def main():
# =========================================
# === Settings
# =========================================
# Get logger
logger = get_logger(__name__)
logger.info('Settings')
# Get argument
parser = argparse.ArgumentParser()
parser.add_argument('--config', default='./configs/model_0.json')
parser.add_argument("--debug", action="store_true")
args = parser.parse_args()
logger.info(f'config: {args.config}')
logger.info(f'debug: {args.debug}')
# Get config
config = json.load(open(args.config))
config.update({'args': {'config': args.config, 'debug': args.debug}})
if config["model"]["name"] == "lightgbm":
config["model"]["model_params"]["nthread"] = cpu_count()
# Create a directory for model output
model_no = pathlib.Path(args.config).stem
model_output_dir = (pathlib.Path(config['dataset']['output_directory']) /
model_no)
if not model_output_dir.exists():
model_output_dir.mkdir()
logger.info(f'model_output_dir: {str(model_output_dir)}')
logger.debug(f'model_output_dir exists: {model_output_dir.exists()}')
config.update({'model_output_dir': str(model_output_dir)})
# =========================================
# === Loading data
# =========================================
logger.info('Loading data')
# Get train and test
input_dir = pathlib.Path(config['dataset']['input_directory'])
train = pd.read_csv(input_dir / 'train.csv')
test = pd.read_csv(input_dir / 'test.csv')
# Get target values
target_column = config['data_type']['target']
y_train = train[target_column].values
# =========================================
# === Loading features
# =========================================
logger.info('Loading features')
# Get features
x_train, x_test = load_features(config)
feature_name = x_test.columns
logger.debug(f'number of features: {len(feature_name)}')
# =========================================
# === Adversarial Validation
# =========================================
logger.info("adversarial validation")
train_adv = x_train
test_adv = x_test
train_adv['target'] = 0
test_adv['target'] = 1
train_test_adv = pd.concat([train_adv, test_adv], axis=0,
sort=False).reset_index(drop=True)
target = train_test_adv['target'].values
train_set, val_set = train_test_split(train_test_adv,
test_size=0.33,
random_state=71,
shuffle=True)
x_train_adv = train_set[feature_name]
y_train_adv = train_set['target']
x_val_adv = val_set[feature_name]
y_val_adv = val_set['target']
logger.debug(f'the number of train set: {len(x_train_adv)}')
logger.debug(f'the number of valid set: {len(x_val_adv)}')
train_lgb = lgb.Dataset(x_train_adv, label=y_train_adv)
val_lgb = lgb.Dataset(x_val_adv, label=y_val_adv)
lgb_model_params = config["adversarial_validation"]["lgb_model_params"]
lgb_train_params = config["adversarial_validation"]["lgb_train_params"]
clf = lgb.train(lgb_model_params,
train_lgb,
valid_sets=[train_lgb, val_lgb],
valid_names=['train', 'valid'],
**lgb_train_params)
feature_imp = pd.DataFrame(sorted(
zip(clf.feature_importance(importance_type='gain'), feature_name)),
columns=['value', 'feature'])
plt.figure(figsize=(20, 10))
sns.barplot(x='value',
y='feature',
data=feature_imp.sort_values(by='value',
ascending=False).head(20))
plt.title('LightGBM Features')
plt.tight_layout()
plt.savefig(model_output_dir / "feature_importance_adv.png")
config.update({
'adversarial_validation_result': {
'score':
clf.best_score,
'feature_importances':
feature_imp.set_index("feature").sort_values(
by="value", ascending=False).head(20).to_dict()["value"]
}
})
# =========================================
# === Train model and predict
# =========================================
logger.info('Train model and predict')
# Get features
x_train, x_test = load_features(config)
feature_name = x_test.columns
logger.debug(f'number of features: {len(feature_name)}')
# Get folds
folds_ids = Fold(
n_splits=config['cv']['n_splits'],
shuffle=config['cv']['shuffle'],
random_state=config['cv']['random_state']).get_stratifiedkfold(
x_train, y_train)
# Train and predict
model_name = config['model']['name']
model_cls = model_map[model_name]
params = config['model']
runner = Runner(model_cls, params, model_output_dir,
f'Train_{model_cls.__name__}')
oof_preds, evals_result = runner.train_cv(x_train, y_train, folds_ids)
config.update(evals_result)
test_preds = runner.predict_cv(x_test)
# =========================================
# === Make submission file
# =========================================
sub = create_submission(test, test_preds, target_column)
sub.to_csv(model_output_dir / 'submission.csv', index=False, header=True)
