DModX

class chemotools.outliers.DModX(model: _BasePCA | _PLS | Pipeline, confidence: float = 0.95, mean_centered: bool = True)

ベースクラス: _ModelResidualsBase

Calculate Distance to Model (DModX) statistics.

DModX measures the distance between an observation and the model plane in the X-space, useful for detecting outliers.

パラメータ:

• model (Union[ModelType, Pipeline]) -- A fitted PCA/PLS model or Pipeline ending with such a model

• confidence (float, default=0.95) -- Confidence level for statistical calculations (between 0 and 1)

• mean_centered (bool, default=True) -- Indicates if the input data was mean-centered before modeling

変数:

• estimator (ModelType) -- The fitted model of type _BasePCA or _PLS

• transformer (Optional[Pipeline]) -- Preprocessing steps before the model

• n_features_in (int) -- Number of features in the input data

• n_components (int) -- Number of components in the model

• n_samples (int) -- Number of samples used to train the model

• critical_value (float) -- The calculated critical value for outlier detection

• train_sse (float) -- The training sum of squared errors (SSE) for the model normalized by degrees of freedom

• A0 (int) -- Adjustment factor for degrees of freedom based on mean centering

参照

[1] Max Bylesjö, Mattias Rantalainen, Oliver Cloarec, Johan K. Nicholson,

Elaine Holmes, Johan Trygg. "OPLS discriminant analysis: combining the strengths of PLS-DA and SIMCA classification." Journal of Chemometrics 20 (8-10), 341-351 (2006).

サンプル

>>> from chemotools.datasets import load_fermentation_train
>>> from chemotools.outliers import DModX
>>> from sklearn.decomposition import PCA
>>> # Load sample data
>>> X, _ = load_fermentation_train()
>>> # Instantiate the PCA model
>>> pca = PCA(n_components=3).fit(X)
>>> # Initialize DModX with the fitted PCA model
>>> dmodx = DModX(model=pca, confidence=0.95, mean_centered=True)
DModX(model=PCA(n_components=3), confidence=0.95, mean_centered=True)
>>> dmodx.fit(X)
>>> # Predict outliers in the dataset
>>> outliers = dmodx.predict(X)
>>> # Calculate DModX residuals
>>> residuals = dmodx.predict_residuals(X)

Attributes

critical_value_