chore: Update measurements

# Conflicts:
#	config/measure.py
#	results/compression_results_auto_small.csv

config/measure.py

@@ -65,12 +65,12 @@ if __name__ == "__main__":
     ]
 
     models = [
-        ("auto-genome-full-256.pt", 256, "autoencoder", files_genome + files_genome_cnn),
-        ("auto-genome-full-128.pt", 128, "autoencoder", files_genome + files_genome_cnn),
+        ("auto-genome-full-256.pt", 256, "autoencoder", files_genome),
+        ("auto-genome-full-128.pt", 128, "autoencoder", files_genome),
         ("cnn-genome-full-256.pt", 256, "cnn", files_genome_cnn),
         ("cnn-genome-full-128.pt", 128, "cnn", files_genome_cnn),
-        ("auto-enwik9-full-256.pt", 256, "autoencoder", files_enwik9 + files_enwik9_cnn),
-        ("auto-enwik9-full-128.pt", 128, "autoencoder", files_enwik9 + files_enwik9_cnn),
+        ("auto-enwik9-full-256.pt", 256, "autoencoder", files_enwik9),
+        ("auto-enwik9-full-128.pt", 128, "autoencoder", files_enwik9),
         ("cnn-enwik9-full-256.pt", 256, "cnn", files_enwik9_cnn),
         ("cnn-enwik9-full-128.pt", 128, "cnn", files_enwik9_cnn),
     ]

make_graphs.py

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

pyproject.toml

@@ -9,8 +9,10 @@ dependencies = [
     "fsspec==2024.9.0",
     "lorem>=0.1.1",
     "arithmeticencodingpython",
-    "pandas-stubs~=2.3.3",
+    "pandas-stubs==2.3.3.251201",
     "seaborn>=0.13.2",
+    "scipy>=1.16.3",
+    "scipy-stubs==1.16.3.3",
 ]
 
 [project.optional-dependencies]

results/compress/compression_results.csv

@@ -1,25 +0,0 @@
-model_type,model_name,context_length,dataset_type,input_file_name,original_file_size,compressed_file_size,mse_loss,compression_time,decompression_time
-autoencoder,auto-genome-full-256.pt,256,genome,genome.fna,4699745,4259288,83.62875366210938,806860910,45252425
-autoencoder,auto-genome-full-256.pt,256,genome,genome_large.fna,23498433,21295512,83.59369659423828,2029608027,7888492
-autoencoder,auto-genome-full-256.pt,256,genome,genome_xlarge.fna,46996793,42591024,83.58621215820312,3893661459,11828570
-autoencoder,auto-genome-full-128.pt,128,genome,genome.fna,4699745,9399552,83.01229095458984,398403410,6667159
-autoencoder,auto-genome-full-128.pt,128,genome,genome_large.fna,23498433,46996992,83.01190185546875,2039699733,18597914
-autoencoder,auto-genome-full-128.pt,128,genome,genome_xlarge.fna,46996793,93993728,83.00253295898438,3914176203,19137168
-cnn,cnn-genome-full-256.pt,256,genome,genome_small.fna,1367,1743,0.0,1066176734,933083718
-cnn,cnn-genome-full-256.pt,256,genome,genome_xsmall.fna,1043,1343,0.0,741888902,796896774
-cnn,cnn-genome-full-256.pt,256,genome,genome_xxsmall.fna,800,1038,0.0,542462371,712222705
-cnn,cnn-genome-full-128.pt,128,genome,genome_small.fna,1367,1682,0.0,967421587,1046043100
-cnn,cnn-genome-full-128.pt,128,genome,genome_xsmall.fna,1043,1300,0.0,632559455,629773982
-cnn,cnn-genome-full-128.pt,128,genome,genome_xxsmall.fna,800,1006,0.0,481589710,488065247
-autoencoder,auto-enwik9-full-256.pt,256,enwik9,text.txt,6488666,6184668,786.6799926757812,564278454,16857177
-autoencoder,auto-enwik9-full-256.pt,256,enwik9,text_large.txt,12977332,12369092,786.6173706054688,1079811645,6431677
-autoencoder,auto-enwik9-full-256.pt,256,enwik9,text_xlarge.txt,25954664,24738184,786.6337890625,2163648216,8789889
-autoencoder,auto-enwik9-full-128.pt,128,enwik9,text.txt,6488666,12774636,206.2792510986328,559830918,20974298
-autoencoder,auto-enwik9-full-128.pt,128,enwik9,text_large.txt,12977332,25549272,206.24131774902344,1166762858,62279675
-autoencoder,auto-enwik9-full-128.pt,128,enwik9,text_xlarge.txt,25954664,51098292,206.33023071289062,2161884920,54375265
-cnn,cnn-enwik9-full-256.pt,256,enwik9,text_small.txt,1022,1561,0.0,827390609,666215221
-cnn,cnn-enwik9-full-256.pt,256,enwik9,text_xsmall.txt,825,1268,0.0,669703293,539488777
-cnn,cnn-enwik9-full-256.pt,256,enwik9,text_xxsmall.txt,492,790,0.0,327867710,327564434
-cnn,cnn-enwik9-full-128.pt,128,enwik9,text_small.txt,1022,1129,0.0,647518999,620040573
-cnn,cnn-enwik9-full-128.pt,128,enwik9,text_xsmall.txt,825,882,0.0,500985855,613062076
-cnn,cnn-enwik9-full-128.pt,128,enwik9,text_xxsmall.txt,492,571,0.0,302816347,308070825