# =========================================
# === Save files
# =========================================
save_path = model_output_dir / 'output.json'
json_dump(config, save_path)
pd.DataFrame(oof_preds,
columns=["target"]).to_csv(model_output_dir / 'oof.csv',
index=False,
header=True)
def main():
# =========================================
# === Settings
# =========================================
# Get logger
logger = get_logger(__name__)
logger.info('Settings')
# Get argument
parser = argparse.ArgumentParser()
parser.add_argument(
'--config', default='model_lgb_hakubishin_20200317/configs/model_0.json')
parser.add_argument("--debug", action="store_true")
args = parser.parse_args()
logger.info(f'config: {args.config}')
logger.info(f'debug: {args.debug}')
# Get config
config = json.load(open(args.config))
config.update({
'args': {
'config': args.config,
'debug': args.debug
}
})
config["model"]["model_params"]["nthread"] = cpu_count()
# Create a directory for model output
model_no = pathlib.Path(args.config).stem
model_output_dir = (
pathlib.Path(config['model_dir_name']) /
pathlib.Path(config['dataset']['output_directory']) / model_no
)
if not model_output_dir.exists():
model_output_dir.mkdir()
logger.info(f'model_output_dir: {str(model_output_dir)}')
logger.debug(f'model_output_dir exists: {model_output_dir.exists()}')
config.update({
'model_output_dir': str(model_output_dir)
})
# =========================================
# === Loading features
# =========================================
logger.info('Loading features')
logger.info(f'targets: {config["target"]}')
logger.info(f'features: {config["features"]}')
logger.info(f'keys: {config["key"]}')
logger.info(f'folds: {config["folds"]}')
# features
x_train = FeatureLoader(
data_type="training", debugging=args.debug
).load_features(config["features"])
x_test = FeatureLoader(
data_type=config["test_data_type"], debugging=args.debug
).load_features(config["features"])
# targets
y_train_set = FeatureLoader(
data_type="training", debugging=args.debug
).load_features(config["target"])
# keys
key_test = FeatureLoader(
data_type=config["test_data_type"], debugging=args.debug
).load_features(config["key"])
# folds
folds_train = FeatureLoader(
data_type="training", debugging=args.debug
).load_features(config["folds"])
logger.debug(f'test_data_type: {config["test_data_type"]}')
logger.debug(f'y_train_set: {y_train_set.shape}')
logger.debug(f'x_train: {x_train.shape}')
logger.debug(f'x_test: {x_test.shape}')
logger.debug(f'key_test: {key_test.shape}')
# =========================================
# === Train model and predict
# =========================================
logger.info('Train model and predict')
# Modeling
target_columns = [
"reply_engagement",
"retweet_engagement",
"retweet_with_comment_engagement",
"like_engagement",
]
for cat in target_columns:
logger.info(f'============= {cat} =============')
# Get target values
y_train = y_train_set[f"TargetCategories_{cat}"].values
# Get folds
folds_col = ["StratifiedGroupKFold_retweet_with_comment_engagement"]
assert len(folds_col) == 1, "The number of fold column must be one"
folds = folds_train[folds_col]
n_fold = folds.max().values[0] + 1
folds_ids = []
logger.debug(f"total pos: {y_train.sum()}")
for i in range(n_fold):
trn_idx = folds[folds != i].dropna().index
val_idx = folds[folds == i].dropna().index
folds_ids.append((trn_idx, val_idx))
logger.debug(f"{i+1}fold: n_trn={len(trn_idx)}, n_val={len(val_idx)}")
logger.debug(f"{i+1}fold: trn_pos={y_train[trn_idx].sum()}, val_pos={y_train[val_idx].sum()}")
# Train and predict
model_cls = model_map[config['model']['name']]
model_params = config['model']
runner = Runner(
model_cls, model_params, model_output_dir, f'Train_{model_cls.__name__}_{cat}'
)
oof_preds, test_preds, evals_result = runner.train_cv(
x_train, y_train, x_test, folds_ids, config)
evals_result[f"evals_result_{cat}"] = evals_result["evals_result"]
evals_result.pop("evals_result")
config.update(evals_result)
# Save oof-pred file
oof_preds_file_name = f"{cat}_oof_pred"
np.save(model_output_dir / oof_preds_file_name, oof_preds)
logger.info(f'Save oof-pred file: {model_output_dir/ oof_preds_file_name}')
# Make submission file
sub = pd.concat([key_test, pd.Series(test_preds).rename("pred")], axis=1)
sub = sub[["KeyCategories_tweet_id", "KeyCategories_engaging_user_id", "pred"]]
sub_file_name = f"{cat}_submission_{config['test_data_type']}.csv"
sub.to_csv(model_output_dir/ sub_file_name, index=False, header=False)
logger.info(f'Save submission file: {model_output_dir/ sub_file_name}')
