stemflow.model.AdaSTEM

---

AdaSTEM

Bases: BaseEstimator

An AdaSTEM model class inherited by AdaSTEMClassifier and AdaSTEMRegressor

Source code in stemflow/model/AdaSTEM.py

class AdaSTEM(BaseEstimator):
    """An AdaSTEM model class inherited by AdaSTEMClassifier and AdaSTEMRegressor"""

    def __init__(
        self,
        base_model: BaseEstimator,
        task: str = "hurdle",
        ensemble_fold: int = 10,
        min_ensemble_required: int = 7,
        grid_len_upper_threshold: Union[float, int] = 25,
        grid_len_lower_threshold: Union[float, int] = 5,
        points_lower_threshold: int = 50,
        stixel_training_size_threshold: int = None,
        temporal_start: Union[float, int] = 1,
        temporal_end: Union[float, int] = 366,
        temporal_step: Union[float, int] = 20,
        temporal_bin_interval: Union[float, int] = 50,
        temporal_bin_start_jitter: Union[float, int, str] = "adaptive",
        spatio_bin_jitter_magnitude: Union[float, int] = "adaptive",
        random_state=None,
        save_gridding_plot: bool = True,
        sample_weights_for_classifier: bool = True,
        Spatio1: str = "longitude",
        Spatio2: str = "latitude",
        Temporal1: str = "DOY",
        use_temporal_to_train: bool = True,
        n_jobs: int = 1,
        subset_x_names: bool = False,
        plot_xlims: Tuple[Union[float, int], Union[float, int]] = None,
        plot_ylims: Tuple[Union[float, int], Union[float, int]] = None,
        verbosity: int = 0,
        plot_empty: bool = False,
        completely_random_rotation: bool = False,
        lazy_loading: bool = False,
        lazy_loading_dir: Union[str, None] = None,
        min_class_sample: int = 1,
        ensemble_bootstrap: bool = False,
        joblib_backend: str = 'loky',
        joblib_temp_folder: Union[None, str] = None
    ):
        """Make an AdaSTEM object

        Args:
            base_model:
                base model estimator
            task:
                task of the model. One of 'classifier', 'regressor' and 'hurdle'. Defaults to 'hurdle'.
            ensemble_fold:
                Ensembles count. Higher, better for the model performance. Time complexity O(N). Defaults to 10.
            min_ensemble_required:
                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 them `unpredictable`. Defaults to 7.
            grid_len_upper_threshold:
                force divide if grid length larger than the threshold. Defaults to 25.
            grid_len_lower_threshold:
                stop divide if grid length **will** be below than the threshold. Defaults to 5.
            points_lower_threshold:
                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:
                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:
                start of the temporal sequence. Defaults to 1.
            temporal_end:
                end of the temporal sequence. Defaults to 366.
            temporal_step:
                step of the sliding window. Defaults to 20.
            temporal_bin_interval:
                size of the sliding window. Defaults to 50.
            temporal_bin_start_jitter:
                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:
                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:
                Whether ot save gridding plots. Defaults to True.
            sample_weights_for_classifier:
                Whether to adjust for unbanlanced data for the classifier. Default to True.
            Spatio1:
                Spatial column name 1 in data. Defaults to 'longitude'.
            Spatio2:
                Spatial column name 2 in data. Defaults to 'latitude'.
            Temporal1:
                Temporal column name 1 in data.  Defaults to 'DOY'.
            use_temporal_to_train:
                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:
                Number of multiprocessing in fitting the model. Defaults to 1.
            subset_x_names:
                Whether to only store variables with std > 0 for each stixel. Set to False will significantly increase the training speed.
            plot_xlims:
                If save_gridding_plot=true, what is the xlims of the plot. Defaults to the extent of input X varibale.
            plot_ylims:
                If save_gridding_plot=true, what is the ylims of the plot. Defaults to the extent of input Y varibale.
            verbosity:
                0 to output nothing and everything otherwise.
            plot_empty:
                Whether to plot the empty grid
            completely_random_rotation:
                If True, the rotation angle will be generated completely randomly, as in paper https://doi.org/10.1002/eap.2056. If False, the ensembles will split the 90 degree with equal angle intervals. e.g., if ensemble_fold=9, then each ensemble will rotate 10 degree futher than the previous ensemble. Defalt to False, because if ensemble fold is small, it will be more robust to equally devide the data; and if ensemble fold is large, they are effectively similar than complete random.
            lazy_loading:
                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:
                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.
            min_class_sample:
                Minimum umber of samples needed to train the classifier in each stixel. If the sample does not satisfy, fit a dummy one. This parameter does not influence regression tasks.
            ensemble_bootstrap:
                Whether to bootstrap the data at each ensemble level to account for uncertainty. Defaults to False.
            joblib_backend:
                The backend of joblib. Defaults to 'loky'. Other options include 'multiprocessing', 'threading'.
            joblib_temp_folder:
                The temporary folder for joblib. If None, falling back to joblib's default directory. If 'lazy_loading_dir', set as the same directory as lazy_loading_dir. If it's string, create a directory and store data into it. Defaults to None.
        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 'random'

        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 (pd.core.frame.DataFrame):
                A dataframe storing the stixel gridding information.
            gridding_plot (matplotlib.figure.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_ (pd.core.frame.DataFrame):
                feature importance dataframe for each stixel.

        """
        # 1. Check random state
        self.random_state = random_state

        # 2. Base model
        check_base_model(base_model)
        base_model = model_wrapper(base_model)
        self.base_model = base_model

        # 3. Model params
        check_task(task)
        self.task = task
        self.Temporal1 = Temporal1
        self.Spatio1 = Spatio1
        self.Spatio2 = Spatio2

        # 4. Gridding params
        if min_ensemble_required > ensemble_fold:
            raise ValueError("Not satisfied: min_ensemble_required <= ensemble_fold")

        self.ensemble_fold = ensemble_fold
        self.min_ensemble_required = min_ensemble_required
        self.grid_len_upper_threshold = grid_len_upper_threshold
        self.grid_len_lower_threshold = grid_len_lower_threshold
        self.points_lower_threshold = points_lower_threshold
        self.temporal_start = temporal_start
        self.temporal_end = temporal_end
        self.temporal_step = temporal_step
        self.temporal_bin_interval = temporal_bin_interval
        self.completely_random_rotation = completely_random_rotation

        check_spatio_bin_jitter_magnitude(spatio_bin_jitter_magnitude)
        self.spatio_bin_jitter_magnitude = spatio_bin_jitter_magnitude
        check_temporal_bin_start_jitter(temporal_bin_start_jitter)
        self.temporal_bin_start_jitter = temporal_bin_start_jitter

        # 5. Training params
        if stixel_training_size_threshold is None:
            self.stixel_training_size_threshold = points_lower_threshold
        else:
            self.stixel_training_size_threshold = stixel_training_size_threshold
        self.use_temporal_to_train = use_temporal_to_train
        self.subset_x_names = subset_x_names
        self.sample_weights_for_classifier = sample_weights_for_classifier
        self.min_class_sample = min_class_sample
        self.ensemble_bootstrap = ensemble_bootstrap

        # 6. Multi-processing params
        n_jobs = check_transform_n_jobs(self, n_jobs)
        self.n_jobs = n_jobs
        self.joblib_backend = joblib_backend
        self.joblib_temp_folder = joblib_temp_folder

        # 7. Plotting params
        self.plot_xlims = plot_xlims
        self.plot_ylims = plot_ylims
        self.save_gridding_plot = save_gridding_plot
        self.plot_empty = plot_empty

        # X. miscellaneous
        self.lazy_loading = lazy_loading
        self.lazy_loading_dir = lazy_loading_dir

        if not verbosity == 0:
            self.verbosity = 1
        else:
            self.verbosity = 0

    def split(self, X_train: pd.core.frame.DataFrame, verbosity: Union[None, int] = None, ax=None, n_jobs: Union[None, int] = None):
        """QuadTree indexing the input data

        Args:
            X_train: Input training data
            verbosity: 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:
            self.grid_dict, a dictionary of one DataFrame for each grid, containing the gridding information
        """
        self.rng = check_random_state(self.random_state)
        n_jobs = check_transform_n_jobs(self, n_jobs)

        if verbosity is None:
            verbosity = self.verbosity

        # Determine grid_len based on conditions
        if "grid_len" not in self.__dir__():
            # We are using AdaSTEM
            self.grid_len = None
            grid_len_upper = self.grid_len_upper_threshold
            grid_len_lower = self.grid_len_lower_threshold
        elif self.grid_len is None:
            # AdaSTEM with predefined thresholds
            grid_len_upper = self.grid_len_upper_threshold
            grid_len_lower = self.grid_len_lower_threshold
        else:
            # We are using STEM
            grid_len_upper = self.grid_len
            grid_len_lower = self.grid_len

        # Call spatial and temporal scale checks
        check_spatial_scale(
            X_train[self.Spatio1].min(),
            X_train[self.Spatio1].max(),
            X_train[self.Spatio2].min(),
            X_train[self.Spatio2].max(),
            grid_len_upper,
            grid_len_lower,
        )

        check_temporal_scale(X_train[self.Temporal1].min(), X_train[self.Temporal1].min(), self.temporal_bin_interval)

        spatio_bin_jitter_magnitude = check_transform_spatio_bin_jitter_magnitude(
            X_train, self.Spatio1, self.Spatio2, self.spatio_bin_jitter_magnitude
        )

        if self.save_gridding_plot:
            if self.plot_xlims is None:
                self.plot_xlims = (X_train[self.Spatio1].min(), X_train[self.Spatio1].max())
            if self.plot_ylims is None:
                self.plot_ylims = (X_train[self.Spatio2].min(), X_train[self.Spatio2].max())

            if ax is None:
                plt.figure(figsize=(20, 20))
                plt.xlim([self.plot_xlims[0], self.plot_xlims[1]])
                plt.ylim([self.plot_ylims[0], self.plot_ylims[1]])
                plt.title("Quadtree", fontsize=20)
            else:
                pass

        X_train_indexes = X_train[[self.Temporal1, self.Spatio1, self.Spatio2]]

        partial_get_one_ensemble_quadtree = partial(
            get_one_ensemble_quadtree,
            size=self.ensemble_fold,
            spatio_bin_jitter_magnitude=spatio_bin_jitter_magnitude,
            temporal_start=self.temporal_start,
            temporal_end=self.temporal_end,
            temporal_step=self.temporal_step,
            temporal_bin_interval=self.temporal_bin_interval,
            temporal_bin_start_jitter=self.temporal_bin_start_jitter,
            data=X_train_indexes,
            Temporal1=self.Temporal1,
            grid_len=self.grid_len,
            grid_len_lon_upper_threshold=self.grid_len_upper_threshold,
            grid_len_lon_lower_threshold=self.grid_len_lower_threshold,
            grid_len_lat_upper_threshold=self.grid_len_upper_threshold,
            grid_len_lat_lower_threshold=self.grid_len_lower_threshold,
            points_lower_threshold=self.points_lower_threshold,
            plot_empty=self.plot_empty,
            Spatio1=self.Spatio1,
            Spatio2=self.Spatio2,
            save_gridding_plot=self.save_gridding_plot,
            ax=ax,
            completely_random_rotation=self.completely_random_rotation,
            ensemble_bootstrap=self.ensemble_bootstrap
        )

        if n_jobs > 1 and isinstance(n_jobs, int):
            parallel = joblib.Parallel(n_jobs=n_jobs, return_as="generator", backend=self.joblib_backend, temp_folder=self.joblib_temp_folder)
            output_generator = parallel(
                joblib.delayed(partial_get_one_ensemble_quadtree)(
                    ensemble_count=ensemble_count, rng=np.random.default_rng(self.rng.integers(1e9) + ensemble_count)
                )
                for ensemble_count in list(range(self.ensemble_fold))
            )
            if verbosity > 0:
                output_generator = tqdm(output_generator, total=self.ensemble_fold, desc="Generating Ensembles: ")

            ensemble_all_df_list = [i for i in output_generator]

        else:
            iter_func_ = (
                tqdm(range(self.ensemble_fold), total=self.ensemble_fold, desc="Generating Ensembles: ")
                if verbosity > 0
                else range(self.ensemble_fold)
            )
            ensemble_all_df_list = [
                partial_get_one_ensemble_quadtree(
                    ensemble_count=ensemble_count, rng=np.random.default_rng(self.rng.integers(1e9) + ensemble_count)
                )
                for ensemble_count in iter_func_
            ]

        # concat
        ensemble_df = pd.concat(ensemble_all_df_list).reset_index(drop=True)

        del ensemble_all_df_list

        # processing
        ensemble_df = ensemble_df.reset_index(drop=True)

        if self.save_gridding_plot:
            if ax is None:
                plt.tight_layout()
                plt.gca().set_aspect("equal")
                ax = plt.gcf()
                plt.close()

            else:
                pass

            self.ensemble_df, self.gridding_plot = ensemble_df, ax

        else:
            self.ensemble_df, self.gridding_plot = ensemble_df, np.nan

    def store_x_names(self, X_train: pd.core.frame.DataFrame):
        """Store the training variables

        Args:
            X_train (pd.core.frame.DataFrame): input training data.
        """
        # store x_names
        self.x_names = list(X_train.columns)
        if not self.use_temporal_to_train:
            if self.Temporal1 in list(self.x_names):
                del self.x_names[self.x_names.index(self.Temporal1)]

        for i in [self.Spatio1, self.Spatio2]:
            if i in self.x_names:
                del self.x_names[self.x_names.index(i)]

    def stixel_fitting(self, stixel):
        """A sub module of SAC training. Fit one stixel

        Args:
            stixel (pd.core.frame.DataFrame): data sjoined with ensemble_df.
            For a single stixel.
        """

        unique_stixel_id = stixel["unique_stixel_id"].iloc[0]
        name = unique_stixel_id

        model, stixel_specific_x_names, status = train_one_stixel(
            stixel_training_size_threshold=self.stixel_training_size_threshold,
            x_names=self.x_names,
            task=self.task,
            base_model=self.base_model,
            sample_weights_for_classifier=self.sample_weights_for_classifier,
            subset_x_names=self.subset_x_names,
            stixel_X_train=stixel,
            min_class_sample=self.min_class_sample
        )

        if not status == "Success":
            # print(f'Fitting: {ensemble_index}. Not pass: {status}')
            pass

        else:
            return (name, model, stixel_specific_x_names)

    def SAC_ensemble_training(self, index_df: pd.core.frame.DataFrame, data: pd.core.frame.DataFrame):
        """A sub-module of SAC training function.
        Train only one ensemble.

