diff --git a/api/examples/heart_failure_report_periodic.html b/api/examples/heart_failure_report_periodic.html index 35eb0fc8f..42d5c8cad 100644 --- a/api/examples/heart_failure_report_periodic.html +++ b/api/examples/heart_failure_report_periodic.html @@ -1064,23 +1064,23 @@
We can visualize the BinaryF1Score
and BinaryPrecision
for the different slices
-2024-06-21 12:37:10,437 INFO cyclops.models.wrappers.sk_model - Best scale_pos_weight: 9
+2024-06-22 11:26:08,664 INFO cyclops.models.wrappers.sk_model - Best scale_pos_weight: 9
-2024-06-21 12:37:10,439 INFO cyclops.models.wrappers.sk_model - Best reg_lambda: 0
+2024-06-22 11:26:08,672 INFO cyclops.models.wrappers.sk_model - Best reg_lambda: 1
-2024-06-21 12:37:10,440 INFO cyclops.models.wrappers.sk_model - Best n_estimators: 500
+2024-06-22 11:26:08,674 INFO cyclops.models.wrappers.sk_model - Best n_estimators: 500
-2024-06-21 12:37:10,442 INFO cyclops.models.wrappers.sk_model - Best max_depth: 5
+2024-06-22 11:26:08,675 INFO cyclops.models.wrappers.sk_model - Best max_depth: 5
-2024-06-21 12:37:10,443 INFO cyclops.models.wrappers.sk_model - Best learning_rate: 0.1
+2024-06-22 11:26:08,676 INFO cyclops.models.wrappers.sk_model - Best learning_rate: 0.1
-2024-06-21 12:37:10,444 INFO cyclops.models.wrappers.sk_model - Best gamma: 2
+2024-06-22 11:26:08,678 INFO cyclops.models.wrappers.sk_model - Best gamma: 0
-2024-06-21 12:37:10,445 INFO cyclops.models.wrappers.sk_model - Best colsample_bytree: 0.8
+2024-06-22 11:26:08,679 INFO cyclops.models.wrappers.sk_model - Best colsample_bytree: 0.7
XGBClassifier(base_score=None, booster=None, callbacks=None, colsample_bylevel=None, colsample_bynode=None, - colsample_bytree=0.8, early_stopping_rounds=None, + colsample_bytree=0.7, early_stopping_rounds=None, enable_categorical=False, eval_metric='logloss', - feature_types=None, gamma=2, gpu_id=None, grow_policy=None, + feature_types=None, gamma=0, gpu_id=None, grow_policy=None, importance_type=None, interaction_constraints=None, learning_rate=0.1, max_bin=None, max_cat_threshold=None, max_cat_to_onehot=None, max_delta_step=None, max_depth=5, @@ -1741,9 +1748,9 @@In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.Training monotone_constraints=None, n_estimators=500, n_jobs=None, num_parallel_tree=None, predictor=None, random_state=123, ...)
XGBClassifier(base_score=None, booster=None, callbacks=None, colsample_bylevel=None, colsample_bynode=None, - colsample_bytree=0.8, early_stopping_rounds=None, + colsample_bytree=0.7, early_stopping_rounds=None, enable_categorical=False, eval_metric='logloss', - feature_types=None, gamma=2, gpu_id=None, grow_policy=None, + feature_types=None, gamma=0, gpu_id=None, grow_policy=None, importance_type=None, interaction_constraints=None, learning_rate=0.1, max_bin=None, max_cat_threshold=None, max_cat_to_onehot=None, max_delta_step=None, max_depth=5, @@ -1765,7 +1772,7 @@Training
-{'objective': 'binary:logistic', 'use_label_encoder': None, 'base_score': None, 'booster': None, 'callbacks': None, 'colsample_bylevel': None, 'colsample_bynode': None, 'colsample_bytree': 0.8, 'early_stopping_rounds': None, 'enable_categorical': False, 'eval_metric': 'logloss', 'feature_types': None, 'gamma': 2, 'gpu_id': None, 'grow_policy': None, 'importance_type': None, 'interaction_constraints': None, 'learning_rate': 0.1, 'max_bin': None, 'max_cat_threshold': None, 'max_cat_to_onehot': None, 'max_delta_step': None, 'max_depth': 5, 'max_leaves': None, 'min_child_weight': 3, 'missing': nan, 'monotone_constraints': None, 'n_estimators': 500, 'n_jobs': None, 'num_parallel_tree': None, 'predictor': None, 'random_state': 123, 'reg_alpha': None, 'reg_lambda': 0, 'sampling_method': None, 'scale_pos_weight': 9, 'subsample': None, 'tree_method': None, 'validate_parameters': None, 'verbosity': None, 'seed': 123}
+{'objective': 'binary:logistic', 'use_label_encoder': None, 'base_score': None, 'booster': None, 'callbacks': None, 'colsample_bylevel': None, 'colsample_bynode': None, 'colsample_bytree': 0.7, 'early_stopping_rounds': None, 'enable_categorical': False, 'eval_metric': 'logloss', 'feature_types': None, 'gamma': 0, 'gpu_id': None, 'grow_policy': None, 'importance_type': None, 'interaction_constraints': None, 'learning_rate': 0.1, 'max_bin': None, 'max_cat_threshold': None, 'max_cat_to_onehot': None, 'max_delta_step': None, 'max_depth': 5, 'max_leaves': None, 'min_child_weight': 3, 'missing': nan, 'monotone_constraints': None, 'n_estimators': 500, 'n_jobs': None, 'num_parallel_tree': None, 'predictor': None, 'random_state': 123, 'reg_alpha': None, 'reg_lambda': 1, 'sampling_method': None, 'scale_pos_weight': 9, 'subsample': None, 'tree_method': None, 'validate_parameters': None, 'verbosity': None, 'seed': 123}
Log the model parameters to the report.
