BoghdadyJR/codesearch_python · Data Studio

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def _apply_func_to_list_of_partitions(self, func, partitions, **kwargs): """Applies a function to a list of remote partitions. Note: The main use for this is to preprocess the func. Args: func: The func to apply partitions: The list of partitions Returns: ...
def apply_func_to_select_indices(self, axis, func, indices, keep_remaining=False): """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 th...
def apply_func_to_select_indices_along_full_axis( self, axis, func, indices, keep_remaining=False ): """Applies a function to a select subset of full columns/rows. Note: This should be used when you need to apply a function that relies on some global information for the entire c...
def apply_func_to_indices_both_axis( self, func, row_indices, col_indices, lazy=False, keep_remaining=True, mutate=False, item_to_distribute=None, ): """ Apply a function to along both axis Important: For your func to operate d...
def inter_data_operation(self, axis, func, other): """Apply a function that requires two BaseFrameManager objects. Args: axis: The axis to apply the function over (0 - rows, 1 - columns) func: The function to apply other: The other BaseFrameManager object to apply fu...
def manual_shuffle(self, axis, shuffle_func, lengths): """Shuffle the partitions based on the `shuffle_func`. Args: axis: The axis to shuffle across. shuffle_func: The function to apply before splitting the result. lengths: The length of each partition to split the r...
def read_parquet(path, engine="auto", columns=None, **kwargs): """Load a parquet object from the file path, returning a DataFrame. Args: path: The filepath of the parquet file. We only support local files for now. engine: This argument doesn't do anything for now. kwargs: ...
def _make_parser_func(sep): """Creates a parser function from the given sep. Args: sep: The separator default to use for the parser. Returns: A function object. """ def parser_func( filepath_or_buffer, sep=sep, delimiter=None, header="infer", ...
def _read(kwargs): """Read csv file from local disk. Args: filepath_or_buffer: The filepath of the csv file. We only support local files for now. kwargs: Keyword arguments in pandas.read_csv """ pd_obj = BaseFactory.read_csv(kwargs) # This happens when...
def read_sql( sql, con, index_col=None, coerce_float=True, params=None, parse_dates=None, columns=None, chunksize=None, ): """ Read SQL query or database table into a DataFrame. Args: sql: string or SQLAlchemy Selectable (select or text object) SQL query to be executed o...
def read_parquet(cls, path, engine, columns, **kwargs): """Load a parquet object from the file path, returning a DataFrame. Ray DataFrame only supports pyarrow engine for now. Args: path: The filepath of the parquet file. We only support local files for now. ...
def _read(cls, *kwargs): """Read csv file from local disk. Args: filepath_or_buffer: The filepath of the csv file. We only support local files for now. kwargs: Keyword arguments in pandas.read_csv """ pd_obj = pandas.read_csv(...
def auto_select_categorical_features(X, threshold=10): """Make a feature mask of categorical features in X. Features with less than 10 unique values are considered categorical. Parameters ---------- X : array-like or sparse matrix, shape=(n_samples, n_features) Dense array or sparse matrix...
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_selected = np.sum(sel) X_sel = X[:, ind[sel]] ...
def _transform_selected(X, transform, selected, copy=True): """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)...
def _matrix_adjust(self, X): """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_feat...
def _fit_transform(self, X): """Assume X contains only categorical features. Parameters ---------- X : array-like or sparse matrix, shape=(n_samples, n_features) Dense array or sparse matrix. """ X = self._matrix_adjust(X) X = check_array( ...
def fit_transform(self, X, y=None): """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 ma...
def _transform(self, X): """Asssume X contains only categorical features. Parameters ---------- X : array-like or sparse matrix, shape=(n_samples, n_features) Dense array or sparse matrix. """ X = self._matrix_adjust(X) X = check_array(X, accept_spar...
def transform(self, X): """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, d...
def fit(self, features, target, sample_weight=None, groups=None): """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 o...
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...
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') for pipeline, pipeline_scores in zip(self._par...
def _summary_of_best_pipeline(self, features, target): """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 fo...
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...
def fit_predict(self, features, target, sample_weight=None, groups=None): """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 predic...
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...
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: {...
def clean_pipeline_string(self, individual): """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 st...
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 """...
def export(self, output_file_name, data_file_path=''): """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: '') ...
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...
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...
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...
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 ...
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...
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 ...
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...
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...
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...
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 ...
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 ...
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 ...
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...
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 ...
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...
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 ...
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 ---...
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...
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 ------- ...
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_...
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 ...
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 ...
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 ...
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...
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...
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) ...
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...
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() ...
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...
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...
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...
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. """ ...
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...
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 ...
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...
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...
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...
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...
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 ...
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...
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 ...
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...
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 ...
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...
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 """ ...
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 ...
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...
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...
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...
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...
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...
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...
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 ...
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: ...
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...
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...
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...
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. ...
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...
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" )
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` ...
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...
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...
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...
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...
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...
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...
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...
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. ...

