894 references to AtMostOnce
Microsoft.ML.Data (222)
Commands\CrossValidationCommand.cs (5)
39[Argument(ArgumentType.AtMostOnce, HelpText = "Results summary filename", ShortName = "sf")] 54[Argument(ArgumentType.AtMostOnce, HelpText = "Name column name", ShortName = "name", SortOrder = 6)] 80[Argument(ArgumentType.AtMostOnce, IsInputFileName = true, HelpText = "The validation data file", ShortName = "valid")] 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")]
Commands\DataCommand.cs (6)
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)] 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)]
Commands\EvaluateCommand.cs (2)
185[Argument(ArgumentType.AtMostOnce, HelpText = "Name column name", ShortName = "name", SortOrder = 6)] 195[Argument(ArgumentType.AtMostOnce, HelpText = "Results summary filename", ShortName = "sf")]
Commands\SaveDataCommand.cs (5)
33[Argument(ArgumentType.AtMostOnce, HelpText = "File to save the data", ShortName = "dout")] 36[Argument(ArgumentType.AtMostOnce, HelpText = "Whether to include hidden columns", ShortName = "keep")] 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")]
Commands\SavePredictorCommand.cs (6)
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")]
Commands\ScoreCommand.cs (3)
49[Argument(ArgumentType.AtMostOnce, HelpText = "Group column name", ShortName = "group")] 66[Argument(ArgumentType.AtMostOnce, HelpText = "Whether to include hidden columns", ShortName = "keep")] 72[Argument(ArgumentType.AtMostOnce, HelpText = "Whether to output all columns or just scores", ShortName = "all")]
Commands\ShowSchemaCommand.cs (4)
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)]
Commands\TestCommand.cs (4)
27[Argument(ArgumentType.AtMostOnce, HelpText = "Column to use for features", ShortName = "feat", SortOrder = 2)] 39[Argument(ArgumentType.AtMostOnce, HelpText = "Name column name", ShortName = "name", SortOrder = 6)] 53[Argument(ArgumentType.AtMostOnce, HelpText = "Results summary filename", ShortName = "sf")] 56[Argument(ArgumentType.AtMostOnce, HelpText = "File to save per-instance predictions and metrics to",
Commands\TrainCommand.cs (3)
55[Argument(ArgumentType.AtMostOnce, HelpText = "Name column name", ShortName = "name", SortOrder = 6)] 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")]
Commands\TrainTestCommand.cs (5)
25[Argument(ArgumentType.AtMostOnce, IsInputFileName = true, HelpText = "The test data file", ShortName = "test", SortOrder = 1)] 37[Argument(ArgumentType.AtMostOnce, HelpText = "Results summary filename", ShortName = "sf")] 52[Argument(ArgumentType.AtMostOnce, HelpText = "Name column name", ShortName = "name", SortOrder = 6)] 62[Argument(ArgumentType.AtMostOnce, IsInputFileName = true, HelpText = "The validation data file", ShortName = "valid")] 74[Argument(ArgumentType.AtMostOnce, HelpText = "File to save per-instance predictions and metrics to",
DataLoadSave\Binary\BinaryLoader.cs (2)
2105[DefaultArgument(ArgumentType.AtMostOnce, IsInputFileName = true, HelpText = "The data file", SortOrder = 0)] 2108[Argument(ArgumentType.AtMostOnce, HelpText = "Verbose?", ShortName = "v", Hide = true)]
DataLoadSave\Database\DatabaseLoader.cs (5)
230[Argument(ArgumentType.AtMostOnce, HelpText = "Name of the column")] 236[Argument(ArgumentType.AtMostOnce, HelpText = "Type of the items in the column")] 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")]
DataLoadSave\Text\TextLoader.cs (21)
106[Argument(ArgumentType.AtMostOnce, HelpText = "Name of the column")] 113[Argument(ArgumentType.AtMostOnce, HelpText = "Type of the items in the column")] 314[Argument(ArgumentType.AtMostOnce, HelpText = "Last index in the range")] 321[Argument(ArgumentType.AtMostOnce, 331[Argument(ArgumentType.AtMostOnce, 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")] 434[Argument(ArgumentType.AtMostOnce, 455[Argument(ArgumentType.AtMostOnce, HelpText = "Whether the input may include sparse representations", ShortName = "sparse")] 461[Argument(ArgumentType.AtMostOnce, 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")] 492[Argument(ArgumentType.AtMostOnce, HelpText = "Remove trailing whitespace from lines", ShortName = "trim")] 499[Argument(ArgumentType.AtMostOnce, ShortName = "header", 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")]
