def _make_rhat_plot(trace, ax, title, labels, varnames, include_transformed):
"""Helper to plot rhat for multiple chains.
Parameters
----------
trace: pymc3 trace
ax: Matplotlib.Axes
title: str
labels: iterable
Same length as the number of chains
include_transformed: bool
Whether to include the transformed variables
Returns
-------
Matplotlib.Axes with a single error bar added
"""
if varnames is None:
varnames = get_default_varnames(trace.varnames, include_transformed)
R = gelman_rubin(trace)
R = {v: R[v] for v in varnames}
ax.set_title(title)
# Set x range
ax.set_xlim(0.9, 2.1)
# X axis labels
ax.set_xticks((1.0, 1.5, 2.0), ("1", "1.5", "2+"))
ax.set_yticks([-(l + 1) for l in range(len(labels))], "")
i = 1
for varname in varnames:
chain = trace.chains[0]
value = trace.get_values(varname, chains=[chain])[0]
k = np.size(value)
if k > 1:
ax.plot([min(r, 2) for r in R[varname]],
[-(j + i) for j in range(k)],
'bo',
markersize=4)
else:
ax.plot(min(R[varname], 2), -i, 'bo', markersize=4)
i += k
# Define range of y-axis
ax.set_ylim(-i + 0.5, -0.5)
# Remove ticklines on y-axes
ax.set_yticks([])
for loc, spine in ax.spines.items():
if loc in ['left', 'right']:
spine.set_color('none') # don't draw spine
return ax
def _make_rhat_plot(trace, ax, title, labels, varnames, include_transformed):
"""Helper to plot rhat for multiple chains.
Parameters
----------
trace: pymc3 trace
ax: Matplotlib.Axes
title: str
labels: iterable
Same length as the number of chains
include_transformed: bool
Whether to include the transformed variables
Returns
-------
Matplotlib.Axes with a single error bar added
"""
if varnames is None:
varnames = get_default_varnames(trace, include_transformed)
R = gelman_rubin(trace)
R = {v: R[v] for v in varnames}
ax.set_title(title)
# Set x range
ax.set_xlim(0.9, 2.1)
# X axis labels
ax.set_xticks((1.0, 1.5, 2.0), ("1", "1.5", "2+"))
ax.set_yticks([-(l + 1) for l in range(len(labels))], "")
i = 1
for varname in varnames:
chain = trace.chains[0]
value = trace.get_values(varname, chains=[chain])[0]
k = np.size(value)
if k > 1:
ax.plot([min(r, 2) for r in R[varname]],
[-(j + i) for j in range(k)], 'bo', markersize=4)
else:
ax.plot(min(R[varname], 2), -i, 'bo', markersize=4)
i += k
# Define range of y-axis
ax.set_ylim(-i + 0.5, -0.5)
# Remove ticklines on y-axes
ax.set_yticks([])
for loc, spine in ax.spines.items():
if loc in ['left', 'right']:
spine.set_color('none') # don't draw spine
return ax
def _run_convergence_checks(self, trace):
if trace.nchains == 1:
msg = ("Only one chain was sampled, this makes it impossible to "
"run some convergence checks")
warn = SamplerWarning(WarningType.BAD_PARAMS, msg, 'info',
None, None, None)
self._add_warnings([warn])
return
from pymc3 import diagnostics
self._effective_n = effective_n = diagnostics.effective_n(trace)
self._gelman_rubin = gelman_rubin = diagnostics.gelman_rubin(trace)
warnings = []
rhat_max = max(val.max() for val in gelman_rubin.values())
if rhat_max > 1.4:
msg = ("The gelman-rubin statistic is larger than 1.4 for some "
"parameters. The sampler did not converge.")
warn = SamplerWarning(
WarningType.CONVERGENCE, msg, 'error', None, None, gelman_rubin)
warnings.append(warn)
elif rhat_max > 1.2:
msg = ("The gelman-rubin statistic is larger than 1.2 for some "
"parameters.")
