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-
-from typing import List
-import re
-import pandas as pd
-import osmnx as ox
-from shapely.geometry import Point, Polygon, MultiPolygon
-from loguru import logger
-import pymorphy2
-from sloyka.src.utils.constants import NUM_CITY_OBJ
-from sloyka.src.geocoder.objects_address_extractor_by_rules import AddressExtractorExtra
-from sloyka.src.utils.data_getter.geo_data_getter import GeoDataGetter
-from rapidfuzz import fuzz
-import numpy as np
-
-
-
-[docs]
-class OtherGeoObjects:
-
-[docs]
- @staticmethod
- def get_and_process_osm_data(osm_id: int, tags: dict) -> pd.DataFrame:
- """
- Retrieves and processes OSM data for different urban objects.
- """
- df = GeoDataGetter.get_osm_data(osm_id, tags)
- df["geometry"] = df["geometry"].apply(OtherGeoObjects.calculate_centroid)
- df.rename(columns={df.columns[-1]: "geo_obj_tag"}, inplace=True)
- return df
-
-
-
-[docs]
- @staticmethod
- def run_osm_dfs(osm_id: int) -> pd.DataFrame:
- """
- Collects dataframes with OSM spatial data, finds centroids and combines them into one.
- """
- tags_list = [
- {"leisure": ["park", "garden", "recreation_ground"]},
- {"amenity": ["hospital", "clinic", "school", "kindergarten"]},
- {"landuse": ["cemetery"]},
- {"natural": ["beach", "water"]},
- {"railway": ["station", "subway"]},
- {"tourism": ["attraction", "museum"]},
- {"historic": ["monument", "memorial"]},
- {"place": ["square"]},
- ]
-
- osm_dfs = list()
- for tags in tags_list:
- logger.debug(f'getting {osm_id, tags}')
- try:
- tmp_df = OtherGeoObjects.get_and_process_osm_data(osm_id, tags)
- osm_dfs.append(tmp_df)
- except RuntimeError:
- logger.warning(f'Runtime error during fetching {osm_id, tags}')
- continue
- if osm_dfs:
- osm_combined_df = pd.concat(osm_dfs, axis=0)
- logger.debug(f'got {osm_id, tags}')
- logger.debug(f'{osm_combined_df.shape}')
- return osm_combined_df
- else:
- logger.warning(f'No data were gathered about city objects in {osm_id}')
- return pd.DataFrame()
-
-
-
-[docs]
- @staticmethod
- def calculate_centroid(geometry) -> Point:
- """
- Calculates the centroid for polygons.
- """
- if isinstance(geometry, (Polygon, MultiPolygon)):
- return geometry.centroid
- elif isinstance(geometry, Point):
- return geometry
- else:
- return None
-
-
-
-[docs]
- @staticmethod
- def extract_geo_obj(text) -> List[str]:
- """
- The function extracts location entities from the text, using the Natasha library.
- """
- if text is None:
- return None
- morph = pymorphy2.MorphAnalyzer()
- extractor = AddressExtractorExtra(morph)
-
- other_geo_obj = []
-
- matches = extractor(text)
- if not matches:
- return other_geo_obj
- try:
- for match in matches:
- if not match:
- continue
- part = match.fact
- if part.value and part.type:
- combined_phrase = f"{part.value} {part.type}"
- other_geo_obj.append(combined_phrase)
- elif part.value:
- other_geo_obj.append(part.value)
- elif part.type:
- other_geo_obj.append(part.type)
- if not other_geo_obj:
- return other_geo_obj
- except Exception as e:
- # logger.warning(f"Error extracting geo objects: {e}")
- return other_geo_obj
- return other_geo_obj
-
-
-
-[docs]
- @staticmethod
- def restoration_of_normal_form(other_geo_obj, osm_combined_df, threshold=0.7) -> List[str]:
- """
- This function compares the extracted location entity with an OSM array and returns a normalized form if the percentage of similarity is at least 70%.
- """
- osm_name_obj = osm_combined_df["name"].tolist()
- similarity_matrix = np.zeros((len(other_geo_obj), len(osm_name_obj)))
-
- def extract_numbers(s):
- return re.findall(r"\d+", s)
-
- for i, word1 in enumerate(other_geo_obj):
- numbers_from_extraction = extract_numbers(word1)
- for j, word2 in enumerate(osm_name_obj):
- numbers_from_OSM_name = extract_numbers(word2)
- if numbers_from_extraction == numbers_from_OSM_name:
- similarity = fuzz.ratio(word1, word2) / 100.0
- else:
- similarity = 0
- similarity_matrix[i, j] = similarity
-
- restoration_list = other_geo_obj.copy()
- for i in range(len(other_geo_obj)):
- max_index = np.argmax(similarity_matrix[i])
- if similarity_matrix[i, max_index] > threshold:
- restoration_list[i] = osm_name_obj[max_index]
- else:
- restoration_list[i] = ""
-
- return restoration_list
-
-
- @staticmethod
- def find_num_city_obj(text, NUM_CITY_OBJ) -> List[str]:
- """
- This function searches for urban objects in the text, the names of which are represented as a number. For example, "school No. 6".
- """
- text = str(text)
- text = text.lower()
- num_obj_list = []
- for key, forms in NUM_CITY_OBJ.items():
- for form in forms:
- pattern = rf"\b{re.escape(form)}\b\s+№?\s*(\d+)"
- matches = re.findall(pattern, text)
- for match in matches:
- num_obj_list.append(f"{key} № {match}")
- num_obj_list = list(set(num_obj_list))
- num_obj_list_clear = {}
- for obj in num_obj_list:
- key = obj.split(" № ")[1]
- if key in num_obj_list_clear:
- if len(obj.split(" № ")[0]) > len(num_obj_list_clear[key].split(" № ")[0]):
- num_obj_list_clear[key] = obj
- else:
- num_obj_list_clear[key] = obj
-
- return list(num_obj_list_clear.values())
-
- @staticmethod
- def combine_city_obj(df_obj) -> pd.DataFrame:
- """
- Combines the found named urban objects and urban objects whose names are in the form of numbers.
- """
- df_obj["other_geo_obj"] = df_obj["other_geo_obj"] + df_obj["other_geo_obj_num"]
- df_obj.drop(columns=["other_geo_obj_num"], inplace=True)
- return df_obj
-
- @staticmethod
- def expand_toponym(df_obj) -> pd.DataFrame:
- """
- Splits the list of found entities into different rows for further analysis.
- """
- expanded_df = df_obj.copy()
- expanded_df["other_geo_obj"] = expanded_df["other_geo_obj"].apply(
- lambda x: x if isinstance(x, list) and x else None
- )
- expanded_df = expanded_df.explode("other_geo_obj").reset_index(drop=True)
- return expanded_df
-
- @staticmethod
- def find_geometry(toponym, osm_combined_df) -> Point:
- """
- Finds the coordinate in the OSM array by the name of the city object.
- """
- if toponym is None:
- return None
- match = osm_combined_df[osm_combined_df["name"] == toponym]
- if not match.empty:
- return match.iloc[0, 1]
- else:
- return None
-
- @staticmethod
- def find_geo_obj_tag(toponym, osm_combined_df) -> str:
- """
- Finds the geo_obj_tag in the OSM array by the name of the city object.
- """
- if toponym is None:
- return None
- match = osm_combined_df[osm_combined_df["name"] == toponym]
- if not match.empty:
- return match.iloc[0, -1]
- else:
- return None
-
- @staticmethod
- def get_unique_part_types(df):
- return df["other_geo_obj"].unique()
-
-
-[docs]
- @staticmethod
- def run(osm_id: int, df: pd.DataFrame, text_column: str) -> pd.DataFrame:
- """
- Launches the module for extracting urban objects from texts that do not relate to streets.
- """
- df_obj = df.copy()
- df_obj["Numbers"] = pd.NA
- # osm_combined_df = OtherGeoObjects.run_osm_dfs(osm_id)
-
- df_obj["other_geo_obj"] = df_obj[text_column].apply(OtherGeoObjects.extract_geo_obj)
- df_obj["other_geo_obj_num"] = df_obj[text_column].apply(
- lambda x: OtherGeoObjects.find_num_city_obj(x, NUM_CITY_OBJ)
- )
-
- df_obj = OtherGeoObjects.combine_city_obj(df_obj)
-
- osm_combined_df = OtherGeoObjects.run_osm_dfs(osm_id)
-
- if not osm_combined_df.empty:
- df_obj["other_geo_obj"] = df_obj["other_geo_obj"].apply(
- lambda x: OtherGeoObjects.restoration_of_normal_form(x, osm_combined_df)
- )
- df_obj = OtherGeoObjects.expand_toponym(df_obj)
-
- df_obj["geometry"] = df_obj["other_geo_obj"].apply(lambda x: OtherGeoObjects.find_geometry(x, osm_combined_df))
- df_obj["geo_obj_tag"] = df_obj["other_geo_obj"].apply(
- lambda x: OtherGeoObjects.find_geo_obj_tag(x, osm_combined_df)
- )
- df_obj = df_obj[df_obj["geometry"].notna()]
-
- return df_obj
-
-
-
-"""
-TODO: add spellchecker since there might be misspelled words.
-
-This module is aimed to provide necessary tools to find mentioned
-location in the text.
-
-@class:Location:
-A class aimed to efficiently geocode addresses using Nominatim. Geocoded addresses are stored in the 'book' dictionary argument.
-Thus, if the address repeats, it would be taken from the book.
-
-@class:Streets:
-A class encapsulating functionality for retrieving street data
-for a specified city from OSM and processing it to extract useful information for geocoding purposes.
-
-@class:Geocoder:
-A class providing functionality for simple geocoding and address extraction.
-"""
-import numpy as np
-import re
-import warnings
-import os
-import flair
-import geopandas as gpd
-import pandas as pd
-import pymorphy2
-import torch
-import string
-import math
-from rapidfuzz import fuzz
-from nltk.stem.snowball import SnowballStemmer
-from sloyka.src.utils.data_getter.historical_geo_data_getter import HistGeoDataGetter
-from sloyka.src.utils.constants import (
- AREA_STOPWORDS,
- GROUP_STOPWORDS,
- REGEX_PATTERN,
- REPLACEMENT_STRING,
-)
-
-from flair.models import SequenceTagger
-from shapely.geometry import Point
-from tqdm import tqdm
-# from natasha import (
-# Segmenter,
-# MorphVocab,
-# NewsEmbedding,
-# NewsMorphTagger,
-# NewsSyntaxParser,
-# NewsNERTagger,
-# Doc,
-# )
-
-from loguru import logger
-
-from pandarallel import pandarallel
-from sloyka.src.geocoder.city_objects_getter import OtherGeoObjects
-from sloyka.src.utils.data_getter.street_getter import Streets
-from sloyka.src.utils.data_getter.location_getter import Location
-from sloyka.src.utils.data_getter.geo_data_getter import GeoDataGetter
-from sloyka.src.geocoder.street_extractor import StreetExtractor
-from sloyka.src.geocoder.word_form_matcher import WordFormFinder
-
-import warnings
-
-warnings.simplefilter("ignore")
-warnings.filterwarnings("ignore", category=DeprecationWarning)
-
-pandarallel.initialize(progress_bar=True, nb_workers=-1)
-
-# segmenter = Segmenter()
-# morph_vocab = MorphVocab()
-morph = pymorphy2.MorphAnalyzer()
-
-# emb = NewsEmbedding()
-# morph_tagger = NewsMorphTagger(emb)
-# syntax_parser = NewsSyntaxParser(emb)
-# ner_tagger = NewsNERTagger(emb)
-warnings.simplefilter(action="ignore", category=FutureWarning)
-warnings.filterwarnings("ignore", category=DeprecationWarning)
-
-
-
-
-stemmer = SnowballStemmer("russian")
-
-tqdm.pandas()
-
-
-
-[docs]
-class Geocoder:
- """
- This class provides a functionality of simple geocoder
- """
-
- dir_path = os.path.dirname(os.path.realpath(__file__))
-
- global_crs: int = 4326
-
- def __init__(
- self,
- model_path: str = "Geor111y/flair-ner-addresses-extractor",
- device: str = "cpu",
- osm_id: int = None,
- city_tags: dict ={"place": ["state"]}
- ):
- self.device = device
- flair.device = torch.device(device)
- self.classifier = SequenceTagger.load(model_path)
- self.osm_id = osm_id
- self.osm_city_name = (
- GeoDataGetter()
- .get_features_from_id(osm_id=self.osm_id,tags=city_tags, selected_columns=["name", "geometry"])
- .iloc[0]["name"]
- )
- self.street_names = Streets.run(self.osm_id)
-
-
- @staticmethod
- def get_stem(street_names_df: pd.DataFrame) -> pd.DataFrame:
- """
- Function finds the stem of the word to find this stem in the street
- names dictionary (df).
- """
- logger.info("get_stem started")
-
- morph = pymorphy2.MorphAnalyzer()
- cases = ["nomn", "gent", "datv", "accs", "ablt", "loct"]
-
- for case in cases:
- street_names_df[case] = street_names_df["street_name"].apply(
- lambda x: morph.parse(x)[0].inflect({case}).word if morph.parse(x)[0].inflect({case}) else None
- )
- return street_names_df
-
-
- @staticmethod
- def get_level(row: pd.Series) -> str:
- """
- Addresses in the messages are recognized on different scales:
- 1. Where we know the street name and house number -- house level;
- 2. Where we know only street name -- street level (with the centroid
- geometry of the street);
- 3. Where we don't know any info but the city -- global level.
- """
-
- if (not pd.isna(row["Street"])) and (row["Numbers"] == ""):
- return "street"
- elif (not pd.isna(row["Street"])) and (row["Numbers"] != ""):
- return "house"
- else:
- return "global"
-
-
- def create_gdf(self, df: pd.DataFrame) -> gpd.GeoDataFrame:
- """
- Function simply creates gdf from the recognised geocoded geometries.
- """
- logger.info("create_gdf started")
-
- df["Location"] = df["addr_to_geocode"].progress_apply(Location().query)
- df = df.dropna(subset=["Location"])
- df["geometry"] = df['Location'].apply(lambda x: Point(x.longitude, x.latitude))
- df["Location"] = df['Location'].apply(lambda x: x.address)
- df["Numbers"].astype(str)
- gdf = gpd.GeoDataFrame(df, geometry="geometry", crs=Geocoder.global_crs)
-
- return gdf
-
- def set_global_repr_point(self, gdf: gpd.GeoDataFrame) -> gpd.GeoDataFrame:
- """
- This function set the centroid (actually, representative point) of the
- geocoded addresses to those texts that weren't geocoded (or didn't
- contain any addresses according to the trained NER model).
- """
-
- try:
- gdf.loc[gdf["level"] == "global", "geometry"] = gdf.loc[
- gdf["level"] != "global", "geometry"
- ].unary_union.representative_point()
- except AttributeError:
- pass
-
- return gdf
-
- def merge_to_initial_df(self, gdf: gpd.GeoDataFrame, initial_df: pd.DataFrame) -> gpd.GeoDataFrame:
- """
- This function merges geocoded df to the initial df in order to keep
- all original attributes.
