def get_date_range_aggregate(request, responder):
"""
When a user asks for a statistic, such as average, sum, count or percentage,
in addition to a date range filter such as date of hire, termination or birth (required),
and categorical filters (if any), this function captures all the relevant entities,
calculates the desired statistic function and returns it.
'function' entities represent the statistic functions - sum, average, percentage, count
"""
# Checks for existing function entity from previous turn
func_entity = request.frame.get('function')
# If the user provides a new function entity, it replaces the one in context from previous turns
func_entities = extract_entities_from_type(request, 'function')
if func_entities:
func_entity = func_entities[0]
if func_entity:
# Resolve the recognized function entity to its canonical form, one that can be used in
# the output dialog as well as in the '_agg_function', which is used to calculate the values
# of these desired function entities
function, responder = _resolve_function_entity(responder, func_entity)
# If there are any categorical entities (eg. race, gender, position etc.) in the query that
# need filtering on, '_resolve_categorical_entities' fetches these entities, resolves them
# to their canonical form and filters the knowledge base on all the conditions requested by
# the user query
qa, size = _resolve_categorical_entities(request, responder)
# Resolve the duration that is explicitly or implicitly mentioned in the query
# and then filtering the KB to retain all employees satisfying that duration criteria
out = _resolve_time(request, responder, qa, size)
if out:
qa, size, field = out
qa_out = qa.execute(size=size)
# Calculate and return desired mathemical value
responder.slots['value'] = _agg_function(qa_out, func=function)
if function == 'pct':
responder.reply("Of the total employees, the {function} "
"that meet your criteria is {value}")
else:
responder.reply("The {function} of employees is {value}")
else:
responder.reply('What time-filtered statistic would you like to know?')
responder.listen()
def get_date_range_employees(request, responder):
"""
When a user asks for a list of employees that satisfy certain criteria in addition
to satisfying a specified date range criterion (date of hire/termination/birth),
this dialogue state filters the knowledge base on those criteria and returns the
shortlisted list of names.
"""
# If there are any categorical entities (eg. race, gender, department etc.) in the query that
# need filtering on, '_resolve_categorical_entities' fetches these entities, resolves them to
# their canonical form and filters the database on all the conditions requested by the user query
qa, size = _resolve_categorical_entities(request, responder)
# Resolve the duration that is explicitly or implicitly mentioned in the query
# and then filtering the KB to retain all employees satisfying that duration criteria
out = _resolve_time(request, responder, qa, size)
if out:
qa, size, field = out
# Finding extreme entities such as 'highest', 'lowest', 'youngest' etc. (if any)
extreme_entity = [
e for e in request.entities if e['type'] == 'extreme'
]
# Filter on the extreme entities if they exist
if extreme_entity:
extreme_entity = extreme_entity[0]
qa, size = _resolve_extremes(request, responder, qa,
extreme_entity, field)
qa_out = qa.execute(size=size)
if qa_out:
responder.slots['emp_list'] = _get_names(qa_out)
# Give the natural language response according to the size of the list to be displayed
if len(qa_out) == 1:
responder.reply(
"Here is the employee you are looking for: {emp_list}")
else:
responder.reply(
"Here are some employees that match your criteria: {emp_list}"
)
else:
responder.listen()
def get_salary_employees(request, responder):
"""
When a user asks for a list of employees that satisfy certain criteria in addition
to satisfying a specified monetary criterion, this dialogue state filters the
knowledge base on those criteria and returns the shortlisted list of names.
"""
money_entities = [e for e in request.entities if e['type'] == 'money']
categorical_entities = [
e for e in request.entities
if e['type'] in ('state', 'sex', 'maritaldesc', 'citizendesc',
'racedesc', 'performance_score', 'employment_status',
'employee_source', 'position', 'department')
]
# Resolve the recognized function entity to its canonical form, one that can be used in
# the output dialog as well as in the '_agg_function', which is used to calculate the values
# of these desired function entities
qa, size = _resolve_categorical_entities(request, responder)
# Filter the knowledge base on ant time related entities that occur in the query
qa = _resolve_time_in_salary(request, responder, qa)
if money_entities:
qa, size = _apply_money_filter(qa, request, responder)
qa_out = qa.execute(size=size)
responder.slots['emp_list'] = _get_names(qa_out)
if qa_out:
if size == 1:
responder.reply(
"Here is the employee you are looking for with their hourly pay: {emp_list}"
)
else:
responder.reply(
"Here are some employees with their hourly pay: {emp_list}")
else:
responder.reply("No such employees found")
def get_salary_employees(request, responder):
"""
When a user asks for a list of employees that satisfy certain criteria in addition
to satisfying a specified monetary criterion, this dialogue state filters the
knowledge base on those criteria and returns the shortlisted list of names.
