AutoPrep 2.0.3

Description:

AutoPrep 2.0.3

AutoPrep - Automated Preprocessing Pipeline with Univariate Anomaly Indicators




This pipeline focuses on data preprocessing, standardization, and cleaning, with additional features to identify univariate anomalies.
Structure of Preprocessing Pipeline
pip install AutoPrep

Dependencies

scikit-learn
category_encoders
bitstring
ydata_profiling

Basic Usage
To utilize this pipeline, you need to import the necessary libraries and initialize the AutoPrep pipeline. Here is a basic example:
import pandas as pd
import numpy as np
import sys
sys.path.append("../")
sys.path.append("./")

data = {

'ID': [1, 2, 3, 4],
'Name': ['Alice', 'Bob', 'Charlie', 42],
'Rank': ['A','B','C','D'],
'Age': [25, 30, 35, np.nan],
'Salary': [50000.00, 60000.50, 75000.75, 80000.00],
'Hire Date': pd.to_datetime(['2020-01-15', '2019-05-22', '2018-08-30', '2021-04-12']),
'Is Manager': [False, True, False, ""]
}
data = pd.DataFrame(data)

from AutoPrep import AutoPrep

pipeline = AutoPrep(
nominal_columns=["ID", "Name", "Is Manager", "Age"],
datetime_columns=["Hire Date"],
pattern_recognition_columns=["Name"],
scaler_option_num="standard",
deactivate_missing_indicator=True
)
#### Automated Preprocessing of data
X_output_preprocessed = pipeline.fit_transform(df=data)

#### Automated Preprocessing + Anomalies in data with pyod library
X_output_anomalies = pipeline.find_anomalies(df=data)


#### Profiling of DataFrame / Visualization of pipeline structure
# pipeline.get_profiling(X=data)
# pipeline.visualize_pipeline_structure_html()

Highlights ⭐
📌 Implementation of univariate methods / Detection of univariate anomalies
Both methods (MOD Z-Value and Tukey Method) are resilient against outliers, ensuring that the position measurement will not be biased. They also support multivariate anomaly detection algorithms in identifying univariate anomalies.
📌 BinaryEncoder instead of OneHotEncoder for nominal columns / Big Data and Performance
Newest research shows similar results for encoding nominal columns with significantly fewer dimensions.

(John T. Hancock and Taghi M. Khoshgoftaar. "Survey on categorical data for neural networks." In: Journal of Big Data 7.1 (2020), pp. 1–41.), Tables 2, 4
(Diogo Seca and João Mendes-Moreira. "Benchmark of Encoders of Nominal Features for Regression." In: World Conference on Information Systems and Technologies. 2021, pp. 146–155.), P. 151

📌 Transformation of time series data and standardization of data with RobustScaler / Normalization for better prediction results
📌 Labeling of NaN values in an extra column instead of removing them / No loss of information

Pipeline - Built-in Logic







Feel free to contribute 🙂
Reference

https://www.researchgate.net/publication/379640146_Detektion_von_Anomalien_in_der_Datenqualitatskontrolle_mittels_unuberwachter_Ansatze (German Thesis)

Further Information

I used sklearn's Pipeline and Transformer concept to create this preprocessing pipeline

Pipeline: https://scikit-learn.org/stable/modules/generated/sklearn.pipeline.Pipeline.html
Transformer: https://scikit-learn.org/stable/modules/generated/sklearn.base.TransformerMixin.html