# -*- coding: utf-8 -*-
"""
Authors: Josef Perktold, Skipper Seabold, Denis A. Engemann
"""
from statsmodels.compat.python import get_function_name, iterkeys, lrange, zip, iteritems
import numpy as np
from statsmodels.graphics.plottools import rainbow
import statsmodels.graphics.utils as utils
[docs]def interaction_plot(x, trace, response, func=np.mean, ax=None, plottype='b',
xlabel=None, ylabel=None, colors=[], markers=[],
linestyles=[], legendloc='best', legendtitle=None,
**kwargs):
"""
Interaction plot for factor level statistics.
Note. If categorial factors are supplied levels will be internally
recoded to integers. This ensures matplotlib compatiblity.
uses pandas.DataFrame to calculate an `aggregate` statistic for each
level of the factor or group given by `trace`.
Parameters
----------
x : array-like
The `x` factor levels constitute the x-axis. If a `pandas.Series` is
given its name will be used in `xlabel` if `xlabel` is None.
trace : array-like
The `trace` factor levels will be drawn as lines in the plot.
If `trace` is a `pandas.Series` its name will be used as the
`legendtitle` if `legendtitle` is None.
response : array-like
The reponse or dependent variable. If a `pandas.Series` is given
its name will be used in `ylabel` if `ylabel` is None.
func : function
Anything accepted by `pandas.DataFrame.aggregate`. This is applied to
the response variable grouped by the trace levels.
plottype : str {'line', 'scatter', 'both'}, optional
The type of plot to return. Can be 'l', 's', or 'b'
ax : axes, optional
Matplotlib axes instance
xlabel : str, optional
Label to use for `x`. Default is 'X'. If `x` is a `pandas.Series` it
will use the series names.
ylabel : str, optional
Label to use for `response`. Default is 'func of response'. If
`response` is a `pandas.Series` it will use the series names.
colors : list, optional
If given, must have length == number of levels in trace.
linestyles : list, optional
If given, must have length == number of levels in trace.
markers : list, optional
If given, must have length == number of lovels in trace
kwargs
These will be passed to the plot command used either plot or scatter.
If you want to control the overall plotting options, use kwargs.
Returns
-------
fig : Figure
The figure given by `ax.figure` or a new instance.
Examples
--------
>>> import numpy as np
>>> np.random.seed(12345)
>>> weight = np.random.randint(1,4,size=60)
>>> duration = np.random.randint(1,3,size=60)
>>> days = np.log(np.random.randint(1,30, size=60))
>>> fig = interaction_plot(weight, duration, days,
... colors=['red','blue'], markers=['D','^'], ms=10)
>>> import matplotlib.pyplot as plt
>>> plt.show()
.. plot::
import numpy as np
from statsmodels.graphics.factorplots import interaction_plot
np.random.seed(12345)
weight = np.random.randint(1,4,size=60)
duration = np.random.randint(1,3,size=60)
days = np.log(np.random.randint(1,30, size=60))
fig = interaction_plot(weight, duration, days,
colors=['red','blue'], markers=['D','^'], ms=10)
import matplotlib.pyplot as plt
#plt.show()
"""
from pandas import DataFrame
fig, ax = utils.create_mpl_ax(ax)
response_name = ylabel or getattr(response, 'name', 'response')
ylabel = '%s of %s' % (get_function_name(func), response_name)
xlabel = xlabel or getattr(x, 'name', 'X')
legendtitle = legendtitle or getattr(trace, 'name', 'Trace')
ax.set_ylabel(ylabel)
ax.set_xlabel(xlabel)
x_values = x_levels = None
if isinstance(x[0], str):
x_levels = [l for l in np.unique(x)]
x_values = lrange(len(x_levels))
x = _recode(x, dict(zip(x_levels, x_values)))
data = DataFrame(dict(x=x, trace=trace, response=response))
plot_data = data.groupby(['trace', 'x']).aggregate(func).reset_index()
# return data
# check plot args
n_trace = len(plot_data['trace'].unique())
if linestyles:
try:
assert len(linestyles) == n_trace
except AssertionError as err:
raise ValueError("Must be a linestyle for each trace level")
else: # set a default
linestyles = ['-'] * n_trace
if markers:
try:
assert len(markers) == n_trace
except AssertionError as err:
raise ValueError("Must be a linestyle for each trace level")
else: # set a default
markers = ['.'] * n_trace
if colors:
try:
assert len(colors) == n_trace
except AssertionError as err:
raise ValueError("Must be a linestyle for each trace level")
else: # set a default
#TODO: how to get n_trace different colors?
colors = rainbow(n_trace)
if plottype == 'both' or plottype == 'b':
for i, (values, group) in enumerate(plot_data.groupby(['trace'])):
# trace label
label = str(group['trace'].values[0])
ax.plot(group['x'], group['response'], color=colors[i],
marker=markers[i], label=label,
linestyle=linestyles[i], **kwargs)
elif plottype == 'line' or plottype == 'l':
for i, (values, group) in enumerate(plot_data.groupby(['trace'])):
# trace label
label = str(group['trace'].values[0])
ax.plot(group['x'], group['response'], color=colors[i],
label=label, linestyle=linestyles[i], **kwargs)
elif plottype == 'scatter' or plottype == 's':
for i, (values, group) in enumerate(plot_data.groupby(['trace'])):
# trace label
label = str(group['trace'].values[0])
ax.scatter(group['x'], group['response'], color=colors[i],
label=label, marker=markers[i], **kwargs)
else:
raise ValueError("Plot type %s not understood" % plottype)
ax.legend(loc=legendloc, title=legendtitle)
ax.margins(.1)
if all([x_levels, x_values]):
ax.set_xticks(x_values)
ax.set_xticklabels(x_levels)
return fig
def _recode(x, levels):
""" Recode categorial data to int factor.
Parameters
----------
x : array-like
array like object supporting with numpy array methods of categorially
coded data.
levels : dict
mapping of labels to integer-codings
Returns
-------
out : instance numpy.ndarray
"""
from pandas import Series
name = None
if isinstance(x, Series):
name = x.name
x = x.values
if x.dtype.type not in [np.str_, np.object_]:
raise ValueError('This is not a categorial factor.'
' Array of str type required.')
elif not isinstance(levels, dict):
raise ValueError('This is not a valid value for levels.'
' Dict required.')
elif not (np.unique(x) == np.unique(list(iterkeys(levels)))).all():
raise ValueError('The levels do not match the array values.')
else:
out = np.empty(x.shape[0], dtype=np.int)
for level, coding in iteritems(levels):
out[x == level] = coding
if name:
out = Series(out)
out.name = name
return out