DataLoadSave\Text\TextSaver.cs (4)
36[Argument(ArgumentType.AtMostOnce, HelpText = "Separator", ShortName = "sep")] 39[Argument(ArgumentType.AtMostOnce, HelpText = "Force dense format", ShortName = "dense")] 47[Argument(ArgumentType.AtMostOnce, HelpText = "Output the comment containing the loader settings", ShortName = "schema")] 50[Argument(ArgumentType.AtMostOnce, HelpText = "Output the header", ShortName = "header")]
EntryPoints\InputBase.cs (1)
34[Argument(ArgumentType.AtMostOnce, HelpText = "Name column name.", ShortName = "name", SortOrder = 2, Visibility = ArgumentAttribute.VisibilityType.EntryPointsOnly)]
Evaluators\AnomalyDetectionEvaluator.cs (10)
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")]
Evaluators\BinaryClassifierEvaluator.cs (12)
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)]
Evaluators\ClusteringEvaluator.cs (4)
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")]
Evaluators\MamlEvaluator.cs (3)
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")]
Evaluators\MulticlassClassificationEvaluator.cs (6)
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")]
Evaluators\MultiOutputRegressionEvaluator.cs (1)
620[Argument(ArgumentType.AtMostOnce, HelpText = "Suppress labels and scores in per-instance outputs?", ShortName = "noScores")]
Evaluators\RankingEvaluator.cs (7)
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)]
Model\Pfa\SavePfaCommand.cs (8)
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)]
Prediction\Calibrator.cs (2)
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)]
Prediction\PredictionEngine.cs (4)
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")]
Scorers\FeatureContributionCalculation.cs (4)
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)]
Scorers\MulticlassClassificationScorer.cs (2)
38[Argument(ArgumentType.AtMostOnce, HelpText = "Score Column Name.", ShortName = "scn")] 41[Argument(ArgumentType.AtMostOnce, HelpText = "Predicted Label Column Name.", ShortName = "plcn")]
Scorers\PredictedLabelScorerBase.cs (2)
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")]
Scorers\RowToRowScorerBase.cs (1)
315[Argument(ArgumentType.AtMostOnce, HelpText = "Output column: The suffix to append to the default column names", ShortName = "ex")]
Training\TrainerInputBase.cs (6)
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)] 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)]
Transforms\BootstrapSamplingTransformer.cs (3)
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")]
Transforms\ColumnBindingsBase.cs (3)
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")]
Transforms\ColumnConcatenatingTransformer.cs (1)
68[Argument(ArgumentType.AtMostOnce, HelpText = "Name of the new column", ShortName = "name")]
Transforms\ColumnSelecting.cs (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)]
Transforms\FeatureContributionCalculationTransformer.cs (4)
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)]
Transforms\GenerateNumberTransform.cs (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")] 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")]
Transforms\Hashing.cs (10)
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.")]
Transforms\KeyToVector.cs (2)
40[Argument(ArgumentType.AtMostOnce, 91[Argument(ArgumentType.AtMostOnce,
Transforms\LabelIndicatorTransform.cs (2)
49[Argument(ArgumentType.AtMostOnce, HelpText = "The positive example class for binary classification.", ShortName = "index")] 74[Argument(ArgumentType.AtMostOnce, HelpText = "Label of the positive class.", ShortName = "index")]
Transforms\NormalizeColumn.cs (12)
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")] 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")] 232[Argument(ArgumentType.AtMostOnce, HelpText = "Max number of bins, power of 2 recommended", ShortName = "bins")] 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)]
Transforms\SkipTakeFilter.cs (2)
55[Argument(ArgumentType.AtMostOnce, HelpText = SkipHelp, ShortName = "s", SortOrder = 1)] 58[Argument(ArgumentType.AtMostOnce, HelpText = TakeHelp, ShortName = "t", SortOrder = 2)]
Transforms\SlotsDroppingTransformer.cs (1)
