B ٻd@sdZddlZddlZddlZddlZddlZddlmZddl m Z ddl m Z ddl m Z ddl mZddl mZdd lmZdd lmZdd lmZdd lmZdd lmZddlmZddlmZddlmZddlmZddlmZddl m!Z!ddl"m#Z#ej$ej%ej&ej'ej(fZ)ddZ*ddZ+ddZ,ddZ-ddZ.dd Z/d!d"Z0d#d$Z1d%d&Z2dHd)d*Z3e#d+gd,dId2d3Z4e#d+gd,dJd4d5Z5e4je5_d6Z6e#d7gd,Gd8d9d9e j7Z8e#d7gd,Gd:d;d;e j9Z:e#dZ;e#de#dCgd,GdFdGdGe j9Z?e@rve8ZAe>ZBe;ZCe4ZDne:ZAe?ZBe_z_infer_type..cSs t|tjS)N)r r float64)rrrrr#`r$cSsdS)NTr)rrrrr#ar$N) r rrr"r%stringrrrangelen)rna_valueZ prev_typeZ type_listZtype_functionsiZ validation_fnrrr _infer_typeFs  r,c csx|D]}||}tj|||r&tjntjd}d} |rHt|| d7} xF|D]>} t| |kr~td| dt| d|d| d7} | VqNWWdQRXqWdS)z3Generator that yields rows of CSV file(s) in order.) delimiterquotingr&z!Problem inferring types: CSV row z has z number of fields. Expected: .N)csvreader QUOTE_MINIMAL QUOTE_NONEnextr)r) filenamesnum_cols field_delimuse_quote_delimheader file_io_fnfnfrdrZrow_numcsv_rowrrr _next_csv_rowks     r?c Cs|dkrt|}dgt|} xbtt||||||D]H\} } |dk rP| |krPPx,t|D] \} } t| | || | | | <qZWq6Wdd| D} dd| DS)z@Infers column types from the first N valid CSV records of files.NcSsg|]}|ptjqSr)r r').0trrr sz*_infer_column_defaults..cSs*g|]"}tj|tjk rdndg|dqS)rr!)dtype)r constantr r')r@rArrrrBs)r(r) enumerater?r,)r5r6r7r8r*r9num_rows_for_inferenceselect_columnsr:Zinferred_typesr+r>jZ col_indexrrr_infer_column_defaultss  rIc Cs||r tjntjd}||dF}yttj|f|}Wn(tk rbtd|ddYnXWdQRXxx|ddD]h}||V}y*ttj|f||krtd|dWn$tk rtd|dYnXWdQRXq|W|S) z-Infers column names from first rows of files.)r-r.rz'Failed when reading the header line of z. Is it an empty file?Nr&zNAll input CSV files should have the same column names in the header row. File z has different column names.)r0r2r3r4r1 StopIterationr)r5r7r8r:Z csv_kwargsr< column_namesnamerrr_infer_column_namess $ $rMcCsddt|D}t|}g}xz|D]r}t|trd|dksB||krXtd|d|d||q$||krtd|d|d q$|||q$Wtt|}t|t|krt|}td d t |d d |dd D}td|d |S)z>Transforms select_columns argument into sorted column indices.cSsi|]\}}||qSrr)r@r+nrrr sz+_get_sorted_col_indices..rz Column index z5 specified in `select_columns` should be > 0 and <= z!, which is the number of columns.zColumn z> specified in `select_columns` must be of one of the columns: r/cSsg|]\}}||kr|qSrr)r@abrrrrBsz+_get_sorted_col_indices..Nr&z:The `select_columns` argument contains duplicate columns: ) rEr) isinstanceintrappendkeyssortedsetzip)rGrKZnames_to_indicesr6resultsv sorted_namesduplicate_columnsrrr_get_sorted_col_indicess(     r^cCs&|r|||}|dkr"||}|S)z4Optionally shuffle and repeat dataset, as requested.r&)shufflerepeat)dataset num_epochsr_shuffle_buffer_size shuffle_seedrrr_maybe_shuffle_and_repeats   reTFc Cs|dkr d}| dkr|} |dkr&tj}tjj|||d} tj| |d} |dkrRd}t| ||||} | pl|dk} |dkr| j|| d} n| j|| d} | j|| d} |dkr| S| j |d SdS) a.Reads and optionally parses TFRecord files into a dataset. Provides common functionality such as batching, optional parsing, shuffling, and performant defaults. Args: file_pattern: List of files or patterns of TFRecord file paths. See `tf.io.gfile.glob` for pattern rules. batch_size: An int representing the number of records to combine in a single batch. parser_fn: (Optional.) A function accepting string input to parse and process the record contents. This function must map records to components of a fixed shape, so they may be batched. By default, uses the record contents unmodified. num_epochs: (Optional.) An int specifying the number of times this dataset is repeated. If None (the default), cycles through the dataset forever. shuffle: (Optional.) A bool that indicates whether the input should be shuffled. Defaults to `True`. shuffle_buffer_size: (Optional.) Buffer size to use for shuffling. A large buffer size ensures better shuffling, but increases memory usage and startup time. shuffle_seed: (Optional.) Randomization seed to use for shuffling. prefetch_buffer_size: (Optional.) An int specifying the number of feature batches to prefetch for performance improvement. Defaults to auto-tune. Set to 0 to disable prefetching. num_parallel_reads: (Optional.) Number of threads used to read records from files. By default or if set to a value >1, the results will be interleaved. Defaults to `24`. num_parallel_parser_calls: (Optional.) Number of parallel records to parse in parallel. Defaults to `batch_size`. drop_final_batch: (Optional.) Whether the last batch should be dropped in case its size is smaller than `batch_size`; the default behavior is not to drop the smaller batch. Returns: A dataset, where each element matches the output of `parser_fn` except it will have an additional leading `batch-size` dimension, or a `batch_size`-length 1-D tensor of strings if `parser_fn` is unspecified. N)r_seed)num_parallel_readsi')drop_remainder)num_parallel_callsr) buffer_size) rAUTOTUNEDataset list_files core_readersTFRecordDatasetrebatchmapprefetch) file_pattern batch_sizeZ parser_fnrbr_rcrdprefetch_buffer_sizerhZnum_parallel_parser_callsdrop_final_batchfilesrarrrmake_tf_record_datasets.4    ryz"data.experimental.make_csv_dataset)Zv1,r!'dutf-8c s dkr d |dkrtj}t|d}tj|}| rD|t|| }dksTdkr҇fdd}dk rt}|dkrt dd|dkrfd d}n2|d krt d d n|d krt dddkrst dt | |ttt krJt }t ddt |dd|ddD}t d|d dk r^t  dk rxddDnt|t | | dk rtt krt dtdt d dk rtt krfdd Ddk r&kr&t ddd fddfdd} tjkr|j d}t} |_||}n fdd }||}t|| | | | }|j|| dkd!}tj||dd"}||}|S)#aReads CSV files into a dataset. Reads CSV files into a dataset, where each element of the dataset is a (features, labels) tuple that corresponds to a batch of CSV rows. The features dictionary maps feature column names to `Tensor`s containing the corresponding feature data, and labels is a `Tensor` containing the batch's label data. By default, the first rows of the CSV files are expected to be headers listing the column names. If the first rows are not headers, set `header=False` and provide the column names with the `column_names` argument. By default, the dataset is repeated indefinitely, reshuffling the order each time. This behavior can be modified by setting the `num_epochs` and `shuffle` arguments. For example, suppose you have a CSV file containing | Feature_A | Feature_B | | --------- | --------- | | 1 | "a" | | 2 | "b" | | 3 | "c" | | 4 | "d" | ``` # No label column specified dataset = tf.data.experimental.make_csv_dataset(filename, batch_size=2) iterator = dataset.as_numpy_iterator() print(dict(next(iterator))) # prints a dictionary of batched features: # OrderedDict([('Feature_A', array([1, 4], dtype=int32)), # ('Feature_B', array([b'a', b'd'], dtype=object))]) ``` ``` # Set Feature_B as label column dataset = tf.data.experimental.make_csv_dataset( filename, batch_size=2, label_name="Feature_B") iterator = dataset.as_numpy_iterator() print(next(iterator)) # prints (features, labels) tuple: # (OrderedDict([('Feature_A', array([1, 2], dtype=int32))]), # array([b'a', b'b'], dtype=object)) ``` See the [Load CSV data guide](https://www.tensorflow.org/tutorials/load_data/csv) for more examples of using `make_csv_dataset` to read CSV data. Args: file_pattern: List of files or patterns of file paths containing CSV records. See `tf.io.gfile.glob` for pattern rules. batch_size: An int representing the number of records to combine in a single