def main():
Xtrain, Ytrain, Xtest, Ytest = getKaggleMNIST()
dbn = DBN([1000, 750, 500], UnsupervisedModel=AutoEncoder)
# dbn = DBN([1000, 750, 500, 10])
output = dbn.fit(Xtrain, pretrain_epochs=2)
print "output.shape", output.shape
# sample before using t-SNE because it requires lots of RAM
sample_size = 600
tsne = TSNE()
reduced = tsne.fit_transform(output[:sample_size])
plt.scatter(reduced[:,0], reduced[:,1], s=100, c=Ytrain[:sample_size], alpha=0.5)
plt.title("t-SNE visualization")
plt.show()
# t-SNE on raw data
reduced = tsne.fit_transform(Xtrain[:sample_size])
plt.scatter(reduced[:,0], reduced[:,1], s=100, c=Ytrain[:sample_size], alpha=0.5)
plt.title("t-SNE visualization")
plt.show()
pca = PCA()
reduced = pca.fit_transform(output)
plt.scatter(reduced[:,0], reduced[:,1], s=100, c=Ytrain, alpha=0.5)
plt.title("PCA visualization")
plt.show()
def vizualize_clusters(X, y, py, hist=None):
""" Using T-SNE to visualize the site clusters.
Plot and save the scatter (and the histogramm).
"""
model = TSNE(n_components=2, random_state=0)
fig = model.fit_transform(X, y)
fig1 = model.fit_transform(X, py)
pyplot.figure(figsize=(16, 8))
pyplot.subplot(121)
classes = list(set(y))
for c, color in zip(classes, plt.colors.cnames.iteritems()):
indeces = [i for i, p in enumerate(y) if p == c]
pyplot.scatter(fig[indeces, 0], fig[indeces, 1], marker="o", c=color[0])
pyplot.subplot(122)
clusters = list(set(py))
for c, color in zip(clusters, plt.colors.cnames.iteritems()):
indeces = [i for i, p in enumerate(py) if p == c]
pyplot.scatter(fig1[indeces, 0], fig1[indeces, 1], marker="o", c=color[0])
# pyplot.show()
pyplot.savefig("clusters" + "_scatter.png")
if hist is not None:
pyplot.figure(figsize=(4, 4))
pyplot.xticks(clusters)
pyplot.bar(clusters, hist, align="center")
# pyplot.show()
pyplot.savefig("clusters" + "_hist.png")
def tsne(similarity, euclid=False, perplexity=30):
if euclid:
model = TSNE(learning_rate=100, perplexity=perplexity, n_iter=200000)
result = model.fit_transform(similarity)
else:
model = TSNE(learning_rate=100, n_iter=100000, init='random', metric='precomputed')
result = model.fit_transform(1 - similarity)
return result.T
def MyTSNE(train,test):
#MyTSNE(train.iloc[:100,:],test.iloc[:20,:])
model = TSNE(n_components=2, random_state=0)
a = np.vstack(
[train.values,
test.values]
)
model.fit_transform(a)
return
def programmer_5(data_zs, r):
# 进行数据降维
tsne = TSNE()
tsne.fit_transform(data_zs)
tsne = pd.DataFrame(tsne.embedding_, index=data_zs.index)
# 不同类别用不同颜色和样式绘图
d = tsne[r[u'聚类类别'] == 0]
plt.plot(d[0], d[1], 'r.')
d = tsne[r[u'聚类类别'] == 1]
plt.plot(d[0], d[1], 'go')
d = tsne[r[u'聚类类别'] == 2]
plt.plot(d[0], d[1], 'b*')
plt.show()
def learn_embedding(precompute_metric=False, use_saved=False):
base = 'datasets/dspace_topics'
new_base = 'datasets/dspace_embeddings'
# Delete previous saved embedding
if os.path.exists(new_base):
shutil.rmtree(new_base)
os.makedirs(new_base)
print 'Embedding: Extracting topics'
# choose a random subset of documents
filename_vec = os.listdir(base)
subsample = 5000
filename_vec = np.random.choice(filename_vec, subsample)
topic_vec = []
for filename in tqdm(filename_vec):
path = os.path.join(base, filename)
with open(path) as f:
d = json.load(f)
topics = d['topics']
topic_vec.append(topics)
print 'Embedding: Computing pairwise distances'
if precompute_metric:
if use_saved:
with open('metric.npy') as f:
metric = np.load(f)
else:
metric = pairwise_distances(np.array(topic_vec), metric=KL, n_jobs=-1)
with open('metric.npy', 'w') as f:
np.save(f, metric)
print 'Embedding: Learning embedding'
tsne = TSNE(n_iter=1000, verbose=10, metric='precomputed')
y = tsne.fit_transform(metric)
else:
print 'Embedding: Learning embedding'
tsne = TSNE(n_iter=1000, verbose=10)
y = tsne.fit_transform(topic_vec)
print 'Embedding: Saving embedding'
for (index, filename) in tqdm(enumerate(filename_vec), total=len(filename_vec)):
path = os.path.join(base, filename)
with open(path, 'r') as f:
d = json.load(f)
d['embedding'] = list(y[index])
new_path = os.path.join(new_base, filename)
with open(new_path, 'w') as new_f:
json.dump(d, new_f)
def add_tsne_features(x_train, x_test):
print('add_tsne_features <<')
x_train_data = x_train.data_
x_test_data = x_test.data_
x = np.vstack((x_train_data, x_test_data))
print('applying pca...')
pca = PCA(n_components=25)
x_pca = pca.fit_transform(x)
print('applying t-SNE...')
tsne_model = TSNE(n_components=2, random_state=0)
x_tsne = tsne_model.fit_transform(x_pca)
x_train_data = np.hstack((x_train_data, x_tsne[:x_train_data.shape[0], :]))
x_test_data = np.hstack((x_test_data, x_tsne[-x_test_data.shape[0]:, :]))
assert(x_train.columns_ == x_test.columns_)
columns = x_train.columns_ + ['tsne_1', 'tsne_2']
x_train = DataSet(x_train.ids_, columns, x_train_data)
x_test = DataSet(x_test.ids_, columns, x_test_data)
print('add_tsne_features >>')
return x_train, x_test
def sendTSNE(self, people):
d = self.getData()
if d is None:
return
else:
(X, y) = d
X_pca = PCA(n_components=50).fit_transform(X, X)
tsne = TSNE(n_components=2, init='random', random_state=0)
X_r = tsne.fit_transform(X_pca)
yVals = list(np.unique(y))
colors = cm.rainbow(np.linspace(0, 1, len(yVals)))
# print(yVals)
plt.figure()
for c, i in zip(colors, yVals):
name = "Unknown" if i == -1 else people[i]
plt.scatter(X_r[y == i, 0], X_r[y == i, 1], c=c, label=name)
plt.legend()
imgdata = StringIO.StringIO()
plt.savefig(imgdata, format='png')
imgdata.seek(0)
content = 'data:image/png;base64,' + \
urllib.quote(base64.b64encode(imgdata.buf))
msg = {
"type": "TSNE_DATA",
"content": content
}
self.sendMessage(json.dumps(msg))
def display_closestwords_tsnescatterplot(model, word):
arr = np.empty((0,300), dtype='f')
word_labels = [word]
# get close words
close_words = model.similar_by_word(word)
# add the vector for each of the closest words to the array
arr = np.append(arr, np.array([model[word]]), axis=0)
for wrd_score in close_words:
wrd_vector = model[wrd_score[0]]
word_labels.append(wrd_score[0])
arr = np.append(arr, np.array([wrd_vector]), axis=0)
# find tsne coords for 2 dimensions
tsne = TSNE(n_components=2, random_state=0)
np.set_printoptions(suppress=True)
Y = tsne.fit_transform(arr)
x_coords = Y[:, 0]
y_coords = Y[:, 1]
# display scatter plot
plt.scatter(x_coords, y_coords)
for label, x, y in zip(word_labels, x_coords, y_coords):
plt.annotate(label, xy=(x, y), xytext=(0, 0), textcoords='offset points')
plt.xlim(x_coords.min()+0.00005, x_coords.max()+0.00005)
plt.ylim(y_coords.min()+0.00005, y_coords.max()+0.00005)
plt.show()
def make_tsne_plot(model, rel_wds, plot_lims, title):
dim = 30
X, keys = make_data_matrix(model)
# first we actually do PCA to reduce the
# dimensionality to make tSNE easier to calculate
X_std = StandardScaler().fit_transform(X)
sklearn_pca = PCA(n_components=2)
X = sklearn_pca.fit_transform(X_std)[:,:dim]
# do downsample
k = 5000
sample = []
important_words = []
r_wds = [word[0] for word in rel_wds]
for i, key in enumerate(keys):
if key in r_wds:
sample.append(i)
sample = np.concatenate((np.array(sample),
np.random.choice(len(keys), k-10, replace = False),
))
X = X[sample,:]
keys = [keys[i] for i in sample]
# Do tSNE
tsne = TSNE(n_components=2, random_state=0, metric="cosine")
