├── .gitignore
├── LICENSE.txt
├── README.md
├── anoms.py
├── breakout.py
├── requirements.txt
├── setup.py
├── src
├── edm-multi.cpp
├── edm-multi.h
├── edm-per.cpp
├── edm-per.h
├── edmTail.cpp
├── edmTail.h
├── edmx.cpp
├── edmx.h
├── helper.cpp
├── helper.h
└── pywrapper.cpp
└── tests
├── __init__.py
├── anom_breakout_data.txt
├── anoms_test.py
├── breakout_test.py
├── expected_both.txt
├── expected_longterm.txt
├── expected_longterm_onlylast.txt
├── expected_neg.txt
├── expected_onlylast.txt
├── expected_pos.txt
├── expected_threshold_med.txt
├── expected_threshold_p95.txt
├── expected_threshold_p99.txt
├── raw_data.txt
└── scribe_data.txt
/.gitignore:
--------------------------------------------------------------------------------
1 | *.pyc
2 | *~
3 | *.iml
4 | build/
5 | dist/
6 | *.egg-info/
7 | *.so
8 | *.swp
9 |
--------------------------------------------------------------------------------
/LICENSE.txt:
--------------------------------------------------------------------------------
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--------------------------------------------------------------------------------
/README.md:
--------------------------------------------------------------------------------
1 | # AnomalyDetection and BreakoutDetection in python
2 | This is a python implementation of Twitter's AnomalyDetection and BreakoutDetection.
3 |
4 | ## Install
5 | The dependencies contain C++ and Fortran code, so that you need gcc installed.
6 | Checkout the code, enter the folder and run:
7 | ```
8 | pip install -r requirements.txt
9 | ```
10 | When use this as a library, please include the line for "pyloess" from "requirements.txt" in your "requirements.txt".
11 |
12 | ## Usage
13 | The parameters are the same as the AnomalyDetectionVec in Twitter's AnomalyDetection (except the plot related ones).
14 | You need to put your time series data into a list of float numbers:
15 | ```
16 | from anoms import detect_anoms
17 | from breakout import detect_breakout
18 |
19 | x = list()
20 |
21 | \# put the data into x
22 |
23 | res = detect_anoms(x, max_anoms=0.02, alpha=0.01, direction='both')
24 | ```
25 | `res` will be a list of int numbers, consists the index of detected anomalies in `x`.
26 | If `e_value=True` is set, `res` will be a tuple,
27 | whose first value is the list of index of detected anomalies
28 | and the second value is the list of expected values.
29 | ```
30 | res = detect_breakout(x, min_size=24, method='multi', beta=0.001, degree=1)
31 | ```
32 | `res` will be a list of int numbers, consists the index of detected breakout in `x`.
33 |
--------------------------------------------------------------------------------
/anoms.py:
--------------------------------------------------------------------------------
1 | import numpy as np
2 | from pyloess import stl
3 | from scipy.stats import t
4 | from pandas import Series
5 | from math import floor
6 | from breakout import detect_breakout
7 | import logging
8 |
9 | logger = logging.getLogger('indeed.anoms')
10 |
11 |
12 | def detect_anoms(x, period, max_anoms=0.10, alpha=0.05, direction='both', longterm_period=None, only_last=None,
13 | threshold=None, e_value=False, breakout_kwargs=None):
14 | """
15 | Anomaly Detection Using Seasonal Hybrid ESD Test.
16 | :param x: a list of floats, which consists of the observations.
17 | :param period: int, the number of observations in a single period.
18 | :param max_anoms: float in (0, 0.49]. Maximum number of anomalies that S-H-ESD will detect as a percentage of the
19 | data
20 | :param alpha: float. The level of statistical significance with which to accept or reject anomalies.
21 | :param direction: string. Directionality of the anomalies to be detected. Options are: 'pos', 'neg', 'both'.
22 | 'pos' only reports positive going anomalies, 'neg' only reports negative going anomalies,
23 | 'both' report anomalies on both direction,
24 | :param longterm_period: int. Split x into lists of given size, and perform anomaly detection on them
25 | individually.
26 | :param only_last: int. Find and report anomalies only within a length in the tail of the time series.
27 | :param threshold: string. Only report positive going anomalies above the threshold specified.
28 | Options are: None, 'med_max', 'p95' and 'p99'.
29 | :param e_value: boolean. Returns an additional list containing the expected value.
30 | :param breakout_kwargs: dict. If given, use it as parameter to call breakout detection to improve the trends.
31 | :return: a list of int, consists of the index of the anomalies in x.
32 | If e_value is set to True, a list of float is returned as the second return value, which consists the
33 | expected values of each detected anomaly.
34 | """
35 | if max_anoms > 0.49 or max_anoms <= 0:
36 | raise ValueError("max_anoms must be >0 and <= 0.49")
37 | if alpha <= 0:
38 | raise ValueError("alpha must greater than 0.")
39 | if longterm_period is None:
40 | longterm_period = len(x)
41 | for v in x:
42 | if np.isnan(v):
43 | raise ValueError("data contains NaN value.")
44 | ret = set()
45 | e_values = None
46 | if e_value:
47 | e_values = [None] * len(x) # To keep the expected values when e_value is set.
48 | for window_start in xrange(0, len(x), longterm_period):
49 | # If the data is too long, split the data into smaller windows, and do the anomaly detection on each window.
50 | window_end = min(len(x), window_start + longterm_period)
51 | if logger.isEnabledFor(logging.DEBUG):
52 | logger.debug("Start to process window: window_start=%s, window_end=%s" %
53 | (window_start, window_end))
54 | # If the window size doesn't divide the total size, the last window doesn't have enough data.
55 | # In this case, adjust the start position of last window to make it having same size as previous windows.
56 | if window_end - window_start < longterm_period:
57 | window_start = window_end - longterm_period
58 | if logger.isEnabledFor(logging.DEBUG):
59 | logger.debug("The last window doesn't contain enough length of data. "
60 | "Adjusted the starting index. window_start=%s, window_end=%s" % (window_start, window_end))
61 | window_x = x[window_start:window_end]
62 | if len(window_x) < period * 2:
63 | raise ValueError("Anom detection needs at least 2 periods worth of data.")
64 | window_ret = _detect_anomaly_for_one_window(window_x, period, max_anoms, alpha, direction, e_values,
65 | window_start, breakout_kwargs)
66 | if threshold:
67 | window_ret = _post_processing_threshold(window_x, period, window_ret, threshold)
68 | ret = ret.union(window_ret)
69 | if only_last:
70 | ret = _post_processing_only_last(x, ret, only_last)
71 | ret = sorted(ret)
72 | if e_value:
73 | return ret, map(lambda i: e_values[i], ret)
74 | return ret
75 |
76 |
77 | def _get_trends_by_median(x):
78 | median = np.median(x)
79 | return [median] * len(x)
80 |
81 |
82 | def _get_trends_by_breakout_detection(x, kwargs):
83 | # divide the data into parts using breakout detection, using the median of each part as the trend.
84 | ret_list = detect_breakout(x, **kwargs)
85 | if logger.isEnabledFor(logging.DEBUG):
86 | logger.debug("detect_breakout result: %s" % ret_list)
87 | last_loc = len(x)
88 | if last_loc not in ret_list:
89 | ret_list.append(last_loc)
90 | prev_loc = 0
91 | trends = []
92 | for loc in ret_list:
93 | median = np.median(x[prev_loc:loc])
94 | trends.extend([median] * (loc - prev_loc))
95 | prev_loc = loc
96 | if logger.isEnabledFor(logging.DEBUG):
97 | logger.debug("detect_breakout trends: %s length=%s" % (trends, len(trends)))
98 | return trends
99 |
100 |
101 | def _detect_anomaly_for_one_window(x, period, max_anoms, alpha, direction, e_values, window_start, breakout_kwargs):
102 | # The core part of anomaly detection:
103 | # 1. Use STL to perform seasonal decomposition.
104 | # parameters are copied from R's stl()
105 | stl_ret = stl(x, np=period, ns=len(x) * 10 + 1, isdeg=0, robust=True, ni=1, no=15)
106 | # 2. Calculate residuals using seasonal from STL result and median as the trends.
107 | seasons = stl_ret['seasonal']
108 | if e_values: # store the expected values if e_value is set
109 | trends = stl_ret['trend']
110 | for i in range(0, len(x)):
111 | if e_values[window_start + i] is None:
112 | e_values[window_start + i] = floor(seasons[i] + trends[i])
113 | if breakout_kwargs:
114 | trends = _get_trends_by_breakout_detection(x, breakout_kwargs)
115 | else:
116 | trends = _get_trends_by_median(x)
117 | residuals = [x[i] - seasons[i] - trends[i] for i in range(0, len(x))]
118 | # 3. Use ESD to find out outliers from residuals. These outliers' corresponding values in x are the anomalies
119 | max_anom_num = max(1, int(len(x) * max_anoms))
120 | anom_index = _esd(residuals, max_anom_num, alpha, direction=direction)
121 | ret = set()
122 | for anom_i in anom_index:
123 | ret.add(window_start + anom_i) # convert the index to the index in x
124 | return ret
125 |
126 |
127 | def _post_processing_threshold(x, period, ret, threshold):
128 | # The threshold is calculated from the max values of each window.
129 | period_maxs = []
130 | for i in xrange(0, len(x), period):
131 | period_maxs.append(max(x[i: min(len(x), i + period)]))
132 | thresh = 0
133 | if threshold == 'med_max':
134 | thresh = np.median(period_maxs)
135 | elif threshold == 'p95':
136 | thresh = np.percentile(period_maxs, 95)
137 | elif threshold == 'p99':
138 | thresh = np.percentile(period_maxs, 99)
139 | if logger.isEnabledFor(logging.DEBUG):
140 | logger.debug("threshold is True. threshold=%s, thresh=%s" % (threshold, thresh))
141 | ret = set(filter(lambda index: x[index] >= thresh, ret))
142 | return ret
143 |
144 |
145 | def _post_processing_only_last(x, ret, only_last):
146 | last_period_start = len(x) - only_last
147 | if logger.isEnabledFor(logging.DEBUG):
148 | logger.debug("only_last is set. Will remove all anomalies before index %s." % last_period_start)
149 | ret = set(filter(lambda value: value >= last_period_start, ret))
150 | return ret
151 |
152 |
153 | _MAD_CONSTANT = 1.4826 # a magic number copied from R's mad() function
154 |
155 |
156 | def _esd(x, max_outlier, alpha, direction):
157 | """
158 | The ESD test using median and MAD in the calculation of the test statistic.
