def detect_anomaly(df): df = df.fillna(0) clf =HBOS() x_values = df.index.values.reshape(df.index.values.shape[0],1) y_values = df.total_traded_quote_asset_volume.values.reshape(df.total_traded_quote_asset_volume.values.shape[0],1) clf.fit(y_values) clf.predict(y_values) df["label_qav"] = clf.predict(y_values) df["score_qav"] = clf.decision_function(y_values)#.round(6) df['change_qav'] = df.total_traded_quote_asset_volume.pct_change(periods=1)*100 df['change_price'] = df.last_price.pct_change(periods=1)*100 return df
def test_hbos(self):
clf = HBOS(contamination=0.05)
clf.fit(self.X_train)
assert_equal(len(clf.decision_scores), self.X_train.shape[0])
pred_scores = clf.decision_function(self.X_test)
assert_equal(pred_scores.shape[0], self.X_test.shape[0])
assert_equal(clf.predict(self.X_test).shape[0],
self.X_test.shape[0])
assert_greater(roc_auc_score(self.y_test, pred_scores), 0.5)
def extract_is_outlier(df: pd.DataFrame,
col: str,
pbar=None,
verbose: bool = True,
model=None,
outliers_fraction: float = 0.05,
replace_with=None) -> pd.DataFrame:
"""
Create an is_outlier column
:param df: the data
:param col: the column name
:param conf: the config dir
:param pbar: tqdm progress bar
:return:
"""
df = df.copy(deep=True)
msg = "Trying to find outliers in " + str(col)
if pbar is None:
print_c(verbose, msg)
else:
pbar.set_description(msg)
if model is None:
model = HBOS(contamination=outliers_fraction)
X = df[col].astype(np.float32)
mask = ~(np.isnan(X) | np.isinf(X) | np.isneginf(X))
model.fit(X[mask].to_frame())
preds = model.predict(X[mask].to_frame())
df[col + '_' + 'isoutlier'] = 0
df.loc[mask, col + '_' + 'isoutlier'] = preds
if replace_with is not None:
msg = "Replacing outliers in " + str(col) + " with " + str(
replace_with)
if pbar is None:
print_c(verbose, msg)
else:
pbar.set_description(msg)
df.loc[df[col + '_' + 'isoutlier'] == 1, col] = replace_with
return df
def get_HBOS_scores(dataframe,
cols,
outliers_fraction=0.01,
standardize=True):
'''Takes df, a list selected column nmaes, outliers_fraction = 0.01 default
Returns:
df with CBOLF scores added
'''
if standardize:
#standardize selected variables
minmax = MinMaxScaler(feature_range=(0, 1))
dataframe[cols] = minmax.fit_transform(dataframe[cols])
#Convert dataframe to a numpy array in order to incorprate our algorithm
arrays = []
for row in cols:
row = dataframe[row].values.reshape(-1, 1)
arrays.append(row)
X = np.concatenate((arrays), axis=1)
#fit
clf = HBOS(contamination=outliers_fraction)
#clf = CBLOF(contamination=outliers_fraction,check_estimator=False, random_state=0)
clf.fit(X)
# predict raw anomaly score
scores_pred = clf.decision_function(X) * -1
# prediction of a datapoint category outlier or inlier
y_pred = clf.predict(X)
n_inliers = len(y_pred) - np.count_nonzero(y_pred)
n_outliers = np.count_nonzero(y_pred == 1)
CheckOutliers.df2 = dataframe
CheckOutliers.df2['outlier'] = y_pred.tolist()
print('OUTLIERS:', n_outliers, 'INLIERS:', n_inliers,
'found with HBOS')
class TestHBOS(unittest.TestCase):
def setUp(self):
self.n_train = 200
self.n_test = 100
self.contamination = 0.1
self.roc_floor = 0.8
self.X_train, self.y_train, self.X_test, self.y_test = generate_data(
n_train=self.n_train,
n_test=self.n_test,
contamination=self.contamination,
random_state=42)
self.clf = HBOS(contamination=self.contamination)
self.clf.fit(self.X_train)
def test_parameters(self):
assert (hasattr(self.clf, 'decision_scores_')
and self.clf.decision_scores_ is not None)
assert (hasattr(self.clf, 'labels_') and self.clf.labels_ is not None)