X_transf = tsne.fit_transform(X)
k_means = KMeans(n_clusters=8)
labels = k_means.fit_predict(X_transf)
scatter_plot(X_transf[:,0], X_transf[:,1], rel_wds, labels, title, keys, plot_lims)
def plot_features(subject, data_path, model_path, test_labels, dataset='test'):
with open(model_path + '/' + subject + '.pickle', 'rb') as f:
state_dict = cPickle.load(f)
cnn = ConvNet(state_dict['params'])
cnn.set_weights(state_dict['weights'])
scalers = state_dict['scalers']
if dataset == 'test':
d = load_test_data(data_path, subject)
x = d['x']
y = test_labels['preictal']
elif dataset == 'train':
d = load_train_data(data_path, subject)
x, y = d['x'], d['y']
else:
raise ValueError('dataset')
x, _ = scale_across_time(x, x_test=None, scalers=scalers) if state_dict['params']['scale_time'] \
else scale_across_features(x, x_test=None, scalers=scalers)
cnn.batch_size.set_value(x.shape[0])
get_features = theano.function([cnn.x, Param(cnn.training_mode, default=0)], cnn.feature_extractor.output,
allow_input_downcast=True)
logits_test = get_features(x)
model = TSNE(n_components=2, random_state=0)
z = model.fit_transform(np.float64(logits_test))
plt.scatter(z[:, 0], z[:, 1], s=60, c=y)
plt.show()
def plot_data(data, has_label=True):
import numpy as np
import seaborn as sns
from sklearn.manifold import TSNE
from sklearn.decomposition import PCA
if not has_label:
data = data.copy()
data['label'] = np.zeros([len(data),1])
LIMIT = 4000
if data.shape[0] > LIMIT:
dt = data.sample(n=LIMIT, replace=False)
X = dt.ix[:,:-1]
labels = dt.ix[:,-1]
else:
X = data.ix[:,:-1]
labels = data.ix[:,-1]
tsne_model = TSNE(n_components=2, random_state=0)
np.set_printoptions(suppress=True)
points1 = tsne_model.fit_transform(X)
df1 = pd.DataFrame(data=np.column_stack([points1,labels]), columns=["x","y","class"])
sns.lmplot("x", "y", data=df1, hue='class', fit_reg=False, palette=sns.color_palette('colorblind'))
sns.plt.title('TNSE')
pca = PCA(n_components=2)
pca.fit(X)
points2 = pca.transform(X)
df2 = pd.DataFrame(data=np.column_stack([points2,labels]), columns=["x","y","class"])
sns.lmplot("x", "y", data=df2, hue='class', fit_reg=False, palette=sns.color_palette('colorblind'))
sns.plt.title('PCA')
def plot_mfi(self, outputfile='embeddings.pdf', nb_clusters=8, weights='NA'):
# collect embeddings for mfi:
X = np.asarray([self.w2v_model[w] for w in self.mfi \
if w in self.w2v_model], dtype='float32')
# dimension reduction:
tsne = TSNE(n_components=2)
coor = tsne.fit_transform(X) # unsparsify
plt.clf()
sns.set_style('dark')
sns.plt.rcParams['axes.linewidth'] = 0.4
fig, ax1 = sns.plt.subplots()
labels = self.mfi
# first plot slices:
x1, x2 = coor[:,0], coor[:,1]
ax1.scatter(x1, x2, 100, edgecolors='none', facecolors='none')
# clustering on top (add some colouring):
clustering = AgglomerativeClustering(linkage='ward',
affinity='euclidean', n_clusters=nb_clusters)
clustering.fit(coor)
# add names:
for x, y, name, cluster_label in zip(x1, x2, labels, clustering.labels_):
ax1.text(x, y, name, ha='center', va="center",
color=plt.cm.spectral(cluster_label / 10.),
fontdict={'family': 'Arial', 'size': 8})
# control aesthetics:
ax1.set_xlabel('')
ax1.set_ylabel('')
ax1.set_xticklabels([])
ax1.set_xticks([])
ax1.set_yticklabels([])
ax1.set_yticks([])
sns.plt.savefig(outputfile, bbox_inches=0)
def perform_AE(X, dim=2, tsne=False):
y = np.zeros(shape=X.shape[0], dtype=int)
if tsne:
hidden_layers = [X.shape[1], 500, 100, 32]
encoder_weights, decoder_weights = pretrain(X, hidden_layers)
X_32d = ae(X, encoder_weights, decoder_weights, hidden_layers)
ae_tsne = TSNE(n_components=dim, learning_rate=800, verbose=1)
X_2d = ae_tsne.fit_transform(X_32d)
method = 'ae_tsne_scaled'
### END - if tsne
else:
hidden_layers = [X.shape[1], 500, 100, 20, dim]
encoder_weights, decoder_weights = pretrain(X, hidden_layers)
X_2d = ae(X, encoder_weights, decoder_weights, hidden_layers)
method = 'ae_scaled'
### END - else
print('***** ' + method + ' *****')
cluster(X_2d, method)
np.save("{0}_{1}_X_2d".format(species, method), X_2d)
def plot_phonemes(path):
phoneme_embeddings = dict()
for line in codecs.open(path,"r"):
line = line.split(",")
key= line[0][1:-1]
emb = line[1:]
emb[-1] = emb[-1][:-1]
emb = np.array([float(e) for e in emb])
phoneme_embeddings[key] = emb
phoneme_embeddings = DataFrame(phoneme_embeddings,columns=phoneme_embeddings.keys())
print(phoneme_embeddings.columns)
m = TSNE()
phoneme_embeddings_tsne = m.fit_transform(phoneme_embeddings.transpose())
print(len(phoneme_embeddings_tsne))
for p,emb in zip(phoneme_embeddings.columns, phoneme_embeddings_tsne):
c = "black"
if regex.search("^[aeiou3E][*]?$", p):
c = "red"
plt.annotate(p,(emb[0],emb[1]),color=c)
if regex.search("^.*w~$", p):
c = "blue"
plt.annotate(p,(emb[0],emb[1]),color=c)
if regex.search("^.*y~$", p):
c = "yellow"
plt.annotate(p,(emb[0],emb[1]),color=c)
if regex.search("^.*h~$", p):
c = "brown"
plt.annotate(p,(emb[0],emb[1]),color=c)
if regex.search("^.*\"$", p):
c = "green"
plt.annotate(p,(emb[0],emb[1]),color=c)
def t_sne_view(norm_table, subj_cond, cohorts, image_type):
# t-SNE analysis: Use stochastic neighbor embedding to reduce dimensionality of
# data set to two dimensions in a non-linear, distance dependent fashion
# Perform PCA data reduction if dimensionality of feature space is large:
if len(norm_table.columns) > 12:
pca = PCA(n_components = 12)
pca.fit(norm_table.as_matrix())
raw_data = pca.transform(norm_table.as_matrix())
else:
raw_data = norm_table.as_matrix()
# Transform data into a two-dimensional embedded space:
tsne = TSNE(n_components = 2, perplexity = 40.0, early_exaggeration= 2.0,
learning_rate = 100.0, init = 'pca')
tsne_data = tsne.fit_transform(raw_data)
# Prepare for normalization and view:
cols = ['t-SNE', 'Cluster Visualization']
tsne_table = pd.DataFrame(tsne_data, index = norm_table.index, columns = cols)
# The output is no longer centered or normalized, so shift & scale it before display:
tsne_avg = ppmi.data_stats(tsne_table, subj_cond, cohorts)
tsne_norm_table = ppmi.normalize_table(tsne_table, tsne_avg)
# Send out to graphics rendering engine:
if (image_type == 'Gauss'):
return scg.scatter_gauss(tsne_norm_table[cols[0]], tsne_norm_table[cols[1]], subj_cond)
elif (image_type == 'Scatter'):
return scg.scatter_plain(tsne_norm_table[cols[0]], tsne_norm_table[cols[1]], subj_cond)
def main():
embedding = WordEmbedding(embeddingpath(default_embeddingconfig))
for old, new in spelling_changes:
print(old, '--', new)
print(embedding.nearest_words([old]))
print()
print()
war, ist = tense_changes[0]
tensediff = embedding[ist] - embedding[war]
for past, present in tense_changes[1 : ]:
print(past, '+ tensediff:', *embedding.nearest_words([embedding[past] + tensediff]))
print('Should be:', present)
print()
# word_diffs = [embedding[new] - embedding[old] for (old, new) in word_changes]
spelling_diffs = [embedding[new] - embedding[old] for (old, new) in spelling_changes[10 : 20]]
tense_diffs = [embedding[present] - embedding[past] for (past, present) in tense_changes]
def metric(u, v):
return max(distance.cosine(u, v), 0)
while True:
try:
model = TSNE(n_components=2, metric=metric)
reduced = model.fit_transform(spelling_diffs + tense_diffs)
print(reduced)
return
except Exception:
pass
def apply_tSNE30(proj_data, proj_weights=None):
model = TSNE(n_components=2, perplexity=30.0, metric="euclidean",
learning_rate=100, early_exaggeration=4.0,
random_state=RANDOM_SEED);
norm_data = normalize_columns(proj_data);
result = model.fit_transform(norm_data.T);
return result;
def plot_mean_activation_and_stuff(some_probs, Y, do_tsne=False):
pyplot.clf()
probs = numpy.float32(some_probs)
xv = numpy.arange(probs.shape[1])#probs.var(axis=0)
yv = probs.mean(axis=0)
pyplot.axis([-0.1, probs.shape[1],0,1])
for k in range(probs.shape[1]):
pyplot.plot(xv[k]*numpy.ones(probs.shape[0]),probs[:,k],'o',ms=4.,
markeredgecolor=(1, 0, 0, 0.01),
markerfacecolor=(1, 0, 0, 0.01),)
pyplot.plot(xv,yv, 'bo')
pyplot.show(block=False)
if do_video:
pyplot.savefig(video.stdin, format='jpeg')
video.stdin.flush()
pyplot.savefig('epoch_probs.png')
if not do_tsne: return
try:
from sklearn.manifold import TSNE
tsne = TSNE(random_state=0)
ps = tsne.fit_transform(numpy.float64(probs[:400]))
pyplot.clf()
Y = numpy.int32(Y)[:400]
for i,c,s in zip(range(10),list('bgrcmyk')+[(.4,.3,.9),(.9,.4,.3),(.3,.9,.4)],'ov'*5):
sub = ps[Y == i]
pyplot.plot(sub[:,0], sub[:,1], s,color=c,ms=3,mec=c)
pyplot.show(block=False)
pyplot.savefig('probs_embed.png')
except ImportError:
print "cant do tsne"
def topic_dimen_reduce(words, word2vec):
dictionary, matrix = terms_analysis.get_words_matrix(words, word2vec)
pca = PCA(n_components=50)
pca_matrix = pca.fit_transform(matrix)
tsne = TSNE(n_components=2)
t_matrix = tsne.fit_transform(pca_matrix)
return dictionary, t_matrix
def tsnePlot(plotname, modelName, word, dest):
"""Plots a tsne graph of words most similar to the word passed in the argument (as represented in the model previously calculated)"""
model = word2vec.Word2Vec.load(modelName)
words = [model.most_similar(word)[i][0] for i in range(0, len(model.most_similar(word)))]
words.append(word)
#nested list constaining 100 dimensional word vectors of each most-similar word
word_vectors = [model[word] for word in words]
word_vectors = np.array(word_vectors)
tsne_model = TSNE(n_components=2, random_state=0)
Y = tsne_model.fit_transform(word_vectors)
sb.plt.plot(Y[:,0], Y[:,1], 'o')
for word, x, y in zip(words, Y[:,0], Y[:,1]):
sb.plt.annotate(word, (x, y), size=12)
#sb.plt.pause(10)
plotname = plotname + ".png"
if not os.path.exists(dest):
os.makedirs(dest)
path = os.path.join(dest, plotname)
sb.plt.savefig(path)
def reduce_dimentionality(self):
self.vectors = []
for key in self.selected_words:
self.vectors.append(self.model[key])
tnse_model = TSNE(n_components=2, random_state=0)
np.set_printoptions(suppress=True)
self.reduced_vectors = tnse_model.fit_transform(self.vectors)
def plotTSNEDecisionBoundaries():
tsne = TSNE()
tsne_data = tsne.fit_transform(feature_set)
x_min,x_max = tsne_data[:,0].min()-1, tsne_data[:,0].max() + 1
y_min,y_max = tsne_data[:,1].min()-1, tsne_data[:,1].max() + 1
step_size = 2.0
xx,yy = np.meshgrid(np.arange(x_min,x_max,step_size),np.arange(y_min,y_max,step_size))
for index,classifier in enumerate(classifiers):
plt.subplot(2,3,index+1)
plt.subplots_adjust(wspace=0.5,hspace=0.5)
classifier.fit(tsne_data,class_labels)
Z = classifier.predict(zip(xx.ravel(),yy.ravel()))
Z = Z.reshape(xx.shape)
plt.contourf(xx,yy,Z,cmap=plt.cm.Paired,alpha=0.7)
plt.scatter(tsne_data[:,0],tsne_data[:,1],c=class_labels,cmap=plt.cm.rainbow,alpha=0.6)
plt.xlabel("Feature 1")
plt.ylabel("Feature 2")
plt.xlim(x_min,x_max)
plt.ylim(y_min,y_max)
plt.xticks(())
plt.yticks(())
plt.title(classifier_names[index])
plt.show()
def visualization(result, word_dict): tsne = TSNE(perplexity=30, n_components=2, init='pca', n_iter=5000) plot_only = 500 low_dim_embs = tsne.fit_transform(result[0:500]) labels = [ word_dict[i] for i in range(500) ] plot_with_labels(low_dim_embs, labels)
def project_in_2D(distance_mat, method='mds'):
"""
Project SDRs onto a 2D space using manifold learning algorithms
:param distance_mat: A square matrix with pairwise distances
:param method: Select method from 'mds' and 'tSNE'
:return: an array with dimension (numSDRs, 2). It contains the 2D projections
of each SDR
"""
seed = np.random.RandomState(seed=3)
if method == 'mds':
mds = MDS(n_components=2, max_iter=3000, eps=1e-9,
random_state=seed,
dissimilarity="precomputed", n_jobs=1)
pos = mds.fit(distance_mat).embedding_
nmds = MDS(n_components=2, metric=False, max_iter=3000, eps=1e-12,
dissimilarity="precomputed", random_state=seed,
n_jobs=1, n_init=1)
pos = nmds.fit_transform(distance_mat, init=pos)
elif method == 'tSNE':
tsne = TSNE(n_components=2, init='pca', random_state=0)
pos = tsne.fit_transform(distance_mat)
else:
raise NotImplementedError
return pos
def PlotTSNE (data, labels): #Takes the data and the labels
# Visualize the results on TSNE reduced data
print "BUSY IN TSNE"
model = TSNE(n_components=2, random_state=0)
reduced_data = model.fit_transform(data)
print "DATA REDUCED"
# Plot the decision boundary. For that, we will assign a color to each
x_min, x_max = reduced_data[:, 0].min(), reduced_data[:, 0].max()
y_min, y_max = reduced_data[:, 1].min(), reduced_data[:, 1].max()
plt.plot(reduced_data[:, 0], reduced_data[:, 1], 'k.', markersize=2)
#Adds labels to the plot
for label, x, y in zip(labels, reduced_data[:, 0], reduced_data[:, 1]):
plt.annotate(
label,
xy = (x, y), xytext = (-20, 20),
textcoords = 'offset points', ha = 'right', va = 'bottom',
bbox = dict(boxstyle = 'round,pad=0.5', fc = 'green', alpha = 0.5),
arrowprops = dict(arrowstyle = '->', connectionstyle = 'arc3,rad=0'))
plt.title('TSNE Plot')
plt.xlim(x_min, x_max)
plt.ylim(y_min, y_max)
plt.xticks(())
plt.yticks(())
plt.show()
def plotly_js_viz(word_2_vec_model):
tsne_model=TSNE(n_components=2,random_state=5)
data=tsne_model.fit_transform(word_2_vec_model.syn0)
xd=list(data[:,0])
yd=list(data[:,1])
names_our=word_2_vec_model.index2word
plot([Scatter(x=xd,y=yd,mode="markers",text=names_our)])
def visualize_latent_rep(args, model, x_latent):
print("pca_on=%r pca_comp=%d tsne_comp=%d tsne_perplexity=%f tsne_lr=%f" % (
args.use_pca,
args.pca_components,
args.tsne_components,
args.tsne_perplexity,
args.tsne_lr
))
if args.use_pca:
pca = PCA(n_components = args.pca_components)
x_latent = pca.fit_transform(x_latent)
figure(figsize=(6, 6))
scatter(x_latent[:, 0], x_latent[:, 1], marker='.')
show()
tsne = TSNE(n_components = args.tsne_components,
perplexity = args.tsne_perplexity,
learning_rate = args.tsne_lr,
n_iter = args.tsne_iterations,
verbose = 4)
x_latent_proj = tsne.fit_transform(x_latent)
del x_latent
figure(figsize=(6, 6))
scatter(x_latent_proj[:, 0], x_latent_proj[:, 1], marker='.')
show()
def tsne_plot(model):
#"Creates and TSNE model and plots it"
labels = []
tokens = []
for word in model.wv.vocab:
tokens.append(model[word])
labels.append(word)
tsne_model = TSNE(perplexity=40, n_components=2, init='pca', n_iter=2500, random_state=23)
new_values = tsne_model.fit_transform(tokens)
x = []
y = []
for value in new_values:
x.append(value[0])
y.append(value[1])
plt.figure(figsize=(16, 16))
for i in range(len(x)):
plt.scatter(x[i], y[i])
plt.annotate(labels[i],
xy=(x[i], y[i]),
xytext=(5, 2),
textcoords='offset points',
ha='right',
va='bottom')
plt.show()
def infer(FLAGS):
"""
Inference.
