Make db a parameter of serialize() (#208)

Previously, `db` is an attribute of class `Expr` and its value
cannot be decided when init a new object. As a result, when the
`Expr` is serialized, we need to explicitly bind it to a db to
create functions or types. However, in some cases we forgot to
do that, which lead to errros.

To avoid forgotting this, this patch moves db to a parameter of
`serialize()` and pass db to it each time we call it.

This patch fixes the following bugs in `serialize()`:

1. Database is not found when trying to create a function or data
   type.
2. Star notation `*` is not serialized correctly in column fileds
   when expanding a composite type result.
3. `count(*)` returns wrong result when dataframe contains `None`.

---------

Co-authored-by: Ruxue Zeng <[email protected]>

doc/source/notebooks/abalone.ipynb

@@ -504,6 +504,7 @@
     "import numpy as np\n",
     "import pickle\n",
     "\n",
+    "\n",
     "@gp.create_column_function\n",
     "def linreg_func(length: List[float], shucked_weight: List[float], rings: List[int]) -> LinregType:\n",
     "    X = np.array([length, shucked_weight]).T\n",
@@ -560,9 +561,8 @@
     "#    ) a\n",
     "# ) DISTRIBUTED BY (sex);\n",
     "\n",
-    "linreg_fitted = (\n",
-    "    abalone_train.group_by(\"sex\")\n",
-    "    .apply(lambda t: linreg_func(t[\"length\"], t[\"shucked_weight\"], t[\"rings\"]), expand=True)\n",
+    "linreg_fitted = abalone_train.group_by(\"sex\").apply(\n",
+    "    lambda t: linreg_func(t[\"length\"], t[\"shucked_weight\"], t[\"rings\"]), expand=True\n",
     ")"
    ]
   },
@@ -800,7 +800,7 @@
     "linreg_test_fit = linreg_fitted.inner_join(\n",
     "    abalone_test,\n",
     "    cond=lambda t1, t2: t1[\"sex\"] == t2[\"sex\"],\n",
-    "    self_columns=[\"col_nm\", \"coef\", \"intercept\", \"serialized_linreg_model\", \"created_dt\"]\n",
+    "    self_columns=[\"col_nm\", \"coef\", \"intercept\", \"serialized_linreg_model\", \"created_dt\"],\n",
     ")"
    ]
   },
@@ -836,12 +836,11 @@
     "\n",
     "\n",
     "linreg_pred = linreg_test_fit.assign(\n",
-    "    rings_pred=lambda t:\n",
-    "        linreg_pred_func(\n",
-    "            t[\"serialized_linreg_model\"],\n",
-    "            t[\"length\"],\n",
-    "            t[\"shucked_weight\"],\n",
-    "        ),\n",
+    "    rings_pred=lambda t: linreg_pred_func(\n",
+    "        t[\"serialized_linreg_model\"],\n",
+    "        t[\"length\"],\n",
+    "        t[\"shucked_weight\"],\n",
+    "    ),\n",
     ")[[\"id\", \"sex\", \"rings\", \"rings_pred\"]]"
    ]
   },
@@ -946,6 +945,7 @@
     "#    , r2_score float8\n",
     "# );\n",
     "\n",
+    "\n",
     "@dataclasses.dataclass\n",
     "class linreg_eval_type:\n",
     "    mae: float\n",
@@ -974,6 +974,7 @@
    "source": [
     "from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score\n",
     "\n",
+    "\n",
     "@gp.create_column_function\n",
     "def linreg_eval(y_actual: List[float], y_pred: List[float]) -> linreg_eval_type:\n",
     "    mae = mean_absolute_error(y_actual, y_pred)\n",
@@ -1066,8 +1067,7 @@
     "# ) a\n",
     "\n",
     "\n",
-    "linreg_pred.group_by(\"sex\").apply(\n",
-    "    lambda t: linreg_eval(t[\"rings\"], t[\"rings_pred\"]), expand=True)"
+    "linreg_pred.group_by(\"sex\").apply(lambda t: linreg_eval(t[\"rings\"], t[\"rings_pred\"]), expand=True)"
    ]
   }
  ],

doc/source/notebooks/basic.ipynb

@@ -787,14 +787,8 @@
     "t_join = t1.join(\n",
     "    t2,\n",
     "    on=\"val\",\n",
-    "    self_columns = {\n",
-    "        \"id\": \"t1_id\",\n",
-    "        \"val\": \"t1_val\"\n",
-    "    },\n",
-    "    other_columns = {\n",
-    "        \"id\": \"t2_id\",\n",
-    "        \"val\": \"t2_val\"\n",
-    "    }\n",
+    "    self_columns={\"id\": \"t1_id\", \"val\": \"t1_val\"},\n",
+    "    other_columns={\"id\": \"t2_id\", \"val\": \"t2_val\"},\n",
     ")\n",
     "t_join"
    ]
@@ -1075,7 +1069,7 @@
     "    numbers.assign(is_even=lambda t: t[\"val\"] % 2 == 0)\n",
     "    .group_by(\"is_even\")\n",
     "    .apply(lambda t: F.sum(t[\"val\"]))\n",
-    ")\n"
+    ")"
    ]
   }
  ],