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def best_actions(self):
"""A tuple containing the actions whose action sets have the best prediction.""" |
if self._best_actions is None:
best_prediction = self.best_prediction
self._best_actions = tuple(
action
for action, action_set in self._action_sets.items()
if action_set.prediction == best_prediction
)
return self._bes... |
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def select_action(self):
"""Select an action according to the action selection strategy of the associated algorithm. If an action has already been selected, rais... |
if self._selected_action is not None:
raise ValueError("The action has already been selected.")
strategy = self._algorithm.action_selection_strategy
self._selected_action = strategy(self)
return self._selected_action |
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def _set_selected_action(self, action):
"""Setter method for the selected_action property.""" |
assert action in self._action_sets
if self._selected_action is not None:
raise ValueError("The action has already been selected.")
self._selected_action = action |
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def _set_payoff(self, payoff):
"""Setter method for the payoff property.""" |
if self._selected_action is None:
raise ValueError("The action has not been selected yet.")
if self._closed:
raise ValueError("The payoff for this match set has already"
"been applied.")
self._payoff = float(payoff) |
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def pay(self, predecessor):
"""If the predecessor is not None, gives the appropriate amount of payoff to the predecessor in payment for its contribution to this ... |
assert predecessor is None or isinstance(predecessor, MatchSet)
if predecessor is not None:
expectation = self._algorithm.get_future_expectation(self)
predecessor.payoff += expectation |
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def add(self, rule):
"""Add a new classifier rule to the classifier set. Return a list containing zero or more rules that were deleted from the classifier by the... |
assert isinstance(rule, ClassifierRule)
condition = rule.condition
action = rule.action
# If the rule already exists in the population, then we virtually
# add the rule by incrementing the existing rule's numerosity. This
# prevents redundancy in the rule set. Otherwi... |
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def get(self, rule, default=None):
"""Return the existing version of the given rule. If the rule is not present in the classifier set, return the default. If no ... |
assert isinstance(rule, ClassifierRule)
if (rule.condition not in self._population or
rule.action not in self._population[rule.condition]):
return default
return self._population[rule.condition][rule.action] |
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def ping(config_file, profile, solver_def, json_output, request_timeout, polling_timeout):
"""Ping the QPU by submitting a single-qubit problem.""" |
now = utcnow()
info = dict(datetime=now.isoformat(), timestamp=datetime_to_timestamp(now), code=0)
def output(fmt, **kwargs):
info.update(kwargs)
if not json_output:
click.echo(fmt.format(**kwargs))
def flush():
if json_output:
click.echo(json.dumps(in... |
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def solvers(config_file, profile, solver_def, list_solvers):
"""Get solver details. Unless solver name/id specified, fetch and display details for all online sol... |
with Client.from_config(
config_file=config_file, profile=profile, solver=solver_def) as client:
try:
solvers = client.get_solvers(**client.default_solver)
except SolverNotFoundError:
click.echo("Solver(s) {} not found.".format(solver_def))
return 1... |
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def sample(config_file, profile, solver_def, biases, couplings, random_problem, num_reads, verbose):
"""Submit Ising-formulated problem and return samples.""" |
# TODO: de-dup wrt ping
def echo(s, maxlen=100):
click.echo(s if verbose else strtrunc(s, maxlen))
try:
client = Client.from_config(
config_file=config_file, profile=profile, solver=solver_def)
except Exception as e:
click.echo("Invalid configuration: {}".format(e... |
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def get_input_callback(samplerate, params, num_samples=256):
"""Return a function that produces samples of a sine. Parameters samplerate : float The sample rate.... |
amplitude = params['mod_amplitude']
frequency = params['mod_frequency']
def producer():
"""Generate samples.
Yields
------
samples : ndarray
A number of samples (`num_samples`) of the sine.
"""
start_time = 0
while True:
time... |
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def get_playback_callback(resampler, samplerate, params):
"""Return a sound playback callback. Parameters resampler The resampler from which samples are read. sa... |
def callback(outdata, frames, time, _):
"""Playback callback.
Read samples from the resampler and modulate them onto a carrier
frequency.
