mervenoyan commited on
Commit
3bfabca
1 Parent(s): ae1692d

misc improvements

Browse files
Files changed (2) hide show
  1. app.py +4 -1
  2. logs.txt +0 -31
app.py CHANGED
@@ -70,7 +70,10 @@ def train_baseline(dataset, dataset_name, token, column):
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  with tempfile.TemporaryDirectory() as tmpdirname:
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  from contextlib import redirect_stdout
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- fc.fit(X, y)
 
 
 
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  username = HfApi().whoami(token=token)["name"]
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  repo_url = create_repo(repo_id = f"{username}/{dataset_name}", token = token)
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  with tempfile.TemporaryDirectory() as tmpdirname:
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  from contextlib import redirect_stdout
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+ with open(f'{tmpdirname}/logs.txt', 'w') as f:
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+ with redirect_stdout(f):
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+ print('Logging training')
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+ fc.fit(X, y)
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  username = HfApi().whoami(token=token)["name"]
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  repo_url = create_repo(repo_id = f"{username}/{dataset_name}", token = token)
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logs.txt DELETED
@@ -1,31 +0,0 @@
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- Logging training
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- Running DummyClassifier()
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- accuracy: 0.643 average_precision: 0.357 roc_auc: 0.500 recall_macro: 0.500 f1_macro: 0.392
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- === new best DummyClassifier() (using recall_macro):
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- accuracy: 0.643 average_precision: 0.357 roc_auc: 0.500 recall_macro: 0.500 f1_macro: 0.392
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-
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- Running GaussianNB()
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- accuracy: 0.623 average_precision: 0.505 roc_auc: 0.590 recall_macro: 0.560 f1_macro: 0.549
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- === new best GaussianNB() (using recall_macro):
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- accuracy: 0.623 average_precision: 0.505 roc_auc: 0.590 recall_macro: 0.560 f1_macro: 0.549
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-
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- Running MultinomialNB()
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- accuracy: 0.647 average_precision: 0.481 roc_auc: 0.609 recall_macro: 0.589 f1_macro: 0.588
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- === new best MultinomialNB() (using recall_macro):
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- accuracy: 0.647 average_precision: 0.481 roc_auc: 0.609 recall_macro: 0.589 f1_macro: 0.588
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-
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- Running DecisionTreeClassifier(class_weight='balanced', max_depth=1)
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- accuracy: 0.586 average_precision: 0.401 roc_auc: 0.568 recall_macro: 0.568 f1_macro: 0.558
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- Running DecisionTreeClassifier(class_weight='balanced', max_depth=5)
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- accuracy: 0.590 average_precision: 0.419 roc_auc: 0.564 recall_macro: 0.576 f1_macro: 0.560
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- Running DecisionTreeClassifier(class_weight='balanced', min_impurity_decrease=0.01)
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- accuracy: 0.582 average_precision: 0.393 roc_auc: 0.563 recall_macro: 0.567 f1_macro: 0.555
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- Running LogisticRegression(C=0.1, class_weight='balanced', max_iter=1000)
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- accuracy: 0.574 average_precision: 0.487 roc_auc: 0.425 recall_macro: 0.548 f1_macro: 0.547
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- Running LogisticRegression(class_weight='balanced', max_iter=1000)
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- accuracy: 0.578 average_precision: 0.470 roc_auc: 0.437 recall_macro: 0.562 f1_macro: 0.557
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-
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- Best model:
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- Pipeline(steps=[('minmaxscaler', MinMaxScaler()), ('multinomialnb', MultinomialNB())])
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- Best Scores:
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- accuracy: 0.647 average_precision: 0.481 roc_auc: 0.609 recall_macro: 0.589 f1_macro: 0.588