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logs.log
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2024-02-21 09:42:31,178:WARNING:
'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install.
2024-02-21 09:42:31,178:WARNING:
'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install.
2024-02-21 09:42:31,179:WARNING:
'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install.
2024-02-21 09:42:31,179:WARNING:
'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install.
2024-02-21 10:49:25,014:WARNING:
'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install.
2024-02-21 10:49:25,015:WARNING:
'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install.
2024-02-21 10:49:25,016:WARNING:
'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install.
2024-02-21 10:49:25,016:WARNING:
'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install.
2024-02-21 11:23:18,131:WARNING:
'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install.
2024-02-21 11:23:18,131:WARNING:
'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install.
2024-02-21 11:23:18,131:WARNING:
'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install.
2024-02-21 11:23:18,131:WARNING:
'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install.
2024-02-21 11:24:24,768:WARNING:/home/vscode/.local/lib/python3.11/site-packages/ydata_profiling/model/correlations.py:66: UserWarning: There was an attempt to calculate the auto correlation, but this failed.
To hide this warning, disable the calculation
(using `df.profile_report(correlations={"auto": {"calculate": False}})`
If this is problematic for your use case, please report this as an issue:
https://github.com/ydataai/ydata-profiling/issues
(include the error message: 'Function <code object pandas_auto_compute at 0x7f46a2352cc0, file "/home/vscode/.local/lib/python3.11/site-packages/ydata_profiling/model/pandas/correlations_pandas.py", line 164>')
warnings.warn(
2024-02-21 11:30:49,924:WARNING:
'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install.
2024-02-21 11:30:49,924:WARNING:
'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install.
2024-02-21 11:30:49,925:WARNING:
'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install.
2024-02-21 11:30:49,925:WARNING:
'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install.
2024-02-21 11:48:15,341:WARNING:
'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install.
2024-02-21 11:48:15,342:WARNING:
'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install.
2024-02-21 11:48:15,342:WARNING:
'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install.
2024-02-21 11:48:15,342:WARNING:
'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install.
2024-02-21 11:48:41,047:WARNING:/home/vscode/.local/lib/python3.11/site-packages/ydata_profiling/model/correlations.py:66: UserWarning: There was an attempt to calculate the auto correlation, but this failed.
To hide this warning, disable the calculation
(using `df.profile_report(correlations={"auto": {"calculate": False}})`
If this is problematic for your use case, please report this as an issue:
https://github.com/ydataai/ydata-profiling/issues
(include the error message: 'Function <code object pandas_auto_compute at 0x7f26363d4110, file "/home/vscode/.local/lib/python3.11/site-packages/ydata_profiling/model/pandas/correlations_pandas.py", line 164>')
warnings.warn(
2024-02-21 11:49:08,585:WARNING:/home/vscode/.local/lib/python3.11/site-packages/ydata_profiling/model/correlations.py:66: UserWarning: There was an attempt to calculate the auto correlation, but this failed.
To hide this warning, disable the calculation
(using `df.profile_report(correlations={"auto": {"calculate": False}})`
If this is problematic for your use case, please report this as an issue:
https://github.com/ydataai/ydata-profiling/issues
(include the error message: 'Function <code object pandas_auto_compute at 0x7f26363d4110, file "/home/vscode/.local/lib/python3.11/site-packages/ydata_profiling/model/pandas/correlations_pandas.py", line 164>')
warnings.warn(
2024-02-21 12:39:15,995:WARNING:
'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install.
2024-02-21 12:39:15,998:WARNING:
'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install.
2024-02-21 12:39:15,998:WARNING:
'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install.
2024-02-21 12:39:15,998:WARNING:
'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install.
2024-02-21 12:39:23,756:WARNING:/workspaces/streamlitapptest/nomanstreamlit/lib/python3.11/site-packages/ydata_profiling/model/correlations.py:66: UserWarning: There was an attempt to calculate the auto correlation, but this failed.
To hide this warning, disable the calculation
(using `df.profile_report(correlations={"auto": {"calculate": False}})`
If this is problematic for your use case, please report this as an issue:
https://github.com/ydataai/ydata-profiling/issues
(include the error message: 'Function <code object pandas_auto_compute at 0x7f77d23a04e0, file "/workspaces/streamlitapptest/nomanstreamlit/lib/python3.11/site-packages/ydata_profiling/model/pandas/correlations_pandas.py", line 164>')
warnings.warn(
2024-02-21 12:42:26,555:INFO:PyCaret ClassificationExperiment
2024-02-21 12:42:26,555:INFO:Logging name: clf-default-name
2024-02-21 12:42:26,555:INFO:ML Usecase: MLUsecase.CLASSIFICATION
2024-02-21 12:42:26,555:INFO:version 3.3.0
2024-02-21 12:42:26,556:INFO:Initializing setup()
2024-02-21 12:42:26,556:INFO:self.USI: be37
2024-02-21 12:42:26,556:INFO:self._variable_keys: {'target_param', 'memory', 'pipeline', 'X_train', 'html_param', 'n_jobs_param', 'fold_groups_param', 'seed', 'fix_imbalance', 'X', 'idx', 'y', 'fold_shuffle_param', 'USI', '_ml_usecase', 'y_test', 'gpu_n_jobs_param', 'log_plots_param', 'exp_id', 'y_train', 'fold_generator', 'is_multiclass', 'logging_param', 'gpu_param', '_available_plots', 'X_test', 'exp_name_log', 'data'}
2024-02-21 12:42:26,556:INFO:Checking environment
2024-02-21 12:42:26,556:INFO:python_version: 3.11.8
2024-02-21 12:42:26,556:INFO:python_build: ('main', 'Feb 7 2024 22:58:49')
2024-02-21 12:42:26,557:INFO:machine: x86_64
2024-02-21 12:42:26,559:INFO:platform: Linux-6.2.0-1019-azure-x86_64-with-glibc2.31
2024-02-21 12:42:26,560:INFO:Memory: svmem(total=8315179008, available=4581572608, percent=44.9, used=3402391552, free=186281984, active=1951911936, inactive=5357084672, buffers=171614208, cached=4554891264, shared=1232896, slab=632111104)
2024-02-21 12:42:26,561:INFO:Physical Core: 1
2024-02-21 12:42:26,561:INFO:Logical Core: 2
2024-02-21 12:42:26,561:INFO:Checking libraries
2024-02-21 12:42:26,561:INFO:System:
2024-02-21 12:42:26,562:INFO: python: 3.11.8 (main, Feb 7 2024, 22:58:49) [GCC 10.2.1 20210110]
2024-02-21 12:42:26,562:INFO:executable: /workspaces/streamlitapptest/nomanstreamlit/bin/python
2024-02-21 12:42:26,562:INFO: machine: Linux-6.2.0-1019-azure-x86_64-with-glibc2.31