assert (hasattr(self.clf, 'threshold_')
and self.clf.threshold_ is not None)
assert (hasattr(self.clf, '_mu') and self.clf._mu is not None)
assert (hasattr(self.clf, '_sigma') and self.clf._sigma is not None)
assert (hasattr(self.clf, 'hist_') and self.clf.hist_ is not None)
assert (hasattr(self.clf, 'bin_edges_')
and self.clf.bin_edges_ is not None)
def test_train_scores(self):
assert_equal(len(self.clf.decision_scores_), self.X_train.shape[0])
def test_prediction_scores(self):
pred_scores = self.clf.decision_function(self.X_test)
# check score shapes
assert_equal(pred_scores.shape[0], self.X_test.shape[0])
# check performance
assert (roc_auc_score(self.y_test, pred_scores) >= self.roc_floor)
def test_prediction_labels(self):
pred_labels = self.clf.predict(self.X_test)
assert_equal(pred_labels.shape, self.y_test.shape)
def test_prediction_proba(self):
pred_proba = self.clf.predict_proba(self.X_test)
assert (pred_proba.min() >= 0)
assert (pred_proba.max() <= 1)
def test_prediction_proba_linear(self):
pred_proba = self.clf.predict_proba(self.X_test, method='linear')
assert (pred_proba.min() >= 0)
assert (pred_proba.max() <= 1)
def test_prediction_proba_unify(self):
pred_proba = self.clf.predict_proba(self.X_test, method='unify')
assert (pred_proba.min() >= 0)
assert (pred_proba.max() <= 1)
def test_prediction_proba_parameter(self):
with assert_raises(ValueError):
self.clf.predict_proba(self.X_test, method='something')
def test_fit_predict(self):
pred_labels = self.clf.fit_predict(self.X_train)
assert_equal(pred_labels.shape, self.y_train.shape)
def test_fit_predict_score(self):
self.clf.fit_predict_score(self.X_test, self.y_test)
self.clf.fit_predict_score(self.X_test,
self.y_test,
scoring='roc_auc_score')
self.clf.fit_predict_score(self.X_test,
self.y_test,
scoring='prc_n_score')
with assert_raises(NotImplementedError):
self.clf.fit_predict_score(self.X_test,
self.y_test,
scoring='something')
# def test_score(self):
# self.clf.score(self.X_test, self.y_test)
# self.clf.score(self.X_test, self.y_test, scoring='roc_auc_score')
# self.clf.score(self.X_test, self.y_test, scoring='prc_n_score')
# with assert_raises(NotImplementedError):
# self.clf.score(self.X_test, self.y_test, scoring='something')
def test_predict_rank(self):
pred_socres = self.clf.decision_function(self.X_test)
pred_ranks = self.clf._predict_rank(self.X_test)
# assert the order is reserved
assert_allclose(rankdata(pred_ranks), rankdata(pred_socres), atol=2)
assert_array_less(pred_ranks, self.X_train.shape[0] + 1)
assert_array_less(-0.1, pred_ranks)
def test_predict_rank_normalized(self):
pred_socres = self.clf.decision_function(self.X_test)
pred_ranks = self.clf._predict_rank(self.X_test, normalized=True)
# assert the order is reserved
assert_allclose(rankdata(pred_ranks), rankdata(pred_socres), atol=2)
assert_array_less(pred_ranks, 1.01)
assert_array_less(-0.1, pred_ranks)
def test_model_clone(self):
clone_clf = clone(self.clf)
def tearDown(self):
pass
n_train = 1000
n_test = 500
X_train, y_train, c_train, X_test, y_test, c_test = generate_data(
n_train=n_train, n_test=n_test, contamination=contamination)
# train a HBOS detector (default version)
clf = HBOS()
clf.fit(X_train)
# get the prediction on the training data
y_train_pred = clf.y_pred
y_train_score = clf.decision_scores
# get the prediction on the test data
y_test_pred = clf.predict(X_test)
y_test_score = clf.decision_function(X_test)
print('Train ROC:{roc}, precision@n:{prn}'.format(
