update notebook

alteryx · Nov 17, 2020 · 2b8e910 · 2b8e910
1 parent 86273fe
commit 2b8e910
Showing 1 changed file with 63 additions and 24 deletions.
diff --git a/docs/source/user_guide/data_checks.ipynb b/docs/source/user_guide/data_checks.ipynb
@@ -33,6 +33,7 @@
    "source": [
     "import numpy as np\n",
     "import pandas as pd\n",
+    "from evalml.data_checks import DataCheckMessageType\n",
     "\n",
     "from evalml.data_checks import HighlyNullDataCheck\n",
     "\n",
@@ -43,9 +44,13 @@
     "                  [8, 6, np.nan]])\n",
     "\n",
     "null_check = HighlyNullDataCheck(pct_null_threshold=0.8)\n",
+    "results = null_check.validate(X)\n",
     "\n",
-    "for message in null_check.validate(X):\n",
-    "    print(message.message)"
+    "for message in results[DataCheckMessageType.WARNING]:\n",
+    "    print(\"Warning:\", message.message)\n",
+    "\n",
+    "for message in results[DataCheckMessageType.ERROR]:\n",
+    "    print(\"Error:\", message.message)"
    ]
   },
   {
@@ -84,9 +89,13 @@
     "                 \"good col\":[0, 4, 1]})\n",
     "y = pd.Series([1, 0, 1])\n",
     "no_variance_data_check = NoVarianceDataCheck()\n",
+    "results = no_variance_data_check.validate(X, y)\n",
+    "\n",
+    "for message in results[DataCheckMessageType.WARNING]:\n",
+    "    print(\"Warning:\", message.message)\n",
     "\n",
-    "for message in no_variance_data_check.validate(X, y):\n",
-    "    print(message.message)"
+    "for message in results[DataCheckMessageType.ERROR]:\n",
+    "    print(\"Error:\", message.message)"
    ]
   },
   {
@@ -110,9 +119,13 @@
     "y = pd.Series([1, 0, 1])\n",
     "\n",
     "no_variance_data_check = NoVarianceDataCheck(count_nan_as_value=True)\n",
+    "results = no_variance_data_check.validate(X, y)\n",
     "\n",
-    "for message in no_variance_data_check.validate(X, y):\n",
-    "    print(message.message)"
+    "for message in results[DataCheckMessageType.WARNING]:\n",
+    "    print(\"Warning:\", message.message)\n",
+    "\n",
+    "for message in results[DataCheckMessageType.ERROR]:\n",
+    "    print(\"Error:\", message.message)"
    ]
   },
   {
@@ -139,10 +152,15 @@
     "                  [4, 4, 8, 3],\n",
     "                  [9, 2, 7, 1]])\n",
     "y = pd.Series([0, 1, 1, 1, 1])\n",
+    "\n",
     "class_imbalance_check = ClassImbalanceDataCheck(threshold=0.25, num_cv_folds=4)\n",
+    "results = class_imbalance_check.validate(X, y)\n",
     "\n",
-    "for message in class_imbalance_check.validate(X, y):\n",
-    "    print(message.message)"
+    "for message in results[DataCheckMessageType.WARNING]:\n",
+    "    print(\"Warning:\", message.message)\n",
+    "\n",
+    "for message in results[DataCheckMessageType.ERROR]:\n",
+    "    print(\"Error:\", message.message)"
    ]
   },
   {
@@ -167,8 +185,13 @@
     "y = pd.Series([10, 42, 31, 51, 40])\n",
     "\n",
     "target_leakage_check = TargetLeakageDataCheck(pct_corr_threshold=0.8)\n",
-    "for message in target_leakage_check.validate(X, y):\n",
-    "    print(message.message)"
+    "results = target_leakage_check.validate(X, y)\n",
+    "\n",
+    "for message in results[DataCheckMessageType.WARNING]:\n",
+    "    print(\"Warning:\", message.message)\n",
+    "\n",
+    "for message in results[DataCheckMessageType.ERROR]:\n",
+    "    print(\"Error:\", message.message)"
    ]
   },
   {
@@ -194,10 +217,15 @@
     "    \n",
     "X = pd.DataFrame({})\n",
     "y = pd.Series([0, 1, None, None])\n",
+    "\n",
     "invalid_target_check = InvalidTargetDataCheck('binary')\n",
+    "results = invalid_target_check.validate(X, y)\n",
+    "\n",
+    "for message in results[DataCheckMessageType.WARNING]:\n",
+    "    print(\"Warning:\", message.message)\n",
     "\n",
