few_shot_NLP.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "few_shot_NLP.ipynb",
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "o4jEF6D8M30S"
      },
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "\n",
        "test = pd.read_csv('/content/final_fewshot_test.csv')\n",
        "train = pd.read_csv('/content/final_fewshot_train.csv')"
      ],
      "execution_count": 38,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "60WwCUEdNNs9"
      },
      "source": [
        "train=train[['text','class']]\n",
        "test=test[['text','class']]"
      ],
      "execution_count": 39,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "s2dnNQecNVs7"
      },
      "source": [
        "import tensorflow as tf\n",
        "import tensorflow_hub as hub\n",
        "module_url = 'https://tfhub.dev/google/universal-sentence-encoder-large/4'\n",
        "# Import the Universal Sentence Encoder's TF Hub module\n",
        "embed = hub.load(module_url)"
      ],
      "execution_count": 40,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LlsVoD4VOZx7",
        "outputId": "78d479a3-5c1a-4a0a-d82c-cb9214f765ff",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 425
        }
      },
      "source": [
        "import numpy as np # linear algebra\n",
        "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
        "\n",
        "import tensorflow as tf\n",
        "import tensorflow_hub as hub\n",
        "import keras\n",
        "import keras.backend as K\n",
        "from keras.layers import *\n",
        "from keras.callbacks import *\n",
        "from keras.optimizers import *\n",
        "from keras import Model\n",
        "from keras.layers.core import Lambda, Flatten, Dense\n",
        "from keras.layers import Bidirectional, LSTM\n",
        "import pickle    \n",
        "import os\n",
        "input_text1 = Input(shape=(512,))\n",
        "x = Dense(256, activation='relu')(input_text1)\n",
        "x = Dropout(0.4)(x)\n",
        "x = BatchNormalization()(x)\n",
        "x = Dense(64, activation='relu', kernel_regularizer=keras.regularizers.l2(0.001))(x)\n",
        "x = Dropout(0.4)(x)\n",
        "dense_layer = Dense(128, name='dense_layer')(x)\n",
        "norm_layer = Lambda(lambda  x: K.l2_normalize(x, axis=1), name='norm_layer')(dense_layer)\n",
        "\n",
        "model=Model(inputs=[input_text1], outputs=norm_layer)\n",
        "\n",
        "model.summary()"
      ],
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"functional_5\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "input_5 (InputLayer)         [(None, 512)]             0         \n",
            "_________________________________________________________________\n",
            "dense_2 (Dense)              (None, 256)               131328    \n",
            "_________________________________________________________________\n",
            "dropout_2 (Dropout)          (None, 256)               0         \n",
            "_________________________________________________________________\n",
            "batch_normalization_1 (Batch (None, 256)               1024      \n",
            "_________________________________________________________________\n",
            "dense_3 (Dense)              (None, 64)                16448     \n",
            "_________________________________________________________________\n",
            "dropout_3 (Dropout)          (None, 64)                0         \n",
            "_________________________________________________________________\n",
            "dense_layer (Dense)          (None, 128)               8320      \n",
            "_________________________________________________________________\n",
            "norm_layer (Lambda)          (None, 128)               0         \n",
            "=================================================================\n",
            "Total params: 157,120\n",
            "Trainable params: 156,608\n",
            "Non-trainable params: 512\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ByWXLtc9PVAg"
      },
      "source": [
        "from keras import backend as K\n",
        "from keras.models import Model\n",
        "from keras.layers import Input, Layer\n",
        "\n",
        "# Input for anchor, positive and negative images\n",
        "in_a = Input(shape=(512,))\n",
        "in_p = Input(shape=(512,))\n",
        "in_n = Input(shape=(512,))\n",
        "\n",
        "# Output for anchor, positive and negative embedding vectors\n",
        "# The nn4_small model instance is shared (Siamese network)\n",
        "emb_a = model(in_a)\n",
        "emb_p = model(in_p)\n",
        "emb_n = model(in_n)\n",
        "\n",
        "class TripletLossLayer(Layer):\n",
        "    def __init__(self, alpha, **kwargs):\n",
        "        self.alpha = alpha\n",
        "        super(TripletLossLayer, self).__init__(**kwargs)\n",
        "    \n",
        "    def triplet_loss(self, inputs):\n",
        "        a, p, n = inputs\n",
        "        p_dist = K.sum(K.square(a-p), axis=-1)\n",
        "        n_dist = K.sum(K.square(a-n), axis=-1)\n",
