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]
# ### ThemeRiver -- 流量图
themeriver = ThemeRiver()
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)