Weibo_sentiment_analysis_training_and_testing.ipynb

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  "metadata": {
    "colab": {
      "name": "Weibo_sentiment_analysis_training_and_testing.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "toc_visible": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
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    "accelerator": "TPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dMqGMoR0KYU4"
      },
      "source": [
        "#Load framework and TPU environment"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rq-Oj6_OxQ-9",
        "outputId": "636dd36e-8ea1-4e2e-d41a-38d305950ef6"
      },
      "source": [
        "%pylab inline\r\n",
        "import tensorflow as tf\r\n",
        "import pandas as pd\r\n",
        "import jieba\r\n",
        "from gensim import corpora\r\n",
        "from keras.preprocessing import sequence\r\n",
        "from scipy import stats\r\n",
        "import ast\r\n",
        "import os\r\n",
        "from keras.models import Model,Sequential\r\n",
        "from keras.layers.embeddings import Embedding\r\n",
        "from keras.layers import Dense, Reshape,Input, LSTM,Bidirectional,GRU, concatenate\r\n",
        "from keras.layers.normalization import BatchNormalization\r\n",
        "from keras.callbacks import ReduceLROnPlateau\r\n",
        "from keras import optimizers\r\n",
        "from sklearn.preprocessing import OneHotEncoder\r\n",
        "\r\n",
        "resolver = tf.distribute.cluster_resolver.TPUClusterResolver(tpu='grpc://' + os.environ['COLAB_TPU_ADDR'])\r\n",
        "tf.config.experimental_connect_to_cluster(resolver)\r\n",
        "tf.tpu.experimental.initialize_tpu_system(resolver)\r\n",
        "strategy = tf.distribute.TPUStrategy(resolver)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Populating the interactive namespace from numpy and matplotlib\n",
            "INFO:tensorflow:Initializing the TPU system: grpc://10.56.1.34:8470\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:tensorflow:Initializing the TPU system: grpc://10.56.1.34:8470\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:tensorflow:Clearing out eager caches\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:tensorflow:Clearing out eager caches\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:tensorflow:Finished initializing TPU system.\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:tensorflow:Finished initializing TPU system.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:tensorflow:Found TPU system:\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:tensorflow:Found TPU system:\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:tensorflow:*** Num TPU Cores: 8\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:tensorflow:*** Num TPU Cores: 8\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:tensorflow:*** Num TPU Workers: 1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:tensorflow:*** Num TPU Workers: 1\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:tensorflow:*** Num TPU Cores Per Worker: 8\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:tensorflow:*** Num TPU Cores Per Worker: 8\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:localhost/replica:0/task:0/device:CPU:0, CPU, 0, 0)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
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            "INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:localhost/replica:0/task:0/device:CPU:0, CPU, 0, 0)\n"
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        },
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        },
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            "INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU:3, TPU, 0, 0)\n"
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        },
        {
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        },
        {
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        },
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        },
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          "text": [
