Python package for data logging and monitoring.
Project description
Raymon: analyse data & model health
What is Raymon?
Raymon helps Machine Learning teams analyse data, data health and model performance. Using data profiles, users can extract features describing data quality, data novelty, model confidence and prediction performance from data.Then, they can use these features to validate production data and monitor for data drift, data degradation and model degradation.
Raymon is open source and can be used standalone but integrates nicely with the rest of the Raymon.ai ML Observability hub, for example to make predictions traceable and debuggable.
Raymon’s focus is on simplicity, practicality and extendability. We offer a set of extractors that are cheap to compute and simple to understand.
We can support any data type. Currently, we offer extractors for structured data and vision data, but you can easily implement your own extractor which means we can any data type and any extractor that you want.
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At a glance
Installation
pip install raymon
Building a model profile
Building a ModelProfile
captures all kinds of data characteristics of your models input,s outputs, actuals and predictions.
profile = ModelProfile(
name="HousePricesCheap",
version="3.0.0",
components=[
InputComponent(
name="outlier_score",
extractor=SequenceSimpleExtractor(
prep=coltf, extractor=KMeansOutlierScorer()),
),
OutputComponent(name="prediction", extractor=ElementExtractor(element=0)),
ActualComponent(name="actual", extractor=ElementExtractor(element=0)),
EvalComponent(name="abs_error", extractor=AbsoluteRegressionError()),
] + generate_components(X_train[feature_selector].dtypes,
complass=InputComponent),
scores=[
MeanScore(
name="MAE",
inputs=["abs_error"],
preference="low",
),
MeanScore(
name="mean_outlier_score",
inputs=["outlier_score"],
preference="low",
),
],
)
profile.build(input=X_val[feature_selector],
output=y_pred_val[:, None],
actual=y_val[:, None])
Validating production data
Profiles can then be used in production code to validate your incoming data and model performance monitoring.
tags = profile.validate_input(request)
output_tags = profile.validate_output(request_pred)
actual_tags = profile.validate_actual(request_actual)
eval_tags = profile.validate_eval(output=request_pred,
actual=request_actual)
# or all at once:
all_tags = profile.validate_all(input=request,
output=request_pred,
actual=request_actual)
Inspect and contrast model profiles
interactive-demo
https://user-images.githubusercontent.com/7951058/132864346-2715fb47-00e9-484c-9f06-c709d4a9847f.mov
Logging text, data and tags
Moreover, if you want to use the rest of the platform, Raymon makes model predictions traceable and debuggable. Raymon enables you to log text, data and tags from anywhere in your code. You can later use these tags and data objects to debug and improve your systems.
import pandas as pd
import numpy as np
from PIL import Image
import raymon.types as rt
from raymon import Trace, RaymonAPILogger, Tag
logger = RaymonAPILogger(project_id=project_id)
trace = Trace(logger=logger, trace_id=None)
# Logging text messages
trace.info("You can log whatever you want here")
# Tagging traces
trace.tag([
Tag(name="sdk_version", value="1.4.2", type="label"),
Tag(name="prediction_time_ms", value="120", type="metric")
])
# Logging data
img = Image.open("./data_sample/castinginspection/def_front/cast_def_0_0.jpeg")
df = pd.DataFrame(arr, columns=['a', 'b'])
trace.log(ref="pandas-ref", data=rt.DataFrame(df))
trace.log(ref="image-ref", data=rt.Image(img))
For more information, check out our docs & examples!
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