|
|
|
|
@ -37,195 +37,197 @@ def raschet():
|
|
|
|
|
)
|
|
|
|
|
except:
|
|
|
|
|
log_to_telegram('error connect to db')
|
|
|
|
|
try:
|
|
|
|
|
query = text('select * from service_placementpoint')
|
|
|
|
|
connection = conn.connect()
|
|
|
|
|
pts = pd.read_sql(query, connection)
|
|
|
|
|
pts['geometry'] = pts['geometry'].apply(wkb.loads, hex=True)
|
|
|
|
|
pts = gpd.GeoDataFrame(pts, geometry='geometry', crs='epsg:4326')
|
|
|
|
|
pts = pts.to_crs('epsg:32637')
|
|
|
|
|
pts = pts.rename(
|
|
|
|
|
columns={
|
|
|
|
|
'target_cnt_nearby_mean': 'target_dist1',
|
|
|
|
|
'target_age_nearby_mean': 'target_dist2',
|
|
|
|
|
'yndxfood_cnt_cst': 'target_dist3',
|
|
|
|
|
},
|
|
|
|
|
)
|
|
|
|
|
|
|
|
|
|
query = text('select * from service_placementpoint')
|
|
|
|
|
connection = conn.connect()
|
|
|
|
|
pts = pd.read_sql(query, connection)
|
|
|
|
|
pts['geometry'] = pts['geometry'].apply(wkb.loads, hex=True)
|
|
|
|
|
pts = gpd.GeoDataFrame(pts, geometry='geometry', crs='epsg:4326')
|
|
|
|
|
pts = pts.to_crs('epsg:32637')
|
|
|
|
|
pts = pts.rename(
|
|
|
|
|
columns={
|
|
|
|
|
'target_cnt_nearby_mean': 'target_dist1',
|
|
|
|
|
'target_age_nearby_mean': 'target_dist2',
|
|
|
|
|
'yndxfood_cnt_cst': 'target_dist3',
|
|
|
|
|
},
|
|
|
|
|
)
|
|
|
|
|
|
|
|
|
|
feats = [
|
|
|
|
|
'id', 'metro_dist', 'target_dist', 'property_price_bargains', 'property_price_offers',
|
|
|
|
|
'property_mean_floor',
|
|
|
|
|
'property_era', 'flats_cnt_2', 'flats_cnt', 'popul_home', 'popul_job', 'other_post_cnt', 'yndxfood_sum',
|
|
|
|
|
'yndxfood_cnt', 'school_cnt', 'kindergar_cnt', 'target_post_cnt', 'public_stop_cnt', 'sport_center_cnt',
|
|
|
|
|
'pharmacy_cnt', 'supermarket_cnt', 'supermarket_premium_cnt', 'clinic_cnt', 'bank_cnt', 'reca_cnt',
|
|
|
|
|
'lab_cnt', 'culture_cnt', 'attraction_cnt', 'mfc_cnt', 'bc_cnt', 'tc_cnt', 'rival_pvz_cnt',
|
|
|
|
|
'rival_post_cnt',
|
|
|
|
|
'business_activity', 'age_day', 'target_cnt_ao_mean', 'target_dist1', 'target_dist2', 'target_dist3',
|
|
|
|
|
]
|
|
|
|
|
|
|
|
|
|
# Записи для обучения
|
|
|
|
|
pts_trn = pts.loc[pts.sample_trn == True].reset_index(drop=True)
|
|
|
|
|
pts_trn = gpd.GeoDataFrame(pts_trn, geometry='geometry', crs='epsg:32637')
|
|
|
|
|
pts_target = pts_trn[['geometry']]
|
|
|
|
|
pts_target['cnt'] = 1
|
|
|
|
|
pts_target = gpd.GeoDataFrame(pts_target, geometry='geometry', crs='epsg:32637')
|
|
|
|
|
|
|
|
|
|
target_feature_coords = []
|
|
|
|
|
for i in range(0, len(pts_target)):
|
|
|
|
|
target_feature_coords.append((pts_target.geometry.x[i], pts_target.geometry.y[i]))
|
|
|
|
|
target_feature_coords = np.array(target_feature_coords)
|
|
|
|
|
|
|
|
|
|