125[Argument(ArgumentType.AtMostOnce, HelpText = "Last index in the range")]
Transforms\TrainAndScoreTransformer.cs (3)
32[Argument(ArgumentType.AtMostOnce, HelpText = "Group column name", ShortName = "group", SortOrder = 100, 44[Argument(ArgumentType.AtMostOnce, IsInputFileName = true, HelpText = "Predictor model file used in scoring", 127[Argument(ArgumentType.AtMostOnce, HelpText = "Name column name", ShortName = "name", SortOrder = 106,
Transforms\TypeConverting.cs (4)
61[Argument(ArgumentType.AtMostOnce, HelpText = "The result type", ShortName = "type")] 67[Argument(ArgumentType.AtMostOnce, HelpText = "For a key column, this defines the range of values", ShortName = "key", Visibility = ArgumentAttribute.VisibilityType.EntryPointsOnly)] 135[Argument(ArgumentType.AtMostOnce, HelpText = "The result type", ShortName = "type", SortOrder = 2)] 141[Argument(ArgumentType.AtMostOnce, HelpText = "For a key column, this defines the range of values", ShortName = "key", Visibility = ArgumentAttribute.VisibilityType.EntryPointsOnly)]
Transforms\ValueMapping.cs (4)
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")] 425[Argument(ArgumentType.AtMostOnce,
Transforms\ValueToKeyMappingTransformer.cs (12)
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)] 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)]
Utilities\TypeParsingUtils.cs (1)
95[Argument(ArgumentType.AtMostOnce, HelpText = "Count of valid key values")]
Utils\LossFunctions.cs (3)
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")]
Microsoft.ML.Ensemble (20)
EntryPoints\CreateEnsemble.cs (6)
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)]
OutputCombiners\BaseMultiCombiner.cs (1)
20[Argument(ArgumentType.AtMostOnce, HelpText = "Whether to normalize the output of base models before combining them",
OutputCombiners\BaseStacking.cs (1)
20[Argument(ArgumentType.AtMostOnce, ShortName = "vp", SortOrder = 50,
OutputCombiners\MultiWeightedAverage.cs (1)
47[Argument(ArgumentType.AtMostOnce, HelpText = "The metric type to be used to find the weights for each model", ShortName = "wn", SortOrder = 50)]
OutputCombiners\WeightedAverage.cs (1)
42[Argument(ArgumentType.AtMostOnce, HelpText = "The metric type to be used to find the weights for each model", ShortName = "wn", SortOrder = 50)]
Selector\FeatureSelector\RandomFeatureSelector.cs (1)
27[Argument(ArgumentType.AtMostOnce, HelpText = "The proportion of features to be selected. The range is 0.0-1.0", ShortName = "fp", SortOrder = 50)]
Selector\SubModelSelector\BestPerformanceRegressionSelector.cs (1)
24[Argument(ArgumentType.AtMostOnce, HelpText = "The metric type to be used to find the best performance", ShortName = "mn", SortOrder = 50)]
Selector\SubModelSelector\BestPerformanceSelector.cs (1)
24[Argument(ArgumentType.AtMostOnce, HelpText = "The metric type to be used to find the best performance", ShortName = "mn", SortOrder = 50)]
Selector\SubModelSelector\BestPerformanceSelectorMulticlass.cs (1)
24[Argument(ArgumentType.AtMostOnce, HelpText = "The metric type to be used to find the best performance", ShortName = "mn", SortOrder = 50)]
Selector\SubModelSelector\SubModelDataSelector.cs (2)
16[Argument(ArgumentType.AtMostOnce, ShortName = "lp", SortOrder = 50, 21[Argument(ArgumentType.AtMostOnce, ShortName = "vp", SortOrder = 50,
Trainer\EnsembleTrainerBase.cs (4)
27[Argument(ArgumentType.AtMostOnce, 33[Argument(ArgumentType.AtMostOnce, HelpText = "Batch size", ShortName = "bs", SortOrder = 107)] 43[Argument(ArgumentType.AtMostOnce, HelpText = "All the base learners will run asynchronously if the value is true", ShortName = "tp", SortOrder = 106)] 47[Argument(ArgumentType.AtMostOnce,
Microsoft.ML.EntryPoints (34)
CrossValidationMacro.cs (12)
34[Argument(ArgumentType.AtMostOnce, HelpText = "The predictor model", SortOrder = 1)] 47[Argument(ArgumentType.AtMostOnce, HelpText = "The transform model from the pipeline before this command. " + 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)] 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)] 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)]
CVSplit.cs (2)
27[Argument(ArgumentType.AtMostOnce, HelpText = "Number of folds to split into", SortOrder = 2)] 30[Argument(ArgumentType.AtMostOnce, ShortName = "strat", HelpText = "Stratification column", SortOrder = 3)]
