Diabetes prediction model

#%%

import pandas as pd

import matplotlib.pyplot as plt

import torch

from torch import nn

from sklearn.model_selection import train_test_split

df = pd.read_csv("diabetes.csv")

y = df["Outcome"]

X = []

for i,rows in df.iterrows():

    X.append([rows["Pregnancies"],rows["Glucose"],rows["BloodPressure"],rows["SkinThickness"],rows["Insulin"],rows["BMI"],rows["DiabetesPedigreeFunction"],rows["Age"]])

X = torch.tensor(X)

X = X.float()

y = torch.tensor(y)

y = y.float()

trainx,testx,trainy,testy = train_test_split(X,y,test_size=0.2,random_state=42)

trainx.shape,trainy.shape

class dmodel(nn.Module):

    def __init__(self,input_features,output_features,hidden_units=16):

        super().__init__()

        self.linear_layer_stack = nn.Sequential(nn.Linear(in_features=input_features,out_features=hidden_units),

        nn.Linear(in_features=hidden_units,out_features=32),

        nn.Linear(in_features=32,out_features=hidden_units),

        nn.Linear(in_features=hidden_units,out_features=output_features))

   

    def forward(self,x):

        return self.linear_layer_stack(x)

model4 = dmodel(input_features=8,output_features=1)

trainx = trainx.float()

trainy = trainy.float()

testx = testx.float()

testy = testy.float()

# model4(trainx)

loss_fn = nn.BCEWithLogitsLoss()

optimizer = torch.optim.SGD(model4.parameters(),lr=0.01)

def accuracy_fn(y_true,y_pred):

    correct = torch.eq(y_true,y_pred).sum().item()

    acc = (correct / len(y_pred)) * 100

    return acc

#%%

epochs = 10000

torch.manual_seed(42)

for epoch in range(epochs):

    y_logits = model4(trainx)

    y_pred = torch.round(torch.sigmoid(y_logits))

    loss = loss_fn(y_logits.squeeze(),trainy)

    acc = accuracy_fn(y_true=trainy,

                      y_pred = y_pred.squeeze())

    optimizer.zero_grad()

    loss.backward()

    optimizer.step()

    model4.eval()

    with torch.inference_mode():

        test_logits = model4(testx)

        test_pred = torch.round(torch.sigmoid(test_logits))

        test_loss = loss_fn(test_logits.squeeze(),testy)

        test_acc = accuracy_fn(y_true=testy,

                               y_pred = test_pred.squeeze())

   

    if epoch % 100 == 0:

        print(f"Epoch : {epoch} | Loss : {loss:.5f}, Accuracy: {acc:.2f}%|Test loss : {test_loss :.5f}, Test acc : {test_acc:.2f}")