kloodia/raw_github · Datasets at Hugging Face

Return the score on the given testing data using the user-specified scoring function. Parameters ---------- testing_features: array-like {n_samples, n_features} Feature matrix of the testing set testing_target: array-like {n_samples} List of class labels for pred...

def score(self, testing_features, testing_target): """Return the score on the given testing data using the user-specified scoring function. Parameters ---------- testing_features: array-like {n_samples, n_features} Feature matrix of the testing set testing_target: ar...

Use the optimized pipeline to estimate the class probabilities for a feature set. Parameters ---------- features: array-like {n_samples, n_features} Feature matrix of the testing set Returns ------- array-like: {n_samples, n_target} The class pro...

def predict_proba(self, features): """Use the optimized pipeline to estimate the class probabilities for a feature set. Parameters ---------- features: array-like {n_samples, n_features} Feature matrix of the testing set Returns ------- array-like: {...

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

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 """...

Impute missing values in a feature set. Parameters ---------- features: array-like {n_samples, n_features} A feature matrix Returns ------- array-like {n_samples, n_features}

def _impute_values(self, features): """Impute missing values in a feature set. Parameters ---------- features: array-like {n_samples, n_features} A feature matrix Returns ------- array-like {n_samples, n_features} """ if self.verbosit...

Use the optimized pipeline to predict the target for a feature set. Parameters ---------- features: array-like {n_samples, n_features} Feature matrix Returns ---------- array-like: {n_samples} Predicted target for the samples in the feature matri...

def predict(self, features): """Use the optimized pipeline to predict the target for a feature set. Parameters ---------- features: array-like {n_samples, n_features} Feature matrix Returns ---------- array-like: {n_samples} Predicted tar...

Setup Memory object for memory caching.

def _setup_memory(self): """Setup Memory object for memory caching. """ if self.memory: if isinstance(self.memory, str): if self.memory == "auto": # Create a temporary folder to store the transformers of the pipeline self._cache...

Check if a dataset has a valid feature set and labels. Parameters ---------- features: array-like {n_samples, n_features} Feature matrix target: array-like {n_samples} or None List of class labels for prediction sample_weight: array-like {n_samples} (opti...

def _check_dataset(self, features, target, sample_weight=None): """Check if a dataset has a valid feature set and labels. Parameters ---------- features: array-like {n_samples, n_features} Feature matrix target: array-like {n_samples} or None List of clas...

Compile a DEAP pipeline into a sklearn pipeline. Parameters ---------- expr: DEAP individual The DEAP pipeline to be compiled Returns ------- sklearn_pipeline: sklearn.pipeline.Pipeline

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...

Recursively iterate through all objects in the pipeline and set a given parameter. Parameters ---------- pipeline_steps: array-like List of (str, obj) tuples from a scikit-learn pipeline or related object parameter: str The parameter to assign a value for in each...

def _set_param_recursive(self, pipeline_steps, parameter, value): """Recursively iterate through all objects in the pipeline and set a given parameter. Parameters ---------- pipeline_steps: array-like List of (str, obj) tuples from a scikit-learn pipeline or related object ...

Stop optimization process once maximum minutes have elapsed.

def _stop_by_max_time_mins(self): """Stop optimization process once maximum minutes have elapsed.""" if self.max_time_mins: total_mins_elapsed = (datetime.now() - self._start_datetime).total_seconds() / 60. if total_mins_elapsed >= self.max_time_mins: raise Keyboa...

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 cv_score: float internal cross validation score individual_stats: dictio...

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 ...

Determine the fit of the provided individuals. Parameters ---------- population: a list of DEAP individual One individual is a list of pipeline operators and model parameters that can be compiled by DEAP into a callable function features: numpy.ndarray {n_samples...

def _evaluate_individuals(self, population, features, target, sample_weight=None, groups=None): """Determine the fit of the provided individuals. Parameters ---------- population: a list of DEAP individual One individual is a list of pipeline operators and model parameters t...

Preprocess DEAP individuals before pipeline evaluation. Parameters ---------- individuals: a list of DEAP individual One individual is a list of pipeline operators and model parameters that can be compiled by DEAP into a callable function Returns -------...

def _preprocess_individuals(self, individuals): """Preprocess DEAP individuals before pipeline evaluation. Parameters ---------- individuals: a list of DEAP individual One individual is a list of pipeline operators and model parameters that can be compiled by DEA...

