feat: add edge case tests, GitHub Actions CI, and ruff linting (#64)

* feat: add edge case tests, GitHub Actions CI, and ruff linting

- Add 37 edge case tests covering empty DataFrames, constant columns,
  infinite values, correlation edge cases, leakage, drift, and more
- Add GitHub Actions CI workflow (pytest on Python 3.10-3.12, ruff lint)
- Add ruff configuration with sensible defaults for the project
- Add ruff as dev dependency
- Fix all ruff lint issues (unused imports, re-exports, unused vars)
- Apply ruff formatting across the entire codebase

* fix: resolve ruff import sorting in markdown.py

Author: maskedsyntax

.github/workflows/ci.yml

@@ -0,0 +1,49 @@
+name: CI
+
+on:
+  push:
+    branches: [main]
+  pull_request:
+    branches: [main]
+
+jobs:
+  test:
+    runs-on: ubuntu-latest
+    strategy:
+      matrix:
+        python-version: ["3.10", "3.11", "3.12"]
+
+    steps:
+      - uses: actions/checkout@v4
+
+      - name: Install uv
+        uses: astral-sh/setup-uv@v4
+
+      - name: Set up Python ${{ matrix.python-version }}
+        run: uv python install ${{ matrix.python-version }}
+
+      - name: Install dependencies
+        run: uv sync --dev
+
+      - name: Run tests
+        run: uv run pytest tests/ -v --tb=short
+
+  lint:
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/checkout@v4
+
+      - name: Install uv
+        uses: astral-sh/setup-uv@v4
+
+      - name: Set up Python
+        run: uv python install 3.12
+
+      - name: Install dependencies
+        run: uv sync --dev
+
+      - name: Run ruff check
+        run: uv run ruff check .
+
+      - name: Run ruff format check
+        run: uv run ruff format --check .

examples/reports/train_hashprep_report_fixes.py

@@ -3,9 +3,6 @@
 Review and adapt before production use.
 """
 
-from sklearn.preprocessing import OneHotEncoder, OrdinalEncoder
-from sklearn.preprocessing import RobustScaler
-import numpy as np
 import pandas as pd
 
 
@@ -14,51 +11,51 @@ def apply_fixes(df):
     df = df.copy()
 
     # Column 'Cabin' has 77% missing values
-    df = df.drop(columns=['Cabin'])
+    df = df.drop(columns=["Cabin"])
 
     # Frequency encode high-cardinality column 'Name'
-    freq_Name = df['Name'].value_counts(normalize=True)
-    df['Name_encoded'] = df['Name'].map(freq_Name)
+    freq_Name = df["Name"].value_counts(normalize=True)
+    df["Name_encoded"] = df["Name"].map(freq_Name)
 
     # Frequency encode high-cardinality column 'Ticket'
-    freq_Ticket = df['Ticket'].value_counts(normalize=True)
-    df['Ticket_encoded'] = df['Ticket'].map(freq_Ticket)
+    freq_Ticket = df["Ticket"].value_counts(normalize=True)
+    df["Ticket_encoded"] = df["Ticket"].map(freq_Ticket)
 
     # Clip outliers in 'Fare' using IQR method
-    q1_Fare, q3_Fare = df['Fare'].quantile([0.25, 0.75])
+    q1_Fare, q3_Fare = df["Fare"].quantile([0.25, 0.75])
     iqr_Fare = q3_Fare - q1_Fare
     lower_Fare, upper_Fare = q1_Fare - 1.5 * iqr_Fare, q3_Fare + 1.5 * iqr_Fare
-    df['Fare'] = df['Fare'].clip(lower=lower_Fare, upper=upper_Fare)
+    df["Fare"] = df["Fare"].clip(lower=lower_Fare, upper=upper_Fare)
 
     # Clip outliers in 'Parch' using IQR method
-    q1_Parch, q3_Parch = df['Parch'].quantile([0.25, 0.75])
+    q1_Parch, q3_Parch = df["Parch"].quantile([0.25, 0.75])
     iqr_Parch = q3_Parch - q1_Parch
     lower_Parch, upper_Parch = q1_Parch - 1.5 * iqr_Parch, q3_Parch + 1.5 * iqr_Parch
-    df['Parch'] = df['Parch'].clip(lower=lower_Parch, upper=upper_Parch)
+    df["Parch"] = df["Parch"].clip(lower=lower_Parch, upper=upper_Parch)
 
