├── .gitignore
├── .travis.yml
├── LICENSE
├── README.md
├── banpei
    ├── __init__.py
    ├── base
    │   ├── __init__.py
    │   └── model.py
    ├── hotelling.py
    ├── sst.py
    └── utils.py
├── demo
    ├── data
    │   └── abnormal_frequency.csv
    └── sst_detect.py
├── docs
    └── images
    │   └── sst_example.png
├── poetry.lock
├── pyproject.toml
└── tests
    ├── __init__.py
    ├── test_data
        ├── davis.csv
        └── periodic_wave.csv
    ├── test_hotelling.py
    ├── test_sst.py
    └── test_utils.py


/.gitignore:
--------------------------------------------------------------------------------
1 | .venv
2 | dist/
3 | .ipynb_checkpoints/
4 | __pycache__/
5 | *egg-info/*
6 | 


--------------------------------------------------------------------------------
/.travis.yml:
--------------------------------------------------------------------------------
 1 | language: python
 2 | python:
 3 |   - "3.8"
 4 | before_install:
 5 |   - pip install poetry
 6 | install:
 7 |   - poetry install
 8 | script:
 9 |   - poetry run python -m unittest -v
10 | 


--------------------------------------------------------------------------------
/LICENSE:
--------------------------------------------------------------------------------
 1 | MIT License
 2 | 
 3 | Copyright (c) 2017 Hirofumi Tsuruta
 4 | 
 5 | Permission is hereby granted, free of charge, to any person obtaining a copy
 6 | of this software and associated documentation files (the "Software"), to deal
 7 | in the Software without restriction, including without limitation the rights
 8 | to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
 9 | copies of the Software, and to permit persons to whom the Software is
10 | furnished to do so, subject to the following conditions:
11 | 
12 | The above copyright notice and this permission notice shall be included in all
13 | copies or substantial portions of the Software.
14 | 
15 | THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
16 | IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
17 | FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
18 | AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
19 | LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
20 | OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
21 | SOFTWARE.
22 | 


--------------------------------------------------------------------------------
/README.md:
--------------------------------------------------------------------------------
 1 | ## Banpei
 2 | [![Build Status](https://travis-ci.org/tsurubee/banpei.svg?branch=master)](https://travis-ci.org/tsurubee/banpei)  
 3 | 
 4 | Banpei is a Python package of the anomaly detection.  
 5 | Anomaly detection is a technique used to identify unusual patterns that do not conform to expected behavior.
 6 | 
 7 | ## System
 8 | Python ^3.6 (2.x is not supported)
 9 | 
10 | ## Installation
11 | ```bash
12 | $ pip install banpei
13 | ```
14 | After installation, you can import banpei in Python.
15 | ```
16 | $ python
17 | >>> import banpei
18 | ```
19 | 
20 | ## Usage
21 | #### Example
22 | *Singular spectrum transformation(sst)*
23 | ```python
24 | import banpei 
25 | model   = banpei.SST(w=50)
26 | results = model.detect(data)
27 | ```
28 | The input 'data' must be one-dimensional array-like object containing a sequence of values.  
29 | The output 'results' is Numpy array with the same size as input data.  
30 | The graph below shows the change-point scoring calculated by sst for the periodic data.
31 | 
32 | <img src="./docs/images/sst_example.png" alt="sst_example" width="700">
33 | 
34 | The data used is placed as '/tests/test_data/periodic_wave.csv'.  You can read a CSV file using the following code.  
35 | ```python
36 | import pandas as pd
37 | raw_data = pd.read_csv('./tests/test_data/periodic_wave.csv')
38 | data = raw_data['y']
39 | ```
40 | 
41 | SST processing can be accelerated using the Lanczos method which is one of Krylov subspace methods by specifying `True` for the `is_lanczos` argument like below.  
42 | ```python
43 | results = model.detect(data, is_lanczos=True)
44 | ```
45 | 
46 | ## Real-time monitoring with Bokeh
47 | Banpei is developed with the goal of constructing the environment of real-time abnormality monitoring.  In order to achieve the goal, Banpei has the function corresponded to the streaming data.  With the help of Bokeh, which is great visualization library, we can construct the simple monitoring tool.   
48 | Here's a simple demonstration movie of change-point detection of the data trends.
49 | 
50 | [![sst detection](https://img.youtube.com/vi/7_woubLAhXk/0.jpg)](https://www.youtube.com/watch?v=7_woubLAhXk)  
51 | https://youtu.be/7_woubLAhXk  
52 | The sample code how to construct real-time monitoring environment is placed in '/demo' folder.
53 | 
54 | ## The implemented algorithm
55 | #### Outlier detection
56 | * Hotelling's theory
57 | #### Change point detection
58 | * Singular spectrum transformation(sst)
59 | 
60 | ## License
61 | This project is licensed under the terms of the MIT license, see LICENSE.


--------------------------------------------------------------------------------
/banpei/__init__.py:
--------------------------------------------------------------------------------
1 | from . import base
2 | from .base import model
3 | from . import hotelling
4 | from .hotelling import Hotelling
5 | from . import sst
6 | from .sst import SST
7 | 


--------------------------------------------------------------------------------
/banpei/base/__init__.py:
--------------------------------------------------------------------------------
1 | from banpei.base import model
2 | 


--------------------------------------------------------------------------------
/banpei/base/model.py:
--------------------------------------------------------------------------------
 1 | import numpy as np
 2 | 
 3 | 
 4 | class BaseModel(object):
 5 |     def __init__(self):
 6 |         pass
 7 | 
 8 |     def convert_to_nparray(self, data):
 9 |         if not isinstance(data, np.ndarray):
10 |             return np.array(data)
11 |         else:
12 |             return data
13 | 
14 |     def detect(self, *args):
15 |         """
16 |         This is a placeholder intended to be overwritten by individual models.
17 |         """
18 |         raise NotImplementedError
19 | 


--------------------------------------------------------------------------------
/banpei/hotelling.py:
--------------------------------------------------------------------------------
 1 | import numpy as np
 2 | from scipy import stats
 3 | from banpei.base.model import BaseModel
 4 | 
 5 | 
 6 | class Hotelling(BaseModel):
 7 |     def __init__(self):
 8 |         pass
 9 | 
10 |     def detect(self, data, threshold):
11 |         """
12 |         Parameters
13 |         ----------
14 |         data : array_like
15 |                Input array or object that can be converted to an array.
16 |         threshold : float
17 | 
18 |         Returns
19 |         -------
20 |         List of tuples where each tuple contains index number and anomalous value.
21 |         """
22 |         data = self.convert_to_nparray(data)
23 | 
24 |         # Set the threshold of abnormality
25 |         abn_th = stats.chi2.interval(1-threshold, 1)[1]
26 | 
27 |         # Covert raw data into the degree of abnormality
28 |         avg = np.average(data)
29 |         var = np.var(data)
30 |         data_abn = [(x - avg)**2 / var for x in data]
31 | 
32 |         # Abnormality determination
33 |         result = []
34 |         for (index, x) in enumerate(data_abn):
35 |             if x > abn_th:
36 |                 result.append((index, data[index]))
37 |         return result
38 | 


