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# Gavin Leech g-leech

Last active Jul 22, 2021
View effect_sizes.py
 import math # assumes bivariate normal, dichotomised groups def dichotomy_r_to_d(r) : d = 2*r / (math.sqrt(1 - r**2)) return d # Equation 9 # https://sci-hub.tw/10.1037/1082-989X.11.4.386 def r_to_d(r, n1, n2) :
Created Jul 9, 2021
View match_data_extrapolation.py
 # You can get your own copy of the MCMC trace: https://github.com/g-leech/masks_v_mandates#run # but I've included the quantiles in this script for reproducibility. import numpy as np sns.set_style("whitegrid") def exp_reduction(a, x): reductions = 1 - np.exp((-1.0) * a * x) return reductions.mean()
Created May 31, 2020
View reported_vs_excess_death.py
 #!/usr/bin/env python # coding: utf-8 # ## Excess COVID-19 mortality vs reported deaths over time # # split bar? # # y-axis: deaths # x-axis: time #
Created May 22, 2020
View pyswip_helpers.py
 def handle_utterance_str(text) : if text[0] != "'" and text[0] != '"' : text = f'"{text}"' text = text.replace('"', '\"') text = text.replace("'", '\"') return "handle_utterance(1,{},Output)".format(text) def escape_and_call_prolexa(text) : libPrefix = "prolexa:"
Last active May 20, 2020
NLP helpers
View utils.py
 #%tensorflow_version 2.x import pandas as pd import numpy as np import re from nltk import word_tokenize from nltk.stem import WordNetLemmatizer from scipy.sparse import hstack from sklearn.feature_extraction.text import CountVectorizer, TfidfVectorizer
Created May 8, 2020
Checking the coactivation of school and uni closures
View school-uni-coactivation.py
 import pandas as pd # From https://www.notion.so/977d5e5be0434bf996704ec361ad621d?v=fe54f89ca9e04ac799af42b39e1efc4b path = "COVID 19 Containment measures data.csv" df = pd.read_csv(path) withoutUS = df[~df["Country"].str.contains("US:")] withoutUS = withoutUS[~withoutUS["Country"].str.contains("United States")] numCountries = withoutUS.Country.unique().shape[0]
Created Oct 21, 2019
View fixed_rbf.py
 from scipy.spatial.distance import cdist import numpy as np import matplotlib.pyplot as plt # First write a covariance function. e.g. rbf def radial_basis_kernel(x1, x2, varSigma, lengthScale): if x2 is None: d = cdist(x1, x1) else: d = cdist(x1, x2)
Created Aug 20, 2019
Kelly bound on house insurance premium
View kelly_house.py
 import numpy as np probability = 1/10000 wealth = 120000 houseValue = 100000 bound = probability * np.log(wealth - houseValue) \ + (1 - probability) * np.log(wealth) maxPremium = wealth - np.exp(bound)
Created Sep 7, 2018
View open_university_degrees.py
 # https://help.open.ac.uk/documents/policies/working-out-your-class-of-honours/files/50/honours-class-working-out.pdf # Only 2nd and 3rd year courses count. # Course, credits, grade, year courses = [ ["M343", 30, "Distinction", 3], ["M249", 30, "Distinction", 2], ["M248", 30, "Distinction", 2], ["MST210", 60, "Grade 2 Pass", 2] ]
Last active Jul 22, 2018