Struct linfa::dataset::DatasetBase [−][src]
DatasetBase
This is the fundamental structure of a dataset. It contains a number of records about the data
and may contain targets, weights and feature names. In order to keep the type complexity low
the dataset base is only generic over the records and targets and introduces a trait bound on
the records. weights
and feature_names
, on the other hand, are always assumed to be owned
and copied when views are created.
Fields
records
: a two-dimensional matrix with dimensionality (nsamples, nfeatures), in case of kernel methods a quadratic matrix with dimensionality (nsamples, nsamples), which may be sparsetargets
: a two-/one-dimension matrix with dimensionality (nsamples, ntargets)weights
: optional weights for each sample with dimensionality (nsamples)feature_names
: optional descriptive feature names with dimensionality (nfeatures)
Trait bounds
R: Records
: generic over feature matrices or kernel matricesT
: generic over anyndarray
matrix which can be used as targets. TheAsTargets
trait bound is omitted here to avoid some repetition in implementationsrc/dataset/impl_dataset.rs
Fields
records: R
targets: T
Implementations
impl<F: Float, D: Data<Elem = F>, T> DatasetBase<ArrayBase<D, Ix2>, T>
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pub fn pearson_correlation(&self) -> PearsonCorrelation<F>
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Calculate the Pearson Correlation Coefficients from a dataset
The PCC describes the linear correlation between two variables. It is the covariance divided by the product of the standard deviations, therefore essentially a normalised measurement of the covariance and in range (-1, 1). A negative coefficient indicates a negative correlation between both variables.
Example
let corr = linfa_datasets::diabetes() .pearson_correlation(); println!("{}", corr);
pub fn pearson_correlation_with_p_value(
&self,
num_iter: usize
) -> PearsonCorrelation<F>
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&self,
num_iter: usize
) -> PearsonCorrelation<F>
Calculate the Pearson Correlation Coefficients and p-values from the dataset
The PCC describes the linear correlation between two variables. It is the covariance divided by the product of the standard deviations, therefore essentially a normalised measurement of the covariance and in range (-1, 1). A negative coefficient indicates a negative correlation between both variables.
The p-value supports or reject the null hypthesis that two variables are not correlated. The smaller the p-value the stronger is the evidence that two variables are correlated. A typical threshold is p < 0.05.
Parameters
num_iter
: number of iterations of the permutation test to estimate the p-value
Example
let corr = linfa_datasets::diabetes() .pearson_correlation_with_p_value(100); println!("{}", corr);
impl<R: Records, S> DatasetBase<R, S>
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Implementation without constraints on records and targets
This implementation block provides methods for the creation and mutation of datasets. This includes swapping the targets, return the records etc.
pub fn new<T: IntoTargets<S>>(records: R, targets: T) -> DatasetBase<R, S>
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Create a new dataset from records and targets
Example
let dataset = Dataset::new(records, targets);
pub fn targets(&self) -> &S
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Returns reference to targets
pub fn weights(&self) -> Option<&[f32]>
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Returns optionally weights
pub fn weight_for(&self, idx: usize) -> f32
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Return a single weight
The weight of the idx
th observation is returned. If no weight is specified, then all
observations are unweighted with default value 1.0
.
pub fn feature_names(&self) -> Vec<String>
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Returns feature names
A feature name gives a human-readable string describing the purpose of a single feature. This allow the reader to understand its purpose while analysing results, for example correlation analysis or feature importance.
pub fn records(&self) -> &R
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Return records of a dataset
The records are data points from which predictions are made. This functions returns a reference to the record field.
pub fn with_records<T: Records>(self, records: T) -> DatasetBase<T, S>
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Updates the records of a dataset
This function overwrites the records in a dataset. It also invalidates the weights and feature names.
pub fn with_targets<T>(self, targets: T) -> DatasetBase<R, T>
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Updates the targets of a dataset
This function overwrites the targets in a dataset.
