From 3901a1e776c8f87e8408ab699754b91e1f80655c Mon Sep 17 00:00:00 2001
From: benjas <909336740@qq.com>
Date: Fri, 5 Feb 2021 13:39:47 +0800
Subject: [PATCH] Update. Feature engineering of Users and Commendities

---
 .../3-特征工程.ipynb                      | 162 ++++++++++--------
 1 file changed, 90 insertions(+), 72 deletions(-)

diff --git a/机器学习竞赛实战_优胜解决方案/京东用户购买意向预测/3-特征工程.ipynb b/机器学习竞赛实战_优胜解决方案/京东用户购买意向预测/3-特征工程.ipynb
index 5b04afa..035fe39 100644
--- a/机器学习竞赛实战_优胜解决方案/京东用户购买意向预测/3-特征工程.ipynb
+++ b/机器学习竞赛实战_优胜解决方案/京东用户购买意向预测/3-特征工程.ipynb
@@ -1021,7 +1021,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 19,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1030,7 +1030,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 20,
    "metadata": {},
    "outputs": [
     {
@@ -1103,7 +1103,7 @@
        "33  272629.0  10.0   6.0"
       ]
      },
-     "execution_count": 21,
+     "execution_count": 20,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1116,7 +1116,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 21,
    "metadata": {},
    "outputs": [
     {
@@ -1221,7 +1221,7 @@
        "200003.0 4.0        8.0       0.0       0.0       0.0       0.0      12.0"
       ]
      },
-     "execution_count": 22,
+     "execution_count": 21,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1235,7 +1235,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 22,
    "metadata": {},
    "outputs": [
     {
@@ -1466,7 +1466,7 @@
        "[5 rows x 48 columns]"
       ]
      },
-     "execution_count": 23,
+     "execution_count": 22,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1478,7 +1478,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 23,
    "metadata": {},
    "outputs": [
     {
@@ -1489,7 +1489,7 @@
        "           names=[None, 'cate'])"
       ]
      },
-     "execution_count": 24,
+     "execution_count": 23,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1500,7 +1500,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 24,
    "metadata": {},
    "outputs": [
     {
@@ -1511,7 +1511,7 @@
        "           names=['cate', None])"
       ]
      },
-     "execution_count": 25,
+     "execution_count": 24,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1523,7 +1523,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 25,
    "metadata": {},
    "outputs": [
     {
@@ -1540,7 +1540,7 @@
        "      dtype='object')"
       ]
      },
-     "execution_count": 26,
+     "execution_count": 25,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1552,7 +1552,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 26,
    "metadata": {},
    "outputs": [
     {
@@ -1573,7 +1573,7 @@
        "      dtype='object')"
       ]
      },
-     "execution_count": 27,
+     "execution_count": 26,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1598,7 +1598,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 27,
    "metadata": {},
    "outputs": [
     {
@@ -1839,7 +1839,7 @@
        "[5 rows x 49 columns]"
       ]
      },
-     "execution_count": 28,
+     "execution_count": 27,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1852,7 +1852,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 28,
    "metadata": {},
    "outputs": [
     {
@@ -2093,7 +2093,7 @@
        "[5 rows x 50 columns]"
       ]
      },
-     "execution_count": 29,
+     "execution_count": 28,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2109,7 +2109,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 29,
    "metadata": {},
    "outputs": [
     {
@@ -2358,7 +2358,7 @@
        "[5 rows x 51 columns]"
       ]
      },
-     "execution_count": 30,
+     "execution_count": 29,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2388,7 +2388,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": 30,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2451,7 +2451,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": 31,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2499,7 +2499,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": 32,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2517,7 +2517,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
+   "execution_count": 33,
    "metadata": {},
    "outputs": [
     {
@@ -2537,7 +2537,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
+   "execution_count": 34,
    "metadata": {},
    "outputs": [
     {
@@ -2634,7 +2634,7 @@
        "4  263587.0   72348.0  2016-01-31 23:59:08       NaN   1.0   5.0  159.0"
       ]
      },
-     "execution_count": 35,
+     "execution_count": 34,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2645,7 +2645,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
+   "execution_count": 35,
    "metadata": {},
    "outputs": [
     {
@@ -2673,7 +2673,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 37,
+   "execution_count": 36,
    "metadata": {},
    "outputs": [
     {
@@ -2682,7 +2682,7 @@
        "(50601736, 7)"
       ]
      },
-     "execution_count": 37,
+     "execution_count": 36,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2693,7 +2693,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 38,
+   "execution_count": 37,
    "metadata": {},
    "outputs": [
     {
@@ -2711,7 +2711,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 39,
+   "execution_count": 38,
    "metadata": {},
    "outputs": [
     {
@@ -2869,7 +2869,7 @@
        "4    0.0           0.0           0.0           0.0           1.0           0.0  "
       ]
      },
-     "execution_count": 39,
+     "execution_count": 38,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2880,7 +2880,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 40,
+   "execution_count": 39,
    "metadata": {},
    "outputs": [
     {
@@ -2898,7 +2898,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 41,
+   "execution_count": 40,
    "metadata": {},
    "outputs": [
     {
@@ -3038,7 +3038,7 @@
        "4     0     1  "
       ]
      },
-     "execution_count": 41,
