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import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
# Import the function from run_multi_seed_analysis.py
from plotting.run_multi_seed_analysis import run_multiple_seeds_and_save_csv
def analyze_model_saliency(
config, seeds=[0, 1, 2, 3, 4], max_steps=200, visualize=True
):
"""
Analyze the saliency maps of a model with the given configuration.
Args:
config (dict): Dictionary containing model configuration with keys:
- ENV_NAME: Environment name
- PARTIAL: Whether to use partial observations
- MEMORY_TYPE: Type of memory to use
- OBS_SIZE: Size of observations
- MODEL_SEED: Seed used for the model (to locate model file)
seeds (list): List of seeds to run the analysis with
max_steps (int): Maximum number of steps per episode
visualize (bool): Whether to create visualization plots
Returns:
dict: A dictionary containing:
- csv_path: Path to the CSV file with results
- avg_plot_path: Path to the average saliency plot
- individual_plot_path: Path to the individual seeds saliency plot
"""
output_csv = f'saliency_results_{config["MEMORY_TYPE"]}_{config["ENV_NAME"]}_Partial={config["PARTIAL"]}.csv'
# Run the analysis for all seeds
output_csv = run_multiple_seeds_and_save_csv(
config, seeds, max_steps=max_steps, output_csv=output_csv
)
result_paths = {"csv_path": output_csv}
if visualize:
# Load the results for visualization
results_df = pd.read_csv(output_csv)
# Create a visualization of the distributions for each seed
plt.figure(figsize=(12, 8))
sns.set_style("whitegrid")
# Filter columns that represent positions
pos_columns = [col for col in results_df.columns if col.startswith("pos_")]
# Plot each seed's distribution
for idx, row in results_df.iterrows():
seed = row["seed"]
positions = [float(col.split("_")[1]) for col in pos_columns]
values = [row[col] for col in pos_columns]
plt.plot(positions, values, marker="o", markersize=3, label=f"Seed {seed}")
plt.xlabel("Normalized Episode Position")
plt.ylabel("Saliency Magnitude")
plt.title(
f"Terminal Saliency Distribution by Seed\n{config['MEMORY_TYPE']} on {config['ENV_NAME']}"
)
plt.legend()
plt.tight_layout()
individual_plot_path = f"saliency_plot_{config['MEMORY_TYPE']}_{config['ENV_NAME']}_Partial={config['PARTIAL']}.png"
plt.savefig(individual_plot_path, dpi=300)
plt.close()
# Calculate average distribution across seeds
avg_values = [results_df[col].mean() for col in pos_columns]
std_values = [results_df[col].std() for col in pos_columns]
positions = [float(col.split("_")[1]) for col in pos_columns]
plt.figure(figsize=(12, 8))
plt.plot(positions, avg_values, "b-", linewidth=2, label="Mean Distribution")
plt.fill_between(
positions,
[avg - std for avg, std in zip(avg_values, std_values)],
[avg + std for avg, std in zip(avg_values, std_values)],
color="b",
alpha=0.2,
label="Standard Deviation",
