simon-library 0.7.0

A library for hw4

simon_library

simon_library is a Python package designed for data preprocessing and model evaluation. The library contains a set of utilities for cleaning data, feature engineering, splitting data, training models, and evaluating performance using ROC AUC. This package is especially useful for data science and machine learning tasks.

Installation

You can install the simon_library package via pip:

pip install simon_library

Available Functions

1. get_data(path) Loads a CSV file into a pandas DataFrame with error handling and logging.

Parameters:

path (str): The path to the CSV file to be loaded. Returns:

pd.DataFrame: The loaded DataFrame if successful, otherwise None.

2. clean_data(df) Cleans the data by handling missing values and preparing it for further analysis.

Parameters:

df (pd.DataFrame): The DataFrame to clean. Returns:

pd.DataFrame: The cleaned DataFrame.

3. create_features(df) Generates additional features from existing columns, including encoding categorical variables and adding new binary features.

Parameters:

df (pd.DataFrame): The DataFrame to create features from. Returns:

pd.DataFrame: The DataFrame with new and transformed features.

4. split_data(df, target, test_size=0.2, random_state=42) Splits the DataFrame into training and testing sets.

Parameters:

df (pd.DataFrame): The DataFrame to split. target (str): The target column to predict. test_size (float): Proportion of the data to be used as the test set. random_state (int): Random seed for reproducibility. Returns:

X_train, X_test, y_train, y_test: Split training and test sets.

5. train_model(X_train, X_test, y_train, y_test, model) Trains the provided model on the training set and evaluates it on the test set.

Parameters:

X_train, X_test: Feature sets for training and testing. y_train, y_test: Target values for training and testing. model: A scikit-learn model to train. Returns:

accuracy: Accuracy score on the test set. train_predictions_proba, test_predictions_proba: Predicted probabilities for training and testing sets.

6. get_roc_auc(y_train, y_test, train_predictions, test_predictions) Calculates the ROC AUC score for both training and test sets.

Parameters:

y_train, y_test: True target values for the training and test sets. train_predictions, test_predictions: Predicted probabilities for the training and test sets. Returns:

roc_auc_train, roc_auc_test: ROC AUC scores for training and test sets.