dagshub_mlops_project/fastapi_with_pycaret/logs.log

2023-06-21 16:49:04,182:WARNING:
'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install.
2023-06-21 16:49:04,183:WARNING:
'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install.
2023-06-21 16:49:04,183:WARNING:
'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install.
2023-06-21 16:49:04,183:WARNING:
'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install.
2023-06-21 16:49:04,471:WARNING:
'prophet' is a soft dependency and not included in the pycaret installation. Please run: `pip install prophet` to install.
2023-06-21 16:49:04,589:INFO:PyCaret RegressionExperiment
2023-06-21 16:49:04,589:INFO:Logging name: diamond
2023-06-21 16:49:04,589:INFO:ML Usecase: MLUsecase.REGRESSION
2023-06-21 16:49:04,589:INFO:version 3.0.2
2023-06-21 16:49:04,589:INFO:Initializing setup()
2023-06-21 16:49:04,589:INFO:self.USI: 70d6
2023-06-21 16:49:04,589:INFO:self._variable_keys: {'USI', 'X', 'html_param', 'memory', 'exp_name_log', '_ml_usecase', 'y_test', 'y', 'target_param', 'log_plots_param', 'logging_param', 'gpu_param', 'fold_generator', 'fold_groups_param', 'fold_shuffle_param', 'y_train', 'pipeline', 'idx', '_available_plots', 'transform_target_param', 'X_test', 'X_train', 'seed', 'exp_id', 'gpu_n_jobs_param', 'data', 'n_jobs_param'}
2023-06-21 16:49:04,589:INFO:Checking environment
2023-06-21 16:49:04,589:INFO:python_version: 3.10.10
2023-06-21 16:49:04,589:INFO:python_build: ('v3.10.10:aad5f6a891', 'Feb  7 2023 08:47:40')
2023-06-21 16:49:04,589:INFO:machine: arm64
2023-06-21 16:49:04,589:INFO:platform: macOS-12.3-arm64-arm-64bit
2023-06-21 16:49:04,589:INFO:Memory: svmem(total=17179869184, available=5329797120, percent=69.0, used=7255687168, free=71467008, active=5269946368, inactive=5149851648, wired=1985740800)
2023-06-21 16:49:04,590:INFO:Physical Core: 8
2023-06-21 16:49:04,590:INFO:Logical Core: 8
2023-06-21 16:49:04,590:INFO:Checking libraries
2023-06-21 16:49:04,590:INFO:System:
2023-06-21 16:49:04,590:INFO:    python: 3.10.10 (v3.10.10:aad5f6a891, Feb  7 2023, 08:47:40) [Clang 13.0.0 (clang-1300.0.29.30)]
2023-06-21 16:49:04,590:INFO:executable: /Users/vu/Downloads/mlops/dagshub_project/.venv/bin/python
2023-06-21 16:49:04,590:INFO:   machine: macOS-12.3-arm64-arm-64bit
2023-06-21 16:49:04,590:INFO:PyCaret required dependencies:
2023-06-21 16:49:04,590:INFO:                 pip: 23.0.1
2023-06-21 16:49:04,590:INFO:          setuptools: 67.8.0
2023-06-21 16:49:04,590:INFO:             pycaret: 3.0.2
2023-06-21 16:49:04,590:INFO:             IPython: 8.14.0
2023-06-21 16:49:04,590:INFO:          ipywidgets: 7.7.5
2023-06-21 16:49:04,590:INFO:                tqdm: 4.65.0
2023-06-21 16:49:04,590:INFO:               numpy: 1.23.0
2023-06-21 16:49:04,590:INFO:              pandas: 1.5.3
2023-06-21 16:49:04,590:INFO:              jinja2: 3.1.2
2023-06-21 16:49:04,590:INFO:               scipy: 1.10.1
2023-06-21 16:49:04,590:INFO:              joblib: 1.2.0
