pypunisher package

Submodules

pypunisher.metrics.criterion module

Information Criterion

pypunisher.metrics.criterion.aic(model, X_train, y_train)[source]

Compute the Akaike Information Criterion (AIC)

AIC’s objective is to prevent model overfitting by adding a penalty term which penalizes more complex models. Its formal definition is:

\[-2ln(L) + 2k\]

where \(L\) is the maximized value of the likelihood function and \(k\) if the number of parameters. A smaller AIC value suggests that the model is a better fit for the data, relative to competing models.

Parameters:
  • model (fitted sklearn model object) – A fitted sklearn model.
  • X_train (2d ndarray) – The data used to train model.
  • y_train (1d numpy array) – the response variable.
Returns:

aic (float)

AIC value if sample size is sufficient. If n/k < 40 where n is the number of observations and k is the number of features, AICc gets returned to adjust for small sample size.

References

pypunisher.metrics.criterion.bic(model, X_train, y_train)[source]

Compute the Bayesian Information Criterion (BIC)

BIC’s objective is to prevent model over-fitting by adding a penalty term which penalizes more complex models. Its formal definition is:

\[-2ln(L) + ln(n)k\]

where \(L\) is the maximized value of the likelihood function and \(k\) if the number of parameters. A smaller BIC value suggests that the model is a better fit for the data, relative to competing models.

Parameters:
  • model (fitted sklearn model object) – A fitted sklearn model.
  • X_train (2d ndarray) – The data used to train model.
  • y_train (1d numpy array) – the response variable.
Returns:

bic (float)

Bayesian Information Criterion value.

References

pypunisher.selection_engines.selection module

Forward and Backward Selection Algorithms

class pypunisher.selection_engines.selection.Selection(model, X_train, y_train, X_val, y_val, criterion=None, verbose=True)[source]

Bases: object

Forward and Backward Selection Algorithms.

Parameters:
  • model (sklearn model) – any sklearn model with .fit(), .predict() and .score() methods.
  • X_train (2d ndarray) – a 2D numpy array of (observations, features).
  • y_train (1d ndarray) – a 1D array of target classes for X_train.
  • X_val (2d ndarray) – a 2D numpy array of (observations, features).
  • y_val (1d ndarray) – a 1D array of target classes for X_validate.
  • criterion (str or None) –

    model selection criterion.

    • ’aic’: use Akaike Information Criterion.
    • ’bic’: use Bayesian Information Criterion.
    • None: Use the model’s default (i.e., call .score()).
  • verbose (bool) – if True, print additional information as selection occurs. Defaults to True.
forward(n_features=0.5, min_change=None, **kwargs)[source]

Perform Forward Selection on a Sklearn model.

Parameters:
  • n_features (int) – the max. number of features to allow. Note: min_change must be None in order for n_features to operate. Floats will be regarded as proportions of the total that must lie on (0, 1).
  • min_change (int or float) – The smallest change to be considered significant. Note: n_features must be None in order for min_change to operate.
  • kwargs (Keyword Args) –
    • _do_not_skip:
      Explore loop exhaustion. For internal use only. Not intended for outside use.
Returns:

S (list)

The column indices of X_train (and X_val) that denote the chosen features.
Raises:

if n_features and min_change are both non-None.
backward(n_features=0.5, min_change=None, **kwargs)[source]

Perform Backward Selection on a Sklearn model.

Parameters:
  • n_features (int or float) – The number of features to select. Floats will be regarded as proportions of the total that must lie on (0, 1). min_change must be None for n_features to operate.
  • min_change (int or float) – The smallest change to be considered significant. n_features must be None for min_change to operate.
  • kwargs (Keyword Args) –
    • _do_not_skip (bool):
      Explore loop exhaustion. For internal use only. Not intended for outside use.
    • _last_score_punt (bool):
      Relax defeated_last_iter_score decision boundary. For internal use only. Not intended for outside use.
Returns:

S (list)

The column indices of X_train (and X_val) that denote the chosen features.
Raises:

if n_features and min_change are both non-None.