はじめに
深層学習day3の「Section3：GRU」の実装演習を以下にまとめる。
実装演習
「3_2_tf_languagemodel/predict_word.ipynb」を試した。
このプログラムは、tensorflowが必要であり、Google colabを使った。Google colabのtensorflowはバージョン2系であり、このプログラムは、バージョン1で作られているので、注意が必要。
from google.colab import drive
drive.mount('/content/drive')
import os
#os.chdir('drive/My Drive/DNN_code/lesson_3/3_2_tf_languagemodel/')
os.chdir('/content/drive/MyDrive/Colab Notebooks/studyai/3_2_tf_languagemodel')
次のようにして、tensorflowのバージョンを1系に変更する。 リスタートが求められたら、メニューの「ランタイム」→「ランタイムを再起動する」で再起動する。
%tensorflow_version 1.x
次でバージョンを確認する。
import tensorflow as tf
print(tf.__version__)
結果は、次の通り、バージョン1系となった。
1.15.0
実際のプログラムを実行する。
import tensorflow as tf
import numpy as np
import re
import glob
import collections
import random
import pickle
import time
import datetime
import os
# logging levelを変更
tf.logging.set_verbosity(tf.logging.ERROR)
class Corpus:
def __init__(self):
self.unknown_word_symbol = "<???>" # 出現回数の少ない単語は未知語として定義しておく
self.unknown_word_threshold = 3 # 未知語と定義する単語の出現回数の閾値
self.corpus_file = "./corpus/**/*.txt"
self.corpus_encoding = "utf-8"
self.dictionary_filename = "./data_for_predict/word_dict.dic"
self.chunk_size = 5
self.load_dict()
words = []
for filename in glob.glob(self.corpus_file, recursive=True):
with open(filename, "r", encoding=self.corpus_encoding) as f:
# word breaking
text = f.read()
# 全ての文字を小文字に統一し、改行をスペースに変換
text = text.lower().replace("\n", " ")
# 特定の文字以外の文字を空文字に置換する
text = re.sub(r"[^a-z '\-]", "", text)
# 複数のスペースはスペース一文字に変換
text = re.sub(r"[ ]+", " ", text)
# 前処理： '-' で始まる単語は無視する
words = [ word for word in text.split() if not word.startswith("-")]
self.data_n = len(words) - self.chunk_size
self.data = self.seq_to_matrix(words)
def prepare_data(self):
"""
訓練データとテストデータを準備する。
data_n = ( text データの総単語数 ) - chunk_size
input: (data_n, chunk_size, vocabulary_size)
output: (data_n, vocabulary_size)
"""
# 入力と出力の次元テンソルを準備
all_input = np.zeros([self.chunk_size, self.vocabulary_size, self.data_n])
all_output = np.zeros([self.vocabulary_size, self.data_n])
# 準備したテンソルに、コーパスの one-hot 表現(self.data) のデータを埋めていく
# i 番目から ( i + chunk_size - 1 ) 番目までの単語が１組の入力となる
# このときの出力は ( i + chunk_size ) 番目の単語
for i in range(self.data_n):
all_output[:, i] = self.data[:, i + self.chunk_size] # (i + chunk_size) 番目の単語の one-hot ベクトル
for j in range(self.chunk_size):
all_input[j, :, i] = self.data[:, i + self.chunk_size - j - 1]
# 後に使うデータ形式に合わせるために転置を取る
all_input = all_input.transpose([2, 0, 1])
all_output = all_output.transpose()
# 訓練データ：テストデータを 4 : 1 に分割する
training_num = ( self.data_n * 4 ) // 5
return all_input[:training_num], all_output[:training_num], all_input[training_num:], all_output[training_num:]
def build_dict(self):
# コーパス全体を見て、単語の出現回数をカウントする
counter = collections.Counter()
for filename in glob.glob(self.corpus_file, recursive=True):
with open(filename, "r", encoding=self.corpus_encoding) as f:
# word breaking
text = f.read()
# 全ての文字を小文字に統一し、改行をスペースに変換
text = text.lower().replace("\n", " ")
# 特定の文字以外の文字を空文字に置換する
text = re.sub(r"[^a-z '\-]", "", text)
# 複数のスペースはスペース一文字に変換
text = re.sub(r"[ ]+", " ", text)
# 前処理： '-' で始まる単語は無視する
words = [word for word in text.split() if not word.startswith("-")]
counter.update(words)
# 出現頻度の低い単語を一つの記号にまとめる
word_id = 0
dictionary = {}
