30[Argument(ArgumentType.Multiple, HelpText = "Trainer to use", ShortName = "tr", SignatureType = typeof(SignatureTrainer))] 33[Argument(ArgumentType.Multiple, HelpText = "Scorer to use", NullName = "<Auto>", SortOrder = 101, SignatureType = typeof(SignatureDataScorer))] 36[Argument(ArgumentType.Multiple, HelpText = "Evaluator to use", ShortName = "eval", NullName = "<Auto>", SortOrder = 102, SignatureType = typeof(SignatureMamlEvaluator))] 39[Argument(ArgumentType.AtMostOnce, HelpText = "Results summary filename", ShortName = "sf")] 42[Argument(ArgumentType.LastOccurrenceWins, HelpText = "Column to use for features", ShortName = "feat", SortOrder = 2)] 45[Argument(ArgumentType.LastOccurrenceWins, HelpText = "Column to use for labels", ShortName = "lab", SortOrder = 3)] 48[Argument(ArgumentType.LastOccurrenceWins, HelpText = "Column to use for example weight", ShortName = "weight", SortOrder = 4)] 51[Argument(ArgumentType.LastOccurrenceWins, HelpText = "Column to use for grouping", ShortName = "group", SortOrder = 5)] 54[Argument(ArgumentType.AtMostOnce, HelpText = "Name column name", ShortName = "name", SortOrder = 6)] 57[Argument(ArgumentType.LastOccurrenceWins, HelpText = "Column to use for stratification", ShortName = "strat", SortOrder = 7)] 60[Argument(ArgumentType.LastOccurrenceWins, HelpText = "Columns with custom kinds declared through key assignments, for example, col[Kind]=Name to assign column named 'Name' kind 'Kind'", 64[Argument(ArgumentType.LastOccurrenceWins, HelpText = "Number of folds in k-fold cross-validation", ShortName = "k")] 67[Argument(ArgumentType.LastOccurrenceWins, HelpText = "Use threads", ShortName = "threads")] 70[Argument(ArgumentType.LastOccurrenceWins, HelpText = "Normalize option for the feature column", ShortName = "norm")] 73[Argument(ArgumentType.LastOccurrenceWins, HelpText = "Whether we should cache input training data", ShortName = "cache")] 76[Argument(ArgumentType.Multiple, HelpText = "Transforms to apply prior to splitting the data into folds", 80[Argument(ArgumentType.AtMostOnce, IsInputFileName = true, HelpText = "The validation data file", ShortName = "valid")] 83[Argument(ArgumentType.Multiple, HelpText = "Output calibrator", ShortName = "cali", NullName = "<None>", SignatureType = typeof(SignatureCalibrator))] 86[Argument(ArgumentType.LastOccurrenceWins, HelpText = "Number of instances to train the calibrator", ShortName = "numcali")] 89[Argument(ArgumentType.LastOccurrenceWins, HelpText = "File to save per-instance predictions and metrics to", 93[Argument(ArgumentType.AtMostOnce, HelpText = "Print the run/fold index in per-instance output", ShortName = "opf")] 96[Argument(ArgumentType.AtMostOnce, HelpText = "Whether we should collate metrics or store them in per-folds files", ShortName = "collate")] 99[Argument(ArgumentType.LastOccurrenceWins, HelpText = "Whether we should load predictor from input model and use it as the initial model state", ShortName = "cont")]

26[Argument(ArgumentType.Multiple, Visibility = ArgumentAttribute.VisibilityType.CmdLineOnly, HelpText = "The data loader", ShortName = "loader", SortOrder = 1, NullName = "<Auto>", SignatureType = typeof(SignatureDataLoader))] 29[Argument(ArgumentType.AtMostOnce, IsInputFileName = true, HelpText = "The data file", ShortName = "data", SortOrder = 0)] 32[Argument(ArgumentType.AtMostOnce, Visibility = ArgumentAttribute.VisibilityType.CmdLineOnly, HelpText = "Model file to save", ShortName = "out")] 35[Argument(ArgumentType.AtMostOnce, Visibility = ArgumentAttribute.VisibilityType.CmdLineOnly, IsInputFileName = true, HelpText = "Model file to load", ShortName = "in", SortOrder = 90)] 38[Argument(ArgumentType.Multiple, Visibility = ArgumentAttribute.VisibilityType.CmdLineOnly, HelpText = "Load transforms from model file?", ShortName = "loadTrans", SortOrder = 91)] 41[Argument(ArgumentType.AtMostOnce, Visibility = ArgumentAttribute.VisibilityType.CmdLineOnly, HelpText = "Random seed", ShortName = "seed", SortOrder = 101)] 44[Argument(ArgumentType.AtMostOnce, Visibility = ArgumentAttribute.VisibilityType.CmdLineOnly, HelpText = "Verbose?", ShortName = "v", Hide = true)] 47[Argument(ArgumentType.AtMostOnce, Visibility = ArgumentAttribute.VisibilityType.CmdLineOnly, HelpText = "The web server to publish the RESTful API", Hide = true)] 54HelpText = "Desired degree of parallelism in the data pipeline", ShortName = "n")] 58HelpText = "Transform", Name = "Transform", ShortName = "xf", SignatureType = typeof(SignatureDataTransform))]

126[Argument(ArgumentType.LastOccurrenceWins, HelpText = "Column to use for labels", ShortName = "lab", SortOrder = 3)] 129[Argument(ArgumentType.LastOccurrenceWins, HelpText = "Column to use for example weight", ShortName = "weight", SortOrder = 4)] 132[Argument(ArgumentType.LastOccurrenceWins, HelpText = "Column to use for grouping", ShortName = "group", SortOrder = 5)] 135[Argument(ArgumentType.LastOccurrenceWins, HelpText = "Columns with custom kinds declared through key assignments, for example, col[Kind]=Name to assign column named 'Name' kind 'Kind'", 139[Argument(ArgumentType.Multiple, HelpText = "Evaluator to use", ShortName = "eval", SignatureType = typeof(SignatureMamlEvaluator))] 176[Argument(ArgumentType.LastOccurrenceWins, HelpText = "Column to use for labels", ShortName = "lab", SortOrder = 3)] 179[Argument(ArgumentType.LastOccurrenceWins, HelpText = "Column to use for example weight", ShortName = "weight", SortOrder = 4)] 182[Argument(ArgumentType.LastOccurrenceWins, HelpText = "Column to use for grouping", ShortName = "group", SortOrder = 5)] 185[Argument(ArgumentType.AtMostOnce, HelpText = "Name column name", ShortName = "name", SortOrder = 6)] 188[Argument(ArgumentType.LastOccurrenceWins, HelpText = "Columns with custom kinds declared through key assignments, for example, col[Kind]=Name to assign column named 'Name' kind 'Kind'", 192[Argument(ArgumentType.Multiple, HelpText = "Evaluator to use", ShortName = "eval", SignatureType = typeof(SignatureMamlEvaluator))] 195[Argument(ArgumentType.AtMostOnce, HelpText = "Results summary filename", ShortName = "sf")] 198[Argument(ArgumentType.LastOccurrenceWins, HelpText = "File to save per-instance predictions and metrics to",

30[Argument(ArgumentType.Multiple, HelpText = "The data saver to use", NullName = "<Auto>", SignatureType = typeof(SignatureDataSaver))] 33[Argument(ArgumentType.AtMostOnce, HelpText = "File to save the data", ShortName = "dout")] 36[Argument(ArgumentType.AtMostOnce, HelpText = "Whether to include hidden columns", ShortName = "keep")] 95[Argument(ArgumentType.Multiple, HelpText = "Comma separated list of columns to display", ShortName = "cols")] 98[Argument(ArgumentType.AtMostOnce, HelpText = "Number of rows")] 101[Argument(ArgumentType.AtMostOnce, HelpText = "Whether to include hidden columns", ShortName = "keep")] 104[Argument(ArgumentType.AtMostOnce, HelpText = "Force dense format")] 107[Argument(ArgumentType.Multiple, HelpText = "The data saver to use", NullName = "<Auto>", SignatureType = typeof(SignatureDataSaver))]

28[Argument(ArgumentType.AtMostOnce, HelpText = "Model file containing the predictor", ShortName = "in")] 32[Argument(ArgumentType.AtMostOnce, HelpText = "File to save model summary", ShortName = "sum")] 36[Argument(ArgumentType.AtMostOnce, HelpText = "File to save in text format", ShortName = "text")] 40[Argument(ArgumentType.AtMostOnce, HelpText = "File to save in INI format", ShortName = "ini")] 44[Argument(ArgumentType.AtMostOnce, HelpText = "File to save in C++ code", ShortName = "code")] 48[Argument(ArgumentType.AtMostOnce, HelpText = "File to save in binary format", ShortName = "bin")]

46[Argument(ArgumentType.LastOccurrenceWins, HelpText = "Column to use for features when scorer is not defined", ShortName = "feat")] 49[Argument(ArgumentType.AtMostOnce, HelpText = "Group column name", ShortName = "group")] 53HelpText = "Input columns: Columns with custom kinds declared through key assignments, for example, col[Kind]=Name to assign column named 'Name' kind 'Kind'", 57[Argument(ArgumentType.Multiple, HelpText = "Scorer to use", SignatureType = typeof(SignatureDataScorer))] 60[Argument(ArgumentType.Multiple, HelpText = "The data saver to use", SignatureType = typeof(SignatureDataSaver))] 63[Argument(ArgumentType.LastOccurrenceWins, HelpText = "File to save the data", ShortName = "dout")] 66[Argument(ArgumentType.AtMostOnce, HelpText = "Whether to include hidden columns", ShortName = "keep")] 69[Argument(ArgumentType.Multiple, HelpText = "Post processing transform", ShortName = "pxf", SignatureType = typeof(SignatureDataTransform))] 72[Argument(ArgumentType.AtMostOnce, HelpText = "Whether to output all columns or just scores", ShortName = "all")] 75[Argument(ArgumentType.Multiple, HelpText = "What columns to output beyond score columns, if outputAllColumns=-.",

29[Argument(ArgumentType.AtMostOnce, HelpText = "Show all steps in transform chain", ShortName = "steps")] 32[Argument(ArgumentType.AtMostOnce, HelpText = "Show the metadata types", ShortName = "metaTypes")] 36[Argument(ArgumentType.AtMostOnce, HelpText = "Show the metadata types and values", ShortName = "meta,metaVals,metaValues")] 41[Argument(ArgumentType.AtMostOnce, HelpText = "Show slot names", ShortName = "slots", Hide = true)]

27[Argument(ArgumentType.AtMostOnce, HelpText = "Column to use for features", ShortName = "feat", SortOrder = 2)] 30[Argument(ArgumentType.LastOccurrenceWins, HelpText = "Column to use for labels", ShortName = "lab", SortOrder = 3)] 33[Argument(ArgumentType.LastOccurrenceWins, HelpText = "Column to use for example weight", ShortName = "weight", SortOrder = 4)] 36[Argument(ArgumentType.LastOccurrenceWins, HelpText = "Column to use for grouping", ShortName = "group", SortOrder = 5)] 39[Argument(ArgumentType.AtMostOnce, HelpText = "Name column name", ShortName = "name", SortOrder = 6)] 43HelpText = "Columns with custom kinds declared through key assignments, for example, col[Kind]=Name to assign column named 'Name' kind 'Kind'", 47[Argument(ArgumentType.Multiple, HelpText = "Scorer to use", NullName = "<Auto>", SortOrder = 101, SignatureType = typeof(SignatureDataScorer))] 50[Argument(ArgumentType.Multiple, HelpText = "Evaluator to use", ShortName = "eval", NullName = "<Auto>", SortOrder = 102, SignatureType = typeof(SignatureMamlEvaluator))] 53[Argument(ArgumentType.AtMostOnce, HelpText = "Results summary filename", ShortName = "sf")] 56[Argument(ArgumentType.AtMostOnce, HelpText = "File to save per-instance predictions and metrics to",

43[Argument(ArgumentType.LastOccurrenceWins, HelpText = "Column to use for features", ShortName = "feat", SortOrder = 2)] 46[Argument(ArgumentType.LastOccurrenceWins, HelpText = "Column to use for labels", ShortName = "lab", SortOrder = 3)] 49[Argument(ArgumentType.LastOccurrenceWins, HelpText = "Column to use for example weight", ShortName = "weight", SortOrder = 4)] 52[Argument(ArgumentType.LastOccurrenceWins, HelpText = "Column to use for grouping", ShortName = "group", SortOrder = 5)] 55[Argument(ArgumentType.AtMostOnce, HelpText = "Name column name", ShortName = "name", SortOrder = 6)] 58[Argument(ArgumentType.LastOccurrenceWins, HelpText = "Columns with custom kinds declared through key assignments, for example, col[Kind]=Name to assign column named 'Name' kind 'Kind'", 62[Argument(ArgumentType.LastOccurrenceWins, HelpText = "Normalize option for the feature column", ShortName = "norm")] 65[Argument(ArgumentType.Multiple, HelpText = "Trainer to use", ShortName = "tr", SignatureType = typeof(SignatureTrainer))] 68[Argument(ArgumentType.AtMostOnce, IsInputFileName = true, HelpText = "The validation data file", ShortName = "valid")] 71[Argument(ArgumentType.AtMostOnce, IsInputFileName = true, HelpText = "The test data file", ShortName = "test")] 74[Argument(ArgumentType.LastOccurrenceWins, HelpText = "Whether we should cache input training data", ShortName = "cache")] 77[Argument(ArgumentType.Multiple, HelpText = "Output calibrator", ShortName = "cali", NullName = "<None>", SignatureType = typeof(SignatureCalibrator))] 80[Argument(ArgumentType.LastOccurrenceWins, HelpText = "Number of instances to train the calibrator", ShortName = "numcali")] 83[Argument(ArgumentType.LastOccurrenceWins, HelpText = "Whether we should load predictor from input model and use it as the initial model state", ShortName = "cont")]

25[Argument(ArgumentType.AtMostOnce, IsInputFileName = true, HelpText = "The test data file", ShortName = "test", SortOrder = 1)] 28[Argument(ArgumentType.Multiple, HelpText = "Trainer to use", ShortName = "tr", SignatureType = typeof(SignatureTrainer))] 31[Argument(ArgumentType.Multiple, HelpText = "Scorer to use", NullName = "<Auto>", SortOrder = 101, SignatureType = typeof(SignatureDataScorer))] 34[Argument(ArgumentType.Multiple, HelpText = "Evaluator to use", ShortName = "eval", NullName = "<Auto>", SortOrder = 102, SignatureType = typeof(SignatureMamlEvaluator))] 37[Argument(ArgumentType.AtMostOnce, HelpText = "Results summary filename", ShortName = "sf")] 40[Argument(ArgumentType.LastOccurrenceWins, HelpText = "Column to use for features", ShortName = "feat", SortOrder = 2)] 43[Argument(ArgumentType.LastOccurrenceWins, HelpText = "Column to use for labels", ShortName = "lab", SortOrder = 3)] 46[Argument(ArgumentType.LastOccurrenceWins, HelpText = "Column to use for example weight", ShortName = "weight", SortOrder = 4)] 49[Argument(ArgumentType.LastOccurrenceWins, HelpText = "Column to use for grouping", ShortName = "group", SortOrder = 5)] 52[Argument(ArgumentType.AtMostOnce, HelpText = "Name column name", ShortName = "name", SortOrder = 6)] 55[Argument(ArgumentType.LastOccurrenceWins, HelpText = "Columns with custom kinds declared through key assignments, for example, col[Kind]=Name to assign column named 'Name' kind 'Kind'", 59[Argument(ArgumentType.LastOccurrenceWins, HelpText = "Normalize option for the feature column", ShortName = "norm")] 62[Argument(ArgumentType.AtMostOnce, IsInputFileName = true, HelpText = "The validation data file", ShortName = "valid")] 65[Argument(ArgumentType.LastOccurrenceWins, HelpText = "Whether we should cache input training data", ShortName = "cache")] 68[Argument(ArgumentType.Multiple, HelpText = "Output calibrator", ShortName = "cali", NullName = "<None>", SignatureType = typeof(SignatureCalibrator))] 71[Argument(ArgumentType.LastOccurrenceWins, HelpText = "Number of instances to train the calibrator", ShortName = "numcali")] 74[Argument(ArgumentType.AtMostOnce, HelpText = "File to save per-instance predictions and metrics to", 78[Argument(ArgumentType.LastOccurrenceWins, HelpText = "Whether we should load predictor from input model and use it as the initial model state", ShortName = "cont")]

43[Argument(ArgumentType.LastOccurrenceWins, HelpText = "The number of worker decompressor threads to use", ShortName = "t")] 48[Argument(ArgumentType.LastOccurrenceWins, HelpText = "When shuffling, the number of blocks worth of data to keep in the shuffle pool. " + 2105[DefaultArgument(ArgumentType.AtMostOnce, IsInputFileName = true, HelpText = "The data file", SortOrder = 0)] 2108[Argument(ArgumentType.AtMostOnce, HelpText = "Verbose?", ShortName = "v", Hide = true)]

36[Argument(ArgumentType.LastOccurrenceWins, HelpText = "The compression scheme to use for the blocks", ShortName = "comp")] 39[Argument(ArgumentType.LastOccurrenceWins, HelpText = "The block-size heuristic will choose no more than this many rows to have per block, can be set to null to indicate that there is no inherent limit", ShortName = "rpb")] 42[Argument(ArgumentType.LastOccurrenceWins, HelpText = "The block-size heuristic will attempt to have about this many bytes across all columns per block, can be set to null to accept the indicated max-rows-per-block as the number of rows per block", ShortName = "bpb")] 45[Argument(ArgumentType.LastOccurrenceWins, HelpText = "If true, this forces a deterministic block order during writing", ShortName = "det")] 48[Argument(ArgumentType.LastOccurrenceWins, HelpText = "Suppress any info output (not warnings or errors)", Hide = true)]

230[Argument(ArgumentType.AtMostOnce, HelpText = "Name of the column")] 236[Argument(ArgumentType.AtMostOnce, HelpText = "Type of the items in the column")] 242[Argument(ArgumentType.Multiple, HelpText = "Source index range(s) of the column", ShortName = "src")] 248[Argument(ArgumentType.Multiple, HelpText = "For a key column, this defines the range of values", ShortName = "key")] 306[Argument(ArgumentType.Required, HelpText = "First index in the range")] 315[Argument(ArgumentType.AtMostOnce, HelpText = "Last index in the range")] 324[Argument(ArgumentType.AtMostOnce, HelpText = "Name of the column")] 330[Argument(ArgumentType.AtMostOnce, HelpText = "Force scalar columns to be treated as vectors of length one", ShortName = "vector")] 351[Argument(ArgumentType.Multiple, HelpText = "Column groups. Each group is specified as name:type:numeric-ranges, eg, col=Features:R4:1-17,26,35-40",

35[Argument(ArgumentType.Multiple, HelpText = "The data loader", ShortName = "loader", SignatureType = typeof(SignatureDataLoader))] 38[Argument(ArgumentType.Multiple, HelpText = "Transform", Name = "Transform", ShortName = "xf", SignatureType = typeof(SignatureDataTransform))]

106[Argument(ArgumentType.AtMostOnce, HelpText = "Name of the column")] 113[Argument(ArgumentType.AtMostOnce, HelpText = "Type of the items in the column")] 130[Argument(ArgumentType.Multiple, HelpText = "Source index range(s) of the column", ShortName = "src")] 136[Argument(ArgumentType.Multiple, HelpText = "For a key column, this defines the range of values", ShortName = "key")] 305[Argument(ArgumentType.Required, HelpText = "First index in the range")] 314[Argument(ArgumentType.AtMostOnce, HelpText = "Last index in the range")] 322HelpText = "This range extends to the end of the line, but should be a fixed number of items", 332HelpText = "This range extends to the end of the line, which can vary from line to line", 339[Argument(ArgumentType.AtMostOnce, HelpText = "This range includes only other indices not specified", ShortName = "other")] 345[Argument(ArgumentType.AtMostOnce, HelpText = "Force scalar columns to be treated as vectors of length one", ShortName = "vector")] 435HelpText = 455[Argument(ArgumentType.AtMostOnce, HelpText = "Whether the input may include sparse representations", ShortName = "sparse")] 462HelpText = "Number of source columns in the text data. Default is that sparse rows contain their size information.", 466[Argument(ArgumentType.AtMostOnce, Visibility = ArgumentAttribute.VisibilityType.CmdLineOnly, HelpText = "Source column separator. Options: tab, space, comma, single character", ShortName = "sep")] 473[Argument(ArgumentType.AtMostOnce, Name = nameof(Separator), Visibility = ArgumentAttribute.VisibilityType.EntryPointsOnly, HelpText = "Source column separator.", ShortName = "sep")] 479[Argument(ArgumentType.AtMostOnce, Name = "Decimal Marker", HelpText = "Character symbol used to separate the integer part from the fractional part of a number written in decimal form.", ShortName = "decimal")] 485[Argument(ArgumentType.Multiple, HelpText = "Column groups. Each group is specified as name:type:numeric-ranges, eg, col=Features:R4:1-17,26,35-40", 492[Argument(ArgumentType.AtMostOnce, HelpText = "Remove trailing whitespace from lines", ShortName = "trim")] 500HelpText = "Data file has header with feature names. Header is read only if options 'hs' and 'hf' are not specified.")] 506[Argument(ArgumentType.AtMostOnce, HelpText = "Use separate parsing threads?", ShortName = "threads", Hide = true)] 513[Argument(ArgumentType.AtMostOnce, HelpText = "Escape new line characters inside a quoted field? If AllowQuoting is false, this argument is ignored.", ShortName = "multilines", Hide = true)] 519[Argument(ArgumentType.AtMostOnce, HelpText = "File containing a header with feature names. If specified, header defined in the data file (header+) is ignored.", 526[Argument(ArgumentType.AtMostOnce, HelpText = "Maximum number of rows to produce", ShortName = "rows", Hide = true)] 532[Argument(ArgumentType.AtMostOnce, HelpText = "Character to use to escape quotes inside quoted fields. It can't be a character used as separator.", ShortName = "escapechar")] 541[Argument(ArgumentType.AtMostOnce, HelpText = "If true, empty float fields will be loaded as NaN. If false, they'll be loaded as 0. Default is false.", ShortName = "missingrealnan")]

36[Argument(ArgumentType.AtMostOnce, HelpText = "Separator", ShortName = "sep")] 39[Argument(ArgumentType.AtMostOnce, HelpText = "Force dense format", ShortName = "dense")] 44[Argument(ArgumentType.LastOccurrenceWins, HelpText = "Suppress any info output (not warnings or errors)", Hide = true)] 47[Argument(ArgumentType.AtMostOnce, HelpText = "Output the comment containing the loader settings", ShortName = "schema")] 50[Argument(ArgumentType.AtMostOnce, HelpText = "Output the header", ShortName = "header")]

38[Argument(ArgumentType.LastOccurrenceWins, HelpText = "The number of worker decompresser threads to use", ShortName = "t")]

35[Argument(ArgumentType.LastOccurrenceWins, HelpText = "Write a copy of the data in row-wise format, in addition to the transposed data. This will increase performance for mixed applications while taking significantly more space.", ShortName = "row")] 38[Argument(ArgumentType.LastOccurrenceWins, HelpText = "Suppress any info output (not warnings or errors)", Hide = true)]

27[Argument(ArgumentType.Multiple | ArgumentType.Required, HelpText = "Column indices to select", Name = "Index", ShortName = "ind")] 30[Argument(ArgumentType.LastOccurrenceWins, HelpText = "If true, selected columns are dropped instead of kept, with the order of kept columns being the same as the original", ShortName = "d")]

31[Argument(ArgumentType.Required, ShortName = "data", HelpText = "The data to be used for evaluation.", SortOrder = 1, Visibility = ArgumentAttribute.VisibilityType.EntryPointsOnly)] 34[Argument(ArgumentType.AtMostOnce, HelpText = "Name column name.", ShortName = "name", SortOrder = 2, Visibility = ArgumentAttribute.VisibilityType.EntryPointsOnly)]

23[Argument(ArgumentType.Required, ShortName = "predictorModel", HelpText = "The predictor to summarize")]

