def lisa_geo(df, df_class, time_series_id, maximize_score=F1_SCORE):
df_corr_dist = get_df_corr_geo_distance(df)
df_class_copy = df_class.copy()
df_class_copy = df_class_copy.rename(columns={time_series_id: 'class'})
# append mean values of each row to dataframe
df_val_mean = df_copy_with_mean(df)
# LISA Time Series
df_results = df_lisa_time_series(time_series_id, df_val_mean, df_corr_dist, global_correlation=True)
thresholds = np.linspace(0, 1, 200)
thresholds = np.round(thresholds, 7) # round thresholds
scores = min_max_normalization(df_results[time_series_id].values)
scores = np.round(scores, 7) # round scores
threshold_scores = get_threshold_scores(thresholds, scores, df_class_copy['class'])
selected_index = get_max_score_index_for_score_type(threshold_scores, maximize_score)
selected_threshold = thresholds[selected_index]
y_hat_results = (scores < selected_threshold).astype(int)
y_truth = df_class_copy['class'].values.astype(int)
info = get_detection_meta(selected_threshold, y_hat_results, y_truth)
info['thresholds'] = thresholds.tolist()
info['detection_threshold_scores'] = threshold_scores.tolist()
return scores, y_hat_results, info
def isolation_forest(df,
df_class,
df_train,
df_train_class,
maximize_score=F1_SCORE,
n_jobs=-1,
bootstrap=True,
n_estimators=40,
train_size=0.5,
random_seed=10):
df_train_common_class = df_anomaly_instances(df_train_class)
df_train_with_common_class = df_train.join(df_train_common_class)
df_common_class = df_anomaly_instances(df_class)
train, valid = get_train_valid_sets(df_train_with_common_class,
train_size=train_size,
random_seed=random_seed)
model = IsolationForest(random_state=random_seed,
n_jobs=n_jobs,
max_samples=train.shape[0],
bootstrap=bootstrap,
n_estimators=n_estimators)
model.fit(train.drop('class', axis=1).values)
thresholds = np.linspace(0, 1, 200)
thresholds = np.round(thresholds, 7) # round thresholds
y_scores = min_max_normalization(
model.decision_function(valid.drop('class', axis=1).values))
y_scores = np.round(y_scores, 7) # round scores
training_threshold_scores = get_threshold_scores(thresholds, y_scores,
valid['class'])
selected_index = get_max_score_index_for_score_type(
training_threshold_scores, maximize_score)
selected_threshold = thresholds[selected_index]
# detection on dataset
scores = min_max_normalization(model.decision_function(df.values))
scores = np.round(scores, 7) # round scores
y_hat_results = (scores < selected_threshold).astype(int)
y_truth = df_common_class['class'].values.astype(int)
detection_threshold_scores = get_threshold_scores(thresholds, scores,
df_common_class['class'])
info = get_detection_meta(selected_threshold, y_hat_results, y_truth)
info['thresholds'] = thresholds.tolist()
info['training_threshold_scores'] = training_threshold_scores.tolist()
info['detection_threshold_scores'] = detection_threshold_scores.tolist()
return scores, y_hat_results, df_common_class, info
def cluster_gaussian_mixture(df,
df_class,
df_train,
df_train_class,
maximize_score=F1_SCORE,
n_components=3,
n_init=3,
train_size=0.5,
random_seed=10):
df_train_common_class = df_anomaly_instances(df_train_class)
df_train_with_common_class = df_train.join(df_train_common_class)
df_common_class = df_anomaly_instances(df_class)
train, valid = get_train_valid_sets(df_train_with_common_class,
train_size=train_size,
random_seed=random_seed)
gmm = GaussianMixture(n_components=n_components,
n_init=n_init,
random_state=random_seed)
gmm.fit(train.drop('class', axis=1).values)
thresholds = np.linspace(0, 1, 200)
thresholds = np.round(thresholds, 7) # round thresholds
y_scores = min_max_normalization(
gmm.score_samples(valid.drop('class', axis=1).values))
y_scores = np.round(y_scores, 7) # round scores
training_threshold_scores = get_threshold_scores(thresholds, y_scores,
valid['class'])
selected_index = get_max_score_index_for_score_type(
training_threshold_scores, maximize_score)
selected_threshold = thresholds[selected_index]
# detection on dataset
scores = min_max_normalization(gmm.score_samples(df.values))
scores = np.round(scores, 7) # round scores
y_hat_results = (scores < selected_threshold).astype(int)