2024-02-21 12:42:26,562:INFO:PyCaret required dependencies:
2024-02-21 12:42:26,609:INFO: pip: 24.0
2024-02-21 12:42:26,609:INFO: setuptools: 65.5.0
2024-02-21 12:42:26,609:INFO: pycaret: 3.3.0
2024-02-21 12:42:26,609:INFO: IPython: 8.21.0
2024-02-21 12:42:26,609:INFO: ipywidgets: 8.1.2
2024-02-21 12:42:26,610:INFO: tqdm: 4.66.2
2024-02-21 12:42:26,610:INFO: numpy: 1.25.2
2024-02-21 12:42:26,610:INFO: pandas: 2.1.4
2024-02-21 12:42:26,610:INFO: jinja2: 3.1.3
2024-02-21 12:42:26,610:INFO: scipy: 1.11.4
2024-02-21 12:42:26,610:INFO: joblib: 1.3.2
2024-02-21 12:42:26,611:INFO: sklearn: 1.4.1.post1
2024-02-21 12:42:26,611:INFO: pyod: 1.1.3
2024-02-21 12:42:26,611:INFO: imblearn: 0.12.0
2024-02-21 12:42:26,611:INFO: category_encoders: 2.6.3
2024-02-21 12:42:26,611:INFO: lightgbm: 4.3.0
2024-02-21 12:42:26,611:INFO: numba: 0.58.1
2024-02-21 12:42:26,612:INFO: requests: 2.31.0
2024-02-21 12:42:26,612:INFO: matplotlib: 3.7.5
2024-02-21 12:42:26,612:INFO: scikitplot: 0.3.7
2024-02-21 12:42:26,612:INFO: yellowbrick: 1.5
2024-02-21 12:42:26,612:INFO: plotly: 5.19.0
2024-02-21 12:42:26,612:INFO: plotly-resampler: Not installed
2024-02-21 12:42:26,612:INFO: kaleido: 0.2.1
2024-02-21 12:42:26,613:INFO: schemdraw: 0.15
2024-02-21 12:42:26,613:INFO: statsmodels: 0.14.1
2024-02-21 12:42:26,613:INFO: sktime: 0.26.0
2024-02-21 12:42:26,613:INFO: tbats: 1.1.3
2024-02-21 12:42:26,613:INFO: pmdarima: 2.0.4
2024-02-21 12:42:26,613:INFO: psutil: 5.9.8
2024-02-21 12:42:26,614:INFO: markupsafe: 2.1.5
2024-02-21 12:42:26,614:INFO: pickle5: Not installed
2024-02-21 12:42:26,614:INFO: cloudpickle: 3.0.0
2024-02-21 12:42:26,614:INFO: deprecation: 2.1.0
2024-02-21 12:42:26,614:INFO: xxhash: 3.4.1
2024-02-21 12:42:26,614:INFO: wurlitzer: 3.0.3
2024-02-21 12:42:26,615:INFO:PyCaret optional dependencies:
2024-02-21 12:42:26,630:INFO: shap: Not installed
2024-02-21 12:42:26,630:INFO: interpret: Not installed
2024-02-21 12:42:26,631:INFO: umap: Not installed
2024-02-21 12:42:26,631:INFO: ydata_profiling: 4.6.4
2024-02-21 12:42:26,631:INFO: explainerdashboard: Not installed
2024-02-21 12:42:26,631:INFO: autoviz: Not installed
2024-02-21 12:42:26,631:INFO: fairlearn: Not installed
2024-02-21 12:42:26,632:INFO: deepchecks: Not installed
2024-02-21 12:42:26,632:INFO: xgboost: Not installed
2024-02-21 12:42:26,632:INFO: catboost: Not installed
2024-02-21 12:42:26,632:INFO: kmodes: Not installed
2024-02-21 12:42:26,632:INFO: mlxtend: Not installed
2024-02-21 12:42:26,632:INFO: statsforecast: Not installed
2024-02-21 12:42:26,633:INFO: tune_sklearn: Not installed
2024-02-21 12:42:26,633:INFO: ray: Not installed
2024-02-21 12:42:26,633:INFO: hyperopt: Not installed
2024-02-21 12:42:26,633:INFO: optuna: Not installed
2024-02-21 12:42:26,633:INFO: skopt: Not installed
2024-02-21 12:42:26,633:INFO: mlflow: Not installed
2024-02-21 12:42:26,634:INFO: gradio: Not installed
2024-02-21 12:42:26,634:INFO: fastapi: Not installed
2024-02-21 12:42:26,634:INFO: uvicorn: Not installed
2024-02-21 12:42:26,634:INFO: m2cgen: Not installed
2024-02-21 12:42:26,634:INFO: evidently: Not installed
2024-02-21 12:42:26,634:INFO: fugue: Not installed
2024-02-21 12:42:26,635:INFO: streamlit: 1.31.1
2024-02-21 12:42:26,635:INFO: prophet: Not installed
2024-02-21 12:42:26,635:INFO:None
2024-02-21 12:42:26,635:INFO:Set up data.
2024-02-21 12:42:26,640:INFO:Set up folding strategy.
2024-02-21 12:42:26,640:INFO:Set up train/test split.
2024-02-21 12:42:26,645:INFO:Set up index.
2024-02-21 12:42:26,645:INFO:Assigning column types.
2024-02-21 12:42:26,648:INFO:Engine successfully changes for model 'lr' to 'sklearn'.
2024-02-21 12:42:26,685:INFO:Engine for model 'knn' has not been set explicitly, hence returning None.
2024-02-21 12:42:26,687:INFO:Engine for model 'rbfsvm' has not been set explicitly, hence returning None.
2024-02-21 12:42:26,714:WARNING:
'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install.
Alternately, you can install this by running `pip install pycaret[models]`
2024-02-21 12:42:26,714:WARNING:
'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install.
Alternately, you can install this by running `pip install pycaret[models]`
2024-02-21 12:42:26,762:INFO:Engine for model 'knn' has not been set explicitly, hence returning None.
2024-02-21 12:42:26,763:INFO:Engine for model 'rbfsvm' has not been set explicitly, hence returning None.
2024-02-21 12:42:26,786:WARNING:
'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install.
Alternately, you can install this by running `pip install pycaret[models]`
2024-02-21 12:42:26,786:WARNING:
'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install.
Alternately, you can install this by running `pip install pycaret[models]`
2024-02-21 12:42:26,787:INFO:Engine successfully changes for model 'knn' to 'sklearn'.
2024-02-21 12:42:26,824:INFO:Engine for model 'rbfsvm' has not been set explicitly, hence returning None.
2024-02-21 12:42:26,847:WARNING:
'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install.
Alternately, you can install this by running `pip install pycaret[models]`
2024-02-21 12:42:26,847:WARNING:
'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install.
Alternately, you can install this by running `pip install pycaret[models]`
2024-02-21 12:42:26,887:INFO:Engine for model 'rbfsvm' has not been set explicitly, hence returning None.
2024-02-21 12:42:26,910:WARNING:
'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install.
Alternately, you can install this by running `pip install pycaret[models]`
2024-02-21 12:42:26,911:WARNING:
'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install.
Alternately, you can install this by running `pip install pycaret[models]`
2024-02-21 12:42:26,911:INFO:Engine successfully changes for model 'rbfsvm' to 'sklearn'.
2024-02-21 12:42:26,972:WARNING:
'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install.
Alternately, you can install this by running `pip install pycaret[models]`
2024-02-21 12:42:26,973:WARNING:
'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install.
Alternately, you can install this by running `pip install pycaret[models]`
2024-02-21 12:42:27,034:WARNING:
'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install.
Alternately, you can install this by running `pip install pycaret[models]`
2024-02-21 12:42:27,034:WARNING:
'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install.
Alternately, you can install this by running `pip install pycaret[models]`
2024-02-21 12:42:27,036:INFO:Preparing preprocessing pipeline...
2024-02-21 12:42:27,037:INFO:Set up label encoding.
2024-02-21 12:42:27,037:INFO:Set up simple imputation.
2024-02-21 12:42:27,040:INFO:Set up encoding of ordinal features.
2024-02-21 12:42:27,041:INFO:Set up encoding of categorical features.
2024-02-21 12:42:27,042:INFO:Set up column name cleaning.
2024-02-21 12:42:27,163:INFO:Finished creating preprocessing pipeline.