results/compress/compression_results_auto_small.csv

@@ -1,13 +0,0 @@
-model_type,model_name,context_length,dataset_type,input_file_name,original_file_size,compressed_file_size,mse_loss,compression_time,decompression_time
-autoencoder,auto-genome-full-256.pt,256,genome,genome_small.fna,1367,1392,220.29188537597656,237708436,29378622
-autoencoder,auto-genome-full-256.pt,256,genome,genome_xsmall.fna,1043,1160,263.97027587890625,5293080,4488761
-autoencoder,auto-genome-full-256.pt,256,genome,genome_xxsmall.fna,800,928,319.5350036621094,4873340,4381310
-autoencoder,auto-genome-full-128.pt,128,genome,genome_small.fna,1367,2816,120.52304077148438,251659317,5284661
-autoencoder,auto-genome-full-128.pt,128,genome,genome_xsmall.fna,1043,2304,133.8379669189453,152590538,4432469
-autoencoder,auto-genome-full-128.pt,128,genome,genome_xxsmall.fna,800,1792,159.2862548828125,153622653,4532139
-autoencoder,auto-enwik9-full-256.pt,256,enwik9,text_small.txt,1022,976,746.8033447265625,6699780,22065927
-autoencoder,auto-enwik9-full-256.pt,256,enwik9,text_xsmall.txt,825,976,787.5345458984375,3715750,3665629
-autoencoder,auto-enwik9-full-256.pt,256,enwik9,text_xxsmall.txt,492,488,828.8658447265625,4892560,4176979
-autoencoder,auto-enwik9-full-128.pt,128,enwik9,text_small.txt,1022,2016,231.42955017089844,5005880,4400159
-autoencoder,auto-enwik9-full-128.pt,128,enwik9,text_xsmall.txt,825,1764,236.55636596679688,5024449,4180100
-autoencoder,auto-enwik9-full-128.pt,128,enwik9,text_xxsmall.txt,492,1008,237.20529174804688,4856779,4145850

results/compression_results.csv

@@ -0,0 +1,37 @@
+model_type,model_name,context_length,dataset_type,input_file_name,original_file_size,compressed_file_size,mse_loss,compression_time,decompression_time
+autoencoder,auto-genome-full-256.pt,256,genome,genome.fna,4699745,4259288,83.62875366210938,636915773,27887947
+autoencoder,auto-genome-full-256.pt,256,genome,genome_large.fna,23498433,21295512,83.59369659423828,1932602305,7778175
+autoencoder,auto-genome-full-256.pt,256,genome,genome_xlarge.fna,46996793,42591024,83.58621215820312,3850901316,10996509
+autoencoder,auto-genome-full-128.pt,128,genome,genome.fna,4699745,9399552,83.01229095458984,390656081,5804539
+autoencoder,auto-genome-full-128.pt,128,genome,genome_large.fna,23498433,46996992,83.01190185546875,1932561312,10575739
+autoencoder,auto-genome-full-128.pt,128,genome,genome_xlarge.fna,46996793,93993728,83.00253295898438,3873777067,18670984
+cnn,cnn-genome-full-256.pt,256,genome,genome_small.fna,1367,1743,0.0,1029290599,890595665
+cnn,cnn-genome-full-256.pt,256,genome,genome_xsmall.fna,1043,1343,0.0,686878467,683701323
+cnn,cnn-genome-full-256.pt,256,genome,genome_xxsmall.fna,800,1038,0.0,531354486,527072394
