statinf 1.0.7

A library for statistics and causal inference

STATINF

statinf is a library for statistics and causal inference. It provides main the statistical models ranging from the traditional OLS to Neural Networks.

Regressions

OLS

statinf comes with the OLS regression implemented with analytic formula.

from statinf.regressions.LinearModels import OLS
from statinf.data.GenerateData import generate_dataset

# Generate a synthetic dataset
data = generate_dataset(coeffs=[1.2556, -6.465, 1.665414, 1.5444], n=1000, std_dev=1.6, intercept=.0)

# We set the OLS formula
formula = "Y ~ X0 + X1 + X2 + X3"
# We fit the OLS with the data, the formula and without intercept
ols = OLS(formula, df, fit_intercept=True)

ols.summary()

The output:

=========================================================================
                               OLS summary                               
=========================================================================
| R² = 0.99129                  | Adjusted-R² = 0.99126
| n  =   1000                   | p =     4
| Fisher = 37790.52477                         
=========================================================================
Variables  Coefficients  Standard Errors    t values  Probabilites
       X0      1.234759         0.020300   60.824688           0.0
       X1     -6.475338         0.009052 -715.327289           0.0
       X2      1.662661         0.020141   82.552778           0.0
       X3      1.519622         0.020319   74.787592           0.0

Deep Learning

Multi Layer Perceptron

You can train a Neural Network using the MLP class. The below example shows how to train an MLP with 1 single linear layer. It is equivalent to implement an OLS with Gradient Descent.

from statinf.data.GenerateData import generate_dataset
from statinf.ml.neuralnetwork import MLP, Layer

# Generate the synthetic dataset
data = generate_dataset(coeffs=[1.2556, -6.465, 1.665414, 1.5444], n=1000, std_dev=1.6, intercept=.0)

X = [c for c in data.columns if c not in ['Y']] # equivalent to['X0', 'X1', 'X2', 'X3']

# Initialize the network and its architecture
nn = MLP()
nn.add(Layer(4, 1, activation='linear'))

# Train the neural network
nn.train(data=data, X=X, Y='Y', epochs=1, learning_rate=0.001)

# Extract the network's weights
print(nn.get_weights())

Output:

{'weights 0': array([[ 1.32005564],
       [-6.38121934],
       [ 1.64515704],
       [ 1.48571785]]), 'bias 0': array([0.81190412])}