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', 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 
None 
total_time_limit 
int 
The total time limit in seconds for AutoML training.
It is not used when 
3600 
mode 
str 
Can be {

'Explain' 
ml_task 
str 
Can be {"auto", "binary_classification", "multiclass_classification", "regression"}.

'auto' 
model_time_limit 
int 
The time limit for training a single model, in seconds.
If For example, in the case of 10fold crossvalidation, one model will have 10 learners.
The 
None 
algorithms 
list of str 
The list of algorithms that will be used in the training. The algorithms can be:

'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 optimized. If "auto", then:
Note:
Still not implemented, please left 
'auto' 
validation_strategy 
dict 
Dictionary with validation type. Right now train/test split and crossvalidation are supported. Example:

'auto' 
explain_level 
int 
The level of explanations included to each model:
If left 
'auto' 
golden_features 
boolean 
Whether to use golden features
If left

'auto' 
features_selection 
boolean 
Whether to do features_selection
If left

'auto' 
start_random_models 
int 
Number of starting random models to try.
If left

'auto' 
hill_climbing_steps 
int 
Number of steps to perform during hill climbing.
If left

'auto' 
top_models_to_improve 
int 
Number of best models to improve in

'auto' 
verbose 
int 
Controls the verbosity when fitting and predicting. Note:
Still not implemented, please left 
1 
random_state 
int 
Controls the randomness of the 
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/datasetsforstart/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}%")
MultiClass 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))
Scikitlearn 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",
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`}. 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`, `Artificial 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 reallife use cases.
 Uses 5fold CV (CrossValidation).
 Uses the following models: `Linear`, `Random Forest`, `LightGBM`, `XGBoost`, `CatBoost`, `Artificial Neural Network`, and `Ensemble`.
 Has learning curves and importance plots in reports.
 `Compete` : To be used for machine learning competitions (maximum performance).
 Uses 10fold CV (CrossValidation).
 Uses the following models: `Linear`, `DecisionTree`, `Random Forest`, `Extra Trees`, `XGBoost`, `CatBoost`, `Artificial Neural Network`,
`Artificial Neural Network`, `Nearest Neighbors`, `Ensemble`, and `Stacking`.
 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 10fold crossvalidation, 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 optimized.
If "auto", then:
 `logloss` is used for classifications taks.
 `rmse` is used for regression taks.
Note:
Still not implemented, please left `None`
validation_strategy (dict): Dictionary with validation type. Right now train/test split and crossvalidation are supported.
Example:
Crossvalidation 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.
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/datasetsforstart/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}%")
MultiClass 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))
Scikitlearn 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.random_state = random_state
fit(self, X, y)
¶
Fit the AutoML model.
Parameters:
Name  Type  Description  Default 

X 
list or numpy.ndarray or pandas.DataFrame 
Training data 
required 
y 
list or numpy.ndarray or pandas.DataFrame 
Training targets 
required 
Returns:
Type  Description 

AutoML object 
Returns 
Source code in supervised/automl.py
def fit(self, X, y):
"""Fit the AutoML model.
Arguments:
X (list or numpy.ndarray or pandas.DataFrame): Training data
y (list or numpy.ndarray or pandas.DataFrame): Training targets
Returns:
AutoML object: Returns `self`
"""
return self._fit(X, y)
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 

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:
 Onedimensional array of class labels for classification.
 Onedimensional 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)
¶
Calculates a goodness of fit
for an AutoML instance.
Parameters:
Name  Type  Description  Default 

X 
list or numpy.ndarray or pandas.DataFrame 
Test values to make predictions on. 
required 
y 
list or numpy.ndarray or pandas.DataFrame 
True labels for X. 
None 
Returns:
Type  Description 

float 
Returns a goodness of fit measure (higher is better):

Source code in supervised/automl.py
def score(self, X, y=None):
"""Calculates a goodness of `fit` for an AutoML instance.
Arguments:
X (list or numpy.ndarray or pandas.DataFrame):
Test values to make predictions on.
y (list or numpy.ndarray or pandas.DataFrame):
True labels for X.
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)