MWM size prediction research: scripts, descriptions and data

Author: alexey-zakharenkov

mwm_size_prediction/README.md

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+# MWM size prediction model
+
+The web application uses a data-science-based model to predict size MWM files
+compiled from the borders. Here described are the efforts that were undertaken
+to build such a prediction model. The serialized model resides at `web/app/data/`
+in the `model.pkl` and `scaler.pkl` files. Its first variant was trained only
+on county-level data and is valid at limited parameters range (see web/app/config.py
+for the model limitations). Now we try to extend the model to predict also
+province-level regions.
+
+## Data gathering
+
+We chosen countries/regions with dense OSM data and took them as the training
+dataset. As a first try Germany, Austria, Belgium and Netherlands where taken 
+giving about 950 borders of different admin levels. The sample was found to
+be too small for good training.
+Then Norway, Switzerland, Ile-de-France of France, Japan, United Kingdom, Belarus,
+4 states: California, Texas, New York, Washington – of the United States
+were added.
+
+#### Geographic data gathering
+
+First, with the help of the web app I split the forementioned countries/regions down to the
+"county"-subregions in general sense of a "county" – it's an admin level
+which is too small for MWMs, but regions of one level higher are too big,
+so that a usual MWM would be a cluster of "counties".
+
+Japan was a special case, so `extract_mwm_geo_data.py` script contains a
+function to split the country into subregions.
+
+The `extract_mwm_geo_data.py` script, endowed with a valid connection
+to the database with borders, gathers information about all borders of a
+given country/region and its descendants: id, parent_id, admin level, name,
+full area, land area (so the table with land borders of the planet is necessary),
+city/town count and population, hamlet/village count and population.
+
+One should keep in mind that some borders may be absent in OSM, so a region may
+not be fully covered by subregions. So, a region area (or places cout,
+or population) may be greater than the sum of areas of its subregions.
+One way is to fix borders by hand. Another way, that I followed, is to select
+areas, cities and population from the database even for upper-level regions
+(except countries, for which the calculation would run too long and is not useful).
+
+#### Mwm size data gathering
+
+Having borders division of the training countries in the web app, I download all
+borders, changing the poly-file naming procedure so that the name to contain
+the region id. The id would be the link between files with geodata and mwm sizes data.
+So we have many border file with names like _03565917_Japan_Gunma Prefecture_Numata.poly_
+that I place into the `omim/data/borders/` directory instead of original
+borders.
+
+Also, I did a *.o5m-extract for each country to supply the maps_generator
+not with the whole planet-latest.o5m file. I used https://boundingbox.klokantech.com 
+to find a polygon for an extract, first getting geojson of ten points or so at the
+website and then composing a *.poly file in a text editor. With this
+`country.poly` file I got a country extract with `osmconvert` tool:
+```bash
+osmctools/osmconvert planet-latest.o5m -B=country.poly -o=country.o5m
+```
+
+Then
+```bash
+md5sum country.o5m > country.o5m.md5
+```
+In `maps_generation.ini` I changed the path to the planet and md5sum file and run
+the MWMs generation with
+```bash
+nohup python -m maps_generator --order="" --skip="Routing,RoutingTransit" \
+    --without_countries="World*" --countries="*_Switzerland_*" &
+```
+
+For the asterisk to work at the beginning of the mask in the `--countries` option,
+I made some changes to `omim/tools/generator/maps_generator/__main__.py`:
+
+```python
+    def end_star_compare(prefix, full):
+        return full.startswith(prefix)
+
+    def start_star_compare(suffix, full):
+        return full.endswith(suffix)
+
+    def both_star_compare(substr, full):
+        return substr in full
+
+    ...
+            cmp = compare
+            _raw_country = country_item[:]
+            if _raw_country:
+                if all(_raw_country[i] == "*" for i in (0, -1)):
+                    _raw_country = _raw_country.replace("*", "") 
+                    cmp = both_star_compare
+                elif _raw_country[-1] == "*":
+                    _raw_country = _raw_country.replace("*", "") 
+                    cmp = end_star_compare
+                elif _raw_country[0] == "*":
+                    _raw_country = _raw_country.replace("*", "") 
+                    cmp = start_star_compare
+
+```
+
+After all mwms for a country had beed generated in a directory like
+`maps_build/2021_01_20__18_06_38/210120`
+I got their sizes (in Kb) with this command:
+
+```bash
+du maps_build/maps_build/2021_01_20__18_06_38/210120/*.mwm | sort -k2 > Norway.sizes
+```
+
+In fact, I renamed directory to some 2021_01_20__18_06_38-Norway and used command
+```bash
+du maps_build/*-Norway/[0-9]*/*.mwm | sort -k2 > Norway.sizes
+```
+
+#### Combining geo data with sizes data
+
+Now I had a set of `<Country>_regions.json` and `<Country>.sizes` files
+with geo- and sizes-data respectively on several large regions with subregions.
+I used the `combine_data.py` script  to generate one big `7countries.csv`.
+
+Yet another `4countries.csv` file with Germany, Austria, Belgium and Netherlands
+subregions was already prepared before, it has excluded=1 flag for those
+Netherland subregions which contain much water (inner waters, not ocean). Also,
+there were not data for upper-lever regions, and the values of area, cities,
+population and mwm_size were obtained as the sum of subregions defined by
+parent_id column.
+
+Since data for country-level regions was not collected (due to long sql queries and
+mwm generation time), we enrich the `7countries.csv` dataset with country-level
+by summing up data of subregions:
+```python
+import pandas as pd
+data7 = pd.read_csv('data/7countries.csv', sep=';')
+
+# Drop data for countries if it present
+data7 = data7[data7['al'] != 2]
+
+countries = {'id':   [-59065, -2978650, -51701, -382313, -62149],
+             'name': ['Belarus', 'Norway', 'Switzerland', 'Japan', 'United Kingdom'],
+             'excluded': [0]*5,
+             'al': [2]*5,
+        }
+sum_fields = ('full_area', 'land_area', 'city_cnt', 'hamlet_cnt', 'city_pop', 'hamlet_pop', 'mwm_size_sum')
+
+for field in sum_fields:
+    field_values = [data7[data7['parent_id'] == c_id][field].sum() for c_id in countries['id']]
+    countries[field] = field_values
+
+countries_df = pd.DataFrame(countries, columns = list(countries.keys()))
+data7 = pd.concat([data7, countries_df])
+data7.to_csv('data/7countries-1.csv', index=False, sep=';')
+
+# Check, and if all right, do
+# import os; os.rename('data/7countries-1.csv', 'data/7countries.csv')
+```
+
+The union of `4countries.csv` and `7countries.csv` data is the
+dataset for data science experiments on mwm size prediction. Keep in mind
+that _mwm_size_ field may be NULL (for countries), or _mwm_size_sum_ may be NULL
+(in 4countries.csv). Make corrections when getting combined dataset:
+
+```python
+import pandas as pd
+import numpy as np
+
+def fit_mwm_size(df):
+    df['mwm_size'] = np.where(df['mwm_size'].isnull(), df['mwm_size_sum'], df['mwm_size'])
+
+data1 = pd.read_csv('data/4countries.csv', sep=';')  # Austria, Belgium, Netherlands, Germany
+data2 = pd.read_csv('data/7countries.csv', sep=';')  # Norway, UK, US(4 states), Switzerland, Japan, Belarus, Ile-de-France
+
+data = pd.concat([data1, data2])
+
+data = data[data.excluded.eq(0) & data.id.notnull()]
+
+fit_mwm_size(data)
+```

mwm_size_prediction/combine_data.py

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+import json
+import csv
+
+
+def get_combined_info(region_name):
+    with open(f'data/{region_name}_regions.json', newline='') as f:
+        regions = json.load(f)
+        regions = {int(k):v for k, v in regions.items()}
+
+    with open(f'data/{region_name}.sizes') as sizes_file:
+        for line in sizes_file:
+            mwm_name = line.split('/')[-1][:-4]
+            #print(f"mwm_name = {mwm_name}")
+            r_id = -int(mwm_name.split('_')[0])
+            if r_id not in regions:
+                raise Exception(f'id {r_id} not in {region_name} data')
+            size = int(line.split()[0])
+            name = mwm_name.split('_')[-1]
+            country = mwm_name.split('_')[1]
+
+            regions[r_id].update({
+                'mwm_name': mwm_name,
+                'country': country,
+                'mwm_size': size,
+            })
+
+
+    admin_levels = set(x['al'] for x in regions.values())
+
+    ids_to_remove = []  # Far oversea regions may be counted but no mwm generated for
+    for al in sorted(admin_levels, reverse=True):
+        for r_id, r_data in ((r_id, r_data) for r_id, r_data in regions.items() if r_data['al'] == al):
+            children = [ch for ch in regions.values() if ch['parent_id'] == r_id]
+            is_leaf = not bool(children)
+            r_data['is_leaf'] = int(is_leaf)
+            r_data['excluded'] = 0
+            if is_leaf:
+                if 'mwm_size' not in r_data:
+                    print(f"Mwm not generated for {r_data['name']}")
+                    ids_to_remove.append(r_id)
+                else:
+                    r_data['mwm_size_sum'] = r_data['mwm_size']
+            else:
+                r_data['mwm_size_sum'] = sum(ch['mwm_size'] for ch in children)
+
+    return {k:v for k,v in regions.items() if k not in ids_to_remove}
+
+
+def main():
+    region_names = [
+        'Belarus', 'Switzerland', 'Ile-de-France',
+        'United Kingdom', 'Norway', 'Japan', 'United States'
+    ]
+
+    rows = []
+
+    # full_area includes ocean.
+    fieldnames = ['id', 'parent_id', 'al', 'is_leaf', 'excluded', 'name', 'mwm_name', 'country',
+                  'city_cnt', 'city_pop', 'hamlet_cnt', 'hamlet_pop',
+                  'full_area', 'land_area', 'mwm_size', 'mwm_size_sum']
+
+    with open('data/7countries.csv', 'w', newline='') as csvfile:
+        writer = csv.DictWriter(csvfile, delimiter=';', fieldnames=fieldnames)
+        writer.writeheader()
+
+        for region_name in region_names:
+            regions = get_combined_info(region_name)
+            rows = sorted(regions.values(), key=lambda reg: (reg['al'], reg['name']))
+            writer.writerows(rows)
+
+
+if __name__ == '__main__':
+    main()