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 time
import logging
from riiid.core.data import DataLoader
from riiid.saint.model import SaintModel
from riiid.utils import configure_console_logging
from riiid.config import INPUT_PATH, MODELS_PATH
configure_console_logging()
logging.info('Loading model')
MODEL_ID = 'saint_20210104_024103'
model: SaintModel = SaintModel.load(MODELS_PATH, MODEL_ID)
tests = DataLoader(INPUT_PATH).load_tests_examples()
for i, test in enumerate(tests):
if model.test_batch == 1:
start = time.perf_counter()
test = model.update(test)
_, predictions = model.predict(test)
end = time.perf_counter()
total = end - start
logging.info('Time spent: {:.1f}s ({:.3f}s by batch)'.format(total, total / model.test_batch))
import os from riiid.config import INPUT_PATH from riiid.core.data import DataLoader, save_pkl, load_pkl from riiid.validation import generate_test loader = DataLoader(INPUT_PATH) train, _, _ = loader.load() test = generate_test(train, size=2_500_000, N=10_000, seed=0) save_pkl(test, os.path.join(INPUT_PATH, 'tests_0.pkl')) test = generate_test(train, size=5_000_000, N=20_000, seed=0) save_pkl(test, os.path.join(INPUT_PATH, 'tests_1.pkl'))
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)
import os
from riiid.core.data import DataLoader, preprocess_questions, preprocess_lectures, save_pkl
from riiid.core.model import RiiidModel
from riiid.validation import merge_test
from riiid.utils import configure_console_logging
from riiid.config import INPUT_PATH, MODELS_PATH, PARAMS
configure_console_logging()
# Load and preprocess data
loader = DataLoader(INPUT_PATH)
train, questions, lectures = loader.load_first_users(30000)
questions = preprocess_questions(questions)
lectures = preprocess_lectures(lectures)
# Load and merge validation set
test = loader.load_tests('tests_0.pkl')
train = merge_test(train, test)
# Compute features
model = RiiidModel(questions, lectures, params=PARAMS)
X, y, train, valid = model.fit_transform(train)
save_pkl((X, y, train, valid),
path=os.path.join(MODELS_PATH, model.get_name('data.pkl')))
# Fit models
model.fit_lgbm(X[train], y[train], X[valid], y[valid])
model.fit_catboost(X[train], y[train], X[valid], y[valid])
model.fit_neural(X[train], y[train], X[valid], y[valid])
model.fit_blender(X[valid], y[valid])
from doppel.aws.s3 import S3Bucket
from riiid.core.data import DataLoader, preprocess_questions, preprocess_lectures
from riiid.core.model import RiiidModel
from riiid.validation import merge_test
from riiid.config import PARAMS
from riiid import cache
from riiid.aws.cache import S3CacheManager
from riiid.aws.config import CONTEXT
CONTEXT.get_logger()
try:
cache.CACHE_MANAGER = S3CacheManager('kaggle-riiid-cache')
loader = DataLoader(CONTEXT.data_path())
train, questions, lectures = loader.load()
questions = preprocess_questions(questions)
lectures = preprocess_lectures(lectures)
test = loader.load_tests('tests_0.pkl')
train = merge_test(train, test)
del test
PARAMS['question_embedding']['workers'] = 32
PARAMS['answers_embedding']['workers'] = 32
model = RiiidModel(questions, lectures, params=PARAMS)
X, y, train, valid = model.fit_transform(train)
bucket = S3Bucket(model.get_normalized_name())
import logging
from doppel import terminate
from doppel.aws.s3 import S3Bucket
from riiid.core.data import DataLoader
from riiid.saint.model import SaintModel
from riiid.aws.config import CONTEXT
CONTEXT.get_logger()
try:
loader = DataLoader(CONTEXT.data_path())
train, questions, lectures = loader.load()
model = SaintModel(questions, lectures)
train = model.fit_transform(train)
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)
bucket = S3Bucket(model.get_normalized_name())
logging.info('Saving model')
bucket.save_pickle(model, model.get_name(ext='pkl'))
logging.info('Saving data')
bucket.save_pickle_multiparts((X_train, y_train, X_test, y_test),
model.get_name('data.pkl'))