3_2_JS_divergence_distance.ipynb

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  "cells": [
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import os\n",
        "import numpy as np\n",
        "import gzip\n",
        "from scipy.spatial import distance\n",
        "from transformers import AutoTokenizer\n",
        "from tqdm.notebook import trange, tqdm\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt"
      ],
      "metadata": {
        "id": "O-k5IjzGLeJG"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "d_dir = \"./data/en-cola/\"\n",
        "subset = \"test.csv\"\n",
        "model_path_tr = \"./roberta-large-cased-en-cola_32_2e-05_lr_0.0001_decay_balanced/\"\n",
        "model_path_nt = \"./roberta-large-cased-en-cola_32_2e-05_lr_0.0001_decay_balanced_frozen/\""
      ],
      "metadata": {
        "id": "SDcELkGB-foN"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data = pd.read_csv(d_dir+subset)"
      ],
      "metadata": {
        "id": "j_71pVGTPKWf"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "output_dir=model_path_tr\n",
        "adj_filenames = [\n",
        "    output_dir + 'attentions/' + filename \n",
        "    for filename in os.listdir(output_dir + 'attentions/') \n",
        "        if subset.split(\".\")[0] + '_part' in (output_dir + 'attentions/' + filename) and '.npy' in filename]\n",
        "# sorted by part number\n",
        "adj_filenames = sorted(adj_filenames, key = lambda x: int(x.split('_')[-1].split('of')[0][4:].strip())) \n",
        "adj_filenames"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "za1wvUtx_J9c",
        "outputId": "3b7db465-4b45-461d-e001-bd9a981fe02a"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['./roberta-large-cased-en-cola_32_2e-05_lr_0.0001_decay_balanced/attentions/test_part1of1.npy.gz']"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "output_dir=model_path_nt\n",
        "adj_filenames_nt = [\n",
        "    output_dir + 'attentions/' + filename \n",
        "    for filename in os.listdir(output_dir + 'attentions/') \n",
        "        if subset.split(\".\")[0] + '_part' in (output_dir + 'attentions/' + filename) and '.npy' in filename\n",
        "]\n",
        "adj_filenames_nt = sorted(adj_filenames_nt, key = lambda x: int(x.split('_')[-1].split('of')[0][4:].strip())) \n",
        "adj_filenames_nt"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EOuWIxDGCYil",
        "outputId": "1f02655b-f51f-40b8-b4cf-eca70620043a"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['./roberta-large-cased-en-cola_32_2e-05_lr_0.0001_decay_balanced_frozen/attentions/test_part1of1.npy.gz']"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def load_matrix(filepath):\n",
        "    loaded_adj=[]\n",
        "    if 'gz' in filepath:\n",
        "        with gzip.GzipFile(filepath, 'rb') as f:\n",
        "            loaded_adj=np.load(f, allow_pickle=True)\n",
        "    else:\n",
        "        loaded_adj=np.load(filepath)\n",
        "    return loaded_adj"
      ],
      "metadata": {
        "id": "qO9s5btcBoM-"
      },
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "id": "_bqQDfLJ-Tki"
      },
      "outputs": [],
      "source": [
        "if len(adj_filenames) > 1:\n",
        "    tr_adj = np.concatenate((load_matrix(adj_filenames[0]), load_matrix(adj_filenames[1])), axis=0)\n",
        "    nt_adj = np.concatenate((load_matrix(adj_filenames_nt[0]), load_matrix(adj_filenames_nt[1])), axis=0)\n",
        "else:\n",
        "    tr_adj = load_matrix(adj_filenames[0])\n",
        "    nt_adj = load_matrix(adj_filenames_nt[0])\n",
        "n_samples = tr_adj.shape[0]"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "cola_gr_major = pd.read_csv(d_dir + \"phenomenon_minor.tsv\", sep='\\t')\n",
        "cola_gr_major = cola_gr_major[cola_gr_major['Sentence'].isin(data.sentence.values)]\n",
        "cola_gr_major = cola_gr_major.reset_index(drop=True)"
      ],
      "metadata": {
        "id": "hdNr8dmYWH1k"
      },
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df=cola_gr_major\n",
        "tokenizer = AutoTokenizer.from_pretrained(model_path_tr)"
      ],
      "metadata": {
        "id": "bxsO3pehXOW-"
      },
