savioratharv/fyp_eval · Datasets at Hugging Face

Code stringlengths 103 85.9k	Summary listlengths 0 94
Please provide a description of the function:def sort_index( self, axis=0, level=None, ascending=True, inplace=False, kind="quicksort", na_position="last", sort_remaining=True, by=None, ): axis = self._get_axis_numb...	[ "Sort a DataFrame by one of the indices (columns or index).\r\n\r\n Args:\r\n axis: The axis to sort over.\r\n level: The MultiIndex level to sort over.\r\n ascending: Ascending or descending\r\n inplace: Whether or not to update this DataFrame inplace.\r\n ...
Please provide a description of the function:def sort_values( self, by, axis=0, ascending=True, inplace=False, kind="quicksort", na_position="last", ): axis = self._get_axis_number(axis) if not is_list_like(by): ...	[ "Sorts by a column/row or list of columns/rows.\r\n\r\n Args:\r\n by: A list of labels for the axis to sort over.\r\n axis: The axis to sort.\r\n ascending: Sort in ascending or descending order.\r\n inplace: If true, do the operation inplace.\r\n kind: ...
Please provide a description of the function:def sub(self, other, axis="columns", level=None, fill_value=None): return self._binary_op( "sub", other, axis=axis, level=level, fill_value=fill_value )	[ "Subtract a DataFrame/Series/scalar from this DataFrame.\r\n\r\n Args:\r\n other: The object to use to apply the subtraction to this.\r\n axis: The axis to apply the subtraction over.\r\n level: Mutlilevel index level to subtract over.\r\n fill_value: The value to ...
Please provide a description of the function:def to_numpy(self, dtype=None, copy=False): return self._default_to_pandas("to_numpy", dtype=dtype, copy=copy)	[ "Convert the DataFrame to a NumPy array.\r\n\r\n Args:\r\n dtype: The dtype to pass to numpy.asarray()\r\n copy: Whether to ensure that the returned value is a not a view on another\r\n array.\r\n\r\n Returns:\r\n A numpy array.\r\n " ]
Please provide a description of the function:def truediv(self, other, axis="columns", level=None, fill_value=None): return self._binary_op( "truediv", other, axis=axis, level=level, fill_value=fill_value )	[ "Divides this DataFrame against another DataFrame/Series/scalar.\r\n\r\n Args:\r\n other: The object to use to apply the divide against this.\r\n axis: The axis to divide over.\r\n level: The Multilevel index level to apply divide over.\r\n fill_value: The value to...
Please provide a description of the function:def var( self, axis=None, skipna=None, level=None, ddof=1, numeric_only=None, **kwargs ): axis = self._get_axis_number(axis) if axis is not None else 0 if numeric_only is not None and not numeric_only: self._validate_dty...	[ "Computes variance across the DataFrame.\r\n\r\n Args:\r\n axis (int): The axis to take the variance on.\r\n skipna (bool): True to skip NA values, false otherwise.\r\n ddof (int): degrees of freedom\r\n\r\n Returns:\r\n The variance of the DataFrame.\r\n ...
Please provide a description of the function:def size(self): return len(self._query_compiler.index) * len(self._query_compiler.columns)	[ "Get the number of elements in the DataFrame.\r\n\r\n Returns:\r\n The number of elements in the DataFrame.\r\n " ]
Please provide a description of the function:def get(self): if self.call_queue: return self.apply(lambda df: df).data else: return self.data.copy()	[ "Flushes the call_queue and returns the data.\n\n Note: Since this object is a simple wrapper, just return the data.\n\n Returns:\n The object that was `put`.\n " ]
Please provide a description of the function:def apply(self, func, **kwargs): self.call_queue.append((func, kwargs)) def call_queue_closure(data, call_queues): result = data.copy() for func, kwargs in call_queues: try: result = func(r...	[ "Apply some callable function to the data in this partition.\n\n Note: It is up to the implementation how kwargs are handled. They are\n an important part of many implementations. As of right now, they\n are not serialized.\n\n Args:\n func: The lambda to apply (may al...
Please provide a description of the function:def apply(self, func, kwargs): import dask # applies the func lazily delayed_call = self.delayed_call self.delayed_call = self.dask_obj return self.__class__(dask.delayed(func)(delayed_call, kwargs))	[ "Apply some callable function to the data in this partition.\n\n Note: It is up to the implementation how kwargs are handled. They are\n an important part of many implementations. As of right now, they\n are not serialized.\n\n Args:\n func: The lambda to apply (may al...
Please provide a description of the function:def add_to_apply_calls(self, func, kwargs): import dask self.delayed_call = dask.delayed(func)(self.delayed_call, kwargs) return self	[ "Add the function to the apply function call stack.\n\n This function will be executed when apply is called. It will be executed\n in the order inserted; apply's func operates the last and return\n " ]
Please provide a description of the function:def _read_csv_with_offset_pyarrow_on_ray( fname, num_splits, start, end, kwargs, header ): # pragma: no cover bio = open(fname, "rb") # The header line for the CSV file first_line = bio.readline() bio.seek(start) to_read = header + first_line + ...	[ "Use a Ray task to read a chunk of a CSV into a pyarrow Table.\n Note: Ray functions are not detected by codecov (thus pragma: no cover)\n Args:\n fname: The filename of the file to open.\n num_splits: The number of splits (partitions) to separate the DataFrame into.\n start: The start ...
Please provide a description of the function:def compute_chunksize(df, num_splits, default_block_size=32, axis=None): if axis == 0 or axis is None: row_chunksize = get_default_chunksize(len(df.index), num_splits) # Take the min of the default and the memory-usage chunksize first to avoid a ...	[ "Computes the number of rows and/or columns to include in each partition.\n\n Args:\n df: The DataFrame to split.\n num_splits: The maximum number of splits to separate the DataFrame into.\n default_block_size: Minimum number of rows/columns (default set to 32x32).\n axis: The axis to...
Please provide a description of the function:def _get_nan_block_id(partition_class, n_row=1, n_col=1, transpose=False): global _NAN_BLOCKS if transpose: n_row, n_col = n_col, n_row shape = (n_row, n_col) if shape not in _NAN_BLOCKS: arr = np.tile(np.array(np.NaN), shape) # T...	[ "A memory efficient way to get a block of NaNs.\n\n Args:\n partition_class (BaseFramePartition): The class to use to put the object\n in the remote format.\n n_row(int): The number of rows.\n n_col(int): The number of columns.\n transpose(bool): If true, swap rows and colu...
Please provide a description of the function:def split_result_of_axis_func_pandas(axis, num_splits, result, length_list=None): if num_splits == 1: return result if length_list is not None: length_list.insert(0, 0) sums = np.cumsum(length_list) if axis == 0: retur...	[ "Split the Pandas result evenly based on the provided number of splits.\n\n Args:\n axis: The axis to split across.\n num_splits: The number of even splits to create.\n result: The result of the computation. This should be a Pandas\n DataFrame.\n length_list: The list of le...
Please provide a description of the function:def _parse_tuple(tup): row_loc, col_loc = slice(None), slice(None) if is_tuple(tup): row_loc = tup[0] if len(tup) == 2: col_loc = tup[1] if len(tup) > 2: raise IndexingError("Too many indexers") else: ...	[ "Unpack the user input for getitem and setitem and compute ndim\n\n loc[a] -> ([a], :), 1D\n loc[[a,b],] -> ([a,b], :),\n loc[a,b] -> ([a], [b]), 0D\n " ]
Please provide a description of the function:def _is_enlargement(locator, global_index): if ( is_list_like(locator) and not is_slice(locator) and len(locator) > 0 and not is_boolean_array(locator) and (isinstance(locator, type(global_index[0])) and locator not in global_...	[ "Determine if a locator will enlarge the global index.\n\n Enlargement happens when you trying to locate using labels isn't in the\n original index. In other words, enlargement == adding NaNs !\n " ]
Please provide a description of the function:def _compute_ndim(row_loc, col_loc): row_scaler = is_scalar(row_loc) col_scaler = is_scalar(col_loc) if row_scaler and col_scaler: ndim = 0 elif row_scaler ^ col_scaler: ndim = 1 else: ndim = 2 return ndim	[ "Compute the ndim of result from locators\n " ]
Please provide a description of the function:def _broadcast_item(self, row_lookup, col_lookup, item, to_shape): # It is valid to pass a DataFrame or Series to __setitem__ that is larger than # the target the user is trying to overwrite. This if isinstance(item, (pandas.Series, pandas.Da...	[ "Use numpy to broadcast or reshape item.\n\n Notes:\n - Numpy is memory efficient, there shouldn't be performance issue.\n " ]
Please provide a description of the function:def _write_items(self, row_lookup, col_lookup, item): self.qc.write_items(row_lookup, col_lookup, item)	[ "Perform remote write and replace blocks.\n " ]
Please provide a description of the function:def _handle_enlargement(self, row_loc, col_loc): if _is_enlargement(row_loc, self.qc.index) or _is_enlargement( col_loc, self.qc.columns ): _warn_enlargement() self.qc.enlarge_partitions( new_row_la...	[ "Handle Enlargement (if there is one).\n\n Returns:\n None\n " ]
Please provide a description of the function:def _compute_enlarge_labels(self, locator, base_index): # base_index_type can be pd.Index or pd.DatetimeIndex # depending on user input and pandas behavior # See issue #2264 base_index_type = type(base_index) locator_as_index ...	[ "Helper for _enlarge_axis, compute common labels and extra labels.\n\n Returns:\n nan_labels: The labels needs to be added\n " ]
Please provide a description of the function:def _split_result_for_readers(axis, num_splits, df): # pragma: no cover splits = split_result_of_axis_func_pandas(axis, num_splits, df) if not isinstance(splits, list): splits = [splits] return splits	[ "Splits the DataFrame read into smaller DataFrames and handles all edge cases.\n\n Args:\n axis: Which axis to split over.\n num_splits: The number of splits to create.\n df: The DataFrame after it has been read.\n\n Returns:\n A list of pandas DataFrames.\n " ]
Please provide a description of the function:def _read_parquet_columns(path, columns, num_splits, kwargs): # pragma: no cover import pyarrow.parquet as pq df = pq.read_pandas(path, columns=columns, **kwargs).to_pandas() # Append the length of the index here to build it externally return _split_re...	[ "Use a Ray task to read columns from Parquet into a Pandas DataFrame.\n\n Note: Ray functions are not detected by codecov (thus pragma: no cover)\n\n Args:\n path: The path of the Parquet file.\n columns: The list of column names to read.\n num_splits: The number of partitions to split th...
Please provide a description of the function:def _read_csv_with_offset_pandas_on_ray( fname, num_splits, start, end, kwargs, header ): # pragma: no cover index_col = kwargs.get("index_col", None) bio = file_open(fname, "rb") bio.seek(start) to_read = header + bio.read(end - start) bio.clos...	[ "Use a Ray task to read a chunk of a CSV into a Pandas DataFrame.\n\n Note: Ray functions are not detected by codecov (thus pragma: no cover)\n\n Args:\n fname: The filename of the file to open.\n num_splits: The number of splits (partitions) to separate the DataFrame into.\n start: The s...
Please provide a description of the function:def _read_hdf_columns(path_or_buf, columns, num_splits, kwargs): # pragma: no cover df = pandas.read_hdf(path_or_buf, columns=columns, **kwargs) # Append the length of the index here to build it externally return _split_result_for_readers(0, num_splits, df...	[ "Use a Ray task to read columns from HDF5 into a Pandas DataFrame.\n\n Note: Ray functions are not detected by codecov (thus pragma: no cover)\n\n Args:\n path_or_buf: The path of the HDF5 file.\n columns: The list of column names to read.\n num_splits: The number of partitions to split t...
