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Makefile

@@ -0,0 +1,29 @@
+.PHONY: lint format test coverage complexity maintainability audit security check all
+
+lint:
+	uv run ruff check .
+
+format:
+	uv run ruff format .
+
+test:
+	uv run pytest
+
+coverage:
+	uv run pytest --cov=src --cov-report=term-missing
+
+complexity:
+	uv run radon cc . -a -s -nb
+
+maintainability:
+	uv run radon mi . -s
+
+audit:
+	uv run pip-audit
+
+security:
+	uv run bandit -r . -x ./.venv
+
+check: lint test complexity maintainability audit security
+
+all: check

README.md

@@ -1,17 +1,3 @@
----
-title: Developer Salary Prediction
-emoji: 🚀
-colorFrom: red
-colorTo: red
-sdk: docker
-app_port: 8501
-tags:
-- streamlit
-pinned: false
-short_description: Developer salary prediction using 2025 Stackoverflow survey
-license: apache-2.0
----
-
 # Developer Salary Prediction
 
 A minimal, local-first ML application that predicts developer salaries using Stack Overflow Developer Survey data. Built with Python, scikit-learn, Pydantic, and Streamlit.
@@ -44,7 +30,7 @@ Download the Stack Overflow Developer Survey CSV file:
    data/survey_results_public.csv
    ```
 
-**Required columns:** `Country`, `YearsCode`, `EdLevel`, `DevType`, `Industry`, `ConvertedCompYearly`
+**Required columns:** `Country`, `YearsCode`, `WorkExp`, `EdLevel`, `DevType`, `Industry`, `Age`, `ICorPM`, `ConvertedCompYearly`
 
 ### 3. Train the Model
 
@@ -57,7 +43,7 @@ This will:
 - Load and preprocess the survey data (with cardinality reduction)
 - Train an XGBoost model with early stopping
 - Save the model to `models/model.pkl`
-- Generate `config/valid_categories.yaml` with valid country, education, developer type, and industry values
+- Generate `config/valid_categories.yaml` with valid country, education, developer type, industry, age, and IC/PM values
 
 ### 4. Run the Streamlit App
 
@@ -74,9 +60,12 @@ The app will open in your browser at `http://localhost:8501`
 Launch the Streamlit app and enter:
 - **Country**: Developer's country
 - **Years of Coding (Total)**: Total years coding including education
+- **Years of Professional Work Experience**: Years of professional work experience
 - **Education Level**: Highest degree completed
 - **Developer Type**: Primary developer role
 - **Industry**: Industry the developer works in
+- **Age**: Developer's age range
+- **IC or PM**: Individual contributor or people manager
 
 Click "Predict Salary" to see the estimated annual salary.
 
@@ -92,9 +81,12 @@ from src.infer import predict_salary
 input_data = SalaryInput(
     country="United States of America",
     years_code=5.0,
+    work_exp=3.0,
     education_level="Bachelor's degree (B.A., B.S., B.Eng., etc.)",
     dev_type="Developer, full-stack",
-    industry="Software Development"
+    industry="Software Development",
+    age="25-34 years old",
+    ic_or_pm="Individual contributor"
 )
 
 # Get prediction
@@ -118,6 +110,8 @@ The model validates inputs against actual training data categories:
 - **Valid Education Levels**: Only education levels from training data (~9 levels)
 - **Valid Developer Types**: Only developer types from training data (~20 types)
 - **Valid Industries**: Only industries from training data (~15 industries)
+- **Valid Age Ranges**: Only age ranges from training data (~7 ranges)
+- **Valid IC/PM Values**: Only IC/PM values from training data (~3 values)
 
 The Streamlit app uses dropdown menus with only valid options. If you use the programmatic API with invalid values, you'll get a helpful error message pointing to the valid categories file.
 
@@ -130,9 +124,12 @@ from src.schema import SalaryInput
 invalid_input = SalaryInput(
     country="Japan",  # Invalid!
     years_code=5.0,
+    work_exp=3.0,
     education_level="Bachelor's degree (B.A., B.S., B.Eng., etc.)",
     dev_type="Developer, back-end",
-    industry="Software Development"
+    industry="Software Development",
+    age="25-34 years old",
+    ic_or_pm="Individual contributor"
 )
 ```
 
@@ -224,7 +221,7 @@ uv run python -m src.train
 
 **Quick one-liner test:**
 ```bash
-uv run python -c "from src.schema import SalaryInput; from src.infer import predict_salary; test = SalaryInput(country='United States of America', years_code=5.0, education_level='Bachelor'\''s degree (B.A., B.S., B.Eng., etc.)', dev_type='Developer, full-stack', industry='Software Development'); print(f'Prediction: \${predict_salary(test):,.0f}')"
+uv run python -c "from src.schema import SalaryInput; from src.infer import predict_salary; test = SalaryInput(country='United States of America', years_code=5.0, work_exp=3.0, education_level='Bachelor'\''s degree (B.A., B.S., B.Eng., etc.)', dev_type='Developer, full-stack', industry='Software Development', age='25-34 years old', ic_or_pm='Individual contributor'); print(f'Prediction: \${predict_salary(test):,.0f}')"
 ```
 
 **Or run the full example script:**

app.py

@@ -32,6 +32,7 @@ with st.sidebar:
         - Developer type
         - Industry
         - Age
+        - Individual contributor or people manager
         """
     )
     st.info("💡 Tip: Results are estimates based on survey averages.")
@@ -43,6 +44,7 @@ with st.sidebar:
     st.write(f"**Developer Types:** {len(valid_categories['DevType'])} available")
     st.write(f"**Industries:** {len(valid_categories['Industry'])} available")
     st.write(f"**Age Ranges:** {len(valid_categories['Age'])} available")
+    st.write(f"**IC/PM Roles:** {len(valid_categories['ICorPM'])} available")
     st.caption("Only values from the training data are shown in the dropdowns.")
 
 # Main input form
@@ -56,13 +58,35 @@ valid_education_levels = valid_categories["EdLevel"]
 valid_dev_types = valid_categories["DevType"]
 valid_industries = valid_categories["Industry"]
 valid_ages = valid_categories["Age"]
+valid_icorpm = valid_categories["ICorPM"]
 
 # Set default values (if available)
-default_country = "United States of America" if "United States of America" in valid_countries else valid_countries[0]
-default_education = "Bachelor's degree (B.A., B.S., B.Eng., etc.)" if "Bachelor's degree (B.A., B.S., B.Eng., etc.)" in valid_education_levels else valid_education_levels[0]
-default_dev_type = "Developer, back-end" if "Developer, back-end" in valid_dev_types else valid_dev_types[0]
-default_industry = "Software Development" if "Software Development" in valid_industries else valid_industries[0]
+default_country = (
+    "United States of America"
+    if "United States of America" in valid_countries
+    else valid_countries[0]
+)
+default_education = (
+    "Bachelor's degree (B.A., B.S., B.Eng., etc.)"
+    if "Bachelor's degree (B.A., B.S., B.Eng., etc.)" in valid_education_levels
+    else valid_education_levels[0]
+)
+default_dev_type = (
+    "Developer, back-end"
+    if "Developer, back-end" in valid_dev_types
+    else valid_dev_types[0]
+)
+default_industry = (
+    "Software Development"
+    if "Software Development" in valid_industries
+    else valid_industries[0]
+)
 default_age = "25-34 years old" if "25-34 years old" in valid_ages else valid_ages[0]
+default_icorpm = (
+    "Individual contributor"
+    if "Individual contributor" in valid_icorpm
+    else valid_icorpm[0]
+)
 
 with col1:
     country = st.selectbox(
@@ -119,6 +143,13 @@ age = st.selectbox(
     help="Developer's age range",
 )
 
+ic_or_pm = st.selectbox(
+    "Individual Contributor or People Manager",
+    options=valid_icorpm,
+    index=valid_icorpm.index(default_icorpm),
+    help="Are you an individual contributor or people manager?",
+)
+
 # Prediction button
 if st.button("🔮 Predict Salary", type="primary", use_container_width=True):
     try:
@@ -131,6 +162,7 @@ if st.button("🔮 Predict Salary", type="primary", use_container_width=True):
             dev_type=dev_type,
             industry=industry,
             age=age,
+            ic_or_pm=ic_or_pm,
         )
 
         # Make prediction

config/currency_rates.yaml

@@ -46,10 +46,6 @@ Netherlands:
   code: EUR
   name: European Euro
   rate: 0.86
-Other:
-  code: EUR
-  name: European Euro
-  rate: 0.86
 Poland:
   code: PLN
   name: Polish zloty

config/model_parameters.yaml

@@ -29,6 +29,19 @@ features:
     # Minimum occurrences for a category to be kept
     min_frequency: 50
 
+    # Default name for the catch-all "other" category
+    # Variants like "Other (please specify):" and "Other:" are normalized to this
+    other_category: "Other"
+
+    # Features where rows with the "Other" category should be dropped
+    # These catch-all categories hurt model quality (low R2, high prediction error)
+    drop_other_from:
+      - Country
+      - DevType
+      - Industry
+      - Age
+      - ICorPM
+
   # One-hot encoding settings
   encoding:
     # Drop first category to avoid multicollinearity
@@ -79,6 +92,14 @@ training:
   # Model output path (relative to project root)
   model_path: "models/model.pkl"
 
+# Guardrail Evaluation Thresholds
+guardrails:
+  # Minimum R2 score per category (below this triggers a warning)
+  min_r2_per_category: 0.20
+
+  # Maximum absolute percentage difference between mean actual and predicted salary
+  max_abs_pct_diff: 20
+
 # Notes:
 # - All paths are relative to project root
 # - Modify these parameters to experiment with different model configurations

config/valid_categories.yaml

@@ -11,7 +11,6 @@ Country:
 - India
 - Italy
 - Netherlands
-- Other
 - Poland
 - Portugal
 - Spain
@@ -25,7 +24,6 @@ EdLevel:
 - Bachelor's degree (B.A., B.S., B.Eng., etc.)
 - Master's degree (M.A., M.S., M.Eng., MBA, etc.)
 - Other
-- 'Other (please specify):'
 - Primary/elementary school
 - Professional degree (JD, MD, Ph.D, Ed.D, etc.)
 - Secondary school (e.g. American high school, German Realschule or Gymnasium, etc.)
@@ -47,8 +45,6 @@ DevType:
 - Developer, game or graphics
 - Developer, mobile
 - Engineering manager
-- Other
-- 'Other (please specify):'
 - Senior executive (C-suite, VP, etc.)
