53 lines
2.6 KiB
Python
53 lines
2.6 KiB
Python
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directory = '/home/nuno/Documents/Jobs/IDInsight'
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import pandas as pd
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import numpy as np
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import matplotlib.pyplot as plt
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import seaborn as sns
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# Import the algorithms
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from sklearn.cluster import KMeans
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from sklearn.cluster import AffinityPropagation
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from sklearn.cluster import MeanShift
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from sklearn.cluster import SpectralClustering
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from sklearn.cluster import AgglomerativeClustering
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from sklearn.cluster import DBSCAN
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from sklearn.cluster import Birch
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from sklearn.mixture import GaussianMixture
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## Install the dataframe
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insuranceDataFrame = pd.read_csv(directory + '/insurance_clean_continuous.csv')
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## Gather the algorithms
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clusterings = [KMeans(n_clusters=3, random_state=0).fit(insuranceDataFrame), AffinityPropagation(affinity='euclidean', convergence_iter=100, copy=True, damping=.9, max_iter=200, preference=None, verbose=False).fit(insuranceDataFrame), MeanShift().fit(insuranceDataFrame), SpectralClustering(n_clusters=3, affinity='nearest_neighbors', n_neighbors = 20, random_state=0).fit(insuranceDataFrame), AgglomerativeClustering(n_clusters=3).fit(insuranceDataFrame), DBSCAN(eps=500, min_samples=3).fit(insuranceDataFrame), Birch(branching_factor=50, n_clusters=3, threshold=0.5, compute_labels=True).fit(insuranceDataFrame)]
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# This takes a while
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names = ["KMeans", "AffinityPropagation", "MeanShift", "SpectralClustering", "AgglomerativeClustering", "DBSCAN", "Birch"]
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assert(len(names)==len(clusterings))
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## Plot the plots
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sns.set()
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## This produces *a lot* of plots!
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for k in range(len(names)):
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insuranceDataFrame["cluster"] = clusterings[k].labels_
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n= len(np.unique(clusterings[k].labels_))
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for variable in insuranceDataFrame.columns:
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sns_plot = sns.relplot(x="charges", y=variable, hue="cluster", data=insuranceDataFrame, palette = sns.color_palette("hls", n))
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plt.subplots_adjust(top=0.9) ## Makes more room at the top
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plt.title(names[k] + ": charges ~ " + variable, y=1.3)
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sns_plot.savefig("figures/" + names[k] + "-" + variable + ".png")
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## Gaussian Requires different syntax
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clustering = GaussianMixture(n_components=3).fit(insuranceDataFrame)
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n= len(np.unique(clustering.predict(insuranceDataFrame)))
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insuranceDataFrame["cluster"] = clustering.predict(insuranceDataFrame)
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for variable in insuranceDataFrame.columns:
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sns_plot = sns.relplot(x="charges", y=variable, hue="cluster", data=insuranceDataFrame,palette=sns.color_palette("hls", n))
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plt.subplots_adjust(top=0.9) ## Makes more room at the top
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plt.title("GaussianMixture" + ": charges ~ " + variable, y=1.3)
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sns_plot.savefig("figures/" +"GaussianMixture" + "-" + variable + ".png")
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