y_truth = df_common_class['class'].values.astype(int)
detection_threshold_scores = get_threshold_scores(thresholds, scores,
df_common_class['class'])
info = get_detection_meta(selected_threshold, y_hat_results, y_truth)
info['thresholds'] = thresholds.tolist()
info['training_threshold_scores'] = training_threshold_scores.tolist()
info['detection_threshold_scores'] = detection_threshold_scores.tolist()
return scores, y_hat_results, df_common_class, info
def histogram(df, df_class, df_train, df_train_class, maximize_score=F1_SCORE, train_size=0.5, random_seed=10):
df_train_common_class = df_anomaly_instances(df_train_class)
df_train_with_common_class = df_train.join(df_train_common_class)
df_common_class = df_anomaly_instances(df_class)
train, valid = get_train_valid_sets(df_train_with_common_class, train_size=train_size, random_seed=random_seed)
# square root of number of instances as number of bins
num_bins = (np.sqrt(train.shape[0])).astype(int)
logging.debug('Number of bins %d' % num_bins)
# create and train model
model = hist_model(bins=num_bins)
model.fit(train.drop('class', axis=1).values)
thresholds = np.linspace(0, 1, 200)
thresholds = np.round(thresholds, 7) # round thresholds
y_scores = min_max_normalization(model.predict(valid.drop('class', axis=1).values))
y_scores = np.round(y_scores, 7) # round scores
training_threshold_scores = get_threshold_scores(thresholds, y_scores, valid['class'])
selected_index = get_max_score_index_for_score_type(training_threshold_scores, maximize_score)
selected_threshold = thresholds[selected_index]
# detection on dataset
scores = min_max_normalization(model.predict(df.values))
scores = np.round(scores, 7) # round scores
y_hat_results = (scores < selected_threshold).astype(int)
y_truth = df_common_class['class'].values.astype(int)
detection_threshold_scores = get_threshold_scores(thresholds, scores, df_common_class['class'])
info = get_detection_meta(selected_threshold, y_hat_results, y_truth)
info['thresholds'] = thresholds.tolist()
info['training_threshold_scores'] = training_threshold_scores.tolist()
info['detection_threshold_scores'] = detection_threshold_scores.tolist()
return scores, y_hat_results, df_common_class, info
def lisa_dtw(df, df_class, time_series_id, maximize_score=F1_SCORE, window_size=10, distance_function=EUCLIDEAN):
df_correlation = dtw_pearson(df, time_series_id, distance_function, window_size=window_size)
# after correlation have been computed we remove the size of the window from the head of the data frame, as those correlations cannot be computed
offset = window_size - 1
df_correlation = df_correlation.iloc[offset:]
df = df.iloc[offset:]
df_class = df_class.iloc[offset:]
# mean values of each row of dataframe
df_mean = df_copy_with_mean(df)
df_class_copy = df_class.copy()
df_class_copy = df_class_copy.rename(columns={time_series_id: 'class'})
# LISA Time Series
df_results = df_lisa_time_series(time_series_id, df_mean, df_correlation)
thresholds = np.linspace(0, 1, 200)
thresholds = np.round(thresholds, 7) # round thresholds
scores = min_max_normalization(df_results[time_series_id].values)
scores = np.round(scores, 7) # round scores
threshold_scores = get_threshold_scores(thresholds, scores, df_class_copy['class'])
selected_index = get_max_score_index_for_score_type(threshold_scores, maximize_score)
selected_threshold = thresholds[selected_index]
y_hat_results = (scores < selected_threshold).astype(int)
y_truth = df_class_copy['class'].values.astype(int)
info = get_detection_meta(selected_threshold, y_hat_results, y_truth)
info['thresholds'] = thresholds.tolist()
info['detection_threshold_scores'] = threshold_scores.tolist()
return scores, y_hat_results, info, df, df_class
def svm(df, df_class, df_train, df_train_class, maximize_score=F1_SCORE, nu=0.5, kernel='rbf', train_size=0.5, random_seed=10):
df_train_common_class = df_anomaly_instances(df_train_class)
df_train_with_common_class = df_train.join(df_train_common_class)
df_common_class = df_anomaly_instances(df_class)
train, valid = get_train_valid_sets(df_train_with_common_class, train_size=train_size, random_seed=random_seed)
