"""ML-ENSEMBLE
:author: Sebastian Flennerhag
:copyright: 2017
:licence: MIT
Explained variance plots.
"""
from __future__ import division, print_function
import numpy as np
import warnings
try:
from pandas import DataFrame, Series
except ImportError:
DataFrame = Series = None
warnings.warn("Pandas not installed. Visualization module may not work "
"as intended.", ImportWarning)
try:
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
from sklearn.decomposition import KernelPCA
from matplotlib.colors import ListedColormap
from seaborn import color_palette
except:
warnings.warn("Matplotlib and Seaborn not installed. Cannot load "
"visualization module.", ImportWarning)
[docs]def pca_comp_plot(X, y=None, figsize=(10, 8),
title='Principal Components Comparison', title_font_size=14,
show=True, **kwargs):
"""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 :obj:`matplotlib.pyplot.show`.
**kwargs : optional
optional arguments to pass to :class:`mlens.visualization.pca_plot`.
Returns
-------
ax :
axis object.
See Also
--------
:class:`mlens.visualization.pca_plot`
"""
comp = ['linear', 'rbf']
f = plt.figure(figsize=figsize)
ax = []
subplot_kwarg = {}
for dim, frame in [(2, 221), (3, 223)]:
if dim is 3:
# Need to specify projection
subplot_kwarg = {'projection': '3d'}
for i, kernel in enumerate(comp):
# Create subplot
ax.append(f.add_subplot(frame + i, **subplot_kwarg))
# Build figure
ax[-1] = pca_plot(X, KernelPCA(dim, kernel), y,
ax=ax[-1], show=False, **kwargs)
ax[-1].set_title('%s kernel, %i dims' % (kernel, dim))
# Whiten background if dim is 3
if dim is 3:
ax[-1].set_facecolor((1, 1, 1))
if show:
f.suptitle(title, fontsize=title_font_size)
plt.show()
return ax
[docs]def pca_plot(X, estimator, y=None, cmap=None, figsize=(10, 8),
title='Principal Components Analysis', title_font_size=14,
show=True, ax=None, **kwargs):
"""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 :obj:`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 :obj:`matplotlib.pyplot.show`.
ax : object, optional
axis to attach plot to.
**kwargs : optional
arguments to pass to :obj:`matplotlib.pyplot.scatter`.
Returns
-------
ax : optional
if ``ax`` was specified, returns ``ax`` with plot attached.
"""
Z = X.values if isinstance(X, (DataFrame, Series)) else X
Z = estimator.fit_transform(Z)
# prep figure if no axis supplied
if ax is None:
f, ax = plt.subplots(figsize=figsize)
if estimator.n_components == 3:
ax = f.add_subplot(111, projection='3d')
if cmap is None:
cmap = ListedColormap(color_palette('husl'))
if estimator.n_components == 1:
ax.scatter(Z, c=y, cmap=cmap, **kwargs)
elif estimator.n_components == 2:
ax.scatter(Z[:, 0], Z[:, 1], c=y, cmap=cmap, **kwargs)
elif estimator.n_components == 3:
ax.scatter(Z[:, 0], Z[:, 1], Z[:, 2], c=y, cmap=cmap, **kwargs)
else:
raise ValueError("'n_components' is too large to visualize. "
"Set to one of [1, 2, 3].")
if show:
plt.title(title, fontsize=title_font_size)
plt.show()
return ax
[docs]def 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):
"""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 :obj:`matplotlib.pyplot.show`.
ax : object, optional
axis to attach plot to.
**kwargs : optional
optional arguments passed to the :obj:`matplotlib.pyplot.step`
function.
Returns
-------
ax : optional
if ``ax`` was specified, returns ``ax`` with plot attached.
"""
estimator.set_params(**{'n_components': None})
ind_var_exp = estimator.fit(X).explained_variance_ratio_
cum_var_exp = np.cumsum(ind_var_exp)
x = range(1, len(ind_var_exp) + 1)
if ax is None:
_, ax = plt.subplots(figsize=figsize)
ax.step(x, cum_var_exp, **kwargs)
buffer_ = buffer * len(ind_var_exp)
ax.set_ylim(0, 1 + buffer_ / 3)
ax.set_xlim(1 - buffer_, len(ind_var_exp) + buffer_)
ax.set_xticks([i for i in x])
if set_labels:
ax.set_xlabel('Number of Components')
ax.set_ylabel('Explained variance ratio')
if show:
plt.title(title, fontsize=title_font_size)
plt.show()
return ax