159 | """
160 | x = Series(x)
161 | n = len(x)
162 | outlier_index = []
163 | for i in range(1, max_outlier + 1):
164 | median = x.median()
165 | mad = np.median([abs(value - median) for value in x]) * _MAD_CONSTANT
166 | if mad == 0:
167 | break
168 | if direction == 'both':
169 | ares = x.map(lambda value: abs(value - median) / mad)
170 | elif direction == 'pos':
171 | ares = x.map(lambda value: (value - median) / mad)
172 | elif direction == 'neg':
173 | ares = x.map(lambda value: (median - value) / mad)
174 | r_idx = ares.idxmax()
175 | r = ares[r_idx]
176 | if direction == 'both':
177 | p = 1.0 - alpha / (2 * (n - i + 1))
178 | else:
179 | p = 1.0 - alpha / (n - i + 1)
180 | crit = t.ppf(p, n-i-1)
181 | lam = (n-i)*crit / np.sqrt((n-i-1+crit**2) * (n-i+1))
182 | if logger.isEnabledFor(logging.DEBUG):
183 | logger.debug("%s/%s outlier. median=%s, mad=%s, r_idx=%s, r=%s, crit=%s, lam=%s" %
184 | (i, max_outlier, median, mad, r_idx, r, crit, lam))
185 | if r > lam:
186 | outlier_index.append(r_idx)
187 | x = x.drop(r_idx)
188 | else:
189 | # The r keeps decreasing while lam keeps increasing. Therefore, when r is less than lam for the first time,
190 | # we can stop.
191 | break
192 | return outlier_index
193 |
--------------------------------------------------------------------------------
/breakout.py:
--------------------------------------------------------------------------------
1 | from edm import edm_multi, edm_x, edm_tail, edm_percent
2 | import numpy as np
3 | import logging
4 |
5 | logger = logging.getLogger("indeed.breakout")
6 |
7 | _EDM_TAIL_QUANT = 0.5
8 |
9 |
10 | def detect_breakout(z, min_size=30, method='amoc', alpha=2, exact=True, sig_level=0.05, nperm=0,
11 | degree=1, beta=None, percent=None):
12 | """
13 | Breakout Detector: Energy Divisive with Medians
14 | A technique for robustly, i.e., in the presence of anomalies, detecting single or multiple change points in
15 | univariate time series.
16 | :param z: list of floats. The input time series.
17 | :param min_size: int. The minimum number of observations between change points.
18 | :param method: string. Method must be one of either 'amoc' (At Most One Change) or
19 | 'multi' (Multiple Changes). For 'amoc' at most one change point location will be returned.
20 | :param alpha: float in (0, 2]. For 'amoc' method. The alpha parameter used to weight the distance
21 | between observations.
22 | :param exact: boolean. For 'amoc' method. True to use truemedians, False to use approximate medians
23 | when determining change points.
24 | :param sig_level: float in (0, 1). For 'amoc' method. Once a change point is found its statistical significance is
25 | determined through a hypothesis test. This is the significance.
26 | :param nperm: int >= 0. For 'amoc' method. The number of permutations to perform in order to obtain an approximate
27 | p-value. If 0 then then permutation test is not performed.
28 | :param degree: int, can take the values 0, 1 or 2. For 'multi' method. The degree of the penalization polynomial.
29 | :param beta: float. For 'multi' method. Used to further control the amount of penalization.
30 | :param percent: float. For 'multi' method. This value specifies the minimum percent change in the goodness of fit
31 | statistic to consider adding an additional change point.
32 | :return: list of int, containing the index of change points.
33 | """
34 | if not isinstance(min_size, int) or min_size < 2:
35 | raise ValueError("min_size must be an int >= 2.")
36 | if method == 'amoc':
37 | multi = False
38 | if alpha > 2 or alpha <= 0:
39 | raise ValueError("alpha must be in the interval (0, 2]")
40 | if sig_level <= 0 or sig_level >= 1:
41 | raise ValueError("sig_level must be in interval (0, 1)")
42 | if not isinstance(nperm, int) or nperm < 0:
43 | raise ValueError("nperm must be an int greater than 0.")
44 | elif method == 'multi':
45 | multi = True
46 | if degree not in [0, 1, 2]:
47 | raise ValueError("degree must be 0, 1 or 2.")
48 | if beta is None and percent is None:
49 | raise ValueError("beta and percent can not be both None.")
50 | else:
51 | raise ValueError("method must be 'amoc' or 'multi'")
52 | for value in z:
53 | if np.isnan(value):
54 | raise ValueError("data contains NaN.")
55 | if not z:
56 | return []
57 | z_max = max(z)
58 | z_min = min(z)
59 | distance = z_max - z_min
60 | if distance == 0:
61 | return []
62 | z = [float(value - z_min) / distance for value in z]
63 | if multi:
64 | return _detect_multiple_breakout(z, min_size, beta, percent, degree)
65 | else:
66 | ret = _detect_single_breakout(z, min_size, exact, alpha, nperm, sig_level)
67 | return [ret] if ret is not None else []
68 |
69 |
70 | def _detect_multiple_breakout(z, min_size, beta, percent, degree):
71 | if beta is None:
72 | if logger.isEnabledFor(logging.DEBUG):
73 | logger.debug("calling edm_percent")
74 | return edm_percent(z, min_size, percent, degree)
75 | if logger.isEnabledFor(logging.DEBUG):
76 | logger.debug("calling edm_multi")
77 | return edm_multi(z, min_size, beta, degree)
78 |
79 |
80 | def _detect_single_breakout(z, min_size, exact, alpha, nperm, sig_level):
81 | if exact:
82 | if logger.isEnabledFor(logging.DEBUG):
83 | logger.debug("calling edm_x")
84 | ret, stat = edm_x(z, min_size, alpha)
85 | else:
86 | if logger.isEnabledFor(logging.DEBUG):
87 | logger.debug("calling edm_tail")
88 | ret, stat = edm_tail(z, min_size, alpha, _EDM_TAIL_QUANT)
89 | if nperm == 0:
90 | return ret
91 | p_val = _permutation_test(z, min_size, stat, exact, alpha, nperm)
92 | return ret if p_val <= sig_level else None
93 |
94 |
95 | def _permutation_test(z, min_size, stat, exact, alpha, nperm):
96 | over = 1
97 | for i in range(0, nperm):
98 | z_perm = list(np.random.permutation(z))
99 | if exact:
100 | if logger.isEnabledFor(logging.DEBUG):
101 | logger.debug("calling edm_x for nperm %s" % i)
102 | _, stat_perm = edm_x(z_perm, min_size, alpha)
103 | else:
104 | if logger.isEnabledFor(logging.DEBUG):
105 | logger.debug("calling edm_tail for nperm %s" % i)
106 | _, stat_perm = edm_tail(z_perm, min_size, alpha, _EDM_TAIL_QUANT)
107 | if stat_perm > stat:
108 | over += 1
109 | if logger.isEnabledFor(logging.DEBUG):
110 | logger.debug("over=%s, stat_perm=%s, stat=%s" % (over, stat_perm, stat))
111 | p_val = float(over) / (nperm + 1)
112 | if logger.isEnabledFor(logging.DEBUG):
113 | logger.debug("over=%s, p_val=%s" % (over, p_val))
114 | return p_val
115 |
116 |
--------------------------------------------------------------------------------
/requirements.txt:
--------------------------------------------------------------------------------
1 | -e git+git://github.com/andreas-h/pyloess.git@7415090e00c3987eecc44be2efcfbdaf038656e0#egg=pyloess
2 | -e .
3 |
--------------------------------------------------------------------------------
/setup.py:
--------------------------------------------------------------------------------
1 | import os
2 | from setuptools import setup, Extension
3 |
4 |
5 | def read(fname):
6 | return open(os.path.join(os.path.dirname(__file__), fname)).read()
7 |
8 | # pyloess requires numpy already installed before its installation.
9 | try:
10 | import numpy
11 | except ImportError:
12 | import pip
13 | pip.main(['install', 'numpy>=1.9.2'])
14 |
15 |
16 | _CPP_PATH = './src/'
17 | sources = []
18 | for filename in ['pywrapper.cpp', 'edm-multi.cpp', 'edm-per.cpp', 'edmTail.cpp', 'edmx.cpp', 'helper.cpp']:
19 | source_file = _CPP_PATH + filename
20 | sources.append(_CPP_PATH + filename)
21 |
22 | module = Extension('edm', sources=sources)
23 |
24 | setup(
25 | name="anomaly-detection",
26 | version="0.0.1",
27 | description="A python implementation of https://github.com/twitter/AnomalyDetection",
28 | py_modules=['anoms', 'breakout'],
29 | install_requires=[
30 | 'pandas>=0.12.0',
31 | 'scipy>=0.15.1',
32 | 'numpy>=1.9.2',
33 | 'pyloess'
34 | ],
35 | long_description=read('README.md'),
36 | ext_modules=[module]
37 | )
38 |
--------------------------------------------------------------------------------
/src/edm-multi.cpp:
--------------------------------------------------------------------------------
1 | #include
2 | #include
3 | #include
4 | #include
5 | #include"helper.h"
6 |
7 | //Z: time series
8 | //min_size: minimum segment size
9 | //beta: penalization term for the addition of a change point
10 |
11 | // [[Rcpp::export]]
12 | extern "C" std::vector EDM_multi(const std::vector& Z, const int min_size=24, double beta=0, const int degree=0){
13 |
14 | //identify which type of penalization to use
15 | double (*G)(double);
16 | switch(degree){
17 | case 1: G=Linear;
18 | break;
19 | case 2: G=Quadratic;
20 | break;
21 | default: G=Const;
22 | break;
23 | }
24 |
25 | int n = Z.size();
26 | if(beta < 0)//assume that beta is a positive number
27 | beta = -beta;
28 | std::vector prev(n+1,0);//store optimal location of previous change point
29 | std::vector number(n+1,0);//store the number of change points in optimal segmentation
30 | std::vector F(n+1,-3);//store optimal statistic value
31 | //F[s] is calculated using observations { Z[0], Z[1], ..., Z[s-1] }
32 |
33 | //trees used to store the "upper half" of the considered observations
34 | std::multiset right_min, left_min;
35 | //trees used to store the "lower half" of the considered observations
36 | std::multiset > right_max, left_max;
37 |
38 | //Iterate over possible locations for the last change
39 | for(int s=2*min_size; s prev[t-1]){
59 | for(int i=prev[t-1]; i F[s]){
73 | number[s] = number[t] + 1;
74 | F[s] = tmp;
75 | prev[s] = t;
76 | }
77 | }
78 | }
79 |
80 | //obtain list of optimal change point estimates
81 | std::vector ret;
82 | int at = n;
83 | while(at){
84 | if(prev[at])//don't insert 0 as a change point estimate
85 | ret.push_back(prev[at]);
86 | at = prev[at];
87 | }
88 | sort(ret.begin(),ret.end());
89 | return ret;
90 | }
91 |
--------------------------------------------------------------------------------
/src/edm-multi.h:
--------------------------------------------------------------------------------
1 | #ifndef _EDM_MULTI_
2 | #define _EDM_MULTI_
3 | #include
4 |
5 |
6 | extern "C" std::vector EDM_multi(const std::vector& Z, const int min_size, double beta, const int degree);
7 |
8 | #endif
9 |
--------------------------------------------------------------------------------
/src/edm-per.cpp:
--------------------------------------------------------------------------------
1 | /*
2 | Penalizes based on percent chagne in the statistic value.
