JorgeVB/code_unlearning_dataset · Datasets at Hugging Face

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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: A list of BaseFramePartition objects. def _apply_func_to_list_of_partitions(...
Applies a function to select indices. Note: Your internal function must take a kwarg `internal_indices` for this to work correctly. This prevents information leakage of the internal index to the external representation. Args: axis: The axis to apply the func over. ...
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 column/row, but only need to apply a function to a subset. Important: For your func to operate directly ...
Apply a function to along both axis Important: For your func to operate directly on the indices provided, it must use `row_internal_indices, col_internal_indices` as keyword arguments. def apply_func_to_indices_both_axis( self, func, row_indices, col_ind...
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 func to. Returns: A new BaseFrameManager ob...
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 result into. Returns: A new BaseFrameManager ...
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: Pass into parquet's read_pandas function. def read_parquet(path, engi...
Creates a parser function from the given sep. Args: sep: The separator default to use for the parser. Returns: A function object. def _make_parser_func(sep): """Creates a parser function from the given sep. Args: sep: The separator default to use for the parser. Returns:...
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 def _read(**kwargs): """Read csv file from local disk. Args: filepath_or_buffer: ...
Read SQL query or database table into a DataFrame. Args: sql: string or SQLAlchemy Selectable (select or text object) SQL query to be executed or a table name. con: SQLAlchemy connectable (engine/connection) or database string URI or DBAPI2 connection (fallback mode) index_col: Column(s) to...
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. engine: Ray only support pyarrow reader. ...
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 def _read(cls, **kwargs): """Read csv file from local disk. Args: ...
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) Dense array or sparse matrix. threshold : int Maximum number of unique values...
Split X into selected features and other features 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) sel[np.asarray(selected)] = True non_sel = np.logical_not(sel) n...
Apply a transform function to portion of selected features. Parameters ---------- X : array-like or sparse matrix, shape=(n_samples, n_features) Dense array or sparse matrix. transform : callable A callable transform(X) -> X_transformed copy : boolean, optional Copy X even...
Adjust all values in X to encode for NaNs and infinities in the data. Parameters ---------- X : array-like, shape=(n_samples, n_feature) Input array of type int. Returns ------- X : array-like, shape=(n_samples, n_feature) Input array without any...
Assume X contains only categorical features. Parameters ---------- X : array-like or sparse matrix, shape=(n_samples, n_features) Dense array or sparse matrix. def _fit_transform(self, X): """Assume X contains only categorical features. Parameters ---------...
Fit OneHotEncoder to X, then transform X. Equivalent to self.fit(X).transform(X), but more convenient and more efficient. See fit for the parameters, transform for the return value. Parameters ---------- X : array-like or sparse matrix, shape=(n_samples, n_features) ...
Asssume X contains only categorical features. Parameters ---------- X : array-like or sparse matrix, shape=(n_samples, n_features) Dense array or sparse matrix. def _transform(self, X): """Asssume X contains only categorical features. Parameters ---------- ...
Transform X using one-hot encoding. Parameters ---------- X : array-like or sparse matrix, shape=(n_samples, n_features) Dense array or sparse matrix. Returns ------- X_out : sparse matrix if sparse=True else a 2-d array, dtype=int Transformed in...
Fit an optimized machine learning pipeline. Uses genetic programming to optimize a machine learning pipeline that maximizes score on the provided features and target. Performs internal k-fold cross-validaton to avoid overfitting on the provided data. The best pipeline is then trained on...
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...
Helper function to update the _optimized_pipeline field. def _update_top_pipeline(self): """Helper function to update the _optimized_pipeline field.""" # Store the pipeline with the highest internal testing score if self._pareto_front: self._optimized_pipeline_score = -float('inf') ...
Print out best pipeline at the end of optimization process. Parameters ---------- features: array-like {n_samples, n_features} Feature matrix target: array-like {n_samples} List of class labels for prediction Returns ------- self: object...
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...
Call fit and predict in sequence. Parameters ---------- features: array-like {n_samples, n_features} Feature matrix target: array-like {n_samples} List of class labels for prediction sample_weight: array-like {n_samples}, optional Per-sample w...
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...
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...
Provide a string of the individual without the parameter prefixes. Parameters ---------- individual: individual Individual which should be represented by a pretty string Returns ------- A string like str(individual), but with parameter prefixes removed. def...
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 ...
Export the optimized pipeline as Python code. Parameters ---------- output_file_name: string String containing the path and file name of the desired output file data_file_path: string (default: '') By default, the path of input dataset is 'PATH/TO/DATA/FILE' by d...
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 ...
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...
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...
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...
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_mi...
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...
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...
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 -------...
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...
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...
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) ...
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 ...
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 ...
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 ...
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...
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,...
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...
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...
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...
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:...
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 -...
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 = ...
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...
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...
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 ---...
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 intege...
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 ' 'l...
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: ...
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_tes...
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 ...
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...
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 ma...
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. ...
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...
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...
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...
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...
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 selec...
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...
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 ...
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(...
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 ...
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: ...
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...
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 ...
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, o...
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 space...
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. s...
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. """ ...
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 :...
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 = {} ...
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. ...
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. d...
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 ...
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. """ ...
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 comp...
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 ...
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...
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 ``...
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...
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...
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...
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...
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:`GoogleAPICa...
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...
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 ...
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...
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...
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...

