How to use init_data method in hypothesis

Best Python code snippet using hypothesis

MOMspot.py

Source:MOMspot.py

1# -*- coding: utf-8 -*-2import numpy as np3import pandas as pd4import matplotlib.pyplot as plt5from math import log,floor6import tqdm7from scipy.optimize import minimize8# colors for plot9deep_saffron = '#FF9933'10air_force_blue = '#5D8AA8'11def backMean(X,d):12    M = []13    w = X[:d].sum()14    M.append(w/d)15    for i in range(d,len(X)):16        w = w - X[i-d] + X[i]17        M.append(w/d)18    return np.array(M)19class momSPOT:20    """21    This class allows to run biSPOT algorithm on univariate dataset (upper and lower bounds)22    23    Attributes24    ----------25    proba : float26        Detection level (risk), chosen by the user27        28    extreme_quantile : float29        current threshold (bound between normal and abnormal events)30        31    data : numpy.array32        stream33    34    init_data : numpy.array35        initial batch of observations (for the calibration/initialization step)36    37    init_threshold : float38        initial threshold computed during the calibration step39    40    peaks : numpy.array41        array of peaks (excesses above the initial threshold)42    43    n : int44        number of observed values45    46    Nt : int47        number of observed peaks48    """49    def __init__(self, q = 1e-4):50        """51        Constructor52        Parameters53        ----------54        q55            Detection level (risk)56    57        Returns58        ----------59        biSPOT object60        """61        self.proba = q62        self.data = None63        self.init_data = None64        self.n = 065        nonedict =  {'up':None,'down':None}66        67        self.extreme_quantile = dict.copy(nonedict)68        self.init_threshold = dict.copy(nonedict)69        self.peaks = dict.copy(nonedict)70        self.gamma = dict.copy(nonedict)71        self.sigma = dict.copy(nonedict)72        self.Nt = {'up':0,'down':0}73        74        75    def __str__(self):76        s = ''77        s += 'Streaming Peaks-Over-Threshold Object\n'78        s += 'Detection level q = %s\n' % self.proba79        if self.data is not None:80            s += 'Data imported : Yes\n'81            s += '\t initialization  : %s values\n' % self.init_data.size82            s += '\t stream : %s values\n' % self.data.size83        else:84            s += 'Data imported : No\n'85            return s86            87        if self.n == 0:88            s += 'Algorithm initialized : No\n'89        else:90            s += 'Algorithm initialized : Yes\n'91            s += '\t initial threshold : %s\n' % self.init_threshold92            93            r = self.n-self.init_data.size94            if r > 0:95                s += 'Algorithm run : Yes\n'96                s += '\t number of observations : %s (%.2f %%)\n' % (r,100*r/self.n)97                s += '\t triggered alarms : %s (%.2f %%)\n' % (len(self.alarm),100*len(self.alarm)/self.n)98            else:99                s += '\t number of peaks  : %s\n' % self.Nt100                s += '\t upper extreme quantile : %s\n' % self.extreme_quantile['up']101                s += '\t lower extreme quantile : %s\n' % self.extreme_quantile['down']102                s += 'Algorithm run : No\n'103        return s104    105    106    def fit(self,init_data,data):107        """108        Import data to biSPOT object109        110        Parameters111        ----------112        init_data : list, numpy.array or pandas.Series113            initial batch to calibrate the algorithm ()114            115        data : numpy.array116            data for the run (list, np.array or pd.series)117    118        """119        if isinstance(data,list):120            self.data = np.array(data)121        elif isinstance(data,np.ndarray):122            self.data = data123        elif isinstance(data,pd.Series):124            self.data = data.values125        else:126            print('This data format (%s) is not supported' % type(data))127            return128            129        if isinstance(init_data,list):130            self.init_data = np.array(init_data)131        elif isinstance(init_data,np.ndarray):132            self.init_data = init_data133        elif isinstance(init_data,pd.Series):134            self.init_data = init_data.values135        elif isinstance(init_data,int):136            self.init_data = self.data[:init_data]137            self.data = self.data[init_data:]138        elif isinstance(init_data,float) & (init_data<1) & (init_data>0):139            r = int(init_data*data.size)140            self.init_data = self.data[:r]141            self.data = self.data[r:]142        else:143            print('The initial