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fix: code cleanup

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This commit is contained in:
Volodymyr Orlov
2020-03-26 16:23:50 -07:00
parent 4d967858a5
commit 1257d2c19b
2 changed files with 4 additions and 37 deletions
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src/ensemble/random_forest_regressor.rs
+3 -7
View File
@@ -144,7 +144,7 @@ mod tests {
}
#[test]
fn my_fit_longley1() {
fn my_fit_longley_ndarray() {
let x = arr2(&[
[ 234.289, 235.6, 159., 107.608, 1947., 60.323],
@@ -163,9 +163,7 @@ mod tests {
[ 502.601, 393.1, 251.4, 125.368, 1960., 69.564],
[ 518.173, 480.6, 257.2, 127.852, 1961., 69.331],
[ 554.894, 400.7, 282.7, 130.081, 1962., 70.551]]);
let y = arr1(&[83.0, 88.5, 88.2, 89.5, 96.2, 98.1, 99.0, 100.0, 101.2, 104.6, 108.4, 110.8, 112.6, 114.2, 115.7, 116.9]);
println!("{:?}", y.shape());
let y = arr1(&[83.0, 88.5, 88.2, 89.5, 96.2, 98.1, 99.0, 100.0, 101.2, 104.6, 108.4, 110.8, 112.6, 114.2, 115.7, 116.9]);
let expected_y: Vec<f64> = vec![85., 88., 88., 89., 97., 98., 99., 99., 102., 104., 109., 110., 113., 114., 115., 116.];
@@ -174,9 +172,7 @@ mod tests {
min_samples_leaf: 1,
min_samples_split: 2,
n_trees: 1000,
mtry: Option::None}).predict(&x);
println!("{:?}", y_hat);
mtry: Option::None}).predict(&x);
for i in 0..y_hat.len() {
assert!((y_hat[i] - expected_y[i]).abs() < 1.0);
src/tree/decision_tree_regressor.rs
+1 -30
View File
@@ -94,7 +94,7 @@ impl<T: FloatExt + Debug> DecisionTreeRegressor<T> {
pub fn fit_weak_learner<M: Matrix<T>>(x: &M, y: &M::RowVector, samples: Vec<usize>, mtry: usize, parameters: DecisionTreeRegressorParameters) -> DecisionTreeRegressor<T> {
let y_m = M::from_row_vector(y.clone());
// println!("{:?}", y_m);
let (_, y_ncols) = y_m.shape();
let (_, num_attributes) = x.shape();
let classes = y_m.unique();
@@ -355,35 +355,6 @@ mod tests {
assert!((y_hat[i] - expected_y[i]).abs() < 0.1);
}
}
#[test]
fn fit_longley1() {
let x = DenseMatrix::from_array(&[
&[ 234.289, 235.6, 159., 107.608, 1947., 60.323],
&[ 259.426, 232.5, 145.6, 108.632, 1948., 61.122],
&[ 258.054, 368.2, 161.6, 109.773, 1949., 60.171],
&[ 284.599, 335.1, 165., 110.929, 1950., 61.187],
&[ 328.975, 209.9, 309.9, 112.075, 1951., 63.221],
&[ 346.999, 193.2, 359.4, 113.27 , 1952., 63.639],
&[ 365.385, 187., 354.7, 115.094, 1953., 64.989],
&[ 363.112, 357.8, 335., 116.219, 1954., 63.761],
&[ 397.469, 290.4, 304.8, 117.388, 1955., 66.019],
&[ 419.18 , 282.2, 285.7, 118.734, 1956., 67.857],
&[ 442.769, 293.6, 279.8, 120.445, 1957., 68.169],
&[ 444.546, 468.1, 263.7, 121.95 , 1958., 66.513],
&[ 482.704, 381.3, 255.2, 123.366, 1959., 68.655],
&[ 502.601, 393.1, 251.4, 125.368, 1960., 69.564],
&[ 518.173, 480.6, 257.2, 127.852, 1961., 69.331],
&[ 554.894, 400.7, 282.7, 130.081, 1962., 70.551]]);
let y: Vec<f64> = vec![83.0, 88.5, 88.2, 89.5, 96.2, 98.1, 99.0, 100.0, 101.2, 104.6, 108.4, 110.8, 112.6, 114.2, 115.7, 116.9];
let y_hat = DecisionTreeRegressor::fit(&x, &y, Default::default()).predict(&x);
for i in 0..y_hat.len() {
assert!((y_hat[i] - y[i]).abs() < 0.1);
}
}
}