30[Argument(ArgumentType.AtMostOnce, HelpText = "Expected number of false positives")] 33[Argument(ArgumentType.AtMostOnce, HelpText = "Expected false positive rate")] 36[Argument(ArgumentType.AtMostOnce, HelpText = "Number of top-scored predictions to display", ShortName = "n")] 39[Argument(ArgumentType.AtMostOnce, HelpText = "Whether to calculate metrics in one pass")] 42[Argument(ArgumentType.AtMostOnce, HelpText = "The number of samples to use for AUC calculation. If 0, AUC is not computed. If -1, the whole dataset is used", ShortName = "numauc")] 624[Argument(ArgumentType.AtMostOnce, HelpText = "Expected number of false positives")] 627[Argument(ArgumentType.AtMostOnce, HelpText = "Expected false positive rate")] 630[Argument(ArgumentType.AtMostOnce, HelpText = "Number of top-scored predictions to display", ShortName = "n")] 633[Argument(ArgumentType.AtMostOnce, HelpText = "Whether to calculate metrics in one pass")] 636[Argument(ArgumentType.AtMostOnce, HelpText = "The number of samples to use for AUC calculation. If 0, AUC is not computed. If -1, the whole dataset is used", ShortName = "numauc")]

35[Argument(ArgumentType.AtMostOnce, HelpText = "Probability value for classification thresholding")] 38[Argument(ArgumentType.AtMostOnce, HelpText = "Use raw score value instead of probability for classification thresholding", ShortName = "useRawScore")] 41[Argument(ArgumentType.AtMostOnce, HelpText = "The number of samples to use for p/r curve generation. Specify 0 for no p/r curve generation", ShortName = "numpr")] 44[Argument(ArgumentType.AtMostOnce, HelpText = "The number of samples to use for AUC calculation. If 0, AUC is not computed. If -1, the whole dataset is used", ShortName = "numauc")] 47[Argument(ArgumentType.AtMostOnce, HelpText = "The number of samples to use for AUPRC calculation. Specify 0 for no AUPRC calculation", ShortName = "numauprc")] 1289[Argument(ArgumentType.AtMostOnce, HelpText = "Probability column name", ShortName = "prob")] 1292[Argument(ArgumentType.AtMostOnce, HelpText = "Probability value for classification thresholding")] 1295[Argument(ArgumentType.AtMostOnce, HelpText = "Use raw score value instead of probability for classification thresholding", ShortName = "useRawScore")] 1298[Argument(ArgumentType.AtMostOnce, HelpText = "The number of samples to use for p/r curve generation. Specify 0 for no p/r curve generation", ShortName = "numpr")] 1301[Argument(ArgumentType.AtMostOnce, HelpText = "The number of samples to use for AUC calculation. If 0, AUC is not computed. If -1, the whole dataset is used", ShortName = "numauc")] 1304[Argument(ArgumentType.AtMostOnce, HelpText = "The number of samples to use for AUPRC calculation. Specify 0 for no AUPRC calculation", ShortName = "numauprc")] 1307[Argument(ArgumentType.AtMostOnce, HelpText = "Precision-Recall results filename", ShortName = "pr", Visibility = ArgumentAttribute.VisibilityType.CmdLineOnly)]

36[Argument(ArgumentType.AtMostOnce, HelpText = "Calculate DBI? (time-consuming unsupervised metric)", 767[Argument(ArgumentType.AtMostOnce, HelpText = "Features column name", ShortName = "feat")] 770[Argument(ArgumentType.AtMostOnce, HelpText = "Calculate DBI? (time-consuming unsupervised metric)", ShortName = "dbi")] 773[Argument(ArgumentType.AtMostOnce, HelpText = "Output top K clusters", ShortName = "topk")]

60[Argument(ArgumentType.AtMostOnce, HelpText = "Column to use for labels.", ShortName = "lab")] 63[Argument(ArgumentType.AtMostOnce, HelpText = "Weight column name.", ShortName = "weight")] 68[Argument(ArgumentType.AtMostOnce, HelpText = "Score column name.", ShortName = "score")] 73[Argument(ArgumentType.Multiple, HelpText = "Stratification column name.", Name = "StratColumn", ShortName = "strat")]

34[Argument(ArgumentType.AtMostOnce, HelpText = "Output top K accuracy", ShortName = "topkacc")] 37[Argument(ArgumentType.AtMostOnce, HelpText = "Use the textual class label names in the report, if available", ShortName = "n")] 874[Argument(ArgumentType.AtMostOnce, HelpText = "Output top-K accuracy.", ShortName = "topkacc")] 877[Argument(ArgumentType.AtMostOnce, HelpText = "Output top-K classes.", ShortName = "topk")] 880[Argument(ArgumentType.AtMostOnce, HelpText = "Maximum number of classes in confusion matrix.", ShortName = "nccf")] 883[Argument(ArgumentType.AtMostOnce, HelpText = "Output per class statistics and confusion matrix.", ShortName = "opcs")]

617[Argument(ArgumentType.Multiple, HelpText = "Loss function", ShortName = "loss")] 620[Argument(ArgumentType.AtMostOnce, HelpText = "Suppress labels and scores in per-instance outputs?", ShortName = "noScores")]

465[Argument(ArgumentType.Multiple, HelpText = "Loss function", ShortName = "loss")] 468[Argument(ArgumentType.LastOccurrenceWins, HelpText = "Quantile index to select", ShortName = "ind")]

37[Argument(ArgumentType.AtMostOnce, HelpText = "Maximum truncation level for computing (N)DCG", ShortName = "t")] 43[Argument(ArgumentType.AtMostOnce, HelpText = "Label relevance gains", ShortName = "gains")] 46[Argument(ArgumentType.AtMostOnce, HelpText = "Generate per-group (N)DCG", ShortName = "ogs")] 857[Argument(ArgumentType.AtMostOnce, HelpText = "Column to use for the group ID", ShortName = "group")] 860[Argument(ArgumentType.AtMostOnce, HelpText = "Maximum truncation level for computing (N)DCG", ShortName = "t")] 863[Argument(ArgumentType.AtMostOnce, HelpText = "Label relevance gains", ShortName = "gains")] 866[Argument(ArgumentType.AtMostOnce, HelpText = "Group summary filename", ShortName = "gsf", Visibility = ArgumentAttribute.VisibilityType.CmdLineOnly)]

343[Argument(ArgumentType.Multiple, HelpText = "Loss function", ShortName = "loss")]

20[Argument(ArgumentType.Multiple, HelpText = "Loss function", ShortName = "loss")]

30[Argument(ArgumentType.AtMostOnce, HelpText = "The path to write the output PFA too. Leave empty for stdout.", SortOrder = 1)] 33[Argument(ArgumentType.AtMostOnce, HelpText = "The 'name' property in the output PFA program. By default this will be the extension-less name ", NullName = "<Auto>", SortOrder = 3)] 37[Argument(ArgumentType.AtMostOnce, HelpText = "Whether we should allow set operations.", ShortName = "set", SortOrder = 3, Hide = true)] 40[Argument(ArgumentType.AtMostOnce, HelpText = "Comma delimited list of input column names to drop", ShortName = "idrop", SortOrder = 4)] 43[Argument(ArgumentType.AtMostOnce, HelpText = "Comma delimited list of output column names to drop", ShortName = "odrop", SortOrder = 5)] 46[Argument(ArgumentType.AtMostOnce, HelpText = "Whether the inputs should also map to the outputs.", ShortName = "input", SortOrder = 6)] 49[Argument(ArgumentType.AtMostOnce, HelpText = "Whether we should attempt to load the predictor and attach the scorer to the pipeline if one is present.", ShortName = "pred", SortOrder = 7)] 52[Argument(ArgumentType.AtMostOnce, HelpText = "Format option for the JSON exporter.", ShortName = "format", SortOrder = 8)]

1637[Argument(ArgumentType.LastOccurrenceWins, HelpText = "The slope parameter of f(x) = 1 / (1 + exp(slope * x + offset)", ShortName = "a")] 1640[Argument(ArgumentType.LastOccurrenceWins, HelpText = "The offset parameter of f(x) = 1 / (1 + exp(slope * x + offset)", ShortName = "b")] 2130[Argument(ArgumentType.Required, ShortName = "uncalibratedPredictorModel", HelpText = "The predictor to calibrate", SortOrder = 2)] 2133[Argument(ArgumentType.Required, ShortName = "maxRows", HelpText = "The maximum number of examples to train the calibrator on", SortOrder = 3)] 2144[Argument(ArgumentType.AtMostOnce, ShortName = "slope", HelpText = "The slope parameter of the calibration function 1 / (1 + exp(slope * x + offset)", SortOrder = 1)] 2147[Argument(ArgumentType.AtMostOnce, ShortName = "offset", HelpText = "The offset parameter of the calibration function 1 / (1 + exp(slope * x + offset)", SortOrder = 3)]

184[Argument(ArgumentType.AtMostOnce, HelpText = "Whether to throw an error if a column exists in the output schema but not the output object.", ShortName = "ignore", SortOrder = 50)] 187[Argument(ArgumentType.AtMostOnce, HelpText = "Additional settings of the input schema.", ShortName = "input", SortOrder = 50)] 190[Argument(ArgumentType.AtMostOnce, HelpText = "Additional settings of the output schema.", ShortName = "output")] 193[Argument(ArgumentType.AtMostOnce, HelpText = "Whether the prediction engine owns the transformer and should dispose of it.", ShortName = "own")]

40[Argument(ArgumentType.AtMostOnce, HelpText = "Number of top contributions", SortOrder = 1)] 43[Argument(ArgumentType.AtMostOnce, HelpText = "Number of bottom contributions", SortOrder = 2)] 46[Argument(ArgumentType.AtMostOnce, HelpText = "Whether or not output of Features contribution should be normalized", ShortName = "norm", SortOrder = 3)] 49[Argument(ArgumentType.AtMostOnce, HelpText = "Whether or not output of Features contribution in string key-value format", ShortName = "str", SortOrder = 4)]

38[Argument(ArgumentType.AtMostOnce, HelpText = "Score Column Name.", ShortName = "scn")] 41[Argument(ArgumentType.AtMostOnce, HelpText = "Predicted Label Column Name.", ShortName = "plcn")]

24[Argument(ArgumentType.AtMostOnce, HelpText = "Value for classification thresholding", ShortName = "t")] 27[Argument(ArgumentType.AtMostOnce, HelpText = "Specify which predictor output to use for classification thresholding", ShortName = "tcol")]

25[Argument(ArgumentType.Multiple, HelpText = "List of numbers between 0 and 1 (comma-separated) to get quantile statistics. The default value outputs Five point summary")]

315[Argument(ArgumentType.AtMostOnce, HelpText = "Output column: The suffix to append to the default column names", ShortName = "ex")]

26[Argument(ArgumentType.Required, ShortName = "data", HelpText = "The data to be used for training", SortOrder = 1, Visibility = ArgumentAttribute.VisibilityType.EntryPointsOnly)] 32[Argument(ArgumentType.AtMostOnce, HelpText = "Column to use for features", ShortName = "feat", SortOrder = 2, Visibility = ArgumentAttribute.VisibilityType.EntryPointsOnly)] 39[Argument(ArgumentType.AtMostOnce, HelpText = "Normalize option for the feature column", ShortName = "norm", SortOrder = 5, Visibility = ArgumentAttribute.VisibilityType.EntryPointsOnly)] 48[Argument(ArgumentType.LastOccurrenceWins, HelpText = "Whether trainer should cache input training data", ShortName = "cache", SortOrder = 6, Visibility = ArgumentAttribute.VisibilityType.EntryPointsOnly)] 63[Argument(ArgumentType.AtMostOnce, HelpText = "Column to use for labels", ShortName = "lab", SortOrder = 3, Visibility = ArgumentAttribute.VisibilityType.EntryPointsOnly)] 79[Argument(ArgumentType.AtMostOnce, HelpText = "Column to use for example weight", ShortName = "weight", SortOrder = 4, Visibility = ArgumentAttribute.VisibilityType.EntryPointsOnly)] 94[Argument(ArgumentType.AtMostOnce, HelpText = "Column to use for example weight", ShortName = "weight", SortOrder = 4, Visibility = ArgumentAttribute.VisibilityType.EntryPointsOnly)] 109[Argument(ArgumentType.AtMostOnce, HelpText = "Column to use for example groupId", ShortName = "groupId", SortOrder = 5, Visibility = ArgumentAttribute.VisibilityType.EntryPointsOnly)]

40[Argument(ArgumentType.AtMostOnce, HelpText = "Whether this is the out-of-bag sample, that is, all those rows that are not selected by the transform.", 44[Argument(ArgumentType.AtMostOnce, HelpText = "The random seed. If unspecified random state will be instead derived from the environment.")] 47[Argument(ArgumentType.AtMostOnce, HelpText = "Whether we should attempt to shuffle the source data. By default on, but can be turned off for efficiency.", ShortName = "si")] 50[Argument(ArgumentType.LastOccurrenceWins, HelpText = "When shuffling the output, the number of output rows to keep in that pool. Note that shuffling of output is completely distinct from shuffling of input.", ShortName = "pool")]

19[Argument(ArgumentType.AtMostOnce, HelpText = "Name of the new column", ShortName = "name")] 22[Argument(ArgumentType.AtMostOnce, HelpText = "Name of the source column", ShortName = "src")] 116[Argument(ArgumentType.AtMostOnce, HelpText = "Name of the new column", ShortName = "name")] 119[Argument(ArgumentType.Multiple, HelpText = "Name of the source column", ShortName = "src")]

68[Argument(ArgumentType.AtMostOnce, HelpText = "Name of the new column", ShortName = "name")] 76[Argument(ArgumentType.Multiple, HelpText = "Names of the source columns", ShortName = "src")] 120[Argument(ArgumentType.Multiple | ArgumentType.Required, HelpText = "New column definition(s) (optional form: name:srcs)", 128[Argument(ArgumentType.Multiple, HelpText = "New column definition(s) (optional form: name:srcs)",

141[Argument(ArgumentType.Multiple | ArgumentType.Required, HelpText = "New column definition(s) (optional form: name:src)",

211[Argument(ArgumentType.Multiple, HelpText = "List of columns to keep.", ShortName = "keepcol", SortOrder = 1)] 214[Argument(ArgumentType.Multiple, HelpText = "List of columns to drop.", ShortName = "dropcol", SortOrder = 2)] 217[Argument(ArgumentType.AtMostOnce, HelpText = "Specifies whether to keep or remove hidden columns.", ShortName = "hidden", SortOrder = 3)] 220[Argument(ArgumentType.AtMostOnce, HelpText = "Specifies whether to ignore columns that are missing from the input.", ShortName = "ignore", SortOrder = 4)]

35[Argument(ArgumentType.Required, HelpText = "The predictor model to apply to data", SortOrder = 1)] 38[Argument(ArgumentType.AtMostOnce, HelpText = "Name of feature column", SortOrder = 2)] 41[Argument(ArgumentType.AtMostOnce, HelpText = "Number of top contributions", SortOrder = 3)] 44[Argument(ArgumentType.AtMostOnce, HelpText = "Number of bottom contributions", SortOrder = 4)] 47[Argument(ArgumentType.AtMostOnce, HelpText = "Whether or not output of Features contribution should be normalized", ShortName = "norm", SortOrder = 5)]

37[Argument(ArgumentType.AtMostOnce, HelpText = "Name of the new column", ShortName = "name")] 40[Argument(ArgumentType.AtMostOnce, HelpText = "Use an auto-incremented integer starting at zero instead of a random number", ShortName = "cnt")] 43[Argument(ArgumentType.AtMostOnce, HelpText = "The random seed")] 89[Argument(ArgumentType.Multiple | ArgumentType.Required, HelpText = "New column definition(s) (optional form: name:seed)", 93[Argument(ArgumentType.AtMostOnce, HelpText = "Use an auto-incremented integer starting at zero instead of a random number", ShortName = "cnt")] 96[Argument(ArgumentType.AtMostOnce, HelpText = "The random seed")]

39[Argument(ArgumentType.Multiple, HelpText = "New column definition(s) (optional form: name:src)", 43[Argument(ArgumentType.AtMostOnce, HelpText = "Number of bits to hash into. Must be between 1 and 31, inclusive", 47[Argument(ArgumentType.AtMostOnce, HelpText = "Hashing seed")] 50[Argument(ArgumentType.AtMostOnce, HelpText = "Whether the position of each term should be included in the hash", 54[Argument(ArgumentType.AtMostOnce, HelpText = "Limit the number of keys used to generate the slot name to this many. 0 means no invert hashing, -1 means no limit.", 58[Argument(ArgumentType.AtMostOnce, HelpText = "Whether the slots of a vector column should be hashed into a single value.")] 64[Argument(ArgumentType.AtMostOnce, HelpText = "Number of bits to hash into. Must be between 1 and 31, inclusive", ShortName = "bits")] 67[Argument(ArgumentType.AtMostOnce, HelpText = "Hashing seed")] 70[Argument(ArgumentType.AtMostOnce, HelpText = "Whether the position of each term should be included in the hash", 74[Argument(ArgumentType.AtMostOnce, HelpText = "Limit the number of keys used to generate the slot name to this many. 0 means no invert hashing, -1 means no limit.", 78[Argument(ArgumentType.AtMostOnce, HelpText = "Whether the slots of a vector column should be hashed into a single value.")]

59[Argument(ArgumentType.Multiple | ArgumentType.Required, HelpText = "New column definition(s) (optional form: name:src)",

41HelpText = "Whether to combine multiple indicator vectors into a single bag vector instead of concatenating them. This is only relevant when the input is a vector.")] 87[Argument(ArgumentType.Multiple, HelpText = "New column definition(s) (optional form: name:src)", 92HelpText = "Whether to combine multiple indicator vectors into a single bag vector instead of concatenating them. This is only relevant when the input is a vector.")]

48[Argument(ArgumentType.Multiple | ArgumentType.Required, HelpText = "New column definition(s) (optional form: name:src)",

49[Argument(ArgumentType.AtMostOnce, HelpText = "The positive example class for binary classification.", ShortName = "index")] 71[Argument(ArgumentType.Multiple, HelpText = "New column definition(s) (optional form: name:src)", Name = "Column", ShortName = "col", SortOrder = 1)] 74[Argument(ArgumentType.AtMostOnce, HelpText = "Label of the positive class.", ShortName = "index")]

38[Argument(ArgumentType.Multiple | ArgumentType.Required, HelpText = "Column", Name = "Column", ShortName = "col", SortOrder = 1)] 41[Argument(ArgumentType.Multiple, HelpText = "If true, keep only rows that contain NA values, and filter the rest.")]

55[Argument(ArgumentType.AtMostOnce, HelpText = "Max number of examples used to train the normalizer", 76[Argument(ArgumentType.AtMostOnce, Name = "FixZero", HelpText = "Whether to map zero to zero, preserving sparsity", ShortName = "zero")] 109[Argument(ArgumentType.AtMostOnce, HelpText = "Max number of bins, power of 2 recommended", ShortName = "bins")] 167[Argument(ArgumentType.AtMostOnce, Name = "FixZero", HelpText = "Whether to map zero to zero, preserving sparsity", ShortName = "zero")] 173[Argument(ArgumentType.Multiple | ArgumentType.Required, HelpText = "New column definition(s) (optional form: name:src)", Name = "Column", ShortName = "col", SortOrder = 1)] 185[Argument(ArgumentType.AtMostOnce, HelpText = "Whether to use CDF as the output", ShortName = "cdf")] 191[Argument(ArgumentType.AtMostOnce, HelpText = "Max number of examples used to train the normalizer", 218[Argument(ArgumentType.AtMostOnce, HelpText = "Whether to use CDF as the output", ShortName = "cdf")] 221[Argument(ArgumentType.Multiple, HelpText = "New column definition(s) (optional form: name:src)", Name = "Column", ShortName = "col", SortOrder = 1)] 229[Argument(ArgumentType.Multiple, HelpText = "New column definition(s) (optional form: name:src)", Name = "Column", ShortName = "col", SortOrder = 1)] 232[Argument(ArgumentType.AtMostOnce, HelpText = "Max number of bins, power of 2 recommended", ShortName = "bins")] 246[Argument(ArgumentType.Required, HelpText = "Label column for supervised binning", ShortName = "label,lab", 250[Argument(ArgumentType.AtMostOnce, HelpText = "Minimum number of examples per bin")] 256[Argument(ArgumentType.AtMostOnce, HelpText = "Should the data be centered around 0", Name = "CenterData", ShortName = "center", SortOrder = 1)] 259[Argument(ArgumentType.AtMostOnce, HelpText = "Minimum quantile value. Defaults to 25", Name = "QuantileMin", ShortName = "qmin", SortOrder = 2)] 262[Argument(ArgumentType.AtMostOnce, HelpText = "Maximum quantile value. Defaults to 75", Name = "QuantileMax", ShortName = "qmax", SortOrder = 3)]

35[Argument(ArgumentType.Multiple | ArgumentType.Required, HelpText = "Column", ShortName = "col", SortOrder = 1, Purpose = SpecialPurpose.ColumnName)] 38[Argument(ArgumentType.Multiple, HelpText = "Minimum value (0 to 1 for key types)")] 41[Argument(ArgumentType.Multiple, HelpText = "Maximum value (0 to 1 for key types)")] 44[Argument(ArgumentType.Multiple, HelpText = "If true, keep the values that fall outside the range.")] 47[Argument(ArgumentType.Multiple, HelpText = "If true, include in the range the values that are equal to min.")] 50[Argument(ArgumentType.Multiple, HelpText = "If true, include in the range the values that are equal to max.")]

