Open and Automated Machine Learning
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This document summarizes an presentation about OpenML, an online platform for sharing machine learning data and experiments. OpenML allows users to search datasets, build machine learning models using various tools/APIs, run experiments on tasks, and automatically upload results. This facilitates reproducibility, benchmarking, and reuse of prior work. OpenML also aims to advance automated machine learning through meta-learning techniques that leverage the large amount of shared data and experiments.
![Search (API)
import pandas as pd
datalist = pd.DataFrame.from_dict(openml_list)
datalist[datalist.NumberOfInstances>10000
].sort_values(['NumberOfInstances'])
Python, R, Java, C#](https://image.slidesharecdn.com/openmltechtalk-180828065132/85/Open-and-Automated-Machine-Learning-15-320.jpg)
![Get (API)
dataset = oml.datasets.get_dataset(1471)
dataset.description[:500]
Python, R, Java, C#](https://image.slidesharecdn.com/openmltechtalk-180828065132/85/Open-and-Automated-Machine-Learning-17-320.jpg)
![Get (API)
X, y, attribute_names = dataset.get_data(
return_attribute_names=True)
Python, R, Java, C#
eeg = pd.DataFrame(X, columns=attribute_names)
eeg['class'] = y](https://image.slidesharecdn.com/openmltechtalk-180828065132/85/Open-and-Automated-Machine-Learning-18-320.jpg)
![Fit and share pipelines
Uploaded to http://www.openml.org/r/7943199
from sklearn import pipeline, ensemble, preprocessing
from openml import tasks,runs, datasets
task = tasks.get_task(59)
pipe = pipeline.Pipeline(steps=[
('Imputer', preprocessing.Imputer()),
('OneHotEncoder', preprocessing.OneHotEncoder(),
('Classifier', ensemble.RandomForestClassifier())
])
flow = oml.flows.sklearn_to_flow(pipe)
run = oml.runs.run_flow_on_task(task, flow)
myrun = run.publish()](https://image.slidesharecdn.com/openmltechtalk-180828065132/85/Open-and-Automated-Machine-Learning-30-320.jpg)
![Fit and share deep learning models
import keras
from keras.models import Sequential,
from keras.layers import Dense, Dropout, Flatten, Conv2D, MaxPooling
from keras.layers.core import Activation
model = Sequential()
model.add(Reshape((28, 28, 1), input_shape=(784,)))
model.add(Conv2D(20, (5, 5), padding=“same", input_shape=(28,28,1),
activation='relu'))
model.add(MaxPooling2D(pool_size=(2, 2), strides=(2, 2)))
model.add(Conv2D(50, (5, 5), padding="same", activation='relu'))
model.add(MaxPooling2D(pool_size=(2, 2), strides=(2, 2)))
model.add(Flatten())
model.add(Dense(500))
model.add(Activation(‘relu'))
model.add(Dense(10))
model.add(Activation('softmax'))
model.compile(loss=keras.losses.categorical_crossentropy,
optimizer=keras.optimizers.Adadelta(),
metrics=['accuracy'])](https://image.slidesharecdn.com/openmltechtalk-180828065132/85/Open-and-Automated-Machine-Learning-31-320.jpg)
![Download, reuse runs
myruns = oml.runs.list_runs(task=[14951])
sns.violinplot(x="score", y="flow", data=pd.DataFrame(](https://image.slidesharecdn.com/openmltechtalk-180828065132/85/Open-and-Automated-Machine-Learning-36-320.jpg)
Open and Automated Machine Learning
- 1. OpenML O P E N , A U T O M AT E D M A C H I N E L E A R N I N G J O A Q U I N VA N S C H O R E N , T U / E @open_mlwww.openml.org
- 2. OpenML You can be part of this presentation :) Follow the code examples: • On Google Colab: goo.gl/VwbKb4 • On Github: https://git.io/fA3eL J O A Q U I N VA N S C H O R E N , T U / E @open_mlwww.openml.org
- 5. (Not so) Automatic Machine Learning It’s hard to find and learn from prior machine learning data
- 6. (Auto)ML: manual work, unnecessary friction Hard to find and reuse prior results No standards / hubs for sharing and organizing results Scattered, ill-described, datasets Manual searching, reformatting, making assumptions Hard to automate model building end-to-end Requires automated data organization, clean APIs,… Myriad algorithms, versions, languages Write code, set up experiments, store results,… Reproducibility is hard Manually tracking every detail is error-prone
- 7. Easy to use: Integrated in many ML tools/environments Easy to contribute: Automated sharing of data, code, results Organized data: APIs to find & reuse data, models, experiments Reward structure: Track your impact, build reputation Self-learning: Learn from many experiments to help people OpenML S H A R E A N D R E U S E M A C H I N E L E A R N I N G D ATA O N L I N E
