Sentiment_Analysis.ipynb

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        "<a href=\"https://colab.research.google.com/github/VGODIE/ML_kaggle_competitions/blob/master/Sentiment_Analysis.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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        "import numpy as np\n",
        "import pandas as pd\n",
        "import tensorflow as tf"
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              "<p style=\"color: red;\">\n",
              "The default version of TensorFlow in Colab will soon switch to TensorFlow 2.x.<br>\n",
              "We recommend you <a href=\"https://www.tensorflow.org/guide/migrate\" target=\"_blank\">upgrade</a> now \n",
              "or ensure your notebook will continue to use TensorFlow 1.x via the <code>%tensorflow_version 1.x</code> magic:\n",
              "<a href=\"https://colab.research.google.com/notebooks/tensorflow_version.ipynb\" target=\"_blank\">more info</a>.</p>\n"
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      },
      "source": [
        "print(tf.__version__)"
      ],
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        {
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          "text": [
            "1.15.0\n"
          ],
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wdXRFbGVtZW50LmRpc2FibGVkID0gdHJ1ZTsKCiAgZm9yIChjb25zdCBmaWxlIG9mIGZpbGVzKSB7CiAgICBjb25zdCBsaSA9IGRvY3VtZW50LmNyZWF0ZUVsZW1lbnQoJ2xpJyk7CiAgICBsaS5hcHBlbmQoc3BhbihmaWxlLm5hbWUsIHtmb250V2VpZ2h0OiAnYm9sZCd9KSk7CiAgICBsaS5hcHBlbmQoc3BhbigKICAgICAgICBgKCR7ZmlsZS50eXBlIHx8ICduL2EnfSkgLSAke2ZpbGUuc2l6ZX0gYnl0ZXMsIGAgKwogICAgICAgIGBsYXN0IG1vZGlmaWVkOiAkewogICAgICAgICAgICBmaWxlLmxhc3RNb2RpZmllZERhdGUgPyBmaWxlLmxhc3RNb2RpZmllZERhdGUudG9Mb2NhbGVEYXRlU3RyaW5nKCkgOgogICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAnbi9hJ30gLSBgKSk7CiAgICBjb25zdCBwZXJjZW50ID0gc3BhbignMCUgZG9uZScpOwogICAgbGkuYXBwZW5kQ2hpbGQocGVyY2VudCk7CgogICAgb3V0cHV0RWxlbWVudC5hcHBlbmRDaGlsZChsaSk7CgogICAgY29uc3QgZmlsZURhdGFQcm9taXNlID0gbmV3IFByb21pc2UoKHJlc29sdmUpID0+IHsKICAgICAgY29uc3QgcmVhZGVyID0gbmV3IEZpbGVSZWFkZXIoKTsKICAgICAgcmVhZGVyLm9ubG9hZCA9IChlKSA9PiB7CiAgICAgICAgcmVzb2x2ZShlLnRhcmdldC5yZXN1bHQpOwogICAgICB9OwogICAgICByZWFkZXIucmVhZEFzQXJyYXlCdWZmZXIoZmlsZSk7CiAgICB9KTsKICAgIC8vIFdhaXQgZm9yIHRoZSBkYXRhIHRvIGJlIHJlYWR5LgogICAgbGV0IGZpbGVEYXRhID0geWllbGQgewogICAgICBwcm9taXNlOiBmaWxlRGF0YVByb21pc2UsCiAgICAgIHJlc3BvbnNlOiB7CiAgICAgICAgYWN0aW9uOiAnY29udGludWUnLAogICAgICB9CiAgICB9OwoKICAgIC8vIFVzZSBhIGNodW5rZWQgc2VuZGluZyB0byBhdm9pZCBtZXNzYWdlIHNpemUgbGltaXRzLiBTZWUgYi82MjExNTY2MC4KICAgIGxldCBwb3NpdGlvbiA9IDA7CiAgICB3aGlsZSAocG9zaXRpb24gPCBmaWxlRGF0YS5ieXRlTGVuZ3RoKSB7CiAgICAgIGNvbnN0IGxlbmd0aCA9IE1hdGgubWluKGZpbGVEYXRhLmJ5dGVMZW5ndGggLSBwb3NpdGlvbiwgTUFYX1BBWUxPQURfU0laRSk7CiAgICAgIGNvbnN0IGNodW5rID0gbmV3IFVpbnQ4QXJyYXkoZmlsZURhdGEsIHBvc2l0aW9uLCBsZW5ndGgpOwogICAgICBwb3NpdGlvbiArPSBsZW5ndGg7CgogICAgICBjb25zdCBiYXNlNjQgPSBidG9hKFN0cmluZy5mcm9tQ2hhckNvZGUuYXBwbHkobnVsbCwgY2h1bmspKTsKICAgICAgeWllbGQgewogICAgICAgIHJlc3BvbnNlOiB7CiAgICAgICAgICBhY3Rpb246ICdhcHBlbmQnLAogICAgICAgICAgZmlsZTogZmlsZS5uYW1lLAogICAgICAgICAgZGF0YTogYmFzZTY0LAogICAgICAgIH0sCiAgICAgIH07CiAgICAgIHBlcmNlbnQudGV4dENvbnRlbnQgPQogICAgICAgICAgYCR7TWF0aC5yb3VuZCgocG9zaXRpb24gLyBmaWxlRGF0YS5ieXRlTGVuZ3RoKSAqIDEwMCl9JSBkb25lYDsKICAgIH0KICB9CgogIC8vIEFsbCBkb25lLgogIHlpZWxkIHsKICAgIHJlc3BvbnNlOiB7CiAgICAgIGFjdGlvbjogJ2NvbXBsZXRlJywKICAgIH0KICB9Owp9CgpzY29wZS5nb29nbGUgPSBzY29wZS5nb29nbGUgfHwge307CnNjb3BlLmdvb2dsZS5jb2xhYiA9IHNjb3BlLmdvb2dsZS5jb2xhYiB8fCB7fTsKc2NvcGUuZ29vZ2xlLmNvbGFiLl9maWxlcyA9IHsKICBfdXBsb2FkRmlsZXMsCiAgX3VwbG9hZEZpbGVzQ29udGludWUsCn07Cn0pKHNlbGYpOwo=",
              "ok": true,
              "headers": [
                [
                  "content-type",
                  "application/javascript"
                ]
              ],
              "status": 200,
              "status_text": ""
            }
          },
          "base_uri": "https://localhost:8080/",
          "height": 88
        }
      },
      "source": [
        "from google.colab import files\n",
        "files.upload()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-eb90cdb9-3635-4ca7-bf4a-64bc6cbb993a\" name=\"files[]\" multiple disabled />\n",
