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Run cargo clippy --fix (#250)

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* Run `cargo clippy --fix`
* Run `cargo clippy --all-features --fix`
* Fix other clippy warnings
* cargo fmt

Co-authored-by: Luis Moreno <morenol@users.noreply.github.com>
This commit is contained in:
morenol
2023-01-27 06:41:18 -04:00
committed by GitHub
parent 83dcf9a8ac
commit c7353d0b57
47 changed files with 146 additions and 222 deletions
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src/naive_bayes/bernoulli.rs
+4 -8
View File
@@ -271,21 +271,18 @@ impl<TY: Number + Ord + Unsigned> BernoulliNBDistribution<TY> {
let y_samples = y.shape();
if y_samples != n_samples {
return Err(Failed::fit(&format!(
"Size of x should equal size of y; |x|=[{}], |y|=[{}]",
n_samples, y_samples
"Size of x should equal size of y; |x|=[{n_samples}], |y|=[{y_samples}]"
)));
}
if n_samples == 0 {
return Err(Failed::fit(&format!(
"Size of x and y should greater than 0; |x|=[{}]",
n_samples
"Size of x and y should greater than 0; |x|=[{n_samples}]"
)));
}
if alpha < 0f64 {
return Err(Failed::fit(&format!(
"Alpha should be greater than 0; |alpha|=[{}]",
alpha
"Alpha should be greater than 0; |alpha|=[{alpha}]"
)));
}
@@ -318,8 +315,7 @@ impl<TY: Number + Ord + Unsigned> BernoulliNBDistribution<TY> {
feature_in_class_counter[class_index][idx] +=
row_i.to_usize().ok_or_else(|| {
Failed::fit(&format!(
"Elements of the matrix should be 1.0 or 0.0 |found|=[{}]",
row_i
"Elements of the matrix should be 1.0 or 0.0 |found|=[{row_i}]"
))
})?;
}
src/naive_bayes/categorical.rs
+4 -9
View File
@@ -158,8 +158,7 @@ impl<T: Number + Unsigned> CategoricalNBDistribution<T> {
pub fn fit<X: Array2<T>, Y: Array1<T>>(x: &X, y: &Y, alpha: f64) -> Result<Self, Failed> {
if alpha < 0f64 {
return Err(Failed::fit(&format!(
"alpha should be >= 0, alpha=[{}]",
alpha
"alpha should be >= 0, alpha=[{alpha}]"
)));
}
@@ -167,15 +166,13 @@ impl<T: Number + Unsigned> CategoricalNBDistribution<T> {
let y_samples = y.shape();
if y_samples != n_samples {
return Err(Failed::fit(&format!(
"Size of x should equal size of y; |x|=[{}], |y|=[{}]",
n_samples, y_samples
"Size of x should equal size of y; |x|=[{n_samples}], |y|=[{y_samples}]"
)));
}
if n_samples == 0 {
return Err(Failed::fit(&format!(
"Size of x and y should greater than 0; |x|=[{}]",
n_samples
"Size of x and y should greater than 0; |x|=[{n_samples}]"
)));
}
let y: Vec<usize> = y.iterator(0).map(|y_i| y_i.to_usize().unwrap()).collect();
@@ -202,8 +199,7 @@ impl<T: Number + Unsigned> CategoricalNBDistribution<T> {
.max()
.ok_or_else(|| {
Failed::fit(&format!(
"Failed to get the categories for feature = {}",
feature
"Failed to get the categories for feature = {feature}"
))
})?;
n_categories.push(feature_max + 1);
@@ -429,7 +425,6 @@ mod tests {
fn search_parameters() {
let parameters = CategoricalNBSearchParameters {
alpha: vec![1., 2.],
..Default::default()
};
let mut iter = parameters.into_iter();
let next = iter.next().unwrap();
src/naive_bayes/gaussian.rs
+2 -5
View File
@@ -185,15 +185,13 @@ impl<TY: Number + Ord + Unsigned> GaussianNBDistribution<TY> {
let y_samples = y.shape();
if y_samples != n_samples {
return Err(Failed::fit(&format!(
"Size of x should equal size of y; |x|=[{}], |y|=[{}]",
n_samples, y_samples
"Size of x should equal size of y; |x|=[{n_samples}], |y|=[{y_samples}]"
)));
}
if n_samples == 0 {
return Err(Failed::fit(&format!(
"Size of x and y should greater than 0; |x|=[{}]",
n_samples
"Size of x and y should greater than 0; |x|=[{n_samples}]"
)));
}
let (class_labels, indices) = y.unique_with_indices();
@@ -375,7 +373,6 @@ mod tests {
fn search_parameters() {
let parameters = GaussianNBSearchParameters {
priors: vec![Some(vec![1.]), Some(vec![2.])],
..Default::default()
};
let mut iter = parameters.into_iter();
let next = iter.next().unwrap();
src/naive_bayes/multinomial.rs
+4 -8
View File
@@ -220,21 +220,18 @@ impl<TY: Number + Ord + Unsigned> MultinomialNBDistribution<TY> {
let y_samples = y.shape();
if y_samples != n_samples {
return Err(Failed::fit(&format!(
"Size of x should equal size of y; |x|=[{}], |y|=[{}]",
n_samples, y_samples
"Size of x should equal size of y; |x|=[{n_samples}], |y|=[{y_samples}]"
)));
}
if n_samples == 0 {
return Err(Failed::fit(&format!(
"Size of x and y should greater than 0; |x|=[{}]",
n_samples
"Size of x and y should greater than 0; |x|=[{n_samples}]"
)));
}
if alpha < 0f64 {
return Err(Failed::fit(&format!(
"Alpha should be greater than 0; |alpha|=[{}]",
alpha
"Alpha should be greater than 0; |alpha|=[{alpha}]"
)));
}
@@ -266,8 +263,7 @@ impl<TY: Number + Ord + Unsigned> MultinomialNBDistribution<TY> {
feature_in_class_counter[class_index][idx] +=
row_i.to_usize().ok_or_else(|| {
Failed::fit(&format!(
"Elements of the matrix should be convertible to usize |found|=[{}]",
row_i
"Elements of the matrix should be convertible to usize |found|=[{row_i}]"
))
})?;
}