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How to use update_func method in autotest

Best Python code snippet using autotest_python

sampling.py

Source:sampling.py

1import time2from typing import List, Dict3import numpy as np4import pymc as mc5import theano as th6import theano.tensor as tt7class Sampler(object):8    def __init__(self, n_query:int, dim_features:int, update_func:str="pick_best", beta_demo:float=0.1, beta_pref:float=1.):9        """10        Initializes the sampler.11        :param n_query: Number of queries.12        :param dim_features: Dimension of feature vectors.13        :param update_func: options are "rank", "pick_best", and "approx". To use "approx", n_query must be 2. Will throw an assertion14            error otherwise.15        :param beta_demo: parameter measuring irrationality of human in providing demonstrations16        :param beta_pref: parameter measuring irrationality of human in selecting preferences17        """18        self.n_query = n_query19        self.dim_features = dim_features20        self.update_func = update_func21        self.beta_demo = beta_demo22        self.beta_pref = beta_pref23        if self.update_func=="approx":24            assert self.n_query == 2, "Cannot use approximation to update function if n_query > 2"25        elif not (self.update_func=="rank" or self.update_func=="pick_best"):26            raise Exception(update_func + " is not a valid update function.")27        # feature vectors from demonstrated trajectories28        self.phi_demos = np.zeros((1, self.dim_features))29        # a list of np.arrays containing feature difference vectors and which encode the ranking from the preference30        # queries31        self.phi_prefs = []32        self.f = None33    def load_demo(self, phi_demos:np.ndarray):34        """35        Loads the demonstrations into the Sampler.36        :param demos: a Numpy array containing feature vectors for each demonstration.37            Has dimension n_dem x self.dim_features.38        """39        self.phi_demos = phi_demos40    def load_prefs(self, phi: Dict, rank):41        """42        Loads the results of a preference query into the sampler.43        :param phi: a dictionary mapping rankings (0,...,n_query-1) to feature vectors.44        """45        result = []46        if self.update_func == "rank":47            result = [None] * len(rank)48            for i in range(len(rank)):49                result[i] = phi[rank[i]]50        elif self.update_func == "approx":51            result = phi[rank] - phi[1-rank]52        elif self.update_func == "pick_best":53            result, tmp = [phi[rank] - phi[rank]], []54            for key in sorted(phi.keys()):55                if key != rank:56                    tmp.append(phi[key] - phi[rank])57            result.extend(tmp)58        self.phi_prefs.append(np.array(result))59    def clear_pref(self):60        """61        Clears all preference information from the sampler.62        """63        self.phi_prefs = []64    def sample(self, N:int, T:int=1, burn:int=1000) -> List:65        """66        Returns N samples from the distribution defined by applying update_func on the demonstrations and preferences67        observed thus far.68        :param N: number of samples to draw.69        :param T: if greater than 1, all samples except each T^{th} sample are discarded.70        :param burn: how many samples before the chain converges; these initial samples are discarded.71        :return: list of samples drawn.72        """73        x = tt.vector()74        x.tag.test_value = np.random.uniform(-1, 1, self.dim_features)75        # define update function76        start = time.time()77        if self.update_func=="approx":78            self.f = th.function([x], tt.sum([-tt.nnet.relu(-self.beta_pref * tt.dot(self.phi_prefs[i], x)) for i in range(len(self.phi_prefs))])79                            + tt.sum(self.beta_demo * tt.dot(self.phi_demos, x)))80        elif self.update_func=="pick_best":81            self.f = th.function([x], tt.sum(82                [-tt.log(tt.sum(tt.exp(self.beta_pref * tt.dot(self.phi_prefs[i], x)))) for i in range(len(self.phi_prefs))])83                            + tt.sum(self.beta_demo * tt.dot(self.phi_demos, x)))84        elif self.update_func=="rank":85            self.f = th.function([x], tt.sum( # summing across different queries86                [tt.sum( # summing across different terms in PL-update87                    -tt.log(88                        [tt.sum( # summing down different feature-differences in a single term in PL-update89                            tt.exp(self.beta_pref * tt.dot(self.phi_prefs[i][j:, :] - self.phi_prefs[i][j], x))90                        ) for j in range(self.n_query)]91                    )92                ) for i in range(len(self.phi_prefs))])93                            + tt.sum(self.beta_demo * tt.dot(self.phi_demos, x)))94        print("Finished constructing sampling function in " + str(time.time() - start) + "seconds")95        # perform sampling96        x = mc.Uniform('x', -np.ones(self.dim_features), np.ones(self.dim_features), value=np.zeros(self.dim_features))97        def sphere(x):98            if (x**2).sum()>=1.:99                return -np.inf100            else:101                return self.f(x)102        p = mc.Potential(103            logp = sphere,104            name = 'sphere',105            parents = {'x': x},106            doc = 'Sphere potential',107            verbose = 0)108        chain = mc.MCMC([x])109        chain.use_step_method(mc.AdaptiveMetropolis, x, delay=burn, cov=np.eye(self.dim_features)/5000)110        chain.sample(N*T+burn, thin=T, burn=burn, verbose=-1)111        samples = x.trace()112        samples = np.array([x/np.linalg.norm(x) for x in samples])113        # print("Finished MCMC after drawing " + str(N*T+burn) + " samples")...

