小编Har*_*riS的帖子

我想获得稍后在笔记本中使用的最佳模型，以使用不同的测试批次进行预测。

可重现的示例（取自 Optuna Github）：

import lightgbm as lgb
import numpy as np
import sklearn.datasets
import sklearn.metrics
from sklearn.model_selection import train_test_split

import optuna


# FYI: Objective functions can take additional arguments
# (https://optuna.readthedocs.io/en/stable/faq.html#objective-func-additional-args).
def objective(trial):
    data, target = sklearn.datasets.load_breast_cancer(return_X_y=True)
    train_x, valid_x, train_y, valid_y = train_test_split(data, target, test_size=0.25)
    dtrain = lgb.Dataset(train_x, label=train_y)
    dvalid = lgb.Dataset(valid_x, label=valid_y)

    param = {
        "objective": "binary",
        "metric": "auc",
        "verbosity": -1,
        "boosting_type": "gbdt",
        "lambda_l1": trial.suggest_loguniform("lambda_l1", 1e-8, 10.0),
        "lambda_l2": trial.suggest_loguniform("lambda_l2", 1e-8, 10.0),
        "num_leaves": trial.suggest_int("num_leaves", 2, 256),
        "feature_fraction": trial.suggest_uniform("feature_fraction", 0.4, …

我想更改图例中项目的名称。下面是一个可重现的例子。

import plotly.express as px
df = px.data.iris()
colorsIdx = {'setosa': '#c9cba3', 'versicolor': '#ffe1a8',
             'virginica': '#e26d5c'}
cols      = df['species'].map(colorsIdx)

fig = px.scatter_3d(df, x='sepal_length', y='sepal_width', z='petal_width',
              color=cols)
fig.show()

因为我将自己的颜色分配给了我想重命名图例的物种，所以它不会显示为“#c9cba3”、“#ffe1a8”和“#e26d5c”。相反，我想成为“setosa”“versicolor”和“virginica”

我想在同一个图上用不同的颜色绘制多个预测，但是比例尺不正确。我对任何其他方法持开放态度。

可重现的例子：

require(forecast)     

# MAKING DATA
data <- c(3.86000,  19.55810,  19.51091,  20.74048,  20.71333,  29.04191,  30.28864, 25.64300,  23.33368,  23.70870 , 26.16600  ,27.61286 , 27.88409 , 28.41400 , 24.81957 , 24.60952,  27.49857,  32.08000 , 29.98000, 27.49000 , 237.26150, 266.35478, 338.30000, 377.69476, 528.65905, 780.00000  )
a.ts <- ts(data,start=c(2005,1),frequency=12)

# FORECASTS
arima011_css =stats::arima(x = a.ts, order = c(0, 1, 1), method = "CSS") # css estimate
arima011_forecast = forecast(arima011_css, h=10, level=c(99.5))

arima321_css =stats::arima(x = a.ts, order = c(3, 2, 1), method = "CSS") # …

我想在两行之间填充红色，红色的 alpha 值根据列表而变化。本质上是红色的自定义渐变，但 Alpha 使红色显得更暗或更亮。

可重现的例子：

import matplotlib.pyplot as plt

fig, ax = plt.subplots()
ax.plot(range(20))
y = [8,9,12,14] # respective y values
a = [0.3,0.1,0.5,0.7]
ax.axhspan(8, 14, alpha=0.5, color='red')

plt.show()

这可能有助于确定将 RGB 转换为 RGBA 时所需的颜色。也许有一种方法可以使用 axhspan 在两条线之间映射这些颜色？

from matplotlib.colors import to_rgb
r, g, b = to_rgb('red')
alpha_arr = a
c = [(r, g, b, alpha) for alpha in alpha_arr]