From a52996f8dde0d05e6ff2e04e7cd3d13bededc289 Mon Sep 17 00:00:00 2001
From: shreeya-cy <cy.shreeya@gmail.com>
Date: Mon, 29 Jul 2024 20:52:29 +0200
Subject: [PATCH] Added: user_guide and PSI

---
 stream_viz/feature_drift/f_drift_detector.py | 112 ++++--
 stream_viz/tutorial/UserGuide.ipynb          | 369 +++++--------------
 2 files changed, 172 insertions(+), 309 deletions(-)

diff --git a/stream_viz/feature_drift/f_drift_detector.py b/stream_viz/feature_drift/f_drift_detector.py
index babe6f1..ad24b36 100644
--- a/stream_viz/feature_drift/f_drift_detector.py
+++ b/stream_viz/feature_drift/f_drift_detector.py
@@ -26,15 +26,19 @@ class FeatureDriftDetector(DriftDetector):
         Size of the gap between segments when computing gradual drift (default is 50).
     p_val_threshold : float, optional
         P-value threshold for gradual drift detection (default is 0.0001).
+    psi_threshold : float, optional
+        psi threshold for Population Stability Index (default is 0.0001).
     """
 
     def __init__(
         self,
         features_list: List[str],
+        categorical_features: List[str],
         window_size: int = 300,
         ks_test_pval: float = 0.001,
         gap_size: int = 50,
         p_val_threshold: float = 0.0001,
+        psi_threshold: float = 0.12,
     ) -> None:
         self._drift_records: List[Dict[str, str]] = []
         self._valid_keys: set[str] = get_fd_drift_type_keys()
@@ -45,8 +49,10 @@ def __init__(
         self._moving_avg: pd.DataFrame = pd.DataFrame(columns=features_list)
         self.p_val: float = ks_test_pval
         self.p_val_grad: float = p_val_threshold
+        self.psi_threshold: float = psi_threshold
         self._drift_tp_df: pd.DataFrame = pd.DataFrame(columns=features_list)
         self._feature_data_df: pd.DataFrame = pd.DataFrame(columns=features_list)
+        self.categorical_features: List[str] = categorical_features
 
     def update(self, x_i: Dict[str, float], y_i: int, tpt: int) -> None:
         """
@@ -78,9 +84,14 @@ def detect_drift(self, tpt: int) -> None:
         """
         window_df = pd.DataFrame(self._window)
         for feature in window_df.columns:
-            drift_detected, drift_type = self._detect_drift_using_ks(
-                window_df[feature].values
-            )
+            if feature in self.categorical_features:
+                drift_detected, drift_type = self._detect_drift_using_psi(
+                    window_df[feature].values
+                )
+            else:
+                drift_detected, drift_type = self._detect_drift_using_ks(
+                    window_df[feature].values
+                )
             if drift_detected:
                 self._drift_tp_df.loc[tpt, feature] = drift_type
 
@@ -125,6 +136,44 @@ def _detect_drift_using_ks(
 
         return False, None
 
+    def _detect_drift_using_psi(
+        self, window_data: np.ndarray
+    ) -> Tuple[bool, Optional[str]]:
+        first_half = window_data[: self.window_size // 2]
+        second_half = window_data[self.window_size // 2 :]
+
+        grad_first_part = window_data[: (self.window_size // 2) - (self.gap_size // 2)]
+        grad_second_part = window_data[(self.window_size // 2) + (self.gap_size // 2) :]
+
+        psi_value = self.calculate_psi(first_half, second_half)
+        grad_psi_value = self.calculate_psi(grad_first_part, grad_second_part)
+
+        if psi_value > self.psi_threshold:
+            mean_diff = np.mean(second_half) - np.mean(first_half)
+            if np.abs(mean_diff) > np.std(window_data):
+                return True, "sudden_drift"
+            elif mean_diff > 0:
+                return True, "linear_drift"
+
+        if grad_psi_value > self.psi_threshold:
+            return True, "gradual_drift"
+
+        return False, None
+
+    def calculate_psi(self, expected, actual, buckets=10):
+        expected_percents = np.histogram(expected, bins=buckets, range=(0, 1))[0] / len(
+            expected
+        )
+        actual_percents = np.histogram(actual, bins=buckets, range=(0, 1))[0] / len(
+            actual
+        )
+        expected_percents = np.where(expected_percents == 0, 0.01, expected_percents)
+        actual_percents = np.where(actual_percents == 0, 0.01, actual_percents)
+        psi_values = (actual_percents - expected_percents) * np.log(
+            actual_percents / expected_percents
+        )
+        return np.sum(psi_values)
+
     def plot(self, feature_name: str, window_size: Optional[int] = None) -> None:
         """
         Plot the feature values over time, highlighting detected drift points.
@@ -228,40 +277,35 @@ def drift_records(self, drift_record: FeatureDriftType) -> None:
     normal.read_csv_data(_NORMAL_DATA_PATH)
     normal.encode_data()
 
