KaneAI - World’s First E2E Software Testing Agent.Book a Demo Join TestMu Conference

How to use _reset_class method in tempest

Best Python code snippet using tempest_python

SplineInterpolationMethod.py

Source:SplineInterpolationMethod.py

...17                                xe=bbox[1], s=s)18        if data[-1] == 1:19            data = self._reset_nest(data)20        self._data = data21        self._reset_class()22    @staticmethod23    def validate_input(x, y, w, bbox, k, s, ext, check_finite):24        x, y, bbox = np.asarray(x), np.asarray(y), np.asarray(bbox)25        if w is not None:26            w = np.asarray(w)27        if check_finite:28            w_finite = np.isfinite(w).all() if w is not None else True29            if (not np.isfinite(x).all() or not np.isfinite(y).all() or30                    not w_finite):31                raise ValueError("x and y array must not contain "32                                 "NaNs or infs.")33        if s is None or s > 0:34            if not np.all(diff(x) >= 0.0):35                raise ValueError("x must be increasing if s > 0")36        else:37            if not np.all(diff(x) > 0.0):38                raise ValueError("x must be strictly increasing if s = 0")39        if x.size != y.size:40            raise ValueError("x and y should have a same length")41        elif w is not None and not x.size == y.size == w.size:42            raise ValueError("x, y, and w should have a same length")43        elif bbox.shape != (2,):44            raise ValueError("bbox shape should be (2,)")45        elif not (1 <= k <= 5):46            raise ValueError("k should be 1 <= k <= 5")47        elif s is not None and not s >= 0.0:48            raise ValueError("s should be s >= 0.0")49        try:50            ext = _extrap_modes[ext]51        except KeyError:52            raise ValueError("Unknown extrapolation mode %s." % ext)53        return x, y, w, bbox, ext54    @classmethod55    def _from_tck(cls, tck, ext=0):56        self = cls.__new__(cls)57        t, c, k = tck58        self._eval_args = tck59        self._data = (None, None, None, None, None, k, None, len(t), t,60                      c, None, None, None, None)61        self.ext = ext62        return self63    def _reset_class(self):64        data = self._data65        n, t, c, k, ier = data[7], data[8], data[9], data[5], data[-1]66        self._eval_args = t[:n], c[:n], k67        if ier == 0:68            pass69        elif ier == -1:70            self._set_class(InterpolatedUnivariateSpline)71    def _set_class(self, cls):72        self._spline_class = cls73        if self.__class__ in (UnivariateSpline, InterpolatedUnivariateSpline):74            self.__class__ = cls75        else:76            pass77    def _reset_nest(self, data, nest=None):78        n = data[10]79        if nest is None:80            k, m = data[5], len(data[0])81            nest = m+k+182        else:83            if not n <= nest:84                raise ValueError("`nest` can only be increased")85        t, c, fpint, nrdata = [np.resize(data[j], nest) for j in86                               [8, 9, 11, 12]]87        args = data[:8] + (t, c, n, fpint, nrdata, data[13])88        data = dfitpack.fpcurf1(*args)89        return data90    def set_smoothing_factor(self, s):91        data = self._data92        if data[6] == -1:93            warnings.warn('smoothing factor unchanged for'94                          'spline with fixed knots')95            return96        args = data[:6] + (s,) + data[7:]97        data = dfitpack.fpcurf1(*args)98        if data[-1] == 1:99            data = self._reset_nest(data)100        self._data = data101        self._reset_class()102    def __call__(self, x, nu=0, ext=None):103        x = np.asarray(x)104        if x.size == 0:105            return array([])106        if ext is None:107            ext = self.ext108        else:109            try:110                ext = _extrap_modes[ext]111            except KeyError:112                raise ValueError("Unknown extrapolation mode %s." % ext)113        return fitpack.splev(x, self._eval_args, der=nu, ext=ext)114    def get_knots(self):115        data = self._data116        k, n = data[5], data[7]117        return data[8][k:n-k]118    def get_coeffs(self):119        data = self._data120        k, n = data[5], data[7]121        return data[9][:n-k-1]122    def get_residual(self):123        return self._data[10]124    def integral(self, a, b):125        return dfitpack.splint(*(self._eval_args+(a, b)))126    def derivatives(self, x):127        d, ier = dfitpack.spalde(*(self._eval_args+(x,)))128        if not ier == 0:129            raise ValueError("Error code returned by spalde: %s" % ier)130        return d131    def roots(self):132        k = self._data[5]133        if k == 3:134            z, m, ier = dfitpack.sproot(*self._eval_args[:2])135            if not ier == 0:136                raise ValueError("Error code returned by spalde: %s" % ier)137            return z[:m]138        raise NotImplementedError('finding roots unsupported for '139                                  'non-cubic splines')140    def derivative(self, n=1):141        tck = fitpack.splder(self._eval_args, n)142        # if self.ext is 'const', derivative.ext will be 'zeros'143        ext = 1 if self.ext == 3 else self.ext144        return UnivariateSpline._from_tck(tck, ext=ext)145    def antiderivative(self, n=1):146        tck = fitpack.splantider(self._eval_args, n)147        return UnivariateSpline._from_tck(tck, self.ext)148class InterpolatedUnivariateSpline(UnivariateSpline):149    def __init__(self, x, y, w=None, bbox=[None]*2, k=3,150                 ext=0, check_finite=False):151        x, y, w, bbox, self.ext = self.validate_input(x, y, w, bbox, k, None,152                                            ext, check_finite)153        if not np.all(diff(x) > 0.0):154            raise ValueError('x must be strictly increasing')155        # _data == x,y,w,xb,xe,k,s,n,t,c,fp,fpint,nrdata,ier156        self._data = dfitpack.fpcurf0(x, y, k, w=w, xb=bbox[0],157                                      xe=bbox[1], s=0)...