2024-02-21 12:42:27,178:INFO:Pipeline: Pipeline(memory=FastMemory(location=/tmp/joblib),
steps=[('label_encoding',
TransformerWrapperWithInverse(exclude=None, include=None,
transformer=LabelEncoder())),
('numerical_imputer',
TransformerWrapper(exclude=None,
include=['Unnamed: 0', 'PassengerId', 'Age',
'SibSp', 'Parch', 'Fare'],
transformer=SimpleImputer(add_indicator=False,
copy=True,
fill_value=None,
k...
transformer=TargetEncoder(cols=['Name',
'Ticket',
'Cabin'],
drop_invariant=False,
handle_missing='return_nan',
handle_unknown='value',
hierarchy=None,
min_samples_leaf=20,
return_df=True,
smoothing=10,
verbose=0))),
('clean_column_names',
TransformerWrapper(exclude=None, include=None,
transformer=CleanColumnNames(match='[\\]\\[\\,\\{\\}\\"\\:]+')))],
verbose=False)
2024-02-21 12:42:27,178:INFO:Creating final display dataframe.
2024-02-21 12:42:27,571:INFO:Setup _display_container: Description Value
0 Session id 7161
1 Target Pclass
2 Target type Multiclass
3 Target mapping 1: 0, 2: 1, 3: 2
4 Original data shape (418, 12)
5 Transformed data shape (418, 14)
6 Transformed train set shape (292, 14)
7 Transformed test set shape (126, 14)
8 Numeric features 6
9 Categorical features 5
10 Rows with missing values 79.2%
11 Preprocess True
12 Imputation type simple
13 Numeric imputation mean
14 Categorical imputation mode
15 Maximum one-hot encoding 25
16 Encoding method None
17 Fold Generator StratifiedKFold
18 Fold Number 10
19 CPU Jobs -1
20 Use GPU False
21 Log Experiment False
22 Experiment Name clf-default-name
23 USI be37
2024-02-21 12:42:27,639:WARNING:
'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install.
Alternately, you can install this by running `pip install pycaret[models]`
2024-02-21 12:42:27,639:WARNING:
'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install.
Alternately, you can install this by running `pip install pycaret[models]`
2024-02-21 12:42:27,708:WARNING:
'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install.
Alternately, you can install this by running `pip install pycaret[models]`
2024-02-21 12:42:27,708:WARNING:
'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install.
Alternately, you can install this by running `pip install pycaret[models]`
2024-02-21 12:42:27,709:WARNING:/workspaces/streamlitapptest/nomanstreamlit/lib/python3.11/site-packages/pycaret/internal/metrics.py:51: FutureWarning: The `needs_threshold` and `needs_proba` parameter are deprecated in version 1.4 and will be removed in 1.6. You can either let `response_method` be `None` or set it to `predict` to preserve the same behaviour.
warnings.warn(
2024-02-21 12:42:27,710:INFO:setup() successfully completed in 1.16s...............
2024-02-21 12:42:27,719:INFO:Initializing compare_models()
2024-02-21 12:42:27,719:INFO:compare_models(self=<pycaret.classification.oop.ClassificationExperiment object at 0x7f77b8cf1310>, include=None, exclude=None, fold=None, round=4, cross_validation=True, sort=Accuracy, n_select=1, budget_time=None, turbo=True, errors=ignore, fit_kwargs=None, groups=None, experiment_custom_tags=None, probability_threshold=None, verbose=True, parallel=None, caller_params={'self': <pycaret.classification.oop.ClassificationExperiment object at 0x7f77b8cf1310>, 'include': None, 'exclude': None, 'fold': None, 'round': 4, 'cross_validation': True, 'sort': 'Accuracy', 'n_select': 1, 'budget_time': None, 'turbo': True, 'errors': 'ignore', 'fit_kwargs': None, 'groups': None, 'experiment_custom_tags': None, 'probability_threshold': None, 'engine': None, 'verbose': True, 'parallel': None, '__class__': <class 'pycaret.classification.oop.ClassificationExperiment'>})
2024-02-21 12:42:27,719:INFO:Checking exceptions
2024-02-21 12:42:27,722:INFO:Preparing display monitor
2024-02-21 12:42:27,724:INFO:Initializing Logistic Regression
2024-02-21 12:42:27,724:INFO:Total runtime is 2.562999725341797e-06 minutes
2024-02-21 12:42:27,724:INFO:SubProcess create_model() called ==================================
2024-02-21 12:42:27,725:INFO:Initializing create_model()
2024-02-21 12:42:27,725:INFO:create_model(self=<pycaret.classification.oop.ClassificationExperiment object at 0x7f77b8cf1310>, estimator=lr, fold=StratifiedKFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=<pycaret.internal.display.display.CommonDisplay object at 0x7f77b8949250>, model_only=True, return_train_score=False, error_score=0.0, kwargs={})
2024-02-21 12:42:27,725:INFO:Checking exceptions
2024-02-21 12:42:27,725:INFO:Importing libraries
2024-02-21 12:42:27,725:INFO:Copying training dataset
2024-02-21 12:42:27,730:INFO:Defining folds
2024-02-21 12:42:27,730:INFO:Declaring metric variables
2024-02-21 12:42:27,731:INFO:Importing untrained model
2024-02-21 12:42:27,731:INFO:Logistic Regression Imported successfully
2024-02-21 12:42:27,731:INFO:Starting cross validation
2024-02-21 12:42:27,733:INFO:Cross validating with StratifiedKFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1
2024-02-21 12:42:31,060:WARNING:/workspaces/streamlitapptest/nomanstreamlit/lib/python3.11/site-packages/sklearn/linear_model/_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
n_iter_i = _check_optimize_result(
2024-02-21 12:42:31,179:WARNING:/workspaces/streamlitapptest/nomanstreamlit/lib/python3.11/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below:
Traceback (most recent call last):
File "/workspaces/streamlitapptest/nomanstreamlit/lib/python3.11/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
^^^^^^^^^^^^^^^
File "/workspaces/streamlitapptest/nomanstreamlit/lib/python3.11/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
^^^^^^^^^^^^^^
File "/workspaces/streamlitapptest/nomanstreamlit/lib/python3.11/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
^^^^^^^^^^^^^^^^^^^^^
File "/workspaces/streamlitapptest/nomanstreamlit/lib/python3.11/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
^^^^^^^^^^^^^^^^^^^^
File "/workspaces/streamlitapptest/nomanstreamlit/lib/python3.11/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
^^^^^^^^^^^^^^^^^^^^^^^
File "/workspaces/streamlitapptest/nomanstreamlit/lib/python3.11/site-packages/sklearn/utils/_set_output.py", line 295, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/workspaces/streamlitapptest/nomanstreamlit/lib/python3.11/site-packages/pycaret/internal/preprocess/transformers.py", line 233, in transform
X = to_df(X, index=getattr(y, "index", None))
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/workspaces/streamlitapptest/nomanstreamlit/lib/python3.11/site-packages/pycaret/utils/generic.py", line 103, in to_df
data = pd.DataFrame(data, index, columns)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/workspaces/streamlitapptest/nomanstreamlit/lib/python3.11/site-packages/pandas/core/frame.py", line 822, in __init__
mgr = ndarray_to_mgr(
^^^^^^^^^^^^^^^
File "/workspaces/streamlitapptest/nomanstreamlit/lib/python3.11/site-packages/pandas/core/internals/construction.py", line 319, in ndarray_to_mgr
values = _prep_ndarraylike(values, copy=copy_on_sanitize)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/workspaces/streamlitapptest/nomanstreamlit/lib/python3.11/site-packages/pandas/core/internals/construction.py", line 575, in _prep_ndarraylike
values = np.array([convert(v) for v in values])
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
ValueError: setting an array element with a sequence. The requested array has an inhomogeneous shape after 1 dimensions. The detected shape was (2,) + inhomogeneous part.
