diff --git "a/subproduct_prediction/Pipeline.ipynb" "b/subproduct_prediction/Pipeline.ipynb"
new file mode 100644--- /dev/null
+++ "b/subproduct_prediction/Pipeline.ipynb"
@@ -0,0 +1,6074 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "id": "299ffd7f-502b-4183-9536-4e47654baae8",
+ "metadata": {
+ "id": "299ffd7f-502b-4183-9536-4e47654baae8"
+ },
+ "source": [
+ "#### Importing the necessary libraries"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "id": "e27f22a3-f39e-4007-a048-56ccc9af915e",
+ "metadata": {
+ "id": "e27f22a3-f39e-4007-a048-56ccc9af915e"
+ },
+ "outputs": [],
+ "source": [
+ "import torch\n",
+ "import pickle\n",
+ "import pandas as pd\n",
+ "from tqdm import tqdm\n",
+ "from sklearn.pipeline import Pipeline\n",
+ "from transformers import pipeline\n",
+ "from sklearn.metrics import accuracy_score, precision_score"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "7b6553e4-339a-4003-b6f9-4aa52d2818c0",
+ "metadata": {
+ "id": "7b6553e4-339a-4003-b6f9-4aa52d2818c0"
+ },
+ "source": [
+ "#### Loading 5 product models"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "id": "bd40a9e0-faab-4999-9ad5-f74e7ae8b272",
+ "metadata": {
+ "id": "bd40a9e0-faab-4999-9ad5-f74e7ae8b272"
+ },
+ "outputs": [],
+ "source": [
+ "with open('models/Credit_Reporting_model.pkl', 'rb') as f:\n",
+ " trained_model_cr= pickle.load(f)\n",
+ "\n",
+ "with open('models/Credit_Prepaid_Card_model.pkl', 'rb') as f:\n",
+ " trained_model_cp= pickle.load(f)\n",
+ "\n",
+ "with open('models/Checking_saving_model.pkl', 'rb') as f:\n",
+ " trained_model_cs=pickle.load(f)\n",
+ "\n",
+ "with open('models/loan_model.pkl', 'rb') as f:\n",
+ " trained_model_l= pickle.load(f)\n",
+ "\n",
+ "with open('models/Debt_model.pkl', 'rb') as f:\n",
+ " trained_model_d= pickle.load(f)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "8dd19c5a-5e4f-457c-88b7-5efa18964a8b",
+ "metadata": {
+ "id": "8dd19c5a-5e4f-457c-88b7-5efa18964a8b"
+ },
+ "source": [
+ "#### Loading 17 issue models"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "id": "3dae2131-cfa4-4887-a30a-00d6caf547e8",
+ "metadata": {
+ "id": "3dae2131-cfa4-4887-a30a-00d6caf547e8"
+ },
+ "outputs": [],
+ "source": [
+ "# Path to the models and their corresponding names\n",
+ "issue_model_files = {\n",
+ " 'trained_model_account_operations': 'issue_models/account_operations_and_unauthorized_transaction_issues.pkl',\n",
+ " 'trained_model_collect_debt': 'issue_models/attempts_to_collect_debt_not_owed.pkl',\n",
+ " 'trained_model_closing_account': 'issue_models/closing_an_account.pkl',\n",
+ " 'trained_model_closing_your_account': 'issue_models/closing_your_account.pkl',\n",
+ " 'trained_model_credit_report': 'issue_models/credit_report_and_monitoring_issues.pkl',\n",
+ " 'trained_model_lender': 'issue_models/dealing_with_your_lender_or_servicer.pkl',\n",
+ " 'trained_model_disputes': 'issue_models/disputes_and_misrepresentations.pkl',\n",
+ " 'trained_model_improper_use_report': 'issue_models/improper_use_of_your_report.pkl',\n",
+ " 'trained_model_incorrect_info': 'issue_models/incorrect_information_on_your_report.pkl',\n",
+ " 'trained_model_legal_and_threat': 'issue_models/legal_and_threat_actions.pkl',\n",
+ " 'trained_model_managing_account': 'issue_models/managing_an_account.pkl',\n",
+ " 'trained_model_payment_funds': 'issue_models/payment_and_funds_management.pkl',\n",
+ " 'trained_model_investigation_wrt_issue': 'issue_models/problem_with_a_company\\'s_investigation_into_an_existing_issue.pkl',\n",
+ " 'trained_model_investigation_wrt_problem': 'issue_models/problem_with_a_company\\'s_investigation_into_an_existing_problem.pkl',\n",
+ " 'trained_model_credit_investigation_wrt_problem': 'issue_models/problem_with_a_credit_reporting_company\\'s_investigation_into_an_existing_problem.pkl',\n",
+ " 'trained_model_purchase_shown': 'issue_models/problem_with_a_purchase_shown_on_your_statement.pkl',\n",
+ " 'trained_model_notification_about_debt': 'issue_models/written_notification_about_debt.pkl',\n",
+ "}\n",
+ "\n",
+ "issue_models = {}\n",
+ "\n",
+ "for model_name, file_path in issue_model_files.items():\n",
+ " with open(file_path, 'rb') as f:\n",
+ " issue_models[model_name] = pickle.load(f)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "bf41b143-2ff3-4a79-83a9-afcc0d352dd0",
+ "metadata": {
+ "id": "bf41b143-2ff3-4a79-83a9-afcc0d352dd0",
+ "jp-MarkdownHeadingCollapsed": true
+ },
+ "source": [
+ "#### LLM to classify the product based on the narrative"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "id": "b946427b-b259-4eb2-a40b-ed7b7e476354",
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 333,
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+ "outputId": "2e051817-f883-4873-ce39-83682533642b"
