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run_synthetic_experiments.sh
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run_synthetic_experiments.sh
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# Copyright 2022 Juan L. Gamella
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions
# are met:
# 1. Redistributions of source code must retain the above copyright
# notice, this list of conditions and the following disclaimer.
# 2. Redistributions in binary form must reproduce the above copyright
# notice, this list of conditions and the following disclaimer in the
# documentation and/or other materials provided with the distribution.
# 3. Neither the name of the copyright holder nor the names of its
# contributors may be used to endorse or promote products derived from
# this software without specific prior written permission.
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
# "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
# LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS
# FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE
# COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT,
# INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING,
# BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
# LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT
# LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN
# ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
# POSSIBILITY OF SUCH DAMAGE.
# Commands to run the synthetic experiments
# --------------------------------------------------------------------
# Figure 1: Model match, standardized data.
DATASET="synthetic_experiments/dataset_1661334599_runs:10_seed:42_tag:I3s_G:100_k:2.7_p:10_w_min:0.5_w_max:1_v_min:1_v_max:2_envs:4_i_type:noise_i_size:1_i_v_min:5_i_v_max:10_n:10,100,1000_obs:1_standardize:1/"
# The dataset was generated using the command:
# python3 -m src.generate_synthetic_data --G 100 --runs 10 --n 10,100,1000 --i_size 1 --e 4 --p 10 --i_type noise --obs --standardize --seed 42 --tag I3s
# Run the methods: GnIES, UT-IGSP, GES and sortnregress
python3 -m src.run_gnies --n_workers 49 --lambdas 0.01,0.25,0.5,0.75,1,2 --directory $DATASET
python3 -m src.run_gnies --rank --phases f --n_workers 49 --lambdas 0.01,0.25,0.5,0.75,1,2 --directory $DATASET
python3 -m src.run_gnies --rank --phases b --n_workers 49 --lambdas 0.01,0.25,0.5,0.75,1,2 --directory $DATASET
python3 -m src.run_utigsp --n_workers 49 --alpha_lo 0.00001 --alpha_hi 0.1 --n_alphas 5 --beta_lo 0.00001 --beta_hi 0.1 --n_betas 5 --directory $DATASET
python3 -m src.run_ges --n_workers 49 --lambdas 0.01,0.25,0.5,0.75,1,2 --directory $DATASET
python3 -m src.run_sortnregress --n_workers 49 --pool --tag pool --directory $DATASET
./compute_metrics.sh $DATASET
# --------------------------------------------------------------------
# Figure 2: Model mismatch, standardized data.
DATASET="synthetic_experiments/dataset_1661334633_runs:10_seed:42_tag:I3sd_G:100_k:2.7_p:10_w_min:0.5_w_max:1_v_min:1_v_max:2_envs:4_i_type:do_i_size:1_i_v_min:5_i_v_max:10_n:10,100,1000_obs:1_standardize:1/"
# The dataset was generated using the command:
# python3 -m src.generate_synthetic_data --G 100 --runs 10 --n 10,100,1000 --i_size 1 --e 4 --p 10 --i_type do --obs --standardize --seed 42 --tag I3sd
# Run the methods: GnIES, UT-IGSP, GES, GIES and sortnregress
python3 -m src.run_gnies --n_workers 49 --lambdas 0.01,0.25,0.5,0.75,1,2 --directory $DATASET