"""
last_fmphase = getattr(callback, 'last_fmphase', 0)
df = params['fm_gain'] * resampler.read(frames)
df = np.pad(df,... |
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def main(source_samplerate, target_samplerate, params, converter_type):
"""Setup the resampling and audio output callbacks and start playback.""" |
from time import sleep
ratio = target_samplerate / source_samplerate
with sr.CallbackResampler(get_input_callback(source_samplerate, params),
ratio, converter_type) as resampler, \
sd.OutputStream(channels=1, samplerate=target_samplerate,
... |
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def max_num_reads(self, **params):
"""Returns the maximum number of reads for the given solver parameters. Args: **params: Parameters for the sampling method. Re... |
# dev note: in the future it would be good to have a way of doing this
# server-side, as we are duplicating logic here.
properties = self.properties
if self.software or not params:
# software solvers don't use any of the above parameters
return properties['num_... |
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def _sample(self, type_, linear, quadratic, params):
"""Internal method for both sample_ising and sample_qubo. Args: linear (list/dict):
Linear terms of the mod... |
# Check the problem
if not self.check_problem(linear, quadratic):
raise ValueError("Problem graph incompatible with solver.")
# Mix the new parameters with the default parameters
combined_params = dict(self._params)
combined_params.update(params)
# Check th... |
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def _format_params(self, type_, params):
"""Reformat some of the parameters for sapi.""" |
if 'initial_state' in params:
# NB: at this moment the error raised when initial_state does not match lin/quad (in
# active qubits) is not very informative, but there is also no clean way to check here
# that they match because lin can be either a list or a dict. In the futu... |
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def check_problem(self, linear, quadratic):
"""Test if an Ising model matches the graph provided by the solver. Args: linear (list/dict):
Linear terms of the mo... |
for key, value in uniform_iterator(linear):
if value != 0 and key not in self.nodes:
return False
for key, value in uniform_iterator(quadratic):
if value != 0 and tuple(key) not in self.edges:
return False
return True |
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def _retrieve_problem(self, id_):
"""Resume polling for a problem previously submitted. Args: id_: Identification of the query. Returns: :obj: `Future` """ |
future = Future(self, id_, self.return_matrix, None)
self.client._poll(future)
return future |
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def _get_converter_type(identifier):
"""Return the converter type for `identifier`.""" |
if isinstance(identifier, str):
return ConverterType[identifier]
if isinstance(identifier, ConverterType):
return identifier
return ConverterType(identifier) |
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def resample(input_data, ratio, converter_type='sinc_best', verbose=False):
"""Resample the signal in `input_data` at once. Parameters input_data : ndarray Input... |
from samplerate.lowlevel import src_simple
from samplerate.exceptions import ResamplingError
input_data = np.require(input_data, requirements='C', dtype=np.float32)
if input_data.ndim == 2:
num_frames, channels = input_data.shape
output_shape = (int(num_frames * ratio), channels)
e... |
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def set_ratio(self, new_ratio):
"""Set a new conversion ratio immediately.""" |
from samplerate.lowlevel import src_set_ratio
return src_set_ratio(self._state, new_ratio) |
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def process(self, input_data, ratio, end_of_input=False, verbose=False):
"""Resample the signal in `input_data`. Parameters input_data : ndarray Input data. A si... |
from samplerate.lowlevel import src_process
from samplerate.exceptions import ResamplingError
input_data = np.require(input_data, requirements='C', dtype=np.float32)
if input_data.ndim == 2:
num_frames, channels = input_data.shape
output_shape = (int(num_frames ... |
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def _create(self):
"""Create new callback resampler.""" |
from samplerate.lowlevel import ffi, src_callback_new, src_delete
from samplerate.exceptions import ResamplingError
state, handle, error = src_callback_new(
self._callback, self._converter_type.value, self._channels)
if error != 0:
raise ResamplingError(error)
... |
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def set_starting_ratio(self, ratio):
""" Set the starting conversion ratio for the next `read` call. """ |
from samplerate.lowlevel import src_set_ratio
if self._state is None:
self._create()
src_set_ratio(self._state, ratio)
self.ratio = ratio |
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def reset(self):
"""Reset state.""" |
from samplerate.lowlevel import src_reset
if self._state is None:
self._create()
src_reset(self._state) |
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def read(self, num_frames):
"""Read a number of frames from the resampler. Parameters num_frames : int Number of frames to read. Returns ------- output_data : nd... |
from samplerate.lowlevel import src_callback_read, src_error
from samplerate.exceptions import ResamplingError
if self._state is None:
self._create()
if self._channels > 1:
output_shape = (num_frames, self._channels)
