diff --git a/README.md b/README.md
index 3286bf8..2acc7d7 100644
--- a/README.md
+++ b/README.md
@@ -24,12 +24,12 @@ Corrections or suggestions? Please file a [GitHub issue](https://github.com/jpha
* [Lecture Notes](tex/lecture_1.pdf)
* [Assignment 1](https://raw.githubusercontent.com/jphall663/GWU_rml/master/assignments/tex/assignment_1.pdf)
* [Model evaluation notebook](https://nbviewer.jupyter.org/github/jphall663/GWU_rml/blob/master/assignments/eval.ipynb)
- * [Full evaluations results](https://github.com/jphall663/GWU_rml/blob/master/assignments/model_eval_2022_05_27_09_41_34.csv)
+ * [Full evaluations results](https://github.com/jphall663/GWU_rml/blob/master/assignments/model_eval_2022_06_13_09_47_08.csv)
* Software Examples:
* [Building from Penalized GLM to Monotonic GBM (simple)](https://nbviewer.jupyter.org/github/jphall663/GWU_rml/blob/master/lecture_1.ipynb?flush_cache=true)
* [Building from Penalized GLM to Monotonic GBM](https://nbviewer.org/github/jphall663/interpretable_machine_learning_with_python/blob/master/glm_mgbm_gbm.ipynb?flush_cache=true)
* [Simple Explainable Boosting Machine Example](https://nbviewer.jupyter.org/github/jphall663/GWU_rml/blob/master/lecture_1_ebm_example.ipynb?flush_cache=true)
- * [PiML Credit Card Data Example](https://colab.research.google.com/github/SelfExplainML/PiML-Toolbox/blob/main/examples/Example_TaiwanCredit.ipynb)
+ * [PiML Assignment 1 Example](https://github.com/jphall663/GWU_rml/blob/master/assignments/assignment_1/group6_PiML_example.ipynb) and simple [requirements.txt](https://github.com/jphall663/GWU_rml/blob/master/assignments/assignment_1/piml_requirements.txt)
### Lecture 1 Suggested Software
@@ -236,6 +236,8 @@ Python:
* **Introduction and Background**:
+ * [NIST AI Risk Management Framework](https://www.nist.gov/itl/ai-risk-management-framework)
+ * [Interagency Guidance on Model Risk Management (SR 11-7)](https://www.federalreserve.gov/supervisionreg/srletters/sr1107a1.pdf)
* [Eight Principles of Responsible Machine Learning](https://ethical.institute/principles.html)
* [Principles for Accountable Algorithms and a Social Impact Statement for Algorithms](https://www.fatml.org/resources/principles-for-accountable-algorithms)
* [Responsible AI Practices](https://ai.google/responsibilities/responsible-ai-practices/)
diff --git a/assignments/assignment_3/assign_3_template.ipynb b/assignments/assignment_3/assign_3_template.ipynb
index d4a6891..43c7f0c 100644
--- a/assignments/assignment_3/assign_3_template.ipynb
+++ b/assignments/assignment_3/assign_3_template.ipynb
@@ -188,7 +188,10 @@
" print(protected_key.title() + ' proportion accepted: %.3f' % protected_prop)\n",
"\n",
" # return adverse impact ratio\n",
- " return ((protected_prop + eps)/(reference_prop + eps))\n"
+ " if np.isclose(protected_accepted, 0.0):\n",
+ " return np.nan\n",
+ " else:\n",
+ " return ((protected_prop + eps)/(reference_prop + eps))\n"
]
},
{
@@ -206,7 +209,7 @@
"metadata": {},
"outputs": [],
"source": [
- "def get_max_f1_frame(frame, y, yhat, res=0.01, air_reference=None, air_protected=None): \n",
+ "def get_max_f1_frame(frame, y, yhat, res=0.01, air_reference=None, air_protected=None, verbose=False): \n",
" \n",
" \"\"\" Utility function for finding max. F1. \n",
" Coupled to get_confusion_matrix() and air(). \n",
@@ -242,9 +245,9 @@
" row_dict = {'cut': cut, 'f1': f1, 'acc': acc}\n",
" if do_air:\n",
" # conditionally calculate AIR \n",
- " cm_ref = get_confusion_matrix(frame, y, yhat, by=air_reference, level=1, cutoff=cut, verbose=False)\n",
- " cm_pro = get_confusion_matrix(frame, y, yhat, by=air_protected, level=1, cutoff=cut, verbose=False)\n",
- " air_ = air({air_reference: cm_ref, air_protected: cm_pro}, air_reference, air_protected, verbose=False)\n",
+ " cm_ref = get_confusion_matrix(frame, y, yhat, by=air_reference, level=1, cutoff=cut, verbose=verbose)\n",
+ " cm_pro = get_confusion_matrix(frame, y, yhat, by=air_protected, level=1, cutoff=cut, verbose=verbose)\n",
+ " air_ = air({air_reference: cm_ref, air_protected: cm_pro}, air_reference, air_protected, verbose=verbose)\n",
" row_dict['air'] = air_\n",
" \n",
" f1_frame = f1_frame.append(row_dict, ignore_index=True)\n",
@@ -270,7 +273,7 @@
"outputs": [],
"source": [
"def ebm_grid(train, valid, x_names, y_name, gs_params=None, n_models=None, early_stopping_rounds=None, seed=None,\n",
- " air_reference=None, air_protected=None, air_cut=None):\n",
+ " air_reference=None, air_protected=None, air_cut=None, verbose=False):\n",
" \n",
" \"\"\" Performs a random grid search over n_models and gs_params.\n",
" Optionally considers random feature sets and AIR.\n",
@@ -287,6 +290,7 @@
" :param air_reference: Reference group for AIR calculation, optional.\n",
" :param air_protected: Protected group for AIR calculation, optional. \n",
" :param air_cut: Cutoff for AIR calculation, optional.\n",
+ " :param verbose: Whether to print intermediate acceptance rates, default False. \n",
" :return: Tuple: (Best EBM model, Pandas DataFrame of models to select from)\n",
"\n",
" \"\"\"\n",
@@ -349,9 +353,9 @@
" # conditionally calculate AIR \n",
" valid_phat = valid.copy(deep=True)\n",
" valid_phat['phat'] = candidate.predict_proba(valid[features])[:, 1]\n",
- " cm_ref = get_confusion_matrix(valid_phat, y_name, 'phat', by=air_reference, level=1, cutoff=air_cut, verbose=False)\n",
- " cm_pro = get_confusion_matrix(valid_phat, y_name, 'phat', by=air_protected, level=1, cutoff=air_cut, verbose=False)\n",
- " air_ = air({air_reference: cm_ref, air_protected: cm_pro}, air_reference, air_protected, verbose=False)\n",
+ " cm_ref = get_confusion_matrix(valid_phat, y_name, 'phat', by=air_reference, level=1, cutoff=air_cut, verbose=verbose)\n",
+ " cm_pro = get_confusion_matrix(valid_phat, y_name, 'phat', by=air_protected, level=1, cutoff=air_cut, verbose=verbose)\n",
+ " air_ = air({air_reference: cm_ref, air_protected: cm_pro}, air_reference, air_protected, verbose=verbose)\n",
" row_dict['air'] = air_\n",
" del valid_phat\n",
"\n",
@@ -539,7 +543,7 @@
"Grid search run 10/10:\n",
"Training with parameters: {'n_jobs': 4, 'early_stopping_rounds': 100, 'random_state': 12345, 'max_bins': 512, 'max_interaction_bins': 64, 'interactions': 5, 'outer_bags': 4, 'inner_bags': 0, 'learning_rate': 0.001, 'validation_size': 0.25, 'min_samples_leaf': 2, 'max_leaves': 3}\n",
"---------- ----------\n",
- "EBM training completed in 332.29 s.\n"
+ "EBM training completed in 324.90 s.\n"
]
}
],
@@ -1210,7 +1214,7 @@
"text": [
"Grid search run 1/500:\n",
"Training with parameters: {'n_jobs': 4, 'early_stopping_rounds': 100, 'random_state': 12345, 'max_bins': 512, 'max_interaction_bins': 16, 'interactions': 5, 'outer_bags': 4, 'inner_bags': 0, 'learning_rate': 0.05, 'validation_size': 0.25, 'min_samples_leaf': 1, 'max_leaves': 3}\n",
- "Grid search new best score discovered at iteration 1/500: 0.7997.\n",
+ "Grid search new best score discovered at iteration 1/500: 0.8217.\n",
"---------- ----------\n",
"Grid search run 2/500:\n",
"Training with parameters: {'n_jobs': 4, 'early_stopping_rounds': 100, 'random_state': 12345, 'max_bins': 128, 'max_interaction_bins': 32, 'interactions': 5, 'outer_bags': 8, 'inner_bags': 0, 'learning_rate': 0.001, 'validation_size': 0.25, 'min_samples_leaf': 2, 'max_leaves': 5}\n",
@@ -1226,14 +1230,12 @@
"---------- ----------\n",
"Grid search run 6/500:\n",
"Training with parameters: {'n_jobs': 4, 'early_stopping_rounds': 100, 'random_state': 12345, 'max_bins': 256, 'max_interaction_bins': 16, 'interactions': 15, 'outer_bags': 12, 'inner_bags': 4, 'learning_rate': 0.01, 'validation_size': 0.1, 'min_samples_leaf': 2, 'max_leaves': 5}\n",
- "Grid search new best score discovered at iteration 6/500: 0.8019.\n",
"---------- ----------\n",
"Grid search run 7/500:\n",
"Training with parameters: {'n_jobs': 4, 'early_stopping_rounds': 100, 'random_state': 12345, 'max_bins': 512, 'max_interaction_bins': 32, 'interactions': 15, 'outer_bags': 4, 'inner_bags': 4, 'learning_rate': 0.05, 'validation_size': 0.25, 'min_samples_leaf': 10, 'max_leaves': 1}\n",
"---------- ----------\n",
"Grid search run 8/500:\n",
"Training with parameters: {'n_jobs': 4, 'early_stopping_rounds': 100, 'random_state': 12345, 'max_bins': 256, 'max_interaction_bins': 16, 'interactions': 15, 'outer_bags': 8, 'inner_bags': 4, 'learning_rate': 0.001, 'validation_size': 0.5, 'min_samples_leaf': 5, 'max_leaves': 3}\n",
- "Grid search new best score discovered at iteration 8/500: 0.8209.\n",
"---------- ----------\n",
"Grid search run 9/500:\n",
"Training with parameters: {'n_jobs': 4, 'early_stopping_rounds': 100, 'random_state': 12345, 'max_bins': 256, 'max_interaction_bins': 16, 'interactions': 10, 'outer_bags': 8, 'inner_bags': 0, 'learning_rate': 0.05, 'validation_size': 0.5, 'min_samples_leaf': 5, 'max_leaves': 1}\n",
@@ -1261,13 +1263,14 @@
"---------- ----------\n",
"Grid search run 17/500:\n",
"Training with parameters: {'n_jobs': 4, 'early_stopping_rounds': 100, 'random_state': 12345, 'max_bins': 512, 'max_interaction_bins': 64, 'interactions': 10, 'outer_bags': 8, 'inner_bags': 4, 'learning_rate': 0.05, 'validation_size': 0.25, 'min_samples_leaf': 5, 'max_leaves': 3}\n",
+ "Grid search new best score discovered at iteration 17/500: 0.8224.\n",
"---------- ----------\n",
"Grid search run 18/500:\n",
"Training with parameters: {'n_jobs': 4, 'early_stopping_rounds': 100, 'random_state': 12345, 'max_bins': 512, 'max_interaction_bins': 64, 'interactions': 15, 'outer_bags': 12, 'inner_bags': 4, 'learning_rate': 0.01, 'validation_size': 0.25, 'min_samples_leaf': 1, 'max_leaves': 3}\n",
- "Grid search new best score discovered at iteration 18/500: 0.8244.\n",
"---------- ----------\n",
"Grid search run 19/500:\n",
"Training with parameters: {'n_jobs': 4, 'early_stopping_rounds': 100, 'random_state': 12345, 'max_bins': 128, 'max_interaction_bins': 32, 'interactions': 15, 'outer_bags': 8, 'inner_bags': 0, 'learning_rate': 0.01, 'validation_size': 0.25, 'min_samples_leaf': 5, 'max_leaves': 3}\n",
+ "Grid search new best score discovered at iteration 19/500: 0.8243.\n",
"---------- ----------\n",
"Grid search run 20/500:\n",
"Training with parameters: {'n_jobs': 4, 'early_stopping_rounds': 100, 'random_state': 12345, 'max_bins': 256, 'max_interaction_bins': 32, 'interactions': 15, 'outer_bags': 8, 'inner_bags': 4, 'learning_rate': 0.05, 'validation_size': 0.5, 'min_samples_leaf': 10, 'max_leaves': 5}\n",
@@ -1298,7 +1301,6 @@
"---------- ----------\n",
"Grid search run 27/500:\n",
"Training with parameters: {'n_jobs': 4, 'early_stopping_rounds': 100, 'random_state': 12345, 'max_bins': 512, 'max_interaction_bins': 64, 'interactions': 15, 'outer_bags': 4, 'inner_bags': 4, 'learning_rate': 0.01, 'validation_size': 0.25, 'min_samples_leaf': 10, 'max_leaves': 5}\n",
- "Grid search new best score discovered at iteration 27/500: 0.8246.\n",
"---------- ----------\n",
"Grid search run 28/500:\n",
"Training with parameters: {'n_jobs': 4, 'early_stopping_rounds': 100, 'random_state': 12345, 'max_bins': 512, 'max_interaction_bins': 64, 'interactions': 5, 'outer_bags': 4, 'inner_bags': 4, 'learning_rate': 0.01, 'validation_size': 0.5, 'min_samples_leaf': 10, 'max_leaves': 5}\n",
