ravenclaw 0.1.0

Data wrangling and feature engineering toolkit for pandas DataFrames

Ravenclaw - Intelligent Data Preprocessing and Clustering

A comprehensive toolkit for automated feature engineering and clustering with pandas DataFrames. Ravenclaw intelligently detects column types, converts temporal strings, creates cyclical encodings, and performs automatic clustering - all with minimal configuration.

🎯 Key Features

Intelligent Preprocessing Pipeline

• Automatic Detection: Identifies datetime, date, time, and categorical columns in string format
• Smart Conversion: Converts string temporal data to proper types with format learning
• Cyclical Encoding: Creates sin/cos features for temporal data (perfect for ML models)
• One-Hot Encoding: Handles categorical variables with temporal safety checks
• Sklearn Compatible: Standard fit/transform interface for seamless integration

Advanced Clustering

• AutoKMeans: Automatically determines optimal number of clusters
• Multiple Methods: Silhouette, Calinski-Harabasz, Davies-Bouldin, and Elbow methods
• Built-in Preprocessing: Integrated scaling and imputation
• Column Selection: Flexible include/exclude functionality
• Sklearn Interface: Drop-in replacement for standard KMeans

🚀 Quick Start

Installation

pip install ravenclaw

Complete ML Pipeline Example

Here's how to preprocess mixed data and cluster it in a single pipeline:

import pandas as pd
import numpy as np
from ravenclaw.preprocessing.df_encoders import FeatureEngineeringPipeline
from ravenclaw.clustering.kmeans import AutoKMeans
from sklearn.pipeline import Pipeline

# Create sample mixed data (the kind you get in real life!)
np.random.seed(42)
df = pd.DataFrame({
    'timestamp': ['2023-01-15 14:30:00', '2023-06-30 09:15:00', '2023-12-25 18:45:00'] * 50,
    'date_created': ['2023-01-15', '2023-06-30', '2023-12-25'] * 50,
    'time_of_day': ['14:30:00', '09:15:00', '18:45:00'] * 50,
    'category': ['Premium', 'Standard', 'Basic'] * 50,
    'region': ['North', 'South', 'East', 'West'] * 37 + ['North', 'South', 'East'],
    'revenue': np.random.normal(1000, 200, 150),
    'customer_age': np.random.randint(18, 80, 150),
    'satisfaction': np.random.uniform(1, 5, 150)
})

# Add some missing values (real data is messy!)
df.loc[np.random.choice(150, 10), 'revenue'] = np.nan
df.loc[np.random.choice(150, 5), 'satisfaction'] = np.nan

print(f"Original data: {df.shape[1]} columns")
print(f"Column types: {df.dtypes.value_counts().to_dict()}")

# Step 1: Feature Engineering Pipeline
# Automatically detects and converts temporal strings, creates cyclical features
preprocessor = FeatureEngineeringPipeline.create_default()
processed_df = preprocessor.fit_transform(df)

print(f"After preprocessing: {processed_df.shape[1]} columns")
print(f"New features: {list(processed_df.columns)}")

# Step 2: Automatic Clustering with Built-in Imputation
# AutoKMeans handles missing values and scaling automatically
clusterer = AutoKMeans(
    max_k=8,                           # Try up to 8 clusters
    method='silhouette',               # Use silhouette score for k-selection
    scale=True,                        # Scale features (important for clustering)
    impute=True,                       # Handle missing values
    impute_strategy='median',          # Use median for imputation
    ignore_non_numeric=True,           # Silently skip non-numeric columns
    random_state=42
)

# Fit and predict clusters
cluster_labels = clusterer.fit_predict(processed_df)

print(f"Optimal clusters found: {clusterer.n_clusters_}")
print(f"Cluster distribution: {np.bincount(cluster_labels)}")

# Add clusters back to original data for analysis
df['cluster'] = cluster_labels
print("\nCluster summary:")
print(df.groupby('cluster')[['revenue', 'customer_age', 'satisfaction']].mean())

Sklearn Pipeline Integration

from sklearn.pipeline import Pipeline
from sklearn.preprocessing import StandardScaler

# Create a complete sklearn pipeline
ml_pipeline = Pipeline([
    ('preprocessing', FeatureEngineeringPipeline.create_default()),
    ('clustering', AutoKMeans(max_k=5, scale=True, impute=True))
])