batch. column_names: An optional list of strings that corresponds to the CSV columns, in order. One per column of the input record. If this is not provided, infers the column names from the first row of the records. These names will be the keys of the features dict of each dataset element. column_defaults: A optional list of default values for the CSV fields. One item per selected column of the input record. Each item in the list is either a valid CSV dtype (float32, float64, int32, int64, or string), or a `Tensor` with one of the aforementioned types. The tensor can either be a scalar default value (if the column is optional), or an empty tensor (if the column is required). If a dtype is provided instead of a tensor, the column is also treated as required. If this list is not provided, tries to infer types based on reading the first num_rows_for_inference rows of files specified, and assumes all columns are optional, defaulting to `0` for numeric values and `""` for string values. If both this and `select_columns` are specified, these must have the same lengths, and `column_defaults` is assumed to be sorted in order of increasing column index. label_name: A optional string corresponding to the label column. If provided, the data for this column is returned as a separate `Tensor` from the features dictionary, so that the dataset complies with the format expected by a `tf.Estimator.train` or `tf.Estimator.evaluate` input function. select_columns: An optional list of integer indices or string column names, that specifies a subset of columns of CSV data to select. If column names are provided, these must correspond to names provided in `column_names` or inferred from the file header lines. When this argument is specified, only a subset of CSV columns will be parsed and returned, corresponding to the columns specified. Using this results in faster parsing and lower memory usage. If both this and `column_defaults` are specified, these must have the same lengths, and `column_defaults` is assumed to be sorted in order of increasing column index. field_delim: An optional `string`. Defaults to `","`. Char delimiter to separate fields in a record. use_quote_delim: An optional bool. Defaults to `True`. If false, treats double quotation marks as regular characters inside of the string fields. na_value: Additional string to recognize as NA/NaN. header: A bool that indicates whether the first rows of provided CSV files correspond to header lines with column names, and should not be included in the data. num_epochs: An int specifying the number of times this dataset is repeated. If None, cycles through the dataset forever. shuffle: A bool that indicates whether the input should be shuffled. shuffle_buffer_size: Buffer size to use for shuffling. A large buffer size ensures better shuffling, but increases memory usage and startup time. shuffle_seed: Randomization seed to use for shuffling. prefetch_buffer_size: An int specifying the number of feature batches to prefetch for performance improvement. Recommended value is the number of batches consumed per training step. Defaults to auto-tune. num_parallel_reads: Number of threads used to read CSV records from files. If >1, the results will be interleaved. Defaults to `1`. sloppy: If `True`, reading performance will be improved at the cost of non-deterministic ordering. If `False`, the order of elements produced is deterministic prior to shuffling (elements are still randomized if `shuffle=True`. Note that if the seed is set, then order of elements after shuffling is deterministic). Defaults