"""
===================
Isotonic Regression
===================

An illustration of the isotonic regression on generated data (non-linear
monotonic trend with homoscedastic uniform noise).

The isotonic regression algorithm finds a non-decreasing approximation of a
function while minimizing the mean squared error on the training data. The
benefit of such a non-parametric model is that it does not assume any shape for
the target function besides monotonicity. For comparison a linear regression is
also presented.

The plot on the right-hand side shows the model prediction function that
results from the linear interpolation of thresholds points. The thresholds
points are a subset of the training input observations and their matching
target values are computed by the isotonic non-parametric fit.

"""
print(__doc__)

# Author: Nelle Varoquaux <nelle.varoquaux@gmail.com>
#         Alexandre Gramfort <alexandre.gramfort@inria.fr>
# License: BSD

import numpy as np
import matplotlib.pyplot as plt
from matplotlib.collections import LineCollection

from sklearn.linear_model import LinearRegression
from sklearn.isotonic import IsotonicRegression
from sklearn.utils import check_random_state

n = 100
x = np.arange(n)
rs = check_random_state(0)
y = rs.randint(-50, 50, size=(n,)) + 50. * np.log1p(np.arange(n))

# %%
# Fit IsotonicRegression and LinearRegression models:

ir = IsotonicRegression(out_of_bounds="clip")
y_ = ir.fit_transform(x, y)

lr = LinearRegression()
lr.fit(x[:, np.newaxis], y)  # x needs to be 2d for LinearRegression

# %%
# Plot results:

segments = [[[i, y[i]], [i, y_[i]]] for i in range(n)]
lc = LineCollection(segments, zorder=0)
lc.set_array(np.ones(len(y)))
lc.set_linewidths(np.full(n, 0.5))

fig, (ax0, ax1) = plt.subplots(ncols=2, figsize=(12, 6))

ax0.plot(x, y, 'C0.', markersize=12)
ax0.plot(x, y_, 'C1.-', markersize=12)
ax0.plot(x, lr.predict(x[:, np.newaxis]), 'C2-')
ax0.add_collection(lc)
ax0.legend(('Training data', 'Isotonic fit', 'Linear fit'), loc='lower right')
ax0.set_title('Isotonic regression fit on noisy data (n=%d)' % n)

x_test = np.linspace(-10, 110, 1000)
ax1.plot(x_test, ir.predict(x_test), 'C1-')
ax1.plot(ir.X_thresholds_, ir.y_thresholds_, 'C1.', markersize=12)
ax1.set_title("Prediction function (%d thresholds)" % len(ir.X_thresholds_))

plt.show()

# %%
# Note that we explicitly passed `out_of_bounds="clip"` to the constructor of
# `IsotonicRegression` to control the way the model extrapolates outside of the
# range of data observed in the training set. This "clipping" extrapolation can
# be seen on the plot of the decision function on the right-hand.
