piex 0.1.2

Pipeline Explorer

An open source project from Data to AI Lab at MIT.

Pipeline Explorer

Classes and functions to explore and reproduce the performance obtained by thousands of MLBlocks pipelines and templates across hundreds of datasets.

• Free software: MIT license
• Documentation: https://HDI-Project.github.io/piex
• Homepage: https://github.com/HDI-Project/piex

Overview

This repository contains a collection of classes and functions which allows a user to easily explore the results of a series of experiments run by team MIT using MLBlocks pipelines over a large collection of Datasets.

Along with this library we are releasing a number of fitted pipelines, their performance on cross validation, test data and metrics. The results of these experiments were stored in a Database and later on uploaded to Amazon S3, from where they can be downloaded and analyzed using the Pipeline Explorer.

We will continuously add more pipelines, templates and datasets to our experiments and make them publicly available to the community.

These can be used for the following purposes:

• Find what is the best score we found so far for a given dataset and task type (given the search space we defined and our tuners)
• Use information about pipeline performance to do meta learning

Current summary of our experiments is:

# of
datasets	453
pipelines	2115907
templates	63
tests	2152

Concepts

Before diving into the software usage, we briefly explain some concepts and terminology.

Primitives

We call the smallest computational blocks used in a Machine Learning process primitives, which:

• Can be either classes or functions.
• Have some initialization arguments, which MLBlocks calls init_params.
• Have some tunable hyperparameters, which have types and a list or range of valid values.

Templates

Primitives can be combined to form what we call Templates, which:

• Have a list of primitives.
• Have some initialization arguments, which correspond to the initialization arguments of their primitives.
• Have some tunable hyperparameters, which correspond to the tunable hyperparameters of their primitives.

Pipelines

Templates can be used to build Pipelines by taking and fixing a set of valid hyperparameters for a Template. Hence, Pipelines:

• Have a list of primitives, which corresponds to the list of primitives of their template.
• Have some initialization arguments, which correspond to the initialization arguments of their template.
• Have some hyperparameter values, which fall within the ranges of valid tunable hyperparameters of their template.

A pipeline can be fitted and evaluated using the MLPipeline API in MLBlocks.

Datasets

A collection of ~450 datasets was used covering 6 different data modalities and 17 task types.

Each dataset was split using a holdout method in two parts, training and testing, which were used respectively to find and fit the optimal pipeline for each dataset, and to later on evaluate the goodness-of-fit of each pipeline against a specific metric for each dataset.

This collection of datasets is stored in an Amazon S3 Bucket in the D3M format, including the training and testing partitioning, and can be downloaded both using piex or a web browser following this link: https://d3m-data-dai.s3.amazonaws.com/index.html

What is an experiment/test?

Throughout our description we will refer to a search process as an experiment or a test. An experiment/test is defined as follows:

• It is given a dataset and a task
• It is given a template
• It then searches using a Bayesian tuning algorithm (using a tuner from our BTB library). Tuning algorithm tests multiple pipelines derived from the template and tries to find the best set of hyperparameters possible for that template on each dataset.
• During the search process, a collection of information is stored in the database and is available through piex. They are:
  • Cross Validation score obtained over the training partition by each pipeline fitted during the search process.
  • In parallel, at some points in time the best pipeline already found was validated against the testing data, and the obtained score was also stored in the database.

Each experiment was given one or more of the following configuration values:

• Timeout: Maximum time that the search process is allowed to run.
• Budget: Maximum number of tuning iterations that the search process is allowed to perform.
• Checkpoints: List of points in time, in seconds, where the best pipeline so far was scored against the testing data.
• Pipeline: The name of the template to use to build the pipelines.
• Tuner Type: The type of tuner to use, gp or uniform.

Getting Started

Installation

The simplest and recommended way to install the Pipeline Explorer is using pip:

pip install piex

Alternatively, you can also clone the repository and install it from sources

git clone [e-mail]:HDI-Project/piex.git
cd piex
pip install -e .

Usage

The S3PipelineExplorer

The S3PipelineExplorer class provides methods to download the results from previous tests executions from S3, see which pipelines obtained the best scores and load them as a dictionary, ready to be used by an MLPipeline.

