2017-7-31 实验数据收集：长文章svm.ipynb

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   "metadata": {
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Reading english Data: 63386\n",
      "Reading english Data: 63386\n",
      "Merging the English and Japanes news dataframe...\n",
      "Drop the null line...\n",
      "False    63340\n",
      "True         3\n",
      "Name: en_article, dtype: int64\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "base_w2v_bow_svm.py:257: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  df_pairs_sample['word2vec_en'] = df_pairs_sample['en_article'].apply(doc2vec_en)\n",
      "base_w2v_bow_svm.py:258: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  df_pairs_sample['word2vec_jp'] = df_pairs_sample['jp_article'].apply(doc2vec_jp)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n",
      "1000\n",
      "2000\n",
      "3000\n",
      "4000\n",
      "0\n",
      "1000\n",
      "2000\n",
      "3000\n",
      "4000\n",
      "C value = 1\n",
      "classification report of TRAINING data:\n",
      "             precision    recall  f1-score   support\n",
      "\n",
      "        0.0       0.52      0.14      0.22      4000\n",
      "        1.0       0.50      0.87      0.64      4000\n",
      "\n",
      "avg / total       0.51      0.51      0.43      8000\n",
      "\n",
      "classification report of TEST data:\n",
      "             precision    recall  f1-score   support\n",
      "\n",
      "        0.0       0.00      0.00      0.00         0\n",
      "        1.0       1.00      0.79      0.88      1000\n",
      "\n",
      "avg / total       1.00      0.79      0.88      1000\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Process Process-11:\n",
      "Traceback (most recent call last):\n",
      "  File \"/home/M2015eliu/.pyenv/versions/anaconda2-4.1.1/lib/python2.7/multiprocessing/process.py\", line 258, in _bootstrap\n",
      "    self.run()\n",
      "  File \"/home/M2015eliu/.pyenv/versions/anaconda2-4.1.1/lib/python2.7/multiprocessing/process.py\", line 114, in run\n",
      "    self._target(*self._args, **self._kwargs)\n",
      "  File \"/home/M2015eliu/cas/2017.1.1~LiuSTM/base_w2v_bow_svm.py\", line 90, in find_ranking_quick\n",
      "    sys.stdout.write(\"Doc.\" + str(n * step + i) + \": \" + str(rank) + \"||\")\n",
      "NameError: global name 'sys' is not defined\n",
      "Process Process-13:\n",
      "Traceback (most recent call last):\n",
      "  File \"/home/M2015eliu/.pyenv/versions/anaconda2-4.1.1/lib/python2.7/multiprocessing/process.py\", line 258, in _bootstrap\n",
      "    self.run()\n",
      "  File \"/home/M2015eliu/.pyenv/versions/anaconda2-4.1.1/lib/python2.7/multiprocessing/process.py\", line 114, in run\n",
      "    self._target(*self._args, **self._kwargs)\n",
      "  File \"/home/M2015eliu/cas/2017.1.1~LiuSTM/base_w2v_bow_svm.py\", line 90, in find_ranking_quick\n",
      "    sys.stdout.write(\"Doc.\" + str(n * step + i) + \": \" + str(rank) + \"||\")\n",
      "NameError: global name 'sys' is not defined\n",
      "Process Process-18:\n",
      "Traceback (most recent call last):\n",
      "  File \"/home/M2015eliu/.pyenv/versions/anaconda2-4.1.1/lib/python2.7/multiprocessing/process.py\", line 258, in _bootstrap\n",
      "    self.run()\n",
      "  File \"/home/M2015eliu/.pyenv/versions/anaconda2-4.1.1/lib/python2.7/multiprocessing/process.py\", line 114, in run\n",
      "    self._target(*self._args, **self._kwargs)\n",
      "  File \"/home/M2015eliu/cas/2017.1.1~LiuSTM/base_w2v_bow_svm.py\", line 90, in find_ranking_quick\n",
      "    sys.stdout.write(\"Doc.\" + str(n * step + i) + \": \" + str(rank) + \"||\")\n",
      "NameError: global name 'sys' is not defined\n",
      "Process Process-12:\n",
      "Traceback (most recent call last):\n",
      "  File \"/home/M2015eliu/.pyenv/versions/anaconda2-4.1.1/lib/python2.7/multiprocessing/process.py\", line 258, in _bootstrap\n",
      "    self.run()\n",
      "  File \"/home/M2015eliu/.pyenv/versions/anaconda2-4.1.1/lib/python2.7/multiprocessing/process.py\", line 114, in run\n",
      "    self._target(*self._args, **self._kwargs)\n",
      "  File \"/home/M2015eliu/cas/2017.1.1~LiuSTM/base_w2v_bow_svm.py\", line 90, in find_ranking_quick\n",
      "    sys.stdout.write(\"Doc.\" + str(n * step + i) + \": \" + str(rank) + \"||\")\n",
      "NameError: global name 'sys' is not defined\n",
      "Process Process-15:\n",
      "Traceback (most recent call last):\n",
      "  File \"/home/M2015eliu/.pyenv/versions/anaconda2-4.1.1/lib/python2.7/multiprocessing/process.py\", line 258, in _bootstrap\n",
      "    self.run()\n",
      "  File \"/home/M2015eliu/.pyenv/versions/anaconda2-4.1.1/lib/python2.7/multiprocessing/process.py\", line 114, in run\n",
      "    self._target(*self._args, **self._kwargs)\n",
      "  File \"/home/M2015eliu/cas/2017.1.1~LiuSTM/base_w2v_bow_svm.py\", line 90, in find_ranking_quick\n",
      "    sys.stdout.write(\"Doc.\" + str(n * step + i) + \": \" + str(rank) + \"||\")\n",
      "NameError: global name 'sys' is not defined\n",
      "Process Process-17:\n",
      "Traceback (most recent call last):\n",
      "  File \"/home/M2015eliu/.pyenv/versions/anaconda2-4.1.1/lib/python2.7/multiprocessing/process.py\", line 258, in _bootstrap\n",
      "    self.run()\n",
      "  File \"/home/M2015eliu/.pyenv/versions/anaconda2-4.1.1/lib/python2.7/multiprocessing/process.py\", line 114, in run\n",
      "    self._target(*self._args, **self._kwargs)\n",
      "  File \"/home/M2015eliu/cas/2017.1.1~LiuSTM/base_w2v_bow_svm.py\", line 90, in find_ranking_quick\n",
      "    sys.stdout.write(\"Doc.\" + str(n * step + i) + \": \" + str(rank) + \"||\")\n",
      "NameError: global name 'sys' is not defined\n",
      "Process Process-14:\n",
      "Traceback (most recent call last):\n",
      "  File \"/home/M2015eliu/.pyenv/versions/anaconda2-4.1.1/lib/python2.7/multiprocessing/process.py\", line 258, in _bootstrap\n",
      "    self.run()\n",
      "  File \"/home/M2015eliu/.pyenv/versions/anaconda2-4.1.1/lib/python2.7/multiprocessing/process.py\", line 114, in run\n",
      "    self._target(*self._args, **self._kwargs)\n",
      "  File \"/home/M2015eliu/cas/2017.1.1~LiuSTM/base_w2v_bow_svm.py\", line 90, in find_ranking_quick\n",
      "    sys.stdout.write(\"Doc.\" + str(n * step + i) + \": \" + str(rank) + \"||\")\n",
      "NameError: global name 'sys' is not defined\n",
      "Process Process-16:\n",
      "Traceback (most recent call last):\n",
      "  File \"/home/M2015eliu/.pyenv/versions/anaconda2-4.1.1/lib/python2.7/multiprocessing/process.py\", line 258, in _bootstrap\n",
      "    self.run()\n",
      "  File \"/home/M2015eliu/.pyenv/versions/anaconda2-4.1.1/lib/python2.7/multiprocessing/process.py\", line 114, in run\n",
      "    self._target(*self._args, **self._kwargs)\n",
      "  File \"/home/M2015eliu/cas/2017.1.1~LiuSTM/base_w2v_bow_svm.py\", line 90, in find_ranking_quick\n",
      "    sys.stdout.write(\"Doc.\" + str(n * step + i) + \": \" + str(rank) + \"||\")\n",
      "NameError: global name 'sys' is not defined\n",
      "Process Process-19:\n",
      "Traceback (most recent call last):\n",
      "  File \"/home/M2015eliu/.pyenv/versions/anaconda2-4.1.1/lib/python2.7/multiprocessing/process.py\", line 258, in _bootstrap\n",
      "    self.run()\n",
      "  File \"/home/M2015eliu/.pyenv/versions/anaconda2-4.1.1/lib/python2.7/multiprocessing/process.py\", line 114, in run\n",
      "    self._target(*self._args, **self._kwargs)\n",
      "  File \"/home/M2015eliu/cas/2017.1.1~LiuSTM/base_w2v_bow_svm.py\", line 90, in find_ranking_quick\n",
      "    sys.stdout.write(\"Doc.\" + str(n * step + i) + \": \" + str(rank) + \"||\")\n",
      "NameError: global name 'sys' is not defined\n",
      "Process Process-20:\n",
      "Traceback (most recent call last):\n",
      "  File \"/home/M2015eliu/.pyenv/versions/anaconda2-4.1.1/lib/python2.7/multiprocessing/process.py\", line 258, in _bootstrap\n",
      "    self.run()\n",
      "  File \"/home/M2015eliu/.pyenv/versions/anaconda2-4.1.1/lib/python2.7/multiprocessing/process.py\", line 114, in run\n",
      "    self._target(*self._args, **self._kwargs)\n",
      "  File \"/home/M2015eliu/cas/2017.1.1~LiuSTM/base_w2v_bow_svm.py\", line 90, in find_ranking_quick\n",
      "    sys.stdout.write(\"Doc.\" + str(n * step + i) + \": \" + str(rank) + \"||\")\n",
      "NameError: global name 'sys' is not defined\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5.57616305351\n",
      "count    0.0\n",
      "mean     NaN\n",
      "std      NaN\n",
      "min      NaN\n",
      "25%      NaN\n",
      "50%      NaN\n",
      "75%      NaN\n",
      "max      NaN\n",
      "dtype: float64\n",
      "('TOP1', 0)\n",
      "('TOP5', 0)\n",
      "('TOP10', 0)\n"
     ]
    }
   ],
   "source": [
    "run base_w2v_bow_svm.py"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Reading english Data: 63386\n",
      "Reading english Data: 63386\n",
      "Merging the English and Japanes news dataframe...\n",
      "Drop the null line...\n",
      "False    63340\n",
      "True         3\n",
      "Name: en_article, dtype: int64\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/M2015eliu/.pyenv/versions/anaconda2-4.1.1/lib/python2.7/site-packages/ipykernel/__main__.py:194: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "/home/M2015eliu/.pyenv/versions/anaconda2-4.1.1/lib/python2.7/site-packages/ipykernel/__main__.py:195: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n",
      "1000\n",
      "2000\n",
      "3000\n",
      "4000\n",
      "0\n",
