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plot_posterior.py
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plot_posterior.py
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import numpy as np
import pandas as pn
import scipy.stats as stats
from .joyplot import joyplot
from typing import Union
import matplotlib.gridspec as gridspect
# Create cmap
from matplotlib.colors import ListedColormap
import matplotlib.pyplot as plt
import matplotlib.colors as colors
import matplotlib.cm as cmx
import seaborn as sns
from arviz.plots.jointplot import *
from arviz.plots.plot_utils import make_label
from arviz import plot_kde, plot_dist
from arviz.plots.jointplot import _var_names, _scale_fig_size
from arviz.plots.kdeplot import _fast_kde_2d
from arviz.stats import hpd
import arviz
# Seaborn style
sns.set(style="white", rc={"axes.facecolor": (0, 0, 0, 0)})
# Discrete cmap
# seaborn palette
pal_disc = sns.cubehelix_palette(10, rot=-.25, light=.7)
pal_disc_l = sns.cubehelix_palette(10)
# matplotlib cmap
my_cmap = ListedColormap(pal_disc)
my_cmap_l = ListedColormap(pal_disc_l)
# Continuous cmap
# seaborn palette
pal_cont_light = sns.cubehelix_palette(250, rot=-.25, light=1)
pal_cont = sns.cubehelix_palette(250, rot=-.25, light=.7)
pal_cont_l = sns.cubehelix_palette(250)
pal_cont_l_light = sns.cubehelix_palette(250, light=1)
# matplotlib cmap
my_cmap_full = ListedColormap(pal_cont)
my_cmap_full_light = ListedColormap(pal_cont_light)
my_cmap_full_l = ListedColormap(pal_cont_l)
my_cmap_full_l_light = ListedColormap(pal_cont_l_light)
# Default individual colors
default_red = '#DA8886'
default_blue = pal_cont[50]
default_l = pal_disc_l.as_hex()[4]
class PlotPosterior:
def __init__(self, data: arviz.data.inference_data.InferenceData = None):
self.data = data
self.iteration = 1
self.likelihood_axes = None
self.marginal_axes = None
self.joy = None
def create_figure(self, marginal=True, likelihood=True, joyplot=True,
figsize=None, textsize=None,
n_samples=11):
figsize, self.ax_labelsize, _, self.xt_labelsize, self.linewidth, _ = _scale_fig_size(figsize, textsize)
self.fig, axes = plt.subplots(0, 0, figsize=figsize, constrained_layout=False)
gs_0 = gridspect.GridSpec(3, 6, figure=self.fig, hspace=.1)
if marginal is True:
# Testing
if likelihood is False:
self.marginal_axes = self._create_joint_axis(figure=self.fig, subplot_spec=gs_0[0:2, 0:4])
elif likelihood is False and joyplot is False:
self.marginal_axes = self._create_joint_axis(figure=self.fig, subplot_spec=gs_0[:, :])
else:
self.marginal_axes = self._create_joint_axis(figure=self.fig, subplot_spec=gs_0[0:2, 0:3])
if likelihood is True:
if marginal is False:
self.likelihood_axes = self._create_likelihood_axis(figure=self.fig, subplot_spec=gs_0[0:2, 0:4])
elif joyplot is False:
self.likelihood_axes = self._create_likelihood_axis(figure=self.fig, subplot_spec=gs_0[0:2, 4:])
else:
self.likelihood_axes = self._create_likelihood_axis(figure=self.fig, subplot_spec=gs_0[0:1, 4:])
if joyplot is True:
self.n_samples = n_samples
if marginal is False and likelihood is False:
self.joy = self._create_joy_axis(self.fig, gs_0[:, :])
else:
self.joy = self._create_joy_axis(self.fig, gs_0[1:2, 4:])
def _create_joint_axis(self, figure=None, subplot_spec=None, figsize=None, textsize=None):