              "     <output id=\"result-eb90cdb9-3635-4ca7-bf4a-64bc6cbb993a\">\n",
              "      Upload widget is only available when the cell has been executed in the\n",
              "      current browser session. Please rerun this cell to enable.\n",
              "      </output>\n",
              "      <script src=\"/nbextensions/google.colab/files.js\"></script> "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Saving kaggle.json to kaggle.json\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'kaggle.json': b'{\"username\":\"vgodie\",\"key\":\"914a11543650585c542451372ade778c\"}'}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QDGoTUSWo3sF",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "! mkdir -p ~/.kaggle\n",
        "! cp kaggle.json ~/.kaggle\n",
        "! chmod 600 ~/.kaggle/kaggle.json"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "unfd-OzUo6PQ",
        "colab_type": "code",
        "outputId": "404f5e5b-a514-4def-9508-ffd5354a7e3c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 238
        }
      },
      "source": [
        "! kaggle competitions download -c iad-deep-learning-sentiment"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Warning: Looks like you're using an outdated API Version, please consider updating (server 1.5.6 / client 1.5.4)\n",
            "Downloading x_train.txt.zip to /content\n",
            " 98% 581M/595M [00:04<00:00, 108MB/s]\n",
            "100% 595M/595M [00:04<00:00, 133MB/s]\n",
            "Downloading x_test.txt.zip to /content\n",
            " 72% 49.0M/68.0M [00:00<00:00, 107MB/s]\n",
            "100% 68.0M/68.0M [00:00<00:00, 139MB/s]\n",
            "Downloading random_prediction.csv.zip to /content\n",
            "  0% 0.00/2.08M [00:00<?, ?B/s]\n",
            "100% 2.08M/2.08M [00:00<00:00, 141MB/s]\n",
            "Downloading y_train.csv.zip to /content\n",
            "  0% 0.00/8.37M [00:00<?, ?B/s]\n",
            "100% 8.37M/8.37M [00:00<00:00, 77.0MB/s]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VFNgngRQpLys",
        "colab_type": "code",
        "outputId": "817890e3-6eea-45c1-bcfe-9227296bfde8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 119
        }
      },
      "source": [
        "! unzip x_train.txt.zip\n",
        "! unzip x_test.txt.zip\n",
        "! unzip y_train.csv.zip"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Archive:  x_train.txt.zip\n",
            "  inflating: x_train.txt             \n",
            "Archive:  x_test.txt.zip\n",
            "  inflating: x_test.txt              \n",
            "Archive:  y_train.csv.zip\n",
            "  inflating: y_train.csv             \n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KYQI5fyTqEwZ",
        "colab_type": "text"
      },
      "source": [
        "# Download data and preprocess"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2NKyEo-5pYy5",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "f = open(\"x_train.txt\", \"r\")\n",
        "x_train = f.readlines()\n",
        "f.close()\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UUz70-WQqACI",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "y_train = pd.read_csv(\"y_train.csv\")"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Lxo9J4BwqOT5",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def delete_punctuation(x):\n",
        "    punctuation = list(string.punctuation)\n",
        "    return ''.join([a if a not in punctuation + ['\\n'] else ' ' for a in x])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_sgXf5AJs8Pq",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from tensorflow.keras.preprocessing.text import Tokenizer\n",
        "from sklearn.model_selection import train_test_split\n",
        "from tensorflow.keras.preprocessing.text import text_to_word_sequence\n",
        "from tensorflow.keras.preprocessing.sequence import pad_sequences"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YBtJsVLit-WL",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "x_train, x_test, y_train, y_test = train_test_split(x_train, y_train.Probability.values)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eY7rgWfUrvEl",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "max_features = 100000\n",
