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def _declare_consumer(self, consumer, nowait=False):
"""Declare consumer so messages can be received from it using
:meth:`iterconsume`."""
if consumer.queue not in self._open_consumers:
# Use the ConsumerSet's consumer by default, but if the
# child consumer has a callbac... | Declare consumer so messages can be received from it using
:meth:`iterconsume`. | entailment |
def consume(self):
"""Declare consumers."""
head = self.consumers[:-1]
tail = self.consumers[-1]
[self._declare_consumer(consumer, nowait=True)
for consumer in head]
self._declare_consumer(tail, nowait=False) | Declare consumers. | entailment |
def iterconsume(self, limit=None):
"""Cycle between all consumers in consume mode.
See :meth:`Consumer.iterconsume`.
"""
self.consume()
return self.backend.consume(limit=limit) | Cycle between all consumers in consume mode.
See :meth:`Consumer.iterconsume`. | entailment |
def cancel(self):
"""Cancel a running :meth:`iterconsume` session."""
for consumer_tag in self._open_consumers.values():
try:
self.backend.cancel(consumer_tag)
except KeyError:
pass
self._open_consumers.clear() | Cancel a running :meth:`iterconsume` session. | entailment |
def convert_md_to_rst(source, destination=None, backup_dir=None):
"""Try to convert the source, an .md (markdown) file, to an .rst
(reStructuredText) file at the destination. If the destination isn't
provided, it defaults to be the same as the source path except for the
filename extension. If the destin... | Try to convert the source, an .md (markdown) file, to an .rst
(reStructuredText) file at the destination. If the destination isn't
provided, it defaults to be the same as the source path except for the
filename extension. If the destination file already exists, it will be
overwritten. In the event of an... | entailment |
def build_readme(base_path=None):
"""Call the conversion routine on README.md to generate README.rst.
Why do all this? Because pypi requires reStructuredText, but markdown
is friendlier to work with and is nicer for GitHub."""
if base_path:
path = os.path.join(base_path, 'README.md')
else:
... | Call the conversion routine on README.md to generate README.rst.
Why do all this? Because pypi requires reStructuredText, but markdown
is friendlier to work with and is nicer for GitHub. | entailment |
def sense(self):
"""Return a situation, encoded as a bit string, which represents
the observable state of the environment.
Usage:
situation = scenario.sense()
assert isinstance(situation, BitString)
Arguments: None
Return:
The current situati... | Return a situation, encoded as a bit string, which represents
the observable state of the environment.
Usage:
situation = scenario.sense()
assert isinstance(situation, BitString)
Arguments: None
Return:
The current situation. | entailment |
def execute(self, action):
"""Execute the indicated action within the environment and
return the resulting immediate reward dictated by the reward
program.
Usage:
immediate_reward = scenario.execute(selected_action)
Arguments:
action: The action to be ex... | Execute the indicated action within the environment and
return the resulting immediate reward dictated by the reward
program.
Usage:
immediate_reward = scenario.execute(selected_action)
Arguments:
action: The action to be executed within the current situation.
... | entailment |
def reset(self):
"""Reset the scenario, starting it over for a new run.
Usage:
if not scenario.more():
scenario.reset()
Arguments: None
Return: None
"""
self.remaining_cycles = self.initial_training_cycles
self.needle_index = random.r... | Reset the scenario, starting it over for a new run.
Usage:
if not scenario.more():
scenario.reset()
Arguments: None
Return: None | entailment |
def sense(self):
"""Return a situation, encoded as a bit string, which represents
the observable state of the environment.
Usage:
situation = scenario.sense()
assert isinstance(situation, BitString)
Arguments: None
Return:
The current situati... | Return a situation, encoded as a bit string, which represents
the observable state of the environment.
Usage:
situation = scenario.sense()
assert isinstance(situation, BitString)
Arguments: None
Return:
The current situation. | entailment |
def execute(self, action):
"""Execute the indicated action within the environment and
return the resulting immediate reward dictated by the reward
program.
Usage:
immediate_reward = scenario.execute(selected_action)
Arguments:
action: The action to be ex... | Execute the indicated action within the environment and
return the resulting immediate reward dictated by the reward
program.
Usage:
immediate_reward = scenario.execute(selected_action)
Arguments:
action: The action to be executed within the current situation.
... | entailment |
def get_possible_actions(self):
"""Return a sequence containing the possible actions that can be
executed within the environment.