+    # Create a mapping of original to encoded column names
+    encoded_categorical_cols = normal.X_encoded_data.columns[
+        normal.X_encoded_data.columns.str.startswith("c")
+    ]
+    original_to_encoded_categorical_cols = {
+        original: encoded
+        for original, encoded in zip(
+            normal.original_categorical_cols, encoded_categorical_cols
+        )
+    }
+
     # As the KS test is only for numerical features
     X_numerical = normal.X_encoded_data[normal.original_numerical_cols]
-    # X_categorical = normal.X_encoded_data[normal.original_categorical_cols]
-    dt_streamer = DataStreamer(
-        fd_detector_obj=FeatureDriftDetector(X_numerical.columns)
+    X_categorical = normal.X_encoded_data[encoded_categorical_cols]
+    all_features = X_numerical.columns.tolist() + X_categorical.columns.tolist()
+    fd_detector = FeatureDriftDetector(
+        features_list=all_features,
+        categorical_features=encoded_categorical_cols.tolist(),
     )
-    dt_streamer.stream_data(X_df=X_numerical, y_df=normal.y_encoded_data)
 
+    dt_streamer = DataStreamer(fd_detector_obj=fd_detector)
+    dt_streamer.stream_data(X_df=normal.X_encoded_data, y_df=normal.y_encoded_data)
+
+    # Plot feature drift for a numerical features
     dt_streamer.fd_detector_obj.plot(feature_name=X_numerical.columns[0])
 
-    # ----- Test: Feature Drift Detection for numerical variables on Dummy drift data -----
-    # features_list = ["n_feature_1", "n_feature_2"]
-    # drift_detector = FeatureDriftDetector(
-    #     features_list=features_list, window_size=100, ks_test_pval=0.001
-    # )
-    #
-    # # Generate data for 3 distributions for each feature
-    # random_state = np.random.RandomState(seed=42)
-    # dist_a_f1 = random_state.normal(0.8, 0.05, 1000)
-    # dist_b_f1 = random_state.normal(0.4, 0.02, 1000)
-    # dist_c_f1 = random_state.normal(0.6, 0.1, 1000)
-    #
-    # dist_a_f2 = random_state.normal(0.3, 0.04, 1000)
-    # dist_b_f2 = random_state.normal(0.7, 0.03, 1000)
-    # dist_c_f2 = random_state.normal(0.5, 0.05, 1000)
-    #
-    # # Concatenate data to simulate a data stream with 2 drifts for each feature
-    # stream_f1 = np.concatenate((dist_a_f1, dist_b_f1, dist_c_f1))
-    # stream_f2 = np.concatenate((dist_a_f2, dist_b_f2, dist_c_f2))
-    #
-    # # Simulate streaming data update
-    # for i, (val_f1, val_f2) in enumerate(zip(stream_f1, stream_f2)):
-    #     x_i = {"n_feature_1": val_f1, "n_feature_2": val_f2}
-    #     drift_detector.update(x_i, 1, i)
-    #
-    # drift_detector._drift_tp_df.head()
-    # drift_detector._moving_avg_df.head()
+    # Plot feature drift for a categorical features
+    dt_streamer.fd_detector_obj.plot(feature_name=X_categorical.columns[0])
+
+    # dt = FeatureDriftDetector(fd_detector_obj=normal)
+    # dt.plot("n0")
+    # dt.plot("c5_b")
diff --git a/stream_viz/tutorial/UserGuide.ipynb b/stream_viz/tutorial/UserGuide.ipynb
index 41a2466..633d4ca 100644
--- a/stream_viz/tutorial/UserGuide.ipynb
+++ b/stream_viz/tutorial/UserGuide.ipynb
@@ -6,8 +6,8 @@
    "id": "cccfcee9-3f1b-4036-a67e-e71ea4d8fc27",
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2024-07-26T15:05:23.577620Z",
-     "start_time": "2024-07-26T15:05:23.573711Z"
+     "end_time": "2024-07-29T18:50:14.438404Z",
+     "start_time": "2024-07-29T18:50:14.425996Z"
     }
    },
    "outputs": [
@@ -15,7 +15,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Current Directory: G:\\github-aditya0by0\\stream-viz\\stream_viz\n"
+      "Current Directory: /Users/shreeyacy/GitHub/stream-viz-1/stream_viz\n"
      ]
     }
    ],
@@ -35,8 +35,8 @@
    "id": "b8eb7a87-0ea0-44ca-9a29-b26885cda470",
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2024-07-26T15:05:26.497041Z",
-     "start_time": "2024-07-26T15:05:26.488055Z"
+     "end_time": "2024-07-29T18:50:17.289843Z",
+     "start_time": "2024-07-29T18:50:17.273457Z"
     }
    },
    "outputs": [
@@ -44,7 +44,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Current Directory: G:\\github-aditya0by0\\stream-viz\n"
+      "Current Directory: /Users/shreeyacy/GitHub/stream-viz-1\n"
      ]
     }
    ],
@@ -116,129 +116,15 @@
    "id": "21e4215b-5dc5-4773-8f3d-f78ac3c4fbc9",
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2024-07-26T15:06:14.381562Z",
-     "start_time": "2024-07-26T15:06:14.329113Z"
+     "end_time": "2024-07-29T18:50:20.741166Z",
+     "start_time": "2024-07-29T18:50:20.686728Z"
     }
    },
    "outputs": [
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+     "end_time": "2024-07-29T18:50:23.196805Z",
+     "start_time": "2024-07-29T18:50:22.980401Z"
     }
    },
    "outputs": [
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-       "      <td>0.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0.474236</td>\n",
-       "      <td>0.566046</td>\n",
-       "      <td>0.499081</td>\n",
-       "      <td>0.416457</td>\n",
-       "      <td>0.552283</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.363202</td>\n",
-       "      <td>0.554642</td>\n",
-       "      <td>0.495610</td>\n",
-       "      <td>0.216550</td>\n",
-       "      <td>0.397683</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   c5_b  c6_b  c7_b  c8_b  c9_b        n0        n1        n2        n3  \\\n",
-       "0   0.0   0.0   1.0   0.0   0.0  0.530356  0.598345  0.519161  0.478557   \n",
-       "1   0.0   0.0   0.0   1.0   1.0  0.672618  0.423329  0.442055  0.449888   \n",
-       "2   0.0   0.0   0.0   1.0   1.0  0.567192  0.576429  0.505532  0.364614   \n",
-       "3   0.0   0.0   0.0   1.0   1.0  0.474236  0.566046  0.499081  0.416457   \n",
-       "4   1.0   0.0   0.0   1.0   0.0  0.363202  0.554642  0.495610  0.216550   \n",
-       "\n",
-       "         n4  \n",
-       "0  0.620371  \n",
-       "1  0.458838  \n",
-       "2  0.550814  \n",
-       "3  0.552283  \n",
-       "4  0.397683  "
-      ]
+      "text/plain": "   c5_b  c6_b  c7_b  c8_b  c9_b        n0        n1        n2        n3  \\\n0   0.0   0.0   1.0   0.0   0.0  0.530356  0.598345  0.519161  0.478557   \n1   0.0   0.0   0.0   1.0   1.0  0.672618  0.423329  0.442055  0.449888   \n2   0.0   0.0   0.0   1.0   1.0  0.567192  0.576429  0.505532  0.364614   \n3   0.0   0.0   0.0   1.0   1.0  0.474236  0.566046  0.499081  0.416457   \n4   1.0   0.0   0.0   1.0   0.0  0.363202  0.554642  0.495610  0.216550   \n\n         n4  \n0  0.620371  \n1  0.458838  \n2  0.550814  \n3  0.552283  \n4  0.397683  ",