        "max_len = 100\n",
        "embedding_size = 300\n",
        "\n",
        "tokenizer = Tokenizer(num_words=max_features)\n",
        "tokenizer.fit_on_texts(x_train)\n",
        "list_tokenized_train = tokenizer.texts_to_sequences(x_train)\n",
        "list_tokenized_test = tokenizer.texts_to_sequences(x_test)\n",
        "x_train_pad = pad_sequences(list_tokenized_train, maxlen=max_len)\n",
        "x_test_pad = pad_sequences(list_tokenized_test, maxlen=max_len)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LUvd0bqA7Aff",
        "colab_type": "code",
        "outputId": "8c407388-a28d-435c-b290-0c00aa8cd939",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 357
        }
      },
      "source": [
        "!wget http://nlp.stanford.edu/data/wordvecs/glove.42B.300d.zip"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "--2019-12-21 22:13:51--  http://nlp.stanford.edu/data/wordvecs/glove.42B.300d.zip\n",
            "Resolving nlp.stanford.edu (nlp.stanford.edu)... 171.64.67.140\n",
            "Connecting to nlp.stanford.edu (nlp.stanford.edu)|171.64.67.140|:80... connected.\n",
            "HTTP request sent, awaiting response... 302 Found\n",
            "Location: https://nlp.stanford.edu/data/wordvecs/glove.42B.300d.zip [following]\n",
            "--2019-12-21 22:13:51--  https://nlp.stanford.edu/data/wordvecs/glove.42B.300d.zip\n",
            "Connecting to nlp.stanford.edu (nlp.stanford.edu)|171.64.67.140|:443... connected.\n",
            "HTTP request sent, awaiting response... 301 Moved Permanently\n",
            "Location: http://downloads.cs.stanford.edu/nlp/data/wordvecs/glove.42B.300d.zip [following]\n",
            "--2019-12-21 22:13:51--  http://downloads.cs.stanford.edu/nlp/data/wordvecs/glove.42B.300d.zip\n",
            "Resolving downloads.cs.stanford.edu (downloads.cs.stanford.edu)... 171.64.64.22\n",
            "Connecting to downloads.cs.stanford.edu (downloads.cs.stanford.edu)|171.64.64.22|:80... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 1877802108 (1.7G) [application/zip]\n",
            "Saving to: ‘glove.42B.300d.zip’\n",
            "\n",
            "glove.42B.300d.zip  100%[===================>]   1.75G  2.10MB/s    in 14m 33s \n",
            "\n",
            "2019-12-21 22:28:24 (2.05 MB/s) - ‘glove.42B.300d.zip’ saved [1877802108/1877802108]\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "n-WiPem0oIDq",
        "colab_type": "code",
        "outputId": "d115dedf-74b5-4c68-f5d7-5ee8c749e760",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        }
      },
      "source": [
        "! unzip glove.42B.300d.zip"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Archive:  glove.42B.300d.zip\n",
            "  inflating: glove.42B.300d.txt      \n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "txN0QQKHjAWT",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from tqdm import tqdm_notebook"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7I2G1VNWjQ3X",
        "colab_type": "code",
        "outputId": "11893191-6bbb-44ec-f3f1-5666cd3eed51",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 66,
          "referenced_widgets": [
            "1dae5ae23a7f416a8c66fbb2e1cdf241",
            "f20299429cdc46f692d98efdd2b0fcec",
            "498add6ce4b94cf6a5384136f8e072ce",
            "cda59fdb28654a6e8eaa9a3251f2a321",
            "95bae674a62d4717ab7db3ab0a8d6125",
            "8a3081f009924aeebb225a453947820e",
            "091440ca7be742bda8e10ac44a5eb095",
            "8ebdc558a7a749e0928027a9ff561729"
          ]
        }
      },
      "source": [
        "f = open('./glove.42B.300d.txt')\n",
        "\n",
        "embeddings_index = dict() \n",
        "for line in tqdm_notebook(f):\n",
        "    values = line.split(' ')\n",
        "    word = values[0]\n",
        "    coefs = np.asarray(values[1:], dtype='float32')\n",
        "    embeddings_index[word] = coefs\n",
        "f.close()\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "1dae5ae23a7f416a8c66fbb2e1cdf241",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(IntProgress(value=1, bar_style='info', max=1), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Tf8BoSk2jRsZ",