figsize, ax_labelsize, _, xt_labelsize, linewidth, _ = _scale_fig_size(figsize, textsize)
# Instantiate figure and grid
if figure is None:
fig, _ = plt.subplots(0, 0, figsize=figsize, constrained_layout=True)
else:
fig = figure
if subplot_spec is None:
grid = plt.GridSpec(4, 4, hspace=0.1, wspace=0.1, figure=fig)
else:
grid = gridspect.GridSpecFromSubplotSpec(4, 4, subplot_spec=subplot_spec)
# Set up main plot
self.axjoin = fig.add_subplot(grid[1:, :-1])
# Set up top KDE
self.ax_hist_x = fig.add_subplot(grid[0, :-1], sharex=self.axjoin)
self.ax_hist_x.tick_params(labelleft=False, labelbottom=False)
# Set up right KDE
self.ax_hist_y = fig.add_subplot(grid[1:, -1], sharey=self.axjoin)
self.ax_hist_y.tick_params(labelleft=False, labelbottom=False)
sns.despine(left=True, bottom=True)
return self.axjoin, self.ax_hist_x, self.ax_hist_y
def _create_likelihood_axis(self, figure=None, subplot_spec=None, **kwargs):
# Making the axes:
if figure is None:
figsize = kwargs.get('figsize', None)
fig, _ = plt.subplots(0, 0, figsize=figsize, constrained_layout=False)
else:
fig = figure
if subplot_spec is None:
grid = plt.GridSpec(1, 1, hspace=0.1, wspace=0.1, figure=fig)
else:
grid = gridspect.GridSpecFromSubplotSpec(1, 1, subplot_spec=subplot_spec)
ax_like = fig.add_subplot(grid[0, 0])
ax_like.spines['bottom'].set_position(('data', 0.0))
ax_like.yaxis.tick_right()
ax_like.spines['right'].set_position(('axes', 1.03))
ax_like.spines['top'].set_color('none')
ax_like.spines['left'].set_color('none')
ax_like.set_xlabel('Thickness Obs.')
ax_like.set_title('Likelihood')
return ax_like
def _create_joy_axis(self, figure=None, subplot_spec=None, n_samples=None, overlap=.85):
if n_samples is None:
n_samples = self.n_samples
grid = gridspect.GridSpecFromSubplotSpec(n_samples, 1, hspace=-overlap, subplot_spec=subplot_spec)
ax_joy = [figure.add_subplot(grid[i, 0]) for i in range(n_samples)]
ax_joy[0].set_title('Foo Likelihood')
return ax_joy
def create_color_map(self):
cNorm = colors.Normalize(0, self.y_max_like)
scalarMap = cmx.ScalarMappable(norm=cNorm, cmap=my_cmap_full)
return scalarMap
def evaluate_cmap(self, cmap, draw_mu, draw_sigma, obs: Union[float, list] = None):
likelihood_at_observation = stats.norm.pdf(obs, loc=draw_mu, scale=draw_sigma)
color_fill = colors.to_hex(cmap.to_rgba(np.atleast_1d(likelihood_at_observation)[0]))
return color_fill
def plot_marginal_posterior(self, plotters, iteration=-1, **marginal_kwargs):
marginal_kwargs.setdefault("plot_kwargs", {})
marginal_kwargs["plot_kwargs"]["linewidth"] = self.linewidth
marginal_kwargs.setdefault('fill_kwargs', {})
marginal_kwargs["plot_kwargs"].setdefault('color', default_l)
marginal_kwargs['fill_kwargs'].setdefault('color', default_l)
marginal_kwargs['fill_kwargs'].setdefault('alpha', .8)
# Flatten data
x = plotters[0][2].flatten()[:iteration]
y = plotters[1][2].flatten()[:iteration]
for val, ax, rotate in ((x, self.ax_hist_x, False), (y, self.ax_hist_y, True)):
plot_dist(val, textsize=self.xt_labelsize, rotated=rotate, ax=ax, **marginal_kwargs)
def plot_joint_posterior(self, plotters, iteration=-1, kind='kde', **joint_kwargs):
# Set labels for axes
x_var_name = make_label(plotters[0][0], plotters[0][1])
y_var_name = make_label(plotters[1][0], plotters[1][1])
self.axjoin.set_xlabel(x_var_name, fontsize=self.ax_labelsize)
self.axjoin.set_ylabel(y_var_name, fontsize=self.ax_labelsize)