 - Student
 - System administrator
@@ -64,8 +60,6 @@ Industry:
 - Internet, Telecomm or Information Services
 - Manufacturing
 - Media & Advertising Services
-- Other
-- 'Other:'
 - Retail and Consumer Services
 - Software Development
 - Transportation, or Supply Chain
@@ -76,4 +70,6 @@ Age:
 - 45-54 years old
 - 55-64 years old
 - 65 years or older
-- Other
+ICorPM:
+- Individual contributor
+- People manager

debug_prepare_features.py

@@ -11,12 +11,14 @@ with open(config_path, "r") as f:
     config = yaml.safe_load(f)
 
 # Create test input
-df = pd.DataFrame({
-    'Country': ['United States of America'],
-    'YearsCode': [5.0],
-    'EdLevel': ["Bachelor's degree (B.A., B.S., B.Eng., etc.)"],
-    'DevType': ['Developer, full-stack']
-})
+df = pd.DataFrame(
+    {
+        "Country": ["United States of America"],
+        "YearsCode": [5.0],
+        "EdLevel": ["Bachelor's degree (B.A., B.S., B.Eng., etc.)"],
+        "DevType": ["Developer, full-stack"],
+    }
+)
 
 print("=" * 70)
 print("STEP-BY-STEP DEBUGGING OF prepare_features()")
@@ -32,7 +34,7 @@ df_processed = df.copy()
 # Step 3: Unicode normalization
 for col in ["Country", "EdLevel", "DevType"]:
     if col in df_processed.columns:
-        df_processed[col] = df_processed[col].str.replace('\u2019', "'", regex=False)
+        df_processed[col] = df_processed[col].str.replace("\u2019", "'", regex=False)
 
 print("\n2. After unicode normalization:")
 print(f"   Columns: {list(df_processed.columns)}")
@@ -72,7 +74,7 @@ print(f"   Columns: {list(df_features.columns)}")
 print(f"   Values: {df_features.iloc[0].to_dict()}")
 
 # Step 7: One-hot encode
-drop_first = config['features']['encoding']['drop_first']
+drop_first = config["features"]["encoding"]["drop_first"]
 print(f"\n6. One-hot encoding with drop_first={drop_first}:")
 df_encoded = pd.get_dummies(df_features, drop_first=drop_first)
 

diagnose_encoding.py

@@ -4,19 +4,23 @@ from src.preprocessing import prepare_features
 import pandas as pd
 
 # Create two inputs that differ ONLY in Country
-input1 = pd.DataFrame({
-    'Country': ['United States of America'],
-    'YearsCode': [5.0],
-    'EdLevel': ["Bachelor's degree (B.A., B.S., B.Eng., etc.)"],
-    'DevType': ['Developer, full-stack']
-})
+input1 = pd.DataFrame(
+    {
+        "Country": ["United States of America"],
+        "YearsCode": [5.0],
+        "EdLevel": ["Bachelor's degree (B.A., B.S., B.Eng., etc.)"],
+        "DevType": ["Developer, full-stack"],
+    }
+)
 
-input2 = pd.DataFrame({
-    'Country': ['Germany'],  # Different!
-    'YearsCode': [5.0],
-    'EdLevel': ["Bachelor's degree (B.A., B.S., B.Eng., etc.)"],
-    'DevType': ['Developer, full-stack']
-})
+input2 = pd.DataFrame(
+    {
+        "Country": ["Germany"],  # Different!
+        "YearsCode": [5.0],
+        "EdLevel": ["Bachelor's degree (B.A., B.S., B.Eng., etc.)"],
+        "DevType": ["Developer, full-stack"],
+    }
+)
 
 print("=" * 70)
 print("ENCODING DIAGNOSIS")
@@ -26,13 +30,13 @@ print("=" * 70)
 features1 = prepare_features(input1)
 features2 = prepare_features(input2)
 
-print(f"\nInput 1 (USA):")
+print("\nInput 1 (USA):")
 print(f"  Shape: {features1.shape}")
 print(f"  Columns: {list(features1.columns)}")
 non_zero1 = [col for col in features1.columns if features1[col].iloc[0] != 0]
 print(f"  Non-zero features ({len(non_zero1)}): {non_zero1}")
 
-print(f"\nInput 2 (Germany):")
+print("\nInput 2 (Germany):")
 print(f"  Shape: {features2.shape}")
 non_zero2 = [col for col in features2.columns if features2[col].iloc[0] != 0]
 print(f"  Non-zero features ({len(non_zero2)}): {non_zero2}")
@@ -51,15 +55,17 @@ print("COUNTRY ENCODING CHECK")
 print("=" * 70)
 
 # Test just Country encoding
-test_countries = ['United States of America', 'Germany', 'India']
+test_countries = ["United States of America", "Germany", "India"]
 for country in test_countries:
-    test_df = pd.DataFrame({
-        'Country': [country],
-        'YearsCode': [5.0],
-        'EdLevel': ["Bachelor's degree (B.A., B.S., B.Eng., etc.)"],
-        'DevType': ['Developer, full-stack']
-    })
+    test_df = pd.DataFrame(
+        {
+            "Country": [country],
+            "YearsCode": [5.0],
+            "EdLevel": ["Bachelor's degree (B.A., B.S., B.Eng., etc.)"],
+            "DevType": ["Developer, full-stack"],
+        }
+    )
     encoded = prepare_features(test_df)
-    country_cols = [col for col in encoded.columns if col.startswith('Country_')]
+    country_cols = [col for col in encoded.columns if col.startswith("Country_")]
     non_zero_countries = [col for col in country_cols if encoded[col].iloc[0] != 0]
     print(f"{country:40s} -> {non_zero_countries}")

example_inference.py

@@ -23,6 +23,7 @@ def main():
         dev_type="Developer, full-stack",
         industry="Software Development",
         age="25-34 years old",
+        ic_or_pm="Individual contributor",
     )
 
     print(f"Country: {input_data_1.country}")
@@ -32,6 +33,7 @@ def main():
     print(f"Developer Type: {input_data_1.dev_type}")
     print(f"Industry: {input_data_1.industry}")
     print(f"Age: {input_data_1.age}")
+    print(f"IC or PM: {input_data_1.ic_or_pm}")
 
     salary_1 = predict_salary(input_data_1)
     print(f"💰 Predicted Salary: ${salary_1:,.2f} USD/year")
@@ -48,6 +50,7 @@ def main():
         dev_type="Developer, front-end",
         industry="Fintech",
         age="18-24 years old",
+        ic_or_pm="Individual contributor",
     )
 
     print(f"Country: {input_data_2.country}")
@@ -57,6 +60,7 @@ def main():
     print(f"Developer Type: {input_data_2.dev_type}")
     print(f"Industry: {input_data_2.industry}")
     print(f"Age: {input_data_2.age}")
+    print(f"IC or PM: {input_data_2.ic_or_pm}")
 
     salary_2 = predict_salary(input_data_2)
     print(f"💰 Predicted Salary: ${salary_2:,.2f} USD/year")
@@ -73,6 +77,7 @@ def main():
         dev_type="Engineering manager",
         industry="Banking/Financial Services",
         age="35-44 years old",
+        ic_or_pm="People manager",
     )
 
     print(f"Country: {input_data_3.country}")
@@ -82,6 +87,7 @@ def main():
     print(f"Developer Type: {input_data_3.dev_type}")
     print(f"Industry: {input_data_3.industry}")
     print(f"Age: {input_data_3.age}")
+    print(f"IC or PM: {input_data_3.ic_or_pm}")
 
     salary_3 = predict_salary(input_data_3)
     print(f"💰 Predicted Salary: ${salary_3:,.2f} USD/year")
@@ -98,6 +104,7 @@ def main():
         dev_type="Developer, back-end",
         industry="Manufacturing",
         age="25-34 years old",
+        ic_or_pm="Individual contributor",
     )
 
     print(f"Country: {input_data_4.country}")
@@ -107,6 +114,7 @@ def main():
     print(f"Developer Type: {input_data_4.dev_type}")
     print(f"Industry: {input_data_4.industry}")
     print(f"Age: {input_data_4.age}")
+    print(f"IC or PM: {input_data_4.ic_or_pm}")
 
     salary_4 = predict_salary(input_data_4)
     print(f"💰 Predicted Salary: ${salary_4:,.2f} USD/year")

guardrail_evaluation.py

@@ -0,0 +1,261 @@
+"""Per-category guardrail evaluation for the salary prediction model.
+
+Runs cross-validation and computes R2 scores and predicted vs actual salary
+comparisons broken down by each categorical feature value. Flags categories
+that fall below configurable thresholds.
+"""
+
+import sys
+from pathlib import Path
+
+import numpy as np
+import pandas as pd
+import yaml
+from sklearn.metrics import r2_score
+from sklearn.model_selection import KFold
+from xgboost import XGBRegressor
+
+from src.preprocessing import prepare_features, reduce_cardinality
+
+
+CATEGORICAL_FEATURES = ["Country", "EdLevel", "DevType", "Industry", "Age", "ICorPM"]
+
+
+def load_and_preprocess(config: dict) -> tuple[pd.DataFrame, pd.DataFrame, pd.Series]:
+    """Load data and apply same preprocessing as train.py.
+
+    Returns:
+        (df, X, y) where df has original categorical columns (after cardinality
+        reduction), X is one-hot encoded features, y is the target.
+    """
+    data_path = Path("data/survey_results_public.csv")
+    if not data_path.exists():
+        print(f"Error: Data file not found at {data_path}")
+        sys.exit(1)
+
+    df = pd.read_csv(
+        data_path,
+        usecols=[
+            "Country",
+            "YearsCode",
+            "WorkExp",
+            "EdLevel",
+            "DevType",
+            "Industry",
+            "Age",
+            "ICorPM",
+            "ConvertedCompYearly",
+        ],
+    )
+
+    main_label = "ConvertedCompYearly"
+    min_salary = config["data"]["min_salary"]
+    df = df[df[main_label] > min_salary]
+
+    # Per-country percentile outlier removal
+    lower_pct = config["data"]["lower_percentile"] / 100
+    upper_pct = config["data"]["upper_percentile"] / 100
+    lower_bound = df.groupby("Country")[main_label].transform("quantile", lower_pct)
+    upper_bound = df.groupby("Country")[main_label].transform("quantile", upper_pct)
+    df = df[(df[main_label] > lower_bound) & (df[main_label] < upper_bound)]
+
+    df = df.dropna(subset=[main_label])
+
+    # Cardinality reduction (same as train.py)
+    for col in CATEGORICAL_FEATURES:
+        df[col] = reduce_cardinality(df[col])
+
+    # Drop rows with "Other" in specified features (same as train.py)
+    other_name = config["features"]["cardinality"].get("other_category", "Other")
+    drop_other_from = config["features"]["cardinality"].get("drop_other_from", [])
+    if drop_other_from:
+        before_drop = len(df)
+        for col in drop_other_from:
+            df = df[df[col] != other_name]
+        print(
+            f"Dropped {before_drop - len(df):,} rows with '{other_name}' in {drop_other_from}"
+        )
+
+    X = prepare_features(df)
+    y = df[main_label]
+
+    return df, X, y
+
+
+def run_cv_predictions(
+    X: pd.DataFrame,
+    y: pd.Series,
+    config: dict,
+) -> np.ndarray:
+    """Run KFold CV and return out-of-fold predictions for every row.
+
+    Each row gets exactly one prediction (from the fold where it was in the
+    test set).
