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SvenKlaassenSvenKlaassen
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update quantile scripts
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‎scripts/irm/cvar.py‎

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from montecover.irm import CVARCoverageSimulation
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# Create and run simulation with config file
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sim = CVARCoverageSimulation(
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config_file="scripts/irm/cvar_config.yml",
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log_level="INFO",
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log_file="logs/irm/cvar_sim.log",
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)
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sim.run_simulation()
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sim.save_results(output_path="results/irm/", file_prefix="cvar")
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# Save config file for reproducibility
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sim.save_config("results/irm/cvar_config.yml")

‎scripts/irm/cvar_config.yml‎

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# Simulation parameters for CVAR Coverage
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simulation_parameters:
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repetitions: 200
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max_runtime: 19800 # 5.5 hours in seconds
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random_seed: 42
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n_jobs: -2
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dgp_parameters:
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n_obs: [5000] # Sample size
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dim_x: [5] # Number of covariates
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# Define reusable learner configurations
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learner_definitions:
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linear: &linear
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name: "Linear"
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logit: &logit
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name: "Logistic"
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lgbmr: &lgbmr
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name: "LGBM Regr."
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params:
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n_estimators: 200 # Fewer trees — faster
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learning_rate: 0.05 # Balanced speed and stability
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num_leaves: 15 # Modest complexity for smaller data
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max_depth: 5 # Limit tree depth to avoid overfitting
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min_child_samples: 10 # Minimum samples per leaf — conservative
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subsample: 0.9 # Slightly randomized rows
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colsample_bytree: 0.9 # Slightly randomized features
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reg_alpha: 0.0 # No L1 regularization (faster)
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reg_lambda: 0.1 # Light L2 regularization
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random_state: 42 # Reproducible
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lgbmc: &lgbmc
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name: "LGBM Clas."
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params:
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n_estimators: 200 # Fewer trees — faster
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learning_rate: 0.05 # Balanced speed and stability
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num_leaves: 15 # Modest complexity for smaller data
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max_depth: 5 # Limit tree depth to avoid overfitting
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min_child_samples: 10 # Minimum samples per leaf — conservative
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subsample: 0.9 # Slightly randomized rows
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colsample_bytree: 0.9 # Slightly randomized features
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reg_alpha: 0.0 # No L1 regularization (faster)
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reg_lambda: 0.1 # Light L2 regularization
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random_state: 42 # Reproducible
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dml_parameters:
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tau_vec: [[0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8]] # Quantiles
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trimming_threshold: [0.01]
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learners:
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- ml_g: *linear
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ml_m: *logit
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- ml_g: *lgbmr
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ml_m: *lgbmc
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- ml_g: *lgbmr
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ml_m: *logit
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- ml_g: *linear
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ml_m: *lgbmc
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confidence_parameters:
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level: [0.95, 0.90] # Confidence levels

‎scripts/irm/lpq.py‎

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from montecover.irm import LPQCoverageSimulation
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# Create and run simulation with config file
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sim = LPQCoverageSimulation(
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config_file="scripts/irm/lpq_config.yml",
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log_level="INFO",
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log_file="logs/irm/lpq_sim.log",
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)
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sim.run_simulation()
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sim.save_results(output_path="results/irm/", file_prefix="lpq")
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# Save config file for reproducibility
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sim.save_config("results/irm/lpq_config.yml")

‎scripts/irm/lpq_config.yml‎

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# Simulation parameters for LPQ Coverage
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simulation_parameters:
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repetitions: 200
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max_runtime: 19800 # 5.5 hours in seconds
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random_seed: 42
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n_jobs: -2
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dgp_parameters:
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n_obs: [5000] # Sample size
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dim_x: [5] # Number of covariates
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# Define reusable learner configurations
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learner_definitions:
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logit: &logit
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name: "Logistic"
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lgbmc: &lgbmc
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name: "LGBM Clas."
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params:
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n_estimators: 200 # Fewer trees — faster
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learning_rate: 0.05 # Balanced speed and stability
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num_leaves: 15 # Modest complexity for smaller data
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max_depth: 5 # Limit tree depth to avoid overfitting
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min_child_samples: 10 # Minimum samples per leaf — conservative
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subsample: 0.9 # Slightly randomized rows
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colsample_bytree: 0.9 # Slightly randomized features
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reg_alpha: 0.0 # No L1 regularization (faster)
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reg_lambda: 0.1 # Light L2 regularization
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random_state: 42 # Reproducible
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dml_parameters:
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tau_vec: [[0.3, 0.4, 0.5, 0.6, 0.7]] # Quantiles
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trimming_threshold: [0.01]
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learners:
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- ml_g: *logit
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ml_m: *logit
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- ml_g: *lgbmc
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ml_m: *lgbmc
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- ml_g: *lgbmc
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ml_m: *logit
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- ml_g: *logit
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ml_m: *lgbmc
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confidence_parameters:
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level: [0.95, 0.90] # Confidence levels

‎scripts/irm/pq.py‎

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from montecover.irm import PQCoverageSimulation
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# Create and run simulation with config file
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sim = PQCoverageSimulation(
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config_file="scripts/irm/pq_config.yml",
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log_level="INFO",
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log_file="logs/irm/pq_sim.log",
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)
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sim.run_simulation()
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sim.save_results(output_path="results/irm/", file_prefix="pq")
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# Save config file for reproducibility
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sim.save_config("results/irm/pq_config.yml")

‎scripts/irm/pq_config.yml‎

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# Simulation parameters for PQ Coverage
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simulation_parameters:
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repetitions: 200
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max_runtime: 19800 # 5.5 hours in seconds
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random_seed: 42
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n_jobs: -2
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dgp_parameters:
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n_obs: [5000] # Sample size
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dim_x: [5] # Number of covariates
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# Define reusable learner configurations
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learner_definitions:
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logit: &logit
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name: "Logistic"
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lgbmc: &lgbmc
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name: "LGBM Clas."
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params:
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n_estimators: 200 # Fewer trees — faster
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learning_rate: 0.05 # Balanced speed and stability
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num_leaves: 15 # Modest complexity for smaller data
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max_depth: 5 # Limit tree depth to avoid overfitting
25+
min_child_samples: 10 # Minimum samples per leaf — conservative
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subsample: 0.9 # Slightly randomized rows
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colsample_bytree: 0.9 # Slightly randomized features
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reg_alpha: 0.0 # No L1 regularization (faster)
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reg_lambda: 0.1 # Light L2 regularization
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random_state: 42 # Reproducible
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dml_parameters:
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tau_vec: [[0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8]] # Quantiles
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trimming_threshold: [0.01]
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learners:
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- ml_g: *logit
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ml_m: *logit
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- ml_g: *lgbmc
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ml_m: *lgbmc
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- ml_g: *lgbmc
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ml_m: *logit
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- ml_g: *logit
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ml_m: *lgbmc
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confidence_parameters:
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level: [0.95, 0.90] # Confidence levels

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