prompt: We are currently solving the following issue within our repository. Here is the issue text:
  --- BEGIN ISSUE ---
  Bug in SHAP analysis block
  Trying out this module, the following error arises with the cloud tutorial for home rentals:
  
  ```python
    File "/tmp/e74830c1ef5914dfafe52537b8b33cd0479265f508bfd6c616614666305205822.py", line 392, in predict
      insights, global_insights = explain(data=data,encoded_data=encoded_data,predictions=df,ts_analysis=None,problem_definition=self.problem_definition,stat_analysis=self.statistical_analysis,runtime_analysis=self.runtime_analyzer,target_name=self.target,target_dtype=self.dtype_dict[self.target],explainer_blocks=self.analysis_blocks,pred_args=self.pred_args)
    File "/MindsDB/lightwood/lightwood/analysis/explain.py", line 81, in explain
      row_insights, global_insights = block.explain(row_insights, global_insights, **kwargs)
    File "/MindsDB/lightwood/lightwood/analysis/helpers/shap.py", line 79, in explain
      shap_values = shap_explainer.shap_values(ns.data, silent=True)
    File "/MindsDB/nenv/lib/python3.8/site-packages/shap/explainers/_kernel.py", line 186, in shap_values
      explanations.append(self.explain(data, **kwargs))
    File "/MindsDB/nenv/lib/python3.8/site-packages/shap/explainers/_kernel.py", line 207, in explain
      match_instance_to_data(instance, self.data)
    File "/MindsDB/nenv/lib/python3.8/site-packages/shap/utils/_legacy.py", line 87, in match_instance_to_data
      instance.group_display_values = [instance.x[0, group[0]] if len(group) == 1 else "" for group in data.groups]
    File "/MindsDB/nenv/lib/python3.8/site-packages/shap/utils/_legacy.py", line 87, in <listcomp>
      instance.group_display_values = [instance.x[0, group[0]] if len(group) == 1 else "" for group in data.groups]
  IndexError: index 7 is out of bounds for axis 1 with size 7
  
  ```
  
