Top Insights (Snowflake ML Functions)¶

About Top Insights¶

Top Insights uses a decision tree model that separates a dataset into segments that have different behavior in relation to the metric you want to analyze. The algorithm analyzes inter-segment differences between the metric in the control group and the test group.

• The control group consists of the data points the model will use as a baseline.

• The test group consists of points of interest to be analyzed.

Top Insights then produces a number of possible contributor combinations, which are filtered based on their significance and distinctiveness. Top Insights does not return redundant segments.

Good candidate datasets for analysis with Top Insights typically have a large number of columns or dimensions used to segment data that make it difficult to intuitively identify what segments influence a metric. Dimensions can be categorical (location, market segment, etc.) or continuous (that is, quantitative, such as temperature or attendance).

A Top Insights model is a schema-level object. You only need one instance, since the instance does not hold any state.

Required privileges¶

A TOP_INSIGHTS instance is a schema-level object. Therefore, the role you use to create the instance must have the CREATE SNOWFLAKE.ML.TOP_INSIGHTS privilege on the schema where the instance is created. This privilege is similar to other schema privileges like CREATE TABLE or CREATE VIEW.

If you are not the owner of the instance, you must have the USAGE privilege on it to be able to call its GET_DRIVERS method.

Using Top Insights¶

To use Top Insights in your queries and pipelines, first create an instance of the TOP_INSIGHTS (SNOWFLAKE.ML) class. The SQL statement below creates an instance named my_insights. Creating the instance does not require any arguments.

CREATE SNOWFLAKE.ML.TOP_INSIGHTS IF NOT EXISTS my_insights();

After creating an instance, you can use the GET_DRIVERS method to extract key drivers from the dataset you want to perform key driver analytics on. You pass the input data all in one piece (a reference to a single table, view, or query) and provide the names of the metric and label columns within the input data as additional arguments. Categorical and continuous dimensions are inferred by their type and do not need to be specified explicitly.

CALL my_insights!get_drivers (
  INPUT_DATA => TABLE(my_table),
  LABEL_COLNAME => 'label',
  METRIC_COLNAMe => 'sales');

Preparing data for Top Insights¶

To use Top Insights, make sure you have a Boolean label column that distinguishes rows that are part of the control group (labeled FALSE) from rows in the test group (labeled TRUE). This column is usually derived from other values in the dataset, such as a timestamp or the name of a vertical, so it is common to create a view to do this. The view is also a good place to filter out columns that are not part of your analysis.

The example below, for time-series analysis, creates a view with a label column based on a date range. Specifically, it labels records in the latest month as TRUE (test data) and all previous records as FALSE (control data). Top Insights can then analyze the continuous and categorical dimensions that explain differences in month-to-month changes for the specified metric.

CREATE VIEW input_table_time_series_label (
  ds, metric, dim_country, dim_vertical, label ) AS
  SELECT
    ds,
    metric,
    dim_country,
    dim_vertical,
    ds >= dateadd(month, -1, current_date) AS label
  FROM input_table;

The following example, for vertical analysis, creates a view with a label column based on the country. Specifically, it labels records in non-US countries as TRUE, and labels records in the USA as FALSE. Top Insights will then analyze the continuous and categorical dimensions that explain differences in a metric between these population groups.

CREATE VIEW input_table_vertical_label (
  ds, metric,  dim_country, dim_vertical, label ) AS
  SELECT
    ds,
    metric,
    dim_country,
    dim_vertical,
    dim_country <> 'USA' as label
  FROM input_table;

Interpreting the results¶

Top Insights returns a row for each segment of interest it finds in your data. Each row contains a plain-English description of the segment, which can contain multiple criteria (for example, “COUNTRY = france, not VERTICAL = fashion, not VERTICAL = tech” might describe a single segment). For each segment, Top Insights provides the following values which quantify how much the segment contributes to the changes between the control and the test group.

The contribution, relative contribution, and growth rate may be negative, indicating that a segment has a negative impact.

Cost considerations¶

Using Top Insights incurs compute costs. Execution time scales with the number of rows and dimensions processed. See Understanding compute cost for general information about Snowflake compute costs.