46[Argument(ArgumentType.LastOccurrenceWins, HelpText = "The pool will have this many rows", ShortName = "rows")] 50[Argument(ArgumentType.LastOccurrenceWins, HelpText = "If true, the transform will not attempt to shuffle the input cursor but only shuffle based on the pool. This parameter has no effect if the input data was not itself shufflable.", ShortName = "po")] 53[Argument(ArgumentType.LastOccurrenceWins, HelpText = "If true, the transform will always provide a shuffled view.", ShortName = "force")] 56[Argument(ArgumentType.LastOccurrenceWins, HelpText = "If true, the transform will always shuffle the input. The default value is the same as forceShuffle.", ShortName = "forceSource")] 59[Argument(ArgumentType.LastOccurrenceWins, HelpText = "The random seed to use for forced shuffling.", ShortName = "seed")]

55[Argument(ArgumentType.AtMostOnce, HelpText = SkipHelp, ShortName = "s", SortOrder = 1)] 58[Argument(ArgumentType.AtMostOnce, HelpText = TakeHelp, ShortName = "t", SortOrder = 2)] 64[Argument(ArgumentType.Required, HelpText = Options.TakeHelp, ShortName = "c,n,t", SortOrder = 1)] 70[Argument(ArgumentType.Required, HelpText = Options.SkipHelp, ShortName = "c,n,s", SortOrder = 1)]

43[Argument(ArgumentType.Multiple | ArgumentType.Required, HelpText = "Columns to drop the slots for", 51[Argument(ArgumentType.Multiple, HelpText = "Source slot index range(s) of the column to drop")] 119[Argument(ArgumentType.Required, HelpText = "First index in the range")] 125[Argument(ArgumentType.AtMostOnce, HelpText = "Last index in the range")]

27[Argument(ArgumentType.LastOccurrenceWins, HelpText = "Column to use for features when scorer is not defined", 32[Argument(ArgumentType.AtMostOnce, HelpText = "Group column name", ShortName = "group", SortOrder = 100, 37HelpText = "Input columns: Columns with custom kinds declared through key assignments, for example, col[Kind]=Name to assign column named 'Name' kind 'Kind'", 41[Argument(ArgumentType.Multiple, HelpText = "Scorer to use", NullName = "<Auto>", SignatureType = typeof(SignatureDataScorer))] 44[Argument(ArgumentType.AtMostOnce, IsInputFileName = true, HelpText = "Predictor model file used in scoring", 111[Argument(ArgumentType.LastOccurrenceWins, HelpText = "Column to use for features when scorer is not defined", 115[Argument(ArgumentType.LastOccurrenceWins, HelpText = "Column to use for labels", ShortName = "lab", SortOrder = 103, 119[Argument(ArgumentType.LastOccurrenceWins, HelpText = "Column to use for grouping", ShortName = "group", 123[Argument(ArgumentType.LastOccurrenceWins, HelpText = "Column to use for example weight", ShortName = "weight", 127[Argument(ArgumentType.AtMostOnce, HelpText = "Name column name", ShortName = "name", SortOrder = 106, 132HelpText = "Input columns: Columns with custom kinds declared through key assignments, for example, col[Kind]=Name to assign column named 'Name' kind 'Kind'", 149[Argument(ArgumentType.Multiple, HelpText = "Trainer to use", ShortName = "tr", NullName = "<None>", SortOrder = 1, SignatureType = typeof(SignatureTrainer))] 152[Argument(ArgumentType.Multiple, HelpText = "Output calibrator", ShortName = "cali", NullName = "<None>", SignatureType = typeof(SignatureCalibrator))] 155[Argument(ArgumentType.LastOccurrenceWins, HelpText = "Number of instances to train the calibrator", ShortName = "numcali")] 158[Argument(ArgumentType.Multiple, HelpText = "Scorer to use", NullName = "<Auto>", SignatureType = typeof(SignatureDataScorer))]

23[Argument(ArgumentType.Required, HelpText = "Input dataset", Visibility = ArgumentAttribute.VisibilityType.EntryPointsOnly, SortOrder = 1)]

61[Argument(ArgumentType.AtMostOnce, HelpText = "The result type", ShortName = "type")] 64[Argument(ArgumentType.Multiple, HelpText = "For a key column, this defines the cardinality/count of valid key values", ShortName = "key", Visibility = ArgumentAttribute.VisibilityType.CmdLineOnly)] 67[Argument(ArgumentType.AtMostOnce, HelpText = "For a key column, this defines the range of values", ShortName = "key", Visibility = ArgumentAttribute.VisibilityType.EntryPointsOnly)] 132[Argument(ArgumentType.Multiple | ArgumentType.Required, HelpText = "New column definition(s) (optional form: name:type:src)", Name = "Column", ShortName = "col", SortOrder = 1)] 135[Argument(ArgumentType.AtMostOnce, HelpText = "The result type", ShortName = "type", SortOrder = 2)] 138[Argument(ArgumentType.Multiple, HelpText = "For a key column, this defines the range of values", ShortName = "key", Visibility = ArgumentAttribute.VisibilityType.CmdLineOnly)] 141[Argument(ArgumentType.AtMostOnce, HelpText = "For a key column, this defines the range of values", ShortName = "key", Visibility = ArgumentAttribute.VisibilityType.EntryPointsOnly)]

410[Argument(ArgumentType.Multiple | ArgumentType.Required, HelpText = "New column definition(s) (optional form: name:src)", Name = "Column", ShortName = "col", SortOrder = 1)] 413[Argument(ArgumentType.AtMostOnce, IsInputFileName = true, HelpText = "The data file containing the terms", ShortName = "data", SortOrder = 2)] 416[Argument(ArgumentType.AtMostOnce, HelpText = "The name of the column containing the keys", ShortName = "keyCol, term, TermColumn")] 419[Argument(ArgumentType.AtMostOnce, HelpText = "The name of the column containing the values", ShortName = "valueCol, value")] 422[Argument(ArgumentType.Multiple, HelpText = "The data loader", NullName = "<Auto>", SignatureType = typeof(SignatureDataLoader))] 426HelpText = "Specifies whether the values are key values or numeric, only valid when loader is not specified and the type of data is not an idv.",

45[Argument(ArgumentType.AtMostOnce, HelpText = "Maximum number of terms to keep when auto-training", ShortName = "max")] 48[Argument(ArgumentType.AtMostOnce, HelpText = "Comma separated list of terms", Name = "Terms", Visibility = ArgumentAttribute.VisibilityType.CmdLineOnly)] 51[Argument(ArgumentType.AtMostOnce, HelpText = "List of terms", Name = "Term", Visibility = ArgumentAttribute.VisibilityType.EntryPointsOnly)] 54[Argument(ArgumentType.AtMostOnce, HelpText = "How items should be ordered when vectorized. By default, they will be in the order encountered. " + 58[Argument(ArgumentType.AtMostOnce, HelpText = "Whether key value metadata should be text, regardless of the actual input type", ShortName = "textkv", Hide = true)] 99[Argument(ArgumentType.AtMostOnce, HelpText = "Maximum number of keys to keep per column when auto-training", ShortName = "max", SortOrder = 5)] 102[Argument(ArgumentType.AtMostOnce, HelpText = "Comma separated list of terms", Name = "Terms", SortOrder = 105, Visibility = ArgumentAttribute.VisibilityType.CmdLineOnly)] 105[Argument(ArgumentType.AtMostOnce, HelpText = "List of terms", Name = "Term", SortOrder = 106, Visibility = ArgumentAttribute.VisibilityType.EntryPointsOnly)] 108[Argument(ArgumentType.AtMostOnce, IsInputFileName = true, HelpText = "Data file containing the terms", ShortName = "data", SortOrder = 110, Visibility = ArgumentAttribute.VisibilityType.CmdLineOnly)] 111[Argument(ArgumentType.Multiple, HelpText = "Data loader", NullName = "<Auto>", SortOrder = 111, Visibility = ArgumentAttribute.VisibilityType.CmdLineOnly, SignatureType = typeof(SignatureDataLoader))] 115[Argument(ArgumentType.AtMostOnce, HelpText = "Name of the text column containing the terms", ShortName = "termCol", SortOrder = 112, Visibility = ArgumentAttribute.VisibilityType.CmdLineOnly)] 123[Argument(ArgumentType.AtMostOnce, HelpText = "How items should be ordered when vectorized. By default, they will be in the order encountered. " + 129[Argument(ArgumentType.AtMostOnce, HelpText = "Whether key value metadata should be text, regardless of the actual input type", ShortName = "textkv", SortOrder = 114, Hide = true)] 136[Argument(ArgumentType.Multiple, HelpText = "New column definition(s) (optional form: name:src)", Name = "Column", ShortName = "col", SortOrder = 1)]

95[Argument(ArgumentType.AtMostOnce, HelpText = "Count of valid key values")]

212[Argument(ArgumentType.AtMostOnce, HelpText = "Margin value", ShortName = "marg")] 311[Argument(ArgumentType.AtMostOnce, HelpText = "Smoothing constant", ShortName = "smooth")] 427[Argument(ArgumentType.AtMostOnce, HelpText = "Beta (dilation)", ShortName = "beta")] 582[Argument(ArgumentType.LastOccurrenceWins, HelpText =

47[Argument(ArgumentType.Required, ShortName = "models", HelpText = "The models to combine into an ensemble", SortOrder = 1)] 53[Argument(ArgumentType.Required, ShortName = "models", HelpText = "The models to combine into an ensemble", SortOrder = 1)] 56[Argument(ArgumentType.AtMostOnce, ShortName = "validate", HelpText = "Whether to validate that all the pipelines are identical", SortOrder = 5)] 62[Argument(ArgumentType.AtMostOnce, ShortName = "combiner", HelpText = "The combiner used to combine the scores", SortOrder = 2)] 68[Argument(ArgumentType.AtMostOnce, ShortName = "combiner", HelpText = "The combiner used to combine the scores", SortOrder = 2)] 74[Argument(ArgumentType.AtMostOnce, ShortName = "combiner", HelpText = "The combiner used to combine the scores", SortOrder = 2)] 80[Argument(ArgumentType.AtMostOnce, ShortName = "combiner", HelpText = "The combiner used to combine the scores", SortOrder = 2)] 86[Argument(ArgumentType.AtMostOnce, ShortName = "combiner", HelpText = "The combiner used to combine the scores", SortOrder = 2)]

20[Argument(ArgumentType.AtMostOnce, HelpText = "Whether to normalize the output of base models before combining them",

21HelpText = "The proportion of instances to be selected to test the individual base learner. If it is 0, it uses training set")]

45[Argument(ArgumentType.Multiple, HelpText = "Base predictor for meta learning", ShortName = "bp", SortOrder = 50,

47[Argument(ArgumentType.AtMostOnce, HelpText = "The metric type to be used to find the weights for each model", ShortName = "wn", SortOrder = 50)]

43[Argument(ArgumentType.Multiple, HelpText = "Base predictor for meta learning", ShortName = "bp", SortOrder = 50,

42[Argument(ArgumentType.Multiple, HelpText = "Base predictor for meta learning", ShortName = "bp", SortOrder = 50,

42[Argument(ArgumentType.AtMostOnce, HelpText = "The metric type to be used to find the weights for each model", ShortName = "wn", SortOrder = 50)]

27[Argument(ArgumentType.AtMostOnce, HelpText = "The proportion of features to be selected. The range is 0.0-1.0", ShortName = "fp", SortOrder = 50)]

28[Argument(ArgumentType.Multiple, HelpText = "The metric type to be used to find the diversity among base learners", ShortName = "dm", SortOrder = 50)]

29[Argument(ArgumentType.Multiple, HelpText = "The metric type to be used to find the diversity among base learners", ShortName = "dm", SortOrder = 50)]

28[Argument(ArgumentType.Multiple, HelpText = "The metric type to be used to find the diversity among base learners", ShortName = "dm", SortOrder = 50)]

24[Argument(ArgumentType.AtMostOnce, HelpText = "The metric type to be used to find the best performance", ShortName = "mn", SortOrder = 50)]

24[Argument(ArgumentType.AtMostOnce, HelpText = "The metric type to be used to find the best performance", ShortName = "mn", SortOrder = 50)]

24[Argument(ArgumentType.AtMostOnce, HelpText = "The metric type to be used to find the best performance", ShortName = "mn", SortOrder = 50)]

17HelpText = "The proportion of best base learners to be selected. The range is 0.0-1.0")] 22HelpText = "The proportion of instances to be selected to test the individual base learner. If it is 0, it uses training set")]

19[Argument(ArgumentType.Multiple, HelpText = "The Feature selector", ShortName = "fs", SortOrder = 1)]

41[Argument(ArgumentType.Multiple, HelpText = "Algorithm to prune the base learners for selective Ensemble", ShortName = "pt", SortOrder = 4)] 46[Argument(ArgumentType.Multiple, HelpText = "Output combiner", ShortName = "oc", SortOrder = 5)] 51[Argument(ArgumentType.Multiple, HelpText = "Base predictor type", ShortName = "bp,basePredictorTypes", SortOrder = 1, Visibility = ArgumentAttribute.VisibilityType.CmdLineOnly, SignatureType = typeof(SignatureBinaryClassifierTrainer))]

28HelpText = "Number of models per batch. If not specified, will default to 50 if there is only one base predictor, " + 33[Argument(ArgumentType.AtMostOnce, HelpText = "Batch size", ShortName = "bs", SortOrder = 107)] 39[Argument(ArgumentType.Multiple, HelpText = "Sampling Type", ShortName = "st", SortOrder = 2)] 43[Argument(ArgumentType.AtMostOnce, HelpText = "All the base learners will run asynchronously if the value is true", ShortName = "tp", SortOrder = 106)] 48HelpText = "True, if metrics for each model need to be evaluated and shown in comparison table. This is done by using validation set if available or the training set",

43[Argument(ArgumentType.Multiple, HelpText = "Algorithm to prune the base learners for selective Ensemble", ShortName = "pt", SortOrder = 4)] 47[Argument(ArgumentType.Multiple, HelpText = "Output combiner", ShortName = "oc", SortOrder = 5)] 52[Argument(ArgumentType.Multiple, HelpText = "Base predictor type", ShortName = "bp,basePredictorTypes", SortOrder = 1, Visibility = ArgumentAttribute.VisibilityType.CmdLineOnly, SignatureType = typeof(SignatureMulticlassClassifierTrainer))]

37[Argument(ArgumentType.Multiple, HelpText = "Algorithm to prune the base learners for selective Ensemble", ShortName = "pt", SortOrder = 4)] 41[Argument(ArgumentType.Multiple, HelpText = "Output combiner", ShortName = "oc", SortOrder = 5)] 46[Argument(ArgumentType.Multiple, HelpText = "Base predictor type", ShortName = "bp,basePredictorTypes", SortOrder = 1, Visibility = ArgumentAttribute.VisibilityType.CmdLineOnly, SignatureType = typeof(SignatureRegressorTrainer))]

28[Argument(ArgumentType.Required, HelpText = "The data to be used for training", SortOrder = 1)] 34[Argument(ArgumentType.AtMostOnce, HelpText = "The predictor model", SortOrder = 1)] 43[Argument(ArgumentType.Required, HelpText = "The data set", SortOrder = 1)] 47[Argument(ArgumentType.AtMostOnce, HelpText = "The transform model from the pipeline before this command. " + 53[Argument(ArgumentType.Required, HelpText = "The training subgraph", SortOrder = 3)] 58[Argument(ArgumentType.Required, HelpText = "The training subgraph inputs", SortOrder = 4)] 63[Argument(ArgumentType.Required, HelpText = "The training subgraph outputs", SortOrder = 5)] 68[Argument(ArgumentType.AtMostOnce, HelpText = "Column to use for stratification", ShortName = "strat", SortOrder = 6)] 72[Argument(ArgumentType.AtMostOnce, HelpText = "Number of folds in k-fold cross-validation", ShortName = "k", SortOrder = 7)] 77[Argument(ArgumentType.Required, HelpText = "Specifies the trainer kind, which determines the evaluator to be used.", SortOrder = 8)] 80[Argument(ArgumentType.AtMostOnce, HelpText = "Column to use for labels", ShortName = "lab", SortOrder = 9)] 83[Argument(ArgumentType.AtMostOnce, HelpText = "Column to use for example weight", ShortName = "weight", SortOrder = 10)] 86[Argument(ArgumentType.AtMostOnce, HelpText = "Column to use for grouping", ShortName = "group", SortOrder = 11)] 89[Argument(ArgumentType.AtMostOnce, HelpText = "Name column name", ShortName = "name", SortOrder = 12)] 116[Argument(ArgumentType.Multiple, HelpText = "Overall metrics datasets", SortOrder = 1)] 119[Argument(ArgumentType.Multiple, HelpText = "Per instance metrics datasets", SortOrder = 2)] 122[Argument(ArgumentType.Multiple, HelpText = "Confusion matrix datasets", SortOrder = 3)] 125[Argument(ArgumentType.Multiple, HelpText = "Warning datasets", SortOrder = 4)] 128[Argument(ArgumentType.AtMostOnce, HelpText = "The label column name", ShortName = "Label", SortOrder = 6)] 131[Argument(ArgumentType.AtMostOnce, HelpText = "Column to use for example weight", ShortName = "weight", SortOrder = 7)] 134[Argument(ArgumentType.AtMostOnce, HelpText = "Column to use for grouping", ShortName = "group", SortOrder = 8)] 137[Argument(ArgumentType.AtMostOnce, HelpText = "Name column name", ShortName = "name", SortOrder = 9)] 140[Argument(ArgumentType.Required, HelpText = "Specifies the trainer kind, which determines the evaluator to be used.", SortOrder = 5)]

24[Argument(ArgumentType.Required, HelpText = "Input dataset", SortOrder = 1)] 27[Argument(ArgumentType.AtMostOnce, HelpText = "Number of folds to split into", SortOrder = 2)] 30[Argument(ArgumentType.AtMostOnce, ShortName = "strat", HelpText = "Stratification column", SortOrder = 3)]

18[Argument(ArgumentType.Required, HelpText = "Pointer to IDataView in memory", SortOrder = 1)]

25[Argument(ArgumentType.Multiple, HelpText = "Features", SortOrder = 2)] 207[Argument(ArgumentType.Required, HelpText = "The label column", SortOrder = 2)] 217[Argument(ArgumentType.AtMostOnce, HelpText = "Convert the key values to text", SortOrder = 3)] 223[Argument(ArgumentType.Required, HelpText = "The predicted label column", SortOrder = 2)]

22[Argument(ArgumentType.Required, ShortName = "data", HelpText = "Location of the input file", SortOrder = 1)] 25[Argument(ArgumentType.AtMostOnce, ShortName = "schema", HelpText = "Custom schema to use for parsing", SortOrder = 2)] 48[Argument(ArgumentType.Required, ShortName = "data", HelpText = "Location of the input file", SortOrder = 1)] 51[Argument(ArgumentType.Required, ShortName = "args", HelpText = "Arguments", SortOrder = 2)]

27[DefaultArgument(ArgumentType.Required, HelpText = "Path to the graph to run")] 30[Argument(ArgumentType.AtMostOnce, HelpText = "Random seed")]

78[Argument(ArgumentType.Required, HelpText = "The models", SortOrder = 1)] 100[Argument(ArgumentType.Required, HelpText = "The data sets", SortOrder = 1)]

22[Argument(ArgumentType.Multiple, HelpText = "Input models", SortOrder = 1)] 34[Argument(ArgumentType.Multiple | ArgumentType.Required, HelpText = "Transform model", SortOrder = 1)] 37[Argument(ArgumentType.Required, HelpText = "Predictor model", SortOrder = 2)] 43[Argument(ArgumentType.Required, HelpText = "Transform model", SortOrder = 1)] 46[Argument(ArgumentType.Required, HelpText = "Predictor model", SortOrder = 2)] 58[Argument(ArgumentType.Multiple, HelpText = "Input models", SortOrder = 1)] 61[Argument(ArgumentType.AtMostOnce, HelpText = "Use probabilities from learners instead of raw values.", SortOrder = 2)] 67[Argument(ArgumentType.Multiple, HelpText = "Input models", SortOrder = 1)] 73[Argument(ArgumentType.Required, HelpText = "Transform model", SortOrder = 2)]

27[Argument(ArgumentType.Required, HelpText = "The predictor model for the subgraph exemplar.", SortOrder = 1)] 35[Argument(ArgumentType.Required, HelpText = "The subgraph for the binary trainer used to construct the OVA learner. This should be a TrainBinary node.", SortOrder = 1)] 38[Argument(ArgumentType.Required, HelpText = "The training subgraph output.", SortOrder = 2)] 41[Argument(ArgumentType.AtMostOnce, HelpText = "Use probabilities in OVA combiner", SortOrder = 3)]

44[Argument(ArgumentType.Required, HelpText = "The path to the model file", ShortName = "path", Visibility = ArgumentAttribute.VisibilityType.EntryPointsOnly)] 47[Argument(ArgumentType.AtMostOnce, HelpText = "Use feature weights to pre-filter features", ShortName = "usefw", Visibility = ArgumentAttribute.VisibilityType.EntryPointsOnly)] 50[Argument(ArgumentType.AtMostOnce, HelpText = "Limit the number of examples to evaluate on", ShortName = "numexamples", Visibility = ArgumentAttribute.VisibilityType.EntryPointsOnly)] 53[Argument(ArgumentType.AtMostOnce, HelpText = "The number of permutations to perform", ShortName = "permutations", Visibility = ArgumentAttribute.VisibilityType.EntryPointsOnly)]

19[Argument(ArgumentType.Multiple, HelpText = "Extra columns to write", SortOrder = 2)] 60[Argument(ArgumentType.Required, HelpText = "The predictor model used in scoring", SortOrder = 2)]

27[Argument(ArgumentType.Required, HelpText = "The dataset to be scored", SortOrder = 1)] 30[Argument(ArgumentType.Required, HelpText = "The predictor model to apply to data", SortOrder = 2)] 33[Argument(ArgumentType.AtMostOnce, HelpText = "Suffix to append to the score columns", SortOrder = 3)] 39[Argument(ArgumentType.Required, HelpText = "The dataset to be scored", SortOrder = 1)] 42[Argument(ArgumentType.Required, HelpText = "The transform model to apply to data", SortOrder = 2)] 57[Argument(ArgumentType.Required, HelpText = "The predictor model to turn into a transform", SortOrder = 1)]

22[Argument(ArgumentType.Required, HelpText = "The data to be used for training", SortOrder = 1)] 28[Argument(ArgumentType.AtMostOnce, HelpText = "The predictor model", SortOrder = 1)] 35[Argument(ArgumentType.Required, ShortName = "train", HelpText = "The data to be used for training", SortOrder = 1)] 39[Argument(ArgumentType.Required, ShortName = "test", HelpText = "The data to be used for testing", SortOrder = 2)] 43[Argument(ArgumentType.AtMostOnce, HelpText = "The aggregated transform model from the pipeline before this command, to apply to the test data, and also include in the final model, together with the predictor model.", SortOrder = 3)] 46[Argument(ArgumentType.Required, HelpText = "The training subgraph", SortOrder = 4)] 49[Argument(ArgumentType.Required, HelpText = "The training subgraph inputs", SortOrder = 5)] 52[Argument(ArgumentType.Required, HelpText = "The training subgraph outputs", SortOrder = 6)] 55[Argument(ArgumentType.AtMostOnce, HelpText = "Specifies the trainer kind, which determines the evaluator to be used.", SortOrder = 7)] 58[Argument(ArgumentType.AtMostOnce, HelpText = "Identifies which pipeline was run for this train test.", SortOrder = 8)] 61[Argument(ArgumentType.AtMostOnce, HelpText = "Indicates whether to include and output training dataset metrics.", SortOrder = 9)] 64[Argument(ArgumentType.AtMostOnce, HelpText = "Column to use for labels", ShortName = "lab", SortOrder = 10)] 67[Argument(ArgumentType.AtMostOnce, HelpText = "Column to use for example weight", ShortName = "weight", SortOrder = 11)] 70[Argument(ArgumentType.AtMostOnce, HelpText = "Column to use for grouping", ShortName = "group", SortOrder = 12)] 73[Argument(ArgumentType.AtMostOnce, HelpText = "Name column name", ShortName = "name", SortOrder = 13)]

20[Argument(ArgumentType.Required, HelpText = "Input dataset", SortOrder = 1)] 23[Argument(ArgumentType.AtMostOnce, HelpText = "Fraction of training data", SortOrder = 2)] 26[Argument(ArgumentType.AtMostOnce, ShortName = "strat", HelpText = "Stratification column", SortOrder = 3)]

