API documentation

AutoML class

Automated Machine Learning for supervised tasks (binary classification, multiclass classification, regression).

__init__(self, results_path=None, total_time_limit=3600, mode='Explain', ml_task='auto', model_time_limit=None, algorithms='auto', train_ensemble=True, stack_models='auto', eval_metric='auto', validation_strategy='auto', explain_level='auto', golden_features='auto', features_selection='auto', start_random_models='auto', hill_climbing_steps='auto', top_models_to_improve='auto', boost_on_errors='auto', kmeans_features='auto', mix_encoding='auto', max_single_prediction_time=None, optuna_time_budget=None, optuna_init_params={}, optuna_verbose=True, n_jobs=-1, verbose=1, random_state=1234) special

Initialize AutoML object.

Parameters:

Name	Type	Description	Default
results_path	str	The path with results. If None, then the name of directory will be generated with the template: AutoML_{number}, where the number can be from 1 to 1,000 - depends which direcory name will be available. If the results_path will point to directory with AutoML results (params.json must be present), then all models will be loaded.	None
total_time_limit	int	The total time limit in seconds for AutoML training. It is not used when model_time_limit is not None.	3600
mode	str	Can be {Explain, Perform, Compete, Optuna}. This parameter defines the goal of AutoML and how intensive the AutoML search will be. Explain : To to be used when the user wants to explain and understand the data. Uses 75%/25% train/test split. Uses the following models: Baseline, Linear, Decision Tree, Random Forest, XGBoost, Neural Network, and Ensemble. Has full explanations in reports: learning curves, importance plots, and SHAP plots. Perform : To be used when the user wants to train a model that will be used in real-life use cases. Uses 5-fold CV (Cross-Validation). Uses the following models: Linear, Random Forest, LightGBM, XGBoost, CatBoost, Neural Network, and Ensemble. Has learning curves and importance plots in reports. Compete : To be used for machine learning competitions (maximum performance). Uses 80/20 train/test split, or 5-fold CV, or 10-fold CV (Cross-Validation) - it depends on total_time_limit. If not set directly, AutoML will select validation automatically. Uses the following models: Decision Tree, Random Forest, Extra Trees, LightGBM, XGBoost, CatBoost, Neural Network, Nearest Neighbors, Ensemble, and Stacking. It has only learning curves in the reports. Optuna : To be used for creating highly-tuned machine learning models. Uses 10-fold CV (Cross-Validation). It tunes with Optuna the following algorithms: Random Forest, Extra Trees, LightGBM, XGBoost, CatBoost, Neural Network. It applies Ensemble and Stacking for trained models. It has only learning curves in the reports.	'Explain'
ml_task	str	Can be {"auto", "binary_classification", "multiclass_classification", "regression"}. If left auto AutoML will try to guess the task based on target values. If there will be only 2 values in the target, then task will be set to "binary_classification". If number of values in the target will be between 2 and 20 (included), then task will be set to "multiclass_classification". In all other casses, the task is set to "regression".	'auto'
model_time_limit	int	The time limit for training a single model, in seconds. If model_time_limit is set, the total_time_limit is not respected. The single model can contain several learners. The time limit for subsequent learners is computed based on model_time_limit. For example, in the case of 10-fold cross-validation, one model will have 10 learners. The model_time_limit is the time for all 10 learners.	None
algorithms	list of str	The list of algorithms that will be used in the training. The algorithms can be: Baseline, Linear, Decision Tree, Random Forest, Extra Trees, LightGBM, Xgboost, CatBoost, Neural Network, Nearest Neighbors,	'auto'
train_ensemble	boolean	Whether an ensemble gets created at the end of the training.	True
stack_models	boolean	Whether a models stack gets created at the end of the training. Stack level is 1.	'auto'
eval_metric	str	The metric to be used in early stopping and to compare models. for binary classification: logloss, auc, f1, average_precision, accuracy - default is logloss (if left "auto") for mutliclass classification: logloss, f1, accuracy - default is logloss (if left "auto") for regression: rmse, mse, mae, r2, mape, spearman, pearson - default is rmse (if left "auto")	'auto'
validation_strategy	dict	Dictionary with validation type. Right now train/test split and cross-validation are supported. Example: Cross-validation exmaple: { "validation_type": "kfold", "k_folds": 5, "shuffle": True, "stratify": True, "random_seed": 123 } Train/test example: { "validation_type": "split", "train_ratio": 0.75, "shuffle": True, "stratify": True }	'auto'
