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update dllib-sacred.py
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This commit is contained in:
Maciej Czajka 2022-05-09 21:12:46 +02:00
parent 8e85f90e0a
commit 7a0bf1af21
1 changed files with 11 additions and 19 deletions
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Biblioteka_DL/dllib-sacred.py
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@ -2,7 +2,6 @@ import numpy as np
import sys import sys
import os import os
import torch import torch
import mlflow
import pandas as pd import pandas as pd
from torch import nn from torch import nn
from torch.autograd import Variable from torch.autograd import Variable
@ -17,15 +16,13 @@ from sacred.observers import MongoObserver
# EPOCHS = int(sys.argv[1]) # EPOCHS = int(sys.argv[1])
#ex = Experiment() ex = Experiment()
#ex.observers.append(FileStorageObserver('my_res')) ex.observers.append(FileStorageObserver('my_res'))
#ex.observers.append(MongoObserver(url='mongodb://admin:IUM_2021@172.17.0.1:27017', db_name='sacred')) 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():
#@ex.config epochs = 100
#def my_config():
# epochs = 100
class Model(nn.Module): class Model(nn.Module):
def __init__(self, input_dim): def __init__(self, input_dim):
@ -245,11 +242,8 @@ def remove_list(games):
# features_g = pd.DataFrame(features_g, dtype=np.float64) # features_g = pd.DataFrame(features_g, dtype=np.float64)
# features_g = features_g.to_numpy() # 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):
#@ex.automain
#def my_main(epochs, _run):
def my_main(epochs):
platform = pd.read_csv('all_games.train.csv', sep=',', usecols=[1], header=None).values.tolist() 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() 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() 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() loss_fn = nn.CrossEntropyLoss()
# epochs = 1000 # epochs = 1000
# epochs = epochs # epochs = epochs
#_run.info['epochs'] = epochs _run.info['epochs'] = epochs
mlflow.log_param("epochs", epochs)
def print_(loss): def print_(loss):
print ("The loss calculated: ", loss) print ("The loss calculated: ", loss)
@ -329,15 +322,14 @@ def my_main(epochs):
pred = pred.detach().numpy() pred = pred.detach().numpy()
print("The accuracy is", accuracy_score(labels_test_g, np.argmax(pred, axis=1))) 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))) _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 = pd.DataFrame(pred)
pred.to_csv('result.csv') pred.to_csv('result.csv')
# save model # save model
torch.save(model, "games_model.pkl") torch.save(model, "games_model.pkl")
#ex.add_artifact("games_model.pkl") ex.add_artifact("games_model.pkl")