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#!/usr/bin/env python
coding: utf-8
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k-means clustering
from numpy import unique
from numpy import where
from sklearn.datasets import make_classification
from sklearn.cluster import KMeans
from matplotlib import pyplot
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
get_ipython().run_line_magic(‘matplotlib’, ‘inline’)
import pandas as pd
import numpy as np
import numpy as np
from sklearn.cluster import KMeans
from sklearn import datasets
from sklearn.preprocessing import StandardScaler
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import warnings
warnings.filterwarnings(‘ignore’)
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the combined data
data_folder = ‘./nhanes_input_data/’
import the CSV as a pandas dataframe
df = pd.read_csv( data_folder + ‘0_dietaryIntakeDataForClassificationAndAnalysisData.csv’)
df.shape
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df.head(5)
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parameters to be used for KMeans clustring: centres
X and/or kdf will have only features we want to create cluster around
kdf = df[
[
'RIDAGEYR_Age_in_years_at_screening'
,'URDACT_Albumin_creatinine_ratio_mg_g'
]
]
X = kdf
X[:5]
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ref: internet (not my code, using as a library)
def clean_dataset(df):
assert isinstance(df, pd.DataFrame), "df needs to be a pd.DataFrame"
df.dropna(inplace=True)
indices_to_keep = ~df.isin([np.nan, np.inf, -np.inf]).any(1)
return df[indices_to_keep].astype(np.float64)
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X has the features to cluster around (centres: Age, ACR). df has the complete data
after clustering is done using features in X, we find positions (index) for each data
in a cluster then we use those index positions to cluster the data from df
X.shape, df.shape
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X = clean_dataset(X)
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define the model
model = KMeans(n_clusters = 10) #,random_state=0, n_init="auto"
fit the model
model.fit(X)
#model.labels_
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Create csv files with the cluster daya
One csv for one Cluster
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howManyClusters = 10
for clusterId in range (howManyClusters):
ind_list = np.where(model.labels_ == clusterId )[0]
cluster = df.iloc[ind_list]
cluster.to_csv(‘./nhanes_output_data/classifiedGroups/kmeanscluster/cluster-‘
+ str(clusterId) + ‘.csv’);
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model.cluster_centers_
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Scatter plot to see each cluster points visually
std_data = StandardScaler().fit_transform(X)
plt.scatter(std_data[:,0], std_data[:,1], c = model.labels_, cmap = "rainbow")
plt.title("K-means Clustering of Diet and ACR data")
plt.show()
# References:
# print("Shape of cluster:", model.cluster_centers_.shape)
# https://stackoverflow.com/questions/50297142/get-cluster-points-after-kmeans-in-a-list-format
#
# https://machinelearningmastery.com/clustering-algorithms-with-python/
# https://stackoverflow.com/questions/50297142/get-cluster-points-after-kmeans-in-a-list-format
# https://scikit-learn.org/stable/modules/generated/sklearn.cluster.KMeans.html
# https://datascience.stackexchange.com/questions/48693/perform-k-means-clustering-over-multiple-columns
# https://scikit-learn.org/stable/modules/generated/sklearn.cluster.KMeans.html
#
>>> from sklearn.cluster import KMeans
>>> import numpy as np
>>> X = np.array([[1, 2], [1, 4], [1, 0],
… [10, 2], [10, 4], [10, 0]])
>>> kmeans = KMeans(n_clusters=2, random_state=0, n_init="auto").fit(X)
>>> kmeans.labels_
array([1, 1, 1, 0, 0, 0], dtype=int32)
>>> kmeans.predict([[0, 0], [12, 3]])
array([1, 0], dtype=int32)
>>> kmeans.cluster_centers_
array([[10., 2.],
[ 1., 2.]])
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