Update self.evaluated_individuals_ and error message during pipeline evaluation. Parameters ---------- result_score_list: list A list of CV scores for evaluated pipelines eval_individuals_str: list A list of strings for evaluated pipelines operator_counts...

def _update_evaluated_individuals_(self, result_score_list, eval_individuals_str, operator_counts, stats_dicts): """Update self.evaluated_individuals_ and error message during pipeline evaluation. Parameters ---------- result_score_list: list A list of CV scores for evaluate...

Update self._pbar and error message during pipeline evaluation. Parameters ---------- pbar_num: int How many pipelines has been processed pbar_msg: None or string Error message Returns ------- None

def _update_pbar(self, pbar_num=1, pbar_msg=None): """Update self._pbar and error message during pipeline evaluation. Parameters ---------- pbar_num: int How many pipelines has been processed pbar_msg: None or string Error message Returns ...

Perform a replacement, insertion, or shrink mutation on an individual. Parameters ---------- individual: DEAP individual A list of pipeline operators and model parameters that can be compiled by DEAP into a callable function allow_shrink: bool (True) ...

def _random_mutation_operator(self, individual, allow_shrink=True): """Perform a replacement, insertion, or shrink mutation on an individual. Parameters ---------- individual: DEAP individual A list of pipeline operators and model parameters that can be compiled ...

Generate an expression where each leaf might have a different depth between min_ and max_. Parameters ---------- pset: PrimitiveSetTyped Primitive set from which primitives are selected. min_: int Minimum height of the produced trees. max_: int ...

def _gen_grow_safe(self, pset, min_, max_, type_=None): """Generate an expression where each leaf might have a different depth between min_ and max_. Parameters ---------- pset: PrimitiveSetTyped Primitive set from which primitives are selected. min_: int ...

Count the number of pipeline operators as a measure of pipeline complexity. Parameters ---------- individual: list A grown tree with leaves at possibly different depths dependending on the condition function. Returns ------- operator_count: int ...

def _operator_count(self, individual): """Count the number of pipeline operators as a measure of pipeline complexity. Parameters ---------- individual: list A grown tree with leaves at possibly different depths dependending on the condition function. Ret...

Update values in the list of result scores and self._pbar during pipeline evaluation. Parameters ---------- val: float or "Timeout" CV scores result_score_list: list A list of CV scores Returns ------- result_score_list: list ...

def _update_val(self, val, result_score_list): """Update values in the list of result scores and self._pbar during pipeline evaluation. Parameters ---------- val: float or "Timeout" CV scores result_score_list: list A list of CV scores Returns ...

Generate a Tree as a list of lists. The tree is build from the root to the leaves, and it stop growing when the condition is fulfilled. Parameters ---------- pset: PrimitiveSetTyped Primitive set from which primitives are selected. min_: int Mini...

def _generate(self, pset, min_, max_, condition, type_=None): """Generate a Tree as a list of lists. The tree is build from the root to the leaves, and it stop growing when the condition is fulfilled. Parameters ---------- pset: PrimitiveSetTyped Primitive s...

Select categorical features and transform them using OneHotEncoder. 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 ------- array-like,...

def transform(self, X): """Select categorical features and transform them using OneHotEncoder. 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 ...

Select continuous features and transform them using PCA. 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 ------- array-like, {n_samples...

def transform(self, X): """Select continuous features and transform them using PCA. 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 ---...

Fit the StackingEstimator meta-transformer. 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 classes in classification, real numbers i...

def fit(self, X, y=None, **fit_params): """Fit the StackingEstimator meta-transformer. 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 corr...

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 ------- X_transformed: array-like, s...

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 ------- ...

Default scoring function: balanced accuracy. Balanced accuracy computes each class' accuracy on a per-class basis using a one-vs-rest encoding, then computes an unweighted average of the class accuracies. Parameters ---------- y_true: numpy.ndarray {n_samples} True class labels y_pred:...

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 one-vs-rest encoding, then computes an unweighted average of the class accuracies. Parameters ---------- y_true: numpy.ndarray {n_...

