Implementation:Online ml River Utils Inspect

Knowledge Sources	Online_ml_River
Domains	Online_Learning, Model_Introspection
Last Updated	2026-02-08 16:00 GMT

Overview

Utilities for runtime inspection of model types and capabilities.

Description

Provides functions to determine a model's type at runtime, handling pipelines and wrappers where isinstance checks fail. Includes isclassifier, isregressor, istransformer, isanomalydetector, and more. Essential for River's internal routing and validation logic.

Usage

Use when writing generic code that needs to detect model capabilities, validate pipeline compatibility, or route data appropriately based on model type.

Code Reference

Source Location

Repository: Online_ml_River
File: river/utils/inspect.py

Signature

def extract_relevant(model: base.Estimator):
    ...

def isclassifier(model):
    ...

def isregressor(model):
    ...

def istransformer(model):
    ...

def isanomalydetector(model):
    ...

def isclusterer(model):
    ...

def isdriftdetector(model):
    ...

Import

from river import utils

Usage Examples

from river import linear_model, preprocessing, utils, tree

# Check model types
model = linear_model.LogisticRegression()
print(f"Is classifier: {utils.inspect.isclassifier(model)}")  # True
print(f"Is regressor: {utils.inspect.isregressor(model)}")  # False

# Works with pipelines
pipeline = (
    preprocessing.StandardScaler() |
    tree.HoeffdingTreeClassifier()
)
print(f"Pipeline is classifier: {utils.inspect.isclassifier(pipeline)}")  # True

# Anomaly detection
from river import anomaly
detector = anomaly.HalfSpaceTrees()
print(f"Is anomaly detector: {utils.inspect.isanomalydetector(detector)}")  # True

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