To start working with it, it needs to be given the name of the S3 Bucket from which the data will be downloaded.

For this examples, we will be using the ml-pipelines-2018 bucket, where the results of the experiments run for the Machine Learning Bazaar paper can be found.

from piex.explorer import S3PipelineExplorer

piex = S3PipelineExplorer('ml-pipelines-2018')

The Datasets

The get_datasets method returns a pandas.DataFrame with information about the available datasets, their data modalities, task types and task subtypes.

datasets = piex.get_datasets()
datasets.shape

(453, 4)

datasets.head()

	dataset	data_modality	task_type	task_subtype
314	124_120_mnist	image	classification	multi_class
315	124_138_cifar100	image	classification	multi_class
316	124_153_svhn_cropped	image	classification	multi_class
317	124_174_cifar10	image	classification	multi_class
318	124_178_coil100	image	classification	multi_class

datasets = piex.get_datasets(data_modality='multi_table', task_type='regression')
datasets.head()

	dataset	data_modality	task_type	task_subtype
311	uu2_gp_hyperparameter_estimation	multi_table	regression	multivariate
312	uu3_world_development_indicators	multi_table	regression	univariate

The Experiments

The list of tests that have been executed can be obtained with the method get_tests.

This method returns a pandas.DataFrame that contains a row for each experiment that has been run on each dataset. This dataset includes information about the dataset, the configuration used for the experiment, such as the template, the checkpoints or the budget, and information about the execution, such as the timestamp, the exact software version, the host that executed the test and whether there was an error or not.

Just like the get_datasets, any keyword arguments will be used to filter the results.

import pandas as pd

tests = piex.get_tests()
tests.head().T

	0	1	2	3	4
budget	NaN	NaN	NaN	NaN	NaN
checkpoints	[900, 1800, 3600, 7200]	[900, 1800, 3600, 7200]	[900, 1800, 3600, 7200]	[900, 1800, 3600, 7200]	[900, 1800, 3600, 7200]
commit	4c7c29f	4c7c29f	4c7c29f	4c7c29f	4c7c29f
dataset	196_autoMpg	26_radon_seed	LL0_1027_esl	LL0_1028_swd	LL0_1030_era
docker	False	False	False	False	False
error	NaN	NaN	NaN	NaN	NaN
hostname	ec2-52-14-97-167.us-east-2.compute.amazonaws.com	ec2-18-223-109-53.us-east-2.compute.amazonaws.com	ec2-18-217-79-23.us-east-2.compute.amazonaws.com	ec2-18-217-239-54.us-east-2.compute.amazonaws.com	ec2-18-225-32-252.us-east-2.compute.amazonaws.com
image	NaN	NaN	NaN	NaN	NaN
insert_ts	2018-10-24 20:05:01.872	2018-10-24 20:05:02.778	2018-10-24 20:05:02.879	2018-10-24 20:05:02.980	2018-10-24 20:05:03.081
pipeline	categorical_encoder/imputer/standard_scaler/xg...	categorical_encoder/imputer/standard_scaler/xg...	categorical_encoder/imputer/standard_scaler/xg...	categorical_encoder/imputer/standard_scaler/xg...	categorical_encoder/imputer/standard_scaler/xg...
status	done	done	done	done	done
test_id	20181024200501872083	20181024200501872083	20181024200501872083	20181024200501872083	20181024200501872083
timeout	NaN	NaN	NaN	NaN	NaN
traceback	NaN	NaN	NaN	NaN	NaN
tuner_type	NaN	NaN	NaN	NaN	NaN
update_ts	2018-10-24 22:05:55.386	2018-10-24 22:05:57.508	2018-10-24 22:05:56.337	2018-10-24 22:05:56.112	2018-10-24 22:05:56.164
data_modality	single_table	single_table	single_table	single_table	single_table
task_type	regression	regression	regression	regression	regression
task_subtype	univariate	univariate	univariate	univariate	univariate
metric	meanSquaredError	rootMeanSquaredError	meanSquaredError	meanSquaredError	meanSquaredError
dataset_id	196_autoMpg_dataset_TRAIN	26_radon_seed_dataset_TRAIN	LL0_1027_esl_dataset_TRAIN	LL0_1028_swd_dataset_TRAIN	LL0_1030_era_dataset_TRAIN
problem_id	196_autoMpg_problem_TRAIN	26_radon_seed_problem_TRAIN	LL0_1027_esl_problem_TRAIN	LL0_1028_swd_problem_TRAIN	LL0_1030_era_problem_TRAIN
target	class	log_radon	out1	Out1	out1
size	24	160	16	52	32
size_human	24K	160K	16K	52K	32K
test_features	7	28	4	10	4
test_samples	100	183	100	199	199
train_features	7	28	4	10	4
train_samples	298	736	388	801	801

