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feat: enhance test scenarios and configuration for regression models

Browse Source 
- Updated `pyproject.toml` to include new linting rules for end-to-end tests.
- Modified `requirements-dev.txt` to add dependencies for E2E testing with `testcontainers` and `requests`.
- Refactored multiple JSON test scenario files to standardize structure, including new fields for `experiment_run_id`, `bucket_name`, and `file_name`.
- Improved model training parameters in `train_model_params.py` to use `experiment_name` directly.
- Adjusted `data_manager_repository.py` to utilize the updated `experiment_name` for logging.

These changes improve the organization and clarity of regression model tests and enhance the overall testing framework.
This commit is contained in:
vitor-aignosi
2026-05-04 11:11:16 -03:00
parent 50d0ea6f32
commit 6bd30e3328
26 changed files with 2024 additions and 397 deletions
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64
docs/test-scenarios/01-linear-regression-basic.json
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@@ -1,30 +1,40 @@
{
"_description": "Cenário básico de regressão linear sem scaler",
"experimentName": "test-linear-regression-basic",
"username": "bruno.domingues@aignosi.com.br",
"modelName": "Linear Regression",
"targetVariable": "03CV020/CORRENTE_N_M1_PV(Value)",
"variableColumns": ["303-WIT-200(Value)"],
"lagTrain": {"303-WIT-200(Value)": 0},
"lagVal": {"303-WIT-200(Value)": 0},
"remStaticWin": false,
"lowLim": {},
"uppLim": {},
"window": 0,
"useScaler": false,
"includeAr": false,
"trainSize": 80,
"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,
"lineSeparator": ",",
"decimalSeparator": ".",
"dateColumn": "timestamp",
"dateFormat": "yyyy-MM-dd HH:mm:ss",
"removedIntervals": [],
"degree": 1,
"interactionOnly": false,
"nanTreatment": "drop",
"startDate": null,
"endDate": null,
"scalerName": "None",
"supportFilters": {}
}
"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": {}
}

64
docs/test-scenarios/02-linear-regression-with-scaler.json
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@@ -1,30 +1,40 @@
{
"_description": "Regressão linear com Standard Scaler habilitado",
"experimentName": "test-linear-regression-scaler",
"username": "bruno.domingues@aignosi.com.br",
"modelName": "Linear Regression",
"targetVariable": "03CV020/CORRENTE_N_M1_PV(Value)",
"variableColumns": ["303-WIT-200(Value)"],
"lagTrain": {"303-WIT-200(Value)": 0},
"lagVal": {"303-WIT-200(Value)": 0},
"remStaticWin": false,
"lowLim": {},
"uppLim": {},
"window": 0,
"useScaler": true,
"includeAr": false,
"trainSize": 80,
"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,
"lineSeparator": ",",
"decimalSeparator": ".",
"dateColumn": "timestamp",
"dateFormat": "yyyy-MM-dd HH:mm:ss",
"removedIntervals": [],
"degree": 1,
"interactionOnly": false,
"nanTreatment": "drop",
"startDate": null,
"endDate": null,
"scalerName": "Standard Scaler",
"supportFilters": {}
}
"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": {}
}

64
docs/test-scenarios/03-polynomial-regression-degree2.json
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@@ -1,30 +1,40 @@
{
"_description": "Regressão polinomial de grau 2 com scaler (obrigatório para evitar overflow)",
"experimentName": "test-polynomial-degree2",
"username": "bruno.domingues@aignosi.com.br",
"modelName": "Polynomial Regression",
"targetVariable": "03CV020/CORRENTE_N_M1_PV(Value)",
"variableColumns": ["303-WIT-200(Value)"],
"lagTrain": {"303-WIT-200(Value)": 0},
"lagVal": {"303-WIT-200(Value)": 0},
"remStaticWin": false,
"lowLim": {},
"uppLim": {},
"window": 0,
"useScaler": true,
"includeAr": false,
"trainSize": 80,
"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,
"lineSeparator": ",",
"decimalSeparator": ".",
"dateColumn": "timestamp",
"dateFormat": "yyyy-MM-dd HH:mm:ss",
"removedIntervals": [],
"degree": 2,
"interactionOnly": false,
"nanTreatment": "drop",
"startDate": null,
"endDate": null,
"scalerName": "Standard Scaler",
"supportFilters": {}
}
"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": {}
}