--------------------------------------------------------------------------------
/banpei/sst.py:
--------------------------------------------------------------------------------
  1 | import numpy as np
  2 | from banpei.base.model import BaseModel
  3 | from .utils import *
  4 | 
  5 | class SST(BaseModel):
  6 |     def __init__(self, w, m=2, k=None, L=None):
  7 |         """
  8 |         Parameters
  9 |         ----------
 10 |         w    : int
 11 |                Window size
 12 |         m    : int
 13 |                Number of basis vectors
 14 |         k    : int
 15 |                Number of columns for the trajectory and test matrices
 16 |         L    : int
 17 |                Lag time
 18 |         """
 19 |         self.w = w
 20 |         self.m = m
 21 |         if k is None:
 22 |             self.k = self.w // 2
 23 |         else:
 24 |             self.k = k
 25 |         if L is None:
 26 |             self.L = self.k // 2
 27 |         else:
 28 |             self.L = L
 29 | 
 30 |     def detect(self, data, is_lanczos=False):
 31 |         """
 32 |         Batch mode detection
 33 | 
 34 |         Parameters
 35 |         ----------
 36 |         data : array_like
 37 |                Input array or object that can be converted to an array.
 38 |         is_lanczos : boolean
 39 |                If true, the change score is calculated based on the lanczos method
 40 | 
 41 |         Returns
 42 |         -------
 43 |             Numpy array contains the degree of change.
 44 |             The size of Numpy array is the same as input array.
 45 |         """
 46 |         # Set variables
 47 |         data = self.convert_to_nparray(data)
 48 |         T = len(data)
 49 | 
 50 |         # Check the size of input data
 51 |         if not len(data) > self.L + self.w + self.k - 2:
 52 |             raise ValueError("Input data is too small.")
 53 | 
 54 |         # Calculation range
 55 |         start_cal = self.k + self.w
 56 |         end_cal = T - self.L + 1
 57 | 
 58 |         # Calculate the degree of change
 59 |         change_scores = np.zeros(len(data))
 60 |         for t in range(start_cal, end_cal + 1):
 61 |             # Trajectory matrix
 62 |             start_tra = t - self.w - self.k + 1
 63 |             end_tra = t - self.w
 64 |             tra_matrix = self._extract_matrix(data, start_tra, end_tra, self.w)
 65 | 
 66 |             # Test matrix
 67 |             start_test = start_tra + self.L
 68 |             end_test = end_tra + self.L
 69 |             test_matrix = self._extract_matrix(data, start_test, end_test, self.w)
 70 | 
 71 |             # Calculate the score by singular value decomposition(SVD)
 72 |             if is_lanczos:
 73 |                 change_scores[t] = self._calculate_score_by_lanczos(tra_matrix, test_matrix)
 74 |             else:
 75 |                 change_scores[t] = self._calculate_score_by_svd(tra_matrix, test_matrix)
 76 | 
 77 |         return change_scores
 78 | 
 79 |     def stream_detect(self, data, is_lanczos=False):
 80 |         """
 81 |         Stream mode detection for live monitoring.
 82 | 
 83 |         Parameters
 84 |         ----------
 85 |         data : array_like
 86 |                Input array or object that can be converted to an array.
 87 |         is_lanczos : boolean
 88 |                If true, the change score is calculated based on the lanczos method
 89 | 
 90 |         Returns
 91 |         -------
 92 |         tuple: (score, delay)
 93 |               score means the degree of change in the latest we can calculate.
 94 |               delay means the time lag between the latest data point and calculation point.
 95 |         """
 96 |         # Set variables
 97 |         data = self.convert_to_nparray(data)
 98 |         T = len(data)
 99 | 
100 |         # Check the size of input data
101 |         if not len(data) > self.L + self.w + self.k - 2:
102 |             return 0
103 | 
104 |         # Calculation range
105 |         t = T - self.L + 1
106 | 
107 |         # Trajectory matrix
108 |         start_tra = t - self.w - self.k + 1
109 |         end_tra = t - self.w
110 |         tra_matrix = self._extract_matrix(data, start_tra, end_tra, self.w)
111 | 
112 |         # Test matrix
113 |         start_test = start_tra + self.L
114 |         end_test = end_tra + self.L
115 |         test_matrix = self._extract_matrix(data, start_test, end_test, self.w)
116 | 
117 |         # Calculate the change score
118 |         if is_lanczos:
119 |             return self._calculate_score_by_lanczos(tra_matrix, test_matrix)
120 |         else:
121 |             return self._calculate_score_by_svd(tra_matrix, test_matrix)
122 | 
123 |     def _calculate_score_by_svd(self, tra_matrix, test_matrix):
124 |         U_tra, _, _ = np.linalg.svd(tra_matrix, full_matrices=False)
125 |         U_test, _, _ = np.linalg.svd(test_matrix, full_matrices=False)
126 |         U_tra_m = U_tra[:, :self.m]
127 |         U_test_m = U_test[:, :self.m]
128 |         s = np.linalg.svd(np.dot(U_tra_m.T, U_test_m), full_matrices=False, compute_uv=False)
129 |         return 1 - s[0]
130 | 
131 |     def _calculate_score_by_lanczos(self, tra_matrix, test_matrix):
132 |         m, _, _ = power_method(test_matrix)
133 |         k = 2 * self.m if self.m % 2 == 0 else 2 * self.m - 1
134 |         P = np.dot(tra_matrix, tra_matrix.T)
135 |         T = tridiagonalize_by_lanczos(P, m, k)
136 |         eigenvalue, eigenvectors = tridiag_eigen(T)
137 |         return 1 - np.sum(eigenvectors[0, np.argsort(eigenvalue)[::-1][:self.m]] ** 2)
138 | 
139 |     def _extract_matrix(self, data, start, end, w):
140 |         row = w
141 |         column = end - start + 1
142 |         matrix = np.empty((row, column))
143 |         i = 0
144 |         for t in range(start, end+1):
145 |             matrix[:, i] = data[t-1:t-1+row]
146 |             i += 1
147 | 
148 |         return matrix
149 | 


