Update action-exclude-records-v1

scripts/match-and-exclude-records-actions_v1.py

@@ -18,6 +18,11 @@ DRIVER_MEMORY = "4g"
 EXECUTOR_MEMORY = "4g"
 MYSQL_JAR_PATH = "jars/mysql-connector-java-8.0.30.jar"
 
+# EXCLUDE VALIDATION FIELD
+EXCLUDE_ROWS_FIELD = "EXCLUDE_VALID"
+# REDONDEO DE DECIMALES
+ROUND_DECIMAL = 2
+
 
 class MatchAndExcludeRecordsAction(ActionInterface):
 
@@ -31,7 +36,7 @@ class MatchAndExcludeRecordsAction(ActionInterface):
         self.exclude_pivot = None
         self.pivot_params = None
         self.ctp_params = None
-        self.tmp_df = None
+        self.output = None
         self.config_params = ["max-records-per-combinations", "max-timeout-per-combinations", "exclude-entity-pivot"]
 
     def parser(self, descriptor: Dict[str, Any]):
@@ -89,9 +94,13 @@ class MatchAndExcludeRecordsAction(ActionInterface):
         # Agregar un prefijo a cada columna, tanto del pivot como contraparte. Actualizar campos del input
         # pivot: 'PIVOT_', contraparte: 'COUNTERPART_'
         for column in pivot_df.columns:
+            if column == EXCLUDE_ROWS_FIELD:
+                continue
             pivot_df = pivot_df.withColumnRenamed(column, "PIVOT_"+column)
 
         for column in ctp_df.columns:
+            if column == EXCLUDE_ROWS_FIELD:
+                continue
             ctp_df = ctp_df.withColumnRenamed(column, "COUNTERPART_"+column)
 
         for key_p, key_c in zip(self.pivot_params.keys(), self.ctp_params.keys()):
@@ -102,8 +111,19 @@ class MatchAndExcludeRecordsAction(ActionInterface):
                 self.pivot_params[key_p] = ["PIVOT_"+column for column in self.pivot_params[key_p]]
                 self.ctp_params[key_c] = ["COUNTERPART_" + column for column in self.ctp_params[key_c]]
 
-        from pyspark.sql.functions import sum, collect_list, round, udf, array_except
-        from pyspark.sql.types import StringType, ArrayType, IntegerType
+        from pyspark.sql.functions import sum, collect_list, round, udf, array_except, size, col, array
+        from pyspark.sql.types import ArrayType, IntegerType, LongType
+
+        pivot_cols = self.pivot_params["columns-transaction"].copy()
+        if self.pivot_params["amount-column"] in pivot_cols:
+            pivot_cols.remove(self.pivot_params["amount-column"])
+
+        ctp_cols = self.ctp_params["columns-transaction"].copy()
+        if self.ctp_params["amount-column"] in ctp_cols:
+            ctp_cols.remove(self.ctp_params["amount-column"])
+
+        timeout = self.timeout
+        max_combinations = self.max_combinations
 
         # Ejecutamos lógica de excluir registros
         if len(self.pivot_params["columns-group"]) == 0 and len(self.ctp_params["columns-group"]) == 0:
@@ -111,63 +131,68 @@ class MatchAndExcludeRecordsAction(ActionInterface):
 
         # Caso: 1 - Muchos
         elif len(self.pivot_params["columns-group"]) == 0 and len(self.ctp_params["columns-group"]) > 0:
-            ctp_df_2 = ctp_df.groupby(self.ctp_params["columns-group"]). \
-                agg(round(sum(self.ctp_params["amount-column"]), 2).alias(self.ctp_params["amount-column"]),
+            ctp_df2 = ctp_df.groupby(self.ctp_params["columns-group"]). \
+                agg(round(sum(self.ctp_params["amount-column"]), ROUND_DECIMAL).alias(self.ctp_params["amount-column"]),
                     collect_list(self.ctp_params["id-column"]).alias(self.ctp_params["id-column"]))
-            ctp_df_2.show()
+
+            pivot_df2 = pivot_df.dropDuplicates(subset=pivot_cols)
 
