batman

iris_flowers/visualize.py

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+import matplotlib.pyplot as plt
+import seaborn as sns
+from sklearn import datasets
+import pandas as pd
+import os
+
+def load_data():
+    """Loads the Iris dataset into a Pandas DataFrame."""
+    iris = datasets.load_iris(as_frame=True)
+    df = iris.frame
+    # Map target integers to target names for better labeling
+    df['species'] = df['target'].map(lambda x: iris.target_names[x])
+    # Drop the numeric target column as we have the species name now
+    df = df.drop(columns=['target'])
+    return df
+
+def setup_plots_dir():
+    """Creates the plots directory if it doesn't exist."""
+    if not os.path.exists('plots'):
+        os.makedirs('plots')
+
+def plot_pairplot(df):
+    """Generates and saves a pairplot."""
+    plt.figure(figsize=(10, 8))
+    sns.pairplot(df, hue='species')
+    plt.savefig('plots/pairplot.png')
+    plt.close()
+    print("Saved plots/pairplot.png")
+
+def plot_correlation_heatmap(df):
+    """Generates and saves a correlation heatmap."""
+    plt.figure(figsize=(8, 6))
+    # Select only numeric columns for correlation
+    numeric_df = df.select_dtypes(include=['float64', 'int64'])
+    corr = numeric_df.corr()
+    sns.heatmap(corr, annot=True, cmap='coolwarm', fmt=".2f")
+    plt.title('Feature Correlation Heatmap')
+    plt.tight_layout()
+    plt.savefig('plots/heatmap.png')
+    plt.close()
+    print("Saved plots/heatmap.png")
+
+def plot_histograms(df):
+    """Generates and saves histograms for each feature."""
+    df.hist(figsize=(10, 8), bins=20)
+    plt.suptitle('Feature Distributions')
+    plt.tight_layout()
+    plt.savefig('plots/histograms.png')
+    plt.close()
+    print("Saved plots/histograms.png")
+
+def main():
+    setup_plots_dir()
+    df = load_data()
+    
+    print("Generating plots...")
+    plot_pairplot(df)
+    plot_correlation_heatmap(df)
+    plot_histograms(df)
+    print("Done!")
+
+if __name__ == "__main__":
+    main()