+    """
+    n_splits = config["data"].get("cv_splits", 5)
+    random_state = config["data"]["random_state"]
+    model_config = config["model"]
+
+    kf = KFold(n_splits=n_splits, shuffle=True, random_state=random_state)
+    oof_predictions = np.empty(len(y))
+    oof_predictions[:] = np.nan
+
+    print(f"Running {n_splits}-fold cross-validation...")
+    for fold, (train_idx, test_idx) in enumerate(kf.split(X), 1):
+        X_train, X_test = X.iloc[train_idx], X.iloc[test_idx]
+        y_train, y_test = y.iloc[train_idx], y.iloc[test_idx]
+
+        model = XGBRegressor(
+            n_estimators=model_config["n_estimators"],
+            learning_rate=model_config["learning_rate"],
+            max_depth=model_config["max_depth"],
+            min_child_weight=model_config["min_child_weight"],
+            random_state=model_config["random_state"],
+            n_jobs=model_config["n_jobs"],
+            early_stopping_rounds=model_config["early_stopping_rounds"],
+        )
+        model.fit(
+            X_train,
+            y_train,
+            eval_set=[(X_test, y_test)],
+            verbose=False,
+        )
+
+        test_r2 = model.score(X_test, y_test)
+        print(
+            f"  Fold {fold}: Test R2 = {test_r2:.4f} (best iter: {model.best_iteration + 1})"
+        )
+
+        oof_predictions[test_idx] = model.predict(X_test)
+
+    overall_r2 = r2_score(y, oof_predictions)
+    print(f"\nOverall OOF R2: {overall_r2:.4f}")
+
+    return oof_predictions
+
+
+def compute_category_metrics(
+    df: pd.DataFrame,
+    y: pd.Series,
+    predictions: np.ndarray,
+    feature: str,
+) -> pd.DataFrame:
+    """Compute per-category R2, mean actual/predicted, and abs % diff."""
+    results = []
+    categories = df[feature].values
+    actuals = y.values
+
+    for cat in sorted(df[feature].unique()):
+        mask = categories == cat
+        cat_actual = actuals[mask]
+        cat_pred = predictions[mask]
+        count = int(mask.sum())
+
+        if count < 2:
+            cat_r2 = float("nan")
+        else:
+            cat_r2 = r2_score(cat_actual, cat_pred)
+
+        mean_actual = cat_actual.mean()
+        mean_pred = cat_pred.mean()
+        abs_pct_diff = abs(mean_pred - mean_actual) / mean_actual * 100
+
+        results.append(
+            {
+                "Category": cat,
+                "Count": count,
+                "R2": cat_r2,
+                "Mean Actual ($)": mean_actual,
+                "Mean Predicted ($)": mean_pred,
+                "Abs % Diff": abs_pct_diff,
+            }
+        )
+
+    return pd.DataFrame(results)
+
+
+def format_table(metrics_df: pd.DataFrame) -> str:
+    """Format metrics DataFrame as a markdown table."""
+    lines = []
+    header = (
+        "| Category | Count | R2 | Mean Actual ($) | Mean Predicted ($) | Abs % Diff |"
+    )
+    sep = (
+        "|----------|------:|----:|----------------:|-------------------:|-----------:|"
+    )
+    lines.append(header)
+    lines.append(sep)
+
+    for _, row in metrics_df.iterrows():
+        r2_str = f"{row['R2']:.2f}" if not np.isnan(row["R2"]) else "N/A"
+        lines.append(
+            f"| {row['Category'][:45]:45s} | {row['Count']:5,d} | {r2_str:>4s} "
+            f"| {row['Mean Actual ($)']:>15,.0f} | {row['Mean Predicted ($)']:>18,.0f} "
+            f"| {row['Abs % Diff']:>9.1f}% |"
+        )
+
+    return "\n".join(lines)
+
+
+def main():
+    """Run per-category guardrail evaluation."""
+    config_path = Path("config/model_parameters.yaml")
+    with open(config_path, "r") as f:
+        config = yaml.safe_load(f)
+
+    guardrails = config.get("guardrails", {})
+    min_r2 = guardrails.get("min_r2_per_category", 0.30)
+    max_pct_diff = guardrails.get("max_abs_pct_diff", 10)
+
+    print("=" * 80)
+    print("GUARDRAIL EVALUATION - Per-Category Model Quality")
+    print(f"Thresholds: min R2 = {min_r2}, max abs % diff = {max_pct_diff}%")
+    print("=" * 80)
+
+    df, X, y = load_and_preprocess(config)
+    print(f"Dataset: {len(df):,} rows, {X.shape[1]} features\n")
+
+    predictions = run_cv_predictions(X, y, config)
+
+    # Reset index alignment: df and y may have non-contiguous indices
+    # predictions array is positional, so align everything by position
+    df_eval = df.reset_index(drop=True)
+    y_eval = y.reset_index(drop=True)
+
+    warnings = []
+
+    for feature in CATEGORICAL_FEATURES:
+        print(f"\n## {feature}\n")
+        metrics = compute_category_metrics(df_eval, y_eval, predictions, feature)
+        print(format_table(metrics))
+
+        # Check guardrails
+        for _, row in metrics.iterrows():
+            cat = row["Category"]
+            if not np.isnan(row["R2"]) and row["R2"] < min_r2:
+                warnings.append(
+                    f'{feature} "{cat}": R2 = {row["R2"]:.2f} (threshold: {min_r2})'
+                )
+            if row["Abs % Diff"] > max_pct_diff:
+                warnings.append(
+                    f'{feature} "{cat}": Abs % Diff = {row["Abs % Diff"]:.1f}% '
+                    f"(threshold: {max_pct_diff}%)"
+                )
+
+    # Summary
+    print("\n" + "=" * 80)
+    if warnings:
+        print("### Guardrail Warnings\n")
+        for w in warnings:
+            print(f"  - {w}")
+        print(f"\n{len(warnings)} guardrail violation(s) found.")
+    else:
+        print("All categories pass guardrail thresholds.")
+
+    print("=" * 80)
+
+    sys.exit(1 if warnings else 0)
+
+
+if __name__ == "__main__":
+    main()

models/model.pkl

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:efa053c467c2c1ea33a65f04aedc32fb7a6c47658238f26d88cd0a10986c0c98
-size 3985657
+oid sha256:4166bad87e5b67d7b498637d8ef286a3e3f74a82450db02d2aa7e4e09d750090
+size 4146513

pyproject.toml

@@ -12,4 +12,20 @@ dependencies = [
     "xgboost>=3.1.0",
     "ruff>=0.15.0",
     "pyyaml>=6.0.0",
+    "numpy>=2.4.2",
+    "radon>=6.0.1",
+    "pip-audit>=2.10.0",
+    "bandit>=1.9.3",
 ]
+
+[project.optional-dependencies]
+dev = [
+    "pytest>=8.0.0",
+    "pytest-cov>=6.0.0",
+    "radon>=6.0.0",
+    "pip-audit>=2.7.0",
+    "bandit>=1.8.0",
+]
+
+[tool.pytest.ini_options]
+testpaths = ["tests"]

src/infer.py

@@ -106,6 +106,13 @@ def predict_salary(data: SalaryInput) -> float:
             f"Check config/valid_categories.yaml for all valid values."
         )
 
+    if data.ic_or_pm not in valid_categories["ICorPM"]:
+        raise ValueError(
+            f"Invalid IC or PM value: '{data.ic_or_pm}'. "
+            f"Must be one of {len(valid_categories['ICorPM'])} valid values. "
+            f"Check config/valid_categories.yaml for all valid values."
+        )
+
     # Create a DataFrame with the input data
     input_df = pd.DataFrame(
         {
@@ -116,6 +123,7 @@ def predict_salary(data: SalaryInput) -> float:
             "DevType": [data.dev_type],
             "Industry": [data.industry],
             "Age": [data.age],
+            "ICorPM": [data.ic_or_pm],
         }
     )
 

src/preprocessing.py

@@ -10,10 +10,28 @@ with open(_config_path, "r") as f:
     _config = yaml.safe_load(f)
 
 
+def _get_other_category() -> str:
+    """Get the standard 'Other' category name from config."""
+    return _config["features"]["cardinality"].get("other_category", "Other")
+
+
+def normalize_other_categories(series: pd.Series) -> pd.Series:
+    """
+    Normalize variants of 'Other' to the standard category name.
+
+    Replaces values like 'Other (please specify):', 'Other:', etc.
+    with the standard 'Other' category from config.
+    """
+    other_name = _get_other_category()
+    return series.replace(
+        to_replace=r"^Other\b.*$",
+        value=other_name,
+        regex=True,
+    )
+
+
 def reduce_cardinality(
-    series: pd.Series,
-    max_categories: int = None,
-    min_frequency: int = None
+    series: pd.Series, max_categories: int = None, min_frequency: int = None
 ) -> pd.Series:
     """
     Reduce cardinality by grouping rare categories into 'Other'.
@@ -28,11 +46,16 @@ def reduce_cardinality(
     Returns:
         Series with rare categories replaced by 'Other'
     """
+    other_name = _get_other_category()
+
     # Use config defaults if not provided
     if max_categories is None:
-        max_categories = _config['features']['cardinality']['max_categories']
+        max_categories = _config["features"]["cardinality"]["max_categories"]
     if min_frequency is None:
-        min_frequency = _config['features']['cardinality']['min_frequency']
+        min_frequency = _config["features"]["cardinality"]["min_frequency"]
+
+    # Normalize "Other" variants before counting frequencies
+    series = normalize_other_categories(series)
 
     # Count value frequencies
     value_counts = series.value_counts()
@@ -43,8 +66,8 @@ def reduce_cardinality(
     top_categories = value_counts.head(max_categories)
     kept_categories = top_categories[top_categories >= min_frequency].index.tolist()
 
-    # Replace rare categories with 'Other'
-    return series.apply(lambda x: x if x in kept_categories else 'Other')
+    # Replace rare categories with the standard 'Other' name
+    return series.apply(lambda x: x if x in kept_categories else other_name)
 
 
 def prepare_features(df: pd.DataFrame) -> pd.DataFrame:
@@ -55,7 +78,7 @@ def prepare_features(df: pd.DataFrame) -> pd.DataFrame:
     during training and inference, preventing data leakage and inconsistencies.
 
     Args:
-        df: DataFrame with columns: Country, YearsCode, WorkExp, EdLevel, DevType, Industry, Age
+        df: DataFrame with columns: Country, YearsCode, WorkExp, EdLevel, DevType, Industry, Age, ICorPM
             NOTE: During training, cardinality reduction should be applied to df
             BEFORE calling this function. During inference, valid_categories.yaml
             ensures only valid (already-reduced) categories are used.