# Save files (override)
logger.info('Save files')
save_path = model_output_dir / 'output.json'
json_dump(config, save_path)
logger.info(f'Save model log: {save_path}')
# =========================================
# === Upload to GCS
# =========================================
if not args.debug:
logger.info('Upload to GCS')
bucket_dir_name = config["model_dir_name"] + "/" + model_no
logger.info(f'bucket_dir_name: {bucket_dir_name}')
files = list(model_output_dir.iterdir())
upload_to_gcs(bucket_dir_name, files)
def main():
# =========================================
# === Settings
# =========================================
# Get logger
logger = get_logger(__name__)
logger.info('Settings')
# Get argument
parser = argparse.ArgumentParser()
parser.add_argument(
'--config', default='model_lgb_hakubishin_20200317/configs/model_0.json')
parser.add_argument("--debug", action="store_true")
args = parser.parse_args()
logger.info(f'config: {args.config}')
logger.info(f'debug: {args.debug}')
# Get config
config = json.load(open(args.config))
config.update({
'args': {
'config': args.config,
'debug': args.debug
}
})
config["model"]["model_params"]["nthread"] = cpu_count()
# Create a directory for model output
model_no = pathlib.Path(args.config).stem
model_output_dir = (
pathlib.Path(config['model_dir_name']) /
pathlib.Path(config['dataset']['output_directory']) / model_no
)
if not model_output_dir.exists():
model_output_dir.mkdir()
logger.info(f'model_output_dir: {str(model_output_dir)}')
logger.debug(f'model_output_dir exists: {model_output_dir.exists()}')
config.update({
'model_output_dir': str(model_output_dir)
})
# =========================================
# === Loading features
# =========================================
logger.info('Loading features')
logger.info(f'targets: {config["target"]}')
logger.info(f'features: {config["features"]}')
# features
x_train = FeatureLoader(
data_type="training", debugging=args.debug
).load_features(config["features"])
# targets
y_train_set = FeatureLoader(
data_type="training", debugging=args.debug
).load_features(config["target"])
# folds
folds_train = FeatureLoader(
data_type="training", debugging=args.debug
).load_features(config["folds"])
logger.debug(f'y_train_set: {y_train_set.shape}')
logger.debug(f'x_train: {x_train.shape}')
# =========================================
# === Train model and predict
# =========================================
logger.info('Train model and predict')
# Get target values
y_train = y_train_set["Target_answered_correctly"].values
# Get folds
trn_idx = folds_train.query("Fold_val != 1").index
val_idx = folds_train.query("Fold_val == 1").index
folds_ids = [(trn_idx, val_idx)]
logger.debug(f"n_trn={len(trn_idx)}, n_val={len(val_idx)}")
logger.debug(f"trn_pos={y_train[trn_idx].sum()}, val_pos={y_train[val_idx].sum()}")
# Train and predict
model_cls = model_map[config['model']['name']]
model_params = config['model']
runner = Runner(
model_cls, model_params, model_output_dir, f'{model_cls.__name__}', n_fold=1,
)
oof_preds, evals_result, importances = runner.train_cv(
x_train, y_train, folds_ids)
config.update(evals_result)
# Save importances
importances.mean(axis=1).reset_index().rename(
columns={"index": "feature", 0: "value"}
).sort_values("value", ascending=False).to_csv(
model_output_dir / "importances.csv", index=False
)
# Save oof-pred file
oof_preds_file_name = f"oof_pred"
np.save(model_output_dir / oof_preds_file_name, oof_preds)
logger.info(f'Save oof-pred file: {model_output_dir/ oof_preds_file_name}')
# Save files (override)
logger.info('Save files')
save_path = model_output_dir / 'output.json'
json_dump(config, save_path)
logger.info(f'Save model log: {save_path}')
# =========================================
# === Upload to GCS
# =========================================
if not args.debug:
logger.info('Upload to GCS')
bucket_dir_name = config["model_dir_name"] + "/" + model_no
logger.info(f'bucket_dir_name: {bucket_dir_name}')
files = list(model_output_dir.iterdir())
upload_to_gcs(bucket_dir_name, files)
def main():
# =========================================
# === Settings
# =========================================
# Get logger
logger = get_logger(__name__)
logger.info('Settings')
# Get argument
parser = argparse.ArgumentParser()
parser.add_argument('--config', default='./configs/model_1dcnn_0.json')
parser.add_argument("--debug", action="store_true")
args = parser.parse_args()
logger.info(f'config: {args.config}')
logger.info(f'debug: {args.debug}')
# Get config
config = json.load(open(args.config))
