diff --git a/blocksnet/analysis/land_use/prediction/artifacts/model.joblib b/blocksnet/analysis/land_use/prediction/artifacts/model.joblib index 5a530b81..b8e1fbf0 100644 Binary files a/blocksnet/analysis/land_use/prediction/artifacts/model.joblib and b/blocksnet/analysis/land_use/prediction/artifacts/model.joblib differ diff --git a/blocksnet/analysis/land_use/prediction/artifacts/scaler.joblib b/blocksnet/analysis/land_use/prediction/artifacts/scaler.joblib new file mode 100644 index 00000000..1137d4dc Binary files /dev/null and b/blocksnet/analysis/land_use/prediction/artifacts/scaler.joblib differ diff --git a/blocksnet/analysis/land_use/prediction/core.py b/blocksnet/analysis/land_use/prediction/core.py index 8e3fdda5..9bfe07cf 100644 --- a/blocksnet/analysis/land_use/prediction/core.py +++ b/blocksnet/analysis/land_use/prediction/core.py @@ -4,12 +4,15 @@ import numpy as np import pandas as pd import geopandas as gpd +import joblib from shapely import Point, unary_union +from sklearn.preprocessing import RobustScaler from blocksnet.enums import LandUseCategory, LandUse from blocksnet.machine_learning.context import BaseContext from blocksnet.machine_learning.strategy import BaseStrategy from ._strategy import get_default_strategy +from ._strategy import ARTIFACTS_DIRECTORY as DEFAULT_ARTIFACTS_DIR from .schemas import BlocksInputSchema from .preprocessing import DataProcessor @@ -35,21 +38,55 @@ def land_use_to_category(lu: LandUse) -> LandUseCategory | None: return LandUseCategory.from_land_use(lu) def category_to_index(val) -> int | None: - """Convert category (enum or str) to index.""" + """Convert category (enum or str) to index (case-insensitive for strings).""" if isinstance(val, LandUseCategory): return CATEGORY_TO_INDEX.get(val) if isinstance(val, str): + s = val.strip() + enum_val = None try: - enum_val = LandUseCategory(val.lower()) - return CATEGORY_TO_INDEX.get(enum_val) - except ValueError: - return None + enum_val = LandUseCategory(s) + except Exception: + try: + enum_val = next(v for v in LandUseCategory if v.value.lower() == s.lower()) + except Exception: + try: + enum_val = LandUseCategory[s.upper()] + except Exception: + enum_val = None + return CATEGORY_TO_INDEX.get(enum_val) if enum_val is not None else None return None CATEGORY_TO_INDEX = {cat: i for i, cat in enumerate(LandUseCategory)} INDEX_TO_CATEGORY = {i: cat for cat, i in CATEGORY_TO_INDEX.items()} +PREFERRED_FEATURE_ORDER = [ + "area", + "perimeter", + "compactness", + "solidity", + "bbox_width", + "elongation", + "mrr_height", + "mrr_area", + "mrr_aspect_ratio", + "rectangularity_index", + "shape_index", + "fractal_dimension", + "median_dist", + "mean_k3_dist", + "deg_10m", + "clust_10m", + "avg_n_deg_10m", + "component_id_10m", + "component_size_10m", + "pagerank_10m", + "h3_density", + "h3_mean_area", + "h3_entropy", +] + class SpatialClassifier(BaseContext): def __init__( @@ -69,42 +106,17 @@ def __init__( super().__init__(strategy=strategy) self.data_processor = DataProcessor(buffer_distance=buffer_distance, k_neighbors=k_neighbors) - self.feature_cols: list[str] | None = ['x_local', - 'y_local', - 'compactness', - 'fractal_dimension', - 'rectangularity_index', - 'squareness_index', - 'shape_index_log', - 'mbr_area_log', - 'mbr_aspect_ratio_log', - 'solidity_log', - 'asymmetry_x_log', - 'asymmetry_y_log', - 'nbr_mean_x_local', - 'nbr_mean_y_local', - 'nbr_mean_compactness', - 'nbr_mean_fractal_dimension', - 'nbr_mean_shape_index', - 'nbr_mean_mbr_area', - 'nbr_mean_rectangularity_index', - 'nbr_mean_mbr_aspect_ratio', - 'nbr_mean_squareness_index', - 'nbr_mean_solidity', - 'nbr_mean_asymmetry_x', - 'nbr_mean_asymmetry_y', - 'nbr_mean_shape_index_log', - 'nbr_mean_mbr_area_log', - 'nbr_mean_mbr_aspect_ratio_log', - 'nbr_mean_solidity_log', - 'nbr_mean_asymmetry_x_log', - 'nbr_mean_asymmetry_y_log'] + self.feature_cols: list[str] | None = None self.target_col = 'target_label' self.is_fitted = False self.class_names_: list[LandUseCategory] | None = None # context for inference (if need to store train processing) self.processed_train_for_context: gpd.GeoDataFrame | None = None + # keep normalized training gdf to compute nearby_* against known zones + self.known_gdf_for_rec_zones: gpd.GeoDataFrame | None = None + self._last_normalized_train: gpd.GeoDataFrame | None = None + self.scaler: RobustScaler | None = None # ---------- auxiliary input normalization methods ---------- @@ -136,19 +148,27 @@ def _stack_city_list(city_gdfs: Iterable[gpd.GeoDataFrame], for g in city_gdfs: if not isinstance(g, gpd.GeoDataFrame): raise TypeError("All elements in the list must be GeoDataFrame") - all_cols |= set(g.columns) - all_cols |= {"city"} # ensure presence + cols = set(g.columns) + cols.add("city") + all_cols |= cols ref_crs = city_gdfs[0].crs parts: list[gpd.GeoDataFrame] = [] - for i, g in enumerate(city_gdfs, start): + name_counter = start + for g in city_gdfs: gi = g.copy() - if ref_crs is not None and gi.crs != ref_crs: + if ref_crs is not None and gi.crs not in (None, ref_crs): gi = gi.to_crs(ref_crs) - # overwrite/create 'city' - if "city" in gi.columns: - gi = gi.drop(columns=["city"]) - gi["city"] = name_fmt.format(i) + + if "city" not in gi.columns: + gi["city"] = name_fmt.format(name_counter) + name_counter += 1 + else: + missing_mask = gi["city"].isna() + if missing_mask.any(): + gi.loc[missing_mask, "city"] = name_fmt.format(name_counter) + name_counter += 1 + gi = gi.reindex(columns=sorted(all_cols)) parts.append(gi) return gpd.GeoDataFrame(pd.concat(parts, ignore_index=True), crs=ref_crs) @@ -203,6 +223,104 @@ def _ensure_city_and_center(df: gpd.GeoDataFrame) -> gpd.GeoDataFrame: raise ValueError(f"Failed to compute 'city_center' for cities: {bad}. Check geometry.") return out + def _select_feature_columns(self, df: gpd.GeoDataFrame) -> list[str]: + """ + Determine feature columns (numeric only) + """ + numeric_cols = df.select_dtypes(include=[np.number]).columns.tolist() + exclude = {'category', 'category_true', 'city', 'city_center', self.target_col} + numeric_cols = [c for c in numeric_cols if c not in exclude] + + if not numeric_cols: + raise ValueError("No numeric feature columns available after preprocessing.") + + preferred = [col for col in PREFERRED_FEATURE_ORDER if col in numeric_cols] + return preferred + + remaining = [col for col in numeric_cols if col not in preferred] + return preferred + sorted(remaining) + + def _ensure_scaler(self, fit_scaler: bool, feature_frame: pd.DataFrame) -> None: + """ + Fit scaler when required and make sure feature columns are scaled. + """ + if self.feature_cols is None: + raise ValueError("Feature columns are not initialized.") + + if fit_scaler or self.scaler is None: + self.scaler = RobustScaler() + self.scaler.fit(feature_frame[self.feature_cols]) + if self.scaler is None: + raise ValueError("Scaler is not initialized. Train the classifier or load a scaler state.") + + transformed = self.scaler.transform(feature_frame[self.feature_cols]) + feature_frame.loc[:, self.feature_cols] = transformed + + def preprocess_data( + self, + data: gpd.GeoDataFrame | list | tuple, + *, + fit_scaler: bool = False, + require_strict_categories: bool = False, + ) -> gpd.GeoDataFrame: + """ + Unified preprocessing pipeline for both training and inference data. + + Args: + data: GeoDataFrame or list/tuple of GeoDataFrames. + fit_scaler: When True, fit RobustScaler on the resulting features (training mode). + require_strict_categories: Whether to enforce category presence. + + Returns: + GeoDataFrame with engineered (and scaled) features. + """ + normalized = self._normalize_input(data) + if fit_scaler: + self._last_normalized_train = normalized.copy() + self.known_gdf_for_rec_zones = normalized.copy() + reference = normalized + else: + reference = self.known_gdf_for_rec_zones + if reference is None: + reference = self._last_normalized_train + if reference is None: + reference = normalized + + processed = self.data_processor.prepare_data( + normalized, + known_gdf_for_rec_zones=reference, + require_strict_categories=require_strict_categories, + ) + + processed_df = processed.copy() + if 'category' not in processed_df and 'category' in normalized.columns: + processed_df['category'] = normalized['category'].values + if 'city' in normalized.columns: + processed_df['city'] = normalized['city'].values + if 'city_center' in normalized.columns: + processed_df['city_center'] = normalized['city_center'].values + processed_df['geometry'] = normalized.geometry.values + + processed_gdf = gpd.GeoDataFrame(processed_df, geometry='geometry', crs=normalized.crs) + + if fit_scaler or self.feature_cols is None: + self.feature_cols = self._select_feature_columns(processed_gdf) + if self.feature_cols is None: + raise ValueError("Feature columns are not initialized. Train the classifier first.") + + for feature in self.feature_cols: + if feature not in processed_gdf.columns: + processed_gdf[feature] = 0.0 + + self._ensure_scaler(fit_scaler, processed_gdf) + + meta_cols = [c for c in ['geometry', 'category', 'category_true', 'city', 'city_center'] if c in processed_gdf.columns] + other_cols = [c for c in processed_gdf.columns if c not in self.feature_cols + meta_cols] + ordered_cols = self.feature_cols + other_cols + meta_cols + processed_gdf = processed_gdf[ordered_cols] + + return processed_gdf + def _normalize_input(self, data: gpd.GeoDataFrame | list | tuple) -> gpd.GeoDataFrame: """ 1) Validates input(s) using BlocksInputSchema (without additional checks). @@ -218,7 +336,6 @@ def _normalize_input(self, data: gpd.GeoDataFrame | list | tuple) -> gpd.GeoData TypeError: If input is not GeoDataFrame, list[GeoDataFrame] or tuple[GeoDataFrame] """ if isinstance(data, (list, tuple)): - # validate EACH GDF validated = [BlocksInputSchema(g) for g in data] merged = self._stack_city_list(validated, start=0, name_fmt="{:03d}") return self._ensure_city_and_center(merged) @@ -228,138 +345,100 @@ def _normalize_input(self, data: gpd.GeoDataFrame | list | tuple) -> gpd.GeoData else: raise TypeError("Expecting GeoDataFrame, list[GeoDataFrame] or tuple[GeoDataFrame].") - # ---------------------- L/U split ---------------------- - def split_l_u_per_city( - self, - gdf: gpd.GeoDataFrame, - target_col: str = 'category', - test_size: float = 0.2, - random_state: int = 42 - ) -> pd.Series: - """ - Returns pd.Series (index=gdf.index) with values: - 'L' — labeled part for training, - 'U' — hidden part for validation within city, - 'X' — initially unlabeled (if any). - Splitting is done independently for each city. - Args: - gdf (gpd.GeoDataFrame): Input GeoDataFrame - target_col (str, optional): Name of target column. Defaults to 'category'. - test_size (float, optional): Proportion of data for validation. Defaults to 0.2. - random_state (int, optional): Random seed. Defaults to 42. + # ---------------------- train / predict ---------------------- - Returns: - pd.Series: Series with split assignments + def preprocess_training_data( + self, + train_gdf: gpd.GeoDataFrame | list | tuple, + *, + require_strict_categories: bool = True, + **_, + ) -> gpd.GeoDataFrame: """ - if 'city' not in gdf.columns: - gdf = gdf.assign(city="__one__") - - assign = pd.Series('X', index=gdf.index, dtype=object) - rng = np.random.default_rng(random_state) - labeled_all = gdf.index[gdf[target_col].notna()] - - for _, idx in gdf.groupby('city').indices.items(): - idx = pd.Index(idx) - - labeled_idx = idx.intersection(labeled_all) - n_lab = len(labeled_idx) - if n_lab == 0: - continue - - n_val = int(round(test_size * n_lab)) - n_val = max(1, min(n_val, n_lab)) - - if n_lab == 1: - val_idx = labeled_idx - train_idx = idx.difference(val_idx) - else: - val_idx = pd.Index(rng.choice(labeled_idx.to_numpy(), size=n_val, replace=False)) - train_idx = labeled_idx.difference(val_idx) - - assign.loc[train_idx] = 'L' - assign.loc[val_idx] = 'U' - - return assign + Backward-compatible wrapper around ``preprocess_data`` for training datasets. + """ + return self.preprocess_data( + train_gdf, + fit_scaler=True, + require_strict_categories=require_strict_categories, + ) - # ---------------------- train / predict ---------------------- + def preprocess_run_data( + self, + gdf: gpd.GeoDataFrame | list | tuple, + *, + require_strict_categories: bool = False, + **_, + ) -> gpd.GeoDataFrame: + """ + Backward-compatible wrapper around ``preprocess_data`` for inference datasets. + """ + return self.preprocess_data( + gdf, + fit_scaler=False, + require_strict_categories=require_strict_categories, + ) - def train(self, train_gdf: gpd.GeoDataFrame | list | tuple) -> None: + def train( + self, + train_gdf: gpd.GeoDataFrame | list | tuple, + ) -> float: """ - Training with city-wise L/U split: - - FIRST validates input using BlocksInputSchema (for GDF or each list/tuple element), - then adds 'city' and 'city_center' (without extra validations). - - Train strategy on L, validate on U. + Train the spatial classifier using all available labeled data. Args: - train_gdf (gpd.GeoDataFrame | list | tuple): Training data + train_gdf: Raw training data (GeoDataFrame or list of GeoDataFrames). Returns: - float: Training score + float: Training score computed on the same dataset (for reference). """ - # 1) input validation and normalization (city + city_center) - train_gdf = self._normalize_input(train_gdf) - - # 2) L/U labeling by cities - assign = self.split_l_u_per_city(train_gdf, target_col='category', test_size=0.2, random_state=42) - - # 3) preprocessing - processed_train = self.data_processor.prepare_data(train_gdf) - self.processed_train_for_context = processed_train + # processed_train = self.preprocess_data( + # train_gdf, + # fit_scaler=True, + # require_strict_categories=True, + # ) - excluded_columns = ['geometry', 'category', 'city', 'city_center'] - excluded_columns += getattr(self.data_processor, 'columns_to_log', []) - self.feature_cols = [c for c in processed_train.columns if c not in excluded_columns] + self.processed_train_for_context = train_gdf.copy() - processed_train[self.target_col] = processed_train["category"].map(category_to_index) - if processed_train[self.target_col].isnull().any(): - raise ValueError("Some category values could not be mapped to index.") + if 'category' not in train_gdf.columns: + raise ValueError("Training data must contain the 'category' column.") - # align assign and processed_train - assign = assign.reindex(processed_train.index) - is_L = (assign == 'L') - is_U = (assign == 'U') + y_series = train_gdf['category'].map(category_to_index) + if y_series.isnull().any(): + missing = train_gdf.loc[y_series.isnull(), 'category'].unique() + raise ValueError(f"Unknown categories encountered during training: {missing}") - X_L = processed_train.loc[is_L, self.feature_cols].values - y_L = processed_train.loc[is_L, self.target_col].values - - if is_U.any(): - X_U = processed_train.loc[is_U, self.feature_cols].values - y_U = processed_train.loc[is_U, self.target_col].values - else: - X_U = np.empty((0, len(self.feature_cols)), dtype=float) - y_U = np.empty((0,), dtype=int) + X = train_gdf[self.feature_cols].values + y = y_series.to_numpy(dtype=int) - score = self.strategy.train(X_L, y_L, X_U, y_U) - unique_labels = np.unique(np.concatenate([y_L, y_U]) if y_U.size else y_L) - self.class_names_ = [INDEX_TO_CATEGORY[i] for i in unique_labels] + score = self.strategy.train(X, y, X, y) + self.is_fitted = True + self.classes_ = list(self.strategy.model.classes_) + self.class_names_ = [INDEX_TO_CATEGORY[i] for i in self.classes_] return score - def _prepare_for_prediction(self, new_data: gpd.GeoDataFrame | list | tuple) -> gpd.GeoDataFrame: + def _prepare_for_prediction( + self, + new_data: gpd.GeoDataFrame | list | tuple, + *, + require_strict_categories: bool = False, + ) -> gpd.GeoDataFrame: """ Common input normalizer + preprocessing for inference. Args: new_data (gpd.GeoDataFrame | list | tuple): New data to prepare + require_strict_categories (bool, optional): Whether category validation is strict. Returns: gpd.GeoDataFrame: Processed data ready for prediction """ - # 1) validation (BlocksInputSchema) and adding city/city_center - gdf_norm = self._normalize_input(new_data) - - # 2) preprocessing (features); can pass context if needed - processed_new = self.data_processor.prepare_data(gdf_norm) - - # 3) add missing features to match training set - if self.feature_cols: - for c in self.feature_cols: - if c not in processed_new.columns: - processed_new[c] = 0.0 - processed_new = processed_new[self.feature_cols + [col for col in processed_new.columns if col not in self.feature_cols]] - - return processed_new + if isinstance(new_data, gpd.GeoDataFrame): + if self.feature_cols and all(col in new_data.columns for col in self.feature_cols): + return new_data.copy() + return self.preprocess_run_data(new_data, require_strict_categories=require_strict_categories) def _as_list(self, gdf_or_list) -> tuple[list[gpd.GeoDataFrame], bool]: """ @@ -394,7 +473,7 @@ def predict(self, new_gdf: Union[gpd.GeoDataFrame, list, tuple]) -> Union[np.nda out: List[np.ndarray] = [] for g in items: - processed_new = self._prepare_for_prediction(g) + processed_new = self._prepare_for_prediction(g, require_strict_categories=False) X = processed_new[self.feature_cols].values out.append(self.strategy.predict(X)) @@ -414,7 +493,7 @@ def predict_proba(self, new_gdf: Union[gpd.GeoDataFrame, list, tuple]) -> Union[ out: List[np.ndarray] = [] for g in items: - processed_new = self._prepare_for_prediction(g) + processed_new = self._prepare_for_prediction(g, require_strict_categories=False) X = processed_new[self.feature_cols].values out.append(self.strategy.predict_proba(X)) @@ -436,30 +515,64 @@ def run(self, gdf: Union[gpd.GeoDataFrame, list, tuple]) -> Union[gpd.GeoDataFra # ensure class order for prob columns if self.class_names_ is None: self.classes_ = list(self.strategy.model.classes_) + # optional guard: ensure current model is trained with our category set size + if len(self.classes_) != len(LandUseCategory) - 1: + raise ValueError( + "Loaded model classes do not match current LandUseCategory set. " + "Please retrain the model with updated categories." + ) self.class_names_ = [INDEX_TO_CATEGORY[c] for c in self.classes_] for g in items: - processed_new = self._prepare_for_prediction(g) + processed_new = self._prepare_for_prediction(g, require_strict_categories=False) X = processed_new[self.feature_cols].values - preds = self.strategy.predict(X) probs = self.strategy.predict_proba(X) # shape: (n, n_classes) in model.classes_ order - # restore original GeoDataFrame (with 'city', 'city_center' after normalize) - gdf_norm = self._normalize_input(g).copy() + if not isinstance(processed_new, gpd.GeoDataFrame): + gdf_out = gpd.GeoDataFrame(processed_new.copy(), geometry='geometry') + else: + gdf_out = processed_new.copy() # labels and names - gdf_norm['pred_class'] = preds - gdf_norm['pred_name'] = [INDEX_TO_CATEGORY[c].value for c in preds] + gdf_out['pred_class'] = preds + gdf_out['pred_name'] = [INDEX_TO_CATEGORY[c].value for c in preds] - # probabilities — strictly in self.classes_/self.class_names_ order + # probabilities - strictly in self.classes_/self.class_names_ order for j, cls in enumerate(self.class_names_): - gdf_norm[f'prob_{cls.value}'] = probs[:, j] if probs.size else np.array([], dtype=float) + gdf_out[f'prob_{cls.value}'] = probs[:, j] if probs.size else np.array([], dtype=float) - results.append(gdf_norm[['geometry','category','pred_name','prob_urban','prob_non_urban','prob_industrial']]) + prob_cols = [f'prob_{cls.value}' for cls in self.class_names_] + base_cols = ['geometry'] + if 'category' in gdf_out.columns: + base_cols.append('category') + base_cols.append('pred_name') + results.append(gdf_out[base_cols + prob_cols]) return results if was_list else results[0] + def save_scaler(self, path: str | Path) -> None: + """ + Persist the fitted scaler and feature list to disk. + """ + if self.scaler is None or not self.feature_cols: + raise ValueError("Scaler is not initialized. Train the classifier before saving.") + state = { + "scaler": self.scaler, + "feature_cols": self.feature_cols, + } + joblib.dump(state, str(path)) + + def load_scaler(self, path: str | Path) -> None: + """ + Load a previously saved scaler + feature column order. + """ + state = joblib.load(str(path)) + self.scaler = state.get("scaler") + self.feature_cols = state.get("feature_cols") + if self.scaler is None or not self.feature_cols: + raise ValueError("Loaded scaler state is incomplete.") + @classmethod def default(cls) -> "SpatialClassifier": """ @@ -468,7 +581,14 @@ def default(cls) -> "SpatialClassifier": Returns: SpatialClassifier: Default classifier instance """ - return cls(get_default_strategy()) + inst = cls(get_default_strategy()) + try: + p = Path(DEFAULT_ARTIFACTS_DIR) / "scaler.joblib" + if p.exists(): + inst.load_scaler(p) + except Exception: + pass + return inst def save_mistakes(self, test_gdf: gpd.GeoDataFrame, predictions: np.ndarray, @@ -496,7 +616,34 @@ def save_mistakes(self, test_gdf: gpd.GeoDataFrame, ) self._save_geojson(mistakes, filename) + # ---------------------- persistence ---------------------- + def save(self, path: str | Path) -> None: + """ + Save strategy (model) and scaler state in the same directory. + + - strategy artifacts are managed by the strategy itself + - scaler state (scaler + feature_cols) is saved via joblib in scaler.joblib + """ + path = Path(path) + path.mkdir(parents=True, exist_ok=True) + # Save strategy artifacts + self.strategy.save(str(path)) + # Save scaler state + scaler_path = path / "scaler.joblib" + self.save_scaler(scaler_path) + + def load(self, path: str | Path) -> None: + """ + Load strategy (model) and scaler state from the same directory. + """ + path = Path(path) + # Load strategy artifacts + self.strategy.load(str(path)) + # Load scaler state if present + scaler_path = path / "scaler.joblib" + if scaler_path.exists(): + self.load_scaler(scaler_path) def save_predictions_to_geojson(self, gdf: gpd.GeoDataFrame, predictions: np.ndarray, probabilities: np.ndarray, @@ -550,3 +697,5 @@ def _save_geojson(self, gdf: gpd.GeoDataFrame, filename: str) -> None: save_gdf.to_file(filename, driver='GeoJSON', encoding='utf-8') except Exception as e: raise RuntimeError(f"Error saving {filename}: {str(e)}") from e + + diff --git a/blocksnet/analysis/land_use/prediction/preprocessing.py b/blocksnet/analysis/land_use/prediction/preprocessing.py index 72df2bc6..ce8cd6a9 100644 --- a/blocksnet/analysis/land_use/prediction/preprocessing.py +++ b/blocksnet/analysis/land_use/prediction/preprocessing.py @@ -1,11 +1,16 @@ +import h3 import numpy as np import pandas as pd +import networkx as nx import geopandas as gpd from typing import Optional from scipy import sparse +from scipy.spatial.distance import cdist from shapely import make_valid from sklearn.neighbors import radius_neighbors_graph, kneighbors_graph +from blocksnet.enums import LandUse, LandUseCategory + class DataProcessor: @@ -24,9 +29,9 @@ def __init__(self, buffer_distance: float = 1000, k_neighbors: int = 5): self.feature_names_for_spatial_context = [ 'mbr_area', 'solidity', 'compactness', 'shape_index', 'mbr_aspect_ratio', 'squareness_index', 'fractal_dimension', - 'rectangularity_index', 'nearby_bus_res_count', - 'nearby_transport_count', 'nearby_industrial_count', - 'nearby_rec_spec_agri_count' + 'rectangularity_index', + 'nearby_residential_count', 'nearby_business_count', + 'nearby_recreation_count', 'nearby_industrial_count', ] self.columns_to_log = [ @@ -34,6 +39,12 @@ def __init__(self, buffer_distance: float = 1000, k_neighbors: int = 5): 'solidity', 'asymmetry_x', 'asymmetry_y' ] + # Advanced feature settings inspired by prediction_v2.ipynb + self.rings: tuple[tuple[int, int], ...] = ((0, 150), (150, 500), (500, 1000)) + self.graph_neighbor_buffer: float = 10.0 + self.distance_batch_size: int = 512 + self.h3_resolution: int = 9 + def build_city_graph(self, city_gdf: gpd.GeoDataFrame, mode: str = "radius", radius: float = 1000.0, @@ -205,77 +216,371 @@ def neighbor_label_counts_and_proportions(self, return pd.concat([df_counts, df_props], axis=1) def calc_polygon_features(self, gdf: gpd.GeoDataFrame) -> pd.DataFrame: - """Вычисляет геометрические характеристики полигонов. - - Calculates geometric features of polygons including: - - Basic metrics: area, perimeter, centroid coordinates - - Shape metrics: compactness, fractal dimension, rectangularity - - Bounding box metrics: aspect ratio, squareness - - Advanced metrics: solidity, asymmetry measures - - Args: - gdf: GeoDataFrame containing polygon geometries to analyze - - Returns: - pd.DataFrame: DataFrame containing computed features with columns: - - compactness: Measure of circularity (4π*area/perimeter²) - - fractal_dimension: Complexity measure (log(area)/log(perimeter)) - - shape_index: Area to perimeter ratio - - mbr_area: Area of minimum bounding rectangle - - rectangularity_index: Ratio of polygon area to MBR area - - mbr_aspect_ratio: Ratio of MBR dimensions - - squareness_index: Inverse of aspect ratio - - solidity: Ratio of polygon area to convex hull area - - asymmetry_x: Horizontal asymmetry measure - - asymmetry_y: Vertical asymmetry measure - - Raises: - Exception: If any geometric computation fails + """ + Compute geometric descriptors for polygons (area, perimeter, shape indices, etc.). + """ + if gdf.empty: + return pd.DataFrame(index=gdf.index) + + eps = 1e-12 + geo = gdf.geometry + area = np.nan_to_num(geo.area.to_numpy(), nan=0.0) + perimeter = np.nan_to_num(geo.length.to_numpy(), nan=0.0) + convex_area = np.nan_to_num(geo.convex_hull.area.to_numpy(), nan=0.0) + centroids = geo.centroid + cx = np.nan_to_num(centroids.x.to_numpy(), nan=0.0) + cy = np.nan_to_num(centroids.y.to_numpy(), nan=0.0) + + bounds = geo.bounds + minx = bounds["minx"].to_numpy() + maxx = bounds["maxx"].to_numpy() + miny = bounds["miny"].to_numpy() + maxy = bounds["maxy"].to_numpy() + bbox_width = np.nan_to_num(maxx - minx, nan=0.0) + bbox_height = np.nan_to_num(maxy - miny, nan=0.0) + + compactness = np.where( + perimeter > 0, + (4.0 * np.pi * area) / (np.square(perimeter) + eps), + 0.0, + ) + solidity = np.where(convex_area > 0, area / (convex_area + eps), 0.0) + shape_index = np.where( + area > 0, + 0.25 * perimeter / np.sqrt(area + eps), + 0.0, + ) + fractal_dimension = np.where( + (area > 0) & (perimeter > 0), + 2.0 * np.log((perimeter + eps) / 4.0) / np.log(area + eps), + 0.0, + ) + fractal_dimension = np.nan_to_num(fractal_dimension, nan=0.0, posinf=0.0, neginf=0.0) + fractal_dimension = np.clip(fractal_dimension, 0.0, 2.5) + + asym_x = np.abs(((minx + maxx) / 2.0) - cx) + asym_y = np.abs(((miny + maxy) / 2.0) - cy) + elongation = np.where(bbox_height > 0, bbox_width / (bbox_height + eps), 1.0) + + mrr_metrics = geo.apply(self._mrr_metrics_single) + mrr_width = np.array([vals[0] for vals in mrr_metrics], dtype=float) + mrr_height = np.array([vals[1] for vals in mrr_metrics], dtype=float) + mrr_area = np.array([vals[2] for vals in mrr_metrics], dtype=float) + mrr_aspect_ratio = np.array([vals[3] for vals in mrr_metrics], dtype=float) + rectangularity_index = np.where(mrr_area > 0, area / (mrr_area + eps), 0.0) + + result = pd.DataFrame({ + 'area': area, + 'perimeter': perimeter, + 'compactness': compactness, + 'solidity': solidity, + 'bbox_width': bbox_width, + 'bbox_height': bbox_height, + 'elongation': elongation, + 'mrr_height': mrr_height, + 'mrr_area': mrr_area, + 'mrr_aspect_ratio': mrr_aspect_ratio, + 'rectangularity_index': rectangularity_index, + 'shape_index': shape_index, + 'fractal_dimension': fractal_dimension, + 'asymmetry_x': asym_x, + 'asymmetry_y': asym_y, + }, index=gdf.index) + + # Maintain legacy feature names for downstream compatibility + result['mbr_area'] = result['mrr_area'] + result['mbr_aspect_ratio'] = result['mrr_aspect_ratio'] + result['squareness_index'] = np.where( + result['mrr_aspect_ratio'] > 0, + 1.0 / (result['mrr_aspect_ratio'] + eps), + 0.0, + ) + + return result + + @staticmethod + def _mrr_metrics_single(geom): + """ + Return width, height, area and aspect ratio of the minimum rotated rectangle. """ try: - geo = gdf.geometry - area = geo.area.to_numpy() - length = geo.length.to_numpy() - centroids = geo.centroid - cx, cy = centroids.x.to_numpy(), centroids.y.to_numpy() - - min_env = geo.minimum_rotated_rectangle() - mbr_area = min_env.area.to_numpy() - convex = geo.convex_hull - convex_area = convex.area.to_numpy() - - compactness = np.where(length>0, 4*np.pi*area/length**2, 0) - fractal_dim = np.where((area>0)&(length>0)&(np.log(length)!