def correlation_matching(I_tr, T_tr, I_te, T_te, n_comps):
""" Learns correlation matching (CM) over I_tr and T_tr
and applies it to I_tr, T_tr, I_te, T_te
Parameters
----------
I_tr: np.ndarray [shape=(n_tr, d_I)]
image data matrix for training
T_tr: np.ndarray [shape=(n_tr, d_T)]
text data matrix for training
I_te: np.ndarray [shape=(n_te, d_I)]
image data matrix for testing
T_te: np.ndarray [shape=(n_te, d_T)]
text data matrix for testing
n_comps: int > 0 [scalar]
number of canonical componens to use
Returns
-------
I_tr_cca : np.ndarray [shape=(n_tr, n_comps)]
image data matrix represetned in correlation space
T_tr_cca : np.ndarray [shape=(n_tr, n_comps)]
text data matrix represetned in correlation space
I_te_cca : np.ndarray [shape=(n_te, n_comps)]
image data matrix represetned in correlation space
T_te_cca : np.ndarray [shape=(n_te, n_comps)]
text data matrix represetned in correlation space
"""
# sclale image and text data
I_scaler = StandardScaler()
I_tr = I_scaler.fit_transform(I_tr)
I_te = I_scaler.transform(I_te)
T_scaler = StandardScaler()
T_tr = T_scaler.fit_transform(T_tr)
T_te = T_scaler.transform(T_te)
cca = PLSCanonical(n_components=n_comps, scale=False)
cca.fit(I_tr, T_tr)
I_tr_cca, T_tr_cca = cca.transform(I_tr, T_tr)
I_te_cca, T_te_cca = cca.transform(I_te, T_te)
return I_tr_cca, T_tr_cca, I_te_cca, T_te_cca
def feature_action_sensitivity(feature_type='TD4'):
''' 对每个特征，分析其在不移位和移位情况下的协方差 '''
results = []
subjects = ['subject_' + str(i + 1) for i in range(1)]
channel_pos_list = ['S0', # 中心位置
'U1', 'U2', 'D1', 'D2', 'L1', 'L2', 'R1', 'R2'] # 上 下 左 右
pos_num = len(channel_pos_list)
actions = [i+1 for i in range(7)]
action_num = len(actions) # 7 动作类型个数
if feature_type == 'TD4':
feature_list = ['MAV', 'ZC', 'SSC', 'WL']
elif feature_type == 'TD5':
feature_list = ['MAV', 'ZC', 'SSC', 'WL','RMS']
feat_num = len(feature_list) # 4 特征维度
groups = [i+1 for i in range(4)]
group_num = len(groups) # 4 通道数
group_span = group_num*feat_num
# print group_span
action_span = feat_num*group_num # 16
# print groups, channel_num, channel_span, feat_num
train_dir = 'train4_250_100'
results.append(['subject', 'action', 'feature', 'group', 'means_shift', 'std_shift'] )
plsca = PLSCanonical(n_components=2)
# pos = 1
k=0
for pos_idx, pos_name in enumerate(channel_pos_list[1:]):
pos = pos_idx+1
for subject in subjects:
# shift_simulation = np.ones((action_num,action_span,2))
trains, classes = data_load.load_feature_dataset(train_dir, subject, feature_type)
# m = trains.shape[0]
# print trains.shape, classes.shape, m
# print group_span, group_span*2
# sys.exit(0)
# m = trains.shape[0]*2/3
m = trains.shape[0]/2
X_train = trains[:m, group_span*pos: group_span*(pos+1)]
Y_train = trains[:m:, :group_span]
X_test = trains[m:, group_span*pos: group_span*(pos+1)]
Y_test = trains[m:, :group_span]
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)
filename=subject+'_'+pos_name
# plot_plsc_figure(X_train_r,Y_train_r,X_test_r, Y_test_r, filename)
plot_plsc_figure_two(X_train_r,Y_train_r,X_test_r, Y_test_r, filename)
def generate_transform_equations(trains_S0, trains_shift, **kw):
print 'generate transform equations.........'
new_fold(transform_fold)
chan_len = kw['chan_len']
for idx, channel_pos in enumerate(kw['pos_list']):
X_trains = trains_shift[:,idx*chan_len:idx*chan_len+chan_len]
plsca = PLSCanonical(n_components=12)
plsca.fit(X_trains, trains_S0)
joblib.dump(plsca, transform_fold+'/cca_transform_'+kw['subject']+'_'+channel_pos+'.model')
print 'generate transform equations finished.........'
def drawFaces(emb1, emb2, wordRanking, n, reduction="cut"):
"""
Plot Chernoff faces for n most/less interesting words
From: https://gist.github.com/aflaxman/4043086
:param n: if negative: less interesting
:param reduction:
:return:
"""
s1 = None
s2 = None
if reduction=="cut":
s1 = emb1.getSimMatrix()[0:,0:18]
s2 = emb2.getSimMatrix()[0:,0:18]
elif reduction=="svd":
s1 = TruncatedSVD(n_components=k).fit_transform(emb1.getSimMatrix())
s2 = TruncatedSVD(n_components=k).fit_transform(emb2.getSimMatrix())
elif reduction=="cca": #use orginal embeddings, not similarity matrix for reduction
cca = PLSCanonical(n_components=18)
cca.fit(emb1.m, emb2.m)
s1, s2 = cca.transform(emb1.m, emb2.m)
interesting = list()
name = str(n)+"."+reduction
if n<0: #plot uninteresting words
n *= -1
interesting = [wordRanking[::-1][i] for i in xrange(n)]
else:
interesting = [wordRanking[i] for i in xrange(n)]
fig = plt.figure(figsize=(11,11))
c = 0
for i in range(n):
word = interesting[i]
j = emb1.d[word]
ax = fig.add_subplot(n,2,c+1,aspect='equal')
mpl_cfaces.cface(ax, *s1[j]) #nice for similarity matrix *s1[j][:18]
ax.axis([-1.2,1.2,-1.2,1.2])
ax.set_xticks([])
ax.set_yticks([])
ax.set_title(word)
ax2 = fig.add_subplot(n,2,c+2,aspect='equal')
mpl_cfaces.cface(ax2, *s2[j])
ax2.axis([-1.2,1.2,-1.2,1.2])
ax2.set_xticks([])
ax2.set_yticks([])
ax2.set_title(word)
c += 2
plotname = "plots/"+NAME+".cface_s1s2_"+name+".png"
fig.savefig(plotname)
print("\tSaved Chernoff faces plot in '%s'" % (plotname))
def getCCARanking(self, filter=None):
"""
Compare how far apart words are in projection into common space by CCA
:return:
"""
cca = PLSCanonical(n_components=self.n)
cca.fit(self.emb1.m, self.emb2.m)
m1transformed, m2transformed = cca.transform(self.emb1.m, self.emb2.m)
#get distances between vectors
assert self.emb1.vocab_size == self.emb2.vocab_size
distDict = dict()
for i in xrange(self.emb1.vocab_size):
v1 = m1transformed[i]
v2 = m2transformed[i]
w = self.emb1.rd[i]
distDict[w] = 1-Similarity.euclidean(v1,v2)
ranked = sorted(distDict.iteritems(), key=itemgetter(1), reverse=True)
if filter is not None:
ranked = [(w, s) for (w, s) in distDict.iteritems() if w in filter]
return ranked
def plotClustersCCA(self, filter=None):
"""
Plot clusters in 2dim CCA space: Comparable across embeddings
:return:
"""
if len(self.cluster1) <= 1:
cmap1 = plt.get_cmap('jet', 2)
else:
cmap1 = plt.get_cmap('jet', len(self.cluster1))
cmap1.set_under('gray')
if len(self.cluster2) <= 1:
cmap2 = plt.get_cmap('jet', 2)
else:
cmap2 = plt.get_cmap('jet', len(self.cluster2))
cmap2.set_under('gray')
cca = PLSCanonical(n_components=2)
cca.fit(self.emb1.m, self.emb2.m)
m1transformed, m2transformed = cca.transform(self.emb1.m, self.emb2.m)
labels1 = [self.emb1.rd[i] for i in xrange(self.emb1.vocab_size)]
colors1 = [self.word2cluster1[self.emb1.rd[i]] for i in xrange(self.emb1.vocab_size)]
labels2 = [self.emb2.rd[i] for i in xrange(self.emb2.vocab_size)]
colors2 = [self.word2cluster2[self.emb2.rd[i]] for i in xrange(self.emb2.vocab_size)]
if filter is not None:
print("\tFiltering samples to plot")
filteredIds = [self.emb1.d[w] for w in filter] #get ids for words in filter
m1transformed = m1transformed[filteredIds]
m2transformed = m2transformed[filteredIds]
labels1 = [l for l in labels1 if l in filter]
labels2 = [l for l in labels2 if l in filter]
elif m1transformed.shape[0] > 100: #sample indices to display, otherwise it's too messy
filteredIds = np.random.randint(low=0, high=m1.transformed.shape[0]) #sample filteredIds
m1transformed = m1transformed[filteredIds]
m2transformed = m2transformed[filteredIds]
labels1 = [l for l in labels1 if l in filter]
labels2 = [l for l in labels2 if l in filter]
plotWithLabelsAndColors(m1transformed, labels1, colors=colors1, cmap=cmap1, filename="plots/"+NAME+".cca1.png", dimRed="CCA")
plotWithLabelsAndColors(m2transformed, labels2, colors=colors2, cmap=cmap2, filename="plots/"+NAME+".cca2.png", dimRed="CCA")
def test_pls_canonical_basics():