elif self._channels == 1:
... |
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def get_variance(seq):
""" Batch variance calculation. """ |
m = get_mean(seq)
return sum((v-m)**2 for v in seq)/float(len(seq)) |
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def mean_absolute_error(seq, correct):
""" Batch mean absolute error calculation. """ |
assert len(seq) == len(correct)
diffs = [abs(a-b) for a, b in zip(seq, correct)]
return sum(diffs)/float(len(diffs)) |
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def normalize(seq):
""" Scales each number in the sequence so that the sum of all numbers equals 1. """ |
s = float(sum(seq))
return [v/s for v in seq] |
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def entropy_variance(data, class_attr=None, method=DEFAULT_CONTINUOUS_METRIC):
""" Calculates the variance fo a continuous class attribute, to be used as an entr... |
assert method in CONTINUOUS_METRICS, "Unknown entropy variance metric: %s" % (method,)
assert (class_attr is None and isinstance(data, dict)) \
or (class_attr is not None and isinstance(data, list))
if isinstance(data, dict):
lst = data
else:
lst = [record.get(class_attr) for re... |
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def majority_value(data, class_attr):
""" Creates a list of all values in the target attribute for each record in the data list object, and returns the value tha... |
if is_continuous(data[0][class_attr]):
return CDist(seq=[record[class_attr] for record in data])
else:
return most_frequent([record[class_attr] for record in data]) |
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def most_frequent(lst):
""" Returns the item that appears most frequently in the given list. """ |
lst = lst[:]
highest_freq = 0
most_freq = None
for val in unique(lst):
if lst.count(val) > highest_freq:
most_freq = val
highest_freq = lst.count(val)
return most_freq |
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def unique(lst):
""" Returns a list made up of the unique values found in lst. i.e., it removes the redundant values in lst. """ |
lst = lst[:]
unique_lst = []
# Cycle through the list and add each value to the unique list only once.
for item in lst:
if unique_lst.count(item) <= 0:
unique_lst.append(item)
# Return the list with all redundant values removed.
return unique_lst |
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def create_decision_tree(data, attributes, class_attr, fitness_func, wrapper, **kwargs):
""" Returns a new decision tree based on the examples given. """ |
split_attr = kwargs.get('split_attr', None)
split_val = kwargs.get('split_val', None)
assert class_attr not in attributes
node = None
data = list(data) if isinstance(data, Data) else data
if wrapper.is_continuous_class:
stop_value = CDist(seq=[r[class_attr] for r in data])
... |
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def add(self, k, count=1):
""" Increments the count for the given element. """ |
self.counts[k] += count
self.total += count |
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def best(self):
""" Returns the element with the highest probability. """ |
b = (-1e999999, None)
for k, c in iteritems(self.counts):
b = max(b, (c, k))
return b[1] |
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def update(self, dist):
""" Adds the given distribution's counts to the current distribution. """ |
assert isinstance(dist, DDist)
for k, c in iteritems(dist.counts):
self.counts[k] += c
self.total += dist.total |
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def probability_lt(self, x):
""" Returns the probability of a random variable being less than the given value. """ |
if self.mean is None:
return
return normdist(x=x, mu=self.mean, sigma=self.standard_deviation) |
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def probability_in(self, a, b):
""" Returns the probability of a random variable falling between the given values. """ |
if self.mean is None:
return
p1 = normdist(x=a, mu=self.mean, sigma=self.standard_deviation)
p2 = normdist(x=b, mu=self.mean, sigma=self.standard_deviation)
return abs(p1 - p2) |
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def probability_gt(self, x):
""" Returns the probability of a random variable being greater than the given value. """ |
if self.mean is None:
return
p = normdist(x=x, mu=self.mean, sigma=self.standard_deviation)
return 1-p |
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def copy_no_data(self):
""" Returns a copy of the object without any data. """ |
return type(self)(
[],
order=list(self.header_modes),
types=self.header_types.copy(),
modes=self.header_modes.copy()) |
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def is_valid(self, name, value):
""" Returns true if the given value matches the type for the given name according to the schema. Returns false otherwise. """ |
if name not in self.header_types:
return False
t = self.header_types[name]
if t == ATTR_TYPE_DISCRETE:
return isinstance(value, int)
elif t == ATTR_TYPE_CONTINUOUS:
return isinstance(value, (float, Decimal))
return True |
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def _read_header(self):
""" When a CSV file is given, extracts header information the file. Otherwise, this header data must be explicitly given when the object ... |
if not self.filename or self.header_types:
return
rows = csv.reader(open(self.filename))
#header = rows.next()
header = next(rows)
self.header_types = {} # {attr_name:type}
self._class_attr_name = None
self.header_order = [] # [attr_name,...]
