Struct linfa::dataset::DatasetBase
source · pub struct DatasetBase<R, T>where
R: Records,{
pub records: R,
pub targets: T,
pub weights: Array1<f32>,
/* private fields */
}
Expand description
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
§weights: Array1<f32>
Implementations§
source§impl<F: Float, D: Data<Elem = F>, T> DatasetBase<ArrayBase<D, Ix2>, T>
impl<F: Float, D: Data<Elem = F>, T> DatasetBase<ArrayBase<D, Ix2>, T>
sourcepub fn pearson_correlation(&self) -> PearsonCorrelation<F>
pub fn pearson_correlation(&self) -> PearsonCorrelation<F>
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);
sourcepub fn pearson_correlation_with_p_value(
&self,
num_iter: usize
) -> PearsonCorrelation<F>
pub fn pearson_correlation_with_p_value(
&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);
source§impl<R: Records, S> DatasetBase<R, S>
impl<R: Records, S> DatasetBase<R, S>
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.
sourcepub fn new(records: R, targets: S) -> DatasetBase<R, S>
pub fn new(records: R, targets: S) -> DatasetBase<R, S>
Create a new dataset from records and targets
Example
let dataset = Dataset::new(records, targets);
sourcepub fn weight_for(&self, idx: usize) -> f32
pub fn weight_for(&self, idx: usize) -> f32
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
.
sourcepub fn feature_names(&self) -> Vec<String>
pub fn feature_names(&self) -> Vec<String>
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.
sourcepub fn records(&self) -> &R
pub fn records(&self) -> &R
Return records of a dataset
The records are data points from which predictions are made. This functions returns a reference to the record field.
sourcepub fn with_records<T: Records>(self, records: T) -> DatasetBase<T, S>
pub fn with_records<T: Records>(self, records: T) -> DatasetBase<T, S>
Updates the records of a dataset
This function overwrites the records in a dataset. It also invalidates the weights and feature names.
sourcepub fn with_targets<T>(self, targets: T) -> DatasetBase<R, T>
pub fn with_targets<T>(self, targets: T) -> DatasetBase<R, T>
Updates the targets of a dataset
This function overwrites the targets in a dataset.
sourcepub fn with_weights(self, weights: Array1<f32>) -> DatasetBase<R, S>
pub fn with_weights(self, weights: Array1<f32>) -> DatasetBase<R, S>
Updates the weights of a dataset
sourcepub fn with_feature_names<I: Into<String>>(
self,
names: Vec<I>
) -> DatasetBase<R, S>
pub fn with_feature_names<I: Into<String>>(
self,
names: Vec<I>
) -> DatasetBase<R, S>
Updates the feature names of a dataset
source§impl<L, R: Records, T: AsTargets<Elem = L>> DatasetBase<R, T>
impl<L, R: Records, T: AsTargets<Elem = L>> DatasetBase<R, T>
sourcepub fn map_targets<S, G: FnMut(&L) -> S>(
self,
fnc: G
) -> DatasetBase<R, Array<S, T::Ix>>
pub fn map_targets<S, G: FnMut(&L) -> S>(
self,
fnc: G
) -> DatasetBase<R, Array<S, T::Ix>>
source§impl<'a, F, L, D, T> DatasetBase<ArrayBase<D, Ix2>, T>where
D: Data<Elem = F>,
T: AsTargets<Elem = L>,
impl<'a, F, L, D, T> DatasetBase<ArrayBase<D, Ix2>, T>where
D: Data<Elem = F>,
T: AsTargets<Elem = L>,
sourcepub fn sample_iter(&'a self) -> Iter<'a, '_, F, T::Elem, T::Ix>
pub fn sample_iter(&'a self) -> Iter<'a, '_, F, T::Elem, T::Ix>
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.
For multi-target datasets, the yielded target value is ArrayView1
consisting of the
different targets. For single-target datasets, the target value is ArrayView0
containing
the single target.
