File size: 9,293 Bytes
c9cfa33 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 |
import textwrap
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
class ComplexRadar:
"""Create a complex radar chart with different scales for each variable
Args:
fig (`matplotlib.figure`) : A matplotlib figure object to add the axes on.
variables (`list`) : a list of variables to. plot
ranges (`list` of `tuples`): A list of ranges (min, max) for each variable
n_ring_levels (`int): Number of ordinate or ring levels to draw.
Default: 5.
show_scales (`bool`): Indicates if we the ranges for each variable are plotted.
Default: True.
format_cfg (`dict`): A dictionary with formatting configurations.
Default: None.
Returns:
`matplotlib.figure.Figure`: a radar plot.
"""
def __init__(self, fig, variables, ranges, n_ring_levels=5, show_scales=True, format_cfg=None):
self.format_cfg = format_cfg
# Calculate angles and create for each variable an axes
# Consider here the trick with having the first axes element twice (len+1)
angles = np.arange(0, 360, 360.0 / len(variables))
axes = [
fig.add_axes([0.1, 0.1, 0.9, 0.9], polar=True, label="axes{}".format(i), **self.format_cfg["axes_args"])
for i in range(len(variables) + 1)
]
# Ensure clockwise rotation (first variable at the top N)
for ax in axes:
ax.set_theta_zero_location("N")
ax.set_theta_direction(-1)
ax.set_axisbelow(True)
# Writing the ranges on each axes
for i, ax in enumerate(axes):
# Here we do the trick by repeating the first iteration
j = 0 if (i == 0 or i == 1) else i - 1
ax.set_ylim(*ranges[j])
# Set endpoint to True if you like to have values right before the last circle
grid = np.linspace(*ranges[j], num=n_ring_levels, endpoint=self.format_cfg["incl_endpoint"])
gridlabel = ["{}".format(round(x, 2)) for x in grid]
gridlabel[0] = "" # remove values from the center
lines, labels = ax.set_rgrids(
grid, labels=gridlabel, angle=angles[j], **self.format_cfg["rgrid_tick_lbls_args"]
)
ax.set_ylim(*ranges[j])
ax.spines["polar"].set_visible(False)
ax.grid(visible=False)
if show_scales is False:
ax.set_yticklabels([])
# Set all axes except the first one unvisible
for ax in axes[1:]:
ax.patch.set_visible(False)
ax.xaxis.set_visible(False)
# Setting the attributes
self.angle = np.deg2rad(np.r_[angles, angles[0]])
self.ranges = ranges
self.ax = axes[0]
self.ax1 = axes[1]
self.plot_counter = 0
# Draw (inner) circles and lines
self.ax.yaxis.grid(**self.format_cfg["rad_ln_args"])
# Draw outer circle
self.ax.spines["polar"].set(**self.format_cfg["outer_ring"])
# Draw angle lines
self.ax.xaxis.grid(**self.format_cfg["angle_ln_args"])
# ax1 is the duplicate of axes[0] (self.ax)
# Remove everything from ax1 except the plot itself
self.ax1.axis("off")
self.ax1.set_zorder(9)
# Create the outer labels for each variable
l, text = self.ax.set_thetagrids(angles, labels=variables)
# Beautify them
labels = [t.get_text() for t in self.ax.get_xticklabels()]
labels = [
"\n".join(
textwrap.wrap(
label,
self.format_cfg["theta_tick_lbls_txt_wrap"],
break_long_words=self.format_cfg["theta_tick_lbls_brk_lng_wrds"],
)
)
for label in labels
]
self.ax.set_xticklabels(labels, **self.format_cfg["theta_tick_lbls"])
for t, a in zip(self.ax.get_xticklabels(), angles):
if a == 0:
t.set_ha("center")
elif a > 0 and a < 180:
t.set_ha("left")
elif a == 180:
t.set_ha("center")
else:
t.set_ha("right")
self.ax.tick_params(axis="both", pad=self.format_cfg["theta_tick_lbls_pad"])
def _scale_data(self, data, ranges):
"""Scales data[1:] to ranges[0]"""
for d, (y1, y2) in zip(data[1:], ranges[1:]):
assert (y1 <= d <= y2) or (y2 <= d <= y1)
x1, x2 = ranges[0]
d = data[0]
sdata = [d]
for d, (y1, y2) in zip(data[1:], ranges[1:]):
sdata.append((d - y1) / (y2 - y1) * (x2 - x1) + x1)
return sdata
def plot(self, data, *args, **kwargs):
"""Plots a line"""
sdata = self._scale_data(data, self.ranges)
self.ax1.plot(self.angle, np.r_[sdata, sdata[0]], *args, **kwargs)
self.plot_counter = self.plot_counter + 1
def use_legend(self, *args, **kwargs):
"""Shows a legend"""
self.ax1.legend(*args, **kwargs)
def radar_plot(data, model_names, invert_range=[], config=None, fig=None):
"""Create a complex radar chart with different scales for each variable
Source: https://towardsdatascience.com/how-to-create-and-visualize-complex-radar-charts-f7764d0f3652
Args:
data (`List[dict]`): the results (list of metric + value pairs).
