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Domain Toolkits

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Toolkit Contents

In line with Artifact-ML's overall organization, artifact-core provides distinct domain toolkits.

Each toolkit implements its own flavour of all core interfaces.

Thereby, toolkits provide their own:

ResourceSpec: schema definitions that describe the structural and semantic properties of validation resources (e.g., feature types and data formats for tabular data).

ArtifactType: enumeration system that assigns unique identifiers to artifact implementations.

ArtifactEngine: unified interface for executing validation artifacts declaratively.

import pandas as pd

from artifact_core.table_comparison import (
    TableComparisonEngine,
    TableComparisonPlotType,
    TabularDataSpec
)

df_real = pd.read_csv("real_data.csv")

df_synthetic = pd.read_csv("synthetic_data.csv")

data_spec = TabularDataSpec.from_df(
    df=df_real, 
    cat_features=categorical_features, 
    cont_features=continuous_features
)

engine = TableComparisonEngine(resource_spec=data_spec)

pca_plot = engine.produce_dataset_comparison_plot(
    plot_type=TableComparisonPlotType.PCA_JUXTAPOSITION,
    dataset_real=df_real,
    dataset_synthetic=df_synthetic,
)

pca_plot

PCA Comparison Artifact

Supported Toolkits