File thresholds.py has 467 lines of code (exceeds 250 allowed). Consider refactoring.

Open

def AUCP(**kwargs):
    """AUCP class for Area Under Curve Precentage thresholder.

       Use the area under the curve to evaluate a non-parametric means
       to threshold scores generated by the decision_scores where outliers

File torch_utility.py has 374 lines of code (exceeds 250 allowed). Consider refactoring.

Open

# -*- coding: utf-8 -*-
"""Utility functions for PyTorch models
"""
# Author: Tiankai Yang <tiankaiy@usc.edu>
# License: BSD 2 clause

Function generate_data_clusters has a Cognitive Complexity of 30 (exceeds 5 allowed). Consider refactoring.

Open

def generate_data_clusters(n_train=1000, n_test=500, n_clusters=2,
                           n_features=2, contamination=0.1, size='same',
                           density='same', dist=0.25, random_state=None,
                           return_in_clusters=False):
    """Utility function to generate synthesized data in clusters.

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File dif.py has 326 lines of code (exceeds 250 allowed). Consider refactoring.

Open

# -*- coding: utf-8 -*-
"""Deep Isolation Forest for Anomaly Detection (DIF)
"""
# Author: Hongzuo Xu <hongzuoxu@126.edu>
# License: BSD 2 clause

File kpca.py has 325 lines of code (exceeds 250 allowed). Consider refactoring.

Open

# -*- coding: utf-8 -*-
"""Kernel Principal Component Analysis (KPCA) Outlier Detector
"""
# Author: Akira Tamamori <tamamori5917@gmail.com>
# License: BSD 2 clause

File deep_svdd.py has 317 lines of code (exceeds 250 allowed). Consider refactoring.

Open

# -*- coding: utf-8 -*-
"""Deep One-Class Classification for outlier detection
"""
# Author: Rafal Bodziony <bodziony.rafal@gmail.com> for the TensorFlow version
# Author: Yuehan Qin <yuehanqi@usc.edu> for the PyTorch version

Function generate_data_categorical has a Cognitive Complexity of 24 (exceeds 5 allowed). Consider refactoring.

Open

def generate_data_categorical(n_train=1000, n_test=500, n_features=2,
                              n_informative=2, n_category_in=2,
                              n_category_out=2, contamination=0.1,
                              shuffle=True, random_state=None):
    """Utility function to generate synthesized categorical data.

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File ae1svm.py has 292 lines of code (exceeds 250 allowed). Consider refactoring.

Open

# -*- coding: utf-8 -*-
"""Using AE-1SVM with Outlier Detection (PyTorch)
   Source: https://arxiv.org/pdf/1804.04888
   There is another implementation of this model by Minh Nghia: https://github.com/minh-nghia/AE-1SVM (Tensorflow)
"""

Consider simplifying this complex logical expression.

Open

    if (include_left and include_right) and (param < low or param > high):
        raise ValueError(
            '{param_name} is set to {param}. '
            'Not in the range of [{low}, {high}].'.format(
                param=param, low=low, high=high, param_name=param_name))

Function _calculate_outlier_scores has a Cognitive Complexity of 18 (exceeds 5 allowed). Consider refactoring.

Open

def _calculate_outlier_scores(X, bin_edges, hist, n_bins, alpha,
                              tol):  # pragma: no cover
    """The internal function to calculate the outlier scores based on
    the bins and histograms constructed with the training data. The program
    is optimized through numba. It is excluded from coverage test for

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Function _calculate_outlier_scores_auto has a Cognitive Complexity of 18 (exceeds 5 allowed). Consider refactoring.

Open

def _calculate_outlier_scores_auto(X, bin_edges, hist, alpha,
                                   tol):  # pragma: no cover
    """The internal function to calculate the outlier scores based on
    the bins and histograms constructed with the training data. The program
    is optimized through numba. It is excluded from coverage test for

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Function decision_function has a Cognitive Complexity of 18 (exceeds 5 allowed). Consider refactoring.

Open

    def decision_function(self, X):
        """Predict raw anomaly score of X using the fitted detector.

        The anomaly score of an input sample is computed based on different
        detector algorithms. For consistency, outliers are assigned with

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Function init_weights has 19 arguments (exceeds 4 allowed). Consider refactoring.

Open

def init_weights(layer, name='kaiming_uniform',

File lunar.py has 265 lines of code (exceeds 250 allowed). Consider refactoring.

Open

# -*- coding: utf-8 -*-
"""LUNAR: Unifying Local Outlier Detection Methods via Graph Neural Networks
"""
# Author: Adam Goodge <a.goodge@u.nus.edu>
#

Function fit has a Cognitive Complexity of 17 (exceeds 5 allowed). Consider refactoring.

Open

    def fit(self, X, y=None):
        """Fit detector. y is ignored in unsupervised methods.

        Parameters
        ----------

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Function fit has a Cognitive Complexity of 17 (exceeds 5 allowed). Consider refactoring.

Open

    def fit(self, X, y=None):
        """Fit detector. y is ignored in unsupervised methods.

        Parameters
        ----------

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Function __init__ has 18 arguments (exceeds 4 allowed). Consider refactoring.

Open

    def __init__(

Function train has a Cognitive Complexity of 16 (exceeds 5 allowed). Consider refactoring.

Open

    def train(self, train_loader):
        """Train the deep learning model.

        Parameters
        ----------

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Function scale_angles has a Cognitive Complexity of 16 (exceeds 5 allowed). Consider refactoring.

Open

def scale_angles(gammas, scaler1=None, scaler2=None):
    """
    Scale all angles in which angles <= 90
    degree will be scaled within [0 - 54.7] and
    angles > 90 will be scaled within [90 - 126]

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Function init_weights has a Cognitive Complexity of 16 (exceeds 5 allowed). Consider refactoring.

Open

def init_weights(layer, name='kaiming_uniform',
                 uniform_a=0.0, uniform_b=1.0,
                 normal_mean=0.0, normal_std=1.0,
                 constant_val=0.0,
                 xavier_gain=1.0,

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