Usage:
possible_actions = scenario.get_possible_actions()
Arguments: None
Return:
A sequence containing the possible actions... | Return a sequence containing the possible actions that can be
executed within the environment.
Usage:
possible_actions = scenario.get_possible_actions()
Arguments: None
Return:
A sequence containing the possible actions which can be
executed within t... | entailment |
def sense(self):
"""Return a situation, encoded as a bit string, which represents
the observable state of the environment.
Usage:
situation = scenario.sense()
assert isinstance(situation, BitString)
Arguments: None
Return:
The current situati... | Return a situation, encoded as a bit string, which represents
the observable state of the environment.
Usage:
situation = scenario.sense()
assert isinstance(situation, BitString)
Arguments: None
Return:
The current situation. | entailment |
def execute(self, action):
"""Execute the indicated action within the environment and
return the resulting immediate reward dictated by the reward
program.
Usage:
immediate_reward = scenario.execute(selected_action)
Arguments:
action: The action to be ex... | Execute the indicated action within the environment and
return the resulting immediate reward dictated by the reward
program.
Usage:
immediate_reward = scenario.execute(selected_action)
Arguments:
action: The action to be executed within the current situation.
... | entailment |
def more(self):
"""Return a Boolean indicating whether additional actions may be
executed, per the reward program.
Usage:
while scenario.more():
situation = scenario.sense()
selected_action = choice(possible_actions)
reward = scenario.... | Return a Boolean indicating whether additional actions may be
executed, per the reward program.
Usage:
while scenario.more():
situation = scenario.sense()
selected_action = choice(possible_actions)
reward = scenario.execute(selected_action)
... | entailment |
def execute(self, action):
"""Execute the indicated action within the environment and
return the resulting immediate reward dictated by the reward
program.
Usage:
immediate_reward = scenario.execute(selected_action)
Arguments:
action: The action to be ex... | Execute the indicated action within the environment and
return the resulting immediate reward dictated by the reward
program.
Usage:
immediate_reward = scenario.execute(selected_action)
Arguments:
action: The action to be executed within the current situation.
... | entailment |
def get_classifications(self):
"""Return the classifications made by the algorithm for this
scenario.
Usage:
model.run(scenario, learn=False)
classifications = scenario.get_classifications()
Arguments: None
Return:
An indexable sequence conta... | Return the classifications made by the algorithm for this
scenario.
Usage:
model.run(scenario, learn=False)
classifications = scenario.get_classifications()
Arguments: None
Return:
An indexable sequence containing the classifications made by
... | entailment |
def new_model(self, scenario):
"""Create and return a new classifier set initialized for handling
the given scenario.
Usage:
scenario = MUXProblem()
model = algorithm.new_model(scenario)
model.run(scenario, learn=True)
Arguments:
scenario... | Create and return a new classifier set initialized for handling
the given scenario.
Usage:
scenario = MUXProblem()
model = algorithm.new_model(scenario)
model.run(scenario, learn=True)
Arguments:
scenario: A Scenario instance.
Return:
... | entailment |
def run(self, scenario):
"""Run the algorithm, utilizing a classifier set to choose the
most appropriate action for each situation produced by the
scenario. Improve the situation/action mapping on each reward
cycle to maximize reward. Return the classifier set that was
created.
... | Run the algorithm, utilizing a classifier set to choose the
most appropriate action for each situation produced by the
scenario. Improve the situation/action mapping on each reward
cycle to maximize reward. Return the classifier set that was
created.
Usage:
scenario ... | entailment |
def _compute_prediction(self):
"""Compute the combined prediction and prediction weight for this
action set. The combined prediction is the weighted average of the
individual predictions of the classifiers. The combined prediction
weight is the sum of the individual prediction weights of... | Compute the combined prediction and prediction weight for this
action set. The combined prediction is the weighted average of the
individual predictions of the classifiers. The combined prediction
weight is the sum of the individual prediction weights of the
classifiers.
Usage:
... | entailment |
def best_prediction(self):
"""The highest value from among the predictions made by the action
sets in this match set."""
if self._best_prediction is None and self._action_sets:
self._best_prediction = max(
action_set.prediction
for action_set in self._... | The highest value from among the predictions made by the action
sets in this match set. | entailment |
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... | A tuple containing the actions whose action sets have the best
prediction. | entailment |
def select_action(self):
"""Select an action according to the action selection strategy of
the associated algorithm. If an action has already been selected,
raise a ValueError instead.
