Start automation of running filter combinations

03_quant_report.script.py

@@ -5,6 +5,8 @@ __generated_with = "0.19.7"
 import marimo as mo
 import polars as pl
 from pathlib import Path
+import argparse
+import json
 
 from validation import check_progress, duration_validation, check_straight_liners
 from utils import QualtricsSurvey, combine_exclusive_columns, calculate_weighted_ranking_scores
@@ -12,6 +14,35 @@ import utils
 
 from speaking_styles import SPEAKING_STYLES
 
+# %%
+# CLI argument parsing for batch automation
+# When run as script: python 03_quant_report.script.py --age '["18 to 21 years"]' --consumer '["Starter"]'
+# When run in Jupyter: args will use defaults (all filters = None = all options selected)
+def parse_cli_args():
+    parser = argparse.ArgumentParser(description='Generate quant report with optional filters')
+    parser.add_argument('--age', type=str, default=None, help='JSON list of age groups')
+    parser.add_argument('--gender', type=str, default=None, help='JSON list of genders')
+    parser.add_argument('--ethnicity', type=str, default=None, help='JSON list of ethnicities')
+    parser.add_argument('--income', type=str, default=None, help='JSON list of income groups')
+    parser.add_argument('--consumer', type=str, default=None, help='JSON list of consumer segments')
+    
+    # Only parse if running as script (not in Jupyter/interactive)
+    try:
+        # Check if running in Jupyter by looking for ipykernel
+        get_ipython()  # noqa: F821
+        return argparse.Namespace(age=None, gender=None, ethnicity=None, income=None, consumer=None)
+    except NameError:
+        args = parser.parse_args()
+        # Parse JSON strings to lists
+        args.age = json.loads(args.age) if args.age else None
+        args.gender = json.loads(args.gender) if args.gender else None
+        args.ethnicity = json.loads(args.ethnicity) if args.ethnicity else None
+        args.income = json.loads(args.income) if args.income else None
+        args.consumer = json.loads(args.consumer) if args.consumer else None
+        return args
+
+cli_args = parse_cli_args()
+
 # %%
 
 # file_browser = mo.ui.file_browser(
@@ -68,7 +99,14 @@ BEST_CHOSEN_CHARACTER = "the_coach"
 
 # %%
 # mo.stop(filter_form.value is None, mo.md("**Please submit filter above to proceed**"))
-_d = S.filter_data(data_validated, age=filter_form.value['age'], gender=filter_form.value['gender'], income=filter_form.value['income'], ethnicity=filter_form.value['ethnicity'], consumer=filter_form.value['consumer'])
+# CLI args: None means "all options selected" (use S.options_* defaults)
+_filter_age = cli_args.age if cli_args.age is not None else S.options_age
+_filter_gender = cli_args.gender if cli_args.gender is not None else S.options_gender
+_filter_ethnicity = cli_args.ethnicity if cli_args.ethnicity is not None else S.options_ethnicity
+_filter_income = cli_args.income if cli_args.income is not None else S.options_income
+_filter_consumer = cli_args.consumer if cli_args.consumer is not None else S.options_consumer
+
+_d = S.filter_data(data_all, age=_filter_age, gender=_filter_gender, income=_filter_income, ethnicity=_filter_ethnicity, consumer=_filter_consumer)
 
 # Stop execution and prevent other cells from running if no data is selected
 # mo.stop(len(_d.collect()) == 0, mo.md("**No Data available for current filter combination**"))

README.md

@@ -1,5 +1,147 @@
+# Voice Branding Quantitative Analysis
+
+## Running Marimo Notebooks
+
 Running on Ct-105 for shared access:
 