--------------------------------------------------------------------------------
/banpei/utils.py:
--------------------------------------------------------------------------------
  1 | import numpy as np
  2 | 
  3 | 
  4 | def power_method(A, iter_num=1):
  5 |     """
  6 |     Calculate the first singular vector/value of a target matrix based on the power method.
  7 |     Parameters
  8 |     ----------
  9 |     A : numpy array
 10 |         Target matrix
 11 |     iter_num : int
 12 |                Number of iterations
 13 | 
 14 |     Returns
 15 |     -------
 16 |     u : numpy array
 17 |         first left singular vector of A
 18 |     s : float
 19 |         first singular value of A
 20 |     v : numpy array
 21 |         first right singular vector of A
 22 |     """
 23 |     # set initial vector q
 24 |     q = np.random.normal(size=A.shape[1])
 25 |     q = q / np.linalg.norm(q)
 26 | 
 27 |     for i in range(iter_num):
 28 |         q = np.dot(np.dot(A.T, A), q)
 29 | 
 30 |     v = q / np.linalg.norm(q)
 31 |     Av = np.dot(A, v)
 32 |     s = np.linalg.norm(Av)
 33 |     u = Av / s
 34 | 
 35 |     return u, s, v
 36 | 
 37 | def tridiagonalize_by_lanczos(P, m, k):
 38 |     """
 39 |     Tridiagonalize matrix by lanczos method
 40 |     Parameters
 41 |     ----------
 42 |     P : numpy array
 43 |         Target matrix
 44 |     q : numpy array
 45 |         Initial vector
 46 |     k : int
 47 |         Size of the tridiagonal matrix
 48 | 
 49 |     Returns
 50 |     -------
 51 |     T : numpy array
 52 |         tridiagonal matrix
 53 |     """
 54 |     # Initialize variables
 55 |     T = np.zeros((k, k))
 56 |     r0 = m
 57 |     beta0 = 1
 58 |     q0 = np.zeros(m.shape)
 59 | 
 60 |     for i in range(k):
 61 |         q1 = r0 / beta0
 62 |         C = np.dot(P, q1)
 63 |         alpha1 = np.dot(q1, C)
 64 |         r1 = C - alpha1 * q1 - beta0 * q0
 65 |         beta1 = np.linalg.norm(r1)
 66 | 
 67 |         T[i, i] = alpha1
 68 |         if i + 1 < k:
 69 |           T[i, i + 1] = beta1
 70 |           T[i + 1, i] = beta1
 71 | 
 72 |         q0 = q1
 73 |         beta0 = beta1
 74 |         r0 = r1
 75 | 
 76 |     return T
 77 | 
 78 | def tridiag_eigen(T, iter_num=1, tol=1e-3):
 79 |     """
 80 |     Calculate eigenvalues and eigenvectors of tridiagonal matrix
 81 |     Parameters
 82 |     ----------
 83 |     P : numpy array
 84 |         Target matrix (tridiagonal)
 85 |     iter_num : int
 86 |         Number of iterations
 87 |     tol : float
 88 |         Stop iteration if the target matrix converges to a diagonal matrix with acceptable tolerance `tol`
 89 | 
 90 |     Returns
 91 |     -------
 92 |     eigenvalue : numpy array
 93 |                  Calculated eigenvalues
 94 |     eigenvectors : numpy array
 95 |                  Calculated eigenvectors
 96 |     """
 97 |     eigenvectors = np.identity(T.shape[0])
 98 | 
 99 |     for i in range(iter_num):
100 |         Q, R = tridiag_qr_decomposition(T)
101 |         T = np.dot(R, Q)
102 |         eigenvectors = np.dot(eigenvectors, Q)
103 |         eigenvalue = np.diag(T)
104 |         if np.all((T - np.diag(eigenvalue) < tol)):
105 |             break
106 | 
107 |     return eigenvalue, eigenvectors
108 | 
109 | def tridiag_qr_decomposition(T):
110 |     """
111 |     QR decomposition for a tridiagonal matrix
112 |     Ref. http://www.ericmart.in/blog/optimizing_julia_tridiag_qr
113 |     Parameters
114 |     ----------
115 |     T : numpy array
116 |         Target matrix (tridiagonal)
117 | 
118 |     Returns
119 |     -------
120 |     Qt.T : numpy array
121 |     R    : numpy array
122 |     """
123 |     R = T.copy()
124 |     Qt = np.eye(T.shape[0])
125 | 
126 |     for i in range(T.shape[0] - 1):
127 |         u = householder(R[i:i + 2, i])
128 |         M = np.outer(u, u)
129 |         R[i:i + 2, :(i + 3)] -= 2 * np.dot(M, R[i:i + 2, :(i + 3)])
130 |         Qt[i:i + 2, :(i + 3)] -= 2 * np.dot(M, Qt[i:i + 2, :(i + 3)])
131 | 
132 |     return Qt.T, R
133 | 
134 | def householder(x):
135 |     """
136 |     Householder projection for vector.
137 |     Parameters
138 |     ----------
139 |     x : numpy array
140 |         Target vector
141 | 
142 |     Returns
143 |     -------
144 |     x : numpy array
145 |     """
146 |     x[0] = x[0] + np.sign(x[0]) * np.linalg.norm(x)
147 |     x = x / np.linalg.norm(x)
148 |     return x
149 | 