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def get_token_length(batch_texts):\n",
        "    inputs = tokenizer.batch_encode_plus(batch_texts,\n",
        "       return_tensors='pt',\n",
        "       add_special_tokens=True,\n",
        "       max_length=64,             # Max length to truncate/pad\n",
        "       pad_to_max_length=True,         # Pad sentence to max length\n",
        "       truncation=True\n",
        "    )\n",
        "    inputs = inputs['input_ids'].numpy()\n",
        "    n_tokens = []\n",
        "    indexes = np.argwhere(inputs == tokenizer.pad_token_id)\n",
        "    for i in range(inputs.shape[0]):\n",
        "        ids = indexes[(indexes == i)[:, 0]]\n",
        "        if not len(ids):\n",
        "            n_tokens.append(max_length)\n",
        "        else:\n",
        "            n_tokens.append(ids[0, 1])\n",
        "    return n_tokens"
      ],
      "metadata": {
        "id": "0m8dZvT8hL9M"
      },
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "ntokens_array = get_token_length(list(data['sentence'].values))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ptqF0hZdbIgJ",
        "outputId": "43d5ebbe-441e-4159-bcf5-174b3a30e2f4"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.9/dist-packages/transformers/tokenization_utils_base.py:2346: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n",
            "  warnings.warn(\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def cutoff_matrix(matrix, ntokens):\n",
        "    \"\"\"Return normalized submatrix of first n_tokens\"\"\"\n",
        "    matrix = matrix[:ntokens]\n",
        "    matrix /= matrix.sum()\n",
        "    return matrix"
      ],
      "metadata": {
        "id": "aQbInimphwQ0"
      },
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from scipy.special import rel_entr\n",
        "def jensenshannon_divergence(p, q, base=None, *, axis=0, keepdims=False):\n",
        "    \"\"\"\n",
        "    Compute the Jensen-Shannon distance (metric) between\n",
        "    two probability arrays. This is the square root\n",
        "    of the Jensen-Shannon divergence.\n",
        "    The Jensen-Shannon distance between two probability\n",
        "    vectors `p` and `q` is defined as,\n",
        "    .. math::\n",
        "       \\\\sqrt{\\\\frac{D(p \\\\parallel m) + D(q \\\\parallel m)}{2}}\n",
        "    where :math:`m` is the pointwise mean of :math:`p` and :math:`q`\n",
        "    and :math:`D` is the Kullback-Leibler divergence.\n",
        "    This routine will normalize `p` and `q` if they don't sum to 1.0.\n",
        "    Parameters\n",
        "    ----------\n",
        "    p : (N,) array_like\n",
        "        left probability vector\n",
        "    q : (N,) array_like\n",
        "        right probability vector\n",
        "    base : double, optional\n",
        "        the base of the logarithm used to compute the output\n",
        "        if not given, then the routine uses the default base of\n",
        "        scipy.stats.entropy.\n",
        "    axis : int, optional\n",
        "        Axis along which the Jensen-Shannon distances are computed. The default\n",
        "        is 0.\n",
        "        .. versionadded:: 1.7.0\n",
        "    keepdims : bool, optional\n",
        "        If this is set to `True`, the reduced axes are left in the\n",
        "        result as dimensions with size one. With this option,\n",
        "        the result will broadcast correctly against the input array.\n",
        "        Default is False.\n",
        "        .. versionadded:: 1.7.0\n",
        "    Returns\n",
        "    -------\n",
        "    js : double or ndarray\n",
        "        The Jensen-Shannon distances between `p` and `q` along the `axis`.\n",
        "    Notes\n",
        "    -----\n",
        "    .. versionadded:: 1.2.0\n",
        "    Examples\n",
        "    --------\n",
        "    >>> from scipy.spatial import distance\n",
        "    >>> import numpy as np\n",
        "    >>> distance.jensenshannon([1.0, 0.0, 0.0], [0.0, 1.0, 0.0], 2.0)\n",
        "    1.0\n",
        "    >>> distance.jensenshannon([1.0, 0.0], [0.5, 0.5])\n",
        "    0.46450140402245893\n",