      "1000\n",
      "2000\n",
      "3000\n",
      "4000\n",
      "C value = 5\n",
      "classification report of TRAINING data:\n",
      "             precision    recall  f1-score   support\n",
      "\n",
      "        0.0       0.44      0.02      0.04      4000\n",
      "        1.0       0.50      0.97      0.66      4000\n",
      "\n",
      "avg / total       0.47      0.50      0.35      8000\n",
      "\n",
      "classification report of TEST data:\n",
      "             precision    recall  f1-score   support\n",
      "\n",
      "        0.0       0.00      0.00      0.00         0\n",
      "        1.0       1.00      0.96      0.98      1000\n",
      "\n",
      "avg / total       1.00      0.96      0.98      1000\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/M2015eliu/.pyenv/versions/anaconda2-4.1.1/lib/python2.7/site-packages/ipykernel/__main__.py:302: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "/home/M2015eliu/.pyenv/versions/anaconda2-4.1.1/lib/python2.7/site-packages/ipykernel/__main__.py:303: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n",
      "0\n",
      "classification report of TRAINING data:\n",
      "             precision    recall  f1-score   support\n",
      "\n",
      "        0.0       0.38      0.02      0.04      1000\n",
      "        1.0       0.50      0.97      0.66      1000\n",
      "\n",
      "avg / total       0.44      0.49      0.35      2000\n",
      "\n",
      "Find answer for doc. 0\n",
      "Find answer for doc. 1\n",
      "Find answer for doc. 2\n",
      "Find answer for doc. 3\n",
      "Find answer for doc. 4\n",
      "Find answer for doc. 5\n",
      "Find answer for doc. 6\n",
      "Find answer for doc. 7\n",
      "Find answer for doc. 8\n",
      "Find answer for doc. 9\n",
      "Find answer for doc. 10\n",
      "Find answer for doc. 11\n",
      "Find answer for doc. 12\n",
      "Find answer for doc. 13\n",
      "Find answer for doc. 14\n",
      "Find answer for doc. 15\n",
      "Find answer for doc. 16\n",
      "Find answer for doc. 17\n",
      "Find answer for doc. 18\n",
      "Find answer for doc. 19\n",
      "Find answer for doc. 20\n",
      "Find answer for doc. 21\n"
     ]
    }
   ],
   "source": [
    "# coding: utf-8\n",
    "\n",
    "\"\"\"\n",
    "created on 2017/05/11\n",
    "@author: liuenda\n",
    "\"\"\"\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "from sklearn import cross_validation\n",
    "from sklearn.metrics import classification_report\n",
    "from gensim import corpora, models, similarities\n",
    "from sklearn import datasets\n",
    "from sklearn import linear_model\n",
    "from sklearn import svm\n",
    "from gensim.models import word2vec\n",
    "from sklearn import preprocessing\n",
    "import random\n",
    "import pickle\n",
    "import time\n",
    "\n",
    "\n",
    "model_name_en = \"./data/model-en/W2Vmodle.bin\"\n",
    "model_name_jp = \"./data/model-jp/W2Vmodle.bin\"\n",
    "\n",
    "model_en = word2vec.Word2Vec.load(model_name_en)\n",
    "model_jp = word2vec.Word2Vec.load(model_name_jp)\n",
    "\n",
    "\n",
    "maxlen = 0 # Default: 0 -> infinite\n",
    "epoch = 50\n",
    "random.seed(1234)\n",
    "\n",
    "\n",
    "\"\"\"\n",
    "Find the ranking results with respect to real pairs\n",
    "Defaulty, projection1 should be JP\n",
    "Whiile, projection2 should be EN->JP\n",
    "\"\"\"\n",
    "def find_ranking(projection1, projection2, clf):\n",
    "\tsim_results = []\n",
    "\trank_results = []\n",
    "\n",
    "\t# Iterate each of the ariticle from projection1 (999) as proj1\n",
    "\t# Calculate the simialrity of proj1 with all ariticles in projection2 (999)\n",
    "\tfor i, proj1 in enumerate(projection1):\n",
    "\t\tprint \"Find answer for doc.\", i\n",
    "\t\tproj1_tile = np.tile(proj1, (len(projection2),1))\n",
    "\t\tfeatures_test = np.concatenate((proj1_tile, projection2), axis=1)\n",
    "\t\tsim = clf.predict_proba(features_test)[:,1]\n",
    "\t\trank = pd.Series(sim).rank(ascending = False)[i]\n",
    "\t\tsim_results.append(sim)\n",
    "\t\trank_results.append(rank)\n",
    "\n",
    "\t# sim_results contains 999*999 similairty matrix\n",
    "\treturn sim_results, rank_results\n",
    "\n",
    "\"\"\"\n",
    "rank_results should be list of (999,)\n",
    "\"\"\"\n",
    "def find_top(rank_results, top):\n",
    "\ts = pd.Series(rank_results)\n",
    "\tn_top = (s <= top).sum()\n",
    "\treturn n_top\n",
    "\n",
    "\n",
    "def average_docment(document_embedding):\n",
    "\treturn np.average(document_embedding, axis=0)\n",
    "\n",
    "def sum_docment(document_embedding):\n",
    "\treturn np.sum(document_embedding, axis=0)\n",
    "\n",
    "def doc2feature(corpus, tfidf, dictionary, w2v):\n",
    "    doc_features = []\n",
    "    for index, doc_bof in enumerate(corpus):\n",
    "\n",
    "        if index % 1000 == 0:\n",
    "            print(index)\n",
    "\n",
    "        doc_tfidf = tfidf[doc_bof]\n",
    "\n",
    "        doc_feature = np.zeros((200,))\n",
    "\n",
    "        for (token_id, token_tfidf) in doc_tfidf:\n",
    "            token = dictionary.get(token_id, \"[unknown-id]\").encode(\"utf-8\")\n",
    "            # if token in w2v:\n",
    "            if True:\n",
    "                token_w2v = w2v[token]\n",
    "            else:\n",
    "                print(\"No word:\", token)\n",
    "                continue\n",
    "            doc_feature += token_w2v * token_tfidf\n",
    "\n",
    "        average = True\n",
    "        if average:\n",
    "            doc_feature = np.true_divide(doc_feature, len(doc_tfidf))\n",
    "        doc_features.append(doc_feature)\n",
    "\n",
    "    return doc_features\n",
    "\n",
    "def doc2vec_en(doc):\n",
    "\tr = [model_en[token] for token in doc.split()]\n",
    "\treturn r\n",
    "\n",
    "def doc2vec_jp(doc):\n",
    "\tr = [model_jp[token] for token in doc.split()]\n",
    "\treturn r\n",
    "\n",
    "\n",
    "def prepare_train(dir_en, dir_jp):\n",
    "\n",
    "\tdf_en_mapping = pd.read_csv(dir_en)\n",
    "\tdf_jp_mapping = pd.read_csv(dir_jp)\n",
    "\n",
    "\tprint \"Reading english Data:\", len(df_en_mapping)\n",
    "\tprint \"Reading english Data:\", len(df_jp_mapping)\n",
    "\n",
    "\tsample_size = len(df_en_mapping)\n",
    "\n",
    "\tassert len(df_en_mapping) == len(df_jp_mapping)\n",
    "\n",
    "\t# Convert mapping to list type and then concat to the a list\n",
    "\tprint \"Merging the English and Japanes news dataframe...\"\n",
    "\tdf_train_1 = pd.concat([df_en_mapping, df_jp_mapping], axis = 1)\n",
    "\tdf_train_1['similarity'] = pd.Series(np.ones(sample_size,)*5)\n",
    "\tdf_train_1['dis_similarity'] = pd.Series(np.ones(sample_size,)*1)\n",
    "\n",
    "\t# Remove null line\n",
    "\tprint \"Drop the null line...\"\n",
    "\t# df_train_1 = df_train_1.dropna(subset=['en_article'])\n",
    "\tdf_train_1 = df_train_1[df_train_1['en_article'] != '<NULL>']\n",
    "\n",
    "\t# Expand the training data\n",
    "\ten_article_wrong = df_train_1.en_article.iloc[random.sample(xrange(len(df_train_1)),len(df_train_1))]\n",
    "\ten_article_wrong.index = df_train_1.index\n",
    "\tprint (en_article_wrong == df_train_1.en_article).value_counts()\n",
    "\tdf_train_1['en_article_wrong'] = en_article_wrong\n",
    "\n",
    "\t# Convert dateframe to list\n",
    "\ttrain_1 = df_train_1[['en_article','jp_article','similarity']].values.tolist()\n",
    "\ttrain_2 = df_train_1[['en_article_wrong','jp_article','dis_similarity']].values.tolist()\n",
    "\n",
    "\treturn train_1, train_2, df_train_1\n",
    "\n",
    "if __name__ == \"__main__\":\n",
    "\n",
    "\n",
    "\tinput = 2\n",
    "\tk = 10\n",
    "\n",
    "\t# --- Prepare and Loading the training data --- #\n",
    "\n",
    "\tif input == 1:\n",
    "\t\t# Prepare For the training data\n",
    "\t\tsample_size = \"_1000\"\n",
    "\t\tdir_en = \"./data/mapping/en_mapped_\"+str(k) + sample_size + \".csv\"\n",
    "\t\tdir_jp = \"./data/mapping/jp_mapped_\" + str(k) + sample_size + \".csv\"\n",
    "\n",
    "\t\t# Prepare For the test data\n",
    "\t\tsample_size = \"_1k2k\"\n",
    "\t\tdir_en_test = \"./data/mapping/en_mapped_\"+str(k) + sample_size + \".csv\"\n",
    "\t\tdir_jp_test = \"./data/mapping/jp_mapped_\" + str(k) + sample_size + \".csv\"\n",
    "\n",
    "\t\ttrain_1, train_2, df_train_1 = \tprepare_train(dir_en, dir_jp)\n",
    "\t\ttest_1, test_2, df_test_1 = prepare_train(dir_en_test, dir_jp_test)\n",
    "\n",
    "\tif input == 2:\n",
    "\t\t# split_line = 5000\n",
    "\t\t# end_line = 6000\n",
    "\t\t# Prepare For the training data\n",
    "\t\tdir_en = \"./data/news/en_news.csv\"\n",
    "\t\tdir_jp = \"./data/news/jp_news.csv\"\n",
    "\n",
    "\t\tpairs_correct, pairs_wrong, df_pairs = prepare_train(dir_en, dir_jp)\n",
    "\t\ttrain_1 = pairs_correct[0:2000] + pairs_correct[3000:5000]\n",
    "\t\ttest_1 = pairs_correct[2000:3000]\n",
    "\n",
    "\t\ttrain_2 = pairs_wrong[0:2000] + pairs_wrong[3000:5000]\n",
    "\t# test_2 = pairs_wrong[split_line:end_line]\n",
    "\n",
    "\n",
    "\t# Expand the training data\n",
    "\ttrain = train_1 + train_2\n",
    "\n",
    "\n",
    "\t# --- Apply the word2vec model to the data sets --- #\n",
    "\n",
    "\tmodel_name_en = \"./data/model-en/W2Vmodle.bin\"\n",
    "\tmodel_name_jp = \"./data/model-jp/W2Vmodle.bin\"\n",
    "\n",
    "\tdf_pairs_sample = df_pairs.iloc[0:5000]\n",
    "\n",
    "\tdf_pairs_sample['word2vec_en'] = df_pairs_sample['en_article'].apply(doc2vec_en)\n",
    "\tdf_pairs_sample['word2vec_jp'] = df_pairs_sample['jp_article'].apply(doc2vec_jp)\n",