Usage:
if match_set.selected_action is None:
match_set.select_action()
... | Select an action according to the action selection strategy of
the associated algorithm. If an action has already been selected,
raise a ValueError instead.
Usage:
if match_set.selected_action is None:
match_set.select_action()
Arguments: None
Return... | entailment |
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 | Setter method for the selected_action property. | entailment |
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"
... | Setter method for the payoff property. | entailment |
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
match set's expected future payoff. The predecessor argument should
be either None or a MatchSet instance whose selected action... | If the predecessor is not None, gives the appropriate amount of
payoff to the predecessor in payment for its contribution to this
match set's expected future payoff. The predecessor argument should
be either None or a MatchSet instance whose selected action led
directly to this match set... | entailment |
def apply_payoff(self):
"""Apply the payoff that has been accumulated from immediate
reward and/or payments from successor match sets. Attempting to
call this method before an action has been selected or after it
has already been called for the same match set will result in a
Val... | Apply the payoff that has been accumulated from immediate
reward and/or payments from successor match sets. Attempting to
call this method before an action has been selected or after it
has already been called for the same match set will result in a
ValueError.
Usage:
... | entailment |
def match(self, situation):
"""Accept a situation (input) and return a MatchSet containing the
classifier rules whose conditions match the situation. If
appropriate per the algorithm managing this classifier set, create
new rules to ensure sufficient coverage of the possible actions.
... | Accept a situation (input) and return a MatchSet containing the
classifier rules whose conditions match the situation. If
appropriate per the algorithm managing this classifier set, create
new rules to ensure sufficient coverage of the possible actions.
Usage:
match_set = mo... | entailment |
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 algorithm in order to make room for the new rule. The rule
argument should be a ClassifierRule instance. The behavior of this
... | 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 algorithm in order to make room for the new rule. The rule
argument should be a ClassifierRule instance. The behavior of this
method depends on whethe... | entailment |
def discard(self, rule, count=1):
"""Remove one or more instances of a rule from the classifier set.
Return a Boolean indicating whether the rule's numerosity dropped
to zero. (If the rule's numerosity was already zero, do nothing and
return False.)
Usage:
if rule in... | Remove one or more instances of a rule from the classifier set.
Return a Boolean indicating whether the rule's numerosity dropped
to zero. (If the rule's numerosity was already zero, do nothing and
return False.)
Usage:
if rule in model and model.discard(rule, count=3):
... | entailment |
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
default was given, use None. This is useful for eliminating
duplicate copies of rules.
Usage:
unique_rule ... | Return the existing version of the given rule. If the rule is
not present in the classifier set, return the default. If no
default was given, use None. This is useful for eliminating
duplicate copies of rules.
Usage:
unique_rule = model.get(possible_duplicate, possible_dupli... | entailment |
def run(self, scenario, learn=True):
"""Run the algorithm, utilizing the classifier set to choose the
most appropriate action for each situation produced by the
scenario. If learn is True, improve the situation/action mapping to
maximize reward. Otherwise, ignore any reward received.
... | Run the algorithm, utilizing the classifier set to choose the
most appropriate action for each situation produced by the
scenario. If learn is True, improve the situation/action mapping to
maximize reward. Otherwise, ignore any reward received.
Usage:
model.run(scenario, lea... | entailment |
def ls(system, user, local, include_missing):
"""List configuration files detected (and/or examined paths)."""
# default action is to list *all* auto-detected files
if not (system or user or local):
system = user = local = True
for path in get_configfile_paths(system=system, user=user, local=l... | List configuration files detected (and/or examined paths). | entailment |
def inspect(config_file, profile):
"""Inspect existing configuration/profile."""
try:
section = load_profile_from_files(
[config_file] if config_file else None, profile)
click.echo("Configuration file: {}".format(config_file if config_file else "auto-detected"))
click.echo(... | Inspect existing configuration/profile. | entailment |
def create(config_file, profile):
"""Create and/or update cloud client configuration file."""
# determine the config file path
if config_file:
click.echo("Using configuration file: {}".format(config_file))
else:
# path not given, try to detect; or use default, but allow user to override... | Create and/or update cloud client configuration file. | entailment |
def _ping(config_file, profile, solver_def, request_timeout, polling_timeout, output):
"""Helper method for the ping command that uses `output()` for info output
and raises `CLIError()` on handled errors.