"""
money_entities = extract_entities_from_type(request, 'money')
# Resolve the recognized function entity to its canonical form, one that can be used in
# the output dialog as well as in the '_agg_function', which is used to calculate the values
# of these desired function entities
qa, size = _resolve_categorical_entities(request, responder)
# Filter the knowledge base on ant time related entities that occur in the query
qa = _resolve_time_in_salary(request, responder, qa)
if money_entities:
out = _apply_money_filter(qa, request, responder)
if not out:
return
qa, size = out
qa_out = qa.execute(size=size)
responder.slots['emp_list'] = _get_names_with_money(qa_out)
if qa_out:
if size == 1:
reply = "Here is the employee you are looking for with their hourly pay: {emp_list}"
responder.reply(reply)
else:
responder.reply(
"Here are some employees with their hourly pay: {emp_list}")
else:
responder.reply("No such employees found")
def get_salary_aggregate(request, responder):
"""
When a user asks for a statistic, such as average, sum, count or percentage,
in addition to an income related filter (required) and categorical filters (if any),
this function captures all the relevant entities, calculates the desired
statistic function and returns it.
'function' entities represent the statistic functions - sum, average, percentage, count
"""
# Checks for existing function entity from previous turn
func_entity = request.frame.get('function')
# If the user provides a new function entity, it replaces the one in context from previous turns
func_entities = [e for e in request.entities if e['type'] == 'function']
money_entities = [e for e in request.entities if e['type'] == 'money']
recur_ent = [
e['value'][0]['cname'] for e in request.entities
if e['type'] == 'time_recur'
]
extreme_entity = [e for e in request.entities if e['type'] == 'extreme']
# If there are any categorical entities (eg. race, gender, department etc.) in the query that
# need filtering on, '_resolve_categorical_entities' fetches these entities, resolves them to
# their canonical form and filters the database on all the conditions requested by the user query
qa, size = _resolve_categorical_entities(request, responder)
# Filter the knowledge base on ant time related entities that occur in the query
qa = _resolve_time_in_salary(request, responder, qa)
# default reply
salary_response = "Hmm, looks like you want a salary statistic. You can ask me about averages, sums, counts and percentages. For eg. what is the average salary for women?"
if func_entities:
func_entity = func_entities[0]
if func_entity:
# Resolve the recognized function entity to its canonical form, one that can be used in
# the output dialog as well as in the '_agg_function', which is used to calculate the values
# of these desired function entities
function, responder = _resolve_function_entity(responder, func_entity)
if money_entities:
out = _apply_money_filter(qa, request, responder)
if out:
qa, size = out
else:
return
qa_out = qa.execute(size=size)
# If there is a multiplicative factor or recurring duration over which the amount is to
# be returned, convert default (hourly) to that duration. For eg. 'what the average monthly salary'
if recur_ent and function in ('avg', 'sum'):
responder = _calculate_agg_salary(responder, qa_out, function,
recur_ent[0])
if np.isnan(responder.slots['value']):
responder.reply(salary_response)
responder.listen()
return
responder.reply(
"the {function} {interval} salary, based on your criteria, is ${value}"
)
# Else calculate aggregate value on default duration (i.e. hourly)
else:
responder = _calculate_agg_salary(responder, qa_out, function)
# default return if NaN
if np.isnan(responder.slots['value']):
responder.reply(salary_response)
responder.listen()
return
# Responses specific to type functions
if function in ('avg', 'sum'):
responder.reply(
'Based on your criteria, the {function} salary is ${value}'
)
else:
responder.reply("The {function} of employees is {value}")
# Capture any implicit mentions of money and resolve count or percentage queries (eg. how many people get more than 30)
elif function not in ('avg', 'sum'):
out = _apply_money_filter(qa, request, responder)
if out:
qa, size = out
else:
return
qa_out = qa.execute(size=size)
responder = _calculate_agg_salary(responder, qa_out, function)
# default return if NaN
if np.isnan(responder.slots['value']):
responder.reply(salary_response)
responder.listen()
return
responder.reply("The {function} of employees is {value}")
else:
responder.reply(
"I see you are looking for the {function}, can you be more specific?"
)
responder.frame['function'] = func_entity
responder.params.allowed_intents = (
'general.get_aggregate', 'salary.get_salary_aggregate',
'date.get_date_range_aggregate')
responder.listen()
else:
responder.reply(salary_response)
responder.listen()
