Histogram bin interval approximation
Using approximated bin intervals to label the histograms provides clarity and allows for the histogram to be more intuitively understood. A dataset may comprise a plurality of records having a plurality of features including one or more continuous features. A selection of a continuous feature may be obtained. A bin width based on a number of bins and feature statistics of the continuous feature may be determined. An approximated bin interval range is determined by applying a bin mask based on the bin width to the feature statistics. An approximated bin width is determined based on the number of bins and the approximated bin interval range. Approximated bin intervals for the histogram are determined based on the approximated bin width. A histogram is generated having bins with intervals based the approximated bin intervals.
1. A computer system, comprising:
one or more processors; and
one or more machine-readable medium coupled to the one or more processors and storing computer program code comprising sets of instructions for executable by the one or more processors to:
determine an approximated bin width for a histogram representing a continuous feature of a dataset, the approximated bin width based on a number of bins and an approximated bin interval range, the approximated bin interval range determined by applying a bin mask to feature statistics of the continuous feature;
determine approximated bin intervals for the histogram based on the approximated bin width;
generate the histogram representing the continuous feature, the histogram having the number of bins, the bins of the histogram having intervals based the approximated bin intervals, each bin of the histogram representing a count of values of the continuous feature in the dataset within the corresponding approximated bin interval.
2. The computer system of claim 1 , wherein the feature statistics of the continuous feature include a maximum value of the continuous feature in the dataset and a minimum value of the continuous feature in the dataset.
3. The computer system of claim 2 , wherein the computer program code further comprises sets of instructions executable by the one or more processors to:
round the maximum value up to determine a rounded maximum value;
multiply the rounded maximum value by the bin mask to determine a treated maximum value, the determination of—the approximated bin interval range being based on the treated maximum value;
round down the minimum value to determine a rounded minimum value; and
multiply the rounded minimum value by the bin mask to determine a treated minimum value, the determination of the approximated bin interval range being based on the treated minimum value.
4. The computer system of claim 3 , wherein the determination of the approximated bin interval range is based on an absolute value of a difference between the rounded maximum value and the rounded minimum value.
5. The computer system of claim 1 , wherein the computer program code further comprises sets of instructions executable by the one or more processors to:
determine whether the bin width is divisible by ten; and
apply the bin mask to the approximated bin width if the bin width is not divisible by ten.
6. The computer system of claim 1 , wherein the computer program code further comprises sets of instructions executable by the one or more processors to:
sort the approximated bin intervals for the histogram in ascending order or descending order based on configuration information; and
truncate a first sorted bin interval and a last sorted bin interval based on the configuration information.
7. The computer system of claim 1 , wherein the computer program code further comprises sets of instructions executable by the one or more processors to:
determine a set of features that impact values a target feature selected via the user interface, the set of features including the continuous feature;
generate histograms for each other feature in the set of features besides the continuous feature; and
present the histograms for each other feature in the user interface.
8. One or more non-transitory computer-readable medium storing computer program code comprising sets of instructions to:
determine an approximated bin width for a histogram representing a continuous feature of a dataset, the approximated bin width based on a number of bins and an approximated bin interval range, the approximated bin interval range determined by applying a bin mask to feature statistics of the continuous feature;
determine approximated bin intervals for the histogram based on the approximated bin width;
generate the histogram representing the continuous feature, the histogram having the number of bins, the bins of the histogram having intervals based the approximated bin intervals, each bin of the histogram representing a count of values of the continuous feature in the dataset within the corresponding approximated bin interval.
9. The non-transitory computer-readable medium of claim 8 , wherein the feature statistics of the continuous feature include a maximum value of the continuous feature in the dataset and a minimum value of the continuous feature in the dataset.
10. The non-transitory computer-readable medium of claim 9 , wherein the computer program code further comprises sets of instructions to:
round the maximum value up to determine a rounded maximum value;
multiply the rounded maximum value by the bin mask to determine a treated maximum value, the determination of the approximated bin interval range being based on the treated maximum value;
round down the minimum value to determine a rounded minimum value; and
multiply the rounded minimum value by the bin mask to determine a treated minimum value, the determination of the approximated bin interval range being based on the treated minimum value.
11. The non-transitory computer-readable medium of claim 10 , wherein the determination of the approximated bin interval range is based on an absolute value of a difference between the rounded maximum value and the rounded minimum value.
12. The non-transitory computer-readable medium of claim 8 , wherein the computer program code further comprises sets of instructions to:
determine whether the bin width is divisible by ten; and
apply the bin mask to the approximated bin width if the bin width is not divisible by ten.
13. The non-transitory computer-readable medium of claim 8 , wherein the computer program code further comprises sets of instructions to:
sort the approximated bin intervals for the histogram in ascending order or descending order based on configuration information; and
truncate a first sorted bin interval and a last sorted bin interval based on the configuration information.
14. The non-transitory computer-readable medium of claim 8 , wherein the computer program code further comprises sets of instructions to:
determine a set of features that impact a target feature selected via the user interface, the set of features including the continuous feature;
generate histograms for each other feature in the set of features besides the continuous feature; and
present the histograms for each other feature in the user interface.
15. A computer-implemented method, comprising:
determine an approximated bin width for a histogram representing a continuous feature of a dataset, the approximated bin width based on a number of bins and an approximated bin interval range, the approximated bin interval range determined by applying a bin mask to feature statistics of the continuous feature;
determine approximated bin intervals for the histogram based on the approximated bin width;
generate the histogram representing the continuous feature, the histogram having the number of bins, the bins of the histogram having intervals based the approximated bin intervals, each bin of the histogram representing a count of values of the continuous feature in the dataset within the corresponding approximated bin interval.
16. The computer-implemented method of claim 15 , wherein the feature statistics of the continuous feature include a maximum value of the continuous feature in the dataset and a minimum value of the continuous feature in the dataset.
17. The computer-implemented method of claim 16 , further comprising:
rounding the maximum value up to determine a rounded maximum value;
multiplying the rounded maximum value by the bin mask to determine a treated maximum value, the determination of the approximated bin interval range being based on the treated maximum value;
rounding down the minimum value to determine a rounded minimum value; and
multiplying the rounded minimum value by the bin mask to determine a treated minimum value, the determination of the approximated bin interval range being based on the treated minimum value.
18. The computer-implemented method of claim 17 , wherein the determination of the approximated bin interval range is based on an absolute value of a difference between the rounded maximum value and the rounded minimum value.
19. The computer-implemented method of claim 15 , further comprising:
determining whether the bin width is divisible by ten; and
applying the bin mask to the approximated bin width if the bin width is not divisible by ten.
20. The computer-implemented method of claim 15 , further comprising:
sorting the approximated bin intervals for the histogram in ascending order or descending order based on configuration information; and
truncating a first sorted bin interval and a last sorted bin interval based on the configuration information.