=0), np.log(area)/np.log(length), 0) - rectangularity = np.where(mbr_area>0, area/mbr_area, 0) - - bounds = min_env.bounds - dx = bounds.maxx - bounds.minx - dy = bounds.maxy - bounds.miny - - aspect_ratio = np.where((dx>0)&(dy>0), np.maximum(dx,dy)/np.minimum(dx,dy), 0) - squareness = np.where(np.maximum(dx,dy)>0, np.minimum(dx,dy)/np.maximum(dx,dy), 0) - shape_index = np.where(length>0, area / length, 0) - solidity = np.where(convex_area>0, area/convex_area, 0) - - asym_x = np.abs((bounds.minx+bounds.maxx)/2 - cx) - asym_y = np.abs((bounds.miny+bounds.maxy)/2 - cy) - - result = pd.DataFrame({ - 'compactness': compactness, - 'fractal_dimension': fractal_dim, - 'shape_index': shape_index, - 'mbr_area': mbr_area, - 'rectangularity_index': rectangularity, - 'mbr_aspect_ratio': aspect_ratio, - 'squareness_index': squareness, - 'solidity': solidity, - 'asymmetry_x': asym_x, - 'asymmetry_y': asym_y, - }, index=gdf.index) - return result - - except Exception as e: - raise e + if geom is None or geom.is_empty: + return (0.0, 0.0, 0.0, 1.0) + mrr = geom.minimum_rotated_rectangle + coords = np.array(mrr.exterior.coords) + edges = np.sqrt(((coords[1:] - coords[:-1]) ** 2).sum(axis=1)) + lengths = np.sort(np.unique(np.round(edges, 12))) + if len(lengths) >= 2: + width, height = float(lengths[-1]), float(lengths[-2]) + elif len(lengths) == 1: + width = height = float(lengths[0]) + else: + width = height = 0.0 + area = float(mrr.area) + w, h = (width, height) if width >= height else (height, width) + aspect = (w / (h + 1e-12)) if (w > 0 and h > 0) else 1.0 + return w, h, area, aspect + except Exception: + return (0.0, 0.0, 0.0, 1.0) + + @staticmethod + def _label_slug(value) -> str: + if value is None: + return "__none__" + if isinstance(value, LandUseCategory): + return value.value.lower() + if isinstance(value, LandUse): + cat = LandUseCategory.from_land_use(value) + return cat.value.lower() if cat else value.value.lower() + if isinstance(value, str): + return value.strip().lower() + return str(value).lower() + + @staticmethod + def _to_metric(gdf: gpd.GeoDataFrame) -> gpd.GeoDataFrame: + if gdf.empty: + return gdf.copy() + try: + utm_crs = gdf.estimate_utm_crs() + except Exception: + utm_crs = None + if utm_crs: + return gdf.to_crs(utm_crs) + return gdf.copy() + + def _compute_ring_statistics(self, city_gdf: gpd.GeoDataFrame, target_col: str) -> pd.DataFrame: + out = pd.DataFrame(index=city_gdf.index) + if city_gdf.empty or city_gdf.geometry.is_empty.all(): + return out + + working = city_gdf.copy() + if target_col not in working.columns: + working[target_col] = None + + for r_min, r_max in self.rings: + buf_max = working.geometry.buffer(r_max) + if r_min > 0: + buf_min = working.geometry.buffer(r_min) + ring_geom = buf_max.difference(buf_min) + else: + ring_geom = buf_max + + right = gpd.GeoDataFrame( + working[[target_col, "area"]].copy(), + geometry=ring_geom, + crs=working.crs, + ) + joined = gpd.sjoin( + working[[target_col, "geometry", "area"]], + right, + how="left", + predicate="intersects", + lsuffix="A", + rsuffix="B", + ).reset_index() + + left_index = "index" + right_index = "index_B" + if left_index not in joined.columns and joined.index.name: + joined = joined.reset_index(names="index_left") + left_index = "index_left" + if right_index not in joined.columns: + right_index = "index_right" + + joined = joined[joined[right_index].notna()] + joined = joined[joined[left_index] != joined[right_index]] + if joined.empty: + band = f"{int(r_min)}_{int(r_max)}m" + for col in ( + f"n_neighbors_{band}m", + f"avg_area_neighbors_{band}m", + f"n_neighbors_none_{band}m", + f"avg_area_neighbors_none_{band}m", + ): + out[col] = 0 if "avg" not in col else 0.0 + continue + + joined[f"{target_col}_B"] = joined[f"{target_col}_B"].fillna("__none__") + joined["_neighbor_class"] = joined[f"{target_col}_B"].map(self._label_slug) + band = f"{int(r_min)}_{int(r_max)}m" + + n_neighbors = joined.groupby(left_index).size() + avg_area_neighbors = joined.groupby(left_index)["area_B"].mean() + + none_mask = joined["_neighbor_class"] == "__none__" + n_neighbors_none = none_mask.groupby(joined[left_index]).sum() + avg_area_none = ( + joined.loc[none_mask].groupby(left_index)["area_B"].mean() + if none_mask.any() + else pd.Series(dtype=float) + ) + + out[f"n_neighbors_{band}m"] = out.index.map(n_neighbors).fillna(0).astype(int) + out[f"avg_area_neighbors_{band}m"] = out.index.map(avg_area_neighbors).fillna(0.0) + out[f"n_neighbors_none_{band}m"] = out.index.map(n_neighbors_none).fillna(0).astype(int) + out[f"avg_area_neighbors_none_{band}m"] = out.index.map(avg_area_none).fillna(0.0) + + pct_by_class = ( + joined[joined["_neighbor_class"] != "__none__"] + .groupby([left_index, "_neighbor_class"]) + .size() + .unstack(fill_value=0) + ) + if pct_by_class.empty: + continue + pct_by_class = pct_by_class.div(pct_by_class.sum(axis=1), axis=0) + for cls in pct_by_class.columns: + slug = self._label_slug(cls) + if slug == "__none__": + continue + col_name = f"pct_neighbor_{slug}_{band}m" + out[col_name] = out.index.map(pct_by_class[cls]).fillna(0.0) + + return out + + def _compute_distance_metrics(self, city_gdf: gpd.GeoDataFrame) -> pd.DataFrame: + out = pd.DataFrame(index=city_gdf.index) + n = len(city_gdf) + if n == 0: + out["median_dist"] = pd.Series(dtype=float) + out["mean_k3_dist"] = pd.Series(dtype=float) + return out + + reps = city_gdf.geometry.representative_point() + coords = np.column_stack((reps.x.values, reps.y.values)).astype(float) + median_dist = np.zeros(n, dtype=float) + mean_k3_dist = np.zeros(n, dtype=float) + + if n > 1: + for start in range(0, n, self.distance_batch_size): + end = min(start + self.distance_batch_size, n) + batch = coords[start:end] + dist_block = cdist(batch, coords) + for local_row, global_idx in enumerate(range(start, end)): + dist_block[local_row, global_idx] = np.nan + median_dist[start:end] = np.nanmedian(dist_block, axis=1) + sorted_block = np.sort(dist_block, axis=1) + k_lim = min(3, sorted_block.shape[1] - 1) + if k_lim > 0: + mean_k3_dist[start:end] = np.nanmean(sorted_block[:, :k_lim], axis=1) + median_dist = np.nan_to_num(median_dist, nan=0.0) + mean_k3_dist = np.nan_to_num(mean_k3_dist, nan=0.0) + out["median_dist"] = median_dist + out["mean_k3_dist"] = mean_k3_dist + return out + + def _compute_local_graph_features(self, city_gdf: gpd.GeoDataFrame) -> pd.DataFrame: + out = pd.DataFrame(index=city_gdf.index) + if city_gdf.empty: + return out + + buffers = city_gdf.geometry.buffer(self.graph_neighbor_buffer) + right = gpd.GeoDataFrame(index=city_gdf.index, geometry=buffers, crs=city_gdf.crs) + pairs = gpd.sjoin( + city_gdf[["geometry"]], + right, + how="left", + predicate="intersects", + lsuffix="L", + rsuffix="R", + ).reset_index() + + left_index = "index" + right_index = "index_R" + if left_index not in pairs.columns and pairs.index.name: + pairs = pairs.reset_index(names="index_left") + left_index = "index_left" + if right_index not in pairs.columns: + right_index = "index_right" + + pairs = pairs[pairs[right_index].notna()] + pairs = pairs[pairs[left_index] != pairs[right_index]] + edges = list(zip(pairs[left_index], pairs[right_index])) + + G = nx.Graph() + G.add_nodes_from(city_gdf.index.tolist()) + if edges: + G.add_edges_from(edges) + + deg_dict = dict(G.degree()) + clust_dict = nx.clustering(G) + avg_neighbor = nx.average_neighbor_degree(G) if G.number_of_edges() > 0 else {node: 0.0 for node in G.nodes()} + + comp_id = {} + comp_size = {} + for cid, comp_nodes in enumerate(nx.connected_components(G), start=1): + size = len(comp_nodes) + for node in comp_nodes: + comp_id[node] = cid + comp_size[node] = size + + try: + pagerank = nx.pagerank(G, alpha=0.85, max_iter=200) + except Exception: + pagerank = {node: 0.0 for node in G.nodes()} + + out["deg_10m"] = out.index.map(deg_dict).fillna(0).astype(int) + out["clust_10m"] = out.index.map(clust_dict).fillna(0.0) + out["avg_n_deg_10m"] = out.index.map(avg_neighbor).fillna(0.0) + out["component_id_10m"] = out.index.map(comp_id).fillna(0).astype(int) + out["component_size_10m"] = out.index.map(comp_size).fillna(1).astype(int) + out["pagerank_10m"] = out.index.map(pagerank).fillna(0.0) + return out + + def _compute_h3_features(self, city_gdf: gpd.GeoDataFrame, target_col: str) -> pd.DataFrame: + if h3 is None: + raise ImportError("h3 package is required to compute hexagonal features. Install via `pip install h3`.") # pragma: no cover + + out = pd.DataFrame(index=city_gdf.index) + if city_gdf.empty: + return out + + latlon = city_gdf.to_crs(4326) + centroids = latlon.geometry.centroid + + def _cell(point): + if point is None or point.is_empty: + return None + return h3.latlng_to_cell(point.y, point.x, self.h3_resolution) + + h3_index = centroids.apply(_cell) + h3_series = pd.Series(h3_index.to_numpy(), index=city_gdf.index) + valid = h3_series.dropna() + + density = h3_series.map(valid.value_counts()) + area_mean = h3_series.map(city_gdf.groupby(h3_series)["area"].mean()) + + if target_col in city_gdf.columns: + targets = city_gdf[target_col].apply(self._label_slug) + else: + targets = pd.Series("__none__", index=city_gdf.index) + + def _entropy(series: pd.Series) -> float: + freq = series.value_counts(normalize=True) + return float(-(freq * np.log(freq + 1e-12)).sum()) + + entropy = targets.groupby(h3_series).apply(_entropy) + out["h3_density"] = density.reindex(city_gdf.index).fillna(0).astype(int) + out["h3_mean_area"] = area_mean.reindex(city_gdf.index).fillna(0.0) + out["h3_entropy"] = h3_series.map(entropy).fillna(0.0) + return out + + def _compute_city_features(self, city_gdf: gpd.GeoDataFrame, target_col: str) -> pd.DataFrame: + metric = self._to_metric(city_gdf) + if metric.empty: + return pd.DataFrame(index=city_gdf.index) + + poly_feats = self.calc_polygon_features(metric) + metric = metric.join(poly_feats) + + ring_feats = self._compute_ring_statistics(metric, target_col=target_col) + dist_feats = self._compute_distance_metrics(metric) + graph_feats = self._compute_local_graph_features(metric) + h3_feats = self._compute_h3_features(metric, target_col=target_col) + + components = [poly_feats, ring_feats, dist_feats, graph_feats, h3_feats] + combined = pd.concat(components, axis=1) + return combined def count_nearby_zones(self, gdf: gpd.GeoDataFrame, rec_gdf: Optional[gpd.GeoDataFrame], buffer_distance: float) -> pd.Series: @@ -327,18 +632,109 @@ def count_nearby_zones(self, gdf: gpd.GeoDataFrame, rec_gdf: Optional[gpd.GeoDat except Exception as e: raise e - def transform_features(self, gdf, known_gdf_for_rec_zones=None): + def _map_land_use_to_category(self, ser: pd.Series) -> pd.Series: + """Map raw land_use values to LandUseCategory using enum mapping. + + Accepts strings or LandUse; returns LandUseCategory or NaN if unmapped. + """ + def to_category(v): + try: + if isinstance(v, LandUse): + lu = v + elif isinstance(v, str): + lu = LandUse(v.lower()) + else: + return np.nan + cat = LandUseCategory.from_land_use(lu) + return cat if cat is not None else np.nan + except Exception: + return np.nan + return ser.map(to_category) + + def count_nearby_by_category( + self, + gdf: gpd.GeoDataFrame, + known_gdf: Optional[gpd.GeoDataFrame], + buffer_distance: float, + exclude_self: bool = True, + require_strict: bool = False, + ) -> pd.DataFrame: + """Count nearby zones per LandUseCategory within a buffer. + + Returns columns: nearby__count for each category in LandUseCategory + where is lowercased (e.g., nearby_industrial_count). + If known_gdf is None or lacks land_use, returns zeros. + """ + # Helper to build safe suffix from category + def _cat_suffix(c): + val = getattr(c, 'value', c) + return str(val).lower() if val is not None else 'unknown' + + # Prepare zero frame in fallback cases + zero_cols = [f"nearby_{_cat_suffix(c)}_count" for c in LandUseCategory] + if known_gdf is None or len(known_gdf) == 0 or 'land_use' not in known_gdf.columns: + return pd.DataFrame(0, index=gdf.index, columns=zero_cols) + + # Ensure same CRS + if gdf.crs != known_gdf.crs: + rec = known_gdf.to_crs(gdf.crs) + else: + rec = known_gdf + + # Map to categories + rec = rec.copy() + rec['__lu_cat__'] = self._map_land_use_to_category(rec['land_use']) + if require_strict and rec['__lu_cat__'].isna().any(): + bad_vals = ( + rec.loc[rec['__lu_cat__'].isna(), 'land_use'] + .astype(str) + .value_counts() + .head(5) + .to_dict() + ) + raise ValueError(f"Unmapped land_use to LandUseCategory in training data: {bad_vals}") + # If all NaN, return zeros + if rec['__lu_cat__'].isna().all(): + return pd.DataFrame(0, index=gdf.index, columns=zero_cols) + + # Build buffers once + buffers = gdf.geometry.buffer(buffer_distance) + buff_gdf = gpd.GeoDataFrame(geometry=buffers, crs=gdf.crs) + + out = {} + for cat in LandUseCategory: + cat_mask = rec['__lu_cat__'] == cat + rec_cat = rec.loc[cat_mask, ['geometry']] + if rec_cat.empty: + out[f"nearby_{_cat_suffix(cat)}_count"] = pd.Series(0, index=gdf.index) + continue + + joined = gpd.sjoin(buff_gdf, rec_cat, how='left', predicate='intersects') + if exclude_self and 'index_right' in joined.columns: + # Drop self-joins for overlapping indices + overlap = rec_cat.index.intersection(gdf.index) + if len(overlap) > 0: + joined = joined[joined.index != joined['index_right']] + counts = joined.groupby(joined.index).size() + out[f"nearby_{_cat_suffix(cat)}_count"] = counts.reindex(gdf.index, fill_value=0).astype(int) + + return pd.DataFrame(out) + + def transform_features(self, gdf, target_col: str = 'category', + known_gdf_for_rec_zones=None, + require_strict_categories: bool = False): """ Transform features of a GeoDataFrame with invalid geometry handling. This method performs several transformations on the input GeoDataFrame: 1. Validates and fixes invalid geometries 2. Calculates local coordinates relative to city centers - 3. Computes geometric features - 4. Counts nearby zones of different types + 3. Computes advanced geometric, network, and contextual features + 4. Counts nearby zones of different types (using known_gdf_for_rec_zones) Args: gdf (GeoDataFrame): Input geographic data to be transformed + target_col (str): Column describing current class labels (default: 'category') known_gdf_for_rec_zones (GeoDataFrame, optional): Reference data for counting nearby zones. Defaults to None. @@ -352,59 +748,86 @@ def transform_features(self, gdf, known_gdf_for_rec_zones=None): gdf = gdf.copy() - gdf.geometry = gdf.geometry.apply(lambda geom: make_valid(geom) if not geom.is_valid else geom) + gdf.geometry = gdf.geometry.apply( + lambda geom: make_valid(geom) if geom is not None and not geom.is_valid else geom + ) - centroids = gdf.geometry.centroid - gdf['x_local'] = 0.0 - gdf['y_local'] = 0.0 + # centroids = gdf.geometry.centroid + # gdf['x_local'] = 0.0 + # gdf['y_local'] = 0.0 - if 'city' in gdf: + # if 'city' in gdf: - def get_city_center(group): - """ - Calculate the center point of a city from its geometries. + # def get_city_center(group): + # """ + # Calculate the center point of a city from its geometries. - Args: - group (Series): Group of geometries belonging to a city + # Args: + # group (Series): Group of geometries belonging to a city - Returns: - Point: Center point of the city - """ - try: - valid_geoms = group.apply(lambda geom: make_valid(geom) if not geom.is_valid else geom) - union = valid_geoms.unary_union - if not union.is_valid: - union = make_valid(union) - return union.centroid - except Exception as e: - return group.iloc[0].centroid + # Returns: + # Point: Center point of the city + # """ + # try: + # valid_geoms = group.apply(lambda geom: make_valid(geom) if not geom.is_valid else geom) + # union = valid_geoms.unary_union + # if not union.is_valid: + # union = make_valid(union) + # return union.centroid + # except Exception as e: + # return group.iloc[0].centroid - cc_geom = gdf.groupby('city')['geometry'].apply(get_city_center) - ccdf = cc_geom.apply(lambda p: pd.Series({'x': p.x, 'y': p.y})) + # cc_geom = gdf.groupby('city')['geometry'].apply(get_city_center) + # ccdf = cc_geom.apply(lambda p: pd.Series({'x': p.x, 'y': p.y})) - gdf = gdf.join(ccdf, on='city') - gdf['x_local'] = centroids.x - gdf['x'] - gdf['y_local'] = centroids.y - gdf['y'] - gdf = gdf.drop(columns=['x', 'y']) + # gdf = gdf.join(ccdf, on='city') + # gdf['x_local'] = centroids.x - gdf['x'] + # gdf['y_local'] = centroids.y - gdf['y'] + # gdf = gdf.drop(columns=['x', 'y']) - calc = self.calc_polygon_features(gdf) - gdf = pd.concat([gdf, calc], axis=1) - - for z in ['rec_spec_agri','bus_res','industrial','transport']: - if known_gdf_for_rec_zones is not None and 'land_use' in known_gdf_for_rec_zones: - rec_z = known_gdf_for_rec_zones[known_gdf_for_rec_zones['land_use']==z] - else: - rec_z = None - - gdf[f'nearby_{z}_count'] = self.count_nearby_zones(gdf, rec_z, self.buffer_distance) + feature_blocks = [] + if 'city' in gdf.columns: + city_iter = gdf.groupby('city').indices.items() + else: + city_iter = [(None, gdf.index)] + for _, idx in city_iter: + idx = pd.Index(idx) + city_slice = gdf.loc[idx].copy() + city_features = self._compute_city_features(city_slice, target_col=target_col) + feature_blocks.append(city_features) + if feature_blocks: + features_df = pd.concat(feature_blocks, axis=0) + features_df = features_df.reindex(gdf.index) + gdf = pd.concat([gdf, features_df], axis=1) + # Nearby counts by LandUseCategory + # try: + # nearby_df = self.count_nearby_by_category( + # gdf, + # known_gdf_for_rec_zones, + # buffer_distance=self.buffer_distance, + # exclude_self=True, + # require_strict=require_strict_categories, + # ) + # except Exception: + # # If something goes wrong, fall back to zeros to avoid breaking pipeline + # nearby_df = pd.DataFrame( + # 0, + # index=gdf.index, + # columns=[f"nearby_{c.value.lower()}_count" for c in LandUseCategory], + # ) + + # gdf = pd.concat([gdf, nearby_df], axis=1) + return gdf def prepare_data(self, gdf: gpd.GeoDataFrame, - target_col: str = 'land_use_code', + target_col: str = 'category', radius: float = 1000.0, k_neighbors: int = None, - classes_: np.ndarray = None) -> pd.DataFrame: + classes_: np.ndarray = None, + known_gdf_for_rec_zones: Optional[gpd.GeoDataFrame] = None, + require_strict_categories: bool = False) -> pd.DataFrame: """ Prepare feature DataFrame from input GeoDataFrame by computing node features and neighbor aggregates. @@ -413,7 +836,7 @@ def prepare_data(self, gdf: gpd.GeoDataFrame, gdf : gpd.GeoDataFrame Input GeoDataFrame containing geometries and target values target_col : str, optional - Name of the target column (default: 'land_use_code') + Name of the target column (default: 'category') radius : float, optional Search radius for neighbor detection in meters (default: 1000.0) k_neighbors : int, optional @@ -439,11 +862,12 @@ def prepare_data(self, gdf: gpd.GeoDataFrame, """ gdf = gdf.copy() gdf.reset_index(drop=True, inplace=True) - base = self.transform_features(gdf, known_gdf_for_rec_zones=None) - - for col in self.columns_to_log: - if col in base.columns: - base[f'{col}_log'] = np.log1p(base[col]) + base = self.transform_features( + gdf, + target_col=target_col, + known_gdf_for_rec_zones=known_gdf_for_rec_zones, + require_strict_categories=require_strict_categories, + ) pieces = [] for city, idx in gdf.groupby('city').indices.items() if 'city' in gdf.columns else {None: gdf.index}.items(): @@ -452,14 +876,17 @@ def prepare_data(self, gdf: gpd.GeoDataFrame, A = self.build_city_graph(city_base, mode="radius" if k_neighbors is None else "knn", radius=radius, k=k_neighbors or 8) + # Base feature columns used for modeling and neighbor aggregation. + # We exclude service columns and already-derived neighbor/probability columns, + # but we KEEP nearby_* counts so they are included in the model and can also + # be aggregated to nbr_mean_nearby_* if numeric. geom_cols = [c for c in city_base.columns if c not in ('geometry', target_col, 'land_use', 'city', 'city_center')] - geom_cols = [c for c in geom_cols if not c.startswith('nbr_') and not c.startswith('prob_') and not c.startswith('nearby_')] - geom_df = city_base[geom_cols] - - nbr_geom = self.neighbor_geom_aggregates(A, geom_df, agg="mean") + geom_cols = [c for c in geom_cols if not c.startswith('nbr_') and not c.startswith('prob_')] + block = city_base[geom_cols] - block = pd.concat([city_base[geom_cols], nbr_geom], axis=1) + # nbr_geom = self.neighbor_geom_aggregates(A, geom_df, agg="mean") + # block = pd.concat([city_base[geom_cols], nbr_geom], axis=1) if classes_ is not None: labels = gdf.loc[city_idx, target_col] diff --git a/blocksnet/analysis/land_use/prediction/schemas.py b/blocksnet/analysis/land_use/prediction/schemas.py index 4aa88de0..184258f4 100644 --- a/blocksnet/analysis/land_use/prediction/schemas.py +++ b/blocksnet/analysis/land_use/prediction/schemas.py @@ -1,4 +1,5 @@ import pandas as pd +from pandera import Field from pandera.typing import Series from shapely import MultiPolygon, Polygon from blocksnet.enums import LandUseCategory @@ -6,7 +7,7 @@ class BlocksInputSchema(GdfSchema): - category: Series + category: Series = Field(nullable=True) @classmethod def _geometry_types(cls): @@ -20,7 +21,22 @@ def _before_validate(cls, df: pd.DataFrame) -> pd.DataFrame: def parse_category(c): if isinstance(c, LandUseCategory): return c - return LandUseCategory(c.lower()) + if isinstance(c, str): + s = c.strip() + # try value (exact), then case-insensitive by value, then by name + try: + return LandUseCategory(s) + except Exception: + pass + try: + return next(v for v in LandUseCategory if v.value.lower() == s.lower()) + except Exception: + pass + try: + return LandUseCategory[s.upper()] + except Exception: + pass + return None df["category"] = df["category"].map(parse_category) diff --git a/blocksnet/enums/land_use_category.py b/blocksnet/enums/land_use_category.py index 172e7a09..a33189e1 100644 --- a/blocksnet/enums/land_use_category.py +++ b/blocksnet/enums/land_use_category.py @@ -1,15 +1,18 @@ from enum import Enum from blocksnet.enums import LandUse - class LandUseCategory(Enum): - """Enumeration class representing different categories of land use. - - This enum defines three main categories of land use: urban, non-urban, and industrial. - It provides methods to convert between LandUse objects and LandUseCategory instances. + """High-level land use categories used for prediction. + Supported categories: + - INDUSTRIAL + - RECREATION + - BUSINESS + - RESIDENTIAL """ - URBAN = "urban" - NON_URBAN = "non_urban" - INDUSTRIAL = "industrial" + RESIDENTIAL = "RESIDENTIAL" + BUSINESS = "BUSINESS" + RECREATION = "RECREATION" + INDUSTRIAL = "INDUSTRIAL" + _REVERSE_MAP = None @classmethod @@ -40,11 +43,12 @@ def to_land_use(self) -> set[LandUse]: return LandUseCategory._REVERSE_MAP.get(self, set()) LU_MAPPING = { - LandUse.RESIDENTIAL: LandUseCategory.URBAN, - LandUse.BUSINESS: LandUseCategory.URBAN, - LandUse.RECREATION: LandUseCategory.NON_URBAN, - LandUse.TRANSPORT: LandUseCategory.NON_URBAN, - LandUse.SPECIAL: LandUseCategory.NON_URBAN, - LandUse.AGRICULTURE: LandUseCategory.NON_URBAN, + LandUse.RESIDENTIAL: LandUseCategory.RESIDENTIAL, + LandUse.BUSINESS: LandUseCategory.BUSINESS, + LandUse.RECREATION: LandUseCategory.RECREATION, + LandUse.AGRICULTURE: LandUseCategory.RECREATION, + LandUse.SPECIAL: LandUseCategory.RECREATION, LandUse.INDUSTRIAL: LandUseCategory.INDUSTRIAL, -} \ No newline at end of file + LandUse.TRANSPORT: LandUseCategory.INDUSTRIAL, + # Other LandUse values (e.g., TRANSPORT, SPECIAL, AGRICULTURE) map to None +} diff --git a/examples/analysis/land_use/artifacts/meta.json b/examples/analysis/land_use/artifacts/meta.json new file mode 100644 index 00000000..e6a5b15b --- /dev/null +++ b/examples/analysis/land_use/artifacts/meta.json @@ -0,0 +1 @@ +{"strategy_cls": "SKLearnVotingClassificationStrategy", "model_cls": "VotingClassifier", "model_params": {"voting": "soft", "n_jobs": -1}} \ No newline at end of file diff --git a/examples/analysis/land_use/artifacts/model.joblib b/examples/analysis/land_use/artifacts/model.joblib new file mode 100644 index 00000000..56bb9c14 Binary files /dev/null and b/examples/analysis/land_use/artifacts/model.joblib differ diff --git a/examples/analysis/land_use/check.ipynb b/examples/analysis/land_use/check.ipynb new file mode 100644 index 00000000..98fee5ea --- /dev/null +++ b/examples/analysis/land_use/check.ipynb @@ -0,0 +1,460 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "53fa74e8", + "metadata": {}, + "source": [ + "# City Coverage Check" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "3e881402", + "metadata": {}, + "outputs": [], + "source": [ + "import pickle\n", + "from pathlib import Path\n", + "\n", + "import geopandas as gpd\n", + "import osmnx as ox\n", + "import pandas as pd\n", + "from shapely.ops import unary_union\n", + "from tqdm.auto import tqdm\n", + "\n", + "from blocksnet.analysis.land_use.prediction.core import (\n", + " category_to_index,\n", + " land_use_to_category,\n", + " str_to_land_use,\n", + ")\n", + "from blocksnet.analysis.land_use.prediction.schemas import BlocksInputSchema\n", + "\n", + "ox.settings.use_cache = True\n", + "\n", + "\n", + "def load_gdfs(root: str | Path, pattern: str = \"*.pkl\", target_crs: str | None = None) -> list[gpd.GeoDataFrame]:\n", + " \"\"\"Load GeoDataFrames from pickle files located under ``root``.\"\"\"\n", + " root_path = Path(root)\n", + " if root_path.is_file() and root_path.suffix.lower() == \".pkl\":\n", + " files = [root_path]\n", + " elif root_path.is_dir():\n", + " files = sorted(p for p in root_path.rglob(pattern) if p.suffix.lower() == \".pkl\")\n", + " else:\n", + " return []\n", + "\n", + " def ensure_gdf(obj):\n", + " if isinstance(obj, gpd.GeoDataFrame):\n", + " return obj\n", + " if isinstance(obj, pd.DataFrame) and \"geometry\" in obj.columns:\n", + " return gpd.GeoDataFrame(obj, geometry=\"geometry\", crs=getattr(obj, \"crs\", None))\n", + " return None\n", + "\n", + " def infer_city_country(fp: Path):\n", + " city = fp.stem\n", + " country = fp.parent.name if fp.parent != fp else None\n", + " return city, country\n", + "\n", + " gdfs: list[gpd.GeoDataFrame] = []\n", + " for fp in files:\n", + " try:\n", + " with open(fp, \"rb\") as f:\n", + " obj = pickle.load(f)\n", + " except Exception as exc: # pragma: no cover - logging only\n", + " print(f'Failed to load {fp}: {exc}')\n", + " continue\n", + "\n", + " gdf = ensure_gdf(obj)\n", + " if gdf is None and isinstance(obj, dict):\n", + " for key, value in obj.items():\n", + " gi = ensure_gdf(value)\n", + " if gi is None:\n", + " continue\n", + " gi = gi.copy()\n", + " if \"city\" not in gi.columns or gi[\"city\"].isna().all():\n", + " gi[\"city\"] = str(key)\n", + " _, country = infer_city_country(fp)\n", + " if \"country\" not in gi.columns and country:\n", + " gi[\"country\"] = country\n", + " if target_crs:\n", + " gi = gi.to_crs(target_crs) if gi.crs else gi.set_crs(target_crs)\n", + " gdfs.append(gi)\n", + " continue\n", + "\n", + " if gdf is None:\n", + " print(f'Skip {fp}: unsupported payload type')\n", + " continue\n", + "\n", + " gdf = gdf.copy()\n", + " city, country = infer_city_country(fp)\n", + " if \"city\" not in gdf.columns:\n", + " gdf[\"city\"] = city\n", + " if \"country\" not in gdf.columns and country:\n", + " gdf[\"country\"] = country\n", + " if target_crs:\n", + " gdf = gdf.to_crs(target_crs) if gdf.crs else gdf.set_crs(target_crs)\n", + " gdfs.append(gdf)\n", + "\n", + " return gdfs\n", + "\n", + "\n", + "def map_and_filter_blocks(blocks: list[gpd.GeoDataFrame]) -> list[gpd.GeoDataFrame]:\n", + " filtered: list[gpd.GeoDataFrame] = []\n", + " for gdf in blocks:\n", + " gi = gdf.copy()\n", + " if \"land_use\" in gi.columns:\n", + " gi[\"category\"] = gi[\"land_use\"].map(str_to_land_use).map(land_use_to_category)\n", + " if \"category\" in gi and gi[\"category\"].notna().sum() > 0:\n", + " filtered.append(gi)\n", + " return filtered\n", + "\n", + "\n", + "def normalize_and_filter(data):\n", + " if isinstance(data, (list, tuple)):\n", + " result = []\n", + " for gdf in data:\n", + " gi = BlocksInputSchema(gdf)\n", + " if \"land_use\" in gi:\n", + " gi[\"category\"] = gi[\"land_use\"].map(str_to_land_use).map(land_use_to_category)\n", + " gi = gi[gi[\"category\"].map(category_to_index).notna()]\n", + " if not gi.empty:\n", + " gi['city'] = gdf['city'].iloc[0]\n", + " gi['country'] = gdf['country'].iloc[0]\n", + " result.append(gi)\n", + " return result\n", + " gi = BlocksInputSchema(data)\n", + " if \"land_use\" in gi:\n", + " gi[\"category\"] = gi[\"land_use\"].map(str_to_land_use).map(land_use_to_category)\n", + " return gi[gi[\"category\"].map(category_to_index).notna()]\n" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "052d33de", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Loaded 2126 city GeoDataFrames\n" + ] + } + ], + "source": [ + "data_root = Path(\"data/TANYA-2025/\")\n", + "blocks_gdf = load_gdfs(data_root)\n", + "if isinstance(blocks_gdf, gpd.GeoDataFrame):\n", + " blocks_gdf = [blocks_gdf]\n", + "blocks_gdf_copy = normalize_and_filter(map_and_filter_blocks(blocks_gdf))\n", + "print(f\"Loaded {len(blocks_gdf_copy)} city GeoDataFrames\")\n" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "b3b4c0c3", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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geometrycategorycitycountry
0POLYGON ((7695233.28 4347830.673, 7695239.313 ...LandUseCategory.RESIDENTIALBaghlānAfghanistan
1POLYGON ((7695357.312 4345125.413, 7695335.716...LandUseCategory.RESIDENTIALBaghlānAfghanistan
2POLYGON ((7695305.481 4339252.936, 7695237.554...LandUseCategory.RESIDENTIALBaghlānAfghanistan
3POLYGON ((7696979.816 4333980.991, 7696948.112...LandUseCategory.RESIDENTIALBaghlānAfghanistan
4POLYGON ((7646651.829 4297389.817, 7646653.98 ...LandUseCategory.RESIDENTIALBaghlānAfghanistan
...............