@@ -1367,16 +1369,16 @@
"Training with parameters: {'n_jobs': 4, 'early_stopping_rounds': 100, 'random_state': 12345, 'max_bins': 128, 'max_interaction_bins': 16, 'interactions': 5, 'outer_bags': 8, 'inner_bags': 0, 'learning_rate': 0.01, 'validation_size': 0.5, 'min_samples_leaf': 2, 'max_leaves': 1}\n",
"---------- ----------\n",
"Grid search run 50/500:\n",
- "Training with parameters: {'n_jobs': 4, 'early_stopping_rounds': 100, 'random_state': 12345, 'max_bins': 256, 'max_interaction_bins': 16, 'interactions': 10, 'outer_bags': 4, 'inner_bags': 0, 'learning_rate': 0.05, 'validation_size': 0.1, 'min_samples_leaf': 1, 'max_leaves': 5}\n"
+ "Training with parameters: {'n_jobs': 4, 'early_stopping_rounds': 100, 'random_state': 12345, 'max_bins': 256, 'max_interaction_bins': 16, 'interactions': 10, 'outer_bags': 4, 'inner_bags': 0, 'learning_rate': 0.05, 'validation_size': 0.1, 'min_samples_leaf': 1, 'max_leaves': 5}\n",
+ "---------- ----------\n",
+ "Grid search run 51/500:\n",
+ "Training with parameters: {'n_jobs': 4, 'early_stopping_rounds': 100, 'random_state': 12345, 'max_bins': 128, 'max_interaction_bins': 16, 'interactions': 15, 'outer_bags': 8, 'inner_bags': 4, 'learning_rate': 0.05, 'validation_size': 0.25, 'min_samples_leaf': 2, 'max_leaves': 1}\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
- "---------- ----------\n",
- "Grid search run 51/500:\n",
- "Training with parameters: {'n_jobs': 4, 'early_stopping_rounds': 100, 'random_state': 12345, 'max_bins': 128, 'max_interaction_bins': 16, 'interactions': 15, 'outer_bags': 8, 'inner_bags': 4, 'learning_rate': 0.05, 'validation_size': 0.25, 'min_samples_leaf': 2, 'max_leaves': 1}\n",
"---------- ----------\n",
"Grid search run 52/500:\n",
"Training with parameters: {'n_jobs': 4, 'early_stopping_rounds': 100, 'random_state': 12345, 'max_bins': 256, 'max_interaction_bins': 64, 'interactions': 5, 'outer_bags': 8, 'inner_bags': 4, 'learning_rate': 0.05, 'validation_size': 0.5, 'min_samples_leaf': 1, 'max_leaves': 5}\n",
@@ -1451,16 +1453,16 @@
"Training with parameters: {'n_jobs': 4, 'early_stopping_rounds': 100, 'random_state': 12345, 'max_bins': 128, 'max_interaction_bins': 16, 'interactions': 15, 'outer_bags': 8, 'inner_bags': 4, 'learning_rate': 0.001, 'validation_size': 0.25, 'min_samples_leaf': 1, 'max_leaves': 1}\n",
"---------- ----------\n",
"Grid search run 76/500:\n",
- "Training with parameters: {'n_jobs': 4, 'early_stopping_rounds': 100, 'random_state': 12345, 'max_bins': 256, 'max_interaction_bins': 64, 'interactions': 5, 'outer_bags': 12, 'inner_bags': 4, 'learning_rate': 0.01, 'validation_size': 0.25, 'min_samples_leaf': 1, 'max_leaves': 3}\n"
+ "Training with parameters: {'n_jobs': 4, 'early_stopping_rounds': 100, 'random_state': 12345, 'max_bins': 256, 'max_interaction_bins': 64, 'interactions': 5, 'outer_bags': 12, 'inner_bags': 4, 'learning_rate': 0.01, 'validation_size': 0.25, 'min_samples_leaf': 1, 'max_leaves': 3}\n",
+ "---------- ----------\n",
+ "Grid search run 77/500:\n",
+ "Training with parameters: {'n_jobs': 4, 'early_stopping_rounds': 100, 'random_state': 12345, 'max_bins': 512, 'max_interaction_bins': 32, 'interactions': 5, 'outer_bags': 12, 'inner_bags': 0, 'learning_rate': 0.01, 'validation_size': 0.1, 'min_samples_leaf': 5, 'max_leaves': 5}\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
- "---------- ----------\n",
- "Grid search run 77/500:\n",
- "Training with parameters: {'n_jobs': 4, 'early_stopping_rounds': 100, 'random_state': 12345, 'max_bins': 512, 'max_interaction_bins': 32, 'interactions': 5, 'outer_bags': 12, 'inner_bags': 0, 'learning_rate': 0.01, 'validation_size': 0.1, 'min_samples_leaf': 5, 'max_leaves': 5}\n",
"---------- ----------\n",
"Grid search run 78/500:\n",
"Training with parameters: {'n_jobs': 4, 'early_stopping_rounds': 100, 'random_state': 12345, 'max_bins': 512, 'max_interaction_bins': 32, 'interactions': 10, 'outer_bags': 8, 'inner_bags': 4, 'learning_rate': 0.001, 'validation_size': 0.5, 'min_samples_leaf': 1, 'max_leaves': 3}\n",
@@ -1535,16 +1537,16 @@
"Training with parameters: {'n_jobs': 4, 'early_stopping_rounds': 100, 'random_state': 12345, 'max_bins': 512, 'max_interaction_bins': 16, 'interactions': 10, 'outer_bags': 12, 'inner_bags': 0, 'learning_rate': 0.001, 'validation_size': 0.5, 'min_samples_leaf': 1, 'max_leaves': 1}\n",
"---------- ----------\n",
"Grid search run 102/500:\n",
- "Training with parameters: {'n_jobs': 4, 'early_stopping_rounds': 100, 'random_state': 12345, 'max_bins': 256, 'max_interaction_bins': 16, 'interactions': 15, 'outer_bags': 4, 'inner_bags': 4, 'learning_rate': 0.05, 'validation_size': 0.1, 'min_samples_leaf': 10, 'max_leaves': 5}\n"
+ "Training with parameters: {'n_jobs': 4, 'early_stopping_rounds': 100, 'random_state': 12345, 'max_bins': 256, 'max_interaction_bins': 16, 'interactions': 15, 'outer_bags': 4, 'inner_bags': 4, 'learning_rate': 0.05, 'validation_size': 0.1, 'min_samples_leaf': 10, 'max_leaves': 5}\n",
+ "---------- ----------\n",
+ "Grid search run 103/500:\n",
+ "Training with parameters: {'n_jobs': 4, 'early_stopping_rounds': 100, 'random_state': 12345, 'max_bins': 256, 'max_interaction_bins': 64, 'interactions': 10, 'outer_bags': 4, 'inner_bags': 0, 'learning_rate': 0.05, 'validation_size': 0.1, 'min_samples_leaf': 10, 'max_leaves': 3}\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
- "---------- ----------\n",
- "Grid search run 103/500:\n",
- "Training with parameters: {'n_jobs': 4, 'early_stopping_rounds': 100, 'random_state': 12345, 'max_bins': 256, 'max_interaction_bins': 64, 'interactions': 10, 'outer_bags': 4, 'inner_bags': 0, 'learning_rate': 0.05, 'validation_size': 0.1, 'min_samples_leaf': 10, 'max_leaves': 3}\n",
"---------- ----------\n",
"Grid search run 104/500:\n",
"Training with parameters: {'n_jobs': 4, 'early_stopping_rounds': 100, 'random_state': 12345, 'max_bins': 128, 'max_interaction_bins': 64, 'interactions': 10, 'outer_bags': 8, 'inner_bags': 0, 'learning_rate': 0.01, 'validation_size': 0.1, 'min_samples_leaf': 1, 'max_leaves': 3}\n",
@@ -1596,6 +1598,7 @@
"---------- ----------\n",
"Grid search run 120/500:\n",
"Training with parameters: {'n_jobs': 4, 'early_stopping_rounds': 100, 'random_state': 12345, 'max_bins': 256, 'max_interaction_bins': 64, 'interactions': 15, 'outer_bags': 12, 'inner_bags': 0, 'learning_rate': 0.01, 'validation_size': 0.5, 'min_samples_leaf': 2, 'max_leaves': 5}\n",
+ "Grid search new best score discovered at iteration 120/500: 0.8246.\n",
"---------- ----------\n",
"Grid search run 121/500:\n",
"Training with parameters: {'n_jobs': 4, 'early_stopping_rounds': 100, 'random_state': 12345, 'max_bins': 256, 'max_interaction_bins': 64, 'interactions': 10, 'outer_bags': 12, 'inner_bags': 4, 'learning_rate': 0.001, 'validation_size': 0.25, 'min_samples_leaf': 10, 'max_leaves': 5}\n",
@@ -2523,7 +2526,6 @@
"---------- ----------\n",
"Grid search run 407/500:\n",
"Training with parameters: {'n_jobs': 4, 'early_stopping_rounds': 100, 'random_state': 12345, 'max_bins': 512, 'max_interaction_bins': 64, 'interactions': 15, 'outer_bags': 4, 'inner_bags': 4, 'learning_rate': 0.01, 'validation_size': 0.25, 'min_samples_leaf': 5, 'max_leaves': 5}\n",
- "Grid search new best score discovered at iteration 407/500: 0.8249.\n",
"---------- ----------\n",
"Grid search run 408/500:\n",
"Training with parameters: {'n_jobs': 4, 'early_stopping_rounds': 100, 'random_state': 12345, 'max_bins': 128, 'max_interaction_bins': 64, 'interactions': 5, 'outer_bags': 12, 'inner_bags': 4, 'learning_rate': 0.01, 'validation_size': 0.25, 'min_samples_leaf': 5, 'max_leaves': 3}\n",
@@ -2541,16 +2543,16 @@
"Training with parameters: {'n_jobs': 4, 'early_stopping_rounds': 100, 'random_state': 12345, 'max_bins': 128, 'max_interaction_bins': 64, 'interactions': 5, 'outer_bags': 8, 'inner_bags': 0, 'learning_rate': 0.05, 'validation_size': 0.1, 'min_samples_leaf': 2, 'max_leaves': 5}\n",
"---------- ----------\n",
"Grid search run 413/500:\n",
- "Training with parameters: {'n_jobs': 4, 'early_stopping_rounds': 100, 'random_state': 12345, 'max_bins': 128, 'max_interaction_bins': 64, 'interactions': 10, 'outer_bags': 4, 'inner_bags': 0, 'learning_rate': 0.01, 'validation_size': 0.5, 'min_samples_leaf': 2, 'max_leaves': 3}\n"
+ "Training with parameters: {'n_jobs': 4, 'early_stopping_rounds': 100, 'random_state': 12345, 'max_bins': 128, 'max_interaction_bins': 64, 'interactions': 10, 'outer_bags': 4, 'inner_bags': 0, 'learning_rate': 0.01, 'validation_size': 0.5, 'min_samples_leaf': 2, 'max_leaves': 3}\n",
+ "---------- ----------\n",
+ "Grid search run 414/500:\n",
+ "Training with parameters: {'n_jobs': 4, 'early_stopping_rounds': 100, 'random_state': 12345, 'max_bins': 512, 'max_interaction_bins': 64, 'interactions': 5, 'outer_bags': 8, 'inner_bags': 0, 'learning_rate': 0.001, 'validation_size': 0.5, 'min_samples_leaf': 5, 'max_leaves': 3}\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
- "---------- ----------\n",
- "Grid search run 414/500:\n",
- "Training with parameters: {'n_jobs': 4, 'early_stopping_rounds': 100, 'random_state': 12345, 'max_bins': 512, 'max_interaction_bins': 64, 'interactions': 5, 'outer_bags': 8, 'inner_bags': 0, 'learning_rate': 0.001, 'validation_size': 0.5, 'min_samples_leaf': 5, 'max_leaves': 3}\n",
"---------- ----------\n",
"Grid search run 415/500:\n",
"Training with parameters: {'n_jobs': 4, 'early_stopping_rounds': 100, 'random_state': 12345, 'max_bins': 128, 'max_interaction_bins': 32, 'interactions': 10, 'outer_bags': 12, 'inner_bags': 4, 'learning_rate': 0.05, 'validation_size': 0.1, 'min_samples_leaf': 5, 'max_leaves': 3}\n",
@@ -2625,16 +2627,16 @@
"Training with parameters: {'n_jobs': 4, 'early_stopping_rounds': 100, 'random_state': 12345, 'max_bins': 512, 'max_interaction_bins': 32, 'interactions': 15, 'outer_bags': 4, 'inner_bags': 4, 'learning_rate': 0.001, 'validation_size': 0.25, 'min_samples_leaf': 1, 'max_leaves': 1}\n",
"---------- ----------\n",
"Grid search run 439/500:\n",
- "Training with parameters: {'n_jobs': 4, 'early_stopping_rounds': 100, 'random_state': 12345, 'max_bins': 256, 'max_interaction_bins': 32, 'interactions': 5, 'outer_bags': 4, 'inner_bags': 4, 'learning_rate': 0.01, 'validation_size': 0.1, 'min_samples_leaf': 2, 'max_leaves': 1}\n"
+ "Training with parameters: {'n_jobs': 4, 'early_stopping_rounds': 100, 'random_state': 12345, 'max_bins': 256, 'max_interaction_bins': 32, 'interactions': 5, 'outer_bags': 4, 'inner_bags': 4, 'learning_rate': 0.01, 'validation_size': 0.1, 'min_samples_leaf': 2, 'max_leaves': 1}\n",
+ "---------- ----------\n",
+ "Grid search run 440/500:\n",
+ "Training with parameters: {'n_jobs': 4, 'early_stopping_rounds': 100, 'random_state': 12345, 'max_bins': 512, 'max_interaction_bins': 32, 'interactions': 5, 'outer_bags': 12, 'inner_bags': 4, 'learning_rate': 0.05, 'validation_size': 0.1, 'min_samples_leaf': 10, 'max_leaves': 5}\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
- "---------- ----------\n",
- "Grid search run 440/500:\n",