69[Argument(ArgumentType.LastOccurrenceWins, HelpText = "Option for using derivatives optimized for unbalanced sets", ShortName = "us")] 172[Argument(ArgumentType.LastOccurrenceWins, HelpText = 227[Argument(ArgumentType.LastOccurrenceWins, HelpText = "Comma-separated list of gains associated to each relevance label.", ShortName = "gains")] 234[Argument(ArgumentType.LastOccurrenceWins, HelpText = "Train DCG instead of NDCG", ShortName = "dcg")] 240HelpText = "The sorting algorithm to use for DCG and LambdaMart calculations [DescendingStablePessimistic/DescendingStable/DescendingReverse/DescendingDotNet]", 250[Argument(ArgumentType.AtMostOnce, HelpText = "max-NDCG truncation to use in the LambdaMART algorithm", ShortName = "n", Hide = true)] 255[Argument(ArgumentType.LastOccurrenceWins, HelpText = "Use shifted NDCG", Hide = true)] 260[Argument(ArgumentType.AtMostOnce, HelpText = "Cost function parameter (w/c)", ShortName = "cf", Hide = true)] 265[Argument(ArgumentType.LastOccurrenceWins, HelpText = "Distance weight 2 adjustment to cost", ShortName = "dw", Hide = true)] 270[Argument(ArgumentType.LastOccurrenceWins, HelpText = "Normalize query lambdas", ShortName = "nql", Hide = true)] 353[Argument(ArgumentType.Multiple, HelpText = "Allows to choose Parallel FastTree Learning Algorithm", ShortName = "parag")] 359[Argument(ArgumentType.LastOccurrenceWins, HelpText = "The number of threads to use", ShortName = "t", NullName = "<Auto>")] 372[Argument(ArgumentType.LastOccurrenceWins, HelpText = "The seed of the random number generator", ShortName = "r1")] 379[Argument(ArgumentType.LastOccurrenceWins, HelpText = "The seed of the active feature selection", ShortName = "r3", Hide = true)] 386[Argument(ArgumentType.LastOccurrenceWins, HelpText = "The entropy (regularization) coefficient between 0 and 1", ShortName = "e")] 393[Argument(ArgumentType.LastOccurrenceWins, HelpText = "The number of histograms in the pool (between 2 and numLeaves)", ShortName = "ps")] 399[Argument(ArgumentType.LastOccurrenceWins, HelpText = "Whether to utilize the disk or the data's native transposition facilities (where applicable) when performing the transpose", ShortName = "dt")] 405[Argument(ArgumentType.LastOccurrenceWins, HelpText = "Whether to collectivize features during dataset preparation to speed up training", ShortName = "flocks", Hide = true)] 411[Argument(ArgumentType.LastOccurrenceWins, HelpText = "Whether to do split based on multiple categorical feature values.", ShortName = "cat")] 417[Argument(ArgumentType.LastOccurrenceWins, HelpText = "Maximum categorical split groups to consider when splitting on a categorical feature. " + 425[Argument(ArgumentType.LastOccurrenceWins, HelpText = "Maximum categorical split points to consider when splitting on a categorical feature.", ShortName = "maxcat")] 431[Argument(ArgumentType.LastOccurrenceWins, HelpText = "Minimum categorical example percentage in a bin to consider for a split.", ShortName = "mdop")] 437[Argument(ArgumentType.LastOccurrenceWins, HelpText = "Minimum categorical example count in a bin to consider for a split.", ShortName = "mdo")] 443[Argument(ArgumentType.LastOccurrenceWins, HelpText = "Bias for calculating gradient for each feature bin for a categorical feature.", ShortName = "bias")] 449[Argument(ArgumentType.LastOccurrenceWins, HelpText = "Bundle low population bins. " + 460[Argument(ArgumentType.LastOccurrenceWins, HelpText = "Maximum number of distinct values (bins) per feature", ShortName = "mb")] 466[Argument(ArgumentType.LastOccurrenceWins, HelpText = "Sparsity level needed to use sparse feature representation", ShortName = "sp")] 472[Argument(ArgumentType.LastOccurrenceWins, HelpText = "The feature first use penalty coefficient", ShortName = "ffup")] 478[Argument(ArgumentType.LastOccurrenceWins, HelpText = "The feature re-use penalty (regularization) coefficient", ShortName = "frup")] 488[Argument(ArgumentType.LastOccurrenceWins, HelpText = "Tree fitting gain confidence requirement (should be in the range [0,1) ).", ShortName = "gainconf")] 494[Argument(ArgumentType.AtMostOnce, HelpText = "The temperature of the randomized softmax distribution for choosing the feature", ShortName = "smtemp")] 500[Argument(ArgumentType.AtMostOnce, HelpText = "Print execution time breakdown to stdout", ShortName = "et")] 506[Argument(ArgumentType.AtMostOnce, HelpText = "Print memory statistics to stdout", ShortName = "memstats")] 513[Argument(ArgumentType.LastOccurrenceWins, HelpText = "The max number of leaves in each regression tree", ShortName = "nl", SortOrder = 2)] 523[Argument(ArgumentType.LastOccurrenceWins, HelpText = "The minimal number of examples allowed in a leaf of a regression tree, out of the subsampled data", ShortName = "mil", SortOrder = 3)] 532[Argument(ArgumentType.LastOccurrenceWins, HelpText = "Total number of decision trees to create in the ensemble", ShortName = "iter", SortOrder = 1)] 541[Argument(ArgumentType.AtMostOnce, HelpText = "The fraction of features (chosen randomly) to use on each iteration", ShortName = "ff")] 547[Argument(ArgumentType.AtMostOnce, HelpText = "Number of trees in each bag (0 for disabling bagging)", ShortName = "bag")] 553[Argument(ArgumentType.AtMostOnce, HelpText = "Percentage of training examples used in each bag", ShortName = "bagfrac")] 563[Argument(ArgumentType.AtMostOnce, HelpText = "The fraction of features (chosen randomly) to use on each split", ShortName = "sf")] 569[Argument(ArgumentType.AtMostOnce, HelpText = "Smoothing paramter for tree regularization", ShortName = "s")] 575[Argument(ArgumentType.AtMostOnce, HelpText = "When a root split is impossible, allow training to proceed", ShortName = "allowempty,dummies", Hide = true)] 583[Argument(ArgumentType.LastOccurrenceWins, HelpText = "The level of feature compression to use", ShortName = "fcomp", Hide = true)] 590[Argument(ArgumentType.LastOccurrenceWins, HelpText = "Compress the tree Ensemble", ShortName = "cmp", Hide = true)] 598[Argument(ArgumentType.LastOccurrenceWins, HelpText = "Print metrics graph for the first test set", ShortName = "graph", Hide = true)] 607[Argument(ArgumentType.LastOccurrenceWins, HelpText = "Print Train and Validation metrics in graph", ShortName = "graphtv", Hide = true)] 614[Argument(ArgumentType.LastOccurrenceWins, HelpText = "Calculate metric values for train/valid/test every k rounds", ShortName = "tf")] 655[Argument(ArgumentType.LastOccurrenceWins, HelpText = "Option for using best regression step trees", ShortName = "bsr")] 661[Argument(ArgumentType.LastOccurrenceWins, HelpText = "Should we use line search for a step size", ShortName = "ls")] 667[Argument(ArgumentType.LastOccurrenceWins, HelpText = "Number of post-bracket line search steps", ShortName = "lssteps")] 673[Argument(ArgumentType.LastOccurrenceWins, HelpText = "Minimum line search step size", ShortName = "minstep")] 687[Argument(ArgumentType.LastOccurrenceWins, HelpText = "Optimization algorithm to be used (GradientDescent, AcceleratedGradientDescent)", ShortName = "oa")] 694[Argument(ArgumentType.Multiple, HelpText = "Early stopping rule. (Validation set (/valid) is required.)", Name = "EarlyStoppingRule", ShortName = "esr", NullName = "<Disable>")] 721[Argument(ArgumentType.AtMostOnce, HelpText = "Early stopping metrics. (For regression, 1: L1, 2:L2; for ranking, 1:NDCG@1, 3:NDCG@3)", ShortName = "esmt")] 728[Argument(ArgumentType.AtMostOnce, HelpText = "Enable post-training pruning to avoid overfitting. (a validation set is required)", ShortName = "pruning")] 734[Argument(ArgumentType.LastOccurrenceWins, HelpText = "Use window and tolerance for pruning", ShortName = "prtol")] 740[Argument(ArgumentType.AtMostOnce, HelpText = "The tolerance threshold for pruning", ShortName = "prth")] 747[Argument(ArgumentType.AtMostOnce, HelpText = "The moving window size for pruning", ShortName = "prws")] 754[Argument(ArgumentType.LastOccurrenceWins, HelpText = "The learning rate", ShortName = "lr", SortOrder = 4)] 762[Argument(ArgumentType.AtMostOnce, HelpText = "Shrinkage", ShortName = "shrk")] 770[Argument(ArgumentType.AtMostOnce, HelpText = "Dropout rate for tree regularization", ShortName = "tdrop")] 778[Argument(ArgumentType.AtMostOnce, HelpText = "Sample each query 1 in k times in the GetDerivatives function", ShortName = "sr")] 784[Argument(ArgumentType.AtMostOnce, HelpText = "Write the last ensemble instead of the one determined by early stopping", ShortName = "hl")] 790[Argument(ArgumentType.AtMostOnce, HelpText = "Upper bound on absolute value of single tree output", ShortName = "mo")] 796[Argument(ArgumentType.LastOccurrenceWins, HelpText = "Training starts from random ordering (determined by /r1)", ShortName = "rs", Hide = true)] 803[Argument(ArgumentType.AtMostOnce, HelpText = "Filter zero lambdas during training", ShortName = "fzl", Hide = true)] 826[Argument(ArgumentType.LastOccurrenceWins, HelpText = "Freeform defining the scores that should be used as the baseline ranker", ShortName = "basescores", Hide = true)] 834[Argument(ArgumentType.LastOccurrenceWins, HelpText = "Baseline alpha for tradeoffs of risk (0 is normal training)", ShortName = "basealpha", Hide = true)] 842[Argument(ArgumentType.LastOccurrenceWins, HelpText = "The discount freeform which specifies the per position discounts of examples in a query (uses a single variable P for position where P=0 is first position)",

69[Argument(ArgumentType.LastOccurrenceWins, HelpText = "Should we use derivatives optimized for unbalanced sets", ShortName = "us")]

557[Argument(ArgumentType.AtMostOnce, HelpText = "Whether to open the GAM visualization page URL", ShortName = "o", SortOrder = 3)]

66[Argument(ArgumentType.AtMostOnce, HelpText = "Metric for pruning. (For regression, 1: L1, 2:L2; default L2)", ShortName = "pmetric")]

40[Argument(ArgumentType.LastOccurrenceWins, HelpText = "The entropy (regularization) coefficient between 0 and 1", ShortName = "e")] 50[Argument(ArgumentType.LastOccurrenceWins, HelpText = "Tree fitting gain confidence requirement (should be in the range [0,1) ).", ShortName = "gainconf")] 56[Argument(ArgumentType.LastOccurrenceWins, HelpText = "Total number of iterations over all features", ShortName = "iter", SortOrder = 1)] 64[Argument(ArgumentType.LastOccurrenceWins, HelpText = "The number of threads to use", ShortName = "t", NullName = "<Auto>")] 70[Argument(ArgumentType.LastOccurrenceWins, HelpText = "The learning rate", ShortName = "lr", SortOrder = 4)] 78[Argument(ArgumentType.LastOccurrenceWins, HelpText = "Whether to utilize the disk or the data's native transposition facilities (where applicable) when performing the transpose", ShortName = "dt")] 84[Argument(ArgumentType.LastOccurrenceWins, HelpText = "Maximum number of distinct values (bins) per feature", ShortName = "mb")] 90[Argument(ArgumentType.AtMostOnce, HelpText = "Upper bound on absolute value of single output", ShortName = "mo")] 96[Argument(ArgumentType.AtMostOnce, HelpText = "Sample each query 1 in k times in the GetDerivatives function", ShortName = "sr")] 102[Argument(ArgumentType.LastOccurrenceWins, HelpText = "The seed of the random number generator", ShortName = "r1")] 108[Argument(ArgumentType.LastOccurrenceWins, HelpText = "Minimum number of training instances required to form a partition", ShortName = "mi", SortOrder = 3)] 116[Argument(ArgumentType.LastOccurrenceWins, HelpText = "Whether to collectivize features during dataset preparation to speed up training", ShortName = "flocks", Hide = true)] 122[Argument(ArgumentType.AtMostOnce, HelpText = "Enable post-training pruning to avoid overfitting. (a validation set is required)", ShortName = "pruning")]

44[Argument(ArgumentType.AtMostOnce, HelpText = "Number of labels to be sampled from each leaf to make the distribution", ShortName = "qsc")] 158[Argument(ArgumentType.AtMostOnce, HelpText = "Upper bound on absolute value of single tree output", ShortName = "mo")] 161[Argument(ArgumentType.AtMostOnce, HelpText = "The calibrator kind to apply to the predictor. Specify null for no calibration", Visibility = ArgumentAttribute.VisibilityType.EntryPointsOnly)] 164[Argument(ArgumentType.AtMostOnce, HelpText = "The maximum number of examples to use when training the calibrator", Visibility = ArgumentAttribute.VisibilityType.EntryPointsOnly)]

305[Argument(ArgumentType.LastOccurrenceWins, HelpText = "Shuffle the labels on every iteration. " +

37[Argument(ArgumentType.AtMostOnce, HelpText = "The type of IntArray to construct", ShortName = "type", SortOrder = 0)] 40[Argument(ArgumentType.AtMostOnce, HelpText = "The length of each int arrays", ShortName = "len", SortOrder = 1)] 43[Argument(ArgumentType.AtMostOnce, HelpText = "The number of int arrays to create", ShortName = "c", SortOrder = 2)] 46[Argument(ArgumentType.AtMostOnce, HelpText = "The number of bins to have in the int array", ShortName = "b", SortOrder = 3)] 49[Argument(ArgumentType.AtMostOnce, HelpText = "The random parameter, which will differ depending on the type of the feature", ShortName = "p", SortOrder = 4)] 52[Argument(ArgumentType.AtMostOnce, HelpText = "The number of seconds to run sumups in each trial", ShortName = "s", SortOrder = 5)] 55[Argument(ArgumentType.AtMostOnce, HelpText = "Random seed", ShortName = "seed", SortOrder = 101)] 58[Argument(ArgumentType.AtMostOnce, HelpText = "Verbose?", ShortName = "v", Hide = true)] 65HelpText = "Desired degree of parallelism in the data pipeline", ShortName = "n", SortOrder = 6)]

122[Argument(ArgumentType.AtMostOnce, HelpText = "Tolerance threshold. (Non negative value)", ShortName = "th")] 184[Argument(ArgumentType.AtMostOnce, HelpText = "Threshold in range [0,1].", ShortName = "th")] 188[Argument(ArgumentType.AtMostOnce, HelpText = "The window size.", ShortName = "w")] 290[Argument(ArgumentType.AtMostOnce, HelpText = "Threshold in range [0,1].", ShortName = "th")] 474[Argument(ArgumentType.AtMostOnce, HelpText = "The window size.", ShortName = "w")]

539[Argument(ArgumentType.Multiple, HelpText = "Trainer to use", ShortName = "tr", NullName = "<None>", SortOrder = 1, SignatureType = typeof(SignatureTreeEnsembleTrainer))] 542[Argument(ArgumentType.AtMostOnce, IsInputFileName = true, HelpText = "Predictor model file used in scoring", 546[Argument(ArgumentType.AtMostOnce, HelpText = "Output column: The suffix to append to the default column names", 550[Argument(ArgumentType.AtMostOnce, HelpText = "If specified, determines the permutation seed for applying this featurizer to a multiclass problem.", 563[Argument(ArgumentType.AtMostOnce, HelpText = "Output column: The suffix to append to the default column names", 567[Argument(ArgumentType.AtMostOnce, HelpText = "If specified, determines the permutation seed for applying this featurizer to a multiclass problem.", 571[Argument(ArgumentType.Required, HelpText = "Trainer to use", SortOrder = 10, Visibility = ArgumentAttribute.VisibilityType.EntryPointsOnly)]

56[Argument(ArgumentType.Multiple | ArgumentType.Required, HelpText = "New column definition(s) (optional form: name:src)", Name = "Column", ShortName = "col", SortOrder = 1)]

59[Argument(ArgumentType.Multiple | ArgumentType.Required, HelpText = "New column definition(s) (optional form: name:src)", Name = "Column", ShortName = "col", SortOrder = 1)] 62[Argument(ArgumentType.AtMostOnce, HelpText = "Folder where to search for images", ShortName = "folder")]

39[Argument(ArgumentType.AtMostOnce, HelpText = "Whether to use alpha channel", ShortName = "alpha")] 42[Argument(ArgumentType.AtMostOnce, HelpText = "Whether to use red channel", ShortName = "red")] 45[Argument(ArgumentType.AtMostOnce, HelpText = "Whether to use green channel", ShortName = "green")] 48[Argument(ArgumentType.AtMostOnce, HelpText = "Whether to use blue channel", ShortName = "blue")] 51[Argument(ArgumentType.AtMostOnce, HelpText = "Order of channels")] 55[Argument(ArgumentType.AtMostOnce, HelpText = "Whether to separate each channel or interleave in specified order")] 58[Argument(ArgumentType.AtMostOnce, HelpText = "Whether to convert to floating point", ShortName = "conv")] 61[Argument(ArgumentType.AtMostOnce, HelpText = "Offset (pre-scale)")] 64[Argument(ArgumentType.AtMostOnce, HelpText = "Scale factor")] 92[Argument(ArgumentType.Multiple | ArgumentType.Required, HelpText = "New column definition(s) (optional form: name:src)", Name = "Column", ShortName = "col", SortOrder = 1)] 95[Argument(ArgumentType.AtMostOnce, HelpText = "Whether to use alpha channel", ShortName = "alpha")] 98[Argument(ArgumentType.AtMostOnce, HelpText = "Whether to use red channel", ShortName = "red")] 101[Argument(ArgumentType.AtMostOnce, HelpText = "Whether to use green channel", ShortName = "green")] 104[Argument(ArgumentType.AtMostOnce, HelpText = "Whether to use blue channel", ShortName = "blue")] 107[Argument(ArgumentType.AtMostOnce, HelpText = "Order of colors.")] 110[Argument(ArgumentType.AtMostOnce, HelpText = "Whether to separate each channel or interleave in specified order")] 113[Argument(ArgumentType.AtMostOnce, HelpText = "Whether to convert to floating point", ShortName = "conv")] 116[Argument(ArgumentType.AtMostOnce, HelpText = "Offset (pre-scale)")] 119[Argument(ArgumentType.AtMostOnce, HelpText = "Scale factor")]

39[Argument(ArgumentType.AtMostOnce, HelpText = "Width of the resized image", ShortName = "width")] 42[Argument(ArgumentType.AtMostOnce, HelpText = "Height of the resized image", ShortName = "height")] 45[Argument(ArgumentType.AtMostOnce, HelpText = "Resizing method", ShortName = "scale")] 48[Argument(ArgumentType.AtMostOnce, HelpText = "Anchor for cropping", ShortName = "anchor")] 72[Argument(ArgumentType.Multiple | ArgumentType.Required, HelpText = "New column definition(s) (optional form: name:src)", Name = "Column", ShortName = "col", SortOrder = 1)] 75[Argument(ArgumentType.Required, HelpText = "Resized width of the image", ShortName = "width")] 78[Argument(ArgumentType.Required, HelpText = "Resized height of the image", ShortName = "height")] 81[Argument(ArgumentType.AtMostOnce, HelpText = "Resizing method", ShortName = "scale")] 84[Argument(ArgumentType.AtMostOnce, HelpText = "Anchor for cropping", ShortName = "anchor")]

38[Argument(ArgumentType.AtMostOnce, HelpText = "Whether to use alpha channel", ShortName = "alpha")] 41[Argument(ArgumentType.AtMostOnce, HelpText = "Whether to use red channel", ShortName = "red")] 44[Argument(ArgumentType.AtMostOnce, HelpText = "Whether to use green channel", ShortName = "green")] 47[Argument(ArgumentType.AtMostOnce, HelpText = "Whether to use blue channel", ShortName = "blue")] 50[Argument(ArgumentType.AtMostOnce, HelpText = "Order of channels")] 54[Argument(ArgumentType.AtMostOnce, HelpText = "Whether to separate each channel or interleave in specified order")] 57[Argument(ArgumentType.AtMostOnce, HelpText = "Width of the image", ShortName = "width")] 60[Argument(ArgumentType.AtMostOnce, HelpText = "Height of the image", ShortName = "height")] 63[Argument(ArgumentType.AtMostOnce, HelpText = "Offset (pre-scale)")] 66[Argument(ArgumentType.AtMostOnce, HelpText = "Scale factor")] 69[Argument(ArgumentType.AtMostOnce, HelpText = "Default value for alpha channel. Will be used if ContainsAlpha set to false")] 72[Argument(ArgumentType.AtMostOnce, HelpText = "Default value for red channel. Will be used if ContainsRed set to false")] 75[Argument(ArgumentType.AtMostOnce, HelpText = "Default value for green channel. Will be used if ContainsGreen set to false")] 78[Argument(ArgumentType.AtMostOnce, HelpText = "Default value for blue channel. Will be used if ContainsGreen set to false")] 106[Argument(ArgumentType.Multiple | ArgumentType.Required, HelpText = "New column definition(s) (optional form: name:src)", Name = "Column", ShortName = "col", SortOrder = 1)] 109[Argument(ArgumentType.AtMostOnce, HelpText = "Whether to use alpha channel", ShortName = "alpha")] 112[Argument(ArgumentType.AtMostOnce, HelpText = "Whether to use red channel", ShortName = "red")] 115[Argument(ArgumentType.AtMostOnce, HelpText = "Whether to use green channel", ShortName = "green")] 118[Argument(ArgumentType.AtMostOnce, HelpText = "Whether to use blue channel", ShortName = "blue")] 121[Argument(ArgumentType.AtMostOnce, HelpText = "Order of colors.")] 124[Argument(ArgumentType.AtMostOnce, HelpText = "Whether to separate each channel or interleave in specified order")] 127[Argument(ArgumentType.AtMostOnce, HelpText = "Width of the image", ShortName = "width")] 130[Argument(ArgumentType.AtMostOnce, HelpText = "Height of the image", ShortName = "height")] 133[Argument(ArgumentType.AtMostOnce, HelpText = "Offset (pre-scale)")] 136[Argument(ArgumentType.AtMostOnce, HelpText = "Scale factor")] 139[Argument(ArgumentType.AtMostOnce, HelpText = "Default value for alpha channel. Will be used if ContainsAlpha set to false")] 142[Argument(ArgumentType.AtMostOnce, HelpText = "Default value for red channel. Will be used if ContainsRed set to false")] 145[Argument(ArgumentType.AtMostOnce, HelpText = "Default value for green channel. Will be used if ContainsGreen set to false")] 148[Argument(ArgumentType.AtMostOnce, HelpText = "Default value for blue channel. Will be used if ContainsBlue set to false")]

110[Argument(ArgumentType.AtMostOnce, HelpText = "The number of clusters", SortOrder = 50, Name = "K")] 118[Argument(ArgumentType.AtMostOnce, HelpText = "Cluster initialization algorithm", ShortName = "init")] 124[Argument(ArgumentType.AtMostOnce, HelpText = "Tolerance parameter for trainer convergence. Low = slower, more accurate", 132[Argument(ArgumentType.AtMostOnce, HelpText = "Maximum number of iterations.", ShortName = "maxiter, NumberOfIterations")] 139[Argument(ArgumentType.AtMostOnce, HelpText = "Memory budget (in MBs) to use for KMeans acceleration", 147[Argument(ArgumentType.AtMostOnce, HelpText = "Degree of lock-free parallelism. Defaults to automatic. Determinism not guaranteed.", ShortName = "nt,t,threads", SortOrder = 50)]

68HelpText = "Minimum loss reduction required to make a further partition on a leaf node of the tree. the larger, " + 80HelpText = "Maximum depth of a tree. 0 means no limit. However, tree still grows by best-first.")] 93HelpText = "Minimum sum of instance weight(hessian) needed in a child. If the tree partition step results in a leaf " + 107HelpText = "Subsample frequency for bagging. 0 means no subsample. " 121HelpText = "Subsample ratio of the training instance. Setting it to 0.5 means that LightGBM randomly collected " + 135HelpText = "Subsample ratio of columns when constructing each tree. Range: (0,1].", 147HelpText = "L2 regularization term on weights, increasing this value will make model more conservative.", 161HelpText = "L1 regularization term on weights, increase this value will make model more conservative.", 262[Argument(ArgumentType.AtMostOnce, HelpText = "The drop ratio for trees. Range:(0,1).")] 269[Argument(ArgumentType.AtMostOnce, HelpText = "Maximum number of dropped trees in a boosting round.")] 276[Argument(ArgumentType.AtMostOnce, HelpText = "Probability for not dropping in a boosting round.")] 283[Argument(ArgumentType.AtMostOnce, HelpText = "True will enable xgboost dart mode.")] 289[Argument(ArgumentType.AtMostOnce, HelpText = "True will enable uniform drop.")] 327[Argument(ArgumentType.AtMostOnce, HelpText = "Retain ratio for large gradient instances.")] 334[Argument(ArgumentType.AtMostOnce, HelpText = "Retain ratio for small gradient instances.")]