1860POLYGON ((7701818.262 4339531.938, 7701841.55 ...LandUseCategory.RESIDENTIALBaghlānAfghanistan
1861POLYGON ((7680725.868 4333333.253, 7680717.652...LandUseCategory.RESIDENTIALBaghlānAfghanistan
1862POLYGON ((7680704.149 4333363.948, 7680705.697...LandUseCategory.RESIDENTIALBaghlānAfghanistan
1863POLYGON ((7670397.066 4360671.545, 7670427.278...LandUseCategory.RESIDENTIALBaghlānAfghanistan
1865POLYGON ((7647646.825 4294758.468, 7647645.6 4...LandUseCategory.RESIDENTIALBaghlānAfghanistan
\n", + "

1395 rows × 4 columns

\n", + "
" + ], + "text/plain": [ + " geometry \\\n", + "0 POLYGON ((7695233.28 4347830.673, 7695239.313 ... \n", + "1 POLYGON ((7695357.312 4345125.413, 7695335.716... \n", + "2 POLYGON ((7695305.481 4339252.936, 7695237.554... \n", + "3 POLYGON ((7696979.816 4333980.991, 7696948.112... \n", + "4 POLYGON ((7646651.829 4297389.817, 7646653.98 ... \n", + "... ... \n", + "1860 POLYGON ((7701818.262 4339531.938, 7701841.55 ... \n", + "1861 POLYGON ((7680725.868 4333333.253, 7680717.652... \n", + "1862 POLYGON ((7680704.149 4333363.948, 7680705.697... \n", + "1863 POLYGON ((7670397.066 4360671.545, 7670427.278... \n", + "1865 POLYGON ((7647646.825 4294758.468, 7647645.6 4... \n", + "\n", + " category city country \n", + "0 LandUseCategory.RESIDENTIAL Baghlān Afghanistan \n", + "1 LandUseCategory.RESIDENTIAL Baghlān Afghanistan \n", + "2 LandUseCategory.RESIDENTIAL Baghlān Afghanistan \n", + "3 LandUseCategory.RESIDENTIAL Baghlān Afghanistan \n", + "4 LandUseCategory.RESIDENTIAL Baghlān Afghanistan \n", + "... ... ... ... \n", + "1860 LandUseCategory.RESIDENTIAL Baghlān Afghanistan \n", + "1861 LandUseCategory.RESIDENTIAL Baghlān Afghanistan \n", + "1862 LandUseCategory.RESIDENTIAL Baghlān Afghanistan \n", + "1863 LandUseCategory.RESIDENTIAL Baghlān Afghanistan \n", + "1865 LandUseCategory.RESIDENTIAL Baghlān Afghanistan \n", + "\n", + "[1395 rows x 4 columns]" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "blocks_gdf_copy[0]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "afb3664e", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Cities: 1%| | 14/2126 [03:49<30:57:38, 52.77s/it]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "OSM lookup failed for Annaba, Algeria: ('Connection broken: IncompleteRead(16171 bytes read, 297196 more expected)', IncompleteRead(16171 bytes read, 297196 more expected))\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Cities: 3%|▎ | 67/2126 [06:24<1:35:24, 2.78s/it] " + ] + } + ], + "source": [ + "def choose_area_crs(gdf: gpd.GeoDataFrame, fallback: str = \"EPSG:3857\") -> str:\n", + " try:\n", + " crs = gdf.estimate_utm_crs()\n", + " if crs:\n", + " authority = getattr(crs, \"to_authority\", None)\n", + " if authority:\n", + " epsg = authority()\n", + " if epsg:\n", + " return f\"{epsg[0]}:{epsg[1]}\"\n", + " return str(crs)\n", + " except Exception:\n", + " pass\n", + " return fallback\n", + "\n", + "\n", + "def get_city_country(gdf: gpd.GeoDataFrame) -> tuple[str | None, str | None]:\n", + " city = None\n", + " if \"city\" in gdf.columns:\n", + " values = [v for v in gdf[\"city\"].unique() if isinstance(v, str) and v.strip()]\n", + " if values:\n", + " city = values[0]\n", + " country = None\n", + " if \"country\" in gdf.columns:\n", + " values = [v for v in gdf[\"country\"].unique() if isinstance(v, str) and v.strip()]\n", + " if values:\n", + " country = values[0]\n", + " return city, country\n", + "\n", + "\n", + "def geocode_city_boundary(city: str | None, country: str | None) -> gpd.GeoDataFrame | None:\n", + " if not city:\n", + " return None\n", + " query = \", \".join(part for part in (city, country) if part)\n", + " try:\n", + " boundary = ox.geocode_to_gdf(query)\n", + " except Exception as exc:\n", + " print(f\"OSM lookup failed for {query}: {exc}\")\n", + " return None\n", + " return boundary if not boundary.empty else None\n", + "\n", + "\n", + "def compute_coverage(city_gdf: gpd.GeoDataFrame) -> dict | None:\n", + " city, country = get_city_country(city_gdf)\n", + " boundary_gdf = geocode_city_boundary(city, country)\n", + " if boundary_gdf is None:\n", + " return None\n", + "\n", + " crs = choose_area_crs(city_gdf)\n", + " boundary_proj = boundary_gdf.to_crs(crs)\n", + " city_proj = city_gdf.to_crs(crs)\n", + "\n", + " city_boundary = unary_union(boundary_proj.geometry)\n", + " if city_boundary.is_empty:\n", + " return None\n", + "\n", + " source_union = unary_union(city_proj.geometry)\n", + " if source_union.is_empty:\n", + " return None\n", + "\n", + " covered_area = source_union.intersection(city_boundary).area\n", + " source_area = source_union.area\n", + " city_area = city_boundary.area\n", + " coverage_pct = 0.0 if city_area == 0 else (covered_area / city_area) * 100\n", + "\n", + " return {\n", + " \"city\": city or \"\",\n", + " \"country\": country,\n", + " \"city_area_km2\": city_area / 1_000_000,\n", + " \"covered_area_km2\": covered_area / 1_000_000,\n", + " \"source_area_km2\": source_area / 1_000_000,\n", + " \"coverage_pct\": coverage_pct,\n", + " }\n", + "\n", + "\n", + "results = []\n", + "for gdf in tqdm(blocks_gdf_copy, desc=\"Cities\"):\n", + " metrics = compute_coverage(gdf)\n", + " if metrics is None:\n", + " continue\n", + " results.append(metrics)\n", + "\n", + "coverage_df = pd.DataFrame(results)\n", + "coverage_df\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "64bc9071", + "metadata": {}, + "outputs": [], + "source": [ + "ax = coverage_df[\"coverage_pct\"].plot.hist(bins=20, figsize=(8, 4))\n", + "ax.set_xlabel(\"% покрытия городской территории\")\n", + "ax.set_ylabel(\"Количество городов\")\n", + "ax.set_title(\"Распределение покрытия блоками\")\n", + "ax.grid(True, linestyle=\"--\", alpha=0.5)\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": ".venv", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.11" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/examples/analysis/land_use/prediction.ipynb b/examples/analysis/land_use/prediction.ipynb index 8eba3c96..af1c2c44 100644 --- a/examples/analysis/land_use/prediction.ipynb +++ b/examples/analysis/land_use/prediction.ipynb @@ -37,12 +37,13 @@ "outputs": [], "source": [ "classifier = SpatialClassifier.default()\n", - "result = classifier.run(blocks_gdf)" + "test_ready = classifier.preprocess_run_data(blocks_gdf)\n", + "result = classifier.run(test_ready)" ] }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 3, "id": "93f96afc", "metadata": {}, "outputs": [ @@ -70,56 +71,172 @@ " geometry\n", " category\n", " pred_name\n", - " prob_urban\n", - " prob_non_urban\n", - " prob_industrial\n", + " prob_RESIDENTIAL\n", + " prob_BUSINESS\n", + " prob_RECREATION\n", + " prob_INDUSTRIAL\n", " \n", " \n", " \n", " \n", " 0\n", - " POLYGON ((0 0, 1 0, 1 1, 0 1, 0 0))\n", - " LandUseCategory.URBAN\n", - " non_urban\n", - " 0.351396\n", - " 0.480452\n", - " 0.168152\n", + " MULTIPOLYGON (((349424.859 6631180.891, 349424...\n", + " LandUseCategory.INDUSTRIAL\n", + " INDUSTRIAL\n", + " 0.045165\n", + " 0.028159\n", + " 0.305649\n", + " 0.621027\n", " \n", " \n", " 1\n", - " POLYGON ((1 0, 2 0, 2 1, 1 1, 1 0))\n", - " LandUseCategory.URBAN\n", - " non_urban\n", - " 0.345072\n", - " 0.492949\n", - " 0.161979\n", + " MULTIPOLYGON (((352083.617 6633950.146, 352240...\n", + " LandUseCategory.RECREATION\n", + " RESIDENTIAL\n", + " 0.376333\n", + " 0.209277\n", + " 0.238064\n", + " 0.176327\n", " \n", " \n", " 2\n", - " POLYGON ((0 1, 1 1, 1 2, 0 2, 0 1))\n", + " MULTIPOLYGON (((346700.642 6618453.176, 346681...\n", + " LandUseCategory.RESIDENTIAL\n", + " RESIDENTIAL\n", + " 0.724700\n", + " 0.118614\n", + " 0.118224\n", + " 0.038462\n", + " \n", + " \n", + " 3\n", + " MULTIPOLYGON (((347043.363 6618261.219, 347042...\n", + " LandUseCategory.RESIDENTIAL\n", + " RECREATION\n", + " 0.256970\n", + " 0.138033\n", + " 0.451670\n", + " 0.153327\n", + " \n", + " \n", + " 4\n", + " MULTIPOLYGON (((354879.039 6618859.116, 354845...\n", + " LandUseCategory.RESIDENTIAL\n", + " RESIDENTIAL\n", + " 0.579051\n", + " 0.133522\n", + " 0.208631\n", + " 0.078796\n", + " \n", + " \n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " \n", + " \n", + " 9244\n", + " MULTIPOLYGON (((346635.461 6647492.048, 346473...\n", + " LandUseCategory.RESIDENTIAL\n", + " RECREATION\n", + " 0.123647\n", + " 0.019748\n", + " 0.839957\n", + " 0.016648\n", + " \n", + " \n", + " 9245\n", + " MULTIPOLYGON (((346361.221 6647603.446, 346328...\n", " LandUseCategory.INDUSTRIAL\n", - " non_urban\n", - " 0.344711\n", - " 0.485752\n", - " 0.169537\n", + " BUSINESS\n", + " 0.163405\n", + " 0.432571\n", + " 0.302783\n", + " 0.101240\n", + " \n", + " \n", + " 9246\n", + " MULTIPOLYGON (((344109.285 6649134.367, 344000...\n", + " LandUseCategory.RECREATION\n", + " RECREATION\n", + " 0.296557\n", + " 0.160168\n", + " 0.402465\n", + " 0.140809\n", + " \n", + " \n", + " 9247\n", + " MULTIPOLYGON (((346323.488 6649497.386, 346199...\n", + " LandUseCategory.INDUSTRIAL\n", + " RESIDENTIAL\n", + " 0.479835\n", + " 0.183405\n", + " 0.296642\n", + " 0.040117\n", + " \n", + " \n", + " 9248\n", + " MULTIPOLYGON (((346670.461 6649890.747, 346543...\n", + " LandUseCategory.RESIDENTIAL\n", + " RESIDENTIAL\n", + " 0.477204\n", + " 0.277776\n", + " 0.166046\n", + " 0.078974\n", " \n", " \n", "\n", + "

9249 rows × 7 columns

\n", "" ], "text/plain": [ - " geometry category pred_name \\\n", - "0 POLYGON ((0 0, 1 0, 1 1, 0 1, 0 0)) LandUseCategory.URBAN non_urban \n", - "1 POLYGON ((1 0, 2 0, 2 1, 1 1, 1 0)) LandUseCategory.URBAN non_urban \n", - "2 POLYGON ((0 1, 1 1, 1 2, 0 2, 0 1)) LandUseCategory.INDUSTRIAL non_urban \n", + " geometry \\\n", + "0 MULTIPOLYGON (((349424.859 6631180.891, 349424... \n", + "1 MULTIPOLYGON (((352083.617 6633950.146, 352240... \n", + "2 MULTIPOLYGON (((346700.642 6618453.176, 346681... \n", + "3 MULTIPOLYGON (((347043.363 6618261.219, 347042... \n", + "4 MULTIPOLYGON (((354879.039 6618859.116, 354845... \n", + "... ... \n", + "9244 MULTIPOLYGON (((346635.461 6647492.048, 346473... \n", + "9245 MULTIPOLYGON (((346361.221 6647603.446, 346328... \n", + "9246 MULTIPOLYGON (((344109.285 6649134.367, 344000... \n", + "9247 MULTIPOLYGON (((346323.488 6649497.386, 346199... \n", + "9248 MULTIPOLYGON (((346670.461 6649890.747, 346543... \n", "\n", - " prob_urban prob_non_urban prob_industrial \n", - "0 0.351396 0.480452 0.168152 \n", - "1 0.345072 0.492949 0.161979 \n", - "2 0.344711 0.485752 0.169537 " + " category pred_name prob_RESIDENTIAL \\\n", + "0 LandUseCategory.INDUSTRIAL INDUSTRIAL 0.045165 \n", + "1 LandUseCategory.RECREATION RESIDENTIAL 0.376333 \n", + "2 LandUseCategory.RESIDENTIAL RESIDENTIAL 0.724700 \n", + "3 LandUseCategory.RESIDENTIAL RECREATION 0.256970 \n", + "4 LandUseCategory.RESIDENTIAL RESIDENTIAL 0.579051 \n", + "... ... ... ... \n", + "9244 LandUseCategory.RESIDENTIAL RECREATION 0.123647 \n", + "9245 LandUseCategory.INDUSTRIAL BUSINESS 0.163405 \n", + "9246 LandUseCategory.RECREATION RECREATION 0.296557 \n", + "9247 LandUseCategory.INDUSTRIAL RESIDENTIAL 0.479835 \n", + "9248 LandUseCategory.RESIDENTIAL RESIDENTIAL 0.477204 \n", + "\n", + " prob_BUSINESS prob_RECREATION prob_INDUSTRIAL \n", + "0 0.028159 0.305649 0.621027 \n", + "1 0.209277 0.238064 0.176327 \n", + "2 0.118614 0.118224 0.038462 \n", + "3 0.138033 0.451670 0.153327 \n", + "4 0.133522 0.208631 0.078796 \n", + "... ... ... ... \n", + "9244 0.019748 0.839957 0.016648 \n", + "9245 0.432571 0.302783 0.101240 \n", + "9246 0.160168 0.402465 0.140809 \n", + "9247 0.183405 0.296642 0.040117 \n", + "9248 0.277776 0.166046 0.078974 \n", + "\n", + "[9249 rows x 7 columns]" ] }, - "execution_count": 25, + "execution_count": 3, "metadata": {}, "output_type": "execute_result" } @@ -143,10 +260,9 @@ "metadata": {}, "outputs": [], "source": [ - "import os\n", "from xgboost import XGBClassifier\n", "from lightgbm import LGBMClassifier\n", - "from sklearn.ensemble import RandomForestClassifier, HistGradientBoostingClassifier\n", + "from sklearn.ensemble import RandomForestClassifier\n", "\n", "from blocksnet.analysis.land_use.prediction import SpatialClassifier\n", "from blocksnet.machine_learning.strategy.sklearn.ensemble.voting.classification_strategy import SKLearnVotingClassificationStrategy" @@ -160,61 +276,301 @@ "outputs": [], "source": [ "import pandas as pd\n", - "blocks_gdf = pd.read_pickle('./../../data/saint_petersburg/blocks.pickle')" + "blocks_gdf = pd.read_pickle('./../../data/saint_petersburg/blocks.pickle')\n" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "id": "32125c7e", "metadata": {}, "outputs": [], "source": [ - "# 1. Инициализация и обучение\n", - "BASE_PARAMS = {\"random_state\": 42, \"n_jobs\": -1}\n", - "CPU = max(1, min(8, os.cpu_count() or 1))\n", - "MODEL_PARAMS = {\n", - " \"rf\": {\n", - " \"n_estimators\": 120, # было 200\n", - " \"max_depth\": 7,\n", - " \"class_weight\": \"balanced\",\n", - " \"max_samples\": 0.25, # 🔴 бэггинг на подвыборке\n", - " \"min_samples_leaf\": 10, # стабилизация и меньше узлов\n", - " **BASE_PARAMS\n", - " },\n", - " \"xgb\": {\n", - " \"n_estimators\": 150, # меньше\n", - " \"max_depth\": 7,\n", - " \"learning_rate\": 0.05,\n", - " \"subsample\": 0.8, # стахастичность\n", - " \"colsample_bytree\": 0.8,\n", - " \"tree_method\": \"hist\", # память/скорость\n", - " \"n_jobs\": CPU # XGB игнорирует BASE_PARAMS если его стерли\n", - " },\n", - " \"lgb\": {\n", - " \"n_estimators\": 200,\n", - " \"max_depth\": 7,\n", - " \"learning_rate\": 0.05,\n", - " \"class_weight\": \"balanced\",\n", - " \"num_threads\": CPU # у LGB параметр другое имя\n", - " },\n", - " \"hgb\": {\n", - " \"max_iter\": 200,\n", - " \"max_depth\": 7,\n", - " \"learning_rate\": 0.05,\n", - " \"random_state\": 42\n", - " }\n", - "}\n", "estimators = [\n", - " (\"rf\", RandomForestClassifier(**MODEL_PARAMS[\"rf\"])),\n", - " (\"xgb\", XGBClassifier(**MODEL_PARAMS[\"xgb\"])),\n", - " (\"lgb\", LGBMClassifier(**MODEL_PARAMS[\"lgb\"])),\n", - " (\"hgb\", HistGradientBoostingClassifier(**MODEL_PARAMS[\"hgb\"])),\n", + " ('XGB', XGBClassifier(\n", + " n_estimators=200,\n", + " max_depth=8,\n", + " learning_rate=0.3,\n", + " eval_metric='mlogloss',\n", + " use_label_encoder=False,\n", + " n_jobs=-1,\n", + " random_state=42\n", + " )),\n", + " ('LGBM', LGBMClassifier(\n", + " n_estimators=200,\n", + " max_depth=8,\n", + " learning_rate=0.3,\n", + " class_weight='balanced',\n", + " verbose=-1,\n", + " n_jobs=-1,\n", + " random_state=42\n", + " )),\n", + " ('RF', RandomForestClassifier(\n", + " n_estimators=200,\n", + " max_depth=8,\n", + " class_weight='balanced',\n", + " n_jobs=-1,\n", + " random_state=42\n", + " )),\n", "]\n", "\n", "strategy = SKLearnVotingClassificationStrategy(estimators, {\"voting\": \"soft\", \"n_jobs\": -1})\n", - "classifier = SpatialClassifier(strategy, 1000, 5)\n", - "score = classifier.train(blocks_gdf)" + "classifier = SpatialClassifier(strategy, 1000, 5)\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "73744c0d", + "metadata": {}, + "outputs": [], + "source": [ + "train_ready = classifier.preprocess_training_data(blocks_gdf)\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "cc5bbd80", + "metadata": {}, + "outputs": [], + "source": [ + "classifier.train(train_ready)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "076594a6", + "metadata": {}, + "outputs": [], + "source": [ + "raw_test = pd.read_pickle('./../../data/saint_petersburg/test_blocks.pickle')\n", + "test_ready = classifier.preprocess_run_data(raw_test)\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "25272750", + "metadata": {}, + "outputs": [], + "source": [ + "result = classifier.run(test_ready)\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "5a020e4d", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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geometrycategorypred_nameprob_RESIDENTIALprob_BUSINESSprob_RECREATIONprob_INDUSTRIAL
0MULTIPOLYGON (((349424.859 6631180.891, 349424...LandUseCategory.INDUSTRIALINDUSTRIAL0.0451650.0281590.3056490.621027
1MULTIPOLYGON (((352083.617 6633950.146, 352240...LandUseCategory.RECREATIONRESIDENTIAL0.3763330.2092770.2380640.176327
2MULTIPOLYGON (((346700.642 6618453.176, 346681...LandUseCategory.RESIDENTIALRESIDENTIAL0.7247000.1186140.1182240.038462
3MULTIPOLYGON (((347043.363 6618261.219, 347042...LandUseCategory.RESIDENTIALRECREATION0.2569700.1380330.4516700.153327
4MULTIPOLYGON (((354879.039 6618859.116, 354845...LandUseCategory.RESIDENTIALRESIDENTIAL0.5790510.1335220.2086310.078796
........................
