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2026-08-05 13:53:37 +00:00
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40
docs/test-scenarios/01-linear-regression-basic.json Normal file
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{
"_description": "Cenário básico de regressão linear sem scaler",
"experiment_run_id": 1001,
"variable_columns": [
"303-WIT-200(Value)"
],
"target_variable": "03CV020/CORRENTE_N_M1_PV(Value)",
"bucket_name": "model-training",
"file_name": "training_data.csv",
"line_separator": ",",
"decimal_separator": ".",
"date_column": "timestamp",
"date_format": "yyyy-MM-dd HH:mm:ss",
"train_size": 80,
"shuffle": true,
"random_state": 42,
"model_name": "Linear Regression",
"model_type": "linear_regression",
"data_model_kwargs": {
"lag_train": {
"303-WIT-200(Value)": 0
},
"lag_val": {
"303-WIT-200(Value)": 0
},
"nan_treatment": "drop",
"rem_static_win": false,
"static_threshold": null,
"start_date": null,
"end_date": null,
"support_filters": {},
"removed_intervals": []
},
"model_kwargs": {
"degree": 1,
"interaction_only": false,
"scaler_name": "None"
},
"opt_params": {}
}

40
docs/test-scenarios/02-linear-regression-with-scaler.json Normal file
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{
"_description": "Regressão linear com Standard Scaler habilitado",
"experiment_run_id": 1002,
"variable_columns": [
"303-WIT-200(Value)"
],
"target_variable": "03CV020/CORRENTE_N_M1_PV(Value)",
"bucket_name": "model-training",
"file_name": "training_data.csv",
"line_separator": ",",
"decimal_separator": ".",
"date_column": "timestamp",
"date_format": "yyyy-MM-dd HH:mm:ss",
"train_size": 80,
"shuffle": true,
"random_state": 42,
"model_name": "Linear Regression",
"model_type": "linear_regression",
"data_model_kwargs": {
"lag_train": {
"303-WIT-200(Value)": 0
},
"lag_val": {
"303-WIT-200(Value)": 0
},
"nan_treatment": "drop",
"rem_static_win": false,
"static_threshold": null,
"start_date": null,
"end_date": null,
"support_filters": {},
"removed_intervals": []
},
"model_kwargs": {
"degree": 1,
"interaction_only": false,
"scaler_name": "Standard Scaler"
},
"opt_params": {}
}

40
docs/test-scenarios/03-polynomial-regression-degree2.json Normal file
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{
"_description": "Regressão polinomial de grau 2 com scaler (obrigatório para evitar overflow)",
"experiment_run_id": 1003,
"variable_columns": [
"303-WIT-200(Value)"
],
"target_variable": "03CV020/CORRENTE_N_M1_PV(Value)",
"bucket_name": "model-training",
"file_name": "training_data.csv",
"line_separator": ",",
"decimal_separator": ".",
"date_column": "timestamp",
"date_format": "yyyy-MM-dd HH:mm:ss",
"train_size": 80,
"shuffle": true,
"random_state": 42,
"model_name": "Polynomial Regression",
"model_type": "polynomial_regression",
"data_model_kwargs": {
"lag_train": {
"303-WIT-200(Value)": 0
},
"lag_val": {
"303-WIT-200(Value)": 0
},
"nan_treatment": "drop",
"rem_static_win": false,
"static_threshold": null,
"start_date": null,
"end_date": null,
"support_filters": {},
"removed_intervals": []
},
"model_kwargs": {
"degree": 2,
"interaction_only": false,
"scaler_name": "Standard Scaler"
},
"opt_params": {}
}

40
docs/test-scenarios/04-polynomial-regression-degree3.json Normal file
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{
"_description": "Regressão polinomial de grau 3 com scaler",
"experiment_run_id": 1004,
"variable_columns": [
"303-WIT-200(Value)"
],
"target_variable": "03CV020/CORRENTE_N_M1_PV(Value)",
"bucket_name": "model-training",
"file_name": "training_data.csv",
"line_separator": ",",
"decimal_separator": ".",
"date_column": "timestamp",
"date_format": "yyyy-MM-dd HH:mm:ss",
"train_size": 80,
"shuffle": true,
"random_state": 42,
"model_name": "Polynomial Regression",
"model_type": "polynomial_regression",
"data_model_kwargs": {
"lag_train": {
"303-WIT-200(Value)": 0
},
"lag_val": {
"303-WIT-200(Value)": 0
},
"nan_treatment": "drop",
"rem_static_win": false,
"static_threshold": null,
"start_date": null,
"end_date": null,
"support_filters": {},
"removed_intervals": []
},
"model_kwargs": {
"degree": 3,
"interaction_only": false,
"scaler_name": "Standard Scaler"
},
"opt_params": {}
}