    "\n",
    "\n",
    "\t# Feature 1: TF-IDF + Average word2vec\n",
    "\n",
    "\t# --- Find tf-idf * word2vec features --- #\n",
    "\n",
    "    #  For English text:\n",
    "    # texts_en = [doc.split() for doc in list(df_pairs_sample[\"en_article\"])]\n",
    "\ttexts_en = [doc.split() for doc in list(df_pairs[\"en_article\"])]\n",
    "\tdictionary_en = corpora.Dictionary(texts_en)\n",
    "\tcorpus_en = [dictionary_en.doc2bow(text) for text in texts_en]\n",
    "\ttfidf_en = models.TfidfModel(corpus_en)\n",
    "\n",
    "\t#  For Japanese text:\n",
    "\t# texts_jp = [doc.split() for doc in list(df_pairs_sample[\"jp_article\"])]\n",
    "\ttexts_jp = [doc.split() for doc in list(df_pairs[\"jp_article\"])]\n",
    "\tdictionary_jp = corpora.Dictionary(texts_jp)\n",
    "\tcorpus_jp = [dictionary_jp.doc2bow(text) for text in texts_jp]\n",
    "\ttfidf_jp = models.TfidfModel(corpus_jp)\n",
    "\n",
    "\tfeatures_en = doc2feature(corpus_en[:5000], tfidf_en, dictionary_en, model_en)\n",
    "\tfeatures_jp = doc2feature(corpus_jp[:5000], tfidf_jp, dictionary_jp, model_jp)\n",
    "\n",
    "\t# --- When do not apply the tfidf re-weighting --- #\n",
    "\tflag_NO_tfidf = False\n",
    "\tif flag_NO_tfidf:\n",
    "\n",
    "\t\tdf_pairs_sample[\"sum_vector_en\"] = df_pairs_sample['word2vec_en'].apply(sum_docment)\n",
    "\t\tdf_pairs_sample[\"sum_vector_jp\"] = df_pairs_sample['word2vec_jp'].apply(sum_docment)\n",
    "\t\tdf_pairs_sample[\"average_vector_en\"] = df_pairs_sample['word2vec_en'].apply(average_docment)\n",
    "\t\tdf_pairs_sample[\"average_vector_jp\"] = df_pairs_sample['word2vec_jp'].apply(average_docment)\n",
    "\n",
    "\t\tfeatures_en = list(df_pairs_sample[\"average_vector_en\"])\n",
    "\t\tfeatures_jp = list(df_pairs_sample[\"average_vector_jp\"])\n",
    "\n",
    "\n",
    "\tfeatures_merge = np.concatenate((features_en,features_jp), axis = 1)\n",
    "\n",
    "\t# --- Expanding the training data (dissimilar paris)\n",
    "\tfeatures_en_wrong = np.array(features_en)\n",
    "\tnp.random.shuffle((features_en_wrong))\n",
    "\tc = np.all(features_en_wrong == features_en, axis=1)\n",
    "\tprint \"C value =\", c.sum() # check the duplicated amount\n",
    "\n",
    "\tfeatures_merge_wrong = np.concatenate((features_en_wrong,features_jp), axis = 1)\n",
    "\n",
    "\t# --- Prepare the final training and test data --- #\n",
    "\n",
    "\tX = np.concatenate((features_merge, features_merge_wrong), axis = 0)\n",
    "\ty = np.concatenate((np.ones(len(features_merge)), np.zeros(len(features_en_wrong))), axis = 0)\n",
    "\n",
    "\t# --- Split into test data and training data --- #\n",
    "\n",
    "\tX_train1, X_test, X_train2, X_train3_wrong, X_o = np.split(X, [2000, 3000, 5000, 9000])\n",
    "\ty_train1, y_test, y_train2, y_train3_wrong, Y_o = np.split(y, [2000, 3000, 9000, 9000])\n",
    "\n",
    "\tX_train = np.concatenate((X_train1, X_train2, X_train3_wrong), axis = 0)\n",
    "\ty_train = np.concatenate((y_train1, y_train2, y_train3_wrong), axis = 0)\n",
    "\tX_train_correct = np.concatenate((X_train1, X_train2), axis = 0)\n",
    "\ty_train_correct = np.concatenate((y_train1, y_train2), axis = 0)\n",
    "\n",
    "\t# --- SVM Training --- #\n",
    "\n",
    "\tclf = svm.SVC(kernel=\"rbf\", gamma=0.001, C=100, probability=True)\n",
    "\n",
    "\t# 在使用\n",
    "\tstanderlization = 0\n",
    "\tif standerlization == 1:\n",
    "\t\tscaler = preprocessing.StandardScaler().fit(X_train)\n",
    "\t\tX_scaled = scaler.transform(X_train)\n",
    "\t\tX_test_scaled = scaler.transform(X_test)\n",
    "\t\tclf.fit(X_scaled, y_train)\n",
    "\n",
    "\t\ty_test_predict = clf.predict(X_test_scaled)\n",
    "\t\ty_train_predict = clf.predict(X_scaled)\n",
    "\n",
    "\tif standerlization == 2:\n",
    "\t\tmin_max_scaler = preprocessing.MinMaxScaler()\n",
    "\t\tX_scaled = min_max_scaler.fit_transform(X_train)\n",
    "\t\tX_test_scaled = min_max_scaler.transform(X_test)\n",
    "\t\tclf.fit(X_scaled, y_train)\n",
    "\n",
    "\t\ty_test_predict = clf.predict(X_test_scaled)\n",
    "\t\ty_train_predict = clf.predict(X_scaled)\n",
    "\telse:\n",
    "\t\tclf.fit(X_train, y_train)\n",
    "\t\t# clf.score(X_train, y_train)\n",
    "\t\t# clf.score(X_test, y_test)\n",
    "\t\ty_test_predict = clf.predict(X_test)\n",
    "\t\ty_train_predict = clf.predict(X_train)\n",
    "\n",
    "\n",
    "\n",
    "\tprint \"classification report of TRAINING data:\"\n",
    "\tprint(classification_report(y_train, y_train_predict))\n",
    "\n",
    "\tprint \"classification report of TEST data:\"\n",
    "\tprint(classification_report(y_test, y_test_predict))\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\t# --- Prepare for a new independent evaluation balanced data --- #\n",
    "\n",
    "\tdf_pairs_evaluate = df_pairs.iloc[55000:60000]\n",
    "\n",
    "\tdf_pairs_evaluate['word2vec_en'] = df_pairs_evaluate['en_article'].apply(doc2vec_en)\n",
    "\tdf_pairs_evaluate['word2vec_jp'] = df_pairs_evaluate['jp_article'].apply(doc2vec_jp)\n",
    "\n",
    "\tfeatures_en_eva = doc2feature(corpus_en[60000:61000], tfidf_en, dictionary_en, model_en)\n",
    "\tfeatures_jp_eva = doc2feature(corpus_jp[60000:61000], tfidf_jp, dictionary_jp, model_jp)\n",
    "\n",
    "\tfeatures_merge_eva = np.concatenate((features_en_eva,features_jp_eva), axis = 1)\n",
    "\n",
    "\tfeatures_en_wrong_eva =  features_en[:1000]\n",
    "\t# features_en_wrong_eva = np.array(features_en_eva)\n",
    "\t# np.random.shuffle((features_en_wrong_eva))\n",
    "\t# c = np.all(features_en_wrong_eva == features_en_eva, axis=1)\n",
    "\t# print \"C value =\", c.sum() # check the duplicated amount\n",
    "\n",
    "\tfeatures_merge_wrong = np.concatenate((features_en_wrong_eva,features_jp_eva), axis = 1)\n",
    "\n",
    "\tX_eva = np.concatenate((features_merge_eva, features_merge_wrong), axis = 0)\n",
    "\ty_eva = np.concatenate((np.ones(len(features_merge_eva)), np.zeros(len(features_en_wrong_eva))), axis = 0)\n",
    "\n",
    "\ty_eva_predict = clf.predict(X_eva)\n",
    "\n",
    "\tprint \"classification report of TRAINING data:\"\n",
    "\tprint(classification_report(y_eva, y_eva_predict))\n",
    "\n",
    "\n",
    "\t# --- Evaluation for SVM --- #\n",
    "\n",
    "\ty_test_proba = clf.predict_proba(X_test)\n",
    "\ty_train_proba = clf.predict_proba(X_train)\n",
    "\n",
    "\t# sim_results_train, rank_results_train = find_ranking(projection1_train, projection2_train)\n",
    "\tsim_results_test, rank_results_test = find_ranking(X_test[:,:200] ,X_test[:,200:], clf)\n",
    "\n",
    "\n",
    "\tprint pd.Series(rank_results_test).describe()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Reading english Data: 63386\n",
      "Reading english Data: 63386\n",
      "Merging the English and Japanes news dataframe...\n",
      "Drop the null line...\n",
      "False    63340\n",
      "True         3\n",
      "Name: en_article, dtype: int64\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/M2015eliu/.pyenv/versions/anaconda2-4.1.1/lib/python2.7/site-packages/ipykernel/__main__.py:184: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "/home/M2015eliu/.pyenv/versions/anaconda2-4.1.1/lib/python2.7/site-packages/ipykernel/__main__.py:185: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n",
      "1000\n",
      "2000\n",
      "3000\n",
      "4000\n",
      "0\n",
      "1000\n",
      "2000\n",
      "3000\n",
      "4000\n",
      "C value = 5\n",
      "classification report of TRAINING data:\n",
      "             precision    recall  f1-score   support\n",
      "\n",
      "        0.0       0.96      1.00      0.98      4819\n",
      "        1.0       1.00      0.96      0.98      4181\n",
      "\n",
      "avg / total       0.98      0.98      0.98      9000\n",
      "\n",
      "classification report of TEST data:\n",
      "             precision    recall  f1-score   support\n",
      "\n",
      "        0.0       0.00      0.00      0.00        57\n",
      "        1.0       0.94      1.00      0.97       943\n",
      "\n",
      "avg / total       0.89      0.94      0.92      1000\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/M2015eliu/.pyenv/versions/anaconda2-4.1.1/lib/python2.7/site-packages/sklearn/metrics/classification.py:1113: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Find answer for doc. 0\n",
      "Find answer for doc. 1\n",
      "Find answer for doc. 2\n",
      "Find answer for doc. 3\n",
      "Find answer for doc. 4\n",
      "Find answer for doc. 5\n",
      "Find answer for doc. 6\n",
      "Find answer for doc. 7\n",
      "Find answer for doc. 8\n",
      "Find answer for doc. 9\n",
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      "Find answer for doc. 12\n",
      "Find answer for doc. 13\n",
      "Find answer for doc. 14\n",
      "Find answer for doc. 15\n",
      "Find answer for doc. 16\n",
      "Find answer for doc. 17\n",
      "Find answer for doc. 18\n",
      "Find answer for doc. 19\n",
      "Find answer for doc. 20\n",
      "Find answer for doc. 21\n",
      "Find answer for doc. 22\n",
      "Find answer for doc. 23\n",
      "Find answer for doc. 24\n",
      "Find answer for doc. 25\n",
      "Find answer for doc. 26\n",
      "Find answer for doc. 27\n",
      "Find answer for doc. 28\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0mTraceback (most recent call last)",
