Add : Event Embedding code & data, 최종 보고서 수정

+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "name": "stock-prediction.ipynb",
+      "provenance": []
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "accelerator": "GPU"
+  },
+  "cells": [
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "9EBLJGRkA7au",
+        "colab_type": "code",
+        "outputId": "6aa769f2-a86f-463c-893c-7a8b0b3aca08",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 122
+        }
+      },
+      "source": [
+        "from google.colab import auth\n",
+        "auth.authenticate_user()\n",
+        "\n",
+        "from google.colab import drive\n",
+        "drive.mount('/content/gdrive')"
+      ],
+      "execution_count": 0,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "Go to this URL in a browser: https://accounts.google.com/o/oauth2/auth?client_id=947318989803-6bn6qk8qdgf4n4g3pfee6491hc0brc4i.apps.googleusercontent.com&redirect_uri=urn%3aietf%3awg%3aoauth%3a2.0%3aoob&response_type=code&scope=email%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdocs.test%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive.photos.readonly%20https%3a%2f%2fwww.googleapis.com%2fauth%2fpeopleapi.readonly\n",
+            "\n",
+            "Enter your authorization code:\n",
+            "··········\n",
+            "Mounted at /content/gdrive\n"
+          ],
+          "name": "stdout"
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "xz6TIi8x-3wI",
+        "colab_type": "code",
+        "outputId": "47fb88a4-fa0e-4327-b5d0-9ab7f42041f2",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 34
+        }
+      },
+      "source": [
+        "%tensorflow_version 1.x"
+      ],
+      "execution_count": 0,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "TensorFlow 1.x selected.\n"
+          ],
+          "name": "stdout"
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "ykRUwvkvIoKH",
+        "colab_type": "code",
+        "outputId": "6d116660-4a7f-4c85-a733-6956210958c9",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 68
+        }
+      },
+      "source": [
+        "!cd gdrive/'My Drive'/'capstone 2' && ls"
+      ],
+      "execution_count": 0,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "main.go\t\t       preprocessed_data.gsheet  title.csv     Word2vec.model\n",
+            "metadata.tsv\t       title2020.csv\t\t title.gsheet\n",
+            "preprocessed_data.csv  title2.csv\t\t word2vec\n"
+          ],
+          "name": "stdout"
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "C-V3pgCEX5xR",
+        "colab_type": "text"
+      },
+      "source": [
+        "### 데이터 전처리 \n",
+        "[~ 포토 \\~]가 들어간 기사 제목은 데이터 리스트에서 삭제 하고 [\\~]가 들어간 기사 제목은 [\\~] 삭제\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "oZ2Q2_uWViO3",
+        "colab_type": "code",
+        "outputId": "0fc44251-8920-4e1a-9472-5603d0ce1264",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 785
+        }
+      },
+      "source": [
+        "!pip3 install hanja==0.13.0\n",
+        "!pip3 install git+https://github.com/haven-jeon/PyKoSpacing.git"
+      ],
+      "execution_count": 0,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "Requirement already satisfied: hanja==0.13.0 in /usr/local/lib/python3.6/dist-packages (0.13.0)\n",
+            "Collecting git+https://github.com/haven-jeon/PyKoSpacing.git\n",
+            "  Cloning https://github.com/haven-jeon/PyKoSpacing.git to /tmp/pip-req-build-n_sau7zy\n",
+            "  Running command git clone -q https://github.com/haven-jeon/PyKoSpacing.git /tmp/pip-req-build-n_sau7zy\n",
+            "Requirement already satisfied (use --upgrade to upgrade): pykospacing==0.1 from git+https://github.com/haven-jeon/PyKoSpacing.git in /usr/local/lib/python3.6/dist-packages\n",
+            "Collecting tensorflow<=1.6.0,>=1.4.0\n",
+            "  Using cached https://files.pythonhosted.org/packages/d9/0f/fbd8bb92459c75db93040f80702ebe4ba83a52cdb6ad930654c31dc0b711/tensorflow-1.6.0-cp36-cp36m-manylinux1_x86_64.whl\n",
+            "Requirement already satisfied: keras>=2.1.5 in /usr/local/lib/python3.6/dist-packages (from pykospacing==0.1) (2.2.5)\n",
+            "Requirement already satisfied: h5py>=2.7.1 in /usr/local/lib/python3.6/dist-packages (from pykospacing==0.1) (2.10.0)\n",
+            "Requirement already satisfied: argparse>=1.4.0 in /usr/local/lib/python3.6/dist-packages (from pykospacing==0.1) (1.4.0)\n",
+            "Requirement already satisfied: six>=1.10.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow<=1.6.0,>=1.4.0->pykospacing==0.1) (1.12.0)\n",
+            "Requirement already satisfied: wheel>=0.26 in /usr/local/lib/python3.6/dist-packages (from tensorflow<=1.6.0,>=1.4.0->pykospacing==0.1) (0.34.2)\n",
+            "Requirement already satisfied: astor>=0.6.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow<=1.6.0,>=1.4.0->pykospacing==0.1) (0.8.1)\n",
+            "Requirement already satisfied: numpy>=1.13.3 in /usr/local/lib/python3.6/dist-packages (from tensorflow<=1.6.0,>=1.4.0->pykospacing==0.1) (1.18.2)\n",
+            "Collecting tensorboard<1.7.0,>=1.6.0\n",
+            "  Using cached https://files.pythonhosted.org/packages/b0/67/a8c91665987d359211dcdca5c8b2a7c1e0876eb0702a4383c1e4ff76228d/tensorboard-1.6.0-py3-none-any.whl\n",
+            "Requirement already satisfied: protobuf>=3.4.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow<=1.6.0,>=1.4.0->pykospacing==0.1) (3.10.0)\n",
+            "Requirement already satisfied: termcolor>=1.1.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow<=1.6.0,>=1.4.0->pykospacing==0.1) (1.1.0)\n",
+            "Requirement already satisfied: grpcio>=1.8.6 in /usr/local/lib/python3.6/dist-packages (from tensorflow<=1.6.0,>=1.4.0->pykospacing==0.1) (1.27.2)\n",
+            "Requirement already satisfied: gast>=0.2.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow<=1.6.0,>=1.4.0->pykospacing==0.1) (0.3.3)\n",
+            "Requirement already satisfied: absl-py>=0.1.6 in /usr/local/lib/python3.6/dist-packages (from tensorflow<=1.6.0,>=1.4.0->pykospacing==0.1) (0.9.0)\n",
+            "Requirement already satisfied: pyyaml in /usr/local/lib/python3.6/dist-packages (from keras>=2.1.5->pykospacing==0.1) (3.13)\n",
+            "Requirement already satisfied: scipy>=0.14 in /usr/local/lib/python3.6/dist-packages (from keras>=2.1.5->pykospacing==0.1) (1.4.1)\n",
+            "Requirement already satisfied: keras-applications>=1.0.8 in /usr/local/lib/python3.6/dist-packages (from keras>=2.1.5->pykospacing==0.1) (1.0.8)\n",
+            "Requirement already satisfied: keras-preprocessing>=1.1.0 in /usr/local/lib/python3.6/dist-packages (from keras>=2.1.5->pykospacing==0.1) (1.1.0)\n",
+            "Requirement already satisfied: markdown>=2.6.8 in /usr/local/lib/python3.6/dist-packages (from tensorboard<1.7.0,>=1.6.0->tensorflow<=1.6.0,>=1.4.0->pykospacing==0.1) (3.2.1)\n",
+            "Requirement already satisfied: html5lib==0.9999999 in /usr/local/lib/python3.6/dist-packages (from tensorboard<1.7.0,>=1.6.0->tensorflow<=1.6.0,>=1.4.0->pykospacing==0.1) (0.9999999)\n",
+            "Requirement already satisfied: bleach==1.5.0 in /usr/local/lib/python3.6/dist-packages (from tensorboard<1.7.0,>=1.6.0->tensorflow<=1.6.0,>=1.4.0->pykospacing==0.1) (1.5.0)\n",
+            "Requirement already satisfied: werkzeug>=0.11.10 in /usr/local/lib/python3.6/dist-packages (from tensorboard<1.7.0,>=1.6.0->tensorflow<=1.6.0,>=1.4.0->pykospacing==0.1) (1.0.0)\n",
+            "Requirement already satisfied: setuptools in /usr/local/lib/python3.6/dist-packages (from protobuf>=3.4.0->tensorflow<=1.6.0,>=1.4.0->pykospacing==0.1) (46.0.0)\n",
+            "Building wheels for collected packages: pykospacing\n",
+            "  Building wheel for pykospacing (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
+            "  Created wheel for pykospacing: filename=pykospacing-0.1-cp36-none-any.whl size=2255598 sha256=249ac07d0d8b26e4b9d4a1821995b953f9f90c6206cb378f183ff2be5001b607\n",
+            "  Stored in directory: /tmp/pip-ephem-wheel-cache-th23h_qr/wheels/4d/45/58/e26cb2b7f6a063d234158c6fd1e5700f6e15b99d67154340ba\n",
+            "Successfully built pykospacing\n",
+            "\u001b[31mERROR: magenta 0.3.19 has requirement tensorflow>=1.12.0, but you'll have tensorflow 1.6.0 which is incompatible.\u001b[0m\n",
+            "Installing collected packages: tensorboard, tensorflow\n",
+            "  Found existing installation: tensorboard 2.2.0\n",
+            "    Uninstalling tensorboard-2.2.0:\n",
+            "      Successfully uninstalled tensorboard-2.2.0\n",
+            "  Found existing installation: tensorflow 2.2.0rc2\n",
+            "    Uninstalling tensorflow-2.2.0rc2:\n",
+            "      Successfully uninstalled tensorflow-2.2.0rc2\n",
+            "Successfully installed tensorboard-1.6.0 tensorflow-1.6.0\n"
+          ],
+          "name": "stdout"
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "Ddf2Fp7-JAYI",
+        "colab_type": "code",
+        "outputId": "0d3029dd-6db6-41eb-8142-24d42bd516b6",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 34
+        }
+      },
+      "source": [
+        "import hanja\n",
+        "import pandas as pd\n",
+        "import re\n",
+        "from pykospacing import spacing\n",
+        "\n",
+        "news_list = pd.read_csv('gdrive/My Drive/capstone 2/title.csv', encoding='utf-8')\n",
+        "print(len(news_list))\n",
+        "photo_regexp = \"\\[.*포토.*\\]\"\n",
+        "brac_regexp = \"\\[.*\\]\"\n",
+        "spechar_with_regexp = \"[^가-힣ㄱ-ㅎㅏ-ㅣa-zA-Z0-9\\s\\+\\.\\-]\"\n",
+        "spechar_without_regexp = \"[^가-힣ㄱ-ㅎㅏ-ㅣa-zA-Z0-9\\s\\.]\"\n",
+        "percentage = \"(\\+\\d*\\.\\d*)|(\\-\\d*\\.\\d*)\"\n",
+        "is_exist_regexp = \"[가-힣ㄱ-ㅎㅏ-ㅣa-zA-Z0-9\\s\\.]\"\n",
+        "\n",
+        "\n",
+        "for i, title in enumerate(news_list['title']):\n",
+        "  if re.search(photo_regexp,title):\n",
+        "    news_list.drop(i, inplace=True)\n",
+        "  else :\n",
+        "    if \"↑\" in title:\n",
+        "      title = title.replace(\"↑\",\"상승\")\n",
+        "    if \"↓\" in title:\n",
+        "      title = title.replace(\"↓\",\"하락\")\n",
+        "\n",
+        "    title = hanja.translate(title, 'substitution')\n",
+        "    title = re.sub(brac_regexp, '', title)\n",
+        "    title = re.sub(\"\\.{3}|\\.{2}\", '', title)\n",
+        "\n",
+        "    if re.search(percentage,title):\n",
+        "      title = re.sub(spechar_with_regexp, '',title).lstrip()\n",
+        "    else:\n",
+        "      title = re.sub(spechar_without_regexp, '', title).lstrip()\n",
+        " \n",
+        "    if ( not re.search(is_exist_regexp,title) ) or (len(title) == 0):\n",
+        "      news_list.drop(i, inplace=True)\n",
+        "    \n",
+        "    news_list[\"title\"][i] = spacing(title)\n",
+        "    \n",
+        "\n",
+        "\n",
+        "df = pd.DataFrame(news_list, columns=['title','date','publication'])\n",
+        "df.to_csv('gdrive/My Drive/capstone 2/without_percentage_preprocessed_data.csv',sep=',',encoding='UTF-8',index=False) \n"
+      ],
+      "execution_count": 0,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "100960\n"
+          ],
+          "name": "stdout"
+        }
+      ]
+    }
+  ]
+}
\ No newline at end of file