pd.DataFrame(tests.groupby(['data_modality', 'task_type']).size(), columns=['count'])

		count
data_modality	task_type
graph	community_detection	5
graph_matching	18
link_prediction	2
vertex_nomination	2
image	classification	57
regression	1
multi_table	classification	1
regression	1
single_table	classification	1405
collaborative_filtering	1
regression	430
time_series_forecasting	175
text	classification	17
timeseries	classification	37

tests = piex.get_tests(data_modality='graph', task_type='link_prediction')
tests[['dataset', 'pipeline', 'checkpoints', 'test_id']]

	dataset	pipeline	checkpoints	test_id
1716	59_umls	NaN	[900, 1800, 3600, 7200]	20181031040541366347
2141	59_umls	graph/link_prediction/random_forest_classifier	[900, 1800, 3600, 7200]	20181031182305995728

The Experiment Results

The results of the experiments can be seen using the get_experiment_results method.

These results include both the cross validation score obtained by the pipeline during the tuning, as well as the score obtained by this pipeline once it has been fitted using the training data and then used to make predictions over the test data.

Just like the get_datasets, any keyword arguments will be used to filter the results, including the test_id.

results = piex.get_test_results(test_id='20181031182305995728')
results[['test_id', 'pipeline', 'score', 'cv_score', 'elapsed', 'iterations']]

	test_id	pipeline	score	cv_score	elapsed	iterations
7464	20181031182305995728	graph/link_prediction/random_forest_classifier	0.499853	0.843175	900.255511	435.0
7465	20181031182305995728	graph/link_prediction/random_forest_classifier	0.499853	0.854603	1800.885417	805.0
7466	20181031182305995728	graph/link_prediction/random_forest_classifier	0.499853	0.854603	3600.005072	1432.0
7467	20181031182305995728	graph/link_prediction/random_forest_classifier	0.785568	0.860000	7200.225256	2366.0

The Best Pipeline

Information about the best pipeline for a dataset can be obtained using the get_best_pipeline method.

This method returns a pandas.Series object with information about the pipeline that obtained the best cross validation score during the tuning, as well as the template that was used to build it.

Note: This call will download some data in the background the first time that it is run, so it might take a while to return.

piex.get_best_pipeline('185_baseball')

id                            17385666-31da-4b6e-ab7f-8ac7080a4d55
dataset                                 185_baseball_dataset_TRAIN
metric                                                     f1Macro
name             categorical_encoder/imputer/standard_scaler/xg...
rank                                                      0.307887
score                                                     0.692113
template                                  5bd0ce5249e71569e8bf8003
test_id                                       20181024234726559170
pipeline         categorical_encoder/imputer/standard_scaler/xg...
data_modality                                         single_table
task_type                                           classification
Name: 1149699, dtype: object

Apart from obtaining this information, we can use the load_best_pipeline method to load its JSON specification, ready to be using in an mlblocks.MLPipeline object.

pipeline = piex.load_best_pipeline('185_baseball')
pipeline['primitives']

['mlprimitives.feature_extraction.CategoricalEncoder',
 'sklearn.preprocessing.Imputer',
 'sklearn.preprocessing.StandardScaler',
 'mlprimitives.preprocessing.ClassEncoder',
 'xgboost.XGBClassifier',
 'mlprimitives.preprocessing.ClassDecoder']

The Best Template

Just like the best pipeline, the best template for a given dataset can be obtained using the get_best_template method.

