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feat: refactoring, adds Result to most public API

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This commit is contained in:
Volodymyr Orlov
2020-09-18 15:20:32 -07:00
parent 4921ae76f5
commit a9db970195
24 changed files with 389 additions and 298 deletions
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src/tree/decision_tree_classifier.rs
+20 -11
View File
@@ -50,9 +50,9 @@
//! let y = vec![ 0., 0., 0., 0., 0., 0., 0., 0.,
//! 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.];
//!
//! let tree = DecisionTreeClassifier::fit(&x, &y, Default::default());
//! let tree = DecisionTreeClassifier::fit(&x, &y, Default::default()).unwrap();
//!
//! let y_hat = tree.predict(&x); // use the same data for prediction
//! let y_hat = tree.predict(&x).unwrap(); // use the same data for prediction
//! ```
//!
//!
@@ -71,6 +71,7 @@ use rand::seq::SliceRandom;
use serde::{Deserialize, Serialize};
use crate::algorithm::sort::quick_sort::QuickArgSort;
use crate::error::Failed;
use crate::linalg::Matrix;
use crate::math::num::RealNumber;
@@ -276,7 +277,7 @@ impl<T: RealNumber> DecisionTreeClassifier<T> {
x: &M,
y: &M::RowVector,
parameters: DecisionTreeClassifierParameters,
) -> DecisionTreeClassifier<T> {
) -> Result<DecisionTreeClassifier<T>, Failed> {
let (x_nrows, num_attributes) = x.shape();
let samples = vec![1; x_nrows];
DecisionTreeClassifier::fit_weak_learner(x, y, samples, num_attributes, parameters)
@@ -288,14 +289,17 @@ impl<T: RealNumber> DecisionTreeClassifier<T> {
samples: Vec<usize>,
mtry: usize,
parameters: DecisionTreeClassifierParameters,
) -> DecisionTreeClassifier<T> {
) -> Result<DecisionTreeClassifier<T>, Failed> {
let y_m = M::from_row_vector(y.clone());
let (_, y_ncols) = y_m.shape();
let (_, num_attributes) = x.shape();
let classes = y_m.unique();
let k = classes.len();
if k < 2 {
panic!("Incorrect number of classes: {}. Should be >= 2.", k);
return Err(Failed::fit(&format!(
"Incorrect number of classes: {}. Should be >= 2.",
k
)));
}
let mut yi: Vec<usize> = vec![0; y_ncols];
@@ -343,12 +347,12 @@ impl<T: RealNumber> DecisionTreeClassifier<T> {
};
}
tree
Ok(tree)
}
/// Predict class value for `x`.
/// * `x` - _KxM_ data where _K_ is number of observations and _M_ is number of features.
pub fn predict<M: Matrix<T>>(&self, x: &M) -> M::RowVector {
pub fn predict<M: Matrix<T>>(&self, x: &M) -> Result<M::RowVector, Failed> {
let mut result = M::zeros(1, x.shape().0);
let (n, _) = x.shape();
@@ -357,7 +361,7 @@ impl<T: RealNumber> DecisionTreeClassifier<T> {
result.set(0, i, self.classes[self.predict_for_row(x, i)]);
}
result.to_row_vector()
Ok(result.to_row_vector())
}
pub(in crate) fn predict_for_row<M: Matrix<T>>(&self, x: &M, row: usize) -> usize {
@@ -637,7 +641,9 @@ mod tests {
assert_eq!(
y,
DecisionTreeClassifier::fit(&x, &y, Default::default()).predict(&x)
DecisionTreeClassifier::fit(&x, &y, Default::default())
.and_then(|t| t.predict(&x))
.unwrap()
);
assert_eq!(
@@ -652,6 +658,7 @@ mod tests {
min_samples_split: 2
}
)
.unwrap()
.depth
);
}
@@ -686,7 +693,9 @@ mod tests {
assert_eq!(
y,
DecisionTreeClassifier::fit(&x, &y, Default::default()).predict(&x)
DecisionTreeClassifier::fit(&x, &y, Default::default())
.and_then(|t| t.predict(&x))
.unwrap()
);
}
@@ -718,7 +727,7 @@ mod tests {
1., 1., 0., 0., 0., 1., 1., 0., 0., 0., 1., 1., 0., 0., 0., 1., 1., 0., 0., 0.,
];
let tree = DecisionTreeClassifier::fit(&x, &y, Default::default());
let tree = DecisionTreeClassifier::fit(&x, &y, Default::default()).unwrap();
let deserialized_tree: DecisionTreeClassifier<f64> =
bincode::deserialize(&bincode::serialize(&tree).unwrap()).unwrap();
src/tree/decision_tree_regressor.rs
+16 -16
View File
@@ -45,9 +45,9 @@
//! 101.2, 104.6, 108.4, 110.8, 112.6, 114.2, 115.7, 116.9,
//! ];
//!
