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Patch to version 0.4.0 (#257)

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* uncomment test

* Add random test for logistic regression

* linting

* Bump version

* Add test for logistic regression

* linting

* initial commit

* final

* final-clean

* Bump to 0.4.0

* Fix linter

* cleanup

* Update CHANDELOG with breaking changes

* Update CHANDELOG date

* Add functional methods to DenseMatrix implementation

* linting

* add type declaration in test

* Fix Wasm tests failing

* linting

* fix tests

* linting

* Add type annotations on BBDTree constructor

* fix clippy

* fix clippy

* fix tests

* bump version

* run fmt. fix changelog

---------

Co-authored-by: Edmund Cape <edmund@Edmunds-MacBook-Pro.local>
This commit is contained in:
Lorenzo
2024-03-04 13:51:27 +00:00
committed by GitHub
parent 80a93c1a0e
commit 239c00428f
45 changed files with 759 additions and 406 deletions
Download Patch File Download Diff File Expand all files Collapse all files
src/preprocessing/categorical.rs
+11 -6
View File
@@ -12,7 +12,7 @@
//! &[1.5, 2.0, 1.5, 4.0],
//! &[1.5, 1.0, 1.5, 5.0],
//! &[1.5, 2.0, 1.5, 6.0],
//! ]);
//! ]).unwrap();
//! let encoder_params = OneHotEncoderParams::from_cat_idx(&[1, 3]);
//! // Infer number of categories from data and return a reusable encoder
//! let encoder = OneHotEncoder::fit(&data, encoder_params).unwrap();
@@ -240,14 +240,16 @@ mod tests {
&[2.0, 1.5, 4.0],
&[1.0, 1.5, 5.0],
&[2.0, 1.5, 6.0],
]);
])
.unwrap();
let oh_enc = DenseMatrix::from_2d_array(&[
&[1.0, 0.0, 1.5, 1.0, 0.0, 0.0, 0.0],
&[0.0, 1.0, 1.5, 0.0, 1.0, 0.0, 0.0],
&[1.0, 0.0, 1.5, 0.0, 0.0, 1.0, 0.0],
&[0.0, 1.0, 1.5, 0.0, 0.0, 0.0, 1.0],
]);
])
.unwrap();
(orig, oh_enc)
}
@@ -259,14 +261,16 @@ mod tests {
&[1.5, 2.0, 1.5, 4.0],
&[1.5, 1.0, 1.5, 5.0],
&[1.5, 2.0, 1.5, 6.0],
]);
])
.unwrap();
let oh_enc = DenseMatrix::from_2d_array(&[
&[1.5, 1.0, 0.0, 1.5, 1.0, 0.0, 0.0, 0.0],
&[1.5, 0.0, 1.0, 1.5, 0.0, 1.0, 0.0, 0.0],
&[1.5, 1.0, 0.0, 1.5, 0.0, 0.0, 1.0, 0.0],
&[1.5, 0.0, 1.0, 1.5, 0.0, 0.0, 0.0, 1.0],
]);
])
.unwrap();
(orig, oh_enc)
}
@@ -334,7 +338,8 @@ mod tests {
&[2.0, 1.5, 4.0],
&[1.0, 1.5, 5.0],
&[2.0, 1.5, 6.0],
]);
])
.unwrap();
let params = OneHotEncoderParams::from_cat_idx(&[1]);
let result = OneHotEncoder::fit(&m, params);
src/preprocessing/numerical.rs
+47 -38
View File
@@ -11,7 +11,7 @@
//! vec![0.0, 0.0],
//! vec![1.0, 1.0],
//! vec![1.0, 1.0],
//! ]);
//! ]).unwrap();
//!
//! let standard_scaler =
//! numerical::StandardScaler::fit(&data, numerical::StandardScalerParameters::default())
@@ -24,7 +24,7 @@
//! vec![-1.0, -1.0],
//! vec![1.0, 1.0],
//! vec![1.0, 1.0],
//! ])
//! ]).unwrap()
//! );
//! ```
use std::marker::PhantomData;
@@ -197,15 +197,18 @@ mod tests {
fn combine_three_columns() {
assert_eq!(
build_matrix_from_columns(vec![
DenseMatrix::from_2d_vec(&[vec![1.0], vec![1.0], vec![1.0]]),
DenseMatrix::from_2d_vec(&[vec![2.0], vec![2.0], vec![2.0]]),
DenseMatrix::from_2d_vec(&[vec![3.0], vec![3.0], vec![3.0]])
DenseMatrix::from_2d_vec(&vec![vec![1.0], vec![1.0], vec![1.0],]).unwrap(),
DenseMatrix::from_2d_vec(&vec![vec![2.0], vec![2.0], vec![2.0],]).unwrap(),
DenseMatrix::from_2d_vec(&vec![vec![3.0], vec![3.0], vec![3.0],]).unwrap()
]),
Some(DenseMatrix::from_2d_vec(&[
vec![1.0, 2.0, 3.0],
vec![1.0, 2.0, 3.0],
vec![1.0, 2.0, 3.0]
]))
Some(
DenseMatrix::from_2d_vec(&vec![
vec![1.0, 2.0, 3.0],
vec![1.0, 2.0, 3.0],
vec![1.0, 2.0, 3.0]
])
.unwrap()
)
)
}
@@ -287,13 +290,15 @@ mod tests {
/// sklearn.
#[test]
fn fit_transform_random_values() {
let transformed_values =