warnings.warn(
2024-02-21 12:42:31,182:WARNING:/workspaces/streamlitapptest/nomanstreamlit/lib/python3.11/site-packages/sklearn/metrics/_classification.py:1561: UserWarning: Note that pos_label (set to 3) is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class.
warnings.warn(
2024-02-21 12:42:31,184:WARNING:/workspaces/streamlitapptest/nomanstreamlit/lib/python3.11/site-packages/sklearn/metrics/_classification.py:1561: UserWarning: Note that pos_label (set to 3) is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class.
warnings.warn(
2024-02-21 12:42:31,187:WARNING:/workspaces/streamlitapptest/nomanstreamlit/lib/python3.11/site-packages/sklearn/metrics/_classification.py:1561: UserWarning: Note that pos_label (set to 3) is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class.
warnings.warn(
2024-02-21 12:42:31,580:WARNING:/workspaces/streamlitapptest/nomanstreamlit/lib/python3.11/site-packages/sklearn/linear_model/_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
n_iter_i = _check_optimize_result(
2024-02-21 12:42:31,630:WARNING:/workspaces/streamlitapptest/nomanstreamlit/lib/python3.11/site-packages/sklearn/linear_model/_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
n_iter_i = _check_optimize_result(
2024-02-21 12:42:31,631:WARNING:/workspaces/streamlitapptest/nomanstreamlit/lib/python3.11/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below:
Traceback (most recent call last):
File "/workspaces/streamlitapptest/nomanstreamlit/lib/python3.11/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
^^^^^^^^^^^^^^^
File "/workspaces/streamlitapptest/nomanstreamlit/lib/python3.11/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
^^^^^^^^^^^^^^
File "/workspaces/streamlitapptest/nomanstreamlit/lib/python3.11/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
^^^^^^^^^^^^^^^^^^^^^
File "/workspaces/streamlitapptest/nomanstreamlit/lib/python3.11/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
^^^^^^^^^^^^^^^^^^^^
File "/workspaces/streamlitapptest/nomanstreamlit/lib/python3.11/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
^^^^^^^^^^^^^^^^^^^^^^^
File "/workspaces/streamlitapptest/nomanstreamlit/lib/python3.11/site-packages/sklearn/utils/_set_output.py", line 295, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/workspaces/streamlitapptest/nomanstreamlit/lib/python3.11/site-packages/pycaret/internal/preprocess/transformers.py", line 233, in transform
X = to_df(X, index=getattr(y, "index", None))
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/workspaces/streamlitapptest/nomanstreamlit/lib/python3.11/site-packages/pycaret/utils/generic.py", line 103, in to_df
data = pd.DataFrame(data, index, columns)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/workspaces/streamlitapptest/nomanstreamlit/lib/python3.11/site-packages/pandas/core/frame.py", line 822, in __init__
mgr = ndarray_to_mgr(
^^^^^^^^^^^^^^^
File "/workspaces/streamlitapptest/nomanstreamlit/lib/python3.11/site-packages/pandas/core/internals/construction.py", line 319, in ndarray_to_mgr
values = _prep_ndarraylike(values, copy=copy_on_sanitize)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/workspaces/streamlitapptest/nomanstreamlit/lib/python3.11/site-packages/pandas/core/internals/construction.py", line 575, in _prep_ndarraylike
values = np.array([convert(v) for v in values])
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
ValueError: setting an array element with a sequence. The requested array has an inhomogeneous shape after 1 dimensions. The detected shape was (2,) + inhomogeneous part.
warnings.warn(
2024-02-21 12:42:31,633:WARNING:/workspaces/streamlitapptest/nomanstreamlit/lib/python3.11/site-packages/sklearn/metrics/_classification.py:1561: UserWarning: Note that pos_label (set to 3) is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class.
warnings.warn(
2024-02-21 12:42:31,636:WARNING:/workspaces/streamlitapptest/nomanstreamlit/lib/python3.11/site-packages/sklearn/metrics/_classification.py:1561: UserWarning: Note that pos_label (set to 3) is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class.
warnings.warn(
2024-02-21 12:42:31,640:WARNING:/workspaces/streamlitapptest/nomanstreamlit/lib/python3.11/site-packages/sklearn/metrics/_classification.py:1561: UserWarning: Note that pos_label (set to 3) is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class.
warnings.warn(
2024-02-21 12:42:31,665:WARNING:/workspaces/streamlitapptest/nomanstreamlit/lib/python3.11/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below:
Traceback (most recent call last):
File "/workspaces/streamlitapptest/nomanstreamlit/lib/python3.11/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
^^^^^^^^^^^^^^^
File "/workspaces/streamlitapptest/nomanstreamlit/lib/python3.11/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
^^^^^^^^^^^^^^
File "/workspaces/streamlitapptest/nomanstreamlit/lib/python3.11/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
^^^^^^^^^^^^^^^^^^^^^
File "/workspaces/streamlitapptest/nomanstreamlit/lib/python3.11/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
^^^^^^^^^^^^^^^^^^^^
File "/workspaces/streamlitapptest/nomanstreamlit/lib/python3.11/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
^^^^^^^^^^^^^^^^^^^^^^^
File "/workspaces/streamlitapptest/nomanstreamlit/lib/python3.11/site-packages/sklearn/utils/_set_output.py", line 295, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/workspaces/streamlitapptest/nomanstreamlit/lib/python3.11/site-packages/pycaret/internal/preprocess/transformers.py", line 233, in transform
X = to_df(X, index=getattr(y, "index", None))
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/workspaces/streamlitapptest/nomanstreamlit/lib/python3.11/site-packages/pycaret/utils/generic.py", line 103, in to_df
data = pd.DataFrame(data, index, columns)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/workspaces/streamlitapptest/nomanstreamlit/lib/python3.11/site-packages/pandas/core/frame.py", line 822, in __init__
mgr = ndarray_to_mgr(
^^^^^^^^^^^^^^^
File "/workspaces/streamlitapptest/nomanstreamlit/lib/python3.11/site-packages/pandas/core/internals/construction.py", line 319, in ndarray_to_mgr
values = _prep_ndarraylike(values, copy=copy_on_sanitize)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/workspaces/streamlitapptest/nomanstreamlit/lib/python3.11/site-packages/pandas/core/internals/construction.py", line 575, in _prep_ndarraylike
values = np.array([convert(v) for v in values])
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
ValueError: setting an array element with a sequence. The requested array has an inhomogeneous shape after 1 dimensions. The detected shape was (2,) + inhomogeneous part.
warnings.warn(
2024-02-21 12:42:31,667:WARNING:/workspaces/streamlitapptest/nomanstreamlit/lib/python3.11/site-packages/sklearn/metrics/_classification.py:1561: UserWarning: Note that pos_label (set to 3) is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class.
warnings.warn(
2024-02-21 12:42:31,670:WARNING:/workspaces/streamlitapptest/nomanstreamlit/lib/python3.11/site-packages/sklearn/metrics/_classification.py:1561: UserWarning: Note that pos_label (set to 3) is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class.
warnings.warn(
2024-02-21 12:42:31,673:WARNING:/workspaces/streamlitapptest/nomanstreamlit/lib/python3.11/site-packages/sklearn/metrics/_classification.py:1561: UserWarning: Note that pos_label (set to 3) is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class.