This function is invariant to output format and/or error signaling mechanism.
"""
config = di... | Helper method for the ping command that uses `output()` for info output
and raises `CLIError()` on handled errors.
This function is invariant to output format and/or error signaling mechanism. | entailment |
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)
... | Ping the QPU by submitting a single-qubit problem. | entailment |
def solvers(config_file, profile, solver_def, list_solvers):
"""Get solver details.
Unless solver name/id specified, fetch and display details for
all online solvers available on the configured endpoint.
"""
with Client.from_config(
config_file=config_file, profile=profile, solver=solv... | Get solver details.
Unless solver name/id specified, fetch and display details for
all online solvers available on the configured endpoint. | entailment |
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 = Cl... | Submit Ising-formulated problem and return samples. | entailment |
def get_input_callback(samplerate, params, num_samples=256):
"""Return a function that produces samples of a sine.
Parameters
----------
samplerate : float
The sample rate.
params : dict
Parameters for FM generation.
num_samples : int, optional
Number of samples to be ge... | Return a function that produces samples of a sine.
Parameters
----------
samplerate : float
The sample rate.
params : dict
Parameters for FM generation.
num_samples : int, optional
Number of samples to be generated on each call. | entailment |
def get_playback_callback(resampler, samplerate, params):
"""Return a sound playback callback.
Parameters
----------
resampler
The resampler from which samples are read.
samplerate : float
The sample rate.
params : dict
Parameters for FM generation.
"""
def call... | Return a sound playback callback.
Parameters
----------
resampler
The resampler from which samples are read.
samplerate : float
The sample rate.
params : dict
Parameters for FM generation. | entailment |
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),
... | Setup the resampling and audio output callbacks and start playback. | entailment |
def max_num_reads(self, **params):
"""Returns the maximum number of reads for the given solver parameters.
Args:
**params:
Parameters for the sampling method. Relevant to num_reads:
- annealing_time
- readout_thermalization
- ... | Returns the maximum number of reads for the given solver parameters.
Args:
**params:
Parameters for the sampling method. Relevant to num_reads:
- annealing_time
- readout_thermalization
- num_reads
- programming_therma... | entailment |
def sample_ising(self, linear, quadratic, **params):
"""Sample from the specified Ising model.
Args:
linear (list/dict): Linear terms of the model (h).
quadratic (dict of (int, int):float): Quadratic terms of the model (J).
**params: Parameters for the sampling metho... | Sample from the specified Ising model.
Args:
linear (list/dict): Linear terms of the model (h).
quadratic (dict of (int, int):float): Quadratic terms of the model (J).
**params: Parameters for the sampling method, specified per solver.
Returns:
:obj:`Fut... | entailment |
def sample_qubo(self, qubo, **params):
"""Sample from the specified QUBO.
Args:
qubo (dict of (int, int):float): Coefficients of a quadratic unconstrained binary
optimization (QUBO) model.
**params: Parameters for the sampling method, specified per solver.
... | Sample from the specified QUBO.
Args:
qubo (dict of (int, int):float): Coefficients of a quadratic unconstrained binary
optimization (QUBO) model.
**params: Parameters for the sampling method, specified per solver.
Returns:
:obj:`Future`
Exa... | entailment |
def _sample(self, type_, linear, quadratic, params):
"""Internal method for both sample_ising and sample_qubo.
Args:
linear (list/dict): Linear terms of the model.
quadratic (dict of (int, int):float): Quadratic terms of the model.
**params: Parameters for the sampli... | Internal method for both sample_ising and sample_qubo.
Args:
linear (list/dict): Linear terms of the model.
quadratic (dict of (int, int):float): Quadratic terms of the model.
**params: Parameters for the sampling method, specified per solver.
Returns:
:... | entailment |
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 ... | Reformat some of the parameters for sapi. | entailment |
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 model (h).
quadratic (dict of (int, int):float): Quadratic terms of the model (J).
Returns:
boolean
... | Test if an Ising model matches the graph provided by the solver.
Args:
linear (list/dict): Linear terms of the model (h).
quadratic (dict of (int, int):float): Quadratic terms of the model (J).
Returns:
boolean
Examples:
This example creates a c... | entailment |
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)
ret... | Resume polling for a problem previously submitted.
Args:
id_: Identification of the query.