40
docs/test-scenarios/05-linear-regression-with-lags.json Normal file
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{
"_description": "Regressão linear com lags de treino e validação",
"experiment_run_id": 1005,
"variable_columns": [
"303-WIT-200(Value)"
],
"target_variable": "03CV020/CORRENTE_N_M1_PV(Value)",
"bucket_name": "model-training",
"file_name": "training_data.csv",
"line_separator": ",",
"decimal_separator": ".",
"date_column": "timestamp",
"date_format": "yyyy-MM-dd HH:mm:ss",
"train_size": 80,
"shuffle": true,
"random_state": 42,
"model_name": "Linear Regression",
"model_type": "linear_regression",
"data_model_kwargs": {
"lag_train": {
"303-WIT-200(Value)": 5
},
"lag_val": {
"303-WIT-200(Value)": 3
},
"nan_treatment": "drop",
"rem_static_win": false,
"static_threshold": null,
"start_date": null,
"end_date": null,
"support_filters": {},
"removed_intervals": []
},
"model_kwargs": {
"degree": 1,
"interaction_only": false,
"scaler_name": "None"
},
"opt_params": {}
}

40
docs/test-scenarios/06-linear-regression-nan-interpolation.json Normal file
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{
"_description": "Regressão linear com tratamento de NaN por interpolação linear",
"experiment_run_id": 1006,
"variable_columns": [
"303-WIT-200(Value)"
],
"target_variable": "03CV020/CORRENTE_N_M1_PV(Value)",
"bucket_name": "model-training",
"file_name": "training_data.csv",
"line_separator": ",",
"decimal_separator": ".",
"date_column": "timestamp",
"date_format": "yyyy-MM-dd HH:mm:ss",
"train_size": 80,
"shuffle": true,
"random_state": 42,
"model_name": "Linear Regression",
"model_type": "linear_regression",
"data_model_kwargs": {
"lag_train": {
"303-WIT-200(Value)": 0
},
"lag_val": {
"303-WIT-200(Value)": 0
},
"nan_treatment": "linear interpolation",
"rem_static_win": false,
"static_threshold": null,
"start_date": null,
"end_date": null,
"support_filters": {},
"removed_intervals": []
},
"model_kwargs": {
"degree": 1,
"interaction_only": false,
"scaler_name": "None"
},
"opt_params": {}
}

40
docs/test-scenarios/07-linear-regression-static-window-removal.json Normal file
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{
"_description": "Regressão linear com remoção de janelas estáticas",
"experiment_run_id": 1007,
"variable_columns": [
"303-WIT-200(Value)"
],
"target_variable": "03CV020/CORRENTE_N_M1_PV(Value)",
"bucket_name": "model-training",
"file_name": "training_data.csv",
"line_separator": ",",
"decimal_separator": ".",
"date_column": "timestamp",
"date_format": "yyyy-MM-dd HH:mm:ss",
"train_size": 80,
"shuffle": true,
"random_state": 42,
"model_name": "Linear Regression",
"model_type": "linear_regression",
"data_model_kwargs": {
"lag_train": {
"303-WIT-200(Value)": 0
},
"lag_val": {
"303-WIT-200(Value)": 0
},
"nan_treatment": "drop",
"rem_static_win": true,
"static_threshold": null,
"start_date": null,
"end_date": null,
"support_filters": {},
"removed_intervals": []
},
"model_kwargs": {
"degree": 1,
"interaction_only": false,
"scaler_name": "None"
},
"opt_params": {}
}