Example
let dataset = linfa_datasets::iris();
for (x, y) in dataset.sample_iter() {
println!("{} => {}", x, y);
}
source§impl<'a, F: 'a, L: 'a, D, T> DatasetBase<ArrayBase<D, Ix2>, T>where
D: Data<Elem = F>,
T: AsTargets<Elem = L> + FromTargetArray<'a>,
impl<'a, F: 'a, L: 'a, D, T> DatasetBase<ArrayBase<D, Ix2>, T>where
D: Data<Elem = F>,
T: AsTargets<Elem = L> + FromTargetArray<'a>,
sourcepub fn view(&'a self) -> DatasetBase<ArrayView2<'a, F>, T::View>
pub fn view(&'a self) -> DatasetBase<ArrayView2<'a, F>, T::View>
Creates a view of a dataset
sourcepub fn feature_iter(&'a self) -> DatasetIter<'a, '_, ArrayBase<D, Ix2>, T>
pub fn feature_iter(&'a self) -> DatasetIter<'a, '_, ArrayBase<D, Ix2>, T>
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.
sourcepub fn target_iter(&'a self) -> DatasetIter<'a, '_, ArrayBase<D, Ix2>, T>
pub fn target_iter(&'a self) -> DatasetIter<'a, '_, ArrayBase<D, Ix2>, T>
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.
source§impl<'a, L: 'a, F, T> DatasetBase<ArrayView2<'a, F>, T>where
T: AsTargets<Elem = L> + FromTargetArray<'a>,
impl<'a, L: 'a, F, T> DatasetBase<ArrayView2<'a, F>, T>where
T: AsTargets<Elem = L> + FromTargetArray<'a>,
sourcepub fn split_with_ratio(
&'a self,
ratio: f32
) -> (DatasetBase<ArrayView2<'a, F>, T::View>, DatasetBase<ArrayView2<'a, F>, T::View>)
pub fn split_with_ratio(
&'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.
source§impl<'a, 'b: 'a, F, L: Label, T, D> DatasetBase<ArrayBase<D, Ix2>, T>where
D: Data<Elem = F>,
T: AsSingleTargets<Elem = L> + Labels<Elem = L>,
impl<'a, 'b: 'a, F, L: Label, T, D> DatasetBase<ArrayBase<D, Ix2>, T>where
D: Data<Elem = F>,
T: AsSingleTargets<Elem = L> + Labels<Elem = L>,
sourcepub fn one_vs_all(
&self
) -> Result<Vec<(L, DatasetBase<ArrayView2<'_, F>, CountedTargets<bool, Array1<bool>>>)>>
pub fn one_vs_all(
&self
) -> Result<Vec<(L, DatasetBase<ArrayView2<'_, F>, CountedTargets<bool, Array1<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 single-target views of the same dataset.
source§impl<L: Label, R: Records, S: AsTargets<Elem = L>> DatasetBase<R, S>
impl<L: Label, R: Records, S: AsTargets<Elem = L>> DatasetBase<R, S>
sourcepub fn label_frequencies_with_mask(&self, mask: &[bool]) -> HashMap<L, f32>
pub fn label_frequencies_with_mask(&self, mask: &[bool]) -> HashMap<L, f32>
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
sourcepub fn label_frequencies(&self) -> HashMap<L, f32>
pub fn label_frequencies(&self) -> HashMap<L, f32>
Calculates label frequencies from a dataset
source§impl<'b, F: Clone, E: Copy + 'b, D, T> DatasetBase<ArrayBase<D, Ix2>, T>where
D: Data<Elem = F>,
T: FromTargetArray<'b, Elem = E>,
T::Owned: AsTargets,
impl<'b, F: Clone, E: Copy + 'b, D, T> DatasetBase<ArrayBase<D, Ix2>, T>where
D: Data<Elem = F>,
T: FromTargetArray<'b, Elem = E>,
T::Owned: AsTargets,
sourcepub 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>>::Owned>> + 'b
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>>::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
sourcepub fn bootstrap_samples<R: Rng>(
&'b self,
num_samples: usize,
rng: &'b mut R
) -> impl Iterator<Item = DatasetBase<Array2<F>, <T as FromTargetArray<'b>>::Owned>> + 'b
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>>::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
sourcepub fn bootstrap_features<R: Rng>(
&'b self,
num_features: usize,
rng: &'b mut R
) -> impl Iterator<Item = DatasetBase<Array2<F>, <T as FromTargetArray<'b>>::Owned>> + 'b
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>>::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
sourcepub fn shuffle<R: Rng>(&self, rng: &mut R) -> DatasetBase<Array2<F>, T::Owned>
pub fn shuffle<R: Rng>(&self, rng: &mut R) -> DatasetBase<Array2<F>, T::Owned>
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