      "\u001b[0;32m<ipython-input-8-f781f30b9aa3>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m    252\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    253\u001b[0m         \u001b[0;31m# sim_results_train, rank_results_train = find_ranking(projection1_train, projection2_train)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 254\u001b[0;31m         \u001b[0msim_results_test\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrank_results_test\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfind_ranking\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_test\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;36m200\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m,\u001b[0m\u001b[0mX_test\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m200\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mclf\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m<ipython-input-8-f781f30b9aa3>\u001b[0m in \u001b[0;36mfind_ranking\u001b[0;34m(projection1, projection2, clf)\u001b[0m\n\u001b[1;32m     49\u001b[0m                 \u001b[0mproj1_tile\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtile\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mproj1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mprojection2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     50\u001b[0m                 \u001b[0mfeatures_test\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconcatenate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mproj1_tile\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mprojection2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 51\u001b[0;31m                 \u001b[0msim\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mclf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpredict_proba\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfeatures_test\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     52\u001b[0m                 \u001b[0mrank\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mSeries\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msim\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrank\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mascending\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     53\u001b[0m                 \u001b[0msim_results\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msim\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/home/M2015eliu/.pyenv/versions/anaconda2-4.1.1/lib/python2.7/site-packages/sklearn/svm/base.pyc\u001b[0m in \u001b[0;36m_predict_proba\u001b[0;34m(self, X)\u001b[0m\n\u001b[1;32m    623\u001b[0m         pred_proba = (self._sparse_predict_proba\n\u001b[1;32m    624\u001b[0m                       if self._sparse else self._dense_predict_proba)\n\u001b[0;32m--> 625\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mpred_proba\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    626\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    627\u001b[0m     \u001b[0;34m@\u001b[0m\u001b[0mproperty\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/home/M2015eliu/.pyenv/versions/anaconda2-4.1.1/lib/python2.7/site-packages/sklearn/svm/base.pyc\u001b[0m in \u001b[0;36m_dense_predict_proba\u001b[0;34m(self, X)\u001b[0m\n\u001b[1;32m    671\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprobA_\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprobB_\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    672\u001b[0m             \u001b[0msvm_type\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msvm_type\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkernel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mkernel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdegree\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdegree\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 673\u001b[0;31m             cache_size=self.cache_size, coef0=self.coef0, gamma=self._gamma)\n\u001b[0m\u001b[1;32m    674\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    675\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mpprob\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "# coding: utf-8\n",
    "\n",
    "\"\"\"\n",
    "created on 2017/05/11\n",
    "@author: liuenda\n",
    "\"\"\"\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "from sklearn import cross_validation\n",
    "from sklearn.metrics import classification_report\n",
    "from gensim import corpora, models, similarities\n",
    "from sklearn import datasets\n",
    "from sklearn import linear_model\n",
    "from sklearn import svm\n",
    "from gensim.models import word2vec\n",
    "\n",
    "import random\n",
    "import pickle\n",
    "import time\n",
    "\n",
    "\n",
    "model_name_en = \"./data/model-en/W2Vmodle.bin\"\n",
    "model_name_jp = \"./data/model-jp/W2Vmodle.bin\"\n",
    "\n",
    "model_en = word2vec.Word2Vec.load(model_name_en)\n",
    "model_jp = word2vec.Word2Vec.load(model_name_jp)\n",
    "\n",
    "\n",
    "maxlen = 0 # Default: 0 -> infinite\n",
    "epoch = 50\n",
    "random.seed(1234)\n",
    "\n",
    "\n",
    "\"\"\"\n",
    "Find the ranking results with respect to real pairs\n",
    "Defaulty, projection1 should be JP\n",
    "Whiile, projection2 should be EN->JP\n",
    "\"\"\"\n",
    "def find_ranking(projection1, projection2, clf):\n",
    "\tsim_results = []\n",
    "\trank_results = []\n",
    "\n",
    "\t# Iterate each of the ariticle from projection1 (999) as proj1\n",
    "\t# Calculate the simialrity of proj1 with all ariticles in projection2 (999)\n",
    "\tfor i, proj1 in enumerate(projection1):\n",
    "\t\tprint \"Find answer for doc.\", i\n",
    "\t\tproj1_tile = np.tile(proj1, (len(projection2),1))\n",
    "\t\tfeatures_test = np.concatenate((proj1_tile, projection2), axis=1)\n",
    "\t\tsim = clf.predict_proba(features_test)[:,1]\n",
    "\t\trank = pd.Series(sim).rank(ascending = False)[i]\n",
    "\t\tsim_results.append(sim)\n",
    "\t\trank_results.append(rank)\n",
    "\n",
    "\t# sim_results contains 999*999 similairty matrix\n",
    "\treturn sim_results, rank_results\n",
    "\n",
    "\"\"\"\n",
    "rank_results should be list of (999,)\n",
    "\"\"\"\n",
    "def find_top(rank_results, top):\n",
    "\ts = pd.Series(rank_results)\n",
    "\tn_top = (s <= top).sum()\n",
    "\treturn n_top\n",
    "\n",
    "\n",
    "def doc2feature(corpus, tfidf, dictionary, w2v):\n",
    "    doc_features = []\n",
    "    for index, doc_bof in enumerate(corpus):\n",
    "\n",
    "        if index % 1000 == 0:\n",
    "            print(index)\n",
    "\n",
    "        doc_tfidf = tfidf[doc_bof]\n",
    "\n",
    "        doc_feature = np.zeros((200,))\n",
    "\n",
    "        for (token_id, token_tfidf) in doc_tfidf:\n",
    "            token = dictionary.get(token_id, \"[unknown-id]\").encode(\"utf-8\")\n",
    "            # if token in w2v:\n",
    "            if True:\n",
    "                token_w2v = w2v[token]\n",
    "            else:\n",
    "                print(\"No word:\", token)\n",
    "                continue\n",
    "            doc_feature += token_w2v * token_tfidf\n",
    "        doc_features.append(doc_feature)\n",
    "\n",
    "    return doc_features\n",
    "\n",
    "def doc2vec_en(doc):\n",
    "\tr = [model_en[token] for token in doc.split()]\n",
    "\treturn r\n",
    "\n",
    "def doc2vec_jp(doc):\n",
    "\tr = [model_jp[token] for token in doc.split()]\n",
    "\treturn r\n",
    "\n",
    "\n",
    "def prepare_train(dir_en, dir_jp):\n",
    "\n",
    "\tdf_en_mapping = pd.read_csv(dir_en)\n",
    "\tdf_jp_mapping = pd.read_csv(dir_jp)\n",
    "\n",
    "\tprint \"Reading english Data:\", len(df_en_mapping)\n",
    "\tprint \"Reading english Data:\", len(df_jp_mapping)\n",
    "\n",
    "\tsample_size = len(df_en_mapping)\n",
    "\n",
    "\tassert len(df_en_mapping) == len(df_jp_mapping)\n",
    "\n",
    "\t# Convert mapping to list type and then concat to the a list\n",
    "\tprint \"Merging the English and Japanes news dataframe...\"\n",
    "\tdf_train_1 = pd.concat([df_en_mapping, df_jp_mapping], axis = 1)\n",
    "\tdf_train_1['similarity'] = pd.Series(np.ones(sample_size,)*5)\n",
    "\tdf_train_1['dis_similarity'] = pd.Series(np.ones(sample_size,)*1)\n",
    "\n",
    "\t# Remove null line\n",
    "\tprint \"Drop the null line...\"\n",
    "\t# df_train_1 = df_train_1.dropna(subset=['en_article'])\n",
    "\tdf_train_1 = df_train_1[df_train_1['en_article'] != '<NULL>']\n",
    "\n",
    "\t# Expand the training data\n",
    "\ten_article_wrong = df_train_1.en_article.iloc[random.sample(xrange(len(df_train_1)),len(df_train_1))]\n",
    "\ten_article_wrong.index = df_train_1.index\n",
    "\tprint (en_article_wrong == df_train_1.en_article).value_counts()\n",
    "\tdf_train_1['en_article_wrong'] = en_article_wrong\n",
    "\n",
    "\t# Convert dateframe to list\n",
    "\ttrain_1 = df_train_1[['en_article','jp_article','similarity']].values.tolist()\n",
    "\ttrain_2 = df_train_1[['en_article_wrong','jp_article','dis_similarity']].values.tolist()\n",
    "\n",
    "\treturn train_1, train_2, df_train_1\n",
    "\n",
    "if __name__ == \"__main__\":\n",
    "\n",
    "\n",
    "\tinput = 2\n",
    "\tk = 10\n",
    "\n",
    "\t# --- Prepare and Loading the training data --- #\n",
    "\n",
    "\tif input == 1:\n",
    "\t\t# Prepare For the training data\n",
    "\t\tsample_size = \"_1000\"\n",
    "\t\tdir_en = \"./data/mapping/en_mapped_\"+str(k) + sample_size + \".csv\"\n",
    "\t\tdir_jp = \"./data/mapping/jp_mapped_\" + str(k) + sample_size + \".csv\"\n",
    "\n",
    "\t\t# Prepare For the test data\n",
    "\t\tsample_size = \"_1k2k\"\n",
    "\t\tdir_en_test = \"./data/mapping/en_mapped_\"+str(k) + sample_size + \".csv\"\n",
    "\t\tdir_jp_test = \"./data/mapping/jp_mapped_\" + str(k) + sample_size + \".csv\"\n",
    "\n",