+     "execution_count": 40,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3049,7 +3049,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 42,
+   "execution_count": 41,
    "metadata": {},
    "outputs": [
     {
@@ -3058,7 +3058,7 @@
        "datetime.datetime(2016, 3, 4, 0, 0)"
       ]
      },
-     "execution_count": 42,
+     "execution_count": 41,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3071,7 +3071,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 43,
+   "execution_count": 42,
    "metadata": {},
    "outputs": [
     {
@@ -3092,7 +3092,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 53,
+   "execution_count": 43,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -3159,7 +3159,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 54,
+   "execution_count": 44,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -3189,7 +3189,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 56,
+   "execution_count": 45,
    "metadata": {},
    "outputs": [
     {
@@ -3205,7 +3205,27 @@
       "get_accumulate_product_feat finsihed\n",
       "get_accumulate_cate_feat finsihed\n",
       "get_comments_product_feat finished\n",
-      "get labels\n",
+      "get labels\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "D:\\Anaconda3\\lib\\site-packages\\pandas\\core\\frame.py:6692: FutureWarning: Sorting because non-concatenation axis is not aligned. A future version\n",
+      "of pandas will change to not sort by default.\n",
+      "\n",
+      "To accept the future behavior, pass 'sort=False'.\n",
+      "\n",
+      "To retain the current behavior and silence the warning, pass 'sort=True'.\n",
+      "\n",
+      "  sort=sort)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
       "round 1/20 over!\n",
       "2016-02-02\n",
       "2016-02-05\n",
@@ -3396,7 +3416,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 60,
+   "execution_count": 46,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -3457,14 +3477,14 @@
     "    val_start_date = train_end_date\n",
     "    val_end_date = datetime.strptime(val_start_date, '%Y-%m-%d') + timedelta(days=5)\n",
     "    val_end_date = val_end_date.strftime('%Y-%m-%d')\n",
-    "    make_val_answer(val_start_date, val_end_date, all_actions, 'label_'+val_s1_path)\n",
+    "    make_val_answer(val_start_date, val_end_date, all_actions, val_s1_path)\n",
     "    \n",
     "    actions.to_csv(val_s1_path, index=False)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 61,
+   "execution_count": 47,
    "metadata": {},
    "outputs": [
     {
@@ -3480,22 +3500,6 @@
       "get_accumulate_cate_feat finsihed\n",
       "get_comments_product_feat finished\n"
      ]
-    },
-    {
-     "ename": "FileNotFoundError",
-     "evalue": "[Errno 2] No such file or directory: 'label_data/val_set.csv'",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[1;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
-      "\u001b[1;32m<ipython-input-61-e7de241c1782>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[1;31m# 验证集\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[0mmake_val_set\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'2016-02-23'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'2016-02-26'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'data/val_set.csv'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
-      "\u001b[1;32m<ipython-input-60-6f88f89bdf35>\u001b[0m in \u001b[0;36mmake_val_set\u001b[1;34m(train_start_date, train_end_date, val_s1_path)\u001b[0m\n\u001b[0;32m     56\u001b[0m     \u001b[0mval_end_date\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdatetime\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstrptime\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mval_start_date\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'%Y-%m-%d'\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m+\u001b[0m \u001b[0mtimedelta\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdays\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m5\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     57\u001b[0m     \u001b[0mval_end_date\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mval_end_date\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstrftime\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'%Y-%m-%d'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 58\u001b[1;33m     \u001b[0mmake_val_answer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mval_start_date\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mval_end_date\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mall_actions\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'label_'\u001b[0m\u001b[1;33m+\u001b[0m\u001b[0mval_s1_path\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     59\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     60\u001b[0m     \u001b[0mactions\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mto_csv\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mval_s1_path\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mindex\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mFalse\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
-      "\u001b[1;32m<ipython-input-60-6f88f89bdf35>\u001b[0m in \u001b[0;36mmake_val_answer\u001b[1;34m(val_start_date, val_end_date, all_actions, label_val_s1_path)\u001b[0m\n\u001b[0;32m      4\u001b[0m     \u001b[0mactions\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mactions\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'user_id'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'sku_id'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m     \u001b[0mactions\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mactions\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdrop_duplicates\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 6\u001b[1;33m     \u001b[0mactions\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mto_csv\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlabel_val_s1_path\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mindex\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mFalse\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      7\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      8\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mmake_val_set\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtrain_start_date\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtrain_end_date\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mval_s1_path\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
