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doc/12_segmentation.md
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## Segmentation Functions
### CDB_CreateAndPredictSegment (query TEXT,variable_name TEXT,target_query TEXT)
### CDB_CreateAndPredictSegment (query TEXT, variable_name TEXT, target_query TEXT)
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.
@ -9,14 +9,14 @@ This function trains a [Gradient Boosting](http://scikit-learn.org/stable/module
| Name | Type | Description |
|------|------|-------------|
| 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|
| variablei\_name| TEXT | Specify the variable in the query to predict, all other columns are assumed to be features |
| target\_table | TEXT | The query which returns the cartodb\_id and features for the rows your would like to predict the target variable for |
| n\_estimators(optional) | INTEGER DEFAULT 1200| Number of estimators to be used |
| max\_depth (optional) | INTEGER DEFAULT 3 | Max tree depth |
| subsample (optional) | DOUBLE PRECISION DEFAULT 0.5 | Subsample paramter for GradientBooster|
| learning\_rate(optional) | DOUBLE PRECISION DEFAULT 0.01| Learning rate for the GradientBooster|
| min\_samples\_leaf(optional) | INTEGER DEFAULT 1 | Minimum samples to use per leaf|
| query | TEXT | The input query to train the algorithm, which should have both the variable of interest and the features that will be used to predict it |
| variable\_name| TEXT | Specify the variable in the query to predict, all other columns are assumed to be features |
| target\_table | TEXT | The query which returns the `cartodb_id` and features for the rows your would like to predict the target variable for |
| n\_estimators (optional) | INTEGER DEFAULT 1200 | Number of estimators to be used. Values should be between 1 and x. |
| max\_depth (optional) | INTEGER DEFAULT 3 | Max tree depth. Values should be between 1 and n. |
| subsample (optional) | DOUBLE PRECISION DEFAULT 0.5 | Subsample parameter for GradientBooster. Values should be within the range 0 to 1. |
| learning\_rate (optional) | DOUBLE PRECISION DEFAULT 0.01 | Learning rate for the GradientBooster. Values should be between 0 and 1 (??) |
| min\_samples\_leaf (optional) | INTEGER DEFAULT 1 | Minimum samples to use per leaf. Values should range from x to y |
#### Returns
@ -24,21 +24,20 @@ A table with the following columns.
| Column Name | Type | Description |
|-------------|------|-------------|
| cartodb\_id | INTEGER | The CartoDB id of the row in the target\_query|
| cartodb\_id | INTEGER | The CartoDB id of the row in the target\_query |
| prediction | NUMERIC | The predicted value of the variable of interest |
| accuracy | NUMERIC | The mean squared accuracy of the model. |
#### Example Usage
```sql
SELECT * from cdb_crankshaft.CDB_CreateAndPredictSegment(
'SELECT agg, median_rent::numeric, male_pop::numeric, female_pop::numeric from late_night_agg',
SELECT * from cdb_crankshaft.CDB_CreateAndPredictSegment(
'SELECT agg, median_rent::numeric, male_pop::numeric, female_pop::numeric FROM late_night_agg',
'agg',
'select ROW_NUMBER ( ) over () as cartodb_id, median_rent, male_pop, female_pop from ml_learning_ny');
'SELECT row_number() OVER () As cartodb_id, median_rent, male_pop, female_pop FROM ml_learning_ny');
```
### CDB_CreateAndPredictSegment (target NUMERIC[],train_features NUMERIC[], prediction_features Numeric[], prediction_ids NUMERIC[])
### CDB_CreateAndPredictSegment(target numeric[], train_features numeric[], prediction_features numeric[], prediction_ids numeric[])
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.
@ -47,11 +46,10 @@ This function trains a [Gradient Boosting](http://scikit-learn.org/stable/module
| Name | Type | Description |
|------|------|-------------|
| target | NUMERIC[] | An array of target values of the variable you want to predict|
| 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. |
| 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. |
| prediction\_ids | NUMERIC[]] | 1D array of length n\_rows with the ids that can use used to rejoin the data with inputs |
| target | numeric[] | An array of target values of the variable you want to predict|
| train\_features| numeric[] | 1D array of length n features \* 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[feature1, feature2, feature3]::numeric[]) can be used to construct this. |
| 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[feature1, feature2, feature3]::numeric[]) can be used to construct this. |
| prediction\_ids | numeric[] | 1D array of length n\_rows with the ids that can use used to re-join the data with inputs |
#### Returns
@ -62,23 +60,23 @@ A table with the following columns.
| cartodb\_id | INTEGER | The CartoDB id of the row in the target\_query|
| prediction | NUMERIC | The predicted value of the variable of interest |
| accuracy | NUMERIC | The mean squared accuracy of the model. |
| n\_estimators(optional) | INTEGER DEFAULT 1200| Number of estimators to be used |
| n\_estimators (optional) | INTEGER DEFAULT 1200 | Number of estimators to be used |
| max\_depth (optional) | INTEGER DEFAULT 3 | Max tree depth |
| subsample (optional) | DOUBLE PRECISION DEFAULT 0.5 | Subsample paramter for GradientBooster|
| learning\_rate(optional) | DOUBLE PRECISION DEFAULT 0.01| Learning rate for the GradientBooster|
| min\_samples\_leaf(optional) | INTEGER DEFAULT 1 | Minimum samples to use per leaf|
| subsample (optional) | DOUBLE PRECISION DEFAULT 0.5 | Subsample parameter for GradientBooster|
| learning\_rate (optional) | DOUBLE PRECISION DEFAULT 0.01 | Learning rate for the GradientBooster |
| min\_samples\_leaf (optional) | INTEGER DEFAULT 1 | Minimum samples to use per leaf |
#### Example Usage
```sql
WITH training AS (
SELECT array_agg(agg) AS target,
cdb_crankshaft.CDB_PyAgg(Array[median_rent, male_pop, female_pop]::Numeric[]) AS features
FROM late_night_agg),
WITH training As (
SELECT array_agg(agg) As target,
cdb_crankshaft.CDB_PyAgg(Array[median_rent, male_pop, female_pop]::Numeric[]) As features
FROM late_night_agg),
target AS (
SELECT cdb_crankshaft.CDB_PyAgg(Array[median_rent, male_pop, female_pop]::Numeric[]) AS features,
array_agg(cartodb_id ) AS cartodb_ids FROM late_night_agg)
SELECT cdb_crankshaft.CDB_PyAgg(Array[median_rent, male_pop, female_pop]::Numeric[]) As features,
array_agg(cartodb_id) As cartodb_ids FROM late_night_agg)
SELECT cdb_crankshaft.CDB_CreateAndPredictSegment2(training.target, training.features, target.features, targetcartodb_ids)
FROM training, target;
`````
SELECT cdb_crankshaft.CDB_CreateAndPredictSegment2(training.target, training.features, target.features, target.cartodb_ids)
FROM training, target;
```