2023-06-21 16:49:04,590:INFO:             sklearn: 1.2.2
2023-06-21 16:49:04,590:INFO:                pyod: 1.0.9
2023-06-21 16:49:04,590:INFO:            imblearn: 0.10.1
2023-06-21 16:49:04,590:INFO:   category_encoders: 2.6.1
2023-06-21 16:49:04,590:INFO:            lightgbm: 3.3.5
2023-06-21 16:49:04,590:INFO:               numba: 0.57.0
2023-06-21 16:49:04,590:INFO:            requests: 2.31.0
2023-06-21 16:49:04,590:INFO:          matplotlib: 3.7.1
2023-06-21 16:49:04,590:INFO:          scikitplot: 0.3.7
2023-06-21 16:49:04,590:INFO:         yellowbrick: 1.5
2023-06-21 16:49:04,590:INFO:              plotly: 5.15.0
2023-06-21 16:49:04,590:INFO:             kaleido: 0.2.1
2023-06-21 16:49:04,590:INFO:         statsmodels: 0.14.0
2023-06-21 16:49:04,590:INFO:              sktime: 0.17.0
2023-06-21 16:49:04,590:INFO:               tbats: 1.1.3
2023-06-21 16:49:04,590:INFO:            pmdarima: 2.0.3
2023-06-21 16:49:04,590:INFO:              psutil: 5.9.5
2023-06-21 16:49:04,590:INFO:PyCaret optional dependencies:
2023-06-21 16:49:05,146:INFO:                shap: 0.41.0
2023-06-21 16:49:05,146:INFO:           interpret: Not installed
2023-06-21 16:49:05,146:INFO:                umap: Not installed
2023-06-21 16:49:05,146:INFO:    pandas_profiling: 3.6.6
2023-06-21 16:49:05,146:INFO:  explainerdashboard: 0.4.2.2
2023-06-21 16:49:05,146:INFO:             autoviz: Not installed
2023-06-21 16:49:05,146:INFO:           fairlearn: Not installed
2023-06-21 16:49:05,146:INFO:             xgboost: Not installed
2023-06-21 16:49:05,146:INFO:            catboost: Not installed
2023-06-21 16:49:05,146:INFO:              kmodes: Not installed
2023-06-21 16:49:05,146:INFO:             mlxtend: Not installed
2023-06-21 16:49:05,146:INFO:       statsforecast: Not installed
2023-06-21 16:49:05,146:INFO:        tune_sklearn: Not installed
2023-06-21 16:49:05,146:INFO:                 ray: Not installed
2023-06-21 16:49:05,146:INFO:            hyperopt: Not installed
2023-06-21 16:49:05,146:INFO:              optuna: Not installed
2023-06-21 16:49:05,146:INFO:               skopt: Not installed
2023-06-21 16:49:05,146:INFO:              mlflow: 2.4.1
2023-06-21 16:49:05,146:INFO:              gradio: 3.35.2
2023-06-21 16:49:05,146:INFO:             fastapi: 0.97.0
2023-06-21 16:49:05,146:INFO:             uvicorn: 0.22.0
2023-06-21 16:49:05,146:INFO:              m2cgen: Not installed
2023-06-21 16:49:05,146:INFO:           evidently: Not installed
2023-06-21 16:49:05,146:INFO:               fugue: Not installed
2023-06-21 16:49:05,146:INFO:           streamlit: Not installed
2023-06-21 16:49:05,146:INFO:             prophet: Not installed
2023-06-21 16:49:05,146:INFO:None
2023-06-21 16:49:05,146:INFO:Set up data.
2023-06-21 16:49:05,151:INFO:Set up train/test split.
2023-06-21 16:49:05,154:INFO:Set up index.
2023-06-21 16:49:05,155:INFO:Set up folding strategy.
2023-06-21 16:49:05,155:INFO:Assigning column types.
