def test_train():
"""
Test that the classifier correctly extracts all observations from the test dataset.
"""
#train the classifier
data = load_dataset(data_file)
cls = TemporalEvidencesClassifier(data.features, data.target_names)
cls = cls.fit(data.data, data.target)
#load expected sources and their observations from json file
expected_sources = sources_from_json(sources_file)
#compare expected with actual sources
assert_array_equal(sorted(cls.sources.keys()), sorted(expected_sources.keys()),)
for name in expected_sources.keys():
assert_source_equal(cls.sources[name], expected_sources[name])
def test_recommend():
"""
Test that the classifier generates the correct recommendations for the test dataset.
"""
#train the classifier and calculate recommendations
data = load_dataset(data_file)
cls = TemporalEvidencesClassifier(data.features, data.target_names)
cls = cls.fit(data.data, data.target)
actual_recommendations = cls.predict(data.data, include_conflict_theta=True)
#load expected results from json file
with open(recommendations_file, 'r') as infile:
expected_recommendations = json.load(infile)
#compare expected with actual results
for actual, expected in zip(actual_recommendations, expected_recommendations):
assert_recommendations_equal(actual, expected)
def test_train():
"""
Test that the classifier correctly extracts all observations from the test dataset.
"""
#train the classifier
data = load_dataset(data_file)
cls = TemporalEvidencesClassifier(data.features, data.target_names)
cls = cls.fit(data.data, data.target)
#load expected sources and their observations from json file
expected_sources = sources_from_json(sources_file)
#compare expected with actual sources
assert_array_equal(
sorted(cls.sources.keys()),
sorted(expected_sources.keys()),
)
for name in expected_sources.keys():
assert_source_equal(cls.sources[name], expected_sources[name])
def test_recommend():
"""
Test that the classifier generates the correct recommendations for the test dataset.
"""
#train the classifier and calculate recommendations
data = load_dataset(data_file)
cls = TemporalEvidencesClassifier(data.features, data.target_names)
cls = cls.fit(data.data, data.target)
actual_recommendations = cls.predict(data.data,
include_conflict_theta=True)
#load expected results from json file
with open(recommendations_file, 'r') as infile:
expected_recommendations = json.load(infile)
#compare expected with actual results
for actual, expected in zip(actual_recommendations,
expected_recommendations):
assert_recommendations_equal(actual, expected)
import pandas
from recsys.classifiers.temporal import TemporalEvidencesClassifier
from recsys.dataset import load_dataset
from evaluation.metrics import results_as_dataframe
from evaluation import plot
import config
#configuration
data = load_dataset("../datasets/houseA.csv", "../datasets/houseA.config")
#run the classifier on the whole dataset
cls = TemporalEvidencesClassifier(data.features, data.target_names)
cls = cls.fit(data.data, data.target)
results = cls.predict(data.data, include_conflict_theta=True)
#extract conflict and uncertainty and convert recommendations to pandas representation
recommendations, conflict, uncertainty = zip(*results)
results = results_as_dataframe(data.target, list(recommendations))
#for each row, mark correct recommendations with "1", false recommendations with "0"
find_matches_in_row = lambda row: [1 if col == row.name else 0 for col in row]
results = results.apply(find_matches_in_row, axis=1)
#set uncertainty and conflict as multi-index
results.index = pandas.MultiIndex.from_tuples(zip(conflict, uncertainty),
names=["Conflict", "Uncertainty"])
#found_within: the correct service was found within X recommendations
sys.path.append("..")
import pandas
from recsys.classifiers.temporal import TemporalEvidencesClassifier
from recsys.classifiers.binning import initialize_bins
from recsys.dataset import load_dataset
from evaluation import plot
import config
#configuration
data = load_dataset("../datasets/houseA.csv", "../datasets/houseA.config")
#fit classifier to dataset
cls = TemporalEvidencesClassifier(data.features,
data.target_names,
bins=initialize_bins(0, 300, 10))
cls = cls.fit(data.data, data.target)
#create visualizations of habits around each user action
plot_conf = plot.plot_config(config.plot_directory,
sub_dirs=[data.name, "habits"],
img_type=config.img_type)
for source in cls.sources.values():
observations = pandas.DataFrame(source.temporal_counts)
observations.columns = data.target_names
observations.index = cls.bins
plot.plot_observations(source.name(), observations, plot_conf)
print "Results can be found in the \"%s\" directory" % config.plot_directory
