Machine Learning pipeline evaluation framework
We use Hatch for environment management and packaging evalem.
To start the development env, you'll first need to install hatch. We recommend installing with pipx so it doesn't interfere with other python envs.
python3 -m pip install --user pipx
python3 -m pipx ensurepath
This globally installs hatch, restart the terminal for changes to take effect. Then,
pipx install hatch
With hatch installed, you can start a shell environment with test dependencies, creating the env if it doesn't already exist.
hatch -e test shell
See the pyproject.toml for the test dependencies. The test environment inherits dependencies from the default environment.
If you need to remove dependencies, you'll need to delete the environments and recreate them. Use
hatch env prune to remove all environments and `hatch -e test shell' to recreate the default and test environments.
[email protected]:NASA-IMPACT/evalem.git
pip install -e .
Base DTOs exist at evalem._base.structures, primarily which consists of PredictionDTO and ReferenceDTO.
Different evaluation mode can happen during metric calculation:
- Single Reference, Single Prediction (SRSP)
- Single Reference, Multiple Predictions (SRMP)
- Multiple References, Single Prediction (MRSP)
- Multiple References, Multiple Prediction (MRMP)
out of which, SRSP and MRSP seems like common mode of evaluation.
Out-of-box, evalem metrics bake in all these and they transform these references/predictions internally based on the metric. For multiple references, common mode is to flatten everything to single list, duplicating the prediction value to different ground truths.
Evalem provides a handful of evaluation metrics out-of-box, in a single place so that users won't have to switch between different implementation, between different external modules and packages.
All the metrics are based off the main base evalem._base.metrics.Metric.
All the metrics object take in references and predictionsduring execution/runtime/evaluation-time. Each metric could have different way of building. For instance, some metrics could be run on different devices other than cpu(such as cuda: 0, mps, etc based on their implementation). Each metric evaluation gives result in a evalem._base.structures.MetricResult structure which consist of score and other parameters.
evalem._base.metrics module has some base metrics (of type BasicMetric) such as:
PrecisionMetricRecallMetricF1MetricAccuracyMetricConfusionMatrix
from evalem._base.structures import (
PredictionDTO,
ReferenceDTO,
)
from evalem._base.structures import MetricResult
from evalem._base.metrics import (
Metric,
AccuracyMetric,
PrecisionMetric,
RecallMetric,
F1Metric,
BasicMetric,
ConfusionMatrix
)
# Single References (SR)
references = [
ReferenceDTO(value="Reference 1"),
ReferenceDTO(value="Reference 2")
]
# Multiple references
references = [
[
ReferenceDTO("Reference 1"),
dict(value="Reference 1.1"),
dict(value="Reference 2"),
],
[
dict(value="Reference 2"),
ReferenceDTO("Dummy 1"),
ReferenceDTO("Dummy 2"),
"Dummy 3"
]
]
predictions = [
PredictionDTO(value="Reference 1", score=1.0),
PredictionDTO(value="Reference 2.5", score=0.75)
]
print(PrecisionMetric()(references=references, predictions=predictions)) # Output of type MetricResult
print(RecallMetric()(references=references, predictions=predictions))
print(AccuracyMetric()(references=references, predictions=predictions))
print(ConfusionMatrix()(references=references, predictions=predictions))evalem.nlp.metrics separates out the namespace for NLP.
All the NLP-based metrics are derived from evalem.nlp.metrics._base.NLPMetric which itself inherits from the base Metric.
Some of the available metrics are:
evalem.nlp.metrics.BartScoreevalem.nlp.metrics.BertScoreevalem.nlp.metrics.BleuMetricevalem.nlp.metrics.ExactMatchMetricevalem.nlp.metrics.MeteorMetricevalem.nlp.metrics.RougeMetricevalem.nlp.metrics.SacredBleuMetric
All the semantic metrics are of type evalem.nlp.metrics.semantics.SemanticMetric which is obviously inherited from NLPMetric
from evalem.nlp.metrics import NLPMetric, SemanticMetric
from evalem.nlp.metrics import (
BartScore,
BertScore,
BleuMetric,
ExactMatchMetric,
MeteorMetric,
RougeMetric,
SacreBleuMetric,
)
references = [
"I love NLP",
"I love working with language models",
"I love my cat"
]
predictions = [
"I don't really like doing NLP",
"Language models are okay",
"I absolutely love my cat"
]
print(BertScore(device="cpu")(references=references, predictions=predictions))
print(ExactMatchMetric()(references=references, predictions=predictions))
...Evaluators in evalem help in containerizing metrics to run them in single go instead of having to create separate instances for each metric. It's one level of abstraction above the metric.
The base evaluator container can be : evalem._base.evaluators.Evaluator which takes in a list of metric objects to run in single go.
