def test_decorate():
@proto.decorate
def test():
r'''Test function
test 1 x = x + 1
test 2 x = x - 2
test x y = 10
'''
expected = [
(1, var('x')),
(2, var('x')),
(var('x'), var('y')),
]
actual = [pattern for pattern, _, _ in test.patterns]
for e, a in zip(expected, actual):
assert match(e, a)
assert match(a, e)
assert test(1, 2) == 3
assert test(2, 1) == -1
assert test(5, 6) == 10
def get_refmod_mine_nlm_matches(model1, model2):
matches = []
nodes1temp = commonFunctions.get_nodes(model1, consideredComponents)
nodes2temp = commonFunctions.get_nodes(model2, consideredComponents)
nodes1 = create_refmod_nodes(nodes1temp)
nodes2 = create_refmod_nodes(nodes2temp)
entries1 = [node_a.labelTokens for node_a in nodes1]
entries2 = [node_b.labelTokens for node_b in nodes2]
global wiki_entries
wiki_entries = fetch_entry_from_wiktionary(entries1 + entries2)
for n1 in nodes1:
for n2 in nodes2:
if is_identical_condition(n1, n2):
matches.append(
matcher.match(node1=model1.diagram_graph.node[n1.id],
node2=model2.diagram_graph.node[n2.id],
score=1.0))
continue
if is_cross_category_condition(n1, n2):
matches.append(
matcher.match(node1=model1.diagram_graph.node[n1.id],
node2=model2.diagram_graph.node[n2.id],
score=1.0))
continue
return matches
def test_decorate_method():
class TestClass(object):
def __init__(self, value):
self.value = value
@proto.decorate_method
def get_value():
r'''Returns first argument, gets instance attribute 'value' otherwise.
value x = x
value = self.value
'''
matcher = TestClass.__dict__['get_value']
expected = [(var('x'), ), tuple()]
actual = [pattern for pattern, _, _ in matcher.patterns]
for e, a in zip(expected, actual):
assert match(e, a)
assert match(a, e)
v = TestClass(2)
assert v.get_value(1) == 1
assert v.get_value() == 2
def test_matcher_gets_reset_after_complex_match(self):
matcher = self.example_comment_matcher()
self.assertEqual(
' first comment ',
matcher.match('/* first comment */').raw_value)
self.assertEqual(
' second comment ',
matcher.match('/* second comment */').raw_value)
def main():
st.title("Dataset")
data, titles, texts = utils.load_data() # careful, loads 12 GB of data
preprocess_wiki_text(data, "preped_wikitexts.jsonl") # Prep text from Wiki
get_infobox_data(
"./data/preped_wikitexts.jsonl",
"./data/matched_texts.jsonl") # Get Articles and Infoboxs mapped
# or use for speedup => get_infobox_data_multi("./data/preped_wikitexts.jsonl", "./data/matched_texts.jsonl")
matcher.match(
"./data/matched_texts.jsonl", "/data/train_data.jsonl"
) # match sentences and triple (infobox values) => saving in data as jsonl
def tampilDeadline(usrMsg):
deadline = db.getList_Daftar_Tugas_Status(False)
for i in range(len(deadline)):
#General untuk tugas
if(matcher.match(usrMsg,"tugas")):
if(matcher.match(usrMsg,deadline[i][2].lower())):
return deadline[i][1]
#Spesifik, tucil, tubes atau pr
else:
if(matcher.match(usrMsg,deadline[i][2].lower()) and matcher.match(usrMsg,deadline[i][3].lower())):
return deadline[i][1]
return "Tidak ada deadline itu"
def callback_boxes(message):
d = jsonpickle.decode(message.data)
print("@", time.time(), "\n", d)
matched = match(d, clusters)
fus_pub.publish(jsonpickle.encode(matched))
def test_link_is_encoded_as_phrase(self):
links_match = []
for para in paras_from('bookmark with link.docx'):
phrase_contents = in_para_allcontent.pick_contents\
(in_para_allcontent.contentlist(para), lambda x: x["type"] == "phrase")
for content in phrase_contents:
links_match.append(
matcher.match(in_para_phrase.content_regex, content))
self.assertAllAreOk(links_match)
def test_bookmark_is_encoded_as_anchor(self):
anchors_match = []
for para in paras_from('anchor.docx'):
anchor_contents = in_para_allcontent.pick_contents\
(in_para_allcontent.contentlist(para), lambda x: x['type'] == "anchor")
for content in anchor_contents:
anchors_match.append(
matcher.match(in_para_bookmark.content_regex, content))
self.assertAllAreOk(anchors_match)
def test_reference_is_encoded_as_external(self):
extrefs_match = []
for para in paras_from('externalref.docx'):
extref_contents = in_para_allcontent.pick_contents\
(in_para_allcontent.contentlist(para), lambda x: x["type"] == "extref")
for content in extref_contents:
extrefs_match.append(
matcher.match(in_para_externalref.content_regex, content))
self.assertAllAreOk(extrefs_match)
def test_link_to_html_is_encoded_as_phrase(self):
links_match = []
para_with_link = paras_from('link to html.docx')[0]
phrase_contents = in_para_allcontent.pick_contents\
(in_para_allcontent.contentlist(para_with_link), lambda x: x["type"] == "phrase")
for content in phrase_contents:
links_match.append(
matcher.match(in_para_phrase.content_regex, content))
self.assertAllAreOk(links_match)
def daftar_katakunci(text):
data = []
textlist = text.split(" ")
for kata in bd.getList_Kata_Penting()[1:]:
for i in range(len(textlist)):
index = matcher.match(textlist[i].lower(), kata.lower())
if (index and len(kata) + 1 >= len(textlist[i]) + 1):
data.append(kata)
break
if (index):
break
for kata in bd.getList_Kata_Tampil_Deadline():
for i in range(len(textlist)):
index = matcher.match(textlist[i].lower(), kata.lower())
