def load_rf_data(cur_path):
data_folder = "data\\titanic"
processed_data_folder = os.path.join(cur_path, data_folder)
# Note: Not using test.csv as it does not provide whether or not the passenger survived; therefore we cannot assess
# how well the model performed.
data_file_path = os.path.join(processed_data_folder, "train.csv")
data = DataProcessor(data_file_path, processed_data_folder)
try:
#Try to load data
data.load_processed_data()
except FileNotFoundError:
#No data found, so process it
# 10% test, 10% validation, 80% training samples from data
splits = (0.1, 0.1, 0.8)
# Only use certain columns
use_cols = ( # 0, #PassengerID
1, # Survived
2, # Pclass
# 3, #Name
4, # Sex
5, # Age
6, # SibSp
7, # Parch
# 8, #Ticket
9, # Fare
# 10, #Cabin
11, # Embarked
)
# Mark features as categorical (so we can one-hot-encode them later)
# categorical_cols = ()
categorical_cols = (2, # Pclass
4, # Sex
11 # Embarked
)
# Convert certain columns to float values (so we can use numpy arrays)
converters = {4: lambda sex: {'male': 0.0, 'female': 1.0}[sex],
11: lambda embarked: {'S': 0.0, 'C': 1.0, 'Q': 2.0}[embarked]}
data.process_data(splits=splits, use_cols=use_cols, categorical_cols=categorical_cols, converters=converters,
filter_missing=True)
return data
import sys
from data_processing import DataProcessor
from matplotlib import pyplot
cur_path = os.path.dirname(__file__)
data_folder = "data\\titanic"
processed_data_folder = os.path.join(cur_path, data_folder)
# Note: Not using test.csv as it does not provide whether or not the passenger survived; therefore we cannot assess
# how well the model performed.
data_file_path = os.path.join(processed_data_folder, "train.csv")
data_processor = DataProcessor(data_file_path, processed_data_folder, "ffnn_processed.npz")
# Load data
try:
# Try to load data
data_processor.load_processed_data()
except FileNotFoundError:
# No data found, so process it
# 20% test, 20% validation, 60% training samples from data
splits = (0.2, 0.2, 0.6)
# Only use certain columns
use_cols = ( # 0, #PassengerID
1, # Survived
2, # Pclass
# 3, #Name
4, # Sex
5, # Age
6, # SibSp
7, # Parch
# 8, #Ticket