FeatureCombiner.cs (1)
217[Argument(ArgumentType.AtMostOnce, HelpText = "Convert the key values to text", SortOrder = 3)]
ImportTextData.cs (1)
25[Argument(ArgumentType.AtMostOnce, ShortName = "schema", HelpText = "Custom schema to use for parsing", SortOrder = 2)]
JsonUtils\ExecuteGraphCommand.cs (1)
30[Argument(ArgumentType.AtMostOnce, HelpText = "Random seed")]
ModelOperations.cs (1)
61[Argument(ArgumentType.AtMostOnce, HelpText = "Use probabilities from learners instead of raw values.", SortOrder = 2)]
OneVersusAllMacro.cs (1)
41[Argument(ArgumentType.AtMostOnce, HelpText = "Use probabilities in OVA combiner", SortOrder = 3)]
PermutationFeatureImportance.cs (3)
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)]
ScoreModel.cs (1)
33[Argument(ArgumentType.AtMostOnce, HelpText = "Suffix to append to the score columns", SortOrder = 3)]
TrainTestMacro.cs (9)
28[Argument(ArgumentType.AtMostOnce, HelpText = "The predictor model", SortOrder = 1)] 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)] 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)]
TrainTestSplit.cs (2)
23[Argument(ArgumentType.AtMostOnce, HelpText = "Fraction of training data", SortOrder = 2)] 26[Argument(ArgumentType.AtMostOnce, ShortName = "strat", HelpText = "Stratification column", SortOrder = 3)]
Microsoft.ML.FastTree (48)
FastTreeArguments.cs (21)
250[Argument(ArgumentType.AtMostOnce, HelpText = "max-NDCG truncation to use in the LambdaMART algorithm", ShortName = "n", Hide = true)] 260[Argument(ArgumentType.AtMostOnce, HelpText = "Cost function parameter (w/c)", ShortName = "cf", Hide = true)] 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")] 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)] 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")] 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")] 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")] 803[Argument(ArgumentType.AtMostOnce, HelpText = "Filter zero lambdas during training", ShortName = "fzl", Hide = true)]
GamModelParameters.cs (1)
557[Argument(ArgumentType.AtMostOnce, HelpText = "Whether to open the GAM visualization page URL", ShortName = "o", SortOrder = 3)]
GamRegression.cs (1)
66[Argument(ArgumentType.AtMostOnce, HelpText = "Metric for pruning. (For regression, 1: L1, 2:L2; default L2)", ShortName = "pmetric")]
GamTrainer.cs (3)
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")] 122[Argument(ArgumentType.AtMostOnce, HelpText = "Enable post-training pruning to avoid overfitting. (a validation set is required)", ShortName = "pruning")]
RandomForestClassification.cs (4)
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)]
SumupPerformanceCommand.cs (8)
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)]
Training\EarlyStoppingCriteria.cs (5)
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")]
TreeEnsembleFeaturizer.cs (5)
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.",
Microsoft.ML.ImageAnalytics (53)
ImageLoader.cs (1)
62[Argument(ArgumentType.AtMostOnce, HelpText = "Folder where to search for images", ShortName = "folder")]
ImagePixelExtractor.cs (18)
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")] 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")]
ImageResizer.cs (6)
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")] 81[Argument(ArgumentType.AtMostOnce, HelpText = "Resizing method", ShortName = "scale")] 84[Argument(ArgumentType.AtMostOnce, HelpText = "Anchor for cropping", ShortName = "anchor")]
VectorToImageTransform.cs (28)
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")] 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")]
Microsoft.ML.KMeansClustering (6)
KMeansPlusPlusTrainer.cs (6)
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)]
Microsoft.ML.LightGbm (47)
LightGbmArguments.cs (15)
67[Argument(ArgumentType.AtMostOnce, 79[Argument(ArgumentType.AtMostOnce, 92[Argument(ArgumentType.AtMostOnce, 106[Argument(ArgumentType.AtMostOnce, 120[Argument(ArgumentType.AtMostOnce, 134[Argument(ArgumentType.AtMostOnce, 146[Argument(ArgumentType.AtMostOnce, 160[Argument(ArgumentType.AtMostOnce, 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.")]
