import numpy as np
from sklearn.decomposition import PCA
from sklearn.base import BaseEstimator, OutlierMixin
from sklearn.utils.validation import check_is_fitted, check_array, FLOAT_DTYPES
class PCAOutlierDetection(BaseEstimator, OutlierMixin):
"""
Does outlier detection based on the reconstruction error from PCA.
"""
def __init__(
self,
n_components=None,
threshold=None,
variant="relative",
whiten=False,
svd_solver="auto",
tol=0.0,
iterated_power="auto",
random_state=None,
):
self.n_components = n_components
self.threshold = threshold
self.whiten = whiten
self.variant = variant
self.svd_solver = svd_solver
self.tol = tol
self.iterated_power = iterated_power
self.random_state = random_state
def fit(self, X, y=None):
"""
Fit the model using X as training data.
:param X: array-like, shape=(n_columns, n_samples,) training data.
:param y: ignored but kept in for pipeline support
:return: Returns an instance of self.
"""
X = check_array(X, estimator=self, dtype=FLOAT_DTYPES)
if not self.threshold:
raise ValueError(f"The `threshold` value cannot be `None`.")
self.pca_ = PCA(
n_components=self.n_components,
whiten=self.whiten,
svd_solver=self.svd_solver,
tol=self.tol,
iterated_power=self.iterated_power,
random_state=self.random_state,
)
self.pca_.fit(X, y)
self.offset_ = -self.threshold
return self
def transform(self, X):
"""
Uses the underlying PCA method to transform the data.
"""
X = check_array(X, estimator=self, dtype=FLOAT_DTYPES)
check_is_fitted(self, ["pca_", "offset_"])
return self.pca_.transform(X)
def difference(self, X):
"""
Shows the calculated difference between original and reconstructed data. Row by row.
:param X: array-like, shape=(n_columns, n_samples, ) training data.
:return: array, shape=(n_samples,) the difference
"""
check_is_fitted(self, ["pca_", "offset_"])
reduced = self.pca_.transform(X)
diff = np.sum(np.abs(self.pca_.inverse_transform(reduced) - X), axis=1)
if self.variant == "relative":
diff = diff / X.sum(axis=1)
return diff
def decision_function(self, X):
return self.threshold - self.difference(X)
def score_samples(self, X):
return -self.difference(X)
def predict(self, X):
"""
Predict if a point is an outlier.
:param X: array-like, shape=(n_columns, n_samples, ) training data.
:return: array, shape=(n_samples,) the predicted data. 1 for inliers, -1 for outliers.
"""
X = check_array(X, estimator=self, dtype=FLOAT_DTYPES)
check_is_fitted(self, ["pca_", "offset_"])
result = np.ones(X.shape[0])
result[self.difference(X) > self.threshold] = -1
return result.astype(np.int)