  --- END ISSUE ---
  
  Below are some code segments, each from a relevant file. One or more of these files may contain bugs.
  --- BEGIN FILES ---
  Path: `lightwood/analysis/helpers/shap.py`
  Content:
  ```
  1 import warnings
  2 from types import SimpleNamespace
  3 from typing import Dict, Optional, Tuple
  4 
  5 import numpy as np
  6 import pandas as pd
  7 from lightwood.analysis.base import BaseAnalysisBlock
  8 from lightwood.api import dtype
  9 from lightwood.api.types import PredictionArguments
  10 from lightwood.data.encoded_ds import EncodedDs
  11 from lightwood.helpers.log import log
  12 from sklearn.preprocessing import LabelEncoder
  13 
  14 import shap
  15 
  16 
  17 class ShapleyValues(BaseAnalysisBlock):
  18     """
  19     Analysis block that estimates column importance with SHAP (SHapley Additive exPlanations), a game theoretic approach
  20     to explain the ouput of any machine learning model. SHAP assigns each feature an importance value for a particular
  21     prediction.
  22 
  23     Reference:
  24         https://shap.readthedocs.io/en/stable/
  25         https://proceedings.neurips.cc/paper/2017/file/8a20a8621978632d76c43dfd28b67767-Paper.pdf
  26     """
  27     label_encoder: LabelEncoder
  28 
  29     def __init__(self, deps: Optional[Tuple] = ...):
  30         super().__init__(deps=deps)
  31         self.label_encoder = LabelEncoder()
  32 
  33     def analyze(self, info: Dict[str, object], **kwargs) -> Dict[str, object]:
  34         log.info('Preparing to compute feature importance values with SHAP')
  35         ns = SimpleNamespace(**kwargs)
  36 
  37         output_dtype = ns.dtype_dict[ns.target]
  38         train_data: EncodedDs = ns.train_data
  39 
  40         if output_dtype in (dtype.integer, dtype.float, dtype.quantity):
  41             pass
  42         elif output_dtype in (dtype.binary, dtype.categorical, dtype.tags):
  43             self.label_encoder.fit(train_data.data_frame[ns.target].values)
  44         else:
  45             log.warning(f'ShapleyValues analyzers not supported for type: {output_dtype}')
  46             return info
  47 
  48         def model(x: np.ndarray) -> np.ndarray:
  49             assert(isinstance(x, np.ndarray))
  50             df = pd.DataFrame(data=x, columns=train_data.data_frame.columns)
  51             ds = EncodedDs(encoders=train_data.encoders, data_frame=df, target=train_data.target)
  52 
  53             decoded_predictions = ns.predictor(ds=ds, args=PredictionArguments())
  54             if output_dtype in (dtype.integer, dtype.float, dtype.quantity):
  55                 encoded_predictions = decoded_predictions['prediction'].values
  56             elif output_dtype in (dtype.binary, dtype.categorical, dtype.tags):
  57                 encoded_predictions = self.label_encoder.transform(decoded_predictions['prediction'].values)
  58 
  59             return encoded_predictions
  60 
  61         info['shap_explainer'] = shap.KernelExplainer(model=model, data=train_data.data_frame)
  62 
  63         return info
  64 
  65     def explain(self,
  66                 row_insights: pd.DataFrame,
  67                 global_insights: Dict[str, object],
  68                 **kwargs
  69                 ) -> Tuple[pd.DataFrame, Dict[str, object]]:
  70         log.info('Computing feature importance values with Kernel SHAP method')
  71         ns = SimpleNamespace(**kwargs)
  72 
  73         shap_explainer = ns.analysis.get('shap_explainer', None)
  74         if shap_explainer is None:
  75             return row_insights, global_insights
  76 
  77         with warnings.catch_warnings():
  78             warnings.filterwarnings("ignore", category=DeprecationWarning)
  79             shap_values = shap_explainer.shap_values(ns.data, silent=True)
  80 
  81         shap_values_df = pd.DataFrame(shap_values).rename(
  82             mapper=lambda i: f"feature_{i}_impact", axis='columns')
  83 
  84         if kwargs.get('target_dtype', None) in (dtype.binary, dtype.categorical, dtype.tags):
  85             predictions = self.label_encoder.transform(row_insights['prediction'])
  86         else:
  87             predictions = row_insights['prediction']
  88 
  89         base_response = (predictions - shap_values_df.sum(axis='columns')).mean()
  90         global_insights['base_response'] = base_response
  91 
  92         row_insights = pd.concat([row_insights, shap_values_df], axis='columns')
  93 
  94         return row_insights, global_insights
  95
  ```
  --- END FILES ---
  
  Please first localize the bug based on the issue statement, and then generate a patch according to the `git diff` format fenced by three backticks.
  
  Here is an example:
  
  ```diff
  diff --git a/examples/server_async.py b/examples/server_async.py
  --- a/examples/server_async.py
  +++ b/examples/server_async.py
  @@ -313,4 +313,4 @@
   
   
   if __name__ == "__main__":
  -    asyncio.run(run_async_server("."), debug=True)
  +    asyncio.run(run_async_server(), debug=True)
  diff --git a/examples/server_sync.py b/examples/server_sync.py
  --- a/examples/server_sync.py
  +++ b/examples/server_sync.py
  @@ -313,5 +313,5 @@
   
   
   if __name__ == "__main__":
  -    server = run_sync_server(".")
  +    server = run_sync_server()
       server.shutdown()
  
  ```
  
golden_diff: diff --git a/lightwood/analysis/helpers/shap.py b/lightwood/analysis/helpers/shap.py
  --- a/lightwood/analysis/helpers/shap.py
  +++ b/lightwood/analysis/helpers/shap.py
  @@ -29,6 +29,8 @@
       def __init__(self, deps: Optional[Tuple] = ...):
           super().__init__(deps=deps)
           self.label_encoder = LabelEncoder()
  +        self.columns = []
  +        self.target = None
   
       def analyze(self, info: Dict[str, object], **kwargs) -> Dict[str, object]:
           log.info('Preparing to compute feature importance values with SHAP')
  @@ -45,9 +47,13 @@
               log.warning(f'ShapleyValues analyzers not supported for type: {output_dtype}')
               return info
   
