autocure 0.2.0

Automated data cleaning and auto ML training with hyperparameter tuning.

Autocure

Automated data cleaning + AutoML model training with hyperparameter tuning.

Key features

• Data scan

  • Missing values summary
  • Dtype inference
  • Outlier counts (IQR method)
  • Duplicate rows count

• Data cleaning (cure)

  • Numeric imputation (median)
  • Categorical imputation (mode / fallback "Unknown")
  • Text preprocessing (applies only to text columns):
    • remove punctuation
    • lowercase
    • tokenize
    • remove stopwords
    • lemmatize
  • Drop duplicates

• Pipeline & AutoML

  • Automatic ColumnTransformer (StandardScaler for numeric, OneHotEncoder for categorical)
  • Support for classification and regression
  • Multiple algorithms with GridSearchCV hyperparameter search
  • Model leaderboard and best-model selection

• CLI

  • scan-data, fix, train-model commands for quick workflows

Installation

From project root (editable install for development):

python -m pip install -e .

Or install dependencies:

pip install -r requirements.txt
pip install nltk

NLTK data used by cure() is downloaded at runtime (punkt, stopwords, wordnet).

Quick usage

Python:

import autocure as ac
import pandas as pd

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

# 1) Inspect dataset
report = ac.scan(df)
print(report)

# 2) Clean dataset
clean = ac.cure(df)

# 3) Create pipeline (optional)
pipeline = ac.make_pipeline(clean, target="target_column")

# 4) Train models (returns a dict)
result = ac.train(clean, target="target_column")

print(result["best_model_name"])
print("score:", result["best_score"])
# Best model object for predict:
best_model = result["best_model"]
preds = best_model.predict(clean.drop(columns=["target_column"]).iloc[:5])

Returned keys from train():

• best_model (fitted sklearn estimator / pipeline)
• best_model_name
• best_score
• best_params
• leaderboard (list/dict of model results)

CLI

Examples:

# Scan a CSV
autocure scan-data data.csv

# Clean and save
autocure fix data.csv --out cleaned.csv

# Train via CLI
autocure train-model data.csv --target target_column

Tips & notes

• cure() applies text preprocessing only to columns detected as object/string dtype.
• For small categorical cardinality detection and problem-type heuristics, train() uses simple rules; override by preparing the DataFrame appropriately.
• If you use large text datasets, consider vectorization or specialized NLP pipelines before heavy GridSearchCV.

Contributing

Bug reports and PRs welcome. Follow repository guidelines and add tests for new behavior.

License

MIT License