         # Caso: Muchos - 1
         elif len(self.pivot_params["columns-group"]) > 0 and len(self.ctp_params["columns-group"]) == 0:
             pivot_df2 = pivot_df.groupby(self.pivot_params["columns-group"]).\
-                agg(round(sum(self.pivot_params["amount-column"]), 2).alias(self.pivot_params["amount-column"]),
+                agg(round(sum(self.pivot_params["amount-column"]), ROUND_DECIMAL).alias(self.pivot_params["amount-column"]),
                     collect_list(self.pivot_params["id-column"]).alias(self.pivot_params["id-column"]))
-            pivot_df2.show()
+
+            ctp_df2 = ctp_df.dropDuplicates(subset=ctp_cols)
+
         # Caso: Muchos - Muchos
         elif len(self.pivot_params["columns-group"]) > 0 and len(self.ctp_params["columns-group"]) > 0:
             pivot_df2 = pivot_df.groupby(self.pivot_params["columns-group"]). \
-                agg(round(sum(self.pivot_params["amount-column"]), 2).alias(self.pivot_params["amount-column"]),
+                agg(round(sum(self.pivot_params["amount-column"]), ROUND_DECIMAL).alias(self.pivot_params["amount-column"]),
                     collect_list(self.pivot_params["id-column"]).alias(self.pivot_params["id-column"]))
-            ctp_df_2 = ctp_df.groupby(self.ctp_params["columns-group"]). \
-                agg(round(sum(self.ctp_params["amount-column"]), 2).alias(self.ctp_params["amount-column"]),
+            ctp_df2 = ctp_df.groupby(self.ctp_params["columns-group"]). \
+                agg(round(sum(self.ctp_params["amount-column"]), ROUND_DECIMAL).alias(self.ctp_params["amount-column"]),
                     collect_list(self.ctp_params["id-column"]).alias(self.ctp_params["id-column"]))
 
-            pivot_cols = self.pivot_params["columns-transaction"].copy()
-            if self.pivot_params["amount-column"] in pivot_cols:
-                pivot_cols.remove(self.pivot_params["amount-column"])
-
-            ctp_cols = self.ctp_params["columns-transaction"].copy()
-            if self.ctp_params["amount-column"] in ctp_cols:
-                ctp_cols.remove(self.ctp_params["amount-column"])
-
-            condition = [pivot_df2[col1] == ctp_df_2[col2] for col1, col2 in zip(pivot_cols, ctp_cols)]
-            merged = pivot_df2.join(ctp_df_2, condition)
-
-            merged = merged.withColumn("DIFF", pivot_df2[self.pivot_params["amount-column"]] - ctp_df_2[self.ctp_params["amount-column"]])
-
-            merged_df = merged.withColumn("DIFF", round(merged["DIFF"], 2))
-            if self.exclude_pivot:
-                df_pandas = pivot_df.toPandas()
-                group_cols = self.pivot_params["columns-group"]
-                amount_col = self.pivot_params["amount-column"]
-                id_col = self.pivot_params["id-column"]
-            else:
-                df_pandas = ctp_df.toPandas()
-                group_cols = self.ctp_params["columns-group"]
-                amount_col = self.ctp_params["amount-column"]
-                id_col = self.ctp_params["id-column"]
-            timeout = self.timeout
-            max_combinations = self.max_combinations
-            custom_udf = udf(lambda diff: custom_apply(diff, df_pandas, group_cols, amount_col, id_col, timeout,
-                                                       max_combinations), ArrayType(IntegerType()))
-            merged_df = merged_df.withColumn("LISTA_DIFF", custom_udf(merged_df["DIFF"]))
-            merged_df = merged_df.withColumn("LIST_PIVOT_ID", array_except(merged_df[self.pivot_params["id-column"]],
-                                                                           merged_df["LISTA_DIFF"]))
-            merged_df = merged_df.withColumn("LIST_COUNTERPART_ID", array_except(merged_df[self.ctp_params["id-column"]],
-                                                                                 merged_df["LISTA_DIFF"]))
-            merged_df.show()
+        condition = [pivot_df2[col1] == ctp_df2[col2] for col1, col2 in zip(pivot_cols, ctp_cols)]
+        merged = pivot_df2.join(ctp_df2, condition)
+
+        merged = merged.withColumn("DIFF", pivot_df2[self.pivot_params["amount-column"]] - ctp_df2[self.ctp_params["amount-column"]])
+
+        merged_df = merged.withColumn("DIFF", round(merged["DIFF"], ROUND_DECIMAL))
+        if self.exclude_pivot:
+            df_pandas = pivot_df.toPandas()
+            group_cols = self.pivot_params["columns-group"]
+            amount_col = self.pivot_params["amount-column"]
+            id_col = self.pivot_params["id-column"]
+        else:
+            df_pandas = ctp_df.toPandas()
+            group_cols = self.ctp_params["columns-group"]
+            amount_col = self.ctp_params["amount-column"]
+            id_col = self.ctp_params["id-column"]
+
+        custom_udf = udf(lambda diff: custom_apply(diff, df_pandas, group_cols, amount_col, id_col, timeout,
+                                                   max_combinations), ArrayType(IntegerType()))
+        merged_df = merged_df.withColumn("LISTA_DIFF", custom_udf(merged_df["DIFF"]))
+        merged_df = merged_df.filter((col("DIFF") == 0) | ((col("DIFF") != 0) & (size(col("LISTA_DIFF")) > 0)))
+
+        if self.exclude_pivot:
+            merged_df = merged_df.withColumn("INTER_PIVOT_ID", array_except(merged_df[self.pivot_params["id-column"]],
+                                                                            merged_df["LISTA_DIFF"]))
+            merged_df = merged_df.withColumnRenamed(self.ctp_params["id-column"], "INTER_CTP_ID")
+            if merged_df.schema["INTER_CTP_ID"].dataType == LongType():
+                merged_df = merged_df.withColumn("INTER_CTP_ID", array(col("INTER_CTP_ID")).cast(ArrayType(LongType())))
+        else:
+            merged_df = merged_df.withColumn("INTER_CTP_ID", array_except(merged_df[self.ctp_params["id-column"]],
+                                                                          merged_df["LISTA_DIFF"]))
+            merged_df = merged_df.withColumnRenamed(self.pivot_params["id-column"], "INTER_PIVOT_ID")
+            if merged_df.schema["INTER_PIVOT_ID"].dataType == LongType():
+                merged_df = merged_df.withColumn("INTER_PIVOT_ID", array(col("INTER_PIVOT_ID")).cast(ArrayType(LongType())))
+        self.output = merged_df
 