+ },
+ "outputs": [],
+ "source": [
+ "device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n",
+ "\n",
+ "# Define the pipeline for classifying product\n",
+ "product_classifier = pipeline(\"text-classification\", model=\"Mahesh9/distil-bert-fintuned-product-cfpb-complaints\",\n",
+ " max_length = 512, truncation = True, device = device)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "0f0c40cd-f23e-4e0a-8c03-34b517a4c727",
+ "metadata": {
+ "id": "0f0c40cd-f23e-4e0a-8c03-34b517a4c727",
+ "jp-MarkdownHeadingCollapsed": true
+ },
+ "source": [
+ "#### Function to choose the appropriate product model to classify the sub-product"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "id": "619d9c58-1a83-4279-b452-63f3cb69998f",
+ "metadata": {
+ "id": "619d9c58-1a83-4279-b452-63f3cb69998f"
+ },
+ "outputs": [],
+ "source": [
+ "# Define a function to select the appropriate subproduct prediction model based on the predicted product\n",
+ "def select_subproduct_model(predicted_product):\n",
+ " if predicted_product == 'Credit Reporting' :\n",
+ " return trained_model_cr\n",
+ " elif predicted_product == 'Credit/Prepaid Card':\n",
+ " return trained_model_cp\n",
+ " elif predicted_product == 'Checking or savings account':\n",
+ " return trained_model_cs\n",
+ " elif predicted_product == 'Loans / Mortgage':\n",
+ " return trained_model_l\n",
+ " elif predicted_product == 'Debt collection':\n",
+ " return trained_model_d\n",
+ " else:\n",
+ " raise ValueError(\"Invalid predicted product category\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "2f361468-ab6d-4d9a-a665-2c9dbce42e93",
+ "metadata": {
+ "id": "2f361468-ab6d-4d9a-a665-2c9dbce42e93"
+ },
+ "source": [
+ "#### LLM to classify the issue based on the narrative"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "id": "0a8da273-8dfb-43b8-abf9-cf06871f2763",
+ "metadata": {
+ "colab": {
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+ ]
+ },
+ "id": "0a8da273-8dfb-43b8-abf9-cf06871f2763",
+ "outputId": "6551f092-b9a2-4574-b270-94a3cf78a21b"
+ },
+ "outputs": [],
+ "source": [
+ "# Define the pipeline for classifying issue\n",
+ "issue_classifier = pipeline(\"text-classification\", model=\"Mahesh9/distil-bert-fintuned-issues-cfpb-complaints\",\n",
+ " max_length = 512, truncation = True, device = device)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "df05c0c0-c4cc-4287-b129-75f60dd88348",
+ "metadata": {
+ "id": "df05c0c0-c4cc-4287-b129-75f60dd88348"
+ },
+ "source": [
+ "#### Function to choose the appropriate issue model to classify the sub-issue"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "id": "f55a787b-ce6a-49dd-96dd-1cbfda8a68a5",
+ "metadata": {
+ "id": "f55a787b-ce6a-49dd-96dd-1cbfda8a68a5"
+ },
+ "outputs": [],
+ "source": [
+ "# Define a function to select the appropriate subissue prediction model based on the predicted issue\n",
+ "def select_subissue_model(predicted_issue):\n",
+ " if predicted_issue == \"Problem with a company's investigation into an existing problem\":\n",
+ " return issue_models['trained_model_investigation_wrt_problem']\n",
+ "\n",
+ " elif predicted_issue == \"Problem with a credit reporting company's investigation into an existing problem\":\n",
+ " return issue_models['trained_model_credit_investigation_wrt_problem']\n",
+ "\n",
+ " elif predicted_issue == \"Problem with a company's investigation into an existing issue\":\n",
+ " return issue_models['trained_model_investigation_wrt_issue']\n",
+ "\n",
+ " elif predicted_issue == \"Problem with a purchase shown on your statement\":\n",
+ " return issue_models['trained_model_purchase_shown']\n",
+ "\n",
+ " elif predicted_issue == \"Incorrect information on your report\":\n",
+ " return issue_models['trained_model_incorrect_info']\n",
+ "\n",
+ " elif predicted_issue == \"Improper use of your report\":\n",
+ " return issue_models['trained_model_improper_use_report']\n",
+ "\n",
+ " elif predicted_issue == \"Account Operations and Unauthorized Transaction Issues\":\n",
+ " return issue_models['trained_model_account_operations']\n",
+ "\n",
+ " elif predicted_issue == \"Payment and Funds Management\":\n",
+ " return issue_models['trained_model_payment_funds']\n",
+ "\n",
+ " elif predicted_issue == \"Managing an account\":\n",
+ " return issue_models['trained_model_managing_account']\n",
+ "\n",
+ " elif predicted_issue == \"Attempts to collect debt not owed\":\n",
+ " return issue_models['trained_model_collect_debt']\n",
+ "\n",
+ " elif predicted_issue == \"Written notification about debt\":\n",
+ " return issue_models['trained_model_notification_about_debt']\n",
+ "\n",
+ " elif predicted_issue == \"Dealing with your lender or servicer\":\n",
