Playing around with sci-kit learn on donations data

analysis.py

#!/usr/bin/env python3

import pdb

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from sklearn.decomposition import PCA
from sklearn.cluster import KMeans
from mpl_toolkits.mplot3d import Axes3D

# a matrix where the rows are donors and columns are donees, and the values
# are the total amount given by donor to donee
df = pd.read_csv("temp.tsv", sep="\t").pivot(index="donor", columns="donee", values="sum_amount").fillna(0.0)
df_norm = ((df - df.mean(0)) / df.std(0)).fillna(0.0)

kmeans = KMeans(n_clusters=10, random_state=0).fit(df)

for i, label in enumerate(kmeans.labels_):
    if label == 2:
        print(df.index[i])

# pca = PCA(n_components=3)
# pca.fit(df_norm)

# df_trans = pca.transform(df_norm)

# fig = plt.figure(1)
# ax = Axes3D(fig)
# ax.scatter(df_trans[:, 0], df_trans[:, 1], df_trans[:, 2])

# pdb.set_trace()

temp.tsv