3 | Linear penalty means that each new breakout must result in an at least X% increast
4 | Quadratic penalty means that each new brekaout must result in at least an (X*k)% increase for k breakouts
5 | */
6 |
7 | #include
8 | #include
9 | #include
10 | #include
11 | #include"helper.h"
12 |
13 | // [[Rcpp::export]]
14 | extern "C" std::vector EDM_percent(const std::vector& Z, const int min_size=24, const double percent=0, const int degree=0){
15 | //Z: time series
16 | //min_size: minimum segment size
17 | //beta: penalization term for the addition of a change point
18 |
19 | //identify which type of penalization to use
20 | double (*G)(double);
21 | switch(degree){
22 | case 1: G=Linear;
23 | break;
24 | case 2: G=Quadratic;
25 | break;
26 | default: G=Const;
27 | break;
28 | }
29 |
30 | int n = Z.size();
31 |
32 | std::vector prev(n+1,0);//store optimal location of previous change point
33 | std::vector number(n+1,0);//store the number of change points in optimal segmentation
34 | std::vector F(n+1,0);//store optimal statistic value
35 | //F[s] is calculated using observations { Z[0], Z[1], ..., Z[s-1] }
36 |
37 | //trees used to store the "upper half" of the considered observations
38 | std::multiset right_min, left_min;
39 | //trees used to store the "lower half" of the considered observations
40 | std::multiset > right_max, left_max;
41 |
42 | //Iterate over possible locations for the last change
43 | for(int s=2*min_size; s prev[t-1]){
63 | for(int i=prev[t-1]; i(s-prev[t]),2) );
74 | double tmp = F[t] + normalize * std::pow(static_cast(left_median - right_median),2);
75 | //Find best location for change point. check % condition later
76 | if(tmp > F[s]){
77 | number[s] = number[t] + 1;
78 | F[s] = tmp;
79 | prev[s] = t;
80 | }
81 | }
82 | //check to make sure we meet the percent change requirement
83 | if( prev[s]){
84 | if(F[s] - F[prev[s]] < percent*G(number[prev[s]])*F[prev[s]]){
85 | number[s] = number[prev[s]];
86 | F[s] = F[prev[s]];
87 | prev[s] = prev[prev[s]];
88 | }
89 | }
90 | }
91 |
92 | //obtain list of optimal change point estimates
93 | std::vector ret;
94 | int at = n;
95 | while(at){
96 | if(prev[at])//don't insert 0 as a change point estimate
97 | ret.push_back(prev[at]);
98 | at = prev[at];
99 | }
100 | sort(ret.begin(),ret.end());
101 | return ret;
102 | }
103 |
--------------------------------------------------------------------------------
/src/edm-per.h:
--------------------------------------------------------------------------------
1 | #ifndef _EDM_PER_
2 | #define _EDM_PER_
3 | #include
4 |
5 |
6 | extern "C" std::vector EDM_percent(const std::vector& Z, const int min_size, const double percent, const int degree);
7 |
8 | #endif
9 |
--------------------------------------------------------------------------------
/src/edmTail.cpp:
--------------------------------------------------------------------------------
1 | /*
2 | This version calculates the between distance using the delta points around the change point estimate.
3 | */
4 |
5 | #include
6 | #include
7 | #include
8 | #include
9 | #include"helper.h"
10 |
11 | //Class used to hold all the information about the
12 | //breakout location and the interval trees
13 | struct Information{
14 | std::vector A, B, AB;
15 | double best_stat;
16 | int best_loc, best_t2;
17 | int min_size, b;
18 |
19 | Information(int, int);
20 | };
21 |
22 | Information::Information(int bb, int m){
23 | A = std::vector(1<<(bb+1));
24 | B = std::vector(1<<(bb+1));
25 | AB = std::vector(1<<(bb+1));
26 | b = bb;
27 | best_stat = best_loc = best_t2 = -3;
28 | min_size = m;
29 | }
30 |
31 | /*void printInformation(Information& info){
32 | std::cout<<"best_stat: "<& Z, Information& info, int& tau1, double quant, double alpha);
38 | void ForwardUpdate(std::vector& Z, Information& info, int& tau1, double quant, double alpha);
39 |
40 | int GetIndex(int B, double x){
41 | //Get index of leaf node interval containing x
42 | return (int)std::ceil(std::abs(x) * (1<& x, double quant){
46 | //Return approximate quantile based on the interval tree
47 |
48 | int N = x.size();
49 | int k = std::ceil(x[1]*quant);
50 | double l=0, u=1;
51 | int i=1,j;
52 | while(i < N){ //Make sure that we do not go beyond the array bounds
53 | j = i<<1;
54 | if(j >= N)
55 | break;
56 | if(x[i] == k){//Exactly k elements in this node's subtree. So can terminate early
57 | //Return a weighted combination of the child node medians
58 | double lWeight = x[j]/(x[j]+x[j+1]);
59 | double rWeight = 1 - lWeight;
60 | double lu, rl;
61 | lu = (u+l)/2;
62 | rl = (u+lu)/2;
63 | return lWeight*(quant*(lu-l)+l) + rWeight*(quant*(u-rl)+rl);
64 | }
65 | else if(x[j] >= k){//More than k elements in node's left child's subtree, move to left child
66 | i = j;
67 | u = (l+u)/2;
68 | }
69 | else if(x[j] < k){//Not enough elements in node's left child's subtree, move to right child
70 | k -= x[j];
71 | i = j+1;
72 | l = (l+u)/2;
73 | }
74 | }
75 | return quant*(u-l)+l;
76 | }
77 |
78 | std::vector AddToTree(int B, std::vector& x){
79 | std::vector A(1<<(B+1));
80 | std::vector::iterator i;
81 | for(i = x.begin(); i < x.end(); ++i){//Iterage over items we wish to add to the tree
82 | int index = GetIndex(B,*i);
83 | while(index){
84 | ++A[index];
85 | index /= 2;
86 | }
87 | }
88 | return A;
89 | }
90 |
91 | // [[Rcpp::export]]
92 | extern "C" EDMResult EDM_tail(std::vector& Z, const int min_size=24, const double alpha=2, const double quant=0.5){
93 |
94 | int N = Z.size();
95 | int eps = (int)std::ceil( std::log(N) );
96 | eps = std::max( eps, 10 );
97 |
98 | Information info(eps,min_size);
99 |
100 | int tau1 = info.min_size;
101 | int tau2 = tau1 * 2;
102 |
103 |
104 |
105 | //Populate trees and calculate statistic value for starting configuration of
106 | //2 min_size segments
107 | for(int i=0; i info.best_stat){
167 | info.best_stat = stat;
168 | info.best_loc = tau1;
169 | info.best_t2 = tau2;
170 | }
171 | }
172 |
173 | bool forward_move = false;
174 | //Initial consideration of other possible locations for tau1
175 | while(tau1 < N-min_size){
176 | //"warm start" to update tree and statistic value for other prefix series
177 | if(forward_move){
178 | ForwardUpdate(Z, info, tau1, quant, alpha);
179 | }
180 | else{
181 | BackwardUpdate(Z, info, tau1, quant, alpha);
182 | }
183 | forward_move = !forward_move;
184 | }
185 | return EDMResult(info.best_loc, info.best_stat);
186 | }
187 |
188 | void ForwardUpdate(std::vector& Z, Information& info, int& tau1, double quant, double alpha){
189 |
190 | int min_size = info.min_size;
191 | int tau2 = tau1 + min_size;
192 | ++tau1;
193 | int N = Z.size(), index;
194 | //Update A tree
195 | for(int i=tau1-min_size; i info.best_stat){
281 | info.best_stat = stat;
282 | info.best_loc = tau1;
283 | info.best_t2 = tau2;
284 | }
285 | }
286 | }
287 |
288 | void BackwardUpdate(std::vector& Z, Information& info, int& tau1, double quant, double alpha){
289 |
290 | int min_size = info.min_size;
291 | int tau2 = tau1 + min_size;
292 | ++tau1;
293 | int N = Z.size(), index;
294 | //Update A tree
295 | for(int i=tau1-min_size; i=tau1+min_size; --tau2){
373 | index = GetIndex(info.b,Z[tau2-1]-Z[tau2-2]);
374 | while(index){
375 | --info.B[index];
376 | index /= 2;
377 | }
378 | qb = std::pow( GetQuantile(info.B,quant), alpha);
379 |
380 | double stat = 2*qc - qa - qb;
381 | stat *= (double)(tau2-tau1)*tau1/tau2;
382 |
383 | if(stat > info.best_stat){
384 | info.best_stat = stat;
385 | info.best_loc = tau1;
386 | info.best_t2 = tau2;
387 | }
388 | }
389 | }
390 |
--------------------------------------------------------------------------------
/src/edmTail.h:
--------------------------------------------------------------------------------
1 | #ifndef _EDM_TAIL_
2 | #define _EDM_TAIL_
3 | #include
4 | #include"helper.h"
5 |
6 |
7 | extern "C" EDMResult EDM_tail(std::vector& Z, const int min_size, const double alpha, const double quant);
8 |
9 | #endif
10 |
--------------------------------------------------------------------------------
/src/edmx.cpp:
--------------------------------------------------------------------------------
1 | /*
2 | Robust estimation of 2[mean(X)-mean(Y)]^2 time normalization factor
3 | This is the E-Divisive E-statistic when alpha = 2
4 | Instead of calculating mean(X) we calculate median(X), and similarly for Y
5 | */
6 |
7 |
8 | #include
9 | #include
10 | #include
11 | #include
12 | #include"helper.h"
13 |
14 |
15 | void AddToHeaps(std::priority_queue, std::greater >& m,
16 | std::priority_queue& M, double x);
17 |
18 | double getMedian(const std::priority_queue, std::greater >& m,
19 | const std::priority_queue& M);
20 |
21 | double Median(const std::vector& Z, int a, int b){
22 | //Calculate the median of the values in { Z[i] : a <= i < b }
23 | std::vector x(Z.begin()+a, Z.begin()+b);
24 | int n = x.size(), h=n/2;
25 |
26 | if( n&1){// n is odd
27 | std::nth_element(x.begin(),x.begin()+h,x.end());
28 | return x[h];
29 | }
30 | else{// n is even
31 | double y1,y2;
32 | std::nth_element(x.begin(),x.begin()+h,x.end());
33 | y1 = x[h];
34 | std::nth_element(x.begin(),x.begin()+h-1,x.end());
35 | y2 = x[h-1];
36 | return (y1+y2)/2;
37 | }
38 | }
39 |
40 | // [[Rcpp::export]]
41 | extern "C" EDMResult EDMX(const std::vector& Z, int min_size = 24, double alpha=2){
42 |
43 | alpha = 2; //Not used, just here for uniform funciton signature
44 |
45 | std::priority_queue LeftMax;