pub fn with_weights(self, weights: Array1<f32>) -> DatasetBase<R, S>
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Updates the weights of a dataset
pub fn with_feature_names<I: Into<String>>(
self,
names: Vec<I>
) -> DatasetBase<R, S>
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self,
names: Vec<I>
) -> DatasetBase<R, S>
Updates the feature names of a dataset
impl<L, R: Records, T: AsTargets<Elem = L>> DatasetBase<R, T>
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pub fn map_targets<S, G: FnMut(&L) -> S>(
self,
fnc: G
) -> DatasetBase<R, Array2<S>>
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self,
fnc: G
) -> DatasetBase<R, Array2<S>>
Map targets with a function f
Example
let dataset = linfa_datasets::winequality() .map_targets(|x| *x > 6); // dataset has now boolean targets println!("{:?}", dataset.targets());
Returns
A modified dataset with new target type.
pub fn ntargets(&self) -> usize
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Return the number of targets in the dataset
Example
let dataset = linfa_datasets::winequality(); println!("#targets {}", dataset.ntargets());
impl<'a, F: Float, L, D, T> DatasetBase<ArrayBase<D, Ix2>, T> where
D: Data<Elem = F>,
T: AsTargets<Elem = L>,
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D: Data<Elem = F>,
T: AsTargets<Elem = L>,
pub fn sample_iter(&'a self) -> Iter<'a, '_, F, T::Elem>
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Iterate over observations
This function creates an iterator which produces tuples of data points and target value. The iterator runs once for each data point and, while doing so, holds an reference to the owned dataset.
Example
let dataset = linfa_datasets::iris(); for (x, y) in dataset.sample_iter() { println!("{} => {}", x, y); }
impl<'a, F: Float, L: 'a, D, T> DatasetBase<ArrayBase<D, Ix2>, T> where
D: Data<Elem = F>,
T: AsTargets<Elem = L> + FromTargetArray<'a, L>,
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D: Data<Elem = F>,
T: AsTargets<Elem = L> + FromTargetArray<'a, L>,
pub fn view(&'a self) -> DatasetBase<ArrayView2<'a, F>, T::View>
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Creates a view of a dataset
pub fn feature_iter(&'a self) -> DatasetIter<'a, '_, ArrayBase<D, Ix2>, T>
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Iterate over features
This iterator produces dataset views with only a single feature, while the set of targets remain complete. It can be useful to compare each feature individual to all targets.
pub fn target_iter(&'a self) -> DatasetIter<'a, '_, ArrayBase<D, Ix2>, T>
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Iterate over targets
This functions creates an iterator which produces dataset views complete records, but only a single target each. Useful to train multiple single target models for a multi-target dataset.
impl<'a, L: 'a, F: Float, T> DatasetBase<ArrayView2<'a, F>, T> where
T: AsTargets<Elem = L> + FromTargetArray<'a, L>,
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T: AsTargets<Elem = L> + FromTargetArray<'a, L>,
pub fn split_with_ratio(
&'a self,
ratio: f32
) -> (DatasetBase<ArrayView2<'a, F>, T::View>, DatasetBase<ArrayView2<'a, F>, T::View>)
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&'a self,
ratio: f32
) -> (DatasetBase<ArrayView2<'a, F>, T::View>, DatasetBase<ArrayView2<'a, F>, T::View>)
Split dataset into two disjoint chunks
This function splits the observations in a dataset into two disjoint chunks. The splitting
threshold is calculated with the ratio
. For example a ratio of 0.9
allocates 90% to the
first chunks and 9% to the second. This is often used in training, validation splitting
procedures.