LightGbmBinaryTrainer.cs (4)
140[Argument(ArgumentType.AtMostOnce, HelpText = "Use for binary classification when training data is not balanced.", ShortName = "us")] 149[Argument(ArgumentType.AtMostOnce, 158[Argument(ArgumentType.AtMostOnce, HelpText = "Parameter for the sigmoid function.", ShortName = "sigmoid")] 165[Argument(ArgumentType.AtMostOnce,
LightGbmMulticlassTrainer.cs (4)
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")] 108[Argument(ArgumentType.AtMostOnce,
LightGbmRankingTrainer.cs (2)
138[Argument(ArgumentType.AtMostOnce, HelpText = "Parameter for the sigmoid function.", ShortName = "sigmoid")] 145[Argument(ArgumentType.AtMostOnce,
LightGbmRegressionTrainer.cs (1)
135[Argument(ArgumentType.AtMostOnce,
LightGbmTrainerBase.cs (21)
82[Argument(ArgumentType.AtMostOnce, HelpText = "Number of iterations.", SortOrder = 1, ShortName = "iter")] 93[Argument(ArgumentType.AtMostOnce, 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")] 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.")]
Microsoft.ML.Maml (5)
HelpCommand.cs (5)
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")] 50[Argument(ArgumentType.AtMostOnce, ShortName = "all", Hide = true)] 435[Argument(ArgumentType.AtMostOnce, IsInputFileName = true, HelpText = "The path of the XML documentation file",
Microsoft.ML.Mkl.Components (21)
OlsLinearRegression.cs (2)
82[Argument(ArgumentType.AtMostOnce, HelpText = "L2 regularization weight", ShortName = "l2", SortOrder = 50)] 96[Argument(ArgumentType.AtMostOnce, HelpText = "Number of entries in a batch when loading data (0 = auto).", Hide = true)]
SymSgdClassificationTrainer.cs (9)
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")]
VectorWhitening.cs (10)
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")]
Microsoft.ML.OnnxConverter (11)
SaveOnnxCommand.cs (11)
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)]
Microsoft.ML.OnnxTransformer (5)
OnnxTransform.cs (5)
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)] 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)]
Microsoft.ML.Parquet (4)
PartitionedFileLoader.cs (3)
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")] 89[Argument(ArgumentType.AtMostOnce, HelpText = "Data type of the column.")]
PartitionedPathParser.cs (1)
84[Argument(ArgumentType.AtMostOnce, HelpText = "Data type of each column.")]
Microsoft.ML.PCA (12)
PcaTrainer.cs (4)
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")]
PcaTransformer.cs (8)
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")] 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")]
Microsoft.ML.Predictor.Tests (9)
CmdLine\CmdLine.cs (5)
165[Argument(ArgumentType.AtMostOnce)] 168[Argument(ArgumentType.AtMostOnce)] 180[Argument(ArgumentType.AtMostOnce)] 195[Argument(ArgumentType.AtMostOnce)] 293[Argument(ArgumentType.AtMostOnce, ShortName = "val")]
CmdLine\CmdLineReverseTest.cs (4)
108[Argument(ArgumentType.AtMostOnce)] 114[Argument(ArgumentType.AtMostOnce)] 117[Argument(ArgumentType.AtMostOnce)] 120[Argument(ArgumentType.AtMostOnce)]
Microsoft.ML.Recommender (10)
MatrixFactorizationTrainer.cs (10)
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")]
Microsoft.ML.ResultProcessor (6)
ResultProcessor.cs (6)
312[Argument(ArgumentType.AtMostOnce, HelpText = "Output file name", ShortName = "o")] 316[Argument(ArgumentType.AtMostOnce, HelpText = "Output to a visualization HTML", ShortName = "html")] 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")] 338[Argument(ArgumentType.AtMostOnce, HelpText = "Internal setting set if called from unit test suite")]
Microsoft.ML.StandardTrainers (76)
FactorizationMachine\FactorizationMachineTrainer.cs (9)
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)] 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)]
LdSvm\LdSvmTrainer.cs (10)
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")] 131[Argument(ArgumentType.AtMostOnce, 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")]
Standard\LogisticRegression\LbfgsPredictorBase.cs (11)
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.", 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)]
Standard\LogisticRegression\LogisticRegression.cs (1)
105[Argument(ArgumentType.AtMostOnce, HelpText = "Show statistics of training examples.", ShortName = "stat, ShowTrainingStats", SortOrder = 50)]
Standard\LogisticRegression\MulticlassLogisticRegression.cs (1)
106[Argument(ArgumentType.AtMostOnce, HelpText = "Show statistics of training examples.", ShortName = "stat, ShowTrainingStats", SortOrder = 50)]
Standard\MulticlassClassification\OneVersusAllTrainer.cs (1)
105[Argument(ArgumentType.AtMostOnce, HelpText = "Use probability or margins to determine max", ShortName = "useprob")]
Standard\Online\AveragedLinear.cs (9)
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")]
Standard\Online\AveragedPerceptron.cs (2)
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)]
Standard\Online\LinearSvm.cs (7)
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)]
