def test_train_eval():
dataset_path = generate_dataset_file(5, 7)
with tempdir() as dirname:
models_dir = os.path.join(dirname, 'models')
result_path = os.path.join(dirname, 'tune_clusters.pkz')
validate.train(dataset_path, PARAM_CSV, models_dir)
validate.eval(dataset_path, PARAM_CSV, models_dir, result_path)
assert os.path.exists(result_path)
def test_train():
dataset_path = generate_dataset_file(10, 10)
with tempdir() as dirname:
ensemble_path = os.path.join(dirname, 'ensemble.pkz')
train(
dataset_path,
ensemble_path,
model_ensemble_size='3',
learning_init_epochs='3')
assert os.path.exists(ensemble_path)
def test_guess_schema():
with tempdir() as dirname:
types_csv_out = os.path.join(dirname, 'types.csv')
values_csv_out = os.path.join(dirname, 'values.csv')
guess_schema(DATA_CSV, types_csv_out, values_csv_out)
expected_types = open(TYPES_CSV).read()
expected_values = open(VALUES_CSV).read()
actual_types = open(types_csv_out).read()
actual_values = open(values_csv_out).read()
assert actual_types == expected_types
assert actual_values == expected_values
def serve(rows=100, cols=10, cats=4, tool='timers'):
"""Profile TreeCatServer on a random dataset.
Available tools: timers, time, snakeviz, line_profiler, pdb
"""
from treecat.generate import generate_model_file
config = make_config()
model_path = generate_model_file(rows, cols, cats)
with tempdir() as dirname:
config_path = os.path.join(dirname, 'config.pkz')
pickle_dump(config, config_path)
cmd = [FILE, 'serve_files', model_path, config_path, str(rows)]
run_with_tool(cmd, tool, dirname)
def train(rows=100,
cols=10,
epochs=5,
clusters=32,
parallel=False,
tool='timers'):
"""Profile TreeCatTrainer on a random dataset.
Available tools: timers, time, snakeviz, line_profiler, pdb
"""
from treecat.generate import generate_dataset_file
config = make_config(learning_init_epochs=epochs,
model_num_clusters=clusters,
model_ensemble_size=1,
learning_parallel=parallel)
dataset_path = generate_dataset_file(rows, cols)
with tempdir() as dirname:
config_path = os.path.join(dirname, 'config.pkz')
pickle_dump(config, config_path)
cmd = [FILE, 'train_files', dataset_path, config_path]
run_with_tool(cmd, tool, dirname)
def eval(rows=100, cols=10, cats=4, tool='timers'):
"""Profile treecat.validate.eval on a random dataset.
Available tools: timers, time, snakeviz, line_profiler, pdb
"""
from treecat.generate import generate_dataset_file
from treecat.validate import train
dataset_path = generate_dataset_file(rows, cols)
validate_py = os.path.join(os.path.dirname(FILE), 'validate.py')
with tempdir() as dirname:
param_csv_path = os.path.join(dirname, 'param.csv')
with open(param_csv_path, 'w') as f:
f.write('learning_init_epochs\n2')
train(dataset_path, param_csv_path, dirname, learning_init_epochs=2)
cmd = [
validate_py,
'eval',
dataset_path,
param_csv_path,
dirname,
os.path.join(dirname, 'tuning.pkz'),
'learning_init_epochs=2',
]
run_with_tool(cmd, tool, dirname)
def test_e2e(model_type):
with tempdir() as dirname:
data_csv = os.path.join(TESTDATA, 'tiny_data.csv')
config = TINY_CONFIG.copy()
print('Guess schema.')
types_csv = os.path.join(dirname, 'types.csv')
values_csv = os.path.join(dirname, 'values.csv')
guess_schema(data_csv, types_csv, values_csv)
print('Load schema')
groups_csv = os.path.join(TESTDATA, 'tiny_groups.csv')
schema = load_schema(types_csv, values_csv, groups_csv)
ragged_index = schema['ragged_index']
tree_prior = schema['tree_prior']
print('Load data')
data = load_data(schema, data_csv)
feature_types = [TY_MULTINOMIAL] * len(schema['feature_names'])
table = Table(feature_types, ragged_index, data)
dataset = {
'schema': schema,
'table': table,
}
print('Train model')
if model_type == 'single':
model = train_model(table, tree_prior, config)
elif model_type == 'ensemble':
model = train_ensemble(table, tree_prior, config)
else:
raise ValueError(model_type)
print('Serve model')
server = serve_model(dataset, model)
print('Query model')
evidence = {'genre': 'drama'}
server.logprob([evidence])
samples = server.sample(100)
server.logprob(samples)
samples = server.sample(100, evidence)
server.logprob(samples)
try:
median = server.median([evidence])
server.logprob(median)
except NotImplementedError:
warn('{} median not implemented'.format(model_type))
pass
try:
mode = server.mode([evidence])
server.logprob(mode)
except NotImplementedError:
warn('{} mode not implemented'.format(model_type))
pass
print('Examine latent structure')
server.feature_density()
server.observed_perplexity()
server.latent_perplexity()
server.latent_correlation()
server.estimate_tree()
server.sample_tree(10)
print('Plotting latent structure')
plot_circular(server)
def test_import_data():
with tempdir() as dirname:
dataset_out = os.path.join(dirname, 'dataset.pkz')
assert not os.path.exists(dataset_out)
import_data(DATA_CSV, TYPES_CSV, VALUES_CSV, GROUPS_CSV, dataset_out)
assert os.path.exists(dataset_out)
def test_pickle(data, ext):
with tempdir() as dirname:
filename = os.path.join(dirname, 'test.{}'.format(ext))
pickle_dump(data, filename)
actual = pickle_load(filename)
assert_equal(actual, data)