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        },
        {
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        },
        {
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          "text": [
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        },
        {
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        },
        {
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          "text": [
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        },
        {
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          "text": [
            "INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU_SYSTEM:0, TPU_SYSTEM, 0, 0)\n"
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        },
        {
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          "text": [
            "INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:XLA_CPU:0, XLA_CPU, 0, 0)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:XLA_CPU:0, XLA_CPU, 0, 0)\n"
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          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Pn2bpeiQKf6x"
      },
      "source": [
        "#Load the saved training set, model, and so on. But I only need the training set. The file includes the model because I packaged everything in a zip for convenience."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FB1YSlH5DIE4",
        "outputId": "a5231b82-424e-40f3-cb99-a37c1ca29925"
      },
      "source": [
        "!gdown https://drive.google.com/uc?id=1nbWJO9wGMODfnmYGCASi9kEozpgtIRjo\r\n",
        "!unzip weibo_baselines.zip"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Downloading...\n",
            "From: https://drive.google.com/uc?id=1nbWJO9wGMODfnmYGCASi9kEozpgtIRjo\n",
            "To: /content/weibo_baselines.zip\n",
            "38.9MB [00:00, 105MB/s] \n",
            "Archive:  weibo_baselines.zip\n",
            "   creating: weibo0.15split_testAcc90.48%/\n",
            "   creating: weibo0.50split_testAcc90.14%/\n",
            "   creating: weibo0.85split_testAcc98.84%/\n",
            "  inflating: uids_for_detection.csv  \n",
            "  inflating: weibo0.50split_testAcc90.14%/metrics_means_and_stds.csv  \n",
            "  inflating: weibo0.50split_testAcc90.14%/weibo_model.h5  \n",
            "  inflating: weibo0.50split_testAcc90.14%/token2idW.csv  \n",
            "  inflating: weibo0.50split_testAcc90.14%/token2idTR.csv  \n",
            "  inflating: weibo0.50split_testAcc90.14%/weibo_dataset.csv  \n",
            "   creating: weibo0.85split_testAcc98.84%/.ipynb_checkpoints/\n",
            "  inflating: weibo0.85split_testAcc98.84%/metrics_means_and_stds.csv  \n",
            "  inflating: weibo0.85split_testAcc98.84%/weibo_model.h5  \n",
            "  inflating: weibo0.85split_testAcc98.84%/token2idW.csv  \n",
            "  inflating: weibo0.85split_testAcc98.84%/token2idTR.csv  \n",
            "  inflating: weibo0.85split_testAcc98.84%/weibo_dataset.csv  \n",
            "   creating: weibo0.15split_testAcc90.48%/.ipynb_checkpoints/\n",
            "  inflating: weibo0.15split_testAcc90.48%/metrics_means_and_stds.csv  \n",
            "  inflating: weibo0.15split_testAcc90.48%/weibo_model.h5  \n",
            "  inflating: weibo0.15split_testAcc90.48%/token2idW.csv  \n",
            "  inflating: weibo0.15split_testAcc90.48%/token2idTR.csv  \n",
            "  inflating: weibo0.15split_testAcc90.48%/weibo_dataset.csv  \n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uOXyrjNgliNl",
        "outputId": "068a7ccc-4a20-4621-8a28-e178becbeab8"
      },
      "source": [
        "cd weibo0.85split_testAcc98.84%"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/content/weibo0.85split_testAcc98.84%\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TH2wfO4i0Q5x"
      },
      "source": [
        "#Split the data set into training set and test set. The split_pos in the third line of code means that 85% will be the training set and 15% will be the test set. This code cell is responsible for processing the training set."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "E4LLQFa7kn0U",