64
docs/test-scenarios/04-polynomial-regression-degree3.json
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@@ -1,30 +1,40 @@
{
"_description": "Regressão polinomial de grau 3 com scaler",
"experimentName": "test-polynomial-degree3",
"username": "bruno.domingues@aignosi.com.br",
"modelName": "Polynomial Regression",
"targetVariable": "03CV020/CORRENTE_N_M1_PV(Value)",
"variableColumns": ["303-WIT-200(Value)"],
"lagTrain": {"303-WIT-200(Value)": 0},
"lagVal": {"303-WIT-200(Value)": 0},
"remStaticWin": false,
"lowLim": {},
"uppLim": {},
"window": 0,
"useScaler": true,
"includeAr": false,
"trainSize": 80,
"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,
"lineSeparator": ",",
"decimalSeparator": ".",
"dateColumn": "timestamp",
"dateFormat": "yyyy-MM-dd HH:mm:ss",
"removedIntervals": [],
"degree": 3,
"interactionOnly": false,
"nanTreatment": "drop",
"startDate": null,
"endDate": null,
"scalerName": "Standard Scaler",
"supportFilters": {}
}
"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": {}
}

64
docs/test-scenarios/05-linear-regression-with-lags.json
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@@ -1,30 +1,40 @@
{
"_description": "Regressão linear com lags de treino e validação",
"experimentName": "test-linear-with-lags",
"username": "bruno.domingues@aignosi.com.br",
"modelName": "Linear Regression",
"targetVariable": "03CV020/CORRENTE_N_M1_PV(Value)",
"variableColumns": ["303-WIT-200(Value)"],
"lagTrain": {"303-WIT-200(Value)": 5},
"lagVal": {"303-WIT-200(Value)": 3},
"remStaticWin": false,
"lowLim": {},
"uppLim": {},
"window": 0,
"useScaler": false,
"includeAr": false,
"trainSize": 80,
"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,
"lineSeparator": ",",
"decimalSeparator": ".",
"dateColumn": "timestamp",
"dateFormat": "yyyy-MM-dd HH:mm:ss",
"removedIntervals": [],
"degree": 1,
"interactionOnly": false,
"nanTreatment": "drop",
"startDate": null,
"endDate": null,
"scalerName": "None",
"supportFilters": {}
}
"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": {}
}

64
docs/test-scenarios/06-linear-regression-nan-interpolation.json
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@@ -1,30 +1,40 @@
{
"_description": "Regressão linear com tratamento de NaN por interpolação linear",
"experimentName": "test-linear-nan-interpolation",
"username": "bruno.domingues@aignosi.com.br",
"modelName": "Linear Regression",
"targetVariable": "03CV020/CORRENTE_N_M1_PV(Value)",
"variableColumns": ["303-WIT-200(Value)"],
"lagTrain": {"303-WIT-200(Value)": 0},
"lagVal": {"303-WIT-200(Value)": 0},
"remStaticWin": false,
"lowLim": {},
"uppLim": {},
"window": 0,
"useScaler": false,
"includeAr": false,
"trainSize": 80,
"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,
"lineSeparator": ",",
"decimalSeparator": ".",
"dateColumn": "timestamp",
"dateFormat": "yyyy-MM-dd HH:mm:ss",
"removedIntervals": [],
"degree": 1,
"interactionOnly": false,
"nanTreatment": "linear interpolation",
"startDate": null,
"endDate": null,
"scalerName": "None",
"supportFilters": {}
}
"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": {}
}

64
docs/test-scenarios/07-linear-regression-static-window-removal.json
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@@ -1,30 +1,40 @@
{
"_description": "Regressão linear com remoção de janelas estáticas",
"experimentName": "test-linear-static-removal",
"username": "bruno.domingues@aignosi.com.br",
"modelName": "Linear Regression",
"targetVariable": "03CV020/CORRENTE_N_M1_PV(Value)",
"variableColumns": ["303-WIT-200(Value)"],
"lagTrain": {"303-WIT-200(Value)": 0},
"lagVal": {"303-WIT-200(Value)": 0},
"remStaticWin": true,
"lowLim": {},
"uppLim": {},
"window": 10,
"useScaler": false,
"includeAr": false,
"trainSize": 80,
"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,
"lineSeparator": ",",
"decimalSeparator": ".",
"dateColumn": "timestamp",
"dateFormat": "yyyy-MM-dd HH:mm:ss",
"removedIntervals": [],
"degree": 1,
"interactionOnly": false,
"nanTreatment": "drop",
"startDate": null,
"endDate": null,
"scalerName": "None",
"supportFilters": {}
}
"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": {}
}