--------------------------------------------------------------------------------
/demo/data/abnormal_frequency.csv:
--------------------------------------------------------------------------------
  1 | data
  2 | -0.001309202
  3 | 0.004999935
  4 | 0.009931528
  5 | 0.015606947
  6 | 0.012165483
  7 | 0.021053507
  8 | 0.023163775
  9 | 0.025547385
 10 | 0.028555434
 11 | 0.030887427
 12 | 0.032064662
 13 | 0.033960539
 14 | 0.0326049
 15 | 0.031226098
 16 | 0.034767635
 17 | 0.03303784
 18 | 0.03234975
 19 | 0.027418702
 20 | 0.023798397
 21 | 0.020915627
 22 | 0.019327269
 23 | 0.01454095
 24 | 0.010557806
 25 | 0.007512618
 26 | 0.006836284
 27 | -0.002651396
 28 | -0.003046795
 29 | -0.010335278
 30 | -0.0146095
 31 | -0.017036011
 32 | -0.019010741
 33 | -0.024092515
 34 | -0.027755231
 35 | -0.03110136
 36 | -0.032917381
 37 | -0.033296106
 38 | -0.032929639
 39 | -0.033075737
 40 | -0.030500874
 41 | -0.034934815
 42 | -0.028259315
 43 | -0.028238817
 44 | -0.027178242
 45 | -0.024271168
 46 | -0.019204542
 47 | -0.017044947
 48 | -0.013517739
 49 | -0.011475614
 50 | -0.00348009
 51 | -0.001660824
 52 | -0.000921096
 53 | 0.005556799
 54 | 0.009794498
 55 | 0.012295517
 56 | 0.01942764
 57 | 0.022312101
 58 | 0.022835518
 59 | 0.025761897
 60 | 0.028085268
 61 | 0.033081434
 62 | 0.03113548
 63 | 0.031536889
 64 | 0.03057832
 65 | 0.032093899
 66 | 0.031921851
 67 | 0.031325857
 68 | 0.027817311
 69 | 0.028331133
 70 | 0.026584308
 71 | 0.023274172
 72 | 0.01818309
 73 | 0.01516139
 74 | 0.011563823
 75 | 0.007731249
 76 | 0.00327767
 77 | -0.000746272
 78 | -0.00669743
 79 | -0.009837405
 80 | -0.010247843
 81 | -0.017803313
 82 | -0.01886644
 83 | -0.020821345
 84 | -0.026799818
 85 | -0.030547137
 86 | -0.031516376
 87 | -0.033459392
 88 | -0.033763293
 89 | -0.03389861
 90 | -0.035717321
 91 | -0.032535404
 92 | -0.029922304
 93 | -0.028164797
 94 | -0.028975866
 95 | -0.022134656
 96 | -0.021243628
 97 | -0.018295366
 98 | -0.014864763
 99 | -0.014050062
100 | -0.008011062
101 | -0.000618961
102 | 0.003234213
103 | 0.005161978
104 | 0.008938239
105 | 0.015331692
106 | 0.015932804
107 | 0.022870991
108 | 0.023878567
109 | 0.0251499
110 | 0.029679182
111 | 0.030490088
112 | 0.03122618
113 | 0.034707709
114 | 0.03441347
115 | 0.036531618
116 | 0.031726109
117 | 0.033933381
118 | 0.03115202
119 | 0.025105061
120 | 0.023435103
121 | 0.020750652
122 | 0.021009958
123 | 0.010743649
124 | 0.01101995
125 | 0.007161684
126 | 0.005493388
127 | 0.000245278
128 | -0.00690161
129 | -0.011979052
130 | -0.012423812
131 | -0.017526446
132 | -0.021742785
133 | -0.027585554
134 | -0.028822655
135 | -0.028996518
136 | -0.032731958
137 | -0.033525345
138 | -0.033554684
139 | -0.029818587
140 | -0.035188085
141 | -0.03220005
142 | -0.031241327
143 | -0.027282746
144 | -0.027700642
145 | -0.026120087
146 | -0.02338027
147 | -0.018610725
148 | -0.015007348
149 | -0.011066312
150 | -0.005323548
151 | -0.001018248
152 | 0.000815861
153 | 0.009971936
154 | 0.016915742
155 | 0.027132757
156 | 0.033037507
157 | 0.03495552
158 | 0.033280928
159 | 0.025572179
160 | 0.019097057
161 | 0.00557636
162 | -0.006944917
163 | -0.018406279
164 | -0.025949341
165 | -0.030357156
166 | -0.035645733
167 | -0.032162279
168 | -0.027722067
169 | -0.019245603
170 | -0.014092031
171 | 0.000165405
172 | 0.007904563
173 | 0.017105049
174 | 0.024191588
175 | 0.03161611
176 | 0.030284469
177 | 0.03132785
178 | 0.024902248
179 | 0.017648845
180 | 0.010967636
181 | -0.002313734
182 | -0.015563618
183 | -0.020554285
184 | -0.029492202
185 | -0.034362231
186 | -0.032133823
187 | -0.02729204
188 | -0.023402121
189 | -0.014989887
190 | -0.00319565
191 | 0.004577492
192 | 0.014273079
193 | 0.026820204
194 | 0.030785014
195 | 0.033032699
196 | 0.031919472
197 | 0.028040579
198 | 0.020086733
199 | 0.009936895
200 | -0.000468274
201 | -0.010490489
202 | -0.01966071
203 | -0.027137653
204 | -0.033728572
205 | -0.031967484
206 | -0.027973383
207 | -0.025460551
208 | -0.016841856
209 | -0.00600687
210 | 0.004727111
211 | 0.011614699
212 | 0.02325396
213 | 0.028660073
214 | 0.034046468
215 | 0.032366019
216 | 0.026800272
217 | 0.025799719
218 | 0.01402146
219 | 0.002776668
220 | -0.005233022
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--------------------------------------------------------------------------------
/demo/sst_detect.py:
--------------------------------------------------------------------------------
 1 | import banpei
 2 | import numpy as np
 3 | import pandas as pd
 4 | from bokeh.io import curdoc
 5 | from bokeh.models import ColumnDataSource, DatetimeTickFormatter
 6 | from bokeh.plotting import figure
 7 | from datetime import datetime
 8 | from math import radians
 9 | from pytz import timezone
10 | 
11 | raw_data = pd.read_csv('./data/abnormal_frequency.csv')
12 | raw_data = np.array(raw_data['data'])
13 | data = []
14 | results = []
15 | 
16 | 
17 | def get_new_data():
18 |     global data
19 |     global raw_data
20 |     data.append(raw_data[0])
21 |     raw_data = np.delete(raw_data, 0)
22 | 
23 | 
24 | def update_data():
25 |     global results
26 |     get_new_data()
27 |     ret = model.stream_detect(data)
28 |     results.append(ret)
29 |     now = datetime.now(tz=timezone("Asia/Tokyo"))
30 |     new_data = dict(x=[now], y=[data[-1]], ret=[results[-1]])
31 |     source.stream(new_data, rollover=500)
32 | 
33 | # Create Data Source
34 | source = ColumnDataSource(dict(x=[], y=[], ret=[]))
35 | 
36 | 
37 | # Create Banpei instance
38 | model = banpei.SST(w=30)
39 | 
40 | # Draw a graph
41 | fig = figure(x_axis_type="datetime",
42 |              x_axis_label="Datetime",
43 |              plot_width=950,
44 |              plot_height=650)
45 | fig.title.text = "Realtime monitoring with Banpei"
46 | fig.line(source=source, x='x', y='y', line_width=2, alpha=.85, color='blue', legend='observed data')
47 | fig.line(source=source, x='x', y='ret', line_width=2, alpha=.85, color='red', legend='change-point score')
48 | fig.circle(source=source, x='x', y='y', line_width=2, line_color='blue', color='blue')
49 | fig.legend.location = "top_left"
50 | 
51 | # Configuration of the axis
52 | format = "%Y-%m-%d-%H-%M-%S"
53 | fig.xaxis.formatter = DatetimeTickFormatter(
54 |     seconds=[format],
55 |     minsec =[format],
56 |     minutes=[format],
57 |     hourmin=[format],
58 |     hours  =[format],
59 |     days   =[format],
60 |     months =[format],
61 |     years  =[format]
62 | )
63 | fig.xaxis.major_label_orientation=radians(90)
64 | 
65 | # Configuration of the callback
66 | curdoc().add_root(fig)
67 | curdoc().add_periodic_callback(update_data, 1000) #ms
68 | 


--------------------------------------------------------------------------------
/docs/images/sst_example.png:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/tsurubee/banpei/d962460284481f098874f73aae226a3dd9af1dc9/docs/images/sst_example.png


