GarrettMooney

"""View remote jobs in a 3 day interval."""

BASE_URL = "https://www.linkedin.com/jobs/search/"

def get_linkedin_jobs(search, n_days=3, remote=True):
    # time
    seconds_in_day = 86400
    n_seconds = n_days * seconds_in_day
    query = BASE_URL + "?f_TPR=r" + str(n_seconds)

GarrettMooney

from typing import Union

import numpy as np
import pandas as pd

def create_sample_weights(
    y_train: np.ndarray, 
    X_train: Union[np.ndarray, pd.DataFrame]
) -> pd.Series:
    y_series = pd.DataFrame({"y": y_train})["y"]

GarrettMooney

from numpy import log, exp
from scipy.special import betaln as logbeta


def probability_B_beats_A(α_A, β_A, α_B, β_B):
    total = 0.0
    for i in range(α_B):
        total += exp(
            logbeta(α_A + i, β_B + β_A)
            - log(β_B + i)

GarrettMooney

%pip install "whatlies[sentence_tfm]"  # quotes for my fellow zsh users

import numpy as np
from whatlies.language import SentenceTFMLanguage
from sklearn.pipeline import Pipeline
from sklearn.linear_model import LogisticRegression

pipe = Pipeline([
 ("embed", SentenceTFMLanguage('distilbert-base-nli-stsb-mean-tokens')),

GarrettMooney

# venv
# usage:
#   $ venv .recsys
function venv {
    default_envdir=".env"
    envdir=${1:-$default_envdir}

    if [ ! -d $envdir ]; then
        python3.7 -m virtualenv -p python3.7 $envdir
        echo -e "\x1b[38;5;2m✔ Created virtualenv $envdir\x1b[0m"

GarrettMooney

#!/bin/bash
flag=$1
if [[ $flag == 'DL' ]]; then
	# -------------
	# Deep Learning
	# -------------
	echo "Configuring GPU for Deep Learning..."
	sudo apt-get install -y libnvidia-common-440
	sudo apt-get install -y cuda
	sudo apt-get install -y cuda-drivers

GarrettMooney

#pragma once

#include <algorithm>
#include <functional>
#include <future>
#include <thread>
#include <vector>

// adapted from https://stackoverflow.com/a/49188371/2055486

GarrettMooney

library(ggplot2)
theme_set(theme_classic())

# Visualizing entropy ---------------------------------------------------------
entropy <- function(p) -sum(p * log(p))
n <- 1e6
p <- runif(n)
q <- 1 - p
z <- matrix(c(p, q), ncol = 2)
ent <- apply(z, 1, entropy)

GarrettMooney

li(ggplot2)
theme_set(theme_classic())

entropy <- function(p) -sum(p * log(p))
n <- 1e6
p <- runif(n)
q <- 1 - p
z <- matrix(c(p, q), ncol = 2)
ent <- apply(z, 1, entropy)
qplot(q, ent, geom = "line") +

GarrettMooney

li(ggplot2)
theme_set(theme_classic())

kld <- function(p, q) sum(p * log(p / q))
n <- 1e6
p <- c(0.6, 0.4)
q1 <- runif(n)
q2 <- 1 - q1
z <- matrix(c(q1, q2), ncol = 2)
kl <- apply(z, 1, kld, p = p)