"""
# Retrieve embeddings for docs
words = ["tennis", "wimbledon", "icecream", "cake", "bear", "pie"]
# Get index in doc embeddings
with open(os.path.join(basedir, FLAGS.data_dir, "doc_to_idx.json"), 'r') as f:
doc_to_idx = json.load(f)
# Load the trained model
model = torch.load(os.path.join(basedir, FLAGS.data_dir, "model.pt"))
doc_embeddings = model.doc_embeddings.weight.data
my_embeddings = np.array(
[doc_embeddings[doc_to_idx[word]].numpy() for word in words])
# Use TSNE model to reduce dimensionality
model = TSNE(n_components=2, random_state=0)
points = model.fit_transform(my_embeddings)
# Visualize
for i, word in enumerate(words):
x, y = points[i, 0]*1e4, points[i, 1]*1e4
plt.scatter(x, y)
plt.annotate(word, xy=(x, y), xytext=(25, 5),
textcoords='offset points', ha='right', va='bottom')
plt.show()
bottleneck_representation = encoder.predict([X_scRNAseq, X_scProteomics])
print(pd.DataFrame(bottleneck_representation).shape)
print(pd.DataFrame(bottleneck_representation).iloc[0:5,0:5])
# Dimensionality reduction plot
#plt.figure(figsize=(20, 15))
plt.scatter(bottleneck_representation[:, 0], bottleneck_representation[:, 1], c = Y_scRNAseq, cmap = 'tab20', s = 10)
plt.title('Autoencoder Data Integration')
plt.xlabel('Dimension 1')
plt.ylabel('Dimension 2')
#plt.colorbar()
plt.show()
# tSNE on Autoencoder bottleneck representation
model_tsne_auto = TSNE(learning_rate = 200, n_components = 2, random_state = 123, perplexity = 90, n_iter = 1000, verbose = 1)
tsne_auto = model_tsne_auto.fit_transform(bottleneck_representation)
plt.scatter(tsne_auto[:, 0], tsne_auto[:, 1], c = Y_scRNAseq, cmap = 'tab20', s = 10)
plt.title('tSNE on Autoencoder: Data Integration, CITEseq')
plt.xlabel("tSNE1")
plt.ylabel("tSNE2")
plt.show()
# UNIFORM MANIFOLD APPROXIMATION AND PROJECTION (UMAP)
#model_umap = UMAP(n_neighbors = 20, min_dist = 0.3, n_components = 2)
#umap = model_umap.fit_transform(bottleneck_representation)
#plt.scatter(umap[:, 0], umap[:, 1], c = Y_scRNAseq, cmap = 'tab20', s = 10)
#plt.title('UMAP on Autoencoder')
#plt.xlabel("UMAP1")
#plt.ylabel("UMAP2")
#plt.show()
'spleenV5_rel', 'spleenV10_rel', 'spleenV15_rel', 'spleenV20_rel',
'spleenV25_rel', 'spleenV30_rel', 'spleenV35_rel', 'spleenV40_rel',
'spleenV45_rel', 'spleenV50_rel', 'meanspleendose', 'spleenvolume'
]
new_data_dense_features2 = string_float(new_data[dense_features2]).values
N = 20000
n_components = 3
tsne = TSNE(n_components=n_components,
perplexity=50,
n_iter=N,
init='pca',
random_state=0,
verbose=1,
method='exact')
tsne_results = tsne.fit_transform(new_data_dense_features2)
new_data = new_data.drop(dense_features2, axis=1)
new_data["DoseComp1"] = tsne_results[:, 0]
new_data["DoseComp2"] = tsne_results[:, 1]
new_data["DoseComp3"] = tsne_results[:, 2]
dense_features2_new = ["DoseComp1", "DoseComp2", "DoseComp3"]
#new_data=np.concatenate([new_data.values,tsne_results],axis=1)
new_data.to_csv('Data_tsne_20K_3c.csv', index=False)
data_train, data_test = train_test_split(new_data,
test_size=.3,
random_state=8)
y_train_class = data_train.pop('G4RIL').values
def project(cls1_data,
cls2_data,
projection='mds',
setsize=None,
with_debiasing=None,
figname=None):
if type(cls1_data) == list:
fig, axes = plt.subplots(3, 4, figsize=(9, 12))
fig.tight_layout(pad=0.5)
flataxes = [ax for tmp in axes for ax in tmp]
setsize = len(cls1_data)
if setsize > 500: setsize = 500
X = np.r_[np.concatenate(cls1_data, axis=0)[:setsize, :, :],
np.concatenate(cls2_data, axis=0)[:setsize, :, :]]
if with_debiasing:
X = with_debiasing.clean_data(X)
for layer in range(0, 12):
print('plotting layer %d' % (layer + 1))
if projection == 'mds':
mds = MDS(n_components=2)
X_transformed = mds.fit_transform(X[:, layer, :].astype(
np.float64))
if projection == 'pca':
pca = PCA(n_components=2)
X_transformed = pca.fit_transform(X[:, layer, :].astype(
np.float64))
if projection == 'tsne':
tsne = TSNE(n_components=2, verbose=1)
X_transformed = tsne.fit_transform(X[:, layer, :].astype(
np.float64))
colors = ['red'] * setsize + ['blue'] * setsize
ax = flataxes[layer]
ax.set_aspect('equal', adjustable='box')
ax.scatter(X_transformed[:, 0], X_transformed[:, 1], s=2, c=colors)
#ax.set_xlim((-20, 20))
#ax.set_ylim((-20, 20))
ax.set_title('Layer %d' % (layer + 1), fontsize=12)
print('plotting done.')
else:
if not setsize:
setsize = cls1_data.shape[0]
colors = ['red'] * setsize + ['blue'] * setsize
X = np.r_[cls1_data[:setsize, :], cls2_data[:setsize, :]]
mds = MDS(n_components=2)
X_transformed = mds.fit_transform(X)
fig = plt.plot()
plt.scatter(X_transformed[:, 0], X_transformed[:, 1], s=2, c=colors)
if figname:
plt.savefig(figname)
else:
plt.show()
#Set df4 equal to a set of a sample of 1000 deafault and 1000 non-default observations.
df2 = tsne_data[tsne_data.default == 0].sample(n = 1000)
df3 = tsne_data[tsne_data.default == 1].sample(n = 1000)
df4 = pd.concat([df2, df3], axis = 0)
#Scale features to improve the training ability of TSNE.
standard_scaler = StandardScaler()
df4_std = standard_scaler.fit_transform(df4)
#Set y equal to the target values.
y = df4.ix[:,-1].values
tsne = TSNE(n_components=2, random_state=0)
x_test_2d = tsne.fit_transform(df4_std)
#Build the scatter plot with the two types of transactions.