explain_level	int	The level of explanations included to each model: if explain_level is 0 no explanations are produced. if explain_level is 1 the following explanations are produced: importance plot (with permutation method), for decision trees produce tree plots, for linear models save coefficients. if explain_level is 2 the following explanations are produced: the same as 1 plus SHAP explanations. If left auto AutoML will produce explanations based on the selected mode.	'auto'
golden_features	boolean	Whether to use golden features If left auto AutoML will use golden features based on the selected mode: If mode is "Explain", golden_features = False. If mode is "Perform", golden_features = True. If mode is "Compete", golden_features = True.	'auto'
features_selection	boolean	Whether to do features_selection If left auto AutoML will do feature selection based on the selected mode: If mode is "Explain", features_selection = False. If mode is "Perform", features_selection = True. If mode is "Compete", features_selection = True.	'auto'
start_random_models	int	Number of starting random models to try. If left auto AutoML will select it based on the selected mode: If mode is "Explain", start_random_models = 1. If mode is "Perform", start_random_models = 5. If mode is "Compete", start_random_models = 10.	'auto'
hill_climbing_steps	int	Number of steps to perform during hill climbing. If left auto AutoML will select it based on the selected mode: If mode is "Explain", hill_climbing_steps = 0. If mode is "Perform", hill_climbing_steps = 2. If mode is "Compete", hill_climbing_steps = 2.	'auto'
top_models_to_improve	int	Number of best models to improve in hill_climbing steps. If left auto AutoML will select it based on the selected mode: If mode is "Explain", top_models_to_improve = 0. If mode is "Perform", top_models_to_improve = 2. If mode is "Compete", top_models_to_improve = 3.	'auto'
boost_on_errors	boolean	Whether a model with boost on errors from previous best model should be trained. By default available in the Compete mode.	'auto'
kmeans_features	boolean	Whether a model with k-means generated features should be trained. By default available in the Compete mode.	'auto'
mix_encoding	boolean	Whether a model with mixed encoding should be trained. Mixed encoding is the encoding that uses label encoding for categoricals with more than 25 categories, and one-hot binary encoding for other categoricals. It is only applied if there are categorical features with cardinality smaller than 25. By default it is available in the Compete mode.	'auto'
max_single_prediction_time	int or float	The limit for prediction time for single sample. Use it if you want to have a model with fast predictions. Ideal for creating ML pipelines used as REST API. Time is in seconds. By default (max_single_prediction_time=None) models are not optimized for fast predictions, except the mode Perform. For the mode Perform the default is 0.5 seconds.	None
optuna_time_budget	int	The time in seconds which should be used by Optuna to tune each algorithm. It is time for tuning single algorithm. If you select two algorithms: Xgboost and CatBoost, and set optuna_time_budget=1000, then Xgboost will be tuned for 1000 seconds and CatBoost will be tuned for 1000 seconds. What is more, the tuning is made for each data type, for example for raw data and for data with inserted Golden Features. This parameter is only used when mode="Optuna". If you set mode="Optuna" and forget to set this parameter, it will be set to 3600 seconds.	None
optuna_init_params	dict	If you have already tuned parameters from Optuna you can reuse them by setting this parameter. This parameter is only used when mode="Optuna". The dict should have structure and params as specified in the MLJAR AutoML .	{}
optuna_verbose	boolean	If true the Optuna tuning details are displayed. Set to True by default.	True
n_jobs	int	Number of CPU cores to be used. By default is set to -1 which means using all processors.	-1
verbose	int	Controls the verbosity when fitting and predicting. Note: Still not implemented, please left 1	1
random_state	int	Controls the randomness of the AutoML	1234

Examples:

Binary Classification Example:

>>> import pandas as pd
>>> from sklearn.model_selection import train_test_split
>>> from sklearn.metrics import roc_auc_score
>>> from supervised import AutoML
>>> df = pd.read_csv(
...        "https://raw.githubusercontent.com/pplonski/datasets-for-start/master/adult/data.csv",
...       skipinitialspace=True
...    )
>>> X_train, X_test, y_train, y_test = train_test_split(
... df[df.columns[:-1]], df["income"], test_size=0.25
... )
>>> automl = AutoML()
>>> automl.fit(X_train, y_train)
>>> y_pred_prob = automl.predict_proba(X_test)
>>> print(f"AUROC: {roc_auc_score(y_test, y_pred_prob):.2f}%")