        Args:
            index_df (pd.core.frame.DataFrame): ensemble data (model.ensemble_df)
            data (pd.core.frame.DataFrame): input covariates to train
        """

        # Calculate the start indices for the sliding window

        unique_start_indices = np.sort(index_df[f"{self.Temporal1}_start"].unique())
        # training, window by window

        if self.ensemble_bootstrap:
            bootstrap_random_state = index_df['bootstrap_random_state'].iloc[0]
            rng = np.random.default_rng(bootstrap_random_state)  # NumPy's random generator
            bootstrap_indices = rng.choice(data.index, size=len(data), replace=True)  # Full bootstrap sample
        else:
            bootstrap_indices = None # Place holder

        res_list = []
        for start in unique_start_indices:

            if self.ensemble_bootstrap:
                valid_index_window_data_df = data.index[
                    (data[self.Temporal1] >= start) & (data[self.Temporal1] < start + self.temporal_bin_interval)
                ]
                window_data_df_index = bootstrap_indices[np.isin(bootstrap_indices, valid_index_window_data_df)]
                window_data_df = data.loc[window_data_df_index] # So that we don't need to make a whole copy of the data
                del window_data_df_index, valid_index_window_data_df

            else:
                window_data_df = data[
                    (data[self.Temporal1] >= start) & (data[self.Temporal1] < start + self.temporal_bin_interval)
                ]

            window_data_df = transform_pred_set_to_STEM_quad(self.Spatio1, self.Spatio2, window_data_df, index_df)
            window_index_df = index_df[index_df[f"{self.Temporal1}_start"] == start]

            # Merge
            def find_belonged_points(df, df_a):
                return df_a[
                    (df_a[f"{self.Spatio1}_new"] >= df["stixel_calibration_point_transformed_left_bound"].iloc[0])
                    & (df_a[f"{self.Spatio1}_new"] < df["stixel_calibration_point_transformed_right_bound"].iloc[0])
                    & (df_a[f"{self.Spatio2}_new"] >= df["stixel_calibration_point_transformed_lower_bound"].iloc[0])
                    & (df_a[f"{self.Spatio2}_new"] < df["stixel_calibration_point_transformed_upper_bound"].iloc[0])
                ]

            query_results = (
                window_index_df[
                    [
                        "ensemble_index",
                        "unique_stixel_id",
                        "stixel_calibration_point_transformed_left_bound",
                        "stixel_calibration_point_transformed_right_bound",
                        "stixel_calibration_point_transformed_lower_bound",
                        "stixel_calibration_point_transformed_upper_bound",
                    ]
                ]
                .groupby(["ensemble_index", "unique_stixel_id"])
                .apply(find_belonged_points, df_a=window_data_df)
            )

            if len(query_results) == 0:
                """All points fall out of the grids"""
                continue

            # train
            res = (
                query_results.reset_index(drop=False, level=[0, 1])
                .dropna(subset="unique_stixel_id")
                .groupby("unique_stixel_id")
                .apply(lambda stixel: self.stixel_fitting(stixel))
            ).values

            res_list.append(list(res))

        return res_list

    def SAC_training(
        self, ensemble_df: pd.core.frame.DataFrame, data: pd.core.frame.DataFrame, verbosity: int = 0, n_jobs: int = 1
    ):
        """This function is a training function with SAC strategy:
        Split (S), Apply(A), Combine (C). At ensemble level.
        It is built on pandas `apply` method.

        Args:
            ensemble_df (pd.core.frame.DataFrame): gridding information for all ensemble
            data (pd.core.frame.DataFrame): data
            verbosity (int, optional): Defaults to 0.

        """
        assert isinstance(n_jobs, int)

        groups = ensemble_df.groupby("ensemble_index")

        # Parallel wrapper
        if n_jobs == 1:
            output_generator = (self.SAC_ensemble_training(index_df=ensemble[1], data=data) for ensemble in groups)
        else:

            def mp_train(ensemble, self=self, data=data):
                res = self.SAC_ensemble_training(index_df=ensemble[1], data=data)
                return res

            parallel = joblib.Parallel(n_jobs=n_jobs, return_as="generator", backend=self.joblib_backend, temp_folder=self.joblib_temp_folder)
            output_generator = parallel(joblib.delayed(mp_train)(i) for i in groups)

        # tqdm wrapper
        if verbosity > 0:
            output_generator = tqdm(
                output_generator, total=len(ensemble_df["ensemble_index"].unique()), desc="Training: "
            )

        # iterate through
        if self.lazy_loading:
            self.model_dict = LazyLoadingEnsembleDict(self.lazy_loading_dir)
        else:
            self.model_dict = {}

        stixel_specific_x_names = {}

        for ensemble_id, ensemble in enumerate(output_generator):
            for time_block in ensemble:
                for feature_tuple in time_block:
                    if feature_tuple is None:
                        continue
                    name = feature_tuple[0]
                    model = feature_tuple[1]
                    x_names = feature_tuple[2]
                    self.model_dict[f"{name}_model"] = model
                    stixel_specific_x_names[name] = x_names

            # dump here if lazy_loading_ensemble = True
            if self.lazy_loading:
                self.model_dict.dump_ensemble(ensemble_id)

        self.stixel_specific_x_names = stixel_specific_x_names
        return self

    def fit(
        self,
        X_train: pd.core.frame.DataFrame,
        y_train: Union[pd.core.frame.DataFrame, np.ndarray],
        verbosity: Union[None, int] = None,
        ax=None,
        n_jobs: Union[None, int] = None,
    ):
        """Fitting method

        Args:
            X_train: Training variables
            y_train: Training target
            ax: matplotlib Axes to add to
            verbosty: whether to show progress bar. 0 for no and 1 for yes.
            ax: matplotlib ax for adding grid plot on that.
            n_jobs: multiprocessing thread count. Default the n_jobs of model object.

        Raises:
            TypeError: X_train is not a type of pd.core.frame.DataFrame
            TypeError: y_train is not a type of np.ndarray or pd.core.frame.DataFrame
        """
        # setup random state
        self.rng = check_random_state(self.random_state)

        # Setup lazyloading dir
        if self.lazy_loading_dir is None:
            saving_code = ''.join(np.random.choice(list(string.ascii_letters + string.digits)) for _ in range(16))
            self.lazy_loading_dir = f'./stemflow_model_{saving_code}'
        else:
            if os.path.exists(self.lazy_loading_dir):
                shutil.rmtree(self.lazy_loading_dir)
        self.lazy_loading_dir = str(Path(self.lazy_loading_dir.rstrip('/\\')))

        # Setup joblib_temp_folder
        if self.joblib_temp_folder is None:
            pass
        elif self.joblib_temp_folder=='lazy_loading_dir':
            self.joblib_temp_folder = self.lazy_loading_dir
        else:
            if not os.path.exists(self.joblib_temp_folder):
                os.makedirs(self.joblib_temp_folder)

        verbosity = check_verbosity(self, verbosity)
        check_X_train(X_train)
        check_y_train(y_train)
        n_jobs = check_transform_n_jobs(self, n_jobs)
        self.store_x_names(X_train)

        # quadtree
        X_train = X_train.reset_index(drop=True)  # I reset index here!! caution! This step is important for the following indexing.
        X_train["true_y"] = np.array(y_train).flatten()
        self.split(X_train, verbosity=verbosity, ax=ax, n_jobs=n_jobs)

        # define model dict
        self.model_dict = {}
        # stixel specific x_names list
        self.stixel_specific_x_names = {}

        self.SAC_training(self.ensemble_df, X_train, verbosity, n_jobs)
        self.classes_ = np.unique(y_train)

        return self

    def stixel_predict(self, stixel: pd.core.frame.DataFrame) -> Union[None, pd.core.frame.DataFrame]:
        """A sub module of SAC prediction. Predict one stixel

        Args:
            stixel (pd.core.frame.DataFrame): data joined with ensemble_df.
            For a single stixel.

        Returns:
            pd.core.frame.DataFrame: the prediction result of this stixel
        """

        stixel['unique_stixel_id'] = stixel.name
        unique_stixel_id = stixel["unique_stixel_id"].iloc[0]

        model_x_names_tuple = get_model_and_stixel_specific_x_names(
            self.model_dict,
            unique_stixel_id,
            self.stixel_specific_x_names,
            self.x_names,
        )

        if model_x_names_tuple[0] is None:
            return None

        pred = predict_one_stixel(X_test_stixel=stixel,
                                  task=self.task,
                                  model_x_names_tuple=model_x_names_tuple,
                                  base_model_method=self.base_model_method,
                                  **self.base_model_prediction_param)

        if pred is None:
            return None
        else:
            return pred

    def SAC_ensemble_predict(
        self, index_df: pd.core.frame.DataFrame, data: pd.core.frame.DataFrame
    ) -> pd.core.frame.DataFrame:
        """A sub-module of SAC prediction function.
        Predict only one ensemble.

        Args:
            index_df (pd.core.frame.DataFrame): ensemble data (model.ensemble_df)
            data (pd.core.frame.DataFrame): input covariates to predict
        Returns:
            pd.core.frame.DataFrame: Prediction result of one ensemble.
        """

        # Calculate the start indices for the sliding window
        start_indices = sorted(index_df[f"{self.Temporal1}_start"].unique())

        # prediction, window by window
        window_prediction_list = []
        for start in start_indices:
            window_data_df = data[
                (data[self.Temporal1] >= start) & (data[self.Temporal1] < start + self.temporal_bin_interval)
            ]
            window_data_df = transform_pred_set_to_STEM_quad(self.Spatio1, self.Spatio2, window_data_df, index_df)
            window_index_df = index_df[index_df[f"{self.Temporal1}_start"] == start]

            def find_belonged_points(df, df_a):
                return df_a[
                    (df_a[f"{self.Spatio1}_new"] >= df["stixel_calibration_point_transformed_left_bound"].iloc[0])
                    & (df_a[f"{self.Spatio1}_new"] < df["stixel_calibration_point_transformed_right_bound"].iloc[0])
                    & (df_a[f"{self.Spatio2}_new"] >= df["stixel_calibration_point_transformed_lower_bound"].iloc[0])
                    & (df_a[f"{self.Spatio2}_new"] < df["stixel_calibration_point_transformed_upper_bound"].iloc[0])
                ]

            query_results = (
                window_index_df[
                    [
                        "ensemble_index",
                        "unique_stixel_id",
                        "stixel_calibration_point_transformed_left_bound",
                        "stixel_calibration_point_transformed_right_bound",
                        "stixel_calibration_point_transformed_lower_bound",
                        "stixel_calibration_point_transformed_upper_bound",
                    ]
                ]
                .groupby(["ensemble_index", "unique_stixel_id"])
                .apply(find_belonged_points, df_a=window_data_df)
            )

            if len(query_results) == 0:
                """All points fall out of the grids"""
                continue

            # predict            
            window_prediction = (
                query_results.reset_index(drop=False, level=[0, 1])
                .dropna(subset="unique_stixel_id")
                .groupby("unique_stixel_id")
                .apply(lambda stixel: self.stixel_predict(stixel)) #
            )

            window_prediction_list.append(window_prediction)


        if self.lazy_loading:
            ensemble_id = index_df['ensemble_index'].iloc[0]
            self.model_dict.dump_ensemble(ensemble_id)

        if any([i is not None for i in window_prediction_list]):
            ensemble_prediction = pd.concat(window_prediction_list, axis=0)
            ensemble_prediction = ensemble_prediction.droplevel(0, axis=0)
            ensemble_prediction = ensemble_prediction.groupby("index").mean().reset_index(drop=False)
        else:
            ensmeble_index = list(window_index_df["ensemble_index"])[0]
            warnings.warn(f"No prediction for this ensemble: {ensmeble_index}")
            ensemble_prediction = None

        return ensemble_prediction

    def SAC_predict(
        self, ensemble_df: pd.core.frame.DataFrame, data: pd.core.frame.DataFrame, verbosity: int = 0, n_jobs: int = 1
    ) -> pd.core.frame.DataFrame:
        """This function is a prediction function with SAC strategy:
        Split (S), Apply(A), Combine (C). At ensemble level.
        It is built on pandas `apply` method.

        Args:
            ensemble_df (pd.core.frame.DataFrame): gridding information for all ensemble
            data (pd.core.frame.DataFrame): data
            verbosity (int, optional): Defaults to 0.

        Returns:
            pd.core.frame.DataFrame: prediction results.
        """
        assert isinstance(n_jobs, int)

        groups = ensemble_df.groupby("ensemble_index")

        # Parallel maker
        if n_jobs == 1:
            output_generator = (self.SAC_ensemble_predict(index_df=ensemble[1], data=data) for ensemble in groups)
        else:

            def mp_predict(ensemble, self=self, data=data):
                res = self.SAC_ensemble_predict(index_df=ensemble[1], data=data)
                return res

            parallel = joblib.Parallel(n_jobs=n_jobs, return_as="generator", backend=self.joblib_backend, temp_folder=self.joblib_temp_folder)
            output_generator = parallel(joblib.delayed(mp_predict)(i) for i in groups)

        # tqdm wrapper
        if verbosity > 0:
            output_generator = tqdm(
                output_generator, total=len(ensemble_df["ensemble_index"].unique()), desc="Predicting: "
            )

        # Prediction
        pred = [i.set_index("index") for i in output_generator]
        pred = pd.concat(pred, axis=1)
        if len(pred) == 0:
            raise ValueError(
                "All samples are not predictable based on current settings!\nTry adjusting the 'points_lower_threshold', increase the grid size, or increase sample size!"
            )

        pred.columns = list(range(self.ensemble_fold))
        return pred

    def predict_proba(
        self,
        X_test: pd.core.frame.DataFrame,
        verbosity: Union[int, None] = None,
        return_std: bool = False,
        n_jobs: Union[None, int] = None,
        aggregation: str = "mean",
        return_by_separate_ensembles: bool = False,
        logit_agg: bool = False,
        base_model_method: Union[None, str] = None,
        **base_model_prediction_param
    ) -> Union[np.ndarray, Tuple[np.ndarray]]:
        """Predict probability

        Args:
            X_test (pd.core.frame.DataFrame):
                Testing variables.
            verbosity (int, optional):
                show progress bar or not. Yes for 0, and No for other. Defaults to None, which set it as the verbosity of the main model class.
            return_std (bool, optional):
                Whether return the standard deviation among ensembles. Defaults to False.
            n_jobs (Union[int, None], optional):
                Number of processes used in this task. If None, use the self.n_jobs. Default to 1.
                I do not recommend setting value larger than 1.
                In practice, multi-processing seems to slow down the process instead of speeding up.
                Could be more practical with large amount of data.
                Still in experiment.
            aggregation (str, optional):
                'mean' or 'median' for aggregation method across ensembles.
            return_by_separate_ensembles (bool, optional):
                Experimental function. return not by aggregation, but by separate ensembles.
            logit_agg:
                Whether to use logit aggregation for the classification task. Most likely only used when you are predicting "real" calibrated probability. If True, the model is averaging the probability prediction estimated by all ensembles in logit scale, and then back-tranforms it to probability scale. It's recommended to be jointly used with the CalibratedClassifierCV class in sklearn as a wrapper of the classifier to estimate the calibrated probability. Default is False, but can be set to true for "real" probability averaging.
            base_model_method:
                The name of the prediction method for base models. If None, `predict` or `predict_proba` will be used depending on the tasks. This argument is handy if you have a custom base model class that has a special prediction function. Notice that dummy model will still predict 0, so the ensemble-aggregated result is still an average of zeros and your special prediction function output. Therefore, it may only make sense if your special prediction function predicts 0 as the absense/control value. Defaults to None.
            base_model_prediction_param:
                Any other paramters to pass into the prediction method of the base models. e.g., base_model_prediction_param={'n_jobs':1}.
        Raises:
            TypeError:
                X_test is not of type pd.core.frame.DataFrame.
            ValueError:
                aggregation is not in ['mean','median'].

        Returns:
            predicted results. (pred_mean, pred_std) if return_std==true, and pred_mean if return_std==False.