color_map = {0:'red', 1:'blue'}
plt.figure()
for idx, cl in enumerate(np.unique(y)):
plt.scatter(x = x_test_2d[y==cl,0],
y = x_test_2d[y==cl,1],
c = color_map[idx],
label = cl)
plt.xlabel('X in t-SNE')
plt.ylabel('Y in t-SNE')
plt.legend(loc='upper left')
plt.title('t-SNE visualization of test data')
plt.show()
pBarLen = 20
sys.stdout.write("|%s| - Training(%s/%s)-%s\r"%(progressBar((i//200)%pBarLen, pBarLen),i,steps,
estimate_time(startTime, steps, i)))
if i % logStep == 0:
print("Loss: %s" % lossVal)
sim = similarity.eval()
for i in range(len(valid_set)):
word = WORDS[valid_set[i]]
top_k = 5
nearest = (-sim[i,:]).argsort()[1:1+top_k]
msg = "%s: "% word
for k in range(top_k):
close_word = WORDS[nearest[k]]
if k > 0: msg += ", "#"\t"
msg += close_word #+ ": %.08f\n"%sim[i,k]
print(msg)
print("------------------------------------------")
saveModel(sess, LOG_DIR+"model.ckpt")
final_embeddings = normalized_embeddings.eval()
writeToFile(final_embeddings, "savedEmbeddings/embeddings.pkl")
# plotting using t-SNE
# two_d_embeddings = tsne.fit_transform(final_embeddings)
# plot(two_d_embeddings, WORDS)
final_embeddings = normalized_embeddings.eval()
two_d_embeddings = tsne.fit_transform(final_embeddings)
plot(two_d_embeddings, WORDS)
writeToFile(final_embeddings, "savedEmbeddings/embeddings.pkl")
print(y.shape)
z_vec.flatten()
z_vec = z_vec[:, 0, :]
type(z_vec)
print(z_vec.shape)
b.flatten()
b = b[:, 0, :]
type(b)
print(b.shape)
from sklearn.manifold import TSNE
tsne = TSNE(n_components=2, random_state=0)
# Convert the image to a numpy array
X_z_2d = tsne.fit_transform(z_vec)
print(X_z_2d.shape)
X_b_2d = tsne.fit_transform(b)
print(X_b_2d.shape)
# Plot z's
from matplotlib import pyplot as plt
target_ids = range(10)
plt.figure(figsize=(6, 5))
target_names = np.array([0, 1, 2,3,4,5,6,7,8,9])
print(target_names)
colors = 'k', 'b', 'y', 'r', 'g', 'm', 'c', 'orange', 'purple', 'brown'
for i, c, label in zip(target_ids, colors, target_names):
plt.scatter(X_z_2d[y == i, 0], X_z_2d[y == i, 1], s=15, c=c, label=label)
plt.legend()
plt.show()
np.savetxt('{0}_c{1}_labels.tsv'.format(arguments.fout, k),
(nmf.cluster_labels, nmf.remain_cell_inds),
fmt='%u',
delimiter='\t')
# --------------------------------------------------
# 3.4. T-SNE PLOT
# --------------------------------------------------
if arguments.tsne:
model = TSNE(n_components=2,
random_state=0,
init='pca',
method='exact',
metric='euclidean',
perplexity=30)
ret = model.fit_transform(nmf.pp_data.T)
plt.title('{0} cluster (Euclidean)'.format(k))
plt.scatter(ret[:, 0], ret[:, 1], 20, nmf.cluster_labels)
plt.xticks([])
plt.yticks([])
plt.savefig('{0}_c{1}_tsne.png'.format(arguments.fout, k),
format='png',
bbox_inches=None,
pad_inches=0.1)
# plt.show()
# --------------------------------------------------
# 6. SUMMARIZE RESULTS
# --------------------------------------------------
print '\n------------------------------ Summary:'
def process(self):
# parse parameters
input_words = self.parameters.get("words", "")
if not input_words or not input_words.split(","):
self.dataset.update_status(
"No input words provided, cannot look for similar words.",
is_final=True)
self.dataset.finish(0)
return
input_words = input_words.split(",")
try:
threshold = float(self.parameters.get("threshold"))
except ValueError:
threshold = float(self.get_options()["threshold"]["default"])
threshold = max(-1.0, min(1.0, threshold))
num_words = convert_to_int(self.parameters.get("num-words"))
overlay = self.parameters.get("overlay")
reduction_method = self.parameters.get("method")
all_words = self.parameters.get("all-words")
# load model files and initialise
self.dataset.update_status("Unpacking word embedding models")
staging_area = self.unpack_archive_contents(self.source_file)
common_vocab = None
vector_size = None
models = {}
# find words that are common to all models
self.dataset.update_status("Determining cross-model common vocabulary")
for model_file in staging_area.glob("*.model"):
if self.interrupted:
shutil.rmtree(staging_area)
raise ProcessorInterruptedException(
"Interrupted while processing word embedding models")
model = KeyedVectors.load(str(model_file)).wv
models[model_file.stem] = model
if vector_size is None:
vector_size = model.vector_size # needed later for dimensionality reduction
if common_vocab is None:
common_vocab = set(model.vocab.keys())
else:
common_vocab &= set(model.vocab.keys()) # intersect
# sort common vocabulary by combined frequency across all models
# this should make filtering for common words a bit faster further down
self.dataset.update_status("Sorting vocabulary")
common_vocab = list(common_vocab)
common_vocab.sort(key=lambda w: sum(
[model.vocab[w].count for model in models.values()]),
reverse=True)
# initial boundaries of 2D space (to be adjusted later based on t-sne
# outcome)
max_x = 0.0 - sys.float_info.max
max_y = 0.0 - sys.float_info.max
min_x = sys.float_info.max
min_y = sys.float_info.max
# for each model, find the words that we may want to plot - these are
# the nearest neighbours for the given query words
relevant_words = {}
# the vectors need to be reduced all at once - but the vectors are
# grouped by model. To solve this, keep one numpy array of vectors,
# but also keep track of which indexes of this array belong to which
# model, by storing the index of the first vector for a model
vectors = numpy.empty((0, vector_size))
vector_offsets = {}
# now process each model
for model_name, model in models.items():
relevant_words[model_name] = set(
) # not a set, since order needs to be preserved
self.dataset.update_status("Finding similar words in model '%s'" %
model_name)
for query in input_words:
if query not in model.vocab:
self.dataset.update_status(
"Query '%s' was not found in model %s; cannot find nearest neighbours."
% (query, model_name),
is_final=True)
self.dataset.finish(0)
return
if self.interrupted:
shutil.rmtree(staging_area)
raise ProcessorInterruptedException(
"Interrupted while finding similar words")
# use a larger sample (topn) than required since some of the
# nearest neighbours may not be in the common vocabulary and
# will therefore need to be ignored
context = set([
word[0] for word in model.most_similar(query, topn=1000)
if word[0] in common_vocab and word[1] >= threshold
][:num_words])
relevant_words[model_name] |= {
query
} | context # always include query word
# now do another loop to determine which words to plot for each model
# this is either the same as relevant_words, or a superset which
# combines all relevant words for all models
plottable_words = {}
last_model = max(relevant_words.keys())
all_relevant_words = set().union(*relevant_words.values())
for model_name, words in relevant_words.items():
plottable_words[model_name] = []
vector_offsets[model_name] = len(vectors)
# determine which words to plot for this model. either the nearest
# neighbours for this model, or all nearest neighbours found across
# all models
words_to_include = all_relevant_words if all_words else relevant_words[
model_name]
for word in words_to_include:
if word in plottable_words[model_name] or (
not overlay and model_name != last_model
and word not in input_words):
# only plot each word once per model, or if 'overlay'
# is not set, only once overall (for the most recent
# model)
continue
vector = models[model_name][word]
plottable_words[model_name].append(word)
vectors = numpy.append(vectors, [vector], axis=0)
del models # no longer needed
# reduce the vectors of all words to be plotted for this model to
# a two-dimensional coordinate with the previously initialised tsne
# transformer. here the two-dimensional vectors are interpreted as
# cartesian coordinates
if reduction_method == "PCA":
pca = PCA(n_components=2, random_state=0)
vectors = pca.fit_transform(vectors)
elif reduction_method == "t-SNE":