Returns:
:obj: `Future` | entailment |
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) | Return the converter type for `identifier`. | entailment |
def resample(input_data, ratio, converter_type='sinc_best', verbose=False):
"""Resample the signal in `input_data` at once.
Parameters
----------
input_data : ndarray
Input data. A single channel is provided as a 1D array of `num_frames` length.
Input data with several channels is repre... | Resample the signal in `input_data` at once.
Parameters
----------
input_data : ndarray
Input data. A single channel is provided as a 1D array of `num_frames` length.
Input data with several channels is represented as a 2D array of shape
(`num_frames`, `num_channels`). For use with ... | entailment |
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) | Set a new conversion ratio immediately. | entailment |
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 single channel is provided as a 1D array of `num_frames` length.
Input data with several chan... | Resample the signal in `input_data`.
Parameters
----------
input_data : ndarray
Input data. A single channel is provided as a 1D array of `num_frames` length.
Input data with several channels is represented as a 2D array of shape
(`num_frames`, `num_channels`... | entailment |
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)
... | Create new callback resampler. | entailment |
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 | Set the starting conversion ratio for the next `read` call. | entailment |
def reset(self):
"""Reset state."""
from samplerate.lowlevel import src_reset
if self._state is None:
self._create()
src_reset(self._state) | Reset state. | entailment |
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 : ndarray
Resampled frames as a (`num_output_frames`, `num_channels`) ... | Read a number of frames from the resampler.
Parameters
----------
num_frames : int
Number of frames to read.
Returns
-------
output_data : ndarray
Resampled frames as a (`num_output_frames`, `num_channels`) or
(`num_output_frames`,) a... | entailment |
def get_variance(seq):
"""
Batch variance calculation.
"""
m = get_mean(seq)
return sum((v-m)**2 for v in seq)/float(len(seq)) | Batch variance calculation. | entailment |
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)) | Batch mean absolute error calculation. | entailment |
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] | Scales each number in the sequence so that the sum of all numbers equals 1. | entailment |
def normcdf(x, mu, sigma):
"""
Describes the probability that a real-valued random variable X with a given
probability distribution will be found at a value less than or equal to X
in a normal distribution.
http://en.wikipedia.org/wiki/Cumulative_distribution_function
"""
t = x-mu
y... | Describes the probability that a real-valued random variable X with a given
probability distribution will be found at a value less than or equal to X
in a normal distribution.
http://en.wikipedia.org/wiki/Cumulative_distribution_function | entailment |
def normpdf(x, mu, sigma):
"""
Describes the relative likelihood that a real-valued random variable X will
take on a given value.
http://en.wikipedia.org/wiki/Probability_density_function
"""
u = (x-mu)/abs(sigma)
y = (1/(math.sqrt(2*pi)*abs(sigma)))*math.exp(-u*u/2)
return y | Describes the relative likelihood that a real-valued random variable X will
take on a given value.
http://en.wikipedia.org/wiki/Probability_density_function | entailment |
def entropy(data, class_attr=None, method=DEFAULT_DISCRETE_METRIC):
"""
Calculates the entropy of the attribute attr in given data set data.
Parameters:
data<dict|list> :=
if dict, treated as value counts of the given attribute name
if list, treated as a raw list from which the valu... | Calculates the entropy of the attribute attr in given data set data.
Parameters:
data<dict|list> :=
if dict, treated as value counts of the given attribute name
if list, treated as a raw list from which the value counts will be generated
attr<string> := the name of the class attribute | entailment |
def entropy_variance(data, class_attr=None,
method=DEFAULT_CONTINUOUS_METRIC):
"""
Calculates the variance fo a continuous class attribute, to be used as an
entropy metric.
"""
assert method in CONTINUOUS_METRICS, "Unknown entropy variance metric: %s" % (method,)
assert (class_attr is None a... | Calculates the variance fo a continuous class attribute, to be used as an
entropy metric. | entailment |
def get_gain(data, attr, class_attr,
method=DEFAULT_DISCRETE_METRIC,
only_sub=0, prefer_fewer_values=False, entropy_func=None):
"""
Calculates the information gain (reduction in entropy) that would
result by splitting the data on the chosen attribute (attr).
Parameters:
prefer_fewe... | Calculates the information gain (reduction in entropy) that would
result by splitting the data on the chosen attribute (attr).
Parameters:
prefer_fewer_values := Weights the gain by the count of the attribute's
unique values. If multiple attributes have the same gain, but one has
s... | entailment |
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 that appears in this list
the most frequently.