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 is the number of components. y: None Unused Returns -...

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 is the number of components. y: None ...

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 it's running. 0 = none, 1 = minimal, 2 = ...

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 ...

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 List of sample weight Returns ------- sample_w...

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 ...

Dynamically create operator class. Parameters ---------- opsourse: string operator source in config dictionary (key) opdict: dictionary operator params in config dictionary (value) regression: bool True if it can be used in TPOTRegressor classification: bool True...

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...

Ensure that the provided value is a positive integer. Parameters ---------- value: int The number to evaluate Returns ------- value: int Returns a 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: value = int(value) except Exception: rais...

Ensure that the provided value is a float integer in the range [0., 1.]. Parameters ---------- value: float The number to evaluate Returns ------- value: float Returns a float in the range (0., 1.)

def float_range(value): """Ensure that the provided value is a float integer in the range [0., 1.]. Parameters ---------- value: float The number to evaluate Returns ------- value: float Returns a float in the range (0., 1.) """ try: value = float(value) ...

Main function that is called when TPOT is run on the command line.

def _get_arg_parser(): """Main function that is called when TPOT is run on the command line.""" parser = argparse.ArgumentParser( description=( 'A Python tool that automatically creates and optimizes machine ' 'learning pipelines using genetic programming.' ), add...

converts mymodule.myfunc in the myfunc object itself so tpot receives a 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: module_name, func_name = scoring_func.rsplit('.', 1) module_path = os.getcwd() ...

Perform a TPOT run.

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...

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 classes in classification, real numbers ...

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...

Make subset after fit Parameters ---------- 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. Returns ------- X_transformed: array-like, shape (n_samples, n_features + 1) or...

def transform(self, X): """Make subset after fit Parameters ---------- 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. Returns ------- X_transformed: array-like, s...

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.

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. """ ...

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) Two individuals which are not the same, but share at least one primitive. ...

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) Two individual...

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 of the individuals in population, the returned ind...

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 ...

Part of an evolutionary algorithm applying only the variation part (crossover, mutation **or** reproduction). The modified individuals have their fitness invalidated. The individuals are cloned so returned population is independent of the input population. :param population: A list of individuals to var...

def varOr(population, toolbox, lambda_, cxpb, mutpb): """Part of an evolutionary algorithm applying only the variation part (crossover, mutation **or** reproduction). The modified individuals have their fitness invalidated. The individuals are cloned so returned population is independent of the input po...

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 cumulatively. Initialized as: 0 'crossover...

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...

This is the :math:`(\mu + \lambda)` evolutionary algorithm. :param population: A list of individuals. :param toolbox: A :class:`~deap.base.Toolbox` that contains the evolution operators. :param mu: The number of individuals to select for the next generation. :param lambda\_: The numb...

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...

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.

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...

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 of pipeline operators and model parameters that c...

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 ...

Fit estimator and compute scores for a given dataset split. Parameters ---------- sklearn_pipeline : pipeline object implementing 'fit' The object to use to fit the data. features : array-like of shape at least 2D The data to fit. target : array-like, optional, default: None ...

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...

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 An operator class

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 ...

Generate source code for a TPOT Pipeline. Parameters ---------- exported_pipeline: deap.creator.Individual The pipeline that is being exported operators: List of operator classes from operator library pipeline_score: Optional pipeline score to be saved to the exported file ...

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...

Convert the unstructured DEAP pipeline into a tree data-structure. Parameters ---------- ind: deap.creator.Individual The pipeline that is being exported Returns ------- pipeline_tree: list List of operators in the current optimized pipeline EXAMPLE: pipeline: ...

def expr_to_tree(ind, pset): """Convert the unstructured DEAP pipeline into a tree data-structure. Parameters ---------- ind: deap.creator.Individual The pipeline that is being exported Returns ------- pipeline_tree: list List of operators in the current optimized pipeline ...

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 impute : bool Whether to impute new values in the feature set. Re...

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 impute : b...

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

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 """ ...

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 ------- Source code for the sklearn pipeline

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 ------- Source code for the ...

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

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...

Get the next value in the page.

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 # iterator, we update the number of remaining. self._remaining -= 1 retur...

