def parallel_base() -> Parallel:
rL = []
with open("iris.data", "r") as f:
rL = f.read().split("\n")
nameSet, data = set(), {}
for i in range(len(rL) - 1):
tmp = rL[i].split(",")
if tmp[-1] not in nameSet:
nameSet.add(tmp[-1])
data[tmp[-1]] = []
data[tmp[-1]].append(list(map(float, tmp[:-1])))
c = (Parallel(init_opts=opts.InitOpts(theme=ThemeType.CHALK)).add_schema([
{
"dim": 0,
"name": "calyx length"
},
{
"dim": 1,
"name": "calyx width"
},
{
"dim": 2,
"name": "petal length"
},
{
"dim": 3,
"name": "petal width"
},
]).set_global_opts(title_opts=opts.TitleOpts(title="鸢尾花数据集")))
for k in data.keys():
c.add(k, data[k])
return c
def parallel_base() -> Parallel:
data = [
[1, 91, 45, 125, 0.82, 34],
[2, 65, 27, 78, 0.86, 45],
[3, 83, 60, 84, 1.09, 73],
[4, 109, 81, 121, 1.28, 68],
[5, 106, 77, 114, 1.07, 55],
[6, 109, 81, 121, 1.28, 68],
[7, 106, 77, 114, 1.07, 55],
[8, 89, 65, 78, 0.86, 51, 26],
[9, 53, 33, 47, 0.64, 50, 17],
[10, 80, 55, 80, 1.01, 75, 24],
[11, 117, 81, 124, 1.03, 45],
]
c = (
Parallel()
.add_schema(
[
{"dim": 0, "name": "data"},
{"dim": 1, "name": "AQI"},
{"dim": 2, "name": "PM2.5"},
{"dim": 3, "name": "PM10"},
{"dim": 4, "name": "CO"},
{"dim": 5, "name": "NO2"},
]
)
.add("parallel", data)
.set_global_opts(title_opts=opts.TitleOpts(title="Parallel-基本示例"))
)
return c
def parallel_category() -> Parallel:
data = [
[1, 91, 45, 125, 0.82, 34, 23, "良"],
[2, 65, 27, 78, 0.86, 45, 29, "良"],
[3, 83, 60, 84, 1.09, 73, 27, "良"],
[4, 109, 81, 121, 1.28, 68, 51, "轻度污染"],
[5, 106, 77, 114, 1.07, 55, 51, "轻度污染"],
[6, 109, 81, 121, 1.28, 68, 51, "轻度污染"],
[7, 106, 77, 114, 1.07, 55, 51, "轻度污染"],
[8, 89, 65, 78, 0.86, 51, 26, "良"],
[9, 53, 33, 47, 0.64, 50, 17, "良"],
[10, 80, 55, 80, 1.01, 75, 24, "良"],
[11, 117, 81, 124, 1.03, 45, 24, "轻度污染"],
[12, 99, 71, 142, 1.1, 62, 42, "良"],
[13, 95, 69, 130, 1.28, 74, 50, "良"],
[14, 116, 87, 131, 1.47, 84, 40, "轻度污染"],
]
c = (Parallel().add_schema([
opts.ParallelAxisOpts(dim=0, name="data"),
opts.ParallelAxisOpts(dim=1, name="AQI"),
opts.ParallelAxisOpts(dim=2, name="PM2.5"),
opts.ParallelAxisOpts(dim=3, name="PM10"),
opts.ParallelAxisOpts(dim=4, name="CO"),
opts.ParallelAxisOpts(dim=5, name="NO2"),
opts.ParallelAxisOpts(dim=6, name="CO2"),
opts.ParallelAxisOpts(
dim=7,
name="等级",
type_="category",
data=["优", "良", "轻度污染", "中度污染", "重度污染", "严重污染"],
),
]).add("parallel", data).set_global_opts(title_opts=opts.TitleOpts(
title="Parallel-Category")))
return c
def parallel_base(dataSet) -> Parallel:
schema = [{
"dim": i - 1,
"name": dataSet[0][i]
} for i in range(1, len(dataSet[0]))]
DS, data = dataSet[1:], {0: [], 1: []}
for ds in DS:
data[ds[0]].append(list(map(float, ds[1:])))
c = (
Parallel(init_opts=opts.InitOpts(width="4444px", height="666px"))
#下面是暗黑主题
#Parallel(init_opts=opts.InitOpts(width = "3000px", height = "800px", theme=ThemeType.CHALK))
.add_schema(schema).set_global_opts(
title_opts=opts.TitleOpts(title="学生体侧数据"),
legend_opts=opts.LegendOpts(is_show=True),
toolbox_opts=opts.ToolboxOpts(),
))
for k in data.keys():
c.add(str(k), data[k])
return c
def test_parallel_base(fake_writer):