@@ -67,7 +90,7 @@ def prepare_features(df: pd.DataFrame) -> pd.DataFrame:
         - Fills missing values with defaults (0 for numeric, "Unknown" for categorical)
         - Normalizes Unicode apostrophes to regular apostrophes
         - Applies one-hot encoding with drop_first=True to avoid multicollinearity
-        - Column names in output will be like: YearsCode, WorkExp, Country_X, EdLevel_Y, DevType_Z, Industry_W, Age_V
+        - Column names in output will be like: YearsCode, WorkExp, Country_X, EdLevel_Y, DevType_Z, Industry_W, Age_V, ICorPM_U
         - Does NOT apply cardinality reduction (must be done before calling this)
     """
     # Create a copy to avoid modifying the original
@@ -75,12 +98,22 @@ def prepare_features(df: pd.DataFrame) -> pd.DataFrame:
 
     # Normalize Unicode apostrophes to regular apostrophes for consistency
     # This handles cases where data has \u2019 (') instead of '
-    for col in ["Country", "EdLevel", "DevType", "Industry", "Age"]:
+    for col in ["Country", "EdLevel", "DevType", "Industry", "Age", "ICorPM"]:
+        if col in df_processed.columns:
+            df_processed[col] = df_processed[col].str.replace(
+                "\u2019", "'", regex=False
+            )
+
+    # Normalize "Other" category variants (e.g. "Other (please specify):" -> "Other")
+    for col in ["Country", "EdLevel", "DevType", "Industry", "Age", "ICorPM"]:
         if col in df_processed.columns:
-            df_processed[col] = df_processed[col].str.replace('\u2019', "'", regex=False)
+            df_processed[col] = normalize_other_categories(df_processed[col])
 
     # Handle legacy column name (YearsCodePro -> YearsCode)
-    if "YearsCodePro" in df_processed.columns and "YearsCode" not in df_processed.columns:
+    if (
+        "YearsCodePro" in df_processed.columns
+        and "YearsCode" not in df_processed.columns
+    ):
         df_processed.rename(columns={"YearsCodePro": "YearsCode"}, inplace=True)
 
     # Fill missing values with defaults
@@ -91,13 +124,23 @@ def prepare_features(df: pd.DataFrame) -> pd.DataFrame:
     df_processed["DevType"] = df_processed["DevType"].fillna("Unknown")
     df_processed["Industry"] = df_processed["Industry"].fillna("Unknown")
     df_processed["Age"] = df_processed["Age"].fillna("Unknown")
+    df_processed["ICorPM"] = df_processed["ICorPM"].fillna("Unknown")
 
     # NOTE: Cardinality reduction is NOT applied here
     # It should be applied during training BEFORE calling this function
     # During inference, valid_categories.yaml ensures only valid values are used
 
     # Select only the features we need
-    feature_cols = ["Country", "YearsCode", "WorkExp", "EdLevel", "DevType", "Industry", "Age"]
+    feature_cols = [
+        "Country",
+        "YearsCode",
+        "WorkExp",
+        "EdLevel",
+        "DevType",
+        "Industry",
+        "Age",
+        "ICorPM",
+    ]
     df_features = df_processed[feature_cols]
 
     # Apply one-hot encoding for categorical variables
@@ -105,7 +148,9 @@ def prepare_features(df: pd.DataFrame) -> pd.DataFrame:
     # The reindex in infer.py will align with training columns
     # For training (many rows), we use the config value
     is_inference = len(df_features) == 1
-    drop_first = False if is_inference else _config['features']['encoding']['drop_first']
+    drop_first = (
+        False if is_inference else _config["features"]["encoding"]["drop_first"]
+    )
     df_encoded = pd.get_dummies(df_features, drop_first=drop_first)
 
     return df_encoded

src/schema.py

@@ -21,6 +21,7 @@ class SalaryInput(BaseModel):
     dev_type: str = Field(..., description="Developer type")
     industry: str = Field(..., description="Industry the developer works in")
     age: str = Field(..., description="Developer's age range")
+    ic_or_pm: str = Field(..., description="Individual contributor or people manager")
 
     class Config:
         """Pydantic configuration."""
@@ -34,5 +35,6 @@ class SalaryInput(BaseModel):
                 "dev_type": "Developer, back-end",
                 "industry": "Software Development",
                 "age": "25-34 years old",
+                "ic_or_pm": "Individual contributor",
             }
         }

src/train.py

@@ -25,15 +25,28 @@ def main():
 
     if not data_path.exists():
         print(f"Error: Data file not found at {data_path}")
-        print("Please download the Stack Overflow Developer Survey CSV and place it in the data/ directory.")
+        print(
+            "Please download the Stack Overflow Developer Survey CSV and place it in the data/ directory."
+        )
         print("Download from: https://insights.stackoverflow.com/survey")
         return
 
     # Load only required columns to save memory
     df = pd.read_csv(
         data_path,
-        usecols=["Country", "YearsCode", "WorkExp", "EdLevel", "DevType", "Industry", "Age",
-                 "Currency", "CompTotal", "ConvertedCompYearly"],
+        usecols=[
+            "Country",
+            "YearsCode",
+            "WorkExp",
+            "EdLevel",
+            "DevType",
+            "Industry",
+            "Age",
+            "ICorPM",
+            "Currency",
+            "CompTotal",
+            "ConvertedCompYearly",
+        ],
     )
 
     print(f"Loaded {len(df):,} rows")
@@ -42,13 +55,13 @@ def main():
     # select main label
     main_label = "ConvertedCompYearly"
     # select records with main label more than min_salary threshold
-    min_salary = config['data']['min_salary']
+    min_salary = config["data"]["min_salary"]
     df = df[df[main_label] > min_salary]
     # Exclude outliers based on percentile bounds PER COUNTRY
     # This preserves records from lower-paid and higher-paid countries
     # that would otherwise be removed by global percentile filtering
-    lower_pct = config['data']['lower_percentile'] / 100
-    upper_pct = config['data']['upper_percentile'] / 100
+    lower_pct = config["data"]["lower_percentile"] / 100
+    upper_pct = config["data"]["upper_percentile"] / 100
     lower_bound = df.groupby("Country")[main_label].transform("quantile", lower_pct)
     upper_bound = df.groupby("Country")[main_label].transform("quantile", upper_pct)
     df = df[(df[main_label] > lower_bound) & (df[main_label] < upper_bound)]
@@ -63,11 +76,12 @@ def main():
     df_copy = df.copy()
 
     # Normalize Unicode apostrophes to regular apostrophes for consistency
-    df_copy["Country"] = df_copy["Country"].str.replace('\u2019', "'", regex=False)
-    df_copy["EdLevel"] = df_copy["EdLevel"].str.replace('\u2019', "'", regex=False)
-    df_copy["DevType"] = df_copy["DevType"].str.replace('\u2019', "'", regex=False)
-    df_copy["Industry"] = df_copy["Industry"].str.replace('\u2019', "'", regex=False)
-    df_copy["Age"] = df_copy["Age"].str.replace('\u2019', "'", regex=False)
+    df_copy["Country"] = df_copy["Country"].str.replace("\u2019", "'", regex=False)
+    df_copy["EdLevel"] = df_copy["EdLevel"].str.replace("\u2019", "'", regex=False)
+    df_copy["DevType"] = df_copy["DevType"].str.replace("\u2019", "'", regex=False)
+    df_copy["Industry"] = df_copy["Industry"].str.replace("\u2019", "'", regex=False)
+    df_copy["Age"] = df_copy["Age"].str.replace("\u2019", "'", regex=False)
+    df_copy["ICorPM"] = df_copy["ICorPM"].str.replace("\u2019", "'", regex=False)
 
     # Apply cardinality reduction
     df_copy["Country"] = reduce_cardinality(df_copy["Country"])
@@ -75,6 +89,7 @@ def main():
     df_copy["DevType"] = reduce_cardinality(df_copy["DevType"])
     df_copy["Industry"] = reduce_cardinality(df_copy["Industry"])
     df_copy["Age"] = reduce_cardinality(df_copy["Age"])
+    df_copy["ICorPM"] = reduce_cardinality(df_copy["ICorPM"])
 
     # Apply cardinality reduction to the actual training data as well
     # (prepare_features no longer does this internally)
@@ -83,6 +98,20 @@ def main():
     df["DevType"] = reduce_cardinality(df["DevType"])
     df["Industry"] = reduce_cardinality(df["Industry"])
     df["Age"] = reduce_cardinality(df["Age"])
+    df["ICorPM"] = reduce_cardinality(df["ICorPM"])
+
+    # Drop rows with "Other" in specified features (low-quality catch-all categories)
+    other_name = config["features"]["cardinality"].get("other_category", "Other")
+    drop_other_from = config["features"]["cardinality"].get("drop_other_from", [])
+    if drop_other_from:
+        before_drop = len(df)
+        for col in drop_other_from:
+            df = df[df[col] != other_name]
+            df_copy = df_copy[df_copy[col] != other_name]
+        print(
+            f"Dropped {before_drop - len(df):,} rows with '{other_name}' in {drop_other_from}"
+        )
+        print(f"After dropping 'Other': {len(df):,} rows")
 
     # Now apply full feature transformations for model training
     X = prepare_features(df)
@@ -95,6 +124,7 @@ def main():
     devtype_values = df_copy["DevType"].dropna().unique().tolist()
     industry_values = df_copy["Industry"].dropna().unique().tolist()
     age_values = df_copy["Age"].dropna().unique().tolist()
+    icorpm_values = df_copy["ICorPM"].dropna().unique().tolist()
 
     valid_categories = {
         "Country": sorted(country_values),
@@ -102,13 +132,16 @@ def main():
         "DevType": sorted(devtype_values),
         "Industry": sorted(industry_values),
         "Age": sorted(age_values),
+        "ICorPM": sorted(icorpm_values),
     }
 
     valid_categories_path = Path("config/valid_categories.yaml")
     with open(valid_categories_path, "w") as f:
         yaml.dump(valid_categories, f, default_flow_style=False, sort_keys=False)
 
-    print(f"\nSaved {len(valid_categories['Country'])} valid countries, {len(valid_categories['EdLevel'])} valid education levels, {len(valid_categories['DevType'])} valid developer types, {len(valid_categories['Industry'])} valid industries, and {len(valid_categories['Age'])} valid age ranges to {valid_categories_path}")
+    print(
+        f"\nSaved {len(valid_categories['Country'])} valid countries, {len(valid_categories['EdLevel'])} valid education levels, {len(valid_categories['DevType'])} valid developer types, {len(valid_categories['Industry'])} valid industries, {len(valid_categories['Age'])} valid age ranges, and {len(valid_categories['ICorPM'])} valid IC/PM values to {valid_categories_path}"
+    )
 
     # Compute currency conversion rates per country
     # Use the original data with Currency and CompTotal columns
@@ -121,7 +154,9 @@ def main():
     # Compute conversion rate: local currency / USD
     currency_df["rate"] = currency_df["CompTotal"] / currency_df[main_label]
     # Filter out unreasonable rates (negative, zero, or extreme)
-    currency_df = currency_df[(currency_df["rate"] > 0.001) & (currency_df["rate"] < 100000)]
+    currency_df = currency_df[
+        (currency_df["rate"] > 0.001) & (currency_df["rate"] < 100000)
+    ]
 
     currency_rates = {}
     for country in valid_categories["Country"]:
@@ -147,12 +182,21 @@ def main():
 
     currency_rates_path = Path("config/currency_rates.yaml")
     with open(currency_rates_path, "w") as f:
-        yaml.dump(currency_rates, f, default_flow_style=False, sort_keys=True,
-                  allow_unicode=True)
+        yaml.dump(
+            currency_rates,
+            f,
+            default_flow_style=False,
+            sort_keys=True,
+            allow_unicode=True,
+        )
 
-    print(f"Saved currency rates for {len(currency_rates)} countries to {currency_rates_path}")
+    print(
+        f"Saved currency rates for {len(currency_rates)} countries to {currency_rates_path}"
+    )
     for country, info in sorted(currency_rates.items()):
-        print(f"  {country:45s} -> {info['code']} ({info['name']}, rate: {info['rate']})")
+        print(