self.axjoin.tick_params(labelsize=self.xt_labelsize)
# Flatten data
x = plotters[0][2].flatten()[:iteration]
y = plotters[1][2].flatten()[:iteration]
if kind == "scatter":
self.axjoin.scatter(x, y, **joint_kwargs)
elif kind == "kde":
if False:
gridsize = (128, 128)# if contour else (256, 256)
density, xmin, xmax, ymin, ymax = _fast_kde_2d(x, y, gridsize=gridsize)
# self.axjoin.scatter(x, y, density)
self.axjoin.imshow(density)
else:
if 'contour' not in joint_kwargs:
joint_kwargs.setdefault('contour', True)
fill_last = joint_kwargs.get('fill_last', False)
try:
self.foo = plot_kde(x, y, fill_last=fill_last, ax=self.axjoin, **joint_kwargs)
except ValueError:
pass
except np.linalg.LinAlgError:
pass
else:
gridsize = joint_kwargs.get('grid_size', 'auto')
if gridsize == "auto":
gridsize = int(len(x) ** 0.35)
self.axjoin.hexbin(x, y, mincnt=1, gridsize=gridsize, **joint_kwargs)
self.axjoin._grid(False)
def plot_trace(self, plotters, iteration, n_iterations=20):
i_0 = np.max([0, (iteration - n_iterations)])
theta1_val_trace = plotters[0][2].flatten()[i_0:iteration+1]
theta2_val_trace = plotters[1][2].flatten()[i_0:iteration+1]
theta1_val = theta1_val_trace[-1]
theta2_val = theta2_val_trace[-1]
# Plot point of the given iteration
self.axjoin.plot(theta1_val, theta2_val, 'bo', ms=6, color='k')
# Plot a trace of n_iterations
pair_x_array = np.vstack(
(theta1_val_trace[:-1], theta1_val_trace[1:])).T
pair_y_array = np.vstack((theta2_val_trace[:-1], theta2_val_trace[1:])).T
for i, pair_x in enumerate(pair_x_array):
alpha_val = i / pair_x_array.shape[0]
pair_y = pair_y_array[i]
self.axjoin.plot(pair_x, pair_y, linewidth=1, alpha=alpha_val, color='k')
def plot_marginal(self, var_names=None, data=None, iteration=-1,
group='both',
plot_trace=True, n_iterations=20,
kind='kde',
coords=None, credible_interval=.98,
marginal_kwargs=None, marginal_kwargs_prior=None,
joint_kwargs=None, joint_kwargs_prior=None):
self.axjoin.clear()
self.ax_hist_x.clear()
self.ax_hist_y.clear()
if data is None:
data = self.data
valid_kinds = ["scatter", "kde", "hexbin"]
if kind not in valid_kinds:
raise ValueError(
("Plot type {} not recognized." "Plot type must be in {}").format(kind, valid_kinds)
)
if coords is None:
coords = {}
if joint_kwargs is None:
joint_kwargs = {}
if marginal_kwargs is None:
marginal_kwargs = {}
data_0 = convert_to_dataset(data, group="posterior")
var_names = _var_names(var_names, data_0)
plotters = list(xarray_var_iter(get_coords(data_0, coords), var_names=var_names, combined=True))
if len(plotters) != 2:
raise Exception(
"Number of variables to be plotted must 2 (you supplied {})".format(len(plotters))
)
if kind == 'kde':
joint_kwargs.setdefault('contourf_kwargs', {})
joint_kwargs.setdefault('contour_kwargs', {})
joint_kwargs.setdefault('pcolormesh_kwargs', {})
joint_kwargs['contourf_kwargs'].setdefault('cmap', my_cmap_full_l_light)
joint_kwargs['contourf_kwargs'].setdefault('levels', 11)
joint_kwargs['contourf_kwargs'].setdefault('alpha', 1)
joint_kwargs['contour_kwargs'].setdefault('alpha', 0)
joint_kwargs['pcolormesh_kwargs'].setdefault('cmap', my_cmap_full_l_light)
joint_kwargs['pcolormesh_kwargs'].setdefault('alpha', 1)
marginal_kwargs.setdefault('fill_kwargs', {})
marginal_kwargs.setdefault("plot_kwargs", {})
marginal_kwargs["plot_kwargs"]["linewidth"] = self.linewidth
