# Presenting Univariate Drift Detection Methods¶

Univariate Drift Detection looks at each feature individually and checks whether its distribution has changed compared to reference data. There are many ways to compare two samples of data and measure their similarity. NannyML provides several drift detection methods so that the users can choose the one that suits their data best or the one they are familiar with. Additionally more than one method can be used together to gain different perspectives on how the distribution of your data is changing.

This page explains which aspects of a distribution change each drift detection method is able to capture, what are the important implementation details and in which situations a specific method can be a good choice.

We are grouping the drift detection methods presented according to whether they apply to categorical (discrete) or continuous features. When a method is used for both it is mentioned in both places because of implementation differences between the two types of features.

Lastly let’s note that we are always performing two sample tests or comparisons. Probability density functions (PDF) and cumulative density functions (CDF) are always estimated from the data samples that are being compared.

## Methods for Continuous Features¶

### Kolmogorov-Smirnov Test¶

The Kolmogorov-Smirnov Test is a two-sample, non-parametric statistical test. It is used to test for the equality of one-dimensional continuous distributions. The test outputs the test statistic, called D-statistic, and an associated p-value. The test statistic is the maximum distance of the cumulative distribution functions (CDF) of the two samples.

The D-statistic is robust to small changes in the data, easy to interpret and falls into 0-1 range. This makes the Kolmogorov-Smirnov test a popular choice for many data distribution monitoring practitioners. You can see on the image below how the value of D-statistic changes with the change of data distribution to build some intuition on it’s behavior.

### Jensen-Shannon Distance¶

Jensen-Shannon Distance is a metric that tells us how different two probability distributions are. It is based on Kullback-Leibler divergence but is created in such a way that it is symmetric and ranges between 0 and 1.

Between two distributions $$P,Q$$ of a continuous feature Kullback-Leibler divergence is defined as:

$D_{KL} \left(P || Q \right) = \int_{-\infty}^{\infty}p(x)\ln \left( \frac{p(x)}{q(x)} \right) dx$

where $$p(x)$$ and $$q(x)$$ are the probability density functions of the distributions $$P,Q$$ respectively. And Jensen-Shannon Divergence is defined as:

$D_{JS} \left(P || Q \right) = \frac{1}{2} \left[ D_{KL} \left(P \Bigg|\Bigg| \frac{1}{2}(P+Q) \right) + D_{KL} \left(Q \Bigg|\Bigg| \frac{1}{2}(P+Q) \right)\right]$

and is a method of measuring the similarity between two probability distributions. Jensen-Shannon Distance is the squared root of Jensen-Shannon divergence and is a proper distance metric.

As mentioned, NannyML calculates drift performing two sample set comparisons. One sample is usually the whole reference data while the other comes from the data of the chunk we are calculating drift for. In order to calculate Jensen-Shannon Distance NannyML splits a continuous feature into bins using information from the reference sample. The binning is done using Doane’s formula from numpy. If a continuous feature has relatively low amount of unique values, meaning that unique values are less then 10% of the reference dataset size up to a maximum of 50, each value becomes a bin. If the any data from the chunk sample are outside those ranges a new bin created for them. The new bins relative frequency for the reference sample is set to 0. The relative frequency for each bin is calculated for the reference and chunk samples. Those results are then used to calculate the Jensen-Shannon Distance.

Unlike KS D-static that looks at maximum difference between two empirical CDFs, JS distance looks at the total difference between empirical Probability Density Functions (PDF). This makes it more sensitive to changes that may be ignored by KS. This effect can be observed in the plot below to get the intuition:

In the two rows we see two different changes been induced to the reference dataset. We can see from the cumulative density functions on the right that the resulting KS distance is the same. On the left we see the probability density functions of the samples and the resulting Jensen-Shannon Divergence at each point. Integrating over it and taking the square root gives the Jensen-Shannon distance showed. We can see that the resulting Jensen-Shannon distance is able to differentiate the two changes.

### Wasserstein Distance¶

The Wasserstein Distance, also known as earth mover’s distance and the Kantorovich-Rubinstein metric, is a measure of the difference between two probability distributions. Wasserstein distance can be thought of as the minimum amount of work needed to transform one distribution into the other. Informally, if the PDF of each distribution is imagined as a pile of dirt, the Wasserstein distance is the amount of work it would take to transform one pile of dirt into the other (which is why it is also called the earth mover’s distance).

