CODEWITHSUNDEEP
pythonscikit-learnk-meansdecision-tree
Jessica Chambers
Jessica Chambers

Reputation: 1316

Get values from k-means cluster after clustering

I have a dataset that I have run a K-means algorithm on (scikit-learn), and I want to build a decision tree on each cluster. I can recuperate the values from the cluster, but not the "class" values (I'm doing supervised learning, each element can belong to one of two classes and I need the value associated with the data to build my trees)

Ex: unfiltered data set:

[val1 val2 class]
X_train=[val1 val2]
y_train=[class]

The clustering code is this:

X = clusterDF[clusterDF.columns[clusterDF.columns.str.contains('\'AB\'')]]
y = clusterDF['Class']
(X_train, X_test, y_train, y_test) = train_test_split(X, y,
        test_size=0.30)

kmeans = KMeans(n_clusters=3, n_init=5, max_iter=3000, random_state=1)
kmeans.fit(X_train, y_train)
y_pred = kmeans.predict(X_test)

And this is my (unbelievably clunky!) code for extracting the values to build the tree. The issue is the Y values; they aren't consistent with the X values

cl={i: np.where(kmeans.labels_ == i)[0] for i in range(kmeans.n_clusters)}
for j in range(0,len(k_means_labels_unique)):
    Xc=None
    Y=None
    #for i in range(0,len(k_means_labels_unique)):
    indexes = cl.get(j,0)
    for i, row in X.iterrows():
        if i in indexes:
            if Xc is not None:
                Xc = np.vstack([Xc, [row['first occurrence of \'AB\''],row['similarity to \'AB\'']]])
            else:
                Xc = np.array([row['first occurrence of \'AB\''],row['similarity to \'AB\'']])
            if Y is not None:
                Y = np.vstack([Y, y[i]])
            else:
                Y = np.array(y[i])
    Xc = pd.DataFrame(data=Xc, index=range(0, len(X)),
                     columns=['first occurrence of \'AB\'',
        'similarity to \'AB\''])  # 1st row as the column names


    Y = pd.DataFrame(data=Y, index=range(0, len(Y)),columns=['Class'])


        print("\n\t-----Classifier ", j + 1,"----")

        (X_train, X_test, y_train, y_test) = train_test_split(X, Y,
            test_size=0.30)

        classifier = DecisionTreeClassifier(criterion='entropy',max_depth = 2)
        classifier = getResults(
            X_train,
        y_train,
        X_test,
        y_test,
        classifier,
        filename='classif'+str(3 + i),
        )

Any ideas (or downright more efficient ways) of taking the clustered data to make a decision tree from?

Upvotes: 0

Views: 743

Answers (1)

Jan K
Jan K

Reputation: 4150

Did not read all the code but my guess is that passing an index vector into the train_test_split function would help you keep track of the samples.

X = clusterDF[clusterDF.columns[clusterDF.columns.str.contains('\'AB\'')]]
y = clusterDF['Class']
indices = clusterDF.index
X_train, X_test, y_train, y_test, indices_train, indices_test = train_test_split(X, y, indices)

Upvotes: 1

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