import torch
import sys
import mlflow
import torch.nn.functional as F
from torch import nn
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score, mean_squared_error
import numpy as np
import pandas as pd
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)

with mlflow.start_run():
    data_train = pd.read_csv("data_train.csv")
    data_test  = pd.read_csv("data_test.csv")
    data_val   = pd.read_csv("data_val.csv")
    FEATURES   =  ['age','hypertension','heart_disease','ever_married', 'avg_glucose_level', 'bmi']

    x_train = data_train[FEATURES].astype(np.float32)
    y_train = data_train['stroke'].astype(np.float32)

    x_test = data_test[FEATURES].astype(np.float32)
    y_test = data_test['stroke'].astype(np.float32)

    fTrain = torch.from_numpy(x_train.values)
    tTrain = torch.from_numpy(y_train.values.reshape(2945,1))

    fTest= torch.from_numpy(x_test.values)
    tTest = torch.from_numpy(y_test.values)

    batch_size = int(sys.argv[1]) if len(sys.argv) > 1 else 16
    num_epochs = int(sys.argv[2]) if len(sys.argv) > 2 else 5
    learning_rate = 0.001
    input_dim = 6
    output_dim = 1

    model = LogisticRegressionModel(input_dim, output_dim)

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

    for epoch in range(num_epochs):
        # print ("Epoch #",epoch)
        model.train()
        optimizer.zero_grad()
        # Forward pass
        y_pred = model(fTrain)
        # Compute Loss
        loss = criterion(y_pred, tTrain)
        # print(loss.item())
        # Backward pass
        loss.backward()
        optimizer.step()
    y_pred = model(fTest)
    # print("predicted Y value: ", y_pred.data)

    torch.save(model.state_dict(), 'stroke.pth')

    rmse = mean_squared_error(tTest, y_pred.detach().numpy())
    mlflow.log_metric("rmse", rmse)
    mlflow.log_param("Last loss", loss.item())
    mlflow.log_param("epochs", num_epochs)
    mlflow.log_param("batch size", batch_size)