            If return_by_separate_ensembles == True:
                Return numpy.ndarray of shape (n_samples, n_ensembles)

        """
        check_X_test(X_test)
        check_prediciton_aggregation(aggregation)
        return_by_separate_ensembles, return_std = check_prediction_return(return_by_separate_ensembles, return_std)
        verbosity = check_verbosity(self, verbosity)
        n_jobs = check_transform_n_jobs(self, n_jobs)
        self.base_model_method = base_model_method
        self.base_model_prediction_param = base_model_prediction_param

        # predict
        res = self.SAC_predict(self.ensemble_df, X_test, verbosity=verbosity, n_jobs=n_jobs)

        # Experimental Function
        if return_by_separate_ensembles:
            new_res = pd.DataFrame({"index": list(X_test.index)}).set_index("index")
            new_res = new_res.merge(res, left_on="index", right_on="index", how="left")
            return new_res.values

        # Transform to logit space if classification:
        if self.task=='classification' and logit_agg:
            for col_index in range(res.shape[1]):
                prob = np.clip(res.iloc[:,col_index], 1e-6, 1 - 1e-6)
                res.iloc[:,col_index] = np.log(prob / (1-prob)) # logit space

            # Aggregate
            if aggregation == "mean":
                res_mean = res.mean(axis=1, skipna=True)  # mean of all grid model that predicts this stixel
            elif aggregation == "median":
                res_mean = res.median(axis=1, skipna=True)

             # Transform to logit space if classification:
            res_mean = 1/(1+np.exp(-res_mean)) # notice that the res_std is not transformed!
            res_mean = res_mean.where(res_mean<=1e-6, 0)

        else:
            # don't need to aggregate at logit scale
            # Aggregate
            if aggregation == "mean":
                res_mean = res.mean(axis=1, skipna=True)  # mean of all grid model that predicts this stixel
            elif aggregation == "median":
                res_mean = res.median(axis=1, skipna=True)

        res_std = res.std(axis=1, skipna=True)

        # Nan count
        res_nan_count = res.isnull().sum(axis=1)
        pred_mean = np.where(
            self.ensemble_fold - res_nan_count.values >= self.min_ensemble_required, res_mean.values, np.nan
        )
        pred_std = np.where(
            self.ensemble_fold - res_nan_count.values >= self.min_ensemble_required, res_std.values, np.nan
        )

        res = pd.DataFrame({"index": list(res_mean.index), "pred_mean": pred_mean, "pred_std": pred_std}).set_index(
            "index"
        )

        # Preparing output (formatting)
        new_res = pd.DataFrame({"index": list(X_test.index)}).set_index("index")
        new_res = new_res.merge(res, left_on="index", right_on="index", how="left")

        nan_count = np.sum(np.isnan(new_res["pred_mean"].values))
        nan_frac = nan_count / len(new_res["pred_mean"].values)
        warnings.warn(f"There are {nan_frac}% points ({nan_count} points) falling out of predictable range.")

        if return_std:
            if self.task=='classification':
                return np.array([1-new_res["pred_mean"].values.flatten(), new_res["pred_mean"].values.flatten()]).T, new_res["pred_std"].values
            else:
                return new_res["pred_mean"].values.flatten(), new_res["pred_std"].values.flatten()
        else:
            if self.task=='classification':
                return np.array([1-new_res["pred_mean"].values.flatten(), new_res["pred_mean"].values.flatten()]).T
            else:
                return new_res["pred_mean"].values.flatten()


    @abstractmethod
    def predict(
        self,
        X_test: pd.core.frame.DataFrame,
        verbosity: Union[None, int] = None,
        return_std: bool = False,
        n_jobs: Union[None, int] = 1,
        aggregation: str = "mean",
        return_by_separate_ensembles: bool = False,
        logit_agg: bool = False,
        base_model_method: Union[None, str] = None,
        **base_model_prediction_param
    ) -> Union[np.ndarray, Tuple[np.ndarray]]:
        pass


    @classmethod
    def eval_STEM_res(
        self,
        task: str,
        y_test: Union[pd.core.series.Series, np.ndarray],
        y_pred: Union[pd.core.series.Series, np.ndarray],
        cls_threshold: Union[float, None] = None,
    ) -> dict:
        """Evaluation using multiple metrics

        Classification metrics used:
        1. AUC
        2. Cohen's Kappa
        3. F1
        4. precision
        5. recall
        6. average precision

        Regression metrics used:
        1. spearman's r
        2. peason's r
        3. R2
        4. mean absolute error (MAE)
        5. mean squared error (MSE)
        6. poisson deviance explained (PDE)

        Args:
            task (str):
                one of 'regression', 'classification' or 'hurdle'.
            y_test (Union[pd.core.series.Series, np.ndarray]):
                y true
            y_pred (Union[pd.core.series.Series, np.ndarray]):
                y predicted
            cls_threshold (Union[float, None], optional):
                Cutting threshold for the classification.
                Values above cls_threshold will be labeled as 1 and 0 otherwise.
                Defaults to None (0.5 for classification and 0 for hurdle).

        Raises:
            AttributeError: task not one of 'regression', 'classification' or 'hurdle'.

        Returns:
            dict: dictionary containing the metric names and their values.
        """

        if task not in ["regression", "classification", "hurdle"]:
            raise AttributeError(
                f"task type must be one of 'regression', 'classification', or 'hurdle'! Now it is {task}"
            )

        if cls_threshold is None:
            if task == "classification":
                cls_threshold = 0.5
            elif task == "hurdle":
                cls_threshold = 0

        if task == "regression":
            auc, kappa, f1, precision, recall, average_precision = [np.nan] * 6
        else:
            a = pd.DataFrame({"y_true": np.array(y_test).flatten(), "pred": np.array(y_pred).flatten()}).dropna()

            y_test_b = np.where(a.y_true > cls_threshold, 1, 0)
            y_pred_b = np.where(a.pred > cls_threshold, 1, 0)

            if len(np.unique(y_test_b)) == 1 and len(np.unique(y_pred_b)) == 1:
                auc, kappa, f1, precision, recall, average_precision = [np.nan] * 6

            else:
                auc = roc_auc_score(y_test_b, np.array(a.pred)) # AUC can be calculated with probability
                kappa = cohen_kappa_score(y_test_b, y_pred_b, weights='linear')
                f1 = f1_score(y_test_b, y_pred_b)
                precision = precision_score(y_test_b, y_pred_b)
                recall = recall_score(y_test_b, y_pred_b)
                average_precision = average_precision_score(y_test_b, y_pred_b)

        if not task == "classification":
            a = pd.DataFrame({"y_true": y_test, "pred": y_pred}).dropna()
            s_r, _ = spearmanr(np.array(a.y_true), np.array(a.pred))
            p_r, _ = pearsonr(np.array(a.y_true), np.array(a.pred))
            r2 = r2_score(a.y_true, a.pred)
            MAE = mean_absolute_error(a.y_true, a.pred)
            MSE = mean_squared_error(a.y_true, a.pred)
            try:
                poisson_deviance_explained = d2_tweedie_score(a[a.pred > 0].y_true, a[a.pred > 0].pred, power=1)
            except Exception as e:
                warnings.warn(f"PED estimation fail: {e}")
                poisson_deviance_explained = np.nan
        else:
            s_r, p_r, r2, MAE, MSE, poisson_deviance_explained = [np.nan] * 6

        return {
            "AUC": auc,
            "kappa": kappa,
            "f1": f1,
            "precision": precision,
            "recall": recall,
            "average_precision": average_precision,
            "Spearman_r": s_r,
            "Pearson_r": p_r,
            "R2": r2,
            "MAE": MAE,
            "MSE": MSE,
            "poisson_deviance_explained": poisson_deviance_explained,
        }

    def score(self, X_test: pd.core.frame.DataFrame, y_test: Union[pd.core.series.Series, np.ndarray]) -> dict:
        """Combine predicting and evaluating in one method

        Args:
            X_test (pd.core.frame.DataFrame): Testing variables
            y_test (Union[pd.core.series.Series, np.ndarray]): y true

        Returns:
            dict: dictionary containing the metric names and their values.
        """

        y_pred = self.predict(X_test)
        score_dict = AdaSTEM.eval_STEM_res(self.task, np.array(y_test).flatten(), np.array(y_pred).flatten())
        self.score_dict = score_dict
        return self.score_dict

    def calculate_feature_importances(self):
        """A method to generate feature importance values for each stixel.

        feature importances are saved in self.feature_importances_.

        Attribute dependence:
            1. self.ensemble_df
            2. self.model_dict
            3. self.stixel_specific_x_names
            4. The input base model should have attribute `feature_importances_`

        """
        # generate feature importance dict
        feature_importance_list = []

        for ensemble_id in self.ensemble_df['ensemble_index'].unique():
            for index, ensemble_row in self.ensemble_df[self.ensemble_df['ensemble_index']==ensemble_id][
                self.ensemble_df["stixel_checklist_count"] >= self.stixel_training_size_threshold
            ].iterrows():
                if ensemble_row["stixel_checklist_count"] < self.stixel_training_size_threshold:
                    continue

                try:
                    stixel_index = ensemble_row["unique_stixel_id"]
                    the_model = self.model_dict[f"{stixel_index}_model"]
                    x_names = self.stixel_specific_x_names[stixel_index]

                    if isinstance(the_model, dummy_model1):
                        importance_dict = dict(zip(self.x_names, [1 / len(self.x_names)] * len(self.x_names)))
                    elif isinstance(the_model, Hurdle):
                        if "feature_importances_" in the_model.__dir__():
                            importance_dict = dict(zip(x_names, the_model.feature_importances_))
                        else:
                            if isinstance(the_model.classifier, dummy_model1):
                                importance_dict = dict(zip(self.x_names, [1 / len(self.x_names)] * len(self.x_names)))
                            else:
                                importance_dict = dict(zip(x_names, the_model.classifier.feature_importances_))
                    else:
                        importance_dict = dict(zip(x_names, the_model.feature_importances_))

                    importance_dict["stixel_index"] = stixel_index
                    feature_importance_list.append(importance_dict)

                except Exception as e:
                    warnings.warn(f"{e}")
                    continue

            if self.lazy_loading:
                self.model_dict.dump_ensemble(ensemble_id)

        self.feature_importances_ = (
            pd.DataFrame(feature_importance_list).set_index("stixel_index").reset_index(drop=False).fillna(0)
        )

    def assign_feature_importances_by_points(
        self,
        Sample_ST_df: Union[pd.core.frame.DataFrame, None] = None,
        verbosity: Union[None, int] = None,
        aggregation: str = "mean",
        n_jobs: Union[int, None] = 1,
        assign_function: Callable = assign_points_to_one_ensemble,
    ) -> pd.core.frame.DataFrame:
        """Assign feature importance to the input spatio-temporal points

        Args:
            Sample_ST_df (Union[pd.core.frame.DataFrame, None], optional):
                Dataframe that indicate the spatio-temporal points of interest.
                Must contain `self.Spatio1`, `self.Spatio2`, and `self.Temporal1` in columns.
                If None, the resolution will be:

                | variable|values|
                |---------|--------|
                |Spatio_var1|np.arange(-180,180,1)|
                |Spatio_var2|np.arange(-90,90,1)|
                |Temporal_var1|np.arange(1,366,7)|

                Defaults to None.
            verbosity (Union[None, int], optional):
                0 to output nothing, everything other wise. Default None set it to the verbosity of AdaSTEM model class.
            aggregation (str, optional):
                One of 'mean' and 'median' to aggregate feature importance across ensembles.
            n_jobs (Union[int, None], optional):
                Number of processes used in this task. If None, use the self.n_jobs. Default to 1.

        Raises:
            NameError:
                feature_importances_ attribute is not calculated. Try model.calculate_feature_importances() first.
            ValueError:
                f'aggregation not one of [\'mean\',\'median\'].'
            KeyError:
                One of [`self.Spatio1`, `self.Spatio2`, `self.Temporal1`] not found in `Sample_ST_df.columns`

        Returns:
            DataFrame with feature importance assigned.
        """
        #
        verbosity = check_verbosity(self, verbosity=verbosity)
        n_jobs = check_transform_n_jobs(self, n_jobs)
        check_prediciton_aggregation(aggregation)

        #
        if "feature_importances_" not in dir(self):
            raise NameError(
                "feature_importances_ attribute is not calculated. Try model.calculate_feature_importances() first."
            )

        #
        if Sample_ST_df is None:
            Spatio_var1 = np.arange(-180, 180, 1)
            Spatio_var2 = np.arange(-90, 90, 1)
            Temporal_var1 = np.arange(1, 366, 7)
            new_Spatio_var1, new_Spatio_var2, new_Temporal_var1 = np.meshgrid(Spatio_var1, Spatio_var2, Temporal_var1)

            Sample_ST_df = pd.DataFrame(
                {
                    self.Temporal1: new_Temporal_var1.flatten(),
                    self.Spatio1: new_Spatio_var1.flatten(),
                    self.Spatio2: new_Spatio_var2.flatten(),
                }
            )
        else:
            for var_name in [self.Spatio1, self.Spatio2, self.Temporal1]:
                if var_name not in Sample_ST_df.columns:
                    raise KeyError(f"{var_name} not found in Sample_ST_df.columns")

        partial_assign_func = partial(
            assign_function,
            ensemble_df=self.ensemble_df,
            Sample_ST_df=Sample_ST_df,
            Temporal1=self.Temporal1,
            Spatio1=self.Spatio1,
            Spatio2=self.Spatio2,
            feature_importances_=self.feature_importances_,
        )

        # assign input spatio-temporal points to stixels
        if n_jobs > 1:
            parallel = joblib.Parallel(n_jobs=n_jobs, return_as="generator", backend=self.joblib_backend, temp_folder=self.joblib_temp_folder)
            output_generator = parallel(joblib.delayed(partial_assign_func)(i) for i in list(range(self.ensemble_fold)))
            if verbosity > 0:
                output_generator = tqdm(output_generator, total=self.ensemble_fold, desc="Querying ensembles: ")
            round_res_list = [i for i in output_generator]

        else:
            iter_func_ = (
                tqdm(range(self.ensemble_fold), total=self.ensemble_fold, desc="Querying ensembles: ")
                if verbosity > 0
                else range(self.ensemble_fold)
            )
            round_res_list = [partial_assign_func(ensemble_count) for ensemble_count in iter_func_]

        round_res_df = pd.concat(round_res_list, axis=0)
        del round_res_list

        ensemble_available_count = round_res_df.groupby("sample_index").count().iloc[:, 0]

        # Only points with more than self.min_ensemble_required ensembles available are used
        usable_sample = ensemble_available_count[ensemble_available_count >= self.min_ensemble_required]  #
        round_res_df = round_res_df[round_res_df["sample_index"].isin(list(usable_sample.index))]

        # aggregate across ensembles
        if aggregation == "mean":
            mean_feature_importances_across_ensembles = round_res_df.groupby("sample_index").mean()
        elif aggregation == "median":
            mean_feature_importances_across_ensembles = round_res_df.groupby("sample_index").median()

        if self.use_temporal_to_train:
            mean_feature_importances_across_ensembles = mean_feature_importances_across_ensembles.rename(
                columns={self.Temporal1: f"{self.Temporal1}_predictor"}
            )
        out_ = pd.concat([Sample_ST_df, mean_feature_importances_across_ensembles], axis=1).dropna()
        return out_

    @staticmethod
    def load(tar_gz_file, new_lazy_loading_path=None, remove_original_file=False):

        if new_lazy_loading_path is None:
            saving_code = int(np.random.uniform(1, 1e8))
            new_lazy_loading_path = f'./stemflow_model_{saving_code}'
        new_lazy_loading_path = str(Path(new_lazy_loading_path.rstrip('/\\')))

        file = tarfile.open(tar_gz_file) 
        file.extractall(new_lazy_loading_path, filter=tarfile.data_filter) 
        file.close()

        with open(os.path.join(new_lazy_loading_path, 'model.pkl'), 'rb') as f:
            model = pickle.load(f)

        if model.lazy_loading:
            # then this is lazy loading
            if not len(os.listdir(new_lazy_loading_path))>1:
                raise FileExistsError('Your model is not a lazy_loading model, but more than 1 files/folders are found in the .tar.gz file?')
            else:
                model.set_params(lazy_loading_dir=new_lazy_loading_path)
                model.model_dict.directory = new_lazy_loading_path
                model.lazy_loading_dir = new_lazy_loading_path

        if remove_original_file:
            os.remove(tar_gz_file)

        return model

    def save(self, tar_gz_file, remove_temporary_file = True):
        if not os.path.exists(self.lazy_loading_dir):
            os.makedirs(self.lazy_loading_dir, exist_ok=False)

        if self.lazy_loading:
            ensemble_ids = list(self.model_dict.ensemble_models.keys())
            for current_in_memory_ensemble in ensemble_ids:
                self.model_dict.dump_ensemble(current_in_memory_ensemble)

            # check all ensemble on disk
            for ensemble_id in range(self.ensemble_fold):
                if not f'ensemble_{ensemble_id}_dict.pkl' in os.listdir(self.lazy_loading_dir):
                    raise FileNotFoundError(f'Ensemble models file ensemble_{ensemble_id}_dict.pkl is missing in lazyloading directory {self.lazy_loading_dir}!')

