Fixed preproc test

SmokinCaterpillar · Feb 20, 2018 · 671a2b9 · 671a2b9
1 parent c9b52de
commit 671a2b9
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Showing 3 changed files with 9 additions and 7 deletions.
diff --git a/scripts/do_cross_val.py b/scripts/do_cross_val.py
@@ -29,7 +29,7 @@ def main():
 
     gc.collect()
 
-    regressor_kwargs = dict(n_estimators=256, max_leaf_nodes=4096,
+    regressor_kwargs = dict(n_estimators=256, max_leaf_nodes=10000,
                               max_features=0.2, n_jobs=-1, verbose=1,
                               random_state=42)
 

diff --git a/trufflepig/main.py b/trufflepig/main.py
@@ -65,7 +65,7 @@ def main():
     if not tpmo.model_exists(current_datetime, model_directoy):
         post_frame = tpgd.load_or_scrape_training_data(steem_kwargs, data_directory,
                                                        current_datetime=current_datetime,
-                                                       days=7,
+                                                       days=8,
                                                        offset_days=8,
                                                        ncores=32)
 
@@ -75,7 +75,7 @@ def main():
     else:
         post_frame = None
 
-    regressor_kwargs = dict(n_estimators=256, max_leaf_nodes=4096,
+    regressor_kwargs = dict(n_estimators=256, max_leaf_nodes=10000,
                               max_features=0.2, n_jobs=-1, verbose=1,
                               random_state=42)
 

diff --git a/trufflepig/model.py b/trufflepig/model.py
@@ -364,9 +364,11 @@ def load_or_train_pipeline(post_frame, directory, current_datetime=None,
 def find_truffles(post_frame, pipeline, min_max_reward=(1.0, 10), min_votes=5, k=10):
     logger.info('Looking for truffles and filtering preprocessed data further. '
                 'min max reward {} and min votes {}'.format(min_max_reward, min_votes))
-    post_frame = post_frame.loc[(post_frame.reward >= min_max_reward[0]) &
-                                (post_frame.reward <= min_max_reward[1]) &
-                                (post_frame.votes >= min_votes)]
+    to_drop = post_frame.loc[(post_frame.reward < min_max_reward[0]) |
+                                (post_frame.reward > min_max_reward[1]) |
+                                (post_frame.votes < min_votes)]
+
+    post_frame.drop(to_drop.index, inplace=True)
 
     logger.info('Predicting truffles')
     predicted_rewards_and_votes = pipeline.predict(post_frame)
@@ -405,4 +407,4 @@ def log_pipeline_info(pipeline):
     feature_importance_string = 'Feature importances \n'
     for kdx, importance in enumerate(pipeline.named_steps['regressor'].feature_importances_):
         feature_importance_string += '{:03d}: {:.3f}\n'.format(kdx, importance)
-    logger.info(feature_importance_string)
+    logger.info(feature_importance_string)