From 1257d2c19b7238d847c8b591191ab3438f9bf8a6 Mon Sep 17 00:00:00 2001 From: Volodymyr Orlov Date: Thu, 26 Mar 2020 16:23:50 -0700 Subject: [PATCH] fix: code cleanup --- src/ensemble/random_forest_regressor.rs | 10 +++----- src/tree/decision_tree_regressor.rs | 31 +------------------------ 2 files changed, 4 insertions(+), 37 deletions(-) diff --git a/src/ensemble/random_forest_regressor.rs b/src/ensemble/random_forest_regressor.rs index ecf4a48..db16935 100644 --- a/src/ensemble/random_forest_regressor.rs +++ b/src/ensemble/random_forest_regressor.rs @@ -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 = 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); diff --git a/src/tree/decision_tree_regressor.rs b/src/tree/decision_tree_regressor.rs index e9808cf..c347c93 100644 --- a/src/tree/decision_tree_regressor.rs +++ b/src/tree/decision_tree_regressor.rs @@ -94,7 +94,7 @@ impl DecisionTreeRegressor { pub fn fit_weak_learner>(x: &M, y: &M::RowVector, samples: Vec, mtry: usize, parameters: DecisionTreeRegressorParameters) -> DecisionTreeRegressor { 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 = 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); - } } } \ No newline at end of file