def __init__(self,
embeddings, # embeddings to be visualized
# shape (n, dim)
domains, # name of the domain for each embedding
# shape (n,)
labels, # name of the label/class for each embedding
# shape (n,)
method='tsne', # dimensionality reduction method
# pca and tsne are supported
markers=['.', '*', 'X', 'd'], # markers to scatter different domains
# first N_DOMAINS markers are used
# where N_DOMAINS is the number of different domains
# more markers available at
# https://matplotlib.org/3.1.1/api/markers_api.html
colors=['black', # colors to scatter different labels
'brown', # first N_LABELS colors are used
'red', # first N_LABELS colors are used
'yellow', # where N_LABELS is the number of different labels
'green', # https://matplotlib.org/3.1.1/api/colors_api.html
'lightseagreen',
'aqua',
'dodgerblue',
'darkblue',
'blue',
'orchid',
'hotpink'],
use_css_colors=False,
**kwargs # arguments to be passed into T-SNE/PCA constructor
# for example you n_jobs for T-SNE
):
embeddings = np.asarray(embeddings)
assert embeddings.ndim == 2, 'wrong embeddings shape'
assert method in ['tsne', 'pca'], 'method should be one of: "tsne", "pca"'
if method == 'tsne':
self.embeddings_transformed = TSNE(**kwargs).fit_transform(embeddings)
elif method == 'pca':
self.embeddings_transformed = PCA(**kwargs).fit_transform(embeddings)
self.domains = np.asarray(domains)
assert domains.ndim == 1 and domains.shape[0] == embeddings.shape[0], 'wrong domains shape'
self.labels = np.asarray(labels)
assert labels.ndim == 1 and labels.shape[0] == embeddings.shape[0], 'wrong labels shape'
assert len(markers) >= len(np.unique(domains)), 'not enough markers for domains'
self.markers = markers
self.colors = colors
if use_css_colors:
self.colors = list(CSS4_COLORS.keys())
assert len(self.colors) >= len(np.unique(labels)), 'not enough colors for labels'
def add_each_chaps(g, chapter, captions):
c = mcolors.keys()[chapter * 6]
for i in range(len(captions)):
g.add_node(str(chapter + 1) + '.' + str(i + 1),
caption=captions[i],
color=c)
import math
import numpy as np
import random
from typing import Union
from operator import itemgetter
# from PIL import Image
from matplotlib.colors import CSS4_COLORS
import matplotlib.pyplot as plt
import matplotlib.patches as patches
from .preprocess import Image
RANDOM_COLORS = list((CSS4_COLORS.keys()))
random.shuffle(RANDOM_COLORS)
def filter_predict(detection: dict, name: list):
if not name:
return detection
filtered_detection = dict()
filtered_list = [
i for i, d in enumerate(detection["detection_classes"]) if d["name"] in name
]
if not filtered_list:
return filtered_detection
for key in detection.keys():
if not hasattr(detection[key], "__getitem__"):
filtered_detection[key] = detection[key]
else:
def barplot(df: pd.DataFrame,
bar_width: float = 0.3,
space_width: float = 0.3,
height: float = None,
dpi: int = 200,
min_std: float = 0,
min_value: float = None,
max_value: float = None,
show_legend: bool = True,
show_title: str = True,
legend_position: str = "best",
data_label: str = None,
title: str = None,
path: str = None,
colors: Dict[str, str] = None,
alphas: Dict[str, float] = None,
facecolors: Dict[str, str] = None,
orientation: str = "vertical",
subplots: bool = False,
plots_per_row: Union[int, str] = "auto",
minor_rotation: float = 0,
major_rotation: float = 0,
unique_minor_labels: bool = False,
unique_major_labels: bool = True,
unique_data_label: bool = True,
auto_normalize_metrics: bool = True,
placeholder: bool = False,
scale: str = "linear",
custom_defaults: Dict[str, List[str]] = None,
sort_subplots: Callable[[List], List] = None,
sort_bars: Callable[[pd.DataFrame], pd.DataFrame] = None,
letter: str = None) -> Tuple[Figure, Axes]:
"""Plot barplot corresponding to given dataframe, containing y value and optionally std.
Parameters
----------
df: pd.DataFrame,
Dataframe from which to extrat data for plotting barplot.
bar_width: float = 0.3,
Width of the bar of the barplot.
height: float = None,
Height of the barplot. By default golden ratio of the width.
dpi: int = 200,
DPI for plotting the barplots.
min_std: float = 0.001,
Minimum standard deviation for showing error bars.
min_value: float = None,
Minimum value for the barplot.
max_value: float = 0,
Maximum value for the barplot.
show_legend: bool = True,
Whetever to show or not the legend.
If legend is hidden, the bar ticks are shown alternatively.
show_title: str = True,
Whetever to show or not the barplot title.
legend_position: str = "best",
Legend position, by default "best".
data_label: str = None,
Barplot's data_label.
Use None for not showing any data_label (default).
title: str = None,
Barplot's title.
Use None for not showing any title (default).
path: str = None,
Path where to save the barplot.
Use None for not saving it (default).
colors: Dict[str, str] = None,
Dict of colors to be used for innermost index of dataframe.
By default None, using the default color tableau from matplotlib.
alphas: Dict[str, float] = None,
Dict of alphas to be used for innermost index of dataframe.
By default None, using the default alpha.
orientation: str = "vertical",
Orientation of the bars.
