def trian_out_of_date(format_dir, model_dir):
"""
聚类去除参考文献时间与文献发表时间差的时间的异常点
参考文献
http://scikit-learn.org/stable/auto_examples/neighbors/plot_lof.html#sphx-glr-auto-examples-neighbors-plot-lof-py
https://zhuanlan.zhihu.com/p/37753692
"""
print("[START] train out of date")
origin_dates = []
for paper in os.listdir(format_dir):
with open(format_dir + paper, 'r', encoding="utf-8") as format_data:
_format_data = format_data.read()
_format_data = json.loads(_format_data)
origin_dates += _format_data["out_of_date"]
# 一维数据转二维
origin_dates = list(zip(origin_dates, np.zeros_like(origin_dates)))
clf = LocalOutlierFactor(n_neighbors=20)
# fit非监督训练
clf.fit(origin_dates)
# 离群系数
outlier = clf.kneighbors(origin_dates)[0].max(axis=1)
# 参考文献发表日期 与 文献发表日期的时间差中 最后一个非离群点
out_of_date = 0
for i, _d in enumerate(origin_dates):
if outlier[i] == 0 and _d[0] > out_of_date:
out_of_date = _d[0]
with open(model_dir + 'outlier.txt', 'w') as data:
data.write(str(out_of_date))
print("[DONE] train out of date")
def LOF(data, predict, k):
clf = LocalOutlierFactor(n_neighbors=k+1, algorithm='auto', contamination=0.1,n_jobs=-1)
clf.fit(data)
predict['k distances'] = clf.kneighbors(predict)[0].max(axis=1)
predict['local outlier factor'] = -clf._decision_function(predict.iloc[:,:-1])
return predict
def localoutlierfactor(data, predict, k):
lof_clf = LocalOutlierFactor(n_neighbors=k + 1,
contamination=0.2,
n_jobs=-1)
lof_clf.fit(data)
# 记录 k 邻域距离
predict['k_distances'] = lof_clf.kneighbors(predict)[0].max(axis=1)
# 记录 LOF 离群因子，做相反数处理
predict['local_outlier_factor'] = -lof_clf._decision_function(
predict.iloc[:, :-1])
return predict
def LOF_anomaly_score(x):
"""To figure out anomaly scores."""
# must calibrate it for all measurements
outliers = []
outliers_list = []
for i, j in x:
pd_i = pd.DataFrame(i)
method = 1
k = 30
clf = LocalOutlierFactor(n_neighbors=k,
algorithm='auto',
contamination=0.1,
n_jobs=-1)
clf.fit(pd_i)
# Record k neighborhood distance
pd_i['k distances'] = clf.kneighbors(pd_i)[0].max(axis=1)
# Record LOF factor，take negative
pd_i['local outlier factor'] = -clf._decision_function(
pd_i.iloc[:, :-1])
# Separate group points and normal points according to the threshold
outliers = pd_i[pd_i['local outlier factor'] > method].sort_values(
by='local outlier factor')
inliers = pd_i[pd_i['local outlier factor'] <= method].sort_values(
by='local outlier factor')
# Figure
plt.rcParams['axes.unicode_minus'] = False # display the negative sign
plt.figure(figsize=(8, 4)).add_subplot(111)
plt.scatter(pd_i[pd_i['local outlier factor'] > method].index,
pd_i[pd_i['local outlier factor'] > method]
['local outlier factor'],
c='red',
s=50,
marker='.',
alpha=None,
label='outliers')
plt.scatter(pd_i[pd_i['local outlier factor'] <= method].index,
pd_i[pd_i['local outlier factor'] <= method]
['local outlier factor'],
c='black',
s=50,
marker='.',
alpha=None,
label='inliers')
plt.hlines(method, -2, 2 + max(pd_i.index), linestyles='--')
plt.xlim(-2, 2 + max(pd_i.index))
plt.title(f'LOF Local outlier detection of {j}', fontsize=13)
plt.ylabel('Anamoly Score', fontsize=15) # Local outlier Factors
plt.legend()
plt.savefig(f'LOF_images/LOF_{j}', format='png', dpi=1200)
plt.show()
outliers_list.append(list(outliers.index))
return outliers_list
def localoutlierfactor(data, predict, k):
from sklearn.neighbors import LocalOutlierFactor
clf = LocalOutlierFactor(n_neighbors=k + 1,
algorithm='auto',
contamination=0.1,
n_jobs=-1)
clf.fit(data)
# 记录 k 邻域距离
predict['k distances'] = clf.kneighbors(predict)[0].max(axis=1)
# 记录 LOF 离群因子，做相反数处理
predict['local outlier factor'] = -clf._decision_function(
predict.iloc[:, :-1])
return predict
def LOF(datastream, kpar):
#not sure to use euclidean or minkowski