... |
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def validate_row(self, row):
""" Ensure each element in the row matches the schema. """ |
clean_row = {}
if isinstance(row, (tuple, list)):
assert self.header_order, "No attribute order specified."
assert len(row) == len(self.header_order), \
"Row length does not match header length."
itr = zip(self.header_order, row)
else:
... |
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def split(self, ratio=0.5, leave_one_out=False):
""" Returns two Data instances, containing the data randomly split between the two according to the given ratio.... |
a_labels = set()
a = self.copy_no_data()
b = self.copy_no_data()
for row in self:
if leave_one_out and not self.is_continuous_class:
label = row[self.class_attribute_name]
if label not in a_labels:
a_labels.add(label)
... |
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def _get_attribute_value_for_node(self, record):
""" Gets the closest value for the current node's attribute matching the given record. """ |
# Abort if this node has not get split on an attribute.
if self.attr_name is None:
return
# Otherwise, lookup the attribute value for this node in the
# given record.
attr = self.attr_name
attr_value = record[attr]
attr_values = sel... |
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def get_values(self, attr_name):
""" Retrieves the unique set of values seen for the given attribute at this node. """ |
ret = list(self._attr_value_cdist[attr_name].keys()) \
+ list(self._attr_value_counts[attr_name].keys()) \
+ list(self._branches.keys())
ret = set(ret)
return ret |
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def get_best_splitting_attr(self):
""" Returns the name of the attribute with the highest gain. """ |
best = (-1e999999, None)
for attr in self.attributes:
best = max(best, (self.get_gain(attr), attr))
best_gain, best_attr = best
return best_attr |
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def get_gain(self, attr_name):
""" Calculates the information gain from splitting on the given attribute. """ |
subset_entropy = 0.0
for value in iterkeys(self._attr_value_counts[attr_name]):
value_prob = self.get_value_prob(attr_name, value)
e = self.get_entropy(attr_name, value)
subset_entropy += value_prob * e
return (self.main_entropy - subset_entropy) |
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def get_value_ddist(self, attr_name, attr_value):
""" Returns the class value probability distribution of the given attribute value. """ |
assert not self.tree.data.is_continuous_class, \
"Discrete distributions are only maintained for " + \
"discrete class types."
ddist = DDist()
cls_counts = self._attr_class_value_counts[attr_name][attr_value]
for cls_value, cls_count in iteritems(cls_counts):
... |
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def get_value_prob(self, attr_name, value):
""" Returns the value probability of the given attribute at this node. """ |
if attr_name not in self._attr_value_count_totals:
return
n = self._attr_value_counts[attr_name][value]
d = self._attr_value_count_totals[attr_name]
return n/float(d) |
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def predict(self, record, depth=0):
""" Returns the estimated value of the class attribute for the given record. """ |
# Check if we're ready to predict.
if not self.ready_to_predict:
raise NodeNotReadyToPredict
# Lookup attribute value.
attr_value = self._get_attribute_value_for_node(record)
# Propagate decision to leaf node.
if self.attr_name:
... |
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def ready_to_split(self):
""" Returns true if this node is ready to branch off additional nodes. Returns false otherwise. """ |