Verifies the parameters don't use any reserved parameter. Raises: ValueError: If a reserved parameter is used.

def _verify_params(self): """Verifies the parameters don't use any reserved parameter. Raises: ValueError: If a reserved parameter is used. """ reserved_in_use = self._RESERVED_PARAMS.intersection(self.extra_params) if reserved_in_use: raise ValueError("U...

Get the next page in the iterator. Returns: Optional[Page]: The next page in the iterator or :data:`None` if there are no pages left.

def _next_page(self): """Get the next page in the iterator. Returns: Optional[Page]: The next page in the iterator or :data:`None` if there are no pages left. """ if self._has_next_page(): response = self._get_next_page_response() item...

Getter for query parameters for the next request. Returns: dict: A dictionary of query parameters.

def _get_query_params(self): """Getter for query parameters for the next request. Returns: dict: A dictionary of query parameters. """ result = {} if self.next_page_token is not None: result[self._PAGE_TOKEN] = self.next_page_token if self.max_res...

Requests the next page from the path provided. Returns: dict: The parsed JSON response of the next page's contents. Raises: ValueError: If the HTTP method is not ``GET`` or ``POST``.

def _get_next_page_response(self): """Requests the next page from the path provided. Returns: dict: The parsed JSON response of the next page's contents. Raises: ValueError: If the HTTP method is not ``GET`` or ``POST``. """ params = self._get_query_para...

Get the next page in the iterator. Wraps the response from the :class:`~google.gax.PageIterator` in a :class:`Page` instance and captures some state at each page. Returns: Optional[Page]: The next page in the iterator or :data:`None` if there are no pages left.

def _next_page(self): """Get the next page in the iterator. Wraps the response from the :class:`~google.gax.PageIterator` in a :class:`Page` instance and captures some state at each page. Returns: Optional[Page]: The next page in the iterator or :data:`None` if ...

Get the next page in the iterator. Returns: Page: The next page in the iterator or :data:`None` if there are no pages left.

def _next_page(self): """Get the next page in the iterator. Returns: Page: The next page in the iterator or :data:`None` if there are no pages left. """ if not self._has_next_page(): return None if self.next_page_token is not None: ...

Determines whether or not there are more pages with results. Returns: bool: Whether the iterator has more pages.

def _has_next_page(self): """Determines whether or not there are more pages with results. Returns: bool: Whether the iterator has more pages. """ if self.page_number == 0: return True if self.max_results is not None: if self.num_results >= se...

Main comparison function for all Firestore types. @return -1 is left < right, 0 if left == right, otherwise 1

def compare(cls, left, right): """ Main comparison function for all Firestore types. @return -1 is left < right, 0 if left == right, otherwise 1 """ # First compare the types. leftType = TypeOrder.from_value(left).value rightType = TypeOrder.from_value(right).valu...

Service that performs image detection and annotation for a batch of files. Now only "application/pdf", "image/tiff" and "image/gif" are supported. This service will extract at most the first 10 frames (gif) or pages (pdf or tiff) from each file provided and perform detection and annotation ...

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...

Run asynchronous image detection and annotation for a list of images. Progress and results can be retrieved through the ``google.longrunning.Operations`` interface. ``Operation.metadata`` contains ``OperationMetadata`` (metadata). ``Operation.response`` contains ``AsyncBatchAnnotateImag...

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. ...

Run asynchronous image detection and annotation for a list of generic files, such as PDF files, which may contain multiple pages and multiple images per page. Progress and results can be retrieved through the ``google.longrunning.Operations`` interface. ``Operation.metadata`` contains ``...

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, ): """ Run asynchronous image detection and annotation for a list of generic files, such as P...

Called by IPython when this module is loaded as an 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" )

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` constructor. Returns: GoogleAPICallError: An in...

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` ...

Create a :class:`GoogleAPICallError` from a :class:`requests.Response`. Args: response (requests.Response): The HTTP response. Returns: GoogleAPICallError: An instance of the appropriate subclass of :class:`GoogleAPICallError`, with the message and errors populated from...

def from_http_response(response): """Create a :class:`GoogleAPICallError` from a :class:`requests.Response`. Args: response (requests.Response): The HTTP response. Returns: GoogleAPICallError: An instance of the appropriate subclass of :class:`GoogleAPICallError`, with the mess...