warn = SamplerWarning(
WarningType.CONVERGENCE, msg, 'warn', None, None, gelman_rubin)
warnings.append(warn)
elif rhat_max > 1.05:
msg = ("The gelman-rubin statistic is larger than 1.05 for some "
"parameters. This indicates slight problems during "
"sampling.")
warn = SamplerWarning(
WarningType.CONVERGENCE, msg, 'info', None, None, gelman_rubin)
warnings.append(warn)
eff_min = min(val.min() for val in effective_n.values())
n_samples = len(trace) * trace.nchains
if eff_min < 200 and n_samples >= 500:
msg = ("The estimated number of effective samples is smaller than "
"200 for some parameters.")
warn = SamplerWarning(
WarningType.CONVERGENCE, msg, 'error', None, None, effective_n)
warnings.append(warn)
elif eff_min / n_samples < 0.1:
msg = ("The number of effective samples is smaller than "
"10% for some parameters.")
warn = SamplerWarning(
WarningType.CONVERGENCE, msg, 'warn', None, None, effective_n)
warnings.append(warn)
elif eff_min / n_samples < 0.25:
msg = ("The number of effective samples is smaller than "
"25% for some parameters.")
warn = SamplerWarning(
WarningType.CONVERGENCE, msg, 'info', None, None, effective_n)
warnings.append(warn)
self._add_warnings(warnings)
def _run_convergence_checks(self, trace):
if trace.nchains == 1:
msg = ("Only one chain was sampled, this makes it impossible to "
"run some convergence checks")
warn = SamplerWarning(WarningType.BAD_PARAMS, msg, 'info',
None, None, None)
self._add_warnings([warn])
return
from pymc3 import diagnostics
self._effective_n = effective_n = diagnostics.effective_n(trace)
self._gelman_rubin = gelman_rubin = diagnostics.gelman_rubin(trace)
warnings = []
rhat_max = max(val.max() for val in gelman_rubin.values())
if rhat_max > 1.4:
msg = ("The gelman-rubin statistic is larger than 1.4 for some "
"parameters. The sampler did not converge.")
warn = SamplerWarning(
WarningType.CONVERGENCE, msg, 'error', None, None, gelman_rubin)
warnings.append(warn)
elif rhat_max > 1.2:
msg = ("The gelman-rubin statistic is larger than 1.2 for some "
"parameters.")
warn = SamplerWarning(
WarningType.CONVERGENCE, msg, 'warn', None, None, gelman_rubin)
warnings.append(warn)
elif rhat_max > 1.05:
msg = ("The gelman-rubin statistic is larger than 1.05 for some "
"parameters. This indicates slight problems during "
"sampling.")
warn = SamplerWarning(
WarningType.CONVERGENCE, msg, 'info', None, None, gelman_rubin)
warnings.append(warn)
eff_min = min(val.min() for val in effective_n.values())
n_samples = len(trace) * trace.nchains
if eff_min < 200 and n_samples >= 500:
msg = ("The estimated number of effective samples is smaller than "
"200 for some parameters.")
warn = SamplerWarning(
WarningType.CONVERGENCE, msg, 'error', None, None, effective_n)
warnings.append(warn)
elif eff_min / n_samples < 0.25:
msg = ("The number of effective samples is smaller than "
"25% for some parameters.")
warn = SamplerWarning(
WarningType.CONVERGENCE, msg, 'warn', None, None, effective_n)
warnings.append(warn)
self._add_warnings(warnings)
def forestplot(trace,
models=None,
varnames=None,
transform=identity_transform,
alpha=0.05,
quartiles=True,
rhat=True,
main=None,
xtitle=None,
xlim=None,
ylabels=None,
colors='C0',
chain_spacing=0.1,
vline=0,
gs=None,
plot_transformed=False,
plot_kwargs=None):
"""
Forest plot (model summary plot).
Generates a "forest plot" of 100*(1-alpha)% credible intervals from a trace
or list of traces.
