Histogram error handling (#25)

* Use Option as return type where things might fail

* Test suite aligned with docs

* Equispaced does not panic anymore

* Fixed some tests

* Fixed FD tests

* Fixed wrong condition in IF

* Fixed wrong test

* Added new test for EquiSpaced and fixed old one

* Fixed doc tests

* Fix docs.

* Fix docs.

* Fix docs.

* Fmt

* Create StrategyError

* Fmt

* Return Result. Fix Equispaced, Sqrt and Rice

* Fix Rice

* Fixed Sturges

* Fix strategies

* Fix match

* Tests compile

* Fix assertion

* Fmt

* Add more error types

* Rename StrategyError to BinsBuildError

* Make GridBuilder::from_array return Result

This is nice because it doesn't lose information. (Returning an
`Option` combines the two error variants into a single case.)

* Make BinsBuildError enum non-exhaustive

Once the `#[non_exhaustive]` attribute is stable, we should replace
the hidden enum variant with that attribute on the enum.

* Use lazy OR operator.

* Use lazy OR operator.

src/entropy.rs

@@ -1,5 +1,5 @@
 //! Information theory (e.g. entropy, KL divergence, etc.).
-use crate::errors::ShapeMismatch;
+use crate::errors::{EmptyInput, MultiInputError, ShapeMismatch};
 use ndarray::{Array, ArrayBase, Data, Dimension, Zip};
 use num_traits::Float;
 
@@ -19,7 +19,7 @@ where
     ///      i=1
     /// ```
     ///
-    /// If the array is empty, `None` is returned.
+    /// If the array is empty, `Err(EmptyInput)` is returned.
     ///
     /// **Panics** if `ln` of any element in the array panics (which can occur for negative values for some `A`).
     ///
@@ -38,7 +38,7 @@ where
     ///
     /// [entropy]: https://en.wikipedia.org/wiki/Entropy_(information_theory)
     /// [Information Theory]: https://en.wikipedia.org/wiki/Information_theory
-    fn entropy(&self) -> Option<A>
+    fn entropy(&self) -> Result<A, EmptyInput>
     where
         A: Float;
 
@@ -53,8 +53,9 @@ where
     ///             i=1
     /// ```
     ///
-    /// If the arrays are empty, Ok(`None`) is returned.
-    /// If the array shapes are not identical, `Err(ShapeMismatch)` is returned.
+    /// If the arrays are empty, `Err(MultiInputError::EmptyInput)` is returned.
+    /// If the array shapes are not identical,
+    /// `Err(MultiInputError::ShapeMismatch)` is returned.
     ///
     /// **Panics** if, for a pair of elements *(pᵢ, qᵢ)* from *p* and *q*, computing
     /// *ln(qᵢ/pᵢ)* is a panic cause for `A`.
@@ -73,7 +74,7 @@ where
     ///
     /// [Kullback-Leibler divergence]: https://en.wikipedia.org/wiki/Kullback%E2%80%93Leibler_divergence
     /// [Information Theory]: https://en.wikipedia.org/wiki/Information_theory
-    fn kl_divergence<S2>(&self, q: &ArrayBase<S2, D>) -> Result<Option<A>, ShapeMismatch>
+    fn kl_divergence<S2>(&self, q: &ArrayBase<S2, D>) -> Result<A, MultiInputError>
     where
         S2: Data<Elem = A>,
         A: Float;
@@ -89,8 +90,9 @@ where
     ///           i=1
     /// ```
     ///
-    /// If the arrays are empty, Ok(`None`) is returned.
-    /// If the array shapes are not identical, `Err(ShapeMismatch)` is returned.
+    /// If the arrays are empty, `Err(MultiInputError::EmptyInput)` is returned.
+    /// If the array shapes are not identical,
+    /// `Err(MultiInputError::ShapeMismatch)` is returned.
     ///
     /// **Panics** if any element in *q* is negative and taking the logarithm of a negative number
     /// is a panic cause for `A`.
@@ -114,7 +116,7 @@ where
     /// [Information Theory]: https://en.wikipedia.org/wiki/Information_theory
     /// [optimization problems]: https://en.wikipedia.org/wiki/Cross-entropy_method
     /// [machine learning]: https://en.wikipedia.org/wiki/Cross_entropy#Cross-entropy_error_function_and_logistic_regression
-    fn cross_entropy<S2>(&self, q: &ArrayBase<S2, D>) -> Result<Option<A>, ShapeMismatch>
+    fn cross_entropy<S2>(&self, q: &ArrayBase<S2, D>) -> Result<A, MultiInputError>
     where
         S2: Data<Elem = A>,
         A: Float;
@@ -125,14 +127,14 @@ where
     S: Data<Elem = A>,
     D: Dimension,
 {
-    fn entropy(&self) -> Option<A>
+    fn entropy(&self) -> Result<A, EmptyInput>
     where
         A: Float,
     {
         if self.len() == 0 {
-            None
+            Err(EmptyInput)
         } else {
-            let entropy = self
+            let entropy = -self
                 .mapv(|x| {
                     if x == A::zero() {
                         A::zero()
@@ -141,23 +143,24 @@ where
                     }
                 })
                 .sum();
-            Some(-entropy)
+            Ok(entropy)
         }
     }
 