for word, count in counter.items():
if count <= self.unknown_word_threshold:
continue
dictionary[word] = word_id
word_id += 1
dictionary[self.unknown_word_symbol] = word_id
print("総単語数：", len(dictionary))
# 辞書を pickle を使って保存しておく
with open(self.dictionary_filename, "wb") as f:
pickle.dump(dictionary, f)
print("Dictionary is saved to", self.dictionary_filename)
self.dictionary = dictionary
print(self.dictionary)
def load_dict(self):
with open(self.dictionary_filename, "rb") as f:
self.dictionary = pickle.load(f)
self.vocabulary_size = len(self.dictionary)
self.input_layer_size = len(self.dictionary)
self.output_layer_size = len(self.dictionary)
print("総単語数: ", self.input_layer_size)
def get_word_id(self, word):
# print(word)
# print(self.dictionary)
# print(self.unknown_word_symbol)
# print(self.dictionary[self.unknown_word_symbol])
# print(self.dictionary.get(word, self.dictionary[self.unknown_word_symbol]))
return self.dictionary.get(word, self.dictionary[self.unknown_word_symbol])
# 入力された単語を one-hot ベクトルにする
def to_one_hot(self, word):
index = self.get_word_id(word)
data = np.zeros(self.vocabulary_size)
data[index] = 1
return data
def seq_to_matrix(self, seq):
print(seq)
data = np.array([self.to_one_hot(word) for word in seq]) # (data_n, vocabulary_size)
return data.transpose() # (vocabulary_size, data_n)
class Language:
"""
input layer: self.vocabulary_size
hidden layer: rnn_size = 30
output layer: self.vocabulary_size
"""
def __init__(self):
self.corpus = Corpus()
self.dictionary = self.corpus.dictionary
self.vocabulary_size = len(self.dictionary) # 単語数
self.input_layer_size = self.vocabulary_size # 入力層の数
self.hidden_layer_size = 30 # 隠れ層の RNN ユニットの数
self.output_layer_size = self.vocabulary_size # 出力層の数
self.batch_size = 128 # バッチサイズ
self.chunk_size = 5 # 展開するシーケンスの数。c_0, c_1, ..., c_(chunk_size - 1) を入力し、c_(chunk_size) 番目の単語の確率が出力される。
self.learning_rate = 0.005 # 学習率
self.epochs = 1000 # 学習するエポック数
self.forget_bias = 1.0 # LSTM における忘却ゲートのバイアス
self.model_filename = "./data_for_predict/predict_model.ckpt"
self.unknown_word_symbol = self.corpus.unknown_word_symbol
def inference(self, input_data, initial_state):
"""
:param input_data: (batch_size, chunk_size, vocabulary_size) 次元のテンソル
:param initial_state: (batch_size, hidden_layer_size) 次元の行列
:return:
"""
# 重みとバイアスの初期化
hidden_w = tf.Variable(tf.truncated_normal([self.input_layer_size, self.hidden_layer_size], stddev=0.01))
hidden_b = tf.Variable(tf.ones([self.hidden_layer_size]))
output_w = tf.Variable(tf.truncated_normal([self.hidden_layer_size, self.output_layer_size], stddev=0.01))
output_b = tf.Variable(tf.ones([self.output_layer_size]))
# BasicLSTMCell, BasicRNNCell は (batch_size, hidden_layer_size) が chunk_size 数ぶんつながったリストを入力とする。
# 現時点での入力データは (batch_size, chunk_size, input_layer_size) という３次元のテンソルなので
# tf.transpose や tf.reshape などを駆使してテンソルのサイズを調整する。
input_data = tf.transpose(input_data, [1, 0, 2]) # 転置。(chunk_size, batch_size, vocabulary_size)
input_data = tf.reshape(input_data, [-1, self.input_layer_size]) # 変形。(chunk_size * batch_size, input_layer_size)
input_data = tf.matmul(input_data, hidden_w) + hidden_b # 重みWとバイアスBを適用。 (chunk_size, batch_size, hidden_layer_size)