fit_transform_with_default_standard_scaler(&DenseMatrix::from_2d_array(&[
let transformed_values = fit_transform_with_default_standard_scaler(
&DenseMatrix::from_2d_array(&[
&[0.1004222429, 0.2194113576, 0.9310663354, 0.3313593793],
&[0.2045493861, 0.1683865411, 0.5071506765, 0.7257355264],
&[0.5708488802, 0.1846414616, 0.9590802982, 0.5591871046],
&[0.8387612750, 0.5754861361, 0.5537109852, 0.1077646442],
]));
])
.unwrap(),
);
println!("{transformed_values}");
assert!(transformed_values.approximate_eq(
&DenseMatrix::from_2d_array(&[
@@ -301,7 +306,8 @@ mod tests {
&[-0.7615464283, -0.7076698384, -1.1075452562, 1.2632979631],
&[0.4832504303, -0.6106747444, 1.0630075435, 0.5494084257],
&[1.3936980634, 1.7215431158, -0.8839228078, -1.3855590021],
]),
])
.unwrap(),
1.0
))
}
@@ -310,13 +316,10 @@ mod tests {
#[test]
fn fit_transform_with_zero_variance() {
assert_eq!(
fit_transform_with_default_standard_scaler(&DenseMatrix::from_2d_array(&[
&[1.0],
&[1.0],
&[1.0],
&[1.0]
])),
DenseMatrix::from_2d_array(&[&[0.0], &[0.0], &[0.0], &[0.0]]),
fit_transform_with_default_standard_scaler(
&DenseMatrix::from_2d_array(&[&[1.0], &[1.0], &[1.0], &[1.0]]).unwrap()
),
DenseMatrix::from_2d_array(&[&[0.0], &[0.0], &[0.0], &[0.0]]).unwrap(),
"When scaling values with zero variance, zero is expected as return value"
)
}
@@ -331,7 +334,8 @@ mod tests {
&[1.0, 2.0, 5.0],
&[1.0, 1.0, 1.0],
&[1.0, 2.0, 5.0]
]),
])
.unwrap(),
StandardScalerParameters::default(),
),
Ok(StandardScaler {
@@ -354,7 +358,8 @@ mod tests {
&[0.2045493861, 0.1683865411, 0.5071506765, 0.7257355264],
&[0.5708488802, 0.1846414616, 0.9590802982, 0.5591871046],
&[0.8387612750, 0.5754861361, 0.5537109852, 0.1077646442],
]),
])
.unwrap(),
StandardScalerParameters::default(),
)
.unwrap();
@@ -364,17 +369,18 @@ mod tests {
vec![0.42864544605, 0.2869813741, 0.737752073825, 0.431011663625],
);
assert!(
&DenseMatrix::<f64>::from_2d_vec(&[fitted_scaler.stds]).approximate_eq(
assert!(&DenseMatrix::<f64>::from_2d_vec(&vec![fitted_scaler.stds])
.unwrap()
.approximate_eq(
&DenseMatrix::from_2d_array(&[&[
0.29426447500954,
0.16758497615485,
0.20820945786863,
0.23329718831165
],]),
],])
.unwrap(),
0.00000000000001
)
)
))
}
/// If `with_std` is set to `false` the values should not be
@@ -392,8 +398,9 @@ mod tests {
};
assert_eq!(
standard_scaler.transform(&DenseMatrix::from_2d_array(&[&[0.0, 2.0], &[2.0, 4.0]])),
Ok(DenseMatrix::from_2d_array(&[&[-1.0, -1.0], &[1.0, 1.0]]))
standard_scaler
.transform(&DenseMatrix::from_2d_array(&[&[0.0, 2.0], &[2.0, 4.0]]).unwrap()),
Ok(DenseMatrix::from_2d_array(&[&[-1.0, -1.0], &[1.0, 1.0]]).unwrap())
)
}
@@ -413,8 +420,8 @@ mod tests {
assert_eq!(
standard_scaler
.transform(&DenseMatrix::from_2d_array(&[&[0.0, 9.0], &[4.0, 12.0]])),
Ok(DenseMatrix::from_2d_array(&[&[0.0, 3.0], &[2.0, 4.0]]))
.transform(&DenseMatrix::from_2d_array(&[&[0.0, 9.0], &[4.0, 12.0]]).unwrap()),
Ok(DenseMatrix::from_2d_array(&[&[0.0, 3.0], &[2.0, 4.0]]).unwrap())
)
}
@@ -433,7 +440,8 @@ mod tests {
&[0.2045493861, 0.1683865411, 0.5071506765, 0.7257355264],
&[0.5708488802, 0.1846414616, 0.9590802982, 0.5591871046],
&[0.8387612750, 0.5754861361, 0.5537109852, 0.1077646442],
]),
])
.unwrap(),
StandardScalerParameters::default(),
)
.unwrap();
@@ -446,17 +454,18 @@ mod tests {
vec![0.42864544605, 0.2869813741, 0.737752073825, 0.431011663625],
);
assert!(
&DenseMatrix::from_2d_vec(&vec![deserialized_scaler.stds]).approximate_eq(
assert!(&DenseMatrix::from_2d_vec(&vec![deserialized_scaler.stds])
.unwrap()
.approximate_eq(
&DenseMatrix::from_2d_array(&[&[
0.29426447500954,
0.16758497615485,
0.20820945786863,
0.23329718831165
],]),
],])
.unwrap(),
0.00000000000001
)
)
))
}
}
}