# Basic checks for PLSCanonical
d = load_linnerud()
X = d.data
Y = d.target
pls = PLSCanonical(n_components=X.shape[1])
pls.fit(X, Y)
assert_matrix_orthogonal(pls.x_weights_)
assert_matrix_orthogonal(pls.y_weights_)
assert_matrix_orthogonal(pls._x_scores)
assert_matrix_orthogonal(pls._y_scores)
# Check X = TP' and Y = UQ'
T = pls._x_scores
P = pls.x_loadings_
U = pls._y_scores
Q = pls.y_loadings_
# Need to scale first
Xc, Yc, x_mean, y_mean, x_std, y_std = _center_scale_xy(X.copy(),
Y.copy(),
scale=True)
assert_array_almost_equal(Xc, np.dot(T, P.T))
assert_array_almost_equal(Yc, np.dot(U, Q.T))
# Check that rotations on training data lead to scores
Xt = pls.transform(X)
assert_array_almost_equal(Xt, pls._x_scores)
Xt, Yt = pls.transform(X, Y)
assert_array_almost_equal(Xt, pls._x_scores)
assert_array_almost_equal(Yt, pls._y_scores)
# Check that inverse_transform works
X_back = pls.inverse_transform(Xt)
assert_array_almost_equal(X_back, X)
_, Y_back = pls.inverse_transform(Xt, Yt)
assert_array_almost_equal(Y_back, Y)
class _PLSCanonicalImpl:
def __init__(self, **hyperparams):
self._hyperparams = hyperparams
self._wrapped_model = Op(**self._hyperparams)
def fit(self, X, y=None):
if y is not None:
self._wrapped_model.fit(X, y)
else:
self._wrapped_model.fit(X)
return self
def transform(self, X):
return self._wrapped_model.transform(X)
def predict(self, X):
return self._wrapped_model.predict(X)
def plot_compare_cross_decomposition():
# Dataset based latent variables model
n = 500
# 2 latents vars:
l1 = np.random.normal(size=n)
l2 = np.random.normal(size=n)
latents = np.array([l1, l1, l2, l2]).T
X = latents + np.random.normal(size=4 * n).reshape((n, 4))
Y = latents + np.random.normal(size=4 * n).reshape((n, 4))
X_train = X[:n // 2]
Y_train = Y[:n // 2]
X_test = X[n // 2:]
Y_test = Y[n // 2:]
print("Corr(X)")
print(np.round(np.corrcoef(X.T), 2))
print("Corr(Y)")
print(np.round(np.corrcoef(Y.T), 2))
# #############################################################################
# Canonical (symmetric) PLS
# Transform data
# ~~~~~~~~~~~~~~
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
plt.figure(figsize=(12, 8))
plt.subplot(221)
plt.scatter(X_train_r[:, 0],
Y_train_r[:, 0],
label="train",
marker="o",
s=25)
plt.scatter(X_test_r[:, 0], Y_test_r[:, 0], label="test", marker="o", s=25)
plt.xlabel("x scores")
plt.ylabel("y scores")
plt.title('Comp. 1: X vs Y (test corr = %.2f)' %
np.corrcoef(X_test_r[:, 0], Y_test_r[:, 0])[0, 1])
plt.xticks(())
plt.yticks(())
plt.legend(loc="best")
plt.subplot(224)
plt.scatter(X_train_r[:, 1],
Y_train_r[:, 1],
label="train",
marker="o",
s=25)
plt.scatter(X_test_r[:, 1], Y_test_r[:, 1], label="test", marker="o", s=25)
plt.xlabel("x scores")
plt.ylabel("y scores")
plt.title('Comp. 2: X vs Y (test corr = %.2f)' %
np.corrcoef(X_test_r[:, 1], Y_test_r[:, 1])[0, 1])
plt.xticks(())
plt.yticks(())
plt.legend(loc="best")
# 2) 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)
plt.xlabel("X comp. 1")
plt.ylabel("X comp. 2")
plt.title('X comp. 1 vs X comp. 2 (test corr = %.2f)' %
np.corrcoef(X_test_r[:, 0], X_test_r[:, 1])[0, 1])
plt.legend(loc="best")
plt.xticks(())
plt.yticks(())
plt.subplot(223)
plt.scatter(Y_train_r[:, 0],
Y_train_r[:, 1],
label="train",
marker="*",
s=50)
plt.scatter(Y_test_r[:, 0], Y_test_r[:, 1], label="test", marker="*", s=50)
plt.xlabel("Y comp. 1")
plt.ylabel("Y comp. 2")
plt.title('Y comp. 1 vs Y comp. 2 , (test corr = %.2f)' %
np.corrcoef(Y_test_r[:, 0], Y_test_r[:, 1])[0, 1])
plt.legend(loc="best")
plt.xticks(())
plt.yticks(())
plt.show()
# #############################################################################
# PLS regression, with multivariate response, a.k.a. PLS2
n = 1000
q = 3
p = 10
X = np.random.normal(size=n * p).reshape((n, p))
B = np.array([[1, 2] + [0] * (p - 2)] * q).T
# each Yj = 1*X1 + 2*X2 + noize
Y = np.dot(X, B) + np.random.normal(size=n * q).reshape((n, q)) + 5
pls2 = PLSRegression(n_components=3)
pls2.fit(X, Y)
print("True B (such that: Y = XB + Err)")
print(B)
# compare pls2.coef_ with B
print("Estimated B")
print(np.round(pls2.coef_, 1))
pls2.predict(X)
# PLS regression, with univariate response, a.k.a. PLS1
n = 1000
p = 10
X = np.random.normal(size=n * p).reshape((n, p))
y = X[:, 0] + 2 * X[:, 1] + np.random.normal(size=n * 1) + 5
pls1 = PLSRegression(n_components=3)
pls1.fit(X, y)
# note that the number of components exceeds 1 (the dimension of y)
print("Estimated betas")
print(np.round(pls1.coef_, 1))
# #############################################################################
# CCA (PLS mode B with symmetric deflation)
cca = CCA(n_components=2)
cca.fit(X_train, Y_train)
X_train_r, Y_train_r = cca.transform(X_train, Y_train)
X_test_r, Y_test_r = cca.transform(X_test, Y_test)
X_train = X[:n // 2]
Y_train = Y[:n // 2]
X_test = X[n // 2:]
Y_test = Y[n // 2:]
print("Corr(X)")
print(np.round(np.corrcoef(X.T), 2))
print("Corr(Y)")
print(np.round(np.corrcoef(Y.T), 2))
# #############################################################################
# Canonical (symmetric) PLS
# Transform data
# ~~~~~~~~~~~~~~
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
plt.figure(figsize=(12, 8))
plt.subplot(221)
plt.plot(X_train_r[:, 0], Y_train_r[:, 0], "ob", label="train")
plt.plot(X_test_r[:, 0], Y_test_r[:, 0], "or", label="test")
plt.xlabel("x scores")
plt.ylabel("y scores")
plt.title('Comp. 1: X vs Y (test corr = %.2f)' %
np.corrcoef(X_test_r[:, 0], Y_test_r[:, 0])[0, 1])
################################################################################
#
# PLS
#
################################################################################
import scipy.linalg
from sklearn.cross_decomposition import PLSCanonical
Xim_tgt_s_ = StandardScaler().fit_transform(Xim[msk_tgt, :])
Xdemoclin_tgt_s_ = StandardScaler().fit_transform(Xdemoclin[msk_tgt, :])
_, s_, _ = scipy.linalg.svd(Xdemoclin_tgt_s_, full_matrices=False)
rank_ = np.sum(s_ > 1e-6)
plsca = PLSCanonical(n_components=rank_)
%time PLSim_scores, PLSclin_scores = plsca.fit_transform(Xim_tgt_s_, Xdemoclin_tgt_s_)
# Imaging components
df_ = pd.DataFrame(PLSim_scores)
df_["respond_wk8"] = pop["respond_wk8"][msk_tgt].values
df_["respond_wk16"] = pop["respond_wk16"][msk_tgt].values
df_["GM_frac"] = pop["GM_frac"][msk_tgt].values
sns.pairplot(df_, hue="respond_wk8")
print("PC1 capture global GM atrophy")
# Demo/Clinic components
df_ = pd.DataFrame(PLSclin_scores)
for var in vars_demo + vars_clinic + ["respond_wk8", "respond_wk16"]:
df_[var] = pop[var][msk_tgt].values
vec_c.append(i)
for i in vec2:
vec_c.append(i)
if j < len_train:
l_p.append(vec_p)
l_c.append(vec_c)
else:
l_p_t.append(vec_p)
l_c_t.append(vec_c)
j += 1
sorted_p = np.asarray(l_p)
sorted_c = np.asarray(l_c) #Convert the input to an array
plc = PLSCanonical()
plc.fit_transform(sorted_c, sorted_p)
sorted_c, sorted_p = plc.transform(sorted_c, sorted_p)
sorted_c_test = np.asarray(l_c_t)
sorted_p_test = np.asarray(l_p_t)
sorted_c_test, sorted_p_test = plc.transform(sorted_c_test, sorted_p_test)
plr = PLSRegression()
plr.fit(sorted_c, sorted_p)
params = plr.get_params()
plr.set_params(**params)
y_score = plr.predict(sorted_c_test)
sim_count = 0
print("Test Similarity: ")
class Wrapper:
"""
This is a wrapper class for linear, regularised and kernel CCA, Multiset CCA and Generalized CCA.
We create an instance with a method and number of latent dimensions.
If we have more than 2 views we need to use generalized methods, but we can override in the 2 view case also with
the generalized parameter.
The class has a number of methods:
fit(): gives us train correlations and stores the variables needed for out of sample prediction as well as some
method-specific variables
cv_fit(): allows us to perform a hyperparameter search and then fit the model using the optimal hyperparameters
predict_corr(): allows us to predict the out of sample correlation for supplied views
predict_view(): allows us to predict a reconstruction of missing views from the supplied views
transform_view(): allows us to transform given views to the latent variable space
remaining methods are used to
"""
def __init__(self, latent_dims: int = 1, method: str = 'l2', generalized: bool = False, max_iter: int = 500,
tol=1e-6):
self.latent_dims = latent_dims
self.method = method
self.generalized = generalized
self.max_iter = max_iter
self.tol = tol
def fit(self, *args, params=None):
if params is None:
params = {}
self.params = params
if len(args) > 2:
self.generalized = True
print('more than 2 views therefore switched to generalized')
if 'c' not in self.params:
self.params = {'c': [0] * len(args)}
if self.method == 'kernel':
#Linear kernel by default
if 'kernel' not in self.params:
self.params['kernel'] = 'linear'
#First order polynomial by default
if 'degree' not in self.params:
self.params['degree'] = 1
# First order polynomial by default
if 'sigma' not in self.params:
self.params['sigma'] = 1.0
# Fit returns in-sample score vectors and correlations as well as models with transform functionality
self.dataset_list = []
self.dataset_means = []
for dataset in args:
self.dataset_means.append(dataset.mean(axis=0))
self.dataset_list.append(dataset - dataset.mean(axis=0))
if self.method == 'kernel':
self.fit_kcca = cca_zoo.KCCA.KCCA(self.dataset_list[0], self.dataset_list[1], params=self.params,
latent_dims=self.latent_dims)
self.score_list = [self.fit_kcca.U, self.fit_kcca.V]
elif self.method == 'pls':
self.fit_scikit_pls(self.dataset_list[0], self.dataset_list[1])
elif self.method == 'scikit':
self.fit_scikit_cca(self.dataset_list[0], self.dataset_list[1])
elif self.method == 'mcca':
self.fit_mcca(*self.dataset_list)
elif self.method == 'gcca':
self.fit_gcca(*self.dataset_list)
else:
self.outer_loop(*self.dataset_list)
if self.method[:4] == 'tree':
self.tree_list = [self.tree_list[i] for i in range(len(args))]
self.weights_list = [np.expand_dims(tree.feature_importances_, axis=1) for tree in self.tree_list]
else:
self.rotation_list = []
for i in range(len(args)):
self.rotation_list.append(
self.weights_list[i] @ pinv2(self.loading_list[i].T @ self.weights_list[i], check_finite=False))
self.train_correlations = self.predict_corr(*args)
return self
def cv_fit(self, *args, param_candidates=None, folds: int = 5, verbose: bool = False):
best_params = cross_validate(*args, max_iter=self.max_iter, latent_dims=self.latent_dims, method=self.method,
param_candidates=param_candidates, folds=folds,
verbose=verbose, tol=self.tol)
self.fit(*args, params=best_params)
return self
def bayes_cv_fit(self, *args, param_candidates=None, folds: int = 5, verbose: bool = False):
space = {
"n_estimators": hp.choice("n_estimators", [100, 200, 300, 400, 500, 600]),
"max_depth": hp.quniform("max_depth", 1, 15, 1),
"criterion": hp.choice("criterion", ["gini", "entropy"]),
}
trials = Trials()
best_params = fmin(
fn=Wrapper(),
space=space,
algo=tpe.suggest,
max_evals=100,
trials=trials
)
self.fit(*args, params=best_params)
return self
def predict_corr(self, *args):
# Takes two datasets and predicts their out of sample correlation using trained model
transformed_views = self.transform_view(*args)
all_corrs = []
for x, y in itertools.product(transformed_views, repeat=2):
all_corrs.append(np.diag(np.corrcoef(x.T, y.T)[:self.latent_dims, self.latent_dims:]))
all_corrs = np.array(all_corrs).reshape((len(args), len(args), self.latent_dims))
return all_corrs
def predict_view(self, *args):
# Regress original given views onto target
transformed_views = self.transform_view(*args)
# Get the regression from the training data with available views
predicted_target = np.mean([transformed_views[i] for i in range(len(args)) if args[i] is not None], axis=0)
predicted_views = []
for i, view in enumerate(args):
if view is None:
predicted_views.append(predicted_target @ pinv2(self.weights_list[i]))
else:
predicted_views.append(view)
for i, predicted_view in enumerate(predicted_views):
predicted_views[i] += self.dataset_means[i]
return predicted_views
def transform_view(self, *args):
# Demeaning
new_views = []
for i, new_view in enumerate(args):
if new_view is None:
new_views.append(None)
else:
new_views.append(new_view - self.dataset_means[i])
if self.method == 'kernel':
transformed_views = list(self.fit_kcca.transform(new_views[0], new_views[1]))
elif self.method == 'pls':
transformed_views = list(self.PLS.transform(new_views[0], new_views[1]))
elif self.method[:4] == 'tree':
transformed_views = []
for i, new_view in enumerate(new_views):
if new_view is None:
transformed_views.append(None)
else:
transformed_views.append(self.tree_list[i].predict(new_view))
else:
transformed_views = []
for i, new_view in enumerate(new_views):
if new_view is None:
transformed_views.append(None)
else:
transformed_views.append(new_view @ self.rotation_list[i])
# d x n x k
return transformed_views
def outer_loop(self, *args):
# list of d: p x k
self.weights_list = [np.zeros((args[i].shape[1], self.latent_dims)) for i in range(len(args))]
# list of d: n x k
self.score_list = [np.zeros((args[i].shape[0], self.latent_dims)) for i in range(len(args))]
# list of d:
self.loading_list = [np.zeros((args[i].shape[1], self.latent_dims)) for i in range(len(args))]
if len(args) == 2:
C_train = args[0].T @ args[1]
C_train_res = C_train.copy()
else:
C_train_res = None
residuals = list(args)
# For each of the dimensions
for k in range(self.latent_dims):
self.inner_loop = cca_zoo.alternating_least_squares.ALS_inner_loop(*residuals, C=C_train_res,
generalized=self.generalized,
params=self.params,
method=self.method,
max_iter=self.max_iter)
for i in range(len(args)):
if self.method[:4] == 'tree':
self.tree_list = self.inner_loop.weights
else:
self.weights_list[i][:, k] = self.inner_loop.weights[i]
self.score_list[i][:, k] = self.inner_loop.targets[i, :]
self.loading_list[i][:, k] = residuals[i].T @ self.score_list[i][:, k] / np.linalg.norm(
self.score_list[i][:, k])
residuals[i] -= np.outer(self.score_list[i][:, k] / np.linalg.norm(self.score_list[i][:, k]),
self.loading_list[i][:, k])
return self
def fit_scikit_cca(self, train_set_1, train_set_2):
self.cca = CCA(n_components=self.latent_dims, scale=False)
self.cca.fit(train_set_1, train_set_2)
self.score_list = [self.cca.x_scores_, self.cca.y_scores_]
self.weights_list = [self.cca.x_weights_, self.cca.y_weights_]
self.loading_list = [self.cca.x_loadings_, self.cca.y_loadings_]
self.rotation_list = [self.cca.x_rotations_, self.cca.y_rotations_]
return self
def fit_scikit_pls(self, train_set_1, train_set_2):
self.PLS = PLSCanonical(n_components=self.latent_dims, scale=False)
self.PLS.fit(train_set_1, train_set_2)
self.score_list = [self.PLS.x_scores_, self.PLS.y_scores_]
self.weights_list = [self.PLS.x_weights_, self.PLS.y_weights_]
return self
def fit_mcca(self, *args):
all_views = np.concatenate(args, axis=1)
C = all_views.T @ all_views
# Can regularise by adding to diagonal
D = block_diag(*[(1 - self.params['c'][i]) * m.T @ m + self.params['c'][i] * np.eye(m.shape[1]) for i, m in
enumerate(args)])
R = cholesky(D, lower=False)
whitened = np.linalg.inv(R.T) @ C @ np.linalg.inv(R)
[eigvals, eigvecs] = np.linalg.eig(whitened)
idx = np.argsort(eigvals, axis=0)[::-1]
eigvecs = eigvecs[:, idx].real
eigvals = eigvals[idx].real
eigvecs = np.linalg.inv(R) @ eigvecs
splits = np.cumsum([0] + [view.shape[1] for view in args])
self.weights_list = [eigvecs[splits[i]:splits[i + 1], :self.latent_dims] for i in range(len(args))]
self.rotation_list = self.weights_list
self.score_list = [self.dataset_list[i] @ self.weights_list[i] for i in range(len(args))]
def fit_gcca(self, *args):
Q = []
for i, view in enumerate(args):
view_cov = view.T @ view
view_cov = (1 - self.params['c'][i]) * view_cov + self.params['c'][i] * np.eye(view_cov.shape[0])
Q.append(view @ np.linalg.inv(view_cov) @ view.T)
Q = np.sum(Q, axis=0)
[eigvals, eigvecs] = np.linalg.eig(Q)
idx = np.argsort(eigvals, axis=0)[::-1]
eigvecs = eigvecs[:, idx].real
eigvals = eigvals[idx].real
self.weights_list = [np.linalg.pinv(view) @ eigvecs[:, :self.latent_dims] for view in args]
self.rotation_list = self.weights_list
self.score_list = [self.dataset_list[i] @ self.weights_list[i] for i in range(len(args))]
def run_pls_loop(n_seeds: int = 10, equal_dims: bool = False, **kwargs):
default_args = {
'sample_sizes': [int(10 ** i) for i in range(2, 5)],
'seeds': [int(2 ** i) for i in range(n_seeds)],
'sigmas': np.linspace(0, 5, num=11),
'orthogonal': [False],
'normal': [True],
'three_d': [False, True],
'dims_x': np.logspace(1, 4, num=4, base=4, dtype=int),
'dims_y': np.logspace(1, 4, num=4, base=4, dtype=int),
}
for k in default_args:
if k in kwargs:
default_args[k] = list(kwargs[k]) if isinstance(kwargs[k], (list, tuple, np.ndarray)) else [kwargs[k]]
df = pd.DataFrame()
for n_samples in tqdm(default_args['sample_sizes']):
for three_d in default_args['three_d']:
train, test = generate_source_signal(n_samples=n_samples, three_d=three_d)
for seed in tqdm(default_args['seeds'], leave=False):
for orthogonal in default_args['orthogonal']:
for normal in default_args['normal']:
for sigma in default_args['sigmas']:
for dim_x in default_args['dims_x']:
for dim_y in [dim_x] if equal_dims else default_args['dims_y']:
# create sim
sim = create_pls_simulation(
train=train,
test=test,
n_samples=n_samples,
three_d=three_d,
angle_spacing=1.0,
magnitude_range=None,
dim_x=dim_x,
dim_y=dim_y,
sigma=sigma,
orthogonal=orthogonal,
normal=normal,
seed=seed,
)
# fit PLS
pls = PLSCanonical(
n_components=sim['metadata']['dim_z'],
scale=True,
algorithm='svd',
max_iter=int(1e9),
tol=1e-15,
).fit(sim['x_train'], sim['y_train'])
# get results
results = visualize_pls_results(pls, sim, verbose=False)
results.update({
'n_samples': n_samples,
'three_d': three_d,
'seed': seed,
'orthogonal': orthogonal,
'normal': normal,
'sigma': sigma,
'dim_x': dim_x,
'dim_y': dim_y,
})
results = {k: [v] for k, v in results.items()}
df = pd.concat([df, pd.DataFrame.from_dict(results)])
return reset_df(df), default_args
def pls_decomposition(videos, audios, n_components=256):
plsca = PLSCanonical(n_components=n_components)
plsca.fit(audios, videos)
videos_c, audios_c = plsca.transform(videos, audios)
return videos_c, audios_c
reg = linear_model.Lasso(alpha=0.1)
#弹性网络回归（Elastic Net）
from sklearn.linear_model import ElasticNet
regr = ElasticNet(random_state=0)
#贝叶斯回归（Bayesian Regression）
from sklearn import linear_model
reg = linear_model.BayesianRidge()
#多项式回归（Polynomial regression——多项式基函数回归）
from sklearn.preprocessing import PolynomialFeatures
poly = PolynomialFeatures(degree=2)
poly.fit_transform(X)
#偏最小二乘回归（PLS）
from sklearn.cross_decomposition import PLSCanonical
PLSCanonical(algorithm='nipals',
copy=True,
max_iter=500,
n_components=2,
scale=True,
tol=1e-06)
#典型相关分析（CCA）
from sklearn.cross_decomposition import CCA
cca = CCA(n_components=2)
#B聚类分析
#KNN算法
from sklearn.neighbors import KNeighborsClassifier
nbrs = NearestNeighbors(n_neighbors=2, algorithm='ball_tree').fit(X)
#Kmeans算法
from sklearn.cluster import KMeans
kmeans = KMeans(init='k-means++', n_clusters=n_digits, n_init=10)
#层次聚类（Hierarchical clustering）——支持多种距离
from sklearn.cluster import AgglomerativeClustering
plt.plot(fpr, tpr)
plt.plot([0, 1], [0, 1])
plt.xlim([0, 1])
plt.gca().set_aspect('equal', adjustable='box')
plt.legend(['Cell volume', 'Age', 'Both'])
#NB: Strong colinearity between Age and Volume
# Transition rate prediction using PLS
X = dfc_g1[['vol_sm', 'Age', 'gr_sm']] # Design matrix
y = dfc_g1['G1S_logistic'] # Response var
# Drop NaN rows
I = np.isnan(dfc_g1['gr_sm'])
X = X.loc[~I].copy()
y = y[~I]
pls_model = PLSCanonical()
pls_model.fit(scale(X), y)
X_c, y_c = pls_model.transform(scale(X), y)
# Multiple linearregression on birth size and growth rate
df['bvol'] = df['Birth volume']
df['exp_gr'] = df['Exponential growth rate']
df['g1_len'] = df['G1 length']
model = smf.ols('g1_len ~ exp_gr + bvol', data=df).fit()
model.summary()
print model.pvalues
# Delete S/G2 after first time point
g1s_marked = []
for c in collated_filtered:
def training_lda_TD4_intra(my_clfs, trains, classes, **kw):
start_time = time.time()
if (kw.has_key('log_fold')):
log_fold = root_path + '/result/' + kw['log_fold']
new_fold(log_fold)
chan_len = kw['chan_len']
action_num = kw['action_num']
cv = 3
results = []
results.append([
'Feat', 'Algorithm', 'n_components', 'Channel_Pos', 'Accuracy', 'std'
])
log_file = 'feat_' + kw['feature_type'] + '_intra'
clf = sklearn.lda.LDA(solver='svd',
shrinkage=None,
priors=None,
n_components=None,
store_covariance=False,
tol=0.0001)
data_num = trains.shape[0] / action_num
scores = sklearn.cross_validation.cross_val_score(clf,
trains,
classes,
cv=cv)
results.append([
'feat_TD4_cv_' + str(cv), 'lda', 'ALL', 0,
scores.mean(),
scores.std()
])
# 组内训练策略 9组数据
print '组内训练.............'
for idx, channel_pos in enumerate(kw['pos_list']):
# print '----training TD4 intra , channel_pos: ', channel_pos,'......'
trains_intra = trains[:, idx * chan_len:idx * chan_len + chan_len]
scores = sklearn.cross_validation.cross_val_score(clf,
trains_intra,
classes,
cv=cv)
results.append([
'feat_TD4_cv_' + str(cv), 'lda', 0, channel_pos,
scores.mean(),
scores.std()
])
# 中心训练策略
print '中心训练策略.............'
trains_intra_S0 = trains[:, 0:chan_len]
for idx, channel_pos in enumerate(kw['pos_list']):
if channel_pos == 'S0':
continue
tests_shift = trains[:, idx * chan_len:idx * chan_len + chan_len]
# if channel_pos == 'L2':
# print idx*chan_len, idx*chan_len+chan_len, tests_shift.shape, trains.shape
# sys.exit(0)
scores = clf.fit(trains_intra_S0, classes).score(tests_shift, classes)
results.append([
'feat_TD4_cv_' + str(cv), 'lda', 0,
'train S0' + ' test ' + channel_pos,
scores.mean(),
scores.std()
])
# 组训练策略（不同于组内训练策略） 5-fold
print '组训练策略.............'
trains_intra_S0 = trains[:, 0:chan_len]
kf = KFold(data_num, n_folds=cv)
for idx, channel_pos in enumerate(kw['pos_list']):
if channel_pos == 'S0':
continue
itera = cv
scores = np.zeros((itera, ))
# stds = np.zeros( (itera,) )
itera -= 1
trains_shift = trains[:, idx * chan_len:idx * chan_len + chan_len]
for train_idx, test_idx in kf:
train_idx_all = np.array([], np.int)
test_idx_all = np.array([], np.int)
for action_idx in range(action_num):
train_idx_all = np.concatenate(
(train_idx_all, train_idx * (action_idx + 1)), axis=0)
test_idx_all = np.concatenate(
(test_idx_all, test_idx * (action_idx + 1)), axis=0)
X_train = np.concatenate(
(trains_intra_S0[train_idx_all], trains_shift[train_idx_all]),
axis=0)
y_train = np.concatenate(
(classes[train_idx_all], classes[train_idx_all]), axis=0)
X_test = trains_shift[test_idx_all]
y_test = classes[test_idx_all]
# X_test = trains_shift
# y_test = classes
score = clf.fit(X_train, y_train).score(X_test, y_test)
scores[itera] = score.mean()
itera -= 1
# print scores
results.append([
'feat_TD4_cv_' + str(cv), 'lda', 0, 'S0 + ' + channel_pos,
np.mean(scores),
np.std(scores)
])
# 基于CCA的训练策略 5-fold 交叉验证
print 'CCA训练策略.............'
trains_S0 = trains[:, 0:chan_len]
n_components_list = [6, 8, 10, 12, 14, 16] # 子空间维数
# n_components_list = [12,14,16]
kf = KFold(data_num, n_folds=cv)
for n_components in n_components_list:
for idx, channel_pos in enumerate(kw['pos_list']):
if channel_pos == 'S0':
continue
itera = cv
scores = np.zeros((itera, ))
stds = np.zeros((itera, ))
itera -= 1
trains_shift = trains[:, idx * chan_len:idx * chan_len + chan_len]
for train_idx, test_idx in kf:
train_idx_all = np.array([], np.int)
test_idx_all = np.array([], np.int)
for action_idx in range(action_num):
train_idx_all = np.concatenate(
(train_idx_all, train_idx * (action_idx + 1)), axis=0)
test_idx_all = np.concatenate(
(test_idx_all, test_idx * (action_idx + 1)), axis=0)
# print train_idx_all.shape, train_idx_all, test_idx_all.shape, test_idx_all
# plsca.fit(trains_shift[train_idx_all], trains_S0[train_idx_all])
plsca = PLSCanonical(n_components=n_components)
plsca.fit(trains_shift, trains_S0)
trains_shift_cca, trains_S0_cca = plsca.transform(
trains_shift, trains_S0)
X_trains = np.concatenate(
(trains_S0_cca, trains_shift_cca[train_idx_all]), axis=0)
y_trains = np.concatenate((classes, classes[train_idx_all]),
axis=0)
score = clf.fit(X_trains,
y_trains).score(trains_shift_cca[test_idx_all],
classes[test_idx_all])
scores[itera] = score.mean()
# stds[itera] = score.std()
itera -= 1
results.append([
'feat_TD4_cv_' + str(cv), 'lda_cca', n_components,
'S0 + ' + channel_pos,
np.mean(scores),
np.std(scores)
])
log_result(results,
log_fold + '/' + log_file + '_action_1-' + str(action_num), 2)
print '----Log Fold:', log_fold, ', log_file: ', log_file + '_action_1-' + str(
action_num)
print '----training TD4 time elapsed:', time.time() - start_time
def training_lda_TD4_inter(my_clfs, trains_S0, trains_shift, classes, **kw):
print 'training_lda_TD4_inter.........'
start_time = time.time()
log_fold = root_path + '/result/' + kw['log_fold']
new_fold(log_fold)
chan_len = kw['chan_len']
action_num = kw['action_num']
print "----training " + kw[
'feature_type'] + " inter, training by position O, testing by electrode shift "
cv = 5
results = []
results.append(['Feat', 'Algorithm', 'Channel_Pos', 'Accuracy', 'std'])
log_file = 'feat_' + kw['feature_type'] + '_inter'
clf = sklearn.lda.LDA(solver='svd',
shrinkage=None,
priors=None,
n_components=None,
store_covariance=False,
tol=0.0001)
data_num = trains_S0.shape[0] / action_num
# print data_num
scores = sklearn.cross_validation.cross_val_score(clf,
trains_S0,
classes,
cv=cv)
results.append(
['feat_TD4_cv_' + str(cv), 'lda', 'S0',
scores.mean(),
scores.std()])
kf = KFold(data_num, n_folds=cv)
for idx, channel_pos in enumerate(kw['pos_list']):
X_test = trains_shift[:, idx * chan_len:idx * chan_len + chan_len]
y_test = classes
iteration = cv
scores = np.zeros((iteration, ))
cca_scores = np.zeros((iteration, ))
iteration -= 1
for train_idx, test_idx in kf:
train_idx_all = np.array([], np.int)
test_idx_all = np.array([], np.int)
for action_idx in range(action_num):
train_idx_all = np.concatenate(
(train_idx_all, train_idx * action_idx), axis=0)
test_idx_all = np.concatenate(
(test_idx_all, test_idx * action_idx), axis=0)
# X_train, y_train = trains_S0[train_idx_all], classes[train_idx_all]
X_train, y_train = trains_S0, classes
X_train_shift, y_train_shift = X_test[train_idx_all], classes[
train_idx_all]
X_train_all = np.concatenate((X_train, X_train_shift), axis=0)
y_train_all = np.concatenate((y_train, y_train_shift), axis=0)
sys.exit(0)
score_inter = clf.fit(X_train_all,
y_train_all).score(X_test, y_test)
scores[iteration] = score_inter.mean()
# print X_train.shape, y_train.shape
if channel_pos != 'S0':
# plsca = joblib.load(transform_fold+'/cca_transform_'+kw['subject']+'_'+channel_pos+'.model')
plsca = PLSCanonical(n_components=14)
# print X_test.shape, X_train.shape
# sys.exit(0)
plsca.fit(X_test[train_idx], X_train)
X_test_cca, X_train_cca = plsca.transform(X_test, X_train)
cca_score = clf.fit(X_train_cca,
y_train).score(X_test_cca, y_test)
cca_scores[iteration] = cca_score.mean()
iteration -= 1
# print scores
# print cca_scores
# sys.exit(0)
results.append(
['feat_TD4', 'lda', channel_pos,
np.mean(scores),
np.std(scores)])
results.append([
'feat_TD4', 'lda_cca', channel_pos,
np.mean(cca_scores),
np.std(cca_scores)
])
log_result(results, log_fold + '/' + log_file + '_' + str(kw['num']), 2)
print '----Log Fold:', log_fold, ', log_file: ', log_file + '_' + channel_pos + '_' + str(
kw['num'])
print '----training TD4 time elapsed:', time.time() - start_time
connectivity_data)
if not include_negative_weights:
# set negative connectivities to 0
edge_data = np.apply_along_axis(
lambda x: [0 if element < 0 else element for element in x], 1,
edge_data)
# re-split data (3 ways) for CCA
X1_train = edge_data[:140, :]
X2_train = edge_data[140:280, :]
X2_remain = edge_data[280:, :]
#cca = CCA(n_components =2)
#cca.fit(X1_train, X2_train)
cca = PLSCanonical(n_components=100)
cca.fit(X1_train, X2_train)
block_1_transformed, block_2_transformed = cca.transform(X1_train,
X2_train,
copy=False)
block_3_transformed = np.dot(X2_remain, cca.y_rotations_)
edge_data_transformed = np.vstack(
(block_1_transformed, block_2_transformed, block_3_transformed))
# initialise the classifier
clf = svm.SVC(kernel='precomputed')
# optional shuffle
perm = np.random.permutation(n_subjects)
#print perm
#correct not accurate
from sklearn.cross_validation import train_test_split
from sklearn.neighbors import KNeighborsClassifier
from sklearn import metrics
from sklearn.svm import SVC
import numpy as np
import pandas as pd
from sklearn.cross_decomposition import PLSRegression
from sklearn.cross_decomposition import PLSCanonical
df = pd.read_csv('newdata.csv')
x = df.drop(['tag'], axis=1)
y = df.drop(['kx', 'ky', 'kz', 'wa', 'wb', 'wc', 'wd', 'we', 'wf'], axis=1)
X_train, X_test, Y_train, Y_test = train_test_split(x, y, random_state=5)
plsr = PLSRegression()
plsr.fit(X_train, Y_train)
plsc = PLSCanonical()
plsc.fit(X_train, Y_train)
print(plsr.score(X_test, Y_test))
print(plsc.score(X_test, Y_test))
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)
dataTestT = plscan.transform(dataTest)
plsCanScores[:,i] = util.classify(dataTrainT,dataTestT,labelsTrain,labelsTest)
fig = plt.figure()
util.plotAccuracy(fig,nComponents,plsCanScores)
plt.title('PLS Canonical accuracy',figure=fig)
plscan = PLSCanonical(n_components=2)
xt,yt = plscan.fit_transform(dataTrain,Ytrain)
fig = plt.figure()
util.plotData(fig,xt,labelsTrain,classColors)
u = plscan.x_weights_
plt.quiver(u[0,0],u[1,0],color='k',edgecolor='k',lw=1,scale=0.1,figure=fig)
def __init__(self, **hyperparams):
self._hyperparams = hyperparams
self._wrapped_model = Op(**self._hyperparams)
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)
if (0):
#%% PLS Cannonical
nComponents = np.arange(1, nClasses + 1)
plsCanScores = np.zeros((5, np.alen(nComponents)))
for i, n in enumerate(nComponents):
plscan = PLSCanonical(n_components=n)
plscan.fit(Xtrain, Ytrain)
XtrainT = plscan.transform(Xtrain)
XtestT = plscan.transform(Xtest)
plsCanScores[:, i] = util.classify(XtrainT, XtestT, labelsTrain,
labelsTest)
plscan = PLSCanonical(n_components=2)
plscan.fit(Xtrain, Ytrain)
xt = plscan.transform(Xtrain)
fig = plt.figure()
util.plotData(fig, xt, labelsTrain, classColors)
plt.title('First 2 components of projected data')
#%% Plot accuracies for PLSSVD
plt.figure()
from sklearn import datasets
import numpy as np
from sklearn.model_selection import train_test_split
from sklearn.cross_decomposition import PLSCanonical
from sklearn.neighbors import KNeighborsClassifier
import math
from mlxtend.feature_selection import SequentialFeatureSelector as SFS
dataSet = datasets.load_digits()
data = dataSet["data"]
target = dataSet["target"]
plsca = PLSCanonical(n_components=2)
plsca.fit(data, target)
X_train_r, Y_train_r = plsca.transform(data, target)
knn = math.sqrt(len(X_train_r))
knn = KNeighborsClassifier(n_neighbors=int(knn))
Y_train_r = [int(Y_train_r[i]) for i in range(0, len(Y_train_r))]
k = knn.fit(X_train_r, Y_train_r)
print(k.score(X_train_r, Y_train_r))
knn = KNeighborsClassifier(n_neighbors=4)
sfs = SFS(knn,
k_features=3,
forward=True,
floating=False,
verbose=2,
def _create_model(self):
return PLSCanonical()
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)
if (0):
#%% PLS Cannonical
nComponents = np.arange(1,nClasses+1)
plsCanScores = np.zeros((5,np.alen(nComponents)))
for i,n in enumerate(nComponents):
plscan = PLSCanonical(n_components=n)
plscan.fit(Xtrain,Ytrain)
XtrainT = plscan.transform(Xtrain)
XtestT = plscan.transform(Xtest)
plsCanScores[:,i] = util.classify(XtrainT,XtestT,labelsTrain,labelsTest)
plscan = PLSCanonical(n_components=2)
plscan.fit(Xtrain,Ytrain)
xt = plscan.transform(Xtrain)
fig = plt.figure()
util.plotData(fig,xt,labelsTrain,classColors)
plt.title('First 2 components of projected data')
#%% Plot accuracies for PLSSVD
def fit_scikit_pls(self, train_set_1, train_set_2):
self.PLS = PLSCanonical(n_components=self.latent_dims, scale=False)
self.PLS.fit(train_set_1, train_set_2)
self.score_list = [self.PLS.x_scores_, self.PLS.y_scores_]
self.weights_list = [self.PLS.x_weights_, self.PLS.y_weights_]
return self
def __init__(self, allow_missing_values=False):
# Explicitly initialise both constructors:
super(MultiCurvePlsPredictor,
self).__init__(classic_estimator=PLSCanonical(),
allow_missing_values=allow_missing_values)
#Następnie sprawdź sprawność klasyfikatora kNN dla zbioru testowego ograniczonego do wybranego
#podzbioru cech. Parametr kk przyjmij jako pierwiastek z liczby obiektów w zbiorze.
#Dla jakiej liczby cech osiągnięto najlepsze rezultaty?
from sklearn import datasets
from sklearn import model_selection
from sklearn.neighbors import KNeighborsClassifier
import math
from sklearn.cross_decomposition import PLSCanonical
mnist_dataset = datasets.load_digits()
X = mnist_dataset.data
Y = mnist_dataset.target
target_names = mnist_dataset.target_names
train, test, train_targets, test_targets = model_selection.train_test_split(X, Y, train_size=0.5,test_size=0.5)
max = 0
max_n_components = 0
for i in range(1, 10):
plsca = PLSCanonical(n_components=i)
plsca.fit(train, train_targets)
X_r = plsca.fit(train, train_targets).transform(train)
Y_r = plsca.fit(test, test_targets).transform(test)
clf = KNeighborsClassifier(round(math.sqrt(X.shape[0])),weights="uniform", metric="euclidean")
clf.fit(X_r, train_targets)
print(i, ":", clf.score(Y_r, test_targets))
if max < clf.score(Y_r, test_targets):
max = clf.score(Y_r, test_targets)
max_n_components = i
print("Best result for:", max_n_components)
def get_algorithm(self):
'''
Inputs:
algorithm (string) - Name of the regressor to run. Follows Sklearn naming conventions.
Available keys: ARDRegression | AdaBoostRegressor | BaggingRegressor | BayesianRidge | CCA
DecisionTreeRegressor | ElasticNet | ExtraTreeRegressor
ExtraTreesRegressor | GaussianProcessRegressor | GradientBoostingRegressor
HuberRegressor | KNeighborsRegressor | KernelRidge | Lars | Lasso
LassoLars | LinearRegression | LinearSVR | MLPRegressor | NuSVR |
OrthogonalMatchingPursuit | PLSCanonical | PLSRegression |
PassiveAggressiveRegressor | RANSACRegressor | RandomForestRegressor |
Ridge | SGDRegressor | SVR | TheilSenRegressor | TransformedTargetRegressor
Currently not supporting: ElasticNetCV | LarsCV | LassoCV | LassoLarsCV | LassoLarsIC |
MultiTaskElasticNet | MultiTaskElasticNetCV | MultiTaskLasso | MultiTaskLassoCV |
OrthogonalMatchingPursuitCV | RidgeCV | RadiusNeighborsRegressor
Outputs:
Notes:
Scoring Metrics: https://scikit-learn.org/stable/modules/model_evaluation.html#scoring-parameter
'''
if (self.algorithmName == "ARDRegression"): algorithm = ARDRegression()
elif (self.algorithmName == "AdaBoostRegressor"):
algorithm = AdaBoostRegressor()
elif (self.algorithmName == "BaggingRegressor"):
algorithm = BaggingRegressor()
elif (self.algorithmName == "BayesianRidge"):
algorithm = BayesianRidge()
elif (self.algorithmName == "CCA"):
algorithm = CCA()
elif (self.algorithmName == "DecisionTreeRegressor"):
algorithm = DecisionTreeRegressor()
elif (self.algorithmName == "ElasticNet"):
algorithm = ElasticNet()
elif (self.algorithmName == "ExtraTreeRegressor"):
algorithm = ExtraTreeRegressor()
elif (self.algorithmName == "ExtraTreesRegressor"):
algorithm = ExtraTreesRegressor()
elif (self.algorithmName == "GaussianProcessRegressor"):
algorithm = GaussianProcessRegressor()
elif (self.algorithmName == "GradientBoostingRegressor"):
algorithm = GradientBoostingRegressor()
elif (self.algorithmName == "HuberRegressor"):
algorithm = HuberRegressor()
elif (self.algorithmName == "KNeighborsRegressor"):
algorithm = KNeighborsRegressor()
elif (self.algorithmName == "KernelRidge"):
algorithm = KernelRidge()
elif (self.algorithmName == "Lars"):
algorithm = Lars()
elif (self.algorithmName == "Lasso"):
algorithm = Lasso()
elif (self.algorithmName == "LassoLars"):
algorithm = LassoLars()
elif (self.algorithmName == "LinearRegression"):
algorithm = LinearRegression()
elif (self.algorithmName == "LinearSVR"):
algorithm = LinearSVR()
elif (self.algorithmName == "MLPRegressor"):
algorithm = MLPRegressor()
elif (self.algorithmName == "NuSVR"):
algorithm = NuSVR()
elif (self.algorithmName == "OrthogonalMatchingPursuit"):
algorithm = OrthogonalMatchingPursuit()
elif (self.algorithmName == "PLSCanonical"):
algorithm = PLSCanonical()
elif (self.algorithmName == "PLSRegression"):
algorithm = PLSRegression()
elif (self.algorithmName == "PassiveAggressiveRegressor"):
algorithm = PassiveAggressiveRegressor()
elif (self.algorithmName == "RANSACRegressor"):
algorithm = RANSACRegressor()
elif (self.algorithmName == "RandomForestRegressor"):
algorithm = RandomForestRegressor()
elif (self.algorithmName == "Ridge"):
algorithm = Ridge()
elif (self.algorithmName == "SGDRegressor"):
algorithm = SGDRegressor()
elif (self.algorithmName == "SVR"):
algorithm = SVR()
elif (self.algorithmName == "TheilSenRegressor"):
algorithm = TheilSenRegressor()
elif (self.algorithmName == "TransformedTargetRegressor"):
algorithm = TransformedTargetRegressor()
else:
return None
return algorithm
ursis = []
graph_features = []
# label_col = df1.loc['URSI']
# print('label col:')
# print(label_col)
ursi_ids = df1.iloc[:, 0]
linreg = LinearRegression(normalize=True)
lasso = Lasso(fit_intercept=True, normalize=True)
ransac = RANSACRegressor()
pls = PLSRegression()
cca = CCA()
pls_ca = PLSCanonical()
rf = RandomForestRegressor(n_estimators=50, n_jobs=4)
gp = GaussianProcessRegressor()
ir = IsotonicRegression()
svr_lin = SVR(kernel='linear')
svr_rbf = SVR()
classifiers = [linreg, lasso, pls, svr_lin, svr_rbf, rf, gp]
classifier_names = ['LR', 'Lasso', 'PLS', 'SVR (lin)', 'SVR (rbf)', 'RF', 'GP']
prediction_targets = ['all', 'CCI']
r = {}
mse = {}
#correct not accurate
from sklearn.cross_validation import train_test_split
from sklearn.neighbors import KNeighborsClassifier
from sklearn import metrics
from sklearn.svm import SVC
import numpy as np
import pandas as pd
from sklearn.cross_decomposition import PLSRegression
from sklearn.cross_decomposition import PLSCanonical
df=pd.read_csv('newdata.csv')
x=df.drop(['tag'],axis=1)
y=df.drop(['kx','ky','kz','wa','wb','wc','wd','we','wf'],axis=1)
X_train , X_test , Y_train , Y_test = train_test_split(x,y , random_state=5)
plsr=PLSRegression()
plsr.fit(X_train,Y_train)
plsc=PLSCanonical()
plsc.fit(X_train,Y_train)
print (plsr.score(X_test,Y_test))
print (plsc.score(X_test,Y_test))
def GetAllModelsForComparison(X_train, Y_train):
models = {
'ARDRegression': ARDRegression(),
'BayesianRidge': BayesianRidge(),
'ElasticNet': ElasticNet(),
'ElasticNetCV': ElasticNetCV(),
'Hinge': Hinge(),
#'Huber': Huber(),
'HuberRegressor': HuberRegressor(),
'Lars': Lars(),
'LarsCV': LarsCV(),
'Lasso': Lasso(),
'LassoCV': LassoCV(),
'LassoLars': LassoLars(),
'LassoLarsCV': LassoLarsCV(),
'LinearRegression': LinearRegression(),
'Log': Log(),
'LogisticRegression': LogisticRegression(),
'LogisticRegressionCV': LogisticRegressionCV(),
'ModifiedHuber': ModifiedHuber(),
'MultiTaskElasticNet': MultiTaskElasticNet(),
'MultiTaskElasticNetCV': MultiTaskElasticNetCV(),
'MultiTaskLasso': MultiTaskLasso(),
'MultiTaskLassoCV': MultiTaskLassoCV(),
'OrthogonalMatchingPursuit': OrthogonalMatchingPursuit(),
'OrthogonalMatchingPursuitCV': OrthogonalMatchingPursuitCV(),
'PassiveAggressiveClassifier': PassiveAggressiveClassifier(),
'PassiveAggressiveRegressor': PassiveAggressiveRegressor(),
'Perceptron': Perceptron(),
'RANSACRegressor': RANSACRegressor(),
#'RandomizedLasso': RandomizedLasso(),
#'RandomizedLogisticRegression': RandomizedLogisticRegression(),
'Ridge': Ridge(),
'RidgeCV': RidgeCV(),
'RidgeClassifier': RidgeClassifier(),
'SGDClassifier': SGDClassifier(),
'SGDRegressor': SGDRegressor(),
'SquaredLoss': SquaredLoss(),
'TheilSenRegressor': TheilSenRegressor(),
'BaseEstimator': BaseEstimator(),
'ClassifierMixin': ClassifierMixin(),
'LinearClassifierMixin': LinearClassifierMixin(),
'LinearDiscriminantAnalysis': LinearDiscriminantAnalysis(),
'QuadraticDiscriminantAnalysis': QuadraticDiscriminantAnalysis(),
'StandardScaler': StandardScaler(),
'TransformerMixin': TransformerMixin(),
'BaseEstimator': BaseEstimator(),
'KernelRidge': KernelRidge(),
'RegressorMixin': RegressorMixin(),
'LinearSVC': LinearSVC(),
'LinearSVR': LinearSVR(),
'NuSVC': NuSVC(),
'NuSVR': NuSVR(),
'OneClassSVM': OneClassSVM(),
'SVC': SVC(),
'SVR': SVR(),
'SGDClassifier': SGDClassifier(),
'SGDRegressor': SGDRegressor(),
#'BallTree': BallTree(),
#'DistanceMetric': DistanceMetric(),
#'KDTree': KDTree(),
'KNeighborsClassifier': KNeighborsClassifier(),
'KNeighborsRegressor': KNeighborsRegressor(),
'KernelDensity': KernelDensity(),
#'LSHForest': LSHForest(),
'LocalOutlierFactor': LocalOutlierFactor(),
'NearestCentroid': NearestCentroid(),
'NearestNeighbors': NearestNeighbors(),
'RadiusNeighborsClassifier': RadiusNeighborsClassifier(),
'RadiusNeighborsRegressor': RadiusNeighborsRegressor(),
#'GaussianProcess': GaussianProcess(),
'GaussianProcessRegressor': GaussianProcessRegressor(),
'GaussianProcessClassifier': GaussianProcessClassifier(),
'CCA': CCA(),
'PLSCanonical': PLSCanonical(),
'PLSRegression': PLSRegression(),
'PLSSVD': PLSSVD(),
#'ABCMeta': ABCMeta(),
#'BaseDiscreteNB': BaseDiscreteNB(),
'BaseEstimator': BaseEstimator(),
#'BaseNB': BaseNB(),
'BernoulliNB': BernoulliNB(),
'ClassifierMixin': ClassifierMixin(),
'GaussianNB': GaussianNB(),
