feat: Graphs checkpoint
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4 changed files with 453 additions and 4 deletions
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@ -11,6 +11,8 @@ dependencies = [
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"arithmeticencodingpython",
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"pandas-stubs==2.3.3.251201",
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"seaborn>=0.13.2",
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"scipy>=1.16.3",
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"scipy-stubs==1.16.3.3",
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]
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[project.optional-dependencies]
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@ -1,9 +1,290 @@
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import pandas as pd
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import os
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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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import pandas as pd
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import scipy
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import seaborn as sns
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from matplotlib.figure import Figure
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ALGORITHM_COL = 'compressor'
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LABEL_COL = 'label'
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CONTEXT_COL = 'context_size'
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INPUT_SIZE_COL = 'input_size'
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OUTPUT_SIZE_COL = 'compressed_size'
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COMPRESS_TIME_COL = 'compression_time'
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DECOMPRESS_TIME_COL = 'decompression_time'
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RATE_COL = 'compression_ratio'
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DISTORTION_COL = 'mse_loss'
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def original_v_compressed_filesize(
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df: pd.DataFrame,
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unique_labels: list[str],
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palette_dict,
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markers_dict
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) -> Figure:
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"""The "rate" graph"""
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plt.figure()
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break_point = 0.1
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ax_small, ax_large = split_graph(df, INPUT_SIZE_COL, 'Input size (MB)',
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OUTPUT_SIZE_COL, 'Compressed size (log, MB)',
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break_point, 'Compressor', 'upper left', LABEL_COL,
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unique_labels, palette_dict, markers_dict)
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# Add Baseline (y=x)
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df_small, df_large = df[df[INPUT_SIZE_COL] < break_point], df[df[INPUT_SIZE_COL] > break_point]
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baseline_label = 'Compression ratio 1.0'
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baseline_alpha = 0.5
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min_xy, max_xy = df_small[INPUT_SIZE_COL].min(), df_small[INPUT_SIZE_COL].max()
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ax_small.plot([min_xy, max_xy], [min_xy, max_xy],
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color='gray', linestyle='--', label=baseline_label, alpha=baseline_alpha)
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min_xy, max_xy = df_large[INPUT_SIZE_COL].min(), df_large[INPUT_SIZE_COL].max()
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ax_large.plot([min_xy, max_xy], [min_xy, max_xy],
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color='gray', linestyle='--', label=baseline_label, alpha=baseline_alpha)
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plt.yscale('log')
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return plt.gcf()
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def filesize_v_compression_time(
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df: pd.DataFrame,
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unique_labels: list[str],
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palette_dict,
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markers_dict
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) -> Figure:
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"""The "execution time" graph"""
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plt.figure()
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split_graph(df, INPUT_SIZE_COL, 'Input size (MB)',
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COMPRESS_TIME_COL, 'Compression time (log, s)',
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0.1, 'Compressor', 'center left', LABEL_COL,
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unique_labels, palette_dict, markers_dict)
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plt.yscale('log')
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return plt.gcf()
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def filesize_v_decompression_time(
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df: pd.DataFrame,
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unique_labels: list[str],
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palette_dict,
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markers_dict
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) -> Figure:
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"""The "execution time" graph"""
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plt.figure()
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split_graph(df, INPUT_SIZE_COL, 'Input size (MB)',
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DECOMPRESS_TIME_COL, 'Decompression time (log, s)',
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0.1, 'Compressor', 'center left', LABEL_COL,
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unique_labels, palette_dict, markers_dict)
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plt.yscale('log')
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return plt.gcf()
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def split_graph(
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df, x, x_axis_label, y, y_axis_label,