if (index):
data.append(kata)
return data
def process(usrMsg):
result = at.ValidasiInput(usrMsg)
if(result =="-1"):
text = str(usrMsg).lower()
#Menampilkan help
for pattern in kata_help:
if (matcher.match(text, pattern)) :
return help()
#Menandai task selesai
for pattern in kata_task_selesai:
if (matcher.match(text, pattern)) :
return tandaiTask(text)
#Menampilkan tanggal deadline suatu task
for pattern in kata_tampil_deadline:
if (matcher.match(text, pattern)) :
return tampilDeadline(text)
kata_penting = db.getList_Kata_Help()
kata_penting += db.getList_Kata_Tampil_Deadline()
kata_penting += db.getList_Kata_Task_Selesai()
kata_input = text.split(" ")
found = False
for kata in kata_input:
for pattern in kata_penting:
if kata not in kata_penting:
if matcher.similarity(pattern, kata) >= 0.75:
text = text.replace(kata, pattern)
found = True
if found:
return "Mungkin maksud kamu:\n" + text
return "Maaf, pesan tidak dikenali"
else:
return result
def diundurTask(usrMsg):
found = False
text = str(usrMsg).split(" ")
for i in range(len(text)):
if (matcher.match(text[i], "undur") or matcher.match(text[i], "ubah")):
if (len(text[i + 2]) > 2):
tanggal = text[i + 2]
else:
bulan_int = bulan.get(text[i + 3].lower())
tanggal = text[i + 2] + "/" + bulan_int + "/" + text[i + 4]
(tgl, bln, th) = re.split("/", tanggal)
date = datetime.date(int(th), int(bln), int(tgl))
bd.update_Daftar_Tugas(text[i - 1], date)
found = True
output = "Deadline Tugas ID " + text[i - 1] + "<br>"
output += "berhasil diperbarui menjadi " + str(date) + " <br>"
return output
if (found == False):
return "-1"
def prune_results(res_1, res_2, threshold=3.0):
to_return = set()
res_1_all_values = []
for value in res_1.values():
res_1_all_values = res_1_all_values + value
res_2_all_values = []
for value in res_1.values():
res_2_all_values = res_2_all_values + value
for entity in res_1.keys():
do_rule_3 = res_1[entity].__len__()
for match in res_1[entity]:
# RULE 1
if res_1_all_values.__contains__(match) and res_2_all_values.__contains__(match):
to_return.add(
matcher.match(node1=entity.node, node2=get_node_from_entities(match.id2, res_2.keys()), score=1.0))
# RULE 2
elif res_1_all_values.__contains__(match) or res_2_all_values.__contains__(match):
if match.score >= threshold:
to_return.add(
matcher.match(node1=entity.node, node2=get_node_from_entities(match.id2, res_2.keys()),
score=1.0))
else:
do_rule_3 = do_rule_3 - 1
# RULE 3
if do_rule_3 == 0:
for match in get_two_best(res_1[entity]):
to_return.add(
matcher.match(node1=entity.node, node2=get_node_from_entities(match.id2, res_2.keys()), score=1.0))
for entity in res_2.keys():
do_rule_3 = res_2[entity].__len__()
for match in res_2[entity]:
# RULE 1
if res_1_all_values.__contains__(match) and res_2_all_values.__contains__(
match):
to_return.add(
matcher.match(node1=entity.node, node2=get_node_from_entities(match.id2, res_1.keys()), score=1.0))
# RULE 2
elif res_1_all_values.__contains__(match) or res_2_all_values.__contains__(match):
if match.score >= threshold:
to_return.add(
matcher.match(node1=entity.node, node2=get_node_from_entities(match.id2, res_1.keys()),
score=1.0))
else:
do_rule_3 = do_rule_3 - 1
# RULE 3
if do_rule_3 == 0:
for match in get_two_best(res_2[entity]):
to_return.add(
matcher.match(node1=entity.node, node2=get_node_from_entities(match.id2, res_1.keys()), score=1.0))
return to_return
def getMatchSSSmatches(diagram1, diagram2, threshold=0.5):
# bag has structure: {nodeId: normalizedLabel}
bag1 = extract_normalize_step(diagram1, activeComponents)
bag2 = extract_normalize_step(diagram2, activeComponents)
matches_alpha = calculate_similarity_step(bag1, bag2)
# Identify step
matches_final = []
for m in matches_alpha:
if m.similiraty_score >= threshold:
matches_final.append(
matcher.match(node1=diagram1.diagram_graph.node[m.id1],
node2=diagram2.diagram_graph.node[m.id2],
score=m.similiraty_score))
return matches_final
def student_listing_matcher(student, listing):
"""
Match student to listing by student fields and desired listing fields
:param student: Student obj
:param listing: Listing obj
:return: ratio between 0 and 1
"""
same_job_type = False
for i in student.looking_for:
if i in listing.job_type:
same_job_type = True
if not same_job_type:
return 0
if len(student.skills) == 0:
return 0
skill_ratio = matcher.match(student.skills, listing.desired_skills)
return skill_ratio
def antaraTanggal_Jenis(text, jenis):
data = []
textlist = text.split(" ")
for i in range(len(textlist)):
index = matcher.match(textlist[i].lower(), "antara")
if (index):
if len(textlist[i + 1]) > 2:
data.append(textlist[i + 1])
data.append(str(textlist[i + 3]).replace("?", ""))
else:
bulan_int = bulan.get(textlist[i + 1].lower())
tanggal = textlist[i +
1] + "/" + bulan_int + "/" + textlist[i + 3]
data.append(tanggal)
bulan_int = bulan.get(textlist[i + 3].lower())
tanggal = textlist[i +
1] + "/" + bulan_int + "/" + textlist[i + 3]
data.append(tanggal)
break
if (len(data) == 2):
(tgl, bln, th) = re.split("/", data[0])
date1 = datetime.date(int(th), int(bln), int(tgl))
(tgl, bln, th) = re.split("/", data[1])