# Never split if we're a leaf that predicts adequately.
threshold = self._tree.leaf_threshold
if self._tree.data.is_continuous_class:
var = self._class_cdist.variance
if var is not None and threshold is not None \
and var <= threshold:
return ... |
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def set_leaf_dist(self, attr_value, dist):
""" Sets the probability distribution at a leaf node. """ |
assert self.attr_name
assert self.tree.data.is_valid(self.attr_name, attr_value), \
"Value %s is invalid for attribute %s." \
% (attr_value, self.attr_name)
if self.is_continuous_class:
assert isinstance(dist, CDist)
assert self.attr_name
... |
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def train(self, record):
""" Incrementally update the statistics at this node. """ |
self.n += 1
class_attr = self.tree.data.class_attribute_name
class_value = record[class_attr]
# Update class statistics.
is_con = self.tree.data.is_continuous_class
if is_con:
# For a continuous class.
self._class_cdist += class_value
... |
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def build(cls, data, *args, **kwargs):
""" Constructs a classification or regression tree in a single batch by analyzing the given data. """ |
assert isinstance(data, Data)
if data.is_continuous_class:
fitness_func = gain_variance
else:
fitness_func = get_gain
t = cls(data=data, *args, **kwargs)
t._data = data
t.sample_count = len(data)
t._tree = create_decision_tree(
... |
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def out_of_bag_mae(self):
""" Returns the mean absolute error for predictions on the out-of-bag samples. """ |
if not self._out_of_bag_mae_clean:
try:
self._out_of_bag_mae = self.test(self.out_of_bag_samples)
self._out_of_bag_mae_clean = True
except NodeNotReadyToPredict:
return
return self._out_of_bag_mae.copy() |
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def out_of_bag_samples(self):
""" Returns the out-of-bag samples list, inside a wrapper to keep track of modifications. """ |
#TODO:replace with more a generic pass-through wrapper?
class O(object):
def __init__(self, tree):
self.tree = tree
def __len__(self):
return len(self.tree._out_of_bag_samples)
def append(self, v):
self.tree._out_of_bag... |
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def set_missing_value_policy(self, policy, target_attr_name=None):
""" Sets the behavior for one or all attributes to use when traversing the tree using a query ... |
assert policy in MISSING_VALUE_POLICIES, \
"Unknown policy: %s" % (policy,)
for attr_name in self.data.attribute_names:
if target_attr_name is not None and target_attr_name != attr_name:
continue
self.missing_value_policy[attr_name] = policy |
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def train(self, record):
""" Incrementally updates the tree with the given sample record. """ |
assert self.data.class_attribute_name in record, \
"The class attribute must be present in the record."
record = record.copy()
self.sample_count += 1
self.tree.train(record) |
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def _fell_trees(self):
""" Removes trees from the forest according to the specified fell method. """ |
if callable(self.fell_method):
for tree in self.fell_method(list(self.trees)):
self.trees.remove(tree) |
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def _get_best_prediction(self, record, train=True):
""" Gets the prediction from the tree with the lowest mean absolute error. """ |
if not self.trees:
return
best = (+1e999999, None)
for tree in self.trees:
best = min(best, (tree.mae.mean, tree))
_, best_tree = best
prediction, tree_mae = best_tree.predict(record, train=train)
return prediction.mean |
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def best_oob_mae_weight(trees):
""" Returns weights so that the tree with smallest out-of-bag mean absolute error """ |
best = (+1e999999, None)
for tree in trees:
oob_mae = tree.out_of_bag_mae
if oob_mae is None or oob_mae.mean is None:
continue
best = min(best, (oob_mae.mean, tree))
best_mae, best_tree = best
if best_tree is None:
return
... |
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def mean_oob_mae_weight(trees):
""" Returns weights proportional to the out-of-bag mean absolute error for each tree. """ |
weights = []
active_trees = []
for tree in trees:
oob_mae = tree.out_of_bag_mae
if oob_mae is None or oob_mae.mean is None:
continue
weights.append(oob_mae.mean)
active_trees.append(tree)
if not active_trees:
re... |
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def _grow_trees(self):
""" Adds new trees to the forest according to the specified growth method. """ |
if self.grow_method == GROW_AUTO_INCREMENTAL:
self.tree_kwargs['auto_grow'] = True
while len(self.trees) < self.size:
self.trees.append(Tree(data=self.data, **self.tree_kwargs)) |
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def train(self, record):
""" Updates the trees with the given training record. """ |
self._fell_trees()
self._grow_trees()
for tree in self.trees:
if random.random() < self.sample_ratio:
tree.train(record)
else:
tree.out_of_bag_samples.append(record)
while len(tree.out_of_bag_samples) > self.max_out_of_bag_... |
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def get_configfile_paths(system=True, user=True, local=True, only_existing=True):
"""Return a list of local configuration file paths. Search paths for configurat... |
candidates = []
# system-wide has the lowest priority, `/etc/dwave/dwave.conf`
if system:
candidates.extend(homebase.site_config_dir_list(
app_author=CONF_AUTHOR, app_name=CONF_APP,
use_virtualenv=False, create=False))
# user-local will override it, `~/.config/dwave/d... |
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def get_default_configfile_path():
"""Return the default configuration-file path. Typically returns a user-local configuration file; e.g: ``~/.config/dwave/dwave... |
base = homebase.user_config_dir(
app_author=CONF_AUTHOR, app_name=CONF_APP, roaming=False,
use_virtualenv=False, create=False)
path = os.path.join(base, CONF_FILENAME)
return path |
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def load_config_from_files(filenames=None):
"""Load D-Wave Cloud Client configuration from a list of files. .. note:: This method is not standardly used to set u... |
if filenames is None:
filenames = get_configfile_paths()
config = configparser.ConfigParser(default_section="defaults")
for filename in filenames:
try:
with open(filename, 'r') as f:
config.read_file(f, filename)
except (IOError, OSError):
ra... |
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def load_profile_from_files(filenames=None, profile=None):
"""Load a profile from a list of D-Wave Cloud Client configuration files. .. note:: This method is not... |
# progressively build config from a file, or a list of auto-detected files
# raises ConfigFileReadError/ConfigFileParseError on error
config = load_config_from_files(filenames)
# determine profile name fallback:
# (1) profile key under [defaults],
# (2) first non-[defaults] section
# (... |
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def load_config(config_file=None, profile=None, client=None, endpoint=None, token=None, solver=None, proxy=None):
"""Load D-Wave Cloud Client configuration based... |
if profile is None:
profile = os.getenv("DWAVE_PROFILE")
if config_file == False:
# skip loading from file altogether
section = {}
elif config_file == True:
# force auto-detection, disregarding DWAVE_CONFIG_FILE
section = load_profile_from_files(None, profile)
... |
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def legacy_load_config(profile=None, endpoint=None, token=None, solver=None, proxy=None, **kwargs):
"""Load configured URLs and token for the SAPI server. .. war... |
def _parse_config(fp, filename):
fields = ('endpoint', 'token', 'proxy', 'solver')
config = OrderedDict()
for line in fp:
# strip whitespace, skip blank and comment lines
line = line.strip()
if not line or line.startswith('#'):
continue
... |
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def src_simple(input_data, output_data, ratio, converter_type, channels):
"""Perform a single conversion from an input buffer to an output buffer. Simple interfa... |
input_frames, _ = _check_data(input_data)
output_frames, _ = _check_data(output_data)
data = ffi.new('SRC_DATA*')
data.input_frames = input_frames
data.output_frames = output_frames
data.src_ratio = ratio
data.data_in = ffi.cast('float*', ffi.from_buffer(input_data))
data.data_out = ffi... |
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def src_new(converter_type, channels):
"""Initialise a new sample rate converter. Parameters converter_type : int Converter to be used. channels : int Number of ... |
error = ffi.new('int*')
state = _lib.src_new(converter_type, channels, error)
return state, error[0] |
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def src_process(state, input_data, output_data, ratio, end_of_input=0):
"""Standard processing function. Returns non zero on error. """ |
input_frames, _ = _check_data(input_data)
output_frames, _ = _check_data(output_data)
data = ffi.new('SRC_DATA*')
data.input_frames = input_frames
data.output_frames = output_frames
data.src_ratio = ratio
data.data_in = ffi.cast('float*', ffi.from_buffer(input_data))
data.data_out = ffi... |
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def _src_input_callback(cb_data, data):
"""Internal callback function to be used with the callback API. Pulls the Python callback function from the handle contai... |
cb_data = ffi.from_handle(cb_data)
ret = cb_data['callback']()
if ret is None:
cb_data['last_input'] = None
return 0 # No frames supplied
input_data = _np.require(ret, requirements='C', dtype=_np.float32)
input_frames, channels = _check_data(input_data)
# Check whether the cor... |
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def src_callback_new(callback, converter_type, channels):
"""Initialisation for the callback based API. Parameters callback : function Called whenever new frames... |
cb_data = {'callback': callback, 'channels': channels}
