DTMClassifier#

class skfda.ml.classification.DTMClassifier(proportiontocut, depth_method=None, metric=LpDistance(p=2, vector_norm=None))[source]#

Distance to trimmed means (DTM) classification.

Test samples are classified to the class that minimizes the distance of the observation to the trimmed mean of the group [1].

Parameters:
  • proportiontocut (float) – Indicates the percentage of functions to remove. It is not easy to determine as it varies from dataset to dataset.

  • depth_method (Depth[Input] | None) – The depth class used to order the data. See the documentation of the depths module for a list of available depths. By default it is ModifiedBandDepth.

  • metric (Metric[Input]) – Distance function between two functional objects. See the documentation of the metrics module for a list of available metrics. L2 distance is used by default.

Examples

Firstly, we will import and split the Berkeley Growth Study dataset

>>> from skfda.datasets import fetch_growth
>>> from sklearn.model_selection import train_test_split
>>> dataset = fetch_growth()
>>> fd = dataset['data']
>>> y = dataset['target']
>>> X_train, X_test, y_train, y_test = train_test_split(
...     fd, y, test_size=0.25, stratify=y, random_state=0)

We will fit a Distance to trimmed means classifier

>>> from skfda.ml.classification import DTMClassifier
>>> clf = DTMClassifier(proportiontocut=0.25)
>>> clf.fit(X_train, y_train)
DTMClassifier(...)

We can predict the class of new samples

>>> clf.predict(X_test) # Predict labels for test samples
array([1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 1,
        1, 0, 0, 0, 0, 1, 1, 1, 1, 0, 1, 1])

Finally, we calculate the mean accuracy for the test data

>>> clf.score(X_test, y_test)
0.875

References

Methods

fit(X, y)

Fit the model using X as training data and y as target values.

get_metadata_routing()

Get metadata routing of this object.

get_params([deep])

Get parameters for this estimator.

predict(X)

Predict the class labels for the provided data.

score(X, y[, sample_weight])

Return the mean accuracy on the given test data and labels.

set_params(**params)

Set the parameters of this estimator.

set_score_request(*[, sample_weight])

Request metadata passed to the score method.

fit(X, y)[source]#

Fit the model using X as training data and y as target values.

Parameters:
  • X (Input) – FDataGrid with the training data or array matrix with shape (n_samples, n_samples) if metric=’precomputed’.

  • y (Target) – Target values of shape = (n_samples) or (n_samples, n_outputs).

Returns:

self

Return type:

NearestCentroid[Input, Target]

get_metadata_routing()#

Get metadata routing of this object.

Please check User Guide on how the routing mechanism works.

Returns:

routing – A MetadataRequest encapsulating routing information.

Return type:

MetadataRequest

get_params(deep=True)#

Get parameters for this estimator.

Parameters:

deep (bool, default=True) – If True, will return the parameters for this estimator and contained subobjects that are estimators.

Returns:

params – Parameter names mapped to their values.

Return type:

dict

predict(X)[source]#

Predict the class labels for the provided data.

Parameters:

X (Input) – FDataGrid with the test samples.

Returns:

Array of shape (n_samples) or

(n_samples, n_outputs) with class labels for each data sample.

Return type:

Target

score(X, y, sample_weight=None)[source]#

Return the mean accuracy on the given test data and labels.

In multi-label classification, this is the subset accuracy which is a harsh metric since you require for each sample that each label set be correctly predicted.

Parameters:
  • X (array-like of shape (n_samples, n_features)) – Test samples.

  • y (array-like of shape (n_samples,) or (n_samples, n_outputs)) – True labels for X.

  • sample_weight (array-like of shape (n_samples,), default=None) – Sample weights.

Returns:

score – Mean accuracy of self.predict(X) w.r.t. y.

Return type:

float

set_params(**params)#

Set the parameters of this estimator.

The method works on simple estimators as well as on nested objects (such as Pipeline). The latter have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object.

Parameters:

**params (dict) – Estimator parameters.

Returns:

self – Estimator instance.

Return type:

estimator instance

set_score_request(*, sample_weight='$UNCHANGED$')#

Request metadata passed to the score method.

Note that this method is only relevant if enable_metadata_routing=True (see sklearn.set_config()). Please see User Guide on how the routing mechanism works.

The options for each parameter are:

  • True: metadata is requested, and passed to score if provided. The request is ignored if metadata is not provided.

  • False: metadata is not requested and the meta-estimator will not pass it to score.

  • None: metadata is not requested, and the meta-estimator will raise an error if the user provides it.

  • str: metadata should be passed to the meta-estimator with this given alias instead of the original name.

The default (sklearn.utils.metadata_routing.UNCHANGED) retains the existing request. This allows you to change the request for some parameters and not others.

New in version 1.3.

Note

This method is only relevant if this estimator is used as a sub-estimator of a meta-estimator, e.g. used inside a Pipeline. Otherwise it has no effect.

Parameters:
  • sample_weight (str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED) – Metadata routing for sample_weight parameter in score.

  • self (DTMClassifier) –

Returns:

self – The updated object.

Return type:

object