date2 = datetime.date(int(th), int(bln), int(tgl))
# rubah rertun list
output = "[Menampilkan daftar " + jenis + str(date1) + " - " + str(
date2) + "] <br>"
daftar = bd.getList_Daftar_Tugas_Jenis_tgl(jenis, date1, date2, False)
if (len(daftar) == 0):
return "Tidak ada " + jenis + " antara " + str(
date1) + " - " + str(date2)
for tugas in daftar:
output += "(ID: " + tugas[0] + ") " + tugas[1] + " " + tugas[
2] + " " + tugas[3] + " <br>"
return output
else:
return "-1"
def filelist(self, roots, **kwargs):
'''
Parameters
----------
roots:
file_roots, pillar_roots, cache_roots, etc to walk
kwargs:
Contains any extra variables to pass to element
'''
for root, abspath in walk(roots):
element = self.element(root, abspath, **kwargs)
if self.match_each and not all(
matcher.match([element], self.pattern)):
continue
self.add_element(element, **kwargs)
return self.as_sequence
def get_matches_by_combine(activity_pairs_all, bots_results, bot_thresholds,
best, second_best, model_pairs):
final_matches = dict()
for pair in model_pairs:
final_matches[pair] = []
for act_pair in activity_pairs_all:
# bots_results[best][act_pair] , return similarity score of act_pair in best BOT configufarion
if bots_results[best][act_pair] >= bot_thresholds[best][0] or bots_results[second_best][act_pair] >= \
bot_thresholds[second_best][0]:
sim_score = max(bots_results[best][act_pair],
bots_results[second_best][act_pair])
node1 = act_pair.model_pair.bpmn1.diagram_graph.node[
act_pair.node1_id]
node2 = act_pair.model_pair.bpmn2.diagram_graph.node[
act_pair.node2_id]
final_matches[act_pair.model_pair].append(
matcher.match(node1=node1, node2=node2, score=sim_score))
return final_matches
def get_triple_s_matches(bpmn1,
bpmn2,
syntactic_weight=0.5,
semantic_weight=0.35,
ratio_weight=0.05,
position_weight=0.1,
threshold=0.5):
matches = []
nodes1 = commonFunctions.get_nodes(bpmn1, activeComponents)
nodes2 = commonFunctions.get_nodes(bpmn2, activeComponents)
graph1 = commonFunctions.get_graph_with_id_nodes(bpmn1)
graph2 = commonFunctions.get_graph_with_id_nodes(bpmn2)
for node_a in nodes1:
for node_b in nodes2:
l1 = node_a['node_name'].lower()
l2 = node_b['node_name'].lower()
syntactic_score = get_syntactic_score(l1, l2)
semantic_score = get_semantic_score(l1, l2)
structural_score = get_structural_score(
node_a,
node_b,
graph_a=graph1,
graph_b=graph2,
bpmn_a=bpmn1,
bpmn_b=bpmn2,
ratio_weight=ratio_weight,
position_weight=position_weight)
final_score = syntactic_weight * syntactic_score + semantic_weight * semantic_score + structural_score
if final_score >= threshold:
matches.append(
matcher.match(node1=node_a,
node2=node_b,
score=final_score))
return matches
def haritask(text):
found = False
textlist = text.split(" ")
for i in range(len(textlist)):
index = matcher.match(textlist[i].lower(), "hari")
if (index):
found = True
N = int(textlist[i - 1])
dateEnd = nHariKedepan(N)
output = "[Menampilkan Tugas " + str(N) + " hari ke depan]<br>"
daftar = bd.getList_Daftar_Tugas_tgl(datetime.date.today(),
dateEnd, 0)
if (len(daftar) == 0):
return "Tidak ada " + "deadline" + " " + str(
N) + " hari ke depan"
for tugas in daftar:
output += "(ID: " + tugas[0] + ") " + tugas[1] + " " + tugas[
2] + " " + tugas[3] + " <br>"
return output
if (not found):
return "-1"
def filelist(self, roots, **kwargs):
'''
Parameters
----------
roots:
file_roots, pillar_roots, cache_roots, etc to walk
kwargs:
Contains any extra variables to pass to element
'''
for root, abspath in walk(roots):
element = self.element(root, abspath, **kwargs)
if self.match_each and not all(
matcher.match(
[element], self.pattern
)
):
continue
self.add_element(element, **kwargs)
return self.as_sequence
def minggutask_Jenis(text, jenis):
found = False
textlist = text.split(" ")
for i in range(len(textlist)):
index = matcher.match(textlist[i].lower(), "minggu")
if (index):
found = True
N = int(textlist[i - 1])
dateEnd = nHariKedepan(N * 7)
output = "[Menampilkan " + jenis + " " + str(
N) + " minggu ke depan]<br>"
daftar = bd.getList_Daftar_Tugas_Jenis_tgl(jenis,
datetime.date.today(),
dateEnd, 0)
if (len(daftar) == 0):
return "Tidak ada " + jenis + " " + str(N) + " minggu ke depan"
for tugas in daftar:
output += "(ID: " + tugas[0] + ")" + tugas[1] + " " + tugas[
2] + " " + tugas[3] + "<br>"
return output
if (not found):
return "-1"
def parse(lines): """Parses the given text lines and returns an AST that represents the simple HTML document from the text. Raises a ParseError if parsing fails. Raises a TokenizeError if tokenizing fails.""" return match(SimpHtmlParser().parse(lines))
import matcher
classSize = 4
matcher.studentMaker('A', 90, 'male', 5, 4, 4, [2], [4])
matcher.studentMaker('B', 90, 'female', 5, 4, 4, [1], [])
matcher.studentMaker('C', 65, 'male', 3, 2, 1, [4], [])
matcher.studentMaker('D', 65, 'female', 3, 2, 1, [3], [1])
#A and C
#B and D
matcher.groupMaker(classSize)
matcher.match()
matcher.printMatch()
numPics = len(imageFiles)
print numPics
button_pressed = 1
button_released = 0
i = 0
while True:
try:
button_status = controller.check_button()
if button_status == button_pressed:
print "Got new photo!"