marginal_kwargs["plot_kwargs"].setdefault('color', default_l)
marginal_kwargs['fill_kwargs'].setdefault('color', default_l)
marginal_kwargs['fill_kwargs'].setdefault('alpha', .8)
if group == 'both' or group == 'posterior':
self.plot_joint_posterior(plotters, kind=kind, iteration=iteration, **joint_kwargs)
self.plot_marginal_posterior(plotters, iteration=iteration, **marginal_kwargs)
if group == 'both' or group == 'prior':
alpha_p = .8 if group == 'prior' else .5
if joint_kwargs_prior is None:
joint_kwargs_prior = {}
if marginal_kwargs_prior is None:
marginal_kwargs_prior = {}
marginal_kwargs_prior.setdefault('fill_kwargs', {})
marginal_kwargs_prior.setdefault("plot_kwargs", {})
marginal_kwargs_prior["plot_kwargs"]["linewidth"] = self.linewidth
if kind == 'kde':
joint_kwargs_prior.setdefault('contourf_kwargs', {})
joint_kwargs_prior.setdefault('contour_kwargs', {})
joint_kwargs_prior.setdefault('pcolormesh_kwargs', {})
joint_kwargs_prior['contourf_kwargs'].setdefault('cmap', my_cmap_full_light)
joint_kwargs_prior['contourf_kwargs'].setdefault('levels', 11)
joint_kwargs_prior['contourf_kwargs'].setdefault('alpha', alpha_p)
joint_kwargs_prior['contour_kwargs'].setdefault('alpha', 0)
joint_kwargs_prior['pcolormesh_kwargs'].setdefault('cmap', my_cmap_full_light)
joint_kwargs_prior['pcolormesh_kwargs'].setdefault('alpha', alpha_p)
marginal_kwargs_prior["plot_kwargs"].setdefault('color', default_blue)
marginal_kwargs_prior['fill_kwargs'].setdefault('color', default_blue)
marginal_kwargs_prior['fill_kwargs'].setdefault('alpha', alpha_p)
data_1 = convert_to_dataset(data, group="prior")
plotters_prior = list(xarray_var_iter(get_coords(data_1, coords), var_names=var_names, combined=True))
self.plot_joint_posterior(plotters_prior, kind=kind, **joint_kwargs_prior)
self.plot_marginal_posterior(plotters_prior, **marginal_kwargs_prior)
x_min, x_max, y_min, y_max = self.compute_hpd(plotters_prior, credible_interval=credible_interval)
else:
x_min, x_max, y_min, y_max = self.compute_hpd(plotters, iteration=iteration,
credible_interval=credible_interval)
if plot_trace is True:
self.plot_trace(plotters, iteration, n_iterations)
self.axjoin.set_xlim(x_min, x_max)
self.axjoin.set_ylim(y_min, y_max)
self.ax_hist_x.set_xlim(self.axjoin.get_xlim())
self.ax_hist_y.set_ylim(self.axjoin.get_ylim())
return self.axjoin, self.ax_hist_x, self.ax_hist_y
@staticmethod
def compute_hpd(plotters, iteration=-1, credible_interval=.98):
x = plotters[0][2].flatten()[:iteration]
y = plotters[1][2].flatten()[:iteration]
x_min, x_max = hpd(x, credible_interval=credible_interval)
y_min, y_max = hpd(y, credible_interval=credible_interval)
return x_min, x_max, y_min, y_max
def set_likelihood_limits(self, val, type):
val = np.repeat(np.atleast_1d(val), 1)
if type == 'x_max':
val = val + val * .2
try:
self._xma_list = np.append(self._xma_list[:25], val)
self.x_max_like = np.max(self._xma_list)
except AttributeError:
self._xma_list = val
self.x_max_like = np.max(self._xma_list)
if type == 'x_min':
val = val - val * .2
try:
self._xmi_list = np.append(self._xmi_list[:25], val)
self.x_min_like = np.min(self._xmi_list)
except AttributeError:
self._xmi_list = val
self.x_min_like = np.min(self._xmi_list)
if type == 'y_max':
try:
self._yma_list = np.append(self._yma_list[:25], val)