- "Training with parameters: {'n_jobs': 4, 'early_stopping_rounds': 100, 'random_state': 12345, 'max_bins': 512, 'max_interaction_bins': 32, 'interactions': 5, 'outer_bags': 12, 'inner_bags': 4, 'learning_rate': 0.05, 'validation_size': 0.1, 'min_samples_leaf': 10, 'max_leaves': 5}\n",
"---------- ----------\n",
"Grid search run 441/500:\n",
"Training with parameters: {'n_jobs': 4, 'early_stopping_rounds': 100, 'random_state': 12345, 'max_bins': 256, 'max_interaction_bins': 64, 'interactions': 15, 'outer_bags': 8, 'inner_bags': 4, 'learning_rate': 0.05, 'validation_size': 0.25, 'min_samples_leaf': 10, 'max_leaves': 1}\n",
@@ -2709,16 +2711,16 @@
"Training with parameters: {'n_jobs': 4, 'early_stopping_rounds': 100, 'random_state': 12345, 'max_bins': 256, 'max_interaction_bins': 16, 'interactions': 5, 'outer_bags': 4, 'inner_bags': 4, 'learning_rate': 0.01, 'validation_size': 0.1, 'min_samples_leaf': 1, 'max_leaves': 5}\n",
"---------- ----------\n",
"Grid search run 465/500:\n",
- "Training with parameters: {'n_jobs': 4, 'early_stopping_rounds': 100, 'random_state': 12345, 'max_bins': 256, 'max_interaction_bins': 64, 'interactions': 15, 'outer_bags': 4, 'inner_bags': 0, 'learning_rate': 0.01, 'validation_size': 0.5, 'min_samples_leaf': 5, 'max_leaves': 5}\n"
+ "Training with parameters: {'n_jobs': 4, 'early_stopping_rounds': 100, 'random_state': 12345, 'max_bins': 256, 'max_interaction_bins': 64, 'interactions': 15, 'outer_bags': 4, 'inner_bags': 0, 'learning_rate': 0.01, 'validation_size': 0.5, 'min_samples_leaf': 5, 'max_leaves': 5}\n",
+ "---------- ----------\n",
+ "Grid search run 466/500:\n",
+ "Training with parameters: {'n_jobs': 4, 'early_stopping_rounds': 100, 'random_state': 12345, 'max_bins': 512, 'max_interaction_bins': 32, 'interactions': 15, 'outer_bags': 12, 'inner_bags': 4, 'learning_rate': 0.001, 'validation_size': 0.25, 'min_samples_leaf': 2, 'max_leaves': 3}\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
- "---------- ----------\n",
- "Grid search run 466/500:\n",
- "Training with parameters: {'n_jobs': 4, 'early_stopping_rounds': 100, 'random_state': 12345, 'max_bins': 512, 'max_interaction_bins': 32, 'interactions': 15, 'outer_bags': 12, 'inner_bags': 4, 'learning_rate': 0.001, 'validation_size': 0.25, 'min_samples_leaf': 2, 'max_leaves': 3}\n",
"---------- ----------\n",
"Grid search run 467/500:\n",
"Training with parameters: {'n_jobs': 4, 'early_stopping_rounds': 100, 'random_state': 12345, 'max_bins': 128, 'max_interaction_bins': 64, 'interactions': 5, 'outer_bags': 12, 'inner_bags': 0, 'learning_rate': 0.01, 'validation_size': 0.25, 'min_samples_leaf': 1, 'max_leaves': 3}\n",
@@ -2793,16 +2795,16 @@
"Training with parameters: {'n_jobs': 4, 'early_stopping_rounds': 100, 'random_state': 12345, 'max_bins': 256, 'max_interaction_bins': 64, 'interactions': 15, 'outer_bags': 4, 'inner_bags': 0, 'learning_rate': 0.05, 'validation_size': 0.1, 'min_samples_leaf': 5, 'max_leaves': 5}\n",
"---------- ----------\n",
"Grid search run 491/500:\n",
- "Training with parameters: {'n_jobs': 4, 'early_stopping_rounds': 100, 'random_state': 12345, 'max_bins': 512, 'max_interaction_bins': 32, 'interactions': 5, 'outer_bags': 8, 'inner_bags': 4, 'learning_rate': 0.001, 'validation_size': 0.5, 'min_samples_leaf': 10, 'max_leaves': 3}\n"
+ "Training with parameters: {'n_jobs': 4, 'early_stopping_rounds': 100, 'random_state': 12345, 'max_bins': 512, 'max_interaction_bins': 32, 'interactions': 5, 'outer_bags': 8, 'inner_bags': 4, 'learning_rate': 0.001, 'validation_size': 0.5, 'min_samples_leaf': 10, 'max_leaves': 3}\n",
+ "---------- ----------\n",
+ "Grid search run 492/500:\n",
+ "Training with parameters: {'n_jobs': 4, 'early_stopping_rounds': 100, 'random_state': 12345, 'max_bins': 128, 'max_interaction_bins': 64, 'interactions': 5, 'outer_bags': 8, 'inner_bags': 4, 'learning_rate': 0.01, 'validation_size': 0.25, 'min_samples_leaf': 1, 'max_leaves': 3}\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
- "---------- ----------\n",
- "Grid search run 492/500:\n",
- "Training with parameters: {'n_jobs': 4, 'early_stopping_rounds': 100, 'random_state': 12345, 'max_bins': 128, 'max_interaction_bins': 64, 'interactions': 5, 'outer_bags': 8, 'inner_bags': 4, 'learning_rate': 0.01, 'validation_size': 0.25, 'min_samples_leaf': 1, 'max_leaves': 3}\n",
"---------- ----------\n",
"Grid search run 493/500:\n",
"Training with parameters: {'n_jobs': 4, 'early_stopping_rounds': 100, 'random_state': 12345, 'max_bins': 128, 'max_interaction_bins': 16, 'interactions': 5, 'outer_bags': 8, 'inner_bags': 0, 'learning_rate': 0.05, 'validation_size': 0.1, 'min_samples_leaf': 2, 'max_leaves': 3}\n",
@@ -2828,7 +2830,7 @@
"Grid search run 500/500:\n",
"Training with parameters: {'n_jobs': 4, 'early_stopping_rounds': 100, 'random_state': 12345, 'max_bins': 512, 'max_interaction_bins': 32, 'interactions': 5, 'outer_bags': 12, 'inner_bags': 4, 'learning_rate': 0.001, 'validation_size': 0.1, 'min_samples_leaf': 5, 'max_leaves': 3}\n",
"---------- ----------\n",
- "EBM training completed in 14344.42 s.\n"
+ "EBM training completed in 14575.71 s.\n"
]
}
],
@@ -2910,9 +2912,9 @@
" | 0.25 | \n",
" 1 | \n",
" 3 | \n",
- " [property_value_std, debt_to_income_ratio_miss... | \n",
- " 0.799714 | \n",
- " 0.655594 | \n",
+ " [loan_amount_std, no_intro_rate_period_std, lo... | \n",
+ " 0.821680 | \n",
+ " 0.730429 | \n",
" 100.0 | \n",
" 4.0 | \n",
" 12345.0 | \n",
@@ -2928,9 +2930,9 @@
" 0.25 | \n",
" 2 | \n",
" 5 | \n",
- " [debt_to_income_ratio_missing, conforming, int... | \n",
- " 0.555938 | \n",
- " 1.004718 | \n",
+ " [loan_amount_std, debt_to_income_ratio_std, pr... | \n",
+ " 0.802044 | \n",
+ " 0.727615 | \n",
" 100.0 | \n",
" 4.0 | \n",
" 12345.0 | \n",
@@ -2946,9 +2948,9 @@
" 0.50 | \n",
" 1 | \n",
" 3 | \n",
- " [property_value_std, debt_to_income_ratio_std,... | \n",
- " 0.762612 | \n",
- " 0.831124 | \n",
+ " [debt_to_income_ratio_std] | \n",
+ " 0.626502 | \n",
+ " 0.897869 | \n",
" 100.0 | \n",
" 4.0 | \n",
" 12345.0 | \n",
@@ -2964,9 +2966,9 @@
" 0.50 | \n",
" 1 | \n",
" 5 | \n",
- " [debt_to_income_ratio_std, conforming] | \n",
- " 0.644341 | \n",
- " 0.902798 | \n",
+ " [income_std, property_value_std] | \n",
+ " 0.714950 | \n",
+ " 0.926718 | \n",
" 100.0 | \n",
" 4.0 | \n",
" 12345.0 | \n",
@@ -2982,9 +2984,9 @@
" 0.10 | \n",
" 10 | \n",
" 3 | \n",
- " [loan_amount_std, term_360, no_intro_rate_peri... | \n",
- " 0.739317 | \n",
- " 0.861719 | \n",
+ " [loan_to_value_ratio_std] | \n",
+ " 0.757156 | \n",
+ " 0.588048 | \n",
" 100.0 | \n",
" 4.0 | \n",
" 12345.0 | \n",
@@ -3018,9 +3020,9 @@
" 0.50 | \n",
" 10 | \n",
" 1 | \n",
- " [term_360, conforming, no_intro_rate_period_st... | \n",
- " 0.798385 | \n",
- " 0.675978 | \n",
+ " [debt_to_income_ratio_std, property_value_std,... | \n",
+ " 0.816408 | \n",
+ " 0.714912 | \n",
" 100.0 | \n",
" 4.0 | \n",
" 12345.0 | \n",
@@ -3036,9 +3038,9 @@
" 0.10 | \n",
" 10 | \n",
" 3 | \n",
- " [term_360] | \n",
- " 0.518910 | \n",
- " 1.000000 | \n",
+ " [income_std, loan_to_value_ratio_std, debt_to_... | \n",
+ " 0.780121 | \n",
+ " 0.722661 | \n",
" 100.0 | \n",
" 4.0 | \n",
" 12345.0 | \n",
@@ -3054,9 +3056,9 @@
" 0.25 | \n",
" 5 | \n",
" 3 | \n",
- " [debt_to_income_ratio_std, loan_to_value_ratio... | \n",
- " 0.824514 | \n",
- " 0.721801 | \n",
+ " [debt_to_income_ratio_missing] | \n",
+ " 0.500391 | \n",
+ " 1.000754 | \n",
" 100.0 | \n",
" 4.0 | \n",
" 12345.0 | \n",
@@ -3072,9 +3074,9 @@
" 0.50 | \n",
" 10 | \n",
" 3 | \n",
- " [property_value_std, loan_to_value_ratio_std, ... | \n",
- " 0.796788 | \n",
- " 0.720372 | \n",
+ " [no_intro_rate_period_std, intro_rate_period_s... | \n",
+ " 0.820667 | \n",
+ " 0.725268 | \n",
" 100.0 | \n",
" 4.0 | \n",
" 12345.0 | \n",
@@ -3090,9 +3092,9 @@
" 0.10 | \n",
" 5 | \n",
" 3 | \n",
- " [no_intro_rate_period_std, debt_to_income_rati... | \n",
- " 0.817335 | \n",
- " 0.727576 | \n",
+ " [intro_rate_period_std, term_360, debt_to_inco... | \n",
+ " 0.664358 | \n",
+ " 0.902079 | \n",
" 100.0 | \n",
" 4.0 | \n",
" 12345.0 | \n",
@@ -3130,17 +3132,17 @@
"499 0.001 0.10 5 3 \n",
"\n",
" features auc air \\\n",
- "0 [property_value_std, debt_to_income_ratio_miss... 0.799714 0.655594 \n",
- "1 [debt_to_income_ratio_missing, conforming, int... 0.555938 1.004718 \n",
- "2 [property_value_std, debt_to_income_ratio_std,... 0.762612 0.831124 \n",
- "3 [debt_to_income_ratio_std, conforming] 0.644341 0.902798 \n",
- "4 [loan_amount_std, term_360, no_intro_rate_peri... 0.739317 0.861719 \n",
+ "0 [loan_amount_std, no_intro_rate_period_std, lo... 0.821680 0.730429 \n",
+ "1 [loan_amount_std, debt_to_income_ratio_std, pr... 0.802044 0.727615 \n",
+ "2 [debt_to_income_ratio_std] 0.626502 0.897869 \n",
+ "3 [income_std, property_value_std] 0.714950 0.926718 \n",
+ "4 [loan_to_value_ratio_std] 0.757156 0.588048 \n",
".. ... ... ... \n",
- "495 [term_360, conforming, no_intro_rate_period_st... 0.798385 0.675978 \n",
- "496 [term_360] 0.518910 1.000000 \n",
- "497 [debt_to_income_ratio_std, loan_to_value_ratio... 0.824514 0.721801 \n",
- "498 [property_value_std, loan_to_value_ratio_std, ... 0.796788 0.720372 \n",
- "499 [no_intro_rate_period_std, debt_to_income_rati... 0.817335 0.727576 \n",
+ "495 [debt_to_income_ratio_std, property_value_std,... 0.816408 0.714912 \n",
+ "496 [income_std, loan_to_value_ratio_std, debt_to_... 0.780121 0.722661 \n",
+ "497 [debt_to_income_ratio_missing] 0.500391 1.000754 \n",
+ "498 [no_intro_rate_period_std, intro_rate_period_s... 0.820667 0.725268 \n",
+ "499 [intro_rate_period_std, term_360, debt_to_inco... 0.664358 0.902079 \n",
"\n",
" early_stopping_rounds n_jobs random_state \n",
"0 100.0 4.0 12345.0 \n",
@@ -3185,7 +3187,7 @@
"outputs": [
{
"data": {
- "image/png": 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\n",
+ "image/png": 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\n",
"text/plain": [
""
]
@@ -3224,8 +3226,8 @@
"name": "stdout",
"output_type": "stream",
"text": [
- "Best AUC: 0.7863 above 0.8 AIR (0.8008).\n",
- "Remediated EBM retrained with AUC: 0.7863.\n"
+ "Best AUC: 0.7852 above 0.8 AIR (0.8043).\n",
+ "Remediated EBM retrained with AUC: 0.7852.\n"
]
}
],
@@ -3272,9 +3274,9 @@
"name": "stdout",
"output_type": "stream",
"text": [
- "Adverse impact ratio for Asian people vs. White people: 1.157\n",
- "Adverse impact ratio for Black people vs. White people: 0.801\n",
- "Adverse impact ratio for Females vs. Males: 0.958\n"
+ "Adverse impact ratio for Asian people vs. White people: 1.154\n",
+ "Adverse impact ratio for Black people vs. White people: 0.804\n",
+ "Adverse impact ratio for Females vs. Males: 0.963\n"
]
}
],
@@ -3312,7 +3314,7 @@
},
{
"cell_type": "code",
- "execution_count": 26,
+ "execution_count": 25,
"id": "972cef55",
"metadata": {},
"outputs": [
@@ -3322,18 +3324,18 @@
"{'max_bins': 512,\n",
" 'max_interaction_bins': 16,\n",
" 'interactions': 10,\n",
- " 'outer_bags': 4,\n",
- " 'inner_bags': 0,\n",
+ " 'outer_bags': 8,\n",
+ " 'inner_bags': 4,\n",
" 'learning_rate': 0.001,\n",
" 'validation_size': 0.25,\n",
" 'min_samples_leaf': 5,\n",
- " 'max_leaves': 5,\n",
+ " 'max_leaves': 3,\n",
" 'early_stopping_rounds': 100.0,\n",