- """
-
- # initial_df.drop(columns=['key_0'], inplace=True)
- gdf = initial_df.join(
- gdf[
- [
- "Street",
- "initial_street",
- "only_full_street_name",
- "Numbers",
- "Score",
- "location_options",
- "Location",
- "geometry",
- ]
- ],
- how="outer",
- )
- gdf = gpd.GeoDataFrame(gdf, geometry="geometry", crs=Geocoder.global_crs)
-
- return
-
- def assign_street(variable):
- if isinstance(variable, float) and math.isnan(variable):
- return "street"
- return variable
-
- def get_df_areas(self, osm_id, tags):
- """
- Retrieves the GeoDataFrame of areas corresponding to the given OSM ID and tags.
-
- Args:
- osm_id (int): The OpenStreetMap ID.
- tags (dict): The tags to filter by.
- date (str): The date of the data to retrieve.
-
- Returns:
- gpd.GeoDataFrame: The GeoDataFrame containing the areas.
-
- This function first checks if the GeoDataFrame corresponding to the given OSM ID is already in the cache.
- If it is, it returns the cached GeoDataFrame. Otherwise, it retrieves the GeoDataFrame from the HistGeoDataGetter,
- filters out the 'way' elements, and adds it to the cache. Finally, it returns the GeoDataFrame from the cache.
- """
- area_cache = {}
- if osm_id not in area_cache:
- geo_data_getter = HistGeoDataGetter()
- df_areas = geo_data_getter.get_features_from_id(osm_id=osm_id, tags=tags)
- df_areas = df_areas[df_areas["element_type"] != "way"]
- area_cache[osm_id] = df_areas
- return area_cache[osm_id]
-
- def preprocess_group_name(self, group_name):
- """
- Preprocesses a group name by converting it to lowercase, removing special characters, and removing specified stopwords.
-
- Args:
- group_name (str): The group name to preprocess.
-
- Returns:
- str: The preprocessed group name.
- """
- group_name = group_name.lower()
- group_name = re.sub(REGEX_PATTERN, REPLACEMENT_STRING, group_name)
- words_to_remove = GROUP_STOPWORDS
- for word in words_to_remove:
- group_name = re.sub(word, "", group_name, flags=re.IGNORECASE)
- return group_name
-
- def preprocess_area_names(self, df_areas):
- """
- Preprocesses the area names in the given DataFrame by removing specified stopwords, converting the names to lowercase,
- and stemming them.
-
- Parameters:
- df_areas (DataFrame): The DataFrame containing the area names.
-
- Returns:
- DataFrame: The DataFrame with preprocessed area names, where the 'area_name' column contains the original names
- with stopwords removed, the 'area_name_processed' column contains the lowercase names with special characters
- removed, and the 'area_stems' column contains the stemmed names.
- """
- words_to_remove = AREA_STOPWORDS
- for word in words_to_remove:
- df_areas["area_name"] = df_areas["name"].str.replace(word, "", regex=True)
-
- df_areas["area_name_processed"] = df_areas["area_name"].str.lower()
- df_areas["area_name_processed"] = df_areas["area_name_processed"].str.replace(
- REGEX_PATTERN, REPLACEMENT_STRING, regex=True
- )
- df_areas["area_stems"] = df_areas["area_name_processed"].apply(
- lambda x: [stemmer.stem(word) for word in x.split()]
- )
- return df_areas
-
- def match_group_to_area(self, group_name, df_areas):
- """
- Matches a given group name to an area in a DataFrame of areas.
-
- Args:
- group_name (str): The name of the group to match.
- df_areas (DataFrame): The DataFrame containing the areas to match against.
-
- Returns:
- tuple: A tuple containing the best match for the group name and the admin level of the match.
- If no match is found, returns (None, None).
- """
- group_name_stems = [stemmer.stem(word) for word in group_name.split()]
- max_partial_ratio = 20
- max_token_sort_ratio = 20
- best_match = None
- admin_level = None
-
- for _, row in df_areas.iterrows():
- area_stems = row["area_stems"]
-
- partial_ratio = fuzz.partial_ratio(group_name, row["area_name_processed"])
- token_sort_ratio = fuzz.token_sort_ratio(group_name_stems, area_stems)
-
- if partial_ratio > max_partial_ratio and token_sort_ratio > max_token_sort_ratio:
- max_partial_ratio = partial_ratio
- max_token_sort_ratio = token_sort_ratio
- best_match = row["area_name"]
- admin_level = row["key"]
-
- return best_match, admin_level
-
-
-[docs]
- def run(
- self, df: pd.DataFrame, tags:dict|None=None, text_column: str = "text", group_column: str | None = "group_name", search_for_objects=False
- ):
- """
- Runs the data processing pipeline on the input DataFrame.
-
- Args:
- tags (dict): The tags to filter by.
- date (str): The date of the data to retrieve.
- df (pd.DataFrame): The input DataFrame.
- text_column (str, optional): The name of the text column in the DataFrame. Defaults to "text".
-
- Returns:
- gpd.GeoDataFrame: The processed DataFrame after running the data processing pipeline.
-
- This function retrieves the GeoDataFrame of areas corresponding to the given OSM ID and tags.
- It then preprocesses the area names and matches each group name to an area. The best match
- and admin level are assigned to the DataFrame. The function also retrieves other geographic
- objects and street names, preprocesses the street names, finds the word form, creates a GeoDataFrame,
- merges it with the other geographic objects, assigns the street tag, and returns the final GeoDataFrame.
- """
-
- # initial_df = df.copy()
- if tags:
- df_areas = self.get_df_areas(self.osm_id, tags)
- df_areas = self.preprocess_area_names(df_areas)
-
- if group_column and group_column in df.columns:
- for i, group_name in enumerate(df[group_column]):
- processed_group_name = self.preprocess_group_name(group_name)
- best_match, admin_level = self.match_group_to_area(processed_group_name, df_areas)
- df.at[i, "territory"] = best_match
- df.at[i, "key"] = admin_level
- del df_areas
- # df = AreaMatcher.run(self, df, osm_id, tags, date)
-
- df[text_column] = df[text_column].astype(str).str.replace('\n', ' ')
- df_reconstruction = df.copy()
- df[text_column] = df[text_column].apply(str)
-
-
- df = StreetExtractor.process_pipeline(df, text_column, self.classifier)
- street_names = self.get_stem(self.street_names)
-
- df = WordFormFinder(self.osm_city_name).find_word_form(df, street_names)
-
- del street_names
- gdf = self.create_gdf(df)
- del df
-
- if search_for_objects:
- df_obj = OtherGeoObjects.run(self.osm_id, df, text_column)
- gdf = pd.concat([gdf, df_obj], ignore_index=True)
- del df_obj
- gdf["geo_obj_tag"] = gdf["geo_obj_tag"].apply(Geocoder.assign_street)
-
- gdf = pd.concat(
- [gdf, df_reconstruction[~df_reconstruction[text_column].isin(gdf[text_column])]], ignore_index=True
- )
-
- # gdf2 = self.merge_to_initial_df(gdf, initial_df)
-
- # # Add a new 'level' column using the get_level function
- # gdf2["level"] = gdf2.progress_apply(self.get_level, axis=1)
- # gdf2 = self.set_global_repr_point(gdf2)
- gdf.set_crs(4326, inplace=True)
- return gdf
-
-
-
-
- # def extract_ner_street(self, text: str) -> pd.Series:
- # """
- # Function calls the pre-trained custom NER model to extract mentioned
- # addresses from the texts (usually comment) in social networks in
- # russian language.
- # The model scores 0.8 in F1 and other metrics.
- # """
-
- # try:
- # text = re.sub(r"\[.*?\]", "", text)
- # except Exception:
- # return pd.Series([None, None])
-
- # sentence = Sentence(text)
- # self.classifier.predict(sentence)
- # try:
- # res = sentence.get_labels("ner")[0].labeled_identifier.split("]: ")[1].split("/")[0].replace('"', "")
- # score = round(sentence.get_labels("ner")[0].score, 3)
- # if score > 0.7:
- # return pd.Series([res, score])
- # else:
- # return pd.Series([None, None])
-
- # except IndexError:
- # return pd.Series([None, None])
-
- # @staticmethod
- # def extract_building_num(text, street_name, number) -> string:
- # """
- # The function finds the already extracted street name in the text
- # and searches for numbers related to building numbers in a certain range of indexes
- # around the street name.
- # """
- # if pd.notna(number) and number != "":
- # return number
- # if isinstance(text, float) and math.isnan(text):
- # return ""
-
- # clear_text = str(text).translate(str.maketrans("", "", string.punctuation))
- # clear_text = clear_text.lower().split(" ")
- # positions = [index for index, item in enumerate(clear_text) if item == street_name]
-
- # if not positions:
- # return ""
-
- # position = positions[0]
- # search_start = max(0, position)
- # search_end = min(len(clear_text), position + END_INDEX_POSITION)
-
- # num_result = []
-
- # for f_index in range(max(0, search_start), min(len(clear_text), search_end)):
- # element = clear_text[f_index]
- # if any(character.isdigit() for character in str(element)) and len(str(element)) <= 3:
- # num_result.append(element)
- # break
-
- # if num_result:
- # return num_result[0]
- # else:
- # return ""
-
- # @staticmethod
- # def extract_toponym(text, street_name) -> string:
- # """
- # The function finds the already extracted street name in the text
- # and searches for words related to toponyms in a certain range of indexes
- # around the street name.
- # """
- # if isinstance(text, float) and math.isnan(text):
- # return None
-
- # clear_text = str(text).translate(str.maketrans("", "", string.punctuation))
- # clear_text = clear_text.lower().split(" ")
- # positions = [index for index, item in enumerate(clear_text) if item == street_name]
-
- # if not positions:
- # return None
-
- # position = positions[0]
- # search_start = max(0, position - int(START_INDEX_POSITION))
- # search_end = min(len(clear_text), position + int(END_INDEX_POSITION))
-
- # ad_result = []
- # for i in range(search_start, min(search_end + 1, len(clear_text))):
- # word = clear_text[i]
- # normal_form = morph.parse(word)[0].normal_form
- # if normal_form in TARGET_TOPONYMS:
- # ad_result.append(REPLACEMENT_DICT.get(normal_form, normal_form))
-
- # if ad_result:
- # return ad_result[0]
- # else:
- # return None
-
- # def get_street(self, df: pd.DataFrame, text_column: str) -> gpd.GeoDataFrame:
- # """
- # Function calls NER model and post-process result in order to extract
- # the address mentioned in the text.
- # """
- # logger.info("get_street started")
-
- # df[text_column].dropna(inplace=True)
- # df[text_column] = df[text_column].astype(str)
-
- # logger.info("extract_ner_street started")
-
- # df[["Street", "Score"]] = df[text_column].progress_apply(lambda t: self.extract_ner_street(t))
- # df["Street"] = df[[text_column, "Street"]].progress_apply(
- # lambda row: Geocoder.get_ner_address_natasha(row, text_column),
- # axis=1,
- # )
-
- # df = df[df.Street.notna()]
- # df = df[df["Street"].str.contains("[а-яА-Я]")]
-
- # logger.info("pattern1.sub started")
-
- # pattern1 = re.compile(r"(\D)(\d)(\D)")
- # df["Street"] = df["Street"].progress_apply(lambda x: pattern1.sub(r"\1 \2\3", x))
-
- # logger.info("pattern2.findall started")
-
- # pattern2 = re.compile(r"\d+")
- # df["Numbers"] = df["Street"].progress_apply(lambda x: " ".join(pattern2.findall(x)))
-
- # logger.info("pattern2.sub started")
-
- # df["Street"] = df["Street"].progress_apply(lambda x: pattern2.sub("", x).strip())
-
- # df["initial_street"] = df["Street"].copy()
-
- # df["Street"] = df["Street"].str.lower()
-
- # logger.info("extract_building_num started")
-
- # df["Numbers"] = df.progress_apply(
- # lambda row: Geocoder.extract_building_num(row[text_column], row["Street"], row["Numbers"]),
- # axis=1,
- # )
-
- # logger.info("extract_toponym started")
-
- # df["Toponims"] = df.progress_apply(
- # lambda row: Geocoder.extract_toponym(row[text_column], row["Street"]),
- # axis=1,
- # )
- # return df
-
- # df = pd.DataFrame(data={'text': 'На биржевой 14 что-то произошло'}, index=[0])
- # print(Geocoder().run(df=df, text_column='text'))
-
-
- # def find_word_form(self, df: pd.DataFrame, strts_df: pd.DataFrame) -> pd.DataFrame:
- # """
- # In Russian language any word has different forms.
- # Since addresses are extracted from the texts in social networks,
- # they might be in any possible form. This function is aimed to match that
- # free form to the one that is used in the OSM database.
-
- # Since the stem is found there would be several streets with that stem
- # in their name.
- # However, the searching street name has its specific ending (form) and
- # not each matched street name could have it.
- # """
-
- # df["full_street_name"] = None
-
- # for idx, row in df.iterrows():
- # search_val = row["Street"]
- # search_top = row["Toponims"]
- # val_num = row["Numbers"]
-
- # for col in strts_df.columns[2:]:
- # search_rows = strts_df.loc[strts_df[col] == search_val]
- # matching_rows = search_rows[search_rows["toponim_name"] == search_top]
-
- # if not matching_rows.empty:
- # only_streets_full = matching_rows["street"].values
- # streets_full = [
- # street + f" {val_num}" + f" {self.osm_city_name}" + " Россия" for street in only_streets_full
- # ]
-
- # df.loc[idx, "full_street_name"] = ",".join(streets_full)
- # df.loc[idx, "only_full_street_name"] = ",".join(only_streets_full)
-
- # else:
- # if search_val in strts_df[col].values:
- # only_streets_full = strts_df.loc[strts_df[col] == search_val, "street"].values
- # streets_full = [
- # street + f" {val_num}" + f" {self.osm_city_name}" + " Россия"
- # for street in only_streets_full
- # ]
-
- # df.loc[idx, "full_street_name"] = ",".join(streets_full)
- # df.loc[idx, "only_full_street_name"] = ",".join(only_streets_full)
-
- # df.dropna(subset=["full_street_name", "only_full_street_name"], inplace=True)
- # df["location_options"] = df["full_street_name"].str.split(",")
- # df["only_full_street_name"] = df["only_full_street_name"].str.split(",")
-
- # tmp_df_1 = df["location_options"].explode()
- # tmp_df_1.name = "addr_to_geocode"
- # tmp_df_2 = df["only_full_street_name"].explode()
- # tmp_df_2.name = "only_full_street_name"
- # new_df = tmp_df_1.to_frame().join(tmp_df_2.to_frame())
-
- # df.drop(columns=["only_full_street_name"], inplace=True)
- # df = df.merge(new_df, left_on=df.index, right_on=new_df.index)
- # df.drop(columns=["key_0"], inplace=True)
-
- # # new_df = df["only_full_street_name"].explode()
- # # new_df.name = "only_full_street_name"
- # # df.drop(columns=['key_0', 'only_full_street_name'], inplace=True)
- # # df = df.merge(new_df, left_on=df.index, right_on=new_df.index)
-
- # # print(df.head())
- # df["only_full_street_name"] = df["only_full_street_name"].astype(str)
- # df["location_options"] = df["location_options"].astype(str)
-
- # return df
-
-import pandas as pd
-import re
-from tqdm import tqdm
-from loguru import logger
-from typing import Tuple, List, Optional
-
-import string
-import math
-from typing import Optional
-from loguru import logger
-from flair.data import Sentence
-
-# Initialize morphological analyzer (use the correct library for your context)
-import pymorphy2
-morph = pymorphy2.MorphAnalyzer()
-
-from sloyka.src.geocoder.text_address_extractor_by_rules import NatashaExtractor
-from sloyka.src.utils.constants import (
- START_INDEX_POSITION,
- REPLACEMENT_DICT,
- TARGET_TOPONYMS,
- END_INDEX_POSITION,
- SCORE_THRESHOLD
-)
-
-
-
-[docs]
-class StreetExtractor:
-
- extractor = NatashaExtractor()
-
-
-[docs]
- @staticmethod
- def process_pipeline(df: pd.DataFrame, text_column: str, classifier) -> pd.DataFrame:
-
- local_df = df.copy()
- """
- Execute the address extraction pipeline on the DataFrame.