Standard\Online\OnlineLinear.cs (4)
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")]
Standard\SdcaBinary.cs (21)
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")] 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")] 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)]
Microsoft.ML.Sweeper (17)
Algorithms\KdoSweeper.cs (1)
44[Argument(ArgumentType.AtMostOnce, HelpText = "Seed for the random number generator for the first batch sweeper", ShortName = "seed")]
Algorithms\NelderMead.cs (1)
29[Argument(ArgumentType.AtMostOnce, HelpText = "Seed for the random number generator for the first batch sweeper", ShortName = "seed")]
Algorithms\SmacSweeper.cs (1)
31[Argument(ArgumentType.AtMostOnce, HelpText = "Seed for the random number generator for the first batch sweeper", ShortName = "seed")]
AsyncSweeper.cs (3)
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")]
ConfigRunner.cs (6)
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)")] 51[Argument(ArgumentType.AtMostOnce, Hide = true)] 185[Argument(ArgumentType.AtMostOnce, HelpText = "The number of threads to use for the sweep (default auto determined by the number of cores)", ShortName = "t")]
SweepCommand.cs (4)
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")]
SynthConfigRunner.cs (1)
27[Argument(ArgumentType.AtMostOnce, HelpText = "The number of threads to use for the sweep (default auto determined by the number of cores)", ShortName = "t")]
Microsoft.ML.TensorFlow (3)
TensorflowTransform.cs (3)
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)]
Microsoft.ML.TimeSeries (65)
AdaptiveSingularSpectrumSequenceModeler.cs (2)
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)]
ExponentialAverageTransform.cs (1)
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].",
IidChangePointDetector.cs (3)
48[Argument(ArgumentType.AtMostOnce, HelpText = "The length of the sliding window on p-values for computing the martingale score.", ShortName = "wnd", 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.",
IidSpikeDetector.cs (2)
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",
MovingAverageTransform.cs (3)
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. " +
PercentileThresholdTransform.cs (2)
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. " +
PValueTransform.cs (4)
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.",
SequentialAnomalyDetectionTransformBase.cs (6)
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",
SequentialForecastingTransformBase.cs (2)
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 " +
SlidingWindowTransformBase.cs (3)
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)]
SRCNNAnomalyDetector.cs (1)
47[Argument(ArgumentType.AtMostOnce, HelpText = "The size of the sliding window for computing spectral residual", ShortName = "wnd",
SrCnnEntireAnomalyDetector.cs (6)
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.",
SrCnnTransformBase.cs (5)
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.", 49[Argument(ArgumentType.AtMostOnce, HelpText = "The threshold to determine anomaly, score larger than the threshold is considered as anomaly.",
SsaAnomalyDetectionBase.cs (3)
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)]
SsaChangePointDetector.cs (4)
48[Argument(ArgumentType.AtMostOnce, HelpText = "The length of the sliding window on p-values for computing the martingale score.", ShortName = "wnd", 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.",
SSaForecasting.cs (12)
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)] 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)] 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)]
SsaForecastingBase.cs (3)
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)]
SsaSpikeDetector.cs (3)
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", 66[Argument(ArgumentType.AtMostOnce, HelpText = "The function used to compute the error between the expected and the observed value.", ShortName = "err", SortOrder = 103)]
Microsoft.ML.Transforms (190)
Dracula\CMCountTable.cs (2)
182[Argument(ArgumentType.AtMostOnce, HelpText = "Count-Min Sketch table depth", ShortName = "d")] 185[Argument(ArgumentType.AtMostOnce, HelpText = "Count-Min Sketch width", ShortName = "w")]
Dracula\CountTableTransformer.cs (8)
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")] 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")] 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")]
Dracula\CountTargetEncodingTransformer.cs (12)
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")] 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")] 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")]
Dracula\DictCountTable.cs (1)
142[Argument(ArgumentType.AtMostOnce, HelpText = "Garbage threshold (counts below or equal to the threshold are assigned to the garbage bin)", ShortName = "gb")]
ExpressionTransformer.cs (2)
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.")]