Please provide a description of the function:def _read_feather_columns(path, columns, num_splits): # pragma: no cover from pyarrow import feather df = feather.read_feather(path, columns=columns) # Append the length of the index here to build it externally return _split_result_for_readers(0, num_s...	[ "Use a Ray task to read columns from Feather into a Pandas DataFrame.\n\n Note: Ray functions are not detected by codecov (thus pragma: no cover)\n\n Args:\n path: The path of the Feather file.\n columns: The list of column names to read.\n num_splits: The number of partitions to split th...
Please provide a description of the function:def _read_sql_with_limit_offset( num_splits, sql, con, index_col, kwargs ): # pragma: no cover pandas_df = pandas.read_sql(sql, con, index_col=index_col, **kwargs) if index_col is None: index = len(pandas_df) else: index = pandas_df.inde...	[ "Use a Ray task to read a chunk of SQL source.\n\n Note: Ray functions are not detected by codecov (thus pragma: no cover)\n " ]
Please provide a description of the function:def get_index(index_name, *partition_indices): # pragma: no cover index = partition_indices[0].append(partition_indices[1:]) index.names = index_name return index	[ "Get the index from the indices returned by the workers.\n\n Note: Ray functions are not detected by codecov (thus pragma: no cover)" ]
Please provide a description of the function:def read_parquet(cls, path, engine, columns, kwargs): from pyarrow.parquet import ParquetFile if cls.read_parquet_remote_task is None: return super(RayIO, cls).read_parquet(path, engine, columns, kwargs) if not columns: ...	[ "Load a parquet object from the file path, returning a DataFrame.\n Ray DataFrame only supports pyarrow engine for now.\n\n Args:\n path: The filepath of the parquet file.\n We only support local files for now.\n engine: Ray only support pyarrow reader.\n ...
Please provide a description of the function:def _read_csv_from_file_pandas_on_ray(cls, filepath, kwargs={}): names = kwargs.get("names", None) index_col = kwargs.get("index_col", None) if names is None: # For the sake of the empty df, we assume no `index_col` to get the cor...	[ "Constructs a DataFrame from a CSV file.\n\n Args:\n filepath (str): path to the CSV file.\n npartitions (int): number of partitions for the DataFrame.\n kwargs (dict): args excluding filepath provided to read_csv.\n\n Returns:\n DataFrame or Series construc...
Please provide a description of the function:def _read(cls, filepath_or_buffer, **kwargs): # The intention of the inspection code is to reduce the amount of # communication we have to do between processes and nodes. We take a quick # pass over the arguments and remove those that are def...	[ "Read csv file from local disk.\n Args:\n filepath_or_buffer:\n The filepath of the csv file.\n We only support local files for now.\n kwargs: Keyword arguments in pandas.read_csv\n " ]
Please provide a description of the function:def read_hdf(cls, path_or_buf, kwargs): if cls.read_hdf_remote_task is None: return super(RayIO, cls).read_hdf(path_or_buf, kwargs) format = cls._validate_hdf_format(path_or_buf=path_or_buf) if format is None: Er...	[ "Load a h5 file from the file path or buffer, returning a DataFrame.\n\n Args:\n path_or_buf: string, buffer or path object\n Path to the file to open, or an open :class:`pandas.HDFStore` object.\n kwargs: Pass into pandas.read_hdf function.\n\n Returns:\n ...
Please provide a description of the function:def read_feather(cls, path, columns=None, use_threads=True): if cls.read_feather_remote_task is None: return super(RayIO, cls).read_feather( path, columns=columns, use_threads=use_threads ) if columns is None:...	[ "Read a pandas.DataFrame from Feather format.\n Ray DataFrame only supports pyarrow engine for now.\n\n Args:\n path: The filepath of the feather file.\n We only support local files for now.\n multi threading is set to True by default\n columns:...
Please provide a description of the function:def to_sql(cls, qc, **kwargs): # we first insert an empty DF in order to create the full table in the database # This also helps to validate the input against pandas # we would like to_sql() to complete only when all rows have been inserted i...	[ "Write records stored in a DataFrame to a SQL database.\n Args:\n qc: the query compiler of the DF that we want to run to_sql on\n kwargs: parameters for pandas.to_sql(**kwargs)\n " ]
Please provide a description of the function:def read_sql(cls, sql, con, index_col=None, kwargs): if cls.read_sql_remote_task is None: return super(RayIO, cls).read_sql(sql, con, index_col=index_col, kwargs) row_cnt_query = "SELECT COUNT(*) FROM ({})".format(sql) row_cn...	[ "Reads a SQL query or database table into a DataFrame.\n Args:\n sql: string or SQLAlchemy Selectable (select or text object) SQL query to be\n executed or a table name.\n con: SQLAlchemy connectable (engine/connection) or database string URI or\n DBAPI2 co...
Please provide a description of the function:def to_datetime( arg, errors="raise", dayfirst=False, yearfirst=False, utc=None, box=True, format=None, exact=True, unit=None, infer_datetime_format=False, origin="unix", cache=False, ): if not isinstance(arg, DataFram...	[ "Convert the arg to datetime format. If not Ray DataFrame, this falls\n back on pandas.\n\n Args:\n errors ('raise' or 'ignore'): If 'ignore', errors are silenced.\n Pandas blatantly ignores this argument so we will too.\n dayfirst (bool): Date format is passed in as day first.\n ...
Please provide a description of the function:def read_sql( sql, con, index_col=None, coerce_float=True, params=None, parse_dates=None, columns=None, chunksize=None, partition_column=None, lower_bound=None, upper_bound=None, max_sessions=None, ): _, _, _, kwargs =...	[ " Read SQL query or database table into a DataFrame.\n\n Args:\n sql: string or SQLAlchemy Selectable (select or text object) SQL query to be executed or a table name.\n con: SQLAlchemy connectable (engine/connection) or database string URI or DBAPI2 connection (fallback mode)\n index_col: C...
Please provide a description of the function:def block_lengths(self): if self._lengths_cache is None: try: # The first column will have the correct lengths. We have an # invariant that requires that all blocks be the same length in a # row of ...	[ "Gets the lengths of the blocks.\n\n Note: This works with the property structure `_lengths_cache` to avoid\n having to recompute these values each time they are needed.\n " ]
Please provide a description of the function:def block_widths(self): if self._widths_cache is None: try: # The first column will have the correct lengths. We have an # invariant that requires that all blocks be the same width in a # column of ...	[ "Gets the widths of the blocks.\n\n Note: This works with the property structure `_widths_cache` to avoid\n having to recompute these values each time they are needed.\n " ]
Please provide a description of the function:def deploy_ray_func(func, partition, kwargs): # pragma: no cover try: return func(partition, **kwargs) # Sometimes Arrow forces us to make a copy of an object before we operate # on it. We don't want the error to propagate to the user, and we want t...	[ "Deploy a function to a partition in Ray.\n\n Note: Ray functions are not detected by codecov (thus pragma: no cover)\n\n Args:\n func: The function to apply.\n partition: The partition to apply the function to.\n kwargs: A dictionary of keyword arguments for the function.\n\n Returns:...
Please provide a description of the function:def get(self): if len(self.call_queue): return self.apply(lambda x: x).get() try: return ray.get(self.oid) except RayTaskError as e: handle_ray_task_error(e)	[ "Gets the object out of the plasma store.\n\n Returns:\n The object from the plasma store.\n " ]
Please provide a description of the function:def block_lengths(self): if self._lengths_cache is None: # The first column will have the correct lengths. We have an # invariant that requires that all blocks be the same length in a # row of blocks. self._len...	[ "Gets the lengths of the blocks.\n\n Note: This works with the property structure `_lengths_cache` to avoid\n having to recompute these values each time they are needed.\n " ]
Please provide a description of the function:def block_widths(self): if self._widths_cache is None: # The first column will have the correct lengths. We have an # invariant that requires that all blocks be the same width in a # column of blocks. self._wid...	[ "Gets the widths of the blocks.\n\n Note: This works with the property structure `_widths_cache` to avoid\n having to recompute these values each time they are needed.\n " ]
Please provide a description of the function:def map_across_blocks(self, map_func): preprocessed_map_func = self.preprocess_func(map_func) new_partitions = np.array( [ [part.apply(preprocessed_map_func) for part in row_of_parts] for row_of_parts in se...	[ "Applies `map_func` to every partition.\n\n Args:\n map_func: The function to apply.\n\n Returns:\n A new BaseFrameManager object, the type of object that called this.\n " ]
Please provide a description of the function:def copartition_datasets(self, axis, other, left_func, right_func): if left_func is None: new_self = self else: new_self = self.map_across_full_axis(axis, left_func) # This block of code will only shuffle if absolutel...	[ "Copartition two BlockPartitions objects.\n\n Args:\n axis: The axis to copartition.\n other: The other BlockPartitions object to copartition with.\n left_func: The function to apply to left. If None, just use the dimension\n of self (based on axis).\n ...
Please provide a description of the function:def map_across_full_axis(self, axis, map_func): # Since we are already splitting the DataFrame back up after an # operation, we will just use this time to compute the number of # partitions as best we can right now. num_splits = self....	[ "Applies `map_func` to every partition.\n\n Note: This method should be used in the case that `map_func` relies on\n some global information about the axis.\n\n Args:\n axis: The axis to perform the map across (0 - index, 1 - columns).\n map_func: The function to apply...
Please provide a description of the function:def take(self, axis, n): # These are the partitions that we will extract over if not axis: partitions = self.partitions bin_lengths = self.block_lengths else: partitions = self.partitions.T bin_...	[ "Take the first (or last) n rows or columns from the blocks\n\n Note: Axis = 0 will be equivalent to `head` or `tail`\n Axis = 1 will be equivalent to `front` or `back`\n\n Args:\n axis: The axis to extract (0 for extracting rows, 1 for extracting columns)\n n: The n...
Please provide a description of the function:def concat(self, axis, other_blocks): if type(other_blocks) is list: other_blocks = [blocks.partitions for blocks in other_blocks] return self.__constructor__( np.concatenate([self.partitions] + other_blocks, axis=axis...	[ "Concatenate the blocks with another set of blocks.\n\n Note: Assumes that the blocks are already the same shape on the\n dimension being concatenated. A ValueError will be thrown if this\n condition is not met.\n\n Args:\n axis: The axis to concatenate to.\n ...
Please provide a description of the function:def to_pandas(self, is_transposed=False): # In the case this is transposed, it is easier to just temporarily # transpose back then transpose after the conversion. The performance # is the same as if we individually transposed the blocks and ...	[ "Convert this object into a Pandas DataFrame from the partitions.\n\n Args:\n is_transposed: A flag for telling this object that the external\n representation is transposed, but not the internal.\n\n Returns:\n A Pandas DataFrame\n " ]
Please provide a description of the function:def get_indices(self, axis=0, index_func=None, old_blocks=None): ErrorMessage.catch_bugs_and_request_email(not callable(index_func)) func = self.preprocess_func(index_func) if axis == 0: # We grab the first column of blocks and ex...	[ "This gets the internal indices stored in the partitions.\n\n Note: These are the global indices of the object. This is mostly useful\n when you have deleted rows/columns internally, but do not know\n which ones were deleted.\n\n Args:\n axis: This axis to extract the ...