input_data = tf.split(input_data, self.chunk_size, 0) # リストに分割。chunk_size * (batch_size, hidden_layer_size)
# RNN のセルを定義する。RNN Cell の他に LSTM のセルや GRU のセルなどが利用できる。
cell = tf.nn.rnn_cell.BasicRNNCell(self.hidden_layer_size)
outputs, states = tf.nn.static_rnn(cell, input_data, initial_state=initial_state)
# 最後に隠れ層から出力層につながる重みとバイアスを処理する
# 最終的に softmax 関数で処理し、確率として解釈される。
# softmax 関数はこの関数の外で定義する。
output = tf.matmul(outputs[-1], output_w) + output_b
return output
def loss(self, logits, labels):
cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=logits, labels=labels))
return cost
def training(self, cost):
# 今回は最適化手法として Adam を選択する。
# ここの AdamOptimizer の部分を変えることで、Adagrad, Adadelta などの他の最適化手法を選択することができる
optimizer = tf.train.AdamOptimizer(learning_rate=self.learning_rate).minimize(cost)
return optimizer
def train(self):
# 変数などの用意
input_data = tf.placeholder("float", [None, self.chunk_size, self.input_layer_size])
actual_labels = tf.placeholder("float", [None, self.output_layer_size])
initial_state = tf.placeholder("float", [None, self.hidden_layer_size])
prediction = self.inference(input_data, initial_state)
cost = self.loss(prediction, actual_labels)
optimizer = self.training(cost)
correct = tf.equal(tf.argmax(prediction, 1), tf.argmax(actual_labels, 1))
accuracy = tf.reduce_mean(tf.cast(correct, tf.float32))
# TensorBoard で可視化するため、クロスエントロピーをサマリーに追加
tf.summary.scalar("Cross entropy: ", cost)
summary = tf.summary.merge_all()
# 訓練・テストデータの用意
# corpus = Corpus()
trX, trY, teX, teY = self.corpus.prepare_data()
training_num = trX.shape[0]
# ログを保存するためのディレクトリ
timestamp = time.time()
dirname = datetime.datetime.fromtimestamp(timestamp).strftime("%Y%m%d%H%M%S")
# ここから実際に学習を走らせる
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
summary_writer = tf.summary.FileWriter("./log/" + dirname, sess.graph)
# エポックを回す
for epoch in range(self.epochs):
step = 0
epoch_loss = 0
epoch_acc = 0
# 訓練データをバッチサイズごとに分けて学習させる (= optimizer を走らせる)
# エポックごとの損失関数の合計値や（訓練データに対する）精度も計算しておく
while (step + 1) * self.batch_size < training_num:
start_idx = step * self.batch_size
end_idx = (step + 1) * self.batch_size
batch_xs = trX[start_idx:end_idx, :, :]
batch_ys = trY[start_idx:end_idx, :]
_, c, a = sess.run([optimizer, cost, accuracy],
feed_dict={input_data: batch_xs,
actual_labels: batch_ys,
initial_state: np.zeros([self.batch_size, self.hidden_layer_size])
}
)
epoch_loss += c
epoch_acc += a
step += 1
# コンソールに損失関数の値や精度を出力しておく
print("Epoch", epoch, "completed ouf of", self.epochs, "-- loss:", epoch_loss, " -- accuracy:",
epoch_acc / step)
# Epochが終わるごとにTensorBoard用に値を保存
summary_str = sess.run(summary, feed_dict={input_data: trX,
actual_labels: trY,
initial_state: np.zeros(
[trX.shape[0],
self.hidden_layer_size]
)
}
)
summary_writer.add_summary(summary_str, epoch)
summary_writer.flush()
# 学習したモデルも保存しておく
saver = tf.train.Saver()
saver.save(sess, self.model_filename)
# 最後にテストデータでの精度を計算して表示する
a = sess.run(accuracy, feed_dict={input_data: teX, actual_labels: teY,
initial_state: np.zeros([teX.shape[0], self.hidden_layer_size])})
print("Accuracy on test:", a)
def predict(self, seq):
"""
文章を入力したときに次に来る単語を予測する
:param seq: 予測したい単語の直前の文字列。chunk_size 以上の単語数が必要。
:return:
"""
# 最初に復元したい変数をすべて定義してしまいます
tf.reset_default_graph()
input_data = tf.placeholder("float", [None, self.chunk_size, self.input_layer_size])
