# Path: loom/util.py
# def LOG(message):
#     if VERBOSITY:
#         sys.stdout.write('{}\n'.format(message))
#         sys.stdout.flush()
# 
# Path: loom/test/util.py
# def for_each_dataset(fun):
#     @functools.wraps(fun)
#     def test_one(dataset):
#         paths = loom.store.get_paths(dataset, sample_count=2)
#         for key, path in loom.store.iter_paths(dataset, paths):
#             if not os.path.exists(path):
#                 raise ValueError(
#                     'missing {} at {},\n  first `python -m loom.datasets test`'
#                     .format(key, path))
#         kwargs = {'name': dataset}
#         kwargs.update(paths['query'])
#         kwargs.update(paths['consensus'])
#         kwargs.update(paths['samples'][0])
#         kwargs.update(paths['ingest'])
#         kwargs.update(paths)
#         fun(**kwargs)
# 
#     @functools.wraps(fun)
#     def test_all():
#         for dataset in loom.datasets.TEST_CONFIGS:
#             yield test_one, dataset
# 
#     return test_all
# 
# Path: loom/test/test_query.py
# def get_example_requests(model, rows, query_type='mixed'):
#     assert query_type in ['sample', 'score', 'mixed']
#     cross_cat = CrossCat()
#     with open_compressed(model, 'rb') as f:
#         cross_cat.ParseFromString(f.read())
#     feature_count = sum(len(kind.featureids) for kind in cross_cat.kinds)
#     featureids = range(feature_count)
# 
#     nontrivials = [True] * feature_count
#     for kind in cross_cat.kinds:
#         fs = iter(kind.featureids)
#         for model in loom.schema.MODELS.iterkeys():
#             for shared in getattr(kind.product_model, model):
#                 f = fs.next()
#                 if model == 'dd':
#                     if len(shared.alphas) == 0:
#                         nontrivials[f] = False
#                 elif model == 'dpd':
#                     if len(shared.betas) == 0:
#                         nontrivials[f] = False
#     all_observed = nontrivials[:]
#     none_observed = [False] * feature_count
# 
#     observeds = []
#     observeds.append(all_observed)
#     for f, nontrivial in izip(featureids, nontrivials):
#         if nontrivial:
#             observed = all_observed[:]
#             observed[f] = False
#             observeds.append(observed)
#     for f in featureids:
#         observed = [
#             nontrivial and sample_bernoulli(0.5)
#             for nontrivial in nontrivials
#         ]
#         observeds.append(observed)
#     for f, nontrivial in izip(featureids, nontrivials):
#         if nontrivial:
#             observed = none_observed[:]
#             observed[f] = True
#             observeds.append(observed)
#     observeds.append(none_observed)
# 
#     requests = []
#     for i, observed in enumerate(observeds):
#         request = Query.Request()
#         request.id = "example-{}".format(i)
#         if query_type in ['sample', 'mixed']:
#             set_diff(request.sample.data, none_observed)
#             request.sample.to_sample.sparsity = DENSE
#             request.sample.to_sample.dense[:] = observed
#             request.sample.sample_count = 1
#         if query_type in ['score', 'mixed']:
#             set_diff(request.score.data, none_observed)
#         requests.append(request)
#     for row in load_rows(rows)[:20]:
#         i += 1
#         request = Query.Request()
#         request.id = "example-{}".format(i)
#         if query_type in ['sample', 'mixed']:
#             request.sample.sample_count = 1
#             request.sample.data.MergeFrom(row.diff)
#             request.sample.to_sample.sparsity = DENSE
#             conditions = izip(nontrivials, row.diff.pos.observed.dense)
#             to_sample = [
#                 nontrivial and not is_observed
#                 for nontrivial, is_observed in conditions
#             ]
#             set_observed(request.sample.to_sample, to_sample)
#         if query_type in ['score', 'mixed']:
#             request.score.data.MergeFrom(row.diff)
#         requests.append(request)
#     return requests
# 
# def check_response(request, response):
#     assert_equal(request.id, response.id)
#     assert_equal(len(response.error), 0)


import os
import loom.store
import loom.datasets
import loom.tasks
import loom.query
from loom.util import LOG
from loom.test.util import for_each_dataset
from loom.test.test_query import get_example_requests, check_response
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
# "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
# LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS
# FOR A PARTICULAR PURPOSE ARE DISCLAIMED.  IN NO EVENT SHALL THE
# COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT,
# INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING,
# BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS
# OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
# ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR
# TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE
# USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.