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:`GoogleAPICallError` constructor. Returns: Google...

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...

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`.

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...

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 :param method: The API call method name (ie, ``runQuery...

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...

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 name in the cloud console. :type method: str :param method: The name of ...

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...

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 your project name in the cloud console. :type m...

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...

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] :param keys: The keys to retrieve from the datastore. :type re...

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...

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. :type partition_id: :class:`.entity_pb2.PartitionId` :param partition_id: Partition ID corresponding to...

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. ...

Perform a ``beginTransaction`` request. :type project_id: str :param project_id: The project to connect to. This is usually your project name in the cloud console. :type transaction_options: ~.datastore_v1.types.TransactionOptions :param transaction_options: ...

def begin_transaction(self, project_id, transaction_options=None): """Perform a ``beginTransaction`` request. :type project_id: str :param project_id: The project to connect to. This is usually your project name in the cloud console. :type transaction_options...

Perform a ``commit`` request. :type project_id: str :param project_id: The project to connect to. This is usually your project name in the cloud console. :type mode: :class:`.gapic.datastore.v1.enums.CommitRequest.Mode` :param mode: The type of commit to perf...

def commit(self, project_id, mode, mutations, transaction=None): """Perform a ``commit`` request. :type project_id: str :param project_id: The project to connect to. This is usually your project name in the cloud console. :type mode: :class:`.gapic.datastore....

Perform a ``rollback`` request. :type project_id: str :param project_id: The project to connect to. This is usually your project name in the cloud console. :type transaction: bytes :param transaction: The transaction ID to rollback. :rtype: :class:`....

def rollback(self, project_id, transaction): """Perform a ``rollback`` request. :type project_id: str :param project_id: The project to connect to. This is usually your project name in the cloud console. :type transaction: bytes :param transaction: Th...

Perform an ``allocateIds`` 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] :param keys: The keys for which the backend should allocate IDs. ...

def allocate_ids(self, project_id, keys): """Perform an ``allocateIds`` 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] :param keys:...

Creates a request to read rows in a table. :type table_name: str :param table_name: The name of the table to read from. :type start_key: bytes :param start_key: (Optional) The beginning of a range of row keys to read from. The range will include ``start_key``. If ...

def _create_row_request( table_name, start_key=None, end_key=None, filter_=None, limit=None, end_inclusive=False, app_profile_id=None, row_set=None, ): """Creates a request to read rows in a table. :type table_name: str :param table_name: The name of the table to read from. ...

Creates a request to mutate rows in a table. :type table_name: str :param table_name: The name of the table to write to. :type rows: list :param rows: List or other iterable of :class:`.DirectRow` instances. :type: app_profile_id: str :param app_profile_id: (Optional) The unique name of the A...

def _mutate_rows_request(table_name, rows, app_profile_id=None): """Creates a request to mutate rows in a table. :type table_name: str :param table_name: The name of the table to write to. :type rows: list :param rows: List or other iterable of :class:`.DirectRow` instances. :type: app_profil...

Checks that a row belongs to a table. :type table_name: str :param table_name: The name of the table. :type row: :class:`~google.cloud.bigtable.row.Row` :param row: An instance of :class:`~google.cloud.bigtable.row.Row` subclasses. :raises: :exc:`~.table.TableMismatchError` if the...

def _check_row_table_name(table_name, row): """Checks that a row belongs to a table. :type table_name: str :param table_name: The name of the table. :type row: :class:`~google.cloud.bigtable.row.Row` :param row: An instance of :class:`~google.cloud.bigtable.row.Row` subclasses. ...

Table name used in requests. For example: .. literalinclude:: snippets_table.py :start-after: [START bigtable_table_name] :end-before: [END bigtable_table_name] .. note:: This property will not change if ``table_id`` does not, but the return value ...

def name(self): """Table name used in requests. For example: .. literalinclude:: snippets_table.py :start-after: [START bigtable_table_name] :end-before: [END bigtable_table_name] .. note:: This property will not change if ``table_id`` does not, but ...

