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classify-samples-from-dist: Run k-nearest-neighbors on a labeled distance matrix.ΒΆ

Docstring:

Usage: qiime sample-classifier classify-samples-from-dist [OPTIONS]

  Run k-nearest-neighbors on a labeled distance matrix. Return cross-validated
  (leave one out) predictions and  accuracy. k = 1 by default

Inputs:
  --i-distance-matrix ARTIFACT
    DistanceMatrix        a distance matrix                         [required]
Parameters:
  --m-metadata-file METADATA
  --m-metadata-column COLUMN  MetadataColumn[Categorical]
                          Categorical metadata column to use as prediction
                          target.                                   [required]
  --p-k INTEGER           Number of nearest neighbors             [default: 1]
  --p-cv INTEGER          Number of k-fold cross-validations to perform.
    Range(1, None)                                                [default: 5]
  --p-random-state INTEGER
                          Seed used by random number generator.     [optional]
  --p-n-jobs NTHREADS     Number of jobs to run in parallel.      [default: 1]
  --p-palette TEXT Choices('YellowOrangeBrown', 'YellowOrangeRed',
    'OrangeRed', 'PurpleRed', 'RedPurple', 'BluePurple', 'GreenBlue',
    'PurpleBlue', 'YellowGreen', 'summer', 'copper', 'viridis', 'cividis',
    'plasma', 'inferno', 'magma', 'sirocco', 'drifting', 'melancholy',
    'enigma', 'eros', 'spectre', 'ambition', 'mysteriousstains', 'daydream',
    'solano', 'navarro', 'dandelions', 'deepblue', 'verve', 'greyscale')
                          The color palette to use for plotting.
                                                          [default: 'sirocco']
Outputs:
  --o-predictions ARTIFACT SampleData[ClassifierPredictions]
                          leave one out predictions for each sample [required]
  --o-accuracy-results VISUALIZATION
                          Accuracy results visualization.           [required]
Miscellaneous:
  --output-dir PATH       Output unspecified results to a directory
  --verbose / --quiet     Display verbose output to stdout and/or stderr
                          during execution of this action. Or silence output
                          if execution is successful (silence is golden).
  --recycle-pool TEXT     Use a cache pool for pipeline resumption. QIIME 2
                          will cache your results in this pool for reuse by
                          future invocations. These pool are retained until
                          deleted by the user. If not provided, QIIME 2 will
                          create a pool which is automatically reused by
                          invocations of the same action and removed if the
                          action is successful. Note: these pools are local to
                          the cache you are using.
  --no-recycle            Do not recycle results from a previous failed
                          pipeline run or save the results from this run for
                          future recycling.
  --parallel              Execute your action in parallel. This flag will use
                          your default parallel config.
  --parallel-config FILE  Execute your action in parallel using a config at
                          the indicated path.
  --use-cache DIRECTORY   Specify the cache to be used for the intermediate
                          work of this pipeline. If not provided, the default
                          cache under $TMP/qiime2/ will be used.
                          IMPORTANT FOR HPC USERS: If you are on an HPC system
                          and are using parallel execution it is important to
                          set this to a location that is globally accessible
                          to all nodes in the cluster.
  --example-data PATH     Write example data and exit.
  --citations             Show citations and exit.
  --help                  Show this message and exit.

Import:

from qiime2.plugins.sample_classifier.pipelines import classify_samples_from_dist

Docstring:

Run k-nearest-neighbors on a labeled distance matrix.

Run k-nearest-neighbors on a labeled distance matrix. Return cross-
validated (leave one out) predictions and  accuracy. k = 1 by default

Parameters
----------
distance_matrix : DistanceMatrix
    a distance matrix
metadata : MetadataColumn[Categorical]
    Categorical metadata column to use as prediction target.
k : Int, optional
    Number of nearest neighbors
cv : Int % Range(1, None), optional
    Number of k-fold cross-validations to perform.
random_state : Int, optional
    Seed used by random number generator.
n_jobs : Threads, optional
    Number of jobs to run in parallel.
palette : Str % Choices('YellowOrangeBrown', 'YellowOrangeRed', 'OrangeRed', 'PurpleRed', 'RedPurple', 'BluePurple', 'GreenBlue', 'PurpleBlue', 'YellowGreen', 'summer', 'copper', 'viridis', 'cividis', 'plasma', 'inferno', 'magma', 'sirocco', 'drifting', 'melancholy', 'enigma', 'eros', 'spectre', 'ambition', 'mysteriousstains', 'daydream', 'solano', 'navarro', 'dandelions', 'deepblue', 'verve', 'greyscale'), optional
    The color palette to use for plotting.

Returns
-------
predictions : SampleData[ClassifierPredictions]
    leave one out predictions for each sample
accuracy_results : Visualization
    Accuracy results visualization.