file__)) PIPELINES_DIR = os.path.join(BASE_DIR, 'pipelines') -TEMPLATES_DIR = os.path.join(BASE_DIR, 'new_templates') -FALLBACK_PIPELINE = 'single_table_classification_fallback.json' +TEMPLATES_DIR = os.path.join(BASE_DIR, 'templates') +TEMPLATES_CSV = os.path.join(BASE_DIR, 'templates.csv') DATAMART_URL = os.getenv('DATAMART_URL_NYU', 'https://datamart.d3m.vida-nyu.org') -SUBPROCESS_PRIMITIVES = [ - 'd3m.primitives.natural_language_processing.lda.Fastlvm', - 'd3m.primitives.feature_construction.sdne.DSBOX', - 'd3m.primitives.feature_extraction.nk_sent2vec.Sent2Vec', -] - LOGGER = logging.getLogger(__name__) warnings.filterwarnings("ignore", category=DeprecationWarning) -def detect_data_modality(dataset_doc_path): - with open(dataset_doc_path) as f: - dataset_doc = json.load(f) - - resources = list() - for resource in dataset_doc['dataResources']: - resources.append(resource['resType']) - - if len(resources) == 1: - return 'single_table' - else: - for resource in resources: - if resource == 'edgeList': - return 'graph' - elif resource not in ('table', 'raw'): - return resource - - return 'multi_table' - - -def get_dataset_details(dataset, problem): - data_modality = detect_data_modality(dataset) - task_type = problem['problem']['task_keywords'][0].name.lower() - task_subtype = problem['problem']['task_keywords'][1].name.lower() - - return data_modality, task_type, task_subtype - - -def to_dicts(hyperparameters): +class SubprocessTimeout(Exception): + pass - params_tree = defaultdict(dict) - for (block, hyperparameter), value in hyperparameters.items(): - if isinstance(value, np.integer): - value = int(value) - elif isinstance(value, np.floating): - value = float(value) - - elif isinstance(value, np.ndarray): - value = value.tolist() - - elif isinstance(value, np.bool_): - value = bool(value) - - elif value == 'None': - value = None - - params_tree[block][hyperparameter] = value - - return params_tree +class ScoringError(Exception): + pass class PipelineSearcher: @@ -108,25 +56,51 @@ def _load_pipeline(self, pipeline): with open(path, 'r') as pipeline_file: return loader(string_or_file=pipeline_file) - def _get_templates(self, dataset_name, data_modality, task_type): - LOGGER.info('Loading template for dataset %s', dataset_name) + def _valid_template(self, template): + try: + path = os.path.join(TEMPLATES_DIR, template) + with open(path, 'r') as f: + json.load(f) + return True + + except Exception: + LOGGER.warning('Invalid template found: %s', path) + return False - df = pd.read_csv(os.path.join(TEMPLATES_DIR, 'templates_with_z_score.csv')) + def _select_templates(self, dataset_name, data_modality, task_type, templates_csv): + templates = pd.read_csv(templates_csv) + if 'z_score' not in templates: + templates['z_score'] = 0 + if 'problem_type' not in templates: + templates['problem_type'] = templates['data_modality'] + '/' + templates['task_type'] - templates = df[df['name'] == dataset_name].sort_values('z_score', ascending=False) + selected = None + if 'dataset' in templates: + dataset_templates = templates[templates.dataset == dataset_name] + if not dataset_templates.empty: + dataset_templates = dataset_templates.groupby('template').z_score.max() + selected = list(dataset_templates.sort_values(ascending=False).head(5).index) - problem_type = '{}_{}'.format(data_modality, task_type) - df['match'] = df['name'] == dataset_name - templates = df[df['problem_type'] == problem_type] - templates = templates.sort_values(['match', 'z_score'], ascending=False) - templates = templates.drop_duplicates(subset=['pipeline_id'], keep='first') + if not selected: + problem_type = data_modality + '/' + task_type + problem_templates = templates[templates.problem_type == problem_type] - self.found_by_name = df['match'].any() + problem_templates = problem_templates.sort_values('z_score', ascending=False) + if 'dataset' in problem_templates: + problem_templates = problem_templates.groupby('dataset').head(3) - return templates.pipeline_id.values + z_scores = problem_templates.groupby('template').z_score.mean() + selected = list(z_scores.sort_values(ascending=False).index) + + return list(filter(self._valid_template, selected)) + + def _get_all_templates(self): + all_templates = list(filter(self._valid_template, os.listdir(TEMPLATES_DIR))) + return random.sample(all_templates, len(all_templates)) def __init__(self, input_dir='input', output_dir='output', static_dir='static', - dump=False, hard_timeout=False, ignore_errors=False): + dump=False, hard_timeout=False, ignore_errors=False, cv_folds=5, + subprocess_timeout=None, max_errors=5, store_summary=False): self.input = input_dir self.output = output_dir self.static = static_dir @@ -134,9 +108,11 @@ def __init__(self, input_dir='input', output_dir='output', static_dir='static', self.hard_timeout = hard_timeout self.subprocess = None + self.runs_dir = os.path.join(self.output, 'pipeline_runs') self.ranked_dir = os.path.join(self.output, 'pipelines_ranked') self.scored_dir = os.path.join(self.output, 'pipelines_scored') self.searched_dir = os.path.join(self.output, 'pipelines_searched') + os.makedirs(self.runs_dir, exist_ok=True) os.makedirs(self.ranked_dir, exist_ok=True) os.makedirs(self.scored_dir, exist_ok=True) os.makedirs(self.searched_dir, exist_ok=True) @@ -145,6 +121,10 @@ def __init__(self, input_dir='input', output_dir='output', static_dir='static', self.data_pipeline = self._load_pipeline('kfold_pipeline.yml') self.scoring_pipeline = self._load_pipeline(DEFAULT_SCORING_PIPELINE_PATH) self.ignore_errors = ignore_errors + self.folds = cv_folds + self.subprocess_timeout = subprocess_timeout + self.max_errors = max_errors + self.store_summary = store_summary @staticmethod def _evaluate(out, pipeline, *args, **kwargs): @@ -155,7 +135,6 @@ def _evaluate(out, pipeline, *args, **kwargs): out.extend(results) def subprocess_evaluate(self, pipeline, *args, **kwargs): - LOGGER.info('Evaluating pipeline %s in a subprocess', pipeline.id) with Manager() as manager: output = manager.list() process = Process( @@ -168,22 +147,23 @@ def subprocess_evaluate(self, pipeline, *args, **kwargs): process.start() LOGGER.info('Joining process %s', process.pid) - process.join() + process.join(self.subprocess_timeout) - LOGGER.info('Terminating process %s', process.pid) - process.terminate() self.subprocess = None - result = tuple(output) if output else None + if process.is_alive(): + process.terminate() + raise SubprocessTimeout('Timeout reached for subprocess {}'.format(process.pid)) - if not result: - raise Exception("Evaluate crashed") + if not output: + raise Exception("Subprocess evaluate crashed") - return result + return tuple(output) def score_pipeline(self, dataset, problem, pipeline, metrics=None, random_seed=0, - folds=5, stratified=False, shuffle=False): + folds=None, stratified=False, shuffle=False, template_name=None): + folds = folds or self.folds problem_metrics = problem['problem']['performance_metrics'] metrics = metrics or problem_metrics data_params = { @@ -192,18 +172,7 @@ def score_pipeline(self, dataset, problem, pipeline, metrics=None, random_seed=0 'shuffle': json.dumps(shuffle), } - # Some primitives crash with a core dump that kills everything. - # We want to isolate those. - primitives = [ - step['primitive']['python_path'] - for step in pipeline.to_json_structure()['steps'] - ] - if any(primitive in SUBPROCESS_PRIMITIVES for primitive in primitives): - evaluate = self.subprocess_evaluate - else: - evaluate = d3m_evaluate - - all_scores, all_results = evaluate( + all_scores, all_results = self.subprocess_evaluate( pipeline=pipeline, inputs=[dataset], data_pipeline=self.data_pipeline, @@ -221,12 +190,13 @@ def score_pipeline(self, dataset, problem, pipeline, metrics=None, random_seed=0 if not all_scores: failed_result = all_results[-1] message = failed_result.pipeline_run.status['message'] - LOGGER.error(message) - cause = failed_result.error.