     # Clip outliers in 'SibSp' using IQR method
-    q1_SibSp, q3_SibSp = df['SibSp'].quantile([0.25, 0.75])
+    q1_SibSp, q3_SibSp = df["SibSp"].quantile([0.25, 0.75])
     iqr_SibSp = q3_SibSp - q1_SibSp
     lower_SibSp, upper_SibSp = q1_SibSp - 1.5 * iqr_SibSp, q3_SibSp + 1.5 * iqr_SibSp
-    df['SibSp'] = df['SibSp'].clip(lower=lower_SibSp, upper=upper_SibSp)
+    df["SibSp"] = df["SibSp"].clip(lower=lower_SibSp, upper=upper_SibSp)
 
     # Drop highly correlated column 'Survived,Sex'
-    df = df.drop(columns=['Survived,Sex'])
+    df = df.drop(columns=["Survived,Sex"])
 
     return df
 
 
-if __name__ == '__main__':
+if __name__ == "__main__":
     import sys
 
     if len(sys.argv) < 2:
-        print('Usage: python fixes.py <input.csv> [output.csv]')
+        print("Usage: python fixes.py <input.csv> [output.csv]")
         sys.exit(1)
 
     input_file = sys.argv[1]
-    output_file = sys.argv[2] if len(sys.argv) > 2 else 'cleaned_data.csv'
+    output_file = sys.argv[2] if len(sys.argv) > 2 else "cleaned_data.csv"
 
     df = pd.read_csv(input_file)
     df_clean = apply_fixes(df)
     df_clean.to_csv(output_file, index=False)
-    print(f'Cleaned data saved to {output_file}')
+    print(f"Cleaned data saved to {output_file}")

examples/reports/train_hashprep_report_pipeline.py

@@ -5,27 +5,27 @@
 
 from sklearn.compose import ColumnTransformer
 from sklearn.pipeline import Pipeline
-from sklearn.preprocessing import OneHotEncoder, OrdinalEncoder
-import numpy as np
 
 
 def build_preprocessing_pipeline():
     """Build sklearn preprocessing pipeline."""
 
     transformers = [
-        ('drop_column_Cabin', 'drop', ['Cabin']),
-        ('drop_column_Survived,S', 'drop', ['Survived,Sex']),
+        ("drop_column_Cabin", "drop", ["Cabin"]),
+        ("drop_column_Survived,S", "drop", ["Survived,Sex"]),
     ]
 
     preprocessor = ColumnTransformer(
         transformers=transformers,
-        remainder='passthrough',
+        remainder="passthrough",
         verbose_feature_names_out=False,
     )
 
-    pipeline = Pipeline([
-        ('preprocessor', preprocessor),
-    ])
+    pipeline = Pipeline(
+        [
+            ("preprocessor", preprocessor),
+        ]
+    )
 
     return pipeline
 
@@ -37,20 +37,18 @@ def get_pre_pipeline_steps():
     """
     steps = []
     # Outlier clipping for ['Fare']
-    steps.append(('clip_outliers_Fare', None))  # Implement manually
+    steps.append(("clip_outliers_Fare", None))  # Implement manually
     # Outlier clipping for ['Parch']
-    steps.append(('clip_outliers_Parch', None))  # Implement manually
+    steps.append(("clip_outliers_Parch", None))  # Implement manually
     # Outlier clipping for ['SibSp']
-    steps.append(('clip_outliers_SibSp', None))  # Implement manually
+    steps.append(("clip_outliers_SibSp", None))  # Implement manually
     return steps
 
 
-if __name__ == '__main__':
-    import joblib
-
+if __name__ == "__main__":
     pipeline = build_preprocessing_pipeline()
     if pipeline:
-        print('Pipeline created successfully')
+        print("Pipeline created successfully")
         print(pipeline)
         # Example: Save pipeline
-        # joblib.dump(pipeline, 'preprocessing_pipeline.joblib')
+        # joblib.dump(pipeline, 'preprocessing_pipeline.joblib')

hashprep/__init__.py

@@ -1,3 +1,3 @@
-from .core.analyzer import DatasetAnalyzer
+from .core.analyzer import DatasetAnalyzer as DatasetAnalyzer
 