        "    >>> distance.jensenshannon([1.0, 0.0, 0.0], [1.0, 0.0, 0.0])\n",
        "    0.0\n",
        "    >>> a = np.array([[1, 2, 3, 4],\n",
        "    ...               [5, 6, 7, 8],\n",
        "    ...               [9, 10, 11, 12]])\n",
        "    >>> b = np.array([[13, 14, 15, 16],\n",
        "    ...               [17, 18, 19, 20],\n",
        "    ...               [21, 22, 23, 24]])\n",
        "    >>> distance.jensenshannon(a, b, axis=0)\n",
        "    array([0.1954288, 0.1447697, 0.1138377, 0.0927636])\n",
        "    >>> distance.jensenshannon(a, b, axis=1)\n",
        "    array([0.1402339, 0.0399106, 0.0201815])\n",
        "    \"\"\"\n",
        "    p = np.asarray(p)\n",
        "    q = np.asarray(q)\n",
        "    p = p / np.sum(p, axis=axis, keepdims=True)\n",
        "    q = q / np.sum(q, axis=axis, keepdims=True)\n",
        "    m = (p + q) / 2.0\n",
        "    left = rel_entr(p, m)\n",
        "    right = rel_entr(q, m)\n",
        "    left_sum = np.sum(left, axis=axis, keepdims=keepdims)\n",
        "    right_sum = np.sum(right, axis=axis, keepdims=keepdims)\n",
        "    js = left_sum + right_sum\n",
        "    if base is not None:\n",
        "        js /= np.log(base)\n",
        "    return js"
      ],
      "metadata": {
        "id": "I-UW_cHWmuUi"
      },
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "all_data=dict()\n",
        "arr=[]\n",
        "for features in ['Sem  Violation', \"Infl/Agr Violation\", \"Extra/Mising Expr\", \"Correct\", 'Syntax', 'All']:\n",
        "    inds=data[data['label']==1].index[:150]\n",
        "    all_data[features]=[]\n",
        "    if features not in {'Correct', 'All', 'Syntax'}:\n",
        "        inds=cola_gr_major[cola_gr_major[features]==1].index\n",
        "        inds = [i for i in list(inds) if i in df[df['Acceptability']==0].index]\n",
        "        arr.extend(list(inds))\n",
        "    if features == \"Syntax\":\n",
        "        syntax_ph = df.drop(arr)\n",
        "        inds = syntax_ph[syntax_ph['Acceptability']==0].index\n",
        "    if features == \"All\":\n",
        "        inds = data.index\n",
        "    distances_layers=[]\n",
        "    for layer in trange(tr_adj.shape[1]):\n",
        "        for sentence in inds:\n",
        "            ntokens=ntokens_array[sentence]\n",
        "            for head in range(tr_adj.shape[2]):\n",
        "                av_words=[]\n",
        "                for word in range(ntokens):\n",
        "                    tr_ex = tr_adj[sentence,layer,head,:][word]\n",
        "                    tr_ex = cutoff_matrix(tr_ex, ntokens) \n",
        "                    nt_ex = nt_adj[sentence,layer,head,:][word]\n",
        "                    nt_ex = cutoff_matrix(nt_ex, ntokens) \n",
        "                    distance_=jensenshannon_divergence(tr_ex, nt_ex)\n",
        "                    # zero arrays case \n",
        "                    if abs(distance_)!=np.inf:\n",
        "                        av_words.append(distance_)\n",
        "                    else:\n",
        "                        if np.isnan(distance_):\n",
        "                            print(tr_ex, nt_ex, distance_)\n",
        "                        av_words.append(0)\n",
        "            av_heads = np.mean(np.array(av_words))\n",
        "        av_sents = np.mean(av_heads)\n",
        "        distances_layers.append(np.mean(av_sents))\n",
        "    all_data[features]=distances_layers"
      ],
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            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "5dc816d6fb924b8490f4a9c2f8153a34"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/24 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "91383fcbe887423f80a0a7d7b1e232df"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data_plot_dist=pd.DataFrame.from_dict(all_data)\n",
        "# data_plot_dist.to_csv(\"en-roberta-js-test.csv\")\n",
        "sns.lineplot(data=data_plot_dist)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 430
        },
        "id": "-E1RVlTqR-tR",
        "outputId": "d3e40efc-88b0-404f-a687-a10a8cd6460f"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    }
  ]
}