def __init__ (self): """ PUBLIC: Constructor ------------------- constructs member objects """ #=====[ Step 1: create member objects ]===== self.preprocess = Preprocess () self.storage_delegate = StorageDelegate () self.semantic_analysis = SemanticAnalysis () self.user_analysis = UserAnalysis () self.inference = None
class SpotOn:
def __init__ (self):
"""
PUBLIC: Constructor
-------------------
constructs member objects
"""
#=====[ Step 1: create member objects ]=====
self.preprocess = Preprocess ()
self.storage_delegate = StorageDelegate ()
self.semantic_analysis = SemanticAnalysis ()
self.user_analysis = UserAnalysis ()
self.inference = None
def load (self):
"""
PUBLIC: load
------------
loads in all parameters
"""
#=====[ Step 1: load in semantic analysis ]=====
print_status ("Initialization", "Loading ML parameters (Begin)")
self.semantic_analysis.load ()
print_status ("Initialization", "Loading ML parameters (End)")
#=====[ Step 2: transfer over models to inference ]=====
print_status ("Initialization", "Constructing Inference instance (Begin)")
self.inference = Inference (self.semantic_analysis.lda_model, self.semantic_analysis.lda_model_topics)
print_status ("Initialization", "Constructing Inference instance (End)")
####################################################################################################
######################[ --- Getting Users --- ]#####################################################
####################################################################################################
def get_users (self):
"""
PUBLIC: get_users
-----------------
constructs self.u_df from all available
calendar dataframes
"""
self.u_df = self.user_analysis.extract_users (self.storage_delegate.iter_calendar_dfs)
self.u_df = self.semantic_analysis.analyze (self.u_df, 'all_event_names')
def load_users (self, filepath='../data/pandas/users/users.df'):
"""
PUBLIC: load_users
------------------
constructs self.u_df from a saved file
"""
self.u_df = pd.read_pickle(filepath)
####################################################################################################
######################[ --- Training --- ]#########################################################
####################################################################################################
def extract_text (self, activity_row):
"""
PRIVATE: extract_text
---------------------
given a row representing an activity, this returns
a list of words representing it as a 'text'
"""
text = []
if type(activity_row['name']) == list:
text += activity_row['name']
if type(activity_row['words']) == list:
text += activity_row['words']
return text
def get_corpus_dictionary (self):
"""
PRIVATE: get_corpus_dictionary
------------------------------
Assembles a gensim corpus and dictionary from activities_df,
where each text is name || words.
"""
#=====[ Step 1: iterate through all activity dataframes ]=====
print_status ("get_corpus", "assembling texts")
texts = []
for df in self.storage_delegate.iter_activity_dfs ():
print_inner_status ("assembling texts", "next df")
texts += list(df.apply(self.extract_text, axis=1))
#=====[ Step 3: get dictionary ]=====
print_status ("get_corpus", "assembling dictionary")
dictionary = gensim.corpora.Dictionary(texts)
#=====[ Step 4: get corpus ]=====
print_status ("get_corpus", "assembling corpus")
corpus = [dictionary.doc2bow (text) for text in texts]
return corpus, dictionary
def train_semantic_analysis (self):
"""
PUBLIC: train_semantic_analysis
-------------------------------
finds parameters for self.semantic_analysis
"""
#=====[ Step 1: get the corpus ]=====
print_status ("train_semantic_analysis", "getting corpus/dictionary")
corpus, dictionary = self.get_corpus_dictionary ()
#=====[ Step 2: train ]=====
print_status ("train_semantic_analysis", "training semantic analysis")
self.semantic_analysis.train (corpus, dictionary)
####################################################################################################
######################[ --- Inference --- ]#########################################################
####################################################################################################
def score_activities_old (self, user_activities, recommend_activities):
"""
PUBLIC: score_activities
------------------------
Given a user and a list of activities, both represented as json, this will return
