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Description:

pytorchmnl 0.0.1

A simple pytorch based MNL lib

Fit your Multinomial Logistic Regression with Pytorch

Install
pip install pytorch_mnl
How to use
import the lib
import pandas as pd
from pytorch_mnl.core import *

load data
data = pd.read_csv("./data/Iris.csv").drop("Id", axis=1)

choose x, y cols:
x_cols=['SepalLengthCm', 'SepalWidthCm', 'PetalLengthCm', 'PetalWidthCm']
target_col = 'Species'

the number of classes to predict:
n_targets = len(data[target_col].unique())
n_targets

3

X, y = prepare_data(data, x_cols=x_cols, target_col=target_col)

we get pytorch tensors ready to use!
type(X), type(y)

(torch.Tensor, torch.Tensor)

let's split in train/valid choosing a percenage as holdout, and choose a batch size to fit our model
dls = DataLoaders.from_Xy(X, y, pct=0.2, batch_size=8)

as our model has 4 variables, we will fit a 4 MNL, with 3 targets.
model = LinearMNL(len(x_cols), n_targets)

learn = Learner(dls, model)

learn.fit(25)

epoch = 0, val_loss = 2.072, accuracy = 0.53
epoch = 1, val_loss = 1.908, accuracy = 0.53
epoch = 2, val_loss = 1.770, accuracy = 0.80
epoch = 3, val_loss = 1.657, accuracy = 0.80
epoch = 4, val_loss = 1.564, accuracy = 0.80
epoch = 5, val_loss = 1.487, accuracy = 0.80
epoch = 6, val_loss = 1.422, accuracy = 0.80
epoch = 7, val_loss = 1.368, accuracy = 0.80
epoch = 8, val_loss = 1.321, accuracy = 0.80
epoch = 9, val_loss = 1.282, accuracy = 0.83
epoch = 10, val_loss = 1.247, accuracy = 0.83
epoch = 11, val_loss = 1.217, accuracy = 0.83
epoch = 12, val_loss = 1.190, accuracy = 0.83
epoch = 13, val_loss = 1.166, accuracy = 0.83
epoch = 14, val_loss = 1.144, accuracy = 0.87
epoch = 15, val_loss = 1.125, accuracy = 0.87
epoch = 16, val_loss = 1.107, accuracy = 0.90
epoch = 17, val_loss = 1.091, accuracy = 0.90
epoch = 18, val_loss = 1.076, accuracy = 0.90
epoch = 19, val_loss = 1.063, accuracy = 0.90
epoch = 20, val_loss = 1.050, accuracy = 0.90
epoch = 21, val_loss = 1.038, accuracy = 0.90
epoch = 22, val_loss = 1.027, accuracy = 0.90
epoch = 23, val_loss = 1.016, accuracy = 0.90
epoch = 24, val_loss = 1.007, accuracy = 0.90