def find_correlation_cca_method1(signal, reference_signals, n_components=2):
r"""
Perform canonical correlation analysis (CCA)
Reference: https://github.com/aaravindravi/Brain-computer-interfaces/blob/master/notebook_12_class_cca.ipynb
Args:
signal : ndarray, shape (channel,time)
Input signal in time domain
reference_signals : ndarray, shape (len(flick_freq),2*num_harmonics,time)
Required sinusoidal reference templates corresponding to the flicker frequency for SSVEP classification
n_components : int, default: 2
number of components to keep (for sklearn.cross_decomposition.CCA)
Returns:
result : array, size: len(flick_freq)
Probability for each reference signals
Dependencies:
CCA : sklearn.cross_decomposition.CCA
np : numpy package
"""
cca = CCA(n_components)
corr = np.zeros(n_components)
result = np.zeros(reference_signals.shape[0])
for freq_idx in range(0, reference_signals.shape[0]):
cca_x = signal.T
cca_y = np.squeeze(reference_signals[freq_idx, :, :]).T
cca.fit(cca_x, cca_y)
a, b = cca.transform(cca_x, cca_y)
for ind_val in range(0, n_components):
corr[ind_val] = np.corrcoef(a[:, ind_val], b[:, ind_val])[0, 1]
result[freq_idx] = np.max(corr)
return result
def map_spaces(self, algo, src_mapped_embed=None, trg_mapped_embed=None):
# (There may be duplicates in self.shared_vocab_src and/or self.shared_vocab_trg,
# swap_vocab can be used to only inspect one-to-one translations)
src_embed = self.model_src[self.shared_vocab_src]
trg_embed = self.model_trg[self.shared_vocab_trg]
os.makedirs(algo, exist_ok=True)
if algo == "procrustes":
logging.info(
"Calculating Rotation Matrix (Procrustes Problem) and applying it to first embedding"
)
#ortho, _ = orthogonal_procrustes(src_embed, trg_embed)
# does the same as
u, _, vt = np.linalg.svd(trg_embed.T.dot(src_embed))
w = vt.T.dot(u.T)
self.model_src.vectors.dot(w, out=self.model_src.vectors)
elif algo == "noise":
logging.info(
"Calculating Rotation Matrix with noise aware algorithm and applying it to first embedding"
)
transform_matrix, alpha, clean_indices, noisy_indices = noise_aware(
src_embed, trg_embed)
#write cleaned vocab to file
with open("vocab.clean.txt", 'w') as v:
for src, trg in np.asarray(self.shared_vocab)[clean_indices]:
v.write("{}\t{}\n".format(src, trg))
self.model_src.vectors.dot(transform_matrix,
out=self.model_src.vectors)
logging.info("Percentage of clean indices: {}".format(alpha))
elif algo == "cca":
logging.info(
"Calculating Mapping based on CCA and applying it to both embeddings"
)
cca = CCA(n_components=100, max_iter=5000)
cca.fit(src_embed, trg_embed)
self.model_src.vectors, self.model_trg.vectors = cca.transform(
self.model_src.vectors, self.model_trg.vectors)
elif algo == "gcca":
logging.info(
"Calculating Mapping based on GCCA and applying it to both embeddings"
)
gcca = GCCA()
gcca.fit([src_embed, trg_embed])
transform_l = gcca.transform_as_list(
(self.model_src.vectors, self.model_trg.vectors))
# gcca computes positive and negative correlations (eigenvalues), sorted in ascending order.
# We are only interested in the positive portion
self.model_src.vectors = transform_l[0][:, 100:]
self.model_trg.vectors = transform_l[1][:, 100:]
# save transformed model(s)
if src_mapped_embed:
self.model_src.save(os.path.join(algo, src_mapped_embed))
if trg_mapped_embed:
self.model_trg.save(os.path.join(algo, trg_mapped_embed))
def CCA_project_vectors(args,
src_dico,
tgt_dico,
src_full,
tgt_full,
src_train,
tgt_train,
NUM_dim=100):
print('Exporting embeddings...')
OutputDir = "output/{}-{}/".format(args.src_lang, args.tgt_lang)
if not os.path.exists(OutputDir):
os.makedirs(OutputDir)
cca = CCA(n_components=NUM_dim)
print("Fitting...")
cca.fit(src_train, tgt_train)
print(cca.get_params())
X_c, Y_c = cca.transform(src_full, tgt_full)
src_out, tgt_out = utils.norm_embeddings(X_c), utils.norm_embeddings(Y_c)
print("Exporting embeddings...")
utils.export_embeddings(src_dico[0], src_out,
OutputDir + 'projected.{}'.format(args.src_lang))
utils.export_embeddings(tgt_dico[0], tgt_out,
OutputDir + 'projected.{}'.format(args.tgt_lang))
print("work over!")
