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303 lines
12 KiB
303 lines
12 KiB
import os
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import catboost
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import geopandas as gpd
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import numpy as np
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import pandas as pd
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import psycopg2
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import sqlalchemy
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from celery import shared_task
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from django.db.models import F
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from scipy import interpolate
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from scipy.spatial import distance
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from shapely import wkb
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from sklearn import metrics
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from sklearn import model_selection as ms
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from sqlalchemy import text
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from postamates.settings import AGE_DAY_LIMIT
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from postamates.settings import DB_URL
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from service.models import PlacementPoint
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@shared_task()
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def raschet():
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conn = sqlalchemy.create_engine(
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DB_URL,
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connect_args={'options': '-csearch_path=public'},
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)
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query = text('select * from service_placementpoint')
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connection = conn.connect()
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pts = pd.read_sql(query, connection)
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pts['geometry'] = pts['geometry'].apply(wkb.loads, hex=True)
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pts = gpd.GeoDataFrame(pts, geometry='geometry', crs='epsg:4326')
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pts = pts.to_crs('epsg:32637')
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pts = pts.rename(
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columns={
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'target_cnt_nearby_mean': 'target_dist1',
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'target_age_nearby_mean': 'target_dist2',
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'yndxfood_cnt_cst': 'target_dist3',
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},
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)
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feats = [
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'id', 'metro_dist', 'target_dist', 'property_price_bargains', 'property_price_offers',
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'property_mean_floor',
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'property_era', 'flats_cnt_2', 'flats_cnt', 'popul_home', 'popul_job', 'other_post_cnt', 'yndxfood_sum',
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'yndxfood_cnt', 'school_cnt', 'kindergar_cnt', 'target_post_cnt', 'public_stop_cnt', 'sport_center_cnt',
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'pharmacy_cnt', 'supermarket_cnt', 'supermarket_premium_cnt', 'clinic_cnt', 'bank_cnt', 'reca_cnt',
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'lab_cnt', 'culture_cnt', 'attraction_cnt', 'mfc_cnt', 'bc_cnt', 'tc_cnt', 'rival_pvz_cnt',
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'rival_post_cnt',
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'business_activity', 'age_day', 'target_cnt_ao_mean', 'target_dist1', 'target_dist2', 'target_dist3',
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]
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# Записи для обучения
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pts_trn = pts.loc[pts.sample_trn == True].reset_index(drop=True)
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pts_trn = gpd.GeoDataFrame(pts_trn, geometry='geometry', crs='epsg:32637')
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pts_target = pts_trn[['geometry']]
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pts_target['cnt'] = 1
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pts_target = gpd.GeoDataFrame(pts_target, geometry='geometry', crs='epsg:32637')
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target_feature_coords = []
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for i in range(0, len(pts_target)):
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target_feature_coords.append((pts_target.geometry.x[i], pts_target.geometry.y[i]))
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target_feature_coords = np.array(target_feature_coords)
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pts_trn['target_dist'] = pts_trn.apply(
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lambda x: ((sorted(distance.cdist([[x['geometry'].x, x['geometry'].y]], target_feature_coords)[0])[1])),
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axis=1,
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)
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pts_trn.loc[pts_trn.target_dist > 700, 'target_dist'] = 700
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pts_trn['target_dist1'] = pts_trn.apply(
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lambda x: ((sorted(distance.cdist([[x['geometry'].x, x['geometry'].y]], target_feature_coords)[0])[2])),
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axis=1,
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)
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pts_trn.loc[pts_trn.target_dist1 > 700, 'target_dist1'] = 700
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pts_trn['target_dist2'] = pts_trn.apply(
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lambda x: ((sorted(distance.cdist([[x['geometry'].x, x['geometry'].y]], target_feature_coords)[0])[3])),
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axis=1,
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)
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pts_trn.loc[pts_trn.target_dist2 > 700, 'target_dist2'] = 700
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pts_trn['target_dist3'] = pts_trn.apply(
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lambda x: ((sorted(distance.cdist([[x['geometry'].x, x['geometry'].y]], target_feature_coords)[0])[4])),
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axis=1,
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)
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pts_trn.loc[pts_trn.target_dist3 > 700, 'target_dist3'] = 700
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pts_trn['buf'] = pts_trn.buffer(500)
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pts_trn = gpd.GeoDataFrame(pts_trn, geometry='buf', crs='epsg:32637')
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target_post = gpd.sjoin(pts_trn, pts_target, op='contains').groupby('id', as_index=False).agg({'cnt': 'count'})
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target_post = target_post.rename(columns={'cnt': 'target_post_cnt'})
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pts_trn = pts_trn.drop(columns=['target_post_cnt'])
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pts_trn = pts_trn.join(target_post.set_index('id'), on='id')
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pts_trn['target_post_cnt'] = pts_trn['target_post_cnt'] - 1
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pts_trn = pts_trn.sort_values(by='id').reset_index(drop=True)
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X_trn = pts_trn[feats].drop(columns=['id'])
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Y_trn = pts_trn[['fact']]
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# Записи для инференса