Top Insights performance does not generally benefit from using a larger warehouse than is needed to load all the data being analyzed, which must fit into memory. Datasets that surpass about 1,000,000 rows and 1,000 columns may exhaust memory. Snowflake recommends using a Snowpark-optimized warehouse rather than a larger standard warehouse. Snowpark-optimized warehouses have more memory than standard warehouses of the corresponding size.

While instances of the Top Insights class are schema-level objects, they do not store any data and have negligible impact on storage costs.

Examples¶

The following examples demonstrate how to use Top Insights for time-series analysis and vertical analysis.

• Time-series analysis example

• Vertical analysis example

CREATE OR REPLACE TABLE input_table(
  ds DATE, metric NUMBER, dim_country VARCHAR, dim_vertical VARCHAR);

INSERT INTO input_table
  SELECT
    DATEADD(day, SEQ4(), DATE_FROM_PARTS(2020, 4, 1)) AS ds,
    UNIFORM(1, 10, RANDOM()) AS metric,
    'usa' AS dim_country,
    'tech' AS dim_vertical
  FROM TABLE(GENERATOR(ROWCOUNT => 365));

INSERT INTO input_table
  SELECT
    DATEADD(day, SEQ4(), DATE_FROM_PARTS(2020, 4, 1)) AS ds,
    UNIFORM(1, 10, RANDOM()) AS metric,
    'usa' AS dim_country,
    'auto' AS dim_vertical
  FROM TABLE(GENERATOR(ROWCOUNT => 365));

INSERT INTO input_table
  SELECT
    DATEADD(day, seq4(), DATE_FROM_PARTS(2020, 4, 1)) AS ds,
    UNIFORM(1, 10, RANDOM()) AS metric,
    'usa' AS dim_country,
    'fashion' AS dim_vertical
  FROM TABLE(GENERATOR(ROWCOUNT => 365));

INSERT INTO input_table
  SELECT
    DATEADD(day, SEQ4(), DATE_FROM_PARTS(2020, 4, 1)) AS ds,
    UNIFORM(1, 10, RANDOM()) AS metric,
    'usa' AS dim_country,
    'finance' AS dim_vertical
  FROM TABLE(GENERATOR(ROWCOUNT => 365));

INSERT INTO input_table
  SELECT
    DATEADD(day, SEQ4(), DATE_FROM_PARTS(2020, 4, 1)) AS ds,
    UNIFORM(1, 10, RANDOM()) AS metric,
    'canada' AS dim_country,
    'fashion' AS dim_vertical
  FROM TABLE(GENERATOR(ROWCOUNT => 365));

INSERT INTO input_table
  SELECT
    DATEADD(day, SEQ4(), DATE_FROM_PARTS(2020, 4, 1)) AS ds,
    UNIFORM(1, 10, RANDOM()) AS metric,
    'canada' AS dim_country,
    'finance' AS dim_vertical
  FROM TABLE(GENERATOR(ROWCOUNT => 365));

INSERT INTO input_table
  SELECT
    DATEADD(day, SEQ4(), DATE_FROM_PARTS(2020, 4, 1)) AS ds,
    UNIFORM(1, 10, RANDOM()) AS metric,
    'canada' AS dim_country,
    'tech' AS dim_vertical
  FROM TABLE(GENERATOR(ROWCOUNT => 365));

INSERT INTO input_table
  SELECT
    DATEADD(day, SEQ4(), DATE_FROM_PARTS(2020, 4, 1)) AS ds,
    UNIFORM(1, 10, RANDOM()) AS metric,
    'canada' AS dim_country,
    'auto' AS dim_vertical
  FROM TABLE(GENERATOR(ROWCOUNT => 365));

INSERT INTO input_table
  SELECT
    DATEADD(day, SEQ4(), DATE_FROM_PARTS(2020, 4, 1)) AS ds,
    UNIFORM(1, 10, RANDOM()) AS metric,
    'france' AS dim_country,
    'fashion' AS dim_vertical
  FROM TABLE(GENERATOR(ROWCOUNT => 365));

