ml-audit 0.1.4

Solves Data Lineage Blindness by tracking granular preprocessing steps unlike standard experiment trackers.

ML Audit

Solves Data Lineage Blindness by tracking granular preprocessing steps.

ml-audit is a lightweight Python library designed to bring transparency and reproducibility to data preprocessing. Unlike standard experiment trackers that treat preprocessing as a black box, this library records every granular transformation applied to your pandas DataFrame.

Why ML Audit?

It solves "Data Lineage Blindness".

Most data science teams suffer from a gap in their experiment tracking:

• MLflow/W&B track metrics (accuracy, loss) and hyperparameters. They often treat the cleaned dataset as a static artifact.
• DVC tracks file versions. It tells you that the data changed from Version A to Version B.
• ML Audit tells you why and how it changed. It logs: "Imputed column 'Age' with mean (42.5), then Scaled with StandardScaler, then OneHotEncoded 'Gender'."

Features

• Full Audit Trail: Automatically logs every step (Imputation, Scaling, Encoding, etc.) into a JSON audit file.
• Reproducibility: Verify if your data pipeline produces the exact same result every time using hash validation.
• Visualization: Auto-generates an interactive HTML timeline of your preprocessing steps.
• Comprehensive Operations:
  • Imputation: mean, median, mode, constant, ffill, bfill.
  • Scaling: minmax, standard, robust, maxabs.
  • Encoding: onehot, label, target encoding.
  • Balancing: smote (via imblearn), oversample, undersample.
  • Transformation: log, sqrt, boxcox.
  • Date Extraction: Extract year, month, day from timestamps.
  • Binning: Discretize continuous variables into quantiles or uniform bins.
• Multi-Column Support: Apply operations to lists of columns efficiently.
• Generic Support: Track any arbitrary pandas method (e.g., dropna, rename).

Installation

You can install ml-audit via pip:

pip install ml-audit

For SMOTE balancing support, install with the balance extra:

pip install ml-audit[balance]

Interactive Demo

Try out the library instantly in your browser:

Quick Start

1. Initialize the Recorder

import pandas as pd
from ml_audit import AuditTrialRecorder

# Load your data
df = pd.read_csv("data.csv")

# Initialize the auditor wrapped around your dataframe
auditor = AuditTrialRecorder(df, name="experiment_v1")

2. Apply Preprocessing

Chain methods fluently. Operations are applied immediately to auditor.current_df.

auditor.filter_rows("age", ">=", 18) \
       .impute(["salary", "score"], strategy='median') \
       .scale(["salary", "age"], method='minmax') \
       .encode("gender", method='onehot') \
       .balance_classes("churn", strategy='oversample') # Handles imbalanced data

3. Access Data

processed_df = auditor.current_df
print(processed_df.head())

4. Export & Visualize

Save the audit trail. This will generate a JSON file (audit_trails/) and an HTML visualization (visualizations/).

auditor.export_audit_trail("audit.json")
# Output:
# - audit_trails/audit.json
# - visualizations/audit.html

Detailed API Documentation

All methods support Method Chaining (returning self).

1. Imputation (impute)

Fill missing values in one or more columns using statistical strategies or specific methods.

Signature:

auditor.impute(column, strategy='mean', fill_value=None, method=None)

Parameters:

• column (str | list): The column(s) to impute.
• strategy (str):
  • 'mean': Fill with mean.
  • 'median': Fill with median.
  • 'mode': Fill with mode (most frequent).
  • 'constant': Fill with fill_value.
• method (str):
  • 'ffill': Forward fill (propagates last valid observation).
  • 'bfill': Backward fill (use next valid observation).
  • Note: If method is provided, strategy is ignored.
• fill_value (any): Value to use when strategy='constant'.

Examples:

# Impute multiple columns with median
auditor.impute(["age", "salary"], strategy='median')

# Fill with a constant value (e.g., 0)
auditor.impute("bonus", strategy='constant', fill_value=0)

# Forward fill for time-series data
auditor.impute("stock_price", method='ffill')

2. Scaling (scale)

Scale numerical features to a specific range or distribution.

Signature:

auditor.scale(column, method='standard')

Parameters:

• column (str | list): The column(s) to scale.
• method (str):
  • 'standard': Standardize features (mean=0, std=1). Uses StandardScaler.
  • 'minmax': Scale to range [0, 1]. Uses MinMaxScaler.
  • 'robust': Scale using statistics that are robust to outliers. Uses RobustScaler.
  • 'maxabs': Scale by maximum absolute value. Uses MaxAbsScaler.

