tmasjc

library(tidyverse)
library(tidymodels)

sf_trees <- read_csv("https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2020/2020-01-28/sf_trees.csv")

trees_df <- sf_trees %>%
    mutate(
        legal_status = case_when(
            legal_status == "DPW Maintained" ~ legal_status,
            TRUE ~ "Other"

tmasjc

Primary Approaches to Local Interpretation

source: https://bradleyboehmke.github.io/HOML/iml.html

Local Interpretable Model-Agnostic Explanations

Assumptions:

• Every model is linear on a local scale;
• It is possible to fit a simple surrogate model around a single observation that will mimic how the global model behaves at the locality;

tmasjc

# %%
import configparser
import mysql.connector
from mysql.connector import Error
import pandas as pd
from openpyxl import load_workbook
from openpyxl.utils.dataframe import dataframe_to_rows

config = configparser.ConfigParser()
config.read("config.ini")

tmasjc

# %%
import configparser
import pandas as pd
import smtplib
from socket import gaierror
from email import encoders
from email.mime.base import MIMEBase
from email.mime.multipart import MIMEMultipart
from email.mime.text import MIMEText

tmasjc

library(tidyverse)
library(rlang)
set.seed(123)

df <- tibble(id = rep(c(1:2), 10),
             grade = sample(c("A", "B", "C"), 20, replace = TRUE))

df %>% 
    group_by(id) %>% 
    summarise(

tmasjc

library(tidyverse)
library(janitor)
library(EnvStats)
library(infer)
library(ggthemes)
set.seed(123)
old = theme_set(theme_minimal(base_family = "Noto Sans CJK SC") +
                    theme(legend.position = "top"))

# DID ----

tmasjc

library(feather)
library(dplyr)
library(purrr)
library(furrr)
library(uaparserjs)

raw <- feather::read_feather("raw.feather") %>% 
    `[[`('ua') %>% 
    purrr::discard(is.na) %>% 
    unique()

tmasjc

library(tidyverse)
library(ggthemes)
library(lubridate)
library(gganimate)

# ggplot theme
old <- theme_set(
    theme_minimal() +
        theme(
            text = element_text(family = 'Menlo'),

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set.seed(1234)

vec   = c(13.1, 7.5, 4.4, 10.0, 7.8, 3.4, 3.5) / 100
rb    = EnvStats::ebeta(vec)
alpha = rb$parameters['shape1'] 
beta  = rb$parameters['shape2']

x <- rbeta(1000, shape1 = alpha, shape2 = beta)
hist(
    x,

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cuts = 0.8 # cutoff level
maxN = 100 # number of trials
    
run_game <- function(vec, cutoff, N) {
    # browser()
    if (length(vec) > N) {
        return(vec)
    }
    pass = ifelse(mean(vec) >= cutoff, 0, 1)
    vec  = c(vec, pass)