data = [
[1, 91, 45, 125, 0.82, 34],
[2, 65, 27, 78, 0.86, 45],
[3, 83, 60, 84, 1.09, 73],
[4, 109, 81, 121, 1.28, 68],
[5, 106, 77, 114, 1.07, 55],
[6, 109, 81, 121, 1.28, 68],
]
c = (Parallel().add_schema([
{
"dim": 0,
"name": "data"
},
{
"dim": 1,
"name": "AQI"
},
{
"dim": 2,
"name": "PM2.5"
},
{
"dim": 3,
"name": "PM10"
},
{
"dim": 4,
"name": "CO"
},
{
"dim": 5,
"name": "NO2"
},
]).add("parallel", data))
c.render()
_, content = fake_writer.call_args[0]
eq_(c.theme, "white")
eq_(c.renderer, "canvas")
def test_parallel_base():
data = [
[1, 91, 45, 125, 0.82, 34],
[2, 65, 27, 78, 0.86, 45],
[3, 83, 60, 84, 1.09, 73],
[4, 109, 81, 121, 1.28, 68],
[5, 106, 77, 114, 1.07, 55],
[6, 109, 81, 121, 1.28, 68],
]
c = (Parallel().add_schema([
{
"dim": 0,
"name": "data"
},
{
"dim": 1,
"name": "AQI"
},
{
"dim": 2,
"name": "PM2.5"
},
{
"dim": 3,
"name": "PM10"
},
{
"dim": 4,
"name": "CO"
},
{
"dim": 5,
"name": "NO2"
},
]).add("parallel", data))
assert c.theme == "white"
assert c.renderer == "canvas"
c.render("render.html")
def parallel_base(dataSet) -> Parallel:
nameSet, data = set(), {}
for ds in dataSet:
if ds[-1] not in nameSet:
nameSet.add(ds[-1])
data[ds[-1]] = []
data[ds[-1]].append(list(map(float, ds[:-1])))
c = (
Parallel(init_opts=opts.InitOpts(theme=ThemeType.CHALK))
.add_schema(
[
{"dim": 0, "name": "calyx length"},
{"dim": 1, "name": "calyx width"},
{"dim": 2, "name": "petal length"},
{"dim": 3, "name": "petal width"},
]
)
.set_global_opts(title_opts=opts.TitleOpts(title="鸢尾花数据集"))
)
for k in data.keys():
c.add(k, data[k])
return c
(Parallel().add_schema([
{
"dim": 0,
"name": "排名"
},
{
"dim": 1,
"name": "教育质量"
},
{
"dim": 2,
"name": "校友就业"
},
{
"dim": 3,
"name": "教师质量"
},
{
"dim": 4,
"name": "影响"
},
{
"dim": 5,
"name": "引文"
},
{
"dim": 6,
"name": "广泛影响"
},
]).add("parallel", data).set_global_opts(title_opts=opts.TitleOpts(
title="2017cwur前10大学具体得分排名")).render("2017cwur前10大学具体得分排名.html"))
{
"dim": 2,
"name": "C"
},
{
"dim": 3,
"name": "Score",
"type": "category",
"data": ["Excellent", "Good", "OK", "Bad"],
},
]
parallel_data = [[12.99, 100, 82, "Good"], [9.99, 80, 77, "OK"],
[20, 120, 60, "Excellent"]]
# init_opts=opts.InitOpts(width="800px",height="300px")
parallel = Parallel(init_opts=opts.InitOpts(width="800px", height="300px"))
parallel.add_schema(parallel_axis)
parallel.add(series_name="Related Indexes",
data=parallel_data,
linestyle_opts=opts.LineStyleOpts(width=4, opacity=0.5))
liquid = Liquid(init_opts=opts.InitOpts(width="300px", height="300px"))
liquid.add("liquid", [0.6, 0.7, 0.3])
liquid.set_global_opts(title_opts=opts.TitleOpts(title="Basic Example"))
heatmap_data = [[i, j, random.randint(0, 50)] for i in range(7)
for j in range(7)]
heatmap = HeatMap()
heatmap.add_xaxis(Faker.week)
heatmap.add_yaxis(
"相关表",
[11, 117, 81, 124, 1.03, 45, 24, "轻度污染"],
[12, 99, 71, 142, 1.1, 62, 42, "良"],
[13, 95, 69, 130, 1.28, 74, 50, "良"],
[14, 116, 87, 131, 1.47, 84, 40, "轻度污染"],
]
c = (Parallel().add_schema([
opts.ParallelAxisOpts(dim=0, name="data"),
opts.ParallelAxisOpts(dim=1, name="AQI"),
opts.ParallelAxisOpts(dim=2, name="PM2.5"),
