131 lines
4.1 KiB
Python
131 lines
4.1 KiB
Python
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import torch
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import torch.nn as nn
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import pandas as pd
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import numpy as np
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import matplotlib.pyplot as plt
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from sklearn.model_selection import train_test_split
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from sklearn.metrics import accuracy_score
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def drop_relevant_columns(imbd_data):
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imbd_data.drop(columns=["Poster_Link"], inplace=True)
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imbd_data.drop(columns=["Overview"], inplace=True)
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imbd_data.drop(columns=["Certificate"], inplace=True)
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return imbd_data
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def lowercase_columns_names(imbd_data):
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imbd_data["Series_Title"] = imbd_data["Series_Title"].str.lower()
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imbd_data["Genre"] = imbd_data["Genre"].str.lower()
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imbd_data["Director"] = imbd_data["Director"].str.lower()
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imbd_data["Star1"] = imbd_data["Star1"].str.lower()
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imbd_data["Star2"] = imbd_data["Star2"].str.lower()
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imbd_data["Star3"] = imbd_data["Star3"].str.lower()
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imbd_data["Star4"] = imbd_data["Star4"].str.lower()
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return imbd_data
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def data_to_numeric(imbd_data):
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imbd_data = imbd_data.replace(np.nan, '', regex=True)
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imbd_data["Gross"] = imbd_data["Gross"].str.replace(',', '')
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imbd_data["Gross"] = pd.to_numeric(imbd_data["Gross"], errors='coerce')
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imbd_data["Runtime"] = imbd_data["Runtime"].str.replace(' min', '')
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imbd_data["Runtime"] = pd.to_numeric(imbd_data["Runtime"], errors='coerce')
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imbd_data["IMDB_Rating"] = pd.to_numeric(imbd_data["IMDB_Rating"], errors='coerce')
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imbd_data["Meta_score"] = pd.to_numeric(imbd_data["Meta_score"], errors='coerce')
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imbd_data["Released_Year"] = pd.to_numeric(imbd_data["Released_Year"], errors='coerce')
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imbd_data = imbd_data.dropna()
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imbd_data = imbd_data.reset_index()
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imbd_data.drop(columns=["index"], inplace=True)
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return imbd_data
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def create_train_dev_test(imbd_data):
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data_train, data_test = train_test_split(imbd_data, test_size=230, random_state=1, shuffle=True)
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data_test, data_dev = train_test_split(data_test, test_size=115, random_state=1, shuffle=True)
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data_test.to_csv("data_test.csv", encoding="utf-8", index=False)
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data_dev.to_csv("data_dev.csv", encoding="utf-8", index=False)
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data_train.to_csv("data_train.csv", encoding="utf-8", index=False)
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def normalize_gross(imbd_data):
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imbd_data[["Gross"]] = imbd_data[["Gross"]] / 10000000
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return imbd_data
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def prepare_dataset():
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df = pd.read_csv('../imdb_top_1000.csv')
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df = drop_relevant_columns(df)
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df_lowercase = lowercase_columns_names(df)
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df = data_to_numeric(df_lowercase)
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df = normalize_gross(df)
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return df
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class LinearRegressionModel(torch.nn.Module):
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def __init__(self):
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super(LinearRegressionModel, self).__init__()
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self.linear = torch.nn.Linear(1, 1) # One in and one out
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def forward(self, x):
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y_pred = self.linear(x)
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return y_pred
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df = prepare_dataset()
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data_train, data_test = train_test_split(df, random_state=1, shuffle=True)
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X_train = pd.DataFrame(data_train["Meta_score"], dtype=np.float64)
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X_train = X_train.to_numpy()
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y_train = pd.DataFrame(data_train["Gross"], dtype=np.float64)
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y_train = y_train.to_numpy()
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X_train = X_train.reshape(-1, 1)
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y_train = y_train.reshape(-1, 1)
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X_train = torch.from_numpy(X_train.astype(np.float32)).view(-1, 1)
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y_train = torch.from_numpy(y_train.astype(np.float32)).view(-1, 1)
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input_size = 1
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output_size = 1
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model = nn.Linear(input_size, output_size)
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learning_rate = 0.0001
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l = nn.MSELoss()
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optimizer = torch.optim.SGD(model.parameters(), lr=learning_rate)
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num_epochs = 1000
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for epoch in range(num_epochs):
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# forward feed
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y_pred = model(X_train.requires_grad_())
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# calculate the loss
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loss = l(y_pred, y_train)
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# backward propagation: calculate gradients
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loss.backward()
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# update the weights
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optimizer.step()
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# clear out the gradients from the last step loss.backward()
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optimizer.zero_grad()
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if epoch % 100 == 0:
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print('epoch {}, loss {}'.format(epoch, loss.item()))
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predicted = model(X_train).detach().numpy()
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pred = pd.DataFrame(predicted)
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pred.to_csv('result.csv')
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# plt.scatter(X_train.detach().numpy() , y_train.detach().numpy())
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# plt.plot(X_train.detach().numpy() , predicted , "red")
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# plt.xlabel("Meta_score")
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# plt.ylabel("Gross")
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# plt.show()
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