'LabelBinarizer': LabelBinarizer(),
'MultinomialNB': MultinomialNB(),
'DecisionTreeClassifier': DecisionTreeClassifier(),
'DecisionTreeRegressor': DecisionTreeRegressor(),
'ExtraTreeClassifier': ExtraTreeClassifier(),
'AdaBoostClassifier': AdaBoostClassifier(),
'AdaBoostRegressor': AdaBoostRegressor(),
'BaggingClassifier': BaggingClassifier(),
'BaggingRegressor': BaggingRegressor(),
#'BaseEnsemble': BaseEnsemble(),
'ExtraTreesClassifier': ExtraTreesClassifier(),
'ExtraTreesRegressor': ExtraTreesRegressor(),
'GradientBoostingClassifier': GradientBoostingClassifier(),
'GradientBoostingRegressor': GradientBoostingRegressor(),
'IsolationForest': IsolationForest(),
'RandomForestClassifier': RandomForestClassifier(),
'RandomForestRegressor': RandomForestRegressor(),
'RandomTreesEmbedding': RandomTreesEmbedding(),
#'VotingClassifier': VotingClassifier(),
'BaseEstimator': BaseEstimator(),
'ClassifierMixin': ClassifierMixin(),
'LabelBinarizer': LabelBinarizer(),
'MetaEstimatorMixin': MetaEstimatorMixin(),
#'OneVsOneClassifier': OneVsOneClassifier(),
#'OneVsRestClassifier': OneVsRestClassifier(),
#'OutputCodeClassifier': OutputCodeClassifier(),
'Parallel': Parallel(),
#'ABCMeta': ABCMeta(),
'BaseEstimator': BaseEstimator(),
#'ClassifierChain': ClassifierChain(),
'ClassifierMixin': ClassifierMixin(),
'MetaEstimatorMixin': MetaEstimatorMixin(),
#'MultiOutputClassifier': MultiOutputClassifier(),
#'MultiOutputEstimator': MultiOutputEstimator(),
#'MultiOutputRegressor': MultiOutputRegressor(),
'Parallel': Parallel(),
'RegressorMixin': RegressorMixin(),
'LabelPropagation': LabelPropagation(),
'LabelSpreading': LabelSpreading(),
'BaseEstimator': BaseEstimator(),
'IsotonicRegression': IsotonicRegression(),
'RegressorMixin': RegressorMixin(),
'TransformerMixin': TransformerMixin(),
'BernoulliRBM': BernoulliRBM(),
'MLPClassifier': MLPClassifier(),
'MLPRegressor': MLPRegressor()
}
return models
def test_sanity_check_pls_canonical_random():
# Sanity check for PLSCanonical on random data
# The results were checked against the R-package plspm
n = 500
p_noise = 10
q_noise = 5
# 2 latents vars:
rng = check_random_state(11)
l1 = rng.normal(size=n)
l2 = rng.normal(size=n)
latents = np.array([l1, l1, l2, l2]).T
X = latents + rng.normal(size=4 * n).reshape((n, 4))
Y = latents + rng.normal(size=4 * n).reshape((n, 4))
X = np.concatenate((X, rng.normal(size=p_noise * n).reshape(n, p_noise)),
axis=1)
Y = np.concatenate((Y, rng.normal(size=q_noise * n).reshape(n, q_noise)),
axis=1)
pls = PLSCanonical(n_components=3)
pls.fit(X, Y)
expected_x_weights = np.array([
[0.65803719, 0.19197924, 0.21769083],
[0.7009113, 0.13303969, -0.15376699],
[0.13528197, -0.68636408, 0.13856546],
[0.16854574, -0.66788088, -0.12485304],
[-0.03232333, -0.04189855, 0.40690153],
[0.1148816, -0.09643158, 0.1613305],
[0.04792138, -0.02384992, 0.17175319],
[-0.06781, -0.01666137, -0.18556747],
[-0.00266945, -0.00160224, 0.11893098],
[-0.00849528, -0.07706095, 0.1570547],
[-0.00949471, -0.02964127, 0.34657036],
[-0.03572177, 0.0945091, 0.3414855],
[0.05584937, -0.02028961, -0.57682568],
[0.05744254, -0.01482333, -0.17431274],
])
expected_x_loadings = np.array([
[0.65649254, 0.1847647, 0.15270699],
[0.67554234, 0.15237508, -0.09182247],
[0.19219925, -0.67750975, 0.08673128],
[0.2133631, -0.67034809, -0.08835483],
[-0.03178912, -0.06668336, 0.43395268],
[0.15684588, -0.13350241, 0.20578984],
[0.03337736, -0.03807306, 0.09871553],
[-0.06199844, 0.01559854, -0.1881785],
[0.00406146, -0.00587025, 0.16413253],
[-0.00374239, -0.05848466, 0.19140336],
[0.00139214, -0.01033161, 0.32239136],
[-0.05292828, 0.0953533, 0.31916881],
[0.04031924, -0.01961045, -0.65174036],
[0.06172484, -0.06597366, -0.1244497],
])
expected_y_weights = np.array([
[0.66101097, 0.18672553, 0.22826092],
[0.69347861, 0.18463471, -0.23995597],
[0.14462724, -0.66504085, 0.17082434],
[0.22247955, -0.6932605, -0.09832993],
[0.07035859, 0.00714283, 0.67810124],
[0.07765351, -0.0105204, -0.44108074],
[-0.00917056, 0.04322147, 0.10062478],
[-0.01909512, 0.06182718, 0.28830475],
[0.01756709, 0.04797666, 0.32225745],
])
expected_y_loadings = np.array([
[0.68568625, 0.1674376, 0.0969508],
[0.68782064, 0.20375837, -0.1164448],
[0.11712173, -0.68046903, 0.12001505],
[0.17860457, -0.6798319, -0.05089681],
[0.06265739, -0.0277703, 0.74729584],
[0.0914178, 0.00403751, -0.5135078],
[-0.02196918, -0.01377169, 0.09564505],
[-0.03288952, 0.09039729, 0.31858973],
[0.04287624, 0.05254676, 0.27836841],
])
assert_array_almost_equal(np.abs(pls.x_loadings_),
np.abs(expected_x_loadings))
assert_array_almost_equal(np.abs(pls.x_weights_),
np.abs(expected_x_weights))
assert_array_almost_equal(np.abs(pls.y_loadings_),
np.abs(expected_y_loadings))
assert_array_almost_equal(np.abs(pls.y_weights_),
np.abs(expected_y_weights))
x_loadings_sign_flip = np.sign(pls.x_loadings_ / expected_x_loadings)
x_weights_sign_flip = np.sign(pls.x_weights_ / expected_x_weights)
y_weights_sign_flip = np.sign(pls.y_weights_ / expected_y_weights)
y_loadings_sign_flip = np.sign(pls.y_loadings_ / expected_y_loadings)
assert_array_almost_equal(x_loadings_sign_flip, x_weights_sign_flip)
assert_array_almost_equal(y_loadings_sign_flip, y_weights_sign_flip)
assert_matrix_orthogonal(pls.x_weights_)
assert_matrix_orthogonal(pls.y_weights_)
assert_matrix_orthogonal(pls._x_scores)
assert_matrix_orthogonal(pls._y_scores)
tol=tol,
shuffle=True,
verbose=0,
epsilon=0.1,
random_state=random_state,
learning_rate='invscaling',
eta0=0.01,
power_t=0.25,
early_stopping=False,
validation_fraction=0.1,
n_iter_no_change=5,
warm_start=False,
average=False),
PLSCanonical(n_components=9,
scale=False,
algorithm='nipals',
max_iter=1000,
tol=1e-3,
copy=True),
CCA(n_components=9, scale=False, max_iter=1000, tol=1e-3, copy=True),
MLPRegressor(hidden_layer_sizes=(500, 30),
activation='relu',
solver='lbfgs',
alpha=0.0001,
batch_size='auto',
learning_rate='constant',
learning_rate_init=0.00000001,
power_t=0.5,
max_iter=100000,
shuffle=True,
random_state=random_state,
tol=tol,
def training_lda_TD4_intra(my_clfs, trains, classes, **kw):
start_time = time.time()
if(kw.has_key('log_fold')):
log_fold = root_path + '/result/' + kw['log_fold']
new_fold(log_fold)
chan_len = kw['chan_len']
action_num = kw['action_num']
cv = 3
results = []
results.append(
['Feat', 'Algorithm','n_components', 'Channel_Pos', 'Accuracy', 'std'])
log_file = 'feat_'+kw['feature_type']+'_intra'
clf = sklearn.lda.LDA(solver='svd', shrinkage=None, priors=None,
n_components=None, store_covariance=False,
tol=0.0001)
data_num = trains.shape[0]/action_num
scores = sklearn.cross_validation.cross_val_score(clf, trains, classes, cv=cv)
results.append(['feat_TD4_cv_'+str(cv), 'lda', 'ALL', 0, scores.mean(), scores.std()])
# 组内训练策略 9组数据
print '组内训练.............'
for idx, channel_pos in enumerate(kw['pos_list']):
# print '----training TD4 intra , channel_pos: ', channel_pos,'......'
trains_intra = trains[:,idx*chan_len: idx*chan_len+chan_len]
scores = sklearn.cross_validation.cross_val_score(
clf, trains_intra, classes, cv=cv)
results.append(['feat_TD4_cv_'+str(cv), 'lda', 0, channel_pos, scores.mean(), scores.std()])
# 中心训练策略
print '中心训练策略.............'
trains_intra_S0 = trains[:,0:chan_len]
for idx, channel_pos in enumerate(kw['pos_list']):
if channel_pos == 'S0':
continue
tests_shift = trains[:,idx*chan_len: idx*chan_len+chan_len]
# if channel_pos == 'L2':
# print idx*chan_len, idx*chan_len+chan_len, tests_shift.shape, trains.shape
# sys.exit(0)
scores = clf.fit(trains_intra_S0, classes).score(tests_shift, classes)
results.append(['feat_TD4_cv_'+str(cv), 'lda', 0, 'train S0' + ' test ' + channel_pos, scores.mean(), scores.std()])
# 组训练策略（不同于组内训练策略） 5-fold
print '组训练策略.............'