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pts_inf = pts.loc[(pts.status == 'Pending') |
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(pts.status == 'Installation') |
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(pts.status == 'Cancelled') |
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((pts.status == 'Working') & (pts.sample_trn == False))].reset_index(drop=True)
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pts_inf = gpd.GeoDataFrame(pts_inf, geometry='geometry', crs='epsg:32637')
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pts_inf['buf'] = pts_inf.buffer(500)
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pts_inf = gpd.GeoDataFrame(pts_inf, geometry='buf', crs='epsg:32637')
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pts_target = pts.loc[(pts.status == 'Working') |
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(pts.status == 'Installation') |
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(pts.sample_trn == True)].reset_index(drop=True)
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pts_target = pts_target[['geometry']]
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pts_target['cnt'] = 1
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pts_target = gpd.GeoDataFrame(pts_target, geometry='geometry', crs='epsg:32637')
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target_feature_coords = []
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for i in range(0, len(pts_target)):
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target_feature_coords.append((pts_target.geometry.x[i], pts_target.geometry.y[i]))
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target_feature_coords = np.array(target_feature_coords)
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pts_inf['target_dist'] = pts_inf.apply(
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lambda x: ((sorted(distance.cdist([[x['geometry'].x, x['geometry'].y]], target_feature_coords)[0])[0])),
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axis=1,
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)
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pts_inf.loc[pts_inf.target_dist > 700, 'target_dist'] = 700
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pts_inf['target_dist1'] = pts_inf.apply(
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lambda x: ((sorted(distance.cdist([[x['geometry'].x, x['geometry'].y]], target_feature_coords)[0])[1])),
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axis=1,
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)
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pts_inf.loc[pts_inf.target_dist1 > 700, 'target_dist1'] = 700
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pts_inf['target_dist2'] = pts_inf.apply(
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lambda x: ((sorted(distance.cdist([[x['geometry'].x, x['geometry'].y]], target_feature_coords)[0])[2])),
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axis=1,
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)
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pts_inf.loc[pts_inf.target_dist2 > 700, 'target_dist2'] = 700
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pts_inf['target_dist3'] = pts_inf.apply(
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lambda x: ((sorted(distance.cdist([[x['geometry'].x, x['geometry'].y]], target_feature_coords)[0])[3])),
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axis=1,
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)
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pts_inf.loc[pts_inf.target_dist3 > 700, 'target_dist3'] = 700
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pts_inf = pts_inf.sort_values(by='id').reset_index(drop=True)
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target_post = gpd.sjoin(pts_inf, pts_target, op='contains').groupby('id', as_index=False).agg({'cnt': 'count'})
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target_post = target_post.rename(columns={'cnt': 'target_post_cnt'})
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pts_inf = pts_inf.drop(columns=['target_post_cnt'])
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pts_inf = pts_inf.join(target_post.set_index('id'), on='id')
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pts_inf['age_day_init'] = pts_inf['age_day']
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pts_inf['age_day'] = 240
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X_inf = pts_inf[feats]
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seeds = [3, 99, 87, 21, 15]
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# Обучение, инференс
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r2_scores = []
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mapes = []
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y_infers = []
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for i in seeds:
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x_trn, x_test, y_trn, y_test = ms.train_test_split(X_trn, Y_trn, test_size=0.2, random_state=i)
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model = catboost.CatBoostRegressor(cat_features=['property_era'], random_state=i)
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model.fit(x_trn, y_trn, verbose=False)
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r2_score = metrics.r2_score(y_test, model.predict(x_test))
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mape = metrics.mean_absolute_percentage_error(y_test, model.predict(x_test))
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if ((r2_score > 0.45) & (mape < 0.25)):
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r2_scores.append(r2_score)
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mapes.append(mape)
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y_infers.append(model.predict(X_inf.drop(columns=['id'])))
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current_pred = sum(y_infers) / 5
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# Обновление полей по результатам работы модели
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update_fields = pts_inf[
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[
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'id', 'age_day_init', 'status', 'fact', 'delta_current', 'delta_first', 'plan_current', 'plan_first',
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'prediction_first',
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]
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]
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update_fields = update_fields.join(
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pd.concat(
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[
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X_inf[['id']],
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pd.DataFrame({'prediction_current': current_pred}),
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],
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axis=1,
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).set_index('id'),
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on='id',
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)
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update_fields['prediction_current'] = update_fields['prediction_current'].astype(int)
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days_x = np.array([0, 30, 60, 90, 120, 150, 180, 210, 240, 270])
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perc_y = np.array([0, 0.15, 0.20, 0.30, 0.60, 0.70, 0.70, 0.75, 0.75, 0.80])
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spl = interpolate.splrep(days_x, perc_y)
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update_fields['plan_first'] = update_fields.apply(
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lambda x: (x.prediction_first * interpolate.splev(x.age_day_init, spl) if x.status == 'Working' else 0),
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axis=1,
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)
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update_fields['plan_current'] = update_fields.apply(
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lambda x: (x.prediction_current * interpolate.splev(x.age_day_init, spl) if x.status == 'Working' else 0),
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axis=1,