+ " return issue_models['trained_model_lender']\n",
+ "\n",
+ " elif predicted_issue == \"Disputes and Misrepresentations\":\n",
+ " return issue_models['trained_model_disputes']\n",
+ "\n",
+ " elif predicted_issue == \"Closing your account\":\n",
+ " return issue_models['trained_model_closing_your_account']\n",
+ "\n",
+ " elif predicted_issue == \"Closing an account\":\n",
+ " return issue_models['trained_model_closing_account']\n",
+ "\n",
+ " elif predicted_issue == \"Credit Report and Monitoring Issues\":\n",
+ " return issue_models['trained_model_credit_report']\n",
+ "\n",
+ " elif predicted_issue == \"Legal and Threat Actions\":\n",
+ " return issue_models['trained_model_legal_and_threat']\n",
+ "\n",
+ " else:\n",
+ " raise ValueError(\"Invalid predicted issue category\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "d87974e1-1bf8-44ea-bfee-75de8e2960b4",
+ "metadata": {
+ "id": "d87974e1-1bf8-44ea-bfee-75de8e2960b4"
+ },
+ "source": [
+ "#### Driver code to classify the complaint into various categories"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "id": "dc785511-d68f-4341-a080-23f8f27eefc4",
+ "metadata": {
+ "id": "dc785511-d68f-4341-a080-23f8f27eefc4"
+ },
+ "outputs": [],
+ "source": [
+ "def classify_complaint(narrative):\n",
+ " # Predict product category\n",
+ " predicted_product = product_classifier(narrative)[0]['label']\n",
+ "\n",
+ " # Load the appropriate subproduct prediction model\n",
+ " subproduct_model = select_subproduct_model(predicted_product)\n",
+ " # Predict subproduct category using the selected model\n",
+ " predicted_subproduct = subproduct_model.predict([narrative])[0]\n",
+ "\n",
+ "\n",
+ "\n",
+ " # Predict the appropriate issue category using the narrative\n",
+ " predicted_issue = issue_classifier(narrative)[0]['label']\n",
+ "\n",
+ " # Load the appropriate subissue prediction model\n",
+ " subissue_model = select_subissue_model(predicted_issue)\n",
+ " # Predict subissue category using the selected model\n",
+ " predicted_subissue = subissue_model.predict([narrative])[0]\n",
+ "\n",
+ " return {\n",
+ " \"Product\" : predicted_product,\n",
+ " \"Sub-product\" : predicted_subproduct,\n",
+ " \"Issue\" : predicted_issue,\n",
+ " \"Sub-issue\" : predicted_subissue\n",
+ " }"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "id": "982521ea-364e-4521-889e-fe586c186701",
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "982521ea-364e-4521-889e-fe586c186701",
+ "outputId": "57634353-228e-4333-cb04-4d0fcdbe55f1"
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "{'Product': 'Credit/Prepaid Card',\n",
+ " 'Sub-product': 'General-purpose credit card or charge card',\n",
+ " 'Issue': \"Problem with a company's investigation into an existing problem\",\n",
+ " 'Sub-issue': 'Was not notified of investigation status or results'}"
+ ]
+ },
+ "execution_count": 9,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "narrative = \"\"\"It is absurd that I have consistently made timely payments for this account and have never been\n",
+ " overdue. I kindly request that you promptly update my account to reflect this accurately.\"\"\"\n",
+ "\n",
+ "classify_complaint(narrative)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "id": "a80b68d8",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "
\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " | \n",
+ " Complaint Narrative | \n",
+ " Actual Product | \n",
+ " Predicted Product | \n",
+ " Actual Sub-product | \n",
+ " Predicted Sub-product | \n",
+ " Actual Issue | \n",
+ " Predicted Issue | \n",
+ " Actual Sub-issue | \n",
+ " Predicted Sub-issue | \n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ " 0 | \n",
+ " 2nd and final notice Notice to agent is notice... | \n",
+ " Credit Reporting | \n",
+ " Credit Reporting | \n",
+ " Credit reporting | \n",
+ " Credit reporting | \n",
+ " Problem with a company's investigation into an... | \n",
+ " Improper use of your report | \n",
+ " Was not notified of investigation status or re... | \n",
+ " Reporting company used your report improperly | \n",
+ "
\n",
+ " \n",
+ " 1 | \n",
+ " It has come to my attention that this bankrupt... | \n",
+ " Credit Reporting | \n",
+ " Credit Reporting | \n",
+ " Credit reporting | \n",
+ " Credit reporting | \n",
+ " Incorrect information on your report | \n",
+ " Incorrect information on your report | \n",
+ " Information belongs to someone else | \n",
+ " Information belongs to someone else | \n",
+ "
\n",
+ " \n",
+ " 2 | \n",
+ " per 15 1666B no lates should be furnished per ... | \n",
+ " Credit Reporting | \n",
+ " Credit Reporting | \n",
+ " Credit reporting | \n",
+ " Credit reporting | \n",
+ " Incorrect information on your report | \n",