140[Argument(ArgumentType.AtMostOnce, HelpText = "Use for binary classification when training data is not balanced.", ShortName = "us")] 150HelpText = "Control the balance of positive and negative weights, useful for unbalanced classes." + 158[Argument(ArgumentType.AtMostOnce, HelpText = "Parameter for the sigmoid function.", ShortName = "sigmoid")] 166HelpText = "Evaluation metrics.",

88[Argument(ArgumentType.AtMostOnce, HelpText = "Use for multi-class classification when training data is not balanced", ShortName = "us")] 94[Argument(ArgumentType.AtMostOnce, HelpText = "Use softmax loss for the multi classification.")] 101[Argument(ArgumentType.AtMostOnce, HelpText = "Parameter for the sigmoid function.", ShortName = "sigmoid")] 109HelpText = "Evaluation metrics.",

131[Argument(ArgumentType.Multiple, HelpText = "An array of gains associated to each relevance label.", ShortName = "gains")] 138[Argument(ArgumentType.AtMostOnce, HelpText = "Parameter for the sigmoid function.", ShortName = "sigmoid")] 146HelpText = "Evaluation metrics.",

136HelpText = "Evaluation metrics.",

82[Argument(ArgumentType.AtMostOnce, HelpText = "Number of iterations.", SortOrder = 1, ShortName = "iter")] 94HelpText = "Shrinkage rate for trees, used to prevent over-fitting. Range: (0,1].", 103[Argument(ArgumentType.AtMostOnce, HelpText = "Maximum leaves for trees.", 112[Argument(ArgumentType.AtMostOnce, HelpText = "Minimum number of instances needed in a child.", 124[Argument(ArgumentType.AtMostOnce, HelpText = "Maximum number of bucket bin for features.", ShortName = "mb")] 134HelpText = "Which booster to use, can be gbtree, gblinear or dart. gbtree and dart use tree based model while gblinear uses linear function.", 142[Argument(ArgumentType.AtMostOnce, HelpText = "Verbose", ShortName = "v")] 151[Argument(ArgumentType.AtMostOnce, HelpText = "Printing running messages.")] 157[Argument(ArgumentType.AtMostOnce, HelpText = "Number of parallel threads used to run LightGBM.", ShortName = "nt")] 166[Argument(ArgumentType.AtMostOnce, HelpText = "Rounds of early stopping, 0 will disable it.", 173[Argument(ArgumentType.AtMostOnce, HelpText = "Number of entries in a batch when loading data.", Hide = true)] 179[Argument(ArgumentType.AtMostOnce, HelpText = "Enable categorical split or not.", ShortName = "cat")] 186[Argument(ArgumentType.AtMostOnce, HelpText = "Enable special handling of missing value or not.", ShortName = "hmv")] 193[Argument(ArgumentType.AtMostOnce, HelpText = "Enable usage of zero (0) as missing value.", ShortName = "uzam")] 200[Argument(ArgumentType.AtMostOnce, HelpText = "Minimum number of instances per categorical group.", ShortName = "mdpg")] 208[Argument(ArgumentType.AtMostOnce, HelpText = "Max number of categorical thresholds.", ShortName = "maxcat")] 220[Argument(ArgumentType.AtMostOnce, HelpText = "Lapalace smooth term in categorical feature spilt. Avoid the bias of small categories.")] 228[Argument(ArgumentType.AtMostOnce, HelpText = "L2 Regularization for categorical split.")] 239[Argument(ArgumentType.AtMostOnce, HelpText = "Sets the random seed for LightGBM to use.")] 245[Argument(ArgumentType.AtMostOnce, HelpText = "Whether to use deterministic algorithm.")] 251[Argument(ArgumentType.AtMostOnce, HelpText = "Whether to force column-wise histogram building.")] 257[Argument(ArgumentType.AtMostOnce, HelpText = "Whether to force row-wise histogram building.")] 260[Argument(ArgumentType.Multiple, HelpText = "Parallel LightGBM Learning Algorithm", ShortName = "parag")]

25[Argument(ArgumentType.Multiple, HelpText = "Command", Name = "Command", ShortName = "cmd", SignatureType = typeof(SignatureCommand))]

41[DefaultArgument(ArgumentType.AtMostOnce, HelpText = "The component name to get help for")] 44[Argument(ArgumentType.AtMostOnce, HelpText = "The kind of component to look for", ShortName = "kind")] 47[Argument(ArgumentType.AtMostOnce, HelpText = "List the component kinds", ShortName = "list")] 54[Argument(ArgumentType.Multiple, HelpText = "Extra DLLs", ShortName = "dll")] 435[Argument(ArgumentType.AtMostOnce, IsInputFileName = true, HelpText = "The path of the XML documentation file",

82[Argument(ArgumentType.AtMostOnce, HelpText = "L2 regularization weight", ShortName = "l2", SortOrder = 50)] 90[Argument(ArgumentType.LastOccurrenceWins, HelpText = "Whether to calculate per parameter significance statistics", ShortName = "sig")] 96[Argument(ArgumentType.AtMostOnce, HelpText = "Number of entries in a batch when loading data (0 = auto).", Hide = true)]

88[Argument(ArgumentType.AtMostOnce, HelpText = "Degree of lock-free parallelism. Determinism not guaranteed. " + 95[Argument(ArgumentType.AtMostOnce, HelpText = "Number of passes over the data.", ShortName = "iter", SortOrder = 50)] 104[Argument(ArgumentType.AtMostOnce, HelpText = "Tolerance for difference in average loss in consecutive passes.", ShortName = "tol")] 110[Argument(ArgumentType.AtMostOnce, HelpText = "Learning rate", ShortName = "lr", NullName = "<Auto>", SortOrder = 51)] 118[Argument(ArgumentType.AtMostOnce, HelpText = "L2 regularization", ShortName = "l2", SortOrder = 52)] 127[Argument(ArgumentType.AtMostOnce, HelpText = "The number of iterations each thread learns a local model until combining it with the " + 136[Argument(ArgumentType.AtMostOnce, HelpText = "The acceleration memory budget in MB", ShortName = "accelMemBudget")] 142[Argument(ArgumentType.AtMostOnce, HelpText = "Shuffle data?", ShortName = "shuf")] 148[Argument(ArgumentType.AtMostOnce, HelpText = "Apply weight to the positive class, for imbalanced data", ShortName = "piw")]

54[Argument(ArgumentType.Multiple | ArgumentType.Required, HelpText = "New column definition(s) (optional form: name:src)", Name = "Column", ShortName = "col", SortOrder = 1)] 57[Argument(ArgumentType.AtMostOnce, HelpText = "Whitening kind (PCA/ZCA)")] 60[Argument(ArgumentType.AtMostOnce, HelpText = "Scaling regularizer")] 63[Argument(ArgumentType.AtMostOnce, HelpText = "Max number of rows", ShortName = "rows")] 66[Argument(ArgumentType.AtMostOnce, HelpText = "Whether to save inverse (recovery) matrix", ShortName = "saveInv")] 69[Argument(ArgumentType.AtMostOnce, HelpText = "PCA components to retain")] 79[Argument(ArgumentType.AtMostOnce, HelpText = "Whitening kind (PCA/ZCA)")] 82[Argument(ArgumentType.AtMostOnce, HelpText = "Scaling regularizer")] 85[Argument(ArgumentType.AtMostOnce, HelpText = "Max number of rows", ShortName = "rows")] 88[Argument(ArgumentType.AtMostOnce, HelpText = "Whether to save inverse (recovery) matrix", ShortName = "saveInv")] 91[Argument(ArgumentType.AtMostOnce, HelpText = "PCA components to keep/drop")]

36[Argument(ArgumentType.Required, HelpText = "The path to write the output ONNX to.", SortOrder = 1)] 39[Argument(ArgumentType.AtMostOnce, HelpText = "The path to write the output JSON to.", SortOrder = 2)] 42[Argument(ArgumentType.AtMostOnce, HelpText = "The 'name' property in the output ONNX. By default this will be the ONNX extension-less name.", NullName = "<Auto>", SortOrder = 3)] 45[Argument(ArgumentType.AtMostOnce, HelpText = "The 'domain' property in the output ONNX.", NullName = "<Auto>", SortOrder = 4)] 48[Argument(ArgumentType.AtMostOnce, Visibility = ArgumentAttribute.VisibilityType.CmdLineOnly, HelpText = "Comma delimited list of input column names to drop", ShortName = "idrop", SortOrder = 5)] 51[Argument(ArgumentType.AtMostOnce, Visibility = ArgumentAttribute.VisibilityType.EntryPointsOnly, HelpText = "Array of input column names to drop", Name = nameof(InputsToDrop), SortOrder = 6)] 54[Argument(ArgumentType.AtMostOnce, Visibility = ArgumentAttribute.VisibilityType.CmdLineOnly, HelpText = "Comma delimited list of output column names to drop", ShortName = "odrop", SortOrder = 7)] 57[Argument(ArgumentType.AtMostOnce, Visibility = ArgumentAttribute.VisibilityType.EntryPointsOnly, HelpText = "Array of output column names to drop", Name = nameof(OutputsToDrop), SortOrder = 8)] 60[Argument(ArgumentType.AtMostOnce, Visibility = ArgumentAttribute.VisibilityType.CmdLineOnly, HelpText = "Whether we should attempt to load the predictor and attach the scorer to the pipeline if one is present.", ShortName = "pred", SortOrder = 9)] 67[Argument(ArgumentType.AtMostOnce, Visibility = ArgumentAttribute.VisibilityType.EntryPointsOnly, HelpText = "Model that needs to be converted to ONNX format.", SortOrder = 10)] 74[Argument(ArgumentType.AtMostOnce, Visibility = ArgumentAttribute.VisibilityType.EntryPointsOnly, HelpText = "Predictor model that needs to be converted to ONNX format.", SortOrder = 12)] 77[Argument(ArgumentType.AtMostOnce, HelpText = "The targeted ONNX version. It can be either \"Stable\" or \"Experimental\". If \"Experimental\" is used, produced model can contain components that is not officially supported in ONNX standard.", SortOrder = 11)]

64[Argument(ArgumentType.Required, HelpText = "Name of the column")] 67[Argument(ArgumentType.Multiple, HelpText = "Shape of the column")] 73[Argument(ArgumentType.Required, HelpText = "Path to the onnx model file.", ShortName = "model", SortOrder = 0)] 76[Argument(ArgumentType.Multiple | ArgumentType.Required, HelpText = "Name of the input column.", SortOrder = 1)] 79[Argument(ArgumentType.Multiple | ArgumentType.Required, HelpText = "Name of the output column.", SortOrder = 2)] 82[Argument(ArgumentType.AtMostOnce, HelpText = "GPU device id to run on (e.g. 0,1,..). Null for CPU. Requires CUDA 9.1.", SortOrder = 3)] 85[Argument(ArgumentType.AtMostOnce, HelpText = "If true, resumes execution on CPU upon GPU error. If false, will raise the GPU exception.", SortOrder = 4)] 88[Argument(ArgumentType.Multiple, HelpText = "Shapes used to overwrite shapes loaded from ONNX file.", SortOrder = 5)] 91[Argument(ArgumentType.AtMostOnce, HelpText = "Protobuf CodedInputStream recursion limit.", SortOrder = 6)] 94[Argument(ArgumentType.AtMostOnce, HelpText = "Controls the number of threads used to parallelize the execution of the graph (across nodes).", SortOrder = 7)] 97[Argument(ArgumentType.AtMostOnce, HelpText = "Controls the number of threads to use to run the model.", SortOrder = 8)]

80[Argument(ArgumentType.LastOccurrenceWins, HelpText = "Number of column chunk values to cache while reading from parquet file", ShortName = "chunkSize")] 83[Argument(ArgumentType.LastOccurrenceWins, HelpText = "If true, will read large numbers as dates", ShortName = "bigIntDates")]

71[Argument(ArgumentType.Required, HelpText = "Base path to the directory of your partitioned files.", ShortName = "bp")] 74[Argument(ArgumentType.AtMostOnce, HelpText = "Append a column with the file path.", ShortName = "path")] 77[Argument(ArgumentType.AtMostOnce, HelpText = "Path parser to extract column name/value pairs from the file path.", ShortName = "parser")] 80[Argument(ArgumentType.Multiple, HelpText = "The data loader.", SignatureType = typeof(SignatureDataLoader))] 86[Argument(ArgumentType.Required, HelpText = "Name of the column.")] 89[Argument(ArgumentType.AtMostOnce, HelpText = "Data type of the column.")] 92[Argument(ArgumentType.Required, HelpText = "Index of the directory representing this column.")]

80[Argument(ArgumentType.Multiple, HelpText = "Column definitions used to override the Partitioned Path Parser. Expected with the format name:type:numeric-source, for example, col=MyFeature:R4:1", 84[Argument(ArgumentType.AtMostOnce, HelpText = "Data type of each column.")]

96[Argument(ArgumentType.AtMostOnce, HelpText = "The number of components in the PCA", ShortName = "k", SortOrder = 50)] 101[Argument(ArgumentType.AtMostOnce, HelpText = "Oversampling parameter for randomized PCA training", SortOrder = 50)] 106[Argument(ArgumentType.AtMostOnce, HelpText = "If enabled, data is centered to be zero mean", Name = "Center", ShortName = "center")] 110[Argument(ArgumentType.AtMostOnce, HelpText = "The seed for random number generation", ShortName = "seed")]

40[Argument(ArgumentType.Multiple | ArgumentType.Required, HelpText = "New column definition(s) (optional form: name:src)", Name = "Column", ShortName = "col", SortOrder = 1)] 43[Argument(ArgumentType.Multiple, HelpText = "The name of the weight column", ShortName = "weight", Purpose = SpecialPurpose.ColumnName)] 46[Argument(ArgumentType.AtMostOnce, HelpText = "The number of components in the PCA", ShortName = "k")] 49[Argument(ArgumentType.AtMostOnce, HelpText = "Oversampling parameter for randomized PCA training", ShortName = "over")] 52[Argument(ArgumentType.AtMostOnce, HelpText = "If enabled, data is centered to be zero mean")] 55[Argument(ArgumentType.AtMostOnce, HelpText = "The seed for random number generation")] 61[Argument(ArgumentType.Multiple, HelpText = "The name of the weight column", ShortName = "weight")] 64[Argument(ArgumentType.AtMostOnce, HelpText = "The number of components in the PCA", ShortName = "k")] 67[Argument(ArgumentType.AtMostOnce, HelpText = "Oversampling parameter for randomized PCA training", ShortName = "over")] 70[Argument(ArgumentType.AtMostOnce, HelpText = "If enabled, data is centered to be zero mean", ShortName = "center")] 73[Argument(ArgumentType.AtMostOnce, HelpText = "The seed for random number generation", ShortName = "seed")]

169[Argument(ArgumentType.AtMostOnce, HelpText = "Loss function minimized for finding factor matrices.")] 180[Argument(ArgumentType.AtMostOnce, HelpText = "Regularization parameter. " + 193[Argument(ArgumentType.AtMostOnce, HelpText = "Latent space dimension (denoted by k). If the factorized matrix is m-by-n, " + 203[Argument(ArgumentType.AtMostOnce, HelpText = "Training iterations; that is, the times that the training algorithm iterates through the whole training data once.", ShortName = "iter,numiterations")] 215[Argument(ArgumentType.AtMostOnce, HelpText = "Initial learning rate. It specifies the speed of the training algorithm. " + 236[Argument(ArgumentType.AtMostOnce, HelpText = "Importance of unobserved entries' loss in one-class matrix factorization.")] 248[Argument(ArgumentType.AtMostOnce, HelpText = "Desired negative entries' value in one-class matrix factorization")] 256[Argument(ArgumentType.AtMostOnce, HelpText = "Number of threads can be used in the training procedure.", ShortName = "t,numthreads")] 262[Argument(ArgumentType.AtMostOnce, HelpText = "Suppress writing additional information to output.")] 268[Argument(ArgumentType.AtMostOnce, HelpText = "Force the factor matrices to be non-negative.", ShortName = "nn")]

308[DefaultArgument(ArgumentType.Multiple, HelpText = "Result file pattern")] 312[Argument(ArgumentType.AtMostOnce, HelpText = "Output file name", ShortName = "o")] 316[Argument(ArgumentType.AtMostOnce, HelpText = "Output to a visualization HTML", ShortName = "html")] 320[Argument(ArgumentType.Multiple, HelpText = "Which metrics should be processed (default=all)?", ShortName = "a")] 324[Argument(ArgumentType.AtMostOnce, HelpText = "Include columns for standard deviations?", ShortName = "stdev")] 328[Argument(ArgumentType.AtMostOnce, HelpText = "Output per-fold results", ShortName = "opf")] 332[Argument(ArgumentType.AtMostOnce, HelpText = "Separator for per-fold results. Can be: actual char, 'tab', 'colon', 'space','comma'", ShortName = "opfsep")] 336[Argument(ArgumentType.Multiple, HelpText = "Extra DLLs", ShortName = "dll")] 338[Argument(ArgumentType.AtMostOnce, HelpText = "Internal setting set if called from unit test suite")] 341[Argument(ArgumentType.Multiple, HelpText = "Result file pattern with customized tag", ShortName = "in")]

111[Argument(ArgumentType.AtMostOnce, HelpText = "Initial learning rate", ShortName = "lr", SortOrder = 1)] 118[Argument(ArgumentType.AtMostOnce, HelpText = "Number of training iterations", ShortName = "iters,iter", SortOrder = 2)] 125[Argument(ArgumentType.AtMostOnce, HelpText = "Latent space dimension", ShortName = "d", SortOrder = 3)] 132[Argument(ArgumentType.AtMostOnce, HelpText = "Regularization coefficient of linear weights", ShortName = "lambdaLinear", SortOrder = 4)] 139[Argument(ArgumentType.AtMostOnce, HelpText = "Regularization coefficient of latent weights", ShortName = "lambdaLatent", SortOrder = 5)] 146[Argument(ArgumentType.AtMostOnce, HelpText = "Whether to normalize the input vectors so that the concatenation of all fields' feature vectors is unit-length", ShortName = "norm", SortOrder = 6)] 153[Argument(ArgumentType.Multiple, HelpText = "Extra columns to use for feature vectors. The i-th specified string denotes the column containing features form the (i+1)-th field." + 161[Argument(ArgumentType.AtMostOnce, HelpText = "Whether to shuffle for each training iteration", ShortName = "shuf", SortOrder = 90)] 167[Argument(ArgumentType.AtMostOnce, HelpText = "Report traning progress or not", ShortName = "verbose", SortOrder = 91)] 173[Argument(ArgumentType.AtMostOnce, HelpText = "Radius of initial latent factors", ShortName = "rad", SortOrder = 110)]

78[Argument(ArgumentType.AtMostOnce, HelpText = "Depth of Local Deep SVM tree", ShortName = "depth", SortOrder = 50)] 87[Argument(ArgumentType.AtMostOnce, HelpText = "Regularizer for classifier parameter W", ShortName = "lw", SortOrder = 50)] 96[Argument(ArgumentType.AtMostOnce, HelpText = "Regularizer for kernel parameter Theta", ShortName = "lt", SortOrder = 50)] 105[Argument(ArgumentType.AtMostOnce, HelpText = "Regularizer for kernel parameter Thetaprime", ShortName = "lp", SortOrder = 50)] 114[Argument(ArgumentType.AtMostOnce, HelpText = "Parameter for sigmoid sharpness", ShortName = "s", SortOrder = 50)] 123[Argument(ArgumentType.AtMostOnce, HelpText = "No bias", ShortName = "bias")] 132HelpText = "Number of iterations", ShortName = "iter,NumIterations", SortOrder = 50)] 138[Argument(ArgumentType.AtMostOnce, HelpText = "The calibrator kind to apply to the predictor. Specify null for no calibration", Visibility = ArgumentAttribute.VisibilityType.EntryPointsOnly)] 141[Argument(ArgumentType.AtMostOnce, HelpText = "The maximum number of examples to use when training the calibrator", Visibility = ArgumentAttribute.VisibilityType.EntryPointsOnly)] 144[Argument(ArgumentType.AtMostOnce, HelpText = "Whether to cache the data before the first iteration")]

45[Argument(ArgumentType.AtMostOnce, HelpText = "L2 regularization weight", ShortName = "l2, L2Weight", SortOrder = 50)] 54[Argument(ArgumentType.AtMostOnce, HelpText = "L1 regularization weight", ShortName = "l1, L1Weight", SortOrder = 50)] 63[Argument(ArgumentType.AtMostOnce, HelpText = "Tolerance parameter for optimization convergence. Low = slower, more accurate", 73[Argument(ArgumentType.AtMostOnce, HelpText = "Memory size for L-BFGS. Low=faster, less accurate", ShortName = "m, MemorySize", SortOrder = 50)] 82[Argument(ArgumentType.AtMostOnce, HelpText = "Maximum iterations.", ShortName = "maxiter, MaxIterations, NumberOfIterations")] 91[Argument(ArgumentType.AtMostOnce, HelpText = "Run SGD to initialize LR weights, converging to this tolerance", 101[Argument(ArgumentType.AtMostOnce, HelpText = "If set to true, produce no output during training.", 112[Argument(ArgumentType.LastOccurrenceWins, HelpText = "Init weights diameter", ShortName = "initwts, InitWtsDiameter", SortOrder = 140)] 119[Argument(ArgumentType.AtMostOnce, HelpText = "Whether or not to use threads. Default is true", 126[Argument(ArgumentType.AtMostOnce, HelpText = "Number of threads", ShortName = "nt, NumThreads")] 132[Argument(ArgumentType.AtMostOnce, HelpText = "Force densification of the internal optimization vectors", ShortName = "do")] 140[Argument(ArgumentType.AtMostOnce, HelpText = "Enforce non-negative weights", ShortName = "nn", SortOrder = 90)]

105[Argument(ArgumentType.AtMostOnce, HelpText = "Show statistics of training examples.", ShortName = "stat, ShowTrainingStats", SortOrder = 50)]

106[Argument(ArgumentType.AtMostOnce, HelpText = "Show statistics of training examples.", ShortName = "stat, ShowTrainingStats", SortOrder = 50)]

23[Argument(ArgumentType.Multiple, HelpText = "Base predictor", ShortName = "p", SortOrder = 4, SignatureType = typeof(SignatureBinaryClassifierTrainer))] 27[Argument(ArgumentType.Multiple, HelpText = "Output calibrator", ShortName = "cali", SortOrder = 150, NullName = "<None>", SignatureType = typeof(SignatureCalibrator))] 30[Argument(ArgumentType.LastOccurrenceWins, HelpText = "Number of instances to train the calibrator", SortOrder = 150, ShortName = "numcali")] 33[Argument(ArgumentType.Multiple, HelpText = "Whether to treat missing labels as having negative labels, or exclude their rows from dataview.", SortOrder = 150, ShortName = "missNeg")]

105[Argument(ArgumentType.AtMostOnce, HelpText = "Use probability or margins to determine max", ShortName = "useprob")]

27[Argument(ArgumentType.AtMostOnce, HelpText = "Learning rate", ShortName = "lr", SortOrder = 50)] 40[Argument(ArgumentType.AtMostOnce, HelpText = "Decrease learning rate", ShortName = "decreaselr", SortOrder = 50)] 52[Argument(ArgumentType.AtMostOnce, HelpText = "Number of examples after which weights will be reset to the current average", ShortName = "numreset")] 63[Argument(ArgumentType.AtMostOnce, HelpText = "Instead of updating averaged weights on every example, only update when loss is nonzero", ShortName = "lazy,DoLazyUpdates")] 69[Argument(ArgumentType.AtMostOnce, HelpText = "L2 Regularization Weight", ShortName = "reg,L2RegularizerWeight", SortOrder = 50)] 81[Argument(ArgumentType.AtMostOnce, HelpText = "Extra weight given to more recent updates", ShortName = "rg")] 92[Argument(ArgumentType.AtMostOnce, HelpText = "Whether Recency Gain is multiplicative (vs. additive)", ShortName = "rgm,RecencyGainMulti")] 102[Argument(ArgumentType.AtMostOnce, HelpText = "Do averaging?", ShortName = "avg")] 108[Argument(ArgumentType.AtMostOnce, HelpText = "The inexactness tolerance for averaging", ShortName = "avgtol")]

99[Argument(ArgumentType.Multiple, Name = "LossFunction", HelpText = "Loss Function", ShortName = "loss", SortOrder = 50)] 110[Argument(ArgumentType.AtMostOnce, HelpText = "The calibrator kind to apply to the predictor. Specify null for no calibration", Visibility = ArgumentAttribute.VisibilityType.EntryPointsOnly)] 116[Argument(ArgumentType.AtMostOnce, HelpText = "The maximum number of examples to use when training the calibrator", Visibility = ArgumentAttribute.VisibilityType.EntryPointsOnly)]