controller.turn_light_on()
if (i >= numPics):
i = 0
imagePath = (imageFiles)[i]
print "Matching photo " + imagePath
template = cv2.imread('badguy.jpg')
matcher.match(imagePath, template)
i = i + 1
button_status = button_released
else:
controller.turn_light_off()
except KeyboardInterrupt, SystemExit:
controller.turn_light_off()
controller.turn_buzzer_off()
sys.exit()
break
except (IOError, TypeError) as e:
print("Error")
def test_matches_and_has_remaining_text(self):
matcher = self.example_string_matcher()
matcher.match("'foo\\'s bar and fig\\'s foo' and stuff")
self.assertEqual(
" and stuff",
matcher.remaining_text)
def test_match_for_complex_delims_remaining_text(self):
matcher = self.example_comment_matcher()
matcher.match('/* comment */this text remains')
self.assertEqual('this text remains', matcher.remaining_text)
def test_match_calculates_correct_remaining_text(self):
matcher = self.example_exact_literal_matcher()
matcher.match('->later text')
self.assertEqual('later text', matcher.remaining_text)
def test_matches_has_correct_remaining_value(self):
matcher = self.example_while_matcher()
matcher.match("foobar and stuff")
self.assertEqual(
" and stuff",
matcher.remaining_text)
def validate_model(
prob_thresh=load_config()["machine_learning"]["prboability_thresholds"]["general"],
test=False
):
"""Compares new model with status quo production model and compiles/reports the results.
Based on results, will either replace model and archive old one or just maintain status quo.
Parameters:
- `prob_thresh` (float): probability threshold which the classifier will use to determine
whether or not there is a match.
- `test` (bool): whether in testing or not, will dtermine flow of operations and mute emails appropriately.
"""
match_query = """
SELECT
company_projects.job_number,
company_projects.city,
company_projects.address,
company_projects.title,
company_projects.owner,
company_projects.contractor,
company_projects.engineer,
company_projects.address_lat,
company_projects.address_lng,
company_projects.receiver_emails_dump,
web_certificates.url_key,
web_certificates.cert_id,
attempted_matches.ground_truth,
attempted_matches.multi_phase,
web_certificates.pub_date,
web_certificates.source,
CONCAT(base_urls.base_url, web_certificates.url_key) AS link
FROM
web_certificates
LEFT JOIN
attempted_matches
ON
web_certificates.cert_id = attempted_matches.cert_id
LEFT JOIN
company_projects
ON
attempted_matches.project_id = company_projects.project_id
LEFT JOIN
base_urls
ON
base_urls.source = web_certificates.source
WHERE
company_projects.closed=1
AND
attempted_matches.ground_truth=1
AND
attempted_matches.multi_phase=0
AND
attempted_matches.validate=1
"""
corr_web_certs_query = """
SELECT
web_certificates.*
FROM
web_certificates
LEFT JOIN
attempted_matches
ON
web_certificates.cert_id = attempted_matches.cert_id
LEFT JOIN
company_projects
ON
attempted_matches.project_id = company_projects.project_id
LEFT JOIN
base_urls
ON
base_urls.source = web_certificates.source
WHERE
company_projects.closed=1
AND
attempted_matches.ground_truth=1
AND
attempted_matches.multi_phase=0
AND
attempted_matches.validate=1
"""
with create_connection() as conn:
validate_company_projects = pd.read_sql(match_query, conn)
validate_web_df = pd.read_sql(corr_web_certs_query, conn)
new_results = match(
version="new",
company_projects=validate_company_projects,
df_web=validate_web_df,
test=True,
prob_thresh=prob_thresh,
)
analysis_df = pd.merge(
new_results[['job_number', 'cert_id', 'pred_prob', 'pred_match', 'total_score']],
validate_company_projects[['job_number', 'cert_id', 'ground_truth']],
how='left',
on=['job_number', 'cert_id']
)
analysis_df['ground_truth'] = analysis_df.ground_truth.apply(lambda x: 1 if x == 1.0 else 0)
tp = len(analysis_df[(analysis_df.pred_match == 1) & (analysis_df.ground_truth == 1)])
fp = len(analysis_df[(analysis_df.pred_match == 1) & (analysis_df.ground_truth == 0)])
tn = len(analysis_df[(analysis_df.pred_match == 0) & (analysis_df.ground_truth == 0)])
fn = len(analysis_df[(analysis_df.pred_match == 0) & (analysis_df.ground_truth == 1)])
if fn:
logger.warning(f"match for project #{list(analysis_df[(analysis_df.pred_match == 0) & (analysis_df.ground_truth == 1)]['job_number'])} was not detected.")