factor_test.py

Source:factor_test.py

...36        self.portfolio = self.portfolio_cls(self.data_handler, self.events, self.start_date,37                                            self.initial_capital, self.stock_num)38        self.execution_handler = self.execution_handler_cls(self.events)3940    def _reset_class(self):4142        self.strategy = self.strategy_cls(self.data_handler, self.events, self.stock_num, self.factor, self.layer)43        self.portfolio = self.portfolio_cls(self.data_handler, self.events, self.start_date,44                                            self.initial_capital, self.stock_num)45        self.execution_handler = self.execution_handler_cls(self.events)4647    def _run_factortest(self, cur_layer):4849        i = 050        while True:51            i += 152            print(i)5354            if self.data_handler.continue_backtest:55                self.data_handler.update_bars_monthly()56                self.portfolio.update_timeindex()57            else:58                break5960            while True:61                try:62                    event = self.events.get(False)63                except queue.Empty:64                    break65                else:66                    if event is not None:67                        if event.type == 'MARKET':68                            self.strategy.calculate_signals(event, cur_layer)69                        elif event.type == 'SIGNAL':70                            self.signals += 171                            self.portfolio.update_signal(event)72                        elif event.type == 'ORDER':73                            self.orders += 174                            self.execution_handler.execute_order(event)75                        elif event.type == 'FILL':76                            self.fills += 177                            self.portfolio.update_fill(event)7879                if self.events.empty() and self.strategy.transferring == True:80                    self.events.put(MarketEvent())8182            time.sleep(self.heartbeat)8384    def run_trading(self):8586        for cur_layer in range(self.layer):87            self._run_factortest(cur_layer)88            self._output_performance()89            my_plot = plot_performance(self.portfolio.equity_curve,90                                       self.data_handler.symbol_data[self.symbol_list[0]],91                                       self.execution_handler.execution_records)92            my_plot.plot_equity_curve()93            self.data_handler.reset_latest_data()94            self._reset_class()95            self.data_handler.continue_backtest = True9697    def factor_equity_curve(self):9899        return self.portfolio.equity_curve['equity_curve']100101    '''def run_trading(self):102103        self._run_factortest()104        self._output_performance()105        my_plot = plot_performance(self.portfolio.equity_curve,106                                   self.data_handler.symbol_data[self.symbol_list[0]],107                                   self.execution_handler.execution_records)108        my_plot.plot_equity_curve()'''

test_routine.py

Source:test_routine.py

2'''3import torch.optim as optim4from cortex.plugins import ModelPlugin5def test_routine(model_class, arguments, data_class):6    ModelPlugin._reset_class()7    kwargs = {arguments['arg1']: 11, arguments['arg2']: 13}8    data = data_class(11)9    model = model_class(contract=dict(inputs=dict(A='test')))10    model._data = data11    model.kwargs.update(**kwargs)12    model.build()13    model.eval_step()14    print('Training nets: ', model._training_nets)15    assert 'net' in list(model._training_nets.values())[0]16    params = list(model.nets.net.parameters())17    op = optim.SGD(params, lr=0.0001)18    model._optimizers = dict(net=op)19    A = model.inputs('A')20    model.routine(A)21    model._reset_epoch()22    model.train_step()23    model.train_step()24    model.train_step()25    print('Results:', model._all_epoch_results)26    print('Losses:', model._all_epoch_losses)27    print('Times:', model._all_epoch_times)28    assert len(list(model._all_epoch_results.values())[0]) == 329    assert len(list(model._all_epoch_losses.values())[0]) == 330    assert len(list(model._all_epoch_times.values())[0]) == 331def test_routine_with_submodels(model_with_submodel):32    model = model_with_submodel33    model.build()34    params = list(model.nets.net.parameters())35    op = optim.SGD(params, lr=0.0001)36    params2 = list(model.nets.net2.parameters())37    op2 = optim.SGD(params2, lr=0.001)38    model._optimizers = dict(net=op, net2=op2)39    model.submodel._optimizers = dict(net=op, net2=op2)40    assert model._get_training_nets() == []41    model.train_step()42    assert model._get_training_nets() == ['net', 'net2']43    model.train_step()44    model.train_step()45def test_routine_with_submodels_2(model_class_with_submodel_2, data_class):46    ModelPlugin._reset_class()47    kwargs = {'d': 11, 'c': 13}48    data = data_class(11)49    contract = dict(inputs=dict(B='test'))50    sub_contract = dict(51        kwargs=dict(a='d'),52        nets=dict(net='net2'),53        inputs=dict(A='test')54    )55    sub_contract2 = dict(56        kwargs=dict(a='d'),57        nets=dict(net='net3'),58        inputs=dict(A='test')59    )60    model = model_class_with_submodel_2(sub_contract1=sub_contract,...

Automation Testing Tutorials

Learn to execute automation testing from scratch with LambdaTest Learning Hub. Right from setting up the prerequisites to run your first automation test, to following best practices and diving deeper into advanced test scenarios. LambdaTest Learning Hubs compile a list of step-by-step guides to help you be proficient with different test automation frameworks i.e. Selenium, Cypress, TestNG etc.