def get_data(n=1000):
loader = DataLoader(INPUT_PATH)
train, questions, lectures = loader.load_first_users(n)
questions = preprocess_questions(questions)
lectures = preprocess_lectures(lectures)
test = loader.load_tests('tests_1.pkl')
return train, questions, lectures, test
def score_params(params, n_users=30000):
loader = DataLoader(CONTEXT.data_path())
train, questions, lectures = loader.load_first_users(n_users)
questions = preprocess_questions(questions)
lectures = preprocess_lectures(lectures)
test = loader.load_tests('tests_0.pkl')
train = merge_test(train, test)
del test
model = RiiidModel(questions, lectures, params)
X, y, train, valid = model.fit_transform(train)
model.fit_lgbm(X[train], y[train], X[valid], y[valid])
return model.best_score, model.best_iteration
import os
from riiid.core.data import DataLoader, save_pkl
from riiid.saint.model import SaintModel
from riiid.utils import configure_console_logging
from riiid.config import INPUT_PATH, MODELS_PATH
configure_console_logging()
# Load data
loader = DataLoader(INPUT_PATH)
train, questions, lectures = loader.load_first_users(30000)
# Compute features
model = SaintModel(questions, lectures)
train = model.fit_transform(train)
# Create train and validation datasets
train, test = model.split_train_test(train)
train = model.create_features(train)
test = model.create_features(test)
X_train, y_train = model.create_dataset(train)
X_test, y_test = model.create_dataset(test)
save_pkl((X_train, y_train, X_test, y_test), os.path.join(MODELS_PATH, model.get_name('data.pkl')))
# Fit model
model.fit(X_train, y_train, X_test, y_test)
model.score(X_test, y_test)
# Save model
model.save(MODELS_PATH)