        "outputId": "e1241a1f-2478-4ff4-d0db-9bf8a818b910"
      },
      "source": [
        "dataset=pd.read_csv('weibo_dataset.csv',sep=',',keep_default_na=False)\r\n",
        "split_rate=0.85\r\n",
        "split_pos=int(len(dataset)*split_rate)\r\n",
        "training_dataset=dataset[:split_pos]\r\n",
        "n_weibos=training_dataset['n_weibos'].values\r\n",
        "labels=training_dataset['label'].values\r\n",
        "num_weibo=20\r\n",
        "def merge_two_list(a,b):\r\n",
        "  if isinstance(a[0],ndarray) == False and isinstance(a[0],list)==False:\r\n",
        "    return np.concatenate((array(a).reshape(-1,1),array(b).reshape(-1,1)),axis=1).reshape(len(a)*2)\r\n",
        "  return np.concatenate((a,b),axis=1).reshape(len(a)*2,len(a[0]),-1)\r\n",
        "def chinese_cut_token(sentences):\r\n",
        "  sentences_cut=[]\r\n",
        "  for input in sentences:\r\n",
        "    input=ast.literal_eval(input)\r\n",
        "    sentence_cut=[[\"None\"]]*num_weibo\r\n",
        "    for i,weibo in enumerate(input):\r\n",
        "      if (i>=num_weibo): break\r\n",
        "      tokens=jieba.cut(weibo, cut_all=True)\r\n",
        "      sentence_cut[i]=[word for word in tokens]\r\n",
        "    sentences_cut.append(sentence_cut[:num_weibo])\r\n",
        "  dic=corpora.Dictionary([token for sentence_cut in sentences_cut for token in sentence_cut]).token2id\r\n",
        "  X=[]\r\n",
        "  X_mask=[]\r\n",
        "  for user in sentences_cut:\r\n",
        "    x,x_mask=[],[]\r\n",
        "    for weibo_sentence in user:\r\n",
        "      x.append([dic[token]+2 for token in weibo_sentence])\r\n",
        "      ids=array([dic[token]+2 for token in weibo_sentence])\r\n",
        "      if len(ids)>=2:\r\n",
        "        ids[randint(len(ids)-1,size=int(ceil(len(ids)/(10 if rand()>0.68 else 8))))]=1\r\n",
        "      x_mask.append(ids)\r\n",
        "    X.append(x)\r\n",
        "    X_mask.append(x_mask)\r\n",
        "  maxLen=max(len(max(x,key=len)) for x in X)\r\n",
        "  wordsSize =len(dic)+1\r\n",
        "  return sentences_cut,dic,merge_two_list(X,X_mask),maxLen,wordsSize\r\n",
        "_,dicW,XW,maxLenW,wordsSizeW=chinese_cut_token(n_weibos)\r\n",
        "n_weibos_seq = sequence.pad_sequences(XW.reshape(-1), maxLenW).reshape(len(n_weibos)*2,num_weibo,maxLenW)\r\n",
        "n_topicsAndRetwefeatuers=np.concatenate((training_dataset['n_topics'],training_dataset['n_retwefeatuers']))\r\n",
        "_,dicTR,XTR,maxLenTR,wordsSizeTR=chinese_cut_token(n_topicsAndRetwefeatuers)\r\n",
        "n_TR_seq = sequence.pad_sequences(XTR.reshape(-1), maxLenTR).reshape(len(n_topicsAndRetwefeatuers)*2,num_weibo,maxLenTR)\r\n",
        "gap=len(training_dataset['n_retwefeatuers'])*2\r\n",
        "n_topics_seq=n_TR_seq[:gap]\r\n",
        "n_retwefeatuers_seq=n_TR_seq[gap:]\r\n",
        "user_metrics_dataset=training_dataset[['follow_count', 'followers_count', 'mbrank', 'mbtype', 'statuses_count', 'urank', 'verified', 'verified_type', 'verified_type_ext']]\r\n",
        "umd_mean=user_metrics_dataset.mean(axis=0).values\r\n",
        "umd_std=user_metrics_dataset.std(axis=0).values\r\n",
        "user_metrics_dataset=(user_metrics_dataset.values-umd_mean)/umd_std\r\n",
        "user_metrics_dataset=merge_two_list(user_metrics_dataset,user_metrics_dataset)\r\n",
        "tmp=[ast.literal_eval(row) for row in training_dataset['n_weibo_infos']]\r\n",
        "weibo_metrics_dataset=ones((len(tmp),num_weibo,len(tmp[0][0])))*-2#e.g if some users has less than num_weibo, then -2 means empty weibo\r\n",
        "for i in range(weibo_metrics_dataset.shape[0]):\r\n",
        "  weibo_metrics_dataset[i,:len(tmp[i])]=tmp[i][:num_weibo]\r\n",
        "wmd_mean=weibo_metrics_dataset.reshape(-1,5).mean(axis=0)\r\n",
        "wmd_std=weibo_metrics_dataset.reshape(-1,5).std(axis=0)\r\n",
        "weibo_metrics_dataset=array([(wi-wmd_mean)/wmd_std for wi in weibo_metrics_dataset])\r\n",
        "weibo_metrics_dataset=merge_two_list(weibo_metrics_dataset,weibo_metrics_dataset)\r\n",
