cytopy.flow.cell_classifier.sklearn_classifier¶

This module contains the SklearnCellClassifier for using supervised classification methods, trained on some labeled FileGroup (has existing Populations) to predict single cell classifications.

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Classes:

SklearnCellClassifier(model, params, …)

Use supervised machine learning to predict the classification of single cell data.

class cytopy.flow.cell_classifier.sklearn_classifier.SklearnCellClassifier(model, params: dict, multi_label: bool = False, **kwargs)¶

Use supervised machine learning to predict the classification of single cell data. This class allows the user to apply an Scikit-Learn classifier or classifier that follows the conventions of Scikit-Learn i.e. contains the methods ‘fit’, ‘fit_predict’ and ‘predict’. Training data should be provided in the form of a FileGroup with existing Populations. Supports multi-class and multi-label classification; if multi-label classification is chosen, the tree structure of training data is NOT conserved - all resulting populations will have the same parent population.

Parameters

model (Scikit-Learn Classifier) – Should be a valid Scikit-Learn class or similar e.g. XGBClassifier
params (dict) – Parameters to initiate class with
features (list) – List of channels/markers to use as features in prediction
target_populations (list) – List of populations from training data to predict
multi_label (bool (default=False)) – If True, single cells can belong to more than one population. The tree structure of training data is NOT conserved - all resulting populations will have the same parent population.
logging_level (int (default=logging.INFO)) – Level to log events at
log (str, optional) – Path to log output to; if not given, will log to stdout
population_prefix (str (default=”CellClassifier_”)) – Prefix applied to populations generated

scaler¶

Scaler object

Type: Scaler

transformer¶

Transformer object

Type: Transformer

class_weights¶

Sample class weights; key is sample index, value is weight. Set by calling compute_class_weights.

Type: dict

x¶

Training feature space

Type: Pandas.DataFrame

y¶

Target labels

Type: numpy.ndarray

logger¶

Type: logging.Logger

features¶

Type: list

target_populations¶

Type: list

Methods:

`load_model`(path, **kwargs)	Load a pickled model from disk.
`plot_confusion_matrix`([cmap, figsize, x, y])	Wraps cytopy.flow.supervised.confusion_matrix_plots (see for more details).
`plot_learning_curve`([experiment, …])	This method will generate a learning curve using the Scikit-Learn utility function sklearn.model_selection.learning_curve.
`save_model`(path, **kwargs)	Pickle the associated model and save to disk.

load_model(path: str, **kwargs)¶

Load a pickled model from disk. WARNING: be aware of continuity issues. Compatibility with new releases of Scikit-Learn and cytopy are not guaranteed. The loaded model must correspond to the expected method for this CellClassifier.

Parameters

path (str) – Where to save on disk
kwargs – Additional keyword arguments passed to pickle.dump call

Returns

Return type

None

plot_confusion_matrix(cmap: Optional[str] = None, figsize: tuple = (10, 5), x: Optional[pandas.core.frame.DataFrame] = None, y: Optional[numpy.ndarray] = None, **kwargs)¶

Wraps cytopy.flow.supervised.confusion_matrix_plots (see for more details). Given some feature space and target labels, use the model to generate a confusion matrix heatmap. If x and y are not provided, will use associated training data.

Parameters

cmap (str (optional)) – Colour scheme
figsize (tuple (default=(10, 5))) – Figure size
x (Pandas.DataFrame (optional)) – Feature space. If not given, will use associated training data. To use a validation dataset, use the ‘load_validation’ method to get relevant data.
y (numpy.ndarray (optional)) – Target labels. If not given, will use associated training data. To use a validation dataset, use the ‘load_validation’ method to get relevant data.
kwargs – Additional keyword arguments passed to cytopy.flow.supervised.confusion_matrix_plots

Returns

Return type

Matplotlib.Figure

Raises

AssertionError – Invalid x, y input

plot_learning_curve(experiment: Optional[cytopy.data.experiment.Experiment] = None, validation_id: Optional[str] = None, root_population: Optional[str] = None, ax: Optional[matplotlib.axes._axes.Axes] = None, x_label: str = 'Training examples', y_label: str = 'Score', train_sizes: Optional[numpy.array] = None, verbose: int = 1, **kwargs)¶

This method will generate a learning curve using the Scikit-Learn utility function sklearn.model_selection.learning_curve. Either use the associated training data or a validation FileGroup by providing the Experiment object and the ID for the validation sample (validation_id). This validation sample should contain the same populations as the training data, which must be downstream of the ‘root_population’.

Parameters

experiment (Experiment (optional)) – If provided, should be the same Experiment training data was derived from
validation_id (str (optional)) – Name of the sample to use for validation
root_population (str (optional)) – If not given, will use the same root_population as training data
ax (Matplotlib.Axes (optional)) – Axes object to use to draw plot
x_label (str (default="Training examples")) – X-axis labels
y_label (str (default="Score")) – Y-axis labels
train_sizes (numpy.ndarray (optional)) – Defaults to linear range between 0.1 and 1.0, with 10 steps
verbose (int (default=1)) – Passed to learning_curve function
kwargs – Additional keyword arguments passed to sklearn.model_selection.learning_curve

Returns

Return type

Matplotlib.Axes

Raises

AssertionError – If plotting learning curve for validation and Experiment, validation_id or root_population not provided

save_model(path: str, **kwargs)¶

Pickle the associated model and save to disk. WARNING: be aware of continuity issues. Compatibility with new releases of Scikit-Learn and cytopy are not guaranteed.

Parameters

path (str) – Where to save on disk
kwargs – Additional keyword arguments passed to pickle.dump call

Returns

Return type

None