Old engine for Continuous Time Bayesian Networks. Superseded by reCTBN. 🐍
https://github.com/madlabunimib/PyCTBN
You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
452 lines
14 KiB
452 lines
14 KiB
from collections import namedtuple
|
|
import warnings
|
|
|
|
from matplotlib.artist import setp
|
|
import numpy as np
|
|
|
|
from pandas.core.dtypes.common import is_dict_like
|
|
from pandas.core.dtypes.missing import remove_na_arraylike
|
|
|
|
import pandas as pd
|
|
|
|
from pandas.io.formats.printing import pprint_thing
|
|
from pandas.plotting._matplotlib.core import LinePlot, MPLPlot
|
|
from pandas.plotting._matplotlib.style import _get_standard_colors
|
|
from pandas.plotting._matplotlib.tools import _flatten, _subplots
|
|
|
|
|
|
class BoxPlot(LinePlot):
|
|
_kind = "box"
|
|
_layout_type = "horizontal"
|
|
|
|
_valid_return_types = (None, "axes", "dict", "both")
|
|
# namedtuple to hold results
|
|
BP = namedtuple("Boxplot", ["ax", "lines"])
|
|
|
|
def __init__(self, data, return_type="axes", **kwargs):
|
|
# Do not call LinePlot.__init__ which may fill nan
|
|
if return_type not in self._valid_return_types:
|
|
raise ValueError("return_type must be {None, 'axes', 'dict', 'both'}")
|
|
|
|
self.return_type = return_type
|
|
MPLPlot.__init__(self, data, **kwargs)
|
|
|
|
def _args_adjust(self):
|
|
if self.subplots:
|
|
# Disable label ax sharing. Otherwise, all subplots shows last
|
|
# column label
|
|
if self.orientation == "vertical":
|
|
self.sharex = False
|
|
else:
|
|
self.sharey = False
|
|
|
|
@classmethod
|
|
def _plot(cls, ax, y, column_num=None, return_type="axes", **kwds):
|
|
if y.ndim == 2:
|
|
y = [remove_na_arraylike(v) for v in y]
|
|
# Boxplot fails with empty arrays, so need to add a NaN
|
|
# if any cols are empty
|
|
# GH 8181
|
|
y = [v if v.size > 0 else np.array([np.nan]) for v in y]
|
|
else:
|
|
y = remove_na_arraylike(y)
|
|
bp = ax.boxplot(y, **kwds)
|
|
|
|
if return_type == "dict":
|
|
return bp, bp
|
|
elif return_type == "both":
|
|
return cls.BP(ax=ax, lines=bp), bp
|
|
else:
|
|
return ax, bp
|
|
|
|
def _validate_color_args(self):
|
|
if "color" in self.kwds:
|
|
if self.colormap is not None:
|
|
warnings.warn(
|
|
"'color' and 'colormap' cannot be used "
|
|
"simultaneously. Using 'color'"
|
|
)
|
|
self.color = self.kwds.pop("color")
|
|
|
|
if isinstance(self.color, dict):
|
|
valid_keys = ["boxes", "whiskers", "medians", "caps"]
|
|
for key, values in self.color.items():
|
|
if key not in valid_keys:
|
|
raise ValueError(
|
|
f"color dict contains invalid key '{key}'. "
|
|
f"The key must be either {valid_keys}"
|
|
)
|
|
else:
|
|
self.color = None
|
|
|
|
# get standard colors for default
|
|
colors = _get_standard_colors(num_colors=3, colormap=self.colormap, color=None)
|
|
# use 2 colors by default, for box/whisker and median
|
|
# flier colors isn't needed here
|
|
# because it can be specified by ``sym`` kw
|
|
self._boxes_c = colors[0]
|
|
self._whiskers_c = colors[0]
|
|
self._medians_c = colors[2]
|
|
self._caps_c = "k" # mpl default
|
|
|
|
def _get_colors(self, num_colors=None, color_kwds="color"):
