module metrics.correlations#

Short summary#

module mlinsights.metrics.correlations


source on GitHub



truncated documentation


Computes non linear correlations.



source on GitHub

mlinsights.metrics.correlations.non_linear_correlations(df, model, draws=5, minmax=False)#

Computes non linear correlations.

  • dfpandas.DataFrame or numpy.array

  • model – machine learned model used to compute the correlations

  • draws – number of tries for bootstrap, the correlation is the average of the results obtained at each draw

  • minmax – if True, returns three matrices correlations, min, max, only the correlation matrix if False


see parameter minmax

Pearson Correlations is:

cor(X_i, X_j) = \frac{cov(X_i, Y_i)}{\sigma(X_i)\sigma(X_j)}

If variables are centered, \mathbb{E}X_i=\mathbb{E}X_j=0, it becomes:

cor(X_i, X_j) = \frac{\mathbb{E}(X_i X_j)}{\sqrt{\mathbb{E}X_i^2 \mathbb{E}X_j^2}}

If rescaled, \mathbb{E}X_i^2=\mathbb{E}X_j^2=1, then it becomes cor(X_i, X_j) = \mathbb{E}(X_i X_j). Let’s assume we try to find a coefficient such as \alpha_{ij} minimizes the standard deviation of noise \epsilon_{ij}:

X_j = \alpha_{ij}X_i + \epsilon_{ij}

It is like if coefficient \alpha_{ij} comes from a a linear regression which minimizes \mathbb{E}(X_j - \alpha_{ij}X_i)^2. If variable X_i, X_j are centered and rescaled: \alpha_{ij}^* = \mathbb{E}(X_i X_j) = cor(X_i, X_j). We extend that definition to function f of parameter \omega defined as: f(\omega, X) \rightarrow \mathbb{R}. f is not linear anymore. Let’s assume parameter \omega^* minimizes quantity \min_\omega (X_j  - f(\omega, X_i))^2. Then X_j = \alpha_{ij} \frac{f(\omega^*, X_i)}{\alpha_{ij}} + \epsilon_{ij} and we choose \alpha_{ij} such as \mathbb{E}\left(\frac{f(\omega^*, X_i)^2}{\alpha_{ij}^2}\right) = 1. Let’s define a non linear correlation bounded by f as:

cor^f(X_i, X_j) = \sqrt{ \mathbb{E} (f(\omega, X_i)^2 )}

We can verify that this value is in interval`:math:[0,1]`. That also means that there is no negative correlation. f is a machine learned model and most of them usually overfit the data. The database is split into two parts, one is used to train the model, the other one to compute the correlation. The same split are used for every coefficient. The returned matrix is not necessarily symmetric.

Compute non linear correlations

The following example compute non linear correlations on Iris datasets based on a RandomForestRegressor model.


import pandas
from sklearn import datasets
from sklearn.ensemble import RandomForestRegressor
from mlinsights.metrics import non_linear_correlations

iris = datasets.load_iris()
X =[:, :4]
df = pandas.DataFrame(X)
df.columns = ["X1", "X2", "X3", "X4"]
cor = non_linear_correlations(df, RandomForestRegressor())


              X1        X2        X3        X4
    X1  0.998006  0.000000  0.852165  0.788926
    X2  0.000000  0.993317  0.289960  0.254784
    X3  0.885267  0.525742  0.998749  0.951968
    X4  0.728800  0.559557  0.966172  0.999212

source on GitHub