config.update({'args': {'config': args.config, 'debug': args.debug}})
if config["model"]["name"] == "lightgbm":
config["model"]["model_params"]["nthread"] = cpu_count()
# Create a directory for model output
model_no = pathlib.Path(args.config).stem
model_output_dir = (pathlib.Path(config['dataset']['output_directory']) /
model_no)
if not model_output_dir.exists():
model_output_dir.mkdir()
logger.info(f'model_output_dir: {str(model_output_dir)}')
logger.debug(f'model_output_dir exists: {model_output_dir.exists()}')
config.update({'model_output_dir': str(model_output_dir)})
# =========================================
# === Loading data
# =========================================
logger.info('Loading data')
# Get train and test
input_dir = pathlib.Path(config['dataset']['input_directory'])
train = pd.read_csv(input_dir / 'train.csv')
test = pd.read_csv(input_dir / 'test.csv')
spectrum = pd.read_csv(input_dir / 'spectrum_stack.csv')
spectrum_fitting = pd.read_csv(input_dir / 'spectrum_fitting_stack.csv')
wv_cols = [f"wavelength_{i}" for i in range(512)]
wv_fit_cols = [f"fitting_wavelength_{i}" for i in range(512)]
train_spectrum = pd.merge(train,
spectrum,
on="spectrum_filename",
how="left")
test_spectrum = pd.merge(test,
spectrum,
on="spectrum_filename",
how="left")
train_spectrum = pd.merge(train_spectrum,
spectrum_fitting,
on="spectrum_filename",
how="left")
test_spectrum = pd.merge(test_spectrum,
spectrum_fitting,
on="spectrum_filename",
how="left")
train_std = np.std(train_spectrum[wv_cols].values, axis=1, keepdims=True)
test_std = np.std(test_spectrum[wv_cols].values, axis=1, keepdims=True)
train_spectrum[wv_cols] = train_spectrum[wv_cols].values / train_std
test_spectrum[wv_cols] = test_spectrum[wv_cols].values / test_std
spectrum_cols = wv_cols + wv_fit_cols
train_spectrum = train_spectrum[spectrum_cols]
test_spectrum = test_spectrum[spectrum_cols]
# Get target values
target_column = config['data_type']['target']
y_train = train[target_column].values
# =========================================
# === Loading features
# =========================================
logger.info('Loading features')
# Get features
x_train, x_test = load_features(config)
feature_name = x_test.columns
logger.debug(f'number of features: {len(feature_name)}')
# =========================================
# === features preprocess
# =========================================
x_total = x_train.append(x_test).reset_index(drop=True)
remove_features = [c for c in x_total.columns if c.find("layout_x") != -1]
remove_features += [c for c in x_total.columns if c.find("layout_y") != -1]
x_total.drop(columns=remove_features, inplace=True)
x_total = pd.get_dummies(
x_total, columns=["LabelEncoding_exc_wl", "LabelEncoding_layout_a"])
x_total.fillna(0, inplace=True)
from sklearn.preprocessing import StandardScaler
numeric_features = [
c for c in x_total.columns if c.find("LabelEncoding_") == -1
]
sc = StandardScaler()
x_total[numeric_features] = sc.fit_transform(x_total[numeric_features])
x_train = x_total.iloc[:len(train)]
x_test = x_total.iloc[len(train):].reset_index(drop=True)
x_train = pd.concat([x_train, train_spectrum], axis=1)
x_test = pd.concat([x_test, test_spectrum], axis=1)
logger.debug(f'number of features with spec in train: {x_train.shape}')
logger.debug(f'number of features with spec in test: {x_test.shape}')
# =========================================
# === Train model and predict
# =========================================
logger.info('Train model and predict')
# Get folds
folds_ids = Fold(
n_splits=config['cv']['n_splits'],
shuffle=config['cv']['shuffle'],
random_state=config['cv']['random_state']).get_stratifiedkfold(
x_train, y_train)
# Train and predict
model_name = config['model']['name']
model_cls = model_map[model_name]
params = config['model']
runner = Runner(model_cls, params, model_output_dir,
f'Train_{model_cls.__name__}')
oof_preds, evals_result = runner.train_cv(x_train, y_train, folds_ids)
config.update(evals_result)
test_preds = runner.predict_cv(x_test)
# =========================================
# === Make submission file
# =========================================
sub = create_submission(test, test_preds, target_column)
sub.to_csv(model_output_dir / 'submission.csv', index=False, header=True)
# =========================================
# === Save files
# =========================================
save_path = model_output_dir / 'output.json'
json_dump(config, save_path)
pd.DataFrame(oof_preds,
columns=["target"]).to_csv(model_output_dir / 'oof.csv',
index=False,
header=True)
