stemflow.model.SphereAdaSTEM
SphereAdaSTEM
Bases: AdaSTEM
A SphereAdaSTEm model class (allow fixed grid size)
Parents
stemflow.model.AdaSTEM
Children
stemflow.model.SphereAdaSTEM.SphereAdaSTEMClassifier
stemflow.model.SphereAdaSTEM.SphereAdaSTEMRegressor
Source code in stemflow/model/SphereAdaSTEM.py
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SAC_ensemble_predict(index_df, data)
A sub-module of SAC prediction function. Predict only one ensemble.
Parameters:
-
index_df
(DataFrame
) –ensemble data (model.ensemble_df)
-
data
(DataFrame
) –input covariates to predict
Returns: pd.core.frame.DataFrame: Prediction result of one ensemble.
Source code in stemflow/model/SphereAdaSTEM.py
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SAC_ensemble_training(index_df, data)
A sub-module of SAC training function. Train only one ensemble.
Parameters:
-
index_df
(DataFrame
) –ensemble data (model.ensemble_df)
-
data
(DataFrame
) –input covariates to train
Source code in stemflow/model/SphereAdaSTEM.py
__init__(base_model, task='hurdle', ensemble_fold=10, min_ensemble_required=7, grid_len_upper_threshold=8000, grid_len_lower_threshold=500, points_lower_threshold=50, stixel_training_size_threshold=None, temporal_start=1, temporal_end=366, temporal_step=20, temporal_bin_interval=50, temporal_bin_start_jitter='adaptive', spatio_bin_jitter_magnitude='adaptive', random_state=None, save_gridding_plot=True, sample_weights_for_classifier=True, Spatio1='longitude', Spatio2='latitude', Temporal1='DOY', use_temporal_to_train=True, n_jobs=1, subset_x_names=False, plot_xlims=(-180, 180), plot_ylims=(-90, 90), verbosity=0, plot_empty=False, radius=6371.0, lazy_loading=False, lazy_loading_dir=None)
Make a Spherical AdaSTEM object
Parameters:
-
base_model
(BaseEstimator
) –base model estimator
-
task
(str
, default:'hurdle'
) –task of the model. One of 'classifier', 'regressor' and 'hurdle'. Defaults to 'hurdle'.
-
ensemble_fold
(int
, default:10
) –Ensembles count. Higher, better for the model performance. Time complexity O(N). Defaults to 10.
-
min_ensemble_required
(int
, default:7
) –Only points with more than this number of model ensembles available are predicted. In the training phase, if stixels contain less than
points_lower_threshold
of data records, the results are set to np.nan, making themunpredictable
. Defaults to 7. -
grid_len_upper_threshold
(Union[float, int]
, default:8000
) –force divide if grid length (km) larger than the threshold. Defaults to 8000 km.
-
grid_len_lower_threshold
(Union[float, int]
, default:500
) –stop divide if grid length (km) will be below than the threshold. Defaults to 500 km.
-
points_lower_threshold
(int
, default:50
) –Do not further split the gird if split results in less samples than this threshold. Overriden by grid_len_*_upper_threshold parameters. Defaults to 50.
-
stixel_training_size_threshold
(int
, default:None
) –Do not train the model if the available data records for this stixel is less than this threshold, and directly set the value to np.nan. Defaults to 50.
-
temporal_start
(Union[float, int]
, default:1
) –start of the temporal sequence. Defaults to 1.
-
temporal_end
(Union[float, int]
, default:366
) –end of the temporal sequence. Defaults to 366.
-
temporal_step
(Union[float, int]
, default:20
) –step of the sliding window. Defaults to 20.
-
temporal_bin_interval
(Union[float, int]
, default:50
) –size of the sliding window. Defaults to 50.
-
temporal_bin_start_jitter
(Union[float, int, str]
, default:'adaptive'
) –jitter of the start of the sliding window. If 'adaptive', a random jitter of range (-bin_interval, 0) will be generated for the start. Defaults to 'adaptive'.
-
spatio_bin_jitter_magnitude
(Union[float, int]
, default:'adaptive'
) –jitter of the spatial gridding. Defaults to 'adaptive'.
-
random_state
–None or int. After setting the same seed, the model will generate the same results each time. For reproducibility.
-
save_gridding_plot
(bool
, default:True
) –Whether ot save gridding plots. Defaults to True.
-
sample_weights_for_classifier
(bool
, default:True
) –Whether to adjust for unbanlanced data for the classifier. Default to True.
-
Spatio1
(str
, default:'longitude'
) –Spatial column name 1 in data. For SphereAdaSTEM, this HAS to be 'longitude'.