self.y_max_like = np.max(self._yma_list)
except AttributeError:
self._yma_list = val
self.y_max_like = np.max(self._yma_list)
def plot_normal_likelihood(self, mean:Union[str, float], std:Union[str, float], obs:Union[str, float],
data=None, iteration=-1, x_range=None, color='auto', **kwargs):
self.likelihood_axes.clear()
x_limits = kwargs.get('x_limits', 4)
if data is None:
data = self.data
draw = data.posterior[{'chain':0, 'draw':iteration}]
draw_mu = draw[mean] if type(mean) is str else mean
draw_sigma = draw[std] if type(std) is str else std
obs = data.observed_data[obs] if type(obs) is str else obs
self.set_likelihood_limits(draw_mu + x_limits * draw_sigma, 'x_max')
self.set_likelihood_limits(draw_mu - x_limits * draw_sigma, 'x_min')
if x_range is not None:
thick_min = x_range[0]
thick_max = x_range[1]
else:
thick_max = self.x_max_like # draw_mu + 3 * draw_sigma
thick_min = self.x_min_like # draw_mu - 3 * draw_sigma
thick_vals = np.linspace(thick_min, thick_max, 100)
observation = np.asarray(obs)
thick_model = draw_mu
thick_std = draw_sigma
nor_l = stats.norm.pdf(thick_vals, loc=thick_model, scale=thick_std)
self.set_likelihood_limits(nor_l.max(), 'y_max')
likelihood_at_observation = stats.norm.pdf(observation, loc=thick_model, scale=thick_std)
if color == 'auto':
# This operations are for getting the same color in the likelihood plot as in the joy plot
self.cmap_l = self.create_color_map()
color_fill = self.evaluate_cmap(self.cmap_l, draw_mu, draw_sigma, obs)
elif color is None:
color_fill = default_l
else:
color_fill = color
y_min = (nor_l.min() - nor_l.max()) * .01
y_max = nor_l.max() + nor_l.max() * .05
# This is the bell
if not 'hide_bell' in kwargs:
self.likelihood_axes.plot(thick_vals, nor_l, color='#7eb1bc', linewidth=.5)
self.likelihood_axes.fill_between(thick_vals, nor_l, 0, color=color_fill, alpha=.8)
if not 'hide_bell' in kwargs and not 'hide_lines' in kwargs:
# This are the lines spawning from the observations
self.likelihood_axes.vlines(observation, 0.000000000001, likelihood_at_observation, linestyles='dashdot',
color='#DA8886', alpha=.5)
self.likelihood_axes.hlines(likelihood_at_observation, observation, thick_max,
linestyle='dashdot', color='#DA8886', alpha=.5)
# This are the observations
observation_size = self.xt_labelsize * 3
self.likelihood_axes.scatter(observation, np.zeros_like(observation), s=observation_size, c='#DA8886')
self.likelihood_axes.set_ylim(y_min, y_max)
self.likelihood_axes.set_xlim(thick_min, thick_max)
# Make the axis sexy
self.likelihood_axes.spines['bottom'].set_position(('data', 0.0))
self.likelihood_axes.yaxis.tick_right()
self.likelihood_axes.spines['right'].set_position(('axes', 1.03))
self.likelihood_axes.spines['top'].set_color('none')
self.likelihood_axes.spines['left'].set_color('none')
self.likelihood_axes.set_xlabel('Thickness Obs.')
self.likelihood_axes.set_title('Likelihood')
# self.likelihood_axes.set_ylim(y_min, y_max)
self.likelihood_axes.set_xlim(thick_min, thick_max)
# self.likelihood_axes.set_xlim(self.x_min_like, self.x_max_like)
self.likelihood_axes.set_ylim(0, self.y_max_like)
return self.likelihood_axes, self.cmap_l
def plot_joy(self, var_names: tuple = None, obs: Union[str, float] = None,
data=None, iteration=-1, samples_size=1000, cmap='auto'):
"""
A0rgs:
var_names: mu and sigma of the likelihood!