roc=roc_auc_score(y_train, y_train_score),
prn=precision_n_scores(y_train, y_train_score)))
print('Test ROC:{roc}, precision@n:{prn}'.format(
roc=roc_auc_score(y_test, y_test_score),
prn=precision_n_scores(y_test, y_test_score)))
#######################################################################
# Visualizations
# initialize the log directory if it does not exist
pathlib.Path('example_figs').mkdir(parents=True, exist_ok=True)
class TestHBOS(unittest.TestCase):
def setUp(self):
self.n_train = 100
self.n_test = 50
self.contamination = 0.1
self.roc_floor = 0.6
self.X_train, self.y_train, self.X_test, self.y_test = generate_data(
n_train=self.n_train, n_test=self.n_test,
contamination=self.contamination, random_state=42)
self.clf = HBOS(contamination=self.contamination)
self.clf.fit(self.X_train)
def test_sklearn_estimator(self):
check_estimator(self.clf)
def test_parameters(self):
assert_true(hasattr(self.clf, 'decision_scores_') and
self.clf.decision_scores_ is not None)
assert_true(hasattr(self.clf, 'labels_') and
self.clf.labels_ is not None)
assert_true(hasattr(self.clf, 'threshold_') and
self.clf.threshold_ is not None)
assert_true(hasattr(self.clf, '_mu') and
self.clf._mu is not None)
assert_true(hasattr(self.clf, '_sigma') and
self.clf._sigma is not None)
assert_true(hasattr(self.clf, 'hist_') and
self.clf.hist_ is not None)
assert_true(hasattr(self.clf, 'bin_edges_') and
self.clf.bin_edges_ is not None)
def test_train_scores(self):
assert_equal(len(self.clf.decision_scores_), self.X_train.shape[0])
def test_prediction_scores(self):
pred_scores = self.clf.decision_function(self.X_test)
# check score shapes
assert_equal(pred_scores.shape[0], self.X_test.shape[0])
# check performance
assert_greater(roc_auc_score(self.y_test, pred_scores), self.roc_floor)
def test_prediction_labels(self):
pred_labels = self.clf.predict(self.X_test)
assert_equal(pred_labels.shape, self.y_test.shape)
def test_prediction_proba(self):
pred_proba = self.clf.predict_proba(self.X_test)
assert_greater_equal(pred_proba.min(), 0)
assert_less_equal(pred_proba.max(), 1)
def test_prediction_proba_linear(self):
pred_proba = self.clf.predict_proba(self.X_test, method='linear')
assert_greater_equal(pred_proba.min(), 0)
assert_less_equal(pred_proba.max(), 1)
def test_prediction_proba_unify(self):
pred_proba = self.clf.predict_proba(self.X_test, method='unify')
assert_greater_equal(pred_proba.min(), 0)
assert_less_equal(pred_proba.max(), 1)
def test_prediction_proba_parameter(self):
with assert_raises(ValueError):
self.clf.predict_proba(self.X_test, method='something')
def test_fit_predict(self):
pred_labels = self.clf.fit_predict(self.X_train)
assert_equal(pred_labels.shape, self.y_train.shape)
def test_fit_predict_score(self):
self.clf.fit_predict_score(self.X_test, self.y_test)
self.clf.fit_predict_score(self.X_test, self.y_test,
scoring='roc_auc_score')
self.clf.fit_predict_score(self.X_test, self.y_test,
scoring='prc_n_score')
with assert_raises(NotImplementedError):
self.clf.fit_predict_score(self.X_test, self.y_test,
scoring='something')
# def test_score(self):
# self.clf.score(self.X_test, self.y_test)
# self.clf.score(self.X_test, self.y_test, scoring='roc_auc_score')
# self.clf.score(self.X_test, self.y_test, scoring='prc_n_score')
# with assert_raises(NotImplementedError):
# self.clf.score(self.X_test, self.y_test, scoring='something')
def test_predict_rank(self):
pred_socres = self.clf.decision_function(self.X_test)
pred_ranks = self.clf._predict_rank(self.X_test)
