Python sklearn poly regression

Question

I'm stuck solving this issue for two days now. I have some datapoints I put in a scatter plot and get this:

Which is nice, but now I also want to add a regression line, so I had a look at this example from sklearn and changed the code to this

import numpy as np
import matplotlib.pyplot as plt
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import PolynomialFeatures
from sklearn.linear_model import LinearRegression
from sklearn.model_selection import cross_val_score

degrees = [3, 4, 5]
X = combined[['WPI score']]
y = combined[['CPI score']]

plt.figure(figsize=(14, 5))
for i in range(len(degrees)):
    ax = plt.subplot(1, len(degrees), i + 1)
    plt.setp(ax, xticks=(), yticks=())

    polynomial_features = PolynomialFeatures(degree=degrees[i], include_bias=False)
    linear_regression = LinearRegression()
    pipeline = Pipeline([("polynomial_features", polynomial_features), ("linear_regression", linear_regression)])
    pipeline.fit(X, y)

    # Evaluate the models using crossvalidation
    scores = cross_val_score(pipeline, X, y, scoring="neg_mean_squared_error", cv=10)

    X_test = X #np.linspace(0, 1, len(combined))
    plt.plot(X, pipeline.predict(X_test), label="Model")
    plt.scatter(X, y, label="CPI-WPI")
    plt.xlabel("X")
    plt.ylabel("y")
    plt.legend(loc="best")
    plt.title("Degree {}\nMSE = {:.2e}(+/- {:.2e})".format(degrees[i], -scores.mean(), scores.std()))
plt.savefig(pic_path + 'multi.png', bbox_inches='tight')
plt.show()

which has the following output:

Note that X and y are both DataFrames of size (151, 1). I can post the content of X and y too, if necessary.

What I want is a nice smooth line, but I seem not to be able to figure out, how to do this.

[Edit]

The question here is: How do I get a single smooth, curvy polynomial line instead of multiple ones with seemingly random pattern.

[Edit 2]

The problem is, when I use the linspace like this:

X_test = np.linspace(1, 4, 151)
X_test = X_test[:, np.newaxis]

I get a even more random pattern:

Answer 1

The trick was to set the code like following:

X_test = np.linspace(min(X['GPI score']), max(X['GPI score']), X.shape[0])
X_test = X_test[:, np.newaxis]
plt.plot(X_test, pipeline.predict(X_test), label="Model")

Which yields the following result (a much nicer, single smooth line)