def test_custom_priors():
output_model_path = yatest.common.test_output_path('model.bin')
output_eval_path = yatest.common.test_output_path('test.eval')
cmd = (
CATBOOST_PATH,
'fit',
'--loss-function', 'Logloss',
'-f', data_file('adult', 'train_small'),
'--column-description', data_file('adult', 'train.cd'),
'-i', '10',
'-T', '4',
'-r', '0',
'-m', output_model_path,
'--priors', '-2:0:8',
'--feature-priors', '0:1,1:2,2:-1:0',
'--eval-file', output_eval_path,
)
yatest.common.execute(cmd)
return [local_canonical_file(output_eval_path)]
def test_crossentropy():
output_model_path = yatest.common.test_output_path('model.bin')
output_eval_path = yatest.common.test_output_path('test.eval')
cmd = (
CATBOOST_PATH,
'fit',
'--loss-function', 'CrossEntropy',
'-f', data_file('adult_crossentropy', 'train_proba'),
'-t', data_file('adult_crossentropy', 'test_proba'),
'--column-description', data_file('adult_crossentropy', 'train.cd'),
'-i', '10',
'-T', '4',
'-r', '0',
'-m', output_model_path,
'--eval-file', output_eval_path,
)
yatest.common.execute(cmd)
return [local_canonical_file(output_eval_path)]
def test_train_dir():
pool = 'no_split'
output_model_path = 'model.bin'
train_dir_path = 'trainDir'
params = (
'--use-best-model', 'false',
'--loss-function', 'RMSE',
'-f', data_file(pool, 'train_full3'),
'-t', data_file(pool, 'test3'),
'--column-description', data_file(pool, 'train_full3.cd'),
'-i', '10',
'-T', '4',
'-r', '0',
'-m', output_model_path,
'--train-dir', train_dir_path,
)
fit_catboost_gpu(params)
outputs = ['time_left.tsv', 'learn_error.tsv', 'test_error.tsv', 'meta.tsv', output_model_path]
for output in outputs:
assert os.path.isfile(train_dir_path + '/' + output)
def test_meta(loss_function, boosting_type):
output_model_path = yatest.common.test_output_path('model.bin')
meta_path = 'meta.tsv'
params = (
'--use-best-model', 'false',
'--loss-function', loss_function,
'-f', data_file('adult', 'train_small'),
'-t', data_file('adult', 'test_small'),
'--column-description', data_file('adult', 'train.cd'),
'--boosting-type', boosting_type,
'-i', '10',
'-T', '4',
'-r', '0',
'-m', output_model_path,
'--name', 'test experiment',
)
# meta_path is implicit output file
fit_catboost_gpu(params, output_data={meta_path: meta_path})
return [local_canonical_file(meta_path)]
def test_deep_tree_classification(depth):
output_model_path = yatest.common.test_output_path('model.bin')
output_eval_path = yatest.common.test_output_path('test.eval')
cmd = (
CATBOOST_PATH,
'fit',
'--loss-function', 'Logloss',
'-f', data_file('adult', 'train_small'),
'-t', data_file('adult', 'test_small'),
'--column-description', data_file('adult', 'train.cd'),
'-i', '10',
'-T', '4',
'-r', '0',
'--depth', str(depth),
'-m', output_model_path,
'--eval-file', output_eval_path,
)
yatest.common.execute(cmd)
return [local_canonical_file(output_eval_path)]
def test_queryaverage():
learn_error_path = yatest.common.test_output_path('learn_error.tsv')
test_error_path = yatest.common.test_output_path('test_error.tsv')
cmd = (
CATBOOST_PATH,
'fit',
'--loss-function', 'QueryRMSE',
'-f', data_file('querywise_pool', 'train_full3'),
'-t', data_file('querywise_pool', 'test3'),
'--column-description', data_file('querywise_pool', 'train_full3.cd'),
'-i', '20',
'-T', '4',
'-r', '0',
'--custom-metric', 'QueryAverage:top=2',
'--learn-err-log', learn_error_path,
'--test-err-log', test_error_path,
)
yatest.common.execute(cmd)
return [local_canonical_file(learn_error_path), local_canonical_file(test_error_path)]
def test_weights(boosting_type):
output_model_path = yatest.common.test_output_path('model.bin')
output_eval_path = yatest.common.test_output_path('test.eval')