@@ -1807,7 +1814,7 @@Log the performance metrics to the report.
We can add a performance metric to the model card using the log_performance_metric
method, which expects a dictionary where the keys are in the following format: slice_name/metric_name
. For instance, overall/accuracy
.
-2024-06-21 12:38:04,978 INFO cyclops.models.wrappers.sk_model - Best alpha: 0.001
+2024-06-22 11:27:03,972 INFO cyclops.models.wrappers.sk_model - Best alpha: 0.001
-2024-06-21 12:38:04,980 INFO cyclops.models.wrappers.sk_model - Best eta0: 0.01
+2024-06-22 11:27:03,974 INFO cyclops.models.wrappers.sk_model - Best eta0: 0.01
-2024-06-21 12:38:04,982 INFO cyclops.models.wrappers.sk_model - Best learning_rate: adaptive
+2024-06-22 11:27:03,975 INFO cyclops.models.wrappers.sk_model - Best learning_rate: adaptive
Log the performance metrics to the report.
We can add a performance metric to the model card using the log_performance_metric
method, which expects a dictionary where the keys are in the following format: slice_name/metric_name
. For instance, overall/accuracy
.
-2024-06-21 12:38:43,893 INFO cycquery.orm - Database setup, ready to run queries!
+2024-06-22 11:27:44,063 INFO cycquery.orm - Database setup, ready to run queries!
-2024-06-21 12:39:06,620 INFO cycquery.orm - Query returned successfully!
+2024-06-22 11:28:05,493 INFO cycquery.orm - Query returned successfully!
-2024-06-21 12:39:06,622 INFO cycquery.utils.profile - Finished executing function run_query in 20.457117 s
+2024-06-22 11:28:05,496 INFO cycquery.utils.profile - Finished executing function run_query in 19.191247 s
-2024-06-21 12:41:53,915 INFO cycquery.orm - Query returned successfully!
+2024-06-22 11:30:49,671 INFO cycquery.orm - Query returned successfully!
-2024-06-21 12:41:53,916 INFO cycquery.utils.profile - Finished executing function run_query in 164.333240 s
+2024-06-22 11:30:49,673 INFO cycquery.utils.profile - Finished executing function run_query in 161.002521 s
-2024-06-21 12:46:39,158 INFO cyclops.models.wrappers.sk_model - Best reg_lambda: 0
+2024-06-22 11:59:59,281 INFO cyclops.models.wrappers.sk_model - Best reg_lambda: 10
-2024-06-21 12:46:39,160 INFO cyclops.models.wrappers.sk_model - Best n_estimators: 500
+2024-06-22 11:59:59,284 INFO cyclops.models.wrappers.sk_model - Best n_estimators: 250
-2024-06-21 12:46:39,161 INFO cyclops.models.wrappers.sk_model - Best max_depth: 5
+2024-06-22 11:59:59,285 INFO cyclops.models.wrappers.sk_model - Best max_depth: 5
-2024-06-21 12:46:39,162 INFO cyclops.models.wrappers.sk_model - Best learning_rate: 0.1
+2024-06-22 11:59:59,287 INFO cyclops.models.wrappers.sk_model - Best learning_rate: 0.1
-2024-06-21 12:46:39,164 INFO cyclops.models.wrappers.sk_model - Best gamma: 2
+2024-06-22 11:59:59,288 INFO cyclops.models.wrappers.sk_model - Best gamma: 1
-2024-06-21 12:46:39,165 INFO cyclops.models.wrappers.sk_model - Best colsample_bytree: 0.8
+2024-06-22 11:59:59,289 INFO cyclops.models.wrappers.sk_model - Best colsample_bytree: 1
XGBClassifier(base_score=None, booster=None, callbacks=None, + colsample_bylevel=None, colsample_bynode=None, colsample_bytree=1, + early_stopping_rounds=None, enable_categorical=False, + eval_metric='logloss', feature_types=None, gamma=1, gpu_id=None, + grow_policy=None, importance_type=None, + interaction_constraints=None, learning_rate=0.1, max_bin=None, + max_cat_threshold=None, max_cat_to_onehot=None, + max_delta_step=None, max_depth=5, max_leaves=None, + min_child_weight=3, missing=nan, monotone_constraints=None, + n_estimators=250, n_jobs=None, num_parallel_tree=None, + predictor=None, random_state=123, ...)