+import numpy as np
+
+print(np.load("./resultEmbeding.pickle", allow_pickle=True))
+D
\ No newline at end of file

+{"class_name": "Sequential", "config": [{"class_name": "Flatten", "config": {"name": "flatten_1", "trainable": true, "batch_input_shape": [null, 5, 80], "dtype": "float32"}}, {"class_name": "Dense", "config": {"name": "dense_1", "trainable": true, "units": 512, "activation": "sigmoid", "use_bias": true, "kernel_initializer": {"class_name": "VarianceScaling", "config": {"scale": 1.0, "mode": "fan_avg", "distribution": "uniform", "seed": null}}, "bias_initializer": {"class_name": "Zeros", "config": {}}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}}, {"class_name": "Dropout", "config": {"name": "dropout_1", "trainable": true, "rate": 0.8}}, {"class_name": "Dense", "config": {"name": "dense_2", "trainable": true, "units": 1024, "activation": "sigmoid", "use_bias": true, "kernel_initializer": {"class_name": "VarianceScaling", "config": {"scale": 1.0, "mode": "fan_avg", "distribution": "uniform", "seed": null}}, "bias_initializer": {"class_name": "Zeros", "config": {}}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}}, {"class_name": "Dropout", "config": {"name": "dropout_2", "trainable": true, "rate": 0.8}}, {"class_name": "Dropout", "config": {"name": "dropout_3", "trainable": true, "rate": 0.8}}, {"class_name": "Dense", "config": {"name": "dense_3", "trainable": true, "units": 2, "activation": "softmax", "use_bias": true, "kernel_initializer": {"class_name": "VarianceScaling", "config": {"scale": 1.0, "mode": "fan_avg", "distribution": "uniform", "seed": null}}, "bias_initializer": {"class_name": "Zeros", "config": {}}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}}], "keras_version": "2.0.2", "backend": "theano"}
\ No newline at end of file

+{"class_name": "Sequential", "config": [{"class_name": "Conv1D", "config": {"name": "conv1d_1", "trainable": true, "batch_input_shape": [null, 5, 80], "dtype": "float32", "filters": 128, "kernel_size": [1], "strides": [1], "padding": "valid", "dilation_rate": [1], "activation": "relu", "use_bias": true, "kernel_initializer": {"class_name": "VarianceScaling", "config": {"scale": 1.0, "mode": "fan_avg", "distribution": "uniform", "seed": null}}, "bias_initializer": {"class_name": "Zeros", "config": {}}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}}, {"class_name": "Conv1D", "config": {"name": "conv1d_2", "trainable": true, "filters": 128, "kernel_size": [3], "strides": [1], "padding": "same", "dilation_rate": [1], "activation": "relu", "use_bias": true, "kernel_initializer": {"class_name": "VarianceScaling", "config": {"scale": 1.0, "mode": "fan_avg", "distribution": "uniform", "seed": null}}, "bias_initializer": {"class_name": "Zeros", "config": {}}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}}, {"class_name": "MaxPooling1D", "config": {"name": "max_pooling1d_1", "trainable": true, "strides": [2], "pool_size": [2], "padding": "valid"}}, {"class_name": "Flatten", "config": {"name": "flatten_1", "trainable": true}}, {"class_name": "Dropout", "config": {"name": "dropout_1", "trainable": true, "rate": 0.8}}, {"class_name": "Dense", "config": {"name": "dense_1", "trainable": true, "units": 2, "activation": "softmax", "use_bias": true, "kernel_initializer": {"class_name": "VarianceScaling", "config": {"scale": 1.0, "mode": "fan_avg", "distribution": "uniform", "seed": null}}, "bias_initializer": {"class_name": "Zeros", "config": {}}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}}], "keras_version": "2.0.2", "backend": "theano"}
\ No newline at end of file