Examples:

# Standardize normally distributed features
auditor.scale(["height", "weight"], method='standard')

# Normalize image pixel values
auditor.scale("pixels", method='minmax')

3. Encoding (encode)

Encode categorical features into numeric form.

Signature:

auditor.encode(column, method='onehot', target_col=None)

Parameters:

• column (str | list): The column(s) to encode.
• method (str):
  • 'onehot': Creates binary columns for each category (e.g., color_red, color_blue).
  • 'label': Assigns a unique integer to each category.
  • 'target': Encodes categories based on the mean of the target_col.
• target_col (str): Required only for method='target'.

Examples:

# One-hot encode low-cardinality nominal data
auditor.encode("color", method='onehot') 
# Result: color_red, color_blue, ...

# Label encode ordinal data
auditor.encode("quality", method='label')
# Result: 0, 1, 2...

# Target encode high-cardinality data
auditor.encode("zip_code", method='target', target_col="house_price")

4. Transformation (transform)

Apply mathematical transformations to columns.

Signature:

auditor.transform(column, func='log')

Parameters:

• column (str | list): The column(s) to transform.
• func (str):
  • 'log': Applies np.log1p (natural log(1+x)). Safer for zero values.
  • 'sqrt': Applies square root.
  • 'cbrt': Applies cube root.
  • 'square': Squares the values.

Examples:

# Log transform skewed data
auditor.transform("income", func='log')

5. Binning (bin_numeric)

Discretize continuous variables into bins (buckets).

Signature:

auditor.bin_numeric(column, bins=5, strategy='quantile', labels=None)

Parameters:

• column (str | list): The column(s) to bin.
• bins (int): Number of bins to create.
• strategy (str):
  • 'quantile': Bins have equal number of data points (Equal Frequency).
  • 'uniform': Bins have equal width (Equal Width).
• labels (list, optional): Custom labels for the bins.

Examples:

# Create 4 quartiles for age
auditor.bin_numeric("age", bins=4, strategy='quantile')

6. Date Extraction (extract_date_features)

Extract features from datetime columns.

Signature:

auditor.extract_date_features(column, features=['year', 'month', 'day', 'weekday'])

Parameters:

• column (str | list): The datetime column(s).
• features (list): List of features to extract. Options: 'year', 'month', 'day', 'weekday', 'hour'.

Examples:

# Extract year and month from 'joined_date'
auditor.extract_date_features("joined_date", features=['year', 'month'])
# Creates columns: joined_date_year, joined_date_month

7. Balancing (balance_classes)

Balance the dataset based on the target variable.

Signature:

auditor.balance_classes(target, strategy='oversample', random_state=42)

Parameters:

• target (str): The target column name (variables to predict).
• strategy (str):
  • 'oversample': Randomly duplicate samples from minority class.
  • 'undersample': Randomly remove samples from majority class.
  • 'smote': Synthetic Minority Over-sampling Technique. (Requires imblearn).

Examples:

# Handle imbalanced dataset using SMOTE
auditor.balance_classes("is_fraud", strategy='smote')

8. Filtering & Dropping

Basic dataframe manipulations.

Signatures:

auditor.filter_rows(column, operator, value)
auditor.drop_columns(columns)

Examples:

# Keep only adults
auditor.filter_rows("age", ">=", 18)

# Remove PII
auditor.drop_columns(["ssn", "email"])

9. Generic Operations (track_pandas)

Track any pandas dataframe method that isn't natively built-in.

Signature:

auditor.track_pandas(method_name, *args, **kwargs)

Examples:

# Track a rename operation
auditor.track_pandas("rename", columns={"old_name": "new_name"})

# Track dropping NaNs
auditor.track_pandas("dropna", subset=["critical_col"])

10. Reproducibility

Verify Lineage: You can check if re-running the recorded operations on the original data produces the exact same result (by hash).

if auditor.verify_reproducibility():
    print("Pipeline is scientifically reproducible!")
else:
    print("Pipeline result mismatch!")
# Also available: auditor.replay() returns the re-computed dataframe independently

Visualization

When you run export_audit_trail(), an HTML file is generated in the visualizations/ folder. This interactive timeline shows:

1. Step Sequence: The order of operations.
2. Parameters: What strategy/method was used (e.g., strategy='median').
3. Data Shape: How the row/column count changed.
4. Schema: How columns were added or removed.

License

MIT License. Free to use for personal and commercial projects.