)
plt.xlabel("Normalized Episode Position")
plt.ylabel("Average Saliency Magnitude")
plt.title(
f"Average Terminal Saliency Distribution Across Seeds\n{config['MEMORY_TYPE']} on {config['ENV_NAME']}"
)
plt.legend()
plt.tight_layout()
avg_plot_path = f"avg_saliency_plot_{config['MEMORY_TYPE']}_{config['ENV_NAME']}_Partial={config['PARTIAL']}.png"
plt.savefig(avg_plot_path, dpi=300)
plt.close()
result_paths["individual_plot_path"] = individual_plot_path
result_paths["avg_plot_path"] = avg_plot_path
print(f"Analysis complete. Results saved to: {output_csv}")
return result_paths
# Example usage
if __name__ == "__main__":
configs = [
# fart models
{
"ENV_NAME": "AutoEncodeEasy",
"PARTIAL": False,
"MEMORY_TYPE": "fart",
"OBS_SIZE": 128,
"MODEL_SEED": 3,
},
{
"ENV_NAME": "AutoEncodeEasy",
"PARTIAL": True,
"MEMORY_TYPE": "fart",
"OBS_SIZE": 128,
"MODEL_SEED": 4,
},
{
"ENV_NAME": "BattleShipEasy",
"PARTIAL": False,
"MEMORY_TYPE": "fart",
"OBS_SIZE": 128,
"MODEL_SEED": 0,
},
{
"ENV_NAME": "BattleShipEasy",
"PARTIAL": True,
"MEMORY_TYPE": "fart",
"OBS_SIZE": 128,
"MODEL_SEED": 0,
},
{
"ENV_NAME": "CartPoleEasy",
"PARTIAL": False,
"MEMORY_TYPE": "fart",
"OBS_SIZE": 128,
"MODEL_SEED": 0,
},
{
"ENV_NAME": "CartPoleEasy",
"PARTIAL": True,
"MEMORY_TYPE": "fart",
"OBS_SIZE": 128,
"MODEL_SEED": 1,
},
{
"ENV_NAME": "CountRecallEasy",
"PARTIAL": False,
"MEMORY_TYPE": "fart",
"OBS_SIZE": 128,
"MODEL_SEED": 0,
},
{
"ENV_NAME": "CountRecallEasy",
"PARTIAL": True,
"MEMORY_TYPE": "fart",
"OBS_SIZE": 128,
"MODEL_SEED": 0,
},
{
"ENV_NAME": "MineSweeperEasy",
"PARTIAL": False,
"MEMORY_TYPE": "fart",
"OBS_SIZE": 128,
"MODEL_SEED": 4,
},
{
"ENV_NAME": "MineSweeperEasy",
"PARTIAL": True,
"MEMORY_TYPE": "fart",
"OBS_SIZE": 128,
"MODEL_SEED": 3,
},
{
"ENV_NAME": "NavigatorEasy",
"PARTIAL": False,
"MEMORY_TYPE": "fart",
"OBS_SIZE": 128,
"MODEL_SEED": 2,
},
{
"ENV_NAME": "NavigatorEasy",
"PARTIAL": True,
"MEMORY_TYPE": "fart",
"OBS_SIZE": 128,
"MODEL_SEED": 1,
},
{
"ENV_NAME": "NoisyCartPoleEasy",
"PARTIAL": False,
"MEMORY_TYPE": "fart",
"OBS_SIZE": 128,
"MODEL_SEED": 4,
},
{
"ENV_NAME": "NoisyCartPoleEasy",
"PARTIAL": True,
"MEMORY_TYPE": "fart",
"OBS_SIZE": 128,
"MODEL_SEED": 0,
},
# lru models
{
"ENV_NAME": "AutoEncodeEasy",
"PARTIAL": False,
"MEMORY_TYPE": "lru",
"OBS_SIZE": 128,
"MODEL_SEED": 0,
},
{
"ENV_NAME": "AutoEncodeEasy",
"PARTIAL": True,
"MEMORY_TYPE": "lru",
"OBS_SIZE": 128,
"MODEL_SEED": 0,
},
{
"ENV_NAME": "BattleShipEasy",
"PARTIAL": False,
"MEMORY_TYPE": "lru",
"OBS_SIZE": 128,
"MODEL_SEED": 1,
},
{
"ENV_NAME": "BattleShipEasy",
"PARTIAL": True,
"MEMORY_TYPE": "lru",
"OBS_SIZE": 128,
"MODEL_SEED": 2,
},
{
"ENV_NAME": "CartPoleEasy",
"PARTIAL": False,
"MEMORY_TYPE": "lru",
"OBS_SIZE": 128,
"MODEL_SEED": 3,
},
{
"ENV_NAME": "CartPoleEasy",
"PARTIAL": True,
"MEMORY_TYPE": "lru",
"OBS_SIZE": 128,
"MODEL_SEED": 3,