        "trainInputs=[*[(n_weibos_seq[:,i],n_retwefeatuers_seq[:,i],n_topics_seq[:,i],weibo_metrics_dataset[:,i])for i in range(num_weibo)],user_metrics_dataset]\r\n",
        "trainOutputs=OneHotEncoder().fit_transform(merge_two_list(labels,labels).reshape((-1,1))).toarray()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Building prefix dict from the default dictionary ...\n",
            "DEBUG:jieba:Building prefix dict from the default dictionary ...\n",
            "Dumping model to file cache /tmp/jieba.cache\n",
            "DEBUG:jieba:Dumping model to file cache /tmp/jieba.cache\n",
            "Loading model cost 1.017 seconds.\n",
            "DEBUG:jieba:Loading model cost 1.017 seconds.\n",
            "Prefix dict has been built successfully.\n",
            "DEBUG:jieba:Prefix dict has been built successfully.\n",
            "<string>:6: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "t-iRRPIh08kU"
      },
      "source": [
        "#This code cell is responsible for processing the test set."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "w_YsMwwsTrMB",
        "outputId": "37ab6055-1910-4942-9328-7d124a249e0e"
      },
      "source": [
        "testset_dataset=dataset[split_pos:]\r\n",
        "n_weibos=testset_dataset['n_weibos'].values\r\n",
        "labels=testset_dataset['label'].values\r\n",
        "def chinese_cut_token(sentences,dic=None):\r\n",
        "  sentences_cut=[]\r\n",
        "  for input in sentences:\r\n",
        "    input=ast.literal_eval(input)\r\n",
        "    sentence_cut=[[\"None\"]]*num_weibo\r\n",
        "    for i,weibo in enumerate(input):\r\n",
        "      if (i>=num_weibo): break\r\n",
        "      tokens=jieba.cut(weibo, cut_all=True)\r\n",
        "      sentence_cut[i]=[word for word in tokens]\r\n",
        "    sentences_cut.append(sentence_cut[:num_weibo])\r\n",
        "  dic=corpora.Dictionary([token for sentence_cut in sentences_cut for token in sentence_cut]).token2id if dic==None else dic\r\n",
        "  X=[]\r\n",
        "  for user in sentences_cut:\r\n",
        "    x=[]\r\n",
        "    for weibo_sentence in user:\r\n",
        "      x.append([dic.get(token,-1)+2 for token in weibo_sentence])\r\n",
        "    X.append(x)\r\n",
        "  maxLen=max(len(max(x,key=len)) for x in X)\r\n",
        "  wordsSize =len(dic)+1\r\n",
        "  return sentences_cut,dic,array(X),maxLen,wordsSize\r\n",
        "_,_,XW,_,_=chinese_cut_token(n_weibos,dicW)\r\n",
        "n_weibos_seq2 = sequence.pad_sequences(XW.reshape(-1), maxLenW).reshape(len(n_weibos),num_weibo,maxLenW)\r\n",
        "n_topicsAndRetwefeatuers2=append(testset_dataset['n_retwefeatuers'],testset_dataset['n_topics'])\r\n",
        "_,_,XTR,_,_=chinese_cut_token(n_topicsAndRetwefeatuers2,dicTR)\r\n",
        "n_topicsAndRetwefeatuers_seq2 = sequence.pad_sequences(XTR.reshape(-1), maxLenTR).reshape(len(n_topicsAndRetwefeatuers2),num_weibo,maxLenTR)\r\n",
        "n_retwefeatuers_seq2=n_topicsAndRetwefeatuers_seq2[:len(testset_dataset['n_retwefeatuers'])]\r\n",
        "n_topics_seq2=n_topicsAndRetwefeatuers_seq2[len(testset_dataset['n_retwefeatuers']):]\r\n",
        "user_metrics_dataset2=testset_dataset[['follow_count', 'followers_count', 'mbrank', 'mbtype', 'statuses_count', 'urank', 'verified', 'verified_type', 'verified_type_ext']]\r\n",
        "user_metrics_dataset2=(user_metrics_dataset2-umd_mean)/umd_std\r\n",
        "tmp=[ast.literal_eval(row) for row in testset_dataset['n_weibo_infos']]\r\n",
        "weibo_metrics_dataset2=ones((len(tmp),num_weibo,len(tmp[0][0])))*-2\r\n",
        "for i in range(weibo_metrics_dataset2.shape[0]):\r\n",
        "  weibo_metrics_dataset2[i,:len(tmp[i])]=tmp[i][:num_weibo]\r\n",
        "weibo_metrics_dataset2=array([(wi-wmd_mean)/wmd_std for wi in weibo_metrics_dataset2])\r\n",
        "testInputs=[*[(n_weibos_seq2[:,i],n_retwefeatuers_seq2[:,i],n_topics_seq2[:,i],weibo_metrics_dataset2[:,i])for i in range(num_weibo)],user_metrics_dataset2]\r\n",