logger.info(f"true postives: {tp}")
logger.info(f"false postives: {fp}")
logger.info(f"true negatives: {tn}")
logger.info(f"false negatives: {fn}")
recall = tp / (tp + fn)
precision = tp / (tp + fp)
logger.info(f"recall: {recall}")
logger.info(f"precision: {precision}")
min_prob = min(analysis_df[analysis_df.ground_truth == 1.0]['pred_prob'])
logger.info(f"minimum probability threshhold to acheive 100% recall: {min_prob}")
analysis_df['adj_pred_match'] = analysis_df.pred_prob.apply(lambda x: x >= min_prob)
avg_prob = mean(analysis_df[analysis_df.ground_truth == 1.0]['pred_prob'])
logger.debug(analysis_df[analysis_df.adj_pred_match])
signal_and_noise = analysis_df[analysis_df.pred_prob > -0.1]
signal = signal_and_noise[signal_and_noise.ground_truth == 1.0]['pred_prob']
noise = signal_and_noise[signal_and_noise.ground_truth != 1.0]['pred_prob']
interval = 0.1
bottom_ranges = np.arange(0, 1, interval)
ground_truths, false_matches = [], []
for bottom_range in bottom_ranges:
bottom_range = round(bottom_range, 1)
upper_range = round((bottom_range + interval), 1)
if bottom_range == 0.0: # capture all the false matches scored at exactly 0
bottom_range = -0.1
ground_truths.append(len([value for value in signal if value <= upper_range and value > bottom_range]))
false_matches.append(len([value for value in noise if value <= upper_range and value > bottom_range]))
df = pd.DataFrame({
'probability score' : bottom_ranges,
'true match' : ground_truths,
'false match' : false_matches
})
p1 = plt.bar(df['probability score'], df['true match'], width=0.07, align='edge', color=(112/255, 94/255, 204/255, 1))
p2 = plt.bar(df['probability score'], df['false match'], width=0.07, align='edge', bottom=df['true match'], color=(112/255, 94/255, 134/255, 1))
t = plt.axvline(x=prob_thresh, color=(70/255, 70/255, 80/255, 1), linestyle='--')
plt.ylabel('# of matches')
plt.xlabel('predicted probability of match')
ax = plt.axes()
ax.xaxis.set_major_locator(MaxNLocator(integer=True))
# ax.set_yscale('log', nonposy='clip') # too glitchy to use
plt.xticks([0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1])
plt.title('Precision Spread on Validation Data\n')
plt.legend((p1[0], p2[0]), ('true match', 'false match'))
# ax = plt.axes()
# for spine in ax.spines:
# ax.spines[spine].set_visible(False)
legend = plt.legend((p1[0], p2[0], t), ('true match', 'false match', 'decision threshold'), frameon=1)
frame = legend.get_frame()
frame.set_alpha(0)
if not test: # will also display inside jupyter notebook regardless (if %matplotlib inline)
plt.savefig('static/precision_spread.png', transparent=True, dpi=300)
if recall < 1.0:
adj_tp = len(analysis_df[(analysis_df.adj_pred_match == 1) & (analysis_df.ground_truth == 1)])
adj_fp = len(analysis_df[(analysis_df.adj_pred_match == 1) & (analysis_df.ground_truth == 0)])
adj_tn = len(analysis_df[(analysis_df.adj_pred_match == 0) & (analysis_df.ground_truth == 0)])
adj_fn = len(analysis_df[(analysis_df.adj_pred_match == 0) & (analysis_df.ground_truth == 1)])
logger.info(f"adjusted true postives: {adj_tp}")
logger.info(f"adjusted false postives: {adj_fp}")
logger.info(f"adjusted true negatives: {adj_tn}")
logger.info(f"adjusted false negatives: {adj_fn}")
adj_recall = adj_tp / (adj_tp + adj_fn)
adj_precision = adj_tp / (adj_tp + adj_fp)
logger.info(f"adjusted recall: {adj_recall}")
logger.info(f"adjusted precision: {adj_precision}")
logger.info(f"Would have had {adj_fp} false positives ({adj_precision}% precision) if threshold was adjusted down to acheive 100%")
try:
sq_results = match(
version="status_quo",
company_projects=validate_company_projects,
df_web=validate_web_df,
test=True,
prob_thresh=prob_thresh,
)
except FileNotFoundError:
logger.info(
"could not find any status quo models to use for baseline validation."
)
if not test:
logger.info("adopting new model by default and skipping rest of validation")
for filename in ["rf_model.pkl", "rf_features.pkl"]:
os.rename("new_" + filename, filename)
return # exit function because there is no basline to validate against
else:
logger.info(
"will keep testing validation using new model as baseline. Just for testing purposes."
)
sq_results = match(
version="new",
company_projects=validate_company_projects,
df_web=validate_web_df,
test=True,
prob_thresh=prob_thresh,
)
sq_analysis_df = pd.merge(
sq_results[['job_number', 'cert_id', 'pred_prob', 'pred_match', 'total_score']],
validate_company_projects[['job_number', 'cert_id', 'ground_truth']],
how='left',
on=['job_number', 'cert_id']
)
sq_analysis_df['ground_truth'] = sq_analysis_df.ground_truth.apply(lambda x: 1 if x == 1.0 else 0)
sq_tp = len(sq_analysis_df[(sq_analysis_df.pred_match == 1) & (sq_analysis_df.ground_truth == 1)])
sq_fp = len(sq_analysis_df[(sq_analysis_df.pred_match == 1) & (sq_analysis_df.ground_truth == 0)])
sq_tn = len(sq_analysis_df[(sq_analysis_df.pred_match == 0) & (sq_analysis_df.ground_truth == 0)])
sq_fn = len(sq_analysis_df[(sq_analysis_df.pred_match == 0) & (sq_analysis_df.ground_truth == 1)])
if sq_fn:
logger.warning(f"match for project #{list(sq_analysis_df[(sq_analysis_df.pred_match == 0) & (sq_analysis_df.ground_truth == 1)]['job_number'])} was not detected.")
logger.info(f"true postives: {sq_tp}")
logger.info(f"false postives: {sq_fp}")
logger.info(f"true negatives: {sq_tn}")
logger.info(f"false negatives: {sq_fn}")
sq_recall = sq_tp / (sq_tp + sq_fn)
sq_precision = sq_tp / (sq_tp + sq_fp)
logger.info(f"recall: {sq_recall}")
logger.info(f"precision: {sq_precision}")
sq_min_prob = min(sq_analysis_df[sq_analysis_df.ground_truth == 1.0]['pred_prob'])
logger.info(f"minimum probability threshhold to acheive 100% recall: {sq_min_prob}")
sq_analysis_df['adj_pred_match'] = sq_analysis_df.pred_prob.apply(lambda x: x >= sq_min_prob)
sq_avg_prob = mean(sq_analysis_df[sq_analysis_df.ground_truth == 1.0]['pred_prob'])
logger.debug(sq_analysis_df[sq_analysis_df.adj_pred_match])
update_results({
"probability threshold": prob_thresh,
"SMOTE": load_config()["machine_learning"]["use_smote"],
"100% recall acheived" : True if int(recall) == 1 else False,
'minimum probability required for status quo model' : sq_min_prob,
'minimum probability required for new model' : min_prob,
'average probability required for status quo model' : sq_avg_prob,
'average probability required for new model' : avg_prob,
'false positives with status quo' : sq_fp,
'false positives with new' : fp,
'precision': precision,
})
if recall < 1.0:
logger.warning(
"100% recall not acheived with new model - archiving it "
"and maintaining status quo!"