  +        self.target = ns.target
  +        self.columns = list(set(ns.dtype_dict.keys()) - {self.target})
  +        input_df = train_data.data_frame[self.columns]
  +
           def model(x: np.ndarray) -> np.ndarray:
               assert(isinstance(x, np.ndarray))
  -            df = pd.DataFrame(data=x, columns=train_data.data_frame.columns)
  +            df = pd.DataFrame(data=x, columns=self.columns)
               ds = EncodedDs(encoders=train_data.encoders, data_frame=df, target=train_data.target)
   
               decoded_predictions = ns.predictor(ds=ds, args=PredictionArguments())
  @@ -58,7 +64,7 @@
   
               return encoded_predictions
   
  -        info['shap_explainer'] = shap.KernelExplainer(model=model, data=train_data.data_frame)
  +        info['shap_explainer'] = shap.KernelExplainer(model=model, data=input_df)
   
           return info
   
  @@ -76,10 +82,10 @@
   
           with warnings.catch_warnings():
               warnings.filterwarnings("ignore", category=DeprecationWarning)
  -            shap_values = shap_explainer.shap_values(ns.data, silent=True)
  +            shap_values = shap_explainer.shap_values(ns.data[self.columns], silent=True)
   
           shap_values_df = pd.DataFrame(shap_values).rename(
  -            mapper=lambda i: f"feature_{i}_impact", axis='columns')
  +            mapper=lambda i: f"shap_contribution_{self.columns[i]}", axis='columns')
   
           if kwargs.get('target_dtype', None) in (dtype.binary, dtype.categorical, dtype.tags):
               predictions = self.label_encoder.transform(row_insights['prediction'])
  @@ -87,8 +93,9 @@
               predictions = row_insights['prediction']
   
           base_response = (predictions - shap_values_df.sum(axis='columns')).mean()
  -        global_insights['base_response'] = base_response
   
           row_insights = pd.concat([row_insights, shap_values_df], axis='columns')
  +        row_insights['shap_base_response'] = base_response
  +        row_insights['shap_final_response'] = predictions
   