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break_point, legend_title, legend_loc, hue, unique_labels, palette_dict, markers_dict
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) -> tuple:
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df = df.sort_values(by=x)
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f, (ax_left, ax_right) = plt.subplots(1, 2, sharey=True, figsize=(10, 5))
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df_left = df[df[x] < break_point]
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sns.scatterplot(
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data=df_left,
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x=x,
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y=y,
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ax=ax_left,
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hue=hue,
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hue_order=unique_labels,
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palette=palette_dict,
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style=hue,
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style_order=unique_labels,
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markers=markers_dict
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)
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ax_left.set_xlabel('')
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df_right = df[df[x] > break_point]
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sns.scatterplot(
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data=df_right,
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x=x,
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y=y,
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ax=ax_right,
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hue=hue,
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hue_order=unique_labels,
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palette=palette_dict,
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style=hue,
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style_order=unique_labels,
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markers=markers_dict
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)
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ax_right.set_xlabel('')
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ax_right.set_ylabel('')
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# Combine both plots into one
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ax_left.spines['right'].set_visible(False)
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ax_right.spines['left'].set_visible(False)
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ax_right.yaxis.tick_right()
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ax_right.tick_params(labelright=False)
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ax_right.yaxis.set_ticks_position('none')
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# Add diagonal slash lines to indicate the break (with help from Gemini)
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d = .015 # proportion of vertical to horizontal extent of the slanted line
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kwargs = dict(transform=ax_left.transAxes, color='k', clip_on=False)
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ax_left.plot((1 - d, 1 + d), (-d, +d), **kwargs) # Top-right diagonal
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ax_left.plot((1 - d, 1 + d), (1 - d, 1 + d), **kwargs) # Bottom-right diagonal
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kwargs.update(transform=ax_right.transAxes) # Switch to the other axes
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ax_right.plot((-d, +d), (1 - d, 1 + d), **kwargs) # Top-left diagonal
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ax_right.plot((-d, +d), (-d, +d), **kwargs) # Bottom-left diagonal
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# Fix legends
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handles_left, labels_left = ax_left.get_legend_handles_labels()
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handles_right, labels_right = ax_right.get_legend_handles_labels()
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unique_legend = dict(zip(labels_left + labels_right, handles_left + handles_right))
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ax_left.get_legend().remove()
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ax_right.get_legend().remove()
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ax_left.legend(unique_legend.values(), unique_legend.keys(), title=legend_title, loc=legend_loc)
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f.text(0.5, 0, x_axis_label, ha='center', va='center')
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ax_left.set_ylabel(y_axis_label)
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ax_left.grid(True)
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ax_right.grid(True)
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plt.tight_layout()
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return ax_left, ax_right
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def compression_v_mse_scatter(df: pd.DataFrame) -> Figure:
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"""The "distortion" graph"""
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plt.figure()
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sns.scatterplot(
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data=df,
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x=RATE_COL,
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y=DISTORTION_COL
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)
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plt.xscale('log')
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plt.xlabel('Compression ratio (log)')
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# TODO This does not work properly
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plt.yscale('log')
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plt.ylabel('MSE (log)')
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return plt.gcf()
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def compression_ratios(df: pd.DataFrame) -> Figure:
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"""The "distortion" graph"""
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plt.figure()
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fig, ax = plt.subplots()
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sns.boxplot(
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data=df,
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x=RATE_COL,
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y=LABEL_COL,
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ax=ax