-    fn kl_divergence<S2>(&self, q: &ArrayBase<S2, D>) -> Result<Option<A>, ShapeMismatch>
+    fn kl_divergence<S2>(&self, q: &ArrayBase<S2, D>) -> Result<A, MultiInputError>
     where
         A: Float,
         S2: Data<Elem = A>,
     {
         if self.len() == 0 {
-            return Ok(None);
+            return Err(MultiInputError::EmptyInput);
         }
         if self.shape() != q.shape() {
             return Err(ShapeMismatch {
                 first_shape: self.shape().to_vec(),
                 second_shape: q.shape().to_vec(),
-            });
+            }
+            .into());
         }
 
         let mut temp = Array::zeros(self.raw_dim());
@@ -174,22 +177,23 @@ where
                 }
             });
         let kl_divergence = -temp.sum();
-        Ok(Some(kl_divergence))
+        Ok(kl_divergence)
     }
 
-    fn cross_entropy<S2>(&self, q: &ArrayBase<S2, D>) -> Result<Option<A>, ShapeMismatch>
+    fn cross_entropy<S2>(&self, q: &ArrayBase<S2, D>) -> Result<A, MultiInputError>
     where
         S2: Data<Elem = A>,
         A: Float,
     {
         if self.len() == 0 {
-            return Ok(None);
+            return Err(MultiInputError::EmptyInput);
         }
         if self.shape() != q.shape() {
             return Err(ShapeMismatch {
                 first_shape: self.shape().to_vec(),
                 second_shape: q.shape().to_vec(),
-            });
+            }
+            .into());
         }
 
         let mut temp = Array::zeros(self.raw_dim());
@@ -206,15 +210,15 @@ where
                 }
             });
         let cross_entropy = -temp.sum();
-        Ok(Some(cross_entropy))
+        Ok(cross_entropy)
     }
 }
 
 #[cfg(test)]
 mod tests {
     use super::EntropyExt;
     use approx::assert_abs_diff_eq;
-    use errors::ShapeMismatch;
+    use errors::{EmptyInput, MultiInputError};
     use ndarray::{array, Array1};
     use noisy_float::types::n64;
     use std::f64;
@@ -228,7 +232,7 @@ mod tests {
     #[test]
     fn test_entropy_with_empty_array_of_floats() {
         let a: Array1<f64> = array![];
-        assert!(a.entropy().is_none());
+        assert_eq!(a.entropy(), Err(EmptyInput));
     }
 
     #[test]
@@ -251,13 +255,13 @@ mod tests {
     }
 
     #[test]
-    fn test_cross_entropy_and_kl_with_nan_values() -> Result<(), ShapeMismatch> {
+    fn test_cross_entropy_and_kl_with_nan_values() -> Result<(), MultiInputError> {
         let a = array![f64::NAN, 1.];
         let b = array![2., 1.];
-        assert!(a.cross_entropy(&b)?.unwrap().is_nan());
-        assert!(b.cross_entropy(&a)?.unwrap().is_nan());
-        assert!(a.kl_divergence(&b)?.unwrap().is_nan());
-        assert!(b.kl_divergence(&a)?.unwrap().is_nan());
+        assert!(a.cross_entropy(&b)?.is_nan());
+        assert!(b.cross_entropy(&a)?.is_nan());
+        assert!(a.kl_divergence(&b)?.is_nan());
+        assert!(b.kl_divergence(&a)?.is_nan());
         Ok(())
     }
 