46 | std::priority_queue, std::greater > LeftMin;
47 |
48 | double stat = -3, stat_best = -3, t1=0.0, t2;
49 | int tau1, tau2;
50 | int N = Z.size();
51 | for(int i=0; i RightMax;
57 | std::priority_queue, std::greater > RightMin;
58 | double medL = getMedian(LeftMin, LeftMax);
59 |
60 | //Add first set of elements to the heaps for the right segment
61 | for(std::vector::const_iterator i=Z.begin()+tau1; i!=Z.begin()+tau1+min_size-1; ++i)
62 | AddToHeaps(RightMin, RightMax, *i);
63 |
64 | for(tau2=tau1+min_size; tau2 stat_best){
72 | t1 = tau1;
73 | t2 = tau2;
74 | stat_best = stat;
75 | }
76 | }
77 | }
78 | return EDMResult(t1, stat_best);
79 | }
80 |
81 | // Use 2 heaps to keep track of the median (can also be adjusted for other quantiles). One heap
82 | // for the "larger" and one heap for the "smaller" observations. Simple to update for streaming
83 | // data ( O(log n) ) and find median ( O(1) ).
84 |
85 | double getMedian(const std::priority_queue, std::greater >& m,
86 | const std::priority_queue& M){
87 |
88 | if(m.size() > M.size()) // There are an odd number of observations
89 | return m.top();
90 | else if(M.size() > m.size()) // There are an odd number of observations
91 | return M.top();
92 | else // There are an even number of obersations
93 | return (m.top()+M.top())/2;
94 | }
95 |
96 | void AddToHeaps(std::priority_queue, std::greater >& m,
97 | std::priority_queue& M, double x){
98 |
99 | // decide on initial heap to place element into
100 | if(m.empty() || x < m.top())
101 | M.push(x);
102 | else
103 | m.push(x);
104 | // make sure that heaps are balanced
105 | if(m.size() > M.size() + 1){
106 | M.push( m.top() );
107 | m.pop();
108 | }
109 | else if(M.size() > m.size() + 1){
110 | m.push( M.top() );
111 | M.pop();
112 | }
113 | }
114 |
--------------------------------------------------------------------------------
/src/edmx.h:
--------------------------------------------------------------------------------
1 | #ifndef _EDM_X_
2 | #define _EDM_X_
3 | #include
4 | #include"helper.h"
5 |
6 |
7 | extern "C" EDMResult EDMX(const std::vector& Z, const int min_size, const double alpha);
8 |
9 | #endif
10 |
--------------------------------------------------------------------------------
/src/helper.cpp:
--------------------------------------------------------------------------------
1 | #include
2 | #include
3 | #include
4 | #include"helper.h"
5 |
6 | extern double Linear(const double x){ return 1;}
7 | extern double Const(const double x){ return 0;}
8 | extern double Quadratic(const double x){ return 2*x+1;}
9 |
10 |
11 | /*
12 | Use 2 multisets (red-black trees) to keep track of the median. One tree for the larger (m) and
13 | one for the smaller (M) observations. Insertion and deletion in O(log(n)) and find
14 | the median in O(1), additional memory use is O(n).
15 | */
16 |
17 | //insert x into the appropriate tree
18 | extern void insert_element(std::multiset& m, std::multiset >& M, double x){
19 |
20 | if(m.empty() || x < *(m.begin()))
21 | M.insert(x);
22 | else
23 | m.insert(x);
24 | if(m.size() > M.size() + 1){
25 | std::multiset::iterator i;
26 | i = m.begin();
27 | M.insert(*i);
28 | m.erase(m.begin());
29 | }
30 | else if(M.size() > m.size() + 1){
31 | std::multiset >::iterator i;
32 | i = M.begin();
33 | m.insert(*i);
34 | M.erase(M.begin());
35 | }
36 | }
37 |
38 | //given a pair of trees obtain the median
39 | extern double get_median(std::multiset& m, std::multiset >& M){
40 |
41 | if(m.size() > M.size())
42 | return *(m.begin());
43 | else if(M.size() > m.size())
44 | return *(M.begin());
45 | else
46 | return ( *(M.begin()) + *(m.begin()) )/2;
47 | }
48 |
49 | //remove x from the tree, if multiple copies of x exist only remove 1
50 | //since this method is never called by the user directly it is assumed
51 | //that there is at least 1 copy of x
52 | extern void remove_element(std::multiset& m, std::multiset >& M, const double x){
53 |
54 | if(x < *(m.begin())){
55 | std::multiset >::iterator i = M.find(x);
56 | M.erase(i);
57 | }
58 | else{
59 | std::multiset::iterator i = m.find(x);
60 | m.erase(i);
61 | }
62 | if(m.size() > M.size() + 1){
63 | std::multiset::iterator i;
64 | i = m.begin();
65 | M.insert(*i);
66 | m.erase(m.begin());
67 | }
68 | else if(M.size() > m.size() + 1){
69 | std::multiset >::iterator i;
70 | i = M.begin();
71 | m.insert(*i);
72 | M.erase(M.begin());
73 | }
74 | }
75 |
76 | EDMResult::EDMResult(const int best_loc, const double best_stat) {
77 | this->best_loc = best_loc;
78 | this->best_stat = best_stat;
79 | }
80 |
--------------------------------------------------------------------------------
/src/helper.h:
--------------------------------------------------------------------------------
1 | #ifndef my_help_func
2 | #define my_help_func
3 |
4 | #include
5 | #include
6 | #include
7 |
8 |
9 | double get_median(std::multiset&, std::multiset >&);
10 | void insert_element(std::multiset&, std::multiset >&, const double);
11 | void remove_element(std::multiset&, std::multiset >&, const double);
12 |
13 | extern double Linear(const double x);
14 | extern double Const(const double x);
15 | extern double Quadratic(const double x);
16 |
17 | class EDMResult {
18 | public:
19 | int best_loc;
20 | double best_stat;
21 | EDMResult(const int, const double);
22 | };
23 |
24 | #endif
25 |
--------------------------------------------------------------------------------
/src/pywrapper.cpp:
--------------------------------------------------------------------------------
1 | #include
2 | #include
3 | #include"edm-multi.h"
4 | #include"edm-per.h"
5 | #include"edmTail.h"
6 | #include"edmx.h"
7 |
8 |
9 | std::vector to_vector(PyObject *pyList) {
10 | Py_ssize_t list_len = PyList_Size(pyList);
11 | std::vector valueVector;
12 | for(Py_ssize_t i=0; i &ret) {
21 | int ret_len = ret.size();
22 | PyObject *pyRetList = PyList_New(ret_len);
23 | for(int i=0; i Z = to_vector(pyList);
44 | std::vector ret = EDM_multi(Z, min_size, beta, degree);
45 | return to_pylist(ret);
46 | }
47 |
48 | static PyObject* EDM_percent_wrapper(PyObject *self, PyObject *args) {
49 | PyObject *pyList;
50 | int min_size;
51 | double percent;
52 | int degree;
53 | PyArg_ParseTuple(args, "Oidi", &pyList, &min_size, &percent, °ree);
54 | std::vector Z = to_vector(pyList);
55 | std::vector ret = EDM_percent(Z, min_size, percent, degree);
56 | return to_pylist(ret);
57 | }
58 |
59 | static PyObject* EDM_tail_wrapper(PyObject *self, PyObject *args) {
60 | PyObject *pyList;
61 | int min_size;
62 | double alpha;
63 | double quant;
64 | PyArg_ParseTuple(args, "Oidd", &pyList, &min_size, &alpha, &quant);
65 | std::vector Z = to_vector(pyList);
66 | EDMResult ret = EDM_tail(Z, min_size, alpha, quant);
67 | return to_pytuple(ret.best_loc, ret.best_stat);
68 | }
69 |
70 | static PyObject* EDM_x_wrapper(PyObject *self, PyObject *args) {
71 | PyObject *pyList;
72 | int min_size;
73 | double alpha;
74 | PyArg_ParseTuple(args, "Oid", &pyList, &min_size, &alpha);
75 | std::vector Z = to_vector(pyList);
76 | EDMResult ret = EDMX(Z, min_size, alpha);
77 | return to_pytuple(ret.best_loc, ret.best_stat);
78 | }
79 |
80 | static PyMethodDef edmMethods[] = {
81 | {"edm_multi", EDM_multi_wrapper, METH_VARARGS, "EDM Multi"},
82 | {"edm_percent", EDM_percent_wrapper, METH_VARARGS, "EDM Percent"},
83 | {"edm_tail", EDM_tail_wrapper, METH_VARARGS, "EDM Tail"},
84 | {"edm_x", EDM_x_wrapper, METH_VARARGS, "EDM X"},
85 | {NULL, NULL, 0, NULL} /* Sentinel */
86 | };
87 | extern "C" void initedm(void) {
88 | (void) Py_InitModule("edm", edmMethods);
89 | }
90 |
--------------------------------------------------------------------------------
/tests/__init__.py:
--------------------------------------------------------------------------------
1 | import logging
2 | import sys
3 |
4 | logger = logging.getLogger('indeed')
5 | logger.setLevel(logging.DEBUG)
6 | logger.addHandler(logging.StreamHandler(sys.stdout))
7 |
--------------------------------------------------------------------------------
/tests/anom_breakout_data.txt:
--------------------------------------------------------------------------------
1 | 98661
2 | 90838
3 | 115577
4 | 156157
5 | 149326
6 | 137430
7 | 128477
8 | 101070
9 | 89014
10 | 111482
11 | 152514
12 | 140990
13 | 129535
14 | 117823
15 | 107921
16 | 99013
17 | 122146
18 | 157169
19 | 142685
20 | 136091
21 | 132255
22 | 105010
23 | 91416
24 | 109886
25 | 150593
26 | 151661
27 | 137599
28 | 117487
29 | 89129
30 | 75168
31 | 103702
32 | 162456
33 | 201787
34 | 209951
35 | 193875
36 | 169426
37 | 151683
38 | 195004
39 | 243620
40 | 247397
41 | 262154
42 | 246290
43 | 215785
44 | 195394
45 | 233457
46 | 296383
47 | 287097
48 | 287857
49 | 270806
50 | 237375
51 | 207232
52 | 236679
53 | 302098
54 | 286822
55 | 282890
56 | 278101
57 | 238554
58 | 214978
59 | 247097
60 | 308226
61 | 284886
62 | 257507
63 | 232215
64 | 215723
65 | 195825
66 | 257706
67 | 328275
68 | 311134
69 | 308784
70 | 272147
71 | 227808
72 | 201230
73 | 245762
74 | 304675
75 |
--------------------------------------------------------------------------------
/tests/anoms_test.py:
--------------------------------------------------------------------------------
1 | import tests
2 | import unittest
3 | from anoms import detect_anoms
4 | import numpy as np
5 |
6 |
7 | # Test that we can get exactly the same result as Twitter's AnomalyDetection library.
