import fixed and dependencies simplified

Author: vm-aifluence-jro

.gitignore

@@ -6,3 +6,4 @@ __pycache__/
 /figures
 /robust_pca.egg-info
 /dist
+/build

robust_pca/benchmark/comparator.py

@@ -1,6 +1,7 @@
 import pandas as pd
 import numpy as np
-import cross_validation
+from robust_pca.benchmark import cross_validation
+from robust_pca.benchmark import  utils
 from sklearn.metrics import (
     mean_squared_error,
     mean_absolute_error,
@@ -11,7 +12,7 @@
 from math import floor
 from skopt.space import Categorical, Real, Integer
 import matplotlib.pyplot as plt
-import utils
+
 
 
 class Comparator:

robust_pca/benchmark/cross_validation.py

@@ -4,7 +4,7 @@
 from typing import Optional
 from sklearn.utils import resample
 from math import floor
-import utils
+from robust_pca.benchmark import utils
 
 
 class CrossValidation:

robust_pca/imputations/models.py

@@ -9,12 +9,10 @@
 from sklearn.impute import KNNImputer, IterativeImputer
 from robust_pca.imputations.rpca.pcp_rpca import RPCA
 from robust_pca.imputations.rpca.temporal_rpca import TemporalRPCA
-
+from robust_pca.benchmark import utils
 import os
-import utils
 import sys
 
-
 class suppress_stdout_stderr(object):
     """
     A context manager for doing a "deep suppression" of stdout and stderr in

robust_pca/imputations/rpca/utils/utils.py

@@ -11,7 +11,6 @@
 from sklearn.neighbors import kneighbors_graph
 import scipy
 from scipy.linalg import toeplitz
-# from statsmodels import robust
 
 
 def get_period(signal: NDArray, max_period: Optional[int] = None) -> int:

robust_pca/utils/utils.py

@@ -9,7 +9,6 @@
 import pandas as pd
 from sklearn.neighbors import kneighbors_graph
 import scipy
-from statsmodels import robust
 
 def get_period(signal: List) -> int:
     """Retrieve the "period" of a series based on the ACF
@@ -291,7 +290,7 @@ def get_anomaly(A, X, e=3):
         np.ndarray: filtered A
         np.ndarray: noise
     """
-    mad = robust.mad(X)
+    mad = np.median(np.abs(X - np.median(X)))
     return np.where(np.abs(A) > (e * mad), A, 0), np.where(np.abs(A) <= (e * mad), A, 0)
 
 def resultRPCA_to_signal(

setup.py

@@ -25,8 +25,7 @@
     "scipy",
     "tqdm",
     "pillow",
-    "scikit-learn",
-    "statsmodels"
+    "scikit-learn"
 ]
 PYTHON_REQUIRES = ">=3.8"