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)
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ax.set_xlabel('Compression ratio')
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ax.set_ylabel('')
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ax.grid(True)
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return plt.gcf()
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def generate(
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df: pd.DataFrame, unique_labels, palette_dict, markers_dict,
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tgt_dir: str, dpi: int = 300
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) -> None:
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"""Generate all the plots"""
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# Make plots
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original_v_compressed_filesize(df, unique_labels, palette_dict, markers_dict).savefig(
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os.path.join(tgt_dir, 'original_v_compressed_filesize.png'),
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bbox_inches='tight',
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dpi=dpi
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)
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filesize_v_compression_time(df, unique_labels, palette_dict, markers_dict).savefig(
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os.path.join(tgt_dir, 'filesize_v_compression_time.png'),
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bbox_inches='tight',
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dpi=dpi
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)
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filesize_v_decompression_time(df, unique_labels, palette_dict, markers_dict).savefig(
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os.path.join(tgt_dir, 'filesize_v_decompression_time.png'),
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bbox_inches='tight',
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dpi=dpi
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)
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# compression_v_mse_scatter(df).savefig(os.path.join(tgt_dir, 'compression_v_mse.png'), bbox_inches='tight')
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compression_ratios(df).savefig(os.path.join(tgt_dir, 'compression_ratios.png'), bbox_inches='tight')
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def setup(tgt_dir):
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# Create the targ directory if it does not exist
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os.makedirs(tgt_dir, exist_ok=True)
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# Prepare matplotlib for use with LaTeX (makes it look less out of place, less Pythonesque)
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params = {'text.usetex': True,
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'font.size': 11,
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'font.family': 'serif',
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}
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plt.rcParams.update(params)
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def preprocessing(df: pd.DataFrame) -> tuple:
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# Convert byts to MB
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df[INPUT_SIZE_COL] /= 1e6
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df[OUTPUT_SIZE_COL] /= 1e6
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# Convert ns to s
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df[COMPRESS_TIME_COL] /= 1e9
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# Add labels to differentiate between algorithms with context lengths
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def create_label(row):
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compressor = row[ALGORITHM_COL]
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return compressor if pd.isna(row[CONTEXT_COL]) else f"{compressor} ($L = {int(row[CONTEXT_COL])}$)"
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df[LABEL_COL] = df.apply(create_label, axis=1)
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# Add the compression ratio
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df[RATE_COL] = df[INPUT_SIZE_COL] / df[OUTPUT_SIZE_COL]
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# Identify all categories upfront
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unique_labels = sorted(df[LABEL_COL].unique())
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n_labels = len(unique_labels)
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# Create fixed palette and marker mapping
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palette_dict = dict(zip(unique_labels, sns.color_palette("tab10", n_labels)))
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markers_dict = dict(zip(unique_labels, ['x', '+', '1', '2', '3', '4']))
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return df, unique_labels, palette_dict, markers_dict
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def main():
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"""Load the data and generate the plots."""
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df = pd.read_csv("measurements.csv")
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tgt_dir = "figures"
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setup(tgt_dir)
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generate(*preprocessing(df), tgt_dir=tgt_dir, dpi=150)
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if __name__ == "__main__":
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main()
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exit()
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# read in the csv
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df = pd.read_csv("compression_results.csv")
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@ -43,7 +324,8 @@ if __name__ == "__main__":
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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.legend(
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[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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@ -60,7 +342,6 @@ if __name__ == "__main__":
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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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49
results/measurements.csv
Normal file
49
results/measurements.csv
Normal file
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@ -0,0 +1,49 @@
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compressor,training_dataset,context_size,input_filename,input_size,compressed_size,compression_time,decompressed_size,decompression_time,mse_loss