+{"class_name": "Sequential", "config": [{"class_name": "LSTM", "config": {"name": "lstm_1", "trainable": true, "batch_input_shape": [null, 5, 80], "dtype": "float32", "return_sequences": false, "go_backwards": false, "stateful": false, "unroll": false, "implementation": 0, "units": 256, "activation": "tanh", "recurrent_activation": "hard_sigmoid", "use_bias": true, "kernel_initializer": {"class_name": "VarianceScaling", "config": {"scale": 1.0, "mode": "fan_avg", "distribution": "uniform", "seed": null}}, "recurrent_initializer": {"class_name": "Orthogonal", "config": {"gain": 1.0, "seed": null}}, "bias_initializer": {"class_name": "Zeros", "config": {}}, "unit_forget_bias": true, "kernel_regularizer": {"class_name": "L1L2", "config": {"l1": 0.0, "l2": 0.0010000000474974513}}, "recurrent_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "recurrent_constraint": null, "bias_constraint": null, "dropout": 0.0, "recurrent_dropout": 0.0}}, {"class_name": "Dropout", "config": {"name": "dropout_1", "trainable": true, "rate": 0.6}}, {"class_name": "Dense", "config": {"name": "dense_1", "trainable": true, "units": 2, "activation": "softmax", "use_bias": true, "kernel_initializer": {"class_name": "VarianceScaling", "config": {"scale": 1.0, "mode": "fan_avg", "distribution": "uniform", "seed": null}}, "bias_initializer": {"class_name": "Zeros", "config": {}}, "kernel_regularizer": {"class_name": "L1L2", "config": {"l1": 0.0, "l2": 0.0010000000474974513}}, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}}], "keras_version": "2.0.2", "backend": "theano"}
\ No newline at end of file

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+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""
+Created on Mon Mar 20 23:41:51 2017
+
+@author: red-sky
+"""
+
+
+import numpy as np
+import theano
+from theano import tensor as T
+
+
+class EmbeddingLayer(object):
+    def __init__(self, num_vocab, word_dim, rng, embedding_w=None):
+        '''
+        word_dim :: dimension of the word embeddings
+        num_vocab :: number of word embeddings in the vocabulary
+        embedding_w :: pre-train word vector
+        '''
+
+        if embedding_w is None:
+            word_vectors = rng.uniform(-1.0, 1.0, (num_vocab, word_dim))
+            self.embedding_w = theano.shared(word_vectors,
+                                             name="EmbeddingLayer_W") \
+                .astype(theano.config.floatX)
+        else:
+            self.embedding_w = theano.shared(embedding_w,
+                                             name="EmbeddingLayer_W") \
+                .astype(theano.config.floatX)
+
+        self.params = [self.embedding_w]
+        self.infor = [num_vocab, word_dim]
+
+    def words_ind_2vec(self, index):
+        map_word_vectors = self.embedding_w[index]
+        output = T.mean(map_word_vectors,  axis=0)
+        return output, map_word_vectors
+
+
+if __name__ == "__main__":
+    rng = np.random.RandomState(220495)
+    arrWords = T.ivector("words")
+    EMBD = EmbeddingLayer(100, 150, rng=rng)
+    Word2Vec = theano.function(
+        inputs=[arrWords],
+        outputs=EMBD.words_ind_2vec(arrWords)
+    )
+    Vec = Word2Vec([1, 2, 3, 4])
+    Vec = Word2Vec([2, 3, 4])
+    print("Dim: ", Vec.shape)
+    print("Val: ", Vec)