+cnn,cnn-genome-full-128.pt,128,genome,genome_small.fna,1367,1682,0.0,829554150,851934528
+cnn,cnn-genome-full-128.pt,128,genome,genome_xsmall.fna,1043,1300,0.0,654742547,637221301
+cnn,cnn-genome-full-128.pt,128,genome,genome_xxsmall.fna,800,1006,0.0,483840337,488870786
+autoencoder,auto-enwik9-full-256.pt,256,enwik9,text.txt,6488666,6184668,786.6799926757812,551986635,10536259
+autoencoder,auto-enwik9-full-256.pt,256,enwik9,text_large.txt,12977332,12369092,786.6173706054688,1065897991,5763879
+autoencoder,auto-enwik9-full-256.pt,256,enwik9,text_xlarge.txt,25954664,24738184,786.6337890625,2139223055,8369164
+autoencoder,auto-enwik9-full-128.pt,128,enwik9,text.txt,6488666,12774636,206.2792510986328,545577194,20624030
+autoencoder,auto-enwik9-full-128.pt,128,enwik9,text_large.txt,12977332,25549272,206.24131774902344,1073396133,60871642
+autoencoder,auto-enwik9-full-128.pt,128,enwik9,text_xlarge.txt,25954664,51098292,206.33023071289062,2145601924,59481825
+cnn,cnn-enwik9-full-256.pt,256,enwik9,text_small.txt,1022,1561,0.0,693378115,671294958
+cnn,cnn-enwik9-full-256.pt,256,enwik9,text_xsmall.txt,825,1268,0.0,550333502,550062973
+cnn,cnn-enwik9-full-256.pt,256,enwik9,text_xxsmall.txt,492,790,0.0,333745012,332073466
+cnn,cnn-enwik9-full-128.pt,128,enwik9,text_small.txt,1022,1129,0.0,629310179,621317553
+cnn,cnn-enwik9-full-128.pt,128,enwik9,text_xsmall.txt,825,882,0.0,504538600,504907940
+cnn,cnn-enwik9-full-128.pt,128,enwik9,text_xxsmall.txt,492,571,0.0,305443187,308964670
+autoencoder,auto-genome-full-256.pt,256,genome,genome_small.fna,1367,1392,220.29188537597656,237708436,29378622
+autoencoder,auto-genome-full-256.pt,256,genome,genome_xsmall.fna,1043,1160,263.97027587890625,5293080,4488761
+autoencoder,auto-genome-full-256.pt,256,genome,genome_xxsmall.fna,800,928,319.5350036621094,4873340,4381310
+autoencoder,auto-genome-full-128.pt,128,genome,genome_small.fna,1367,2816,120.52304077148438,251659317,5284661
+autoencoder,auto-genome-full-128.pt,128,genome,genome_xsmall.fna,1043,2304,133.8379669189453,152590538,4432469
+autoencoder,auto-genome-full-128.pt,128,genome,genome_xxsmall.fna,800,1792,159.2862548828125,153622653,4532139
+autoencoder,auto-enwik9-full-256.pt,256,enwik9,text_small.txt,1022,976,746.8033447265625,6699780,22065927
+autoencoder,auto-enwik9-full-256.pt,256,enwik9,text_xsmall.txt,825,976,787.5345458984375,3715750,3665629
+autoencoder,auto-enwik9-full-256.pt,256,enwik9,text_xxsmall.txt,492,488,828.8658447265625,4892560,4176979
+autoencoder,auto-enwik9-full-128.pt,128,enwik9,text_small.txt,1022,2016,231.42955017089844,5005880,4400159
+autoencoder,auto-enwik9-full-128.pt,128,enwik9,text_xsmall.txt,825,1764,236.55636596679688,5024449,4180100
+autoencoder,auto-enwik9-full-128.pt,128,enwik9,text_xxsmall.txt,492,1008,237.20529174804688,4856779,4145850