|
|
pass
|
|
|
|
def maybe_color_bp(self, bp):
|
|
if isinstance(self.color, dict):
|
|
boxes = self.color.get("boxes", self._boxes_c)
|
|
whiskers = self.color.get("whiskers", self._whiskers_c)
|
|
medians = self.color.get("medians", self._medians_c)
|
|
caps = self.color.get("caps", self._caps_c)
|
|
else:
|
|
# Other types are forwarded to matplotlib
|
|
# If None, use default colors
|
|
boxes = self.color or self._boxes_c
|
|
whiskers = self.color or self._whiskers_c
|
|
medians = self.color or self._medians_c
|
|
caps = self.color or self._caps_c
|
|
|
|
# GH 30346, when users specifying those arguments explicitly, our defaults
|
|
# for these four kwargs should be overridden; if not, use Pandas settings
|
|
if not self.kwds.get("boxprops"):
|
|
setp(bp["boxes"], color=boxes, alpha=1)
|
|
if not self.kwds.get("whiskerprops"):
|
|
setp(bp["whiskers"], color=whiskers, alpha=1)
|
|
if not self.kwds.get("medianprops"):
|
|
setp(bp["medians"], color=medians, alpha=1)
|
|
if not self.kwds.get("capprops"):
|
|
setp(bp["caps"], color=caps, alpha=1)
|
|
|
|
def _make_plot(self):
|
|
if self.subplots:
|
|
self._return_obj = pd.Series(dtype=object)
|
|
|
|
for i, (label, y) in enumerate(self._iter_data()):
|
|
ax = self._get_ax(i)
|
|
kwds = self.kwds.copy()
|
|
|
|
ret, bp = self._plot(
|
|
ax, y, column_num=i, return_type=self.return_type, **kwds
|
|
)
|
|
self.maybe_color_bp(bp)
|
|
self._return_obj[label] = ret
|
|
|
|
label = [pprint_thing(label)]
|
|
self._set_ticklabels(ax, label)
|
|
else:
|
|
y = self.data.values.T
|
|
ax = self._get_ax(0)
|
|
kwds = self.kwds.copy()
|
|
|
|
ret, bp = self._plot(
|
|
ax, y, column_num=0, return_type=self.return_type, **kwds
|
|
)
|
|
self.maybe_color_bp(bp)
|
|
self._return_obj = ret
|
|
|
|
labels = [l for l, _ in self._iter_data()]
|
|
labels = [pprint_thing(l) for l in labels]
|
|
if not self.use_index:
|
|
labels = [pprint_thing(key) for key in range(len(labels))]
|
|
self._set_ticklabels(ax, labels)
|
|
|
|
def _set_ticklabels(self, ax, labels):
|
|
if self.orientation == "vertical":
|
|
ax.set_xticklabels(labels)
|
|
else:
|
|
ax.set_yticklabels(labels)
|
|
|
|
def _make_legend(self):
|
|
pass
|
|
|
|
def _post_plot_logic(self, ax, data):
|
|
pass
|
|
|
|
@property
|
|
def orientation(self):
|
|
if self.kwds.get("vert", True):
|
|
return "vertical"
|
|
else:
|
|
return "horizontal"
|
|
|
|
@property
|
|
def result(self):
|
|
if self.return_type is None:
|
|
return super().result
|
|
else:
|
|
return self._return_obj
|
|
|
|
|
|
def _grouped_plot_by_column(
|
|
plotf,
|
|
data,
|
|
columns=None,
|
|
by=None,
|
|
numeric_only=True,
|
|
grid=False,
|
|
figsize=None,
|
|
ax=None,
|
|
layout=None,
|
|
return_type=None,
|
|
**kwargs,
|
|
):
|
|
grouped = data.groupby(by)
|
|
if columns is None:
|
|
if not isinstance(by, (list, tuple)):
|
|
by = [by]
|
|
columns = data._get_numeric_data().columns.difference(by)
|
|
naxes = len(columns)
|
|
fig, axes = _subplots(
|
|
naxes=naxes, sharex=True, sharey=True, figsize=figsize, ax=ax, layout=layout
|
|
)
|
|
|
|
_axes = _flatten(axes)
|
|
|
|