impl<'a, 'b: 'a, F: Float, L: Label, T, D> DatasetBase<ArrayBase<D, Ix2>, T> where
D: Data<Elem = F>,
T: AsTargets<Elem = L> + Labels<Elem = L>,
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D: Data<Elem = F>,
T: AsTargets<Elem = L> + Labels<Elem = L>,
pub fn one_vs_all(
&self
) -> Result<Vec<DatasetBase<ArrayView2<'_, F>, CountedTargets<bool, Array2<bool>>>>>
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&self
) -> Result<Vec<DatasetBase<ArrayView2<'_, F>, CountedTargets<bool, Array2<bool>>>>>
Produce N boolean targets from multi-class targets
Some algorithms (like SVM) don’t support multi-class targets. This function splits a dataset into multiple binary target view of the same dataset.
impl<L: Label, R: Records, S: AsTargets<Elem = L>> DatasetBase<R, S>
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pub fn label_frequencies_with_mask(&self, mask: &[bool]) -> HashMap<L, f32>
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Calculates label frequencies from a dataset while masking certain samples.
Parameters
mask
: a boolean array that specifies which samples to include in the count
Returns
A mapping of the Dataset’s samples to their frequencies
pub fn label_frequencies(&self) -> HashMap<L, f32>
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Calculates label frequencies from a dataset
impl<'b, F: Float, E: Copy + 'b, D, T> DatasetBase<ArrayBase<D, Ix2>, T> where
D: Data<Elem = F>,
T: AsTargets<Elem = E> + FromTargetArray<'b, E>,
T::Owned: AsTargets,
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D: Data<Elem = F>,
T: AsTargets<Elem = E> + FromTargetArray<'b, E>,
T::Owned: AsTargets,
pub fn bootstrap<R: Rng>(
&'b self,
sample_feature_size: (usize, usize),
rng: &'b mut R
) -> impl Iterator<Item = DatasetBase<Array2<F>, <T as FromTargetArray<'b, E>>::Owned>> + 'b
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&'b self,
sample_feature_size: (usize, usize),
rng: &'b mut R
) -> impl Iterator<Item = DatasetBase<Array2<F>, <T as FromTargetArray<'b, E>>::Owned>> + 'b
Apply bootstrapping for samples and features
Bootstrap aggregating is used for sub-sample generation and improves the accuracy and stability of machine learning algorithms. It samples data uniformly with replacement and generates datasets where elements may be shared. This selects a subset of observations as well as features.
Parameters
sample_feature_size
: The number of samples and features per bootstraprng
: The random number generator used in the sampling procedure
Returns
An infinite Iterator yielding at each step a new bootstrapped dataset
pub fn bootstrap_samples<R: Rng>(
&'b self,
num_samples: usize,
rng: &'b mut R
) -> impl Iterator<Item = DatasetBase<Array2<F>, <T as FromTargetArray<'b, E>>::Owned>> + 'b
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&'b self,
num_samples: usize,
rng: &'b mut R
) -> impl Iterator<Item = DatasetBase<Array2<F>, <T as FromTargetArray<'b, E>>::Owned>> + 'b
Apply sample bootstrapping
Bootstrap aggregating is used for sub-sample generation and improves the accuracy and stability of machine learning algorithms. It samples data uniformly with replacement and generates datasets where elements may be shared. Only a sample subset is selected which retains all features and targets.
Parameters
num_samples
: The number of samples per bootstraprng
: The random number generator used in the sampling procedure
Returns
An infinite Iterator yielding at each step a new bootstrapped dataset
pub fn bootstrap_features<R: Rng>(
&'b self,
num_features: usize,
rng: &'b mut R
) -> impl Iterator<Item = DatasetBase<Array2<F>, <T as FromTargetArray<'b, E>>::Owned>> + 'b
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&'b self,
num_features: usize,
rng: &'b mut R
) -> impl Iterator<Item = DatasetBase<Array2<F>, <T as FromTargetArray<'b, E>>::Owned>> + 'b
Apply feature bootstrapping
Bootstrap aggregating is used for sub-sample generation and improves the accuracy and stability of machine learning algorithms. It samples data uniformly with replacement and generates datasets where elements may be shared. Only a feature subset is selected while retaining all samples and targets.
