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modin-project/modin | modin/engines/base/frame/partition_manager.py | BaseFrameManager._apply_func_to_list_of_partitions | def _apply_func_to_list_of_partitions(self, func, partitions, **kwargs):
"""Applies a function to a list of remote partitions.
Note: The main use for this is to preprocess the func.
Args:
func: The func to apply
partitions: The list of partitions
Returns:
... | python | def _apply_func_to_list_of_partitions(self, func, partitions, **kwargs):
"""Applies a function to a list of remote partitions.
Note: The main use for this is to preprocess the func.
Args:
func: The func to apply
partitions: The list of partitions
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modin-project/modin | modin/engines/base/frame/partition_manager.py | BaseFrameManager.apply_func_to_select_indices | def apply_func_to_select_indices(self, axis, func, indices, keep_remaining=False):
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Note: Your internal function must take a kwarg `internal_indices` for
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modin-project/modin | modin/engines/base/frame/partition_manager.py | BaseFrameManager.apply_func_to_select_indices_along_full_axis | def apply_func_to_select_indices_along_full_axis(
self, axis, func, indices, keep_remaining=False
):
"""Applies a function to a select subset of full columns/rows.
Note: This should be used when you need to apply a function that relies
on some global information for the entire c... | python | def apply_func_to_select_indices_along_full_axis(
self, axis, func, indices, keep_remaining=False
):
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modin-project/modin | modin/engines/base/frame/partition_manager.py | BaseFrameManager.apply_func_to_indices_both_axis | def apply_func_to_indices_both_axis(
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Apply a function to along both axis
Important: For your func to operate d... | python | def apply_func_to_indices_both_axis(
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lazy=False,
keep_remaining=True,
mutate=False,
item_to_distribute=None,
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modin-project/modin | modin/engines/base/frame/partition_manager.py | BaseFrameManager.inter_data_operation | def inter_data_operation(self, axis, func, other):
"""Apply a function that requires two BaseFrameManager objects.
Args:
axis: The axis to apply the function over (0 - rows, 1 - columns)
func: The function to apply
other: The other BaseFrameManager object to apply fu... | python | def inter_data_operation(self, axis, func, other):
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modin-project/modin | modin/engines/base/frame/partition_manager.py | BaseFrameManager.manual_shuffle | def manual_shuffle(self, axis, shuffle_func, lengths):
"""Shuffle the partitions based on the `shuffle_func`.
Args:
axis: The axis to shuffle across.
shuffle_func: The function to apply before splitting the result.
lengths: The length of each partition to split the r... | python | def manual_shuffle(self, axis, shuffle_func, lengths):
"""Shuffle the partitions based on the `shuffle_func`.
Args:
axis: The axis to shuffle across.
shuffle_func: The function to apply before splitting the result.
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modin-project/modin | modin/pandas/io.py | read_parquet | def read_parquet(path, engine="auto", columns=None, **kwargs):
"""Load a parquet object from the file path, returning a DataFrame.
Args:
path: The filepath of the parquet file.
We only support local files for now.
engine: This argument doesn't do anything for now.
kwargs: ... | python | def read_parquet(path, engine="auto", columns=None, **kwargs):
"""Load a parquet object from the file path, returning a DataFrame.
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path: The filepath of the parquet file.
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modin-project/modin | modin/pandas/io.py | _make_parser_func | def _make_parser_func(sep):
"""Creates a parser function from the given sep.
Args:
sep: The separator default to use for the parser.
Returns:
A function object.
"""
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Args:
sep: The separator default to use for the parser.
Returns:
A function object.
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modin-project/modin | modin/pandas/io.py | _read | def _read(**kwargs):
"""Read csv file from local disk.
Args:
filepath_or_buffer:
The filepath of the csv file.
We only support local files for now.
kwargs: Keyword arguments in pandas.read_csv
"""
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Args:
filepath_or_buffer:
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kwargs: Keyword arguments in pandas.read_csv
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modin-project/modin | modin/pandas/io.py | read_sql | def read_sql(
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columns=None,
chunksize=None,
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""" Read SQL query or database table into a DataFrame.
Args:
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chunksize=None,
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modin-project/modin | modin/engines/base/io.py | BaseIO.read_parquet | def read_parquet(cls, path, engine, columns, **kwargs):
"""Load a parquet object from the file path, returning a DataFrame.
Ray DataFrame only supports pyarrow engine for now.
Args:
path: The filepath of the parquet file.
We only support local files for now.
... | python | def read_parquet(cls, path, engine, columns, **kwargs):
"""Load a parquet object from the file path, returning a DataFrame.
