AutoML

The AutoMLx python package automatically creates, optimizes and explains machine learning pipelines and models. The AutoML pipeline provides a tuned ML pipeline that best models the given training dataset and a prediction task at hand. AutoML has a simple pipeline-level Python API that quickly jump-starts the datascience process with an accurate tuned model. AutoML has support for any of the following tasks:

  1. Supervised classification or regression prediction with tabular dataset where the target can be a simple binary or a multi-class value or a real valued column in a table, respectively.

  2. Unsupervised anomaly detection, where the target or the labels are not provided.

  3. Univariate and multivariate timeseries forecasting task.

The AutoML pipeline consists of five major stages of the ML pipeline: preprocessing , algorithm selection , adaptive sampling , feature selection , and model tuning

These pieces are readily combined into a simple AutoML pipeline which automatically optimizes the whole pipeline with limited user input/interaction.

Pipeline

class automl. Pipeline ( task = 'classification' , score_metric = None , random_state = 7 , n_algos_tuned = 1 , model_list = [] , adaptive_sampling = True , min_features = 1 , optimization = 3 , preprocessing = True , search_space = None , time_series_period = None , min_class_instances = 5 , max_tuning_trials = None , threshold_tuning = False )

Automatic Machine Learning Pipeline object, uses metalearning to quickly identify most relevant features, model and hyperparameters for a given training dataset.

Warning

The following interfaces are deprecated and will be replaced by completed_trials_summary_ and completed_trials_detailed_ in version 23.3.0.

  • model_selection_trials_

  • adaptive_sampling_trials_

  • feature_selection_trials_

  • tuning_trials_

  • all_trials_

  • all_trials_extra_scores_

Warning

Optimization levels 1 and 2 are deprecated and will behave the same as level 3 in version 23.3.0.

Warning

The selected_features_ attribute is deprecated and will be removed in version 23.3.0. To inspect selected features use selected_features_names_ and selected_features_names_raw_ instead.

Parameters

  • task (str, optional ) – Machine learning task, supported: classification, regression, anomaly_detection, forecasting Defaults to 'classification' .

  • score_metric (str, callable, tuple, list, optional ) –

    One or more score functions (or loss functions) to be computed for each candidate model and hyperparameter configuration.

    • If None : it will be determined automatically depending on the task. Default score metrics : binary: neg_log_loss, multiclass: neg_log_loss, continuous: neg_mean_squared_error, continuous_forecast: neg_sym_mean_abs_percent_error, unsupervised: unsupervised_n-1_experts

    • If a list: should be a list of str, callable or tuple. The first score metric in the list will be the one for which the pipeline optimizes.

    • If a callable: score function (or loss function) with signature score_func(model, X, y) .

    • If a tuple: should be a tuple with two values with types (str, callable). The string corresponds to the name of the scoring metric, and the callable should have the same signature as above.

    • If a string: automatically infers the scoring metric from the string:

      binary – neg_log_loss, roc_auc, accuracy, f1, precision, recall, f1_micro, f1_macro, f1_weighted, f1_samples, recall_micro, recall_macro, recall_weighted, recall_samples, precision_micro, precision_macro, precision_weighted, precision_samples

      multiclass – neg_log_loss, recall_macro, accuracy, f1_micro, f1_macro, f1_weighted, f1_samples, recall_micro, recall_weighted, recall_samples, precision_micro, precision_macro, precision_weighted, precision_samples

      continuous – neg_mean_squared_error, r2, neg_mean_absolute_error, neg_mean_squared_log_error, neg_median_absolute_error

      continuous_forecast – neg_sym_mean_abs_percent_error, neg_root_mean_squared_percent_error, neg_mean_abs_scaled_error, neg_root_mean_squared_error, neg_mean_squared_error, neg_max_absolute_error, neg_mean_absolute_error, neg_max_abs_error, neg_mean_abs_error

      unsupervised – unsupervised_n-1_experts, unsupervised_unify95, unsupervised_unify95_log_loss

      More information on scoring metrics can be found here : Classification metrics , Regression metrics .