results/cpu_compression_graphs.py

@@ -110,5 +110,5 @@ def plot_compression_metrics(csv_path, output_dir="results/compress/plots"):
 
 if __name__ == "__main__":
     # You can modify this path to point to your CSV file
-    csv_path = "results/compress/compression_metrics_20251216_182603.csv"
+    csv_path = "compression_metrics_20251216_182603.csv"
     plot_compression_metrics(csv_path)

results/make_graphs.py

@@ -0,0 +1,549 @@
+import glob
+import os
+
+import matplotlib.pyplot as plt
+import numpy as np
+import pandas as pd
+import seaborn as sns
+from matplotlib.figure import Figure
+
+ALGORITHM_COL = 'compressor'
+LABEL_COL = 'label'
+CONTEXT_COL = 'context_size'
+INPUT_SIZE_COL = 'input_size'
+OUTPUT_SIZE_COL = 'compressed_size'
+COMPRESS_TIME_COL = 'compression_time'
+DECOMPRESS_TIME_COL = 'decompression_time'
+RATE_COL = 'compression_ratio'
+DISTORTION_COL = 'mse_loss'
+
+
+def original_v_compressed_filesize(df: pd.DataFrame,
+                                   unique_labels: list[str], palette_dict, markers_dict
+                                   ) -> Figure:
+    """The "rate" graph"""
+    plt.figure()
+
+    break_point = 0.1
+
+    _, ax_small, ax_large = split_graph(df, INPUT_SIZE_COL, 'Input size (MB)',
+                                        OUTPUT_SIZE_COL, 'Compressed size (log, MB)',
+                                        break_point, 'Compressor', 'upper left', LABEL_COL,
+                                        unique_labels, palette_dict, markers_dict)
+
+    # Add Baseline (y=x)
+    df_small, df_large = df[df[INPUT_SIZE_COL] < break_point], df[df[INPUT_SIZE_COL] > break_point]
+    baseline_label = 'Compression ratio 1.0'
+    baseline_alpha = 0.5
+    min_xy, max_xy = df_small[INPUT_SIZE_COL].min(), df_small[INPUT_SIZE_COL].max()
+    ax_small.plot([min_xy, max_xy], [min_xy, max_xy],
+                  color='gray', linestyle='--', label=baseline_label, alpha=baseline_alpha)
+    min_xy, max_xy = df_large[INPUT_SIZE_COL].min(), df_large[INPUT_SIZE_COL].max()
+    ax_large.plot([min_xy, max_xy], [min_xy, max_xy],
+                  color='gray', linestyle='--', label=baseline_label, alpha=baseline_alpha)
+
+    plt.yscale('log')
+
+    return plt.gcf()
+
+
+def compression_ratios(df: pd.DataFrame, unique_labels, palette_dict) -> Figure:
+    """The "rate" graph"""
+    plt.figure()
+
+    fig, ax = plt.subplots()
+    sns.boxplot(
+        data=df,
+        x=RATE_COL,
+        y=LABEL_COL,
+        hue=LABEL_COL,
+        hue_order=unique_labels,
+        palette=palette_dict,
+        ax=ax,
+        fill=False
+    )
+
+    ax.set_xlabel('Compression ratio')
+    ax.set_ylabel('Compressor')
+
+    plt.yticks(rotation=45, ha="right")
+
+    ax.grid(True, alpha=0.3)
+
+    return plt.gcf()
+
+
+def compression_ratio_v_compression_time(df: pd.DataFrame, unique_labels, palette_dict, markers_dict) -> Figure:
+    plt.figure()
+
+    sns.scatterplot(
+        data=df,
+        x=RATE_COL,
+        y=COMPRESS_TIME_COL,
+        hue=LABEL_COL,
+        hue_order=unique_labels,
+        palette=palette_dict,
+        style=LABEL_COL,
+        style_order=unique_labels,
+        markers=markers_dict,
+    )
+
+    plt.legend(title='Compressor')
+
+    plt.xlabel('Compression ratio')
+    plt.ylabel('Compression time (s)')
+
+    plt.grid(True, alpha=0.3)
+
+    return plt.gcf()
+
+
+def filesize_v_compression_time(df: pd.DataFrame,
+                                unique_labels: list[str], palette_dict, markers_dict
+                                ) -> Figure:
+    """The "execution time" graph"""
+    plt.figure()
+
+    f, _, _ = split_graph(df, INPUT_SIZE_COL, 'Input size (MB)',
+                          COMPRESS_TIME_COL, 'Runtime (log, s)',
+                          0.1, 'Compressor', 'center left', LABEL_COL,
+                          unique_labels, palette_dict, markers_dict)
+
+    f.text(0.5, 1, 'Compression runtime for different filesizes using each compressor', va='center', ha='center')
+    plt.yscale('log')
+
+    return plt.gcf()
+
+
+def filesize_v_decompression_time(df: pd.DataFrame,
+                                  unique_labels: list[str], palette_dict, markers_dict
+                                  ) -> Figure:
+    """The "execution time" graph"""
+    plt.figure()
+
+    f, _, _ = split_graph(df, INPUT_SIZE_COL, 'Input size (MB)',
+                          DECOMPRESS_TIME_COL, 'Runtime (log, s)',
+                          0.1, 'Compressor', 'center left', LABEL_COL,
+                          unique_labels, palette_dict, markers_dict)
+
+    f.text(0.5, 1, 'Decompression runtime for different filesizes using each compressor', va='center', ha='center')
+    plt.yscale('log')
