import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns

# Properly structured data for all 4 datasets
data = {
    "Dataset": ["Brain tumor"]*9 + ["Brain tumor*"]*9 + ["SRBCT"]*9 + ["SRBCT*"]*9,
    "Model": ["OVA SVM", "BDT SVM", "K-SVCR", "TKSVC", "LSK-SVCR", "KWMSVM", "RSSVM", "SRSSVM", "Our model"]*4,
    "Linear Kernel": [
        # Letter Recognition (non-starred)
        0.9145, 0.9332, 0.9462, 0.9611, 0.9744, 0.9597, 0.9774, 0.9695, 0.9902,
        # Letter Recognition (starred)
        0.9145, 0.9332, 0.9462, 0.9611, 0.9744, 0.9597, 0.9774, 0.9695, 0.9788,
        # Yeast (non-starred)
        0.9225, 0.9374, 0.9281, 0.9501, 0.9719, 0.9623, 0.9713, 0.9727, 0.9936,
        # Yeast (starred)
        0.9225, 0.9374, 0.9281, 0.9501, 0.9719, 0.9623, 0.9713, 0.9727, 0.9841
    ],
    "Polynomial Kernel": [
        # Letter Recognition (non-starred)
        0.9276, 0.9281, 0.9497, 0.9571, 0.9819, 0.9702, 0.9621, 0.9743, 0.9976,
        # Letter Recognition (starred)
        0.9276, 0.9281, 0.9497, 0.9571, 0.9819, 0.9702, 0.9621, 0.9743, 0.9813,
        # Yeast (non-starred)
        0.9189, 0.9311, 0.9375, 0.9442, 0.9683, 0.9591, 0.9857, 0.9734, 0.9994,
        # Yeast (starred)
        0.9189, 0.9311, 0.9375, 0.9442, 0.9683, 0.9591, 0.9857, 0.9734, 0.9873
    ],
    "RBF Kernel": [
        # Letter Recognition (non-starred)
        0.9197, 0.9332, 0.9503, 0.9648, 0.9764, 0.9627, 0.9709, 0.9725, 0.9943,
        # Letter Recognition (starred)
        0.9197, 0.9332, 0.9503, 0.9648, 0.9764, 0.9627, 0.9709, 0.9725, 0.9853,
        # Yeast (non-starred)
        0.9286, 0.9297, 0.9361, 0.9543, 0.9751, 0.9654, 0.9836, 0.9888, 0.9985,
        # Yeast (starred)
        0.9286, 0.9297, 0.9361, 0.9543, 0.9751, 0.9654, 0.9836, 0.9888, 0.9907
    ]
}

df = pd.DataFrame(data)

# Melt the DataFrame for seaborn
df_melted = df.melt(id_vars=["Dataset", "Model"], 
                    var_name="Kernel", 
                    value_name="Accuracy")

# Define consistent dash styles for all models
dash_styles = {model: (4, 2) for model in df["Model"].unique()}

# Create plots for each dataset
for dataset in df["Dataset"].unique():
    plt.figure(figsize=(10, 6))
    subset = df_melted[df_melted["Dataset"] == dataset]
    
    sns.lineplot(
        x="Kernel", 
        y="Accuracy", 
        hue="Model", 
        style="Model",
        data=subset,
        markers=True,
        dashes=dash_styles,
        palette="tab10",
        linewidth=3.5,
        markersize=12,
        ci=None
    )
    
    plt.title(f"Accuracy Comparison for {dataset.replace('*', '⋆')}", fontsize=15, fontweight="bold")
    plt.xlabel("Kernel Type", fontsize=14)
    plt.ylabel("Accuracy", fontsize=14)
    plt.ylim(0.9, 1.02)
    plt.legend(title="Model", bbox_to_anchor=(1.05, 1), loc='upper left')
    plt.tight_layout()
    plt.show()<pre>An Error occurred while handling another error:
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