+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""
+Created on Sat Mar 25 17:36:33 2017
+
+@author: red-sky
+"""
+import sys
+import json
+import theano
+import pickle
+import os.path
+import numpy as np
+import theano.tensor as T
+from SmallUtils import createShareVar, ADAM_OPTIMIZER
+from EmbeddingLayer import EmbeddingLayer
+from RoleDependentLayer import RoleDependentLayer
+
+
+class Input(object):
+    def __init__(self, object1, object1_fake, action, object2, rng,
+                 vovab_length=4000, wordDim=100, trainedWordsVectors=None,):
+        # Init Embeding layer, input vector of index and ouput average
+        # of word vector as ref Ding et al 2014
+        self.EMBD = EmbeddingLayer(vovab_length, wordDim, rng=rng,
+                                   embedding_w=trainedWordsVectors)
+
+        object1_vector, _ = self.EMBD.words_ind_2vec(object1)
+        action_vector, _ = self.EMBD.words_ind_2vec(action)
+        object2_vector, _ = self.EMBD.words_ind_2vec(object2)
+        object1_vector_fake, _ = self.EMBD.words_ind_2vec(object1_fake)
+
+        self.output = [object1_vector, object1_vector_fake,
+                       action_vector, object2_vector]
+        self.params = self.EMBD.params
+
+    def get_params(self):
+        trainParams = {
+            "WordWvec": self.EMBD.embedding_w.get_value()
+        }
+        return(trainParams)
+
+
+class ModelBody(object):
+    def __init__(self, vectorObjects, rng, n_out, n_in,
+                 trainedModelParams=None):
+        if trainedModelParams is None:
+            trainedModelParams = {
+                "roleDependentLayer1_": {
+                    "T": None, "W1": None, "W2": None, "b": None
+                },
+                "roleDependentLayer2_": {
+                    "T": None, "W1": None, "W2": None, "b": None
+                },
+                "roleDependentLayer3_": {
+                    "T": None, "W1": None, "W2": None, "b": None
+                }
+            }
+
+        Obj1, Ob1_fake, Act, Obj2 = vectorObjects
+
+        self.RoleDepen1 = RoleDependentLayer(
+            left_dependent=T.stack([Obj1, Ob1_fake], axis=0),
+            right_dependent=Act,
+            n_in=n_in, n_out=n_out, rng=rng,
+            trainedParams=trainedModelParams,
+            name="roleDependentLayer1_"
+        )
+        self.RoleDepen1_output = self.RoleDepen1.output
+
+        self.RoleDepen2 = RoleDependentLayer(
+            left_dependent=Obj2,
+            right_dependent=Act,
+            n_in=n_in, n_out=n_out, rng=rng,
+            trainedParams=trainedModelParams,
+            name="roleDependentLayer2_"
+        )
+        self.RoleDepen2_output = T.flatten(self.RoleDepen2.output, outdim=1)
+
+        self.RoleDepen3 = RoleDependentLayer(
+            left_dependent=self.RoleDepen1_output,
+            right_dependent=self.RoleDepen2_output,
+            n_in=n_out, n_out=n_out, rng=rng,
+            trainedParams=trainedModelParams,
+            name="roleDependentLayer3_"
+        )
+
+        self.params = self.RoleDepen1.params + self.RoleDepen2.params + \
+            self.RoleDepen3.params
+
+        self.L2 = (
+            self.RoleDepen1.L2 +
+            self.RoleDepen2.L2 +
+            self.RoleDepen3.L2
+        )
+        self.output = self.RoleDepen3.output
+
+    def get_params(self):
+        trainedModelParams = {
+            "roleDependentLayer1_": self.RoleDepen1.get_params(),
+            "roleDependentLayer2_": self.RoleDepen2.get_params(),
+            "roleDependentLayer3_": self.RoleDepen3.get_params()
+        }
+        return(trainedModelParams)
+
+
+class LogisticRegression(object):
+
+    def __init__(self, rng, layerInput, n_in, n_out,
+                 paramsLayer=None,
+                 name="LogisticRegression_"):
+
+        self.layerInput = layerInput
+        if paramsLayer is None:
+            self.W = createShareVar(rng=rng, name=name+"W",
+                                    factor_for_init=n_out + n_in,
+                                    dim=(n_in, n_out))
+        else:
+            self.W = theano.shared(value=paramsLayer["W"],
+                                   name=name+"W", borrow=True)
+
+        if paramsLayer is None:
+            b_values = np.zeros((n_out,), dtype=theano.config.floatX)
+            self.b = theano.shared(value=b_values,
+                                   name=name+"b", borrow=True)
+        else:
+            self.b = theano.shared(value=paramsLayer["b"],
+                                   name=name+"b", borrow=True)
+
+        step1 = T.dot(self.layerInput, self.W)
+        self.prob_givenX = T.tanh(step1 + self.b)
+        self.y_predict = T.argmax(self.prob_givenX, axis=1)
+
+        self.params = [self.W, self.b]
+        self.L2 = sum([(param**2).sum() for param in self.params])
+
+    def get_params(self):
+        trainedParams = {
+            "W": self.W.get_value(), "b": self.b.get_value()
+        }
+        return(trainedParams)
+
+    def neg_log_likelihood(self, y_true):
+        y_true = T.cast(y_true, "int32")
+        log_prob = T.log(self.prob_givenX)
+        nll = -T.mean(log_prob[T.arange(y_true.shape[0]), y_true])
+        return nll
+
+    def margin_loss(self):
+        loss = T.max([0, 1 - self.prob_givenX[0, 0] + self.prob_givenX[1, 0]])
+        return loss
+
+    def cal_errors(self, y_true):
+        if y_true.ndim != self.y_predict.ndim:
+            raise TypeError(
+                "y should have the same shape as self.y_pred",
+                ("y_true", y_true.ndim, "y_pred", self.y_predict.ndim)
+            )
+        if y_true.dtype.startswith("int"):
+            return T.mean(T.neq(self.y_predict, y_true))
+        else:
+            raise TypeError(
+                "y_true should have type int ...",
+                ("y_true", y_true.type, "y_pred", self.y_predict.type)
+            )
+
+
+def main(dataPath, trainedParamsPath="modelTrained.pickle",
+         outputVectorPath="resultEmbeding.pickle",
+         learning_rate=0.005, L2_reg=0.0001,
+         n_epochs=500, num_K=150, word_dim=150):
+    # CONSTANT VARIABLES
+    RNG = np.random.RandomState(220495 + 280295 + 1)
+    LABEL_NUM = 2
+    if os.path.isfile(trainedParamsPath):
+        with open(trainedParamsPath, 'rb') as handle:
+            trainedParams = pickle.load(handle)
+    else:
+        print("No Trained Model, create new")
+        trainedParams = {
+            "Input": {"WordWvec": None}, "Body": None, "Output": None
+        }
+
+    OPTIMIZER = ADAM_OPTIMIZER
+    # INPUT DATA
+    data_indexed_events = np.load(dataPath,allow_pickle=True)
+    N_sample = len(data_indexed_events)
+#    N_sample = 1
+    all_index = list(set(sum(np.concatenate(data_indexed_events).ravel(), [])))
+#    all_train_index = list(set(np.hstack(data_indexed_events[0:NNN].flat)))
+    # Snip tensor at begin
+    object1 = T.ivector("object1")
+    object1_fake = T.ivector("object1_fake")
+    action = T.ivector("action")
+    object2 = T.ivector("object2")
+
+    constainY = theano.shared(
+        np.asarray([1, 0], dtype=theano.config.floatX),
+        borrow=True
+    )
+
+    # WORDS EMBEDING VECTOR
+    wordsEmbedLayer = Input(
+        object1=object1, object1_fake=object1_fake,
+        action=action, object2=object2, rng=RNG,
+        wordDim=word_dim, vovab_length=len(all_index),
+        trainedWordsVectors=trainedParams["Input"]["WordWvec"]
+    )
+
+    Obj1, Ob1_fake, Act, Obj2 = wordsEmbedLayer.output
+
+    # EVENTS EMBEDING LAYER - THREE ROLE DEPENTDENT LAYER
+    eventsEmbedingLayer = ModelBody(
+        vectorObjects=wordsEmbedLayer.output,
+        n_out=num_K, n_in=word_dim, rng=RNG,
+        trainedModelParams=trainedParams["Body"]
+    )
+
+    # CLASSIFY LAYER
+    predict_layers = LogisticRegression(
+        layerInput=eventsEmbedingLayer.output,
+        rng=RNG, n_in=num_K, n_out=1,
+        paramsLayer=trainedParams["Output"]
+    )
+
+    # COST FUNCTION
+    COST = (
+        predict_layers.margin_loss() +
+        L2_reg * predict_layers.L2 +
+        L2_reg * eventsEmbedingLayer.L2
+    )
+
+    # GRADIENT CALCULATION and UPDATE
+    all_params = wordsEmbedLayer.params + \
+        eventsEmbedingLayer.params + predict_layers.params
+    print("TRAIN: ", all_params)
+
+    UPDATE = OPTIMIZER(COST, all_params, learning_rate=learning_rate)
+
+    # TRAIN MODEL
+    GET_COST = theano.function(
+        inputs=[object1, object1_fake, action, object2],
+        outputs=[predict_layers.margin_loss(),
+                 predict_layers.prob_givenX],
+    )
+
+#    TEST = theano.function(
+#        inputs=[object1, object1_fake, action, object2],
+#        outputs=eventsEmbedingLayer.RoleDepen2.test,
+#        on_unused_input='warn'
+#    )
+
+    TRAIN = theano.function(
+        inputs=[object1, object1_fake, action, object2],
+        outputs=[predict_layers.margin_loss()],
+        updates=UPDATE
+    )
+
+    GET_EVENT_VECTOR = theano.function(
+        inputs=[object1, object1_fake, action, object2],
+        outputs=[predict_layers.margin_loss(),
+                 eventsEmbedingLayer.output],
+    )
+
+    def generate_fake_object(all_index, RNG, obj):
+        fake_obj = list(RNG.choice(all_index, len(obj)))
+        while sorted(fake_obj) == sorted(obj):
+            print("WRONG faking object 1", obj)
+            fake_obj = list(RNG.choice(all_index, len(obj)))
+        return(fake_obj)
+
+    def generate_list_object(data_indexed_events, all_index, RNG):
+        list_fake_object1 = [
+            generate_fake_object(all_index, RNG, events[0])
+            for events in data_indexed_events
+        ]
+        list_real_object = set([
+            "_".join([str(a) for a in sorted(events[0])])
+            for events in data_indexed_events
+        ])
+        wrong = 0
+        while True:
+            valid = True
+            wrong += 1
+            for i, obj in enumerate(list_fake_object1):
+                s = "_".join([str(a) for a in sorted(obj)])
+                if s in list_real_object:
+                    valid = valid and False
+                    list_fake_object1[i] = \
+                        generate_fake_object(all_index, RNG, s)
+                else:
+                    valid = valid and True
+            if valid:
+                break
+        print("There are %d wrong random loops" % wrong)
+        return(list_fake_object1)
+
+    print("*"*72)
+    print("Begin Training process")
+
+    for epoch in range(n_epochs):
+        # create false label
+        print("Begin new epoch: %d" % epoch)
+
+        list_fake_object1 = generate_list_object(data_indexed_events,
+                                                 all_index, RNG)
+        cost_of_epoch = []
+        set_index = set(range(N_sample))
+        temp_variable = N_sample
+        print("*" * 72+"\n")
+        print("*" * 72+"\n")
+        # train
+        model_train = {
+            "Input": wordsEmbedLayer.get_params(),
+            "Body": eventsEmbedingLayer.get_params(),
+            "Output": predict_layers.get_params()
+        }
+        RESULT = {}
+        outCOST = []
+        Max_inter = len(set_index)*2
+        iter_num = 0
+        while len(set_index) > 0 and iter_num <= Max_inter:
+            iter_num += 1
+            index = set_index.pop()
+            ob1_real, act, obj2 = data_indexed_events[index]
+            ob1_fake = list_fake_object1[index]
+            cost, probY = GET_COST(ob1_real, ob1_fake, act, obj2)
+            outCOST.append(cost)
+#            test = TEST(ob1_real, ob1_fake, act, obj2)
+#            for a in test:
+#                print(a, a.shape)
+
+            if cost > 0:
+                set_index.add(index)
+                c = TRAIN(ob1_real, ob1_fake, act, obj2)
+            else:
+                RESULT[index] = GET_EVENT_VECTOR(ob1_real, ob1_fake, act, obj2)
+
+            if (len(set_index) % 50 == 0 and
+                    temp_variable != len(set_index)):
+                temp_variable = len(set_index)
+                print("There are %f %% left in this %d "
+                      "epoch with average cost %f"
+                      % (len(set_index)/float(N_sample)*100,
+                         epoch, np.mean(outCOST[-50:])))
+            if iter_num > Max_inter - 5:
+                print(set_index, ob1_real, ob1_fake, act, obj2)
+
+        with open(trainedParamsPath, 'wb') as handle:
+            pickle.dump(model_train, handle,
+                        protocol=pickle.HIGHEST_PROTOCOL)
+
+        with open(outputVectorPath, 'wb') as handle:
+            pickle.dump(RESULT, handle,
+                        protocol=pickle.HIGHEST_PROTOCOL)
+
+if __name__ == "__main__":
+#    arg = ["", "Data/Query_Apple/2005-2010/IndexedEvents.npy",
+#           "Data/Query_Apple/2005-2010/linhtinh/", "20"]
+    arg = sys.argv
+    main(dataPath="../../Thesis_data/IndexedEvents.npy", trainedParamsPath="TrainedParams.pickle",
+         outputVectorPath="resultEmbeding.pickle", n_epochs=20)
+

