Add underscores for the classifier names in the docs

docs/plasticc.rst

@@ -101,20 +101,20 @@ Training the classifier
 =======================
 
 Several different classifiers can be trained using the same augmented dataset.
-To train a standard classifier with flat weights named "flatweight", run: ::
+To train a standard classifier with flat weights named "flat_weight", run: ::
 
-   avocado_train_classifier plasticc_augment flatweight
+   avocado_train_classifier plasticc_augment flat_weight
 
 This will take approximately 30 minutes.
 
 
 Generating predictions
 ======================
 
-To generate predictions for the full dataset with our "flatweight" classifier,
+To generate predictions for the full dataset with our "flat_weight" classifier,
 run: ::
 
-   avocado_predict plasticc_test flatweight
+   avocado_predict plasticc_test flat_weight
 
 This will take approximately 1 hour to run.
 
@@ -126,7 +126,7 @@ The predictions generated by avocado will be saved in an HDF5 file by default.
 These can be converted to a CSV file used by kaggle with the following command:
 ::
 
-   avocado_convert_kaggle plasticc_test flatweight
+   avocado_convert_kaggle plasticc_test flat_weight
 
 
 (optional) Training a redshift-weighted classifier
@@ -139,8 +139,8 @@ datasets where the exact form of augmentation will otherwise leak into the
 classification. To train and generate predictions with a redshift-weighted
 classifier, run the following commands: ::
 
-   avocado_train_classifier plasticc_augment redshiftweight --object_weighting redshift
-   avocado_predict plasticc_test redshiftweight
+   avocado_train_classifier plasticc_augment redshift_weight --object_weighting redshift
+   avocado_predict plasticc_test redshift_weight
 
 
 (optional) Training classifiers on biased samples
@@ -150,12 +150,12 @@ In Boone et al. 2019, we illustrate the bias of a classically trained
 classifier when the redshift distributions of the training samples are
 modified. To reproduce these results, run the following commands: ::
 
-   avocado_train_classifier plasticc_augment flatweight_bias_high --simulate_plasticc_bias high_redshift
-   avocado_train_classifier plasticc_augment flatweight_bias_low --simulate_plasticc_bias low_redshift
-   avocado_train_classifier plasticc_augment redshiftweight_bias_high --object_weighting redshift --simulate_plasticc_bias high_redshift
-   avocado_train_classifier plasticc_augment redshiftweight_bias_low --object_weighting redshift --simulate_plasticc_bias low_redshift
+   avocado_train_classifier plasticc_augment flat_weight_bias_high --simulate_plasticc_bias high_redshift
+   avocado_train_classifier plasticc_augment flat_weight_bias_low --simulate_plasticc_bias low_redshift
+   avocado_train_classifier plasticc_augment redshift_weight_bias_high --object_weighting redshift --simulate_plasticc_bias high_redshift
+   avocado_train_classifier plasticc_augment redshift_weight_bias_low --object_weighting redshift --simulate_plasticc_bias low_redshift
 
-   avocado_predict plasticc_test flatweight_bias_high
-   avocado_predict plasticc_test flatweight_bias_low
-   avocado_predict plasticc_test redshiftweight_bias_high
-   avocado_predict plasticc_test redshiftweight_bias_low
+   avocado_predict plasticc_test flat_weight_bias_high
+   avocado_predict plasticc_test flat_weight_bias_low
+   avocado_predict plasticc_test redshift_weight_bias_high
+   avocado_predict plasticc_test redshift_weight_bias_low