FourierDistributionSampler.cs (2)
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")]
GcnTransform.cs (9)
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)] 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")]
HashJoiningTransform.cs (9)
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")]
LearnerFeatureSelection.cs (2)
35[Argument(ArgumentType.AtMostOnce, HelpText = "The number of slots to preserve", ShortName = "topk", SortOrder = 1)] 58[Argument(ArgumentType.AtMostOnce, HelpText = "Name column name", ShortName = "name", Purpose = SpecialPurpose.ColumnName)]
LoadTransform.cs (1)
39[Argument(ArgumentType.AtMostOnce, HelpText = "Whether to load all transforms except those marked by tags", ShortName = "comp", SortOrder = 3)]
MissingValueHandlingTransformer.cs (6)
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")]
MissingValueReplacing.cs (5)
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")] 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")]
MutualInformationFeatureSelection.cs (2)
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",
OneHotEncoding.cs (2)
32[Argument(ArgumentType.AtMostOnce, HelpText = "Output kind: Bag (multi-set vector), Ind (indicator vector), Key (index), or Binary encoded indicator vector", ShortName = "kind")] 77[Argument(ArgumentType.AtMostOnce, HelpText = "Output kind: Bag (multi-set vector), Ind (indicator vector), or Key (index)",
OneHotHashEncoding.cs (10)
27[Argument(ArgumentType.AtMostOnce, 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")] 38[Argument(ArgumentType.AtMostOnce, 43[Argument(ArgumentType.AtMostOnce, HelpText = "Output kind: Bag (multi-set vector), Ind (indicator vector), or Key (index)", 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")] 104[Argument(ArgumentType.AtMostOnce, 109[Argument(ArgumentType.AtMostOnce, HelpText = "Output kind: Bag (multi-set vector), Ind (indicator vector), or Key (index)",
ProduceIdTransform.cs (1)
33[Argument(ArgumentType.AtMostOnce, HelpText = "Name of the column to produce", ShortName = "col", SortOrder = 1)]
RandomFourierFeaturizing.cs (4)
41[Argument(ArgumentType.AtMostOnce, HelpText = "The number of random Fourier features to create", ShortName = "dim")] 47[Argument(ArgumentType.AtMostOnce, HelpText = "Create two features for every random Fourier frequency? (one for cos and one for sin)")] 58[Argument(ArgumentType.AtMostOnce, HelpText = "The number of random Fourier features to create", ShortName = "dim")] 64[Argument(ArgumentType.AtMostOnce, HelpText = "create two features for every random Fourier frequency? (one for cos and one for sin)")]
SvmLight\SvmLightLoader.cs (2)
88[Argument(ArgumentType.AtMostOnce, HelpText = "The size of the feature vectors.", ShortName = "size")] 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")]
SvmLight\SvmLightSaver.cs (4)
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)]
Text\LdaTransform.cs (22)
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")] 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")] 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")]
Text\NgramHashingTransformer.cs (16)
37[Argument(ArgumentType.AtMostOnce, HelpText = "Maximum n-gram length", ShortName = "ngram")] 40[Argument(ArgumentType.AtMostOnce, HelpText = 44[Argument(ArgumentType.AtMostOnce, 49[Argument(ArgumentType.AtMostOnce, 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.", 117[Argument(ArgumentType.AtMostOnce, HelpText = "Maximum n-gram length", ShortName = "ngram", SortOrder = 3)] 120[Argument(ArgumentType.AtMostOnce, 125[Argument(ArgumentType.AtMostOnce, 130[Argument(ArgumentType.AtMostOnce, 135[Argument(ArgumentType.AtMostOnce, HelpText = "Hashing seed")] 138[Argument(ArgumentType.AtMostOnce, HelpText = "Whether to rehash unigrams", ShortName = "rehash")] 141[Argument(ArgumentType.AtMostOnce, 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.",
Text\NgramTransform.cs (8)