Parameters
num_features
: The number of features per bootstraprng
: The random number generator used in the sampling procedure
Returns
An infinite Iterator yielding at each step a new bootstrapped dataset
pub fn shuffle<R: Rng>(&self, rng: &mut R) -> DatasetBase<Array2<F>, T::Owned>
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Produces a shuffled version of the current Dataset.
Parameters
rng
: the random number generator that will be used to shuffle the samples
Returns
A new shuffled version of the current Dataset
pub fn fold(
&self,
k: usize
) -> Vec<(DatasetBase<Array2<F>, T::Owned>, DatasetBase<Array2<F>, T::Owned>)>
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&self,
k: usize
) -> Vec<(DatasetBase<Array2<F>, T::Owned>, DatasetBase<Array2<F>, T::Owned>)>
Performs K-folding on the dataset.
The dataset is divided into k
“fold”, each containing
(dataset size)/k
samples, used to generate k
training-validation
dataset pairs. Each pair contains a validation Dataset
with k
samples,
the ones contained in the i-th fold, and a training Dataset
composed by the
union of all the samples in the remaining folds.
Parameters
k
: the number of folds to apply
Returns
A vector of k
training-validation Dataset pairs.
Example
use linfa::dataset::DatasetView; use ndarray::array; let records = array![[1.,1.], [2.,1.], [3.,2.], [4.,1.],[5., 3.], [6.,2.]]; let targets = array![1, 1, 0, 1, 0, 0]; let dataset : DatasetView<f64, usize> = (records.view(), targets.view()).into(); let accuracies = dataset.fold(3).into_iter().map(|(train, valid)| { // Here you can train your model and perform validation // let model = params.fit(&dataset); // let predi = model.predict(&valid); // predi.confusion_matrix(&valid).accuracy() });
pub fn sample_chunks<'a: 'b>(
&'b self,
chunk_size: usize
) -> ChunksIter<'b, 'a, F, T>
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&'b self,
chunk_size: usize
) -> ChunksIter<'b, 'a, F, T>
pub fn to_owned(&self) -> DatasetBase<Array2<F>, T::Owned>
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impl<'a, F: Float, E: Copy + 'a, D, S> DatasetBase<ArrayBase<D, Ix2>, ArrayBase<S, Ix2>> where
D: DataMut<Elem = F>,
S: DataMut<Elem = E>,
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D: DataMut<Elem = F>,
S: DataMut<Elem = E>,
pub fn iter_fold<O, C: Fn(DatasetView<'_, F, E>) -> O>(
&'a mut self,
k: usize,
fit_closure: C
) -> impl Iterator<Item = (O, DatasetBase<ArrayView2<'_, F>, ArrayView2<'_, E>>)>
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&'a mut self,
k: usize,
fit_closure: C
) -> impl Iterator<Item = (O, DatasetBase<ArrayView2<'_, F>, ArrayView2<'_, E>>)>
Allows to perform k-folding cross validation on fittable algorithms.
Given in input a dataset, a value of k and the desired params for the fittable algorithm, returns an iterator over the k trained models and the associated validation set.
The models are trained according to a closure specified as an input.
Parameters
k
: the number of folds to apply to the datasetparams
: the desired parameters for the fittable algorithm at handfit_closure
: a closure of the type(params, training_data) -> fitted_model
that will be used to produce the trained model for each fold. The training data given in input won’t outlive the closure.
Returns
An iterator over couples (trained_model, validation_set)
.
