new_ml_func

service/tasks.py

@@ -20,6 +20,7 @@ from service.models import PlacementPoint
 # Запустить scheduler
 # celery -A postamates beat -l INFO.
 
+
 @shared_task()
 def raschet():
     conn = sqlalchemy.create_engine(
@@ -27,11 +28,26 @@ def raschet():
         connect_args={'options': '-csearch_path=public'},
     )
     query = text('select * from service_placementpoint')
-    pts = pd.read_sql(query, conn.connect())
-    feats = ['id', 'popul_home', 'popul_job', 'other_post_cnt', 'yndxfood_sum', 'target_post_cnt']
+    connection = conn.connect()
+    pts = pd.read_sql(query, connection)
+    pts.loc[pts.target_dist > 700, 'target_dist'] = 700
+    pts = pts.sort_values(by='id').reset_index(drop=True)
+
+    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_age_nearby_mean', 'target_cnt_ao_mean',
+        # 'target_cnt_nearby_mean'
+    ]
 
     # Записи для обучения
     pts_trn = pts.loc[pts.sample_trn == True].reset_index(drop=True)
+    # pts_trn = pts_trn.loc[pts_trn.fact < 450].reset_index(drop=True)
     X_trn = pts_trn[feats].drop(columns=['id'])
     Y_trn = pts_trn[['fact']]
 
@@ -39,24 +55,27 @@ def raschet():
     pts_inf = pts.loc[(pts.status == 'Pending') |
                       (pts.status == 'Installation') |
                       (pts.status == 'Cancelled') |
-                      ((pts.status == 'Pending') & (pts.sample_trn == False))].reset_index(drop=True)
+                      ((pts.status == 'Working') & (pts.sample_trn == False))].reset_index(drop=True)
+    pts_inf['age_day'] = 240
     X_inf = pts_inf[feats]
+    seeds = [39, 85, 15, 1, 59]
 
     # Обучение, инференс
     r2_scores = []
     mapes = []
     y_infers = []
 
-    while len(r2_scores) < 5:
-        x_trn, x_test, y_trn, y_test = ms.train_test_split(X_trn, Y_trn, test_size=0.2)
-        model = catboost.CatBoostRegressor(cat_features=[])
+    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) & (mape < 0.5)):
+        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
@@ -65,7 +84,7 @@ def raschet():
         pd.concat(
             [
                 X_inf[['id']],
-                pd.DataFrame({'prediction_current': current_pred}),
+                pd.DataFrame([{'prediction_current': current_pred}]),
             ],
             axis=1,
         ).set_index('id'),
@@ -86,6 +105,11 @@ def raschet():
         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"""
+    try:
+        cursor.executemany(sql_update_query, update_records1)
+        conn2.commit()
+    except Exception:
+        cursor.execute('ROLLBACK')
         cursor.executemany(sql_update_query, update_records1)
         conn2.commit()