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@ -1,11 +1,16 @@
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import os
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import os
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import catboost
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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 pandas as pd
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import psycopg2
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import psycopg2
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import sqlalchemy
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import sqlalchemy
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from celery import shared_task
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from celery import shared_task
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from django.db.models import F
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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 metrics
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from sklearn import model_selection as ms
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from sklearn import model_selection as ms
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from sqlalchemy import text
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from sqlalchemy import text
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@ -15,12 +20,6 @@ from postamates.settings import DB_URL
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from service.models import PlacementPoint
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from service.models import PlacementPoint
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# Запустить worker
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# celery -A postamates worker -l info
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# Запустить scheduler
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# celery -A postamates beat -l INFO.
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@shared_task()
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@shared_task()
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def raschet():
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def raschet():
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conn = sqlalchemy.create_engine(
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conn = sqlalchemy.create_engine(
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@ -30,8 +29,16 @@ def raschet():
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query = text('select * from service_placementpoint')
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query = text('select * from service_placementpoint')
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connection = conn.connect()
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connection = conn.connect()
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pts = pd.read_sql(query, connection)
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pts = pd.read_sql(query, connection)
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pts.loc[pts.target_dist > 700, 'target_dist'] = 700
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pts['geometry'] = pts['geometry'].apply(wkb.loads, hex=True)
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pts = pts.sort_values(by='id').reset_index(drop=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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feats = [
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'id', 'metro_dist', 'target_dist', 'property_price_bargains', 'property_price_offers',
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'id', 'metro_dist', 'target_dist', 'property_price_bargains', 'property_price_offers',
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@ -41,13 +48,53 @@ def raschet():
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'pharmacy_cnt', 'supermarket_cnt', 'supermarket_premium_cnt', 'clinic_cnt', 'bank_cnt', 'reca_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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'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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'rival_post_cnt',
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'business_activity', 'age_day', 'target_age_nearby_mean', 'target_cnt_ao_mean',
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'business_activity', 'age_day', 'target_cnt_ao_mean', 'target_dist1', 'target_dist2', 'target_dist3',
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# 'target_cnt_nearby_mean'
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]
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]
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# Записи для обучения
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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 = pts.loc[pts.sample_trn == True].reset_index(drop=True)
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# pts_trn = pts_trn.loc[pts_trn.fact < 450].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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X_trn = pts_trn[feats].drop(columns=['id'])
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Y_trn = pts_trn[['fact']]
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Y_trn = pts_trn[['fact']]
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@ -56,9 +103,56 @@ def raschet():
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(pts.status == 'Installation') |
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(pts.status == 'Installation') |
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(pts.status == 'Cancelled') |
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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.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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pts_inf['age_day'] = 240
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X_inf = pts_inf[feats]
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X_inf = pts_inf[feats]
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seeds = [39, 85, 15, 1, 59]
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seeds = [3, 99, 87, 21, 15]
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# Обучение, инференс
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# Обучение, инференс
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r2_scores = []
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r2_scores = []
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@ -79,12 +173,17 @@ def raschet():
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current_pred = sum(y_infers) / 5
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current_pred = sum(y_infers) / 5
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# Обновление полей по результатам работы модели
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# Обновление полей по результатам работы модели
