ium_444356/Biblioteka_DL/dllib-mlflow.py
Maciej Czajka b865d30572
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update dllib-mlflow.py
2022-05-11 19:06:07 +02:00

352 lines
12 KiB
Python

from urllib.parse import urlparse
import numpy as np
import sys
import os
import torch
import mlflow
import pandas as pd
from mlflow.models import infer_signature
from torch import nn
from torch.autograd import Variable
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score
import torch.nn.functional as F
from datetime import datetime
from sacred import Experiment
from sacred.observers import FileStorageObserver
from sacred.observers import MongoObserver
# EPOCHS = int(sys.argv[1])
# mlflow.set_tracking_uri("http://172.17.0.1:5000")
mlflow.set_experiment("s444356")
class Model(nn.Module):
def __init__(self, input_dim):
super(Model, self).__init__()
self.layer1 = nn.Linear(input_dim, 50)
self.layer2 = nn.Linear(50, 40)
self.layer3 = nn.Linear(40, 3)
def forward(self, x):
x = F.relu(self.layer1(x.float()))
x = F.relu(self.layer2(x.float()))
x = F.softmax(self.layer3(x.float())) # To check with the loss function
return x.float()
# funkcja usuwająca wiersze zawierające platformę "Stadia"
def delete_stadia(games):
index_list = []
for i in range(0, len(games["platform"])):
try:
if games["platform"][i] == " Stadia":
index_list.append(i)
except:
continue
games.drop(index_list, inplace=True)
return games.reset_index()
# funkcja usuwająca wiersze zawierające "tbd" w kolumnie "user_review"
def delete_tbd(games):
index_list = []
for i in range(0, len(games["platform"])):
try:
if games["user_review"][i] == 'tbd':
index_list.append(i)
except:
continue
games.drop(index_list, inplace=True)
return games.reset_index()
def delete_PC(games):
index_list = []
for i in range(0, len(games["platform"])):
try:
if games["platform"][i] == " PC":
index_list.append(i)
except:
continue
games.drop(index_list, inplace=True)
return games.reset_index()
# funkcja zmieniająca kolumnę "user_review" ze stringa na numeric
def user_review_to_numeric(games):
games["user_review"] = pd.to_numeric(games["user_review"])
return games
# funkcja normalizująca wartości w kolumnie "meta_score" i "user_review"
def normalization(games):
games['meta_score'] = games['meta_score'] / 100.0
games['user_review'] = games['user_review'] / 10.0
return games
# PlayStation - 0
# PlayStation 2 - 1
# PlayStation 3 - 2
# PlayStation 4 - 3
# PlayStation 5 - 4
# PlayStation Vita - 5
# Xbox - 6
# Xbox 360 - 7
# Xbox Series X - 8
# Nintendo 64 - 9
# GameCube - 10
# DS - 11
# 3DS - 12
# Wii - 13
# Wii U - 14
# Switch - 15
# PC - 16
# Dreamcast - 17
# Game Boy Advance - 18
# PSP - 19
# Xbox One - 20
# def platform_to_number(games):
# for i in range(0, len(games["platform"])):
#
# if games["platform"][i] == " PlayStation":
# games["platform"][i] = 0
# elif games["platform"][i] == " PlayStation 2":
# games["platform"][i] = 1
# elif games["platform"][i] == " PlayStation 3":
# games["platform"][i] = 2
# elif games["platform"][i] == " PlayStation 4":
# games["platform"][i] = 3
# elif games["platform"][i] == " PlayStation 5":
# games["platform"][i] = 4
# elif games["platform"][i] == " PlayStation Vita":
# games["platform"][i] = 5
# elif games["platform"][i] == " Xbox":
# games["platform"][i] = 6
# elif games["platform"][i] == " Xbox 360":
# games["platform"][i] = 7
# elif games["platform"][i] == " Xbox Series X":
# games["platform"][i] = 8
# elif games["platform"][i] == " Nintendo 64":
# games["platform"][i] = 9
# elif