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| """ | |
| Road Recommender Module | |
| ----------------------- | |
| Translates user questionnaire responses into a numerical vector tailored for | |
| road running shoe features. Refactored to use unified dictionary lookups | |
| to minimize cyclomatic complexity while preserving 100% of the original logic. | |
| """ | |
| from .content_based import get_priority_val, run_recommendation_pipeline | |
| from typing import List, Dict, Any, Tuple | |
| def preprocess_road_input(user_input: Dict[str, Any], | |
| binary_cols: List[str], | |
| continuous_cols: List[str]) -> Tuple[List[float], List[int]]: | |
| """ | |
| Translates road running preferences into a standardized numerical vector. | |
| Uses dictionary-based mapping to eliminate ternary operators and if-else | |
| branching, ensuring the lowest possible cyclomatic complexity. | |
| Args: | |
| user_input (Dict[str, Any]): Raw questionnaire data from the frontend. | |
| binary_cols (List[str]): List of binary feature names. | |
| continuous_cols (List[str]): List of continuous feature names. | |
| Returns: | |
| Tuple[List[float], List[int]]: | |
| - The full numerical vector (N-dimensional). | |
| - A list of indices for masked similarity calculation. | |
| """ | |
| all_cols = binary_cols + continuous_cols | |
| feats = {col: 0.0 for col in all_cols} | |
| # 1. Heuristic Priority Mappings | |
| feats['lightweight'] = get_priority_val(user_input, ['pace'], {'pace': {'Easy': 0.5, 'Steady': 0.5, 'Fast': 1.0}}) | |
| feats['rocker'] = get_priority_val(user_input, ['running_purpose'], {'running_purpose': {'Race': 1.0, 'Tempo': 0.5, 'Daily': 0.0}}) | |
| feats['removable_insole'] = get_priority_val(user_input, ['orthotic_usage'], {'orthotic_usage': {'Yes': 1.0, 'No': 0.5}}) | |
| # 2. Unified Dictionary Lookups (Replacing If-Else Ternaries) | |
| purp = user_input.get('running_purpose', 'Daily') | |
| feats['pace_daily_running'] = {'Daily': 1.0, 'Tempo': 0.5}.get(purp, 0.0) | |
| feats['pace_tempo'] = {'Tempo': 1.0}.get(purp, 0.5) | |
| feats['pace_competition'] = {'Race': 1.0, 'Tempo': 0.5}.get(purp, 0.0) | |
| feats['arch_neutral'] = get_priority_val(user_input, ['arch_type'], {'arch_type': {'Flat': 0.0, 'Normal': 0.8, 'High': 1.0}}) | |
| feats['arch_stability'] = get_priority_val(user_input, ['arch_type'], {'arch_type': {'Flat': 1.0, 'Normal': 0.2, 'High': 0.0}}) | |
| feats['drop_lab_mm'] = get_priority_val(user_input, ['pace'], {'pace': {'Easy': 1.0, 'Steady': 0.5, 'Fast': 0.0}}) | |
| prio_strike = ['strike_pattern', 'pace'] | |
| feats['strike_heel'] = get_priority_val(user_input, prio_strike, {'strike_pattern': {'Heel': 1.0, 'Mid': 0.5, 'Forefoot': 0.0}, 'pace': {'Easy': 1.0, 'Steady': 0.5, 'Fast': 0.0}}) | |
| feats['strike_mid'] = get_priority_val(user_input, prio_strike, {'strike_pattern': {'Heel': 0.5, 'Mid': 1.0, 'Forefoot': 0.5}, 'pace': {'Easy': 0.5, 'Steady': 1.0, 'Fast': 0.5}}) | |
| feats['strike_forefoot'] = get_priority_val(user_input, prio_strike, {'strike_pattern': {'Heel': 0.0, 'Mid': 0.0, 'Forefoot': 1.0}, 'pace': {'Easy': 0.0, 'Steady': 0.5, 'Fast': 1.0}}) | |
| feats['midsole_softness'] = get_priority_val(user_input, ['cushion_preferences', 'pace'], {'cushion_preferences': {'Soft': 1.0, 'Balanced': 0.6, 'Firm': 0.2}, 'pace': {'Easy': 1.0, 'Steady': 0.6, 'Fast': 0.2}}) | |
| feats['width_fit'] = get_priority_val(user_input, ['stability_need', 'foot_width'], {'stability_need': {'Neutral': 0.5, 'Guided': 0.2}, 'foot_width': {'Narrow': 0.2, 'Regular': 0.6, 'Wide': 1}}) | |
| feats['toebox_width'] = get_priority_val(user_input, ['stability_need'], {'stability_need': {'Neutral': 0.5, 'Guided': 0.2}}) | |