INSERT INTO input_table
  SELECT
    DATEADD(day, SEQ4(), DATE_FROM_PARTS(2020, 4, 1)) AS ds,
    UNIFORM(1, 10, RANDOM()) AS metric,
    'france' AS dim_country,
    'finance' AS dim_vertical
  FROM TABLE(GENERATOR(ROWCOUNT => 365));

INSERT INTO input_table
  SELECT
    DATEADD(day, SEQ4(), DATE_FROM_PARTS(2020, 4, 1)) AS ds,
    UNIFORM(1, 10, RANDOM()) AS metric,
    'france' AS dim_country,
    'tech' AS dim_vertical
  FROM TABLE(GENERATOR(ROWCOUNT => 365));

INSERT INTO input_table
  SELECT
    DATEADD(day, SEQ4(), DATE_FROM_PARTS(2020, 4, 1)) AS ds,
    UNIFORM(1, 10, RANDOM()) AS metric,
    'france' AS dim_country,
    'auto' AS dim_vertical
  FROM TABLE(GENERATOR(ROWCOUNT => 365));

-- Data for the test group

INSERT INTO input_table
  SELECT
    DATEADD(day, SEQ4(), DATE_FROM_PARTS(2020, 8, 1)) AS ds,
    UNIFORM(300, 320, RANDOM()) AS metric,
    'usa' AS dim_country,
    'auto' AS dim_vertica
  FROM TABLE(GENERATOR(ROWCOUNT => 365));

INSERT INTO input_table
  SELECT
    DATEADD(day, SEQ4(), DATE_FROM_PARTS(2020, 8, 1))  AS ds,
    UNIFORM(400, 420, RANDOM()) AS metric,
    'usa' AS dim_country,
    'finance' AS dim_vertical
  FROM TABLE(GENERATOR(ROWCOUNT => 365));

CREATE OR REPLACE VIEW input_view AS (
    SELECT
        metric,
        dim_country as country,
        dim_vertical as vertical,
        ds >= '2021-01-01' AS label
    FROM input_table
);

CREATE OR REPLACE SNOWFLAKE.ML.TOP_INSIGHTS my_insights_model()

CALL my_insights_model!GET_DRIVERS(
  INPUT_DATA => TABLE(input_view),
  LABEL_COLNAME => 'label',
  METRIC_COLNAME => 'metric'
)

+---------------------------------------------------------------------+----------------+-------------+--------------+-----------------------+---------------+
| CONTRIBUTOR                                                         | METRIC_CONTROL | METRIC_TEST | CONTRIBUTION | RELATIVE_CONTRIBUTION |   GROWTH_RATE |
|---------------------------------------------------------------------+----------------+-------------+--------------+-----------------------+---------------|
| ["Overall"]                                                         |         128445 |      158456 |        30011 |         1             |  0.2336486434 |
| ["COUNTRY = usa"]                                                   |         116238 |      154574 |        38336 |         1.277398287   |  0.3298060875 |
| ["COUNTRY = usa","VERTICAL = finance"]                              |          64281 |       87423 |        23142 |         0.771117257   |  0.3600130676 |
| ["COUNTRY = usa","VERTICAL = auto"]                                 |          48930 |       66131 |        17201 |         0.5731565093  |  0.3515430206 |
| ["COUNTRY = usa","VERTICAL = tech"]                                 |           1543 |         503 |        -1040 |        -0.03465396021 | -0.6740116656 |
| ["COUNTRY = canada","VERTICAL = finance"]                           |           1538 |         482 |        -1056 |        -0.03518709806 | -0.6866059818 |
| ["COUNTRY = canada","VERTICAL = fashion"]                           |           1519 |         446 |        -1073 |        -0.03575355703 | -0.7063857801 |
| ["COUNTRY = france","VERTICAL = auto"]                              |           1534 |         460 |        -1074 |        -0.03578687814 | -0.7001303781 |
| ["COUNTRY = usa","not VERTICAL = auto","not VERTICAL = finance"]    |           3027 |        1020 |        -2007 |        -0.06687547899 | -0.6630327056 |
| ["COUNTRY = france","not VERTICAL = fashion","not VERTICAL = tech"] |           3100 |         962 |        -2138 |        -0.07124054513 | -0.6896774194 |
| ["COUNTRY = france","not VERTICAL = fashion"]                       |           4687 |        1456 |        -3231 |        -0.1076605245  | -0.689353531  |
| ["COUNTRY = france"]                                                |           6202 |        1947 |        -4255 |        -0.1417813468  | -0.68606901   |
| ["not COUNTRY = usa"]                                               |          12207 |        3882 |        -8325 |        -0.2773982873  | -0.6819857459 |
+---------------------------------------------------------------------+----------------+-------------+--------------+-----------------------+---------------+