8 | # The 'raw_data.txt' is containing the same data as 'raw_data.R'.
9 | # The expected_*.txt files are containing the same result from 'vec_anom_detection.R' using same parameters.
10 | def read_twitter_raw_data(filename):
11 | x = []
12 | with open(filename, 'r') as f:
13 | for line in f.readlines():
14 | x.append(float(line))
15 | return x
16 |
17 |
18 | def _read_twitter_test_result(filename):
19 | index = []
20 | e_values = []
21 | with open(filename, 'r') as f:
22 | for line in f.readlines():
23 | parts = line.split(' ')
24 | index.append(int(parts[0]) - 1) # The 'index' column. R's array index is from 1, not 0
25 | e_values.append(float(parts[2])) # The 'expected_value' column
26 | return index, e_values
27 |
28 |
29 | class TestDetectAnoms(unittest.TestCase):
30 | def test_seasonal_data(self):
31 | """
32 | An example from real Indeed data. Numbers are the click count of one of Indeed pages.
33 | The last number is an anomaly caused by a holiday.
34 | """
35 | x = [534592, 854369, 868702, 852728, 773757, 618216, 423549, 497898, 836237, 883591, 888337, 818443, 660449,
36 | 482778, 477392, 904671, 943225, 918105, 843145, 685644, 511239, 558484, 894195, 927928, 919406, 852359,
37 | 658974, 473478, 458006, 587811]
38 | anoms_index = detect_anoms(x, 7)
39 | self.assertEqual([29], anoms_index)
40 |
41 | def test_constants(self):
42 | ret = detect_anoms([1] * 1000, 14, direction='both')
43 | self.assertEqual([], ret)
44 |
45 | def test_twitter_data_both(self):
46 | """
47 | Use the same test data from Twitter's library. The result will be exactly the same as Twitter's.
48 | Set the direction=both
49 | """
50 | x = read_twitter_raw_data('tests/raw_data.txt')
51 | expected_index, expected_e_values = _read_twitter_test_result('tests/expected_both.txt')
52 | index, e_values = detect_anoms(x, 1440, max_anoms=0.02, direction='both', e_value=True)
53 | self.assertListEqual(expected_index, index)
54 | self.assertListEqual(expected_e_values, e_values)
55 |
56 | def test_twitter_data_pos(self):
57 | """
58 | Use the same test data from Twitter's library. The result will be exactly the same as Twitter's.
59 | Set the direction=pos
60 | """
61 | x = read_twitter_raw_data('tests/raw_data.txt')
62 | expected_index, expected_e_values = _read_twitter_test_result('tests/expected_pos.txt')
63 | index, e_values = detect_anoms(x, 1440, max_anoms=0.02, direction='pos', e_value=True)
64 | self.assertListEqual(expected_index, index)
65 | self.assertListEqual(expected_e_values, e_values)
66 |
67 | def test_twitter_data_neg(self):
68 | """
69 | Use the same test data from Twitter's library. The result will be exactly the same as Twitter's.
70 | Set the direction=neg
71 | """
72 | x = read_twitter_raw_data('tests/raw_data.txt')
73 | expected_index, expected_e_values = _read_twitter_test_result('tests/expected_neg.txt')
74 | index, e_values = detect_anoms(x, 1440, max_anoms=0.02, direction='neg', e_value=True)
75 | self.assertListEqual(expected_index, index)
76 | self.assertListEqual(expected_e_values, e_values)
77 |
78 | def test_twitter_data_onlylast(self):
79 | """
80 | Use the same test data from Twitter's library. The result will be exactly the same as Twitter's.
81 | Set the only_last=True
82 | """
83 | x = read_twitter_raw_data('tests/raw_data.txt')
84 | expected_index, expected_e_values = _read_twitter_test_result('tests/expected_onlylast.txt')
85 | index, e_values = detect_anoms(x, 1440, max_anoms=0.02, direction='both', only_last=1440, e_value=True)
86 | self.assertListEqual(expected_index, index)
87 | self.assertListEqual(expected_e_values, e_values)
88 |
89 | def test_twitter_data_threshold_med(self):
90 | """
91 | Use the same test data from Twitter's library. The result will be exactly the same as Twitter's.
92 | Set the threshold=med_max
93 | """
94 | x = read_twitter_raw_data('tests/raw_data.txt')
95 | expected_index, expected_e_values = _read_twitter_test_result('tests/expected_threshold_med.txt')
96 | index, e_values = detect_anoms(x, 1440, max_anoms=0.02, direction='both', threshold='med_max', e_value=True)
97 | self.assertListEqual(expected_index, index)
98 | self.assertListEqual(expected_e_values, e_values)
99 |
100 | def test_twitter_data_threshold_p95(self):
101 | """
102 | Use the same test data from Twitter's library. The result will be exactly the same as Twitter's.
103 | Set the threshold=p95
104 | """
105 | x = read_twitter_raw_data('tests/raw_data.txt')
106 | expected_index, expected_e_values = _read_twitter_test_result('tests/expected_threshold_p95.txt')
107 | index, e_values = detect_anoms(x, 1440, max_anoms=0.02, direction='both', threshold='p95', e_value=True)
108 | self.assertListEqual(expected_index, index)
109 | self.assertListEqual(expected_e_values, e_values)
110 |
111 | def test_twitter_data_threshold_p99(self):
112 | """
113 | Use the same test data from Twitter's library. The result will be exactly the same as Twitter's.
114 | Set the threshold=p99
115 | """
116 | x = read_twitter_raw_data('tests/raw_data.txt')
117 | expected_index, expected_e_values = _read_twitter_test_result('tests/expected_threshold_p99.txt')
118 | index, e_values = detect_anoms(x, 1440, max_anoms=0.02, direction='both', threshold='p99', e_value=True)
119 | self.assertListEqual(expected_index, index)
120 | self.assertListEqual(expected_e_values, e_values)
121 |
122 | def test_twitter_data_longterm(self):
123 | """
124 | Use the same test data from Twitter's library. The result will be exactly the same as Twitter's.
125 | Set the longterm_period to 1440 * 7
126 | """
127 | x = read_twitter_raw_data('tests/raw_data.txt')
128 | expected_index, expected_e_values = _read_twitter_test_result('tests/expected_longterm.txt')
129 | index, e_values = detect_anoms(x, 1440, max_anoms=0.02, direction='both', longterm_period=1440 * 7,
130 | e_value=True)
131 | self.assertListEqual(expected_index, index)
132 | self.assertListEqual(expected_e_values, e_values)
133 |
134 | def test_twitter_data_longterm_onlylast(self):
135 | """
136 | Use the same test data from Twitter's library. The result will be exactly the same as Twitter's.
137 | Set the longterm_period to 1440 * 7
138 | """
139 | x = read_twitter_raw_data('tests/raw_data.txt')
140 | expected_index, expected_e_values = _read_twitter_test_result('tests/expected_longterm_onlylast.txt')
141 | index, e_values = detect_anoms(x, 1440, max_anoms=0.02, direction='both', longterm_period=1440 * 7,
142 | only_last=1440, e_value=True)
143 | self.assertListEqual(expected_index, index)
144 | self.assertListEqual(expected_e_values, e_values)
145 |
146 | def test_illegal_data_and_parameters(self):
147 | self.assertRaises(ValueError, detect_anoms, [1] * 1000, 14, max_anoms=0.5)
148 | self.assertRaises(ValueError, detect_anoms, [1] * 1000, 14, max_anoms=0)
149 | self.assertRaises(ValueError, detect_anoms, [1] * 1000, 14, alpha=0)
150 | self.assertRaises(ValueError, detect_anoms, [1] * 27, 14) # time series' length is less than period * 2.