-{'objective': 'binary:logistic', 'use_label_encoder': None, 'base_score': None, 'booster': None, 'callbacks': None, 'colsample_bylevel': None, 'colsample_bynode': None, 'colsample_bytree': 0.8, 'early_stopping_rounds': None, 'enable_categorical': False, 'eval_metric': 'logloss', 'feature_types': None, 'gamma': 2, 'gpu_id': None, 'grow_policy': None, 'importance_type': None, 'interaction_constraints': None, 'learning_rate': 0.1, 'max_bin': None, 'max_cat_threshold': None, 'max_cat_to_onehot': None, 'max_delta_step': None, 'max_depth': 5, 'max_leaves': None, 'min_child_weight': 3, 'missing': nan, 'monotone_constraints': None, 'n_estimators': 500, 'n_jobs': None, 'num_parallel_tree': None, 'predictor': None, 'random_state': 123, 'reg_alpha': None, 'reg_lambda': 0, 'sampling_method': None, 'scale_pos_weight': None, 'subsample': None, 'tree_method': None, 'validate_parameters': None, 'verbosity': None, 'seed': 123}
+{'objective': 'binary:logistic', 'use_label_encoder': None, 'base_score': None, 'booster': None, 'callbacks': None, 'colsample_bylevel': None, 'colsample_bynode': None, 'colsample_bytree': 1, 'early_stopping_rounds': None, 'enable_categorical': False, 'eval_metric': 'logloss', 'feature_types': None, 'gamma': 1, 'gpu_id': None, 'grow_policy': None, 'importance_type': None, 'interaction_constraints': None, 'learning_rate': 0.1, 'max_bin': None, 'max_cat_threshold': None, 'max_cat_to_onehot': None, 'max_delta_step': None, 'max_depth': 5, 'max_leaves': None, 'min_child_weight': 3, 'missing': nan, 'monotone_constraints': None, 'n_estimators': 250, 'n_jobs': None, 'num_parallel_tree': None, 'predictor': None, 'random_state': 123, 'reg_alpha': None, 'reg_lambda': 10, 'sampling_method': None, 'scale_pos_weight': None, 'subsample': None, 'tree_method': None, 'validate_parameters': None, 'verbosity': None, 'seed': 123}
Log the model parameters to the report.
@@ -1729,7 +1729,7 @@Log the performance metrics to the report.
We can add a performance metric to the model card using the log_performance_metric
method, which expects a dictionary where the keys are in the following format: slice_name/metric_name
. For instance, overall/accuracy
.
-2024-06-21 13:11:46,839 INFO cycquery.orm - Database setup, ready to run queries!
+2024-06-22 12:27:06,982 INFO cycquery.orm - Database setup, ready to run queries!
-2024-06-21 13:11:57,397 INFO cycquery.orm - Query returned successfully!
+2024-06-22 12:27:33,373 INFO cycquery.orm - Query returned successfully!
-2024-06-21 13:12:02,439 INFO cycquery.orm - Query returned successfully!
+2024-06-22 12:27:38,633 INFO cycquery.orm - Query returned successfully!
-2024-06-21 13:12:07,253 INFO cycquery.orm - Query returned successfully!
+2024-06-22 12:27:44,560 INFO cycquery.orm - Query returned successfully!
-2024-06-21 13:12:07,689 INFO cycquery.orm - Query returned successfully!
+2024-06-22 12:27:47,055 INFO cycquery.orm - Query returned successfully!
-2024-06-21 13:12:07,885 INFO cycquery.orm - Query returned successfully!
+2024-06-22 12:27:47,243 INFO cycquery.orm - Query returned successfully!