-__version__ = "0.1.0b1"
+__version__ = "0.1.0b1"

hashprep/checks/__init__.py

@@ -1,29 +1,31 @@
-from typing import List, Optional
-
-from .core import Issue
+from .columns import _check_duplicates, _check_high_cardinality, _check_mixed_data_types, _check_single_value_columns
+from .core import Issue as Issue
+from .correlations import calculate_correlations
+from .distribution import _check_uniform_distribution, _check_unique_values
 from .drift import check_drift
+from .imbalance import _check_class_imbalance
 from .leakage import _check_data_leakage, _check_target_leakage_patterns
-from .missing_values import _check_high_missing_values, _check_empty_columns, _check_dataset_missingness, \
-    _check_missing_patterns
-from .columns import _check_single_value_columns, _check_high_cardinality, _check_duplicates, _check_mixed_data_types
+from .missing_values import (
+    _check_dataset_missingness,
+    _check_empty_columns,
+    _check_high_missing_values,
+    _check_missing_patterns,
+)
 from .outliers import (
-    _check_outliers,
-    _check_high_zero_counts,
-    _check_extreme_text_lengths,
-    _check_datetime_skew,
-    _check_skewness,
-    _check_infinite_values,
     _check_constant_length,
+    _check_datetime_skew,
     _check_empty_dataset,
+    _check_extreme_text_lengths,
+    _check_high_zero_counts,
+    _check_infinite_values,
+    _check_outliers,
+    _check_skewness,
 )
-from .correlations import calculate_correlations
-from .imbalance import _check_class_imbalance
-from .distribution import _check_uniform_distribution, _check_unique_values
 
 
 def _check_dataset_drift(analyzer):
     """Wrapper for drift detection that uses analyzer's comparison_df."""
-    if hasattr(analyzer, 'comparison_df') and analyzer.comparison_df is not None:
+    if hasattr(analyzer, "comparison_df") and analyzer.comparison_df is not None:
         return check_drift(analyzer.df, analyzer.comparison_df)
     return []
 
@@ -56,7 +58,7 @@ def _check_dataset_drift(analyzer):
 CORRELATION_CHECKS = {"feature_correlation", "categorical_correlation", "mixed_correlation"}
 
 
-def run_checks(analyzer, checks_to_run: List[str]):
+def run_checks(analyzer, checks_to_run: list[str]):
     issues = []
     correlation_requested = False
 
@@ -70,4 +72,4 @@ def run_checks(analyzer, checks_to_run: List[str]):
     if correlation_requested:
         issues.extend(calculate_correlations(analyzer))
 
-    return issues
+    return issues

hashprep/checks/columns.py

@@ -1,8 +1,9 @@
-from .core import Issue
 from ..config import DEFAULT_CONFIG
+from .core import Issue
 
 _COL_THRESHOLDS = DEFAULT_CONFIG.columns
 
+
 def _check_single_value_columns(analyzer):
     issues = []
     for col in analyzer.df.columns:
@@ -26,7 +27,12 @@ def _check_single_value_columns(analyzer):
             )
     return issues
 
-def _check_high_cardinality(analyzer, threshold: int = _COL_THRESHOLDS.high_cardinality_count, critical_threshold: float = _COL_THRESHOLDS.high_cardinality_ratio_critical):
+
+def _check_high_cardinality(
+    analyzer,
+    threshold: int = _COL_THRESHOLDS.high_cardinality_count,
+    critical_threshold: float = _COL_THRESHOLDS.high_cardinality_ratio_critical,
+):
     issues = []
     categorical_cols = analyzer.df.select_dtypes(include="object").columns.tolist()
     for col in categorical_cols:
@@ -52,6 +58,7 @@ def _check_high_cardinality(analyzer, threshold: int = _COL_THRESHOLDS.high_card
             )
     return issues
 
+
 def _check_duplicates(analyzer):
     issues = []
     duplicate_rows = int(analyzer.df.duplicated().sum())
@@ -76,6 +83,7 @@ def _check_duplicates(analyzer):
         )
     return issues
 
+
 def _check_mixed_data_types(analyzer):
     issues = []
     for col in analyzer.df.columns:
@@ -91,4 +99,4 @@ def _check_mixed_data_types(analyzer):
                     quick_fix="Options: \n- Cast to single type: Ensure consistency (Pros: Simplifies processing; Cons: May lose nuance).\n- Split column: Separate types into new features (Pros: Preserves info; Cons: Adds complexity).\n- Investigate source: Check data collection errors (Pros: Improves quality; Cons: Time-consuming).",
                 )
             )
-    return issues
+    return issues

hashprep/checks/core.py

@@ -1,9 +1,8 @@
 from dataclasses import dataclass
 
-@dataclass
 
+@dataclass
 class Issue:
-
     category: str
 
     severity: str  # critical or warning