sourcepub fn fold(
&self,
k: usize
) -> Vec<(DatasetBase<Array2<F>, T::Owned>, DatasetBase<Array2<F>, T::Owned>)>
pub fn fold(
&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
“folds”, 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::{Ix1, 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, Ix1> = (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>
pub fn to_owned(&self) -> DatasetBase<Array2<F>, T::Owned>
source§impl<'a, F: 'a + Clone, E: Copy + 'a, D, S, I: TargetDim> DatasetBase<ArrayBase<D, Ix2>, ArrayBase<S, I>>where
D: DataMut<Elem = F>,
S: DataMut<Elem = E>,
impl<'a, F: 'a + Clone, E: Copy + 'a, D, S, I: TargetDim> DatasetBase<ArrayBase<D, Ix2>, ArrayBase<S, I>>where
D: DataMut<Elem = F>,
S: DataMut<Elem = E>,
sourcepub fn iter_fold<O, C: Fn(&DatasetView<'_, F, E, I>) -> O>(
&'a mut self,
k: usize,
fit_closure: C
) -> impl Iterator<Item = (O, DatasetBase<ArrayView2<'_, F>, ArrayView<'_, E, I>>)>
pub fn iter_fold<O, C: Fn(&DatasetView<'_, F, E, I>) -> O>(
&'a mut self,
k: usize,
fit_closure: C
) -> impl Iterator<Item = (O, DatasetBase<ArrayView2<'_, F>, ArrayView<'_, E, I>>)>
Performs k-folding cross validation on fittable algorithms.
Given a dataset as input, 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, Records};
use ndarray::{array, ArrayView1, ArrayView2, Ix1};
use linfa::Error;
struct MockFittable {}
struct MockFittableResult {
mock_var: usize,
}
impl<'a> Fit<ArrayView2<'a,f64>, ArrayView1<'a, f64>, linfa::error::Error> for MockFittable {
type Object = MockFittableResult;
fn fit(&self, training_data: &DatasetView<f64, f64, Ix1>) -> Result<Self::Object, linfa::error::Error> {
Ok(MockFittableResult {
mock_var: training_data.nsamples(),
})
}
}
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, Ix1> = (records, targets).into();
let params = MockFittable {};
for (model,validation_set) in dataset.iter_fold(5, |v| params.fit(v).unwrap()){
// Here you can use `model` and `validation_set` to
// assert the performance of the chosen algorithm
}
sourcepub fn cross_validate<O, ER, M, FACC, C>(
&'a mut self,
k: usize,
parameters: &[M],
eval: C
) -> Result<Array<FACC, I>, ER>where
ER: Error + From<Error>,
M: for<'c> Fit<ArrayView2<'c, F>, ArrayView<'c, E, I>, ER, Object = O>,
O: for<'d> PredictInplace<ArrayView2<'a, F>, Array<E, I>>,
FACC: Float,
C: Fn(&Array<E, I>, &ArrayView<'_, E, I>) -> Result<Array<FACC, I::Smaller>, Error>,
pub fn cross_validate<O, ER, M, FACC, C>(
&'a mut self,
k: usize,
parameters: &[M],
eval: C
) -> Result<Array<FACC, I>, ER>where
ER: Error + From<Error>,
M: for<'c> Fit<ArrayView2<'c, F>, ArrayView<'c, E, I>, ER, Object = O>,
O: for<'d> PredictInplace<ArrayView2<'a, F>, Array<E, I>>,
FACC: Float,
C: Fn(&Array<E, I>, &ArrayView<'_, E, I>) -> Result<Array<FACC, I::Smaller>, Error>,
Cross validation for single and multi-target algorithms
Given a list of fittable models, cross validation is used to compare their performance according to some performance metric. To do so, k-folding is applied to the dataset and, for each fold, each model is trained on the training set and its performance is evaluated on the validation set. The performances collected for each model are then averaged over the number of folds.
For single-target datasets, Dataset::cross_validate_single
is recommended.
Parameters:
k
: the number of folds to applyparameters
: a list of models to compareeval
: closure used to evaluate the performance of each trained model. This closure is called on the model output and validation targets of each fold and outputs the performance score for each target. For single-target dataset the signature is(Array1, Array1) -> Array0
. For multi-target dataset the signature is(Array2, Array2) -> Array1
.
Returns
An array of model performances, for each model and each target, if no errors occur.