data cannot be set')144            return145        146    def add(self,data):147        """148        This function allows to append data to the already fitted data149        150        Parameters151        ----------152        data : list, numpy.array, pandas.Series153            data to append154        """155        if isinstance(data,list):156            data = np.array(data)157        elif isinstance(data,np.ndarray):158            data = data159        elif isinstance(data,pd.Series):160            data = data.values161        else:162            print('This data format (%s) is not supported' % type(data))163            return164        165        self.data = np.append(self.data,data)166        return167    def initialize(self, verbose = True):168        """169        Run the calibration (initialization) step170        171        Parameters172        ----------173        verbose : bool174            (default = True) If True, gives details about the batch initialization175        """176        n_init = self.init_data.size177        178        S = np.sort(self.init_data)     # we sort X to get the empirical quantile179        self.init_threshold['up'] = S[int(0.98*n_init)] # t is fixed for the whole algorithm180        self.init_threshold['down'] = S[int(0.02*n_init)] # t is fixed for the whole algorithm181        # initial peaks182        self.peaks['up'] = self.init_data[self.init_data>self.init_threshold['up']]-self.init_threshold['up']183        self.peaks['down'] = -(self.init_data[self.init_data<self.init_threshold['down']]-self.init_threshold['down'])184        self.Nt['up'] = self.peaks['up'].size185        self.Nt['down'] = self.peaks['down'].size186        self.n = n_init187        188        if verbose:189            print('Initial threshold : %s' % self.init_threshold)190            print('Number of peaks : %s' % self.Nt)191            #print('Grimshaw maximum log-likelihood estimation ... ', end = '')192            193        l = {'up':None,'down':None}194        for side in ['up','down']:195            g,s,l[side] = self._MOM(side)196            self.extreme_quantile[side] = self._quantile(side,g,s)197            self.gamma[side] = g198            self.sigma[side] = s199        200        ltab = 20201        form = ('\t'+'%20s' + '%20.2f' + '%20.2f')202        '''203        if verbose:204            print('[done]')205            print('\t' + 'Parameters'.rjust(ltab) + 'Upper'.rjust(ltab) + 'Lower'.rjust(ltab))206            print('\t' + '-'*ltab*3)207            print(form % (chr(0x03B3),self.gamma['up'],self.gamma['down']))208            print(form % (chr(0x03C3),self.sigma['up'],self.sigma['down']))209            print(form % ('likelihood',l['up'],l['down']))210            print(form % ('Extreme quantile',self.extreme_quantile['up'],self.extreme_quantile['down']))211            print('\t' + '-'*ltab*3)212        '''213        return 214    215    def _MOM(self,side,epsilon = 1e-8, n_points = 10):216        Yi = self.peaks[side]217        avg = np.mean(Yi)218        var = np.var(Yi)219        sigma = 0.5*avg*(avg**2/var + 1)220        gamma = 0.5*(avg**2/var - 1)221        print gamma, sigma222        return gamma,sigma,100223    224    def _quantile(self,side,gamma,sigma):225        """226        Compute the quantile at level 1-q for a given side227        228        Parameters229        ----------230        side : str231            'up' or 'down'232        gamma : float233            GPD parameter234        sigma : float235            GPD parameter236        Returns237        ----------238        float239            quantile at level 1-q for the GPD(Î³,Ï,Î¼=0)240        """241        if side == 'up':242            r = self.n * self.proba / self.Nt[side]243            if gamma != 0:244                return self.init_threshold['up'] + (sigma/gamma)*(pow(r,-gamma)-1)245            else:246                return self.init_threshold['up'] - sigma*log(r)247        elif side == 'down':248            r = self.n * self.proba / self.Nt[side]249            if gamma != 0:250                return self.init_threshold['down'] - (sigma/gamma)*(pow(r,-gamma)-1)251            else:252                return self.init_threshold['down'] + sigma*log(r)253        else:254            print('error : the side is not right')255        256    def run(self, with_alarm = True):257        """258        Run biSPOT on the stream259        260        Parameters261        ----------262        with_alarm : bool263            (default = True) If False, SPOT will adapt the threshold assuming \264            there is no abnormal values265        Returns266        ----------267        dict268            keys : 'upper_thresholds', 'lower_thresholds' and 'alarms'269            270            '***-thresholds' contains the extreme quantiles and 'alarms' contains \271            the indexes of the values which have triggered alarms272            273        """274        if (self.n>self.init_data.size):275            print('Warning : the algorithm seems to have already been run, you \276            should initialize before running again')277            return {}278        279        # list of the thresholds280        thup = []281        thdown = []282        alarm = []283        # Loop over the stream284        for i in tqdm.tqdm(range(self.data.size)):285    286            # If the observed value exceeds the current threshold (alarm case)287            if self.data[i]>self.extreme_quantile['up'] :288                # if we want to alarm, we put it in the alarm list289                if with_alarm:290                    alarm.append(i)291                # otherwise we add it in the peaks292                else:293                    self.peaks['up'] = np.append(self.peaks['up'],self.data[i]-self.init_threshold['up'])294                    self.Nt['up'] += 1295                    self.n += 1296                    # and we update the thresholds297                    g,s,l = self._MOM('up')298                    self.extreme_quantile['up'] = self._quantile('up',g,s)299            # case where the value exceeds the initial threshold but not the alarm ones300            elif self.data[i]>self.init_threshold['up']:301                    # we add it in the peaks302                    self.peaks['up'] = np.append(self.peaks['up'],self.data[i]-self.init_threshold['up'])303                    self.Nt['up'] += 1304                    self.n += 1305                    # and we update the thresholds306                    g,s,l = self._MOM('up')307                    self.extreme_quantile['up'] = self._quantile('up',g,s)308                    309            elif self.data[i]<self.extreme_quantile['down'] :310                # if we want to alarm, we put it in the alarm list311                if with_alarm:312                    alarm.append(i)313                # otherwise we add it in the peaks314                else:315                    self.peaks['down'] = np.append(self.peaks['down'],-(self.data[i]-self.init_threshold['down']))316                    self.Nt['down'] += 1317                    self.n += 1318                    # and we update the thresholds319                    g,s,l = self._MOM('down')320                    self.extreme_quantile['down'] = self._quantile('down',g,s)321            # case where the value exceeds the initial threshold but not the alarm ones322            elif self.data[i]<self.init_threshold['down']:323                    # we add it in the peaks324                    self.peaks['down'] = np.append(self.peaks['down'],-(self.data[i]-self.init_threshold['down']))325                    self.Nt['down'] += 1326                    self.n += 1327                    # and we update the thresholds328                    g,s,l = self._MOM('down')329                    self.extreme_quantile['down'] = self._quantile('down',g,s)330            else:331                self.n += 1332                333            thup.append(self.extreme_quantile['up']) # thresholds record334            thdown.append(self.extreme_quantile['down']) # thresholds record335        336        return {'upper_thresholds' : thup,'lower_thresholds' : thdown, 'alarms': alarm}337    338    def plot(self,run_results,with_alarm = True):339        """340        Plot the results of given by the run341        342        Parameters343        ----------344        run_results : dict345            results given by the 'run' method346        with_alarm : bool347            (default = True) If True, alarms are plotted.348        Returns349        ----------350        list351            list of the plots352            353        """354        x = range(self.data.size)355        K = run_results.keys()356        357        ts_fig, = plt.plot(x,self.data,color=air_force_blue)358        fig = [ts_fig]359        360        if 'upper_thresholds' in K:361            thup = run_results['upper_thresholds']362            uth_fig, = plt.plot(x,thup,color=deep_saffron,lw=2,ls='dashed')363            fig.append(uth_fig)364            365        if 'lower_thresholds' in K:366            thdown = run_results['lower_thresholds']367            lth_fig, = plt.plot(x,thdown,color=deep_saffron,lw=2,ls='dashed')368            fig.append(lth_fig)369        370        if with_alarm and ('alarms' in K):371            alarm = run_results['alarms']372            al_fig = plt.scatter(alarm,self.data[alarm],color='red')373            fig.append(al_fig)374            375        plt.xlim((0,self.data.size))376        plt.show()377        ...