        "colab_type": "code",
        "outputId": "fc86f69b-629a-4c38-c9ed-220faa9882f4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 88
        }
      },
      "source": [
        "all_embs = np.stack(embeddings_index.values())\n",
        "emb_mean, emb_std = all_embs.mean(), all_embs.std()\n",
        "emb_mean, emb_std"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/IPython/core/interactiveshell.py:2822: FutureWarning: arrays to stack must be passed as a \"sequence\" type such as list or tuple. Support for non-sequence iterables such as generators is deprecated as of NumPy 1.16 and will raise an error in the future.\n",
            "  if self.run_code(code, result):\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(0.005720101, 0.2951066)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xAQ7_L88jX0Q",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "word_index = tokenizer.word_index\n",
        "unknown_words = set()\n",
        "nb_words = min(max_features, len(word_index))\n",
        "embedding_matrix = np.random.normal(emb_mean, emb_std, (nb_words, embedding_size))\n",
        "for word, i in word_index.items():\n",
        "    if i >= max_features: continue\n",
        "    embedding_vector = embeddings_index.get(word)\n",
        "    if embedding_vector is not None: embedding_matrix[i] = embedding_vector\n",
        "    else: unknown_words.add(word)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vo8kqFvUpVVT",
        "colab_type": "text"
      },
      "source": [
        "# Definind and training model\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8gW6iVnwpZRM",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from tensorflow.keras.layers import Input, Bidirectional, Dropout, Dense, GRU, Embedding, LayerNormalization, LeakyReLU\n",
        "from tensorflow.keras import Model\n",
        "from tensorflow.keras.optimizers import Adam, RMSprop\n",
        "from tensorflow.keras.callbacks import EarlyStopping, ReduceLROnPlateau, ModelCheckpoint, LearningRateScheduler"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NuNJix_yl6fy",
        "colab_type": "code",
        "outputId": "4e640ee9-b6f4-4e08-8244-febb6b1c7774",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "input_layer = Input((max_len,), name = 'comment_text')\n",
        "embedding_layer = Embedding(max_features, embedding_size, input_length=max_len, \n",
        "                            weights=[embedding_matrix], \n",
        "                            trainable = False)(input_layer)\n",
        "x = Bidirectional(GRU(128, return_sequences=True))(embedding_layer)\n",
        "x = Dropout(0.3)(x)\n",
        "x = Bidirectional(GRU(128, return_sequences=False))(x)\n",
        "x = Dense(64, activation=LeakyReLU(0.3))(x)\n",
        "output_layer = Dense(1, activation=\"sigmoid\")(x)\n",
        "\n",
        "model = Model(inputs=input_layer, outputs=output_layer)\n",
        "model.compile(loss='binary_crossentropy',\n",
        "                  optimizer=Adam(clipvalue=1, clipnorm=1, amsgrad=True),\n",
        "                  metrics=['accuracy'])\n",
        "print(model.summary())\n",
        "\n",
        "def schedule(ind):\n",
        "    a = [0.001, 0.001, 0.0001, 0.0001, 0.00001, 0.00001, 0.000001]\n",
        "    return a[ind]\n",
        "\n",
        "lr = LearningRateScheduler(schedule)\n",
        "    \n",
        "early_stop = EarlyStopping(monitor='val_loss',\n",
        "                           patience=4,\n",
        "                           verbose=1,\n",
        "                           min_delta=1e-4)\n",
        "\n",
        "\n",
        "history = model.fit(x_train_pad, y_train, batch_size=1500, epochs = 3, \n",