For multi-target dataset, the array has dimensions (n_models, n_targets)
. For
single-target dataset, the array has dimensions (n_models)
.
Otherwise, it might return an Error in one of the following cases:
- An error occurred during the fitting of one model
- An error occurred inside the evaluation closure
Example
use linfa::prelude::*;
use ndarray::arr0;
// mutability needed for fast cross validation
let mut dataset = linfa_datasets::diabetes();
let models = vec![model1, model2];
let r2_scores = dataset.cross_validate(5, &models, |prediction, truth| prediction.r2(truth).map(arr0))?;
source§impl<'a, F: 'a + Clone, E: Copy + 'a, D, S> DatasetBase<ArrayBase<D, Ix2>, ArrayBase<S, Ix1>>where
D: DataMut<Elem = F>,
S: DataMut<Elem = E>,
impl<'a, F: 'a + Clone, E: Copy + 'a, D, S> DatasetBase<ArrayBase<D, Ix2>, ArrayBase<S, Ix1>>where
D: DataMut<Elem = F>,
S: DataMut<Elem = E>,
sourcepub fn cross_validate_single<O, ER, M, FACC, C>(
&'a mut self,
k: usize,
parameters: &[M],
eval: C
) -> Result<Array1<FACC>, ER>where
ER: Error + From<Error>,
M: for<'c> Fit<ArrayView2<'c, F>, ArrayView1<'c, E>, ER, Object = O>,
O: for<'d> PredictInplace<ArrayView2<'a, F>, Array1<E>>,
FACC: Float,
C: Fn(&Array1<E>, &ArrayView1<'_, E>) -> Result<FACC, Error>,
pub fn cross_validate_single<O, ER, M, FACC, C>(
&'a mut self,
k: usize,
parameters: &[M],
eval: C
) -> Result<Array1<FACC>, ER>where
ER: Error + From<Error>,
M: for<'c> Fit<ArrayView2<'c, F>, ArrayView1<'c, E>, ER, Object = O>,
O: for<'d> PredictInplace<ArrayView2<'a, F>, Array1<E>>,
FACC: Float,
C: Fn(&Array1<E>, &ArrayView1<'_, E>) -> Result<FACC, Error>,
Specialized version of cross_validate
for single-target datasets. Allows the evaluation
closure to return a float without wrapping it in arr0
. See [Dataset.cross_validate
] for
more details.
source§impl<F, E, I: TargetDim> DatasetBase<ArrayBase<OwnedRepr<D>, Dim<[usize; 2]>>, ArrayBase<OwnedRepr<T>, I>>
impl<F, E, I: TargetDim> DatasetBase<ArrayBase<OwnedRepr<D>, Dim<[usize; 2]>>, ArrayBase<OwnedRepr<T>, I>>
sourcepub fn split_with_ratio(self, ratio: f32) -> (Self, Self)
pub fn split_with_ratio(self, ratio: f32) -> (Self, Self)
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.
Panics
Panic occurs when the input record or targets are not in row-major layout.
source§impl<F: Copy, L: Copy + Label, D, T> DatasetBase<ArrayBase<D, Ix2>, T>where
D: Data<Elem = F>,
T: AsTargets<Elem = L>,
impl<F: Copy, L: Copy + Label, D, T> DatasetBase<ArrayBase<D, Ix2>, T>where
D: Data<Elem = F>,
T: AsTargets<Elem = L>,
sourcepub fn with_labels(
&self,
labels: &[L]
) -> DatasetBase<Array2<F>, CountedTargets<L, Array<L, T::Ix>>>
pub fn with_labels(
&self,
labels: &[L]
) -> DatasetBase<Array2<F>, CountedTargets<L, Array<L, T::Ix>>>
Transforms the input dataset by keeping only those samples whose label appears in labels
.
In the multi-target case a sample is kept if any of its targets appears in labels
.
Sample weights and feature names are preserved by this transformation.