This returns just the name of the template that was used to build the best pipeline.

template_name = piex.get_best_template('185_baseball')
template_name

'categorical_encoder/imputer/standard_scaler/xgbclassifier'

This can be later on used to explore the template, obtaining its default hyperparameters:

defaults = piex.get_default_hyperparameters(template_name)
defaults

{'mlprimitives.feature_extraction.CategoricalEncoder#1': {'copy': True,
  'features': 'auto',
  'max_labels': 0},
 'sklearn.preprocessing.Imputer#1': {'missing_values': 'NaN',
  'axis': 0,
  'copy': True,
  'strategy': 'mean'},
 'sklearn.preprocessing.StandardScaler#1': {'with_mean': True,
  'with_std': True},
 'mlprimitives.preprocessing.ClassEncoder#1': {},
 'xgboost.XGBClassifier#1': {'n_jobs': -1,
  'n_estimators': 100,
  'max_depth': 3,
  'learning_rate': 0.1,
  'gamma': 0,
  'min_child_weight': 1},
 'mlprimitives.preprocessing.ClassDecoder#1': {}}

Or obtaining the corresponding tunable ranges, ready to be used with a tuner:

tunable = piex.get_tunable_hyperparameters(template_name)
tunable

{'mlprimitives.feature_extraction.CategoricalEncoder#1': {'max_labels': {'type': 'int',
   'default': 0,
   'range': [0, 100]}},
 'sklearn.preprocessing.Imputer#1': {'strategy': {'type': 'str',
   'default': 'mean',
   'values': ['mean', 'median', 'most_frequent']}},
 'sklearn.preprocessing.StandardScaler#1': {'with_mean': {'type': 'bool',
   'default': True},
  'with_std': {'type': 'bool', 'default': True}},
 'mlprimitives.preprocessing.ClassEncoder#1': {},
 'xgboost.XGBClassifier#1': {'n_estimators': {'type': 'int',
   'default': 100,
   'range': [10, 1000]},
  'max_depth': {'type': 'int', 'default': 3, 'range': [3, 10]},
  'learning_rate': {'type': 'float', 'default': 0.1, 'range': [0, 1]},
  'gamma': {'type': 'float', 'default': 0, 'range': [0, 1]},
  'min_child_weight': {'type': 'int', 'default': 1, 'range': [1, 10]}},
 'mlprimitives.preprocessing.ClassDecoder#1': {}}

Scoring Templates and Pipelines

The S3PipelineExplorer class also allows cross validating templates and pipelines over any of the datasets.

Scoring a Pipeline

The simplest use case is cross validating a pipeline over a dataset. For this, we must pass the ID of the pipeline and the name of the dataset to the method score_pipeline.

The dataset can be the one that was used during the experiments or a different one.

piex.score_pipeline(pipeline['id'], '185_baseball')

(0.6921128080904511, 0.09950216269594728)

piex.score_pipeline(pipeline['id'], 'uu4_SPECT')

(0.8897656842904123, 0.037662864373452655)

Optionally, the cross validation configuration can be changed

piex.score_pipeline(pipeline['id'], 'uu4_SPECT', n_splits=3, random_state=43)

(0.8869488536155202, 0.019475563687443638)

Scoring a Template

A Template can also be tested over any dataset by passing its name, the dataset and, optionally, the cross validation specification. You have to make sure to choose template that is relevant for the task/data modality for which you want to use it.

If no hyperparameters are passed, the default ones will be used:

piex.score_template(template_name, 'uu4_SPECT', n_splits=3, random_state=43)

(0.8555346666968675, 0.028343173498423108)

You can get the default hyperparameters, and update the hyperparameters by setting values in the dictionary:

With this anyone can tune the templates that we have for different task/data modality types using their own AutoML routine. If you choose to do so, let us know the score you are getting and the pipeline and we will add to our database.

hyperparameters = piex.get_default_hyperparameters(template_name)
hyperparameters['xgboost.XGBClassifier#1']['learning_rate'] = 1

piex.score_template(template_name, 'uu4_SPECT', hyperparameters, n_splits=3, random_state=43)

(0.8754554700753094, 0.019151608028236813)

History

0.1.2

• Pin dependency versions to ensure reproducibility
• Allow S3PipelineExplorer usage without AWS credentials
• Fix minor bug in S3PipelineExplorer

0.1.1

• Improved documentation
• Minor bugfixes and improvements to the explorer API

0.1.0

• First release on PyPI