handle = ffi.new_handle(cb_data)
error = ffi.new('int*')
state = _lib.src_callback_new(_src_input_callback, converter_type,
channels, error, handle)
if state == ffi.NULL:
return None, handle, error[0]
... |
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def src_callback_read(state, ratio, frames, data):
"""Read up to `frames` worth of data using the callback API. Returns ------- frames : int Number of frames rea... |
data_ptr = ffi.cast('float*f', ffi.from_buffer(data))
return _lib.src_callback_read(state, ratio, frames, data_ptr) |
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def from_config(cls, config_file=None, profile=None, client=None, endpoint=None, token=None, solver=None, proxy=None, legacy_config_fallback=False, **kwargs):
""... |
# try loading configuration from a preferred new config subsystem
# (`./dwave.conf`, `~/.config/dwave/dwave.conf`, etc)
config = load_config(
config_file=config_file, profile=profile, client=client,
endpoint=endpoint, token=token, solver=solver, proxy=proxy)
_LO... |
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def close(self):
"""Perform a clean shutdown. Waits for all the currently scheduled work to finish, kills the workers, and closes the connection pool. .. note:: ... |
# Finish all the work that requires the connection
_LOGGER.debug("Joining submission queue")
self._submission_queue.join()
_LOGGER.debug("Joining cancel queue")
self._cancel_queue.join()
_LOGGER.debug("Joining poll queue")
self._poll_queue.join()
_LOGGER.... |
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def get_solver(self, name=None, refresh=False, **filters):
"""Load the configuration for a single solver. Makes a blocking web call to `{endpoint}/solvers/remote... |
_LOGGER.debug("Requested a solver that best matches feature filters=%r", filters)
# backward compatibility: name as the first feature
if name is not None:
filters.setdefault('name', name)
# in absence of other filters, config/env solver filters/name are used
if not... |
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def _submit(self, body, future):
"""Enqueue a problem for submission to the server. This method is thread safe. """ |
self._submission_queue.put(self._submit.Message(body, future)) |
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def _do_submit_problems(self):
"""Pull problems from the submission queue and submit them. Note: This method is always run inside of a daemon thread. """ |
try:
while True:
# Pull as many problems as we can, block on the first one,
# but once we have one problem, switch to non-blocking then
# submit without blocking again.
# `None` task is used to signal thread termination
... |
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def _handle_problem_status(self, message, future):
"""Handle the results of a problem submission or results request. This method checks the status of the problem... |
try:
_LOGGER.trace("Handling response: %r", message)
_LOGGER.debug("Handling response for %s with status %s", message.get('id'), message.get('status'))
# Handle errors in batch mode
if 'error_code' in message and 'error_msg' in message:
raise Sol... |
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def _do_cancel_problems(self):
"""Pull ids from the cancel queue and submit them. Note: This method is always run inside of a daemon thread. """ |
try:
while True:
# Pull as many problems as we can, block when none are available.
# `None` task is used to signal thread termination
item = self._cancel_queue.get()
if item is None:
break
item_lis... |
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def _poll(self, future):
"""Enqueue a problem to poll the server for status.""" |
if future._poll_backoff is None:
# on first poll, start with minimal back-off
future._poll_backoff = self._POLL_BACKOFF_MIN
# if we have ETA of results, schedule the first poll for then
if future.eta_min and self._is_clock_diff_acceptable(future):
... |
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def _do_poll_problems(self):
"""Poll the server for the status of a set of problems. Note: This method is always run inside of a daemon thread. """ |
try:
# grouped futures (all scheduled within _POLL_GROUP_TIMEFRAME)
frame_futures = {}
def task_done():
self._poll_queue.task_done()
def add(future):
# add future to query frame_futures
# returns: worker lives on?... |
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def _do_load_results(self):
"""Submit a query asking for the results for a particular problem. To request the results of a problem: ``GET /problems/{problem_id}/... |
try:
while True:
# Select a problem
future = self._load_queue.get()
# `None` task signifies thread termination
if future is None:
break
_LOGGER.debug("Loading results of: %s", future.id)
... |
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def encode_bqm_as_qp(solver, linear, quadratic):
"""Encode the binary quadratic problem for submission to a given solver, using the `qp` format for data. Args: s... |
active = active_qubits(linear, quadratic)