    "\t\ttrain_1, train_2, df_train_1 = \tprepare_train(dir_en, dir_jp)\n",
    "\t\ttest_1, test_2, df_test_1 = prepare_train(dir_en_test, dir_jp_test)\n",
    "\n",
    "\tif input == 2:\n",
    "\t\t# split_line = 5000\n",
    "\t\t# end_line = 6000\n",
    "\t\t# Prepare For the training data\n",
    "\t\tdir_en = \"./data/news/en_news.csv\"\n",
    "\t\tdir_jp = \"./data/news/jp_news.csv\"\n",
    "\n",
    "\t\tpairs_correct, pairs_wrong, df_pairs = prepare_train(dir_en, dir_jp)\n",
    "\t\ttrain_1 = pairs_correct[0:2000] + pairs_correct[3000:5000]\n",
    "\t\ttest_1 = pairs_correct[2000:3000]\n",
    "\n",
    "\t\ttrain_2 = pairs_wrong[0:2000] + pairs_wrong[3000:5000]\n",
    "\t# test_2 = pairs_wrong[split_line:end_line]\n",
    "\n",
    "\n",
    "\t# Expand the training data\n",
    "\ttrain = train_1 + train_2\n",
    "\n",
    "\n",
    "\t# --- Apply the word2vec model to the data sets --- #\n",
    "\n",
    "\tmodel_name_en = \"./data/model-en/W2Vmodle.bin\"\n",
    "\tmodel_name_jp = \"./data/model-jp/W2Vmodle.bin\"\n",
    "\n",
    "\tdf_pairs_sample = df_pairs.iloc[0:5000]\n",
    "\n",
    "\tdf_pairs_sample['word2vec_en'] = df_pairs_sample['en_article'].apply(doc2vec_en)\n",
    "\tdf_pairs_sample['word2vec_jp'] = df_pairs_sample['jp_article'].apply(doc2vec_jp)\n",
    "\n",
    "\n",
    "\t# Feature 1: TF-IDF + Average word2vec\n",
    "\n",
    "\t# --- Find tf-idf * word2vec features --- #\n",
    "\n",
    "    #  For English text:\n",
    "\ttexts_en = [doc.split() for doc in list(df_pairs_sample[\"en_article\"])]\n",
    "\tdictionary_en = corpora.Dictionary(texts_en)\n",
    "\tcorpus_en = [dictionary_en.doc2bow(text) for text in texts_en]\n",
    "\ttfidf_en = models.TfidfModel(corpus_en)\n",
    "\n",
    "\t#  For Japanese text:\n",
    "\ttexts_jp = [doc.split() for doc in list(df_pairs_sample[\"jp_article\"])]\n",
    "\tdictionary_jp = corpora.Dictionary(texts_jp)\n",
    "\tcorpus_jp = [dictionary_jp.doc2bow(text) for text in texts_jp]\n",
    "\ttfidf_jp = models.TfidfModel(corpus_jp)\n",
    "\n",
    "\tfeatures_en = doc2feature(corpus_en, tfidf_en, dictionary_en, model_en)\n",
    "\tfeatures_jp = doc2feature(corpus_jp, tfidf_jp, dictionary_jp, model_jp)\n",
    "\n",
    "\tfeatures_merge = np.concatenate((features_en,features_jp), axis = 1)\n",
    "\n",
    "\t# --- Expanding the training data (dissimilar paris)\n",
    "\tfeatures_en_wrong = np.array(features_en)\n",
    "\tnp.random.shuffle((features_en_wrong))\n",
    "\tc = np.all(features_en_wrong == features_en, axis=1)\n",
    "\tprint \"C value =\", c.sum() # check the duplicated amount\n",
    "\n",
    "\tfeatures_merge_wrong = np.concatenate((features_en_wrong,features_jp), axis = 1)\n",
    "\n",
    "\t# --- Prepare the final training and test data --- #\n",
    "\n",
    "\tX = np.concatenate((features_merge, features_merge_wrong), axis = 0)\n",
    "\ty = np.concatenate((np.ones(len(features_merge)), np.zeros(len(features_en_wrong))), axis = 0)\n",
    "\n",
    "\t# --- Split into test data and training data --- #\n",
    "\n",
    "\tX_train1, X_test, X_train2, X_train3_wrong = np.split(X, [2000, 3000, 5000])\n",
    "\ty_train1, y_test, y_train2, y_train3_wrong = np.split(y, [2000, 3000, 5000])\n",
    "\n",
    "\tX_train = np.concatenate((X_train1, X_train2, X_train3_wrong), axis = 0)\n",
    "\ty_train = np.concatenate((y_train1, y_train2, y_train3_wrong), axis = 0)\n",
    "\tX_train_correct = np.concatenate((X_train1, X_train2), axis = 0)\n",
    "\ty_train_correct = np.concatenate((y_train1, y_train2), axis = 0)\n",
    "\n",
    "\t# --- SVM Training --- #\n",
    "\n",
    "\tclf = svm.SVC(kernel=\"rbf\", gamma=0.001, C=100, probability=True)\n",
    "\tclf.fit(X_train, y_train)\n",
    "\t# clf.score(X_train, y_train)\n",
    "\t# clf.score(X_test, y_test)\n",
    "\n",
    "\ty_test_predict = clf.predict(X_test)\n",
    "\ty_train_predict = clf.predict(X_train)\n",
    "\n",
    "\tprint \"classification report of TRAINING data:\"\n",
    "\tprint(classification_report(y_train_predict, y_train))\n",
    "\n",
    "\tprint \"classification report of TEST data:\"\n",
    "\tprint(classification_report(y_test_predict, y_test))\n",
    "\n",
    "\ty_test_proba = clf.predict_proba(X_test)\n",
    "\ty_train_proba = clf.predict_proba(X_train)\n",
    "\n",
    "\t# --- Evaluation for SVM --- #\n",
    "\n",
    "\t# sim_results_train, rank_results_train = find_ranking(projection1_train, projection2_train)\n",
    "\tsim_results_test, rank_results_test = find_ranking(X_test[:,:200] ,X_test[:,200:], clf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Reading english Data: 63386\n",
      "Reading english Data: 63386\n",
      "Merging the English and Japanes news dataframe...\n",
      "Drop the null line...\n",
      "False    63340\n",
      "True         3\n",
      "Name: en_article, dtype: int64\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "base_w2v_bow_svm.py:258: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  df_pairs_sample['word2vec_jp'] = df_pairs_sample['jp_article'].apply(doc2vec_jp)\n",
      "base_w2v_bow_svm.py:259: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n",
      "1000\n",
      "2000\n",
      "3000\n",
      "4000\n",
      "0\n",
      "1000\n",
      "2000\n",
      "3000\n",
      "4000\n",
      "C value = 3\n",
      "Usinge the None\n",
      "classification report of TRAINING data:\n",
      "             precision    recall  f1-score   support\n",
      "\n",
      "        0.0       1.00      0.96      0.98      4000\n",
      "        1.0       0.96      1.00      0.98      4000\n",
      "\n",
      "avg / total       0.98      0.98      0.98      8000\n",
      "\n",
      "classification report of TEST data:\n",
      "             precision    recall  f1-score   support\n",
      "\n",
      "        0.0       0.00      0.00      0.00         0\n",
      "        1.0       1.00      0.93      0.97      1000\n",
      "\n",
      "avg / total       1.00      0.93      0.97      1000\n",
      "\n",
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      "count    1000.000000\n",
      "mean       14.175000\n",
      "std        33.818872\n",
      "min         1.000000\n",
      "25%         2.000000\n",
      "50%         6.000000\n",
      "75%        14.000000\n",
      "max       629.000000\n",
      "dtype: float64\n",
      "('TOP1', 134)\n",
      "('TOP5', 470)\n",
      "('TOP10', 662)\n"
     ]
    }
   ],
   "source": [
    "run base_w2v_bow_svm.py"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Reading english Data: 63386\n",
      "Reading english Data: 63386\n",
      "Merging the English and Japanes news dataframe...\n",
      "Drop the null line...\n",
      "False    63340\n",
      "True         3\n",
      "Name: en_article, dtype: int64\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "base_w2v_bow_svm.py:258: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  df_pairs_sample['word2vec_jp'] = df_pairs_sample['jp_article'].apply(doc2vec_jp)\n",
      "base_w2v_bow_svm.py:259: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n",
      "1000\n",
      "2000\n",
      "3000\n",
      "4000\n",
      "0\n",
      "1000\n",
      "2000\n",
      "3000\n",
      "4000\n",
      "C value = 4\n",
      "Usinge the None\n",
      "classification report of TRAINING data:\n",
      "             precision    recall  f1-score   support\n",
      "\n",
      "        0.0       1.00      0.50      0.67      7937\n",
      "        1.0       0.01      0.73      0.02        63\n",
      "\n",
      "avg / total       0.99      0.50      0.66      8000\n",
      "\n",
      "classification report of TEST data:\n",
      "             precision    recall  f1-score   support\n",
      "\n",
      "        0.0       0.00      0.00      0.00       995\n",
      "        1.0       0.01      1.00      0.01         5\n",
      "\n",
      "avg / total       0.00      0.01      0.00      1000\n",
      "\n",
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     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Process Process-34:\n",
      "Traceback (most recent call last):\n",
      "  File \"/home/M2015eliu/.pyenv/versions/anaconda2-4.1.1/lib/python2.7/multiprocessing/process.py\", line 258, in _bootstrap\n",
      "    self.run()\n",
      "  File \"/home/M2015eliu/.pyenv/versions/anaconda2-4.1.1/lib/python2.7/multiprocessing/process.py\", line 114, in run\n",
      "    self._target(*self._args, **self._kwargs)\n",
      "  File \"/home/M2015eliu/cas/2017.1.1~LiuSTM/base_w2v_bow_svm.py\", line 86, in find_ranking_quick\n",
      "    sim = clf.predict_proba(features_test)[:, 1]\n",
      "  File \"/home/M2015eliu/.pyenv/versions/anaconda2-4.1.1/lib/python2.7/site-packages/sklearn/svm/base.py\", line 625, in _predict_proba\n",
      "    return pred_proba(X)\n",
      "  File \"/home/M2015eliu/.pyenv/versions/anaconda2-4.1.1/lib/python2.7/site-packages/sklearn/svm/base.py\", line 673, in _dense_predict_proba\n",
      "    cache_size=self.cache_size, coef0=self.coef0, gamma=self._gamma)\n",
      "KeyboardInterrupt\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0mTraceback (most recent call last)",
      "\u001b[0;32m/home/M2015eliu/cas/2017.1.1~LiuSTM/base_w2v_bow_svm.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m    361\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    362\u001b[0m         \u001b[0;31m# Quick way\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 363\u001b[0;31m         \u001b[0mq\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mwrapper_find_ranking_quick\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_test\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mclf\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    364\u001b[0m         \u001b[0mdic_rank_results_test_new\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mq\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    365\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/home/M2015eliu/cas/2017.1.1~LiuSTM/base_w2v_bow_svm.py\u001b[0m in \u001b[0;36mwrapper_find_ranking_quick\u001b[0;34m(X_test_scaled, clf)\u001b[0m\n\u001b[1;32m     64\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     65\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mj\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mjobs\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 66\u001b[0;31m                 \u001b[0mj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mjoin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     67\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     68\u001b[0m         \u001b[0;32mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtime\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtime\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0ms\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/home/M2015eliu/.pyenv/versions/anaconda2-4.1.1/lib/python2.7/multiprocessing/process.pyc\u001b[0m in \u001b[0;36mjoin\u001b[0;34m(self, timeout)\u001b[0m\n\u001b[1;32m    143\u001b[0m         \u001b[0;32massert\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_parent_pid\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0mos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgetpid\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'can only join a child process'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    144\u001b[0m         \u001b[0;32massert\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_popen\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'can only join a started process'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 145\u001b[0;31m         \u001b[0mres\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_popen\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwait\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    146\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mres\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    147\u001b[0m             \u001b[0m_current_process\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_children\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdiscard\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/home/M2015eliu/.pyenv/versions/anaconda2-4.1.1/lib/python2.7/multiprocessing/forking.pyc\u001b[0m in \u001b[0;36mwait\u001b[0;34m(self, timeout)\u001b[0m\n\u001b[1;32m    152\u001b[0m         \u001b[0;32mdef\u001b[0m \u001b[0mwait\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtimeout\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    153\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mtimeout\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 154\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpoll\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    155\u001b[0m             \u001b[0mdeadline\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtime\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtime\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mtimeout\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    156\u001b[0m             \u001b[0mdelay\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m0.0005\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/home/M2015eliu/.pyenv/versions/anaconda2-4.1.1/lib/python2.7/multiprocessing/forking.pyc\u001b[0m in \u001b[0;36mpoll\u001b[0;34m(self, flag)\u001b[0m\n\u001b[1;32m    133\u001b[0m                 \u001b[0;32mwhile\u001b[0m \u001b[0mTrue\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    134\u001b[0m                     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 135\u001b[0;31m                         \u001b[0mpid\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msts\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwaitpid\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpid\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mflag\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    136\u001b[0m                     \u001b[0;32mexcept\u001b[0m \u001b[0mos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0merror\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    137\u001b[0m                         \u001b[0;32mif\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0merrno\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0merrno\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mEINTR\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Process Process-37:\n",
      "Traceback (most recent call last):\n",
      "  File \"/home/M2015eliu/.pyenv/versions/anaconda2-4.1.1/lib/python2.7/multiprocessing/process.py\", line 258, in _bootstrap\n",
      "    self.run()\n",
      "  File \"/home/M2015eliu/.pyenv/versions/anaconda2-4.1.1/lib/python2.7/multiprocessing/process.py\", line 114, in run\n",
      "    self._target(*self._args, **self._kwargs)\n",
      "  File \"/home/M2015eliu/cas/2017.1.1~LiuSTM/base_w2v_bow_svm.py\", line 86, in find_ranking_quick\n",
      "    sim = clf.predict_proba(features_test)[:, 1]\n",
      "  File \"/home/M2015eliu/.pyenv/versions/anaconda2-4.1.1/lib/python2.7/site-packages/sklearn/svm/base.py\", line 625, in _predict_proba\n",
      "    return pred_proba(X)\n",
      "  File \"/home/M2015eliu/.pyenv/versions/anaconda2-4.1.1/lib/python2.7/site-packages/sklearn/svm/base.py\", line 673, in _dense_predict_proba\n",
      "    cache_size=self.cache_size, coef0=self.coef0, gamma=self._gamma)\n",
      "KeyboardInterrupt\n",
      "Process Process-32:\n",
      "Traceback (most recent call last):\n",
      "  File \"/home/M2015eliu/.pyenv/versions/anaconda2-4.1.1/lib/python2.7/multiprocessing/process.py\", line 258, in _bootstrap\n",
      "    self.run()\n",
      "  File \"/home/M2015eliu/.pyenv/versions/anaconda2-4.1.1/lib/python2.7/multiprocessing/process.py\", line 114, in run\n",
      "    self._target(*self._args, **self._kwargs)\n",
      "  File \"/home/M2015eliu/cas/2017.1.1~LiuSTM/base_w2v_bow_svm.py\", line 86, in find_ranking_quick\n",
      "    sim = clf.predict_proba(features_test)[:, 1]\n",
      "  File \"/home/M2015eliu/.pyenv/versions/anaconda2-4.1.1/lib/python2.7/site-packages/sklearn/svm/base.py\", line 625, in _predict_proba\n",
      "    return pred_proba(X)\n",
      "  File \"/home/M2015eliu/.pyenv/versions/anaconda2-4.1.1/lib/python2.7/site-packages/sklearn/svm/base.py\", line 673, in _dense_predict_proba\n",
      "    cache_size=self.cache_size, coef0=self.coef0, gamma=self._gamma)\n",
      "KeyboardInterrupt\n"
     ]
    }
   ],
   "source": [
    "run base_w2v_bow_svm.py"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('average=', True)\n",
      "Reading english Data: 63386\n",
      "Reading english Data: 63386\n",
      "Merging the English and Japanes news dataframe...\n",
      "Drop the null line...\n",
      "False    63340\n",
      "True         3\n",
      "Name: en_article, dtype: int64\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "base_w2v_bow_svm.py:257: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  df_pairs_sample['word2vec_en'] = df_pairs_sample['en_article'].apply(doc2vec_en)\n",
      "base_w2v_bow_svm.py:258: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  df_pairs_sample['word2vec_jp'] = df_pairs_sample['jp_article'].apply(doc2vec_jp)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n",
      "1000\n",
      "2000\n",
      "3000\n",
      "4000\n",
      "0\n",
      "1000\n",
      "2000\n",
      "3000\n",
      "4000\n",
      "C value = 3\n",
      "Usinge the None\n",
      "classification report of TRAINING data:\n",
      "             precision    recall  f1-score   support\n",
      "\n",
      "        0.0       0.53      0.14      0.22      4000\n",
      "        1.0       0.50      0.88      0.64      4000\n",
      "\n",
      "avg / total       0.52      0.51      0.43      8000\n",
      "\n",
      "classification report of TEST data:\n",
      "             precision    recall  f1-score   support\n",
      "\n",
      "        0.0       0.23      0.06      0.09      1000\n",
      "        1.0       0.46      0.81      0.59      1000\n",
      "\n",
      "avg / total       0.35      0.43      0.34      2000\n",
      "\n",
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501.5||475.277245998\n",
      "count    1000.000000\n",
      "mean      500.585500\n",
      "std        22.452406\n",
      "min         1.000000\n",
      "25%       500.500000\n",
      "50%       500.500000\n",
      "75%       500.500000\n",
      "max      1000.000000\n",
      "dtype: float64\n",
      "('TOP1', 1)\n",
      "('TOP5', 1)\n",
      "('TOP10', 1)\n"
     ]
    }
   ],
   "source": [
    "run base_w2v_bow_svm.py"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('average=', False)\n",
      "Reading english Data: 63386\n",
      "Reading english Data: 63386\n",
      "Merging the English and Japanes news dataframe...\n",
      "Drop the null line...\n",
      "False    63340\n",
      "True         3\n",
      "Name: en_article, dtype: int64\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "base_w2v_bow_svm.py:257: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  df_pairs_sample['word2vec_en'] = df_pairs_sample['en_article'].apply(doc2vec_en)\n",