2023-06-21 16:49:05,156:INFO:Engine successfully changes for model 'lr' to 'sklearn'.
2023-06-21 16:49:05,156:INFO:Engine for model 'lasso' has not been set explicitly, hence returning None.
2023-06-21 16:49:05,158:INFO:Engine for model 'ridge' has not been set explicitly, hence returning None.
2023-06-21 16:49:05,159:INFO:Engine for model 'en' has not been set explicitly, hence returning None.
2023-06-21 16:49:05,182:INFO:Engine for model 'svm' has not been set explicitly, hence returning None.
2023-06-21 16:49:05,200:INFO:Engine for model 'knn' has not been set explicitly, hence returning None.
2023-06-21 16:49:05,200: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]`
2023-06-21 16:49:05,231: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]`
2023-06-21 16:49:05,231:INFO:Engine for model 'lasso' has not been set explicitly, hence returning None.
2023-06-21 16:49:05,233:INFO:Engine for model 'ridge' has not been set explicitly, hence returning None.
2023-06-21 16:49:05,235:INFO:Engine for model 'en' has not been set explicitly, hence returning None.
2023-06-21 16:49:05,258:INFO:Engine for model 'svm' has not been set explicitly, hence returning None.
2023-06-21 16:49:05,275:INFO:Engine for model 'knn' has not been set explicitly, hence returning None.
2023-06-21 16:49:05,275: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]`
2023-06-21 16:49:05,275: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]`
2023-06-21 16:49:05,275:INFO:Engine successfully changes for model 'lasso' to 'sklearn'.
2023-06-21 16:49:05,277:INFO:Engine for model 'ridge' has not been set explicitly, hence returning None.
2023-06-21 16:49:05,279:INFO:Engine for model 'en' has not been set explicitly, hence returning None.
2023-06-21 16:49:05,302:INFO:Engine for model 'svm' has not been set explicitly, hence returning None.
2023-06-21 16:49:05,338:INFO:Engine for model 'knn' has not been set explicitly, hence returning None.
2023-06-21 16:49:05,339: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]`
2023-06-21 16:49:05,339: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]`
2023-06-21 16:49:05,341:INFO:Engine for model 'ridge' has not been set explicitly, hence returning None.
2023-06-21 16:49:05,343:INFO:Engine for model 'en' has not been set explicitly, hence returning None.
2023-06-21 16:49:05,382:INFO:Engine for model 'svm' has not been set explicitly, hence returning None.
2023-06-21 16:49:05,416:INFO:Engine for model 'knn' has not been set explicitly, hence returning None.
2023-06-21 16:49:05,417: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]`
2023-06-21 16:49:05,417: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]`
2023-06-21 16:49:05,417:INFO:Engine successfully changes for model 'ridge' to 'sklearn'.
2023-06-21 16:49:05,420:INFO:Engine for model 'en' has not been set explicitly, hence returning None.
2023-06-21 16:49:05,443:INFO:Engine for model 'svm' has not been set explicitly, hence returning None.
2023-06-21 16:49:05,461:INFO:Engine for model 'knn' has not been set explicitly, hence returning None.
2023-06-21 16:49:05,461: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]`
2023-06-21 16:49:05,461: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]`
2023-06-21 16:49:05,464:INFO:Engine for model 'en' has not been set explicitly, hence returning None.
2023-06-21 16:49:05,487:INFO:Engine for model 'svm' has not been set explicitly, hence returning None.
2023-06-21 16:49:05,506:INFO:Engine for model 'knn' has not been set explicitly, hence returning None.
2023-06-21 16:49:05,506: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]`
2023-06-21 16:49:05,506: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]`
2023-06-21 16:49:05,507:INFO:Engine successfully changes for model 'en' to 'sklearn'.
2023-06-21 16:49:05,534:INFO:Engine for model 'svm' has not been set explicitly, hence returning None.
2023-06-21 16:49:05,552:INFO:Engine for model 'knn' has not been set explicitly, hence returning None.