Multi-Class Classification Example:

>>> import pandas as pd
>>> from sklearn.datasets import load_digits
>>> from sklearn.metrics import accuracy_score
>>> from sklearn.model_selection import train_test_split
>>> from supervised import AutoML
>>> digits = load_digits()
>>> X_train, X_test, y_train, y_test = train_test_split(
...     digits.data, digits.target, stratify=digits.target, test_size=0.25,
...     random_state=123
... )
>>> automl = AutoML(mode="Perform")
>>> automl.fit(X_train, y_train)
>>> y_pred = automl.predict(X_test)
>>> print(f"Accuracy: {accuracy_score(y_test, y_pred):.2f}%")

Regression Example:

>>> import pandas as pd
>>> from sklearn.datasets import load_boston
>>> from sklearn.model_selection import train_test_split
>>> from sklearn.metrics import mean_squared_error
>>> from supervised import AutoML
>>> housing = load_boston()
>>> X_train, X_test, y_train, y_test = train_test_split(
...       pd.DataFrame(housing.data, columns=housing.feature_names),
...       housing.target,
...       test_size=0.25,
...       random_state=123,
... )
>>> automl = AutoML(mode="Compete")
>>> automl.fit(X_train, y_train)
>>> print("Test R^2:", automl.score(X_test, y_test))

Scikit-learn Pipeline Integration Example:

>>> from imblearn.over_sampling import RandomOverSampler
>>> from sklearn.pipeline import make_pipeline
>>> from sklearn.datasets import make_classification
>>> from sklearn.model_selection import train_test_split
>>> from supervised import AutoML
>>> X, y = make_classification()
>>> X_train, X_test, y_train, y_test = train_test_split(X,y)
>>> pipeline = make_pipeline(RandomOverSampler(), AutoML())
>>> print(pipeline.fit(X_train, y_train).score(X_test, y_test))

Source code in supervised/automl.py

def __init__(
    self,
    results_path=None,
    total_time_limit=60 * 60,
    mode="Explain",
    ml_task="auto",
    model_time_limit=None,
    algorithms="auto",
    train_ensemble=True,
    stack_models="auto",
    eval_metric="auto",
    validation_strategy="auto",
    explain_level="auto",
    golden_features="auto",
    features_selection="auto",
    start_random_models="auto",
    hill_climbing_steps="auto",
    top_models_to_improve="auto",
    boost_on_errors="auto",
    kmeans_features="auto",
    mix_encoding="auto",
    max_single_prediction_time=None,
    optuna_time_budget=None,
    optuna_init_params={},
    optuna_verbose=True,
    n_jobs=-1,
    verbose=1,
    random_state=1234,
):
    """
    Initialize `AutoML` object.

    Arguments:
        results_path (str): The path with results. If None, then the name of directory will be generated with the template: AutoML_{number},
            where the number can be from 1 to 1,000 - depends which direcory name will be available.
            If the `results_path` will point to directory with AutoML results (`params.json` must be present),
            then all models will be loaded.

        total_time_limit (int): The total time limit in seconds for AutoML training.
            It is not used when `model_time_limit` is not `None`.

        mode (str): Can be {`Explain`, `Perform`, `Compete`, `Optuna`}. This parameter defines the goal of AutoML and how intensive the AutoML search will be.

            - `Explain` : To to be used when the user wants to explain and understand the data.
                - Uses 75%/25% train/test split.
                - Uses the following models: `Baseline`, `Linear`, `Decision Tree`, `Random Forest`, `XGBoost`, `Neural Network`, and `Ensemble`.
                - Has full explanations in reports: learning curves, importance plots, and SHAP plots.
            - `Perform` : To be used when the user wants to train a model that will be used in real-life use cases.
                - Uses 5-fold CV (Cross-Validation).
                - Uses the following models: `Linear`, `Random Forest`, `LightGBM`, `XGBoost`, `CatBoost`, `Neural Network`, and `Ensemble`.
                - Has learning curves and importance plots in reports.
            - `Compete` : To be used for machine learning competitions (maximum performance).
                - Uses 80/20 train/test split, or 5-fold CV, or 10-fold CV (Cross-Validation) - it depends on `total_time_limit`. If not set directly, AutoML will select validation automatically.
                - Uses the following models: `Decision Tree`, `Random Forest`, `Extra Trees`, `LightGBM`,  `XGBoost`, `CatBoost`, `Neural Network`,
                    `Nearest Neighbors`, `Ensemble`, and `Stacking`.
                - It has only learning curves in the reports.
            - `Optuna` : To be used for creating highly-tuned machine learning models.
                - Uses 10-fold CV (Cross-Validation).
                - It tunes with Optuna the following algorithms: `Random Forest`, `Extra Trees`, `LightGBM`, `XGBoost`, `CatBoost`, `Neural Network`.
                - It applies `Ensemble` and `Stacking` for trained models.
                - It has only learning curves in the reports.               