+
+    return plt.gcf()
+
+
+def filesize_v_mse(df: pd.DataFrame) -> Figure:
+    """The "distortion" graph"""
+    plt.figure()
+
+    df = df[df[DISTORTION_COL] != 0]
+    df = df[df[ALGORITHM_COL] == 'Autoencoder']
+
+    df.sort_values(by=INPUT_SIZE_COL, inplace=True)
+
+    def filename_and_size(row):
+        filename = row['input_filename']
+        size = row[INPUT_SIZE_COL]
+        return f"{filename} ({size:.4f} MB)"
+
+    def size(row):
+        full_name = row['input_filename']
+        # Strip prefix
+        size_name = full_name.lstrip('text').lstrip('genome').rstrip('txt').rstrip('fna')
+        size_name = f"*{size_name}*"
+        return size_name
+
+    df['input_filename_size'] = df.apply(filename_and_size, axis=1)
+    df['input_size'] = df.apply(size, axis=1)
+
+    g = sns.catplot(
+        data=df,
+        kind="bar",
+        x=DISTORTION_COL,
+        y='input_size',
+        col='training_dataset',
+        hue=CONTEXT_COL,
+        palette='Set2',
+        height=5,
+        aspect=0.6
+    )
+
+    g.set_axis_labels("MSE Loss", "Filename")
+    g.set_titles("Autoencoder trained on {col_name}")
+
+    # plt.title('MSE for autoencoder')
+    # plt.yticks(rotation=45, ha="right")
+    # plt.legend(title='Context size')
+    g.tight_layout()
+
+    return plt.gcf()
+
+
+def mse_losses(df: pd.DataFrame, unique_labels, palette_dict) -> Figure:
+    """The "distortion" graph"""
+    plt.figure()
+
+    fig, ax = plt.subplots()
+    sns.boxplot(
+        data=df,
+        x=DISTORTION_COL,
+        y=LABEL_COL,
+        hue=LABEL_COL,
+        hue_order=unique_labels,
+        palette=palette_dict,
+        ax=ax,
+        fill=False
+    )
+
+    ax.set_xlabel('MSE')
+    ax.set_ylabel('Compressor')
+
+    plt.yticks(rotation=45, ha="right")
+
+    ax.grid(True, alpha=0.3)
+
+    return plt.gcf()
+
+
+def split_graph(
+        df, x, x_axis_label, y, y_axis_label,
+        break_point, legend_title, legend_loc, hue, unique_labels, palette_dict, markers_dict
+) -> tuple:
+    df = df.sort_values(by=x)
+
+    f, (ax_left, ax_right) = plt.subplots(1, 2, sharey=True, figsize=(8, 4))
+
+    df_left = df[df[x] < break_point]
+    sns.scatterplot(
+        data=df_left,
+        x=x,
+        y=y,
+        ax=ax_left,
+        hue=hue,
+        hue_order=unique_labels,
+        palette=palette_dict,
+        style=hue,
+        style_order=unique_labels,
+        markers=markers_dict,
+        # s=150
+    )
+    ax_left.set_xlabel('')
+
+    df_right = df[df[x] > break_point]
+    sns.scatterplot(
+        data=df_right,
+        x=x,
+        y=y,
+        ax=ax_right,
+        hue=hue,
+        hue_order=unique_labels,
+        palette=palette_dict,
+        style=hue,
+        style_order=unique_labels,
+        markers=markers_dict,
+        # s=150
+    )
+    ax_right.set_xlabel('')
+    ax_right.set_ylabel('')
+
+    # Combine both plots into one
+    ax_left.spines['right'].set_visible(False)
+    ax_right.spines['left'].set_visible(False)
+    ax_right.yaxis.tick_right()
+    ax_right.tick_params(labelright=False)
+    ax_right.yaxis.set_ticks_position('none')
+
+    # Add diagonal slash lines to indicate the break (with help from Gemini)
+    d = .015  # proportion of vertical to horizontal extent of the slanted line
+    kwargs = dict(transform=ax_left.transAxes, color='k', clip_on=False)
+    ax_left.plot((1 - d, 1 + d), (-d, +d), **kwargs)  # Top-right diagonal
+    ax_left.plot((1 - d, 1 + d), (1 - d, 1 + d), **kwargs)  # Bottom-right diagonal
+
+    kwargs.update(transform=ax_right.transAxes)  # Switch to the other axes
+    ax_right.plot((-d, +d), (1 - d, 1 + d), **kwargs)  # Top-left diagonal
+    ax_right.plot((-d, +d), (-d, +d), **kwargs)  # Bottom-left diagonal
+
+    # Fix legends
+    handles_left, labels_left = ax_left.get_legend_handles_labels()
+    handles_right, labels_right = ax_right.get_legend_handles_labels()
+    unique_legend = dict(zip(labels_left + labels_right, handles_left + handles_right))
+    ax_left.get_legend().remove()
+    ax_right.get_legend().remove()
+    ax_left.legend(unique_legend.values(), unique_legend.keys(), title=legend_title, loc=legend_loc)
+
+    f.text(0.5, 0, x_axis_label, ha='center', va='center')
+    ax_left.set_ylabel(y_axis_label)
+
+    ax_left.grid(True, alpha=0.3)
+    ax_right.grid(True, alpha=0.3)
+
+    plt.tight_layout()
+    return f, ax_left, ax_right
+
+
+def generate(
+        df: pd.DataFrame, unique_labels, palette_dict, markers_dict,
+        tgt_dir: str
+) -> None:
+    """Generate all the plots"""
+    # Make plots
+
+    original_v_compressed_filesize(df, unique_labels, palette_dict, markers_dict).savefig(
