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deneme.py
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deneme.py
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import torch
import torch.nn as nn
import torch.optim as optim
import os
import pickle
import numpy as np
import pandas as pd
# Define a basic feedforward neural network
class BasicModel(nn.Module):
def __init__(self, input_size, hidden_size, output_size):
super(BasicModel, self).__init__()
self.fc1 = nn.Linear(input_size, hidden_size)
self.relu = nn.ReLU()
self.fc2 = nn.Linear(hidden_size, output_size)
def forward(self, x):
x = self.fc1(x)
x = self.relu(x)
x = self.fc2(x)
return x
if __name__=="__main__":
"""
# I nstantiate the model
input_size = 10
hidden_size = 20
output_size = 1
model = BasicModel(input_size, hidden_size, output_size)
# Save the entire model
torch.save(model, 'basic_model.pth')
# Load the entire model
loaded_model = torch.load('basic_model.pth')
print(loaded_model)
"""
acc_list=[]
for i in [1,2,3,0,5,6,7,8,9]:
file_path = os.path.join(f"/auto/k2/aykut3/scgpt/scGPT/scgpt_gcn/save_scgcn/scgpt_myeloid_run_{i}/results.pkl")
with open(file_path, "rb") as file:
results= pickle.load(file)
acc_list.append(100*results["results"]["test/macro_f1"]) #macro_f1
print(results["seed_numbers"])
file_path = os.path.join(f"/auto/k2/aykut3/scgpt/scGPT/scgpt_gcn/save_scgcn/scgpt_myeloid_median/results.pkl")
with open(file_path, "rb") as file:
results= pickle.load(file)
acc_list.append(100*results["results"]["test/macro_f1"])
acc_np= np.array(acc_list)
print("All accuracy:", acc_np)
print("Median:",np.median(acc_np))
print("Average:",np.mean(acc_np))
print("Standard Deviation", np.std(acc_list))
print("Seed numbers:", results["seed_numbers"])
# Load DataFrame from a CSV file
loaded_df = pd.read_csv('results_ms_type3.csv', index_col=0) # Ensure the first column is used as the DataFrame index
# Print the loaded DataFrame
print(loaded_df)