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K-Means Clustering with Tensorflow
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import numpy as np | |
import pandas as pd | |
import tensorflow as tf | |
import matplotlib.pyplot as plt | |
import seaborn as sns | |
#uncomment below if using Jupyter | |
#%config InlineBackend.figure_format = 'retina' | |
# get data | |
df = pd.read_csv('../some/data/path') | |
def k_means_clustering(df_col_1, df_col_2, clusters, steps=100): | |
''' | |
Takes in two dataframe columns and outputs plot of clusters. | |
''' | |
vec_vals = [] | |
for i in range(len(df_col_1)): | |
a = df_col_1[i] | |
b = df_col_2[i] | |
vec_vals.append([a, b]) | |
v_vals = np.array(vec_vals) | |
np.random.shuffle(v_vals) | |
sess = tf.Session() | |
k = clusters | |
points = v_vals | |
data = tf.constant(points) | |
# random initial centroids (points shuffled above) | |
centroids = tf.Variable(data[:k, :]) | |
# add k dim to data and n dim to centroids to make matrices compatible | |
# for array operations instead of loops | |
data_expanded = tf.expand_dims(data, 0) | |
centroids_expanded = tf.expand_dims(centroids, 1) | |
# computes squared Euclidean distance between every point and every centroid | |
# and get closest centroid for each point | |
allocations = tf.argmin(tf.reduce_sum(tf.square(data_expanded - centroids_expanded), 2), 0) | |
sess.run(tf.global_variables_initializer()) | |
c = 0 # index of centroid | |
tf.equal(allocations, c) | |
tf.gather(data, tf.where(tf.equal(allocations, c))) | |
means = tf.concat( | |
[tf.reduce_mean( | |
tf.gather(data, | |
tf.where(tf.equal(allocations, c))), 0) for c in range(k)], 0) | |
update_centroids = tf.assign(centroids, means) | |
for step in range(steps): | |
_, centroid_values, allocation_values = sess.run([update_centroids, centroids, allocations]) | |
clusters_df = pd.DataFrame({df_col_1.name: points[:,0], df_col_2.name: points[:,1], "cluster": allocation_values}) | |
sns.lmplot(df_col_1.name, df_col_2.name, data=clusters_df, fit_reg=False, size=6, hue="cluster") | |
plt.show() | |
k_means_clustering(df['col_name_a'], df['col_name_b'], 3) |
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