+ " Improper use of your report | \n",
+ " Account status incorrect | \n",
+ " Reporting company used your report improperly | \n",
+ "
\n",
+ " \n",
+ " 3 | \n",
+ " XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX, XXXX ... | \n",
+ " Credit Reporting | \n",
+ " Credit Reporting | \n",
+ " Credit reporting | \n",
+ " Credit reporting | \n",
+ " Problem with a credit reporting company's inve... | \n",
+ " Improper use of your report | \n",
+ " Investigation took more than 30 days | \n",
+ " Reporting company used your report improperly | \n",
+ "
\n",
+ " \n",
+ " 4 | \n",
+ " On XX/XX/2022 I sent a letter disputing a bill... | \n",
+ " Credit/Prepaid Card | \n",
+ " Credit Reporting | \n",
+ " General-purpose credit card or charge card | \n",
+ " Credit reporting | \n",
+ " Problem with a purchase shown on your statement | \n",
+ " Attempts to collect debt not owed | \n",
+ " Credit card company isn't resolving a dispute ... | \n",
+ " Debt is not yours | \n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " Complaint Narrative Actual Product \\\n",
+ "0 2nd and final notice Notice to agent is notice... Credit Reporting \n",
+ "1 It has come to my attention that this bankrupt... Credit Reporting \n",
+ "2 per 15 1666B no lates should be furnished per ... Credit Reporting \n",
+ "3 XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX, XXXX ... Credit Reporting \n",
+ "4 On XX/XX/2022 I sent a letter disputing a bill... Credit/Prepaid Card \n",
+ "\n",
+ " Predicted Product Actual Sub-product \\\n",
+ "0 Credit Reporting Credit reporting \n",
+ "1 Credit Reporting Credit reporting \n",
+ "2 Credit Reporting Credit reporting \n",
+ "3 Credit Reporting Credit reporting \n",
+ "4 Credit Reporting General-purpose credit card or charge card \n",
+ "\n",
+ " Predicted Sub-product Actual Issue \\\n",
+ "0 Credit reporting Problem with a company's investigation into an... \n",
+ "1 Credit reporting Incorrect information on your report \n",
+ "2 Credit reporting Incorrect information on your report \n",
+ "3 Credit reporting Problem with a credit reporting company's inve... \n",
+ "4 Credit reporting Problem with a purchase shown on your statement \n",
+ "\n",
+ " Predicted Issue \\\n",
+ "0 Improper use of your report \n",
+ "1 Incorrect information on your report \n",
+ "2 Improper use of your report \n",
+ "3 Improper use of your report \n",
+ "4 Attempts to collect debt not owed \n",
+ "\n",
+ " Actual Sub-issue \\\n",
+ "0 Was not notified of investigation status or re... \n",
+ "1 Information belongs to someone else \n",
+ "2 Account status incorrect \n",
+ "3 Investigation took more than 30 days \n",
+ "4 Credit card company isn't resolving a dispute ... \n",
+ "\n",
+ " Predicted Sub-issue \n",
+ "0 Reporting company used your report improperly \n",
+ "1 Information belongs to someone else \n",
+ "2 Reporting company used your report improperly \n",
+ "3 Reporting company used your report improperly \n",
+ "4 Debt is not yours "
+ ]
+ },
+ "execution_count": 10,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "test_df = pd.read_csv('../data_splits/test_data_results.csv')\n",
+ "test_df.head()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 29,
+ "id": "dd18d7f1",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "{'Complaint Narrative': 'I have been on SAVE repayment plab for over XXXX month, which I was told would have interest waived, unfortunately this has not happened, instead have been accruing nearly XXXXXXXX XXXX interest per month Please help',\n",
+ " 'Actual Product': 'Loans / Mortgage',\n",
+ " 'Predicted Product': 'Loans / Mortgage',\n",
+ " 'Actual Sub-product': 'Federal student loan servicing',\n",
+ " 'Predicted Sub-product': 'Federal student loan servicing',\n",
+ " 'Actual Issue': 'Dealing with your lender or servicer',\n",
+ " 'Predicted Issue': 'Disputes and Misrepresentations',\n",
+ " 'Actual Sub-issue': 'Trouble with how payments are being handled',\n",
+ " 'Predicted Sub-issue': 'Problem with fees'}"
+ ]
+ },
+ "execution_count": 29,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "import random\n",
+ "a = random.randint(0, 100)\n",
+ "test_df[(test_df['Actual Product'] == 'Loans / Mortgage') & \n",
+ " (test_df['Predicted Product'] == 'Loans / Mortgage') &\n",
+ " (test_df['Actual Issue'] != test_df['Predicted Issue']) \n",
+ " ].iloc[a,:].to_dict()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 28,