)
if test:
logger.info("skipping files transfers because running in test mode")
else:
for artifact in ["model", "features"]:
os.rename(
f"new_rf_{artifact}.pkl",
f"model_archive/rf_new_{artifact}-{datetime.datetime.now().date()}.pkl",
)
else:
logger.info("100% recall acheived! Adopting new model and archiving old one.")
if test:
logger.info("skipping files transfers because running in test mode")
else:
for artifact in ["model", "features"]:
os.rename(
f"rf_{artifact}.pkl",
f"model_archive/rf_{artifact}-{datetime.datetime.now().date()}.pkl",
)
os.rename(f"new_rf_{artifact}.pkl", f"rf_{artifact}.pkl")
for metric, new, sq in zip(
("false positive(s)", "max threshold", "average prediction probability"),
(fp, min_prob, avg_prob),
(sq_fp, sq_min_prob, sq_avg_prob),
):
if metric == "false positive(s)":
if new <= sq:
good_outcome = True
else:
good_outcome = False
elif new >= sq:
good_outcome = True
else:
good_outcome = False
if good_outcome:
logger.info(
f"New model produced {new} {metric}, "
f"which is better or equal to status quo of {sq}."
)
else:
logger.warning(
f"Might want to investigate new model - new model produced "
f"{new} {metric}, compared to status quo of {sq}"
)
def do(row):
a, b = row
if a[1] and b[1]:
if match(a[1], b[1]) > 0:
return a[0], b[0]
def test_truth_table(self):
build_train_set()
train_model(prob_thresh=prob_thresh)
match_query = """
SELECT
company_projects.*,
web_certificates.url_key
FROM
web_certificates
LEFT JOIN
attempted_matches
ON
web_certificates.cert_id = attempted_matches.cert_id
LEFT JOIN
company_projects
ON
attempted_matches.project_id = company_projects.project_id
LEFT JOIN
base_urls
ON
base_urls.source = web_certificates.source
WHERE
company_projects.closed=1
AND
attempted_matches.ground_truth=1
AND
attempted_matches.multi_phase=0
AND
attempted_matches.validate=0
"""
corr_web_certs_query = """
SELECT
web_certificates.*
FROM
web_certificates
LEFT JOIN
attempted_matches
ON
web_certificates.cert_id = attempted_matches.cert_id
LEFT JOIN
company_projects
ON
attempted_matches.project_id = company_projects.project_id
LEFT JOIN
base_urls
ON
base_urls.source = web_certificates.source
WHERE
company_projects.closed=1
AND
attempted_matches.ground_truth=1
AND
attempted_matches.multi_phase=0
AND
attempted_matches.validate=0
"""
with create_connection() as conn:
test_company_projects = pd.read_sql(match_query, conn)
test_web_df = pd.read_sql(corr_web_certs_query, conn)
test_web_df = wrangle(test_web_df)
results = match(
company_projects=test_company_projects,
df_web=test_web_df,
test=True,
prob_thresh=prob_thresh,
version="new",
)
# confrim 100% recall with below assert
qty_actual_matches = int(len(results)**0.5)
qty_found_matches = results[results.pred_match == 1].title.nunique()
self.assertTrue(
qty_found_matches == qty_actual_matches,
msg=
f"qty_found_matches({qty_found_matches}) not equal qty_actual_matches({qty_actual_matches})",
)
# make sure not more than 25% false positives with below assert
false_positives = len(
results[results.pred_match == 1]) - qty_found_matches
self.assertTrue(
false_positives <= round(qty_actual_matches * 0.25, 1),
msg=
f"found too many false positives ({false_positives}) out of total test projects ({qty_actual_matches})",
)
# test single sample
sample_company = pd.DataFrame(
{
"cert_id": "99999",
"project_id": "99999",
"job_number": "2387",
"city": "Ottawa",
"address": "2562 Del Zotto Ave., Ottawa, Ontario",
"title": "DWS Building Expansion",
"owner": "Douglas Stalker",
"contractor": "GNC",
"engineer": "Goodkey",
"address_lat": 45.312234,
"address_lng": -75.623789,
"receiver_emails_dump": "{'alex': '*****@*****.**'}",
"closed": "0",
},
index=range(1),
)
sample_web = pd.DataFrame(
{
"cert_id": "99998",
"pub_date": "2019-03-06",
"city": "Ottawa-Carleton",
"address": "2562 Del Zotto Avenue, Gloucester, Ontario",
"title":
"Construct a 1 storey storage addition to a 2 storey office/industrial building",
"owner": "Doug Stalker, DWS Roofing",
"contractor": "GNC Constructors Inc.",
"engineer": None,
"address_lat": 45.312234,
"address_lng": -75.623789,
"url_key": "B0046A36-3F1C-11E9-9A87-005056AA6F02",
"source": "dcn",
},
index=range(1),
)
is_match, prob = match(
company_projects=sample_company,
df_web=sample_web,
test=True,
version="new").iloc[0][["pred_match", "pred_prob"]]
self.assertTrue(
is_match,
msg=
f"Project #{sample_company.job_number} did not match successfully. Match probability returned was {prob}.",
)
# test same sample but using db retreival
results = match(
company_projects=sample_company,
since="2019-03-05",
until="2019-03-07",
test=True,
version="new",
)
prob_from_db_cert = (
results[results.contractor == "gnc"].iloc[0].pred_prob
) #'gnc' is what is returned from the wrangling funcs