+      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>c5_b</th>\n      <th>c6_b</th>\n      <th>c7_b</th>\n      <th>c8_b</th>\n      <th>c9_b</th>\n      <th>n0</th>\n      <th>n1</th>\n      <th>n2</th>\n      <th>n3</th>\n      <th>n4</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.530356</td>\n      <td>0.598345</td>\n      <td>0.519161</td>\n      <td>0.478557</td>\n      <td>0.620371</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>0.672618</td>\n      <td>0.423329</td>\n      <td>0.442055</td>\n      <td>0.449888</td>\n      <td>0.458838</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>0.567192</td>\n      <td>0.576429</td>\n      <td>0.505532</td>\n      <td>0.364614</td>\n      <td>0.550814</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>0.474236</td>\n      <td>0.566046</td>\n      <td>0.499081</td>\n      <td>0.416457</td>\n      <td>0.552283</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.363202</td>\n      <td>0.554642</td>\n      <td>0.495610</td>\n      <td>0.216550</td>\n      <td>0.397683</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
      },
      "execution_count": 5,
      "metadata": {},
@@ -749,7 +521,7 @@
    "source": [
     "### FeatureDriftDetector Class\n",
     "\n",
-    "The `FeatureDriftDetector` class is designed to monitor and detect feature drift in streaming data using the Kolmogorov-Smirnov (KS) test. This class can handle numerical features and provides functionality to identify sudden, linear, and gradual drifts in the data. Drift detection is crucial for maintaining the reliability and accuracy of machine learning models, especially in dynamic environments where data distributions can change over time.\n",
+    "The `FeatureDriftDetector` class is designed to monitor and detect feature drift in streaming data using the Kolmogorov-Smirnov (KS) test and Population Stability Index Test (PSI test). This class can handle numerical features and categorical features, and provides functionality to identify sudden, linear, and gradual drifts in the data. Drift detection is crucial for maintaining the reliability and accuracy of machine learning models, especially in dynamic environments where data distributions can change over time.\n",
     "\n",
     "#### Parameters\n",
     "\n",
@@ -768,21 +540,29 @@
     "- **p_val_threshold** (`float`, optional): \n",
     "  The p-value threshold for gradual drift detection. The default value is 0.0001. This threshold is used to determine the significance of gradual changes in the data, providing an additional layer of drift detection sensitivity.\n",
     "\n",
+    "- **psi_threshold** (`float`, optional):\n",
+    "  The psi threshold for the Population Stability Index. The default value is 0.12. This threshold helps in determining the significance of the detected drift, ensuring that meaningfu changes are detected or flagged.\n",
+    "\n",
     "#### Drift Detection Methodology\n",
     "\n",
-    "Drift detection in the `FeatureDriftDetector` is performed using the Kolmogorov-Smirnov (KS) test, which compares the distributions of feature values within a sliding window of data. The following steps outline the drift detection process:\n",
+    "Drift detection in the `FeatureDriftDetector` is performed using the Kolmogorov-Smirnov (KS) test for numerical features and Population Stability Index Test (PSI test) for categorical features, which compares the distributions of feature values within a sliding window of data. The following steps outline the drift detection process:\n",
     "\n",
     "1. **Window Data Segmentation**:\n",
     "    - The feature values within the current window are split into two halves: the first half and the second half.\n",
     "    - Additionally, for detecting gradual drift, the data is further segmented into overlapping parts by introducing a gap between segments. Specifically, the first half of the data ends before the gap, and the second half of the data starts after the gap. This method ensures that gradual changes are not masked by immediate changes around the midpoint of the window.\n",
     "\n",
     "2. **Kolmogorov-Smirnov Test**:\n",
-    "    - The KS test is applied to compare the distributions of the first and second halves.\n",
+    "    - The KS test is applied for numerical features to compare the distributions of the first and second halves.\n",
     "    - If the p-value from the KS test is below the specified threshold (`ks_test_pval`), it indicates a significant difference between the two halves, suggesting potential drift.\n",
+    "\n",
+    "3. **Population Stability Index Test**:\n",
+    "    - The PSI test is applied for categorical features to compare the distributions of the first and second halves.\n",