--------------------------------------------------------------------------------
/poetry.lock:
--------------------------------------------------------------------------------
  1 | [[package]]
  2 | category = "main"
  3 | description = "NumPy is the fundamental package for array computing with Python."
  4 | name = "numpy"
  5 | optional = false
  6 | python-versions = ">=3.6"
  7 | version = "1.19.2"
  8 | 
  9 | [[package]]
 10 | category = "main"
 11 | description = "Powerful data structures for data analysis, time series, and statistics"
 12 | name = "pandas"
 13 | optional = false
 14 | python-versions = ">=3.6.1"
 15 | version = "1.1.2"
 16 | 
 17 | [package.dependencies]
 18 | numpy = ">=1.15.4"
 19 | python-dateutil = ">=2.7.3"
 20 | pytz = ">=2017.2"
 21 | 
 22 | [package.extras]
 23 | test = ["pytest (>=4.0.2)", "pytest-xdist", "hypothesis (>=3.58)"]
 24 | 
 25 | [[package]]
 26 | category = "main"
 27 | description = "Extensions to the standard Python datetime module"
 28 | name = "python-dateutil"
 29 | optional = false
 30 | python-versions = "!=3.0.*,!=3.1.*,!=3.2.*,>=2.7"
 31 | version = "2.8.1"
 32 | 
 33 | [package.dependencies]
 34 | six = ">=1.5"
 35 | 
 36 | [[package]]
 37 | category = "main"
 38 | description = "World timezone definitions, modern and historical"
 39 | name = "pytz"
 40 | optional = false
 41 | python-versions = "*"
 42 | version = "2020.1"
 43 | 
 44 | [[package]]
 45 | category = "main"
 46 | description = "SciPy: Scientific Library for Python"
 47 | name = "scipy"
 48 | optional = false
 49 | python-versions = ">=3.6"
 50 | version = "1.5.2"
 51 | 
 52 | [package.dependencies]
 53 | numpy = ">=1.14.5"
 54 | 
 55 | [[package]]
 56 | category = "main"
 57 | description = "Python 2 and 3 compatibility utilities"
 58 | name = "six"
 59 | optional = false
 60 | python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*"
 61 | version = "1.15.0"
 62 | 
 63 | [metadata]
 64 | content-hash = "c5e845a685da64b7df92315e5cb73d00549fafa9e3f768ea0b071dbac0f89db6"
 65 | python-versions = "^3.8"
 66 | 
 67 | [metadata.files]
 68 | numpy = [
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112 |     {file = "pandas-1.1.2.tar.gz", hash = "sha256:b64ffd87a2cfd31b40acd4b92cb72ea9a52a48165aec4c140e78fd69c45d1444"},
113 | ]
114 | python-dateutil = [
115 |     {file = "python-dateutil-2.8.1.tar.gz", hash = "sha256:73ebfe9dbf22e832286dafa60473e4cd239f8592f699aa5adaf10050e6e1823c"},
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117 | ]
118 | pytz = [
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121 | ]
122 | scipy = [
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125 |     {file = "scipy-1.5.2-cp36-cp36m-manylinux1_x86_64.whl", hash = "sha256:07e52b316b40a4f001667d1ad4eb5f2318738de34597bd91537851365b6c61f1"},
126 |     {file = "scipy-1.5.2-cp36-cp36m-win32.whl", hash = "sha256:d56b10d8ed72ec1be76bf10508446df60954f08a41c2d40778bc29a3a9ad9bce"},
127 |     {file = "scipy-1.5.2-cp36-cp36m-win_amd64.whl", hash = "sha256:8e28e74b97fc8d6aa0454989db3b5d36fc27e69cef39a7ee5eaf8174ca1123cb"},
128 |     {file = "scipy-1.5.2-cp37-cp37m-macosx_10_9_x86_64.whl", hash = "sha256:6e86c873fe1335d88b7a4bfa09d021f27a9e753758fd75f3f92d714aa4093768"},
129 |     {file = "scipy-1.5.2-cp37-cp37m-manylinux1_i686.whl", hash = "sha256:a0afbb967fd2c98efad5f4c24439a640d39463282040a88e8e928db647d8ac3d"},
130 |     {file = "scipy-1.5.2-cp37-cp37m-manylinux1_x86_64.whl", hash = "sha256:eecf40fa87eeda53e8e11d265ff2254729d04000cd40bae648e76ff268885d66"},
131 |     {file = "scipy-1.5.2-cp37-cp37m-win32.whl", hash = "sha256:315aa2165aca31375f4e26c230188db192ed901761390be908c9b21d8b07df62"},
132 |     {file = "scipy-1.5.2-cp37-cp37m-win_amd64.whl", hash = "sha256:ec5fe57e46828d034775b00cd625c4a7b5c7d2e354c3b258d820c6c72212a6ec"},
133 |     {file = "scipy-1.5.2-cp38-cp38-macosx_10_9_x86_64.whl", hash = "sha256:fc98f3eac993b9bfdd392e675dfe19850cc8c7246a8fd2b42443e506344be7d9"},
134 |     {file = "scipy-1.5.2-cp38-cp38-manylinux1_i686.whl", hash = "sha256:a785409c0fa51764766840185a34f96a0a93527a0ff0230484d33a8ed085c8f8"},
135 |     {file = "scipy-1.5.2-cp38-cp38-manylinux1_x86_64.whl", hash = "sha256:0a0e9a4e58a4734c2eba917f834b25b7e3b6dc333901ce7784fd31aefbd37b2f"},
136 |     {file = "scipy-1.5.2-cp38-cp38-win32.whl", hash = "sha256:dac09281a0eacd59974e24525a3bc90fa39b4e95177e638a31b14db60d3fa806"},
137 |     {file = "scipy-1.5.2-cp38-cp38-win_amd64.whl", hash = "sha256:92eb04041d371fea828858e4fff182453c25ae3eaa8782d9b6c32b25857d23bc"},
138 |     {file = "scipy-1.5.2.tar.gz", hash = "sha256:066c513d90eb3fd7567a9e150828d39111ebd88d3e924cdfc9f8ce19ab6f90c9"},
139 | ]
140 | six = [
141 |     {file = "six-1.15.0-py2.py3-none-any.whl", hash = "sha256:8b74bedcbbbaca38ff6d7491d76f2b06b3592611af620f8426e82dddb04a5ced"},
142 |     {file = "six-1.15.0.tar.gz", hash = "sha256:30639c035cdb23534cd4aa2dd52c3bf48f06e5f4a941509c8bafd8ce11080259"},
143 | ]
144 | 


--------------------------------------------------------------------------------
/pyproject.toml:
--------------------------------------------------------------------------------
 1 | [tool.poetry]
 2 | name = "banpei"
 3 | version = "0.1.2"
 4 | description = "Anomaly detection library with Python"
 5 | authors = ["Hirofumi Tsuruta"]
 6 | license = "MIT"
 7 | 
 8 | [tool.poetry.dependencies]
 9 | python = "^3.6"
10 | numpy = "^1.19.2"
11 | pandas = "^1.1.2"
12 | scipy = "^1.5.2"
13 | 
14 | [tool.poetry.dev-dependencies]
15 | 
16 | [build-system]
17 | requires = ["poetry>=0.12"]
18 | build-backend = "poetry.masonry.api"
19 | 


--------------------------------------------------------------------------------
/tests/__init__.py:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/tsurubee/banpei/d962460284481f098874f73aae226a3dd9af1dc9/tests/__init__.py


