feat: Improved plots
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1 changed files with 98 additions and 25 deletions
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@ -1,3 +1,4 @@
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import glob
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import os
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import matplotlib.pyplot as plt
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@ -67,7 +68,32 @@ def compression_ratios(df: pd.DataFrame, unique_labels, palette_dict) -> Figure:
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plt.yticks(rotation=45, ha="right")
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ax.grid(True)
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ax.grid(True, alpha=0.3)
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return plt.gcf()
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def compression_ratio_v_compression_time(df: pd.DataFrame, unique_labels, palette_dict, markers_dict) -> Figure:
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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=COMPRESS_TIME_COL,
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hue=LABEL_COL,
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hue_order=unique_labels,
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palette=palette_dict,
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style=LABEL_COL,
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style_order=unique_labels,
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markers=markers_dict,
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)
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plt.legend(title='Compressor')
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plt.xlabel('Compression ratio')
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plt.ylabel('Compression time (s)')
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plt.grid(True, alpha=0.3)
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return plt.gcf()
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@ -120,25 +146,35 @@ def filesize_v_mse(df: pd.DataFrame) -> Figure:
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size = row[INPUT_SIZE_COL]
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return f"{filename} ({size:.4f} MB)"
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df['input_filename_size'] = df.apply(filename_and_size, axis=1)
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def size(row):
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full_name = row['input_filename']
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# Strip prefix
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size_name = full_name.lstrip('text').lstrip('genome').rstrip('txt').rstrip('fna')
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size_name = f"*{size_name}*"
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return size_name
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fig, ax = plt.subplots()
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sns.barplot(
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df['input_filename_size'] = df.apply(filename_and_size, axis=1)
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df['input_size'] = df.apply(size, axis=1)
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g = sns.catplot(
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data=df,
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y='input_filename',
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kind="bar",
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x=DISTORTION_COL,
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y='input_size',
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col='training_dataset',
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hue=CONTEXT_COL,
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ax=ax,
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palette='Set2'
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palette='Set2',
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height=5,
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aspect=0.6
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)
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plt.title('MSE for autoencoder')
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plt.xlabel('MSE')
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plt.ylabel('Filename')
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plt.yticks(rotation=45, ha="right")
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plt.legend(title='Context size')
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g.set_axis_labels("MSE Loss", "Filename")
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g.set_titles("Autoencoder trained on {col_name}")
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plt.grid(True)
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# plt.title('MSE for autoencoder')
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# plt.yticks(rotation=45, ha="right")
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# plt.legend(title='Context size')
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g.tight_layout()
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return plt.gcf()
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@ -164,7 +200,7 @@ def mse_losses(df: pd.DataFrame, unique_labels, palette_dict) -> Figure:
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plt.yticks(rotation=45, ha="right")
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ax.grid(True)
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ax.grid(True, alpha=0.3)
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return plt.gcf()
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@ -238,8 +274,8 @@ def split_graph(
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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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ax_left.grid(True, alpha=0.3)
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ax_right.grid(True, alpha=0.3)
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plt.tight_layout()
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return f, ax_left, ax_right
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@ -247,7 +283,7 @@ def split_graph(
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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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tgt_dir: str
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) -> None:
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"""Generate all the plots"""
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# Make plots
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@ -270,6 +306,11 @@ def generate(
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os.path.join(tgt_dir, 'compression_ratios.png'),
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bbox_inches='tight'
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)
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compression_ratio_v_compression_time(df, unique_labels, palette_dict, markers_dict).savefig(
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os.path.join(tgt_dir, 'compression_ratio_v_compression_time.png'),
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bbox_inches='tight'
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)
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filesize_v_mse(df).savefig(
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os.path.join(tgt_dir, 'filesize_mse.png'),
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bbox_inches='tight'
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@ -280,7 +321,7 @@ def generate(
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)
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def setup(tgt_dir):
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def setup(tgt_dir, dpi = 300):
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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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@ -288,7 +329,7 @@ def setup(tgt_dir):
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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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'figure.dpi': 300,
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'figure.dpi': dpi,
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}
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plt.rcParams.update(params)
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@ -327,8 +368,8 @@ def main():
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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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setup(tgt_dir, 300)
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generate(*preprocessing(df), tgt_dir=tgt_dir)
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def old_results():
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@ -395,7 +436,7 @@ def old_results():
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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.ylim(0, model_df["compressed_file_size"].max() / 1e6)
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plt.ylim(0, df[df["model_type"] == model_type]["compressed_file_size"].max() / 1e6)
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plt.legend(title="Context size")
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plt.tight_layout()
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plt.savefig(f"./graphs/{model_type}_{dataset_type}_compression_ratio.png")
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@ -428,7 +469,7 @@ def old_results():
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linestyle=linestyle
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)
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plt.grid(True)
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plt.grid(True, alpha=0.3)
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plt.legend()
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plt.title(f"(Log-linear) Extrapolated execution time for CNN")
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# plt.xscale('log')
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@ -466,11 +507,43 @@ def old_results():
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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.grid(True)
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plt.grid(True, alpha=0.3)
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plt.tight_layout()
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plt.savefig(f"./graphs/{model_type}_loss.png")
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def training_loss(df, loss) -> Figure:
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plt.figure(figsize=(4, 3))
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plt.plot(df['train_loss'], label="Training loss")
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plt.plot(df['validation_loss'], label="Validation losses")
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plt.xlabel("Epoch")
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plt.ylabel(loss)
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if loss == 'MSE Loss':
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ylim = 0.01
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else:
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ylim = 6
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plt.ylim(0, ylim)
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plt.legend()
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plt.tight_layout()
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return plt.gcf()
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def make_training_graphs(models_dir, loss):
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for csv in glob.glob(models_dir + '/*.csv'):
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df = pd.read_csv(csv)
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training_loss(df, loss).savefig(
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csv.replace('.csv', '.png'),
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bbox_inches='tight',
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)
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if __name__ == "__main__":
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main()
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old_results()
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# old_results()
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make_training_graphs('../models/autoencoder', 'MSE Loss')
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make_training_graphs('../models/cnn', 'Cross Entropy Loss')
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