Ray DataFrame only supports pyarrow engine for now.
Args:
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We only support local files for now.
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modin-project/modin | modin/engines/base/io.py | BaseIO._read | def _read(cls, **kwargs):
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We only support local files for now.
kwargs: Keyword arguments in pandas.read_csv
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kwargs: Keyword arguments in pandas.read_csv
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EpistasisLab/tpot | tpot/builtins/one_hot_encoder.py | auto_select_categorical_features | def auto_select_categorical_features(X, threshold=10):
"""Make a feature mask of categorical features in X.
Features with less than 10 unique values are considered categorical.
Parameters
----------
X : array-like or sparse matrix, shape=(n_samples, n_features)
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EpistasisLab/tpot | tpot/builtins/one_hot_encoder.py | _X_selected | def _X_selected(X, selected):
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n_features = X.shape[1]
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sel = np.zeros(n_features, dtype=bool)
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non_sel = np.logical_not(sel)
n_selected = np.sum(sel)
X_sel = X[:, ind[sel]]
... | python | def _X_selected(X, selected):
"""Split X into selected features and other features"""
n_features = X.shape[1]
ind = np.arange(n_features)
sel = np.zeros(n_features, dtype=bool)
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EpistasisLab/tpot | tpot/base.py | TPOTBase.fit | def fit(self, features, target, sample_weight=None, groups=None):
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features: array-like {n_samples, n_features}
Feature matrix
target: array-like {n_samples}
List of class labels fo... | python | def _summary_of_best_pipeline(self, features, target):
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Feature matrix
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array-like: {n_samples}
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EpistasisLab/tpot | tpot/base.py | TPOTBase.score | def score(self, testing_features, testing_target):
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testing_features: array-like {n_samples, n_features}
Feature matrix of the testing set
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features: array-like {n_samples, n_features}
Feature matrix of the testing set
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EpistasisLab/tpot | tpot/base.py | TPOTBase.clean_pipeline_string | def clean_pipeline_string(self, individual):
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EpistasisLab/tpot | tpot/base.py | TPOTBase._check_periodic_pipeline | def _check_periodic_pipeline(self, gen):
"""If enough time has passed, save a new optimized pipeline. Currently used in the per generation hook in the optimization loop.
Parameters
----------
gen: int
Generation number
Returns
-------
None
"""... | python | def _check_periodic_pipeline(self, gen):
"""If enough time has passed, save a new optimized pipeline. Currently used in the per generation hook in the optimization loop.
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EpistasisLab/tpot | tpot/base.py | TPOTBase.export | def export(self, output_file_name, data_file_path=''):
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String containing the path and file name of the desired output file
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output_file_name: string
String containing the path and file name of the desired output file
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EpistasisLab/tpot | tpot/base.py | TPOTBase._impute_values | def _impute_values(self, features):
"""Impute missing values in a feature set.
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features: array-like {n_samples, n_features}
A feature matrix
Returns
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array-like {n_samples, n_features}
"""
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"""Impute missing values in a feature set.
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A feature matrix
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array-like {n_samples, n_features}
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EpistasisLab/tpot | tpot/base.py | TPOTBase._check_dataset | def _check_dataset(self, features, target, sample_weight=None):
"""Check if a dataset has a valid feature set and labels.
Parameters
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features: array-like {n_samples, n_features}
Feature matrix
target: array-like {n_samples} or None
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"""Check if a dataset has a valid feature set and labels.
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EpistasisLab/tpot | tpot/base.py | TPOTBase._compile_to_sklearn | def _compile_to_sklearn(self, expr):
"""Compile a DEAP pipeline into a sklearn pipeline.
Parameters
----------
expr: DEAP individual
The DEAP pipeline to be compiled
Returns
-------
sklearn_pipeline: sklearn.pipeline.Pipeline
"""
skle... | python | def _compile_to_sklearn(self, expr):
"""Compile a DEAP pipeline into a sklearn pipeline.
Parameters
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expr: DEAP individual
The DEAP pipeline to be compiled
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sklearn_pipeline: sklearn.pipeline.Pipeline
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EpistasisLab/tpot | tpot/base.py | TPOTBase._set_param_recursive | def _set_param_recursive(self, pipeline_steps, parameter, value):
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Parameters
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pipeline_steps: array-like
List of (str, obj) tuples from a scikit-learn pipeline or related object
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EpistasisLab/tpot | tpot/base.py | TPOTBase._stop_by_max_time_mins | def _stop_by_max_time_mins(self):
"""Stop optimization process once maximum minutes have elapsed."""