9244MULTIPOLYGON (((346635.461 6647492.048, 346473...LandUseCategory.RESIDENTIALRECREATION0.1236470.0197480.8399570.016648
9245MULTIPOLYGON (((346361.221 6647603.446, 346328...LandUseCategory.INDUSTRIALBUSINESS0.1634050.4325710.3027830.101240
9246MULTIPOLYGON (((344109.285 6649134.367, 344000...LandUseCategory.RECREATIONRECREATION0.2965570.1601680.4024650.140809
9247MULTIPOLYGON (((346323.488 6649497.386, 346199...LandUseCategory.INDUSTRIALRESIDENTIAL0.4798350.1834050.2966420.040117
9248MULTIPOLYGON (((346670.461 6649890.747, 346543...LandUseCategory.RESIDENTIALRESIDENTIAL0.4772040.2777760.1660460.078974
\n", + "

9249 rows × 7 columns

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" + ], + "text/plain": [ + " geometry \\\n", + "0 MULTIPOLYGON (((349424.859 6631180.891, 349424... \n", + "1 MULTIPOLYGON (((352083.617 6633950.146, 352240... \n", + "2 MULTIPOLYGON (((346700.642 6618453.176, 346681... \n", + "3 MULTIPOLYGON (((347043.363 6618261.219, 347042... \n", + "4 MULTIPOLYGON (((354879.039 6618859.116, 354845... \n", + "... ... \n", + "9244 MULTIPOLYGON (((346635.461 6647492.048, 346473... \n", + "9245 MULTIPOLYGON (((346361.221 6647603.446, 346328... \n", + "9246 MULTIPOLYGON (((344109.285 6649134.367, 344000... \n", + "9247 MULTIPOLYGON (((346323.488 6649497.386, 346199... \n", + "9248 MULTIPOLYGON (((346670.461 6649890.747, 346543... \n", + "\n", + " category pred_name prob_RESIDENTIAL \\\n", + "0 LandUseCategory.INDUSTRIAL INDUSTRIAL 0.045165 \n", + "1 LandUseCategory.RECREATION RESIDENTIAL 0.376333 \n", + "2 LandUseCategory.RESIDENTIAL RESIDENTIAL 0.724700 \n", + "3 LandUseCategory.RESIDENTIAL RECREATION 0.256970 \n", + "4 LandUseCategory.RESIDENTIAL RESIDENTIAL 0.579051 \n", + "... ... ... ... \n", + "9244 LandUseCategory.RESIDENTIAL RECREATION 0.123647 \n", + "9245 LandUseCategory.INDUSTRIAL BUSINESS 0.163405 \n", + "9246 LandUseCategory.RECREATION RECREATION 0.296557 \n", + "9247 LandUseCategory.INDUSTRIAL RESIDENTIAL 0.479835 \n", + "9248 LandUseCategory.RESIDENTIAL RESIDENTIAL 0.477204 \n", + "\n", + " prob_BUSINESS prob_RECREATION prob_INDUSTRIAL \n", + "0 0.028159 0.305649 0.621027 \n", + "1 0.209277 0.238064 0.176327 \n", + "2 0.118614 0.118224 0.038462 \n", + "3 0.138033 0.451670 0.153327 \n", + "4 0.133522 0.208631 0.078796 \n", + "... ... ... ... \n", + "9244 0.019748 0.839957 0.016648 \n", + "9245 0.432571 0.302783 0.101240 \n", + "9246 0.160168 0.402465 0.140809 \n", + "9247 0.183405 0.296642 0.040117 \n", + "9248 0.277776 0.166046 0.078974 \n", + "\n", + "[9249 rows x 7 columns]" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "result\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e2beb4dd", + "metadata": {}, + "outputs": [], + "source": [ + "classifier.save('artifacts')\n" ] } ], diff --git a/examples/analysis/land_use/prediction_v2.ipynb b/examples/analysis/land_use/prediction_v2.ipynb new file mode 100644 index 00000000..c509fcc4 --- /dev/null +++ b/examples/analysis/land_use/prediction_v2.ipynb @@ -0,0 +1,2646 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 113, + "id": "1fd9d09b", + "metadata": {}, + "outputs": [], + "source": [ + "import pickle\n", + "import osmnx as ox\n", + "import numpy as np\n", + "import pandas as pd\n", + "import geopandas as gpd\n", + "from pathlib import Path\n", + "from tqdm.auto import tqdm\n", + "from shapely.geometry import Point, MultiPolygon, Polygon\n", + "from copy import deepcopy\n", + "\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "import h3\n" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "99cd69eb", + "metadata": {}, + "outputs": [], + "source": [ + "def load_gdfs(root: str | Path, pattern: str = \"*.pkl\", target_crs: str | None = None) -> list[gpd.GeoDataFrame]:\n", + " \"\"\"\n", + " Загружает GeoDataFrame'ы из .pkl файлов:\n", + " - если root указывает на один .pkl файл — читаем его и возвращаем список [GDF]\n", + " - если root — папка — рекурсивно ищем по маске pattern и возвращаем список GDF\n", + "\n", + " Пытаемся заполнить столбцы 'city' и 'country' из структуры путей:\n", + " ...///.pkl\n", + "\n", + " Args:\n", + " root: путь к файлу .pkl или папке\n", + " pattern: маска файлов (по умолчанию \"*.pkl\") — используется только если root папка\n", + " target_crs: если указан, приводим все геоданные к этому CRS\n", + "\n", + " Returns:\n", + " list[GeoDataFrame]\n", + " \"\"\"\n", + " p = Path(root)\n", + "\n", + " def _ensure_gdf(obj):\n", + " \"\"\"Преобразует объект в GeoDataFrame, если это DataFrame с колонкой 'geometry'.\"\"\"\n", + " if isinstance(obj, gpd.GeoDataFrame):\n", + " return obj\n", + " if isinstance(obj, pd.DataFrame) and \"geometry\" in obj.columns:\n", + " return gpd.GeoDataFrame(obj, geometry=\"geometry\", crs=getattr(obj, \"crs\", None))\n", + " return None\n", + "\n", + " def _infer_city_country(fp: Path):\n", + " # ожидаем ...///.pkl\n", + " city = fp.name[:-4] if fp else None\n", + " country = fp.parent.name if len(fp.parents) >= 2 else None\n", + " return city, country\n", + "\n", + " # Соберём список файлов\n", + " if p.is_file() and p.suffix.lower() == \".pkl\":\n", + " files = [p]\n", + " elif p.is_dir():\n", + " files = sorted(p.rglob(pattern))\n", + " files = [f for f in files if f.suffix.lower() == \".pkl\"]\n", + " else:\n", + " return []\n", + "\n", + " gdfs: list[gpd.GeoDataFrame] = []\n", + "\n", + " for fp in files:\n", + " try:\n", + " with open(fp, \"rb\") as f:\n", + " obj = pickle.load(f)\n", + " except Exception as e:\n", + " print(f\"Пропускаю {fp}: {e}\")\n", + " continue\n", + "\n", + " # Если GeoDataFrame\n", + " gdf = _ensure_gdf(obj)\n", + "\n", + " # Если dict с GeoDataFrame\n", + " if gdf is None and isinstance(obj, dict):\n", + " for k, v in obj.items():\n", + " gi = _ensure_gdf(v)\n", + " if gi is None:\n", + " continue\n", + " gi = gi.copy()\n", + " if \"city\" not in gi.columns or gi[\"city\"].isna().all():\n", + " gi[\"city\"] = str(k)\n", + " _, country = _infer_city_country(fp)\n", + " if (\"country\" not in gi.columns) and country:\n", + " gi[\"country\"] = country\n", + " if target_crs:\n", + " gi = gi.to_crs(target_crs) if gi.crs else gi.set_crs(target_crs)\n", + " gdfs.append(gi)\n", + " continue\n", + "\n", + " if gdf is None:\n", + " print(f\"Пропускаю {fp}: объект не GeoDataFrame и не dict с GeoDataFrame.\")\n", + " continue\n", + "\n", + " gdf = gdf.copy()\n", + " city, country = _infer_city_country(fp)\n", + " if \"city\" not in gdf.columns:\n", + " gdf[\"city\"] = city or fp.stem\n", + " if (\"country\" not in gdf.columns) and country:\n", + " gdf[\"country\"] = country\n", + "\n", + " if target_crs:\n", + " gdf = gdf.to_crs(target_crs) if gdf.crs else gdf.set_crs(target_crs)\n", + "\n", + " gdfs.append(gdf)\n", + "\n", + " return gdfs\n", + "\n", + "blocks_gdf = load_gdfs('data/TANYA-2025 v2/Russia')\n", + "spb_test = gpd.read_file('data/blocks_SPB_test.gpkg')[['land_use', 'geometry']]\n", + "kem_test = gpd.read_file('data/blocks_KEM_test.gpkg')[['land_use', 'geometry']]\n", + "\n", + "kem_test.loc[kem_test['land_use'] == 'businnes', 'land_use'] = 'business'" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "41f3fec6", + "metadata": {}, + "outputs": [], + "source": [ + "def get_city_boundary(city_name, country_name):\n", + " \"\"\"\n", + " Возвращает GeoDataFrame с границей города.\n", + " Пытается найти именно город (place=city), а не административный район.\n", + " \"\"\"\n", + " query = f\"city {city_name}, {country_name}\"\n", + " \n", + " # --- 1. Попробуем найти как \"place=city\" ---\n", + " try:\n", + " gdf_city = ox.geocode_to_gdf(\n", + " query,\n", + " which_result=1, # первый результат\n", + " structure='polygon', # только полигоны\n", + " tags={'place': 'city'} # фильтр по типу\n", + " )\n", + " if not gdf_city.empty:\n", + " print(f\"✅ Найден город как place=city: {city_name}\")\n", + " return gdf_city\n", + " except Exception as e:\n", + " pass # пробуем следующий вариант\n", + " \n", + " # --- 2. Попробуем admin_level=8 (городской уровень) ---\n", + " try:\n", + " gdf_city = ox.geocode_to_gdf(\n", + " query,\n", + " which_result=1,\n", + " structure='polygon',\n", + " tags={'admin_level': '8'}\n", + " )\n", + " if not gdf_city.empty:\n", + " print(f\"✅ Найден город как admin_level=8: {city_name}\")\n", + " return gdf_city\n", + " except Exception as e:\n", + " pass\n", + " \n", + " # --- 3. Если ничего не нашли — попробуем просто первую границу ---\n", + " try:\n", + " gdf_city = ox.geocode_to_gdf(query, which_result=1)\n", + " if not gdf_city.empty:\n", + " print(f\"⚠️ Используем первую найденную границу (возможно, район): {city_name}\")\n", + " return gdf_city\n", + " except Exception as e:\n", + " print(f\"❌ Не удалось найти границу для: {query}\")\n", + " pass\n", + " \n", + " try:\n", + " query = f\"{city_name}, {country_name}\"\n", + " gdf_city = ox.geocode_to_gdf(query, which_result=1)\n", + " if not gdf_city.empty:\n", + " print(f\"⚠️ Используем первую найденную границу (возможно, район): {city_name}\")\n", + " return gdf_city\n", + " except Exception as e:\n", + " print(f\"❌ Не удалось найти границу для: {query}\")\n", + " return None\n", + "\n", + "# --- Обработка каждого города ---\n", + "city_gdfs_with_coverage = []\n", + "\n", + "for index, gdf in enumerate(blocks_gdf):\n", + " gdf = gdf.copy()\n", + " \n", + " # Получаем имя города и страны из первой строки (предполагаем, что весь gdf — один город)\n", + " city_name = gdf['city'].iloc[0]\n", + " country_name = gdf['country'].iloc[0]\n", + " \n", + " # Определяем UTM CRS\n", + " utm_crs = gdf.estimate_utm_crs()\n", + " gdf.to_crs(utm_crs, inplace=True)\n", + " \n", + " # --- Шаг 1: Получаем границу города ---\n", + " city_boundary = get_city_boundary(city_name, country_name)\n", + " \n", + " if city_boundary is None:\n", + " print(f\"❌ Пропуск города {city_name}, {country_name} — не найдена граница\")\n", + " # Добавляем пустые колонки\n", + " gdf['area_pct_of_city'] = np.nan\n", + " gdf['city_boundary_area_m2'] = np.nan\n", + " gdf['covered_area_ratio'] = np.nan\n", + " city_gdfs_with_coverage.append(gdf)\n", + " continue\n", + " \n", + " # Переводим границу в UTM, если нужно\n", + " if city_boundary.crs != utm_crs:\n", + " city_boundary.to_crs(utm_crs, inplace=True)\n", + " \n", + " # --- Шаг 2: Площадь города ---\n", + " total_city_area = city_boundary.area.values # в м²\n", + " \n", + " # --- Шаг 3: Площадь каждого квартала ---\n", + " gdf['area_m2'] = gdf.geometry.area\n", + " \n", + " # Доля квартала от общей площади города (%)\n", + " gdf['area_pct_of_city'] = (gdf['area_m2'] / total_city_area) * 100\n", + " \n", + " # --- Шаг 4: Доля непокрытой территории ---\n", + " total_blocks_area = gdf['area_m2'].sum()\n", + " uncovered_ratio = max(0.0, (total_city_area[0] - total_blocks_area) / total_city_area[0])\n", + " \n", + " # Добавляем как колонку (одинаковое значение для всех строк города)\n", + " gdf['city_boundary_area_m2'] = total_city_area[0]\n", + " gdf['covered_area_ratio'] = 1 - uncovered_ratio\n", + " \n", + " # Сохраняем геометрию границы (опционально)\n", + " gdf['city_boundary_geometry'] = city_boundary['geometry'].iloc[0]\n", + " \n", + " # --- Лог ---\n", + " # print(f\"{index:>3}. 🏙️ {city_name}, {country_name}\")\n", + " # print(f\" Площадь города: {total_city_area[0]:,.0f} м²\")\n", + " # print(f\" Сумма кварталов: {total_blocks_area:,.0f} м²\")\n", + " # print(f\" Покрытие: {100*(1 - uncovered_ratio):.1f}% | Не размечено: {100*uncovered_ratio:.1f}%\")\n", + " # print(\"-\" * 50)\n", + " \n", + " city_gdfs_with_coverage.append(gdf)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "770996c3", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Собираем метаданные по городам\n", + "city_metadata = []\n", + "\n", + "for gdf in city_gdfs_with_coverage:\n", + " city = gdf['city'].iloc[0]\n", + " country = gdf['country'].iloc[0]\n", + " covered_ratio = gdf['covered_area_ratio'].iloc[0]\n", + " total_city_area = gdf['city_boundary_area_m2'].iloc[0]\n", + " total_block_area = gdf['area_m2'].sum()\n", + " \n", + " city_metadata.append({\n", + " 'city': city,\n", + " 'country': country,\n", + " 'covered_ratio': covered_ratio, # доля покрытия\n", + " 'total_city_area_km2': total_city_area / 1e6, # м² → км²\n", + " 'total_block_area_km2': total_block_area / 1e6\n", + " })\n", + "\n", + "# Преобразуем в DataFrame\n", + "city_coverage_df = pd.DataFrame(city_metadata)\n", + "\n", + "# Гистограмма доли покрытия\n", + "plt.figure(figsize=(10, 6))\n", + "sns.histplot(city_coverage_df['covered_ratio'] * 100, bins=20, kde=True, color='skyblue')\n", + "plt.title('Распределение доли покрытия кварталами по городам')\n", + "plt.xlim(left=80, right=100)\n", + "plt.ylim(bottom=0)\n", + "plt.xlabel('Доля покрытия территории (%)')\n", + "plt.ylabel('Число городов')\n", + "plt.grid(True)\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "de0bf4a6", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "✅ Готово! Покрытие по типам землепользования для всех городов:\n" + ] + }, + { + "data": { + "text/html": [ + "
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citycountryland_use_namecoverage_pcttotal_covered_pct
0AbakanRussiaagriculture32.72186.617
1AbakanRussiabusiness0.99986.617
2AbakanRussiaindustrial12.82186.617
3AbakanRussiarecreation1.88286.617
4AbakanRussiaresidential32.21186.617
5AbakanRussiaspecial2.33586.617
6AbakanRussiatransport3.64986.617
7AchinskRussiaagriculture19.29669.298
8AchinskRussiabusiness0.18469.298
9AchinskRussiaindustrial11.68669.298
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" + ], + "text/plain": [ + " city country land_use_name coverage_pct total_covered_pct\n", + "0 Abakan Russia agriculture 32.721 86.617\n", + "1 Abakan Russia business 0.999 86.617\n", + "2 Abakan Russia industrial 12.821 86.617\n", + "3 Abakan Russia recreation 1.882 86.617\n", + "4 Abakan Russia residential 32.211 86.617\n", + "5 Abakan Russia special 2.335 86.617\n", + "6 Abakan Russia transport 3.649 86.617\n", + "7 Achinsk Russia agriculture 19.296 69.298\n", + "8 Achinsk Russia business 0.184 69.298\n", + "9 Achinsk Russia industrial 11.686 69.298" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Собираем результаты\n", + "city_landuse_coverage = []\n", + "\n", + "for gdf in city_gdfs_with_coverage:\n", + " gdf['land_use_name'] = gdf['land_use'].apply(lambda x: x.value)\n", + " # Группируем по land_use и суммируем area_pct_of_city\n", + " coverage_by_type = (\n", + " gdf.groupby('land_use_name')['area_pct_of_city']\n", + " .sum()\n", + " .reset_index()\n", + " .rename(columns={'area_pct_of_city': 'coverage_pct'})\n", + " )\n", + " \n", + " # Добавляем информацию о городе\n", + " city_name = gdf['city'].iloc[0]\n", + " country_name = gdf['country'].iloc[0]\n", + " total_covered_ratio = gdf['covered_area_ratio'].iloc[0] # доля покрытия (1 - uncovered)\n", + " \n", + " coverage_by_type['city'] = city_name\n", + " coverage_by_type['country'] = country_name\n", + " coverage_by_type['total_covered_pct'] = total_covered_ratio * 100 # в процентах\n", + " \n", + " city_landuse_coverage.append(coverage_by_type)\n", + "\n", + "# Объединяем всё в один DataFrame\n", + "all_coverage_df = pd.concat(city_landuse_coverage, ignore_index=True)\n", + "\n", + "# Переводим в удобный формат\n", + "all_coverage_df = all_coverage_df[['city', 'country', 'land_use_name', 'coverage_pct', 'total_covered_pct']]\n", + "all_coverage_df = all_coverage_df.round(3)\n", + "\n", + "print(\"✅ Готово! Покрытие по типам землепользования для всех городов:\")\n", + "all_coverage_df.head(10)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "a83ab783", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "❌ Города с RESIDENTIAL > 60%, RESIDENTIAL < 10%, AGRICULTURE > 40% (будут удалены): 78 шт.\n" + ] + } + ], + "source": [ + "# Шаг 1: Найдём все города, где residential > 60%\n", + "cities_to_remove = all_coverage_df[\n", + " (all_coverage_df['land_use_name'].str.upper() == 'RESIDENTIAL') & (all_coverage_df['coverage_pct'] > 50) |\n", + " (all_coverage_df['land_use_name'].str.upper() == 'RESIDENTIAL') & (all_coverage_df['coverage_pct'] < 10) |\n", + " (all_coverage_df['land_use_name'].str.upper() == 'AGRICULTURE') & (all_coverage_df['coverage_pct'] > 40) |\n", + " (all_coverage_df['total_covered_pct'] < 80) \n", + "]['city']\n", + "\n", + "# Преобразуем в множество для быстрого поиска\n", + "cities_to_remove_set = set(cities_to_remove)\n", + "\n", + "print(f\"❌ Города с RESIDENTIAL > 60%, RESIDENTIAL < 10%, AGRICULTURE > 40% (будут удалены): {len(cities_to_remove_set)} шт.\")" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "f9f14968", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "✅ Осталось городов: 82\n" + ] + } + ], + "source": [ + "# Удаляем ВСЕ строки, где city в списке cities_to_remove_set\n", + "all_coverage_df_filter = all_coverage_df[\n", + " ~all_coverage_df['city'].isin(cities_to_remove_set)\n", + "].copy()\n", + "\n", + "print(f\"✅ Осталось городов: {all_coverage_df_filter['city'].nunique()}\")\n", + "\n", + "UNIQUE_CITIES = all_coverage_df_filter['city'].unique()" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "aaf851d9", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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9dLgjh1yug/qcadKheuvrX2vt9tluhwQArqFcBAAAgllAJaTozDhwlFbk66P5D9v58UN/odT47m6HhBBgkp4TD/qVBnY7SrW+ar365Y3aVrDC7bAAwBWUiwAAQDALqCZ7xsyZM5WSktLk/40bN87v8YSqzxY9roqqImUkD9ShA853OxyEEK83TKcfcp9enP5Lbcibp1dmXKdLj3lBCTHpbocGAH5HuQgAAASrgEtInX766c3WnKitrfV7PKFoS/4SzV39lp0/ceSt8noD7jJBkIsIj9ZPDn9Yz316iXYUr9UrX1yni476qyIjYt0ODQD8inIRAAAIVgHVZM/YunWrfD7fHhOFLv81D/jguwdtHz7DekxUj/SRboeEEBUTlaRzj3xMsVEp2lqwVK/P+pV8vhq3wwIAv6JcBAAAglVAJaToNNt9i9a/r4075isiPEbHjLjO7XAQ4lLic/STIx62Heuv3DLDjr5HnyoAQgXlIgAAEMwCKiHFD013VVWX6ZMFj9r5IwZdqsTYrm6HBCinywhNOuRe89NMc1b9W7OW/cPtkADALygXAQCAYBZQCSlTBb1rV5Igbpmx5FkVl+cqJS5Hhw68wO1wgHqDc47RcQfcYOc/XvCIvt/wkdshAUCHo1wEAACCWUAlpKZOnaq//e1veyw3yx544AFXYgoVO4vXa9byf9r54w68yTaRAgLJIQPO15h+P7Hzb319hzbkzXc7JADoUJSLAABAMAuohNRTTz2lQYMG7bF86NChevLJJ12JKVR8NP9h1fqq1TdzrAZkM4w0ArMvleMPvFn9s8ep1lelV7+8wSZSASBYUS4CAADBLFwBNpJMVlbWHsvT09O1ZcsWV2IKBetzv9PyzdPk8YTZ2lF0oopA5fWG6YxDp+rvn/1MW/KX6JUZ1+vSY15QdGSC26EBQLujXBRY1q1b1+ptk5KS7HkDAAABmpDq3r27vvzyS/Xu3Xu35WZZdna2a3EFe4epnyx4zM6P7D1J6Yl93A4J2KvI8Bj95IhH9OzHF2pH8Vq9PvM2nXvkY/J6A+rjDADajHJRYKgoKjbVdHXssce2+jkSEhO1auVKklIAADQQUL/gLr/8cl1//fWqrq7W0UcfbZd98sknuvXWW3XTTTe5HV5QMjWjNu5YoIiwaI0beoXb4QD7JSEm3SalXvj0Uq3eNksfzvuDJh70K7fDAoB2RbkoMFSVVZg7eDr5wduU3qt7i7cv2pqrN66dosLCQhJSAAAEakLqlltu0Y4dO/TLX/5SVVVVdll0dLRuu+02TZ482e3wgo7PV6NPF/6pvsNo8yMf6CyyUgZp0iH36rWvbtbsla8qLaG3xvTf1ek5AAQDykWBJT4jTck5ezahBAAAQZCQMn0XmVFj7rjjDi1ZskQxMTHq37+/oqIY8a0jzF/7rvKK1igmMlljB/7U7XCAFhuUc7SOHn6NTayaWlKpCT1sx/wAEAwoFwEAgGAWUKPs1YmPj9eYMWM0bNgwCl0dpMZXqWmLd43Qc8TgS+kUGp3WYYMu1oiep8hxam1/UibJCgDBhHIRAAAIRgFVQ8qYPXu2Xn31Va1fv76+enqdN954w7W4gs3y7e+ruHy7kmKzNLrf2W6HA7SpBsHJo3+j/NKN2pA3T6/MuE6XHvN3xUYlux0aALQZ5SIAABCsAqqG1CuvvKLDDjvMVkt/8803bSeeixcv1qeffmqHy0X7iIkL0/fb3rLzE4ZdqfCwSLdDAtrEXMNnHfaQkuOylV+yUf/+6hbV1la7HRYAtAnlIgAAEMwCKiH1u9/9Tg8//LD+85//KDIyUo8++qiWLl2qs88+Wz169HA7vKBxxGkZqq4tU9ek/hrWY6Lb4QDtIi46RT854lFFhsdpXe4c/fe7qXIcx+2wAKDVKBcBAIBgFlAJqVWrVunkk0+286bgVVpaapvj3HDDDXr66afdDi8o+FSjQ05Is/NHDb9KXm+Y2yEB7aZrUl+dOfZ+eTxezVvzlmYt/6fbIQFAq1EuAgAAwSygElIpKSkqLi628926ddOiRYvsfEFBgcrKylyOLjhUeXIVGR2m1Ni+6p91pNvhAO2uX9bhOu6AG+z8x/Mf0fLN09wOCQBahXIRAAAIZgGVkBo3bpw++ugjO3/WWWfpuuuu0+WXX65zzz1XxxxzjNvhdXo1tVWq9uyw88OzzrJ3WYFgdHD/83RQnzMkOXpz1q+VW7ja7ZAAoMUoFwEAgGAWUKPsPf7446qoqLDzv/71rxUREaGvvvpKZ555pn7zm9+4HV6nZ0Yhk8fRhhWlyho50u1wgA5jkq0nHnSbdhSv17rc2Xr1yxt06bH/UExkotuhAcB+o1wEAACCWUAkpIqKiuxjeHi44uPj6//+5S9/aSe0T+2owtItdv6z17bo1p9QOwrBLcwboTPHPqBnP75AO0s26M1Zt+ucIx6l3zQAAY9yEQAACAUB0WQvOTnZ9pOwrwltqx3lyCevE6OV83f1RwGEwsh7Zx/+R4WHRWvV1q/06cLH3Q4JAPxeLpo+fbpOPfVUZWdn2xqkb7311m7/b0YkvfPOO5WVlaWYmBgde+yxWrFiRQfsGQAAQIDVkPrss8/2KBiddNJJ+utf/2o78UT71Y6K8mW4HQ7gV5kpA/WjMXfpjVmTNXPZC/bvYT1OdDssAPBbuciMznfAAQfo0ksv1RlnmP71dvfggw/qscce0wsvvKDevXvrjjvu0AknnKDvv/9e0dHRbdoXAACAgE5IjR8/fo9lYWFhOvTQQ9WnTx9XYgom+SUbbO2o6IgEhdXGux0O4HdDe5ygrQXL9NXS5/Wfb3+rLgm9lJUyyO2wAMAv5aKJEyfaqSkm2fXII4/YPqlOO+00u+zvf/+7MjIybE2qc845pxV7AAAA0Ema7KGDa0eVbbXzqQk95RF9RyE0HTXsKvXLPFw1tRV67csbVVqx0+2QAMB1a9as0datW20zvTpJSUk65JBDNHPmTFdjAwAAwS0gakg1tmHDBpWVlalLly5uhxI0fUdFRyQqNjJZxSp0OyTAFaYz89MP/Z2e/fhC7SxZr3/PvFUXjH/Cdn4OAIGsI8tFJhllmBpRDZm/6/6vKZWVlXaqU9fxus/ns1N7MzW5vF6vva3mcVq2rdmmtdu25/ZmHzri2AAAECha+j0XEAkp029Bnby8PL388ss6+uij7R06tF6tr1qFZbv6jkqN7247MgVCWXRkgs4+4o/628cXaX3ud/rfvIc08aBfuR0WAHS6ctHUqVM1ZcqUPZbn5uaqoqKi3V+vpKREo0aNUoYnWnFVLdu2NibRbpsVHqfUFm7bHttHeaLt9mYftm/f3vInAACgkyguLu58CamHH37YPpqESVpamh0JxvRlgLYpKN0sx/EpMjxOsVGMUggY6Yl9NOmQe/Xqlzdo9spXlZk8SCP7THI7LABwpVyUmZlpH7dt22ZH2atj/j7wwAOb3W7y5Mm68cYbd