# Source code for pyod.models.cd

```
# -*- coding: utf-8 -*-
"""Cook's distance outlier detection (CD)
"""
# Author: D Kulik
# License: BSD 2 clause
from __future__ import division
from __future__ import print_function
import numpy as np
from sklearn.linear_model import LinearRegression
from sklearn.utils import check_array
from sklearn.utils.validation import check_is_fitted
from .base import BaseDetector
def _Cooks_dist(X, y, model):
"""Calculated the Cook's distance
Parameters
----------
X : numpy array of shape (n_samples, n_features)
The training dataset.
y : numpy array of shape (n_samples)
The training datset
model : object
Regression model used to calculate the Cook's distance
Returns
-------
distances_ : numpy array of shape (n_samples)
Cook's distance
"""
# Leverage is computed as the diagonal of the projection matrix of X
leverage = (X * np.linalg.pinv(X).T).sum(1)
# Compute the rank and the degrees of freedom of the model
rank = np.linalg.matrix_rank(X)
df = X.shape[0] - rank
# Compute the MSE from the residuals
residuals = y - model.predict(X)
mse = np.dot(residuals, residuals) / df
# Compute Cook's distance
if (mse != 0) or (mse != np.nan):
residuals_studentized = residuals / np.sqrt(mse) / np.sqrt(
1 - leverage)
distance_ = residuals_studentized ** 2 / X.shape[1]
distance_ *= leverage / (1 - leverage)
distance_ = ((distance_ - distance_.min())
/ (distance_.max() - distance_.min()))
else:
distance_ = np.ones(len(y)) * np.nan
return distance_
def _process_distances(X, model):
"""Calculated the mean Cook's distances for
each feature
Parameters
----------
X : numpy array of shape (n_samples, n_features)
The training dataset.
model : object
Regression model used to calculate the Cook's distance
Returns
-------
distances_ : numpy array of shape (n_samples)
mean Cook's distance
"""
distances_ = []
for i in range(X.shape[1]):
mod = model
# Extract new X and y inputs
exp = np.delete(X.copy(), i, axis=1)
resp = X[:, i]
exp = exp.reshape(-1, 1) if exp.ndim == 1 else exp
# Fit the model
mod.fit(exp, resp)
# Get Cook's Distance
distance_ = _Cooks_dist(exp, resp, mod)
distances_.append(distance_)
distances_ = np.nanmean(distances_, axis=0)
return distances_
[docs]class CD(BaseDetector):
"""Cook's distance can be used to identify points that negatively
affect a regression model. A combination of each observation’s
leverage and residual values are used in the measurement. Higher
leverage and residuals relate to higher Cook’s distances. Note
that this method is unsupervised and requires at least two
features for X with which to calculate the mean Cook's distance
for each datapoint. Read more in the :cite:`cook1977detection`.
Parameters
----------
contamination : float in (0., 0.5), optional (default=0.1)
The amount of contamination of the data set, i.e.
the proportion of outliers in the data set. Used when fitting to
define the threshold on the decision function.
model : object, optional (default=LinearRegression())
Regression model used to calculate the Cook's distance
Attributes
----------
decision_scores_ : numpy array of shape (n_samples,)
The outlier scores of the training data.
The higher, the more abnormal. Outliers tend to have higher
scores. This value is available once the detector is
fitted.
threshold_ : float
The modified z-score to use as a threshold. Observations with
a modified z-score (based on the median absolute deviation) greater
than this value will be classified as outliers.
labels_ : int, either 0 or 1
The binary labels of the training data. 0 stands for inliers
and 1 for outliers/anomalies. It is generated by applying
``threshold_`` on ``decision_scores_``.
"""
def __init__(self, contamination=0.1, model=LinearRegression()):
super(CD, self).__init__(contamination=contamination)
self.model = model
[docs] def fit(self, X, y=None):
""""Fit detector. y is ignored in unsupervised methods.
Parameters
----------
X : numpy array of shape (n_samples, n_features)
The input samples.
y : Ignored
Not used, present for API consistency by convention.
Returns
-------
self : object
Fitted estimator.
"""
# Validate inputs X and y
X = check_array(X)
self._set_n_classes(y)
# Get Cook's distance
distances_ = _process_distances(X, self.model)
self.decision_scores_ = distances_
self._process_decision_scores()
return self
[docs] def decision_function(self, X):
"""Predict raw anomaly score of X using the fitted detector.
For consistency, outliers are assigned with larger anomaly scores.
Parameters
----------
X : numpy array of shape (n_samples, n_features)
The training input samples. Sparse matrices are accepted only
if they are supported by the base estimator.
Returns
-------
anomaly_scores : numpy array of shape (n_samples,)
The anomaly score of the input samples.
"""
check_is_fitted(self, ['decision_scores_', 'threshold_', 'labels_'])
# Validate input X
X = check_array(X)
# Get Cook's distance
distances_ = _process_distances(X, self.model)
return distances_
```