pts_trn['target_dist'] = pts_trn.apply(
|
|
|
|
|
lambda x: ((sorted(distance.cdist([[x['geometry'].x, x['geometry'].y]], target_feature_coords)[0])[1])),
|
|
|
|
|
axis=1,
|
|
|
|
|
)
|
|
|
|
|
pts_trn.loc[pts_trn.target_dist > 700, 'target_dist'] = 700
|
|
|
|
|
|
|
|
|
|
pts_trn['target_dist1'] = pts_trn.apply(
|
|
|
|
|
lambda x: ((sorted(distance.cdist([[x['geometry'].x, x['geometry'].y]], target_feature_coords)[0])[2])),
|
|
|
|
|
axis=1,
|
|
|
|
|
)
|
|
|
|
|
pts_trn.loc[pts_trn.target_dist1 > 700, 'target_dist1'] = 700
|
|
|
|
|
|
|
|
|
|
pts_trn['target_dist2'] = pts_trn.apply(
|
|
|
|
|
lambda x: ((sorted(distance.cdist([[x['geometry'].x, x['geometry'].y]], target_feature_coords)[0])[3])),
|
|
|
|
|
axis=1,
|
|
|
|
|
)
|
|
|
|
|
pts_trn.loc[pts_trn.target_dist2 > 700, 'target_dist2'] = 700
|
|
|
|
|
|
|
|
|
|
pts_trn['target_dist3'] = pts_trn.apply(
|
|
|
|
|
lambda x: ((sorted(distance.cdist([[x['geometry'].x, x['geometry'].y]], target_feature_coords)[0])[4])),
|
|
|
|
|
axis=1,
|
|
|
|
|
)
|
|
|
|
|
pts_trn.loc[pts_trn.target_dist3 > 700, 'target_dist3'] = 700
|
|
|
|
|
|
|
|
|
|
pts_trn['buf'] = pts_trn.buffer(500)
|
|
|
|
|
pts_trn = gpd.GeoDataFrame(pts_trn, geometry='buf', crs='epsg:32637')
|
|
|
|
|
target_post = gpd.sjoin(pts_trn, pts_target, op='contains').groupby('id', as_index=False).agg({'cnt': 'count'})
|
|
|
|
|
target_post = target_post.rename(columns={'cnt': 'target_post_cnt'})
|
|
|
|
|
pts_trn = pts_trn.drop(columns=['target_post_cnt'])
|
|
|
|
|
pts_trn = pts_trn.join(target_post.set_index('id'), on='id')
|
|
|
|
|
pts_trn['target_post_cnt'] = pts_trn['target_post_cnt'] - 1
|
|
|
|
|
pts_trn = pts_trn.sort_values(by='id').reset_index(drop=True)
|
|
|
|
|
X_trn = pts_trn[feats].drop(columns=['id'])
|
|
|
|
|
Y_trn = pts_trn[['fact']]
|
|
|
|
|
|
|
|
|
|
# Записи для инференса
|
|
|
|
|
pts_inf = pts.loc[(pts.status == 'Pending') |
|
|
|
|
|
(pts.status == 'Installation') |
|
|
|
|
|
(pts.status == 'Cancelled') |
|
|
|
|
|
((pts.status == 'Working') & (pts.sample_trn == False))].reset_index(drop=True)
|
|
|
|
|
pts_inf = gpd.GeoDataFrame(pts_inf, geometry='geometry', crs='epsg:32637')
|
|
|
|
|
|
|
|
|
|
pts_inf['buf'] = pts_inf.buffer(500)
|
|
|
|
|
pts_inf = gpd.GeoDataFrame(pts_inf, geometry='buf', crs='epsg:32637')
|
|
|
|
|
pts_target = pts.loc[(pts.status == 'Working') |
|
|
|
|
|
(pts.status == 'Installation') |
|
|
|
|
|
(pts.sample_trn == True)].reset_index(drop=True)
|
|
|
|
|
pts_target = pts_target[['geometry']]
|
|
|
|
|
pts_target['cnt'] = 1
|
|
|
|
|
pts_target = gpd.GeoDataFrame(pts_target, geometry='geometry', crs='epsg:32637')
|
|
|
|
|
|
|
|
|
|
target_feature_coords = []
|
|
|
|
|