69
docs/test-scenarios/08-linear-regression-with-limits.json
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@@ -1,30 +1,45 @@
{
"_description": "Regressão linear com limites inferior e superior para variáveis",
"experimentName": "test-linear-with-limits",
"username": "bruno.domingues@aignosi.com.br",
"modelName": "Linear Regression",
"targetVariable": "03CV020/CORRENTE_N_M1_PV(Value)",
"variableColumns": ["303-WIT-200(Value)"],
"lagTrain": {"303-WIT-200(Value)": 0},
"lagVal": {"303-WIT-200(Value)": 0},
"remStaticWin": false,
"lowLim": {"303-WIT-200(Value)": 0.0},
"uppLim": {"303-WIT-200(Value)": 1000.0},
"window": 0,
"useScaler": false,
"includeAr": false,
"trainSize": 80,
"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,
"lineSeparator": ",",
"decimalSeparator": ".",
"dateColumn": "timestamp",
"dateFormat": "yyyy-MM-dd HH:mm:ss",
"removedIntervals": [],
"degree": 1,
"interactionOnly": false,
"nanTreatment": "drop",
"startDate": null,
"endDate": null,
"scalerName": "None",
"supportFilters": {}
}
"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": {}
}

64
docs/test-scenarios/09-polynomial-degree2-with-scaler-and-lags.json
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@@ -1,30 +1,40 @@
{
"_description": "Cenário completo: regressão polinomial grau 2 com scaler e lags",
"experimentName": "test-polynomial-complete",
"username": "bruno.domingues@aignosi.com.br",
"modelName": "Polynomial Regression",
"targetVariable": "03CV020/CORRENTE_N_M1_PV(Value)",
"variableColumns": ["303-WIT-200(Value)"],
"lagTrain": {"303-WIT-200(Value)": 3},
"lagVal": {"303-WIT-200(Value)": 2},
"remStaticWin": false,
"lowLim": {},
"uppLim": {},
"window": 0,
"useScaler": true,
"includeAr": false,
"trainSize": 80,
"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,
"lineSeparator": ",",
"decimalSeparator": ".",
"dateColumn": "timestamp",
"dateFormat": "yyyy-MM-dd HH:mm:ss",
"removedIntervals": [],
"degree": 2,
"interactionOnly": false,
"nanTreatment": "drop",
"startDate": null,
"endDate": null,
"scalerName": "Standard Scaler",
"supportFilters": {}
}
"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": {}
}

64
docs/test-scenarios/10-linear-regression-with-ar.json
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@@ -1,30 +1,40 @@
{
"_description": "Regressão linear com variável autoregressiva (AR)",
"experimentName": "test-linear-with-ar",
"username": "bruno.domingues@aignosi.com.br",
"modelName": "Linear Regression",
"targetVariable": "03CV020/CORRENTE_N_M1_PV(Value)",
"variableColumns": ["303-WIT-200(Value)"],
"lagTrain": {"303-WIT-200(Value)": 0},
"lagVal": {"303-WIT-200(Value)": 0},
"remStaticWin": false,
"lowLim": {},
"uppLim": {},
"window": 0,
"useScaler": false,
"includeAr": true,
"trainSize": 80,
"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,
"lineSeparator": ",",
"decimalSeparator": ".",
"dateColumn": "timestamp",
"dateFormat": "yyyy-MM-dd HH:mm:ss",
"removedIntervals": [],
"degree": 1,
"interactionOnly": false,
"nanTreatment": "drop",
"startDate": null,
"endDate": null,
"scalerName": "None",
"supportFilters": {}
}
"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": {}
}

65
docs/test-scenarios/11-linear-regression-static-threshold-custom.json
View File

@@ -1,31 +1,40 @@
{
"_description": "Regressão linear com remoção de janelas estáticas e static_threshold customizado",
"experimentName": "test-linear-static-threshold",
"username": "bruno.domingues@aignosi.com.br",
"modelName": "Linear Regression",
"targetVariable": "03CV020/CORRENTE_N_M1_PV(Value)",
"variableColumns": ["303-WIT-200(Value)"],
"lagTrain": {"303-WIT-200(Value)": 0},
"lagVal": {"303-WIT-200(Value)": 0},
"remStaticWin": true,
"staticThreshold": 100,
"lowLim": {},
"uppLim": {},
"window": 10,
"useScaler": false,
"includeAr": false,
"trainSize": 80,
"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,
"lineSeparator": ",",
"decimalSeparator": ".",
"dateColumn": "timestamp",
"dateFormat": "yyyy-MM-dd HH:mm:ss",
"removedIntervals": [],
"degree": 1,
"interactionOnly": false,
"nanTreatment": "drop",
"startDate": null,
"endDate": null,
"scalerName": "None",
"supportFilters": {}
}
"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": {}
}