},
{
"ENV_NAME": "CountRecallEasy",
"PARTIAL": False,
"MEMORY_TYPE": "lru",
"OBS_SIZE": 128,
"MODEL_SEED": 0,
},
{
"ENV_NAME": "CountRecallEasy",
"PARTIAL": True,
"MEMORY_TYPE": "lru",
"OBS_SIZE": 128,
"MODEL_SEED": 1,
},
{
"ENV_NAME": "MineSweeperEasy",
"PARTIAL": False,
"MEMORY_TYPE": "lru",
"OBS_SIZE": 128,
"MODEL_SEED": 4,
},
{
"ENV_NAME": "MineSweeperEasy",
"PARTIAL": True,
"MEMORY_TYPE": "lru",
"OBS_SIZE": 128,
"MODEL_SEED": 1,
},
{
"ENV_NAME": "NavigatorEasy",
"PARTIAL": False,
"MEMORY_TYPE": "lru",
"OBS_SIZE": 128,
"MODEL_SEED": 2,
},
{
"ENV_NAME": "NavigatorEasy",
"PARTIAL": True,
"MEMORY_TYPE": "lru",
"OBS_SIZE": 128,
"MODEL_SEED": 4,
},
{
"ENV_NAME": "NoisyCartPoleEasy",
"PARTIAL": False,
"MEMORY_TYPE": "lru",
"OBS_SIZE": 128,
"MODEL_SEED": 2,
},
{
"ENV_NAME": "NoisyCartPoleEasy",
"PARTIAL": True,
"MEMORY_TYPE": "lru",
"OBS_SIZE": 128,
"MODEL_SEED": 2,
},
# mingru models
{
"ENV_NAME": "AutoEncodeEasy",
"PARTIAL": False,
"MEMORY_TYPE": "mingru",
"OBS_SIZE": 128,
"MODEL_SEED": 1,
},
{
"ENV_NAME": "AutoEncodeEasy",
"PARTIAL": True,
"MEMORY_TYPE": "mingru",
"OBS_SIZE": 128,
"MODEL_SEED": 1,
},
{
"ENV_NAME": "BattleShipEasy",
"PARTIAL": False,
"MEMORY_TYPE": "mingru",
"OBS_SIZE": 128,
"MODEL_SEED": 2,
},
{
"ENV_NAME": "BattleShipEasy",
"PARTIAL": True,
"MEMORY_TYPE": "mingru",
"OBS_SIZE": 128,
"MODEL_SEED": 2,
},
{
"ENV_NAME": "CartPoleEasy",
"PARTIAL": False,
"MEMORY_TYPE": "mingru",
"OBS_SIZE": 128,
"MODEL_SEED": 4,
},
{
"ENV_NAME": "CartPoleEasy",
"PARTIAL": True,
"MEMORY_TYPE": "mingru",
"OBS_SIZE": 128,
"MODEL_SEED": 4,
},
{
"ENV_NAME": "CountRecallEasy",
"PARTIAL": False,
"MEMORY_TYPE": "mingru",
"OBS_SIZE": 128,
"MODEL_SEED": 2,
},
{
"ENV_NAME": "CountRecallEasy",
"PARTIAL": True,
"MEMORY_TYPE": "mingru",
"OBS_SIZE": 128,
"MODEL_SEED": 2,
},
{
"ENV_NAME": "MineSweeperEasy",
"PARTIAL": False,
"MEMORY_TYPE": "mingru",
"OBS_SIZE": 128,
"MODEL_SEED": 0,
},
{
"ENV_NAME": "MineSweeperEasy",
"PARTIAL": True,
"MEMORY_TYPE": "mingru",
"OBS_SIZE": 128,
"MODEL_SEED": 2,
},
{
"ENV_NAME": "NavigatorEasy",
"PARTIAL": False,
"MEMORY_TYPE": "mingru",
"OBS_SIZE": 128,
"MODEL_SEED": 0,
},
{
"ENV_NAME": "NavigatorEasy",
"PARTIAL": True,
"MEMORY_TYPE": "mingru",
"OBS_SIZE": 128,
"MODEL_SEED": 0,
},
{
"ENV_NAME": "NoisyCartPoleEasy",
"PARTIAL": False,
"MEMORY_TYPE": "mingru",
"OBS_SIZE": 128,
"MODEL_SEED": 3,
},
{
"ENV_NAME": "NoisyCartPoleEasy",
"PARTIAL": True,
"MEMORY_TYPE": "mingru",
"OBS_SIZE": 128,
"MODEL_SEED": 4,
},
]
# Example usage
seeds = [0, 1, 2, 3, 4]
for config in configs:
print(
f"Analyzing {config['MEMORY_TYPE']} on {config['ENV_NAME']} (Partial={config['PARTIAL']}, Seed={config['MODEL_SEED']})"
)
results = analyze_model_saliency(
config=config, seeds=seeds, max_steps=200, visualize=True
)
print(f"Successfully analyzed: {results}")
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