+ "id": "ed261949",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "{'Complaint Narrative': 'I have contacted Alpine Credit XXXX XXXX Multiple times to verify the debt or send debt validation with no response first on XX/XX/XXXX with no response after 30 days, I sent another letter in XX/XX/XXXX stating it has been over 30 days since my letter and I have not received a response, they marked the account as disputed which means they have received my letters but have yet to contact me. I contacted them a third time XX/XX/XXXX and still no response.',\n",
+ " 'Actual Product': 'Debt collection',\n",
+ " 'Predicted Product': 'Debt collection',\n",
+ " 'Actual Sub-product': 'Medical debt',\n",
+ " 'Predicted Sub-product': 'Credit card debt',\n",
+ " 'Actual Issue': 'Written notification about debt',\n",
+ " 'Predicted Issue': 'Written notification about debt',\n",
+ " 'Actual Sub-issue': \"Didn't receive enough information to verify debt\",\n",
+ " 'Predicted Sub-issue': \"Didn't receive enough information to verify debt\"}"
+ ]
+ },
+ "execution_count": 28,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "import random\n",
+ "test_df[(test_df['Actual Product'] == 'Debt collection') & \n",
+ " (test_df['Predicted Product'] == 'Debt collection') &\n",
+ " (test_df['Actual Issue'] == test_df['Predicted Issue']) &\n",
+ " (test_df['Actual Sub-issue'] == test_df['Predicted Sub-issue']) \n",
+ " ].iloc[random.randint(0, 100),:].to_dict()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "2f40eda7",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "\"\"\"\n",
+ "I have called MOHELA many times trying to get an update on my payments for public loan forgiveness.\n",
+ "When my loan was transferred to them over a year ago, I had 98 payments.\n",
+ "I have payments that under the temporary waiver count towards loan forgiveness and have not been updated or applied.\n",
+ "My payments have almost a year that have not been updated on their systems.\n",
+ "A few months is understandable, but over a year is unacceptable.\n",
+ "I have enough payments if they count the payments that have not been counted to get my loans forgiven under the public service program.\n",
+ "I have been working as a XXXX for 13 years.\n",
+ "The lack of accountability on their part has cause me a lot of stress and is affecting my mental health.\n",
+ "These payments should have been counted a long time ago. Calling them on the phone is a nightmare.\n",
+ "\"\"\""
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 17,
+ "id": "7efb17e0",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "{'Product': 'Credit Reporting',\n",
+ " 'Sub-product': 'Credit reporting',\n",
+ " 'Issue': 'Improper use of your report',\n",
+ " 'Sub-issue': 'Reporting company used your report improperly'}"
+ ]
+ },
+ "execution_count": 17,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "narrative_1 = \"\"\"in accordance with the Fair Credit Reporting Act this creditor has violated my Rights! Under Act 15 U.S.C. 1681 section 602 says \" I have the right to Privacy \\'\\'. 15 U.S.C 1681 section 604 A section 2 \\'It also states a consumer reporting agency can not refurnish an account without my written instructions \\'\\'. Under 15 U.S.C 1666B \" A creditor may not treat a payment on a credit card account under an open and consumer credit plan as late for any purpose \\'\\'.\"\"\"\n",
+ "classify_complaint(narrative_1)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 26,
+ "id": "a6df5fd5",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "{'Product': 'Loans / Mortgage',\n",
+ " 'Sub-product': 'Federal student loan servicing',\n",
+ " 'Issue': 'Dealing with your lender or servicer',\n",
+ " 'Sub-issue': 'Received bad information about your loan'}"
+ ]
+ },
+ "execution_count": 26,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "narrative_2 = \"\"\" Despite numerous calls, MOHELA hasn't updated my account with payments eligible for public loan forgiveness.\n",
+ " This lack of action over a year is causing significant stress.\"\"\"\n",
+ "\n",
+ "classify_complaint(narrative_2)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "3bd1cbd4",
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "markdown",
+ "id": "6a68ebbc-de80-4176-ac38-bfe5fd84b86c",
+ "metadata": {
+ "id": "6a68ebbc-de80-4176-ac38-bfe5fd84b86c",
+ "jp-MarkdownHeadingCollapsed": true
+ },
+ "source": [
+ "### Evaluating on the test set"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 17,
+ "id": "w76l5Y606Xyc",
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 275,
+ "referenced_widgets": [
+ "215ca9707ca345daaa2f258f0ac448b4",
+ "9b9e28f7d25c47ffb27964235abdbab6",
+ "2c847326537e4c6180fb06f5e2149edd",
+ "c33233f6538149d9aa4e5dc12606e8d8",
+ "a4e9b673f63b47f4ba3cf0b785149f92",
+ "be8d38ead5344c0fa0e3e8f356d1ee21",
+ "1247d5f2624e4bdb9260b86c3040558a",
+ "262e189bdf254433822527730e0858fe",
+ "5cc60b7f47274fb3a21ffd1d58269729",
+ "a5dd0ebfc34645e19a8e439012353d92",