games["platform"][i] == " GameCube":
# games["platform"][i] = 10
# elif games["platform"][i] == " DS":
# games["platform"][i] = 11
# elif games["platform"][i] == " 3DS":
# games["platform"][i] = 12
# elif games["platform"][i] == " Wii":
# games["platform"][i] = 13
# elif games["platform"][i] == " Wii U":
# games["platform"][i] = 14
# elif games["platform"][i] == " Switch":
# games["platform"][i] = 15
# elif games["platform"][i] == " PC":
# games["platform"][i] = 16
# elif games["platform"][i] == " Dreamcast":
# games["platform"][i] = 17
# elif games["platform"][i] == " Game Boy Advance":
# games["platform"][i] = 18
# elif games["platform"][i] == " PSP":
# games["platform"][i] = 19
# elif games["platform"][i] == " Xbox One":
# games["platform"][i] = 20
#
# return games
# old - 0
# mid - 1
# new - 2
def platform_to_number(games):
for i in range(0, len(games["platform"])):
if games["platform"][i] == " PlayStation":
games["platform"][i] = 0
elif games["platform"][i] == " PlayStation 2":
games["platform"][i] = 0
elif games["platform"][i] == " PlayStation 3":
games["platform"][i] = 1
elif games["platform"][i] == " PlayStation 4":
games["platform"][i] = 2
elif games["platform"][i] == " PlayStation 5":
games["platform"][i] = 2
elif games["platform"][i] == " PlayStation Vita":
games["platform"][i] = 1
elif games["platform"][i] == " Xbox":
games["platform"][i] = 0
elif games["platform"][i] == " Xbox 360":
games["platform"][i] = 1
elif games["platform"][i] == " Xbox Series X":
games["platform"][i] = 2
elif games["platform"][i] == " Nintendo 64":
games["platform"][i] = 0
elif games["platform"][i] == " GameCube":
games["platform"][i] = 0
elif games["platform"][i] == " DS":
games["platform"][i] = 0
elif games["platform"][i] == " 3DS":
games["platform"][i] = 1
elif games["platform"][i] == " Wii":
games["platform"][i] = 0
elif games["platform"][i] == " Wii U":
games["platform"][i] = 1
elif games["platform"][i] == " Switch":
games["platform"][i] = 2
elif games["platform"][i] == " PC":
dt = datetime.strptime(games["release_date"][i], '%B %d, %Y')
if (dt.year == 1995 or dt.year == 1996 or dt.year == 1997 or dt.year == 1998
or dt.year == 1999 or dt.year == 2000 or dt.year == 2001 or dt.year == 2002
or dt.year == 2003 or dt.year == 2004 or dt.year == 2005):
games["platform"][i] = 0
if (dt.year == 2006 or dt.year == 2007 or dt.year == 2008 or dt.year == 2009
or dt.year == 2010 or dt.year == 2011 or dt.year == 2012 or dt.year == 2013
or dt.year == 2014 or dt.year == 2015 or dt.year == 2016):
games["platform"][i] = 1
if (dt.year == 2017 or dt.year == 2018 or dt.year == 2019
or dt.year == 2020 or dt.year == 2021):
games["platform"][i] = 2
# games["platform"][i] = 0
elif games["platform"][i] == " Dreamcast":
games["platform"][i] = 0
elif games["platform"][i] == " Game Boy Advance":
games["platform"][i] = 0
elif games["platform"][i] == " PSP":
games["platform"][i] = 1
elif games["platform"][i] == " Xbox One":
games["platform"][i] = 2
return games
def remove_list(games):
for i in range(0, len(games)):
games['platform'][i] = games['platform'][i][0]
games['release_date'][i] = games['release_date'][i][0]
games['meta_score'][i] = games['meta_score'][i][0]
games['user_review'][i] = games['user_review'][i][0]
return games