+        os.path.join(tgt_dir, 'original_v_compressed_filesize.png'),
+        bbox_inches='tight',
+    )
+
+    filesize_v_compression_time(df, unique_labels, palette_dict, markers_dict).savefig(
+        os.path.join(tgt_dir, 'filesize_v_compression_time.png'),
+        bbox_inches='tight',
+    )
+    filesize_v_decompression_time(df, unique_labels, palette_dict, markers_dict).savefig(
+        os.path.join(tgt_dir, 'filesize_v_decompression_time.png'),
+        bbox_inches='tight',
+    )
+
+    compression_ratios(df, unique_labels, palette_dict).savefig(
+        os.path.join(tgt_dir, 'compression_ratios.png'),
+        bbox_inches='tight'
+    )
+    compression_ratio_v_compression_time(df, unique_labels, palette_dict, markers_dict).savefig(
+        os.path.join(tgt_dir, 'compression_ratio_v_compression_time.png'),
+        bbox_inches='tight'
+    )
+
+    filesize_v_mse(df).savefig(
+        os.path.join(tgt_dir, 'filesize_mse.png'),
+        bbox_inches='tight'
+    )
+    mse_losses(df, unique_labels, palette_dict).savefig(
+        os.path.join(tgt_dir, 'mse_losses.png'),
+        bbox_inches='tight'
+    )
+
+
+def setup(tgt_dir, dpi = 300):
+    # Create the targ directory if it does not exist
+    os.makedirs(tgt_dir, exist_ok=True)
+
+    # Prepare matplotlib for use with LaTeX (makes it look less out of place, less Pythonesque)
+    params = {'text.usetex': True,
+              'font.size': 11,
+              'font.family': 'serif',
+              'figure.dpi': dpi,
+              }
+    plt.rcParams.update(params)
+
+
+def preprocessing(df: pd.DataFrame) -> tuple:
+    # Convert byts to MB
+    df[INPUT_SIZE_COL] /= 1e6
+    df[OUTPUT_SIZE_COL] /= 1e6
+
+    # Convert ns to s
+    df[COMPRESS_TIME_COL] /= 1e9
+
+    # Add labels to differentiate between algorithms with context lengths
+    def create_label(row):
+        compressor = row[ALGORITHM_COL]
+        return compressor if pd.isna(row[CONTEXT_COL]) else f"{compressor} ($L = {int(row[CONTEXT_COL])}$)"
+
+    df[LABEL_COL] = df.apply(create_label, axis=1)
+
+    # Add the compression ratio
+    df[RATE_COL] = df[INPUT_SIZE_COL] / df[OUTPUT_SIZE_COL]
+
+    # Identify all categories upfront
+    unique_labels = sorted(df[LABEL_COL].unique())
+    n_labels = len(unique_labels)
+
+    # Create fixed palette and marker mapping
+    palette_dict = dict(zip(unique_labels, sns.color_palette("Set2", n_labels)))
+    markers_dict = dict(zip(unique_labels, ['o', '^', 'v', 's', 'D', 'H', 'X']))
+
+    return df, unique_labels, palette_dict, markers_dict
+
+
+def main():
+    """Load the data and generate the plots."""
+    df = pd.read_csv("measurements.csv")
+
+    tgt_dir = "figures"
+    setup(tgt_dir, 300)
+    generate(*preprocessing(df), tgt_dir=tgt_dir)
+
+
+def old_results():
+    # read in the csv
+    df = pd.read_csv("compression_results.csv")
+
+    # Make compatible with new code
+    df[INPUT_SIZE_COL] = df['original_file_size']
+    df[OUTPUT_SIZE_COL] = df['compressed_file_size']
+    df['compressor'] = df['model_type']
+    df[CONTEXT_COL] = df['context_length']
+    #
+
+    df, unique_labels, palette_dict, markers_dict = preprocessing(df)
+
+    for dataset_type in df["dataset_type"].unique():
+        for model_type in df["model_type"].unique():
+            dataset_df = df[df["dataset_type"] == dataset_type]
+            model_df = dataset_df[dataset_df["model_type"] == model_type].copy()
+
+            # execution time
+            plt.figure(figsize=(4, 3))
+            model_df["original_file_size_mb"] = model_df["original_file_size"] / 1e6
+            model_df["compression_time_s"] = model_df["compression_time"] / 1e9
+            model_df["decompression_time_s"] = model_df["decompression_time"] / 1e9
+            # compression
+            sns.lineplot(
+                data=model_df,
+                x="original_file_size_mb",
+                y="compression_time_s",
+                hue="context_length",
+                palette="Set2",
+                markers=True,
+                legend="brief",
+                linestyle="-"
+            )
+            # decompression
+            sns.lineplot(
+                data=model_df,
+                x="original_file_size_mb",
+                y="decompression_time_s",
+                hue="context_length",
+                palette="Set2",
+                markers=True,
+                legend=False,
+                linestyle="--"
+            )
+            # plt.title(f"{model_type.capitalize()} compression and decompression time: {dataset_type}")
+            plt.xlabel("File size (MB)")
+            plt.ylabel("Time (log, s)")
+            plt.yscale("log")
+            plt.legend(