cd_file = data_file('adult_weight', 'train.cd')
test_file = data_file('adult_weight', 'test_weight')
params = {
'--use-best-model': 'false',
'--loss-function': 'Logloss',
'-f': data_file('adult_weight', 'train_weight'),
'-t': test_file,
'--column-description': cd_file,
'--boosting-type': boosting_type,
'-i': '10',
'-T': '4',
'-r': '0',
'-m': output_model_path,
}
fit_catboost_gpu(params)
apply_catboost(output_model_path, test_file, cd_file, output_eval_path)
return [local_canonical_file(output_eval_path)]
def test_prediction_type(prediction_type, loss_function):
output_model_path = yatest.common.test_output_path('model.bin')
output_eval_path = yatest.common.test_output_path('test.eval')
cmd = (
CATBOOST_PATH,
'fit',
'--loss-function', loss_function,
'-f', data_file('adult', 'train_small'),
'-t', data_file('adult', 'test_small'),
'--column-description', data_file('adult', 'train.cd'),
'-i', '10',
'-T', '4',
'-r', '0',
'-m', output_model_path,
'--eval-file', output_eval_path,
'--prediction-type', prediction_type
)
yatest.common.execute(cmd)
return [local_canonical_file(output_eval_path)]
def test_feature_border_types(border_type):
output_model_path = yatest.common.test_output_path('model.bin')
output_eval_path = yatest.common.test_output_path('test.eval')
cmd = (
CATBOOST_PATH,
'fit',
'--loss-function', 'Logloss',
'-f', data_file('adult', 'train_small'),
'-t', data_file('adult', 'test_small'),
'--column-description', data_file('adult', 'train.cd'),
'-i', '10',
'-T', '4',
'-r', '0',
'-m', output_model_path,
'--eval-file', output_eval_path,
'--feature-border-type', border_type,
)
yatest.common.execute(cmd)
return [local_canonical_file(output_eval_path)]
def test_queryrmse_approx_on_full_history():
output_model_path = yatest.common.test_output_path('model.bin')
output_eval_path = yatest.common.test_output_path('test.eval')
cmd = (
CATBOOST_PATH,
'fit',
'--loss-function', 'QueryRMSE',
'-f', data_file('querywise_pool', 'train_full3'),
'-t', data_file('querywise_pool', 'test3'),
'--column-description', data_file('querywise_pool', 'train_full3.cd'),
'--approx-on-full-history',
'-i', '20',
'-T', '4',
'-r', '0',
'-m', output_model_path,
'--eval-file', output_eval_path,
)
yatest.common.execute(cmd)
return [local_canonical_file(output_eval_path)]
def test_baseline():
output_model_path = yatest.common.test_output_path('model.bin')
output_eval_path = yatest.common.test_output_path('test.eval')
cmd = (
CATBOOST_PATH,
'fit',
'--loss-function', 'Logloss',
'-f', data_file('adult_weight', 'train_weight'),
'-t', data_file('adult_weight', 'test_weight'),
'--column-description', data_file('train_adult_baseline.cd'),
'-i', '10',
'-T', '4',
'-r', '0',
'-m', output_model_path,
'--eval-file', output_eval_path,
)
yatest.common.execute(cmd)
return [local_canonical_file(output_eval_path)]
def test_newton():
output_model_path = yatest.common.test_output_path('model.bin')
output_eval_path = yatest.common.test_output_path('test.eval')
cmd = (
CATBOOST_PATH,
'fit',
'--loss-function', 'Logloss',
'-f', data_file('adult', 'train_small'),
'-t', data_file('adult', 'test_small'),
'--column-description', data_file('adult', 'train.cd'),
'-i', '10',
'-T', '4',
'-r', '0',
'-m', output_model_path,
'--leaf-estimation-method', 'Newton',
'--eval-file', output_eval_path,
)
yatest.common.execute(cmd)
return [local_canonical_file(output_eval_path)]
def test_fold_len_multiplier():
output_model_path = yatest.common.test_output_path('model.bin')
output_eval_path = yatest.common.test_output_path('test.eval')
cmd = (
CATBOOST_PATH,
'fit',
'--loss-function', 'MultiClass',
'-f', data_file('adult', 'train_small'),
'-t', data_file('adult', 'test_small'),
