Tibo De Peuter
2f869a8a7a
# Conflicts: # config/measure.py # results/compression_results_auto_small.csv
132 lines
5.2 KiB
Python
132 lines
5.2 KiB
Python
import pandas as pd
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import matplotlib.pyplot as plt
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import seaborn as sns
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import numpy as np
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if __name__ == "__main__":
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# read in the csv
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df = pd.read_csv("compression_results.csv")
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for dataset_type in df["dataset_type"].unique():
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for model_type in df["model_type"].unique():
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dataset_df = df[df["dataset_type"] == dataset_type]
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model_df = dataset_df[dataset_df["model_type"] == model_type].copy()
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# execution time
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plt.figure()
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model_df["original_file_size_mb"] = model_df["original_file_size"] / 1e6
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model_df["compression_time_s"] = model_df["compression_time"] / 1e9
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model_df["decompression_time_s"] = model_df["decompression_time"] / 1e9
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# compression
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sns.lineplot(
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data=model_df,
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x="original_file_size_mb",
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y="compression_time_s",
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hue="context_length",
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palette="Set1",
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markers=True,
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legend="brief",
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linestyle="-"
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)
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# decompression
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sns.lineplot(
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data=model_df,
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x="original_file_size_mb",
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y="decompression_time_s",
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hue="context_length",
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palette="Set1",
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markers=True,
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legend=False,
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linestyle="--"
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)
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plt.title(f"{model_type.capitalize()} compression and decompression time: {dataset_type}")
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plt.xlabel("file size [MB]")
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plt.ylabel("Time [s]")
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plt.yscale("log")
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plt.legend([f"{style}, {c_type}" for style, c_type in zip(["Solid", "Dashed"], ["compression", "decompression"])])
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plt.tight_layout()
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plt.savefig(f"./graphs/{model_type}_{dataset_type}_execution_time.png")
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# compression ratio
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plt.figure()
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c256 = model_df[model_df["context_length"] == 256]
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c128 = model_df[model_df["context_length"] == 128]
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plt.plot(c256["original_file_size"] / 1e6, c256["compressed_file_size"] / 1e6, label="256")
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plt.plot(c128["original_file_size"] / 1e6, c128["compressed_file_size"] / 1e6, label="128")
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plt.title(f"{model_type.capitalize()} compressed file evolution: {dataset_type}")
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plt.xlabel("Original file size [MB]")
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plt.ylabel("Compressed file size [MB]")
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plt.legend()
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plt.savefig(f"./graphs/{model_type}_{dataset_type}_compression_ratio.png")
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# if model_type == "cnn":
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# import numpy as np
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#
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# plt.figure()
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# for length, linestyle in [(128, '-'), (256, '--')]:
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# # extrapolate execution time to larger files
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# x = model_df[model_df["context_length"] == length]["original_file_size"] / 1e6
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# y = model_df[model_df["context_length"] == length]["compression_time"]
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# y_decom = model_df[model_df["context_length"] == length]["decompression_time"]
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#
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# b1, loga1 = np.polyfit(x, np.log(y), 1)
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# b2, loga2 = np.polyfit(x, np.log(y_decom), 1)
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#
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# x_comp = np.linspace(0, 40, 1000)
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# x_decomp = np.linspace(0, 40, 1000)
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# a1 = np.exp(loga1)
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# a2 = np.exp(loga2)
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#
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#
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# plt.plot(
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# x_comp, a1 * np.exp(x_comp),
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# label=f"{length} compression",
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# linestyle=linestyle
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# )
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# plt.plot(
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# x_decomp, a2 * np.exp(x_decomp),
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# label=f"{length} decompression",
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# linestyle=linestyle
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# )
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#
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#
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#
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# plt.legend()
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# plt.title(f"Extrapolated execution time for CNN compression and decompression")
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# plt.xlabel("File size [MB]")
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# plt.ylabel("Time [s]")
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# plt.tight_layout()
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# plt.savefig(f"./graphs/{model_type}_{dataset_type}_extrapolated_execution_time.png")
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for model_type in df["model_type"].unique():
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model_df = df[df["model_type"] == model_type]
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plt.figure(figsize=(10, 4))
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bar_height = 0.25
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files = model_df["input_file_name"].unique()
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y = np.arange(len(files))
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c256 = model_df[model_df["context_length"] == 256]
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c128 = model_df[model_df["context_length"] == 128]
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plt.barh(
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y - bar_height / 2,
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c256["mse_loss"],
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height=bar_height,
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label="256"
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)
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plt.barh(
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y + bar_height / 2,
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c128["mse_loss"],
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height=bar_height,
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label="128"
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)
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plt.yticks(y, files, rotation=45, ha="right")
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plt.title(f"MSE loss for different context lengths")
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plt.xlabel("MSE loss")
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plt.ylabel("Filename")
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plt.legend()
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plt.tight_layout()
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plt.savefig(f"./graphs/{model_type}_loss.png")
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