stemflow.model.Hurdle
Hurdle
Bases: BaseEstimator
A simple Hurdle model class
Source code in stemflow/model/Hurdle.py
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__init__(classifier, regressor)
Make a Hurdle class object
Parameters:
-
classifier
(BaseEstimator
) –A sklearn style classifier estimator. Must have
fit
andpredict
methods. Will be better if it haspredict_proba
method, which returns a numpy array of shape (n_sample, 2) -
regressor
(BaseEstimator
) –A sklearn style regressor estimator. Must have
fit
andpredict
methods.
Example
>> from xgboost import XGBClassifier, XGBRegressor
>> from stemflow.model.Hurdle import Hurdle
>> model = Hurdle(classifier = XGBClassifier(tree_method='hist',random_state=42, verbosity = 0, n_jobs=1),
regressor = XGBRegressor(tree_method='hist',random_state=42, verbosity = 0, n_jobs=1))
>> model.fit(X_train, y_train)
>> pred = model.predict(X_test)
>> ...
Source code in stemflow/model/Hurdle.py
fit(X_train, y_train, sample_weight=None)
Fitting model
Parameters:
-
X_train
(Union[DataFrame, ndarray]
) –Training variables
-
y_train
(Sequence
) –Training target
Source code in stemflow/model/Hurdle.py
predict(X_test)
Predicting
Parameters:
-
X_test
(Union[DataFrame, ndarray]
) –Test variables
Returns:
-
ndarray
–A prediction array with shape (-1,1)
Source code in stemflow/model/Hurdle.py
predict_proba(X_test)
Predicting probability
This method output a numpy array with shape (n_sample, 2) However, user should notice that this is only for structuring the sklearn predict_proba-like method Only the res[:,1] is meaningful, aka the last dimension in the two dimensions. The first dimension is always zero.
Parameters:
-
X_test
(Union[DataFrame, ndarray]
) –Testing variables
Returns:
-
ndarray
–Prediction results with shape (n_samples, 2)
Source code in stemflow/model/Hurdle.py
Hurdle_for_AdaSTEM
Bases: BaseEstimator
Source code in stemflow/model/Hurdle.py
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__init__(classifier, regressor)
Make a Hurdle_for_AdaSTEM class object
Normally speaking, AdaSTEMClassifier and AdaSTEMRegressor should be passed here if using this class.
Parameters:
-
classifier
(BaseEstimator
) –A sklearn style classifier estimator (should be AdaSTEMClassifier here). Must have
fit
andpredict
methods. Will be better if it haspredict_proba
method, which returns a numpy array of shape (n_sample, 2) -
regressor
(BaseEstimator
) –A sklearn style regressor estimator (should be AdaSTEMRegressor here). Must have
fit
andpredict
methods.
Example
>> from stemflow.model.AdaSTEM import AdaSTEM, AdaSTEMClassifier, AdaSTEMRegressor
>> from stemflow.model.Hurdle import Hurdle_for_AdaSTEM
>> from xgboost import XGBClassifier, XGBRegressor
>> SAVE_DIR = './'
>> model = Hurdle_for_AdaSTEM(
... classifier=AdaSTEMClassifier(base_model=XGBClassifier(tree_method='hist',random_state=42, verbosity = 0, n_jobs=1),
... save_gridding_plot = True,
... ensemble_fold=10,
... min_ensemble_required=7,
... grid_len_lon_upper_threshold=25,
... grid_len_lon_lower_threshold=5,
... grid_len_lat_upper_threshold=25,
... grid_len_lat_lower_threshold=5,
... points_lower_threshold=50,
... Spatio1='longitude',
... Spatio2 = 'latitude',
... Temporal1 = 'DOY',
... use_temporal_to_train=True),
... regressor=AdaSTEMRegressor(base_model=XGBRegressor(tree_method='hist',random_state=42, verbosity = 0, n_jobs=1),
... save_gridding_plot = True,
... ensemble_fold=10,
... min_ensemble_required=7,
... grid_len_lon_upper_threshold=25,
... grid_len_lon_lower_threshold=5,
... grid_len_lat_upper_threshold=25,
... grid_len_lat_lower_threshold=5,
... points_lower_threshold=50,
... Spatio1='longitude',
... Spatio2 = 'latitude',
... Temporal1 = 'DOY',
... use_temporal_to_train=True)
... )
>> ## fit
>> model.fit(X_train.reset_index(drop=True), y_train)
>> ## predict
>> pred = model.predict(X_test)
>> pred = np.where(pred<0, 0, pred)
>> eval_metrics = AdaSTEM.eval_STEM_res('hurdle',y_test, pred_mean)
>> print(eval_metrics)
Source code in stemflow/model/Hurdle.py
fit(X_train, y_train, verbosity=1)
Fitting model Args: X_train: Training variables y_train: Training target verbosity: Whether to show progress bar. 0 for No, and Yes other wise.
Source code in stemflow/model/Hurdle.py
predict(X_test, n_jobs=1, verbosity=1, return_by_separate_ensembles=False)
Predict
Parameters:
-
X_test
(Union[DataFrame, ndarray]
) –Test variables
-
n_jobs
(int
, default:1
) –Multi-processing in prediction.
-
verbosity
(int
, default:1
) –Whether to show progress bar. 0 for No, and Yes other wise.
-
return_by_separate_ensembles
(bool
, default:False
) –Test function. return not by aggregation, but by separate ensembles.
Returns:
-
ndarray
–A prediction array with shape (-1,1)
Source code in stemflow/model/Hurdle.py
predict_proba(X_test, n_jobs=1, verbosity=0, return_by_separate_ensembles=False)
Just a rewrite of predict
method
Parameters:
-
X_test
(Union[DataFrame, ndarray]
) –Testing variables
-
n_jobs
(int
, default:1
) –Multi-processing in prediction.
-
verbosity
(int
, default:0
) –Whether to show progress bar. 0 for No, and Yes other wise.
-
return_by_separate_ensembles
(bool
, default:False
) –Test function. return not by aggregation, but by separate ensembles.
Returns:
-
ndarray
–A prediction array with shape (-1,1)