Please provide a description of the function:def _get_blocks_containing_index(self, axis, index): if not axis: ErrorMessage.catch_bugs_and_request_email(index > sum(self.block_widths)) cumulative_column_widths = np.array(self.block_widths).cumsum() block_idx = int(np...	[ "Convert a global index to a block index and local index.\n\n Note: This method is primarily used to convert a global index into a\n partition index (along the axis provided) and local index (useful\n for `iloc` or similar operations.\n\n Args:\n axis: The axis along w...
Please provide a description of the function:def _get_dict_of_block_index(self, axis, indices, ordered=False): # Get the internal index and create a dictionary so we only have to # travel to each partition once. all_partitions_and_idx = [ self._get_blocks_containing_index(ax...	[ "Convert indices to a dict of block index to internal index mapping.\n\n Note: See `_get_blocks_containing_index` for primary usage. This method\n accepts a list of indices rather than just a single value, and uses\n `_get_blocks_containing_index`.\n\n Args:\n axis: Th...
Please provide a description of the function:def _apply_func_to_list_of_partitions(self, func, partitions, kwargs): preprocessed_func = self.preprocess_func(func) return [obj.apply(preprocessed_func, kwargs) for obj in partitions]	[ "Applies a function to a list of remote partitions.\n\n Note: The main use for this is to preprocess the func.\n\n Args:\n func: The func to apply\n partitions: The list of partitions\n\n Returns:\n A list of BaseFramePartition objects.\n " ]
Please provide a description of the function:def apply_func_to_select_indices(self, axis, func, indices, keep_remaining=False): if self.partitions.size == 0: return np.array([[]]) # Handling dictionaries has to be done differently, but we still want # to figure out the parti...	[ "Applies a function to select indices.\n\n Note: Your internal function must take a kwarg `internal_indices` for\n this to work correctly. This prevents information leakage of the\n internal index to the external representation.\n\n Args:\n axis: The axis to apply the ...
Please provide a description of the function:def apply_func_to_select_indices_along_full_axis( self, axis, func, indices, keep_remaining=False ): if self.partitions.size == 0: return self.__constructor__(np.array([[]])) if isinstance(indices, dict): dict_indi...	[ "Applies a function to a select subset of full columns/rows.\n\n Note: This should be used when you need to apply a function that relies\n on some global information for the entire column/row, but only need\n to apply a function to a subset.\n\n Important: For your func to operat...
Please provide a description of the function:def apply_func_to_indices_both_axis( self, func, row_indices, col_indices, lazy=False, keep_remaining=True, mutate=False, item_to_distribute=None, ): if keep_remaining: row_parti...	[ "\n Apply a function to along both axis\n\n Important: For your func to operate directly on the indices provided,\n it must use `row_internal_indices, col_internal_indices` as keyword\n arguments.\n " ]
Please provide a description of the function:def inter_data_operation(self, axis, func, other): if axis: partitions = self.row_partitions other_partitions = other.row_partitions else: partitions = self.column_partitions other_partitions = other.co...	[ "Apply a function that requires two BaseFrameManager objects.\n\n Args:\n axis: The axis to apply the function over (0 - rows, 1 - columns)\n func: The function to apply\n other: The other BaseFrameManager object to apply func to.\n\n Returns:\n A new BaseFr...
Please provide a description of the function:def manual_shuffle(self, axis, shuffle_func, lengths): if axis: partitions = self.row_partitions else: partitions = self.column_partitions func = self.preprocess_func(shuffle_func) result = np.array([part.shuff...	[ "Shuffle the partitions based on the `shuffle_func`.\n\n Args:\n axis: The axis to shuffle across.\n shuffle_func: The function to apply before splitting the result.\n lengths: The length of each partition to split the result into.\n\n Returns:\n A new Base...
Please provide a description of the function:def read_parquet(path, engine="auto", columns=None, kwargs): return DataFrame( query_compiler=BaseFactory.read_parquet( path=path, columns=columns, engine=engine, kwargs ) )	[ "Load a parquet object from the file path, returning a DataFrame.\n\n Args:\n path: The filepath of the parquet file.\n We only support local files for now.\n engine: This argument doesn't do anything for now.\n kwargs: Pass into parquet's read_pandas function.\n " ]
Please provide a description of the function:def _make_parser_func(sep): def parser_func( filepath_or_buffer, sep=sep, delimiter=None, header="infer", names=None, index_col=None, usecols=None, squeeze=False, prefix=None, mangle_du...	[ "Creates a parser function from the given sep.\n\n Args:\n sep: The separator default to use for the parser.\n\n Returns:\n A function object.\n " ]
Please provide a description of the function:def _read(kwargs): pd_obj = BaseFactory.read_csv(kwargs) # This happens when `read_csv` returns a TextFileReader object for iterating through if isinstance(pd_obj, pandas.io.parsers.TextFileReader): reader = pd_obj.read pd_obj.read = lamb...	[ "Read csv file from local disk.\n Args:\n filepath_or_buffer:\n The filepath of the csv file.\n We only support local files for now.\n kwargs: Keyword arguments in pandas.read_csv\n " ]
Please provide a description of the function:def read_sql( sql, con, index_col=None, coerce_float=True, params=None, parse_dates=None, columns=None, chunksize=None, ): _, _, _, kwargs = inspect.getargvalues(inspect.currentframe()) return DataFrame(query_compiler=BaseFactory....	[ " Read SQL query or database table into a DataFrame.\n\n Args:\n sql: string or SQLAlchemy Selectable (select or text object) SQL query to be executed or a table name.\n con: SQLAlchemy connectable (engine/connection) or database string URI or DBAPI2 connection (fallback mode)\n index_col: C...
Please provide a description of the function:def read_parquet(cls, path, engine, columns, kwargs): ErrorMessage.default_to_pandas("`read_parquet`") return cls.from_pandas(pandas.read_parquet(path, engine, columns, kwargs))	[ "Load a parquet object from the file path, returning a DataFrame.\n Ray DataFrame only supports pyarrow engine for now.\n\n Args:\n path: The filepath of the parquet file.\n We only support local files for now.\n engine: Ray only support pyarrow reader.\n ...
Please provide a description of the function:def _read(cls, kwargs): pd_obj = pandas.read_csv(kwargs) if isinstance(pd_obj, pandas.DataFrame): return cls.from_pandas(pd_obj) if isinstance(pd_obj, pandas.io.parsers.TextFileReader): # Overwriting the read metho...	[ "Read csv file from local disk.\n Args:\n filepath_or_buffer:\n The filepath of the csv file.\n We only support local files for now.\n kwargs: Keyword arguments in pandas.read_csv\n " ]
Please provide a description of the function:def auto_select_categorical_features(X, threshold=10): feature_mask = [] for column in range(X.shape[1]): if sparse.issparse(X): indptr_start = X.indptr[column] indptr_end = X.indptr[column + 1] unique = np.unique(X.d...	[ "Make a feature mask of categorical features in X.\n\n Features with less than 10 unique values are considered categorical.\n\n Parameters\n ----------\n X : array-like or sparse matrix, shape=(n_samples, n_features)\n Dense array or sparse matrix.\n\n threshold : int\n Maximum number o...
Please provide a description of the function:def _X_selected(X, selected): 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]] X_not_...	[ "Split X into selected features and other features" ]
Please provide a description of the function:def _transform_selected(X, transform, selected, copy=True): if selected == "all": return transform(X) if len(selected) == 0: return X X = check_array(X, accept_sparse='csc', force_all_finite=False) X_sel, X_not_sel, n_selected, n_featur...	[ "Apply a transform function to portion of selected features.\n\n Parameters\n ----------\n X : array-like or sparse matrix, shape=(n_samples, n_features)\n Dense array or sparse matrix.\n\n transform : callable\n A callable transform(X) -> X_transformed\n\n copy : boolean, optional\n ...
Please provide a description of the function:def _matrix_adjust(self, X): data_matrix = X.data if sparse.issparse(X) else X # Shift all values to specially encode for NAN/infinity/OTHER and 0 # Old value New Value # --------- --------- # N (0..int_max)...	[ "Adjust all values in X to encode for NaNs and infinities in the data.\n\n Parameters\n ----------\n X : array-like, shape=(n_samples, n_feature)\n Input array of type int.\n\n Returns\n -------\n X : array-like, shape=(n_samples, n_feature)\n Input ar...
Please provide a description of the function:def _fit_transform(self, X): X = self._matrix_adjust(X) X = check_array( X, accept_sparse='csc', force_all_finite=False, dtype=int ) if X.min() < 0: raise ValueError("X nee...	[ "Assume X contains only categorical features.\n\n Parameters\n ----------\n X : array-like or sparse matrix, shape=(n_samples, n_features)\n Dense array or sparse matrix.\n " ]
Please provide a description of the function:def fit_transform(self, X, y=None): if self.categorical_features == "auto": self.categorical_features = auto_select_categorical_features(X, threshold=self.threshold) return _transform_selected( X, self._fit_transf...	[ "Fit OneHotEncoder to X, then transform X.\n\n Equivalent to self.fit(X).transform(X), but more convenient and more\n efficient. See fit for the parameters, transform for the return value.\n\n Parameters\n ----------\n X : array-like or sparse matrix, shape=(n_samples, n_features)...
Please provide a description of the function:def _transform(self, X): X = self._matrix_adjust(X) X = check_array(X, accept_sparse='csc', force_all_finite=False, dtype=int) if X.min() < 0: raise ValueError("X needs to contain only non-negative integer...	[ "Asssume X contains only categorical features.\n\n Parameters\n ----------\n X : array-like or sparse matrix, shape=(n_samples, n_features)\n Dense array or sparse matrix.\n " ]
Please provide a description of the function:def transform(self, X): return _transform_selected( X, self._transform, self.categorical_features, copy=True )	[ "Transform X using one-hot encoding.\n\n Parameters\n ----------\n X : array-like or sparse matrix, shape=(n_samples, n_features)\n Dense array or sparse matrix.\n\n Returns\n -------\n X_out : sparse matrix if sparse=True else a 2-d array, dtype=int\n ...
Please provide a description of the function:def fit(self, features, target, sample_weight=None, groups=None): self._fit_init() features, target = self._check_dataset(features, target, sample_weight) self.pretest_X, _, self.pretest_y, _ = train_test_split(features, ...	[ "Fit an optimized machine learning pipeline.\n\n Uses genetic programming to optimize a machine learning pipeline that\n maximizes score on the provided features and target. Performs internal\n k-fold cross-validaton to avoid overfitting on the provided data. The\n best pipeline is then ...
Please provide a description of the function:def _setup_memory(self): if self.memory: if isinstance(self.memory, str): if self.memory == "auto": # Create a temporary folder to store the transformers of the pipeline self._cachedir = mkd...	[ "Setup Memory object for memory caching.\n " ]
Please provide a description of the function:def _update_top_pipeline(self): # 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._pareto_front.items, ...	[ "Helper function to update the _optimized_pipeline field." ]