+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""
+Created on Sat Mar 25 16:13:18 2017
+
+@author: red-sky
+"""
+
+import theano
+import numpy as np
+import theano.tensor as T
+from SmallUtils import createShareVar
+
+
+class RoleDependentLayer(object):
+    def __init__(self, left_dependent, right_dependent, rng,
+                 n_in=100, n_out=4, trainedParams=None,
+                 name="RoleDependentEmbedding_"):
+        if trainedParams is None:
+            trainedParams = {
+                name: {
+                    "T": None, "W1": None, "W2": None, "b": None
+                }
+            }
+
+        if trainedParams[name]["T"] is not None:
+            assert trainedParams[name]["T"].shape == (n_out, n_in, n_in)
+            self.T = theano.shared(value=trainedParams[name]["T"],
+                                   name=name+"T", borrow=True)
+        else:
+            self.T = createShareVar(rng=rng, name=name+"T",
+                                    factor_for_init=n_out + n_in,
+                                    dim=(n_out, n_in, n_in))
+
+        if trainedParams[name]["W1"] is not None:
+            assert trainedParams[name]["W1"].shape == (n_in, n_out)
+            self.W1 = theano.shared(value=trainedParams[name]["W1"],
+                                    name=name+"W1", borrow=True)
+        else:
+            self.W1 = createShareVar(rng=rng, name=name+"W1",
+                                     factor_for_init=n_out + n_in,
+                                     dim=(n_in, n_out))
+
+        if trainedParams[name]["W2"] is not None:
+            assert trainedParams[name]["W2"].shape == (n_in, n_out)
+            self.W2 = theano.shared(value=trainedParams[name]["W2"],
+                                    name=name+"W2", borrow=True)
+        else:
+            self.W2 = createShareVar(rng=rng, name=name+"W2",
+                                     factor_for_init=n_out + n_in,
+                                     dim=(n_in, n_out))
+
+        if trainedParams[name]["b"] is not None:
+            assert trainedParams[name]["b"].shape == (n_out,)
+            self.b = theano.shared(value=trainedParams[name]["b"],
+                                   name=name+"b", borrow=True)
+        else:
+            b_values = np.zeros(shape=(n_out,), dtype=theano.config.floatX)
+            self.b = theano.shared(value=b_values, name=name+"b", borrow=True)
+
+        # list of layer params
+        self.params = [self.T, self.W1, self.W2, self.b]
+
+        # L2 regulation
+        self.L2 = sum([(param**2).sum() for param in self.params])
+
+        # Bi-linear step
+        def one_kernel(Tk, left, right):
+            first_bi_libear = theano.dot(left, Tk)
+            seccon_bi_linear = theano.dot(first_bi_libear, right)
+            return(seccon_bi_linear.flatten())
+
+        bi_1, _ = theano.scan(
+            fn=one_kernel,
+            sequences=[self.T],
+            non_sequences=[left_dependent, right_dependent],
+            n_steps=n_out
+        )
+
+        # Feed forward network step
+        feedforward_step1 = theano.dot(left_dependent, self.W1)
+        feedforward_step2 = theano.dot(right_dependent, self.W2)
+        feedforward_step3 = (feedforward_step1 +
+                             feedforward_step2.dimshuffle("x", 0) +
+                             self.b.dimshuffle("x", 0))
+        feedforward_step4 = bi_1.dimshuffle(1, 0) + feedforward_step3
+        self.output = theano.tensor.tanh(feedforward_step4)
+        self.test = [feedforward_step3]
+
+    def output_(self, left_dependent, right_dependent):
+
+        def one_kernel(Tk, left, right):
+            first_bi_libear = theano.dot(left, Tk)
+            seccon_bi_linear = theano.dot(first_bi_libear, right)
+            return(seccon_bi_linear.flatten())
+
+        bi_linear_tensor, _ = theano.scan(
+            fn=one_kernel,
+            sequences=[self.T],
+            non_sequences=[left_dependent, right_dependent],
+            n_steps=n_out
+        )
+
+        bi_linear_tensor = bi_linear_tensor.dimshuffle(1, 0)
+        feedforward_step1 = theano.dot(left_dependent, self.W1)
+        feedforward_step2 = theano.dot(right_dependent, self.W2)
+        feedforward_step3 = (feedforward_step1 +
+                             feedforward_step2.dimshuffle("x", 0) +
+                             self.b.dimshuffle("x", 0))
+        feedforward_step4 = bi_linear_tensor + feedforward_step3
+        output = theano.tensor.tanh(feedforward_step4)
+        return(output)
+
+    def get_params(self):
+        trainedParams = {
+            "T": self.T.get_value(), "W1": self.W1.get_value(),
+            "W2": self.W2.get_value(), "b": self.b.get_value()
+        }
+        return(trainedParams)

+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""
+Created on Sat Mar 25 15:55:14 2017
+
+@author: red-sky
+"""
+import theano
+import theano.tensor as T
+import numpy as np
+
+def createShareVar(rng, dim, name, factor_for_init):
+    var_values = np.asarray(
+        rng.uniform(
+            low=-np.sqrt(6.0 / factor_for_init),
+            high=np.sqrt(6.0 / factor_for_init),
+            size=dim,
+        )
+    )
+    Var = theano.shared(value=var_values, name=name, borrow=True)
+    return Var
+
+
+def adadelta(lr, tparams, cost, grads, listInput):
+    """
+    An adaptive learning rate optimizer
+
+    Parameters
+    ----------
+    lr : Theano SharedVariable
+        Initial learning rate
+    tpramas: Theano SharedVariable
+        Model parameters
+    grads: Theano variable
+        Gradients of cost w.r.t to parameres
+
+    cost: Theano variable
+        Objective fucntion to minimize
+
+    Notes
+    -----
+    For more information, see [ADADELTA]_.
+
+    .. [ADADELTA] Matthew D. Zeiler, *ADADELTA: An Adaptive Learning
+       Rate Method*, arXiv:1212.5701.
+    """
+    np_float = np.asarray(0., dtype=theano.config.floatX)
+    zipped_grads = [theano.shared(p.get_value() * np_float,
+                                  name='%s_grad' % k)
+                    for k, p in enumerate(tparams)]
+    running_up2 = [theano.shared(p.get_value() * np_float,
+                                 name='%s_rup2' % k)
+                   for k, p in enumerate(tparams)]
+    running_grads2 = [theano.shared(p.get_value() * np_float,
+                                    name='%s_rgrad2' % k)
+                      for k, p in enumerate(tparams)]
+
+    zgup = [(zg, g) for zg, g in zip(zipped_grads, grads)]
+    rg2up = [(rg2, 0.95 * rg2 + 0.05 * (g ** 2))
+             for rg2, g in zip(running_grads2, grads)]
+
+    f_grad_shared = theano.function(inputs=listInput,
+                                    outputs=cost,
+                                    updates=zgup + rg2up,
+                                    name='adadelta_f_grad_shared')
+
+    updir = [-T.sqrt(ru2 + 1e-6) / T.sqrt(rg2 + 1e-6) * zg
+             for zg, ru2, rg2 in zip(zipped_grads,
+                                     running_up2,
+                                     running_grads2)]
+    ru2up = [(ru2, 0.95 * ru2 + 0.05 * (ud ** 2))
+             for ru2, ud in zip(running_up2, updir)]
+    param_up = [(p, p + ud) for p, ud in zip(tparams, updir)]
+
+    f_update = theano.function([lr], [], updates=ru2up + param_up,
+                               on_unused_input='ignore',
+                               name='adadelta_f_update')
+
+    return f_grad_shared, f_update
+
+
+def ADAM_OPTIMIZER(loss, all_params, learning_rate=0.001,
+                   b1=0.9, b2=0.999, e=1e-8, gamma=1-1e-8):
+    """
+    CITE: http://sebastianruder.com/optimizing-gradient-descent/index.html#adam
+    ADAM update rules
+    Default values are taken from [Kingma2014]
+    References:
+    [Kingma2014] Kingma, Diederik, and Jimmy Ba.
+    "Adam: A Method for Stochastic Optimization."
+    arXiv preprint arXiv:1412.6980 (2014).
+    http://arxiv.org/pdf/1412.6980v4.pdf
+    """
+    updates = []
+    all_grads = theano.grad(loss, all_params)
+    alpha = learning_rate
+    t = theano.shared(np.float32(1))
+    # (Decay the first moment running average coefficient)
+    b1_t = b1*gamma**(t-1)
+
+    for params_previous, g in zip(all_params, all_grads):
+        init_moment = np.zeros(params_previous.get_value().shape,
+                               dtype=theano.config.floatX)
+        # (the mean)
+        first_moment = theano.shared(init_moment)
+        # (the uncentered variance)
+        second_moment = theano.shared(init_moment)
+
+        # (Update biased first moment estimate)
+        bias_m = b1_t*first_moment + (1 - b1_t)*g
+
+        # (Update biased second raw moment estimate)
+        bias_v = b2*second_moment + (1 - b2)*g**2
+
+        # (Compute bias-corrected first moment estimate)
+        unbias_m = bias_m / (1-b1**t)
+
+        # (Compute bias-corrected second raw moment estimate)
+        unbias_v = bias_v / (1-b2**t)
+
+        # (Update parameters)
+        update_term = (alpha * unbias_m) / (T.sqrt(unbias_v) + e)
+        params_new = params_previous - update_term
+
+        updates.append((first_moment, bias_m))
+        updates.append((second_moment, bias_v))
+        updates.append((params_previous, params_new))
+    updates.append((t, t + 1.))
+    return updates
\ No newline at end of file

