fix: Perform the last action with the column name modified.

sapientml_core/explain/main.py

@@ -12,11 +12,13 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
+import collections
 from typing import Literal, Optional
 
 import pandas as pd
 from sapientml.params import CancellationToken
 from sapientml.util.logging import setup_logger
+from sapientml_preprocess.generator import check_cols_has_symbols, remove_symbols
 
 from .AutoEDA import EDA
 from .AutoVisualization import AutoVisualization_Class
@@ -81,12 +83,46 @@ def process(
     if visualization:
         # Call AutoVisualization to generate visualization codes
         AV = AutoVisualization_Class()
-        visualization_code = AV.AutoVisualization(
-            df=dataframe,
-            target_columns=target_columns,
-            problem_type=problem_type,
-            ignore_columns=ignore_columns,
-        )
+        cols_has_symbols = check_cols_has_symbols(dataframe.columns.to_list())
+        no_symbol_columns = [col for col in dataframe.columns.values if col not in cols_has_symbols]
+        rename_dict = {}
+        if cols_has_symbols:
+            df = list(
+                dataframe.rename(columns=lambda col: remove_symbols(col) if col in cols_has_symbols else col).columns
+            )
+            rename_dict = {}
+            same_column = {k: v for k, v in collections.Counter(df).items() if v > 1 and k in no_symbol_columns}
+            for target, org_column in zip(df, dataframe.columns.tolist()):
+                if target in same_column.keys():
+                    rename_dict[target + str(same_column[target] - 1)] = org_column
+                    same_column[target] = same_column[target] - 1
+                else:
+                    rename_dict[target] = org_column
+
+            df = list(rename_dict.values())
+
+            if len(rename_dict) != 0:
+                col_has_target = [target for target in rename_dict.keys() if rename_dict.values() == target_columns]
+                visualization_code = AV.AutoVisualization(
+                    df=dataframe,
+                    target_columns=col_has_target,
+                    problem_type=problem_type,
+                    ignore_columns=ignore_columns,
+                )
+            else:
+                visualization_code = AV.AutoVisualization(
+                    df=dataframe,
+                    target_columns=target_columns,
+                    problem_type=problem_type,
+                    ignore_columns=ignore_columns,
+                )
+        else:
+            visualization_code = AV.AutoVisualization(
+                df=dataframe,
+                target_columns=target_columns,
+                problem_type=problem_type,
+                ignore_columns=ignore_columns,
+            )
     else:
         visualization_code = None
 

sapientml_core/generator.py

@@ -221,8 +221,43 @@ def generate_pipeline(self, dataset: Dataset, task: Task):
         for pipeline in sapientml_results:
             pipeline.validation = code_block.validation + pipeline.validation
             pipeline.test = code_block.test + pipeline.test
-            pipeline.train = code_block.train + pipeline.train
             pipeline.predict = code_block.predict + pipeline.predict
+            if "cols_has_symbols" in pipeline.test:
+                pipeline.test = pipeline.test.replace(
+                    '"feature": feature_train.columns',
+                    '"feature": feature_train.rename(columns=rename_symbol_cols).columns',
+                )
+                pipeline.test = pipeline.test.replace(
+                    "prediction.to_csv", "prediction.rename(columns=rename_symbol_cols).to_csv"
+                )
+
+                pipeline.predict = pipeline.predict.replace(
+                    '"feature": feature_train.columns',
+                    '"feature": feature_train.rename(columns=rename_symbol_cols).columns',
+                )
+                pipeline.predict = pipeline.predict.replace(
+                    "prediction.to_csv", "prediction.rename(columns=rename_symbol_cols).to_csv"
+                )
+
+                pipeline.validation = pipeline.validation.replace(
+                    '"feature": feature_train.columns',
+                    '"feature": feature_train.rename(columns=rename_symbol_cols).columns',
+                )
+                pipeline.validation = pipeline.validation.replace(
+                    "prediction.to_csv", "prediction.rename(columns=rename_symbol_cols).to_csv"
+                )
+
+                def replace_targets(match_obj):
+                    return match_obj[0].replace(
+                        "TARGET_COLUMNS", "[rename_symbol_cols.get(v, v) for v in TARGET_COLUMNS]"
+                    )
+
+                pat = r"prediction = pd.DataFrame\(y_prob, columns=.?TARGET_COLUMNS.*, index=feature_test.index\)"
+                pipeline.test = re.sub(pat, replace_targets, pipeline.test)
+                pipeline.predict = re.sub(pat, replace_targets, pipeline.predict)
+                pipeline.validation = re.sub(pat, replace_targets, pipeline.validation)
+
+            pipeline.train = code_block.train + pipeline.train
             result_pipelines.append(pipeline)
 
         logger.info("Executing generated pipelines...")