opts.ParallelAxisOpts(dim=3, name="PM10"),
opts.ParallelAxisOpts(dim=4, name="CO"),
opts.ParallelAxisOpts(dim=5, name="NO2"),
opts.ParallelAxisOpts(dim=6, name="CO2"),
opts.ParallelAxisOpts(
dim=7,
name="等级",
type_="category",
data=["优", "良", "轻度污染", "中度污染", "重度污染", "严重污染"],
),
]).add(
"parallel",
data,
).set_global_opts(title_opts=opts.TitleOpts(
title="Parallel-Category")).render("parallel_category.html"))
# Section3 极坐标系
# 爱心图
import math
from pyecharts.charts import Polar
data = []
parallel_axis = [
{
"dim": 0,
"name": "Price"
},
{
"dim": 1,
"name": "Net Weight"
},
{
"dim": 2,
"name": "Amount"
},
{
"dim": 3,
"name": "Score",
"type": "category",
"data": ["Excellent", "Good", "OK", "Bad"],
},
]
data = [[12.99, 100, 82, "Good"], [9.99, 80, 77, "OK"],
[20, 120, 60, "Excellent"]]
(Parallel(init_opts=opts.InitOpts(width="1400px", height="800px")).add_schema(
schema=parallel_axis).add(
series_name="",
data=data,
linestyle_opts=opts.LineStyleOpts(width=4, opacity=0.5),
).render("Basic_parallel.html"))
schema = [
opts.ParallelAxisOpts(dim=0, name="data"),
opts.ParallelAxisOpts(dim=1, name="AQI"),
opts.ParallelAxisOpts(dim=2, name="PM2.5"),
opts.ParallelAxisOpts(dim=3, name="PM10"),
opts.ParallelAxisOpts(dim=4, name="CO"),
opts.ParallelAxisOpts(dim=5, name="NO2"),
opts.ParallelAxisOpts(dim=6, name="CO2"),
opts.ParallelAxisOpts(
dim=7,
name="等级",
type_="category",
data=["优", "良", "轻度污染", "中度污染", "重度污染", "严重污染"],
),
]
parallel = Parallel()
# 添加坐标轴和数据
parallel.add_schema(schema=schema).add("", data)
parallel.render_notebook()
# %% [markdown]
# ### Radar -- 雷达图
radar = Radar()
radar.add_schema(schema=[
opts.RadarIndicatorItem(name=_k, max_=200) for _k in list("ABCDFG")
])
radar.add("Expectation", [Faker.values()]).add("Reality", [Faker.values()])
radar.render_notebook()
# %% [markdown]
parallel_axis = [
{
"dim": 0,
"name": "Price"
},
{
"dim": 1,
"name": "Net Weight"
},
{
"dim": 2,
"name": "Amount"
},
{
"dim": 3,
"name": "Score",
"type": "category",
"data": ["Excellent", "Good", "OK", "Bad"],
},
]
data = [[12.99, 100, 82, "Good"], [9.99, 80, 77, "OK"],
[20, 120, 60, "Excellent"]]
(Parallel().add_schema(schema=parallel_axis).add(
series_name="",
data=data,
linestyle_opts=opts.LineStyleOpts(width=4, opacity=0.5),
).render("basic_parallel.html"))
"dim": 0,
"name": "Price"
},
{
"dim": 1,
"name": "Net Weight"
},
{
"dim": 2,
"name": "Amount"
},
{
"dim": 3,
"name": "Score",
"type": "category",
"data": ["Excellent", "Good", "OK", "Bad"],
},
]
data = [[12.99, 100, 82, "Good"], [9.99, 80, 77, "OK"],
[20, 120, 60, "Excellent"]]
c = (Parallel().add_schema(schema=parallel_axis).add(
series_name="",
data=data,
linestyle_opts=opts.LineStyleOpts(width=4, opacity=0.5),
))
c.width = "100%"
put_html(c.render_notebook())
def find_value_render(query_filter, x_label, x, result, Data, query,
table_path, answer):
colorList = ['#f36c6c', '#e6cf4e', '#20d180', '#0093ff']
# x = ['GDP', 'Industry', 'Architecture', 'Service']
# y = [990865, 317109, 70904, 534233]
# x1 = ["周一"]
# y1 = [11]
Y = []
