Atlantic is an automated preprocessing framework for Supervised Machine Learning
Project description
Atlantic - Automated Data Preprocessing Framework for Supervised Machine Learning
Framework Contextualization
The Atlantic
project constitutes an comprehensive and objective approach to simplify and automate data processing through the integration and objectively validated application of various preprocessing mechanisms, ranging from feature engineering, automated feature selection, multiple encoding versions and null imputation methods. The optimization methodology of this framework follows a evaluation structured in tree based models ensembles.
This project aims at providing the following application capabilities:
-
General applicability on tabular datasets: The developed preprocessing procedures are applicable on any data table associated with Supervised Machine Learning scopes regardless of the properties or specifications of the Dataset features.
-
Automated treatment of tabular data associated with predictive analysis: It implements a global and carefully validated tested data treatment based on the characteristics of each Dataset input columns, assuming its identification with the associated target column.
-
Robustness and improvement of predictive results: The implementation of the
atlantic
automated data preprocessing pipeline aims at improving predictive performance directly associated with the processing methods implemented based on the Dataset properties.
Main Development Tools
Major frameworks used to built this project:
Framework Architecture
Where to get it
Binary installer for the latest released version is available at the Python Package Index PyPI.
The source code is currently hosted on GitHub at: https://github.com/TsLu1s/Atlantic
Installation
To install this package from Pypi repository run the following command:
pip install atlantic
Usage Examples
1. Atlantic - Automated Data Preprocessing Pipeline
In order to be able to apply the automated preprocessing atlantic
pipeline you need first to import the package.
The following needed step is to load a dataset and define your to be predicted target column name into the variable target
and define split ratio for your Train and Validation subsets.
You can customize the fit_processing
method by altering the following running pipeline parameters:
- split_ratio: Division ratio in which the preprocessing methods will be evaluated within the loaded Dataset.
- relevance: Minimal value of the total sum of relative variable\feature importance percentage selected in the
H2O AutoML feature selection
step. - h2o_fs_models: Quantity of models generated for competition in step
H2O AutoML feature selection
to evaluate the relative importance of each feature (only leaderboard model is selected for evaluation). - encoding_fs: You can choose if you want to encond your features in order to reduce loading time in
H2O AutoML feature selection
step. If inTrue
mode label encoding is applied to categorical features. - vif_ratio: This value defines the minimal
threshold
for Variance Inflation Factor filtering (default value=10).
Importante Notes:
- Default predictive evaluation metric for regression contexts is
Mean Absolute Error
and classification isAccuracy
. - Although functional,
Atlantic
data processing is not optimized for big data purposes yet. - Major update is now available in versions>=1.1.0
from atlantic.pipeline import ATLpipeline
import pandas as pd
from sklearn.model_selection import train_test_split
import warnings
warnings.filterwarnings("ignore", category=Warning) # -> For a clean console
data = pd.read_csv('csv_directory_path', encoding='latin', delimiter=',') # Dataframe Loading Example
train,test = train_test_split(data, train_size=0.8)
train,test = train.reset_index(drop=True), test.reset_index(drop=True) # -> Required
### Fit Data Processing
atl = ATLpipeline(X=train, # X:pd.DataFrame, target:str="Target_Column"
target="Target Column")
atl.fit_processing(split_ratio=0.75, # split_ratio:float=0.75, relevance:float=0.99 [0.5,1]
relevance=0.99, # h2o_fs_models:int [1,50], encoding_fs:bool=True\False
h2o_fs_models=7, # vif_ratio:float=10.0 [3,30]
encoding_fs=True,
vif_ratio=10.0)
### Transform Data Processing
train = atl.data_processing(X=train)
test = atl.data_processing(X=test)
### Export Atlantic Preprocessing Metadata
import pickle
output = open("fit_atl.pkl", 'wb')
pickle.dump(atl, output)
2. Atlantic - Preprocessing Data
2.1 Encoding Versions
There are multiple preprocessing methods available to direct use. This package provides upgrated encoding LabelEncoder
, OneHotEncoder
and IDF methods with an automatic multicolumn application.