Parameters
----------
trace : trace or list of traces
Trace(s) from an MCMC sample.
models : list (optional)
List with names for the models in the list of traces. Useful when
plotting more that one trace.
varnames: list
List of variables to plot (defaults to None, which results in all
variables plotted).
transform : callable
Function to transform data (defaults to identity)
alpha : float, optional
Alpha value for (1-alpha)*100% credible intervals (defaults to 0.05).
quartiles : bool, optional
Flag for plotting the interquartile range, in addition to the
(1-alpha)*100% intervals (defaults to True).
rhat : bool, optional
Flag for plotting Gelman-Rubin statistics. Requires 2 or more chains
(defaults to True).
main : string, optional
Title for main plot. Passing False results in titles being suppressed;
passing None (default) results in default titles.
xtitle : string, optional
Label for x-axis. Defaults to no label
xlim : list or tuple, optional
Range for x-axis. Defaults to matplotlib's best guess.
ylabels : list or array, optional
User-defined labels for each variable. If not provided, the node
__name__ attributes are used.
colors : list or string, optional
list with valid matplotlib colors, one color per model. Alternative a
string can be passed. If the string is `cycle `, it will automatically
chose a color per model from the matyplolib's cycle. If a single color
is passed, eg 'k', 'C2', 'red' this color will be used for all models.
Defauls to 'C0' (blueish in most matplotlib styles)
chain_spacing : float, optional
Plot spacing between chains (defaults to 0.1).
vline : numeric, optional
Location of vertical reference line (defaults to 0).
gs : GridSpec
Matplotlib GridSpec object. Defaults to None.
plot_transformed : bool
Flag for plotting automatically transformed variables in addition to
original variables (defaults to False).
plot_kwargs : dict
Optional arguments for plot elements. Currently accepts 'fontsize',
'linewidth', 'marker', and 'markersize'.
Returns
-------
gs : matplotlib GridSpec
"""
if plot_kwargs is None:
plot_kwargs = {}
if not isinstance(trace, (list, tuple)):
trace = [trace]
if models is None:
if len(trace) > 1:
models = ['m_{}'.format(i) for i in range(len(trace))]
else:
models = ['']
elif len(models) != len(trace):
raise ValueError("The number of names for the models does not match "
"the number of models")
if colors == 'cycle':
colors = ['C{}'.format(i % 10) for i in range(len(models))]
elif isinstance(colors, str):
colors = [colors for i in range(len(models))]
# Quantiles to be calculated
if quartiles:
qlist = [100 * alpha / 2, 25, 50, 75, 100 * (1 - alpha / 2)]
else:
qlist = [100 * alpha / 2, 50, 100 * (1 - alpha / 2)]
nchains = [tr.nchains for tr in trace]
if varnames is None:
varnames = []
for idx, tr in enumerate(trace):
varnames_tmp = get_default_varnames(tr.varnames, plot_transformed)
for v in varnames_tmp:
if v not in varnames:
varnames.append(v)
plot_rhat = [rhat and nch > 1 for nch in nchains]
# Empty list for y-axis labels
if gs is None:
# Initialize plot
if np.any(plot_rhat):
gs = gridspec.GridSpec(1, 2, width_ratios=[3, 1])
gr_plot = plt.subplot(gs[1])
gr_plot.set_xticks((1.0, 1.5, 2.0), ("1", "1.5", "2+"))
gr_plot.set_xlim(0.9, 2.1)
gr_plot.set_yticks([])
gr_plot.set_title('R-hat')
else:
gs = gridspec.GridSpec(1, 1)
# Subplot for confidence intervals
interval_plot = plt.subplot(gs[0])
trace_quantiles = []
hpd_intervals = []
for tr in trace:
trace_quantiles.append(
quantiles(tr, qlist, transform=transform, squeeze=False))
hpd_intervals.append(hpd(tr, alpha, transform=transform,
squeeze=False))
labels = []
