def plot_violinplot(ax,data, log, group_labels=None):
'''
create violin plots on an axis
'''
if log:
warning("log option ignored for violin plot")
pos = range(len(data))
dist = max(pos)-min(pos)
w = min(0.15*max(dist,1.0),0.5)
for data,p in zip(data,pos):
if len(data)>0:
kde = gaussian_kde(data) #calculates the kernel density
x = np.linspace(np.min(data),np.max(data),250.) # support for violin
v = kde.evaluate(x) #violin profile (density curve)
scl = 1 / (v.max() / 0.4)
v = v*scl #scaling the violin to the available space
ax.fill_betweenx(x,p-v,p+v,facecolor=COLOR_LIST[p],alpha=0.6, lw=1.5)
for percentile in [25, 75]:
quant = scoreatpercentile(data.ravel(), percentile)
q_x = kde.evaluate(quant) * scl
q_x = [p - q_x, p + q_x]
ax.plot(q_x, [quant, quant], linestyle=":", c='k')
med = np.median(data)
m_x = kde.evaluate(med) * scl
m_x = [p - m_x, p + m_x]
ax.plot(m_x, [med, med], linestyle="--", c='k', lw=1.5)
if group_labels:
labels = group_labels[:]
labels.insert(0, '')
ax.set_xticklabels(labels, rotation='vertical')
def plot_violinplot(ax, value, log, group_labels=None):
'''
helper function for plotting violin plots on axes
Parameters
----------
ax : axes instance
value : ndarray
log : bool
group_labels : list of str, optional
'''
if log:
warning("log option ignored for violin plot")
pos = range(len(value))
dist = max(pos) - min(pos)
_ = min(0.15 * max(dist, 1.0), 0.5)
for data, p in zip(value, pos):
if len(data) > 0:
kde = gaussian_kde(data) #calculates the kernel density
x = np.linspace(np.min(data), np.max(data),
250.) # support for violin
v = kde.evaluate(x) #violin profile (density curve)
scl = 1 / (v.max() / 0.4)
v = v * scl #scaling the violin to the available space
ax.fill_betweenx(x,
p - v,
p + v,
facecolor=COLOR_LIST[p],
alpha=0.6,
lw=1.5)
for percentile in [25, 75]:
quant = scoreatpercentile(data.ravel(), percentile)
q_x = kde.evaluate(quant) * scl
q_x = [p - q_x, p + q_x]
ax.plot(q_x, [quant, quant], linestyle=":", c='k')
med = np.median(data)
m_x = kde.evaluate(med) * scl
m_x = [p - m_x, p + m_x]
ax.plot(m_x, [med, med], linestyle="--", c='k', lw=1.5)
if group_labels:
labels = group_labels[:]
labels.insert(0, '')
ax.set_xticklabels(labels, rotation='vertical')
def plot_violinplot(ax, value, log, group_labels=None):
'''
helper function for plotting violin plots on axes
Parameters
----------
ax : axes instance
value : ndarray
log : bool
group_labels : list of str, optional
'''
if log:
_logger.warning("log option ignored for violin plot")
pos = range(len(value))
dist = max(pos) - min(pos)
_ = min(0.15 * max(dist, 1.0), 0.5)
for data, p in zip(value, pos):
if len(data) > 0:
kde = gaussian_kde(data) # calculates the kernel density
x = np.linspace(np.min(data), np.max(data),
250.) # support for violin
v = kde.evaluate(x) # violin profile (density curve)
scl = 1 / (v.max() / 0.4)
v = v * scl # scaling the violin to the available space
ax.fill_betweenx(
x, p - v, p + v, facecolor=get_color(p), alpha=0.6, lw=1.5)
for percentile in [25, 75]:
quant = scoreatpercentile(data.ravel(), percentile)
q_x = kde.evaluate(quant) * scl
q_x = [p - q_x, p + q_x]
ax.plot(q_x, [quant, quant], linestyle=":", c='k')
med = np.median(data)
m_x = kde.evaluate(med) * scl
m_x = [p - m_x, p + m_x]
ax.plot(m_x, [med, med], linestyle="--", c='k', lw=1.5)
if group_labels:
labels = group_labels[:]
labels.insert(0, '')
ax.set_xticklabels(labels, rotation='vertical')
def kernel_weighted_samples(x, color, x_grid, **kwargs): from statsmodels.nonparametric.kde import KDEUnivariate import matplotlib.pyplot as plt """Univariate Kernel Density Estimation with Statsmodels""" kde = KDEUnivariate(x) kde.fit(**kwargs) # kernel, bw, fft, weights, gridsize, ...] pdf = kde.evaluate(x_grid) plt.plot(x_grid, pdf, color=color, alpha=0.5, lw=3) plt.fill_between(x_grid, pdf, where=None, color=color, alpha = 0.2) return pdf, x_grid
def kernel_weighted_samples(x, color, x_grid, **kwargs):
from statsmodels.nonparametric.kde import KDEUnivariate
import matplotlib.pyplot as plt
"""Univariate Kernel Density Estimation with Statsmodels"""
kde = KDEUnivariate(x)
kde.fit(**kwargs) # kernel, bw, fft, weights, gridsize, ...]
pdf = kde.evaluate(x_grid)
plt.plot(x_grid, pdf, color=color, alpha=0.5, lw=3)
plt.fill_between(x_grid, pdf, where=None, color=color, alpha=0.2)
return pdf, x_grid
def bayes_classifier(x_vec, kdes):
"""
Classifies an input sample into class w_j determined by
maximizing the class conditional probability for p(x|w_j).
Keyword arguments:
x_vec: A dx1 dimensional numpy array representing the sample.
kdes: List of the gausssian_kde (kernel density) estimates
Returns a tuple ( p(x|w_j)_value, class label ).
"""
p_vals = []
for kde in kdes:
p_vals.append(kde.evaluate(x_vec))
max_index, max_value = max(enumerate(p_vals), key=operator.itemgetter(1))
return max_value, max_index + 1