to `False`. num_rows_for_inference: Number of rows of a file to use for type inference if record_defaults is not provided. If None, reads all the rows of all the files. Defaults to 100. compression_type: (Optional.) A `tf.string` scalar evaluating to one of `""` (no compression), `"ZLIB"`, or `"GZIP"`. Defaults to no compression. ignore_errors: (Optional.) If `True`, ignores errors with CSV file parsing, such as malformed data or empty lines, and moves on to the next valid CSV record. Otherwise, the dataset raises an error and stops processing when encountering any invalid records. Defaults to `False`. encoding: Encoding to use when reading. Defaults to `UTF-8`. Returns: A dataset, where each element is a (features, labels) tuple that corresponds to a batch of `batch_size` CSV rows. The features dictionary maps feature column names to `Tensor`s containing the corresponding column data, and labels is a `Tensor` containing the column data for the label column specified by `label_name`. Raises: ValueError: If any of the arguments is malformed. Nr&Fcstj|ddS)Nr)encoding)rFileIO)filename)rrrr#sz%make_csv_dataset_v2..z$Received unknown `compression_type` z*. Expected: GZIP, ZLIB or (empty string).ZGZIPcstj|ddS)Nrt)r)gzipopen)r)rrrr#sZZLIBz`compression_type` z& is not supported for probing columns.r!zCExpected `column_names` or `header` arguments. Neither is provided.cSsg|]\}}||kr|qSrr)r@rPrQrrrrB sz'make_csv_dataset_v2..rRzREither `column_names` argument or CSV header row contains duplicate column names: r/cSs0g|](}t|s(|tkr(tjg|dn|qS))rC)r is_tf_type_ACCEPTABLE_CSV_TYPESr rD)r@xrrrrBszmIf specified, `column_defaults` and `select_columns` must have the same length: `column_defaults` has length z, `select_columns` has length csg|] }|qSrr)r@r+)rKrrrB*sz2`label_name` provided must be one of the columns: z . Received: c s.t|d}r*|t}|S)N)record_defaultsr7r8r* select_colsr9compression_type) CsvDatasetapplyr ignore_errors)rra)column_defaultsrr7r9rr*rGr8rrfilename_to_dataset0sz0make_csv_dataset_v2..filename_to_datasetcs.tt|}dk r*|}||fS|S)aNOrganizes columns into a features dictionary. Args: *columns: list of `Tensor`s corresponding to one csv record. Returns: An OrderedDict of feature names to values for that particular record. If label_name is provided, extracts the label feature to be returned as the second element of the tuple. N) collections OrderedDictrYpop)columnsfeatureslabel)rK label_namerrmap_fn?s  z#make_csv_dataset_v2..map_fn)rjc stj|ddddS)Nr&) cycle_length block_lengthsloppybuffer_output_elementsprefetch_input_elements)roParallelInterleaveDataset)ra)rrhrrrapply_fnWsz%make_csv_dataset_v2..apply_fn)ruri)use_inter_op_parallelism)rrl_get_file_namesrmZfrom_tensor_slicesr_r)rZconstant_valuerrMrXrWrYr^rI interleave options_libOptions deterministic with_optionsrrerq MapDatasetrs)rtrurKrrrGr7r8r*r9rbr_rcrdrvrhrrFrrrr5rar:Zcompression_type_valuer\r]rrrr)rrKrrr7rr9rrr*rhrGrr8rmake_csv_dataset_v2Es                 rcCs6tt|||||||||| | | | | |||||||S)N)rDatasetV1Adapterr)rtrurKrrrGr7r8r*r9rbr_rcrdrvrhrrFrrrrrrmake_csv_dataset_v1usri@zdata.experimental.CsvDatasetcs.eZdZdZd fdd Zed d ZZS) CsvDatasetV2a A Dataset comprising lines from one or more CSV files. The `tf.data.experimental.CsvDataset` class provides a minimal CSV Dataset interface. There is also a richer `tf.data.experimental.make_csv_dataset` function which provides additional convenience features such as column header parsing, column type-inference, automatic shuffling, and file interleaving. The elements of this dataset correspond to records from the