+            f"  {country:45s} -> {info['code']} ({info['name']}, rate: {info['rate']})"
+        )
 
     print(f"\nFeature matrix shape: {X.shape}")
     print(f"Total features: {X.shape[1]}")
@@ -166,31 +210,37 @@ def main():
     print("\n📍 Top 10 Countries:")
     top_countries = df["Country"].value_counts().head(10)
     for country, count in top_countries.items():
-        print(f"  - {country}: {count:,} ({count/len(df)*100:.1f}%)")
+        print(f"  - {country}: {count:,} ({count / len(df) * 100:.1f}%)")
 
     # Show top education levels
     print("\n🎓 Top Education Levels:")
     top_edu = df["EdLevel"].value_counts().head(10)
     for edu, count in top_edu.items():
-        print(f"  - {edu}: {count:,} ({count/len(df)*100:.1f}%)")
+        print(f"  - {edu}: {count:,} ({count / len(df) * 100:.1f}%)")
 
     # Show top developer types
     print("\n👨‍💻 Top Developer Types:")
     top_devtype = df["DevType"].value_counts().head(10)
     for devtype, count in top_devtype.items():
-        print(f"  - {devtype}: {count:,} ({count/len(df)*100:.1f}%)")
+        print(f"  - {devtype}: {count:,} ({count / len(df) * 100:.1f}%)")
 
     # Show top industries
     print("\n🏢 Top Industries:")
     top_industry = df["Industry"].value_counts().head(10)
     for industry, count in top_industry.items():
-        print(f"  - {industry}: {count:,} ({count/len(df)*100:.1f}%)")
+        print(f"  - {industry}: {count:,} ({count / len(df) * 100:.1f}%)")
 
     # Show age distribution
     print("\n🎂 Age Distribution:")
     top_age = df["Age"].value_counts().head(10)
     for age, count in top_age.items():
-        print(f"  - {age}: {count:,} ({count/len(df)*100:.1f}%)")
+        print(f"  - {age}: {count:,} ({count / len(df) * 100:.1f}%)")
+
+    # Show IC or PM distribution
+    print("\n👥 IC or PM Distribution:")
+    top_icorpm = df["ICorPM"].value_counts().head(10)
+    for icorpm, count in top_icorpm.items():
+        print(f"  - {icorpm}: {count:,} ({count / len(df) * 100:.1f}%)")
 
     # Show YearsCode statistics
     print("\n💼 Years of Coding Experience:")
@@ -217,47 +267,81 @@ def main():
     feature_counts = X.sum().sort_values(ascending=False)
 
     # Exclude numeric features (YearsCode)
-    categorical_features = feature_counts[~feature_counts.index.str.startswith('YearsCode')]
+    categorical_features = feature_counts[
+        ~feature_counts.index.str.startswith("YearsCode")
+    ]
 
     # Country features
     print("\n🌍 Top 15 Country Features (most common):")
-    country_features = categorical_features[categorical_features.index.str.startswith('Country_')]
+    country_features = categorical_features[
+        categorical_features.index.str.startswith("Country_")
+    ]
     for i, (feature, count) in enumerate(country_features.head(15).items(), 1):
         percentage = (count / len(X)) * 100
-        country_name = feature.replace('Country_', '')
-        print(f"  {i:2d}. {country_name:45s} - {count:6.0f} occurrences ({percentage:5.1f}%)")
+        country_name = feature.replace("Country_", "")
+        print(
+            f"  {i:2d}. {country_name:45s} - {count:6.0f} occurrences ({percentage:5.1f}%)"
+        )
 
     # Education level features
     print("\n🎓 Top 10 Education Level Features (most common):")
-    edlevel_features = categorical_features[categorical_features.index.str.startswith('EdLevel_')]
+    edlevel_features = categorical_features[
+        categorical_features.index.str.startswith("EdLevel_")
+    ]
     for i, (feature, count) in enumerate(edlevel_features.head(10).items(), 1):
         percentage = (count / len(X)) * 100
-        edu_name = feature.replace('EdLevel_', '')
-        print(f"  {i:2d}. {edu_name:45s} - {count:6.0f} occurrences ({percentage:5.1f}%)")
+        edu_name = feature.replace("EdLevel_", "")
+        print(
+            f"  {i:2d}. {edu_name:45s} - {count:6.0f} occurrences ({percentage:5.1f}%)"
+        )
 
     # Developer type features
     print("\n👨‍💻 Top 10 Developer Type Features (most common):")
-    devtype_features = categorical_features[categorical_features.index.str.startswith('DevType_')]
+    devtype_features = categorical_features[
+        categorical_features.index.str.startswith("DevType_")
+    ]
     for i, (feature, count) in enumerate(devtype_features.head(10).items(), 1):
         percentage = (count / len(X)) * 100
-        devtype_name = feature.replace('DevType_', '')
-        print(f"  {i:2d}. {devtype_name:45s} - {count:6.0f} occurrences ({percentage:5.1f}%)")
+        devtype_name = feature.replace("DevType_", "")
+        print(
+            f"  {i:2d}. {devtype_name:45s} - {count:6.0f} occurrences ({percentage:5.1f}%)"
+        )
 
     # Industry features
     print("\n🏢 Top 10 Industry Features (most common):")
-    industry_features = categorical_features[categorical_features.index.str.startswith('Industry_')]
+    industry_features = categorical_features[
+        categorical_features.index.str.startswith("Industry_")
+    ]
     for i, (feature, count) in enumerate(industry_features.head(10).items(), 1):
         percentage = (count / len(X)) * 100
-        industry_name = feature.replace('Industry_', '')
-        print(f"  {i:2d}. {industry_name:45s} - {count:6.0f} occurrences ({percentage:5.1f}%)")
+        industry_name = feature.replace("Industry_", "")
+        print(
+            f"  {i:2d}. {industry_name:45s} - {count:6.0f} occurrences ({percentage:5.1f}%)"
+        )
 
     # Age features
     print("\n🎂 Top 10 Age Features (most common):")
-    age_features = categorical_features[categorical_features.index.str.startswith('Age_')]
+    age_features = categorical_features[
+        categorical_features.index.str.startswith("Age_")
+    ]
     for i, (feature, count) in enumerate(age_features.head(10).items(), 1):
         percentage = (count / len(X)) * 100
-        age_name = feature.replace('Age_', '')
-        print(f"  {i:2d}. {age_name:45s} - {count:6.0f} occurrences ({percentage:5.1f}%)")
+        age_name = feature.replace("Age_", "")
+        print(
+            f"  {i:2d}. {age_name:45s} - {count:6.0f} occurrences ({percentage:5.1f}%)"
+        )
+
+    # ICorPM features
+    print("\n👥 Top 10 IC/PM Features (most common):")
+    icorpm_features = categorical_features[
+        categorical_features.index.str.startswith("ICorPM_")
+    ]
+    for i, (feature, count) in enumerate(icorpm_features.head(10).items(), 1):
+        percentage = (count / len(X)) * 100
+        icorpm_name = feature.replace("ICorPM_", "")
+        print(
+            f"  {i:2d}. {icorpm_name:45s} - {count:6.0f} occurrences ({percentage:5.1f}%)"
+        )
 
     print(f"\n📊 Total one-hot encoded features: {len(X.columns)}")
     print("   - Numeric: 2 (YearsCode, WorkExp)")
@@ -266,13 +350,14 @@ def main():
     print(f"   - DevType: {len(devtype_features)}")
     print(f"   - Industry: {len(industry_features)}")
     print(f"   - Age: {len(age_features)}")
+    print(f"   - ICorPM: {len(icorpm_features)}")
 
     print("=" * 60 + "\n")
 
     # Cross-validation for robust evaluation
-    n_splits = config['data'].get('cv_splits', 5)
-    random_state = config['data']['random_state']
-    model_config = config['model']
+    n_splits = config["data"].get("cv_splits", 5)
+    random_state = config["data"]["random_state"]
+    model_config = config["model"]
 
     print(f"Running {n_splits}-fold cross-validation...")
     kf = KFold(n_splits=n_splits, shuffle=True, random_state=random_state)
@@ -286,16 +371,17 @@ def main():
         y_train, y_test = y.iloc[train_idx], y.iloc[test_idx]
 
         model = XGBRegressor(
-            n_estimators=model_config['n_estimators'],
-            learning_rate=model_config['learning_rate'],
-            max_depth=model_config['max_depth'],
-            min_child_weight=model_config['min_child_weight'],
-            random_state=model_config['random_state'],
-            n_jobs=model_config['n_jobs'],
-            early_stopping_rounds=model_config['early_stopping_rounds'],
+            n_estimators=model_config["n_estimators"],
+            learning_rate=model_config["learning_rate"],
+            max_depth=model_config["max_depth"],
+            min_child_weight=model_config["min_child_weight"],
+            random_state=model_config["random_state"],
+            n_jobs=model_config["n_jobs"],
+            early_stopping_rounds=model_config["early_stopping_rounds"],
         )
         model.fit(
-            X_train, y_train,
+            X_train,
+            y_train,
             eval_set=[(X_test, y_test)],
             verbose=False,
         )
@@ -305,7 +391,9 @@ def main():
         train_scores.append(train_r2)
         test_scores.append(test_r2)
         best_iterations.append(model.best_iteration + 1)
-        print(f"  Fold {fold}: Train R2 = {train_r2:.4f}, Test R2 = {test_r2:.4f} (best iter: {model.best_iteration + 1})")
+        print(
+            f"  Fold {fold}: Train R2 = {train_r2:.4f}, Test R2 = {test_r2:.4f} (best iter: {model.best_iteration + 1})"
+        )
 
     avg_train = np.mean(train_scores)
     avg_test = np.mean(test_scores)
@@ -323,23 +411,24 @@ def main():
     )
 
     final_model = XGBRegressor(
-        n_estimators=model_config['n_estimators'],
-        learning_rate=model_config['learning_rate'],
-        max_depth=model_config['max_depth'],
-        min_child_weight=model_config['min_child_weight'],
-        random_state=model_config['random_state'],
-        n_jobs=model_config['n_jobs'],
-        early_stopping_rounds=model_config['early_stopping_rounds'],
+        n_estimators=model_config["n_estimators"],
+        learning_rate=model_config["learning_rate"],
+        max_depth=model_config["max_depth"],
+        min_child_weight=model_config["min_child_weight"],
+        random_state=model_config["random_state"],
+        n_jobs=model_config["n_jobs"],
+        early_stopping_rounds=model_config["early_stopping_rounds"],
     )
     final_model.fit(
-        X_train_final, y_train_final,
+        X_train_final,
+        y_train_final,
         eval_set=[(X_es, y_es)],
-        verbose=config['training']['verbose'],
+        verbose=config["training"]["verbose"],
     )
     print(f"Final model best iteration: {final_model.best_iteration + 1}")
 
     # Save model and feature columns for inference
-    model_path = Path(config['training']['model_path'])
+    model_path = Path(config["training"]["model_path"])
     model_path.parent.mkdir(parents=True, exist_ok=True)
 
     artifacts = {

test_feature_impact.py

@@ -17,6 +17,7 @@ def test_years_experience_impact():
         "dev_type": "Developer, full-stack",
         "industry": "Software Development",
         "age": "25-34 years old",
+        "ic_or_pm": "Individual contributor",
     }
 
     # Test with different years of experience
@@ -35,7 +36,9 @@ def test_years_experience_impact():
         print(f"\n✅ PASS: All {len(predictions)} predictions are different")
         return True
     else:
-        print(f"\n❌ FAIL: Only {unique_predictions}/{len(predictions)} unique predictions")
+        print(
+            f"\n❌ FAIL: Only {unique_predictions}/{len(predictions)} unique predictions"
+        )
         return False
 
 
@@ -52,6 +55,7 @@ def test_country_impact():
         "dev_type": "Developer, full-stack",
         "industry": "Software Development",
         "age": "25-34 years old",
+        "ic_or_pm": "Individual contributor",
     }
 
     # Test with different countries (select diverse ones)
@@ -60,7 +64,7 @@ def test_country_impact():
         "Germany",
         "India",
         "Brazil",
-        "Poland"
+        "Poland",
     ]
 
     # Filter to only countries that exist in valid categories
@@ -83,8 +87,10 @@ def test_country_impact():
         print("   This indicates the model is NOT using country as a feature!")