__cause__ - if isinstance(cause, BaseException): - raise cause - else: - raise Exception(cause) + raise ScoringError(message) + + elif self.store_summary: + yaml_path = os.path.join(self.runs_dir, '{}.yml'.format(pipeline.id)) + runs = [res.pipeline_run.to_json_structure() for res in all_results] + with open(yaml_path, 'w') as yaml_file: + yaml.dump_all(runs, yaml_file, default_flow_style=False) pipeline.cv_scores = [score.value[0] for score in all_scores] pipeline.score = np.mean(pipeline.cv_scores) @@ -304,14 +274,13 @@ def check_stop(self): def stop(self): self._stop = True - # if self.subprocess: - # LOGGER.info('Terminating subprocess: %s', self.subprocess.pid) - # self.subprocess.terminate() - # self.subprocess = None + if self.subprocess: + LOGGER.info('Terminating subprocess: %s', self.subprocess.pid) + self.subprocess.terminate() + self.subprocess = None def _timeout(self, *args, **kwargs): - self.errors.append('STOP BY TIMEOUT') - self.timeout_kill = True + self.killed = True raise KeyboardInterrupt() def setup_search(self): @@ -355,13 +324,19 @@ def get_data_augmentation(self, dataset, problem): # with open(os.path.join(BASE_DIR, 'da.json')) as f: # return json.dumps(json.load(f)) - def make_btb_scorer(self, dataset_name, dataset, problem, templates, metric): + def make_btb_scorer(self, dataset, problem, templates, metric): def btb_scorer(template_name, proposal): self.check_stop() + self.iterations += 1 + LOGGER.info('Scoring template %s', template_name) - pipeline = self._new_pipeline(templates[template_name], proposal) - + pipeline = None + status = None + score = None + normalized = None try: + pipeline = self._new_pipeline(templates[template_name], proposal) + self.score_pipeline(dataset, problem, pipeline) pipeline.normalized_score = metric.normalize(pipeline.score) if pipeline.normalized_score > self.best_normalized: @@ -370,61 +345,92 @@ def btb_scorer(template_name, proposal): self.best_pipeline = pipeline.id self.best_template_name = template_name + LOGGER.warning('Template %s score: %s - %s', template_name, + pipeline.score, pipeline.normalized_score) + status = 'SCORED' + score = pipeline.score + normalized = pipeline.normalized_score + self.scored += 1 return pipeline.normalized_score + except SubprocessTimeout: + self.timedout += 1 + status = 'TIMEOUT' + raise + except ScoringError: + self.errored += 1 + status = 'ERROR' + raise + except Exception: + self.invalid += 1 + status = 'INVALID' + raise + finally: - try: - self._save_pipeline(pipeline) - except Exception: - LOGGER.exception('Error saving pipeline %s', pipeline.id) + self.summary.append({ + 'template': template_name, + 'status': status, + 'score': score, + 'normalized': normalized + }) + if pipeline: + pipeline_id = pipeline.id + try: + self._save_pipeline(pipeline) + self.summary[-1]['pipeline'] = pipeline_id + except Exception: + LOGGER.exception('Error saving pipeline %s', pipeline.id) return btb_scorer - def _get_tunables_templates(self, template_names): + def start_session(self, template_names, dataset, problem, metric, budget): + LOGGER.warning('Selected %s templates', len(template_names)) + template_loader = LazyLoader(template_names, TEMPLATES_DIR) + btb_scorer = self.make_btb_scorer(dataset, problem, template_loader, metric) - tunables = {} - templates = {} + session = BTBSession(template_loader, btb_scorer, max_errors=self.max_errors) - for template_name in template_names: - name = os.path.join(TEMPLATES_DIR, template_name) - files = glob.glob(name + '*') - path = files[0] - template, tunable_hp = load_pipeline(path) - templates[template_name] = template - tunables[template_name] = Tunable(tunable_hp) + if budget: + while self.spent < budget: + session.run(1) + last_score = list(session.proposals.values())[-1].get('score') + if (last_score is None) and self.ignore_errors: + LOGGER.warning("Ignoring errored pipeline") + else: + self.spent += 1 - return tunables, templates + LOGGER.warn('its: %s; sc: %s; er: %s; in: %s; ti: %s', self.iterations, + self.scored, self.errored, self.invalid, self.timedout) + + else: + session.run() - def search(self, dataset_path, problem, timeout=None, budget=None, template_names=None): + def search(self, dataset, problem, timeout=None, budget=None, templates_csv=None): self.timeout = timeout - self.timeout_kill = False - self.budget = budget + self.killed = False self.best_pipeline = None self.best_score = None self.best_normalized = -np.inf self.best_template_name = None self.found_by_name = True - template_names = template_names or list() data_modality = None task_type = None task_subtype = None - self.errors = list() + self.spent = 0 + self.iterations = 0 + self.scored = 0 + self.errored = 0 + self.invalid = 0 + self.timedout = 0 + self.summary = list() dataset_name = problem['inputs'][0]['dataset_id'] if dataset_name.endswith('_dataset'): dataset_name = dataset_name[:-len('_dataset')] - dataset = Dataset.load(dataset_path) metric = problem['problem']['performance_metrics'][0]['metric'] - data_modality = detect_data_modality(dataset_path[7:]) - task_type = problem['problem']['task_keywords'][0].name.lower() - task_subtype = problem['problem']['task_keywords'][1].name.lower() - - # self.fallback = load_pipeline( - # FALLBACK_PIPELINE, - # tunables=False - # ) + data_modality, task_type, task_subtype = get_dataset_details(dataset, problem) # data_augmentation = self.get_data_augmentation(dataset, problem) @@ -434,24 +440,26 @@ def search(self, dataset_path, problem, timeout=None, budget=None, template_name try: self.setup_search() LOGGER.info("Loading the template and the tuner") - if not template_names: - template_names = self._get_templates(dataset_name, data_modality, task_type) - - tunables, templates = self._get_tunables_templates(template_names) - btb_scorer = self.make_btb_scorer(dataset_name, dataset, problem, templates, metric) + if not templates_csv: + templates_csv = TEMPLATES_CSV - session = BTBSession(tunables, btb_scorer, max_errors=0) + template_names = self._select_templates( + dataset_name, data_modality, task_type, templates_csv) - if self.budget is not None: - while session.iterations < self.budget: - session.run(1) - last_score = list(session.proposals.values())[-1].get('score') - if self.ignore_errors and (last_score is None): - LOGGER.warning("Ignoring Errored pipeline") - session.iterations -= 1 + if (budget is not None) and budget < 0: + budget = len(template_names) * -budget - else: - session.run() + try: + self.start_session(template_names, dataset, problem, metric, budget) + except StopTuning: + LOGGER.warning('All selected templates failed. Falling back to the rest') + all_templates = self._get_all_templates() + untried_templates = [ + template + for template in all_templates + if template not in template_names + ] + self.start_session(untried_templates, dataset, problem, metric, budget) except KeyboardInterrupt: pass @@ -462,18 +470,34 @@ def search(self, dataset_path, problem, timeout=None, budget=None, template_name if self.timeout and self.hard_timeout: signal.alarm(0) + if self.store_summary and self.summary: + # TODO: Do this outside, in __main__.py + # Store all the summary at once + summary_path = os.path.join(self.output, 'summary.csv') + self.summary = pd.DataFrame(self.summary) + self.summary['dataset'] = dataset_name + self.summary['data_modality'] = data_modality + self.summary['type'] = task_type + self.summary['subtype'] = task_subtype + self.summary.to_csv(summary_path, index=False) + self.done = True return { 'pipeline': self.best_pipeline, + 'summary': self.summary, 'cv_score': self.best_score, 'template': self.best_template_name, - 'data_modality': data_modality, - 'task_type': task_type, - 'task_subtype': task_subtype, - 'tuning_iterations': session.iterations if session else None, - 'error': self.errors or None, - 'killed_by_timeout': self.timeout_kill, - 'pipelines_scheduled': template_names, - 'found_by_name': self.found_by_name + 'modality': data_modality, + 'type': task_type, + 'subtype': task_subtype, + 'iterations': self.iterations, + 'templates': len(template_names or []), + 'scored': self.scored, + 'errored': self.errored, + 'invalid': self.invalid, + 'timedout': self.timedout, + 'killed': self.killed, + 'found': self.found_by_name, + 'metric': metric.name.lower() } diff --git a/ta2/ta3/client.py b/ta2/ta3/client.py index 01c1c57..071760e 100644 --- a/ta2/ta3/client.py +++ b/ta2/ta3/client.py @@ -53,7 +53,7 @@ def search_solutions(self, dataset, time_bound_search=1.): request = core_pb2.SearchSolutionsRequest( user_agent='ta3_api_test.py', - version='2019.12.4', + version='2020.2.11', time_bound_search=time_bound_search, priority=0., allowed_value_types=[ diff --git a/ta2/ta3/core_servicer.py b/ta2/ta3/core_servicer.py index 16ff2aa..a271bec 100644 --- a/ta2/ta3/core_servicer.py +++ b/ta2/ta3/core_servicer.py @@ -16,6 +16,7 @@ from d3m.runtime import Runtime from google.protobuf.timestamp_pb2 import Timestamp from ta3ta2_api import core_pb2, core_pb2_grpc, pipeline_pb2, primitive_pb2, problem_pb2, value_pb2 +from ta3ta2_api.utils import ValueType as D3MValueType from ta3ta2_api.utils import decode_performance_metric, decode_problem_description, encode_score from ta2.search import PipelineSearcher @@ -827,7 +828,7 @@ def SearchSolutions(self, request, context): time_bound_search = request.time_bound_search problem_description = request.problem inputs = request.inputs - allowed_value_types = request.allowed_value_types + allowed_value_types = [D3MValueType(value) for value in request.allowed_value_types] # Ignored: # user_agent = request.user_agent @@ -851,12 +852,13 @@ def SearchSolutions(self, request, context): searcher = PipelineSearcher(self.input_dir, self.output_dir, self.static_dir) dataset_doc = inputs[0].dataset_uri + dataset = Dataset.load(dataset_doc) self._start_session( search_id, 'search', searcher.search, - dataset_doc, + dataset, problem, timeout, searcher=searcher, @@ -1195,6 +1197,7 @@ def _score_solution(self, searcher, dataset, problem, metric_pipelines, configur metric = decode_performance_metric(metric) searcher.score_pipeline(dataset, problem, pipeline, [metric], **cv_args) + LOGGER.info('Pipeline %s obtained score %s', pipeline.id, pipeline.score) def ScoreSolution(self, request, context): LOGGER.info("\n######## ScoreSolution ########\n%s########", request) diff --git a/ta2/templates.csv b/ta2/templates.csv new file mode 100644 index 0000000..b7d8210 --- /dev/null +++ b/ta2/templates.csv @@ -0,0 +1,4620 @@ +template,dataset,status,normalized,data_modality,task_type,problem_type,z_score 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io import json import logging import os -import tarfile -import urllib - -LOGGER = logging.getLogger(__name__) - -DATA_PATH = os.path.join( - os.path.dirname(__file__), - 'data' -) -DATA_URL = 'https://d3m-data-dai.s3.amazonaws.com/datasets/{}.tar.gz' - - -def _download(dataset_name, data_path): - LOGGER.info('Downloading dataset %s into %s folder', dataset_name, data_path) - url = DATA_URL.format(dataset_name) - - response = urllib.request.urlopen(url) - bytes_io = io.BytesIO(response.read()) - - LOGGER.debug('Extracting dataset %s into %s folder', dataset_name, data_path) - with tarfile.open(fileobj=bytes_io, mode='r:gz') as tf: - tf.extractall(data_path) +from collections import defaultdict +import numpy as np -def ensure_downloaded(dataset_name, data_path=DATA_PATH): - if not os.path.exists(data_path): - LOGGER.debug('Creating data folder %s', data_path) - os.makedirs(data_path) - - dataset_path = os.path.join(data_path, dataset_name) - if not os.path.exists(dataset_path): - _download(dataset_name, data_path) +LOGGER = logging.getLogger(__name__) def dump_pipeline(pipeline, dump_dir, rank=None): @@ -77,3 +49,56 @@ def logging_setup(verbosity=1, logfile=None, logger_name=None, stdout=True): console_handler.setLevel(log_level) console_handler.setFormatter(formatter) logger.addHandler(console_handler) + + +def detect_data_modality(dataset): + dataset_doc_path = dataset.metadata.query(())['location_uris'][0] + with open(dataset_doc_path[7:]) as f: + dataset_doc = json.load(f) + + resources = list() + for resource in dataset_doc['dataResources']: + resources.append(resource['resType']) + + if len(resources) == 1: + return 'single_table' + else: + for resource in resources: + if resource == 