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-# stemflow
-A package for Adaptive Spatio-Temporal Model (AdaSTEM) in python
-
-
-## Installation
-
-```py
-pip install stemflow
-```
-
-## Brief introduction
-stemflow is a toolkit for Adaptive Spatio-Temporal Model (AdaSTEM) in python. A typical usage is daily abundance estimation using eBird citizen science data. It leverages the "adjacency" information of surrounding target values in space and time, to predict the classes/continues values of target spatial-temporal point. In the demo, we use a two-step hurdle model as "base model", with XGBoostClassifier for occurence modeling and XGBoostRegressor for abundance modeling.
-
-User can define the size of stixel (spatial temporal pixel) in terms of space and time. Larger stixel guarantees generalizability but loses precision in fine resolution; Smaller stixel may have better predictability in the exact area but reduced extrapolability for points outside the stixel.
-
-In the demo, we first split the training data using temporal sliding windows with size of 50 day of year (DOY) and step of 20 DOY (`temporal_start = 1`, `temporal_end=366`, `temporal_step=20`, `temporal_bin_interval=50`). For each temporal slice, a spatial gridding is applied, where we force the stixel to be split into smaller 1/4 pieces if the edge is larger than 25 units (measured in longitude and latitude, `grid_len_lon_upper_threshold=25`, `grid_len_lat_upper_threshold=25`), and stop splitting to prevent the edge length to shrink below 5 units (`grid_len_lon_lower_threshold=5`, `grid_len_lat_lower_threshold=5`) or containing less than 25 checklists (`points_lower_threshold=50`).
-
-This process is excecuted 10 times (`ensemble_fold = 10`), each time with random jitter and random rotation of the gridding, generating 10 ensembles. In the prediciton phase, only spatial-temporal points with more than 7 (`min_ensemble_required = 7`) ensembles usable are predicted (otherwise, set as `np.nan`).
-
-Fitting and prediction methods follow the convention of sklearn `estimator` class:
-
-```py
-## fit
-model.fit(X_train.reset_index(drop=True), y_train)
-
-## predict
-pred = model.predict(X_test)
-pred = np.where(pred<0, 0, pred)
-```
-
-Where the pred is the mean of the predicted values across ensembles.
-
-
-
-## Usage
-
-```py
-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)
-
-```
-
-
-# Plot QuadTree ensembles
-```
-model.classifier.gridding_plot
-# or model.regressor.gridding_plot
-```
-
-
-
-----
-## Documentation:
-[stemflow Documentation](https://chenyangkang.github.io/stemflow/)
-
-
-----
-
-
-----
-
-
-
-
------
-References:
-
-1. [Fink, D., Damoulas, T., & Dave, J. (2013, June). Adaptive Spatio-Temporal Exploratory Models: Hemisphere-wide species distributions from massively crowdsourced eBird data. In Proceedings of the AAAI Conference on Artificial Intelligence (Vol. 27, No. 1, pp. 1284-1290).](https://ojs.aaai.org/index.php/AAAI/article/view/8484)
-
-2. [Fink, D., Auer, T., Johnston, A., Ruiz‐Gutierrez, V., Hochachka, W. M., & Kelling, S. (2020). Modeling avian full annual cycle distribution and population trends with citizen science data. Ecological Applications, 30(3), e02056.](https://esajournals.onlinelibrary.wiley.com/doi/full/10.1002/eap.2056)
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-# Welcome to stemflow
+# stemflow
+
+ 
+
+
+
+ A package for Adaptive Spatio-Temporal Model (AdaSTEM) in python.
+
+
-## stemflow
-A package for Adaptive Spatio-Temporal Model (AdaSTEM) in python
+
+
+
+
+
## Installation
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