83[Argument(ArgumentType.AtMostOnce, HelpText = "Regularizer constant", ShortName = "lambda", SortOrder = 50)] 89[Argument(ArgumentType.AtMostOnce, HelpText = "Batch size", ShortName = "batch", SortOrder = 190)] 94[Argument(ArgumentType.AtMostOnce, HelpText = "Perform projection to unit-ball? Typically used with batch size > 1.", ShortName = "project", SortOrder = 50)] 99[Argument(ArgumentType.AtMostOnce, HelpText = "No bias")] 104[Argument(ArgumentType.AtMostOnce, HelpText = "The calibrator kind to apply to the predictor. Specify null for no calibration", Visibility = ArgumentAttribute.VisibilityType.EntryPointsOnly)] 107[Argument(ArgumentType.AtMostOnce, HelpText = "The maximum number of examples to use when training the calibrator", Visibility = ArgumentAttribute.VisibilityType.EntryPointsOnly)] 113[Argument(ArgumentType.AtMostOnce, HelpText = "Column to use for example weight", ShortName = "weight,WeightColumn", SortOrder = 4, Visibility = ArgumentAttribute.VisibilityType.EntryPointsOnly)]

72[Argument(ArgumentType.Multiple, Name = "LossFunction", HelpText = "Loss Function", ShortName = "loss", SortOrder = 50)]

27[Argument(ArgumentType.AtMostOnce, HelpText = "Number of iterations", ShortName = "iter,numIterations", SortOrder = 50)] 36[Argument(ArgumentType.AtMostOnce, HelpText = "Initial Weights and bias, comma-separated", ShortName = "initweights")] 47[Argument(ArgumentType.AtMostOnce, HelpText = "Init weights diameter", ShortName = "initwts,initWtsDiameter", SortOrder = 140)] 60[Argument(ArgumentType.AtMostOnce, HelpText = "Whether to shuffle for each training iteration", ShortName = "shuf")]

163[Argument(ArgumentType.AtMostOnce, HelpText = "L2 regularizer constant. By default the l2 constant is automatically inferred based on data set.", NullName = "<Auto>", ShortName = "l2, L2Const", SortOrder = 1)] 173[Argument(ArgumentType.AtMostOnce, HelpText = "L1 soft threshold (L1/L2). Note that it is easier to control and sweep using the threshold parameter than the raw L1-regularizer constant. By default the l1 threshold is automatically inferred based on data set.", 186[Argument(ArgumentType.AtMostOnce, HelpText = "Degree of lock-free parallelism. Defaults to automatic. Determinism not guaranteed.", NullName = "<Auto>", ShortName = "nt,t,threads, NumThreads", SortOrder = 50)] 193[Argument(ArgumentType.AtMostOnce, HelpText = "The tolerance for the ratio between duality gap and primal loss for convergence checking.", ShortName = "tol")] 205[Argument(ArgumentType.AtMostOnce, HelpText = "Maximum number of iterations; set to 1 to simulate online learning. Defaults to automatic.", NullName = "<Auto>", ShortName = "iter, MaxIterations, NumberOfIterations")] 218[Argument(ArgumentType.AtMostOnce, HelpText = "Shuffle data every epoch?", ShortName = "shuf")] 228[Argument(ArgumentType.AtMostOnce, HelpText = "Convergence check frequency (in terms of number of iterations). Set as negative or zero for not checking at all. If left blank, it defaults to check after every 'numThreads' iterations.", NullName = "<Auto>", ShortName = "checkFreq, CheckFrequency")] 234[Argument(ArgumentType.AtMostOnce, HelpText = "The learning rate for adjusting bias from being regularized.", ShortName = "blr")] 1472[Argument(ArgumentType.AtMostOnce, HelpText = "Apply weight to the positive class, for imbalanced data", ShortName = "piw")] 1690[Argument(ArgumentType.Multiple, Name = "LossFunction", HelpText = "Loss Function", ShortName = "loss", SortOrder = 50)] 1762[Argument(ArgumentType.Multiple, HelpText = "Loss Function", ShortName = "loss", SortOrder = 50)] 1765[Argument(ArgumentType.AtMostOnce, HelpText = "The calibrator kind to apply to the predictor. Specify null for no calibration", Visibility = ArgumentAttribute.VisibilityType.EntryPointsOnly)] 1768[Argument(ArgumentType.AtMostOnce, HelpText = "The maximum number of examples to use when training the calibrator", Visibility = ArgumentAttribute.VisibilityType.EntryPointsOnly)] 1840[Argument(ArgumentType.AtMostOnce, HelpText = "L2 Regularization constant", ShortName = "l2, L2Weight", SortOrder = 50)] 1852[Argument(ArgumentType.AtMostOnce, HelpText = "Degree of lock-free parallelism. Defaults to automatic depending on data sparseness. Determinism not guaranteed.", ShortName = "nt,t,threads, NumThreads", SortOrder = 50)] 1860[Argument(ArgumentType.AtMostOnce, HelpText = "Exponential moving averaged improvement tolerance for convergence", ShortName = "tol")] 1872[Argument(ArgumentType.AtMostOnce, HelpText = "Maximum number of iterations; set to 1 to simulate online learning.", ShortName = "iter, MaxIterations")] 1881[Argument(ArgumentType.AtMostOnce, HelpText = "Initial learning rate (only used by SGD)", Name = "InitialLearningRate", ShortName = "ilr,lr,InitLearningRate")] 1893[Argument(ArgumentType.AtMostOnce, HelpText = "Shuffle data every epoch?", ShortName = "shuf")] 1903[Argument(ArgumentType.AtMostOnce, HelpText = "Apply weight to the positive class, for imbalanced data", ShortName = "piw")] 1912[Argument(ArgumentType.AtMostOnce, HelpText = "Convergence check frequency (in terms of number of iterations). Default equals number of threads", ShortName = "checkFreq")] 2362[Argument(ArgumentType.Multiple, HelpText = "Loss Function", ShortName = "loss", SortOrder = 50)] 2415[Argument(ArgumentType.Multiple, HelpText = "Loss Function", ShortName = "loss", SortOrder = 50)] 2418[Argument(ArgumentType.AtMostOnce, HelpText = "The calibrator kind to apply to the predictor. Specify null for no calibration", Visibility = ArgumentAttribute.VisibilityType.EntryPointsOnly)] 2421[Argument(ArgumentType.AtMostOnce, HelpText = "The maximum number of examples to use when training the calibrator", Visibility = ArgumentAttribute.VisibilityType.EntryPointsOnly)]

99[Argument(ArgumentType.Multiple, Name = "LossFunction", HelpText = "Loss Function", ShortName = "loss", SortOrder = 50)]

72[Argument(ArgumentType.Multiple, Name = "LossFunction", HelpText = "Loss Function", ShortName = "loss", SortOrder = 50)]

32[Argument(ArgumentType.Multiple, HelpText = "Swept parameters", ShortName = "p", SignatureType = typeof(SignatureSweeperParameter))] 35[Argument(ArgumentType.LastOccurrenceWins, HelpText = "Number of tries to generate distinct parameter sets.", ShortName = "r")] 116[Argument(ArgumentType.LastOccurrenceWins, HelpText = "Limit for the number of combinations to generate the entire grid.", ShortName = "maxpoints")]

41[Argument(ArgumentType.Multiple | ArgumentType.Required, HelpText = "Swept parameters", ShortName = "p", SignatureType = typeof(SignatureSweeperParameter))] 44[Argument(ArgumentType.AtMostOnce, HelpText = "Seed for the random number generator for the first batch sweeper", ShortName = "seed")] 47[Argument(ArgumentType.LastOccurrenceWins, HelpText = "If iteration point is outside parameter definitions, should it be projected?", ShortName = "project")] 50[Argument(ArgumentType.LastOccurrenceWins, HelpText = "Number of points to use for random initialization", ShortName = "nip")] 53[Argument(ArgumentType.LastOccurrenceWins, HelpText = "Minimum mutation spread", ShortName = "mms")] 56[Argument(ArgumentType.LastOccurrenceWins, HelpText = "Maximum length of history to retain", ShortName = "hlen")] 59[Argument(ArgumentType.LastOccurrenceWins, HelpText = "If true, draws samples from independent Beta distributions, rather than multivariate Gaussian", ShortName = "beta")] 62[Argument(ArgumentType.LastOccurrenceWins, HelpText = "If true, uses simpler mutation and concentration model")] 65[Argument(ArgumentType.LastOccurrenceWins, HelpText = "Proportion of trials, between 0 and 1, that are uniform random draws", ShortName = "prand")] 68[Argument(ArgumentType.LastOccurrenceWins, HelpText = "Maximum power for rescaling (the larger the number, the stronger the exploitation of good points)", ShortName = "wrp")] 74[Argument(ArgumentType.LastOccurrenceWins, HelpText = "(Deprecated) Use legacy discrete parameter behavior.", ShortName = "legacy", Hide = true, Visibility = ArgumentAttribute.VisibilityType.CmdLineOnly)]

23[Argument(ArgumentType.Multiple | ArgumentType.Required, HelpText = "Swept parameters", ShortName = "p", SignatureType = typeof(SignatureSweeperParameter))] 26[Argument(ArgumentType.LastOccurrenceWins, HelpText = "The sweeper used to get the initial results.", ShortName = "init", SignatureType = typeof(SignatureSweeperFromParameterList))] 29[Argument(ArgumentType.AtMostOnce, HelpText = "Seed for the random number generator for the first batch sweeper", ShortName = "seed")] 32[Argument(ArgumentType.LastOccurrenceWins, HelpText = "Simplex diameter for stopping", ShortName = "dstop")] 36HelpText = "If iteration point is outside parameter definitions, should it be projected?", ShortName = "project")] 40[Argument(ArgumentType.LastOccurrenceWins, HelpText = "Reflection parameter", ShortName = "dr")] 43[Argument(ArgumentType.LastOccurrenceWins, HelpText = "Expansion parameter", ShortName = "de")] 46[Argument(ArgumentType.LastOccurrenceWins, HelpText = "Inside contraction parameter", ShortName = "dic")] 49[Argument(ArgumentType.LastOccurrenceWins, HelpText = "Outside contraction parameter", ShortName = "doc")] 52[Argument(ArgumentType.LastOccurrenceWins, HelpText = "Shrinkage parameter", ShortName = "ds")]

28[Argument(ArgumentType.Multiple | ArgumentType.Required, HelpText = "Swept parameters", ShortName = "p", SignatureType = typeof(SignatureSweeperParameter))] 31[Argument(ArgumentType.AtMostOnce, HelpText = "Seed for the random number generator for the first batch sweeper", ShortName = "seed")] 34[Argument(ArgumentType.LastOccurrenceWins, HelpText = "If iteration point is outside parameter definitions, should it be projected?", ShortName = "project")] 37[Argument(ArgumentType.LastOccurrenceWins, HelpText = "Number of regression trees in forest", ShortName = "numtrees")] 40[Argument(ArgumentType.LastOccurrenceWins, HelpText = "Minimum number of data points required to be in a node if it is to be split further", ShortName = "nmin")] 43[Argument(ArgumentType.LastOccurrenceWins, HelpText = "Number of points to use for random initialization", ShortName = "nip")] 46[Argument(ArgumentType.LastOccurrenceWins, HelpText = "Number of search parents to use for local search in maximizing EI acquisition function", ShortName = "lsp")] 49[Argument(ArgumentType.LastOccurrenceWins, HelpText = "Number of random configurations when maximizing EI acquisition function", ShortName = "nrcan")] 52[Argument(ArgumentType.LastOccurrenceWins, HelpText = "Fraction of eligible dimensions to split on (i.e., split ratio)", ShortName = "sr")] 55[Argument(ArgumentType.LastOccurrenceWins, HelpText = "Epsilon threshold for ending local searches", ShortName = "eps")] 58[Argument(ArgumentType.LastOccurrenceWins, HelpText = "Number of neighbors to sample for locally searching each numerical parameter", ShortName = "nnnp")]

155[Argument(ArgumentType.Multiple | ArgumentType.Required, HelpText = "Base sweeper", ShortName = "sweeper", SignatureType = typeof(SignatureSweeper))] 158[Argument(ArgumentType.AtMostOnce, HelpText = "Sweep batch size", ShortName = "batchsize")] 161[Argument(ArgumentType.AtMostOnce, HelpText = "Synchronization relaxation", ShortName = "relaxation")] 164[Argument(ArgumentType.AtMostOnce, HelpText = "Random seed", ShortName = "seed")]

35[Argument(ArgumentType.AtMostOnce, HelpText = "Command pattern for the sweeps", ShortName = "pattern")] 38[Argument(ArgumentType.AtMostOnce, HelpText = "output folder for the outputs of the sweeps", ShortName = "outfolder")] 41[Argument(ArgumentType.AtMostOnce, HelpText = "prefix to add to the output file names", ShortName = "pre")] 44[Argument(ArgumentType.AtMostOnce, HelpText = "The executable name, including the path (the default is MAML.exe)")] 47[Argument(ArgumentType.Multiple, HelpText = "Specify how to extract the metrics from the result file.", ShortName = "ev", SignatureType = typeof(SignatureSweepResultEvaluator))] 185[Argument(ArgumentType.AtMostOnce, HelpText = "The number of threads to use for the sweep (default auto determined by the number of cores)", ShortName = "t")]

29[Argument(ArgumentType.Required, HelpText = "Parameter name", ShortName = "n")] 35[Argument(ArgumentType.LastOccurrenceWins, HelpText = "Number of steps for grid runthrough.", ShortName = "steps")] 38[Argument(ArgumentType.LastOccurrenceWins, HelpText = "Amount of increment between steps (multiplicative if log).", ShortName = "inc")] 41[Argument(ArgumentType.LastOccurrenceWins, HelpText = "Log scale.", ShortName = "log")] 47[Argument(ArgumentType.Required, HelpText = "Minimum value")] 50[Argument(ArgumentType.Required, HelpText = "Maximum value")] 56[Argument(ArgumentType.Required, HelpText = "Minimum value")] 59[Argument(ArgumentType.Required, HelpText = "Maximum value")] 65[Argument(ArgumentType.Multiple, HelpText = "Values", ShortName = "v")]

26[Argument(ArgumentType.Multiple, HelpText = "Config runner", ShortName = "run,ev,evaluator", SignatureType = typeof(SignatureConfigRunner))] 30[Argument(ArgumentType.Multiple, HelpText = "Sweeper", ShortName = "s", SignatureType = typeof(SignatureSweeper))] 33[Argument(ArgumentType.AtMostOnce, HelpText = "Initial Sweep batch size (for instantiating sweep algorithm)", ShortName = "isbs")] 36[Argument(ArgumentType.AtMostOnce, HelpText = "Sweep batch size", ShortName = "sbs")] 39[Argument(ArgumentType.AtMostOnce, HelpText = "Sweep number of batches", ShortName = "snb")] 42[Argument(ArgumentType.AtMostOnce, HelpText = "Random seed", ShortName = "seed")]

23[Argument(ArgumentType.LastOccurrenceWins, HelpText = "The sweeper used to get the initial results.", ShortName = "m")]

27[Argument(ArgumentType.AtMostOnce, HelpText = "The number of threads to use for the sweep (default auto determined by the number of cores)", ShortName = "t")]

937[Argument(ArgumentType.Required, HelpText = "TensorFlow model used by the transform. Please see https://www.tensorflow.org/mobile/prepare_models for more details.", SortOrder = 0)] 943[Argument(ArgumentType.Multiple | ArgumentType.Required, HelpText = "The names of the model inputs", ShortName = "inputs", SortOrder = 1)] 949[Argument(ArgumentType.Multiple | ArgumentType.Required, HelpText = "The name of the outputs", ShortName = "outputs", SortOrder = 2)] 955[Argument(ArgumentType.AtMostOnce, HelpText = "Number of samples to use for mini-batch training.", SortOrder = 9)] 965[Argument(ArgumentType.AtMostOnce, HelpText = "Add a batch dimension to the input e.g. input = [224, 224, 3] => [-1, 224, 224, 3].", SortOrder = 16)] 974[Argument(ArgumentType.AtMostOnce, HelpText = "If the first dimension of the output is unknown, should it be treated as batched or not. e.g. output = [-1] will be read as a vector of unknown length when this is false.", SortOrder = 17)]

38[Argument(ArgumentType.AtMostOnce, HelpText = "Time span of growth ratio. Must be strictly positive.", SortOrder = 1)] 41[Argument(ArgumentType.AtMostOnce, HelpText = "Growth. Must be non-negative.", SortOrder = 2)]

33[Argument(ArgumentType.Required, HelpText = "The name of the source column", ShortName = "src", 37[Argument(ArgumentType.Required, HelpText = "The name of the new column", ShortName = "name", 41[Argument(ArgumentType.AtMostOnce, HelpText = "Coefficient d in: d m(y_t) = d * y_t + (1-d) * m(y_(t-1)), it should be in [0, 1].",

40[Argument(ArgumentType.Required, HelpText = "The name of the source column.", ShortName = "src", 44[Argument(ArgumentType.Required, HelpText = "The name of the new column.", 48[Argument(ArgumentType.AtMostOnce, HelpText = "The length of the sliding window on p-values for computing the martingale score.", ShortName = "wnd", 52[Argument(ArgumentType.Required, HelpText = "The confidence for change point detection in the range [0, 100].", 56[Argument(ArgumentType.AtMostOnce, HelpText = "The martingale used for scoring.", ShortName = "mart", SortOrder = 103)] 59[Argument(ArgumentType.AtMostOnce, HelpText = "The epsilon parameter for the Power martingale.",

39[Argument(ArgumentType.Required, HelpText = "The name of the source column.", ShortName = "src", 43[Argument(ArgumentType.Required, HelpText = "The name of the new column.", 47[Argument(ArgumentType.AtMostOnce, HelpText = "The argument that determines whether to detect positive or negative anomalies, or both.", ShortName = "side", 51[Argument(ArgumentType.AtMostOnce, HelpText = "The size of the sliding window for computing the p-value.", ShortName = "wnd", 55[Argument(ArgumentType.Required, HelpText = "The confidence for spike detection in the range [0, 100].",

32[Argument(ArgumentType.Required, HelpText = "The name of the source column", ShortName = "src", 36[Argument(ArgumentType.Required, HelpText = "The name of the new column", ShortName = "name", 40[Argument(ArgumentType.AtMostOnce, HelpText = "The size of the sliding window for computing the moving average", ShortName = "wnd", SortOrder = 3)] 43[Argument(ArgumentType.AtMostOnce, HelpText = "Lag between current observation and last observation from the sliding window", ShortName = "l", SortOrder = 4)] 46[Argument(ArgumentType.AtMostOnce, HelpText = "(optional) Comma separated list of weights, the first weight is applied to the oldest value. " +

33[Argument(ArgumentType.Required, HelpText = "The name of the source column", ShortName = "src", 37[Argument(ArgumentType.Required, HelpText = "The name of the new column", ShortName = "name", 41[Argument(ArgumentType.AtMostOnce, HelpText = "The percentile value for thresholding in the range [0, 100]", ShortName = "pcnt", 45[Argument(ArgumentType.AtMostOnce, HelpText = "The size of the sliding window for computing the percentile threshold. " +

33[Argument(ArgumentType.Required, HelpText = "The name of the source column", ShortName = "src", 37[Argument(ArgumentType.Required, HelpText = "The name of the new column", ShortName = "name", 41[Argument(ArgumentType.AtMostOnce, HelpText = "The seed value of the random generator", ShortName = "seed", 45[Argument(ArgumentType.AtMostOnce, HelpText = "The flag that determines whether the p-values are calculated on the positive side", ShortName = "pos", 49[Argument(ArgumentType.AtMostOnce, HelpText = "The size of the sliding window for computing the p-value", ShortName = "wnd", 53[Argument(ArgumentType.AtMostOnce, HelpText = "The size of the initial window for computing the p-value. The default value is set to 0, which means there is no initial window considered.",

78[Argument(ArgumentType.Required, HelpText = "The name of the source column", ShortName = "src", 82[Argument(ArgumentType.Required, HelpText = "The name of the new column", ShortName = "name", 86[Argument(ArgumentType.AtMostOnce, HelpText = "The argument that determines whether to detect positive or negative anomalies, or both", ShortName = "side", 90[Argument(ArgumentType.AtMostOnce, HelpText = "The size of the sliding window for computing the p-value.", ShortName = "wnd", 94[Argument(ArgumentType.AtMostOnce, HelpText = "The size of the initial window for computing the p-value as well as training if needed. The default value is set to 0, which means there is no initial window considered.", 98[Argument(ArgumentType.AtMostOnce, HelpText = "The martingale used for scoring", 102[Argument(ArgumentType.AtMostOnce, HelpText = "The argument that determines whether anomalies should be detected based on the raw anomaly score, the p-value or the martingale score", 106[Argument(ArgumentType.AtMostOnce, HelpText = "The epsilon parameter for the Power martingale", 110[Argument(ArgumentType.Required, HelpText = "The threshold for alerting",

21[Argument(ArgumentType.Required, HelpText = "The name of the source column", ShortName = "src", 25[Argument(ArgumentType.Required, HelpText = "The name of the new column", ShortName = "name", 29[Argument(ArgumentType.Required, HelpText = "The name of the confidence interval lower bound column.", ShortName = "cnfminname", 33[Argument(ArgumentType.Required, HelpText = "The name of the confidence interval upper bound column.", ShortName = "cnfmaxnname", 37[Argument(ArgumentType.AtMostOnce, HelpText = "The length of series from the beginning used for training.", ShortName = "wnd", 41[Argument(ArgumentType.AtMostOnce, HelpText = "The size of the initial window. The default value " +

41[Argument(ArgumentType.Required, HelpText = "The name of the source column", ShortName = "src", 45[Argument(ArgumentType.Required, HelpText = "The name of the new column", 49[Argument(ArgumentType.AtMostOnce, HelpText = "The size of the sliding window for computing the moving average", ShortName = "wnd", SortOrder = 3)] 52[Argument(ArgumentType.AtMostOnce, HelpText = "Lag between current observation and last observation from the sliding window", ShortName = "l", SortOrder = 4)] 55[Argument(ArgumentType.AtMostOnce, HelpText = "Define how to populate the first rows of the produced series", SortOrder = 5)]

39[Argument(ArgumentType.Required, HelpText = "The name of the source column.", ShortName = "src", 43[Argument(ArgumentType.Required, HelpText = "The name of the new column.", 47[Argument(ArgumentType.AtMostOnce, HelpText = "The size of the sliding window for computing spectral residual", ShortName = "wnd", 51[Argument(ArgumentType.Required, HelpText = "The number of points to the back of training window.", 55[Argument(ArgumentType.Required, HelpText = "The number of pervious points used in prediction.", 59[Argument(ArgumentType.Required, HelpText = "The size of sliding window to generate a saliency map for the series.", 63[Argument(ArgumentType.Required, HelpText = "The size of sliding window to calculate the anomaly score for each data point.", 67[Argument(ArgumentType.Required, HelpText = "The threshold to determine anomaly, score larger than the threshold is considered as anomaly.",

59[Argument(ArgumentType.AtMostOnce, HelpText = "The threshold to determine anomaly, score larger than the threshold is considered as anomaly.", 63[Argument(ArgumentType.AtMostOnce, HelpText = "The number of data points to be detected in each batch. It should be at least 12. Set this parameter to -1 to detect anomaly on the entire series.", 67[Argument(ArgumentType.AtMostOnce, HelpText = "This parameter is used in AnomalyAndMargin mode the determine the range of the boundaries.", 71[Argument(ArgumentType.AtMostOnce, HelpText = "Specify the detect mode as one of AnomalyOnly, AnomalyAndExpectedValue and AnomalyAndMargin.", 75[Argument(ArgumentType.AtMostOnce, HelpText = "If there is circular pattern in the series, set this value to the number of points in one cycle.", 79[Argument(ArgumentType.AtMostOnce, HelpText = "Specify the deseasonality mode as one of stl, mean and median.",

17[Argument(ArgumentType.Required, HelpText = "The name of the source column.", ShortName = "src", 21[Argument(ArgumentType.Required, HelpText = "The name of the new column.", 25[Argument(ArgumentType.AtMostOnce, HelpText = "The size of the sliding window for computing spectral residual", ShortName = "wnd", 29[Argument(ArgumentType.AtMostOnce, HelpText = "The size of the initial window for computing. The default value is set to 0, which means there is no initial window considered.", ShortName = "iwnd", 33[Argument(ArgumentType.AtMostOnce, HelpText = "The number of points to the back of training window.", 37[Argument(ArgumentType.AtMostOnce, HelpText = "The number of pervious points used in prediction.", 41[Argument(ArgumentType.Required, HelpText = "The size of sliding window to generate a saliency map for the series.", 45[Argument(ArgumentType.Required, HelpText = "The size of sliding window to generate a saliency map for the series.", 49[Argument(ArgumentType.AtMostOnce, HelpText = "The threshold to determine anomaly, score larger than the threshold is considered as anomaly.",

