Refactored the code, added a description

roerich/algorithms/calc_metrics.py

@@ -1,38 +1,68 @@
-from abc import ABCMeta, abstractmethod
-import numpy as np
-import pandas as pd
-from sklearn.metrics import pairwise_distances, roc_curve, roc_auc_score
-from sklearn.model_selection import StratifiedKFold, KFold, train_test_split
-from sklearn.discriminant_analysis import QuadraticDiscriminantAnalysis
-from copy import deepcopy
-from joblib import Parallel, delayed
-from scipy import interpolate
-from scipy.signal import argrelmax
-
 from .cpdc import ChangePointDetectionBase
 from roerich.scores.fd import frechet_distance
 from roerich.scores.mmd import maximum_mean_discrepancy
+from roerich.scores.energy import energy_distance
 
 
-class ScoreCalculator(ChangePointDetectionBase):
+class SlidingWindows(ChangePointDetectionBase):
 
-    def __init__(self, metric=None, func=None, periods=1, window_size=100, step=1, n_runs=1):
+    def __init__(self, metric=None, periods=1, window_size=100, step=1, n_runs=1):
         super().__init__(periods=periods, window_size=window_size, step=step, n_runs=n_runs)
         self.metric = metric
-        self.func = func
+
+        """
+        Change point detection algorithm based on binary classification.
+
+        Parameters:
+        -----------
+        periods: int, default=1
+            Number of consecutive observations of a time series, considered as one input vector.
+        The signal is considered as an autoregression process (AR) for classification. In the most cases periods=1
+        will be a good choice.
+
+        window_size: int, default=100
+            Number of consecutive observations of a time series in test and reference
+        windows. Recommendation: select the value so that there is only one change point within 2*window_size
+        observations of the signal.
+
+        step: int, default=1
+            Algorithm estimates change point detection score for each <step> observation. step > 1 helps
+        to speed up the algorithm.
+
+        n_runs: int, default=1
+            Number of times, the binary classifier runs on each pair of test and reference
+        windows. Observations in the windows are divided randomly between train and validation sample every time.
+        n_runs > 1 helps to reduce noise in the change point detection score.
+        
+        metric: str/function, default=None
+            Function that gives the measure of dissimilarity between data points in windows.
+        Metric should be one of: EnergyDist, FrechetDist, MaxMeanDisc; or a function should be passed.
+        Function must be in the following format:
+
+            Parameters:
+            -----------
+            X_ref: numpy.ndarray
+                Matrix of reference observations.
+            X_test: numpy.ndarray
+                Matrix of test observations.
+    
+            Returns:
+            --------
+            score: float
+                Estimated change point detection score for a pair of window.
+
+        """
 
     def reference_test_predict(self, X_ref, X_test):
 
-        if self.metric == "EnergyDist":
-            n = X_ref.shape[0]
-            E = 2*pairwise_distances(X_ref, X_test, metric='euclidean') - pairwise_distances(X_test, metric='euclidean') - pairwise_distances(X_ref, metric='euclidean')
-            return np.sum(E) / n ** 2
-        elif self.metric == "FrechetDist":
+        if self.metric == "energy":
+            return energy_distance(X_ref, X_test)
+        elif self.metric == "fd":
             return frechet_distance(X_ref, X_test)
-        elif self.metric == "MaxMeanDisc":
+        elif self.metric == "mmd":
             return maximum_mean_discrepancy(X_ref, X_test)
-        elif self.func is not None:
-            return self.func(X_ref, X_test)
+        elif callable(self.metric):
+            return self.metric(X_ref, X_test)
         else:
             raise ValueError("metric should be one of: EnergyDist, FrechetDist, MaxMeanDisc; or a function should be "
                              "passed")

roerich/algorithms/cpdc.py

@@ -115,7 +115,7 @@ def reference_test_predict(self, X_ref, X_test):
         X_test: numpy.ndarray
             Matrix of test observations.
 
-        Retunrs:
+        Returns:
         --------
         score: float
             Estimated change point detection score for a pair of window.

roerich/change_point/__init__.py

@@ -2,7 +2,7 @@
 from ..algorithms.cpdc import ChangePointDetectionClassifier, ChangePointDetectionRuLSIF
 from ..algorithms.cpdc_cv import ChangePointDetectionClassifierCV
 from ..algorithms.enrg_dist import EnergyDistanceCalculator
-from ..algorithms.calc_metrics import ScoreCalculator
+from ..algorithms.calc_metrics import SlidingWindows
 
 
 __all__ = [
@@ -12,7 +12,7 @@
     'ChangePointDetectionRuLSIF',
     'ChangePointDetectionClassifierCV',
     'EnergyDistanceCalculator',
-    'ScoreCalculator'
+    'SlidingWindows'
 ]
 
 

roerich/scores/__init__.py

@@ -1,10 +1,11 @@
 from .mmd import maximum_mean_discrepancy
 from .fd import frechet_distance
-
+from .energy import energy_distance
 
 __all__ = [
     'maximum_mean_discrepancy',
-    'frechet_distance'
+    'frechet_distance',
+    'energy_distance'
 ]
 
 

roerich/scores/energy.py

@@ -0,0 +1,10 @@
+import numpy as np
+from sklearn.metrics import pairwise_distances
+
+
+def energy_distance(x, y):
+    n = x.shape[0]
+    e = 2*pairwise_distances(x, y, metric='euclidean')\
+        - pairwise_distances(y, metric='euclidean') \
+        - pairwise_distances(x, metric='euclidean')
+    return np.sum(e) / n ** 2