mlinsights/_doc/examples/plot_quantile_mlpregression.py at 37347dcbfe5eb26abf87a5a42f2b73a78bccefe9 · sdpython/mlinsights · GitHub

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"""
Quantile MLPRegressor
=====================

`scikit-learn <http://scikit-learn.org/stable/>`_ does not have a
quantile regression for multi-layer perceptron.
`mlinsights <https://sdpython.github.io/doc/dev/mlinsights/>`_
implements a version of it based on the *scikit-learn* model. The
implementation overwrites method ``_backprop``.

We first generate some dummy data.
"""

import numpy
from pandas import DataFrame
import matplotlib.pyplot as plt
from sklearn.neural_network import MLPRegressor
from mlinsights.mlmodel import QuantileMLPRegressor

X = numpy.random.random(1000)
eps1 = (numpy.random.random(900) - 0.5) * 0.1
eps2 = (numpy.random.random(100)) * 10
eps = numpy.hstack([eps1, eps2])
X = X.reshape((1000, 1))
Y = X.ravel() * 3.4 + 5.6 + eps

########################################
#


clr = MLPRegressor(hidden_layer_sizes=(30,), activation="tanh")
clr.fit(X, Y)

########################################
#


clq = QuantileMLPRegressor(hidden_layer_sizes=(30,), activation="tanh")
clq.fit(X, Y)

########################################
#


data = dict(X=X.ravel(), Y=Y, clr=clr.predict(X), clq=clq.predict(X))
df = DataFrame(data)
df.head()

########################################
#


fig, ax = plt.subplots(1, 1, figsize=(10, 4))
choice = numpy.random.choice(X.shape[0] - 1, size=100)
xx = X.ravel()[choice]
yy = Y[choice]
ax.plot(xx, yy, ".", label="data")
xx = numpy.array([[0], [1]])
y1 = clr.predict(xx)
y2 = clq.predict(xx)
ax.plot(xx, y1, "--", label="L2")
ax.plot(xx, y2, "--", label="L1")
ax.set_title("Quantile (L1) vs Square (L2) for MLPRegressor")
ax.legend()