-2024-06-21 13:12:31,796 INFO cyclops.models.wrappers.sk_model - Best reg_lambda: 0
+2024-06-22 12:36:38,435 INFO cyclops.models.wrappers.sk_model - Best reg_lambda: 1
-2024-06-21 13:12:31,798 INFO cyclops.models.wrappers.sk_model - Best n_estimators: 500
+2024-06-22 12:36:38,437 INFO cyclops.models.wrappers.sk_model - Best n_estimators: 100
-2024-06-21 13:12:31,799 INFO cyclops.models.wrappers.sk_model - Best max_depth: 2
+2024-06-22 12:36:38,438 INFO cyclops.models.wrappers.sk_model - Best max_depth: 5
-2024-06-21 13:12:31,800 INFO cyclops.models.wrappers.sk_model - Best learning_rate: 0.1
+2024-06-22 12:36:38,440 INFO cyclops.models.wrappers.sk_model - Best learning_rate: 0.01
-2024-06-21 13:12:31,802 INFO cyclops.models.wrappers.sk_model - Best gamma: 2
+2024-06-22 12:36:38,441 INFO cyclops.models.wrappers.sk_model - Best gamma: 2
-2024-06-21 13:12:31,803 INFO cyclops.models.wrappers.sk_model - Best colsample_bytree: 1
+2024-06-22 12:36:38,442 INFO cyclops.models.wrappers.sk_model - Best colsample_bytree: 1
XGBClassifier(base_score=None, booster=None, callbacks=None, colsample_bylevel=None, colsample_bynode=None, colsample_bytree=1, early_stopping_rounds=None, enable_categorical=False, eval_metric='logloss', feature_types=None, gamma=2, gpu_id=None, grow_policy=None, importance_type=None, - interaction_constraints=None, learning_rate=0.1, max_bin=None, + interaction_constraints=None, learning_rate=0.01, max_bin=None, max_cat_threshold=None, max_cat_to_onehot=None, - max_delta_step=None, max_depth=2, max_leaves=None, + max_delta_step=None, max_depth=5, max_leaves=None, min_child_weight=3, missing=nan, monotone_constraints=None, - n_estimators=500, n_jobs=None, num_parallel_tree=None, + n_estimators=100, n_jobs=None, num_parallel_tree=None, predictor=None, random_state=123, ...)
-{'objective': 'binary:logistic', 'use_label_encoder': None, 'base_score': None, 'booster': None, 'callbacks': None, 'colsample_bylevel': None, 'colsample_bynode': None, 'colsample_bytree': 1, 'early_stopping_rounds': None, 'enable_categorical': False, 'eval_metric': 'logloss', 'feature_types': None, 'gamma': 2, 'gpu_id': None, 'grow_policy': None, 'importance_type': None, 'interaction_constraints': None, 'learning_rate': 0.1, 'max_bin': None, 'max_cat_threshold': None, 'max_cat_to_onehot': None, 'max_delta_step': None, 'max_depth': 2, 'max_leaves': None, 'min_child_weight': 3, 'missing': nan, 'monotone_constraints': None, 'n_estimators': 500, 'n_jobs': None, 'num_parallel_tree': None, 'predictor': None, 'random_state': 123, 'reg_alpha': None, 'reg_lambda': 0, 'sampling_method': None, 'scale_pos_weight': None, 'subsample': None, 'tree_method': None, 'validate_parameters': None, 'verbosity': None, 'seed': 123}
+{'objective': 'binary:logistic', 'use_label_encoder': None, 'base_score': None, 'booster': None, 'callbacks': None, 'colsample_bylevel': None, 'colsample_bynode': None, 'colsample_bytree': 1, 'early_stopping_rounds': None, 'enable_categorical': False, 'eval_metric': 'logloss', 'feature_types': None, 'gamma': 2, 'gpu_id': None, 'grow_policy': None, 'importance_type': None, 'interaction_constraints': None, 'learning_rate': 0.01, 'max_bin': None, 'max_cat_threshold': None, 'max_cat_to_onehot': None, 'max_delta_step': None, 'max_depth': 5, 'max_leaves': None, 'min_child_weight': 3, 'missing': nan, 'monotone_constraints': None, 'n_estimators': 100, 'n_jobs': None, 'num_parallel_tree': None, 'predictor': None, 'random_state': 123, 'reg_alpha': None, 'reg_lambda': 1, 'sampling_method': None, 'scale_pos_weight': None, 'subsample': None, 'tree_method': None, 'validate_parameters': None, 'verbosity': None, 'seed': 123}
Log the model parameters to the report.
@@ -1787,7 +1787,7 @@Log the performance metrics to the report.
We can add a performance metric to the model card using the log_performance_metric
method, which expects a dictionary where the keys are in the following format: slice_name/metric_name
. For instance, overall/accuracy
.