129 lines
5.3 KiB
Python
129 lines
5.3 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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import pickle
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# List of the 96 features that Neurobiber can predict
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BIBER_FEATURES = [
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"BIN_QUAN","BIN_QUPR","BIN_AMP","BIN_PASS","BIN_XX0","BIN_JJ",
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"BIN_BEMA","BIN_CAUS","BIN_CONC","BIN_COND","BIN_CONJ","BIN_CONT",
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"BIN_DPAR","BIN_DWNT","BIN_EX","BIN_FPP1","BIN_GER","BIN_RB",
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"BIN_PIN","BIN_INPR","BIN_TO","BIN_NEMD","BIN_OSUB","BIN_PASTP",
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"BIN_VBD","BIN_PHC","BIN_PIRE","BIN_PLACE","BIN_POMD","BIN_PRMD",
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"BIN_WZPRES","BIN_VPRT","BIN_PRIV","BIN_PIT","BIN_PUBV","BIN_SPP2",
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"BIN_SMP","BIN_SERE","BIN_STPR","BIN_SUAV","BIN_SYNE","BIN_TPP3",
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"BIN_TIME","BIN_NOMZ","BIN_BYPA","BIN_PRED","BIN_TOBJ","BIN_TSUB",
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"BIN_THVC","BIN_NN","BIN_DEMP","BIN_DEMO","BIN_WHQU","BIN_EMPH",
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"BIN_HDG","BIN_WZPAST","BIN_THAC","BIN_PEAS","BIN_ANDC","BIN_PRESP",
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"BIN_PROD","BIN_SPAU","BIN_SPIN","BIN_THATD","BIN_WHOBJ","BIN_WHSUB",
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"BIN_WHCL","BIN_ART","BIN_AUXB","BIN_CAP","BIN_SCONJ","BIN_CCONJ",
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"BIN_DET","BIN_EMOJ","BIN_EMOT","BIN_EXCL","BIN_HASH","BIN_INF",
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"BIN_UH","BIN_NUM","BIN_LAUGH","BIN_PRP","BIN_PREP","BIN_NNP",
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"BIN_QUES","BIN_QUOT","BIN_AT","BIN_SBJP","BIN_URL","BIN_WH",
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"BIN_INDA","BIN_ACCU","BIN_PGAS","BIN_CMADJ","BIN_SPADJ","BIN_X"
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]
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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/analysis_data/092925_unified_phab.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_vec_df = biber_vec_df[biber_vec_df['AuthorPHID'] != "PHID-USER-idceizaw6elwiwm5xshb"]
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#biber_vec_df = biber_vec_df[biber_vec_df['comment_text'] != 'nan']
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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_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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argmax_components = np.argmax(cumulative_variance >= 0.90) + 1
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print(f"Number of PCs explaining 90% variance: {argmax_components}")
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pca = PCA(n_components=argmax_components)
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biber_vecs_pca = pca.fit_transform(biber_vecs)
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with open('100125_subcomment_pca.pkl', 'wb') as f:
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pickle.dump(pca, f)
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selected_axis = "closed_relevance"
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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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for i, component in enumerate(pca.components_):
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print(f"PC{i+1}:")
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indices = np.argsort(np.abs(component))[::-1]
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for idx in indices[:10]: # Top 10
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print(f" {BIBER_FEATURES[idx]}: {component[idx]:.3f}")
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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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pc_dict = {f"PC{i+1}": biber_vecs_pca[:, i] for i in range(18)}
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pc_dict[selected_axis] = biber_vec_df[selected_axis].astype(str)
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pc_dict["source"] = biber_vec_df['source'].astype(str)
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pc_dict["phase"] = biber_vec_df['phase'].astype(str)
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pc_dict["text"] = biber_vec_df['comment_text'].astype(str)
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pc_dict['id'] = biber_vec_df['id']
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pc_dict['week_index'] = biber_vec_df['week_index']
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pc_dict['priority'] = biber_vec_df['priority']
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pc_dict['closed_relevance'] = biber_vec_df['closed_relevance']
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pc_dict['TaskPHID'] = biber_vec_df['TaskPHID']
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pc_dict['AuthorPHID'] = biber_vec_df['AuthorPHID']
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pc_dict['date_created'] = biber_vec_df['date_created']
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plot_df = pd.DataFrame(pc_dict)
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plot_df.to_csv("100125_subcomment_PCA_df.csv", index=False)
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print("Top 10 PC1 values:")
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print(plot_df.nlargest(10, "PC1"))
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print("\nBottom 10 PC1 values:")
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print(plot_df.nsmallest(10, "PC1"))
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print("Top 10 PC2 values:")
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print(plot_df.nlargest(10, "PC2"))
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print("\nBottom 10 PC2 values:")
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print(plot_df.nsmallest(10, "PC2"))
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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}_100125_biber_pca_final.png", dpi=300)
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#plt.show()
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