45
docs/test-scenarios/08-linear-regression-with-limits.json Normal file
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{
"_description": "Regressão linear com limites inferior e superior para variáveis",
"experiment_run_id": 1008,
"variable_columns": [
"303-WIT-200(Value)"
],
"target_variable": "03CV020/CORRENTE_N_M1_PV(Value)",
"bucket_name": "model-training",
"file_name": "training_data.csv",
"line_separator": ",",
"decimal_separator": ".",
"date_column": "timestamp",
"date_format": "yyyy-MM-dd HH:mm:ss",
"train_size": 80,
"shuffle": true,
"random_state": 42,
"model_name": "Linear Regression",
"model_type": "linear_regression",
"data_model_kwargs": {
"lag_train": {
"303-WIT-200(Value)": 0
},
"lag_val": {
"303-WIT-200(Value)": 0
},
"nan_treatment": "drop",
"rem_static_win": false,
"static_threshold": null,
"start_date": null,
"end_date": null,
"support_filters": {
"303-WIT-200(Value)": {
"min": 0.0,
"max": 1000.0
}
},
"removed_intervals": []
},
"model_kwargs": {
"degree": 1,
"interaction_only": false,
"scaler_name": "None"
},
"opt_params": {}
}

40
docs/test-scenarios/09-polynomial-degree2-with-scaler-and-lags.json Normal file
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{
"_description": "Cenário completo: regressão polinomial grau 2 com scaler e lags",
"experiment_run_id": 1009,
"variable_columns": [
"303-WIT-200(Value)"
],
"target_variable": "03CV020/CORRENTE_N_M1_PV(Value)",
"bucket_name": "model-training",
"file_name": "training_data.csv",
"line_separator": ",",
"decimal_separator": ".",
"date_column": "timestamp",
"date_format": "yyyy-MM-dd HH:mm:ss",
"train_size": 80,
"shuffle": true,
"random_state": 42,
"model_name": "Polynomial Regression",
"model_type": "polynomial_regression",
"data_model_kwargs": {
"lag_train": {
"303-WIT-200(Value)": 3
},
"lag_val": {
"303-WIT-200(Value)": 2
},
"nan_treatment": "drop",
"rem_static_win": false,
"static_threshold": null,
"start_date": null,
"end_date": null,
"support_filters": {},
"removed_intervals": []
},
"model_kwargs": {
"degree": 2,
"interaction_only": false,
"scaler_name": "Standard Scaler"
},
"opt_params": {}
}

42
docs/test-scenarios/10-linear-regression-with-ar.json Normal file
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{
"_description": "Linear regression placeholder for autoregressive features; include_ar is reserved for future wrapper support (see opt_params).",
"experiment_run_id": 1010,
"variable_columns": [
"303-WIT-200(Value)"
],
"target_variable": "03CV020/CORRENTE_N_M1_PV(Value)",
"bucket_name": "model-training",
"file_name": "training_data.csv",
"line_separator": ",",
"decimal_separator": ".",
"date_column": "timestamp",
"date_format": "yyyy-MM-dd HH:mm:ss",
"train_size": 80,
"shuffle": true,
"random_state": 42,
"model_name": "Linear Regression",
"model_type": "linear_regression",
"data_model_kwargs": {
"lag_train": {
"303-WIT-200(Value)": 0
},
"lag_val": {
"303-WIT-200(Value)": 0
},
"nan_treatment": "drop",
"rem_static_win": false,
"static_threshold": null,
"start_date": null,
"end_date": null,
"support_filters": {},
"removed_intervals": []
},
"model_kwargs": {
"degree": 1,
"interaction_only": false,
"scaler_name": "None"
},
"opt_params": {
"include_ar": true
}
}

40
docs/test-scenarios/11-linear-regression-static-threshold-custom.json Normal file
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{
"_description": "Regressão linear com remoção de janelas estáticas e static_threshold customizado",
"experiment_run_id": 1011,
"variable_columns": [
"303-WIT-200(Value)"
],
"target_variable": "03CV020/CORRENTE_N_M1_PV(Value)",
"bucket_name": "model-training",
"file_name": "training_data.csv",
"line_separator": ",",
"decimal_separator": ".",
"date_column": "timestamp",
"date_format": "yyyy-MM-dd HH:mm:ss",
"train_size": 80,
"shuffle": true,
"random_state": 42,
"model_name": "Linear Regression",
"model_type": "linear_regression",
"data_model_kwargs": {
"lag_train": {
"303-WIT-200(Value)": 0
},
"lag_val": {
"303-WIT-200(Value)": 0
},
"nan_treatment": "drop",
"rem_static_win": true,
"static_threshold": 100,
"start_date": null,
"end_date": null,
"support_filters": {},
"removed_intervals": []
},
"model_kwargs": {
"degree": 1,
"interaction_only": false,
"scaler_name": "None"
},
"opt_params": {}
}

