autosweep-preprocessing 0.1.1

Flexible tabular data preprocessing utility with a single AutoSweep API

autosweep-preprocessing

A lightweight preprocessing library built around a single flexible API: AutoSweep.

Usage

from autosweep_preprocessing import AutoSweep

result = AutoSweep(
    file_path="data.csv",
    target_column="target",
    encode_categorical="onehot",
    remove_correlated=True,
    structured_output=True,
)

X = result["X"]
y = result["y"]
info = result["info"]

Function

AutoSweep supports:

• CSV/Excel loading
• Missing value handling and imputation
• Numeric scaling (standard, minmax, robust)
• Categorical encoding (onehot, ordinal, label)
• Optional datetime feature extraction
• Optional outlier handling (iqr, zscore)
• Optional correlation and low-variance filtering
• Structured output for pipeline diagnostics

AutoSweep Arguments Guide

Required / Core

• file_path (required)

  • What it does: Path to input dataset (.csv or Excel file).
  • Use case: Point to your raw training file before preprocessing.
  • Example: file_path="data/train.csv"

• target_column (default: None)

  • What it does: Separates target variable from features and returns it as y.
  • Use case: Set this when you want to train/evaluate models after preprocessing.
  • Example: target_column="price"

Column cleaning

• drop_columns (default: None)

  • What it does: Drops specific columns by name.
  • Use case: Remove IDs, leakage columns, or metadata fields.
  • Example: drop_columns=["id", "created_at"]

• drop_threshold (default: 1.0)

  • What it does: Drops columns whose missing-value fraction is greater than this threshold.
  • Use case: Use 0.4/0.5 to remove heavily incomplete columns.
  • Example: drop_threshold=0.5

Missing values

• impute_strategy_num (default: 'mean')

  • What it does: Numeric imputation strategy.
  • Allowed: 'mean', 'median', 'most_frequent', 'constant', 'knn', 'mode'.
  • Use case: Use 'median' for skewed numeric data, 'knn' for richer local patterns.
  • Example: impute_strategy_num="median"

• impute_strategy_cat (default: 'most_frequent')

  • What it does: Categorical imputation strategy.
  • Allowed: any SimpleImputer categorical strategy (commonly 'most_frequent', 'constant').
  • Use case: Use 'most_frequent' for stable categories.
  • Example: impute_strategy_cat="most_frequent"

Scaling and encoding

• scaler (default: 'standard')

  • What it does: Scales numeric features.
  • Allowed: 'standard', 'minmax', 'robust', or any other value for passthrough.
  • Use case: Use 'robust' when outliers are present.
  • Example: scaler="robust"

• encode_categorical (default: None)

  • What it does: Encodes categorical columns.
  • Allowed: None, 'none', 'passthrough', 'onehot', 'ordinal', 'label'.
  • Use case: Use 'onehot' for linear/tree models; 'label' for compact numeric conversion.
  • Example: encode_categorical="onehot"

Feature selection

• remove_low_variance (default: False)

  • What it does: Removes low-variance numeric features after preprocessing.
  • Use case: Enable when many near-constant numeric features exist.
  • Example: remove_low_variance=True

• variance_thresh (default: 0.0)

  • What it does: Variance cutoff used by low-variance filtering.
  • Use case: Increase (e.g., 0.01) to remove weak/noisy features.
  • Example: variance_thresh=0.01

• remove_correlated (default: False)

  • What it does: Drops highly correlated numeric features.
  • Use case: Reduce multicollinearity and redundant columns.
  • Example: remove_correlated=True

• corr_threshold (default: 0.95)

  • What it does: Absolute correlation threshold for dropping features.
  • Use case: Use 0.85-0.95 depending on how aggressively you want feature pruning.
  • Example: corr_threshold=0.9

Outlier handling

• outlier_method (default: None)

  • What it does: Enables outlier detection.
  • Allowed: None, 'iqr', 'zscore' (also 'z-score', 'z_score').
  • Use case: Use 'iqr' for non-normal data; 'zscore' for roughly normal distributions.
  • Example: outlier_method="iqr"

• outlier_threshold (default: 1.5)

  • What it does: Threshold used by outlier method.
  • Use case: Increase to keep more rows, decrease to be stricter.
  • Example: outlier_threshold=3.0 (common for z-score)

• cap_outliers (default: False)

  • What it does: Caps outliers to bounds instead of dropping rows.
  • Use case: Set True when you want to preserve dataset size.
  • Example: cap_outliers=True

Datetime features

• extract_datetime (default: False)

  • What it does: Parses datetime-like columns and extracts year/month/day/weekday/hour.
  • Use case: Enable when date fields carry predictive signal.
  • Example: extract_datetime=True

• drop_datetime_original (default: False)

  • What it does: Drops original datetime columns after extraction.
  • Use case: Keep only engineered datetime parts to simplify model input.
  • Example: drop_datetime_original=True

Target encoding and output format

• target_encode (default: False)

  • What it does: Applies mean target encoding to categorical features.
  • Use case: Helpful for high-cardinality categorical variables.
  • Important: Requires target_column; avoid leakage by fitting only on training data in production workflows.
  • Example: target_encode=True

• structured_output (default: True)

  • What it does: Controls return format.
  • If True: returns { 'X', 'y', 'feature_names', 'info' }.
  • If False: returns tuple(s) (X, y, feature_names or X, feature_names).
  • Use case: Keep True for debugging and pipeline introspection.

• verbose (default: True)

  • What it does: Prints detailed preprocessing diagnostics.
  • Use case: Set False for cleaner logs in training pipelines.
  • Example: verbose=False

Notes

• If you use Excel input, keep openpyxl installed.
• If target_encode=True, provide a valid target_column.