code.py

import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score, precision_score, recall_score
import numpy as np

import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.autograd import Variable


class Net(nn.Module):
    # define nn
    def __init__(self):
        super(Net, self).__init__()
        self.fc1 = nn.Linear(4, 100)
        self.fc2 = nn.Linear(100, 100)
        self.fc3 = nn.Linear(100, 3)
        self.softmax = nn.Softmax(dim=1)

    def forward(self, X):
        X = F.relu(self.fc1(X))
        X = self.fc2(X)
        X = self.fc3(X)
        X = self.softmax(X)

        return X

# load IRIS dataset
dataset = pd.read_csv('pytorch_iris/dataset/iris.csv')

# transform species to numerics
dataset.loc[dataset.species=='Iris-setosa', 'species'] = 0
dataset.loc[dataset.species=='Iris-versicolor', 'species'] = 1
dataset.loc[dataset.species=='Iris-virginica', 'species'] = 2


train_X, test_X, train_y, test_y = train_test_split(dataset[dataset.columns[0:4]].values,
                                                    dataset.species.values, test_size=0.8)


train_y=train_y.astype('int32')
test_y=test_y.astype('int32')


train_X = Variable(torch.Tensor(train_X).float())
test_X = Variable(torch.Tensor(test_X).float())

train_y = Variable(torch.Tensor(train_y).long())
test_y = Variable(torch.Tensor(test_y).long())


net = Net()

criterion = nn.CrossEntropyLoss()# cross entropy loss

optimizer = torch.optim.SGD(net.parameters(), lr=0.01)

for epoch in range(1000):
    optimizer.zero_grad()
    out = net(train_X)
    loss = criterion(out, train_y)
    loss.backward() #compute the gradients for all trainable parameters
    optimizer.step()

    if epoch % 100 == 0:

        print("number of epoch", epoch, "loss", loss.data)

predict_out = net(test_X)
_, predict_y = torch.max(predict_out, 1)

print("prediction accuracy", accuracy_score(test_y.data, predict_y.data))

print("macro precision", precision_score(test_y.data, predict_y.data, average='macro'))
print("micro precision", precision_score(test_y.data, predict_y.data, average='micro'))
print ("macro recall", recall_score(test_y.data, predict_y.data, average='macro'))
print ("micro recall", recall_score(test_y.data, predict_y.data, average='micro'))