helpers2.py

import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
from sklearn.cluster import KMeans
from sklearn.datasets import make_blobs

def simulate_data(n = 500, features = 10, centroids = 3):
    '''
    Simulates n data points, each with number of features equal to features, with a number of centers equal to centroids
    INPUT (defaults)
        n = number of rows (500)
        features = number of columns (10)
        centroids = number of centers (3)
    Output
        dataset = a dataset with the the specified characteristics
    '''
    dataset, y = make_blobs(n_samples=n, n_features=features, centers=centroids, random_state=42)

    return dataset

def plot_data(data, labels):
    '''
    Plot data with colors associated with labels
    '''
    fig = plt.figure();
    ax = Axes3D(fig)
    ax.scatter(data[:, 0], data[:, 1], data[:, 2], c=labels, cmap='tab10');

data = simulate_data(200, 5, 4)

def get_kmeans_score(data, center):
    '''
    returns the kmeans score regarding SSE for points to centers
    INPUT:
        data - the dataset you want to fit kmeans to
        center - the number of centers you want (the k value)
    OUTPUT:
        score - the SSE score for the kmeans model fit to the data
    '''
    #instantiate kmeans
    kmeans = KMeans(n_clusters=center)

    # Then fit the model to your data using the fit method
    model = kmeans.fit(data)

    # Obtain a score related to the model fit
    score = np.abs(model.score(data))

    return score

def fit_mods():
    scores = []
    centers = list(range(1,11))

    for center in centers:
        scores.append(get_kmeans_score(data, center))

    return centers, scores