class CCAFusion(TransformerMixin, BaseEstimator):
def __init__(self, c1, c2):
self.pipes = [c1, c2]
self.max_iter = 500
self.cca = None
def fit(self, X, y=None, **fit_params):
C = []
n_components = None
for pipe in self.pipes:
c = pipe.fit_transform(X, y)
if hasattr(c, 'toarray'):
c = c.toarray()
if n_components is None:
n_components = c.shape[1]
else:
n_components = min(c.shape[1], n_components)
C += [c]
self.cca = CCA(n_components=n_components, max_iter=self.max_iter)
self.cca.fit(*C)
return self
def transform(self, X, y=None):
C = []
for pipe in self.pipes:
c = pipe.transform(X, y)
if hasattr(c, 'toarray'):
c = c.toarray()
C += [c]
return self.cca.transform(*C)[0]
def fit_transform(self, X, y=None, **fit_params):
return self.fit(X, y, **fit_params).transform(X, y)
def train_eval(self, train_index, test_index, ignore_eval=False):
normalized_train, normalized_test = normalize_by_train(self.source[train_index], self.source[test_index])
if self.comp is not None:
if self.use_scikit is not None:
if self.use_scikit == 'cca':
dim_reduction = CCA(n_components=self.comp)
else:
dim_reduction = PCA(n_components=self.comp)
# fit cca according to train data only
dim_reduction.fit(normalized_train, self.target[train_index])
# convert source into lower dimensional representation
normalized_train = dim_reduction.transform(normalized_train)
normalized_test = dim_reduction.transform(normalized_test)
else:
_, wa, _ = tutorial_on_cca(normalized_train, self.target[train_index])
normalized_train = normalized_train @ wa[:, :self.comp]
normalized_test = normalized_test @ wa[:, :self.comp]
model = self.build_model()
model.fit(normalized_train, self.target[train_index])
prediction = model.predict(normalized_test)
# res_df.to_csv(f"{self.out_name}/res1.csv")
if not ignore_eval:
return self.evaluate_regression(prediction, test_index)
else:
return prediction
def train_eval(self, train_index, test_index):
train_source, test_source = self.source[train_index], self.source[
test_index]
train_target, test_target = self.target[train_index], self.target[
test_index]
train_source, test_source = scale_train_test(train_source, test_source)
train_target, _ = scale_train_test(train_target, test_target)
# rho, w_t, w_s, _ = evaluate_cca_wa_wb(train_target, train_source)
cca = CCA(n_components=min(train_source.shape[1],
train_target.shape[1]),
max_iter=1000)
cca.fit(train_source, train_target)
w_s = cca.x_rotations_
w_t = cca.y_rotations_
predicted_target = test_source @ w_s @ np.linalg.pinv(w_t)
predicted_target = unscale_prediction(train_target, predicted_target)
if self.target_encoder is not None:
test_target = self.original_target[test_index]
predicted_target = self.target_encoder.decode(
torch.as_tensor(predicted_target)).detach().numpy()
scores = np.zeros(self.original_target.shape[1])
for i in range(self.original_target.shape[1]):
predicted = predicted_target[:, i]
actual = test_target[:, i]
r, pval = pearsonr(predicted, actual)
scores[i] = r
return scores
def fit_cca(self, outfile=''):
# fits linear CCA constraint and replaces pretrained name embeddings with CCA transformed embeddings
self.load_embeddings()
self.extract_pretrained_prototype_embeddings()
items, vectors = zip(
*[(k, v) for k, v in self.pretrained_prototype_embeddings.items()
if k in self.exemplar_to_concept])
concept_embs = Reach(vectors, items)
train_vectors = []
for x in items:
train_vectors.append(self.train_embeddings[x])
train_vectors = Reach.normalize(train_vectors)
cca = CCA(n_components=self.train_embeddings.size, max_iter=10000)
cca.fit(train_vectors, concept_embs.norm_vectors)
# transform all name embeddings using the CCA mapping
all_name_embeddings = deepcopy(self.pretrained_name_embeddings)
items = [x for _, x in sorted(all_name_embeddings.indices.items())]
projected_name_embeddings = cca.transform(
all_name_embeddings.norm_vectors)
new_name_embeddings = Reach(projected_name_embeddings, items)
self.pretrained_name_embeddings = new_name_embeddings
self.load_embeddings()
if outfile:
with open('{}_cca.p', 'wb') as f:
pickle.dump(cca, f)
class CCA_method():
def __init__(self, n_latents):
self._n_latents = n_latents
self._cca = CCA(n_components=n_latents,
scale=False,
max_iter=10000,
tol=1e-8)
self._Q = np.eye(self._n_latents)
def fit(self, X, Y):
# projections U'X, V'Y such that U'X and V'Y are maximally correlated
self._cca.fit(X, Y)
# get time-course of projected data
UX, VY = self._cca.transform(X, Y)
# learn linear regression VY = UX * Q
# (Q will be optimal in least-squares sense)
self._Q = np.linalg.pinv(UX).dot(VY)
def predict(self, X):
# transform source data into latent space
UX = self._cca.transform(X)
# predict latent activity in target space
QUX = UX.dot(self._Q)
# predict observed activity in target space
Ypred = QUX.dot(self._cca.y_loadings_.T)
return Ypred
def visualize_with_cca(X, y, title):
cca = CCA(n_components=2)
cca.fit(X, y)
X_cca = cca.transform(X)
Xax = X_cca[:, 0]
Yax = X_cca[:, 1]
labels = (y > 0).astype(int)
cdict = {0: 'red', 1: 'green'}
labl = {0: 'home_loss', 1: 'home_win'}
marker = {0: '*', 1: 'o'}
alpha = {0: .3, 1: .5}
fig, ax = plt.subplots(figsize=(7, 5))
fig.patch.set_facecolor('white')
for l in np.unique(labels):
ix = np.where(labels == l)
ax.scatter(Xax[ix],
Yax[ix],
c=cdict[l],
s=40,
label=labl[l],
marker=marker[l],
alpha=alpha[l])
plt.xlabel("First Principal Component", fontsize=14)
plt.ylabel("Second Principal Component", fontsize=14)
plt.legend()
plt.title(title)
plt.show()
def cca_classify(X_eeg_signals, Yi_frequency_signals):
cca = CCA(1)
corr_results = []
for fr in range(0, Yi_frequency_signals.shape[0]):
X = X_eeg_signals
Yi = Yi_frequency_signals[fr, :, :]
#计算X与Yi之间的相关性
cca.fit(X.T, np.squeeze(Yi).T)
X_train_r, Yi_train_r = cca.transform(X.T, np.squeeze(Yi).T)
corr = np.corrcoef(X_train_r[:, 0], Yi_train_r[:, 0])[0, 1]
#得出X与每个Yi的相关性
corr_results.append(corr)
if corr_results[np.argmax(corr_results)] > 0.50:
#设置阈值
global index
global all_data
classify_result = np.argmax(corr_results) + 1
print(corr_results)
index += 1
#保存数据
TT = pd.DataFrame(X_eeg_signals)
all_data = all_data.append(np.transpose(TT[1:9]))
if index == 50:
#保存数据
all_data = pd.DataFrame(all_data)
all_data.to_csv('./j_8_all_data.csv', index=False)
return classify_result
else:
return -1
def doCCA(metrics, color):
inp = np.array([metrics[m] for m in metricsInput2]).T.astype(float)
out = np.array([metrics[m] for m in metricsOutput2]).T.astype(float)
inp0 = np.zeros(len(metricsInput2))
out0 = np.zeros(len(metricsOutput2))
inp = np.vstack((inp, inp0))
out = np.vstack((out, out0))
cca = CCA(n_components=1, scale=False)
cca.fit(inp, out)
inp_cca = inp.dot(cca.x_weights_)
out_cca = out.dot(cca.y_weights_)
# Create linear regression object
regr = linear_model.LinearRegression()
# Train the model using the training sets
regr.fit(inp_cca, out_cca)
cca_regr = regr.predict(inp_cca)
# The coefficients
print('Coefficients: \n', regr.coef_)
plt.scatter(inp_cca, out_cca, c=color)
plt.plot(inp_cca, cca_regr, color=color, linewidth=0.5)
logging.info('cca')
logging.info(cca.x_rotations_)
logging.info(cca.y_rotations_)
def cca(m1, m2, preprocessing=None):
"""
Use CCA to decompose two views and plot result.
Params:
m1, m2: Every column is a example with every row as a feature.
preprocessing: If None, we don't do pre-processing; if 'orth', we adjust center to 0 and perform PCA.
"""
# Adjust means to be 0 and perform PCA.
if preprocessing == "orth":
# Zero means.
m1 -= np.mean(m1, axis=1, keepdims=True)
# print("m1=", np.sum(m1, axis=1))
m2 -= np.mean(m2, axis=1, keepdims=True)
# PCA.
cca = CCA(n_components=3, max_iter=100)
cca.fit(m1.T, m2.T)
X_c = cca.transform(m1.T)
fig, ax = plt.subplots()
ax.set_title('Fig.2.(c)')
# ax.set_color_cycle(['blue', 'green', 'red'])
ax.set_prop_cycle('color', ['blue', 'red', 'green'])
ax.plot(X_c)
# ax.plot(Y_c)
plt.show()
def perform(arrs):
blocks_cnt = sum([arr.shape[1] * arr.shape[2] for arr in arrs])
X = np.zeros((blocks_cnt, 16))
Y = np.zeros((blocks_cnt, 64))
for c in range(3):
for i, arr in enumerate(arrs):
height = arr.shape[1]
width = arr.shape[2]
for y in range(height):
for x in range(width):
X[y * width + x] = np.hstack(
[arr[c][y][x - 1][:][-1], arr[c][y - 1][x][-1][:]])
Y[y * width + x] = arr[c][y][x].ravel()
X_mc = (X - X.mean()) / (X.std())
Y_mc = (Y - Y.mean()) / (Y.std())
ca = CCA(n_components=1)
ca.fit(X_mc, Y_mc)
print(f'\nColor {c}:')
weights = ca.x_weights_.ravel()
print(weights.shape)
print(', '.join(map(lambda a: str(a), weights)))
print(ca.n_iter_)
def project_vectors(origForeignVecFile,
origEnVecFile,
subsetEnVecFile,
subsetForeignVecFile,
outputEnFile,
outputForeignFile,
NUMCC=40):
'''
将词典的向量输入到CCA中，生成投影向量，再生成双语向量
:param origForeignVecFile: 外语向量矩阵
:param origEnVecFile: 英语向量矩阵
:param subsetEnVecFile: 词典中的英语向量矩阵
:param subsetForeignVecFile: 词典中的外语向量矩阵
:param outputEnFile: 重新获得的英语词向量
:param outputForeignFile: 重新获得的外语词向量
:param truncRatio: 模型的训练系数
'''
'''数据读入，处理掉开头的英文单词，只保留词向量'''
tmp = np.loadtxt(origEnVecFile, dtype=np.str, delimiter=' ')
origEnVecs = tmp[:, 1:].astype(np.float)
tmp2 = np.loadtxt(origForeignVecFile, dtype=np.str, delimiter=' ')
origForeignVecs = tmp2[:, 1:].astype(np.float)
tmp3 = np.loadtxt(subsetEnVecFile, dtype=np.str, delimiter=' ')
subsetEnVecs = tmp3[:, 1:].astype(np.float)
tmp4 = np.loadtxt(subsetForeignVecFile, dtype=np.str, delimiter=' ')
subsetForeignVecs = tmp4[:, 1:].astype(np.float)
'''预处理，使每行正则化'''
#origEnVecs=preprocessing.normalize(origEnVecs)
#origForeignVecs=preprocessing.normalize(origForeignVecs)
subsetEnVecs = preprocessing.normalize(subsetEnVecs)
subsetForeignVecs = preprocessing.normalize(subsetForeignVecs)
'''训练CCA'''
'''
num = [NUMCC]
regs = [1e-1]
cca = rcca.CCACrossValidate(regs=regs,numCCs=num,kernelcca=False,cutoff=0.1)
cca.train([subsetEnVecs, subsetForeignVecs])
'''
cca = CCA(n_components=NUMCC)
cca.fit(subsetEnVecs, subsetForeignVecs)
print cca.get_params()
X_c, Y_c = cca.transform(origEnVecs, origForeignVecs)
'''生成投影后的向量'''
#tmpOutput = rcca._listdot([d.T for d in [origEnVecs, origForeignVecs]], cca.ws)
origEnVecsProjected = preprocessing.normalize(X_c)
#origEnVecsProjected = preprocessing.scale(tmpOutput[0])
origEnVecsProjected = np.column_stack(
(tmp[:, :1], origEnVecsProjected.astype(np.str)))
origForeignVecsProjected = preprocessing.normalize(Y_c)
#origForeignVecsProjected = preprocessing.scale(tmpOutput[1])
origForeignVecsProjected = np.column_stack(
(tmp2[:, :1], origForeignVecsProjected.astype(np.str)))
np.savetxt(outputEnFile, origEnVecsProjected, fmt="%s", delimiter=' ')
np.savetxt(outputForeignFile,
origForeignVecsProjected,
fmt="%s",
delimiter=' ')
print "work over!"
def cca_fit(X, Y):
cca = CCA(n_components=1)
cca.fit(X, Y)
X = list(itertools.islice(X, 10))
Y = list(itertools.islice(Y, 10))
return cca.score(X, Y)
def _CCA(data, graph, n):
cca = CCA(n_components=n)
adjacencyMatrix = createAffinityMatrix(graph)
cca.fit(data, adjacencyMatrix)
X_c, Y_c = cca.transform(data, adjacencyMatrix)
writeCSV(X_c, 'CCA_X')
writeCSV(Y_c, 'CCA_Y')
def cca_d_h(d_var, h_var, components_num): cca=CCA(n_components=components_num, scale=True, max_iter=2000) cca.fit(d_var, h_var) d_c,h_c=cca.transform(d_var, h_var) ah = np.linalg.inv((h_var.T).dot(h_var)).dot(h_var.T).dot(h_c) ad = np.linalg.inv((d_var.T).dot(d_var)).dot(d_var.T).dot(d_c) return d_c, h_c, ad, ah
def mean_canonical_correlations(scaled_features, df):
cca = CCA(1)
cca.fit(scaled_features, df.iloc[:,-1])
X_c, Y_c = cca.transform(scaled_features, df.iloc[:,-1])
mean_canonical_correlation = np.mean(X_c)
return mean_canonical_correlation
def canonical_correlation_analysis(occurences_a, occurences_b):
occurences_a = pd.Series(occurences_a, dtype="category")
occurences_a = pd.get_dummies(occurences_a)
occurences_b = pd.DataFrame.from_items(occurences_b)
occurences_b = pd.get_dummies(occurences_b)
cca = CCA(n_components=1)
cca.fit(occurences_a, occurences_b)
return cca.score(occurences_a, occurences_b)
def cca_analysis(X, Y, X_dev, Y_dev): cca = CCA(n_components=1, max_iter=2000) cca.fit(X, Y) X_dev_c, Y_dev_c = cca.transform(X_dev, Y_dev) corrcoef = np.corrcoef(X_dev_c.T, Y_dev_c.T)[0,1] return corrcoef
def cca_score(X, Y):
# Calculate the CCA score of the first component pair
ca = CCA(n_components=1)
ca.fit(X, Y)
Xc, Yc = ca.transform(X, Y)
score = np.corrcoef(Xc[:, 0], Yc[:, 0])
return score[0][1]
def CCA_analysis(self, recordings_index, trial_index, brain_area):
path = self.all_data_path + '/' + self.selected_recordings[
recordings_index]
#Prepare rates
rates = self.convert_one_population_to_rates(recordings_index,
trial_index, brain_area).T
#Prepare behavior
trials = np.load(path + '/' + 'trials.intervals.npy')
#Behavioral data
mot_timestamps = np.load(path + '/' + 'face.timestamps.npy')
mot_energy = np.load(path + '/' + 'face.motionEnergy.npy')
beh_range = np.bitwise_and(
mot_timestamps[:, 1] >= trials[trial_index][0],
mot_timestamps[:, 1] <= trials[trial_index][1])
#print(np.where(beh_range==True))
#print(mot_timestamps[beh_range])
beh_subset = mot_energy[beh_range]
beh_subset_aligned = self.align_rate_and_behavior(
beh_subset, rates[:, 0]).reshape(-1, 1)
from sklearn.cross_decomposition import CCA
cca = CCA(n_components=2)
cca.fit(rates, beh_subset_aligned)
X_train_r, Y_train_r = cca.transform(rates, beh_subset_aligned)
print(X_train_r.shape)
print(Y_train_r.shape)
plt.scatter(X_train_r[:, 0],
Y_train_r[:],
label="train",
marker="*",
c="b",
s=50)
plt.show()
plt.scatter(X_train_r[:, 1],
Y_train_r[:],
label="train",
marker="*",
c="b",
s=50)
plt.show()
#rates_test=self.convert_one_population_to_rates(recordings_index,2,brain_area).T
#X_test_r, Y_test_r = cca.transform(rates_test, beh_subset_aligned)
#plt.scatter(X_test_r[:, 0], Y_test_r[:], label="test",
#marker="^", c="b", s=50)
#plt.show()
print(beh_subset_aligned.shape)
print(rates.shape)
def get_cca(chip_cors, rna_vec):
Y_vec = np.array([[each_val / max(chip_cors) for each_val in chip_cors]])
X_vec = np.array([[each_val / max(rna_vec) for each_val in rna_vec]])
Y_vec = Y_vec.transpose()
X_vec = X_vec.transpose()
cca_obj = CCA(n_components=1)
cca_obj.fit(X_vec, Y_vec)
r_squared_canonical = cca_obj.score(X_vec, Y_vec)
return r_squared_canonical
def cca(src_dict, tgt_dict, bi_dict, dim=250):
#with open('../data/seed_embedding.dat', 'wb') as f:
# pickle.dump(x, f)
# pickle.dump(y, f)
cca_model = CCA(n_components=dim)
src_mat, tgt_mat = make_training_matrices(src_dict, tgt_dict, bi_dict)
cca_model.fit(src_mat, tgt_mat)
return cca_model.transform(src_dict.embed, tgt_dict.embed)
def do_cca(X, y, X_orig, n_components=10, permutations=10):
'''
Performs a CCA using components
Projects scores back to edge space
'''
cca = CCA(n_components=n_components)
cca.fit(X, y)
# save the latent component correlation
cca.mode_r = []
for component in range(n_components):
cca.mode_r.append(
np.corrcoef(cca.x_scores_[:, component],
cca.y_scores_[:, component])[0, 1])
# correlate behaviour with LC score
cca.y_score_correlation = np.zeros((np.shape(y)[1], n_components))
for component in range(n_components):
for beh in range(np.shape(y)[1]):
cca.y_score_correlation[beh, component] = np.corrcoef(
y[:, beh].T, cca.y_scores_[:, component])[0, 1]
# correlate edges with LC score
cca.x_score_correlation = np.zeros((np.shape(X_orig)[1], n_components))
for component in range(n_components):
cca.x_score_correlation[:, component] = np.corrcoef(
cca.x_scores_[:, component], X_orig.T)[1::, 0]
# non parametric max T tests for component significance
max_r = []
for perm in tqdm(range(permutations)):
#shuffle the behaviour for each permutation
y_shuffle = shuffle(y)
#perform a new CCA with shuffled data
cca_perm = []
cca_perm = CCA(n_components=n_components)
cca_perm.fit(X, y_shuffle)
# save the latent component correlation
mode_r_perm = []
for component in range(n_components):
mode_r_perm.append(
np.corrcoef(cca_perm.x_scores_[:, component],
cca_perm.y_scores_[:, component])[0, 1])
# take the max r value
max_r.append(np.max(mode_r_perm))
# Compute adjusted p-values via percentile
p_adj = []
for component in range(n_components):
p_adj.append(np.mean(max_r >= cca.mode_r[component]))
return cca, p_adj
def cca_feature(self, data, parameter_list):
cca = CCA(1)
result = []
for i in range(parameter_list[-1]):
# reference_signals = self.reference_signals(parameter_list[1][i], parameter_list[2], parameter_list[3])
reference_signals = self.reference_signals(parameter_list[1][i], parameter_list[2], parameter_list[3])
cca.fit(data.T, reference_signals.T)
x, y = cca.transform(data.T, np.squeeze(reference_signals).T)
corr = np.corrcoef(x[:, 0], y[:, 0])[0, 1]
result.append(corr)
return result
def fit_CCA(tr_block, data_builder):
'''We fit a CCA to some 100 odd points???
'''
# train on number of points
num_points = 100
PixelPoints = data_builder.sample_random_pixels()
points_array_ipw = []
points_array_refl = []
for yr in [14, 15]:
doy_strings = data_builder.club_days(tr_block[tr_block[0][:, 1] == yr])
days_in_sorted = doy_strings.keys()
days_in_sorted.sort()
ipw_files, refl_files = data_builder.sort_IPW_refl_files_imgs(yr)
for set_ in days_in_sorted:
print 'Building data set for year: %d and string of days %s' % (
yr, set_)
# Get the required files only
temp_ipw_files = filter(
lambda x: re.findall('\d+', x)[1] in doy_strings[set_],
ipw_files)
temp_refl_files = filter(
lambda x: re.findall('\d+', x)[1] in doy_strings[set_],
refl_files)
temp_ipw_files = map(
lambda x: '../data/dataset/20' + str(yr) + os.sep + x,
temp_ipw_files)
temp_refl_files = map(
lambda x: '../data/dataset/20' + str(yr) + os.sep + x,
temp_refl_files)
for x_, y_ in zip(PixelPoints[:num_points, 0],
PixelPoints[:num_points, 1]):
temp_array = data_builder.build_features_and_truth_imgs(
temp_ipw_files, temp_refl_files, x_, y_)
points_array_ipw.append(temp_array[1])
points_array_refl.append(temp_array[2])
X_ = np.vstack(points_array_ipw)
Y_ = np.vstack(points_array_refl)
mdl = CCA(n_components=10)
print 'Fitting a CCA...'
mdl.fit(X_[:, :1089], Y_[:, :1089])
ipw_frames = X_[:, 2178:-1]
refl_frames = Y_[:, 2178:]
del X_
del Y_
ipw_frames = ipw_frames[~np.any(np.isnan(ipw_frames), axis=1), :]
refl_frames = refl_frames[~np.any(np.isnan(refl_frames), axis=1), :]
# indices = [(x*1089,(x+1)*1089)for x in range(4) ]
# # the number of components times 4
# ipw_refl_fusion = np.zeros((ipw_frames.shape[0],80))
print 'Building the feature fusion..'
return mdl
def load_mutation_data():
if os.path.isfile(mutation_pickle_path):
pickle_load = pickle.load(open(mutation_pickle_path, 'rb'))
return pickle_load[0], pickle_load[1]
gene_effect_df = pd.read_csv(
r"C:\Users\Nitay\Documents\courses\roded-seminar\Achilles_gene_dependency.csv"
)
mutations_df = pd.read_csv(
r"C:\Users\Nitay\Documents\courses\roded-seminar\CCLE_mutations.csv")
mutations_df = mutations_df[mutations_df["isDeleterious"].fillna(False)]
gene_effect_df = gene_effect_df.set_index("Unnamed: 0").T
gene_effect_df.columns.names = ["cell_line"]
gene_effect_df.index.names = ["gene"]
def clean_gene_name(name):
return name.split("(")[0].strip()
clean_gene_effect_df = gene_effect_df.rename(index=clean_gene_name)
common_genes = set(clean_gene_effect_df.index).intersection(
set(mutations_df['Hugo_Symbol']))
mutations_cell_line = set(mutations_df['DepMap_ID'])
new_mutations_df = pd.DataFrame(np.zeros(
(len(common_genes), len(mutations_cell_line))),
columns=mutations_cell_line,
index=common_genes)
for i, row in mutations_df.iterrows():
cell_line = row["DepMap_ID"]
gene = row['Hugo_Symbol']
if gene in common_genes and cell_line in mutations_cell_line:
new_mutations_df.loc[gene, cell_line] = 1
filtered_gene_effect_df = clean_gene_effect_df.filter(items=common_genes,
axis=0)
filtered_mutations_df = new_mutations_df.loc[new_mutations_df.sum(1) > 0,
new_mutations_df.sum(0) > 0]
from sklearn.cross_decomposition import CCA
Y = filtered_gene_effect_df.values
X = filtered_mutations_df.values
cca = CCA(n_components=10)
cca.fit(X, Y)
X_c = cca.transform(X)
filtered_mutations_df = pd.DataFrame(X_c)
pickle.dump([filtered_gene_effect_df, filtered_mutations_df],
open(mutation_pickle_path, "wb"))
return filtered_gene_effect_df, filtered_mutations_df
def cca(vocab1, vocab2, cca_model=None, dim=300, max_iter=1000, thre=0.5):
if not cca_model:
cca_model = CCA(n_components=dim, max_iter=max_iter)
try:
cca_model.fit(vocab1, vocab2)
[cca_vec1, cca_vec2] = cca_model.transform(vocab1, vocab2)
except:
print('svd cannot converge, try smaller dim')
else:
[cca_vec1, cca_vec2] = cca_model.transform(vocab1, vocab2)
comb_cca = (thre * cca_vec1 + (1 - thre) * cca_vec2)
return comb_cca, cca_vec1, cca_vec2, cca_model
def predict(self):
if self.k_CCA is None:
if self.verbose: print('Going to compute best components first')
self.determine_CCA_components()
# self.cca_predictions, _ = self.ccaCV.predict(self.features, self.ccaCV.ws)
cca = CCA(n_components=self.k_CCA)
cca.fit(self.features[:6000], self.graph[:6000])
self.cca_predictions = cca.transform(self.features)
if self.verbose:
print('Produced predictions')
print('Size of predictions {}'.format(self.cca_predictions.shape))
def canonical_approach():
from sklearn.cross_decomposition import CCA
(X, Y), cities = pull_xy_data()
cca = CCA(n_components=2)
cca.fit(X, Y)
ccaX, ccaY = cca.transform(X, Y)
plot(ccaX, cities, ["CC01", "CC02", "CC03"], 1)
return "OK What Now?"
def __init__(self, dataset, n=None, tol=1e-4):
if n is None:
n = int(numpy.ceil(numpy.sqrt(len(dataset.attributes))))
self.dataset = dataset
self.attributes = random.sample(dataset.attributes, n)
cca = CCA(n_components=1, tol=tol)
cca.fit(
dataset.X.take([a.index for a in self.attributes], 1),
dataset.y)
self.linear_combination = LinearCombination(
self.attributes,
cca.x_weights_.transpose()[0])
def cca_for_ssvep(input_data, sampling_rate, compared_frequencies):
# TODO: Strick input checks, exceptions and avoid crashing and processing errors
# Pre-allocate SSVEP signals matrix to be compared with original EEG recordings using CCA
number_time_points = input_data.shape[1]
number_harmonics = 2
cca_base_signal_matrix = [[] for loop_var in compared_frequencies]
# Pre-allocate output: one correlation coefficient (Rho) for each target SSVEP frequency
# Note: Row 1 is for default Rho scores, Row 2 is for the Rho scores After cca transformation
cca_rho_values = numpy.zeros([1, len(compared_frequencies)], dtype='float')
# For each target frequency, fill Y matrix with sine and cosine signals for every harmonic
for loop_frequencies in range(len(compared_frequencies)):
# For this current SSVEP frequency, pre-allocate the harmonics matrix
cca_base_signal_matrix[loop_frequencies] = numpy.zeros([number_harmonics * 2, number_time_points])
time_points_count = numpy.arange(number_time_points, dtype='float')
time_points_count = time_points_count / sampling_rate
# Generate sine and cosine reference signals, for every harmonic
for loop_harmonics in range(number_harmonics):
# Compute the reference signals for current harmonic
base_constant = 2 * numpy.pi * (loop_harmonics + 1) * compared_frequencies[loop_frequencies]
base_sine_signal = numpy.sin((base_constant * time_points_count))
base_cosine_signal = numpy.cos((base_constant * time_points_count))
# Copy signals back to reference matrix
base_position = loop_harmonics + 1
sine_position = (2 * (base_position - 1) + 1)
cosine_position = 2 * base_position
cca_base_signal_matrix[loop_frequencies][sine_position - 1, :] = base_sine_signal
cca_base_signal_matrix[loop_frequencies][cosine_position - 1, :] = base_cosine_signal
# After the loop, extract the y_matrix from reference matrix for current SSVEP frequency
y_matrix = cca_base_signal_matrix[loop_frequencies]
# Create a CCA object and compute the correlation score
cca_object = CCA(n_components=number_harmonics)
cca_object.fit(numpy.transpose(input_data), numpy.transpose(y_matrix))
values_x, values_y = cca_object.transform(input_data, y_matrix)
cca_rho_values[0, loop_frequencies] = cca_object.score(input_data, y_matrix, values_y) # Score = Rho value?
# After loop return and exit
return cca_rho_values
def fit_CCA(tr_block,data_builder):
'''We fit a CCA to some 100 odd points???
'''
# train on number of points
num_points = 100
PixelPoints = data_builder.sample_random_pixels()
points_array_ipw = []
points_array_refl = []
for yr in [14,15]:
doy_strings = data_builder.club_days(tr_block[tr_block[0][:,1] == yr])
days_in_sorted = doy_strings.keys()
days_in_sorted.sort()
ipw_files,refl_files = data_builder.sort_IPW_refl_files_imgs(yr)
for set_ in days_in_sorted:
print 'Building data set for year: %d and string of days %s'%(yr,set_)
# Get the required files only
temp_ipw_files = filter(lambda x: re.findall('\d+',x)[1] in doy_strings[set_],ipw_files)
temp_refl_files = filter(lambda x: re.findall('\d+',x)[1] in doy_strings[set_],refl_files)
temp_ipw_files = map(lambda x: '../data/dataset/20' + str(yr) + os.sep + x,temp_ipw_files)
temp_refl_files = map(lambda x: '../data/dataset/20' + str(yr) + os.sep + x,temp_refl_files)
for x_,y_ in zip(PixelPoints[:num_points,0],PixelPoints[:num_points,1]):
temp_array = data_builder.build_features_and_truth_imgs(temp_ipw_files,temp_refl_files,x_,y_)
points_array_ipw.append(temp_array[1])
points_array_refl.append(temp_array[2])
X_ = np.vstack(points_array_ipw)
Y_ = np.vstack(points_array_refl)
mdl = CCA(n_components = 10)
print 'Fitting a CCA...'
mdl.fit(X_[:,:1089],Y_[:,:1089])
ipw_frames = X_[:,2178:-1]
refl_frames = Y_[:,2178:]
del X_
del Y_
ipw_frames = ipw_frames[~np.any(np.isnan(ipw_frames),axis = 1),:]
refl_frames = refl_frames[~np.any(np.isnan(refl_frames),axis = 1),:]
# indices = [(x*1089,(x+1)*1089)for x in range(4) ]
# # the number of components times 4
# ipw_refl_fusion = np.zeros((ipw_frames.shape[0],80))
print 'Building the feature fusion..'
return mdl
def test_cca_implementation():
X = np.random.multivariate_normal(np.random.randint(50,100,(10)).astype('float'),np.identity(10),200)
Y = np.random.multivariate_normal(np.random.randint(80,200,(6)).astype('float'),np.identity(6),200)
X_test = np.random.multivariate_normal(np.random.randint(50,100,(10)).astype('float'),np.identity(10),20)
Y_test = np.random.multivariate_normal(np.random.randint(50,100,(6)).astype('float'),np.identity(6),20)
mdl_test = CCA(n_components = 6)
mdl_test.fit(X,Y)
Y_pred = mdl_test.predict(X)
print Y_pred
print '-'*50
# print Y_test
from sklearn.cross_decomposition import CCA as CCA_sklearn
mdl_actual = CCA_sklearn(n_components = 6)
mdl_actual.fit(X,Y)
print '-'*50
Y_actual = mdl_actual.predict(X)
print Y_actual
[ 138, 33, 68, 2, 110, 43] ] print X.shape #X = N.array(Z)[:,0:3].tolist() #Y = N.array(Z)[:,3:6].tolist() print 'X=\n',X print 'Y=\n',Y Rx = N.corrcoef(X.T) Ry = N.corrcoef(Y.T) cca = CCA(n_components=1) cca.fit(X, Y) print "Rx:\n", Rx print "Ry:\n", Ry print "x_weights:\n", cca.x_weights_ print "y_weights:\n", cca.y_weights_ print "x_loadings:\n", cca.x_loadings_ print "y_loadings:\n", cca.y_loadings_ print "x_scores_:\n", cca.x_scores_ print "y_scores_:\n", cca.y_scores_ loadings_man_x = N.dot(Rx, cca.x_weights_) loadings_man_y = N.dot(Ry, cca.y_weights_) print "loadings_man_x:\n",loadings_man_x print "loadings_man_y:\n",loadings_man_y
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)
def mainExec(name_file, features):
'''
Based on a list of image names and image features, learn a CCA model based on Stacked Auxiliary Embedding and
save this model to disk.
:param name_file
:param features
:return:
'''
print "Creating vocabulary"
voc = readVocabulary()
print "Generating document vectors"
occurrenceVectors, idf = createOccurrenceVectors(voc)
print "Weighing vectors"
weightedVectors = weight_tfidf(occurrenceVectors, idf)
sentenceMatrix = []
imagematrix = []
print "Creating matrices"
currentSentence = 0
for i in weightedVectors.keys():
if isLargeEnough(i):
currentSentence += 1
print "current Sentence: " + str(currentSentence)
for j in range(len(weightedVectors[i])):
weightedVectors[i][j] = float(weightedVectors[i][j])
if currentSentence == 1:
sentenceMatrix = weightedVectors[i]
imagematrix = getImage(i,name_file, features)
elif currentSentence ==2:
sentenceMatrix = np.concatenate(([sentenceMatrix], [weightedVectors[i]]), axis = 0)
imagematrix = np.concatenate(([imagematrix], [getImage(i,name_file, features)]), axis = 0)
else:
sentenceMatrix = np.concatenate((sentenceMatrix, [weightedVectors[i]]), axis = 0)
imagematrix = np.concatenate((imagematrix, [getImage(i,name_file, features)]), axis = 0)
print "Modelling cca"
cca = CCA(n_components=128)
cca.fit(sentenceMatrix, imagematrix)
pickle.dump(cca, open("ccasnippetmodel.p",'w+'))
idf = np.zeros(len(voc))
trainingimages = []
trainingsentences = []
dp = getDataProvider('flickr30k')
currentPair = 0
for pair in dp.sampleImageSentencePair():
currentPair += 1
if currentPair % 100 == 0:
print "Current pair: " + str(currentPair)
img = pair['image']['feat']
trainingimages.append(img)
sentence = getFullSentence(pair)
for i in range(len(sentence)):
if sentence[i] > 0:
idf[i] += 1
trainingsentences.append(sentence)
for i in range(len(trainingsentences)):
trainingsentences[i] = trainingsentences[i]*idf
trans_img, trans_sent = cca.transform(trainingimages, trainingsentences)
nn_img = nearest_neighbor(trainingimages)
nn_sent = nearest_neighbor(trainingsentences)
augmented_imgs = []
augmented_sentences = []
for i in range(len(trans_img)):
augm_img = trainingimages[i].extend(phi(3000,nn_img, trans_img[i]))
augmented_imgs.append(augm_img)
for i in range(len(trans_sent)):
augm_sent = trainingsentences[i].extend(phi(3000, nn_sent, trans_sent[i]))
augmented_sentences.append(augm_sent)
augmentedcca = CCA(n_components= 96)
augmentedcca.fit(augmented_sentences, augmented_imgs)
pickle.dump(cca, open("augmentedcca.p",'w+'))
def main():
sess = tf.InteractiveSession()
X1_data, X2_data, Y_data, baseline_data, labels_data = read_inputs()
# set up the DCCA network
keep_input = tf.placeholder("float")
keep_hidden = tf.placeholder("float")
X1_in, X1_out = build_network(273, 1500, 1500, 1500, 50, keep_input, keep_hidden)
X2_in, X2_out = build_network(112, 1500, 1500, 1500, 50, keep_input, keep_hidden)
# define the DCCA cost function
U = tf.placeholder("float", [50, 40])
V = tf.placeholder("float", [50, 40])
UtF = tf.matmul(tf.transpose(U), tf.transpose(X1_out))
GtV = tf.matmul(X2_out, V)
canon_corr = tf.mul(1./BATCH, tf.reduce_sum(tf.mul(tf.matmul(UtF, GtV), tf.constant(np.eye(40), dtype = tf.float32))))
corr_step = tf.train.AdamOptimizer(1e-6).minimize(- canon_corr)
sess.run(tf.initialize_all_variables())
# train the network
print "Training DCCA"
for i in range(0, EPOCHS):
for j in range(0, len(X1_data.train), int(BATCH)):
X1_in_batch = X1_data.train[j:(j + BATCH)]
X2_in_batch = X2_data.train[j:(j + BATCH)]
X1_out_batch = X1_out.eval(feed_dict = {
X1_in : X1_in_batch,
keep_input : 1.0,
keep_hidden : 1.0})
X2_out_batch = X2_out.eval(feed_dict = {
X2_in : X2_in_batch,
keep_input : 1.0,
keep_hidden : 1.0})
# compute CCA on the output layers
cca = CCA(n_components = 40)
cca.fit(X1_out_batch, X2_out_batch)
U_batch = cca.x_weights_
V_batch = cca.y_weights_
# perform gradient step
corr_step.run(feed_dict = {
X1_in : X1_in_batch,
X2_in : X2_in_batch,
U : U_batch,
V : V_batch,
keep_input : 0.9,
keep_hidden : 0.8})
# print useful info
print "EPOCH", i, "/ COST", canon_corr.eval(feed_dict = {
X1_in : X1_in_batch,
X2_in : X2_in_batch,
U : U_batch,
V : V_batch,
keep_input : 1.0,
keep_hidden : 1.0})
# train the softmax classifier
print "Training softmax"
W_s = weight_variable([89, 39])
b_s = bias_variable([39])
baseline = tf.placeholder("float", [None, 39])
y_true = tf.placeholder("float", [None, 39])
# define the cost
X1_baseline_combo = tf.concat(1, [X1_out, baseline])
y_pred = tf.nn.softmax(tf.matmul(X1_baseline_combo, W_s) + b_s)
lr_cost = - tf.reduce_sum(y_true * tf.log(tf.clip_by_value(y_pred, 1e-10, 1.0)))
lr_step = tf.train.AdamOptimizer(1e-4).minimize(lr_cost)
# set up accuracy checking
correct_prediction = tf.equal(tf.argmax(y_pred, 1), tf.argmax(y_true, 1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, "float"))
sess.run(tf.initialize_all_variables())
for i in range(0, EPOCHS):
for j in range(0, len(X1_data.train), int(BATCH)):
lr_step.run(feed_dict = {
X1_in : X1_data.train[j:(j + BATCH)],
y_true : Y_data.train[j:(j + BATCH)],
baseline : baseline_data.train[j:(j + BATCH)],
keep_input : 1.0,
keep_hidden : 1.0})
print i, accuracy.eval(feed_dict = {
X1_in : X1_data.dev,
y_true : Y_data.dev,
baseline : baseline_data.dev,
keep_input : 1.0,
keep_hidden : 1.0})
print "Test accuracy:", accuracy.eval(feed_dict = {
X1_in : X1_data.test,
y_true : Y_data.test,
baseline : baseline_data.test,
keep_input : 1.0,
keep_hidden : 1.0})
# project the data and print it to file
X1_train_proj = X1_baseline_combo.eval(feed_dict = {
X1_in : X1_data.train,
baseline : baseline_data.train,
keep_input : 1.0,
keep_hidden : 1.0})
X1_dev_proj = X1_baseline_combo.eval(feed_dict = {
X1_in : X1_data.dev,
baseline : baseline_data.dev,
keep_input : 1.0,
keep_hidden : 1.0})
X1_test_proj = X1_baseline_combo.eval(feed_dict = {
X1_in : X1_data.test,
baseline : baseline_data.test,
keep_input : 1.0,
keep_hidden : 1.0})