# initialise t-sne transformer
# parameters taken from Hamilton et al.
# https://github.com/williamleif/histwords/blob/master/viz/common.py
tsne = TSNE(n_components=2,
random_state=0,
learning_rate=150,
init="pca")
vectors = tsne.fit_transform(vectors)
elif reduction_method == "TruncatedSVD":
# standard sklearn parameters made explicit
svd = TruncatedSVD(n_components=2,
algorithm="randomized",
n_iter=5,
random_state=0)
vectors = svd.fit_transform(vectors)
else:
shutil.rmtree(staging_area)
self.dataset.update_status(
"Invalid dimensionality reduction technique selected",
is_final=True)
self.dataset.finish(0)
return
# also keep track of the boundaries of our 2D space, so we can plot
# them properly later
for position in vectors:
max_x = max(max_x, position[0])
max_y = max(max_y, position[1])
min_x = min(min_x, position[0])
min_y = min(min_y, position[1])
# now we know for each model which words should be plotted and at what
# position
# with this knowledge, we can normalize the positions, and start
# plotting them in a graph
# a palette generated with https://medialab.github.io/iwanthue/
colours = [
"#d58eff", "#cf9000", "#3391ff", "#a15700", "#911ca7", "#00ddcb",
"#cc25a9", "#d5c776", "#6738a8", "#ff9470", "#47c2ff", "#a4122c",
"#00b0ca", "#9a0f76", "#ff70c8", "#713c88"
]
colour_index = 0
# make sure all coordinates are positive
max_x -= min_x
max_y -= min_y
# determine graph dimensions and proportions
width = 1000 # arbitrary
height = width * (max_y / max_x) # retain proportions
scale = width / max_x
# margin around the plot to give room for labels and to look better
margin = width * 0.1
width += 2 * margin
height += 2 * margin
# normalize all known positions to fit within the graph
vectors = [(margin + ((position[0] - min_x) * scale),
margin + ((position[1] - min_y) * scale))
for position in vectors]
# now all positions are finalised, we can determine the "journey" of
# each query - the sequence of positions in the graph it takes, so we
# can draw lines from position to position later
journeys = {}
for query in input_words:
journeys[query] = []
for model_name, words in plottable_words.items():
index = words.index(query)
journeys[query].append(vectors[vector_offsets[model_name] +
index])
# font sizes proportional to width (which is static and thus predictable)
fontsize_large = width / 50
fontsize_normal = width / 75
fontsize_small = width / 100
# now we have the dimensions, the canvas can be instantiated
model_type = self.source_dataset.parameters.get(
"model-type", "word2vec")
canvas = get_4cat_canvas(
self.dataset.get_results_path(),
width,
height,
header="%s nearest neighbours (fitting: %s) - '%s'" %
(model_type, reduction_method, ",".join(input_words)),
fontsize_normal=fontsize_normal,
fontsize_large=fontsize_large,
fontsize_small=fontsize_small)
# use colour-coded backgrounds to distinguish the query words in the
# graph, each model (= interval) with a separate colour
for model_name in plottable_words:
solid = Filter(id="solid-%s" % model_name)
solid.feFlood(flood_color=colours[colour_index])
solid.feComposite(in_="SourceGraphic")
canvas.defs.add(solid)
# this can get kind of confusing, but you shouldn't be using this
# with more than 16 models anyway
colour_index = 0 if colour_index >= len(
colours) - 1 else colour_index + 1
# now plot each word for each model
self.dataset.update_status("Plotting graph")
words = SVG(insert=(0, 0), size=(width, height))
queries = SVG(insert=(0, 0), size=(width, height))
colour_index = 0
for model_name, labels in plottable_words.items():
positions = vectors[
vector_offsets[model_name]:vector_offsets[model_name] +
len(labels)]
label_index = 0
for position in positions:
word = labels[label_index]
is_query = word in input_words
label_index += 1
filter = ("url(#solid-%s)" %
model_name) if is_query else "none"
colour = "#FFF" if is_query else colours[colour_index]
fontsize = fontsize_normal if is_query else fontsize_small
if word in input_words:
word += " (" + model_name + ")"
label_container = SVG(insert=position,
size=(1, 1),
overflow="visible")
label_container.add(
Text(insert=("50%", "50%"),
text=word,
dominant_baseline="middle",
text_anchor="middle",
style="fill:%s;font-size:%ipx" % (colour, fontsize),
filter=filter))
# we make sure the queries are always rendered on top by
# putting them in a separate SVG container
if is_query:
queries.add(label_container)
else:
words.add(label_container)
colour_index = 0 if colour_index >= len(
colours) - 1 else colour_index + 1
# plot a line between positions for query words
lines = SVG(insert=(0, 0), size=(width, height))
for query, journey in journeys.items():
previous_position = None
for position in journey:
if previous_position is None:
previous_position = position
continue
lines.add(
Line(start=previous_position,
end=position,
stroke="#CE1B28",
stroke_width=2))
previous_position = position
canvas.add(lines)
canvas.add(words)
canvas.add(queries)
canvas.save(pretty=True)
shutil.rmtree(staging_area)
self.dataset.finish(len(journeys))
w2v_model.wv.most_similar('人民', topn=5)
w2v_model.wv.most_similar('台灣', topn=5)
similar_words = {key_word:[similar_word[0] for similar_word in w2v_model.wv.most_similar(key_word, topn=6)]
for key_word in ['台灣','人民','國家','民主','中共','大陸','共匪','自由']}
similar_words
## Visualization
from sklearn.manifold import TSNE
all_words = sum([[key_word]+similar_words for key_word, similar_words in similar_words.items()], [])
all_words_vec = w2v_model.wv[all_words]
tsne = TSNE(n_components=2, random_state=0, n_iter=10000, perplexity=2)
np.set_printoptions(suppress=True)
T = tsne.fit_transform(all_words_vec)
labels=all_words
## Chinese Font Issues in Plotting
from matplotlib import rcParams
from matplotlib.font_manager import FontProperties
import matplotlib.pyplot as plt
# 解决负号'-'显示为方块的问题
# rcParams['axes.unicode_minus']=False
myfont = FontProperties(fname='/System/Library/Fonts/PingFang.ttc',
size=12)
# plt.title('乘客等级分布', fontproperties=myfont)
# plt.ylabel('人数', fontproperties=myfont)
# plt.legend(('头等舱', '二等舱', '三等舱'), loc='best', prop=myfont)
### ~ Data Viz ~ ###
#######################
df_train_both = pd.merge(df_train_genetic_scaled,
df_train_non_genetic_pre_processed,
left_index=True,
right_index=True,
how='outer')
df_train_both_with_target = pd.merge(df_train_both,
df_solution,
left_index=True,
right_index=True,
how='outer')
#Applying t-SNE to vizualize data
tsne = TSNE(n_components=3, verbose=1, perplexity=40, n_iter=300)
x = df_train_both.values
x_tsne = tsne.fit_transform(x)
df_tsne = pd.DataFrame()
df_tsne['x-tsne'] = x_tsne[:, 0]
df_tsne['y-tsne'] = x_tsne[:, 1]
df_tsne['label'] = df_solution.values
chart = ggplot( df_tsne, aes(x='x-tsne', y='y-tsne', color='label') ) \
+ geom_point(size=70,alpha=0.1) \
+ ggtitle("tSNE dimensions colored by cancer stage")
print(chart)
b_sight=3000,
b_maxl=1500,
n_jobs=20)
#print('fit gammas')
vlm.fit_gammas()
print('calculate velocity')
vlm.predict_U()
vlm.calculate_velocity()
vlm.calculate_shift(assumption="constant_velocity")
vlm.extrapolate_cell_at_t(delta_t=1.)
print('running tsne')
bh_tsne = TSNE(random_state=1)
vlm.ts = bh_tsne.fit_transform(vlm.pcs[:, :20])
print('projection of velocity onto embeddings')
vlm.estimate_transition_prob(hidim="Sx_sz",
embed="ts",
transform="sqrt",
psc=1,
n_neighbors=3500,
knn_random=True,
sampled_fraction=0.5)
print('calculate embedding shift')
vlm.calculate_embedding_shift(sigma_corr=0.05, expression_scaling=True)
print('calculate grid arrows')
vlm.calculate_grid_arrows(smooth=0.8, steps=(40, 40), n_neighbors=100)
log_str="Nearest to %s:" % valid_word
