Shoeboxam

# Pseudocode of an OpenDP combinator for privatizing a certain class of vector-valued transformations

def make_some_transformation_stable(trans_query: Transformation, scale, threshold):
    assert trans_query.input_metric == SymmetricDistance
    assert trans_query.output_metric == L1Distance
    # must be equivalent to representation 2 in https://arxiv.org/pdf/1709.05396.pdf, Section 2.2.1
    assert trans_query.output_domain == HashMapDomain[AllDomain[KeyType], AllDomain[FloatCountType]]

    def function(data):
        noised = {k: v + sample_laplace(scale) for k, v in trans_query.invoke(data)}

Shoeboxam

import pandas as pd
import matplotlib.pyplot as plt
from dateutil import parser
plt.style.use("bmh")

data = pd.read_excel("/Users/michael/Downloads/MD 4 year.XLS", parse_dates=['Gift Date'])


def plot_cumulative_per_year(event_days, min_delta, max_delta):
    cumulative_sums = []

Shoeboxam

WhiteNoise Core
Differential Privacy Library Python Bindings

This repository contains python bindings to Whitenoise-Core and its underlying Rust binaries.

• For examples of this library in action, please see the Python notebooks in WhiteNoise-Samples.
• In addition, see the accompanying WhiteNoise-System repository which includes WhiteNoise tools for differential privacy.

---

Shoeboxam

import d3m
from d3m.metadata.pipeline import Pipeline, PrimitiveStep
from d3m.metadata.base import ArgumentType

from google.protobuf.json_format import MessageToJson
from ta3ta2_api import utils
import json

types = [
    utils.ValueType.RAW,

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import d3m
from d3m.metadata.pipeline import Pipeline, PrimitiveStep
from d3m.metadata.base import ArgumentType

from google.protobuf.json_format import MessageToJson
from ta3ta2_api import utils
import json

types = [
    utils.ValueType.RAW,

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{
  "hardManipulations": [],
  "problems": {
    "DA_college_debt_problem": {
      "description": "The task here is to predict the median debt-to-earnings ratio of colleges across the US. The supplied dataset consists of the College Scorecards dataset which make it easier for students to search for a college that is a good fit for them. They can use the College Scorecard to find out more about a college's affordability and value so they can make more informed decisions about which college to attend, including factors such as median debt-to-earning ratio of graduates. This is a regression problem.",
      "firstTarget": {
        "colIndex": 4,
        "colName": "DEBT_EARNINGS_RATIO",
        "resID": "0",
        "targetIndex": 0

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import Popper from 'popper.js';
import m from 'mithril';

class PopperWrapper {
    onupdate({state}) {
        if (state.popper) state.popper.update()
    }

    view(vnode) {
        let {attrs, children, state} = vnode;

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---
title: "Statistical/Machine Learning Mini-Project VI"
author: "Michael Shoemate"
date: "April 23, 2019"
output: pdf_document
---

# Section 1
```{R echo = F, results = 'hide', message=F, warning=F}
# installs each package if not exists, and attaches

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---
title: "Statistical/Machine Learning Mini-Project IV"
author: "Michael Shoemate"
date: "March 26, 2019"
output: pdf_document
---

# Section 1
```{R echo = F, results = 'hide', message=F, warning=F}
# installs each package if not exists, and attaches

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#!/bin/bash

# On Ubuntu, I installed Redis with apt. It autostarts and requires `/etc/init.d/redis-server stop` to kill
# disable autostart with this: `sudo update-rc.d redis-server disable`
# The config file referenced in `fab redis_run` doesn't exist; I just start redis explicitly

cd ~/TwoRavens/
. `which virtualenvwrapper.sh`
workon 2ravens
: $(fab celery_restart)