discrimintools.MDA#

class discrimintools.MDA(n_components=2, priors=None, classes=False)[source]#

Mixed Discriminant Analysis (MDA)

Performs a linear discrimination analysis (LDA) on mixed predictors. It’s a classical linear discriminant analysis carried out on the principal factors of a mixed principal component analysis (MPCA) of explanatory variables. Discriminat analysis on mixed predictors consists in two steps:

  1. Computation of mixed principal component analysis (MPCA) on features.

  2. Performns linear discriminant analysis (LDA) on principal components extract in step 1.

Parameters:

  • n_components (int or None, default = 2) – Number of components to keep. If None, keep all the components.

  • priors (str, 1-D array or Series of shape (n_classes,), default = None) – The priors statement specifies the class prior probabilities of group membership, possibles values:

    • ‘equal’ to set the prior probabilities equal.

    • ‘prop’ to set the prior probabilities proportional to the sample sizes.

    • 1-D array or Series which specify the prior probability for each level of the classification variable.

  • classes (None, tuple or list, default = None) – Name of level in order to return. If None, classes are sorted in unique values in y.

Returns:

  • call_ (NamedTuple) – Call informations.

    • XDataFrame of shape (n_samples, n_features)

      Training data

    • ySeries of shape (n_samples,)

      Target values. True values for X.

    • targetstr

      Name of target.

    • featureslist

      Names of features seen during fit.

    • classeslist

      Names of classes.

    • priorsSeries of shape (n_classes,)

      Priors probabilities.

    • n_samplesint

      Number of samples.

    • n_featuresint

      Number of features.

    • n_classesint

      Number of target values.

    • max_componentsint

      Maximum number of components.

    • n_componentsint

      Number of components kept.

  • cancoef_ (NamedTuple) – Canonical coefficients:

    • standardizedDataFrame of shape (n_variables, n_components)

      The standardized canonical coefficients

    • rawDataFrame of shape (n_variables+1, n_componets)

      The raw canonical coefficients

    • projectionDataFrame of shape (n_variables+1, n_components)

      The projection canonical coefficients.

  • coef_ (NamedTuple) – Linear discriminant coefficients:

    • standardizedDataFrame of shape (n_variables, n_classes)

      The standardized coefficients.

    • rawDataFrame of shape (n_variables+1, n_classes)

      The raw coefficients.

    • projectionDataFrame of shape (n_variables+1, n_classes)

      The projection coefficients.

  • ind_ (NamedTuple) – Individuals informations:

    • scoresDataFrame of shape (n_samples, n_classes)

      The scores of individuals.

    • projectionDataFrame of shape (n_samples, n_classes)

      The projection of individuals.

  • model_ (str, default = ‘mda’) – The model fitted.

  • pipe_ (a sequence of data transformers with two named_steps :) –

    • mpca : mixed principal components analysis (MPCA)

    • lda : linear discriminant analysis (LDA)

See also

GFA

General Factor Analysis (GFA)

GFALDA

General Factor Analysis Linear Discriminant Analysis (GFALDA)

MPCA

Mixed Principal Component Analysis (MPCA)

summaryGFA

Printing summaries of General Factor Analysis model.

summaryGFALDA

Printing summaries of General Factor Analysis Linear Discriminant Analysis model.

summaryMDA

Printing summaries of Mixed Discriminant Analysis model.

summaryMPCA

Printing summaries of Mixed Principal Component Analysis model.

References

[1] Abdesselam, R. (2006), « Mixed principal component analysis ». In M. Nadif & F. X. Jollis (Eds), Actes des XIIIémes Rencontres SFC-2006 (pp. 27-31). Metz, France.

[2] Abdesselam, R. (2010), « Discriminant Analysis on Mixed Predictors ».

Examples

>>> from discrimintools.datasets import load_dataset
>>> from discrimintools import MDA
>>> D = load_dataset("heart") # load training data
>>> y, X = D["disease"], D.drop(columns=["disease"]) # split into X and y
>>> clf = MDA(n_components=5)
>>> clf.fit(X,y)
MDA(n_components=5)
```
__init__(n_components=2, priors=None, classes=False)[source]#

Methods

__init__([n_components, priors, classes])

decision_function(X)

Apply decision function to an input data

eval_predict(X, y[, verbose])

Evaluation of the prediction' quality

fit(X, y)

Fit the Mixed Discriminant Analysis model

fit_transform(X, y)

Fit to data, then transform it

get_metadata_routing()

Get metadata routing of this object.

get_params([deep])

Get parameters for this estimator.

pred_table(X, y)

Prediction table

predict(X)

Predict class labels for samples in X

predict_log_proba(X)

Return log of posterior probabilities

predict_proba(X)

Estimate probability

score(X, y)

Return accuracy on the given input data

set_output(*[, transform])

Set output container.

set_params(**params)

Set the parameters of this estimator.

transform(X)

Project data to maximize class separation
On this page

This Page