for i in range(0, len(pts_target)):
|
|
|
|
|
target_feature_coords.append((pts_target.geometry.x[i], pts_target.geometry.y[i]))
|
|
|
|
|
target_feature_coords = np.array(target_feature_coords)
|
|
|
|
|
|
|
|
|
|
pts_inf['target_dist'] = pts_inf.apply(
|
|
|
|
|
lambda x: ((sorted(distance.cdist([[x['geometry'].x, x['geometry'].y]], target_feature_coords)[0])[0])),
|
|
|
|
|
axis=1,
|
|
|
|
|
)
|
|
|
|
|
pts_inf.loc[pts_inf.target_dist > 700, 'target_dist'] = 700
|
|
|
|
|
|
|
|
|
|
pts_inf['target_dist1'] = pts_inf.apply(
|
|
|
|
|
lambda x: ((sorted(distance.cdist([[x['geometry'].x, x['geometry'].y]], target_feature_coords)[0])[1])),
|
|
|
|
|
axis=1,
|
|
|
|
|
)
|
|
|
|
|
pts_inf.loc[pts_inf.target_dist1 > 700, 'target_dist1'] = 700
|
|
|
|
|
|
|
|
|
|
pts_inf['target_dist2'] = pts_inf.apply(
|
|
|
|
|
lambda x: ((sorted(distance.cdist([[x['geometry'].x, x['geometry'].y]], target_feature_coords)[0])[2])),
|
|
|
|
|
axis=1,
|
|
|
|
|
)
|
|
|
|
|
pts_inf.loc[pts_inf.target_dist2 > 700, 'target_dist2'] = 700
|
|
|
|
|
|
|
|
|
|
pts_inf['target_dist3'] = pts_inf.apply(
|
|
|
|
|
lambda x: ((sorted(distance.cdist([[x['geometry'].x, x['geometry'].y]], target_feature_coords)[0])[3])),
|
|
|
|
|
axis=1,
|
|
|
|
|
)
|
|
|
|
|
pts_inf.loc[pts_inf.target_dist3 > 700, 'target_dist3'] = 700
|
|
|
|
|
|
|
|
|
|
pts_inf = pts_inf.sort_values(by='id').reset_index(drop=True)
|
|
|
|
|
target_post = gpd.sjoin(pts_inf, pts_target, op='contains').groupby('id', as_index=False).agg({'cnt': 'count'})
|
|
|
|
|
target_post = target_post.rename(columns={'cnt': 'target_post_cnt'})
|
|
|
|
|
pts_inf = pts_inf.drop(columns=['target_post_cnt'])
|
|
|
|
|
pts_inf = pts_inf.join(target_post.set_index('id'), on='id')
|
|
|
|
|
pts_inf['age_day_init'] = pts_inf['age_day']
|
|
|
|
|
pts_inf['age_day'] = 240
|
|
|
|
|
X_inf = pts_inf[feats]
|
|
|
|
|
|
|
|
|
|
seeds = [3, 99, 87, 21, 15]
|
|
|
|
|
|
|
|
|
|
# Обучение, инференс
|
|
|
|
|
r2_scores = []
|
|
|
|
|
mapes = []
|
|
|
|
|
y_infers = []
|
|
|
|
|
|
|
|
|
|
for i in seeds:
|
|
|
|
|
x_trn, x_test, y_trn, y_test = ms.train_test_split(X_trn, Y_trn, test_size=0.2, random_state=i)
|
|
|
|
|
model = catboost.CatBoostRegressor(cat_features=['property_era'], random_state=i)
|
|
|
|
|
model.fit(x_trn, y_trn, verbose=False)
|
|
|
|
|
r2_score = metrics.r2_score(y_test, model.predict(x_test))
|
|
|
|
|
mape = metrics.mean_absolute_percentage_error(y_test, model.predict(x_test))
|
|
|
|
|
if ((r2_score > 0.45) & (mape < 0.25)):
|
|
|
|
|
r2_scores.append(r2_score)
|
|
|
|
|
mapes.append(mape)
|
|
|
|
|
y_infers.append(model.predict(X_inf.drop(columns=['id'])))
|
|
|
|
|
|
|
|
|
|
current_pred = sum(y_infers) / 5
|
