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Run: cargo clippy --fix -Z unstable-options and cargo fmt

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This commit is contained in:
Luis Moreno
2020-11-08 19:39:11 -04:00
parent 8281a1620e
commit 860056c3ba
48 changed files with 367 additions and 395 deletions
Download Patch File Download Diff File Expand all files Collapse all files
src/tree/decision_tree_classifier.rs
+21 -22
View File
@@ -126,7 +126,7 @@ impl<T: RealNumber> PartialEq for DecisionTreeClassifier<T> {
|| self.num_classes != other.num_classes
|| self.nodes.len() != other.nodes.len()
{
return false;
false
} else {
for i in 0..self.classes.len() {
if (self.classes[i] - other.classes[i]).abs() > T::epsilon() {
@@ -138,7 +138,7 @@ impl<T: RealNumber> PartialEq for DecisionTreeClassifier<T> {
return false;
}
}
return true;
true
}
}
}
@@ -174,8 +174,8 @@ impl Default for DecisionTreeClassifierParameters {
impl<T: RealNumber> Node<T> {
fn new(index: usize, output: usize) -> Self {
Node {
index: index,
output: output,
index,
output,
split_feature: 0,
split_value: Option::None,
split_score: Option::None,
@@ -206,7 +206,7 @@ fn impurity<T: RealNumber>(criterion: &SplitCriterion, count: &Vec<usize>, n: us
for i in 0..count.len() {
if count[i] > 0 {
let p = T::from(count[i]).unwrap() / T::from(n).unwrap();
impurity = impurity - p * p;
impurity -= p * p;
}
}
}
@@ -215,7 +215,7 @@ fn impurity<T: RealNumber>(criterion: &SplitCriterion, count: &Vec<usize>, n: us
for i in 0..count.len() {
if count[i] > 0 {
let p = T::from(count[i]).unwrap() / T::from(n).unwrap();
impurity = impurity - p * p.log2();
impurity -= p * p.log2();
}
}
}
@@ -229,7 +229,7 @@ fn impurity<T: RealNumber>(criterion: &SplitCriterion, count: &Vec<usize>, n: us
}
}
return impurity;
impurity
}
impl<'a, T: RealNumber, M: Matrix<T>> NodeVisitor<'a, T, M> {
@@ -242,14 +242,14 @@ impl<'a, T: RealNumber, M: Matrix<T>> NodeVisitor<'a, T, M> {
level: u16,
) -> Self {
NodeVisitor {
x: x,
y: y,
x,
y,
node: node_id,
samples: samples,
order: order,
samples,
order,
true_child_output: 0,
false_child_output: 0,
level: level,
level,
phantom: PhantomData,
}
}
@@ -266,7 +266,7 @@ pub(in crate) fn which_max(x: &Vec<usize>) -> usize {
}
}
return which;
which
}
impl<T: RealNumber> DecisionTreeClassifier<T> {
@@ -325,10 +325,10 @@ impl<T: RealNumber> DecisionTreeClassifier<T> {
}
let mut tree = DecisionTreeClassifier {
nodes: nodes,
parameters: parameters,
nodes,
parameters,
num_classes: k,
classes: classes,
classes,
depth: 0,
};
@@ -376,19 +376,18 @@ impl<T: RealNumber> DecisionTreeClassifier<T> {
let node = &self.nodes[node_id];
if node.true_child == None && node.false_child == None {
result = node.output;
} else if x.get(row, node.split_feature) <= node.split_value.unwrap_or(T::nan())
{
queue.push_back(node.true_child.unwrap());
} else {
if x.get(row, node.split_feature) <= node.split_value.unwrap_or(T::nan()) {
queue.push_back(node.true_child.unwrap());
} else {
queue.push_back(node.false_child.unwrap());
}
queue.push_back(node.false_child.unwrap());
}
}
None => break,
};
}