           return row_insights, global_insights
  
verification_info: {"golden_diff": "diff --git a/lightwood/analysis/helpers/shap.py b/lightwood/analysis/helpers/shap.py\n--- a/lightwood/analysis/helpers/shap.py\n+++ b/lightwood/analysis/helpers/shap.py\n@@ -29,6 +29,8 @@\n     def __init__(self, deps: Optional[Tuple] = ...):\n         super().__init__(deps=deps)\n         self.label_encoder = LabelEncoder()\n+        self.columns = []\n+        self.target = None\n \n     def analyze(self, info: Dict[str, object], **kwargs) -> Dict[str, object]:\n         log.info('Preparing to compute feature importance values with SHAP')\n@@ -45,9 +47,13 @@\n             log.warning(f'ShapleyValues analyzers not supported for type: {output_dtype}')\n             return info\n \n+        self.target = ns.target\n+        self.columns = list(set(ns.dtype_dict.keys()) - {self.target})\n+        input_df = train_data.data_frame[self.columns]\n+\n         def model(x: np.ndarray) -> np.ndarray:\n             assert(isinstance(x, np.ndarray))\n-            df = pd.DataFrame(data=x, columns=train_data.data_frame.columns)\n+            df = pd.DataFrame(data=x, columns=self.columns)\n             ds = EncodedDs(encoders=train_data.encoders, data_frame=df, target=train_data.target)\n \n             decoded_predictions = ns.predictor(ds=ds, args=PredictionArguments())\n@@ -58,7 +64,7 @@\n \n             return encoded_predictions\n \n-        info['shap_explainer'] = shap.KernelExplainer(model=model, data=train_data.data_frame)\n+        info['shap_explainer'] = shap.KernelExplainer(model=model, data=input_df)\n \n         return info\n \n@@ -76,10 +82,10 @@\n \n         with warnings.catch_warnings():\n             warnings.filterwarnings(\"ignore\", category=DeprecationWarning)\n-            shap_values = shap_explainer.shap_values(ns.data, silent=True)\n+            shap_values = shap_explainer.shap_values(ns.data[self.columns], silent=True)\n \n         shap_values_df = pd.DataFrame(shap_values).rename(\n-            mapper=lambda i: f\"feature_{i}_impact\", axis='columns')\n+            mapper=lambda i: f\"shap_contribution_{self.columns[i]}\", axis='columns')\n \n         if kwargs.get('target_dtype', None) in (dtype.binary, dtype.categorical, dtype.tags):\n             predictions = self.label_encoder.transform(row_insights['prediction'])\n@@ -87,8 +93,9 @@\n             predictions = row_insights['prediction']\n \n         base_response = (predictions - shap_values_df.sum(axis='columns')).mean()\n-        global_insights['base_response'] = base_response\n \n         row_insights = pd.concat([row_insights, shap_values_df], axis='columns')\n+        row_insights['shap_base_response'] = base_response\n+        row_insights['shap_final_response'] = predictions\n \n         return row_insights, global_insights\n", "issue": "Bug in SHAP analysis block\nTrying out this module, the following error arises with the cloud tutorial for home rentals:\r\n\r\n```python\r\n  File \"/tmp/e74830c1ef5914dfafe52537b8b33cd0479265f508bfd6c616614666305205822.py\", line 392, in predict\r\n    insights, global_insights = explain(data=data,encoded_data=encoded_data,predictions=df,ts_analysis=None,problem_definition=self.problem_definition,stat_analysis=self.statistical_analysis,runtime_analysis=self.runtime_analyzer,target_name=self.target,target_dtype=self.dtype_dict[self.target],explainer_blocks=self.analysis_blocks,pred_args=self.pred_args)\r\n  File \"/MindsDB/lightwood/lightwood/analysis/explain.py\", line 81, in explain\r\n    row_insights, global_insights = block.explain(row_insights, global_insights, **kwargs)\r\n  File \"/MindsDB/lightwood/lightwood/analysis/helpers/shap.py\", line 79, in explain\r\n    shap_values = shap_explainer.shap_values(ns.data, silent=True)\r\n  File \"/MindsDB/nenv/lib/python3.8/site-packages/shap/explainers/_kernel.py\", line 186, in shap_values\r\n    explanations.append(self.explain(data, **kwargs))\r\n  File \"/MindsDB/nenv/lib/python3.8/site-packages/shap/explainers/_kernel.py\", line 207, in explain\r\n    match_instance_to_data(instance, self.data)\r\n  File \"/MindsDB/nenv/lib/python3.8/site-packages/shap/utils/_legacy.py\", line 87, in match_instance_to_data\r\n    instance.group_display_values = [instance.x[0, group[0]] if len(group) == 1 else \"\" for group in