pyxel_framework.py

Source:pyxel_framework.py

...54    def update(self):55        if not self.is_update:56            return57        # print(f"update '{self.name}' background")58        self.update_func(self)59    def draw(self):60        if not self.is_draw:61            return62        pyxel.cls(self.col)63class ObjRect:64    def __init__(self, name, xy, wh, col, update_func=_pass):65        self.name = name66        self.xy = xy67        self.wh = wh68        self.col = col69        self.update_func = update_func70        self.is_update = True71        self.is_draw = True72    73    def is_hover(self, xy):74        return all(self.xy[i] <= xy[i] < self.xy[i]+self.wh[i] for i in range(2))75    def update(self):76        if not self.is_update:77            return78        self.update_func(self)79    def draw(self):80        if not self.is_draw:81            return82        pyxel.rect(*self.xy, *self.wh, self.col)83class ObjRectFrame:84    def __init__(self, name, xy, wh, col, update_func=_pass):85        self.name = name86        self.xy = xy87        self.wh = wh88        self.col = col89        self.update_func = update_func90        self.is_update = True91        self.is_draw = True92    93    def update(self):94        if not self.is_update:95            return96        self.update_func(self)97    def draw(self):98        if not self.is_draw:99            return100        pyxel.rectb(*self.xy, *self.wh, self.col)101class ObjImg:102    def __init__(self, name, xy, bank_num, uvwh, colkey=-1, update_func=_pass):103        self.name = name104        self.xy = xy105        self.bank_num = bank_num106        self.uvwh = uvwh107        self.colkey = colkey108        self.update_func = update_func109        self.is_update = True110        self.is_draw = True111    112    def is_hover(self, xy):113        return all(self.xy[i] <= xy[i] < self.xy[i]+self.uvwh[2+i] for i in range(2))114    def update(self):115        if not self.is_update:116            return117        self.update_func(self)118    def draw(self):119        if not self.is_draw:120            return121        pyxel.blt(*self.xy, self.bank_num, *self.uvwh, self.colkey)122class ObjText:123    def __init__(self, name, xy, text, col, update_func=_pass):124        self.name = name125        self.xy = xy126        self.text = text127        self.col = col128        self.update_func = update_func129        self.is_update = True130        self.is_draw = True131    132    def update(self):133        if not self.is_draw:134            return135        self.update_func(self)136    def draw(self):137        if not self.is_draw:138            return...

test_adapters.py

Source:test_adapters.py

1import logging2from unittest import mock3import uuid4from driftwood.adapters import StatusUpdateAdapter5class TestStatusAdapter:6    def test_1(self):7        update_func = mock.MagicMock()8        log = logging.getLogger(uuid.uuid4().hex)9        adapter = StatusUpdateAdapter(update_func, log)10        update_func.assert_call_count == 011        adapter.info("test")12        update_func.assert_called_with(20, "INFO")13        update_func.assert_call_count == 114        adapter.debug("test")15        update_func.assert_call_count == 116        adapter.error("test")17        update_func.assert_called_with(40, "ERROR")18        update_func.assert_call_count == 219        adapter.warning("test")20        adapter.info("test")21        adapter.warning("test")22        update_func.assert_call_count == 223        ...

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