@@ -284,20 +288,19 @@ mod tests {
     }
 
     #[test]
-    fn test_cross_entropy_and_kl_with_empty_array_of_floats() -> Result<(), ShapeMismatch> {
+    fn test_cross_entropy_and_kl_with_empty_array_of_floats() {
         let p: Array1<f64> = array![];
         let q: Array1<f64> = array![];
-        assert!(p.cross_entropy(&q)?.is_none());
-        assert!(p.kl_divergence(&q)?.is_none());
-        Ok(())
+        assert!(p.cross_entropy(&q).unwrap_err().is_empty_input());
+        assert!(p.kl_divergence(&q).unwrap_err().is_empty_input());
     }
 
     #[test]
-    fn test_cross_entropy_and_kl_with_negative_qs() -> Result<(), ShapeMismatch> {
+    fn test_cross_entropy_and_kl_with_negative_qs() -> Result<(), MultiInputError> {
         let p = array![1.];
         let q = array![-1.];
-        let cross_entropy: f64 = p.cross_entropy(&q)?.unwrap();
-        let kl_divergence: f64 = p.kl_divergence(&q)?.unwrap();
+        let cross_entropy: f64 = p.cross_entropy(&q)?;
+        let kl_divergence: f64 = p.kl_divergence(&q)?;
         assert!(cross_entropy.is_nan());
         assert!(kl_divergence.is_nan());
         Ok(())
@@ -320,26 +323,26 @@ mod tests {
     }
 
     #[test]
-    fn test_cross_entropy_and_kl_with_zeroes_p() -> Result<(), ShapeMismatch> {
+    fn test_cross_entropy_and_kl_with_zeroes_p() -> Result<(), MultiInputError> {
         let p = array![0., 0.];
         let q = array![0., 0.5];
-        assert_eq!(p.cross_entropy(&q)?.unwrap(), 0.);
-        assert_eq!(p.kl_divergence(&q)?.unwrap(), 0.);
+        assert_eq!(p.cross_entropy(&q)?, 0.);
+        assert_eq!(p.kl_divergence(&q)?, 0.);
         Ok(())
     }
 
     #[test]
     fn test_cross_entropy_and_kl_with_zeroes_q_and_different_data_ownership(
-    ) -> Result<(), ShapeMismatch> {
+    ) -> Result<(), MultiInputError> {
         let p = array![0.5, 0.5];
         let mut q = array![0.5, 0.];
-        assert_eq!(p.cross_entropy(&q.view_mut())?.unwrap(), f64::INFINITY);
-        assert_eq!(p.kl_divergence(&q.view_mut())?.unwrap(), f64::INFINITY);
+        assert_eq!(p.cross_entropy(&q.view_mut())?, f64::INFINITY);
+        assert_eq!(p.kl_divergence(&q.view_mut())?, f64::INFINITY);
         Ok(())
     }
 
     #[test]
-    fn test_cross_entropy() -> Result<(), ShapeMismatch> {
+    fn test_cross_entropy() -> Result<(), MultiInputError> {
         // Arrays of probability values - normalized and positive.
         let p: Array1<f64> = array![
             0.05340169, 0.02508511, 0.03460454, 0.00352313, 0.07837615, 0.05859495, 0.05782189,
@@ -356,16 +359,12 @@ mod tests {
         // Computed using scipy.stats.entropy(p) + scipy.stats.entropy(p, q)
         let expected_cross_entropy = 3.385347705020779;
 
-        assert_abs_diff_eq!(
-            p.cross_entropy(&q)?.unwrap(),
-            expected_cross_entropy,
-            epsilon = 1e-6
-        );
+        assert_abs_diff_eq!(p.cross_entropy(&q)?, expected_cross_entropy, epsilon = 1e-6);
         Ok(())
     }
 