--------------------------------------------------------------------------------
/tests/test_data/davis.csv:
--------------------------------------------------------------------------------
  1 | "x","sex","weight","height","repwt","repht"
  2 | "1","M",77,182,77,180
  3 | "2","F",58,161,51,159
  4 | "3","F",53,161,54,158
  5 | "4","M",68,177,70,175
  6 | "5","F",59,157,59,155
  7 | "6","M",76,170,76,165
  8 | "7","M",76,167,77,165
  9 | "8","M",69,186,73,180
 10 | "9","M",71,178,71,175
 11 | "10","M",65,171,64,170
 12 | "11","M",70,175,75,174
 13 | "12","F",166,57,56,163
 14 | "13","F",51,161,52,158
 15 | "14","F",64,168,64,165
 16 | "15","F",52,163,57,160
 17 | "16","F",65,166,66,165
 18 | "17","M",92,187,101,185
 19 | "18","F",62,168,62,165
 20 | "19","M",76,197,75,200
 21 | "20","F",61,175,61,171
 22 | "21","M",119,180,124,178
 23 | "22","F",61,170,61,170
 24 | "23","M",65,175,66,173
 25 | "24","M",66,173,70,170
 26 | "25","F",54,171,59,168
 27 | "26","F",50,166,50,165
 28 | "27","F",63,169,61,168
 29 | "28","F",58,166,60,160
 30 | "29","F",39,157,41,153
 31 | "30","M",101,183,100,180
 32 | "31","F",71,166,71,165
 33 | "32","M",75,178,73,175
 34 | "33","M",79,173,76,173
 35 | "34","F",52,164,52,161
 36 | "35","F",68,169,63,170
 37 | "36","M",64,176,65,175
 38 | "37","F",56,166,54,165
 39 | "38","M",69,174,69,171
 40 | "39","M",88,178,86,175
 41 | "40","M",65,187,67,188
 42 | "41","F",54,164,53,160
 43 | "42","M",80,178,80,178
 44 | "43","F",63,163,59,159
 45 | "44","M",78,183,80,180
 46 | "45","M",85,179,82,175
 47 | "46","F",54,160,55,158
 48 | "47","M",73,180,NA,NA
 49 | "48","F",49,161,NA,NA
 50 | "49","F",54,174,56,173
 51 | "50","F",75,162,75,158
 52 | "51","M",82,182,85,183
 53 | "52","F",56,165,57,163
 54 | "53","M",74,169,73,170
 55 | "54","M",102,185,107,185
 56 | "55","M",64,177,NA,NA
 57 | "56","M",65,176,64,172
 58 | "57","F",66,170,65,NA
 59 | "58","M",73,183,74,180
 60 | "59","M",75,172,70,169
 61 | "60","M",57,173,58,170
 62 | "61","M",68,165,69,165
 63 | "62","M",71,177,71,170
 64 | "63","M",71,180,76,175
 65 | "64","F",78,173,75,169
 66 | "65","M",97,189,98,185
 67 | "66","F",60,162,59,160
 68 | "67","F",64,165,63,163
 69 | "68","F",64,164,62,161
 70 | "69","F",52,158,51,155
 71 | "70","M",80,178,76,175
 72 | "71","F",62,175,61,171
 73 | "72","M",66,173,66,175
 74 | "73","F",55,165,54,163
 75 | "74","F",56,163,57,159
 76 | "75","F",50,166,50,161
 77 | "76","F",50,171,NA,NA
 78 | "77","F",50,160,55,150
 79 | "78","F",63,160,64,158
 80 | "79","M",69,182,70,180
 81 | "80","M",69,183,70,183
 82 | "81","F",61,165,60,163
 83 | "82","M",55,168,56,170
 84 | "83","F",53,169,52,175
 85 | "84","F",60,167,55,163
 86 | "85","F",56,170,56,170
 87 | "86","M",59,182,61,183
 88 | "87","M",62,178,66,175
 89 | "88","F",53,165,53,165
 90 | "89","F",57,163,59,160
 91 | "90","F",57,162,56,160
 92 | "91","M",70,173,68,170
 93 | "92","F",56,161,56,161
 94 | "93","M",84,184,86,183
 95 | "94","M",69,180,71,180
 96 | "95","M",88,189,87,185
 97 | "96","F",56,165,57,160
 98 | "97","M",103,185,101,182
 99 | "98","F",50,169,50,165
100 | "99","F",52,159,52,153
101 | "100","F",55,155,NA,154
102 | "101","F",55,164,55,163
103 | "102","M",63,178,63,175
104 | "103","F",47,163,47,160
105 | "104","F",45,163,45,160
106 | "105","F",62,175,63,173
107 | "106","F",53,164,51,160
108 | "107","F",52,152,51,150
109 | "108","F",57,167,55,164
110 | "109","F",64,166,64,165
111 | "110","F",59,166,55,163
112 | "111","M",84,183,90,183
113 | "112","M",79,179,79,171
114 | "113","F",55,174,57,171
115 | "114","M",67,179,67,179
116 | "115","F",76,167,77,165
117 | "116","F",62,168,62,163
118 | "117","M",83,184,83,181
119 | "118","M",96,184,94,183
120 | "119","M",75,169,76,165
121 | "120","M",65,178,66,178
122 | "121","M",78,178,77,175
123 | "122","M",69,167,73,165
124 | "123","F",68,178,68,175
125 | "124","F",55,165,55,163
126 | "125","M",67,179,NA,NA
127 | "126","F",52,169,56,NA
128 | "127","F",47,153,NA,154
129 | "128","F",45,157,45,153
130 | "129","F",68,171,68,169
131 | "130","F",44,157,44,155
132 | "131","F",62,166,61,163
133 | "132","M",87,185,89,185
134 | "133","F",56,160,53,158
135 | "134","F",50,148,47,148
136 | "135","M",83,177,84,175
137 | "136","F",53,162,53,160
138 | "137","F",64,172,62,168
139 | "138","F",62,167,NA,NA
140 | "139","M",90,188,91,185
141 | "140","M",85,191,83,188
142 | "141","M",66,175,68,175
143 | "142","F",52,163,53,160
144 | "143","F",53,165,55,163
145 | "144","F",54,176,55,176
146 | "145","F",64,171,66,171
147 | "146","F",55,160,55,155
148 | "147","F",55,165,55,165
149 | "148","F",59,157,55,158
150 | "149","F",70,173,67,170
151 | "150","M",88,184,86,183
152 | "151","F",57,168,58,165
153 | "152","F",47,162,47,160
154 | "153","F",47,150,45,152
155 | "154","F",55,162,NA,NA
156 | "155","F",48,163,44,160
157 | "156","M",54,169,58,165
158 | "157","M",69,172,68,174
159 | "158","F",59,170,NA,NA
160 | "159","F",58,169,NA,NA
161 | "160","F",57,167,56,165
162 | "161","F",51,163,50,160
163 | "162","F",54,161,54,160
164 | "163","F",53,162,52,158
165 | "164","F",59,172,58,171
166 | "165","M",56,163,58,161
167 | "166","F",59,159,59,155
168 | "167","F",63,170,62,168
169 | "168","F",66,166,66,165
170 | "169","M",96,191,95,188
171 | "170","F",53,158,50,155
172 | "171","M",76,169,75,165
173 | "172","F",54,163,NA,NA
174 | "173","M",61,170,61,170
175 | "174","M",82,176,NA,NA
176 | "175","M",62,168,64,168
177 | "176","M",71,178,68,178
178 | "177","F",60,174,NA,NA
179 | "178","M",66,170,67,165
180 | "179","M",81,178,82,175
181 | "180","M",68,174,68,173
182 | "181","M",80,176,78,175
183 | "182","F",43,154,NA,NA
184 | "183","M",82,181,NA,NA
185 | "184","F",63,165,59,160
186 | "185","M",70,173,70,173
187 | "186","F",56,162,56,160
188 | "187","F",60,172,55,168
189 | "188","F",58,169,54,166
190 | "189","M",76,183,75,180
191 | "190","F",50,158,49,155
192 | "191","M",88,185,93,188
193 | "192","M",89,173,86,173
194 | "193","F",59,164,59,165
195 | "194","F",51,156,51,158
196 | "195","F",62,164,61,161
197 | "196","M",74,175,71,175
198 | "197","M",83,180,80,180
199 | "198","M",81,175,NA,NA
200 | "199","M",90,181,91,178
201 | "200","M",79,177,81,178
202 | 