151 | x = [1] * 1000
152 | x[999] = np.nan
153 | self.assertRaises(ValueError, detect_anoms, x, 14)
154 |
155 |
156 | class TestAnomWithBreakout(unittest.TestCase):
157 | def setUp(self):
158 | # A breakout happened in the position 33:
159 | self.data = read_twitter_raw_data('tests/anom_breakout_data.txt')
160 |
161 | def test_anom_without_breakout(self):
162 | # when the breakout just happened, reports it as anomaly, which is correct.
163 | ret = detect_anoms(self.data[3:33], 7, max_anoms=0.01, only_last=1)
164 | self.assertEqual([29], ret)
165 | # after the time windows moved forward, still reports the last data is anomaly, which is bad.
166 | ret = detect_anoms(self.data[8:38], 7, max_anoms=0.01, only_last=1)
167 | self.assertEqual([29], ret)
168 | ret = detect_anoms(self.data[12:42], 7, max_anoms=0.01, only_last=1)
169 | self.assertEqual([29], ret)
170 |
171 | def test_anom_with_breakout(self):
172 | breakout_kwargs = {'min_size': 7, 'method': 'multi', 'beta': 0.008}
173 | # when the breakout just happened, reports it as anomaly, which is correct.
174 | ret = detect_anoms(self.data[3:33], 7, max_anoms=0.01, only_last=1, breakout_kwargs=breakout_kwargs)
175 | self.assertEqual([29], ret)
176 | # after the time window moved forward, detects the breakout and stops reporting the last point as anomaly.
177 | ret = detect_anoms(self.data[8:38], 7, max_anoms=0.01, only_last=1, breakout_kwargs=breakout_kwargs)
178 | self.assertEqual([], ret)
179 | ret = detect_anoms(self.data[12:42], 7, max_anoms=0.01, only_last=1, breakout_kwargs=breakout_kwargs)
180 | self.assertEqual([], ret)
181 |
--------------------------------------------------------------------------------
/tests/breakout_test.py:
--------------------------------------------------------------------------------
1 | import unittest
2 | from tests.anoms_test import read_twitter_raw_data
3 | from breakout import detect_breakout
4 | import numpy as np
5 |
6 |
7 | class TestBreakoutDetection(unittest.TestCase):
8 | def setUp(self):
9 | self.data = read_twitter_raw_data('tests/scribe_data.txt')
10 |
11 | def test_edm_multi(self):
12 | ret_list = detect_breakout(self.data, min_size=24, method='multi', beta=0.001, degree=1)
13 | self.assertEqual([47, 87], ret_list)
14 |
15 | def test_edm_percent(self):
16 | ret_list = detect_breakout(self.data, min_size=24, method='multi', percent=0.1, degree=1)
17 | self.assertEqual([26, 51, 106], ret_list)
18 |
19 | def test_edm_x_no_nperm(self):
20 | ret_list = detect_breakout(self.data, min_size=24, method='amoc', exact=True, sig_level=0.05, nperm=0)
21 | self.assertEqual([95], ret_list)
22 |
23 | def test_edm_x_nperm_has_ret(self):
24 | ret_list = detect_breakout(self.data, min_size=24, method='amoc', exact=True, sig_level=0.9, nperm=10)
25 | self.assertEqual([95], ret_list)
26 |
27 | def test_edm_x_nperm_no_ret(self):
28 | ret_list = detect_breakout(self.data, min_size=24, method='amoc', exact=True, sig_level=0.001, nperm=10)
29 | self.assertEqual([], ret_list)
30 |
31 | def test_edm_tail_no_nperm(self):
32 | ret_list = detect_breakout(self.data, min_size=24, method='amoc', exact=False, sig_level=0.05, nperm=0)
33 | self.assertEqual([47], ret_list)
34 |
35 | def test_edm_tail_nperm_has_ret(self):
36 | ret_list = detect_breakout(self.data, min_size=24, method='amoc', exact=False, sig_level=0.9, nperm=10)
37 | self.assertEqual([47], ret_list)
38 |
39 | def test_edm_tail_nperm_no_ret(self):
40 | ret_list = detect_breakout(self.data, min_size=24, method='amoc', exact=False, sig_level=0.001, nperm=10)
41 | self.assertEqual([], ret_list)
42 |
43 | def test_invalid_parameters(self):
44 | self.assertRaises(ValueError, detect_breakout, self.data, min_size=0,
45 | method='amoc', exact=False, sig_level=0.001, nperm=10)
46 | self.assertRaises(ValueError, detect_breakout, self.data, min_size=1.5,
47 | method='amoc', exact=False, sig_level=0.001, nperm=10)
48 | self.assertRaises(ValueError, detect_breakout, self.data, min_size=30,
49 | method='amoc', exact=False, alpha=0, sig_level=0.001, nperm=10)
50 | self.assertRaises(ValueError, detect_breakout, self.data, min_size=30,
51 | method='amoc', exact=False, alpha=2.1, sig_level=0.001, nperm=10)
52 | self.assertRaises(ValueError, detect_breakout, self.data, min_size=30,
53 | method='amoc', exact=False, sig_level=0.0, nperm=10)
54 | self.assertRaises(ValueError, detect_breakout, self.data, min_size=30,
55 | method='amoc', exact=False, sig_level=1.0, nperm=10)
56 | self.assertRaises(ValueError, detect_breakout, self.data, min_size=30,
57 | method='amoc', exact=False, sig_level=0.001, nperm=-1)
58 | self.assertRaises(ValueError, detect_breakout, self.data, min_size=30,
59 | method='amoc', exact=False, sig_level=0.001, nperm=1.1)
60 | self.assertRaises(ValueError, detect_breakout, self.data, min_size=30,
61 | method='multi', beta=0.008, degree=3)
62 | self.assertRaises(ValueError, detect_breakout, self.data, min_size=30,
63 | method='multi', degree=0)
64 | self.assertRaises(ValueError, detect_breakout, self.data, min_size=30,
65 | method='wrong_method', exact=False, sig_level=0.001, nperm=10)
66 |
67 | def test_empty(self):
68 | ret_list = detect_breakout([], min_size=24, method='multi', beta=0.001, degree=1)
69 | self.assertEqual([], ret_list)
70 |
71 | def test_constant(self):
72 | ret_list = detect_breakout([10] * 100, min_size=24, method='multi', beta=0.001, degree=1)
73 | self.assertEqual([], ret_list)
74 |
75 | def test_nan(self):
76 | self.data[10] = np.nan
77 | self.assertRaises(ValueError, detect_breakout, self.data, min_size=30,
78 | method='amoc', exact=False, sig_level=0.001, nperm=10)
79 |
80 | def test_int_values(self):
81 | # make sure the code still works if the values are int.