+ "ae77e7d380374288b5ba80aa7afe4c53",
+ "9d1ead93dcca4b1493bb558cb1e95045",
+ "671c9eaf696e49529817248842570983",
+ "c3adeb56cb2d4ca79f4227c334cfb6f2",
+ "fe3589a0e5f947f087977719bff83f34",
+ "706310ab6a4d412cbaf978e5eef0d209",
+ "577f070a1f864bacbc0fc855e1729f74",
+ "1fe072e24afa4496b9fd000261460933",
+ "167fa10ab49c46968c34bb5d14ee2931",
+ "a723803381aa412b8cb5b52f2310fe8e",
+ "8b848a78513346bbb41f77b81b86d227",
+ "a6e06a1b728d4e5e88b27f6859b29459"
+ ]
+ },
+ "id": "w76l5Y606Xyc",
+ "outputId": "f3830c46-4340-4049-8072-f0f138d385a0"
+ },
+ "outputs": [
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "215ca9707ca345daaa2f258f0ac448b4",
+ "version_major": 2,
+ "version_minor": 0
+ },
+ "text/plain": [
+ "Generating train split: 0 examples [00:00, ? examples/s]"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "9d1ead93dcca4b1493bb558cb1e95045",
+ "version_major": 2,
+ "version_minor": 0
+ },
+ "text/plain": [
+ " 0%| | 0/61880 [00:00, ?it/s]"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "/usr/local/lib/python3.10/dist-packages/transformers/pipelines/base.py:1157: UserWarning: You seem to be using the pipelines sequentially on GPU. In order to maximize efficiency please use a dataset\n",
+ " warnings.warn(\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Product Prediction Accuracy: 0.959502262443439\n",
+ "Product Prediction Precision: 0.8787916313109703\n",
+ "Subproduct Prediction Accuracy: 0.9265675500969619\n",
+ "Subproduct Prediction Precision: 0.6017085660720637\n",
+ "Issue Prediction Accuracy: 0.7651745313510019\n",
+ "Issue Prediction Precision: 0.7111296882245811\n",
+ "Sub-issue Prediction Accuracy: 0.7402876535229477\n",
+ "Sub-issue Prediction Precision: 0.6149852547976704\n"
+ ]
+ }
+ ],
+ "source": [
+ "from datasets import load_dataset\n",
+ "import pandas as pd\n",
+ "from tqdm.auto import tqdm\n",
+ "from sklearn.metrics import accuracy_score, precision_score\n",
+ "\n",
+ "# Load your dataset from a csv file\n",
+ "dataset = load_dataset('csv', data_files='/content/drive/MyDrive/Customer_Complaints data/test-data-split.csv')\n",
+ "test_data = dataset['train'].to_pandas()\n",
+ "\n",
+ "# Initialize lists to store predicted and actual labels\n",
+ "predicted_products = []\n",
+ "predicted_subproducts = []\n",
+ "predicted_issues = []\n",
+ "predicted_subissues = []\n",
+ "\n",
+ "actual_products = test_data['Product']\n",
+ "actual_subproducts = test_data['Sub-product']\n",
+ "actual_issues = test_data['Issue']\n",
+ "actual_subissues = test_data['Sub-issue']\n",
+ "\n",
+ "# Iterate over each complaint narrative in the test set\n",
+ "for narrative in tqdm(test_data['Consumer complaint narrative']):\n",
+ " # Predict product and subproduct using the custom_predict function\n",
+ " prediction = classify_complaint(narrative)\n",
+ "\n",
+ " # Append predicted labels to lists\n",
+ " predicted_products.append(prediction['Product'])\n",
+ " predicted_subproducts.append(prediction['Sub-product'])\n",
+ " predicted_issues.append(prediction['Issue'])\n",
+ " predicted_subissues.append(prediction['Sub-issue'])\n",
+ "\n",
+ "# Calculate accuracy and precision\n",
+ "accuracy_product = accuracy_score(actual_products, predicted_products)\n",
+ "precision_product = precision_score(actual_products, predicted_products, average='macro', zero_division=1)\n",
+ "accuracy_subproduct = accuracy_score(actual_subproducts, predicted_subproducts)\n",
+ "precision_subproduct = precision_score(actual_subproducts, predicted_subproducts, average='macro', zero_division=1)\n",
+ "\n",
+ "accuracy_issue = accuracy_score(actual_issues, predicted_issues)\n",
+ "precision_issue = precision_score(actual_issues, predicted_issues, average='macro', zero_division=1)\n",
+ "accuracy_subissue = accuracy_score(actual_subissues, predicted_subissues)\n",
+ "precision_subissue = precision_score(actual_subissues, predicted_subissues, average='macro', zero_division=1)\n",
+ "\n",
+ "# Print the results\n",
+ "print(\"Product Prediction Accuracy:\", accuracy_product)\n",
+ "print(\"Product Prediction Precision:\", precision_product)\n",
+ "\n",
+ "print(\"Subproduct Prediction Accuracy:\", accuracy_subproduct)\n",
+ "print(\"Subproduct Prediction Precision:\", precision_subproduct)\n",
+ "\n",
+ "print(\"Issue Prediction Accuracy:\", accuracy_issue)\n",
+ "print(\"Issue Prediction Precision:\", precision_issue)\n",
+ "\n",
+ "print(\"Sub-issue Prediction Accuracy:\", accuracy_subissue)\n",
+ "print(\"Sub-issue Prediction Precision:\", precision_subissue)\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 51,
+ "id": "D-TYtc_2LNOH",
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "D-TYtc_2LNOH",
+ "outputId": "a77627cc-e82c-411e-d8d1-26fd461b8548"