| feats['stiffness_scaled'] = get_priority_val(user_input, ['arch_type', 'pace', 'running_purpose'], {'arch_type': {'Flat': 0.0, 'Normal': 0.5, 'High': 0.5}, 'pace': {'Easy': 0.2, 'Steady': 0.6, 'Fast': 1.0}, 'running_purpose': {'Daily': 0.2, 'Tempo': 0.6, 'Race': 1}}) | |
| feats['torsional_rigidity'] = get_priority_val(user_input, ['arch_type', 'pace'], {'arch_type': {'Flat': 1.0, 'Normal': 0.5, 'High': 0.5}, 'pace': {'Easy': 0.2, 'Steady': 0.6, 'Fast': 1.0}}) | |
| feats['heel_stiff'] = get_priority_val(user_input, ['arch_type'], {'arch_type': {'Flat': 1.0, 'Normal': 0.6, 'High': 0.2}}) | |
| feats['plate_rock_plate'] = 0.5 | |
| feats['plate_carbon_plate'] = get_priority_val(user_input, ['pace', 'running_purpose'], {'pace': {'Easy': 0.5, 'Steady': 0.5, 'Fast': 1.0}, 'running_purpose': {'Daily': 0.5, 'Tempo': 0.5, 'Race': 1.0}}) | |
| feats['heel_lab_mm'] = get_priority_val(user_input, ['strike_pattern', 'pace'], {'strike_pattern': {'Heel': 1.0, 'Mid': 0.5, 'Forefoot': 0.0}, 'pace': {'Easy': 1.0, 'Steady': 0.5, 'Fast': 0.0}}) | |
| feats['forefoot_lab_mm'] = get_priority_val(user_input, ['strike_pattern', 'pace'], {'strike_pattern': {'Heel': 0.0, 'Mid': 0.5, 'Forefoot': 1.0}, 'pace': {'Easy': 0.0, 'Steady': 0.5, 'Fast': 1.0}}) | |
| feats['weight_lab_oz'] = 1.0 - feats.get('lightweight', 0.5) | |
| feats['season_summer'] = get_priority_val(user_input, ['season'], {'season': {'Summer': 1.0, 'Spring & Fall': 0.5, 'Winter': 0.0}}) | |
| feats['season_winter'] = get_priority_val(user_input, ['season'], {'season': {'Summer': 0.0, 'Spring & Fall': 0.0, 'Winter': 1.0}}) | |
| feats['season_all'] = get_priority_val(user_input, ['season'], {'season': {'Summer': 0.5, 'Spring & Fall': 1.0, 'Winter': 0.0}}) | |
| # Static Column Assignment | |
| for col in ['toebox_durability', 'heel_durability', 'outsole_durability', 'breathability_scaled']: | |
| feats[col] = 1.0 | |
| # 3. Vector and Masking Setup | |
| binary_set = set(binary_cols) | |
| full_vector_raw = [feats.get(c, 0.0 if c in binary_set else 0.5) for c in all_cols] | |
| provided_inputs = {k for k, v in user_input.items() if v} | |
| feature_sources = { | |
| 'lightweight': ['pace'], 'rocker': ['running_purpose'], 'removable_insole': ['orthotic_usage'], | |
| 'pace_daily_running': ['running_purpose'], 'pace_tempo': ['running_purpose'], 'pace_competition': ['running_purpose'], | |
| 'arch_neutral': ['arch_type'], 'arch_stability': ['arch_type'], 'drop_lab_mm': ['pace'], | |
| 'strike_heel': ['strike_pattern', 'pace'], 'strike_mid': ['strike_pattern', 'pace'], 'strike_forefoot': ['strike_pattern', 'pace'], | |
| 'midsole_softness': ['cushion_preferences', 'pace'], 'width_fit': ['stability_need', 'foot_width'], | |
| 'toebox_width': ['stability_need'], 'stiffness_scaled': ['arch_type', 'pace', 'running_purpose'], | |
| 'torsional_rigidity': ['arch_type', 'pace'], 'heel_stiff': ['arch_type'], | |
| 'plate_rock_plate': ['pace', 'running_purpose'], 'plate_carbon_plate': ['pace', 'running_purpose'], | |
| 'heel_lab_mm': ['strike_pattern', 'pace'], 'forefoot_lab_mm': ['strike_pattern', 'pace'], | |
| 'weight_lab_oz': ['pace'], 'season_summer': ['season'], 'season_winter': ['season'], 'season_all': ['season'] | |
| } | |
| valid_indices = [ | |
| i for i, col in enumerate(all_cols) | |
| if not feature_sources.get(col) or not set(feature_sources[col]).isdisjoint(provided_inputs) | |
| ] | |
| return full_vector_raw, valid_indices or list(range(len(all_cols))) | |
| def get_recommendations(user_input: Dict[str, Any], artifacts: Dict[str, Any]) -> List[str]: | |
| """Wrapper entry point for road recommendation.""" | |
| full_vector, valid_idx = preprocess_road_input(user_input, artifacts['binary_cols'], artifacts['continuous_cols']) | |
| return run_recommendation_pipeline(full_vector, valid_idx, artifacts) |