ium_444018/lab7/biblioteka_DL/dllib.py
Szymon Parafiński fa51d4a87a move sol to lab7
2022-05-16 01:13:58 +02:00

176 lines
5.7 KiB
Python

import sys
import torch
import torch.nn as nn
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score, mean_squared_error
from sacred.observers import MongoObserver, FileStorageObserver
from sacred import Experiment
ex = Experiment(save_git_info=False)
ex.observers.append(MongoObserver(url='mongodb://admin:IUM_2021@172.17.0.1:27017',
db_name='sacred'))
ex.observers.append(FileStorageObserver('s444018_sacred_FileObserver'))
@ex.config
def my_config():
epochs = "1000"
def drop_relevant_columns(imbd_data):
imbd_data.drop(columns=["Poster_Link"], inplace=True)
imbd_data.drop(columns=["Overview"], inplace=True)
imbd_data.drop(columns=["Certificate"], inplace=True)
return imbd_data
def lowercase_columns_names(imbd_data):
imbd_data["Series_Title"] = imbd_data["Series_Title"].str.lower()
imbd_data["Genre"] = imbd_data["Genre"].str.lower()
imbd_data["Director"] = imbd_data["Director"].str.lower()
imbd_data["Star1"] = imbd_data["Star1"].str.lower()
imbd_data["Star2"] = imbd_data["Star2"].str.lower()
imbd_data["Star3"] = imbd_data["Star3"].str.lower()
imbd_data["Star4"] = imbd_data["Star4"].str.lower()
return imbd_data
def data_to_numeric(imbd_data):
imbd_data = imbd_data.replace(np.nan, '', regex=True)
imbd_data["Gross"] = imbd_data["Gross"].str.replace(',', '')
imbd_data["Gross"] = pd.to_numeric(imbd_data["Gross"], errors='coerce')
imbd_data["Runtime"] = imbd_data["Runtime"].str.replace(' min', '')
imbd_data["Runtime"] = pd.to_numeric(imbd_data["Runtime"], errors='coerce')
imbd_data["IMDB_Rating"] = pd.to_numeric(imbd_data["IMDB_Rating"], errors='coerce')
imbd_data["Meta_score"] = pd.to_numeric(imbd_data["Meta_score"], errors='coerce')
imbd_data["Released_Year"] = pd.to_numeric(imbd_data["Released_Year"], errors='coerce')
imbd_data = imbd_data.dropna()
imbd_data = imbd_data.reset_index()
imbd_data.drop(columns=["index"], inplace=True)
return imbd_data
def create_train_dev_test(imbd_data):
data_train, data_test = train_test_split(imbd_data, test_size=230, random_state=1, shuffle=True)
data_test, data_dev = train_test_split(data_test, test_size=115, random_state=1, shuffle=True)
data_test.to_csv("data_test.csv", encoding="utf-8", index=False)
data_dev.to_csv("data_dev.csv", encoding="utf-8", index=False)
data_train.to_csv("data_train.csv", encoding="utf-8", index=False)
def normalize_gross(imbd_data):
imbd_data[["Gross"]] = imbd_data[["Gross"]] / 10000000
return imbd_data
def prepare_dataset():
df = pd.read_csv('biblioteka_DL/imdb_top_1000.csv')
df = drop_relevant_columns(df)
df_lowercase = lowercase_columns_names(df)
df = data_to_numeric(df_lowercase)
df = normalize_gross(df)
return df
class LinearRegressionModel(torch.nn.Module):
def __init__(self):
super(LinearRegressionModel, self).__init__()
self.linear = torch.nn.Linear(1, 1) # One in and one out
def forward(self, x):
y_pred = self.linear(x)
return y_pred
@ex.automain
def my_main(epochs, _run):
# num_epochs = 1000
# num_epochs = int(sys.argv[1])
# number of epochs is parametrized
try:
num_epochs = int(epochs)
except Exception as e:
print(e)
print("Setting default epochs value to 1000.")
num_epochs = 1000
df = prepare_dataset()
data_train, data_test = train_test_split(df, random_state=1, shuffle=True)
X_train = pd.DataFrame(data_train["Meta_score"], dtype=np.float64)
X_train = X_train.to_numpy()
y_train = pd.DataFrame(data_train["Gross"], dtype=np.float64)
y_train = y_train.to_numpy()
X_train = X_train.reshape(-1, 1)
y_train = y_train.reshape(-1, 1)
X_train = torch.from_numpy(X_train.astype(np.float32)).view(-1, 1)
y_train = torch.from_numpy(y_train.astype(np.float32)).view(-1, 1)
input_size = 1
output_size = 1
model = nn.Linear(input_size, output_size)
learning_rate = 0.0001
l = nn.MSELoss()
optimizer = torch.optim.SGD(model.parameters(), lr=learning_rate)
for epoch in range(num_epochs):
# forward feed
y_pred = model(X_train.requires_grad_())
# calculate the loss
loss = l(y_pred, y_train)
# backward propagation: calculate gradients
loss.backward()
# update the weights
optimizer.step()
# clear out the gradients from the last step loss.backward()
optimizer.zero_grad()
if epoch % 100 == 0:
print('epoch {}, loss {}'.format(epoch, loss.item()))
X_test = pd.DataFrame(data_test["Meta_score"], dtype=np.float64)
X_test = X_test.to_numpy()
X_test = X_test.reshape(-1, 1)
X_test = torch.from_numpy(X_test.astype(np.float32)).view(-1, 1)
predictedSet = model(X_test).detach().numpy()
gross_test_g = pd.DataFrame(data_test["Gross"], dtype=np.float64)
gross_test_g = gross_test_g.to_numpy()
gross_test_g = gross_test_g.reshape(-1, 1)
pred = pd.DataFrame(predictedSet)
pred.to_csv('result.csv')
# save model
torch.save(model, "model.pkl")
predicted = []
expected = []
for i in range(0, len(X_test)):
predicted.append(np.argmax(model(X_test[i]).detach().numpy(), axis=0))
expected.append(gross_test_g[i])
for i in range(0, len(expected)):
expected[i] = expected[i][0]
rmse = mean_squared_error(gross_test_g, pred, squared=False)
mse = mean_squared_error(gross_test_g, pred)
_run.log_scalar("RMSE", rmse)
_run.log_scalar("MSE", mse)
_run.info['epochs'] = epochs
# ex.run()
ex.add_artifact("model.pkl")