|
|
|
|
|
|
|
|
|
# Обновление полей по результатам работы модели
|
|
|
|
|
update_fields = pts_inf[
|
|
|
|
|
[
|
|
|
|
|
'id', 'age_day_init', 'status', 'fact', 'delta_current', 'delta_first', 'plan_current', 'plan_first',
|
|
|
|
|
'prediction_first',
|
|
|
|
|
feats = [
|
|
|
|
|
'id', 'metro_dist', 'target_dist', 'property_price_bargains', 'property_price_offers',
|
|
|
|
|
'property_mean_floor',
|
|
|
|
|
'property_era', 'flats_cnt_2', 'flats_cnt', 'popul_home', 'popul_job', 'other_post_cnt', 'yndxfood_sum',
|
|
|
|
|
'yndxfood_cnt', 'school_cnt', 'kindergar_cnt', 'target_post_cnt', 'public_stop_cnt', 'sport_center_cnt',
|
|
|
|
|
'pharmacy_cnt', 'supermarket_cnt', 'supermarket_premium_cnt', 'clinic_cnt', 'bank_cnt', 'reca_cnt',
|
|
|
|
|
'lab_cnt', 'culture_cnt', 'attraction_cnt', 'mfc_cnt', 'bc_cnt', 'tc_cnt', 'rival_pvz_cnt',
|
|
|
|
|
'rival_post_cnt',
|
|
|
|
|
'business_activity', 'age_day', 'target_cnt_ao_mean', 'target_dist1', 'target_dist2', 'target_dist3',
|
|
|
|
|
]
|
|
|
|
|
]
|
|
|
|
|
update_fields = update_fields.join(
|
|
|
|
|
pd.concat(
|
|
|
|
|
|
|
|
|
|
# Записи для обучения
|
|
|
|
|
pts_trn = pts.loc[pts.sample_trn == True].reset_index(drop=True)
|
|
|
|
|
pts_trn = gpd.GeoDataFrame(pts_trn, geometry='geometry', crs='epsg:32637')
|
|
|
|
|
pts_target = pts_trn[['geometry']]
|
|
|
|
|
pts_target['cnt'] = 1
|
|
|
|
|
pts_target = gpd.GeoDataFrame(pts_target, geometry='geometry', crs='epsg:32637')
|
|
|
|
|
|
|
|
|
|
target_feature_coords = []
|
|
|
|
|
for i in range(0, len(pts_target)):
|
|
|
|
|
target_feature_coords.append((pts_target.geometry.x[i], pts_target.geometry.y[i]))
|
|
|
|
|
target_feature_coords = np.array(target_feature_coords)
|
|
|
|
|
|
|
|
|
|
pts_trn['target_dist'] = pts_trn.apply(
|
|
|
|
|
lambda x: ((sorted(distance.cdist([[x['geometry'].x, x['geometry'].y]], target_feature_coords)[0])[1])),
|
|
|
|
|
axis=1,
|
|
|
|
|
)
|
|
|
|
|
pts_trn.loc[pts_trn.target_dist > 700, 'target_dist'] = 700
|
|
|
|
|
|
|
|
|
|
pts_trn['target_dist1'] = pts_trn.apply(
|
|
|
|
|
lambda x: ((sorted(distance.cdist([[x['geometry'].x, x['geometry'].y]], target_feature_coords)[0])[2])),
|
|
|
|
|
axis=1,
|
|
|
|
|
)
|
|
|
|
|
pts_trn.loc[pts_trn.target_dist1 > 700, 'target_dist1'] = 700
|
|
|
|
|
|
|
|
|
|
pts_trn['target_dist2'] = pts_trn.apply(
|
|
|
|
|
lambda x: ((sorted(distance.cdist([[x['geometry'].x, x['geometry'].y]], target_feature_coords)[0])[3])),
|
|
|
|
|
axis=1,
|
|
|
|
|
)
|
|
|
|
|
pts_trn.loc[pts_trn.target_dist2 > 700, 'target_dist2'] = 700
|
|
|
|
|
|
|
|
|
|
pts_trn['target_dist3'] = pts_trn.apply(
|
|
|
|
|
lambda x: ((sorted(distance.cdist([[x['geometry'].x, x['geometry'].y]], target_feature_coords)[0])[4])),