data.groups]\r\n  File \"/MindsDB/nenv/lib/python3.8/site-packages/shap/utils/_legacy.py\", line 87, in <listcomp>\r\n    instance.group_display_values = [instance.x[0, group[0]] if len(group) == 1 else \"\" for group in data.groups]\r\nIndexError: index 7 is out of bounds for axis 1 with size 7\r\n\r\n```\n", "before_files": [{"content": "import warnings\nfrom types import SimpleNamespace\nfrom typing import Dict, Optional, Tuple\n\nimport numpy as np\nimport pandas as pd\nfrom lightwood.analysis.base import BaseAnalysisBlock\nfrom lightwood.api import dtype\nfrom lightwood.api.types import PredictionArguments\nfrom lightwood.data.encoded_ds import EncodedDs\nfrom lightwood.helpers.log import log\nfrom sklearn.preprocessing import LabelEncoder\n\nimport shap\n\n\nclass ShapleyValues(BaseAnalysisBlock):\n    \"\"\"\n    Analysis block that estimates column importance with SHAP (SHapley Additive exPlanations), a game theoretic approach\n    to explain the ouput of any machine learning model. SHAP assigns each feature an importance value for a particular\n    prediction.\n\n    Reference:\n        https://shap.readthedocs.io/en/stable/\n        https://proceedings.neurips.cc/paper/2017/file/8a20a8621978632d76c43dfd28b67767-Paper.pdf\n    \"\"\"\n    label_encoder: LabelEncoder\n\n    def __init__(self, deps: Optional[Tuple] = ...):\n        super().__init__(deps=deps)\n        self.label_encoder = LabelEncoder()\n\n    def analyze(self, info: Dict[str, object], **kwargs) -> Dict[str, object]:\n        log.info('Preparing to compute feature importance values with SHAP')\n        ns = SimpleNamespace(**kwargs)\n\n        output_dtype = ns.dtype_dict[ns.target]\n        train_data: EncodedDs = ns.train_data\n\n        if output_dtype in (dtype.integer, dtype.float, dtype.quantity):\n            pass\n        elif output_dtype in (dtype.binary, dtype.categorical, dtype.tags):\n            self.label_encoder.fit(train_data.data_frame[ns.target].values)\n        else:\n            log.warning(f'ShapleyValues analyzers not supported for type: {output_dtype}')\n            return info\n\n        def model(x: np.ndarray) -> np.ndarray:\n            assert(isinstance(x, np.ndarray))\n            df = pd.DataFrame(data=x, columns=train_data.data_frame.columns)\n            ds = EncodedDs(encoders=train_data.encoders, data_frame=df, target=train_data.target)\n\n            decoded_predictions = ns.predictor(ds=ds, args=PredictionArguments())\n            if output_dtype in (dtype.integer, dtype.float, dtype.quantity):\n                encoded_predictions = decoded_predictions['prediction'].values\n            elif output_dtype in (dtype.binary, dtype.categorical, dtype.tags):\n                encoded_predictions = self.label_encoder.transform(decoded_predictions['prediction'].values)\n\n            return encoded_predictions\n\n        info['shap_explainer'] = shap.KernelExplainer(model=model, data=train_data.data_frame)\n\n        return info\n\n    def explain(self,\n                row_insights: pd.DataFrame,\n                global_insights: Dict[str, object],\n                **kwargs\n                ) -> Tuple[pd.DataFrame, Dict[str, object]]:\n        log.info('Computing feature importance values with Kernel SHAP method')\n        ns = SimpleNamespace(**kwargs)\n\n        shap_explainer = ns.analysis.get('shap_explainer', None)\n        if shap_explainer is None:\n            return row_insights, global_insights\n\n        with warnings.catch_warnings():\n            warnings.filterwarnings(\"ignore\", category=DeprecationWarning)\n            shap_values = shap_explainer.shap_values(ns.data, silent=True)\n\n        shap_values_df = pd.DataFrame(shap_values).rename(\n            mapper=lambda i: f\"feature_{i}_impact\", axis='columns')\n\n        if kwargs.get('target_dtype', None) in (dtype.binary, dtype.categorical, dtype.tags):\n            predictions = self.label_encoder.transform(row_insights['prediction'])\n        else:\n            predictions = row_insights['prediction']\n\n        base_response = (predictions - shap_values_df.sum(axis='columns')).mean()\n        global_insights['base_response'] = base_response\n\n        row_insights = pd.concat([row_insights, shap_values_df], axis='columns')\n\n        return row_insights, global_insights\n", "path": "lightwood/analysis/helpers/shap.py"}], "after_files": [{"content": "import