Please provide a description of the function:def _summary_of_best_pipeline(self, features, target): if not self._optimized_pipeline: raise RuntimeError('There was an error in the TPOT optimization ' 'process. This could be because the data was ' ...	[ "Print out best pipeline at the end of optimization process.\n\n Parameters\n ----------\n features: array-like {n_samples, n_features}\n Feature matrix\n\n target: array-like {n_samples}\n List of class labels for prediction\n\n Returns\n -------\n ...
Please provide a description of the function:def predict(self, features): if not self.fitted_pipeline_: raise RuntimeError('A pipeline has not yet been optimized. Please call fit() first.') features = self._check_dataset(features, target=None, sample_weight=None) return se...	[ "Use the optimized pipeline to predict the target for a feature set.\n\n Parameters\n ----------\n features: array-like {n_samples, n_features}\n Feature matrix\n\n Returns\n ----------\n array-like: {n_samples}\n Predicted target for the samples in th...
Please provide a description of the function:def fit_predict(self, features, target, sample_weight=None, groups=None): self.fit(features, target, sample_weight=sample_weight, groups=groups) return self.predict(features)	[ "Call fit and predict in sequence.\n\n Parameters\n ----------\n features: array-like {n_samples, n_features}\n Feature matrix\n target: array-like {n_samples}\n List of class labels for prediction\n sample_weight: array-like {n_samples}, optional\n ...
Please provide a description of the function:def score(self, testing_features, testing_target): if self.fitted_pipeline_ is None: raise RuntimeError('A pipeline has not yet been optimized. Please call fit() first.') testing_features, testing_target = self._check_dataset(testing_fea...	[ "Return the score on the given testing data using the user-specified scoring function.\n\n Parameters\n ----------\n testing_features: array-like {n_samples, n_features}\n Feature matrix of the testing set\n testing_target: array-like {n_samples}\n List of class lab...
Please provide a description of the function:def predict_proba(self, features): if not self.fitted_pipeline_: raise RuntimeError('A pipeline has not yet been optimized. Please call fit() first.') else: if not (hasattr(self.fitted_pipeline_, 'predict_proba')): ...	[ "Use the optimized pipeline to estimate the class probabilities for a feature set.\n\n Parameters\n ----------\n features: array-like {n_samples, n_features}\n Feature matrix of the testing set\n\n Returns\n -------\n array-like: {n_samples, n_target}\n ...
Please provide a description of the function:def clean_pipeline_string(self, individual): dirty_string = str(individual) # There are many parameter prefixes in the pipeline strings, used solely for # making the terminal name unique, eg. LinearSVC__. parameter_prefixes = [(m.star...	[ "Provide a string of the individual without the parameter prefixes.\n\n Parameters\n ----------\n individual: individual\n Individual which should be represented by a pretty string\n\n Returns\n -------\n A string like str(individual), but with parameter prefixes...
Please provide a description of the function:def _check_periodic_pipeline(self, gen): self._update_top_pipeline() if self.periodic_checkpoint_folder is not None: total_since_last_pipeline_save = (datetime.now() - self._last_pipeline_write).total_seconds() if total_since_...	[ "If enough time has passed, save a new optimized pipeline. Currently used in the per generation hook in the optimization loop.\n Parameters\n ----------\n gen: int\n Generation number\n\n Returns\n -------\n None\n " ]
Please provide a description of the function:def export(self, output_file_name, data_file_path=''): if self._optimized_pipeline is None: raise RuntimeError('A pipeline has not yet been optimized. Please call fit() first.') to_write = export_pipeline(self._optimized_pipeline, ...	[ "Export the optimized pipeline as Python code.\n\n Parameters\n ----------\n output_file_name: string\n String containing the path and file name of the desired output file\n data_file_path: string (default: '')\n By default, the path of input dataset is 'PATH/TO/DAT...
Please provide a description of the function:def _impute_values(self, features): if self.verbosity > 1: print('Imputing missing values in feature set') if self._fitted_imputer is None: self._fitted_imputer = Imputer(strategy="median") self._fitted_imputer.fi...	[ "Impute missing values in a feature set.\n\n Parameters\n ----------\n features: array-like {n_samples, n_features}\n A feature matrix\n\n Returns\n -------\n array-like {n_samples, n_features}\n " ]
Please provide a description of the function:def _check_dataset(self, features, target, sample_weight=None): # Check sample_weight if sample_weight is not None: try: sample_weight = np.array(sample_weight).astype('float') except ValueError as e: raise Val...	[ "Check if a dataset has a valid feature set and labels.\n\n Parameters\n ----------\n features: array-like {n_samples, n_features}\n Feature matrix\n target: array-like {n_samples} or None\n List of class labels for prediction\n sample_weight: array-like {n_s...
Please provide a description of the function:def _compile_to_sklearn(self, expr): sklearn_pipeline_str = generate_pipeline_code(expr_to_tree(expr, self._pset), self.operators) sklearn_pipeline = eval(sklearn_pipeline_str, self.operators_context) sklearn_pipeline.memory = self._memory ...	[ "Compile a DEAP pipeline into a sklearn pipeline.\n\n Parameters\n ----------\n expr: DEAP individual\n The DEAP pipeline to be compiled\n\n Returns\n -------\n sklearn_pipeline: sklearn.pipeline.Pipeline\n " ]
Please provide a description of the function:def _set_param_recursive(self, pipeline_steps, parameter, value): for (_, obj) in pipeline_steps: recursive_attrs = ['steps', 'transformer_list', 'estimators'] for attr in recursive_attrs: if hasattr(obj, attr): ...	[ "Recursively iterate through all objects in the pipeline and set a given parameter.\n\n Parameters\n ----------\n pipeline_steps: array-like\n List of (str, obj) tuples from a scikit-learn pipeline or related object\n parameter: str\n The parameter to assign a value...
Please provide a description of the function:def _stop_by_max_time_mins(self): 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 KeyboardInterrupt('{} minut...	[ "Stop optimization process once maximum minutes have elapsed." ]
Please provide a description of the function:def _combine_individual_stats(self, operator_count, cv_score, individual_stats): stats = deepcopy(individual_stats) # Deepcopy, since the string reference to predecessor should be cloned stats['operator_count'] = operator_count stats['intern...	[ "Combine the stats with operator count and cv score and preprare to be written to _evaluated_individuals\n\n Parameters\n ----------\n operator_count: int\n number of components in the pipeline\n cv_score: float\n internal cross validation score\n individual_...
Please provide a description of the function:def _evaluate_individuals(self, population, features, target, sample_weight=None, groups=None): # Evaluate the individuals with an invalid fitness individuals = [ind for ind in population if not ind.fitness.valid] # update pbar for valid ind...	[ "Determine the fit of the provided individuals.\n\n Parameters\n ----------\n population: a list of DEAP individual\n One individual is a list of pipeline operators and model parameters that can be\n compiled by DEAP into a callable function\n features: numpy.ndarra...
Please provide a description of the function:def _preprocess_individuals(self, individuals): # update self._pbar.total if not (self.max_time_mins is None) and not self._pbar.disable and self._pbar.total <= self._pbar.n: self._pbar.total += self._lambda # Check we do not eval...	[ "Preprocess DEAP individuals before pipeline evaluation.\n\n Parameters\n ----------\n individuals: a list of DEAP individual\n One individual is a list of pipeline operators and model parameters that can be\n compiled by DEAP into a callable function\n\n Returns\n ...
Please provide a description of the function:def _update_evaluated_individuals_(self, result_score_list, eval_individuals_str, operator_counts, stats_dicts): for result_score, individual_str in zip(result_score_list, eval_individuals_str): if type(result_score) in [float, np.float64, np.flo...	[ "Update self.evaluated_individuals_ and error message during pipeline evaluation.\n\n Parameters\n ----------\n result_score_list: list\n A list of CV scores for evaluated pipelines\n eval_individuals_str: list\n A list of strings for evaluated pipelines\n op...
Please provide a description of the function:def _update_pbar(self, pbar_num=1, pbar_msg=None): if not isinstance(self._pbar, type(None)): if self.verbosity > 2 and pbar_msg is not None: self._pbar.write(pbar_msg, file=self._file) if not self._pbar.disable: ...	[ "Update self._pbar and error message during pipeline evaluation.\n\n Parameters\n ----------\n pbar_num: int\n How many pipelines has been processed\n pbar_msg: None or string\n Error message\n\n Returns\n -------\n None\n " ]
Please provide a description of the function:def _random_mutation_operator(self, individual, allow_shrink=True): if self.tree_structure: mutation_techniques = [ partial(gp.mutInsert, pset=self._pset), partial(mutNodeReplacement, pset=self._pset) ]...	[ "Perform a replacement, insertion, or shrink mutation on an individual.\n\n Parameters\n ----------\n individual: DEAP individual\n A list of pipeline operators and model parameters that can be\n compiled by DEAP into a callable function\n\n allow_shrink: bool (True...
Please provide a description of the function:def _gen_grow_safe(self, pset, min_, max_, type_=None): def condition(height, depth, type_): return type_ not in self.ret_types or depth == height return self._generate(pset, min_, max_, condition, type_)	[ "Generate an expression where each leaf might have a different depth between min_ and max_.\n\n Parameters\n ----------\n pset: PrimitiveSetTyped\n Primitive set from which primitives are selected.\n min_: int\n Minimum height of the produced trees.\n max_: i...
Please provide a description of the function:def _operator_count(self, individual): operator_count = 0 for i in range(len(individual)): node = individual[i] if type(node) is deap.gp.Primitive and node.name != 'CombineDFs': operator_count += 1 retu...	[ "Count the number of pipeline operators as a measure of pipeline complexity.\n\n Parameters\n ----------\n individual: list\n A grown tree with leaves at possibly different depths\n dependending on the condition function.\n\n Returns\n -------\n operat...
Please provide a description of the function:def _update_val(self, val, result_score_list): self._update_pbar() if val == 'Timeout': self._update_pbar(pbar_msg=('Skipped pipeline #{0} due to time out. ' 'Continuing to the next pipeline.'.forma...	[ "Update values in the list of result scores and self._pbar during pipeline evaluation.\n\n Parameters\n ----------\n val: float or \"Timeout\"\n CV scores\n result_score_list: list\n A list of CV scores\n\n Returns\n -------\n result_score_list:...
Please provide a description of the function:def _generate(self, pset, min_, max_, condition, type_=None): if type_ is None: type_ = pset.ret expr = [] height = np.random.randint(min_, max_) stack = [(0, type_)] while len(stack) != 0: depth, type_...	[ "Generate a Tree as a list of lists.\n\n The tree is build from the root to the leaves, and it stop growing when\n the condition is fulfilled.\n\n Parameters\n ----------\n pset: PrimitiveSetTyped\n Primitive set from which primitives are selected.\n min_: int\n ...
Please provide a description of the function:def transform(self, X): selected = auto_select_categorical_features(X, threshold=self.threshold) X_sel, _, n_selected, _ = _X_selected(X, selected) if n_selected == 0: # No features selected. raise ValueError('No cate...	[ "Select categorical features and transform them using OneHotEncoder.\n\n Parameters\n ----------\n X: numpy ndarray, {n_samples, n_components}\n New data, where n_samples is the number of samples and n_components is the number of components.\n\n Returns\n -------\n ...