warnings.warn(
2024-02-21 12:42:32,002:WARNING:/workspaces/streamlitapptest/nomanstreamlit/lib/python3.11/site-packages/sklearn/linear_model/_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
n_iter_i = _check_optimize_result(
2024-02-21 12:42:32,036:WARNING:/workspaces/streamlitapptest/nomanstreamlit/lib/python3.11/site-packages/sklearn/linear_model/_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
n_iter_i = _check_optimize_result(
2024-02-21 12:42:32,041:WARNING:/workspaces/streamlitapptest/nomanstreamlit/lib/python3.11/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below:
Traceback (most recent call last):
File "/workspaces/streamlitapptest/nomanstreamlit/lib/python3.11/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
^^^^^^^^^^^^^^^
File "/workspaces/streamlitapptest/nomanstreamlit/lib/python3.11/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
^^^^^^^^^^^^^^
File "/workspaces/streamlitapptest/nomanstreamlit/lib/python3.11/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
^^^^^^^^^^^^^^^^^^^^^
File "/workspaces/streamlitapptest/nomanstreamlit/lib/python3.11/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
^^^^^^^^^^^^^^^^^^^^
File "/workspaces/streamlitapptest/nomanstreamlit/lib/python3.11/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
^^^^^^^^^^^^^^^^^^^^^^^
File "/workspaces/streamlitapptest/nomanstreamlit/lib/python3.11/site-packages/sklearn/utils/_set_output.py", line 295, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/workspaces/streamlitapptest/nomanstreamlit/lib/python3.11/site-packages/pycaret/internal/preprocess/transformers.py", line 233, in transform
X = to_df(X, index=getattr(y, "index", None))
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/workspaces/streamlitapptest/nomanstreamlit/lib/python3.11/site-packages/pycaret/utils/generic.py", line 103, in to_df
data = pd.DataFrame(data, index, columns)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/workspaces/streamlitapptest/nomanstreamlit/lib/python3.11/site-packages/pandas/core/frame.py", line 822, in __init__
mgr = ndarray_to_mgr(
^^^^^^^^^^^^^^^
File "/workspaces/streamlitapptest/nomanstreamlit/lib/python3.11/site-packages/pandas/core/internals/construction.py", line 319, in ndarray_to_mgr
values = _prep_ndarraylike(values, copy=copy_on_sanitize)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/workspaces/streamlitapptest/nomanstreamlit/lib/python3.11/site-packages/pandas/core/internals/construction.py", line 575, in _prep_ndarraylike
values = np.array([convert(v) for v in values])
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
ValueError: setting an array element with a sequence. The requested array has an inhomogeneous shape after 1 dimensions. The detected shape was (2,) + inhomogeneous part.
warnings.warn(
2024-02-21 12:42:32,043:WARNING:/workspaces/streamlitapptest/nomanstreamlit/lib/python3.11/site-packages/sklearn/metrics/_classification.py:1561: UserWarning: Note that pos_label (set to 3) is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class.
warnings.warn(
2024-02-21 12:42:32,046:WARNING:/workspaces/streamlitapptest/nomanstreamlit/lib/python3.11/site-packages/sklearn/metrics/_classification.py:1561: UserWarning: Note that pos_label (set to 3) is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class.
warnings.warn(
2024-02-21 12:42:32,050:WARNING:/workspaces/streamlitapptest/nomanstreamlit/lib/python3.11/site-packages/sklearn/metrics/_classification.py:1561: UserWarning: Note that pos_label (set to 3) is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class.
warnings.warn(
2024-02-21 12:42:32,073:WARNING:/workspaces/streamlitapptest/nomanstreamlit/lib/python3.11/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below:
Traceback (most recent call last):
File "/workspaces/streamlitapptest/nomanstreamlit/lib/python3.11/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
^^^^^^^^^^^^^^^
File "/workspaces/streamlitapptest/nomanstreamlit/lib/python3.11/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
^^^^^^^^^^^^^^
File "/workspaces/streamlitapptest/nomanstreamlit/lib/python3.11/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
^^^^^^^^^^^^^^^^^^^^^
File "/workspaces/streamlitapptest/nomanstreamlit/lib/python3.11/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
^^^^^^^^^^^^^^^^^^^^
File "/workspaces/streamlitapptest/nomanstreamlit/lib/python3.11/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
^^^^^^^^^^^^^^^^^^^^^^^
File "/workspaces/streamlitapptest/nomanstreamlit/lib/python3.11/site-packages/sklearn/utils/_set_output.py", line 295, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/workspaces/streamlitapptest/nomanstreamlit/lib/python3.11/site-packages/pycaret/internal/preprocess/transformers.py", line 233, in transform
X = to_df(X, index=getattr(y, "index", None))
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/workspaces/streamlitapptest/nomanstreamlit/lib/python3.11/site-packages/pycaret/utils/generic.py", line 103, in to_df
data = pd.DataFrame(data, index, columns)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/workspaces/streamlitapptest/nomanstreamlit/lib/python3.11/site-packages/pandas/core/frame.py", line 822, in __init__
mgr = ndarray_to_mgr(
^^^^^^^^^^^^^^^
File "/workspaces/streamlitapptest/nomanstreamlit/lib/python3.11/site-packages/pandas/core/internals/construction.py", line 319, in ndarray_to_mgr
values = _prep_ndarraylike(values, copy=copy_on_sanitize)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/workspaces/streamlitapptest/nomanstreamlit/lib/python3.11/site-packages/pandas/core/internals/construction.py", line 575, in _prep_ndarraylike
values = np.array([convert(v) for v in values])
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
ValueError: setting an array element with a sequence. The requested array has an inhomogeneous shape after 1 dimensions. The detected shape was (2,) + inhomogeneous part.
warnings.warn(
2024-02-21 12:42:32,075:WARNING:/workspaces/streamlitapptest/nomanstreamlit/lib/python3.11/site-packages/sklearn/metrics/_classification.py:1561: UserWarning: Note that pos_label (set to 3) is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class.
warnings.warn(
2024-02-21 12:42:32,078:WARNING:/workspaces/streamlitapptest/nomanstreamlit/lib/python3.11/site-packages/sklearn/metrics/_classification.py:1561: UserWarning: Note that pos_label (set to 3) is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class.
warnings.warn(
2024-02-21 12:42:32,081:WARNING:/workspaces/streamlitapptest/nomanstreamlit/lib/python3.11/site-packages/sklearn/metrics/_classification.py:1561: UserWarning: Note that pos_label (set to 3) is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class.
warnings.warn(
2024-02-21 12:42:32,379:WARNING:/workspaces/streamlitapptest/nomanstreamlit/lib/python3.11/site-packages/sklearn/linear_model/_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
n_iter_i = _check_optimize_result(
2024-02-21 12:42:32,400:WARNING:/workspaces/streamlitapptest/nomanstreamlit/lib/python3.11/site-packages/sklearn/linear_model/_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
n_iter_i = _check_optimize_result(
2024-02-21 12:42:32,416:WARNING:/workspaces/streamlitapptest/nomanstreamlit/lib/python3.11/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below:
Traceback (most recent call last):
File "/workspaces/streamlitapptest/nomanstreamlit/lib/python3.11/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
^^^^^^^^^^^^^^^
File "/workspaces/streamlitapptest/nomanstreamlit/lib/python3.11/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
^^^^^^^^^^^^^^
File "/workspaces/streamlitapptest/nomanstreamlit/lib/python3.11/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
^^^^^^^^^^^^^^^^^^^^^
File "/workspaces/streamlitapptest/nomanstreamlit/lib/python3.11/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
^^^^^^^^^^^^^^^^^^^^
File "/workspaces/streamlitapptest/nomanstreamlit/lib/python3.11/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
^^^^^^^^^^^^^^^^^^^^^^^
File "/workspaces/streamlitapptest/nomanstreamlit/lib/python3.11/site-packages/sklearn/utils/_set_output.py", line 295, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/workspaces/streamlitapptest/nomanstreamlit/lib/python3.11/site-packages/pycaret/internal/preprocess/transformers.py", line 233, in transform
X = to_df(X, index=getattr(y, "index", None))
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/workspaces/streamlitapptest/nomanstreamlit/lib/python3.11/site-packages/pycaret/utils/generic.py", line 103, in to_df
data = pd.DataFrame(data, index, columns)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/workspaces/streamlitapptest/nomanstreamlit/lib/python3.11/site-packages/pandas/core/frame.py", line 822, in __init__
mgr = ndarray_to_mgr(
^^^^^^^^^^^^^^^
File "/workspaces/streamlitapptest/nomanstreamlit/lib/python3.11/site-packages/pandas/core/internals/construction.py", line 319, in ndarray_to_mgr
values = _prep_ndarraylike(values, copy=copy_on_sanitize)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/workspaces/streamlitapptest/nomanstreamlit/lib/python3.11/site-packages/pandas/core/internals/construction.py", line 575, in _prep_ndarraylike
values = np.array([convert(v) for v in values])
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
ValueError: setting an array element with a sequence. The requested array has an inhomogeneous shape after 1 dimensions. The detected shape was (2,) + inhomogeneous part.