2023-06-21 16:49:05,552: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]`
2023-06-21 16:49:05,552: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]`
2023-06-21 16:49:05,579:INFO:Engine for model 'svm' has not been set explicitly, hence returning None.
2023-06-21 16:49:05,597:INFO:Engine for model 'knn' has not been set explicitly, hence returning None.
2023-06-21 16:49:05,597: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]`
2023-06-21 16:49:05,597: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]`
2023-06-21 16:49:05,597:INFO:Engine successfully changes for model 'knn' to 'sklearn'.
2023-06-21 16:49:05,624:INFO:Engine for model 'svm' has not been set explicitly, hence returning None.
2023-06-21 16:49:05,641: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]`
2023-06-21 16:49:05,642: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]`
2023-06-21 16:49:05,667:INFO:Engine for model 'svm' has not been set explicitly, hence returning None.
2023-06-21 16:49:05,685: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]`
2023-06-21 16:49:05,685: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]`
2023-06-21 16:49:05,685:INFO:Engine successfully changes for model 'svm' to 'sklearn'.
2023-06-21 16:49:05,729: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]`
2023-06-21 16:49:05,729: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]`
2023-06-21 16:49:05,773: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]`
2023-06-21 16:49:05,773: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]`
2023-06-21 16:49:05,775:INFO:Preparing preprocessing pipeline...
2023-06-21 16:49:05,775:INFO:Set up target transformation.
2023-06-21 16:49:05,775:INFO:Set up simple imputation.
2023-06-21 16:49:05,777:INFO:Set up encoding of ordinal features.
2023-06-21 16:49:05,777:INFO:Set up encoding of categorical features.
2023-06-21 16:49:05,777:INFO:Set up column name cleaning.
2023-06-21 16:49:05,848:INFO:Finished creating preprocessing pipeline.
2023-06-21 16:49:05,857:INFO:Pipeline: Pipeline(memory=FastMemory(location=/var/folders/4p/4dsrcqhn1xzdbvntydthbzkc0000gn/T/joblib),
         steps=[('target_transformation',
                 TransformerWrapperWithInverse(transformer=TargetTransformer(estimator=PowerTransformer(standardize=False)))),
                ('numerical_imputer',
                 TransformerWrapper(include=['Carat Weight'],
                                    transformer=SimpleImputer())),
                ('categorical_imputer'...
                                                                         'data_type': dtype('O'),
                                                                         'mapping': AGSL    0
GIA     1
NaN    -1
dtype: int64}]))),
                ('onehot_encoding',
                 TransformerWrapper(include=['Cut', 'Color', 'Clarity',
                                             'Polish', 'Symmetry'],
                                    transformer=OneHotEncoder(cols=['Cut',
                                                                    'Color',
                                                                    'Clarity',
                                                                    'Polish',
                                                                    'Symmetry'],
                                                              handle_missing='return_nan',
                                                              use_cat_names=True))),
                ('clean_column_names',
                 TransformerWrapper(transformer=CleanColumnNames()))])
2023-06-21 16:49:05,857:INFO:Creating final display dataframe.