'--column-description', data_file('adult', 'train.cd'),
'-i', '10',
'-T', '4',
'-r', '0',
'-m', output_model_path,
'--eval-file', output_eval_path,
'--fold-len-multiplier', '1.5'
)
yatest.common.execute(cmd)
return [local_canonical_file(output_eval_path)]
def test_custom_loss(custom_loss_function):
pool = 'no_split'
output_model_path = yatest.common.test_output_path('model.bin')
output_eval_path = yatest.common.test_output_path('test.eval')
learn_error_path = yatest.common.test_output_path('learn_error.tsv')
test_error_path = yatest.common.test_output_path('test_error.tsv')
cmd = (
CATBOOST_PATH,
'fit',
'--loss-function',
'RMSE',
'-f',
data_file(pool, 'train_full3'),
'-t',
data_file(pool, 'test3'),
'--column-description',
data_file(pool, 'train_full3.cd'),
'-i',
'10',
'-T',
'4',
'-r',
'0',
'-m',
output_model_path,
'--eval-file',
output_eval_path,
'--custom-loss',
custom_loss_function,
'--learn-err-log',
learn_error_path,
'--test-err-log',
test_error_path,
)
yatest.common.execute(cmd)
return [
local_canonical_file(learn_error_path),
local_canonical_file(test_error_path)
]
def test_bootstrap(boosting_type):
bootstrap_option = {
'no': {'--bootstrap-type': 'No'},
'bayes': {'--bootstrap-type': 'Bayesian', '--bagging-temperature': '0.0'},
'bernoulli': {'--bootstrap-type': 'Bernoulli', '--subsample': '1.0'}
}
test_file = data_file('adult', 'test_small')
cd_file = data_file('adult', 'train.cd')
params = {
'--use-best-model': 'false',
'--loss-function': 'Logloss',
'--random-strength': '0',
'-f': data_file('adult', 'train_small'),
'-t': test_file,
'--column-description': cd_file,
'--boosting-type': boosting_type,
'-i': '10',
'-w': '0.03',
'-T': '4',
'-r': '0',
}
for bootstrap in bootstrap_option:
model_path = yatest.common.test_output_path('model_' + bootstrap + '.bin')
eval_path = yatest.common.test_output_path('test_' + bootstrap + '.eval')
model_option = {'-m': model_path}
run_params = combine_dicts(params,
bootstrap_option[bootstrap],
model_option)
fit_catboost_gpu(run_params)
apply_catboost(model_path, test_file, cd_file, eval_path)
ref_eval_path = yatest.common.test_output_path('test_no.eval')
assert (filecmp.cmp(ref_eval_path, yatest.common.test_output_path('test_bayes.eval')))
assert (filecmp.cmp(ref_eval_path, yatest.common.test_output_path('test_bernoulli.eval')))
return [local_canonical_file(ref_eval_path)]
def test_has_time(boosting_type):
output_model_path = yatest.common.test_output_path('model.bin')
output_eval_path = yatest.common.test_output_path('test.eval')
cd_file = data_file('adult', 'train.cd')
test_file = data_file('adult', 'test_small')
params = (
'--use-best-model', 'false',
'--loss-function', 'Logloss',
'-f', data_file('adult', 'train_small'),
'-t', test_file,
'--column-description', cd_file,
'--boosting-type', boosting_type,
'-i', '10',
'-T', '4',
'-r', '0',
'--has-time',
'-m', output_model_path,
)
fit_catboost_gpu(params)
apply_catboost(output_model_path, test_file, cd_file, output_eval_path)
return [local_canonical_file(output_eval_path)]
def test_custom_priors(boosting_type):
output_model_path = yatest.common.test_output_path('model.bin')
output_eval_path = yatest.common.test_output_path('test.eval')
test_file = data_file('adult', 'test_small')
cd_file = data_file('adult', 'train.cd')
params = (
'--use-best-model', 'false', '--loss-function', 'Logloss', '-f',
data_file('adult', 'train_small'), '-t', test_file,
'--column-description', cd_file, '--boosting-type', boosting_type,
'-i', '10', '-T', '4', '-r', '0', '-m', output_model_path, '--ctr',
'Borders:Prior=-2:Prior=0:Prior=8/3:Prior=1:Prior=-1:Prior=3,'
'FeatureFreq:Prior=0', '--per-feature-ctr',
'4:Borders:Prior=0.444,FeatureFreq:Prior=0.444;'