" 'n_jobs': 4,\n",
" 'random_state': 12345}"
]
},
- "execution_count": 26,
+ "execution_count": 25,
"metadata": {},
"output_type": "execute_result"
}
@@ -3352,24 +3354,25 @@
},
{
"cell_type": "code",
- "execution_count": 27,
+ "execution_count": 26,
"id": "f173ac4f",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
- "['property_value_std',\n",
- " 'no_intro_rate_period_std',\n",
- " 'loan_amount_std',\n",
+ "['conforming',\n",
" 'income_std',\n",
- " 'conforming',\n",
" 'intro_rate_period_std',\n",
+ " 'no_intro_rate_period_std',\n",
+ " 'debt_to_income_ratio_missing',\n",
+ " 'loan_amount_std',\n",
" 'debt_to_income_ratio_std',\n",
+ " 'property_value_std',\n",
" 'term_360']"
]
},
- "execution_count": 27,
+ "execution_count": 26,
"metadata": {},
"output_type": "execute_result"
}
@@ -3388,7 +3391,7 @@
},
{
"cell_type": "code",
- "execution_count": 25,
+ "execution_count": 27,
"id": "e978d190",
"metadata": {},
"outputs": [
@@ -3396,7 +3399,7 @@
"name": "stdout",
"output_type": "stream",
"text": [
- "All tasks completed in 14724.53 s.\n"
+ "All tasks completed in 15049.52 s.\n"
]
}
],
diff --git a/assignments/assignment_4/assign_4_template.ipynb b/assignments/assignment_4/assign_4_template.ipynb
index 28a5dd3..a8d9293 100644
--- a/assignments/assignment_4/assign_4_template.ipynb
+++ b/assignments/assignment_4/assign_4_template.ipynb
@@ -55,13 +55,14 @@
"text": [
"Checking whether there is an H2O instance running at http://localhost:54321 ..... not found.\n",
"Attempting to start a local H2O server...\n",
- " Java Version: openjdk version \"11.0.11\" 2021-04-20; OpenJDK Runtime Environment (build 11.0.11+9-Ubuntu-0ubuntu2.18.04); OpenJDK 64-Bit Server VM (build 11.0.11+9-Ubuntu-0ubuntu2.18.04, mixed mode, sharing)\n",
+ " Java Version: openjdk version \"11.0.15\" 2022-04-19; OpenJDK Runtime Environment (build 11.0.15+10-Ubuntu-0ubuntu0.18.04.1); OpenJDK 64-Bit Server VM (build 11.0.15+10-Ubuntu-0ubuntu0.18.04.1, mixed mode, sharing)\n",
" Starting server from /home/patrickh/Workspace/GWU_rml/assignments/assign_env/lib/python3.6/site-packages/h2o/backend/bin/h2o.jar\n",
- " Ice root: /tmp/tmpn_p9he8h\n",
- " JVM stdout: /tmp/tmpn_p9he8h/h2o_patrickh_started_from_python.out\n",
- " JVM stderr: /tmp/tmpn_p9he8h/h2o_patrickh_started_from_python.err\n",
+ " Ice root: /tmp/tmpsak1_gm4\n",
+ " JVM stdout: /tmp/tmpsak1_gm4/h2o_patrickh_started_from_python.out\n",
+ " JVM stderr: /tmp/tmpsak1_gm4/h2o_patrickh_started_from_python.err\n",
" Server is running at http://127.0.0.1:54321\n",
- "Connecting to H2O server at http://127.0.0.1:54321 ... successful.\n"
+ "Connecting to H2O server at http://127.0.0.1:54321 ... successful.\n",
+ "Warning: Your H2O cluster version is too old (1 year and 1 month)! Please download and install the latest version from http://h2o.ai/download/\n"
]
},
{
@@ -76,9 +77,9 @@
"| H2O_cluster_version: | \n",
"3.32.1.3 |
\n",
"| H2O_cluster_version_age: | \n",
- "25 days |
\n",
+ "1 year and 1 month !!! | \n",
"| H2O_cluster_name: | \n",
- "H2O_from_python_patrickh_w27v4r |
\n",
+ "H2O_from_python_patrickh_9d3qoe | \n",
"| H2O_cluster_total_nodes: | \n",
"1 |
\n",
"| H2O_cluster_free_memory: | \n",
@@ -106,8 +107,8 @@
"H2O_cluster_timezone: America/New_York\n",
"H2O_data_parsing_timezone: UTC\n",
"H2O_cluster_version: 3.32.1.3\n",
- "H2O_cluster_version_age: 25 days\n",
- "H2O_cluster_name: H2O_from_python_patrickh_w27v4r\n",
+ "H2O_cluster_version_age: 1 year and 1 month !!!\n",
+ "H2O_cluster_name: H2O_from_python_patrickh_9d3qoe\n",
"H2O_cluster_total_nodes: 1\n",
"H2O_cluster_free_memory: 2 Gb\n",
"H2O_cluster_total_cores: 24\n",
@@ -236,10 +237,10 @@
"outputs": [],
"source": [
"def get_gv(title, model_id, mojo_path):\n",
- " \n",
+ "\n",
" \"\"\" Utility function to generate graphviz dot file from h2o MOJO using\n",
" a subprocess.\n",
- " \n",
+ " \n",
" Args:\n",
" title: Title for displayed decision tree.\n",
" model_id: h2o model identifier.\n",
@@ -256,13 +257,17 @@
" # tree, see for more information:\n",
" # http://docs.h2o.ai/h2o/latest-stable/h2o-genmodel/javadoc/index.html\n",
" gv_file_name = model_id + '.gv'\n",
+ " # if the line below fails for you, try instead:\n",
+ " #gv_args = str('-cp ' + h2o_jar_path +\n",
+ " # ' hex.genmodel.tools.PrintMojo --tree 0 -i \"'\n",
+ " # + mojo_path + '\" -o').split()\n",
" gv_args = str('-cp ' + h2o_jar_path +\n",
" ' hex.genmodel.tools.PrintMojo --tree 0 -i '\n",
" + mojo_path + ' -o').split()\n",
" gv_args.insert(0, 'java')\n",
" gv_args.append(gv_file_name)\n",
" if title is not None:\n",
- " gv_args = gv_args + ['--title', title]\n",
+ " gv_args = gv_args + ['--title', '\"' + str(title) + '\"']\n",
"\n",
" # call constructed command\n",
" print()\n",
@@ -270,7 +275,8 @@
" print(' '.join(gv_args))\n",
" # if the line below is failing for you, try instead:\n",
" # _ = subprocess.call(gv_args, shell=True)\n",
- " _ = subprocess.call(gv_args)\n"
+ " _ = subprocess.call(gv_args)\n",
+ "\n"
]
},
{
@@ -739,7 +745,7 @@
" /home/patrickh/Workspace/GWU_rml/assignments/assign_env/lib/python3.6/site-packages/h2o/backend/bin/h2o.jar\n",
"\n",
"Calling external process ...\n",
- "java -cp /home/patrickh/Workspace/GWU_rml/assignments/assign_env/lib/python3.6/site-packages/h2o/backend/bin/h2o.jar hex.genmodel.tools.PrintMojo --tree 0 -i /home/patrickh/Workspace/GWU_rml/assignments/assignment_4/stolen_dt.zip -o stolen_dt.gv --title Stolen Model\n",
+ "java -cp /home/patrickh/Workspace/GWU_rml/assignments/assign_env/lib/python3.6/site-packages/h2o/backend/bin/h2o.jar hex.genmodel.tools.PrintMojo --tree 0 -i /home/patrickh/Workspace/GWU_rml/assignments/assignment_4/stolen_dt.zip -o stolen_dt.gv --title \"Stolen Model\"\n",
"Calling external process ...\n",
"dot -Tpng stolen_dt.gv -o stolen_dt.png\n"
]
@@ -771,7 +777,7 @@
"outputs": [
{
"data": {
- "image/png": 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xAwAAAIBuKCoqCg8PVxwDfuLEiVWrVmVlZQUFBfXs2VPboQFQ17ZtW29v7xs3bshkMktLy7Fjx1pZWfn4+KSlpWk7NAAAAACAbiPHDwAAAAAN3Y0bN3x8fCQSyYQJEywtLWUy2fXr1+fMmfPSSy9pOzQAVdHX13d2dg4LC0tNTfXy8goJCbGxsXFxcYmIiJDL5dqODgAAAACgk8jxAwAAAEADVVZWFhkZ6erq2rVr1+3bt0+dOjU5OTksLMzZ2VnboQGoGYlEsnz58vT09N27dwshRowY0a1bN6lUmpOTo+3QAAAAAAA6hhw/AAAAADQ4d+7ckUqltra2Q4cOLSwsDA0NTU1N9fPzk0gk2g4NQO0ZGxu7u7srjuIYNWqUVCqVSCQeHh7R0dHaDg0AAAAAoDPI8QMAAABAAxIbG+vl5dWxY8fvvvtuyJAhV69elclk7u7uhoaG2g4NQL2xs7Pz8/NLTU0NDAxMTEx0cnJydHQMDg4uKCjQdmgAAAAAgIaOHD8AAAAAaN/Dhw+Dg4P79Onj6OgYGxu7fv36rKysoKCgHj16aDs0AM+KmZmZp6fnpUuXYmJiHBwc5s6dK5FIvLy84uLitB0aAAAAAKDhIscPAAAAANqUkJDg4+NjbW09e/bsrl27njp1KiYmxtPTs1mzZtoODcBz4uDgEBQUlJKS4uvrK5PJ7O3tnZycwsPDS0pKtB0aAAAAAKDBIccPAAAAAFpQXFwcHh7u4uLSvXv3X3/91dvbOyMjIyws7K233tJ2aAC0o23btt7e3jdu3JDJZJaWlmPHjrWysvLx8UlLS9N2aAAAAACABoQcPwAAAAA8V7dv35ZKpZ07dx4zZowQYv/+/YmJid7e3q1bt9Z2aAC0T19f39nZOSwsLDU11cvLKyQkxMbGxsXFJSIiQi6Xazs6AAAAAID2keMHAAAAgOfk1KlTHh4eVlZW69atGzdu3M2bN2Uymaurq56enrZDw//H3r3HRVXuexxfg4IpkdewGAURlYtY6lBWjta2Ga2TYrGdUdSskzqza6dgljO0tUizZnR7AS8101Z3Wgkz3RRtdxq62Ja0gqQUEQRkAMksDFEUHGHOH7MPh1AQEFiAn/cfvpi1nvU834W9ev1cv1lrAe2OVCqNjY0tLCxMSEgQBCE8PDwwMNBoNJaUlIgdDQAAAAAgJnr8AAAAANC6zp49azabQ0NDx40bl5eXt2nTpvz8fIPBMGjQILGjAWjvPDw8VCqVzWbLzMyMiIgwGo1SqVStVqekpIgdDQAAAAAgDnr8AAAAANBafvjhB61WK5VKn3/++bFjx6anp6empmo0mu7du3fp0kWn0xmNxpUrV44cOVIikYSGhl68eLHm2M8///yhhx6SSCR33XWXxWKpM3N1dfV9991XWVlZZ/vx48eNRuPChQslEgmPBwA6k6CgIIPBYLfb4+Pjs7Oz5XJ5WFiY2WwuLy8XOxoAAAAAoE1JmvQuN5VKJQiC1WpttTwAAAAA0OFVVlbu3r3bbDYnJycHBgb+93//t0aj6d27d80AiUQyZMiQ48ePuz6WlZX17NlTEASNRmMymWqG2e32QYMGZWVlDRs2rM4Su3btevTRR//xj3/MnTv3qhn8/f3z8/N5ezfQWaWlpZnN5h07dnh4eEyfPn3hwoXDhw8XOxQAAAAAoMksFsv06dObdA2H+/gBAAAAoMXk5ubq9foBAwbMnj27d+/ersdr63S62g1+l65du9b8fMsttwiCMH78eLPZXPuWfalUKgiCv7//lQtt3brV19d37dq19f0L8Kabbrr+0wHQbslkMpPJlJ+fHxMTY7PZQkND5XK51Wp1OBxiRwMAAAAAtC56/AAAAABwvaqrq5OTk9VqdWBg4Pbt2+fOnZuTk2OxWBQKReMfmJ+YmHj77bfPnz//xIkTri2u7wG4u7vXGfnT5IrRZgAAIABJREFUTz8NGTJk8eLFR48e/fTTT1vwRAB0LN7e3jqdLicnx2az+fj4REZG+vr66vX6goICsaMBAAAAAFoLPX4AAAAAaL7S0tK4uLghQ4Yolcri4uKdO3cWFBQYDIaBAwc2darbbrvNYrFcuHBh+vTpDd+Ju3nz5ujo6Llz5/bp02fNmjXXER9AZ+Dm5qZQKCwWi91u12q127Zt8/f3VyqVSUlJvLADAAAAADofevwAAAAA0BxpaWlardbHx+fll19WKpVHjhzZv3+/SqWq/RD+ppLL5atXr/7+++9ffPHF+sb89ttvVVVVAwcO9PT0fPrppz///PP09PRmrwigM5FKpbGxsYWFhQkJCYIghIeHBwYGGo3GkpISsaMBAAAAAFoMPX4AAAAAaIKKiort27ePGjUqLCwsNTV1/fr1J0+eNJlMw4cPb5H5o6Oj1Wr1mjVrPvnkk6sOMJvNzz77rOvnBQsW3HTTTWvXrm2RpQF0Dh4eHiqVymazZWZmRkREGI1GqVSqVqtTUlLEjgYAAAAAaAH0+AEAAACgUbKzs/V6vVQq1Wg0Q4cOtdlsaWlpGo3G09OzZRfasmVLUFDQk08+WVxcXGeXw+HYtGnTyJEjJRKJRCK57bbbKioqEhISTp482bIZAHQCQUFBBoPBbrfHx8dnZ2fL5fKwsDCz2VxeXi52NAAAAABA89HjBwAAAICGVFVVJSUlKZXKoKCg999/f8mSJUVFRRaLRaFQtNKKN99884cffnjx4sXZs2fX2WW1Wl944QVnLe+++67D4diwYUMrhQHQ0Xl5eWk0mvT09NTUVJlMFh0dLZVKtVptRkaG2NEAAAAAAM1Bjx8AAAAAru7UqVNGo3Hw4MGPPvqoIAiJiYlZWVk6na5fv34tu1B1dXXNny5BQUFbt2798ssv6wxbvXp1ncb/tGnTvL29TSbT+fPnWzYVgE5GJpOZTKb8/PyYmBibzRYaGiqXy61Wq8PhEDsaAAAAAKAJ6PEDAAAAQF379+9Xq9W+vr4Gg+HRRx/Nycmx2WwqlapLly6tsdzp06cFQfjll19qb1SpVIsWLaq95b333uvbt2+dbxh4eHhMnjy5tLR07dq1rZENQCfj7e2t0+lc/1vz8fGJjIz09fXV6/UFBQViRwMAAAAANAo9fgAAAAD4j7KyMrPZPGLEiHHjxuXl5W3cuLG4uDguLs7f37/1Fv3oo4/mzp0rCIJGo9m/f3/tXatWrZLL5TXDnn/++fT09DfffLP2mI8//vjHH38UBMFgMKxatar1cgLoTNzc3BQKhcVisdvtWq1227Zt/v7+SqUyKSnJ6XSKnQ4AAAAA0BBJk/7lplKpBEGwWq2tlgcAAAAARJCZmfnmm29u3br18uXLKpXqueeeGzlyZOstJ5FIgoKCMjMzW2+J4ODgY8eO0asD0BiXLl3atWuX2WxOTk4eOnTo3Llz582b17dvX7Fz1SshIeHSpUuRkZHu7u5iZwEAAACA62KxWKZPn96kazjcxw8AAADgxnXp0iWr1apUKkNCQj799NOlS5eePHly+/btrdrgd6msrGzV+S9fvtyq8wPoTDw8PFQqlc1my8zMjIiIMBqNUqlUrVanpKSIHe3qPD09Fy1aNHjw4LVr154/f17sOAAAAADQpriPHwAAAMCN6OTJk2+99dbmzZvPnDnz8MMPR0VFPfjggxKJpG1Wdy20cOFCHx+fiIiIoUOHttTMx48f//DDD8+cObN69Wqn08l9/ACa4dy5czt37ty8efOPP/4ok8k0Gs2sWbM8PT3FzvUH5eXlb7311tq1a8+dO/fMM88sXLiwf//+NXvPnDnz66+/XvXA7t27+/r6tlVMAAAAALiGZtzHT48fAAAAwA2kurr6iy++MJvNH330Ub9+/Z544olnnnmGZg8AXFVaWprZbN6xY4eHh8f06dMXLlw4fPhwsUP9gcPheO+991avXp2bm/vEE08sXrzY9a2pNWvWPP/881c9RC6X//vf/27bmAAAAABQL57VDwAAAABXV1paGhcXN3ToUKVSmZeXt2XLloKCAoPBQIMfAOojk8lMJlN+fn5MTIzNZgsNDZXL5Var1eFwiB3tP9zd3Z944onDhw9bLJYjR44EBQX99a9/FQRh8eLFznrQ4AcAAADQ0dHjBwAAANDJpaWlabVaqVT60ksvKRSKw4cPp6amzpkzx93dXexoANABeHt763S6nJwcm83m4+MTGRnp6+ur1+sLCgrEjvYfEokkNDR01KhRbm5ueXl5YscBAAAAgNbVVewAAAAAANAqKioqkpKS1q9f/8033wQHB7/22mtz5869+eabxc4FAB2Sm5ubQqFQKBQnT55866233njjjdWrV0+YMGHhwoWTJ0+WSCRiBUtPT1+1apXFYhkzZsz7778fHh4uCMKZM2d+/fXXq47v3r07T3ABAAAA0KFxHz8AAACAzub48eN6vX7AgAGzZ8+WSqU2m+3o0aNRUVE0+AHg+kml0tjY2MLCwoSEBEEQwsPDAwMDjUZjSUlJGyf54osvJk2aNHr06PPnz3/11VcpKSlTp051fdtg27ZtQfWYNWtWG+cEAAAAgJZFjx8AAABAJ1FdXZ2cnDxlypTAwMB33nnn2WefLSoqslgsCoVC7GgA0Nl4eHioVCqbzZaZmRkREWE0GqVSqVqtTklJaYPVDx48GBYW9tBDD/n4+GRkZOzevVsul9cesHjxYmc9/v3vf7dBQgAAAABoPfT4AQAAAHR4v/zyi9FoHDx48KRJkyoqKhITE/Pz82NjY2+99VaxowFAJxcUFGQwGOx2e3x8fHZ2tlwuDwsLM5vN5eXlrbfod99996c//enEiRPbtm0LDg5uvYUAAAAAoB2ixw8AAACgA0tLS5szZ87AgQMNBsPUqVOPHz9us9lUKlXXrl3FjtZaDh48uHTpUrFTAMAfeHl5aTSa9PT01NRUmUwWHR0tlUq1Wm1GRkZrLLdw4cLVq1dLpdLWmBwAAAAA2jl6/AAAAAA6nnPnzpnN5jvvvDMsLOzo0aMbN248efJkXFzc4MGDxY7WWpxO5969e++//365XF5WViZ2HAC4OplMZjKZ8vPzY2JibDZbaGioXC63Wq0Oh0PsaAAAAADQSdDjBwAAANCRHDt2TK/X+/r6RkVFBQYGpqSkpKamajSaHj16iB2ttTgcju3bt48YMWLatGkhISFZWVnx8fFihwKAhnh7e+t0upycHJvN5uPjExkZ6evrq9frCwoKxI4GAAAAAB0ePX4AAAAAHcClS5esVqtSqQwJCfnggw/0en1RUZHFYrnvvvvEjtaKzp8/v27dusGDB0dFRYWHh+fn57/xxhsBAQFi5wKARnFzc1MoFBaLxW63a7Xabdu2+fv7K5XKpKQkp9MpdjoAAAAA6Kjo8QMAAABo14qLi41GY0BAwIwZMwRB2LVrV3Z2tk6n69u3r9jRWteuXbsGDhy4Zs2aqKiogoKC1157rX///mKHAoDmkEqlsbGxhYWFCQkJgiCEh4cHBgYajcaSkhKxowEAAABAx0OPHwAAAEB75HQ6k5OT1Wq1n5/funXrZs2alZeXZ7PZpkyZIpFIxE7XFk6fPn327NmhQ4eOHDnSy8tL7DgAcL08PDxUKpXNZsvMzIyIiDAajVKpVK1Wp6SkiB0NAAAAADoSevwAAAAA2pezZ8+azebQ0FClUpmXl7dly5bCwkKDweDn5yd2tDY1f/78I0eO+Pv7P/LIIzKZzGKxVFVViR0KAFpAUFCQwWCw2+3x8fHZ2dlyuTwsLMxsNpeXl4sdDQAAAAA6AHr8AAAAANqLtLQ0rVYrlUqff/55uVz+448/pqamzpkzx93dXexo4ggJCdm6dWteXt6ECRPmzZs3bNiwN9544+LFi2LnAoAW4OXlpdFo0tPTU1NTZTJZdHS0VCrVarUZGRliRwMAAACAdo0ePwAAAACRVVZWWq1WpVIZFha2b9++ZcuW2e12k8l0xx13iB2tXZBKpatXry4sLJw/f/7y5cv9/Pzi4+PFDgUALUYmk5lMpvz8/JiYGJvNFhoaKpfLrVarw+EQOxoAAAAAtEf0+AEAAACIJjc3V6/XDxgwYPbs2b1793a9pFmn0/Xu3VvsaO1Oz5499Xp9fn7+a6+99s0334gdBwBamLe3t06ny8nJsdlsPj4+kZGRvr6+er2+oKBA7GgAAAAA0L7Q4wcAAADQ1qqrq5OTk9VqdWBg4I4dO+bOnZubm2uxWBQKhUQiETtdu9atW7d58+YlJCSIHQQAWoWbm5tCobBYLHa7XavVbtu2zd/fX6lUJiUlOZ1OsdMBAAAAQLtAjx8AAABA2zl9+rTRaAwICJg4ceLvv/++c+dOu91uMBgGDBggdjQAQDsilUpjY2MLCwtd32oKDw8PDAw0Go0lJSViRwMAAAAAkdHjBwAAANAW0tLStFrtoEGDXn/99YkTJx45csRms6lUqq5du4odDQDQTnl4eKhUKtebXCIiIoxGo1QqVavVKSkpYkcDAAAAANHQ4wcAAADQis6fP282m0eOHBkWFpaWlrZ+/fqTJ0+aTKaQkBCxowEAOoygoCCDwWC32+Pj47Ozs+VyeVhYmNlsLi8vFzsaAAAAALQ1evwAAAAAWkV2drZer/f19V24cOGwYcNsNltqaqpGo/H09BQ7GgCgQ/Ly8tJoNOnp6ampqTKZLDo6WiqVarXajIwMsaMBAAAAQNuhxw8AAACgJVVVVSUlJSmVyqCgoPfff1+n0xUVFVksFoVCIXY0AEAnIZPJTCZTfn5+TEyMzWYLDQ2Vy+VWq9XhcIgdDQAAAABaHT1+AAAAAC3j559/NhqN/v7+jz76qCAIiYmJWVlZOp2uX79+YkcDAHRC3t7eOp0uJyfHZrP5+PhERkb6+vrq9fqCggKxowEAAABAK6LHDwAAAOB67d+/X61W+/n5rV27dubMmbm5uTabTaVSdenSRexoAIBOzs3NTaFQWCwWu92u1Wq3bdvm7++vVCqTkpKcTqfY6QAAAACg5dHjBwAAANBMZWVlZrN5xIgR48aNy8vL27hxY35+vsFgGDRokNjRAAA3HKlUGhsbW1hYmJCQIAhCeHh4YGCg0WgsKSkROxoAAAAAtCR6/AAAAACa7IcfftBqtT4+PlFRUaNGjUpPT09NTdVoNN27dxc7GgDghubh4aFSqWw2W2ZmZkREhNFolEqlarU6JSVF7GgAAAAA0DLo8QMAAABorMrKSqvVqlQqZTLZV199tWzZspMnT27fvv3OO+8UOxoAAH8QFBRkMBjsdnt8fHx2drZcLg8LCzObzeXl5WJHAwAAAIDrQo8fAAAAwLWdPHkyNjZ24MCBkZGRN910k81mO3bsmE6n69Onj9jRAACol5eXl0ajcT1vRiaTRUdHS6VSrVabkZEhdjQAAAAAaCZ6/AAAAADqVV1dnZycrFar/fz8zGbzU089deLEiaSkJIVCIZFIxE4HAEBjyWQyk8mUn58fExNjs9lCQ0PlcrnVanU4HGJHAwAAAICmoccPAAAA4CpKS0vj4uKGDBmiVCqLi4t37txpt9sNBsPAgQPFjgYAQDN5e3vrdLqcnBybzebj4xMZGenr66vX6wsKCsSOBgAAAACNRY8fAAAAwB+kpaVptVofH5+XXnpJqVQePnx4//79KpXK3d1d7GgAALQANzc3hUJhsVjsdrtWq922bZu/v79SqUxKSnI6nWKnAwAAAIBroMcPAAAAQBAEoaKiYvv27aNHjw4LC/v+++/Xr19fXFxsMplCQ0PFjgYAQKuQSqWxsbGFhYUJCQmCIISHhwcGBhqNxpKSErGjAQAAAEC96PEDAAAAN7rjx4/r9XqpVKrRaIYMGWKz2X744QeNRuPp6Sl2NAAAWp2Hh4dKpbLZbJmZmREREUajUSqVqtXqlJQUsaMBAAAAwFXQ4wcAAABuUNXV1cnJyVOmTAkMDHznnXcWLFhQWFhosVgUCoXY0QAAEEFQUJDBYLDb7fHx8dnZ2XK5PCwszGw2l5eXix0NAAAAAP4fPX4AAADghnPq1Cmj0ejv7z9p0qSKiorExES73R4bG3vrrbeKHQ0AAJF5eXlpNJr09PTU1FSZTBYdHS2VSrVabUZGhtjRAAAAAEAQ6PEDAAAAN5S0tLQ5c+b4+voaDIZHH330+PHjNptNpVJ16dJF7GgAALQvMpnMZDLl5+fHxMTYbLbQ0FC5XG61Wh0Oh9jRAAAAANzQ6PEDAAAAnV9ZWZnZbL7jjjvCwsKOHj26cePG4uLiuLi4wYMHix0NAIB2zdvbW6fT5eTk2Gw2Hx+fyMhIX19fvV5fUFAgdjQAAAAANyh6/AAAAEBnduzYsaioKKlUGhUVNXLkyB9++CE1NVWj0XTv3l3saAAAdBhubm4KhcJisdjtdq1Wu23bNn9/f6VSmZSU5HQ6xU4HAAAA4MZCjx8AAADohC5dumS1WpVKZXBw8L/+9a+lS5eePHly+/bto0aNEjsaAAAdmFQqjY2NLSwsTEhIEAQhPDw8MDDQaDSWlJSIHQ0AAADAjYIePwAAANCpFBcXG43GwYMHz5gxQxCE3bt3Z2Vl6XS6Pn36iB0NAIBOwsPDQ6VS2Wy2zMzMiIgIo9EolUrVanVKSorY0QAAAAB0fvT4AQAAgM6guro6OTlZrVb7+fmtX79+9uzZJ06csNlsU6ZMkUgkYqcDAKBzCgoKMhgMdrs9Pj4+OztbLpeHhYWZzeby8nKxowEAAADotOjxAwAAAB3b2bNnzWZzaGioUqnMy8vbsmVLQUGBwWDw9fUVOxoAADcELy8vjUaTnp6empoqk8mio6OlUqlWq83IyBA7GgAAAIBOiB4/AAAA0FGlpaVptVofH5/nn39+3LhxP/30U2pq6pw5c9zd3cWOBgDAjUgmk5lMpvz8/JiYGJvNFhoaKpfLrVarw+EQOxoAAACAzoMePwAAANDBVFZWWq1W19OAv/7669dee624uNhkMo0YMULsaAAAQPD29tbpdDk5OTabzcfHJzIy0s/PT6/XFxQUiB0NAAAAQGdAjx8AAADoMHJycvR6vVQqnT17to+Pj81my8zMjIqKuvnmm8WOBgAA/sDNzU2hUFgsFrvdrtFotm7d6u/vr1Qqk5KSnE6n2OkAAAAAdGD0+AEAAID2rrq6Ojk5ecqUKcOGDduxY8e8efPy8vIsFotCoRA7GgAAuAapVBobG1tUVJSQkCAIQnh4eGBgoNFoLCkpETsaAAAAgA6JHj8AAADQfv3yyy9GozEgIGDSpEkVFRWJiYl2u91gMEilUrGjAQCAJvDw8FCpVK5n8ERERBiNRqlUqlarU1JSxI4GAAAAoIOhxw8AAAC0R2lpaVqtdtCgQa+//vrEiRMPHz5ss9lUKlXXrl3FjgYAAJovKCjIYDDY7fb4+Pjs7Gy5XB4WFmY2m8vLy8WOBgAAAKBjoMcPAAAAtCPnzp0zm80jR44MCwtLS0uLi4srLi42mUwhISFiRwMAAC3Gy8tLo9Gkp6enpqbKZLLo6GipVKrVajMyMsSOBgAAAKC9o8cPAAAAtAtZWVl6vd7Pz2/hwoXDhg3bv39/amqqRqPp0aOH2NEAAEBrkclkJpMpPz8/JibGZrOFhobK5XKr1epwOMSOBgAAAKCdoscPAAAAiOnSpUtWq1WpVAYHB3/wwQc6na6oqMhisYwdO1bsaAAAoI14e3vrdLqcnBybzebj4xMZGenn56fX6wsKCsSOBgAAAKDdoccPAAAAiOPnn382Go1DhgyZMWOGIAi7du3Kzs7W6XT9+vUTOxoAABCBm5ubQqGwWCx2u12j0WzdutXf31+pVCYlJTmdTrHTAQAAAGgv6PEDAAAAbW3//v1qtdrX13fdunUzZ87Mzc212WxTpkyRSCRiRwMAAOKTSqWxsbFFRUUJCQmCIISHhwcGBhqNxpKSErGjAQAAABAfPX4AAACgjZw9e9ZsNoeGho4bNy4vL2/Tpk35+fkGg2HQoEFiRwMAAO2Oh4eHSqWy2WyZmZkRERFGo1EqlarV6pSUFLGjAQA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\n",
"text/plain": [
""
]
@@ -781,7 +787,14 @@
}
],
"source": [
- "display(Image(('stolen_dt.png')))"
+ "display(Image(('stolen_dt.png')))\n",
+ "# if this is failing, you may need graphviz\n",
+ "# on Colab or linux, install with:\n",
+ "# \"!apt-get install graphviz\" (inside notebook)\n",
+ "# \"apt-get install graphviz\" (outside notebook)\n",
+ "# on mac, install with: \n",
+ "# \"!brew install graphviz\" (inside notebook)\n",
+ "# \"brew install graphviz\" (outside notebook)"
]
},
{
@@ -1640,7 +1653,7 @@
"name": "stdout",
"output_type": "stream",
"text": [
- "All tasks completed in 48.85 s.\n"
+ "All tasks completed in 49.83 s.\n"
]
}
],
@@ -1659,10 +1672,19 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 22,
"id": "e4cca3fd",
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Are you sure you want to shutdown the H2O instance running at http://127.0.0.1:54321 (Y/N)? y\n",
+ "H2O session _sid_b993 closed.\n"
+ ]
+ }
+ ],
"source": [
"# be careful, this can erase your work!\n",
"h2o.cluster().shutdown(prompt=True)"
diff --git a/assignments/eval.ipynb b/assignments/eval.ipynb
index e2bf962..c9f1422 100644
--- a/assignments/eval.ipynb
+++ b/assignments/eval.ipynb
@@ -7,7 +7,7 @@
"source": [
"## License \n",
"\n",
- "Copyright 2021 Patrick Hall (jphall@gwu.edu)\n",
+ "Copyright 2021--2022 Patrick Hall (jphall@gwu.edu)\n",
"\n",
"Licensed under the Apache License, Version 2.0 (the \"License\");\n",
"you may not use this file except in compliance with the License.\n",
@@ -125,13 +125,13 @@
" group6_mxgb | \n",
" group8_mxgb | \n",
" ph_mxgb | \n",
+ " group7_piml_ebm | \n",
" group2_ebm | \n",
- " ph_glm | \n",
" ... | \n",
- " group1_glm | \n",
- " group8_ebm | \n",
" group2_glm | \n",
" group3_mxgb | \n",
+ " group7_mxgb2 | \n",
+ " group6_piml_gaminet | \n",
" group9_ebm | \n",
" group6_glm | \n",
" group9_glm | \n",
@@ -151,13 +151,13 @@
" 0.086361 | \n",
" 0.078233 | \n",
" 0.059522 | \n",
+ " 0.074151 | \n",
" 0.081364 | \n",
- " 0.142090 | \n",
" ... | \n",
" 0.142090 | \n",
- " 0.081364 | \n",
- " 0.142090 | \n",
" 0.059522 | \n",
+ " 0.080985 | \n",
+ " 0.034641 | \n",
" 0.081364 | \n",
" 0.142090 | \n",
" 0.142090 | \n",
@@ -175,13 +175,13 @@
" 0.033920 | \n",
" 0.022467 | \n",
" 0.036210 | \n",
+ " 0.031165 | \n",
" 0.026177 | \n",
- " 0.081674 | \n",
" ... | \n",
" 0.081674 | \n",
- " 0.026177 | \n",
- " 0.081674 | \n",
" 0.036210 | \n",
+ " 0.033100 | \n",
+ " 0.032192 | \n",
" 0.026177 | \n",
" 0.081674 | \n",
" 0.081674 | \n",
@@ -199,13 +199,13 @@
" 0.183323 | \n",
" 0.179896 | \n",
" 0.180734 | \n",
+ " 0.182987 | \n",
" 0.184662 | \n",
- " 0.125823 | \n",
" ... | \n",
" 0.125823 | \n",
- " 0.184662 | \n",
- " 0.125823 | \n",
" 0.180734 | \n",
+ " 0.177759 | \n",
+ " 0.257928 | \n",
" 0.184662 | \n",
" 0.125823 | \n",
" 0.125823 | \n",
@@ -223,13 +223,13 @@
" 0.030934 | \n",
" 0.010161 | \n",
" 0.027677 | \n",
+ " 0.018368 | \n",
" 0.030049 | \n",
- " 0.006973 | \n",
" ... | \n",
" 0.006973 | \n",
- " 0.030049 | \n",
- " 0.006973 | \n",
" 0.027677 | \n",
+ " 0.023707 | \n",
+ " 0.000700 | \n",
" 0.030049 | \n",
" 0.006973 | \n",
" 0.006973 | \n",
@@ -247,13 +247,13 @@
" 0.178491 | \n",
" 0.214328 | \n",
" 0.177813 | \n",
+ " 0.188477 | \n",
" 0.205948 | \n",
- " 0.130426 | \n",
" ... | \n",
" 0.130426 | \n",
- " 0.205948 | \n",
- " 0.130426 | \n",
" 0.177813 | \n",
+ " 0.210616 | \n",
+ " 0.124897 | \n",
" 0.205948 | \n",
" 0.130426 | \n",
" 0.130426 | \n",
@@ -295,13 +295,13 @@
" 0.255826 | \n",
" 0.236099 | \n",
" 0.274767 | \n",
+ " 0.203201 | \n",
" 0.227771 | \n",
- " 0.160032 | \n",
" ... | \n",
" 0.160032 | \n",
- " 0.227771 | \n",
- " 0.160032 | \n",
" 0.274767 | \n",
+ " 0.257779 | \n",
+ " 0.218879 | \n",
" 0.227771 | \n",
" 0.160032 | \n",
" 0.160032 | \n",
@@ -319,13 +319,13 @@
" 0.176984 | \n",
" 0.255476 | \n",
" 0.182039 | \n",
+ " 0.207900 | \n",
" 0.253325 | \n",
- " 0.123836 | \n",
" ... | \n",
" 0.123836 | \n",
- " 0.253325 | \n",
- " 0.123836 | \n",
" 0.182039 | \n",
+ " 0.252247 | \n",
+ " 0.145454 | \n",
" 0.253325 | \n",
" 0.123836 | \n",
" 0.123836 | \n",
@@ -343,13 +343,13 @@
" 0.236894 | \n",
" 0.224857 | \n",
" 0.212740 | \n",
+ " 0.215871 | \n",
" 0.225811 | \n",
- " 0.169604 | \n",
" ... | \n",
" 0.169604 | \n",
- " 0.225811 | \n",
- " 0.169604 | \n",
" 0.212740 | \n",
+ " 0.204329 | \n",
+ " 0.062602 | \n",
" 0.225811 | \n",
" 0.169604 | \n",
" 0.169604 | \n",
@@ -367,13 +367,13 @@
" 0.001113 | \n",
" 0.001779 | \n",
" 0.001323 | \n",
+ " 0.007910 | \n",
" 0.001488 | \n",
- " 0.002538 | \n",
" ... | \n",
" 0.002538 | \n",
- " 0.001488 | \n",
- " 0.002538 | \n",
" 0.001323 | \n",
+ " 0.000406 | \n",
+ " 0.000042 | \n",
" 0.001488 | \n",
" 0.002538 | \n",
" 0.002538 | \n",
@@ -391,13 +391,13 @@
" 0.233696 | \n",
" 0.185213 | \n",
" 0.259442 | \n",
+ " 0.194421 | \n",
" 0.207258 | \n",
- " 0.156659 | \n",
" ... | \n",
" 0.156659 | \n",
- " 0.207258 | \n",
- " 0.156659 | \n",
" 0.259442 | \n",
+ " 0.195232 | \n",
+ " 0.212237 | \n",
" 0.207258 | \n",
" 0.156659 | \n",
" 0.156659 | \n",
@@ -407,7 +407,7 @@
"
|---|
\n",
" \n",
"\n",
- "19831 rows × 28 columns
\n",
+ "19831 rows × 36 columns
\n",
""
],
"text/plain": [
@@ -424,31 +424,31 @@
"19829 0.0 1 0.001348 0.001113 0.001488 0.001113 \n",
"19830 0.0 0 0.259751 0.233696 0.207258 0.233696 \n",
"\n",
- " group8_mxgb ph_mxgb group2_ebm ph_glm ... group1_glm \\\n",
- "0 0.078233 0.059522 0.081364 0.142090 ... 0.142090 \n",
- "1 0.022467 0.036210 0.026177 0.081674 ... 0.081674 \n",
- "2 0.179896 0.180734 0.184662 0.125823 ... 0.125823 \n",
- "3 0.010161 0.027677 0.030049 0.006973 ... 0.006973 \n",
- "4 0.214328 0.177813 0.205948 0.130426 ... 0.130426 \n",
- "... ... ... ... ... ... ... \n",
- "19826 0.236099 0.274767 0.227771 0.160032 ... 0.160032 \n",
- "19827 0.255476 0.182039 0.253325 0.123836 ... 0.123836 \n",
- "19828 0.224857 0.212740 0.225811 0.169604 ... 0.169604 \n",
- "19829 0.001779 0.001323 0.001488 0.002538 ... 0.002538 \n",
- "19830 0.185213 0.259442 0.207258 0.156659 ... 0.156659 \n",
+ " group8_mxgb ph_mxgb group7_piml_ebm group2_ebm ... group2_glm \\\n",
+ "0 0.078233 0.059522 0.074151 0.081364 ... 0.142090 \n",
+ "1 0.022467 0.036210 0.031165 0.026177 ... 0.081674 \n",
+ "2 0.179896 0.180734 0.182987 0.184662 ... 0.125823 \n",
+ "3 0.010161 0.027677 0.018368 0.030049 ... 0.006973 \n",