        #
        path_tar_gz_file, basename_tar_gz_file = os.path.split(Path(tar_gz_file.rstrip('/\\')))

        # temporary save the model using pickle
        model_path = os.path.join(self.lazy_loading_dir, f'model.pkl')
        with open(model_path, 'wb') as f:
            pickle.dump(self, f)

        # save the main model class and potentially lazyloading pieces to the tar.gz file
        with tarfile.open(tar_gz_file, "w:gz") as tar:
            tar.add(model_path, arcname=basename_tar_gz_file)
            if self.lazy_loading:
                for pieces in os.listdir(self.lazy_loading_dir):
                    tar.add(os.path.join(self.lazy_loading_dir, pieces), arcname=pieces)

        if remove_temporary_file:
            os.remove(model_path)
            if self.lazy_loading:
                shutil.rmtree(self.lazy_loading_dir)

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/AdaSTEM.py

def SAC_ensemble_predict(
    self, index_df: pd.core.frame.DataFrame, data: pd.core.frame.DataFrame
) -> pd.core.frame.DataFrame:
    """A sub-module of SAC prediction function.
    Predict only one ensemble.

    Args:
        index_df (pd.core.frame.DataFrame): ensemble data (model.ensemble_df)
        data (pd.core.frame.DataFrame): input covariates to predict
    Returns:
        pd.core.frame.DataFrame: Prediction result of one ensemble.
    """

    # Calculate the start indices for the sliding window
    start_indices = sorted(index_df[f"{self.Temporal1}_start"].unique())

    # prediction, window by window
    window_prediction_list = []
    for start in start_indices:
        window_data_df = data[
            (data[self.Temporal1] >= start) & (data[self.Temporal1] < start + self.temporal_bin_interval)
        ]
        window_data_df = transform_pred_set_to_STEM_quad(self.Spatio1, self.Spatio2, window_data_df, index_df)
        window_index_df = index_df[index_df[f"{self.Temporal1}_start"] == start]

        def find_belonged_points(df, df_a):
            return df_a[
                (df_a[f"{self.Spatio1}_new"] >= df["stixel_calibration_point_transformed_left_bound"].iloc[0])
                & (df_a[f"{self.Spatio1}_new"] < df["stixel_calibration_point_transformed_right_bound"].iloc[0])
                & (df_a[f"{self.Spatio2}_new"] >= df["stixel_calibration_point_transformed_lower_bound"].iloc[0])
                & (df_a[f"{self.Spatio2}_new"] < df["stixel_calibration_point_transformed_upper_bound"].iloc[0])
            ]

        query_results = (
            window_index_df[
                [
                    "ensemble_index",
                    "unique_stixel_id",
                    "stixel_calibration_point_transformed_left_bound",
                    "stixel_calibration_point_transformed_right_bound",
                    "stixel_calibration_point_transformed_lower_bound",
                    "stixel_calibration_point_transformed_upper_bound",
                ]
            ]
            .groupby(["ensemble_index", "unique_stixel_id"])
            .apply(find_belonged_points, df_a=window_data_df)
        )

        if len(query_results) == 0:
            """All points fall out of the grids"""
            continue

        # predict            
        window_prediction = (
            query_results.reset_index(drop=False, level=[0, 1])
            .dropna(subset="unique_stixel_id")
            .groupby("unique_stixel_id")
            .apply(lambda stixel: self.stixel_predict(stixel)) #
        )

        window_prediction_list.append(window_prediction)


    if self.lazy_loading:
        ensemble_id = index_df['ensemble_index'].iloc[0]
        self.model_dict.dump_ensemble(ensemble_id)

    if any([i is not None for i in window_prediction_list]):
        ensemble_prediction = pd.concat(window_prediction_list, axis=0)
        ensemble_prediction = ensemble_prediction.droplevel(0, axis=0)
        ensemble_prediction = ensemble_prediction.groupby("index").mean().reset_index(drop=False)
    else:
        ensmeble_index = list(window_index_df["ensemble_index"])[0]
        warnings.warn(f"No prediction for this ensemble: {ensmeble_index}")
        ensemble_prediction = None

    return ensemble_prediction

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/AdaSTEM.py

def SAC_ensemble_training(self, index_df: pd.core.frame.DataFrame, data: pd.core.frame.DataFrame):
    """A sub-module of SAC training function.
    Train only one ensemble.

    Args:
        index_df (pd.core.frame.DataFrame): ensemble data (model.ensemble_df)
        data (pd.core.frame.DataFrame): input covariates to train
    """

    # Calculate the start indices for the sliding window

    unique_start_indices = np.sort(index_df[f"{self.Temporal1}_start"].unique())
    # training, window by window

    if self.ensemble_bootstrap:
        bootstrap_random_state = index_df['bootstrap_random_state'].iloc[0]
        rng = np.random.default_rng(bootstrap_random_state)  # NumPy's random generator
        bootstrap_indices = rng.choice(data.index, size=len(data), replace=True)  # Full bootstrap sample
    else:
        bootstrap_indices = None # Place holder

    res_list = []
    for start in unique_start_indices:

        if self.ensemble_bootstrap:
            valid_index_window_data_df = data.index[
                (data[self.Temporal1] >= start) & (data[self.Temporal1] < start + self.temporal_bin_interval)
            ]
            window_data_df_index = bootstrap_indices[np.isin(bootstrap_indices, valid_index_window_data_df)]
            window_data_df = data.loc[window_data_df_index] # So that we don't need to make a whole copy of the data
            del window_data_df_index, valid_index_window_data_df

        else:
            window_data_df = data[
                (data[self.Temporal1] >= start) & (data[self.Temporal1] < start + self.temporal_bin_interval)
            ]

        window_data_df = transform_pred_set_to_STEM_quad(self.Spatio1, self.Spatio2, window_data_df, index_df)
        window_index_df = index_df[index_df[f"{self.Temporal1}_start"] == start]

        # Merge
        def find_belonged_points(df, df_a):
            return df_a[
                (df_a[f"{self.Spatio1}_new"] >= df["stixel_calibration_point_transformed_left_bound"].iloc[0])
                & (df_a[f"{self.Spatio1}_new"] < df["stixel_calibration_point_transformed_right_bound"].iloc[0])
                & (df_a[f"{self.Spatio2}_new"] >= df["stixel_calibration_point_transformed_lower_bound"].iloc[0])
                & (df_a[f"{self.Spatio2}_new"] < df["stixel_calibration_point_transformed_upper_bound"].iloc[0])
            ]

        query_results = (
            window_index_df[
                [
                    "ensemble_index",
                    "unique_stixel_id",
                    "stixel_calibration_point_transformed_left_bound",
                    "stixel_calibration_point_transformed_right_bound",
                    "stixel_calibration_point_transformed_lower_bound",
                    "stixel_calibration_point_transformed_upper_bound",
                ]
            ]
            .groupby(["ensemble_index", "unique_stixel_id"])
            .apply(find_belonged_points, df_a=window_data_df)
        )

        if len(query_results) == 0:
            """All points fall out of the grids"""
            continue

        # train
        res = (
            query_results.reset_index(drop=False, level=[0, 1])
            .dropna(subset="unique_stixel_id")
            .groupby("unique_stixel_id")
            .apply(lambda stixel: self.stixel_fitting(stixel))
        ).values

        res_list.append(list(res))

    return res_list

SAC_predict(ensemble_df, data, verbosity=0, n_jobs=1)

This function is a prediction function with SAC strategy: Split (S), Apply(A), Combine (C). At ensemble level. It is built on pandas apply method.

Parameters:

• ensemble_df (DataFrame) –

  gridding information for all ensemble

• data (DataFrame) –

  data

• verbosity (int, default: 0 ) –

  Defaults to 0.

Returns:

• DataFrame –

  pd.core.frame.DataFrame: prediction results.

Source code in stemflow/model/AdaSTEM.py

def SAC_predict(
    self, ensemble_df: pd.core.frame.DataFrame, data: pd.core.frame.DataFrame, verbosity: int = 0, n_jobs: int = 1
) -> pd.core.frame.DataFrame:
    """This function is a prediction function with SAC strategy:
    Split (S), Apply(A), Combine (C). At ensemble level.
    It is built on pandas `apply` method.

    Args:
        ensemble_df (pd.core.frame.DataFrame): gridding information for all ensemble
        data (pd.core.frame.DataFrame): data
        verbosity (int, optional): Defaults to 0.

    Returns:
        pd.core.frame.DataFrame: prediction results.
    """
    assert isinstance(n_jobs, int)

    groups = ensemble_df.groupby("ensemble_index")

    # Parallel maker
    if n_jobs == 1:
        output_generator = (self.SAC_ensemble_predict(index_df=ensemble[1], data=data) for ensemble in groups)
    else:

        def mp_predict(ensemble, self=self, data=data):
            res = self.SAC_ensemble_predict(index_df=ensemble[1], data=data)
            return res

        parallel = joblib.Parallel(n_jobs=n_jobs, return_as="generator", backend=self.joblib_backend, temp_folder=self.joblib_temp_folder)
        output_generator = parallel(joblib.delayed(mp_predict)(i) for i in groups)

    # tqdm wrapper
    if verbosity > 0:
        output_generator = tqdm(
            output_generator, total=len(ensemble_df["ensemble_index"].unique()), desc="Predicting: "
        )

    # Prediction
    pred = [i.set_index("index") for i in output_generator]
    pred = pd.concat(pred, axis=1)
    if len(pred) == 0:
        raise ValueError(
            "All samples are not predictable based on current settings!\nTry adjusting the 'points_lower_threshold', increase the grid size, or increase sample size!"
        )

    pred.columns = list(range(self.ensemble_fold))
    return pred

SAC_training(ensemble_df, data, verbosity=0, n_jobs=1)

This function is a training function with SAC strategy: Split (S), Apply(A), Combine (C). At ensemble level. It is built on pandas apply method.

Parameters:

• ensemble_df (DataFrame) –

  gridding information for all ensemble

• data (DataFrame) –

  data

• verbosity (int, default: 0 ) –

  Defaults to 0.

Source code in stemflow/model/AdaSTEM.py

def SAC_training(
    self, ensemble_df: pd.core.frame.DataFrame, data: pd.core.frame.DataFrame, verbosity: int = 0, n_jobs: int = 1
):
    """This function is a training function with SAC strategy:
    Split (S), Apply(A), Combine (C). At ensemble level.
    It is built on pandas `apply` method.

    Args:
        ensemble_df (pd.core.frame.DataFrame): gridding information for all ensemble
        data (pd.core.frame.DataFrame): data
        verbosity (int, optional): Defaults to 0.

    """
    assert isinstance(n_jobs, int)

    groups = ensemble_df.groupby("ensemble_index")

    # Parallel wrapper
    if n_jobs == 1:
        output_generator = (self.SAC_ensemble_training(index_df=ensemble[1], data=data) for ensemble in groups)
    else:

        def mp_train(ensemble, self=self, data=data):
            res = self.SAC_ensemble_training(index_df=ensemble[1], data=data)
            return res

        parallel = joblib.Parallel(n_jobs=n_jobs, return_as="generator", backend=self.joblib_backend, temp_folder=self.joblib_temp_folder)
        output_generator = parallel(joblib.delayed(mp_train)(i) for i in groups)

    # tqdm wrapper
    if verbosity > 0:
        output_generator = tqdm(
            output_generator, total=len(ensemble_df["ensemble_index"].unique()), desc="Training: "
        )

    # iterate through
    if self.lazy_loading:
        self.model_dict = LazyLoadingEnsembleDict(self.lazy_loading_dir)
    else:
        self.model_dict = {}

    stixel_specific_x_names = {}

    for ensemble_id, ensemble in enumerate(output_generator):
        for time_block in ensemble:
            for feature_tuple in time_block:
                if feature_tuple is None:
                    continue
                name = feature_tuple[0]
                model = feature_tuple[1]
                x_names = feature_tuple[2]
                self.model_dict[f"{name}_model"] = model
                stixel_specific_x_names[name] = x_names

        # dump here if lazy_loading_ensemble = True
        if self.lazy_loading:
            self.model_dict.dump_ensemble(ensemble_id)

    self.stixel_specific_x_names = stixel_specific_x_names
    return self

__init__(base_model, task='hurdle', ensemble_fold=10, min_ensemble_required=7, grid_len_upper_threshold=25, grid_len_lower_threshold=5, 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=None, plot_ylims=None, verbosity=0, plot_empty=False, completely_random_rotation=False, lazy_loading=False, lazy_loading_dir=None, min_class_sample=1, ensemble_bootstrap=False, joblib_backend='loky', joblib_temp_folder=None)

Make an 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 them unpredictable. Defaults to 7.

• grid_len_upper_threshold (Union[float, int], default: 25 ) –

  force divide if grid length larger than the threshold. Defaults to 25.

• grid_len_lower_threshold (Union[float, int], default: 5 ) –

  stop divide if grid length will be below than the threshold. Defaults to 5.

• 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. Defaults to 'longitude'.

• Spatio2 (str, default: 'latitude' ) –

  Spatial column name 2 in data. Defaults to '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: None ) –

  If save_gridding_plot=true, what is the xlims of the plot. Defaults to the extent of input X varibale.

• plot_ylims (Tuple[Union[float, int], Union[float, int]], default: None ) –

  If save_gridding_plot=true, what is the ylims of the plot. Defaults to the extent of input Y varibale.

• verbosity (int, default: 0 ) –

  0 to output nothing and everything otherwise.

• plot_empty (bool, default: False ) –

  Whether to plot the empty grid

• completely_random_rotation (bool, default: False ) –

  If True, the rotation angle will be generated completely randomly, as in paper https://doi.org/10.1002/eap.2056. If False, the ensembles will split the 90 degree with equal angle intervals. e.g., if ensemble_fold=9, then each ensemble will rotate 10 degree futher than the previous ensemble. Defalt to False, because if ensemble fold is small, it will be more robust to equally devide the data; and if ensemble fold is large, they are effectively similar than complete random.

• 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.

• min_class_sample (int, default: 1 ) –

  Minimum umber of samples needed to train the classifier in each stixel. If the sample does not satisfy, fit a dummy one. This parameter does not influence regression tasks.

• ensemble_bootstrap (bool, default: False ) –

  Whether to bootstrap the data at each ensemble level to account for uncertainty. Defaults to False.

• joblib_backend (str, default: 'loky' ) –

  The backend of joblib. Defaults to 'loky'. Other options include 'multiprocessing', 'threading'.

• joblib_temp_folder (Union[None, str], default: None ) –

  The temporary folder for joblib. If None, falling back to joblib's default directory. If 'lazy_loading_dir', set as the same directory as lazy_loading_dir. If it's string, create a directory and store data into it. Defaults to None.