-
Spatio2
(str
, default:'latitude'
) –Spatial column name 2 in data. For SphereAdaSTEM, this HAS to be 'latitude'.
-
Temporal1
(str
, default:'DOY'
) –Temporal column name 1 in data. Defaults to 'DOY'.
-
use_temporal_to_train
(bool
, default:True
) –Whether to use temporal variable to train. For example in modeling the daily abundance of bird population, whether use 'day of year (DOY)' as a training variable. Defaults to True.
-
n_jobs
(int
, default:1
) –Number of multiprocessing in fitting the model. Defaults to 1.
-
subset_x_names
(bool
, default:False
) –Whether to only store variables with std > 0 for each stixel. Set to False will significantly increase the training speed.
-
plot_xlims
(Tuple[Union[float, int], Union[float, int]]
, default:(-180, 180)
) –If save_gridding_plot=true, what is the xlims of the plot. Defaults to (-180,180).
-
plot_ylims
(Tuple[Union[float, int], Union[float, int]]
, default:(-90, 90)
) –If save_gridding_plot=true, what is the ylims of the plot. Defaults to (-90,90).
-
verbosity
(int
, default:0
) –0 to output nothing and everything otherwise.
-
plot_empty
(bool
, default:False
) –Whether to plot the empty grid
-
radius
(float
, default:6371.0
) –radius of earth in km.
-
lazy_loading
(bool
, default:False
) –If True, ensembles of models will be saved in disk, and only loaded when being used (e.g., prediction phase), and the ensembles of models are dump to disk once it is used.
-
lazy_loading_dir
(Union[str, None]
, default:None
) –If lazy_loading, the directory of the model to temporary save to. Default to None, where a random number will be generated as folder name.
Raises:
-
AttributeError
–Base model do not have method 'fit' or 'predict'
-
AttributeError
–task not in one of ['regression', 'classification', 'hurdle']
-
AttributeError
–temporal_bin_start_jitter not in one of [str, float, int]
-
AttributeError
–temporal_bin_start_jitter is type str, but not 'adaptive'
Attributes:
-
x_names
(list
) –All training variables used.
-
stixel_specific_x_names
(dict
) –stixel specific x_names (predictor variable names) for each stixel. We remove the variables that have no variation for each stixel. Therefore, the x_names are different for each stixel.
-
ensemble_df
(DataFrame
) –A dataframe storing the stixel gridding information.
-
gridding_plot
(Figure
) –Ensemble plot.
-
model_dict
(dict
) –Dictionary of {stixel_index: trained_model}.
-
grid_dict
(dict
) –An array of stixels assigned to each ensemble.
-
feature_importances_
(DataFrame
) –feature importance dataframe for each stixel.
Source code in stemflow/model/SphereAdaSTEM.py
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split(X_train, verbosity=None, ax=None, n_jobs=1)
QuadTree indexing the input data
Parameters:
-
X_train
(DataFrame
) –Input training data
-
verbosity
(Union[None, int]
, default:None
) –0 to output nothing, everything other wise. Default None set it to the verbosity of AdaSTEM model class.
-
ax
–matplotlit Axes to add to.
Returns:
-
dict
–self.grid_dict, a dictionary of one DataFrame for each grid, containing the gridding information
Source code in stemflow/model/SphereAdaSTEM.py
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SphereAdaSTEMClassifier
Bases: SphereAdaSTEM
SphereAdaSTEM model Classifier interface
Example
>>> from stemflow.model.SphereAdaSTEM import SphereAdaSTEMClassifier
>>> from xgboost import XGBClassifier
>>> model = SphereAdaSTEMClassifier(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_upper_threshold=8000,
grid_len_lower_threshold=500,
points_lower_threshold=50,
Spatio1='longitude',
Spatio2 = 'latitude',
Temporal1 = 'DOY',
use_temporal_to_train=True)
>>> model.fit(X_train, y_train)
>>> pred = model.predict(X_test)
Source code in stemflow/model/SphereAdaSTEM.py
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SphereAdaSTEMRegressor
Bases: SphereAdaSTEM
SphereAdaSTEM model Regressor interface
Example:
>>> from stemflow.model.SphereAdaSTEM import SphereAdaSTEMRegressor
>>> from xgboost import XGBRegressor
>>> model = SphereAdaSTEMRegressor(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_upper_threshold=8000,
grid_len_lower_threshold=500,
points_lower_threshold=50,
Spatio1='longitude',
Spatio2 = 'latitude',
Temporal1 = 'DOY',
use_temporal_to_train=True)
>>> model.fit(X_train, y_train)
>>> pred = model.predict(X_test)
Source code in stemflow/model/SphereAdaSTEM.py
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