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How to use __truncate method in yandex-tank

Best Python code snippet using yandex-tank

Trading.py

Source:Trading.py

...647        diff = price_max - price_min648        649        data = {}650        if price != 0 and (price <= price_min):651            data['ratio1'] = float(self.__truncate(price_min, 2))652        elif price == 0:653            data['ratio1'] = float(self.__truncate(price_min, 2))654        if price != 0 and (price > price_min) and (price <= (price_max - 0.768 * diff)):655            data['ratio1'] = float(self.__truncate(price_min, 2))656            data['ratio0_768'] = float(self.__truncate(price_max - 0.768 * diff, 2))657        elif price == 0:658            data['ratio0_768'] = float(self.__truncate(price_max - 0.768 * diff, 2))      659        if price != 0 and (price > (price_max - 0.768 * diff)) and (price <= (price_max - 0.618 * diff)):660            data['ratio0_768'] = float(self.__truncate(price_max - 0.768 * diff, 2))661            data['ratio0_618'] = float(self.__truncate(price_max - 0.618 * diff, 2))662        elif price == 0:663            data['ratio0_618'] = float(self.__truncate(price_max - 0.618 * diff, 2))          664        if price != 0 and (price > (price_max - 0.618 * diff)) and (price <= (price_max - 0.5 * diff)):665            data['ratio0_618'] = float(self.__truncate(price_max - 0.618 * diff, 2))666            data['ratio0_5'] = float(self.__truncate(price_max - 0.5 * diff, 2))667        elif price == 0:668            data['ratio0_5'] = float(self.__truncate(price_max - 0.5 * diff, 2))669        if price != 0 and (price > (price_max - 0.5 * diff)) and (price <= (price_max - 0.382 * diff)):670            data['ratio0_5'] = float(self.__truncate(price_max - 0.5 * diff, 2))671            data['ratio0_382'] = float(self.__truncate(price_max - 0.382 * diff, 2))672        elif price == 0:673            data['ratio0_382'] = float(self.__truncate(price_max - 0.382 * diff, 2))674        if price != 0 and (price > (price_max - 0.382 * diff)) and (price <= (price_max - 0.286 * diff)):675            data['ratio0_382'] = float(self.__truncate(price_max - 0.382 * diff, 2))676            data['ratio0_286'] = float(self.__truncate(price_max - 0.286 * diff, 2))677        elif price == 0:678            data['ratio0_286'] = float(self.__truncate(price_max - 0.286 * diff, 2))679        if price != 0 and (price > (price_max - 0.286 * diff)) and (price <= price_max):680            data['ratio0_286'] = float(self.__truncate(price_max - 0.286 * diff, 2))           681            data['ratio0'] = float(self.__truncate(price_max, 2))682        elif price == 0:683            data['ratio0'] = float(self.__truncate(price_max, 2))684        if price != 0 and (price < (price_max + 0.272 * diff)) and (price >= price_max):685            data['ratio0'] = float(self.__truncate(price_max, 2))686            data['ratio1_272'] = float(self.__truncate(price_max + 0.272 * diff, 2))687        elif price == 0:688            data['ratio1_272'] = float(self.__truncate(price_max + 0.272 * diff, 2))689        if price != 0 and (price < (price_max + 0.414 * diff)) and (price >= (price_max + 0.272 * diff)):690            data['ratio1_272'] = float(self.__truncate(price_max, 2))691            data['ratio1_414'] = float(self.__truncate(price_max + 0.414 * diff, 2))692        elif price == 0:693            data['ratio1_414'] = float(self.__truncate(price_max + 0.414 * diff, 2))694        if price != 0 and (price < (price_max + 0.618 * diff)) and (price >= (price_max + 0.414 * diff)):695            data['ratio1_618'] = float(self.__truncate(price_max + 0.618 * diff, 2))696        elif price == 0:697            data['ratio1_618'] = float(self.__truncate(price_max + 0.618 * diff, 2))698        return data699    def saveCSV(self, filename: str='tradingdata.csv') -> None:700        """Saves the DataFrame to an uncompressed CSV."""701        p = compile(r"^[\w\-. ]+$")702        if not p.match(filename):703            raise TypeError('Filename required.')704        if not isinstance(self.df, DataFrame):705            raise TypeError('Pandas DataFrame required.')706        try:707            self.df.to_csv(filename)708        except OSError:709            Logger.critical(f'Unable to save: {filename}')710    def __calculateSupportResistenceLevels(self):711        """Support and Resistance levels. (private function)"""712        for i in range(2, self.df.shape[0] - 2):713            if self.__isSupport(self.df, i):714                l = self.df['low'][i]715                if self.__isFarFromLevel(l):716                    self.levels.append((i, l))717            elif self.__isResistance(self.df, i):718                l = self.df['high'][i]719                if self.__isFarFromLevel(l):720                    self.levels.append((i, l))721        return self.levels722    def __isSupport(self, df, i) -> bool:723        """Is support level? (private function)"""724        c1 = df['low'][i] < df['low'][i - 1]725        c2 = df['low'][i] < df['low'][i + 1]726        c3 = df['low'][i + 1] < df['low'][i + 2]727        c4 = df['low'][i - 1] < df['low'][i - 2]728        support = c1 and c2 and c3 and c4729        return support730    def __isResistance(self, df, i) -> bool:731        """Is resistance level? (private function)"""732        c1 = df['high'][i] > df['high'][i - 1]733        c2 = df['high'][i] > df['high'][i + 1]734        c3 = df['high'][i + 1] > df['high'][i + 2]735        c4 = df['high'][i - 1] > df['high'][i - 2]736        resistance = c1 and c2 and c3 and c4737        return resistance738    def __isFarFromLevel(self, l) -> float:739        """Is far from support level? (private function)"""740        s = mean(self.df['high'] - self.df['low'])741        return np_sum([abs(l-x) < s for x in self.levels]) == 0742    def __truncate(self, f, n) -> float:...

Writer.py

Source:Writer.py

...24        write_to_terminal()25            Print metrics for each class to the terminal26        write_to_csv()27            Print metrics for each class to the .csv file28        __truncate(number, digits=4)29            Trim number to n-th decimal place30    """31    def __init__(self, data):32        self._precision = data["data"]["pr"]33        self._recall = data["data"]["rc"]34        self._score = data["data"]["sc"]35        self._f1 = data["data"]["f1_m"]36        self._ap = data["data"]["ap"]37        self._classes = data["classes"]38    def write_to_terminal(self) -> None:39        """40        Print metrics for each class to the terminal41        """42        table = BeautifulTable()43        table.column_headers = [44            f"{colored('Class', 'blue', attrs=['bold'])}",45            f"{colored('Precision', 'blue', attrs=['bold'])}",46            f"{colored('Recall', 'blue', attrs=['bold'])}",47            f"{colored('F1', 'blue', attrs=['bold'])}",48            f"{colored('AP', 'blue', attrs=['bold'])}",49        ]50        for i, j, k, z, cl in zip(51            self._f1, self._precision, self._recall, range(len(self._ap)), self._classes52        ):53            index = np.argmax(i)54            table.append_row(55                [56                    f"{colored(f'{cl}', 'green', attrs=['bold'])}",57                    self.__truncate(j[index]),58                    self.__truncate(k[index]),59                    self.__truncate(2 * j[index] * k[index] / (k[index] + j[index])),60                    self.__truncate(self._ap[z]),61                ]62            )63        print(table)64        print(65            f"{colored('mAP:', 'red', attrs=['bold'])} {self.__truncate(sum(self._ap) / len(self._ap))}"66        )67    def write_to_csv(self) -> None:68        """69        Print metrics for each class to the .csv file70        """71        precisions_list = [self.__truncate(i[np.argmax(i)]) for i in self._precision]72        recall_list = [self.__truncate(i[np.argmax(i)]) for i in self._recall]73        f1_list = [self.__truncate(i[np.argmax(i)]) for i in self._f1]74        ap_list = [self.__truncate(self._ap[i]) for i in range(len(self._ap))]75        frame = pd.DataFrame(76            {77                " ": self._classes,78                "precision": precisions_list,79                "recall": recall_list,80                "f1": f1_list,81                "ap": ap_list,82                "map": self.__truncate(sum(self._ap) / len(self._ap)),83            }84        )85        if not os.path.exists("./results"):86            os.mkdir("./results")87        frame.to_csv(88            f"results/meters_{datetime.datetime.today().strftime('%Y-%m-%d_%H:%M:%S')}.csv",89            sep=";",90            index=False,91            index_label=True,92        )93    @classmethod94    def __truncate(cls, number: float, digits=4) -> float:95        """96        Trim number to nth decimal place97        :param number: floating point number98        :param digits: the digit to which the number is to be truncated99        :return: truncated number100        """101        stepper = 10.0**digits...

vocab.py

Source:vocab.py

...12        self.__tok2id = OrderedDict()13        if add_null:14            self.__tok2id[NULL] = 015        for t in tokens:16            t = self.__truncate(t)17            if t not in self.__tok2id:18                self.__tok2id[t] = len(self.__tok2id)19        self.__id2tok = OrderedDict({i: t for (t, i) in self.__tok2id.items()})20    def has(self, token):21        token = self.__truncate(token)22        if isinstance(token, int):23            return token in self.__id2tok24        elif isinstance(token, str):25            return token in self.__tok2id26        else:27            raise TypeError(f"VocabDict only accepts str or int. "28                            f"{type(token)} was given.")29    def to_id(self, token, str_cast=False):30        if str_cast:31            token = str(token)32        token = self.__truncate(token)33        return self.__tok2id[token]34    def to_id_list(self, tokens, str_cast=False):35        return [self.to_id(t, str_cast) for t in tokens]36    def to_id_batch(self, list_of_tokens, str_cast=False):37        return [self.to_id_list(lst, str_cast) for lst in list_of_tokens]38    def to_str(self, id, int_cast=False):39        if int_cast:40            id = int(id)41        return self.__id2tok[id]42    def to_str_list(self, ids, int_cast=False):43        return [self.to_str(i, int_cast=int_cast) for i in ids]44    def to_str_batch(self, list_of_ids, int_cast=False):45        return [self.to_str_list(lst, int_cast) for lst in list_of_ids]46    def __truncate(self, t):47        return t[:self.max_token_length]48    @property49    def tok2id(self):50        return self.__tok2id51    @property52    def id2tok(self):53        return self.__id2tok54    @property55    def size(self):56        return len(self.__tok2id)57    @property58    def is_empty(self):59        return self.size == 060    @property...

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