         return False
     else:
-        print(f"\n⚠️  PARTIAL: Only {unique_predictions}/{len(predictions)} unique predictions")
-        print(f"   Duplicate salaries found - possible feature issue")
+        print(
+            f"\n⚠️  PARTIAL: Only {unique_predictions}/{len(predictions)} unique predictions"
+        )
+        print("   Duplicate salaries found - possible feature issue")
         return False
 
 
@@ -101,6 +107,7 @@ def test_education_impact():
         "dev_type": "Developer, full-stack",
         "industry": "Software Development",
         "age": "25-34 years old",
+        "ic_or_pm": "Individual contributor",
     }
 
     # Test with different education levels
@@ -133,8 +140,10 @@ def test_education_impact():
         print("   This indicates the model is NOT using education level as a feature!")
         return False
     else:
-        print(f"\n⚠️  PARTIAL: Only {unique_predictions}/{len(predictions)} unique predictions")
-        print(f"   Duplicate salaries found - possible feature issue")
+        print(
+            f"\n⚠️  PARTIAL: Only {unique_predictions}/{len(predictions)} unique predictions"
+        )
+        print("   Duplicate salaries found - possible feature issue")
         return False
 
 
@@ -151,6 +160,7 @@ def test_devtype_impact():
         "education_level": "Bachelor's degree (B.A., B.S., B.Eng., etc.)",
         "industry": "Software Development",
         "age": "25-34 years old",
+        "ic_or_pm": "Individual contributor",
     }
 
     # Test with different developer types (using actual values from trained model)
@@ -183,8 +193,10 @@ def test_devtype_impact():
         print("   This indicates the model is NOT using developer type as a feature!")
         return False
     else:
-        print(f"\n⚠️  PARTIAL: Only {unique_predictions}/{len(predictions)} unique predictions")
-        print(f"   Duplicate salaries found - possible feature issue")
+        print(
+            f"\n⚠️  PARTIAL: Only {unique_predictions}/{len(predictions)} unique predictions"
+        )
+        print("   Duplicate salaries found - possible feature issue")
         return False
 
 
@@ -201,6 +213,7 @@ def test_industry_impact():
         "education_level": "Bachelor's degree (B.A., B.S., B.Eng., etc.)",
         "dev_type": "Developer, full-stack",
         "age": "25-34 years old",
+        "ic_or_pm": "Individual contributor",
     }
 
     # Test with different industries (using actual values from trained model)
@@ -233,8 +246,10 @@ def test_industry_impact():
         print("   This indicates the model is NOT using industry as a feature!")
         return False
     else:
-        print(f"\n⚠️  PARTIAL: Only {unique_predictions}/{len(predictions)} unique predictions")
-        print(f"   Duplicate salaries found - possible feature issue")
+        print(
+            f"\n⚠️  PARTIAL: Only {unique_predictions}/{len(predictions)} unique predictions"
+        )
+        print("   Duplicate salaries found - possible feature issue")
         return False
 
 
@@ -251,6 +266,7 @@ def test_age_impact():
         "education_level": "Bachelor's degree (B.A., B.S., B.Eng., etc.)",
         "dev_type": "Developer, full-stack",
         "industry": "Software Development",
+        "ic_or_pm": "Individual contributor",
     }
 
     # Test with different age ranges (using actual values from trained model)
@@ -282,8 +298,10 @@ def test_age_impact():
         print("   This indicates the model is NOT using age as a feature!")
         return False
     else:
-        print(f"\n⚠️  PARTIAL: Only {unique_predictions}/{len(predictions)} unique predictions")
-        print(f"   Duplicate salaries found - possible feature issue")
+        print(
+            f"\n⚠️  PARTIAL: Only {unique_predictions}/{len(predictions)} unique predictions"
+        )
+        print("   Duplicate salaries found - possible feature issue")
         return False
 
 
@@ -300,6 +318,7 @@ def test_work_exp_impact():
         "dev_type": "Developer, full-stack",
         "industry": "Software Development",
         "age": "25-34 years old",
+        "ic_or_pm": "Individual contributor",
     }
 
     # Test with different years of work experience
@@ -322,34 +341,142 @@ def test_work_exp_impact():
         print("   This indicates the model is NOT using work experience as a feature!")
         return False
     else:
-        print(f"\n⚠️  PARTIAL: Only {unique_predictions}/{len(predictions)} unique predictions")
-        print(f"   Duplicate salaries found - possible feature issue")
+        print(
+            f"\n⚠️  PARTIAL: Only {unique_predictions}/{len(predictions)} unique predictions"
+        )
+        print("   Duplicate salaries found - possible feature issue")
+        return False
+
+
+def test_icorpm_impact():
+    """Test that changing IC or PM changes prediction."""
+    print("\n" + "=" * 70)
+    print("TEST 8: IC or PM Impact")
+    print("=" * 70)
+
+    base_input = {
+        "country": "United States of America",
+        "years_code": 5.0,
+        "work_exp": 3.0,
+        "education_level": "Bachelor's degree (B.A., B.S., B.Eng., etc.)",
+        "dev_type": "Developer, full-stack",
+        "industry": "Software Development",
+        "age": "25-34 years old",
+    }
+
+    # Test with different IC/PM values (using actual values from trained model)
+    test_icorpm = [
+        "Individual contributor",
+        "People manager",
+    ]
+
+    # Filter to only values that exist in valid categories
+    test_icorpm = [v for v in test_icorpm if v in valid_categories["ICorPM"]]
+
+    predictions = []
+    for icorpm in test_icorpm:
+        input_data = SalaryInput(**base_input, ic_or_pm=icorpm)
+        salary = predict_salary(input_data)
+        predictions.append(salary)
+        print(f"  IC/PM: {icorpm[:50]:50s} -> Salary: ${salary:,.2f}")
+
+    # Check if predictions are different
+    unique_predictions = len(set(predictions))
+    if unique_predictions == len(predictions):
+        print(f"\n✅ PASS: All {len(predictions)} predictions are different")
+        return True
+    elif unique_predictions == 1:
+        print(f"\n❌ FAIL: All predictions are IDENTICAL (${predictions[0]:,.2f})")
+        print("   This indicates the model is NOT using IC/PM as a feature!")
+        return False
+    else:
+        print(
+            f"\n⚠️  PARTIAL: Only {unique_predictions}/{len(predictions)} unique predictions"
+        )
+        print("   Duplicate salaries found - possible feature issue")
         return False
 
 
 def test_combined_features():
     """Test that combining different features produces expected variations."""