6sh1b17d6WnpysxMbFDCrhz5szRMKdCyZEt23ZTeZHdtm/N2VILt22P7QucCrt9fHy8unbt2vInAACgk2jpYCjhgdJ/AdqXz1drE1IGtaOA3Q3sNl7jh16paYuf0H+/+53SE3srJ+0At8MCAL+Xi8yoeiYp9cknn9QnoExy6euvv9aVV17Z7HZRUVF2asw0TTNTezPlGNMMwLSYc1pYpDHbtHbb9tze7ENHHBsAAAJFS7/n+FYMUqapns+pUURYtOKj09wOBwg4Rw65TIO6HS2fr0avfXWListz3Q4JADqEaSo2b948O9UlvMz8+vXrbZLk+uuv17333qt33nlHCxcu1E9/+lNlZ2dr0iRqjwIAgI5DQioI+Ryf8ks32fkUakcBTfJ4vDrt4N8qPamfSiry9PrM22y/awAQbGbPnq2RI0fayTBN7cz8nXfeaf++9dZbdc011+iKK67QmDFjbALrgw8+aHG1ewAAAIV6QuqJJ57QiBEjbB8GZho7dqzef/99hYri8m2q9VUp3BupxBj6KgCaExkRq7MP+4OiIuK1IW+ePp7/iNshAUC7mzBhgh3hrfH0/PPP2/83N65++9vf2lH1TIfkH3/8sQYMGOB22AAAIMgFZUIqJydH999/v+1A0twVNCPTnHbaaVq8eLGCnSlg5pdstPPJ8Tm2FgiA5qUm9NBpB99j579Z8bIWrf/A7ZAAAAAAIOgFZbbCjEZz0kknqX///vYO33333WdHNpk1a5aCXUlFrqprK+T1hCspZtfIOQD23cn54YMvtfPvfvtbbS9c5XZIAAAAABDUgjIh1VBtba1eeeUVlZaW2qZ7wV47amfJBjufEtdNXm+Y2yEBncaEoVeqd8YhNqH72lc3q6q2zO2QAAAAACBohStImVFiTALK9IVgake9+eabGjJkSJPrVlZW2qmOGe5YPwzRa6ZASzrtGkrRs8fYw2WV+aqqKZPHE6ak2OxmxibeNeTw2rVr7XO1xLp165p97f3jaZftWxO7UV1drYiIiFa8rtv77u5xa9u+73pt87qtfS/l5eWpsLCwVdsmJSUpLW1/R5n0aNLB9+nZj8/XzuJ1+nrtn9vluLdl3wG4i/cuAABAxwnahNTAgQPtkMbmh+y///1vXXTRRZo2bVqTSampU6dqypQpeyzPzc21Ca1AYka+GTVqlGLCMuStjtvt/wpKltjH5KheiqjNkGr33N5T6bXb33777a16fbNtpJMmb3Vii7dNjq2128dHZMtbndLi7dsauxlssBX5GNf33e3j1pZ9jwmLstua63b79u0tfl3z/jWjPpWXl7d4W/v6MTF6+umnbWJqfx018Ff6z/xbtLHwW539i5FNvtf8se8A3FdcXOx2CAAAAEEraBNSkZGR6tevn503Pwq//fZbPfroo3rqqaf2WHfy5Ml2COSGNaS6d++u9PR0O0pfoBWOTWft59UOUVJEcv3yiupilVXn2vmkhC7yhe1scvvt+evt9jfce6JyeqW36LVXLNqiJ+//WJMqeis1IqvFsReUbbKvPam6t3wRLc8MtUfsrdnW7X1387i1dd/Lawvsa5tail27dm3V9T5jxgxNfmiS0jISWrRt3rZiTb3pLVtLqSWvbdat8t6q97/7nQYdWavi2lVKishpcext3XcA7ouOjnY7BAAAgKAVtAmppqrdN2yW11BUVJSdGjM/ZHc12QkcZmjmXU0IHMnz/8mJgtJdI+slRKcrItzsS3OJi13Nh1Iz4pSZs/+1RozcrYVNvvb+c9pl+7bE3ppt3d93945b2/d912ub67Y176W66z0tI74Vsbf+tUf1PVNL136lNTs/V4WzTtW+NEWERfnt9QEEBt67AAAAHScoS1qmxtP06dNtnzmmLynz9+eff67zzz9fwai6pkLFFbtqRyXHt7wmB4DdmSTS6B4/05Y1ZXI8tdqav0Q+h75kAAAAAKC9BGVCyvTX8tOf/tT2I3XMMcfY5noffvihjjvuOAWjgrLN9jEmMlnREfFuhwMEhXBvpF754xrJ8domsXlFq90OCQAAAACCRlA22Xv22WcVKmp9NSos22rnU+K6uR0OEFTyt1cpxtdd5WHrVFi2RdERCUqMzXA7LAAAAADo9IKyhlQoMT+SHadWkeGxio1q+QhsAPYuXIlKje9h57cXrVRldanbIQEAAABAp0dCqhNzHJ8KSnc110uJy7H93gBofyYhZZrEmvfc1oKl8vlq3Q4JAAAAADo1ElKdWHF5rmp9VQrzRiohJt3tcICgZZK9mckD7XutqqbM1pRynNaMeAgAAAAAMEhIdVKOHOWXbrLzyXHZ8ng4lUBHCg+LVFbyIDtfXL5dReXb3A4JAAAAADqtoOzUPBTUqlRVNaU2EZUUm+l2OEBIiIlKUpeEXtpRvFa5hatsJ+dREXFuhwUAaKNax6f8yjI7lddWq6K2Wj7HUbjXK1/XaOVccopKwx27Xhg3AQEAaBckpDqpau8O+5gY01Vh3gi3wwFChumvrbyqUGWV+dqSv0Q90g6U18tHKQB0RpW1NdpQmq/t5cXyac+m2DW1PikqTJk/GiczpnFe7jplxiQqKzZJUWF89gMA0BZ8k3ZCyemRqlGRnU+KzXY7HCAk+5Nan/udqmvLta1wpf2bQQUAoPMwNZ3Wl+Rrc1mh7QbBiPKGq0t0nBIiohUdFi6vx6tan0+bVq7S0tnzlHXcoarx+rSxrECbygrVPS5ZOXHJdj0AANByJKQ6oUNOSJM8UmxkMs2FABeYWomZKYO0cccClVTkqqgsSUlxWW6HBQDYD6XVlVpWtF1lNVX278SIaPWIT1VSRHSTNxcKymq17sk3dMjYQxXVPVObygpUVF2h9aZmVUWJBiR2VWJktAt7AgBA50ZCqpOprq3QQUd3sfPJcd3cDgcIWTGRSUpL6K284jXKLVqlqMgERUfEux0WAGAvTNO8FUW5tlZUhDdM/RLSlRoVu1+1XD3y2BpUZv28ylKtKc6zfU0tzN+kPglptikftWUBANh/1DHuZNbunK6YuHB5nEjFRqW4HQ4Q0kxSOC4q1f6w2Zq/RLW+GrdDAgA0oyDS0fKi7fYzOyUyViO75NgEU0uTSGb99Oh4HdSlu7pExdkGf6uK87TSJLqcPfuhAgAATSMh1Yk4jk/Lt79v5yOdLtyFA1xm3oMZyQMUHhZlay9uL1zBjxEACEDdzj9BO35oVZcdm6QhyZmKbOOAFOHeMA1KylCv+F0117dVFGtp4TY7Oh8AANg3ElKdyOptX6uocpMqymoV4VA7CgiY/qSSB9nGHCUVeSos2+J2SACABooSIpT142PsfK/4VPWOb7+beuZ5TMfmg5MyTfee2lFZqu8LtthO0wEAwN6RkOpEvlnxsn2c+/kOeRTmdjgAfhATmai0xN52PrdotSqqit0OCQAgaWt5kQqSI+18lwopJy6lQ2qYm6Z/Q5Oz5PV4VFBVrmUF26gxCwDAPpCQ6iR2FK/Tyi0zbC2Mrz/IdTscAI0kx2YrLso023C0tWCpHNW6HRIAhLSCyjLbr5Ox5Y3PlFzVsV0dJEfF7kpKyaOdVWW7Ok8nKQUAQLNISHUS3674l33MThypndt2DVMMIND6k+pf359UhWeT2yEBQMgyo9+Z/pyMuNJqbfrHrj44O1pSZIwGJmfY+e0VxVpXstMvrwsAQGdEQqoTMM1/5q99x84P6HqS2+EA2Et/Ulk/9CdV4y3U6GN3dXQLAPAf03/TkoKtqnF8ig+PUupO/97IMyPv9U9Mt/MbywpU4GEEVgAAmkJCqhMwyaiqmjKlJfZRZsJwt8MBsBfRpj+phF52fuJFOcovW+t2SAAQUtYU71BpTZXCPV4NSs6wnY37W0ZMonJik+38prAqxfbNcSEKAAACGwmpAOfz1dY31zu4/zkd0hEngPaVHNdNYU6CIiK9+nLNw6qsLnU7JAAICTsrS21H5sbApAxFh0W4FkvP+FSlRMbK8Uj9fvVTFdVUuhYLAACBiIRUgFu5dYbySzcqOiJBw3ue7HY4APaDSRzH+HJUmFel4sot+u+c39GxLQB0sCpfje1I3MiOTVJKVKzr3wUmKRbpeBSZlqwnN82Rj+8CAADqkZAKcN8sf9k+juxzuiLDY9wOB8B+8ihcrz22Vh55tWj9+5q35m23QwKAoGWS/mZEvWpfrWLDI9UrPlWBINzrVfeaKPmqqjW3eJteXPmt2yEBABAwSEgFsO2Fq7Rm+zfyeLwa3e9st8MB0ELrl5VqRPY5dv6DuQ9qe+FKt0MCgKC0o7JUOyvLbH9RAxO7yusJnCJujLza8Lf/2Pk/fz9di/O3uB0SAAABIXC+rbGHb1fsqh01MHuCkuOy3Q4HQCsMzviR+maOVU1thV6feZuqasrdDgkAgkqNr1arivPsfE5ciuIiohRocj+cpUMSu6nWcXTnnPdUUVPtdkgAALiOhFSAKq8s1IJ1/7XzBw841+1wALSSqeF42sH3KD46TXlFa/T+d/e7HRIABJW1JTttU72YsAh1j9s1sl0g+ln2gUqPjtf6knz9afE0t8MBAMB1JKQC1Nw1b9oaFRnJA9Qj7SC3wwHQBnHRqTrj0Kk2ObVg7X80f+2uphsAgLYpqqqoH1WvX2J6QDXVayw+PFJ3HnSinX91zVx9vX2t2yEBAOCqwP3WDmE+X42+XfmqnT+4/7l2lBYAnVvPrqM0bsjP7fz7c6Yqt2i12yEBQKfvyHz1D031ukYnKCky8Ad/ObRrb53Ve6Sdv2/uhyqvqXI7JAAAXENCKgAt2zxNRWVbFRuVrGE9dt1JA9D5HTH4UvXuerCqbX9Sv1I1/UkBQKttryhWSU2lwjyegBlVb39cPXScsmIStaW8SE8t/dLtcAAAcA0JqQD0zfKX7ONBfc5UeFjgdcwJoHW83jBNOuRexUV3UW7hSn049w9uhwQAnVKNz2f7jjK6x6UoMixcnUVseKRuO+A4O//yyjlaUrDV7ZAAAHAFCakAsyV/qdbnzZXXE65Rfc9yOxwA7Sw+Jk2nH3Kv6e7c9hW3cN37bocEAJ3OxtJ825F5dFiEsmMDtyPz5hye2UfHdxsknxzbdM8k2AAACDUkpALMtyteto+Dc45RYmxXt8MB0AF6ZxyiI4f8zM7/d8592lG8zu2QAKDTqKyt0eayQjvfO76LvJ20r80bhx+thIgoLSvcrldWzXE7HAAA/I6EVAAprdipRes/sPMHDzjX7XAAdKBxQ65Qz/RRqqop0+szb1NNbaXbIQFAp7C+dKetWZQYEa3UqFh1Vl2i43Td0Al23vQltam0wO2QAADwKxJSAWTOqtdV66tWduow5XQZ4XY4ADq8P6n77OAF2wqW63/z/uh2SAAQ8Crk07byYjtvOjLv7CMR/6jncI1K666K2mo9MP9jO3IgAAChgoRUgKitrdacVa/Z+YP7n+N2OAD8wDTLPe3ge+y8ef9/v+Ejt0MCgIC2LazaPpqaUYmRMersTEJt8oHHK9Ibppnb1+ijTcvcDgkAAL8hIRUgvt/4sUoq8hQfnaYhObtGXgEQ/PplHa7DBl1i59+dfY92lmxwOyQACEixfXNU7K21873iuyhY9IxP1cUDDrHzjy7+XOU1VW6HBACAX5CQCrDOzEf1/bHCwiLcDgeAHx017Ep1TztQldUlemPmr1RTy48RAGgs+5xdN+zSo+MVGx6pYHJh/4OVFZOo7eXFen75N26HAwCAX5CQCgCbdizUpp2LFOaN0EF9z3Q7HAB+5vWG6/RDf6eYyCRtyV+iTxY86nZIABBQVpTtVPLowZIj9YhLUbCJDovQ9cOPsvP/XPmNNtLBOQAgBJCQCgBf/1A7amiPExUfHTxV0AHsv6TYTP3o4Cl2/psVL2vpps/cDgkAAsa/ty+xj8lOmGKCrHZUnaOy+mtMeg9V+Wr16CK+AwAAwY+ElMuKy3O1ZMPHdp7OzIHQNiB7nA4dcKGd/883d6ugdLPbIQGA6+bt2KgFJdvlq6lV19rg7dbAdHB+0/BjFObx6PMtKzVr+xq3QwIAoEORkHLZ7JWvyefU2P5jslIGux0OAJcdPeJqdUsdporqYtuflBmBEwBC2bbyYsV5I7Tj09mKDPKia9/ENJ3V+yA7/9CCT1Xj29WJOwAAwSi4v9UDXE1tpb5b/bqdP7j/eW6HAyAAmL7kzhh7v6IjEmzfcp8ufNztkADAVSfkDNZjA0/Qphc/UCi4YtBhSomM1dqSnXp19Vy3wwEAoMOQkHLR4vUfqqwyX4mxmRrUbYLb4QAIEMlx2Tr14Lvt/Kzl/9DyzdPdDgkAXBUbFqGaolKFgoTIaF015Eg7//TSL7WzMjT2GwAQekhIucRxnPrOzEf3PduOsgUAdQZ1O0oH9z/Xzr/zzV0qLNvqdkgAAD85tedwDUrKUGlNlZ5Z+pXb4QAA0CFISLlkQ95cbStYpvCwaI3sM8ntcAAEoGNGXGf7liuvKtSbs25XrY/+pAAgFHg9Hl0/bFft+TfXztea4h1uhwQAQLsjIeWSutpRw3tOVGxUstvhAAhA4WGRtj+pqIh4bcibp88XPel2SAAAPxmV3kPjM/up1nH02KLP3Q4HAIB2R0LKBWYo92WbPrPzdU1yAKApqfHddcroO+z8V0uf06qtNN0AgFBxzbDxCvN4NWPban2Tu87tcAAAaFckpFwwe+VrchyfenUdo65J/dwOB0CAG9L9OI3qe5adf+vrO1Rcnut2SAAAP+gZn6of9z7Qzj+y8DPVOj63QwIAoN2QkPKzqppyzV3zpp0/uP95bocDoJM4/sAblZE80I7MafqT8vlq3A4JAOAHPxs4VvHhUVpRlKv31i92OxwAANpNUCakpk6dqjFjxighIUFdu3bVpEmTtGzZMgWCheveU0VVkZLjuql/1hFuhwOgkwgPi9KZY+9XZHis1uXO0fTvn3E7JACAHyRHxerSgYfa+SeWfKHymiq3QwIAoF0EZUJq2rRpuuqqqzRr1ix99NFHqq6u1vHHH6/S0lK3Q9PGvAX2cUy/n8jrDXM7HACdSJeEnjpp1K/t/Bff/1Vrtn3tdkgAAD/4SZ+DlB2bpLyKUv1z5bduhwMAQLsIyoTUBx98oIsvvlhDhw7VAQccoOeff17r16/XnDlz3A5Npx3yW116zN91YO/T3A4FQCdkRuYc2ft0SY7e/Po3KinPczskAEAHiwwL19VDx9n5v6/4VrnlJW6HBABAm4UrBBQWFtrH1NTUJv+/srLSTnWKiorso8/ns1N7i6jtqg3rtkoyU8usW7dOXq/JI3okx9OKV/e0Yfu2bOv29sTuZuxr166V4zh+vt7dfO22v76p2RkREdHk//VNPF2ro2ersGKDXvrsRo3v92t5PbvfX0hKSlJaWppCUV5eXv3nfku19bi15bX3dd73JZTPeUfpiDIA0FrHZg/Uy6lztHDnZtt0786DJrodEgAAbRIeCoXJ66+/XocffriGDRvWbJ9TU6ZM2WN5bm6uKioq2jUe80PliiuuUHl5eaufY9SoUYp00uStTmzxtsmxtXb7+IhseatT/Lat29sTuzvbeyq9dtvbb79d/r7e3Xzt9nh9j8mD7SWPFZ/q6MgLpW0li3TvE+dp2Ze7NwGOiYnR008/bZMUoaStn7FtOW7t8fm+r/O+N6F6zjtScXGx2yEA9Twej24YdpQunf6i3l2/yDbjG5ic4XZYAAC0WtAnpExfUosWLdKMGTOaXWfy5Mm68cYbd6sh1b17d6WnpysxseU/RPdVuDWxTH5oktIyElq8/YpFW/Tk/R9rUkVvpUZktXj7grJNtunipOre8kU4ftvW7e2J3Z3tt+fvaip7w70nKqdXul+vdzdfu62vX/fa+9q22pOvCm3QgLGORhzcTRHalYjI21asqTe9ZWtomYEdQklbPmPbetza6/O9NddMKJ/zjhQdHe12CMBuhqdm67hug/TRpqV6dPHn+vNhZ9tEFQAAnVFQJ6Suvvpqvfvuu5o+fbpycnKaXS8qKspOjZmC/a4mO+3HFBpMra20jHhl5rT8Lnbu1sIfmhA4kqc1t9GdNmzflm3d3p7Y3Yw9NSPOhevdzddu2+vXvfa+t01SbmGtCso2qyp8ozK6pCkyIrb+tc3nTXt/hgW6tn3Gtu24tdfne+uu2dA95x2JY4lAdNWQI/X5lhX6Nne9vty2Wkdk9nU7JAAAWiUoS1qmvxaTjHrzzTf16aefqnfv3m6HBAAdIi2xt2Iik+RzarU5/3vV+mrcDgkA0IG6xSXrnD4H2fnHFk9TDX2dAQA6KW+wNtP75z//qZdeekkJCQnaunWrndrSrwcABCKPx6vM5EEK90aqurZc2wqWyTG1ugAAQeuSAYcqKTJGa4p36K11890OBwCAVgnKJntPPPGEfZwwYcJuy5977jldfPHFLkUFAB0jPCxSWSlDtHHHfJVW7lSkJyg/2gGgUzOjt7aGGajA9GvaUEJktK4YdJh+v+ATPb3kK52YM0TxEXt2PwEAQCALyl8trRliHQA6s+jIBHVN6q9thctV5d2uwQcz0hoABIKKomI7hOaxxx7bqu0TEhO1auXKPZJSZ/Q6QK+unqt1JTv13PJZumbo+HaKGAAA/wjKhBQAhKLE2AxVVpfYTs7PvKqndpatltTP7bAAIKRVlVWYu6U6+cHblN6re4u2LdqaqzeunaLCwsI9ElLh3jBdO3S8bvr6Tb28ao5O73WAcuKS2zl6AAA6DgkpAAgiaYl9VFxapMjoEk1f9aAG9x+phJjdf8QAAPwvPiNNyTlZ7fqcR2b21cHpPfVN7jo9vnia7j/4tHZ9fgAAOlJQdmoOAKHK4/EoxtdD2zeWq7x6p/414wZV1zCgAwAE62f+DcOOklcefbJ5ub7L2+B2SAAA7DcSUgAQZDwK04sPrlZkWIK25H+vd769W47DsOAAEIz6JaXr9F4j7PzDCz+Tj75UAQCdBAkpAAhC+duqdGSfm+T1huv7DR9p2uKn3A4JANBBfj74CMWFR2pp4Ta9t36x2+EAALBfSEgBQJDqmjBEJ4/6tZ3/4vtnNHf1W26HBADoAClRsbps4Fg7/+fvp6uspsrtkAAA2MpOT7cAAQAASURBVCcSUgAQxA7sfZqOGHyZnX9vzn1asfkLt0MCAHSAn/Q5yI6yt6OyVC8s/9rtcAAA2CcSUgAQ5CYM+6VG9DpVjlOr12fepk07FrkdEgCgnUWGhevaoePt/IsrZ2tLWaHbIQEAsFckpAAgBEZhOmX0b9Q3c6yqayv0yoxrtbN4vdthAQDa2YSs/hqV1l2Vvho9vni62+EAALBXJKQAIASEeSP047G/V1bKYJVVFuil6VerpGKH22EBANr5BsQNw46SR9L/Ni3V/B2b3A4JAIBmkZACgBARGRGrc458TMlx3ZRfulEvTb9K5ZU06QCAYDIwOUM/6jnCzj+86DP5HMftkAAAaBIJKQAIIfHRXXTeuD8rLrqLthUs10tfXKPK6hK3wwIAtKNfDD5cseERWpy/RR9s+N7tcAAAaBIJKQAIMV0SeuiC8U8oJjJJm3cu0itfXKfqmnK3wwLgorvvvts292o4DRo0yO2w0Epp0fG6ZMBYO//Y4mkqqa50OyQAAPZAQgoAQlDXpH46f9yfFRURr/V5c/XqlzepprbK7bAAuGjo0KHasmVL/TRjxgy3Q0IbnNd3lHrEp2hHZameXvql2+EAALAHElIAEKKyUofo3CMfU0RYtFZvm6XXZ96m2tpqt8MC4JLw8HBlZmbWT2lpaW6HhDaIDAvXLSOOsfOvrv5OKwq3ux0SAAC7Cd/9TwBAKOmedqB+csTDevmL67R88zS99tXN+vFhDyo8LMrt0AD42YoVK5Sdna3o6GiNHTtWU6dOVY8ePZpct7Ky0k51ioqK7KPP57NTe3McR16v144e52lhH91mm9Zu6/b2ddua/W/NcT04raeOzhqgT7cs1wPzP9ZTh//ENscEAKAjtPS7ioQUAIS43hmH6OzDH7LJqBVbvtArM67X2Yf/UZHhMW6HBsBPDjnkED3//PMaOHCgba43ZcoUHXnkkVq0aJESEhL2WN8kq8w6jeXm5qqioqLd4yspKdGoUaOU4YlWXAtbF9fGJNpts8LjlNqKlslubh/libbbmv3fvr11NZwuyBymL7et0vydm/Sv77/W0el9WvU8AADsS3FxsVqChBQAQP2yDrfN90wyas22r/XS9Kt0zhGPKjpyzx+iAILPxIkT6+dHjBhhE1Q9e/bUq6++qssuu2yP9SdPnqwbb7xxtxpS3bt3V3p6uhITEzukgDtnzhwNcyqUHNmybTeVF9lt+9acLbVwW7e3L3Aq7Lbx8fHq2rVr6/oMlPSzisP05yVf6PkNc3XygAOVEBHdqucCAGBvTC3rliAhBQCwenUdowvG/UUvfXGNNuTN0z+nXWk7Po+JSnI7NAB+lpycrAEDBmjlypVN/n9UVJSdGjPNy8zU3kwzM9MMwLR4c1rY4sxs09pt3d6+bluz/205ruf3H6N3NyzWupKdembZTN38Q99SAAC0p5Z+V9GpOQCgXk7aAbpwwtOKjUrWlvzv9cJnP1Nh6Ra3wwLgZ6aJ2KpVq5SVleV2KGgHEd6w+g7OX1s9V0sLtrkdEgAAJKQAALvLShmkn074q+Kj05RbtEp/++Qibclf4nZYADrQzTffrGnTpmnt2rX66quvdPrppyssLEznnnuu26GhnRzStZeO6zZIpq7WvXM/UE0HdD4PAEBLkJACAOwhPamPLj3mBaUn9lVJRZ6tKbV883S3wwLQQTZu3GiTT6ZT87PPPltdunTRrFmzbJ9QCB43DT9aiRHRWla4XS+u/NbtcAAAIY6EFACgSUlxWbr46L+pT8ahqq4p16tf3qhvVrzidlgAOsArr7yizZs3q7Ky0ianzN99+/Z1Oyy0sy7Rcbp+2FF2/pmlX2l9Sb7bIQEAQhgJKQBAs8woe+cc+ahG9j5djuPTh3Mf1PtzpqqmthVjnwMAXHdKj6E6JL2nKn01+t28D+U4put0AAD8j4QUAGCvwrwROnn0b3T08Gvs37NXvWab8BWUbnY7NABAC5kR+yYfeLyiwyI0J2+D3lq3wO2QAAAhioQUAGC/fsAcPvgSnXPEo4qOTNTmnYv014/O18otX7odGgCghbrFJevKwUfY+ccWTVNueYnbIQEAQhAJKQDAfuuffaQuP+4lZaUMUXlVoV7+4lp9vugJ+Xy1bocGAGiBn/Q9SENTslRSU6kH5n9E0z0AgN+RkAIAtEhyXLbt7Hx037MkOfri+2f0/GeXKq9ojduhAQD2U5jHq98ceIJ9nLZ1pd7bsNjtkAAAIYaEFACgxcLDIjVx1GRNOuQ+RUXEa9OOhXr6f+dq5rJ/UFsKADqJfknp+vngw+38HxZ8ok2lBW6HBAAIISSkAACtNrznRP38hFfVN3Osan1V+nj+w7bD8x3F69wODQCwH37a/2Ad2KWbSmuqdNec/6rW8bkdEgAgRJCQAgC0SVJsps498nGdMvoORYbHaeOO+Xrqw5/os4V/VlV1mdvhAQD2wjTZu/ugkxQXHqn5Ozfp78u/cTskAECIICEFAGiXUfhG9jldvzjhVfXJONTWlpqx5Fn95YPTtWDde3SWCwABPureLSOOsfNPLf1SSwq2uh0SACAEkJACALSbpLgsnTfuzzrrsD8oOa6bistz9fbXd+i5Ty/Wxrz5bocHAGjGSd2H6pjsAbbJ3h2z31NFTbXbIQEAgly42wEAAIKvttSgnKPVL+twzVr+oq0pZTo9f+7TS2ztqSOH/Ew90g9yO0wA6DTWrWt9v3xJSUlKT0/fr8/uXx14vBbs3Kx1JTv1x0Wf6vYDT1Bnlpubq8LCwg49bgCA1iMhBQDoEOFhUTpi8KU6oNep+nzRX7Rg7XtavW2WnXqmj7aJqV5dx9gfQQCAPVUUFZtMkY499thWP0dCYqJWrVy5X8mV5MgY3XXQRF3z1Wt6c+0CHZDaTSf3GKbOmozq26+fiouKOvy4AQBah4QUAKBDJcSk69Qxd+mIwT/TV0uf07y172hd7mytmzZbGckDNbrvWRrWc6Iiw2PcDhUAAkpVWYXkODr5wduU3qt7i7cv2pqrN66dYmsJ7W9i5ZCuvfSzgYfpmWVfaeq8j9Q/qasGJHVVZ2P22SSjznjsLiVmpnf4cQMAtBwJKQCAX6TEd9PJo3+jI4aYxNTzmrv6LW0rWKb35tyrjxc8ohE9T9Govj9WelIft0MFgIASn5Gm5Jwsv73ezwYdpkX5WzRz+xrd9s3b+vv4C5UQGa3OyCSj/HnsAAD7j07NAQB+lRSbqYkH/UrXn/qBjj3gBqXE56iyukTfrnxFT374Yz3zv/NswqqgdLPboQJASPJ6PPrt6JOVFZOojaUFuuu7/8rHaKkAgHZGQgoA4IrYqGSNHXihrpr4lh2Zb0D2BHk8YdpasFSfLHhMf3rvFD33ySWateyf2lG8Tg4/hgDAb0x/Ug8ccpoivWH6YusqvbD8a7dDAgAEGZrsAQBc5fF41TdzrJ3KKvO1ZOMnWrz+f1qXO0cbd8y300fz/2hrUvXLPMKO3tcz/SBF0OcUAHSowcmZumXEsbpv3od6YskXGpDcVYdn0KwaANA+SEgBAAJGbFSK7UfKTMXlufp+w0daseULm5zKL9lom/WZyesNV3bKUJuY6tl1lLp3OVCREbFuhw8AQWdSrxFalL9Zb69bqNu/fUdPH3GuBiZnuB0WACAIkJACAATs6HyHDDjPTpXVpVq7/Vut3DJDK7d+paKyrfW1p75c+pxt6tc1qa/iw3to5IRU1arCNvHzeDxu7wYAdHq3HXCcNpUWanbeel0/63U9N+4CZcYmuh0WAKCTIyEFAAh4URFxGthtgp1MoqmgdJOtNbUu9zutz51jO0DfVrBc27Rcp1/ZU2VaodXbVisqIl7REQmKjkxQVESCwr2RJKkAoIUivGF68ODT9LMvXtbq4jxdN/N1/fXIczvtyHsAgMBAQgoA0KmYhJLpT8pMB/Y+zS4rKtumTTsXadHKL/T+Zy+q95Ak+VSr8qpCO6l017Zh3giboDLJqWiTrIpMsMsAAHtnkk+Pjj1Tl0z/p01K3frN23rssB/bZBUAAK3BKHsAgE4vMTZDg3OO0cicC/Tcb1cq3jdEPdIOUtekfkqMyVRkeJxdr9ZXrdLKndpZsk6b8xdr9bZZtinglvwlqvLkqufgOFXXVri9OwAQkEwzvUcOPVOx4RG2+d69cz+QjxFQAQCtFJQJqenTp+vUU09Vdna2vZP+1ltvuR0SAMCPPPLYZn5JsVnKSO5vOz/vm3mYcrqMUFpiHyVEpysibNcofSYBVVKRp0rvVl129wC9Pv9iPfvxhfpo3sNatmmayisL3d4dAAgYpkPzqWNOU5jHo/9u+F4PLfjENqUGAKClgrLJXmlpqQ444ABdeumlOuOMM9wOBwAQALyeMMVEJtlJuypM2RpTldUlqqguUWHRTu3YkaektEht3rnYTrOW/8OuZ2pa9TAj+qWPUu+uBysmKsndnQEAFx2W0Vt3jDxRd3/3vl5dM1dRYeG6Zuh4+ugDALRIUCakJk6caCcAAPbG9B8VG5Vip6qCBD101TTNWzxT3rh8rc/9znacvqN4rbYXrrTT7JWvyuPxqlvqMFvjykzZqUPsMgAIJSf3GKbK2hpNnf+R/rHyW4V5vfrl4CNJSgEAQjshBQBAa8VFpqlfz0M1vOeuGxulFTu1Pm+uTVCt2faNcotWaeOOBXaatvhJxUWlakC38UryDlZYGD/EAISOM3ofqEpfrf648FM9v/xrVdXW6PphR5GUAgDsFxJSkiorK+1Up6ioyD76fD47tSfTxt7rNXfSPZLTmi9rj4vbu/nabd2e2EMvdo7b2rVrW92vR3V1tSIiWjf6XFJSktLS0tRaeXl5KixsXb9N69ata/NxN8es4Wd/TGSyBmYfZSejqGyrVm2dqVXbvtLabd/YTtLnrn5T0pu67ZnhKvdsUEm5T3FRKS2sOeVp99g7i7ac8/a45prTGY8l4G/n9h1l+5P6/YJP9NKqOaqordGtBxyrMGqOAgD2gYSUpKlTp2rKlCl7LM/NzVVFRfuOtlRSUqJRo0YpJixD3uofOjFpgeTYWrt9fES2vNUpft3ezddu6/bEHnqxh/Jx81R67fa33357i7etfw6TE2llH7UxMTF6+umnbZKgpUxS4oorrlB5eXnrXlyy+x7ppMlbndii7WLCouy25nN6+/bte1nTq27xh9vpiN412ly4QGvzvtLq7TOkuCLVqEBbCgoU5olUQmQ3JUZ1V3S4SU55Ouy873/sgac9znlbrrm9KS4ubtfnA4LV2X0OUnRYhB11742185VfWaZ7Rp9i+5YCAKA5fEtImjx5sm688cbdakh1795d6enpSkxs2Q+a/SnczpkzR+fVDlFSRHKLty8o22S3n1TdW74Ix6/bu/nabd2e2EMv9lA+btvz19vtb7j3ROX0Sm/x9isWbdGT93/cqu3zthVr6k1v2do6Xbt2bdVn5IwZMzT5oUlKy0hodeyTKnorNSKrRduW1xbY4xYfH9+i2DMzs3XQwBO1YuUKnThpjH5+56HyhReq1lelgso1djIj+iXGZCoxJkPhYZHtft5bG3sgaOs5b+s1tzfR0dHt+nyAW0zt0dYyiV5TJt6XH/UcrtjwSN055z19tmWFrvnqNf3+kElKitw1ommoMTe2W1vzc3+POQB0diSkJEVFRdmpMVO43dV8ov2YO+S7mgA4kqc11Q8cF7d387Xbuj2xh17sHLfUjDhl5rS8xkju1sI2bL/rtc1nXWs+P+s+I9My4tsUe1uOe2tjD/OGae2SYkU7WcroOkhllfkqLt+uksodqq4t146SNdpRslbx0V2UFJtlR/vbvdaU41rsbmrrOe/Ife9sxxJorKKo2FZ5PfbYY1v9HAmJiVq1cuV+JUiO7TZQKZExuunrNzV3x0ZdMu2f+uOhZ6hXQheFWjKqb79+Kv6hG5COPOYA0JkFZULKNFlYuXJl/d9r1qzRvHnzlJqaqh49ergaGwAg+JnkSFx0qp18vhoVV+TZvqcqqotVUpFnJ1NrKjku29aa8nrD3A4ZQBCqKquw7a9PfvA2pffq3uLti7bm6o1rp9iaPvubHBmV3kN/HXeebpz5hjaUFuiSaS/qntEn64jMvgoV5niZZNQZj92lxMz0Dj/mANBZBWVCavbs2TrqqF2dzxp1zfEuuugiPf/88y5GBgAINV5vuJJiM+1UWV2iwrItKirPtbWmzIh9O4rX2f+Tt5WddgHAPsRnpCk5p2XNmNuiX2K6np9wgW79+m3N37lJN8x6Qxf0G6Orhhyp8BBKwJtklD+POwB0NkGZkJowYUKrR5YCAKCjREXEq2tSf6Ul9FZR+XYVlG5SdW2F8ks3KipTOvPqnnK8/z/qKwB0VqlRcfrL4WfrscXT9K/V3+mfK7/VvB0bdd/oU5Ud174DEAAAOic6RwAAwIVaU6a5Xs/00cpKGfJDf1LSAUemyhe7QZvzv1dFdYnbYQJAm0SGhevmEcfowYNPU0JElBblb9EFn7+gTzcvdzs0AEAAICEFAICLfU2ZTs5zuoxQ5fZMLf66wC4vrdihDXlztWnnIpVXta5TXAAIFEdlD9A/J1yk4SlZKq6u1G3fvK3fzH5XOytL3Q4NAOAiElIAAAQApzpK//rjGnnLuisheldHtmakvo075mvjjgUqq9yVrAKAzsg003v6yHP10/4HyyuPPty4RGd9/De9vXYBXW0AQIgiIQUAQADx+KKUmTLINuczI/BJHpVXFWrTzoXauGOhKqqK3Q4RAFrFdGh+zdDxtsPzgUldVVRdoXvnfaifz3hFa4t3uB0eAMDPSEgBABCAIsNjlJE8QL3SRyspNuuHxFSBNuyYp807v1dlNU1dAHROg5Mz9fz4C3Xd0AmKDovQ3B0bdc6nz2nqvP8pt5z+8wAgVJCQAgAggEWER6trUj+bmEqwNaak0sodWp/3nbbmL1VVTbnbIQJAi4V7vbqg/xj96+hLdERGX9U6jt5YO1+nf/yMHl88XUVVFW6HCADoYCSkAADoJImpzOQB6pk2SvHRaXZZcUWu1uXOVoVnkxJSItwOEQBa1bfUw2PP0FNHnKMRqdmqrK3RCyu+1qSPntYzS79UfmWZ2yECADoICSkAADqRyIhYZaUMVve0kYqNSrHLqr07df1jQzRv04uMygegUzoorbv+euR5euiQ09U3Mc2Oxvf00q906odP6XfzPqSPKQAIQuFuBwAAAFouOiJe3VKH2Q7Pt+StlCLLtGTb21rz3qc6fPAlGtPvHFurCgA6C4/Ho3FZ/XR4Zh99smmZ/rlytpYUbNWbaxfY6YiMPjq91wE6LKOPbfIHAOjc+CQHAKATi4lMUoyvj158YJWSorurorpYnyx4TH9+/zR9t+oN+Xw1bocIAC0S5vHq+JzBemH8BbYp37jMfvJImrFttW76+k2d8uGTemzR51pDrSkA6NSoIQUAQCfnkUfLvivSiYN/r/KIZZq26AkVlm3Ve3Pu1azl/9RRw6/SoG5H29oHANBZmM8s05TPTOtKduqNNfP0/oYl2lFZqn+s/NZOQ1OydEz2AB2dPUDd4pLdDhkA0AIkpAAACBJej1cH9DpVQ7ufoDmrXtMX3z+rHcVr9e+vblF26jAdM+Ia9eo6xu0wAaDFesan6obhR+vqoeP15dbVemf9Qn21bbUW52+x02OLp2lQUoZNTI3P6ifHcdwOGQCwDySkAAAIMuFhkTpkwPk6sPdpmrnsH7aW1Oadi/SPz3+uvpljddTwa5SVMsjtMAGgxSK8YZqQ3d9OeRUl+mzzCn26ebm+y9ugpYXb7PSXJV8oLSJGPX5xhoo8NYr3+ehzCgACEAkpAACCVFREvCYMu1Kj+52tGd//VXNWv65VW2faydSimjD8l0qN7+52mACCVG5urgoLC1u9fVJSktLT05v9/7ToeJ3VZ6Sd8ivLNG3LSpucmpO3XnnV5ep6wqFaryptyF2jhIhoJUZGKykixj6afqqCVUcfdwBoLySkAAAIcvHRXXTiQbfZWlOfL3pCi9a/r8UbPtSSjZ/ooD6n68ghlys+Js3tMAEEWVKkb79+Ki4qavVzJCQmatXKlfuVHEmJitWkXiPsVFFTrf8s/Fo3/eUP6n7SEaryOCqqrrDTRhXYDtLjI0xyKlpJkTE2WRUsNaj8fdwBoC1ISAEAECJS4nN0+qH3aezAn+rThX/Sqq1fafaq1zR/7X90yIALdNign9paVQDQVqaGjkmKnPHYXUrMbHlio2hrrt64dop9npYmRqLDIzQyIVMb/vq2jjn+OEVlpqmwulyFVeUqqqpQpa9GxdUVdtpYVmC3iQ2PVEJElE1OST7To7o6IzePOwC0FAkpAABCTGbKQJ037nGt3T5bnyx4zPYvNWPJXzVn1b91xOBLNbrfWQoPi3I7TABBwCRFknOyXI0hJjzCTpkxibazc5OQMsmpwqoK+2j+LqupstO28mIpQjrwH3dr6tovdXD1Vg1LzdawlCxbm6qzCITjDgD7QkIKAIAQ1avraF16zAtauulTfbbwz3ZEvo/m/1HfrHhJ44f+QsN7niyvN8ztMAGg3Xg8HkWHRSg6JkIZMYl2WVWtqTFVaWtMmWZ9xVUVCo+L0YKS7VqwbPtuI/2ZxNTQH6b+Sem2k3UAQOuQkAIAIMR/nA3OOUYDs8dr/tp3NW3xkyos26p3vr3bjtB31PCrNSB7nF0PAIJRZFi4upgpOs7+nb9xs169baoeevk5bQ2v1qKdW7S+NF/rSnba6b0Ni+16Jhk1IKmrhiZn2gTVkJRM9YhPdXlvAKDzICEFAADk9YZrZJ9JGtbjRH278l/6culzyi1apVe/vEHd0w7U0cOvUY/0kW6HCQAdziOPytds1nFd+qhfv352WUFlmRbnb9WCnZv1fcEWLcnfqsLqCi3O32InrZlr14sLj1TvqCR1u+BEFXlqFFNbo6gwfnIBQFP4dAQAAPUiwqN12KCLdFCfM/Tl0uf1zYqXtSFvnl747DL1yzpCRw6+TDlpB7gdJgD4VXJUrA7P7GMnw/RFtamswCapTELq+/ytWlqwTaU1VVpUk6usM4/WelVpfd46RXrDFP9Dh+kJ4VF2PpymfgBAQgoAAOwpOjJBx4y4Rgf3P0fTFz+tuWve0sotM+xk+p46fPBl6t31YJryAQhJ5rMvJy7FTifkDLbLany1WlWUp8+XL9DUF55S92MPVYXHUZWvVjsry+xUJyYs4ockVZTiw6MVHxEpr8fr4h4BgP+RkAIAAM1KiEnXyaN/rUMHXqivlj6vBevetaPzmSk7dZgdlW9XH1P8kAIQ2kytp4HJGQpL7a1f/OV1HTXhKCV0y1BJdaWdTMfpJTUVqqitUXlttZ1yK0rstia1HxcepUhvrbocPVobKorU2/EpjM9WAEGMhBQAANinLgk9dOqYOzVu6BWatewf+m71m9q8c5Fe/fJGpSf21WGDL9bQnOMVFhbhdqgAEDBMQikpMsZOdap9tXZEv/okVXWFqh2fSmoqpTCp9zVn69aVnyh27XQNSjIdpv9/p+mZMYnUTAUQNEhIAQCA/ZYUm6kTRt6iIwZfpq+Xv6jZq16znZ+//fUd+mT+oxrd72wd1OdMxUWnuB0qAAQkMzpfalScner6o6r01dgEVd7OHVqzdLnShvdXWU21vtuxwU51UqNiNSQ5yyaphvyQpEpukOwCgM6EhBQAAGixuOhUHT3iGh026GLNXvmqHZmvpCJPny/6i774/q8a3nOiDu5/rjKSB7gdKgAENFPjKToswk7hvmJ98Jsn9c7y5fJmJNvO0r+3I/lt1YqiXNsP1Yxtq+xUJycuWUOSd9WiSiqtlTeSmqoAOgcSUgAAoE2dnx8x5DKNHfhTfb/xI329/CVtyf9e89a8bSfTAfqY/udqQNaR8nopdgDA/vB6POqXmG6nH/UcbpdV1FZreeF2m6SqG9lvfWm+NpYW2Ol/m5ba9Ua+9Fut9JYrqWi7EmyH6VGKC4+kqR+AgEPJEAAAtJnpO2p4z5M0rMdEbdyxQN+seElLNn5a3wF6fHSaDuh1qg7sfZpSE3q4HS4AdDqmBtWI1G52qlNUVaElBbsSVKYW1YK8DSpQpSrkqKK8WNtUbNfzymMTU3Uj+5lEVVRYOEkqAK4iIQUAANqN+XHTPe0AOxWWbbXN+UxNKdOc78ulz9mpZ/pojewzSYO6He12uADQqSVGRuuQrr3sZKxYsUJDx47RKU9OkZMYq+KaXZ2m1zqOiqor7FQn3OO1yan4iGj7aEb5c+S4uDcAQg0JKQAA0GEdoB8z4lpNGHqllm+Zrnmr39LKrV9pXe5sO0VHJKh70lj1GBjHjyAAaKebAtU7CpXkhCs5oUt9p+nltdX1I/qZx9KaStU4PuVXldupjmlZPfC+K/Xs5nka6S2yTQb7JqbtNkogALQXElIAAKDDm/MNzjnGTqbW1Pw1/9G8NW+psGyLVuT9Tz/77QCVOsuUV5Sh+Jh0RYXH0YwEANqJ+TyNDY+0U0ZMgl3mcxyblNqVpNqVqCqrrZbPIyUM6a2Pd66xU5206Dj1TUhTn8Q09YhPUY/4VPWIS1HXmATb3xUAtAYJKQAA4NdaU+OGXq4jh1ymNdu+0ZeLXtGyTZ8pKkbKL91op4iwGCXEpNspMjzW7ZCBkLdu3Tq/bNOez9Ner++Wjt5vk0RKsE31ouuXmSTV9k2b9b/Hn9P1992lnWG1Wl2cp81lhcqrKLXT17m7P7/ph6p7XPKuBFV8ilJ84UqqDVN6ZKySw6NbnKxKSkpSenq6Wis3N1eFhYWt2ra6uloREa0fodDN7dt63ND55LbhWg+ka4aEFAAA8DuPx6s+mYfKV5KmS096TL974UcKjytTacVOVdeWa2fJejuZ5FR8dBfFRacqOiKRmlOAH1UUFZs3q4499thWP0d1VZVrr92W13eLm/ttkkfR8mrn9Lk6N3Oo+vXrZ5ebGlRrindoZVGu1hbv1IbSfK0v2TW6X2VtjVYW5dmpMV91jap2FKoqN19VuQX2sdLMby9QVV6BqnYUyKmq2W2bhMRErVq5slU/lM0P9L79+qm4qEit4fF65fh8rdrW7e3bctzQ+eS28VoPpGuGhBQAAHBVTbWjCCUpM6Wnan01NilVXJGrssp8m5yqqzkV5g1XbFQXxUenKjYyRV5vmNuhA0GtqqzCdECkkx+8Tem9urdo280Ll+l/9/xJNbW1fn/t9nh9twTifpuR+YanZtupoRqfT1vKCrX+hwTVwk1r9PqnHyplWD/VhnnljQhXdGYXOzXH65gfpB6FOx6pokprPp6pf6z8VgPKe6hLVJy6RMfZ/quSIqPtKIN7Y2qLmB/oZzx2lxIz01t13Np63N3Yvmhrrt64dordf7eTC/CPwjZc64F2zZCQAgAAAcMknRJju9rJJKdMUqq0YodKK/Pt38Xl2+zkkUcxkUmq9UQrvVu07bQXQMeIz0hTck5Wi3/wuPXa7fn6bukM+x3u9ap7fIqdDs+QxjhJmjrlLF386uNKyshUpa/G1qCyU/18tSp+WOaTY/usqpKjKo8jxYYr40dH6uVtiyUzNWKaB9rkVES0fUz+IVG1K2EVo/L8QiWNHqzwrFRFZnSx8YV7wvar2WDdcWvrcXdre4SmxMz0Tn+9kJACAAABm5yq60vKcXwqrypSaeUOlVTsVE1thcqqCiSvdPEdu5qVAAACg2lebWo0NVerydxEqHV8qvLV2qnaV6PCnfma+/b/dPalP1V1ZJjyKkq0s7JMhVXlqnUcm8TaXl5sp+b0//UlWq3/Y+9O4KOqzj6O/2cm+x6SQNhBQFA2FcS6477VulWttYpLta1LXepSrRu1im1dW61LW7Wbddf61tZdUauooCioICA7hCSQfU9m3s9zwsQkJIFsM5PJ7+vnmpk7c+89dxnmzHPPeU6NtHlt0zyfx+MCUy5A5fUp1tP4N8b9bXxcmehTysSdVONt3I7N93m8vXJsAHyDgBQAAOgTOaeS4jPclJ26k+rqq1yrqaLSfC2Zv0qeQ8gtBQB9KWDVGCTyKTh0Ray/XOv/9l9dcMPvmvJXBYNXFfW1Kq6tcsGpktrqrX8bp1J7XletjcWb9f7Cj5UxZrj8Xo8LeBkLZjUErJVWBwXKTtCEX/1Y6yStK2xM3O61roSuldXWAJbXq1ivT3HeGMVZYMs9tr8xrqUXgM4jIAUAAPrcD5m42CQ31RQn6//+9I7uvDrcpQIA9Na/+ZbHyqZhyRntvm/58uUad9hZrsugdWOyQFZ9wK96f8PWv37VNT1u/Oue+/2qKCtTSUGhUoYMkt8rWSdw61LoWnCpQWqo67iQw5K1+2M3a01cQAVb1rvuhfEWuLK/Wx/bXwtuMTgH8A0CUgAAAACAqGKBn1hPY0um7VmzokDv/fQOnfqX3yp37GjXqqo+0BisCv614FVwat7VsLahwQWvfInxsrBVXV213IM2WKsrF6DyxWzt0tj4tzbWK29CfM8fBCDCEZACAAAAAKCpO6FHMZakcAcHc1214DP995Z7dcTtVyt1yKAWydxrt/61QJYFrqoa6twkVX2zgtxE7fHPm7XSH1D+lnVN+beCAatEX6wLrNG6CtGGgBQAAAAAAF3kDUg1eZuV2OBRTkJKm+/xB/yqaWjYOuJg42iD1e5vnSqqq+X3eVx3wbK6Gje1ZsnZg8GpxBgLVsW5x/WugyHQNxGQAgAAAACgF3k9XiXGeJUoG3kwscVra+Z/puevvk3feehmpQwd5IJUVcGgVX2dC2I1bE3ubpMNJNgkVtrtbzfpuhVvaVzxEo1IydSIlAEanmx/M13uLSBSEZACAAAAACCM/FU1ivd7lN1GCyt/INAUpKqqr2v2uNblsopJSdKKqiKtWFe0zbKZcUkuMDXcAlVbg1T2eHhyhhJj4kK0d0DbCEgBAAAAABChvB6PkmLi3KRWDZ62rNugp664RX969nHVpiZobXmR1lYUaXX5Fm2pqVRRbaWKtlTq0y3rt1mvdS90Aaqtgarg46HJGS7xOtDbuMoAAAAAAOiDbOS+qjV5mpE+VGPHjm3xWnldjdZVFGtNeZHWlG9xgSoLWK2pKFZJbZUKqsvdtKBwbYvlLHV6bmJas9ZUmRpp3QBTMjUkKU0xOzByIbAjCEgBAAAAABBlLH/UhIxBbmrNAlKNwSkLVjUGqtZufWx5qjZWlbrpg4LVLZbzebwuKGV5qoYlZ2hgYooGJqRqYOLWKSFFcbSuwg6K2ivlvvvu029/+1vl5eVp6tSp+v3vf68ZM2aEu1gAAAARi/oTAPQP6XGJSh+QqEkDhrSYHwgEXFe/YHCq6e/W4FVNQ73WVhS7qT2Wt8oFqrYGqAbEJyszPumbKS7R/U2LS3ABrv6s3t/QYsTF6vr6rQntm43E6ObVumNvzzcWFmj4D4/TOl+NNpVsUiAgBYL/NT1uPJff/G18bBpi6jTxnstd/rFwi8qA1BNPPKHLL79cDzzwgPbaay/dfffdOuKII7R06VINHDgw3MUDAACIONSfAAAej0dZCclu2i1r2DbJ1a2LXzA4tb6iWAVV5cqvLlN+lU3lbkRAl7eqtlJLS/K3293QAmMZ8YnKiEtUckyckmPjlbL1rz23Vl5ufky8kmLjFO+NUZzPpzj76/Up1utz+a6Cf2M8XrcPnWEBm4aAX/V+v+rtr3vc4J67+QG/6hoa3L7ZqIcWKKoJBoy2zmsMFjUGjGramFfdZpCpzq2/KwYds6+K1SBVl3d+YY+UOCJXkSAqA1J33nmnzjvvPJ199tnuuVWsXnzxRT388MP6+c9/Hu7iAQAARBzqTwCA7SVXH5SY6qbpOSPaDOyU1FVvDU41TpuqylVcW+laXdnfopoqFdVUqrSuWn4FmoJXPcVCUV4LSm0Nrrm/9l/wsafxuQkGoLoaFOpJXnmUEBOjBF9ss6nxeWLMN4/tb1VZhf50/wPa/aSjlJSe1rRPjfsZ3N825tmGPFJ5wRa9PPt3inn2+HDvdvQFpGpra7VgwQJdc801TfO8Xq8OPfRQvf/++20uU1NT46agkpIS97e4uFh+f89enGVlZe7DsH51kaor6zq9fP76Urf8xtXF8gZiQrp8OLfd3eUpe/8rO8ctPMtvLih3y37++efu37vOWrNmTdj+jezPZQ+n7h634L7bftv3dk8qLS1tqmBHu87Wn0JZd2pef9qycp3qKqs6tWzJujy3bPHq9YrpwqkM5/KUPTzLl23a3CPfB33xeu3Ovndnv7u77b5+3MN5vfaWZEmj3WSPkqU4NU4pja9bIKi8vlZl/lqV1dWqsqFWlf46VfsbVOWvV5W/TlWuBVKdqmyetSoK1Ktua0sm9zfgV22gwbXc6q0AXIy88tlfj1dxXptiFCdrodU4xbr5PsV57LlXcR5rreV1Lbnca57Gx42v+xS/dRmbF28tu+yxx7fjLbsC0potVdr4+KuavtseihtU39Fbm7rpNVe7abPKP/9aFeXl4a87BaLM+vXr3XF/7733Wsy/8sorAzNmzGhzmRtvvDF4rpiYmJiYmJiYWkxr164NRLvO1p+oOzExMTExMTGpm3WnqGsh1RV2N9ByJgTZnb0tW7YoKyur0/1Pw8UikcOHD9fatWuVlpYW7uKAcxKROCeRh3MSmTgvarq7Z3ebhwxpmfAVoa87cU12H8ewezh+3ccx7D6OYfdxDHv3GHa27hR1Aans7Gz5fD5t2rSpxXx7npvbduKu+Ph4NzWXkZGhvsguCD5YkYVzEnk4J5GHcxKZOC9Senq6+oPO1p/CVXfimuw+jmH3cPy6j2PYfRzD7uMY9t4x7EzdKerGWIyLi9O0adP0+uuvt7hrZ8/33nvvsJYNAAAgElF/AgAAoRZ1LaSMNSGfNWuWpk+frhkzZrhhiysqKppGjQEAAEBL1J8AAEAoRWVA6tRTT1VBQYFuuOEG5eXlabfddtNLL72kQYMGKVpZs/kbb7xxm+bzCB/OSeThnEQezklk4rz0T5Fcf+Ka7D6OYfdw/LqPY9h9HMPu4xhG1jH0WGbzHigTAAAAAAAA0D9zSAEAAAAAACCyEZACAAAAAABASBGQAgAAAAAAQEgRkAIAAAAAAEBIEZDqQ95++20de+yxGjJkiDwej55//vkWr1t+ehsZZ/DgwUpMTNShhx6qZcuWha28/cGcOXO05557KjU1VQMHDtTxxx+vpUuXtnhPdXW1LrzwQmVlZSklJUUnnXSSNm3aFLYyR7v7779fU6ZMUVpampv23ntv/fe//216nfMRfrfddpv7N+zSSy9tmsd5Cb2bbrrJnYfm04QJE5pe55wgUtx3330aNWqUEhIStNdee+nDDz8Md5Gi5nOObVHf7v1jeNZZZ21zXR555JFhK2+k4fdFaI7hzJkzt7kOf/zjH4etzP31NxUBqT6koqJCU6dOdRWztvzmN7/R7373Oz3wwAP64IMPlJycrCOOOMJdLOgdc+fOdR/EefPm6dVXX1VdXZ0OP/xwd66CLrvsMv3f//2fnnrqKff+DRs26MQTTwxruaPZsGHDXMBjwYIFmj9/vg4++GAdd9xx+vzzz93rnI/w+uijj/Tggw+6L7jmOC/hMXHiRG3cuLFpevfdd5te45wgEjzxxBO6/PLL3fDSH3/8sasHWd0mPz8/3EWLis85tkV9u/ePobEAVPPr8p///GdIyxjJ+H0RmmNozjvvvBbXoX2+EeLfVAH0SXbqnnvuuabnfr8/kJubG/jtb3/bNK+4uDgQHx8f+Oc//xmmUvY/+fn57tzMnTu36RzExsYGnnrqqab3fPnll+4977//fhhL2r9kZmYG/vSnP3E+wqysrCwwbty4wKuvvho48MADA5dccombz3kJjxtvvDEwderUNl/jnCBSzJgxI3DhhRc2PW9oaAgMGTIkMGfOnLCWKxo+59g+6ts9fwzNrFmzAscdd1zYytTX8Pui54+haV4XRfh+U9FCKkqsXLlSeXl5rtlwUHp6umva/v7774e1bP1JSUmJ+ztgwAD31yLKFpFvfl6sqfyIESM4LyHQ0NCgxx9/3N0NsWamnI/wsjtVxxxzTIvjbzgv4WPdTKxLxU477aTTTz9da9ascfM5J4gEtbW17lpsfh16vV73nOuw+59zdB717Z7z1ltvua5U48eP109+8hNt3rw53EWKWPy+6PljGPSPf/xD2dnZmjRpkq655hpVVlaGqYT99zdVTC+UF2FgX45m0KBBLebb8+Br6F1+v9/lxNl3333dP2rGjn1cXJwyMjJavJfz0rsWLVrk/rG05vPWp/m5557TrrvuqoULF3I+wsS+xKy7jXXZa43PSXjYD6hHH33U/RiwZuqzZ8/W/vvvr8WLF3NOEBEKCwtdJbitus2SJUvCVq5o+ZxbbhV0DvXtnmHd9axrz+jRo7VixQpde+21Ouqoo9wPWZ/PF+7iRRR+X/TOMTTf//73NXLkSBew/+yzz3T11Ve7PFPPPvtsWMvb335TEZACerD1h1XwyM0Qflbxtn8o7W7I008/rVmzZrm+zQiPtWvX6pJLLnF9+C0pMSKDVf6DLKeX/XC1itmTTz7pEvUCiO7P+bnnnhvWsqH/+t73vtf0ePLkye7aHDNmjGs1dcghh4S1bJGG3xe9dwzPP//8FtehDVRg158FSe16hELym4oue1EiNzfX/W2d2d6eB19D77nooov073//W2+++aZLABdkx966HBQXF7d4P+eld1nEfuzYsZo2bZobZcMSa95zzz2cjzCxZr2WgHiPPfZQTEyMm+zLzJLC2mO7m8J5CT+7y7Xzzjtr+fLlfFYQEawbhbWWoG7TO59zdB717d5h3Unt88512RK/L3rvGLbFAvaG6zC0v6kISEUJa/JqJ//1119vmldaWupG/7BmdugdlqvR/qGz5otvvPGGOw/N2Yc3Nja2xXmxpqCWv4HzEtqmujU1NZyPMLG7Tdbk1+6wBKfp06e7XCbBx5yX8CsvL3d3Be0OIZ8VREpF2K7F5teh/Xtuz7kOu/85R+dR3+4d69atczmkuC4b8fui949hW6xOargOQ/ubii57fawS0Txia4kV7YNjydksgZj1jf3Vr36lcePGuQ/d9ddf7/rEHn/88WEtd7Q3AX3sscf0r3/9y+ViCPaZtQSX1uXF/lqTeBuy2s5TWlqaLr74YvdB/da3vhXu4kclS0hoXRTsM1FWVubOjzUBf/nllzkfYWKfjeZ99o0Nk52VldU0n/MSeldccYWOPfZY133Hhuq98cYbXWuU0047jc8KIoZdg9ZFwALXM2bM0N133+2Sqp599tnhLlqf/5yjbdS3e/cY2mS5zE466SQX3LMA6VVXXeVaYRxxxBFhLXek4PdF7x9Du+7s9aOPPtrVRy2H1GWXXaYDDjjAdSGFQvebqlNj8iGs3nzzTTeUYuvJhk4NDkV7/fXXBwYNGuSGnz3kkEMCS5c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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Получаем список уникальных типов землепользования\n", + "land_use_types = all_coverage_df_filter['land_use_name'].unique()\n", + "n_types = len(land_use_types)\n", + "\n", + "# Настройка сетки графиков\n", + "cols = 2\n", + "rows = (n_types + cols - 1) // cols # округление вверх\n", + "fig, axes = plt.subplots(rows, cols, figsize=(12, 5 * rows))\n", + "axes = axes.flatten() if n_types > 1 else [axes]\n", + "\n", + "# Цвета — можно использовать палитру\n", + "colors = sns.color_palette(\"husl\", n_types)\n", + "\n", + "for idx, land_use in enumerate(land_use_types):\n", + " ax = axes[idx]\n", + " data = all_coverage_df_filter[all_coverage_df_filter['land_use_name'] == land_use]['coverage_pct'].dropna()\n", + " \n", + " if len(data) == 0:\n", + " ax.text(0.5, 0.5, 'Нет данных', transform=ax.transAxes, ha='center', va='center')\n", + " ax.set_title(f\"{land_use} (пусто)\")\n", + " else:\n", + " # Гистограмма + KDE\n", + " sns.histplot(data, bins=30, kde=True, ax=ax, color=colors[idx], alpha=0.7)\n", + " ax.set_title(f\"Распределение: {land_use}\", fontsize=14)\n", + " ax.set_xlabel('Доля от площади города (%)')\n", + " ax.set_ylabel('Частота')\n", + " ax.grid(True, axis='y', alpha=0.3)\n", + "\n", + "# Скрыть лишние подграфики\n", + "for idx in range(n_types, len(axes)):\n", + " axes[idx].set_visible(False)\n", + "\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "1b0da630", + "metadata": {}, + "outputs": [], + "source": [ + "# Маппинг 7 → 2\n", + "mapping_7_to_2 = {\n", + " \"INDUSTRIAL\": \"NOT_LIVING\",\n", + " \"RECREATION\": \"NOT_LIVING\",\n", + " \"BUSINESS\": \"NOT_LIVING\",\n", + " \"RESIDENTIAL\": \"LIVING\",\n", + " \"AGRICULTURE\": \"NOT_LIVING\",\n", + " \"SPECIAL\": \"NOT_LIVING\",\n", + " \"TRANSPORT\": \"NOT_LIVING\"\n", + "}\n", + "\n", + "mapping_7_to_3 = {\n", + " \"INDUSTRIAL\": \"LARGEAREA\",\n", + " \"RECREATION\": \"LARGEAREA\",\n", + " \"BUSINESS\": \"MIXEDUSE\",\n", + " \"RESIDENTIAL\": \"MIXEDUSE\",\n", + " \"AGRICULTURE\": \"LARGEAREA\",\n", + " \"SPECIAL\": \"ENGINEERING\",\n", + " \"TRANSPORT\": \"ENGINEERING\"\n", + "}\n", + "\n", + "mapping_7_to_4 = {\n", + " \"INDUSTRIAL\": \"INDUSTRIAL\",\n", + " \"RECREATION\": \"RECREATION\",\n", + " \"BUSINESS\": \"MIXEDUSE\",\n", + " \"RESIDENTIAL\": \"MIXEDUSE\",\n", + " \"AGRICULTURE\": \"RECREATION\",\n", + " \"SPECIAL\": \"ENGINEERING\",\n", + " \"TRANSPORT\": \"ENGINEERING\"\n", + "}\n", + "\n", + "mapping_7_to_7 = {\n", + " \"INDUSTRIAL\": \"INDUSTRIAL\",\n", + " \"RECREATION\": \"RECREATION\",\n", + " \"BUSINESS\": \"BUSINESS\",\n", + " \"RESIDENTIAL\": \"RESIDENTIAL\",\n", + " \"AGRICULTURE\": \"AGRICULTURE\",\n", + " \"SPECIAL\": \"SPECIAL\",\n", + " \"TRANSPORT\": \"TRANSPORT\"\n", + "}\n", + "\n", + "# Создаём новый список GeoDataFrame'ов с бинарными метками\n", + "city_gdfs_binary = []\n", + "\n", + "for gdf_city in blocks_gdf: # ваш list(gdf) по городам\n", + " # Глубокая копия, чтобы не изменять оригинал\n", + " gdf_bin = deepcopy(gdf_city)\n", + " \n", + " # Предполагаем, что категория хранится в колонке 'CATEGORY' как строка\n", + " # Если это Enum — конвертируем в строку\n", + " if gdf_bin['land_use'].dtype == 'object':\n", + " cat_str = gdf_bin['land_use'].astype(str).str.split('.').str[-1] # если Enum: 'land_use.RESIDENTIAL' → 'RESIDENTIAL'\n", + " else:\n", + " cat_str = gdf_bin['land_use'].apply(lambda x: x.value if hasattr(x, 'value') else x)\n", + " \n", + " # Маппинг в бинарный класс\n", + " gdf_bin['target'] = cat_str.map(mapping_7_to_7)\n", + " \n", + " # Опционально: сохраняем оригинальную категорию\n", + " # gdf_bin['land_use_ORIGINAL'] = cat_str\n", + " \n", + " city_gdfs_binary.append(gdf_bin)\n", + "\n", + "spb_test['target'] = spb_test['land_use'].str.upper().map(mapping_7_to_7)\n", + "kem_test['target'] = kem_test['land_use'].str.upper().map(mapping_7_to_7)\n", + "\n", + "spb_test.dropna(inplace=True)\n", + "kem_test.dropna(inplace=True)\n", + "\n", + "spb_test.reset_index(drop=True, inplace=True)\n", + "kem_test.reset_index(drop=True, inplace=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "0106eef8", + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "63dd83e9b05144ca94544c5c8ef1ba5b", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Masking targets: 0%| | 0/160 [00:003}: скрыто {frac*100:.1f}% меток (≈ {gdf['target'].isna().sum():>3} из {len(gdf):>4})\")\n", + "\n", + "print(f\"Город СПб: скрыто {spb_test_mask.attrs.get('masked_frac', None)*100:.1f}% меток (≈ {spb_test_mask['target'].isna().sum():>3} из {len(spb_test_mask):>4})\")\n", + "print(f\"Город Кемерово: скрыто {kem_test_mask.attrs.get('masked_frac', None)*100:.1f}% меток (≈ {kem_test_mask['target'].isna().sum():>3} из {len(kem_test_mask):>4})\")" + ] + }, + { + "cell_type": "code", + "execution_count": 121, + "id": "5e752b8f", + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "fc4acac74ad3421bab05a3fd933a9fb8", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + " 0%| | 0/160 [00:00=1).\n", + " Возвращает (width, height, area, aspect_ratio).\n", + " \"\"\"\n", + " try:\n", + " mrr = geom.minimum_rotated_rectangle\n", + " coords = np.array(mrr.exterior.coords)\n", + " # четыре уникальные длины сторон\n", + " edges = np.sqrt(((coords[1:] - coords[:-1])**2).sum(axis=1))\n", + " # в MRR есть две пары равных сторон; берём две уникальные длины\n", + " lengths = np.sort(np.unique(np.round(edges, 12)))\n", + " if len(lengths) >= 2:\n", + " width, height = float(lengths[-1]), float(lengths[-2])\n", + " elif len(lengths) == 1:\n", + " width, height = float(lengths[0]), float(lengths[0])\n", + " else:\n", + " width = height = 0.0\n", + " area = float(mrr.area)\n", + " w, h = (width, height) if width >= height else (height, width)\n", + " aspect = (w / (h + 1e-12)) if (w > 0 and h > 0) else 1.0\n", + " return w, h, area, aspect\n", + " except Exception:\n", + " return 0.0, 0.0, 0.0, 1.0\n", + "\n", + "def add_spatial_features(\n", + " gdf: gpd.GeoDataFrame,\n", + " target_col: str = \"target\",\n", + " rings=((0, 150), (150, 500), (500, 1000)),\n", + " batch_size: int = 500,\n", + " h3_resolution: int = 9,\n", + ") -> gpd.GeoDataFrame:\n", + " \"\"\"\n", + " Признаки для классификации типа землепользования:\n", + " • геометрия формы (в т.ч. фрактальность/прямоугольность/ориентация MRR),\n", + " • пространственные признаки соседства в радиусах (100, 500, 1000 м),\n", + " • локальные метрические признаки (межквартальные расстояния),\n", + " • графовые признаки по соседству через буфер 10 м (networkx).\n", + " \"\"\"\n", + " gdf = gdf.copy()\n", + " utm_crs = gdf.estimate_utm_crs()\n", + " gdf = gdf.to_crs(utm_crs)\n", + "\n", + " # --- Геометрические признаки ---\n", + " gdf[\"area\"] = gdf.geometry.area\n", + " gdf[\"perimeter\"] = gdf.geometry.length\n", + " # компактность (округлость)\n", + " gdf[\"compactness\"] = (4 * np.pi * gdf[\"area\"]) / (gdf[\"perimeter\"]**2 + 1e-12)\n", + " # выпуклая оболочка и «заполненность»\n", + " gdf[\"convex_hull_area\"] = gdf.geometry.convex_hull.area\n", + " gdf[\"solidity\"] = gdf[\"area\"] / (gdf[\"convex_hull_area\"] + 1e-12)\n", + "\n", + " # ограничивающий bbox\n", + " bounds = gdf.geometry.bounds\n", + " gdf[\"bbox_width\"] = bounds[\"maxx\"] - bounds[\"minx\"]\n", + " gdf[\"bbox_height\"] = bounds[\"maxy\"] - bounds[\"miny\"]\n", + " gdf[\"elongation\"] = np.where(\n", + " gdf[\"bbox_height\"] > 0,\n", + " gdf[\"bbox_width\"] / (gdf[\"bbox_height\"] + 1e-12),\n", + " 1.0\n", + " )\n", + "\n", + " # --- Форма по минимальному повёрнутому прямоугольнику (MRR) ---\n", + " mrr_vals = gdf.geometry.apply(_mrr_metrics)\n", + " # gdf[\"mrr_width\"] = [t[0] for t in mrr_vals]\n", + " gdf[\"mrr_height\"] = [t[1] for t in mrr_vals]\n", + " gdf[\"mrr_area\"] = [t[2] for t in mrr_vals]\n", + " gdf[\"mrr_aspect_ratio\"] = [t[3] for t in mrr_vals]\n", + " # прямоугольность: насколько форма заполняет MRR\n", + " gdf[\"rectangularity_index\"] = gdf[\"area\"] / (gdf[\"mrr_area\"] + 1e-12)\n", + " # индекс сложности формы (Miller): чем выше, тем граница «извилистее»\n", + " gdf[\"shape_index\"] = 0.25 * gdf[\"perimeter\"] / np.sqrt(gdf[\"area\"] + 1e-12)\n", + " # приближённая фрактальная размерность границы (масштабно-инвариантная эвристика)\n", + " # FD ~ 2 * log(P/4) / log(A)\n", + " gdf[\"fractal_dimension\"] = (\n", + " 2.0 * np.log((gdf[\"perimeter\"] + 1e-12) / 4.0) / np.log(gdf[\"area\"] + 1e-12)\n", + " ).replace([np.inf, -np.inf], np.nan).fillna(0.0).clip(lower=0.0, upper=2.5)\n", + "\n", + " # --- Пространственные признаки для нескольких колец (r_min, r_max) ---\n", + " for r_min, r_max in rings:\n", + " buf_max = gdf.geometry.buffer(r_max)\n", + " if r_min and r_min > 0:\n", + " buf_min = gdf.geometry.buffer(r_min)\n", + " ring_geom = buf_max.difference(buf_min)\n", + " else:\n", + " ring_geom = buf_max\n", + "\n", + " right = gpd.GeoDataFrame(\n", + " gdf[[target_col, \"area\"]].copy(),\n", + " geometry=ring_geom,\n", + " crs=gdf.crs\n", + " )\n", + "\n", + " joined = gpd.sjoin(\n", + " gdf[[target_col, \"geometry\", \"area\"]],\n", + " right,\n", + " how=\"left\",\n", + " predicate=\"intersects\",\n", + " lsuffix=\"A\",\n", + " rsuffix=\"B\",\n", + " ).reset_index()\n", + "\n", + " left_index = \"index\"\n", + " right_index = \"index_B\"\n", + " if left_index not in joined.columns and joined.index.name:\n", + " joined = joined.reset_index(names=\"index_left\")\n", + " left_index = \"index_left\"\n", + "\n", + " joined = joined[joined[left_index] != joined[right_index]]\n", + "\n", + " # --- обработка пропусков ---\n", + " joined[f\"{target_col}_B\"] = joined[f\"{target_col}_B\"].fillna(\"__none__\")\n", + "\n", + " # базовые агрегаты\n", + " n_neighbors = joined.groupby(left_index).size()\n", + " avg_area_neighbors = joined.groupby(left_index)[\"area_B\"].mean()\n", + "\n", + " # количество и площадь соседей без категории\n", + " none_mask = joined[f\"{target_col}_B\"] == \"__none__\"\n", + " n_neighbors_none = none_mask.groupby(joined[left_index]).sum()\n", + " avg_area_none = (\n", + " joined.loc[none_mask].groupby(left_index)[\"area_B\"].mean()\n", + " if none_mask.any()\n", + " else pd.Series(dtype=float)\n", + " )\n", + " band = f\"{int(r_min)}_{int(r_max)}m\"\n", + " gdf[f\"n_neighbors_{band}m\"] = gdf.index.map(n_neighbors).fillna(0).astype(int)\n", + " gdf[f\"avg_area_neighbors_{band}m\"] = gdf.index.map(avg_area_neighbors).fillna(0.0)\n", + " gdf[f\"n_neighbors_none_{band}m\"] = gdf.index.map(n_neighbors_none).fillna(0).astype(int)\n", + " gdf[f\"avg_area_neighbors_none_{band}m\"] = gdf.index.map(avg_area_none).fillna(0.0)\n", + "\n", + " # --- распределение по классам (без None) ---\n", + " pct_by_class = (\n", + " joined[joined[f\"{target_col}_B\"] != \"__none__\"]\n", + " .groupby([left_index, f\"{target_col}_B\"])\n", + " .size()\n", + " .unstack(fill_value=0)\n", + " )\n", + " pct_by_class = pct_by_class.div(pct_by_class.sum(axis=1), axis=0)\n", + "\n", + " for cls in pct_by_class.columns:\n", + " gdf[f\"pct_neighbor_{str(cls).lower()}_{band}m\"] = (\n", + " gdf.index.map(pct_by_class[cls]).fillna(0.0)\n", + " )\n", + "\n", + " # --- Локальные метрические признаки (cdist, representative_point) ---\n", + " reps = gdf.geometry.representative_point()\n", + " coords = np.column_stack((reps.x.values, reps.y.values)).astype(float)\n", + " n = len(coords)\n", + "\n", + " # создаём заготовки под метрики\n", + " median_dist = np.zeros(n)\n", + " mean_k3_dist = np.zeros(n)\n", + "\n", + " if n > 1:\n", + " for i in range(0, n, batch_size):\n", + " batch = coords[i:i + batch_size]\n", + " dist_block = cdist(batch, coords)\n", + " # маскируем «самого себя»\n", + " row_idx, col_idx = np.indices(dist_block.shape)\n", + " self_mask = (row_idx + i == col_idx)\n", + " dist_block[self_mask] = np.nan\n", + "\n", + " # медианное расстояние до всех остальных\n", + " median_dist[i:i + batch_size] = np.nanmedian(dist_block, axis=1)\n", + "\n", + " # сортируем и берём разные k\n", + " sorted_dist = np.sort(dist_block, axis=1)\n", + " k_lim = min(3, sorted_dist.shape[1] - 1)\n", + " mean_k3_dist[i:i + batch_size] = np.nanmean(sorted_dist[:, :k_lim], axis=1)\n", + "\n", + " # добавляем в GeoDataFrame\n", + " gdf[\"median_dist\"] = median_dist\n", + " gdf[\"mean_k3_dist\"] = mean_k3_dist\n", + "\n", + " # --- Граф по пересечениям с буфером 10 м (networkx) ---\n", + " # левая — исходная геометрия, правая — буфер 10 м\n", + " buf10 = gdf.geometry.buffer(10.0)\n", + " right10 = gpd.GeoDataFrame(\n", + " index=gdf.index,\n", + " geometry=buf10,\n", + " crs=gdf.crs\n", + " )\n", + " pairs = gpd.sjoin(\n", + " gdf[[\"geometry\"]], right10,\n", + " how=\"left\", predicate=\"intersects\",\n", + " lsuffix=\"L\", rsuffix=\"R\"\n", + " ).reset_index()\n", + "\n", + " li = \"index\"\n", + " ri = \"index_R\"\n", + " if li not in pairs.columns and pairs.index.name:\n", + " pairs = pairs.reset_index(names=\"index_left\")\n", + " li = \"index_left\"\n", + "\n", + " # удаляем самосоединения\n", + " pairs = pairs[pairs[li] != pairs[ri]]\n", + " # формируем уникальные неориентированные рёбра\n", + " edges = {(int(a), int(b)) if a < b else (int(b), int(a))\n", + " for a, b in zip(pairs[li], pairs[ri])}\n", + " G = nx.Graph()\n", + " G.add_nodes_from(gdf.index.astype(int).tolist())\n", + " G.add_edges_from(edges)\n", + "\n", + " # степень\n", + " deg = dict(G.degree())\n", + " gdf[\"deg_10m\"] = gdf.index.map(deg).fillna(0).astype(int)\n", + "\n", + " # кластерный коэффициент\n", + " clust = nx.clustering(G)\n", + " gdf[\"clust_10m\"] = gdf.index.map(clust).fillna(0.0)\n", + "\n", + " # средняя степень соседей\n", + " andeg = nx.average_neighbor_degree(G)\n", + " gdf[\"avg_n_deg_10m\"] = gdf.index.map(andeg).fillna(0.0)\n", + "\n", + " # компоненты связности\n", + " comp_id = {}\n", + " comp_size = {}\n", + " for cid, comp_nodes in enumerate(nx.connected_components(G), start=1):\n", + " size = len(comp_nodes)\n", + " for u in comp_nodes:\n", + " comp_id[u] = cid\n", + " comp_size[u] = size\n", + " gdf[\"component_id_10m\"] = gdf.index.map(comp_id).fillna(0).astype(int)\n", + " gdf[\"component_size_10m\"] = gdf.index.map(comp_size).fillna(1).astype(int)\n", + "\n", + " # PageRank (быстрый, устойчивый)\n", + " try:\n", + " pr = nx.pagerank(G, alpha=0.85, max_iter=200)\n", + " except Exception:\n", + " pr = {u: 0.0 for u in G.nodes()}\n", + " gdf[\"pagerank_10m\"] = gdf.index.map(pr).fillna(0.0)\n", + "\n", + " # --- H3-гексагональные признаки ---\n", + " # Переводим обратно в EPSG:4326, чтобы h3 принимал долготу/широту\n", + " gdf = gdf.to_crs(4326)\n", + " gdf[\"h3_index\"] = gdf.geometry.centroid.apply(\n", + " lambda p: h3.latlng_to_cell(p.y, p.x, h3_resolution)\n", + " )\n", + "\n", + " # Плотность зданий/участков по гексагону\n", + " hex_density = gdf.groupby(\"h3_index\").size().rename(\"h3_density\")\n", + " gdf[\"h3_density\"] = gdf[\"h3_index\"].map(hex_density)\n", + "\n", + " # Средняя площадь по гексагону\n", + " hex_area_mean = gdf.groupby(\"h3_index\")[\"area\"].mean().rename(\"h3_mean_area\")\n", + " gdf[\"h3_mean_area\"] = gdf[\"h3_index\"].map(hex_area_mean)\n", + "\n", + " # Энтропия классов по гексагону (разнообразие типов)\n", + " def _entropy(series):\n", + " freq = series.value_counts(normalize=True)\n", + " return -(freq * np.log(freq + 1e-12)).sum()\n", + "\n", + " hex_entropy = gdf.groupby(\"h3_index\")[target_col].apply(_entropy).rename(\"h3_entropy\")\n", + " gdf[\"h3_entropy\"] = gdf[\"h3_index\"].map(hex_entropy)\n", + "\n", + "\n", + " feature_cols = [\n", + " # геометрия формы\n", + " \"area\", \"perimeter\", \"compactness\", \"solidity\",\n", + " \"bbox_width\", \n", + " # \"bbox_height\", \n", + " \"elongation\",\n", + " # \"mrr_width\", \n", + " \"mrr_height\", \"mrr_area\", \"mrr_aspect_ratio\",\n", + " \"rectangularity_index\", \"shape_index\", \"fractal_dimension\",\n", + "\n", + " # локальные дистанции\n", + " \"median_dist\", \"mean_k3_dist\",\n", + " \n", + " # графовые по 10 м\n", + " \"deg_10m\", \"clust_10m\", \"avg_n_deg_10m\",\n", + " \"component_id_10m\", \"component_size_10m\", \"pagerank_10m\",\n", + "\n", + " #h3 признаки\n", + " \"h3_density\", \"h3_mean_area\", \"h3_entropy\"\n", + " ]\n", + " # мультирадиусные соседства и доли по классам\n", + " feature_cols += [c for c in gdf.columns if c.startswith((\"n_neighbors_\", \"avg_area_neighbors_\", \"pct_neighbor_\"))]\n", + "\n", + " return gdf[feature_cols + [target_col, \"geometry\"]]\n", + "\n", + "city_gdfs_with_features = [\n", + " add_spatial_features(gdf) for gdf in tqdm(city_gdfs_binary_mask)\n", + "]\n", + "\n", + "test_gdf_spb = add_spatial_features(spb_test_mask)\n", + "test_gdf_kem = add_spatial_features(kem_test_mask)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "6911be56", + "metadata": {}, + "outputs": [], + "source": [ + "test_gdf_kem[['geometry', 'h3_density', 'h3_mean_area', 'h3_entropy']].explore('h3_mean_area')" + ] + }, + { + "cell_type": "code", + "execution_count": 133, + "id": "9b63d838", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Используемые признаки: ['area', 'perimeter', 'compactness', 'solidity', 'bbox_width', 'elongation', 'mrr_height', 'mrr_area', 'mrr_aspect_ratio', 'rectangularity_index', 'shape_index', 'fractal_dimension', 'median_dist', 'mean_k3_dist', 'deg_10m', 'clust_10m', 'avg_n_deg_10m', 'component_id_10m', 'component_size_10m', 'pagerank_10m', 'h3_density', 'h3_mean_area', 'h3_entropy', 'n_neighbors_0_150mm', 'avg_area_neighbors_0_150mm', 'n_neighbors_none_0_150mm', 'avg_area_neighbors_none_0_150mm', 'pct_neighbor_agriculture_0_150mm', 'pct_neighbor_business_0_150mm', 'pct_neighbor_industrial_0_150mm', 'pct_neighbor_recreation_0_150mm', 'pct_neighbor_residential_0_150mm', 'pct_neighbor_special_0_150mm', 'pct_neighbor_transport_0_150mm', 'n_neighbors_150_500mm', 'avg_area_neighbors_150_500mm', 'n_neighbors_none_150_500mm', 'avg_area_neighbors_none_150_500mm', 'pct_neighbor_agriculture_150_500mm', 'pct_neighbor_business_150_500mm', 'pct_neighbor_industrial_150_500mm', 'pct_neighbor_recreation_150_500mm', 'pct_neighbor_residential_150_500mm', 'pct_neighbor_special_150_500mm', 'pct_neighbor_transport_150_500mm', 'n_neighbors_500_1000mm', 'avg_area_neighbors_500_1000mm', 'n_neighbors_none_500_1000mm', 'avg_area_neighbors_none_500_1000mm', 'pct_neighbor_agriculture_500_1000mm', 'pct_neighbor_business_500_1000mm', 'pct_neighbor_industrial_500_1000mm', 'pct_neighbor_recreation_500_1000mm', 'pct_neighbor_residential_500_1000mm', 'pct_neighbor_special_500_1000mm', 'pct_neighbor_transport_500_1000mm']\n", + "Классы: {'AGRICULTURE': np.int64(0), 'BUSINESS': np.int64(1), 'INDUSTRIAL': np.int64(2), 'RECREATION': np.int64(3), 'RESIDENTIAL': np.int64(4), 'SPECIAL': np.int64(5), 'TRANSPORT': np.int64(6)}\n", + "Размер X_train: (163285, 56)\n", + "Размер X_test SPb: (9249, 56)\n", + "Размер X_test Kem: (1918, 56)\n", + "Количество признаков: 56\n" + ] + } + ], + "source": [ + "from sklearn.preprocessing import LabelEncoder, RobustScaler\n", + "import pandas as pd\n", + "import numpy as np\n", + "\n", + "# --- 1. Объединение всех городов ---\n", + "train_combined = pd.concat([\n", + " gdf.drop(columns=['geometry']) for gdf in city_gdfs_with_features\n", + "], ignore_index=True)\n", + "\n", + "# --- 2. Автоматическое определение числовых признаков ---\n", + "# Оставляем только колонки с числовыми типами (int, float), исключая целевую\n", + "feature_cols = train_combined.select_dtypes(include=[np.number]).columns.tolist()\n", + "\n", + "\n", + "print(\"Используемые признаки:\", feature_cols)\n", + "\n", + "# --- 3. X и y ---\n", + "X_train = train_combined[feature_cols].fillna(0).values\n", + "y_train_str = pd.concat([\n", + " gdf.drop(columns=['geometry']) for gdf in city_gdfs_binary\n", + "], ignore_index=True)['target'] # 'LIVING' или 'NOT_LIVING'\n", + "\n", + "# Кодируем метки\n", + "le = LabelEncoder()\n", + "y_train = le.fit_transform(y_train_str)\n", + "print(\"Классы:\", dict(zip(le.classes_, le.transform(le.classes_)))) # например: {'LIVING': 0, 'NOT_LIVING': 1}\n", + "\n", + "# --- 4. Нормализация ---\n", + "scaler = RobustScaler()\n", + "X_train_scaled = scaler.fit_transform(X_train)\n", + "\n", + "# --- 5. Подготовка тестовых данных ---\n", + "# Извлекаем X_test\n", + "X_test_spb = test_gdf_spb[feature_cols].fillna(0).values\n", + "X_test_kem = test_gdf_kem[feature_cols].fillna(0).values\n", + "X_test_spb_scaled = scaler.transform(X_test_spb)\n", + "X_test_kem_scaled = scaler.transform(X_test_kem)\n", + "\n", + "# Преобразуем y_test\n", + "if 'target' in spb_test.columns and 'target' in kem_test.columns:\n", + " y_test_spb = le.transform(spb_test['target'].values)\n", + " y_test_kem = le.transform(kem_test['target'].values)\n", + "else:\n", + " raise ValueError(\"Тестовый DataFrame должен содержать колонку 'target' для оценки\")\n", + "\n", + "print(f\"Размер X_train: {X_train_scaled.shape}\")\n", + "print(f\"Размер X_test SPb: {X_test_spb.shape}\")\n", + "print(f\"Размер X_test Kem: {X_test_kem.shape}\")\n", + "print(f\"Количество признаков: {len(feature_cols)}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 134, + "id": "c5b1ad0d", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "🚀 Старт сравнения моделей\n", + "\n", + "Модель | F1-Macro | Время (сек) \n", + "---------------------------------------------\n", + "RFast | 0.6216 | 36.6\n", + "\n", + "==================================================\n", + "🏆 Рейтинг моделей (по F1-Macro)\n", + "==================================================\n", + "Model F1-Macro Std Time (s)\n", + "RFast 0.6216 0.0014 36.6392\n", + "\n", + "✅ Лучшая модель: RFast\n" + ] + } + ], + "source": [ + "# Импорты моделей\n", + "from sklearn.ensemble import RandomForestClassifier\n", + "from sklearn.svm import LinearSVC, SVC\n", + "from sklearn.calibration import CalibratedClassifierCV\n", + "from lightgbm import LGBMClassifier\n", + "from xgboost import XGBClassifier\n", + "from catboost import CatBoostClassifier\n", + "import time\n", + "\n", + "from sklearn.ensemble import VotingClassifier\n", + "from sklearn.metrics import classification_report, confusion_matrix\n", + "# Метрики и утилиты\n", + "from sklearn.model_selection import cross_val_score, StratifiedKFold\n", + "import warnings\n", + "warnings.filterwarnings('ignore', category=UserWarning)\n", + "\n", + "# --- Список моделей ---\n", + "models = []\n", + "models.append(('RFast', RandomForestClassifier(\n", + " n_estimators=200, # меньше деревьев\n", + " max_depth=8, # ограничим глубину\n", + " class_weight='balanced',\n", + " n_jobs=-1,\n", + " random_state=42\n", + ")))\n", + "# models.append(('LGBM-Fast', LGBMClassifier(\n", + "# n_estimators=100, # уменьшаем число деревьев\n", + "# max_depth=6,\n", + "# learning_rate=0.1,\n", + "# class_weight='balanced',\n", + "# verbose=-1,\n", + "# n_jobs=-1,\n", + "# random_state=42\n", + "# )))\n", + "# models.append(('XGB-Fast', XGBClassifier(\n", + "# n_estimators=100,\n", + "# max_depth=6,\n", + "# learning_rate=0.1,\n", + "# eval_metric='mlogloss',\n", + "# use_label_encoder=False,\n", + "# n_jobs=-1,\n", + "# random_state=42\n", + "# )))\n", + "# models.append(('CatBoost-Fast', CatBoostClassifier(\n", + "# iterations=100, # уменьшаем число итераций\n", + "# depth=6,\n", + "# learning_rate=0.1,\n", + "# verbose=False,\n", + "# random_seed=42,\n", + "# )))\n", + "\n", + "# Настройка CV (можно уменьшить до 3 фолдов для скорости!)\n", + "cv = StratifiedKFold(n_splits=3, shuffle=True, random_state=42) # было 5 → стало 3 (снижает время на 40%)\n", + "\n", + "scoring = 'f1_macro'\n", + "results = []\n", + "names = []\n", + "\n", + "print(\"🚀 Старт сравнения моделей\\n\")\n", + "print(f\"{'Модель':<12} | {'F1-Macro':<10} | {'Время (сек)':<12}\")\n", + "print(\"-\" * 45)\n", + "\n", + "# Оборачиваем модели в tqdm для прогресс-бара\n", + "for name, model in models:\n", + " start_time = time.time()\n", + " try:\n", + " # Кросс-валидация\n", + " cv_scores = cross_val_score(model, X_train, y_train, cv=cv, scoring=scoring, n_jobs=-1)\n", + " mean_f1 = cv_scores.mean()\n", + " std_f1 = cv_scores.std()\n", + " \n", + " elapsed = time.time() - start_time\n", + " \n", + " results.append((name, mean_f1, std_f1, elapsed))\n", + " names.append(name)\n", + " \n", + " # Вывод строки сразу\n", + " print(f\"{name:<12} | {mean_f1:.4f} | {elapsed:8.1f}\")\n", + " \n", + " except Exception as e:\n", + " elapsed = time.time() - start_time\n", + " print(f\"{name:<12} | Ошибка | {elapsed:8.1f} | {str(e)}\")\n", + " continue\n", + "\n", + "# Таблица результатов\n", + "df_results = pd.DataFrame(results, columns=['Model', 'F1-Macro', 'Std', 'Time (s)'])\n", + "df_results.sort_values(by='F1-Macro', ascending=False, inplace=True)\n", + "\n", + "print(\"\\n\" + \"=\"*50)\n", + "print(\"🏆 Рейтинг моделей (по F1-Macro)\")\n", + "print(\"=\"*50)\n", + "print(df_results[['Model', 'F1-Macro', 'Std', 'Time (s)']].round(4).to_string(index=False))\n", + "\n", + "best_model_name = df_results.iloc[0]['Model']\n", + "print(f\"\\n✅ Лучшая модель: {best_model_name}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 141, + "id": "7715d516", + "metadata": {}, + "outputs": [], + "source": [ + "# --- Определяем лучшие базовые модели ---\n", + "voting_models = [\n", + " ('XGB', XGBClassifier(\n", + " n_estimators=200,\n", + " max_depth=8,\n", + " learning_rate=0.3,\n", + " eval_metric='mlogloss',\n", + " use_label_encoder=False,\n", + " n_jobs=-1,\n", + " random_state=42\n", + " )),\n", + " ('LGBM', LGBMClassifier(\n", + " n_estimators=200,\n", + " max_depth=8,\n", + " learning_rate=0.3,\n", + " class_weight='balanced',\n", + " verbose=-1,\n", + " n_jobs=-1,\n", + " random_state=42\n", + " )),\n", + " ('RF', RandomForestClassifier(\n", + " n_estimators=200,\n", + " max_depth=8,\n", + " class_weight='balanced',\n", + " n_jobs=-1,\n", + " random_state=42\n", + " )),\n", + " ('CB', CatBoostClassifier(\n", + " iterations=200,\n", + " depth=8,\n", + " learning_rate=0.3,\n", + " verbose=False,\n", + " random_seed=42,\n", + " ))\n", + "]\n", + "\n", + "# --- Инициализация VotingClassifier ---\n", + "voting = VotingClassifier(\n", + " estimators=voting_models,\n", + " voting='soft', # используем вероятности, а не метки (лучше для несбалансированных данных)\n", + " weights=[1, 1, 1, 1], # большее влияние лучшей модели XGB\n", + " n_jobs=-1\n", + ")\n", + "\n", + "# # --- Кросс-валидация ---\n", + "# cv = StratifiedKFold(n_splits=3, shuffle=True, random_state=42)\n", + "# scoring = 'f1_macro'\n", + "\n", + "# print(\"🚀 Оценка Voting-модели\\n\")\n", + "# start_time = time.time()\n", + "\n", + "# cv_scores = cross_val_score(voting, X_train_scaled, y_train, cv=cv, scoring=scoring, n_jobs=-1)\n", + "\n", + "# elapsed = time.time() - start_time\n", + "# mean_f1 = np.mean(cv_scores)\n", + "# std_f1 = np.std(cv_scores)\n", + "\n", + "# print(f\"Voting (soft) | F1-Macro: {mean_f1:.4f} ± {std_f1:.4f} | Время: {elapsed:.1f} сек\")\n", + "\n", + "# --- Обучение на всех данных (для финальной модели) ---\n", + "voting.fit(X_train_scaled, y_train)\n", + "\n", + "# Прогноз\n", + "y_pred_spb = voting.predict(X_test_spb_scaled)\n", + "y_pred_kem = voting.predict(X_test_kem_scaled)" + ] + }, + { + "cell_type": "code", + "execution_count": 142, + "id": "3d1794f3", + "metadata": {}, + "outputs": [], + "source": [ + "test_gdf_spb['predict'] = le.inverse_transform(y_pred_spb)\n", + "test_gdf_kem['predict'] = le.inverse_transform(y_pred_kem)" + ] + }, + { + "cell_type": "code", + "execution_count": 143, + "id": "9fe14667", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Отчёт по тестовой выборке Санкт-Петербурга:\n", + " precision recall f1-score support\n", + "\n", + " AGRICULTURE 0.39 0.50 0.44 146\n", + " BUSINESS 0.22 0.24 0.23 314\n", + " INDUSTRIAL 0.32 0.51 0.39 507\n", + " RECREATION 0.56 0.34 0.42 1894\n", + " RESIDENTIAL 0.70 0.91 0.79 4993\n", + " SPECIAL 0.42 0.13 0.20 134\n", + " TRANSPORT 0.89 0.19 0.31 1261\n", + "\n", + " accuracy 0.63 9249\n", + " macro avg 0.50 0.40 0.40 9249\n", + "weighted avg 0.65 0.63 0.60 9249\n", + "\n", + "\n", + "Отчёт по тестовой выборке Кемерово:\n", + " precision recall f1-score support\n", + "\n", + " AGRICULTURE 0.32 0.28 0.30 29\n", + " BUSINESS 0.35 0.20 0.25 96\n", + " INDUSTRIAL 0.47 0.53 0.50 179\n", + " RECREATION 0.45 0.20 0.27 210\n", + " RESIDENTIAL 0.81 0.94 0.87 1300\n", + " SPECIAL 0.42 0.32 0.37 34\n", + " TRANSPORT 0.86 0.09 0.16 70\n", + "\n", + " accuracy 0.73 1918\n", + " macro avg 0.52 0.36 0.39 1918\n", + "weighted avg 0.70 0.73 0.69 1918\n", + "\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 143, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from sklearn.metrics import classification_report, confusion_matrix, ConfusionMatrixDisplay\n", + "\n", + "print(\"\\nОтчёт по тестовой выборке Санкт-Петербурга:\")\n", + "print(classification_report(y_test_spb, y_pred_spb, target_names=le.classes_))\n", + "\n", + "\n", + "cm = confusion_matrix(y_test_spb, y_pred_spb, labels=range(len(le.classes_)))\n", + "disp = ConfusionMatrixDisplay(confusion_matrix=cm, display_labels=le.classes_)\n", + "disp.plot(cmap='Blues', xticks_rotation=45)\n", + "\n", + "print(\"\\nОтчёт по тестовой выборке Кемерово:\")\n", + "print(classification_report(y_test_kem, y_pred_kem, target_names=le.classes_))\n", + "\n", + "\n", + "cm = confusion_matrix(y_test_kem, y_pred_kem, labels=range(len(le.classes_)))\n", + "disp = ConfusionMatrixDisplay(confusion_matrix=cm, display_labels=le.classes_)\n", + "disp.plot(cmap='Blues', xticks_rotation=45)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 130, + "id": "60382bce", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# корреляционная матрица\n", + "corr = pd.DataFrame(X_train, columns=feature_cols).corr(method='pearson')\n", + "\n", + "# визуализация\n", + "plt.figure(figsize=(15, 15))\n", + "sns.heatmap(\n", + " corr[(corr > 0.8) | (corr < -0.6)].fillna(0),\n", + " center=0,\n", + " linewidths=0.5,\n", + " cbar_kws={'label': 'Correlation (Pearson)'},\n", + ")\n", + "plt.title(\"Корреляционная матрица признаков\")\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 131, + "id": "a9a3a647", + "metadata": {}, + "outputs": [], + "source": [ + "XGB_fi = voting.named_estimators_['XGB'].feature_importances_ / max(voting.named_estimators_['XGB'].feature_importances_)\n", + "LGBM_fi = voting.named_estimators_['LGBM'].feature_importances_ / max(voting.named_estimators_['LGBM'].feature_importances_)\n", + "RF_fi = voting.named_estimators_['RF'].feature_importances_ / max(voting.named_estimators_['RF'].feature_importances_)\n", + "CB_fi = voting.named_estimators_['CB'].feature_importances_ / max(voting.named_estimators_['CB'].feature_importances_)" + ] + }, + { + "cell_type": "code", + "execution_count": 132, + "id": "ad125382", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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XGB_importanceLGBM_importanceRF_importanceCB_importanceWeighted_mean
feature
pct_neighbor_transport_0_150mm1.0000000.3188561.0000000.6394190.739569
pct_neighbor_residential_0_150mm0.7242140.4565680.6110621.0000000.697961
pct_neighbor_agriculture_0_150mm0.9666600.2658900.7957180.6391790.666862
fractal_dimension0.0745401.0000000.5078650.5252510.526914
area0.1034210.7139830.4602410.5306760.452080
pct_neighbor_industrial_0_150mm0.4459300.3368640.6308440.3030740.429178
mean_k3_dist0.0612070.9099580.3801340.2892520.410138
pct_neighbor_recreation_0_150mm0.3607280.3432200.4629760.4244200.397836
pct_neighbor_agriculture_150_500mm0.0647310.2510590.7500380.1914730.314325
pct_neighbor_special_0_150mm0.0735590.2658900.6219340.0897640.262787
mrr_area0.0762080.4322030.3635150.1657810.259427
pct_neighbor_agriculture_500_1000mm0.0173220.3167370.4963800.0907780.230304
pct_neighbor_residential_150_500mm0.0535460.3707630.3076470.1871990.229789
pct_neighbor_industrial_150_500mm0.0244380.3283900.3663020.0915770.202677
pct_neighbor_transport_150_500mm0.0097060.1927970.5793230.0243670.201548
h3_mean_area0.0194310.4851690.2192540.0816680.201381
median_dist0.0099420.7372880.0171230.0246050.197239
shape_index0.1385700.1980930.3029680.1441130.195936
avg_n_deg_10m0.0097420.6313560.1026740.0231010.191718
n_neighbors_0_150mm0.0290990.3209750.3125250.0729450.183886
rectangularity_index0.0142610.6419490.0266820.0471210.182503
pct_neighbor_recreation_500_1000mm0.0374600.3824150.1447570.1524880.179280
compactness0.0199960.4120760.1501940.1344060.179168
perimeter0.0331130.2627120.2577080.1583030.177959
pagerank_10m0.0112930.6281780.0186530.0472890.176353
deg_10m0.0212170.4639830.0827670.1261600.173532
h3_entropy0.0344740.4332630.0530310.1621230.170723
clust_10m0.0175930.4925850.0396350.1042060.163505
pct_neighbor_business_0_150mm0.0745360.2807200.1785870.1046560.159625
pct_neighbor_industrial_500_1000mm0.0175960.3771190.1797340.0188440.148323
pct_neighbor_recreation_150_500mm0.0375590.2733050.1686040.1023570.145456
mrr_height0.0116290.4163140.0912240.0407440.139978
solidity0.0115830.4618640.0559680.0039620.133344
bbox_width0.0138780.3559320.1298140.0320310.132914
component_size_10m0.0088250.4947030.0198470.0000000.130844
pct_neighbor_special_150_500mm0.0105280.2690680.2226930.0159470.129559
mrr_aspect_ratio0.0069960.4925850.0088800.0013520.127453
avg_area_neighbors_0_150mm0.0097230.4798730.0146460.0036490.126973
pct_neighbor_business_150_500mm0.0382340.2521190.1410430.0571910.122147
pct_neighbor_transport_500_1000mm0.0085870.1843220.2792010.0149820.121773
pct_neighbor_business_500_1000mm0.0196860.3093220.0837020.0620650.118694
pct_neighbor_residential_500_1000mm0.0107080.3961860.0440760.0170460.117004
avg_area_neighbors_none_150_500mm0.0076320.4406780.0076590.0032970.114817
avg_area_neighbors_150_500mm0.0088930.4120760.0237250.0063890.112771
elongation0.0070540.4131360.0094670.0000000.107414
avg_area_neighbors_none_0_150mm0.0087880.4067800.0079620.0057780.107327
n_neighbors_150_500mm0.0132010.3548730.0412330.0192390.107137
h3_density0.0259420.1949150.1183650.0699020.102281
n_neighbors_500_1000mm0.0115720.3580510.0305580.0071930.101843
avg_area_neighbors_500_1000mm0.0091080.3675850.0138080.0151180.101405
n_neighbors_none_500_1000mm0.0105250.3559320.0115160.0136780.097913
avg_area_neighbors_none_500_1000mm0.0080540.3612290.0101210.0000000.094851
n_neighbors_none_0_150mm0.0151240.2447030.0642740.0000000.081025
pct_neighbor_special_500_1000mm0.0065990.2044490.1056160.0015770.079560
n_neighbors_none_150_500mm0.0137820.2478810.0115450.0186280.072959
component_id_10m0.0083760.1864410.0048740.0000000.049923
\n", + "