      "base_w2v_bow_svm.py:258: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  df_pairs_sample['word2vec_jp'] = df_pairs_sample['jp_article'].apply(doc2vec_jp)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n",
      "1000\n",
      "2000\n",
      "3000\n",
      "4000\n",
      "0\n",
      "1000\n",
      "2000\n",
      "3000\n",
      "4000\n",
      "C value = 1\n",
      "Usinge the None\n",
      "classification report of TRAINING data:\n",
      "             precision    recall  f1-score   support\n",
      "\n",
      "        0.0       1.00      0.96      0.98      4000\n",
      "        1.0       0.96      1.00      0.98      4000\n",
      "\n",
      "avg / total       0.98      0.98      0.98      8000\n",
      "\n",
      "classification report of TEST data:\n",
      "             precision    recall  f1-score   support\n",
      "\n",
      "        0.0       0.95      0.87      0.91      1000\n",
      "        1.0       0.88      0.96      0.92      1000\n",
      "\n",
      "avg / total       0.92      0.91      0.91      2000\n",
      "\n",
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23.0||Doc.697: 53.0||Doc.299: 24.5||Doc.595: 77.0||Doc.794: 5.0||Doc.396: 39.5||Doc.999: 4.0||Doc.99: 1.0||Doc.698: 13.5||Doc.596: 5.0||Doc.795: 7.5||Doc.397: 2.0||Doc.699: 3.0||Doc.597: 13.5||Doc.796: 37.5||Doc.398: 39.0||Doc.598: 13.0||Doc.797: 30.0||Doc.399: 20.0||Doc.599: 1.5||Doc.798: 31.0||Doc.799: 7.5||151.588675022\n",
      "count    1000.000000\n",
      "mean       12.408000\n",
      "std        26.154648\n",
      "min         1.000000\n",
      "25%         2.000000\n",
      "50%         5.000000\n",
      "75%        12.000000\n",
      "max       458.000000\n",
      "dtype: float64\n",
      "('TOP1', 131)\n",
      "('TOP5', 526)\n",
      "('TOP10', 701)\n",
      "Using the new test data to evaluate.......\n",
      "0\n",
      "0\n",
      "Doc.0: 11.0||Doc.100: 12.0||Doc.300: 10.0||Doc.200: 3.0||Doc.400: 1.0||Doc.500: 2.0||Doc.600: 71.0||Doc.700: 1.5||Doc.800: 68.5||Doc.900: 1.0||Doc.101: 2.0||Doc.301: 2.0||Doc.1: 1.0||Doc.201: 2.5||Doc.401: 14.0||Doc.501: 24.0||Doc.601: 5.0||Doc.701: 3.0||Doc.901: 2.0||Doc.801: 105.5||Doc.302: 4.0||Doc.2: 2.0||Doc.402: 9.5||Doc.102: 9.0||Doc.502: 45.0||Doc.602: 2.5||Doc.202: 2.0||Doc.702: 2.0||Doc.902: 7.0||Doc.303: 22.0||Doc.3: 1.0||Doc.802: 48.0||Doc.503: 10.0||Doc.603: 12.0||Doc.403: 17.0||Doc.203: 152.0||Doc.103: 13.0||Doc.703: 16.0||Doc.304: 3.0||Doc.903: 2.5||Doc.504: 3.0||Doc.4: 13.0||Doc.604: 2.0||Doc.204: 2.5||Doc.803: 56.5||Doc.404: 17.0||Doc.104: 4.0||Doc.305: 1.0||Doc.704: 9.0||Doc.505: 1.5||Doc.904: 3.5||Doc.205: 73.0||Doc.605: 21.0||Doc.5: 1.0||Doc.405: 1.5||Doc.804: 37.5||Doc.306: 7.0||Doc.105: 4.0||Doc.506: 2.0||Doc.206: 1.0||Doc.705: 4.0||Doc.905: 1.0||Doc.606: 44.5||Doc.6: 3.0||Doc.406: 4.0||Doc.805: 7.0||Doc.307: 1.0||Doc.106: 6.0||Doc.207: 3.0||Doc.507: 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4.0||Doc.299: 4.0||Doc.896: 1.5||Doc.997: 1.0||Doc.199: 1.0||Doc.395: 3.0||Doc.795: 2.0||Doc.498: 1.0||Doc.596: 17.0||Doc.97: 33.0||Doc.998: 4.0||Doc.897: 36.0||Doc.796: 25.5||Doc.499: 10.0||Doc.396: 1.5||Doc.999: 14.0||Doc.597: 6.0||Doc.98: 1.0||Doc.797: 85.5||Doc.898: 13.0||Doc.397: 2.5||Doc.798: 165.5||Doc.598: 13.0||Doc.99: 9.0||Doc.899: 68.5||Doc.398: 2.0||Doc.799: 312.5||Doc.599: 10.0||Doc.399: 1.0||150.916481018\n",
      "count    1000.000000\n",
      "mean       22.445000\n",
      "std        57.543625\n",
      "min         1.000000\n",
      "25%         2.000000\n",
      "50%         6.000000\n",
      "75%        19.125000\n",
      "max       811.500000\n",
      "dtype: float64\n",
      "('TOP1', 141)\n",
      "('TOP5', 461)\n",
      "('TOP10', 639)\n"
     ]
    }
   ],
   "source": [
    "run base_w2v_bow_svm.py"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('average=', False)\n",
      "Reading english Data: 63386\n",
      "Reading english Data: 63386\n",
      "Merging the English and Japanes news dataframe...\n",
      "Drop the null line...\n",
      "False    63340\n",
      "True         3\n",
      "Name: en_article, dtype: int64\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "base_w2v_bow_svm.py:257: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  df_pairs_sample['word2vec_en'] = df_pairs_sample['en_article'].apply(doc2vec_en)\n",
      "base_w2v_bow_svm.py:258: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  df_pairs_sample['word2vec_jp'] = df_pairs_sample['jp_article'].apply(doc2vec_jp)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n",
      "1000\n",
      "2000\n",
      "3000\n",
      "4000\n",
      "0\n",
      "1000\n",
      "2000\n",
      "3000\n",
      "4000\n",
      "C value = 3\n",
      "Usinge the None\n",
      "classification report of TRAINING data:\n",
      "             precision    recall  f1-score   support\n",
      "\n",
      "        0.0       1.00      0.96      0.98      4000\n",
      "        1.0       0.96      1.00      0.98      4000\n",
      "\n",
      "avg / total       0.98      0.98      0.98      8000\n",
      "\n",
      "classification report of TEST data:\n",
      "             precision    recall  f1-score   support\n",
      "\n",
      "        0.0       0.95      0.88      0.91      1000\n",
      "        1.0       0.89      0.95      0.92      1000\n",
      "\n",
      "avg / total       0.92      0.92      0.92      2000\n",
      "\n",
      "Doc.0: 5.0||Doc.200: 60.0||Doc.100: 8.0||Doc.300: 5.0||Doc.400: 3.0||Doc.600: 5.5||Doc.500: 1.5||Doc.700: 58.5||Doc.800: 8.5||Doc.900: 1.5||Doc.1: 3.0||Doc.201: 6.0||Doc.401: 13.0||Doc.101: 7.0||Doc.301: 2.5||Doc.501: 2.5||Doc.601: 9.0||Doc.701: 11.0||Doc.801: 11.5||Doc.2: 1.0||Doc.901: 1.5||Doc.102: 3.0||Doc.502: 2.0||Doc.402: 3.0||Doc.202: 35.0||Doc.302: 6.0||Doc.602: 6.0||Doc.702: 8.0||Doc.103: 1.0||Doc.503: 2.0||Doc.802: 3.5||Doc.3: 1.0||Doc.902: 2.0||Doc.203: 1.5||Doc.403: 7.5||Doc.303: 3.0||Doc.603: 3.0||Doc.703: 3.5||Doc.504: 319.0||Doc.104: 9.0||Doc.903: 1.5||Doc.803: 40.5||Doc.204: 4.0||Doc.4: 31.5||Doc.304: 1.0||Doc.404: 1.0||Doc.505: 53.0||Doc.604: 3.0||Doc.704: 7.5||Doc.205: 27.5||Doc.105: 2.0||Doc.904: 3.0||Doc.305: 3.5||Doc.804: 138.5||Doc.5: 2.0||Doc.405: 12.5||Doc.506: 30.0||Doc.705: 3.0||Doc.605: 50.5||Doc.206: 9.0||Doc.306: 33.0||Doc.905: 6.0||Doc.106: 3.5||Doc.805: 33.5||Doc.6: 1.0||Doc.507: 2.0||Doc.406: 1.0||Doc.706: 11.0||Doc.307: 2.0||Doc.207: 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1.5||Doc.582: 8.0||Doc.282: 44.5||Doc.785: 3.0||Doc.982: 4.0||Doc.184: 11.0||Doc.683: 4.0||Doc.487: 3.0||Doc.87: 1.0||Doc.385: 21.0||Doc.883: 1.0||Doc.283: 1.5||Doc.583: 4.0||Doc.786: 16.0||Doc.983: 8.0||Doc.684: 4.0||Doc.386: 2.5||Doc.185: 2.0||Doc.884: 2.0||Doc.488: 1.5||Doc.88: 1.5||Doc.584: 33.0||Doc.284: 26.5||Doc.984: 2.0||Doc.787: 4.0||Doc.387: 22.0||Doc.685: 2.0||Doc.885: 1.0||Doc.489: 25.0||Doc.186: 2.0||Doc.89: 9.5||Doc.285: 18.5||Doc.985: 1.0||Doc.585: 16.5||Doc.388: 8.0||Doc.788: 36.5||Doc.686: 36.5||Doc.886: 34.0||Doc.490: 1.0||Doc.187: 4.0||Doc.286: 3.5||Doc.90: 5.0||Doc.986: 5.5||Doc.586: 17.0||Doc.389: 15.0||Doc.687: 13.5||Doc.887: 2.5||Doc.789: 45.5||Doc.188: 24.0||Doc.491: 2.5||Doc.287: 2.0||Doc.91: 4.0||Doc.390: 3.0||Doc.987: 8.5||Doc.888: 11.0||Doc.587: 21.0||Doc.688: 3.5||Doc.790: 12.0||Doc.492: 19.0||Doc.189: 1.5||Doc.391: 2.0||Doc.288: 5.0||Doc.92: 5.0||Doc.889: 3.0||Doc.988: 2.0||Doc.588: 7.0||Doc.689: 20.0||Doc.791: 17.5||Doc.190: 2.5||Doc.493: 68.5||Doc.392: 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6.5||Doc.697: 30.0||Doc.595: 46.0||Doc.297: 3.5||Doc.197: 8.0||Doc.799: 33.5||Doc.997: 10.5||Doc.898: 44.0||Doc.298: 6.0||Doc.596: 3.0||Doc.698: 20.5||Doc.198: 16.5||Doc.998: 2.5||Doc.899: 19.0||Doc.299: 28.5||Doc.597: 5.5||Doc.699: 11.0||Doc.999: 1.0||Doc.199: 1.5||Doc.598: 49.0||Doc.599: 2.5||150.159080982\n",
      "count    1000.000000\n",
      "mean       12.681500\n",
      "std        24.767039\n",
      "min         1.000000\n",
      "25%         2.000000\n",
      "50%         5.000000\n",
      "75%        13.000000\n",
      "max       383.500000\n",
      "dtype: float64\n",
      "('TOP1', 128)\n",
      "('TOP5', 520)\n",
      "('TOP10', 685)\n",
      "Using the new test data to evaluate.......\n",
      "0\n",
      "0\n",
      "Doc.0: 27.0||Doc.200: 3.0||Doc.100: 4.0||Doc.300: 4.0||Doc.400: 1.0||Doc.600: 50.0||Doc.500: 1.0||Doc.700: 6.5||Doc.800: 21.5||Doc.900: 1.0||Doc.201: 2.5||Doc.301: 2.0||Doc.101: 1.0||Doc.1: 2.0||Doc.401: 69.0||Doc.601: 1.0||Doc.501: 34.0||Doc.701: 3.0||Doc.801: 79.5||Doc.302: 4.0||Doc.102: 16.0||Doc.2: 29.0||Doc.901: 2.0||Doc.202: 2.0||Doc.402: 7.5||Doc.602: 2.5||Doc.502: 21.0||Doc.702: 1.0||Doc.303: 1.0||Doc.802: 12.0||Doc.103: 3.0||Doc.203: 146.0||Doc.3: 1.0||Doc.902: 5.0||Doc.603: 15.0||Doc.403: 10.0||Doc.503: 15.0||Doc.703: 9.0||Doc.304: 3.0||Doc.803: 19.5||Doc.204: 7.5||Doc.104: 3.0||Doc.4: 2.0||Doc.604: 1.0||Doc.903: 2.5||Doc.404: 67.0||Doc.504: 5.0||Doc.704: 2.0||Doc.305: 2.0||Doc.804: 19.5||Doc.205: 95.0||Doc.5: 1.0||Doc.105: 1.0||Doc.605: 6.0||Doc.904: 3.5||Doc.705: 4.0||Doc.505: 7.5||Doc.405: 1.5||Doc.805: 1.0||Doc.306: 1.0||Doc.6: 8.0||Doc.206: 5.0||Doc.606: 67.5||Doc.106: 6.0||Doc.905: 1.0||Doc.706: 4.5||Doc.506: 1.0||Doc.806: 6.0||Doc.406: 5.0||Doc.607: 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22.0||Doc.878: 11.5||Doc.177: 52.0||Doc.475: 30.0||Doc.678: 22.0||Doc.75: 6.0||Doc.775: 77.5||Doc.380: 2.5||Doc.277: 4.0||Doc.577: 5.0||Doc.879: 2.0||Doc.976: 33.0||Doc.476: 1.5||Doc.178: 2.0||Doc.776: 13.0||Doc.679: 9.5||Doc.381: 10.0||Doc.76: 4.0||Doc.278: 11.5||Doc.578: 34.0||Doc.880: 4.5||Doc.477: 13.5||Doc.977: 3.5||Doc.179: 2.0||Doc.680: 8.5||Doc.777: 2.0||Doc.279: 11.5||Doc.382: 16.0||Doc.77: 3.0||Doc.579: 1.0||Doc.881: 21.0||Doc.478: 1.5||Doc.978: 17.5||Doc.681: 25.0||Doc.280: 8.0||Doc.383: 37.0||Doc.180: 6.0||Doc.78: 53.0||Doc.580: 42.5||Doc.778: 2.0||Doc.882: 8.0||Doc.479: 1.0||Doc.281: 45.0||Doc.979: 70.0||Doc.682: 16.0||Doc.581: 3.0||Doc.384: 1.0||Doc.883: 4.0||Doc.181: 12.5||Doc.79: 26.0||Doc.779: 1.0||Doc.480: 18.0||Doc.282: 21.0||Doc.683: 3.0||Doc.385: 1.0||Doc.582: 2.0||Doc.980: 13.0||Doc.884: 3.5||Doc.182: 51.5||Doc.481: 38.0||Doc.80: 40.0||Doc.780: 19.5||Doc.283: 1.0||Doc.386: 4.0||Doc.981: 8.0||Doc.684: 25.5||Doc.183: 1.0||Doc.482: 37.0||Doc.583: 3.0||Doc.781: 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28.0||Doc.394: 1.0||Doc.291: 12.0||Doc.789: 498.5||Doc.892: 1.0||Doc.591: 1.5||Doc.88: 4.0||Doc.988: 4.0||Doc.490: 7.0||Doc.191: 1.0||Doc.395: 1.0||Doc.292: 2.0||Doc.693: 27.5||Doc.790: 207.5||Doc.893: 3.0||Doc.989: 10.0||Doc.491: 2.0||Doc.592: 4.0||Doc.89: 12.0||Doc.192: 8.0||Doc.396: 1.5||Doc.293: 16.0||Doc.694: 10.5||Doc.492: 11.0||Doc.894: 32.0||Doc.791: 190.5||Doc.990: 1.0||Doc.593: 7.0||Doc.90: 32.0||Doc.397: 1.5||Doc.193: 4.0||Doc.294: 1.0||Doc.695: 31.5||Doc.895: 5.0||Doc.792: 1.0||Doc.991: 2.0||Doc.493: 1.5||Doc.594: 4.0||Doc.398: 2.0||Doc.194: 5.0||Doc.91: 3.0||Doc.295: 1.0||Doc.696: 8.5||Doc.896: 1.5||Doc.595: 1.0||Doc.793: 245.5||Doc.992: 1.0||Doc.494: 21.0||Doc.195: 3.0||Doc.399: 3.0||Doc.92: 37.0||Doc.897: 28.0||Doc.697: 9.5||Doc.296: 17.0||Doc.596: 2.0||Doc.196: 2.0||Doc.794: 106.5||Doc.993: 7.0||Doc.495: 1.0||Doc.898: 5.0||Doc.698: 18.5||Doc.93: 60.0||Doc.297: 16.0||Doc.597: 27.0||Doc.197: 7.0||Doc.899: 79.5||Doc.795: 1.0||Doc.699: 97.0||Doc.994: 1.0||Doc.496: 4.0||Doc.94: 7.0||Doc.198: 4.0||Doc.298: 16.0||Doc.598: 29.0||Doc.796: 19.5||Doc.995: 8.0||Doc.497: 10.0||Doc.199: 1.0||Doc.95: 5.0||Doc.299: 2.0||Doc.599: 6.0||Doc.797: 19.5||Doc.498: 1.0||Doc.996: 1.0||Doc.96: 79.0||Doc.798: 97.5||Doc.499: 3.0||Doc.997: 1.0||Doc.97: 23.0||Doc.799: 107.5||Doc.998: 34.0||Doc.98: 1.0||Doc.999: 23.0||Doc.99: 53.0||151.576575041\n",
      "count    1000.00000\n",
      "mean       17.72750\n",
      "std        37.45753\n",
      "min         1.00000\n",
      "25%         2.00000\n",
      "50%         6.00000\n",
      "75%        18.00000\n",
      "max       498.50000\n",
      "dtype: float64\n",
      "('TOP1', 179)\n",
      "('TOP5', 491)\n",
      "('TOP10', 654)\n"
     ]
    }
   ],
   "source": [
    "run base_w2v_bow_svm.py"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using the new test data to evaluate.......\n",
      "0\n",
      "0\n",
      "C value = 0\n",
      "classification report of TEST data:\n",
      "             precision    recall  f1-score   support\n",
      "\n",
      "        0.0       0.93      0.91      0.92      1000\n",
      "        1.0       0.91      0.93      0.92      1000\n",
      "\n",
      "avg / total       0.92      0.92      0.92      2000\n",
      "\n"
     ]
    }
   ],
   "source": [
    "\t# ---- Independent test data ---- #\n",
    "\tprint(\"Using the new test data to evaluate.......\")\n",
    "\tdf_pairs_evaluate = df_pairs.iloc[50000:55000:5]\n",
    "\n",
    "\ttexts_en_new = [doc.split() for doc in list(df_pairs_evaluate[\"en_article\"])]\n",
    "\tcorpus_en_new = [dictionary_en.doc2bow(text) for text in texts_en_new]\n",
    "\n",
    "\ttexts_jp_new = [doc.split() for doc in list(df_pairs_evaluate[\"jp_article\"])]\n",
    "\tcorpus_jp_new = [dictionary_jp.doc2bow(text) for text in texts_jp_new]\n",
    "\n",
    "\tfeatures_en_new = doc2feature(corpus_en_new[:1000], tfidf_en, dictionary_en, model_en)\n",
    "\tfeatures_jp_new = doc2feature(corpus_jp_new[:1000], tfidf_jp, dictionary_jp, model_jp)\n",
    "\n",
    "\tfeatures_merge_1_new = np.concatenate((features_en_new, features_jp_new), axis=1)\n",
    "\n",
    "\t# X_test_scaled_new = scaler.transform(features_merge_new)\n",
    "\tX_test_scaled_1_new = features_merge_1_new\n",
    "\t# --- Expanding the training data (dissimilar paris)\n",
    "\tfeatures_en_wrong_new = np.array(features_en_new)\n",
    "\tnp.random.shuffle((features_en_wrong_new))\n",
    "\tc = np.all(features_en_wrong_new == features_en_new, axis=1)\n",
    "\tprint \"C value =\", c.sum() # check the duplicated amount\n",
    "\n",
    "\tfeatures_merge_wrong_new = np.concatenate((features_en_wrong_new,features_jp_new), axis = 1)\n",
    "\n",
    "\t# --- Prepare the final training and test data --- #\n",
    "\tX_test_scaled_new = np.concatenate((features_merge_1_new, features_merge_wrong_new), axis = 0)\n",
    "\t\n",
    "\ty_test_predict_new = clf.predict(X_test_scaled_new)\n",
    "\tprint \"classification report of TEST data:\"\n",
    "\tprint(classification_report(y_test, y_test_predict_new))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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OF5HTgdmAAIuicyep6lOqenvyAzwJ/FlVe1T1EQBV3YVTwq4UkXYROSU6ngKsa0rkjIoJ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\noJRCGKdSLVtJh23v+3LVVbmWo3IV4VA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k7r23U4IXLRo8gr3wwtxGoJAVJ2y8Q6theE++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\nIiIiKfT/hsySqAAnsD8AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5a72f27950>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# %matplotlib inline\n",
    "from matplotlib import gridspec\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib\n",
    "\n",
    "fig = plt.figure(figsize=(8.5, 10)) \n",
    "gs = gridspec.GridSpec(2, 1, height_ratios=[1, 2.5]) \n",
    "ax1 = fig.add_subplot(gs[0])\n",
    "\n",
    "\n",
    "plt.plot(rank_results_test,'bx')\n",
    "ax1.set_title('Ranks for TEST-1L, SVM, Long\\n', fontsize=16)\n",
    "ax1.set_ylabel('Rank (Linear)', fontsize=16)\n",
    "# ax1.set_ylim([100,0])\n",
    "ax1.set_ylim([0,150])\n",
    "ax1.plot([0, 1000], [11, 11], 'r-', lw=2, label = \"TOP-10 line\")\n",
    "# ax.plot([0, 1000], [6, 6], 'g-', lw=2, label =\"TOP-5 line\")\n",
    "# ax.plot([0, 1000], [1.2, 1.2], 'y:', lw=2, label = \"TOP-1 line\")\n",
    "ax1.tick_params(axis='y', labelsize=16)\n",
    "# plt.ylim([30,0.5])\n",
    "ax1.grid(True)\n",
    "# plt.show()\n",
    "ax1.xaxis.set_ticklabels([])\n",
    "\n",
    "\n",
    "# fig = plt.figure(figsize=(10, 17))\n",
    "ax2 = fig.add_subplot(gs[1])\n",
    "ax2.plot(rank_results_test,'bx')\n",
    "\n",
    "ax2.set_xlabel('Document Number', fontsize=16)\n",
    "ax2.set_ylabel('Rank (Log)', fontsize=16)\n",
    "ax2.set_ylim([1000,0.9])\n",
    "# plt.ylim([0.9,1000])\n",
    "ax2.set_yscale('log')\n",
    "ax2.grid()\n",
    "# plt.ylim([30,0.5])\n",
    "ax2.plot([0, 1000], [11, 11], 'r-', lw=2, label = \"TOP-10 line\")\n",
    "ax2.plot([0, 1000], [6, 6], 'g-', lw=2, label =\"TOP-5 line\")\n",
    "ax2.plot([0, 1000], [1.2, 1.2], 'y-', lw=2, label = \"TOP-1 line\")\n",
    "lgd = plt.legend(loc='upper center', ncol=3, fontsize=14, bbox_to_anchor=(0.5, -0.1))\n",
    "ax2.tick_params(axis='x', labelsize=16)\n",
    "ax2.tick_params(axis='y', labelsize=16)\n",
    "plt.tight_layout()\n",
    "plt.savefig('long-svm-test1.pdf', format='pdf', dpi=1000, bbox_extra_artists=(lgd,), bbox_inches='tight')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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o4UREJNXViMaYGcDHgGnAi4GfA1eLyHWemw7gU8CCyM09wAdE5Ccl/FZha5yj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jKTJZPrbVHqEKJDzzKyyl5uTIPL5iVCD8PV9ipl6fKpHxpb8UJjq7nRNbQxtgaDgRgOh06HkmOZ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\nxlYgY2swGDw5n25uL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PnK2D7wvIO8L/ruU0H/hnihbvxSVLfL6n0buinykelbO1XHH0j1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\nRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5a7408ef50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "from matplotlib import gridspec\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib\n",
    "\n",
    "fig = plt.figure(figsize=(8.5, 10)) \n",
    "gs = gridspec.GridSpec(2, 1, height_ratios=[1, 2.5]) \n",
    "ax1 = fig.add_subplot(gs[0])\n",
    "\n",
    "\n",
    "plt.plot(rank_results_test_new,'bx')\n",
    "ax1.set_title('Ranks for TEST-2L, SVM, Long\\n', fontsize=16)\n",
    "ax1.set_ylabel('Rank (Linear)', fontsize=16)\n",
    "# ax1.set_ylim([100,0])\n",
    "ax1.set_ylim([0,150])\n",
    "ax1.plot([0, 1000], [11, 11], 'r-', lw=2, label = \"TOP-10 line\")\n",
    "# ax.plot([0, 1000], [6, 6], 'g-', lw=2, label =\"TOP-5 line\")\n",
    "# ax.plot([0, 1000], [1.2, 1.2], 'y:', lw=2, label = \"TOP-1 line\")\n",
    "ax1.tick_params(axis='y', labelsize=16)\n",
    "# plt.ylim([30,0.5])\n",
    "ax1.grid(True)\n",
    "# plt.show()\n",
    "ax1.xaxis.set_ticklabels([])\n",
    "\n",
    "\n",
    "# fig = plt.figure(figsize=(10, 17))\n",
    "ax2 = fig.add_subplot(gs[1])\n",
    "ax2.plot(rank_results_test_new,'bx')\n",
    "\n",
    "ax2.set_xlabel('Document Number', fontsize=16)\n",
    "ax2.set_ylabel('Rank (Log)', fontsize=16)\n",
    "ax2.set_ylim([1000,0.9])\n",
    "# plt.ylim([0.9,1000])\n",
    "ax2.set_yscale('log')\n",
    "ax2.grid()\n",
    "# plt.ylim([30,0.5])\n",
    "ax2.plot([0, 1000], [11, 11], 'r-', lw=2, label = \"TOP-10 line\")\n",
    "ax2.plot([0, 1000], [6, 6], 'g-', lw=2, label =\"TOP-5 line\")\n",
    "ax2.plot([0, 1000], [1.2, 1.2], 'y-', lw=2, label = \"TOP-1 line\")\n",
    "ax2.tick_params(axis='x', labelsize=16)\n",
    "ax2.tick_params(axis='y', labelsize=16)\n",
    "lgd = plt.legend(loc='upper center', ncol=3, fontsize=14, bbox_to_anchor=(0.5, -0.1))\n",
    "plt.tight_layout()\n",
    "plt.savefig('long-svm-test2.pdf', format='pdf', dpi=1000, bbox_extra_artists=(lgd,), bbox_inches='tight')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
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