def main():
if (len(sys.argv) != 2):
print("Usage: python ML-Agglomerative.py path-name")
sys.exit()
path = sys.argv[1] # "../ames/test.csv"
df = pd.read_csv(path)
arr1 = df.columns.values
arr2 = df.columns.values
NL = NormalizedLevenshtein()
vectors = pd.DataFrame([[NL.distance(i, j) for j in arr2] for i in arr1])
clusters = 5
agg = AgglomerativeClustering(affinity='euclidean',
compute_full_tree='auto',
connectivity=None,
linkage='ward',
memory=None,
n_clusters=clusters,
pooling_func='deprecated').fit(vectors)
for i in range(clusters):
print("Cluster " + str(i) + ":")
print(pd.Series(arr1[agg.labels_ == i]))
print('\n')
def simDif(w1,w2):
normalized_levenshtein = NormalizedLevenshtein()
dis = normalized_levenshtein.distance(w1,w2)
sim = normalized_levenshtein.similarity(w1,w2)
print('distance: '+str(dis)+' similarity: '+ str(sim))
# 此线表示的是固定文本的一段和活动文本各段比较之后的分隔
print('----------------------------')
def similarity(outputs_batch, labels_batch, dic):
norm_lev = NormalizedLevenshtein()
outputs_batch = torch.argmax(outputs_batch, -1)
avg_sim = 0
for j in range(outputs_batch.size(-1)):
pred = [dic[int(k)] for k in outputs_batch[:, j]]
pred = utils.clear(pred)
avg_sim += norm_lev.distance(pred, labels_batch[j])
avg_sim = 1 - avg_sim / outputs_batch.size(-1)
return avg_sim
def similarity_plus(outputs_batch, labels_batch, dic):
d = enchant.Dict("en_US")
norm_lev = NormalizedLevenshtein()
outputs_batch = torch.argmax(outputs_batch, -1)
avg_sim = 0
for j in range(outputs_batch.size(-1)):
pred = [dic[int(k)] for k in outputs_batch[:, j]]
pred = utils.clear(pred)
if not d.check(pred):
pred = d.suggest(pred)
avg_sim += norm_lev.distance(pred, labels_batch[j])
avg_sim = 1 - avg_sim / outputs_batch.size(-1)
return avg_sim
def data_classifier(arr, sources):
NL = NormalizedLevenshtein()
vectors = pd.DataFrame([[NL.distance(i, j) for j in arr] for i in arr])
clusters = int(len(arr)**.5)
if clusters <= 1:
clusters = 2
kmeans = KMeans(n_clusters=clusters, random_state=0).fit(vectors)
field_ids = ["field-" + str(uuid.uuid4()) for field in arr]
uncert = pd.DataFrame()
for i in range(clusters):
uncert[i] = vectors.apply(dist,
args=(kmeans.cluster_centers_[i], ),
axis=0)
uncert.index = field_ids
classifications_obj = {}
fields_obj = {}
for i in range(clusters):
cluster = {}
fields = pd.Series(field_ids)[kmeans.labels_ == i]
for field_id in fields:
cluster[field_id] = uncert.loc[field_id][i]
idx = field_ids.index(field_id)
fields_obj[field_id] = {"name": arr[idx], "source": sources[idx]}
cid_obj = {}
cid_obj["name"] = "classification" + str(i)
cid_obj["metadata"] = None
cid_obj["values"] = cluster
cid_obj["distribution"] = np.array(list(cluster.values())).mean()
classifications_obj["classification-" + str(uuid.uuid4())] = cid_obj
data = {}
data["Classifications"] = classifications_obj
data["Fields"] = fields_obj
return (json.dumps(data, sort_keys=True, indent=4))
def similarity(self, question, answer):
stopword = self.read_from(folder_path + '上证专用停用词.txt')
stopwords = []
for sw in stopword:
sw = sw.strip('\n')
sw = sw.strip(' ')
stopwords.append(sw)
# print(stopwords)
meaningful_words1 = []
meaningful_words2 = []
words2 = jieba.cut(str(question))
words3 = jieba.cut(str(answer))
for word in words2:
if word not in stopwords:
meaningful_words1.append(word)
for word in words3:
if word not in stopwords:
meaningful_words2.append(word)
s2 = ''.join(meaningful_words1)
# print(s2)
s3 = ''.join(meaningful_words2)
a1 = Cosine(1)
b1 = Damerau()
c1 = Jaccard(1)
d1 = JaroWinkler()
e1 = Levenshtein()
f1 = LongestCommonSubsequence()
g1 = MetricLCS()
h1 = NGram(2)
i1 = NormalizedLevenshtein()
j1 = OptimalStringAlignment()
k1 = QGram(1)
l1 = SorensenDice(2)
m1 = WeightedLevenshtein(character_substitution=CharSub())
line_sim = []
cos_s = a1.similarity(s2, s3)
line_sim.append(cos_s)
cos_d = a1.distance(s2, s3)
line_sim.append(cos_d)
dam = b1.distance(s2, s3)
line_sim.append(dam)
jac_d = c1.distance(s2, s3)
line_sim.append(jac_d)
jac_s = c1.similarity(s2, s3)
line_sim.append(jac_s)
jar_d = d1.distance(s2, s3)
line_sim.append(jar_d)
jar_s = d1.similarity(s2, s3)
line_sim.append(jar_s)
lev = e1.distance(s2, s3)
line_sim.append(lev)
lon = f1.distance(s2, s3)
line_sim.append(lon)
met = g1.distance(s2, s3)
line_sim.append(met)
ngr = h1.distance(s2, s3)
line_sim.append(ngr)
nor_d = i1.distance(s2, s3)
line_sim.append(nor_d)
nor_s = i1.similarity(s2, s3)
line_sim.append(nor_s)
opt = j1.distance(s2, s3)
line_sim.append(opt)
qgr = k1.distance(s2, s3)
line_sim.append(qgr)
sor_d = l1.distance(s2, s3)
line_sim.append(sor_d)
sor_s = l1.similarity(s2, s3)
line_sim.append(sor_s)
wei = m1.distance(s2, s3)
line_sim.append(wei)
return line_sim
from similarity.normalized_levenshtein import NormalizedLevenshtein
from similarity.jaccard import Jaccard
s1 = '中华人民共和国'
s2 = '中国'
normalized_levenshtein = NormalizedLevenshtein()
print('Levenshtein: ', normalized_levenshtein.distance(s1, s2))
jaccard_distance = Jaccard(1)
print('Jaccard: ', jaccard_distance.distance(s1, s2))
# print(jaccard_similarity_score(list(s1), list(s2)))
jarowinkler = JaroWinkler()
sim = jarowinkler.similarity(my_string, temp_article[i])
if sim > 0.45:
filter_thresh_45.append(data[i])
normalized_levenshtein = NormalizedLevenshtein()
filter_normalized_levenshtein = []
for i in range(len(filter_thresh_45)):
sim = normalized_levenshtein.distance(my_string, filter_thresh_45[i][0])
if sim >= 0.7:
filter_normalized_levenshtein.append(filter_thresh_45[i])
with open('filtered_levenshtein_human_mobility.txt',
'w',
encoding="ISO-8859-1") as outfile:
json.dump(filter_normalized_levenshtein, outfile)
outfile.close()
with open("filtered_levenshtein_mobility_prediction.csv", 'w') as resultFile:
wr = csv.writer(resultFile, dialect='excel')