warnings.warn(
2024-02-21 12:42:32,418:WARNING:/workspaces/streamlitapptest/nomanstreamlit/lib/python3.11/site-packages/sklearn/metrics/_classification.py:1561: UserWarning: Note that pos_label (set to 3) is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class.
warnings.warn(
2024-02-21 12:42:32,421:WARNING:/workspaces/streamlitapptest/nomanstreamlit/lib/python3.11/site-packages/sklearn/metrics/_classification.py:1561: UserWarning: Note that pos_label (set to 3) is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class.
warnings.warn(
2024-02-21 12:42:32,426:WARNING:/workspaces/streamlitapptest/nomanstreamlit/lib/python3.11/site-packages/sklearn/metrics/_classification.py:1561: UserWarning: Note that pos_label (set to 3) is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class.
warnings.warn(
2024-02-21 12:42:32,440:WARNING:/workspaces/streamlitapptest/nomanstreamlit/lib/python3.11/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below:
Traceback (most recent call last):
File "/workspaces/streamlitapptest/nomanstreamlit/lib/python3.11/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
^^^^^^^^^^^^^^^
File "/workspaces/streamlitapptest/nomanstreamlit/lib/python3.11/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
^^^^^^^^^^^^^^
File "/workspaces/streamlitapptest/nomanstreamlit/lib/python3.11/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
^^^^^^^^^^^^^^^^^^^^^
File "/workspaces/streamlitapptest/nomanstreamlit/lib/python3.11/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
^^^^^^^^^^^^^^^^^^^^
File "/workspaces/streamlitapptest/nomanstreamlit/lib/python3.11/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
^^^^^^^^^^^^^^^^^^^^^^^
File "/workspaces/streamlitapptest/nomanstreamlit/lib/python3.11/site-packages/sklearn/utils/_set_output.py", line 295, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/workspaces/streamlitapptest/nomanstreamlit/lib/python3.11/site-packages/pycaret/internal/preprocess/transformers.py", line 233, in transform
X = to_df(X, index=getattr(y, "index", None))
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/workspaces/streamlitapptest/nomanstreamlit/lib/python3.11/site-packages/pycaret/utils/generic.py", line 103, in to_df
data = pd.DataFrame(data, index, columns)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/workspaces/streamlitapptest/nomanstreamlit/lib/python3.11/site-packages/pandas/core/frame.py", line 822, in __init__
mgr = ndarray_to_mgr(
^^^^^^^^^^^^^^^
File "/workspaces/streamlitapptest/nomanstreamlit/lib/python3.11/site-packages/pandas/core/internals/construction.py", line 319, in ndarray_to_mgr
values = _prep_ndarraylike(values, copy=copy_on_sanitize)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/workspaces/streamlitapptest/nomanstreamlit/lib/python3.11/site-packages/pandas/core/internals/construction.py", line 575, in _prep_ndarraylike
values = np.array([convert(v) for v in values])
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
ValueError: setting an array element with a sequence. The requested array has an inhomogeneous shape after 1 dimensions. The detected shape was (2,) + inhomogeneous part.
warnings.warn(
2024-02-21 12:42:32,442:WARNING:/workspaces/streamlitapptest/nomanstreamlit/lib/python3.11/site-packages/sklearn/metrics/_classification.py:1561: UserWarning: Note that pos_label (set to 3) is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class.
warnings.warn(
2024-02-21 12:42:32,445:WARNING:/workspaces/streamlitapptest/nomanstreamlit/lib/python3.11/site-packages/sklearn/metrics/_classification.py:1561: UserWarning: Note that pos_label (set to 3) is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class.
warnings.warn(
2024-02-21 12:42:32,449:WARNING:/workspaces/streamlitapptest/nomanstreamlit/lib/python3.11/site-packages/sklearn/metrics/_classification.py:1561: UserWarning: Note that pos_label (set to 3) is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class.
warnings.warn(
2024-02-21 12:42:32,752:WARNING:/workspaces/streamlitapptest/nomanstreamlit/lib/python3.11/site-packages/sklearn/linear_model/_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
n_iter_i = _check_optimize_result(
2024-02-21 12:42:32,768:WARNING:/workspaces/streamlitapptest/nomanstreamlit/lib/python3.11/site-packages/sklearn/linear_model/_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
n_iter_i = _check_optimize_result(
2024-02-21 12:42:32,791:WARNING:/workspaces/streamlitapptest/nomanstreamlit/lib/python3.11/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below:
Traceback (most recent call last):
File "/workspaces/streamlitapptest/nomanstreamlit/lib/python3.11/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
^^^^^^^^^^^^^^^
File "/workspaces/streamlitapptest/nomanstreamlit/lib/python3.11/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
^^^^^^^^^^^^^^
File "/workspaces/streamlitapptest/nomanstreamlit/lib/python3.11/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
^^^^^^^^^^^^^^^^^^^^^
File "/workspaces/streamlitapptest/nomanstreamlit/lib/python3.11/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
^^^^^^^^^^^^^^^^^^^^
File "/workspaces/streamlitapptest/nomanstreamlit/lib/python3.11/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
^^^^^^^^^^^^^^^^^^^^^^^
File "/workspaces/streamlitapptest/nomanstreamlit/lib/python3.11/site-packages/sklearn/utils/_set_output.py", line 295, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/workspaces/streamlitapptest/nomanstreamlit/lib/python3.11/site-packages/pycaret/internal/preprocess/transformers.py", line 233, in transform
X = to_df(X, index=getattr(y, "index", None))
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/workspaces/streamlitapptest/nomanstreamlit/lib/python3.11/site-packages/pycaret/utils/generic.py", line 103, in to_df
data = pd.DataFrame(data, index, columns)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/workspaces/streamlitapptest/nomanstreamlit/lib/python3.11/site-packages/pandas/core/frame.py", line 822, in __init__
mgr = ndarray_to_mgr(
^^^^^^^^^^^^^^^
File "/workspaces/streamlitapptest/nomanstreamlit/lib/python3.11/site-packages/pandas/core/internals/construction.py", line 319, in ndarray_to_mgr
values = _prep_ndarraylike(values, copy=copy_on_sanitize)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/workspaces/streamlitapptest/nomanstreamlit/lib/python3.11/site-packages/pandas/core/internals/construction.py", line 575, in _prep_ndarraylike
values = np.array([convert(v) for v in values])
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
ValueError: setting an array element with a sequence. The requested array has an inhomogeneous shape after 1 dimensions. The detected shape was (2,) + inhomogeneous part.