+                [f"{style}, {c_type}" for style, c_type in zip(["Solid", "Dashed"], ["compression", "decompression"])])
+            plt.tight_layout()
+            plt.savefig(f"./graphs/{model_type}_{dataset_type}_execution_time.png")
+
+            # compression ratio
+            plt.figure(figsize=(4, 3))
+            c256 = model_df[model_df["context_length"] == 256]
+            c128 = model_df[model_df["context_length"] == 128]
+
+            plt.plot(c256["original_file_size"] / 1e6, c256["compressed_file_size"] / 1e6, label="256")
+            plt.plot(c128["original_file_size"] / 1e6, c128["compressed_file_size"] / 1e6, label="128")
+            # plt.title(f"{model_type.capitalize()} compressed file evolution: {dataset_type}")
+            plt.xlabel("Original file size (MB)")
+            plt.ylabel("Compressed file size (MB)")
+            plt.ylim(0, df[df["model_type"] == model_type]["compressed_file_size"].max() / 1e6)
+            plt.legend(title="Context size")
+            plt.tight_layout()
+            plt.savefig(f"./graphs/{model_type}_{dataset_type}_compression_ratio.png")
+
+            if model_type == "cnn":
+
+                plt.figure()
+                for length, linestyle in [(128, '-'), (256, '--')]:
+                    # extrapolate execution time to larger files
+                    x = model_df[model_df["context_length"] == length]["original_file_size"] / 1e6
+                    y = model_df[model_df["context_length"] == length]["compression_time"]
+                    y_decom = model_df[model_df["context_length"] == length]["decompression_time"]
+
+                    b1, loga1 = np.polyfit(x, np.log(y), 1)
+                    b2, loga2 = np.polyfit(x, np.log(y_decom), 1)
+
+                    x_comp = np.linspace(0, 40, 1000)
+                    x_decomp = np.linspace(0, 40, 1000)
+                    a1 = np.exp(loga1)
+                    a2 = np.exp(loga2)
+
+                    plt.plot(
+                        x_comp, a1 * np.exp(x_comp),
+                        label=f"{length} compression",
+                        linestyle=linestyle
+                    )
+                    plt.plot(
+                        x_decomp, a2 * np.exp(x_decomp),
+                        label=f"{length} decompression",
+                        linestyle=linestyle
+                    )
+
+                plt.grid(True, alpha=0.3)
+                plt.legend()
+                plt.title(f"(Log-linear) Extrapolated execution time for CNN")
+                # plt.xscale('log')
+                plt.xlabel("File size (MB)")
+                plt.yscale('log')
+                plt.ylabel("Time (log, s)")
+                plt.tight_layout()
+                plt.savefig(f"./graphs/{model_type}_{dataset_type}_extrapolated_execution_time.png")
+
+    for model_type in df["model_type"].unique():
+        model_df = df[df["model_type"] == model_type]
+
+        plt.figure(figsize=(10, 4))
+        bar_height = 0.25
+        files = model_df["input_file_name"].unique()
+        y = np.arange(len(files))
+        c256 = model_df[model_df["context_length"] == 256]
+        c128 = model_df[model_df["context_length"] == 128]
+
+        plt.barh(
+            y - bar_height / 2,
+            c256["mse_loss"],
+            height=bar_height,
+            label="256",
+        )
+
+        plt.barh(
+            y + bar_height / 2,
+            c128["mse_loss"],
+            height=bar_height,
+            label="128",
+        )
+        plt.yticks(y, files, rotation=45, ha="right")
+        plt.title(f"MSE loss for different context lengths")
+        plt.xlabel("MSE loss")
+        plt.ylabel("Filename")
+        plt.legend()
+        plt.grid(True, alpha=0.3)
+        plt.tight_layout()
+        plt.savefig(f"./graphs/{model_type}_loss.png")
+
+
+def training_loss(df, loss) -> Figure:
+    plt.figure(figsize=(4, 3))
+
+    plt.plot(df['train_loss'], label="Training loss")
+    plt.plot(df['validation_loss'], label="Validation losses")
+
+    plt.xlabel("Epoch")
+    plt.ylabel(loss)
+
+    if loss == 'MSE Loss':
+        ylim = 0.01
+    else:
+        ylim = 6
+    plt.ylim(0, ylim)
+
+    plt.legend()
+    plt.tight_layout()
+
+    return plt.gcf()
+
+
+def make_training_graphs(models_dir, loss):
+    for csv in glob.glob(models_dir + '/*.csv'):
+        df = pd.read_csv(csv)
+        training_loss(df, loss).savefig(
+            csv.replace('.csv', '.png'),
+            bbox_inches='tight',
+        )
+
+
+if __name__ == "__main__":
+    main()
+    # old_results()
+    make_training_graphs('../models/autoencoder', 'MSE Loss')
+    make_training_graphs('../models/cnn', 'Cross Entropy Loss')