-
- Args:
- df (pd.DataFrame): DataFrame containing the text data.
- text_column (str): Column name in the DataFrame with text data for address extraction.
-
- Returns:
- pd.DataFrame: DataFrame with extracted street addresses and additional processing columns.
- """
- texts = StreetExtractor._preprocess_text_column(local_df, text_column)
- extracted_streets = StreetExtractor._extract_streets(texts, classifier)
- refined_streets = StreetExtractor._refine_street_data(extracted_streets)
- building_numbers = StreetExtractor._extract_building_numbers(texts, refined_streets)
- toponyms = StreetExtractor._extract_toponyms(texts, refined_streets)
-
- # Combine results into a DataFrame
- processed_df = pd.DataFrame({
- text_column: texts,
- 'Street': refined_streets,
- 'Numbers': building_numbers,
- 'Toponyms': toponyms
- })
-
- StreetExtractor._check_df_len_didnt_change(local_df, processed_df)
-
- return processed_df
-
-
- @staticmethod
- def _check_df_len_didnt_change(df1, df2):
- try:
- assert len(df1) == len(df2)
- except Exception as e:
- logger.critical('dfs lengths differ')
- raise e
-
-
- @staticmethod
- def _preprocess_text_column(df: pd.DataFrame, text_column: str) -> List[str]:
- """
- Preprocess the text column by ensuring non-null values and converting to string type.
-
- Args:
- df (pd.DataFrame): DataFrame containing the text data.
- text_column (str): Column name in the DataFrame with text data.
-
- Returns:
- List[str]: List of preprocessed text entries.
- """
- try:
- text_series = df[text_column].dropna().astype(str)
- return text_series.tolist()
- except Exception as e:
- logger.warning(f"Error in _preprocess_text_column: {e}")
- return []
-
- @staticmethod
- def _extract_streets(texts: List[str], classifier) -> List[Tuple[Optional[str], Optional[float]]]:
- """
- Extract street names from the text column using NER model.
-
- Args:
- texts (List[str]): List of text entries.
-
- Returns:
- List[Tuple[Optional[str], Optional[float]]]: List of tuples with extracted street names and confidence scores.
- """
- tqdm.pandas()
- extracted_streets = []
- for text in tqdm(texts):
- try:
- extracted_streets.append(StreetExtractor.extract_ner_street(text, classifier))
- except Exception as e:
- logger.warning(f"Error extracting NER street from text '{text}': {e}")
- extracted_streets.append((None, None))
- return extracted_streets
-
- @staticmethod
- def _refine_street_data(street_data: List[Tuple[Optional[str], Optional[float]]]) -> List[Optional[str]]:
- """
- Refine street data by normalizing and cleaning up street names.
-
- Args:
- street_data (List[Tuple[Optional[str], Optional[float]]]): List of tuples with extracted street names and confidence scores.
-
- Returns:
- List[Optional[str]]: List of refined street names.
- """
- refined_streets = []
- for street, _ in street_data:
- if street:
- try:
- refined_streets.append(StreetExtractor._refine_street_name(street))
- except Exception as e:
- logger.warning(f"Error refining street '{street}': {e}")
- refined_streets.append(None)
- else:
- refined_streets.append(None)
- return refined_streets
-
- @staticmethod
- def _refine_street_name(street: str) -> str:
- """
- Refine street name by normalizing and cleaning up the street string.
-
- Args:
- street (str): Raw street name.
-
- Returns:
- str: Refined street name.
- """
- try:
- street = re.sub(r"(\D)(\d)(\D)", r"\1 \2\3", street)
- street = re.sub(r"\d+", "", street).strip().lower()
- return street
- except Exception as e:
- logger.warning(f"Error in _refine_street_name with street '{street}': {e}")
- return ""
-
- @staticmethod
- def _extract_building_numbers(texts: List[str], streets: List[Optional[str]]) -> List[Optional[str]]:
- """
- Extract building numbers from the text data.
-
- Args:
- texts (List[str]): List of text entries.
- streets (List[Optional[str]]): List of refined street names.
-
- Returns:
- List[Optional[str]]: List of extracted building numbers.
- """
- building_numbers = []
- for text, street in zip(texts, streets):
- if street:
- try:
- building_numbers.append(StreetExtractor._extract_building_number(text, street))
- except Exception as e:
- logger.warning(f"Error extracting building number from text '{text}' with street '{street}': {e}")
- building_numbers.append(None)
- else:
- building_numbers.append(None)
- return building_numbers
-
- @staticmethod
- def _extract_building_number(text: str, street: str) -> str:
- """
- Extract building number from the text.
-
- Args:
- text (str): Input text for address extraction.
- street (str): Extracted and refined street name.
-
- Returns:
- str: Extracted building number.
- """
- try:
- numbers = " ".join(re.findall(r"\d+", text))
- return StreetExtractor.extract_building_num(text, street, numbers)
- except Exception as e:
- logger.warning(f"Error in _extract_building_number with text '{text}' and street '{street}': {e}")
- return ""
-
- @staticmethod
- def _extract_toponyms(texts: List[str], streets: List[Optional[str]]) -> List[Optional[str]]:
- """
- Extract toponyms from the text data.
-
- Args:
- texts (List[str]): List of text entries.
- streets (List[Optional[str]]): List of refined street names.
-
- Returns:
- List[Optional[str]]: List of extracted toponyms.
- """
- toponyms = []
- for text, street in zip(texts, streets):
- if street:
- try:
- toponyms.append(StreetExtractor.extract_toponym(text, street))
- except Exception as e:
- logger.warning(f"Error extracting toponym from text '{text}' with street '{street}': {e}")
- toponyms.append(None)
- else:
- toponyms.append(None)
- return toponyms
-
-
-
-[docs]
- @staticmethod
- def extract_toponym(text: str, street_name: str) -> Optional[str]:
- """
- Extract toponyms near the specified street name in the text.
-
- This function identifies the position of a street name in the text and searches for related toponyms
- within a specified range around the street name.
-
- Args:
- text (str): The text containing the address.
- street_name (str): The name of the street to search around.
-
- Returns:
- Optional[str]: The first toponym found if present, otherwise None.
- """
- try:
- # Handle the case where text is NaN
- if isinstance(text, float) and math.isnan(text):
- return None
-
- # Clean and split the text into words
- cleaned_text = StreetExtractor._clean_text(text)
- words = cleaned_text.split()
-
- # Find positions of the street name
- positions = StreetExtractor._find_street_name_positions(words, street_name)
- if not positions:
- return None
-
- # Search for toponyms in the range around the street name
- toponym = StreetExtractor._search_toponyms(words, positions[0])
- return toponym
-
- except Exception as e:
- logger.warning(f"Error in extract_toponym with text '{text}' and street_name '{street_name}': {e}")
- return None
-
-
- @staticmethod
- def _clean_text(text: str) -> str:
- """
- Clean the input text by removing punctuation and converting to lowercase.
-
- Args:
- text (str): The input text.
-
- Returns:
- str: The cleaned text.
- """
- return text.translate(str.maketrans("", "", string.punctuation)).lower()
-
- @staticmethod
- def _find_street_name_positions(words: List[str], street_name: str) -> List[int]:
- """
- Find positions of the street name in the list of words.
-
- Args:
- words (List[str]): List of words from the cleaned text.
- street_name (str): The name of the street to find.
-
- Returns:
- List[int]: List of positions where the street name occurs.
- """
- return [index for index, word in enumerate(words) if word == street_name]
-
- @staticmethod
- def _search_toponyms(words: List[str], position: int) -> Optional[str]:
- """
- Search for toponyms within a specified range around the given position.
-
- Args:
- words (List[str]): List of words from the cleaned text.
- position (int): The position around which to search for toponyms.
-
- Returns:
- Optional[str]: The first toponym found if present, otherwise None.
- """
- search_start = max(0, position - START_INDEX_POSITION)
- search_end = min(len(words), position + END_INDEX_POSITION)
-
- for i in range(search_start, search_end + 1):
- word = words[i]
- try:
- normal_form = morph.parse(word)[0].normal_form
- except Exception as e:
- logger.warning(f"Error parsing word '{word}': {e}")
- continue
-
- if normal_form in TARGET_TOPONYMS:
- return REPLACEMENT_DICT.get(normal_form, normal_form)
-
- return None
-
-
-[docs]
- @staticmethod
- def extract_building_num(text: str, street_name: str, number: Optional[str]) -> str:
- """
- Extract building numbers near the specified street name in the text.
-
- This function identifies the position of a street name in the text and searches for related building numbers
- within a specified range of indexes around the street name.
-
- Args:
- text (str): The text containing the address.
- street_name (str): The name of the street to search around.
- number (Optional[str]): Previously extracted building number.
-
- Returns:
- str: The first building number found if present, otherwise an empty string.
- """
- try:
- if pd.notna(number) and number != "":
- return number
-
- if isinstance(text, float) and math.isnan(text):
- return ""
-
- cleaned_text = StreetExtractor._clean_text(text)
- words = cleaned_text.split()
-
- positions = StreetExtractor._find_street_name_positions(words, street_name)
- if not positions:
- return ""
-
- building_number = StreetExtractor._search_building_number(words, positions[0])
- return building_number
-
- except Exception as e:
- logger.warning(f"Error in extract_building_num with text '{text}', street_name '{street_name}', number '{number}': {e}")
- return ""
-
-
-
- @staticmethod
- def _find_street_name_positions(words: List[str], street_name: str) -> List[int]:
- """
- Find positions of the street name in the list of words.
-
- Args:
- words (List[str]): List of words from the cleaned text.
- street_name (str): The name of the street to find.
-
- Returns:
- List[int]: List of positions where the street name occurs.
- """
- return [index for index, word in enumerate(words) if word == street_name]
-
- @staticmethod
- def _search_building_number(words: List[str], position: int) -> str:
- """
- Search for building numbers within a specified range around the given position.
-
- Args:
- words (List[str]): List of words from the cleaned text.
- position (int): The position around which to search for building numbers.
-
- Returns:
- str: The first building number found if present, otherwise an empty string.
- """
- search_start = max(0, position)
- search_end = min(len(words), position + END_INDEX_POSITION)
-
- for index in range(search_start, search_end):
- word = words[index]
- if StreetExtractor._is_building_number(word):
- return word
-
- return ""
-
- @staticmethod
- def _is_building_number(word: str) -> bool:
- """
- Check if a word is a valid building number.
-
- Args:
- word (str): The word to check.
-
- Returns:
- bool: True if the word is a valid building number, otherwise False.
- """
- return any(character.isdigit() for character in word) and len(word) <= 3
-
-#---------
-
-[docs]
- @staticmethod
- def extract_ner_street(text: str, classifier) -> pd.Series:
- """
- Extract street addresses from text using a pre-trained custom NER model.
-
- This function processes text by removing unnecessary content, applies a custom NER model
- to extract mentioned addresses, and returns the address with a confidence score.
-
- Args:
- text (str): The input text to process and extract addresses from.
-
- Returns:
- pd.Series: A Series containing the extracted address and confidence score,
- or [None, None] if extraction fails or the score is below the threshold.
- """
- try:
- cleaned_text = StreetExtractor._clean_text(text)
- sentence = Sentence(cleaned_text)
-
- # Predict entities using the classifier
- classifier.predict(sentence)
-
- address, score = StreetExtractor._extract_address_and_score(sentence)
-
- if not address or score < SCORE_THRESHOLD:
- address = StreetExtractor.extractor.get_ner_address_natasha(text)
- if address:
- score = 1
-
- # Return the result if the score is above the threshold
- return pd.Series([address, score] if score is not None and score > SCORE_THRESHOLD else [None, None])
-
- except Exception as e:
- logger.warning(f"Error in extract_ner_street with text '{text}': {e}")
- return pd.Series([None, None])
-
-
- @staticmethod
- def _clean_text(text: str) -> str:
- """
- Clean the input text by removing unwanted patterns.
-
- Args:
- text (str): The input text.
-
- Returns:
- str: The cleaned text.
- """
- try:
- return re.sub(r"\[.*?\]", "", text)
- except Exception as e:
- logger.warning(f"Error in _clean_text with text '{text}': {e}")
- return text
-
- @staticmethod
- def _extract_address_and_score(sentence: Sentence) -> Tuple[Optional[str], Optional[float]]:
- """
- Extract address and score from the NER model's predictions.
-
- Args:
- sentence (Sentence): The Sentence object containing NER predictions.
-
- Returns:
- Tuple[Optional[str], Optional[float]]: Extracted address and its confidence score.
- """
- try:
- labels = sentence.get_labels("ner")
- if labels:
- label = labels[0]
- address = StreetExtractor._parse_address(label.labeled_identifier)
- score = round(label.score, 3)
- return address, score
- return None, None
- except IndexError as e:
- logger.warning(f"Error in _extract_address_and_score: {e}")
- return None, None
-
- @staticmethod
- def _parse_address(label_value: str) -> str:
- """
- Parse the address from the label value string.
-
- Args:
- label_value (str): The labeled identifier from the NER model.
-
- Returns:
- str: Parsed address.
- """
- try:
- return label_value.split("]: ")[1].split("/")[0].replace('"', "")
- except IndexError as e:
- logger.warning(f"Error in _parse_address with label value '{label_value}': {e}")
- return ""
-
-
-
-
-"""
-This module contains the EmotionClassifiers class, which is designed to categorise input texts into emotion categories.
-It uses a Huggingface transformer model trained on Bert_Large by default.
-The EmotionClassifiers class has the following method:
-@method:recognize_emotion: Adding an emotion category.
-@method:recognize_average_emotion_from_multiple_models: Adding an average emotion category or the most likely emotion
-category using multiple models.
-"""
-
-from aniemore.recognizers.text import TextRecognizer
-from aniemore.models import HuggingFaceModel
-import torch
-import pandas as pd
-from tqdm import tqdm
-import gc
-
-
-
-[docs]
-class EmotionRecognizer:
- """
- This class is designed to categorise input texts into emotion categories.
-
- Attributes:
-
- - model: This attribute holds the model used for emotion recognition. It defaults to HuggingFaceModel.Text.Bert_Large,
- but can be set to any other compatible model during the instantiation of the class.
-
- - device: the device to use for inference. It automatically selects 'cuda' (GPU) if a compatible GPU
- is available and CUDA is enabled, otherwise, it falls back to 'cpu'.
-
- - text: The text to be analyzed.
-
- - df: The DataFrame containing the text to be analyzed.
-
- - text_column: The name of the column containing the text to be analyzed.