40
docs/test-scenarios/12-angular-test-date-format.json Normal file
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{
"_description": "Alternate date column (DATA) and dd/MM/yyyy HH:mm:ss format; uses MinIO object training_data_dd_mm_yyyy.csv from E2E fixtures.",
"experiment_run_id": 1012,
"variable_columns": [
"303-WIT-230(Value)"
],
"target_variable": "03CV022/CORRENTE_N_M1_PV(Value)",
"bucket_name": "model-training",
"file_name": "training_data_dd_mm_yyyy.csv",
"line_separator": ",",
"decimal_separator": ".",
"date_column": "DATA",
"date_format": "dd/MM/yyyy HH:mm:ss",
"train_size": 80,
"shuffle": true,
"random_state": 42,
"model_name": "Linear Regression",
"model_type": "linear_regression",
"data_model_kwargs": {
"lag_train": {
"303-WIT-230(Value)": 3
},
"lag_val": {
"303-WIT-230(Value)": 0
},
"nan_treatment": "drop",
"rem_static_win": false,
"static_threshold": null,
"start_date": "01/05/2022",
"end_date": "31/07/2022",
"support_filters": {},
"removed_intervals": []
},
"model_kwargs": {
"degree": 1,
"interaction_only": false,
"scaler_name": "None"
},
"opt_params": {}
}

40
docs/test-scenarios/13-angular-test-double-date-column.json Normal file
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{
"_description": "Same alternate CSV as scenario 12 (DATA + dd/MM/yyyy); narrow date window for regression coverage. Not a multi-date-column dataset.",
"experiment_run_id": 1013,
"variable_columns": [
"303-WIT-230(Value)"
],
"target_variable": "03CV022/CORRENTE_N_M1_PV(Value)",
"bucket_name": "model-training",
"file_name": "training_data_dd_mm_yyyy.csv",
"line_separator": ",",
"decimal_separator": ".",
"date_column": "DATA",
"date_format": "dd/MM/yyyy HH:mm:ss",
"train_size": 80,
"shuffle": true,
"random_state": 42,
"model_name": "Linear Regression",
"model_type": "linear_regression",
"data_model_kwargs": {
"lag_train": {
"303-WIT-230(Value)": 0
},
"lag_val": {
"303-WIT-230(Value)": 0
},
"nan_treatment": "drop",
"rem_static_win": false,
"static_threshold": null,
"start_date": "01/05/2022 00:00:00",
"end_date": "31/05/2022 23:59:59",
"support_filters": {},
"removed_intervals": []
},
"model_kwargs": {
"degree": 1,
"interaction_only": false,
"scaler_name": "None"
},
"opt_params": {}
}

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docs/test-scenarios/14-angular-test-polynomial-support-filters.json Normal file
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{
"_description": "Cenário angular-test-01: regressão polinomial degree 4, scaler, support filters em 303-WIT-200",
"experiment_run_id": 1014,
"variable_columns": [
"303-WIT-200(Value)"
],
"target_variable": "03CV020/CORRENTE_N_M1_PV(Value)",
"bucket_name": "model-training",
"file_name": "training_data.csv",
"line_separator": ",",
"decimal_separator": ".",
"date_column": "timestamp",
"date_format": "yyyy-MM-dd HH:mm:ss",
"train_size": 80,
"shuffle": true,
"random_state": 42,
"model_name": "Polynomial Regression",
"model_type": "polynomial_regression",
"data_model_kwargs": {
"lag_train": {
"303-WIT-200(Value)": 0
},
"lag_val": {
"303-WIT-200(Value)": 0
},
"nan_treatment": "drop",
"rem_static_win": false,
"static_threshold": null,
"start_date": "2025-06-02 00:00:00",
"end_date": "2025-06-08 23:59:59",
"support_filters": {
"303-WIT-200(Value)": {
"upper_line": {
"intercept": 40.400002,
"angle": 0
},
"lower_line": {
"intercept": 30.5,
"angle": 0
}
}
},
"removed_intervals": []
},
"model_kwargs": {
"degree": 4,
"interaction_only": false,
"scaler_name": "Standard Scaler"
},
"opt_params": {}
}