# One-line fit and predict
cluster_labels = ml_pipeline.fit_predict(df)

🔧 Advanced Usage

Custom Preprocessing Pipeline

from ravenclaw.preprocessing.df_encoders import (
    StringTemporalConverter,
    DateTimeConverter, 
    DayOfYearEncoder,
    HourOfDayEncoder,
    OneHotEncoder,
    FeatureEngineeringPipeline
)

# Build custom pipeline with specific encoders
custom_pipeline = FeatureEngineeringPipeline([
    StringTemporalConverter(),          # String temporal → proper types
    DateTimeConverter(),                # datetime → day_of_year + hour_of_day
    DayOfYearEncoder(),                 # day_of_year → sin/cos features
    HourOfDayEncoder(),                 # hour_of_day → sin/cos features
    OneHotEncoder(exclude_columns=['timestamp'])  # Categorical → binary (skip temporal)
])

result = custom_pipeline.fit_transform(df)

Clustering with Column Selection

# Cluster only specific columns
clusterer = AutoKMeans(
    include_columns=['revenue', 'customer_age', 'satisfaction'],
    max_k=6,
    method='calinski_harabasz',
    impute=True,
    impute_strategy='mean'
)

labels = clusterer.fit_predict(df)

# Get clustering diagnostics
diagnostics = clusterer.get_diagnostics()
print(f"Tried k values: {diagnostics['candidate_ks']}")
print(f"Scores: {diagnostics['scores']}")

Individual Encoder Usage

from ravenclaw.preprocessing.df_encoders import StringTemporalConverter

# Just convert temporal strings
converter = StringTemporalConverter()
converter.fit(df)

# See what formats were learned
formats = converter.get_learned_formats()
print("Detected formats:", formats)

# Transform the data
converted_df = converter.transform(df)

🧠 How It Works

Intelligent Column Detection

Ravenclaw uses priority-based classification to handle ambiguous cases:

datetime > date > time > day_of_week > day_of_year > hour_of_day > categorical

Automatic K-Selection Methods

• Silhouette Score: Measures cluster cohesion and separation
• Calinski-Harabasz: Ratio of between-cluster to within-cluster variance
• Davies-Bouldin: Average similarity between clusters
• Elbow Method: Finds the "elbow" in the inertia curve

Built-in Imputation

AutoKMeans includes sklearn's SimpleImputer with strategies:

• 'mean': Replace with column mean (numeric only)
• 'median': Replace with column median (numeric only)
• 'most_frequent': Replace with mode (works for all types)
• 'constant': Replace with a constant value

📊 What You Get

Before Ravenclaw

# Manual feature engineering pain
df['hour'] = pd.to_datetime(df['timestamp']).dt.hour
df['day_of_year'] = pd.to_datetime(df['timestamp']).dt.dayofyear
df['hour_sin'] = np.sin(2 * np.pi * df['hour'] / 24)
df['hour_cos'] = np.cos(2 * np.pi * df['hour'] / 24)
# ... repeat for every temporal column
# ... handle missing values
# ... scale features
# ... try different k values
# ... compare clustering metrics

After Ravenclaw

# One-liner magic ✨
pipeline = FeatureEngineeringPipeline.create_default()
processed_df = pipeline.fit_transform(df)
labels = AutoKMeans(max_k=8).fit_predict(processed_df)

🛠️ Development

# Clone and setup
git clone https://github.com/idin/ravenclaw.git
cd ravenclaw

# Create conda environment  
conda env create -f environment.yml
conda activate ravenclaw

# Install in development mode
pip install -e .

# Run tests (244 tests, all passing!)
pytest tests/

📈 Project Status

• ✅ 244 Tests Passing - Comprehensive test coverage
• ✅ Production Ready - Used in real ML workflows
• ✅ Zero Warnings - Clean, maintainable codebase
• ✅ Sklearn Compatible - Drop-in replacement for standard tools
• ✅ Type Safe - Full type hints and error handling

🎯 Perfect For

• Data Scientists: Spend less time on preprocessing, more on insights
• ML Engineers: Robust, tested pipelines for production
• Analysts: Intelligent automation for common data tasks
• Anyone: Who works with messy, real-world pandas DataFrames

📄 License

MIT License - Use it, love it, contribute to it!

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Ravenclaw: Because your data deserves intelligent preprocessing 🧙‍♂️✨