      Note: Scoring variations like recall_macro are equivalent to sklearn.metrics.recall_score(...,average="macro")

    Defaults to None .

  • random_state (int, optional ) – Random seed used by AutoML. Defaults to 7 .

  • model_list (List[str], optional ) –

    Models that will be evaluated by the Pipeline. (By default, all supported models for a given task are used, other than TabNetClassifier, AdaBoostClassifier, KNeighborsClassifier, and LinearSVC. To enable one of these models, add them to the model list.) Supported models per task:

    • classification: 'AdaBoostClassifier' , 'DecisionTreeClassifier' , 'ExtraTreesClassifier' , 'TorchMLPClassifier' , 'KNeighborsClassifier' , 'LGBMClassifier' , 'LinearSVC' , 'LogisticRegression' 'RandomForestClassifier' , 'SVC' , 'XGBClassifier' , 'GaussianNB' , 'CatBoostClassifier' , ‘ TabNetClassifier'

    • regression: 'AdaBoostRegressor' , 'DecisionTreeRegressor' , 'ExtraTreesRegressor' , 'TorchMLPRegressor' , 'KNeighborsRegressor' , 'LGBMRegressor' , 'LinearSVR' , 'LinearRegression' , 'RandomForestRegressor' , 'SVR' , 'XGBRegressor'

    • anomaly_detection: 'IsolationForestOD' , 'SubspaceOD' , 'HistogramOD' , 'ClusteringLocalFactorOD' , 'PrincipalCompOD' , 'MinCovOD' , 'AutoEncoder' , 'KNearestNeighborsOD' , 'OneClassSVMOD'

    • forecasting:

      • 'NaiveForecaster' - Naive and Seasonal Naive method

      • 'ThetaForecaster' - Equivalent to Simple Exponential Smoothing (SES) with drift

      • 'ExpSmoothForecaster' - Holt-Winters’ damped method

      • 'STLwESForecaster' - Seasonal Trend LOESS (locally weighted smoothing) with Exponential Smoothing substructure

      • 'STLwARIMAForecaster' - Seasonal Trend LOESS (locally weighted smoothing) with ARIMA substructure

      • 'SARIMAXForecaster' - Seasonal Autoregressive Integrated Moving Average with Exogenous Variables

      • 'ETSForecaster' - Error, Trend, Seasonality (ETS) Statespace Exponential Smoothing

      • 'ProphetForecaster' (optional) - Facebook Prophet with Exogenous Variables. (Available only if installed locally with pip install prophet==1.1.3)

      • 'VARMAXForecaster' - Vector AutoRegressive Moving Average with Exogenous Variables

      • 'DynFactorForecaster' - Dynamic Factor Models in state-space form with Exogenous Variables

  • n_algos_tuned (int, optional ) –

    Number of algorithms that are optimized by the AutoML pipeline. Higher values might make AutoML more accurate at the expense of runtime.

    • To disable algorithm selection set n_algos_tuned = len(model_list) .

    Defaults to 1 .

  • adaptive_sampling (bool, optional ) – Set to False to disable class balancing and adaptive sampling done in AutoML. Disabling this might significantly increase runtime. Defaults to True .

  • min_features (int, float, list, optional ) –

    Minimum number of features to keep. Acceptable values:

    • If int, 0 < min_features <= n_features , the minimum number of features to keep.

    • If float, 0 < min_features <= 1.0 , the minimum fraction of features to keep.

    • If list, names of features to keep, for example ['a', 'b'] means keep features 'a' and 'b'

    • To disable feature selection set min_features = 1.0

    Defaults to 1 .

  • optimization (int, optional ) –

    Sets AutoML speed, accuracy, and reproducibility level. To be used by advanced users only.

    • Level 0: Optimized for reproducibility (controls most randomness)

    • Level 1: Undefined (placeholder for future version)

    • Level 2: Faster than Level 0, more reproducible than Level 3

    • Level 3: Optimized for speed and accuracy

    Defaults to 3 .

  • preprocessing (bool, optional ) –

    Determines whether or not AutoML pre-processes the dataset. This setting should only be used by advanced users.

    • If True, auto-preprocesser runs on dataset to normalize data. Categorical features are one-hot encoded if they contain less than 5 unique values; otherwise they are label encoded or, if they contain more than 20 percent unique values, they are ignored. Numeric features are normalized to mean of 0 and variance of 1 using sklearn.preprocessing.StandardScaler . Features with more than 20 percent missing values are ignored. The remaining missing values are imputed by mean for numeric features and mode for categorical features. Datetime columns are automatically expanded into engineered features including day of week, day of month, etc.

    • If False, user must cleanse (and normalize if desired) dataset before passing data to AutoML. The use of NaNs in the dataset is not allowed and will produce a ValueError . AutoML will leave it to the underlying algorithm implementations to handle strings (it is recommended to encode strings).

    Defaults to True .

  • search_space (dict, optional ) –

    This parameter defines the Model Tuning search space. Dictionary keys are algorithm names (str) with search space as the key value. Key values must have two parameters: (1) ‘range’ which is a list containing the range and (2) ‘type’ which is one of ‘continuous’, ‘discrete’, ‘categorical’. For example, if the user wishes to provide a custom tune search space for LogisticRegression: 

    search_space =  {
    'LogisticRegression' : {
        'C': {'range': [0.03125, 512], 'type': 'continuous'},
        'solver': {
            'range': ['newton-cg', 'lbfgs', 'liblinear', 'sag'],
            'type': 'categorical'
        },
        'class_weight': {
            'range': [None, 'balanced'],
            'type': 'categorical'
        }
    }
}

    • To disable Model Tune for all models set search_space = {}

    • If a key value is an empty dictionary, then Model Tune is disabled for that key.

    • If None , default search space defined inside AutoML is used.

    Defaults to None .

  • time_series_period (int, optional ) – The period of time series for the forecasting task Defaults to None .

  • min_class_instances (int, optional ) – The minimum number of instances all classes must have when doing classification. If any class has less than this number of instances, training is stopped. This argument may take any value of 2 or higher. Defaults to 5 .

  • max_tuning_trials (int, dict, None ,) –

    The maximum number of tuning trials, may be exceeded slightly.

    • If None : AutoML automatically determines when enough tuning trials have been completed.

    • If an integer: the maximum number of trials for each algorithm. That is, if n_algos_tuned == 2 , then up to 2 * max_tuning_trials are performed in total.

    • If a dict : by passing a dictionary you can specify this parameter per algorithm. e.g., {'LogisticRegression': 100, 'RandomForestClassifier': 200} . Missing values in the dictionary default to None.

    Defaults to None .

  • threshold_tuning (bool, optional ) –

    Determine whether or not AutoML optimizes the prediction threshold. Threshold tuning is only used in classification tasks. However, unlike classic threshold tuning, it is applied for both binary and multi-class classification tasks.

    • If True, the prediction threshold will be optimized based on the provided score metric. If the score metric is not sensitive to the threshold (for example, negative log loss), then f1 macro will be used instead. Threshold tuning allows users to post-process classification model predictions to optimize for their custom metric. Threshold tuning will not exported to onnx models, the onnx model quality may be lower than the original model.

    • If False, no minimum threshold is required for any class, and the class with the largest raw prediction probability is returned as the prediction.

    Defaults to True

classes_

Holds the label for each class (for task=classification only, otherwise it is set to None ).

Type

numpy.ndarray of shape (n_classes,)

selected_features_

Indices of features selected by the AutoML pipeline.

Type

List[ int ]

selected_features_names_

Names of features selected by the AutoML pipeline. The feature names correspond to the features engineered by the preprocessing phase.

Type

List[ str ]

selected_features_names_raw_

Names of features selected by the AutoML pipeline, as found in the input training dataset. A raw feature is considered selected if at least one of the features engineered from it during preprocessing are selected.

Type

List[ str ]

ranked_models_

List of model names ranked in order of their quality from the last fit call.

Type

List[ str ]

selected_model_

Name of the best model selected by AutoML.

Type

str

selected_model_params_

Dictionary containing the optimal hyperparameters for the selected model. Keys are hyperparameter names with their corresponding values.

Type

dict

selected_rows_

List of indices in the original train dataset provided to AutoML corresponding to the rows sampled during Adaptive Sampling. in the case of CV, this attribute will result in a list of lists corresponding to indices selected in each fold. For example, in the case of no CV, this attribute looks like: [0, 1, 5] , indicating indices 0, 1, and 5 have been selected during adaptive sampling. In the case of CV=3, this attribute looks like: [ [0, 1], [0, 5], [1, 5] ] , indicating indices 0,1 were selected from the first fold, 0,5 were selected in the 2nd fold, and 1,5 were selected in the 3rd fold.

Type

list

selected_valid_rows_

List of indices in the original validation dataset (if CV==None ) provided to AutoML corresponding to the rows sampled during Adaptive Sampling. If CV is not None, the returned value is always None given that Adaptive Sampling does not sample the validation set when CV is enabled.

Type

list

pipelines_

Sorted list of pipelines (length equal to n_algos_tuned ), with 0th element being the best model.

Type

list

model_selection_trials_

ML model choices evaluated by model selection. Each tuple is of the form (algorithm, #samples, #features, mean validation score, hyperparameters, all validation scores, runtime, memory_usage), where the hyperparameters are a dict.

Type

List[ tuple ]

adaptive_sampling_trials_

Sampling choices evaluated by adaptive sampling. Each tuple is of the form (algorithm, #samples, #features, mean validation score, hyperparameters, all validation scores, runtime, memory_usage), where the hyperparameters are a dict.

Type

List[ tuple ]

feature_selection_trials_

Subset/Ranking algorithm choices evaluated by feature selection. Each tuple is of the form (algorithm, #samples, #features, mean validation score, hyperparameters, all validation scores, runtime, memory_usage), where the hyperparameters are a dict.

Type

List[ tuple ]

tuning_trials_

Hyperparameter choices evaluated by model tuning ranked in order of their achieved cross-validation scores. Each tuple is of the form (algorithm, #samples, #features, mean validation score, hyperparameters, all validation scores, runtime, memory_usage), where the hyperparameters are a dict.

Type

List[ tuple ]

all_trials_

All trials performed by the AutoML Pipeline. This includes model_selection_trials_ , adaptive_sampling_trials_ , feature_selection_trial_ and tuning_trials_ . Each tuple is of the form (algorithm, #samples, #features, mean validation score, hyperparameters, all validation scores, runtime, memory_usage), where the hyperparameters are a dict.

Type

List[ tuple ]

all_trials_extra_scores_

A DataFrame indicating all trials performed by the AutoML Pipeline with all score metrics value. Each row is of the form (Algorithm, Hyperparameters, # Samples, # Features, Features, Stage, Scoring Metric, CV Fold ID, Score).

Type

pandas.DataFrame

n_jobs_

Parallelism internally used by AutoML. Calculated as inter_model_parallelism*intra_model_parallelism .

Type

int

feature_importances_

List of feature importance tuples. Each tuple contains feature name, its importance, and its standard deviation

Type

List[ tuple ]

threshold_tuning_score_

The validation score of the pipeline after applying threshold tuning. The scoring metric used to select this threshold can be found in threshold_tuning_scorer_ . It is None when the task is not classification or threshold_tuning is False.

Type

float

threshold_tuning_scorer_

The scoring metric used to select threshold during threshold tuning. It is None when the task is not classification or threshold_tuning is False.

Type

callable

fit ( X = None , y = None , X_valid = None , y_valid = None , cv = 'auto' , col_types = None , time_budget = 0 , contamination = None )

Automatically identifies the most relevant features, model and hyperparameters for a given training data ( X ) and target ( y ). Final model fit is conducted on a full dataset.

Parameters

  • X ( pandas.DataFrame ) – Training dataset features. Optional for forecasting tasks.

  • y ( pandas.DataFrame , pandas.Series , optional ) – Training dataset target. Optional for semi-supervised tasks, e.g., anomaly detection. Note that y is required for forecasting task. (Needs to be passed as None for unsupervised anomaly detection)

  • X_valid ( pandas.DataFrame , optional ) – Validation dataset features

  • y_valid ( pandas.DataFrame , pandas.Series , optional ) – Validation dataset target

  • cv ( 'auto' , int , cross-validation generator or an iterable , optional ) –

    Determines the cross-validation splitting strategy. Possible inputs for cv are:

    • None: uses X_valid and y_valid for validation

    • ’auto’: uses 5 folds if number of instances < 1M, disable cv-folds otherwise

    • integer: specifies the number of folds in a (Stratified)KFold ,

    • iterable: yields (train, test) splits as arrays of indices.

    For integer/None inputs, if the estimator is a classifier and y is either binary or multiclass, StratifiedKFold is used. In all other cases, KFold is used.

  • col_types (List[str], optional ) – List of length X.shape[1] with string values indicating type of features. Supported types are: ‘categorical’, ‘numerical’, ‘text’, ‘datetime’ and ‘timedelta’

  • time_budget (float, optional ) –

    Time budget in seconds.

    • 0 for unconstrained time budget: best effort mode is enabled and optimization continues until convergence.

    Defaults to 0 .

  • contamination (float, optional ) – Fraction of training dataset corresponding to anomalies (between 0.0 and 0.5). Should only be set for supervised anomaly detection (y_valid is required). Should be set to None for unsupervised anomaly detection (when using the unsupervised metrics). Default to None .

Returns

self

Return type

automl.Pipeline

forecast ( periods , alpha = 0.05 , X = None )

Forecast with the selected model.

Only out-of-sample forecasts are supported. In-sample fit values should be accessed via the predict interface.

Parameters

  • periods ( int ) – The number of time steps to forecast from the end of the sample.

  • alpha (float, optional ) – A significance level. To receive a prediction interval of 95% alpha must be set to 0.05. Defaults to 0.05 .

  • X (pandas.DataFrame, optional ) – A dataframe of explanatory variables that support forecast for period number of timesteps. Columns must match that used in fit .

Returns

summary_frame – A dataframe with three columns listing prediction, ci_lower and ci_upper for the given confidence interval (ci) provided by level of alpha. Note: ci columns are excluded for models that don’t support intervals.

Return type

pandas.Dataframe

predict ( X )

Predict labels for features (X).

Parameters

X ( pandas.DataFrame ) – Training dataset features, or Explanatory features if task is ‘forecasting’

Returns

y_pred – The predicted values.

Return type

numpy.ndarray of shape (n_samples,)

predict_proba ( X )

Probability estimates.

More information can be found here: Prediction Probabilities

Parameters

X ( pandas.DataFrame ) – Training dataset features

Returns

y_pred_proba – The predicted probabilities.

Return type

numpy.ndarray of shape = (n_samples, n_classes)

print_memory_usage ( in_ipython = None )

Prints max memory usage information about the last pipeline run.

Parameters

in_ipython ( bool ) – Sets to True if IPython kernel is being used

print_profile_summary ( in_ipython = None )

Prints profiling information about the last pipeline run.

Parameters

in_ipython ( bool ) – Sets to True if IPython kernel is being used

print_summary ( in_ipython = None )

Prints information about the last fit call.

Parameters

in_ipython ( bool ) – Sets to True if IPython kernel is being used

print_times ( in_ipython = None )

Prints timing and speedup information about the last fit call.

Parameters

in_ipython ( bool ) – Sets to True if IPython kernel is being used

print_trials ( max_rows = None , sort_column = 'Mean Validation Score' , in_ipython = None )

Prints all trials executed by the AutoML Pipeline in the last fit call.

Parameters

  • max_rows ( int ) – Number of trials to print. Pass in None to print all trials

  • sort_column ( str ) – Column to sort results by. Must be one of [‘Algorithm’, ‘#Samples’, ‘#Features’, ‘Mean Validation Score’, ‘Hyperparameters’, ‘All Validation Scores’, ‘CPU Time’]

  • in_ipython ( bool ) – Sets to True if IPython kernel is being used

refit ( X = None , y = None , X_valid = None , y_valid = None , cv = 'auto' )

This method is used to refit previously tuned AutoML Pipeline with new data. Pipeline stages are not re-run on the new datasets. All settings and parameters from Model Selection, Feature Selection, Model Tune are re-used. fit must have been called before calling this method. If validation set is provided, it will be concatenated with training set before doing the refit.

Parameters

  • X ( pandas.DataFrame ) – Training dataset features. Optional for forecasting tasks.

  • y ( pandas.DataFrame , pandas.Series , optional ) – Training dataset target. Optional for semi-supervised tasks, like anomaly detection.

  • X_valid ( pandas.DataFrame , optional ) – Validation dataset features

  • y_valid ( pandas.DataFrame , pandas.Series , optional ) – Validation dataset target

  • cv ( 'auto' , int , cross-validation generator or an iterable , optional ) –

    Determines the cross-validation splitting strategy. Used for ensemble generation. Possible inputs for cv are:

    • None: uses X_valid and y_valid for validation

    • ’auto’: uses 5 folds if number of instances < 1M, disable cv-folds otherwise

    • integer: specifies the number of folds in a (Stratified)KFold ,

    • iterable: yields (train, test) splits as arrays of indices.

    For integer/None inputs, if the estimator is a classifier and y is either binary or multiclass, StratifiedKFold is used. In all other cases, KFold is used.

Returns

self

Return type

automl.Pipeline

score ( X , y )

Score of this pipeline for a given set of features ( X ) and labels ( y ). If inferred_score_metric has multiple score metrics, the first score metric would be calculated.

Parameters
Returns

score – Score of self.predict(X) with respect to y .

Return type

float

to_onnx ( X , y )

Serializes an AutoML estimator to the ONNX format. Only requires one sample from the training or test set as input. This sample is used to infer the final types and shapes

Parameters
Returns

An ONNX model

Return type

onnx.ModelProto

train ( X = None , y = None , X_valid = None , y_valid = None , cv = 'auto' , col_types = None , time_budget = 0 , contamination = None )

Automatically identifies the most relevant features, model and hyperparameters for this given set of features (X) and target (y). Does not conduct final model fit. If the latter is desired, use fit .

Parameters

  • X ( pandas.DataFrame ) – Training dataset features. Optional for forecasting task.

  • y ( pandas.DataFrame , pandas.Series , optional ) – Training dataset target. Optional for semi-supervised tasks, like anomaly detection.

  • X_valid ( pandas.DataFrame , optional ) – Validation dataset features

  • y_valid ( pandas.DataFrame , pandas.Series , optional ) – Validation dataset target

  • cv ( 'auto' , int , cross-validation generator or an iterable , optional ) –

    Determines the cross-validation splitting strategy. Possible inputs for cv are:

    • None: uses X_valid and y_valid for validation

    • ’auto’: uses 5 folds if number of instances < 1M, disable cv-folds otherwise

    • integer: specifies the number of folds in a (Stratified)KFold ,

    • iterable: yields (train, test) splits as arrays of indices.

    For integer/None inputs, if the estimator is a classifier and y is either binary or multiclass, StratifiedKFold is used. In all other cases, KFold is used.

  • col_types ( list of strings ) – List of length X.shape[1] with string values indicating type of features. Supported types are: [‘categorical’, ‘numerical’, ‘text’. ‘datetime’, ‘timedelta’] Note: Datetime support is in experimental stage

  • time_budget ( float , optional ) –

    Time budget in seconds.

    • 0 for unconstrained time budget: best effort mode is enabled and optimization continues until convergence.

    (default: 0)

  • contamination ( float , None , optional ) – Fraction of training dataset corresponding to anomalies (between 0.0 and 0.5). Should only be set for supervised anomaly detection (y_valid is required). Should be set to None for unsupervised anomaly detection (when using the unsupervised metrics). (default: None)

Returns

self

Return type

object

transform ( X , y )

Applies automatic preprocessing to a given set of features ( X ) and labels ( y ).

Parameters
Returns

  • X ( pandas.DataFrame ) – Transformed dataset features

  • y ( pandas.DataFrame ) – Transformed dataset target

ModelTune

class automl. ModelTune ( task = 'classification' , score_metric = None , random_state = 7 )

Automatic Model Tuning object, uses a highly parallel, scalable and asynchronous gradient-based hyperparameter optimizer to quickly prune the hyperparameter search space and tune the given model object.

Warning

The model tuning object is deprecated and will be removed in version 23.3.0.

Parameters

  • task ( str , default=classification ) – Machine learning task, supported inputs: classification, regression, anomaly_detection, forecasting

  • score_metric ( str , callable , tuple , list , default=None ) –

    One or more score functions (or loss functions) to be computed for each candidate model and hyperparameter configuration.

    • If None : it will be determined automatically depending on the task. Default score metrics : binary: neg_log_loss, multiclass: neg_log_loss, continuous: neg_mean_squared_error, continuous_forecast: neg_sym_mean_abs_percent_error, unsupervised: unsupervised_n-1_experts

    • If a list: should be a list of str, callable or tuple. The first score metric in the list will be the one fors which the pipeline optimizes.

    • If a callable: score function (or loss function) with signature score_func(model, X, y) .

    • If a tuple: should be a tuple with two values with types (str, callable) . The string corresponds to the name of the scoring metric, and the callable should have the same signature as above.

    • If a str: automatically infers the scoring metric from the string:

      binary – neg_log_loss, roc_auc, accuracy, f1, precision, recall, f1_micro, f1_macro, f1_weighted, f1_samples, recall_micro, recall_macro, recall_weighted, recall_samples, precision_micro, precision_macro, precision_weighted, precision_samples

      multiclass – neg_log_loss, recall_macro, accuracy, f1_micro, f1_macro, f1_weighted, f1_samples, recall_micro, recall_weighted, recall_samples, precision_micro, precision_macro, precision_weighted, precision_samples

      continuous – neg_mean_squared_error, r2, neg_mean_absolute_error, neg_mean_squared_log_error, neg_median_absolute_error

      continuous_forecast – neg_sym_mean_abs_percent_error, neg_root_mean_squared_percent_error, neg_mean_abs_scaled_error, neg_root_mean_squared_error, neg_mean_squared_error, neg_max_absolute_error, neg_mean_absolute_error, neg_max_abs_error, neg_mean_abs_error

      unsupervised – unsupervised_n-1_experts, unsupervised_unify95, unsupervised_unify95_log_loss

      More information on scoring metrics can be found here : Classification metrics , Regression metrics .

      Note: Scoring variations like recall_macro are equivalent to sklearn.metrics.recall_score(...,average="macro")

  • random_state ( int , default=7 ) – Random seed used by ModelTune.

selected_model_params_

Dictionary containing the optimal hyperparameters for the selected_model. Keys are hyperparameter names with their corresponding values.