     print("\n" + "=" * 70)
-    print("TEST 8: Combined Feature Variations")
+    print("TEST 9: Combined Feature Variations")
     print("=" * 70)
 
     # Create diverse combinations (using actual values from trained model)
     test_cases = [
-        ("India", 2, 1, "Bachelor's degree (B.A., B.S., B.Eng., etc.)", "Developer, back-end", "Software Development", "18-24 years old"),
-        ("Germany", 5, 3, "Master's degree (M.A., M.S., M.Eng., MBA, etc.)", "Developer, full-stack", "Manufacturing", "25-34 years old"),
-        ("United States of America", 10, 8, "Master's degree (M.A., M.S., M.Eng., MBA, etc.)", "Engineering manager", "Fintech", "35-44 years old"),
-        ("Poland", 15, 12, "Bachelor's degree (B.A., B.S., B.Eng., etc.)", "Developer, front-end", "Healthcare", "45-54 years old"),
-        ("Brazil", 5, 3, "Some college/university study without earning a degree", "DevOps engineer or professional", "Government", "25-34 years old"),
+        (
+            "India",
+            2,
+            1,
+            "Bachelor's degree (B.A., B.S., B.Eng., etc.)",
+            "Developer, back-end",
+            "Software Development",
+            "18-24 years old",
+            "Individual contributor",
+        ),
+        (
+            "Germany",
+            5,
+            3,
+            "Master's degree (M.A., M.S., M.Eng., MBA, etc.)",
+            "Developer, full-stack",
+            "Manufacturing",
+            "25-34 years old",
+            "Individual contributor",
+        ),
+        (
+            "United States of America",
+            10,
+            8,
+            "Master's degree (M.A., M.S., M.Eng., MBA, etc.)",
+            "Engineering manager",
+            "Fintech",
+            "35-44 years old",
+            "People manager",
+        ),
+        (
+            "Poland",
+            15,
+            12,
+            "Bachelor's degree (B.A., B.S., B.Eng., etc.)",
+            "Developer, front-end",
+            "Healthcare",
+            "45-54 years old",
+            "Individual contributor",
+        ),
+        (
+            "Brazil",
+            5,
+            3,
+            "Some college/university study without earning a degree",
+            "DevOps engineer or professional",
+            "Government",
+            "25-34 years old",
+            "Individual contributor",
+        ),
     ]
 
     predictions = []
-    for country, years, work_exp, education, devtype, industry, age in test_cases:
+    for (
+        country,
+        years,
+        work_exp,
+        education,
+        devtype,
+        industry,
+        age,
+        icorpm,
+    ) in test_cases:
         # Skip if not in valid categories
-        if (country not in valid_categories["Country"]
-                or education not in valid_categories["EdLevel"]
-                or devtype not in valid_categories["DevType"]
-                or industry not in valid_categories["Industry"]
-                or age not in valid_categories["Age"]):
+        if (
+            country not in valid_categories["Country"]
+            or education not in valid_categories["EdLevel"]
+            or devtype not in valid_categories["DevType"]
+            or industry not in valid_categories["Industry"]
+            or age not in valid_categories["Age"]
+            or icorpm not in valid_categories["ICorPM"]
+        ):
             continue
 
         input_data = SalaryInput(
@@ -360,10 +487,13 @@ def test_combined_features():
             dev_type=devtype,
             industry=industry,
             age=age,
+            ic_or_pm=icorpm,
         )
         salary = predict_salary(input_data)
         predictions.append(salary)
-        print(f"  {country[:15]:15s} | {years:2d}y | {work_exp:2d}w | {education[:25]:25s} | {devtype[:25]:25s} | {industry[:20]:20s} | {age[:15]:15s} -> ${salary:,.2f}")
+        print(
+            f"  {country[:15]:15s} | {years:2d}y | {work_exp:2d}w | {education[:25]:25s} | {devtype[:25]:25s} | {industry[:20]:20s} | {age[:15]:15s} | {icorpm[:5]:5s} -> ${salary:,.2f}"
+        )
 
     # Check if predictions are different
     unique_predictions = len(set(predictions))
@@ -372,7 +502,7 @@ def test_combined_features():
         return True
     else:
         print(f"\n⚠️  Only {unique_predictions}/{len(predictions)} unique predictions")
-        print(f"   Some combinations produce identical salaries")
+        print("   Some combinations produce identical salaries")
         return False
 
 
@@ -387,12 +517,19 @@ def print_feature_analysis():
     print(f"\nTotal features in model: {len(feature_columns)}")
 
     # Count by type
-    country_features = [f for f in feature_columns if f.startswith('Country_')]
-    edlevel_features = [f for f in feature_columns if f.startswith('EdLevel_')]
-    devtype_features = [f for f in feature_columns if f.startswith('DevType_')]
-    industry_features = [f for f in feature_columns if f.startswith('Industry_')]
-    age_features = [f for f in feature_columns if f.startswith('Age_')]
-    numeric_features = [f for f in feature_columns if not f.startswith(('Country_', 'EdLevel_', 'DevType_', 'Industry_', 'Age_'))]
+    country_features = [f for f in feature_columns if f.startswith("Country_")]
+    edlevel_features = [f for f in feature_columns if f.startswith("EdLevel_")]
+    devtype_features = [f for f in feature_columns if f.startswith("DevType_")]
+    industry_features = [f for f in feature_columns if f.startswith("Industry_")]
+    age_features = [f for f in feature_columns if f.startswith("Age_")]
+    icorpm_features = [f for f in feature_columns if f.startswith("ICorPM_")]
+    numeric_features = [
+        f
+        for f in feature_columns
+        if not f.startswith(
+            ("Country_", "EdLevel_", "DevType_", "Industry_", "Age_", "ICorPM_")
+        )
+    ]
 
     print(f"  - Numeric features: {len(numeric_features)} -> {numeric_features}")
     print(f"  - Country features: {len(country_features)}")
@@ -400,32 +537,38 @@ def print_feature_analysis():
     print(f"  - DevType features: {len(devtype_features)}")
     print(f"  - Industry features: {len(industry_features)}")
     print(f"  - Age features: {len(age_features)}")
+    print(f"  - ICorPM features: {len(icorpm_features)}")
 
     if len(country_features) > 0:
-        print(f"\nSample country features:")
+        print("\nSample country features:")
         for feat in country_features[:5]:
             print(f"    - {feat}")
 
     if len(edlevel_features) > 0:
-        print(f"\nSample education features:")
+        print("\nSample education features:")
         for feat in edlevel_features[:5]:
             print(f"    - {feat}")
 
     if len(devtype_features) > 0:
-        print(f"\nSample developer type features:")
+        print("\nSample developer type features:")
         for feat in devtype_features[:5]:
             print(f"    - {feat}")
 
     if len(industry_features) > 0:
-        print(f"\nSample industry features:")
+        print("\nSample industry features:")
         for feat in industry_features[:5]:
             print(f"    - {feat}")
 
     if len(age_features) > 0:
-        print(f"\nSample age features:")
+        print("\nSample age features:")
         for feat in age_features[:5]:
             print(f"    - {feat}")
 
+    if len(icorpm_features) > 0:
+        print("\nSample IC/PM features:")
+        for feat in icorpm_features[:5]:
+            print(f"    - {feat}")
+
     # Check if there are any features at all
     if len(country_features) == 0:
         print("\n⚠️  WARNING: No country features found!")
@@ -437,6 +580,8 @@ def print_feature_analysis():
         print("\n⚠️  WARNING: No industry features found!")
     if len(age_features) == 0:
         print("\n⚠️  WARNING: No age features found!")
+    if len(icorpm_features) == 0:
+        print("\n⚠️  WARNING: No IC/PM features found!")
 
 
 def main():
@@ -458,6 +603,7 @@ def main():
         "Industry": test_industry_impact(),
         "Age": test_age_impact(),
         "Work Experience": test_work_exp_impact(),
+        "IC or PM": test_icorpm_impact(),
         "Combined Features": test_combined_features(),
     }
 
@@ -478,8 +624,12 @@ def main():
     if passed_count == total_count:
         print("\n🎉 All tests passed! The model is using all features correctly.")
     else:
-        print("\n⚠️  Some tests failed. The model may not be using all features properly.")
-        print("   This indicates potential training-testing skew or feature engineering issues.")
+        print(
+            "\n⚠️  Some tests failed. The model may not be using all features properly."
+        )
+        print(
+            "   This indicates potential training-testing skew or feature engineering issues."
+        )
 
 
 if __name__ == "__main__":

test_fix.py

@@ -2,28 +2,33 @@
 
 # Force reload of modules
 import sys
-if 'src.preprocessing' in sys.modules:
-    del sys.modules['src.preprocessing']
-if 'src.infer' in sys.modules:
-    del sys.modules['src.infer']
+
+if "src.preprocessing" in sys.modules:
+    del sys.modules["src.preprocessing"]
+if "src.infer" in sys.modules:
+    del sys.modules["src.infer"]
 
 from src.preprocessing import prepare_features
 import pandas as pd
 
 # Create test inputs with different countries (values from valid_categories)
-input1 = pd.DataFrame({
-    'Country': ['United States of America'],
-    'YearsCode': [5.0],
-    'EdLevel': ["Bachelor's degree (B.A., B.S., B.Eng., etc.)"],
-    'DevType': ['Developer, full-stack']
-})
-
-input2 = pd.DataFrame({
-    'Country': ['Germany'],
-    'YearsCode': [5.0],
-    'EdLevel': ["Bachelor's degree (B.A., B.S., B.Eng., etc.)"],
-    'DevType': ['Developer, full-stack']
-})
+input1 = pd.DataFrame(
+    {
+        "Country": ["United States of America"],
+        "YearsCode": [5.0],
+        "EdLevel": ["Bachelor's degree (B.A., B.S., B.Eng., etc.)"],
+        "DevType": ["Developer, full-stack"],
+    }
+)
+
+input2 = pd.DataFrame(
+    {
+        "Country": ["Germany"],
+        "YearsCode": [5.0],
+        "EdLevel": ["Bachelor's degree (B.A., B.S., B.Eng., etc.)"],
+        "DevType": ["Developer, full-stack"],
+    }
+)
 
 print("Testing prepare_features with different countries...")
 features1 = prepare_features(input1)

tests/conftest.py

@@ -0,0 +1,37 @@
+"""Shared fixtures for pytest tests."""
+
+from pathlib import Path
+
+import pytest
+import yaml
+
+
+@pytest.fixture
+def sample_salary_input():
+    """Return a dict with valid SalaryInput fields."""
+    return {
+        "country": "United States of America",
+        "years_code": 5.0,
+        "work_exp": 3.0,
+        "education_level": "Bachelor's degree (B.A., B.S., B.Eng., etc.)",
+        "dev_type": "Developer, full-stack",
+        "industry": "Software Development",
+        "age": "25-34 years old",
+        "ic_or_pm": "Individual contributor",
+    }
+
+
+@pytest.fixture
+def valid_categories_data():
+    """Load and return valid categories from config."""
+    path = Path("config/valid_categories.yaml")
+    with open(path, "r") as f:
+        return yaml.safe_load(f)
+
+
+@pytest.fixture
+def model_config():
+    """Load and return model parameters config."""
+    path = Path("config/model_parameters.yaml")
+    with open(path, "r") as f:
+        return yaml.safe_load(f)

tests/test_infer.py

@@ -0,0 +1,73 @@
+"""Tests for src/infer.py - Inference and validation."""
+
+import pytest
+
+from src.infer import get_local_currency, predict_salary
+from src.schema import SalaryInput
+
+
+def test_predict_salary_returns_positive_float(sample_salary_input):
+    """predict_salary returns a positive float."""
+    result = predict_salary(SalaryInput(**sample_salary_input))
+    assert isinstance(result, float)
+    assert result > 0
+
+
+def test_invalid_country(sample_salary_input):
+    """Invalid country raises ValueError."""
+    sample_salary_input["country"] = "Narnia"
+    with pytest.raises(ValueError, match="Invalid country"):
+        predict_salary(SalaryInput(**sample_salary_input))
+
+
+def test_invalid_education_level(sample_salary_input):
+    """Invalid education level raises ValueError."""
+    sample_salary_input["education_level"] = "Fake Degree"
+    with pytest.raises(ValueError, match="Invalid education level"):
+        predict_salary(SalaryInput(**sample_salary_input))
+
+
+def test_invalid_dev_type(sample_salary_input):
+    """Invalid developer type raises ValueError."""
+    sample_salary_input["dev_type"] = "Wizard"
+    with pytest.raises(ValueError, match="Invalid developer type"):
+        predict_salary(SalaryInput(**sample_salary_input))
+
+
+def test_invalid_industry(sample_salary_input):
+    """Invalid industry raises ValueError."""
+    sample_salary_input["industry"] = "Space Tourism"
+    with pytest.raises(ValueError, match="Invalid industry"):
+        predict_salary(SalaryInput(**sample_salary_input))
+
+
+def test_invalid_age(sample_salary_input):
+    """Invalid age raises ValueError."""
+    sample_salary_input["age"] = "100+ years old"
+    with pytest.raises(ValueError, match="Invalid age"):
+        predict_salary(SalaryInput(**sample_salary_input))
+
+
+def test_invalid_ic_or_pm(sample_salary_input):
+    """Invalid IC/PM value raises ValueError."""
+    sample_salary_input["ic_or_pm"] = "CEO"
+    with pytest.raises(ValueError, match="Invalid IC or PM"):
+        predict_salary(SalaryInput(**sample_salary_input))
+
+
+def test_get_local_currency_unknown_country():
+    """get_local_currency returns None for unknown country."""
