From 7a0bf1af217dd4241cb129870f39cf733fe4bdce Mon Sep 17 00:00:00 2001 From: Maciej Czajka Date: Mon, 9 May 2022 21:12:46 +0200 Subject: [PATCH] update dllib-sacred.py --- Biblioteka_DL/dllib-sacred.py | 30 +++++++++++------------------- 1 file changed, 11 insertions(+), 19 deletions(-) diff --git a/Biblioteka_DL/dllib-sacred.py b/Biblioteka_DL/dllib-sacred.py index 139c1eb..17d5c12 100644 --- a/Biblioteka_DL/dllib-sacred.py +++ b/Biblioteka_DL/dllib-sacred.py @@ -2,7 +2,6 @@ import numpy as np import sys import os import torch -import mlflow import pandas as pd from torch import nn from torch.autograd import Variable @@ -17,15 +16,13 @@ from sacred.observers import MongoObserver # EPOCHS = int(sys.argv[1]) -#ex = Experiment() -#ex.observers.append(FileStorageObserver('my_res')) -#ex.observers.append(MongoObserver(url='mongodb://admin:IUM_2021@172.17.0.1:27017', db_name='sacred')) +ex = Experiment() +ex.observers.append(FileStorageObserver('my_res')) +ex.observers.append(MongoObserver(url='mongodb://admin:IUM_2021@172.17.0.1:27017', db_name='sacred')) -mlflow.set_experiment("s444356") - -#@ex.config -#def my_config(): -# epochs = 100 +@ex.config +def my_config(): + epochs = 100 class Model(nn.Module): def __init__(self, input_dim): @@ -245,11 +242,8 @@ def remove_list(games): # features_g = pd.DataFrame(features_g, dtype=np.float64) # features_g = features_g.to_numpy() -epochs = int(sys.argv[1]) if len(sys.argv) > 20 else 20 - -#@ex.automain -#def my_main(epochs, _run): -def my_main(epochs): +@ex.automain +def my_main(epochs, _run): platform = pd.read_csv('all_games.train.csv', sep=',', usecols=[1], header=None).values.tolist() release_date = pd.read_csv('all_games.train.csv', sep=',', usecols=[2], header=None).values.tolist() meta_score = pd.read_csv('all_games.train.csv', sep=',', usecols=[4], header=None).values.tolist() @@ -301,8 +295,7 @@ def my_main(epochs): loss_fn = nn.CrossEntropyLoss() # epochs = 1000 # epochs = epochs - #_run.info['epochs'] = epochs - mlflow.log_param("epochs", epochs) + _run.info['epochs'] = epochs def print_(loss): print ("The loss calculated: ", loss) @@ -329,15 +322,14 @@ def my_main(epochs): pred = pred.detach().numpy() print("The accuracy is", accuracy_score(labels_test_g, np.argmax(pred, axis=1))) - #_run.info['accuracy'] = accuracy_score(labels_test_g, np.argmax(pred, axis=1)) + _run.info['accuracy'] = accuracy_score(labels_test_g, np.argmax(pred, axis=1)) _run.log_scalar("measure.accuracy", accuracy_score(labels_test_g, np.argmax(pred, axis=1))) - mlflow.log_metric("measure.accuracy", accuracy_score(labels_test_g, np.argmax(pred, axis=1))) pred = pd.DataFrame(pred) pred.to_csv('result.csv') # save model torch.save(model, "games_model.pkl") - #ex.add_artifact("games_model.pkl") + ex.add_artifact("games_model.pkl")