def _apply_func_to_list_of_partitions(self, func, partitions, **kwargs): """Applies a function to a list of remote partitions. Note: The main use for this is to preprocess the func. Args: func: The func to apply partitions: The list of partitions Returns: ...

def apply_func_to_select_indices(self, axis, func, indices, keep_remaining=False): """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 th...

def apply_func_to_select_indices_along_full_axis( self, axis, func, indices, keep_remaining=False ): """Applies a function to a select subset of full columns/rows. Note: This should be used when you need to apply a function that relies on some global information for the entire c...

def apply_func_to_indices_both_axis( self, func, row_indices, col_indices, lazy=False, keep_remaining=True, mutate=False, item_to_distribute=None, ): """ Apply a function to along both axis Important: For your func to operate d...

def inter_data_operation(self, axis, func, other): """Apply a function that requires two BaseFrameManager objects. Args: axis: The axis to apply the function over (0 - rows, 1 - columns) func: The function to apply other: The other BaseFrameManager object to apply fu...

def manual_shuffle(self, axis, shuffle_func, lengths): """Shuffle the partitions based on the `shuffle_func`. Args: axis: The axis to shuffle across. shuffle_func: The function to apply before splitting the result. lengths: The length of each partition to split the r...

def read_parquet(path, engine="auto", columns=None, **kwargs): """Load a parquet object from the file path, returning a DataFrame. Args: path: The filepath of the parquet file. We only support local files for now. engine: This argument doesn't do anything for now. kwargs: ...

def _make_parser_func(sep): """Creates a parser function from the given sep. Args: sep: The separator default to use for the parser. Returns: A function object. """ def parser_func( filepath_or_buffer, sep=sep, delimiter=None, header="infer", ...

def _read(**kwargs): """Read csv file from local disk. Args: filepath_or_buffer: The filepath of the csv file. We only support local files for now. kwargs: Keyword arguments in pandas.read_csv """ pd_obj = BaseFactory.read_csv(**kwargs) # This happens when...

def read_sql( sql, con, index_col=None, coerce_float=True, params=None, parse_dates=None, columns=None, chunksize=None, ): """ Read SQL query or database table into a DataFrame. Args: sql: string or SQLAlchemy Selectable (select or text object) SQL query to be executed o...

def read_parquet(cls, path, engine, columns, **kwargs): """Load a parquet object from the file path, returning a DataFrame. Ray DataFrame only supports pyarrow engine for now. Args: path: The filepath of the parquet file. We only support local files for now. ...

def _read(cls, **kwargs): """Read csv file from local disk. Args: filepath_or_buffer: The filepath of the csv file. We only support local files for now. kwargs: Keyword arguments in pandas.read_csv """ pd_obj = pandas.read_csv(*...

def auto_select_categorical_features(X, threshold=10): """Make a feature mask of categorical features in X. Features with less than 10 unique values are considered categorical. Parameters ---------- X : array-like or sparse matrix, shape=(n_samples, n_features) Dense array or sparse matrix...

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_selected = np.sum(sel) X_sel = X[:, ind[sel]] ...

def _transform_selected(X, transform, selected, copy=True): """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)...

def _matrix_adjust(self, X): """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_feat...

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

def fit_transform(self, X, y=None): """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 ma...

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

def transform(self, X): """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, d...

def fit(self, features, target, sample_weight=None, groups=None): """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 o...

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...

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') for pipeline, pipeline_scores in zip(self._par...

def _summary_of_best_pipeline(self, features, target): """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 fo...

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...

def fit_predict(self, features, target, sample_weight=None, groups=None): """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 predic...

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...

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: {...

def clean_pipeline_string(self, individual): """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 st...

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 """...

def export(self, output_file_name, data_file_path=''): """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: '') ...

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...

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...

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...

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 ...

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...

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 ...

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...

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...

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...

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 ...

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 ...

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 ...

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...

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 ...

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...

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 ...

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 ---...

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...

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 ------- ...

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_...

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 ...

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 ...

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 ...

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...

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...

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) ...

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...

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() ...

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...

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...

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...

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. """ ...

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...

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 ...

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...

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...

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...

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...

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 ...

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...

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 ...

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...

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 ...

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...

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 """ ...

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 ...

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...

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...

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...

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...

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...

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...

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 ...

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: ...

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...

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...

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...

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. ...

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...

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" )

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` ...

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...

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...

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...

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...

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...

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...

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...

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. ...