# games = pd.read_csv('all_games.csv', sep=',')
# games = platform_to_number(games)
# games = delete_stadia(games)
# games = delete_tbd(games)
# games = user_review_to_numeric(games)
# games = normalization(games)
# games.drop(['level_0', 'index'], axis='columns', inplace=True)
# labels_g = pd.DataFrame(games["platform"], dtype=np.int64)
# labels_g = labels_g.to_numpy()
# features_g = {'meta_score': games['meta_score'],
# 'user_review': games['user_review']}
# features_g = pd.DataFrame(features_g, dtype=np.float64)
# features_g = features_g.to_numpy()
epochs = int(sys.argv[1]) if len(sys.argv) > 1 else 20
def my_main(epochs):
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()
user_review = pd.read_csv('all_games.train.csv', sep=',', usecols=[5], header=None).values.tolist()
games_train = {'platform': platform,
'release_date': release_date,
'meta_score': meta_score,
'user_review': user_review}
games_train = pd.DataFrame(games_train)
games_test = {'platform': platform,
'release_date': release_date,
'meta_score': meta_score,
'user_review': user_review}
games_test = pd.DataFrame(games_test)
games_train = remove_list(games_train)
games_train = platform_to_number(games_train)
games_train = delete_stadia(games_train)
games_train = delete_tbd(games_train)
games_train = user_review_to_numeric(games_train)
games_train = normalization(games_train)
games_test = remove_list(games_test)
games_test = platform_to_number(games_test)
games_test = delete_stadia(games_test)
games_test = delete_tbd(games_test)
games_test = user_review_to_numeric(games_test)
games_test = normalization(games_test)
labels_train_g = pd.DataFrame(games_train["platform"], dtype=np.int64)
labels_train_g = labels_train_g.to_numpy()
features_train_g = {'meta_score': games_train['meta_score'],
'user_review': games_train['user_review']}
features_train_g = pd.DataFrame(features_train_g, dtype=np.float64)
features_train_g = features_train_g.to_numpy()
labels_test_g = pd.DataFrame(games_test["platform"], dtype=np.int64)
labels_test_g = labels_test_g.to_numpy()
features_test_g = {'meta_score': games_test['meta_score'],
'user_review': games_test['user_review']}
features_test_g = pd.DataFrame(features_test_g, dtype=np.float64)
features_test_g = features_test_g.to_numpy()
# Training
model = Model(features_train_g.shape[1])
optimizer = torch.optim.Adam(model.parameters(), lr=0.01)
loss_fn = nn.CrossEntropyLoss()
# epochs = 1000
# epochs = epochs
mlflow.log_param("epochs", epochs)
def print_(loss):
print ("The loss calculated: ", loss)
x_train, y_train = Variable(torch.from_numpy(features_train_g)).float(), Variable(
torch.from_numpy(labels_train_g)).long()
input_example = features_train_g
siganture = infer_signature(features_train_g, labels_train_g)
tracking_url_type_store = urlparse(mlflow.get_tracking_uri()).scheme
# print(tracking_url_type_store)
if tracking_url_type_store != "file":
mlflow.pytorch.log_model(model, "model", registered_model_name="s444356", signature=siganture,
input_example=input_example)
else:
mlflow.pytorch.log_model(model, "model", signature=siganture, input_example=input_example)
mlflow.pytorch.save_model(model, "my_model", signature=siganture, input_example=input_example)
for epoch in range(1, epochs + 1):
print("Epoch #", epoch)
y_pred = model(x_train)
loss = loss_fn(y_pred, y_train.squeeze(-1))
print_(loss.item())
# Zero gradients
optimizer.zero_grad()
loss.backward() # Gradients
optimizer.step() # Update
mlflow.log_param("loss", loss.item())
# Prediction
x_test = Variable(torch.from_numpy(features_test_g)).float()
pred = model(x_test)
pred = pred.detach().numpy()
print("The accuracy is", accuracy_score(labels_test_g, np.argmax(pred, axis=1)))
mlflow.log_metric("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")
with mlflow.start_run() as run:
my_main(epochs)