+ "4 0.214328 0.177813 0.188477 0.205948 ... 0.130426 \n",
+ "... ... ... ... ... ... ... \n",
+ "19826 0.236099 0.274767 0.203201 0.227771 ... 0.160032 \n",
+ "19827 0.255476 0.182039 0.207900 0.253325 ... 0.123836 \n",
+ "19828 0.224857 0.212740 0.215871 0.225811 ... 0.169604 \n",
+ "19829 0.001779 0.001323 0.007910 0.001488 ... 0.002538 \n",
+ "19830 0.185213 0.259442 0.194421 0.207258 ... 0.156659 \n",
"\n",
- " group8_ebm group2_glm group3_mxgb group9_ebm group6_glm \\\n",
- "0 0.081364 0.142090 0.059522 0.081364 0.142090 \n",
- "1 0.026177 0.081674 0.036210 0.026177 0.081674 \n",
- "2 0.184662 0.125823 0.180734 0.184662 0.125823 \n",
- "3 0.030049 0.006973 0.027677 0.030049 0.006973 \n",
- "4 0.205948 0.130426 0.177813 0.205948 0.130426 \n",
- "... ... ... ... ... ... \n",
- "19826 0.227771 0.160032 0.274767 0.227771 0.160032 \n",
- "19827 0.253325 0.123836 0.182039 0.253325 0.123836 \n",
- "19828 0.225811 0.169604 0.212740 0.225811 0.169604 \n",
- "19829 0.001488 0.002538 0.001323 0.001488 0.002538 \n",
- "19830 0.207258 0.156659 0.259442 0.207258 0.156659 \n",
+ " group3_mxgb group7_mxgb2 group6_piml_gaminet group9_ebm group6_glm \\\n",
+ "0 0.059522 0.080985 0.034641 0.081364 0.142090 \n",
+ "1 0.036210 0.033100 0.032192 0.026177 0.081674 \n",
+ "2 0.180734 0.177759 0.257928 0.184662 0.125823 \n",
+ "3 0.027677 0.023707 0.000700 0.030049 0.006973 \n",
+ "4 0.177813 0.210616 0.124897 0.205948 0.130426 \n",
+ "... ... ... ... ... ... \n",
+ "19826 0.274767 0.257779 0.218879 0.227771 0.160032 \n",
+ "19827 0.182039 0.252247 0.145454 0.253325 0.123836 \n",
+ "19828 0.212740 0.204329 0.062602 0.225811 0.169604 \n",
+ "19829 0.001323 0.000406 0.000042 0.001488 0.002538 \n",
+ "19830 0.259442 0.195232 0.212237 0.207258 0.156659 \n",
"\n",
" group9_glm group8_glm group3_glm ph_ebm \n",
"0 0.142090 0.142090 0.142090 0.082841 \n",
@@ -463,7 +463,7 @@
"19829 0.002538 0.002538 0.002538 0.000993 \n",
"19830 0.156659 0.156659 0.156659 0.222328 \n",
"\n",
- "[19831 rows x 28 columns]"
+ "[19831 rows x 36 columns]"
]
},
"execution_count": 3,
@@ -623,6 +623,7 @@
" | \n",
" fold | \n",
" metric | \n",
+ " group1_ebm | \n",
" group1_glm | \n",
" group1_mxgb | \n",
" group2_ebm | \n",
@@ -630,9 +631,8 @@
" group2_mxgb | \n",
" group3_ebm | \n",
" group3_glm | \n",
- " group3_mxgb | \n",
" ... | \n",
- " group7_mxgb_rank | \n",
+ " group7_piml_reludnn_rank | \n",
" group8_ebm_rank | \n",
" group8_glm_rank | \n",
" group8_mxgb_rank | \n",
@@ -649,6 +649,7 @@
" 0 | \n",
" 0.0 | \n",
" acc | \n",
+ " 0.901 | \n",
" 0.900 | \n",
" 0.902 | \n",
" 0.901 | \n",
@@ -656,23 +657,23 @@
" 0.902 | \n",
" 0.901 | \n",
" 0.900 | \n",
- " 0.902 | \n",
" ... | \n",
- " 4.5 | \n",
- " 13.0 | \n",
- " 22.0 | \n",
- " 13.0 | \n",
- " 13.0 | \n",
- " 22.0 | \n",
- " 4.5 | \n",
- " 13.0 | \n",
- " 22.0 | \n",
- " 4.5 | \n",
+ " 16.0 | \n",
+ " 16.0 | \n",
+ " 28.5 | \n",
+ " 16.0 | \n",
+ " 16.0 | \n",
+ " 28.5 | \n",
+ " 5.0 | \n",
+ " 16.0 | \n",
+ " 28.5 | \n",
+ " 5.0 | \n",
" \n",
" \n",
" | 1 | \n",
" 0.0 | \n",
" auc | \n",
+ " 0.839 | \n",
" 0.775 | \n",
" 0.812 | \n",
" 0.839 | \n",
@@ -680,23 +681,23 @@
" 0.813 | \n",
" 0.839 | \n",
" 0.775 | \n",
- " 0.814 | \n",
" ... | \n",
- " 16.5 | \n",
- " 5.5 | \n",
- " 22.0 | \n",
+ " 14.0 | \n",
+ " 6.0 | \n",
+ " 30.0 | \n",
" 1.0 | \n",
- " 5.5 | \n",
- " 22.0 | \n",
- " 11.5 | \n",
- " 5.5 | \n",
- " 22.0 | \n",
- " 11.5 | \n",
+ " 6.0 | \n",
+ " 30.0 | \n",
+ " 18.0 | \n",
+ " 6.0 | \n",
+ " 30.0 | \n",
+ " 18.0 | \n",
"
\n",
" \n",
" | 2 | \n",
" 0.0 | \n",
" f1 | \n",
+ " 0.404 | \n",
" 0.335 | \n",
" 0.376 | \n",
" 0.404 | \n",
@@ -704,23 +705,23 @@
" 0.374 | \n",
" 0.404 | \n",
" 0.335 | \n",
- " 0.379 | \n",
" ... | \n",
- " 14.5 | \n",
- " 6.5 | \n",
- " 22.0 | \n",
+ " 20.0 | \n",
+ " 7.0 | \n",
+ " 30.0 | \n",
" 2.0 | \n",
- " 6.5 | \n",
- " 22.0 | \n",
- " 11.5 | \n",
+ " 7.0 | \n",
+ " 30.0 | \n",
+ " 16.5 | \n",
" 1.0 | \n",
- " 22.0 | \n",
- " 11.5 | \n",
+ " 30.0 | \n",
+ " 16.5 | \n",
"
\n",
" \n",
" | 3 | \n",
" 0.0 | \n",
" logloss | \n",
+ " 0.251 | \n",
" 0.291 | \n",
" 0.264 | \n",
" 0.251 | \n",
@@ -728,23 +729,23 @@
" 0.263 | \n",
" 0.251 | \n",
" 0.291 | \n",
- " 0.263 | \n",
" ... | \n",
- " 16.5 | \n",
- " 5.5 | \n",
- " 22.0 | \n",
+ " 14.0 | \n",
+ " 6.0 | \n",
+ " 30.0 | \n",
" 1.0 | \n",
- " 5.5 | \n",
- " 22.0 | \n",
- " 12.5 | \n",
- " 5.5 | \n",
- " 22.0 | \n",
- " 12.5 | \n",
+ " 6.0 | \n",
+ " 30.0 | \n",
+ " 18.5 | \n",
+ " 6.0 | \n",
+ " 30.0 | \n",
+ " 18.5 | \n",
"
\n",
" \n",
" | 4 | \n",
" 0.0 | \n",
" mse | \n",
+ " 0.077 | \n",
" 0.084 | \n",
" 0.078 | \n",
" 0.077 | \n",
@@ -752,18 +753,17 @@
" 0.078 | \n",
" 0.077 | \n",
" 0.084 | \n",
- " 0.078 | \n",
" ... | \n",
- " 13.5 | \n",
- " 5.0 | \n",
- " 22.0 | \n",
- " 5.0 | \n",
- " 5.0 | \n",
- " 22.0 | \n",
- " 13.5 | \n",
- " 5.0 | \n",
- " 22.0 | \n",
- " 13.5 | \n",
+ " 18.0 | \n",
+ " 7.0 | \n",
+ " 30.0 | \n",
+ " 7.0 | \n",
+ " 7.0 | \n",
+ " 30.0 | \n",
+ " 18.0 | \n",
+ " 7.0 | \n",
+ " 30.0 | \n",
+ " 18.0 | \n",
"
\n",
" \n",
" | 5 | \n",
@@ -778,21 +778,22 @@
" 0.906 | \n",
" 0.906 | \n",
" ... | \n",
- " 14.0 | \n",
- " 14.0 | \n",
- " 14.0 | \n",
- " 1.0 | \n",
- " 14.0 | \n",
- " 14.0 | \n",
- " 14.0 | \n",
- " 14.0 | \n",
- " 14.0 | \n",
- " 14.0 | \n",
+ " 18.5 | \n",
+ " 18.5 | \n",
+ " 18.5 | \n",
+ " 1.5 | \n",
+ " 18.5 | \n",
+ " 18.5 | \n",
+ " 18.5 | \n",
+ " 18.5 | \n",
+ " 18.5 | \n",
+ " 18.5 | \n",
"
\n",
" \n",
" | 6 | \n",
" 1.0 | \n",
" auc | \n",
+ " 0.829 | \n",
" 0.757 | \n",
" 0.793 | \n",
" 0.829 | \n",
@@ -800,23 +801,23 @@
" 0.792 | \n",
" 0.829 | \n",
" 0.757 | \n",
- " 0.792 | \n",
" ... | \n",
- " 10.5 | \n",
- " 5.0 | \n",
- " 22.0 | \n",
+ " 15.0 | \n",
+ " 5.5 | \n",
+ " 30.0 | \n",
" 1.0 | \n",
- " 5.0 | \n",
- " 22.0 | \n",
- " 14.5 | \n",
- " 9.0 | \n",
- " 22.0 | \n",
- " 14.5 | \n",
+ " 5.5 | \n",
+ " 30.0 | \n",
+ " 20.5 | \n",
+ " 10.5 | \n",
+ " 30.0 | \n",
+ " 20.5 | \n",
"
\n",
" \n",
" | 7 | \n",
" 1.0 | \n",
" f1 | \n",
+ " 0.371 | \n",
" 0.302 | \n",
" 0.339 | \n",
" 0.371 | \n",
@@ -824,23 +825,23 @@
" 0.342 | \n",
" 0.371 | \n",
" 0.302 | \n",
- " 0.337 | \n",
" ... | \n",
- " 12.5 | \n",
- " 5.5 | \n",
- " 22.0 | \n",
+ " 14.0 | \n",
+ " 6.0 | \n",
+ " 30.0 | \n",
" 1.0 | \n",
- " 5.5 | \n",
- " 22.0 | \n",
- " 16.0 | \n",
- " 9.0 | \n",
- " 22.0 | \n",
- " 16.0 | \n",
+ " 6.0 | \n",
+ " 30.0 | \n",
+ " 21.0 | \n",
+ " 10.5 | \n",
+ " 30.0 | \n",
+ " 21.0 | \n",
"
\n",
" \n",
" | 8 | \n",
" 1.0 | \n",
" logloss | \n",
+ " 0.246 | \n",
" 0.281 | \n",
" 0.263 | \n",
" 0.246 | \n",
@@ -848,23 +849,23 @@
" 0.264 | \n",
" 0.246 | \n",
" 0.281 | \n",
- " 0.264 | \n",
" ... | \n",
- " 10.5 | \n",
- " 5.5 | \n",
- " 22.0 | \n",
+ " 14.0 | \n",
+ " 6.0 | \n",
+ " 30.0 | \n",
" 1.0 | \n",
- " 5.5 | \n",
- " 22.0 | \n",
- " 14.5 | \n",
- " 5.5 | \n",
- " 22.0 | \n",
- " 14.5 | \n",
+ " 6.0 | \n",
+ " 30.0 | \n",
+ " 20.5 | \n",
+ " 6.0 | \n",
+ " 30.0 | \n",
+ " 20.5 | \n",
"
\n",
" \n",
" | 9 | \n",
" 1.0 | \n",
" mse | \n",
+ " 0.074 | \n",
" 0.080 | \n",
" 0.078 | \n",
" 0.074 | \n",
@@ -872,18 +873,17 @@
" 0.078 | \n",
" 0.074 | \n",
" 0.080 | \n",
- " 0.078 | \n",
" ... | \n",
- " 13.5 | \n",
- " 5.0 | \n",
- " 22.0 | \n",
- " 5.0 | \n",
- " 5.0 | \n",
- " 22.0 | \n",
- " 13.5 | \n",
- " 5.0 | \n",
- " 22.0 | \n",
- " 13.5 | \n",
+ " 14.0 | \n",
+ " 5.5 | \n",
+ " 29.0 | \n",
+ " 5.5 | \n",
+ " 5.5 | \n",
+ " 29.0 | \n",
+ " 19.5 | \n",
+ " 5.5 | \n",
+ " 29.0 | \n",
+ " 19.5 | \n",
"
\n",
" \n",
" | 10 | \n",
@@ -898,21 +898,22 @@
" 0.908 | \n",
" 0.908 | \n",
" ... | \n",
- " 14.0 | \n",
- " 14.0 | \n",
- " 14.0 | \n",
+ " 18.5 | \n",
+ " 18.5 | \n",
+ " 18.5 | \n",
" 1.0 | \n",
- " 14.0 | \n",
- " 14.0 | \n",
- " 14.0 | \n",
- " 14.0 | \n",
- " 14.0 | \n",
- " 14.0 | \n",
+ " 18.5 | \n",
+ " 18.5 | \n",
+ " 18.5 | \n",
+ " 18.5 | \n",
+ " 18.5 | \n",
+ " 18.5 | \n",
"
\n",
" \n",
" | 11 | \n",
" 2.0 | \n",
" auc | \n",
+ " 0.827 | \n",
" 0.763 | \n",
" 0.796 | \n",
" 0.827 | \n",
@@ -920,23 +921,23 @@
" 0.798 | \n",
" 0.827 | \n",
" 0.763 | \n",
- " 0.797 | \n",
" ... | \n",
- " 16.5 | \n",
- " 5.0 | \n",
- " 22.0 | \n",
+ " 15.0 | \n",
+ " 6.0 | \n",
+ " 29.0 | \n",
" 1.0 | \n",
- " 5.0 | \n",
- " 22.0 | \n",
- " 13.5 | \n",
- " 9.0 | \n",
- " 22.0 | \n",
- " 13.5 | \n",
+ " 6.0 | \n",
+ " 29.0 | \n",
+ " 20.5 | \n",
+ " 11.0 | \n",
+ " 29.0 | \n",
+ " 20.5 | \n",
"
\n",
" \n",
" | 12 | \n",
" 2.0 | \n",
" f1 | \n",
+ " 0.376 | \n",
" 0.312 | \n",
" 0.345 | \n",
" 0.376 | \n",
@@ -944,23 +945,23 @@
" 0.352 | \n",
" 0.376 | \n",
" 0.312 | \n",
- " 0.344 | \n",
" ... | \n",
- " 12.5 | \n",
- " 3.5 | \n",
- " 22.0 | \n",
- " 9.0 | \n",
- " 3.5 | \n",
- " 22.0 | \n",
- " 15.0 | \n",
- " 7.5 | \n",
- " 22.0 | \n",
- " 15.0 | \n",
+ " 14.5 | \n",
+ " 4.0 | \n",
+ " 30.0 | \n",
+ " 10.5 | \n",
+ " 4.0 | \n",
+ " 30.0 | \n",
+ " 21.0 | \n",
+ " 8.5 | \n",
+ " 30.0 | \n",
+ " 21.0 | \n",
"
\n",
" \n",
" | 13 | \n",
" 2.0 | \n",
" logloss | \n",
+ " 0.245 | \n",
" 0.279 | \n",
" 0.260 | \n",
" 0.245 | \n",
@@ -968,23 +969,23 @@
" 0.259 | \n",
" 0.245 | \n",
" 0.279 | \n",
- " 0.259 | \n",
" ... | \n",
- " 16.5 | \n",
- " 5.5 | \n",
- " 22.0 | \n",
+ " 14.0 | \n",
+ " 6.5 | \n",
+ " 29.0 | \n",
" 1.0 | \n",
- " 5.5 | \n",
- " 22.0 | \n",
- " 12.5 | \n",
- " 5.5 | \n",
- " 22.0 | \n",
- " 12.5 | \n",
+ " 6.5 | \n",
+ " 29.0 | \n",
+ " 19.5 | \n",
+ " 6.5 | \n",
+ " 29.0 | \n",
+ " 19.5 | \n",
"
\n",
" \n",
" | 14 | \n",
" 2.0 | \n",
" mse | \n",
+ " 0.073 | \n",
" 0.079 | \n",
" 0.076 | \n",
" 0.073 | \n",
@@ -992,18 +993,17 @@
" 0.076 | \n",
" 0.073 | \n",
" 0.079 | \n",
- " 0.076 | \n",
" ... | \n",
- " 13.5 | \n",
- " 5.5 | \n",
- " 22.0 | \n",
+ " 14.5 | \n",
+ " 7.5 | \n",
+ " 29.0 | \n",
" 1.0 | \n",
- " 5.5 | \n",
- " 22.0 | \n",
- " 13.5 | \n",
- " 5.5 | \n",
- " 22.0 | \n",
- " 13.5 | \n",
+ " 7.5 | \n",
+ " 29.0 | \n",
+ " 20.5 | \n",
+ " 7.5 | \n",
+ " 29.0 | \n",
+ " 20.5 | \n",
"
\n",
" \n",
" | 15 | \n",
@@ -1018,21 +1018,22 @@
" 0.903 | \n",
" 0.903 | \n",
" ... | \n",
- " 14.5 | \n",
- " 14.5 | \n",
- " 14.5 | \n",
- " 1.5 | \n",
- " 14.5 | \n",
- " 14.5 | \n",
- " 14.5 | \n",
- " 1.5 | \n",
- " 14.5 | \n",
- " 14.5 | \n",
+ " 19.0 | \n",
+ " 19.0 | \n",
+ " 19.0 | \n",
+ " 2.0 | \n",
+ " 19.0 | \n",
+ " 19.0 | \n",
+ " 19.0 | \n",
+ " 2.0 | \n",
+ " 19.0 | \n",
+ " 19.0 | \n",
"
\n",
" \n",
" | 16 | \n",
" 3.0 | \n",
" auc | \n",
+ " 0.826 | \n",
" 0.755 | \n",
" 0.794 | \n",
" 0.826 | \n",
@@ -1040,23 +1041,23 @@
" 0.795 | \n",
" 0.826 | \n",
" 0.755 | \n",
- " 0.795 | \n",
" ... | \n",
- " 16.5 | \n",
- " 5.5 | \n",
- " 22.0 | \n",
- " 5.5 | \n",
- " 5.5 | \n",
- " 22.0 | \n",
- " 12.5 | \n",
+ " 14.0 | \n",
+ " 6.0 | \n",
+ " 30.0 | \n",
+ " 6.0 | \n",
+ " 6.0 | \n",
+ " 30.0 | \n",
+ " 19.5 | \n",
" 1.0 | \n",
- " 22.0 | \n",
- " 12.5 | \n",
+ " 30.0 | \n",
+ " 19.5 | \n",
"
\n",
" \n",
" | 17 | \n",
" 3.0 | \n",
" f1 | \n",
+ " 0.373 | \n",
" 0.307 | \n",
" 0.340 | \n",
" 0.373 | \n",
@@ -1064,23 +1065,23 @@
" 0.348 | \n",
" 0.373 | \n",
" 0.307 | \n",
- " 0.345 | \n",
" ... | \n",
- " 16.5 | \n",
- " 4.0 | \n",
- " 22.0 | \n",
- " 9.0 | \n",
- " 4.0 | \n",
- " 22.0 | \n",
