InferMap overview

InferMap maps messy source columns to a known target schema, with a confidence score and a human-readable reason for each mapping. It runs in Python and TypeScript, and the two implementations are verified bit-for-bit against a shared golden-test suite.

Install

pip install infermap

npm install infermap

Python database extras: infermap[postgres], infermap[mysql], infermap[duckdb], infermap[all]. The TypeScript package requires Node 20+ and is edge-runtime compatible.

Native acceleration. The scorers + domain detection share a pyo3-free Rust core (infermap-core). Install infermap[native] to auto-dispatch to the compiled kernels — byte-identical output (CI parity-gated), governed by INFERMAP_NATIVE (auto / 1 / 0). The TypeScript package runs the same kernels via an opt-in WASM backend (enableInfermapWasm()); the MCP infermap://scorer-info resource reports the active backend.

Quickstart

import infermap

result = infermap.map("crm_export.csv", "canonical_customers.csv")
for m in result.mappings:
    print(f"{m.source} -> {m.target}  ({m.confidence:.0%})")

# Apply the mapping to a DataFrame
import polars as pl
df = pl.read_csv("crm_export.csv")
renamed = result.apply(df)

# Save and reload
result.to_config("my_mapping.yaml")
saved = infermap.from_config("my_mapping.yaml")

In TypeScript:

import { map } from "infermap";

const result = map(
  { records: [{ fname: "John", lname: "Doe", email_addr: "j@d.co" }] },
  { records: [{ first_name: "", last_name: "", email: "" }] },
);

for (const m of result.mappings) {
  console.log(`${m.source} → ${m.target}  (${m.confidence.toFixed(2)})`);
}

Key features

7 built-in scorers: exact, alias, initialism, pattern-type, profile, fuzzy-name, and LLM (pluggable).
Optimal 1:1 assignment via the Hungarian algorithm.
Common-prefix canonicalization that strips schema-wide prefixes (for example prospect_City versus City).
Confidence calibration (identity, isotonic, or Platt) into probabilities.
Domain dictionaries for healthcare, finance, and ecommerce.
Custom scorers via the @infermap.scorer decorator (Python) or defineScorer() (TypeScript).
Many input formats: CSV, JSON, in-memory records, database tables, and schema definition files.
Edge-runtime compatible with a minimal-dependency TypeScript core (only goldencheck-types for domain packs).
Accuracy benchmark: 162 test cases, F1 0.84 (Python); TypeScript parity within 0.0005.

CLI

Command	Purpose
`infermap map <source> <target>`	Map two files or schemas and print a report.
`infermap apply <source> --config <mapping> --output <file>`	Apply a saved mapping to rename columns.
`infermap inspect <source>`	Extract and display a schema from a file or DB table.
`infermap validate <source> --config <mapping> --required <fields> --strict`	Validate a saved config against a source.

infermap map crm_export.csv canonical_customers.csv -o mapping.json
infermap apply crm_export.csv --config mapping.json --output renamed.csv
infermap inspect "sqlite:///mydb.db" --table customers

Custom scorer

import infermap
from infermap.types import FieldInfo, ScorerResult

@infermap.scorer("prefix_scorer", weight=0.8)
def prefix_scorer(source: FieldInfo, target: FieldInfo) -> ScorerResult | None:
    if source.name[:3].lower() != target.name[:3].lower():
        return None
    return ScorerResult(score=0.85, reasoning=f"Shared prefix '{source.name[:3]}'")

Scorer weights

The default pipeline (default_scorers()) is the six scorers below. LLMScorer is not in the default set — it is opt-in (and stubbed until you wire a provider).

Scorer	Weight	Default?
ExactScorer	1.0	yes
AliasScorer	0.95	yes
InitialismScorer	0.75	yes
PatternTypeScorer	0.7	yes
ProfileScorer	0.5	yes
FuzzyNameScorer	0.4	yes
LLMScorer	0.8	opt-in

Config

domains:
  - healthcare
  - finance
scorers:
  ProfileScorer:
    enabled: false
  FuzzyNameScorer:
    weight: 0.3
aliases:
  order_id:
    - order_num
    - ord_no

Get started

Concepts

GoldenMatch

GoldenCheck

GoldenFlow

GoldenPipe

GoldenAnalysis

InferMap

SQL extensions

Reference

Research

Install

Quickstart

Key features

CLI

Custom scorer

Scorer weights

Config

​Install

​Quickstart

​Key features

​CLI

​Custom scorer

​Scorer weights

​Config

Install

Quickstart

Key features

CLI

Custom scorer

Scorer weights

Config