|
|
|
|
|
axis=1,
|
|
|
|
|
)
|
|
|
|
|
pts_trn.loc[pts_trn.target_dist3 > 700, 'target_dist3'] = 700
|
|
|
|
|
|
|
|
|
|
pts_trn['buf'] = pts_trn.buffer(500)
|
|
|
|
|
pts_trn = gpd.GeoDataFrame(pts_trn, geometry='buf', crs='epsg:32637')
|
|
|
|
|
target_post = gpd.sjoin(pts_trn, pts_target, op='contains').groupby('id', as_index=False).agg({'cnt': 'count'})
|
|
|
|
|
target_post = target_post.rename(columns={'cnt': 'target_post_cnt'})
|
|
|
|
|
pts_trn = pts_trn.drop(columns=['target_post_cnt'])
|
|
|
|
|
pts_trn = pts_trn.join(target_post.set_index('id'), on='id')
|
|
|
|
|
pts_trn['target_post_cnt'] = pts_trn['target_post_cnt'] - 1
|
|
|
|
|
pts_trn = pts_trn.sort_values(by='id').reset_index(drop=True)
|
|
|
|
|
X_trn = pts_trn[feats].drop(columns=['id'])
|
|
|
|
|
Y_trn = pts_trn[['fact']]
|
|
|
|
|
|
|
|
|
|
# Записи для инференса
|
|
|
|
|
pts_inf = pts.loc[(pts.status == 'Pending') |
|
|
|
|
|
(pts.status == 'Installation') |
|
|
|
|
|
(pts.status == 'Cancelled') |
|
|
|
|
|
((pts.status == 'Working') & (pts.sample_trn == False))].reset_index(drop=True)
|
|
|
|
|
pts_inf = gpd.GeoDataFrame(pts_inf, geometry='geometry', crs='epsg:32637')
|
|
|
|
|
|
|
|
|
|
pts_inf['buf'] = pts_inf.buffer(500)
|
|
|
|
|
pts_inf = gpd.GeoDataFrame(pts_inf, geometry='buf', crs='epsg:32637')
|
|
|
|
|
pts_target = pts.loc[(pts.status == 'Working') |
|
|
|
|
|
(pts.status == 'Installation') |
|
|
|
|
|
(pts.sample_trn == True)].reset_index(drop=True)
|
|
|
|
|
pts_target = pts_target[['geometry']]
|
|
|
|
|
pts_target['cnt'] = 1
|
|
|
|
|
pts_target = gpd.GeoDataFrame(pts_target, geometry='geometry', crs='epsg:32637')
|
|
|
|
|
|
|
|
|
|
target_feature_coords = []
|
|
|
|
|
for i in range(0, len(pts_target)):
|
|
|
|
|
target_feature_coords.append((pts_target.geometry.x[i], pts_target.geometry.y[i]))
|
|
|
|
|
target_feature_coords = np.array(target_feature_coords)
|
|
|
|
|
|
|
|
|
|
pts_inf['target_dist'] = pts_inf.apply(
|
|
|
|
|
lambda x: ((sorted(distance.cdist([[x['geometry'].x, x['geometry'].y]], target_feature_coords)[0])[0])),
|
|
|
|
|
axis=1,
|
|
|
|
|
)
|
|
|
|
|
pts_inf.loc[pts_inf.target_dist > 700, 'target_dist'] = 700
|
|
|
|
|
|
|
|
|
|
pts_inf['target_dist1'] = pts_inf.apply(
|
|
|
|
|
lambda x: ((sorted(distance.cdist([[x['geometry'].x, x['geometry'].y]], target_feature_coords)[0])[1])),
|
|
|
|
|
axis=1,
|
|
|
|
|
)
|
|
|
|
|
pts_inf.loc[pts_inf.target_dist1 > 700, 'target_dist1'] = 700
|
|
|
|
|
|
|
|
|
|
pts_inf['target_dist2'] = pts_inf.apply(
|
|
|
|
|
lambda x: ((sorted(distance.cdist([[x['geometry'].x, x['geometry'].y]], target_feature_coords)[0])[2])),
|
|
|
|
|
axis=1,
|
|
|
|
