def test_reopen(trie):
""" Test training and the re-openning of persistant trie
"""
trie.add_ngram([LE,PETIT,CHAT])
trie.add_ngram([LE,PETIT,CHIEN])
trie.add_ngram([LE,PETIT,RAT])
trie.add_ngram([LE,GROS,RAT])
assert trie.dirty
# store trie param
trie_class = trie.__class__
trie_path = trie.path
print("Will reopen with class:%s and path:%s" % (trie_class, trie_path))
# close and reopen
trie.close()
del trie
trie = trie_class(trie_path)
assert trie.dirty
assert trie.query_count([LE, PETIT]) == 3
assert float_equal(trie.query_entropy([LE, PETIT]), 1.584962500721156)
assert float_equal(trie.query_autonomy([LE, PETIT]), 1.0)
assert trie.query_count([]) == 4
assert not trie.dirty
# close and reopen (again)
trie.close()
trie = trie_class(trie_path)
assert not trie.dirty
assert float_equal(trie.query_autonomy([LE, PETIT]), 1.0)
def test_reopen(trie):
""" Test training and the re-openning of persistant trie
"""
trie.add_ngram([LE, PETIT, CHAT])
trie.add_ngram([LE, PETIT, CHIEN])
trie.add_ngram([LE, PETIT, RAT])
trie.add_ngram([LE, GROS, RAT])
assert trie.dirty
# store trie param
trie_class = trie.__class__
trie_path = trie.path
print("Will reopen with class:%s and path:%s" % (trie_class, trie_path))
# close and reopen
trie.close()
del trie
trie = trie_class(trie_path)
assert trie.dirty
assert trie.query_count([LE, PETIT]) == 3
assert float_equal(trie.query_entropy([LE, PETIT]), 1.584962500721156)
assert float_equal(trie.query_autonomy([LE, PETIT]), 1.0)
assert trie.query_count([]) == 4
assert not trie.dirty
# close and reopen (again)
trie.close()
trie = trie_class(trie_path)
assert not trie.dirty
assert float_equal(trie.query_autonomy([LE, PETIT]), 1.0)
def test_with_tuple(storage):
storage.add_sentence(("le", "petit", "chat"))
storage.add_sentence(("le", "petit", "chien"))
storage.add_sentence(("pour", "le", "petit"), freq=2)
assert storage.query_count(("le", "petit")) == 4
assert storage.query_count(("pour",)) == 2
assert float_equal(storage.query_entropy(("le", "petit")), 1.75)
assert float_equal(storage.query_autonomy(("le", "petit")), 1.89582)
def test_with_tuple(storage):
storage.add_sentence(('le', 'petit', 'chat'))
storage.add_sentence(('le', 'petit', 'chien'))
storage.add_sentence(('pour', 'le', 'petit'), freq=2)
assert storage.query_count(('le', 'petit')) == 4
assert storage.query_count(('pour', )) == 2
assert float_equal(storage.query_entropy(('le', 'petit')), 1.75)
assert float_equal(storage.query_autonomy(('le', 'petit')), 1.89582)
def test_with_tuple(storage):
storage.add_sentence(('le','petit','chat'))
storage.add_sentence(('le','petit','chien'))
storage.add_sentence(('pour','le','petit'), freq=2)
assert storage.query_count(('le', 'petit')) == 4
assert storage.query_count(('pour',)) == 2
assert float_equal(storage.query_entropy(('le', 'petit')), 1.75)
assert float_equal(storage.query_autonomy(('le', 'petit')),1.89582)
def test_basic_trie(trie):
""" Minimal test on simple example
"""
trie.add_ngram([LE,PETIT,CHAT])
trie.add_ngram([LE,PETIT,CHIEN])
trie.add_ngram([LE,PETIT,RAT])
trie.add_ngram([LE,GROS,RAT])
assert trie.query_count([LE, PETIT]) == 3
assert float_equal(trie.query_entropy([LE, PETIT]), 1.584962500721156)
assert float_equal(trie.query_autonomy([LE, PETIT]), 1.0)
# query the empty list gives the total count
assert trie.query_count([]) == 4
# entropy of the "root"
assert trie.query_entropy([]) == 0.0
assert isnan(trie.query_autonomy([]))
def test_basic_trie(trie):
""" Minimal test on simple example
"""
trie.add_ngram([LE, PETIT, CHAT])
trie.add_ngram([LE, PETIT, CHIEN])
trie.add_ngram([LE, PETIT, RAT])
trie.add_ngram([LE, GROS, RAT])
assert trie.query_count([LE, PETIT]) == 3
assert float_equal(trie.query_entropy([LE, PETIT]), 1.584962500721156)
assert float_equal(trie.query_autonomy([LE, PETIT]), 1.0)
# query the empty list gives the total count
assert trie.query_count([]) == 4
# entropy of the "root"
assert trie.query_entropy([]) == 0.0
assert isnan(trie.query_autonomy([]))
def test_basic(storage):
"""
Forward that begins by « le petit »:
- le petit chat
- le petit chien
- le petit $ * 2
Backward that begins by « petit le » :
- petit le ^ * 2
- petit le pour * 2
--> count is the mean of 4 and 4, and entropy is the mean of 2 (the None are counted separately) and 1.5.