152[Argument(ArgumentType.Required, HelpText = "The inner window size for SSA in [2, windowSize]", ShortName = "swnd", SortOrder = 11)] 155[Argument(ArgumentType.AtMostOnce, HelpText = "The discount factor in [0, 1]", ShortName = "disc", SortOrder = 12)] 158[Argument(ArgumentType.AtMostOnce, HelpText = "The function used to compute the error between the expected and the observed value", ShortName = "err", SortOrder = 13)] 161[Argument(ArgumentType.AtMostOnce, HelpText = "The flag determing whether the model is adaptive", ShortName = "adp", SortOrder = 14)]

40[Argument(ArgumentType.Required, HelpText = "The name of the source column.", ShortName = "src", 44[Argument(ArgumentType.Required, HelpText = "The name of the new column.", 48[Argument(ArgumentType.AtMostOnce, HelpText = "The length of the sliding window on p-values for computing the martingale score.", ShortName = "wnd", 52[Argument(ArgumentType.Required, HelpText = "The number of points from the beginning of the sequence used for training.", 56[Argument(ArgumentType.Required, HelpText = "The confidence for change point detection in the range [0, 100].", 60[Argument(ArgumentType.Required, HelpText = "An upper bound on the largest relevant seasonality in the input time-series.", ShortName = "swnd", SortOrder = 5)] 63[Argument(ArgumentType.AtMostOnce, HelpText = "The function used to compute the error between the expected and the observed value.", ShortName = "err", SortOrder = 103)] 66[Argument(ArgumentType.AtMostOnce, HelpText = "The martingale used for scoring.", ShortName = "mart", SortOrder = 104)] 69[Argument(ArgumentType.AtMostOnce, HelpText = "The epsilon parameter for the Power martingale.",

38[Argument(ArgumentType.Required, HelpText = "The name of the source column.", ShortName = "src", SortOrder = 1, Purpose = SpecialPurpose.ColumnName)] 41[Argument(ArgumentType.Required, HelpText = "The name of the new column.", SortOrder = 2)] 44[Argument(ArgumentType.AtMostOnce, HelpText = "The name of the confidence interval lower bound column.", ShortName = "cnfminname", SortOrder = 3)] 47[Argument(ArgumentType.AtMostOnce, HelpText = "The name of the confidence interval upper bound column.", ShortName = "cnfmaxnname", SortOrder = 3)] 50[Argument(ArgumentType.AtMostOnce, HelpText = "The discount factor in [0,1] used for online updates.", ShortName = "disc", SortOrder = 5)] 53[Argument(ArgumentType.AtMostOnce, HelpText = "The flag determing whether the model is adaptive", ShortName = "adp", SortOrder = 6)] 56[Argument(ArgumentType.Required, HelpText = "The length of the window on the series for building the trajectory matrix (parameter L).", SortOrder = 2)] 59[Argument(ArgumentType.AtMostOnce, HelpText = "The rank selection method.", SortOrder = 3)] 62[Argument(ArgumentType.AtMostOnce, HelpText = "The desired rank of the subspace used for SSA projection (parameter r). This parameter should be in the range in [1, windowSize]. " + 66[Argument(ArgumentType.AtMostOnce, HelpText = "The maximum rank considered during the rank selection process. If not provided (i.e. set to null), it is set to windowSize - 1.", SortOrder = 3)] 69[Argument(ArgumentType.AtMostOnce, HelpText = "The flag determining whether the model should be stabilized.", SortOrder = 3)] 72[Argument(ArgumentType.AtMostOnce, HelpText = "The flag determining whether the meta information for the model needs to be maintained.", SortOrder = 3)] 75[Argument(ArgumentType.AtMostOnce, HelpText = "The maximum growth on the exponential trend.", SortOrder = 3)] 78[Argument(ArgumentType.Required, HelpText = "The length of series that is kept in buffer for modeling (parameter N).", SortOrder = 2)] 81[Argument(ArgumentType.Required, HelpText = "The length of series from the beginning used for training.", SortOrder = 2)] 84[Argument(ArgumentType.Required, HelpText = "The number of values to forecast.", SortOrder = 2)] 87[Argument(ArgumentType.AtMostOnce, HelpText = "The confidence level in [0, 1) for forecasting.", SortOrder = 2)] 90[Argument(ArgumentType.AtMostOnce, HelpText = "Set this to true horizon will change at prediction time.", SortOrder = 2)]

85[Argument(ArgumentType.AtMostOnce, HelpText = "The discount factor in [0, 1]", ShortName = "disc", SortOrder = 12)] 88[Argument(ArgumentType.AtMostOnce, HelpText = "The function used to compute the error between the expected and the observed value", ShortName = "err", SortOrder = 13)] 91[Argument(ArgumentType.AtMostOnce, HelpText = "The flag determing whether the model is adaptive", ShortName = "adp", SortOrder = 14)]

39[Argument(ArgumentType.Required, HelpText = "The name of the source column.", ShortName = "src", 43[Argument(ArgumentType.Required, HelpText = "The name of the new column.", 47[Argument(ArgumentType.AtMostOnce, HelpText = "The argument that determines whether to detect positive or negative anomalies, or both.", ShortName = "side", 51[Argument(ArgumentType.AtMostOnce, HelpText = "The size of the sliding window for computing the p-value.", ShortName = "wnd", 55[Argument(ArgumentType.Required, HelpText = "The number of points from the beginning of the sequence used for training.", 59[Argument(ArgumentType.Required, HelpText = "The confidence for spike detection in the range [0, 100].", 63[Argument(ArgumentType.Required, HelpText = "An upper bound on the largest relevant seasonality in the input time-series.", ShortName = "swnd", SortOrder = 5)] 66[Argument(ArgumentType.AtMostOnce, HelpText = "The function used to compute the error between the expected and the observed value.", ShortName = "err", SortOrder = 103)]

83[Argument(ArgumentType.Multiple | ArgumentType.Required, HelpText = "Columns to use for feature selection", Name = "Column", ShortName = "col", SortOrder = 1)] 86[Argument(ArgumentType.Required, HelpText = "If the count of non-default values for a slot is greater than or equal to this threshold, the slot is preserved", ShortName = "c", SortOrder = 1)]

182[Argument(ArgumentType.AtMostOnce, HelpText = "Count-Min Sketch table depth", ShortName = "d")] 185[Argument(ArgumentType.AtMostOnce, HelpText = "Count-Min Sketch width", ShortName = "w")]

356[Argument(ArgumentType.Multiple | ArgumentType.Required, HelpText = "New column definition(s) (optional form: name:src)", ShortName = "col", SortOrder = 1)] 359[Argument(ArgumentType.Multiple, HelpText = "Count table settings", ShortName = "table", SignatureType = typeof(SignatureCountTableBuilder))] 362[Argument(ArgumentType.AtMostOnce, HelpText = "The coefficient with which to apply the prior smoothing to the features", ShortName = "prior")] 365[Argument(ArgumentType.AtMostOnce, HelpText = "Laplacian noise diversity/scale-parameter. Suggest keeping it less than 1.", ShortName = "laplace")] 368[Argument(ArgumentType.AtMostOnce, HelpText = "Seed for the random generator for the laplacian noise.", ShortName = "seed")] 371[Argument(ArgumentType.Required, HelpText = "Label column", ShortName = "label,lab", Purpose = SpecialPurpose.ColumnName)] 374[Argument(ArgumentType.AtMostOnce, HelpText = "Optional model file to load counts from. If this is specified all other options are ignored.", ShortName = "inmodel, extfile")] 377[Argument(ArgumentType.AtMostOnce, HelpText = "Keep counts for all columns in one shared count table", ShortName = "shared")] 383[Argument(ArgumentType.Multiple, HelpText = "Count table settings", ShortName = "table", SignatureType = typeof(SignatureCountTableBuilder))] 386[Argument(ArgumentType.AtMostOnce, HelpText = "The coefficient with which to apply the prior smoothing to the features", ShortName = "prior")] 389[Argument(ArgumentType.AtMostOnce, HelpText = "Laplacian noise diversity/scale-parameter. Suggest keeping it less than 1.", ShortName = "laplace")] 392[Argument(ArgumentType.AtMostOnce, HelpText = "Seed for the random generator for the laplacian noise.", ShortName = "seed")]

56[Argument(ArgumentType.Multiple | ArgumentType.Required, HelpText = "New column definition(s) (optional form: name:src)", 60[Argument(ArgumentType.Multiple, HelpText = "Count table settings", ShortName = "table", SignatureType = typeof(SignatureCountTableBuilder))] 63[Argument(ArgumentType.AtMostOnce, HelpText = "The coefficient with which to apply the prior smoothing to the features", ShortName = "prior")] 66[Argument(ArgumentType.AtMostOnce, HelpText = "Laplacian noise diversity/scale-parameter. Suggest keeping it less than 1.", ShortName = "laplace")] 69[Argument(ArgumentType.AtMostOnce, HelpText = "Seed for the random generator for the laplacian noise.", ShortName = "seed")] 72[Argument(ArgumentType.Required, HelpText = "Label column", ShortName = "label,lab", Purpose = SpecialPurpose.ColumnName)] 75[Argument(ArgumentType.AtMostOnce, HelpText = "Optional model file to load counts from. If this is specified all other options are ignored.", ShortName = "inmodel, extfile")] 78[Argument(ArgumentType.AtMostOnce, HelpText = "Keep counts for all columns in one shared count table", ShortName = "shared")] 81[Argument(ArgumentType.AtMostOnce, HelpText = "Whether the values need to be combined for a single hash", SortOrder = 3)] 84[Argument(ArgumentType.AtMostOnce, HelpText = "Number of bits to hash into. Must be between 1 and 31, inclusive.", 88[Argument(ArgumentType.AtMostOnce, HelpText = "Hashing seed")] 94[Argument(ArgumentType.Multiple, HelpText = "Count table settings", ShortName = "table", SignatureType = typeof(SignatureCountTableBuilder))] 97[Argument(ArgumentType.AtMostOnce, HelpText = "The coefficient with which to apply the prior smoothing to the features", ShortName = "prior")] 100[Argument(ArgumentType.AtMostOnce, HelpText = "Laplacian noise diversity/scale-parameter. Suggest keeping it less than 1.", ShortName = "laplace")] 103[Argument(ArgumentType.AtMostOnce, HelpText = "Seed for the random generator for the laplacian noise.", ShortName = "seed")] 106[Argument(ArgumentType.AtMostOnce, HelpText = "Whether the values need to be combined for a single hash")]

142[Argument(ArgumentType.AtMostOnce, HelpText = "Garbage threshold (counts below or equal to the threshold are assigned to the garbage bin)", ShortName = "gb")]

216[Argument(ArgumentType.Multiple | ArgumentType.Required, HelpText = "New column definition(s)", ShortName = "col", SortOrder = 1)] 219[Argument(ArgumentType.AtMostOnce, ShortName = "expr", SortOrder = 2, HelpText = "Lambda expression which will be applied.")] 225[Argument(ArgumentType.AtMostOnce, ShortName = "expr", SortOrder = 2, HelpText = "Lambda expression which will be applied.")]

84[Argument(ArgumentType.AtMostOnce, HelpText = "gamma in the kernel definition: exp(-gamma*||x-y||^2 / r^2). r is an estimate of the average intra-example distance", ShortName = "g")] 205[Argument(ArgumentType.AtMostOnce, HelpText = "a in the term exp(-a|x| / r). r is an estimate of the average intra-example L1 distance")]

45[Argument(ArgumentType.Multiple | ArgumentType.Required, HelpText = "New column definition(s) (optional form: name:src)", Name = "Column", ShortName = "col", SortOrder = 1)] 48[Argument(ArgumentType.AtMostOnce, HelpText = "The norm to use to normalize each sample", ShortName = "norm", SortOrder = 1)] 51[Argument(ArgumentType.AtMostOnce, HelpText = "Subtract mean from each value before normalizing", SortOrder = 2)] 57[Argument(ArgumentType.Multiple, HelpText = "New column definition(s) (optional form: name:src)", Name = "Column", ShortName = "col", SortOrder = 1)] 60[Argument(ArgumentType.AtMostOnce, HelpText = "Subtract mean from each value before normalizing", SortOrder = 1)] 63[Argument(ArgumentType.AtMostOnce, HelpText = "Normalize by standard deviation rather than L2 norm", ShortName = "useStd")] 66[Argument(ArgumentType.AtMostOnce, HelpText = "Scale features by this value")] 72[Argument(ArgumentType.AtMostOnce, HelpText = "Subtract mean from each value before normalizing")] 90[Argument(ArgumentType.AtMostOnce, HelpText = "The norm to use to normalize each sample", ShortName = "norm", SortOrder = 1)] 114[Argument(ArgumentType.AtMostOnce, HelpText = "Normalize by standard deviation rather than L2 norm")] 117[Argument(ArgumentType.AtMostOnce, HelpText = "Scale features by this value")]

87[Argument(ArgumentType.Multiple, HelpText = "Columns to group by", Name = "GroupKey", ShortName = "g", SortOrder = 1, 92[Argument(ArgumentType.Multiple | ArgumentType.Required, HelpText = "Columns to group together", Name = "Column", ShortName = "col", SortOrder = 2)]

49[Argument(ArgumentType.Multiple | ArgumentType.Required, HelpText = "New column definition(s) (optional form: name:src)", 55[Argument(ArgumentType.AtMostOnce, HelpText = "Whether the values need to be combined for a single hash")] 58[Argument(ArgumentType.AtMostOnce, HelpText = "Number of bits to hash into. Must be between 1 and 31, inclusive.", 62[Argument(ArgumentType.AtMostOnce, HelpText = "Hashing seed")] 65[Argument(ArgumentType.AtMostOnce, HelpText = "Whether the position of each term should be included in the hash", ShortName = "ord")] 72[Argument(ArgumentType.AtMostOnce, HelpText = "Whether the values need to be combined for a single hash")] 76[Argument(ArgumentType.AtMostOnce, HelpText = "Which slots should be combined together. Example: 0,3,5;0,1;3;2,1,0. Overrides 'join'.")] 79[Argument(ArgumentType.AtMostOnce, HelpText = "Number of bits to hash into. Must be between 1 and 31, inclusive.", ShortName = "bits")] 82[Argument(ArgumentType.AtMostOnce, HelpText = "Hashing seed")] 85[Argument(ArgumentType.AtMostOnce, HelpText = "Whether the position of each term should be included in the hash", ShortName = "ord")]

37[Argument(ArgumentType.Multiple | ArgumentType.Required, HelpText = "New column definition(s) (optional form: name:src)",

32[Argument(ArgumentType.LastOccurrenceWins, HelpText = "If the corresponding absolute value of the weight for a slot is greater than this threshold, the slot is preserved", ShortName = "ft", SortOrder = 2)] 35[Argument(ArgumentType.AtMostOnce, HelpText = "The number of slots to preserve", ShortName = "topk", SortOrder = 1)] 40[Argument(ArgumentType.Multiple, HelpText = "Filter", ShortName = "f", SortOrder = 1, SignatureType = typeof(SignatureFeatureScorerTrainer))] 46[Argument(ArgumentType.LastOccurrenceWins, HelpText = "Column to use for features", ShortName = "feat,col", SortOrder = 3, Purpose = SpecialPurpose.ColumnName)] 49[Argument(ArgumentType.LastOccurrenceWins, HelpText = "Column to use for labels", ShortName = "lab", SortOrder = 4, Purpose = SpecialPurpose.ColumnName)] 52[Argument(ArgumentType.LastOccurrenceWins, HelpText = "Column to use for example weight", ShortName = "weight", SortOrder = 5, Purpose = SpecialPurpose.ColumnName)] 55[Argument(ArgumentType.LastOccurrenceWins, HelpText = "Column to use for grouping", ShortName = "group", Purpose = SpecialPurpose.ColumnName)] 58[Argument(ArgumentType.AtMostOnce, HelpText = "Name column name", ShortName = "name", Purpose = SpecialPurpose.ColumnName)] 61[Argument(ArgumentType.LastOccurrenceWins, HelpText = "Columns with custom kinds declared through key assignments, for example, col[Kind]=Name to assign column named 'Name' kind 'Kind'", 65[Argument(ArgumentType.LastOccurrenceWins, HelpText = "Normalize option for the feature column", ShortName = "norm")] 68[Argument(ArgumentType.LastOccurrenceWins, HelpText = "Whether we should cache input training data", ShortName = "cache")]

31[Argument(ArgumentType.Required, HelpText = "Model file to load the transforms from", ShortName = "in", 35[Argument(ArgumentType.Multiple, HelpText = "The tags (comma-separated) to be loaded (or omitted, if " + nameof(Complement) + "+)", 39[Argument(ArgumentType.AtMostOnce, HelpText = "Whether to load all transforms except those marked by tags", ShortName = "comp", SortOrder = 3)]

63[Argument(ArgumentType.Multiple | ArgumentType.Required, HelpText = "Columns to drop the NAs for", Name = "Column", ShortName = "col", SortOrder = 1)]

59[Argument(ArgumentType.Multiple | ArgumentType.Required, HelpText = "New column definition(s) (optional form: name:rep:src)", Name = "Column", ShortName = "col", SortOrder = 1)] 62[Argument(ArgumentType.AtMostOnce, HelpText = "The replacement method to utilize", ShortName = "kind", SortOrder = 2)] 68[Argument(ArgumentType.AtMostOnce, HelpText = "Whether to impute values by slot", ShortName = "slot")] 71[Argument(ArgumentType.AtMostOnce, HelpText = "Whether or not to concatenate an indicator vector column to the value column", ShortName = "ind")] 77[Argument(ArgumentType.AtMostOnce, HelpText = "The replacement method to utilize")] 82[Argument(ArgumentType.AtMostOnce, HelpText = "Whether to impute values by slot", ShortName = "slot")] 85[Argument(ArgumentType.AtMostOnce, HelpText = "Whether or not to concatenate an indicator vector column to the value column", ShortName = "ind")]

45[Argument(ArgumentType.Multiple | ArgumentType.Required, HelpText = "New column definition(s) (optional form: name:src)", Name = "Column", ShortName = "col", SortOrder = 1)]

57[Argument(ArgumentType.Multiple | ArgumentType.Required, HelpText = "New column definition(s) (optional form: name:src)", Name = "Column", ShortName = "col", SortOrder = 1)]

78[Argument(ArgumentType.AtMostOnce, HelpText = "Replacement value for NAs (uses default value if not given)", ShortName = "rep")] 81[Argument(ArgumentType.AtMostOnce, HelpText = "The replacement method to utilize")] 86[Argument(ArgumentType.AtMostOnce, HelpText = "Whether to impute values by slot")] 118[Argument(ArgumentType.Multiple | ArgumentType.Required, HelpText = "New column definition(s) (optional form: name:rep:src)", Name = "Column", ShortName = "col", SortOrder = 1)] 121[Argument(ArgumentType.AtMostOnce, HelpText = "The replacement method to utilize", ShortName = "kind")] 125[Argument(ArgumentType.AtMostOnce, HelpText = "Whether to impute values by slot", ShortName = "slot")]

91[Argument(ArgumentType.Multiple | ArgumentType.Required, HelpText = "Columns to use for feature selection", Name = "Column", ShortName = "col", SortOrder = 1)] 94[Argument(ArgumentType.LastOccurrenceWins, HelpText = "Column to use for labels", ShortName = "lab", 98[Argument(ArgumentType.AtMostOnce, HelpText = "The maximum number of slots to preserve in output", ShortName = "topk,numSlotsToKeep", 102[Argument(ArgumentType.AtMostOnce, HelpText = "Max number of bins for R4/R8 columns, power of 2 recommended",

32[Argument(ArgumentType.AtMostOnce, HelpText = "Output kind: Bag (multi-set vector), Ind (indicator vector), Key (index), or Binary encoded indicator vector", ShortName = "kind")] 74[Argument(ArgumentType.Multiple | ArgumentType.Required, HelpText = "New column definition(s) (optional form: name:src)", Name = "Column", ShortName = "col", SortOrder = 1)] 77[Argument(ArgumentType.AtMostOnce, HelpText = "Output kind: Bag (multi-set vector), Ind (indicator vector), or Key (index)",

28HelpText = "The number of bits to hash into. Must be between 1 and 30, inclusive.", 32[Argument(ArgumentType.AtMostOnce, HelpText = "Hashing seed")] 35[Argument(ArgumentType.AtMostOnce, HelpText = "Whether the position of each term should be included in the hash", ShortName = "ord")] 39HelpText = "Limit the number of keys used to generate the slot name to this many. 0 means no invert hashing, -1 means no limit.", 43[Argument(ArgumentType.AtMostOnce, HelpText = "Output kind: Bag (multi-set vector), Ind (indicator vector), or Key (index)", 91[Argument(ArgumentType.Multiple | ArgumentType.Required, HelpText = "New column definition(s) (optional form: name:numberOfBits:src)", Name = "Column", ShortName = "col", SortOrder = 1)] 94[Argument(ArgumentType.AtMostOnce, HelpText = "Number of bits to hash into. Must be between 1 and 30, inclusive.", 98[Argument(ArgumentType.AtMostOnce, HelpText = "Hashing seed")] 101[Argument(ArgumentType.AtMostOnce, HelpText = "Whether the position of each term should be included in the hash", ShortName = "ord")] 105HelpText = "Limit the number of keys used to generate the slot name to this many. 0 means no invert hashing, -1 means no limit.", 109[Argument(ArgumentType.AtMostOnce, HelpText = "Output kind: Bag (multi-set vector), Ind (indicator vector), or Key (index)",

37[Argument(ArgumentType.Multiple | ArgumentType.Required, HelpText = "New column definition(s)", Name = "Column", ShortName = "col", SortOrder = 1)]

33[Argument(ArgumentType.AtMostOnce, HelpText = "Name of the column to produce", ShortName = "col", SortOrder = 1)]

38[Argument(ArgumentType.Multiple | ArgumentType.Required, HelpText = "New column definition(s) (optional form: name:src)", Name = "Column", ShortName = "col", SortOrder = 1)] 41[Argument(ArgumentType.AtMostOnce, HelpText = "The number of random Fourier features to create", ShortName = "dim")] 44[Argument(ArgumentType.Multiple, HelpText = "Which kernel to use?", ShortName = "kernel", SignatureType = typeof(SignatureKernelBase))] 47[Argument(ArgumentType.AtMostOnce, HelpText = "Create two features for every random Fourier frequency? (one for cos and one for sin)")] 51HelpText = "The seed of the random number generator for generating the new features (if unspecified, " + 58[Argument(ArgumentType.AtMostOnce, HelpText = "The number of random Fourier features to create", ShortName = "dim")] 61[Argument(ArgumentType.Multiple, HelpText = "which kernel to use?", ShortName = "kernel", SignatureType = typeof(SignatureKernelBase))] 64[Argument(ArgumentType.AtMostOnce, HelpText = "create two features for every random Fourier frequency? (one for cos and one for sin)")] 68HelpText = "The seed of the random number generator for generating the new features (if unspecified, " +

88[Argument(ArgumentType.AtMostOnce, HelpText = "The size of the feature vectors.", ShortName = "size")] 91[Argument(ArgumentType.Multiple, HelpText = "Whether the features are indexed by numbers starting at 0, by numbers starting at 1, or by feature names.", ShortName = "indices")] 94[Argument(ArgumentType.AtMostOnce, HelpText = "The number of rows used to train the feature name to index mapping transform. If unspecified, all rows will be used.", ShortName = "numxf")]

