Upload classifier.py

src/classifier.py

@@ -0,0 +1,145 @@
+import pandas as pd
+import joblib
+
+def get_encoding(category, value):
+    encodings = {
+        'person_gender': {
+            'female': 0,
+            'male': 1
+        },
+        'person_education': {
+            'associate': 0,
+            'bachelor': 1,
+            'doctorate': 2,
+            'high school': 3,
+            'master': 4
+        },
+        'person_home_ownership': {
+            'mortgage': 0,
+            'other': 1,
+            'own': 2,
+            'rent': 3
+        },
+        'previous_loan_defaults_on_file': {
+            'no': 0,
+            'yes': 1,
+            0: 0,
+            1: 1
+        },
+        'loan_intent': {
+            'debt_consolidation': 0,
+            'education': 1,
+            'home_improvement': 2,
+            'medical': 3,
+            'personal': 4,
+            'venture': 5
+        }
+    }
+    
+    # Look up the encoding
+    return encodings.get(category, {}).get(value, None)
+
+def classify_loan(
+        person_age: float,
+        person_gender: str,
+        person_education: str,
+        person_income: float,
+        person_emp_exp: float,
+        person_home_ownership: str,
+        loan_amnt: float,
+        loan_intent: str,
+        loan_int_rate: float,
+        loan_percent_income: float,
+        cb_person_cred_hist_length: float,
+        credit_score: float,
+        previous_loan_defaults_on_file: str
+) -> dict[float, str, str, float, float, str, float, str, float, float, float, float, str | str]:
+    """Set the borrower personal details for loan approval classification. (mock API).
+
+    Args:
+        person_age (float): The age of the borrower.
+        person_gender: Gender of the borrower. Either `male` or `female`.
+        person_education: The education level of the borrower.Either 'associate`, `bachelor`, `doctorate`, `master` or `high school`.
+        person_income: The income of the borrower.
+        person_emp_exp: The years of employment experience of the borrower.
+        person_home_ownership: The home ownership status of the borrower. Either `mortage`, `other`, `own` or `rent`.
+        loan_amnt: The amount of loan requested.
+        loan_intent: The intent of the loan. Either `debt_consolidation`, `education`, `home_improvement`, `medical` or `personal`.
+        loan_int_rate: The interest rate of the loan.
+        loan_percent_income: The loan amount as a percentage of annual income.
+        cb_person_cred_hist_length: The length of the credit history of the borrower.
+        credit_score: The credit score of the borrower.
+        previous_loan_defaults_on_file: The indicator of previous loan defaults. Either `yes` or `no`.
+
+    Returns:
+        A dictionary containing the loan approval status.
+    """
+    # make the input data into a dataframe
+    input_data = {
+        "person_age": person_age,
+        "person_gender": person_gender,
+        "person_education": person_education,
+        "person_income": person_income,
+        "person_emp_exp": person_emp_exp,
+        "person_home_ownership": person_home_ownership,
+        "loan_amnt": loan_amnt,
+        "loan_intent": loan_intent, 
+        "loan_int_rate": loan_int_rate,
+        "loan_percent_income": loan_percent_income,
+        "cb_person_cred_hist_length": cb_person_cred_hist_length,
+        "credit_score": credit_score,
+        "previous_loan_defaults_on_file": previous_loan_defaults_on_file
+    }
+    input_df = pd.DataFrame([input_data])
+    print("### This is the input data:")
+    print(input_df.head())
+    # scale the input data
+    means_stds = pd.read_csv("data/means_stds.csv")
+    means_stds.set_index('column', inplace=True)
+    columns = ["person_age", "person_income", "person_emp_exp", "loan_amnt",
+                "loan_int_rate", "loan_percent_income", "cb_person_cred_hist_length",
+                "credit_score"]
+    for column in columns:
+        mean = means_stds.loc[column, 'mean']
+        std = means_stds.loc[column, 'std']
+        input_df[column] = (input_df[column] - mean) / std
+
+    # convert the categorical variables to class
+    categorical_columns = [
+        "person_gender", "person_education", "person_home_ownership",
+        "loan_intent", "previous_loan_defaults_on_file"
+    ]
+    for column in categorical_columns:
+        input_df[column] = input_df[column].apply(lambda x: get_encoding(column, x))
+    
+    print("### This is the processed input data: ")
+    print(input_df.head())
+
+    # load classifier at model/logistic_regression.pkl
+    classifier = joblib.load("model/random_forest_model.pkl")
+
+    # reorder the columns to match the training data
+    ordered_columns = [
+        "person_gender",
+        "person_education",
+        "person_home_ownership",
+        "loan_intent",
+        "previous_loan_defaults_on_file",
+        "person_age",
+        "person_income",
+        "person_emp_exp",
+        "loan_amnt",
+        "loan_int_rate",
+        "loan_percent_income",
+        "cb_person_cred_hist_length",
+        "credit_score"
+    ]
+    input_df = input_df[ordered_columns]
+
+    # make prediction
+    prediction = classifier.predict(input_df)
+
+    if prediction[0] == 1:
+        return "Your loan application has been approved."
+    else:
+        return "Your loan application has been rejected."