from atlantic.processing import AutoLabelEncoder, AutoIdfEncoder, AutoOneHotEncoder
import pandas as pd
from sklearn.model_selection import train_test_split
train,test = train_test_split(data, train_size=0.8)
train,test = train.reset_index(drop=True), test.reset_index(drop=True) # Required
target = "Target_Column" # -> target feature name
cat_cols=[col for col in data.select_dtypes(include=['object']).columns if col != target]
### Encoders
## Create Label Encoder
encoder = AutoLabelEncoder()
## Create IDF Encoder
encoder = AutoIdfEncoder()
## Create One-hot Encoder
encoder = AutoOneHotEncoder()
## Fit
encoder.fit(train[cat_cols])
# Transform the DataFrame using Label\IDF\One-hot Encoding
train = encoder.transform(X=train)
test = encoder.transform(X=test)
# Label Encoding : Perform an inverse transform to convert it back the categorical columns values
test = encoder.inverse_transform(X=test)
# IDF & One-hot Encoding : Perform an inverse transform to convert it back the categorical columns values
# Note: Only decodes the last transformed Dataframe
test = encoder.inverse_transform()
2.2 Feature Selection Methods
You can get filter your most valuable features from the dataset via this 2 feature selection methods:
-
H2O AutoML Feature Selection - This method is based of variable importance evaluation and calculation for tree-based models in H2Os AutoML and it can be customized by use of the following parameters:
- relevance: Minimal value of the total sum of relative variable\feature importance percentage selected.
- h2o_fs_models: Quantity of models generated for competition to evaluate the relative importance of each feature (only leaderboard model will be selected for evaluation).
- encoding_fs: You can choose if you want to encond your features in order to reduce loading time. If in
True
mode label encoding is applied to categorical features.
-
VIF Feature Selection (Variance Inflation Factor) - Variance inflation factor aims at measuring the amount of multicollinearity in a set of multiple regression variables or features, therefore for this filtering method to be applied all input variables need to be of numeric type. It can be customized by changing the column filtering treshold
vif_threshold
designated with a default value of 10.
from atlantic.selector import Selector
fs = Selector(X=train,target="Target_Column")
cols_vif = fs.feature_selection_vif(vif_threshold=10.0) # X: Only numerical values allowed & No nans allowed in VIF
selected_cols, selected_importance = fs.feature_selection_h2o(relevance=0.99, # relevance:float [0.5,1], h2o_fs_models:int [1,50]
h2o_fs_models=7, # encoding_fs:bool=True/False
encoding_fs=True)
2.3 Null Imputation Auxiliar Methods
Simplified and automated multivariate null imputation methods based from Sklearn are also provided and applicable, as following:
## Simplified Null Imputation (Only numeric features)
from atlantic.imputation import AutoSimpleImputer, AutoKNNImputer, AutoIterativeImputer
# Example usage of AutoSimpleImputer
simple_imputer = AutoSimpleImputer(strategy='mean')
simple_imputer.fit(train) # Fit on the Train DataFrame
df_imputed = simple_imputer.transform(train.copy()) # Transform the Train DataFrame
df_imputed_test = simple_imputer.transform(test.copy()) # Transform the Test DataFrame
# Example usage of AutoKNNImputer
knn_imputer = AutoKNNImputer(n_neighbors=3,
weights="uniform")
knn_imputer.fit(train) # Fit on the Train DataFrame
df_imputed = knn_imputer.transform(train.copy()) # Transform the Train DataFrame
df_imputed_test = knn_imputer.transform(test.copy()) # Transform the Test DataFrame
# Example usage of AutoIterativeImputer
iterative_imputer = AutoIterativeImputer(max_iter=10,
random_state=0,
initial_strategy="mean",
imputation_order="ascending")
iterative_imputer.fit(train) # Fit on the Train DataFrame
df_imputed = iterative_imputer.transform(train.copy()) # Transform the Train DataFrame
df_imputed_test = iterative_imputer.transform(test.copy()) # Transform the Test DataFrame
Citation
Feel free to cite Atlantic as following:
@article{SANTOS2023100532,
author = {Luis Santos and Luis Ferreira}
title = {Atlantic - Automated data preprocessing framework for supervised machine learning},
journal = {Software Impacts},
volume = {17},
year = {2023},
issn = {2665-9638},
doi = {http://dx.doi.org/10.1016/j.simpa.2023.100532},
url = {https://www.sciencedirect.com/science/article/pii/S2665963823000696}
}
License
Distributed under the MIT License. See LICENSE for more information.
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