2023-06-21 16:49:05,994:INFO:Setup _display_container:                     Description         Value
0                    Session id          3196
1                        Target         Price
2                   Target type    Regression
3           Original data shape     (6000, 8)
4        Transformed data shape    (6000, 29)
5   Transformed train set shape    (4200, 29)
6    Transformed test set shape    (1800, 29)
7              Ordinal features             1
8              Numeric features             1
9          Categorical features             6
10                   Preprocess          True
11              Imputation type        simple
12           Numeric imputation          mean
13       Categorical imputation          mode
14     Maximum one-hot encoding            25
15              Encoding method          None
16             Transform target          True
17      Transform target method   yeo-johnson
18               Fold Generator         KFold
19                  Fold Number            10
20                     CPU Jobs            -1
21                      Use GPU         False
22               Log Experiment  MlflowLogger
23              Experiment Name       diamond
24                          USI          70d6
2023-06-21 16:49:06,044: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]`
2023-06-21 16:49:06,044: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]`
2023-06-21 16:49:06,089: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]`
2023-06-21 16:49:06,089: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]`
2023-06-21 16:49:06,089:INFO:Logging experiment in loggers
2023-06-21 16:49:06,125:INFO:SubProcess save_model() called ==================================
2023-06-21 16:49:06,144:INFO:Initializing save_model()
2023-06-21 16:49:06,144:INFO:save_model(model=Pipeline(memory=FastMemory(location=/var/folders/4p/4dsrcqhn1xzdbvntydthbzkc0000gn/T/joblib),
         steps=[('target_transformation',
                 TransformerWrapperWithInverse(transformer=TargetTransformer(estimator=PowerTransformer(standardize=False)))),
                ('numerical_imputer',
                 TransformerWrapper(include=['Carat Weight'],
                                    transformer=SimpleImputer())),
                ('categorical_imputer'...
                                                                         'data_type': dtype('O'),
                                                                         'mapping': AGSL    0
GIA     1
NaN    -1
dtype: int64}]))),
                ('onehot_encoding',
                 TransformerWrapper(include=['Cut', 'Color', 'Clarity',
                                             'Polish', 'Symmetry'],
                                    transformer=OneHotEncoder(cols=['Cut',
                                                                    'Color',
                                                                    'Clarity',
                                                                    'Polish',
                                                                    'Symmetry'],
                                                              handle_missing='return_nan',
                                                              use_cat_names=True))),
                ('clean_column_names',
                 TransformerWrapper(transformer=CleanColumnNames()))]), model_name=/var/folders/4p/4dsrcqhn1xzdbvntydthbzkc0000gn/T/tmp0vpbgzem/Transformation Pipeline, prep_pipe_=Pipeline(memory=FastMemory(location=/var/folders/4p/4dsrcqhn1xzdbvntydthbzkc0000gn/T/joblib),
         steps=[('target_transformation',
                 TransformerWrapperWithInverse(transformer=TargetTransformer(estimator=PowerTransformer(standardize=False)))),
                ('numerical_imputer',
                 TransformerWrapper(include=['Carat Weight'],
                                    transformer=SimpleImputer())),
                ('categorical_imputer'...
                                                                         'data_type': dtype('O'),
                                                                         'mapping': AGSL    0
GIA     1
NaN    -1
dtype: int64}]))),
                ('onehot_encoding',
                 TransformerWrapper(include=['Cut', 'Color', 'Clarity',
                                             'Polish', 'Symmetry'],
                                    transformer=OneHotEncoder(cols=['Cut',
                                                                    'Color',
                                                                    'Clarity',
                                                                    'Polish',
                                                                    'Symmetry'],
                                                              handle_missing='return_nan',
                                                              use_cat_names=True))),
                ('clean_column_names',
                 TransformerWrapper(transformer=CleanColumnNames()))]), verbose=False, use_case=MLUsecase.REGRESSION, kwargs={})
2023-06-21 16:49:06,144:INFO:Adding model into prep_pipe
2023-06-21 16:49:06,144:WARNING:Only Model saved as it was a pipeline.
2023-06-21 16:49:06,148:INFO:/var/folders/4p/4dsrcqhn1xzdbvntydthbzkc0000gn/T/tmp0vpbgzem/Transformation Pipeline.pkl saved in current working directory
2023-06-21 16:49:06,157:INFO:Pipeline(memory=FastMemory(location=/var/folders/4p/4dsrcqhn1xzdbvntydthbzkc0000gn/T/joblib),
         steps=[('target_transformation',
                 TransformerWrapperWithInverse(transformer=TargetTransformer(estimator=PowerTransformer(standardize=False)))),
                ('numerical_imputer',
                 TransformerWrapper(include=['Carat Weight'],
                                    transformer=SimpleImputer())),
                ('categorical_imputer'...