- """
-
- def __init__(self, model_name=HuggingFaceModel.Text.Bert_Large, device=None):
- self.device = device if device is not None else ("cuda" if torch.cuda.is_available() else "cpu")
- self.model_name = model_name
-
- # Define the default model names to avoid repeated initialization
- self.default_model_names = [
- HuggingFaceModel.Text.Bert_Tiny,
- HuggingFaceModel.Text.Bert_Base,
- HuggingFaceModel.Text.Bert_Large,
- HuggingFaceModel.Text.Bert_Tiny2,
- ]
-
-
-[docs]
- def recognize_emotion(self, text):
- """
- Return the emotion for a given text.
- """
- recognizer = TextRecognizer(model=self.model_name, device=self.device)
- emotion = recognizer.recognize(text, return_single_label=True)
- return emotion
-
-
-
-[docs]
- def recognize_average_emotion_from_multiple_models(self, df, text_column, models=None, average=True):
- """
- Calculate the prevailing emotion using multiple models for a DataFrame column.
- """
- if models is None:
- models = self.default_model_names
- else:
- # Validate that the provided models are in the default models list
- for model in models:
- if model not in self.default_model_names:
- raise ValueError(
- f"Model {model} is not a valid model. Valid models are: {self.default_model_names}"
- )
-
- # Initialize scores DataFrame
- scores = pd.DataFrame(
- 0, index=df.index, columns=["happiness", "sadness", "anger", "fear", "disgust", "enthusiasm", "neutral"]
- )
-
- # Process each model one by one with progress bar
- for model_name in tqdm(models, desc="Processing models"):
- try:
- print(f"Clearing cache and collecting garbage before loading model: {model_name}")
- torch.cuda.empty_cache()
- gc.collect()
-
- print(f"Loading model: {model_name}")
- recognizer = TextRecognizer(model=model_name, device=self.device)
- model_results = [recognizer.recognize(text, return_single_label=False) for text in df[text_column]]
-
- for idx, result in enumerate(model_results):
- for emotion, score in result.items():
- if average:
- scores.at[df.index[idx], emotion] += score
- else:
- scores.at[df.index[idx], emotion] = max(scores.at[df.index[idx], emotion], score)
-
- # Удаление модели из памяти
- del recognizer
- torch.cuda.empty_cache() # Очистка кеша CUDA (если используется GPU)
- gc.collect() # Сборка мусора
- print(f"Model {model_name} processed and unloaded.")
- except Exception as e:
- print(f"Error processing model {model_name}: {e}")
- torch.cuda.empty_cache()
- gc.collect()
-
- if average:
- # Average the scores by the number of models
- scores = scores.div(len(models))
-
- # Determine the prevailing emotion with the highest score
- prevailing_emotions = scores.idxmax(axis=1)
-
- return prevailing_emotions
-
-
-
-"""
-This module contains the EventDetection class, which is aimed to generate events and their connections based
-on the application of semantic clustering method (BERTopic) on the texts in the context of an urban spatial model.
-
-The EventDetection class has the following methods:
-
-@method:_get_roads:
-Get the road network of a city as road links and roads.
-
-@method:_get_buildings:
-Get the buildings of a city as a GeoDataFrame.
-
-@method:_collect_population:
-Collect population data for each object (building, street, link).
-
-@method:_preprocess:
-Preprocess the data.
-"""
-import re
-from itertools import chain, combinations
-
-import geopandas as gpd
-import osmnx as ox
-import pandas as pd
-import numpy as np
-from bertopic import BERTopic
-from hdbscan import HDBSCAN
-from shapely.geometry import LineString
-from transformers.pipelines import pipeline
-from umap import UMAP
-
-
-
-[docs]
-class EventDetection:
- """
- This class is aimed to generate events and their connections.
- It is based on the application of semantic clustering method (BERTopic)
- on the texts in the context of urban spatial model
- """
-
- def __init__(self):
- np.random.seed(42)
- self.population_filepath = None
- self.levels = ["building", "link", "road", "global"]
- self.levels_scale = dict(zip(self.levels, list(range(2, 10, 2))))
- self.functions_weights = {
- "Безопасность": 0.12,
- "Благоустройство": 0.21,
- "Дороги": 0.18,
- "ЖКХ": 0.2,
- "Здравоохранение": 0.2,
- "Другое": 0.16,
- "Образование": 0.16,
- "Социальная защита": 0.13,
- "Строительство": 0.19,
- "Обращение с отходами": 0.19,
- "Транспорт": 0.17,
- "Экология": 0.22,
- "Энергетика": 0.19,
- }
- self.messages = None
- self.links = None
- self.buildings = None
- self.population = None
- self.topic_model = None
- self.events = None
- self.connections = None
-
- def _get_roads(self, city_name, city_crs) -> gpd.GeoDataFrame:
- """
- Get the road network of a city as road links and roads.
- Args:
- city_name (string): The name of the city.
- city_crs (int): The spatial reference code (CRS) of the city.
- Returns:
- links (GeoDataFrame): GeoDataFrame with the city's road links and roads.
- """
- links = ox.graph_from_place(city_name, network_type="drive")
- links = ox.utils_graph.graph_to_gdfs(links, nodes=False).to_crs(city_crs)
- links = links.reset_index(drop=True)
- links["link_id"] = links.index
- links["geometry"] = links["geometry"].buffer(7)
- links = links.to_crs(4326)
- links = links[["link_id", "name", "geometry"]]
- links.loc[links["name"].map(type) == list, "name"] = links[links["name"].map(type) == list]["name"].map(
- lambda x: ", ".join(x)
- )
- road_id_name = dict(enumerate(links.name.dropna().unique().tolist()))
- road_name_id = {v: k for k, v in road_id_name.items()}
- links["road_id"] = links["name"].replace(road_name_id)
- return links
-
- def _get_buildings(self) -> gpd.GeoDataFrame:
- """
- Get the buildings of a city as a GeoDataFrame
- Args:
- links(GeoDataFrame): GeoDataFrame with the city's road links and roads.
- filepath (string): The path to the GeoJSON file with building data. The default is set to 'population.geojson'.
- Returns:
- buildings (GeoDataFrame): GeoDataFrame with the city's buildings.
- """
- buildings = gpd.read_file(self.population_filepath)
- buildings = buildings[["address", "building_id", "population_balanced", "geometry"]]
- buildings = buildings.to_crs(4326)
- buildings["building_id"] = buildings.index
- buildings = (
- gpd.sjoin_nearest(
- buildings,
- self.links[["link_id", "road_id", "geometry"]],
- how="left",
- max_distance=500,
- )
- .drop(columns=["index_right"])
- .drop_duplicates(subset="building_id")
- )
- self.buildings = buildings
- return buildings
-
- def _collect_population(self) -> dict:
- """
- Collect population data for each object (building, street, link).
- """
- buildings = self.buildings.copy()
- pops_global = {0: buildings.population_balanced.sum()}
- pops_buildings = buildings["population_balanced"].to_dict()
- pops_links = (
- buildings[["population_balanced", "link_id"]].groupby("link_id").sum()["population_balanced"].to_dict()
- )
- pops_roads = (
- buildings[["population_balanced", "road_id"]].groupby("road_id").sum()["population_balanced"].to_dict()
- )
- pops = {
- "global": pops_global,
- "road": pops_roads,
- "link": pops_links,
- "building": pops_buildings,
- }
- self.population = pops
- return pops
-
- def _preprocess(self) -> gpd.GeoDataFrame:
- """
- Preprocess the data
- """
- messages = self.messages[
- [
- "Текст комментария",
- "geometry",
- "Дата и время",
- "message_id",
- "cats",
- ]
- ]
- messages = messages.sjoin(self.buildings, how="left")[
- [
- "Текст комментария",
- "address",
- "geometry",
- "building_id",
- "message_id",
- "Дата и время",
- "cats",
- ]
- ]
- messages.rename(
- columns={"Текст комментария": "text", "Дата и время": "date_time"},
- inplace=True,
- )
- messages = messages.sjoin(self.links, how="left")[
- [
- "text",
- "geometry",
- "building_id",
- "index_right",
- "name",
- "message_id",
- "date_time",
- "cats",
- "road_id",
- ]
- ]
- messages.rename(
- columns={"index_right": "link_id", "name": "road_name"},
- inplace=True,
- )
- messages = messages.join(
- self.buildings[["link_id", "road_id"]],
- on="building_id",
- rsuffix="_from_building",
- )
- messages.loc[messages.link_id.isna(), "link_id"] = messages.loc[messages.link_id.isna()][
- "link_id_from_building"
- ]
- messages.loc[messages.road_id.isna(), "road_id"] = messages.loc[messages.road_id.isna()][
- "road_id_from_building"
- ]
- messages = messages[
- [
- "message_id",
- "text",
- "geometry",
- "building_id",
- "link_id",
- "road_id",
- "date_time",
- "cats",
- ]
- ].dropna(subset="text")
- messages["cats"] = messages.cats.astype(str).str.split("; ").map(lambda x: x[0])
- messages["importance"] = messages["cats"].map(self.functions_weights)
- messages["importance"].fillna(0.16, inplace=True)
- messages["global_id"] = 0
- return messages
-
- def _create_model(self, min_event_size):
- """
- Create a topic model with a UMAP, HDBSCAN, and a BERTopic model.
- """
- umap_model = UMAP(
- n_neighbors=15,
- n_components=5,
- min_dist=0.0,
- metric="cosine",
- random_state=42,
- )
- hdbscan_model = HDBSCAN(
- min_cluster_size=min_event_size,
- min_samples=1,
- metric="euclidean",
- cluster_selection_method="eom",
- prediction_data=True,
- )
- embedding_model = pipeline("feature-extraction", model="cointegrated/rubert-tiny2")
- topic_model = BERTopic(
- embedding_model=embedding_model,
- hdbscan_model=hdbscan_model,
- umap_model=umap_model,
- calculate_probabilities=True,
- verbose=True,
- n_gram_range=(1, 3),
- )
- return topic_model
-
- def _event_from_object(
- self,
- messages,
- topic_model,
- target_column: str,
- population: dict,
- object_id: float,
- event_level: str,
- ):
- """
- Create a list of events for a given object
- (building, street, link, total).
- """
- local_messages = messages[messages[target_column] == object_id]
- message_ids = local_messages.message_id.tolist()
- docs = local_messages.text.tolist()
- if len(docs) >= 5:
- try:
- topics, probs = topic_model.fit_transform(docs)
- except TypeError:
- print("Can't reduce dimensionality or some other problem")
- return
- try:
- topics = topic_model.reduce_outliers(docs, topics)
- topic_model.update_topics(docs, topics=topics)
- except ValueError:
- print("Can't distribute all messages in topics")
- event_model = topic_model.get_topic_info()
- event_model["level"] = event_level
- event_model["object_id"] = str(object_id)
- event_model["id"] = event_model.apply(
- lambda x: f"{str(x.Topic)}_{str(x.level)}_{str(x.object_id)}",
- axis=1,
- )
- try:
- event_model["potential_population"] = population[event_level][object_id]
- except Exception: # need to select type of error
- event_model["potential_population"] = population["global"][0]
-
- clustered_messages = pd.DataFrame(data={"id": message_ids, "text": docs, "topic_id": topics})
- event_model["message_ids"] = [
- clustered_messages[clustered_messages["topic_id"] == topic]["id"].tolist()
- for topic in event_model.Topic
- ]
- event_model["duration"] = event_model.message_ids.map(
- lambda x: (
- pd.to_datetime(messages[messages["message_id"].isin(x)].date_time).max()
- - pd.to_datetime(messages[messages["message_id"].isin(x)].date_time).min()
- ).days
- )
- event_model["category"] = event_model.message_ids.map(
- lambda x: ", ".join(messages[messages["message_id"].isin(x)].cats.mode().tolist())
- )
- event_model["importance"] = event_model.message_ids.map(
- lambda x: messages[messages["message_id"].isin(x)].importance.mean()
- )
- return event_model
- else:
- return
-
- def _get_events(self, min_event_size) -> gpd.GeoDataFrame:
- """
- Create a list of events for all levels.
- """
- messages = self.messages.copy()
- messages_list = messages.text.tolist()
- index_list = messages.message_id.tolist()
- pops = self._collect_population()
- topic_model = self._create_model(min_event_size)
- events = [
- [
- self._event_from_object(messages, topic_model, f"{level}_id", pops, oid, level)
- for oid in messages[f"{level}_id"].unique().tolist()
- ]
- for level in reversed(self.levels)
- ]
- events = [item for sublist in events for item in sublist if item is not None]
- events = pd.concat(list(chain(events)))
- events["geometry"] = events.message_ids.map(
- lambda x: messages[messages.message_id.isin(x)].geometry.unary_union.representative_point()
- )
- events = gpd.GeoDataFrame(events, geometry="geometry").set_crs(4326)
- events.rename(
- columns={
- "Name": "name",
- "Representative_Docs": "docs",
- "Count": "intensity",
- "potential_population": "population",
- },
- inplace=True,
- )
- events["docs"] = events["docs"].map(
- lambda x: ", ".join([str(index_list[messages_list.index(text)]) for text in x])
- )
- events.message_ids = events.message_ids.map(lambda x: ", ".join([str(id) for id in x]))
- events["intensity"] = (events["intensity"] - events["intensity"].min()) / (
- events["intensity"].max() - events["intensity"].min()
- )
- events["duration"] = (events["duration"] - events["duration"].min()) / (
- events["duration"].max() - events["duration"].min()
- )
- events.loc[events.intensity == 0, "intensity"] = 0.1 # fix later
- events.loc[events.duration.isna(), "duration"] = 1 # fix later
- events["risk"] = events.intensity * events.duration * events.importance * events.population
- events["message_ids"] = events.message_ids.map(lambda x: ", ".join(list(set(x.split(", ")))))
- events["docs"] = events.docs.map(lambda x: ", ".join(list(set(x.split(", ")))))
- return events
-
- def _get_event_connections(self) -> gpd.GeoDataFrame:
- """
- Create a list of connections between events.
- """
- events = self.events.copy()
- events.index = events.id
- events.geometry = events.centroid
- weights = [len((set(c[0]) & set(c[1]))) for c in combinations(self.events.message_ids, 2)]
- nodes = [c for c in combinations(events.id, 2)]
- connections = pd.DataFrame(nodes, weights).reset_index()
- connections.columns = ["weight", "a", "b"]
- connections = connections[connections["weight"] > 0]
- connections = connections.join(events.geometry, on="a", rsuffix="_")
- connections = connections.join(events.geometry, on="b", rsuffix="_")
- events.reset_index(drop=True, inplace=True)
- connections["geometry"] = connections.apply(lambda x: LineString([x["geometry"], x["geometry_"]]), axis=1)
- connections.drop(columns=["geometry_"], inplace=True)
- connections = gpd.GeoDataFrame(connections, geometry="geometry").set_crs(32636)
- return connections
-
- def _rebalance(self, connections, events, levels, event_population: int, event_id: str):
- """
- Rebalance the population of an event.
- """
- connections_of_event = connections[connections.a == event_id].b
- if len(connections_of_event) > 0:
- accounted_pops = events[events.id.isin(connections_of_event) & events.level.isin(levels)].population.sum()
- if event_population >= accounted_pops:
- rebalanced_pops = event_population - accounted_pops
- else:
- connections_of_event = connections[connections.b == event_id].a
- accounted_pops = events[
- events.id.isin(connections_of_event) & events.level.isin(levels)
- ].population.sum()
- rebalanced_pops = event_population - accounted_pops
- return rebalanced_pops
- else:
- return event_population
-
- def _rebalance_events(self) -> gpd.GeoDataFrame:
- """
- Rebalance the population of events.