"""
storage.add_sentence(["le", "petit", "chat"])
storage.add_sentence(["le", "petit", "chien"])
storage.add_sentence(["pour", "le", "petit"], freq=2)
assert storage.query_count(["le", "petit"]) == 4
assert storage.query_count(["pour"]) == 2
assert isinstance(storage.query_count(["pour"]), int)
assert float_equal(storage.query_entropy(["le", "petit"]), 1.75)
assert float_equal(storage.query_autonomy(["le", "petit"]), 1.89582)
def test_add_ngram_negativ_freq(trie):
""" Test to add a ngram with negative freq
"""
trie.add_ngram([LE,PETIT,CHAT])
trie.add_ngram([LE,PETIT,CHIEN])
trie.add_ngram([LE,PETIT,RAT])
trie.add_ngram([LE,GROS,RAT])
# test removing a n-gramm
with pytest.raises(ValueError):
trie.add_ngram([LE,PETIT,CHAT], -1)
with pytest.raises(ValueError):
trie.add_ngram([LE,PETIT,CHAT], 0)
return
## The following is noted here for a futur release, see #18
assert trie.query_count([LE, PETIT]) == 2
assert float_equal(trie.query_entropy([LE, PETIT]), 1.0)
assert float_equal(trie.query_autonomy([LE, PETIT]), 1.0)
# test removing more than resonable
trie.add_ngram([LE,PETIT,CHAT], -10)
assert trie.query_count([LE, PETIT]) == 0
def test_add_ngram_negativ_freq(trie):
""" Test to add a ngram with negative freq
"""
trie.add_ngram([LE, PETIT, CHAT])
trie.add_ngram([LE, PETIT, CHIEN])
trie.add_ngram([LE, PETIT, RAT])
trie.add_ngram([LE, GROS, RAT])
# test removing a n-gramm
with pytest.raises(ValueError):
trie.add_ngram([LE, PETIT, CHAT], -1)
with pytest.raises(ValueError):
trie.add_ngram([LE, PETIT, CHAT], 0)
return
## The following is noted here for a futur release, see #18
assert trie.query_count([LE, PETIT]) == 2
assert float_equal(trie.query_entropy([LE, PETIT]), 1.0)
assert float_equal(trie.query_autonomy([LE, PETIT]), 1.0)
# test removing more than resonable
trie.add_ngram([LE, PETIT, CHAT], -10)
assert trie.query_count([LE, PETIT]) == 0
def test_basic(storage):
"""
Forward that begins by « le petit »:
- le petit chat
- le petit chien
- le petit $ * 2
Backward that begins by « petit le » :
- petit le ^ * 2
- petit le pour * 2
--> count is the mean of 4 and 4, and entropy is the mean of 2 (the None are counted separately) and 1.5.
"""
storage.add_sentence(['le','petit','chat'])
storage.add_sentence(['le','petit','chien'])
storage.add_sentence(['pour','le','petit'], freq=2)
assert storage.query_count(['le', 'petit']) == 4
assert storage.query_count(['pour']) == 2
assert isinstance(storage.query_count(['pour']), int)
assert float_equal(storage.query_entropy(['le', 'petit']), 1.75)
assert float_equal(storage.query_autonomy(['le', 'petit']),1.89582)
def run(self):
"""
Checks several things about the requested line to try and analyze why a
line wouldn't be used at its full potential.
"""
line = self._get_relevant_line()
if line is None:
line_name = self._get_line_name()
print("Line "+str(line_name)+" not found")
return BotErrorMessage("I <b>couldn't find the line "+str(line_name)+
"</b>. I'm afraid it doesn't exist.")