--------------------------------------------------------------------------------
/tests/test_data/periodic_wave.csv:
--------------------------------------------------------------------------------
   1 | x,y
   2 | 0.0,0.06506124188645807
   3 | 0.06295776860901389,0.04729658034331435
   4 | 0.12591553721802778,0.11202163914259894
   5 | 0.18887330582704168,0.2808980113772533
   6 | 0.25183107443605557,0.21703401453127058
   7 | 0.3147888430450695,0.3513686153220881
   8 | 0.37774661165408335,0.3506742148551789
   9 | 0.4407043802630972,0.35497329725517246
  10 | 0.5036621488721111,0.4743351610605363
  11 | 0.5666199174811251,0.5952295954664806
  12 | 0.629577686090139,0.5795088552782903
  13 | 0.6925354546991528,0.6659616848105241
  14 | 0.7554932233081667,0.6950754515518492
  15 | 0.8184509919171806,0.7324297547200177
  16 | 0.8814087605261944,0.7507608160400726
  17 | 0.9443665291352084,0.8167148351701229
  18 | 1.0073242977442223,0.7437610413894092
  19 | 1.0702820663532362,0.8549198134863585
  20 | 1.1332398349622501,0.8155925664665619
  21 | 1.196197603571264,0.9441394659920148
  22 | 1.259155372180278,0.9695647248636922
  23 | 1.3221131407892917,0.9294341040910314
  24 | 1.3850709093983056,1.022867934423886
  25 | 1.4480286780073195,1.0122650400933064
  26 | 1.5109864466163334,1.0128905266035009
  27 | 1.5739442152253473,0.9819248523765491
  28 | 1.6369019838343613,1.0214554668108649
  29 | 1.6998597524433752,1.0443932226069927
  30 | 1.7628175210523889,0.9485983494711171
  31 | 1.8257752896614028,0.9268263807017753
  32 | 1.8887330582704167,1.0093329702748166
  33 | 1.9516908268794306,0.8124110518187613
  34 | 2.0146485954884445,0.7722396160549472
  35 | 2.0776063640974582,0.7835609773735198
  36 | 2.1405641327064724,0.8511133033290953
  37 | 2.203521901315486,0.8140374614071098
  38 | 2.2664796699245002,0.7924411714292987
  39 | 2.329437438533514,0.729570373005938
  40 | 2.392395207142528,0.7579203114526158
  41 | 2.4553529757515418,0.7124217275475572
  42 | 2.518310744360556,0.6416536337626744
  43 | 2.5812685129695696,0.4736707862337616
  44 | 2.6442262815785833,0.4952915469320916
  45 | 2.7071840501875974,0.37756387381106926
  46 | 2.770141818796611,0.33793218557638843
  47 | 2.8330995874056253,0.2524550768807747
  48 | 2.896057356014639,0.24361122350131462
  49 | 2.959015124623653,0.2087710657085111
  50 | 3.021972893232667,0.12326929593478533
  51 | 3.0849306618416805,-0.0030367575584913445
  52 | 3.1478884304506947,-0.08259749466936603
  53 | 3.2108461990597084,-0.10730244171993054
  54 | 3.2738039676687225,-0.17070839652770003
  55 | 3.336761736277736,-0.15471875201345356
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  59 | 3.588592810713792,-0.3774199738061773
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  71 | 4.3440860340219585,-1.0257717546598983
  72 | 4.407043802630972,-1.061295398981982
  73 | 4.470001571239987,-0.9639526635214527
  74 | 4.5329593398490005,-0.9497778312381652
  75 | 4.595917108458014,-0.9627958133909896
  76 | 4.658874877067028,-1.0666174507488742
  77 | 4.7218326456760416,-1.0173059063799097
  78 | 4.784790414285056,-0.964050664959146
  79 | 4.84774818289407,-1.0676405325414937
  80 | 4.9107059515030835,-0.9761354067348362
  81 | 4.973663720112097,-0.9553950187876562
  82 | 5.036621488721112,-0.9017077156650961
  83 | 5.0995792573301255,-1.0486682282583353
  84 | 5.162537025939139,-0.8931333314214245
  85 | 5.225494794548153,-0.7703180203796351
  86 | 5.288452563157167,-0.8696737815042043
  87 | 5.351410331766181,-0.8184917845614543
  88 | 5.414368100375195,-0.7158660945920687
  89 | 5.477325868984209,-0.7595831930339249
  90 | 5.540283637593222,-0.6185801175042738
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  93 | 5.729156943420264,-0.5210875866430151
  94 | 5.792114712029278,-0.5445193375098958
  95 | 5.855072480638292,-0.3675272941616243
  96 | 5.918030249247306,-0.44112785712273733
  97 | 5.98098801785632,-0.3882063279784675
  98 | 6.043945786465334,-0.29382909591757844
  99 | 6.106903555074347,-0.2155238028498885
 100 | 6.169861323683361,-0.04733454258048603
 101 | 6.232819092292376,-0.051257799883730824
 102 | 6.295776860901389,0.02714144811697626
 103 | 6.358734629510403,0.12922246145307048
 104 | 6.421692398119417,0.10317416224951799
 105 | 6.484650166728431,0.17118849116205567
 106 | 6.547607935337445,0.21897938039812126
 107 | 6.610565703946459,0.3608088961738647
 108 | 6.673523472555472,0.46216497607766
 109 | 6.736481241164486,0.4508681051072901
 110 | 6.799439009773501,0.46246155023237234
 111 | 6.862396778382514,0.5213461402417442
 112 | 6.925354546991528,0.5433182584571982
 113 | 6.988312315600542,0.684687823867084
 114 | 7.0512700842095555,0.6416125656053029
 115 | 7.11422785281857,0.7259029054604581
 116 | 7.177185621427584,0.7211353019268203
 117 | 7.2401433900365975,0.7224700256396598
 118 | 7.303101158645611,0.7250662082897227
 119 | 7.366058927254626,0.8388105066884541
 120 | 7.429016695863639,0.9219571140288991
 121 | 7.491974464472653,0.919297086502758
 122 | 7.554932233081667,0.9444382343156823
 123 | 7.6178900016906805,0.9186905889900785
 124 | 7.680847770299695,0.9384656554911998
 125 | 7.743805538908709,0.9405516339839456
 126 | 7.8067633075177225,1.0094471049902523
 127 | 7.869721076126736,1.1022588793946153
 128 | 7.932678844735751,0.9900863468730502
 129 | 7.9956366133447645,0.9215196790023258
 130 | 8.058594381953778,1.0427709094642694
 131 | 8.121552150562792,0.9144877503154833
 132 | 8.184509919171806,0.9737476564567903
 133 | 8.24746768778082,0.9014453147806722
 134 | 8.310425456389833,0.9016797468818217
 135 | 8.373383224998848,1.039749533403869
 136 | 8.436340993607862,0.7977910240150363
 137 | 8.499298762216876,0.7695750477742788
 138 | 8.56225653082589,0.7782470000437081
 139 | 8.625214299434903,0.7159546843525625
 140 | 8.688172068043917,0.6514740459909498
 141 | 8.75112983665293,0.5894209499701796
 142 | 8.814087605261944,0.6029578653771741
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1225 | 76.87143547160596,0.9478049432885836
1226 | 76.93439324021497,1.0377118627383373
1227 | 76.99735100882398,0.9458257133521548
1228 | 77.060308777433,1.0055838544058082
1229 | 77.12326654604202,0.9307520677224037
1230 | 77.18622431465103,1.0392461766273737
1231 | 77.24918208326004,0.9599554456735631
1232 | 77.31213985186905,1.0164481122355107
1233 | 77.37509762047807,0.9138129737522115
1234 | 77.43805538908708,0.8107867900855632
1235 | 77.5010131576961,0.8196418897499012
1236 | 77.5639709263051,0.8085675625703137
1237 | 77.62692869491413,0.86034769611376
1238 | 77.68988646352314,0.887707229553625
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1240 | 77.81580200074117,0.6782981970171006
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1244 | 78.06763307517723,0.4883113687080102
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1246 | 78.19354861239525,0.3351733056193025
1247 | 78.25650638100426,0.23066514725173978
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1303 | 81.78214142310904,0.08648719445246597
1304 | 81.84509919171805,0.19196906034507424
1305 | 81.90805696032706,0.1815361571360649
1306 | 81.97101472893608,0.30191505454342726
1307 | 82.0339724975451,0.3283784777803954
1308 | 82.09693026615412,0.3745857583368566
1309 | 82.15988803476313,0.40261077979600407
1310 | 82.22284580337214,0.5490942359416958
1311 | 82.28580357198115,0.5387317427821043
1312 | 82.34876134059016,0.6727845244115852
1313 | 82.41171910919918,0.665698422390865
1314 | 82.4746768778082,0.7124949486523456
1315 | 82.5376346464172,0.7004810099431976
1316 | 82.60059241502623,0.7451008692416863
1317 | 82.66355018363524,0.8590380685490292
1318 | 82.72650795224425,0.8601648167231684
1319 | 82.78946572085326,0.8977700180489494
1320 | 82.85242348946227,0.9536966565039292
1321 | 82.91538125807129,0.9699012852971933
1322 | 82.9783390266803,0.9449408184171869
1323 | 83.04129679528931,1.1225604296925182
1324 | 83.10425456389834,1.1458276037659811
1325 | 83.16721233250735,1.0184059252682822
1326 | 83.23017010111636,0.9277975164324773
1327 | 83.29312786972537,0.9544822822466946
1328 | 83.35608563833439,1.0236962544600976
1329 | 83.4190434069434,0.9572844348036881
1330 | 83.48200117555241,0.9696278016709399
1331 | 83.54495894416144,0.8576155617292479
1332 | 83.60791671277045,0.9592080541455454
1333 | 83.67087448137946,0.9740529251725718
1334 | 83.73383224998847,0.8255237987144511
1335 | 83.79679001859748,0.8381675155773568
1336 | 83.8597477872065,0.8031023515933571
1337 | 83.92270555581551,0.6592661580447448
1338 | 83.98566332442452,0.7190814096993632
1339 | 84.04862109303355,0.6708151680125611
1340 | 84.11157886164256,0.5832342486650762
1341 | 84.17453663025157,0.5602366918616021
1342 | 84.23749439886058,0.5649648736671287
1343 | 84.30045216746959,0.5161604601486114
1344 | 84.36340993607861,0.45982564906818924
1345 | 84.42636770468762,0.35751818772640864
1346 | 84.48932547329663,0.286273420903462
1347 | 84.55228324190566,0.27469314441986387
1348 | 84.61524101051467,0.18661210424121855
1349 | 84.67819877912368,0.1961182139581084
1350 | 84.74115654773269,0.13996918727401716
1351 | 84.80411431634171,-0.004744448660051349
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1368 | 85.87439638269494,-0.8304782885487442
1369 | 85.93735415130396,-0.9084081499023798
1370 | 86.00031191991297,-0.9484994803184525
1371 | 86.06326968852198,-0.961108204678495
1372 | 86.126227457131,-0.9725409311061092
1373 | 86.18918522574,-1.0034633105029527
1374 | 86.25214299434903,-0.985569583729727
1375 | 86.31510076295804,-1.0214849037047022
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1400 | 87.88904497818339,-0.057821691172311486
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1402 | 88.01496051540141,0.03985470427566041
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1404 | 88.14087605261943,0.1424038469923626
1405 | 88.20383382122846,0.20918601958099622
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1407 | 88.32974935844649,0.38183224591387277
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1409 | 88.45566489566451,0.5113967794931951
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1414 | 88.77045373870958,0.62963111091524
1415 | 88.8334115073186,0.7805169625835141
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1418 | 89.02228481314563,0.9211576424416166
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1425 | 89.46298919340873,1.0267526384819043
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1438 | 90.28144018532592,0.7012036781859126
1439 | 90.34439795393493,0.6587771023874212
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1441 | 90.47031349115295,0.6331273744022925
1442 | 90.53327125976196,0.5078474921038905
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1447 | 90.84806010280704,0.3275963419286358
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1479 | 92.86270869829548,-0.8819330349900264
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1486 | 93.30341307855858,-0.8250376526140598
1487 | 93.36637084716759,-0.7519320345897758
1488 | 93.42932861577661,-0.6976009522888763
1489 | 93.49228638438562,-0.703055576569153
1490 | 93.55524415299463,-0.6050624236609172
1491 | 93.61820192160366,-0.6031822454846197
1492 | 93.68115969021267,-0.5025423039079937
1493 | 93.74411745882168,-0.42376343485076234
1494 | 93.80707522743069,-0.4183223943736186
1495 | 93.8700329960397,-0.3571712555765947
1496 | 93.93299076464872,-0.39975179957526313
1497 | 93.99594853325773,-0.2877722181904546
1498 | 94.05890630186674,-0.21888518581137553
1499 | 94.12186407047577,-0.1331160556055342
1500 | 94.18482183908478,-0.1329173304477317
1501 | 94.24777960769379,-0.06505330393788504
1502 | 


