54 lines
1.8 KiB
Python
54 lines
1.8 KiB
Python
from sklearn.decomposition import PCA
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from sklearn.preprocessing import LabelEncoder
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import pandas as pd
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#import torch
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import numpy as np
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import pandas as pd
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import matplotlib.pyplot as plt
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import seaborn as sns
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def format_df_data(df):
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#this accounts for the somewhat idiosyncratic way that I saved my data
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normalized_cols = [col for col in df.columns if col.startswith('normalized_')]
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x = df[normalized_cols].astype(float).values
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#x = np.vstack(df['features'].values)
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return x
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if __name__ == "__main__":
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biber_vec_df = pd.read_csv("/home/nws8519/git/mw-lifecycle-analysis/p2/quest/072525_pp_biberplus_labels.csv", low_memory=False)
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biber_vec_df = biber_vec_df[biber_vec_df['comment_type'] == 'task_description']
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biber_vecs = format_df_data(biber_vec_df)
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#handoff to PCA model
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pca = PCA(2)
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biber_vecs_pca = pca.fit_transform(biber_vecs)
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#first looking at comment_type
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le = LabelEncoder()
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colors = le.fit_transform(biber_vec_df['AuthorWMFAffil'])
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plt.scatter(biber_vecs_pca[:, 0], biber_vecs_pca[:, 1],
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c=colors, edgecolor='none', alpha=0.5, cmap="viridis")
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plt.xlabel('component 1')
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plt.ylabel('component 2')
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plt.colorbar()
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plt.savefig("090225_biber_pca_plot.png", dpi=300)
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plot_df = pd.DataFrame({
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"PC1": biber_vecs_pca[:, 0],
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"PC2": biber_vecs_pca[:, 1],
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"AuthorWMFAffil": biber_vec_df["AuthorWMFAffil"].astype(str)
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})
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plt.figure(figsize=(8,6))
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sns.scatterplot(
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data=plot_df, x="PC1", y="PC2", hue="AuthorWMFAffil",
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palette="tab10", s=40, alpha=0.7, edgecolor=None
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)
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plt.xlabel('component 1')
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plt.ylabel('component 2')
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plt.legend(title='AuthorWMFAffil', bbox_to_anchor=(1.05, 1), loc=2)
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plt.tight_layout()
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plt.savefig("biber_pca_affil.png", dpi=300)
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plt.show()
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