warnings\nfrom types import SimpleNamespace\nfrom typing import Dict, Optional, Tuple\n\nimport numpy as np\nimport pandas as pd\nfrom lightwood.analysis.base import BaseAnalysisBlock\nfrom lightwood.api import dtype\nfrom lightwood.api.types import PredictionArguments\nfrom lightwood.data.encoded_ds import EncodedDs\nfrom lightwood.helpers.log import log\nfrom sklearn.preprocessing import LabelEncoder\n\nimport shap\n\n\nclass ShapleyValues(BaseAnalysisBlock):\n    \"\"\"\n    Analysis block that estimates column importance with SHAP (SHapley Additive exPlanations), a game theoretic approach\n    to explain the ouput of any machine learning model. SHAP assigns each feature an importance value for a particular\n    prediction.\n\n    Reference:\n        https://shap.readthedocs.io/en/stable/\n        https://proceedings.neurips.cc/paper/2017/file/8a20a8621978632d76c43dfd28b67767-Paper.pdf\n    \"\"\"\n    label_encoder: LabelEncoder\n\n    def __init__(self, deps: Optional[Tuple] = ...):\n        super().__init__(deps=deps)\n        self.label_encoder = LabelEncoder()\n        self.columns = []\n        self.target = None\n\n    def analyze(self, info: Dict[str, object], **kwargs) -> Dict[str, object]:\n        log.info('Preparing to compute feature importance values with SHAP')\n        ns = SimpleNamespace(**kwargs)\n\n        output_dtype = ns.dtype_dict[ns.target]\n        train_data: EncodedDs = ns.train_data\n\n        if output_dtype in (dtype.integer, dtype.float, dtype.quantity):\n            pass\n        elif output_dtype in (dtype.binary, dtype.categorical, dtype.tags):\n            self.label_encoder.fit(train_data.data_frame[ns.target].values)\n        else:\n            log.warning(f'ShapleyValues analyzers not supported for type: {output_dtype}')\n            return info\n\n        self.target = ns.target\n        self.columns = list(set(ns.dtype_dict.keys()) - {self.target})\n        input_df = train_data.data_frame[self.columns]\n\n        def model(x: np.ndarray) -> np.ndarray:\n            assert(isinstance(x, np.ndarray))\n            df = pd.DataFrame(data=x, columns=self.columns)\n            ds = EncodedDs(encoders=train_data.encoders, data_frame=df, target=train_data.target)\n\n            decoded_predictions = ns.predictor(ds=ds, args=PredictionArguments())\n            if output_dtype in (dtype.integer, dtype.float, dtype.quantity):\n                encoded_predictions = decoded_predictions['prediction'].values\n            elif output_dtype in (dtype.binary, dtype.categorical, dtype.tags):\n                encoded_predictions = self.label_encoder.transform(decoded_predictions['prediction'].values)\n\n            return encoded_predictions\n\n        info['shap_explainer'] = shap.KernelExplainer(model=model, data=input_df)\n\n        return info\n\n    def explain(self,\n                row_insights: pd.DataFrame,\n                global_insights: Dict[str, object],\n                **kwargs\n                ) -> Tuple[pd.DataFrame, Dict[str, object]]:\n        log.info('Computing feature importance values with Kernel SHAP method')\n        ns = SimpleNamespace(**kwargs)\n\n        shap_explainer = ns.analysis.get('shap_explainer', None)\n        if shap_explainer is None:\n            return row_insights, global_insights\n\n        with warnings.catch_warnings():\n            warnings.filterwarnings(\"ignore\", category=DeprecationWarning)\n            shap_values = shap_explainer.shap_values(ns.data[self.columns], silent=True)\n\n        shap_values_df = pd.DataFrame(shap_values).rename(\n            mapper=lambda i: f\"shap_contribution_{self.columns[i]}\", axis='columns')\n\n        if kwargs.get('target_dtype', None) in (dtype.binary, dtype.categorical, dtype.tags):\n            predictions = self.label_encoder.transform(row_insights['prediction'])\n        else:\n            predictions = row_insights['prediction']\n\n        base_response = (predictions - shap_values_df.sum(axis='columns')).mean()\n\n        row_insights = pd.concat([row_insights, shap_values_df], axis='columns')\n        row_insights['shap_base_response'] = base_response\n        row_insights['shap_final_response'] = predictions\n\n        return row_insights, global_insights\n", "path": "lightwood/analysis/helpers/shap.py"}]}