        "                    validation_split = 0.1, callbacks = [lr])"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow_core/python/keras/initializers.py:119: calling RandomUniform.__init__ (from tensorflow.python.ops.init_ops) with dtype is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Call initializer instance with the dtype argument instead of passing it to the constructor\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow_core/python/ops/resource_variable_ops.py:1630: calling BaseResourceVariable.__init__ (from tensorflow.python.ops.resource_variable_ops) with constraint is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "If using Keras pass *_constraint arguments to layers.\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow_core/python/ops/init_ops.py:97: calling GlorotUniform.__init__ (from tensorflow.python.ops.init_ops) with dtype is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Call initializer instance with the dtype argument instead of passing it to the constructor\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow_core/python/ops/init_ops.py:97: calling Orthogonal.__init__ (from tensorflow.python.ops.init_ops) with dtype is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Call initializer instance with the dtype argument instead of passing it to the constructor\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow_core/python/ops/init_ops.py:97: calling Zeros.__init__ (from tensorflow.python.ops.init_ops) with dtype is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Call initializer instance with the dtype argument instead of passing it to the constructor\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow_core/python/ops/nn_impl.py:183: where (from tensorflow.python.ops.array_ops) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Use tf.where in 2.0, which has the same broadcast rule as np.where\n",
            "Model: \"model\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "comment_text (InputLayer)    [(None, 100)]             0         \n",
            "_________________________________________________________________\n",
            "embedding (Embedding)        (None, 100, 300)          30000000  \n",
            "_________________________________________________________________\n",
            "bidirectional (Bidirectional (None, 100, 256)          329472    \n",
            "_________________________________________________________________\n",
            "dropout (Dropout)            (None, 100, 256)          0         \n",
            "_________________________________________________________________\n",
            "bidirectional_1 (Bidirection (None, 256)               295680    \n",
            "_________________________________________________________________\n",
            "dense (Dense)                (None, 64)                16448     \n",
            "_________________________________________________________________\n",
            "dense_1 (Dense)              (None, 1)                 65        \n",
            "=================================================================\n",
            "Total params: 30,641,665\n",
            "Trainable params: 641,665\n",
            "Non-trainable params: 30,000,000\n",
            "_________________________________________________________________\n",
            "None\n",
            "Train on 2430000 samples, validate on 270000 samples\n",
            "Epoch 1/3\n",
            "2430000/2430000 [==============================] - 884s 364us/sample - loss: 0.2056 - acc: 0.9155 - val_loss: 0.1586 - val_acc: 0.9386\n",
            "Epoch 2/3\n",
            "2430000/2430000 [==============================] - 877s 361us/sample - loss: 0.1482 - acc: 0.9437 - val_loss: 0.1414 - val_acc: 0.9466\n",
            "Epoch 3/3\n",
            "1563000/2430000 [==================>...........] - ETA: 5:08 - loss: 0.1306 - acc: 0.9513"
          ],
          "name": "stdout"
        },
        {
          "output_type": "error",
          "ename": "KeyboardInterrupt",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-27-4ea7377e6437>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     28\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     29\u001b[0m history = model.fit(x_train_pad, y_train, batch_size=1500, epochs = 3, \n\u001b[0;32m---> 30\u001b[0;31m                     validation_split = 0.1, callbacks = [lr])\n\u001b[0m",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/tensorflow_core/python/keras/engine/training.