Fix corrupted csv from EvaluationResult.save() (#2854)

* fix corrupted csv if text contains \r chars; make csv serialization configurable

* Update Documentation & Code Style

* incorporate feedback

* Update Documentation & Code Style

* adjust columns to be converted during loading

Co-authored-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com>

docs/_src/api/api/primitives.md

@@ -523,7 +523,7 @@ In Question Answering, to enforce that the retrieved document is considered corr
 #### EvaluationResult.save
 
 ```python
-def save(out_dir: Union[str, Path])
+def save(out_dir: Union[str, Path], **to_csv_kwargs)
 ```
 
 Saves the evaluation result.
@@ -533,6 +533,9 @@ The result of each node is saved in a separate csv with file name {node_name}.cs
 **Arguments**:
 
 - `out_dir`: Path to the target folder the csvs will be saved.
+- `to_csv_kwargs`: kwargs to be passed to pd.DataFrame.to_csv(). See https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.to_csv.html.
+This method uses different default values than pd.DataFrame.to_csv() for the following parameters:
+index=False, quoting=csv.QUOTE_NONNUMERIC (to avoid problems with \r chars)
 
 <a id="schema.EvaluationResult.load"></a>
 
@@ -540,7 +543,7 @@ The result of each node is saved in a separate csv with file name {node_name}.cs
 
 ```python
 @classmethod
-def load(cls, load_dir: Union[str, Path])
+def load(cls, load_dir: Union[str, Path], **read_csv_kwargs)
 ```
 
 Loads the evaluation result from disk. Expects one csv file per node. See save() for further information.
@@ -548,4 +551,8 @@ Loads the evaluation result from disk. Expects one csv file per node. See save()
 **Arguments**:
 
 - `load_dir`: The directory containing the csv files.
+- `read_csv_kwargs`: kwargs to be passed to pd.read_csv(). See https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.read_csv.html.
+This method uses different default values than pd.read_csv() for the following parameters:
+header=0, converters=CONVERTERS
+where CONVERTERS is a dictionary mapping all array typed columns to ast.literal_eval.
 

haystack/schema.py

@@ -1,4 +1,5 @@
 from __future__ import annotations
+import csv
 
 import typing
 from typing import Any, Optional, Dict, List, Union
@@ -1346,12 +1347,15 @@ class EvaluationResult:
         metrics_df = pd.DataFrame.from_records(metrics, index=documents["multilabel_id"].unique())
         return metrics_df
 
-    def save(self, out_dir: Union[str, Path]):
+    def save(self, out_dir: Union[str, Path], **to_csv_kwargs):
         """
         Saves the evaluation result.
         The result of each node is saved in a separate csv with file name {node_name}.csv to the out_dir folder.
 
         :param out_dir: Path to the target folder the csvs will be saved.
+        :param to_csv_kwargs: kwargs to be passed to pd.DataFrame.to_csv(). See https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.to_csv.html.
+                        This method uses different default values than pd.DataFrame.to_csv() for the following parameters:
+                        index=False, quoting=csv.QUOTE_NONNUMERIC (to avoid problems with \r chars)
         """
         out_dir = out_dir if isinstance(out_dir, Path) else Path(out_dir)
         logger.info(f"Saving evaluation results to {out_dir}")
@@ -1359,29 +1363,45 @@ class EvaluationResult:
             out_dir.mkdir(parents=True)
         for node_name, df in self.node_results.items():
             target_path = out_dir / f"{node_name}.csv"
-            df.to_csv(target_path, index=False, header=True)
+            default_to_csv_kwargs = {
+                "index": False,
+                "quoting": csv.QUOTE_NONNUMERIC,  # avoids problems with \r chars in texts by enclosing all string values in quotes
+            }
+            to_csv_kwargs = {**default_to_csv_kwargs, **to_csv_kwargs}
+            df.to_csv(target_path, **to_csv_kwargs)
 
     @classmethod
-    def load(cls, load_dir: Union[str, Path]):
+    def load(cls, load_dir: Union[str, Path], **read_csv_kwargs):
         """
         Loads the evaluation result from disk. Expects one csv file per node. See save() for further information.
 
         :param load_dir: The directory containing the csv files.
+        :param read_csv_kwargs: kwargs to be passed to pd.read_csv(). See https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.read_csv.html.
+                                This method uses different default values than pd.read_csv() for the following parameters:
+                                header=0, converters=CONVERTERS
+                                where CONVERTERS is a dictionary mapping all array typed columns to ast.literal_eval.
         """
         load_dir = load_dir if isinstance(load_dir, Path) else Path(load_dir)
         csv_files = [file for file in load_dir.iterdir() if file.is_file() and file.suffix == ".csv"]
         cols_to_convert = [
+            "filters",
             "gold_document_ids",
+            "gold_custom_document_ids",
             "gold_contexts",
             "gold_answers",
+            "gold_documents_id_match",
             "gold_offsets_in_documents",
             "gold_answers_exact_match",
             "gold_answers_f1",
-            "gold_answers_document_id_match",
-            "gold_context_similarity",
+            "gold_answers_sas",
+            "gold_answers_match",
+            "gold_contexts_similarity",
+            "offsets_in_document",
         ]
         converters = dict.fromkeys(cols_to_convert, ast.literal_eval)
-        node_results = {file.stem: pd.read_csv(file, header=0, converters=converters) for file in csv_files}
+        default_read_csv_kwargs = {"converters": converters, "header": 0}
+        read_csv_kwargs = {**default_read_csv_kwargs, **read_csv_kwargs}
+        node_results = {file.stem: pd.read_csv(file, **read_csv_kwargs) for file in csv_files}
         # backward compatibility mappings
         for df in node_results.values():
             df.rename(columns={"gold_document_contents": "gold_contexts", "content": "context"}, inplace=True)