'6:Borders:Prior=0.666,FeatureFreq:Prior=0.666;'
'8:Borders:Prior=-0.888:Prior=2/3,FeatureFreq:Prior=-0.888:Prior=0.888'
)
fit_catboost_gpu(params)
apply_catboost(output_model_path, test_file, cd_file, output_eval_path)
return [local_canonical_file(output_eval_path)]
def test_pairlogit_no_target():
output_model_path = yatest.common.test_output_path('model.bin')
output_eval_path = yatest.common.test_output_path('test.eval')
cmd = (
CATBOOST_PATH,
'fit',
'--loss-function', 'PairLogit',
'-f', data_file('zen', 'learn_small.tsv'),
'-t', data_file('zen', 'test_small.tsv'),
'--column-description', data_file('zen', 'zen_target_aux.cd'),
'--learn-pairs', data_file('zen', 'learn_pairs.tsv'),
'--test-pairs', data_file('zen', 'test_pairs.tsv'),
'-i', '20',
'-T', '4',
'-r', '0',
'-m', output_model_path,
'--eval-file', output_eval_path,
)
yatest.common.execute(cmd)
return [local_canonical_file(output_eval_path)]
def test_target_border(border_type):
output_model_path = yatest.common.test_output_path('model.bin')
output_eval_path = yatest.common.test_output_path('test.eval')
cmd = (
CATBOOST_PATH,
'fit',
'--loss-function', 'RMSE',
'-f', data_file('adult_crossentropy', 'train_proba'),
'-t', data_file('adult_crossentropy', 'test_proba'),
'--column-description', data_file('adult_crossentropy', 'train.cd'),
'-i', '3',
'-T', '4',
'-r', '0',
'-m', output_model_path,
'--eval-file', output_eval_path,
'--ctr', 'Borders:TargetBorderCount=3:TargetBorderType=' + border_type
)
yatest.common.execute(cmd)
return [local_canonical_file(output_eval_path)]
def get_cuda_setup_error():
for flag in pytest.config.option.flags:
if re.match('HAVE_CUDA=(0|no|false)', flag, flags=re.IGNORECASE):
return flag
try:
cmd = (CATBOOST_PATH, 'fit', '--task-type', 'GPU', '--devices', '0',
'--gpu-ram-part', '0.25', '--use-best-model', 'false',
'--loss-function', 'Logloss', '-f',
data_file('adult', 'train_small'), '-t',
data_file('adult', 'test_small'), '--column-description',
data_file('adult', 'train.cd'), '--boosting-type', 'Plain',
'-i', '5', '-T', '4', '-r', '0')
yatest.common.execute(cmd)
except Exception as e:
for reason in [
'GPU support was not compiled',
'CUDA driver version is insufficient'
]:
if reason in str(e):
return reason
return None
def test_permutation_block():
output_model_path = yatest.common.test_output_path('model.bin')
output_eval_path = yatest.common.test_output_path('test.eval')
cmd = (
CATBOOST_PATH,
'fit',
'--loss-function', 'Logloss',
'-f', data_file('adult', 'train_small'),
'-t', data_file('adult', 'test_small'),
'--column-description', data_file('adult', 'train.cd'),
'-i', '10',
'-T', '4',
'-r', '0',
'-m', output_model_path,
'--eval-file', output_eval_path,
'--fold-permutation-block', '239'
)
yatest.common.execute(cmd)
return [local_canonical_file(output_eval_path)]
def test_lost_class():
output_model_path = yatest.common.test_output_path('model.bin')
output_eval_path = yatest.common.test_output_path('test.eval')
cmd = (
CATBOOST_PATH,
'fit',
'--loss-function', 'MultiClass',
'-f', data_file('cloudness_lost_class', 'train_small'),
'-t', data_file('cloudness_lost_class', 'test_small'),
'--column-description', data_file('cloudness_lost_class', 'train.cd'),
'-i', '10',
'-T', '4',
'-r', '0',
'-m', output_model_path,
'--eval-file', output_eval_path,
'--classes-count', '3'
)
yatest.common.execute(cmd)
return [local_canonical_file(output_eval_path)]
def test_params_from_file():
output_model_path = yatest.common.test_output_path('model.bin')
output_eval_path = yatest.common.test_output_path('test.eval')
cmd = (
CATBOOST_PATH,
'fit',
'--loss-function', 'Logloss',
'-f', data_file('adult', 'train_small'),
'-t', data_file('adult', 'test_small'),