print("Found line "+str(line.getName()))
# Check if the user believes the line is not used as it really is.
# Results of this checking will be used to display or not a "actually"
# in the bots answer.
print("checking user's beliefs")
user_utilization_ratio = self.context.get_slot_value("utilization")
try:
user_utilization_ratio = float(user_utilization_ratio)
except Exception:
user_utilization_ratio = None
precision_adverb = None
if ( user_utilization_ratio is not None
and not float_equal(user_utilization_ratio*100,
self._get_real_utilization(line))):
precision_adverb = "actually"
# Check 0: is the line saturated?
print("doing check #0")
if float_equal(self._get_real_utilization(line), 100.0):
print("Line "+str(self._get_line_name())+" is saturated")
return {
"fetched-utilization": 100.0,
"precision-adverb": precision_adverb,
"machine-utilization-explanation":
"which means it is <b>saturated</b>. The point is to "+
"fulfill a maximum number of orders, right?",
}
# Check 1: is all demand planned?
print("doing check #1")
nb_orders = Phi.getNumOrders()
horizon_date = \
Phi.getTimeBucket(Phi.getNumberOfTimeBuckets()-1).getEndDate()
nb_orders_planned = 0
orders = []
unplanned_orders = []
nb_orders_forbidden = 0
for i in range(nb_orders):
current_order = Phi.getOrder(i)
orders.append(current_order)
completion_date = current_order.getCompletionDate()
if completion_date < horizon_date: # QUESTION: should the horizon date be included?
nb_orders_planned += 1
else:
unplanned_orders.append(current_order)
if current_order.isForbidden():
nb_orders_forbidden += 1
if nb_orders - nb_orders_forbidden == nb_orders_planned:
return {
"fetched-utilization": self._get_real_utilization(line),
"precision-adverb": precision_adverb,
"machine-utilization-explanation":
"because <b>all the orders are planned</b>: "+
str(nb_orders_planned)+" out of "+str(nb_orders)+" are "+
"planned (where "+str(nb_orders_forbidden)+" orders are "+
"forbidden)",
}
# Check 2: is there a part of the unplanned demand that actually goes through this line?
print("doing check #2")
nb_buckets = Phi.getNumberOfTimeBuckets()
unplanned_goes_through_line = False
for order in unplanned_orders:
current_product_family = order.getFinalPF() # QUESTION: what to do if this returns `None`?
if ( current_product_family is not None
and line.isPFUsed(current_product_family) != 0):
unplanned_goes_through_line = True
break
if not unplanned_goes_through_line:
return {
"fetched-utilization": self._get_real_utilization(line),
"precision-adverb": precision_adverb,
"machine-utilization-explanation":
"because it seems there is <b>no unplanned orders "+
"that need to go through this production line</b>",
}
# Check 3: is it possible to plan the unplanned orders within the horizon (are they already late)?
print("doing check #3")
orders_already_late = False
for order in orders:
due_date = order.getDueDate()
if due_date is not None and due_date > horizon_date:
orders_already_late = True
break
if orders_already_late:
return {
"fetched-utilization": self._get_real_utilization(line),
"precision-adverb": precision_adverb,
"machine-utilization-explanation":
"because <b>some orders have their due date after the time "+
"horizon</b> (the end time of the last time bucket), "+
"therefore the solver decided not to plan them",
}
# Check 4: are there other lines which are saturated over the horizon?
print("doing check #4")
other_saturated_line = None
nb_lines = Phi.getNumberOfLines() # TODO: in the API you also have another function named `getNumLines()` which seems to do the same thing, you might want to look into this.
for i in range(nb_lines):
other_line = Phi.getLine(i)
if ( other_line is not None
and self._is_line_saturated(other_line)):
# Check whether an unplanned goes through this line
for order in unplanned_orders:
current_product_family = order.getFinalPF() # QUESTION: what to do if this returns `None`?
if ( current_product_family is not None
and line.isPFUsed(current_product_family) != 0
and other_line.isPFUsed(current_product_family) != 0):
# current unplanned order should go through both lines
other_saturated_line = other_line
break
if other_saturated_line is not None:
break
if other_saturated_line is not None:
return {
"fetched-utilization": self._get_real_utilization(line),
"precision-adverb": precision_adverb,
"machine-utilization-explanation":
"because <b>production line "+
str(other_saturated_line.getName())+" is saturated</b> "+
"which prevents some of the unplanned commands that go "+
"through line "+str(line.getName())+" to be completed. "+
"The solver does not plan such commands completely",
}
# Check 5: are there any limiting flow constraints on the line?
print("doing check #5")
# Check 6: are there any stock max constraints on some successive lines?
print("doing check #6")
return {
"fetched-utilization": self._get_real_utilization(line),
"precision-adverb": precision_adverb,
"machine-utilization-explanation":
"but I <b>couldn't find out why</b>.\nIf after analyzing the "+
"plan, you still can't understand why this is the case, don't "+
"hesitate to <b>call a consultant</b>",
}
def test_lexer_input_float_scientific_enumeration():
i = '4.2e5'
r = lexer(i)
assert float_equal(4.2 * 10**5, r[0])
def test_lexer_input_float():
i = '4.2'
r = lexer(i)
assert float_equal(4.2, r[0])
def test_lexer_input_scientific_enumeration():
i = '1e4'
r = lexer(i)
assert float_equal(1 * 10**4, r[0])