# Encode linear terms. The coefficients of the linear terms of the objective
# are encoded as an array of little endian 64 bit doubles.
# This array is then base64 encoded into a string safe for json.
# The order of the terms is determined by the _encoding... |
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def decode_qp(msg):
"""Decode SAPI response that uses `qp` format, without numpy. The 'qp' format is the current encoding used for problems and samples. In this ... |
# Decode the simple buffers
result = msg['answer']
result['active_variables'] = _decode_ints(result['active_variables'])
active_variables = result['active_variables']
if 'num_occurrences' in result:
result['num_occurrences'] = _decode_ints(result['num_occurrences'])
result['energies'] =... |
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def _decode_byte(byte):
"""Helper for decode_qp, turns a single byte into a list of bits. Args: byte: byte to be decoded Returns: list of bits corresponding to b... |
bits = []
for _ in range(8):
bits.append(byte & 1)
byte >>= 1
return bits |
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def _decode_ints(message):
"""Helper for decode_qp, decodes an int array. The int array is stored as little endian 32 bit integers. The array has then been base6... |
binary = base64.b64decode(message)
return struct.unpack('<' + ('i' * (len(binary) // 4)), binary) |
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def _decode_doubles(message):
"""Helper for decode_qp, decodes a double array. The double array is stored as little endian 64 bit doubles. The array has then bee... |
binary = base64.b64decode(message)
return struct.unpack('<' + ('d' * (len(binary) // 8)), binary) |
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def decode_qp_numpy(msg, return_matrix=True):
"""Decode SAPI response, results in a `qp` format, explicitly using numpy. If numpy is not installed, the method wi... |
import numpy as np
result = msg['answer']
# Build some little endian type encodings
double_type = np.dtype(np.double)
double_type = double_type.newbyteorder('<')
int_type = np.dtype(np.int32)
int_type = int_type.newbyteorder('<')
# Decode the simple buffers
result['energies'] = n... |
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def evaluate_ising(linear, quad, state):
"""Calculate the energy of a state given the Hamiltonian. Args: linear: Linear Hamiltonian terms. quad: Quadratic Hamilt... |
# If we were given a numpy array cast to list
if _numpy and isinstance(state, np.ndarray):
return evaluate_ising(linear, quad, state.tolist())
# Accumulate the linear and quadratic values
energy = 0.0
for index, value in uniform_iterator(linear):
energy += state[index] * value
... |
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def active_qubits(linear, quadratic):
"""Calculate a set of all active qubits. Qubit is "active" if it has bias or coupling attached. Args: linear (dict[variable... |
active = {idx for idx,bias in uniform_iterator(linear)}
for edge, _ in six.iteritems(quadratic):
active.update(edge)
return active |
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def strip_head(sequence, values):
"""Strips elements of `values` from the beginning of `sequence`.""" |
values = set(values)
return list(itertools.dropwhile(lambda x: x in values, sequence)) |
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def strip_tail(sequence, values):
"""Strip `values` from the end of `sequence`.""" |
return list(reversed(list(strip_head(reversed(sequence), values)))) |
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def datetime_to_timestamp(dt):
"""Convert timezone-aware `datetime` to POSIX timestamp and return seconds since UNIX epoch. Note: similar to `datetime.timestamp(... |
epoch = datetime.utcfromtimestamp(0).replace(tzinfo=UTC)
return (dt - epoch).total_seconds() |
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| def argshash(self, args, kwargs):
"Hash mutable arguments' containers with immutable keys and values."
a = repr(args)
b = repr(sorted((repr(k), repr(v)) for k, v in kwargs.items()))
return a + b |
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