afcarl / catwalk

Training, testing, and evaluating machine learning classifier models

Catwalk

Training, testing, and evaluating machine learning classifier models

Note: Catwalk is now a bundled component of Triage, and future development will take place there. To utilize Catwalk within your custom pipeline, you may still import it from there.

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At the core of many predictive analytics applications is the need to train classifiers on large set of design matrices, test and temporally cross-validate them, and generate evaluation metrics about them.

Python's scikit-learn package provides much of this functionality, but it is not trivial to design large experiments with it in a persistable way. Catwalk builds upon the functionality offered by scikit-learn by implementing:

• Saving of modeling results and metadata in a Postgres database for later analysis
• Exposure of computationally-intensive tasks as discrete workloads that can be used with different parallelization solutions (e.g. multiprocessing, Celery)
• Different model persistence strategies such as on-filesystem or Amazon S3, that can be easily switched between
• Hashing classifier model configuration to only retrain a model if necessary.
• Various best practices in areas like input scaling for different classifier types and feature importance
• Common scikit-learn model evaluation metrics as well as the ability to bundle custom evaluation metrics

Components

This functionality is concentrated in the following components:

• catwalk.ModelTrainer: Train a configured experiment grid on pre-made design matrices, and store each model's metadata and feature importances in a database.
• catwalk.Predictor: Given a trained model and another matrix (ie, a test matrix), generate prediction probabilities and store them in a database.
• catwalk.ModelEvaluator: Given a set of model prediction probabilities, generate metrics (for instance, precision and recall at various thresholds) and store them in a database.

Usage

Below is a complete sample usage of the three Catwalk components.

import datetime

import pandas
from sqlalchemy import create_engine

from metta import metta_io as metta

from catwalk.storage import FSModelStorageEngine, MettaCSVMatrixStore
from catwalk.model_trainers import ModelTrainer
from catwalk.predictors import Predictor
from catwalk.evaluation import ModelEvaluator
from catwalk.utils import save_experiment_and_get_hash


# create a sqlalchemy database engine pointing to a Postgres database
db_engine = create_engine(...)

# A path on your filesystem under which to store matrices and models
project_path = 'mytestproject/modeling'

# create a toy train matrix from scratch
# and saving it using metta to generate a unique id for its metadata
# catwalk uses both the matrix and metadata
train_matrix = pandas.DataFrame.from_dict({
	'entity_id': [1, 2],
	'feature_one': [3, 4],
	'feature_two': [5, 6],
	'label': [7, 8]
}).set_index('entity_id')
train_metadata = {
	'feature_start_time': datetime.date(2012, 12, 20),
	'end_time': datetime.date(2016, 12, 20),
	'label_name': 'label',
	'label_timespan': '1y',
	'feature_names': ['ft1', 'ft2'],
}
train_matrix_uuid = metta.archive_matrix(train_metadata, train_matrix, format='csv')

# The MettaCSVMatrixStore bundles the matrix and metadata together
# for catwalk to use
train_matrix_store = MettaCSVMatrixStore(
	matrix_path='{}.csv'.format(train_matrix_uuid),
	metadata_path='{}.yaml'.format(train_matrix_uuid)
)


# Similarly, create a test matrix
as_of_date = datetime.date(2016, 12, 21)

test_matrix = pandas.DataFrame.from_dict({
	'entity_id': [3],
	'feature_one': [8],
	'feature_two': [5],
	'label': [5]
}).set_index('entity_id')

test_metadata = {
	'label_name': 'label',
	'label_timespan': '1y',
	'end_time': as_of_date,
}
test_matrix_uuid = metta.archive_matrix(test_metadata, test_matrix, format='csv')

# The MettaCSVMatrixStore bundles the matrix and metadata together
# for catwalk to use
test_matrix_store = MettaCSVMatrixStore(
	matrix_path='{}.csv'.format(test_matrix_uuid),
	metadata_path='{}.yaml'.format(test_matrix_uuid)
)

# The ModelStorageEngine handles the persistence of model pickles
# In this case, we are using FSModelStorageEngine to use the local filesystem
model_storage_engine = FSModelStorageEngine(project_path)

# To ensure that we can relate all of our persistent database records with
# each other, we bind them together with an experiment hash. This is based
# on the hash of experiment configuration that you pass in here, so if the
# code fails halfway through and has to run a second time, it will use the
# already-trained models but save the new ones under the same experment
# hash.

# Here, we will just save a trivial experiment configuration.
# You can put any information you want in here, as long as it is hashable
experiment_hash = save_experiment_and_get_hash({'name': 'myexperimentname'}, db_engine)

# instantiate pipeline objects. these will to the brunt of the work
trainer = ModelTrainer(
	project_path=project_path,
	experiment_hash=experiment_hash,
	model_storage_engine=model_storage_engine,
	db_engine=db_engine,
	model_group_keys=['label_name', 'label_timespan']
)
predictor = Predictor(
	project_path,
	model_storage_engine,
	db_engine
)
model_evaluator = ModelEvaluator(
	[{'metrics': ['precision@'], 'thresholds': {'top_n': [5]}}],
	db_engine
)

# run the pipeline
grid_config = {
	'sklearn.linear_model.LogisticRegression': {
		'C': [0.00001, 0.0001],
		'penalty': ['l1', 'l2'],
		'random_state': [2193]
	}
}

# trainer.train_models will run the entire specified grid
# and return database ids for each model
model_ids = trainer.train_models(
	grid_config=grid_config,
	misc_db_parameters=dict(test=True),
	matrix_store=train_matrix_store
)

for model_id in model_ids:
	predictions_proba = predictor.predict(
		model_id=model_id,
		matrix_store=test_matrix_store,
		misc_db_parameters=dict(),
		train_matrix_columns=['feature_one', 'feature_two']
	)

	model_evaluator.evaluate(
		predictions_proba=predictions_proba,
		labels=test_store.labels(),
		model_id=model_id,
		evaluation_start_time=as_of_date,
		evaluation_end_time=as_of_date,
		as_of_date_frequency='6month'
	)

After running the above code, results will be stored in your Postgres database in this structure

In addition to being usable on the design matrices of your current project, Catwalk's functionality is used in triage as a part of an entire modeling experiment that incorporates earlier tasks like feature generation and matrix building.