Points = Point()
clf = LocalOutlierFactor(n_neighbors=kpar,
algorithm="kd_tree",
leaf_size=30,
metric='euclidean')
clf.fit(datastream)
Points.LOF = [-x for x in clf.negative_outlier_factor_.tolist()]
Points.lrd = clf._lrd.tolist()
dist, ind = clf.kneighbors()
Points.kdist = dist.tolist()
Points.knn = ind.tolist()
return Points
def localOutlierFactor(data, predict, k):
# LOF
clf = LocalOutlierFactor(n_neighbors=k + 1,
algorithm='auto',
contamination=0.1,
n_jobs=-1)
clf.fit(data)
# Computer k-neatest-point distance
predict['k distances'] = clf.kneighbors(predict)[0].max(axis=1)
# Record LOF，process negative values
predict['local outlier factor'] = -clf._decision_function(
predict.iloc[:, :-1])
return predict
def calc_max_lof_of_bounds(document_topics, metric, k_min, k_max):
num_docs = document_topics.shape[0]
max_outlier_scores = np.zeros(num_docs)
logger.info('calculating max outlier scores: k_min={}, k_max={}'.format(k_min, k_max))
model = LocalOutlierFactor(n_neighbors=k_max, metric=metric, n_jobs=3)
logger.info('creating k_max-model {}'.format(model))
model.fit(document_topics)
neighbors_distances_k_max, neighbors_indices_k_max = (model.kneighbors(None, n_neighbors=k_max))
max_outlier_scores = np.zeros(num_docs)
for k in range(k_min, k_max+1):
logger.debug('calculating {}-scores'.format(k))
neighbors_distances = neighbors_distances_k_max[:,0:k]
neighbors_indices = neighbors_indices_k_max[:,0:k]
outlier_scores = local_outlier_factor(neighbors_distances, neighbors_indices, k)
max_outlier_scores = np.maximum(max_outlier_scores, outlier_scores)
return max_outlier_scores
cluster_stds = numpy.random.uniform(low=0, high=10, size=[nr_clusters, nr_features]).astype(numpy.float64)
dataset = make_dataset(nr_clusters, cluster_stds, nr_samples, nr_features)
assert dataset.shape == (nr_samples, nr_features), "dimension mismatch"
clf = LocalOutlierFactor(n_neighbors=nr_neighbors, algorithm="brute", metric="euclidean")
start_time = time.clock()
for i in range(0, 5):
clf.fit_predict(dataset)
exec_time = time.clock() - start_time
print("--- %s seconds ---" % (exec_time / 5))
with open("Execution_time.txt", "a") as f:
f.write("{},{},{},{}\n".format(nr_samples, nr_features, nr_neighbors, exec_time / 5))
neigh_dist, neigh_ind = clf.kneighbors()
assert os.getcwd().split("/")[-1] == "team025", "wrighting to the wrong directory, run from team025 root"
dir_name = "./data/n{}_k{}_dim{}".format(int(nr_samples), int(nr_neighbors), int(nr_features))
if not os.path.exists(dir_name):
os.makedirs(dir_name)
print("Writing to directory: {}".format(dir_name))
with open("{}/metadata.txt".format(dir_name), "w+") as f:
f.write("Nr points, dim, nr neigh\n")
f.write("{},{},{}\n".format(nr_samples, nr_features, nr_neighbors))
f.write("Min per dimension\n")
s = ', '.join(str(dim) for dim in numpy.min(dataset, axis=0))
f.write(s)
f.write("\nMax per dimension\n")
s = ', '.join(str(dim) for dim in numpy.max(dataset, axis=0))
class ApplicabilityDomain():
def __init__(self,
method_name='ocsvm',
rate_of_outliers=0.01,
gamma='auto',
nu=0.5,
n_neighbors=10,
metric='minkowski',
p=2):
"""
Applicability Domain (AD)
Parameters
----------
method_name: str, default 'ocsvm'
The name of method to set AD. 'knn', 'lof', or 'ocsvm'
rate_of_outliers: float, default 0.01
Rate of outlier samples. This is used to set threshold
gamma : (only for 'ocsvm') float, default ’auto’
Kernel coefficient for ‘rbf’. Current default is ‘auto’ which optimize gamma to maximize variance in Gram matrix
nu : (only for 'ocsvm') float, default 0.5
An upper bound on the fraction of training errors and a lower bound of the fraction of support vectors. Should be in the interval (0, 1]. By default 0.5 will be taken.
