a804509a00
Some checks failed
s444018-training/pipeline/head There was a failure building this commit
131 lines
4.1 KiB
Python
131 lines
4.1 KiB
Python
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
|
|
|
|
|
|
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('../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
|
|
|
|
|
|
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)
|
|
|
|
|
|
num_epochs = 1000
|
|
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()))
|
|
|
|
predicted = model(X_train).detach().numpy()
|
|
|
|
pred = pd.DataFrame(predicted)
|
|
pred.to_csv('result.csv')
|
|
|
|
# plt.scatter(X_train.detach().numpy() , y_train.detach().numpy())
|
|
# plt.plot(X_train.detach().numpy() , predicted , "red")
|
|
# plt.xlabel("Meta_score")
|
|
# plt.ylabel("Gross")
|
|
# plt.show()
|