'--column-description', data_file('adult', 'train.cd'),
'-i', '6',
'-T', '4',
'-r', '0',
'-m', output_model_path,
'--eval-file', output_eval_path,
'--params-file', data_file('params.json')
)
yatest.common.execute(cmd)
return [local_canonical_file(output_eval_path)]
def test_class_names_multiclass(loss_function):
output_model_path = yatest.common.test_output_path('model.bin')
output_eval_path = yatest.common.test_output_path('test.eval')
cmd = (
CATBOOST_PATH,
'fit',
'--loss-function', loss_function,
'-f', data_file('precipitation_small', 'train_small'),
'-t', data_file('precipitation_small', 'test_small'),
'--column-description', data_file('precipitation_small', 'train.cd'),
'-i', '10',
'-T', '4',
'-r', '0',
'-m', output_model_path,
'--eval-file', output_eval_path,
'--class-names', '0.,0.5,1.,0.25,0.75'
)
yatest.common.execute(cmd)
return [local_canonical_file(output_eval_path)]
def test_quantile_targets(loss_function):
pool = 'no_split'
output_model_path = yatest.common.test_output_path('model.bin')
output_eval_path = yatest.common.test_output_path('test.eval')
cmd = (
CATBOOST_PATH,
'fit',
'--loss-function', loss_function + ':alpha=0.9',
'-f', data_file(pool, 'train_full3'),
'-t', data_file(pool, 'test3'),
'--column-description', data_file(pool, 'train_full3.cd'),
'-i', '10',
'-T', '4',
'-r', '0',
'-m', output_model_path,
'--eval-file', output_eval_path,
)
yatest.common.execute(cmd)
return [local_canonical_file(output_eval_path)]
def test_ctr_type(ctr_type, boosting_type):
output_model_path = yatest.common.test_output_path('model.bin')
output_eval_path = yatest.common.test_output_path('test.eval')
cd_file = data_file('adult_crossentropy', 'train.cd')
test_file = data_file('adult_crossentropy', 'test_proba')
params = (
'--use-best-model', 'false',
'--loss-function', 'RMSE',
'-f', data_file('adult_crossentropy', 'train_proba'),
'-t', test_file,
'--column-description', cd_file,
'--boosting-type', boosting_type,
'-i', '3',
'-T', '4',
'-r', '0',
'-m', output_model_path,
'--ctr', ctr_type
)
fit_catboost_gpu(params)
apply_catboost(output_model_path, test_file, cd_file, output_eval_path)
return [local_canonical_file(output_eval_path)]
def test_feature_id_fstr():
model_path = yatest.common.test_output_path('adult_model.bin')
output_fstr_path = yatest.common.test_output_path('fstr.tsv')
cmd = (
CATBOOST_PATH,
'fit',
'--loss-function',
'Logloss',
'-f',
data_file('adult', 'train_small'),
'--column-description',
data_file('adult', 'train.cd'),
'-i',
'10',
'-T',
'4',
'-r',
'0',
'-m',
model_path,
)
yatest.common.execute(cmd)
fstr_cmd = (
CATBOOST_PATH,
'fstr',
'--input-path',
data_file('adult', 'train_small'),
'--column-description',
data_file('adult_with_id.cd'),
'-m',
model_path,
'-o',
output_fstr_path,
)
yatest.common.execute(fstr_cmd)
return local_canonical_file(output_fstr_path)
def test_allow_writing_files_and_used_ram_limit():
output_model_path = yatest.common.test_output_path('model.bin')
output_eval_path = yatest.common.test_output_path('test.eval')
cmd = (
CATBOOST_PATH,
'fit',
'--allow-writing-files', 'false',
'--used-ram-limit', '1024',
'--loss-function', 'Logloss',
'-f', data_file('adult', 'train_small'),
'-t', data_file('adult', 'test_small'),
'--column-description', data_file('adult', 'train.cd'),
'-i', '100',
'-T', '4',
'-r', '0',
'-m', output_model_path,
'--eval-file', output_eval_path,
)
yatest.common.execute(cmd)
return [local_canonical_file(output_eval_path)]
def test_meta():
pool = 'no_split'
output_model_path = yatest.common.test_output_path('model.bin')
output_eval_path = yatest.common.test_output_path('test.eval')
meta_path = 'meta.tsv'
cmd = (
CATBOOST_PATH,
'fit',
