def knn(X_train, y_train=None, X_test=None, y_test=None):
# train kNN detector
clf_name = 'KNN'
clf = KNN()
clf.fit(X_train)
# get the prediction labels and outlier scores of the training data
y_train_pred = clf.labels_ # binary labels (0: inliers, 1: outliers)
y_train_scores = clf.decision_scores_ # raw outlier scores
# # get the prediction on the test data
y_test_pred = clf.predict(X_test) # outlier labels (0 or 1)
y_test_scores = clf.decision_function(X_test) # outlier scores
#
# # evaluate and print the results
# print("\nOn Training Data:")
# evaluate_print(clf_name, y_train, y_train_scores)
# print("\nOn Test Data:")
# evaluate_print(clf_name, y_test, y_test_scores)
#
# visualize the results
visualize(clf_name,
X_train,
X_test,
y_train_pred,
y_test_pred,
show_figure=True,
save_figure=False)
return y_train_pred, y_train_scores
clf.fit(X_train)
# get the prediction labels and outlier scores of the training data
y_train_pred = clf.labels_ # binary labels (0: inliers, 1: outliers)
y_train_scores = clf.decision_scores_ # raw outlier scores
# get the prediction on the test data
y_test_pred = clf.predict(X_test) # outlier labels (0 or 1)
y_test_scores = clf.decision_function(X_test) # outlier scores
# evaluate and print the results
print("\nOn Training Data:")
evaluate_print(clf_name, y_train, y_train_scores)
print("\nOn Test Data:")
evaluate_print(clf_name, y_test, y_test_scores)
# example of the feature importance
feature_importance = clf.feature_importances_
print("Feature importance", feature_importance)
# visualize the results
visualize(clf_name,
X_train,
y_train,
X_test,
y_test,
y_train_pred,
y_test_pred,
show_figure=True,
save_figure=False)
x_train_pca = kpca.fit_transform(x_train)
clf = KNN(n_neighbors=5, contamination=contam)
x_train_pca = standardizer(x_train_pca)
clf.fit(x_train_pca)
y_pred_pca = clf.labels_ # binary labels (0: inliers, 1: outliers)
y_scores = clf.decision_scores_ # raw outlier scores
evaluation(y, y_scores, 'PCA+KNN')
all_scores['PCA+KNN'] = y_scores
evaluation(y, all_scores, algorithms)
visualize(clf_name,
x_train_pca,
y,
x_train_pca,
y,
y_pred_pca,
y_pred_pca,
show_figure=True,
save_figure=False)
visual_ano = [[], []]
visual_nor = [[], []]
for i in range(0, len(x_train_pca), 10):
if y[i] == 1:
visual_ano[0].append(x_train_pca[i][0])
visual_ano[1].append(x_train_pca[i][1])
else:
visual_nor[0].append(x_train_pca[i][0])
visual_nor[1].append(x_train_pca[i][1])
plt.scatter(visual_ano[0], visual_ano[1], c='r')
def pyod_anomaly_detection(type, contamination):
X_train, y_train, X_test, y_test = data(type=type,
contamination=contamination)
if type == 'MAD':
# train MAD detector
clf_name = 'MAD'
clf = MAD(threshold=3.5)
clf.fit(X_train)
# get the prediction labels and outlier scores of the training data
y_train_pred = clf.labels_ # binary labels (0: inliers, 1: outliers)
y_train_scores = clf.decision_scores_ # raw outlier scores
# get the prediction on the test data
y_test_pred = clf.predict(X_test) # outlier labels (0 or 1)
y_test_scores = clf.decision_function(X_test) # outlier scores
# evaluate and print the results
print("\nOn Training Data:")
evaluate_print(clf_name, y_train, y_train_scores)
print("\nOn Test Data:")
evaluate_print(clf_name, y_test, y_test_scores)
