An artificial neural network for the Titanic challenge on Kaggle
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
import torch | |
import pandas as pd | |
from sklearn.preprocessing import StandardScaler | |
from sklearn.model_selection import train_test_split | |
import numpy as np | |
import matplotlib.pyplot as plt | |
import seaborn as sns | |
np.random.seed(10000) | |
# Load data | |
train = pd.read_csv("train.csv", index_col='PassengerId') | |
test = pd.read_csv("test.csv", index_col='PassengerId') | |
# Merge train and test for wrangling and preprocessing | |
train_test_datasets = [train, test] | |
"""Data wrangling""" | |
# Split cabin into letter and number | |
median_age = train["Age"].median() | |
median_fare = train["Fare"].median() | |
for idx, dataset in enumerate(train_test_datasets): | |
dataset["Age"].fillna(median_age, inplace=True) | |
dataset["Fare"].fillna(median_fare, inplace=True) | |
dataset["Cabin Letter"] = dataset["Cabin"].str.slice(0, 1) | |
dataset.drop("Cabin", axis=1, inplace=True) | |
#dataset["Embarked"] = dataset["Embarked"].cat.codes | |
dataset.drop(["Name", "Ticket"], axis=1, inplace=True) | |
categorical_cols = ["Pclass", "Sex", "Embarked", "Cabin Letter","SibSp"] | |
train_dummies = pd.get_dummies(train, | |
columns=categorical_cols, | |
prefix=categorical_cols, | |
dummy_na=True) | |
test_dummies = pd.get_dummies(test, | |
columns=categorical_cols, | |
prefix=categorical_cols, | |
dummy_na=True) | |
test_dummies["Cabin Letter_T"] = np.zeros(test_dummies.shape[0]) | |
"""BALANCING DATA""" | |
number_surviving = (train_dummies['Survived'] == 1).sum() # Number of survivors | |
bool_survivors = train_dummies['Survived'] == 1 | |
bool_nonsurvivors = train_dummies['Survived'] == 0 | |
all_survivors = train_dummies[bool_survivors] | |
all_nonsurvivors = train_dummies[bool_nonsurvivors] | |
random_nonsurvivors = all_nonsurvivors.sample(number_surviving) | |
train_balanced = pd.concat((all_survivors, random_nonsurvivors)) | |
train_balanced = train_balanced.sample(frac=1) | |
scaler = StandardScaler() | |
scaler.fit(train_dummies.iloc[:,1:]) | |
"""STANDARDIZATION""" | |
train_scaled = scaler.transform(train_balanced.iloc[:,1:]) | |
test_scaled = scaler.transform(test_dummies) | |
train_scaled = pd.DataFrame(train_scaled, | |
index=train_balanced.index, | |
columns=train_balanced.iloc[:,1:].columns) | |
test_scaled = pd.DataFrame(test_scaled, | |
index=test_dummies.index, | |
columns=test_dummies.columns) | |
y = train_balanced["Survived"] | |
"""VALIDATION DATA SPLIT""" | |
# Do a train, validate dataset split | |
X = train_scaled | |
X_train, X_validate, y_train, y_validate = train_test_split(X, y, | |
test_size=0.1) | |
"""Setup the network""" | |
train_features = torch.tensor(X_train.to_numpy()) | |
train_labels = torch.tensor(y_train.to_numpy()) | |
validation_features = torch.tensor(X_validate.to_numpy()) | |
validation_labels = torch.tensor(y_validate.to_numpy()) | |
"""Mini Batches""" | |
n_batches = 41 | |
train_features_batched = train_features.reshape(41, | |
int(train_features.shape[0]/n_batches), | |
train_features.shape[1]) | |
train_labels_batched = train_labels.reshape(n_batches, | |
int(train_labels.shape[0]/n_batches)) | |
n_features = train_features.shape[1] | |
model = torch.nn.Sequential(torch.nn.Linear(n_features, 50), | |
torch.nn.ReLU(), | |
torch.nn.Linear(50, 1), | |
torch.nn.Sigmoid()) | |
model = model.float() | |
criterion = torch.nn.BCELoss() | |
#optimizer = torch.optim.Adam(model.parameters(), lr=0.00001, weight_decay=0.001) | |
optimizer = torch.optim.Adam(model.parameters(), lr=0.0001, weight_decay=0.001) | |
n_epochs = 2000 | |
loss_list = [] | |
validate_loss_list = [] | |
for epoch in range(n_epochs): # loop over the dataset multiple times | |
for batch_idx in range(n_batches): | |
optimizer.zero_grad() | |
outputs = model(train_features_batched[batch_idx].float()) | |
loss = criterion(outputs.flatten().float(), | |
train_labels_batched[batch_idx].float()) | |
loss.backward() | |
optimizer.step() | |
outputs = model(train_features.float()) | |
validation_outputs = model(validation_features.float()) | |
loss = criterion(outputs.flatten().float(), | |
train_labels.float()) | |
validate_loss = criterion(validation_outputs.flatten().float(), | |
validation_labels.float()) | |
loss_list.append(loss.item()) | |
validate_loss_list.append(validate_loss) | |
print('Finished Training') | |
plt.rcParams['svg.fonttype'] = 'none' | |
sns.set(context='paper', | |
style='whitegrid', | |
palette='colorblind', | |
font='Arial', | |
font_scale=2, | |
color_codes=True) | |
plt.plot(loss_list, linewidth=3) | |
plt.plot(validate_loss_list, linewidth=3) | |
plt.legend(("Training Loss", "Validation Loss")) | |
plt.xlabel("Epoch") | |
plt.ylabel("BCE Loss") | |
test_features = torch.tensor(test_scaled.to_numpy()) | |
test_prediction = model(test_features.float()).detach().numpy().flatten() | |
test_prediction_binary = (test_prediction > 0.5).astype(np.int) | |
test_prediction_df = pd.DataFrame(test_prediction_binary, | |
index=test.index, | |
columns=["Survived"]) | |
test_prediction_df.to_csv("prediction_submission_trained.csv") |
Sign up for free
to join this conversation on GitHub.
Already have an account?
Sign in to comment