Geometry Measures
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Calculates the nom-linearity of a linear regressor (L3) measure. |
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Calculates the non-linearity of a nearest neighbor regressor (S4) measure. |
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Calculates the average number of examples per dimension (T2) measure. |
- problexity.regression.l3(X, y, normalize=True)
Calculates the nom-linearity of a linear regressor (L3) measure.
Linearly interpolates both input (X) and output (y) values of each pair of samples with similar output values. Generated l=n-1 synthetic samples and then measures the mean squared error of a linear regressor, fitted with original data and evaluated on synthetic points. By default performs a normalization of samples.
\[L3=\frac{1}{l}\sum_{i=1}^{l}(f(x'_i) - y'_i)^2\]- Parameters:
X (array-like, shape (n_samples, n_features)) – Dataset
y (array-like, shape (n_samples)) – Labels
- Return type:
- Returns:
L3 score
- problexity.regression.s4(X, y, normalize=True)
Calculates the non-linearity of a nearest neighbor regressor (S4) measure.
Linearly interpolates both input (X) and output (y) values of each pair of samples with similar output values. Generated l=n-1 synthetic samples and then measures the mean squared error of a nearest neighbor regessor, fitted with original data and evaluated on synthetic points. By default performs a normalization of samples.
\[S4=\frac{1}{l}\sum_{i=1}^{l}(NN(x'_i) - y'_i)^2\]- Parameters:
X (array-like, shape (n_samples, n_features)) – Dataset
y (array-like, shape (n_samples)) – Labels
- Return type:
- Returns:
S4 score
- problexity.regression.t2(X, y)
Calculates the average number of examples per dimension (T2) measure.
Returns number of samples per number of features. Higher values indicate simpler problems.
\[T2=\frac{n}{d}\]- Parameters:
X (array-like, shape (n_samples, n_features)) – Dataset
y (array-like, shape (n_samples)) – Labels
- Return type:
- Returns:
T2 score