Trait Implementations§
source§impl<L, R: Records, T: AsTargetsMut<Elem = L>> AsTargetsMut for DatasetBase<R, T>
impl<L, R: Records, T: AsTargetsMut<Elem = L>> AsTargetsMut for DatasetBase<R, T>
type Elem = L
type Ix = <T as AsTargetsMut>::Ix
fn as_targets_mut(&mut self) -> ArrayViewMut<'_, Self::Elem, Self::Ix>
source§impl<R: Records, R2: Records, T: AsSingleTargets<Elem = bool>, T2: AsSingleTargets<Elem = Pr>> BinaryClassification<&DatasetBase<R, T>> for DatasetBase<R2, T2>
impl<R: Records, R2: Records, T: AsSingleTargets<Elem = bool>, T2: AsSingleTargets<Elem = Pr>> BinaryClassification<&DatasetBase<R, T>> for DatasetBase<R2, T2>
source§fn log_loss(&self, y: &DatasetBase<R, T>) -> Result<f32>
fn log_loss(&self, y: &DatasetBase<R, T>) -> Result<f32>
Log loss of the probabilities of the binary target
fn roc(&self, y: &DatasetBase<R, T>) -> Result<ReceiverOperatingCharacteristic>
source§impl<R: Clone, T: Clone> Clone for DatasetBase<R, T>where
R: Records,
impl<R: Clone, T: Clone> Clone for DatasetBase<R, T>where
R: Records,
source§fn clone(&self) -> DatasetBase<R, T>
fn clone(&self) -> DatasetBase<R, T>
1.0.0 · source§fn clone_from(&mut self, source: &Self)
fn clone_from(&mut self, source: &Self)
source
. Read moresource§impl<F, E, D, S, I: TargetDim> From<(ArrayBase<D, Dim<[usize; 2]>>, ArrayBase<S, I>)> for DatasetBase<ArrayBase<D, Ix2>, ArrayBase<S, I>>where
D: Data<Elem = F>,
S: Data<Elem = E>,
impl<F, E, D, S, I: TargetDim> From<(ArrayBase<D, Dim<[usize; 2]>>, ArrayBase<S, I>)> for DatasetBase<ArrayBase<D, Ix2>, ArrayBase<S, I>>where
D: Data<Elem = F>,
S: Data<Elem = E>,
source§impl<F, D: Data<Elem = F>, I: Dimension> From<ArrayBase<D, I>> for DatasetBase<ArrayBase<D, I>, Array1<()>>
impl<F, D: Data<Elem = F>, I: Dimension> From<ArrayBase<D, I>> for DatasetBase<ArrayBase<D, I>, Array1<()>>
source§impl<F: Float, T: AsMultiTargets<Elem = F>, T2: AsMultiTargets<Elem = F>, D: Data<Elem = F>> MultiTargetRegression<F, T2> for DatasetBase<ArrayBase<D, Ix2>, T>
impl<F: Float, T: AsMultiTargets<Elem = F>, T2: AsMultiTargets<Elem = F>, D: Data<Elem = F>> MultiTargetRegression<F, T2> for DatasetBase<ArrayBase<D, Ix2>, T>
source§fn max_error(&self, other: &T) -> Result<Array1<F>>
fn max_error(&self, other: &T) -> Result<Array1<F>>
source§fn mean_absolute_error(&self, other: &T) -> Result<Array1<F>>
fn mean_absolute_error(&self, other: &T) -> Result<Array1<F>>
source§fn mean_squared_error(&self, other: &T) -> Result<Array1<F>>
fn mean_squared_error(&self, other: &T) -> Result<Array1<F>>
source§fn mean_squared_log_error(&self, other: &T) -> Result<Array1<F>>
fn mean_squared_log_error(&self, other: &T) -> Result<Array1<F>>
source§fn median_absolute_error(&self, other: &T) -> Result<Array1<F>>
fn median_absolute_error(&self, other: &T) -> Result<Array1<F>>
source§fn mean_absolute_percentage_error(&self, other: &T) -> Result<Array1<F>>
fn mean_absolute_percentage_error(&self, other: &T) -> Result<Array1<F>>
source§impl<R: PartialEq, T: PartialEq> PartialEq<DatasetBase<R, T>> for DatasetBase<R, T>where
R: Records,
impl<R: PartialEq, T: PartialEq> PartialEq<DatasetBase<R, T>> for DatasetBase<R, T>where
R: Records,
source§fn eq(&self, other: &DatasetBase<R, T>) -> bool
fn eq(&self, other: &DatasetBase<R, T>) -> bool
source§impl<F, D: Records<Elem = F>, T> Records for DatasetBase<D, T>
impl<F, D: Records<Elem = F>, T> Records for DatasetBase<D, T>
Implement records for a DatasetBase