-```
+```bash
 uv run marimo run 02_quant_analysis.py --headless --port 8080
 ```
+
+---
+
+## Batch Report Generation
+
+The quant report can be run with different filter combinations via CLI or automated batch processing.
+
+### Single Filter Run (CLI)
+
+Run the report script directly with JSON-encoded filter arguments:
+
+```bash
+# Single consumer segment
+uv run python 03_quant_report.script.py --consumer '["Starter"]'
+
+# Single age group
+uv run python 03_quant_report.script.py --age '["18 to 21 years"]'
+
+# Multiple filters combined
+uv run python 03_quant_report.script.py --age '["18 to 21 years", "22 to 24 years"]' --gender '["Male"]'
+
+# All respondents (no filters = defaults to all options selected)
+uv run python 03_quant_report.script.py
+```
+
+Available filter arguments:
+- `--age` — JSON list of age groups
+- `--gender` — JSON list of genders  
+- `--ethnicity` — JSON list of ethnicities
+- `--income` — JSON list of income groups
+- `--consumer` — JSON list of consumer segments
+
+### Batch Runner (All Combinations)
+
+Run all single-filter combinations automatically with progress tracking:
+
+```bash
+# Preview all combinations without running
+uv run python run_filter_combinations.py --dry-run
+
+# Run all combinations (shows progress bar)
+uv run python run_filter_combinations.py
+
+# Or use the registered CLI entry point
+uv run quant-report-batch
+uv run quant-report-batch --dry-run
+```
+
+This generates reports for:
+- All Respondents (no filters)
+- Each age group individually
+- Each gender individually
+- Each ethnicity individually
+- Each income group individually
+- Each consumer segment individually
+
+Output figures are saved to `figures/<export_date>/<filter_slug>/`.
+
+### Jupyter Notebook Debugging
+
+The script auto-detects Jupyter/IPython environments. When running in VS Code's Jupyter extension, CLI args default to `None` (all options selected), so you can debug cell-by-cell normally.
+
+---
+
+## Adding Custom Filter Combinations
+
+To add new filter combinations to the batch runner, edit `run_filter_combinations.py`:
+
+### Checklist
+
+1. **Open** `run_filter_combinations.py`
+
+2. **Find** the `get_filter_combinations()` function
+
+3. **Add** your combination to the list before the `return` statement:
+
+```python
+# Example: Add a specific age + consumer cross-filter
+combinations.append({
+    'name': 'Age-18to24_Consumer-Starter',  # Used for output folder naming
+    'filters': {
+        'age': ['18 to 21 years', '22 to 24 years'],
+        'consumer': ['Starter']
+    }
+})
+```
+
+4. **Filter keys** must match CLI argument names:
+   - `age` — values from `survey.options_age`
+   - `gender` — values from `survey.options_gender`
+   - `ethnicity` — values from `survey.options_ethnicity`
+   - `income` — values from `survey.options_income`
+   - `consumer` — values from `survey.options_consumer`
+
+5. **Check available values** by running:
+```python
+from utils import QualtricsSurvey
+S = QualtricsSurvey('data/exports/2-2-26/...Labels.csv', 'data/exports/.../....qsf')
+S.load_data()
+print(S.options_age)
+print(S.options_consumer)
+# etc.
+```
+
+6. **Test** with dry-run first:
+```bash
+uv run python run_filter_combinations.py --dry-run
+```
+
+### Example: Adding Multiple Cross-Filters
+
+```python
+# In get_filter_combinations(), before return:
+
+# Young professionals
+combinations.append({
+    'name': 'Young_Professionals',
+    'filters': {
+        'age': ['22 to 24 years', '25 to 34 years'],
+        'consumer': ['Early Professional']
+    }
+})
+
+# High income males
+combinations.append({
+    'name': 'High_Income_Male',
+    'filters': {
+        'income': ['$150,000 - $199,999', '$200,000 or more'],
+        'gender': ['Male']
+    }
+})
+```
+
+### Notes
+
+- **Empty filters dict** = all respondents (no filtering)
+- **Omitted filter keys** = all options for that dimension selected
+- **Output folder names** are auto-generated from active filters by `QualtricsSurvey.filter_data()`

pyproject.toml

@@ -25,8 +25,12 @@ dependencies = [
     "requests>=2.32.5",
     "scipy>=1.14.0",
     "taguette>=1.5.1",
+    "tqdm>=4.66.0",
     "vl-convert-python>=1.9.0.post1",
     "wordcloud>=1.9.5",
 ]
 
+[project.scripts]
+quant-report-batch = "run_filter_combinations:main"
+
 

run_filter_combinations.py

@@ -0,0 +1,165 @@
+#!/usr/bin/env python
+"""
+Batch runner for quant report with different filter combinations.
+
+Runs 03_quant_report.script.py for each single-filter combination:
+- Each age group (with all others active)
+- Each gender (with all others active)
+- Each ethnicity (with all others active)
+- Each income group (with all others active)
+- Each consumer segment (with all others active)
+
+Usage:
+    uv run python run_filter_combinations.py
+    uv run python run_filter_combinations.py --dry-run  # Preview combinations without running
+"""
+
+import subprocess
+import sys
+import json
+from pathlib import Path
+
+from tqdm import tqdm
+
+from utils import QualtricsSurvey
+