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Average Accuracy across all categories : 0.8478829993535876\n",
+ "Average Precision across all categories : 0.7016537851013214\n"
+ ]
+ }
+ ],
+ "source": [
+ "overall_average_accuracy = (accuracy_product + accuracy_subproduct + accuracy_issue + accuracy_subissue) / 4\n",
+ "print(f\"Average Accuracy across all categories : {overall_average_accuracy}\")\n",
+ "\n",
+ "overall_average_precision = (precision_product + precision_subproduct + precision_issue + precision_subissue) / 4\n",
+ "print(f\"Average Precision across all categories : {overall_average_precision}\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "6fpIkBcpMzCB",
+ "metadata": {
+ "id": "6fpIkBcpMzCB"
+ },
+ "source": [
+ "### Classification reports of all 4 categories"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 42,
+ "id": "YERJ53PeK_LY",
+ "metadata": {
+ "id": "YERJ53PeK_LY"
+ },
+ "outputs": [],
+ "source": [
+ "from sklearn.metrics import classification_report\n",
+ "import warnings\n",
+ "warnings.filterwarnings(\"ignore\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 43,
+ "id": "c6pcRJvFJ56Z",
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "c6pcRJvFJ56Z",
+ "outputId": "c22f5075-6ea3-48cd-e67b-87fd72e14b32"
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ " precision recall f1-score support\n",
+ "\n",
+ "Checking or savings account 0.94 0.93 0.93 3071\n",
+ " Credit Reporting 0.98 0.98 0.98 52924\n",
+ " Credit/Prepaid Card 0.80 0.87 0.83 2994\n",
+ " Debt collection 0.79 0.61 0.69 2345\n",
+ " Loans / Mortgage 0.89 0.70 0.78 546\n",
+ "\n",
+ " accuracy 0.96 61880\n",
+ " macro avg 0.88 0.82 0.84 61880\n",
+ " weighted avg 0.96 0.96 0.96 61880\n",
+ "\n"
+ ]
+ }
+ ],
+ "source": [
+ "print(classification_report(actual_products, predicted_products))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 44,
+ "id": "jqlnSF_hKHfI",
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "jqlnSF_hKHfI",
+ "outputId": "4c6e621e-9bf3-43b6-dd2f-ed65aff689f8"
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ " precision recall f1-score support\n",
+ "\n",
+ " Auto debt 0.35 0.17 0.23 140\n",
+ " CD (Certificate of Deposit) 0.87 0.30 0.44 44\n",
+ " Checking account 0.80 0.93 0.86 2596\n",
+ " Conventional home mortgage 0.50 0.03 0.06 29\n",
+ " Credit card debt 0.12 0.35 0.18 386\n",
+ " Credit reporting 0.97 0.98 0.98 52699\n",
+ " Federal student loan servicing 0.97 0.93 0.95 328\n",
+ "General-purpose credit card or charge card 0.73 0.87 0.79 2668\n",
+ " I do not know 0.00 0.00 0.00 616\n",
+ " Loan 0.63 0.39 0.48 189\n",
+ " Medical debt 0.25 0.55 0.34 246\n",
+ " Mortgage debt 0.54 0.20 0.29 35\n",
+ " Other banking product or service 0.67 0.04 0.07 167\n",
+ " Other debt 0.00 0.00 0.00 757\n",
+ " Other personal consumer report 0.21 0.08 0.11 225\n",
+ " Rental debt 0.00 0.00 0.00 43\n",
+ " Savings account 0.80 0.03 0.06 264\n",
+ " Store credit card 0.78 0.06 0.12 326\n",
+ " Telecommunications debt 0.24 0.06 0.09 122\n",
+ "\n",
+ " accuracy 0.93 61880\n",
+ " macro avg 0.50 0.31 0.32 61880\n",
+ " weighted avg 0.91 0.93 0.92 61880\n",
+ "\n"
+ ]
+ }
+ ],
+ "source": [
+ "print(classification_report(actual_subproducts, predicted_subproducts))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 45,
+ "id": "Bc9_CZs_KVlM",
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "Bc9_CZs_KVlM",
+ "outputId": "2027df65-f674-48bd-dc71-cfebf911b2de"
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ " precision recall f1-score support\n",
+ "\n",
+ " Account Operations and Unauthorized Transaction Issues 0.56 0.32 0.41 619\n",
+ " Attempts to collect debt not owed 0.54 0.57 0.56 1381\n",
+ " Closing an account 0.68 0.64 0.66 521\n",
+ " Closing your account 0.58 0.64 0.61 191\n",
+ " Credit Report and Monitoring Issues 0.67 0.53 0.59 412\n",
+ " Dealing with your lender or servicer 0.96 0.97 0.97 314\n",
+ " Disputes and Misrepresentations 0.50 0.38 0.43 550\n",
+ " Improper use of your report 0.84 0.87 0.85 15434\n",
+ " Incorrect information on your report 0.81 0.86 0.83 21807\n",
+ " Legal and Threat Actions 0.00 0.00 0.00 184\n",
+ " Managing an account 0.70 0.77 0.73 1826\n",
+ " Payment and Funds Management 0.53 0.56 0.54 530\n",
+ " Problem with a company's investigation into an existing issue 0.00 0.00 0.00 53\n",
+ " Problem with a company's investigation into an existing problem 0.64 0.46 0.53 5117\n",
+ "Problem with a credit reporting company's investigation into an existing problem 0.69 0.70 0.70 11522\n",
+ " Problem with a purchase shown on your statement 0.73 0.77 0.75 841\n",
+ " Written notification about debt 0.66 0.44 0.53 578\n",
+ "\n",
+ " accuracy 0.77 61880\n",
