def line_plot(data,
atts=None,
percent=100.0,
seed=1,
title=None,
outfile=None,
wait=True):
"""
Uses the internal format to plot the dataset, one line per instance.
:param data: the dataset
:type data: Instances
:param atts: the list of 0-based attribute indices of attributes to plot
:type atts: list
:param percent: the percentage of the dataset to use for plotting
:type percent: float
:param seed: the seed value to use for subsampling
:type seed: int
:param title: an optional title
:type title: str
:param outfile: the (optional) file to save the generated plot to. The extension determines the file format.
:type outfile: str
:param wait: whether to wait for the user to close the plot
:type wait: bool
"""
if not plot.matplotlib_available:
logger.error("Matplotlib is not installed, plotting unavailable!")
return
# create subsample
data = plot.create_subsample(data, percent=percent, seed=seed)
fig = plt.figure()
if atts is None:
x = []
for i in xrange(data.num_attributes):
x.append(i)
else:
x = atts
ax = fig.add_subplot(111)
ax.set_xlabel("attributes")
ax.set_ylabel("value")
ax.grid(True)
for index_y in xrange(data.num_instances):
y = []
for index_x in x:
y.append(data.get_instance(index_y).get_value(index_x))
ax.plot(x, y, "o-", alpha=0.5)
if title is None:
title = data.relationname
if percent != 100:
title += " (%0.1f%%)" % percent
fig.canvas.set_window_title(title)
plt.draw()
if outfile is not None:
plt.savefig(outfile)
if wait:
plt.show()
def line_plot(data, atts=None, percent=100.0, seed=1, title=None, outfile=None, wait=True):
"""
Uses the internal format to plot the dataset, one line per instance.
:param data: the dataset
:type data: Instances
:param atts: the list of 0-based attribute indices of attributes to plot
:type atts: list
:param percent: the percentage of the dataset to use for plotting
:type percent: float
:param seed: the seed value to use for subsampling
:type seed: int
:param title: an optional title
:type title: str
:param outfile: the (optional) file to save the generated plot to. The extension determines the file format.
:type outfile: str
:param wait: whether to wait for the user to close the plot
:type wait: bool
"""
if not plot.matplotlib_available:
logger.error("Matplotlib is not installed, plotting unavailable!")
return
# create subsample
data = plot.create_subsample(data, percent=percent, seed=seed)
fig = plt.figure()
if atts is None:
x = []
for i in xrange(data.num_attributes):
x.append(i)
else:
x = atts
ax = fig.add_subplot(111)
ax.set_xlabel("attributes")
ax.set_ylabel("value")
ax.grid(True)
for index_y in xrange(data.num_instances):
y = []
for index_x in x:
y.append(data.get_instance(index_y).get_value(index_x))
ax.plot(x, y, "o-", alpha=0.5)
if title is None:
title = data.relationname
if percent != 100:
title += " (%0.1f%%)" % percent
fig.canvas.set_window_title(title)
plt.draw()
if outfile is not None:
plt.savefig(outfile)
if wait:
plt.show()
def matrix_plot(data, percent=100.0, seed=1, size=10, title=None, outfile=None, wait=True):
"""
Plots all attributes against each other.
TODO: click events http://matplotlib.org/examples/event_handling/data_browser.html
:param data: the dataset
:type data: Instances
:param percent: the percentage of the dataset to use for plotting
:type percent: float
:param seed: the seed value to use for subsampling
:type seed: int
:param size: the size of the circles in point
:type size: int
:param title: an optional title
:type title: str
:param outfile: the (optional) file to save the generated plot to. The extension determines the file format.