- 9. OpenML: Components Flows: Pipelines/code that build ML models Run locally (or wherever), auto-upload all results Datasets: Auto-annotated, organized, well-formatted Find the datasets you need, share your own Tasks: Auto-generated, machine-readable Everyone’s results are directly comparable Runs:All results from running flows on tasks All details needed for tracking and reproducibility Evaluations can be queried, compared, reused
- 10. It starts with data
- 11. Data (tabular) easily uploaded or referenced (URL) It starts with data
- 12. Data can remain in existing repositories -> registered via URL, transparent to users interoperability For now: only tabular data -> ARFF or CSV import (auto-annotate features) -> FrictionlessData support in the works
- 13. auto-versioned, analysed, organised online
- 14. Search (API) import openml as oml openml_list = oml.datasets.list_datasets() Python, R, Java, C#
- 16. Search (API) datalist.query('name == "eeg-eye-state"') Python, R, Java, C# data id
- 17. Get (API) dataset = oml.datasets.get_dataset(1471) dataset.description[:500] Python, R, Java, C#
- 18. Get (API) X, y, attribute_names = dataset.get_data( return_attribute_names=True) Python, R, Java, C# eeg = pd.DataFrame(X, columns=attribute_names) eeg['class'] = y
- 20. Fit (API) Python, R, Java, C# from sklearn import neighbors clf = neighbors.KNeighborsClassifier() clf.fit(X, y)
- 21. Complete code to build a model, automatically, anywhere import openml as oml from sklearn import neighbors, tree dataset = oml.datasets.get_dataset(1471) X, y = dataset.get_data() clf = neighbors.KNeighborsClassifier() clf.fit(X, y)
- 22. Tasks contain data, goals, procedures. Auto-build + evaluate models correctly All evaluations are directly comparable optimize accuracy Predict target T Tasks benchmarking and collaboration 10-fold Xval
- 23. 10-fold Xval Predict target T Collaborate in real time online optimize accuracy
- 25. Search mytasks = pd.DataFrame.from_dict(task_list) mytasks.query('name=="eeg-eye-state"') task id
- 27. Auto-run algorithms/workflows on any task Integrated in many machine learning tools (+ APIs) Flows Run experiments locally, share them globally
- 28. Integrated in many machine learning tools (+ APIs) import openml as oml from sklearn import tree task = oml.tasks.get_task(14951) clf = tree.ExtraTreeClassifier() flow = oml.flows.sklearn_to_flow(clf) run = oml.runs.run_flow_on_task(task, flow) myrun = run.publish()
- 29. Fit and share (complete code) Uploaded to http://www.openml.org/r/9204488 import openml as oml from sklearn import tree task = oml.tasks.get_task(14951) clf = tree.ExtraTreeClassifier() flow = oml.flows.sklearn_to_flow(clf) run = oml.runs.run_flow_on_task(task, flow) myrun = run.publish()
- 30. Fit and share pipelines Uploaded to http://www.openml.org/r/7943199 from sklearn import pipeline, ensemble, preprocessing from openml import tasks,runs, datasets task = tasks.get_task(59) pipe = pipeline.Pipeline(steps=[ ('Imputer', preprocessing.Imputer()), ('OneHotEncoder', preprocessing.OneHotEncoder(), ('Classifier', ensemble.RandomForestClassifier()) ]) flow = oml.flows.sklearn_to_flow(pipe) run = oml.runs.run_flow_on_task(task, flow) myrun = run.publish()
- 31. Fit and share deep learning models import keras from keras.models import Sequential, from keras.layers import Dense, Dropout, Flatten, Conv2D, MaxPooling from keras.layers.core import Activation model = Sequential() model.add(Reshape((28, 28, 1), input_shape=(784,))) model.add(Conv2D(20, (5, 5), padding=“same", input_shape=(28,28,1), activation='relu')) model.add(MaxPooling2D(pool_size=(2, 2), strides=(2, 2))) model.add(Conv2D(50, (5, 5), padding="same", activation='relu')) model.add(MaxPooling2D(pool_size=(2, 2), strides=(2, 2))) model.add(Flatten()) model.add(Dense(500)) model.add(Activation(‘relu')) model.add(Dense(10)) model.add(Activation('softmax')) model.compile(loss=keras.losses.categorical_crossentropy, optimizer=keras.optimizers.Adadelta(), metrics=['accuracy'])