file(s). RFC 4180 format is expected for CSV files (https://tools.ietf.org/html/rfc4180) Note that we allow leading and trailing spaces for int or float fields. For example, suppose we have a file 'my_file0.csv' with four CSV columns of different data types: >>> with open('/tmp/my_file0.csv', 'w') as f: ... f.write('abcdefg,4.28E10,5.55E6,12\n') ... f.write('hijklmn,-5.3E14,,2\n') We can construct a CsvDataset from it as follows: >>> dataset = tf.data.experimental.CsvDataset( ... "/tmp/my_file0.csv", ... [tf.float32, # Required field, use dtype or empty tensor ... tf.constant([0.0], dtype=tf.float32), # Optional field, default to 0.0 ... tf.int32, # Required field, use dtype or empty tensor ... ], ... select_cols=[1,2,3] # Only parse last three columns ... ) The expected output of its iterations is: >>> for element in dataset.as_numpy_iterator(): ... print(element) (4.28e10, 5.55e6, 12) (-5.3e14, 0.0, 2) See https://www.tensorflow.org/tutorials/load_data/csv#tfdataexperimentalcsvdataset for more in-depth example usage. NFrzTr!c s4tj|tjdd|_tjd|dtjd|_dd|D}tj|dd |_ td |t |_ tj|tj d d|_ tj|tjd d|_tj|tj d d|_tj|tjdd|_tjd| gtjd|_tjd| gtjd|_tdd|j D|_tj|j|j |j |j |j|j|j|j|j|j|jd } tt|| dS)a> Creates a `CsvDataset` by reading and decoding CSV files. Args: filenames: A `tf.string` tensor containing one or more filenames. record_defaults: A list of default values for the CSV fields. Each item in the list is either a valid CSV `DType` (float32, float64, int32, int64, string), or a `Tensor` object with one of the above types. One per column of CSV data, with either a scalar `Tensor` default value for the column if it is optional, or `DType` or empty `Tensor` if required. If both this and `select_columns` are specified, these must have the same lengths, and `column_defaults` is assumed to be sorted in order of increasing column index. If both this and 'exclude_cols' are specified, the sum of lengths of record_defaults and exclude_cols should equal the total number of columns in the CSV file. compression_type: (Optional.) A `tf.string` scalar evaluating to one of `""` (no compression), `"ZLIB"`, or `"GZIP"`. Defaults to no compression. buffer_size: (Optional.) A `tf.int64` scalar denoting the number of bytes to buffer while reading files. Defaults to 4MB. header: (Optional.) A `tf.bool` scalar indicating whether the CSV file(s) have header line(s) that should be skipped when parsing. Defaults to `False`. field_delim: (Optional.) A `tf.string` scalar containing the delimiter character that separates fields in a record. Defaults to `","`. use_quote_delim: (Optional.) A `tf.bool` scalar. If `False`, treats double quotation marks as regular characters inside of string fields (ignoring RFC 4180, Section 2, Bullet 5). Defaults to `True`. na_value: (Optional.) A `tf.string` scalar indicating a value that will be treated as NA/NaN. select_cols: (Optional.) A sorted list of column indices to select from the input data. If specified, only this subset of columns will be parsed. Defaults to parsing all columns. At most one of `select_cols` and `exclude_cols` can be specified. exclude_cols: (Optional.) A sorted list of column indices to exclude from the input data. If specified, only the complement of this set of column will be parsed. Defaults to parsing all columns. At most one of `select_cols` and `exclude_cols` can be specified. Raises: InvalidArgumentError: If exclude_cols is not None and len(exclude_cols) + len(record_defaults) does not match the total number of columns in the file(s) r5)rCrLrr!)argument_defaultZargument_dtypecSs0g|](}t|s(|tkr(tjg|dn|qS))rC)rrrr rD)r@rrrrrBsz)CsvDatasetV2.