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import numpy as np
import numpy.linalg as la
import pandas as pd
import numpy.random as rn
import matplotlib.pyplot as plt
import matplotlib

def calc_coverage(var, est, val):
    ci_l = est - 1.96 * np.sqrt(var)
    ci_r = est + 1.96 * np.sqrt(var)

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def calc_ndcg(a, b, num_rel):
    ranking_len = len(a)
    len_diff = int(num_rel - a.sum())
    a = np.r_[a, np.ones(len_diff)]
    b = np.r_[b, (b.min() - 1) * np.ones(len_diff)]
    if (a.mean() < 1) and (a.mean() > 0): return ndcg_score([a], [b], k=ranking_len)
    else: return np.nan
    
def calc_auc(a, b, num_rel, population_size):
    assert a.sum() <= num_rel

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import numpy as np
from sklearn.metrics import roc_auc_score

y_true = np.r_[np.ones(10000), np.zeros(10000)]
alpha = 0.5
beta = 0.2
y_known = np.r_[(np.random.rand(10000) < alpha).astype(np.float),
                (np.random.rand(10000) < beta).astype(np.float)]
# rankings = [np.random.permutation(2000) for i in range(100)]
rankings = [-np.arange(20000) + 100 *np.random.randn(20000) * i for i in range(100)]

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from collections import defaultdict

import numpy as np
import numpy.linalg as la
import pandas as pd
import numpy.random as rn
import matplotlib.pyplot as plt


def hmean(a, b):

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# in ~/.ipython/profile_default/startup/startup.py
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from itertools import *
from collections import *
from operator import itemgetter

# in ~/.ipython/profile_default/python_config.py
c = get_config()

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def locs_available_here(d, h, cur):
    if cur[0] < d - 1: yield (cur[0] + 1, cur[1])
    if cur[1] < h - 1: yield (cur[0], cur[1] + 1)

def new_paths(d, h, path_so_far, to_loc):
    end_loc = (d - 1, h - 1)
    if to_loc == end_loc:
        yield path_so_far + [end_loc]
    else:
        for new_loc in locs_available_here(d, h, to_loc):

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Fair Ranking Extentions

ideas

• Two sided marketplace extension
  • Recommendation system with fairness to suppliers with inverse-popularity score (like in our presentation)
• Feldman or Celis et al. methods of enforcing fairness constraints
  • Show that problem is exponential in number of protected attributes
  • Use approximation by being fair for each attribute and then performing rank aggregation

notes

• Cant have a ranking that has all the following properties (Arrow 1950):
  • Non-dictatorship

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Three.JS Cubes

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{
  "branchID": 38467,
  "branchLogo": null,
  "branchName": null,
  "ccrAgentID": 337853,
  "ccrEmail": "dibarra@xceligent.com",
  "ccrFirstName": "Dustin",
  "ccrLastName": "Ibarra",
  "ccrPhone": "(816) 303-2868",
  "csrAgentID": 223349,

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{
  "addressList": [
    {
      "address1": "4520 Main St",
      "address2": "Suite 1000",
      "addressID": null,
      "addressType": null,
      "city": "Kansas City",
      "country": "United States",
      "isActive": true,