Document Bootstrapping Examples¶

When estimating machine learning or statistical models on your corpus, you may need to bootstrap documents (randomly sample with replacement). The .bootstrap() method of DocTable will act like a select statement but return a bootstrap object instead of a direct query result. Here I show how to do some basic bootstrapping using an example doctable.

In [1]:
import random
import pandas as pd
import numpy as np
import sys
sys.path.append('..')
import doctable as dt


Create Example DocTable¶

First we define a DocTable that will be used for examples.

In [2]:
schema = (
('integer','id',dict(primary_key=True, autoincrement=True)),
('string','name', dict(nullable=False, unique=True)),
('integer','age'),
('boolean', 'is_old'),
)
db = dt.DocTable(target=':memory:', schema=schema)
print(db)

<DocTable::sqlite:///:memory::_documents_ ct: 0>


Then we add several example rows to the doctable.

In [3]:
for i in range(10):
age = random.random() # number in [0,1]
is_old = age > 0.5
row = {'name':'user_'+str(i), 'age':age, 'is_old':is_old}
db.insert(row, ifnotunique='replace')

for doc in db.select(limit=3):
print(doc)

(1, 'user_0', 0.16086747483303065, False)
(2, 'user_1', 0.14322051505126332, False)
(3, 'user_2', 0.22664393988892395, False)


Create a Bootstrap¶

We can use the doctable method .bootstrap() to return a bootstrap object using the keyword argument n to set the sample size (will use number of docs by default). This method acts like a select query, so we can specify columns and use the where argument to choose columns and rows to be bootstrapped. The bootsrap object contains the rows in the .doc property.

Notice that while our select statement drew three documens, the sample size specified with n is 5. The boostrap object will always return 5 objects, even though the number of docs stays the same.

In [4]:
bs = db.bootstrap(['name','age'], where=db['id'] % 3 == 0, n=4)
print(type(bs))
print(len(bs.docs))
bs.n

<class 'doctable.bootstrap.DocBootstrap'>
3

Out[4]:
4

Use the bootstrap object as an iterator to access the bootstrapped docs. The bootstrap object draws a sample upon instantiation, so the same sample is maintained until reset.

In [5]:
print('first run:')
for doc in bs:
print(doc)
print('second run:')
for doc in bs:
print(doc)

first run:
('user_5', 0.6473182290263347)
('user_2', 0.22664393988892395)
('user_2', 0.22664393988892395)
('user_5', 0.6473182290263347)
second run:
('user_5', 0.6473182290263347)
('user_2', 0.22664393988892395)
('user_2', 0.22664393988892395)
('user_5', 0.6473182290263347)


Draw New Sample¶

You can reset the internal sample of the bootstrap object using the .set_new_sample() method. See that we now sample 2 docs and the output is different from previous runs. The sample will still remain the same each time we iterate until we reset the sample.

In [6]:
bs.set_new_sample(2)
print('first run:')
for doc in bs:
print(doc)
print('second run:')
for doc in bs:
print(doc)

first run:
('user_5', 0.6473182290263347)
('user_8', 0.5270190808172914)
second run:
('user_5', 0.6473182290263347)
('user_8', 0.5270190808172914)


And we can iterate through a new sample using .new_sample(). Equivalent to calling .set_new_sample() and then iterating through elements.

In [7]:
print('drawing new sample:')
for doc in bs.new_sample(3):
print(doc)
print('repeating sample:')
for doc in bs:
print(doc)

drawing new sample:
('user_5', 0.6473182290263347)
('user_5', 0.6473182290263347)
('user_8', 0.5270190808172914)
repeating sample:
('user_5', 0.6473182290263347)
('user_5', 0.6473182290263347)
('user_8', 0.5270190808172914)


I may add additional functionality in the future if I use this in any projects, but that's it for now.