Panics
This method will panic for any of the following three reasons:
- The value of
k
provided is not positive; - The value of
k
provided is greater than the total number of samples in the dataset; - The dataset’s data is not stored contiguously and in standard order;
Example
use linfa::traits::Fit; use linfa::dataset::{Dataset, DatasetView}; use ndarray::{array, ArrayView1, ArrayView2}; struct MockFittable {} struct MockFittableResult { mock_var: usize, } impl<'a> Fit<'a, ArrayView2<'a, f64>, ArrayView2<'a, f64>> for MockFittable { type Object = MockFittableResult; fn fit(&self, training_data: &DatasetView<f64, f64>) -> Self::Object { MockFittableResult { mock_var: training_data.ntargets()} } } let records = array![[1.,1.], [2.,2.], [3.,3.], [4.,4.], [5.,5.]]; let targets = array![1.,2.,3.,4.,5.]; let mut dataset: Dataset<f64, f64> = (records, targets).into(); let params = MockFittable {}; for (model,validation_set) in dataset.iter_fold(5, |v| params.fit(&v)){ // Here you can use `model` and `validation_set` to // assert the performance of the chosen algorithm }
impl<F: Float, E> DatasetBase<ArrayBase<OwnedRepr<D>, Dim<[usize; 2]>>, ArrayBase<OwnedRepr<T>, Dim<[usize; 2]>>>
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pub fn split_with_ratio(self, ratio: f32) -> (Self, Self)
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Split dataset into two disjoint chunks
This function splits the observations in a dataset into two disjoint chunks. The splitting
threshold is calculated with the ratio
. If the input Dataset contains n
samples then the
two new Datasets will have respectively n * ratio
and n - (n*ratio)
samples.
For example a ratio of 0.9
allocates 90% to the
first chunks and 10% to the second. This is often used in training, validation splitting
procedures.
Parameters
ratio
: the ratio of samples in the input Dataset to include in the first output one
Returns
The input Dataset split into two according to the input ratio.
impl<F: Float, L: Copy + Label, D, T> DatasetBase<ArrayBase<D, Ix2>, T> where
D: Data<Elem = F>,
T: AsTargets<Elem = L>,
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D: Data<Elem = F>,
T: AsTargets<Elem = L>,
pub fn with_labels(
&self,
labels: &[&[L]]
) -> DatasetBase<Array2<F>, CountedTargets<L, Array2<L>>>
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&self,
labels: &[&[L]]
) -> DatasetBase<Array2<F>, CountedTargets<L, Array2<L>>>
Trait Implementations
impl<L, R: Records, T: AsTargets<Elem = L>> AsTargets for DatasetBase<R, T>
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type Elem = L
fn as_multi_targets(&self) -> ArrayView2<'_, Self::Elem>
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fn try_single_target(&self) -> Result<ArrayView1<'_, Self::Elem>>
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impl<L, R: Records, T: AsTargetsMut<Elem = L>> AsTargetsMut for DatasetBase<R, T>
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type Elem = L
fn as_multi_targets_mut(&mut self) -> ArrayViewMut2<'_, Self::Elem>
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fn try_single_target_mut(&mut self) -> Result<ArrayViewMut1<'_, Self::Elem>>
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impl<R: Records, R2: Records, T: AsTargets<Elem = bool>, T2: AsTargets<Elem = Pr>> BinaryClassification<&'_ DatasetBase<R, T>> for DatasetBase<R2, T2>
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fn roc(&self, y: &DatasetBase<R, T>) -> Result<ReceiverOperatingCharacteristic>
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impl<F: Float, E, D, S> From<(ArrayBase<D, Dim<[usize; 2]>>, ArrayBase<S, Dim<[usize; 1]>>)> for DatasetBase<ArrayBase<D, Ix2>, ArrayBase<S, Ix2>> where
D: Data<Elem = F>,
S: Data<Elem = E>,
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D: Data<Elem = F>,
S: Data<Elem = E>,