31[Argument(ArgumentType.LastOccurrenceWins, HelpText = "Write the variant of SVM-light format where feature indices start from 0, not 1", ShortName = "z")] 34[Argument(ArgumentType.LastOccurrenceWins, HelpText = "Format output labels for a binary classification problem (-1 for negative, 1 for positive)", ShortName = "b")] 37[Argument(ArgumentType.AtMostOnce, HelpText = "Column to use for features", ShortName = "feat", SortOrder = 2, Visibility = ArgumentAttribute.VisibilityType.EntryPointsOnly)] 40[Argument(ArgumentType.AtMostOnce, HelpText = "Column to use for labels", ShortName = "lab", SortOrder = 3, Visibility = ArgumentAttribute.VisibilityType.EntryPointsOnly)] 43[Argument(ArgumentType.AtMostOnce, HelpText = "Column to use for example weight", ShortName = "weight", SortOrder = 4, Visibility = ArgumentAttribute.VisibilityType.EntryPointsOnly)] 46[Argument(ArgumentType.AtMostOnce, HelpText = "Column to use for example groupId", ShortName = "groupId", SortOrder = 5, Visibility = ArgumentAttribute.VisibilityType.EntryPointsOnly)]

56[Argument(ArgumentType.Multiple | ArgumentType.Required, HelpText = "New column definition(s) (optional form: name:srcs)", Name = "Column", ShortName = "col", SortOrder = 49)] 59[Argument(ArgumentType.AtMostOnce, HelpText = "The number of topics", SortOrder = 50)] 64[Argument(ArgumentType.AtMostOnce, HelpText = "Dirichlet prior on document-topic vectors")] 69[Argument(ArgumentType.AtMostOnce, HelpText = "Dirichlet prior on vocab-topic vectors")] 74[Argument(ArgumentType.Multiple, HelpText = "Number of Metropolis Hasting step")] 79[Argument(ArgumentType.AtMostOnce, HelpText = "Number of iterations", ShortName = "iter")] 84[Argument(ArgumentType.AtMostOnce, HelpText = "Compute log likelihood over local dataset on this iteration interval", ShortName = "llInterval")] 88[Argument(ArgumentType.AtMostOnce, HelpText = "The number of training threads. Default value depends on number of logical processors.", ShortName = "t", SortOrder = 50)] 91[Argument(ArgumentType.AtMostOnce, HelpText = "The threshold of maximum count of tokens per doc", ShortName = "maxNumToken", SortOrder = 50)] 94[Argument(ArgumentType.AtMostOnce, HelpText = "The number of words to summarize the topic", ShortName = "ns")] 97[Argument(ArgumentType.AtMostOnce, HelpText = "The number of burn-in iterations", ShortName = "burninIter")] 102[Argument(ArgumentType.AtMostOnce, HelpText = "Reset the random number generator for each document", ShortName = "reset")] 105[Argument(ArgumentType.AtMostOnce, HelpText = "Whether to output the topic-word summary in text format when saving the model to disk", ShortName = "summary")] 111[Argument(ArgumentType.AtMostOnce, HelpText = "The number of topics")] 114[Argument(ArgumentType.AtMostOnce, HelpText = "Dirichlet prior on document-topic vectors")] 117[Argument(ArgumentType.AtMostOnce, HelpText = "Dirichlet prior on vocab-topic vectors")] 120[Argument(ArgumentType.Multiple, HelpText = "Number of Metropolis Hasting step")] 123[Argument(ArgumentType.AtMostOnce, HelpText = "Number of iterations", ShortName = "iter")] 126[Argument(ArgumentType.AtMostOnce, HelpText = "Compute log likelihood over local dataset on this iteration interval", ShortName = "llInterval")] 129[Argument(ArgumentType.AtMostOnce, HelpText = "The number of training threads", ShortName = "t")] 132[Argument(ArgumentType.AtMostOnce, HelpText = "The threshold of maximum count of tokens per doc", ShortName = "maxNumToken")] 135[Argument(ArgumentType.AtMostOnce, HelpText = "The number of words to summarize the topic", ShortName = "ns")] 138[Argument(ArgumentType.AtMostOnce, HelpText = "The number of burn-in iterations", ShortName = "burninIter")] 141[Argument(ArgumentType.AtMostOnce, HelpText = "Reset the random number generator for each document", ShortName = "reset")] 144[Argument(ArgumentType.AtMostOnce, HelpText = "Whether to output the topic-word summary in text format when saving the model to disk", ShortName = "summary")]

37[Argument(ArgumentType.AtMostOnce, HelpText = "Maximum n-gram length", ShortName = "ngram")] 40[Argument(ArgumentType.AtMostOnce, HelpText = 45HelpText = "Maximum number of tokens to skip when constructing an n-gram", 50HelpText = "Number of bits to hash into. Must be between 1 and 30, inclusive.", 54[Argument(ArgumentType.AtMostOnce, HelpText = "Hashing seed")] 57[Argument(ArgumentType.AtMostOnce, HelpText = "Whether to rehash unigrams", ShortName = "rehash")] 60[Argument(ArgumentType.AtMostOnce, HelpText = "Whether the position of each source column should be included in the hash (when there are multiple source columns).", ShortName = "ord")] 63[Argument(ArgumentType.AtMostOnce, HelpText = "Limit the number of keys used to generate the slot name to this many. 0 means no invert hashing, -1 means no limit.", 112[Argument(ArgumentType.Multiple | ArgumentType.Required, HelpText = "New column definition(s) (optional form: name:numberOfBits:src)", 117[Argument(ArgumentType.AtMostOnce, HelpText = "Maximum n-gram length", ShortName = "ngram", SortOrder = 3)] 121HelpText = "Whether to include all n-gram lengths up to " + nameof(NgramLength) + " or only " + nameof(NgramLength), 126HelpText = "Maximum number of tokens to skip when constructing an n-gram", 131HelpText = "Number of bits to hash into. Must be between 1 and 30, inclusive.", 135[Argument(ArgumentType.AtMostOnce, HelpText = "Hashing seed")] 138[Argument(ArgumentType.AtMostOnce, HelpText = "Whether to rehash unigrams", ShortName = "rehash")] 142HelpText = "Whether the position of each source column should be included in the hash (when there are multiple source columns).", 146[Argument(ArgumentType.AtMostOnce, HelpText = "Limit the number of keys used to generate the slot name to this many. 0 means no invert hashing, -1 means no limit.",

40[Argument(ArgumentType.AtMostOnce, HelpText = "Maximum n-gram length", ShortName = "ngram")] 43[Argument(ArgumentType.AtMostOnce, HelpText = 48HelpText = "Maximum number of tokens to skip when constructing an n-gram", 52[Argument(ArgumentType.Multiple, HelpText = "Maximum number of n-grams to store in the dictionary", ShortName = "max")] 55[Argument(ArgumentType.AtMostOnce, HelpText = "Statistical measure used to evaluate how important a word is to a document in a corpus")] 79[Argument(ArgumentType.Multiple, HelpText = "New column definition(s) (optional form: name:src)", Name = "Column", ShortName = "col", SortOrder = 1)] 82[Argument(ArgumentType.AtMostOnce, HelpText = "Maximum n-gram length", ShortName = "ngram")] 85[Argument(ArgumentType.AtMostOnce, HelpText = 90HelpText = "Maximum number of tokens to skip when constructing an n-gram", 94[Argument(ArgumentType.Multiple, HelpText = "Maximum number of n-grams to store in the dictionary", ShortName = "max")] 97[Argument(ArgumentType.AtMostOnce, HelpText = "The weighting criteria")]

25[Argument(ArgumentType.Required, HelpText = "Name of the source column.", ShortName = "col", Purpose = SpecialPurpose.ColumnName, SortOrder = 1)] 28[Argument(ArgumentType.AtMostOnce, HelpText = "Name of the new column.", ShortName = "dst", SortOrder = 2)]

72HelpText = "Optional column to use for languages. This overrides sentence separator language value.", 76[Argument(ArgumentType.AtMostOnce, HelpText = "Stopword Language (optional).", ShortName = "lang")] 100[Argument(ArgumentType.Multiple | ArgumentType.Required, HelpText = "New column definition(s)", Name = "Column", ShortName = "col", SortOrder = 1)] 104HelpText = "Optional column to use for languages. This overrides language value.", 109[Argument(ArgumentType.AtMostOnce, HelpText = "Language-specific stop words list.", ShortName = "lang", SortOrder = 1)] 709[Argument(ArgumentType.AtMostOnce, HelpText = "Comma separated list of stopwords", Name = "Stopwords", Visibility = ArgumentAttribute.VisibilityType.CmdLineOnly)] 712[Argument(ArgumentType.AtMostOnce, HelpText = "List of stopwords", Name = "Stopword", Visibility = ArgumentAttribute.VisibilityType.EntryPointsOnly)] 715[Argument(ArgumentType.AtMostOnce, IsInputFileName = true, HelpText = "Data file containing the stopwords", ShortName = "data", SortOrder = 2, Visibility = ArgumentAttribute.VisibilityType.CmdLineOnly)] 718[Argument(ArgumentType.Multiple, HelpText = "Data loader", NullName = "<Auto>", SortOrder = 3, Visibility = ArgumentAttribute.VisibilityType.CmdLineOnly, SignatureType = typeof(SignatureDataLoader))] 721[Argument(ArgumentType.AtMostOnce, HelpText = "Name of the text column containing the stopwords", ShortName = "stopwordsCol", SortOrder = 4, Visibility = ArgumentAttribute.VisibilityType.CmdLineOnly)] 727[Argument(ArgumentType.Multiple, HelpText = "New column definition(s)", Name = "Column", ShortName = "col", SortOrder = 1)]

128[Argument(ArgumentType.Required, HelpText = "New column definition (optional form: name:srcs).", Name = "Column", ShortName = "col", SortOrder = 1)] 131[Argument(ArgumentType.AtMostOnce, HelpText = "Dataset language or 'AutoDetect' to detect language per row.", ShortName = "lang", SortOrder = 3)] 134[Argument(ArgumentType.Multiple, Name = "StopWordsRemover", HelpText = "Stopwords remover.", ShortName = "remover", NullName = "<None>", SortOrder = 4)] 185[Argument(ArgumentType.AtMostOnce, HelpText = "Casing text using the rules of the invariant culture.", Name = "TextCase", ShortName = "case", SortOrder = 5)] 188[Argument(ArgumentType.AtMostOnce, HelpText = "Whether to keep diacritical marks or remove them.", ShortName = "diac", SortOrder = 6)] 191[Argument(ArgumentType.AtMostOnce, HelpText = "Whether to keep punctuation marks or remove them.", ShortName = "punc", SortOrder = 7)] 194[Argument(ArgumentType.AtMostOnce, HelpText = "Whether to keep numbers or remove them.", ShortName = "num", SortOrder = 8)] 197[Argument(ArgumentType.AtMostOnce, HelpText = "Column containing the transformed text tokens.", ShortName = "tokens,showtext,showTransformedText", SortOrder = 9)] 200[Argument(ArgumentType.Multiple, HelpText = "A dictionary of allowed terms.", ShortName = "dict", NullName = "<None>", SortOrder = 10, Hide = true)] 204[Argument(ArgumentType.Multiple, Name = "WordFeatureExtractor", HelpText = "Ngram feature extractor to use for words (WordBag/WordHashBag).", ShortName = "wordExtractor", NullName = "<None>", SortOrder = 11)] 215[Argument(ArgumentType.AtMostOnce, HelpText = "Normalize vectors (rows) individually by rescaling them to unit norm.", Name = "VectorNormalizer", ShortName = "norm", SortOrder = 13)] 245[Argument(ArgumentType.Multiple, Name = "CharFeatureExtractor", HelpText = "Ngram feature extractor to use for characters (WordBag/WordHashBag).", ShortName = "charExtractor", NullName = "<None>", SortOrder = 12)]

56[Argument(ArgumentType.Multiple, HelpText = "New column definition(s)", Name = "Column", ShortName = "col", SortOrder = 1)] 59[Argument(ArgumentType.AtMostOnce, HelpText = "Casing text using the rules of the invariant culture.", ShortName = "case", SortOrder = 1)] 62[Argument(ArgumentType.AtMostOnce, HelpText = "Whether to keep diacritical marks or remove them.", 66[Argument(ArgumentType.AtMostOnce, HelpText = "Whether to keep punctuation marks or remove them.", ShortName = "punc", SortOrder = 2)] 69[Argument(ArgumentType.AtMostOnce, HelpText = "Whether to keep numbers or remove them.", ShortName = "num", SortOrder = 2)]

56[Argument(ArgumentType.Multiple | ArgumentType.Required, HelpText = "New column definition(s) (optional form: name:src)", Name = "Column", ShortName = "col", SortOrder = 1)] 59[Argument(ArgumentType.Multiple, HelpText = "Whether to mark the beginning/end of each row/slot with start of text character (0x02)/end of text character (0x03)",

58[Argument(ArgumentType.AtMostOnce, HelpText = "Ngram length", ShortName = "ngram")] 62HelpText = "Maximum number of tokens to skip when constructing an n-gram", 67HelpText = "Whether to include all n-gram lengths up to " + nameof(NgramLength) + " or only " + nameof(NgramLength), 71[Argument(ArgumentType.Multiple, HelpText = "Maximum number of n-grams to store in the dictionary", ShortName = "max")] 74[Argument(ArgumentType.AtMostOnce, HelpText = "Statistical measure used to evaluate how important a word is to a document in a corpus")] 101[Argument(ArgumentType.Multiple, HelpText = "New column definition(s) (optional form: name:srcs)", Name = "Column", ShortName = "col", SortOrder = 1)] 359[Argument(ArgumentType.AtMostOnce, HelpText = "Ngram length (stores all lengths up to the specified Ngram length)", ShortName = "ngram")] 363HelpText = "Maximum number of tokens to skip when constructing an n-gram", 367[Argument(ArgumentType.AtMostOnce, HelpText = 375[Argument(ArgumentType.Multiple, HelpText = "Maximum number of n-grams to store in the dictionary", ShortName = "max")] 378[Argument(ArgumentType.AtMostOnce, HelpText = "The weighting criteria")] 409[Argument(ArgumentType.AtMostOnce, HelpText = "Ngram length", ShortName = "ngram")] 413HelpText = "Maximum number of tokens to skip when constructing an n-gram", 418HelpText = "Whether to include all n-gram lengths up to " + nameof(NgramLength) + " or only " + nameof(NgramLength), 422[Argument(ArgumentType.Multiple, HelpText = "Maximum number of n-grams to store in the dictionary", ShortName = "max")] 425[Argument(ArgumentType.AtMostOnce, HelpText = "The weighting criteria")] 428[Argument(ArgumentType.AtMostOnce, HelpText = "Separator used to separate terms/frequency pairs.")] 431[Argument(ArgumentType.AtMostOnce, HelpText = "Separator used to separate terms from their frequency.")] 448[Argument(ArgumentType.Multiple, HelpText = "New column definition(s) (optional form: name:src)", Name = "Column", ShortName = "col", SortOrder = 1)] 588[Argument(ArgumentType.AtMostOnce, HelpText = "Comma separated list of terms", Name = "Terms", SortOrder = 1, Visibility = ArgumentAttribute.VisibilityType.CmdLineOnly)] 591[Argument(ArgumentType.AtMostOnce, HelpText = "List of terms", Name = "Term", SortOrder = 1, Visibility = ArgumentAttribute.VisibilityType.EntryPointsOnly)] 594[Argument(ArgumentType.AtMostOnce, IsInputFileName = true, HelpText = "Data file containing the terms", ShortName = "data", SortOrder = 2, Visibility = ArgumentAttribute.VisibilityType.CmdLineOnly)] 597[Argument(ArgumentType.Multiple, HelpText = "Data loader", NullName = "<Auto>", SortOrder = 3, Visibility = ArgumentAttribute.VisibilityType.CmdLineOnly, SignatureType = typeof(SignatureDataLoader))] 600[Argument(ArgumentType.AtMostOnce, HelpText = "Name of the text column containing the terms", ShortName = "termCol", SortOrder = 4, Visibility = ArgumentAttribute.VisibilityType.CmdLineOnly)] 603[Argument(ArgumentType.AtMostOnce, HelpText = "How items should be ordered when vectorized. By default, they will be in the order encountered. " + 607[Argument(ArgumentType.AtMostOnce, HelpText = "Drop unknown terms instead of mapping them to NA term.", ShortName = "dropna", SortOrder = 6)]

61[Argument(ArgumentType.Multiple | ArgumentType.Required, HelpText = "New column definition(s) (optional form: name:src)", Name = "Column", ShortName = "col", SortOrder = 0)] 64[Argument(ArgumentType.AtMostOnce, HelpText = "Pre-trained model used to create the vocabulary", ShortName = "model", SortOrder = 1)] 67[Argument(ArgumentType.AtMostOnce, IsInputFileName = true, HelpText = "Filename for custom word embedding model",

77[Argument(ArgumentType.Multiple | ArgumentType.Required, HelpText = "New column definition(s) (optional form: name:numberOfBits:srcs)", 171[Argument(ArgumentType.AtMostOnce, HelpText = "Ngram length (stores all lengths up to the specified Ngram length)", ShortName = "ngram")] 175HelpText = "Maximum number of tokens to skip when constructing an n-gram", 180HelpText = "Number of bits to hash into. Must be between 1 and 30, inclusive.", 184[Argument(ArgumentType.AtMostOnce, HelpText = "Hashing seed")] 187[Argument(ArgumentType.AtMostOnce, HelpText = "Whether the position of each source column should be included in the hash (when there are multiple source columns).", ShortName = "ord")] 191HelpText = "Limit the number of keys used to generate the slot name to this many. 0 means no invert hashing, -1 means no limit.", 196HelpText = "Whether to include all n-gram lengths up to " + nameof(NgramLength) + " or only " + nameof(NgramLength), 259[Argument(ArgumentType.AtMostOnce, HelpText = "Ngram length", ShortName = "ngram", SortOrder = 3)] 263HelpText = "Maximum number of tokens to skip when constructing an n-gram", 268HelpText = "Number of bits to hash into. Must be between 1 and 30, inclusive.", 272[Argument(ArgumentType.AtMostOnce, HelpText = "Hashing seed")] 276HelpText = "Whether the position of each source column should be included in the hash (when there are multiple source columns).", 281HelpText = "Limit the number of keys used to generate the slot name to this many. 0 means no invert hashing, -1 means no limit.", 286HelpText = "Whether to include all n-gram lengths up to ngramLength or only ngramLength", 314[Argument(ArgumentType.Multiple, HelpText = "New column definition(s) (optional form: name:srcs)", Name = "Column", ShortName = "col", SortOrder = 1)]

43HelpText = "Comma separated set of term separator(s). Commonly: 'space', 'comma', 'semicolon' or other single character.", 70HelpText = "Comma separated set of term separator(s). Commonly: 'space', 'comma', 'semicolon' or other single character.", 76HelpText = "Array of single character term separator(s). By default uses space character separator.", 83[Argument(ArgumentType.Multiple, HelpText = "New column definition(s)", Name = "Column", ShortName = "col", SortOrder = 1)]

88[Argument(ArgumentType.Multiple | ArgumentType.Required, HelpText = "Columns to unroll, or 'pivot'", Name = "Column", ShortName = "col")] 91[Argument(ArgumentType.AtMostOnce, HelpText = "Specifies how to unroll multiple pivot columns of different size.")]

1121[Argument(ArgumentType.Required, HelpText = "TensorFlow model used by the transform. Please see https://www.tensorflow.org/mobile/prepare_models for more details.", SortOrder = 0)] 1127[Argument(ArgumentType.Multiple | ArgumentType.Required, HelpText = "The names of the model inputs", ShortName = "inputs", SortOrder = 1)] 1133[Argument(ArgumentType.Multiple | ArgumentType.Required, HelpText = "The name of the outputs", ShortName = "outputs", SortOrder = 2)] 1139[Argument(ArgumentType.AtMostOnce, HelpText = "Training labels.", ShortName = "label", SortOrder = 4)] 1145[Argument(ArgumentType.AtMostOnce, HelpText = "TensorFlow label node.", ShortName = "TFLabel", SortOrder = 5)] 1153[Argument(ArgumentType.AtMostOnce, HelpText = "The name of the optimization operation in the TensorFlow graph.", ShortName = "OptimizationOp", SortOrder = 6)] 1159[Argument(ArgumentType.AtMostOnce, HelpText = "The name of the operation in the TensorFlow graph to compute training loss (Optional)", ShortName = "LossOp", SortOrder = 7)] 1165[Argument(ArgumentType.AtMostOnce, HelpText = "The name of the operation in the TensorFlow graph to compute performance metric during training (Optional)", ShortName = "MetricOp", SortOrder = 8)] 1171[Argument(ArgumentType.AtMostOnce, HelpText = "Number of samples to use for mini-batch training.", SortOrder = 9)] 1177[Argument(ArgumentType.AtMostOnce, HelpText = "Number of training iterations.", SortOrder = 10)] 1183[Argument(ArgumentType.AtMostOnce, HelpText = "The name of the operation in the TensorFlow graph which sets optimizer learning rate (Optional).", SortOrder = 11)] 1189[Argument(ArgumentType.AtMostOnce, HelpText = "Learning rate to use during optimization.", SortOrder = 12)] 1198[Argument(ArgumentType.AtMostOnce, HelpText = "Name of the input in TensorFlow graph that specify the location for saving/restoring models from disk.", SortOrder = 13)] 1207[Argument(ArgumentType.AtMostOnce, HelpText = "Name of the input in TensorFlow graph that specify the location for saving/restoring models from disk.", SortOrder = 14)] 1217[Argument(ArgumentType.AtMostOnce, HelpText = "Add a batch dimension to the input e.g. input = [224, 224, 3] => [-1, 224, 224, 3].", SortOrder = 16)]

351[Argument(ArgumentType.AtMostOnce, HelpText = "Number of samples to use for mini-batch training.", SortOrder = 9)] 357[Argument(ArgumentType.AtMostOnce, HelpText = "Number of training iterations.", SortOrder = 10)] 363[Argument(ArgumentType.AtMostOnce, HelpText = "Learning rate to use during optimization.", SortOrder = 12)] 369[Argument(ArgumentType.AtMostOnce, HelpText = "Early stopping technique parameters to be used to terminate training when training metric stops improving.", SortOrder = 15)] 375[Argument(ArgumentType.AtMostOnce, HelpText = "Model architecture to be used in transfer learning for image classification.", SortOrder = 15)] 381[Argument(ArgumentType.AtMostOnce, HelpText = "Softmax tensor of the last layer in transfer learning.", SortOrder = 15)] 387[Argument(ArgumentType.AtMostOnce, HelpText = "Argmax tensor of the last layer in transfer learning.", SortOrder = 15)] 393[Argument(ArgumentType.AtMostOnce, HelpText = "Final model and checkpoint files/folder prefix for storing graph files.", SortOrder = 15)] 399[Argument(ArgumentType.AtMostOnce, HelpText = "Callback to report metrics during training and validation phase.", SortOrder = 15)] 405[Argument(ArgumentType.AtMostOnce, HelpText = "Indicates the path where the models get downloaded to and cache files saved, default is a new temporary directory.", SortOrder = 15)] 411[Argument(ArgumentType.AtMostOnce, HelpText = "Indicates to evaluate the model on train set after every epoch.", SortOrder = 15)] 417[Argument(ArgumentType.AtMostOnce, HelpText = "Indicates to not re-compute trained cached bottleneck values if already available in the bin folder.", SortOrder = 15)] 423[Argument(ArgumentType.AtMostOnce, HelpText = "Indicates to not re-compute validataionset cached bottleneck validationset values if already available in the bin folder.", SortOrder = 15)] 429[Argument(ArgumentType.AtMostOnce, HelpText = "Validation set.", SortOrder = 15)] 437[Argument(ArgumentType.AtMostOnce, HelpText = "Validation fraction.", SortOrder = 15)] 443[Argument(ArgumentType.AtMostOnce, HelpText = "Indicates the file name to store trainset bottleneck values for caching.", SortOrder = 15)] 449[Argument(ArgumentType.AtMostOnce, HelpText = "Indicates the file name to store validationset bottleneck values for caching.", SortOrder = 15)] 455[Argument(ArgumentType.AtMostOnce, HelpText = "A class that performs learning rate scheduling.", SortOrder = 15)]

1433HelpText = attr.HelpText;

175jo[FieldNames.Desc] = inputAttr.HelpText;