diff --git a/fieldExtraction/src/testbed/testbed_prov_info_to_json.ipynb b/fieldExtraction/src/testbed/testbed_prov_info_to_json.ipynb
new file mode 100644
index 0000000..8179eca
--- /dev/null
+++ b/fieldExtraction/src/testbed/testbed_prov_info_to_json.ipynb
@@ -0,0 +1,9357 @@
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+ "text": [
+ "AWS credentials not found\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "/home/arg/20250505_testbed_providers_to_json/fieldExtraction/.venv/lib/python3.12/site-packages/openpyxl/worksheet/_reader.py:329: UserWarning: Data Validation extension is not supported and will be removed\n",
+ " warn(msg)\n"
+ ]
+ }
+ ],
+ "source": [
+ "import os\n",
+ "os.chdir(\"../..\") # Change to the fieldExtraction directory\n",
+ "\n",
+ "import pandas as pd\n",
+ "from src.testbed.testbed_utils import group_providers_into_json_array\n",
+ "\n",
+ "testbed_filename = \"src/testbed/v5_DoczyAI_all_test_bed_LIVE_4.29.25.xlsx\"\n",
+ "\n",
+ "df = pd.read_excel(testbed_filename)\n",
+ "\n",
+ "df_out = group_providers_into_json_array(df)"
+ ]
+ },
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+ "3173 2014-01-01 COMM PPO - PeaceHealth Multi TIN - ... \n",
+ "3174 2014-01-01 COMM PPO - PeaceHealth Multi TIN - ... \n",
+ "3175 AMD - IV - HSA eff 2017 (amd 4) - Broward MU.txt \n",
+ "3176 CustomFac_AMD - AMD - UNIVERSITY MEDICAL CENTE... \n",
+ "3177 CustomFac_AMD - AMD - UNIVERSITY MEDICAL CENTE... \n",
+ "\n",
+ " CONTRACT_TITLE \\\n",
+ "0 PARTICIPATING PROVIDER AGREEMENT \n",
+ "1 PARTICIPATING PROVIDER AGREEMENT \n",
+ "2 PARTICIPATING PROVIDER AGREEMENT \n",
+ "3 PARTICIPATING PROVIDER AGREEMENT \n",
+ "4 PARTICIPATING PROVIDER AGREEMENT \n",
+ "... ... \n",
+ "3173 ODS HEALTH PLAN, INC. PARTICIPATING PROVIDER A... \n",
+ "3174 ODS HEALTH PLAN, INC. PARTICIPATING PROVIDER A... \n",
+ "3175 FOURTH AMENDMENT TO THE MOLINA HEALTHCARE OF F... \n",
+ "3176 AMENDMENT NUMBER001 TO MOLINA HEALTHCARE OF TE... \n",
+ "3177 AMENDMENT NUMBER001 TO MOLINA HEALTHCARE OF TE... \n",
+ "\n",
+ " CONTRACT_AMENDMENT_NUM CLIENT_NAME PAYER_NAME \\\n",
+ "0 NaN Test-Client Magnolia Health Plan, Inc. \n",
+ "1 NaN Test-Client Magnolia Health Plan, Inc. \n",
+ "2 NaN Test-Client Magnolia Health Plan, Inc. \n",
+ "3 NaN Test-Client Magnolia Health Plan, Inc. \n",
+ "4 NaN Test-Client Magnolia Health Plan, Inc. \n",
+ "... ... ... ... \n",
+ "3173 NaN Test-Client ODS Health Plan, Inc. \n",
+ "3174 NaN Test-Client ODS Health Plan, Inc. \n",
+ "3175 4.0 Test-Client Molina Healthcare of Florida, Inc. \n",
+ "3176 1.0 Test-Client Molina Healthcare of Texas, Inc. \n",
+ "3177 1.0 Test-Client Molina Healthcare of Texas, Inc. \n",
+ "\n",
+ " PROV_GROUP_TIN PROV_GROUP_NPI \\\n",
+ "0 00125434 640908605 \n",
+ "1 00125435 640908605 \n",
+ "2 00125436 640908605 \n",
+ "3 00125437 640908605 \n",
+ "4 00125438 640908605 \n",
+ "... ... ... \n",
+ "3173 930395583 1346237971 \n",
+ "3174 930395583 1346237971 \n",
+ "3175 596012065 1285662239 \n",
+ "3176 746000756 NaN \n",
+ "3177 746000756 NaN \n",
+ "\n",
+ " PROV_GROUP_NAME_FULL \\\n",
+ "0 Central MS Diagnostic, LLC \n",
+ "1 Central MS Diagnostic, LLC \n",
+ "2 Central MS Diagnostic, LLC \n",
+ "3 Central MS Diagnostic, LLC \n",
+ "4 Central MS Diagnostic, LLC \n",
+ "... ... \n",
+ "3173 PeaceHealth \n",
+ "3174 PeaceHealth \n",
+ "3175 North Broward Hospital District DBA Broward He... \n",