var = 0
all_quants = []
bands = [0.05, 0] * len(varnames)
var_old = 0.5
for v_idx, v in enumerate(varnames):
for h, tr in enumerate(trace):
if v not in tr.varnames:
labels.append(models[h] + ' ' + v)
var += 1
else:
for j, chain in enumerate(tr.chains):
var_quantiles = trace_quantiles[h][chain][v]
quants = [var_quantiles[vq] for vq in qlist]
var_hpd = hpd_intervals[h][chain][v].T
# Substitute HPD interval for quantile
quants[0] = var_hpd[0].T
quants[-1] = var_hpd[1].T
# Ensure x-axis contains range of current interval
all_quants.extend(np.ravel(quants))
# Number of elements in current variable
value = tr.get_values(v, chains=[chain])[0]
k = np.size(value)
# Append variable name(s) to list
if j == 0:
if k > 1:
names = _var_str(v, np.shape(value))
names[0] = models[h] + ' ' + names[0]
labels += names
else:
labels.append(models[h] + ' ' + v)
# Add spacing for each chain, if more than one
offset = [0] + [(chain_spacing * ((i + 2) / 2)) * (-1)**i
for i in range(nchains[h] - 1)]
# Y coordinate with offset
y = -var + offset[j]
# Deal with multivariate nodes
if k > 1:
qs = np.moveaxis(np.array(quants), 0, -1).squeeze()
for q in qs.reshape(-1, len(quants)):
# Multiple y values
interval_plot = _plot_tree(interval_plot, y, q,
quartiles, colors[h],
plot_kwargs)
y -= 1
else:
interval_plot = _plot_tree(interval_plot, y, quants,
quartiles, colors[h],
plot_kwargs)
# Genenerate Gelman-Rubin plot
if plot_rhat[h] and v in tr.varnames:
R = gelman_rubin(tr, [v])
if k > 1:
Rval = dict2pd(R, 'rhat').values
gr_plot.plot([min(r, 2) for r in Rval],
[-(j + var) for j in range(k)],
'o',
color=colors[h],
markersize=4)
else:
gr_plot.plot(min(R[v], 2),
-var,
'o',
color=colors[h],
markersize=4)
var += k
if len(trace) > 1:
interval_plot.axhspan(var_old,
y - chain_spacing - 0.5,
facecolor='k',
alpha=bands[v_idx])
gr_plot.axhspan(var_old,
y - chain_spacing - 0.5,
facecolor='k',
alpha=bands[v_idx])
var_old = y - chain_spacing - 0.5
if ylabels is not None:
labels = ylabels
# Update margins
left_margin = np.max([len(x) for x in labels]) * 0.015
gs.update(left=left_margin, right=0.95, top=0.9, bottom=0.05)
# Define range of y-axis for forestplot and R-hat
interval_plot.set_ylim(-var + 0.5, 0.5)
if np.any(plot_rhat):
gr_plot.set_ylim(-var + 0.5, 0.5)
plotrange = [np.min(all_quants), np.max(all_quants)]
datarange = plotrange[1] - plotrange[0]
interval_plot.set_xlim(plotrange[0] - 0.05 * datarange,
plotrange[1] + 0.05 * datarange)
# Add variable labels
interval_plot.set_yticks([-l for l in range(len(labels))])
interval_plot.set_yticklabels(labels,
fontsize=plot_kwargs.get('fontsize', None))
# Add title
if main is None:
plot_title = "{:.0f}% Credible Intervals".format((1 - alpha) * 100)
elif main:
plot_title = main
else:
plot_title = ""
interval_plot.set_title(plot_title,
fontsize=plot_kwargs.get('fontsize', None))
# Add x-axis label
if xtitle is not None:
interval_plot.set_xlabel(xtitle)
# Constrain to specified range
if xlim is not None:
interval_plot.set_xlim(*xlim)
# Remove ticklines on y-axes
for ticks in interval_plot.yaxis.get_major_ticks():
ticks.tick1On = False
ticks.tick2On = False
for loc, spine in interval_plot.spines.items():
if loc in ['left', 'right']:
spine.set_color('none') # don't draw spine
# Reference line
interval_plot.axvline(vline, color='k', linestyle=':')
return gs
def forestplot(trace, models=None, varnames=None, transform=identity_transform,
alpha=0.05, quartiles=True, rhat=True, main=None, xtitle=None,
xlim=None, ylabels=None, colors='C0', chain_spacing=0.1, vline=0,
gs=None, plot_transformed=False, plot_kwargs=None):
"""
Forest plot (model summary plot).
