Machine learning to generate and evaluate visualizations
Methods, systems, and apparatus, including computer programs encoded on computer-readable storage media, for machine learning to generate and evaluate visualizations. In some implementations, a system determines properties of a dataset. The system generates visualization specifications that each define a different visualization for the dataset, wherein the visualization specifications specify different subsets of the dataset being illustrated with different visualization formats. The system evaluates the visualization specifications using a machine learning model trained based on user feedback for visualizations for multiple datasets. The system selects a subset of the visualization specifications based on output of the machine learning model. The system provides, for display, visualization data for the subset of visualization specifications that were selected based on the output of the machine learning model.
1. A method performed by one or more computers, the method comprising:
receiving, by the one or more computers, data indicating a user selection of a portion of a dataset:
determining, by the one or more computers, properties of the selected portion of the dataset;
selecting, by the one or more computers, one or more visualizations for the user-selected portion of the dataset, wherein the one or more visualizations are selected based on evaluation of visualization specifications using a machine learning model trained based on user feedback for visualizations for multiple datasets; and
providing, by the one or more computers, visualization data for display, the visualization data corresponding to the one or more visualizations selected based on output of the machine learning model.
2. The method of claim 1 , wherein the portion of the dataset selected comprises one or more attributes or metrics represented in the dataset; and
wherein selecting the one or more visualizations comprises:
filtering a set of visualizations or visualization specifications to identify candidate visualizations that involve the selected one or more attributes or metrics; and
selecting the one or more visualizations from among the candidate visualizations that involve the selected one or more attributes or metrics.
3. The method of claim 1 , wherein the portion of the dataset selected comprises one or more columns of data in the dataset; and
wherein selecting the one or more visualizations is performed such that the one or more visualizations that each include a representation of data from the selected one or more columns of data in the dataset.
4. The method of claim 1 , wherein the portion of the dataset selected comprises multiple types of data; and
wherein selecting the one or more visualizations comprises selecting multiple visualizations that each represent different subsets of the multiple types of data in the selected portion of the dataset.
5. The method of claim 4 , comprising ranking the a set of visualizations based on scores generated by the machine learning model;
wherein selecting the multiple visualizations comprises selecting the multiple visualizations based on the ranking.
6. The method of claim 1 , wherein the machine learning model comprises an artificial neural network, a classifier, a support vector machine, a decision tree, a regression model, a clustering model, a Gaussian process model, a genetic algorithm, or a reinforcement learning model.
7. The method of claim 1 , wherein the machine learning model has been trained to output scores for visualizations, wherein the trained machine learning model is configured to generate a score for a visualization with respect to a dataset based on input indicating (i) properties of the visualization and (ii) properties of the dataset.
8. The method of claim 1 , wherein the machine learning model has been trained based on examples of actions that users performed after being presented one or more visualizations, wherein at least some of the examples respectively indicate actions performed after presentation of visualizations for different datasets.
9. The method of claim 1 , wherein the machine learning model is configured to evaluate a visualization specification by processing information from the visualization specification including (i) an indication of one or more structural properties of the dataset and (ii) an indication of portions of the dataset represented in the visualization represented by the visualization specification.
10. The method of claim 9 , wherein the one or more structural properties of the dataset include at least one of a number of columns, a number of attributes, a number of metrics, a value for an aggregation for a data range, a span of values for a data range, a data type for a data range, or a statistical measure for a data range.
11. The method of claim 1 , wherein selecting the one or more visualizations comprises filtering the visualization specifications to exclude one or more visualization specifications that do not represent the selected portion of the dataset.
12. The method of claim 11 , wherein the selected portion of the dataset comprises a selected column of the dataset; and
wherein selecting the one or more visualizations comprises selecting only visualizations that are generated from or provide information regarding the selected column.
13. The method of claim 1 , further comprising identifying a user of a computing device;
wherein selecting the one or more visualizations is personalized based on the identity of the user of the computing device; and
wherein providing the visualization data comprises providing the visualization data to the computing device for display by the computing device.
14. A system comprising:
one or more computers; and
one or more computer-readable media storing instructions that are operable, when executed by the one or more computers, to cause the one or more computers to perform operations comprising:
receiving, by the one or more computers, data indicating a user selection of a portion of a dataset;
determining, by the one or more computers, properties of the selected portion of the dataset;
selecting, by the one or more computers, one or more visualizations for the user-selected portion of the dataset, wherein the one or more visualizations are selected based on evaluation of visualization specifications using a machine learning model trained based on user feedback for visualizations for multiple datasets; and
providing, by the one or more computers, visualization data for display, the visualization data corresponding to the one or more visualizations selected based on output of the machine learning model.
15. The system of claim 14 , wherein the portion of the dataset selected comprises one or more attributes or metrics represented in the dataset; and
wherein selecting the one or more visualizations comprises:
filtering a set of visualizations or visualization specifications to identify candidate visualizations that involve the selected one or more attributes or metrics; and
selecting the one or more visualizations from among the candidate visualizations that involve the selected one or more attributes or metrics.
16. The system of claim 14 , wherein the portion of the dataset selected comprises one or more columns of data in the dataset; and
wherein selecting the one or more visualizations is performed such that the one or more visualizations that each include a representation of data from the selected one or more columns of data in the dataset.
17. The system of claim 14 , wherein the portion of the dataset selected comprises multiple types of data; and
wherein selecting the one or more visualizations comprises selecting multiple visualizations that each represent different subsets of the multiple types of data in the selected portion of the dataset.
18. The system of claim 17 , comprising ranking the a set of visualizations based on scores generated by the machine learning model;
wherein selecting the multiple visualizations comprises selecting the multiple visualizations based on the ranking.
19. The system of claim 14 , wherein the machine learning model comprises an artificial neural network, a classifier, a support vector machine, a decision tree, a regression model, a clustering model, a Gaussian process model, a genetic algorithm, or a reinforcement learning model.
20. One or more non-transitory computer-readable media storing instructions that are operable, when executed by one or more computers, to cause the one or more computers to perform operations comprising:
receiving, by the one or more computers, data indicating a user selection of a portion of a dataset;
determining, by the one or more computers, properties of the selected portion of the dataset;
selecting, by the one or more computers, one or more visualizations for the user-selected portion of the dataset, wherein the one or more visualizations are selected based on evaluation of visualization specifications using a machine learning model trained based on user feedback for visualizations for multiple datasets; and
providing, by the one or more computers, visualization data for display, the visualization data corresponding to the one or more visualizations selected based on output of the machine learning model.