__init__..r)rLrkr9r7r8r*r exclude_colscss|]}tg|jVqdS)N)r TensorSpecrC)r@drrr $sz(CsvDatasetV2.__init__..) r5rrkr9Z output_shapesr7r8r*rrrN)r convert_to_tensorr r'Z _filenamesrZoptional_param_to_tensorZ_compression_typeZconvert_n_to_tensorZ_record_defaults!_DEFAULT_READER_BUFFER_SIZE_BYTESZ _buffer_sizeboolZ_headerZ _field_delimZ_use_quote_delimZ _na_valuerZ _select_colsZ _exclude_colstuple _element_specrZcsv_dataset_v2Z _flat_shapessuperr__init__) selfr5rrrkr9r7r8r*rrvariant_tensor) __class__rrrs\8      zCsvDatasetV2.__init__cCs|jS)N)r)rrrr element_spec3szCsvDatasetV2.element_spec)NNFrzTr!NN)__name__ __module__ __qualname____doc__rpropertyr __classcell__rr)rrrs*brcs0eZdZdZeejdd fdd ZZS) CsvDatasetV1z6A Dataset comprising lines from one or more CSV files.)rrNFrzTr!c s,t||||||||| } tt|| dS)a[ Creates a `CsvDataset` by reading and decoding CSV files. The elements of this dataset correspond to records from the file(s). RFC 4180 format is expected for CSV files (https://tools.ietf.org/html/rfc4180) Note that we allow leading and trailing spaces with int or float field. For example, suppose we have a file 'my_file0.csv' with four CSV columns of different data types: ``` abcdefg,4.28E10,5.55E6,12 hijklmn,-5.3E14,,2 ``` We can construct a CsvDataset from it as follows: ```python dataset = tf.data.experimental.CsvDataset( "my_file*.csv", [tf.float32, # Required field, use dtype or empty tensor tf.constant([0.0], dtype=tf.float32), # Optional field, default to 0.0 tf.int32, # Required field, use dtype or empty tensor ], select_cols=[1,2,3] # Only parse last three columns ) ``` The expected output of its iterations is: ```python for element in dataset: print(element) >> (4.28e10, 5.55e6, 12) >> (-5.3e14, 0.0, 2) ``` Args: filenames: A `tf.string` tensor containing one or more filenames. record_defaults: A list of default values for the CSV fields. Each item in the list is either a valid CSV `DType` (float32, float64, int32, int64, string), or a `Tensor` object with one of the above types. One per column of CSV data, with either a scalar `Tensor` default value for the column if it is optional, or `DType` or empty `Tensor` if required. If both this and `select_columns` are specified, these must have the same lengths, and `column_defaults` is assumed to be sorted in order of increasing column index. If both this and 'exclude_cols' are specified, the sum of lengths of record_defaults and exclude_cols should equal the total number of columns in the CSV file. compression_type: (Optional.) A `tf.string` scalar evaluating to one of `""` (no compression), `"ZLIB"`, or `"GZIP"`. Defaults to no compression. buffer_size: (Optional.) A `tf.int64` scalar denoting the number of bytes to buffer while reading files. Defaults to 4MB. header: (Optional.) A `tf.bool` scalar indicating whether the CSV file(s) have header line(s) that should be skipped when parsing. Defaults to `False`. field_delim: (Optional.) A `tf.string` scalar containing the delimiter character that separates fields in a record. Defaults to `","`. use_quote_delim: (Optional.) A `tf.bool` scalar. If `False`, treats double quotation marks as regular characters inside of string fields (ignoring RFC 4180, Section 2, Bullet 5). Defaults to `True`. na_value: (Optional.) A `tf.string` scalar indicating a value that will be treated as NA/NaN. select_cols: (Optional.) A sorted list of column indices to select from the input data. If specified, only this subset of columns will be parsed. Defaults to parsing all columns. At most one of `select_cols` and `exclude_cols` can be specified. N)rrrr) rr5rrrkr9r7r8r*rwrapped)rrrr<sQzCsvDatasetV1.