datawatcher-ml 1.0.0

Production-grade dataset auditing and ML readiness scoring library

DataWatcher

Production-grade dataset auditing and ML readiness scoring library.

DataWatcher runs a comprehensive battery of 22+ audits across your dataset — checking structure, data quality, statistical properties, categorical features, and ML-specific risks — then produces an overall ML Readiness Score (0–100) and a prioritized Risk Summary.

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Installation

pip install datawatcher

For PDF report export support:

pip install "datawatcher[pdf]"

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Quick Start

Python API

import datawatcher

# Audit a CSV file
results = datawatcher.audit_csv("train.csv", target="survived")

print(results["ml_readiness"])
# {'score': 84, 'grade': 'GOOD', 'total_penalty': 16.0, ...}

print(results["risk_summary"])
# {'risk_level': 'LOW', 'top_risks': ['missing_value_audit'], ...}

# Access individual audit results
for audit in results["audit_results"]:
    print(audit.audit_name, audit.severity, audit.passed)

Audit an in-memory DataFrame

import pandas as pd
import datawatcher

df = pd.read_csv("transactions.csv")

results = datawatcher.audit_dataframe(
    df,
    target="churn",
    domain="finance"   # activates finance-specific audits
)

Domain-specific auditing

# Healthcare domain adds: age range, BMI, blood pressure,
# heart rate, lab results, missing diagnosis, medication consistency
results = datawatcher.audit_csv(
    "patients.csv",
    target="readmitted",
    domain="healthcare"
)

# Finance domain adds: negative values, currency consistency,
# interest rate validity, balance consistency
results = datawatcher.audit_csv(
    "loans.csv",
    target="default",
    domain="finance"
)

# Time series domain adds: duplicate timestamp detection
results = datawatcher.audit_csv(
    "sensor_data.csv",
    domain="timeseries"
)

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CLI Usage

After installation, the datawatcher command is available globally:

# Basic audit
datawatcher audit run data.csv

# With target column
datawatcher audit run data.csv --target label

# With domain plugin
datawatcher audit run data.csv --target label --domain healthcare

# Export reports
datawatcher audit run data.csv --target label --export-html --export-pdf --export-json

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Audit Catalog

Structural (4 audits)

Audit	Checks
shape_audit	Row and column counts
dtype_audit	Data type summary per column
memory_usage_audit	Dataset memory footprint
schema_consistency_audit	Mixed types within columns

Quality (5 audits)

Audit	Threshold	Source
missing_value_audit	LOW >3%, MEDIUM >15%	Google TFDV
duplicate_audit	LOW >0.5%, MEDIUM >5%	AWS Deequ
constant_feature_audit	Any constant column	—
near_constant_audit	>95% single value	scikit-learn
invalid_value_audit	Inf/NaN/unrealistic values	—

Statistical (5 audits)

Audit	Threshold	Source
descriptive_stats_audit	Observational (no penalty)	—
variance_audit	Variance < 0.001	scikit-learn VarianceThreshold
skewness_audit	|skew| ≥ 1.0	Hair et al. (2010)
kurtosis_audit	Excess kurtosis > 7	DeCarlo (1997)
outlier_audit	LOW >0.5% rows, MEDIUM >2% rows	IBM Research / TFDV

Categorical (3 audits)

Audit	Threshold
category_frequency_audit	Observational
rare_category_audit	Category < 0.5% frequency
category_imbalance_audit	Dominant category > 70%

ML (5 audits)

Audit	Threshold	Source
cardinality_audit	> 30% unique values	Industry ML best practice
identifier_risk_audit	> 90% unique values	GDPR / ML risk
target_validation_audit	Target column validity	—
class_imbalance_audit	Majority class > 75%	Japkowicz & Stephen (2002)
leakage_audit	|Pearson r| > 0.90 with target	Industry standard

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ML Readiness Score

Score = 100 − Σ(severity_weight × audit_weight)

Severity weights: INFO=0, LOW=3, MEDIUM=7, HIGH=15, CRITICAL=25
Audit weights (examples): leakage=3.0, target_validation=3.0, invalid_values=2.0

Grades:
  ≥ 90 → EXCELLENT
  ≥ 75 → GOOD
  ≥ 60 → FAIR
   < 60 → POOR

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Extending with Custom Audits

from datawatcher import BaseAudit, AuditResult, AuditRegistry, AuditEngine
from datawatcher import audit_dataframe

class MyCustomAudit(BaseAudit):
    audit_name = "my_custom_audit"
    category = "custom"

    def run(self, dataset, context=None):
        df = dataset.df
        # ... your logic ...
        return AuditResult(
            audit_name=self.audit_name,
            category=self.category,
            passed=True,
            severity="INFO",
            findings={"message": "All good"},
            recommendations=[]
        )

# Use programmatically
registry = AuditRegistry()
registry.register(MyCustomAudit())

from datawatcher.core.audit_engine import AuditEngine
from datawatcher.loaders.factory import load_dataset

dataset = load_dataset("data.csv")
engine = AuditEngine(registry)
results = engine.run(dataset, context={"target": "label"})

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Return Value Structure

audit_csv() and audit_dataframe() return:

{
    "audit_results": [AuditResult, ...],   
    "ml_readiness": {
        "score": 84,                        
        "grade": "GOOD",                   
        "total_penalty": 16.0,
        "severity_breakdown": {...}
    },
    "risk_summary": {
        "risk_level": "LOW",              
        "top_risks": ["audit_name", ...],
        "high_risk_audits": [...],
        "medium_risk_audits": [...]
    },
    "metadata": {
        "rows": 10000,
        "columns": 25,
        "memory_usage_mb": 4.2
    },
    "semantic_types": {
        "column_name": "numeric",       
        ...
    }
}

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License

MIT © Ranjeet