initial_state = tf.placeholder("float", [None, self.hidden_layer_size])
prediction = tf.nn.softmax(self.inference(input_data, initial_state))
predicted_labels = tf.argmax(prediction, 1)
# 入力データの作成
# seq を one-hot 表現に変換する。
words = [word for word in seq.split() if not word.startswith("-")]
x = np.zeros([1, self.chunk_size, self.input_layer_size])
for i in range(self.chunk_size):
word = seq[len(words) - self.chunk_size + i]
index = self.dictionary.get(word, self.dictionary[self.unknown_word_symbol])
x[0][i][index] = 1
feed_dict = {
input_data: x, # (1, chunk_size, vocabulary_size)
initial_state: np.zeros([1, self.hidden_layer_size])
}
# tf.Session()を用意
with tf.Session() as sess:
# 保存したモデルをロードする。ロード前にすべての変数を用意しておく必要がある。
saver = tf.train.Saver()
saver.restore(sess, self.model_filename)
# ロードしたモデルを使って予測結果を計算
u, v = sess.run([prediction, predicted_labels], feed_dict=feed_dict)
keys = list(self.dictionary.keys())
# コンソールに文字ごとの確率を表示
for i in range(self.vocabulary_size):
c = self.unknown_word_symbol if i == (self.vocabulary_size - 1) else keys[i]
print(c, ":", u[0][i])
print("Prediction:", seq + " " + ("<???>" if v[0] == (self.vocabulary_size - 1) else keys[v[0]]))
return u[0]
def build_dict():
cp = Corpus()
cp.build_dict()
if __name__ == "__main__":
#build_dict()
ln = Language()
# 学習するときに呼び出す
#ln.train()
# 保存したモデルを使って単語の予測をする
ln.predict("some of them looks like")
結果は、次の通り。
ストリーミング出力は最後の 5000 行に切り捨てられました。
beating : 1.3192087e-14
authentic : 1.4901876e-14
glow : 1.5293808e-14
oy : 1.4680279e-14
emotion : 1.498376e-14
delight : 1.4004225e-14
nuclear : 1.4860068e-14
dropped : 1.4963283e-14
hiroshima : 1.3928438e-14
beings : 1.5822596e-14
tens : 1.5364829e-14
burned : 1.3809718e-14
homeless : 1.5643804e-14
albert : 1.447892e-14
initiated : 1.3293448e-14
bomb : 1.356971e-14
theoretical : 1.457718e-14
radio : 1.4376221e-14
traditionally : 1.3563397e-14
oi : 1.57665e-14
souls : 1.4353673e-14
assigned : 1.4012374e-14
gallery : 1.575054e-14
easily : 1.5530207e-14
coins : 1.4903695e-14
timeless : 1.4624188e-14
inevitably : 1.4411967e-14
trips : 1.6643313e-14
expressions : 1.5180204e-14
raw : 1.2960687e-14
idioms : 1.2821107e-14
magic : 1.40770405e-14
handed : 1.33516635e-14
blonde : 1.2911735e-14
diamond : 1.4749856e-14
charity : 1.5063651e-14
ball : 1.5066438e-14
lipschitz : 1.3815091e-14
stone : 1.3465779e-14
sighed : 1.3959822e-14
feminist : 1.7686968e-14
britain : 1.3061792e-14
movement : 1.476897e-14
largely : 1.40082326e-14
cruise : 1.3874242e-14
missiles : 1.5027064e-14
village : 1.4716864e-14
elsewhere : 2.7652168e-15
ignorant : 1.4561284e-14
jacket : 1.4211227e-14
centre : 1.427959e-14
studies : 1.1551857e-11
bear : 1.2895712e-14
consists : 1.5735616e-14
rewriting : 1.5363599e-14
dawn : 1.621525e-14
demonstrating : 1.3151437e-14
themes : 1.5186896e-14
advancement : 1.6285298e-14
agriculture : 1.494252e-14
despite : 1.4373097e-14
stupidity : 1.4589892e-14
domination : 1.6060978e-14
superiority : 1.5718279e-14
primitive : 1.3121445e-14
religions : 1.4678093e-14
egyptian : 1.3918851e-14
rulers : 1.3896278e-14
thesis : 1.4377593e-14
counting : 1.4958775e-14
track : 1.6071396e-14
accepts : 1.4374193e-14
speculation : 1.4989878e-14
laborers : 1.427812e-14