model = OneClassSVM(gamma='scale', nu=nu, kernel=kernel)
model.fit(train.drop('class', axis=1).values)
thresholds = np.linspace(0, 1, 200)
thresholds = np.round(thresholds, 7) # round thresholds
y_scores = min_max_normalization(model.decision_function(valid.drop('class', axis=1).values))
y_scores = np.round(y_scores, 7) # round scores
training_threshold_scores = get_threshold_scores(thresholds, y_scores, valid['class'])
selected_index = get_max_score_index_for_score_type(training_threshold_scores, maximize_score)
selected_threshold = thresholds[selected_index]
# detection on dataset
scores = min_max_normalization(model.decision_function(df.values))
scores = np.round(scores, 7) # round scores
y_hat_results = (scores < selected_threshold).astype(int)
y_truth = df_common_class['class'].values.astype(int)
detection_threshold_scores = get_threshold_scores(thresholds, scores, df_common_class['class'])
info = get_detection_meta(selected_threshold, y_hat_results, y_truth)
info['thresholds'] = thresholds.tolist()
info['training_threshold_scores'] = training_threshold_scores.tolist()
info['detection_threshold_scores'] = detection_threshold_scores.tolist()
return scores, y_hat_results, df_common_class, info
def robust_pca_huber_loss(df,
df_class,
df_train,
df_train_class,
delta=1,
n_components=2,
maximize_score=F1_SCORE,
train_size=0.5,
random_seed=10):
df_train_common_class = df_anomaly_instances(df_train_class)
df_train_with_common_class = df_train.join(df_train_common_class)
df_common_class = df_anomaly_instances(df_class)
# for supervised detection(!) - stratify parameter makes a split so that the proportion of values in the sample produced will be the same as the proportion of values provided to parameter stratify
#X_train, X_test, y_train, y_test = train_test_split(df_train, df_train_common_class, train_size=train_size, random_state=random_seed, stratify=df_train_common_class)
train, valid = get_train_valid_sets(df_train_with_common_class,
train_size=train_size,
random_seed=random_seed)
X_train = train.drop('class', axis=1)
X_test = valid.drop('class', axis=1)
# Dimensionality reduction with Robust PCA and Huber Loss Function
huber_loss = loss.HuberLoss(delta=delta)
M_rpca = MRobustPCA(n_components, huber_loss)
# Fit R-PCA on Train Set
M_rpca.fit(X_train)
# R-PCA on Test Set
X_test_reduced = M_rpca.transform(X_test)
X_test_reduced = pd.DataFrame(data=X_test_reduced, index=X_test.index)
X_test_reconstructed = M_rpca.inverse_transform(X_test_reduced)
X_test_reconstructed = pd.DataFrame(data=X_test_reconstructed,
index=X_test.index)
y_test_scores = normalized_anomaly_scores(X_test, X_test_reconstructed)
y_test_scores = np.round(y_test_scores, 7) # round scores
# computed scores are always in between 0-1 due to min max normalization
thresholds = np.linspace(0, 1, 200)
thresholds = np.round(thresholds, 7) # round thresholds
training_threshold_scores = get_threshold_scores(
thresholds, y_test_scores, valid['class'], upper_boundary=True
) # or replace valid['class'] with y_test for supervised detection
selected_index = get_max_score_index_for_score_type(
training_threshold_scores, maximize_score)
selected_threshold = thresholds[selected_index]
# Run on Dataset
X_df_reduced = M_rpca.transform(df)
X_df_reduced = pd.DataFrame(data=X_df_reduced, index=df.index)
X_df_reconstructed = M_rpca.inverse_transform(X_df_reduced)
X_df_reconstructed = pd.DataFrame(data=X_df_reconstructed, index=df.index)
# detection on dataset
scores = normalized_anomaly_scores(df, X_df_reconstructed)
scores = np.round(scores, 7) # round scores
y_hat_results = (scores > selected_threshold).astype(int)
y_truth = df_common_class.values.astype(int)
detection_threshold_scores = get_threshold_scores(thresholds,
scores,
df_common_class['class'],
upper_boundary=True)
info = get_detection_meta(selected_threshold,
y_hat_results,
y_truth,
upper_boundary=True)
info['thresholds'] = thresholds.tolist()
info['training_threshold_scores'] = training_threshold_scores.tolist()
info['detection_threshold_scores'] = detection_threshold_scores.tolist()
return scores, y_hat_results, df_common_class, info