(activities, scores) in a sorted list
"""
#=====[ Step 1: preprocess json inputs ]=====
user_events_df = self.preprocess.preprocess_a (user_activities)
activities_df = self.preprocess.preprocess_a (recommend_activities)
#=====[ Step 2: construct a user from user_events_df ]=====
def f():
yield user_events_df
users = self.user_analysis.extract_users (f)
assert len(users) == 1
user = users.iloc[0]
#=====[ Step 3: get scores for each one ]=====
scores = [inference.score_match (user, activities_df.iloc[i]) for i in range(len(activities_df))]
#=====[ Step 4: return sorted list of activity, score ]=====
return sorted(zip(activities_json, scores), key=lambda x: x[1], reverse=True)
def score_activities (self, user_activities, recommend_activities):
"""
PUBLIC: score_activities
------------------------
Given a user and a list of activities, both represented as json, this will return
(activities, scores) in a sorted list
"""
#=====[ Step 1: preprocess user_activities and recommend_activities ]=====
user_activities = self.preprocess.preprocess_a (user_activities)
# print len(recommend_activities)
recommend_activities = self.preprocess.preprocess_a (recommend_activities)
# print len(recommend_activities)
#=====[ Step 2: get scores for each one ]=====
scores,act = self.inference.score_activities (user_activities, recommend_activities)
return scores,act
####################################################################################################
######################[ --- Interface --- ]#########################################################
####################################################################################################
def print_lda_topics (self):
"""
PUBLIC: print_lda_topics
------------------------
prints out a representation of the lda topics found in self.semantic_analysis
"""
self.semantic_analysis.print_lda_topics ()
class SpotOn:
def __init__ (self):
"""
PUBLIC: Constructor
-------------------
constructs member objects
"""
#=====[ Step 1: create member objects ]=====
self.preprocess = Preprocess ()
self.storage_delegate = StorageDelegate ()
self.semantic_analysis = SemanticAnalysis ()
self.user_analysis = UserAnalysis ()
self.inference = None
self.activities_corpus = None
def load (self):
"""
PUBLIC: load
------------
loads in all parameters
"""
#=====[ Step 1: load in semantic analysis ]=====
print_status ("Initialization", "Loading ML parameters (Begin)")
self.semantic_analysis.load ()
print_status ("Initialization", "Loading ML parameters (End)")
#=====[ Step 2: transfer over models to inference ]=====
print_status ("Initialization", "Constructing Inference instance (Begin)")
self.inference = Inference (self.semantic_analysis.lda_model, self.semantic_analysis.lda_model_topics)
print_status ("Initialization", "Constructing Inference instance (End)")
####################################################################################################
######################[ --- Getting Users --- ]#####################################################
####################################################################################################
def get_users (self):
"""
PUBLIC: get_users
-----------------
constructs self.u_df from all available
calendar dataframes
"""
self.u_df = self.user_analysis.extract_users (self.storage_delegate.iter_calendar_dfs)
self.u_df = self.semantic_analysis.analyze (self.u_df, 'all_event_names')
def load_users (self, filepath='../data/pandas/users/users.df'):
"""
PUBLIC: load_users
------------------
constructs self.u_df from a saved file
"""
self.u_df = pd.read_pickle(filepath)
####################################################################################################
######################[ --- Training --- ]#########################################################
####################################################################################################
def extract_text (self, activity_row):
"""
PRIVATE: extract_text
---------------------
given a row representing an activity, this returns
a list of words representing it as a 'text'
"""
text = []
if type(activity_row['name']) == list:
text += activity_row['name']
if type(activity_row['words']) == list:
text += activity_row['words']
return text
def get_corpus_dictionary (self):
"""
PRIVATE: get_corpus_dictionary
------------------------------
Assembles a gensim corpus and dictionary from activities_df,
where each text is name || words.
