View Source Scholar.Metrics.Regression (Scholar v0.2.1)
Regression Metric functions.
Metrics are used to measure the performance and compare the performance of any kind of regressor in easy-to-understand terms.
All of the functions in this module are implemented as
numerical functions and can be JIT or AOT compiled with
any supported Nx compiler.
Summary
Functions
Explained variance regression score function.
Calculates the maximum residual error.
Calculates the mean absolute error of predictions with respect to targets.
Calculates the mean absolute percentage error of predictions
with respect to targets. If y_true values are equal or close
to zero, it returns an arbitrarily large value.
Calculates the mean square error of predictions with respect to targets.
Calculates the mean square logarithmic error of predictions with respect to targets.
Calculates the $R^2$ score of predictions with respect to targets.
Functions
Explained variance regression score function.
Best possible score is 1.0, lower values are worse.
Options
:force_finite(boolean/0) - Flag indicating if NaN and -Inf scores resulting from constant data should be replaced with real numbers (1.0 if prediction is perfect, 0.0 otherwise) The default value istrue.
Examples
iex> y_true = Nx.tensor([3, -0.5, 2, 7], type: {:f, 32})
iex> y_pred = Nx.tensor([2.5, 0.0, 2, 8], type: {:f, 32})
iex> Scholar.Metrics.Regression.explained_variance_score(y_true, y_pred)
#Nx.Tensor<
f32
0.9571734666824341
>
iex> y_true = Nx.tensor([-2.0, -2.0, -2.0], type: :f64)
iex> y_pred = Nx.tensor([-2.0, -2.0, -2.0 + 1.0e-8], type: :f64)
iex> Scholar.Metrics.Regression.explained_variance_score(y_true, y_pred, force_finite: true)
#Nx.Tensor<
f64
0.0
>
iex> y_true = Nx.tensor([-2.0, -2.0, -2.0], type: :f64)
iex> y_pred = Nx.tensor([-2.0, -2.0, -2.0 + 1.0e-8], type: :f64)
iex> Scholar.Metrics.Regression.explained_variance_score(y_true, y_pred, force_finite: false)
#Nx.Tensor<
f64
-Inf
>
iex> y_true = Nx.tensor([-2.0, -2.0, -2.0])
iex> y_pred = Nx.tensor([-2.0, -2.0, -2.0])
iex> Scholar.Metrics.Regression.explained_variance_score(y_true, y_pred, force_finite: false)
#Nx.Tensor<
f32
NaN
>
iex> y_true = Nx.tensor([-2.0, -2.0, -2.0])
iex> y_pred = Nx.tensor([-2.0, -2.0, -2.0])
iex> Scholar.Metrics.Regression.explained_variance_score(y_true, y_pred, force_finite: true)
#Nx.Tensor<
f32
1.0
>Calculates the maximum residual error.
The residual error is defined as $$|y - \hat{y}|$$ where $y$ is a true value
and $\hat{y}$ is a predicted value.
This function returns the maximum residual error over all samples in the
input: $max(|y_i - \hat{y_i}|)$. For perfect predictions, the maximum
residual error is 0.0.
Examples
iex> y_true = Nx.tensor([3, -0.5, 2, 7])
iex> y_pred = Nx.tensor([2.5, 0.0, 2, 8.5])
iex> Scholar.Metrics.Regression.max_residual_error(y_true, y_pred)
#Nx.Tensor<
f32
1.5
>Calculates the mean absolute error of predictions with respect to targets.
$$MAE = \frac{\sum_{i=1}^{n} |\hat{y_i} - y_i|}{n}$$
Examples
iex> y_true = Nx.tensor([[0.0, 1.0], [0.0, 0.0]])
iex> y_pred = Nx.tensor([[1.0, 1.0], [1.0, 0.0]])
iex> Scholar.Metrics.Regression.mean_absolute_error(y_true, y_pred)
#Nx.Tensor<
f32
0.5
>Calculates the mean absolute percentage error of predictions
with respect to targets. If y_true values are equal or close
to zero, it returns an arbitrarily large value.