                                                                         'data_type': dtype('O'),
                                                                         'mapping': AGSL    0
GIA     1
NaN    -1
dtype: int64}]))),
                ('onehot_encoding',
                 TransformerWrapper(include=['Cut', 'Color', 'Clarity',
                                             'Polish', 'Symmetry'],
                                    transformer=OneHotEncoder(cols=['Cut',
                                                                    'Color',
                                                                    'Clarity',
                                                                    'Polish',
                                                                    'Symmetry'],
                                                              handle_missing='return_nan',
                                                              use_cat_names=True))),
                ('clean_column_names',
                 TransformerWrapper(transformer=CleanColumnNames()))])
2023-06-21 16:49:06,157:INFO:save_model() successfully completed......................................
2023-06-21 16:49:06,226:INFO:SubProcess save_model() end ==================================
2023-06-21 16:49:06,229:INFO:setup() successfully completed in 1.54s...............
2023-06-21 16:49:06,232:INFO:Initializing create_model()
2023-06-21 16:49:06,233:INFO:create_model(self=<pycaret.regression.oop.RegressionExperiment object at 0x284457010>, estimator=lightgbm, fold=None, round=4, cross_validation=True, predict=True, fit_kwargs=None, groups=None, refit=True, probability_threshold=None, experiment_custom_tags=None, verbose=True, system=True, add_to_model_list=True, metrics=None, display=None, model_only=True, return_train_score=False, kwargs={})
2023-06-21 16:49:06,233:INFO:Checking exceptions
2023-06-21 16:49:06,277:INFO:Importing libraries
2023-06-21 16:49:06,278:INFO:Copying training dataset
2023-06-21 16:49:06,283:INFO:Defining folds
2023-06-21 16:49:06,283:INFO:Declaring metric variables
2023-06-21 16:49:06,285:INFO:Importing untrained model
2023-06-21 16:49:06,287:INFO:Light Gradient Boosting Machine Imported successfully
2023-06-21 16:49:06,290:INFO:Starting cross validation
2023-06-21 16:49:06,295:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1
2023-06-21 16:49:08,096:INFO:Calculating mean and std
2023-06-21 16:49:08,098:INFO:Creating metrics dataframe
2023-06-21 16:49:08,103:INFO:Finalizing model
2023-06-21 16:49:08,445:INFO:Creating Dashboard logs
2023-06-21 16:49:08,447:INFO:Model: Light Gradient Boosting Machine
2023-06-21 16:49:08,455:INFO:Logged params: {'boosting_type': 'gbdt', 'class_weight': None, 'colsample_bytree': 1.0, 'importance_type': 'split', 'learning_rate': 0.1, 'max_depth': -1, 'min_child_samples': 20, 'min_child_weight': 0.001, 'min_split_gain': 0.0, 'n_estimators': 100, 'n_jobs': -1, 'num_leaves': 31, 'objective': None, 'random_state': 3196, 'reg_alpha': 0.0, 'reg_lambda': 0.0, 'silent': 'warn', 'subsample': 1.0, 'subsample_for_bin': 200000, 'subsample_freq': 0}
2023-06-21 16:49:08,478:INFO:Initializing predict_model()
2023-06-21 16:49:08,478:INFO:predict_model(self=<pycaret.regression.oop.RegressionExperiment object at 0x284457010>, estimator=LGBMRegressor(random_state=3196), probability_threshold=None, encoded_labels=False, raw_score=False, round=4, verbose=False, ml_usecase=None, preprocess=True, encode_labels=<function _SupervisedExperiment.predict_model.<locals>.encode_labels at 0x28ead1ea0>)
2023-06-21 16:49:08,478:INFO:Checking exceptions
2023-06-21 16:49:08,478:INFO:Preloading libraries
2023-06-21 16:49:08,641:WARNING:/Users/vu/Downloads/mlops/dagshub_project/.venv/lib/python3.10/site-packages/_distutils_hack/__init__.py:33: UserWarning: Setuptools is replacing distutils.
  warnings.warn("Setuptools is replacing distutils.")