+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""
+Created on Thu Apr 13 17:01:36 2017
+
+@author: red-sky
+"""
+
+import sys
+import numpy as np
+np.random.seed(280295)
+import keras.backend as K
+from keras.models import Sequential
+from keras.layers import Dense, Activation, Dropout, Flatten
+from keras.layers import Conv1D, GlobalAveragePooling1D, MaxPooling1D
+from keras.callbacks import ModelCheckpoint, EarlyStopping
+from keras import regularizers, optimizers
+
+
+def recall(y_true, y_pred):
+    """Recall metric.
+
+    Only computes a batch-wise average of recall.
+
+    Computes the recall, a metric for multi-label classification of
+    how many relevant items are selected.
+    """
+    true_positives = K.sum(K.round(K.clip(y_true[:, 1] * y_pred[:, 1], 0, 1)))
+    possible_positives = K.sum(K.round(K.clip(y_true[:, 1], 0, 1)))
+    recall = true_positives / (possible_positives + K.epsilon())
+    return recall
+
+
+def precision(y_true, y_pred):
+    """Precision metric.
+
+    Only computes a batch-wise average of precision.
+
+    Computes the precision, a metric for multi-label classification of
+    how many selected items are relevant.
+    """
+    true_positives = K.sum(K.round(K.clip(y_true[:, 1] * y_pred[:, 1], 0, 1)))
+    predicted_positives = K.sum(K.round(K.clip(y_pred[:, 1], 0, 1)))
+    precision = true_positives / (predicted_positives + K.epsilon())
+    return precision
+
+
+def fbeta_score(y_true, y_pred):
+
+    # If there are no true positives, fix the F score at 0 like sklearn.
+    if K.sum(K.round(K.clip(y_true, 0, 1))) == 0:
+        return 0
+
+    p = precision(y_true, y_pred)
+    r = recall(y_true, y_pred)
+    bb = 1 ** 2
+    fbeta_score = (1 + bb) * (p * r) / (bb * p + r + K.epsilon())
+    return fbeta_score
+
+
+def main(dataX_path, dataY_path, result_path,
+         n_epoch, input_dim, days):
+
+    # load data
+    np.random.seed(2204)
+    X = np.load(dataX_path)
+    Y = np.load(dataY_path)
+
+    # build Model
+    model = Sequential()
+    model.add(Conv1D(128, 1, activation='relu', input_shape=(days, input_dim)))
+    model.add(Conv1D(128, 3, activation='relu', padding='same'))
+    model.add(MaxPooling1D(2))
+    model.add(Flatten())
+    model.add(Dropout(0.8))
+    model.add(Dense(2, activation='softmax'))
+    adam = optimizers.Adam(lr=0.001)
+    model.compile(loss='categorical_crossentropy',
+                  optimizer=adam,
+                  metrics=['accuracy', recall, precision, fbeta_score])
+
+    # model Compile
+    model_name = result_path+'model2_price_move_predict.hdf5'
+    checkpointer = ModelCheckpoint(filepath=model_name,
+                                   monitor='val_fbeta_score',
+                                   verbose=2, save_best_only=True)
+    earlystopper = EarlyStopping(monitor='val_loss', patience=20, verbose=2)
+
+    outmodel = open(result_path+'model2_price_move_predict.json', 'w')
+    outmodel.write(model.to_json())
+    outmodel.close()
+
+    # process Training
+    model.fit(X, Y, batch_size=32, verbose=2,
+              validation_split=0.1, epochs=n_epoch,
+              callbacks=[checkpointer])
+
+
+if __name__ == "__main__":
+    dataX = sys.argv[1]
+    dataY = sys.argv[2]
+    model_path = sys.argv[3]
+    n_epoch = int(sys.argv[4])
+    input_dim = int(sys.argv[5])
+    days = int(sys.argv[6])
+    main(dataX, dataY, model_path, n_epoch, input_dim, days)

+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""
+Created on Thu Apr 13 17:01:36 2017
+
+@author: red-sky
+"""
+
+import sys
+import numpy as np
+np.random.seed(280295)
+import keras.backend as K
+from keras.models import Sequential
+from keras import regularizers, optimizers
+from keras.layers import Dense, Activation, LSTM, Dropout
+from keras.callbacks import ModelCheckpoint, EarlyStopping
+
+def recall(y_true, y_pred):
+    """Recall metric.
+
+    Only computes a batch-wise average of recall.
+
+    Computes the recall, a metric for multi-label classification of
+    how many relevant items are selected.
+    """
+    true_positives = K.sum(K.round(K.clip(y_true[:, 0] * y_pred[:, 0], 0, 1)))
+    possible_positives = K.sum(K.round(K.clip(y_true[:, 0], 0, 1)))
+    recall = true_positives / (possible_positives + K.epsilon())
+    return recall
+
+
+def precision(y_true, y_pred):
+    """Precision metric.
+
+    Only computes a batch-wise average of precision.
+
+    Computes the precision, a metric for multi-label classification of
+    how many selected items are relevant.
+    """
+    true_positives = K.sum(K.round(K.clip(y_true[:, 0] * y_pred[:, 0], 0, 1)))
+    predicted_positives = K.sum(K.round(K.clip(y_pred[:, 0], 0, 1)))
+    precision = true_positives / (predicted_positives + K.epsilon())
+    return precision
+
+
+def fbeta_score(y_true, y_pred):
+
+    # If there are no true positives, fix the F score at 0 like sklearn.
+    if K.sum(K.round(K.clip(y_true, 0, 1))) == 0:
+        return 0
+
+    p = precision(y_true, y_pred)
+    r = recall(y_true, y_pred)
+    bb = 1 ** 2
+    fbeta_score = (1 + bb) * (p * r) / (bb * p + r + K.epsilon())
+    return fbeta_score
+
+
+def main(dataX_path, dataY_path, result_path,
+         n_epoch, input_dim, days):
+
+    # load data
+    np.random.seed(2204)
+    X = np.load(dataX_path)
+    Y = np.load(dataY_path)
+
+    # build Model
+    model = Sequential()
+    model.add(LSTM(256, input_shape=(days, input_dim),
+                   kernel_regularizer=regularizers.l2(0.001)))
+
+    model.add(Dropout(0.6))
+    model.add(Dense(2, activation='softmax',
+                    kernel_regularizer=regularizers.l2(0.001)))
+    adam = optimizers.Adam(lr=0.001)
+    model.compile(loss='categorical_crossentropy',
+                  optimizer=adam,
+                  metrics=['accuracy', recall, precision, fbeta_score])
+
+    # model Compile
+    model_name = result_path+'model2_price_move_predict.hdf5'
+    checkpointer = ModelCheckpoint(filepath=model_name,
+                                   monitor='val_fbeta_score', mode="max",
+                                   verbose=2, save_best_only=True)
+    earlystopper = EarlyStopping(monitor='val_loss', patience=20, verbose=2)
+
+    outmodel = open(result_path+'model2_price_move_predict.json', 'w')
+    outmodel.write(model.to_json())
+    outmodel.close()
+
+    # process Training
+    model.fit(X, Y, batch_size=32, verbose=2,
+              validation_split=0.1, epochs=n_epoch,
+              callbacks=[checkpointer])
+
+if __name__ == "__main__":
+    dataX = sys.argv[1]
+    dataY = sys.argv[2]
+    model_path = sys.argv[3]
+    n_epoch = int(sys.argv[4])
+    input_dim = int(sys.argv[5])
+    days = int(sys.argv[6])
+    main(dataX, dataY, model_path, n_epoch, input_dim, days)