obs:
data:
iteration:
samples_size:
cmap:
Returns:
"""
try:
[i.clear() for i in self.joy]
except TypeError:
pass
n_iterations = self.n_samples
iteration_label = [None for i in range(self.n_samples)]
if data is None:
data = self.data
obs = data.observed_data[obs] if type(obs) is str else obs
data = convert_to_dataset(data, group="posterior")
coords = {}
var_names = _var_names(var_names, data)
plotters = list(
xarray_var_iter(get_coords(data, coords), var_names=var_names, combined=True))
x = plotters[0][2].flatten()
y = plotters[1][2].flatten()
n_data = x.shape[0]
# This is the special case if n_samples is smaller than the number of bells to plot
if iteration < self.n_samples / 2:
l_0 = 0
l_1 = int(self.n_samples)
iteration_label[-1] = 0
iteration_label[0] = l_1
elif iteration > n_data - self.n_samples/2:
l_0 = int(n_data - self.n_samples)
l_1 = n_data
iteration_label[-1] = l_0
iteration_label[0] = l_1
else:
l_0 = int(iteration - np.round(self.n_samples / 2)) + 1
l_1 = int(iteration + np.round(self.n_samples / 2))
iteration_label[-1] = l_0
iteration_label[int(np.round(self.n_samples / 2)) - 1] = iteration - 1
iteration_label[0] = l_1
x = x[l_0:l_1]
y = y[l_0:l_1]
self.set_likelihood_limits(x + 4 * y, 'x_max')
self.set_likelihood_limits(x - 4 * y, 'x_min')
df = pn.DataFrame()
color = []
for e in range(l_1-l_0):
e = -e - 1
num = np.random.normal(loc=x[e], scale=y[e], size=samples_size)
name = e + (iteration - int(n_iterations / 2))
df[name] = num
if obs is not None:
self.set_likelihood_limits(stats.norm.pdf(obs, loc=x[e], scale=y[e]), 'y_max')
if cmap is None:
color = default_blue
else:
if cmap == 'auto':
if self.likelihood_axes is None:
cmap = self.create_color_map()
else:
cmap = self.cmap_l
color.append(self.evaluate_cmap(cmap, x[e], y[e], obs))
if self.likelihood_axes is not None:
x_range = self.likelihood_axes.get_xlim()
else:
x_range = (self.x_min_like, self.x_max_like)
f, axes = joyplot(df, bw_method=1, labels=iteration_label, ax=self.joy,
yrot=0,
range_style='all', x_range=x_range,
color=color,
grid='y',
fade=False, last_axis=False,
linewidth=.1, alpha=1);
n_axes = len(axes[:-1])
if int(n_axes / 2) >= iteration:
ax_sel = axes[-iteration-1]
ax_sel.hlines(0, ax_sel.get_xlim()[0], ax_sel.get_xlim()[1], color='#DA8886', linewidth=3)
elif iteration > n_data - int(self.n_samples/2):
ax_sel = axes[-iteration-self.n_samples+n_data-1]
ax_sel.hlines(0, ax_sel.get_xlim()[0], ax_sel.get_xlim()[1], color='#DA8886', linewidth=3)
else:
ax_sel = axes[int(n_axes / 2)]
ax_sel.hlines(0, ax_sel.get_xlim()[0], ax_sel.get_xlim()[1], color='#DA8886', linewidth=3)
if obs is not None:
triangle_size = self.xt_labelsize*5
if self.likelihood_axes is None:
self.joy[0].scatter(obs, np.ones_like(obs) * self.joy[0].get_ylim()[1], marker='v',
s=triangle_size, c='#DA8886')
self.joy[-1].scatter(obs, np.ones_like(obs) * self.joy[-1].get_ylim()[0],
marker='^', s=triangle_size, c='#DA8886')
return axes
def plot_posterior(self, prior_var, like_var, obs, iteration=-1,
marginal_kwargs=None, likelihood_kwargs=None, joy_kwargs = None):
if marginal_kwargs is None:
marginal_kwargs = {}
if likelihood_kwargs is None:
likelihood_kwargs = {}
if joy_kwargs is None:
joy_kwargs = {}
self.plot_marginal(prior_var, iteration=iteration, **marginal_kwargs)
_, cmap = self.plot_normal_likelihood(like_var[0], like_var[1], obs, iteration=iteration,
**likelihood_kwargs)
self.plot_joy(like_var, obs=obs, iteration=iteration, **joy_kwargs)