2023-06-21 16:49:08,735:INFO:Uploading results into container
2023-06-21 16:49:08,735:INFO:Uploading model into container now
2023-06-21 16:49:08,739:INFO:_master_model_container: 1
2023-06-21 16:49:08,739:INFO:_display_container: 2
2023-06-21 16:49:08,739:INFO:LGBMRegressor(random_state=3196)
2023-06-21 16:49:08,739:INFO:create_model() successfully completed......................................
2023-06-21 16:49:08,812:INFO:Soft dependency imported: fastapi: 0.97.0
2023-06-21 16:49:08,812:INFO:Soft dependency imported: uvicorn: 0.22.0
2023-06-21 16:49:08,812:INFO:Soft dependency imported: pydantic: 1.10.9
2023-06-21 16:49:08,822:INFO:Initializing save_model()
2023-06-21 16:49:08,822:INFO:save_model(model=LGBMRegressor(random_state=3196), model_name=diamond_predict_api, prep_pipe_=Pipeline(memory=FastMemory(location=/var/folders/4p/4dsrcqhn1xzdbvntydthbzkc0000gn/T/joblib),
         steps=[('target_transformation',
                 TransformerWrapperWithInverse(transformer=TargetTransformer(estimator=PowerTransformer(standardize=False)))),
                ('numerical_imputer',
                 TransformerWrapper(include=['Carat Weight'],
                                    transformer=SimpleImputer())),
                ('categorical_imputer'...
                                                                         'data_type': dtype('O'),
                                                                         'mapping': AGSL    0
GIA     1
NaN    -1
dtype: int64}]))),
                ('onehot_encoding',
                 TransformerWrapper(include=['Cut', 'Color', 'Clarity',
                                             'Polish', 'Symmetry'],
                                    transformer=OneHotEncoder(cols=['Cut',
                                                                    'Color',
                                                                    'Clarity',
                                                                    'Polish',
                                                                    'Symmetry'],
                                                              handle_missing='return_nan',
                                                              use_cat_names=True))),
                ('clean_column_names',
                 TransformerWrapper(transformer=CleanColumnNames()))]), verbose=False, use_case=MLUsecase.REGRESSION, kwargs={})
2023-06-21 16:49:08,822:INFO:Adding model into prep_pipe
2023-06-21 16:49:08,831:INFO:diamond_predict_api.pkl saved in current working directory
2023-06-21 16:49:08,840:INFO:Pipeline(memory=FastMemory(location=/var/folders/4p/4dsrcqhn1xzdbvntydthbzkc0000gn/T/joblib),
         steps=[('target_transformation',
                 TransformerWrapperWithInverse(transformer=TargetTransformer(estimator=PowerTransformer(standardize=False)))),
                ('numerical_imputer',
                 TransformerWrapper(include=['Carat Weight'],
                                    transformer=SimpleImputer())),
                ('categorical_imputer'...
dtype: int64}]))),
                ('onehot_encoding',
                 TransformerWrapper(include=['Cut', 'Color', 'Clarity',
                                             'Polish', 'Symmetry'],
                                    transformer=OneHotEncoder(cols=['Cut',
                                                                    'Color',
                                                                    'Clarity',
                                                                    'Polish',
                                                                    'Symmetry'],
                                                              handle_missing='return_nan',
                                                              use_cat_names=True))),
                ('clean_column_names',
                 TransformerWrapper(transformer=CleanColumnNames())),
                ('trained_model', LGBMRegressor(random_state=3196))])
2023-06-21 16:49:08,840:INFO:save_model() successfully completed......................................