# assert the order is reserved
assert_allclose(rankdata(pred_ranks), rankdata(pred_socres), atol=2)
assert_array_less(pred_ranks, self.X_train.shape[0] + 1)
assert_array_less(-0.1, pred_ranks)
def test_predict_rank_normalized(self):
pred_socres = self.clf.decision_function(self.X_test)
pred_ranks = self.clf._predict_rank(self.X_test, normalized=True)
# assert the order is reserved
assert_allclose(rankdata(pred_ranks), rankdata(pred_socres), atol=2)
assert_array_less(pred_ranks, 1.01)
assert_array_less(-0.1, pred_ranks)
def tearDown(self):
pass
n_test=n_test,
n_features=2,
contamination=contamination,
random_state=42)
# train HBOS detector
clf_name = 'HBOS'
clf = HBOS()
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,
def detect_anomaly(df, type):
clf = HBOS() #
if type == "forest":
clf = IForest()
x_values = df.index.values.reshape(df.index.values.shape[0], 1)
y_values = df.close.values.reshape(df.close.values.shape[0], 1)
clf.fit(y_values)
clf.predict(y_values)
df["label_close"] = clf.predict(y_values)
df["score_close"] = clf.decision_function(y_values) #.round(6)
y_values = df.volume.values.reshape(df.volume.values.shape[0], 1)
clf.fit(y_values)
clf.predict(y_values)
df["label_volume"] = clf.predict(y_values)
df["score_volume"] = clf.decision_function(y_values) #.round(4)
# x_values = df.index.values.reshape(df.index.values.shape[0],1)
# y_values = df.close.values.reshape(df.close.values.shape[0],1)
# clf = KNN()
# clf.fit(y_values)
# clf.predict(y_values)
# df["label_close_knn"] = clf.predict(y_values)
# df["score_close_knn"] = clf.decision_function(y_values)#.round(6)
# y_values = df.volume.values.reshape(df.volume.values.shape[0],1)
# clf = KNN()
# clf.fit(y_values)
# clf.predict(y_values)
# df["label_volume_knn"] = clf.predict(y_values)
# df["score_volume_knn"] = clf.decision_function(y_values)#.round(4)
# x_values = df.index.values.reshape(df.index.values.shape[0],1)
# y_values = df.close.values.reshape(df.close.values.shape[0],1)
# clf = PCA()
# clf.fit(y_values)
# clf.predict(y_values)
# df["label_close_pca"] = clf.predict(y_values)
# df["score_close_pca"] = clf.decision_function(y_values)#.round(6)
# y_values = df.volume.values.reshape(df.volume.values.shape[0],1)
# clf = PCA()
# clf.fit(y_values)
# clf.predict(y_values)
# df["label_volume_pca"] = clf.predict(y_values)
# df["score_volume_pca"] = clf.decision_function(y_values)#.round(4)
# x_values = df.index.values.reshape(df.index.values.shape[0],1)
# y_values = df.close.values.reshape(df.close.values.shape[0],1)
# clf = IForest()
# clf.fit(y_values)
# clf.predict(y_values)
# df["label_close_iforest"] = clf.predict(y_values)
# df["score_close_iforest"] = clf.decision_function(y_values)#.round(6)
# y_values = df.volume.values.reshape(df.volume.values.shape[0],1)
# clf = IForest()
# clf.fit(y_values)
# clf.predict(y_values)
# df["label_volume_iforest"] = clf.predict(y_values)
# df["score_volume_iforest"] = clf.decision_function(y_values)#.round(4)
return df
class TestHBOS(unittest.TestCase):
def setUp(self):
self.n_train = 100
self.n_test = 50
self.contamination = 0.1
self.roc_floor = 0.6
self.X_train, self.y_train, self.X_test, self.y_test = generate_data(
n_train=self.n_train,
n_test=self.n_test,
contamination=self.contamination)
self.clf = HBOS(contamination=self.contamination)
self.clf.fit(self.X_train)
def test_sklearn_estimator(self):
check_estimator(self.clf)
def test_parameters(self):
if not hasattr(
self.clf,
'decision_scores_') or self.clf.decision_scores_ is None:
self.assertRaises(AttributeError, 'decision_scores_ is not set')
if not hasattr(self.clf, 'labels_') or self.clf.labels_ is None:
self.assertRaises(AttributeError, 'labels_ is not set')
if not hasattr(self.clf, 'threshold_') or self.clf.threshold_ is None:
self.assertRaises(AttributeError, 'threshold_ is not set')
if not hasattr(self.clf, '_mu') or self.clf._mu is None:
self.assertRaises(AttributeError, '_mu is not set')
if not hasattr(self.clf, '_sigma') or self.clf._sigma is None:
self.assertRaises(AttributeError, '_sigma is not set')
if not hasattr(self.clf, 'hist_') or self.clf.hist_ is None:
self.assertRaises(AttributeError, 'hist_ is not set')
if not hasattr(self.clf, 'bin_edges_') or self.clf.bin_edges_ is None:
self.assertRaises(AttributeError, 'bin_edges_ is not set')
def test_train_scores(self):
assert_equal(len(self.clf.decision_scores_), self.X_train.shape[0])
def test_prediction_scores(self):
pred_scores = self.clf.decision_function(self.X_test)
# check score shapes
assert_equal(pred_scores.shape[0], self.X_test.shape[0])
# check performance
assert_greater(roc_auc_score(self.y_test, pred_scores), self.roc_floor)
def test_prediction_labels(self):
pred_labels = self.clf.predict(self.X_test)
assert_equal(pred_labels.shape, self.y_test.shape)
def test_prediction_proba(self):
pred_proba = self.clf.predict_proba(self.X_test)
assert_greater_equal(pred_proba.min(), 0)
assert_less_equal(pred_proba.max(), 1)
def test_prediction_proba_linear(self):
pred_proba = self.clf.predict_proba(self.X_test, method='linear')
assert_greater_equal(pred_proba.min(), 0)
assert_less_equal(pred_proba.max(), 1)
def test_prediction_proba_unify(self):
pred_proba = self.clf.predict_proba(self.X_test, method='unify')
assert_greater_equal(pred_proba.min(), 0)
assert_less_equal(pred_proba.max(), 1)
def test_prediction_proba_parameter(self):
with assert_raises(ValueError):
self.clf.predict_proba(self.X_test, method='something')
def test_fit_predict(self):
pred_labels = self.clf.fit_predict(self.X_train)
assert_equal(pred_labels.shape, self.y_train.shape)
def test_evaluate(self):
self.clf.fit_predict_evaluate(self.X_test, self.y_test)
def tearDown(self):
pass
data['plate'] = f
data = data[data['Metadata_broad_sample'].isin(drugs)]
data = data[data.columns.intersection(selected_cols)]
b = data['Metadata_broad_sample']
w = data['Metadata_Well']
p = data['plate']
del data['Metadata_broad_sample']
del data['Metadata_Well']
del data['plate']
outliers_fraction = 0.01
clf = HBOS (contamination= outliers_fraction)
clf.fit(data)
y_pred = clf.predict(data)
X = pd.DataFrame()
X['outlier'] = y_pred.tolist()
X['Metadata_broad_sample'] = b
X['Metadata_Well'] = w
X['plate'] = p
X.to_csv('outlier_without_regress/'+f)
#target = y_pred.tolist()
#tsne = TSNE(n_components= 2, verbose=1, perplexity=40, n_iter=2000)
#tsne_results = tsne.fit_transform(data)
#fig = plt.figure()
#ax = fig.add_subplot(111, projection='3d')
#ax.scatter(tsne_results[:,0], tsne_results[:,1],tsne_results[:,2], cmap = "coolwarm", edgecolor = "None" , c = target)
#plt.scatter(tsne_results[:,0],tsne_results[:,1], c=target,
# Generate sample data
X_train, y_train, X_test, y_test = \
generate_data(n_train=n_train,
n_test=n_test,
n_features=2,
contamination=contamination,
random_state=42)
# train HBOS detector
clf_name = 'HBOS'
clf = HBOS()
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)
df.loc[df['ground.truth'] == 'anomaly', 'ground.truth'] = 1
df.loc[df['ground.truth'] == 'nominal', 'ground.truth'] = 0
y = df['ground.truth'].values.reshape(-1)