# visualize the results
# making dimensions = 2 for visualising purpose only. By repeating same data each dimension.
visualize(clf_name,
np.hstack((X_train, X_train)),
y_train,
np.hstack((X_test, X_test)),
y_test,
y_train_pred,
y_test_pred,
show_figure=True,
save_figure=False)
elif type == 'ABOD':
# train ABOD detector
clf_name = 'ABOD'
clf = ABOD()
clf.fit(X_train)
# get the prediction labels and outlier scores of the training data
y_train_pred = clf.labels_ # binary labels (0: inliers, 1: outliers)
y_train_scores = clf.decision_scores_ # raw outlier scores
# get the prediction on the test data
y_test_pred = clf.predict(X_test) # outlier labels (0 or 1)
y_test_scores = clf.decision_function(X_test) # outlier scores
# evaluate and print the results
print("\nOn Training Data:")
evaluate_print(clf_name, y_train, y_train_scores)
print("\nOn Test Data:")
evaluate_print(clf_name, y_test, y_test_scores)
# visualize the results
visualize(clf_name,
X_train,
y_train,
X_test,
y_test,
y_train_pred,
y_test_pred,
show_figure=True,
save_figure=False)
elif type == 'AutoEncoder':
# train AutoEncoder detector
clf_name = 'AutoEncoder'
clf = AutoEncoder(epochs=30, contamination=contamination)
clf.fit(X_train)
# get the prediction labels and outlier scores of the training data
y_train_pred = clf.labels_ # binary labels (0: inliers, 1: outliers)
y_train_scores = clf.decision_scores_ # raw outlier scores
# get the prediction on the test data
y_test_pred = clf.predict(X_test) # outlier labels (0 or 1)
y_test_scores = clf.decision_function(X_test) # outlier scores
# evaluate and print the results
print("\nOn Training Data:")
evaluate_print(clf_name, y_train, y_train_scores)
print("\nOn Test Data:")
evaluate_print(clf_name, y_test, y_test_scores)
n_iter=2000).fit_transform(x_train)
X_test_reduced_tsne = TSNE(n_components=2,
random_state=2020,
init='pca',
n_iter=2000).fit_transform(x_test)
# In[14]:
import warnings
warnings.filterwarnings("ignore")
from pyod.utils.example import visualize
visualize('KNN',
X_train_reduced_tsne,
y_train,
X_test_reduced_tsne,
y_test,
y_train_pred,
y_test_pred,
show_figure=True,
save_figure=False)
# ### 用roc和prn评价准确率
# In[21]:
from sklearn.metrics import roc_auc_score
from pyod.utils.utility import precision_n_scores
# In[22]:
train_roc = np.round(roc_auc_score(y_train, y_train_pred), decimals=4)
X_train, y_train, X_test, y_test = \
generate_data(n_train=n_train,
n_test=n_test,
n_features=1,
contamination=contamination,
random_state=42)
# train MAD detector
clf_name = 'MAD'
clf = MAD(threshold=3.5)
clf.fit(X_train)
# get the prediction labels and outlier scores of the training data
y_train_pred = clf.labels_ # binary labels (0: inliers, 1: outliers)
y_train_scores = clf.decision_scores_ # raw outlier scores
# get the prediction on the test data
y_test_pred = clf.predict(X_test) # outlier labels (0 or 1)
y_test_scores = clf.decision_function(X_test) # outlier scores
# evaluate and print the results
print("\nOn Training Data:")
evaluate_print(clf_name, y_train, y_train_scores)
print("\nOn Test Data:")
evaluate_print(clf_name, y_test, y_test_scores)
# visualize the results
# making dimensions = 2 for visualising purpose only. By repeating same data each dimension.
visualize(clf_name, np.hstack((X_train, X_train)), y_train, np.hstack((X_test, X_test)), y_test, y_train_pred,
y_test_pred, show_figure=True, save_figure=False)
read = r"D:\研一下学期\数据挖掘\作业4\pageb\meta_data\pageb.preproc.csv"
data = pd.read_csv(read, header=0, index_col=0)
train_x = data.drop(drop + ground_truth + ["original.label"], axis=1).values
train_y = np.array(
[transfor[x] for x in list(_flatten(data[ground_truth].values.tolist()))])
clf.fit(train_x)
label = clf.labels_
predict = clf.decision_scores_
evaluate_print(clf_name, train_y, predict)
pca = decomposition.PCA(n_components=2)
X = pca.fit_transform(train_x)
visualize(clf_name,
X,
train_y,
X,
train_y,
label,
train_y,
show_figure=True,
save_figure=True)
clf = MCD()
clf_name = "PCA"
read = r"D:\研一下学期\数据挖掘\作业4\abalone\meta_data\abalone.preproc.csv"
data = pd.read_csv(read, header=0, index_col=0)
train_x = data.drop(drop + ground_truth + ["original.label"], axis=1).values
train_y = np.array(
[transfor[x] for x in list(_flatten(data[ground_truth].values.tolist()))])
clf.fit(train_x)
label = clf.labels_
predict = clf.decision_scores_
