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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.

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Install

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

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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)})`);
}

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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 and zero-dependency in the TypeScript core.
  • Accuracy benchmark: 162 test cases, F1 0.84 (Python); TypeScript parity within 0.0005.

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CLI

CommandPurpose
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> --strictValidate 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

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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]}'")

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Default scorer weights

ScorerWeight
ExactScorer1.0
AliasScorer0.95
LLMScorer0.8 (pluggable, stubbed by default)
InitialismScorer0.75
PatternTypeScorer0.7
ProfileScorer0.5
FuzzyNameScorer0.4

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Config

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