def test_ProductRecognizer():
"""Test ``FeatureExtractor`` class."""
from clair.textprocessing import ProductRecognizer, split_random
from clair.coredata import DataStore
print "start"
data_dir = relative("../../example-data")
data = DataStore()
data.read_data(data_dir)
finder = ProductRecognizer("nikon-d70")
print "Test: filter_trainig_samples"
samples, _, _ = finder.filter_trainig_samples(data.listings)
train_samples = samples
print "Number training samples:", len(samples)
# print samples
#Test if search for training samples worked
assert len(samples) > 100
assert all(samples["training_sample"] == 1.0)
pe = samples["products"].map(lambda l: "nikon-d70" in l)
pa = samples["products_absent"].map(lambda l: "nikon-d70" in l)
assert all(pe | pa)
print "\nTest: filter_candidate_listings"
samples = cand_samples = finder.filter_candidate_listings(data.listings)
print "Number candidate samples:", len(samples)
#Test if filter for candidate samples worked
assert len(samples) > 10
assert all(samples["training_sample"] != 1.0)
pe = samples["expected_products"].map(lambda l: "nikon-d70" in l)
assert all(pe)
print "\nTest: train_finder, compute_accuracy"
train_set, test_set = split_random(train_samples, 0.8)
finder.train_finder(train_set)
finder.compute_accuracy(test_set)
print "\nTest: contains_product"
for i, (_, listing) in enumerate(cand_samples.iterrows()):
if i >= 10:
break
contains = finder.contains_product(listing)
print listing["title"]
print "Contains", finder.product_id, ":", contains
print
print "finished"
def test_split_random():
"""Test splitting frame into two fractions randomly."""
from clair.textprocessing import split_random
df = pd.DataFrame({"aaa":[1, 2, 3, 4, 5, 6, 7, 8, 9, 10]})
# print df
frac1, frac2 = split_random(df, 0.3)
# print frac1
# print frac2
assert len(frac1) == 3 and len(frac2) == 7
frac1, frac2 = split_random(df, 0.8)
# print frac1
# print frac2
assert len(frac1) == 8 and len(frac2) == 2
#Simple test for randomness
print split_random(df, 0.4)[0].index
assert any(split_random(df, 0.4)[0].index != split_random(df, 0.4)[0].index)
print "finished"