:type outfile: str
:param wait: whether to wait for the user to close the plot
:type wait: bool
"""
if not plot.matplotlib_available:
logger.error("Matplotlib is not installed, plotting unavailable!")
return
# create subsample
data = plot.create_subsample(data, percent=percent, seed=seed)
fig = plt.figure()
if data.get_class_index() == -1:
c = None
else:
c = []
for i in xrange(data.num_instances()):
inst = data.get_instance(i)
c.append(inst.get_value(inst.get_class_index()))
for index_x in xrange(data.num_attributes()):
x = []
for i in xrange(data.num_instances()):
inst = data.get_instance(i)
x.append(inst.get_value(index_x))
for index_y in xrange(data.num_attributes()):
y = []
for i in xrange(data.num_instances()):
inst = data.get_instance(i)
y.append(inst.get_value(index_y))
ax = fig.add_subplot(
data.num_attributes(), data.num_attributes(), index_x * data.num_attributes() + index_y + 1)
if c is None:
ax.scatter(x, y, s=size, alpha=0.5)
else:
ax.scatter(x, y, c=c, s=size, alpha=0.5)
ax.set_xlabel(data.get_attribute(index_x).get_name())
ax.set_ylabel(data.get_attribute(index_y).get_name())
ax.get_yaxis().set_ticklabels([])
ax.get_xaxis().set_ticklabels([])
ax.plot(ax.get_xlim(), ax.get_ylim(), ls="--", c="0.3")
ax.grid(True)
if title is None:
title = data.get_relationname()
if percent != 100:
title += " (%0.1f%%)" % percent
fig.canvas.set_window_title(title)
plt.draw()
if not outfile is None:
plt.savefig(outfile)
if wait:
plt.show()
def scatter_plot(data,
index_x,
index_y,
percent=100.0,
seed=1,
size=50,
title=None,
outfile=None,
wait=True):
"""
Plots two attributes against each other.
TODO: click events http://matplotlib.org/examples/event_handling/data_browser.html
:param data: the dataset
:type data: Instances
:param index_x: the 0-based index of the attribute on the x axis
:type index_x: int
:param index_y: the 0-based index of the attribute on the y axis
:type index_y: int
:param percent: the percentage of the dataset to use for plotting
:type percent: float
:param seed: the seed value to use for subsampling
:type seed: int
:param size: the size of the circles in point
:type size: int
:param title: an optional title
:type title: str
:param outfile: the (optional) file to save the generated plot to. The extension determines the file format.
:type outfile: str
:param wait: whether to wait for the user to close the plot
:type wait: bool
"""
if not plot.matplotlib_available:
logger.error("Matplotlib is not installed, plotting unavailable!")
return
# create subsample
data = plot.create_subsample(data, percent=percent, seed=seed)
# collect data
x = []
y = []
if data.class_index == -1:
c = None
else:
c = []
for i in xrange(data.num_instances):
inst = data.get_instance(i)
x.append(inst.get_value(index_x))
y.append(inst.get_value(index_y))
if c is not None:
c.append(inst.get_value(inst.class_index))
# plot data
fig, ax = plt.subplots()
if c is None:
ax.scatter(x, y, s=size, alpha=0.5)
else:
ax.scatter(x, y, c=c, s=size, alpha=0.5)
ax.set_xlabel(data.attribute(index_x).name)
ax.set_ylabel(data.attribute(index_y).name)
if title is None:
title = "Attribute scatter plot"
if percent != 100:
title += " (%0.1f%%)" % percent
ax.set_title(title)
ax.plot(ax.get_xlim(), ax.get_ylim(), ls="--", c="0.3")
ax.grid(True)
fig.canvas.set_window_title(data.relationname)
plt.draw()
if outfile is not None:
plt.savefig(outfile)
if wait:
plt.show()
def matrix_plot(data,
percent=100.0,
seed=1,
size=10,
title=None,
outfile=None,
wait=True):
"""
Plots all attributes against each other.
TODO: click events http://matplotlib.org/examples/event_handling/data_browser.html
:param data: the dataset
:type data: Instances
:param percent: the percentage of the dataset to use for plotting
:type percent: float
:param seed: the seed value to use for subsampling
:type seed: int
:param size: the size of the circles in point
:type size: int
:param title: an optional title
:type title: str
:param outfile: the (optional) file to save the generated plot to. The extension determines the file format.