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 'random'

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/AdaSTEM.py

def __init__(
    self,
    base_model: BaseEstimator,
    task: str = "hurdle",
    ensemble_fold: int = 10,
    min_ensemble_required: int = 7,
    grid_len_upper_threshold: Union[float, int] = 25,
    grid_len_lower_threshold: Union[float, int] = 5,
    points_lower_threshold: int = 50,
    stixel_training_size_threshold: int = None,
    temporal_start: Union[float, int] = 1,
    temporal_end: Union[float, int] = 366,
    temporal_step: Union[float, int] = 20,
    temporal_bin_interval: Union[float, int] = 50,
    temporal_bin_start_jitter: Union[float, int, str] = "adaptive",
    spatio_bin_jitter_magnitude: Union[float, int] = "adaptive",
    random_state=None,
    save_gridding_plot: bool = True,
    sample_weights_for_classifier: bool = True,
    Spatio1: str = "longitude",
    Spatio2: str = "latitude",
    Temporal1: str = "DOY",
    use_temporal_to_train: bool = True,
    n_jobs: int = 1,
    subset_x_names: bool = False,
    plot_xlims: Tuple[Union[float, int], Union[float, int]] = None,
    plot_ylims: Tuple[Union[float, int], Union[float, int]] = None,
    verbosity: int = 0,
    plot_empty: bool = False,
    completely_random_rotation: bool = False,
    lazy_loading: bool = False,
    lazy_loading_dir: Union[str, None] = None,
    min_class_sample: int = 1,
    ensemble_bootstrap: bool = False,
    joblib_backend: str = 'loky',
    joblib_temp_folder: Union[None, str] = None
):
    """Make an AdaSTEM object

    Args:
        base_model:
            base model estimator
        task:
            task of the model. One of 'classifier', 'regressor' and 'hurdle'. Defaults to 'hurdle'.
        ensemble_fold:
            Ensembles count. Higher, better for the model performance. Time complexity O(N). Defaults to 10.
        min_ensemble_required:
            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 them `unpredictable`. Defaults to 7.
        grid_len_upper_threshold:
            force divide if grid length larger than the threshold. Defaults to 25.
        grid_len_lower_threshold:
            stop divide if grid length **will** be below than the threshold. Defaults to 5.
        points_lower_threshold:
            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:
            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:
            start of the temporal sequence. Defaults to 1.
        temporal_end:
            end of the temporal sequence. Defaults to 366.
        temporal_step:
            step of the sliding window. Defaults to 20.
        temporal_bin_interval:
            size of the sliding window. Defaults to 50.
        temporal_bin_start_jitter:
            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:
            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:
            Whether ot save gridding plots. Defaults to True.
        sample_weights_for_classifier:
            Whether to adjust for unbanlanced data for the classifier. Default to True.
        Spatio1:
            Spatial column name 1 in data. Defaults to 'longitude'.
        Spatio2:
            Spatial column name 2 in data. Defaults to 'latitude'.
        Temporal1:
            Temporal column name 1 in data.  Defaults to 'DOY'.
        use_temporal_to_train:
            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:
            Number of multiprocessing in fitting the model. Defaults to 1.
        subset_x_names:
            Whether to only store variables with std > 0 for each stixel. Set to False will significantly increase the training speed.
        plot_xlims:
            If save_gridding_plot=true, what is the xlims of the plot. Defaults to the extent of input X varibale.
        plot_ylims:
            If save_gridding_plot=true, what is the ylims of the plot. Defaults to the extent of input Y varibale.
        verbosity:
            0 to output nothing and everything otherwise.
        plot_empty:
            Whether to plot the empty grid
        completely_random_rotation:
            If True, the rotation angle will be generated completely randomly, as in paper https://doi.org/10.1002/eap.2056. If False, the ensembles will split the 90 degree with equal angle intervals. e.g., if ensemble_fold=9, then each ensemble will rotate 10 degree futher than the previous ensemble. Defalt to False, because if ensemble fold is small, it will be more robust to equally devide the data; and if ensemble fold is large, they are effectively similar than complete random.
        lazy_loading:
            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:
            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.
        min_class_sample:
            Minimum umber of samples needed to train the classifier in each stixel. If the sample does not satisfy, fit a dummy one. This parameter does not influence regression tasks.
        ensemble_bootstrap:
            Whether to bootstrap the data at each ensemble level to account for uncertainty. Defaults to False.
        joblib_backend:
            The backend of joblib. Defaults to 'loky'. Other options include 'multiprocessing', 'threading'.
        joblib_temp_folder:
            The temporary folder for joblib. If None, falling back to joblib's default directory. If 'lazy_loading_dir', set as the same directory as lazy_loading_dir. If it's string, create a directory and store data into it. Defaults to None.
    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 'random'

    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 (pd.core.frame.DataFrame):
            A dataframe storing the stixel gridding information.
        gridding_plot (matplotlib.figure.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_ (pd.core.frame.DataFrame):
            feature importance dataframe for each stixel.

    """
    # 1. Check random state
    self.random_state = random_state

    # 2. Base model
    check_base_model(base_model)
    base_model = model_wrapper(base_model)
    self.base_model = base_model

    # 3. Model params
    check_task(task)
    self.task = task
    self.Temporal1 = Temporal1
    self.Spatio1 = Spatio1
    self.Spatio2 = Spatio2

    # 4. Gridding params
    if min_ensemble_required > ensemble_fold:
        raise ValueError("Not satisfied: min_ensemble_required <= ensemble_fold")

    self.ensemble_fold = ensemble_fold
    self.min_ensemble_required = min_ensemble_required
    self.grid_len_upper_threshold = grid_len_upper_threshold
    self.grid_len_lower_threshold = grid_len_lower_threshold
    self.points_lower_threshold = points_lower_threshold
    self.temporal_start = temporal_start
    self.temporal_end = temporal_end
    self.temporal_step = temporal_step
    self.temporal_bin_interval = temporal_bin_interval
    self.completely_random_rotation = completely_random_rotation

    check_spatio_bin_jitter_magnitude(spatio_bin_jitter_magnitude)
    self.spatio_bin_jitter_magnitude = spatio_bin_jitter_magnitude
    check_temporal_bin_start_jitter(temporal_bin_start_jitter)
    self.temporal_bin_start_jitter = temporal_bin_start_jitter

    # 5. Training params
    if stixel_training_size_threshold is None:
        self.stixel_training_size_threshold = points_lower_threshold
    else:
        self.stixel_training_size_threshold = stixel_training_size_threshold
    self.use_temporal_to_train = use_temporal_to_train
    self.subset_x_names = subset_x_names
    self.sample_weights_for_classifier = sample_weights_for_classifier
    self.min_class_sample = min_class_sample
    self.ensemble_bootstrap = ensemble_bootstrap

    # 6. Multi-processing params
    n_jobs = check_transform_n_jobs(self, n_jobs)
    self.n_jobs = n_jobs
    self.joblib_backend = joblib_backend
    self.joblib_temp_folder = joblib_temp_folder

    # 7. Plotting params
    self.plot_xlims = plot_xlims
    self.plot_ylims = plot_ylims
    self.save_gridding_plot = save_gridding_plot
    self.plot_empty = plot_empty

    # X. miscellaneous
    self.lazy_loading = lazy_loading
    self.lazy_loading_dir = lazy_loading_dir

    if not verbosity == 0:
        self.verbosity = 1
    else:
        self.verbosity = 0

assign_feature_importances_by_points(Sample_ST_df=None, verbosity=None, aggregation='mean', n_jobs=1, assign_function=assign_points_to_one_ensemble)

Assign feature importance to the input spatio-temporal points

Parameters:

• Sample_ST_df (Union[DataFrame, None], default: None ) –

  Dataframe that indicate the spatio-temporal points of interest. Must contain self.Spatio1, self.Spatio2, and self.Temporal1 in columns. If None, the resolution will be:

  variable	values
  Spatio_var1	np.arange(-180,180,1)
  Spatio_var2	np.arange(-90,90,1)
  Temporal_var1	np.arange(1,366,7)

  Defaults to None.

• verbosity (Union[None, int], default: None ) –

  0 to output nothing, everything other wise. Default None set it to the verbosity of AdaSTEM model class.

• aggregation (str, default: 'mean' ) –

  One of 'mean' and 'median' to aggregate feature importance across ensembles.

• n_jobs (Union[int, None], default: 1 ) –

  Number of processes used in this task. If None, use the self.n_jobs. Default to 1.

Raises:

• NameError –

  feature_importances_ attribute is not calculated. Try model.calculate_feature_importances() first.

• ValueError –

  f'aggregation not one of ['mean','median'].'

• KeyError –

  One of [self.Spatio1, self.Spatio2, self.Temporal1] not found in Sample_ST_df.columns

Returns:

• DataFrame –

  DataFrame with feature importance assigned.

Source code in stemflow/model/AdaSTEM.py

def assign_feature_importances_by_points(
    self,
    Sample_ST_df: Union[pd.core.frame.DataFrame, None] = None,
    verbosity: Union[None, int] = None,
    aggregation: str = "mean",
    n_jobs: Union[int, None] = 1,
    assign_function: Callable = assign_points_to_one_ensemble,
) -> pd.core.frame.DataFrame:
    """Assign feature importance to the input spatio-temporal points

    Args:
        Sample_ST_df (Union[pd.core.frame.DataFrame, None], optional):
            Dataframe that indicate the spatio-temporal points of interest.
            Must contain `self.Spatio1`, `self.Spatio2`, and `self.Temporal1` in columns.
            If None, the resolution will be:

            | variable|values|
            |---------|--------|
            |Spatio_var1|np.arange(-180,180,1)|
            |Spatio_var2|np.arange(-90,90,1)|
            |Temporal_var1|np.arange(1,366,7)|

            Defaults to None.
        verbosity (Union[None, int], optional):
            0 to output nothing, everything other wise. Default None set it to the verbosity of AdaSTEM model class.
        aggregation (str, optional):
            One of 'mean' and 'median' to aggregate feature importance across ensembles.
        n_jobs (Union[int, None], optional):
            Number of processes used in this task. If None, use the self.n_jobs. Default to 1.

    Raises:
        NameError:
            feature_importances_ attribute is not calculated. Try model.calculate_feature_importances() first.
        ValueError:
            f'aggregation not one of [\'mean\',\'median\'].'
        KeyError:
            One of [`self.Spatio1`, `self.Spatio2`, `self.Temporal1`] not found in `Sample_ST_df.columns`

    Returns:
        DataFrame with feature importance assigned.
    """
    #
    verbosity = check_verbosity(self, verbosity=verbosity)
    n_jobs = check_transform_n_jobs(self, n_jobs)
    check_prediciton_aggregation(aggregation)

    #
    if "feature_importances_" not in dir(self):
        raise NameError(
            "feature_importances_ attribute is not calculated. Try model.calculate_feature_importances() first."
        )

    #
    if Sample_ST_df is None:
        Spatio_var1 = np.arange(-180, 180, 1)
        Spatio_var2 = np.arange(-90, 90, 1)
        Temporal_var1 = np.arange(1, 366, 7)
        new_Spatio_var1, new_Spatio_var2, new_Temporal_var1 = np.meshgrid(Spatio_var1, Spatio_var2, Temporal_var1)

        Sample_ST_df = pd.DataFrame(
            {
                self.Temporal1: new_Temporal_var1.flatten(),
                self.Spatio1: new_Spatio_var1.flatten(),
                self.Spatio2: new_Spatio_var2.flatten(),
            }
        )
    else:
        for var_name in [self.Spatio1, self.Spatio2, self.Temporal1]:
            if var_name not in Sample_ST_df.columns:
                raise KeyError(f"{var_name} not found in Sample_ST_df.columns")

    partial_assign_func = partial(
        assign_function,
        ensemble_df=self.ensemble_df,
        Sample_ST_df=Sample_ST_df,
        Temporal1=self.Temporal1,
        Spatio1=self.Spatio1,
        Spatio2=self.Spatio2,
        feature_importances_=self.feature_importances_,
    )

    # assign input spatio-temporal points to stixels
    if n_jobs > 1:
        parallel = joblib.Parallel(n_jobs=n_jobs, return_as="generator", backend=self.joblib_backend, temp_folder=self.joblib_temp_folder)
        output_generator = parallel(joblib.delayed(partial_assign_func)(i) for i in list(range(self.ensemble_fold)))
        if verbosity > 0:
            output_generator = tqdm(output_generator, total=self.ensemble_fold, desc="Querying ensembles: ")
        round_res_list = [i for i in output_generator]

    else:
        iter_func_ = (
            tqdm(range(self.ensemble_fold), total=self.ensemble_fold, desc="Querying ensembles: ")
            if verbosity > 0
            else range(self.ensemble_fold)
        )
        round_res_list = [partial_assign_func(ensemble_count) for ensemble_count in iter_func_]

    round_res_df = pd.concat(round_res_list, axis=0)
    del round_res_list

    ensemble_available_count = round_res_df.groupby("sample_index").count().iloc[:, 0]

    # Only points with more than self.min_ensemble_required ensembles available are used
    usable_sample = ensemble_available_count[ensemble_available_count >= self.min_ensemble_required]  #
    round_res_df = round_res_df[round_res_df["sample_index"].isin(list(usable_sample.index))]

    # aggregate across ensembles
    if aggregation == "mean":
        mean_feature_importances_across_ensembles = round_res_df.groupby("sample_index").mean()
    elif aggregation == "median":
        mean_feature_importances_across_ensembles = round_res_df.groupby("sample_index").median()

    if self.use_temporal_to_train:
        mean_feature_importances_across_ensembles = mean_feature_importances_across_ensembles.rename(
            columns={self.Temporal1: f"{self.Temporal1}_predictor"}
        )
    out_ = pd.concat([Sample_ST_df, mean_feature_importances_across_ensembles], axis=1).dropna()
    return out_

calculate_feature_importances()

A method to generate feature importance values for each stixel.

feature importances are saved in self.feature_importances_.

Attribute dependence

1. self.ensemble_df
2. self.model_dict
3. self.stixel_specific_x_names
4. The input base model should have attribute feature_importances_

Source code in stemflow/model/AdaSTEM.py

def calculate_feature_importances(self):
    """A method to generate feature importance values for each stixel.

    feature importances are saved in self.feature_importances_.

    Attribute dependence:
        1. self.ensemble_df
        2. self.model_dict
        3. self.stixel_specific_x_names
        4. The input base model should have attribute `feature_importances_`

    """
    # generate feature importance dict
    feature_importance_list = []

    for ensemble_id in self.ensemble_df['ensemble_index'].unique():
        for index, ensemble_row in self.ensemble_df[self.ensemble_df['ensemble_index']==ensemble_id][
            self.ensemble_df["stixel_checklist_count"] >= self.stixel_training_size_threshold
        ].iterrows():
            if ensemble_row["stixel_checklist_count"] < self.stixel_training_size_threshold:
                continue

            try:
                stixel_index = ensemble_row["unique_stixel_id"]
                the_model = self.model_dict[f"{stixel_index}_model"]
                x_names = self.stixel_specific_x_names[stixel_index]

                if isinstance(the_model, dummy_model1):
                    importance_dict = dict(zip(self.x_names, [1 / len(self.x_names)] * len(self.x_names)))
                elif isinstance(the_model, Hurdle):
                    if "feature_importances_" in the_model.__dir__():
                        importance_dict = dict(zip(x_names, the_model.feature_importances_))
                    else:
                        if isinstance(the_model.classifier, dummy_model1):
                            importance_dict = dict(zip(self.x_names, [1 / len(self.x_names)] * len(self.x_names)))
                        else:
                            importance_dict = dict(zip(x_names, the_model.classifier.feature_importances_))
                else:
                    importance_dict = dict(zip(x_names, the_model.feature_importances_))

                importance_dict["stixel_index"] = stixel_index
                feature_importance_list.append(importance_dict)

            except Exception as e:
                warnings.warn(f"{e}")
                continue

        if self.lazy_loading:
            self.model_dict.dump_ensemble(ensemble_id)

    self.feature_importances_ = (
        pd.DataFrame(feature_importance_list).set_index("stixel_index").reset_index(drop=False).fillna(0)
    )

eval_STEM_res(task, y_test, y_pred, cls_threshold=None) classmethod

Evaluation using multiple metrics

Classification metrics used: 1. AUC 2. Cohen's Kappa 3. F1 4. precision 5. recall 6. average precision

Regression metrics used: 1. spearman's r 2. peason's r 3. R2 4. mean absolute error (MAE) 5. mean squared error (MSE) 6. poisson deviance explained (PDE)

Parameters:

• task (str) –

  one of 'regression', 'classification' or 'hurdle'.