     #[test]
-    fn test_kl() -> Result<(), ShapeMismatch> {
+    fn test_kl() -> Result<(), MultiInputError> {
         // Arrays of probability values - normalized and positive.
         let p: Array1<f64> = array![
             0.00150472, 0.01388706, 0.03495376, 0.03264211, 0.03067355, 0.02183501, 0.00137516,
@@ -390,7 +389,7 @@ mod tests {
         // Computed using scipy.stats.entropy(p, q)
         let expected_kl = 0.3555862567800096;
 
-        assert_abs_diff_eq!(p.kl_divergence(&q)?.unwrap(), expected_kl, epsilon = 1e-6);
+        assert_abs_diff_eq!(p.kl_divergence(&q)?, expected_kl, epsilon = 1e-6);
         Ok(())
     }
 }

src/errors.rs

@@ -2,10 +2,50 @@
 use std::error::Error;
 use std::fmt;
 
-#[derive(Debug)]
+/// An error that indicates that the input array was empty.
+#[derive(Clone, Debug, Eq, PartialEq)]
+pub struct EmptyInput;
+
+impl fmt::Display for EmptyInput {
+    fn fmt(&self, f: &mut fmt::Formatter) -> fmt::Result {
+        write!(f, "Empty input.")
+    }
+}
+
+impl Error for EmptyInput {}
+
+/// An error computing a minimum/maximum value.
+#[derive(Clone, Debug, Eq, PartialEq)]
+pub enum MinMaxError {
+    /// The input was empty.
+    EmptyInput,
+    /// The ordering between a tested pair of values was undefined.
+    UndefinedOrder,
+}
+
+impl fmt::Display for MinMaxError {
+    fn fmt(&self, f: &mut fmt::Formatter) -> fmt::Result {
+        match self {
+            MinMaxError::EmptyInput => write!(f, "Empty input."),
+            MinMaxError::UndefinedOrder => {
+                write!(f, "Undefined ordering between a tested pair of values.")
+            }
+        }
+    }
+}
+
+impl Error for MinMaxError {}
+
+impl From<EmptyInput> for MinMaxError {
+    fn from(_: EmptyInput) -> MinMaxError {
+        MinMaxError::EmptyInput
+    }
+}
+
 /// An error used by methods and functions that take two arrays as argument and
 /// expect them to have exactly the same shape
 /// (e.g. `ShapeMismatch` is raised when `a.shape() == b.shape()` evaluates to `False`).
+#[derive(Clone, Debug)]
 pub struct ShapeMismatch {
     pub first_shape: Vec<usize>,
     pub second_shape: Vec<usize>,
@@ -22,3 +62,53 @@ impl fmt::Display for ShapeMismatch {
 }
 
 impl Error for ShapeMismatch {}
+
+/// An error for methods that take multiple non-empty array inputs.
+#[derive(Clone, Debug)]
+pub enum MultiInputError {
+    /// One or more of the arrays were empty.
+    EmptyInput,
+    /// The arrays did not have the same shape.
+    ShapeMismatch(ShapeMismatch),
+}
+
+impl MultiInputError {
+    /// Returns whether `self` is the `EmptyInput` variant.
+    pub fn is_empty_input(&self) -> bool {
+        match self {
+            MultiInputError::EmptyInput => true,
+            _ => false,
+        }
+    }
+
+    /// Returns whether `self` is the `ShapeMismatch` variant.
+    pub fn is_shape_mismatch(&self) -> bool {
+        match self {
+            MultiInputError::ShapeMismatch(_) => true,
+            _ => false,
+        }
+    }
+}
+
+impl fmt::Display for MultiInputError {
+    fn fmt(&self, f: &mut fmt::Formatter) -> fmt::Result {
+        match self {
+            MultiInputError::EmptyInput => write!(f, "Empty input."),
+            MultiInputError::ShapeMismatch(e) => write!(f, "Shape mismatch: {}", e),
+        }
+    }
+}
+
+impl Error for MultiInputError {}
+
+impl From<EmptyInput> for MultiInputError {
+    fn from(_: EmptyInput) -> Self {
+        MultiInputError::EmptyInput
+    }
+}
+
+impl From<ShapeMismatch> for MultiInputError {
+    fn from(err: ShapeMismatch) -> Self {
+        MultiInputError::ShapeMismatch(err)
+    }
+}