Please provide a description of the function:def sort_index( self, axis=0, level=None, ascending=True, inplace=False, kind="quicksort", na_position="last", sort_remaining=True, by=None, ): axis = self._get_axis_numb...

[ "Sort a DataFrame by one of the indices (columns or index).\r\n\r\n Args:\r\n axis: The axis to sort over.\r\n level: The MultiIndex level to sort over.\r\n ascending: Ascending or descending\r\n inplace: Whether or not to update this DataFrame inplace.\r\n ...

Please provide a description of the function:def sort_values( self, by, axis=0, ascending=True, inplace=False, kind="quicksort", na_position="last", ): axis = self._get_axis_number(axis) if not is_list_like(by): ...

[ "Sorts by a column/row or list of columns/rows.\r\n\r\n Args:\r\n by: A list of labels for the axis to sort over.\r\n axis: The axis to sort.\r\n ascending: Sort in ascending or descending order.\r\n inplace: If true, do the operation inplace.\r\n kind: ...

Please provide a description of the function:def sub(self, other, axis="columns", level=None, fill_value=None): return self._binary_op( "sub", other, axis=axis, level=level, fill_value=fill_value )

[ "Subtract a DataFrame/Series/scalar from this DataFrame.\r\n\r\n Args:\r\n other: The object to use to apply the subtraction to this.\r\n axis: The axis to apply the subtraction over.\r\n level: Mutlilevel index level to subtract over.\r\n fill_value: The value to ...

Please provide a description of the function:def to_numpy(self, dtype=None, copy=False): return self._default_to_pandas("to_numpy", dtype=dtype, copy=copy)

[ "Convert the DataFrame to a NumPy array.\r\n\r\n Args:\r\n dtype: The dtype to pass to numpy.asarray()\r\n copy: Whether to ensure that the returned value is a not a view on another\r\n array.\r\n\r\n Returns:\r\n A numpy array.\r\n " ]

Please provide a description of the function:def truediv(self, other, axis="columns", level=None, fill_value=None): return self._binary_op( "truediv", other, axis=axis, level=level, fill_value=fill_value )

[ "Divides this DataFrame against another DataFrame/Series/scalar.\r\n\r\n Args:\r\n other: The object to use to apply the divide against this.\r\n axis: The axis to divide over.\r\n level: The Multilevel index level to apply divide over.\r\n fill_value: The value to...

Please provide a description of the function:def var( self, axis=None, skipna=None, level=None, ddof=1, numeric_only=None, **kwargs ): axis = self._get_axis_number(axis) if axis is not None else 0 if numeric_only is not None and not numeric_only: self._validate_dty...

[ "Computes variance across the DataFrame.\r\n\r\n Args:\r\n axis (int): The axis to take the variance on.\r\n skipna (bool): True to skip NA values, false otherwise.\r\n ddof (int): degrees of freedom\r\n\r\n Returns:\r\n The variance of the DataFrame.\r\n ...

Please provide a description of the function:def size(self): return len(self._query_compiler.index) * len(self._query_compiler.columns)

[ "Get the number of elements in the DataFrame.\r\n\r\n Returns:\r\n The number of elements in the DataFrame.\r\n " ]

Please provide a description of the function:def get(self): if self.call_queue: return self.apply(lambda df: df).data else: return self.data.copy()

[ "Flushes the call_queue and returns the data.\n\n Note: Since this object is a simple wrapper, just return the data.\n\n Returns:\n The object that was `put`.\n " ]

Please provide a description of the function:def apply(self, func, **kwargs): self.call_queue.append((func, kwargs)) def call_queue_closure(data, call_queues): result = data.copy() for func, kwargs in call_queues: try: result = func(r...

[ "Apply some callable function to the data in this partition.\n\n Note: It is up to the implementation how kwargs are handled. They are\n an important part of many implementations. As of right now, they\n are not serialized.\n\n Args:\n func: The lambda to apply (may al...

Please provide a description of the function:def apply(self, func, **kwargs): import dask # applies the func lazily delayed_call = self.delayed_call self.delayed_call = self.dask_obj return self.__class__(dask.delayed(func)(delayed_call, **kwargs))

[ "Apply some callable function to the data in this partition.\n\n Note: It is up to the implementation how kwargs are handled. They are\n an important part of many implementations. As of right now, they\n are not serialized.\n\n Args:\n func: The lambda to apply (may al...

Please provide a description of the function:def add_to_apply_calls(self, func, **kwargs): import dask self.delayed_call = dask.delayed(func)(self.delayed_call, **kwargs) return self

[ "Add the function to the apply function call stack.\n\n This function will be executed when apply is called. It will be executed\n in the order inserted; apply's func operates the last and return\n " ]

Please provide a description of the function:def _read_csv_with_offset_pyarrow_on_ray( fname, num_splits, start, end, kwargs, header ): # pragma: no cover bio = open(fname, "rb") # The header line for the CSV file first_line = bio.readline() bio.seek(start) to_read = header + first_line + ...

[ "Use a Ray task to read a chunk of a CSV into a pyarrow Table.\n Note: Ray functions are not detected by codecov (thus pragma: no cover)\n Args:\n fname: The filename of the file to open.\n num_splits: The number of splits (partitions) to separate the DataFrame into.\n start: The start ...

Please provide a description of the function:def compute_chunksize(df, num_splits, default_block_size=32, axis=None): if axis == 0 or axis is None: row_chunksize = get_default_chunksize(len(df.index), num_splits) # Take the min of the default and the memory-usage chunksize first to avoid a ...

[ "Computes the number of rows and/or columns to include in each partition.\n\n Args:\n df: The DataFrame to split.\n num_splits: The maximum number of splits to separate the DataFrame into.\n default_block_size: Minimum number of rows/columns (default set to 32x32).\n axis: The axis to...

Please provide a description of the function:def _get_nan_block_id(partition_class, n_row=1, n_col=1, transpose=False): global _NAN_BLOCKS if transpose: n_row, n_col = n_col, n_row shape = (n_row, n_col) if shape not in _NAN_BLOCKS: arr = np.tile(np.array(np.NaN), shape) # T...

[ "A memory efficient way to get a block of NaNs.\n\n Args:\n partition_class (BaseFramePartition): The class to use to put the object\n in the remote format.\n n_row(int): The number of rows.\n n_col(int): The number of columns.\n transpose(bool): If true, swap rows and colu...

Please provide a description of the function:def split_result_of_axis_func_pandas(axis, num_splits, result, length_list=None): if num_splits == 1: return result if length_list is not None: length_list.insert(0, 0) sums = np.cumsum(length_list) if axis == 0: retur...

[ "Split the Pandas result evenly based on the provided number of splits.\n\n Args:\n axis: The axis to split across.\n num_splits: The number of even splits to create.\n result: The result of the computation. This should be a Pandas\n DataFrame.\n length_list: The list of le...

Please provide a description of the function:def _parse_tuple(tup): row_loc, col_loc = slice(None), slice(None) if is_tuple(tup): row_loc = tup[0] if len(tup) == 2: col_loc = tup[1] if len(tup) > 2: raise IndexingError("Too many indexers") else: ...

[ "Unpack the user input for getitem and setitem and compute ndim\n\n loc[a] -> ([a], :), 1D\n loc[[a,b],] -> ([a,b], :),\n loc[a,b] -> ([a], [b]), 0D\n " ]

Please provide a description of the function:def _is_enlargement(locator, global_index): if ( is_list_like(locator) and not is_slice(locator) and len(locator) > 0 and not is_boolean_array(locator) and (isinstance(locator, type(global_index[0])) and locator not in global_...

