[Python] Python: loading a kmeans training dataset and using it to predict a new dataset

I have a huge amount of data which I would like to run a kmean classification on. The dataset are so big, that I cannot load the files into memory.

My idea is to run the classifiation on some part of the dataset like a training dataset, and then apply the calssification to the rest of the dataset part by part.

import pandas as pd
import pickle
from sklearn.cluster import KMeans

frames = [pd.read_hdf(fin) for fin in ifiles]
data = pd.concat(frames, ignore_index=True, axis=0)
data.dropna(inplace=True)

k = 12
x = pd.concat(data['A'], data['B'], data['C'], axis=1, keys=['A','B','C'])
model = KMeans(n_clusters=k, random_state=0, n_jobs = -2)
model.fit(x)

pickle.dump(model, open(filename, 'wb'))


x looks like this:

array([[-2.26732099, 0.24895614, 2.34840191],
[-2.26732099, 0.22270912, 1.88942378],
[-1.99246557, 0.04154312, 2.63458941],
...,
[-4.29596287, 1.97036309, -0.22767511],
[-4.26055474, 1.72347591, -0.18185197],
[-4.15980382, 1.73176239, -0.30781225]])


The model look like this:

KMeans(algorithm='auto', copy_x=True, init='k-means++', max_iter=300,
n_clusters=12, n_init=10, n_jobs=-2, precompute_distances='auto',
random_state=0, tol=0.0001, verbose=0)


A plot of two of the model parameters color coded with the model looks like this:

Now I want to load the model and use it for predicting. As a test example I have loaded the same data (not shown here), and trying to predict the new dataset.

modelnew = pickle.load(open('test.pkl', 'rb'))
modelnew.predict(x)


The result:

This data does clearly not cluster. What am I missing? Do I need to fix the model parameters in some way?

I have tried to make an example of a test and a train data set. Here it also goes wrong. There is clearly something I am missing:

## Splitting data in a test and train data set
sample_train, sample_test = train_test_split(x, test_size=0.50)

k = 12 ## Setting number of clusters
model = KMeans(n_clusters=k, random_state=0, n_jobs = -2) ## Kmeans model
train = model.fit(sample_train) ## Fitting the training data
model.predict(sample_test) # Predicting the test data

centroids = model.cluster_centers_
labels = model.labels_

## Figures
cmap_model = np.array(['red', 'lime', 'black', 'green', 'orange', 'blue', 'gray', 'magenta', 'cyan', 'purple', 'pink', 'lightblue', 'brown', 'yellow'])
plt.figure()
plt.scatter(sample_train[:,0], sample_train[:,1], c=cmap_model[train.labels_], s=10, edgecolors='none')
plt.scatter(centroids[:, 0], centroids[:, 1], c=cmap_model, marker = "x", s=150, linewidths = 5, zorder = 10)

plt.figure()
plt.scatter(sample_test[:,0], sample_test[:,1], c=cmap_model[labels], s=10, edgecolors='none')
plt.scatter(centroids[:, 0], centroids[:, 1], c=cmap_model, marker = "x", s=150, linewidths = 5, zorder = 10)
plt.show()


Train data:

Test data:

Continue reading...