+ "3176 El Paso County Hospital District, DBA, Univers... \n",
+ "3177 El Paso County Hospital District, DBA, Univers... \n",
+ "\n",
+ " PROV_OTHER_TIN \\\n",
+ "0 NaN \n",
+ "1 NaN \n",
+ "2 NaN \n",
+ "3 NaN \n",
+ "4 NaN \n",
+ "... ... \n",
+ "3173 261871008| 930975147| 910564987| 931173927| 93... \n",
+ "3174 261871008| 930975147| 910564987| 931173927| 93... \n",
+ "3175 NaN \n",
+ "3176 NaN \n",
+ "3177 NaN \n",
+ "\n",
+ " PROV_OTHER_NPI ... CONTRACT_SID \\\n",
+ "0 NaN ... NaN \n",
+ "1 NaN ... NaN \n",
+ "2 NaN ... NaN \n",
+ "3 NaN ... NaN \n",
+ "4 NaN ... NaN \n",
+ "... ... ... ... \n",
+ "3173 1083888515| 1578552402| 1225019011| 1215918719... ... NaN \n",
+ "3174 1083888515| 1578552402| 1225019011| 1215918719... ... NaN \n",
+ "3175 1578761995| 1174551147| 1265460232| 1568490506... ... NaN \n",
+ "3176 NaN ... NaN \n",
+ "3177 NaN ... NaN \n",
+ "\n",
+ " CONTRACT_CLASS AARETE_DERIVED_CONTRACT_CLASS \\\n",
+ "0 NaN NaN \n",
+ "1 NaN NaN \n",
+ "2 NaN NaN \n",
+ "3 NaN NaN \n",
+ "4 NaN NaN \n",
+ "... ... ... \n",
+ "3173 NaN NaN \n",
+ "3174 NaN NaN \n",
+ "3175 NaN NaN \n",
+ "3176 NaN NaN \n",
+ "3177 NaN NaN \n",
+ "\n",
+ " NUM_AVAILABLE_SIGNATORY_LINE_COUNT NUM_SIGNED_SIGNATORY_LINE_COUNT \\\n",
+ "0 NaN NaN \n",
+ "1 NaN NaN \n",
+ "2 NaN NaN \n",
+ "3 NaN NaN \n",
+ "4 NaN NaN \n",
+ "... ... ... \n",
+ "3173 NaN NaN \n",
+ "3174 NaN NaN \n",
+ "3175 NaN NaN \n",
+ "3176 NaN NaN \n",
+ "3177 NaN NaN \n",
+ "\n",
+ " AARETE_DERIVED_SIGNATORY_COMPLETE_IND \\\n",
+ "0 NaN \n",
+ "1 NaN \n",
+ "2 NaN \n",
+ "3 NaN \n",
+ "4 NaN \n",
+ "... ... \n",
+ "3173 NaN \n",
+ "3174 NaN \n",
+ "3175 NaN \n",
+ "3176 NaN \n",
+ "3177 NaN \n",
+ "\n",
+ " AARETE_DERIVED_ADDITION_RATE_CHANGE_TIMELINE FEE_SCHEDULE \\\n",
+ "0 NaN NaN \n",
+ "1 NaN NaN \n",
+ "2 NaN NaN \n",
+ "3 NaN NaN \n",
+ "4 NaN NaN \n",
+ "... ... ... \n",
+ "3173 NaN NaN \n",
+ "3174 NaN NaN \n",
+ "3175 NaN NaN \n",
+ "3176 NaN NaN \n",
+ "3177 NaN NaN \n",
+ "\n",
+ " FEE_SCHEDULE_VERSION OUTLIER_TERMS \n",
+ "0 NaN NaN \n",
+ "1 NaN NaN \n",
+ "2 NaN NaN \n",
+ "3 NaN NaN \n",
+ "4 NaN NaN \n",
+ "... ... ... \n",
+ "3173 NaN NaN \n",
+ "3174 NaN NaN \n",
+ "3175 NaN NaN \n",
+ "3176 NaN NaN \n",
+ "3177 NaN NaN \n",
+ "\n",
+ "[3178 rows x 144 columns]"
+ ]
+ },
+ "execution_count": 3,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "group_providers_into_json_array(df)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "id": "4504219a",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "group_providers_into_json_array(df).to_excel(\"src/testbed/v5_DoczyAI_all_test_bed_LIVE_4.29.25_W_PROV_INFO_JSON.xlsx\", index=False)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "0b6f9690",
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": ".venv",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.12.7"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/fieldExtraction/src/testbed/testbed_utils.py b/fieldExtraction/src/testbed/testbed_utils.py
index 073f6ee..8f101da 100644
--- a/fieldExtraction/src/testbed/testbed_utils.py
+++ b/fieldExtraction/src/testbed/testbed_utils.py
@@ -1,6 +1,7 @@
import pandas as pd
import src.utils.string_utils as string_utils
+import itertools
def convert_to_float(value):
@@ -27,6 +28,100 @@ def testbed_preprocess(df):
return df
+def group_providers_into_json_array(df):
+ """Groups provider information from a DataFrame into a JSON array format and appends it as a new column.