$$MAPE = \frac{\sum_{i=1}^{n} \frac{|\hat{y_i} - y_i|}{max(\epsilon, \hat{y_i})}}{n}$$
Examples
iex> y_true = Nx.tensor([3, -0.5, 2, 7])
iex> y_pred = Nx.tensor([2.5, 0.0, 2, 8])
iex> Scholar.Metrics.Regression.mean_absolute_percentage_error(y_true, y_pred)
#Nx.Tensor<
f32
0.3273809552192688
>
iex> y_true = Nx.tensor([1.0, 0.0, 2.4, 7.0])
iex> y_pred = Nx.tensor([1.2, 0.1, 2.4, 8.0])
iex> Scholar.Metrics.Regression.mean_absolute_percentage_error(y_true, y_pred)
#Nx.Tensor<
f32
209715.28125
>Calculates the mean square error of predictions with respect to targets.
$$MSE = \frac{\sum_{i=1}^{n} (\hat{y_i} - y_i)^2}{n}$$
Examples
iex> y_true = Nx.tensor([[0.0, 2.0], [0.5, 0.0]])
iex> y_pred = Nx.tensor([[1.0, 1.0], [1.0, 0.0]])
iex> Scholar.Metrics.Regression.mean_square_error(y_true, y_pred)
#Nx.Tensor<
f32
0.5625
>Calculates the mean square logarithmic error of predictions with respect to targets.
$$MSLE = \frac{\sum_{i=1}^{n} (\log(\hat{y_i} + 1) - \log(y_i + 1))^2}{n}$$
Examples
iex> y_true = Nx.tensor([[0.0, 1.0], [0.0, 0.0]])
iex> y_pred = Nx.tensor([[1.0, 1.0], [1.0, 0.0]])
iex> Scholar.Metrics.Regression.mean_square_log_error(y_true, y_pred)
#Nx.Tensor<
f32
0.24022650718688965
>Calculates the $R^2$ score of predictions with respect to targets.
$$R^2 = 1 - \frac{\sum (y_i - \hat{y}_i)^2}{\sum (y_i - \bar{y})^2}$$
Options
:force_finite(boolean/0) - Flag indicating if NaN and -Inf scores resulting from constant data should be replaced with real numbers (1.0 if prediction is perfect, 0.0 otherwise) The default value istrue.
Examples
iex> y_true = Nx.tensor([3, -0.5, 2, 7], type: {:f, 32})
iex> y_pred = Nx.tensor([2.5, 0.0, 2, 8], type: {:f, 32})
iex> Scholar.Metrics.Regression.r2_score(y_true, y_pred)
#Nx.Tensor<
f32
0.9486081600189209
>
iex> y_true = Nx.tensor([-2.0, -2.0, -2.0], type: :f64)
iex> y_pred = Nx.tensor([-2.0, -2.0, -2.0 + 1.0e-8], type: :f64)
iex> Scholar.Metrics.Regression.r2_score(y_true, y_pred, force_finite: true)
#Nx.Tensor<
f64
0.0
>
iex> y_true = Nx.tensor([-2.0, -2.0, -2.0], type: :f64)
iex> y_pred = Nx.tensor([-2.0, -2.0, -2.0 + 1.0e-8], type: :f64)
iex> Scholar.Metrics.Regression.r2_score(y_true, y_pred, force_finite: false)
#Nx.Tensor<
f64
-Inf
>
iex> y_true = Nx.tensor([-2.0, -2.0, -2.0])
iex> y_pred = Nx.tensor([-2.0, -2.0, -2.0])
iex> Scholar.Metrics.Regression.r2_score(y_true, y_pred, force_finite: false)
#Nx.Tensor<
f32
NaN
>
iex> y_true = Nx.tensor([-2.0, -2.0, -2.0])
iex> y_pred = Nx.tensor([-2.0, -2.0, -2.0])
iex> Scholar.Metrics.Regression.r2_score(y_true, y_pred, force_finite: true)
#Nx.Tensor<
f32
1.0
>