# for i in range(len(y)):
# if i==0:
# Y.append(
# opts.BarItem(
# name=x[i],
# value=round(y[i], 2),
# label_opts=opts.LabelOpts(position="insideTop"),
# itemstyle_opts={
# "normal": {
# "color": colorList[0],
# "barBorderRadius": [30, 30, 30, 30],
# }
# }
# ))
# else:
# Y.append(
# opts.BarItem(
# name=x[i],
# value=round(y[i], 2),
# label_opts=opts.LabelOpts(position="insideTop"),
# itemstyle_opts={
# "normal": {
# "color": "blue",
# "barBorderRadius": [30, 30, 30, 30],
# }
# }
# ))
bar1 = Bar()
bar1.add_xaxis(x)
bar1.add_yaxis(result[0][0],
y_axis=result[0][1],
label_opts=opts.LabelOpts(position="insideTop"))
bar1.set_global_opts(
yaxis_opts=opts.AxisOpts(is_show=False),
xaxis_opts=opts.AxisOpts(
axisline_opts=opts.AxisLineOpts(is_show=False),
axispointer_opts=opts.AxisPointerOpts(is_show=False),
axistick_opts=opts.AxisTickOpts(is_show=False)),
title_opts=opts.TitleOpts(subtitle="When the search condition is " +
query_filter,
pos_left='center'),
graphic_opts=[
opts.GraphicText(
graphic_item=opts.GraphicItem(
left="center",
top="bottom",
z=100,
),
graphic_textstyle_opts=opts.GraphicTextStyleOpts(
# 可以通过jsCode添加js代码，也可以直接用字符串
text=[
'\n' + "Q:" + ' ' + query + '\n' + "\n" + 'A:' + ' ' +
answer
],
font="14px Microsoft YaHei",
graphic_basicstyle_opts=opts.GraphicBasicStyleOpts(
fill="#333")))
])
bar2 = Bar()
bar2.add_xaxis(x)
for i in range(len(result)):
bar2.add_yaxis(result[i][0], y_axis=result[i][1])
bar2.set_global_opts(
yaxis_opts=opts.AxisOpts(is_show=False),
xaxis_opts=opts.AxisOpts(
axisline_opts=opts.AxisLineOpts(is_show=False),
axispointer_opts=opts.AxisPointerOpts(is_show=False),
axistick_opts=opts.AxisTickOpts(is_show=False)),
graphic_opts=[
opts.GraphicText(
graphic_item=opts.GraphicItem(
left="center",
top="bottom",
z=100,
),
graphic_textstyle_opts=opts.GraphicTextStyleOpts(
# 可以通过jsCode添加js代码，也可以直接用字符串
text=[
'\n' + "\n" + "Q:" + ' ' + query + '\n' + "\n" + 'A:' +
' ' + answer
],
font="16px Microsoft YaHei",
graphic_basicstyle_opts=opts.GraphicBasicStyleOpts(
fill="#333")))
])
Label = []
data = []
for i in range(len(Data)):
Label.append(str(Data[i][0]))
data.append(Data[i][1])
data = list(map(list, zip(*data)))
parallel = Parallel()
parallel_axis = []
for i in range(len(Label)):
parallel_axis.append({"dim": i, "name": Label[i]})
parallel.add_schema(schema=parallel_axis)
parallel.add("", data, is_smooth=True)
parallel.set_global_opts(graphic_opts=[
opts.GraphicText(
graphic_item=opts.GraphicItem(
left="center",
top="bottom",
z=100,
),
graphic_textstyle_opts=opts.GraphicTextStyleOpts(
# 可以通过jsCode添加js代码，也可以直接用字符串
text=['\n' + "Q:" + ' ' + query + '\n' + 'A:' + ' ' + answer],
font="14px Microsoft YaHei",
graphic_basicstyle_opts=opts.GraphicBasicStyleOpts(
fill="#333")))
])
grid0 = Grid(init_opts=opts.InitOpts(
width="100%",
height="100%",
renderer=globals.RenderType.SVG,
))
grid0.add(bar1, grid_opts={'left': '20%', 'bottom': '34%'})
option0 = grid0.dump_options_with_quotes()
option0 = json.loads(option0)
grid = Grid(init_opts=opts.InitOpts(
width="100%",
height="100%",
renderer=globals.RenderType.SVG,
))
grid.add(bar2, grid_opts={'left': '15%', 'bottom': '50%'})
option1 = grid.dump_options_with_quotes()