        ml_task (str): Can be {"auto", "binary_classification", "multiclass_classification", "regression"}.

            - If left `auto` AutoML will try to guess the task based on target values.
            - If there will be only 2 values in the target, then task will be set to `"binary_classification"`.
            - If number of values in the target will be between 2 and 20 (included), then task will be set to `"multiclass_classification"`.
            - In all other casses, the task is set to `"regression"`.

        model_time_limit (int): The time limit for training a single model, in seconds.
            If `model_time_limit` is set, the `total_time_limit` is not respected.
            The single model can contain several learners. The time limit for subsequent learners is computed based on `model_time_limit`.

            For example, in the case of 10-fold cross-validation, one model will have 10 learners.
            The `model_time_limit` is the time for all 10 learners.

        algorithms (list of str): The list of algorithms that will be used in the training.
            The algorithms can be:

            - `Baseline`,
            - `Linear`,
            - `Decision Tree`,
            - `Random Forest`,
            - `Extra Trees`,
            - `LightGBM`,
            - `Xgboost`,
            - `CatBoost`,
            - `Neural Network`,
            - `Nearest Neighbors`,


        train_ensemble (boolean): Whether an ensemble gets created at the end of the training.

        stack_models (boolean): Whether a models stack gets created at the end of the training. Stack level is 1.

        eval_metric (str): The metric to be used in early stopping and to compare models.

            - for binary classification: `logloss`, `auc`, `f1`, `average_precision`, `accuracy` - default is logloss (if left "auto")
            - for mutliclass classification: `logloss`, `f1`, `accuracy` - default is `logloss` (if left "auto")
            - for regression: `rmse`, `mse`, `mae`, `r2`, `mape`, `spearman`, `pearson` - default is `rmse` (if left "auto")

        validation_strategy (dict): Dictionary with validation type. Right now train/test split and cross-validation are supported.

            Example:

                Cross-validation exmaple:
                {
                    "validation_type": "kfold",
                    "k_folds": 5,
                    "shuffle": True,
                    "stratify": True,
                    "random_seed": 123
                }

                Train/test example:
                {
                    "validation_type": "split",
                    "train_ratio": 0.75,
                    "shuffle": True,
                    "stratify": True
                }

        explain_level (int): The level of explanations included to each model:

            - if `explain_level` is `0` no explanations are produced.
            - if `explain_level` is `1` the following explanations are produced: importance plot (with permutation method), for decision trees produce tree plots, for linear models save coefficients.
            - if `explain_level` is `2` the following explanations are produced: the same as `1` plus SHAP explanations.

            If left `auto` AutoML will produce explanations based on the selected `mode`.

        golden_features (boolean): Whether to use golden features
            If left `auto` AutoML will use golden features based on the selected `mode`:

            - If `mode` is "Explain", `golden_features` = False.
            - If `mode` is "Perform", `golden_features` = True.
            - If `mode` is "Compete", `golden_features` = True.

        features_selection (boolean): Whether to do features_selection
            If left `auto` AutoML will do feature selection based on the selected `mode`:

            - If `mode` is "Explain", `features_selection` = False.
            - If `mode` is "Perform", `features_selection` = True.
            - If `mode` is "Compete", `features_selection` = True.

        start_random_models (int): Number of starting random models to try.
            If left `auto` AutoML will select it based on the selected `mode`:

            - If `mode` is "Explain", `start_random_models` = 1.
            - If `mode` is "Perform", `start_random_models` = 5.
            - If `mode` is "Compete", `start_random_models` = 10.

        hill_climbing_steps (int): Number of steps to perform during hill climbing.
            If left `auto` AutoML will select it based on the selected `mode`:

            - If `mode` is "Explain", `hill_climbing_steps` = 0.
            - If `mode` is "Perform", `hill_climbing_steps` = 2.
            - If `mode` is "Compete", `hill_climbing_steps` = 2.

        top_models_to_improve (int): Number of best models to improve in `hill_climbing` steps.
            If left `auto` AutoML will select it based on the selected `mode`:

            - If `mode` is "Explain", `top_models_to_improve` = 0.
            - If `mode` is "Perform", `top_models_to_improve` = 2.
            - If `mode` is "Compete", `top_models_to_improve` = 3.

        boost_on_errors (boolean): Whether a model with boost on errors from previous best model should be trained. By default available in the `Compete` mode.

        kmeans_features (boolean): Whether a model with k-means generated features should be trained. By default available in the `Compete` mode.

        mix_encoding (boolean): Whether a model with mixed encoding should be trained. Mixed encoding is the encoding that uses label encoding
            for categoricals with more than 25 categories, and one-hot binary encoding for other categoricals. It is only applied if there are
            categorical features with cardinality smaller than 25. By default it is available in the `Compete` mode.

        max_single_prediction_time (int or float): The limit for prediction time for single sample. Use it if you want to have a model with fast predictions.
            Ideal for creating ML pipelines used as REST API. Time is in seconds. By default (`max_single_prediction_time=None`) models are not optimized for fast predictions,
            except the mode `Perform`. For the mode `Perform` the default is `0.5` seconds.

        optuna_time_budget (int): The time in seconds which should be used by Optuna to tune each algorithm. It is time for tuning single algorithm.
            If you select two algorithms: Xgboost and CatBoost, and set optuna_time_budget=1000, then Xgboost will be tuned for 1000 seconds and CatBoost will be tuned for 1000 seconds.
            What is more, the tuning is made for each data type, for example for raw data and for data with inserted Golden Features.
            This parameter is only used when `mode="Optuna"`. If you set `mode="Optuna"` and forget to set this parameter, it will be set to 3600 seconds.

        optuna_init_params (dict): If you have already tuned parameters from Optuna you can reuse them by setting this parameter.
            This parameter is only used when `mode="Optuna"`. The dict should have structure and params as specified in the MLJAR AutoML .

        optuna_verbose (boolean): If true the Optuna tuning details are displayed. Set to `True` by default.

        n_jobs (int): Number of CPU cores to be used. By default is set to `-1` which means using  all processors.

        verbose (int): Controls the verbosity when fitting and predicting.

            Note:
                Still not implemented, please left `1`

        random_state (int): Controls the randomness of the `AutoML`


    Examples:

        Binary Classification Example:

        >>> import pandas as pd
        >>> from sklearn.model_selection import train_test_split
        >>> from sklearn.metrics import roc_auc_score
        >>> from supervised import AutoML
        >>> df = pd.read_csv(
        ...        "https://raw.githubusercontent.com/pplonski/datasets-for-start/master/adult/data.csv",
        ...       skipinitialspace=True
        ...    )
        >>> X_train, X_test, y_train, y_test = train_test_split(
        ... df[df.columns[:-1]], df["income"], test_size=0.25
        ... )
        >>> automl = AutoML()
        >>> automl.fit(X_train, y_train)
        >>> y_pred_prob = automl.predict_proba(X_test)
        >>> print(f"AUROC: {roc_auc_score(y_test, y_pred_prob):.2f}%")


        Multi-Class Classification Example:

        >>> import pandas as pd
        >>> from sklearn.datasets import load_digits
        >>> from sklearn.metrics import accuracy_score
        >>> from sklearn.model_selection import train_test_split
        >>> from supervised import AutoML
        >>> digits = load_digits()
        >>> X_train, X_test, y_train, y_test = train_test_split(
        ...     digits.data, digits.target, stratify=digits.target, test_size=0.25,
        ...     random_state=123
        ... )
        >>> automl = AutoML(mode="Perform")
        >>> automl.fit(X_train, y_train)
        >>> y_pred = automl.predict(X_test)
        >>> print(f"Accuracy: {accuracy_score(y_test, y_pred):.2f}%")

        Regression Example:

        >>> import pandas as pd
        >>> from sklearn.datasets import load_boston
        >>> from sklearn.model_selection import train_test_split
        >>> from sklearn.metrics import mean_squared_error
        >>> from supervised import AutoML
        >>> housing = load_boston()
        >>> X_train, X_test, y_train, y_test = train_test_split(
        ...       pd.DataFrame(housing.data, columns=housing.feature_names),
        ...       housing.target,
        ...       test_size=0.25,
        ...       random_state=123,
        ... )
        >>> automl = AutoML(mode="Compete")
        >>> automl.fit(X_train, y_train)
        >>> print("Test R^2:", automl.score(X_test, y_test))

        Scikit-learn Pipeline Integration Example:

        >>> from imblearn.over_sampling import RandomOverSampler
        >>> from sklearn.pipeline import make_pipeline
        >>> from sklearn.datasets import make_classification
        >>> from sklearn.model_selection import train_test_split
        >>> from supervised import AutoML
        >>> X, y = make_classification()
        >>> X_train, X_test, y_train, y_test = train_test_split(X,y)
        >>> pipeline = make_pipeline(RandomOverSampler(), AutoML())
        >>> print(pipeline.fit(X_train, y_train).score(X_test, y_test))

    """
    super(AutoML, self).__init__()
    # Set user arguments
    self.mode = mode
    self.ml_task = ml_task
    self.results_path = results_path
    self.total_time_limit = total_time_limit
    self.model_time_limit = model_time_limit
    self.algorithms = algorithms
    self.train_ensemble = train_ensemble
    self.stack_models = stack_models
    self.eval_metric = eval_metric
    self.validation_strategy = validation_strategy
    self.verbose = verbose
    self.explain_level = explain_level
    self.golden_features = golden_features
    self.features_selection = features_selection
    self.start_random_models = start_random_models
    self.hill_climbing_steps = hill_climbing_steps
    self.top_models_to_improve = top_models_to_improve
    self.boost_on_errors = boost_on_errors
    self.kmeans_features = kmeans_features
    self.mix_encoding = mix_encoding
    self.max_single_prediction_time = max_single_prediction_time
    self.optuna_time_budget = optuna_time_budget
    self.optuna_init_params = optuna_init_params
    self.optuna_verbose = optuna_verbose
    self.n_jobs = n_jobs
    self.random_state = random_state

fit(self, X, y, sample_weight=None)

Fit the AutoML model.

Parameters:

Name	Type	Description	Default
X	numpy.ndarray or pandas.DataFrame	Training data	required
y	numpy.ndarray or pandas.Series	Training targets	required
sample_weight	numpy.ndarray or pandas.Series	Training sample weights	None

Returns:

Type	Description
AutoML object	Returns self

Source code in supervised/automl.py

def fit(self, X, y, sample_weight=None):
    """Fit the AutoML model.

    Arguments:
        X (numpy.ndarray or pandas.DataFrame): Training data

        y (numpy.ndarray or pandas.Series): Training targets

        sample_weight (numpy.ndarray or pandas.Series): Training sample weights

    Returns:
        AutoML object: Returns `self`
    """
    return self._fit(X, y, sample_weight)

need_retrain(self, X, y, sample_weight=None, decrease=0.1)

Decides about model retraining based on new data.

Parameters:

Name	Type	Description	Default
X	numpy.ndarray or pandas.DataFrame	New data.	required
y	numpy.ndarray or pandas.Series	True labels for X.	required
sample_weight	numpy.ndarray or pandas.Series	Sample weights.	None
decrease	float	The ratio of change in the performance used as a threshold for retraining decision. By default, it is set to 0.1 which means that if the performance of AutoML will decrease by 10% on new data then there is a need to retrain. This value should be set depending on your project needs. Sometimes, 10% is enough, but for some projects, it can be even lower than 1%.	0.1
Returns		boolean: Decides if there is a need to retrain the AutoML.	required

Source code in supervised/automl.py

def need_retrain(self, X, y, sample_weight=None, decrease=0.1):
    """Decides about model retraining based on new data.

    Arguments:
        X (numpy.ndarray or pandas.DataFrame):
            New data.

        y (numpy.ndarray or pandas.Series):
            True labels for X.

        sample_weight (numpy.ndarray or pandas.Series):
            Sample weights.

        decrease (float): The ratio of change in the performance used as a threshold for retraining decision.
            By default, it is set to `0.1` which means that if the performance of AutoML will decrease by 10%
            on new data then there is a need to retrain. This value should be set depending on your project needs.
            Sometimes, 10% is enough, but for some projects, it can be even lower than 1%.