self.assertTrue(round(prob, 2) == round(prob_from_db_cert, 2))
# make sure validation runs
validate_model(prob_thresh=prob_thresh, test=True)
def match_results(content_regex, object_list):
matched_results = []
for list_member in object_list:
matched_results.append(matcher.match(content_regex, list_member))
return matched_results
def get_opbot_matches(dataset, model_pairs=None):
# define models pairs, model1--model2
# get model - activities
# to model1--model add acivity pairs, model1_task1--model2_task1 ... ect
models = dataset # i assume the models are loaded already
if model_pairs is None:
model_pairs = get_model_pairs(models)
model_activity = {} # model_id:{node_id:node}
for model_name in models.keys():
model_activity[model_name] = cf.get_hashable_nodes(
models[model_name], active_components)
final_matches = dict()
for pair in model_pairs:
final_matches[pair] = []
# make copy of nodes kyes, later delete keys of nodes that have been filtered out
nodes_keys = {}
for pair in model_pairs:
nodes_keys[pair] = {}
nodes_keys[pair][pair.model1] = model_activity[
pair.model1].keys() # .copy()
nodes_keys[pair][pair.model2] = model_activity[
pair.model2].keys() # .copy()
# create a list of all tokenised labels from all models
# used to create coocurance dictionary
all_labels = []
for model_id in models.keys():
for node_id in model_activity[model_id].keys():
all_labels.append(
cf.get_tokens_without_stop_words(
model_activity[model_id][node_id]['node_name'].lower()))
all_words_set = set() # used later to create coocurance dict
for m_pair in model_pairs:
model1 = m_pair.model1
model2 = m_pair.model2
for node1 in model_activity[model1].keys():
tokens = cf.get_tokens_without_stop_words(
model_activity[model1][node1]['node_name'])
for node2 in model_activity[model2].keys():
for a_word in tokens + cf.get_tokens_without_stop_words(
model_activity[model2][node2]['node_name']):
all_words_set.add(a_word.lower())
# Filtering step
if model_activity[model1][node1][
'node_name'] == model_activity[model2][node2][
'node_name']:
# nodes with identical labels are matched
final_matches[m_pair].append(
matcher.match(model_activity[model1][node1],
model_activity[model2][node2], 1.0))
if nodes_keys[m_pair][model1].__contains__(node1):
nodes_keys[m_pair][model1].remove(node1)
if nodes_keys[m_pair][model2].__contains__(node2):
nodes_keys[m_pair][model2].remove(node2)
# normalise labels for remaning nodes
# create variable , {model: {node_id:normalised_label}}
labels = {}
for model_name in models:
labels[model_name] = {}
for node in model_activity[model_name].keys():
labels[model_name][node] = cf.stemming(
cf.get_tokens_without_stop_words(
model_activity[model_name][node]['node_name'].lower()))
# Extract activity pairs
activity_pairs_all = []
for pair in model_pairs:
nodes1_keys = nodes_keys[pair][pair.model1]
nodes2_keys = nodes_keys[pair][pair.model2]
for node1 in nodes1_keys:
for node2 in nodes2_keys:
graph1 = cf.get_graph_with_id_nodes(pair.bpmn1)
graph2 = cf.get_graph_with_id_nodes(pair.bpmn2)
n1 = model_activity[pair.model1][node1]
n2 = model_activity[pair.model2][node2]
tokens1 = cf.get_tokens_without_stop_words(
model_activity[pair.model1][node1]['node_name'].lower())
tokens2 = cf.get_tokens_without_stop_words(
model_activity[pair.model2][node2]['node_name'].lower())
st1 = cf.get_current_to_start_node(node=n1,
graph=graph1,
diagram=pair.bpmn1)
st2 = cf.get_current_to_start_node(node=n2,
graph=graph2,
diagram=pair.bpmn2)
new_activity_pair = activity_pair(
pair=pair,
node1=node1,
label1=labels[pair.model1][node1],
st1=st1,
tokens1=tokens1,
node2=node2,
label2=labels[pair.model2][node2],
st2=st2,
tokens2=tokens2)
activity_pairs_all.append(new_activity_pair)
pair.activities_pairs.append(new_activity_pair)
coocurance = cco.get_coocccurance_dict(all_words_set, all_labels)
cco_matcher = cco.cco_occurance_similarity_calculator(coocurance, 2)
# calculate similarities
bots_results = {}
bots_results['B1'] = get_BOT_results(activity_pairs_all, models, 'LIN',
False)
bots_results['B2'] = get_BOT_results(activity_pairs_all, models, 'LIN',
True)
bots_results['B3'] = get_BOT_results(activity_pairs_all, models, 'LEV',
False)
bots_results['B4'] = get_BOT_results(activity_pairs_all, models, 'LEV',
True)
bots_results['B5'] = get_BOT_results(activity_pairs_all, models, 'CCO',
False, cco_matcher)
bots_results['B6'] = get_BOT_results(activity_pairs_all, models, 'CCO',
True, cco_matcher)
# determine thresholds
bot_thresholds = {}
bot_thresholds['B1'] = determine_thresholds(bot_results=bots_results['B1'],
models=model_pairs,
pre_t_min=0.6,
pre_t_max=1)
bot_thresholds['B2'] = determine_thresholds(bot_results=bots_results['B2'],
models=model_pairs,
pre_t_min=0.6,
pre_t_max=1)
bot_thresholds['B3'] = determine_thresholds(bot_results=bots_results['B3'],