40
docs/test-scenarios/15-linear-regression-custom-target-column.json Normal file
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{
"_description": "Target column name is not ``target``; report/Evidently sections must use params.target_variable.",
"experiment_run_id": 1015,
"variable_columns": [
"303-WIT-200(Value)"
],
"target_variable": "MY_CUSTOM_TARGET_COLUMN",
"bucket_name": "model-training",
"file_name": "training_data_custom_target.csv",
"line_separator": ",",
"decimal_separator": ".",
"date_column": "timestamp",
"date_format": "yyyy-MM-dd HH:mm:ss",
"train_size": 80,
"shuffle": true,
"random_state": 42,
"model_name": "Linear Regression",
"model_type": "linear_regression",
"data_model_kwargs": {
"lag_train": {
"303-WIT-200(Value)": 0
},
"lag_val": {
"303-WIT-200(Value)": 0
},
"nan_treatment": "drop",
"rem_static_win": false,
"static_threshold": null,
"start_date": null,
"end_date": null,
"support_filters": {},
"removed_intervals": []
},
"model_kwargs": {
"degree": 1,
"interaction_only": false,
"scaler_name": "None"
},
"opt_params": {}
}

40
docs/test-scenarios/16-linear-regression-naive-timestamp-header.json Normal file
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{
"_description": "Naive Timestamp column header; snake_case date_column/date_format and training_data_timestamp_naive.csv.",
"experiment_run_id": 1016,
"variable_columns": [
"303-WIT-200(Value)"
],
"target_variable": "03CV020/CORRENTE_N_M1_PV(Value)",
"bucket_name": "model-training",
"file_name": "training_data_timestamp_naive.csv",
"line_separator": ",",
"decimal_separator": ".",
"date_column": "Timestamp",
"date_format": "yyyy-MM-dd HH:mm:ss",
"train_size": 80,
"shuffle": true,
"random_state": 42,
"model_name": "Linear Regression",
"model_type": "linear_regression",
"data_model_kwargs": {
"lag_train": {
"303-WIT-200(Value)": 0
},
"lag_val": {
"303-WIT-200(Value)": 0
},
"nan_treatment": "drop",
"rem_static_win": false,
"static_threshold": null,
"start_date": null,
"end_date": null,
"support_filters": {},
"removed_intervals": []
},
"model_kwargs": {
"degree": 1,
"interaction_only": false,
"scaler_name": "None"
},
"opt_params": {}
}

40
docs/test-scenarios/17-linear-regression-blank-timestamp-row.json Normal file
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{
"_description": "One CSV row has an empty timestamp; pipeline should drop it and continue training.",
"experiment_run_id": 1017,
"variable_columns": [
"303-WIT-200(Value)"
],
"target_variable": "03CV020/CORRENTE_N_M1_PV(Value)",
"bucket_name": "model-training",
"file_name": "training_data_blank_timestamp_row.csv",
"line_separator": ",",
"decimal_separator": ".",
"date_column": "timestamp",
"date_format": "yyyy-MM-dd HH:mm:ss",
"train_size": 80,
"shuffle": true,
"random_state": 42,
"model_name": "Linear Regression",
"model_type": "linear_regression",
"data_model_kwargs": {
"lag_train": {
"303-WIT-200(Value)": 0
},
"lag_val": {
"303-WIT-200(Value)": 0
},
"nan_treatment": "drop",
"rem_static_win": false,
"static_threshold": null,
"start_date": null,
"end_date": null,
"support_filters": {},
"removed_intervals": []
},
"model_kwargs": {
"degree": 1,
"interaction_only": false,
"scaler_name": "None"
},
"opt_params": {}
}