return result;
result
}
fn find_best_cutoff<M: Matrix<T>>(
src/tree/decision_tree_regressor.rs
+20 -23
View File
@@ -113,8 +113,8 @@ impl Default for DecisionTreeRegressorParameters {
impl<T: RealNumber> Node<T> {
fn new(index: usize, output: T) -> Self {
Node {
index: index,
output: output,
index,
output,
split_feature: 0,
split_value: Option::None,
split_score: Option::None,
@@ -144,14 +144,14 @@ impl<T: RealNumber> PartialEq for Node<T> {
impl<T: RealNumber> PartialEq for DecisionTreeRegressor<T> {
fn eq(&self, other: &Self) -> bool {
if self.depth != other.depth || self.nodes.len() != other.nodes.len() {
return false;
false
} else {
for i in 0..self.nodes.len() {
if self.nodes[i] != other.nodes[i] {
return false;
}
}
return true;
true
}
}
}
@@ -177,14 +177,14 @@ impl<'a, T: RealNumber, M: Matrix<T>> NodeVisitor<'a, T, M> {
level: u16,
) -> Self {
NodeVisitor {
x: x,
y: y,
x,
y,
node: node_id,
samples: samples,
order: order,
samples,
order,
true_child_output: T::zero(),
false_child_output: T::zero(),
level: level,
level,
}
}
}
@@ -221,7 +221,7 @@ impl<T: RealNumber> DecisionTreeRegressor<T> {
let mut sum = T::zero();
for i in 0..y_ncols {
n += samples[i];
sum = sum + T::from(samples[i]).unwrap() * y_m.get(0, i);
sum += T::from(samples[i]).unwrap() * y_m.get(0, i);
}
let root = Node::new(0, sum / T::from(n).unwrap());
@@ -233,8 +233,8 @@ impl<T: RealNumber> DecisionTreeRegressor<T> {
}
let mut tree = DecisionTreeRegressor {
nodes: nodes,
parameters: parameters,
nodes,
parameters,
depth: 0,
};
@@ -282,19 +282,18 @@ impl<T: RealNumber> DecisionTreeRegressor<T> {
let node = &self.nodes[node_id];
if node.true_child == None && node.false_child == None {
result = node.output;
} else if x.get(row, node.split_feature) <= node.split_value.unwrap_or(T::nan())
{
queue.push_back(node.true_child.unwrap());
} else {
if x.get(row, node.split_feature) <= node.split_value.unwrap_or(T::nan()) {
queue.push_back(node.true_child.unwrap());
} else {
queue.push_back(node.false_child.unwrap());
}
queue.push_back(node.false_child.unwrap());
}
}
None => break,
};
}
return result;
result
}
fn find_best_cutoff<M: Matrix<T>>(
@@ -348,8 +347,7 @@ impl<T: RealNumber> DecisionTreeRegressor<T> {
if prevx.is_nan() || visitor.x.get(*i, j) == prevx {
prevx = visitor.x.get(*i, j);
true_count += visitor.samples[*i];
true_sum =
true_sum + T::from(visitor.samples[*i]).unwrap() * visitor.y.get(0, *i);
true_sum += T::from(visitor.samples[*i]).unwrap() * visitor.y.get(0, *i);
continue;
}
@@ -360,8 +358,7 @@ impl<T: RealNumber> DecisionTreeRegressor<T> {
{
prevx = visitor.x.get(*i, j);
true_count += visitor.samples[*i];
true_sum =
true_sum + T::from(visitor.samples[*i]).unwrap() * visitor.y.get(0, *i);
true_sum += T::from(visitor.samples[*i]).unwrap() * visitor.y.get(0, *i);
continue;
}
@@ -384,7 +381,7 @@ impl<T: RealNumber> DecisionTreeRegressor<T> {
}
prevx = visitor.x.get(*i, j);
true_sum = true_sum + T::from(visitor.samples[*i]).unwrap() * visitor.y.get(0, *i);
true_sum += T::from(visitor.samples[*i]).unwrap() * visitor.y.get(0, *i);
true_count += visitor.samples[*i];
}
}