df[['V1', 'V2', 'V3', 'V4', 'V5', 'V6', 'V7']] = scaler.fit_transform(
df[['V1', 'V2', 'V3', 'V4', 'V5', 'V6', 'V7']])
x1 = df['V1'].values.reshape(-1, 1)
x2 = df['V2'].values.reshape(-1, 1)
x3 = df['V3'].values.reshape(-1, 1)
x4 = df['V4'].values.reshape(-1, 1)
x5 = df['V5'].values.reshape(-1, 1)
x6 = df['V6'].values.reshape(-1, 1)
x7 = df['V7'].values.reshape(-1, 1)
x = np.concatenate((x1, x2, x3, x4, x5, x6, x7), axis=1)
hbos = HBOS(contamination=outliers_fraction)
hbos.fit(x)
y_pred = hbos.predict(x)
fpr, tpr, threshold = roc_curve(y, y_pred) ###计算真阳性率和假阳性率
roc_auc = auc(fpr, tpr) ###计算auc的值
lw = 2
ax = fig.add_subplot(3, 3, i)
plt.plot(fpr,
tpr,
color='darkorange',
lw=lw,
label='ROC curve (area = %0.3f)' % roc_auc) ###假正率为横坐标，真正率为纵坐标做曲线
plt.plot([0, 1], [0, 1], color='navy', lw=lw, linestyle='--')
plt.xlim([0.0, 1.0])
plt.ylim([0.0, 1.05])
plt.xlabel('False Positive Rate')
plt.ylabel('True Positive Rate')
plt.title('AUC')
def do_pyod(model, colnames, arr_baseline, arr_highlight):
# init some counters
n_charts, n_dims, n_bad_data, fit_success, fit_default, fit_fail = init_counters(
colnames)
# dict to collect results into
results = {}
n_lags = model.get('n_lags', 0)
model_level = model.get('model_level', 'dim')
model = model.get('type', 'hbos')
# model init
clf = pyod_init(model)
# get map of cols to loop over
col_map = get_col_map(colnames, model_level)
# build each model
for colname in col_map:
chart = colname.split('|')[0]
dimension = colname.split('|')[1] if '|' in colname else '*'
arr_baseline_dim = arr_baseline[:, col_map[colname]]
arr_highlight_dim = arr_highlight[:, col_map[colname]]
# check for bad data
bad_data = False
# skip if bad data
if bad_data:
n_bad_data += 1
log.info(f'... skipping {colname} due to bad data')
else:
if n_lags > 0:
arr_baseline_dim = add_lags(arr_baseline_dim, n_lags=n_lags)
arr_highlight_dim = add_lags(arr_highlight_dim, n_lags=n_lags)
# remove any nan rows
arr_baseline_dim = arr_baseline_dim[~np.isnan(arr_baseline_dim).
any(axis=1)]
arr_highlight_dim = arr_highlight_dim[~np.isnan(arr_highlight_dim).
any(axis=1)]
log.debug(f'... chart = {chart}')
log.debug(f'... dimension = {dimension}')
log.debug(f'... arr_baseline_dim.shape = {arr_baseline_dim.shape}')
log.debug(
f'... arr_highlight_dim.shape = {arr_highlight_dim.shape}')
log.debug(f'... arr_baseline_dim = {arr_baseline_dim}')
log.debug(f'... arr_highlight_dim = {arr_highlight_dim}')
if model == ['auto_encoder']:
clf = pyod_init(model, n_features=arr_baseline_dim.shape[1])
clf, result = try_fit(clf, colname, arr_baseline_dim,
PyODDefaultModel)
fit_success += 1 if result == 'success' else 0
fit_default += 1 if result == 'default' else 0
# try predictions and if they fail use default model
try:
preds = clf.predict(arr_highlight_dim)
probs = clf.predict_proba(arr_highlight_dim)[:, 1]
except:
fit_success -= 1
fit_default += 1
clf = PyODDefaultModel()
clf.fit(arr_baseline_dim)
preds = clf.predict(arr_highlight_dim)
probs = clf.predict_proba(arr_highlight_dim)[:, 1]
log.debug(f'... preds.shape = {preds.shape}')
log.debug(f'... preds = {preds}')
log.debug(f'... probs.shape = {probs.shape}')
log.debug(f'... probs = {probs}')
# save results
score = (np.mean(probs) + np.mean(preds)) / 2
if chart in results:
results[chart].append({dimension: {'score': score}})
else:
results[chart] = [{dimension: {'score': score}}]
# log some summary stats
log.info(
summary_info(n_charts, n_dims, n_bad_data, fit_success, fit_fail,
fit_default, model_level))
return results