Generates a "forest plot" of 100*(1-alpha)% credible intervals from a trace
or list of traces.
Parameters
----------
trace : trace or list of traces
Trace(s) from an MCMC sample.
models : list (optional)
List with names for the models in the list of traces. Useful when
plotting more that one trace.
varnames: list
List of variables to plot (defaults to None, which results in all
variables plotted).
transform : callable
Function to transform data (defaults to identity)
alpha : float, optional
Alpha value for (1-alpha)*100% credible intervals (defaults to 0.05).
quartiles : bool, optional
Flag for plotting the interquartile range, in addition to the
(1-alpha)*100% intervals (defaults to True).
rhat : bool, optional
Flag for plotting Gelman-Rubin statistics. Requires 2 or more chains
(defaults to True).
main : string, optional
Title for main plot. Passing False results in titles being suppressed;
passing None (default) results in default titles.
xtitle : string, optional
Label for x-axis. Defaults to no label
xlim : list or tuple, optional
Range for x-axis. Defaults to matplotlib's best guess.
ylabels : list or array, optional
User-defined labels for each variable. If not provided, the node
__name__ attributes are used.
colors : list or string, optional
list with valid matplotlib colors, one color per model. Alternative a
string can be passed. If the string is `cycle `, it will automatically
chose a color per model from the matyplolib's cycle. If a single color
is passed, eg 'k', 'C2', 'red' this color will be used for all models.
Defauls to 'C0' (blueish in most matplotlib styles)
chain_spacing : float, optional
Plot spacing between chains (defaults to 0.1).
vline : numeric, optional
Location of vertical reference line (defaults to 0).
gs : GridSpec
Matplotlib GridSpec object. Defaults to None.
plot_transformed : bool
Flag for plotting automatically transformed variables in addition to
original variables (defaults to False).
plot_kwargs : dict
Optional arguments for plot elements. Currently accepts 'fontsize',
'linewidth', 'marker', and 'markersize'.
Returns
-------
gs : matplotlib GridSpec
"""
if plot_kwargs is None:
plot_kwargs = {}
if not isinstance(trace, (list, tuple)):
trace = [trace]
if models is None:
if len(trace) > 1:
models = ['m_{}'.format(i) for i in range(len(trace))]
else:
models = ['']
elif len(models) != len(trace):
raise ValueError("The number of names for the models does not match "
"the number of models")
if colors == 'cycle':
colors = ['C{}'.format(i % 10) for i in range(len(models))]
elif isinstance(colors, str):
colors = [colors for i in range(len(models))]
# Quantiles to be calculated
if quartiles:
qlist = [100 * alpha / 2, 25, 50, 75, 100 * (1 - alpha / 2)]
else:
qlist = [100 * alpha / 2, 50, 100 * (1 - alpha / 2)]
nchains = [tr.nchains for tr in trace]
if varnames is None:
varnames = []
for idx, tr in enumerate(trace):
varnames_tmp = get_default_varnames(tr.varnames, plot_transformed)
for v in varnames_tmp:
if v not in varnames:
varnames.append(v)
plot_rhat = [rhat and nch > 1 for nch in nchains]
# Empty list for y-axis labels
if gs is None:
# Initialize plot
if np.any(plot_rhat):
gs = gridspec.GridSpec(1, 2, width_ratios=[3, 1])
gr_plot = plt.subplot(gs[1])
gr_plot.set_xticks((1.0, 1.5, 2.0), ("1", "1.5", "2+"))
gr_plot.set_xlim(0.9, 2.1)
gr_plot.set_yticks([])
gr_plot.set_title('R-hat')
else:
gs = gridspec.GridSpec(1, 1)
# Subplot for confidence intervals
interval_plot = plt.subplot(gs[0])
trace_quantiles = []
hpd_intervals = []
