+ h2o benchmark's tabular data values (#1106)

* +h2o vals * change csv to html * specified table uuid * change max values style * add df to html converter --------- Co-authored-by: nicl-nno <[email protected]>
aimclub · Jun 28, 2023 · 2972367 · 2972367
1 parent 16d507f
commit 2972367
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diff --git a/cases/credit_scoring/credit_scoring_problem.py b/cases/credit_scoring/credit_scoring_problem.py
@@ -1,6 +1,7 @@
 import logging
 import os
 import random
+from pathlib import Path
 
 import numpy as np
 from sklearn.metrics import roc_auc_score as roc_auc
@@ -39,7 +40,8 @@ def run_credit_scoring_problem(train_file_path, test_file_path,
     metrics = automl.get_metrics()
 
     if automl.history:
-        print(automl.history.get_leaderboard())
+        lb = automl.history.get_leaderboard()
+        Path(os.path.join('D:/', "leaderboard.csv")).write_text(lb)
 
     if visualization:
         automl.current_pipeline.show()
@@ -68,6 +70,6 @@ def get_scoring_data():
     full_path_train, full_path_test = get_scoring_data()
     run_credit_scoring_problem(full_path_train,
                                full_path_test,
-                               timeout=5,
+                               timeout=2,
                                visualization=True,
                                with_tuning=True)
diff --git a/cases/metocean_forecasting_problem.py b/cases/metocean_forecasting_problem.py
@@ -45,6 +45,7 @@ def run_metocean_forecasting_problem(train_file_path, test_file_path,
     fedot = Fedot(problem='ts_forecasting',
                   task_params=TsForecastingParams(forecast_length=forecast_length),
                   timeout=timeout, logging_level=logging.DEBUG)
+    fedot.current_pipeline
 
     pipeline = fedot.fit(features=historical_data, target=ssh_history)
     fedot.forecast(historical_data)

diff --git a/docs/source/benchmarks/amlb_res.csv b/docs/source/benchmarks/amlb_res.csv
@@ -1,32 +1,32 @@
 Dataset name,Metric name,AutoGluon,FEDOT,H2O,LAMA
-APSFailure,auc,0.99,0.991,,0.992
-Amazon_employee_access,auc,0.857,0.865,,0.879
+APSFailure,auc,0.99,0.991,0.992,0.992
+Amazon_employee_access,auc,0.857,0.865,0.873,0.879
 Australian,auc,0.94,0.939,0.939,0.945
-Covertype,neg_logloss,-0.071,-0.117,,
-Fashion-MNIST,neg_logloss,-0.329,-0.373,,-0.248
-Jannis,neg_logloss,-0.728,-0.737,,-0.664
-KDDCup09_appetency,auc,0.804,0.822,,0.85
+Covertype,neg_logloss,-0.071,-0.117,-0.265,
+Fashion-MNIST,neg_logloss,-0.329,-0.373,-0.38,-0.248
+Jannis,neg_logloss,-0.728,-0.737,-0.691,-0.664
+KDDCup09_appetency,auc,0.804,0.822,0.829,0.85
 MiniBooNE,auc,0.982,0.981,,0.988
-Shuttle,neg_logloss,-0.001,-0.001,,-0.001
-Volkert,neg_logloss,-0.917,-1.097,,-0.806
-adult,auc,0.91,0.925,,0.932
-bank-marketing,auc,0.931,0.935,,0.94
+Shuttle,neg_logloss,-0.001,-0.001,-0.0,-0.001
+Volkert,neg_logloss,-0.917,-1.097,-0.976,-0.806
+adult,auc,0.91,0.925,0.931,0.932
+bank-marketing,auc,0.931,0.935,0.939,0.94
 blood-transfusion,auc,0.69,0.759,0.765,0.75
 car,neg_logloss,-0.117,-0.011,-0.004,-0.002
 christine,auc,0.804,0.812,0.823,0.83
 cnae-9,neg_logloss,-0.332,-0.211,-0.175,-0.156
-connect-4,neg_logloss,-0.502,-0.456,,-0.337
+connect-4,neg_logloss,-0.502,-0.456,-0.338,-0.337
 credit-g,auc,0.795,0.778,0.789,0.796
 dilbert,neg_logloss,-0.148,-0.159,-0.05,-0.033
 fabert,neg_logloss,-0.788,-0.895,-0.752,-0.766
 guillermo,auc,0.9,0.891,,0.926
 jasmine,auc,0.883,0.888,0.887,0.88
-jungle chess,neg_logloss,-0.431,-0.193,,-0.149
+jungle chess,neg_logloss,-0.431,-0.193,-0.24,-0.149
 kc1,auc,0.822,0.843,,0.831
 kr-vs-kp,auc,0.999,1.0,,1.0
 mfeat-factors,neg_logloss,-0.161,-0.094,,-0.082
-nomao,auc,0.995,0.994,,0.997
-numerai28_6,auc,0.517,0.529,,0.531
+nomao,auc,0.995,0.994,0.996,0.997
+numerai28_6,auc,0.517,0.529,0.531,0.531
 phoneme,auc,0.965,0.965,,0.965
 segment,neg_logloss,-0.094,-0.062,,-0.061
 sylvine,auc,0.985,0.988,,0.988