# Path: loom/format.py
# def load_encoder(encoder):
#     model = encoder['model']
#     if 'symbols' in encoder:
#         encode = encoder['symbols'].__getitem__
#     elif model == 'bb':
#         encode = BOOLEAN_SYMBOLS.__getitem__
#     else:
#         encode = loom.schema.MODELS[model].Value
#     return encode
# 
# Path: loom/test/util.py
# CLEANUP_ON_ERROR = int(os.environ.get('CLEANUP_ON_ERROR', 1))
# 
# 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/util.py
# def load_rows_csv(dirname):
#     filenames = os.listdir(dirname)
#     rows = [None]
#     for filename in filenames:
#         filename = os.path.join(dirname, filename)
#         with csv_reader(filename) as reader:
#             header = reader.next()
#             rows += reader
#     rows[0] = header
#     return rows


import os
import csv
import numpy
import pandas
import loom.preql
from itertools import izip
from StringIO import StringIO
from nose import SkipTest
from nose.tools import assert_almost_equal
from nose.tools import assert_equal
from nose.tools import assert_raises
from nose.tools import assert_true
from distributions.fileutil import tempdir
from distributions.io.stream import json_load
from distributions.io.stream import open_compressed
from distributions.io.stream import protobuf_stream_load
from distributions.tests.util import assert_close
from loom.format import load_encoder
from loom.test.util import CLEANUP_ON_ERROR
from loom.test.util import for_each_dataset
from loom.test.util import load_rows_csv

def _check_predictions(rows_in, result_out, encoding):
    encoders = json_load(encoding)
    name_to_encoder = {e['name']: load_encoder(e) for e in encoders}
    with open_compressed(rows_in, 'rb') as fin:
        with open(result_out, 'r') as fout:
            in_reader = csv.reader(fin)
            out_reader = csv.reader(fout)
            fnames = in_reader.next()
            out_reader.next()
            for in_row in in_reader:
                for i in range(COUNT):
                    out_row = out_reader.next()
                    bundle = zip(fnames, in_row, out_row)
                    for name, in_val, out_val in bundle:
                        if name == '_id':
                            assert_equal(in_val, out_val)
                            continue
                        encode = name_to_encoder[name]
                        observed = bool(in_val.strip())
                        if observed:
                            assert_almost_equal(
                                encode(in_val),
                                encode(out_val))
                        else:
                            assert_true(bool(out_val.strip()))


@for_each_dataset
def test_predict(root, rows_csv, encoding, **unused):

# Path: loom/format.py
# def load_encoder(encoder):
#     model = encoder['model']
#     if 'symbols' in encoder:
#         encode = encoder['symbols'].__getitem__
#     elif model == 'bb':
#         encode = BOOLEAN_SYMBOLS.__getitem__
#     else:
#         encode = loom.schema.MODELS[model].Value
#     return encode
# 
# Path: loom/test/util.py
# CLEANUP_ON_ERROR = int(os.environ.get('CLEANUP_ON_ERROR', 1))
# 
# 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/util.py
# def load_rows_csv(dirname):
#     filenames = os.listdir(dirname)
#     rows = [None]
#     for filename in filenames:
#         filename = os.path.join(dirname, filename)
#         with csv_reader(filename) as reader:
#             header = reader.next()
#             rows += reader
#     rows[0] = header
#     return rows


# Path: loom/test/test_preql.py
import os
import csv
import numpy
import pandas
import loom.preql
from itertools import izip
from StringIO import StringIO
from nose import SkipTest
from nose.tools import assert_almost_equal
from nose.tools import assert_equal
from nose.tools import assert_raises
from nose.tools import assert_true
from distributions.fileutil import tempdir
from distributions.io.stream import json_load
from distributions.io.stream import open_compressed
from distributions.io.stream import protobuf_stream_load
from distributions.tests.util import assert_close
from loom.format import load_encoder
from loom.test.util import CLEANUP_ON_ERROR
from loom.test.util import for_each_dataset
from loom.test.util import load_rows_csv


def _check_predictions(rows_in, result_out, encoding):
    encoders = json_load(encoding)
    name_to_encoder = {e['name']: load_encoder(e) for e in encoders}
    with open_compressed(rows_in, 'rb') as fin:
        with open(result_out, 'r') as fout:
            in_reader = csv.reader(fin)
            out_reader = csv.reader(fout)
            fnames = in_reader.next()
            out_reader.next()
            for in_row in in_reader:
                for i in range(COUNT):
                    out_row = out_reader.next()
                    bundle = zip(fnames, in_row, out_row)
                    for name, in_val, out_val in bundle:
                        if name == '_id':
                            assert_equal(in_val, out_val)
                            continue
                        encode = name_to_encoder[name]
                        observed = bool(in_val.strip())
                        if observed:
                            assert_almost_equal(
                                encode(in_val),
                                encode(out_val))
                        else:
                            assert_true(bool(out_val.strip()))


@for_each_dataset
def test_predict(root, rows_csv, encoding, **unused):

    with tempdir(cleanup_on_error=CLEANUP_ON_ERROR):