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total_mins_elapsed = (datetime.now() - self._start_datetime).total_seconds() / 60.
if total_mins_elapsed >= self.max_time_mins:
raise Keyboa... | python | def _stop_by_max_time_mins(self):
"""Stop optimization process once maximum minutes have elapsed."""
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EpistasisLab/tpot | tpot/base.py | TPOTBase._combine_individual_stats | def _combine_individual_stats(self, operator_count, cv_score, individual_stats):
"""Combine the stats with operator count and cv score and preprare to be written to _evaluated_individuals
Parameters
----------
operator_count: int
number of components in the pipeline
... | python | def _combine_individual_stats(self, operator_count, cv_score, individual_stats):
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EpistasisLab/tpot | tpot/base.py | TPOTBase._evaluate_individuals | def _evaluate_individuals(self, population, features, target, sample_weight=None, groups=None):
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EpistasisLab/tpot | tpot/base.py | TPOTBase._preprocess_individuals | def _preprocess_individuals(self, individuals):
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individuals: a list of DEAP individual
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EpistasisLab/tpot | tpot/base.py | TPOTBase._update_evaluated_individuals_ | def _update_evaluated_individuals_(self, result_score_list, eval_individuals_str, operator_counts, stats_dicts):
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EpistasisLab/tpot | tpot/base.py | TPOTBase._update_pbar | def _update_pbar(self, pbar_num=1, pbar_msg=None):
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pbar_num: int
How many pipelines has been processed
pbar_msg: None or string
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EpistasisLab/tpot | tpot/base.py | TPOTBase._random_mutation_operator | def _random_mutation_operator(self, individual, allow_shrink=True):
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individual: DEAP individual
A list of pipeline operators and model parameters that can be
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EpistasisLab/tpot | tpot/base.py | TPOTBase._gen_grow_safe | def _gen_grow_safe(self, pset, min_, max_, type_=None):
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pset: PrimitiveSetTyped
Primitive set from which primitives are selected.
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EpistasisLab/tpot | tpot/base.py | TPOTBase._operator_count | def _operator_count(self, individual):
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EpistasisLab/tpot | tpot/base.py | TPOTBase._update_val | def _update_val(self, val, result_score_list):
"""Update values in the list of result scores and self._pbar during pipeline evaluation.
Parameters
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val: float or "Timeout"
CV scores
result_score_list: list
A list of CV scores
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... | python | def _update_val(self, val, result_score_list):
"""Update values in the list of result scores and self._pbar during pipeline evaluation.
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CV scores
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EpistasisLab/tpot | tpot/base.py | TPOTBase._generate | def _generate(self, pset, min_, max_, condition, type_=None):
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EpistasisLab/tpot | tpot/builtins/feature_transformers.py | CategoricalSelector.transform | def transform(self, X):
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EpistasisLab/tpot | tpot/builtins/stacking_estimator.py | StackingEstimator.fit | def fit(self, X, y=None, **fit_params):
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Parameters
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X: array-like of shape (n_samples, n_features)
The training input samples.
y: array-like, shape (n_samples,)
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"""Fit the StackingEstimator meta-transformer.
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EpistasisLab/tpot | tpot/builtins/stacking_estimator.py | StackingEstimator.transform | def transform(self, X):
"""Transform data by adding two synthetic feature(s).
Parameters
----------
X: numpy ndarray, {n_samples, n_components}
New data, where n_samples is the number of samples and n_components is the number of components.
Returns
-------
... | python | def transform(self, X):
"""Transform data by adding two synthetic feature(s).
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X: numpy ndarray, {n_samples, n_components}
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EpistasisLab/tpot | tpot/metrics.py | balanced_accuracy | def balanced_accuracy(y_true, y_pred):
"""Default scoring function: balanced accuracy.
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one-vs-rest encoding, then computes an unweighted average of the class accuracies.
Parameters
----------
y_true: numpy.ndarray {n_... | python | def balanced_accuracy(y_true, y_pred):
"""Default scoring function: balanced accuracy.
Balanced accuracy computes each class' accuracy on a per-class basis using a
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EpistasisLab/tpot | tpot/builtins/zero_count.py | ZeroCount.transform | def transform(self, X, y=None):
"""Transform data by adding two virtual features.
Parameters
----------
X: numpy ndarray, {n_samples, n_components}
New data, where n_samples is the number of samples and n_components
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y: None
... | python | def transform(self, X, y=None):
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EpistasisLab/tpot | tpot/operator_utils.py | source_decode | def source_decode(sourcecode, verbose=0):
"""Decode operator source and import operator class.