[ "Determine if a locator will enlarge the global index.\n\n Enlargement happens when you trying to locate using labels isn't in the\n original index. In other words, enlargement == adding NaNs !\n " ]

Please provide a description of the function:def _compute_ndim(row_loc, col_loc): row_scaler = is_scalar(row_loc) col_scaler = is_scalar(col_loc) if row_scaler and col_scaler: ndim = 0 elif row_scaler ^ col_scaler: ndim = 1 else: ndim = 2 return ndim

[ "Compute the ndim of result from locators\n " ]

Please provide a description of the function:def _broadcast_item(self, row_lookup, col_lookup, item, to_shape): # It is valid to pass a DataFrame or Series to __setitem__ that is larger than # the target the user is trying to overwrite. This if isinstance(item, (pandas.Series, pandas.Da...

[ "Use numpy to broadcast or reshape item.\n\n Notes:\n - Numpy is memory efficient, there shouldn't be performance issue.\n " ]

Please provide a description of the function:def _write_items(self, row_lookup, col_lookup, item): self.qc.write_items(row_lookup, col_lookup, item)

[ "Perform remote write and replace blocks.\n " ]

Please provide a description of the function:def _handle_enlargement(self, row_loc, col_loc): if _is_enlargement(row_loc, self.qc.index) or _is_enlargement( col_loc, self.qc.columns ): _warn_enlargement() self.qc.enlarge_partitions( new_row_la...

[ "Handle Enlargement (if there is one).\n\n Returns:\n None\n " ]

Please provide a description of the function:def _compute_enlarge_labels(self, locator, base_index): # base_index_type can be pd.Index or pd.DatetimeIndex # depending on user input and pandas behavior # See issue #2264 base_index_type = type(base_index) locator_as_index ...

[ "Helper for _enlarge_axis, compute common labels and extra labels.\n\n Returns:\n nan_labels: The labels needs to be added\n " ]

Please provide a description of the function:def _split_result_for_readers(axis, num_splits, df): # pragma: no cover splits = split_result_of_axis_func_pandas(axis, num_splits, df) if not isinstance(splits, list): splits = [splits] return splits

[ "Splits the DataFrame read into smaller DataFrames and handles all edge cases.\n\n Args:\n axis: Which axis to split over.\n num_splits: The number of splits to create.\n df: The DataFrame after it has been read.\n\n Returns:\n A list of pandas DataFrames.\n " ]

Please provide a description of the function:def _read_parquet_columns(path, columns, num_splits, kwargs): # pragma: no cover import pyarrow.parquet as pq df = pq.read_pandas(path, columns=columns, **kwargs).to_pandas() # Append the length of the index here to build it externally return _split_re...

[ "Use a Ray task to read columns from Parquet into a Pandas DataFrame.\n\n Note: Ray functions are not detected by codecov (thus pragma: no cover)\n\n Args:\n path: The path of the Parquet file.\n columns: The list of column names to read.\n num_splits: The number of partitions to split th...

Please provide a description of the function:def _read_csv_with_offset_pandas_on_ray( fname, num_splits, start, end, kwargs, header ): # pragma: no cover index_col = kwargs.get("index_col", None) bio = file_open(fname, "rb") bio.seek(start) to_read = header + bio.read(end - start) bio.clos...

[ "Use a Ray task to read a chunk of a CSV into a Pandas DataFrame.\n\n Note: Ray functions are not detected by codecov (thus pragma: no cover)\n\n Args:\n fname: The filename of the file to open.\n num_splits: The number of splits (partitions) to separate the DataFrame into.\n start: The s...

Please provide a description of the function:def _read_hdf_columns(path_or_buf, columns, num_splits, kwargs): # pragma: no cover df = pandas.read_hdf(path_or_buf, columns=columns, **kwargs) # Append the length of the index here to build it externally return _split_result_for_readers(0, num_splits, df...

[ "Use a Ray task to read columns from HDF5 into a Pandas DataFrame.\n\n Note: Ray functions are not detected by codecov (thus pragma: no cover)\n\n Args:\n path_or_buf: The path of the HDF5 file.\n columns: The list of column names to read.\n num_splits: The number of partitions to split t...

Please provide a description of the function:def _read_feather_columns(path, columns, num_splits): # pragma: no cover from pyarrow import feather df = feather.read_feather(path, columns=columns) # Append the length of the index here to build it externally return _split_result_for_readers(0, num_s...

[ "Use a Ray task to read columns from Feather into a Pandas DataFrame.\n\n Note: Ray functions are not detected by codecov (thus pragma: no cover)\n\n Args:\n path: The path of the Feather file.\n columns: The list of column names to read.\n num_splits: The number of partitions to split th...

Please provide a description of the function:def _read_sql_with_limit_offset( num_splits, sql, con, index_col, kwargs ): # pragma: no cover pandas_df = pandas.read_sql(sql, con, index_col=index_col, **kwargs) if index_col is None: index = len(pandas_df) else: index = pandas_df.inde...

[ "Use a Ray task to read a chunk of SQL source.\n\n Note: Ray functions are not detected by codecov (thus pragma: no cover)\n " ]

Please provide a description of the function:def get_index(index_name, *partition_indices): # pragma: no cover index = partition_indices[0].append(partition_indices[1:]) index.names = index_name return index

[ "Get the index from the indices returned by the workers.\n\n Note: Ray functions are not detected by codecov (thus pragma: no cover)" ]

Please provide a description of the function:def read_parquet(cls, path, engine, columns, **kwargs): from pyarrow.parquet import ParquetFile if cls.read_parquet_remote_task is None: return super(RayIO, cls).read_parquet(path, engine, columns, **kwargs) if not columns: ...

[ "Load a parquet object from the file path, returning a DataFrame.\n Ray DataFrame only supports pyarrow engine for now.\n\n Args:\n path: The filepath of the parquet file.\n We only support local files for now.\n engine: Ray only support pyarrow reader.\n ...

Please provide a description of the function:def _read_csv_from_file_pandas_on_ray(cls, filepath, kwargs={}): names = kwargs.get("names", None) index_col = kwargs.get("index_col", None) if names is None: # For the sake of the empty df, we assume no `index_col` to get the cor...

[ "Constructs a DataFrame from a CSV file.\n\n Args:\n filepath (str): path to the CSV file.\n npartitions (int): number of partitions for the DataFrame.\n kwargs (dict): args excluding filepath provided to read_csv.\n\n Returns:\n DataFrame or Series construc...

Please provide a description of the function:def _read(cls, filepath_or_buffer, **kwargs): # The intention of the inspection code is to reduce the amount of # communication we have to do between processes and nodes. We take a quick # pass over the arguments and remove those that are def...

[ "Read csv file from local disk.\n Args:\n filepath_or_buffer:\n The filepath of the csv file.\n We only support local files for now.\n kwargs: Keyword arguments in pandas.read_csv\n " ]

Please provide a description of the function:def read_hdf(cls, path_or_buf, **kwargs): if cls.read_hdf_remote_task is None: return super(RayIO, cls).read_hdf(path_or_buf, **kwargs) format = cls._validate_hdf_format(path_or_buf=path_or_buf) if format is None: Er...

[ "Load a h5 file from the file path or buffer, returning a DataFrame.\n\n Args:\n path_or_buf: string, buffer or path object\n Path to the file to open, or an open :class:`pandas.HDFStore` object.\n kwargs: Pass into pandas.read_hdf function.\n\n Returns:\n ...

Please provide a description of the function:def read_feather(cls, path, columns=None, use_threads=True): if cls.read_feather_remote_task is None: return super(RayIO, cls).read_feather( path, columns=columns, use_threads=use_threads ) if columns is None:...

[ "Read a pandas.DataFrame from Feather format.\n Ray DataFrame only supports pyarrow engine for now.\n\n Args:\n path: The filepath of the feather file.\n We only support local files for now.\n multi threading is set to True by default\n columns:...

Please provide a description of the function:def to_sql(cls, qc, **kwargs): # we first insert an empty DF in order to create the full table in the database # This also helps to validate the input against pandas # we would like to_sql() to complete only when all rows have been inserted i...

[ "Write records stored in a DataFrame to a SQL database.\n Args:\n qc: the query compiler of the DF that we want to run to_sql on\n kwargs: parameters for pandas.to_sql(**kwargs)\n " ]

Please provide a description of the function:def read_sql(cls, sql, con, index_col=None, **kwargs): if cls.read_sql_remote_task is None: return super(RayIO, cls).read_sql(sql, con, index_col=index_col, **kwargs) row_cnt_query = "SELECT COUNT(*) FROM ({})".format(sql) row_cn...

[ "Reads a SQL query or database table into a DataFrame.\n Args:\n sql: string or SQLAlchemy Selectable (select or text object) SQL query to be\n executed or a table name.\n con: SQLAlchemy connectable (engine/connection) or database string URI or\n DBAPI2 co...

Please provide a description of the function:def to_datetime( arg, errors="raise", dayfirst=False, yearfirst=False, utc=None, box=True, format=None, exact=True, unit=None, infer_datetime_format=False, origin="unix", cache=False, ): if not isinstance(arg, DataFram...

[ "Convert the arg to datetime format. If not Ray DataFrame, this falls\n back on pandas.\n\n Args:\n errors ('raise' or 'ignore'): If 'ignore', errors are silenced.\n Pandas blatantly ignores this argument so we will too.\n dayfirst (bool): Date format is passed in as day first.\n ...

Please provide a description of the function:def read_sql( sql, con, index_col=None, coerce_float=True, params=None, parse_dates=None, columns=None, chunksize=None, partition_column=None, lower_bound=None, upper_bound=None, max_sessions=None, ): _, _, _, kwargs =...

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

Please provide a description of the function:def block_lengths(self): if self._lengths_cache is None: try: # The first column will have the correct lengths. We have an # invariant that requires that all blocks be the same length in a # row of ...

[ "Gets the lengths of the blocks.\n\n Note: This works with the property structure `_lengths_cache` to avoid\n having to recompute these values each time they are needed.\n " ]

Please provide a description of the function:def block_widths(self): if self._widths_cache is None: try: # The first column will have the correct lengths. We have an # invariant that requires that all blocks be the same width in a # column of ...