E.g. data = [{"accuracy": 0.9, "precision":0.8},{"accuracy": 0.7, "precision":0.6}]
names (`List[dict]`): model names.
E.g. names = ["model1", "model 2", ...]
invert_range (`List[dict]`, optional): the metrics to invert (in cases when smaller is better, e.g. speed)
E.g. invert_range=["latency_in_seconds"]
config (`dict`, optional) : a specification of the formatting configurations, namely:
- rad_ln_args (`dict`, default `{"visible": True}`): The visibility of the radial (circle) lines.
- outer_ring (`dict`, default `{"visible": True}`): The visibility of the outer ring.
- angle_ln_args (`dict`, default `{"visible": True}`): The visibility of the angle lines.
- rgrid_tick_lbls_args (`dict`, default `{"fontsize": 12}`): The font size of the tick labels on the scales.
- theta_tick_lbls (`dict`, default `{"fontsize": 12}`): The font size of the variable labels on the plot.
- theta_tick_lbls_pad (`int`, default `3`): The padding of the variable labels on the plot.
- theta_tick_lbls_brk_lng_wrds (`bool`, default `True` ): Whether long words in the label are broken up or not.
- theta_tick_lbls_txt_wrap (`int`, default `15`): Text wrap for tick labels
- incl_endpoint (`bool`, default `False`): Include value endpoints on calse
- marker (`str`, default `"o"`): the shape of the marker used in the radar plot.
- markersize (`int`, default `3`): the shape of the marker used in the radar plot.
- legend_loc (`str`, default `"upper right"`): the location of the legend in the radar plot. Must be one of: 'upper left', 'upper right', 'lower left', 'lower right'.
- bbox_to_anchor (`tuple`, default `(2, 1)`: anchor for the legend.
fig (`matplotlib.figure.Figure`, optional): figure used to plot the radar plot.
Returns:
`matplotlib.figure.Figure`
"""
data = pd.DataFrame(data)
data.index = model_names
variables = data.keys()
if all(x in variables for x in invert_range) is False:
raise ValueError("All of the metrics in `invert_range` should be in the data provided.")
min_max_per_variable = data.describe().T[["min", "max"]]
min_max_per_variable["min"] = min_max_per_variable["min"] - 0.1 * (
min_max_per_variable["max"] - min_max_per_variable["min"]
)
min_max_per_variable["max"] = min_max_per_variable["max"] + 0.1 * (
min_max_per_variable["max"] - min_max_per_variable["min"]
)
ranges = list(min_max_per_variable.itertuples(index=False, name=None))
ranges = [
(max_value, min_value) if var in invert_range else (min_value, max_value)
for var, (min_value, max_value) in zip(variables, ranges)
]
format_cfg = {
"axes_args": {},
"rad_ln_args": {"visible": True},
"outer_ring": {"visible": True},
"angle_ln_args": {"visible": True},
"rgrid_tick_lbls_args": {"fontsize": 12},
"theta_tick_lbls": {"fontsize": 12},
"theta_tick_lbls_pad": 3,
"theta_tick_lbls_brk_lng_wrds": True,
"theta_tick_lbls_txt_wrap": 15,
"incl_endpoint": False,
"marker": "o",
"markersize": 3,
"legend_loc": "upper right",
"bbox_to_anchor": (2, 1),
}
if config is not None:
format_cfg.update(config)
if fig is None:
fig = plt.figure()
radar = ComplexRadar(
fig,
variables,
ranges,
n_ring_levels=3,
show_scales=True,
format_cfg=format_cfg,
)
for g in zip(data.index):
radar.plot(data.loc[g].values, label=g, marker=format_cfg["marker"], markersize=format_cfg["markersize"])
radar.use_legend(**{"loc": format_cfg["legend_loc"], "bbox_to_anchor": format_cfg["bbox_to_anchor"]})
return fig
|