- 32. Fit and share deep learning models Uploaded to https://www.openml.org/r/9204337 task = tasks.get_task(3573) #MNIST flow = oml.flows.keras_to_flow(model) run = oml.runs.run_flow_on_task(task, flow) myrun = run.publish()
- 34. reproducible, linked to data, flows, authors and all other experiments Experiments auto-uploaded, evaluated online Runs Share and reuse results
- 35. Experiments auto-uploaded, evaluated online
- 39. OpenML Community 5600+ registered users, 120000+ yearly users
- 40. A U T O M L : M E TA - L E A R N I N G • Find similar datasets • 20,000+ versioned datasets, with 130+ meta-features • Instead of starting from scratch, start from configurations that worked well on similar datasets
- 41. A U T O M L : M E TA - L E A R N I N G • Find similar datasets • 20,000+ versioned datasets, with 130+ meta-features • Instead of starting from scratch, start from configurations that worked well on similar datasets • Auto-sklearn (AutoML challenge winner, NIPS 2016) • Lookup similar datasets, start with best pipelines Matthias Feurer et al. (2016) NIPS
- 42. A U T O M L : M E TA - L E A R N I N G • Find similar datasets • 20,000+ versioned datasets, with 130+ meta-features • Instead of starting from scratch, start from configurations that worked well on similar datasets • Auto-sklearn (AutoML challenge winner, NIPS 2016) • Lookup similar datasets, start with best pipelines
- 43. A U T O M L : M E TA - L E A R N I N G • Reuse (millions of) prior model evaluations: • Benchmark new algorithms against state-of-the-art • Meta-models: E.g. predict performance or training time • MIT AutoML system (ICBD 2017) • Uses and compares against OpenML results
- 44. A U T O M L : M E TA - L E A R N I N G • Reuse (millions of) prior model evaluations: • Benchmark new algorithms against state-of-the-art • Meta-models: E.g. predict performance or training time • Runtime prediction
- 45. A U T O M L : M E TA - L E A R N I N G • Reuse (millions of) prior model evaluations: • Benchmark new algorithms against state-of-the-art • Meta-models: E.g. predict performance or training time • Faster TPOT (in progress) • Build meta-models (Random Forest works well) • Focus on fast configurations first
- 46. A U T O M L : M E TA - L E A R N I N G • Reuse results on many hyperparameter settings • Surrogate models: predict best hyperparameter settings • Study hyperparameter effects/importance • Amazon’s multi-task learning AutoML (NIPS 2017) • Trains surrogate models per task • On new tasks: learns how to combine them with neural net
- 47. A U T O M L : M E TA - L E A R N I N G • Reuse results on many hyperparameter settings • Surrogate models: predict best hyperparameter settings • Study hyperparameter effects/importance • Hyperparameter space design • Use OpenML data to learn which hyperparameters to tune Jan van Rijn et al. (2017) AutoML@ICML
- 48. A U T O M L : M E TA - L E A R N I N G • Never-ending Automatic Machine Learning: • AutoML methods built on top of OpenML get increasingly better as more meta-data is added • Faster drug discovery (QSAR) • Meta-learning to build better models that recommend drug candidates for rare diseases ChEMBL DB: 1.4M compounds, 10k proteins,12.8M activities Molecule representations MW #LogP #TPSA #b1 #b2 #b3 #b4 #b5 #b6 #b7 #b8 #b9# !! 377.435 !3.883 !77.85 !1 !1 !0 !0 !0 !0 !0 !0 !0 !! 341.361 !3.411 !74.73 !1 !1 !0 !1 !0 !0 !0 !0 !0 !…! 197.188 !-2.089 !103.78 !1 !1 !0 !1 !0 !0 !0 !1 !0 !! 346.813 !4.705 !50.70 !1 !0 !0 !1 !0 !0 !0 !0 !0! ! ! ! ! ! ! !.! ! ! ! ! ! ! !: !! 16,000 regression datasets x52 pipelines (on OpenML) meta-model all data on new protein optimal models to predict activity (Olier et al., Machine Learning 107(1), 2018)
- 49. Learning to learn Bots that learn from all prior experiments Automate drudge work, help people build models
- 50. Join us! (and change the world) Active open source community We need more bright people - ML/DB experts - Developers - UX
- 51. Support is welcome! Workshop sponsorship (hackathons 2x/year) Donations: OpenML foundation Compute time Project ideas
- 52. E I N D H O V E N U N I V E R S I T Y Looking for: • PhD Students • Scientific programmer
- 53. O P E N M L H A C K AT H O N Paris, September 17-21, 2018 meet.openml.org Co-located with COSEAL
- 55. Questions?