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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,...

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