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)
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update_fields['delta_first'] = update_fields.apply(
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lambda x: ((x.fact - x.plan_first) / x.plan_first * 100 if x.status == 'Working' else 0),
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axis=1,
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)
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update_fields['delta_current'] = update_fields.apply(
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lambda x: ((x.fact - x.plan_current) / x.plan_current * 100 if x.status == 'Working' else 0),
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axis=1,
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)
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update_fields_working = update_fields.loc[update_fields.status == 'Working'].reset_index(drop=True)
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update_fields_working = update_fields_working.fillna(0)
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# Загрузка в базу обновленных значений
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conn2 = psycopg2.connect(
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database=os.getenv('POSTGRES_DB', 'postgres'), user=os.getenv('POSTGRES_USER', 'postgres'),
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password=os.getenv('POSTGRES_PASSWORD', 'postgres'),
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host=os.getenv('POSTGRES_HOST', 'postgres'), port=os.getenv('POSTGRES_PORT', 'postgres'),
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options='-c search_path=public',
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)
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cursor = conn2.cursor()
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# prediction_current
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update_records1 = []
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for i in range(0, len(update_fields)):
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update_records1.append((int(update_fields.prediction_current[i]), int(update_fields.id[i])))
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sql_update_query = """Update service_placementpoint set prediction_current = %s where id = %s"""
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try:
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psycopg2.extras.execute_batch(cursor, sql_update_query, update_records1)
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conn2.commit()
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except Exception:
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cursor.execute('ROLLBACK')
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psycopg2.extras.execute_batch(cursor, sql_update_query, update_records1)
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conn2.commit()
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# plan_first
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update_records2 = []
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for i in range(0, len(update_fields_working)):
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update_records2.append((int(update_fields_working.plan_first[i]), int(update_fields_working.id[i])))
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sql_update_query = """Update service_placementpoint set plan_first = %s where id = %s"""
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try:
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psycopg2.extras.execute_batch(cursor, sql_update_query, update_records2)
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conn2.commit()
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except Exception:
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cursor.execute('ROLLBACK')
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psycopg2.extras.execute_batch(cursor, sql_update_query, update_records2)
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conn2.commit()
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# plan_current
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update_records3 = []
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for i in range(0, len(update_fields_working)):
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update_records3.append((int(update_fields_working.plan_current[i]), int(update_fields_working.id[i])))
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sql_update_query = """Update service_placementpoint set plan_current = %s where id = %s"""
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try:
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psycopg2.extras.execute_batch(cursor, sql_update_query, update_records3)
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conn2.commit()
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except Exception:
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cursor.execute('ROLLBACK')
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psycopg2.extras.execute_batch(cursor, sql_update_query, update_records3)
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conn2.commit()
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# delta_first
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update_records4 = []
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for i in range(0, len(update_fields_working)):
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update_records4.append((int(update_fields_working.delta_first[i]), int(update_fields_working.id[i])))
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sql_update_query = """Update service_placementpoint set delta_first = %s where id = %s"""
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try:
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psycopg2.extras.execute_batch(cursor, sql_update_query, update_records4)
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conn2.commit()
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except Exception:
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cursor.execute('ROLLBACK')
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psycopg2.extras.execute_batch(cursor, sql_update_query, update_records4)
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conn2.commit()
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# delta_current
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update_records5 = []
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for i in range(0, len(update_fields_working)):
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update_records5.append((int(update_fields_working.delta_current[i]), int(update_fields_working.id[i])))
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sql_update_query = """Update service_placementpoint set delta_current = %s where id = %s"""
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try:
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psycopg2.extras.execute_batch(cursor, sql_update_query, update_records5)
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conn2.commit()
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except Exception:
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cursor.execute('ROLLBACK')
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psycopg2.extras.execute_batch(cursor, sql_update_query, update_records5)
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conn2.commit()
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@shared_task()
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def add_age_day():
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qs = PlacementPoint.objects
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c1 = qs.filter(sample_trn=True).count()
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qs.update(age_day=F('age_day') + 1)
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qs2 = qs.filter(age_day__gt=AGE_DAY_LIMIT)
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qs2.update(sample_trn=True)
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c2 = PlacementPoint.objects.filter(sample_trn=True).count()
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if c2 - c1 != 0:
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raschet.delay()
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