• y_test (Union[Series, ndarray]) –

  y true

• y_pred (Union[Series, ndarray]) –

  y predicted

• cls_threshold (Union[float, None], default: None ) –

  Cutting threshold for the classification. Values above cls_threshold will be labeled as 1 and 0 otherwise. Defaults to None (0.5 for classification and 0 for hurdle).

Raises:

• AttributeError –

  task not one of 'regression', 'classification' or 'hurdle'.

Returns:

• dict ( dict ) –

  dictionary containing the metric names and their values.

Source code in stemflow/model/AdaSTEM.py

@classmethod
def eval_STEM_res(
    self,
    task: str,
    y_test: Union[pd.core.series.Series, np.ndarray],
    y_pred: Union[pd.core.series.Series, np.ndarray],
    cls_threshold: Union[float, None] = None,
) -> dict:
    """Evaluation using multiple metrics

    Classification metrics used:
    1. AUC
    2. Cohen's Kappa
    3. F1
    4. precision
    5. recall
    6. average precision

    Regression metrics used:
    1. spearman's r
    2. peason's r
    3. R2
    4. mean absolute error (MAE)
    5. mean squared error (MSE)
    6. poisson deviance explained (PDE)

    Args:
        task (str):
            one of 'regression', 'classification' or 'hurdle'.
        y_test (Union[pd.core.series.Series, np.ndarray]):
            y true
        y_pred (Union[pd.core.series.Series, np.ndarray]):
            y predicted
        cls_threshold (Union[float, None], optional):
            Cutting threshold for the classification.
            Values above cls_threshold will be labeled as 1 and 0 otherwise.
            Defaults to None (0.5 for classification and 0 for hurdle).

    Raises:
        AttributeError: task not one of 'regression', 'classification' or 'hurdle'.

    Returns:
        dict: dictionary containing the metric names and their values.
    """

    if task not in ["regression", "classification", "hurdle"]:
        raise AttributeError(
            f"task type must be one of 'regression', 'classification', or 'hurdle'! Now it is {task}"
        )

    if cls_threshold is None:
        if task == "classification":
            cls_threshold = 0.5
        elif task == "hurdle":
            cls_threshold = 0

    if task == "regression":
        auc, kappa, f1, precision, recall, average_precision = [np.nan] * 6
    else:
        a = pd.DataFrame({"y_true": np.array(y_test).flatten(), "pred": np.array(y_pred).flatten()}).dropna()

        y_test_b = np.where(a.y_true > cls_threshold, 1, 0)
        y_pred_b = np.where(a.pred > cls_threshold, 1, 0)

        if len(np.unique(y_test_b)) == 1 and len(np.unique(y_pred_b)) == 1:
            auc, kappa, f1, precision, recall, average_precision = [np.nan] * 6

        else:
            auc = roc_auc_score(y_test_b, np.array(a.pred)) # AUC can be calculated with probability
            kappa = cohen_kappa_score(y_test_b, y_pred_b, weights='linear')
            f1 = f1_score(y_test_b, y_pred_b)
            precision = precision_score(y_test_b, y_pred_b)
            recall = recall_score(y_test_b, y_pred_b)
            average_precision = average_precision_score(y_test_b, y_pred_b)

    if not task == "classification":
        a = pd.DataFrame({"y_true": y_test, "pred": y_pred}).dropna()
        s_r, _ = spearmanr(np.array(a.y_true), np.array(a.pred))
        p_r, _ = pearsonr(np.array(a.y_true), np.array(a.pred))
        r2 = r2_score(a.y_true, a.pred)
        MAE = mean_absolute_error(a.y_true, a.pred)
        MSE = mean_squared_error(a.y_true, a.pred)
        try:
            poisson_deviance_explained = d2_tweedie_score(a[a.pred > 0].y_true, a[a.pred > 0].pred, power=1)
        except Exception as e:
            warnings.warn(f"PED estimation fail: {e}")
            poisson_deviance_explained = np.nan
    else:
        s_r, p_r, r2, MAE, MSE, poisson_deviance_explained = [np.nan] * 6

    return {
        "AUC": auc,
        "kappa": kappa,
        "f1": f1,
        "precision": precision,
        "recall": recall,
        "average_precision": average_precision,
        "Spearman_r": s_r,
        "Pearson_r": p_r,
        "R2": r2,
        "MAE": MAE,
        "MSE": MSE,
        "poisson_deviance_explained": poisson_deviance_explained,
    }

fit(X_train, y_train, verbosity=None, ax=None, n_jobs=None)

Fitting method

Parameters:

• X_train (DataFrame) –

  Training variables

• y_train (Union[DataFrame, ndarray]) –

  Training target

• ax –

  matplotlib Axes to add to

• verbosty –

  whether to show progress bar. 0 for no and 1 for yes.

• ax –

  matplotlib ax for adding grid plot on that.

• n_jobs (Union[None, int], default: None ) –

  multiprocessing thread count. Default the n_jobs of model object.

Raises:

• TypeError –

  X_train is not a type of pd.core.frame.DataFrame

• TypeError –

  y_train is not a type of np.ndarray or pd.core.frame.DataFrame

Source code in stemflow/model/AdaSTEM.py

def fit(
    self,
    X_train: pd.core.frame.DataFrame,
    y_train: Union[pd.core.frame.DataFrame, np.ndarray],
    verbosity: Union[None, int] = None,
    ax=None,
    n_jobs: Union[None, int] = None,
):
    """Fitting method

    Args:
        X_train: Training variables
        y_train: Training target
        ax: matplotlib Axes to add to
        verbosty: whether to show progress bar. 0 for no and 1 for yes.
        ax: matplotlib ax for adding grid plot on that.
        n_jobs: multiprocessing thread count. Default the n_jobs of model object.

    Raises:
        TypeError: X_train is not a type of pd.core.frame.DataFrame
        TypeError: y_train is not a type of np.ndarray or pd.core.frame.DataFrame
    """
    # setup random state
    self.rng = check_random_state(self.random_state)

    # Setup lazyloading dir
    if self.lazy_loading_dir is None:
        saving_code = ''.join(np.random.choice(list(string.ascii_letters + string.digits)) for _ in range(16))
        self.lazy_loading_dir = f'./stemflow_model_{saving_code}'
    else:
        if os.path.exists(self.lazy_loading_dir):
            shutil.rmtree(self.lazy_loading_dir)
    self.lazy_loading_dir = str(Path(self.lazy_loading_dir.rstrip('/\\')))

    # Setup joblib_temp_folder
    if self.joblib_temp_folder is None:
        pass
    elif self.joblib_temp_folder=='lazy_loading_dir':
        self.joblib_temp_folder = self.lazy_loading_dir
    else:
        if not os.path.exists(self.joblib_temp_folder):
            os.makedirs(self.joblib_temp_folder)

    verbosity = check_verbosity(self, verbosity)
    check_X_train(X_train)
    check_y_train(y_train)
    n_jobs = check_transform_n_jobs(self, n_jobs)
    self.store_x_names(X_train)

    # quadtree
    X_train = X_train.reset_index(drop=True)  # I reset index here!! caution! This step is important for the following indexing.
    X_train["true_y"] = np.array(y_train).flatten()
    self.split(X_train, verbosity=verbosity, ax=ax, n_jobs=n_jobs)

    # define model dict
    self.model_dict = {}
    # stixel specific x_names list
    self.stixel_specific_x_names = {}

    self.SAC_training(self.ensemble_df, X_train, verbosity, n_jobs)
    self.classes_ = np.unique(y_train)

    return self

predict_proba(X_test, verbosity=None, return_std=False, n_jobs=None, aggregation='mean', return_by_separate_ensembles=False, logit_agg=False, base_model_method=None, **base_model_prediction_param)

Predict probability

Parameters:

• X_test (DataFrame) –

  Testing variables.

• verbosity (int, default: None ) –

  show progress bar or not. Yes for 0, and No for other. Defaults to None, which set it as the verbosity of the main model class.

• return_std (bool, default: False ) –

  Whether return the standard deviation among ensembles. Defaults to False.

• n_jobs (Union[int, None], default: None ) –

  Number of processes used in this task. If None, use the self.n_jobs. Default to 1. I do not recommend setting value larger than 1. In practice, multi-processing seems to slow down the process instead of speeding up. Could be more practical with large amount of data. Still in experiment.

• aggregation (str, default: 'mean' ) –

  'mean' or 'median' for aggregation method across ensembles.

• return_by_separate_ensembles (bool, default: False ) –

  Experimental function. return not by aggregation, but by separate ensembles.

• logit_agg (bool, default: False ) –

  Whether to use logit aggregation for the classification task. Most likely only used when you are predicting "real" calibrated probability. If True, the model is averaging the probability prediction estimated by all ensembles in logit scale, and then back-tranforms it to probability scale. It's recommended to be jointly used with the CalibratedClassifierCV class in sklearn as a wrapper of the classifier to estimate the calibrated probability. Default is False, but can be set to true for "real" probability averaging.

• base_model_method (Union[None, str], default: None ) –

  The name of the prediction method for base models. If None, predict or predict_proba will be used depending on the tasks. This argument is handy if you have a custom base model class that has a special prediction function. Notice that dummy model will still predict 0, so the ensemble-aggregated result is still an average of zeros and your special prediction function output. Therefore, it may only make sense if your special prediction function predicts 0 as the absense/control value. Defaults to None.

• base_model_prediction_param –

  Any other paramters to pass into the prediction method of the base models. e.g., base_model_prediction_param={'n_jobs':1}.

Raises: TypeError: X_test is not of type pd.core.frame.DataFrame. ValueError: aggregation is not in ['mean','median'].

Returns:

• Union[ndarray, Tuple[ndarray]] –

  predicted results. (pred_mean, pred_std) if return_std==true, and pred_mean if return_std==False.

• Union[ndarray, Tuple[ndarray]] –

  If return_by_separate_ensembles == True: Return numpy.ndarray of shape (n_samples, n_ensembles)

Source code in stemflow/model/AdaSTEM.py

def predict_proba(
    self,
    X_test: pd.core.frame.DataFrame,
    verbosity: Union[int, None] = None,
    return_std: bool = False,
    n_jobs: Union[None, int] = None,
    aggregation: str = "mean",
    return_by_separate_ensembles: bool = False,
    logit_agg: bool = False,
    base_model_method: Union[None, str] = None,
    **base_model_prediction_param
) -> Union[np.ndarray, Tuple[np.ndarray]]:
    """Predict probability

    Args:
        X_test (pd.core.frame.DataFrame):
            Testing variables.
        verbosity (int, optional):
            show progress bar or not. Yes for 0, and No for other. Defaults to None, which set it as the verbosity of the main model class.
        return_std (bool, optional):
            Whether return the standard deviation among ensembles. Defaults to False.
        n_jobs (Union[int, None], optional):
            Number of processes used in this task. If None, use the self.n_jobs. Default to 1.
            I do not recommend setting value larger than 1.
            In practice, multi-processing seems to slow down the process instead of speeding up.
            Could be more practical with large amount of data.
            Still in experiment.
        aggregation (str, optional):
            'mean' or 'median' for aggregation method across ensembles.
        return_by_separate_ensembles (bool, optional):
            Experimental function. return not by aggregation, but by separate ensembles.
        logit_agg:
            Whether to use logit aggregation for the classification task. Most likely only used when you are predicting "real" calibrated probability. If True, the model is averaging the probability prediction estimated by all ensembles in logit scale, and then back-tranforms it to probability scale. It's recommended to be jointly used with the CalibratedClassifierCV class in sklearn as a wrapper of the classifier to estimate the calibrated probability. Default is False, but can be set to true for "real" probability averaging.
        base_model_method:
            The name of the prediction method for base models. If None, `predict` or `predict_proba` will be used depending on the tasks. This argument is handy if you have a custom base model class that has a special prediction function. Notice that dummy model will still predict 0, so the ensemble-aggregated result is still an average of zeros and your special prediction function output. Therefore, it may only make sense if your special prediction function predicts 0 as the absense/control value. Defaults to None.
        base_model_prediction_param:
            Any other paramters to pass into the prediction method of the base models. e.g., base_model_prediction_param={'n_jobs':1}.
    Raises:
        TypeError:
            X_test is not of type pd.core.frame.DataFrame.
        ValueError:
            aggregation is not in ['mean','median'].

    Returns:
        predicted results. (pred_mean, pred_std) if return_std==true, and pred_mean if return_std==False.

        If return_by_separate_ensembles == True:
            Return numpy.ndarray of shape (n_samples, n_ensembles)

    """
    check_X_test(X_test)
    check_prediciton_aggregation(aggregation)
    return_by_separate_ensembles, return_std = check_prediction_return(return_by_separate_ensembles, return_std)
    verbosity = check_verbosity(self, verbosity)
    n_jobs = check_transform_n_jobs(self, n_jobs)
    self.base_model_method = base_model_method
    self.base_model_prediction_param = base_model_prediction_param

    # predict
    res = self.SAC_predict(self.ensemble_df, X_test, verbosity=verbosity, n_jobs=n_jobs)

    # Experimental Function
    if return_by_separate_ensembles:
        new_res = pd.DataFrame({"index": list(X_test.index)}).set_index("index")
        new_res = new_res.merge(res, left_on="index", right_on="index", how="left")
        return new_res.values

    # Transform to logit space if classification:
    if self.task=='classification' and logit_agg:
        for col_index in range(res.shape[1]):
            prob = np.clip(res.iloc[:,col_index], 1e-6, 1 - 1e-6)
            res.iloc[:,col_index] = np.log(prob / (1-prob)) # logit space

        # Aggregate
        if aggregation == "mean":
            res_mean = res.mean(axis=1, skipna=True)  # mean of all grid model that predicts this stixel
        elif aggregation == "median":
            res_mean = res.median(axis=1, skipna=True)

         # Transform to logit space if classification:
        res_mean = 1/(1+np.exp(-res_mean)) # notice that the res_std is not transformed!
        res_mean = res_mean.where(res_mean<=1e-6, 0)

    else:
        # don't need to aggregate at logit scale
        # Aggregate
        if aggregation == "mean":
            res_mean = res.mean(axis=1, skipna=True)  # mean of all grid model that predicts this stixel
        elif aggregation == "median":
            res_mean = res.median(axis=1, skipna=True)

    res_std = res.std(axis=1, skipna=True)

    # Nan count
    res_nan_count = res.isnull().sum(axis=1)
    pred_mean = np.where(
        self.ensemble_fold - res_nan_count.values >= self.min_ensemble_required, res_mean.values, np.nan
    )
    pred_std = np.where(
        self.ensemble_fold - res_nan_count.values >= self.min_ensemble_required, res_std.values, np.nan
    )

    res = pd.DataFrame({"index": list(res_mean.index), "pred_mean": pred_mean, "pred_std": pred_std}).set_index(
        "index"
    )

    # Preparing output (formatting)
    new_res = pd.DataFrame({"index": list(X_test.index)}).set_index("index")
    new_res = new_res.merge(res, left_on="index", right_on="index", how="left")

    nan_count = np.sum(np.isnan(new_res["pred_mean"].values))
    nan_frac = nan_count / len(new_res["pred_mean"].values)
    warnings.warn(f"There are {nan_frac}% points ({nan_count} points) falling out of predictable range.")

    if return_std:
        if self.task=='classification':
            return np.array([1-new_res["pred_mean"].values.flatten(), new_res["pred_mean"].values.flatten()]).T, new_res["pred_std"].values
        else:
            return new_res["pred_mean"].values.flatten(), new_res["pred_std"].values.flatten()
    else:
        if self.task=='classification':
            return np.array([1-new_res["pred_mean"].values.flatten(), new_res["pred_mean"].values.flatten()]).T
        else:
            return new_res["pred_mean"].values.flatten()

score(X_test, y_test)

Combine predicting and evaluating in one method

Parameters:

• X_test (DataFrame) –

  Testing variables

• y_test (Union[Series, ndarray]) –

  y true

Returns:

• dict ( dict ) –

  dictionary containing the metric names and their values.