While finding the Wasserstein distance can be framed as an optimal transport problem, when each distribution is one-dimensional, the CDFs of the two distributions can be used instead. When defined in this way, the Wasserstein distance is the integral of the absolute value of the difference between the two CDFs, or more simply, the area between the CDFS. The figure below illustrates this.

Mathematically we can express this as follows: For the $$i^\text{th}$$ feature of a dataset $$X=(X_1,...,X_i,...,X_n)$$, let $$\hat{F}_{P}$$ and $$\hat{F}_{Q}$$ represent the empirical CDFs of the two samples we are comparing. Further, let $$X_i^{P}$$ and $$X_i^{Q}$$ represent those two samples. Then the Wasserstein distance between the two distributions is given by:

$W_1\left(X_i^{P},X_i^{Q}\right) = \int_\mathbb{R}\left|\hat{F}_{P}(x)-\hat{F}_{Q}(x)\right|dx$

As mentioned, NannyML calculates drift performing two sample set comparisons. One sample is usually the whole reference data while the other comes from the data of the chunk we are calculating drift for. Hence we can view $$P,Q$$ as the reference sample and the chunk sample.

### Hellinger Distance¶

The Hellinger Distance, is a distance metric used to quantify the similarity between two probability distributions. It measures the overlap between the probabilities assigned to the same event by both reference and analysis samples. It ranges from 0 to 1 where a value of 1 is only achieved when reference assigns zero probability to each event to which the analysis sample assigns some positive probability and vice versa. Between two distributions $$P,Q$$ of a continuous feature Hellinger is defined as:

$H\left(P,Q\right) = \frac{1}{\sqrt{2}}\left[\int_{}\left(\sqrt{p(x)}-\sqrt{q(x)}\right)^2dx\right]^{1/2}$

where $$p(x)$$ and $$q(x)$$ are the probability density functions of the distributions $$P,Q$$ respectively.

As mentioned, NannyML calculates drift performing two sample set comparisons. One sample is usually the whole reference data while the other comes from the data of the chunk we are calculating drift for. In order to calculate Hellinger Distance NannyML splits a continuous feature into bins using information from the reference sample. The binning is done using Doane’s formula from numpy. If a continuous feature has relatively low amount of unique values, meaning that unique values are less then 10% of the reference dataset size up to a maximum of 50, each value becomes a bin. If the any data from the chunk sample are outside those ranges a new bin created for them. The new bins’ relative frequency for the reference sample is set to 0. The relative frequency for each bin is calculated for the reference and chunk samples. Those results are then used to calculate the Hellinger Distance.

This distance is very closely related to the Bhattacharya Coefficient. However we choose the former because it follows the triangle inequality and is a proper distance metric. Moreover the division by the squared root of 2 ensures that the distance is always between 0 and 1, which is not the case with the Bhattacharya Coefficient. The relationship between the two can be depicted as follows:

$H^2\left(P,Q\right) = 2(1-BC\left(P,Q\right))$

where

$BC\left(P,Q\right) = \int \sqrt{p(x)q(x)}dx$

## Methods for Categorical Variables¶

### Chi-squared Test¶

The Chi-squared test is a statistical hypothesis test of independence for categorical data. The test outputs the test statistic, sometimes called chi2 statistic, and an associated p-value.

We can understand the Chi-squared test in the following way. We create a contingency table from the categories present in the data and the two samples we are comparing. The expected frequencies, denoted $$m_i$$, are calculated from the marginal sums of the contingency table. The observed frequencies, denoted $$x_i$$, are calculated from the actual frequency entries of the contingency table. The test statistic is then given by the formula:

$\chi^2 = \sum_{i=1}^k \frac{(x_i - m_i)^2}{m_i}$

where we sum over all entries in the contingency table.

This makes the chi-squared statistic sensitive to all changes in the distribution, especially to the ones in low-frequency categories, as the expected frequency is in the denominator. It is therefore not recommended for categorical features with many low-frequency categories or high cardinality features, unless the sample size is really large. Otherwise, in both cases false-positive alarms are expected. Additionally, the statistic is non-negative and not limited which sometimes makes it difficult to interpret. Despite that, the Chi-squared test is a common choice amongst practitioners as it provides p-value together with the statistic that helps to better evaluate its result.