data.py

Source:data.py

...24        self.object_class = object_class25        self.target_visible = target_visible26        self.object_ids = object_ids27        self.multiobj_mode = multiobj_mode28        self.init_data = self._construct_init_data(init_data)29        self._ensure_start_frame()30    def _ensure_start_frame(self):31        # Ensure start frame is 032        start_frame = min(list(self.init_data.keys()))33        if start_frame > 0:34            self.frames = self.frames[start_frame:]35            if self.ground_truth_rect is not None:36                if isinstance(self.ground_truth_rect, (dict, OrderedDict)):37                    for obj_id, gt in self.ground_truth_rect.items():38                        self.ground_truth_rect[obj_id] = gt[start_frame:,:]39                else:40                    self.ground_truth_rect = self.ground_truth_rect[start_frame:,:]41            if self.ground_truth_seg is not None:42                self.ground_truth_seg = self.ground_truth_seg[start_frame:]43                assert len(self.frames) == len(self.ground_truth_seg)44            if self.target_visible is not None:45                self.target_visible = self.target_visible[start_frame:]46            self.init_data = {frame-start_frame: val for frame, val in self.init_data.items()}47    def _construct_init_data(self, init_data):48        if init_data is not None:49            if not self.multiobj_mode:50                assert self.object_ids is None or len(self.object_ids) == 151                for frame, init_val in init_data.items():52                    if 'bbox' in init_val and isinstance(init_val['bbox'], (dict, OrderedDict)):53                        init_val['bbox'] = init_val['bbox'][self.object_ids[0]]54            # convert to list55            for frame, init_val in init_data.items():56                if 'bbox' in init_val:57                    if isinstance(init_val['bbox'], (dict, OrderedDict)):58                        init_val['bbox'] = OrderedDict({obj_id: list(init) for obj_id, init in init_val['bbox'].items()})59                    else:60                        init_val['bbox'] = list(init_val['bbox'])61        else:62            init_data = {0: dict()}     # Assume start from frame 063            if self.object_ids is not None:64                init_data[0]['object_ids'] = self.object_ids65            if self.ground_truth_rect is not None:66                if self.multiobj_mode:67                    assert isinstance(self.ground_truth_rect, (dict, OrderedDict))68                    init_data[0]['bbox'] = OrderedDict({obj_id: list(gt[0,:]) for obj_id, gt in self.ground_truth_rect.items()})69                else:70                    assert self.object_ids is None or len(self.object_ids) == 171                    if isinstance(self.ground_truth_rect, (dict, OrderedDict)):72                        init_data[0]['bbox'] = list(self.ground_truth_rect[self.object_ids[0]][0, :])73                    else:74                        init_data[0]['bbox'] = list(self.ground_truth_rect[0,:])75            if self.ground_truth_seg is not None:76                init_data[0]['mask'] = self.ground_truth_seg[0]77        return init_data78    def init_info(self):79        info = self.frame_info(frame_num=0)80        return info81    def frame_info(self, frame_num):82        info = self.object_init_data(frame_num=frame_num)83        return info84    def init_bbox(self, frame_num=0):85        return self.object_init_data(frame_num=frame_num).get('init_bbox')86    def init_mask(self, frame_num=0):87        return self.object_init_data(frame_num=frame_num).get('init_mask')88    def get_info(self, keys, frame_num=None):89        info = dict()90        for k in keys:91            val = self.get(k, frame_num=frame_num)92            if val is not None:93                info[k] = val94        return info95    def object_init_data(self, frame_num=None) -> dict:96        if frame_num is None:97            frame_num = 098        if frame_num not in self.init_data:99            return dict()100        init_data = dict()101        for key, val in self.init_data[frame_num].items():102            if val is None:103                continue104            init_data['init_'+key] = val105        if 'init_mask' in init_data and init_data['init_mask'] is not None:106            anno = imread_indexed(init_data['init_mask'])107            if not self.multiobj_mode and self.object_ids is not None:108                assert len(self.object_ids) == 1109                anno = (anno == int(self.object_ids[0])).astype(np.uint8)...

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