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gzip,,,genome.fna,4699745,1424004,.681197994,4699745,.015465955,0.0
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gzip,,,genome_large.fna,23498433,7118154,3.384480370,23498433,.067414798,0.0
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gzip,,,genome_small.fna,1367,589,.001937446,1367,.001983156,0.0
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gzip,,,genome_xlarge.fna,46996793,14235842,6.775190783,46996793,.131633333,0.0
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gzip,,,genome_xsmall.fna,1043,475,.002007016,1043,.002012775,0.0
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gzip,,,genome_xxsmall.fna,800,393,.002071485,800,.001958195,0.0
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gzip,,,text_large.txt,12977332,4770044,.613155078,12977332,.043915520,0.0
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gzip,,,text_small.txt,1022,590,.002070305,1022,.001903226,0.0
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gzip,,,text.txt,6488666,2385264,.308393934,6488666,.023656716,0.0
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gzip,,,text_xlarge.txt,25954664,9539638,1.229028819,25954664,.085925486,0.0
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gzip,,,text_xsmall.txt,825,473,.002110205,825,.001980535,0.0
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gzip,,,text_xxsmall.txt,492,325,.001867306,492,.002114055,0.0
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LZ4,,,genome.fna,4699745,2655438,.012701161,4699745,.009190410,0.0
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LZ4,,,genome_large.fna,23498433,13275544,.020719873,23498433,.025022334,0.0
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LZ4,,,genome_small.fna,1367,1041,.001883076,1367,.002144425,0.0
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LZ4,,,genome_xlarge.fna,46996793,26551229,.031734579,46996793,.043495412,0.0
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LZ4,,,genome_xsmall.fna,1043,814,.001954316,1043,.002085566,0.0
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LZ4,,,genome_xxsmall.fna,800,641,.001893416,800,.001943666,0.0
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LZ4,,,text_large.txt,12977332,7879136,.017927300,12977332,.015065196,0.0
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LZ4,,,text_small.txt,1022,857,.001967146,1022,.002040285,0.0
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LZ4,,,text.txt,6488666,3939378,.014891266,6488666,.009709618,0.0
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LZ4,,,text_xlarge.txt,25954664,15758785,.023613977,25954664,.023486747,0.0
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LZ4,,,text_xsmall.txt,825,678,.001757717,825,.002191075,0.0
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LZ4,,,text_xxsmall.txt,492,438,.001869646,492,.002134206,0.0
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Autoencoder,genome,256,genome.fna,4699745,4259288,636915773,,27887947,83.62875366210938
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Autoencoder,genome,256,genome_large.fna,23498433,21295512,1932602305,,7778175,83.59369659423828
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Autoencoder,genome,256,genome_xlarge.fna,46996793,42591024,3850901316,,10996509,83.58621215820312
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Autoencoder,genome,128,genome.fna,4699745,9399552,390656081,,5804539,83.01229095458984
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Autoencoder,genome,128,genome_large.fna,23498433,46996992,1932561312,,10575739,83.01190185546875
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Autoencoder,genome,128,genome_xlarge.fna,46996793,93993728,3873777067,,18670984,83.00253295898438
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Autoencoder,enwik9,256,text.txt,6488666,6184668,551986635,,10536259,786.6799926757812
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Autoencoder,enwik9,256,text_large.txt,12977332,12369092,1065897991,,5763879,786.6173706054688
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Autoencoder,enwik9,256,text_xlarge.txt,25954664,24738184,2139223055,,8369164,786.6337890625
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Autoencoder,enwik9,128,text.txt,6488666,12774636,545577194,,20624030,206.2792510986328
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Autoencoder,enwik9,128,text_large.txt,12977332,25549272,1073396133,,60871642,206.24131774902344
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Autoencoder,enwik9,128,text_xlarge.txt,25954664,51098292,2145601924,,59481825,206.33023071289062
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CNN,genome,256,genome_small.fna,1367,1743,1029290599,,890595665,0.0
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CNN,genome,256,genome_xsmall.fna,1043,1343,686878467,,683701323,0.0
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CNN,genome,256,genome_xxsmall.fna,800,1038,531354486,,527072394,0.0
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CNN,genome,128,genome_small.fna,1367,1682,829554150,,851934528,0.0
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CNN,genome,128,genome_xsmall.fna,1043,1300,654742547,,637221301,0.0
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CNN,genome,128,genome_xxsmall.fna,800,1006,483840337,,488870786,0.0
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CNN,enwik9,256,text_small.txt,1022,1561,693378115,,671294958,0.0
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CNN,enwik9,256,text_xsmall.txt,825,1268,550333502,,550062973,0.0
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CNN,enwik9,256,text_xxsmall.txt,492,790,333745012,,332073466,0.0
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CNN,enwik9,128,text_small.txt,1022,1129,629310179,,621317553,0.0
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CNN,enwik9,128,text_xsmall.txt,825,882,504538600,,504907940,0.0
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CNN,enwik9,128,text_xxsmall.txt,492,571,305443187,,308964670,0.0
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117
uv.lock
generated
117
uv.lock
generated
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@ -1385,6 +1385,18 @@ wheels = [
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{ url = "https://files.pythonhosted.org/packages/2d/ee/346fa473e666fe14c52fcdd19ec2424157290a032d4c41f98127bfb31ac7/numpy-2.3.5-pp311-pypy311_pp73-win_amd64.whl", hash = "sha256:f16417ec91f12f814b10bafe79ef77e70113a2f5f7018640e7425ff979253425", size = 12967213, upload-time = "2025-11-16T22:52:39.38Z" },
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]
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||||
[[package]]
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Reference in a new issue