def folding_in(self, input_id_set):
"""Folding in logic for prediction.
:param input_id_set: A set containing package ids of user's input package list.
:return: Filter companion recommendations and their topics.
"""
manifest_id = int(self.match_manifest(input_id_set))
if manifest_id == -1:
result = np.array(self.dummy_result)
else:
graph_new = tf.Graph()
with graph_new.as_default():
result = Poisson(self.theta[manifest_id])
result = result.prob(self.beta)
with tf.Session(graph=graph_new) as sess_new:
result = sess_new.run(result)
normalised_result = self.normalize_result(result, input_id_set)
if self.USE_FEEDBACK:
alpha_id = int(self.match_feedback_manifest(input_id_set))
return self.filter_recommendation(normalised_result,
alpha_id=alpha_id)
return self.filter_recommendation(normalised_result)
class HPFScoring:
"""The HPF Model scoring class."""
def __init__(self, datastore=None, USE_FEEDBACK=USE_FEEDBACK):
"""Set the variables and load model data."""
self.datastore = datastore
self.USE_FEEDBACK = convert_string2bool_env(USE_FEEDBACK)
self.package_id_dict = OrderedDict()
self.id_package_dict = OrderedDict()
self.beta = None
self.theta = None
self.alpha = None
self.manifest_id_dict = OrderedDict()
self.feedback_id_dict = OrderedDict()
self.manifests = 0
self.packages = 0
self.epsilon = Gamma(tf.constant(
a_c), tf.constant(a_c) / tf.constant(b_c)).\
prob(tf.constant(K, dtype=tf.float32)).eval(session=tf.Session())
self.theta_dummy = Poisson(
np.array([
self.epsilon * Gamma(tf.constant(a), self.epsilon).prob(
tf.constant(K,
dtype=tf.float32)).eval(session=tf.Session())
] * K,
dtype=float))
if isinstance(datastore, S3DataStore): # pragma: no-cover
self.load_s3()
else:
self.load_local()
self.manifests = self.theta.shape[0]
self.packages = self.beta.shape[0]
self.dummy_result = self.theta_dummy.prob(
self.beta).eval(session=tf.Session())
@staticmethod
def _getsizeof(attribute):
"""Return the size of attribute in MBs.
param attribute: The object's attribute.
"""
return "{} MB".format(getsizeof(attribute) / 1024 / 1024)
def model_details(self):
"""Return the model details size."""
details = """The model will be scored against
{} Packages,
{} Manifests,
Theta matrix of size {}, and
Beta matrix of size {}.""".\
format(
len(self.package_id_dict),
len(self.manifest_id_dict),
HPFScoring._getsizeof(self.theta),
HPFScoring._getsizeof(self.beta))
return details
def load_s3(self): # pragma: no cover
"""Load the model data from AWS S3."""
theta_matrix_filename = os.path.join(HPF_SCORING_REGION,
HPF_output_user_matrix)
self.datastore.download_file(theta_matrix_filename,
"/tmp/user_matrix.npz")
sparse_matrix = sparse.load_npz('/tmp/user_matrix.npz')
self.theta = sparse_matrix.toarray()
del (sparse_matrix)
os.remove("/tmp/user_matrix.npz")
beta_matrix_filename = os.path.join(HPF_SCORING_REGION,
HPF_output_item_matrix)
self.datastore.download_file(beta_matrix_filename,
"/tmp/item_matrix.npz")
sparse_matrix = sparse.load_npz('/tmp/item_matrix.npz')
self.beta = sparse_matrix.toarray()
del (sparse_matrix)
os.remove("/tmp/item_matrix.npz")
if self.USE_FEEDBACK:
alpha_matrix_filename = os.path.join(HPF_SCORING_REGION,
HPF_output_feedback_matrix)
self.datastore.download_file(alpha_matrix_filename,
"/tmp/alpha_matrix.npz")
sparse_matrix = sparse.load_npz("/tmp/alpha_matrix.npz")
self.alpha = sparse_matrix.toarray()
del (sparse_matrix)
self.load_jsons()
def load_local(self):
"""Load the model data from AWS S3."""
theta_matrix_filename = os.path.join(self.datastore.src_dir,
HPF_SCORING_REGION,
HPF_output_user_matrix)
sparse_matrix = sparse.load_npz(theta_matrix_filename)
self.theta = sparse_matrix.toarray()
del (sparse_matrix)
beta_matrix_filename = os.path.join(self.datastore.src_dir,
HPF_SCORING_REGION,
HPF_output_item_matrix)
sparse_matrix = sparse.load_npz(beta_matrix_filename)
self.beta = sparse_matrix.toarray()
del (sparse_matrix)
if self.USE_FEEDBACK:
alpha_matrix_filename = os.path.join(self.datastore.src_dir,
HPF_SCORING_REGION,
HPF_output_feedback_matrix)
sparse_matrix = sparse.load_npz(alpha_matrix_filename)
self.alpha = sparse_matrix.toarray()
del (sparse_matrix)
self.load_jsons()
def load_jsons(self):
"""Load Json files via common methods for S3 and local."""