+ " macro avg 0.59 0.56 0.57 61880\n",
+ " weighted avg 0.76 0.77 0.76 61880\n",
+ "\n"
+ ]
+ }
+ ],
+ "source": [
+ "print(classification_report(actual_issues, predicted_issues))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 46,
+ "id": "fWYaS8r3KcUu",
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "fWYaS8r3KcUu",
+ "outputId": "802e06f7-510f-41c1-9fd4-7dc81a56754a"
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ " precision recall f1-score support\n",
+ "\n",
+ " Account information incorrect 0.52 0.50 0.51 2281\n",
+ " Account opened as a result of fraud 0.66 0.67 0.66 135\n",
+ " Account status incorrect 0.68 0.63 0.66 2502\n",
+ " Attempted to collect wrong amount 0.19 0.08 0.11 202\n",
+ " Banking errors 0.63 0.11 0.19 231\n",
+ " Billing problem 0.69 0.37 0.48 126\n",
+ " Can't close your account 0.64 0.05 0.10 165\n",
+ " Card opened as result of identity theft or fraud 0.55 0.28 0.37 120\n",
+ " Card was charged for something you did not purchase with the card 0.28 0.03 0.06 265\n",
+ " Company closed your account 0.43 0.62 0.51 374\n",
+ " Credit card company isn't resolving a dispute about a purchase on your statement 0.52 0.76 0.62 576\n",
+ " Credit inquiries on your report that you don't recognize 0.77 0.78 0.77 3242\n",
+ " Debt is not yours 0.35 0.57 0.44 721\n",
+ " Debt was paid 0.56 0.19 0.29 221\n",
+ " Debt was result of identity theft 0.56 0.28 0.37 439\n",
+ " Deposits and withdrawals 0.32 0.76 0.45 620\n",
+ " Didn't receive enough information to verify debt 0.60 0.53 0.56 429\n",
+ " Didn't receive notice of right to dispute 0.44 0.03 0.05 149\n",
+ "Difficulty submitting a dispute or getting information about a dispute over the phone 0.44 0.25 0.32 435\n",
+ " Fee problem 0.39 0.32 0.35 208\n",
+ " Funds not handled or disbursed as instructed 0.67 0.01 0.02 232\n",
+ " Funds not received from closed account 0.53 0.42 0.47 173\n",
+ " Information belongs to someone else 0.76 0.89 0.82 14486\n",
+ " Information is missing that should be on the report 0.94 0.29 0.44 225\n",
+ " Investigation took more than 30 days 0.90 0.69 0.78 2222\n",
+ " Old information reappears or never goes away 0.93 0.39 0.55 454\n",
+ " Other problem 0.36 0.23 0.28 162\n",
+ " Other problem getting your report or credit score 0.61 0.59 0.60 290\n",
+ " Overdrafts and overdraft fees 0.43 0.51 0.47 215\n",
+ " Personal information incorrect 0.92 0.73 0.81 1497\n",
+ " Problem accessing account 1.00 0.01 0.02 159\n",
+ " Problem canceling credit monitoring or identify theft protection service 0.91 0.33 0.48 122\n",
+ " Problem during payment process 0.45 0.56 0.50 189\n",
+ " Problem using a debit or ATM card 0.44 0.41 0.42 376\n",
+ " Problem with fees 0.49 0.62 0.55 186\n",
+ " Problem with personal statement of dispute 0.49 0.24 0.32 340\n",
+ " Public record information inaccurate 0.80 0.31 0.45 362\n",
+ " Received bad information about your loan 0.72 0.68 0.70 180\n",
+ " Reporting company used your report improperly 0.82 0.85 0.84 12192\n",
+ " Their investigation did not fix an error on your report 0.79 0.78 0.78 11333\n",
+ " Threatened or suggested your credit would be damaged 0.00 0.00 0.00 184\n",
+ " Transaction was not authorized 0.32 0.14 0.19 364\n",
+ " Trouble with how payments are being handled 0.63 0.69 0.66 134\n",
+ " Was not notified of investigation status or results 0.92 0.81 0.86 2362\n",
+ "\n",
+ " accuracy 0.74 61880\n",
+ " macro avg 0.59 0.43 0.45 61880\n",
+ " weighted avg 0.74 0.74 0.73 61880\n",
+ "\n"
+ ]
+ }
+ ],
+ "source": [
+ "print(classification_report(actual_subissues, predicted_subissues))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "MdZrM_BbMf_1",
+ "metadata": {
+ "id": "MdZrM_BbMf_1"
+ },
+ "source": [
+ "### Saving the test reuslts"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 24,
+ "id": "w5g18DmD7ywU",
+ "metadata": {
+ "id": "w5g18DmD7ywU"
+ },
+ "outputs": [],
+ "source": [
+ "# Create a DataFrame from the predicted and actual labels\n",
+ "results_df = pd.DataFrame({\n",
+ " 'Complaint Narrative' : test_data['Consumer complaint narrative'],\n",
+ " 'Actual Product': actual_products,\n",
+ " 'Predicted Product': predicted_products,\n",
+ " 'Actual Sub-product': actual_subproducts,\n",
+ " 'Predicted Sub-product': predicted_subproducts,\n",
+ " 'Actual Issue': actual_issues,\n",
+ " 'Predicted Issue': predicted_issues,\n",
+ " 'Actual Sub-issue': actual_subissues,\n",
+ " 'Predicted Sub-issue': predicted_subissues\n",
+ "})\n",
+ "\n",
+ "# Save the DataFrame to a CSV file\n",
+ "results_df.to_csv('/content/drive/MyDrive/Customer_Complaints data/test_data_results.csv', index=False)"
+ ]
+ }
+ ],
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