- " 13.5 | \n",
- " 4.0 | \n",
- " 22.0 | \n",
- " 13.5 | \n",
+ " 14.0 | \n",
+ " 4.5 | \n",
+ " 30.0 | \n",
+ " 10.0 | \n",
+ " 4.5 | \n",
+ " 30.0 | \n",
+ " 19.5 | \n",
+ " 4.5 | \n",
+ " 30.0 | \n",
+ " 19.5 | \n",
"
\n",
" \n",
" | 18 | \n",
" 3.0 | \n",
" logloss | \n",
+ " 0.251 | \n",
" 0.288 | \n",
" 0.268 | \n",
" 0.251 | \n",
@@ -1088,23 +1089,23 @@
" 0.268 | \n",
" 0.251 | \n",
" 0.288 | \n",
- " 0.268 | \n",
" ... | \n",
- " 13.5 | \n",
- " 5.0 | \n",
- " 22.0 | \n",
- " 5.0 | \n",
- " 5.0 | \n",
- " 22.0 | \n",
- " 13.5 | \n",
- " 5.0 | \n",
- " 22.0 | \n",
- " 13.5 | \n",
+ " 14.0 | \n",
+ " 5.5 | \n",
+ " 30.0 | \n",
+ " 5.5 | \n",
+ " 5.5 | \n",
+ " 30.0 | \n",
+ " 20.5 | \n",
+ " 5.5 | \n",
+ " 30.0 | \n",
+ " 20.5 | \n",
"
\n",
" \n",
" | 19 | \n",
" 3.0 | \n",
" mse | \n",
+ " 0.077 | \n",
" 0.082 | \n",
" 0.080 | \n",
" 0.077 | \n",
@@ -1112,23 +1113,23 @@
" 0.080 | \n",
" 0.077 | \n",
" 0.082 | \n",
- " 0.080 | \n",
" ... | \n",
- " 13.5 | \n",
- " 5.0 | \n",
- " 22.0 | \n",
- " 5.0 | \n",
- " 5.0 | \n",
- " 22.0 | \n",
- " 13.5 | \n",
- " 5.0 | \n",
- " 22.0 | \n",
- " 13.5 | \n",
+ " 14.0 | \n",
+ " 7.0 | \n",
+ " 29.0 | \n",
+ " 7.0 | \n",
+ " 7.0 | \n",
+ " 29.0 | \n",
+ " 20.5 | \n",
+ " 7.0 | \n",
+ " 29.0 | \n",
+ " 20.5 | \n",
"
\n",
" \n",
" | 20 | \n",
" 4.0 | \n",
" acc | \n",
+ " 0.897 | \n",
" 0.895 | \n",
" 0.895 | \n",
" 0.897 | \n",
@@ -1136,23 +1137,23 @@
" 0.895 | \n",
" 0.897 | \n",
" 0.895 | \n",
- " 0.896 | \n",
" ... | \n",
- " 20.0 | \n",
- " 5.0 | \n",
- " 20.0 | \n",
- " 5.0 | \n",
- " 5.0 | \n",
- " 20.0 | \n",
- " 11.5 | \n",
- " 5.0 | \n",
- " 20.0 | \n",
- " 11.5 | \n",
+ " 26.0 | \n",
+ " 6.5 | \n",
+ " 26.0 | \n",
+ " 6.5 | \n",
+ " 6.5 | \n",
+ " 26.0 | \n",
+ " 15.0 | \n",
+ " 6.5 | \n",
+ " 26.0 | \n",
+ " 15.0 | \n",
"
\n",
" \n",
" | 21 | \n",
" 4.0 | \n",
" auc | \n",
+ " 0.831 | \n",
" 0.776 | \n",
" 0.806 | \n",
" 0.831 | \n",
@@ -1160,23 +1161,23 @@
" 0.803 | \n",
" 0.831 | \n",
" 0.776 | \n",
- " 0.807 | \n",
" ... | \n",
- " 14.5 | \n",
- " 5.5 | \n",
- " 22.0 | \n",
+ " 14.0 | \n",
+ " 7.0 | \n",
+ " 29.0 | \n",
" 1.0 | \n",
- " 5.5 | \n",
- " 22.0 | \n",
- " 11.5 | \n",
- " 5.5 | \n",
- " 22.0 | \n",
- " 11.5 | \n",
+ " 7.0 | \n",
+ " 29.0 | \n",
+ " 17.5 | \n",
+ " 7.0 | \n",
+ " 29.0 | \n",
+ " 17.5 | \n",
"
\n",
" \n",
" | 22 | \n",
" 4.0 | \n",
" f1 | \n",
+ " 0.403 | \n",
" 0.358 | \n",
" 0.376 | \n",
" 0.403 | \n",
@@ -1184,23 +1185,23 @@
" 0.371 | \n",
" 0.403 | \n",
" 0.358 | \n",
- " 0.376 | \n",
" ... | \n",
- " 12.5 | \n",
- " 4.5 | \n",
- " 22.0 | \n",
- " 1.0 | \n",
- " 4.5 | \n",
- " 22.0 | \n",
- " 12.5 | \n",
- " 8.0 | \n",
- " 22.0 | \n",
- " 12.5 | \n",
+ " 14.0 | \n",
+ " 6.0 | \n",
+ " 29.0 | \n",
+ " 2.0 | \n",
+ " 6.0 | \n",
+ " 29.0 | \n",
+ " 17.5 | \n",
+ " 10.0 | \n",
+ " 29.0 | \n",
+ " 17.5 | \n",
"
\n",
" \n",
" | 23 | \n",
" 4.0 | \n",
" logloss | \n",
+ " 0.263 | \n",
" 0.300 | \n",
" 0.276 | \n",
" 0.263 | \n",
@@ -1208,23 +1209,23 @@
" 0.277 | \n",
" 0.263 | \n",
" 0.300 | \n",
- " 0.275 | \n",
" ... | \n",
- " 14.5 | \n",
- " 5.5 | \n",
- " 22.0 | \n",
+ " 14.0 | \n",
+ " 6.0 | \n",
+ " 29.0 | \n",
" 1.0 | \n",
- " 5.5 | \n",
- " 22.0 | \n",
- " 11.5 | \n",
- " 5.5 | \n",
- " 22.0 | \n",
- " 11.5 | \n",
+ " 6.0 | \n",
+ " 29.0 | \n",
+ " 16.5 | \n",
+ " 6.0 | \n",
+ " 29.0 | \n",
+ " 16.5 | \n",
"
\n",
" \n",
" | 24 | \n",
" 4.0 | \n",
" mse | \n",
+ " 0.080 | \n",
" 0.087 | \n",
" 0.082 | \n",
" 0.080 | \n",
@@ -1232,134 +1233,160 @@
" 0.083 | \n",
" 0.080 | \n",
" 0.087 | \n",
- " 0.082 | \n",
" ... | \n",
- " 12.5 | \n",
- " 5.5 | \n",
- " 22.0 | \n",
+ " 17.0 | \n",
+ " 7.5 | \n",
+ " 29.0 | \n",
" 1.0 | \n",
- " 5.5 | \n",
- " 22.0 | \n",
- " 12.5 | \n",
- " 5.5 | \n",
- " 22.0 | \n",
- " 12.5 | \n",
+ " 7.5 | \n",
+ " 29.0 | \n",
+ " 17.0 | \n",
+ " 7.5 | \n",
+ " 29.0 | \n",
+ " 17.0 | \n",
"
\n",
" \n",
"\n",
- "25 rows × 54 columns
\n",
+ "25 rows × 70 columns
\n",
""
],
"text/plain": [
- " fold metric group1_glm group1_mxgb group2_ebm group2_glm \\\n",
- "0 0.0 acc 0.900 0.902 0.901 0.900 \n",
- "1 0.0 auc 0.775 0.812 0.839 0.775 \n",
- "2 0.0 f1 0.335 0.376 0.404 0.335 \n",
- "3 0.0 logloss 0.291 0.264 0.251 0.291 \n",
- "4 0.0 mse 0.084 0.078 0.077 0.084 \n",
- "5 1.0 acc 0.906 0.906 0.906 0.906 \n",
- "6 1.0 auc 0.757 0.793 0.829 0.757 \n",
- "7 1.0 f1 0.302 0.339 0.371 0.302 \n",
- "8 1.0 logloss 0.281 0.263 0.246 0.281 \n",
- "9 1.0 mse 0.080 0.078 0.074 0.080 \n",
- "10 2.0 acc 0.908 0.908 0.908 0.908 \n",
- "11 2.0 auc 0.763 0.796 0.827 0.763 \n",
- "12 2.0 f1 0.312 0.345 0.376 0.312 \n",
- "13 2.0 logloss 0.279 0.260 0.245 0.279 \n",
- "14 2.0 mse 0.079 0.076 0.073 0.079 \n",
- "15 3.0 acc 0.903 0.903 0.903 0.903 \n",
- "16 3.0 auc 0.755 0.794 0.826 0.755 \n",
- "17 3.0 f1 0.307 0.340 0.373 0.307 \n",
- "18 3.0 logloss 0.288 0.268 0.251 0.288 \n",
- "19 3.0 mse 0.082 0.080 0.077 0.082 \n",
- "20 4.0 acc 0.895 0.895 0.897 0.895 \n",
- "21 4.0 auc 0.776 0.806 0.831 0.776 \n",
- "22 4.0 f1 0.358 0.376 0.403 0.358 \n",
- "23 4.0 logloss 0.300 0.276 0.263 0.300 \n",
- "24 4.0 mse 0.087 0.082 0.080 0.087 \n",
+ " fold metric group1_ebm group1_glm group1_mxgb group2_ebm \\\n",
+ "0 0.0 acc 0.901 0.900 0.902 0.901 \n",
+ "1 0.0 auc 0.839 0.775 0.812 0.839 \n",
+ "2 0.0 f1 0.404 0.335 0.376 0.404 \n",
+ "3 0.0 logloss 0.251 0.291 0.264 0.251 \n",
+ "4 0.0 mse 0.077 0.084 0.078 0.077 \n",
+ "5 1.0 acc 0.906 0.906 0.906 0.906 \n",
+ "6 1.0 auc 0.829 0.757 0.793 0.829 \n",
+ "7 1.0 f1 0.371 0.302 0.339 0.371 \n",
+ "8 1.0 logloss 0.246 0.281 0.263 0.246 \n",
+ "9 1.0 mse 0.074 0.080 0.078 0.074 \n",
+ "10 2.0 acc 0.908 0.908 0.908 0.908 \n",
+ "11 2.0 auc 0.827 0.763 0.796 0.827 \n",
+ "12 2.0 f1 0.376 0.312 0.345 0.376 \n",
+ "13 2.0 logloss 0.245 0.279 0.260 0.245 \n",
+ "14 2.0 mse 0.073 0.079 0.076 0.073 \n",
+ "15 3.0 acc 0.903 0.903 0.903 0.903 \n",
+ "16 3.0 auc 0.826 0.755 0.794 0.826 \n",
+ "17 3.0 f1 0.373 0.307 0.340 0.373 \n",
+ "18 3.0 logloss 0.251 0.288 0.268 0.251 \n",
+ "19 3.0 mse 0.077 0.082 0.080 0.077 \n",
+ "20 4.0 acc 0.897 0.895 0.895 0.897 \n",
+ "21 4.0 auc 0.831 0.776 0.806 0.831 \n",
+ "22 4.0 f1 0.403 0.358 0.376 0.403 \n",
+ "23 4.0 logloss 0.263 0.300 0.276 0.263 \n",
+ "24 4.0 mse 0.080 0.087 0.082 0.080 \n",
+ "\n",
+ " group2_glm group2_mxgb group3_ebm group3_glm ... \\\n",
+ "0 0.900 0.902 0.901 0.900 ... \n",
+ "1 0.775 0.813 0.839 0.775 ... \n",
+ "2 0.335 0.374 0.404 0.335 ... \n",
+ "3 0.291 0.263 0.251 0.291 ... \n",
+ "4 0.084 0.078 0.077 0.084 ... \n",
+ "5 0.906 0.906 0.906 0.906 ... \n",
+ "6 0.757 0.792 0.829 0.757 ... \n",
+ "7 0.302 0.342 0.371 0.302 ... \n",
+ "8 0.281 0.264 0.246 0.281 ... \n",
+ "9 0.080 0.078 0.074 0.080 ... \n",
+ "10 0.908 0.908 0.908 0.908 ... \n",
+ "11 0.763 0.798 0.827 0.763 ... \n",
+ "12 0.312 0.352 0.376 0.312 ... \n",
+ "13 0.279 0.259 0.245 0.279 ... \n",
+ "14 0.079 0.076 0.073 0.079 ... \n",
+ "15 0.903 0.903 0.903 0.903 ... \n",
+ "16 0.755 0.795 0.826 0.755 ... \n",
+ "17 0.307 0.348 0.373 0.307 ... \n",
+ "18 0.288 0.268 0.251 0.288 ... \n",
+ "19 0.082 0.080 0.077 0.082 ... \n",
+ "20 0.895 0.895 0.897 0.895 ... \n",
+ "21 0.776 0.803 0.831 0.776 ... \n",
+ "22 0.358 0.371 0.403 0.358 ... \n",
+ "23 0.300 0.277 0.263 0.300 ... \n",
+ "24 0.087 0.083 0.080 0.087 ... \n",
"\n",
- " group2_mxgb group3_ebm group3_glm group3_mxgb ... group7_mxgb_rank \\\n",
- "0 0.902 0.901 0.900 0.902 ... 4.5 \n",
- "1 0.813 0.839 0.775 0.814 ... 16.5 \n",
- "2 0.374 0.404 0.335 0.379 ... 14.5 \n",
- "3 0.263 0.251 0.291 0.263 ... 16.5 \n",
- "4 0.078 0.077 0.084 0.078 ... 13.5 \n",
- "5 0.906 0.906 0.906 0.906 ... 14.0 \n",
- "6 0.792 0.829 0.757 0.792 ... 10.5 \n",
- "7 0.342 0.371 0.302 0.337 ... 12.5 \n",
- "8 0.264 0.246 0.281 0.264 ... 10.5 \n",
- "9 0.078 0.074 0.080 0.078 ... 13.5 \n",
- "10 0.908 0.908 0.908 0.908 ... 14.0 \n",
- "11 0.798 0.827 0.763 0.797 ... 16.5 \n",
- "12 0.352 0.376 0.312 0.344 ... 12.5 \n",
- "13 0.259 0.245 0.279 0.259 ... 16.5 \n",
- "14 0.076 0.073 0.079 0.076 ... 13.5 \n",
- "15 0.903 0.903 0.903 0.903 ... 14.5 \n",
- "16 0.795 0.826 0.755 0.795 ... 16.5 \n",
- "17 0.348 0.373 0.307 0.345 ... 16.5 \n",
- "18 0.268 0.251 0.288 0.268 ... 13.5 \n",
- "19 0.080 0.077 0.082 0.080 ... 13.5 \n",
- "20 0.895 0.897 0.895 0.896 ... 20.0 \n",
- "21 0.803 0.831 0.776 0.807 ... 14.5 \n",
- "22 0.371 0.403 0.358 0.376 ... 12.5 \n",
- "23 0.277 0.263 0.300 0.275 ... 14.5 \n",
- "24 0.083 0.080 0.087 0.082 ... 12.5 \n",
+ " group7_piml_reludnn_rank group8_ebm_rank group8_glm_rank \\\n",
+ "0 16.0 16.0 28.5 \n",
+ "1 14.0 6.0 30.0 \n",
+ "2 20.0 7.0 30.0 \n",
+ "3 14.0 6.0 30.0 \n",
+ "4 18.0 7.0 30.0 \n",
+ "5 18.5 18.5 18.5 \n",
+ "6 15.0 5.5 30.0 \n",
+ "7 14.0 6.0 30.0 \n",
+ "8 14.0 6.0 30.0 \n",
+ "9 14.0 5.5 29.0 \n",
+ "10 18.5 18.5 18.5 \n",
+ "11 15.0 6.0 29.0 \n",
+ "12 14.5 4.0 30.0 \n",
+ "13 14.0 6.5 29.0 \n",
+ "14 14.5 7.5 29.0 \n",
+ "15 19.0 19.0 19.0 \n",
+ "16 14.0 6.0 30.0 \n",
+ "17 14.0 4.5 30.0 \n",
+ "18 14.0 5.5 30.0 \n",
+ "19 14.0 7.0 29.0 \n",
+ "20 26.0 6.5 26.0 \n",
+ "21 14.0 7.0 29.0 \n",
+ "22 14.0 6.0 29.0 \n",
+ "23 14.0 6.0 29.0 \n",
+ "24 17.0 7.5 29.0 \n",
"\n",
- " group8_ebm_rank group8_glm_rank group8_mxgb_rank group9_ebm_rank \\\n",
- "0 13.0 22.0 13.0 13.0 \n",
- "1 5.5 22.0 1.0 5.5 \n",
- "2 6.5 22.0 2.0 6.5 \n",
- "3 5.5 22.0 1.0 5.5 \n",
- "4 5.0 22.0 5.0 5.0 \n",
- "5 14.0 14.0 1.0 14.0 \n",
- "6 5.0 22.0 1.0 5.0 \n",
- "7 5.5 22.0 1.0 5.5 \n",
- "8 5.5 22.0 1.0 5.5 \n",
- "9 5.0 22.0 5.0 5.0 \n",
- "10 14.0 14.0 1.0 14.0 \n",
- "11 5.0 22.0 1.0 5.0 \n",
- "12 3.5 22.0 9.0 3.5 \n",
- "13 5.5 22.0 1.0 5.5 \n",
- "14 5.5 22.0 1.0 5.5 \n",
- "15 14.5 14.5 1.5 14.5 \n",
- "16 5.5 22.0 5.5 5.5 \n",
- "17 4.0 22.0 9.0 4.0 \n",
- "18 5.0 22.0 5.0 5.0 \n",
- "19 5.0 22.0 5.0 5.0 \n",
- "20 5.0 20.0 5.0 5.0 \n",
- "21 5.5 22.0 1.0 5.5 \n",
- "22 4.5 22.0 1.0 4.5 \n",
- "23 5.5 22.0 1.0 5.5 \n",
- "24 5.5 22.0 1.0 5.5 \n",
+ " group8_mxgb_rank group9_ebm_rank group9_glm_rank group9_mxgb_rank \\\n",
+ "0 16.0 16.0 28.5 5.0 \n",
+ "1 1.0 6.0 30.0 18.0 \n",
+ "2 2.0 7.0 30.0 16.5 \n",
+ "3 1.0 6.0 30.0 18.5 \n",
+ "4 7.0 7.0 30.0 18.0 \n",
+ "5 1.5 18.5 18.5 18.5 \n",
+ "6 1.0 5.5 30.0 20.5 \n",
+ "7 1.0 6.0 30.0 21.0 \n",
+ "8 1.0 6.0 30.0 20.5 \n",
+ "9 5.5 5.5 29.0 19.5 \n",
+ "10 1.0 18.5 18.5 18.5 \n",
+ "11 1.0 6.0 29.0 20.5 \n",
+ "12 10.5 4.0 30.0 21.0 \n",
+ "13 1.0 6.5 29.0 19.5 \n",
+ "14 1.0 7.5 29.0 20.5 \n",
+ "15 2.0 19.0 19.0 19.0 \n",
+ "16 6.0 6.0 30.0 19.5 \n",
+ "17 10.0 4.5 30.0 19.5 \n",
+ "18 5.5 5.5 30.0 20.5 \n",
+ "19 7.0 7.0 29.0 20.5 \n",
+ "20 6.5 6.5 26.0 15.0 \n",
+ "21 1.0 7.0 29.0 17.5 \n",
+ "22 2.0 6.0 29.0 17.5 \n",
+ "23 1.0 6.0 29.0 16.5 \n",
+ "24 1.0 7.5 29.0 17.0 \n",
"\n",
- " group9_glm_rank group9_mxgb_rank ph_ebm_rank ph_glm_rank ph_mxgb_rank \n",
- "0 22.0 4.5 13.0 22.0 4.5 \n",
- "1 22.0 11.5 5.5 22.0 11.5 \n",
- "2 22.0 11.5 1.0 22.0 11.5 \n",
- "3 22.0 12.5 5.5 22.0 12.5 \n",
- "4 22.0 13.5 5.0 22.0 13.5 \n",
- "5 14.0 14.0 14.0 14.0 14.0 \n",
- "6 22.0 14.5 9.0 22.0 14.5 \n",
- "7 22.0 16.0 9.0 22.0 16.0 \n",
- "8 22.0 14.5 5.5 22.0 14.5 \n",
- "9 22.0 13.5 5.0 22.0 13.5 \n",