package_id_dict_filename = os.path.join(HPF_SCORING_REGION,
HPF_output_package_id_dict)
self.package_id_dict = self.datastore.read_json_file(
package_id_dict_filename)
self.id_package_dict = OrderedDict({
x: n
for n, x in self.package_id_dict[0].get("package_list",
{}).items()
})
self.package_id_dict = OrderedDict(self.package_id_dict[0].get(
"package_list", {}))
manifest_id_dict_filename = os.path.join(HPF_SCORING_REGION,
HPF_output_manifest_id_dict)
self.manifest_id_dict = self.datastore.read_json_file(
manifest_id_dict_filename)
self.manifest_id_dict = OrderedDict({
n: set(x)
for n, x in self.manifest_id_dict[0].get("manifest_list",
{}).items()
})
if self.USE_FEEDBACK:
feedback_id_dict_filename = os.path.join(
HPF_SCORING_REGION, HPF_output_feedback_id_dict)
self.feedback_id_dict = self.datastore.read_json_file(
feedback_id_dict_filename)
self.feedback_id_dict = OrderedDict({
n: set(x)
for n, x in self.feedback_id_dict[0].get("feedback_list",
{}).items()
})
def predict(self, input_stack):
"""Prediction function.
:param input_stack: The user's package list
for which companion recommendation are to be generated.
:return companion_recommendation: The list of recommended companion packages
along with condifence score.
:return package_topic_dict: The topics associated with the packages
in the input_stack+recommendation.
:return missing_packages: The list of packages unknown to the HPF model.
"""
input_stack = set(input_stack)
input_id_set = set()
missing_packages = set()
package_topic_dict = {}
companion_recommendation = []
if not input_stack:
return companion_recommendation, package_topic_dict, list(
missing_packages)
for package_name in input_stack:
package_id = self.package_id_dict.get(package_name)
if package_id:
input_id_set.add(package_id)
package_topic_dict[package_name] = []
else:
missing_packages.add(package_name)
if len(missing_packages) / len(
input_stack) < UNKNOWN_PACKAGES_THRESHOLD:
companion_recommendation = self.folding_in(input_id_set)
else:
_logger.error(
"{} length of missing packages beyond unknow threshold value of {}"
.format(len(missing_packages), UNKNOWN_PACKAGES_THRESHOLD))
return companion_recommendation, package_topic_dict, list(
missing_packages)
def match_manifest(self, input_id_set): # pragma: no cover
"""Find a manifest list that matches user's input package list and return its index.
:param input_id_set: A set containing package ids of user's input package list.
:return manifest_id: The index of the matched manifest.
"""
closest_manifest_id = -1
max_diff = maxsize
for manifest_id, dependency_set in self.manifest_id_dict.items():
curr_diff = len(dependency_set.difference(input_id_set))
if dependency_set == input_id_set:
closest_manifest_id = manifest_id
break
elif input_id_set.issubset(
dependency_set) and curr_diff < max_diff:
closest_manifest_id = manifest_id
max_diff = curr_diff
_logger.debug("input_id_set {} and manifest_id {}".format(
input_id_set, closest_manifest_id))
return closest_manifest_id
def match_feedback_manifest(self, input_id_set):
"""Find a feedback manifest that matches user's input package list and return its index.
:param input_id_set: A set containing package ids of user's input package list.
:return manifest_id: The index of the matched feedback manifest.
"""
for manifest_id, dependency_set in self.feedback_id_dict.items():
if dependency_set == input_id_set:
break
else:
manifest_id = -1
_logger.debug("input_id_set {} and feedback_manifest_id {}".format(
input_id_set, manifest_id))
return manifest_id
def folding_in(self, input_id_set):
"""Folding in logic for prediction.
:param input_id_set: A set containing package ids of user's input package list.
:return: Filter companion recommendations and their topics.
"""
manifest_id = int(self.match_manifest(input_id_set))
if manifest_id == -1:
result = np.array(self.dummy_result)
else:
graph_new = tf.Graph()
with graph_new.as_default():
result = Poisson(self.theta[manifest_id])
result = result.prob(self.beta)
with tf.Session(graph=graph_new) as sess_new:
result = sess_new.run(result)
normalised_result = self.normalize_result(result, input_id_set)
if self.USE_FEEDBACK:
alpha_id = int(self.match_feedback_manifest(input_id_set))
return self.filter_recommendation(normalised_result,
alpha_id=alpha_id)
return self.filter_recommendation(normalised_result)
def normalize_result(self, result, input_id_set, array_len=None):
"""Normalise the probability score of the resulting recommendation.
:param result: The non-normalised recommendation result array.
:param input_id_set: The user's input package ids.
:param array_len: length of normalised result array.
:return normalised_result: The normalised recommendation result array.
"""
if array_len is None:
array_len = self.packages
normalised_result = np.array([
-1.0 if i in input_id_set else result[i].mean()
for i in range(array_len)
])
return normalised_result
def filter_recommendation(self,
result,
alpha_id=-1,
max_count=MAX_COMPANION_REC_COUNT):
"""Filter companion recommendations based on sorted threshold score.
:param result: The unfiltered companion recommendation result.
:param max_count: Maximum number of recommendations to return.
:return companion_recommendation: The filtered list of recommended companion packages
along with condifence score.
:return package_topic_dict: The topics associated with the packages
in the input_stack+recommendation.
"""
highest_indices = set(result.argsort()[-max_count:len(result)])
companion_recommendation = []
if self.USE_FEEDBACK and alpha_id != -1:
alpha_set = set(
np.where(self.alpha[alpha_id] >= feedback_threshold)[0])
highest_indices = highest_indices.intersection(alpha_set)
for package_id in highest_indices:
recommendation = {
"cooccurrence_probability": result[package_id] * 100,
"package_name": self.id_package_dict[package_id],
"topic_list": []
}
companion_recommendation.append(recommendation)
return companion_recommendation