--------------------------------------------------------------------------------
/tests/test_hotelling.py:
--------------------------------------------------------------------------------
 1 | import unittest
 2 | import pandas as pd
 3 | from banpei.hotelling import Hotelling
 4 | 
 5 | 
 6 | class TestHotelling(unittest.TestCase):
 7 | 
 8 |     def setUp(self):
 9 |         self.data = pd.read_csv('tests/test_data/davis.csv')
10 |         self.data_1d = self.data['weight']
11 | 
12 |     def test_hotelling(self):
13 |         expected = 2
14 |         model = Hotelling()
15 |         results = model.detect(self.data_1d, 0.01)
16 |         self.assertEqual(expected, len(results))
17 | 
18 | 
19 | if __name__ == "__main__":
20 |     unittest.main()
21 | 


--------------------------------------------------------------------------------
/tests/test_sst.py:
--------------------------------------------------------------------------------
 1 | import unittest
 2 | import pandas as pd
 3 | from banpei.sst import SST
 4 | 
 5 | 
 6 | class TestSST(unittest.TestCase):
 7 | 
 8 |     def setUp(self):
 9 |         self.raw_data = pd.read_csv('tests/test_data/periodic_wave.csv')
10 |         self.data = self.raw_data['y']
11 | 
12 |     def test_detect_by_svd(self):
13 |         model = SST(w=50)
14 |         results = model.detect(self.data)
15 |         self.assertEqual(len(self.data), len(results))
16 | 
17 |     def test_detect_by_lanczos(self):
18 |         model = SST(w=50)
19 |         results = model.detect(self.data, is_lanczos=True)
20 |         self.assertEqual(len(self.data), len(results))
21 | 
22 |     def test_stream_detect(self):
23 |         model = SST(w=50)
24 |         result = model.stream_detect(self.data)
25 |         self.assertIsInstance(result, float)
26 | 
27 | 
28 | if __name__ == "__main__":
29 |     unittest.main()
30 | 


--------------------------------------------------------------------------------
/tests/test_utils.py:
--------------------------------------------------------------------------------
 1 | import unittest
 2 | import numpy as np
 3 | from banpei.utils import power_method
 4 | 
 5 | 
 6 | class TestUtils(unittest.TestCase):
 7 | 
 8 |     def setUp(self):
 9 |         self.A = np.array([[1, 2, 3],
10 |                            [4, 5, 6],
11 |                            [7, 8, 9],
12 |                            [10, 11, 12]])
13 | 
14 |     def test_power_method(self):
15 |         u, s, v = power_method(self.A, iter_num=1)
16 |         self.assertEqual(len(u), self.A.shape[0])
17 |         self.assertEqual(len(v), self.A.shape[1])
18 | 
19 | if __name__ == "__main__":
20 |     unittest.main()
21 | 


--------------------------------------------------------------------------------