Creates this table. For example: .. literalinclude:: snippets_table.py :start-after: [START bigtable_create_table] :end-before: [END bigtable_create_table] .. note:: A create request returns a :class:`._generated.table_pb2.Table` but we don't u...

def create(self, initial_split_keys=[], column_families={}): """Creates this table. For example: .. literalinclude:: snippets_table.py :start-after: [START bigtable_create_table] :end-before: [END bigtable_create_table] .. note:: A create request r...

Factory to create a row associated with this table. For example: .. literalinclude:: snippets_table.py :start-after: [START bigtable_table_row] :end-before: [END bigtable_table_row] .. warning:: At most one of ``filter_`` and ``append`` can be used in a ...

def row(self, row_key, filter_=None, append=False): """Factory to create a row associated with this table. For example: .. literalinclude:: snippets_table.py :start-after: [START bigtable_table_row] :end-before: [END bigtable_table_row] .. warning:: ...

Check whether the table exists. For example: .. literalinclude:: snippets_table.py :start-after: [START bigtable_check_table_exists] :end-before: [END bigtable_check_table_exists] :rtype: bool :returns: True if the table exists, else False.

def exists(self): """Check whether the table exists. For example: .. literalinclude:: snippets_table.py :start-after: [START bigtable_check_table_exists] :end-before: [END bigtable_check_table_exists] :rtype: bool :returns: True if the table exists, els...

Delete this table. For example: .. literalinclude:: snippets_table.py :start-after: [START bigtable_delete_table] :end-before: [END bigtable_delete_table]

def delete(self): """Delete this table. For example: .. literalinclude:: snippets_table.py :start-after: [START bigtable_delete_table] :end-before: [END bigtable_delete_table] """ table_client = self._instance._client.table_admin_client table_cl...

List the column families owned by this table. For example: .. literalinclude:: snippets_table.py :start-after: [START bigtable_list_column_families] :end-before: [END bigtable_list_column_families] :rtype: dict :returns: Dictionary of column families attached t...

def list_column_families(self): """List the column families owned by this table. For example: .. literalinclude:: snippets_table.py :start-after: [START bigtable_list_column_families] :end-before: [END bigtable_list_column_families] :rtype: dict :return...

List the cluster states owned by this table. For example: .. literalinclude:: snippets_table.py :start-after: [START bigtable_get_cluster_states] :end-before: [END bigtable_get_cluster_states] :rtype: dict :returns: Dictionary of cluster states for this table. ...

def get_cluster_states(self): """List the cluster states owned by this table. For example: .. literalinclude:: snippets_table.py :start-after: [START bigtable_get_cluster_states] :end-before: [END bigtable_get_cluster_states] :rtype: dict :returns: Dict...

Read a single row from this table. For example: .. literalinclude:: snippets_table.py :start-after: [START bigtable_read_row] :end-before: [END bigtable_read_row] :type row_key: bytes :param row_key: The key of the row to read from. :type filter_: :cla...

def read_row(self, row_key, filter_=None): """Read a single row from this table. For example: .. literalinclude:: snippets_table.py :start-after: [START bigtable_read_row] :end-before: [END bigtable_read_row] :type row_key: bytes :param row_key: The key...

Read rows from this table. For example: .. literalinclude:: snippets_table.py :start-after: [START bigtable_read_rows] :end-before: [END bigtable_read_rows] :type start_key: bytes :param start_key: (Optional) The beginning of a range of row keys to ...

def read_rows( self, start_key=None, end_key=None, limit=None, filter_=None, end_inclusive=False, row_set=None, retry=DEFAULT_RETRY_READ_ROWS, ): """Read rows from this table. For example: .. literalinclude:: snippets_table.py...

Read rows from this table. .. warning:: This method will be removed in future releases. Please use ``read_rows`` instead. :type start_key: bytes :param start_key: (Optional) The beginning of a range of row keys to read from. The range will inclu...

def yield_rows(self, **kwargs): """Read rows from this table. .. warning:: This method will be removed in future releases. Please use ``read_rows`` instead. :type start_key: bytes :param start_key: (Optional) The beginning of a range of row keys to ...