https://scikit-learn.org/stable/modules/generated/sklearn.svm.OneClassSVM.html#sklearn.svm.OneClassSVM
n_neighbors: (only for 'knn' and 'lof') int, default 10
Number of neighbors to use for each query
https://scikit-learn.org/stable/modules/generated/sklearn.neighbors.NearestNeighbors.html
metric : string or callable, default ‘minkowski’
Metric to use for distance computation. Any metric from scikit-learn or scipy.spatial.distance can be used.
https://scikit-learn.org/stable/modules/generated/sklearn.neighbors.NearestNeighbors.html
p : integer, default 2
Parameter for the Minkowski metric from sklearn.metrics.pairwise.pairwise_distances. When p = 1, this is equivalent to using manhattan_distance (l1), and euclidean_distance (l2) for p = 2. For arbitrary p, minkowski_distance (l_p) is used.
https://scikit-learn.org/stable/modules/generated/sklearn.neighbors.NearestNeighbors.html
"""
if method_name != 'knn' and method_name != 'lof' and method_name != 'ocsvm':
sys.exit(
'There is no ad method named \'{0}\'. Please check the variable of method_name.'
.format(method_name))
self.method_name = method_name
self.rate_of_outliers = rate_of_outliers
self.gamma = gamma
self.nu = nu
self.n_neighbors = n_neighbors
self.metric = metric
self.p = p
def fit(self, x):
"""
Applicability Domain (AD)
Set AD
Parameters
----------
x : numpy.array or pandas.DataFrame
m x n matrix of X-variables of training data,
m is the number of training sammples and
n is the number of X-variables
"""
x = np.array(x)
if self.method_name == 'ocsvm':
if self.gamma == 'auto':
ocsvm_gammas = 2**np.arange(-20, 11, dtype=float)
variance_of_gram_matrix = []
for index, ocsvm_gamma in enumerate(ocsvm_gammas):
gram_matrix = np.exp(-ocsvm_gamma *
cdist(x, x, metric='seuclidean'))
variance_of_gram_matrix.append(gram_matrix.var(ddof=1))
self.optimal_gamma = ocsvm_gammas[
variance_of_gram_matrix.index(
max(variance_of_gram_matrix))]
else:
self.optimal_gamma = self.gamma
self.ad = OneClassSVM(kernel='rbf',
gamma=self.optimal_gamma,
nu=self.nu)
self.ad.fit(x)
ad_values = np.ndarray.flatten(self.ad.decision_function(x))
elif self.method_name == 'knn':
self.ad = NearestNeighbors(n_neighbors=self.n_neighbors)
self.ad.fit(x)
knn_dist_all, knn_ind_all = self.ad.kneighbors(None)
ad_values = 1 / (knn_dist_all.mean(axis=1) + 1)
elif self.method_name == 'lof':
self.ad = LocalOutlierFactor(novelty=True,
contamination=self.rate_of_outliers)
self.ad.fit(x)
ad_values = self.ad.negative_outlier_factor_ - self.ad.offset_
self.offset = np.percentile(ad_values, 100 * self.rate_of_outliers)
def predict(self, x):
"""
Applicability Domain (AD)
Predict AD-values
Parameters
----------
x : numpy.array or pandas.DataFrame
k x n matrix of X-variables of test data, which is autoscaled with training data,
and k is the number of test samples
Returns
-------
ad_values : numpy.array, shape (n_samples,)
values lower than 0 means outside of AD
"""
x = np.array(x)
if self.method_name == 'ocsvm':
ad_values = np.ndarray.flatten(self.ad.decision_function(x))
elif self.method_name == 'knn':
knn_dist_all, knn_ind_all = self.ad.kneighbors(x)
ad_values = 1 / (knn_dist_all.mean(axis=1) + 1)
elif self.method_name == 'lof':
ad_values = np.ndarray.flatten(self.ad.decision_function(x))
return ad_values - self.offset