+    result = get_local_currency("Narnia", 100000)
+    assert result is None
+
+
+def test_get_local_currency_known_country():
+    """get_local_currency returns dict with expected keys for a known country."""
+    # Use a country that is likely in currency_rates
+    result = get_local_currency("United States of America", 100000)
+    if result is not None:
+        assert "code" in result
+        assert "name" in result
+        assert "rate" in result
+        assert "salary_local" in result
+        assert isinstance(result["salary_local"], float)

tests/test_preprocessing.py

@@ -0,0 +1,140 @@
+"""Tests for src/preprocessing.py - Feature engineering utilities."""
+
+import numpy as np
+import pandas as pd
+
+from src.preprocessing import (
+    normalize_other_categories,
+    prepare_features,
+    reduce_cardinality,
+)
+
+
+class TestNormalizeOtherCategories:
+    """Tests for normalize_other_categories()."""
+
+    def test_replaces_other_please_specify(self):
+        """'Other (please specify):' is replaced with 'Other'."""
+        series = pd.Series(["Other (please specify):", "Developer, back-end"])
+        result = normalize_other_categories(series)
+        assert result.iloc[0] == "Other"
+        assert result.iloc[1] == "Developer, back-end"
+
+    def test_replaces_other_colon(self):
+        """'Other:' is replaced with 'Other'."""
+        series = pd.Series(["Other:", "Software Development"])
+        result = normalize_other_categories(series)
+        assert result.iloc[0] == "Other"
+
+    def test_leaves_non_other_unchanged(self):
+        """Non-Other values are not modified."""
+        values = ["Developer, back-end", "Software Development", "India"]
+        series = pd.Series(values)
+        result = normalize_other_categories(series)
+        assert list(result) == values
+
+    def test_preserves_exact_other(self):
+        """Exact 'Other' is kept as-is."""
+        series = pd.Series(["Other"])
+        result = normalize_other_categories(series)
+        assert result.iloc[0] == "Other"
+
+
+class TestReduceCardinality:
+    """Tests for reduce_cardinality()."""
+
+    def test_groups_rare_categories(self):
+        """Rare categories are grouped into 'Other'."""
+        # Create series with one dominant and many rare categories
+        values = ["Common"] * 100 + ["Rare1", "Rare2", "Rare3"]
+        series = pd.Series(values)
+        result = reduce_cardinality(series, max_categories=5, min_frequency=10)
+        assert "Common" in result.values
+        assert "Rare1" not in result.values
+        assert (result == "Other").sum() == 3
+
+    def test_keeps_frequent_categories(self):
+        """Frequent categories are kept intact."""
+        values = ["A"] * 100 + ["B"] * 80 + ["C"] * 60
+        series = pd.Series(values)
+        result = reduce_cardinality(series, max_categories=5, min_frequency=50)
+        assert set(result.unique()) == {"A", "B", "C"}
+
+
+class TestPrepareFeatures:
+    """Tests for prepare_features()."""
+
+    def test_returns_dataframe_with_numeric_columns(self):
+        """Output contains YearsCode and WorkExp as numeric columns."""
+        df = pd.DataFrame(
+            {
+                "Country": ["India"],
+                "YearsCode": [5.0],
+                "WorkExp": [3.0],
+                "EdLevel": ["Other"],
+                "DevType": ["Developer, back-end"],
+                "Industry": ["Software Development"],
+                "Age": ["25-34 years old"],
+                "ICorPM": ["Individual contributor"],
+            }
+        )
+        result = prepare_features(df)
+        assert "YearsCode" in result.columns
+        assert "WorkExp" in result.columns
+
+    def test_fills_missing_numeric_with_zero(self):
+        """Missing numeric values are filled with 0."""
+        df = pd.DataFrame(
+            {
+                "Country": ["India"],
+                "YearsCode": [np.nan],
+                "WorkExp": [np.nan],
+                "EdLevel": ["Other"],
+                "DevType": ["Developer, back-end"],
+                "Industry": ["Software Development"],
+                "Age": ["25-34 years old"],
+                "ICorPM": ["Individual contributor"],
+            }
+        )
+        result = prepare_features(df)
+        assert result["YearsCode"].iloc[0] == 0.0
+        assert result["WorkExp"].iloc[0] == 0.0
+
+    def test_one_hot_encodes_categorical_columns(self):
+        """Categorical columns are one-hot encoded."""
+        df = pd.DataFrame(
+            {
+                "Country": ["India", "Germany"],
+                "YearsCode": [5.0, 10.0],
+                "WorkExp": [3.0, 8.0],
+                "EdLevel": ["Other", "Other"],
+                "DevType": ["Developer, back-end", "Developer, front-end"],
+                "Industry": ["Software Development", "Healthcare"],
+                "Age": ["25-34 years old", "35-44 years old"],
+                "ICorPM": ["Individual contributor", "People manager"],
+            }
+        )
+        result = prepare_features(df)
+        # Should have one-hot columns for categorical features
+        categorical_cols = [
+            c for c in result.columns if "_" in c and c not in ("YearsCode", "WorkExp")
+        ]
+        assert len(categorical_cols) > 0
+
+    def test_does_not_modify_original(self):
+        """prepare_features does not modify the input DataFrame."""
+        df = pd.DataFrame(
+            {
+                "Country": ["India"],
+                "YearsCode": [5.0],
+                "WorkExp": [3.0],
+                "EdLevel": ["Other"],
+                "DevType": ["Developer, back-end"],
+                "Industry": ["Software Development"],
+                "Age": ["25-34 years old"],
+                "ICorPM": ["Individual contributor"],
+            }
+        )
+        original_country = df["Country"].iloc[0]
+        prepare_features(df)
+        assert df["Country"].iloc[0] == original_country

tests/test_schema.py

@@ -0,0 +1,75 @@
+"""Tests for src/schema.py - Pydantic input validation."""
+
+import pytest
+from pydantic import ValidationError
+
+from src.schema import SalaryInput
+
+
+def test_valid_input(sample_salary_input):
+    """Valid input creates SalaryInput successfully."""
+    result = SalaryInput(**sample_salary_input)
+    assert result.country == sample_salary_input["country"]
+    assert result.years_code == sample_salary_input["years_code"]
+    assert result.work_exp == sample_salary_input["work_exp"]
+    assert result.education_level == sample_salary_input["education_level"]
+    assert result.dev_type == sample_salary_input["dev_type"]
+    assert result.industry == sample_salary_input["industry"]
+    assert result.age == sample_salary_input["age"]
+    assert result.ic_or_pm == sample_salary_input["ic_or_pm"]
+
+
+def test_negative_years_code(sample_salary_input):
+    """Negative years_code raises ValidationError."""
+    sample_salary_input["years_code"] = -1.0
+    with pytest.raises(ValidationError):
+        SalaryInput(**sample_salary_input)
+
+
+def test_negative_work_exp(sample_salary_input):
+    """Negative work_exp raises ValidationError."""
+    sample_salary_input["work_exp"] = -5.0
+    with pytest.raises(ValidationError):
+        SalaryInput(**sample_salary_input)
+
+
+def test_missing_country():
+    """Missing required field raises ValidationError."""
+    with pytest.raises(ValidationError):
+        SalaryInput(
+            years_code=5.0,
+            work_exp=3.0,
+            education_level="Bachelor's degree (B.A., B.S., B.Eng., etc.)",
+            dev_type="Developer, full-stack",
+            industry="Software Development",
+            age="25-34 years old",
+            ic_or_pm="Individual contributor",
+        )
+
+
+def test_missing_education_level():
+    """Missing education_level raises ValidationError."""
+    with pytest.raises(ValidationError):
+        SalaryInput(
+            country="United States of America",
+            years_code=5.0,
+            work_exp=3.0,
+            dev_type="Developer, full-stack",
+            industry="Software Development",
+            age="25-34 years old",
+            ic_or_pm="Individual contributor",
+        )
+
+
+def test_zero_years_code(sample_salary_input):
+    """Zero years_code is valid (ge=0)."""
+    sample_salary_input["years_code"] = 0.0
+    result = SalaryInput(**sample_salary_input)
+    assert result.years_code == 0.0
+
+
+def test_zero_work_exp(sample_salary_input):
+    """Zero work_exp is valid (ge=0)."""
+    sample_salary_input["work_exp"] = 0.0
+    result = SalaryInput(**sample_salary_input)
+    assert result.work_exp == 0.0

uv.lock

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 ]
 
+[[package]]
+name = "bandit"
+version = "1.9.3"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "colorama", marker = "sys_platform == 'win32'" },
+    { name = "pyyaml" },
+    { name = "rich" },
+    { name = "stevedore" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/89/76/a7f3e639b78601118aaa4a394db2c66ae2597fbd8c39644c32874ed11e0c/bandit-1.9.3.tar.gz", hash = "sha256:ade4b9b7786f89ef6fc7344a52b34558caec5da74cb90373aed01de88472f774", size = 4242154, upload-time = "2026-01-19T04:05:22.802Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/e0/0b/8bdc52111c83e2dc2f97403dc87c0830b8989d9ae45732b34b686326fb2c/bandit-1.9.3-py3-none-any.whl", hash = "sha256:4745917c88d2246def79748bde5e08b9d5e9b92f877863d43fab70cd8814ce6a", size = 134451, upload-time = "2026-01-19T04:05:20.938Z" },
+]
+
 [[package]]
 name = "blinker"
 version = "1.9.0"
@@ -53,6 +68,33 @@ wheels = [
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 ]
 
+[[package]]
+name = "boolean-py"
+version = "5.0"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/c4/cf/85379f13b76f3a69bca86b60237978af17d6aa0bc5998978c3b8cf05abb2/boolean_py-5.0.tar.gz", hash = "sha256:60cbc4bad079753721d32649545505362c754e121570ada4658b852a3a318d95", size = 37047, upload-time = "2025-04-03T10:39:49.734Z" }
+wheels = [
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+]
+
+[[package]]
+name = "cachecontrol"
+version = "0.14.4"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "msgpack" },
+    { name = "requests" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/2d/f6/c972b32d80760fb79d6b9eeb0b3010a46b89c0b23cf6329417ff7886cd22/cachecontrol-0.14.4.tar.gz", hash = "sha256:e6220afafa4c22a47dd0badb319f84475d79108100d04e26e8542ef7d3ab05a1", size = 16150, upload-time = "2025-11-14T04:32:13.138Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/ef/79/c45f2d53efe6ada1110cf6f9fca095e4ff47a0454444aefdde6ac4789179/cachecontrol-0.14.4-py3-none-any.whl", hash = "sha256:b7ac014ff72ee199b5f8af1de29d60239954f223e948196fa3d84adaffc71d2b", size = 22247, upload-time = "2025-11-14T04:32:11.733Z" },
+]
+
+[package.optional-dependencies]
+filecache = [
+    { name = "filelock" },
+]
+
 [[package]]
 name = "cachetools"
 version = "6.2.6"
@@ -149,30 +191,171 @@ wheels = [
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+[[package]]
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