warnings.warn(
2024-02-21 12:42:32,793:WARNING:/workspaces/streamlitapptest/nomanstreamlit/lib/python3.11/site-packages/sklearn/metrics/_classification.py:1561: UserWarning: Note that pos_label (set to 3) is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class.
warnings.warn(
2024-02-21 12:42:32,796:WARNING:/workspaces/streamlitapptest/nomanstreamlit/lib/python3.11/site-packages/sklearn/metrics/_classification.py:1561: UserWarning: Note that pos_label (set to 3) is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class.
warnings.warn(
2024-02-21 12:42:32,799:WARNING:/workspaces/streamlitapptest/nomanstreamlit/lib/python3.11/site-packages/sklearn/metrics/_classification.py:1561: UserWarning: Note that pos_label (set to 3) is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class.
warnings.warn(
2024-02-21 12:42:32,805:WARNING:/workspaces/streamlitapptest/nomanstreamlit/lib/python3.11/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below:
Traceback (most recent call last):
File "/workspaces/streamlitapptest/nomanstreamlit/lib/python3.11/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
^^^^^^^^^^^^^^^
File "/workspaces/streamlitapptest/nomanstreamlit/lib/python3.11/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
^^^^^^^^^^^^^^
File "/workspaces/streamlitapptest/nomanstreamlit/lib/python3.11/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
^^^^^^^^^^^^^^^^^^^^^
File "/workspaces/streamlitapptest/nomanstreamlit/lib/python3.11/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
^^^^^^^^^^^^^^^^^^^^
File "/workspaces/streamlitapptest/nomanstreamlit/lib/python3.11/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
^^^^^^^^^^^^^^^^^^^^^^^
File "/workspaces/streamlitapptest/nomanstreamlit/lib/python3.11/site-packages/sklearn/utils/_set_output.py", line 295, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/workspaces/streamlitapptest/nomanstreamlit/lib/python3.11/site-packages/pycaret/internal/preprocess/transformers.py", line 233, in transform
X = to_df(X, index=getattr(y, "index", None))
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/workspaces/streamlitapptest/nomanstreamlit/lib/python3.11/site-packages/pycaret/utils/generic.py", line 103, in to_df
data = pd.DataFrame(data, index, columns)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/workspaces/streamlitapptest/nomanstreamlit/lib/python3.11/site-packages/pandas/core/frame.py", line 822, in __init__
mgr = ndarray_to_mgr(
^^^^^^^^^^^^^^^
File "/workspaces/streamlitapptest/nomanstreamlit/lib/python3.11/site-packages/pandas/core/internals/construction.py", line 319, in ndarray_to_mgr
values = _prep_ndarraylike(values, copy=copy_on_sanitize)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/workspaces/streamlitapptest/nomanstreamlit/lib/python3.11/site-packages/pandas/core/internals/construction.py", line 575, in _prep_ndarraylike
values = np.array([convert(v) for v in values])
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
ValueError: setting an array element with a sequence. The requested array has an inhomogeneous shape after 1 dimensions. The detected shape was (2,) + inhomogeneous part.
warnings.warn(
2024-02-21 12:42:32,807:WARNING:/workspaces/streamlitapptest/nomanstreamlit/lib/python3.11/site-packages/sklearn/metrics/_classification.py:1561: UserWarning: Note that pos_label (set to 3) is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class.
warnings.warn(
2024-02-21 12:42:32,810:WARNING:/workspaces/streamlitapptest/nomanstreamlit/lib/python3.11/site-packages/sklearn/metrics/_classification.py:1561: UserWarning: Note that pos_label (set to 3) is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class.
warnings.warn(
2024-02-21 12:42:32,813:WARNING:/workspaces/streamlitapptest/nomanstreamlit/lib/python3.11/site-packages/sklearn/metrics/_classification.py:1561: UserWarning: Note that pos_label (set to 3) is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class.
warnings.warn(
2024-02-21 12:42:32,992:WARNING:/workspaces/streamlitapptest/nomanstreamlit/lib/python3.11/site-packages/sklearn/linear_model/_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
n_iter_i = _check_optimize_result(
2024-02-21 12:42:33,014:WARNING:/workspaces/streamlitapptest/nomanstreamlit/lib/python3.11/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below:
Traceback (most recent call last):
File "/workspaces/streamlitapptest/nomanstreamlit/lib/python3.11/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
^^^^^^^^^^^^^^^
File "/workspaces/streamlitapptest/nomanstreamlit/lib/python3.11/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
^^^^^^^^^^^^^^
File "/workspaces/streamlitapptest/nomanstreamlit/lib/python3.11/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
^^^^^^^^^^^^^^^^^^^^^
File "/workspaces/streamlitapptest/nomanstreamlit/lib/python3.11/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
^^^^^^^^^^^^^^^^^^^^
File "/workspaces/streamlitapptest/nomanstreamlit/lib/python3.11/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
^^^^^^^^^^^^^^^^^^^^^^^
File "/workspaces/streamlitapptest/nomanstreamlit/lib/python3.11/site-packages/sklearn/utils/_set_output.py", line 295, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/workspaces/streamlitapptest/nomanstreamlit/lib/python3.11/site-packages/pycaret/internal/preprocess/transformers.py", line 233, in transform
X = to_df(X, index=getattr(y, "index", None))
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/workspaces/streamlitapptest/nomanstreamlit/lib/python3.11/site-packages/pycaret/utils/generic.py", line 103, in to_df
data = pd.DataFrame(data, index, columns)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/workspaces/streamlitapptest/nomanstreamlit/lib/python3.11/site-packages/pandas/core/frame.py", line 822, in __init__
mgr = ndarray_to_mgr(
^^^^^^^^^^^^^^^
File "/workspaces/streamlitapptest/nomanstreamlit/lib/python3.11/site-packages/pandas/core/internals/construction.py", line 319, in ndarray_to_mgr
values = _prep_ndarraylike(values, copy=copy_on_sanitize)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/workspaces/streamlitapptest/nomanstreamlit/lib/python3.11/site-packages/pandas/core/internals/construction.py", line 575, in _prep_ndarraylike
values = np.array([convert(v) for v in values])
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
ValueError: setting an array element with a sequence. The requested array has an inhomogeneous shape after 1 dimensions. The detected shape was (2,) + inhomogeneous part.
warnings.warn(
2024-02-21 12:42:33,015:WARNING:/workspaces/streamlitapptest/nomanstreamlit/lib/python3.11/site-packages/sklearn/metrics/_classification.py:1561: UserWarning: Note that pos_label (set to 3) is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class.
warnings.warn(
2024-02-21 12:42:33,017:WARNING:/workspaces/streamlitapptest/nomanstreamlit/lib/python3.11/site-packages/sklearn/metrics/_classification.py:1561: UserWarning: Note that pos_label (set to 3) is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class.
warnings.warn(
2024-02-21 12:42:33,019:WARNING:/workspaces/streamlitapptest/nomanstreamlit/lib/python3.11/site-packages/sklearn/metrics/_classification.py:1561: UserWarning: Note that pos_label (set to 3) is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class.
warnings.warn(
2024-02-21 12:42:33,028:INFO:Calculating mean and std
2024-02-21 12:42:33,028:INFO:Creating metrics dataframe
2024-02-21 12:42:33,031:INFO:Uploading results into container
2024-02-21 12:42:33,032:INFO:Uploading model into container now
2024-02-21 12:42:33,032:INFO:_master_model_container: 1
2024-02-21 12:42:33,032:INFO:_display_container: 2
2024-02-21 12:42:33,033:INFO:LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,
intercept_scaling=1, l1_ratio=None, max_iter=1000,
multi_class='auto', n_jobs=None, penalty='l2',
random_state=7161, solver='lbfgs', tol=0.0001, verbose=0,
warm_start=False)
2024-02-21 12:42:33,033:INFO:create_model() successfully completed......................................