impl<F: Float, E, D, S> From<(ArrayBase<D, Dim<[usize; 2]>>, ArrayBase<S, Dim<[usize; 2]>>)> for DatasetBase<ArrayBase<D, Ix2>, ArrayBase<S, Ix2>> where
D: Data<Elem = F>,
S: Data<Elem = E>,
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D: Data<Elem = F>,
S: Data<Elem = E>,
impl<F: Float, D: Data<Elem = F>, I: Dimension> From<ArrayBase<D, I>> for DatasetBase<ArrayBase<D, I>, Array2<()>>
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impl<L: Label, T: Labels<Elem = L>, R: Records> Labels for DatasetBase<R, T>
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type Elem = L
fn label_count(&self) -> Vec<HashMap<L, usize>>
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fn label_set(&self) -> Vec<HashSet<Self::Elem>>
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fn labels(&self) -> Vec<Self::Elem>
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impl<L: Label, R: Records, T: AsTargets<Elem = L>> Labels for DatasetBase<R, CountedTargets<L, T>>
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A NdArray with discrete labels can act as labels
type Elem = L
fn label_count(&self) -> Vec<HashMap<L, usize>>
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fn label_set(&self) -> Vec<HashSet<Self::Elem>>
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fn labels(&self) -> Vec<Self::Elem>
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impl<F: Float, T: AsTargets<Elem = F>, T2: AsTargets<Elem = F>, D: Data<Elem = F>> MultiTargetRegression<F, T2> for DatasetBase<ArrayBase<D, Ix2>, T>
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fn max_error(&self, other: &T) -> Result<Array1<F>>
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fn mean_absolute_error(&self, other: &T) -> Result<Array1<F>>
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fn mean_squared_error(&self, other: &T) -> Result<Array1<F>>
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fn mean_squared_log_error(&self, other: &T) -> Result<Array1<F>>
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fn median_absolute_error(&self, other: &T) -> Result<Array1<F>>
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fn r2(&self, other: &T) -> Result<Array1<F>>
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fn explained_variance(&self, other: &T) -> Result<Array1<F>>
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impl<'a, F: Float, R, T, S, O> Predict<&'a DatasetBase<R, T>, S> for O where
R: Records<Elem = F>,
O: PredictRef<R, S>,
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R: Records<Elem = F>,
O: PredictRef<R, S>,
fn predict(&self, ds: &'a DatasetBase<R, T>) -> S
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impl<F: Float, D, T, O> Predict<ArrayBase<D, Dim<[usize; 2]>>, DatasetBase<ArrayBase<D, Dim<[usize; 2]>>, T>> for O where
D: Data<Elem = F>,
O: PredictRef<ArrayBase<D, Ix2>, T>,
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D: Data<Elem = F>,
O: PredictRef<ArrayBase<D, Ix2>, T>,
impl<F: Float, R, T, S, O> Predict<DatasetBase<R, T>, DatasetBase<R, S>> for O where
R: Records<Elem = F>,
O: PredictRef<R, S>,
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R: Records<Elem = F>,
O: PredictRef<R, S>,
fn predict(&self, ds: DatasetBase<R, T>) -> DatasetBase<R, S>
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impl<F: Float, D: Records<Elem = F>, T> Records for DatasetBase<D, T>
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Implement records for a DatasetBase
impl<'a, F: Float, L: 'a + Label, D: Data<Elem = F>, T: AsTargets<Elem = L> + Labels<Elem = L>> SilhouetteScore<F> for DatasetBase<ArrayBase<D, Ix2>, T>
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fn silhouette_score(&self) -> Result<F>
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impl<L: Label, R, R2, T, T2> ToConfusionMatrix<L, &'_ DatasetBase<R, T>> for DatasetBase<R2, T2> where
R: Records,