"""
if is_continuous(data[0][class_attr]):
return CDist(seq=[record[class_attr] for... | Creates a list of all values in the target attribute for each record
in the data list object, and returns the value that appears in this list
the most frequently. | entailment |
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)
... | Returns the item that appears most frequently in the given list. | entailment |
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) <= ... | Returns a list made up of the unique values found in lst. i.e., it
removes the redundant values in lst. | entailment |
def choose_attribute(data, attributes, class_attr, fitness, method):
"""
Cycles through all the attributes and returns the attribute with the
highest information gain (or lowest entropy).
"""
best = (-1e999999, None)
for attr in attributes:
if attr == class_attr:
continue
... | Cycles through all the attributes and returns the attribute with the
highest information gain (or lowest entropy). | entailment |
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 =... | Returns a new decision tree based on the examples given. | entailment |
def add(self, k, count=1):
"""
Increments the count for the given element.
"""
self.counts[k] += count
self.total += count | Increments the count for the given element. | entailment |
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] | Returns the element with the highest probability. | entailment |
def probs(self):
"""
Returns a list of probabilities for all elements in the form
[(value1,prob1),(value2,prob2),...].
"""
return [
(k, self.counts[k]/float(self.total))
for k in iterkeys(self.counts)
] | Returns a list of probabilities for all elements in the form
[(value1,prob1),(value2,prob2),...]. | entailment |
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 | Adds the given distribution's counts to the current distribution. | entailment |
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) | Returns the probability of a random variable being less than the
given value. | entailment |
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=s... | Returns the probability of a random variable falling between the given
values. | entailment |
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 | Returns the probability of a random variable being greater than the
given value. | entailment |
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()) | Returns a copy of the object without any data. | entailment |
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 == AT... | Returns true if the given value matches the type for the given name
according to the schema.
Returns false otherwise. | entailment |
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
is instantiated.
"""
if not self.filename or self.header_types:
return
rows = csv.reader(op... | When a CSV file is given, extracts header information the file.
Otherwise, this header data must be explicitly given when the object
is instantiated. | entailment |
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), \
... | Ensure each element in the row matches the schema. | entailment |
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.
The first instance will contain the ratio of data specified.
The second instance will contain the remaining r... | Returns two Data instances, containing the data randomly split between
the two according to the given ratio.
The first instance will contain the ratio of data specified.
The second instance will contain the remaining ratio of data.
If leave_one_out is True, the ratio wi... | entailment |
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
# O... | Gets the closest value for the current node's attribute matching the
given record. | entailment |
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())
... | Retrieves the unique set of values seen for the given attribute
at this node. | entailment |
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 | Returns the name of the attribute with the highest gain. | entailment |
def get_entropy(self, attr_name=None, attr_value=None):
"""
Calculates the entropy of a specific attribute/value combination.
"""
is_con = self.tree.data.is_continuous_class
if is_con:
if attr_name is None:
# Calculate variance of class attribute.
... | Calculates the entropy of a specific attribute/value combination. | entailment |
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_en... | Calculates the information gain from splitting on the given attribute. | entailment |
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 ... | Returns the class value probability distribution of the given
attribute value. | entailment |
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[att... | Returns the value probability of the given attribute at this node. | entailment |
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... | Returns the estimated value of the class attribute for the given
record. | entailment |
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:
... | Returns true if this node is ready to branch off additional nodes.
Returns false otherwise. | entailment |
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_... | Sets the probability distribution at a leaf node. | entailment |
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... | Incrementally update the statistics at this node. | entailment |
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:
fitne... | Constructs a classification or regression tree in a single batch by
analyzing the given data. | entailment |
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 = ... | Returns the mean absolute error for predictions on the out-of-bag
samples. | entailment |
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
... | Returns the out-of-bag samples list, inside a wrapper to keep track
of modifications. | entailment |
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 vector and it encounters a branch that does not
exist.
"""
assert policy in MISSING_VALUE_POLICIES, \
... | Sets the behavior for one or all attributes to use when traversing the
tree using a query vector and it encounters a branch that does not
exist. | entailment |
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.tre... | Incrementally updates the tree with the given sample record. | entailment |
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) | Removes trees from the forest according to the specified fell method. | entailment |
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))
... | Gets the prediction from the tree with the lowest mean absolute error. | entailment |
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