- "10 14.0 14.0 14.0 14.0 14.0 \n",
- "11 22.0 13.5 9.0 22.0 13.5 \n",
- "12 22.0 15.0 7.5 22.0 15.0 \n",
- "13 22.0 12.5 5.5 22.0 12.5 \n",
- "14 22.0 13.5 5.5 22.0 13.5 \n",
- "15 14.5 14.5 1.5 14.5 14.5 \n",
- "16 22.0 12.5 1.0 22.0 12.5 \n",
- "17 22.0 13.5 4.0 22.0 13.5 \n",
- "18 22.0 13.5 5.0 22.0 13.5 \n",
- "19 22.0 13.5 5.0 22.0 13.5 \n",
- "20 20.0 11.5 5.0 20.0 11.5 \n",
- "21 22.0 11.5 5.5 22.0 11.5 \n",
- "22 22.0 12.5 8.0 22.0 12.5 \n",
- "23 22.0 11.5 5.5 22.0 11.5 \n",
- "24 22.0 12.5 5.5 22.0 12.5 \n",
+ " ph_ebm_rank ph_glm_rank ph_mxgb_rank \n",
+ "0 16.0 28.5 5.0 \n",
+ "1 6.0 30.0 18.0 \n",
+ "2 1.0 30.0 16.5 \n",
+ "3 6.0 30.0 18.5 \n",
+ "4 7.0 30.0 18.0 \n",
+ "5 18.5 18.5 18.5 \n",
+ "6 10.5 30.0 20.5 \n",
+ "7 10.5 30.0 21.0 \n",
+ "8 6.0 30.0 20.5 \n",
+ "9 5.5 29.0 19.5 \n",
+ "10 18.5 18.5 18.5 \n",
+ "11 11.0 29.0 20.5 \n",
+ "12 8.5 30.0 21.0 \n",
+ "13 6.5 29.0 19.5 \n",
+ "14 7.5 29.0 20.5 \n",
+ "15 2.0 19.0 19.0 \n",
+ "16 1.0 30.0 19.5 \n",
+ "17 4.5 30.0 19.5 \n",
+ "18 5.5 30.0 20.5 \n",
+ "19 7.0 29.0 20.5 \n",
+ "20 6.5 26.0 15.0 \n",
+ "21 7.0 29.0 17.5 \n",
+ "22 10.0 29.0 17.5 \n",
+ "23 6.0 29.0 16.5 \n",
+ "24 7.5 29.0 17.0 \n",
"\n",
- "[25 rows x 54 columns]"
+ "[25 rows x 70 columns]"
]
},
"execution_count": 6,
@@ -1477,33 +1504,40 @@
{
"data": {
"text/plain": [
- "group8_mxgb_rank 3.16\n",
- "ph_ebm_rank 6.40\n",
- "group1_ebm_rank 6.56\n",
- "group5_ebm_rank 6.56\n",
- "group7_ebm_rank 6.56\n",
- "group9_ebm_rank 6.56\n",
- "group8_ebm_rank 6.56\n",
- "group2_ebm_rank 6.56\n",
- "group3_ebm_rank 6.56\n",
- "group6_ebm_rank 6.78\n",
- "group9_mxgb_rank 12.86\n",
- "group3_mxgb_rank 12.86\n",
- "group5_mxgb_rank 12.86\n",
- "ph_mxgb_rank 12.86\n",
- "group2_mxgb_rank 13.60\n",
- "group6_mxgb_rank 13.60\n",
- "group7_mxgb_rank 13.92\n",
- "group1_mxgb_rank 13.92\n",
- "group9_glm_rank 20.98\n",
- "group1_glm_rank 20.98\n",
- "group7_glm_rank 20.98\n",
- "ph_glm_rank 20.98\n",
- "group5_glm_rank 20.98\n",
- "group3_glm_rank 20.98\n",
- "group2_glm_rank 20.98\n",
- "group8_glm_rank 20.98\n",
- "group6_glm_rank 20.98\n",
+ "group8_mxgb_rank 3.74\n",
+ "ph_ebm_rank 7.84\n",
+ "group1_ebm_rank 8.04\n",
+ "group2_ebm_rank 8.04\n",
+ "group9_ebm_rank 8.04\n",
+ "group3_ebm_rank 8.04\n",
+ "group8_ebm_rank 8.04\n",
+ "group5_ebm_rank 8.04\n",
+ "group7_ebm_rank 8.04\n",
+ "group6_ebm_rank 8.28\n",
+ "group7_mxgb2_rank 9.24\n",
+ "group7_piml_ebm_rank 12.16\n",
+ "group6_piml_ebm_rank 12.16\n",
+ "group7_piml_reludnn_rank 15.76\n",
+ "group7_piml_gaminet_rank 18.00\n",
+ "group9_mxgb_rank 18.34\n",
+ "ph_mxgb_rank 18.34\n",
+ "group3_mxgb_rank 18.34\n",
+ "group5_mxgb_rank 18.36\n",
+ "group6_mxgb_rank 19.46\n",
+ "group2_mxgb_rank 19.46\n",
+ "group1_mxgb_rank 19.66\n",
+ "group7_mxgb_rank 19.66\n",
+ "group6_piml_reludnn_rank 19.72\n",
+ "group6_piml_gaminet_rank 27.66\n",
+ "ph_glm_rank 28.06\n",
+ "group8_glm_rank 28.06\n",
+ "group6_glm_rank 28.06\n",
+ "group2_glm_rank 28.06\n",
+ "group9_glm_rank 28.06\n",
+ "group7_glm_rank 28.06\n",
+ "group1_glm_rank 28.06\n",
+ "group5_glm_rank 28.06\n",
+ "group3_glm_rank 28.06\n",
"dtype: float64"
]
},
diff --git a/assignments/model_eval_2022_05_27_09_41_34.csv b/assignments/model_eval_2022_05_27_09_41_34.csv
deleted file mode 100644
index 439519b..0000000
--- a/assignments/model_eval_2022_05_27_09_41_34.csv
+++ /dev/null
@@ -1,26 +0,0 @@
-fold,metric,group1_glm,group1_mxgb,group2_ebm,group2_glm,group2_mxgb,group3_ebm,group3_glm,group3_mxgb,group5_ebm,group5_glm,group5_mxgb,group6_ebm,group6_glm,group6_mxgb,group7_ebm,group7_glm,group7_mxgb,group8_ebm,group8_glm,group8_mxgb,group9_ebm,group9_glm,group9_mxgb,ph_ebm,ph_glm,ph_mxgb,group1_glm_rank,group1_mxgb_rank,group2_ebm_rank,group2_glm_rank,group2_mxgb_rank,group3_ebm_rank,group3_glm_rank,group3_mxgb_rank,group5_ebm_rank,group5_glm_rank,group5_mxgb_rank,group6_ebm_rank,group6_glm_rank,group6_mxgb_rank,group7_ebm_rank,group7_glm_rank,group7_mxgb_rank,group8_ebm_rank,group8_glm_rank,group8_mxgb_rank,group9_ebm_rank,group9_glm_rank,group9_mxgb_rank,ph_ebm_rank,ph_glm_rank,ph_mxgb_rank
-0.0,acc,0.9,0.902,0.901,0.9,0.902,0.901,0.9,0.902,0.901,0.9,0.902,0.901,0.9,0.902,0.901,0.9,0.902,0.901,0.9,0.901,0.901,0.9,0.902,0.901,0.9,0.902,22.0,4.5,13.0,22.0,4.5,13.0,22.0,4.5,13.0,22.0,4.5,13.0,22.0,4.5,13.0,22.0,4.5,13.0,22.0,13.0,13.0,22.0,4.5,13.0,22.0,4.5
-0.0,auc,0.775,0.812,0.839,0.775,0.813,0.839,0.775,0.814,0.839,0.775,0.814,0.839,0.775,0.813,0.839,0.775,0.812,0.839,0.775,0.841,0.839,0.775,0.814,0.839,0.775,0.814,22.0,16.5,5.5,22.0,14.5,5.5,22.0,11.5,5.5,22.0,11.5,5.5,22.0,14.5,5.5,22.0,16.5,5.5,22.0,1.0,5.5,22.0,11.5,5.5,22.0,11.5
-0.0,f1,0.335,0.376,0.404,0.335,0.374,0.404,0.335,0.379,0.404,0.335,0.379,0.405,0.335,0.374,0.404,0.335,0.376,0.404,0.335,0.406,0.404,0.335,0.379,0.408,0.335,0.379,22.0,14.5,6.5,22.0,16.5,6.5,22.0,11.5,6.5,22.0,11.5,3.0,22.0,16.5,6.5,22.0,14.5,6.5,22.0,2.0,6.5,22.0,11.5,1.0,22.0,11.5
-0.0,logloss,0.291,0.264,0.251,0.291,0.263,0.251,0.291,0.263,0.251,0.291,0.263,0.251,0.291,0.263,0.251,0.291,0.264,0.251,0.291,0.25,0.251,0.291,0.263,0.251,0.291,0.263,22.0,16.5,5.5,22.0,12.5,5.5,22.0,12.5,5.5,22.0,12.5,5.5,22.0,12.5,5.5,22.0,16.5,5.5,22.0,1.0,5.5,22.0,12.5,5.5,22.0,12.5
-0.0,mse,0.084,0.078,0.077,0.084,0.078,0.077,0.084,0.078,0.077,0.084,0.078,0.077,0.084,0.078,0.077,0.084,0.078,0.077,0.084,0.077,0.077,0.084,0.078,0.077,0.084,0.078,22.0,13.5,5.0,22.0,13.5,5.0,22.0,13.5,5.0,22.0,13.5,5.0,22.0,13.5,5.0,22.0,13.5,5.0,22.0,5.0,5.0,22.0,13.5,5.0,22.0,13.5
-1.0,acc,0.906,0.906,0.906,0.906,0.906,0.906,0.906,0.906,0.906,0.906,0.906,0.906,0.906,0.906,0.906,0.906,0.906,0.906,0.906,0.907,0.906,0.906,0.906,0.906,0.906,0.906,14.0,14.0,14.0,14.0,14.0,14.0,14.0,14.0,14.0,14.0,14.0,14.0,14.0,14.0,14.0,14.0,14.0,14.0,14.0,1.0,14.0,14.0,14.0,14.0,14.0,14.0
-1.0,auc,0.757,0.793,0.829,0.757,0.792,0.829,0.757,0.792,0.829,0.757,0.792,0.829,0.757,0.792,0.829,0.757,0.793,0.829,0.757,0.83,0.829,0.757,0.792,0.828,0.757,0.792,22.0,10.5,5.0,22.0,14.5,5.0,22.0,14.5,5.0,22.0,14.5,5.0,22.0,14.5,5.0,22.0,10.5,5.0,22.0,1.0,5.0,22.0,14.5,9.0,22.0,14.5
-1.0,f1,0.302,0.339,0.371,0.302,0.342,0.371,0.302,0.337,0.371,0.302,0.338,0.372,0.302,0.342,0.371,0.302,0.339,0.371,0.302,0.375,0.371,0.302,0.337,0.369,0.302,0.337,22.0,12.5,5.5,22.0,10.5,5.5,22.0,16.0,5.5,22.0,14.0,2.0,22.0,10.5,5.5,22.0,12.5,5.5,22.0,1.0,5.5,22.0,16.0,9.0,22.0,16.0
-1.0,logloss,0.281,0.263,0.246,0.281,0.264,0.246,0.281,0.264,0.246,0.281,0.264,0.246,0.281,0.264,0.246,0.281,0.263,0.246,0.281,0.245,0.246,0.281,0.264,0.246,0.281,0.264,22.0,10.5,5.5,22.0,14.5,5.5,22.0,14.5,5.5,22.0,14.5,5.5,22.0,14.5,5.5,22.0,10.5,5.5,22.0,1.0,5.5,22.0,14.5,5.5,22.0,14.5
-1.0,mse,0.08,0.078,0.074,0.08,0.078,0.074,0.08,0.078,0.074,0.08,0.078,0.074,0.08,0.078,0.074,0.08,0.078,0.074,0.08,0.074,0.074,0.08,0.078,0.074,0.08,0.078,22.0,13.5,5.0,22.0,13.5,5.0,22.0,13.5,5.0,22.0,13.5,5.0,22.0,13.5,5.0,22.0,13.5,5.0,22.0,5.0,5.0,22.0,13.5,5.0,22.0,13.5
-2.0,acc,0.908,0.908,0.908,0.908,0.908,0.908,0.908,0.908,0.908,0.908,0.908,0.908,0.908,0.908,0.908,0.908,0.908,0.908,0.908,0.91,0.908,0.908,0.908,0.908,0.908,0.908,14.0,14.0,14.0,14.0,14.0,14.0,14.0,14.0,14.0,14.0,14.0,14.0,14.0,14.0,14.0,14.0,14.0,14.0,14.0,1.0,14.0,14.0,14.0,14.0,14.0,14.0
-2.0,auc,0.763,0.796,0.827,0.763,0.798,0.827,0.763,0.797,0.827,0.763,0.797,0.827,0.763,0.798,0.827,0.763,0.796,0.827,0.763,0.828,0.827,0.763,0.797,0.826,0.763,0.797,22.0,16.5,5.0,22.0,10.5,5.0,22.0,13.5,5.0,22.0,13.5,5.0,22.0,10.5,5.0,22.0,16.5,5.0,22.0,1.0,5.0,22.0,13.5,9.0,22.0,13.5
-2.0,f1,0.312,0.345,0.376,0.312,0.352,0.376,0.312,0.344,0.376,0.312,0.343,0.374,0.312,0.352,0.376,0.312,0.345,0.376,0.312,0.371,0.376,0.312,0.344,0.374,0.312,0.344,22.0,12.5,3.5,22.0,10.5,3.5,22.0,15.0,3.5,22.0,17.0,7.5,22.0,10.5,3.5,22.0,12.5,3.5,22.0,9.0,3.5,22.0,15.0,7.5,22.0,15.0
-2.0,logloss,0.279,0.26,0.245,0.279,0.259,0.245,0.279,0.259,0.245,0.279,0.259,0.245,0.279,0.259,0.245,0.279,0.26,0.245,0.279,0.244,0.245,0.279,0.259,0.245,0.279,0.259,22.0,16.5,5.5,22.0,12.5,5.5,22.0,12.5,5.5,22.0,12.5,5.5,22.0,12.5,5.5,22.0,16.5,5.5,22.0,1.0,5.5,22.0,12.5,5.5,22.0,12.5
-2.0,mse,0.079,0.076,0.073,0.079,0.076,0.073,0.079,0.076,0.073,0.079,0.076,0.073,0.079,0.076,0.073,0.079,0.076,0.073,0.079,0.072,0.073,0.079,0.076,0.073,0.079,0.076,22.0,13.5,5.5,22.0,13.5,5.5,22.0,13.5,5.5,22.0,13.5,5.5,22.0,13.5,5.5,22.0,13.5,5.5,22.0,1.0,5.5,22.0,13.5,5.5,22.0,13.5
-3.0,acc,0.903,0.903,0.903,0.903,0.903,0.903,0.903,0.903,0.903,0.903,0.903,0.903,0.903,0.903,0.903,0.903,0.903,0.903,0.903,0.904,0.903,0.903,0.903,0.904,0.903,0.903,14.5,14.5,14.5,14.5,14.5,14.5,14.5,14.5,14.5,14.5,14.5,14.5,14.5,14.5,14.5,14.5,14.5,14.5,14.5,1.5,14.5,14.5,14.5,1.5,14.5,14.5
-3.0,auc,0.755,0.794,0.826,0.755,0.795,0.826,0.755,0.795,0.826,0.755,0.795,0.826,0.755,0.795,0.826,0.755,0.794,0.826,0.755,0.826,0.826,0.755,0.795,0.827,0.755,0.795,22.0,16.5,5.5,22.0,12.5,5.5,22.0,12.5,5.5,22.0,12.5,5.5,22.0,12.5,5.5,22.0,16.5,5.5,22.0,5.5,5.5,22.0,12.5,1.0,22.0,12.5
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diff --git a/assignments/model_eval_2022_06_13_09_47_08.csv b/assignments/model_eval_2022_06_13_09_47_08.csv
new file mode 100644
index 0000000..b08372c
--- /dev/null
+++ b/assignments/model_eval_2022_06_13_09_47_08.csv
@@ -0,0 +1,26 @@
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+4.0,acc,0.897,0.895,0.895,0.897,0.895,0.895,0.897,0.895,0.896,0.897,0.895,0.896,0.897,0.895,0.895,0.897,0.895,0.895,0.897,0.895,0.895,0.896,0.897,0.895,0.895,0.897,0.895,0.897,0.897,0.895,0.896,0.897,0.895,0.896,6.5,26.0,26.0,6.5,26.0,26.0,6.5,26.0,15.0,6.5,26.0,15.0,6.5,26.0,26.0,6.5,26.0,26.0,6.5,26.0,26.0,15.0,6.5,26.0,26.0,6.5,26.0,6.5,6.5,26.0,15.0,6.5,26.0,15.0
+4.0,auc,0.831,0.776,0.806,0.831,0.776,0.803,0.831,0.776,0.807,0.831,0.776,0.807,0.831,0.776,0.803,0.825,0.764,0.806,0.831,0.776,0.806,0.832,0.825,0.808,0.815,0.831,0.776,0.836,0.831,0.776,0.807,0.831,0.776,0.807,7.0,29.0,21.0,7.0,29.0,23.5,7.0,29.0,17.5,7.0,29.0,17.5,7.0,29.0,23.5,12.5,34.0,21.0,7.0,29.0,21.0,2.0,12.5,15.0,14.0,7.0,29.0,1.0,7.0,29.0,17.5,7.0,29.0,17.5
+4.0,f1,0.403,0.358,0.376,0.403,0.358,0.371,0.403,0.358,0.376,0.403,0.358,0.376,0.4,0.358,0.371,0.397,0.326,0.374,0.403,0.358,0.376,0.411,0.397,0.371,0.384,0.403,0.358,0.404,0.403,0.358,0.376,0.401,0.358,0.376,6.0,29.0,17.5,6.0,29.0,23.0,6.0,29.0,17.5,6.0,29.0,17.5,11.0,29.0,23.0,12.5,34.0,21.0,6.0,29.0,17.5,1.0,12.5,23.0,14.0,6.0,29.0,2.0,6.0,29.0,17.5,10.0,29.0,17.5
+4.0,logloss,0.263,0.3,0.276,0.263,0.3,0.277,0.263,0.3,0.275,0.263,0.3,0.275,0.263,0.3,0.277,0.266,0.304,0.276,0.263,0.3,0.276,0.264,0.266,0.276,0.271,0.263,0.3,0.261,0.263,0.3,0.275,0.263,0.3,0.275,6.0,29.0,20.5,6.0,29.0,23.5,6.0,29.0,16.5,6.0,29.0,16.5,6.0,29.0,23.5,12.5,34.0,20.5,6.0,29.0,20.5,11.0,12.5,20.5,14.0,6.0,29.0,1.0,6.0,29.0,16.5,6.0,29.0,16.5
+4.0,mse,0.08,0.087,0.082,0.08,0.087,0.083,0.08,0.087,0.082,0.08,0.087,0.082,0.08,0.087,0.083,0.08,0.089,0.083,0.08,0.087,0.082,0.08,0.08,0.083,0.082,0.08,0.087,0.079,0.08,0.087,0.082,0.08,0.087,0.082,7.5,29.0,17.0,7.5,29.0,22.5,7.5,29.0,17.0,7.5,29.0,17.0,7.5,29.0,22.5,7.5,34.0,22.5,7.5,29.0,17.0,7.5,7.5,22.5,17.0,7.5,29.0,1.0,7.5,29.0,17.0,7.5,29.0,17.0