+    "    - If the psi value from the PSI test is above the specified threshold (`psi_threshold`), it indicates a significant difference between two halves, indicating potential drift.\n",
     "    \n",
     "3. **Gradual Drift Detection**:\n",
-    "    - Another KS test is conducted on the overlapping parts of the data to detect gradual drift. The overlapping parts are defined by excluding the gap in the middle of the window.\n",
+    "    - Another KS test and PSI test are conducted on the overlapping parts of the data to detect gradual drift. The overlapping parts are defined by excluding the gap in the middle of the window.\n",
     "    - If the p-value from this test is below the gradual drift threshold (`p_val_threshold`), it suggests gradual changes in the data distribution.\n",
+    "    - Similarly, if the psi value from this test is above the psi threshold, it indicates gradual drift in data distribution.\n",
     "\n",
     "4. **Drift Type Identification**:\n",
     "    - If drift is detected, the type of drift is determined based on the mean difference between the halves:\n",
@@ -829,12 +609,69 @@
    ]
   },
   {
-   "cell_type": "markdown",
-   "id": "f5a0e9f6-1660-40db-bc3a-1c6f0893f3c2",
-   "metadata": {},
+   "cell_type": "code",
+   "execution_count": 6,
+   "outputs": [],
    "source": [
-    "---"
-   ]
+    "from stream_viz.data_streamer import DataStreamer\n",
+    "from stream_viz.feature_drift.f_drift_detector import FeatureDriftDetector\n",
+    "\n",
+    "# Feature Drift Detector for Numerical Columns as of now\n",
+    "# Create a mapping of original to encoded column names\n",
+    "encoded_categorical_cols = normal_encoder.X_encoded_data.columns[\n",
+    "    normal_encoder.X_encoded_data.columns.str.startswith(\"c\")\n",
+    "]\n",
+    "original_to_encoded_categorical_cols = {\n",
+    "    original: encoded\n",
+    "    for original, encoded in zip(\n",
+    "        normal_encoder.original_categorical_cols, encoded_categorical_cols\n",
+    "    )\n",
+    "}\n",
+    "\n",
+    "X_numerical = normal_encoder.X_encoded_data[normal_encoder.original_numerical_cols]\n",
+    "X_categorical = normal_encoder.X_encoded_data[encoded_categorical_cols]\n",
+    "all_features = X_numerical.columns.tolist() + X_categorical.columns.tolist()\n",
+    "fd_detector = FeatureDriftDetector(\n",
+    "    features_list=all_features, categorical_features=encoded_categorical_cols.tolist()\n",
+    ")\n",
+    "dt_streamer = DataStreamer(fd_detector_obj=fd_detector)\n",
+    "dt_streamer.stream_data(\n",
+    "    X_df=normal_encoder.X_encoded_data, y_df=normal_encoder.y_encoded_data\n",
+    ")"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "ExecuteTime": {
+     "end_time": "2024-07-29T18:51:08.913157Z",
+     "start_time": "2024-07-29T18:50:31.390314Z"
+    }
+   },
+   "id": "24eb4aecc0546bc4"
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "<Figure size 1000x600 with 1 Axes>",
+      "image/png": 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"
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "dt_streamer.fd_detector_obj.plot(feature_name=\"c5_b\")"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "ExecuteTime": {
+     "end_time": "2024-07-29T18:51:14.188688Z",
+     "start_time": "2024-07-29T18:51:13.158617Z"
+    }
+   },
+   "id": "d6babfd255f88f3a"
   },
   {
    "cell_type": "markdown",
@@ -936,10 +773,7 @@
    "cell_type": "markdown",
    "id": "66fbcf044689c97c",
    "metadata": {
-    "collapsed": false,
-    "jupyter": {
-     "outputs_hidden": false
-    }
+    "collapsed": false
    },
    "source": [
     "### Stream Graphs \n",
@@ -957,10 +791,7 @@
      "end_time": "2024-07-26T15:08:06.514803Z",
      "start_time": "2024-07-26T15:08:06.358078Z"
     },
-    "collapsed": false,
-    "jupyter": {
-     "outputs_hidden": false
-    }
+    "collapsed": false
    },
    "outputs": [
     {
@@ -1082,10 +913,7 @@
      "end_time": "2024-07-26T15:08:49.978417Z",
      "start_time": "2024-07-26T15:08:49.847105Z"
     },
-    "collapsed": false,
-    "jupyter": {
-     "outputs_hidden": false
-    }
+    "collapsed": false
    },
    "outputs": [
     {
@@ -1111,10 +939,7 @@
    "cell_type": "markdown",
    "id": "f3ccba5ae2b49026",
    "metadata": {
-    "collapsed": false,
-    "jupyter": {
-     "outputs_hidden": false
-    }
+    "collapsed": false
    },
    "source": [
     "### Scatter Plot\n",
@@ -1133,10 +958,7 @@
      "end_time": "2024-07-26T15:11:04.958626Z",
      "start_time": "2024-07-26T15:11:04.796875Z"
     },
-    "collapsed": false,
-    "jupyter": {
-     "outputs_hidden": false
-    }
+    "collapsed": false
    },
    "outputs": [
     {
@@ -1166,10 +988,7 @@
      "end_time": "2024-07-26T15:11:09.053475Z",
      "start_time": "2024-07-26T15:11:08.455548Z"
     },
-    "collapsed": false,
-    "jupyter": {
-     "outputs_hidden": false
-    }
+    "collapsed": false
    },
    "outputs": [
     {