for k in range(top_k):
close_word=reverse_dictionary[nearest[k]]
log_str="%s %s," %(log_str,close_word)
print(log_str)
final_embedding=normalized_embeddings.eval()
'''
可视化Word2Vec散点图并保存
'''
def plot_with_labels(low_dim_embs,labels,filename):
assert low_dim_embs.shape[0]>=len(labels),"more labels than embedding"
plt.figure(figsize=(18,18))
for i,label in enumerate(labels):
x,y=low_dim_embs[i,:]
plt.scatter(x, y)
plt.annotate(label,xy=(x,y),xytext=(5,2),textcoords='offset points',ha='right',va='bottom')
plt.savefig(filename)
'''
tsne实现降维，将原始的128维的嵌入向量降到2维
'''
tsne=TSNE(perplexity=30,n_components=2,init='pca',n_iter=5000)
plot_number=150
low_dim_embs=tsne.fit_transform(final_embedding[:plot_number,:])
labels=[reverse_dictionary[i] for i in range(plot_number)]
plot_with_labels(low_dim_embs, labels, './plot.png')
## Remove duplicates of fighters, only keep the most up to date entry
relevant_data.sort_values(by='date', ascending=False, inplace=True)
relevant_data = relevant_data.drop_duplicates(subset='fighter')
relevant_data.drop(columns='date', inplace=True)
relevant_data.drop(
columns='Stance', inplace=True
) # Removed Stance for dataset simplicity, consider adding in later
relevant_data.fillna(0, inplace=True)
x = relevant_data.iloc[:, 1:]
y = relevant_data.iloc[:, 0]
#y = y.astype('category').cat.codes
model = TSNE(learning_rate=100)
tsne_transformed = model.fit_transform(x)
xs = tsne_transformed[:, 0]
ys = tsne_transformed[:, 1]
## Plot the clusters
#plt.scatter(xs, ys, c = y)
#plt.ylim(-50, 50)
#plt.xlim(-50,50)
#plt.show()
fig, ax = plt.subplots(figsize=(8, 6))
ax.set_ylim(-50, 50)
ax.set_xlim(-50, 50)
ax.scatter(xs, ys)
index = 0
for ine in inertia:
b = (inertia[0] - ine) * (inertia[0] - ine)
c = (ine - inertia[-1]) * (ine - inertia[-1])
a = math.sqrt(b + c)
if a < mininum:
get = index
mininum = a
index += 1
kmeans = KMeans(n_clusters=get, random_state=0).fit(data_sparse)
model = AgglomerativeClustering(n_clusters=get)
gp = model.fit_predict(data_sparse)
tsne = TSNE()
visualisation = tsne.fit_transform(data_sparse)
def count(data, qtd):
all_words = ' '.join([text for text in data])
token_phrase = token_space.tokenize(all_words)
frequency = nltk.FreqDist(token_phrase)
df_frequency = pd.DataFrame({
"Palavra": list(frequency.keys()),
"Frequencia": list(frequency.values())
})
de_frequency = df_frequency.nlargest(columns="Frequencia", n=qtd)
return (de_frequency)
list_city = []
#-*- coding: utf-8 -*- # 接k_means.py from sklearn.manifold import TSNE tsne = TSNE() tsne.fit_transform(data_zs) # 进行数据降维 tsne = pd.DataFrame(tsne.embedding_, index=data_zs.index) # 转换数据格式 import matplotlib.pyplot as plt plt.rcParams['font.sans-serif'] = ['SimHei'] # 用来正常显示中文标签 plt.rcParams['axes.unicode_minus'] = False # 用来正常显示负号 # 不同类别用不同颜色和样式绘图 d = tsne[r[u'聚类类别'] == 0] plt.plot(d[0], d[1], 'r.') d = tsne[r[u'聚类类别'] == 1] plt.plot(d[0], d[1], 'go') d = tsne[r[u'聚类类别'] == 2] plt.plot(d[0], d[1], 'b*') plt.show()
list_avec = all_list_adocs_vec + list_awords_sim_vec
#6
#-------------- PCA ------------------#
#pca = PCA(n_components=2)
#dresult_X_all = pca.fit_transform(list_dvec)
#aresult_X_all = pca.fit_transform(list_avec)
#-------------- TSNE -----------------#
#tsne = TSNE(n_components=2)
#tsne = TSNE(n_components=2, random_state=None, verbose=1, perplexity=40, n_iter=300)
#tsne = TSNE(n_components=2, init='pca', random_state=0, perplexity=40)
tsne = TSNE(n_components=2, perplexity=40, metric='euclidean', init='pca', verbose=0, random_state=0)
dresult_X_all = tsne.fit_transform(list_dvec)
aresult_X_all = tsne.fit_transform(list_avec)
#------------- Setting plot -----------------#
word_vec = "Word Vectors"
docs_vec = "Paragraph Vectors"
list_genre_plot = ["Economic", "Education", "Entertainment", "Foreign", "IT", "Sports"]
list_wovec_plot = [list_genre_plot[0]+' '+word_vec,list_genre_plot[1]+' '+word_vec,list_genre_plot[2]+' '+word_vec,list_genre_plot[3]+' '+word_vec,list_genre_plot[4]+' '+word_vec,list_genre_plot[5]+' '+word_vec]
list_dovec_plot = [list_genre_plot[0]+' '+docs_vec,list_genre_plot[1]+' '+docs_vec,list_genre_plot[2]+' '+docs_vec,list_genre_plot[3]+' '+docs_vec,list_genre_plot[4]+' '+word_vec,list_genre_plot[5]+' '+docs_vec]
plt.rcParams['font.family'] = 'TH SarabunPSK'
plt.rcParams['font.size'] = 14
#with plt.style.context('dark_background'):
#********---------------------------------------------------------********#
# extract output of the final embedding layer (before the softmax)
# in test mode, we should set the 'learning_phase' flag to 0 (e.g., we don't want to use dropout)
get_doc_embedding = K.function(
[model.layers[0].input, K.learning_phase()], [model.layers[9].output])
n_plot = 1000
print('plotting embeddings of first', n_plot, 'documents')
doc_emb = get_doc_embedding([np.array(x_test[:n_plot]), 0])[0]
my_pca = PCA(n_components=10)
my_tsne = TSNE(n_components=2,
perplexity=10) #https://lvdmaaten.github.io/tsne/
doc_emb_pca = my_pca.fit_transform(doc_emb)
doc_emb_tsne = my_tsne.fit_transform(doc_emb_pca)
labels_plt = y_test[:n_plot]
my_colors = ['blue', 'red']
fig, ax = plt.subplots()
for label in list(set(labels_plt)):
idxs = [idx for idx, elt in enumerate(labels_plt) if elt == label]
ax.scatter(doc_emb_tsne[idxs, 0],
doc_emb_tsne[idxs, 1],
c=my_colors[label],
label=str(label),
alpha=0.7,
s=40)
model = word2vec.load(args.model_path)
vocabs = []
vecs = []
for vocab in model.vocab:
vocabs.append(vocab)
vecs.append(model[vocab])
vecs = np.array(vecs)[:args.plot_num]
vocabs = vocabs[:args.plot_num]
'''
Dimensionality Reduction
'''
# from sklearn.decomposition import PCA
tsne = TSNE(n_components=2)
reduced = tsne.fit_transform(vecs)
'''
Plotting
'''
# filtering
use_tags = set(['JJ', 'NNP', 'NN', 'NNS'])
puncts = ["'", '.', ':', ";", ',', "?", "!", u"’"]
plt.figure()
texts = []
for i, label in enumerate(vocabs):
pos = nltk.pos_tag([label])
if (label[0].isupper() and len(label) > 1 and pos[0][1] in use_tags
and all(c not in label for c in puncts)):
x, y = reduced[i, :]
def plot_with_labels(low_dim_embs, labels, filename='tsne.png'):
assert low_dim_embs.shape[0] >= len(labels), 'More labels than embeddings'
plt.figure(figsize=(18, 18)) # in inches
for i, label in enumerate(labels):
x, y = low_dim_embs[i, :]
plt.scatter(x, y)
plt.annotate(label,
xy=(x, y),
xytext=(5, 2),
textcoords='offset points',
ha='right',
va='bottom')
plt.savefig(filename)
try:
# pylint: disable=g-import-not-at-top
from sklearn.manifold import TSNE
import matplotlib.pyplot as plt
tsne = TSNE(perplexity=30, n_components=2, init='pca', n_iter=5000)
plot_only = 500
low_dim_embs = tsne.fit_transform(final_embeddings[:plot_only, :])
labels = [reverse_dictionary[i] for i in xrange(plot_only)]
plot_with_labels(low_dim_embs, labels)
except ImportError:
print('Please install sklearn, matplotlib, and scipy to show embeddings.')
# Threshold filters out unconfident topics
threshold = 0.5
_idx = np.amax(refac_matrix, axis=1) > threshold # idx of doc that above the threshold
refac_matrix = refac_matrix[_idx]
# Dimentionality reduction gives us a better plot
from bokeh.plotting import figure, show
from bokeh.models import HoverTool, CustomJS, ColumnDataSource, Slider
from bokeh.layouts import column
from bokeh.palettes import all_palettes
from sklearn.manifold import TSNE
tsne = TSNE(random_state=2017, perplexity=30) # 5 30 50
tsne_embedding = tsne.fit_transform(refac_matrix)
tsne_embedding = pd.DataFrame(tsne_embedding, columns =['x','y'])
tsne_embedding['hue'] = refac_matrix.argmax(axis=1)
dataset['Date'] = pd.to_datetime(dataset.Date)
'''t-SNE scatter plot made with bokeh.
