mlens.visualization.var_analysis module

ML-ENSEMBLE

author:Sebastian Flennerhag
copyright:2017
licence:MIT

Explained variance plots.

mlens.visualization.var_analysis.exp_var_plot(X, estimator, figsize=(10, 8), buffer=0.01, set_labels=True, title='Explained variance ratio', title_font_size=14, show=True, ax=None, **kwargs)[source]

Function to plot the explained variance using PCA.

Parameters:
  • X (array-like of shape = [n_samples, n_features]) – input matrix to be used for prediction.
  • estimator (class) – PCA estimator, not initiated, assumes a Scikit-learn API.
  • figsize (tuple (default = (10, 8))) – Size of figure.
  • buffer (float (default = 0.01)) – For creating a buffer around the edges of the graph. The buffer added is calculated as num_components * buffer, where num_components determine the length of the x-axis.
  • set_labels (bool) – whether to set axis labels.
  • title (str) – figure title if shown.
  • title_font_size (int) – title font size.
  • show (bool (default = True)) – whether to print figure using matplotlib.pyplot.show.
  • ax (object, optional) – axis to attach plot to.
  • **kwargs (optional) – optional arguments passed to the matplotlib.pyplot.step function.
Returns:

ax – if ax was specified, returns ax with plot attached.
Return type:

optional
mlens.visualization.var_analysis.pca_comp_plot(X, y=None, figsize=(10, 8), title='Principal Components Comparison', title_font_size=14, show=True, **kwargs)[source]

Function for comparing PCA analysis.

Function compares across 2 and 3 dimensions and linear and rbf kernels.

Parameters:
  • X (array-like of shape = [n_samples, n_features]) – input matrix to be used for prediction.
  • y (array-like of shape = [n_samples, ] or None (default = None)) – training labels to be used for color highlighting.
  • figsize (tuple (default = (10, 8))) – Size of figure.
  • title (str) – figure title if shown.
  • title_font_size (int) – title font size.
  • show (bool (default = True)) – whether to print figure matplotlib.pyplot.show.
  • **kwargs (optional) – optional arguments to pass to mlens.visualization.pca_plot.
Returns:

axis object.
Return type:

ax

See also

mlens.visualization.pca_plot

mlens.visualization.var_analysis.pca_plot(X, estimator, y=None, cmap=None, figsize=(10, 8), title='Principal Components Analysis', title_font_size=14, show=True, ax=None, **kwargs)[source]

Function to plot a PCA analysis of 1, 2, or 3 dims.

Parameters:
  • X (array-like of shape = [n_samples, n_features]) – matrix to perform PCA analysis on.
  • estimator (instance) – PCA estimator. Assumes a Scikit-learn API.
  • y (array-like of shape = [n_samples, ] or None (default = None)) – training labels to be used for color highlighting.
  • cmap (object, optional) – cmap object to pass to matplotlib.pyplot.scatter.
  • figsize (tuple (default = (10, 8))) – Size of figure.
  • title (str) – figure title if shown.
  • title_font_size (int) – title font size.
  • show (bool (default = True)) – whether to print figure matplotlib.pyplot.show.
  • ax (object, optional) – axis to attach plot to.
  • **kwargs (optional) – arguments to pass to matplotlib.pyplot.scatter.
Returns:

ax – if ax was specified, returns ax with plot attached.
Return type:

optional