We can compose any metrics together and run them in single go for a set of datasets (references, predictions)
from evalem._base.evaluators import Evaluator
evaluator = Evaluator(metrics=[
BertScore(device="cpu"),
BartScore(device="cpu"),
ExactMatchMetric(),
MeteorMetric(),
RougeMetric(),
SacreBleuMetric(),
BleuMetric()
])
result = evaluator_nlp(references=references, predictions=predictions) # Outputs a list of MetricResult objects
print(result)evalem also provivdes a way to evaluate models directly by runnin inputs through the models, getting predictions and evaluating based on the references. To standardize the model forward-pass, evalem has model wrappers evalem._base.models.ModelWrapper. All model wrappers take in an arbitrary model and the forward pass has to be implemented by downstream wrapper implementation (by implementing _predict(...) method).
evalem._base.models.HFWrapper provides a type for Huggingface-based models
evalem.nlp.models.HFLMWrapper: wrapper for upstream Huggingface language modelevalem.nlp.models.HFPipelineWrapper: wrapper for huggingface pipeline (which itself wraps model + tokenizer)
evalem.nlp.models.QuestionAnsweringHFPipelineWrapperand evalem.nlp.models.TextClassificationHFPipelineWrapperare two impelmentations for QA and Text classification within evalem eco-system.
QuestionAnsweringHFPipelineWrapper provides a wrapper for QA tasks.
from evalem.nlp.models import QuestionAnsweringHFPipelineWrapper
from evalem.nlp.structures import QuestionAnsweringDTO
model = AutoModelForQuestionAnswering.from_pretrained("distilbert-base-cased-distilled-squad")
tokenizer = AutoTokenizer.from_pretrained("distilbert-base-cased-distilled-squad")
wrapped_model = QuestionAnsweringHFPipelineWrapper(
model=model,
tokenizer=tokenizer,
device="mps"
)
data = [
{
"context": "Deep within the labyrinthine caves, echoes tell tales of lost civilizations.",
"question": "What sails toward the unknown?",
"reference": "A lone ship"
},
{
"context": "Beneath the bustling city streets, forgotten catacombs hold secrets of the past.",
"question": "What watches the skies?",
"reference": "The old observatory"
},
{
"context": "In the heart of the ancient forest, a hidden lake glimmers under the moonlight.",
"question": "What lies in the heart of the forest?",
"reference": "A hidden lake"
},
{
"context": "Beneath the bustling city streets, forgotten catacombs hold secrets of the past.",
"question": "What lies in the heart of the forest?",
"reference": "Forgotten catacombs"
},
{
"context": "Deep within the labyrinthine caves, echoes tell tales of lost civilizations.",
"question": "What lies in the heart of the forest?",
"reference": "Labyrinthine caves"
}
]
pprint(wrapped_model([ dict(context=d["context"], question=d["context"]) for d in data]))
We can directly utilize all the previous evaluation metric, do forward-pass manually and provide predictions, references manually to compute the metric.
To abstract the forward pass itself, evalem provides SimpleEvaluationPipeline that takes in a wrapped model and list of evaluators and run them directly.
from evalem import SimpleEvaluationPipeline
from evalem.nlp.evaluators import QAEvaluator
# Out-of-box QA evaluation, consists of basic metrics
evaluator = QAEvaluator()
result = evaluator(
references=references,
predictions=wrapped_model(inputs)
)
# wrap into a pipeline
eval_pipe = SimpleEvaluationPipeline(
model=wrapped_model,
evaluators=[QAEvaluator()]
)
result = eval_pipe(
inputs,
references,
...
)TextClassificationHFPipelineWrapper provides an abstraction for text classification models.
from transformers import AutoModelForSequenceClassification
from evalem.nlp.models import TextClassificationHFPipelineWrapper
model = AutoModelForSequenceClassification.from_pretrained("distilbert-base-uncased-finetuned-sst-2-english")
tokenizer = AutoTokenizer.from_pretrained("distilbert-base-uncased-finetuned-sst-2-english")
wrapped_model = TextClassificationHFPipelineWrapper(
model=model,
tokenizer=tokenizer
)
pprint(wrapped_model("I don't like mangoes"))from evalem import SimpleEvaluationPipeline
from evalem.nlp.evaluators import TextClassificationEvaluator
inputs = [
"I love NLP",
"I love working with language models",
"I love my cat",
"I don't like mangoes"
]
references = [
"POSITIVE",
"POSITIVE",
"POSITIVE",
"NEGATIVE"
]
# Out-of-box QA evaluation, consists of basic metrics
evaluator = TextClassificationEvaluator()
result = evaluator(
references=references,
predictions=wrapped_model(inputs)
)
# wrap into a pipeline
eval_pipe = SimpleEvaluationPipeline(
model=wrapped_model,
evaluators=[TextClassificationEvaluator()]
)
result = eval_pipe(
inputs,
references,
...
)Everywhere that takes in Evaluator type, we can basically compose any evaluators and run them through the EvaluationPipeline.
For isinstance:
from evalem._base.evaluators import Evaluator
from evalem import SimpleEvaluationPipeline
# build model wrapper
wrapped_model = ...
# get dataset
inputs = ...
references = ...
evaluators = [
Evaluator(metrics=[
AccuracyMetric(),
ConfusionMatrix(),
ExactMatchMetric(),
F1Metric(),
]),
Evaluator(metrics=[
BertScore(),
BartScore()
])
]
eval_pipe = SimpleEvaluationPipeline(
model = wrapped_model,
evaluators=evaluators
)
result = eval_pipe(
inputs,
references,
...
)
pprint(result)