R2: Records,
T: AsTargets<Elem = L>,
T2: AsTargets<Elem = L> + Labels<Elem = L>,
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R: Records,
R2: Records,
T: AsTargets<Elem = L>,
T2: AsTargets<Elem = L> + Labels<Elem = L>,
fn confusion_matrix(
&self,
ground_truth: &DatasetBase<R, T>
) -> Result<ConfusionMatrix<L>>
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&self,
ground_truth: &DatasetBase<R, T>
) -> Result<ConfusionMatrix<L>>
impl<L: Label, S: Data<Elem = L>, T: AsTargets<Elem = L> + Labels<Elem = L>, R: Records> ToConfusionMatrix<L, &'_ DatasetBase<R, T>> for ArrayBase<S, Ix1>
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fn confusion_matrix(
&self,
ground_truth: &DatasetBase<R, T>
) -> Result<ConfusionMatrix<L>>
[src]
&self,
ground_truth: &DatasetBase<R, T>
) -> Result<ConfusionMatrix<L>>
Auto Trait Implementations
impl<R, T> RefUnwindSafe for DatasetBase<R, T> where
R: RefUnwindSafe,
T: RefUnwindSafe,
R: RefUnwindSafe,
T: RefUnwindSafe,
impl<R, T> Send for DatasetBase<R, T> where
R: Send,
T: Send,
R: Send,
T: Send,
impl<R, T> Sync for DatasetBase<R, T> where
R: Sync,
T: Sync,
R: Sync,
T: Sync,
impl<R, T> Unpin for DatasetBase<R, T> where
R: Unpin,
T: Unpin,
R: Unpin,
T: Unpin,
impl<R, T> UnwindSafe for DatasetBase<R, T> where
R: UnwindSafe,
T: UnwindSafe,
R: UnwindSafe,
T: UnwindSafe,
Blanket Implementations
impl<T> Any for T where
T: 'static + ?Sized,
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T: 'static + ?Sized,
impl<T> Borrow<T> for T where
T: ?Sized,
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T: ?Sized,
impl<T> BorrowMut<T> for T where
T: ?Sized,
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T: ?Sized,
pub fn borrow_mut(&mut self) -> &mut T
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impl<T> From<T> for T
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impl<T, U> Into<U> for T where
U: From<T>,
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U: From<T>,
impl<'a, F, R, T, S, O> Predict<&'a DatasetBase<R, T>, S> for O where
F: Float,
R: Records<Elem = F>,
O: PredictRef<R, S>,
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F: Float,
R: Records<Elem = F>,
O: PredictRef<R, S>,
pub fn predict(&Self, &'a DatasetBase<R, T>) -> S
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impl<F, D, T, O> Predict<ArrayBase<D, Dim<[usize; 2]>>, DatasetBase<ArrayBase<D, Dim<[usize; 2]>>, T>> for O where
F: Float,
D: Data<Elem = F>,
O: PredictRef<ArrayBase<D, Dim<[usize; 2]>>, T>,
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F: Float,
D: Data<Elem = F>,
O: PredictRef<ArrayBase<D, Dim<[usize; 2]>>, T>,
pub fn predict(
&Self,
ArrayBase<D, Dim<[usize; 2]>>
) -> DatasetBase<ArrayBase<D, Dim<[usize; 2]>>, T>
[src]
&Self,
ArrayBase<D, Dim<[usize; 2]>>
) -> DatasetBase<ArrayBase<D, Dim<[usize; 2]>>, T>
impl<F, R, T, S, O> Predict<DatasetBase<R, T>, DatasetBase<R, S>> for O where
F: Float,
R: Records<Elem = F>,
O: PredictRef<R, S>,
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F: Float,
R: Records<Elem = F>,
O: PredictRef<R, S>,
pub fn predict(&Self, DatasetBase<R, T>) -> DatasetBase<R, S>
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impl<T, U> TryFrom<U> for T where
U: Into<T>,
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U: Into<T>,
type Error = Infallible
The type returned in the event of a conversion error.
pub fn try_from(value: U) -> Result<T, <T as TryFrom<U>>::Error>
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impl<T, U> TryInto<U> for T where
U: TryFrom<T>,
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U: TryFrom<T>,
type Error = <U as TryFrom<T>>::Error
The type returned in the event of a conversion error.
pub fn try_into(self) -> Result<U, <U as TryFrom<T>>::Error>
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impl<V, T> VZip<V> for T where
V: MultiLane<T>,
V: MultiLane<T>,