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: A list of BaseFramePartition objects. def _apply_func_to_list_of_partitions(...

Applies a function to select indices. Note: Your internal function must take a kwarg `internal_indices` for this to work correctly. This prevents information leakage of the internal index to the external representation. Args: axis: The axis to apply the func over. ...

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 column/row, but only need to apply a function to a subset. Important: For your func to operate directly ...

Apply a function to along both axis Important: For your func to operate directly on the indices provided, it must use `row_internal_indices, col_internal_indices` as keyword arguments. def apply_func_to_indices_both_axis( self, func, row_indices, col_ind...

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 func to. Returns: A new BaseFrameManager ob...

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 result into. Returns: A new BaseFrameManager ...

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: Pass into parquet's read_pandas function. def read_parquet(path, engi...

Creates a parser function from the given sep. Args: sep: The separator default to use for the parser. Returns: A function object. def _make_parser_func(sep): """Creates a parser function from the given sep. Args: sep: The separator default to use for the parser. Returns:...

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 def _read(**kwargs): """Read csv file from local disk. Args: filepath_or_buffer: ...

Read SQL query or database table into a DataFrame. Args: sql: string or SQLAlchemy Selectable (select or text object) SQL query to be executed or a table name. con: SQLAlchemy connectable (engine/connection) or database string URI or DBAPI2 connection (fallback mode) index_col: Column(s) to...

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. engine: Ray only support pyarrow reader. ...

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 def _read(cls, **kwargs): """Read csv file from local disk. Args: ...

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) Dense array or sparse matrix. threshold : int Maximum number of unique values...

Split X into selected features and other features 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) sel[np.asarray(selected)] = True non_sel = np.logical_not(sel) n...

Apply a transform function to portion of selected features. Parameters ---------- X : array-like or sparse matrix, shape=(n_samples, n_features) Dense array or sparse matrix. transform : callable A callable transform(X) -> X_transformed copy : boolean, optional Copy X even...

Adjust all values in X to encode for NaNs and infinities in the data. Parameters ---------- X : array-like, shape=(n_samples, n_feature) Input array of type int. Returns ------- X : array-like, shape=(n_samples, n_feature) Input array without any...

Assume X contains only categorical features. Parameters ---------- X : array-like or sparse matrix, shape=(n_samples, n_features) Dense array or sparse matrix. def _fit_transform(self, X): """Assume X contains only categorical features. Parameters ---------...

Fit OneHotEncoder to X, then transform X. Equivalent to self.fit(X).transform(X), but more convenient and more efficient. See fit for the parameters, transform for the return value. Parameters ---------- X : array-like or sparse matrix, shape=(n_samples, n_features) ...

Asssume X contains only categorical features. Parameters ---------- X : array-like or sparse matrix, shape=(n_samples, n_features) Dense array or sparse matrix. def _transform(self, X): """Asssume X contains only categorical features. Parameters ---------- ...

Transform X using one-hot encoding. Parameters ---------- X : array-like or sparse matrix, shape=(n_samples, n_features) Dense array or sparse matrix. Returns ------- X_out : sparse matrix if sparse=True else a 2-d array, dtype=int Transformed in...

Fit an optimized machine learning pipeline. Uses genetic programming to optimize a machine learning pipeline that maximizes score on the provided features and target. Performs internal k-fold cross-validaton to avoid overfitting on the provided data. The best pipeline is then trained on...

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...

Helper function to update the _optimized_pipeline field. def _update_top_pipeline(self): """Helper function to update the _optimized_pipeline field.""" # Store the pipeline with the highest internal testing score if self._pareto_front: self._optimized_pipeline_score = -float('inf') ...

Print out best pipeline at the end of optimization process. Parameters ---------- features: array-like {n_samples, n_features} Feature matrix target: array-like {n_samples} List of class labels for prediction Returns ------- self: object...

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...

Call fit and predict in sequence. Parameters ---------- features: array-like {n_samples, n_features} Feature matrix target: array-like {n_samples} List of class labels for prediction sample_weight: array-like {n_samples}, optional Per-sample w...

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...

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...

Provide a string of the individual without the parameter prefixes. Parameters ---------- individual: individual Individual which should be represented by a pretty string Returns ------- A string like str(individual), but with parameter prefixes removed. def...

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 ...

Export the optimized pipeline as Python code. Parameters ---------- output_file_name: string String containing the path and file name of the desired output file data_file_path: string (default: '') By default, the path of input dataset is 'PATH/TO/DATA/FILE' by d...

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 ...

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...

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...

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...

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_mi...

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...

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...

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 -------...

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...

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...

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) ...

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 ...

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 ...

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 ...

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...

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,...

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...

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...

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...

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:...

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 -...

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 = ...

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...

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...

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 ---...

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 intege...

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 ' 'l...

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: ...

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_tes...

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 ...

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...

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 ma...

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. ...

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...

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...

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...

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...

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 selec...

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...

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 ...

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(...

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 ...

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: ...

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...

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 ...

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, o...

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 space...

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. s...

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. """ ...

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 :...

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 = {} ...

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. ...

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. d...

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 ...

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. """ ...

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 comp...

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 ...

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...

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 ``...

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...

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...

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...

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...

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:`GoogleAPICa...

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...

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 ...

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...

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...

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...