+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""
+Created on Thu Apr 13 17:01:36 2017
+
+@author: red-sky
+"""
+
+import sys
+import numpy as np
+np.random.seed(280295)
+import keras.backend as K
+from keras.models import Sequential
+from keras.layers import Dense, Activation, Dropout, Flatten
+from keras.layers import Conv1D, GlobalAveragePooling1D, MaxPooling1D
+from keras.callbacks import ModelCheckpoint, EarlyStopping
+from keras import regularizers, optimizers
+
+
+def recall(y_true, y_pred):
+    """Recall metric.
+
+    Only computes a batch-wise average of recall.
+
+    Computes the recall, a metric for multi-label classification of
+    how many relevant items are selected.
+    """
+    true_positives = K.sum(K.round(K.clip(y_true[:, 0] * y_pred[:, 0], 0, 1)))
+    possible_positives = K.sum(K.round(K.clip(y_true[:, 0], 0, 1)))
+    recall = true_positives / (possible_positives + K.epsilon())
+    return recall
+
+
+def precision(y_true, y_pred):
+    """Precision metric.
+
+    Only computes a batch-wise average of precision.
+
+    Computes the precision, a metric for multi-label classification of
+    how many selected items are relevant.
+    """
+    true_positives = K.sum(K.round(K.clip(y_true[:, 0] * y_pred[:, 0], 0, 1)))
+    predicted_positives = K.sum(K.round(K.clip(y_pred[:, 0], 0, 1)))
+    precision = true_positives / (predicted_positives + K.epsilon())
+    return precision
+
+
+def fbeta_score(y_true, y_pred):
+
+    # If there are no true positives, fix the F score at 0 like sklearn.
+    if K.sum(K.round(K.clip(y_true, 0, 1))) == 0:
+        return 0
+
+    p = precision(y_true, y_pred)
+    r = recall(y_true, y_pred)
+    bb = 1 ** 2
+    fbeta_score = (1 + bb) * (p * r) / (bb * p + r + K.epsilon())
+    return fbeta_score
+
+
+def main(dataX_path, dataY_path, result_path,
+         n_epoch, input_dim, days):
+
+    # load data
+    np.random.seed(2204)
+    X = np.load(dataX_path)
+    Y = np.load(dataY_path)
+
+    # build Model
+    model = Sequential()
+    model.add(Flatten(input_shape=(days, input_dim)))
+    model.add(Dense(512, activation='sigmoid'))
+    model.add(Dropout(0.8))
+    model.add(Dense(1024, activation='sigmoid'))
+    model.add(Dropout(0.8))
+#    model.add(Dense(1024, activation='sigmoid'))
+    model.add(Dropout(0.8))
+    model.add(Dense(2, activation='softmax'))
+
+    adam = optimizers.Adam(lr=0.001)
+    model.compile(loss='categorical_crossentropy',
+                  optimizer=adam,
+                  metrics=['accuracy', recall, precision, fbeta_score])
+
+    # model Compile
+    model_name = result_path+'model2_price_move_predict.hdf5'
+    checkpointer = ModelCheckpoint(filepath=model_name, monitor='val_acc',
+                                   verbose=2, save_best_only=True)
+    earlystopper = EarlyStopping(monitor='val_loss', patience=20, verbose=2)
+
+    outmodel = open(result_path+'model2_price_move_predict.json', 'w')
+    outmodel.write(model.to_json())
+    outmodel.close()
+
+    # process Training
+    model.fit(X, Y, batch_size=32, verbose=2,
+              validation_split=0.1, epochs=n_epoch,
+              callbacks=[checkpointer])
+
+
+if __name__ == "__main__":
+    dataX = sys.argv[1]
+    dataY = sys.argv[2]
+    model_path = sys.argv[3]
+    n_epoch = int(sys.argv[4])
+    input_dim = int(sys.argv[5])
+    days = int(sys.argv[6])
+    main(dataX, dataY, model_path, n_epoch, input_dim, days)

+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""
+Created on Thu Mar 16 21:57:57 2017
+
+@author: red-sky
+"""
+
+import bs4
+import json
+import sys
+import urllib.request as urlreq
+from bs4 import BeautifulSoup
+import requests
+
+BLOOMBERG_params = {
+    "sort_by_newest": "time:desc",
+    "sort_by_oldest": "time:asc",
+    "source_from_bloomberg": "sites=bview",
+    "end_time": "2017-03-12T15:20:16.240Z"
+}
+
+DATA_TO_EXTRACT = {
+    "query_list_news": ["div", {"class": "search-result-story__container"}],
+    "query_headline": ["h1", {"class": "search-result-story__headline"}],
+    "query_time_published": ["time", {"class": "published-at"}],
+    "query_body": ["div", {"class": "search-result-story__body"}]
+}
+
+
+def parser_url(query_string, page,
+               sort_by="sort_by_oldest",
+               source="source_from_bloomberg"):
+    url = "https://www.bloomberg.com/"
+    # add search query
+    url = url + "search?query=" + query_string + "&"
+    # add sort by
+    url = url + "sort=" + BLOOMBERG_params[sort_by] + "&"
+    # add time to query -- use present time
+    url = url + "sites=" + BLOOMBERG_params[source] + "&"
+    # add page number
+    url = url + "page=" + str(page)
+    return url
+
+
+def get_rid_off_key(list_contents):
+    body_string = ""
+    for substring in list_contents:
+        if (type(substring) == bs4.element.Tag):
+            # join all body string and
+            # eliminate highlight query string key
+            body_string += substring.string
+        else:
+            if (type(substring.string) == bs4.element.NavigableString):
+                body_string += substring.string
+    return(body_string)
+
+
+def extract_from_url(url):
+    try:
+        with requests.get(url) as response:
+            html_of_page = response.read()
+            soup_object = BeautifulSoup(html_of_page, "lxml")
+        # Extract list of news in soup object
+        param_to_find = DATA_TO_EXTRACT["query_list_news"]
+        list_of_news = soup_object.find_all(param_to_find[0],
+                                            attrs=param_to_find[1])
+        if (len(list_of_news) == 0):
+            return None
+        # create list result extracted
+        result = []
+        for block_new in list_of_news:
+            # extract time from block
+            param_to_find = DATA_TO_EXTRACT["query_time_published"]
+            time = block_new.find_all(param_to_find[0],
+                                      attrs=param_to_find[1])
+            time = time[0]["datetime"]
+
+            # extract new headline
+            param_to_find = DATA_TO_EXTRACT["query_headline"]
+            headline = block_new.find_all(param_to_find[0],
+                                          attrs=param_to_find[1])
+            headline = get_rid_off_key(headline[0].a.contents)
+
+            # extract new body list if string
+            param_to_find = DATA_TO_EXTRACT["query_body"]
+            body = block_new.find_all(param_to_find[0],
+                                      attrs=param_to_find[1])
+            print(body)
+
+            body_string = get_rid_off_key(body[0].contents)
+            extracted_from_block = {"time": time,
+                                    "headline": headline,
+                                    "body": body_string}
+            # for debug :
+            # print("\t".join(extracted_from_block))
+            if len(body_string) >= 5:
+                result.append(extracted_from_block)
+    except Exception as inst:
+        print("Something whenwrong :)", inst)
+        print("ULR: ", url)
+        result = []
+    return(result)
+
+
+def Query(key, max_page=5000):
+    # Init page and looping until return None
+    page = 1
+    result = "not None"
+    all_result_query = []
+    error = 0
+    while True and page < max_page:
+        print("Colected: %d articles" % len(all_result_query))
+        new_url = parser_url(key, page)
+        result = extract_from_url(new_url)
+        if len(result) > 0 or error > 10:
+            page += 1
+            error = 0
+        else:
+            error += 1
+
+        if result is not None:
+            all_result_query += result
+        else:
+            break
+    return(all_result_query)
+
+
+if __name__ == "__main__":
+    print("Begin query information about: ", sys.argv[1])
+    print("Then will save result in: ", sys.argv[2])
+
+    News = Query(sys.argv[1], int(sys.argv[4]))
+    file_name1 = sys.argv[2]
+
+    with open(file_name1, "w") as W:
+        json.dump(News, W, indent=1)
+
+    file_name2 = sys.argv[3]
+    with open(file_name2, "w") as W:
+        W.write("\n".join([new["body"] for new in News]))