- """
- levels = self.levels.copy()
- events = self.events.copy()
- connections = self.connections.copy()
- events_rebalanced = []
- for level in levels[1:]:
- levels_to_account = levels[: levels.index(level)]
- events_for_level = events[events.level == level]
- events_for_level["rebalanced_population"] = events_for_level.apply(
- lambda x: self._rebalance(
- connections,
- events,
- levels_to_account,
- x.population,
- x.id,
- ),
- axis=1,
- )
- events_rebalanced.append(events_for_level)
- events_rebalanced = pd.concat(events_rebalanced)
- events_rebalanced.loc[
- events_rebalanced.rebalanced_population.isna(),
- "rebalanced_population",
- ] = 0
- events_rebalanced["population"] = events_rebalanced.rebalanced_population
- events_rebalanced.drop(columns=["rebalanced_population"], inplace=True)
- events_rebalanced.population = events_rebalanced.population.astype(int)
- events_rebalanced["population"] = (events_rebalanced["population"] - events_rebalanced["population"].min()) / (
- events_rebalanced["population"].max() - events_rebalanced["population"].min()
- )
- events_rebalanced.loc[events_rebalanced.population == 0, "population"] = 0.01 # fix later
- events_rebalanced.loc[
- events_rebalanced.population.isna() & events_rebalanced.level.isin(["building", "link"]),
- "population",
- ] = 0.01 # fix later
- events_rebalanced.loc[
- events_rebalanced.population.isna() & events_rebalanced.level.isin(["road", "global"]),
- "population",
- ] = 1 # fix later
- events_rebalanced["risk"] = (
- events_rebalanced.intensity * (events_rebalanced.duration + 1) * events_rebalanced.importance
- )
- events_rebalanced = events_rebalanced[["name", "docs", "level", "id", "risk", "message_ids", "geometry"]]
- return events_rebalanced
-
- def _filter_outliers(self):
- """
- Filter outliers.
- """
- pattern = r"^-1.*"
- events = self.events
- connections = self.connections
- print(
- len(events[events.name.map(lambda x: True if re.match(pattern, x) else False)]),
- "outlier clusters of",
- len(events),
- "total clusters. Filtering...",
- )
- events = events[events.name.map(lambda x: False if re.match(pattern, x) else True)]
- connections = connections[connections.a.map(lambda x: False if re.match(pattern, x) else True)]
- connections = connections[connections.b.map(lambda x: False if re.match(pattern, x) else True)]
- return events, connections
-
- def _prepare_messages(self):
- """
- Prepare messages for export.
- """
- messages = self.messages.copy()
- messages = messages.reset_index(drop=True)
- messages.rename(columns={"cats": "block"}, inplace=True)
- messages = messages[["message_id", "text", "geometry", "date_time", "block"]]
- messages = messages.to_crs(4326)
- return messages
-
-
-[docs]
- def run(
- self,
- target_texts: gpd.GeoDataFrame,
- filepath_to_population: str,
- city_name: str,
- city_crs: int,
- min_event_size: int,
- ):
- """
- Returns a GeoDataFrame of events, a GeoDataFrame of
- connections between events, and a GeoDataFrame of messages.
- """
- self.population_filepath = filepath_to_population
- self.messages = target_texts.copy()
- print("messages loaded")
- self.links = self._get_roads(city_name, city_crs)
- print("road links loaded")
- self.buildings = self._get_buildings()
- print("buildings loaded")
- self.messages = self._preprocess()
- print("messages preprocessed")
- self.events = self._get_events(min_event_size)
- print("events detected")
- self.connections = self._get_event_connections()
- print("connections generated")
- self.events = self._rebalance_events()
- print("population and risk rebalanced")
- self.events, self.connections = self._filter_outliers()
- print("outliers filtered")
- self.messages = self._prepare_messages()
- print("done!")
-
- return self.messages, self.events, self.connections
-
-
-
-"""
-This module contains the TextClassifiers class, which is aimed to classify input texts into themes or structured types of events.
-It uses a Huggingface transformer model trained on rubert-tiny.
-In many cases, the count of messages per theme was too low to efficiently train, so synthetic themes based
-on the categories as the upper level were used (for example, 'unknown_ЖКХ').
-
-Attributes:
-- repository_id (str): The repository ID.
-- number_of_categories (int): The number of categories.
-- device_type (str): The type of device.
-
-The TextClassifiers class has the following methods:
-
-@method:initialize_classifier: Initializes the text classification pipeline with the specified model, tokenizer, and device type.
-
-@method:run_text_classifier_topics:
- Takes a text as input and returns the predicted themes and probabilities.
-
-@method:run_text_classifier:
- Takes a text as input and returns the predicted categories and probabilities.
-"""
-import pandas as pd
-from transformers import pipeline
-from sloyka.src.utils.exceptions import InvalidInputError, ClassifierInitializationError, ClassificationError
-
-
-
-[docs]
-class TextClassifiers:
- def __init__(self, repository_id, number_of_categories=1, device_type=None):
- self.repository_id = repository_id
- self.number_of_categories = number_of_categories
- self.device_type = device_type or -1 # -1 will automatically choose the device based on availability
- self.classifier = None
-
-
-[docs]
- def initialize_classifier(self):
- if not self.classifier:
- try:
- self.classifier = pipeline(
- "text-classification",
- model=self.repository_id,
- tokenizer="cointegrated/rubert-tiny2",
- device=self.device_type,
- )
- except Exception as e:
- raise ClassifierInitializationError(f"Failed to initialize the classifier: {e}")
-
-
-
-[docs]
- def classify_text(self, text, is_topic=False):
- if not isinstance(text, str):
- raise InvalidInputError("Input must be a string.")
-
- self.initialize_classifier()
-
- try:
- predictions = self.classifier(text, top_k=self.number_of_categories)
- preds_df = pd.DataFrame(predictions)
- categories = "; ".join(preds_df["label"].tolist())
- probabilities = "; ".join(preds_df["score"].round(3).astype(str).tolist())
- except Exception as e:
- raise ClassificationError(f"Error during text classification: {e}")
-
- return categories, probabilities
-
-
-
-[docs]
- def run_text_classifier_topics(self, text):
- return self.classify_text(text, is_topic=True)
-
-
-
-
-
-
-"""
-@class:Semgraph:
-The main class of the semantic graph module. It is aimed to build a semantic graph based on the provided data
-and parameters.
-More convenient to use after extracting data from geocoder.
-
-The Semgraph class has the following methods:
-
-@method:clean_from_dublicates:
-A function to clean a DataFrame from duplicates based on specified columns.
-
-@method:clean_from_digits:
-Removes digits from the text in the specified column of the input DataFrame.
-
-@method:clean_from_toponyms:
-Clean the text in the specified text column by removing any words that match the toponyms in the name and
-toponym columns.
-
-@method:aggregate_data:
-Creates a new DataFrame by aggregating the data based on the provided text and toponyms columns.
-"""
-
-import nltk
-import pandas as pd
-import geopandas as gpd
-import networkx as nx
-from transformers import BertTokenizer, BertModel # type: ignore
-
-from .g_attrs_adder import add_attributes
-from .keyword_extracter import extract_keywords
-from .semantic_closeness_annotator import get_semantic_closeness
-from .g_text_data_getter import get_tag, get_coordinates, get_text_ids
-from ..utils.data_preprocessing.preprocessor import (
- clean_from_dublicates,
- clean_from_digits,
- clean_from_toponyms,
- clean_from_links,
-)
-
-nltk.download("stopwords")
-
-
-from sloyka.src.utils.constants import TAG_ROUTER
-
-
-
-[docs]
-class Semgraph:
- """
- This is the main class of semantic graph module.
- It is aimed to build a semantic graph based on the provided data and parameters.
- More convinient to use after extracting data from geocoder.
-
- Param:
- bert_name: the name of the BERT model to use (default is 'DeepPavlov/rubert-base-cased')
- language: the language of the BERT model (default is 'russian')
- device: the device to use for inference (default is 'cpu')
- """
-
- def __init__(
- self, bert_name: str = "DeepPavlov/rubert-base-cased", language: str = "russian", device: str = "cpu"
- ) -> None:
- self.language = language
- self.device = device
- self.tokenizer = BertTokenizer.from_pretrained(bert_name)
- self.model_name = bert_name
- self.model = BertModel.from_pretrained(bert_name).to(device)
-
-
-[docs]
- @staticmethod
- def convert_df_to_edge_df(
- data: pd.DataFrame | gpd.GeoDataFrame, toponym_column: str, word_info_column: str = "words_score"
- ) -> pd.DataFrame | gpd.GeoDataFrame:
- edge_list = []
-
- for i in data[toponym_column]:
- current_df = data.loc[data[toponym_column] == i]
- for j in range(len(current_df)):
- toponym = current_df[toponym_column].iloc[j]
- word_nodes = current_df[word_info_column].iloc[j]
-
- for k in word_nodes:
- if k[2] in TAG_ROUTER.keys():
- edge_list.append([toponym, k[0], k[1], TAG_ROUTER[k[2]]])
-
- edge_df = pd.DataFrame(edge_list, columns=["FROM", "TO", "distance", "type"])
-
- return edge_df
-
-
-
-[docs]
- def build_graph(
- self,
- data: pd.DataFrame | gpd.GeoDataFrame,
- id_column: str,
- text_column: str,
- text_type_column: str,
- toponym_column: str,
- toponym_name_column: str,
- toponym_type_column: str,
- post_id_column: str,
- parents_stack_column: str,
- directed: bool = True,
- location_column: str | None = None,
- geometry_column: str | None = None,
- key_score_filter: float = 0.6,
- semantic_score_filter: float = 0.75,
- top_n: int = 1,
- ) -> nx.classes.graph.Graph:
- """
- Build a graph based on the provided data.
-
- Args:
- data (pd.DataFrame or gpd.GeoDataFrame): The input data to build the graph from.
- id_column (str): The column containing unique identifiers.
- text_column (str): The column containing text information.
- text_type_column (str): The column indicating the type of text.
- toponym_column (str): The column containing toponym information.
- toponym_name_column (str): The column containing toponym names.
- toponym_type_column (str): The column containing toponym types.
- post_id_column (str): The column containing post identifiers.
- parents_stack_column (str): The column containing parent-child relationships.
- directed (bool): Flag indicating if the graph is directed. Defaults to True.
- location_column (str or None): The column containing location information. Defaults to None.
- geometry_column (str or None): The column containing geometry information. Defaults to None.
- key_score_filter (float): The threshold for key score filtering. Defaults to 0.6.
- semantic_score_filter (float): The threshold for semantic score filtering. Defaults to 0.75.
- top_n (int): The number of top keywords to extract. Defaults to 1.
-
- Returns:
- nx.classes.graph.Graph: The constructed graph.
- """
-
- data = clean_from_dublicates(data, id_column)
-
- data = clean_from_digits(data, text_column)
-
- data = clean_from_toponyms(data, text_column, toponym_name_column, toponym_type_column)
-
- data = clean_from_links(data, text_column)
-
- extracted = extract_keywords(
- data,
- text_column,
- text_type_column,
- toponym_column,
- id_column,
- post_id_column,
- parents_stack_column,
- key_score_filter,
- top_n,
- )
-
- df = extracted[0]
- toponyms_attributes = extracted[1]
- words_attributes = extracted[2]
-
- preprocessed_df = self.convert_df_to_edge_df(data=df, toponym_column=toponym_column)
-
- words_df = get_semantic_closeness(preprocessed_df, "TO", semantic_score_filter)
-
- graph_df = pd.concat([preprocessed_df, words_df], ignore_index=True)
- if directed:
- G = nx.from_pandas_edgelist(
- graph_df, source="FROM", target="TO", edge_attr=["distance", "type"], create_using=nx.DiGraph()
- )
-
- else:
- G = nx.from_pandas_edgelist(graph_df, source="FROM", target="TO", edge_attr=["distance", "type"])
-
- nodes = list(G.nodes())
- attributes = get_tag(nodes, list(set(data[toponym_column])))
-
- nx.set_node_attributes(G, attributes, "tag")
- G = add_attributes(G=G, new_attributes=toponyms_attributes, attribute_tag="counts", toponym_attributes=True)
-
- G = add_attributes(G=G, new_attributes=words_attributes, attribute_tag="counts", toponym_attributes=False)
-
- if isinstance(data, gpd.GeoDataFrame):
- G = get_coordinates(
- G=G,
- geocoded_data=data,
- toponym_column=toponym_column,
- location_column=location_column,
- geometry_column=geometry_column,
- )
-
- G = get_text_ids(G=G, filtered_data=df, toponym_column=toponym_column, text_id_column=id_column)
-
- return G
-
-
-
-[docs]
- def update_graph(
- self,
- G: nx.classes.graph.Graph,
- data: pd.DataFrame | gpd.GeoDataFrame,
- id_column: str,
- text_column: str,
- text_type_column: str,
- toponym_column: str,
- toponym_name_column: str,
- toponym_type_column: str,
- post_id_column: str,
- parents_stack_column: str,
- directed: bool = True,
- counts_attribute: str | None = None,
- location_column: str | None = None,
- geometry_column: str | None = None,
- key_score_filter: float = 0.6,
- semantic_score_filter: float = 0.75,
- top_n: int = 1,
- ) -> nx.classes.graph.Graph:
- """
- Update the input graph based on the provided data, returning the updated graph.
-
- Args:
- G (nx.classes.graph.Graph): The input graph to be updated.
- data (pd.DataFrame or gpd.GeoDataFrame): The input data to update the graph.
- id_column (str): The column containing unique identifiers.
- text_column (str): The column containing text information.
- text_type_column (str): The column indicating the type of text.
- toponym_column (str): The column containing toponym information.
- toponym_name_column (str): The column containing toponym names.
- toponym_type_column (str): The column containing toponym types.
- post_id_column (str): The column containing post identifiers.
- parents_stack_column (str): The column containing parent-child relationships.
- directed (bool): Flag indicating if the graph is directed. Defaults to True.
- counts_attribute (str or None): The attribute to be used for counting. Defaults to None.
- location_column (str or None): The column containing location information. Defaults to None.
- geometry_column (str or None): The column containing geometry information. Defaults to None.
- key_score_filter (float): The threshold for key score filtering. Defaults to 0.6.
- semantic_score_filter (float): The threshold for semantic score filtering. Defaults to 0.75.
- top_n (int): The number of top keywords to extract. Defaults to 1.
-
- Returns:
- nx.classes.graph.Graph: The updated graph.
- """
-
- new_G = self.build_graph(
- data,
- id_column,
- text_column,
- text_type_column,
- toponym_column,
- toponym_name_column,
- toponym_type_column,
- post_id_column,
- parents_stack_column,
- directed,
- location_column,
- geometry_column,
- key_score_filter,
- semantic_score_filter,
- top_n,
- )
-
- joined_G = nx.compose(G, new_G)
-
- if counts_attribute is not None:
- nodes = list(set(G.nodes) & set(new_G.nodes))
- for i in nodes:
- joined_G.nodes[i]["total_counts"] = G.nodes[i][counts_attribute] + new_G.nodes[i]["counts"]
-
- return joined_G
-
-
-
-
-# debugging
-# if __name__ == '__main__':
-# file = open("C:\\Users\\thebe\\Downloads\\test.geojson", encoding='utf-8')
-# test_gdf = gpd.read_file(file)
-#
-# sm = Semgraph()
-#
-# G = sm.build_graph(test_gdf[:3000],
-# id_column='id',
-# text_column='text',
-# text_type_column='type',
-# toponym_column='only_full_street_name_numbers',
-# toponym_name_column='initial_street',
-# toponym_type_column='Toponims',
-# post_id_column='post_id',
-# parents_stack_column='parents_stack',
-# location_column='Location',
-# geometry_column='geometry')
-#
-# # print(len(G.nodes))
-# #
-# # G = sm.update_graph(G,
-# # test_gdf[3000:],
-# # id_column='id',
-# # text_column='text',
-# # text_type_column='type',
-# # toponym_column='only_full_street_name',
-# # toponym_name_column='initial_street',
-# # toponym_type_column='Toponims',
-# # post_id_column='post_id',
-# # parents_stack_column='parents_stack',
-# # counts_attribute='counts',
-# # location_column='Location',
-# # geometry_column='geometry')
-# #
-# # print(len(G.nodes))
-# #
-# # nx.write_graphml(G, 'name.graphml', encoding='utf-8')
-
-"""
-This module contains classes for retrieving and working with various types of data.
-
-@class:GeoDataGetter:
-This class is used to retrieve geospatial data from OpenStreetMap (OSM) based on given OSM ID and tags.
-
-@class:VKParser:
-A class for parsing and working with VK comments and posts. Combines posts and comments into one dataframe.
-
-@class:Streets:
-A class for working with street data.
-
-"""
-import osmnx as ox
-import geopandas as gpd
-import pandas as pd
-from sloyka.src.utils.constants import (
- GLOBAL_CRS,
- GLOBAL_METRIC_CRS,
-)
-from shapely.ops import transform
-from tqdm import tqdm
-import requests
-import sys
-import datetime
-import time
-import osm2geojson
-import random
-from typing import List, Optional
-from osmapi import OsmApi
-import networkx as nx
-from loguru import logger
-
-
-
-[docs]
-class VKParser:
- API_VERISON = "5.131"
- COUNT_ITEMS = 100
- # SLEEP_TIME = 0.5
- TIMEOUT_LIMIT = 15
-
-
-[docs]
- @staticmethod
- def get_group_name(domain, accsess_token):
- params = {"group_id": domain, "access_token": accsess_token, "v": VKParser.API_VERISON}
- response = requests.get("https://api.vk.com/method/groups.getById", params=params) # передвинуть повыше
- data = response.json()
- if "response" in data and data["response"]:
- group_name = data["response"][0]["name"]
- return pd.DataFrame({"group_name": [group_name]})
- else:
- print("Error while fetching group name:", data)
- return pd.DataFrame({"group_name": [None]})
-
-
-
-[docs]
- @staticmethod
- def get_owner_id_by_domain(domain, access_token):
- """
- Get the owner ID of a VK group by its domain.
-
- Args:
- domain (str): The domain of the VK group.
- access_token (str): The access token for the VK API.
-
- Returns:
- int: The owner ID of the VK group, or None if the request was not successful.
- """
- url = "https://api.vk.com/method/wall.get"
- params = {
- "domain": domain,
- "access_token": access_token,
- "v": VKParser.API_VERISON,
- }
- response = requests.get(url, params=params)
- if response.ok:
- owner_id = response.json()["response"]["items"][0]["owner_id"]
- else:
- owner_id = None
- return owner_id
-
-
-
-[docs]
- @staticmethod
- def get_group_post_ids(domain, access_token, post_num_limit, step) -> list:
- """
- A static method to retrieve a list of post IDs for a given group, based on the owner ID,
- access token, post number limit, and step size. Returns a list of post IDs.
- """
- offset = 0
- post_ids = []
-
- while offset < post_num_limit:
- print(offset, " | ", post_num_limit, end="\r")
- res = requests.get(
- "https://api.vk.com/method/wall.get",
- params={
- "access_token": access_token,
- "v": VKParser.API_VERISON,
- "domain": domain,
- "count": step,
- "offset": offset,
- },
- timeout=10,
- ).json()["response"]
- # print(res.json().keys())
- time.sleep(random.random())
-
- post_ids_new = [k["id"] for k in res["items"]]
- post_ids += post_ids_new
- offset += step
-
- return post_ids
-
-
-
-[docs]
- @staticmethod
- def get_subcomments(owner_id, post_id, access_token, params):
- """
- Retrieves subcomments from the VK API.
-
- Args:
- owner_id (int): The ID of the owner of the comments.
- post_id (int): The ID of the post.
- access_token (str): The access token for authentication.
- params (dict): Additional parameters for the API request.
-
- Returns:
- list: A list of subcomments retrieved from the API.
- """
- subcomments = []
-
- response = requests.get("https://api.vk.com/method/wall.getComments", params=params)
- # print(response.json().keys())
- time.sleep(random.random())
- data = response.json()
-
- if "response" in data:
- for item in data["response"]["items"]:
- item["date"] = datetime.datetime.utcfromtimestamp(item["date"]).strftime("%Y-%m-%d %H:%M:%S")
- if "likes" in item:
- item["likes.count"] = item["likes"]["count"]
- subcomments.append(item)
-
- return subcomments
-
-
-
-[docs]
- def get_comments(owner_id, post_id, access_token):
- """
- Get comments for a post on VK using the specified owner ID, post ID, and access token.
-
- Parameters:
- owner_id (int): The ID of the post owner.
- post_id (int): The ID of the post.
- access_token (str): The access token for authentication.
-
- Returns:
- list: A list of dictionaries containing comment information.
- """
- params = {
- "owner_id": owner_id,
- "post_id": post_id,
- "access_token": access_token,
- "v": VKParser.API_VERISON,
- "extended": 1,
- "count": 100,
- "need_likes": 1,
- }
-
- comments = []
-
- response = requests.get("https://api.vk.com/method/wall.getComments", params=params)
- # print(response.json().keys())
- time.sleep(random.random())
- data = response.json()
-
- if "response" in data:
- for item in data["response"]["items"]:
- if item["text"] == "":
- continue
- item["date"] = datetime.datetime.utcfromtimestamp(item["date"]).strftime("%Y-%m-%d %H:%M:%S")
- if "likes" in item:
- item["likes.count"] = item["likes"]["count"]
- comments.append(item)
- if item["thread"]["count"] > 0:
- params["comment_id"] = item["id"]
- subcomments = VKParser.get_subcomments(owner_id, post_id, access_token, params)
- comments.extend(subcomments)
- return comments
-
-
-
-[docs]
- @staticmethod
- def comments_to_dataframe(comments):
- """
- Convert comments to a DataFrame.
-
- Args:
- comments: List of comments to be converted.
-
- Returns:
- DataFrame: A DataFrame containing specific columns from the input comments.
- """
- df = pd.DataFrame(comments)
- df = df[["id", "from_id", "date", "text", "post_id", "parents_stack", "likes.count"]]
- return df
-
-
-
-[docs]
- @staticmethod
- def run_posts(domain, access_token, cutoff_date, number_of_messages=float("inf"), step=50):
- """
- A function to retrieve posts from a social media API based on specified parameters.
-
- Parameters:
- owner_id (int): The ID of the owner whose posts are being retrieved.
- access_token (str): The authentication token for accessing the API.
- step (int): The number of posts to retrieve in each API call.
- cutoff_date (str): The date to stop retrieving posts (format: '%Y-%m-%d').
- number_of_messages (float): The maximum number of messages to retrieve (default is infinity).
-
- Returns:
- pandas.DataFrame: A DataFrame containing the retrieved posts.
- """
-
- domain = domain
- offset = 0
- all_posts = []
- if step > number_of_messages:
- step = number_of_messages
- while offset < number_of_messages:
- print(offset, " | ", number_of_messages, end="\r")
-
- response = requests.get(
- "https://api.vk.com/method/wall.get",
- params={
- "access_token": access_token,
- "v": VKParser.API_VERISON,
- "domain": domain,
- "count": step,
- "offset": offset,
- }, timeout=600
- )
- if response.ok:
- # print(response.json().keys())
- data = response.json()["response"]["items"]
- offset += step
- current_posts = pd.json_normalize(data)
- current_posts = current_posts[["date", "id", "text", "views.count", "likes.count", "reposts.count"]]
- current_posts["date"] = [
- datetime.datetime.fromtimestamp(current_posts["date"][i]) for i in range(len(current_posts["date"]))
- ]
- current_posts["type"] = "post"
- all_posts.append(current_posts)
- print(current_posts.date.min())
- if any(current_posts["date"] < datetime.datetime.strptime(cutoff_date, "%Y-%m-%d")):
- print("posts downloaded")
- break
- else:
- continue
- time.sleep(random.random())
- df_posts = pd.concat(all_posts).reset_index(drop=True)
- df_posts = df_posts[df_posts.text.map(lambda x: len(x)) > 0]
- df_posts["text"] = df_posts["text"].str.replace(r"\n", "", regex=True)
- df_posts["link"] = df_posts["text"].str.extract(r"(https://\S+)")
- return df_posts
-
-
-
-[docs]
- @staticmethod
- def run_comments(domain, post_ids, access_token):
- owner_id = VKParser.get_owner_id_by_domain(domain, access_token)
- all_comments = []
- for post_id in tqdm(post_ids):
- comments = VKParser.get_comments(owner_id, post_id, access_token)
- all_comments.extend(comments)
- df = VKParser.comments_to_dataframe(all_comments)
- df["type"] = "comment"
- df = df.reset_index(drop=True)
- print("comments downloaded")
- return df
-
-
-
-[docs]
- @staticmethod
- def run_parser(domain, access_token, cutoff_date, number_of_messages=float("inf"), step=100):
- """
- Runs the parser with the given parameters and returns a combined DataFrame of posts and comments.
-
- :param owner_id: The owner ID for the parser.
- :param access_token: The user token for authentication.
- :param step: The step size for fetching data.
- :param cutoff_date: The cutoff date for fetching data.
- :param number_of_messages: The maximum number of messages to fetch. Defaults to positive infinity.
- :return: A combined DataFrame of posts and comments.
- """
- owner_id = VKParser.get_owner_id_by_domain(domain, access_token)
- df_posts = VKParser.run_posts(domain=owner_id, access_token=access_token, step=step, cutoff_date=cutoff_date, number_of_messages=number_of_messages)
- post_ids = df_posts["id"].tolist()
-
- df_comments = VKParser.run_comments(domain=owner_id, post_ids=post_ids, access_token=access_token)
- df_comments.loc[df_comments["parents_stack"].apply(lambda x: len(x) > 0), "type"] = "reply"
- for i in range(len(df_comments)):
- tmp = df_comments["parents_stack"].iloc[i]
- if tmp is not None:
- if len(tmp) > 0:
- df_comments["parents_stack"].iloc[i] = tmp[0]
- else:
- df_comments["parents_stack"].iloc[i] = None
-
- df_combined = df_comments.join(df_posts, on="post_id", rsuffix="_post")
- df_combined = pd.concat([df_posts, df_comments], ignore_index=True)
- df_group_name = VKParser.get_group_name(domain, access_token)
- df_combined["group_name"] = df_group_name["group_name"][0]
-
- return df_combined
-
-
-
-import pandas as pd
-import numpy as np
-from flair.models import SequenceTagger
-from flair.data import Sentence
-from rapidfuzz import fuzz
-from typing import List
-from sloyka.src.utils.constants import CITY_SERVICES_NAMES
-
-tagger = SequenceTagger.load("Glebosol/city_services")
-
-
-
-[docs]
-class City_services:
-
-[docs]
- def extraction_services(text):
- sentence = Sentence(text)
- tagger.predict(sentence)
- entities = sentence.get_spans("ner")
- entity_names = [entity.text for entity in entities]
- return entity_names
-
-
-
-[docs]
- def remove_last_letter(words):
- reduced_words = [word[:-1] for word in words]
- return reduced_words
-
-
- # def replace_with_most_similar(entity_names: List[str], CITY_SERVICES_NAMES: List[str]) -> List[str]:
- # true_city_services_names = [difflib.get_close_matches(word_entity_names, CITY_SERVICES_NAMES, n=1, cutoff=0.0)[0] for word_entity_names in entity_names]
- # return true_city_services_names
-
-
-[docs]
- def replace_with_most_similar(list_of_entities):
- similarity_matrix = np.zeros((len(list_of_entities), len(CITY_SERVICES_NAMES)))
- for i, word1 in enumerate(list_of_entities):
- for j, word2 in enumerate(CITY_SERVICES_NAMES):
- similarity = fuzz.ratio(word1, word2) / 100.0
- similarity_matrix[i, j] = similarity
- new_list_of_entities = list_of_entities.copy()
- for i in range(len(list_of_entities)):
- max_index = np.argmax(similarity_matrix[i])
- new_list_of_entities[i] = CITY_SERVICES_NAMES[max_index]
- return new_list_of_entities
-
-
-
-[docs]
- def run(self, df, text_column):
- df["City_services_extraced"] = df[text_column].apply(lambda text: City_services.extraction_services(text))
- df["City_services_cuted"] = df["City_services_extraced"].apply(
- lambda row: City_services.remove_last_letter(row)
- )
- df["City_services"] = df["City_services_cuted"].apply(lambda row: City_services.replace_with_most_similar(row))
- df.drop("City_services_cuted", axis=1, inplace=True)
- return df
-
-
-
-import networkx as nx
-import folium
-import random
-from folium.plugins import MarkerCluster
-
-
-
-[docs]
-def draw_graph_folium(graph_path, output_file=None):
- """
- Visualizes a graph from the given graph_path using Folium and MarkerCluster.
-
- Args:
- graph_path (str): The path to the graphml file.
- output_file (str, optional): The file to save the visualization to. Defaults to None.
-
- Returns:
- folium.Map: The folium map object representing the visualized graph.
- """
- G = nx.read_graphml(graph_path)
-
- color_mapping = {
- -1: "gray",
- 0: "blue",
- 1: "green",
- 2: "purple",
- 3: "cyan",
- 4: "brown",
- 5: "orange",
- 6: "pink",
- 7: "darkred",
- 8: "yellow",
- 9: "beige",
- 10: "darkgreen",
- 11: "lightgreen",
- 12: "darkblue",
- 13: "lightblue",
- 14: "darkpurple",
- 15: "cadetblue",
- 16: "red",
- 17: "lightgreen",
- 18: "lightblue",
- }
-
- target_clusters = range(1, 19)
-
- m = folium.Map(
- location=[59.9343, 30.3351],
- zoom_start=10,
- tiles="cartodbdark_matter",
- control_scale=True,
- )
-
- for c in target_clusters:
- mc = MarkerCluster(name=f"{c} | cluster")
-
- for node, data in G.nodes(data=True):
- if "Lat" in data and "Lon" in data:
- main_node_location = [data["Lat"], data["Lon"]]
- for n in G.neighbors(node):
- if "Cluster" in G.nodes[n] and G.nodes[n]["Cluster"] == c:
- neighbor_data = G.nodes[n]
- neighbor_location = [
- main_node_location[1] + random.uniform(-0.0008, 0.0008),
- main_node_location[0] + random.uniform(-0.0008, 0.0008),
- ]
- folium.CircleMarker(
- location=neighbor_location,
- radius=10,
- color=color_mapping[G.nodes[n]["Cluster"]],
- fill=True,
- fill_color=color_mapping[G.nodes[n]["Cluster"]],
- popup=neighbor_data,
- name=f'cluster_{G.nodes[n]["Cluster"]}',
- ).add_to(mc)
- mc.add_to(m)
-
- folium.LayerControl().add_to(m)
- if not output_file is None:
- m.save(output_file)
-
- return m
-
-Attention
-
- You are reading the latest
- (unstable) version of this documentation, which may document features not available
- or compatible with the latest Crest packages released on the Unity Asset Store.