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, validation_freq, max_queue_size, workers, use_multiprocessing, **kwargs)\u001b[0m\n\u001b[1;32m    725\u001b[0m         \u001b[0mmax_queue_size\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmax_queue_size\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    726\u001b[0m         \u001b[0mworkers\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mworkers\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 727\u001b[0;31m         use_multiprocessing=use_multiprocessing)\n\u001b[0m\u001b[1;32m    728\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    729\u001b[0m   def evaluate(self,\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/tensorflow_core/python/keras/engine/training_arrays.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, model, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, validation_freq, **kwargs)\u001b[0m\n\u001b[1;32m    673\u001b[0m         \u001b[0mvalidation_steps\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mvalidation_steps\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    674\u001b[0m         \u001b[0mvalidation_freq\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mvalidation_freq\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 675\u001b[0;31m         steps_name='steps_per_epoch')\n\u001b[0m\u001b[1;32m    676\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    677\u001b[0m   def evaluate(self,\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/tensorflow_core/python/keras/engine/training_arrays.py\u001b[0m in \u001b[0;36mmodel_iteration\u001b[0;34m(model, inputs, targets, sample_weights, batch_size, epochs, verbose, callbacks, val_inputs, val_targets, val_sample_weights, shuffle, initial_epoch, steps_per_epoch, validation_steps, validation_freq, mode, validation_in_fit, prepared_feed_values_from_dataset, steps_name, **kwargs)\u001b[0m\n\u001b[1;32m    392\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    393\u001b[0m         \u001b[0;31m# Get outputs.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 394\u001b[0;31m         \u001b[0mbatch_outs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mins_batch\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    395\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbatch_outs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    396\u001b[0m           \u001b[0mbatch_outs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mbatch_outs\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/tensorflow_core/python/keras/backend.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, inputs)\u001b[0m\n\u001b[1;32m   3474\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3475\u001b[0m     fetched = self._callable_fn(*array_vals,\n\u001b[0;32m-> 3476\u001b[0;31m                                 run_metadata=self.run_metadata)\n\u001b[0m\u001b[1;32m   3477\u001b[0m     \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_call_fetch_callbacks\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfetched\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_fetches\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3478\u001b[0m     output_structure = nest.pack_sequence_as(\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/tensorflow_core/python/client/session.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1470\u001b[0m         ret = tf_session.TF_SessionRunCallable(self._session._session,\n\u001b[1;32m   1471\u001b[0m                                                \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_handle\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1472\u001b[0;31m                                                run_metadata_ptr)\n\u001b[0m\u001b[1;32m   1473\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mrun_metadata\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1474\u001b[0m           \u001b[0mproto_data\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtf_session\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTF_GetBuffer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrun_metadata_ptr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rRxbEt8876A7",
        "colab_type": "code",
        "outputId": "35580de3-7e4f-4e14-809f-19a0eea6e9c6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "y_train"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1., 1., 0., ..., 1., 1., 0.])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qg0doGOtMlku",