CREATE OR REPLACE TABLE vertical_input_table(
  region VARCHAR, industry VARCHAR, num_employee NUMBER, credits FLOAT);

INSERT INTO vertical_input_table
  SELECT
    'USA' as region,
    ['technology', 'finance', 'healthcare', 'consumer'][MOD(ABS(RANDOM()), 4)] as industry,
    UNIFORM(100, 10000, RANDOM()) as num_employee,
    UNIFORM(1000, 3000, RANDOM()) AS credits,
  FROM TABLE(GENERATOR(ROWCOUNT => 450));

INSERT INTO vertical_input_table
  SELECT
    'EMEA' as region,
    ['technology', 'finance', 'healthcare', 'consumer'][MOD(ABS(RANDOM()), 4)] as industry,
    UNIFORM(100, 10000, RANDOM()) as num_employee,
    UNIFORM(100, 5000, RANDOM()) AS credits,
  FROM TABLE(GENERATOR(ROWCOUNT => 350));

CREATE OR REPLACE VIEW vertical_input_view AS (
    SELECT
        credits,
        industry,
        num_employee,
        region = 'EMEA' AS label
    FROM vertical_input_table
);

CREATE OR REPLACE SNOWFLAKE.ML.TOP_INSIGHTS my_insights_model();

CALL my_insights_model!get_drivers(
  INPUT_DATA => TABLE(vertical_input_view),
  LABEL_COLNAME => 'label',
  METRIC_COLNAME => 'credits'
);

+-------------------------------------------------------------------------------------------------------+----------------+-------------+--------------+-----------------------+------------------+|
| CONTRIBUTOR                                                                                           | METRIC_CONTROL | METRIC_TEST | CONTRIBUTION | RELATIVE_CONTRIBUTION |      GROWTH_RATE |
|-------------------------------------------------------------------------------------------------------+----------------+-------------+--------------+-----------------------+------------------|
| ["Overall"]                                                                                           |         896672 |      895326 |        -1346 |           1           |  -0.001501106313 |
| ["not INDUSTRY = consumer","NUM_EMPLOYEE <= 6248.0","NUM_EMPLOYEE > 4235.0"]                          |         141138 |       70337 |       -70801 |          52.601040119 |  -0.5016437813   |
| ["NUM_EMPLOYEE <= 6248.0","NUM_EMPLOYEE > 4235.0"]                                                    |         188770 |      127320 |       -61450 |          45.653789004 |  -0.3255284208   |
| ["not INDUSTRY = technology","NUM_EMPLOYEE <= 8670.0","NUM_EMPLOYEE > 7582.5"]                        |         100533 |       42925 |       -57608 |          42.799405646 |  -0.5730257726   |
| ["not INDUSTRY = consumer","NUM_EMPLOYEE <= 5562.5","NUM_EMPLOYEE > 4235.0"]                          |         103851 |       47052 |       -56799 |          42.198365527 |  -0.54692781     |
+-------------------------------------------------------------------------------------------------------+----------------+-------------+--------------+-----------------------+------------------+

Current limitations¶

• The input metric must be an individual observation or an aggregate.

• For categorical features having more than 25 values, Top Insights uses only the top 25 most influential values to create segments.

• Processing more than 100 million rows in a single job may exhaust memory, even with Snowpark-optimized warehouses.

Legal notices¶