Parameters
----------
sourcecode: string
a string of operator source (e.g 'sklearn.feature_selection.RFE')
verbose: int, optional (default: 0)
How much information TPOT communicates while ... | python | def source_decode(sourcecode, verbose=0):
"""Decode operator source and import operator class.
Parameters
----------
sourcecode: string
a string of operator source (e.g 'sklearn.feature_selection.RFE')
verbose: int, optional (default: 0)
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EpistasisLab/tpot | tpot/operator_utils.py | set_sample_weight | def set_sample_weight(pipeline_steps, sample_weight=None):
"""Recursively iterates through all objects in the pipeline and sets sample weight.
Parameters
----------
pipeline_steps: array-like
List of (str, obj) tuples from a scikit-learn pipeline or related object
sample_weight: array-like
... | python | def set_sample_weight(pipeline_steps, sample_weight=None):
"""Recursively iterates through all objects in the pipeline and sets sample weight.
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----------
pipeline_steps: array-like
List of (str, obj) tuples from a scikit-learn pipeline or related object
sample_weight: array-like
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EpistasisLab/tpot | tpot/operator_utils.py | TPOTOperatorClassFactory | def TPOTOperatorClassFactory(opsourse, opdict, BaseClass=Operator, ArgBaseClass=ARGType, verbose=0):
"""Dynamically create operator class.
Parameters
----------
opsourse: string
operator source in config dictionary (key)
opdict: dictionary
operator params in config dictionary (value... | python | def TPOTOperatorClassFactory(opsourse, opdict, BaseClass=Operator, ArgBaseClass=ARGType, verbose=0):
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opsourse: string
operator source in config dictionary (key)
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EpistasisLab/tpot | tpot/driver.py | positive_integer | def positive_integer(value):
"""Ensure that the provided value is a positive integer.
Parameters
----------
value: int
The number to evaluate
Returns
-------
value: int
Returns a positive integer
"""
try:
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except Exception:
rais... | python | def positive_integer(value):
"""Ensure that the provided value is a positive integer.
Parameters
----------
value: int
The number to evaluate
Returns
-------
value: int
Returns a positive integer
"""
try:
value = int(value)
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EpistasisLab/tpot | tpot/driver.py | float_range | def float_range(value):
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Parameters
----------
value: float
The number to evaluate
Returns
-------
value: float
Returns a float in the range (0., 1.)
"""
try:
value = float(value)
... | python | def float_range(value):
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value: float
The number to evaluate
Returns
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value: float
Returns a float in the range (0., 1.)
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EpistasisLab/tpot | tpot/driver.py | _get_arg_parser | def _get_arg_parser():
"""Main function that is called when TPOT is run on the command line."""
parser = argparse.ArgumentParser(
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add... | python | def _get_arg_parser():
"""Main function that is called when TPOT is run on the command line."""
parser = argparse.ArgumentParser(
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EpistasisLab/tpot | tpot/driver.py | load_scoring_function | def load_scoring_function(scoring_func):
"""
converts mymodule.myfunc in the myfunc
object itself so tpot receives a scoring function
"""
if scoring_func and ("." in scoring_func):
try:
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... | python | def load_scoring_function(scoring_func):
"""
converts mymodule.myfunc in the myfunc
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module_name, func_name = scoring_func.rsplit('.', 1)
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EpistasisLab/tpot | tpot/driver.py | tpot_driver | def tpot_driver(args):
"""Perform a TPOT run."""
if args.VERBOSITY >= 2:
_print_args(args)
input_data = _read_data_file(args)
features = input_data.drop(args.TARGET_NAME, axis=1)
training_features, testing_features, training_target, testing_target = \
train_test_split(features, inp... | python | def tpot_driver(args):
"""Perform a TPOT run."""
if args.VERBOSITY >= 2:
_print_args(args)
input_data = _read_data_file(args)
features = input_data.drop(args.TARGET_NAME, axis=1)
training_features, testing_features, training_target, testing_target = \
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EpistasisLab/tpot | tpot/builtins/feature_set_selector.py | FeatureSetSelector.fit | def fit(self, X, y=None):
"""Fit FeatureSetSelector for feature selection
Parameters
----------
X: array-like of shape (n_samples, n_features)
The training input samples.
y: array-like, shape (n_samples,)
The target values (integers that correspond to cla... | python | def fit(self, X, y=None):
"""Fit FeatureSetSelector for feature selection
Parameters
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X: array-like of shape (n_samples, n_features)
The training input samples.
y: array-like, shape (n_samples,)
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EpistasisLab/tpot | tpot/builtins/feature_set_selector.py | FeatureSetSelector.transform | def transform(self, X):
"""Make subset after fit
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X: numpy ndarray, {n_samples, n_features}
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Returns
-------
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"""Make subset after fit
Parameters
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X: numpy ndarray, {n_samples, n_features}
New data, where n_samples is the number of samples and n_features is the number of features.