        "testOutputs=OneHotEncoder().fit_transform(append(labels,0).reshape((-1,1))).toarray()[:-1]\r\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:23: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pM8BeEkwvvTP"
      },
      "source": [
        "#Model design, build the model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6J_ADIOkvjfX"
      },
      "source": [
        "model = Sequential()\r\n",
        "def biLSTM(wordsSize,maxLen):\r\n",
        "  modelBiLSTM=Sequential()#81\r\n",
        "  modelBiLSTM.add(Embedding(wordsSize+1,32,input_length=maxLen))\r\n",
        "  modelBiLSTM.add(Bidirectional(LSTM(32,dropout=0.4,return_sequences=True)))\r\n",
        "  modelBiLSTM.add(Bidirectional(LSTM(64,dropout=0.4,return_sequences=False)))\r\n",
        "  modelBiLSTM.add(Dense(1,activation='tanh'))\r\n",
        "  return modelBiLSTM\r\n",
        "N=num_weibo\r\n",
        "inputs=[Input(shape=n_weibos_seq[0,0].shape) for i in range(N)]\r\n",
        "inputs_t=[Input(shape=n_topics_seq[0,0].shape) for i in range(N)]\r\n",
        "inputs_r=[Input(shape=n_retwefeatuers_seq[0,0].shape) for i in range(N)]\r\n",
        "inputsm=[Input(shape=(len(weibo_metrics_dataset[0][0]),)) for i in range(N)]\r\n",
        "b=biLSTM(wordsSizeW,maxLenW)\r\n",
        "b_t=biLSTM(wordsSizeTR,maxLenTR)\r\n",
        "b_r=biLSTM(wordsSizeTR,maxLenTR)\r\n",
        "weibo_metrics_importance=2\r\n",
        "weibo_metrics = Sequential()\r\n",
        "weibo_metrics.add(Dense(16, activation='tanh', input_shape=(len(weibo_metrics_dataset[0][0]),)))\r\n",
        "weibo_metrics.add(Dense(weibo_metrics_importance, activation='tanh'))\r\n",
        "concat = concatenate([b.output,b_t.output,b_r.output,weibo_metrics.output], axis=-1)\r\n",
        "output = Dense(16, activation='tanh')(concat)\r\n",
        "output = Dense(1, activation='tanh')(output)\r\n",
        "model = Model(inputs=[b.inputs,b_t.inputs,b_r.inputs,weibo_metrics.inputs], outputs=output)\r\n",
        "biLSTMs=[model([inputs[i],inputs_t[i],inputs_r[i],inputsm[i]]) for i in range(N)]\r\n",
        "concat = concatenate(biLSTMs, axis=-1)\r\n",
        "output = Reshape((-1,1))(concat)\r\n",
        "output=LSTM(32,dropout=0.4,return_sequences=False)(output)\r\n",
        "output = Dense(1, activation='tanh')(output)\r\n",
        "user_metrics_importance=5\r\n",
        "user_metrics = Sequential()\r\n",
        "user_metrics.add(Dense(32, activation='tanh', input_shape=(user_metrics_dataset.shape[1],)))\r\n",
        "user_metrics.add(Dense(user_metrics_importance, activation='tanh'))\r\n",
        "concat = concatenate([output,user_metrics.output], axis=-1)\r\n",
        "output = Dense(16, activation='tanh')(concat)\r\n",
        "output = Dense(2, activation='softmax')(output)\r\n",
        "model = Model(inputs=[*list(zip(inputs,inputs_t,inputs_r,inputsm)),user_metrics.inputs], outputs=output)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 85
        },
        "id": "en-KIshQ6igj",
        "outputId": "b93d2bb3-443d-486b-8912-89a33ab761f6"
      },
      "source": [
        "from keras.utils import plot_model\r\n",
        "plot_model(model,show_shapes=True,show_layer_names=True,expand_nested=True)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dw2GrkNnMCdV"
      },
      "source": [
        "#Train the model, a little slow in the first epoch, please wait."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3iyLKzd0ahkY"
      },
      "source": [
        "model.compile(loss='mse', optimizer='adam',metrics=['accuracy'])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1rNpWrqXbTC0"
      },
      "source": [
        "class AccStop(tf.keras.callbacks.Callback):\r\n",
        "    def __init__(self, threshold):\r\n",
        "        super(AccStop, self).__init__()\r\n",
        "        self.threshold = threshold\r\n",
        "    def on_epoch_end(self, epoch, logs=None): \r\n",
        "        if logs[\"val_accuracy\"] >= self.threshold and logs[\"accuracy\"] >= self.threshold:\r\n",
        "            self.model.stop_training = True\r\n",
        "reduce_lr = ReduceLROnPlateau(monitor='val_loss', patience=10, mode='auto')\r\n",
