How to use update_func method in autotest

Best Python code snippet using autotest_python

sampling.py

Source: sampling.py Github

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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")...

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pyxel_framework.py

Source: pyxel_framework.py Github

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...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...

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test_adapters.py

Source: test_adapters.py Github

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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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