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EpistasisLab/tpot | tpot/builtins/feature_set_selector.py | FeatureSetSelector._get_support_mask | def _get_support_mask(self):
"""
Get the boolean mask indicating which features are selected
Returns
-------
support : boolean array of shape [# input features]
An element is True iff its corresponding feature is selected for
retention.
"""
... | python | def _get_support_mask(self):
"""
Get the boolean mask indicating which features are selected
Returns
-------
support : boolean array of shape [# input features]
An element is True iff its corresponding feature is selected for
retention.
"""
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EpistasisLab/tpot | tpot/gp_deap.py | pick_two_individuals_eligible_for_crossover | def pick_two_individuals_eligible_for_crossover(population):
"""Pick two individuals from the population which can do crossover, that is, they share a primitive.
Parameters
----------
population: array of individuals
Returns
----------
tuple: (individual, individual)
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"""Pick two individuals from the population which can do crossover, that is, they share a primitive.
Parameters
----------
population: array of individuals
Returns
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tuple: (individual, individual)
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EpistasisLab/tpot | tpot/gp_deap.py | mutate_random_individual | def mutate_random_individual(population, toolbox):
"""Picks a random individual from the population, and performs mutation on a copy of it.
Parameters
----------
population: array of individuals
Returns
----------
individual: individual
An individual which is a mutated copy of one ... | python | def mutate_random_individual(population, toolbox):
"""Picks a random individual from the population, and performs mutation on a copy of it.
Parameters
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population: array of individuals
Returns
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individual: individual
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EpistasisLab/tpot | tpot/gp_deap.py | varOr | def varOr(population, toolbox, lambda_, cxpb, mutpb):
"""Part of an evolutionary algorithm applying only the variation part
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their fitness invalidated. The individuals are cloned so returned
population is independent of the input po... | python | def varOr(population, toolbox, lambda_, cxpb, mutpb):
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EpistasisLab/tpot | tpot/gp_deap.py | initialize_stats_dict | def initialize_stats_dict(individual):
'''
Initializes the stats dict for individual
The statistics initialized are:
'generation': generation in which the individual was evaluated. Initialized as: 0
'mutation_count': number of mutation operations applied to the individual and its predecessor... | python | def initialize_stats_dict(individual):
'''
Initializes the stats dict for individual
The statistics initialized are:
'generation': generation in which the individual was evaluated. Initialized as: 0
'mutation_count': number of mutation operations applied to the individual and its predecessor... | [
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EpistasisLab/tpot | tpot/gp_deap.py | eaMuPlusLambda | def eaMuPlusLambda(population, toolbox, mu, lambda_, cxpb, mutpb, ngen, pbar,
stats=None, halloffame=None, verbose=0, per_generation_function=None):
"""This is the :math:`(\mu + \lambda)` evolutionary algorithm.
:param population: A list of individuals.
:param toolbox: A :class:`~deap.bas... | python | def eaMuPlusLambda(population, toolbox, mu, lambda_, cxpb, mutpb, ngen, pbar,
stats=None, halloffame=None, verbose=0, per_generation_function=None):
"""This is the :math:`(\mu + \lambda)` evolutionary algorithm.
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EpistasisLab/tpot | tpot/gp_deap.py | cxOnePoint | def cxOnePoint(ind1, ind2):
"""Randomly select in each individual and exchange each subtree with the
point as root between each individual.
:param ind1: First tree participating in the crossover.
:param ind2: Second tree participating in the crossover.
:returns: A tuple of two trees.
"""
# L... | python | def cxOnePoint(ind1, ind2):
"""Randomly select in each individual and exchange each subtree with the
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:param ind1: First tree participating in the crossover.
:param ind2: Second tree participating in the crossover.
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EpistasisLab/tpot | tpot/gp_deap.py | mutNodeReplacement | def mutNodeReplacement(individual, pset):
"""Replaces a randomly chosen primitive from *individual* by a randomly
chosen primitive no matter if it has the same number of arguments from the :attr:`pset`
attribute of the individual.
Parameters
----------
individual: DEAP individual
A list ... | python | def mutNodeReplacement(individual, pset):
"""Replaces a randomly chosen primitive from *individual* by a randomly
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attribute of the individual.
