AutoLineage

Zero-code data lineage for Python ML pipelines.
One import. Every pandas, scikit-learn and PySpark operation tracked, anomalies caught, root cause localized.

Your model's F1 dropped from 0.97 to 0.00 and nothing crashed. No exception, no error, just a silently broken pipeline. AutoLineage records the complete path from read_csv to f1_score, then tells you the exact operation that caused the regression. No decorators, no config.

import autolineage.auto        # that's the whole setup

import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import f1_score

df = pd.read_csv("data.csv").dropna()
X = df.drop(columns=['target'])
y = df['target']
X_tr, X_te, y_tr, y_te = train_test_split(X, y, test_size=0.2)

model = RandomForestClassifier().fit(X_tr, y_tr)
preds = model.predict(X_te)
score = f1_score(y_te, preds)

# AutoLineage has tracked every line above into one DAG.
from autolineage.auto import get_tracker
get_tracker().visualize()      # opens an interactive lineage graph

Try it in 30 seconds: open the quickstart in Colab (installs the package, tracks a pipeline, plants a bug, watches it get caught), or run the GIF above yourself:

pip install "autolineage[sklearn]"
git clone https://github.com/kishanraj41/autolineage && cd autolineage
python examples/anomaly_demo.py

Install, run, inspect. pip install, one import at the top of your script, then open the graph AutoLineage wrote:

The script in the recording is examples/pipeline.py. Click any node to see operation metadata, shape changes, and upstream dependencies. Export to JSON, Graphviz DOT, Mermaid markup, or self-contained HTML.

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Why AutoLineage?

ML pipelines fail silently. A model whose F1 drops from 0.97 to 0.00 invites hours of print(df.shape) debugging. Existing tools either require explicit instrumentation (MLflow), track only files (DVC), or cover only a single stage (Evidently, Arize). No existing tool records the complete path from read_csv through f1_score in one graph automatically — and then tells you which operation caused a metric to drop.

AutoLineage closes that gap.

Compared to other tools

Capability	AutoLineage	MLflow	Evidently	OpenLineage	DataLineagePy
Zero code changes	Yes	No	No	No	No (wrapper)
Operation-level	Yes	No	No	Job-level	Yes
Cross-framework	pandas + sklearn + PySpark	—	—	Spark only	pandas only
End-to-end trace	Yes	No	No	No	No
Anomaly detection	Yes	No	Drift only	No	No
Root-cause localization	Yes	No	No	No	No
Interactive visualization	Yes	Web UI	Web UI	Web UI	No

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Catching pipeline bugs automatically

This is what AutoLineage is for. Run python examples/anomaly_demo.py and watch a single-line filter bug get detected and localized:

The demo runs the same pipeline twice — once cleanly, once with a corrupted filter — and AutoLineage catches the row-count anomaly, the F1 collapse, and identifies the exact line that caused both. No manual instrumentation, no print statements.

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Installation

# Base install (pandas tracking only)
pip install autolineage

# Recommended: include sklearn support (most common ML stack)
pip install autolineage[sklearn]

# Full install with sklearn + pyspark + Jupyter rich output
pip install autolineage[all]

AutoLineage detects which frameworks you have installed and hooks them automatically. The sklearn and pyspark extras tell pip to install those frameworks alongside AutoLineage if you don't have them already.

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Quick Start

1. Automatic tracking (one line)

import autolineage.auto         # MUST be the first autolineage line in your script

# Use pandas / sklearn / pyspark normally — every operation is tracked
import pandas as pd
df = pd.read_csv("data.csv").dropna().drop_duplicates()

from autolineage.auto import get_tracker
get_tracker().visualize()       # opens HTML graph in your browser

Why first? import autolineage.auto patches framework methods at import time. If you write from sklearn.metrics import f1_score before this line, your local f1_score reference will bypass the wrapper. AutoLineage will warn you when this happens, but the easiest fix is to put import autolineage.auto at the top of your file.

2. Visualize the lineage

tracker = get_tracker()

tracker.visualize()                          # interactive HTML, opens in browser
tracker.visualize("trace.html")              # custom path, no browser pop-up
tracker.to_dot()                             # Graphviz DOT
tracker.to_mermaid()                         # Markdown-friendly Mermaid

In Jupyter notebooks, putting the tracker as the last expression in a cell auto-renders a summary table:

get_tracker()  # in a Jupyter cell — produces a rich HTML table inline

3. Anomaly detection

from autolineage.core.analyzer import LineageAnalyzer

analyzer = LineageAnalyzer(tracker)
analyzer.load_baseline("baseline.json")        # compare against a saved healthy run
anomalies = analyzer.detect_anomalies()

for a in anomalies:
    print(f"[{a.severity}] {a.message}")
# [critical] filter row change: -47,500 (baseline: -50, 94900% deviation)
# [critical] f1_score dropped from 0.9842 to 0.0000 (-100.0%)

4. Root-cause localization

cause = analyzer.localize_root_cause("f1_score")
print(cause.explanation)
# "The most likely cause of f1_score degradation (from 0.9842 to 0.0000)
#  is 'filter' at step 5. Row change was -47,500 (baseline: -50)."