+
+# Default data paths (same as in 03_quant_report.script.py)
+RESULTS_FILE = 'data/exports/2-2-26/JPMC_Chase Brand Personality_Quant Round 1_February 2, 2026_Labels.csv'
+QSF_FILE = 'data/exports/OneDrive_2026-01-21/Soft Launch Data/JPMC_Chase_Brand_Personality_Quant_Round_1.qsf'
+
+REPORT_SCRIPT = Path(__file__).parent / '03_quant_report.script.py'
+
+
+def get_filter_combinations(survey: QualtricsSurvey) -> list[dict]:
+    """
+    Generate all single-filter combinations.
+    
+    Each combination isolates ONE filter value while keeping all others at "all selected".
+    Returns list of dicts with filter kwargs for each run.
+    """
+    combinations = []
+    
+    # Add "All Respondents" run (no filters = all options selected)
+    combinations.append({
+        'name': 'All_Respondents',
+        'filters': {}  # Empty = use defaults (all selected)
+    })
+    
+    # Age groups - one at a time
+    for age in survey.options_age:
+        combinations.append({
+            'name': f'Age-{age}',
+            'filters': {'age': [age]}
+        })
+    
+    # Gender - one at a time
+    for gender in survey.options_gender:
+        combinations.append({
+            'name': f'Gender-{gender}',
+            'filters': {'gender': [gender]}
+        })
+    
+    # Ethnicity - one at a time
+    for ethnicity in survey.options_ethnicity:
+        combinations.append({
+            'name': f'Ethnicity-{ethnicity}',
+            'filters': {'ethnicity': [ethnicity]}
+        })
+    
+    # Income - one at a time
+    for income in survey.options_income:
+        combinations.append({
+            'name': f'Income-{income}',
+            'filters': {'income': [income]}
+        })
+    
+    # Consumer segments - one at a time
+    for consumer in survey.options_consumer:
+        combinations.append({
+            'name': f'Consumer-{consumer}',
+            'filters': {'consumer': [consumer]}
+        })
+    
+    return combinations
+
+
+def run_report(filters: dict, dry_run: bool = False) -> bool:
+    """
+    Run the report script with given filters.
+    
+    Args:
+        filters: Dict of filter_name -> list of values
+        dry_run: If True, just print command without running
+        
+    Returns:
+        True if successful, False otherwise
+    """
+    cmd = [sys.executable, str(REPORT_SCRIPT)]
+    
+    for filter_name, values in filters.items():
+        if values:
+            cmd.extend([f'--{filter_name}', json.dumps(values)])
+    
+    if dry_run:
+        print(f"  Would run: {' '.join(cmd)}")
+        return True
+    
+    try:
+        result = subprocess.run(
+            cmd,
+            capture_output=True,
+            text=True,
+            cwd=Path(__file__).parent
+        )
+        if result.returncode != 0:
+            print(f"\n  ERROR: {result.stderr[:500]}")
+            return False
+        return True
+    except Exception as e:
+        print(f"\n  ERROR: {e}")
+        return False
+
+
+def main():
+    import argparse
+    parser = argparse.ArgumentParser(description='Run quant report for all filter combinations')
+    parser.add_argument('--dry-run', action='store_true', help='Preview combinations without running')
+    args = parser.parse_args()
+    
+    # Load survey to get available filter options
+    print("Loading survey to get filter options...")
+    survey = QualtricsSurvey(RESULTS_FILE, QSF_FILE)
+    survey.load_data()  # Populates options_* attributes
+    
+    # Generate all combinations
+    combinations = get_filter_combinations(survey)
+    print(f"Generated {len(combinations)} filter combinations")
+    
+    if args.dry_run:
+        print("\nDRY RUN - Commands that would be executed:")
+        for combo in combinations:
+            print(f"\n{combo['name']}:")
+            run_report(combo['filters'], dry_run=True)
+        return
+    
+    # Run each combination with progress bar
+    successful = 0
+    failed = []
+    
+    for combo in tqdm(combinations, desc="Running reports", unit="filter"):
+        tqdm.write(f"Running: {combo['name']}")
+        if run_report(combo['filters']):
+            successful += 1
+        else:
+            failed.append(combo['name'])
+    
+    # Summary
+    print(f"\n{'='*50}")
+    print(f"Completed: {successful}/{len(combinations)} successful")
+    if failed:
+        print(f"Failed: {', '.join(failed)}")
+
+
+if __name__ == '__main__':
+    main()

uv.lock

@@ -2075,6 +2075,7 @@ dependencies = [
     { name = "requests" },
     { name = "scipy" },
     { name = "taguette" },
+    { name = "tqdm" },
     { name = "vl-convert-python" },
     { name = "wordcloud" },
 ]
@@ -2101,6 +2102,7 @@ requires-dist = [
     { name = "requests", specifier = ">=2.32.5" },
     { name = "scipy", specifier = ">=1.14.0" },
     { name = "taguette", specifier = ">=1.5.1" },
+    { name = "tqdm", specifier = ">=4.66.0" },
     { name = "vl-convert-python", specifier = ">=1.9.0.post1" },
     { name = "wordcloud", specifier = ">=1.9.5" },
 ]