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"\\", + "paths": { + "ws": "C:\\Users\\gblai\\Documents\\github\\MEDomics\\WS_DEMO\\EXPERIMENTS\\test", + "models": "C:\\Users\\gblai\\Documents\\github\\MEDomics\\WS_DEMO\\EXPERIMENTS\\test\\models", + "notebooks": "C:\\Users\\gblai\\Documents\\github\\MEDomics\\WS_DEMO\\EXPERIMENTS\\test\\notebooks" + }, + "internalPaths": { + "tmp": "C:\\Users\\gblai\\Documents\\github\\MEDomics\\WS_DEMO\\EXPERIMENTS\\test\\test\\tmp", + "exp": "C:\\Users\\gblai\\Documents\\github\\MEDomics\\WS_DEMO\\EXPERIMENTS\\test\\test\\exp" + }, + "configPath": "C:\\Users\\gblai\\Documents\\github\\MEDomics\\WS_DEMO\\EXPERIMENTS\\test\\test.medml", + "nbNodes2Run": 22 +} \ No newline at end of file diff --git a/pythonCode/med_libs/GoExecutionScript.py b/pythonCode/med_libs/GoExecutionScript.py index c3d011ae..88d81e4a 100644 --- a/pythonCode/med_libs/GoExecutionScript.py +++ b/pythonCode/med_libs/GoExecutionScript.py @@ -72,6 +72,10 @@ def __init__(self, json_params: dict, _id: str = "default_id", debug: bool = Fal self._progress = {"now": 0, "currentLabel": ""} self._id = _id self._debug = debug + if self._debug: + # save json_params_dict to a file + with open('json_params_dict.json', 'w') as f: + json.dump(json_params, f, indent=4) def start(self): """ diff --git a/pythonCode/med_libs/MEDml/MEDexperiment_learning.py b/pythonCode/med_libs/MEDml/MEDexperiment_learning.py index dbc544d2..cdd32d27 100644 --- a/pythonCode/med_libs/MEDml/MEDexperiment_learning.py +++ b/pythonCode/med_libs/MEDml/MEDexperiment_learning.py @@ -114,6 +114,13 @@ def setup_dataset(self, node: Node): elif kwargs['use_gpu'] == "False": kwargs['use_gpu'] = False + if 'index' in kwargs: + if kwargs['index'] == "True": + kwargs['index'] = True + elif kwargs['index'] == "False": + kwargs['index'] = False + + # add the imports node.CodeHandler.add_import("import numpy as np") node.CodeHandler.add_import("import pandas as pd") @@ -137,9 +144,15 @@ def setup_dataset(self, node: Node): medml_logger = MEDml_logger() # setup the experiment - pycaret_exp.setup(temp_df, log_experiment=medml_logger, **kwargs) - node.CodeHandler.add_line( - "code", f"pycaret_exp.setup(temp_df, {node.CodeHandler.convert_dict_to_params(kwargs)})") + if 'test_data' in kwargs: + test_data_df = pd.read_csv(kwargs['test_data']['path']) + node.CodeHandler.add_line("code", f"test_data_df = pd.read_csv('{kwargs['test_data']}'") + node.CodeHandler.add_line("code", f"pycaret_exp.setup(temp_df, test_data=test_data_df, {node.CodeHandler.convert_dict_to_params(kwargs)})") + del kwargs['test_data'] + pycaret_exp.setup(temp_df, test_data=test_data_df, log_experiment=medml_logger, **kwargs) + else: + pycaret_exp.setup(temp_df, log_experiment=medml_logger, **kwargs) + node.CodeHandler.add_line("code", f"pycaret_exp.setup(temp_df, {node.CodeHandler.convert_dict_to_params(kwargs)})") node.CodeHandler.add_line( "code", f"dataset = pycaret_exp.get_config('X').join(pycaret_exp.get_config('y'))") dataset_metaData = { diff --git a/pythonCode/med_libs/MEDml/utils/settings_generator/classificationSettings.js b/pythonCode/med_libs/MEDml/utils/settings_generator/classificationSettings.js index 84a8a27f..39fc5409 100644 --- a/pythonCode/med_libs/MEDml/utils/settings_generator/classificationSettings.js +++ b/pythonCode/med_libs/MEDml/utils/settings_generator/classificationSettings.js @@ -122,11 +122,6 @@ const classificationSettings = { }, "dataset": { "options": { - "data_func": { - "type": "data-function", - "tooltip": "

The function that generate data (the dataframe-like input). This\nis useful when the dataset is large, and you need parallel operations\nsuch as compare_models. It can avoid broadcasting large dataset\nfrom driver to workers. Notice one and only one of data and\ndata_func must be set.

\n", - "default_val": "" - }, "index": { "type": "bool-int-str", "tooltip": "
\n
Handle indices in the data dataframe.
\n
\n
\n", @@ -142,11 +137,6 @@ const classificationSettings = { "tooltip": "

If not None, test_data is used as a hold-out set and train_size parameter\nis ignored. The columns of data and test_data must match.

\n", "default_val": "None" }, - "ordinal_features": { - "type": "dict", - "tooltip": "

Categorical features to be encoded ordinally. For example, a categorical\nfeature with \u2018low\u2019, \u2018medium\u2019, \u2018high\u2019 values where low < medium < high can\nbe passed as ordinal_features = {\u2018column_name\u2019 : [\u2018low\u2019, \u2018medium\u2019, \u2018high\u2019]}.

\n", - "default_val": "None" - }, "numeric_features": { "type": "custom-list", "tooltip": "

If the inferred data types are not correct, the numeric_features param can\nbe used to define the data types. It takes a list of strings with column\nnames that are numeric.

\n", @@ -197,11 +187,6 @@ const classificationSettings = { "tooltip": "

Categorical columns with max_encoding_ohe or less unique values are\nencoded using OneHotEncoding. If more, the encoding_method estimator\nis used. Note that columns with exactly two classes are always encoded\nordinally. Set to below 0 to always use OneHotEncoding.

\n", "default_val": "25" }, - "encoding_method": { - "type": "category-encoders estimator", - "tooltip": "

A category-encoders estimator to encode the categorical columns\nwith more than max_encoding_ohe unique values. If None,\ncategory_encoders.target_encoder.TargetEncoder is used.

\n", - "default_val": "None" - }, "rare_to_value": { "type": "float", "tooltip": "

Minimum fraction of category occurrences in a categorical column.\nIf a category is less frequent than rare_to_value * len(X), it is\nreplaced with the string in rare_value. Use this parameter to group\nrare categories before encoding the column. If None, ignores this step.

\n", @@ -217,11 +202,6 @@ const classificationSettings = { "tooltip": "

Remove features with a training-set variance lower than the provided\nthreshold. If 0, keep all features with non-zero variance, i.e. remove\nthe features that have the same value in all samples. If None, skip\nthis transformation step.

\n", "default_val": "None" }, - "group_features": { - "type": "dict", - "tooltip": "

When the dataset contains features with related characteristics,\nadd new fetaures with the following statistical properties of that\ngroup: min, max, mean, std, median and mode. The parameter takes a\ndict with the group name as key and a list of feature names\nbelonging to that group as value.

\n", - "default_val": "None" - }, "drop_groups": { "type": "bool", "tooltip": "

Whether to drop the original features in the group. Ignored when\ngroup_features is None.

\n", @@ -247,11 +227,6 @@ const classificationSettings = { "tooltip": "

Estimator with which to perform class balancing. Choose from the name\nof an imblearn estimator, or a custom instance of such. Ignored when\nfix_imbalance=False.

\n", "default_val": "\u201cSMOTE\u201d" }, - "custom_pipeline": { - "type": "list of (str, transformer), dict or Pipeline", - "tooltip": "

Addidiotnal custom transformers. If passed, they are applied to the\npipeline last, after all the build-in transformers.

\n", - "default_val": "None" - }, "custom_pipeline_position": { "type": "int", "tooltip": "

Position of the custom pipeline in the overal preprocessing pipeline.\nThe default value adds the custom pipeline last.

\n", @@ -317,11 +292,6 @@ const classificationSettings = { "tooltip": "

Name of the experiment for logging. Ignored when log_experiment is False.

\n", "default_val": "None" }, - "experiment_custom_tags": { - "type": "dict", - "tooltip": "

Dictionary of tag_name: String -> value: (String, but will be string-ified\nif not) passed to the mlflow.set_tags to add new custom tags for the experiment.

\n", - "default_val": "None" - }, "log_plots": { "type": "bool", "tooltip": "

When set to True, certain plots are logged automatically in the MLFlow server.\nTo change the type of plots to be logged, pass a list containing plot IDs. Refer\nto documentation of plot_model. Ignored when log_experiment is False.

\n", @@ -337,31 +307,11 @@ const classificationSettings = { "tooltip": "

When set to True, dataset is logged on the MLflow server as a csv file.\nIgnored when log_experiment is False.

\n", "default_val": "False" }, - "engine": { - "type": "Optional[Dict[str, str]] = None", - "tooltip": "

The execution engines to use for the models in the form of a dict\nof model_id: engine - e.g. for Logistic Regression (\u201clr\u201d, users can\nswitch between \u201csklearn\u201d and \u201csklearnex\u201d by specifying\nengine={\u201clr\u201d: \u201csklearnex\u201d}

\n", - "default_val": "" - }, "verbose": { "type": "bool", "tooltip": "

When set to False, Information grid is not printed.

\n", "default_val": "True" }, - "memory": { - "type": "str, bool or Memory", - "tooltip": "
\n
Used to cache the fitted transformers of the pipeline.

If False: No caching is performed.\nIf True: A default temp directory is used.\nIf str: Path to the caching directory.

\n
\n
\n", - "default_val": "rue" - }, - "profile": { - "type": "bool", - "tooltip": "

When set to True, an interactive EDA report is displayed.

\n", - "default_val": "False" - }, - "profile_kwargs": { - "type": "dict", - "tooltip": "

Dictionary of arguments passed to the ProfileReport method used\nto create the EDA report. Ignored if profile is False.

\n", - "default_val": "{} (empty dict)" - }, "time-point": { "type": "string", "default_val": "", @@ -464,11 +414,6 @@ const classificationSettings = { "tooltip": "

When set to \u2018ignore\u2019, will skip the model with exceptions and continue.\nIf \u2018raise\u2019, will break the function when exceptions are raised.

\n", "default_val": "ignore" }, - "fit_kwargs": { - "type": "dict", - "tooltip": "

Dictionary of arguments passed to the fit method of the model.

\n", - "default_val": "{} (empty dict)" - }, "groups": { "type": "string", "tooltip": "

Optional group labels when \u2018GroupKFold\u2019 is used for the cross validation.\nIt takes an array with shape (n_samples, ) where n_samples is the number\nof rows in the training dataset. When string is passed, it is interpreted\nas the column name in the dataset containing group labels.

\n", @@ -484,11 +429,6 @@ const classificationSettings = { "tooltip": "

Threshold for converting predicted probability to class label.\nIt defaults to 0.5 for all classifiers unless explicitly defined\nin this parameter. Only applicable for binary classification.

\n", "default_val": "None" }, - "engine": { - "type": "Optional[Dict[str, str]] = None", - "tooltip": "

The execution engines to use for the models in the form of a dict\nof model_id: engine - e.g. for Logistic Regression (\u201clr\u201d, users can\nswitch between \u201csklearn\u201d and \u201csklearnex\u201d by specifying\nengine={\u201clr\u201d: \u201csklearnex\u201d}

\n", - "default_val": "" - }, "verbose": { "type": "bool", "tooltip": "

Score grid is not printed when verbose is set to False.

\n", @@ -514,11 +454,6 @@ const classificationSettings = { "tooltip": "

When set to False, metrics are evaluated on holdout set. fold param\nis ignored when cross_validation is set to False.

\n", "default_val": "True" }, - "fit_kwargs": { - "type": "dict", - "tooltip": "

Dictionary of arguments passed to the fit method of the model.

\n", - "default_val": "{} (empty dict)" - }, "groups": { "type": "string", "tooltip": "

Optional group labels when GroupKFold is used for the cross validation.\nIt takes an array with shape (n_samples, ) where n_samples is the number\nof rows in training dataset. When string is passed, it is interpreted as\nthe column name in the dataset containing group labels.

\n", @@ -529,16 +464,6 @@ const classificationSettings = { "tooltip": "

Threshold for converting predicted probability to class label.\nIt defaults to 0.5 for all classifiers unless explicitly defined\nin this parameter. Only applicable for binary classification.

\n", "default_val": "None" }, - "experiment_custom_tags": { - "type": "dict", - "tooltip": "

Dictionary of tag_name: String -> value: (String, but will be string-ified\nif not) passed to the mlflow.set_tags to add new custom tags for the experiment.

\n", - "default_val": "None" - }, - "engine": { - "type": "Optional[str] = None", - "tooltip": "

The execution engine to use for the model, e.g. for Logistic Regression (\u201clr\u201d), users can\nswitch between \u201csklearn\u201d and \u201csklearnex\u201d by specifying\nengine=\u201dsklearnex\u201d.

\n", - "default_val": "" - }, "verbose": { "type": "bool", "tooltip": "

Score grid is not printed when verbose is set to False.

\n", @@ -571,16 +496,6 @@ const classificationSettings = { "tooltip": "

Controls cross-validation. If None, the CV generator in the fold_strategy\nparameter of the setup function is used. When an integer is passed,\nit is interpreted as the \u2018n_splits\u2019 parameter of the CV generator in the\nsetup function.

\n", "default_val": "None" }, - "fit_kwargs": { - "type": "dict", - "tooltip": "

Dictionary of arguments passed to the fit method of the model.

\n", - "default_val": "{} (empty dict)" - }, - "plot_kwargs": { - "type": "dict", - "tooltip": "
\n
Dictionary of arguments passed to the visualizer class.
\n
\n
\n", - "default_val": "{} (empty dict)" - }, "groups": { "type": "string", "tooltip": "

Optional group labels when GroupKFold is used for the cross validation.\nIt takes an array with shape (n_samples, ) where n_samples is the number\nof rows in training dataset. When string is passed, it is interpreted as\nthe column name in the dataset containing group labels.

\n", @@ -641,16 +556,6 @@ const classificationSettings = { "type": "string", "tooltip": "

Render mode for the dashboard. The default is set to dash which will\nrender a dashboard in browser. There are four possible options:

\n\n", "default_val": "dash" - }, - "dashboard_kwargs": { - "type": "dict", - "tooltip": "

Dictionary of arguments passed to the ExplainerDashboard class.

\n", - "default_val": "{} (empty dict)" - }, - "run_kwargs": { - "type": "dict", - "tooltip": "

Dictionary of arguments passed to the run method of ExplainerDashboard.

\n", - "default_val": "{} (empty dict)" } }, "code": "dashboard()", @@ -659,11 +564,6 @@ const classificationSettings = { }, "finalize": { "options": { - "fit_kwargs": { - "type": "dict", - "tooltip": "

Dictionary of arguments passed to the fit method of the model.

\n", - "default_val": "{} (empty dict)" - }, "groups": { "type": "string", "tooltip": "

Optional group labels when GroupKFold is used for the cross validation.\nIt takes an array with shape (n_samples, ) where n_samples is the number\nof rows in training dataset. When string is passed, it is interpreted as\nthe column name in the dataset containing group labels.

\n", @@ -673,11 +573,6 @@ const classificationSettings = { "type": "bool", "tooltip": "

Whether to return the complete fitted pipeline or only the fitted model.

\n", "default_val": "False" - }, - "experiment_custom_tags": { - "type": "dict", - "tooltip": "

Dictionary of tag_name: String -> value: (String, but will be string-ified\nif not) passed to the mlflow.set_tags to add new custom tags for the experiment.

\n", - "default_val": "None" } }, "code": "", @@ -706,16 +601,6 @@ const classificationSettings = { }, "load_model": { "options": { - "platform": { - "type": "string", - "tooltip": "

Name of the cloud platform. Currently supported platforms:\n\u2018aws\u2019, \u2018gcp\u2019 and \u2018azure\u2019.

\n", - "default_val": "None" - }, - "authentication": { - "type": "dict", - "tooltip": "

dictionary of applicable authentication tokens.

\n

when platform = \u2018aws\u2019:\n{\u2018bucket\u2019 : \u2018Name of Bucket on S3\u2019, \u2018path\u2019: (optional) folder name under the bucket}

\n

when platform = \u2018gcp\u2019:\n{\u2018project\u2019: \u2018gcp-project-name\u2019, \u2018bucket\u2019 : \u2018gcp-bucket-name\u2019}

\n

when platform = \u2018azure\u2019:\n{\u2018container\u2019: \u2018azure-container-name\u2019}

\n", - "default_val": "None" - }, "verbose": { "type": "bool", "tooltip": "

Success message is not printed when verbose is set to False.

\n", @@ -730,6 +615,10 @@ const classificationSettings = { } } }, + "group_models": { + "options": {}, + "code": "" + }, "tune_model": { "options": { "fold": { @@ -747,21 +636,11 @@ const classificationSettings = { "tooltip": "

Number of iterations in the grid search. Increasing \u2018n_iter\u2019 may improve\nmodel performance but also increases the training time.

\n", "default_val": "10" }, - "custom_grid": { - "type": "dict", - "tooltip": "

To define custom search space for hyperparameters, pass a dictionary with\nparameter name and values to be iterated. Custom grids must be in a format\nsupported by the defined search_library.

\n", - "default_val": "None" - }, "optimize": { "type": "string", "tooltip": "

Metric name to be evaluated for hyperparameter tuning. It also accepts custom\nmetrics that are added through the add_metric function.

\n", "default_val": "Accuracy" }, - "custom_scorer": { - "type": "object", - "tooltip": "

custom scoring strategy can be passed to tune hyperparameters of the model.\nIt must be created using sklearn.make_scorer. It is equivalent of adding\ncustom metric using the add_metric function and passing the name of the\ncustom metric in the optimize parameter.\nWill be deprecated in future.

\n", - "default_val": "None" - }, "search_library": { "type": "string", "tooltip": "

The search library used for tuning hyperparameters. Possible values:

\n\n", @@ -787,11 +666,6 @@ const classificationSettings = { "tooltip": "

When set to True, the returned object is always better performing. The\nmetric used for comparison is defined by the optimize parameter.

\n", "default_val": "True" }, - "fit_kwargs": { - "type": "dict", - "tooltip": "

Dictionary of arguments passed to the fit method of the tuner.

\n", - "default_val": "{} (empty dict)" - }, "groups": { "type": "string", "tooltip": "

Optional group labels when GroupKFold is used for the cross validation.\nIt takes an array with shape (n_samples, ) where n_samples is the number\nof rows in training dataset. When string is passed, it is interpreted as\nthe column name in the dataset containing group labels.

\n", @@ -854,11 +728,6 @@ const classificationSettings = { "tooltip": "

Metric to compare for model selection when choose_better is True.

\n", "default_val": "Accuracy" }, - "fit_kwargs": { - "type": "dict", - "tooltip": "

Dictionary of arguments passed to the fit method of the model.

\n", - "default_val": "{} (empty dict)" - }, "groups": { "type": "string", "tooltip": "

Optional group labels when GroupKFold is used for the cross validation.\nIt takes an array with shape (n_samples, ) where n_samples is the number\nof rows in training dataset. When string is passed, it is interpreted as\nthe column name in the dataset containing group labels.

\n", @@ -916,11 +785,6 @@ const classificationSettings = { "tooltip": "

Sequence of weights (float or int) to weight the occurrences of predicted class\nlabels (hard voting) or class probabilities before averaging (soft voting). Uses\nuniform weights when None.

\n", "default_val": "None" }, - "fit_kwargs": { - "type": "dict", - "tooltip": "

Dictionary of arguments passed to the fit method of the model.

\n", - "default_val": "{} (empty dict)" - }, "groups": { "type": "string", "tooltip": "

Optional group labels when GroupKFold is used for the cross validation.\nIt takes an array with shape (n_samples, ) where n_samples is the number\nof rows in training dataset. When string is passed, it is interpreted as\nthe column name in the dataset containing group labels.

\n", @@ -1008,11 +872,6 @@ const classificationSettings = { "tooltip": "

Metric to compare for model selection when choose_better is True.

\n", "default_val": "Accuracy" }, - "fit_kwargs": { - "type": "dict", - "tooltip": "

Dictionary of arguments passed to the fit method of the model.

\n", - "default_val": "{} (empty dict)" - }, "groups": { "type": "string", "tooltip": "

Optional group labels when GroupKFold is used for the cross validation.\nIt takes an array with shape (n_samples, ) where n_samples is the number\nof rows in training dataset. When string is passed, it is interpreted as\nthe column name in the dataset containing group labels.

\n", @@ -1060,11 +919,6 @@ const classificationSettings = { "tooltip": "

Number of decimal places the metrics in the score grid will be rounded to.

\n", "default_val": "4" }, - "fit_kwargs": { - "type": "dict", - "tooltip": "

Dictionary of arguments passed to the fit method of the model.

\n", - "default_val": "{} (empty dict)" - }, "groups": { "type": "string", "tooltip": "

Optional group labels when GroupKFold is used for the cross validation.\nIt takes an array with shape (n_samples, ) where n_samples is the number\nof rows in training dataset. When string is passed, it is interpreted as\nthe column name in the dataset containing group labels.

\n", diff --git a/pythonCode/med_libs/MEDml/utils/settings_generator/regressionSettings.js b/pythonCode/med_libs/MEDml/utils/settings_generator/regressionSettings.js index b8facb2f..16e5e739 100644 --- a/pythonCode/med_libs/MEDml/utils/settings_generator/regressionSettings.js +++ b/pythonCode/med_libs/MEDml/utils/settings_generator/regressionSettings.js @@ -122,11 +122,6 @@ const regressionSettings = { }, "dataset": { "options": { - "data_func": { - "type": "data-function", - "tooltip": "

The function that generate data (the dataframe-like input). This\nis useful when the dataset is large, and you need parallel operations\nsuch as compare_models. It can avoid broadcasting large dataset\nfrom driver to workers. Notice one and only one of data and\ndata_func must be set.

\n", - "default_val": "" - }, "index": { "type": "bool-int-str", "tooltip": "
\n
Handle indices in the data dataframe.
\n
\n
\n", @@ -142,11 +137,6 @@ const regressionSettings = { "tooltip": "

If not None, test_data is used as a hold-out set and train_size parameter\nis ignored. The columns of data and test_data must match.

\n", "default_val": "None" }, - "ordinal_features": { - "type": "dict", - "tooltip": "

Categorical features to be encoded ordinally. For example, a categorical\nfeature with \u2018low\u2019, \u2018medium\u2019, \u2018high\u2019 values where low < medium < high can\nbe passed as ordinal_features = {\u2018column_name\u2019 : [\u2018low\u2019, \u2018medium\u2019, \u2018high\u2019]}.

\n", - "default_val": "None" - }, "numeric_features": { "type": "custom-list", "tooltip": "

If the inferred data types are not correct, the numeric_features param can\nbe used to define the data types. It takes a list of strings with column\nnames that are numeric.

\n", @@ -197,11 +187,6 @@ const regressionSettings = { "tooltip": "

Categorical columns with max_encoding_ohe or less unique values are\nencoded using OneHotEncoding. If more, the encoding_method estimator\nis used. Note that columns with exactly two classes are always encoded\nordinally. Set to below 0 to always use OneHotEncoding.

\n", "default_val": "25" }, - "encoding_method": { - "type": "category-encoders estimator", - "tooltip": "

A category-encoders estimator to encode the categorical columns\nwith more than max_encoding_ohe unique values. If None,\ncategory_encoders.target_encoder.TargetEncoder is used.

\n", - "default_val": "None" - }, "rare_to_value": { "type": "float", "tooltip": "

Minimum fraction of category occurrences in a categorical column.\nIf a category is less frequent than rare_to_value * len(X), it is\nreplaced with the string in rare_value. Use this parameter to group\nrare categories before encoding the column. If None, ignores this step.

\n", @@ -217,11 +202,6 @@ const regressionSettings = { "tooltip": "

Remove features with a training-set variance lower than the provided\nthreshold. If 0, keep all features with non-zero variance, i.e. remove\nthe features that have the same value in all samples. If None, skip\nthis transformation step.

\n", "default_val": "None" }, - "group_features": { - "type": "dict", - "tooltip": "

When the dataset contains features with related characteristics,\nadd new fetaures with the following statistical properties of that\ngroup: min, max, mean, std, median and mode. The parameter takes a\ndict with the group name as key and a list of feature names\nbelonging to that group as value.

\n", - "default_val": "None" - }, "drop_groups": { "type": "bool", "tooltip": "

Whether to drop the original features in the group. Ignored when\ngroup_features is None.

\n", @@ -247,11 +227,6 @@ const regressionSettings = { "tooltip": "

Defines the method for transformation. By default, the transformation method is\nset to \u2018yeo-johnson\u2019. The other available option for transformation is \u2018quantile\u2019.\nIgnored when transform_target is not True.

\n", "default_val": "yeo-johnson" }, - "custom_pipeline": { - "type": "list of (str, transformer), dict or Pipeline", - "tooltip": "

Addidiotnal custom transformers. If passed, they are applied to the\npipeline last, after all the build-in transformers.

\n", - "default_val": "None" - }, "custom_pipeline_position": { "type": "int", "tooltip": "

Position of the custom pipeline in the overal preprocessing pipeline.\nThe default value adds the custom pipeline last.

\n", @@ -317,11 +292,6 @@ const regressionSettings = { "tooltip": "

Name of the experiment for logging. Ignored when log_experiment is False.

\n", "default_val": "None" }, - "experiment_custom_tags": { - "type": "dict", - "tooltip": "

Dictionary of tag_name: String -> value: (String, but will be string-ified\nif not) passed to the mlflow.set_tags to add new custom tags for the experiment.

\n", - "default_val": "None" - }, "log_plots": { "type": "bool", "tooltip": "

When set to True, certain plots are logged automatically in the MLFlow server.\nTo change the type of plots to be logged, pass a list containing plot IDs. Refer\nto documentation of plot_model. Ignored when log_experiment is False.

\n", @@ -337,31 +307,11 @@ const regressionSettings = { "tooltip": "

When set to True, dataset is logged on the MLflow server as a csv file.\nIgnored when log_experiment is False.

\n", "default_val": "False" }, - "engine": { - "type": "Optional[Dict[str, str]] = None", - "tooltip": "

The execution engines to use for the models in the form of a dict\nof model_id: engine - e.g. for Linear Regression (\u201clr\u201d, users can\nswitch between \u201csklearn\u201d and \u201csklearnex\u201d by specifying\nengine={\u201clr\u201d: \u201csklearnex\u201d}

\n", - "default_val": "" - }, "verbose": { "type": "bool", "tooltip": "

When set to False, Information grid is not printed.

\n", "default_val": "True" }, - "memory": { - "type": "str, bool or Memory", - "tooltip": "
\n
Used to cache the fitted transformers of the pipeline.

If False: No caching is performed.\nIf True: A default temp directory is used.\nIf str: Path to the caching directory.

\n
\n
\n", - "default_val": "rue" - }, - "profile": { - "type": "bool", - "tooltip": "

When set to True, an interactive EDA report is displayed.

\n", - "default_val": "False" - }, - "profile_kwargs": { - "type": "dict", - "tooltip": "

Dictionary of arguments passed to the ProfileReport method used\nto create the EDA report. Ignored if profile is False.

\n", - "default_val": "{} (empty dict)" - }, "time-point": { "type": "string", "default_val": "", @@ -474,21 +424,11 @@ const regressionSettings = { "tooltip": "

When set to \u2018ignore\u2019, will skip the model with exceptions and continue.\nIf \u2018raise\u2019, will break the function when exceptions are raised.

\n", "default_val": "ignore" }, - "fit_kwargs": { - "type": "dict", - "tooltip": "

Dictionary of arguments passed to the fit method of the model.

\n", - "default_val": "{} (empty dict)" - }, "groups": { "type": "string", "tooltip": "

Optional group labels when \u2018GroupKFold\u2019 is used for the cross validation.\nIt takes an array with shape (n_samples, ) where n_samples is the number\nof rows in the training dataset. When string is passed, it is interpreted\nas the column name in the dataset containing group labels.

\n", "default_val": "None" }, - "engine": { - "type": "Optional[Dict[str, str]] = None", - "tooltip": "

The execution engines to use for the models in the form of a dict\nof model_id: engine - e.g. for Linear Regression (\u201clr\u201d, users can\nswitch between \u201csklearn\u201d and \u201csklearnex\u201d by specifying\nengine={\u201clr\u201d: \u201csklearnex\u201d}

\n", - "default_val": "" - }, "verbose": { "type": "bool", "tooltip": "

Score grid is not printed when verbose is set to False.

\n", @@ -514,26 +454,11 @@ const regressionSettings = { "tooltip": "

When set to False, metrics are evaluated on holdout set. fold param\nis ignored when cross_validation is set to False.

\n", "default_val": "True" }, - "fit_kwargs": { - "type": "dict", - "tooltip": "

Dictionary of arguments passed to the fit method of the model.

\n", - "default_val": "{} (empty dict)" - }, "groups": { "type": "string", "tooltip": "

Optional group labels when GroupKFold is used for the cross validation.\nIt takes an array with shape (n_samples, ) where n_samples is the number\nof rows in training dataset. When string is passed, it is interpreted as\nthe column name in the dataset containing group labels.

\n", "default_val": "None" }, - "experiment_custom_tags": { - "type": "dict", - "tooltip": "

Dictionary of tag_name: String -> value: (String, but will be string-ified\nif not) passed to the mlflow.set_tags to add new custom tags for the experiment.

\n", - "default_val": "None" - }, - "engine": { - "type": "Optional[str] = None", - "tooltip": "

The execution engine to use for the model, e.g. for Linear Regression (\u201clr\u201d), users can\nswitch between \u201csklearn\u201d and \u201csklearnex\u201d by specifying\nengine=\u201dsklearnex\u201d.

\n", - "default_val": "" - }, "verbose": { "type": "bool", "tooltip": "

Score grid is not printed when verbose is set to False.

\n", @@ -566,16 +491,6 @@ const regressionSettings = { "tooltip": "

Controls cross-validation. If None, the CV generator in the fold_strategy\nparameter of the setup function is used. When an integer is passed,\nit is interpreted as the \u2018n_splits\u2019 parameter of the CV generator in the\nsetup function.

\n", "default_val": "None" }, - "fit_kwargs": { - "type": "dict", - "tooltip": "

Dictionary of arguments passed to the fit method of the model.

\n", - "default_val": "{} (empty dict)" - }, - "plot_kwargs": { - "type": "dict", - "tooltip": "
\n
Dictionary of arguments passed to the visualizer class.
\n
\n
\n", - "default_val": "{} (empty dict)" - }, "groups": { "type": "string", "tooltip": "

Optional group labels when GroupKFold is used for the cross validation.\nIt takes an array with shape (n_samples, ) where n_samples is the number\nof rows in training dataset. When string is passed, it is interpreted as\nthe column name in the dataset containing group labels.

\n", @@ -637,16 +552,6 @@ const regressionSettings = { "type": "string", "tooltip": "

Render mode for the dashboard. The default is set to dash which will\nrender a dashboard in browser. There are four possible options:

\n\n", "default_val": "dash" - }, - "dashboard_kwargs": { - "type": "dict", - "tooltip": "

Dictionary of arguments passed to the ExplainerDashboard class.

\n", - "default_val": "{} (empty dict)" - }, - "run_kwargs": { - "type": "dict", - "tooltip": "

Dictionary of arguments passed to the run method of ExplainerDashboard.

\n", - "default_val": "{} (empty dict)" } }, "code": "dashboard()", @@ -655,11 +560,6 @@ const regressionSettings = { }, "finalize": { "options": { - "fit_kwargs": { - "type": "dict", - "tooltip": "

Dictionary of arguments passed to the fit method of the model.

\n", - "default_val": "{} (empty dict)" - }, "groups": { "type": "string", "tooltip": "

Optional group labels when GroupKFold is used for the cross validation.\nIt takes an array with shape (n_samples, ) where n_samples is the number\nof rows in training dataset. When string is passed, it is interpreted as\nthe column name in the dataset containing group labels.

\n", @@ -669,11 +569,6 @@ const regressionSettings = { "type": "bool", "tooltip": "

Whether to return the complete fitted pipeline or only the fitted model.

\n", "default_val": "False" - }, - "experiment_custom_tags": { - "type": "dict", - "tooltip": "

Dictionary of tag_name: String -> value: (String, but will be string-ified if\nnot) passed to the mlflow.set_tags to add new custom tags for the experiment.

\n", - "default_val": "None" } }, "code": "", @@ -702,16 +597,6 @@ const regressionSettings = { }, "load_model": { "options": { - "platform": { - "type": "string", - "tooltip": "

Name of the cloud platform. Currently supported platforms:\n\u2018aws\u2019, \u2018gcp\u2019 and \u2018azure\u2019.

\n", - "default_val": "None" - }, - "authentication": { - "type": "dict", - "tooltip": "

dictionary of applicable authentication tokens.

\n

when platform = \u2018aws\u2019:\n{\u2018bucket\u2019 : \u2018Name of Bucket on S3\u2019, \u2018path\u2019: (optional) folder name under the bucket}

\n

when platform = \u2018gcp\u2019:\n{\u2018project\u2019: \u2018gcp-project-name\u2019, \u2018bucket\u2019 : \u2018gcp-bucket-name\u2019}

\n

when platform = \u2018azure\u2019:\n{\u2018container\u2019: \u2018azure-container-name\u2019}

\n", - "default_val": "None" - }, "verbose": { "type": "bool", "tooltip": "

Success message is not printed when verbose is set to False.

\n", @@ -726,6 +611,10 @@ const regressionSettings = { } } }, + "group_models": { + "options": {}, + "code": "" + }, "tune_model": { "options": { "fold": { @@ -743,21 +632,11 @@ const regressionSettings = { "tooltip": "

Number of iterations in the grid search. Increasing \u2018n_iter\u2019 may improve\nmodel performance but also increases the training time.

\n", "default_val": "10" }, - "custom_grid": { - "type": "dict", - "tooltip": "

To define custom search space for hyperparameters, pass a dictionary with\nparameter name and values to be iterated. Custom grids must be in a format\nsupported by the defined search_library.

\n", - "default_val": "None" - }, "optimize": { "type": "string", "tooltip": "

Metric name to be evaluated for hyperparameter tuning. It also accepts custom\nmetrics that are added through the add_metric function.

\n", "default_val": "R2" }, - "custom_scorer": { - "type": "object", - "tooltip": "

custom scoring strategy can be passed to tune hyperparameters of the model.\nIt must be created using sklearn.make_scorer. It is equivalent of adding\ncustom metric using the add_metric function and passing the name of the\ncustom metric in the optimize parameter.\nWill be deprecated in future.

\n", - "default_val": "None" - }, "search_library": { "type": "string", "tooltip": "

The search library used for tuning hyperparameters. Possible values:

\n\n", @@ -783,11 +662,6 @@ const regressionSettings = { "tooltip": "

When set to True, the returned object is always better performing. The\nmetric used for comparison is defined by the optimize parameter.

\n", "default_val": "True" }, - "fit_kwargs": { - "type": "dict", - "tooltip": "

Dictionary of arguments passed to the fit method of the tuner.

\n", - "default_val": "{} (empty dict)" - }, "groups": { "type": "string", "tooltip": "

Optional group labels when GroupKFold is used for the cross validation.\nIt takes an array with shape (n_samples, ) where n_samples is the number\nof rows in training dataset. When string is passed, it is interpreted as\nthe column name in the dataset containing group labels.

\n", @@ -850,11 +724,6 @@ const regressionSettings = { "tooltip": "

Metric to compare for model selection when choose_better is True.

\n", "default_val": "R2" }, - "fit_kwargs": { - "type": "dict", - "tooltip": "

Dictionary of arguments passed to the fit method of the model.

\n", - "default_val": "{} (empty dict)" - }, "groups": { "type": "string", "tooltip": "

Optional group labels when GroupKFold is used for the cross validation.\nIt takes an array with shape (n_samples, ) where n_samples is the number\nof rows in training dataset. When string is passed, it is interpreted as\nthe column name in the dataset containing group labels.

\n", @@ -892,11 +761,6 @@ const regressionSettings = { "tooltip": "

Sequence of weights (float or int) to weight the occurrences of predicted class\nlabels (hard voting) or class probabilities before averaging (soft voting). Uses\nuniform weights when None.

\n", "default_val": "None" }, - "fit_kwargs": { - "type": "dict", - "tooltip": "

Dictionary of arguments passed to the fit method of the model.

\n", - "default_val": "{} (empty dict)" - }, "groups": { "type": "string", "tooltip": "

Optional group labels when GroupKFold is used for the cross validation.\nIt takes an array with shape (n_samples, ) where n_samples is the number\nof rows in training dataset. When string is passed, it is interpreted as\nthe column name in the dataset containing group labels.

\n", @@ -981,11 +845,6 @@ const regressionSettings = { "tooltip": "

Metric to compare for model selection when choose_better is True.

\n", "default_val": "R2" }, - "fit_kwargs": { - "type": "dict", - "tooltip": "

Dictionary of arguments passed to the fit method of the model.

\n", - "default_val": "{} (empty dict)" - }, "groups": { "type": "string", "tooltip": "

Optional group labels when GroupKFold is used for the cross validation.\nIt takes an array with shape (n_samples, ) where n_samples is the number\nof rows in training dataset. When string is passed, it is interpreted as\nthe column name in the dataset containing group labels.

\n", diff --git a/pythonCode/med_libs/MEDml/utils/settings_generator/settings_generator.py b/pythonCode/med_libs/MEDml/utils/settings_generator/settings_generator.py index 3d8b49b2..b9127457 100644 --- a/pythonCode/med_libs/MEDml/utils/settings_generator/settings_generator.py +++ b/pythonCode/med_libs/MEDml/utils/settings_generator/settings_generator.py @@ -12,6 +12,10 @@ import dpath.util as dp from collections.abc import MutableMapping +# EXAMPLE USAGE +# cd pythonCode/med_libs/MEDml/utils/settings_generator +# python settings_generator.py --ml_type classification + # python scripts arguments import argparse parser = argparse.ArgumentParser(description='Script so useful.') @@ -360,6 +364,39 @@ def specific_case(dict_settings: dict) -> dict: del dict_settings['analyze']['interpret_model']['options']['save'] del dict_settings['load_model']['options']['model_name'] + # NOT SUPPORTED + del dict_settings['dataset']['options']['data_func'] + del dict_settings['dataset']['options']['ordinal_features'] + del dict_settings['dataset']['options']['encoding_method'] + del dict_settings['dataset']['options']['group_features'] + del dict_settings['dataset']['options']['custom_pipeline'] + del dict_settings['dataset']['options']['experiment_custom_tags'] + del dict_settings['dataset']['options']['engine'] + del dict_settings['dataset']['options']['memory'] + del dict_settings['dataset']['options']['profile'] + del dict_settings['dataset']['options']['profile_kwargs'] + del dict_settings['compare_models']['options']['engine'] + del dict_settings['compare_models']['options']['fit_kwargs'] + del dict_settings['create_model']['options']['engine'] + del dict_settings['create_model']['options']['fit_kwargs'] + del dict_settings['create_model']['options']['experiment_custom_tags'] + del dict_settings['analyze']['plot_model']['options']['fit_kwargs'] + del dict_settings['analyze']['plot_model']['options']['plot_kwargs'] + del dict_settings['analyze']['dashboard']['options']['dashboard_kwargs'] + del dict_settings['analyze']['dashboard']['options']['run_kwargs'] + del dict_settings['finalize']['options']['fit_kwargs'] + del dict_settings['finalize']['options']['experiment_custom_tags'] + del dict_settings['load_model']['options']['platform'] + del dict_settings['load_model']['options']['authentication'] + del dict_settings['tune_model']['options']['custom_grid'] + del dict_settings['tune_model']['options']['custom_scorer'] + del dict_settings['tune_model']['options']['fit_kwargs'] + del dict_settings['ensemble_model']['options']['fit_kwargs'] + del dict_settings['blend_models']['options']['fit_kwargs'] + del dict_settings['stack_models']['options']['fit_kwargs'] + if ml_type == "classification": + del dict_settings['calibrate_model']['options']['fit_kwargs'] + return dict_settings diff --git a/pythonCode/modules/learning/json_params_dict.json b/pythonCode/modules/learning/json_params_dict.json new file mode 100644 index 00000000..9085faa1 --- /dev/null +++ b/pythonCode/modules/learning/json_params_dict.json @@ -0,0 +1,839 @@ +{ + "MLType": "classification", + "nodes": { + "Dataset_node_7ca364a2-3209-40a8-94c8-5d9da34a5ec4": { + "width": 230, + "height": 99, + "id": "Dataset_node_7ca364a2-3209-40a8-94c8-5d9da34a5ec4", + "type": "datasetNode", + "name": "Dataset", + "position": { + "x": -129.8892918286092, + "y": 143.1886986704983 + }, + "data": { + "internal": { + "name": "Dataset", + "img": "dataset.png", + "type": "dataset", + "results": { + "checked": false, + "contextChecked": true + }, + "hasRun": true, + "code": "", + "settings": { + "files": [ + { + "name": "T1_learning.csv", + "path": "C:\\Users\\gblai\\Documents\\github\\MEDomics\\WS_DEMO\\DATA\\learning\\T1_learning.csv", + "tags": [ + "tslab", + "nradiology", + "image", + "demographic", + "tschart", + "tsprocedure" + ], + "columnsTags": { + "attr_Platelet_Count__mean": [ + "tslab" + ], + "attr_Platelet_Count__minimum": [ + "tslab" + ], + "attr_Platelet_Count__root_mean_square": [ + "tslab" + ], + "attr_Platelet_Count__median": [ + "tslab" + ], + "attr_Platelet_Count__absolute_maximum": [ + "tslab" + ], + "attr_Platelet_Count__maximum": [ + "tslab" + ], + "attr_MCHC__absolute_maximum": [ + "tslab" + ], + "attr_MCHC__maximum": [ + "tslab" + ], + "attr_MCH__maximum": [ + "tslab" + ], + "attr_MCH__absolute_maximum": [ + "tslab" + ], + "attr0": [ + "nradiology" + ], + "attr1": [ + "nradiology" + ], + "attr2": [ + "nradiology" + ], + "attr3": [ + "nradiology" + ], + "attr4": [ + "nradiology" + ], + "attr5": [ + "nradiology" + ], + "attr6": [ + "nradiology" + ], + "attr7": [ + "nradiology" + ], + "attr8": [ + "nradiology" + ], + "attr9": [ + "nradiology" + ], + "attr0_(1)": [ + "image" + ], + "attr1_(1)": [ + "image" + ], + "attr2_(1)": [ + "image" + ], + "attr3_(1)": [ + "image" + ], + "attr4_(1)": [ + "image" + ], + "attr5_(1)": [ + "image" + ], + "attr6_(1)": [ + "image" + ], + "attr7_(1)": [ + "image" + ], + "attr8_(1)": [ + "image" + ], + "attr9_(1)": [ + "image" + ], + "anchor_age": [ + "demographic" + ], + "gender_int": [ + "demographic" + ], + "ethnicity_int": [ + "demographic" + ], + "marital_status_int": [ + "demographic" + ], + "language_int": [ + "demographic" + ], + "insurance_int": [ + "demographic" + ], + "attr_GCS_Eye_Opening__median": [ + "tschart" + ], + "attr_GCS_Verbal_Response__median": [ + "tschart" + ], + "attr_GCS_Motor_Response__median": [ + "tschart" + ], + "attr_GCS_Motor_Response__mean": [ + "tschart" + ], + "attr_GCS_Motor_Response__root_mean_square": [ + "tschart" + ], + "attr_Heart_Rate__root_mean_square": [ + "tschart" + ], + "attr_Heart_Rate__mean": [ + "tschart" + ], + "attr_GCS_Motor_Response__minimum": [ + "tschart" + ], + "attr_GCS_Eye_Opening__root_mean_square": [ + "tschart" + ], + "attr_GCS_Eye_Opening__maximum": [ + "tschart" + ], + "attr_EEG__sum_values": [ + "tsprocedure" + ], + "attr_Dialysis_CRRT__absolute_maximum": [ + "tsprocedure" + ], + "attr_Dialysis_CRRT__maximum": [ + "tsprocedure" + ], + "attr_Dialysis_CRRT__median": [ + "tsprocedure" + ], + "attr_Peritoneal_Dialysis__minimum": [ + "tsprocedure" + ], + "attr_Dialysis_CRRT__root_mean_square": [ + "tsprocedure" + ], + "attr_Dialysis_CRRT__mean": [ + "tsprocedure" + ], + "attr_Peritoneal_Dialysis__length": [ + "tsprocedure" + ], + "attr_Chest_Tube_Removed__length": [ + "tsprocedure" + ], + "attr_Chest_Tube_Removed__sum_values": [ + "tsprocedure" + ] + } + } + ], + "tags": [ + "tslab" + ], + "multipleColumns": { + "T1_subject_id": "T1_subject_id", + "T1_attr_Platelet_Count__mean": "T1_attr_Platelet_Count__mean", + "T1_attr_Platelet_Count__minimum": "T1_attr_Platelet_Count__minimum", + "T1_attr_Platelet_Count__root_mean_square": "T1_attr_Platelet_Count__root_mean_square", + "T1_attr_Platelet_Count__median": "T1_attr_Platelet_Count__median", + "T1_attr_Platelet_Count__absolute_maximum": "T1_attr_Platelet_Count__absolute_maximum", + "T1_attr_Platelet_Count__maximum": 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}, + "1": { + "type": "code", + "content": "for model in trained_models:", + "indent": 0 + }, + "2": { + "type": "code", + "content": " pycaret_exp.plot_model(model, plot='error', plot_kwargs={})", + "indent": 1 + } + }, + "imports": {} + } + } + } + }, + "results": { + "prev_node_id": "node_6d70492f-94b9-46f4-8373-cbc62ce0d167*node_6c590571-e35f-4ba4-acf4-556a1b8a81c8", + "data": { + "VotingClassifier": { + "estimators": {}, + "voting": "soft", + "weights": null, + "n_jobs": -1, + "flatten_transform": true, + "verbose": false, + "classes_": { + "0": 0, + "1": 1 + }, + "estimators_": {}, + "named_estimators_": {}, + "feature_names_in_": { + "0": "subject_id", + "1": "tslab_|_attr_Platelet_Count__mean_T1", + "2": "tslab_|_attr_Platelet_Count__minimum_T1", + "3": "tslab_|_attr_Platelet_Count__root_mean_square_T1", + "4": "tslab_|_attr_Platelet_Count__median_T1", + "5": "tslab_|_attr_Platelet_Count__absolute_maximum_T1", + "6": "tslab_|_attr_Platelet_Count__maximum_T1", + "7": 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{ + "type": "code", + "content": "trained_models_optimized = []", + "indent": 0 + }, + "2": { + "type": "code", + "content": "for model in trained_models:", + "indent": 0 + }, + "3": { + "type": "code", + "content": " optimized_model = pycaret_exp.tune_model(model, )", + "indent": 1 + }, + "4": { + "type": "code", + "content": " trained_models_optimized.append(optimized_model)", + "indent": 1 + }, + "5": { + "type": "code", + "content": "trained_models = trained_models_optimized", + "indent": 0 + } + }, + "imports": {} + } + } + } + }, + "results": { + "prev_node_id": "node_50f78ff3-9c61-438d-9276-98415ac9f661", + "data": { + "LogisticRegression": { + "penalty": "l2", + "dual": false, + "tol": 0.0001, + "C": 1.0, + "fit_intercept": true, + "intercept_scaling": 1, + "class_weight": null, + "random_state": 6281, + "solver": "lbfgs", + "max_iter": 1000, + "multi_class": "auto", + "verbose": 0, + "warm_start": false, + "n_jobs": null, + "l1_ratio": null, + "feature_names_in_": { + "0": 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"type": "code", + "content": "trained_models = []", + "indent": 0 + }, + "2": { + "type": "code", + "content": "trained_models += [pycaret_exp.create_model(estimator='lr')]", + "indent": 0 + }, + "3": { + "type": "code", + "content": "trained_models += [pycaret_exp.create_model(estimator='et')]", + "indent": 0 + } + }, + "imports": {} + } + } + }, + "node_427644ee-74b7-4615-9e0b-e787369ae08e": { + "next_nodes": { + "node_1c1021bc-f108-45ff-8a2d-56a076ad8b8b": { + "next_nodes": {}, + "results": { + "prev_node_id": "node_427644ee-74b7-4615-9e0b-e787369ae08e", + "data": { + "VotingClassifier": "C:\\Users\\gblai\\Documents\\github\\MEDomics\\WS_DEMO\\EXPERIMENTS\\test\\test\\tmp\\5-VotingClassifier.png" + }, + "logs": {}, + "code": { + "content": { + "0": { + "type": "md", + "content": "### This is Analyze", + "indent": 0 + }, + "1": { + "type": "code", + "content": "for model in trained_models:", + "indent": 0 + }, + "2": { + "type": "code", + "content": " pycaret_exp.plot_model(model, 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"tschart_|_attr_GCS_Motor_Response__minimum_T1", + "45": "tschart_|_attr_GCS_Eye_Opening__root_mean_square_T1", + "46": "tschart_|_attr_GCS_Eye_Opening__maximum_T1", + "47": "tsprocedure_|_attr_Dialysis_CRRT__absolute_maximum_T1", + "48": "tsprocedure_|_attr_Dialysis_CRRT__maximum_T1", + "49": "tsprocedure_|_attr_Dialysis_CRRT__median_T1", + "50": "tsprocedure_|_attr_Peritoneal_Dialysis__minimum_T1", + "51": "tsprocedure_|_attr_Dialysis_CRRT__root_mean_square_T1", + "52": "tsprocedure_|_attr_Dialysis_CRRT__mean_T1", + "53": "tsprocedure_|_attr_Peritoneal_Dialysis__length_T1", + "54": "tsprocedure_|_attr_Chest_Tube_Removed__length_T1", + "55": "tsprocedure_|_attr_Chest_Tube_Removed__sum_values_T1" + }, + "n_features_in_": 56, + "n_outputs_": 1, + "classes_": { + "0": 0, + "1": 1 + }, + "n_classes_": 2, + "estimators_": {} + } + }, + "logs": { + "Extra Trees Classifier": { + "params": { + "bootstrap": false, + "ccp_alpha": 0.0, + "class_weight": null, + "criterion": "gini", + "max_depth": null, + "max_features": "sqrt", + "max_leaf_nodes": null, + "max_samples": null, + "min_impurity_decrease": 0.0, + "min_samples_leaf": 1, + "min_samples_split": 2, + "min_weight_fraction_leaf": 0.0, + "n_estimators": 100, + "n_jobs": -1, + "oob_score": false, + "random_state": 6281, + "verbose": 0, + "warm_start": false + }, + "metrics": { + "Accuracy": 0.5833, + "AUC": 0.5394, + "Recall": 0.5567, + "Prec.": 0.6605, + "F1": 0.5787, + "Kappa": 0.1728, + "MCC": 0.1952, + "TT": 0.08 + } + } + }, + "code": { + "content": { + "0": { + "type": "md", + "content": "### This is Train model", + "indent": 0 + }, + "1": { + "type": "code", + "content": "trained_models = [pycaret_exp.create_model(estimator='et')]", + "indent": 0 + } + }, + "imports": {} + } + } + } + }, + "results": { + "prev_node_id": null, + "data": { + "table": 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+ "paths": { + "0": { + "0": { + "name": "T1_learning.csv", + "path": "C:\\Users\\gblai\\Documents\\github\\MEDomics\\WS_DEMO\\DATA\\learning\\T1_learning.csv", + "tags": { + "0": "tslab", + "1": "nradiology", + "2": "image", + "3": "demographic", + "4": "tschart", + "5": "tsprocedure" + }, + "columnsTags": { + "attr_Platelet_Count__mean": { + "0": "tslab" + }, + "attr_Platelet_Count__minimum": { + "0": "tslab" + }, + "attr_Platelet_Count__root_mean_square": { + "0": "tslab" + }, + "attr_Platelet_Count__median": { + "0": "tslab" + }, + "attr_Platelet_Count__absolute_maximum": { + "0": "tslab" + }, + "attr_Platelet_Count__maximum": { + "0": "tslab" + }, + "attr_MCHC__absolute_maximum": { + "0": "tslab" + }, + "attr_MCHC__maximum": { + "0": "tslab" + }, + "attr_MCH__maximum": { + "0": "tslab" + }, + "attr_MCH__absolute_maximum": { + "0": "tslab" + }, + "attr0": { + "0": "nradiology" + }, + "attr1": { + "0": "nradiology" + }, + "attr2": { + "0": "nradiology" + }, + "attr3": { + "0": "nradiology" + }, + "attr4": { + "0": "nradiology" + }, + "attr5": { + "0": "nradiology" + }, + "attr6": { + "0": "nradiology" + }, + "attr7": { + "0": "nradiology" + }, + "attr8": { + "0": "nradiology" + }, + "attr9": { + "0": "nradiology" + }, + "attr0_(1)": { + "0": "image" + }, + "attr1_(1)": { + "0": "image" + }, + "attr2_(1)": { + "0": "image" + }, + "attr3_(1)": { + "0": "image" + }, + "attr4_(1)": { + "0": "image" + }, + "attr5_(1)": { + "0": "image" + }, + "attr6_(1)": { + "0": "image" + }, + "attr7_(1)": { + "0": "image" + }, + "attr8_(1)": { + "0": "image" + }, + "attr9_(1)": { + "0": "image" + }, + "anchor_age": { + "0": "demographic" + }, + "gender_int": { + "0": "demographic" + }, + "ethnicity_int": { + "0": "demographic" + }, + "marital_status_int": { + "0": "demographic" + }, + "language_int": { + "0": "demographic" + }, + "insurance_int": { + "0": "demographic" + }, + "attr_GCS_Eye_Opening__median": { + "0": "tschart" + }, + "attr_GCS_Verbal_Response__median": { + "0": "tschart" + }, + "attr_GCS_Motor_Response__median": { + "0": "tschart" + }, + "attr_GCS_Motor_Response__mean": { + "0": "tschart" + }, + "attr_GCS_Motor_Response__root_mean_square": { + "0": "tschart" + }, + "attr_Heart_Rate__root_mean_square": { + "0": "tschart" + }, + "attr_Heart_Rate__mean": { + "0": "tschart" + }, + "attr_GCS_Motor_Response__minimum": { + "0": "tschart" + }, + "attr_GCS_Eye_Opening__root_mean_square": { + "0": "tschart" + }, + "attr_GCS_Eye_Opening__maximum": { + "0": "tschart" + }, + "attr_EEG__sum_values": { + "0": "tsprocedure" + }, + "attr_Dialysis_CRRT__absolute_maximum": { + "0": "tsprocedure" + }, + "attr_Dialysis_CRRT__maximum": { + "0": "tsprocedure" + }, + "attr_Dialysis_CRRT__median": { + "0": "tsprocedure" + }, + "attr_Peritoneal_Dialysis__minimum": { + "0": "tsprocedure" + }, + "attr_Dialysis_CRRT__root_mean_square": { + "0": "tsprocedure" + }, + "attr_Dialysis_CRRT__mean": { + "0": "tsprocedure" + }, + "attr_Peritoneal_Dialysis__length": { + "0": "tsprocedure" + }, + "attr_Chest_Tube_Removed__length": { + "0": "tsprocedure" + }, + "attr_Chest_Tube_Removed__sum_values": { + "0": "tsprocedure" + } + } + } + } + } + }, + "code": { + "content": { + "0": { + "type": "md", + "content": "### This is Dataset", + "indent": 0 + }, + "1": { + "type": "code", + "content": "df_path_list = ['C:\\\\Users\\\\gblai\\\\Documents\\\\github\\\\MEDomics\\\\WS_DEMO\\\\DATA\\\\learning\\\\T1_learning.csv']", + "indent": 0 + }, + "2": { + "type": "code", + "content": "df_name_list = ['T1_learning.csv']", + "indent": 0 + }, + "3": { + "type": "code", + "content": "df_dict = {} # dict containing time points to their associated files", + "indent": 0 + }, + "4": { + "type": "code", + "content": "for i, name in enumerate(df_name_list): # if the filename not contains T+number we don't keep it, else we associate it to his time point number", + "indent": 0 + }, + "5": { + "type": "code", + "content": " number = ''", + "indent": 1 + }, + "6": { + "type": "code", + "content": " T_in_name = False", + "indent": 1 + }, + "7": { + "type": "code", + "content": " for char in name:", + "indent": 1 + }, + "8": { + "type": "code", + "content": " if char == 'T':", + "indent": 2 + }, + "9": { + "type": "code", + "content": " T_in_name=True", + "indent": 3 + }, + "10": { + "type": "code", + "content": " elif T_in_name and char.isdigit():", + "indent": 2 + }, + "11": { + "type": "code", + "content": " number += char", + "indent": 3 + }, + "12": { + "type": "code", + "content": " elif T_in_name:", + "indent": 2 + }, + "13": { + "type": "code", + "content": " break", + "indent": 3 + }, + "14": { + "type": "code", + "content": " if len(number) > 0:", + "indent": 1 + }, + "15": { + "type": "code", + "content": " df_dict['_T' + number] = pd.read_csv(df_path_list[i], sep=',', encoding='utf-8')", + "indent": 2 + }, + "16": { + "type": "code", + "content": "first_col = 'subject_id'", + "indent": 0 + }, + "17": { + "type": "code", + "content": "target = 'target'", + "indent": 0 + }, + "18": { + "type": "code", + "content": "# for each dataframe, add a suffix to their columns", + "indent": 0 + }, + "19": { + "type": "code", + "content": "for key in df_dict:", + "indent": 0 + }, + "20": { + "type": "code", + "content": " df_dict[key].columns = [f'{col}{key}' if col != target and col != first_col else col for col in df_dict[key].columns]", + "indent": 1 + }, + "21": { + "type": "code", + "content": "sorted_keys = sorted(df_dict.keys(), key=lambda x: int(x.split('_T')[1]))", + "indent": 0 + }, + "22": { + "type": "code", + "content": "df_list = [df_dict[key] for key in sorted_keys]", + "indent": 0 + }, + "23": { + "type": "seperator", + "content": "---------------------", + "indent": 0 + }, + "24": { + "type": "code", + "content": "# merge the dataframes on the first column and the target", + "indent": 0 + }, + "25": { + "type": "code", + "content": "df_merged = df_list[0]", + "indent": 0 + }, + "26": { + "type": "code", + "content": "for i in range(len(df_list) - 1):", + "indent": 0 + }, + "27": { + "type": "code", + "content": " df_merged = df_merged.merge(df_list[i + 1], on=[first_col, target], how='outer')", + "indent": 1 + }, + "28": { + "type": "seperator", + "content": "---------------------", + "indent": 0 + }, + "29": { + "type": "code", + "content": "# Drop all columns not containing tags from tags list and columns (variables) from vars list", + "indent": 0 + }, + "30": { + "type": "code", + "content": "tags_list = ['tslab', 'nradiology', 'image', 'demographic', 'tschart', 'tsprocedure']", + "indent": 0 + }, + "31": { + "type": "code", + "content": "vars_list = ['attr_Platelet_Count__mean_T1', 'attr_Platelet_Count__minimum_T1', 'attr_Platelet_Count__root_mean_square_T1', 'attr_Platelet_Count__median_T1', 'attr_Platelet_Count__absolute_maximum_T1', 'attr_Platelet_Count__maximum_T1', 'attr_MCHC__absolute_maximum_T1', 'attr_MCHC__maximum_T1', 'attr_MCH__maximum_T1', 'attr_MCH__absolute_maximum_T1', 'attr0_T1', 'attr1_T1', 'attr2_T1', 'attr3_T1', 'attr4_T1', 'attr5_T1', 'attr6_T1', 'attr7_T1', 'attr8_T1', 'attr9_T1', 'attr0_(1)_T1', 'attr1_(1)_T1', 'attr2_(1)_T1', 'attr3_(1)_T1', 'attr4_(1)_T1', 'attr5_(1)_T1', 'attr6_(1)_T1', 'attr7_(1)_T1', 'attr8_(1)_T1', 'attr9_(1)_T1', 'anchor_age_T1', 'gender_int_T1', 'ethnicity_int_T1', 'marital_status_int_T1', 'language_int_T1', 'insurance_int_T1', 'attr_GCS_Eye_Opening__median_T1', 'attr_GCS_Verbal_Response__median_T1', 'attr_GCS_Motor_Response__median_T1', 'attr_GCS_Motor_Response__mean_T1', 'attr_GCS_Motor_Response__root_mean_square_T1', 'attr_Heart_Rate__root_mean_square_T1', 'attr_Heart_Rate__mean_T1', 'attr_GCS_Motor_Response__minimum_T1', 'attr_GCS_Eye_Opening__root_mean_square_T1', 'attr_GCS_Eye_Opening__maximum_T1', 'attr_EEG__sum_values_T1', 'attr_Dialysis_CRRT__absolute_maximum_T1', 'attr_Dialysis_CRRT__maximum_T1', 'attr_Dialysis_CRRT__median_T1', 'attr_Peritoneal_Dialysis__minimum_T1', 'attr_Dialysis_CRRT__root_mean_square_T1', 'attr_Dialysis_CRRT__mean_T1', 'attr_Peritoneal_Dialysis__length_T1', 'attr_Chest_Tube_Removed__length_T1', 'attr_Chest_Tube_Removed__sum_values_T1']", + "indent": 0 + }, + "32": { + "type": "code", + "content": "cols_2_keep = [first_col, target]", + "indent": 0 + }, + "33": { + "type": "code", + "content": "for col in df_merged.columns:", + "indent": 0 + }, + "34": { + "type": "code", + "content": " if col in cols_2_keep:", + "indent": 1 + }, + "35": { + "type": "code", + "content": " continue", + "indent": 2 + }, + "36": { + "type": "code", + "content": " col_name = col.split('_|_')[1]", + "indent": 1 + }, + "37": { + "type": "code", + "content": " if col_name in vars_list:", + "indent": 1 + }, + "38": { + "type": "code", + "content": " cols_2_keep.append(col)", + "indent": 2 + }, + "39": { + "type": "code", + "content": "df = df_merged[cols_2_keep]", + "indent": 0 + }, + "40": { + "type": "seperator", + "content": "---------------------", + "indent": 0 + }, + "41": { + "type": "code", + "content": "pycaret_exp = ClassificationExperiment()", + "indent": 0 + }, + "42": { + "type": "code", + "content": "temp_df = df[df['target'].notna()]", + "indent": 0 + }, + "43": { + "type": "code", + "content": "temp_df.dropna(how='all', axis=1, inplace=True)", + "indent": 0 + }, + "44": { + "type": "code", + "content": "pycaret_exp.setup(temp_df, target='target')", + "indent": 0 + }, + "45": { + "type": "code", + "content": "dataset = pycaret_exp.get_config('X').join(pycaret_exp.get_config('y'))", + "indent": 0 + } + }, + "imports": { + "0": { + "type": "code", + "content": "import numpy as np", + "indent": 0 + }, + "1": { + "type": "code", + "content": "import pandas as pd", + "indent": 0 + }, + "2": { + "type": "code", + "content": "from pycaret.classification import *", + "indent": 0 + } + } + }, + "logs": { + "setup": { + "params": { + "Session id": 6281, + "Target": "target", + "Target type": "Binary", + "Original data shape": [ + 159, + 57 + ], + "Transformed data shape": [ + 159, + 57 + ], + "Transformed train set shape": [ + 111, + 57 + ], + "Transformed test set shape": [ + 48, + 57 + ], + "Numeric features": 56, + "Rows with missing values": "100.0%", + "Preprocess": true, + "Imputation type": "simple", + "Numeric imputation": "mean", + "Categorical imputation": "mode", + "Fold Generator": "StratifiedKFold", + "Fold Number": 10, + "CPU Jobs": -1, + "Use GPU": false, + "Experiment Name": "clf-default-name", + "USI": "aa63" + } + } + } + } + } +} \ No newline at end of file diff --git a/pythonCode/modules/learning/run_experiment.py b/pythonCode/modules/learning/run_experiment.py index 613196eb..bcc46e5e 100644 --- a/pythonCode/modules/learning/run_experiment.py +++ b/pythonCode/modules/learning/run_experiment.py @@ -135,10 +135,5 @@ def is_experiment_exist(id_): os.makedirs(local_path) return os.path.exists(os.path.join(local_path, 'MEDexperiment_' + id_ + '.medexp')) - -# save json_params_dict to a file -with open('json_params_dict.json', 'w') as f: - json.dump(json_params_dict, f, indent=4) - run_experiment = GoExecScriptRunExperiment(json_params_dict, id_, True) run_experiment.start() diff --git a/renderer/components/flow/node.jsx b/renderer/components/flow/node.jsx index 06dc8f72..e8009890 100644 --- a/renderer/components/flow/node.jsx +++ b/renderer/components/flow/node.jsx @@ -164,7 +164,7 @@ const NodeObject = ({ id, data, nodeSpecific, nodeBody, defaultSettings, onClick <> {/* here is an overlay panel that is displayed when the user clicks on the node name. It contains the settings of the node*/} op.current.hide(e)}> - +
diff --git a/renderer/components/learning/input.jsx b/renderer/components/learning/input.jsx index 13fde1a3..63404bf8 100644 --- a/renderer/components/learning/input.jsx +++ b/renderer/components/learning/input.jsx @@ -234,7 +234,7 @@ const Input = ({ name, settingInfos, currentValue, onInputChange, disabled, setH return ( <> - { @@ -257,11 +257,7 @@ const Input = ({ name, settingInfos, currentValue, onInputChange, disabled, setH type: settingInfos.type }) }} - > - - - - + /> {createTooltip(settingInfos.tooltip, name)} @@ -424,6 +420,34 @@ const Input = ({ name, settingInfos, currentValue, onInputChange, disabled, setH ) + case "dataframe": + return ( + <> + + { + console.log("e", e, path) + if (path == "") { + setHasWarning({ state: true, tooltip:

No file selected

}) + } else { + setHasWarning({ state: false }) + } + setInputUpdate({ + name: name, + value: { name: e.target.value, path: path }, + type: settingInfos.type + }) + }} + /> + + {createTooltip(settingInfos.tooltip, name)} + + ) + case "data-input-multiple": console.log("currentValue", currentValue) console.log("settingInfos", settingInfos) diff --git a/renderer/components/learning/modalSettingsChooser.jsx b/renderer/components/learning/modalSettingsChooser.jsx index 3774bb11..23880ec5 100644 --- a/renderer/components/learning/modalSettingsChooser.jsx +++ b/renderer/components/learning/modalSettingsChooser.jsx @@ -4,6 +4,7 @@ import Button from "react-bootstrap/Button" import CheckOption from "./checkOption" import { useState, useEffect } from "react" import { FlowFunctionsContext } from "../flow/context/flowFunctionsContext" +import { Message } from "primereact/message" /** * @@ -36,6 +37,7 @@ const ModalSettingsChooser = ({ show, onHide, options, id, data }) => { updatedData: data.internal }) } + console.log("checkedUpdate", data.internal) }, [checkedUpdate]) return ( @@ -44,10 +46,13 @@ const ModalSettingsChooser = ({ show, onHide, options, id, data }) => { {data.setupParam.title + " options"} + {data.internal.type == "model" && } {/* Display all the options available for the node */} - + {Object.entries(options).map(([optionName, optionInfos], i) => { - return + return ( + + ) })} diff --git a/renderer/public/setupVariables/possibleSettings/learning/classificationModelSettings.js b/renderer/public/setupVariables/possibleSettings/learning/classificationModelSettings.js index 490f8011..8c174ee0 100644 --- a/renderer/public/setupVariables/possibleSettings/learning/classificationModelSettings.js +++ b/renderer/public/setupVariables/possibleSettings/learning/classificationModelSettings.js @@ -1,1199 +1,1204 @@ /* eslint-disable */ const classificationModelSettings = { - "lr": { - "options": { - "penalty": { - "type": "string", - "default_val": "l2", - "tooltip": "tooltip not implemented" - }, - "dual": { - "type": "bool", - "default_val": "False", - "tooltip": "tooltip not implemented" - }, - "tol": { - "type": "float", - "default_val": "0.0001", - "tooltip": "tooltip not implemented" - }, - "C": { - "type": "float", - "default_val": "1.0", - "tooltip": "tooltip not implemented" - }, - "fit_intercept": { - "type": "bool", - "default_val": "True", - "tooltip": "tooltip not implemented" - }, - "intercept_scaling": { - "type": "int", - "default_val": "1", - "tooltip": "tooltip not implemented" - }, - "class_weight": { - "type": "NoneType", - "default_val": "None", - "tooltip": "tooltip not implemented" - }, - "random_state": { - "type": "int", - "default_val": "1334", - "tooltip": "tooltip not implemented" - }, - "solver": { - "type": "string", - "default_val": "lbfgs", - "tooltip": "tooltip not implemented" - }, - "max_iter": { - "type": "int", - "default_val": "1000", - "tooltip": "tooltip not implemented" - }, - "multi_class": { - "type": "string", - "default_val": "auto", - "tooltip": "tooltip not implemented" - }, - "verbose": { - "type": "int", - "default_val": "0", - "tooltip": "tooltip not implemented" - }, - "warm_start": { - "type": "bool", - "default_val": "False", - "tooltip": "tooltip not implemented" - }, - "n_jobs": { - "type": "NoneType", - "default_val": "None", - "tooltip": "tooltip not implemented" - }, - "l1_ratio": { - "type": "NoneType", - "default_val": "None", - "tooltip": "tooltip not implemented" - } - }, - "code": "lr" + lr: { + options: { + penalty: { + type: "string", + default_val: "l2", + tooltip: "Specifies the norm used in the penalization ('l1' or 'l2')." + }, + dual: { + type: "bool", + default_val: "False", + tooltip: "Whether to use a dual formulation." + }, + tol: { + type: "float", + default_val: "0.0001", + tooltip: "Tolerance for stopping criteria." + }, + C: { + type: "float", + default_val: "1.0", + tooltip: "Inverse of regularization strength." + }, + fit_intercept: { + type: "bool", + default_val: "True", + tooltip: "Specifies if a constant (a.k.a. bias or intercept) should be added to the decision function." + }, + intercept_scaling: { + type: "int", + default_val: "1", + tooltip: "Scaling of the intercept." + }, + class_weight: { + type: "NoneType", + default_val: "None", + tooltip: "Weights associated with classes." + }, + random_state: { + type: "int", + default_val: "1334", + tooltip: "Seed used by the random number generator." + }, + solver: { + type: "string", + default_val: "lbfgs", + tooltip: "Algorithm to use in the optimization problem." + }, + max_iter: { + type: "int", + default_val: "1000", + tooltip: "Maximum number of iterations taken for the solvers to converge." + }, + multi_class: { + type: "string", + default_val: "auto", + tooltip: "Approach to handle multiple classes." + }, + verbose: { + type: "int", + default_val: "0", + tooltip: "Controls the verbosity." + }, + warm_start: { + type: "bool", + default_val: "False", + tooltip: "Whether to reuse the solution of the previous call to fit as initialization." + }, + n_jobs: { + type: "NoneType", + default_val: "None", + tooltip: "Number of CPU cores used when parallelizing over classes." + }, + l1_ratio: { + type: "NoneType", + default_val: "None", + tooltip: "The mixing parameter, with 0 <= l1_ratio <= 1, for elastic net regularization." + } }, - "knn": { - "options": { - "n_neighbors": { - "type": "int", - "default_val": "5", - "tooltip": "tooltip not implemented" - }, - "radius": { - "type": "NoneType", - "default_val": "None", - "tooltip": "tooltip not implemented" - }, - "algorithm": { - "type": "string", - "default_val": "auto", - "tooltip": "tooltip not implemented" - }, - "leaf_size": { - "type": "int", - "default_val": "30", - "tooltip": "tooltip not implemented" - }, - "metric": { - "type": "string", - "default_val": "minkowski", - "tooltip": "tooltip not implemented" - }, - "metric_params": { - "type": "NoneType", - "default_val": "None", - "tooltip": "tooltip not implemented" - }, - "p": { - "type": "int", - "default_val": "2", - "tooltip": "tooltip not implemented" - }, - "n_jobs": { - "type": "int", - "default_val": "-1", - "tooltip": "tooltip not implemented" - }, - "weights": { - "type": "string", - "default_val": "uniform", - "tooltip": "tooltip not implemented" - } - }, - "code": "knn" + code: "lr" + }, + knn: { + options: { + n_neighbors: { + type: "int", + default_val: "5", + tooltip: "Number of neighbors to use for querying." + }, + radius: { + type: "NoneType", + default_val: "None", + tooltip: "Radius of neighborhoods for radius-based queries." + }, + algorithm: { + type: "string", + default_val: "auto", + tooltip: "Algorithm used to compute the nearest neighbors." + }, + leaf_size: { + type: "int", + default_val: "30", + tooltip: "Leaf size passed to BallTree or KDTree." + }, + metric: { + type: "string", + default_val: "minkowski", + tooltip: "Distance metric used for the tree." + }, + metric_params: { + type: "NoneType", + default_val: "None", + tooltip: "Additional keyword arguments for the metric function." + }, + p: { + type: "int", + default_val: "2", + tooltip: "Parameter for the Minkowski metric." + }, + n_jobs: { + type: "int", + default_val: "-1", + tooltip: "Number of parallel jobs to run for neighbors search." + }, + weights: { + type: "string", + default_val: "uniform", + tooltip: "Weight function used in prediction." + } }, - "nb": { - "options": { - "priors": { - "type": "NoneType", - "default_val": "None", - "tooltip": "tooltip not implemented" - }, - "var_smoothing": { - "type": "float", - "default_val": "1e-09", - "tooltip": "tooltip not implemented" - } - }, - "code": "nb" + code: "knn" + }, + nb: { + options: { + priors: { + type: "NoneType", + default_val: "None", + tooltip: "Prior probabilities of the classes." + }, + var_smoothing: { + type: "float", + default_val: "1e-09", + tooltip: "Portion of the largest variance of all features added to variances for calculation stability." + } }, - "dt": { - "options": { - "criterion": { - "type": "string", - "default_val": "gini", - "tooltip": "tooltip not implemented" - }, - "splitter": { - "type": "string", - "default_val": "best", - "tooltip": "tooltip not implemented" - }, - "max_depth": { - "type": "NoneType", - "default_val": "None", - "tooltip": "tooltip not implemented" - }, - "min_samples_split": { - "type": "int", - "default_val": "2", - "tooltip": "tooltip not implemented" - }, - "min_samples_leaf": { - "type": "int", - "default_val": "1", - "tooltip": "tooltip not implemented" - }, - "min_weight_fraction_leaf": { - "type": "float", - "default_val": "0.0", - "tooltip": "tooltip not implemented" - }, - "max_features": { - "type": "NoneType", - "default_val": "None", - "tooltip": "tooltip not implemented" - }, - "max_leaf_nodes": { - "type": "NoneType", - "default_val": "None", - "tooltip": "tooltip not implemented" - }, - "random_state": { - "type": "int", - "default_val": "1334", - "tooltip": "tooltip not implemented" - }, - "min_impurity_decrease": { - "type": "float", - "default_val": "0.0", - "tooltip": "tooltip not implemented" - }, - "class_weight": { - "type": "NoneType", - "default_val": "None", - "tooltip": "tooltip not implemented" - }, - "ccp_alpha": { - "type": "float", - "default_val": "0.0", - "tooltip": "tooltip not implemented" - } - }, - "code": "dt" + code: "nb" + }, + dt: { + options: { + criterion: { + type: "string", + default_val: "gini", + tooltip: "The function to measure the quality of a split." + }, + splitter: { + type: "string", + default_val: "best", + tooltip: "The strategy used to choose the split at each node." + }, + max_depth: { + type: "NoneType", + default_val: "None", + tooltip: "The maximum depth of the tree. If None, nodes are expanded until all leaves are pure or until all leaves contain less than min_samples_split samples." + }, + min_samples_split: { + type: "int", + default_val: "2", + tooltip: "The minimum number of samples required to split an internal node." + }, + min_samples_leaf: { + type: "int", + default_val: "1", + tooltip: "The minimum number of samples required to be at a leaf node." + }, + min_weight_fraction_leaf: { + type: "float", + default_val: "0.0", + tooltip: "The minimum weighted fraction of the sum total of weights (of all input samples) required to be at a leaf node." + }, + max_features: { + type: "NoneType", + default_val: "None", + tooltip: "The number of features to consider when looking for the best split." + }, + max_leaf_nodes: { + type: "NoneType", + default_val: "None", + tooltip: "Grow a tree with max_leaf_nodes in best-first fashion. Best nodes are defined as relative reduction in impurity." + }, + random_state: { + type: "int", + default_val: "1334", + tooltip: "The seed used by the random number generator." + }, + min_impurity_decrease: { + type: "float", + default_val: "0.0", + tooltip: "A node will be split if this split induces a decrease of the impurity greater than or equal to this value." + }, + class_weight: { + type: "NoneType", + default_val: "None", + tooltip: "Weights associated with classes in the form {class_label: weight}. If None, all classes are supposed to have weight one." + }, + ccp_alpha: { + type: "float", + default_val: "0.0", + tooltip: "Complexity parameter used for Minimal Cost-Complexity Pruning. The subtree with the largest cost complexity that is smaller than ccp_alpha will be chosen." + } }, - "svm": { - "options": { - "loss": { - "type": "string", - "default_val": "hinge", - "tooltip": "tooltip not implemented" - }, - "penalty": { - "type": "string", - "default_val": "l2", - "tooltip": "tooltip not implemented" - }, - "learning_rate": { - "type": "string", - "default_val": "optimal", - "tooltip": "tooltip not implemented" - }, - "epsilon": { - "type": "float", - "default_val": "0.1", - "tooltip": "tooltip not implemented" - }, - "alpha": { - "type": "float", - "default_val": "0.0001", - "tooltip": "tooltip not implemented" - }, - "C": { - "type": "float", - "default_val": "1.0", - "tooltip": "tooltip not implemented" - }, - "l1_ratio": { - "type": "float", - "default_val": "0.15", - "tooltip": "tooltip not implemented" - }, - "fit_intercept": { - "type": "bool", - "default_val": "True", - "tooltip": "tooltip not implemented" - }, - "shuffle": { - "type": "bool", - "default_val": "True", - "tooltip": "tooltip not implemented" - }, - "random_state": { - "type": "int", - "default_val": "1334", - "tooltip": "tooltip not implemented" - }, - "verbose": { - "type": "int", - "default_val": "0", - "tooltip": "tooltip not implemented" - }, - "eta0": { - "type": "float", - "default_val": "0.001", - "tooltip": "tooltip not implemented" - }, - "power_t": { - "type": "float", - "default_val": "0.5", - "tooltip": "tooltip not implemented" - }, - "early_stopping": { - "type": "bool", - "default_val": "False", - "tooltip": "tooltip not implemented" - }, - "validation_fraction": { - "type": "float", - "default_val": "0.1", - "tooltip": "tooltip not implemented" - }, - "n_iter_no_change": { - "type": "int", - "default_val": "5", - "tooltip": "tooltip not implemented" - }, - "warm_start": { - "type": "bool", - "default_val": "False", - "tooltip": "tooltip not implemented" - }, - "average": { - "type": "bool", - "default_val": "False", - "tooltip": "tooltip not implemented" - }, - "max_iter": { - "type": "int", - "default_val": "1000", - "tooltip": "tooltip not implemented" - }, - "tol": { - "type": "float", - "default_val": "0.001", - "tooltip": "tooltip not implemented" - }, - "class_weight": { - "type": "NoneType", - "default_val": "None", - "tooltip": "tooltip not implemented" - }, - "n_jobs": { - "type": "int", - "default_val": "-1", - "tooltip": "tooltip not implemented" - } - }, - "code": "svm" + code: "dt" + }, + svm: { + options: { + loss: { + type: "string", + default_val: "hinge", + tooltip: "The loss function to be used. 'hinge' is the standard SVM loss." + }, + penalty: { + type: "string", + default_val: "l2", + tooltip: "The penalty term ('l2' or 'l1') of the error term." + }, + learning_rate: { + type: "string", + default_val: "optimal", + tooltip: "The learning rate schedule. 'optimal' uses an heuristic proposed by Leon Bottou." + }, + epsilon: { + type: "float", + default_val: "0.1", + tooltip: "Epsilon in the epsilon-insensitive loss function." + }, + alpha: { + type: "float", + default_val: "0.0001", + tooltip: "Constant that multiplies the regularization term." + }, + C: { + type: "float", + default_val: "1.0", + tooltip: "Regularization parameter." + }, + l1_ratio: { + type: "float", + default_val: "0.15", + tooltip: "The ratio of l1 penalty in the 'elasticnet' regularization." + }, + fit_intercept: { + type: "bool", + default_val: "True", + tooltip: "Whether to calculate the intercept for this model." + }, + shuffle: { + type: "bool", + default_val: "True", + tooltip: "Whether or not the training data should be shuffled after each epoch." + }, + random_state: { + type: "int", + default_val: "1334", + tooltip: "The seed used by the random number generator." + }, + verbose: { + type: "int", + default_val: "0", + tooltip: "Enable verbose output when fitting the model." + }, + eta0: { + type: "float", + default_val: "0.001", + tooltip: "The initial learning rate for the 'constant', 'invscaling', or 'adaptive' schedules." + }, + power_t: { + type: "float", + default_val: "0.5", + tooltip: "The exponent for inverse scaling learning rate." + }, + early_stopping: { + type: "bool", + default_val: "False", + tooltip: "Whether to use early stopping to terminate training when validation score is not improving." + }, + validation_fraction: { + type: "float", + default_val: "0.1", + tooltip: "The proportion of training data to set aside as validation set for early stopping." + }, + n_iter_no_change: { + type: "int", + default_val: "5", + tooltip: "Number of iterations with no improvement to wait before stopping training." + }, + warm_start: { + type: "bool", + default_val: "False", + tooltip: "Whether to reuse the solution of the previous call to fit as initialization." + }, + average: { + type: "bool", + default_val: "False", + tooltip: "Whether to average the coefficients of the SVM over all the folds." + }, + max_iter: { + type: "int", + default_val: "1000", + tooltip: "The maximum number of iterations to be run." + }, + tol: { + type: "float", + default_val: "0.001", + tooltip: "Tolerance for stopping criteria." + }, + class_weight: { + type: "NoneType", + default_val: "None", + tooltip: "Weights associated with classes in the form {class_label: weight}." + }, + n_jobs: { + type: "int", + default_val: "-1", + tooltip: "The number of jobs to run in parallel." + } }, - "rbfsvm": { - "options": { - "decision_function_shape": { - "type": "string", - "default_val": "ovr", - "tooltip": "tooltip not implemented" - }, - "break_ties": { - "type": "bool", - "default_val": "False", - "tooltip": "tooltip not implemented" - }, - "kernel": { - "type": "string", - "default_val": "rbf", - "tooltip": "tooltip not implemented" - }, - "degree": { - "type": "int", - "default_val": "3", - "tooltip": "tooltip not implemented" - }, - "gamma": { - "type": "string", - "default_val": "auto", - "tooltip": "tooltip not implemented" - }, - "coef0": { - "type": "float", - "default_val": "0.0", - "tooltip": "tooltip not implemented" - }, - "tol": { - "type": "float", - "default_val": "0.001", - "tooltip": "tooltip not implemented" - }, - "C": { - "type": "float", - "default_val": "1.0", - "tooltip": "tooltip not implemented" - }, - "nu": { - "type": "float", - "default_val": "0.0", - "tooltip": "tooltip not implemented" - }, - "epsilon": { - "type": "float", - "default_val": "0.0", - "tooltip": "tooltip not implemented" - }, - "shrinking": { - "type": "bool", - "default_val": "True", - "tooltip": "tooltip not implemented" - }, - "probability": { - "type": "bool", - "default_val": "True", - "tooltip": "tooltip not implemented" - }, - "cache_size": { - "type": "int", - "default_val": "200", - "tooltip": "tooltip not implemented" - }, - "class_weight": { - "type": "NoneType", - "default_val": "None", - "tooltip": "tooltip not implemented" - }, - "verbose": { - "type": "bool", - "default_val": "False", - "tooltip": "tooltip not implemented" - }, - "max_iter": { - "type": "int", - "default_val": "-1", - "tooltip": "tooltip not implemented" - }, - "random_state": { - "type": "int", - "default_val": "1334", - "tooltip": "tooltip not implemented" - } - }, - "code": "rbfsvm" + code: "svm" + }, + rbfsvm: { + options: { + decision_function_shape: { + type: "string", + default_val: "ovr", + tooltip: "Specifies the shape of the decision function. 'ovr' stands for one-vs-rest, while 'ovo' stands for one-vs-one." + }, + break_ties: { + type: "bool", + default_val: "False", + tooltip: "If true, decision_function_shape='ovr', and number of classes > 2, predict will break ties according to the confidence values." + }, + kernel: { + type: "string", + default_val: "rbf", + tooltip: "Specifies the kernel type to be used in the algorithm. The default is 'rbf' (Radial Basis Function)." + }, + degree: { + type: "int", + default_val: "3", + tooltip: "Degree of the polynomial kernel function ('poly'). Ignored by all other kernels." + }, + gamma: { + type: "string", + default_val: "auto", + tooltip: "Kernel coefficient for 'rbf', 'poly', and 'sigmoid'. If 'auto', it uses 1 / n_features." + }, + coef0: { + type: "float", + default_val: "0.0", + tooltip: "Independent term in kernel function. It is only significant in 'poly' and 'sigmoid'." + }, + tol: { + type: "float", + default_val: "0.001", + tooltip: "Tolerance for stopping criterion." + }, + C: { + type: "float", + default_val: "1.0", + tooltip: "Regularization parameter. The strength of the regularization is inversely proportional to C." + }, + nu: { + type: "float", + default_val: "0.0", + tooltip: "An upper bound on the fraction of margin errors and a lower bound of the fraction of support vectors." + }, + epsilon: { + type: "float", + default_val: "0.0", + tooltip: "Epsilon in the epsilon-SVR model. It specifies the epsilon-tube within which no penalty is associated in the training loss function." + }, + shrinking: { + type: "bool", + default_val: "True", + tooltip: "Whether to use the shrinking heuristic." + }, + probability: { + type: "bool", + default_val: "True", + tooltip: "Whether to enable probability estimates." + }, + cache_size: { + type: "int", + default_val: "200", + tooltip: "Specify the size of the kernel cache (in MB)." + }, + class_weight: { + type: "NoneType", + default_val: "None", + tooltip: "Set the parameter C of class i to class_weight[i]*C for SVC. If not given, all classes are supposed to have weight one." + }, + verbose: { + type: "bool", + default_val: "False", + tooltip: "Enable verbose output." + }, + max_iter: { + type: "int", + default_val: "-1", + tooltip: "Hard limit on iterations within solver, or -1 for no limit." + }, + random_state: { + type: "int", + default_val: "1334", + tooltip: "The seed of the pseudo random number generator used when shuffling the data." + } }, - "gpc": { - "options": { - "kernel": { - "type": "NoneType", - "default_val": "None", - "tooltip": "tooltip not implemented" - }, - "optimizer": { - "type": "string", - "default_val": "fmin_l_bfgs_b", - "tooltip": "tooltip not implemented" - }, - "n_restarts_optimizer": { - "type": "int", - "default_val": "0", - "tooltip": "tooltip not implemented" - }, - "max_iter_predict": { - "type": "int", - "default_val": "100", - "tooltip": "tooltip not implemented" - }, - "warm_start": { - "type": "bool", - "default_val": "False", - "tooltip": "tooltip not implemented" - }, - "copy_X_train": { - "type": "bool", - "default_val": "False", - "tooltip": "tooltip not implemented" - }, - "random_state": { - "type": "int", - "default_val": "1334", - "tooltip": "tooltip not implemented" - }, - "multi_class": { - "type": "string", - "default_val": "one_vs_rest", - "tooltip": "tooltip not implemented" - }, - "n_jobs": { - "type": "int", - "default_val": "-1", - "tooltip": "tooltip not implemented" - } - }, - "code": "gpc" + code: "rbfsvm" + }, + gpc: { + options: { + kernel: { + type: "NoneType", + default_val: "None", + tooltip: "The kernel specifying the covariance function of the GP. If None is passed, the RBF kernel is used as default." + }, + optimizer: { + type: "string", + default_val: "fmin_l_bfgs_b", + tooltip: "A string or callable that specifies the optimizer to use for optimizing the kernel's parameters. The default is 'fmin_l_bfgs_b'." + }, + n_restarts_optimizer: { + type: "int", + default_val: "0", + tooltip: "The number of restarts of the optimizer for finding the kernel’s parameters which maximize the log-marginal likelihood. The default is 0." + }, + max_iter_predict: { + type: "int", + default_val: "100", + tooltip: "The maximum number of iterations in Newton’s method for approximating the posterior during prediction." + }, + warm_start: { + type: "bool", + default_val: "False", + tooltip: "If True, reuse the solution of the last call to fit as initialization for the next call to fit." + }, + copy_X_train: { + type: "bool", + default_val: "False", + tooltip: "If True, a persistent copy of the training data is stored in the object. Otherwise, just a reference to the training data is stored." + }, + random_state: { + type: "int", + default_val: "1334", + tooltip: "The seed of the pseudo-random number generator used when shuffling the data. This affects the optimizer if applicable." + }, + multi_class: { + type: "string", + default_val: "one_vs_rest", + tooltip: "Specifies the strategy to use for multi-class classification. The default is 'one_vs_rest'." + }, + n_jobs: { + type: "int", + default_val: "-1", + tooltip: "The number of jobs to run in parallel. If -1, then the number of jobs is set to the number of cores." + } }, - "mlp": { - "options": { - "activation": { - "type": "string", - "default_val": "relu", - "tooltip": "tooltip not implemented" - }, - "solver": { - "type": "string", - "default_val": "adam", - "tooltip": "tooltip not implemented" - }, - "alpha": { - "type": "float", - "default_val": "0.0001", - "tooltip": "tooltip not implemented" - }, - "batch_size": { - "type": "string", - "default_val": "auto", - "tooltip": "tooltip not implemented" - }, - "learning_rate": { - "type": "string", - "default_val": "constant", - "tooltip": "tooltip not implemented" - }, - "learning_rate_init": { - "type": "float", - "default_val": "0.001", - "tooltip": "tooltip not implemented" - }, - "power_t": { - "type": "float", - "default_val": "0.5", - "tooltip": "tooltip not implemented" - }, - "max_iter": { - "type": "int", - "default_val": "500", - "tooltip": "tooltip not implemented" - }, - "loss": { - "type": "string", - "default_val": "log_loss", - "tooltip": "tooltip not implemented" - }, - "hidden_layer_sizes": { - "type": "tuple", - "default_val": "(100,)", - "tooltip": "tooltip not implemented" - }, - "shuffle": { - "type": "bool", - "default_val": "True", - "tooltip": "tooltip not implemented" - }, - "random_state": { - "type": "int", - "default_val": "1334", - "tooltip": "tooltip not implemented" - }, - "tol": { - "type": "float", - "default_val": "0.0001", - "tooltip": "tooltip not implemented" - }, - "verbose": { - "type": "bool", - "default_val": "False", - "tooltip": "tooltip not implemented" - }, - "warm_start": { - "type": "bool", - "default_val": "False", - "tooltip": "tooltip not implemented" - }, - "momentum": { - "type": "float", - "default_val": "0.9", - "tooltip": "tooltip not implemented" - }, - "nesterovs_momentum": { - "type": "bool", - "default_val": "True", - "tooltip": "tooltip not implemented" - }, - "early_stopping": { - "type": "bool", - "default_val": "False", - "tooltip": "tooltip not implemented" - }, - "validation_fraction": { - "type": "float", - "default_val": "0.1", - "tooltip": "tooltip not implemented" - }, - "beta_1": { - "type": "float", - "default_val": "0.9", - "tooltip": "tooltip not implemented" - }, - "beta_2": { - "type": "float", - "default_val": "0.999", - "tooltip": "tooltip not implemented" - }, - "epsilon": { - "type": "float", - "default_val": "1e-08", - "tooltip": "tooltip not implemented" - }, - "n_iter_no_change": { - "type": "int", - "default_val": "10", - "tooltip": "tooltip not implemented" - }, - "max_fun": { - "type": "int", - "default_val": "15000", - "tooltip": "tooltip not implemented" - } - }, - "code": "mlp" + code: "gpc" + }, + mlp: { + options: { + activation: { + type: "string", + default_val: "relu", + tooltip: "Activation function for the hidden layer. Supported values are 'identity', 'logistic', 'tanh', and 'relu'." + }, + solver: { + type: "string", + default_val: "adam", + tooltip: "The solver for weight optimization. Supported values are 'lbfgs', 'sgd', and 'adam'." + }, + alpha: { + type: "float", + default_val: "0.0001", + tooltip: "L2 penalty (regularization term) parameter." + }, + batch_size: { + type: "string", + default_val: "auto", + tooltip: "Size of minibatches for stochastic optimizers. If 'auto', batch_size=min(200, n_samples)." + }, + learning_rate: { + type: "string", + default_val: "constant", + tooltip: "Learning rate schedule for weight updates. Supported values are 'constant', 'invscaling', and 'adaptive'." + }, + learning_rate_init: { + type: "float", + default_val: "0.001", + tooltip: "The initial learning rate used. It controls the step-size in updating the weights." + }, + power_t: { + type: "float", + default_val: "0.5", + tooltip: "The exponent for inverse scaling learning rate." + }, + max_iter: { + type: "int", + default_val: "500", + tooltip: "Maximum number of iterations. The solver iterates until convergence or this number of iterations is reached." + }, + loss: { + type: "string", + default_val: "log_loss", + tooltip: "The loss function to be optimized. Supported values are 'log_loss' and 'squared_loss'." + }, + hidden_layer_sizes: { + type: "tuple", + default_val: "(100,)", + tooltip: "The ith element represents the number of neurons in the ith hidden layer." + }, + shuffle: { + type: "bool", + default_val: "True", + tooltip: "Whether to shuffle samples in each iteration." + }, + random_state: { + type: "int", + default_val: "1334", + tooltip: "The seed of the pseudo-random number generator used for shuffling the data. It controls the reproducibility." + }, + tol: { + type: "float", + default_val: "0.0001", + tooltip: + "Tolerance for the optimization. When the loss does not improve by at least tol for n_iter_no_change consecutive iterations, convergence is considered to be reached and training stops." + }, + verbose: { + type: "bool", + default_val: "False", + tooltip: "Whether to print progress messages to stdout." + }, + warm_start: { + type: "bool", + default_val: "False", + tooltip: "When set to True, reuse the solution of the previous call to fit as initialization for the next call, otherwise, just erase the previous solution." + }, + momentum: { + type: "float", + default_val: "0.9", + tooltip: "Momentum for gradient descent update. Should be between 0 and 1." + }, + nesterovs_momentum: { + type: "bool", + default_val: "True", + tooltip: "Whether to use Nesterov’s momentum." + }, + early_stopping: { + type: "bool", + default_val: "False", + tooltip: "Whether to use early stopping to terminate training when validation score is not improving." + }, + validation_fraction: { + type: "float", + default_val: "0.1", + tooltip: "The proportion of training data to set aside as validation set for early stopping." + }, + beta_1: { + type: "float", + default_val: "0.9", + tooltip: "Exponential decay rate for estimates of first moment vector in adam." + }, + beta_2: { + type: "float", + default_val: "0.999", + tooltip: "Exponential decay rate for estimates of second moment vector in adam." + }, + epsilon: { + type: "float", + default_val: "1e-08", + tooltip: "Value for numerical stability in adam." + }, + n_iter_no_change: { + type: "int", + default_val: "10", + tooltip: "Maximum number of epochs to not meet tol improvement." + }, + max_fun: { + type: "int", + default_val: "15000", + tooltip: "Maximum number of loss function calls. The solver iterates until convergence (determined by tol) or this number of loss function calls. This only impacts 'lbfgs' solver." + } }, - "ridge": { - "options": { - "alpha": { - "type": "float", - "default_val": "1.0", - "tooltip": "tooltip not implemented" - }, - "fit_intercept": { - "type": "bool", - "default_val": "True", - "tooltip": "tooltip not implemented" - }, - "copy_X": { - "type": "bool", - "default_val": "True", - "tooltip": "tooltip not implemented" - }, - "max_iter": { - "type": "NoneType", - "default_val": "None", - "tooltip": "tooltip not implemented" - }, - "tol": { - "type": "float", - "default_val": "0.0001", - "tooltip": "tooltip not implemented" - }, - "solver": { - "type": "string", - "default_val": "auto", - "tooltip": "tooltip not implemented" - }, - "positive": { - "type": "bool", - "default_val": "False", - "tooltip": "tooltip not implemented" - }, - "random_state": { - "type": "int", - "default_val": "1334", - "tooltip": "tooltip not implemented" - }, - "class_weight": { - "type": "NoneType", - "default_val": "None", - "tooltip": "tooltip not implemented" - } - }, - "code": "ridge" + code: "mlp" + }, + ridge: { + options: { + alpha: { + type: "float", + default_val: "1.0", + tooltip: "Regularization strength; must be a positive float. Larger values specify stronger regularization." + }, + fit_intercept: { + type: "bool", + default_val: "True", + tooltip: "Whether to calculate the intercept for this model. If set to False, no intercept will be used in calculations." + }, + copy_X: { + type: "bool", + default_val: "True", + tooltip: "If True, X will be copied; else, it may be overwritten." + }, + max_iter: { + type: "NoneType", + default_val: "None", + tooltip: "Maximum number of iterations for conjugate gradient solver. For 'sag' and 'lsqr' solvers, the default is 1000." + }, + tol: { + type: "float", + default_val: "0.0001", + tooltip: "Precision of the solution." + }, + solver: { + type: "string", + default_val: "auto", + tooltip: "Solver to use in the computational routines. Options are 'auto', 'svd', 'cholesky', 'lsqr', 'sparse_cg', 'sag', and 'saga'." + }, + positive: { + type: "bool", + default_val: "False", + tooltip: "When set to True, forces the coefficients to be positive." + }, + random_state: { + type: "int", + default_val: "1334", + tooltip: "The seed of the pseudo-random number generator to use when shuffling the data." + }, + class_weight: { + type: "NoneType", + default_val: "None", + tooltip: "Weights associated with classes. If not given, all classes are supposed to have weight one." + } }, - "rf": { - "options": { - "estimator": { - "type": "DecisionTreeClassifier", - "default_val": "DecisionTreeClassifier(ccp_alpha=0.0, class_weight=None, criterion='gini',\n max_depth=None, max_features=None, max_leaf_nodes=None,\n min_impurity_decrease=0.0, min_samples_leaf=1,\n min_samples_split=2, min_weight_fraction_leaf=0.0,\n random_state=None, splitter='best')", - "tooltip": "tooltip not implemented" - }, - "n_estimators": { - "type": "int", - "default_val": "100", - "tooltip": "tooltip not implemented" - }, - "estimator_params": { - "type": "tuple", - "default_val": "('criterion', 'max_depth', 'min_samples_split', 'min_samples_leaf', 'min_weight_fraction_leaf', 'max_features', 'max_leaf_nodes', 'min_impurity_decrease', 'random_state', 'ccp_alpha')", - "tooltip": "tooltip not implemented" - }, - "base_estimator": { - "type": "string", - "default_val": "deprecated", - "tooltip": "tooltip not implemented" - }, - "bootstrap": { - "type": "bool", - "default_val": "True", - "tooltip": "tooltip not implemented" - }, - "oob_score": { - "type": "bool", - "default_val": "False", - "tooltip": "tooltip not implemented" - }, - "n_jobs": { - "type": "int", - "default_val": "-1", - "tooltip": "tooltip not implemented" - }, - "random_state": { - "type": "int", - "default_val": "1334", - "tooltip": "tooltip not implemented" - }, - "verbose": { - "type": "int", - "default_val": "0", - "tooltip": "tooltip not implemented" - }, - "warm_start": { - "type": "bool", - "default_val": "False", - "tooltip": "tooltip not implemented" - }, - "class_weight": { - "type": "NoneType", - "default_val": "None", - "tooltip": "tooltip not implemented" - }, - "max_samples": { - "type": "NoneType", - "default_val": "None", - "tooltip": "tooltip not implemented" - }, - "criterion": { - "type": "string", - "default_val": "gini", - "tooltip": "tooltip not implemented" - }, - "max_depth": { - "type": "NoneType", - "default_val": "None", - "tooltip": "tooltip not implemented" - }, - "min_samples_split": { - "type": "int", - "default_val": "2", - "tooltip": "tooltip not implemented" - }, - "min_samples_leaf": { - "type": "int", - "default_val": "1", - "tooltip": "tooltip not implemented" - }, - "min_weight_fraction_leaf": { - "type": "float", - "default_val": "0.0", - "tooltip": "tooltip not implemented" - }, - "max_features": { - "type": "string", - "default_val": "sqrt", - "tooltip": "tooltip not implemented" - }, - "max_leaf_nodes": { - "type": "NoneType", - "default_val": "None", - "tooltip": "tooltip not implemented" - }, - "min_impurity_decrease": { - "type": "float", - "default_val": "0.0", - "tooltip": "tooltip not implemented" - }, - "ccp_alpha": { - "type": "float", - "default_val": "0.0", - "tooltip": "tooltip not implemented" - } - }, - "code": "rf" + code: "ridge" + }, + rf: { + options: { + estimator: { + type: "DecisionTreeClassifier", + default_val: + "DecisionTreeClassifier(ccp_alpha=0.0, class_weight=None, criterion='gini',\n max_depth=None, max_features=None, max_leaf_nodes=None,\n min_impurity_decrease=0.0, min_samples_leaf=1,\n min_samples_split=2, min_weight_fraction_leaf=0.0,\n random_state=None, splitter='best')", + tooltip: "The base estimator from which the boosted ensemble is built." + }, + n_estimators: { + type: "int", + default_val: "100", + tooltip: "The number of trees in the forest." + }, + estimator_params: { + type: "tuple", + default_val: + "('criterion', 'max_depth', 'min_samples_split', 'min_samples_leaf', 'min_weight_fraction_leaf', 'max_features', 'max_leaf_nodes', 'min_impurity_decrease', 'random_state', 'ccp_alpha')", + tooltip: "The parameters of the base estimator." + }, + base_estimator: { + type: "string", + default_val: "deprecated", + tooltip: "This parameter is deprecated and will be removed in future versions." + }, + bootstrap: { + type: "bool", + default_val: "True", + tooltip: "Whether bootstrap samples are used when building trees. If False, the whole dataset is used to build each tree." + }, + oob_score: { + type: "bool", + default_val: "False", + tooltip: "Whether to use out-of-bag samples to estimate the generalization accuracy." + }, + n_jobs: { + type: "int", + default_val: "-1", + tooltip: "The number of jobs to run in parallel. -1 means using all processors." + }, + random_state: { + type: "int", + default_val: "1334", + tooltip: "The seed used by the random number generator." + }, + verbose: { + type: "int", + default_val: "0", + tooltip: "Controls the verbosity of the tree building process." + }, + warm_start: { + type: "bool", + default_val: "False", + tooltip: "When set to True, reuse the solution of the previous call to fit and add more estimators to the ensemble, otherwise, just fit a whole new forest." + }, + class_weight: { + type: "NoneType", + default_val: "None", + tooltip: "Weights associated with classes in the form {class_label: weight}. If not given, all classes are supposed to have weight one." + }, + max_samples: { + type: "NoneType", + default_val: "None", + tooltip: "If bootstrap is True, the number of samples to draw from X to train each base estimator." + }, + criterion: { + type: "string", + default_val: "gini", + tooltip: "The function to measure the quality of a split. Supported criteria are 'gini' for the Gini impurity and 'entropy' for the information gain." + }, + max_depth: { + type: "NoneType", + default_val: "None", + tooltip: "The maximum depth of the tree. If None, then nodes are expanded until all leaves are pure or until all leaves contain less than min_samples_split samples." + }, + min_samples_split: { + type: "int", + default_val: "2", + tooltip: "The minimum number of samples required to split an internal node." + }, + min_samples_leaf: { + type: "int", + default_val: "1", + tooltip: "The minimum number of samples required to be at a leaf node." + }, + min_weight_fraction_leaf: { + type: "float", + default_val: "0.0", + tooltip: "The minimum weighted fraction of the sum total of weights (of all the input samples) required to be at a leaf node." + }, + max_features: { + type: "string", + default_val: "sqrt", + tooltip: "The number of features to consider when looking for the best split." + }, + max_leaf_nodes: { + type: "NoneType", + default_val: "None", + tooltip: "Grow a tree with max_leaf_nodes in best-first fashion. Best nodes are defined as relative reduction in impurity." + }, + min_impurity_decrease: { + type: "float", + default_val: "0.0", + tooltip: "A node will be split if this split induces a decrease of the impurity greater than or equal to this value." + }, + ccp_alpha: { + type: "float", + default_val: "0.0", + tooltip: "Complexity parameter used for Minimal Cost-Complexity Pruning. The subtree with the largest cost complexity that is smaller than ccp_alpha will be chosen." + } }, - "qda": { - "options": { - "priors": { - "type": "NoneType", - "default_val": "None", - "tooltip": "tooltip not implemented" - }, - "reg_param": { - "type": "float", - "default_val": "0.0", - "tooltip": "tooltip not implemented" - }, - "store_covariance": { - "type": "bool", - "default_val": "False", - "tooltip": "tooltip not implemented" - }, - "tol": { - "type": "float", - "default_val": "0.0001", - "tooltip": "tooltip not implemented" - } - }, - "code": "qda" + code: "rf" + }, + qda: { + options: { + priors: { + type: "NoneType", + default_val: "None", + tooltip: "Priors on the classes. If specified, the priors are not adjusted according to the data." + }, + reg_param: { + type: "float", + default_val: "0.0", + tooltip: "Regularizes the per-class covariance estimates by transforming S2 as (1-reg_param)*S2 + reg_param*np.eye(n_features)." + }, + store_covariance: { + type: "bool", + default_val: "False", + tooltip: "If True, explicitly store the covariance matrix of each class." + }, + tol: { + type: "float", + default_val: "0.0001", + tooltip: "Tolerance used for deciding whether the system is singular. It is used in solving the linear system." + } }, - "ada": { - "options": { - "estimator": { - "type": "NoneType", - "default_val": "None", - "tooltip": "tooltip not implemented" - }, - "n_estimators": { - "type": "int", - "default_val": "50", - "tooltip": "tooltip not implemented" - }, - "estimator_params": { - "type": "tuple", - "default_val": "()", - "tooltip": "tooltip not implemented" - }, - "base_estimator": { - "type": "string", - "default_val": "deprecated", - "tooltip": "tooltip not implemented" - }, - "learning_rate": { - "type": "float", - "default_val": "1.0", - "tooltip": "tooltip not implemented" - }, - "random_state": { - "type": "int", - "default_val": "1334", - "tooltip": "tooltip not implemented" - }, - "algorithm": { - "type": "string", - "default_val": "SAMME.R", - "tooltip": "tooltip not implemented" - } - }, - "code": "ada" + code: "qda" + }, + ada: { + options: { + estimator: { + type: "NoneType", + default_val: "None", + tooltip: "The base estimator from which the boosted ensemble is built." + }, + n_estimators: { + type: "int", + default_val: "50", + tooltip: "The maximum number of estimators at which boosting is terminated. In case of perfect fit, the learning procedure is stopped early." + }, + estimator_params: { + type: "tuple", + default_val: "()", + tooltip: "The parameters of the base estimator." + }, + base_estimator: { + type: "string", + default_val: "deprecated", + tooltip: "This parameter is deprecated and will be removed in future versions." + }, + learning_rate: { + type: "float", + default_val: "1.0", + tooltip: "Weight applied to each classifier at each boosting iteration. A higher learning rate increases the contribution of each classifier." + }, + random_state: { + type: "int", + default_val: "1334", + tooltip: "The seed used by the random number generator." + }, + algorithm: { + type: "string", + default_val: "SAMME.R", + tooltip: "If ‘SAMME.R’ then the underlying estimator must support calculation of class probabilities. If ‘SAMME’ then use the SAMME discrete boosting algorithm." + } }, - "gbc": { - "options": { - "n_estimators": { - "type": "int", - "default_val": "100", - "tooltip": "tooltip not implemented" - }, - "learning_rate": { - "type": "float", - "default_val": "0.1", - "tooltip": "tooltip not implemented" - }, - "loss": { - "type": "string", - "default_val": "log_loss", - "tooltip": "tooltip not implemented" - }, - "criterion": { - "type": "string", - "default_val": "friedman_mse", - "tooltip": "tooltip not implemented" - }, - "min_samples_split": { - "type": "int", - "default_val": "2", - "tooltip": "tooltip not implemented" - }, - "min_samples_leaf": { - "type": "int", - "default_val": "1", - "tooltip": "tooltip not implemented" - }, - "min_weight_fraction_leaf": { - "type": "float", - "default_val": "0.0", - "tooltip": "tooltip not implemented" - }, - "subsample": { - "type": "float", - "default_val": "1.0", - "tooltip": "tooltip not implemented" - }, - "max_features": { - "type": "NoneType", - "default_val": "None", - "tooltip": "tooltip not implemented" - }, - "max_depth": { - "type": "int", - "default_val": "3", - "tooltip": "tooltip not implemented" - }, - "min_impurity_decrease": { - "type": "float", - "default_val": "0.0", - "tooltip": "tooltip not implemented" - }, - "ccp_alpha": { - "type": "float", - "default_val": "0.0", - "tooltip": "tooltip not implemented" - }, - "init": { - "type": "NoneType", - "default_val": "None", - "tooltip": "tooltip not implemented" - }, - "random_state": { - "type": "int", - "default_val": "1334", - "tooltip": "tooltip not implemented" - }, - "alpha": { - "type": "float", - "default_val": "0.9", - "tooltip": "tooltip not implemented" - }, - "verbose": { - "type": "int", - "default_val": "0", - "tooltip": "tooltip not implemented" - }, - "max_leaf_nodes": { - "type": "NoneType", - "default_val": "None", - "tooltip": "tooltip not implemented" - }, - "warm_start": { - "type": "bool", - "default_val": "False", - "tooltip": "tooltip not implemented" - }, - "validation_fraction": { - "type": "float", - "default_val": "0.1", - "tooltip": "tooltip not implemented" - }, - "n_iter_no_change": { - "type": "NoneType", - "default_val": "None", - "tooltip": "tooltip not implemented" - }, - "tol": { - "type": "float", - "default_val": "0.0001", - "tooltip": "tooltip not implemented" - } - }, - "code": "gbc" + code: "ada" + }, + gbc: { + options: { + n_estimators: { + type: "int", + default_val: "100", + tooltip: "The number of boosting stages to be run." + }, + learning_rate: { + type: "float", + default_val: "0.1", + tooltip: "Learning rate shrinks the contribution of each tree by learning_rate." + }, + loss: { + type: "string", + default_val: "log_loss", + tooltip: "The loss function to be optimized." + }, + criterion: { + type: "string", + default_val: "friedman_mse", + tooltip: "The function to measure the quality of a split." + }, + min_samples_split: { + type: "int", + default_val: "2", + tooltip: "The minimum number of samples required to split an internal node." + }, + min_samples_leaf: { + type: "int", + default_val: "1", + tooltip: "The minimum number of samples required to be at a leaf node." + }, + min_weight_fraction_leaf: { + type: "float", + default_val: "0.0", + tooltip: "The minimum weighted fraction of the sum total of weights required to be at a leaf node." + }, + subsample: { + type: "float", + default_val: "1.0", + tooltip: "The fraction of samples to be used for fitting the individual base learners." + }, + max_features: { + type: "NoneType", + default_val: "None", + tooltip: "The number of features to consider when looking for the best split." + }, + max_depth: { + type: "int", + default_val: "3", + tooltip: "The maximum depth of the individual regression estimators." + }, + min_impurity_decrease: { + type: "float", + default_val: "0.0", + tooltip: "A node will be split if this split induces a decrease of the impurity greater than or equal to this value." + }, + ccp_alpha: { + type: "float", + default_val: "0.0", + tooltip: "Complexity parameter used for Minimal Cost-Complexity Pruning." + }, + init: { + type: "NoneType", + default_val: "None", + tooltip: "An estimator object that is used to compute the initial predictions." + }, + random_state: { + type: "int", + default_val: "1334", + tooltip: "The seed used by the random number generator." + }, + alpha: { + type: "float", + default_val: "0.9", + tooltip: "The alpha-quantile of the huber loss function and the quantile loss function." + }, + verbose: { + type: "int", + default_val: "0", + tooltip: "Controls the verbosity when fitting and predicting." + }, + max_leaf_nodes: { + type: "NoneType", + default_val: "None", + tooltip: "Grow trees with max_leaf_nodes in best-first fashion." + }, + warm_start: { + type: "bool", + default_val: "False", + tooltip: "When set to True, reuse the solution of the previous call to fit and add more estimators to the ensemble." + }, + validation_fraction: { + type: "float", + default_val: "0.1", + tooltip: "The proportion of training data to set aside as validation set for early stopping." + }, + n_iter_no_change: { + type: "NoneType", + default_val: "None", + tooltip: "‘n_iter_no_change’ is used to decide if early stopping will be used to terminate training." + }, + tol: { + type: "float", + default_val: "0.0001", + tooltip: "The tolerance for the optimization." + } }, - "lda": { - "options": { - "solver": { - "type": "string", - "default_val": "svd", - "tooltip": "tooltip not implemented" - }, - "shrinkage": { - "type": "NoneType", - "default_val": "None", - "tooltip": "tooltip not implemented" - }, - "priors": { - "type": "NoneType", - "default_val": "None", - "tooltip": "tooltip not implemented" - }, - "n_components": { - "type": "NoneType", - "default_val": "None", - "tooltip": "tooltip not implemented" - }, - "store_covariance": { - "type": "bool", - "default_val": "False", - "tooltip": "tooltip not implemented" - }, - "tol": { - "type": "float", - "default_val": "0.0001", - "tooltip": "tooltip not implemented" - }, - "covariance_estimator": { - "type": "NoneType", - "default_val": "None", - "tooltip": "tooltip not implemented" - } - }, - "code": "lda" + code: "gbc" + }, + lda: { + options: { + solver: { + type: "string", + default_val: "svd", + tooltip: "Solver to use for LDA. ‘svd’ is the default, ‘lsqr’ and ‘eigen’ can be used for shrinkage." + }, + shrinkage: { + type: "NoneType", + default_val: "None", + tooltip: "Shrinkage parameter for ‘lsqr’ and ‘eigen’ solvers." + }, + priors: { + type: "NoneType", + default_val: "None", + tooltip: "Priors on classes. If specified the priors are not adjusted according to the data." + }, + n_components: { + type: "NoneType", + default_val: "None", + tooltip: "Number of components (< n_classes - 1) for dimensionality reduction." + }, + store_covariance: { + type: "bool", + default_val: "False", + tooltip: "If True, explicitly store the covariance matrix of each class." + }, + tol: { + type: "float", + default_val: "0.0001", + tooltip: "Tolerance for rank estimation." + }, + covariance_estimator: { + type: "NoneType", + default_val: "None", + tooltip: "Estimator for covariance matrix." + } }, - "et": { - "options": { - "estimator": { - "type": "ExtraTreeClassifier", - "default_val": "ExtraTreeClassifier(ccp_alpha=0.0, class_weight=None, criterion='gini',\n max_depth=None, max_features='sqrt', max_leaf_nodes=None,\n min_impurity_decrease=0.0, min_samples_leaf=1,\n min_samples_split=2, min_weight_fraction_leaf=0.0,\n random_state=None, splitter='random')", - "tooltip": "tooltip not implemented" - }, - "n_estimators": { - "type": "int", - "default_val": "100", - "tooltip": "tooltip not implemented" - }, - "estimator_params": { - "type": "tuple", - "default_val": "('criterion', 'max_depth', 'min_samples_split', 'min_samples_leaf', 'min_weight_fraction_leaf', 'max_features', 'max_leaf_nodes', 'min_impurity_decrease', 'random_state', 'ccp_alpha')", - "tooltip": "tooltip not implemented" - }, - "base_estimator": { - "type": "string", - "default_val": "deprecated", - "tooltip": "tooltip not implemented" - }, - "bootstrap": { - "type": "bool", - "default_val": "False", - "tooltip": "tooltip not implemented" - }, - "oob_score": { - "type": "bool", - "default_val": "False", - "tooltip": "tooltip not implemented" - }, - "n_jobs": { - "type": "int", - "default_val": "-1", - "tooltip": "tooltip not implemented" - }, - "random_state": { - "type": "int", - "default_val": "1334", - "tooltip": "tooltip not implemented" - }, - "verbose": { - "type": "int", - "default_val": "0", - "tooltip": "tooltip not implemented" - }, - "warm_start": { - "type": "bool", - "default_val": "False", - "tooltip": "tooltip not implemented" - }, - "class_weight": { - "type": "NoneType", - "default_val": "None", - "tooltip": "tooltip not implemented" - }, - "max_samples": { - "type": "NoneType", - "default_val": "None", - "tooltip": "tooltip not implemented" - }, - "criterion": { - "type": "string", - "default_val": "gini", - "tooltip": "tooltip not implemented" - }, - "max_depth": { - "type": "NoneType", - "default_val": "None", - "tooltip": "tooltip not implemented" - }, - "min_samples_split": { - "type": "int", - "default_val": "2", - "tooltip": "tooltip not implemented" - }, - "min_samples_leaf": { - "type": "int", - "default_val": "1", - "tooltip": "tooltip not implemented" - }, - "min_weight_fraction_leaf": { - "type": "float", - "default_val": "0.0", - "tooltip": "tooltip not implemented" - }, - "max_features": { - "type": "string", - "default_val": "sqrt", - "tooltip": "tooltip not implemented" - }, - "max_leaf_nodes": { - "type": "NoneType", - "default_val": "None", - "tooltip": "tooltip not implemented" - }, - "min_impurity_decrease": { - "type": "float", - "default_val": "0.0", - "tooltip": "tooltip not implemented" - }, - "ccp_alpha": { - "type": "float", - "default_val": "0.0", - "tooltip": "tooltip not implemented" - } - }, - "code": "et" + code: "lda" + }, + et: { + options: { + estimator: { + type: "ExtraTreeClassifier", + default_val: + "ExtraTreeClassifier(ccp_alpha=0.0, class_weight=None, criterion='gini',\n max_depth=None, max_features='sqrt', max_leaf_nodes=None,\n min_impurity_decrease=0.0, min_samples_leaf=1,\n min_samples_split=2, min_weight_fraction_leaf=0.0,\n random_state=None, splitter='random')", + tooltip: "The base estimator from which the extra-trees are built." + }, + n_estimators: { + type: "int", + default_val: "100", + tooltip: "The number of trees in the forest." + }, + estimator_params: { + type: "tuple", + default_val: + "('criterion', 'max_depth', 'min_samples_split', 'min_samples_leaf', 'min_weight_fraction_leaf', 'max_features', 'max_leaf_nodes', 'min_impurity_decrease', 'random_state', 'ccp_alpha')", + tooltip: "The parameters of the base estimator." + }, + base_estimator: { + type: "string", + default_val: "deprecated", + tooltip: "This parameter is deprecated and will be removed in future versions." + }, + bootstrap: { + type: "bool", + default_val: "False", + tooltip: "Whether bootstrap samples are used when building trees." + }, + oob_score: { + type: "bool", + default_val: "False", + tooltip: "Whether to use out-of-bag samples to estimate the generalization accuracy." + }, + n_jobs: { + type: "int", + default_val: "-1", + tooltip: "The number of jobs to run in parallel." + }, + random_state: { + type: "int", + default_val: "1334", + tooltip: "The seed used by the random number generator." + }, + verbose: { + type: "int", + default_val: "0", + tooltip: "Controls the verbosity when fitting and predicting." + }, + warm_start: { + type: "bool", + default_val: "False", + tooltip: "When set to True, reuse the solution of the previous call to fit and add more estimators to the ensemble." + }, + class_weight: { + type: "NoneType", + default_val: "None", + tooltip: "Weights associated with classes." + }, + max_samples: { + type: "NoneType", + default_val: "None", + tooltip: "If bootstrap is True, the number of samples to draw from X to train each base estimator." + }, + criterion: { + type: "string", + default_val: "gini", + tooltip: "The function to measure the quality of a split." + }, + max_depth: { + type: "NoneType", + default_val: "None", + tooltip: "The maximum depth of the tree." + }, + min_samples_split: { + type: "int", + default_val: "2", + tooltip: "The minimum number of samples required to split an internal node." + }, + min_samples_leaf: { + type: "int", + default_val: "1", + tooltip: "The minimum number of samples required to be at a leaf node." + }, + min_weight_fraction_leaf: { + type: "float", + default_val: "0.0", + tooltip: "The minimum weighted fraction of the sum total of weights required to be at a leaf node." + }, + max_features: { + type: "string", + default_val: "sqrt", + tooltip: "The number of features to consider when looking for the best split." + }, + max_leaf_nodes: { + type: "NoneType", + default_val: "None", + tooltip: "Grow trees with max_leaf_nodes in best-first fashion." + }, + min_impurity_decrease: { + type: "float", + default_val: "0.0", + tooltip: "A node will be split if this split induces a decrease of the impurity greater than or equal to this value." + }, + ccp_alpha: { + type: "float", + default_val: "0.0", + tooltip: "Complexity parameter used for Minimal Cost-Complexity Pruning." + } }, - "lightgbm": { - "options": { - "boosting_type": { - "type": "string", - "default_val": "gbdt", - "tooltip": "tooltip not implemented" - }, - "objective": { - "type": "NoneType", - "default_val": "None", - "tooltip": "tooltip not implemented" - }, - "num_leaves": { - "type": "int", - "default_val": "31", - "tooltip": "tooltip not implemented" - }, - "max_depth": { - "type": "int", - "default_val": "-1", - "tooltip": "tooltip not implemented" - }, - "learning_rate": { - "type": "float", - "default_val": "0.1", - "tooltip": "tooltip not implemented" - }, - "n_estimators": { - "type": "int", - "default_val": "100", - "tooltip": "tooltip not implemented" - }, - "subsample_for_bin": { - "type": "int", - "default_val": "200000", - "tooltip": "tooltip not implemented" - }, - "min_split_gain": { - "type": "float", - "default_val": "0.0", - "tooltip": "tooltip not implemented" - }, - "min_child_weight": { - "type": "float", - "default_val": "0.001", - "tooltip": "tooltip not implemented" - }, - "min_child_samples": { - "type": "int", - "default_val": "20", - "tooltip": "tooltip not implemented" - }, - "subsample": { - "type": "float", - "default_val": "1.0", - "tooltip": "tooltip not implemented" - }, - "subsample_freq": { - "type": "int", - "default_val": "0", - "tooltip": "tooltip not implemented" - }, - "colsample_bytree": { - "type": "float", - "default_val": "1.0", - "tooltip": "tooltip not implemented" - }, - "reg_alpha": { - "type": "float", - "default_val": "0.0", - "tooltip": "tooltip not implemented" - }, - "reg_lambda": { - "type": "float", - "default_val": "0.0", - "tooltip": "tooltip not implemented" - }, - "random_state": { - "type": "int", - "default_val": "1334", - "tooltip": "tooltip not implemented" - }, - "n_jobs": { - "type": "int", - "default_val": "-1", - "tooltip": "tooltip not implemented" - }, - "importance_type": { - "type": "string", - "default_val": "split", - "tooltip": "tooltip not implemented" - }, - "class_weight": { - "type": "NoneType", - "default_val": "None", - "tooltip": "tooltip not implemented" - } - }, - "code": "lightgbm" + code: "et" + }, + lightgbm: { + options: { + boosting_type: { + type: "string", + default_val: "gbdt", + tooltip: "The type of boosting to be used. Options include 'gbdt', 'dart', 'goss', 'rf'." + }, + objective: { + type: "NoneType", + default_val: "None", + tooltip: "The objective function to be optimized." + }, + num_leaves: { + type: "int", + default_val: "31", + tooltip: "The maximum number of leaves in one tree." + }, + max_depth: { + type: "int", + default_val: "-1", + tooltip: "The maximum depth of the tree. -1 means no limit." + }, + learning_rate: { + type: "float", + default_val: "0.1", + tooltip: "The learning rate shrinks the contribution of each tree by this value." + }, + n_estimators: { + type: "int", + default_val: "100", + tooltip: "The number of boosting iterations." + }, + subsample_for_bin: { + type: "int", + default_val: "200000", + tooltip: "Number of samples for constructing bins." + }, + min_split_gain: { + type: "float", + default_val: "0.0", + tooltip: "The minimum gain to perform a split." + }, + min_child_weight: { + type: "float", + default_val: "0.001", + tooltip: "Minimal sum hessian in one leaf." + }, + min_child_samples: { + type: "int", + default_val: "20", + tooltip: "The minimum number of data needed in a child (leaf)." + }, + subsample: { + type: "float", + default_val: "1.0", + tooltip: "Subsample ratio of the training instance." + }, + subsample_freq: { + type: "int", + default_val: "0", + tooltip: "Frequency of subsample, <=0 means no subsample." + }, + colsample_bytree: { + type: "float", + default_val: "1.0", + tooltip: "Subsample ratio of columns when constructing each tree." + }, + reg_alpha: { + type: "float", + default_val: "0.0", + tooltip: "L1 regularization term on weights." + }, + reg_lambda: { + type: "float", + default_val: "0.0", + tooltip: "L2 regularization term on weights." + }, + random_state: { + type: "int", + default_val: "1334", + tooltip: "The seed used by the random number generator." + }, + n_jobs: { + type: "int", + default_val: "-1", + tooltip: "Number of parallel threads." + }, + importance_type: { + type: "string", + default_val: "split", + tooltip: "The type of feature importance to be used. 'split' or 'gain'." + }, + class_weight: { + type: "NoneType", + default_val: "None", + tooltip: "Weights associated with classes." + } }, - "dummy": { - "options": { - "strategy": { - "type": "string", - "default_val": "prior", - "tooltip": "tooltip not implemented" - }, - "random_state": { - "type": "int", - "default_val": "1334", - "tooltip": "tooltip not implemented" - }, - "constant": { - "type": "NoneType", - "default_val": "None", - "tooltip": "tooltip not implemented" - } - }, - "code": "dummy" - } -}; - export default classificationModelSettings; \ No newline at end of file + code: "lightgbm" + }, + dummy: { + options: { + strategy: { + type: "string", + default_val: "prior", + tooltip: "The strategy to use for predicting. Options are 'stratified', 'most_frequent', 'prior', 'uniform', 'constant'." + }, + random_state: { + type: "int", + default_val: "1334", + tooltip: "The seed used by the random number generator." + }, + constant: { + type: "NoneType", + default_val: "None", + tooltip: "The constant to predict if the strategy is 'constant'." + } + }, + code: "dummy" + } +} +export default classificationModelSettings diff --git a/renderer/public/setupVariables/possibleSettings/learning/classificationModelSettings_old.js b/renderer/public/setupVariables/possibleSettings/learning/classificationModelSettings_old.js new file mode 100644 index 00000000..b4870bd9 --- /dev/null +++ b/renderer/public/setupVariables/possibleSettings/learning/classificationModelSettings_old.js @@ -0,0 +1,1203 @@ +/* eslint-disable */ +const classificationModelSettings = { + lr: { + options: { + penalty: { + type: "string", + default_val: "l2", + tooltip: "" + }, + dual: { + type: "bool", + default_val: "False", + tooltip: "" + }, + tol: { + type: "float", + default_val: "0.0001", + tooltip: "tooltip not implemented" + }, + C: { + type: "float", + default_val: "1.0", + tooltip: "tooltip not implemented" + }, + fit_intercept: { + type: "bool", + default_val: "True", + tooltip: "tooltip not implemented" + }, + intercept_scaling: { + type: "int", + default_val: "1", + tooltip: "tooltip not implemented" + }, + class_weight: { + type: "NoneType", + default_val: "None", + tooltip: "tooltip not implemented" + }, + random_state: { + type: "int", + default_val: "1334", + tooltip: "tooltip not implemented" + }, + solver: { + type: "string", + default_val: "lbfgs", + tooltip: "tooltip not implemented" + }, + max_iter: { + type: "int", + default_val: "1000", + tooltip: "tooltip not implemented" + }, + multi_class: { + type: "string", + default_val: "auto", + tooltip: "tooltip not implemented" + }, + verbose: { + type: "int", + default_val: "0", + tooltip: "tooltip not implemented" + }, + warm_start: { + type: "bool", + default_val: "False", + tooltip: "tooltip not implemented" + }, + n_jobs: { + type: "NoneType", + default_val: "None", + tooltip: "tooltip not implemented" + }, + l1_ratio: { + type: "NoneType", + default_val: "None", + tooltip: "tooltip not implemented" + } + }, + code: "lr" + }, + knn: { + options: { + n_neighbors: { + type: "int", + default_val: "5", + tooltip: "tooltip not implemented" + }, + radius: { + type: "NoneType", + default_val: "None", + tooltip: "tooltip not implemented" + }, + algorithm: { + type: "string", + default_val: "auto", + tooltip: "tooltip not implemented" + }, + leaf_size: { + type: "int", + default_val: "30", + tooltip: "tooltip not implemented" + }, + metric: { + type: "string", + default_val: "minkowski", + tooltip: "tooltip not implemented" + }, + metric_params: { + type: "NoneType", + default_val: "None", + tooltip: "tooltip not implemented" + }, + p: { + type: "int", + default_val: "2", + tooltip: "tooltip not implemented" + }, + n_jobs: { + type: "int", + default_val: "-1", + tooltip: "tooltip not implemented" + }, + weights: { + type: "string", + default_val: "uniform", + tooltip: "tooltip not implemented" + } + }, + code: "knn" + }, + nb: { + options: { + priors: { + type: "NoneType", + default_val: "None", + tooltip: "tooltip not implemented" + }, + var_smoothing: { + type: "float", + default_val: "1e-09", + tooltip: "tooltip not implemented" + } + }, + code: "nb" + }, + dt: { + options: { + criterion: { + type: "string", + default_val: "gini", + tooltip: "tooltip not implemented" + }, + splitter: { + type: "string", + default_val: "best", + tooltip: "tooltip not implemented" + }, + max_depth: { + type: "NoneType", + default_val: "None", + tooltip: "tooltip not implemented" + }, + min_samples_split: { + type: "int", + default_val: "2", + tooltip: "tooltip not implemented" + }, + min_samples_leaf: { + type: "int", + default_val: "1", + tooltip: "tooltip not implemented" + }, + min_weight_fraction_leaf: { + type: "float", + default_val: "0.0", + tooltip: "tooltip not implemented" + }, + max_features: { + type: "NoneType", + default_val: "None", + tooltip: "tooltip not implemented" + }, + max_leaf_nodes: { + type: "NoneType", + default_val: "None", + tooltip: "tooltip not implemented" + }, + random_state: { + type: "int", + default_val: "1334", + tooltip: "tooltip not implemented" + }, + min_impurity_decrease: { + type: "float", + default_val: "0.0", + tooltip: "tooltip not implemented" + }, + class_weight: { + type: "NoneType", + default_val: "None", + tooltip: "tooltip not implemented" + }, + ccp_alpha: { + type: "float", + default_val: "0.0", + tooltip: "tooltip not implemented" + } + }, + code: "dt" + }, + svm: { + options: { + loss: { + type: "string", + default_val: "hinge", + tooltip: "tooltip not implemented" + }, + penalty: { + type: "string", + default_val: "l2", + tooltip: "tooltip not implemented" + }, + learning_rate: { + type: "string", + default_val: "optimal", + tooltip: "tooltip not implemented" + }, + epsilon: { + type: "float", + default_val: "0.1", + tooltip: "tooltip not implemented" + }, + alpha: { + type: "float", + default_val: "0.0001", + tooltip: "tooltip not implemented" + }, + C: { + type: "float", + default_val: "1.0", + tooltip: "tooltip not implemented" + }, + l1_ratio: { + type: "float", + default_val: "0.15", + tooltip: "tooltip not implemented" + }, + fit_intercept: { + type: "bool", + default_val: "True", + tooltip: "tooltip not implemented" + }, + shuffle: { + type: "bool", + default_val: "True", + tooltip: "tooltip not implemented" + }, + random_state: { + type: "int", + default_val: "1334", + tooltip: "tooltip not implemented" + }, + verbose: { + type: "int", + default_val: "0", + tooltip: "tooltip not implemented" + }, + eta0: { + type: "float", + default_val: "0.001", + tooltip: "tooltip not implemented" + }, + power_t: { + type: "float", + default_val: "0.5", + tooltip: "tooltip not implemented" + }, + early_stopping: { + type: "bool", + default_val: "False", + tooltip: "tooltip not implemented" + }, + validation_fraction: { + type: "float", + default_val: "0.1", + tooltip: "tooltip not implemented" + }, + n_iter_no_change: { + type: "int", + default_val: "5", + tooltip: "tooltip not implemented" + }, + warm_start: { + type: "bool", + default_val: "False", + tooltip: "tooltip not implemented" + }, + average: { + type: "bool", + default_val: "False", + tooltip: "tooltip not implemented" + }, + max_iter: { + type: "int", + default_val: "1000", + tooltip: "tooltip not implemented" + }, + tol: { + type: "float", + default_val: "0.001", + tooltip: "tooltip not implemented" + }, + class_weight: { + type: "NoneType", + default_val: "None", + tooltip: "tooltip not implemented" + }, + n_jobs: { + type: "int", + default_val: "-1", + tooltip: "tooltip not implemented" + } + }, + code: "svm" + }, + rbfsvm: { + options: { + decision_function_shape: { + type: "string", + default_val: "ovr", + tooltip: "tooltip not implemented" + }, + break_ties: { + type: "bool", + default_val: "False", + tooltip: "tooltip not implemented" + }, + kernel: { + type: "string", + default_val: "rbf", + tooltip: "tooltip not implemented" + }, + degree: { + type: "int", + default_val: "3", + tooltip: "tooltip not implemented" + }, + gamma: { + type: "string", + default_val: "auto", + tooltip: "tooltip not implemented" + }, + coef0: { + type: "float", + default_val: "0.0", + tooltip: "tooltip not implemented" + }, + tol: { + type: "float", + default_val: "0.001", + tooltip: "tooltip not implemented" + }, + C: { + type: "float", + default_val: "1.0", + tooltip: "tooltip not implemented" + }, + nu: { + type: "float", + default_val: "0.0", + tooltip: "tooltip not implemented" + }, + epsilon: { + type: "float", + default_val: "0.0", + tooltip: "tooltip not implemented" + }, + shrinking: { + type: "bool", + default_val: "True", + tooltip: "tooltip not implemented" + }, + probability: { + type: "bool", + default_val: "True", + tooltip: "tooltip not implemented" + }, + cache_size: { + type: "int", + default_val: "200", + tooltip: "tooltip not implemented" + }, + class_weight: { + type: "NoneType", + default_val: "None", + tooltip: "tooltip not implemented" + }, + verbose: { + type: "bool", + default_val: "False", + tooltip: "tooltip not implemented" + }, + max_iter: { + type: "int", + default_val: "-1", + tooltip: "tooltip not implemented" + }, + random_state: { + type: "int", + default_val: "1334", + tooltip: "tooltip not implemented" + } + }, + code: "rbfsvm" + }, + gpc: { + options: { + kernel: { + type: "NoneType", + default_val: "None", + tooltip: "tooltip not implemented" + }, + optimizer: { + type: "string", + default_val: "fmin_l_bfgs_b", + tooltip: "tooltip not implemented" + }, + n_restarts_optimizer: { + type: "int", + default_val: "0", + tooltip: "tooltip not implemented" + }, + max_iter_predict: { + type: "int", + default_val: "100", + tooltip: "tooltip not implemented" + }, + warm_start: { + type: "bool", + default_val: "False", + tooltip: "tooltip not implemented" + }, + copy_X_train: { + type: "bool", + default_val: "False", + tooltip: "tooltip not implemented" + }, + random_state: { + type: "int", + default_val: "1334", + tooltip: "tooltip not implemented" + }, + multi_class: { + type: "string", + default_val: "one_vs_rest", + tooltip: "tooltip not implemented" + }, + n_jobs: { + type: "int", + default_val: "-1", + tooltip: "tooltip not implemented" + } + }, + code: "gpc" + }, + mlp: { + options: { + activation: { + type: "string", + default_val: "relu", + tooltip: "tooltip not implemented" + }, + solver: { + type: "string", + default_val: "adam", + tooltip: "tooltip not implemented" + }, + alpha: { + type: "float", + default_val: "0.0001", + tooltip: "tooltip not implemented" + }, + batch_size: { + type: "string", + default_val: "auto", + tooltip: "tooltip not implemented" + }, + learning_rate: { + type: "string", + default_val: "constant", + tooltip: "tooltip not implemented" + }, + learning_rate_init: { + type: "float", + default_val: "0.001", + tooltip: "tooltip not implemented" + }, + power_t: { + type: "float", + default_val: "0.5", + tooltip: "tooltip not implemented" + }, + max_iter: { + type: "int", + default_val: "500", + tooltip: "tooltip not implemented" + }, + loss: { + type: "string", + default_val: "log_loss", + tooltip: "tooltip not implemented" + }, + hidden_layer_sizes: { + type: "tuple", + default_val: "(100,)", + tooltip: "tooltip not implemented" + }, + shuffle: { + type: "bool", + default_val: "True", + tooltip: "tooltip not implemented" + }, + random_state: { + type: "int", + default_val: "1334", + tooltip: "tooltip not implemented" + }, + tol: { + type: "float", + default_val: "0.0001", + tooltip: "tooltip not implemented" + }, + verbose: { + type: "bool", + default_val: "False", + tooltip: "tooltip not implemented" + }, + warm_start: { + type: "bool", + default_val: "False", + tooltip: "tooltip not implemented" + }, + momentum: { + type: "float", + default_val: "0.9", + tooltip: "tooltip not implemented" + }, + nesterovs_momentum: { + type: "bool", + default_val: "True", + tooltip: "tooltip not implemented" + }, + early_stopping: { + type: "bool", + default_val: "False", + tooltip: "tooltip not implemented" + }, + validation_fraction: { + type: "float", + default_val: "0.1", + tooltip: "tooltip not implemented" + }, + beta_1: { + type: "float", + default_val: "0.9", + tooltip: "tooltip not implemented" + }, + beta_2: { + type: "float", + default_val: "0.999", + tooltip: "tooltip not implemented" + }, + epsilon: { + type: "float", + default_val: "1e-08", + tooltip: "tooltip not implemented" + }, + n_iter_no_change: { + type: "int", + default_val: "10", + tooltip: "tooltip not implemented" + }, + max_fun: { + type: "int", + default_val: "15000", + tooltip: "tooltip not implemented" + } + }, + code: "mlp" + }, + ridge: { + options: { + alpha: { + type: "float", + default_val: "1.0", + tooltip: "tooltip not implemented" + }, + fit_intercept: { + type: "bool", + default_val: "True", + tooltip: "tooltip not implemented" + }, + copy_X: { + type: "bool", + default_val: "True", + tooltip: "tooltip not implemented" + }, + max_iter: { + type: "NoneType", + default_val: "None", + tooltip: "tooltip not implemented" + }, + tol: { + type: "float", + default_val: "0.0001", + tooltip: "tooltip not implemented" + }, + solver: { + type: "string", + default_val: "auto", + tooltip: "tooltip not implemented" + }, + positive: { + type: "bool", + default_val: "False", + tooltip: "tooltip not implemented" + }, + random_state: { + type: "int", + default_val: "1334", + tooltip: "tooltip not implemented" + }, + class_weight: { + type: "NoneType", + default_val: "None", + tooltip: "tooltip not implemented" + } + }, + code: "ridge" + }, + rf: { + options: { + estimator: { + type: "DecisionTreeClassifier", + default_val: + "DecisionTreeClassifier(ccp_alpha=0.0, class_weight=None, criterion='gini',\n max_depth=None, max_features=None, max_leaf_nodes=None,\n min_impurity_decrease=0.0, min_samples_leaf=1,\n min_samples_split=2, min_weight_fraction_leaf=0.0,\n random_state=None, splitter='best')", + tooltip: "tooltip not implemented" + }, + n_estimators: { + type: "int", + default_val: "100", + tooltip: "tooltip not implemented" + }, + estimator_params: { + type: "tuple", + default_val: + "('criterion', 'max_depth', 'min_samples_split', 'min_samples_leaf', 'min_weight_fraction_leaf', 'max_features', 'max_leaf_nodes', 'min_impurity_decrease', 'random_state', 'ccp_alpha')", + tooltip: "tooltip not implemented" + }, + base_estimator: { + type: "string", + default_val: "deprecated", + tooltip: "tooltip not implemented" + }, + bootstrap: { + type: "bool", + default_val: "True", + tooltip: "tooltip not implemented" + }, + oob_score: { + type: "bool", + default_val: "False", + tooltip: "tooltip not implemented" + }, + n_jobs: { + type: "int", + default_val: "-1", + tooltip: "tooltip not implemented" + }, + random_state: { + type: "int", + default_val: "1334", + tooltip: "tooltip not implemented" + }, + verbose: { + type: "int", + default_val: "0", + tooltip: "tooltip not implemented" + }, + warm_start: { + type: "bool", + default_val: "False", + tooltip: "tooltip not implemented" + }, + class_weight: { + type: "NoneType", + default_val: "None", + tooltip: "tooltip not implemented" + }, + max_samples: { + type: "NoneType", + default_val: "None", + tooltip: "tooltip not implemented" + }, + criterion: { + type: "string", + default_val: "gini", + tooltip: "tooltip not implemented" + }, + max_depth: { + type: "NoneType", + default_val: "None", + tooltip: "tooltip not implemented" + }, + min_samples_split: { + type: "int", + default_val: "2", + tooltip: "tooltip not implemented" + }, + min_samples_leaf: { + type: "int", + default_val: "1", + tooltip: "tooltip not implemented" + }, + min_weight_fraction_leaf: { + type: "float", + default_val: "0.0", + tooltip: "tooltip not implemented" + }, + max_features: { + type: "string", + default_val: "sqrt", + tooltip: "tooltip not implemented" + }, + max_leaf_nodes: { + type: "NoneType", + default_val: "None", + tooltip: "tooltip not implemented" + }, + min_impurity_decrease: { + type: "float", + default_val: "0.0", + tooltip: "tooltip not implemented" + }, + ccp_alpha: { + type: "float", + default_val: "0.0", + tooltip: "tooltip not implemented" + } + }, + code: "rf" + }, + qda: { + options: { + priors: { + type: "NoneType", + default_val: "None", + tooltip: "tooltip not implemented" + }, + reg_param: { + type: "float", + default_val: "0.0", + tooltip: "tooltip not implemented" + }, + store_covariance: { + type: "bool", + default_val: "False", + tooltip: "tooltip not implemented" + }, + tol: { + type: "float", + default_val: "0.0001", + tooltip: "tooltip not implemented" + } + }, + code: "qda" + }, + ada: { + options: { + estimator: { + type: "NoneType", + default_val: "None", + tooltip: "tooltip not implemented" + }, + n_estimators: { + type: "int", + default_val: "50", + tooltip: "tooltip not implemented" + }, + estimator_params: { + type: "tuple", + default_val: "()", + tooltip: "tooltip not implemented" + }, + base_estimator: { + type: "string", + default_val: "deprecated", + tooltip: "tooltip not implemented" + }, + learning_rate: { + type: "float", + default_val: "1.0", + tooltip: "tooltip not implemented" + }, + random_state: { + type: "int", + default_val: "1334", + tooltip: "tooltip not implemented" + }, + algorithm: { + type: "string", + default_val: "SAMME.R", + tooltip: "tooltip not implemented" + } + }, + code: "ada" + }, + gbc: { + options: { + n_estimators: { + type: "int", + default_val: "100", + tooltip: "tooltip not implemented" + }, + learning_rate: { + type: "float", + default_val: "0.1", + tooltip: "tooltip not implemented" + }, + loss: { + type: "string", + default_val: "log_loss", + tooltip: "tooltip not implemented" + }, + criterion: { + type: "string", + default_val: "friedman_mse", + tooltip: "tooltip not implemented" + }, + min_samples_split: { + type: "int", + default_val: "2", + tooltip: "tooltip not implemented" + }, + min_samples_leaf: { + type: "int", + default_val: "1", + tooltip: "tooltip not implemented" + }, + min_weight_fraction_leaf: { + type: "float", + default_val: "0.0", + tooltip: "tooltip not implemented" + }, + subsample: { + type: "float", + default_val: "1.0", + tooltip: "tooltip not implemented" + }, + max_features: { + type: "NoneType", + default_val: "None", + tooltip: "tooltip not implemented" + }, + max_depth: { + type: "int", + default_val: "3", + tooltip: "tooltip not implemented" + }, + min_impurity_decrease: { + type: "float", + default_val: "0.0", + tooltip: "tooltip not implemented" + }, + ccp_alpha: { + type: "float", + default_val: "0.0", + tooltip: "tooltip not implemented" + }, + init: { + type: "NoneType", + default_val: "None", + tooltip: "tooltip not implemented" + }, + random_state: { + type: "int", + default_val: "1334", + tooltip: "tooltip not implemented" + }, + alpha: { + type: "float", + default_val: "0.9", + tooltip: "tooltip not implemented" + }, + verbose: { + type: "int", + default_val: "0", + tooltip: "tooltip not implemented" + }, + max_leaf_nodes: { + type: "NoneType", + default_val: "None", + tooltip: "tooltip not implemented" + }, + warm_start: { + type: "bool", + default_val: "False", + tooltip: "tooltip not implemented" + }, + validation_fraction: { + type: "float", + default_val: "0.1", + tooltip: "tooltip not implemented" + }, + n_iter_no_change: { + type: "NoneType", + default_val: "None", + tooltip: "tooltip not implemented" + }, + tol: { + type: "float", + default_val: "0.0001", + tooltip: "tooltip not implemented" + } + }, + code: "gbc" + }, + lda: { + options: { + solver: { + type: "string", + default_val: "svd", + tooltip: "tooltip not implemented" + }, + shrinkage: { + type: "NoneType", + default_val: "None", + tooltip: "tooltip not implemented" + }, + priors: { + type: "NoneType", + default_val: "None", + tooltip: "tooltip not implemented" + }, + n_components: { + type: "NoneType", + default_val: "None", + tooltip: "tooltip not implemented" + }, + store_covariance: { + type: "bool", + default_val: "False", + tooltip: "tooltip not implemented" + }, + tol: { + type: "float", + default_val: "0.0001", + tooltip: "tooltip not implemented" + }, + covariance_estimator: { + type: "NoneType", + default_val: "None", + tooltip: "tooltip not implemented" + } + }, + code: "lda" + }, + et: { + options: { + estimator: { + type: "ExtraTreeClassifier", + default_val: + "ExtraTreeClassifier(ccp_alpha=0.0, class_weight=None, criterion='gini',\n max_depth=None, max_features='sqrt', max_leaf_nodes=None,\n min_impurity_decrease=0.0, min_samples_leaf=1,\n min_samples_split=2, min_weight_fraction_leaf=0.0,\n random_state=None, splitter='random')", + tooltip: "tooltip not implemented" + }, + n_estimators: { + type: "int", + default_val: "100", + tooltip: "tooltip not implemented" + }, + estimator_params: { + type: "tuple", + default_val: + "('criterion', 'max_depth', 'min_samples_split', 'min_samples_leaf', 'min_weight_fraction_leaf', 'max_features', 'max_leaf_nodes', 'min_impurity_decrease', 'random_state', 'ccp_alpha')", + tooltip: "tooltip not implemented" + }, + base_estimator: { + type: "string", + default_val: "deprecated", + tooltip: "tooltip not implemented" + }, + bootstrap: { + type: "bool", + default_val: "False", + tooltip: "tooltip not implemented" + }, + oob_score: { + type: "bool", + default_val: "False", + tooltip: "tooltip not implemented" + }, + n_jobs: { + type: "int", + default_val: "-1", + tooltip: "tooltip not implemented" + }, + random_state: { + type: "int", + default_val: "1334", + tooltip: "tooltip not implemented" + }, + verbose: { + type: "int", + default_val: "0", + tooltip: "tooltip not implemented" + }, + warm_start: { + type: "bool", + default_val: "False", + tooltip: "tooltip not implemented" + }, + class_weight: { + type: "NoneType", + default_val: "None", + tooltip: "tooltip not implemented" + }, + max_samples: { + type: "NoneType", + default_val: "None", + tooltip: "tooltip not implemented" + }, + criterion: { + type: "string", + default_val: "gini", + tooltip: "tooltip not implemented" + }, + max_depth: { + type: "NoneType", + default_val: "None", + tooltip: "tooltip not implemented" + }, + min_samples_split: { + type: "int", + default_val: "2", + tooltip: "tooltip not implemented" + }, + min_samples_leaf: { + type: "int", + default_val: "1", + tooltip: "tooltip not implemented" + }, + min_weight_fraction_leaf: { + type: "float", + default_val: "0.0", + tooltip: "tooltip not implemented" + }, + max_features: { + type: "string", + default_val: "sqrt", + tooltip: "tooltip not implemented" + }, + max_leaf_nodes: { + type: "NoneType", + default_val: "None", + tooltip: "tooltip not implemented" + }, + min_impurity_decrease: { + type: "float", + default_val: "0.0", + tooltip: "tooltip not implemented" + }, + ccp_alpha: { + type: "float", + default_val: "0.0", + tooltip: "tooltip not implemented" + } + }, + code: "et" + }, + lightgbm: { + options: { + boosting_type: { + type: "string", + default_val: "gbdt", + tooltip: "tooltip not implemented" + }, + objective: { + type: "NoneType", + default_val: "None", + tooltip: "tooltip not implemented" + }, + num_leaves: { + type: "int", + default_val: "31", + tooltip: "tooltip not implemented" + }, + max_depth: { + type: "int", + default_val: "-1", + tooltip: "tooltip not implemented" + }, + learning_rate: { + type: "float", + default_val: "0.1", + tooltip: "tooltip not implemented" + }, + n_estimators: { + type: "int", + default_val: "100", + tooltip: "tooltip not implemented" + }, + subsample_for_bin: { + type: "int", + default_val: "200000", + tooltip: "tooltip not implemented" + }, + min_split_gain: { + type: "float", + default_val: "0.0", + tooltip: "tooltip not implemented" + }, + min_child_weight: { + type: "float", + default_val: "0.001", + tooltip: "tooltip not implemented" + }, + min_child_samples: { + type: "int", + default_val: "20", + tooltip: "tooltip not implemented" + }, + subsample: { + type: "float", + default_val: "1.0", + tooltip: "tooltip not implemented" + }, + subsample_freq: { + type: "int", + default_val: "0", + tooltip: "tooltip not implemented" + }, + colsample_bytree: { + type: "float", + default_val: "1.0", + tooltip: "tooltip not implemented" + }, + reg_alpha: { + type: "float", + default_val: "0.0", + tooltip: "tooltip not implemented" + }, + reg_lambda: { + type: "float", + default_val: "0.0", + tooltip: "tooltip not implemented" + }, + random_state: { + type: "int", + default_val: "1334", + tooltip: "tooltip not implemented" + }, + n_jobs: { + type: "int", + default_val: "-1", + tooltip: "tooltip not implemented" + }, + importance_type: { + type: "string", + default_val: "split", + tooltip: "tooltip not implemented" + }, + class_weight: { + type: "NoneType", + default_val: "None", + tooltip: "tooltip not implemented" + } + }, + code: "lightgbm" + }, + dummy: { + options: { + strategy: { + type: "string", + default_val: "prior", + tooltip: "tooltip not implemented" + }, + random_state: { + type: "int", + default_val: "1334", + tooltip: "tooltip not implemented" + }, + constant: { + type: "NoneType", + default_val: "None", + tooltip: "tooltip not implemented" + } + }, + code: "dummy" + } +} +export default classificationModelSettings diff --git a/renderer/public/setupVariables/possibleSettings/learning/classificationSettings.js b/renderer/public/setupVariables/possibleSettings/learning/classificationSettings.js index 14fe1041..39fc5409 100644 --- a/renderer/public/setupVariables/possibleSettings/learning/classificationSettings.js +++ b/renderer/public/setupVariables/possibleSettings/learning/classificationSettings.js @@ -122,11 +122,6 @@ const classificationSettings = { }, "dataset": { "options": { - "data_func": { - "type": "data-function", - "tooltip": "

The function that generate data (the dataframe-like input). This\nis useful when the dataset is large, and you need parallel operations\nsuch as compare_models. It can avoid broadcasting large dataset\nfrom driver to workers. Notice one and only one of data and\ndata_func must be set.

\n", - "default_val": "" - }, "index": { "type": "bool-int-str", "tooltip": "
\n
Handle indices in the data dataframe.
    \n

  • If False: Reset to RangeIndex.

    • \n

  • If True: Keep the provided index.

    • \n

  • If int: Position of the column to use as index.

    • \n

  • If str: Name of the column to use as index.

    • \n

  • If sequence: Array with shape=(n_samples,) to use as index.

    • \n
\n
\n
\n", @@ -142,11 +137,6 @@ const classificationSettings = { "tooltip": "

If not None, test_data is used as a hold-out set and train_size parameter\nis ignored. The columns of data and test_data must match.

\n", "default_val": "None" }, - "ordinal_features": { - "type": "dict", - "tooltip": "

Categorical features to be encoded ordinally. For example, a categorical\nfeature with \u2018low\u2019, \u2018medium\u2019, \u2018high\u2019 values where low < medium < high can\nbe passed as ordinal_features = {\u2018column_name\u2019 : [\u2018low\u2019, \u2018medium\u2019, \u2018high\u2019]}.

\n", - "default_val": "None" - }, "numeric_features": { "type": "custom-list", "tooltip": "

If the inferred data types are not correct, the numeric_features param can\nbe used to define the data types. It takes a list of strings with column\nnames that are numeric.

\n", @@ -197,11 +187,6 @@ const classificationSettings = { "tooltip": "

Categorical columns with max_encoding_ohe or less unique values are\nencoded using OneHotEncoding. If more, the encoding_method estimator\nis used. Note that columns with exactly two classes are always encoded\nordinally. Set to below 0 to always use OneHotEncoding.

\n", "default_val": "25" }, - "encoding_method": { - "type": "category-encoders estimator", - "tooltip": "

A category-encoders estimator to encode the categorical columns\nwith more than max_encoding_ohe unique values. If None,\ncategory_encoders.target_encoder.TargetEncoder is used.

\n", - "default_val": "None" - }, "rare_to_value": { "type": "float", "tooltip": "

Minimum fraction of category occurrences in a categorical column.\nIf a category is less frequent than rare_to_value * len(X), it is\nreplaced with the string in rare_value. Use this parameter to group\nrare categories before encoding the column. If None, ignores this step.

\n", @@ -217,11 +202,6 @@ const classificationSettings = { "tooltip": "

Remove features with a training-set variance lower than the provided\nthreshold. If 0, keep all features with non-zero variance, i.e. remove\nthe features that have the same value in all samples. If None, skip\nthis transformation step.

\n", "default_val": "None" }, - "group_features": { - "type": "dict", - "tooltip": "

When the dataset contains features with related characteristics,\nadd new fetaures with the following statistical properties of that\ngroup: min, max, mean, std, median and mode. The parameter takes a\ndict with the group name as key and a list of feature names\nbelonging to that group as value.

\n", - "default_val": "None" - }, "drop_groups": { "type": "bool", "tooltip": "

Whether to drop the original features in the group. Ignored when\ngroup_features is None.

\n", @@ -247,11 +227,6 @@ const classificationSettings = { "tooltip": "

Estimator with which to perform class balancing. Choose from the name\nof an imblearn estimator, or a custom instance of such. Ignored when\nfix_imbalance=False.

\n", "default_val": "\u201cSMOTE\u201d" }, - "custom_pipeline": { - "type": "list of (str, transformer), dict or Pipeline", - "tooltip": "

Addidiotnal custom transformers. If passed, they are applied to the\npipeline last, after all the build-in transformers.

\n", - "default_val": "None" - }, "custom_pipeline_position": { "type": "int", "tooltip": "

Position of the custom pipeline in the overal preprocessing pipeline.\nThe default value adds the custom pipeline last.

\n", @@ -317,11 +292,6 @@ const classificationSettings = { "tooltip": "

Name of the experiment for logging. Ignored when log_experiment is False.

\n", "default_val": "None" }, - "experiment_custom_tags": { - "type": "dict", - "tooltip": "

Dictionary of tag_name: String -> value: (String, but will be string-ified\nif not) passed to the mlflow.set_tags to add new custom tags for the experiment.

\n", - "default_val": "None" - }, "log_plots": { "type": "bool", "tooltip": "

When set to True, certain plots are logged automatically in the MLFlow server.\nTo change the type of plots to be logged, pass a list containing plot IDs. Refer\nto documentation of plot_model. Ignored when log_experiment is False.

\n", @@ -337,31 +307,11 @@ const classificationSettings = { "tooltip": "

When set to True, dataset is logged on the MLflow server as a csv file.\nIgnored when log_experiment is False.

\n", "default_val": "False" }, - "engine": { - "type": "Optional[Dict[str, str]] = None", - "tooltip": "

The execution engines to use for the models in the form of a dict\nof model_id: engine - e.g. for Logistic Regression (\u201clr\u201d, users can\nswitch between \u201csklearn\u201d and \u201csklearnex\u201d by specifying\nengine={\u201clr\u201d: \u201csklearnex\u201d}

\n", - "default_val": "" - }, "verbose": { "type": "bool", "tooltip": "

When set to False, Information grid is not printed.

\n", "default_val": "True" }, - "memory": { - "type": "str, bool or Memory", - "tooltip": "
\n
Used to cache the fitted transformers of the pipeline.

If False: No caching is performed.\nIf True: A default temp directory is used.\nIf str: Path to the caching directory.

\n
\n
\n", - "default_val": "rue" - }, - "profile": { - "type": "bool", - "tooltip": "

When set to True, an interactive EDA report is displayed.

\n", - "default_val": "False" - }, - "profile_kwargs": { - "type": "dict", - "tooltip": "

Dictionary of arguments passed to the ProfileReport method used\nto create the EDA report. Ignored if profile is False.

\n", - "default_val": "{} (empty dict)" - }, "time-point": { "type": "string", "default_val": "", @@ -464,11 +414,6 @@ const classificationSettings = { "tooltip": "

When set to \u2018ignore\u2019, will skip the model with exceptions and continue.\nIf \u2018raise\u2019, will break the function when exceptions are raised.

\n", "default_val": "ignore" }, - "fit_kwargs": { - "type": "dict", - "tooltip": "

Dictionary of arguments passed to the fit method of the model.

\n", - "default_val": "{} (empty dict)" - }, "groups": { "type": "string", "tooltip": "

Optional group labels when \u2018GroupKFold\u2019 is used for the cross validation.\nIt takes an array with shape (n_samples, ) where n_samples is the number\nof rows in the training dataset. When string is passed, it is interpreted\nas the column name in the dataset containing group labels.

\n", @@ -484,11 +429,6 @@ const classificationSettings = { "tooltip": "

Threshold for converting predicted probability to class label.\nIt defaults to 0.5 for all classifiers unless explicitly defined\nin this parameter. Only applicable for binary classification.

\n", "default_val": "None" }, - "engine": { - "type": "Optional[Dict[str, str]] = None", - "tooltip": "

The execution engines to use for the models in the form of a dict\nof model_id: engine - e.g. for Logistic Regression (\u201clr\u201d, users can\nswitch between \u201csklearn\u201d and \u201csklearnex\u201d by specifying\nengine={\u201clr\u201d: \u201csklearnex\u201d}

\n", - "default_val": "" - }, "verbose": { "type": "bool", "tooltip": "

Score grid is not printed when verbose is set to False.

\n", @@ -514,11 +454,6 @@ const classificationSettings = { "tooltip": "

When set to False, metrics are evaluated on holdout set. fold param\nis ignored when cross_validation is set to False.

\n", "default_val": "True" }, - "fit_kwargs": { - "type": "dict", - "tooltip": "

Dictionary of arguments passed to the fit method of the model.

\n", - "default_val": "{} (empty dict)" - }, "groups": { "type": "string", "tooltip": "

Optional group labels when GroupKFold is used for the cross validation.\nIt takes an array with shape (n_samples, ) where n_samples is the number\nof rows in training dataset. When string is passed, it is interpreted as\nthe column name in the dataset containing group labels.

\n", @@ -529,16 +464,6 @@ const classificationSettings = { "tooltip": "

Threshold for converting predicted probability to class label.\nIt defaults to 0.5 for all classifiers unless explicitly defined\nin this parameter. Only applicable for binary classification.

\n", "default_val": "None" }, - "experiment_custom_tags": { - "type": "dict", - "tooltip": "

Dictionary of tag_name: String -> value: (String, but will be string-ified\nif not) passed to the mlflow.set_tags to add new custom tags for the experiment.

\n", - "default_val": "None" - }, - "engine": { - "type": "Optional[str] = None", - "tooltip": "

The execution engine to use for the model, e.g. for Logistic Regression (\u201clr\u201d), users can\nswitch between \u201csklearn\u201d and \u201csklearnex\u201d by specifying\nengine=\u201dsklearnex\u201d.

\n", - "default_val": "" - }, "verbose": { "type": "bool", "tooltip": "

Score grid is not printed when verbose is set to False.

\n", @@ -571,16 +496,6 @@ const classificationSettings = { "tooltip": "

Controls cross-validation. If None, the CV generator in the fold_strategy\nparameter of the setup function is used. When an integer is passed,\nit is interpreted as the \u2018n_splits\u2019 parameter of the CV generator in the\nsetup function.

\n", "default_val": "None" }, - "fit_kwargs": { - "type": "dict", - "tooltip": "

Dictionary of arguments passed to the fit method of the model.

\n", - "default_val": "{} (empty dict)" - }, - "plot_kwargs": { - "type": "dict", - "tooltip": "
\n
Dictionary of arguments passed to the visualizer class.
    \n

  • pipeline: fontsize -> int

    • \n
\n
\n
\n", - "default_val": "{} (empty dict)" - }, "groups": { "type": "string", "tooltip": "

Optional group labels when GroupKFold is used for the cross validation.\nIt takes an array with shape (n_samples, ) where n_samples is the number\nof rows in training dataset. When string is passed, it is interpreted as\nthe column name in the dataset containing group labels.

\n", @@ -641,16 +556,6 @@ const classificationSettings = { "type": "string", "tooltip": "

Render mode for the dashboard. The default is set to dash which will\nrender a dashboard in browser. There are four possible options:

\n
    \n

  • \u2018dash\u2019 - displays the dashboard in browser

    • \n

  • \u2018inline\u2019 - displays the dashboard in the jupyter notebook cell.

    • \n

  • \u2018jupyterlab\u2019 - displays the dashboard in jupyterlab pane.

    • \n

  • \u2018external\u2019 - displays the dashboard in a separate tab. (use in Colab)

    • \n
\n", "default_val": "dash" - }, - "dashboard_kwargs": { - "type": "dict", - "tooltip": "

Dictionary of arguments passed to the ExplainerDashboard class.

\n", - "default_val": "{} (empty dict)" - }, - "run_kwargs": { - "type": "dict", - "tooltip": "

Dictionary of arguments passed to the run method of ExplainerDashboard.

\n", - "default_val": "{} (empty dict)" } }, "code": "dashboard()", @@ -659,11 +564,6 @@ const classificationSettings = { }, "finalize": { "options": { - "fit_kwargs": { - "type": "dict", - "tooltip": "

Dictionary of arguments passed to the fit method of the model.

\n", - "default_val": "{} (empty dict)" - }, "groups": { "type": "string", "tooltip": "

Optional group labels when GroupKFold is used for the cross validation.\nIt takes an array with shape (n_samples, ) where n_samples is the number\nof rows in training dataset. When string is passed, it is interpreted as\nthe column name in the dataset containing group labels.

\n", @@ -673,11 +573,6 @@ const classificationSettings = { "type": "bool", "tooltip": "

Whether to return the complete fitted pipeline or only the fitted model.

\n", "default_val": "False" - }, - "experiment_custom_tags": { - "type": "dict", - "tooltip": "

Dictionary of tag_name: String -> value: (String, but will be string-ified\nif not) passed to the mlflow.set_tags to add new custom tags for the experiment.

\n", - "default_val": "None" } }, "code": "", @@ -706,16 +601,6 @@ const classificationSettings = { }, "load_model": { "options": { - "platform": { - "type": "string", - "tooltip": "

Name of the cloud platform. Currently supported platforms:\n\u2018aws\u2019, \u2018gcp\u2019 and \u2018azure\u2019.

\n", - "default_val": "None" - }, - "authentication": { - "type": "dict", - "tooltip": "

dictionary of applicable authentication tokens.

\n

when platform = \u2018aws\u2019:\n{\u2018bucket\u2019 : \u2018Name of Bucket on S3\u2019, \u2018path\u2019: (optional) folder name under the bucket}

\n

when platform = \u2018gcp\u2019:\n{\u2018project\u2019: \u2018gcp-project-name\u2019, \u2018bucket\u2019 : \u2018gcp-bucket-name\u2019}

\n

when platform = \u2018azure\u2019:\n{\u2018container\u2019: \u2018azure-container-name\u2019}

\n", - "default_val": "None" - }, "verbose": { "type": "bool", "tooltip": "

Success message is not printed when verbose is set to False.

\n", @@ -751,21 +636,11 @@ const classificationSettings = { "tooltip": "

Number of iterations in the grid search. Increasing \u2018n_iter\u2019 may improve\nmodel performance but also increases the training time.

\n", "default_val": "10" }, - "custom_grid": { - "type": "dict", - "tooltip": "

To define custom search space for hyperparameters, pass a dictionary with\nparameter name and values to be iterated. Custom grids must be in a format\nsupported by the defined search_library.

\n", - "default_val": "None" - }, "optimize": { "type": "string", "tooltip": "

Metric name to be evaluated for hyperparameter tuning. It also accepts custom\nmetrics that are added through the add_metric function.

\n", "default_val": "Accuracy" }, - "custom_scorer": { - "type": "object", - "tooltip": "

custom scoring strategy can be passed to tune hyperparameters of the model.\nIt must be created using sklearn.make_scorer. It is equivalent of adding\ncustom metric using the add_metric function and passing the name of the\ncustom metric in the optimize parameter.\nWill be deprecated in future.

\n", - "default_val": "None" - }, "search_library": { "type": "string", "tooltip": "

The search library used for tuning hyperparameters. Possible values:

\n
    \n
  • \n
    \u2018scikit-learn\u2019 - default, requires no further installation

    https://github.com/scikit-learn/scikit-learn

    \n
    \n
    \n
    • \n
  • \n
    \u2018scikit-optimize\u2019 - pip install scikit-optimize

    https://scikit-optimize.github.io/stable/

    \n
    \n
    \n
    • \n
  • \n
    \u2018tune-sklearn\u2019 - pip install tune-sklearn ray[tune]

    https://github.com/ray-project/tune-sklearn

    \n
    \n
    \n
    • \n
  • \n
    \u2018optuna\u2019 - pip install optuna

    https://optuna.org/

    \n
    \n
    \n
    • \n
\n", @@ -791,11 +666,6 @@ const classificationSettings = { "tooltip": "

When set to True, the returned object is always better performing. The\nmetric used for comparison is defined by the optimize parameter.

\n", "default_val": "True" }, - "fit_kwargs": { - "type": "dict", - "tooltip": "

Dictionary of arguments passed to the fit method of the tuner.

\n", - "default_val": "{} (empty dict)" - }, "groups": { "type": "string", "tooltip": "

Optional group labels when GroupKFold is used for the cross validation.\nIt takes an array with shape (n_samples, ) where n_samples is the number\nof rows in training dataset. When string is passed, it is interpreted as\nthe column name in the dataset containing group labels.

\n", @@ -858,11 +728,6 @@ const classificationSettings = { "tooltip": "

Metric to compare for model selection when choose_better is True.

\n", "default_val": "Accuracy" }, - "fit_kwargs": { - "type": "dict", - "tooltip": "

Dictionary of arguments passed to the fit method of the model.

\n", - "default_val": "{} (empty dict)" - }, "groups": { "type": "string", "tooltip": "

Optional group labels when GroupKFold is used for the cross validation.\nIt takes an array with shape (n_samples, ) where n_samples is the number\nof rows in training dataset. When string is passed, it is interpreted as\nthe column name in the dataset containing group labels.

\n", @@ -920,11 +785,6 @@ const classificationSettings = { "tooltip": "

Sequence of weights (float or int) to weight the occurrences of predicted class\nlabels (hard voting) or class probabilities before averaging (soft voting). Uses\nuniform weights when None.

\n", "default_val": "None" }, - "fit_kwargs": { - "type": "dict", - "tooltip": "

Dictionary of arguments passed to the fit method of the model.

\n", - "default_val": "{} (empty dict)" - }, "groups": { "type": "string", "tooltip": "

Optional group labels when GroupKFold is used for the cross validation.\nIt takes an array with shape (n_samples, ) where n_samples is the number\nof rows in training dataset. When string is passed, it is interpreted as\nthe column name in the dataset containing group labels.

\n", @@ -1012,11 +872,6 @@ const classificationSettings = { "tooltip": "

Metric to compare for model selection when choose_better is True.

\n", "default_val": "Accuracy" }, - "fit_kwargs": { - "type": "dict", - "tooltip": "

Dictionary of arguments passed to the fit method of the model.

\n", - "default_val": "{} (empty dict)" - }, "groups": { "type": "string", "tooltip": "

Optional group labels when GroupKFold is used for the cross validation.\nIt takes an array with shape (n_samples, ) where n_samples is the number\nof rows in training dataset. When string is passed, it is interpreted as\nthe column name in the dataset containing group labels.

\n", @@ -1064,11 +919,6 @@ const classificationSettings = { "tooltip": "

Number of decimal places the metrics in the score grid will be rounded to.

\n", "default_val": "4" }, - "fit_kwargs": { - "type": "dict", - "tooltip": "

Dictionary of arguments passed to the fit method of the model.

\n", - "default_val": "{} (empty dict)" - }, "groups": { "type": "string", "tooltip": "

Optional group labels when GroupKFold is used for the cross validation.\nIt takes an array with shape (n_samples, ) where n_samples is the number\nof rows in training dataset. When string is passed, it is interpreted as\nthe column name in the dataset containing group labels.

\n", diff --git a/renderer/public/setupVariables/possibleSettings/learning/regressionSettings.js b/renderer/public/setupVariables/possibleSettings/learning/regressionSettings.js index b8facb2f..16e5e739 100644 --- a/renderer/public/setupVariables/possibleSettings/learning/regressionSettings.js +++ b/renderer/public/setupVariables/possibleSettings/learning/regressionSettings.js @@ -122,11 +122,6 @@ const regressionSettings = { }, "dataset": { "options": { - "data_func": { - "type": "data-function", - "tooltip": "

The function that generate data (the dataframe-like input). This\nis useful when the dataset is large, and you need parallel operations\nsuch as compare_models. It can avoid broadcasting large dataset\nfrom driver to workers. Notice one and only one of data and\ndata_func must be set.

\n", - "default_val": "" - }, "index": { "type": "bool-int-str", "tooltip": "
\n
Handle indices in the data dataframe.
    \n

  • If False: Reset to RangeIndex.

    • \n

  • If True: Keep the provided index.

    • \n

  • If int: Position of the column to use as index.

    • \n

  • If str: Name of the column to use as index.

    • \n

  • If sequence: Array with shape=(n_samples,) to use as index.

    • \n
\n
\n
\n", @@ -142,11 +137,6 @@ const regressionSettings = { "tooltip": "

If not None, test_data is used as a hold-out set and train_size parameter\nis ignored. The columns of data and test_data must match.

\n", "default_val": "None" }, - "ordinal_features": { - "type": "dict", - "tooltip": "

Categorical features to be encoded ordinally. For example, a categorical\nfeature with \u2018low\u2019, \u2018medium\u2019, \u2018high\u2019 values where low < medium < high can\nbe passed as ordinal_features = {\u2018column_name\u2019 : [\u2018low\u2019, \u2018medium\u2019, \u2018high\u2019]}.

\n", - "default_val": "None" - }, "numeric_features": { "type": "custom-list", "tooltip": "

If the inferred data types are not correct, the numeric_features param can\nbe used to define the data types. It takes a list of strings with column\nnames that are numeric.

\n", @@ -197,11 +187,6 @@ const regressionSettings = { "tooltip": "

Categorical columns with max_encoding_ohe or less unique values are\nencoded using OneHotEncoding. If more, the encoding_method estimator\nis used. Note that columns with exactly two classes are always encoded\nordinally. Set to below 0 to always use OneHotEncoding.

\n", "default_val": "25" }, - "encoding_method": { - "type": "category-encoders estimator", - "tooltip": "

A category-encoders estimator to encode the categorical columns\nwith more than max_encoding_ohe unique values. If None,\ncategory_encoders.target_encoder.TargetEncoder is used.

\n", - "default_val": "None" - }, "rare_to_value": { "type": "float", "tooltip": "

Minimum fraction of category occurrences in a categorical column.\nIf a category is less frequent than rare_to_value * len(X), it is\nreplaced with the string in rare_value. Use this parameter to group\nrare categories before encoding the column. If None, ignores this step.

\n", @@ -217,11 +202,6 @@ const regressionSettings = { "tooltip": "

Remove features with a training-set variance lower than the provided\nthreshold. If 0, keep all features with non-zero variance, i.e. remove\nthe features that have the same value in all samples. If None, skip\nthis transformation step.

\n", "default_val": "None" }, - "group_features": { - "type": "dict", - "tooltip": "

When the dataset contains features with related characteristics,\nadd new fetaures with the following statistical properties of that\ngroup: min, max, mean, std, median and mode. The parameter takes a\ndict with the group name as key and a list of feature names\nbelonging to that group as value.

\n", - "default_val": "None" - }, "drop_groups": { "type": "bool", "tooltip": "

Whether to drop the original features in the group. Ignored when\ngroup_features is None.

\n", @@ -247,11 +227,6 @@ const regressionSettings = { "tooltip": "

Defines the method for transformation. By default, the transformation method is\nset to \u2018yeo-johnson\u2019. The other available option for transformation is \u2018quantile\u2019.\nIgnored when transform_target is not True.

\n", "default_val": "yeo-johnson" }, - "custom_pipeline": { - "type": "list of (str, transformer), dict or Pipeline", - "tooltip": "

Addidiotnal custom transformers. If passed, they are applied to the\npipeline last, after all the build-in transformers.

\n", - "default_val": "None" - }, "custom_pipeline_position": { "type": "int", "tooltip": "

Position of the custom pipeline in the overal preprocessing pipeline.\nThe default value adds the custom pipeline last.

\n", @@ -317,11 +292,6 @@ const regressionSettings = { "tooltip": "

Name of the experiment for logging. Ignored when log_experiment is False.

\n", "default_val": "None" }, - "experiment_custom_tags": { - "type": "dict", - "tooltip": "

Dictionary of tag_name: String -> value: (String, but will be string-ified\nif not) passed to the mlflow.set_tags to add new custom tags for the experiment.

\n", - "default_val": "None" - }, "log_plots": { "type": "bool", "tooltip": "

When set to True, certain plots are logged automatically in the MLFlow server.\nTo change the type of plots to be logged, pass a list containing plot IDs. Refer\nto documentation of plot_model. Ignored when log_experiment is False.

\n", @@ -337,31 +307,11 @@ const regressionSettings = { "tooltip": "

When set to True, dataset is logged on the MLflow server as a csv file.\nIgnored when log_experiment is False.

\n", "default_val": "False" }, - "engine": { - "type": "Optional[Dict[str, str]] = None", - "tooltip": "

The execution engines to use for the models in the form of a dict\nof model_id: engine - e.g. for Linear Regression (\u201clr\u201d, users can\nswitch between \u201csklearn\u201d and \u201csklearnex\u201d by specifying\nengine={\u201clr\u201d: \u201csklearnex\u201d}

\n", - "default_val": "" - }, "verbose": { "type": "bool", "tooltip": "

When set to False, Information grid is not printed.

\n", "default_val": "True" }, - "memory": { - "type": "str, bool or Memory", - "tooltip": "
\n
Used to cache the fitted transformers of the pipeline.

If False: No caching is performed.\nIf True: A default temp directory is used.\nIf str: Path to the caching directory.

\n
\n
\n", - "default_val": "rue" - }, - "profile": { - "type": "bool", - "tooltip": "

When set to True, an interactive EDA report is displayed.

\n", - "default_val": "False" - }, - "profile_kwargs": { - "type": "dict", - "tooltip": "

Dictionary of arguments passed to the ProfileReport method used\nto create the EDA report. Ignored if profile is False.

\n", - "default_val": "{} (empty dict)" - }, "time-point": { "type": "string", "default_val": "", @@ -474,21 +424,11 @@ const regressionSettings = { "tooltip": "

When set to \u2018ignore\u2019, will skip the model with exceptions and continue.\nIf \u2018raise\u2019, will break the function when exceptions are raised.

\n", "default_val": "ignore" }, - "fit_kwargs": { - "type": "dict", - "tooltip": "

Dictionary of arguments passed to the fit method of the model.

\n", - "default_val": "{} (empty dict)" - }, "groups": { "type": "string", "tooltip": "

Optional group labels when \u2018GroupKFold\u2019 is used for the cross validation.\nIt takes an array with shape (n_samples, ) where n_samples is the number\nof rows in the training dataset. When string is passed, it is interpreted\nas the column name in the dataset containing group labels.

\n", "default_val": "None" }, - "engine": { - "type": "Optional[Dict[str, str]] = None", - "tooltip": "

The execution engines to use for the models in the form of a dict\nof model_id: engine - e.g. for Linear Regression (\u201clr\u201d, users can\nswitch between \u201csklearn\u201d and \u201csklearnex\u201d by specifying\nengine={\u201clr\u201d: \u201csklearnex\u201d}

\n", - "default_val": "" - }, "verbose": { "type": "bool", "tooltip": "

Score grid is not printed when verbose is set to False.

\n", @@ -514,26 +454,11 @@ const regressionSettings = { "tooltip": "

When set to False, metrics are evaluated on holdout set. fold param\nis ignored when cross_validation is set to False.

\n", "default_val": "True" }, - "fit_kwargs": { - "type": "dict", - "tooltip": "

Dictionary of arguments passed to the fit method of the model.

\n", - "default_val": "{} (empty dict)" - }, "groups": { "type": "string", "tooltip": "

Optional group labels when GroupKFold is used for the cross validation.\nIt takes an array with shape (n_samples, ) where n_samples is the number\nof rows in training dataset. When string is passed, it is interpreted as\nthe column name in the dataset containing group labels.

\n", "default_val": "None" }, - "experiment_custom_tags": { - "type": "dict", - "tooltip": "

Dictionary of tag_name: String -> value: (String, but will be string-ified\nif not) passed to the mlflow.set_tags to add new custom tags for the experiment.

\n", - "default_val": "None" - }, - "engine": { - "type": "Optional[str] = None", - "tooltip": "

The execution engine to use for the model, e.g. for Linear Regression (\u201clr\u201d), users can\nswitch between \u201csklearn\u201d and \u201csklearnex\u201d by specifying\nengine=\u201dsklearnex\u201d.

\n", - "default_val": "" - }, "verbose": { "type": "bool", "tooltip": "

Score grid is not printed when verbose is set to False.

\n", @@ -566,16 +491,6 @@ const regressionSettings = { "tooltip": "

Controls cross-validation. If None, the CV generator in the fold_strategy\nparameter of the setup function is used. When an integer is passed,\nit is interpreted as the \u2018n_splits\u2019 parameter of the CV generator in the\nsetup function.

\n", "default_val": "None" }, - "fit_kwargs": { - "type": "dict", - "tooltip": "

Dictionary of arguments passed to the fit method of the model.

\n", - "default_val": "{} (empty dict)" - }, - "plot_kwargs": { - "type": "dict", - "tooltip": "
\n
Dictionary of arguments passed to the visualizer class.
    \n

  • pipeline: fontsize -> int

    • \n
\n
\n
\n", - "default_val": "{} (empty dict)" - }, "groups": { "type": "string", "tooltip": "

Optional group labels when GroupKFold is used for the cross validation.\nIt takes an array with shape (n_samples, ) where n_samples is the number\nof rows in training dataset. When string is passed, it is interpreted as\nthe column name in the dataset containing group labels.

\n", @@ -637,16 +552,6 @@ const regressionSettings = { "type": "string", "tooltip": "

Render mode for the dashboard. The default is set to dash which will\nrender a dashboard in browser. There are four possible options:

\n
    \n

  • \u2018dash\u2019 - displays the dashboard in browser

    • \n

  • \u2018inline\u2019 - displays the dashboard in the jupyter notebook cell.

    • \n

  • \u2018jupyterlab\u2019 - displays the dashboard in jupyterlab pane.

    • \n

  • \u2018external\u2019 - displays the dashboard in a separate tab. (use in Colab)

    • \n
\n", "default_val": "dash" - }, - "dashboard_kwargs": { - "type": "dict", - "tooltip": "

Dictionary of arguments passed to the ExplainerDashboard class.

\n", - "default_val": "{} (empty dict)" - }, - "run_kwargs": { - "type": "dict", - "tooltip": "

Dictionary of arguments passed to the run method of ExplainerDashboard.

\n", - "default_val": "{} (empty dict)" } }, "code": "dashboard()", @@ -655,11 +560,6 @@ const regressionSettings = { }, "finalize": { "options": { - "fit_kwargs": { - "type": "dict", - "tooltip": "

Dictionary of arguments passed to the fit method of the model.

\n", - "default_val": "{} (empty dict)" - }, "groups": { "type": "string", "tooltip": "

Optional group labels when GroupKFold is used for the cross validation.\nIt takes an array with shape (n_samples, ) where n_samples is the number\nof rows in training dataset. When string is passed, it is interpreted as\nthe column name in the dataset containing group labels.

\n", @@ -669,11 +569,6 @@ const regressionSettings = { "type": "bool", "tooltip": "

Whether to return the complete fitted pipeline or only the fitted model.

\n", "default_val": "False" - }, - "experiment_custom_tags": { - "type": "dict", - "tooltip": "

Dictionary of tag_name: String -> value: (String, but will be string-ified if\nnot) passed to the mlflow.set_tags to add new custom tags for the experiment.

\n", - "default_val": "None" } }, "code": "", @@ -702,16 +597,6 @@ const regressionSettings = { }, "load_model": { "options": { - "platform": { - "type": "string", - "tooltip": "

Name of the cloud platform. Currently supported platforms:\n\u2018aws\u2019, \u2018gcp\u2019 and \u2018azure\u2019.

\n", - "default_val": "None" - }, - "authentication": { - "type": "dict", - "tooltip": "

dictionary of applicable authentication tokens.

\n

when platform = \u2018aws\u2019:\n{\u2018bucket\u2019 : \u2018Name of Bucket on S3\u2019, \u2018path\u2019: (optional) folder name under the bucket}

\n

when platform = \u2018gcp\u2019:\n{\u2018project\u2019: \u2018gcp-project-name\u2019, \u2018bucket\u2019 : \u2018gcp-bucket-name\u2019}

\n

when platform = \u2018azure\u2019:\n{\u2018container\u2019: \u2018azure-container-name\u2019}

\n", - "default_val": "None" - }, "verbose": { "type": "bool", "tooltip": "

Success message is not printed when verbose is set to False.

\n", @@ -726,6 +611,10 @@ const regressionSettings = { } } }, + "group_models": { + "options": {}, + "code": "" + }, "tune_model": { "options": { "fold": { @@ -743,21 +632,11 @@ const regressionSettings = { "tooltip": "

Number of iterations in the grid search. Increasing \u2018n_iter\u2019 may improve\nmodel performance but also increases the training time.

\n", "default_val": "10" }, - "custom_grid": { - "type": "dict", - "tooltip": "

To define custom search space for hyperparameters, pass a dictionary with\nparameter name and values to be iterated. Custom grids must be in a format\nsupported by the defined search_library.

\n", - "default_val": "None" - }, "optimize": { "type": "string", "tooltip": "

Metric name to be evaluated for hyperparameter tuning. It also accepts custom\nmetrics that are added through the add_metric function.

\n", "default_val": "R2" }, - "custom_scorer": { - "type": "object", - "tooltip": "

custom scoring strategy can be passed to tune hyperparameters of the model.\nIt must be created using sklearn.make_scorer. It is equivalent of adding\ncustom metric using the add_metric function and passing the name of the\ncustom metric in the optimize parameter.\nWill be deprecated in future.

\n", - "default_val": "None" - }, "search_library": { "type": "string", "tooltip": "

The search library used for tuning hyperparameters. Possible values:

\n
    \n
  • \n
    \u2018scikit-learn\u2019 - default, requires no further installation

    https://github.com/scikit-learn/scikit-learn

    \n
    \n
    \n
    • \n
  • \n
    \u2018scikit-optimize\u2019 - pip install scikit-optimize

    https://scikit-optimize.github.io/stable/

    \n
    \n
    \n
    • \n
  • \n
    \u2018tune-sklearn\u2019 - pip install tune-sklearn ray[tune]

    https://github.com/ray-project/tune-sklearn

    \n
    \n
    \n
    • \n
  • \n
    \u2018optuna\u2019 - pip install optuna

    https://optuna.org/

    \n
    \n
    \n
    • \n
\n", @@ -783,11 +662,6 @@ const regressionSettings = { "tooltip": "

When set to True, the returned object is always better performing. The\nmetric used for comparison is defined by the optimize parameter.

\n", "default_val": "True" }, - "fit_kwargs": { - "type": "dict", - "tooltip": "

Dictionary of arguments passed to the fit method of the tuner.

\n", - "default_val": "{} (empty dict)" - }, "groups": { "type": "string", "tooltip": "

Optional group labels when GroupKFold is used for the cross validation.\nIt takes an array with shape (n_samples, ) where n_samples is the number\nof rows in training dataset. When string is passed, it is interpreted as\nthe column name in the dataset containing group labels.

\n", @@ -850,11 +724,6 @@ const regressionSettings = { "tooltip": "

Metric to compare for model selection when choose_better is True.

\n", "default_val": "R2" }, - "fit_kwargs": { - "type": "dict", - "tooltip": "

Dictionary of arguments passed to the fit method of the model.

\n", - "default_val": "{} (empty dict)" - }, "groups": { "type": "string", "tooltip": "

Optional group labels when GroupKFold is used for the cross validation.\nIt takes an array with shape (n_samples, ) where n_samples is the number\nof rows in training dataset. When string is passed, it is interpreted as\nthe column name in the dataset containing group labels.

\n", @@ -892,11 +761,6 @@ const regressionSettings = { "tooltip": "

Sequence of weights (float or int) to weight the occurrences of predicted class\nlabels (hard voting) or class probabilities before averaging (soft voting). Uses\nuniform weights when None.

\n", "default_val": "None" }, - "fit_kwargs": { - "type": "dict", - "tooltip": "

Dictionary of arguments passed to the fit method of the model.

\n", - "default_val": "{} (empty dict)" - }, "groups": { "type": "string", "tooltip": "

Optional group labels when GroupKFold is used for the cross validation.\nIt takes an array with shape (n_samples, ) where n_samples is the number\nof rows in training dataset. When string is passed, it is interpreted as\nthe column name in the dataset containing group labels.

\n", @@ -981,11 +845,6 @@ const regressionSettings = { "tooltip": "

Metric to compare for model selection when choose_better is True.

\n", "default_val": "R2" }, - "fit_kwargs": { - "type": "dict", - "tooltip": "

Dictionary of arguments passed to the fit method of the model.

\n", - "default_val": "{} (empty dict)" - }, "groups": { "type": "string", "tooltip": "

Optional group labels when GroupKFold is used for the cross validation.\nIt takes an array with shape (n_samples, ) where n_samples is the number\nof rows in training dataset. When string is passed, it is interpreted as\nthe column name in the dataset containing group labels.

\n", diff --git a/renderer/utilities/learning/inputTypesUtils.js b/renderer/utilities/learning/inputTypesUtils.js index 191fc7e4..53e8a642 100644 --- a/renderer/utilities/learning/inputTypesUtils.js +++ b/renderer/utilities/learning/inputTypesUtils.js @@ -109,8 +109,17 @@ const implementedTypesDescription = [ type: "int-float-str", description: "int-float-str", default: "" + }, + // { + // type: "data-function", + // description: "for data function input", + // default: "" + // }, + { + type: "dataframe", + description: "for dataframe input", + default: "" } - ] // this object is used to get the default value and implemeted types of possible settings