8000 DOC Use notebook style for remaining notebooks from notebook-style meta-issue by lesteve · Pull Request #23365 · scikit-learn/scikit-learn · GitHub
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DOC Use notebook style for remaining notebooks from notebook-style meta-issue #23365

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May 13, 2022
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DOC Use notebook style for remaining notebooks from notebook-style meta-issue #23365

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38 changes: 27 additions & 11 deletions examples/cross_decomposition/plot_compare_cross_decomposition.py
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Expand Up @@ -4,6 +4,7 @@
===================================

Simple usage of various cross decomposition algorithms:

- PLSCanonical
- PLSRegression, with multivariate response, a.k.a. PLS2
- PLSRegression, with univariate response, a.k.a. PLS1
Expand All @@ -20,12 +21,11 @@

"""

import numpy as np
import matplotlib.pyplot as plt
from sklearn.cross_decomposition import PLSCanonical, PLSRegression, CCA

# #############################################################################
# %%
# Dataset based latent variables model
# ------------------------------------

import numpy as np

n = 500
# 2 latents vars:
Expand All @@ -46,19 +46,27 @@
print("Corr(Y)")
print(np.round(np.corrcoef(Y.T), 2))

# #############################################################################
# %%
# Canonical (symmetric) PLS

# -------------------------
#
# Transform data
# ~~~~~~~~~~~~~~

from sklearn.cross_decomposition import PLSCanonical

plsca = PLSCanonical(n_components=2)
plsca.fit(X_train, Y_train)
X_train_r, Y_train_r = plsca.transform(X_train, Y_train)
X_test_r, Y_test_r = plsca.transform(X_test, Y_test)

# %%
# Scatter plot of scores
# ~~~~~~~~~~~~~~~~~~~~~~
# 1) On diagonal plot X vs Y scores on each components

import matplotlib.pyplot as plt

# On diagonal plot X vs Y scores on each components
plt.figure(figsize=(12, 8))
plt.subplot(221)
plt.scatter(X_train_r[:, 0], Y_train_r[:, 0], label="train", marker="o", s=25)
Expand Down Expand Up @@ -86,7 +94,7 @@
plt.yticks(())
plt.legend(loc="best")

# 2) Off diagonal plot components 1 vs 2 for X and Y
# Off diagonal plot components 1 vs 2 for X and Y
plt.subplot(222)
plt.scatter(X_train_r[:, 0], X_train_r[:, 1], label="train", marker="*", s=50)
plt.scatter(X_test_r[:, 0], X_test_r[:, 1], label="test", marker="*", s=50)
Expand Down Expand Up @@ -114,8 +122,11 @@
plt.yticks(())
plt.show()

# #############################################################################
# %%
# PLS regression, with multivariate response, a.k.a. PLS2
# -------------------------------------------------------

from sklearn.cross_decomposition import PLSRegression

n = 1000
q = 3
Expand All @@ -134,7 +145,9 @@
print(np.round(pls2.coef_, 1))
pls2.predict(X)

# %%
# PLS regression, with univariate response, a.k.a. PLS1
# -----------------------------------------------------

n = 1000
p = 10
Expand All @@ -146,8 +159,11 @@
print("Estimated betas")
print(np.round(pls1.coef_, 1))

# #############################################################################
# %%
# CCA (PLS mode B with symmetric deflation)
# -----------------------------------------

from sklearn.cross_decomposition import CCA

cca = CCA(n_components=2)
cca.fit(X_train, Y_train)
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20 changes: 14 additions & 6 deletions examples/decomposition/plot_ica_blind_source_separation.py
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Expand Up @@ -14,14 +14,13 @@

"""

# %%
# Generate sample data
# --------------------

import numpy as np
import matplotlib.pyplot as plt
from scipy import signal

from sklearn.decomposition import FastICA, PCA

# #############################################################################
# Generate sample data
np.random.seed(0)
n_samples = 2000
time = np.linspace(0, 8, n_samples)
Expand All @@ -38,6 +37,12 @@
A = np.array([[1, 1, 1], [0.5, 2, 1.0], [1.5, 1.0, 2.0]]) # Mixing matrix
X = np.dot(S, A.T) # Generate observations

# %%
# Fit ICA and PCA models
# ----------------------

from sklearn.decomposition import FastICA, PCA

# Compute ICA
ica = FastICA(n_components=3)
S_ = ica.fit_transform(X) # Reconstruct signals
Expand All @@ -50,8 +55,11 @@
pca = PCA(n_components=3)
H = pca.fit_transform(X) # Reconstruct signals based on orthogonal components

# #############################################################################
# %%
# Plot results
# ------------

import matplotlib.pyplot as plt

plt.figure()

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19 changes: 15 additions & 4 deletions examples/linear_model/plot_multi_task_lasso_support.py
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Expand Up @@ -16,10 +16,11 @@
# Author: Alexandre Gramfort <alexandre.gramfort@inria.fr>
# License: BSD 3 clause

import matplotlib.pyplot as plt
import numpy as np
# %%
# Generate data
# -------------

from sklearn.linear_model import MultiTaskLasso, Lasso
import numpy as np

rng = np.random.RandomState(42)

Expand All @@ -34,11 +35,21 @@
X = rng.randn(n_samples, n_features)
Y = np.dot(X, coef.T) + rng.randn(n_samples, n_tasks)

# %%
# Fit models
# ----------

from sklearn.linear_model import MultiTaskLasso, Lasso

coef_lasso_ = np.array([Lasso(alpha=0.5).fit(X, y).coef_ for y in Y.T])
coef_multi_task_lasso_ = MultiTaskLasso(alpha=1.0).fit(X, Y).coef_

# #############################################################################
# %%
# Plot support and time series
# ----------------------------

import matplotlib.pyplot as plt

fig = plt.figure(figsize=(8, 5))
plt.subplot(1, 2, 1)
plt.spy(coef_lasso_)
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