'--loss-function', 'RMSE',
'-f', data_file(pool, 'train_full3'),
'-t', data_file(pool, 'test3'),
'--column-description', data_file(pool, 'train_full3.cd'),
'-i', '10',
'-T', '4',
'-r', '0',
'-m', output_model_path,
'--eval-file', output_eval_path,
'--name', 'test experiment',
)
yatest.common.execute(cmd)
return [local_canonical_file(meta_path)]
def test_custom_loss_for_multiclassification():
output_model_path = yatest.common.test_output_path('model.bin')
output_eval_path = yatest.common.test_output_path('test.eval')
learn_error_path = yatest.common.test_output_path('learn_error.tsv')
test_error_path = yatest.common.test_output_path('test_error.tsv')
cmd = (
CATBOOST_PATH,
'fit',
'--loss-function',
'MultiClass',
'-f',
data_file('cloudness_small', 'train_small'),
'-t',
data_file('cloudness_small', 'test_small'),
'--column-description',
data_file('cloudness_small', 'train.cd'),
'-i',
'10',
'-T',
'4',
'-r',
'0',
'-m',
output_model_path,
'--eval-file',
output_eval_path,
'--custom-loss',
'AUC,Accuracy,Precision,Recall,F1,TotalF1,MultiClassOneVsAll,MCC',
'--learn-err-log',
learn_error_path,
'--test-err-log',
test_error_path,
)
yatest.common.execute(cmd)
return [
local_canonical_file(learn_error_path),
local_canonical_file(test_error_path)
]
import hashlib
import math
import pytest
import time
import numpy as np
from pandas import read_table, DataFrame, Series
from catboost import Pool, CatBoost, CatBoostClassifier, CatBoostRegressor, CatboostError, cv, train
from catboost_pytest_lib import data_file, local_canonical_file, remove_time_from_json
import yatest.common
EPS = 1e-5
TRAIN_FILE = data_file('adult', 'train_small')
TEST_FILE = data_file('adult', 'test_small')
CD_FILE = data_file('adult', 'train.cd')
NAN_TRAIN_FILE = data_file('adult_nan', 'train_small')
NAN_TEST_FILE = data_file('adult_nan', 'test_small')
NAN_CD_FILE = data_file('adult_nan', 'train.cd')
CLOUDNESS_TRAIN_FILE = data_file('cloudness_small', 'train_small')
CLOUDNESS_TEST_FILE = data_file('cloudness_small', 'test_small')
CLOUDNESS_CD_FILE = data_file('cloudness_small', 'train.cd')
QUERYWISE_TRAIN_FILE = data_file('querywise', 'train')
QUERYWISE_TEST_FILE = data_file('querywise', 'test')
QUERYWISE_CD_FILE = data_file('querywise', 'train.cd')
QUERYWISE_TRAIN_PAIRS_FILE = data_file('querywise', 'train.pairs')
def test_cv_with_not_binarized_target():
train_file = data_file('adult_not_binarized', 'train_small')
cd = data_file('adult_not_binarized', 'train.cd')
pool = Pool(train_file, column_description=cd)
cv(pool, {"iterations": 5, "random_seed": 0, "loss_function": "Logloss"})
return local_canonical_file(remove_time_from_json(JSON_LOG_PATH))
import pytest
import math
import numpy as np
from pandas import read_table, DataFrame, Series
from catboost import Pool, CatBoost, CatBoostClassifier, CatBoostRegressor, CatboostError, cv
from catboost_pytest_lib import data_file, local_canonical_file
import yatest.common
EPS = 1e-5
TRAIN_FILE = data_file('adult', 'train_small')
TEST_FILE = data_file('adult', 'test_small')
CD_FILE = data_file('adult', 'train.cd')
NAN_TRAIN_FILE = data_file('adult_nan', 'train_small')
NAN_TEST_FILE = data_file('adult_nan', 'test_small')
NAN_CD_FILE = data_file('adult_nan', 'train.cd')
CLOUDNESS_TRAIN_FILE = data_file('cloudness_small', 'train_small')
CLOUDNESS_TEST_FILE = data_file('cloudness_small', 'test_small')
CLOUDNESS_CD_FILE = data_file('cloudness_small', 'train.cd')
ZEN_TRAIN_FILE = data_file('zen', 'learn_small.tsv')
ZEN_TRAIN_PAIRS_FILE = data_file('zen', 'learn_pairs.tsv')
ZEN_TEST_FILE = data_file('zen', 'test_small.tsv')
ZEN_TEST_PAIRS_FILE = data_file('zen', 'test_pairs.tsv')
ZEN_CD_FILE = data_file('zen', 'zen.cd')