python3 -m src.run_gnies --rank --phases f --n_workers 49 --lambdas 0.01,0.25,0.5,0.75,1,2 --directory $DATASET
python3 -m src.run_gnies --rank --phases b --n_workers 49 --lambdas 0.01,0.25,0.5,0.75,1,2 --directory $DATASET
python3 -m src.run_utigsp --n_workers 49 --alpha_lo 0.00001 --alpha_hi 0.1 --n_alphas 5 --beta_lo 0.00001 --beta_hi 0.1 --n_betas 5 --directory $DATASET
python3 -m src.run_ges --n_workers 49 --lambdas 0.01,0.25,0.5,0.75,1,2 --directory $DATASET
python3 -m src.run_gies --n_workers 49 --lambdas 0.01,0.25,0.5,0.75,1,2 --directory $DATASET
python3 -m src.run_sortnregress --n_workers 49 --pool --tag pool --directory $DATASET
./compute_metrics.sh $DATASET
# --------------------------------------------------------------------
# Figure 6 (Appendix D): Comparison with raw (unstandardized) data
# -----------
# Model match
DATASET="synthetic_experiments/dataset_1661334649_runs:10_seed:42_tag:I3_G:100_k:2.7_p:10_w_min:0.5_w_max:1_v_min:1_v_max:2_envs:4_i_type:noise_i_size:1_i_v_min:5_i_v_max:10_n:10,100,1000_obs:1_standardize:0/"
# The dataset was generated using the command:
# python3 -m src.generate_synthetic_data --G 100 --runs 10 --n 10,100,1000 --i_size 1 --e 4 --p 10 --i_type noise --obs --seed 42 --tag I3
# Run the methods: GnIES, UT-IGSP, GES and sortnregress
python3 -m src.run_gnies --n_workers 49 --lambdas 0.01,0.25,0.5,0.75,1,2 --directory $DATASET
python3 -m src.run_gnies --rank --phases f --n_workers 49 --lambdas 0.01,0.25,0.5,0.75,1,2 --directory $DATASET
python3 -m src.run_gnies --rank --phases b --n_workers 49 --lambdas 0.01,0.25,0.5,0.75,1,2 --directory $DATASET
python3 -m src.run_utigsp --n_workers 49 --alpha_lo 0.00001 --alpha_hi 0.1 --n_alphas 5 --beta_lo 0.00001 --beta_hi 0.1 --n_betas 5 --directory $DATASET
python3 -m src.run_ges --n_workers 49 --lambdas 0.01,0.25,0.5,0.75,1,2 --directory $DATASET
python3 -m src.run_sortnregress --n_workers 49 --pool --tag pool --directory $DATASET
./compute_metrics.sh $DATASET
# --------------
# Model mismatch
DATASET="synthetic_experiments/dataset_1661334661_runs:10_seed:42_tag:I3d_G:100_k:2.7_p:10_w_min:0.5_w_max:1_v_min:1_v_max:2_envs:4_i_type:do_i_size:1_i_v_min:5_i_v_max:10_n:10,100,1000_obs:1_standardize:0/"
# The dataset was generated using the command:
# python3 -m src.generate_synthetic_data --G 100 --runs 10 --n 10,100,1000 --i_size 1 --e 4 --p 10 --i_type do --obs --seed 42 --tag I3d
# Run the methods: GnIES, UT-IGSP, GES, GIES and sortnregress
python3 -m src.run_gnies --n_workers 49 --lambdas 0.01,0.25,0.5,0.75,1,2 --directory $DATASET
python3 -m src.run_gnies --rank --phases f --n_workers 49 --lambdas 0.01,0.25,0.5,0.75,1,2 --directory $DATASET
python3 -m src.run_gnies --rank --phases b --n_workers 49 --lambdas 0.01,0.25,0.5,0.75,1,2 --directory $DATASET
python3 -m src.run_utigsp --n_workers 49 --alpha_lo 0.00001 --alpha_hi 0.1 --n_alphas 5 --beta_lo 0.00001 --beta_hi 0.1 --n_betas 5 --directory $DATASET
python3 -m src.run_ges --n_workers 49 --lambdas 0.01,0.25,0.5,0.75,1,2 --directory $DATASET
python3 -m src.run_gies --n_workers 49 --lambdas 0.01,0.25,0.5,0.75,1,2 --directory $DATASET
python3 -m src.run_sortnregress --n_workers 49 --pool --tag pool --directory $DATASET
./compute_metrics.sh $DATASET