+
+ This function processes a DataFrame containing provider information, groups the data by the "FILE_NAME" column,
+ and creates a JSON array for each group. The JSON array includes details about the group provider and other
+ associated providers. The resulting JSON array is added as a new column, "PROV_INFO_JSON", to the DataFrame.
+
+ Args:
+ df (pandas.DataFrame): The input DataFrame containing the following columns:
+ - "FILE_NAME": Identifier for grouping rows.
+ - "PROV_GROUP_TIN": Tax Identification Number (TIN) of the group provider.
+ - "PROV_GROUP_NPI": National Provider Identifier (NPI) of the group provider.
+ - "PROV_GROUP_NAME_FULL": Full name of the group provider.
+ - "PROV_OTHER_TIN": Pipe-separated TINs of other providers.
+ - "PROV_OTHER_NPI": Pipe-separated NPIs of other providers.
+ - "PROV_OTHER_NAME_FULL": Pipe-separated full names of other providers.
+
+ Returns:
+ pandas.DataFrame: A modified DataFrame with an additional column "PROV_INFO_JSON",
+ which contains a JSON array for each "FILE_NAME" group. Each JSON array includes:
+ - "TIN": TIN of the provider (or "N/A" if not available).
+ - "NPI": NPI of the provider (or "N/A" if not available).
+ - "NAME": Full name of the provider (or "N/A" if not available).
+ - "IS_GROUP": Boolean indicating whether the provider is the group provider (True)
+ or an associated provider (False).
+
+ Notes:
+ - Missing or NaN values in the input DataFrame are replaced with "N/A" in the JSON output.
+ - If the lengths of "PROV_OTHER_TIN", "PROV_OTHER_NPI", and "PROV_OTHER_NAME_FULL" are not equal,
+ missing values are filled with "N/A" using itertools.zip_longest.
+ - If the OTHER columns are misaligned with one another, the function will NOT
+ be able to correct them. It will simply fill in the gaps with "N/A".
+ """
+
+ df = df.copy()
+ # Create a dictionary to hold provider data in JSON array format for each file
+ group_providers_dict = {}
+
+ for group, grouped_df in df.groupby("FILE_NAME"):
+ json_dict_list = []
+
+ # Get the first row of the grouped DataFrame
+ first_row = grouped_df.iloc[0]
+
+
+ # Extract TIN/NPI/NAME for group provider.
+ group_tin = str(first_row["PROV_GROUP_TIN"]).replace("nan", "N/A")
+ group_npi = str(first_row["PROV_GROUP_NPI"]).replace("nan", "N/A")
+ group_name_full = str(first_row["PROV_GROUP_NAME_FULL"])
+
+ # Append the group provider's TIN/NPI/NAME to the list.
+ json_dict_list.append({
+ "TIN": group_tin,
+ "NPI": group_npi,
+ "NAME": group_name_full, "IS_GROUP": True})
+
+ # Create lists of TIN/NPI/NAME for other providers.
+ # Split on pipe, then strip whitespace.
+ other_tins = str(first_row["PROV_OTHER_TIN"]).split("|")
+ other_npis = str(first_row["PROV_OTHER_NPI"]).split("|")
+ other_names = str(first_row["PROV_OTHER_NAME_FULL"]).split("|")
+ other_tins = [tin.strip().replace("nan", "N/A") for tin in other_tins]
+ other_npis = [npi.strip().replace("nan", "N/A") for npi in other_npis]
+ other_names = [name.strip().replace("nan", "N/A") for name in other_names]
+
+ # Append each other provider's TIN/NPI/NAME to the list.
+ # Use zip to iterate over the three lists in parallel.
+ # IF the lengths of the lists are not equal, use itertools.zip_longest to fill in the gaps with N/A.
+ for tin, npi, name in itertools.zip_longest(other_tins, other_npis, other_names, fillvalue="N/A"):
+ json_dict_list.append({
+ "TIN": tin,
+ "NPI": npi,
+ "NAME": name,
+ "IS_GROUP": False
+ })
+
+ group_providers_dict[group] = json_dict_list
+
+ # Add JSON data back to original DataFrame
+ df["PROV_INFO_JSON"] = df["FILE_NAME"].map(group_providers_dict)
+
+
+ ## Reorder columns to place PROV_INFO_JSON right after PROV_OTHER_NAME_FULL
+ cols = list(df.columns)
+ # Find the position of PROV_OTHER_NAME_FULL
+ prov_other_name_full_pos = cols.index("PROV_OTHER_NAME_FULL")
+ # Remove PROV_INFO_JSON from its current position
+ cols.remove("PROV_INFO_JSON")
+ # Insert it after PROV_OTHER_NAME_FULL
+ cols.insert(prov_other_name_full_pos + 1, "PROV_INFO_JSON")
+ # Reorder the DataFrame
+ df = df[cols]
+
+ return df
def calculate_field_metrics(testbed_df, results_df, field):
"""Calculate precision, recall, and accuracy for a single field.