[ "Gets the widths of the blocks.\n\n Note: This works with the property structure `_widths_cache` to avoid\n having to recompute these values each time they are needed.\n " ]

Please provide a description of the function:def deploy_ray_func(func, partition, kwargs): # pragma: no cover try: return func(partition, **kwargs) # Sometimes Arrow forces us to make a copy of an object before we operate # on it. We don't want the error to propagate to the user, and we want t...

[ "Deploy a function to a partition in Ray.\n\n Note: Ray functions are not detected by codecov (thus pragma: no cover)\n\n Args:\n func: The function to apply.\n partition: The partition to apply the function to.\n kwargs: A dictionary of keyword arguments for the function.\n\n Returns:...

Please provide a description of the function:def get(self): if len(self.call_queue): return self.apply(lambda x: x).get() try: return ray.get(self.oid) except RayTaskError as e: handle_ray_task_error(e)

[ "Gets the object out of the plasma store.\n\n Returns:\n The object from the plasma store.\n " ]

Please provide a description of the function:def block_lengths(self): if self._lengths_cache is None: # The first column will have the correct lengths. We have an # invariant that requires that all blocks be the same length in a # row of blocks. self._len...

[ "Gets the lengths of the blocks.\n\n Note: This works with the property structure `_lengths_cache` to avoid\n having to recompute these values each time they are needed.\n " ]

Please provide a description of the function:def block_widths(self): if self._widths_cache is None: # The first column will have the correct lengths. We have an # invariant that requires that all blocks be the same width in a # column of blocks. self._wid...

[ "Gets the widths of the blocks.\n\n Note: This works with the property structure `_widths_cache` to avoid\n having to recompute these values each time they are needed.\n " ]

Please provide a description of the function:def map_across_blocks(self, map_func): preprocessed_map_func = self.preprocess_func(map_func) new_partitions = np.array( [ [part.apply(preprocessed_map_func) for part in row_of_parts] for row_of_parts in se...

[ "Applies `map_func` to every partition.\n\n Args:\n map_func: The function to apply.\n\n Returns:\n A new BaseFrameManager object, the type of object that called this.\n " ]

Please provide a description of the function:def copartition_datasets(self, axis, other, left_func, right_func): if left_func is None: new_self = self else: new_self = self.map_across_full_axis(axis, left_func) # This block of code will only shuffle if absolutel...

[ "Copartition two BlockPartitions objects.\n\n Args:\n axis: The axis to copartition.\n other: The other BlockPartitions object to copartition with.\n left_func: The function to apply to left. If None, just use the dimension\n of self (based on axis).\n ...

Please provide a description of the function:def map_across_full_axis(self, axis, map_func): # Since we are already splitting the DataFrame back up after an # operation, we will just use this time to compute the number of # partitions as best we can right now. num_splits = self....

[ "Applies `map_func` to every partition.\n\n Note: This method should be used in the case that `map_func` relies on\n some global information about the axis.\n\n Args:\n axis: The axis to perform the map across (0 - index, 1 - columns).\n map_func: The function to apply...

Please provide a description of the function:def take(self, axis, n): # These are the partitions that we will extract over if not axis: partitions = self.partitions bin_lengths = self.block_lengths else: partitions = self.partitions.T bin_...

[ "Take the first (or last) n rows or columns from the blocks\n\n Note: Axis = 0 will be equivalent to `head` or `tail`\n Axis = 1 will be equivalent to `front` or `back`\n\n Args:\n axis: The axis to extract (0 for extracting rows, 1 for extracting columns)\n n: The n...

Please provide a description of the function:def concat(self, axis, other_blocks): if type(other_blocks) is list: other_blocks = [blocks.partitions for blocks in other_blocks] return self.__constructor__( np.concatenate([self.partitions] + other_blocks, axis=axis...

[ "Concatenate the blocks with another set of blocks.\n\n Note: Assumes that the blocks are already the same shape on the\n dimension being concatenated. A ValueError will be thrown if this\n condition is not met.\n\n Args:\n axis: The axis to concatenate to.\n ...

Please provide a description of the function:def to_pandas(self, is_transposed=False): # In the case this is transposed, it is easier to just temporarily # transpose back then transpose after the conversion. The performance # is the same as if we individually transposed the blocks and ...

[ "Convert this object into a Pandas DataFrame from the partitions.\n\n Args:\n is_transposed: A flag for telling this object that the external\n representation is transposed, but not the internal.\n\n Returns:\n A Pandas DataFrame\n " ]

Please provide a description of the function:def get_indices(self, axis=0, index_func=None, old_blocks=None): ErrorMessage.catch_bugs_and_request_email(not callable(index_func)) func = self.preprocess_func(index_func) if axis == 0: # We grab the first column of blocks and ex...

[ "This gets the internal indices stored in the partitions.\n\n Note: These are the global indices of the object. This is mostly useful\n when you have deleted rows/columns internally, but do not know\n which ones were deleted.\n\n Args:\n axis: This axis to extract the ...

Please provide a description of the function:def _get_blocks_containing_index(self, axis, index): if not axis: ErrorMessage.catch_bugs_and_request_email(index > sum(self.block_widths)) cumulative_column_widths = np.array(self.block_widths).cumsum() block_idx = int(np...

[ "Convert a global index to a block index and local index.\n\n Note: This method is primarily used to convert a global index into a\n partition index (along the axis provided) and local index (useful\n for `iloc` or similar operations.\n\n Args:\n axis: The axis along w...

Please provide a description of the function:def _get_dict_of_block_index(self, axis, indices, ordered=False): # Get the internal index and create a dictionary so we only have to # travel to each partition once. all_partitions_and_idx = [ self._get_blocks_containing_index(ax...

[ "Convert indices to a dict of block index to internal index mapping.\n\n Note: See `_get_blocks_containing_index` for primary usage. This method\n accepts a list of indices rather than just a single value, and uses\n `_get_blocks_containing_index`.\n\n Args:\n axis: Th...

Please provide a description of the function:def _apply_func_to_list_of_partitions(self, func, partitions, **kwargs): preprocessed_func = self.preprocess_func(func) return [obj.apply(preprocessed_func, **kwargs) for obj in partitions]

[ "Applies a function to a list of remote partitions.\n\n Note: The main use for this is to preprocess the func.\n\n Args:\n func: The func to apply\n partitions: The list of partitions\n\n Returns:\n A list of BaseFramePartition objects.\n " ]

Please provide a description of the function:def apply_func_to_select_indices(self, axis, func, indices, keep_remaining=False): if self.partitions.size == 0: return np.array([[]]) # Handling dictionaries has to be done differently, but we still want # to figure out the parti...

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

Please provide a description of the function:def apply_func_to_select_indices_along_full_axis( self, axis, func, indices, keep_remaining=False ): if self.partitions.size == 0: return self.__constructor__(np.array([[]])) if isinstance(indices, dict): dict_indi...

[ "Applies a function to a select subset of full columns/rows.\n\n Note: This should be used when you need to apply a function that relies\n on some global information for the entire column/row, but only need\n to apply a function to a subset.\n\n Important: For your func to operat...

Please provide a description of the function:def apply_func_to_indices_both_axis( self, func, row_indices, col_indices, lazy=False, keep_remaining=True, mutate=False, item_to_distribute=None, ): if keep_remaining: row_parti...

[ "\n Apply a function to along both axis\n\n Important: For your func to operate directly on the indices provided,\n it must use `row_internal_indices, col_internal_indices` as keyword\n arguments.\n " ]

Please provide a description of the function:def inter_data_operation(self, axis, func, other): if axis: partitions = self.row_partitions other_partitions = other.row_partitions else: partitions = self.column_partitions other_partitions = other.co...

[ "Apply a function that requires two BaseFrameManager objects.\n\n Args:\n axis: The axis to apply the function over (0 - rows, 1 - columns)\n func: The function to apply\n other: The other BaseFrameManager object to apply func to.\n\n Returns:\n A new BaseFr...

Please provide a description of the function:def manual_shuffle(self, axis, shuffle_func, lengths): if axis: partitions = self.row_partitions else: partitions = self.column_partitions func = self.preprocess_func(shuffle_func) result = np.array([part.shuff...

[ "Shuffle the partitions based on the `shuffle_func`.\n\n Args:\n axis: The axis to shuffle across.\n shuffle_func: The function to apply before splitting the result.\n lengths: The length of each partition to split the result into.\n\n Returns:\n A new Base...

Please provide a description of the function:def read_parquet(path, engine="auto", columns=None, **kwargs): return DataFrame( query_compiler=BaseFactory.read_parquet( path=path, columns=columns, engine=engine, **kwargs ) )

[ "Load a parquet object from the file path, returning a DataFrame.\n\n Args:\n path: The filepath of the parquet file.\n We only support local files for now.\n engine: This argument doesn't do anything for now.\n kwargs: Pass into parquet's read_pandas function.\n " ]

Please provide a description of the function:def _make_parser_func(sep): def parser_func( filepath_or_buffer, sep=sep, delimiter=None, header="infer", names=None, index_col=None, usecols=None, squeeze=False, prefix=None, mangle_du...

[ "Creates a parser function from the given sep.\n\n Args:\n sep: The separator default to use for the parser.\n\n Returns:\n A function object.\n " ]

Please provide a description of the function:def _read(**kwargs): pd_obj = BaseFactory.read_csv(**kwargs) # This happens when `read_csv` returns a TextFileReader object for iterating through if isinstance(pd_obj, pandas.io.parsers.TextFileReader): reader = pd_obj.read pd_obj.read = lamb...

[ "Read csv file from local disk.\n Args:\n filepath_or_buffer:\n The filepath of the csv file.\n We only support local files for now.\n kwargs: Keyword arguments in pandas.read_csv\n " ]

Please provide a description of the function:def read_sql( sql, con, index_col=None, coerce_float=True, params=None, parse_dates=None, columns=None, chunksize=None, ): _, _, _, kwargs = inspect.getargvalues(inspect.currentframe()) return DataFrame(query_compiler=BaseFactory....

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

Please provide a description of the function:def read_parquet(cls, path, engine, columns, **kwargs): ErrorMessage.default_to_pandas("`read_parquet`") return cls.from_pandas(pandas.read_parquet(path, engine, columns, **kwargs))

[ "Load a parquet object from the file path, returning a DataFrame.\n Ray DataFrame only supports pyarrow engine for now.\n\n Args:\n path: The filepath of the parquet file.\n We only support local files for now.\n engine: Ray only support pyarrow reader.\n ...

Please provide a description of the function:def _read(cls, **kwargs): pd_obj = pandas.read_csv(**kwargs) if isinstance(pd_obj, pandas.DataFrame): return cls.from_pandas(pd_obj) if isinstance(pd_obj, pandas.io.parsers.TextFileReader): # Overwriting the read metho...

