8000 DOC: use notebook-style for plot_ols_3d by SamAdamDay · Pull Request #22547 · scikit-learn/scikit-learn · GitHub
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DOC: use notebook-style for plot_ols_3d #22547

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Merged
merged 6 commits into from
Feb 23, 2022
Merged

DOC: use notebook-style for plot_ols_3d #22547

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475948e
Updated sklearn.utils.get_chunk_n_rows docstring to conform with nump…
SamAdamDay Feb 18, 2022
e91d49f
Merge remote-tracking branch 'upstream/main'
SamAdamDay Feb 19, 2022
9bfa3bb
Use notebook-style for plot_ols_3d
SamAdamDay Feb 19, 2022
a1516af
Small tweaks to plot_ols_3d example
SamAdamDay Feb 22, 2022
492c53c
No longer use add_axes() in plot_ols_3d
SamAdamDay Feb 22, 2022
ca03b14
Fix warnings in matplotlib 3.5
lesteve Feb 23, 2022
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28 changes: 18 additions & 10 deletions examples/linear_model/plot_ols_3d.py
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Original file line number Diff line number Diff line change
Expand Up @@ -7,19 +7,18 @@
Features 1 and 2 of the diabetes-dataset are fitted and
plotted below. It illustrates that although feature 2
has a strong coefficient on the full model, it does not
give us much regarding `y` when compared to just feature 1

give us much regarding `y` when compared to just feature 1.
"""

# Code source: Gaël Varoquaux
# Modified for documentation by Jaques Grobler
# License: BSD 3 clause

import matplotlib.pyplot as plt
import numpy as np
from mpl_toolkits.mplot3d import Axes3D
# %%
# First we load the diabetes dataset.

from sklearn import datasets, linear_model
from sklearn import datasets
import numpy as np

X, y = datasets.load_diabetes(return_X_y=True)
indices = (0, 1)
Expand All @@ -29,16 +28,25 @@
y_train = y[:-20]
y_test = y[-20:]

# %%
# Next we fit a linear regression model.

from sklearn import linear_model

ols = linear_model.LinearRegression()
ols.fit(X_train, y_train)
_ = ols.fit(X_train, y_train)


# %%
# Finally we plot the figure from three different views.

import matplotlib.pyplot as plt


# #############################################################################
# Plot the figure
def plot_figs(fig_num, elev, azim, X_train, clf):
fig = plt.figure(fig_num, figsize=(4, 3))
plt.clf()
ax = Axes3D(fig, elev=elev, azim=azim)
ax = fig.add_subplot(111, projection="3d", elev=elev, azim=azim)

ax.scatter(X_train[:, 0], X_train[:, 1], y_train, c="k", marker="+")
ax.plot_surface(
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