小编Xau*_*ume的帖子

我正在尝试将我的 .Rmd 文档编织为 pdf。我尝试过安装tinytex和分离miktex分发，但都没有帮助。当我编译一个 .Rmd 文件时，我仍然得到：

"C:/Program Files/RStudio/bin/pandoc/pandoc" +RTS -K512m -RTS file-R-20200512.utf8.md --to beamer --from markdown+autolink_bare_uris+tex_math_single_backslash --output file-R-20200512.tex --highlight-style tango --pdf-engine pdflatex --self-contained 
This is pdfTeX, Version 3.14159265-2.6-1.40.21 (MiKTeX 2.9.7400 64-bit)
entering extended mode

Sorry, but C:\Users\my_username~1\MIKTEX~1.9\miktex\bin\x64\pdflatex.exe did not succeed.

The log file hopefully contains the information to get MiKTeX going again:

  C:\Users\my_username\AppData\Local\MiKTeX\2.9\miktex\log\pdflatex.log
I was unable to find any missing LaTeX packages from the error log file-R-20200512.log.
This is pdfTeX, Version 3.14159265-2.6-1.40.21 (MiKTeX 2.9.7400 64-bit)
entering …

我的数据框结构如下：

   group  maybe_start  maybe_end
0    ABC        False      False
1    ABC         True      False
2    ABC        False      False
3    ABC        False      False
4    ABC         True      False
5    ABC        False      False
6    ABC        False       True
7    ABC        False      False
8    DEF        False      False
9    DEF        False      False
10   DEF         True      False
11   DEF        False      False
12   DEF        False       True
13   DEF        False      False
14   DEF        False      False
15   DEF        False       True
16   DEF         True      False
17   DEF        False      False
18   DEF        False       True …

我有一个这样的 DataFrame：

import pandas as pd
df = pd.DataFrame.from_dict({'var1': {0: 0.0,
  1: 0.0,
  2: 0.0,
  3: 0.0,
  4: 0.0,
  6: 0.0,
  7: 0.0,
  8: 0.0,
  10: 0.0},
 'var2': {0: 0.0,
  1: 0.0,
  2: 0.0,
  3: 0.0,
  4: 0.0,
  6: 0.0,
  7: 0.0,
  8: 0.0,
  10: 0.0},
 'var3': {0: 0.0,
  1: 0.0,
  2: 0.0,
  3: 0.0,
  4: 0.0,
  6: 0.0,
  7: 0.0,
  8: 0.0,
  10: 0.0},
 'var4': {0: 0.0,
  1: 0.0,
  2: 0.0,
  3: 0.0,
  4: 0.0,
  6: 0.0,
  7: 0.0, …

我有一个结构如下example_df所示的数据集：

example_df = pd.DataFrame({'measurement_id': np.concatenate([[0] * 300, [1] * 300]),\n                           'min': np.concatenate([np.repeat(range(0, 30), 10), \n                                                  np.repeat(range(0, 30), 10)]),\n                           'grp': list(np.repeat(['A', 'B'], 5)) * 60,\n                           'grp2': list(np.random.choice([0, 1, 2], 10)) * 60,\n                           'obj': np.array(list(range(0, 10)) * 60),\n                           'x': np.random.normal(0.0, 10.0, 600),\n                           'y': np.random.normal(50.0, 40.0, 600)})\n

我还有一个函数，它将点组列表作为输入并执行一些计算。我想准备数据并在分组数据框中创建点列表列表。

我目前的解决方案如下：

def df_to_points(df):\n    points = []\n    for index, row in df.iterrows():\n        points.append(tuple(row))\n    return(points)\n\nres = example_df \\\n    .groupby(['measurement_id', 'min', 'grp']) \\\n    .apply(lambda x: [df_to_points(g[['x', 'y']]) for _, g in x.groupby('grp2')])\n\nres.head(5)\nmeasurement_id …

我有一个这样的数据框：

structure(list(x = c(65.11, 65.11, 65.11, 43.72, 43.72, 43.72, 
43.72, 43.72, 43.72, 43.72, 43.72, 59.89, 59.89, 59.89, 59.89, 
36.24, 36.24, 36.24, 36.24, 67.88, 37.89, 37.89, 37.89, 56.05, 
56.05, 56.05, 60.16, 60.16, 60.16, 30.92, 30.92, 30.92, 47.55, 
47.55, 47.55), y = c(32.17, 32.17, 32.17, 56.09, 56.09, 56.09, 
56.09, 56.09, 56.09, 56.09, 56.09, 15.64, 15.64, 15.64, 15.64, 
81.61, 81.61, 81.61, 81.61, 56.96, 21.69, 21.69, 21.69, 86.47, 
86.47, 86.47, 68.31, 68.31, 68.31, 51.56, 51.56, 51.56, 43.44, 
43.44, 43.44), xend = c(59.89, 60.16, 43.72, …

我尝试根据文档设置 XGBoost sklearn APIXGBClassifier以使用自定义目标函数 ( brier)：

    .. note::  Custom objective function

        A custom objective function can be provided for the ``objective``
        parameter. In this case, it should have the signature
        ``objective(y_true, y_pred) -> grad, hess``:

        y_true: array_like of shape [n_samples]
            The target values
        y_pred: array_like of shape [n_samples]
            The predicted values

        grad: array_like of shape [n_samples]
            The value of the gradient for each sample point.
        hess: array_like of shape [n_samples]
            The value of the second derivative for …

我想准备一个子图，其中每个方面都是一个变量相对于其他变量的单独的双 y 轴图。因此，我制作了一个基本图p并在循环中添加辅助 y 轴变量：

library(rlang)
library(plotly)
library(tibble)

dual_axis_lines <- function(data, x, y_left, ..., facets = FALSE, axes = NULL){
  x <- rlang::enquo(x)
  y_left <- rlang::enquo(y_left)
  y_right <- rlang::enquos(...)
  
  y_left_axparms <- list(
    title = FALSE,
    tickfont = list(color = "#1f77b4"),
    side = "left")
  y_right_axparms <- list(
    title = FALSE,
    overlaying = "y",
    side = "right",
    zeroline = FALSE)
  
  p <- plotly::plot_ly(data , x = x) %>%
    plotly::add_trace(y = y_left, name = quo_name(y_left),
                      yaxis = "y1", type = 'scatter', …

有一个lm对象，我需要根据其表示为字符向量的变量创建一个函数。我尝试结合使用eval和expr创建一个f功能，该功能将进一步用于后者obj并对其进行nlm优化。

library(tidyverse)
df <- drop_na(airquality)
model <- lm(Ozone~. - Temp, data = df, x=TRUE, y=TRUE)
base_vars <- all.vars(formula(model)[-2])
k <- length(base_vars)

f <- function(base_df, x, y, parms) {
  with(base_df, parms[1] + 
         eval(expr(paste(paste(paste0('parms[', 2:(k+1), ']'), base_vars, sep = '*'), collapse = '+'))) + 
         log(parms[k+2] * (x - parms[k+3] ^ 2)))
}
obj <- function(parms, y, x) mean((residuals(model) - f(df, x, y, parms))^2) 
fit <- with(data, nlm(obj, c(0, 0, 0, …