models=model_pairs,
pre_t_min=0.6,
pre_t_max=1)
bot_thresholds['B4'] = determine_thresholds(bot_results=bots_results['B4'],
models=model_pairs,
pre_t_min=0.6,
pre_t_max=1)
bot_thresholds['B5'] = determine_thresholds(bot_results=bots_results['B5'],
models=model_pairs,
pre_t_min=0.7,
pre_t_max=1)
bot_thresholds['B6'] = determine_thresholds(bot_results=bots_results['B6'],
models=model_pairs,
pre_t_min=0.7,
pre_t_max=1)
# rank bot results
best, second_best = get_two_best_thresholds(bot_thresholds, bots_results)
# determine alignments
f_matches = get_matches_by_combine(activity_pairs_all, bots_results,
bot_thresholds, best, second_best,
model_pairs)
# return
to_return = {}
for k in final_matches.keys():
to_return[k] = final_matches[k] + f_matches[k]
# it returns {bpmn_models_pair:list_of_matches}
return to_return
def add(text):
data = []
textlist = text.split(" ")
#jenis matkul tugas dan tugas
for kata in bd.getList_Kata_Penting():
for i in range(len(textlist)):
index = matcher.match(textlist[i].lower(), kata.lower())
if (index):
data.append(textlist[i + 1])
data.append(str(textlist[i]).lower())
nama = textlist[i + 2]
pada_tgl = ["pada", "tanggal"]
for kata2 in pada_tgl:
for k in range(len(textlist[i + 2 + 1:])):
index2 = matcher.match(textlist[i + 2 + k].lower(),
kata2.lower())
if (index2):
for j in range(k - 1):
nama += " " + textlist[i + 2 + j + 1]
break
if (index2):
break
data.append(nama)
break
if (index):
break
#tanggal Tugas
for kata in bd.getList_Kata_Tampil_Deadline():
for i in range(len(textlist)):
index = matcher.match(textlist[i].lower(), kata.lower())
if (index):
if (len(textlist[i + 1]) > 2):
data.insert(0, textlist[i + 1])
else:
bulan_int = bulan.get(textlist[i + 2].lower())
tanggal = textlist[
i + 1] + "/" + bulan_int + "/" + textlist[i + 3]
data.insert(0, tanggal)
break
if (index is not False):
break
if (len(data) == 4):
(tgl, bln, th) = re.split("/", data[0])
date = datetime.date(int(th), int(bln), int(tgl))
N = len(bd.getList_Daftar_Tugas()) + 1
if (bd.IsInputValid(date, data[1], data[2], data[3], False)):
bd.upsert_Daftar_Tugas(N, date, data[1], data[2], data[3], False)
output = "[ ===== Berhasil Ditambahkan =====]<br>"
output += "(ID: " + str(N) + ") " + str(
date) + " " + data[1] + " " + data[2] + " " + data[3] + "<br>"
return output
else:
return "Data yang sama telah digunakan"
else:
return "-1"
def ValidasiInput(text):
data = daftar_katakunci(text)
if (matcher.match(str(text).lower(), "undur")
or matcher.match(str(text).lower(), "ubah")):
if ("pada" not in data):
return """Gunakan kata "pada" sebelum tanggal"""
else:
return diundurTask(text)
else:
if len(
data
) <= 2 and "antara" not in data and "depan" not in data and "deadline" not in data:
if (len(data) == 1):
if ("tanggal" not in data and "pada" not in data):
return """Gunakan kata ["tangal", "pada"] sebelum tanggal"""
else:
return """Gunakan kata ["tubes", "tucil", "kuis", "ujian", "pr"]"""
else:
return add(text)
elif ("antara" in data):
if ("tubes" in data):
return antaraTanggal_Jenis(text, "tubes")
elif ("tucil" in data):
return antaraTanggal_Jenis(text, "tucil")
elif ("kuis" in data):
return antaraTanggal_Jenis(text, "kuis")
elif ("ujian" in data):
return antaraTanggal_Jenis(text, "ujian")
elif ("pr" in data):
return antaraTanggal_Jenis(text, "pr")
else:
return antaraTanggal(text)
elif ("depan" in data):
if ("hari" in data):
if ("tubes" in data):
return haritask_Jenis(text, "tubes")
elif ("tucil" in data):
return haritask_Jenis(text, "tucil")
elif ("kuis" in data):
return haritask_Jenis(text, "kuis")
elif ("ujian" in data):
return haritask_Jenis(text, "ujian")
elif ("pr" in data):
return haritask_Jenis(text, "pr")
else:
return haritask(text)
elif ("minggu" in data):
if ("tubes" in data):
return minggutask_Jenis(text, "tubes")
elif ("tucil" in data):
return minggutask_Jenis(text, "tucil")
elif ("kuis" in data):
return minggutask_Jenis(text, "kuis")
elif ("ujian" in data):
return minggutask_Jenis(text, "ujian")
elif ("pr" in data):
return minggutask_Jenis(text, "pr")
else:
return minggutask(text)
else:
return "-1"
elif ("deadline" in data):
if ("hari" in data):
if ("tubes" in data):
return hariIni_Jenis("tubes")
elif ("tucil" in data):
return hariIni_Jenis("tucil")
elif ("kuis" in data):
return hariIni_Jenis("kuis")
elif ("ujian" in data):
return hariIni_Jenis("ujian")
elif ("pr" in data):
return hariIni_Jenis("pr")
else:
return hariIni()
elif ("sejauh" in data):
if ("tubes" in data):
return sejauhIni_Jenis("tubes")
elif ("tucil" in data):
return sejauhIni_Jenis("tucil")
elif ("kuis" in data):
return sejauhIni_Jenis("kuis")
elif ("ujian" in data):
return sejauhIni_Jenis("ujian")
elif ("pr" in data):
return sejauhIni_Jenis("pr")
else:
return sejauhIni()
else:
return "-1"
else:
return "-1"