" + ], + "text/plain": [ + " XGB_importance LGBM_importance \\\n", + "feature \n", + "pct_neighbor_transport_0_150mm 1.000000 0.318856 \n", + "pct_neighbor_residential_0_150mm 0.724214 0.456568 \n", + "pct_neighbor_agriculture_0_150mm 0.966660 0.265890 \n", + "fractal_dimension 0.074540 1.000000 \n", + "area 0.103421 0.713983 \n", + "pct_neighbor_industrial_0_150mm 0.445930 0.336864 \n", + "mean_k3_dist 0.061207 0.909958 \n", + "pct_neighbor_recreation_0_150mm 0.360728 0.343220 \n", + "pct_neighbor_agriculture_150_500mm 0.064731 0.251059 \n", + "pct_neighbor_special_0_150mm 0.073559 0.265890 \n", + "mrr_area 0.076208 0.432203 \n", + "pct_neighbor_agriculture_500_1000mm 0.017322 0.316737 \n", + "pct_neighbor_residential_150_500mm 0.053546 0.370763 \n", + "pct_neighbor_industrial_150_500mm 0.024438 0.328390 \n", + "pct_neighbor_transport_150_500mm 0.009706 0.192797 \n", + "h3_mean_area 0.019431 0.485169 \n", + "median_dist 0.009942 0.737288 \n", + "shape_index 0.138570 0.198093 \n", + "avg_n_deg_10m 0.009742 0.631356 \n", + "n_neighbors_0_150mm 0.029099 0.320975 \n", + "rectangularity_index 0.014261 0.641949 \n", + "pct_neighbor_recreation_500_1000mm 0.037460 0.382415 \n", + "compactness 0.019996 0.412076 \n", + "perimeter 0.033113 0.262712 \n", + "pagerank_10m 0.011293 0.628178 \n", + "deg_10m 0.021217 0.463983 \n", + "h3_entropy 0.034474 0.433263 \n", + "clust_10m 0.017593 0.492585 \n", + "pct_neighbor_business_0_150mm 0.074536 0.280720 \n", + "pct_neighbor_industrial_500_1000mm 0.017596 0.377119 \n", + "pct_neighbor_recreation_150_500mm 0.037559 0.273305 \n", + "mrr_height 0.011629 0.416314 \n", + "solidity 0.011583 0.461864 \n", + "bbox_width 0.013878 0.355932 \n", + "component_size_10m 0.008825 0.494703 \n", + "pct_neighbor_special_150_500mm 0.010528 0.269068 \n", + "mrr_aspect_ratio 0.006996 0.492585 \n", + "avg_area_neighbors_0_150mm 0.009723 0.479873 \n", + "pct_neighbor_business_150_500mm 0.038234 0.252119 \n", + "pct_neighbor_transport_500_1000mm 0.008587 0.184322 \n", + "pct_neighbor_business_500_1000mm 0.019686 0.309322 \n", + "pct_neighbor_residential_500_1000mm 0.010708 0.396186 \n", + "avg_area_neighbors_none_150_500mm 0.007632 0.440678 \n", + "avg_area_neighbors_150_500mm 0.008893 0.412076 \n", + "elongation 0.007054 0.413136 \n", + "avg_area_neighbors_none_0_150mm 0.008788 0.406780 \n", + "n_neighbors_150_500mm 0.013201 0.354873 \n", + "h3_density 0.025942 0.194915 \n", + "n_neighbors_500_1000mm 0.011572 0.358051 \n", + "avg_area_neighbors_500_1000mm 0.009108 0.367585 \n", + "n_neighbors_none_500_1000mm 0.010525 0.355932 \n", + "avg_area_neighbors_none_500_1000mm 0.008054 0.361229 \n", + "n_neighbors_none_0_150mm 0.015124 0.244703 \n", + "pct_neighbor_special_500_1000mm 0.006599 0.204449 \n", + "n_neighbors_none_150_500mm 0.013782 0.247881 \n", + "component_id_10m 0.008376 0.186441 \n", + "\n", + " RF_importance CB_importance \\\n", + "feature \n", + "pct_neighbor_transport_0_150mm 1.000000 0.639419 \n", + "pct_neighbor_residential_0_150mm 0.611062 1.000000 \n", + "pct_neighbor_agriculture_0_150mm 0.795718 0.639179 \n", + "fractal_dimension 0.507865 0.525251 \n", + "area 0.460241 0.530676 \n", + "pct_neighbor_industrial_0_150mm 0.630844 0.303074 \n", + "mean_k3_dist 0.380134 0.289252 \n", + "pct_neighbor_recreation_0_150mm 0.462976 0.424420 \n", + "pct_neighbor_agriculture_150_500mm 0.750038 0.191473 \n", + "pct_neighbor_special_0_150mm 0.621934 0.089764 \n", + "mrr_area 0.363515 0.165781 \n", + "pct_neighbor_agriculture_500_1000mm 0.496380 0.090778 \n", + "pct_neighbor_residential_150_500mm 0.307647 0.187199 \n", + "pct_neighbor_industrial_150_500mm 0.366302 0.091577 \n", + "pct_neighbor_transport_150_500mm 0.579323 0.024367 \n", + "h3_mean_area 0.219254 0.081668 \n", + "median_dist 0.017123 0.024605 \n", + "shape_index 0.302968 0.144113 \n", + "avg_n_deg_10m 0.102674 0.023101 \n", + "n_neighbors_0_150mm 0.312525 0.072945 \n", + "rectangularity_index 0.026682 0.047121 \n", + "pct_neighbor_recreation_500_1000mm 0.144757 0.152488 \n", + "compactness 0.150194 0.134406 \n", + "perimeter 0.257708 0.158303 \n", + "pagerank_10m 0.018653 0.047289 \n", + "deg_10m 0.082767 0.126160 \n", + "h3_entropy 0.053031 0.162123 \n", + "clust_10m 0.039635 0.104206 \n", + "pct_neighbor_business_0_150mm 0.178587 0.104656 \n", + "pct_neighbor_industrial_500_1000mm 0.179734 0.018844 \n", + "pct_neighbor_recreation_150_500mm 0.168604 0.102357 \n", + "mrr_height 0.091224 0.040744 \n", + "solidity 0.055968 0.003962 \n", + "bbox_width 0.129814 0.032031 \n", + "component_size_10m 0.019847 0.000000 \n", + "pct_neighbor_special_150_500mm 0.222693 0.015947 \n", + "mrr_aspect_ratio 0.008880 0.001352 \n", + "avg_area_neighbors_0_150mm 0.014646 0.003649 \n", + "pct_neighbor_business_150_500mm 0.141043 0.057191 \n", + "pct_neighbor_transport_500_1000mm 0.279201 0.014982 \n", + "pct_neighbor_business_500_1000mm 0.083702 0.062065 \n", + "pct_neighbor_residential_500_1000mm 0.044076 0.017046 \n", + "avg_area_neighbors_none_150_500mm 0.007659 0.003297 \n", + "avg_area_neighbors_150_500mm 0.023725 0.006389 \n", + "elongation 0.009467 0.000000 \n", + "avg_area_neighbors_none_0_150mm 0.007962 0.005778 \n", + "n_neighbors_150_500mm 0.041233 0.019239 \n", + "h3_density 0.118365 0.069902 \n", + "n_neighbors_500_1000mm 0.030558 0.007193 \n", + "avg_area_neighbors_500_1000mm 0.013808 0.015118 \n", + "n_neighbors_none_500_1000mm 0.011516 0.013678 \n", + "avg_area_neighbors_none_500_1000mm 0.010121 0.000000 \n", + "n_neighbors_none_0_150mm 0.064274 0.000000 \n", + "pct_neighbor_special_500_1000mm 0.105616 0.001577 \n", + "n_neighbors_none_150_500mm 0.011545 0.018628 \n", + "component_id_10m 0.004874 0.000000 \n", + "\n", + " Weighted_mean \n", + "feature \n", + "pct_neighbor_transport_0_150mm 0.739569 \n", + "pct_neighbor_residential_0_150mm 0.697961 \n", + "pct_neighbor_agriculture_0_150mm 0.666862 \n", + "fractal_dimension 0.526914 \n", + "area 0.452080 \n", + "pct_neighbor_industrial_0_150mm 0.429178 \n", + "mean_k3_dist 0.410138 \n", + "pct_neighbor_recreation_0_150mm 0.397836 \n", + "pct_neighbor_agriculture_150_500mm 0.314325 \n", + "pct_neighbor_special_0_150mm 0.262787 \n", + "mrr_area 0.259427 \n", + "pct_neighbor_agriculture_500_1000mm 0.230304 \n", + "pct_neighbor_residential_150_500mm 0.229789 \n", + "pct_neighbor_industrial_150_500mm 0.202677 \n", + "pct_neighbor_transport_150_500mm 0.201548 \n", + "h3_mean_area 0.201381 \n", + "median_dist 0.197239 \n", + "shape_index 0.195936 \n", + "avg_n_deg_10m 0.191718 \n", + "n_neighbors_0_150mm 0.183886 \n", + "rectangularity_index 0.182503 \n", + "pct_neighbor_recreation_500_1000mm 0.179280 \n", + "compactness 0.179168 \n", + "perimeter 0.177959 \n", + "pagerank_10m 0.176353 \n", + "deg_10m 0.173532 \n", + "h3_entropy 0.170723 \n", + "clust_10m 0.163505 \n", + "pct_neighbor_business_0_150mm 0.159625 \n", + "pct_neighbor_industrial_500_1000mm 0.148323 \n", + "pct_neighbor_recreation_150_500mm 0.145456 \n", + "mrr_height 0.139978 \n", + "solidity 0.133344 \n", + "bbox_width 0.132914 \n", + "component_size_10m 0.130844 \n", + "pct_neighbor_special_150_500mm 0.129559 \n", + "mrr_aspect_ratio 0.127453 \n", + "avg_area_neighbors_0_150mm 0.126973 \n", + "pct_neighbor_business_150_500mm 0.122147 \n", + "pct_neighbor_transport_500_1000mm 0.121773 \n", + "pct_neighbor_business_500_1000mm 0.118694 \n", + "pct_neighbor_residential_500_1000mm 0.117004 \n", + "avg_area_neighbors_none_150_500mm 0.114817 \n", + "avg_area_neighbors_150_500mm 0.112771 \n", + "elongation 0.107414 \n", + "avg_area_neighbors_none_0_150mm 0.107327 \n", + "n_neighbors_150_500mm 0.107137 \n", + "h3_density 0.102281 \n", + "n_neighbors_500_1000mm 0.101843 \n", + "avg_area_neighbors_500_1000mm 0.101405 \n", + "n_neighbors_none_500_1000mm 0.097913 \n", + "avg_area_neighbors_none_500_1000mm 0.094851 \n", + "n_neighbors_none_0_150mm 0.081025 \n", + "pct_neighbor_special_500_1000mm 0.079560 \n", + "n_neighbors_none_150_500mm 0.072959 \n", + "component_id_10m 0.049923 " + ] + }, + "execution_count": 132, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_importances = pd.DataFrame({\n", + " 'feature': feature_cols,\n", + " 'XGB_importance': XGB_fi,\n", + " 'LGBM_importance': LGBM_fi,\n", + " 'RF_importance': RF_fi,\n", + " 'CB_importance': CB_fi\n", + "})\n", + "\n", + "df_importances.set_index('feature', inplace=True)\n", + "df_importances = df_importances.sort_values(by='XGB_importance', ascending=False)\n", + "\n", + "df_importances['Weighted_mean'] = (\n", + " df_importances['XGB_importance'] +\n", + " df_importances['LGBM_importance'] +\n", + " df_importances['RF_importance'] + \n", + " df_importances['CB_importance']\n", + ") / (4)\n", + "\n", + "df_importances.sort_values(by='Weighted_mean', ascending=False, inplace=True)\n", + "\n", + "plt.figure(figsize=(12, 9))\n", + "plt.barh(df_importances.index, df_importances['Weighted_mean'])\n", + "plt.gca().invert_yaxis()\n", + "plt.title(\"Средняя (взвешенная) важность признаков в Voting-ансамбле\")\n", + "plt.xlabel(\"Средняя важность\")\n", + "plt.tight_layout()\n", + "plt.show()\n", + "\n", + "df_importances\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "08d929e7", + "metadata": {}, + "outputs": [], + "source": [ + "m = test_gdf_kem[['geometry', 'target', 'predict']].explore('predict', tiles='Carto DB Positron')\n", + "test_gdf_kem[['geometry', 'target', 'predict']].explore('target', m=m)" + ] + }, + { + "cell_type": "markdown", + "id": "e3ecfc36", + "metadata": {}, + "source": [ + "------------------------------------------------------------------------------------" + ] + }, + { + "cell_type": "code", + "execution_count": 70, + "id": "b7558126", + "metadata": {}, + "outputs": [], + "source": [ + "# --- Иерархия меток ---\n", + "# L1: RESIDENTIAL vs OTHER\n", + "map7_to_L1 = {\n", + " \"RESIDENTIAL\": \"RESIDENTIAL\",\n", + " \"BUSINESS\": \"OTHER\",\n", + " \"SPECIAL\": \"OTHER\",\n", + " \"TRANSPORT\": \"OTHER\",\n", + " \"AGRICULTURE\": \"OTHER\",\n", + " \"RECREATION\": \"OTHER\",\n", + " \"INDUSTRIAL\": \"OTHER\",\n", + "}\n", + "\n", + "# L2: для OTHER → 4 укрупнённые класса\n", + "map7_to_L2 = {\n", + " \"BUSINESS\": \"BUSINESS\",\n", + " \"SPECIAL\": \"SPECIAL\",\n", + " \"TRANSPORT\": \"TRANSPORT\",\n", + " \"AGRICULTURE\": \"LARGEAREA\",\n", + " \"RECREATION\": \"LARGEAREA\",\n", + " \"INDUSTRIAL\": \"LARGEAREA\",\n", + " \"RESIDENTIAL\": None, # не используется на этом уровне\n", + "}\n", + "\n", + "# L3: для LARGEAREA → 3 листа\n", + "map7_to_L3 = {\n", + " \"AGRICULTURE\": \"AGRICULTURE\",\n", + " \"RECREATION\": \"RECREATION\",\n", + " \"INDUSTRIAL\": \"INDUSTRIAL\",\n", + " \"RESIDENTIAL\": None,\n", + " \"BUSINESS\": None,\n", + " \"SPECIAL\": None,\n", + " \"TRANSPORT\": None,\n", + "}\n", + "\n", + "# Целевые листья финальной схемы\n", + "LEAF_CLASSES = [\"RESIDENTIAL\", \"BUSINESS\", \"SPECIAL\", \"TRANSPORT\",\n", + " \"AGRICULTURE\", \"RECREATION\", \"INDUSTRIAL\"]" + ] + }, + { + "cell_type": "code", + "execution_count": 71, + "id": "d76a129a", + "metadata": {}, + "outputs": [], + "source": [ + "y7 = pd.Series(y_train_str).astype(str)\n", + "\n", + "# L1\n", + "y_L1 = y7.map(map7_to_L1)\n", + "mask_L1 = y_L1.notna()\n", + "y_L1 = y_L1[mask_L1]\n", + "X_L1 = X_train_scaled[mask_L1.values]\n", + "\n", + "# L2 (только OTHER)\n", + "mask_OTHER = (y_L1 == \"OTHER\")\n", + "y_L2 = y7[mask_L1].map(map7_to_L2)\n", + "X_L2 = X_L1[mask_OTHER.values]\n", + "y_L2 = y_L2[mask_OTHER.values]\n", + "\n", + "# L3 (только LARGEAREA)\n", + "mask_LARGE = (y_L2 == \"LARGEAREA\")\n", + "y_L3 = y7[mask_L1][mask_OTHER].map(map7_to_L3)\n", + "X_L3 = X_L2[mask_LARGE.values]\n", + "y_L3 = y_L3[mask_LARGE.values]\n", + "\n", + "from sklearn.preprocessing import LabelEncoder\n", + "le_L1 = LabelEncoder().fit(y_L1) # ['OTHER','RESIDENTIAL']\n", + "le_L2 = LabelEncoder().fit(y_L2) # ['BUSINESS','LARGEAREA','SPECIAL','TRANSPORT']\n", + "le_L3 = LabelEncoder().fit(y_L3) # ['AGRICULTURE','INDUSTRIAL','RECREATION']" + ] + }, + { + "cell_type": "code", + "execution_count": 72, + "id": "90c451ee", + "metadata": {}, + "outputs": [], + "source": [ + "clf_L1 = VotingClassifier(\n", + " estimators=voting_models,\n", + " voting='soft',\n", + " weights=[1.8, 1.2, 1.5, 1], \n", + " n_jobs=-1\n", + ").fit(X_L1, le_L1.transform(y_L1))\n", + "\n", + "clf_L2 = VotingClassifier(\n", + " estimators=voting_models,\n", + " voting='soft',\n", + " weights=[1.8, 1.2, 1.5, 1], \n", + " n_jobs=-1\n", + ").fit(X_L2, le_L2.transform(y_L2))\n", + "\n", + "clf_L3 = VotingClassifier(\n", + " estimators=voting_models,\n", + " voting='soft',\n", + " weights=[1.8, 1.2, 1.5, 1], \n", + " n_jobs=-1\n", + ").fit(X_L3, le_L3.transform(y_L3))" + ] + }, + { + "cell_type": "code", + "execution_count": 90, + "id": "51134fbf", + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "\n", + "def hier_predict_labels(X):\n", + " # L1\n", + " p_L1 = clf_L1.predict(X)\n", + " classes_L1 = le_L1.classes_\n", + " idx_RES = np.where(classes_L1 == \"RESIDENTIAL\")[0][0]\n", + " idx_OTH = np.where(classes_L1 == \"OTHER\")[0][0]\n", + " is_RES = (p_L1 == idx_RES)\n", + "\n", + " # Prepare output\n", + " y_pred = np.empty(len(X), dtype=object)\n", + " y_pred[is_RES] = \"RESIDENTIAL\"\n", + "\n", + " # L2 for OTHER\n", + " if (~is_RES).any():\n", + " X_o = X[~is_RES]\n", + " p_L2 = clf_L2.predict(X_o)\n", + " classes_L2 = le_L2.classes_\n", + " pred_L2 = classes_L2[p_L2]\n", + "\n", + " # If LARGEAREA → L3\n", + " is_LA = (pred_L2 == \"LARGEAREA\")\n", + " y_pred_o = pred_L2.copy()\n", + " if is_LA.any():\n", + " X_la = X_o[is_LA]\n", + " p_L3 = clf_L3.predict(X_la)\n", + " classes_L3 = le_L3.classes_\n", + " pred_L3 = classes_L3[p_L3]\n", + " y_pred_o[is_LA] = pred_L3\n", + "\n", + " # write back\n", + " y_pred[~is_RES] = y_pred_o\n", + "\n", + " return y_pred" + ] + }, + { + "cell_type": "code", + "execution_count": 74, + "id": "3547b656", + "metadata": {}, + "outputs": [], + "source": [ + "def hier_predict_proba(X):\n", + " # Приведём к единому вектору вероятностей по LEAF_CLASSES\n", + " proba = np.zeros((len(X), len(LEAF_CLASSES)), dtype=float)\n", + "\n", + " # L1\n", + " p_L1 = clf_L1.predict_proba(X)\n", + " classes_L1 = le_L1.classes_\n", + " p_RES = p_L1[:, np.where(classes_L1 == \"RESIDENTIAL\")[0][0]]\n", + " p_OTH = p_L1[:, np.where(classes_L1 == \"OTHER\")[0][0]]\n", + "\n", + " # P(RESIDENTIAL)\n", + " proba[:, LEAF_CLASSES.index(\"RESIDENTIAL\")] = p_RES\n", + "\n", + " # L2 (можно считать на всех X, веса задаст p_OTH)\n", + " p_L2 = clf_L2.predict_proba(X)\n", + " classes_L2 = le_L2.classes_\n", + " # P(BUSINESS/SPECIAL/TRANSPORT) = p_OTH * p_L2[class]\n", + " for c in [\"BUSINESS\", \"SPECIAL\", \"TRANSPORT\"]:\n", + " proba[:, LEAF_CLASSES.index(c)] = p_OTH * p_L2[:, np.where(classes_L2 == c)[0][0]]\n", + "\n", + " # P(LARGEAREA)\n", + " p_LA = p_OTH * p_L2[:, np.where(classes_L2 == \"LARGEAREA\")[0][0]]\n", + "\n", + " # L3 (на всех X, веса задаст p_LA)\n", + " p_L3 = clf_L3.predict_proba(X)\n", + " classes_L3 = le_L3.classes_\n", + " for c in [\"AGRICULTURE\", \"RECREATION\", \"INDUSTRIAL\"]:\n", + " proba[:, LEAF_CLASSES.index(c)] = p_LA * p_L3[:, np.where(classes_L3 == c)[0][0]]\n", + "\n", + " # нормализация (на случай численных артефактов)\n", + " s = proba.sum(axis=1, keepdims=True)\n", + " s[s == 0] = 1.0\n", + " proba /= s\n", + " return proba\n", + "\n", + "def hier_predict_labels_prob(X):\n", + " proba = hier_predict_proba(X)\n", + " return np.array(LEAF_CLASSES)[proba.argmax(axis=1)]\n" + ] + }, + { + "cell_type": "code", + "execution_count": 85, + "id": "5311ce6f", + "metadata": {}, + "outputs": [], + "source": [ + "def fom_report(y_true, y_pred, labels=None, target_names=None):\n", + " \"\"\"\n", + " Возвращает DataFrame как classification_report, дополнив колонкой 'fom' (IoU/Jaccard).\n", + " Работает для мультикласса и бинарной классификации.\n", + " \"\"\"\n", + " # Базовый отчёт\n", + " rep = classification_report(\n", + " y_true, y_pred,\n", + " labels=labels,\n", + " target_names=target_names,\n", + " output_dict=True,\n", + " zero_division=0\n", + " )\n", + " df = pd.DataFrame(rep).T\n", + "\n", + " # Перекодируем пер-класс FoM (average=None -> вектор по классам)\n", + " # Важно: передать тот же порядок labels, чтобы совпало с target_names/индексами отчёта\n", + " per_class_fom = jaccard_score(\n", + " y_true, y_pred,\n", + " labels=labels,\n", + " average=None\n", + " )\n", + "\n", + " # Сопоставляем индексам отчёта\n", + " if target_names is not None and labels is not None:\n", + " class_rows = target_names\n", + " elif labels is not None:\n", + " class_rows = [str(l) for l in labels]\n", + " else:\n", + " # sklearn сам определил порядок меток; берём те, что выглядят как классы\n", + " class_rows = [idx for idx in df.index if idx not in ('accuracy','macro avg','weighted avg')]\n", + "\n", + " # Добавляем per-class FoM\n", + " for i, row in enumerate(class_rows):\n", + " df.loc[row, 'fom'] = per_class_fom[i] if i < len(per_class_fom) else np.nan\n", + "\n", + " # Добавляем агрегаты FoM\n", + " for avg_name in ('micro', 'macro', 'weighted'):\n", + " try:\n", + " df.loc[f'{avg_name} avg', 'fom'] = jaccard_score(\n", + " y_true, y_pred, labels=labels, average=avg_name\n", + " )\n", + " except Exception:\n", + " df.loc[f'{avg_name} avg', 'fom'] = np.nan\n", + "\n", + " # Заполним NaN нулями для стабильности вывода\n", + " return df.fillna(0.0)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 91, + "id": "8f22efda", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "SPB:\n", + " precision recall f1-score support\n", + "\n", + " RESIDENTIAL 0.75 0.89 0.82 4993\n", + " BUSINESS 0.23 0.21 0.22 314\n", + " SPECIAL 0.45 0.13 0.20 134\n", + " TRANSPORT 0.88 0.22 0.35 1261\n", + " AGRICULTURE 0.41 0.49 0.45 146\n", + " RECREATION 0.58 0.45 0.51 1894\n", + " INDUSTRIAL 0.27 0.55 0.36 507\n", + "\n", + " accuracy 0.65 9249\n", + " macro avg 0.51 0.42 0.41 9249\n", + "weighted avg 0.68 0.65 0.63 9249\n", + "\n", + "KEM:\n", + " precision recall f1-score support\n", + "\n", + " RESIDENTIAL 0.81 0.93 0.86 1300\n", + " BUSINESS 0.39 0.20 0.26 96\n", + " SPECIAL 0.43 0.29 0.35 34\n", + " TRANSPORT 0.89 0.11 0.20 70\n", + " AGRICULTURE 0.30 0.28 0.29 29\n", + " RECREATION 0.42 0.20 0.27 210\n", + " INDUSTRIAL 0.40 0.48 0.43 179\n", + "\n", + " accuracy 0.72 1918\n", + " macro avg 0.52 0.36 0.38 1918\n", + "weighted avg 0.69 0.72 0.69 1918\n", + "\n", + "SPB κ: 0.42750007933385037\n", + "KEM κ: 0.38142039427790575\n", + "\n", + "SPB:\n", + " precision recall f1-score support fom\n", + "RESIDENTIAL 0.754 0.889 0.816 4993.000 0.689\n", + "BUSINESS 0.227 0.210 0.218 314.000 0.122\n", + "SPECIAL 0.447 0.127 0.198 134.000 0.110\n", + "TRANSPORT 0.881 0.222 0.355 1261.000 0.216\n", + "AGRICULTURE 0.411 0.493 0.449 146.000 0.289\n", + "RECREATION 0.581 0.454 0.509 1894.000 0.342\n", + "INDUSTRIAL 0.265 0.554 0.359 507.000 0.219\n", + "accuracy 0.650 0.650 0.650 0.650 0.000\n", + "macro avg 0.509 0.421 0.415 9249.000 0.284\n", + "weighted avg 0.681 0.650 0.630 9249.000 0.493\n", + "micro avg 0.000 0.000 0.000 0.000 0.482\n", + "\n", + "KEM:\n", + " precision recall f1-score support fom\n", + "RESIDENTIAL 0.808 0.926 0.863 1300.000 0.759\n", + "BUSINESS 0.388 0.198 0.262 96.000 0.151\n", + "SPECIAL 0.435 0.294 0.351 34.000 0.213\n", + "TRANSPORT 0.889 0.114 0.203 70.000 0.113\n", + "AGRICULTURE 0.296 0.276 0.286 29.000 0.167\n", + "RECREATION 0.417 0.205 0.275 210.000 0.159\n", + "INDUSTRIAL 0.396 0.480 0.434 179.000 0.277\n", + "accuracy 0.718 0.718 0.718 0.718 0.000\n", + "macro avg 0.519 0.356 0.382 1918.000 0.263\n", + "weighted avg 0.694 0.718 0.687 1918.000 0.576\n", + "micro avg 0.000 0.000 0.000 0.000 0.561\n" + ] + } + ], + "source": [ + "from sklearn.metrics import classification_report, cohen_kappa_score, jaccard_score\n", + "\n", + "y_true_spb = spb_test['target'].astype(str).values\n", + "y_true_kem = kem_test['target'].astype(str).values\n", + "\n", + "# Вариант 1: жадный\n", + "y_pred_spb = hier_predict_labels(X_test_spb)\n", + "y_pred_kem = hier_predict_labels(X_test_kem)\n", + "\n", + "print(\"SPB:\")\n", + "print(classification_report(y_true_spb, y_pred_spb, labels=LEAF_CLASSES, zero_division=0))\n", + "print(\"KEM:\")\n", + "print(classification_report(y_true_kem, y_pred_kem, labels=LEAF_CLASSES, zero_division=0))\n", + "\n", + "print(\"SPB κ:\", cohen_kappa_score(y_true_spb, y_pred_spb))\n", + "print(\"KEM κ:\", cohen_kappa_score(y_true_kem, y_pred_kem))\n", + "\n", + "print(\"\\nSPB:\")\n", + "report = fom_report(y_true_spb, y_pred_spb, labels=LEAF_CLASSES)\n", + "print(report.to_string(float_format=lambda x: f\"{x:.3f}\"))\n", + "\n", + "print(\"\\nKEM:\")\n", + "report = fom_report(y_true_kem, y_pred_kem, labels=LEAF_CLASSES)\n", + "print(report.to_string(float_format=lambda x: f\"{x:.3f}\"))" + ] + }, + { + "cell_type": "code", + "execution_count": 112, + "id": "2205cf26", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " precision recall f1-score support\n", + "\n", + " OTHER 0.83 0.66 0.74 4256\n", + " RESIDENTIAL 0.75 0.89 0.82 4993\n", + "\n", + " accuracy 0.78 9249\n", + " macro avg 0.79 0.77 0.78 9249\n", + "weighted avg 0.79 0.78 0.78 9249\n", + "\n", + "0.6887026691495965\n" + ] + } + ], + "source": [ + "pred_L1 = clf_L1.predict(X_test_spb)\n", + "print(classification_report(le_L1.transform(spb_test['target'].map(map7_to_L1)), pred_L1, target_names=le_L1.classes_, zero_division=0))\n", + "print(jaccard_score(le_L1.transform(spb_test['target'].map(map7_to_L1)), pred_L1))" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": ".venv", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.11" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +}