if (0):
#%% PARTIAL LEAST SQUARES
#%% PLS SVD
nComponents = np.arange(1, nClasses + 1)
plsSvdScores = np.zeros((5, np.alen(nComponents)))
for i, n in enumerate(nComponents):
plssvd = PLSSVD(n_components=n)
plssvd.fit(Xtrain, Ytrain)
XtrainT = plssvd.transform(Xtrain)
XtestT = plssvd.transform(Xtest)
plsSvdScores[:, i] = util.classify(XtrainT, XtestT, labelsTrain,
labelsTest)
plssvd = PLSSVD(n_components=2)
xt, yt = plssvd.fit_transform(Xtrain, Ytrain)
fig = plt.figure()
util.plotData(fig, xt, labelsTrain, classColors)
plt.title('First 2 components of projected data')
#%% Plot accuracies for PLSSVD
plt.figure()
for i in range(5):
plt.plot(nComponents, plsSvdScores[i, :], lw=3)
plt.xlim(1, np.amax(nComponents))
plt.title('PLS SVD accuracy')
plt.xlabel('Number of components')
plt.ylabel('accuracy')
plt.legend(['LR', 'LDA', 'GNB', 'Linear SVM', 'rbf SVM'],
loc='lower right')
#%% PARTIAL LEAST SQUARES
#%% PLS SVD
nComponents = np.arange(1,nClasses+1)
plsSvdScores = np.zeros((2,np.alen(nComponents)))
for i,n in enumerate(nComponents):
plssvd = PLSSVD(n_components=n)
plssvd.fit(dataTrain,Ytrain)
dataTrainT = plssvd.transform(dataTrain)
dataTestT = plssvd.transform(dataTest)
plsSvdScores[:,i] = util.classify(dataTrainT,dataTestT,labelsTrain,labelsTest)
fig = plt.figure()
util.plotAccuracy(fig,nComponents,plsSvdScores)
plt.title('PLS SVD accuracy',figure=fig)
plssvd = PLSSVD(n_components=2)
xt,yt = plssvd.fit_transform(dataTrain,Ytrain)
fig = plt.figure()
util.plotData(fig,xt,labelsTrain,classColors)
u = plssvd.x_weights_
plt.quiver(u[0,0],u[1,0],color='k',edgecolor='k',lw=1,scale=0.1,figure=fig)
plt.quiver(-u[1,0],u[0,0],color='k',edgecolor='k',lw=1,scale=0.4,figure=fig)
#%% PLS mode-A
lda = LDA()
nComponents = np.arange(1,nClasses+1)
plsCanScores = np.zeros((2,np.alen(nComponents)))
for i,n in enumerate(nComponents):
plscan = PLSCanonical(n_components=n)
plscan.fit(dataTrain,Ytrain)
dataTrainT = plscan.transform(dataTrain)
Y_expanded = neuron_types.map(embed_map)
Y_expanded = np.stack(Y_expanded.values)
Y_expanded
# %%
Y_expanded = StandardScaler(with_mean=True, with_std=True, copy=False).fit_transform(
Y_expanded
)
#%%
from sklearn.cross_decomposition import PLSSVD, CCA
currtime = time.time()
n_components = 5
model = PLSSVD(n_components=n_components)
X_scores, Y_scores = model.fit_transform(X_train, Y_expanded)
print(f"{time.time() - currtime} elapsed")
import matplotlib.pyplot as plt
for i in range(n_components):
fig, ax = plt.subplots(1, 1, figsize=(8, 8))
sns.scatterplot(
x=X_scores[:, i],
y=Y_scores[:, i],
hue=neuron_types,
palette=neuron_type_palette,
ax=ax,
legend=False,
)
ax.set(ylabel="Connectivity score", xlabel="Gene score")
if (0):
#%% PARTIAL LEAST SQUARES
#%% PLS SVD
nComponents = np.arange(1,nClasses+1)
plsSvdScores = np.zeros((5,np.alen(nComponents)))
for i,n in enumerate(nComponents):
plssvd = PLSSVD(n_components=n)
plssvd.fit(Xtrain,Ytrain)
XtrainT = plssvd.transform(Xtrain)
XtestT = plssvd.transform(Xtest)
plsSvdScores[:,i] = util.classify(XtrainT,XtestT,labelsTrain,labelsTest)
plssvd = PLSSVD(n_components=2)
xt,yt = plssvd.fit_transform(Xtrain,Ytrain)
fig = plt.figure()
util.plotData(fig,xt,labelsTrain,classColors)
plt.title('First 2 components of projected data')
#%% Plot accuracies for PLSSVD
plt.figure()
for i in range (5):
plt.plot(nComponents,plsSvdScores[i,:],lw=3)
plt.xlim(1,np.amax(nComponents))
plt.title('PLS SVD accuracy')
plt.xlabel('Number of components')
plt.ylabel('accuracy')
plt.legend (['LR','LDA','GNB','Linear SVM','rbf SVM'],loc='lower right')
plt.grid(True)