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library(rdrop2)

## This script uses both local storage and dropbox to store files. When saving, the file is saved in both locations.
## When loading, the file is first looked for locally, and if not found, is looked for on dropbox.
## Could be used in services such as shinyapps in which you don't have access to persistent storage on the machine.
## Note that you have to authenticate your dropbox API before using rdrop2's methods.

## Global param to set dropbox interface on/off
USE_DROPBOX <<- T

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import spacy
en_core_web_lg = spacy.load("en_core_web_lg")

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name = "Simon"
doc=en_core_web_trf(f"My name is {name}")
print(f"Name = {name}. Detected entities: {doc.ents}")

name = "Katy"
doc=en_core_web_trf(f"My name is {name}")
print(f"Name = {name}. Detected entities: {doc.ents}")

name = "Moses"
doc=en_core_web_trf(f"This is what God said to {name}")

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import pprint
import spacy

nlp = spacy.load("en_core_web_trf")
detailed_results = {}
print("Model name: en_core_web_trf")
for template, name_set in itertools.product(templates, name_sets.items()):
    print(f"Name set: {name_set[0]}, Template: \"{template}\"")
    results = names_recall(nlp, name_set[1], template)
    detailed_results[template, name_set[0]] = results

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def names_recall(nlp: spacy.lang.en.English, names: List[str], template: str):
    """
    Run the spaCy NLP model on the template + name, 
    calculate recall for detecting the "PERSON" entity 
    and return a detailed list of detection
    :param nlp: spaCy nlp model
    :param names: list of names to run model on
    :param template: sentence with placeholder for name (e.g. "He calls himself {}")
    """
    results = {}

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import pprint

detailed_results = {}
nlp = en_core_web_lg

print("Model name: en_core_web_lg")
for template, name_set in itertools.product(templates, name_sets.items()):
    print(f"Name set: {name_set[0]}, Template: \"{template}\"")
    results = names_recall(nlp, name_set[1], template)
    detailed_results[template, name_set[0]] = results

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# Databricks notebook source
# install sparklyr (we need this every time we start our cluster as it has to install packages on all workers)
install.packages("sparklyr")

#titanic data
install.packages('titanic')
library(titanic)
# Load sparklyr package.
library(sparklyr)

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biblical_names = ["David", "Moses", "Abraham", "Samuel", "Jacob", 
                  "Isaac", "Jesus", "Matthew", 
                  "John", "Judas","Simon", "Mary"] # Random biblical names

other_names = ["Beyonce", "Ariana", "Katy", # Singers
               "Lebron", "Coby", # NBA players
               "William", "Charles","Robert", "Margaret","Frank", "Helen", # Popular (non biblical) names in 1900 (https://www.ssa.gov/oact/babynames/decades/names1900s.html)
               "Ronald", "George", "Bill", "Barack", "Donald", "Joe" # Presidents
               ]

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from time import time

class TimeTook(object):
    """
    Calculates the time a block took to run.
    Example usage:
    with TimeTook("sample"):
        s = [x for x in range(10000000)]
    Modified from: https://blog.usejournal.com/how-to-create-your-own-timing-context-manager-in-python-a0e944b48cf8
    """