Source code in stemflow/model/AdaSTEM.py

def score(self, X_test: pd.core.frame.DataFrame, y_test: Union[pd.core.series.Series, np.ndarray]) -> dict:
    """Combine predicting and evaluating in one method

    Args:
        X_test (pd.core.frame.DataFrame): Testing variables
        y_test (Union[pd.core.series.Series, np.ndarray]): y true

    Returns:
        dict: dictionary containing the metric names and their values.
    """

    y_pred = self.predict(X_test)
    score_dict = AdaSTEM.eval_STEM_res(self.task, np.array(y_test).flatten(), np.array(y_pred).flatten())
    self.score_dict = score_dict
    return self.score_dict

split(X_train, verbosity=None, ax=None, n_jobs=None)

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:

• –

  self.grid_dict, a dictionary of one DataFrame for each grid, containing the gridding information

Source code in stemflow/model/AdaSTEM.py

def split(self, X_train: pd.core.frame.DataFrame, verbosity: Union[None, int] = None, ax=None, n_jobs: Union[None, int] = None):
    """QuadTree indexing the input data

    Args:
        X_train: Input training data
        verbosity: 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:
        self.grid_dict, a dictionary of one DataFrame for each grid, containing the gridding information
    """
    self.rng = check_random_state(self.random_state)
    n_jobs = check_transform_n_jobs(self, n_jobs)

    if verbosity is None:
        verbosity = self.verbosity

    # Determine grid_len based on conditions
    if "grid_len" not in self.__dir__():
        # We are using AdaSTEM
        self.grid_len = None
        grid_len_upper = self.grid_len_upper_threshold
        grid_len_lower = self.grid_len_lower_threshold
    elif self.grid_len is None:
        # AdaSTEM with predefined thresholds
        grid_len_upper = self.grid_len_upper_threshold
        grid_len_lower = self.grid_len_lower_threshold
    else:
        # We are using STEM
        grid_len_upper = self.grid_len
        grid_len_lower = self.grid_len

    # Call spatial and temporal scale checks
    check_spatial_scale(
        X_train[self.Spatio1].min(),
        X_train[self.Spatio1].max(),
        X_train[self.Spatio2].min(),
        X_train[self.Spatio2].max(),
        grid_len_upper,
        grid_len_lower,
    )

    check_temporal_scale(X_train[self.Temporal1].min(), X_train[self.Temporal1].min(), self.temporal_bin_interval)

    spatio_bin_jitter_magnitude = check_transform_spatio_bin_jitter_magnitude(
        X_train, self.Spatio1, self.Spatio2, self.spatio_bin_jitter_magnitude
    )

    if self.save_gridding_plot:
        if self.plot_xlims is None:
            self.plot_xlims = (X_train[self.Spatio1].min(), X_train[self.Spatio1].max())
        if self.plot_ylims is None:
            self.plot_ylims = (X_train[self.Spatio2].min(), X_train[self.Spatio2].max())

        if ax is None:
            plt.figure(figsize=(20, 20))
            plt.xlim([self.plot_xlims[0], self.plot_xlims[1]])
            plt.ylim([self.plot_ylims[0], self.plot_ylims[1]])
            plt.title("Quadtree", fontsize=20)
        else:
            pass

    X_train_indexes = X_train[[self.Temporal1, self.Spatio1, self.Spatio2]]

    partial_get_one_ensemble_quadtree = partial(
        get_one_ensemble_quadtree,
        size=self.ensemble_fold,
        spatio_bin_jitter_magnitude=spatio_bin_jitter_magnitude,
        temporal_start=self.temporal_start,
        temporal_end=self.temporal_end,
        temporal_step=self.temporal_step,
        temporal_bin_interval=self.temporal_bin_interval,
        temporal_bin_start_jitter=self.temporal_bin_start_jitter,
        data=X_train_indexes,
        Temporal1=self.Temporal1,
        grid_len=self.grid_len,
        grid_len_lon_upper_threshold=self.grid_len_upper_threshold,
        grid_len_lon_lower_threshold=self.grid_len_lower_threshold,
        grid_len_lat_upper_threshold=self.grid_len_upper_threshold,
        grid_len_lat_lower_threshold=self.grid_len_lower_threshold,
        points_lower_threshold=self.points_lower_threshold,
        plot_empty=self.plot_empty,
        Spatio1=self.Spatio1,
        Spatio2=self.Spatio2,
        save_gridding_plot=self.save_gridding_plot,
        ax=ax,
        completely_random_rotation=self.completely_random_rotation,
        ensemble_bootstrap=self.ensemble_bootstrap
    )

    if n_jobs > 1 and isinstance(n_jobs, int):
        parallel = joblib.Parallel(n_jobs=n_jobs, return_as="generator", backend=self.joblib_backend, temp_folder=self.joblib_temp_folder)
        output_generator = parallel(
            joblib.delayed(partial_get_one_ensemble_quadtree)(
                ensemble_count=ensemble_count, rng=np.random.default_rng(self.rng.integers(1e9) + ensemble_count)
            )
            for ensemble_count in list(range(self.ensemble_fold))
        )
        if verbosity > 0:
            output_generator = tqdm(output_generator, total=self.ensemble_fold, desc="Generating Ensembles: ")

        ensemble_all_df_list = [i for i in output_generator]

    else:
        iter_func_ = (
            tqdm(range(self.ensemble_fold), total=self.ensemble_fold, desc="Generating Ensembles: ")
            if verbosity > 0
            else range(self.ensemble_fold)
        )
        ensemble_all_df_list = [
            partial_get_one_ensemble_quadtree(
                ensemble_count=ensemble_count, rng=np.random.default_rng(self.rng.integers(1e9) + ensemble_count)
            )
            for ensemble_count in iter_func_
        ]

    # concat
    ensemble_df = pd.concat(ensemble_all_df_list).reset_index(drop=True)

    del ensemble_all_df_list

    # processing
    ensemble_df = ensemble_df.reset_index(drop=True)

    if self.save_gridding_plot:
        if ax is None:
            plt.tight_layout()
            plt.gca().set_aspect("equal")
            ax = plt.gcf()
            plt.close()

        else:
            pass

        self.ensemble_df, self.gridding_plot = ensemble_df, ax

    else:
        self.ensemble_df, self.gridding_plot = ensemble_df, np.nan

stixel_fitting(stixel)

A sub module of SAC training. Fit one stixel

Parameters:

• stixel (DataFrame) –

  data sjoined with ensemble_df.

Source code in stemflow/model/AdaSTEM.py

def stixel_fitting(self, stixel):
    """A sub module of SAC training. Fit one stixel

    Args:
        stixel (pd.core.frame.DataFrame): data sjoined with ensemble_df.
        For a single stixel.
    """

    unique_stixel_id = stixel["unique_stixel_id"].iloc[0]
    name = unique_stixel_id

    model, stixel_specific_x_names, status = train_one_stixel(
        stixel_training_size_threshold=self.stixel_training_size_threshold,
        x_names=self.x_names,
        task=self.task,
        base_model=self.base_model,
        sample_weights_for_classifier=self.sample_weights_for_classifier,
        subset_x_names=self.subset_x_names,
        stixel_X_train=stixel,
        min_class_sample=self.min_class_sample
    )

    if not status == "Success":
        # print(f'Fitting: {ensemble_index}. Not pass: {status}')
        pass

    else:
        return (name, model, stixel_specific_x_names)

stixel_predict(stixel)

A sub module of SAC prediction. Predict one stixel

Parameters:

• stixel (DataFrame) –

  data joined with ensemble_df.

Returns:

• Union[None, DataFrame] –

  pd.core.frame.DataFrame: the prediction result of this stixel

Source code in stemflow/model/AdaSTEM.py

def stixel_predict(self, stixel: pd.core.frame.DataFrame) -> Union[None, pd.core.frame.DataFrame]:
    """A sub module of SAC prediction. Predict one stixel

    Args:
        stixel (pd.core.frame.DataFrame): data joined with ensemble_df.
        For a single stixel.

    Returns:
        pd.core.frame.DataFrame: the prediction result of this stixel
    """

    stixel['unique_stixel_id'] = stixel.name
    unique_stixel_id = stixel["unique_stixel_id"].iloc[0]

    model_x_names_tuple = get_model_and_stixel_specific_x_names(
        self.model_dict,
        unique_stixel_id,
        self.stixel_specific_x_names,
        self.x_names,
    )

    if model_x_names_tuple[0] is None:
        return None

    pred = predict_one_stixel(X_test_stixel=stixel,
                              task=self.task,
                              model_x_names_tuple=model_x_names_tuple,
                              base_model_method=self.base_model_method,
                              **self.base_model_prediction_param)

    if pred is None:
        return None
    else:
        return pred

store_x_names(X_train)

Store the training variables

Parameters:

• X_train (DataFrame) –

  input training data.

Source code in stemflow/model/AdaSTEM.py

def store_x_names(self, X_train: pd.core.frame.DataFrame):
    """Store the training variables

    Args:
        X_train (pd.core.frame.DataFrame): input training data.
    """
    # store x_names
    self.x_names = list(X_train.columns)
    if not self.use_temporal_to_train:
        if self.Temporal1 in list(self.x_names):
            del self.x_names[self.x_names.index(self.Temporal1)]

    for i in [self.Spatio1, self.Spatio2]:
        if i in self.x_names:
            del self.x_names[self.x_names.index(i)]

AdaSTEMClassifier

Bases: AdaSTEM

AdaSTEM model Classifier interface

Example

>>> from stemflow.model.AdaSTEM import AdaSTEMClassifier
>>> from xgboost import XGBClassifier
>>> model = 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_upper_threshold=25,
                        grid_len_lower_threshold=5,
                        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/AdaSTEM.py

class AdaSTEMClassifier(AdaSTEM):
    """AdaSTEM model Classifier interface

    Example:
        ```
        >>> from stemflow.model.AdaSTEM import AdaSTEMClassifier
        >>> from xgboost import XGBClassifier
        >>> model = 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_upper_threshold=25,
                                grid_len_lower_threshold=5,
                                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)
        ```

    """

    def __init__(
        self,
        base_model,
        task="classification",
        ensemble_fold=10,
        min_ensemble_required=7,
        grid_len_upper_threshold=25,
        grid_len_lower_threshold=5,
        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=False,
        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=None,
        plot_ylims=None,
        verbosity=0,
        plot_empty=False,
        completely_random_rotation=False,
        lazy_loading = False,
        lazy_loading_dir = None,
        min_class_sample = 1,
        ensemble_bootstrap = False,
        joblib_backend = 'loky',
        joblib_temp_folder = None
    ):
        super().__init__(
            base_model=base_model,
            task=task,
            ensemble_fold=ensemble_fold,
            min_ensemble_required=min_ensemble_required,
            grid_len_upper_threshold=grid_len_upper_threshold,
            grid_len_lower_threshold=grid_len_lower_threshold,
            points_lower_threshold=points_lower_threshold,
            stixel_training_size_threshold=stixel_training_size_threshold,
            temporal_start=temporal_start,
            temporal_end=temporal_end,
            temporal_step=temporal_step,
            temporal_bin_interval=temporal_bin_interval,
            temporal_bin_start_jitter=temporal_bin_start_jitter,
            spatio_bin_jitter_magnitude=spatio_bin_jitter_magnitude,
            random_state=random_state,
            save_gridding_plot=save_gridding_plot,
            sample_weights_for_classifier=sample_weights_for_classifier,
            Spatio1=Spatio1,
            Spatio2=Spatio2,
            Temporal1=Temporal1,
            use_temporal_to_train=use_temporal_to_train,
            n_jobs=n_jobs,
            subset_x_names=subset_x_names,
            plot_xlims=plot_xlims,
            plot_ylims=plot_ylims,
            verbosity=verbosity,
            plot_empty=plot_empty,
            completely_random_rotation=completely_random_rotation,
            lazy_loading=lazy_loading,
            lazy_loading_dir=lazy_loading_dir,
            min_class_sample=min_class_sample,
            ensemble_bootstrap=ensemble_bootstrap,
            joblib_backend=joblib_backend,
            joblib_temp_folder = joblib_temp_folder
        )

        self._estimator_type = 'classifier'

    def predict(
        self,
        X_test: pd.core.frame.DataFrame,
        verbosity: Union[None, int] = None,
        return_std: bool = False,
        cls_threshold: float = 0.5,
        n_jobs: Union[int, None] = 1,
        aggregation: str = "mean",
        return_by_separate_ensembles: bool = False,
        logit_agg: bool = False,
        base_model_method: Union[None, str] = None,
        **base_model_prediction_param
    ) -> Union[np.ndarray, Tuple[np.ndarray]]:
        """A rewrite of predict_proba adapted for Classifier

        Args:
            X_test (pd.core.frame.DataFrame):
                Testing variables.
            verbosity (int, optional):
                0 to output nothing, everything other wise. Default None set it to the verbosity of AdaSTEM model class.
            return_std (bool, optional):
                Whether return the standard deviation among ensembles. Defaults to False.
            cls_threshold (float, optional):
                Cutting threshold for the classification.
                Values above cls_threshold will be labeled as 1 and 0 otherwise.
                Defaults to 0.5.
            n_jobs (Union[int, None], optional):
                Number of processes used in this task. If None, use the self.n_jobs. Default to 1.
                I do not recommend setting value larger than 1.
                In practice, multi-processing seems to slow down the process instead of speeding up.
                Could be more practical with large amount of data.
                Still in experiment.
            aggregation (str, optional):
                'mean' or 'median' for aggregation method across ensembles.
            return_by_separate_ensembles (bool, optional):
                Experimental function. return not by aggregation, but by separate ensembles.
            logit_agg:
                Whether to use logit aggregation for the classification task. If True, the model is averaging the probability prediction estimated by all ensembles in logit scale, and then back-tranform it to probability scale. It's recommened to be combinedly used with the CalibratedClassifierCV class in sklearn as a wrapper of the classifier to estimate the calibrated probability.
            base_model_method:
                The name of the prediction method for base models. If None, `predict` or `predict_proba` will be used depending on the tasks. This argument is handy if you have a custom base model class that has a special prediction function. Defaults to None.
            base_model_prediction_param:
                Any other paramters to pass into the prediction method of the base models. e.g., base_model_prediction_param={'n_jobs':1}.
        Raises:
            TypeError:
                X_test is not of type pd.core.frame.DataFrame.
            ValueError:
                aggregation is not in ['mean','median'].

        Returns:
            predicted results. (pred_mean, pred_std) if return_std==true, and pred_mean if return_std==False.

        """
        if return_by_separate_ensembles!=False:
            raise AttributeError('If you want to return by separate ensembles in this classifier, use it in .predict_proba, instead of .predict.')

        if return_std:
            mean, std = self.predict_proba(
                X_test,
                verbosity=verbosity,
                return_std=True,
                n_jobs=n_jobs,
                aggregation=aggregation,
                return_by_separate_ensembles=return_by_separate_ensembles,
                logit_agg=logit_agg,
                base_model_method=base_model_method,
                **base_model_prediction_param
            )
            mean = mean[:,1].flatten()
            mean = np.where(mean < cls_threshold, 0, mean)
            mean = np.where(mean >= cls_threshold, 1, mean)
            warnings.warn('This is a classification task. The standard deviation of the prediction is output at logit scale! The mean prediction is output at probability scale.')
            return mean, std # notice! the std
        else:
            mean = self.predict_proba(
                X_test,
                verbosity=verbosity,
                return_std=False,
                n_jobs=n_jobs,
                aggregation=aggregation,
                return_by_separate_ensembles=return_by_separate_ensembles,
                logit_agg=logit_agg,
                base_model_method=base_model_method,
                **base_model_prediction_param
            )
            mean = mean[:,1].flatten()
            mean = np.where(mean < cls_threshold, 0, mean)
            mean = np.where(mean >= cls_threshold, 1, mean)
            return mean

predict(X_test, verbosity=None, return_std=False, cls_threshold=0.5, n_jobs=1, aggregation='mean', return_by_separate_ensembles=False, logit_agg=False, base_model_method=None, **base_model_prediction_param)

A rewrite of predict_proba adapted for Classifier

Parameters:

• X_test (DataFrame) –

  Testing variables.

