pmbaumgartner

from sklearn.base import BaseEstimator, ClassifierMixin
from scipy.special import expit, logit


class SoftLabelClassifier(BaseEstimator, ClassifierMixin):
    def __init__(self, regressor, eps=0.001):
        self.regressor = regressor
        self.eps = eps

    def fit(self, X, y=None):

pmbaumgartner

#cloud-config
package_upgrade: true

ssh_authorized_keys:
  - <your key>

packages:
  - apt-transport-https
  - ca-certificates
  - curl

pmbaumgartner

Search Contents of Word Documents from the Terminal

You'll need ripgrep and pandoc to get started. You can read more about ripgrep here and pandoc here. I use both of these frequently and they're quite helpful.

You can install them both with homebrew:

brew install pandoc ripgrep

pmbaumgartner

import numpy as np
import numba


@numba.njit()
def tsss(vec1, vec2):
    euclidean_distance = np.linalg.norm(vec1 - vec2)
    cosine_distance = np.dot(vec1, vec2.T) / (
        np.linalg.norm(vec1) * np.linalg.norm(vec2)
    )

pmbaumgartner

Packing Conda Environments

You must be using conda for this approach. You will need conda installed on the Source machine and the Target machine. The Source machine must have an internet connection, the Target does not. The OS in both environments must match; no going from macOS to Win10 for example.

1. (Source) Install conda-pack in your base python environment.

conda install -c conda-forge conda-pack

pmbaumgartner

# ⛔️ BAD EXAMPLE: PRE-REFACTOR
## app.py
import streamlit as st
import pandas as pd

data = pd.read_csv("data.csv")  # no function → no cache, requires pandas import: 👎,👎
sample = data.head(100)  # not input into streamlit object: 👎
described_sample = sample.describe()  # input into streamlit object: ✅
st.write(described_sample)

pmbaumgartner

from time import time

import altair as alt
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import streamlit as st


def mpl_scatter(dataset, x, y):

pmbaumgartner

import altair as alt
import streamlit as st
from vega_datasets import data

cars = data.cars()

quantitative_variables = [
    "Miles_per_Gallon",
    "Cylinders",
    "Displacement",

pmbaumgartner

import streamlit as st
from vega_datasets import data
from time import time
import pandas as pd


@st.cache
def load_data():
    return pd.concat((data.airports() for _ in range(100)))

pmbaumgartner

import streamlit as st
from vega_datasets import data


@st.cache
def load_data():
    return data.birdstrikes()


cols = {