timothyrenner

import numpy as np

def predict(*features):
    """ Performs a prediction on the features.
    
        Parameters
        ----------
        features : List[float]
            The feature values the model needs to make a prediction.
           

timothyrenner

def average_agreement(list1, list2, max_depth):
 
    # Empty lists evaluate to false.
    if (not list1) or (not list2):
        return 0.0
     
    ### NEW CODE ###
    # Truncate the depth
    max_list_len = max(len(list1), len(list2))
    max_depth = min(max_depth, max_list_len)

timothyrenner

from hypothesis import given, settings  # <- NEW CODE
from hypothesis.strategies  import lists, integers
 
@given(
    list1=lists(integers(min_value=1)),
    list2=lists(integers(min_value=1)),
    depth=integers(min_value=1)
)
@settings(deadline=300)  # <- NEW CODE
def test_average_agreement_properties(list1, list2, depth):

timothyrenner

def average_agreement(list1, list2, max_depth):
     
    agreements = []
     
    for depth in range(1, max_depth+1):
        set1 = set(list1[:depth])
        set2 = set(list2[:depth])
         
        intersection = set1 & set2
         

timothyrenner

def average_agreement(list1, list2, max_depth):
 
    ### NEW CODE ###
    # Empty lists evaluate to false.
    if (not list1) or (not list2):
        return 0.0
 
    agreements = []
     
    for depth in range(1, max_depth+1):

timothyrenner

from hypothesis import given
from hypothesis.strategies  import lists, integers
 
@given(
    list1=lists(integers(min_value=1)),
    list2=lists(integers(min_value=1)),
    depth=integers(min_value=1)
)
def test_average_agreement_properties(list1, list2, depth):
     

timothyrenner

import aiohttp
import asyncio
import json

from toolz import get_in, assoc, concat

url = "http://localhost:9200"
index = "stocks"
doc_type = "stock"

timothyrenner

import pandas as pd
import matplotlib.pyplot as plt
import cartopy.crs as ccrs
import cartopy.feature as cfeature
import cartopy.io.shapereader as shpreader

from shapely.ops import cascaded_union

non_conus_states = {'VI', 'AK', 'HI', 'PR', 'GU', 'MP', 'AS'}

timothyrenner

(ns gorilla-to-markdown.core
    (:require [clojure.string :as str]
              [cheshire.core :as json]))

(defn- process-output-json
  "Builds the HTML from the output JSON.
   Assumes the JSON has already been parsed, and that j is a map."
  [j]
  (case (:type j)
        "html" (:content j)

timothyrenner

using DataFrames

keys = rand(1:100000, 500000);
values = randn(length(keys));

df = DataFrame();
df[:KEY] = keys;
df[:VALUE] = values;

@time by(df, :KEY, x -> sum(x[:VALUE]));