5. Save a fingerprint for future comparison

analyzer.save_fingerprint("baseline.json")     # after a healthy run

# Next run, in a different process:
analyzer = LineageAnalyzer(new_tracker)
analyzer.load_baseline("baseline.json")
anomalies = analyzer.detect_anomalies()

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What Gets Tracked

pandas (64 hooks): read_csv, to_csv, read_parquet, to_parquet, dropna, fillna, merge, concat, groupby + aggregations, drop_duplicates, boolean filtering, assign, sort_values, pivot_table, melt, plus 40+ more.

scikit-learn (175 hooks): train_test_split, estimator fit / predict / predict_proba / score across 30+ classes (RandomForest, LogisticRegression, DecisionTree, SVC, KNN, GradientBoosting, etc.), 18 preprocessor classes, 15 metric functions.

PySpark (49 hooks): DataFrame transforms, groupBy + aggregations, join variants, reader / writer methods, actions.

See autolineage/hooks/ for the full list.

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Example Output

On a 284K-row credit card fraud detection pipeline (paper/credit_card_pipeline.py):

 1. [io        ] read_csv -> (284807, 31)                    [1280ms]
 2. [transform ] drop_duplicates (-1,081 rows)                [827ms]
 3. [transform ] filter (-284 rows)
 4. [transform ] assign -> 36 cols                              [1ms]
 5. [transform ] select -> 34 cols
 6. [split     ] train_test_split (80/20)                     [218ms]
 7. [preprocess] StandardScaler.fit_transform                 [201ms]
 8. [preprocess] StandardScaler.transform                      [17ms]
 9. [train     ] RandomForestClassifier.fit                 [88637ms]
10. [train     ] LogisticRegression.fit                      [1138ms]
11. [predict   ] RandomForestClassifier.predict               [332ms]
12. [predict   ] LogisticRegression.predict                     [4ms]
13. [predict   ] RandomForestClassifier.predict_proba         [311ms]
14. [evaluate  ] accuracy_score    = 0.9995
15. [evaluate  ] precision_score   = 0.8824
16. [evaluate  ] recall_score      = 0.7895
17. [evaluate  ] f1_score          = 0.8333
18. [evaluate  ] roc_auc_score     = 0.9871

24 clean records. Zero noise. End-to-end trace from CSV to metrics.

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Architecture

Plugin-based. Each library is a single file implementing BaseHookProvider. Adding new libraries requires ~200 lines and zero changes to the core.

   User Code (unchanged)
           |
   Hook Providers (pandas | sklearn | pyspark | ...)
           |
   UnifiedTracker + TransformationRecord
           |
   LineageAnalyzer  →  anomalies, root causes, fingerprints
   Visualizer       →  HTML / DOT / Mermaid / Jupyter

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Performance

Per-operation instrumentation cost on a 37-operation pipeline (Intel i7-12700H, Python 3.12, pandas 3.0):

Condition	Mean time per call	95% CI
Baseline (no instrumentation)	263.5 µs	± 8.8 µs
With AutoLineage	348.2 µs	± 9.0 µs
Overhead	84.7 µs / op	[78, 91]

At production data scales (≥10⁵ rows), end-to-end overhead becomes indistinguishable from baseline variance because framework computation dominates wall-clock time. See paper/scaling_results.csv for the full scaling study.

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Limitations

• Single-process. Pipelines spanning multiple machines require manual trace correlation. OpenTelemetry export is planned.
• Monkey-patching is version-sensitive. Tested against pandas 2.x / 3.x, scikit-learn 1.x, PySpark 3.x / 4.x.
• Import order matters. import autolineage.auto must come before from sklearn.metrics import f1_score (or any other hooked symbol) — otherwise the local reference will bypass the wrapper. AutoLineage will warn you when this happens.
• C-extension code is invisible. Operations that execute entirely in compiled code without re-entering Python (e.g., certain numpy reductions) are not captured.
• Python-only. R, Julia, Java are out of scope.

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Contributing

Add a new library in 5 steps:

1. Create autolineage/hooks/your_lib_hooks.py
2. Subclass BaseHookProvider
3. Implement install(tracker) and uninstall()
4. Register in autolineage/hooks/registry.py
5. Open a PR

See autolineage/hooks/pandas_io.py for the smallest working example (~110 LoC).

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Development

git clone https://github.com/kishanraj41/autolineage
cd autolineage
pip install -e ".[dev]"
pytest tests/                      # 51 tests
python examples/anomaly_demo.py    # full end-to-end demo

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License

MIT

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Citation

If you use AutoLineage in your research or work, please cite the SSRN preprint:

Plain citation:

VG, K.R. (2026). AutoLineage: Operation-Level Data Lineage for Python ML Pipelines via Import-Time Hooking. SSRN preprint. https://papers.ssrn.com/sol3/papers.cfm?abstract_id=6683825

BibTeX:

@misc{vg2026autolineage,
  title  = {AutoLineage: Operation-Level Data Lineage for Python ML Pipelines via Import-Time Hooking},
  author = {VG, Kishan Raj},
  year   = {2026},
  note   = {SSRN preprint, abstract\_id=6683825},
  url    = {https://papers.ssrn.com/sol3/papers.cfm?abstract_id=6683825}
}