     def response(self):
-        print("Llegue al response3")
+        self.output.show()
+        return self.output
 
     def createSession(self, name: str = "app_engine_spark"):
         try:
@@ -187,17 +212,21 @@ class MatchAndExcludeRecordsAction(ActionInterface):
 
 
 def custom_apply(raw, df_pandas, group_cols, amount_col, id_col, timeout, max_combinations):
-    total_cols = group_cols.copy()
-    total_cols.append(amount_col)
-    total_cols.append(id_col)
-    result = df_pandas[total_cols].groupby(group_cols).apply(custom_func, diff=raw, amount_field=amount_col, id_field=id_col,
-                                                             timeout=timeout, max_combinations=max_combinations).values
-    resp = ",".join(map(str, result)) if len(result) > 0 else ""
-    print(resp)
-    return [int(elem) for elem in resp[1:-1].split(",")]
+    if raw == 0:
+        return []
+    else:
+        total_cols = group_cols.copy()
+        total_cols.append(amount_col)
+        total_cols.append(id_col)
+        total_cols.append(EXCLUDE_ROWS_FIELD)
+        result = df_pandas[total_cols].groupby(group_cols).apply(custom_func, diff=raw, amount_field=amount_col, id_field=id_col,
+                                                                 timeout=timeout, max_combinations=max_combinations).values
+        resp = ",".join(map(str, result)) if len(result) > 0 else ""
+        return [int(elem) for elem in resp[1:-1].split(",")] if resp != "" else []
 
 
 def custom_func(group, diff, amount_field, id_field, timeout, max_combinations):
+    group[amount_field] = group[amount_field].astype(float)
     group = group.reset_index(drop=True)
     values = group[amount_field].values
     indexs = group.index.values
@@ -214,11 +243,13 @@ def custom_func(group, diff, amount_field, id_field, timeout, max_combinations):
             for comb in combinations(indexs, i):
                 if stop_event:  # Verificar si se ha establecido la señal de detención
                     break
-
-                if np.sum(values[list(comb)]).round(2) == diff:
-                    final = group[group.index.isin(comb)][id_field].tolist()
-                    stop_event = True  # Establecer la señal de detención
-                    break
+                if np.sum(values[list(comb)]).round(ROUND_DECIMAL) == diff:
+                    # Validamos si la combinación son de registros validados 'EXCLUDE_VALID'
+                    final = group[group.index.isin(comb)]
+                    if (final[EXCLUDE_ROWS_FIELD] == 'S').all():
+                        final = final[id_field].tolist()
+                        stop_event = True  # Establecer la señal de detención
+                        break
 
         return None
 
@@ -229,7 +260,7 @@ def custom_func(group, diff, amount_field, id_field, timeout, max_combinations):
 
         for future in futures:
             try:
-                future.result(timeout=timeout)  # Esperar hasta 60 segundos por cada tarea
+                future.result(timeout=timeout)
             except TimeoutError:
                 stop_event = True  # Establecer la señal de detención si se alcanza el tiempo límite
                 break
@@ -239,7 +270,6 @@ def custom_func(group, diff, amount_field, id_field, timeout, max_combinations):
 
     if time.time() - start_time >= timeout:
         return None  # Devolver None si se alcanzó el tiempo límite
-
     return final