ium_464914/mlflow/mlflow_prediction.py
Alicja Szulecka ed9927d7a1 mlflow
2024-05-06 17:27:28 +02:00

95 lines
3.3 KiB
Python

import torch
import torch.nn as nn
import torch.optim as optim
from torch.utils.data import DataLoader, Dataset
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import LabelEncoder
import torch.nn.functional as F
from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score, mean_squared_error
import numpy as np
import mlflow
import mlflow.sklearn
mlflow.set_tracking_uri("http://localhost:5000")
mlflow.set_experiment("s464914")
device = (
"cuda"
if torch.cuda.is_available()
else "cpu"
)
class Model(nn.Module):
def __init__(self, input_features=54, hidden_layer1=25, hidden_layer2=30, output_features=8):
super().__init__()
self.fc1 = nn.Linear(input_features,output_features)
self.bn1 = nn.BatchNorm1d(hidden_layer1) # Add batch normalization
self.fc2 = nn.Linear(hidden_layer1, hidden_layer2)
self.bn2 = nn.BatchNorm1d(hidden_layer2) # Add batch normalization
self.out = nn.Linear(hidden_layer2, output_features)
def forward(self, x):
x = F.relu(self.fc1(x))
return x
def load_model(model, model_path):
model.load_state_dict(torch.load(model_path))
model.eval()
def predict(model, input_data):
# Convert input data to PyTorch tensor
# Perform forward pass
with torch.no_grad():
output = model(input_data)
_, predicted_class = torch.max(output, 0)
return predicted_class.item() # Return the predicted class label
def main():
with mlflow.start_run() as run:
forest_test = pd.read_csv('forest_test.csv')
X_test = forest_test.drop(columns=['Cover_Type']).values
y_test = forest_test['Cover_Type'].values
X_test = torch.tensor(X_test, dtype=torch.float32).to(device)
model = Model().to(device)
model_path = 'model.pth' # Path to your saved model file
load_model(model, model_path)
predictions = []
true_labels = []
with torch.no_grad():
for input_data, target in zip(X_test, y_test):
output = model(input_data)
_, predicted_class = torch.max(output, 0)
prediction_entry = f"predicted: {predicted_class.item()} true_label: {target}"
predictions.append(prediction_entry)
true_labels.append()
if predicted_class.item() == target:
true_labels.append(target)
with open(r'predictions.txt', 'w') as fp:
for item in predictions:
# write each item on a new line
fp.write("%s\n" % item)
accuracy = accuracy_score(true_labels, predictions)
precision_micro = precision_score(true_labels, predictions, average='micro')
recall_micro = recall_score(true_labels, predictions, average='micro')
f1_micro = f1_score(true_labels, predictions, average='micro')
rmse = np.sqrt(mean_squared_error(true_labels, predictions))
mlflow.log_metric("accuracy", accuracy)
mlflow.log_metric("precision_micro", precision_micro)
mlflow.log_metric("recall_micro", recall_micro)
mlflow.log_metric("f1_micro", f1_micro)
mlflow.log_metric("rmse", rmse)
if __name__ == "__main__":
main()