        "colab_type": "code",
        "outputId": "6161924b-705d-4123-86d6-3035afbfd778",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "np.sum(y_train)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1349818.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uR7Nzu01MqsW",
        "colab_type": "code",
        "outputId": "e73608ee-e9b0-495e-cb93-3cba8d8c6cfc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "len(y_train) - np.sum(y_train)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1350182.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "b8gQEElmMuzR",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "f = open(\"x_test.txt\", \"r\")\n",
        "x_test = f.readlines()\n",
        "f.close()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "c-iaemD5vaIe",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "list_tokenized_test = tokenizer.texts_to_sequences(x_test)\n",
        "x_test_pad = pad_sequences(list_tokenized_test, maxlen=max_len)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XxpCQ1kjvu-B",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "preds = model.predict(x_test_pad, batch_size=2500)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MREJnIwXxcCo",
        "colab_type": "code",
        "outputId": "ea3b6c4f-adac-49c3-a0d5-da1a69c2afaf",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "preds.shape"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(400000, 1)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 47
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nDe3I_Wawqjd",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "sub = pd.DataFrame()\n",
        "sub[\"Probability\"] = preds.reshape(-1,)\n",
        "sub[\"Id\"] = sub.index + 1"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nqN9NDKSw5mV",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "sub.to_csv(\"submission.csv\", index=None)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gvP573YsxQR2",
        "colab_type": "code",
        "outputId": "d4ae8766-50b1-4447-c1dc-2a6ebb2a9c89",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 419
        }
      },
      "source": [
        "sub"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Probability</th>\n",
              "      <th>Id</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.987192</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.998586</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.001727</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.558569</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.995809</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>399995</th>\n",
              "      <td>0.027100</td>\n",
              "      <td>399996</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>399996</th>\n",
              "      <td>0.001290</td>\n",
              "      <td>399997</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>399997</th>\n",
              "      <td>0.129923</td>\n",
              "      <td>399998</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>399998</th>\n",
              "      <td>0.996316</td>\n",
              "      <td>399999</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>399999</th>\n",
              "      <td>0.046182</td>\n",
              "      <td>400000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>400000 rows × 2 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "        Probability      Id\n",
              "0          0.987192       1\n",
              "1          0.998586       2\n",
              "2          0.001727       3\n",
              "3          0.558569       4\n",
              "4          0.995809       5\n",
              "...             ...     ...\n",
              "399995     0.027100  399996\n",
              "399996     0.001290  399997\n",
              "399997     0.129923  399998\n",
              "399998     0.996316  399999\n",
              "399999     0.046182  400000\n",
              "\n",
              "[400000 rows x 2 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 54
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "B_uO9ryfxtLw",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}