SAMPLE_COUNT = 2
CONFIG = {'schedule': {'extra_passes': 2}}


@for_each_dataset
def test_all(name, schema, rows_csv, **unused):
    name = os.path.join(name, 'test_tasks')
    paths = loom.store.get_paths(name)
    loom.datasets.clean(name)
    loom.tasks.ingest(name, schema, rows_csv, debug=True)
    loom.tasks.infer(
        name,
        sample_count=SAMPLE_COUNT,
        config=CONFIG,
        debug=True)
    loom.tasks.make_consensus(name, debug=True)


# Path: loom/util.py
# def LOG(message):
#     if VERBOSITY:
#         sys.stdout.write('{}\n'.format(message))
#         sys.stdout.flush()
# 
# Path: loom/test/util.py
# def for_each_dataset(fun):
#     @functools.wraps(fun)
#     def test_one(dataset):
#         paths = loom.store.get_paths(dataset, sample_count=2)
#         for key, path in loom.store.iter_paths(dataset, paths):
#             if not os.path.exists(path):
#                 raise ValueError(
#                     'missing {} at {},\n  first `python -m loom.datasets test`'
#                     .format(key, path))
#         kwargs = {'name': dataset}
#         kwargs.update(paths['query'])
#         kwargs.update(paths['consensus'])
#         kwargs.update(paths['samples'][0])
#         kwargs.update(paths['ingest'])
#         kwargs.update(paths)
#         fun(**kwargs)
# 
#     @functools.wraps(fun)
#     def test_all():
#         for dataset in loom.datasets.TEST_CONFIGS:
#             yield test_one, dataset
# 
#     return test_all
# 
# Path: loom/test/test_query.py
# def get_example_requests(model, rows, query_type='mixed'):
#     assert query_type in ['sample', 'score', 'mixed']
#     cross_cat = CrossCat()
#     with open_compressed(model, 'rb') as f:
#         cross_cat.ParseFromString(f.read())
#     feature_count = sum(len(kind.featureids) for kind in cross_cat.kinds)
#     featureids = range(feature_count)
# 
#     nontrivials = [True] * feature_count
#     for kind in cross_cat.kinds:
#         fs = iter(kind.featureids)
#         for model in loom.schema.MODELS.iterkeys():
#             for shared in getattr(kind.product_model, model):
#                 f = fs.next()
#                 if model == 'dd':
#                     if len(shared.alphas) == 0:
#                         nontrivials[f] = False
#                 elif model == 'dpd':
#                     if len(shared.betas) == 0:
#                         nontrivials[f] = False
#     all_observed = nontrivials[:]
#     none_observed = [False] * feature_count
# 
#     observeds = []
#     observeds.append(all_observed)
#     for f, nontrivial in izip(featureids, nontrivials):
#         if nontrivial:
#             observed = all_observed[:]
#             observed[f] = False
#             observeds.append(observed)
#     for f in featureids:
#         observed = [
#             nontrivial and sample_bernoulli(0.5)
#             for nontrivial in nontrivials
#         ]
#         observeds.append(observed)
#     for f, nontrivial in izip(featureids, nontrivials):
#         if nontrivial:
#             observed = none_observed[:]
#             observed[f] = True
#             observeds.append(observed)
#     observeds.append(none_observed)
# 
#     requests = []
#     for i, observed in enumerate(observeds):
#         request = Query.Request()
#         request.id = "example-{}".format(i)
#         if query_type in ['sample', 'mixed']:
#             set_diff(request.sample.data, none_observed)
#             request.sample.to_sample.sparsity = DENSE
#             request.sample.to_sample.dense[:] = observed
#             request.sample.sample_count = 1
#         if query_type in ['score', 'mixed']:
#             set_diff(request.score.data, none_observed)
#         requests.append(request)
#     for row in load_rows(rows)[:20]:
#         i += 1
#         request = Query.Request()
#         request.id = "example-{}".format(i)
#         if query_type in ['sample', 'mixed']:
#             request.sample.sample_count = 1
#             request.sample.data.MergeFrom(row.diff)
#             request.sample.to_sample.sparsity = DENSE
#             conditions = izip(nontrivials, row.diff.pos.observed.dense)
#             to_sample = [
#                 nontrivial and not is_observed
#                 for nontrivial, is_observed in conditions
#             ]
#             set_observed(request.sample.to_sample, to_sample)
#         if query_type in ['score', 'mixed']:
#             request.score.data.MergeFrom(row.diff)
#         requests.append(request)
#     return requests
# 
# def check_response(request, response):
#     assert_equal(request.id, response.id)
#     assert_equal(len(response.error), 0)


# Path: loom/test/test_tasks.py
import os
import loom.store
import loom.datasets
import loom.tasks
import loom.query
from loom.util import LOG
from loom.test.util import for_each_dataset
from loom.test.test_query import get_example_requests, check_response

# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
# "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
# LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS
# FOR A PARTICULAR PURPOSE ARE DISCLAIMED.  IN NO EVENT SHALL THE
# COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT,
# INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING,
# BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS
# OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
# ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR
# TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE
# USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.


SAMPLE_COUNT = 2
CONFIG = {'schedule': {'extra_passes': 2}}


@for_each_dataset
def test_all(name, schema, rows_csv, **unused):
    name = os.path.join(name, 'test_tasks')
    paths = loom.store.get_paths(name)
    loom.datasets.clean(name)
    loom.tasks.ingest(name, schema, rows_csv, debug=True)
    loom.tasks.infer(
        name,
        sample_count=SAMPLE_COUNT,
        config=CONFIG,
        debug=True)
    loom.tasks.make_consensus(name, debug=True)


    LOG('querying')