2024-02-21 12:42:33,175:INFO:SubProcess create_model() end ==================================
2024-02-21 12:42:33,175:INFO:Creating metrics dataframe
2024-02-21 12:42:33,178:INFO:Initializing K Neighbors Classifier
2024-02-21 12:42:33,178:INFO:Total runtime is 0.09090067545572916 minutes
2024-02-21 12:42:33,178:INFO:SubProcess create_model() called ==================================
2024-02-21 12:42:33,179:INFO:Initializing create_model()
2024-02-21 12:42:33,179:INFO:create_model(self=<pycaret.classification.oop.ClassificationExperiment object at 0x7f77b8cf1310>, estimator=knn, fold=StratifiedKFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=<pycaret.internal.display.display.CommonDisplay object at 0x7f77b8949250>, model_only=True, return_train_score=False, error_score=0.0, kwargs={})
2024-02-21 12:42:33,179:INFO:Checking exceptions
2024-02-21 12:42:33,179:INFO:Importing libraries
2024-02-21 12:42:33,180:INFO:Copying training dataset
2024-02-21 12:42:33,183:INFO:Defining folds
2024-02-21 12:42:33,183:INFO:Declaring metric variables
2024-02-21 12:42:33,183:INFO:Importing untrained model
2024-02-21 12:42:33,184:INFO:K Neighbors Classifier Imported successfully
2024-02-21 12:42:33,184:INFO:Starting cross validation
2024-02-21 12:42:33,186:INFO:Cross validating with StratifiedKFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1
2024-02-21 12:42:33,357:WARNING:/workspaces/streamlitapptest/nomanstreamlit/lib/python3.11/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below:
Traceback (most recent call last):
File "/workspaces/streamlitapptest/nomanstreamlit/lib/python3.11/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
^^^^^^^^^^^^^^^
File "/workspaces/streamlitapptest/nomanstreamlit/lib/python3.11/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
^^^^^^^^^^^^^^
File "/workspaces/streamlitapptest/nomanstreamlit/lib/python3.11/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
^^^^^^^^^^^^^^^^^^^^^
File "/workspaces/streamlitapptest/nomanstreamlit/lib/python3.11/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
^^^^^^^^^^^^^^^^^^^^
File "/workspaces/streamlitapptest/nomanstreamlit/lib/python3.11/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
^^^^^^^^^^^^^^^^^^^^^^^
File "/workspaces/streamlitapptest/nomanstreamlit/lib/python3.11/site-packages/sklearn/utils/_set_output.py", line 295, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/workspaces/streamlitapptest/nomanstreamlit/lib/python3.11/site-packages/pycaret/internal/preprocess/transformers.py", line 233, in transform
X = to_df(X, index=getattr(y, "index", None))
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/workspaces/streamlitapptest/nomanstreamlit/lib/python3.11/site-packages/pycaret/utils/generic.py", line 103, in to_df
data = pd.DataFrame(data, index, columns)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/workspaces/streamlitapptest/nomanstreamlit/lib/python3.11/site-packages/pandas/core/frame.py", line 822, in __init__
mgr = ndarray_to_mgr(
^^^^^^^^^^^^^^^
File "/workspaces/streamlitapptest/nomanstreamlit/lib/python3.11/site-packages/pandas/core/internals/construction.py", line 319, in ndarray_to_mgr
values = _prep_ndarraylike(values, copy=copy_on_sanitize)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/workspaces/streamlitapptest/nomanstreamlit/lib/python3.11/site-packages/pandas/core/internals/construction.py", line 575, in _prep_ndarraylike
values = np.array([convert(v) for v in values])
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
ValueError: setting an array element with a sequence. The requested array has an inhomogeneous shape after 1 dimensions. The detected shape was (2,) + inhomogeneous part.
warnings.warn(
2024-02-21 12:42:33,359:WARNING:/workspaces/streamlitapptest/nomanstreamlit/lib/python3.11/site-packages/sklearn/metrics/_classification.py:1561: UserWarning: Note that pos_label (set to 3) is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class.
warnings.warn(
2024-02-21 12:42:33,362:WARNING:/workspaces/streamlitapptest/nomanstreamlit/lib/python3.11/site-packages/sklearn/metrics/_classification.py:1561: UserWarning: Note that pos_label (set to 3) is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class.
warnings.warn(
2024-02-21 12:42:33,364:WARNING:/workspaces/streamlitapptest/nomanstreamlit/lib/python3.11/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below:
Traceback (most recent call last):
File "/workspaces/streamlitapptest/nomanstreamlit/lib/python3.11/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
^^^^^^^^^^^^^^^
File "/workspaces/streamlitapptest/nomanstreamlit/lib/python3.11/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
^^^^^^^^^^^^^^
File "/workspaces/streamlitapptest/nomanstreamlit/lib/python3.11/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
^^^^^^^^^^^^^^^^^^^^^
File "/workspaces/streamlitapptest/nomanstreamlit/lib/python3.11/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
^^^^^^^^^^^^^^^^^^^^
File "/workspaces/streamlitapptest/nomanstreamlit/lib/python3.11/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
^^^^^^^^^^^^^^^^^^^^^^^
File "/workspaces/streamlitapptest/nomanstreamlit/lib/python3.11/site-packages/sklearn/utils/_set_output.py", line 295, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/workspaces/streamlitapptest/nomanstreamlit/lib/python3.11/site-packages/pycaret/internal/preprocess/transformers.py", line 233, in transform
X = to_df(X, index=getattr(y, "index", None))
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/workspaces/streamlitapptest/nomanstreamlit/lib/python3.11/site-packages/pycaret/utils/generic.py", line 103, in to_df
data = pd.DataFrame(data, index, columns)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/workspaces/streamlitapptest/nomanstreamlit/lib/python3.11/site-packages/pandas/core/frame.py", line 822, in __init__
mgr = ndarray_to_mgr(
^^^^^^^^^^^^^^^
File "/workspaces/streamlitapptest/nomanstreamlit/lib/python3.11/site-packages/pandas/core/internals/construction.py", line 319, in ndarray_to_mgr
values = _prep_ndarraylike(values, copy=copy_on_sanitize)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/workspaces/streamlitapptest/nomanstreamlit/lib/python3.11/site-packages/pandas/core/internals/construction.py", line 575, in _prep_ndarraylike
values = np.array([convert(v) for v in values])
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
ValueError: setting an array element with a sequence. The requested array has an inhomogeneous shape after 1 dimensions. The detected shape was (2,) + inhomogeneous part.
warnings.warn(
2024-02-21 12:42:33,365:WARNING:/workspaces/streamlitapptest/nomanstreamlit/lib/python3.11/site-packages/sklearn/metrics/_classification.py:1561: UserWarning: Note that pos_label (set to 3) is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class.
warnings.warn(
2024-02-21 12:42:33,367:WARNING:/workspaces/streamlitapptest/nomanstreamlit/lib/python3.11/site-packages/sklearn/metrics/_classification.py:1561: UserWarning: Note that pos_label (set to 3) is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class.
warnings.warn(
2024-02-21 12:42:33,376:WARNING:/workspaces/streamlitapptest/nomanstreamlit/lib/python3.11/site-packages/sklearn/metrics/_classification.py:1561: UserWarning: Note that pos_label (set to 3) is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class.
warnings.warn(