mirror of
https://github.com/CartoDB/crankshaft.git
synced 2024-11-01 10:20:48 +08:00
85 lines
4.5 KiB
Markdown
85 lines
4.5 KiB
Markdown
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## Segmentation Functions
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### CDB_CreateAndPredictSegment (query TEXT,variable_name TEXT,target_query TEXT)
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This function trains a [Gradient Boosting](http://scikit-learn.org/stable/modules/generated/sklearn.ensemble.GradientBoostingRegressor.html) model to attempt to predict the target data and then generates predictions for new data.
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#### Arguments
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| Name | Type | Description |
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|------|------|-------------|
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| query | TEXT | The input query to train the algorithum, should have both the variable of interest and the features that will be used to predict it|
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| variablei\_name| TEXT | Specify the variable in the query to predict, all other columns are assumed to be features |
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| target\_table | TEXT | The query which returns the cartodb\_id and features for the rows your would like to predict the target variable for |
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| n\_estimators(optional) | INTEGER DEFAULT 1200| Number of estimators to be used |
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| max\_depth (optional) | INTEGER DEFAULT 3 | Max tree depth |
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| subsample (optional) | DOUBLE PRECISION DEFAULT 0.5 | Subsample paramter for GradientBooster|
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| learning\_rate(optional) | DOUBLE PRECISION DEFAULT 0.01| Learning rate for the GradientBooster|
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| min\_samples\_leaf(optional) | INTEGER DEFAULT 1 | Minimum samples to use per leaf|
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#### Returns
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A table with the following columns.
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| Column Name | Type | Description |
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|-------------|------|-------------|
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| cartodb\_id | INTEGER | The CartoDB id of the row in the target\_query|
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| prediction | NUMERIC | The predicted value of the variable of interest |
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| accuracy | NUMERIC | The mean squared accuracy of the model. |
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#### Example Usage
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```sql
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SELECT * from cdb_crankshaft.CDB_CreateAndPredictSegment(
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'SELECT agg, median_rent::numeric, male_pop::numeric, female_pop::numeric from late_night_agg',
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'agg',
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'select ROW_NUMBER ( ) over () as cartodb_id, median_rent, male_pop, female_pop from ml_learning_ny');
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```
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### CDB_CreateAndPredictSegment (target NUMERIC[],train_features NUMERIC[], prediction_features Numeric[], prediction_ids NUMERIC[])
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This function trains a [Gradient Boosting](http://scikit-learn.org/stable/modules/generated/sklearn.ensemble.GradientBoostingRegressor.html) model to attempt to predict the target data and then generates predictions for new data.
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#### Arguments
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| Name | Type | Description |
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|------|------|-------------|
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| target | NUMERIC[] | An array of target values of the variable you want to predict|
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| train\_features| NUMERIC[] | 1D array of length nfeatures\* n\_rows + 1 with the first entry in the array being the number of features in each row. These are the features the model will be trained on. CDB\_Crankshaft.CDB_pyAgg(Array[freature1, feature2, feature3]::Numeric[]) can be used to construct this. |
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| prediction\_features | NUMERIC[]] | 1D array of length nfeatures\* n\_rows\_ + 1 with the first entry in the array being the number of features in each row. These are the features that will be used to predict the target variable CDB\_Crankshaft.CDB\_pyAgg(Array[freature1, feature2, feature3]::Numeric[]) can be used to construct this. |
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| prediction\_ids | NUMERIC[]] | 1D array of length n\_rows with the ids that can use used to rejoin the data with inputs |
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#### Returns
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A table with the following columns.
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| Column Name | Type | Description |
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|-------------|------|-------------|
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| cartodb\_id | INTEGER | The CartoDB id of the row in the target\_query|
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| prediction | NUMERIC | The predicted value of the variable of interest |
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| accuracy | NUMERIC | The mean squared accuracy of the model. |
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| n\_estimators(optional) | INTEGER DEFAULT 1200| Number of estimators to be used |
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| max\_depth (optional) | INTEGER DEFAULT 3 | Max tree depth |
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| subsample (optional) | DOUBLE PRECISION DEFAULT 0.5 | Subsample paramter for GradientBooster|
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| learning\_rate(optional) | DOUBLE PRECISION DEFAULT 0.01| Learning rate for the GradientBooster|
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| min\_samples\_leaf(optional) | INTEGER DEFAULT 1 | Minimum samples to use per leaf|
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#### Example Usage
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```sql
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WITH training AS (
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SELECT array_agg(agg) AS target,
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cdb_crankshaft.CDB_PyAgg(Array[median_rent, male_pop, female_pop]::Numeric[]) AS features
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FROM late_night_agg),
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target AS (
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SELECT cdb_crankshaft.CDB_PyAgg(Array[median_rent, male_pop, female_pop]::Numeric[]) AS features,
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array_agg(cartodb_id ) AS cartodb_ids FROM late_night_agg)
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SELECT cdb_crankshaft.CDB_CreateAndPredictSegment2(training.target, training.features, target.features, targetcartodb_ids)
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FROM training, target;
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`````
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