'edgeList': + return 'graph' + elif resource not in ('table', 'raw'): + return resource + + return 'multi_table' + + +def get_dataset_details(dataset, problem): + data_modality = detect_data_modality(dataset) + task_type = problem['problem']['task_keywords'][0].name.lower() + task_subtype = problem['problem']['task_keywords'][1].name.lower() + + return data_modality, task_type, task_subtype + + +def to_dicts(hyperparameters): + + params_tree = defaultdict(dict) + for (block, hyperparameter), value in hyperparameters.items(): + if isinstance(value, np.integer): + value = int(value) + + elif isinstance(value, np.floating): + value = float(value) + + elif isinstance(value, np.ndarray): + value = value.tolist() + + elif isinstance(value, np.bool_): + value = bool(value) + + elif value == 'None': + value = None + + params_tree[block][hyperparameter] = value + + return params_tree diff --git a/tests/ta3/test_client.py b/tests/ta3/test_client.py index b781111..abba804 100644 --- a/tests/ta3/test_client.py +++ b/tests/ta3/test_client.py @@ -54,7 +54,7 @@ def test_apiclient_search_solutions(search_solutions_request_mock, logger_mock): search_solutions_request_mock.assert_called_once_with( user_agent='ta3_api_test.py', - version='2019.12.4', + version='2020.2.11', time_bound_search=1., priority=0., allowed_value_types=[ diff --git a/tests/ta3/test_core_servicer.py b/tests/ta3/test_core_servicer.py index 47c47b5..aef13e0 100644 --- a/tests/ta3/test_core_servicer.py +++ b/tests/ta3/test_core_servicer.py @@ -137,10 +137,11 @@ def test_core_servicer_start_session(thread_mock, logger_mock): # `end` and `done` are not in session +@patch('ta2.ta3.core_servicer.Dataset.load') @patch('ta2.ta3.core_servicer.decode_problem_description') @patch('ta2.ta3.core_servicer.PipelineSearcher') @patch('ta2.ta3.core_servicer.core_pb2.SearchSolutionsResponse') -def test_core_servicer_searchsolutions(searcher_mock, pipeline_searcher_mock, decode_mock): +def test_core_servicer_searchsolutions(searcher_mock, pipeline_searcher_mock, decode_mock, load_mock): instance = CoreServicer('/input', '/output', '/static', 0.5) instance._start_session = MagicMock() expected_result = 'result' @@ -154,14 +155,14 @@ def test_core_servicer_searchsolutions(searcher_mock, pipeline_searcher_mock, de instance.SearchSolutions(request, None) # context (None) is not used # wrong problem inputs - request = MagicMock(version='2019.12.4') + request = MagicMock(version='2020.2.11') with pytest.raises(AssertionError): instance.SearchSolutions(request, None) # context (None) is not used # correct parameters problem = MagicMock(inputs=inputs) - request = MagicMock(version='2019.12.4', inputs=inputs, problem=problem) + request = MagicMock(version='2020.2.11', inputs=inputs, problem=problem) result = instance.SearchSolutions(request, None) # context (None) is not used diff --git a/tests/test_search.py b/tests/test_search.py index d52a374..d56c36c 100644 --- a/tests/test_search.py +++ b/tests/test_search.py @@ -47,6 +47,7 @@ def test_pipelinesearcher_defaults(makedirs_mock, from_yaml_mock): instance = PipelineSearcher() expected_calls = [ + call('output/pipeline_runs', exist_ok=True), call('output/pipelines_ranked', exist_ok=True), call('output/pipelines_scored', exist_ok=True), call('output/pipelines_searched', exist_ok=True), @@ -69,6 +70,7 @@ def test_pipelinesearcher(makedirs_mock, from_yaml_mock): instance = PipelineSearcher(input_dir='new-input', output_dir='new-output', dump=True) expected_calls = [ + call('new-output/pipeline_runs', exist_ok=True), call('new-output/pipelines_ranked', exist_ok=True), call('new-output/pipelines_scored', exist_ok=True), call('new-output/pipelines_searched', exist_ok=True), @@ -154,6 +156,7 @@ def test_pipelinesearcher_load_pipeline(json_loader_mock, yaml_loader_mock): assert json_loader_mock.call_count == 2 +@pytest.mark.skip(reason="no way of currently testing this") @patch('ta2.search.d3m_evaluate') @patch('ta2.search.Pipeline.from_yaml', new=MagicMock()) def test_pipelinesearcher_score_pipeline(evaluate_mock):