Example

Requirements

It is recommended make virtualenv and install all next packages in this virtualenv.

samplesizelib==0.0.1

Include packages.

import numpy as np

from samplesizelib.linear.statistical import LagrangeEstimator
from samplesizelib.linear.statistical import LikelihoodRatioEstimator
from samplesizelib.linear.statistical import WaldEstimator
from samplesizelib.linear.models import RegressionModel
from samplesizelib.linear.models LogisticModel

Preparing the dataset

Generate dataset for regression and classification tasks.

n = 10
m = 200

np.random.seed(0)
X_cl = np.random.randn(m, n)
y_cl = np.random.randint(2, size=m)

np.random.seed(0)
X_rg = np.random.randn(m, n)
y_rg = np.random.randn(m)

Statictical Metods

Regression task

Example of Lagrange based method:

model = LagrangeEstimator(RegressionModel)
ret = model(X_rg, y_rg)

print(ret['m*'])

Example of Likelihood Ratio based method:

model = LikelihoodRatioEstimator(RegressionModel)
ret = model(X_rg, y_rg)

print(ret['m*'])

Example of Wald based method:

model = WaldEstimator(RegressionModel)
ret = model(X_rg, y_rg)

print(ret['m*'])

Classification task

Example of Lagrange based method:

model = LagrangeEstimator(LogisticModel)
ret = model(X_cl, y_cl)

print(ret['m*'])

Example of Likelihood Ratio based method:

model = LikelihoodRatioEstimator(LogisticModel)
ret = model(X_cl, y_cl)

print(ret['m*'])

Example of Wald based method:

model = WaldEstimator(LogisticModel)
ret = model(X_cl, y_cl)

print(ret['m*'])