A library for statistics and causal inference
Project description
STATINF
1. Installation
You can get statinf from PyPI with:
pip install statinf
statinf
is a library for statistics and causal inference.
It provides main the statistical models ranging from the traditional OLS to Neural Networks.
The library is supported on Windows, Linux and MacOs.
2. Documentation
You can find the full documentation at https://www.florianfelice.com/statinf.
You can also find an FAQ and the latest news of the library on the documentation.
3. Available modules
Here is a nonexhaustive list of available modules on statinf
:

MLP
implements MultiLayer Perceptron (see MLP for more details and examples). 
OLS
allows to use Ordinary Least Squares for linear regressions (see OLS for more details and examples). 
GLM
implements the Generalized Linear Models see GLM for more details and examples). 
stats
allows to use descriptive and tests statistics. 
data
is a module to process data such as data generation, One Hot Encoding and others (see data processing or (see data generation modules for more details).
You can find the below examples and many more on https://www.florianfelice.com/statinf. Stay tuned with the future releases.
3.1. OLS
statinf
comes with the OLS regression implemented with the analytical formula:
from statinf.regressions import OLS
from statinf.data import generate_dataset
# Generate a synthetic dataset
data = generate_dataset(coeffs=[1.2556, 0.465, 1.665414, 2.5444, 7.56445], n=1000, std_dev=1.6)
# We set the OLS formula
formula = "Y ~ X0 + X1 + X2 + X3 + X4 + X1*X2 + exp(X2)"
# We fit the OLS with the data, the formula and without intercept
ols = OLS(formula, df, fit_intercept=True)
ols.summary()
The output will be:
==================================================================================
 OLS summary 
==================================================================================
 R² = 0.98475  R² Adj. = 0.98464 
 n = 999  p = 7 
 Fisher value = 10676.727  
==================================================================================
 Variables  Coefficients  Std. Errors  tvalues  Probabilities 
==================================================================================
 X0  1.3015  0.03079  42.273  0.0 *** 
 X1  0.48712  0.03123  15.597  0.0 *** 
 X2  1.62079  0.04223  38.377  0.0 *** 
 X3  2.55237  0.0326  78.284  0.0 *** 
 X4  7.54776  0.03247  232.435  0.0 *** 
 X1*X2  0.03626  0.02866  1.265  0.206 
 exp(X2)  0.00929  0.01551  0.599  0.549 
==================================================================================
 Significance codes: 0. < *** < 0.001 < ** < 0.01 < * < 0.05 < . < 0.1 < '' < 1 
3.2. GLM
The logistic regression can be used for binary classification where follows a Bernoulli distribution. With being the matrix of regressors, we have:
We then implement the regression with:
from statinf.regressions import GLM
from statinf.data import generate_dataset
# Generate a synthetic dataset
data = generate_dataset(coeffs=[1.2556, 6.465, 1.665414, 1.5444], n=2500, std_dev=10.5, binary=True)
# We split data into train/test/application
train = data.iloc[0:1000]
test = data.iloc[1001:2000]
# We set the linear formula for Xb
formula = "Y ~ X0 + X1 + X2 + X3"
logit = GLM(formula, train, test_set=test)
# Fit the model
logit.fit(plot=False, maxit=10)
logit.get_weights()
The ouput will be:
==================================================================================
 Logit summary 
==================================================================================
 McFadden R² = 0.67128  McFadden R² Adj. = 0.6424 
 LogLikelihood = 227.62  Null LogLikelihood = 692.45 
 LR test pvalue = 0.0  Covariance = nonrobust 
 n = 999  p = 5 
 Iterations = 8  Convergence = True 
==================================================================================
 Variables  Coefficients  Std. Errors  tvalues  Probabilities 
==================================================================================
 X0  1.13024  0.10888  10.381  0.0 *** 
 X1  0.02963  0.07992  0.371  0.711 
 X2  1.40968  0.1261  11.179  0.0 *** 
 X3  0.5253  0.08966  5.859  0.0 *** 
==================================================================================
 Significance codes: 0. < *** < 0.001 < ** < 0.01 < * < 0.05 < . < 0.1 < '' < 1 
==================================================================================
3.3. 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 import generate_dataset
from statinf.ml 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)
Y = ['Y']
X = [c for c in data.columns if c not in Y]
# 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])}
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