Mutates multiple rows in bulk. For example: .. literalinclude:: snippets_table.py :start-after: [START bigtable_mutate_rows] :end-before: [END bigtable_mutate_rows] The method tries to update all specified rows. If some of the rows weren't updated, it would not...

def mutate_rows(self, rows, retry=DEFAULT_RETRY): """Mutates multiple rows in bulk. For example: .. literalinclude:: snippets_table.py :start-after: [START bigtable_mutate_rows] :end-before: [END bigtable_mutate_rows] The method tries to update all specified ro...

Read a sample of row keys in the table. For example: .. literalinclude:: snippets_table.py :start-after: [START bigtable_sample_row_keys] :end-before: [END bigtable_sample_row_keys] The returned row keys will delimit contiguous sections of the table of approxim...

def sample_row_keys(self): """Read a sample of row keys in the table. For example: .. literalinclude:: snippets_table.py :start-after: [START bigtable_sample_row_keys] :end-before: [END bigtable_sample_row_keys] The returned row keys will delimit contiguous sec...

Truncate the table For example: .. literalinclude:: snippets_table.py :start-after: [START bigtable_truncate_table] :end-before: [END bigtable_truncate_table] :type timeout: float :param timeout: (Optional) The amount of time, in seconds, to wait ...

def truncate(self, timeout=None): """Truncate the table For example: .. literalinclude:: snippets_table.py :start-after: [START bigtable_truncate_table] :end-before: [END bigtable_truncate_table] :type timeout: float :param timeout: (Optional) The amoun...

Factory to create a mutation batcher associated with this instance. For example: .. literalinclude:: snippets_table.py :start-after: [START bigtable_mutations_batcher] :end-before: [END bigtable_mutations_batcher] :type table: class :param table: class:`~google...

def mutations_batcher(self, flush_count=FLUSH_COUNT, max_row_bytes=MAX_ROW_BYTES): """Factory to create a mutation batcher associated with this instance. For example: .. literalinclude:: snippets_table.py :start-after: [START bigtable_mutations_batcher] :end-before: [EN...

Mutate all the rows that are eligible for retry. A row is eligible for retry if it has not been tried or if it resulted in a transient error in a previous call. :rtype: list :return: The responses statuses, which is a list of :class:`~google.rpc.status_pb2.Status`. ...

def _do_mutate_retryable_rows(self): """Mutate all the rows that are eligible for retry. A row is eligible for retry if it has not been tried or if it resulted in a transient error in a previous call. :rtype: list :return: The responses statuses, which is a list of ...

Periodically send heartbeats.

def heartbeat(self): """Periodically send heartbeats.""" while self._manager.is_active and not self._stop_event.is_set(): self._manager.heartbeat() _LOGGER.debug("Sent heartbeat.") self._stop_event.wait(timeout=self._period) _LOGGER.info("%s exiting.", _HEART...

Convert a scalar value into a query parameter. :type value: any :param value: A scalar value to convert into a query parameter. :type name: str :param name: (Optional) Name of the query parameter. :rtype: :class:`~google.cloud.bigquery.ScalarQueryParameter` :returns: A query parameter...

def scalar_to_query_parameter(value, name=None): """Convert a scalar value into a query parameter. :type value: any :param value: A scalar value to convert into a query parameter. :type name: str :param name: (Optional) Name of the query parameter. :rtype: :class:`~google.cloud.bigquery.Scala...

Report an individual error event. Example: >>> from google.cloud import errorreporting_v1beta1 >>> >>> client = errorreporting_v1beta1.ReportErrorsServiceClient() >>> >>> project_name = client.project_path('[PROJECT]') >>> >>> ...

def report_error_event( self, project_name, event, retry=google.api_core.gapic_v1.method.DEFAULT, timeout=google.api_core.gapic_v1.method.DEFAULT, metadata=None, ): """ Report an individual error event. Example: >>> from google.clo...

Converts a dictionary of parameter values into query parameters. :type parameters: Mapping[str, Any] :param parameters: Dictionary of query parameter values. :rtype: List[google.cloud.bigquery.query._AbstractQueryParameter] :returns: A list of named query parameters.

def to_query_parameters_dict(parameters): """Converts a dictionary of parameter values into query parameters. :type parameters: Mapping[str, Any] :param parameters: Dictionary of query parameter values. :rtype: List[google.cloud.bigquery.query._AbstractQueryParameter] :returns: A list of named que...