scipy.io.savemat("dcca_projected_data.mat", {'dataTr' : X1_train_proj, "PhonesTr" : labels_data.train, "dataDev" : X1_dev_proj, "PhonesDev" : labels_data.dev, "dataTest" : X1_test_proj, "PhonesTest" : labels_data.test})
session.execute("USE TweetsXiaohu")
# session.execute("DROP TABLE IF EXISTS Tweet")
rows = session.execute("SELECT text, hashtags FROM Tweet limit 1000")
X, Y = [], []
for row in rows:
X.append(row.text)
Y.append([x.lower() for x in row.hashtags])
vectorizer = StemmedTfidfVectorizer(min_df=1, stop_words='english', decode_error='ignore')
# print(vectorizer)
X = vectorizer.fit_transform(X).toarray()
# print '40', X
# print type(X)
Y_indicator = LabelBinarizer().fit(Y).transform(Y)
cca = CCA(n_components = 100, max_iter=10)
cca.fit(X, Y_indicator)
X = cca.transform(X)
# print '45', X
# print type(X)
classif = OneVsRestClassifier(SVC(kernel='linear'))
classif.fit(X, Y)
for row in rows:
# row = rows[0]
# print vectorizer.transform([row.text]).toarray()
# print cca.predict(vectorizer.transform([row.text]).toarray())
transformed = vectorizer.transform([row.text]).toarray()
# print '55', transformed
ccad = cca.transform(transformed)
# print '57', ccad
predicts = classif.predict(ccad)
# check type of array #print(np.dtype(data_selection)) # force dtype = float32 data_selection = data_selection.astype(np.float32, copy=False) # complete cases data_selection = data_selection[~np.isnan(data_selection).any(axis=1)] data_selection = data_selection[np.isfinite(data_selection).any(axis=1)] # target variable / covariates y = data_selection[:,0:3] x = data_selection[:,4:] # split test-train x_train, x_test, y_train, y_test = cross_validation.train_test_split(x,y, test_size=0.2, random_state=0) cca = CCA(n_components=1,scale=True) cca.fit(x_train, y_train) #CCA(copy=True, max_iter=500, n_components=1, scale=True, tol=1e-06), X_train_r, Y_train_r = cca.transform(x_train,y_train) X_test_r, Y_test_r = cca.transform(x_test, y_test) print(type(X_train_r)) print(np.shape(X_train_r)) print(np.shape(Y_train_r)) print(np.shape(x)) print(np.corrcoef(X_train_r[:,0],Y_train_r[:,0])) print(np.corrcoef(X_test_r[:,0],Y_test_r[:,0]))
plt.figure()
for i in range (5):
plt.plot(nComponents,plsRegScores[i,:],lw=3)
plt.xlim(1,np.amax(nComponents))
plt.title('PLS Regression 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):
#%% Canonical Correlation Analysis
nComponents = np.arange(1,nClasses +1)
cca = CCA(n_components=nClasses)
cca.fit(Xtrain,Ytrain)
XtrainT = cca.transform(Xtrain)
XtestT = cca.transform(Xtest)
ccaScores = np.zeros((5,np.alen(nComponents)))
for i,n in enumerate(nComponents):
ccaScores[:,i] = util.classify(XtrainT[:,0:n],XtestT[:,0:n],labelsTrain,labelsTest)
cca = CCA(n_components=3)
cca.fit(Xtrain,Ytrain)
xt = cca.transform(Xtrain)
fig = plt.figure()
util.plotData(fig,xt,labelsTrain,classColors)
plt.title('First 3 components of projected data')
#%% Plot accuracies for CCA
class CCA_Model:
def __init__(self,n_components):
self.n_components = n_components
self.cca = CCA(n_components=n_components)
self.ntop = 10
def learn_model(self,X_chanel, Y_chanel,Y_Distinct=None):
"""
:param X_chanel: array-like for X chanel
:param Y_chanel: array-line for Y chanel
:return:
"""
print "Start learning..."
self.x_dim = len(X_chanel[0])
self.y_dim = len(Y_chanel[0])
self.cca.fit(X_chanel,Y_chanel)
if Y_Distinct == None:
self.X_transform ,self.Y_transform = self.cca.transform(X_chanel,Y_chanel)
else:
self.X_transform ,self.Y_transform = self.cca.transform(X_chanel,Y_Distinct)
print "Learning completed"
def get_bet_match_index_transform_x2y(self,x_transform):
shape = self.Y_transform.shape
scores = np.ndarray(shape[0],dtype=float)
for i in xrange(shape[0]):
scores[i] = np.dot(self.Y_transform[i],x_transform)
#scores[i] = entropy(x_transform,self.Y_transform[i])
indices = (-scores).argsort()[:self.ntop]
return [indices, scores[indices]]
def get_bet_match_index_transform_y2x(self,y_transform):
shape = self.X_transform.shape
scores = np.ndarray(shape[0], dtype=float)
for i in xrange(shape[0]):
scores[i] = np.dot(self.X_transform[i], y_transform)
#scores[i] = entropy(y_transform,self.X_transform[i])
indices = (-scores).argsort()[:self.ntop]
return [indices, scores[indices]]
def get_best_match_cross_indices_x2y(self,x_inputs):
x_transformes = self.cca.transform(x_inputs)
results = []
for x_transform in x_transformes:
results.append(self.get_bet_match_index_transform_x2y(x_transform))
return results
def get_best_match_cross_indices_y2x(self,y_inputs):
_, y_transformes = self.cca.transform([[0 for i in xrange(self.x_dim)]],y_inputs)
results = []
for y_transform in y_transformes:
results.append(self.get_bet_match_index_transform_y2x(y_transform))
return results
plt.xlabel("Y comp. 1")
plt.ylabel("Y comp. 2")
plt.title('Y comp. 1 vs Y comp. 2 , (test corr = %.2f)'% numpy.corrcoef(Y_test_r[:, 0], Y_test_r[:, 1])[0, 1])
plt.legend(loc="best")
plt.xticks(())
plt.yticks(())
plt.savefig(output_file)
plt.close()
# PLSCA
plsca = PLSCanonical(n_components=2)
plsca.fit(Xtrain, Ytrain)
# PLSCanonical(algorithm='nipals', copy=True, max_iter=500, n_components=2,
# scale=True, tol=1e-06)
X_train_r, Y_train_r = plsca.transform(Xtrain, Ytrain)
X_test_r, Y_test_r = plsca.transform(Xtest, Ytest)
do_plot(X_train_r,Y_train_r,X_test_r,Y_test_r,'%s/PLSCA_2comp_norm.pdf' %output_folder)
# CCA
# probably not necessary, but just in case the data was modified in some way
Ytrain = norm.loc[train,:]
Ytest = norm.loc[holdout,:]
Xtrain = numpy.array(X.loc[train,:])
Xtest = X.loc[holdout,:]
cca = CCA(n_components=2)
cca.fit(Xtrain, Ytrain)
# CCA(copy=True, max_iter=500, n_components=2, scale=True, tol=1e-06)
X_train_r, Y_train_r = cca.transform(Xtrain, Ytrain)
X_test_r, Y_test_r = cca.transform(Xtest, Ytest)
do_plot(X_train_r,Y_train_r,X_test_r,Y_test_r,'%s/CCA_2comp_norm.pdf' %output_folder)