[ "Read csv file from local disk.\n Args:\n filepath_or_buffer:\n The filepath of the csv file.\n We only support local files for now.\n kwargs: Keyword arguments in pandas.read_csv\n " ]

Please provide a description of the function:def auto_select_categorical_features(X, threshold=10): feature_mask = [] for column in range(X.shape[1]): if sparse.issparse(X): indptr_start = X.indptr[column] indptr_end = X.indptr[column + 1] unique = np.unique(X.d...

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

Please provide a description of the function:def _X_selected(X, selected): 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]] X_not_...

[ "Split X into selected features and other features" ]

Please provide a description of the function:def _transform_selected(X, transform, selected, copy=True): if selected == "all": return transform(X) if len(selected) == 0: return X X = check_array(X, accept_sparse='csc', force_all_finite=False) X_sel, X_not_sel, n_selected, n_featur...

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

Please provide a description of the function:def _matrix_adjust(self, X): data_matrix = X.data if sparse.issparse(X) else X # Shift all values to specially encode for NAN/infinity/OTHER and 0 # Old value New Value # --------- --------- # N (0..int_max)...

[ "Adjust all values in X to encode for NaNs and infinities in the data.\n\n Parameters\n ----------\n X : array-like, shape=(n_samples, n_feature)\n Input array of type int.\n\n Returns\n -------\n X : array-like, shape=(n_samples, n_feature)\n Input ar...

Please provide a description of the function:def _fit_transform(self, X): X = self._matrix_adjust(X) X = check_array( X, accept_sparse='csc', force_all_finite=False, dtype=int ) if X.min() < 0: raise ValueError("X nee...

[ "Assume X contains only categorical features.\n\n Parameters\n ----------\n X : array-like or sparse matrix, shape=(n_samples, n_features)\n Dense array or sparse matrix.\n " ]

Please provide a description of the function:def fit_transform(self, X, y=None): if self.categorical_features == "auto": self.categorical_features = auto_select_categorical_features(X, threshold=self.threshold) return _transform_selected( X, self._fit_transf...

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

Please provide a description of the function:def _transform(self, X): X = self._matrix_adjust(X) X = check_array(X, accept_sparse='csc', force_all_finite=False, dtype=int) if X.min() < 0: raise ValueError("X needs to contain only non-negative integer...

[ "Asssume X contains only categorical features.\n\n Parameters\n ----------\n X : array-like or sparse matrix, shape=(n_samples, n_features)\n Dense array or sparse matrix.\n " ]

Please provide a description of the function:def transform(self, X): return _transform_selected( X, self._transform, self.categorical_features, copy=True )

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

Please provide a description of the function:def fit(self, features, target, sample_weight=None, groups=None): self._fit_init() features, target = self._check_dataset(features, target, sample_weight) self.pretest_X, _, self.pretest_y, _ = train_test_split(features, ...

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

Please provide a description of the function:def _setup_memory(self): if self.memory: if isinstance(self.memory, str): if self.memory == "auto": # Create a temporary folder to store the transformers of the pipeline self._cachedir = mkd...

[ "Setup Memory object for memory caching.\n " ]

Please provide a description of the function:def _update_top_pipeline(self): # 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._pareto_front.items, ...

[ "Helper function to update the _optimized_pipeline field." ]

Please provide a description of the function:def _summary_of_best_pipeline(self, features, target): if not self._optimized_pipeline: raise RuntimeError('There was an error in the TPOT optimization ' 'process. This could be because the data was ' ...

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

Please provide a description of the function:def predict(self, features): if not self.fitted_pipeline_: raise RuntimeError('A pipeline has not yet been optimized. Please call fit() first.') features = self._check_dataset(features, target=None, sample_weight=None) return se...

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

Please provide a description of the function:def fit_predict(self, features, target, sample_weight=None, groups=None): self.fit(features, target, sample_weight=sample_weight, groups=groups) return self.predict(features)

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

Please provide a description of the function:def score(self, testing_features, testing_target): if self.fitted_pipeline_ is None: raise RuntimeError('A pipeline has not yet been optimized. Please call fit() first.') testing_features, testing_target = self._check_dataset(testing_fea...

[ "Return the score on the given testing data using the user-specified scoring function.\n\n Parameters\n ----------\n testing_features: array-like {n_samples, n_features}\n Feature matrix of the testing set\n testing_target: array-like {n_samples}\n List of class lab...

Please provide a description of the function:def predict_proba(self, features): if not self.fitted_pipeline_: raise RuntimeError('A pipeline has not yet been optimized. Please call fit() first.') else: if not (hasattr(self.fitted_pipeline_, 'predict_proba')): ...

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

Please provide a description of the function:def clean_pipeline_string(self, individual): dirty_string = str(individual) # There are many parameter prefixes in the pipeline strings, used solely for # making the terminal name unique, eg. LinearSVC__. parameter_prefixes = [(m.star...

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

Please provide a description of the function:def _check_periodic_pipeline(self, gen): self._update_top_pipeline() if self.periodic_checkpoint_folder is not None: total_since_last_pipeline_save = (datetime.now() - self._last_pipeline_write).total_seconds() if total_since_...

[ "If enough time has passed, save a new optimized pipeline. Currently used in the per generation hook in the optimization loop.\n Parameters\n ----------\n gen: int\n Generation number\n\n Returns\n -------\n None\n " ]

Please provide a description of the function:def export(self, output_file_name, data_file_path=''): if self._optimized_pipeline is None: raise RuntimeError('A pipeline has not yet been optimized. Please call fit() first.') to_write = export_pipeline(self._optimized_pipeline, ...

[ "Export the optimized pipeline as Python code.\n\n Parameters\n ----------\n output_file_name: string\n String containing the path and file name of the desired output file\n data_file_path: string (default: '')\n By default, the path of input dataset is 'PATH/TO/DAT...

Please provide a description of the function:def _impute_values(self, features): if self.verbosity > 1: print('Imputing missing values in feature set') if self._fitted_imputer is None: self._fitted_imputer = Imputer(strategy="median") self._fitted_imputer.fi...

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

Please provide a description of the function:def _check_dataset(self, features, target, sample_weight=None): # Check sample_weight if sample_weight is not None: try: sample_weight = np.array(sample_weight).astype('float') except ValueError as e: raise Val...

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

Please provide a description of the function:def _compile_to_sklearn(self, expr): sklearn_pipeline_str = generate_pipeline_code(expr_to_tree(expr, self._pset), self.operators) sklearn_pipeline = eval(sklearn_pipeline_str, self.operators_context) sklearn_pipeline.memory = self._memory ...

[ "Compile a DEAP pipeline into a sklearn pipeline.\n\n Parameters\n ----------\n expr: DEAP individual\n The DEAP pipeline to be compiled\n\n Returns\n -------\n sklearn_pipeline: sklearn.pipeline.Pipeline\n " ]

Please provide a description of the function:def _set_param_recursive(self, pipeline_steps, parameter, value): for (_, obj) in pipeline_steps: recursive_attrs = ['steps', 'transformer_list', 'estimators'] for attr in recursive_attrs: if hasattr(obj, attr): ...

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

Please provide a description of the function:def _stop_by_max_time_mins(self): 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 KeyboardInterrupt('{} minut...

[ "Stop optimization process once maximum minutes have elapsed." ]

Please provide a description of the function:def _combine_individual_stats(self, operator_count, cv_score, individual_stats): stats = deepcopy(individual_stats) # Deepcopy, since the string reference to predecessor should be cloned stats['operator_count'] = operator_count stats['intern...

[ "Combine the stats with operator count and cv score and preprare to be written to _evaluated_individuals\n\n Parameters\n ----------\n operator_count: int\n number of components in the pipeline\n cv_score: float\n internal cross validation score\n individual_...

Please provide a description of the function:def _evaluate_individuals(self, population, features, target, sample_weight=None, groups=None): # Evaluate the individuals with an invalid fitness individuals = [ind for ind in population if not ind.fitness.valid] # update pbar for valid ind...

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

Please provide a description of the function:def _preprocess_individuals(self, individuals): # update self._pbar.total if not (self.max_time_mins is None) and not self._pbar.disable and self._pbar.total <= self._pbar.n: self._pbar.total += self._lambda # Check we do not eval...

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

Please provide a description of the function:def _update_evaluated_individuals_(self, result_score_list, eval_individuals_str, operator_counts, stats_dicts): for result_score, individual_str in zip(result_score_list, eval_individuals_str): if type(result_score) in [float, np.float64, np.flo...

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

Please provide a description of the function:def _update_pbar(self, pbar_num=1, pbar_msg=None): if not isinstance(self._pbar, type(None)): if self.verbosity > 2 and pbar_msg is not None: self._pbar.write(pbar_msg, file=self._file) if not self._pbar.disable: ...

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

Please provide a description of the function:def _random_mutation_operator(self, individual, allow_shrink=True): if self.tree_structure: mutation_techniques = [ partial(gp.mutInsert, pset=self._pset), partial(mutNodeReplacement, pset=self._pset) ]...

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

Please provide a description of the function:def _gen_grow_safe(self, pset, min_, max_, type_=None): def condition(height, depth, type_): return type_ not in self.ret_types or depth == height return self._generate(pset, min_, max_, condition, type_)

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

Please provide a description of the function:def _operator_count(self, individual): operator_count = 0 for i in range(len(individual)): node = individual[i] if type(node) is deap.gp.Primitive and node.name != 'CombineDFs': operator_count += 1 retu...

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

Please provide a description of the function:def _update_val(self, val, result_score_list): self._update_pbar() if val == 'Timeout': self._update_pbar(pbar_msg=('Skipped pipeline #{0} due to time out. ' 'Continuing to the next pipeline.'.forma...

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

Please provide a description of the function:def _generate(self, pset, min_, max_, condition, type_=None): if type_ is None: type_ = pset.ret expr = [] height = np.random.randint(min_, max_) stack = [(0, type_)] while len(stack) != 0: depth, type_...

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

Please provide a description of the function:def transform(self, X): selected = auto_select_categorical_features(X, threshold=self.threshold) X_sel, _, n_selected, _ = _X_selected(X, selected) if n_selected == 0: # No features selected. raise ValueError('No cate...

[ "Select categorical features and transform them using OneHotEncoder.\n\n Parameters\n ----------\n X: numpy ndarray, {n_samples, n_components}\n New data, where n_samples is the number of samples and n_components is the number of components.\n\n Returns\n -------\n ...