65
docs/test-scenarios/12-angular-test-date-format.json
View File

@@ -1,31 +1,40 @@
{
"_description": "Cenário angular-test-01: CV022 WIT230 com lag e intervalo de datas",
"experimentName": "angular-test-01",
"username": "lucas.kou@aignosi.com.br",
"modelName": "Linear Regression",
"targetVariable": "03CV022/CORRENTE_N_M1_PV(Value)",
"variableColumns": ["303-WIT-230(Value)"],
"lagTrain": {"303-WIT-230(Value)": 3},
"lagVal": {"303-WIT-230(Value)": 0},
"remStaticWin": false,
"lowLim": {},
"uppLim": {},
"window": 0,
"useScaler": false,
"includeAr": false,
"trainSize": 80,
"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.csv",
"line_separator": ",",
"decimal_separator": ".",
"date_column": "DATA",
"date_format": "dd/MM/yyyy HH:mm:ss",
"train_size": 80,
"shuffle": true,
"lineSeparator": ",",
"decimalSeparator": ".",
"dateColumn": "DATA",
"dateFormat": "dd/MM/yyyy HH:mm:ss",
"removedIntervals": [],
"degree": 1,
"interactionOnly": false,
"nanTreatment": "drop",
"startDate": "01/05/2022",
"endDate": "31/07/2022",
"scalerName": "None",
"supportFilters": {},
"staticThreshold": null
}
"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": {}
}

65
docs/test-scenarios/13-angular-test-double-date-column.json
View File

@@ -1,31 +1,40 @@
{
"_description": "Cenário angular-test: CV022 WIT230 com ficheiro double date column e intervalo curto (00:00 a 00:05)",
"experimentName": "angular-test",
"username": "lucas.kou@aignosi.com.br",
"modelName": "Linear Regression",
"targetVariable": "03CV022/CORRENTE_N_M1_PV(Value)",
"variableColumns": ["303-WIT-230(Value)"],
"lagTrain": {"303-WIT-230(Value)": 0},
"lagVal": {"303-WIT-230(Value)": 0},
"remStaticWin": false,
"lowLim": {},
"uppLim": {},
"window": 0,
"useScaler": false,
"includeAr": false,
"trainSize": 80,
"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.csv",
"line_separator": ",",
"decimal_separator": ".",
"date_column": "DATA",
"date_format": "dd/MM/yyyy HH:mm:ss",
"train_size": 80,
"shuffle": true,
"lineSeparator": ",",
"decimalSeparator": ".",
"dateColumn": "DATA",
"dateFormat": "dd/MM/yyyy HH:mm:ss",
"removedIntervals": [],
"degree": 1,
"interactionOnly": false,
"nanTreatment": "drop",
"startDate": "01/05/2022 00:00:00",
"endDate": "01/05/2022 00:05:10",
"scalerName": "None",
"supportFilters": {},
"staticThreshold": null
}
"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": "01/05/2022 00:05:10",
"support_filters": {},
"removed_intervals": []
},
"model_kwargs": {
"degree": 1,
"interaction_only": false,
"scaler_name": "None"
},
"opt_params": {}
}

83
docs/test-scenarios/14-angular-test-polynomial-support-filters.json
View File

@@ -1,42 +1,51 @@
{
"_description": "Cenário angular-test-01: regressão polinomial degree 4, scaler, support filters em 303-WIT-200",
"experimentName": "angular-test-01",
"username": "lucas.kou@aignosi.com.br",
"modelName": "Polynomial Regression",
"targetVariable": "03CV020/CORRENTE_N_M1_PV(Value)",
"variableColumns": ["303-WIT-200(Value)"],
"lagTrain": {"303-WIT-200(Value)": 0},
"lagVal": {"303-WIT-200(Value)": 0},
"remStaticWin": false,
"lowLim": {},
"uppLim": {},
"window": 0,
"useScaler": true,
"includeAr": false,
"trainSize": 80,
"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,
"lineSeparator": ",",
"decimalSeparator": ".",
"dateColumn": "timestamp",
"dateFormat": "yyyy-MM-dd HH:mm:ss",
"removedIntervals": [],
"degree": 4,
"interactionOnly": false,
"nanTreatment": "drop",
"startDate": "2025-06-02 00:00:05",
"endDate": "2025-06-06 15:02:01",
"scalerName": "Standard Scaler",
"supportFilters": {
"303-WIT-200(Value)": {
"upper_line": {
"intercept": 40.400002,
"angle": 0
},
"lower_line": {
"intercept": 30.5,
"angle": 0
"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 00:00:05",
"end_date": "2025-06-06 15:02:01",
"support_filters": {
"303-WIT-200(Value)": {
"upper_line": {
"intercept": 40.400002,
"angle": 0
},
"lower_line": {
"intercept": 30.5,
"angle": 0
}
}
}
},
"removed_intervals": []
},
"staticThreshold": null
}
"model_kwargs": {
"degree": 4,
"interaction_only": false,
"scaler_name": "Standard Scaler"
},
"opt_params": {}
}