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update_fields = pts_inf[['id', 'delta_current', 'delta_first', 'plan_current', 'plan_first', 'prediction_first']]
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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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update_fields = update_fields.join(
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pd.concat(
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pd.concat(
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[
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[
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X_inf[['id']],
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X_inf[['id']],
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pd.DataFrame([{'prediction_current': current_pred}]),
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pd.DataFrame({'prediction_current': current_pred}),
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],
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],
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axis=1,
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axis=1,
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).set_index('id'),
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).set_index('id'),
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@ -92,6 +191,30 @@ def raschet():
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)
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)
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update_fields['prediction_current'] = update_fields['prediction_current'].astype(int)
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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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|
)
|
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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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|
|
|
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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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|
# Загрузка в базу обновленных значений
|
|
|
|
conn2 = psycopg2.connect(
|
|
|
|
conn2 = psycopg2.connect(
|
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|
|
database=os.getenv('POSTGRES_DB', 'postgres'), user=os.getenv('POSTGRES_USER', 'postgres'),
|
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|
|
database=os.getenv('POSTGRES_DB', 'postgres'), user=os.getenv('POSTGRES_USER', 'postgres'),
|
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|
|
@ -100,17 +223,70 @@ def raschet():
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|
options='-c search_path=public',
|
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|
|
options='-c search_path=public',
|
|
|
|
)
|
|
|
|
)
|
|
|
|
cursor = conn2.cursor()
|
|
|
|
cursor = conn2.cursor()
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
# prediction_current
|
|
|
|
update_records1 = []
|
|
|
|
update_records1 = []
|
|
|
|
for i in range(0, len(update_fields)):
|
|
|
|
for i in range(0, len(update_fields)):
|
|
|
|
update_records1.append((int(update_fields.prediction_current[i]), int(update_fields.id[i])))
|
|
|
|
update_records1.append((int(update_fields.prediction_current[i]), int(update_fields.id[i])))
|
|
|
|
|
|
|
|
|
|
|
|
sql_update_query = """Update service_placementpoint set prediction_current = %s where id = %s"""
|
|
|
|
sql_update_query = """Update service_placementpoint set prediction_current = %s where id = %s"""
|
|
|
|
try:
|
|
|
|
try:
|
|
|
|
cursor.executemany(sql_update_query, update_records1)
|
|
|
|
psycopg2.extras.execute_batch(cursor, sql_update_query, update_records1)
|
|
|
|
|
|
|
|
conn2.commit()
|
|
|
|
|
|
|
|
except Exception:
|
|
|
|
|
|
|
|
cursor.execute('ROLLBACK')
|
|
|
|
|
|
|
|
psycopg2.extras.execute_batch(cursor, sql_update_query, update_records1)
|
|
|
|
|
|
|
|
conn2.commit()
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
# plan_first
|
|
|
|
|
|
|
|
update_records2 = []
|
|
|
|
|
|
|
|
for i in range(0, len(update_fields_working)):
|
|
|
|
|
|
|
|
update_records2.append((int(update_fields_working.plan_first[i]), int(update_fields_working.id[i])))
|
|
|
|
|
|
|
|
sql_update_query = """Update service_placementpoint set plan_first = %s where id = %s"""
|
|
|
|
|
|
|
|
try:
|
|
|
|
|
|
|
|
psycopg2.extras.execute_batch(cursor, sql_update_query, update_records2)
|
|
|
|
|
|
|
|
conn2.commit()
|
|
|
|
|
|
|
|
except Exception:
|
|
|
|
|
|
|
|
cursor.execute('ROLLBACK')
|
|
|
|
|
|
|
|
psycopg2.extras.execute_batch(cursor, sql_update_query, update_records2)
|
|
|
|
|
|
|
|
conn2.commit()
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
# plan_current
|
|
|
|
|
|
|
|
update_records3 = []
|
|
|
|
|
|
|
|
for i in range(0, len(update_fields_working)):
|
|
|
|
|
|
|
|
update_records3.append((int(update_fields_working.plan_current[i]), int(update_fields_working.id[i])))
|
|
|
|
|
|
|
|
sql_update_query = """Update service_placementpoint set plan_current = %s where id = %s"""
|
|
|
|
|
|
|
|
try:
|
|
|
|
|
|
|
|
psycopg2.extras.execute_batch(cursor, sql_update_query, update_records3)
|
|
|
|
|
|
|
|
conn2.commit()
|
|
|
|
|
|
|
|
except Exception:
|
|
|
|
|
|
|
|
cursor.execute('ROLLBACK')
|
|
|
|
|
|
|
|
psycopg2.extras.execute_batch(cursor, sql_update_query, update_records3)
|
|
|
|
|
|
|
|
conn2.commit()
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
# delta_first
|
|
|
|
|
|
|
|
update_records4 = []
|
|
|
|
|
|
|
|
for i in range(0, len(update_fields_working)):
|
|
|
|
|
|
|
|
update_records4.append((int(update_fields_working.delta_first[i]), int(update_fields_working.id[i])))
|
|
|
|
|
|
|
|
sql_update_query = """Update service_placementpoint set delta_first = %s where id = %s"""
|
|
|
|
|
|
|
|
try:
|
|
|
|
|
|
|
|
psycopg2.extras.execute_batch(cursor, sql_update_query, update_records4)
|
|
|
|
|
|
|
|
conn2.commit()
|
|
|
|
|
|
|
|
except Exception:
|
|
|
|
|
|
|
|
cursor.execute('ROLLBACK')
|
|
|
|
|
|
|
|
psycopg2.extras.execute_batch(cursor, sql_update_query, update_records4)
|
|
|
|
|
|
|
|
conn2.commit()
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
# delta_current
|
|
|
|
|
|
|
|
update_records5 = []
|
|
|
|
|
|
|
|
for i in range(0, len(update_fields_working)):
|
|
|
|
|
|
|
|
update_records5.append((int(update_fields_working.delta_current[i]), int(update_fields_working.id[i])))
|
|
|
|
|
|
|
|
sql_update_query = """Update service_placementpoint set delta_current = %s where id = %s"""
|
|
|
|
|
|
|
|
try:
|
|
|
|
|
|
|
|
psycopg2.extras.execute_batch(cursor, sql_update_query, update_records5)
|
|
|
|
conn2.commit()
|
|
|
|
conn2.commit()
|
|
|
|
except Exception:
|
|
|
|
except Exception:
|
|
|
|
cursor.execute('ROLLBACK')
|
|
|
|
cursor.execute('ROLLBACK')
|
|
|
|
cursor.executemany(sql_update_query, update_records1)
|
|
|
|
psycopg2.extras.execute_batch(cursor, sql_update_query, update_records5)
|
|
|
|
conn2.commit()
|
|
|
|
conn2.commit()
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|