• verbosity (int, default: None ) –

  0 to output nothing, everything other wise. Default None set it to the verbosity of AdaSTEM model class.

• return_std (bool, default: False ) –

  Whether return the standard deviation among ensembles. Defaults to False.

• cls_threshold (float, default: 0.5 ) –

  Cutting threshold for the classification. Values above cls_threshold will be labeled as 1 and 0 otherwise. Defaults to 0.5.

• n_jobs (Union[int, None], default: 1 ) –

  Number of processes used in this task. If None, use the self.n_jobs. Default to 1. I do not recommend setting value larger than 1. In practice, multi-processing seems to slow down the process instead of speeding up. Could be more practical with large amount of data. Still in experiment.

• aggregation (str, default: 'mean' ) –

  'mean' or 'median' for aggregation method across ensembles.

• return_by_separate_ensembles (bool, default: False ) –

  Experimental function. return not by aggregation, but by separate ensembles.

• logit_agg (bool, default: False ) –

  Whether to use logit aggregation for the classification task. If True, the model is averaging the probability prediction estimated by all ensembles in logit scale, and then back-tranform it to probability scale. It's recommened to be combinedly used with the CalibratedClassifierCV class in sklearn as a wrapper of the classifier to estimate the calibrated probability.

• base_model_method (Union[None, str], default: None ) –

  The name of the prediction method for base models. If None, predict or predict_proba will be used depending on the tasks. This argument is handy if you have a custom base model class that has a special prediction function. Defaults to None.

• base_model_prediction_param –

  Any other paramters to pass into the prediction method of the base models. e.g., base_model_prediction_param={'n_jobs':1}.

Raises: TypeError: X_test is not of type pd.core.frame.DataFrame. ValueError: aggregation is not in ['mean','median'].

Returns:

• Union[ndarray, Tuple[ndarray]] –

  predicted results. (pred_mean, pred_std) if return_std==true, and pred_mean if return_std==False.

Source code in stemflow/model/AdaSTEM.py

def predict(
    self,
    X_test: pd.core.frame.DataFrame,
    verbosity: Union[None, int] = None,
    return_std: bool = False,
    cls_threshold: float = 0.5,
    n_jobs: Union[int, None] = 1,
    aggregation: str = "mean",
    return_by_separate_ensembles: bool = False,
    logit_agg: bool = False,
    base_model_method: Union[None, str] = None,
    **base_model_prediction_param
) -> Union[np.ndarray, Tuple[np.ndarray]]:
    """A rewrite of predict_proba adapted for Classifier

    Args:
        X_test (pd.core.frame.DataFrame):
            Testing variables.
        verbosity (int, optional):
            0 to output nothing, everything other wise. Default None set it to the verbosity of AdaSTEM model class.
        return_std (bool, optional):
            Whether return the standard deviation among ensembles. Defaults to False.
        cls_threshold (float, optional):
            Cutting threshold for the classification.
            Values above cls_threshold will be labeled as 1 and 0 otherwise.
            Defaults to 0.5.
        n_jobs (Union[int, None], optional):
            Number of processes used in this task. If None, use the self.n_jobs. Default to 1.
            I do not recommend setting value larger than 1.
            In practice, multi-processing seems to slow down the process instead of speeding up.
            Could be more practical with large amount of data.
            Still in experiment.
        aggregation (str, optional):
            'mean' or 'median' for aggregation method across ensembles.
        return_by_separate_ensembles (bool, optional):
            Experimental function. return not by aggregation, but by separate ensembles.
        logit_agg:
            Whether to use logit aggregation for the classification task. If True, the model is averaging the probability prediction estimated by all ensembles in logit scale, and then back-tranform it to probability scale. It's recommened to be combinedly used with the CalibratedClassifierCV class in sklearn as a wrapper of the classifier to estimate the calibrated probability.
        base_model_method:
            The name of the prediction method for base models. If None, `predict` or `predict_proba` will be used depending on the tasks. This argument is handy if you have a custom base model class that has a special prediction function. Defaults to None.
        base_model_prediction_param:
            Any other paramters to pass into the prediction method of the base models. e.g., base_model_prediction_param={'n_jobs':1}.
    Raises:
        TypeError:
            X_test is not of type pd.core.frame.DataFrame.
        ValueError:
            aggregation is not in ['mean','median'].

    Returns:
        predicted results. (pred_mean, pred_std) if return_std==true, and pred_mean if return_std==False.

    """
    if return_by_separate_ensembles!=False:
        raise AttributeError('If you want to return by separate ensembles in this classifier, use it in .predict_proba, instead of .predict.')

    if return_std:
        mean, std = self.predict_proba(
            X_test,
            verbosity=verbosity,
            return_std=True,
            n_jobs=n_jobs,
            aggregation=aggregation,
            return_by_separate_ensembles=return_by_separate_ensembles,
            logit_agg=logit_agg,
            base_model_method=base_model_method,
            **base_model_prediction_param
        )
        mean = mean[:,1].flatten()
        mean = np.where(mean < cls_threshold, 0, mean)
        mean = np.where(mean >= cls_threshold, 1, mean)
        warnings.warn('This is a classification task. The standard deviation of the prediction is output at logit scale! The mean prediction is output at probability scale.')
        return mean, std # notice! the std
    else:
        mean = self.predict_proba(
            X_test,
            verbosity=verbosity,
            return_std=False,
            n_jobs=n_jobs,
            aggregation=aggregation,
            return_by_separate_ensembles=return_by_separate_ensembles,
            logit_agg=logit_agg,
            base_model_method=base_model_method,
            **base_model_prediction_param
        )
        mean = mean[:,1].flatten()
        mean = np.where(mean < cls_threshold, 0, mean)
        mean = np.where(mean >= cls_threshold, 1, mean)
        return mean

AdaSTEMRegressor

Bases: AdaSTEM

AdaSTEM model Regressor interface

Example:

>>> from stemflow.model.AdaSTEM import AdaSTEMRegressor
>>> from xgboost import XGBRegressor
>>> model = 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_upper_threshold=25,
                        grid_len_lower_threshold=5,
                        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/AdaSTEM.py

class AdaSTEMRegressor(AdaSTEM):
    """AdaSTEM model Regressor interface

    Example:
    ```
    >>> from stemflow.model.AdaSTEM import AdaSTEMRegressor
    >>> from xgboost import XGBRegressor
    >>> model = 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_upper_threshold=25,
                            grid_len_lower_threshold=5,
                            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)
    ```

    """

    def __init__(
        self,
        base_model,
        task="regression",
        ensemble_fold=10,
        min_ensemble_required=7,
        grid_len_upper_threshold=25,
        grid_len_lower_threshold=5,
        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=False,
        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=None,
        plot_ylims=None,
        verbosity=0,
        plot_empty=False,
        completely_random_rotation=False,
        lazy_loading=False,
        lazy_loading_dir=None,
        min_class_sample=1,
        ensemble_bootstrap=False,
        joblib_backend='loky',
        joblib_temp_folder=None
    ):
        super().__init__(
            base_model=base_model,
            task=task,
            ensemble_fold=ensemble_fold,
            min_ensemble_required=min_ensemble_required,
            grid_len_upper_threshold=grid_len_upper_threshold,
            grid_len_lower_threshold=grid_len_lower_threshold,
            points_lower_threshold=points_lower_threshold,
            stixel_training_size_threshold=stixel_training_size_threshold,
            temporal_start=temporal_start,
            temporal_end=temporal_end,
            temporal_step=temporal_step,
            temporal_bin_interval=temporal_bin_interval,
            temporal_bin_start_jitter=temporal_bin_start_jitter,
            spatio_bin_jitter_magnitude=spatio_bin_jitter_magnitude,
            random_state=random_state,
            save_gridding_plot=save_gridding_plot,
            sample_weights_for_classifier=sample_weights_for_classifier,
            Spatio1=Spatio1,
            Spatio2=Spatio2,
            Temporal1=Temporal1,
            use_temporal_to_train=use_temporal_to_train,
            n_jobs=n_jobs,
            subset_x_names=subset_x_names,
            plot_xlims=plot_xlims,
            plot_ylims=plot_ylims,
            verbosity=verbosity,
            plot_empty=plot_empty,
            completely_random_rotation=completely_random_rotation,
            lazy_loading=lazy_loading,
            lazy_loading_dir=lazy_loading_dir,
            min_class_sample=min_class_sample,
            ensemble_bootstrap=ensemble_bootstrap,
            joblib_backend=joblib_backend,
            joblib_temp_folder=joblib_temp_folder
        )

        self._estimator_type = 'regressor'


    def predict(
        self,
        X_test: pd.core.frame.DataFrame,
        verbosity: Union[None, int] = None,
        return_std: bool = False,
        n_jobs: Union[None, int] = 1,
        aggregation: str = "mean",
        return_by_separate_ensembles: bool = False,
        base_model_method: Union[None, str] = None,
        **base_model_prediction_param
    ) -> Union[np.ndarray, Tuple[np.ndarray]]:
        """A rewrite of predict_proba

        Args:
            X_test (pd.core.frame.DataFrame):
                Testing variables.
            verbosity (Union[None, int], optional):
                0 to output nothing, everything other wise. Default None set it to the verbosity of AdaSTEM model class.
            return_std (bool, optional):
                Whether return the standard deviation among ensembles. Defaults to False.
            n_jobs (Union[int, None], optional):
                Number of processes used in this task. If None, use the self.n_jobs. Default to 1.
                I do not recommend setting value larger than 1.
                In practice, multi-processing seems to slow down the process instead of speeding up.
                Could be more practical with large amount of data.
                Still in experiment.
            aggregation (str, optional):
                'mean' or 'median' for aggregation method across ensembles.
            return_by_separate_ensembles (bool, optional):
                Experimental function. return not by aggregation, but by separate ensembles.
            base_model_method:
                The name of the prediction method for base models. If None, `predict` or `predict_proba` will be used depending on the tasks. This argument is handy if you have a custom base model class that has a special prediction function. Defaults to None.
            base_model_prediction_param:
                Any other paramters to pass into the prediction method of the base models. e.g., base_model_prediction_param={'n_jobs':1}.

        Raises:
            TypeError:
                X_test is not of type pd.core.frame.DataFrame.
            ValueError:
                aggregation is not in ['mean','median'].

        Returns:
            predicted results. (pred_mean, pred_std) if return_std==true, and pred_mean if return_std==False.

            If return_by_separate_ensembles == True:
                Return numpy.ndarray of shape (n_samples, n_ensembles)

        """

        prediciton = self.predict_proba(
            X_test,
            verbosity=verbosity,
            return_std=return_std,
            n_jobs=n_jobs,
            aggregation=aggregation,
            return_by_separate_ensembles=return_by_separate_ensembles,
            base_model_method = base_model_method,
            **base_model_prediction_param
        )

        # if return_by_separate_ensembles, this will be the dataframe for ensemble
        # if return_std, this wil be a tuple of mean and std of prediction
        # if none of these, then it ill output the mean prediction
        return prediciton

predict(X_test, verbosity=None, return_std=False, n_jobs=1, aggregation='mean', return_by_separate_ensembles=False, base_model_method=None, **base_model_prediction_param)

A rewrite of predict_proba

Parameters:

• X_test (DataFrame) –

  Testing variables.

• verbosity (Union[None, int], default: None ) –

  0 to output nothing, everything other wise. Default None set it to the verbosity of AdaSTEM model class.

• return_std (bool, default: False ) –

  Whether return the standard deviation among ensembles. Defaults to False.

• n_jobs (Union[int, None], default: 1 ) –

  Number of processes used in this task. If None, use the self.n_jobs. Default to 1. I do not recommend setting value larger than 1. In practice, multi-processing seems to slow down the process instead of speeding up. Could be more practical with large amount of data. Still in experiment.

• aggregation (str, default: 'mean' ) –

  'mean' or 'median' for aggregation method across ensembles.

• return_by_separate_ensembles (bool, default: False ) –

  Experimental function. return not by aggregation, but by separate ensembles.

• base_model_method (Union[None, str], default: None ) –

  The name of the prediction method for base models. If None, predict or predict_proba will be used depending on the tasks. This argument is handy if you have a custom base model class that has a special prediction function. Defaults to None.

• base_model_prediction_param –

  Any other paramters to pass into the prediction method of the base models. e.g., base_model_prediction_param={'n_jobs':1}.

Raises:

• TypeError –

  X_test is not of type pd.core.frame.DataFrame.

• ValueError –

  aggregation is not in ['mean','median'].

Returns:

• Union[ndarray, Tuple[ndarray]] –

  predicted results. (pred_mean, pred_std) if return_std==true, and pred_mean if return_std==False.

• Union[ndarray, Tuple[ndarray]] –

  If return_by_separate_ensembles == True: Return numpy.ndarray of shape (n_samples, n_ensembles)

Source code in stemflow/model/AdaSTEM.py

def predict(
    self,
    X_test: pd.core.frame.DataFrame,
    verbosity: Union[None, int] = None,
    return_std: bool = False,
    n_jobs: Union[None, int] = 1,
    aggregation: str = "mean",
    return_by_separate_ensembles: bool = False,
    base_model_method: Union[None, str] = None,
    **base_model_prediction_param
) -> Union[np.ndarray, Tuple[np.ndarray]]:
    """A rewrite of predict_proba

    Args:
        X_test (pd.core.frame.DataFrame):
            Testing variables.
        verbosity (Union[None, int], optional):
            0 to output nothing, everything other wise. Default None set it to the verbosity of AdaSTEM model class.
        return_std (bool, optional):
            Whether return the standard deviation among ensembles. Defaults to False.
        n_jobs (Union[int, None], optional):
            Number of processes used in this task. If None, use the self.n_jobs. Default to 1.
            I do not recommend setting value larger than 1.
            In practice, multi-processing seems to slow down the process instead of speeding up.
            Could be more practical with large amount of data.
            Still in experiment.
        aggregation (str, optional):
            'mean' or 'median' for aggregation method across ensembles.
        return_by_separate_ensembles (bool, optional):
            Experimental function. return not by aggregation, but by separate ensembles.
        base_model_method:
            The name of the prediction method for base models. If None, `predict` or `predict_proba` will be used depending on the tasks. This argument is handy if you have a custom base model class that has a special prediction function. Defaults to None.
        base_model_prediction_param:
            Any other paramters to pass into the prediction method of the base models. e.g., base_model_prediction_param={'n_jobs':1}.

    Raises:
        TypeError:
            X_test is not of type pd.core.frame.DataFrame.
        ValueError:
            aggregation is not in ['mean','median'].

    Returns:
        predicted results. (pred_mean, pred_std) if return_std==true, and pred_mean if return_std==False.

        If return_by_separate_ensembles == True:
            Return numpy.ndarray of shape (n_samples, n_ensembles)

    """

    prediciton = self.predict_proba(
        X_test,
        verbosity=verbosity,
        return_std=return_std,
        n_jobs=n_jobs,
        aggregation=aggregation,
        return_by_separate_ensembles=return_by_separate_ensembles,
        base_model_method = base_model_method,
        **base_model_prediction_param
    )

    # if return_by_separate_ensembles, this will be the dataframe for ensemble
    # if return_std, this wil be a tuple of mean and std of prediction
    # if none of these, then it ill output the mean prediction
    return prediciton

---