82 | z = [int(value) for value in self.data]
83 | ret_list = detect_breakout(z, min_size=24, method='multi', beta=0.001, degree=1)
84 | self.assertEqual([47, 87], ret_list)
85 |
--------------------------------------------------------------------------------
/tests/expected_both.txt:
--------------------------------------------------------------------------------
1 | 125 21.351 129
2 | 5320 193.1036 97
3 | 6224 148.174 96
4 | 7426 52.7478 117
5 | 7428 49.6582 107
6 | 7430 35.6067 104
7 | 7431 32.5045 112
8 | 7432 30.0555 110
9 | 7433 31.2614 107
10 | 7434 30.2551 102
11 | 7435 27.386 102
12 | 7436 28.9807 105
13 | 7437 29.0844 100
14 | 7438 26.9185 101
15 | 7439 26.4621 100
16 | 7440 27.218 99
17 | 7441 40.4268 122
18 | 7442 28.8811 110
19 | 7443 27.1294 109
20 | 7444 26.9913 106
21 | 7445 26.7741 105
22 | 7446 30.6972 111
23 | 7447 27.6085 105
24 | 7448 25.2841 104
25 | 7449 25.2264 104
26 | 7450 25.1927 102
27 | 7451 27.6501 112
28 | 7452 24.8858 106
29 | 7453 24.8099 106
30 | 7454 24.295 103
31 | 7455 24.5221 102
32 | 7456 26.7354 117
33 | 7457 24.7589 108
34 | 7458 25.63 106
35 | 7459 24.6949 100
36 | 7460 23.6518 98
37 | 7461 27.6977 107
38 | 7462 25.8687 104
39 | 7463 24.6654 104
40 | 7464 24.6694 100
41 | 7465 24.4351 98
42 | 7466 25.7723 103
43 | 7467 24.2502 99
44 | 7468 25.0965 96
45 | 7469 23.9362 93
46 | 7470 24.4127 93
47 | 7471 32.8182 114
48 | 7472 27.4586 106
49 | 7473 26.3163 99
50 | 7474 30.3965 99
51 | 7475 26.9516 97
52 | 7476 26.8908 100
53 | 7477 28.9461 97
54 | 7478 28.9742 95
55 | 7479 30.0406 94
56 | 7480 28.8507 93
57 | 7481 32.7811 101
58 | 7482 27.7468 96
59 | 7483 30.8174 101
60 | 7484 28.5658 96
61 | 7485 27.6677 92
62 | 7486 31.0409 102
63 | 7576 154.775 105
64 | 7577 148.862 96
65 | 7583 165.787 94
66 | 7584 167.585 92
67 | 7585 170.349 92
68 | 7586 180.899 93
69 | 7587 170.513 91
70 | 7588 174.678 90
71 | 7589 164.735 88
72 | 7590 178.822 89
73 | 7591 198.326 107
74 | 7592 203.901 100
75 | 7593 200.309 92
76 | 7594 178.491 90
77 | 7595 167.748 90
78 | 7596 183.018 94
79 | 7597 176.769 93
80 | 7598 186.823 91
81 | 7599 183.66 91
82 | 7600 179.276 91
83 | 7601 197.283 96
84 | 7602 191.097 94
85 | 7603 194.67 95
86 | 7604 177.325 91
87 | 7605 173.758 89
88 | 7606 200.816 98
89 | 7607 186.235 92
90 | 7608 185.421 91
91 | 7609 178.958 90
92 | 7610 171.75 88
93 | 7611 203.231 95
94 | 7612 181.354 92
95 | 7613 186.778 87
96 | 7614 175.582 88
97 | 7615 176.125 89
98 | 7616 181.514 90
99 | 7617 175.261 87
100 | 7618 164.719 86
101 | 7621 170.736 106
102 | 7622 151.549 99
103 | 7623 149.412 97
104 | 7624 150.554 94
105 | 7626 149.541 97
106 | 10640 188.2908 88
107 | 13632 56.4691 97
108 | 13633 54.9415 98
109 | 13634 52.0359 93
110 | 13635 47.7313 91
111 | 13636 50.5876 105
112 | 13637 48.2846 99
113 | 13638 44.6438 96
114 | 13639 42.3077 95
115 | 13640 38.8363 94
116 | 13641 41.0145 100
117 | 13642 39.5523 97
118 | 13643 38.9117 96
119 | 13644 37.3052 93
120 | 13645 36.1725 93
121 | 13646 37.515 97
122 | 13647 38.1387 94
123 | 13648 39.5351 91
124 | 13649 38.1834 90
125 | 13650 37.5988 88
126 | 13651 43.6522 105
127 | 13652 47.9571 102
128 | 14348 210 150
129 | 14358 40 154
130 | 14368 250 152
131 | 14378 40 156
132 |
--------------------------------------------------------------------------------
/tests/expected_longterm.txt:
--------------------------------------------------------------------------------
1 | 125 21.351 132
2 | 613 86.6964 112
3 | 2044 138.737 102
4 | 2316 141.448 111
5 | 2973 188.033 157
6 | 5320 193.1036 95
7 | 5732 201.864 171
8 | 5736 198.21 170
9 | 6212 133.898 102
10 | 6218 131.314 94
11 | 6219 136.575 96
12 | 6220 131.547 95
13 | 6221 132.48 102
14 | 6222 135.261 98
15 | 6223 147.37 104
16 | 6224 148.174 98
17 | 6225 129.572 96
18 | 6226 137.275 107
19 | 6227 139.755 103
20 | 6228 131.521 101
21 | 6229 130.279 100
22 | 6231 139.284 108
23 | 6236 129.279 99
24 | 6238 135.028 101
25 | 6239 128.803 99
26 | 6241 153.37 121
27 | 6242 144.616 109
28 | 6243 145.695 110
29 | 6244 147.652 108
30 | 6246 145.458 113
31 | 6253 135.518 105
32 | 7420 72.0985 107
33 | 7421 79.1242 115
34 | 7422 72.3928 110
35 | 7423 77.0147 110
36 | 7424 61.9894 108
37 | 7425 48.589 104
38 | 7426 52.7478 121
39 | 7427 50.9668 108
40 | 7428 49.6582 105
41 | 7429 51.3551 104
42 | 7430 35.6067 102
43 | 7431 32.5045 111
44 | 7432 30.0555 108
45 | 7433 31.2614 107
46 | 7434 30.2551 102
47 | 7435 27.386 101
48 | 7436 28.9807 104
49 | 7437 29.0844 99
50 | 7438 26.9185 99
51 | 7439 26.4621 100
52 | 7440 27.218 97
53 | 7441 40.4268 122
54 | 7442 28.8811 110
55 | 7443 27.1294 109
56 | 7444 26.9913 103
57 | 7445 26.7741 101
58 | 7446 30.6972 106
59 | 7447 27.6085 100
60 | 7448 25.2841 102
61 | 7449 25.2264 100
62 | 7450 25.1927 99
63 | 7451 27.6501 111
64 | 7452 24.8858 104
65 | 7453 24.8099 104
66 | 7454 24.295 102
67 | 7455 24.5221 98
68 | 7456 26.7354 118
69 | 7457 24.7589 106
70 | 7458 25.63 103
71 | 7459 24.6949 98
72 | 7460 23.6518 94
73 | 7461 27.6977 105
74 | 7462 25.8687 101
75 | 7463 24.6654 101
76 | 7464 24.6694 96
77 | 7465 24.4351 96
78 | 7466 25.7723 100
79 | 7467 24.2502 97
80 | 7468 25.0965 93
81 | 7469 23.9362 91
82 | 7470 24.4127 91
83 | 7471 32.8182 112
84 | 7472 27.4586 103
85 | 7473 26.3163 97
86 | 7474 30.3965 96
87 | 7475 26.9516 97
88 | 7476 26.8908 98
89 | 7477 28.9461 96
90 | 7478 28.9742 93
91 | 7479 30.0406 93
92 | 7480 28.8507 91
93 | 7481 32.7811 99
94 | 7482 27.7468 95
95 | 7483 30.8174 100
96 | 7484 28.5658 96
97 | 7485 27.6677 92
98 | 7486 31.0409 102
99 | 7487 42.3687 93
100 | 7488 48.337 94
101 | 7489 47.0822 93
102 | 7490 45.234 93
103 | 7491 54.7834 99
104 | 7575 127.933 89
105 | 7576 154.775 105
106 | 7577 148.862 91
107 | 7578 142.214 91
108 | 7579 138.171 90
109 | 7580 129.121 86
110 | 7581 147.028 95
111 | 7582 143.874 96
112 | 7583 165.787 93
113 | 7584 167.585 89
114 | 7585 170.349 90
115 | 7586 180.899 92
116 | 7587 170.513 88
117 | 7588 174.678 88
118 | 7589 164.735 87
119 | 7590 178.822 87
120 | 7591 198.326 106
121 | 7592 203.901 100
122 | 7593 200.309 91
123 | 7594 178.491 88
124 | 7595 167.748 89
125 | 7596 183.018 93
126 | 7597 176.769 92
127 | 7598 186.823 90
128 | 7599 183.66 88
129 | 7600 179.276 88
130 | 7601 197.283 93
131 | 7602 191.097 92
132 | 7603 194.67 91
133 | 7604 177.325 90
134 | 7605 173.758 87
135 | 7606 200.816 97
136 | 7607 186.235 90
137 | 7608 185.421 90
138 | 7609 178.958 88
139 | 7610 171.75 86
140 | 7611 203.231 93
141 | 7612 181.354 91
142 | 7613 186.778 85
143 | 7614 175.582 86
144 | 7615 176.125 87
145 | 7616 181.514 88
146 | 7617 175.261 85
147 | 7618 164.719 84
148 | 7619 136.325 86
149 | 7620 132.139 85
150 | 7621 170.736 105
151 | 7622 151.549 98
152 | 7623 149.412 95
153 | 7624 150.554 91
154 | 7625 137.062 92
155 | 7626 149.541 96
156 | 8713 200.985 166
157 | 8715 189.45 154
158 | 8717 196.807 159
159 | 8718 193.794 159
160 | 8732 196.37 157
161 | 8734 182.382 146
162 | 8742 184.99 149
163 | 8746 196.799 156
164 | 8751 191.932 151
165 | 8752 190.837 145
166 | 8754 176.116 136
167 | 8755 178.072 137
168 | 8761 193.973 159
169 | 8762 188.091 144
170 | 8763 184.829 144
171 | 8764 179.169 140
172 | 8766 180.098 144
173 | 8767 177.839 141
174 | 8776 184.17 146
175 | 8777 177.707 136
176 | 8784 165.733 130
177 | 8786 174.447 132
178 | 8787 162.432 127
179 | 8791 182.925 142
180 | 8792 172.684 131
181 | 8795 155.527 119
182 | 8796 163.375 127
183 | 8802 154.887 120
184 | 8803 162.636 126
185 | 8811 169.439 128
186 | 8812 160.829 123
187 | 8818 148.581 111
188 | 8821 170.769 132
189 | 8822 156.979 117
190 | 8825 147.02 111
191 | 8827 149.202 112
192 | 8828 148.559 113
193 | 8839 154.461 111
194 | 8841 153.255 116
195 | 8853 143.226 105
196 | 9481 90.8554 118
197 | 10640 188.2908 89
198 | 13636 50.5876 106
199 | 13637 48.2846 102
200 | 13638 44.6438 96
201 | 13639 42.3077 96
202 | 13640 38.8363 94
203 | 13641 41.0145 100
204 | 13642 39.5523 97
205 | 13643 38.9117 97
206 | 13644 37.3052 93
207 | 13645 36.1725 94
208 | 13646 37.515 95
209 | 13647 38.1387 94
210 | 13648 39.5351 92
211 | 13649 38.1834 90
212 | 13651 43.6522 105
213 | 13652 47.9571 103
214 | 14358 40 152
215 | 14368 250 153
216 | 14378 40 153
217 |
--------------------------------------------------------------------------------
/tests/expected_longterm_onlylast.txt:
--------------------------------------------------------------------------------
1 | 13636 50.5876 106
2 | 13637 48.2846 102
3 | 13638 44.6438 96
4 | 13639 42.3077 96