//! let tree = DecisionTreeRegressor::fit(&x, &y, Default::default());
//! let tree = DecisionTreeRegressor::fit(&x, &y, Default::default()).unwrap();
//!
//! let y_hat = tree.predict(&x); // use the same data for prediction
//! let y_hat = tree.predict(&x).unwrap(); // use the same data for prediction
//! ```
//!
//! ## References:
@@ -66,6 +66,7 @@ use rand::seq::SliceRandom;
use serde::{Deserialize, Serialize};
use crate::algorithm::sort::quick_sort::QuickArgSort;
use crate::error::Failed;
use crate::linalg::Matrix;
use crate::math::num::RealNumber;
@@ -196,7 +197,7 @@ impl<T: RealNumber> DecisionTreeRegressor<T> {
x: &M,
y: &M::RowVector,
parameters: DecisionTreeRegressorParameters,
) -> DecisionTreeRegressor<T> {
) -> Result<DecisionTreeRegressor<T>, Failed> {
let (x_nrows, num_attributes) = x.shape();
let samples = vec![1; x_nrows];
DecisionTreeRegressor::fit_weak_learner(x, y, samples, num_attributes, parameters)
@@ -208,16 +209,11 @@ impl<T: RealNumber> DecisionTreeRegressor<T> {
samples: Vec<usize>,
mtry: usize,
parameters: DecisionTreeRegressorParameters,
) -> DecisionTreeRegressor<T> {
) -> Result<DecisionTreeRegressor<T>, Failed> {
let y_m = M::from_row_vector(y.clone());
let (_, y_ncols) = y_m.shape();
let (_, num_attributes) = x.shape();
let classes = y_m.unique();
let k = classes.len();
if k < 2 {
panic!("Incorrect number of classes: {}. Should be >= 2.", k);
}
let mut nodes: Vec<Node<T>> = Vec::new();
@@ -257,12 +253,12 @@ impl<T: RealNumber> DecisionTreeRegressor<T> {
};
}
tree
Ok(tree)
}
/// Predict regression value for `x`.
/// * `x` - _KxM_ data where _K_ is number of observations and _M_ is number of features.
pub fn predict<M: Matrix<T>>(&self, x: &M) -> M::RowVector {
pub fn predict<M: Matrix<T>>(&self, x: &M) -> Result<M::RowVector, Failed> {
let mut result = M::zeros(1, x.shape().0);
let (n, _) = x.shape();
@@ -271,7 +267,7 @@ impl<T: RealNumber> DecisionTreeRegressor<T> {
result.set(0, i, self.predict_for_row(x, i));
}
result.to_row_vector()
Ok(result.to_row_vector())
}
pub(in crate) fn predict_for_row<M: Matrix<T>>(&self, x: &M, row: usize) -> T {
@@ -498,7 +494,9 @@ mod tests {
114.2, 115.7, 116.9,
];
let y_hat = DecisionTreeRegressor::fit(&x, &y, Default::default()).predict(&x);
let y_hat = DecisionTreeRegressor::fit(&x, &y, Default::default())
.and_then(|t| t.predict(&x))
.unwrap();
for i in 0..y_hat.len() {
assert!((y_hat[i] - y[i]).abs() < 0.1);
@@ -517,7 +515,8 @@ mod tests {
min_samples_split: 6,
},
)
.predict(&x);
.and_then(|t| t.predict(&x))
.unwrap();
for i in 0..y_hat.len() {
assert!((y_hat[i] - expected_y[i]).abs() < 0.1);
@@ -536,7 +535,8 @@ mod tests {
min_samples_split: 3,
},
)
.predict(&x);
.and_then(|t| t.predict(&x))
.unwrap();
for i in 0..y_hat.len() {
assert!((y_hat[i] - expected_y[i]).abs() < 0.1);
@@ -568,7 +568,7 @@ mod tests {
114.2, 115.7, 116.9,
];
let tree = DecisionTreeRegressor::fit(&x, &y, Default::default());
let tree = DecisionTreeRegressor::fit(&x, &y, Default::default()).unwrap();
let deserialized_tree: DecisionTreeRegressor<f64> =
bincode::deserialize(&bincode::serialize(&tree).unwrap()).unwrap();