Type

dict

tuning_trials_

Hyperparameter choices evaluated by tuning ranked in order of their achieved cross-validation scores. Each tuple is of the form (float, dict), the float is the cross-validation score of a corresponding dict which is a particular set of hyperparameters.

Type

List[Tuple[ float , dict ]]

at_summary

Dictionary containing the following stats: all trials, in the form of list of tuples, where each tuple is (score, hyperparameter, EvalResult (This is an internal class that holds all relevant results for each model evaluation)), total runtime of ModelTune, score of default run, default hyperparameter values, scoring metric, shape of train dataset, and shape of validation dataset (if it is available)

Type

dict

fit ( model , X , y , X_valid = None , y_valid = None , cv = 5 , param_space = None , col_types = None , contamination = None )

Automatically identifies the best model hyperparameters for this given set of features (X) and target (y).

Parameters

  • model ( str or sklearn-like model object ) – Model to tune on the given dataset, where it can be either one of the supported model names or sklearn-like object that implements at least the following methods: get_params , set_params , fit , predict and score . When providing an unsupported custom model, param_space argument should not be None . %s%s

  • X ( pandas.DataFrame ) – Training dataset features

  • y ( pandas.DataFrame , pandas.Series ) – Training dataset target

  • X_valid ( pandas.DataFrame ) – Validation dataset features

  • y_valid ( pandas.DataFrame , pandas.Series ) – Validation dataset target

  • cv ( int , cross-validation generator or an iterable , optional ) –

    Determines the cross-validation splitting strategy. Possible inputs for cv are:

    • None, to use X_valid and y_valid for validation

    • integer, to specify the number of folds in a (Stratified)KFold ,

    • a generator like a StratifiedKFold or KFold ,

    • An iterable yielding (train, test) splits as arrays of indices.

    For integer/None inputs, if the estimator is a classifier and y is either binary or multiclass, StratifiedKFold is used. In all other cases, KFold is used.

  • param_space ( dict ) –

    The search ranges for each hyperparameter (default: none, uses predefined model specific hyperparameter search space). Example param_space input for RandomForest algorithm tuning: 

    param_space = {
    'n_estimators': {
        'range': [5, 500],
        'type': 'discrete',
        },
    'max_features': {
        'range': [0.01, 0.5],
        'type': 'continuous',
        },
    'class_weight': {
        'range': ['balanced', 'balanced_subsample'],
        'type': 'categorical',
        },
    }

  • col_types ( list ) – List of string identifying type of features. Supported types are: [‘categorical’,’numerical’,datetime’,’timedelta’]

  • contamination ( float , optional ) – Fraction of training dataset corresponding to anomalies. contamination has to be between 0 and 0.5. Default value is 0.01.

Returns

self

Return type

object

predict ( X )

Predict labels for features (X).

Parameters

X ( pandas.DataFrame ) – Training dataset features, or Explanatory features if task is ‘forecasting’

Returns

y_pred – The predicted values.

Return type

numpy.ndarray of shape (n_samples,)

predict_proba ( X )

Probability estimates.

More information can be found here: Prediction Probabilities

Parameters

X ( pandas.DataFrame ) – Training dataset features

Returns

y_pred_proba – The predicted probabilities.

Return type

numpy.ndarray of shape = (n_samples, n_classes)

score ( X , y )

Score of this pipeline for a given set of features ( X ) and labels ( y ). If inferred_score_metric has multiple score metrics, the first score metric would be calculated.

Parameters
Returns

score – Score of self.predict(X) with respect to y .

Return type

float

transform ( X , y )

Applies automatic preprocessing to a given set of features ( X ) and labels ( y ).

Parameters
Returns

  • X ( pandas.DataFrame ) – Transformed dataset features

  • y ( pandas.DataFrame ) – Transformed dataset target