On the image below there is a visualization of the chi-squared statistic for a categorical variable with two categories, a and b. You can see the expected values are calculated from both the reference and analysis data. The red bars represent the difference between the observed and expected frequencies. As mentioned above, in the chi-squared statistic formula, the difference is squared and divided by the expected frequency and the resulting value is then summed over all categories for both samples.

### Jensen-Shannon Distance¶

Jensen-Shannon Distance is a metric that tells us how different two probability distributions are. It is based on Kullback-Leibler divergence but is created in such a way that it is symmetric and ranges between 0 and 1.

Between two distributions $$P,Q$$ of a categorical feature Kullback-Leibler divergence is defined as:

$D_{KL} \left(P || Q \right) = \sum_{x \in X} P(x)\ln \left( \frac{P(x)}{Q(x)} \right)$

where $$p(x)$$ and $$q(x)$$ are the probability mass functions of the distributions $$P,Q$$ respectively. And Jensen-Shannon Divergence is defined as:

$D_{JS} \left(P || Q \right) = \frac{1}{2} \left[ D_{KL} \left(P \Bigg|\Bigg| \frac{1}{2}(P+Q) \right) + D_{KL} \left(Q \Bigg|\Bigg| \frac{1}{2}(P+Q) \right)\right]$

and is a method of measuring the similarity between two probability distributions. Jensen-Shannon Distance is then defined as the squared root of Jensen-Shannon divergence and is a proper distance metric.

As mentioned, NannyML calculates drift performing two sample set comparisons. One sample is usually the whole reference data while the other comes from the data of the chunk we are calculating drift for. When calculating JS Distance for categorical data NannyML uses the reference data to split the data into bins with each categorical value corresponding to a bin in the reference sample. If the any data from the chunk sample have different unique values a new bin created for them. The new bins relative frequency for the reference sample is set to 0. The relative frequency for each bin is calculated for the reference and chunk samples. Those results are then used to calculate the Hellinger Distance.

The intuition behind Jensen-Shannon is that it measures an average of all changes in relative frequencies of categories. Frequencies are compared by dividing one by another, therefore JS distance, just like Chi-squared statistic, is sensitive to changes in less frequent classes. This means that an absolute change of 1 percentage point for less frequent class will have stronger contribution to the final JS distance value than the same change in more frequent class. For this reason it may not be the best choice for categorical variables with many low-frequency classes or high cardinality.

To help our intuition we can look at the image below:

We see how the relative frequencies of three categories have changed between reference and analysis data. We also see that the JS Divergence contribution of each change and the resulting JS distance.

### Hellinger Distance¶

The Hellinger Distance, is a distance metric used to quantify the similarity between two probability distributions. It measures the overlap between the probabilities assigned to the same event by both reference and analysis samples. It ranges from 0 to 1 where a value of 1 is only achieved when reference assigns zero probability to each event to which the analysis sample assigns some positive probability and vice versa.

Between two distributions $$P,Q$$ of a categorical feature Hellinger Distance is defined as:

$H\left(P,Q\right) = \frac{1}{\sqrt{2}}\left[\sum_{x \in X}\left(\sqrt{p(x)}-\sqrt{q(x)}\right)^2\right]^{1/2}$

where $$p(x)$$ and $$q(x)$$ are the probability mass functions of the distributions $$P,Q$$ respectively.

As mentioned, NannyML calculates drift performing two sample set comparisons. One sample is usually the whole reference data while the other comes from the data of the chunk we are calculating drift for. When calculating Hellinger Distance for categorical data NannyML uses the reference data to split the data into bins with each categorical value corresponding to a bin in the reference sample. If the any data from the chunk sample have different unique values a new bin created for them. The new bins relative frequency for the reference sample is set to 0. The relative frequency for each bin is calculated for the reference and chunk samples. Those results are then used to calculate the Hellinger Distance.

### L-Infinity Distance¶

We are using L-Infinity to measure the similarity of categorical features. L-Infinity, for categorical features, is defined as the maximum of the absolute difference between the relative frequencies of each category in the reference and analysis data. You can find more about L-Infinity at Wikipedia. It falls into the range of 0-1 and is easy to interpret as is the greatest change in relative frequency among all categories. This behavior is different compared to Chi Squared test where even small changes in low frequency labels can heavily influence the resulting test statistic.

To help our intuition we can look at the image below:

We see how the relative frequencies of three categories have changed between reference and analysis data. We also see that the resulting L-Infinity distance is the relative frequency change in category c.