40[Argument(ArgumentType.AtMostOnce, HelpText = "Maximum n-gram length", ShortName = "ngram")] 43[Argument(ArgumentType.AtMostOnce, HelpText = 47[Argument(ArgumentType.AtMostOnce, 55[Argument(ArgumentType.AtMostOnce, HelpText = "Statistical measure used to evaluate how important a word is to a document in a corpus")] 82[Argument(ArgumentType.AtMostOnce, HelpText = "Maximum n-gram length", ShortName = "ngram")] 85[Argument(ArgumentType.AtMostOnce, HelpText = 89[Argument(ArgumentType.AtMostOnce, 97[Argument(ArgumentType.AtMostOnce, HelpText = "The weighting criteria")]
Text\SentimentAnalyzingTransform.cs (1)
28[Argument(ArgumentType.AtMostOnce, HelpText = "Name of the new column.", ShortName = "dst", SortOrder = 2)]
Text\StopWordsRemovingTransformer.cs (8)
71[Argument(ArgumentType.AtMostOnce, 76[Argument(ArgumentType.AtMostOnce, HelpText = "Stopword Language (optional).", ShortName = "lang")] 103[Argument(ArgumentType.AtMostOnce, 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)] 721[Argument(ArgumentType.AtMostOnce, HelpText = "Name of the text column containing the stopwords", ShortName = "stopwordsCol", SortOrder = 4, Visibility = ArgumentAttribute.VisibilityType.CmdLineOnly)]
Text\TextFeaturizingEstimator.cs (7)
131[Argument(ArgumentType.AtMostOnce, HelpText = "Dataset language or 'AutoDetect' to detect language per row.", ShortName = "lang", SortOrder = 3)] 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)] 215[Argument(ArgumentType.AtMostOnce, HelpText = "Normalize vectors (rows) individually by rescaling them to unit norm.", Name = "VectorNormalizer", ShortName = "norm", SortOrder = 13)]
Text\TextNormalizing.cs (4)
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)]
Text\WordBagTransform.cs (20)
58[Argument(ArgumentType.AtMostOnce, HelpText = "Ngram length", ShortName = "ngram")] 61[Argument(ArgumentType.AtMostOnce, 66[Argument(ArgumentType.AtMostOnce, 74[Argument(ArgumentType.AtMostOnce, HelpText = "Statistical measure used to evaluate how important a word is to a document in a corpus")] 359[Argument(ArgumentType.AtMostOnce, HelpText = "Ngram length (stores all lengths up to the specified Ngram length)", ShortName = "ngram")] 362[Argument(ArgumentType.AtMostOnce, 367[Argument(ArgumentType.AtMostOnce, HelpText = 378[Argument(ArgumentType.AtMostOnce, HelpText = "The weighting criteria")] 409[Argument(ArgumentType.AtMostOnce, HelpText = "Ngram length", ShortName = "ngram")] 412[Argument(ArgumentType.AtMostOnce, 417[Argument(ArgumentType.AtMostOnce, 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.")] 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)] 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)]
Text\WordEmbeddingsExtractor.cs (2)
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",
Text\WordHashBagProducingTransform.cs (14)
171[Argument(ArgumentType.AtMostOnce, HelpText = "Ngram length (stores all lengths up to the specified Ngram length)", ShortName = "ngram")] 174[Argument(ArgumentType.AtMostOnce, 179[Argument(ArgumentType.AtMostOnce, 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")] 190[Argument(ArgumentType.AtMostOnce, 195[Argument(ArgumentType.AtMostOnce, 259[Argument(ArgumentType.AtMostOnce, HelpText = "Ngram length", ShortName = "ngram", SortOrder = 3)] 262[Argument(ArgumentType.AtMostOnce, 267[Argument(ArgumentType.AtMostOnce, 272[Argument(ArgumentType.AtMostOnce, HelpText = "Hashing seed")] 275[Argument(ArgumentType.AtMostOnce, 280[Argument(ArgumentType.AtMostOnce, 285[Argument(ArgumentType.AtMostOnce,
Text\WordTokenizing.cs (3)
42[Argument(ArgumentType.AtMostOnce, 68[Argument(ArgumentType.AtMostOnce, 74[Argument(ArgumentType.AtMostOnce,
UngroupTransform.cs (1)
91[Argument(ArgumentType.AtMostOnce, HelpText = "Specifies how to unroll multiple pivot columns of different size.")]
Microsoft.ML.Vision (30)
DnnRetrainTransform.cs (12)
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)]
ImageClassificationTrainer.cs (18)
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)]