ax_values = []
|
|
|
|
for i, col in enumerate(columns):
|
|
ax = _axes[i]
|
|
gp_col = grouped[col]
|
|
keys, values = zip(*gp_col)
|
|
re_plotf = plotf(keys, values, ax, **kwargs)
|
|
ax.set_title(col)
|
|
ax.set_xlabel(pprint_thing(by))
|
|
ax_values.append(re_plotf)
|
|
ax.grid(grid)
|
|
|
|
result = pd.Series(ax_values, index=columns)
|
|
|
|
# Return axes in multiplot case, maybe revisit later # 985
|
|
if return_type is None:
|
|
result = axes
|
|
|
|
byline = by[0] if len(by) == 1 else by
|
|
fig.suptitle(f"Boxplot grouped by {byline}")
|
|
fig.subplots_adjust(bottom=0.15, top=0.9, left=0.1, right=0.9, wspace=0.2)
|
|
|
|
return result
|
|
|
|
|
|
def boxplot(
|
|
data,
|
|
column=None,
|
|
by=None,
|
|
ax=None,
|
|
fontsize=None,
|
|
rot=0,
|
|
grid=True,
|
|
figsize=None,
|
|
layout=None,
|
|
return_type=None,
|
|
**kwds,
|
|
):
|
|
|
|
import matplotlib.pyplot as plt
|
|
|
|
# validate return_type:
|
|
if return_type not in BoxPlot._valid_return_types:
|
|
raise ValueError("return_type must be {'axes', 'dict', 'both'}")
|
|
|
|
if isinstance(data, pd.Series):
|
|
data = data.to_frame("x")
|
|
column = "x"
|
|
|
|
def _get_colors():
|
|
# num_colors=3 is required as method maybe_color_bp takes the colors
|
|
# in positions 0 and 2.
|
|
# if colors not provided, use same defaults as DataFrame.plot.box
|
|
result = _get_standard_colors(num_colors=3)
|
|
result = np.take(result, [0, 0, 2])
|
|
result = np.append(result, "k")
|
|
|
|
colors = kwds.pop("color", None)
|
|
if colors:
|
|
if is_dict_like(colors):
|
|
# replace colors in result array with user-specified colors
|
|
# taken from the colors dict parameter
|
|
# "boxes" value placed in position 0, "whiskers" in 1, etc.
|
|
valid_keys = ["boxes", "whiskers", "medians", "caps"]
|
|
key_to_index = dict(zip(valid_keys, range(4)))
|
|
for key, value in colors.items():
|
|
if key in valid_keys:
|
|
result[key_to_index[key]] = value
|
|
else:
|
|
raise ValueError(
|
|
f"color dict contains invalid key '{key}'. "
|
|
f"The key must be either {valid_keys}"
|
|
)
|
|
else:
|
|
result.fill(colors)
|
|
|
|
return result
|
|
|
|
def maybe_color_bp(bp, **kwds):
|
|
# GH 30346, when users specifying those arguments explicitly, our defaults
|
|
# for these four kwargs should be overridden; if not, use Pandas settings
|
|
if not kwds.get("boxprops"):
|
|
setp(bp["boxes"], color=colors[0], alpha=1)
|
|
if not kwds.get("whiskerprops"):
|
|
setp(bp["whiskers"], color=colors[1], alpha=1)
|
|
if not kwds.get("medianprops"):
|
|
setp(bp["medians"], color=colors[2], alpha=1)
|
|
if not kwds.get("capprops"):
|
|
setp(bp["caps"], color=colors[3], alpha=1)
|
|
|
|
def plot_group(keys, values, ax):
|
|
keys = [pprint_thing(x) for x in keys]
|
|
values = [np.asarray(remove_na_arraylike(v)) for v in values]
|
|
bp = ax.boxplot(values, **kwds)
|
|
if fontsize is not None:
|
|
ax.tick_params(axis="both", labelsize=fontsize)
|
|
if kwds.get("vert", 1):
|
|
ticks = ax.get_xticks()
|
|
if len(ticks) != len(keys):
|
|
i, remainder = divmod(len(ticks), len(keys))
|
|
assert remainder == 0, remainder
|
|
keys *= i
|
