Sklearn.KMeans() : Get class centroid labels and reference to a dataset

Question

Sci-Kit learn Kmeans and PCA dimensionality reduction

I have a dataset, 2M rows by 7 columns, with different measurements of home power consumption with a date for each measurement.

• date,
• Global_active_power,
• Global_reactive_power,
• Voltage,
• Global_intensity,
• Sub_metering_1,
• Sub_metering_2,
• Sub_metering_3

I put my dataset into a pandas dataframe, selecting all columns but the date column, then perform cross validation split.

import pandas as pd
from sklearn.cross_validation import train_test_split

data = pd.read_csv('household_power_consumption.txt', delimiter=';')
power_consumption = data.iloc[0:, 2:9].dropna()
pc_toarray = power_consumption.values
hpc_fit, hpc_fit1 = train_test_split(pc_toarray, train_size=.01)
power_consumption.head()

I use K-means classification followed by PCA dimensionality reduction to display.

from sklearn.cluster import KMeans
import matplotlib.pyplot as plt
import numpy as np
from sklearn.decomposition import PCA

hpc = PCA(n_components=2).fit_transform(hpc_fit)
k_means = KMeans()
k_means.fit(hpc)

x_min, x_max = hpc[:, 0].min() - 5, hpc[:, 0].max() - 1
y_min, y_max = hpc[:, 1].min(), hpc[:, 1].max() + 5
xx, yy = np.meshgrid(np.arange(x_min, x_max, .02), np.arange(y_min, y_max, .02))
Z = k_means.predict(np.c_[xx.ravel(), yy.ravel()])
Z = Z.reshape(xx.shape)

plt.figure(1)
plt.clf()
plt.imshow(Z, interpolation='nearest',
          extent=(xx.min(), xx.max(), yy.min(), yy.max()),
          cmap=plt.cm.Paired,
          aspect='auto', origin='lower')

plt.plot(hpc[:, 0], hpc[:, 1], 'k.', markersize=4)
centroids = k_means.cluster_centers_
inert = k_means.inertia_
plt.scatter(centroids[:, 0], centroids[:, 1],
           marker='x', s=169, linewidths=3,
           color='w', zorder=8)
plt.xlim(x_min, x_max)
plt.ylim(y_min, y_max)
plt.xticks(())
plt.yticks(())
plt.show()

Now I would like to find out which rows fell under a given class then which dates fell under a given class.

• Is there any way to relate the points on the graph to an index in my dataset, after PCA?
• Some method I don't know of?
• Or is my approach fundamentally flawed?
• Any recommendations?

I am fairly new to this field and am trying to read through lots of code, this is a compilation of several examples I've seen documented .

My goal is to classify the data and then get the dates that fall under a class.

Thank You

Answer 1

KMeans().predict(X) ..docs here

---

Predict the closest cluster each sample in X belongs to.

In the vector quantization literature, cluster_centers_ is called the code book and each value returned by predict is the index of the closest code in the code book.

Parameters: (New data to predict)

X : {array-like, sparse matrix}, shape = [n_samples, n_features]

Returns: (Index of the cluster each sample belongs to)  

labels : array, shape [n_samples,]

The problem I with the code you submitted is the use of

train_test_split()

which returns two arrays of random rows in your data-set, effectively ruining your dataset order making it difficult to correlate the labels returned from KMeans classification to sequential dates in your data set.

---

Here's an example:

import pandas as pd
import numpy as np
from sklearn.cluster import KMeans

#read data into pandas dataframe
df = pd.read_csv('household_power_consumption.txt', delimiter=';')

#convert merge date and time colums and convert to datetime objects
df['Datetime'] = pd.to_datetime(df['Date'] + ' ' + df['Time'])
df.set_index(pd.DatetimeIndex(df['Datetime'],inplace=True))
df.drop(['Date','Time'], axis=1, inplace=True)

#put last column first
cols = df.columns.tolist()
cols = cols[-1:] + cols[:-1]
df = df[cols]
df = df.dropna()

#convert dataframe to data array and removes date column not to be processed, 
sliced = df.iloc[0:, 1:8].dropna()
hpc = sliced.values

k_means = KMeans()
k_means.fit(hpc)

# array of indexes corresponding to classes around centroids, in the order of your dataset
classified_data = k_means.labels_

#copy dataframe (may be memory intensive but just for illustration)
df_processed = df.copy()
df_processed['Cluster Class'] = pd.Series(classified_data, index=df_processed.index)

---

• Now you can see your result matched with your data-set on the right side.
• Now that it's classified, it's up to you to derive meaning.
• This is just a good overall example of how it can be used, from start to finish.
• Displaying your result, look at PCA or making other graphs dependent on class.