Can either be "vertical" of "horizontal".
subplots: bool = False,
Whetever to slit the top indexing layer to multiple subplots.
plots_per_row: Union[int, str] = "auto",
If subplots is True, specifies the number of plots for row.
If "auto" is used, for vertical the default is 2 plots per row,
while for horizontal the default is 4 plots per row.
minor_rotation: float = 0,
Rotation for the minor ticks of the bars.
major_rotation: float = 0,
Rotation for the major ticks of the bars.
unique_minor_labels: bool = False,
Avoid replicating minor labels on the same axis in multiple subplots settings.
unique_major_labels: bool = True,
Avoid replicating major labels on the same axis in multiple subplots settings.
unique_data_label: bool = True,
Avoid replication of data axis label when using subplots.
auto_normalize_metrics: bool = True,
Whetever to apply or not automatic normalization
to the metrics that are recognized to be between
zero and one. For example AUROC, AUPRC or accuracy.
placeholder: bool = False,
Whetever to add a text on top of the barplots to show
the word "placeholder". Useful when generating placeholder data.
scale: str = "linear",
Scale to use for the barplots.
Can either be "linear" or "log".
custom_defaults: Dict[str, List[str]],
Dictionary to normalize labels.
letter: str = None,
Letter to show on the top left of the figure.
This is sometimes necessary on papers.
By default it is None, that is no letter to be shown.
Raises
------
ValueError:
If the given orientation is nor "vertical" nor "horizontal".
ValueError:
If the given plots_per_row is nor "auto" or a positive integer.
ValueError:
If subplots is True and less than a single index level is provided.
Returns
-------
Tuple containing Figure and Axes of created barplot.
"""
if orientation not in ("vertical", "horizontal"):
raise ValueError(
"Given orientation \"{orientation}\" is not supported.".format(
orientation=orientation))
if not isinstance(plots_per_row,
int) and plots_per_row != "auto" or isinstance(
plots_per_row, int) and plots_per_row < 1:
raise ValueError(
"Given plots_per_row \"{plots_per_row}\" is not 'auto' or a positive integer."
.format(plots_per_row=plots_per_row))
vertical = orientation == "vertical"
levels = get_levels(df)
expected_levels = len(levels) - int(show_legend) - int(subplots)
if len(levels) <= 1 and subplots:
raise ValueError(
"Unable to split plots with only a single index level.")
if plots_per_row == "auto":
if subplots:
plots_per_row = min(len(levels[0]), 2 if vertical else 4)
else:
plots_per_row = min(plots_per_row, len(levels[0]))
if colors is None:
colors = dict(
zip(levels[-1],
list(TABLEAU_COLORS.keys()) + list(CSS4_COLORS.keys())))
if alphas is None:
alphas = dict(zip(levels[-1], (0.9, ) * len(levels[-1])))
if facecolors is None:
facecolors = dict(zip(levels[0], ("white", ) * len(levels[0])))
sorted_level = levels[0]
if sort_subplots is not None:
sorted_level = sort_subplots(sorted_level)
if subplots:
titles = sorted_level
else:
titles = ("", )
figure, axes = get_axes(df, bar_width, space_width, height, dpi, title,
data_label, vertical, subplots, titles,
plots_per_row, custom_defaults, expected_levels,
scale, facecolors, show_title)
for i, (index, ax) in enumerate(zip(titles, axes)):
if subplots:
sub_df = df.loc[index]
else:
sub_df = df
if sort_bars is not None:
sub_df = sort_bars(sub_df)
plot_bars(ax,
sub_df,
bar_width,
space_width,
alphas,
colors,
index,
vertical=vertical,
min_std=min_std)
is_not_first_ax = subplots and (
(not vertical and i % plots_per_row) or
(vertical and i < len(axes) - plots_per_row))
is_not_first_vertical_ax = subplots and (
(vertical and i % plots_per_row) or
(not vertical and i < len(axes) - plots_per_row))
plot_bar_labels(ax, figure, sub_df, vertical, expected_levels,
bar_width, space_width, minor_rotation, major_rotation,
unique_minor_labels and is_not_first_ax,
unique_major_labels and is_not_first_ax,
unique_data_label and is_not_first_vertical_ax,
custom_defaults)
if show_legend:
remove_duplicated_legend_labels(ax, legend_position,
custom_defaults)
max_lenght, min_lenght = get_max_bar_lenght(sub_df, bar_width,
space_width)
max_lenght *= 1.01
min_lenght *= 1.01
min_lenght = min(min_lenght, 0)
if min_value is not None:
min_lenght = min_value
if auto_normalize_metrics and (is_normalized_metric(df.columns[0])
or is_normalized_metric(title)):
max_lenght = max(max_lenght, 1.01)
if max_value is not None:
max_lenght = max_value
if placeholder:
ax.text(0.5,
0.5,
"PLACEHOLDER",
fontsize=30,
alpha=0.75,
color="red",
rotation=8,
horizontalalignment='center',
verticalalignment='center',
transform=ax.transAxes)
if vertical:
ax.set_ylim(min_lenght, max_lenght)
else:
ax.set_xlim(min_lenght, max_lenght)
if letter:
figure.text(0.01,
0.9,
letter,
horizontalalignment='center',
verticalalignment='center',
weight='bold',
fontsize=15)
figure.tight_layout()
if path is not None:
save_picture(path, figure)
return figure, axes