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from sklearn.metrics import (
    average_precision_score, 
    f1_score, 
    roc_auc_score, 
    precision_score, 
    recall_score, 
    accuracy_score
)

def evaluate_bi_cls(y_true, y_pred):

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---
title: "Untitled"
output: html_document
---

```{r setup, include=FALSE}
knitr::opts_chunk$set(message = FALSE)
```

## R Markdown

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Goal

Install working tensorflow or pytorch via standard conda environment workflow.

Basic Setup : Install pytorch in a fresh conda environment

The recommended conda-based install process works smoothly:

$ # Create a fresh environment

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#' Generate fixed width file in R
#' @description This simple function creates fixed width file with no 
#' extra dependencies. 
#' @param justify "l", "r" or something like "lrl" for left, right, left. 
#' @examples dt <- data.frame(a = 1:3, b = NA, c = c('a', 'b', 'c'))
#' write_fwf(dt, "test.txt", width = c(4, 4, 3))
#' @export
write_fwf = function(dt, file, width, 
                     justify = "l", replace_na = "NA") {
  fct_col = which(sapply(dt, is.factor))

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---
title: "Untitled"
author: "Hao"
date: "7/10/2019"
output: pdf_document
---

```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```

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/*!
 * Bootstrap v3.3.7 (http://getbootstrap.com)
 * Copyright 2011-2018 Twitter, Inc.
 * Licensed under MIT (https://github.com/twbs/bootstrap/blob/master/LICENSE)
 */


/*!
 * Generated using the Bootstrap Customizer (<none>)
 * Config saved to config.json and <none>

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library(broom)
library(tidyverse)
library(viridis)


fit <- lm(mpg ~ ., data = mtcars)

fit_dt <- tidy(fit, conf.int = T) 

# reverse the order so it looks right on plot

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library(shiny)
library(imager)
library(colocr)

# Define UI for application that draws a histogram
ui <- navbarPage(
  title = 'colocr',
  tabPanel(
    'Main',
    sidebarLayout(