greek : 1.3171193e-14
historian : 1.4486655e-14
labour : 1.3591832e-14
blows : 1.4370519e-14
modern : 1.41012275e-14
researcher : 1.2356823e-14
commanded : 1.4580488e-14
sum : 1.4188261e-14
sixth : 2.496606e-20
bc : 1.3925889e-14
pan : 1.3654453e-14
flourished : 1.3517735e-14
welfare : 1.4175007e-14
roman : 1.5056241e-14
nurse : 1.486021e-14
fourth : 1.4023979e-14
cultures : 1.3483333e-14
instrument : 1.4368547e-14
blood : 1.6514205e-10
bones : 1.596212e-14
preferred : 1.4236376e-14
wild : 1.2597512e-14
destiny : 1.5494437e-14
paul : 1.36483605e-14
seller : 1.3574474e-14
purple : 1.3854013e-14
ibid : 1.6552928e-14
array : 1.5294362e-14
brilliant : 1.305081e-14
unusual : 1.3509384e-14
inspiring : 1.4097892e-14
preceding : 1.39435896e-14
taste : 1.4553288e-14
stroke : 1.5297805e-14
grim : 1.6048178e-14
clothing : 1.442063e-14
campbell : 1.3260021e-14
analyses : 5.238098e-08
emerging : 1.570036e-14
islamic : 1.5424434e-14
connected : 1.4090634e-14
virgin : 1.3111813e-14
ancient : 1.6127468e-14
treatment : 8.3587763e-16
notably : 1.5323036e-14
etc : 1.4732198e-14
unlikely : 1.4600109e-14
ignored : 1.555603e-14
gods : 1.4307506e-14
identity : 1.452927e-14
meaningful : 1.467717e-14
roots : 1.2780332e-14
wildly : 1.3738993e-14
disease : 1.1581977e-14
investigate : 1.3616508e-14
cure : 1.4511628e-14
blasts : 1.4174952e-14
symptoms : 1.2739835e-14
characterized : 3.2966898e-16
concludes : 1.4210848e-14
engage : 1.5029012e-14
laurence : 1.3182906e-14
urdang : 1.4539886e-14
archaeology : 1.4421041e-14
fragile : 1.4087946e-14
turns : 1.5579189e-14
earliest : 1.5505079e-14
stages : 1.5241885e-14
destroyed : 1.4299595e-14
countless : 1.3952103e-14
generations : 1.3425337e-14
erosion : 1.2757731e-14
prehistoric : 1.3872232e-14
developers : 1.4215808e-14
fashionable : 1.5361488e-14
discover : 1.39421005e-14
greeks : 1.4001742e-14
romans : 1.449246e-14
indians : 1.3721027e-14
ruins : 1.4019752e-14
followed : 8.133046e-13
troy : 1.507722e-14
artifacts : 1.4787572e-14
dating : 1.34629805e-14
pursuit : 1.26823744e-14
museums : 1.3607447e-14
classical : 1.5186865e-14
documented : 1.5514603e-14
tablets : 1.4237272e-14
barely : 1.3549951e-14
identify : 1.3935667e-14
conclusions : 2.9813703e-15
deep : 1.2981147e-14
countryside : 1.4284303e-14
precious : 1.4119689e-14
clever : 1.4005962e-14
linguists : 1.4611418e-14
germany : 1.2804048e-14
ancestors : 1.3964883e-14
jean : 1.4194216e-14
myriad : 1.3865301e-14
revealed : 1.5216064e-14
crete : 1.47619e-14
indo-european : 1.3000425e-14
linguistic : 1.3608174e-14
undoubtedly : 1.4672916e-14
vanished : 1.4893978e-14
unimportant : 1.5161512e-14
establish : 1.2562193e-14
dialect : 1.3347387e-14
examining : 3.0328866e-15
swedish : 1.4965793e-14
dutch : 1.3145619e-14
spanish : 1.2700384e-14
portuguese : 1.4012187e-14
remote : 1.4128149e-14
varieties : 1.4087785e-14
resort : 1.4761113e-14
fingers : 1.5387e-14
toes : 1.5219053e-14
shifts : 1.5819668e-14
nearby : 1.407194e-14
hence : 1.4019299e-14
classified : 2.0795115e-14
finnish : 1.5051159e-14
computers : 1.4321157e-14
emerged : 1.4656245e-14
linked : 1.4465312e-14
showing : 1.533593e-14
constantly : 1.4578015e-14
accuracy : 1.478794e-14
trees : 1.5207997e-14
classification : 1.3247735e-14
grammar : 1.3984713e-14
