Strapping

Strapping is a library containing a fast implementation of bootstrapping sampling algorithm. Along the sampling algorithms you will find a set of helper functions used to compute basic statistics useful in bootstrapping-based analysis.

Library supports:

• single variable sampling
• multi-column variable sampling
• A/B test difference sampling

Installing

Strapping can be installed via pip from PyPI.

pip install strapping

Testing

Tu run tests for the package use tox:

tox

Example

Sample single variable

In this example we will use a bootstrapping algorithm to sample a distribution of mean and std. deviation of the given dataset.

Sample means using bootstrapping

Import bootstrap and stats module.

• bootstrap contains bootstrapping algorithms,
• stats contains helpers for computing basic statistics (e.g. confidence intervals).

from strapping import bootstrap, stats

Generate sample data using normal distribution:

X = np.random.normal(0, 1, size=100).reshape(-1, 1)

Sample a vector containing possible means for given dataset:

mu_sampled = bootstrap.sample(X, iterations=1000, aggrfunc=np.mean)
std_sampled = bootstrap.sample(X, iterations=1000, aggrfunc=np.std)

We can check output values:

>>> np.mean(mu_sampled), np.mean(std_sampled)
(-0.028259915654785906, 1.0099170040429664)

Compute confidence intervals

Now we will compute confidence intervals based on sampled values. This works for both single values and multi-column variables. By default, confidence interval will three values: (5th quantile, mean, 95th quantile).

q05, mean, q95 = stats.confidence_intervals(mu_sampled)

We can check output values:

>>> q05
array([-0.15844911])

>>> mean
array([-0.01509199])

>>> q95
array([0.12659994])

Sample multi-column variables

In this example we will test using bootstrapping for data containing multiple columns.

Generate data containing multiple columns:

X = np.array([
    np.random.normal(0, 1, size=100),
    np.random.normal(10, 5, size=100),
    np.random.normal(-20, 5, size=100),
]).T

Import bootstrap module:

from strapping import bootstrap 

Sample mean for given dataset:

mu_sampled = bootstrap.sample(X, iterations=1000, aggrfunc=np.mean)

We can check output values:

>>> mu_sampled.mean(axis=0)
array([ -0.06588892,   9.97571153, -19.187514  ])

A/B test difference between two variables

In this example we will test using bootstrapping to sample a difference between two given datasets. Then, we will use sampled values to compute percentage confidence intervals for the difference.

Sample means using bootstrapping

Generate data containing multiple columns:

X1 = np.random.normal(5, 2, size=100).reshape(-1, 1)
X2 = np.random.normal(6, 2, size=100).reshape(-1, 1)

Import bootstrap and stats modules:

from strapping import bootstrap, stats 

Sample mean for given dataset:

mu_sampled = bootstrap.sample_diffs(X1, X2, iterations=1000, aggrfunc=np.mean)

We can check output values:

>>> mu_sampled.mean()
-1.2875678613575356

Compute confidence intervals

Now we will compute both confidence intervals and percentage confidence intervals based on sampled values.

>>> stats.confidence_intervals(mu_sampled)
(array([-1.77019123]), array([-1.28756786]), array([-0.79820009]))

Percentage confidence intervals are computed as a percentage difference between sampled values and the mean value of a provided reference (control dataset).

>>> stats.percentage_confidence_intervals(mu_sampled, X1.mean())
(array([-0.36300107]), array([-0.26403278]), array([-0.16368146]))

Other

Compute Cohen's d

Using strapping you can easily compute bootstrapped value of Cohen's d, which is often used for a metric of measuring the effect size.

To do so first compute the difference between two datasets:

diff_sampled = bootstrap.sample_diffs(X1, X2, iterations=1000, aggrfunc=np.mean)

Then, compute the pooled standard deviation using a helper function and finally compute Cohen's d value:

from strapping.stats import pooled_std
pstd = pooled_std(X1, X2)

cohensd = diff_sampled / pstd