Implementation:Online ml River Utils Norm

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

Overview

Dictionary value normalization and scaling utilities.

Description

Provides functions to normalize and scale dictionary values in-place or out-of-place. normalize_values_in_dict divides by a factor (default: sum of values), while scale_values_in_dict multiplies by a constant. Handles edge cases like zero sums gracefully.

Usage

Use for probability normalization, feature scaling, or any scenario requiring proportional adjustment of dictionary values. Essential for softmax implementations and probability distributions.

Code Reference

Source Location

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

Signature

def normalize_values_in_dict(
    dictionary,
    factor=None,
    inplace=True,
    raise_error=False
):
    ...

def scale_values_in_dict(
    dictionary,
    multiplier,
    inplace=True
):
    ...

Import

from river import utils

Usage Examples

from river import utils

# Normalize to sum = 1
scores = {'A': 3, 'B': 1, 'C': 1}
normed = utils.norm.normalize_values_in_dict(scores, inplace=False)
print(f"Normalized: {normed}")  # {'A': 0.6, 'B': 0.2, 'C': 0.2}
print(f"Sum: {sum(normed.values())}")  # 1.0

# Scale all values
values = {'x': 2, 'y': 3, 'z': 5}
scaled = utils.norm.scale_values_in_dict(values, multiplier=10, inplace=False)
print(f"Scaled: {scaled}")  # {'x': 20, 'y': 30, 'z': 50}

# Custom normalization factor
data = {'a': 10, 'b': 20}
normed2 = utils.norm.normalize_values_in_dict(data, factor=100, inplace=False)
print(f"Custom factor: {normed2}")  # {'a': 0.1, 'b': 0.2}

# Handle zero sum gracefully
zeros = {'x': 0, 'y': 0}
result = utils.norm.normalize_values_in_dict(zeros, inplace=False, raise_error=False)
print(f"Zero sum: {result}")  # Returns original

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