        Returns:
            boolean: Decides if there is a need to retrain the AutoML.
    """
    return self._need_retrain(X, y, sample_weight, decrease)

predict(self, X)

Computes predictions from AutoML best model.

Parameters:

Name	Type	Description	Default
X	list or numpy.ndarray or pandas.DataFrame	Input values to make predictions on.	required

Returns:

Type	Description
numpy.ndarray	One-dimensional array of class labels for classification. One-dimensional array of predictions for regression.

Exceptions:

Type	Description
AutoMLException	Model has not yet been fitted.

Source code in supervised/automl.py

def predict(self, X):
    """
    Computes predictions from AutoML best model.

    Arguments:
        X (list or numpy.ndarray or pandas.DataFrame):
            Input values to make predictions on.

    Returns:
        numpy.ndarray:

        - One-dimensional array of class labels for classification.
        - One-dimensional array of predictions for regression.

    Raises:
        AutoMLException: Model has not yet been fitted.
    """
    return self._predict(X)

predict_all(self, X)

Computes both class probabilities and class labels for classification tasks. Computes predictions for regression tasks.

Parameters:

Name	Type	Description	Default
X	list or numpy.ndarray or pandas.DataFrame	Input values to make predictions on.	required

Returns:

Type	Description
pandas.Dataframe	Dataframe (n_samples, n_classes + 1) containing both class probabilities and class labels of the input samples for classification tasks. Dataframe with predictions for regression tasks.

Exceptions:

Type	Description
AutoMLException	Model has not yet been fitted.

Source code in supervised/automl.py

def predict_all(self, X):
    """
    Computes both class probabilities and class labels for classification tasks.
    Computes predictions for regression tasks.

    Arguments:
        X (list or numpy.ndarray or pandas.DataFrame):
            Input values to make predictions on.

    Returns:
        pandas.Dataframe:
            Dataframe (n_samples, n_classes + 1) containing both class probabilities and class
            labels of the input samples for classification tasks.
            Dataframe with predictions for regression tasks.

    Raises:
        AutoMLException: Model has not yet been fitted.

    """
    return self._predict_all(X)

predict_proba(self, X)

Computes class probabilities from AutoML best model. This method can only be used for classification tasks.

Parameters:

Name	Type	Description	Default
X	list or numpy.ndarray or pandas.DataFrame	Input values to make predictions on.	required

Returns:

Type	Description
numpy.ndarray of shape (n_samples, n_classes)	Matrix of containing class probabilities of the input samples

Exceptions:

Type	Description
AutoMLException	Model has not yet been fitted.

Source code in supervised/automl.py

def predict_proba(self, X):
    """
    Computes class probabilities from AutoML best model.
    This method can only be used for classification tasks.

    Arguments:
        X (list or numpy.ndarray or pandas.DataFrame):
            Input values to make predictions on.

    Returns:
        numpy.ndarray of shape (n_samples, n_classes):
            Matrix of containing class probabilities of the input samples

    Raises:
        AutoMLException: Model has not yet been fitted.

    """
    return self._predict_proba(X)

score(self, X, y=None, sample_weight=None)

Calculates a goodness of fit for an AutoML instance.

Parameters:

Name	Type	Description	Default
X	numpy.ndarray or pandas.DataFrame	Test values to make predictions on.	required
y	numpy.ndarray or pandas.Series	True labels for X.	None
sample_weight	numpy.ndarray or pandas.Series	Sample weights.	None

Returns:

Type	Description
float	Returns a goodness of fit measure (higher is better): For classification tasks: returns the mean accuracy on the given test data and labels. For regression tasks: returns the R^2 (coefficient of determination) on the given test data and labels.

Source code in supervised/automl.py

def score(self, X, y=None, sample_weight=None):
    """Calculates a goodness of `fit` for an AutoML instance.

    Arguments:
        X (numpy.ndarray or pandas.DataFrame):
            Test values to make predictions on.

        y (numpy.ndarray or pandas.Series):
            True labels for X.

        sample_weight (numpy.ndarray or pandas.Series):
            Sample weights.
    Returns:
        float: Returns a goodness of fit measure (higher is better):

        - For classification tasks: returns the mean accuracy on the given test data and labels.
        - For regression tasks: returns the R^2 (coefficient of determination) on the given test data and labels.
    """
    return self._score(X, y, sample_weight)