-
- View the stable version of this page. -
-Attention
-- Looks like you are on a version without a published tag. - We will redirect you to the latest documentation automatically. - If it does not redirect automatically in a few seconds, please click the follow: - ${newUrl.href} -
-' + - '' + - _("Hide Search Matches") + - "
" - ) - ); - }, - - /** - * helper function to hide the search marks again - */ - hideSearchWords: () => { - document - .querySelectorAll("#searchbox .highlight-link") - .forEach((el) => el.remove()); - document - .querySelectorAll("span.highlighted") - .forEach((el) => el.classList.remove("highlighted")); - localStorage.removeItem("sphinx_highlight_terms") - }, - - initEscapeListener: () => { - // only install a listener if it is really needed - if (!DOCUMENTATION_OPTIONS.ENABLE_SEARCH_SHORTCUTS) return; - - document.addEventListener("keydown", (event) => { - // bail for input elements - if (BLACKLISTED_KEY_CONTROL_ELEMENTS.has(document.activeElement.tagName)) return; - // bail with special keys - if (event.shiftKey || event.altKey || event.ctrlKey || event.metaKey) return; - if (DOCUMENTATION_OPTIONS.ENABLE_SEARCH_SHORTCUTS && (event.key === "Escape")) { - SphinxHighlight.hideSearchWords(); - event.preventDefault(); - } - }); - }, -}; - -_ready(() => { - /* Do not call highlightSearchWords() when we are on the search page. - * It will highlight words from the *previous* search query. - */ - if (typeof Search === "undefined") SphinxHighlight.highlightSearchWords(); - SphinxHighlight.initEscapeListener(); -}); diff --git a/docs/build/html/_static/styles/furo-extensions.css b/docs/build/html/_static/styles/furo-extensions.css deleted file mode 100644 index bc447f2..0000000 --- a/docs/build/html/_static/styles/furo-extensions.css +++ /dev/null @@ -1,2 +0,0 @@ -#furo-sidebar-ad-placement{padding:var(--sidebar-item-spacing-vertical) var(--sidebar-item-spacing-horizontal)}#furo-sidebar-ad-placement .ethical-sidebar{background:var(--color-background-secondary);border:none;box-shadow:none}#furo-sidebar-ad-placement .ethical-sidebar:hover{background:var(--color-background-hover)}#furo-sidebar-ad-placement .ethical-sidebar a{color:var(--color-foreground-primary)}#furo-sidebar-ad-placement .ethical-callout 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button.copybtn{align-items:center;background-color:var(--color-code-background);border:none;color:var(--color-background-item);cursor:pointer;height:1.25em;opacity:1;right:.5rem;top:.625rem;transition:color .3s,opacity .3s;width:1.25em}.highlight button.copybtn:hover{background-color:var(--color-code-background);color:var(--color-brand-content)}.highlight button.copybtn:after{background-color:transparent;color:var(--color-code-foreground);display:none}.highlight button.copybtn.success{color:#22863a;transition:color 0ms}.highlight button.copybtn.success:after{display:block}.highlight button.copybtn 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Correct the color inheritance from `fieldset` elements in IE.\n * 3. Remove the padding so developers are not caught out when they zero out\n * `fieldset` elements in all browsers.\n */\n\nlegend {\n box-sizing: border-box; /* 1 */\n color: inherit; /* 2 */\n display: table; /* 1 */\n max-width: 100%; /* 1 */\n padding: 0; /* 3 */\n white-space: normal; /* 1 */\n}\n\n/**\n * Add the correct vertical alignment in Chrome, Firefox, and Opera.\n */\n\nprogress {\n vertical-align: baseline;\n}\n\n/**\n * Remove the default vertical scrollbar in IE 10+.\n */\n\ntextarea {\n overflow: auto;\n}\n\n/**\n * 1. Add the correct box sizing in IE 10.\n * 2. Remove the padding in IE 10.\n */\n\n[type=\"checkbox\"],\n[type=\"radio\"] {\n box-sizing: border-box; /* 1 */\n padding: 0; /* 2 */\n}\n\n/**\n * Correct the cursor style of increment and decrement buttons in Chrome.\n */\n\n[type=\"number\"]::-webkit-inner-spin-button,\n[type=\"number\"]::-webkit-outer-spin-button {\n height: auto;\n}\n\n/**\n * 1. Correct the odd appearance in Chrome and Safari.\n * 2. Correct the outline style in Safari.\n */\n\n[type=\"search\"] {\n -webkit-appearance: textfield; /* 1 */\n outline-offset: -2px; /* 2 */\n}\n\n/**\n * Remove the inner padding in Chrome and Safari on macOS.\n */\n\n[type=\"search\"]::-webkit-search-decoration {\n -webkit-appearance: none;\n}\n\n/**\n * 1. Correct the inability to style clickable types in iOS and Safari.\n * 2. Change font properties to `inherit` in Safari.\n */\n\n::-webkit-file-upload-button {\n -webkit-appearance: button; /* 1 */\n font: inherit; /* 2 */\n}\n\n/* Interactive\n ========================================================================== */\n\n/*\n * Add the correct display in Edge, IE 10+, and Firefox.\n */\n\ndetails {\n display: block;\n}\n\n/*\n * Add the correct display in all browsers.\n */\n\nsummary {\n display: list-item;\n}\n\n/* Misc\n ========================================================================== */\n\n/**\n * Add the correct display in IE 10+.\n */\n\ntemplate {\n display: none;\n}\n\n/**\n * Add the correct display in IE 10.\n */\n\n[hidden] {\n display: none;\n}\n","// This file contains styles for managing print media.\n\n////////////////////////////////////////////////////////////////////////////////\n// Hide elements not relevant to print media.\n////////////////////////////////////////////////////////////////////////////////\n@media print\n // Hide icon container.\n .content-icon-container\n display: none !important\n\n // Hide showing header links if hovering over when printing.\n .headerlink\n display: none !important\n\n // Hide mobile header.\n .mobile-header\n display: none !important\n\n // Hide navigation links.\n .related-pages\n display: none !important\n\n////////////////////////////////////////////////////////////////////////////////\n// Tweaks related to decolorization.\n////////////////////////////////////////////////////////////////////////////////\n@media print\n // Apply a border around code which no longer have a color background.\n .highlight\n border: 0.1pt solid var(--color-foreground-border)\n\n////////////////////////////////////////////////////////////////////////////////\n// Avoid page break in some relevant cases.\n////////////////////////////////////////////////////////////////////////////////\n@media print\n ul, ol, dl, a, table, pre, blockquote\n page-break-inside: avoid\n\n h1, h2, h3, h4, h5, h6, img, figure, caption\n page-break-inside: avoid\n page-break-after: avoid\n\n ul, ol, dl\n page-break-before: avoid\n",".visually-hidden\n position: absolute !important\n width: 1px !important\n height: 1px !important\n padding: 0 !important\n margin: -1px !important\n overflow: hidden !important\n clip: rect(0,0,0,0) !important\n white-space: nowrap !important\n border: 0 !important\n color: var(--color-foreground-primary)\n background: var(--color-background-primary)\n\n:-moz-focusring\n outline: auto\n","// This file serves as the \"skeleton\" of the theming logic.\n//\n// This contains the bulk of the logic for handling dark mode, color scheme\n// toggling and the handling of color-scheme-specific hiding of elements.\n\nbody\n @include fonts\n @include spacing\n @include icons\n @include admonitions\n @include default-admonition(#651fff, \"abstract\")\n @include default-topic(#14B8A6, \"pencil\")\n\n @include colors\n\n.only-light\n display: block !important\nhtml body .only-dark\n display: none !important\n\n// Ignore dark-mode hints if print media.\n@media not print\n // Enable dark-mode, if requested.\n body[data-theme=\"dark\"]\n @include colors-dark\n\n html & .only-light\n display: none !important\n .only-dark\n display: block !important\n\n // Enable dark mode, unless explicitly told to avoid.\n @media (prefers-color-scheme: dark)\n body:not([data-theme=\"light\"])\n @include colors-dark\n\n html & .only-light\n display: none !important\n .only-dark\n display: block !important\n\n//\n// Theme toggle presentation\n//\nbody[data-theme=\"auto\"]\n .theme-toggle svg.theme-icon-when-auto-light\n display: block\n\n @media (prefers-color-scheme: dark)\n .theme-toggle svg.theme-icon-when-auto-dark\n display: block\n .theme-toggle svg.theme-icon-when-auto-light\n display: none\n\nbody[data-theme=\"dark\"]\n .theme-toggle svg.theme-icon-when-dark\n display: block\n\nbody[data-theme=\"light\"]\n .theme-toggle svg.theme-icon-when-light\n display: block\n","// Fonts used by this theme.\n//\n// There are basically two things here -- using the system font stack and\n// defining sizes for various elements in %ages. We could have also used `em`\n// but %age is easier to reason about for me.\n\n@mixin fonts {\n // These are adapted from https://systemfontstack.com/\n --font-stack: -apple-system, BlinkMacSystemFont, Segoe UI, Helvetica, Arial,\n sans-serif, Apple Color Emoji, Segoe UI Emoji;\n --font-stack--monospace: \"SFMono-Regular\", Menlo, Consolas, Monaco,\n Liberation Mono, Lucida Console, monospace;\n --font-stack--headings: var(--font-stack);\n\n --font-size--normal: 100%;\n --font-size--small: 87.5%;\n --font-size--small--2: 81.25%;\n --font-size--small--3: 75%;\n --font-size--small--4: 62.5%;\n\n // Sidebar\n --sidebar-caption-font-size: var(--font-size--small--2);\n --sidebar-item-font-size: var(--font-size--small);\n --sidebar-search-input-font-size: var(--font-size--small);\n\n // Table of Contents\n --toc-font-size: var(--font-size--small--3);\n --toc-font-size--mobile: var(--font-size--normal);\n --toc-title-font-size: var(--font-size--small--4);\n\n // Admonitions\n //\n // These aren't defined in terms of %ages, since nesting these is permitted.\n --admonition-font-size: 0.8125rem;\n --admonition-title-font-size: 0.8125rem;\n\n // Code\n --code-font-size: var(--font-size--small--2);\n\n // API\n --api-font-size: var(--font-size--small);\n}\n","// Spacing for various elements on the page\n//\n// If the user wants to tweak things in a certain way, they are permitted to.\n// They also have to deal with the consequences though!\n\n@mixin spacing {\n // Header!\n --header-height: calc(\n var(--sidebar-item-line-height) + 4 * #{var(--sidebar-item-spacing-vertical)}\n );\n --header-padding: 0.5rem;\n\n // Sidebar\n --sidebar-tree-space-above: 1.5rem;\n --sidebar-caption-space-above: 1rem;\n\n --sidebar-item-line-height: 1rem;\n --sidebar-item-spacing-vertical: 0.5rem;\n --sidebar-item-spacing-horizontal: 1rem;\n --sidebar-item-height: calc(\n var(--sidebar-item-line-height) + 2 *#{var(--sidebar-item-spacing-vertical)}\n );\n\n --sidebar-expander-width: var(--sidebar-item-height); // be square\n\n --sidebar-search-space-above: 0.5rem;\n --sidebar-search-input-spacing-vertical: 0.5rem;\n --sidebar-search-input-spacing-horizontal: 0.5rem;\n --sidebar-search-input-height: 1rem;\n --sidebar-search-icon-size: var(--sidebar-search-input-height);\n\n // Table of Contents\n --toc-title-padding: 0.25rem 0;\n --toc-spacing-vertical: 1.5rem;\n --toc-spacing-horizontal: 1.5rem;\n --toc-item-spacing-vertical: 0.4rem;\n --toc-item-spacing-horizontal: 1rem;\n}\n","// Expose theme icons as CSS variables.\n\n$icons: (\n // Adapted from tabler-icons\n // url: https://tablericons.com/\n \"search\":\n url('data:image/svg+xml;charset=utf-8,'),\n // Factored out from mkdocs-material on 24-Aug-2020.\n // url: https://squidfunk.github.io/mkdocs-material/reference/admonitions/\n \"pencil\":\n url('data:image/svg+xml;charset=utf-8,'),\n \"abstract\":\n url('data:image/svg+xml;charset=utf-8,'),\n \"info\":\n url('data:image/svg+xml;charset=utf-8,'),\n \"flame\":\n url('data:image/svg+xml;charset=utf-8,'),\n \"question\":\n url('data:image/svg+xml;charset=utf-8,'),\n \"warning\":\n url('data:image/svg+xml;charset=utf-8,'),\n \"failure\":\n url('data:image/svg+xml;charset=utf-8,'),\n \"spark\":\n url('data:image/svg+xml;charset=utf-8,')\n);\n\n@mixin icons {\n @each $name, $glyph in $icons {\n --icon-#{$name}: #{$glyph};\n }\n}\n","// Admonitions\n\n// Structure of these is:\n// admonition-class: color \"icon-name\";\n//\n// The colors are translated into CSS variables below. The icons are\n// used directly in the main declarations to set the `mask-image` in\n// the title.\n\n// prettier-ignore\n$admonitions: (\n // Each of these has an reST directives for it.\n \"caution\": #ff9100 \"spark\",\n \"warning\": #ff9100 \"warning\",\n \"danger\": #ff5252 \"spark\",\n \"attention\": #ff5252 \"warning\",\n \"error\": #ff5252 \"failure\",\n \"hint\": #00c852 \"question\",\n \"tip\": #00c852 \"info\",\n \"important\": #00bfa5 \"flame\",\n \"note\": #00b0ff \"pencil\",\n \"seealso\": #448aff \"info\",\n \"admonition-todo\": #808080 \"pencil\"\n);\n\n@mixin default-admonition($color, $icon-name) {\n --color-admonition-title: #{$color};\n --color-admonition-title-background: #{rgba($color, 0.2)};\n\n --icon-admonition-default: var(--icon-#{$icon-name});\n}\n\n@mixin default-topic($color, $icon-name) {\n --color-topic-title: #{$color};\n --color-topic-title-background: #{rgba($color, 0.2)};\n\n --icon-topic-default: var(--icon-#{$icon-name});\n}\n\n@mixin admonitions {\n @each $name, $values in $admonitions {\n --color-admonition-title--#{$name}: #{nth($values, 1)};\n --color-admonition-title-background--#{$name}: #{rgba(\n nth($values, 1),\n 0.2\n )};\n }\n}\n","// Colors used throughout this theme.\n//\n// The aim is to give the user more control. Thus, instead of hard-coding colors\n// in various parts of the stylesheet, the approach taken is to define all\n// colors as CSS variables and reusing them in all the places.\n//\n// `colors-dark` depends on `colors` being included at a lower specificity.\n\n@mixin colors {\n --color-problematic: #b30000;\n\n // Base Colors\n --color-foreground-primary: black; // for main text and headings\n --color-foreground-secondary: #5a5c63; // for secondary text\n --color-foreground-muted: #6b6f76; // for muted text\n --color-foreground-border: #878787; // for content borders\n\n --color-background-primary: white; // for content\n --color-background-secondary: #f8f9fb; // for navigation + ToC\n --color-background-hover: #efeff4ff; // for navigation-item hover\n --color-background-hover--transparent: #efeff400;\n --color-background-border: #eeebee; // for UI borders\n --color-background-item: #ccc; // for \"background\" items (eg: copybutton)\n\n // Announcements\n --color-announcement-background: #000000dd;\n --color-announcement-text: #eeebee;\n\n // Brand colors\n --color-brand-primary: #0a4bff;\n --color-brand-content: #2757dd;\n --color-brand-visited: #872ee0;\n\n // API documentation\n --color-api-background: var(--color-background-hover--transparent);\n --color-api-background-hover: var(--color-background-hover);\n --color-api-overall: var(--color-foreground-secondary);\n --color-api-name: var(--color-problematic);\n --color-api-pre-name: var(--color-problematic);\n --color-api-paren: var(--color-foreground-secondary);\n --color-api-keyword: var(--color-foreground-primary);\n\n --color-api-added: #21632c;\n --color-api-added-border: #38a84d;\n --color-api-changed: #046172;\n --color-api-changed-border: #06a1bc;\n --color-api-deprecated: #605706;\n --color-api-deprecated-border: #f0d90f;\n --color-api-removed: #b30000;\n --color-api-removed-border: #ff5c5c;\n\n --color-highlight-on-target: #ffffcc;\n\n // Inline code background\n --color-inline-code-background: var(--color-background-secondary);\n\n // Highlighted text (search)\n --color-highlighted-background: #ddeeff;\n --color-highlighted-text: var(--color-foreground-primary);\n\n // GUI Labels\n --color-guilabel-background: #ddeeff80;\n --color-guilabel-border: #bedaf580;\n --color-guilabel-text: var(--color-foreground-primary);\n\n // Admonitions!\n --color-admonition-background: transparent;\n\n //////////////////////////////////////////////////////////////////////////////\n // Everything below this should be one of:\n // - var(...)\n // - *-gradient(...)\n // - special literal values (eg: transparent, none)\n //////////////////////////////////////////////////////////////////////////////\n\n // Tables\n --color-table-header-background: var(--color-background-secondary);\n --color-table-border: var(--color-background-border);\n\n // Cards\n --color-card-border: var(--color-background-secondary);\n --color-card-background: transparent;\n --color-card-marginals-background: var(--color-background-secondary);\n\n // Header\n --color-header-background: var(--color-background-primary);\n --color-header-border: var(--color-background-border);\n --color-header-text: var(--color-foreground-primary);\n\n // Sidebar (left)\n --color-sidebar-background: var(--color-background-secondary);\n --color-sidebar-background-border: var(--color-background-border);\n\n --color-sidebar-brand-text: var(--color-foreground-primary);\n --color-sidebar-caption-text: var(--color-foreground-muted);\n --color-sidebar-link-text: var(--color-foreground-secondary);\n --color-sidebar-link-text--top-level: var(--color-brand-primary);\n\n --color-sidebar-item-background: var(--color-sidebar-background);\n --color-sidebar-item-background--current: var(\n --color-sidebar-item-background\n );\n --color-sidebar-item-background--hover: linear-gradient(\n 90deg,\n var(--color-background-hover--transparent) 0%,\n var(--color-background-hover) var(--sidebar-item-spacing-horizontal),\n var(--color-background-hover) 100%\n );\n\n --color-sidebar-item-expander-background: transparent;\n --color-sidebar-item-expander-background--hover: var(\n --color-background-hover\n );\n\n --color-sidebar-search-text: var(--color-foreground-primary);\n --color-sidebar-search-background: var(--color-background-secondary);\n --color-sidebar-search-background--focus: var(--color-background-primary);\n --color-sidebar-search-border: var(--color-background-border);\n --color-sidebar-search-icon: var(--color-foreground-muted);\n\n // Table of Contents (right)\n --color-toc-background: var(--color-background-primary);\n --color-toc-title-text: var(--color-foreground-muted);\n --color-toc-item-text: var(--color-foreground-secondary);\n --color-toc-item-text--hover: var(--color-foreground-primary);\n --color-toc-item-text--active: var(--color-brand-primary);\n\n // Actual page contents\n --color-content-foreground: var(--color-foreground-primary);\n --color-content-background: transparent;\n\n // Links\n --color-link: var(--color-brand-content);\n --color-link-underline: var(--color-background-border);\n --color-link--hover: var(--color-brand-content);\n --color-link-underline--hover: var(--color-foreground-border);\n\n --color-link--visited: var(--color-brand-visited);\n --color-link-underline--visited: var(--color-background-border);\n --color-link--visited--hover: var(--color-brand-visited);\n --color-link-underline--visited--hover: var(--color-foreground-border);\n}\n\n@mixin colors-dark {\n --color-problematic: #ee5151;\n\n // Base Colors\n --color-foreground-primary: #cfd0d0; // for main text and headings\n --color-foreground-secondary: #9ca0a5; // for secondary text\n --color-foreground-muted: #81868d; // for muted text\n --color-foreground-border: #666666; // for content borders\n\n --color-background-primary: #131416; // for content\n --color-background-secondary: #1a1c1e; // for navigation + ToC\n --color-background-hover: #1e2124ff; // for navigation-item hover\n --color-background-hover--transparent: #1e212400;\n --color-background-border: #303335; // for UI borders\n --color-background-item: #444; // for \"background\" items (eg: copybutton)\n\n // Announcements\n --color-announcement-background: #000000dd;\n --color-announcement-text: #eeebee;\n\n // Brand colors\n --color-brand-primary: #3d94ff;\n --color-brand-content: #5ca5ff;\n --color-brand-visited: #b27aeb;\n\n // Highlighted text (search)\n --color-highlighted-background: #083563;\n\n // GUI Labels\n --color-guilabel-background: #08356380;\n --color-guilabel-border: #13395f80;\n\n // API documentation\n --color-api-keyword: var(--color-foreground-secondary);\n --color-highlight-on-target: #333300;\n\n --color-api-added: #3db854;\n --color-api-added-border: #267334;\n --color-api-changed: #09b0ce;\n --color-api-changed-border: #056d80;\n --color-api-deprecated: #b1a10b;\n --color-api-deprecated-border: #6e6407;\n --color-api-removed: #ff7575;\n --color-api-removed-border: #b03b3b;\n\n // Admonitions\n --color-admonition-background: #18181a;\n\n // Cards\n --color-card-border: var(--color-background-secondary);\n --color-card-background: #18181a;\n --color-card-marginals-background: var(--color-background-hover);\n}\n","// This file contains the styling for making the content throughout the page,\n// including fonts, paragraphs, headings and spacing among these elements.\n\nbody\n font-family: var(--font-stack)\npre,\ncode,\nkbd,\nsamp\n font-family: var(--font-stack--monospace)\n\n// Make fonts look slightly nicer.\nbody\n -webkit-font-smoothing: antialiased\n -moz-osx-font-smoothing: grayscale\n\n// Line height from Bootstrap 4.1\narticle\n line-height: 1.5\n\n//\n// Headings\n//\nh1,\nh2,\nh3,\nh4,\nh5,\nh6\n line-height: 1.25\n font-family: var(--font-stack--headings)\n font-weight: bold\n\n border-radius: 0.5rem\n margin-top: 0.5rem\n margin-bottom: 0.5rem\n margin-left: -0.5rem\n margin-right: -0.5rem\n padding-left: 0.5rem\n padding-right: 0.5rem\n\n + p\n margin-top: 0\n\nh1\n font-size: 2.5em\n margin-top: 1.75rem\n margin-bottom: 1rem\nh2\n font-size: 2em\n margin-top: 1.75rem\nh3\n font-size: 1.5em\nh4\n font-size: 1.25em\nh5\n font-size: 1.125em\nh6\n font-size: 1em\n\nsmall\n opacity: 75%\n font-size: 80%\n\n// Paragraph\np\n margin-top: 0.5rem\n margin-bottom: 0.75rem\n\n// Horizontal rules\nhr.docutils\n height: 1px\n padding: 0\n margin: 2rem 0\n background-color: var(--color-background-border)\n border: 0\n\n.centered\n text-align: center\n\n// Links\na\n text-decoration: underline\n\n color: var(--color-link)\n text-decoration-color: var(--color-link-underline)\n\n &:visited\n color: var(--color-link--visited)\n text-decoration-color: var(--color-link-underline--visited)\n &:hover\n color: var(--color-link--visited--hover)\n text-decoration-color: var(--color-link-underline--visited--hover)\n\n &:hover\n color: var(--color-link--hover)\n text-decoration-color: var(--color-link-underline--hover)\n &.muted-link\n color: inherit\n &:hover\n color: var(--color-link--hover)\n text-decoration-color: var(--color-link-underline--hover)\n &:visited\n color: var(--color-link--visited--hover)\n text-decoration-color: var(--color-link-underline--visited--hover)\n","// This file contains the styles for the overall layouting of the documentation\n// skeleton, including the responsive changes as well as sidebar toggles.\n//\n// This is implemented as a mobile-last design, which isn't ideal, but it is\n// reasonably good-enough and I got pretty tired by the time I'd finished this\n// to move the rules around to fix this. Shouldn't take more than 3-4 hours,\n// if you know what you're doing tho.\n\n// HACK: Not all browsers account for the scrollbar width in media queries.\n// This results in horizontal scrollbars in the breakpoint where we go\n// from displaying everything to hiding the ToC. We accomodate for this by\n// adding a bit of padding to the TOC drawer, disabling the horizontal\n// scrollbar and allowing the scrollbars to cover the padding.\n// https://www.456bereastreet.com/archive/201301/media_query_width_and_vertical_scrollbars/\n\n// HACK: Always having the scrollbar visible, prevents certain browsers from\n// causing the content to stutter horizontally between taller-than-viewport and\n// not-taller-than-viewport pages.\n\n$icon-size: 1.25rem\n\nhtml\n overflow-x: hidden\n overflow-y: scroll\n scroll-behavior: smooth\n\n.sidebar-scroll, .toc-scroll, article[role=main] *\n // Override Firefox scrollbar style\n scrollbar-width: thin\n scrollbar-color: var(--color-foreground-border) transparent\n\n // Override Chrome scrollbar styles\n &::-webkit-scrollbar\n width: 0.25rem\n height: 0.25rem\n &::-webkit-scrollbar-thumb\n background-color: var(--color-foreground-border)\n border-radius: 0.125rem\n\n//\n// Overalls\n//\nhtml,\nbody\n height: 100%\n color: var(--color-foreground-primary)\n background: var(--color-background-primary)\n\n.skip-to-content\n position: fixed\n padding: 1rem\n border-radius: 1rem\n left: 0.25rem\n top: 0.25rem\n z-index: 40\n background: var(--color-background-primary)\n color: var(--color-foreground-primary)\n\n transform: translateY(-200%)\n transition: transform 300ms ease-in-out\n\n &:focus-within\n transform: translateY(0%)\n\narticle\n color: var(--color-content-foreground)\n background: var(--color-content-background)\n overflow-wrap: break-word\n\n.page\n display: flex\n // fill the viewport for pages with little content.\n min-height: 100%\n\n.mobile-header\n width: 100%\n height: var(--header-height)\n background-color: var(--color-header-background)\n color: var(--color-header-text)\n border-bottom: 1px solid var(--color-header-border)\n\n // Looks like sub-script/super-script have this, and we need this to\n // be \"on top\" of those.\n z-index: 10\n\n // We don't show the header on large screens.\n display: none\n\n // Add shadow when scrolled\n &.scrolled\n border-bottom: none\n box-shadow: 0 0 0.2rem rgba(0, 0, 0, 0.1), 0 0.2rem 0.4rem rgba(0, 0, 0, 0.2)\n\n .header-center\n a\n color: var(--color-header-text)\n text-decoration: none\n\n.main\n display: flex\n flex: 1\n\n// Sidebar (left) also covers the entire left portion of screen.\n.sidebar-drawer\n box-sizing: border-box\n\n border-right: 1px solid var(--color-sidebar-background-border)\n background: var(--color-sidebar-background)\n\n display: flex\n justify-content: flex-end\n // These next two lines took me two days to figure out.\n width: calc((100% - #{$full-width}) / 2 + #{$sidebar-width})\n min-width: $sidebar-width\n\n// Scroll-along sidebars\n.sidebar-container,\n.toc-drawer\n box-sizing: border-box\n width: $sidebar-width\n\n.toc-drawer\n background: var(--color-toc-background)\n // See HACK described on top of this document\n padding-right: 1rem\n\n.sidebar-sticky,\n.toc-sticky\n position: sticky\n top: 0\n height: min(100%, 100vh)\n height: 100vh\n\n display: flex\n flex-direction: column\n\n.sidebar-scroll,\n.toc-scroll\n flex-grow: 1\n flex-shrink: 1\n\n overflow: auto\n scroll-behavior: smooth\n\n// Central items.\n.content\n padding: 0 $content-padding\n width: $content-width\n\n display: flex\n flex-direction: column\n justify-content: space-between\n\n.icon\n display: inline-block\n height: 1rem\n width: 1rem\n svg\n width: 100%\n height: 100%\n\n//\n// Accommodate announcement banner\n//\n.announcement\n background-color: var(--color-announcement-background)\n color: var(--color-announcement-text)\n\n height: var(--header-height)\n display: flex\n align-items: center\n overflow-x: auto\n & + .page\n min-height: calc(100% - var(--header-height))\n\n.announcement-content\n box-sizing: border-box\n padding: 0.5rem\n min-width: 100%\n white-space: nowrap\n text-align: center\n\n a\n color: var(--color-announcement-text)\n text-decoration-color: var(--color-announcement-text)\n\n &:hover\n color: var(--color-announcement-text)\n text-decoration-color: var(--color-link--hover)\n\n////////////////////////////////////////////////////////////////////////////////\n// Toggles for theme\n////////////////////////////////////////////////////////////////////////////////\n.no-js .theme-toggle-container // don't show theme toggle if there's no JS\n display: none\n\n.theme-toggle-container\n vertical-align: middle\n\n.theme-toggle\n cursor: pointer\n border: none\n padding: 0\n background: transparent\n\n.theme-toggle svg\n vertical-align: middle\n height: $icon-size\n width: $icon-size\n color: var(--color-foreground-primary)\n display: none\n\n.theme-toggle-header\n float: left\n padding: 1rem 0.5rem\n\n////////////////////////////////////////////////////////////////////////////////\n// Toggles for elements\n////////////////////////////////////////////////////////////////////////////////\n.toc-overlay-icon, .nav-overlay-icon\n display: none\n cursor: pointer\n\n .icon\n color: var(--color-foreground-secondary)\n height: $icon-size\n width: $icon-size\n\n.toc-header-icon, .nav-overlay-icon\n // for when we set display: flex\n justify-content: center\n align-items: center\n\n.toc-content-icon\n height: 1.5rem\n width: 1.5rem\n\n.content-icon-container\n float: right\n display: flex\n margin-top: 1.5rem\n margin-left: 1rem\n margin-bottom: 1rem\n gap: 0.5rem\n\n .edit-this-page, .view-this-page\n svg\n color: inherit\n height: $icon-size\n width: $icon-size\n\n.sidebar-toggle\n position: absolute\n display: none\n//