for tr in trace:
trace_quantiles.append(quantiles(tr, qlist, transform=transform,
squeeze=False))
hpd_intervals.append(hpd(tr, alpha, transform=transform,
squeeze=False))
labels = []
var = 0
all_quants = []
bands = [0.05, 0] * len(varnames)
var_old = 0.5
for v_idx, v in enumerate(varnames):
for h, tr in enumerate(trace):
if v not in tr.varnames:
labels.append(models[h] + ' ' + v)
var += 1
else:
for j, chain in enumerate(tr.chains):
var_quantiles = trace_quantiles[h][chain][v]
quants = [var_quantiles[vq] for vq in qlist]
var_hpd = hpd_intervals[h][chain][v].T
# Substitute HPD interval for quantile
quants[0] = var_hpd[0].T
quants[-1] = var_hpd[1].T
# Ensure x-axis contains range of current interval
all_quants.extend(np.ravel(quants))
# Number of elements in current variable
value = tr.get_values(v, chains=[chain])[0]
k = np.size(value)
# Append variable name(s) to list
if j == 0:
if k > 1:
names = _var_str(v, np.shape(value))
names[0] = models[h] + ' ' + names[0]
labels += names
else:
labels.append(models[h] + ' ' + v)
# Add spacing for each chain, if more than one
offset = [0] + [(chain_spacing * ((i + 2) / 2)) *
(-1) ** i for i in range(nchains[h] - 1)]
# Y coordinate with offset
y = - var + offset[j]
# Deal with multivariate nodes
if k > 1:
qs = np.moveaxis(np.array(quants), 0, -1).squeeze()
for q in qs.reshape(-1, len(quants)):
# Multiple y values
interval_plot = _plot_tree(interval_plot, y, q,
quartiles, colors[h],
plot_kwargs)
y -= 1
else:
interval_plot = _plot_tree(interval_plot, y, quants,
quartiles, colors[h],
plot_kwargs)
# Genenerate Gelman-Rubin plot
if plot_rhat[h] and v in tr.varnames:
R = gelman_rubin(tr, [v])
if k > 1:
Rval = dict2pd(R, 'rhat').values
gr_plot.plot([min(r, 2) for r in Rval],
[-(j + var) for j in range(k)], 'o',
color=colors[h], markersize=4)
else:
gr_plot.plot(min(R[v], 2), -var, 'o', color=colors[h],
markersize=4)
var += k
if len(trace) > 1:
interval_plot.axhspan(var_old, y - chain_spacing - 0.5,
facecolor='k', alpha=bands[v_idx])
if np.any(plot_rhat):
gr_plot.axhspan(var_old, y - chain_spacing - 0.5,
facecolor='k', alpha=bands[v_idx])
var_old = y - chain_spacing - 0.5
if ylabels is not None:
labels = ylabels
# Update margins
left_margin = np.max([len(x) for x in labels]) * 0.015
gs.update(left=left_margin, right=0.95, top=0.9, bottom=0.05)
# Define range of y-axis for forestplot and R-hat
interval_plot.set_ylim(- var + 0.5, 0.5)
if np.any(plot_rhat):
gr_plot.set_ylim(- var + 0.5, 0.5)
plotrange = [np.min(all_quants), np.max(all_quants)]
datarange = plotrange[1] - plotrange[0]
interval_plot.set_xlim(plotrange[0] - 0.05 * datarange,
plotrange[1] + 0.05 * datarange)
# Add variable labels
interval_plot.set_yticks([- l for l in range(len(labels))])
interval_plot.set_yticklabels(labels,
fontsize=plot_kwargs.get('fontsize', None))
# Add title
if main is None:
plot_title = "{:.0f}% Credible Intervals".format((1 - alpha) * 100)
elif main:
plot_title = main
else:
plot_title = ""
interval_plot.set_title(plot_title,
fontsize=plot_kwargs.get('fontsize', None))
# Add x-axis label
if xtitle is not None:
interval_plot.set_xlabel(xtitle)
# Constrain to specified range
if xlim is not None:
interval_plot.set_xlim(*xlim)
# Remove ticklines on y-axes
for ticks in interval_plot.yaxis.get_major_ticks():
ticks.tick1On = False
ticks.tick2On = False
for loc, spine in interval_plot.spines.items():
if loc in ['left', 'right']:
spine.set_color('none') # don't draw spine
# Reference line
interval_plot.axvline(vline, color='k', linestyle=':')
return gs