79 lines
3.0 KiB
Python
79 lines
3.0 KiB
Python
from sklearn.decomposition import PCA, KernelPCA
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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_subcomment']
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biber_vecs = format_df_data(biber_vec_df)
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#handoff to PCA model
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'''
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pca_trial = PCA()
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biber_vecs_pca_trial = pca_trial.fit_transform(biber_vecs)
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explained_variance = pca_trial.explained_variance_ratio_
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cumulative_variance = np.cumsum(explained_variance)
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n_components = np.argmax(cumulative_variance >= 0.90) + 1
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print(f"Number of PCs explaining 90% variance: {n_components}")
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'''
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pca = PCA(n_components=18)
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biber_vecs_pca = pca.fit_transform(biber_vecs)
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selected_axis = "AuthorWMFAffil"
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component_variances = np.var(biber_vecs_pca, axis=0)
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print("Variance of each PCA component:", component_variances)
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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[selected_axis])
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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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selected_axis: biber_vec_df[selected_axis].astype(str),
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"source":biber_vec_df['source'].astype(str),
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"phase":biber_vec_df['phase'].astype(str)
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})
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g = sns.FacetGrid(plot_df, col="source", row="phase", hue=selected_axis, palette="tab10", height=4, sharex=False, sharey=False)
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g.map_dataframe(sns.scatterplot, x="PC1", y="PC2", alpha=0.7, s=40)
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g.add_legend(title=selected_axis)
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g.set_axis_labels("PC1", "PC2")
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g.fig.subplots_adjust(top=0.9)
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g.fig.suptitle(f"PCA by {selected_axis}, faceted by source")
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#plt.savefig("090225_biber_pca_plot.png", dpi=300)
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'''
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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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selected_axis: biber_vec_df[selected_axis].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="source",
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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=selected_axis, bbox_to_anchor=(1.05, 1), loc=2)
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'''
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g.fig.tight_layout()
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g.savefig(f"subcomment_{selected_axis}_090425_biber_pca.png", dpi=300)
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plt.show()
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