5 | 13640 38.8363 94
6 | 13641 41.0145 100
7 | 13642 39.5523 97
8 | 13643 38.9117 97
9 | 13644 37.3052 93
10 | 13645 36.1725 94
11 | 13646 37.515 95
12 | 13647 38.1387 94
13 | 13648 39.5351 92
14 | 13649 38.1834 90
15 | 13651 43.6522 105
16 | 13652 47.9571 103
17 | 14358 40 152
18 | 14368 250 153
19 | 14378 40 153
20 |
--------------------------------------------------------------------------------
/tests/expected_neg.txt:
--------------------------------------------------------------------------------
1 | 125 21.351 129
2 | 7425 48.589 105
3 | 7426 52.7478 117
4 | 7427 50.9668 107
5 | 7428 49.6582 107
6 | 7430 35.6067 104
7 | 7431 32.5045 112
8 | 7432 30.0555 110
9 | 7433 31.2614 107
10 | 7434 30.2551 102
11 | 7435 27.386 102
12 | 7436 28.9807 105
13 | 7437 29.0844 100
14 | 7438 26.9185 101
15 | 7439 26.4621 100
16 | 7440 27.218 99
17 | 7441 40.4268 122
18 | 7442 28.8811 110
19 | 7443 27.1294 109
20 | 7444 26.9913 106
21 | 7445 26.7741 105
22 | 7446 30.6972 111
23 | 7447 27.6085 105
24 | 7448 25.2841 104
25 | 7449 25.2264 104
26 | 7450 25.1927 102
27 | 7451 27.6501 112
28 | 7452 24.8858 106
29 | 7453 24.8099 106
30 | 7454 24.295 103
31 | 7455 24.5221 102
32 | 7456 26.7354 117
33 | 7457 24.7589 108
34 | 7458 25.63 106
35 | 7459 24.6949 100
36 | 7460 23.6518 98
37 | 7461 27.6977 107
38 | 7462 25.8687 104
39 | 7463 24.6654 104
40 | 7464 24.6694 100
41 | 7465 24.4351 98
42 | 7466 25.7723 103
43 | 7467 24.2502 99
44 | 7468 25.0965 96
45 | 7469 23.9362 93
46 | 7470 24.4127 93
47 | 7471 32.8182 114
48 | 7472 27.4586 106
49 | 7473 26.3163 99
50 | 7474 30.3965 99
51 | 7475 26.9516 97
52 | 7476 26.8908 100
53 | 7477 28.9461 97
54 | 7478 28.9742 95
55 | 7479 30.0406 94
56 | 7480 28.8507 93
57 | 7481 32.7811 101
58 | 7482 27.7468 96
59 | 7483 30.8174 101
60 | 7484 28.5658 96
61 | 7485 27.6677 92
62 | 7486 31.0409 102
63 | 10221 90.3287 133
64 | 10222 87.9705 131
65 | 13632 56.4691 97
66 | 13633 54.9415 98
67 | 13634 52.0359 93
68 | 13635 47.7313 91
69 | 13636 50.5876 105
70 | 13637 48.2846 99
71 | 13638 44.6438 96
72 | 13639 42.3077 95
73 | 13640 38.8363 94
74 | 13641 41.0145 100
75 | 13642 39.5523 97
76 | 13643 38.9117 96
77 | 13644 37.3052 93
78 | 13645 36.1725 93
79 | 13646 37.515 97
80 | 13647 38.1387 94
81 | 13648 39.5351 91
82 | 13649 38.1834 90
83 | 13650 37.5988 88
84 | 13651 43.6522 105
85 | 13652 47.9571 102
86 | 14358 40 154
87 | 14378 40 156
88 |
--------------------------------------------------------------------------------
/tests/expected_onlylast.txt:
--------------------------------------------------------------------------------
1 | 13632 56.4691 97
2 | 13633 54.9415 98
3 | 13634 52.0359 93
4 | 13635 47.7313 91
5 | 13636 50.5876 105
6 | 13637 48.2846 99
7 | 13638 44.6438 96
8 | 13639 42.3077 95
9 | 13640 38.8363 94
10 | 13641 41.0145 100
11 | 13642 39.5523 97
12 | 13643 38.9117 96
13 | 13644 37.3052 93
14 | 13645 36.1725 93
15 | 13646 37.515 97
16 | 13647 38.1387 94
17 | 13648 39.5351 91
18 | 13649 38.1834 90
19 | 13650 37.5988 88
20 | 13651 43.6522 105
21 | 13652 47.9571 102
22 | 14348 210 150
23 | 14358 40 154
24 | 14368 250 152
25 | 14378 40 156
26 |
--------------------------------------------------------------------------------
/tests/expected_pos.txt:
--------------------------------------------------------------------------------
1 | 5320 193.1036 97
2 | 6224 148.174 96
3 | 7576 154.775 105
4 | 7577 148.862 96
5 | 7578 142.214 94
6 | 7581 147.028 97
7 | 7583 165.787 94
8 | 7584 167.585 92
9 | 7585 170.349 92
10 | 7586 180.899 93
11 | 7587 170.513 91
12 | 7588 174.678 90
13 | 7589 164.735 88
14 | 7590 178.822 89
15 | 7591 198.326 107
16 | 7592 203.901 100
17 | 7593 200.309 92
18 | 7594 178.491 90
19 | 7595 167.748 90
20 | 7596 183.018 94
21 | 7597 176.769 93
22 | 7598 186.823 91
23 | 7599 183.66 91
24 | 7600 179.276 91
25 | 7601 197.283 96
26 | 7602 191.097 94
27 | 7603 194.67 95
28 | 7604 177.325 91
29 | 7605 173.758 89
30 | 7606 200.816 98
31 | 7607 186.235 92
32 | 7608 185.421 91
33 | 7609 178.958 90
34 | 7610 171.75 88
35 | 7611 203.231 95
36 | 7612 181.354 92
37 | 7613 186.778 87
38 | 7614 175.582 88
39 | 7615 176.125 89
40 | 7616 181.514 90
41 | 7617 175.261 87
42 | 7618 164.719 86
43 | 7619 136.325 87
44 | 7621 170.736 106
45 | 7622 151.549 99
46 | 7623 149.412 97
47 | 7624 150.554 94
48 | 7626 149.541 97
49 | 10640 188.2908 88
50 | 14348 210 150
51 | 14368 250 152
52 |
--------------------------------------------------------------------------------
/tests/expected_threshold_med.txt:
--------------------------------------------------------------------------------
1 | 7592 203.901 100
2 | 7593 200.309 92
3 | 7606 200.816 98
4 | 7611 203.231 95
5 | 14348 210 150
6 | 14368 250 152
7 |
--------------------------------------------------------------------------------
/tests/expected_threshold_p95.txt:
--------------------------------------------------------------------------------
1 | 14368 250 152
2 |
--------------------------------------------------------------------------------
/tests/expected_threshold_p99.txt:
--------------------------------------------------------------------------------
1 | 14368 250 152
2 |
--------------------------------------------------------------------------------
/tests/scribe_data.txt:
--------------------------------------------------------------------------------
1 | 105.083333333333
2 | 90.9
3 | 763.9
4 | 83.3666666666667
5 | 78.3666666666667
6 | 80.5833333333333
7 | 76.3666666666667
8 | 210.983333333333
9 | 78
10 | 77.5166666666667
11 | 83.0166666666667
12 | 89.2333333333333
13 | 84.8666666666667
14 | 653.166666666667
15 | 70.9166666666667
16 | 72.8333333333333
17 | 75.9166666666667
18 | 73.5333333333333
19 | 548.866666666667
20 | 66.2333333333333
21 | 73.45
22 | 66.9666666666667
23 | 71.1166666666667
24 | 68.3166666666667
25 | 285.383333333333
26 | 317.2
27 | 63.2833333333333
28 | 64.0833333333333
29 | 60.5
30 | 550.883333333333
31 | 399.683333333333
32 | 75.9
33 | 115.35
34 | 78.9333333333333
35 | 88.6833333333333
36 | 475.533333333333
37 | 30.1166666666667
38 | 31.5166666666667
39 | 34.0833333333333
40 | 39.55
41 | 47.5166666666667
42 | 423.633333333333
43 | 52.55
44 | 50.2166666666667
45 | 61.4166666666667
46 | 56.6166666666667
47 | 64.4166666666667
48 | 742.3
49 | 165.85
50 | 122.883333333333
51 | 122.216666666667
52 | 114.666666666667
53 | 565.966666666667
54 | 134.7
55 | 141.166666666667
56 | 160.783333333333
57 | 168.483333333333
58 | 458.65
59 | 513.283333333333
60 | 154.366666666667
61 | 130.666666666667
62 | 125.933333333333
63 | 127.25
64 | 615.583333333333
65 | 122.9
66 | 97.45
67 | 122.766666666667
68 | 115.1
69 | 111.95
70 | 442.783333333333
71 | 113.833333333333
72 | 116.116666666667
73 | 128.7
74 | 135.033333333333
75 | 138.75
76 | 153.383333333333
77 | 143.583333333333
78 | 161.5
79 | 168.116666666667
80 | 152.25
81 | 147.116666666667
82 | 163.916666666667
83 | 161.1
84 | 146.95
85 | 132.65
86 | 127.283333333333
87 | 116.1
88 | 92.2833333333333
89 | 54.8833333333333
90 | 111.35
91 | 114.983333333333
92 | 110.983333333333
93 | 1015.35
94 | 774.583333333333
95 | 232.65
96 | 134.616666666667
97 | 130.25
98 | 98.6666666666667
99 | 102.4
100 | 184.866666666667
101 | 258.766666666667
102 | 70.3333333333333
103 | 81.3833333333333
104 | 81.1
105 | 89.2166666666667
106 | 536.966666666667
107 | 85.8333333333333
108 | 95.6333333333333
109 | 76.1
110 | 94.3833333333333
111 | 73.25
112 | 346.7
113 | 65.3833333333333
114 | 84.7333333333333
115 | 140.566666666667
116 | 120.6
117 | 121.383333333333
118 | 359.233333333333
119 | 55.2833333333333
120 | 54.55
121 | 52.1833333333333
122 | 56.2
123 | 112.116666666667
124 | 208.533333333333
125 | 49.4
126 | 49.0666666666667
127 | 56.0666666666667
128 | 54.0166666666667
129 | 63.5166666666667
130 | 344.416666666667
131 | 42.0666666666667
132 | 55.3666666666667
133 | 55.9666666666667
134 | 55.85
135 | 56.3
136 | 46.5666666666667
137 | 49.25
138 | 43.9
139 | 357.616666666667
140 | 44.1
141 | 44.6833333333333
142 | 43.1333333333333
143 | 40.55
144 | 452.2
145 | 47.0666666666667
146 | 40
147 | 42.35
148 | 48.3666666666667
149 | 44.8666666666667
150 | 48.5166666666667
151 | 244.016666666667
152 | 50.1666666666667
153 | 48.7333333333333
154 | 47.9166666666667
155 | 51.9666666666667
156 | 343.333333333333
157 | 35.25
158 | 45.3333333333333
159 | 46.8666666666667
160 | 48.7833333333333
161 |
--------------------------------------------------------------------------------