Parameters
----------
individual: DEAP individual
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EpistasisLab/tpot | tpot/gp_deap.py | _wrapped_cross_val_score | def _wrapped_cross_val_score(sklearn_pipeline, features, target,
cv, scoring_function, sample_weight=None,
groups=None, use_dask=False):
"""Fit estimator and compute scores for a given dataset split.
Parameters
----------
sklearn_pipeline : pipe... | python | def _wrapped_cross_val_score(sklearn_pipeline, features, target,
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groups=None, use_dask=False):
"""Fit estimator and compute scores for a given dataset split.
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EpistasisLab/tpot | tpot/export_utils.py | get_by_name | def get_by_name(opname, operators):
"""Return operator class instance by name.
Parameters
----------
opname: str
Name of the sklearn class that belongs to a TPOT operator
operators: list
List of operator classes from operator library
Returns
-------
ret_op_class: class
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"""Return operator class instance by name.
Parameters
----------
opname: str
Name of the sklearn class that belongs to a TPOT operator
operators: list
List of operator classes from operator library
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ret_op_class: class
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EpistasisLab/tpot | tpot/export_utils.py | export_pipeline | def export_pipeline(exported_pipeline,
operators, pset,
impute=False, pipeline_score=None,
random_state=None,
data_file_path=''):
"""Generate source code for a TPOT Pipeline.
Parameters
----------
exported_pipeline: deap.cr... | python | def export_pipeline(exported_pipeline,
operators, pset,
impute=False, pipeline_score=None,
random_state=None,
data_file_path=''):
"""Generate source code for a TPOT Pipeline.
Parameters
----------
exported_pipeline: deap.cr... | [
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EpistasisLab/tpot | tpot/export_utils.py | expr_to_tree | def expr_to_tree(ind, pset):
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Parameters
----------
ind: deap.creator.Individual
The pipeline that is being exported
Returns
-------
pipeline_tree: list
List of operators in the current optimized pipeline
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Parameters
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ind: deap.creator.Individual
The pipeline that is being exported
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EpistasisLab/tpot | tpot/export_utils.py | generate_import_code | def generate_import_code(pipeline, operators, impute=False):
"""Generate all library import calls for use in TPOT.export().
Parameters
----------
pipeline: List
List of operators in the current optimized pipeline
operators:
List of operator class from operator library
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"""Generate all library import calls for use in TPOT.export().
Parameters
----------
pipeline: List
List of operators in the current optimized pipeline
operators:
List of operator class from operator library
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EpistasisLab/tpot | tpot/export_utils.py | generate_pipeline_code | def generate_pipeline_code(pipeline_tree, operators):
"""Generate code specific to the construction of the sklearn Pipeline.
Parameters
----------
pipeline_tree: list
List of operators in the current optimized pipeline
Returns
-------
Source code for the sklearn pipeline
"""
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"""Generate code specific to the construction of the sklearn Pipeline.
Parameters
----------
pipeline_tree: list
List of operators in the current optimized pipeline
Returns
-------
Source code for the sklearn pipeline
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EpistasisLab/tpot | tpot/export_utils.py | generate_export_pipeline_code | def generate_export_pipeline_code(pipeline_tree, operators):
"""Generate code specific to the construction of the sklearn Pipeline for export_pipeline.
Parameters
----------
pipeline_tree: list
List of operators in the current optimized pipeline
Returns
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Source code for the ... | python | def generate_export_pipeline_code(pipeline_tree, operators):
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Parameters
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pipeline_tree: list
List of operators in the current optimized pipeline
Returns
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EpistasisLab/tpot | tpot/export_utils.py | _indent | def _indent(text, amount):
"""Indent a multiline string by some number of spaces.
Parameters
----------
text: str
The text to be indented
amount: int
The number of spaces to indent the text
Returns
-------
indented_text
"""
indentation = amount * ' '
return... | python | def _indent(text, amount):
"""Indent a multiline string by some number of spaces.
Parameters
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text: str
The text to be indented
amount: int
The number of spaces to indent the text
Returns
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indented_text
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googleapis/google-cloud-python | api_core/google/api_core/page_iterator.py | Page.next | def next(self):
"""Get the next value in the page."""
item = six.next(self._item_iter)
result = self._item_to_value(self._parent, item)
# Since we've successfully got the next value from the
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self._remaining -= 1
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"""Get the next value in the page."""
item = six.next(self._item_iter)
result = self._item_to_value(self._parent, item)
# Since we've successfully got the next value from the
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googleapis/google-cloud-python | api_core/google/api_core/page_iterator.py | HTTPIterator._verify_params | def _verify_params(self):
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Raises:
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"""
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"""Verifies the parameters don't use any reserved parameter.