results/measurements.csv

@@ -0,0 +1,61 @@
+compressor,training_dataset,context_size,input_filename,input_size,compressed_size,compression_time,decompressed_size,decompression_time,mse_loss
+gzip,,,genome.fna,4699745,1424004,.681197994,4699745,.015465955,0.0
+gzip,,,genome_large.fna,23498433,7118154,3.384480370,23498433,.067414798,0.0
+gzip,,,genome_small.fna,1367,589,.001937446,1367,.001983156,0.0
+gzip,,,genome_xlarge.fna,46996793,14235842,6.775190783,46996793,.131633333,0.0
+gzip,,,genome_xsmall.fna,1043,475,.002007016,1043,.002012775,0.0
+gzip,,,genome_xxsmall.fna,800,393,.002071485,800,.001958195,0.0
+gzip,,,text_large.txt,12977332,4770044,.613155078,12977332,.043915520,0.0
+gzip,,,text_small.txt,1022,590,.002070305,1022,.001903226,0.0
+gzip,,,text.txt,6488666,2385264,.308393934,6488666,.023656716,0.0
+gzip,,,text_xlarge.txt,25954664,9539638,1.229028819,25954664,.085925486,0.0
+gzip,,,text_xsmall.txt,825,473,.002110205,825,.001980535,0.0
+gzip,,,text_xxsmall.txt,492,325,.001867306,492,.002114055,0.0
+LZ4,,,genome.fna,4699745,2655438,.012701161,4699745,.009190410,0.0
+LZ4,,,genome_large.fna,23498433,13275544,.020719873,23498433,.025022334,0.0
+LZ4,,,genome_small.fna,1367,1041,.001883076,1367,.002144425,0.0
+LZ4,,,genome_xlarge.fna,46996793,26551229,.031734579,46996793,.043495412,0.0
+LZ4,,,genome_xsmall.fna,1043,814,.001954316,1043,.002085566,0.0
+LZ4,,,genome_xxsmall.fna,800,641,.001893416,800,.001943666,0.0
+LZ4,,,text_large.txt,12977332,7879136,.017927300,12977332,.015065196,0.0
+LZ4,,,text_small.txt,1022,857,.001967146,1022,.002040285,0.0
+LZ4,,,text.txt,6488666,3939378,.014891266,6488666,.009709618,0.0
+LZ4,,,text_xlarge.txt,25954664,15758785,.023613977,25954664,.023486747,0.0
+LZ4,,,text_xsmall.txt,825,678,.001757717,825,.002191075,0.0
+LZ4,,,text_xxsmall.txt,492,438,.001869646,492,.002134206,0.0
+Autoencoder,genome,256,genome.fna,4699745,4259288,636915773,,27887947,83.62875366210938
+Autoencoder,genome,256,genome_large.fna,23498433,21295512,1932602305,,7778175,83.59369659423828
+Autoencoder,genome,256,genome_xlarge.fna,46996793,42591024,3850901316,,10996509,83.58621215820312
+Autoencoder,genome,128,genome.fna,4699745,9399552,390656081,,5804539,83.01229095458984
+Autoencoder,genome,128,genome_large.fna,23498433,46996992,1932561312,,10575739,83.01190185546875
+Autoencoder,genome,128,genome_xlarge.fna,46996793,93993728,3873777067,,18670984,83.00253295898438
+Autoencoder,enwik9,256,text.txt,6488666,6184668,551986635,,10536259,786.6799926757812
+Autoencoder,enwik9,256,text_large.txt,12977332,12369092,1065897991,,5763879,786.6173706054688
+Autoencoder,enwik9,256,text_xlarge.txt,25954664,24738184,2139223055,,8369164,786.6337890625
+Autoencoder,enwik9,128,text.txt,6488666,12774636,545577194,,20624030,206.2792510986328
+Autoencoder,enwik9,128,text_large.txt,12977332,25549272,1073396133,,60871642,206.24131774902344
+Autoencoder,enwik9,128,text_xlarge.txt,25954664,51098292,2145601924,,59481825,206.33023071289062
+CNN,genome,256,genome_small.fna,1367,1743,1029290599,,890595665,0.0
+CNN,genome,256,genome_xsmall.fna,1043,1343,686878467,,683701323,0.0
+CNN,genome,256,genome_xxsmall.fna,800,1038,531354486,,527072394,0.0
+CNN,genome,128,genome_small.fna,1367,1682,829554150,,851934528,0.0
+CNN,genome,128,genome_xsmall.fna,1043,1300,654742547,,637221301,0.0
+CNN,genome,128,genome_xxsmall.fna,800,1006,483840337,,488870786,0.0
+CNN,enwik9,256,text_small.txt,1022,1561,693378115,,671294958,0.0
+CNN,enwik9,256,text_xsmall.txt,825,1268,550333502,,550062973,0.0
+CNN,enwik9,256,text_xxsmall.txt,492,790,333745012,,332073466,0.0
+CNN,enwik9,128,text_small.txt,1022,1129,629310179,,621317553,0.0
+CNN,enwik9,128,text_xsmall.txt,825,882,504538600,,504907940,0.0
+CNN,enwik9,128,text_xxsmall.txt,492,571,305443187,,308964670,0.0
+Autoencoder,genome,256,genome_small.fna,1367,1392,237708436,,29378622,220.29188537597656
+Autoencoder,genome,256,genome_xsmall.fna,1043,928,4873340,,4381310,319.5350036621094
+Autoencoder,genome,256,genome_xxsmall.fna,800,488,4892560,,4176979,828.8658447265625
+Autoencoder,genome,128,genome_small.fna,1367,1160,5293080,,4488761,263.97027587890625
+Autoencoder,genome,128,genome_xsmall.fna,1043,2816,251659317,,5284661,120.52304077148438
+Autoencoder,genome,128,genome_xxsmall.fna,800,2016,5005880,,4400159,231.42955017089844
+Autoencoder,enwik9,256,text_small.txt,1022,2304,152590538,,4432469,133.8379669189453
+Autoencoder,enwik9,256,text_xsmall.txt,825,976,6699780,,22065927,746.8033447265625
+Autoencoder,enwik9,256,text_xxsmall.txt,492,1764,5024449,,4180100,236.55636596679688
+Autoencoder,enwik9,128,text_small.txt,1022,1792,153622653,,4532139,159.2862548828125
+Autoencoder,enwik9,128,text_xsmall.txt,825,976,3715750,,3665629,787.5345458984375
+Autoencoder,enwik9,128,text_xxsmall.txt,492,1008,4856779,,4145850,237.20529174804688

uv.lock

@@ -1385,6 +1385,18 @@ wheels = [
     { 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" },
 ]
 
+[[package]]
+name = "numpy-typing-compat"
+version = "20251206.2.3"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "numpy" },
+]
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     { name = "pandas-stubs" },
+    { name = "scipy" },
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     { name = "seaborn" },
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