        "acc_stop=AccStop(0.95)\r\n",
        "model.fit(trainInputs, array(trainOutputs).astype(int), batch_size=32, epochs=1000,shuffle=True,validation_data=(testInputs, array(testOutputs).astype(int)),callbacks=[reduce_lr,acc_stop])\r\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KeRjw6MxbQ2N"
      },
      "source": [
        "#Test the model, test set accuracy."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7Cb11hunk356",
        "outputId": "eac8e945-b00f-4f41-ead8-99bd556be127"
      },
      "source": [
        "pred=model.predict(testInputs).argmax(axis=1)\r\n",
        "acc=np.count_nonzero((pred.round().astype(int).reshape(-1)==testOutputs.argmax(axis=1))==True)/len(pred)\r\n",
        "print(\"%d test set accuracy = %.2f%% (test set includes tokens which have never been trained. Model was trained with %d training set)\"%(len(testOutputs),acc*100,len(trainOutputs)//2))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "86 test set accuracy = 98.84% (test set includes tokens which have never been trained. Model was trained with 482 training set)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FTkbNYBbf16s",
        "outputId": "5584c877-359e-48dc-c50b-7b1c03114f23"
      },
      "source": [
        "#This is for those who understand Chinese language or use weibo.\r\n",
        "predict_wrong=where((pred.round().astype(int).reshape(-1)==testOutputs.argmax(axis=1))==False)\r\n",
        "print(\"The ids of users who were identified incorrectly: \"+str(dataset[split_pos:]['uid'].values[predict_wrong]))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "The ids of users who were identified incorrectly: [6432141741]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3acBOKU7zoAy",
        "outputId": "0c0b9a36-0602-484a-f77a-822182e32daa"
      },
      "source": [
        "#these are wrong prediction when test set accuracy is 95.35%, some interesting facts:\r\n",
        "#3977649527 posted a tweet \"Why my weibo was hacked?\"\r\n",
        "#6432141741 and 3977649527 were incorrectly predicted in different models."
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "The ids of users who were identified incorrectly: [3977649527 3308066363 1236000223 6432141741]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hFddftQ91GOW"
      },
      "source": [
        "#Save metrics' means and standard deviations (for input normalization), dictionaries, as well as the model and its weights. which will be the baseline file (not a necessary step, just if you want to save the model in case you will have to retrain it later)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "e3hvClwoOGTC",
        "outputId": "d878e2ab-87b5-4fd7-f4c1-63c3235e11eb"
      },
      "source": [
        "cd .."
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/content\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "F5UrfuO1BXjM"
      },
      "source": [
        "from shutil import make_archive\r\n",
        "!mkdir weibo_model\r\n",
        "pd.DataFrame.from_dict(dicW, orient='index', columns=['id']).to_csv('weibo_model/token2idW.csv', columns=['id'],encoding=\"utf_8_sig\") \r\n",
        "pd.DataFrame.from_dict(dicTR, orient='index', columns=['id']).to_csv('weibo_model/token2idTR.csv', columns=['id'],encoding=\"utf_8_sig\") \r\n",
        "metrics_means_and_stds=pd.DataFrame([[str(wmd_mean.tolist()),str(wmd_std.tolist()),str(umd_mean.tolist()),str(umd_std.tolist())]],columns=[\"weibo_metrics_dataset_mean\",\"weibo_metrics_dataset_std\",\"user_metrics_dataset_mean\",\"user_metrics_dataset_std\"])\r\n",
        "metrics_means_and_stds.to_csv('weibo_model/metrics_means_and_stds.csv') \r\n",
        "dataset.to_csv('weibo_model/weibo_dataset.csv',index=False,encoding=\"utf_8_sig\") \r\n",
        "save_options = tf.saved_model.SaveOptions(experimental_io_device='/job:localhost')\r\n",
        "model.save('weibo_model/weibo_model.h5',options=save_options)\r\n",
        "make_archive(\"weibo%.2fsplit_test%.2f%%\"%(split_rate,acc*100),'zip',root_dir=\"weibo_model\",base_dir=None)   "
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}