|
ax.set_xticklabels(keys, rotation=rot)
|
|
else:
|
|
ax.set_yticklabels(keys, rotation=rot)
|
|
maybe_color_bp(bp, **kwds)
|
|
|
|
# Return axes in multiplot case, maybe revisit later # 985
|
|
if return_type == "dict":
|
|
return bp
|
|
elif return_type == "both":
|
|
return BoxPlot.BP(ax=ax, lines=bp)
|
|
else:
|
|
return ax
|
|
|
|
colors = _get_colors()
|
|
if column is None:
|
|
columns = None
|
|
else:
|
|
if isinstance(column, (list, tuple)):
|
|
columns = column
|
|
else:
|
|
columns = [column]
|
|
|
|
if by is not None:
|
|
# Prefer array return type for 2-D plots to match the subplot layout
|
|
# https://github.com/pandas-dev/pandas/pull/12216#issuecomment-241175580
|
|
result = _grouped_plot_by_column(
|
|
plot_group,
|
|
data,
|
|
columns=columns,
|
|
by=by,
|
|
grid=grid,
|
|
figsize=figsize,
|
|
ax=ax,
|
|
layout=layout,
|
|
return_type=return_type,
|
|
)
|
|
else:
|
|
if return_type is None:
|
|
return_type = "axes"
|
|
if layout is not None:
|
|
raise ValueError("The 'layout' keyword is not supported when 'by' is None")
|
|
|
|
if ax is None:
|
|
rc = {"figure.figsize": figsize} if figsize is not None else {}
|
|
with plt.rc_context(rc):
|
|
ax = plt.gca()
|
|
data = data._get_numeric_data()
|
|
if columns is None:
|
|
columns = data.columns
|
|
else:
|
|
data = data[columns]
|
|
|
|
result = plot_group(columns, data.values.T, ax)
|
|
ax.grid(grid)
|
|
|
|
return result
|
|
|
|
|
|
def boxplot_frame(
|
|
self,
|
|
column=None,
|
|
by=None,
|
|
ax=None,
|
|
fontsize=None,
|
|
rot=0,
|
|
grid=True,
|
|
figsize=None,
|
|
layout=None,
|
|
return_type=None,
|
|
**kwds,
|
|
):
|
|
import matplotlib.pyplot as plt
|
|
|
|
ax = boxplot(
|
|
self,
|
|
column=column,
|
|
by=by,
|
|
ax=ax,
|
|
fontsize=fontsize,
|
|
grid=grid,
|
|
rot=rot,
|
|
figsize=figsize,
|
|
layout=layout,
|
|
return_type=return_type,
|
|
**kwds,
|
|
)
|
|
plt.draw_if_interactive()
|
|
return ax
|
|
|
|
|
|
def boxplot_frame_groupby(
|
|
grouped,
|
|
subplots=True,
|
|
column=None,
|
|
fontsize=None,
|
|
rot=0,
|
|
grid=True,
|
|
ax=None,
|
|
figsize=None,
|
|
layout=None,
|
|
sharex=False,
|
|
sharey=True,
|
|
**kwds,
|
|
):
|
|
if subplots is True:
|
|
naxes = len(grouped)
|
|
fig, axes = _subplots(
|
|
naxes=naxes,
|
|
squeeze=False,
|
|
ax=ax,
|
|
sharex=sharex,
|
|
sharey=sharey,
|
|
figsize=figsize,
|
|
layout=layout,
|
|
)
|
|
axes = _flatten(axes)
|
|
|
|
ret = pd.Series(dtype=object)
|
|
|
|
for (key, group), ax in zip(grouped, axes):
|
|
d = group.boxplot(
|
|
ax=ax, column=column, fontsize=fontsize, rot=rot, grid=grid, **kwds
|
|
)
|
|
ax.set_title(pprint_thing(key))
|
|
ret.loc[key] = d
|
|
fig.subplots_adjust(bottom=0.15, top=0.9, left=0.1, right=0.9, wspace=0.2)
|
|
else:
|
|
keys, frames = zip(*grouped)
|
|
if grouped.axis == 0:
|
|
df = pd.concat(frames, keys=keys, axis=1)
|
|
else:
|
|
if len(frames) > 1:
|
|
df = frames[0].join(frames[1::])
|
|
else:
|
|
df = frames[0]
|
|
ret = df.boxplot(
|
|
column=column,
|
|
fontsize=fontsize,
|
|
rot=rot,
|
|
grid=grid,
|
|
ax=ax,
|
|
figsize=figsize,
|
|
layout=layout,
|
|
**kwds,
|
|
)
|
|
return ret
|
|
|