Raises:
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googleapis/google-cloud-python | api_core/google/api_core/page_iterator.py | HTTPIterator._next_page | def _next_page(self):
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Optional[Page]: The next page in the iterator or :data:`None` if
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"""
if self._has_next_page():
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"""Get the next page in the iterator.
Returns:
Optional[Page]: The next page in the iterator or :data:`None` if
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googleapis/google-cloud-python | api_core/google/api_core/page_iterator.py | HTTPIterator._get_query_params | def _get_query_params(self):
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dict: A dictionary of query parameters.
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googleapis/google-cloud-python | api_core/google/api_core/page_iterator.py | HTTPIterator._get_next_page_response | def _get_next_page_response(self):
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Raises:
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"""
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googleapis/google-cloud-python | firestore/google/cloud/firestore_v1beta1/order.py | Order.compare | def compare(cls, left, right):
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Main comparison function for all Firestore types.
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] | 85e80125a59cb10f8cb105f25ecc099e4b940b50 | https://github.com/googleapis/google-cloud-python/blob/85e80125a59cb10f8cb105f25ecc099e4b940b50/firestore/google/cloud/firestore_v1beta1/order.py#L62-L101 | train |
googleapis/google-cloud-python | vision/google/cloud/vision_v1p4beta1/gapic/image_annotator_client.py | ImageAnnotatorClient.batch_annotate_files | def batch_annotate_files(
self,
requests,
retry=google.api_core.gapic_v1.method.DEFAULT,
timeout=google.api_core.gapic_v1.method.DEFAULT,
metadata=None,
):
"""
Service that performs image detection and annotation for a batch of files.
Now only "applica... | python | def batch_annotate_files(
self,
requests,
retry=google.api_core.gapic_v1.method.DEFAULT,
timeout=google.api_core.gapic_v1.method.DEFAULT,
metadata=None,
):
"""
Service that performs image detection and annotation for a batch of files.
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googleapis/google-cloud-python | vision/google/cloud/vision_v1p4beta1/gapic/image_annotator_client.py | ImageAnnotatorClient.async_batch_annotate_images | def async_batch_annotate_images(
self,
requests,
output_config,
retry=google.api_core.gapic_v1.method.DEFAULT,
timeout=google.api_core.gapic_v1.method.DEFAULT,
metadata=None,
):
"""
Run asynchronous image detection and annotation for a list of images.
... | python | def async_batch_annotate_images(
self,
requests,
output_config,
retry=google.api_core.gapic_v1.method.DEFAULT,
timeout=google.api_core.gapic_v1.method.DEFAULT,
metadata=None,
):
"""
Run asynchronous image detection and annotation for a list of images.
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googleapis/google-cloud-python | vision/google/cloud/vision_v1p4beta1/gapic/image_annotator_client.py | ImageAnnotatorClient.async_batch_annotate_files | def async_batch_annotate_files(
self,
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retry=google.api_core.gapic_v1.method.DEFAULT,
timeout=google.api_core.gapic_v1.method.DEFAULT,
metadata=None,
):
"""
Run asynchronous image detection and annotation for a list of generic
files, such as P... | python | def async_batch_annotate_files(
self,
requests,
retry=google.api_core.gapic_v1.method.DEFAULT,
timeout=google.api_core.gapic_v1.method.DEFAULT,
metadata=None,
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googleapis/google-cloud-python | bigquery/google/cloud/bigquery/__init__.py | load_ipython_extension | def load_ipython_extension(ipython):
"""Called by IPython when this module is loaded as an IPython extension."""
from google.cloud.bigquery.magics import _cell_magic
ipython.register_magic_function(
_cell_magic, magic_kind="cell", magic_name="bigquery"
) | python | def load_ipython_extension(ipython):
"""Called by IPython when this module is loaded as an IPython extension."""
from google.cloud.bigquery.magics import _cell_magic
ipython.register_magic_function(
_cell_magic, magic_kind="cell", magic_name="bigquery"
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googleapis/google-cloud-python | api_core/google/api_core/exceptions.py | from_http_status | def from_http_status(status_code, message, **kwargs):
"""Create a :class:`GoogleAPICallError` from an HTTP status code.
