import torch
import sys
from torch import nn
import numpy as np
import pandas as pd
import mlflow
from sklearn.metrics import accuracy_score

np.set_printoptions(suppress=False)


class LogisticRegressionModel(nn.Module):
    def __init__(self, input_dim, output_dim):
        super(LogisticRegressionModel, self).__init__()
        self.linear = nn.Linear(input_dim, output_dim)
        self.sigmoid = nn.Sigmoid()
    def forward(self, x):
        out = self.linear(x)
        return self.sigmoid(out)



def readAndtrain(epochs, batch_size):
    train = pd.read_csv("train.csv")
    test = pd.read_csv("test.csv")

    xtrain = train[['age','anaemia','creatinine_phosphokinase','diabetes', 'ejection_fraction', 'high_blood_pressure', 'platelets', 'serum_creatinine', 'serum_sodium', 'sex', 'smoking']].astype(np.float32)
    ytrain = train['DEATH_EVENT'].astype(np.float32)
    xtest = test[['age','anaemia','creatinine_phosphokinase','diabetes', 'ejection_fraction', 'high_blood_pressure', 'platelets', 'serum_creatinine', 'serum_sodium', 'sex', 'smoking']].astype(np.float32)
    ytest = test['DEATH_EVENT'].astype(np.float32)
    xTrain = torch.from_numpy(xtrain.values)
    yTrain = torch.from_numpy(ytrain.values.reshape(179,1))
    xTest = torch.from_numpy(xtest.values)
    yTest = torch.from_numpy(ytest.values)

    learning_rate = 0.002
    input_dim = 11
    output_dim = 1

    model = LogisticRegressionModel(input_dim, output_dim)
    model.load_state_dict(torch.load('DEATH_EVENT.pth'))

    criterion = torch.nn.BCELoss(reduction='mean')
    optimizer = torch.optim.SGD(model.parameters(), lr=learning_rate)

    for epoch in range(epochs):
        # print ("Epoch #",epoch)
        model.train()
        optimizer.zero_grad()
        # Forward pass
        y_pred = model(xTrain)
        # Compute Loss
        loss = criterion(y_pred, yTrain)
        # print(loss.item())
        # Backward pass
        loss.backward()
        optimizer.step()


    torch.save(model.state_dict(), 'DEATH_EVENT.pth')
    prediction= model(xTest)

    return prediction, loss.item(), yTest

    print("accuracy_score", accuracy_score(yTest, np.argmax(prediction.detach().numpy(), axis=1)))
    # print("F1", f1_score(yTest, np.argmax(prediction.detach().numpy(), axis=1), average=None))


batch_size = int(sys.argv[1]) if len(sys.argv) > 1 else 10
epochs = int(sys.argv[2]) if len(sys.argv) > 2 else 5
with mlflow.start_run():

    prediction, loss, yTest = readAndtrain(epochs, batch_size)

    mlflow.log_metric("accuracy_score", accuracy_score(yTest, np.argmax(prediction.detach().numpy(), axis=1)))
    mlflow.log_param("Last loss", loss)
    mlflow.log_param("epochs", epochs)
    mlflow.log_param("batch size", batch_size)