option1 = json.loads(option1)
grid1 = Grid(init_opts=opts.InitOpts(
width="100%",
height="100%",
renderer=globals.RenderType.SVG,
))
grid1.add(parallel, grid_opts={'left': '30%', 'bottom': '34%'})
option2 = grid1.dump_options_with_quotes()
option2 = json.loads(option2)
option = {"option": [option1, option2], "query": query}
return option
def plot_hyperparams(self, destination='notebook', output='hyperparams.html'):
"""Plot hyperparams in a parallel line chart
Args:
destination: one of notebook, html
output: the html file path
Returns:
"""
try:
import pyecharts
except Exception as e:
raise Exception("You may not install 'pyecharts',"
"please refer to https://github.com/pyecharts/pyecharts and install it.")
import pyecharts.options as opts
from pyecharts.charts import Parallel
if destination == 'notebook' and not isnotebook():
raise Exception("You may not running in a notebook,"
" try to set 'destination' to 'html' or run it in notebook ")
if self.trials is None or len(self.trials) < 1:
raise Exception("Trials is empty ")
REWARD_METRIC_COL = 'Reward metric'
def get_space_params(trial):
space = trial.space_sample
params_dict = {}
for hyper_param in space.get_all_params():
references = list(hyper_param.references)
if len(references) > 0:
param_name = hyper_param.alias[len(list(hyper_param.references)[0].name) + 1:]
param_value = hyper_param.value
if isinstance(param_value, int) or isinstance(param_value, float):
if not isinstance(param_value, bool):
params_dict[param_name] = param_value
params_dict[REWARD_METRIC_COL] = trial.reward
return params_dict
def make_dims(df_params):
parallel_axis = []
for i, col in enumerate(df_params.columns):
if df_params.dtypes[col].kind == 'O':
parallel_axis.append({
"dim": i,
"name": col,
"type": "category",
"data": df_params[col].unique().tolist(),
})
else:
parallel_axis.append({'dim': i, 'name': col})
return parallel_axis
trials_params = [get_space_params(trial) for trial in self.trials]
param_names = list(set([v for ps in trials_params for v in ps.keys()]))
param_names.remove(REWARD_METRIC_COL)
param_names.insert(len(param_names), REWARD_METRIC_COL)
trial_params_values = []
for t in trials_params:
param_values = [t.get(n) for n in param_names]
trial_params_values.append(param_values)
df_train_params = pd.DataFrame(data=trial_params_values, columns=param_names)
# remove if all is None
df_train_params.dropna(axis=1, how='all', inplace=True)
parallel_axis = make_dims(df_train_params)
chart = \
Parallel(init_opts=opts.InitOpts(width="%dpx" % (len(param_names) * 100), height="400px")) \
.add_schema(schema=parallel_axis).add(series_name="",
data=df_train_params.values.tolist(),
linestyle_opts=opts.LineStyleOpts(width=1, opacity=0.5),
).set_global_opts(
visualmap_opts=[
opts.VisualMapOpts(
type_="color",
is_calculable=True,
precision=2,
# dimension=0,
pos_left="-10",
pos_bottom="30",
max_=df_train_params[REWARD_METRIC_COL].max().tolist(),
min_=df_train_params[REWARD_METRIC_COL].min().tolist()
)
]
)
if destination == 'notebook':
return chart.render_notebook()
else:
return chart.render(output)