resemble : 1.482437e-14
suitable : 1.4846385e-14
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autoimmune : 1.36673255e-14
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kidney : 1.25483035e-14
liver : 1.4317662e-14
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cholestatic : 1.3535486e-14
hcv : 1.34285385e-14
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epithelial : 1.3059474e-14
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glutamate : 1.2958389e-14
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gaba : 1.4501363e-14
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embryonic : 1.4328289e-14
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forelimb : 1.4755765e-14
proximal : 1.4188992e-14
placodes : 1.4175385e-14
mrna : 1.32291e-14
proteins : 1.3583384e-14
e-e : 1.3299129e-14
nucleotide : 1.5688505e-14
formamide : 1.4278773e-14
ssc : 1.5898344e-14
gml : 1.3716892e-14
underweight : 5.6507186e-09
fibroblasts : 1.4062656e-14
ipf : 1.3262171e-14
vitro : 1.31450924e-14
cytokines : 1.606061e-14
collagen : 1.3857581e-14
fibroblast : 1.3105938e-14
fibrotic : 1.4660102e-14
pge : 1.1713384e-14
txa : 1.3920311e-14
pgi : 1.4853522e-14
cox- : 1.3897152e-14
hf-ipf : 1.5616794e-14
hf-nl : 1.3553207e-14
incubated : 1.4347049e-14
serum : 5.0692046e-13
dmem : 1.3968718e-14
txb : 1.4240152e-14
pgf : 1.5516973e-14
il- : 1.3601323e-14
incubation : 1.4994538e-14
trimethylsilyl : 1.4306251e-14
buffer : 1.3149481e-14
gel : 1.471748e-14
membrane : 1.2597104e-14
tgf : 1.461337e-14
vascular : 4.9898637e-16
cardiac : 1.504473e-14
hydrolyze : 1.3613002e-14
divalent : 1.5261726e-14
cations : 1.4541746e-14
e-ntpdase : 1.39132505e-14
ntpdases : 1.4472847e-14
acr : 1.4995055e-14
residues : 1.5782477e-14
phosphate : 1.4743498e-14
vanadyl : 1.566865e-14
cofactor : 1.4113551e-14
equatorial : 1.4141331e-14
tensors : 1.4906142e-14
epr : 1.4190425e-14
vo : 1.3266801e-14
soluble : 1.5729224e-14
atp : 1.3559983e-14
adp : 1.5086797e-14
purified : 1.3827904e-14
nucleotidase : 1.4714563e-14
scd : 1.3904231e-14
adpnp : 1.306523e-14
spectra : 1.4082788e-14
mhz : 1.5197645e-14
carboxyl : 1.5074229e-14
oxygens : 1.456773e-14
hydroxyl : 1.4089773e-14
hydrolysis : 1.4591144e-14
phosphates : 1.5364915e-14
conformations : 1.4613453e-14
conformation : 1.35284655e-14
annan : 1.4627146e-14
unesco : 1.3770264e-14
sci : 1.379508e-14
ricyt : 1.4501307e-14
averaged : 5.898597e-14
ecological : 1.4461973e-14
statins : 1.4468155e-14
coronary : 1.2429156e-14
myocardial : 1.3684097e-14
infarction : 1.445916e-14
revascularization : 1.4107307e-14
lipid-lowering : 1.5685695e-14
incremental : 1.3963711e-14
ischemia : 1.4152476e-14
miracl : 1.51073e-14
atorvastatin : 1.3983299e-14
mgdl : 1.5000432e-14
rehospitalization : 1.4568479e-14
worsening : 1.3476983e-14
endpoints : 1.447928e-14
lipid : 1.5310591e-14
lipoprotein : 1.3669072e-14
ldl : 1.4424536e-14
statin : 1.3786531e-14
a--z : 1.3740905e-14
simvastatin : 1.4133862e-14
bmi : 3.1721308e-07
covariates : 2.8345248e-06
yhl : 2.2330545e-07
yol : 9.993053e-11
obesity : 1.3501665e-09
evgfp : 6.1830607e-09
unintended : 4.6785384e-09
sizes : 2.5699578e-07
obese : 1.9368318e-07
<???> : 2.919577e-05
Prediction: some of them looks like et