:type outfile: str
:param wait: whether to wait for the user to close the plot
:type wait: bool
"""
if not plot.matplotlib_available:
logger.error("Matplotlib is not installed, plotting unavailable!")
return
# create subsample
data = plot.create_subsample(data, percent=percent, seed=seed)
fig = plt.figure()
if data.get_class_index() == -1:
c = None
else:
c = []
for i in xrange(data.num_instances()):
inst = data.get_instance(i)
c.append(inst.get_value(inst.get_class_index()))
for index_x in xrange(data.num_attributes()):
x = []
for i in xrange(data.num_instances()):
inst = data.get_instance(i)
x.append(inst.get_value(index_x))
for index_y in xrange(data.num_attributes()):
y = []
for i in xrange(data.num_instances()):
inst = data.get_instance(i)
y.append(inst.get_value(index_y))
ax = fig.add_subplot(data.num_attributes(), data.num_attributes(),
index_x * data.num_attributes() + index_y + 1)
if c is None:
ax.scatter(x, y, s=size, alpha=0.5)
else:
ax.scatter(x, y, c=c, s=size, alpha=0.5)
ax.set_xlabel(data.get_attribute(index_x).get_name())
ax.set_ylabel(data.get_attribute(index_y).get_name())
ax.get_yaxis().set_ticklabels([])
ax.get_xaxis().set_ticklabels([])
ax.plot(ax.get_xlim(), ax.get_ylim(), ls="--", c="0.3")
ax.grid(True)
if title is None:
title = data.get_relationname()
if percent != 100:
title += " (%0.1f%%)" % percent
fig.canvas.set_window_title(title)
plt.draw()
if not outfile is None:
plt.savefig(outfile)
if wait:
plt.show()
def scatter_plot(data, index_x, index_y, percent=100.0, seed=1, size=50, title=None, outfile=None, wait=True):
"""
Plots two attributes against each other.
TODO: click events http://matplotlib.org/examples/event_handling/data_browser.html
:param data: the dataset
:type data: Instances
:param index_x: the 0-based index of the attribute on the x axis
:type index_x: int
:param index_y: the 0-based index of the attribute on the y axis
:type index_y: int
:param percent: the percentage of the dataset to use for plotting
:type percent: float
:param seed: the seed value to use for subsampling
:type seed: int
:param size: the size of the circles in point
:type size: int
:param title: an optional title
:type title: str
:param outfile: the (optional) file to save the generated plot to. The extension determines the file format.
:type outfile: str
:param wait: whether to wait for the user to close the plot
:type wait: bool
"""
if not plot.matplotlib_available:
logger.error("Matplotlib is not installed, plotting unavailable!")
return
# create subsample
data = plot.create_subsample(data, percent=percent, seed=seed)
# collect data
x = []
y = []
if data.class_index == -1:
c = None
else:
c = []
for i in xrange(data.num_instances):
inst = data.get_instance(i)
x.append(inst.get_value(index_x))
y.append(inst.get_value(index_y))
if c is not None:
c.append(inst.get_value(inst.class_index))
# plot data
fig, ax = plt.subplots()
if c is None:
ax.scatter(x, y, s=size, alpha=0.5)
else:
ax.scatter(x, y, c=c, s=size, alpha=0.5)
ax.set_xlabel(data.attribute(index_x).name)
ax.set_ylabel(data.attribute(index_y).name)
if title is None:
title = "Attribute scatter plot"
if percent != 100:
title += " (%0.1f%%)" % percent
ax.set_title(title)
ax.plot(ax.get_xlim(), ax.get_ylim(), ls="--", c="0.3")
ax.grid(True)
fig.canvas.set_window_title(data.relationname)
plt.draw()
if outfile is not None:
plt.savefig(outfile)
if wait:
plt.show()