|
)
|
|
|
|
|
pts_inf.loc[pts_inf.target_dist2 > 700, 'target_dist2'] = 700
|
|
|
|
|
|
|
|
|
|
pts_inf['target_dist3'] = pts_inf.apply(
|
|
|
|
|
lambda x: ((sorted(distance.cdist([[x['geometry'].x, x['geometry'].y]], target_feature_coords)[0])[3])),
|
|
|
|
|
axis=1,
|
|
|
|
|
)
|
|
|
|
|
pts_inf.loc[pts_inf.target_dist3 > 700, 'target_dist3'] = 700
|
|
|
|
|
|
|
|
|
|
pts_inf = pts_inf.sort_values(by='id').reset_index(drop=True)
|
|
|
|
|
target_post = gpd.sjoin(pts_inf, pts_target, op='contains').groupby('id', as_index=False).agg({'cnt': 'count'})
|
|
|
|
|
target_post = target_post.rename(columns={'cnt': 'target_post_cnt'})
|
|
|
|
|
pts_inf = pts_inf.drop(columns=['target_post_cnt'])
|
|
|
|
|
pts_inf = pts_inf.join(target_post.set_index('id'), on='id')
|
|
|
|
|
pts_inf['age_day_init'] = pts_inf['age_day']
|
|
|
|
|
pts_inf['age_day'] = 240
|
|
|
|
|
X_inf = pts_inf[feats]
|
|
|
|
|
|
|
|
|
|
seeds = [3, 99, 87, 21, 15]
|
|
|
|
|
|
|
|
|
|
# Обучение, инференс
|
|
|
|
|
r2_scores = []
|
|
|
|
|
mapes = []
|
|
|
|
|
y_infers = []
|
|
|
|
|
|
|
|
|
|
for i in seeds:
|
|
|
|
|
x_trn, x_test, y_trn, y_test = ms.train_test_split(X_trn, Y_trn, test_size=0.2, random_state=i)
|
|
|
|
|
model = catboost.CatBoostRegressor(cat_features=['property_era'], random_state=i)
|
|
|
|
|
model.fit(x_trn, y_trn, verbose=False)
|
|
|
|
|
r2_score = metrics.r2_score(y_test, model.predict(x_test))
|
|
|
|
|
mape = metrics.mean_absolute_percentage_error(y_test, model.predict(x_test))
|
|
|
|
|
if ((r2_score > 0.45) & (mape < 0.25)):
|
|
|
|
|
r2_scores.append(r2_score)
|
|
|
|
|
mapes.append(mape)
|
|
|
|
|
y_infers.append(model.predict(X_inf.drop(columns=['id'])))
|
|
|
|
|
|
|
|
|
|
current_pred = sum(y_infers) / 5
|
|
|
|
|
|
|
|
|
|
# Обновление полей по результатам работы модели
|
|
|
|
|
update_fields = pts_inf[
|
|
|
|
|
[
|
|
|
|
|
X_inf[['id']],
|
|
|
|
|
pd.DataFrame({'prediction_current': current_pred}),
|
|
|
|
|
],
|
|
|
|
|
'id', 'age_day_init', 'status', 'fact', 'delta_current', 'delta_first', 'plan_current', 'plan_first',
|
|
|
|
|
'prediction_first',
|
|
|
|
|
]
|
|
|
|
|
]
|
|
|
|
|
update_fields = update_fields.join(
|
|
|
|
|
pd.concat(
|
|
|
|
|
[
|
|
|
|
|
X_inf[['id']],
|
|
|
|
|
pd.DataFrame({'prediction_current': current_pred}),
|
|
|
|
|
],
|
|
|
|
|
axis=1,
|
|
|
|
|
).set_index('id'),
|
|
|
|
|
on='id',
|
|
|
|
|
)
|
|
|
|
|
update_fields['prediction_current'] = update_fields['prediction_current'].astype(int)
|
|
|
|
|
|
|
|
|
|
days_x = np.array([0, 30, 60, 90, 120, 150, 180, 210, 240, 270])
|
|
|
|
|
perc_y = np.array([0, 0.15, 0.20, 0.30, 0.60, 0.70, 0.70, 0.75, 0.75, 0.80])
|
|
|
|
|
spl = interpolate.splrep(days_x, perc_y)