Args:
status_code (int): The HTTP status code.
message (str): The exception message.
kwargs: Additional arguments passed to the :class:`GoogleAPICallError`
... | python | def from_http_status(status_code, message, **kwargs):
"""Create a :class:`GoogleAPICallError` from an HTTP status code.
Args:
status_code (int): The HTTP status code.
message (str): The exception message.
kwargs: Additional arguments passed to the :class:`GoogleAPICallError`
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googleapis/google-cloud-python | api_core/google/api_core/exceptions.py | from_http_response | def from_http_response(response):
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Args:
response (requests.Response): The HTTP response.
Returns:
GoogleAPICallError: An instance of the appropriate subclass of
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"""Create a :class:`GoogleAPICallError` from a :class:`requests.Response`.
Args:
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googleapis/google-cloud-python | api_core/google/api_core/exceptions.py | from_grpc_status | def from_grpc_status(status_code, message, **kwargs):
"""Create a :class:`GoogleAPICallError` from a :class:`grpc.StatusCode`.
Args:
status_code (grpc.StatusCode): The gRPC status code.
message (str): The exception message.
kwargs: Additional arguments passed to the :class:`GoogleAPICal... | python | def from_grpc_status(status_code, message, **kwargs):
"""Create a :class:`GoogleAPICallError` from a :class:`grpc.StatusCode`.
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googleapis/google-cloud-python | api_core/google/api_core/exceptions.py | from_grpc_error | def from_grpc_error(rpc_exc):
"""Create a :class:`GoogleAPICallError` from a :class:`grpc.RpcError`.
Args:
rpc_exc (grpc.RpcError): The gRPC error.
Returns:
GoogleAPICallError: An instance of the appropriate subclass of
:class:`GoogleAPICallError`.
"""
if isinstance(rpc... | python | def from_grpc_error(rpc_exc):
"""Create a :class:`GoogleAPICallError` from a :class:`grpc.RpcError`.
Args:
rpc_exc (grpc.RpcError): The gRPC error.
Returns:
GoogleAPICallError: An instance of the appropriate subclass of
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"""
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googleapis/google-cloud-python | datastore/google/cloud/datastore/_http.py | _request | def _request(http, project, method, data, base_url):
"""Make a request over the Http transport to the Cloud Datastore API.
:type http: :class:`requests.Session`
:param http: HTTP object to make requests.
:type project: str
:param project: The project to make the request for.
:type method: str... | python | def _request(http, project, method, data, base_url):
"""Make a request over the Http transport to the Cloud Datastore API.
:type http: :class:`requests.Session`
:param http: HTTP object to make requests.
:type project: str
:param project: The project to make the request for.
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googleapis/google-cloud-python | datastore/google/cloud/datastore/_http.py | _rpc | def _rpc(http, project, method, base_url, request_pb, response_pb_cls):
"""Make a protobuf RPC request.
:type http: :class:`requests.Session`
:param http: HTTP object to make requests.
:type project: str
:param project: The project to connect to. This is
usually your project na... | python | def _rpc(http, project, method, base_url, request_pb, response_pb_cls):
"""Make a protobuf RPC request.
:type http: :class:`requests.Session`
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:type project: str
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googleapis/google-cloud-python | datastore/google/cloud/datastore/_http.py | build_api_url | def build_api_url(project, method, base_url):
"""Construct the URL for a particular API call.
This method is used internally to come up with the URL to use when
making RPCs to the Cloud Datastore API.
:type project: str
:param project: The project to connect to. This is
usually... | python | def build_api_url(project, method, base_url):
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This method is used internally to come up with the URL to use when
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googleapis/google-cloud-python | datastore/google/cloud/datastore/_http.py | HTTPDatastoreAPI.lookup | def lookup(self, project_id, keys, read_options=None):
"""Perform a ``lookup`` request.
:type project_id: str
:param project_id: The project to connect to. This is
usually your project name in the cloud console.
:type keys: List[.entity_pb2.Key]
:para... | python | def lookup(self, project_id, keys, read_options=None):
"""Perform a ``lookup`` request.
:type project_id: str
:param project_id: The project to connect to. This is
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googleapis/google-cloud-python | datastore/google/cloud/datastore/_http.py | HTTPDatastoreAPI.run_query | def run_query(
self, project_id, partition_id, read_options=None, query=None, gql_query=None
):
"""Perform a ``runQuery`` request.
:type project_id: str
:param project_id: The project to connect to. This is
usually your project name in the cloud console.
... | python | def run_query(
self, project_id, partition_id, read_options=None, query=None, gql_query=None
):
"""Perform a ``runQuery`` request.
:type project_id: str
:param project_id: The project to connect to. This is
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