84 lines
4.6 KiB
Markdown
84 lines
4.6 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 algorithm, which should have both the variable of interest and the features that will be used to predict it |
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| variable\_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. Values should be between 1 and x. |
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| max\_depth (optional) | INTEGER DEFAULT 3 | Max tree depth. Values should be between 1 and n. |
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| subsample (optional) | DOUBLE PRECISION DEFAULT 0.5 | Subsample parameter for GradientBooster. Values should be within the range 0 to 1. |
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| learning\_rate (optional) | DOUBLE PRECISION DEFAULT 0.01 | Learning rate for the GradientBooster. Values should be between 0 and 1 (??) |
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| min\_samples\_leaf (optional) | INTEGER DEFAULT 1 | Minimum samples to use per leaf. Values should range from x to y |
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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 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. |
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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[feature1, 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 re-join the data with inputs |
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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 parameter 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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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_CreateAndPredictSegment(training.target, training.features, target.features, target.cartodb_ids)
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FROM training, target;
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```
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