"""
#=====[ Step 1: iterate through all activity dataframes ]=====
print_status ("get_corpus", "assembling texts")
documents = []
for df in self.storage_delegate.iter_activity_dfs ():
df['lda_doc'] = df['name'] + df['words']
documents += list(df['lda_doc'])
#=====[ Step 2: get dictionary ]=====
print_status ("get_corpus", "assembling dictionary")
dictionary = gensim.corpora.Dictionary(documents)
#=====[ Step 3: get corpus ]=====
print_status ("get_corpus", "assembling corpus")
corpus = [dictionary.doc2bow (d) for d in documents]
return corpus, dictionary
def print_lda_topics (self):
"""
PUBLIC: print_lda_topics
------------------------
prints out a representation of the lda topics found in self.semantic_analysis
"""
print_header ("LDA TOPICS: ")
self.semantic_analysis.print_lda_topics ()
def train_semantic_analysis (self):
"""
PUBLIC: train_semantic_analysis
-------------------------------
finds parameters for self.semantic_analysis
"""
#=====[ Step 1: get the corpus ]=====
print_status ("train_semantic_analysis", "getting corpus/dictionary")
corpus, dictionary = self.get_corpus_dictionary ()
#=====[ Step 2: train ]=====
print_status ("train_semantic_analysis", "training semantic analysis")
self.semantic_analysis.train (corpus, dictionary)
#####[ DEBUG: print out lda topics ]#####
self.print_lda_topics ()
####################################################################################################
######################[ --- Processing --- ]########################################################
####################################################################################################
def activities_json_to_df (self, a_json):
"""
PRIVATE: activities_json_to_df
------------------------------
given: list of json dicts representing activities
returns: dataframe with preprocessing, semantic analysis
"""
a_df = self.preprocess.preprocess_a (a_json)
a_df = self.semantic_analysis.add_lda_vec_column (a_df)
a_df = self.semantic_analysis.add_w2v_sum_column (a_df)
return a_df
def calendar_events_json_to_df (self, ce_json):
"""
PRIVATE: calendar_events_json_to_df
------------------------------
given: list of json dicts representing calendar events
returns: dataframe with preprocessing, semantic analysis
"""
ce_df = self.preprocess.preprocess_ce (ce_json)
ce_df = self.semantic_analysis.add_lda_vec_column (ce_df)
ce_df = self.semantic_analysis.add_w2v_sum_column (ce_df)
return ce_df
def calendar_events_to_user_representation(self, ce_json):
"""
PUBLIC: calendar_events_to_user_representation
----------------------------------------------
given a list containing json dicts representing calendar events belonging
to a single user, this will return a representation that can be passed to
score_activity_for_user and recommend_for_user
"""
user_df = self.calendar_events_json_to_df (ce_json)
lda_vec = self.semantic_analysis.get_user_lda_vec (user_df)
return {'events_df':user_df, 'lda_vec':lda_vec}
def load_activities_corpus(self, activities):
'''
function: load_activities_corpus
params: activities - list of activities to recommend
returns: none
notes: use this function to load a big activities corpus into the SpotOn object, and later when calling
recommend_for_user we will pull activities to recommend from this corpus.
Can be called multiple times to update to different activities
'''
self.activities_corpus = self.activities_json_to_df (activities)
####################################################################################################
######################[ --- Recommending --- ]######################################################
####################################################################################################
def score_activity_for_user(self, user_representation, activity):
"""
PUBLIC: score_activity_for_user
-------------------------------
params: user_representation - representation of the user to score for
(created by calendar_events_to_user_representation)
activity - json of the activity to score
notes: goes from the representation of the user that you use + one activity
-> return a score for how much they'd like it
"""
#=====[ Step 1: get activity dataframe ]=====
activity_df = self.activities_json_to_df ([activity])
#=====[ Step 2: get scored dataframe ]=====
activity_df = self.inference.infer_scores (user_representation, activity_df)
#=====[ Step 3: extract and return score ]=====
return activity_df.iloc[0]['score']
def recommend_for_user(self, user_representation, activities=None, topn=10):
"""
PUBLIC: recommend_for_user
--------------------------
params: user_representation - representation of the user to recommend for
activities - either a list of json activities, or None if
.load_activities_corpus has been called
topn - number of recommendations to return
"""
#=====[ Step 1: get a_df, df of activities to recommend ]=====
if activities is not None:
activities_df = self.activities_json_to_df (activities)
else:
if not (self.activities_corpus is not None):
self.load_activities_corpus ()
activities_df = self.activities_corpus
#=====[ Step 2: get scores, return sorted ]=====
activity_ranks = self.inference.rank_activities (user_representation, activities_df)
return list(activity_ranks)
def recommend_users_for_activity(self, activity, list_of_users, topn=10):
"""
PUBLIC: recommend_users_for_activities
--------------------------------------
params: activity - activity to recommend users for
list_of_users - list of users to filter
topn - number of users to return
notes: goes from an activity and a list of users -> topn users for that activity
"""
scores = [self.score_activity_for_user(user, activity) for user in list_of_users]
sorted_ix = np.argsort(scores)[::-1]
return [list_of_users[sorted_ix[i]] for i in range(topn)]