-    "for message in invalid_target_check.validate(X, y):\n",
-    "    print(message.message)"
+    "for message in results[DataCheckMessageType.ERROR]:\n",
+    "    print(\"Error:\", message.message)"
    ]
   },
   {
@@ -218,10 +246,15 @@
     "from evalml.data_checks import IDColumnsDataCheck\n",
     "\n",
     "X = pd.DataFrame([[0, 53, 6325, 5],[1, 90, 6325, 10],[2, 90, 18, 20]], columns=['user_number', 'cost', 'revenue', 'id'])\n",
+    "\n",
     "id_col_check = IDColumnsDataCheck(id_threshold=0.9)\n",
+    "results = id_col_check.validate(X, y)\n",
     "\n",
-    "for message in id_col_check.validate(X):\n",
-    "    print(message.message)"
+    "for message in results[DataCheckMessageType.WARNING]:\n",
+    "    print(\"Warning:\", message.message)\n",
+    "\n",
+    "for message in results[DataCheckMessageType.ERROR]:\n",
+    "    print(\"Error:\", message.message)"
    ]
   },
   {
@@ -282,9 +315,13 @@
     "from evalml.data_checks import OutliersDataCheck\n",
     "\n",
     "outliers_check = OutliersDataCheck()\n",
+    "results = outliers_check.validate(X, y)\n",
+    "\n",
+    "for message in results[DataCheckMessageType.WARNING]:\n",
+    "    print(\"Warning:\", message.message)\n",
     "\n",
-    "for message in outliers_check.validate(X):\n",
-    "    print(message.message)"
+    "for message in results[DataCheckMessageType.ERROR]:\n",
+    "    print(\"Error:\", message.message)"
    ]
   },
   {
@@ -310,12 +347,13 @@
     "y = pd.Series([1, 0, 1])\n",
     "\n",
     "no_variance_data_check = NoVarianceDataCheck(count_nan_as_value=True)\n",
+    "results = no_variance_data_check.validate(X, y)\n",
+    "\n",
+    "for message in results[DataCheckMessageType.WARNING]:\n",
+    "    print(\"Warning:\", message.message)\n",
     "\n",
-    "for message in no_variance_data_check.validate(X, y):\n",
-    "    if isinstance(message, DataCheckError):\n",
-    "        print(\"ERROR:\", message.message)\n",
-    "    elif isinstance(message, DataCheckWarning):\n",
-    "        print(\"WARNING:\", message.message)"
+    "for message in results[DataCheckMessageType.ERROR]:\n",
+    "    print(\"Error:\", message.message)"
    ]
   },
   {
@@ -329,7 +367,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "If you would prefer to write your own data check, you can do so by extending the `DataCheck` class and implementing the `validate(self, X, y)` class method. Below, we've created a new `DataCheck`, `ZeroVarianceDataCheck`, which is similar to `NoVarianceDataCheck` defined in `EvalML`."
+    "If you would prefer to write your own data check, you can do so by extending the `DataCheck` class and implementing the `validate(self, X, y)` class method. Below, we've created a new `DataCheck`, `ZeroVarianceDataCheck`, which is similar to `NoVarianceDataCheck` defined in `EvalML`. The `validate(self, X, y)` method should return a dictionary with `DataCheckMessageType.WARNING` and `DataCheckMessageType.ERROR` as keys mapping to list of warnings and errors, respectively."
    ]
   },
   {
@@ -345,10 +383,11 @@
     "\n",
     "class ZeroVarianceDataCheck(DataCheck):\n",
     "    def validate(self, X, y):\n",
+    "        messages = {DataCheckMessageType.WARNING: [], DataCheckMessageType.ERROR: []}\n",
     "        if not isinstance(X, pd.DataFrame):\n",
     "            X = pd.DataFrame(X)\n",
     "        warning_msg = \"Column '{}' has zero variance\"\n",
-    "        return [DataCheckError(warning_msg.format(column), self.name) for column in X.columns if len(X[column].unique()) == 1]"
+    "        messages[DataCheckMessageType.WARNING].extend([DataCheckError(warning_msg.format(column), self.name) for column in X.columns if len(X[column].unique()) == 1])"
    ]
   },
   {
@@ -458,4 +497,4 @@
  },
  "nbformat": 4,
  "nbformat_minor": 4
-}
+}