+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""
+Created on Mon Mar 20 17:52:11 2017
+
+@author: red-sky
+"""
+import sys
+import json
+import numpy as np
+
+
+def updateDict(words, dictUp):
+    # update word dictionary with given "words" and the dict "dictUp"
+    for w in words:
+        if w in dictUp:
+            dictUp[w] += 1
+        else:
+            dictUp[w] = 0
+    return dictUp
+
+def extractVocab(eventsFile, fromIndex=0, toIndex="END"):
+    # from Events file, extract infor about words and create a mapping
+    vocab = dict()
+    with open(eventsFile, "r") as file:
+        list_events = file.read().strip().splitlines()
+        if toIndex == -1:
+            list_events = list_events[fromIndex:]
+        else:
+            list_events = sorted(set(list_events[fromIndex:toIndex]))
+    for i, event in enumerate(list_events):
+        if event[0] != "\t":
+            index = i
+            break
+    list_events = list_events[index:]
+    for event in list_events:
+        event = event.split("\t")
+        words = event[1].split(" ") + \
+            event[2].split(" ") + \
+            event[3].split(" ")
+        vocab = updateDict(words, vocab)
+    vocab_words = vocab.keys()
+    support_words = ["NOISEWORDS"]
+    vocab_words = support_words + \
+        sorted(vocab_words, key=lambda x: vocab[x], reverse=True)
+    IndexWords = range(len(vocab_words))
+    Count = ["NOISEWORDS"] + [vocab[w] for w in vocab_words[1:]]
+    result = [dict(zip(vocab_words, Count)),
+              dict(zip(IndexWords, vocab_words)),
+              dict(zip(vocab_words, IndexWords))]
+    return result, list_events
+
+
+def convertEvent(eventsFile, vocabMapping, countMin=5):
+    # convert all Events to index for training
+    wordCount, _, word2index = vocabMapping
+    Events = []
+    with open(eventsFile, "r") as file:
+        list_events = file.read().strip().splitlines()
+
+    for event in list_events:
+        event = event.split("\t")
+        list_obj = [event[1].split(" "),
+                    event[2].split(" "),
+                    event[3].split(" ")]
+
+        # Covert only words that appear more than countMin
+        wordsIndexed = []
+        for obj in list_obj:
+            objIndex = []
+            for w in obj:
+                if wordCount[w] >= countMin:
+                    objIndex.append(word2index[w])
+                else:
+                    objIndex.append(0)
+            wordsIndexed.append(objIndex)
+        Events.append(wordsIndexed)
+    return Events
+
+
+if __name__ == "__main__":
+    # in
+    EventPath = "../../Thesis_data/Apple_query_result_body.txt"
+    fromIndex = 0
+    toIndex = -1
+    minCountWord = 5
+    # out
+    EventNewPath = "./Events_for_training.txt"
+    VocabPath = "./Vocab_in_events_for_training.json"
+    IndexdEventPath = "./IndexedEvents_for_training.npy"
+
+    vocabMapping, EventNew = extractVocab(EventPath, fromIndex, toIndex)
+    with open(VocabPath, "w") as W:
+        json.dump(vocabMapping, W, indent=2)
+
+    with open(EventNewPath, "w") as W:
+        W.write("\n".join(EventNew))
+
+    indexed_events = convertEvent(EventNewPath, vocabMapping, minCountWord)
+    np.save(arr=np.array(indexed_events), file=IndexdEventPath)

+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""
+Created on Mon Mar 20 11:58:54 2017
+
+@author: red-sky
+"""
+
+import sys
+import json
+
+
+def findDate(news_body, list_news):
+    date = ""
+    for ind, new in enumerate(list_news):
+        if news_body in new["body"]:
+            date = new["time"]
+            break
+    return date
+
+
+def extractAllDate(list_events, list_news, choosedInfor=[1, 2, 3, 0, 6]):
+    list_result = []
+    N = len(list_events)
+    i = 0.0
+    for event in list_events:
+        i += 1
+        if i % 1000 == 0:
+            print("Done %f percents" % (i/N*100))
+        date = [findDate(event[6], list_news)]
+        infor = date + [event[i] for i in choosedInfor]
+        list_result.append(infor)
+    return list_result
+
+if __name__ == "__main__":
+    events = open(sys.argv[1], "r").read().strip().splitlines()
+    events = [event.split("\t") for event in events
+              if len(event.split("\t")) > 5]
+    news = json.load(open(sys.argv[2], "r"))
+    result = extractAllDate(events, news)
+
+    with open(sys.argv[3], "w") as W:
+        for line in result[1:]:
+            W.write("\t".join(line)+"\n")

+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""
+Created on Thu Apr 13 16:57:11 2017
+
+@author: red-sky
+"""
+import sys
+import numpy as np
+import pickle
+import pandas as pd
+
+
+def main(VectorsPath, EventPath, StockPricePath, days):
+
+    with open(VectorsPath, "rb") as H:
+        Vec = pickle.load(H)
+        Vectors = np.array([list(b[0]) for a, b in Vec.values()])
+#    Vectors = np.load(VectorsPath)
+    with open(EventPath, "r") as H:
+        F = np.array([a.split("\t")[0:4] for a in H.read().splitlines()])
+
+    D = {}
+    for date, vec in zip(F[:, 0], Vectors):
+        if date[:10] in D:
+            D[date[:10]].append(vec)
+        else:
+            D[date[:10]] = [vec]
+
+    D2 = {}
+    for date in sorted(D.keys()):
+        D2[date] = np.mean(D[date], 0)
+
+    Dates = np.array(sorted(D2.keys()))
+    SampleIndex = [list(range(i-days, i)) for i in range(5, len(Dates))]
+    DataX = []
+    DateX = []
+    for listIndex in SampleIndex:
+        DataX.append([D2[date] for date in Dates[listIndex]])
+        DateX.append(Dates[listIndex[-1]])
+
+    Df = pd.read_csv(StockPricePath)
+    LabelY = []
+    DataX_yesData = []
+    for i, date in enumerate(DateX):
+        retu = list(Df.loc[Df["Date"] == date]["ReturnOpen"])
+        print(retu)
+        if len(retu) > 0:
+            retu = float(retu[0])*100
+            if retu > 0:
+                LabelY.append([1, 0])
+            if retu < -0:
+                LabelY.append([0, 1])
+            if retu <= 0 and retu >= -0:
+                LabelY.append([0, 1])
+            DataX_yesData.append(list(DataX[i]))
+            print(date)
+#        else:
+
+    dataX = np.array(DataX_yesData)
+    dataY = np.array(LabelY)
+    print("DataX:", dataX.shape)
+    print("DataY:", dataY.shape, np.sum(dataY, 0) / np.sum(dataY))
+    return (dataX, dataY)
+
+if __name__ == "__main__":
+    VectorsPath = sys.argv[1]
+    EventPath = sys.argv[2]
+    StockPricePath = sys.argv[3]
+    days = int(sys.argv[5])
+    DataX, LabelY = main(VectorsPath, EventPath, StockPricePath, days)
+    DataPath = sys.argv[4]
+    np.save(arr=DataX, file=DataPath+"/DailyVector" + sys.argv[5] + ".npy")
+    np.save(arr=LabelY, file=DataPath+"/DailyReturn" + sys.argv[5] + ".npy")