__init__)NNFrzTr!N) rrrr functoolswrapsrrrrr)rrr8s rz/data.experimental.make_batched_features_datasetcszdkrtjdkrd| dkr&d} | dkr4tj} tjj||| d}ttrdtt j rdt ddkrpgtjkr|j fddd}t } |_||}nfd d }||}t|tjtjfkrtj|d dd d }t||||| }|j||p|dkd}|tj|| d}rl|krZtdd|d|fdd}|| }|S)aReturns a `Dataset` of feature dictionaries from `Example` protos. If label_key argument is provided, returns a `Dataset` of tuple comprising of feature dictionaries and label. Example: ``` serialized_examples = [ features { feature { key: "age" value { int64_list { value: [ 0 ] } } } feature { key: "gender" value { bytes_list { value: [ "f" ] } } } feature { key: "kws" value { bytes_list { value: [ "code", "art" ] } } } }, features { feature { key: "age" value { int64_list { value: [] } } } feature { key: "gender" value { bytes_list { value: [ "f" ] } } } feature { key: "kws" value { bytes_list { value: [ "sports" ] } } } } ] ``` We can use arguments: ``` features: { "age": FixedLenFeature([], dtype=tf.int64, default_value=-1), "gender": FixedLenFeature([], dtype=tf.string), "kws": VarLenFeature(dtype=tf.string), } ``` And the expected output is: ```python { "age": [[0], [-1]], "gender": [["f"], ["f"]], "kws": SparseTensor( indices=[[0, 0], [0, 1], [1, 0]], values=["code", "art", "sports"] dense_shape=[2, 2]), } ``` Args: file_pattern: List of files or patterns of file paths containing `Example` records. See `tf.io.gfile.glob` for pattern rules. batch_size: An int representing the number of records to combine in a single batch. features: A `dict` mapping feature keys to `FixedLenFeature` or `VarLenFeature` values. See `tf.io.parse_example`. reader: A function or class that can be called with a `filenames` tensor and (optional) `reader_args` and returns a `Dataset` of `Example` tensors. Defaults to `tf.data.TFRecordDataset`. label_key: (Optional) A string corresponding to the key labels are stored in `tf.Examples`. If provided, it must be one of the `features` key, otherwise results in `ValueError`. reader_args: Additional arguments to pass to the reader class. num_epochs: Integer specifying the number of times to read through the dataset. If None, cycles through the dataset forever. Defaults to `None`. shuffle: A boolean, indicates whether the input should be shuffled. Defaults to `True`. shuffle_buffer_size: Buffer size of the ShuffleDataset. A large capacity ensures better shuffling but would increase memory usage and startup time. shuffle_seed: Randomization seed to use for shuffling. prefetch_buffer_size: Number of feature batches to prefetch in order to improve performance. Recommended value is the number of batches consumed per training step. Defaults to auto-tune. reader_num_threads: Number of threads used to read `Example` records. If >1, the results will be interleaved. Defaults to `1`. parser_num_threads: Number of threads to use for parsing `Example` tensors into a dictionary of `Feature` tensors. Defaults to `2`. sloppy_ordering: If `True`, reading performance will be improved at the cost of non-deterministic ordering. If `False`, the order of elements produced is deterministic prior to shuffling (elements are still randomized if `shuffle=True`. Note that if the seed is set, then order of elements after shuffling is deterministic). Defaults to `False`. drop_final_batch: If `True`, and the batch size does not evenly divide the input dataset size, the final smaller batch will be dropped. Defaults to `False`. Returns: A dataset of `dict` elements, (or a tuple of `dict` elements and label). Each `dict` maps feature keys to `Tensor` or `SparseTensor` objects. Raises: TypeError: If `reader` is of the wrong type. ValueError: If `label_key` is not one of the `features` keys. Nr&)r_rgzThe `reader` argument must return a `Dataset` object. `tf.ReaderBase` subclasses are not supported. For example, pass `tf.data.TFRecordDataset` instead of `tf.TFRecordReader`.cs|fS)Nr)r)r1 reader_argsrrr#r$z2make_batched_features_dataset_v2..)rjc s"tj|fddddddS)Ncs|fS)Nr)r)r1rrrr#!r$zDmake_batched_features_dataset_v2..apply_fn..r&)rrrrr)ror)ra)r1rreader_num_threadssloppy_orderingrrrs z2make_batched_features_dataset_v2..apply_fncSs|S)Nr)_r[rrrr#.r$F)r)rizThe `label_key` provided (z&) must be one of the `features` keys: r/cs||fS)N)r)r) label_keyrrr#Er$)rorprrlrmrnrStype issubclassrZ ReaderBase TypeErrorrrrrrrZget_legacy_output_typesr r'rrerqrZparse_example_datasetrrVrrrs)rtrurr1rrrbr_rcrdrvrparser_num_threadsrrwrarrr)rr1rrrr make_batched_features_dataset_v2sRj        rcCs*tt|||||||||| | | | | |S)N)rrr)rtrurr1rrrbr_rcrdrvrrrrwrrr make_batched_features_dataset_v1Ks  rcCslt|tr:|stdg}x,|D]}|t|q Wntt|}|s\td|d|sht|}|S)aOParse list of file names from pattern, optionally shuffled. Args: file_pattern: File glob pattern, or list of glob patterns. shuffle: Whether to shuffle the order of file names. Returns: List of file names matching `file_pattern`. Raises: ValueError: If `file_pattern` is empty, or pattern matches no files. z,Argument `file_pattern` should not be empty.zNo files match `file_pattern` r/)rSlistrextendrZGlobrW)rtr_Z file_namesentryrrrrds  rzdata.experimental.SqlDatasetcs,eZdZdZfddZeddZZS) SqlDatasetV2aA `Dataset` consisting of the results from a SQL query. `SqlDataset` allows a user to read data from the result set of a SQL query. For example: ```python dataset = tf.data.experimental.SqlDataset("sqlite", "/foo/bar.sqlite3", "SELECT name, age FROM people", (tf.string, tf.int32)) # Prints the rows of the result set of the above query. for element in dataset: print(element) ``` cs|tj|tjdd|_tj|tjdd|_tj|tjdd|_tdd||_ t j |j|j|jf|j }t t||dS)aCreates a `SqlDataset`. Args: driver_name: A 0-D `tf.string` tensor containing the database type. Currently, the only supported value is 'sqlite'. data_source_name: A 0-D `tf.string` tensor containing a connection string to connect to the database. query: A 0-D `tf.string` tensor containing the SQL query to execute. output_types: A tuple of `tf.DType` objects representing the types of the columns returned by `query`. driver_name)rCrLdata_source_namequerycSs tg|S)N)r r)rCrrrr#r$z'SqlDatasetV2.__init__..N)r rr r'Z _driver_nameZ_data_source_nameZ_queryr Z map_structurerrZ sql_datasetZ_flat_structurerrr)rrrr output_typesr)rrrrs zSqlDatasetV2.__init__cCs|jS)N)r)rrrrrszSqlDatasetV2.element_spec)rrrrrrrrrr)rrrs rcs,eZdZdZeejfddZZS) SqlDatasetV1z7A `Dataset` consisting of the results from a SQL query.cs"t||||}tt||dS)N)rrrr)rrrrrr)rrrrszSqlDatasetV1.__init__) rrrrrrrrrrr)rrrsr) NNTNNNNNF)NNNNrzTr!TNTr{NNNFr|NFr})NNNNrzTr!TNTr{NNNFr|NFr}) NNNNTr{NNNNFF) NNNNTr{NNNNFF)Errr0rrnumpyrZtensorflow.pythonrZ'tensorflow.python.data.experimental.opsrrZtensorflow.python.data.opsrrrrroZtensorflow.python.data.utilrr Ztensorflow.python.frameworkr r r r rZtensorflow.python.lib.iorZtensorflow.python.opsrrZtensorflow.python.platformrZ tensorflow.python.util.tf_exportrr"r%rrr'rrrr r,r?rIrMr^reryrrrZ DatasetSourcerrrrrrrrenabledrZ SqlDatasetZmake_batched_features_datasetZmake_csv_datasetrrrrs                      %"  X      Z  *   .