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Malte Tammena 2024-06-10 14:43:26 +02:00
parent 7e48504bab
commit 89f25b07d4
3 changed files with 50 additions and 23 deletions
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12
flake.nix
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@ -189,10 +189,20 @@
text = builtins.readFile ./scripts/sc-batch.sh;
};
decode-result-folder = pkgs.writeShellApplication {
name = "decode-result-folder";
runtimeInputs = [
pkgs.python3
];
text = ''
python3 ${./scripts/decode-result-folder.py}
'';
};
aspforaba = pkgs.callPackage ./nix/packages/aspforaba.nix {inherit (self'.packages) clingo;};
in
{
inherit validate aba2sat aspforaba sc-batch;
inherit validate aba2sat aspforaba sc-batch decode-result-folder;
default = aba2sat;
clingo = pkgs.callPackage ./nix/packages/clingo.nix {};
}

3
scripts/decode-result-folder.py
View file

@ -47,7 +47,8 @@ def run():
"assumption_ratio": assumption_ratio,
"max_rules_per_head": max_rules_per_head,
"max_rule_size": max_rule_size,
"time": aba2sat["mean"],
"time_ours": aba2sat["mean"],
"time_theirs": aspforaba['mean'],
"stddev": aba2sat['stddev'],
"speedup": speedup,
})

58
scripts/test.py
View file

@ -11,32 +11,48 @@ def read_and_visualize(csv_file):
# Display the first few rows of the dataframe
print(df.head())
# Identify all the properties (assuming they are all columns except for some timings)
properties = [col for col in df.columns if col != 'speedup' and col != 'time' and col != 'stddev']
plt.figure(figsize=(8,8))
scatterplot = sns.scatterplot(x="time_ours", y="time_theirs", hue="atom_count", data=df)
scatterplot.set(xscale='log', yscale='log')
min_val = min(df['time_ours'].min(), df['time_theirs'].min())
max_val = max(df['time_ours'].max(), df['time_theirs'].max())
plt.plot([min_val, max_val], [min_val, max_val], 'r--')
# ax = plt.gca()
# ax.set_xscale('log')
# ax.set_yscale('log')
# Pairplot to see general pairwise relationships, may help to understand the overall relationship between properties and runtime
sns.pairplot(df)
plt.suptitle('Pairplot of Properties and Runtime', y=1.02)
plt.xlabel("aba2sat")
plt.ylabel("ASPforABA")
plt.legend()
plt.show()
# Create scatter plots for each property against runtime
for prop in properties:
plt.figure(figsize=(10, 6))
sns.scatterplot(x=df[prop], y=df['speedup'])
plt.title(f'Impact of {prop} on Speedup')
plt.xlabel(prop)
plt.ylabel('Speedup')
# # Identify all the properties (assuming they are all columns except for some timings)
# properties = [col for col in df.columns if col != 'speedup' and col != 'time_ours' and col != 'time_theirs' and col != 'stddev']
# Create box plots for categorical properties if any (e.g., difficulty level or type) against runtime
for prop in properties:
if df[prop].dtype == 'object':
plt.figure(figsize=(10, 6))
sns.boxplot(x=df[prop], y=df['speedup'])
plt.title(f'Impact of {prop} on Speedup')
plt.xlabel(prop)
plt.ylabel('Speedup')
# # Pairplot to see general pairwise relationships, may help to understand the overall relationship between properties and runtime
# sns.pairplot(df)
# plt.suptitle('Pairplot of Properties and Runtime', y=1.02)
# plt.show()
plt.show()
# # Create scatter plots for each property against runtime
# for prop in properties:
# plt.figure(figsize=(10, 6))
# sns.scatterplot(x=df[prop], y=df['speedup'])
# plt.title(f'Impact of {prop} on Speedup')
# plt.xlabel(prop)
# plt.ylabel('Speedup')
# # Create box plots for categorical properties if any (e.g., difficulty level or type) against runtime
# for prop in properties:
# if df[prop].dtype == 'object':
# plt.figure(figsize=(10, 6))
# sns.boxplot(x=df[prop], y=df['speedup'])
# plt.title(f'Impact of {prop} on Speedup')
# plt.xlabel(prop)
# plt.ylabel('Speedup')
# plt.show()
# Example usage
csv_file = 'all.csv' # Replace with your actual CSV file path