CODEWITHSUNDEEP
pythonscikit-learn
mmulibra
mmulibra

Reputation: 345

Python Sklearn - Deprecation Warning

I am a beginner in Python and Sklearn. Wondering whether I am missing something here. I am getting the following warning message:

DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and willraise ValueError in 0.19.

Here is the code:

import numpy as np
import matplotlib.pyplot as plt
from sklearn.linear_model import SGDClassifier
from sklearn.datasets.samples_generator import make_blobs

def plot_sgd_separator():
    # we create 50 separable points
    X, Y = make_blobs(n_samples=50, centers=2,random_state=0, cluster_std=0.60)
    X = np.array(X).reshape((1, -1))


    # fit the model
    clf = SGDClassifier(loss="hinge", alpha=0.01,
                        n_iter=200, fit_intercept=True)
    clf.fit(X, Y)

    # plot the line, the points, and the nearest vectors to the plane
    xx = np.linspace(-1, 5, 10)
    yy = np.linspace(-1, 5, 10)

    X1, X2 = np.meshgrid(xx, yy)
    Z = np.empty(X1.shape)
    for (i, j), val in np.ndenumerate(X1):
        x1 = val
        x2 = X2[i, j]
        p = clf.decision_function([x1, x2])
        Z[i, j] = p[0]
    levels = [-1.0, 0.0, 1.0]
    linestyles = ['dashed', 'solid', 'dashed']
    colors = 'k'

    ax = plt.axes()
    ax.contour(X1, X2, Z, levels, colors=colors, linestyles=linestyles)
    ax.scatter(X[:, 0], X[:, 1], c=Y, cmap=plt.cm.Paired)

    ax.axis('tight')


if __name__ == '__main__':
    plot_sgd_separator()
    plt.show()

Thanks again for your kind attention. By the way, I am using Python 3.5.1.

Upvotes: 1

Views: 10741

Answers (2)

RoyaumeIX
RoyaumeIX

Reputation: 1977

I guess your question was answered here, this is probably a duplicate.

Upvotes: 1

Nitin Kashyap
Nitin Kashyap

Reputation: 184

If you read the warning message and do a bit of debugging you will realize that the warning arises because your inputs to the model are unidimensional. You can see this link: Sklearn train model with single sample raises a DeprecationWarning to rectify this.

I feel that you have other issues with your code. When I ran it, I saw that the number of data points in X and Y are not the same. X has 100 and Y has 50, this is a more serious issue and I feel this needs to be rectified first.

Upvotes: 0

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