trains_intra_S0 = trains[:,0:chan_len]
kf = KFold(data_num, n_folds=cv)
for idx, channel_pos in enumerate(kw['pos_list']):
if channel_pos == 'S0':
continue
itera = cv
scores = np.zeros( (itera,) )
# stds = np.zeros( (itera,) )
itera -= 1
trains_shift = trains[:,idx*chan_len: idx*chan_len+chan_len]
for train_idx, test_idx in kf:
train_idx_all = np.array([], np.int)
test_idx_all = np.array([], np.int)
for action_idx in range(action_num):
train_idx_all = np.concatenate( (train_idx_all, train_idx*(action_idx+1)), axis=0)
test_idx_all = np.concatenate( (test_idx_all, test_idx*(action_idx+1)), axis=0)
X_train = np.concatenate( (trains_intra_S0[train_idx_all], trains_shift[train_idx_all]), axis=0)
y_train = np.concatenate( (classes[train_idx_all], classes[train_idx_all]), axis=0)
X_test = trains_shift[test_idx_all]
y_test = classes[test_idx_all]
# X_test = trains_shift
# y_test = classes
score = clf.fit(X_train, y_train).score(X_test, y_test)
scores[itera] = score.mean()
itera -= 1
# print scores
results.append(['feat_TD4_cv_'+str(cv), 'lda', 0, 'S0 + '+channel_pos, np.mean(scores), np.std(scores)])
# 基于CCA的训练策略 5-fold 交叉验证
print 'CCA训练策略.............'
trains_S0 = trains[:,0:chan_len]
n_components_list = [6, 8, 10, 12, 14, 16] # 子空间维数
# n_components_list = [12,14,16]
kf = KFold(data_num, n_folds=cv)
for n_components in n_components_list:
for idx, channel_pos in enumerate(kw['pos_list']):
if channel_pos == 'S0':
continue
itera = cv
scores = np.zeros( (itera,) )
stds = np.zeros( (itera,) )
itera -= 1
trains_shift = trains[:,idx*chan_len: idx*chan_len+chan_len]
for train_idx, test_idx in kf:
train_idx_all = np.array([], np.int)
test_idx_all = np.array([], np.int)
for action_idx in range(action_num):
train_idx_all = np.concatenate( (train_idx_all, train_idx*(action_idx+1)), axis=0)
test_idx_all = np.concatenate( (test_idx_all, test_idx*(action_idx+1)), axis=0)
# print train_idx_all.shape, train_idx_all, test_idx_all.shape, test_idx_all
# plsca.fit(trains_shift[train_idx_all], trains_S0[train_idx_all])
plsca = PLSCanonical(n_components=n_components)
plsca.fit(trains_shift, trains_S0)
trains_shift_cca, trains_S0_cca = plsca.transform(trains_shift, trains_S0)
X_trains = np.concatenate( (trains_S0_cca, trains_shift_cca[train_idx_all]), axis=0)
y_trains = np.concatenate( (classes, classes[train_idx_all]), axis=0)
score = clf.fit(X_trains, y_trains).score(trains_shift_cca[test_idx_all], classes[test_idx_all])
scores[itera] = score.mean()
# stds[itera] = score.std()
itera -= 1
results.append(['feat_TD4_cv_'+str(cv), 'lda_cca', n_components, 'S0 + '+channel_pos, np.mean(scores), np.std(scores)])
log_result(results, log_fold + '/' + log_file + '_action_1-'+str(action_num), 2)
print '----Log Fold:', log_fold, ', log_file: ', log_file + '_action_1-'+str(action_num)
print '----training TD4 time elapsed:', time.time() - start_time
def training_lda_TD4_inter(my_clfs, trains_S0, trains_shift, classes, **kw):
print 'training_lda_TD4_inter.........'
start_time = time.time()
log_fold = root_path + '/result/' + kw['log_fold']
new_fold(log_fold)
chan_len = kw['chan_len']
action_num = kw['action_num']
print "----training "+kw['feature_type']+" inter, training by position O, testing by electrode shift "
cv = 5
results = []
results.append(['Feat', 'Algorithm','Channel_Pos', 'Accuracy', 'std'])
log_file = 'feat_'+kw['feature_type']+'_inter'
clf = sklearn.lda.LDA(solver='svd', shrinkage=None, priors=None,
n_components=None, store_covariance=False,
tol=0.0001)
data_num = trains_S0.shape[0]/action_num
# print data_num
scores = sklearn.cross_validation.cross_val_score(
clf, trains_S0, classes, cv=cv)
results.append(['feat_TD4_cv_'+str(cv), 'lda', 'S0',
scores.mean(), scores.std()])
kf = KFold(data_num, n_folds=cv)
for idx, channel_pos in enumerate(kw['pos_list']):
X_test = trains_shift[:,idx*chan_len:idx*chan_len+chan_len]
y_test = classes
iteration = cv
scores = np.zeros((iteration,))
cca_scores = np.zeros((iteration,))
iteration -= 1
for train_idx, test_idx in kf:
train_idx_all = np.array([], np.int)
test_idx_all = np.array([], np.int)
for action_idx in range(action_num):
train_idx_all = np.concatenate( (train_idx_all, train_idx*action_idx), axis=0)
test_idx_all = np.concatenate( (test_idx_all, test_idx*action_idx), axis=0)
# X_train, y_train = trains_S0[train_idx_all], classes[train_idx_all]
X_train, y_train = trains_S0, classes
X_train_shift, y_train_shift = X_test[train_idx_all], classes[train_idx_all]
X_train_all = np.concatenate( (X_train, X_train_shift), axis=0)
y_train_all = np.concatenate( (y_train, y_train_shift), axis=0)
sys.exit(0)
score_inter = clf.fit(X_train_all, y_train_all).score(X_test, y_test)
scores[iteration] = score_inter.mean()
# print X_train.shape, y_train.shape
if channel_pos != 'S0':
# plsca = joblib.load(transform_fold+'/cca_transform_'+kw['subject']+'_'+channel_pos+'.model')
plsca = PLSCanonical(n_components=14)
# print X_test.shape, X_train.shape
# sys.exit(0)
plsca.fit(X_test[train_idx], X_train)
X_test_cca, X_train_cca = plsca.transform(X_test, X_train)
cca_score = clf.fit(X_train_cca, y_train).score(X_test_cca, y_test)
cca_scores[iteration] = cca_score.mean()
iteration -= 1
# print scores
# print cca_scores
# sys.exit(0)
results.append(['feat_TD4', 'lda',
channel_pos, np.mean(scores), np.std(scores)])
results.append(['feat_TD4', 'lda_cca',
channel_pos, np.mean(cca_scores), np.std(cca_scores)])
log_result(results, log_fold + '/' + log_file + '_' + str(kw['num']), 2)
print '----Log Fold:', log_fold, ', log_file: ', log_file + '_' + channel_pos + '_' + str(kw['num'])
print '----training TD4 time elapsed:', time.time() - start_time
# mean_shift = 0
# std_shift = 0
# for i in range(2, 10):
# mean_shift += results[i][4]
# std_shift += results[i][5]
# mean_shift /= 9
# std_shift /= 9
# results.append(['feat_TD4','lda(svd;tol=0.0001)', 'Shift_means', '1.0', mean_shift, std_shift])
# mean_all = 0
# std_all = 0
# for i in range(1, 10):
# mean_all += results[i][4]
# std_all += results[i][5]
# mean_all /= 9
# std_all /= 9
temp4 = []
for e in temp1[:]:
temp4.append(e)
for e in temp2[:]:
temp4.append(e)
if len(temp4) == 600 and len(temp3) == 300:
x_n.append(temp4)
y_n.append(temp3)
npx = np.asarray(x, dtype=np.float64)
npy = np.asarray(y, dtype=np.float64)
npxn = np.asarray(x_n, dtype=np.float64)
npyn = np.asarray(y_n, dtype=np.float64)
cca = PLSCanonical(n_components=2)
cca.fit_transform(npx, npy)
npx, npy = cca.transform(npx, npy)
npxn, npyn = cca.transform(npxn, npyn)
pls.fit(npx, npy)
params = pls.get_params(deep=True)
print(params)
pls.set_params(**params)
y_score = pls.predict(npxn)
sim_count = 0
tol = 0.1
for index in range(len(y_score)):
X_train = X[:n // 2]
Y_train = Y[:n // 2]
X_test = X[n // 2:]
Y_test = Y[n // 2:]
print("Corr(X)")
print(np.round(np.corrcoef(X.T), 2))
print("Corr(Y)")
print(np.round(np.corrcoef(Y.T), 2))
# #############################################################################
# Canonical (symmetric) PLS
# Transform data
# ~~~~~~~~~~~~~~
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
plt.figure(figsize=(12, 8))
plt.subplot(221)
plt.scatter(X_train_r[:, 0],
Y_train_r[:, 0],
label="train",
marker="o",
c="b",
s=25)
linear_model.LassoLarsCV(),
linear_model.LassoLarsIC(),
linear_model.LinearRegression(),
LinearSVR(),
#linear_model.LogisticRegression(),
#linear_model.LogisticRegressionCV(),
MLPRegressor(),
#linear_model.ModifiedHuber(),
#linear_model.MultiTaskElasticNet(),
#linear_model.MultiTaskElasticNetCV(),
#linear_model.MultiTaskLasso(),
#linear_model.MultiTaskLassoCV(),
NuSVR(),
linear_model.OrthogonalMatchingPursuit(),
linear_model.OrthogonalMatchingPursuitCV(),
PLSCanonical(),
PLSRegression(),
linear_model.PassiveAggressiveRegressor(),
linear_model.RANSACRegressor(),
RadiusNeighborsRegressor(),
RandomForestRegressor(),
#linear_model.RandomizedLasso(),
#linear_model.RandomizedLogisticRegression(),
linear_model.RANSACRegressor(),
linear_model.Ridge(),
linear_model.RidgeCV(),
linear_model.SGDRegressor(),
SVR(),
linear_model.TheilSenRegressor()
]