|
|
|
|
|
|
|
|
|
|
update_fields['plan_first'] = update_fields.apply(
|
|
|
|
|
lambda x: (x.prediction_first * interpolate.splev(x.age_day_init, spl) if x.status == 'Working' else 0),
|
|
|
|
|
axis=1,
|
|
|
|
|
)
|
|
|
|
|
update_fields['plan_current'] = update_fields.apply(
|
|
|
|
|
lambda x: (x.prediction_current * interpolate.splev(x.age_day_init, spl) if x.status == 'Working' else 0),
|
|
|
|
|
axis=1,
|
|
|
|
|
)
|
|
|
|
|
update_fields['delta_first'] = update_fields.apply(
|
|
|
|
|
lambda x: ((x.fact - x.plan_first) / x.plan_first * 100 if x.status == 'Working' else 0),
|
|
|
|
|
axis=1,
|
|
|
|
|
).set_index('id'),
|
|
|
|
|
on='id',
|
|
|
|
|
)
|
|
|
|
|
update_fields['prediction_current'] = update_fields['prediction_current'].astype(int)
|
|
|
|
|
|
|
|
|
|
days_x = np.array([0, 30, 60, 90, 120, 150, 180, 210, 240, 270])
|
|
|
|
|
perc_y = np.array([0, 0.15, 0.20, 0.30, 0.60, 0.70, 0.70, 0.75, 0.75, 0.80])
|
|
|
|
|
spl = interpolate.splrep(days_x, perc_y)
|
|
|
|
|
|
|
|
|
|
update_fields['plan_first'] = update_fields.apply(
|
|
|
|
|
lambda x: (x.prediction_first * interpolate.splev(x.age_day_init, spl) if x.status == 'Working' else 0),
|
|
|
|
|
axis=1,
|
|
|
|
|
)
|
|
|
|
|
update_fields['plan_current'] = update_fields.apply(
|
|
|
|
|
lambda x: (x.prediction_current * interpolate.splev(x.age_day_init, spl) if x.status == 'Working' else 0),
|
|
|
|
|
axis=1,
|
|
|
|
|
)
|
|
|
|
|
update_fields['delta_first'] = update_fields.apply(
|
|
|
|
|
lambda x: ((x.fact - x.plan_first) / x.plan_first * 100 if x.status == 'Working' else 0),
|
|
|
|
|
axis=1,
|
|
|
|
|
)
|
|
|
|
|
update_fields['delta_current'] = update_fields.apply(
|
|
|
|
|
lambda x: ((x.fact - x.plan_current) / x.plan_current * 100 if x.status == 'Working' else 0),
|
|
|
|
|
axis=1,
|
|
|
|
|
)
|
|
|
|
|
|
|
|
|
|
update_fields_working = update_fields.loc[update_fields.status == 'Working'].reset_index(drop=True)
|
|
|
|
|
update_fields_working = update_fields_working.fillna(0)
|
|
|
|
|
)
|
|
|
|
|
update_fields['delta_current'] = update_fields.apply(
|
|
|
|
|
lambda x: ((x.fact - x.plan_current) / x.plan_current * 100 if x.status == 'Working' else 0),
|
|
|
|
|
axis=1,
|
|
|
|
|
)
|
|
|
|
|
|
|
|
|
|
update_fields_working = update_fields.loc[update_fields.status == 'Working'].reset_index(drop=True)
|
|
|
|
|
update_fields_working = update_fields_working.fillna(0)
|
|
|
|
|
except Exception as e:
|
|
|
|
|
log_to_telegram(f'Ошибка при обновлении полей в базе данных: {e}')
|
|
|
|
|
log_to_telegram('Начинается обновление полей в базе')
|
|
|
|
|
# Загрузка в базу обновленных значений
|
|
|
|
|
try:
|
|
|
|
|
|
timofejmalinin
3 years ago