-      "\u001b[1;32mD:\\Anaconda3\\lib\\site-packages\\pandas\\core\\generic.py\u001b[0m in \u001b[0;36mto_csv\u001b[1;34m(self, path_or_buf, sep, na_rep, float_format, columns, header, index, index_label, mode, encoding, compression, quoting, quotechar, line_terminator, chunksize, tupleize_cols, date_format, doublequote, escapechar, decimal)\u001b[0m\n\u001b[0;32m   3018\u001b[0m                                  \u001b[0mdoublequote\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mdoublequote\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3019\u001b[0m                                  escapechar=escapechar, decimal=decimal)\n\u001b[1;32m-> 3020\u001b[1;33m         \u001b[0mformatter\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msave\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   3021\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3022\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mpath_or_buf\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
-      "\u001b[1;32mD:\\Anaconda3\\lib\\site-packages\\pandas\\io\\formats\\csvs.py\u001b[0m in \u001b[0;36msave\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    155\u001b[0m             f, handles = _get_handle(self.path_or_buf, self.mode,\n\u001b[0;32m    156\u001b[0m                                      \u001b[0mencoding\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mencoding\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 157\u001b[1;33m                                      compression=self.compression)\n\u001b[0m\u001b[0;32m    158\u001b[0m             \u001b[0mclose\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mTrue\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    159\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
-      "\u001b[1;32mD:\\Anaconda3\\lib\\site-packages\\pandas\\io\\common.py\u001b[0m in \u001b[0;36m_get_handle\u001b[1;34m(path_or_buf, mode, encoding, compression, memory_map, is_text)\u001b[0m\n\u001b[0;32m    422\u001b[0m         \u001b[1;32melif\u001b[0m \u001b[0mencoding\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    423\u001b[0m             \u001b[1;31m# Python 3 and encoding\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 424\u001b[1;33m             \u001b[0mf\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mopen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mpath_or_buf\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmode\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mencoding\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mencoding\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mnewline\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m\"\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    425\u001b[0m         \u001b[1;32melif\u001b[0m \u001b[0mis_text\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    426\u001b[0m             \u001b[1;31m# Python 3 and no explicit encoding\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
-      "\u001b[1;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: 'label_data/val_set.csv'"
-     ]
     }
    ],
    "source": [
@@ -3505,7 +3509,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 49,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -3513,22 +3517,22 @@
     "    start_days = datetime.strptime(train_end_date, '%Y-%m-%d') - timedelta(days=30)\n",
     "    start_days = start_days.strftime('%Y-%m-%d')\n",
     "    all_actions = get_all_action()\n",
-    "    print \"get all actions!\"\n",
+    "    print(\"get all actions!\")\n",
     "    user = get_basic_user_feat()\n",
-    "    print 'get_basic_user_feat finsihed'\n",
+    "    print('get_basic_user_feat finsihed')\n",
     "    product = get_basic_product_feat()\n",
-    "    print 'get_basic_product_feat finsihed'\n",
+    "    print('get_basic_product_feat finsihed')\n",
     "    \n",
     "    user_acc = get_recent_user_feat(train_end_date, all_actions)\n",
-    "    print 'get_accumulate_user_feat finsihed'\n",
+    "    print('get_accumulate_user_feat finsihed')\n",
     "    \n",
     "    user_cate = get_user_cate_feature(train_start_date, train_end_date, all_actions)\n",
-    "    print 'get_user_cate_feature finished'\n",
+    "    print('get_user_cate_feature finished')\n",
     "    \n",
     "    product_acc = get_accumulate_product_feat(start_days, train_end_date, all_actions)\n",
-    "    print 'get_accumulate_product_feat finsihed'\n",
+    "    print('get_accumulate_product_feat finsihed')\n",
     "    cate_acc = get_accumulate_cate_feat(start_days, train_end_date, all_actions)\n",
-    "    print 'get_accumulate_cate_feat finsihed'\n",
+    "    print('get_accumulate_cate_feat finsihed')\n",
     "    comment_acc = get_comments_product_feat(train_end_date)\n",
     "\n",
     "    actions = None\n",
@@ -3559,9 +3563,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 50,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "get all actions!\n",
+      "get_basic_user_feat finsihed\n",
+      "get_basic_product_feat finsihed\n",
+      "get_accumulate_user_feat finsihed\n",
+      "get_user_cate_feature finished\n",
+      "get_accumulate_product_feat finsihed\n",
+      "get_accumulate_cate_feat finsihed\n"
+     ]
+    }
+   ],
    "source": [
     "# 预测结果\n",
     "sub_start_date = '2016-04-13'\n",