tomquisel

import { extrapolateFromXdr } from "./xdr";

jest.unmock("stellar-sdk");

const deleteOfferXdr =
  "AAAAAAAAAGQAAAAAAAAAAQAAAAAAAAADAAAAAAAAAAAAAAACAAAAAA==";
const createOfferXdr =
  "AAAAAAAAAGQAAAAAAAAAAQAAAAAAAAADAAAAAAAAAAAAAAAAAAAAAAVvtc2vNsygpxOUKCQKkmSJSQItaJ0LxMot3/9ups4/AAAAAAK1OXcAAAAAAAAAAVhDTgAAAAAAm466+JY4VR3PnqT3QyBxEGuHqw4ts9abdaU4InL3WdgAAAAAAOThwAAAABQAAAADAAAAAAAAAAAAAAAA";

describe("extrapolateFromXdr", () => {

tomquisel

// adapted from: https://github.com/stellar/laboratory/blob/master/src/utilities/extrapolateFromXdr.js
import * as _ from "lodash";
import { xdr, StrKey, Keypair, Operation } from "stellar-sdk";

/**
 * Convert XDR string to minimal JSON object
 * @param input input xdr string
 * @param type type of XDR (TransactionResult, TransactionMeta, etc...)
 */
export function extrapolateFromXdr(input: string, type: string) {

tomquisel

class VotingClassifier(object):
    """Stripped-down version of VotingClassifier that uses prefit estimators"""
    def __init__(self, estimators, voting='hard', weights=None):
        self.estimators = [e[1] for e in estimators]
        self.named_estimators = dict(estimators)
        self.voting = voting
        self.weights = weights

    def fit(self, X, y, sample_weight=None):
        raise NotImplementedError

tomquisel

def do_R_wilcoxon(before, after, paired=True, two_tailed=False):
    b_mat = before.as_matrix()
    a_mat = after.as_matrix()
    alternative = 'g'
    if two_tailed:
        alternative = 'two.sided'
    %R -i b_mat,a_mat,paired,alternative -o wilcox_res wilcox_res=wilcox.test(b_mat, a_mat, paired=paired, exact=TRUE, conf.int=TRUE, alternative=alternative)
    #print list(enumerate(wilcox_res))
    # this returns just the p-value and the conf interval
    return {

tomquisel

Keybase proof

I hereby claim:

• I am tomquisel on github.
• I am tom (https://keybase.io/tom) on keybase.
• I have a public key whose fingerprint is 5772 C3AE 84D2 175D 3C2C DE71 1BA1 B957 7D34 77A2

To claim this, I am signing this object:

tomquisel

df.to_hdf('/home/tom/data/behaviorXoutcomes/user_features.h5', 'data', complevel=1, complib='zlib')

tomquisel

from pyspark.sql import SQLContext
sqlContext = SQLContext(sc)
df = sqlContext.read.format("org.elasticsearch.spark.sql").load("index/type")
df.printSchema()

tomquisel

How to direct logs to a file using a logger tag

ls /etc/rsyslog.d/

20-ufw.conf  21-cloudinit.conf  50-default.conf  60-etl.conf  postfix.conf

cat /etc/rsyslog.d/60-etl.conf

:syslogtag, isequal, "claims_etl:" /var/log/etl/claims.log

tomquisel

%reload_ext rpy2.ipython
%R -n library(dplyr)

%%R
do_regression = function(mpr, features) {
    df = read.csv("activity_quantity_df.csv", header=T)
    df$mem_gender = as.factor(df$mem_gender)
    df$mem_age = as.factor(df$mem_age)
    feature_str = paste(features, collapse=" + ")
    print(paste(mpr, "~", feature_str, " + mem_gender*mem_age"))

tomquisel

#!/usr/bin/env python
import sys, os, re, random
import time, datetime
from collections import defaultdict
import pandas as pd
import numpy as np
from argparse import ArgumentParser

def main():
    args = handle_args()