You can move your mouse over a point
to see specific words clustered in
their respective topics'''
source = ColumnDataSource(
data=dict(
x = tsne_embedding.x,
y = tsne_embedding.y,
for k, col in zip(tstInd['clust'].unique(), colors):
tmp = np.vstack(np.asarray(tstInd[tstInd['clust'] == k]['PCA']))
if len(tmp) > 0:
plt.scatter(tmp[:, 0], tmp[:, 1], c=col)
#draw indications of same drug in same color
tstInd = pn.read_csv("/home/galiasn/DATA/MechanismBasedRepurposing/full.csv")
tstInd = tstInd[tstInd['status'] == 'Approved']
tstInd['w2v'] = tstInd['ind_id'].apply(getIndVector)
pca_model = PCA(n_components=30)
tsne_model = TSNE(n_components=2, perplexity=10, n_iter=1000)
XOne = pca_model.fit_transform(list(tstInd['w2v']))
tsne_pca = tsne_model.fit_transform(XOne)
tstInd['PCA'] = list(tsne_pca)
tstInd['sum'] = len(tstInd) * [1]
tstIndgb = tstInd.groupby('drug_id', as_index=False).sum()
tstIndgb = tstIndgb[tstIndgb['sum'] > 1]
tstIndgb = tstIndgb[(tstIndgb['sum'] < 10)]
drugIds = tstIndgb['drug_id'].unique()
drugSampl = random.sample(set(drugIds), 400)
for d in drugSampl:
tmp = np.vstack(np.asarray(tstInd[tstInd['drug_id'] == d]['PCA']))
if len(tmp) > 0:
plt.scatter(tmp[:, 0], tmp[:, 1])
########################
#Mol2Vec
for i in range(10):
idx_by_label = np.array(data.index[data['label'] == i].tolist())
idx_by_label_sample = np.random.choice(idx_by_label,
size=100,
replace=False)
if i == 0:
idx_all = idx_by_label_sample
else:
idx_all = np.append(idx_all, idx_by_label_sample)
data_sample = data.iloc[np.sort(idx_all)]
""" Divide data to features and labels """
data_features = data_sample.iloc[:, 1:]
data_labels = data_sample.iloc[:, 0]
points = tsne.fit_transform(data_features)
points_3d = tsne_3d.fit_transform(data_features)
"plotting function 2d space"
def plot_embedding(x, y, title):
plt.figure()
plt.scatter(x[:, 0], x[:, 1], color=plt.cm.Set1(y / 10))
plt.xticks([])
plt.yticks([])
plt.title(title)
plt.show()
plt.figure(figsize=(16, 2))
plt.plot(list(clust_dict.keys()), list(clust_dict.values()), '+-')
plt.grid()
plt.title('Silhouette average vs number of clusters (using K-means)')
plt.show()
# In[15]:
from sklearn.manifold import TSNE
NUM_CLUSTERS = 8
# loop for all assets and create plot of the system
df_labels = pd.DataFrame(index=df.index, columns=assets)
for a in assets:
dfa = df[[c for c in df.columns if a in c]]
X_clust = dfa.interpolate(axis=0).values
clf = clf = KMeans(n_clusters=NUM_CLUSTERS)
df_labels[a] = clf.fit_predict(X_clust)
model = TSNE(n_components=2, random_state=0)
Y_clust = model.fit_transform(X_clust)
plt.scatter(x=Y_clust.transpose()[0],
y=Y_clust.transpose()[1],
c=df_labels[a],
cmap='jet')
plt.title("T-SNE representation of {} clusters for asset {}".format(
NUM_CLUSTERS, a))
plt.show()
# The analysis does not conclude that the state of each asset can just be describe on two dimensions that can be used to explain the full system behavior.
def getTSNE(data):
ts = TSNE(n_components=2)
ts_data = ts.fit_transform(data)
return ts_data
df['pca-one'] = pca_result[:, 0]
df['pca-two'] = pca_result[:, 1]
from plotnine import *
chart = (ggplot(df, aes(x='pca-one', y='pca-two', color='label')) +
geom_point(size=2, alpha=1) +
ggtitle("First and Second Principal Components by Label"))
ggplot.save(chart, 'PCA.png')
print("Image save as PCA.png in current dir")
import time
from sklearn.manifold import TSNE
time_start = time.time()
tsne = TSNE(n_components=2, verbose=1, perplexity=40, n_iter=500)
tsne_results = tsne.fit_transform(df[feat_cols].values)
df_tsne = df.copy()
df_tsne['x-tsne'] = tsne_results[:, 0]
df_tsne['y-tsne'] = tsne_results[:, 1]
chart = ggplot(df_tsne, aes(
x='x-tsne', y='y-tsne', color='label')) + geom_point(
size=2, alpha=1) + ggtitle("t-SNE dimensions colored by Label")
ggplot.save(chart, 'tsne.png')
print("plot has been save in current dir as t-SNE.png")
torch.manual_seed(42)
for epoch in range(num_epochs):
train_images = overall_train_data
train_images = train_images.requires_grad_()
# Clear gradients w.r.t. parameters
optimizer4_1.zero_grad()
# Forward pass to get output/logits
train_outputs = model4(train_images)
# Get predictions from the maximum value
_, predicted_labels = torch.max(train_outputs.data, 1)
f = train_outputs.detach().numpy()
tsne = TSNE(n_components=2, verbose=1)
t1 = tsne.fit_transform(f)
fig, ax = plt.subplots()
groups = predicted_labels.numpy()
for g in np.unique(groups):
i = np.where(groups == g)
ax.scatter(t1[i, 0], t1[i, 1], label=g)
ax.legend([
'airplane', 'automobile', 'bird', 'cat', 'deer', 'dog', 'frog',
'horse', 'ship', 'truck'
])
plt.show()
train_labels = overall_train_labels
# Calculate Loss: softmax --> cross entropy loss
train_loss = criterion(train_outputs, train_labels)
'Disciplinary failure', 'Education',
'Social drinker', 'Social smoker'
])
cdf = cdf.drop(labels=[
'Reason for absence', 'Month of absence', 'Day of the week', 'Seasons',
'Disciplinary failure', 'Education', 'Social drinker', 'Social smoker'
]).astype(np.float64)
# Standardize the dataset
ss = StandardScaler(with_std=False)
sdf = ss.fit_transform(cdf)
# Perform the TSNE non-linear dimensionality reduction
tsne = TSNE(n_components=2, perplexity=15, random_state=1000)
data_tsne = tsne.fit_transform(sdf)
df_tsne = pd.DataFrame(data_tsne, columns=['x', 'y'], index=cdf.index)
dff = pd.concat([cdf, df_tsne], axis=1)
# Show the dataset
sns.set()
fig, ax = plt.subplots(figsize=(18, 11))
with sns.plotting_context("notebook", font_scale=1.5):
sns.scatterplot(x='x',
y='y',
size='Age',
sizes=(30, 400),
palette=sns.color_palette("husl", 2),
optimizer.zero_grad() # clear gradients for this training step
loss.backward() # back propagation, compute gradients
optimizer.step() # apply gradients
if step % BATCH_SIZE == 0:
test_output, last_layer = cnn(test_x)
pred_y = torch.max(test_output, 1)[1].data.numpy()
accuracy = float(
(pred_y == test_y.data.numpy()).astype(int).sum()) / float(
test_y.size(0))
print('Epoch: ', epoch, '| train loss: %.4f' % loss.data.numpy(),
'| test accuracy: %.2f' % accuracy)
if HAS_SK:
# Visualization of trained flatten layer (T-SNE)
tsne = TSNE(perplexity=30,
n_components=2,
init='pca',
n_iter=5000)
plot_only = 500
low_dim_embs = tsne.fit_transform(
last_layer.data.numpy()[:plot_only, :])
labels = test_y.numpy()[:plot_only]
plot_with_labels(low_dim_embs, labels)
plt.ioff()
# print 10 predictions from test data
test_output, _ = cnn(test_x[:10])
pred_y = torch.max(test_output, 1)[1].data.numpy()
print(pred_y, 'prediction number')
print(test_y[:10].numpy(), 'real number')
classes = classes.numpy()
#outputs = outputs.cpu()
#X = X.to(device)
X = np.vstack((X, outputs))
Y_class = np.concatenate((Y_class, classes))
Y_domain = np.concatenate((Y_domain, np.array([1 for _ in range(inputs.size(0))], dtype=np.int16)))
print("Target stpes: [{}/{}]".format(i, steps))
print(X.shape)
print(Y_class.shape)
print(Y_domain.shape)
X_tsne = tsne.fit_transform(X)
print("Org data dimension is {}. Embedded data dimension is {}".format(X.shape[-1], X_tsne.shape[-1]))
x_min, x_max = X_tsne.min(0), X_tsne.max(0)
X_norm = (X_tsne - x_min) / (x_max - x_min)
color = ['r', 'g', 'b', 'k', 'gold', 'm', 'c', 'orange', 'cyan', 'pink']
class_color = [color[label] for label in Y_class]
domain_color = [color[label] for label in Y_domain]
plt.figure(1, figsize=(8, 8))
plt.scatter(X_norm[:, 0], X_norm[:, 1], c=class_color, s=1)
plt.savefig("./dann{}_{}_class.png".format(source, target))
plt.close("all")
X = model[model.wv.vocab]
label = list(reversed(sorted(l_k, key=f_key)))
X_sort = X
for j in range(0, len(label)):
X_sort[j, :] = model[label[j]]
#tsne visualize
plot_i = 0
plot_f = 500
tsne = TSNE(n_components=2)
X_tsne = tsne.fit_transform(X_sort[plot_i:plot_f, :])
def plot_with_labels(low_dim_embs, labels):
assert low_dim_embs.shape[0] >= len(labels), 'More labels than embeddings'
plt.figure(figsize=(18, 18)) # in inches
for i, label in enumerate(labels):
x, y = low_dim_embs[i, :]
plt.scatter(x, y)
plt.annotate(label,
xy=(x, y),
xytext=(5, 2),
textcoords='offset points',
ha='right',
va='bottom')