        "        return K.sum(K.maximum(p_dist - n_dist + self.alpha, 0), axis=0)\n",
        "    \n",
        "    def call(self, inputs):\n",
        "        loss = self.triplet_loss(inputs)\n",
        "        self.add_loss(loss)\n",
        "        return loss\n",
        "\n",
        "# Layer that computes the triplet loss from anchor, positive and negative embedding vectors\n",
        "triplet_loss_layer = TripletLossLayer(alpha=0.4, name='triplet_loss_layer')([emb_a, emb_p, emb_n])\n",
        "\n",
        "# Model that can be trained with anchor, positive negative images\n",
        "nn4_small2_train = Model([in_a, in_p, in_n], triplet_loss_layer)"
      ],
      "execution_count": 42,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RoQGegqNPsFo",
        "outputId": "ebfb1fd1-eb40-4a99-f340-701284867357",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 391
        }
      },
      "source": [
        "nn4_small2_train.summary()"
      ],
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"functional_7\"\n",
            "__________________________________________________________________________________________________\n",
            "Layer (type)                    Output Shape         Param #     Connected to                     \n",
            "==================================================================================================\n",
            "input_6 (InputLayer)            [(None, 512)]        0                                            \n",
            "__________________________________________________________________________________________________\n",
            "input_7 (InputLayer)            [(None, 512)]        0                                            \n",
            "__________________________________________________________________________________________________\n",
            "input_8 (InputLayer)            [(None, 512)]        0                                            \n",
            "__________________________________________________________________________________________________\n",
            "functional_5 (Functional)       (None, 128)          157120      input_6[0][0]                    \n",
            "                                                                 input_7[0][0]                    \n",
            "                                                                 input_8[0][0]                    \n",
            "__________________________________________________________________________________________________\n",
            "triplet_loss_layer (TripletLoss ()                   0           functional_5[0][0]               \n",
            "                                                                 functional_5[1][0]               \n",
            "                                                                 functional_5[2][0]               \n",
            "==================================================================================================\n",
            "Total params: 157,120\n",
            "Trainable params: 156,608\n",
            "Non-trainable params: 512\n",
            "__________________________________________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MbTlUbgMMhqD"
      },
      "source": [
        "unique_train_label=np.array(train['class'].unique().tolist())\n",
        "labels_train=np.array(train['class'].tolist())\n",
        "map_train_label_indices = {label: np.flatnonzero(labels_train == label) for label in unique_train_label}"
      ],
      "execution_count": 44,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pkyZSYt3QMif"
      },
      "source": [
        "def get_triplets(unique_train_label,map_train_label_indices):\n",
        "      label_l, label_r = np.random.choice(unique_train_label, 2, replace=False)\n",
        "      a, p = np.random.choice(map_train_label_indices[label_l],2, replace=False)\n",
        "      n = np.random.choice(map_train_label_indices[label_r])\n",
        "      return a, p, n\n",
        "\n",
        "def get_triplets_batch(k,train_set,unique_train_label,map_train_label_indices,embed):\n",
        "\n",
        "    while True:\n",
        "      idxs_a, idxs_p, idxs_n = [], [], []\n",
        "      for _ in range(k):\n",
        "          a, p, n = get_triplets(unique_train_label,map_train_label_indices)\n",
        "          idxs_a.append(a)\n",
        "          idxs_p.append(p)\n",
        "          idxs_n.append(n)\n",
        "\n",
        "      a=train_set.iloc[idxs_a].values.tolist()\n",
        "      b=train_set.iloc[idxs_p].values.tolist()\n",
        "      c=train_set.iloc[idxs_n].values.tolist()\n",
        "\n",
        "      a = embed(a)\n",
        "      p = embed(b)\n",
        "      n = embed(c)\n",
        "        # return train_set[idxs_a], train_set[idxs_p], train_set[idxs_n]\n",
        "      yield [a,p,n], []"
      ],
      "execution_count": 45,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "V8StfXHYkPLr",
        "outputId": "a51991c9-c15d-4647-9b18-55880ff2aec3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "nn4_small2_train.compile(loss=None, optimizer='adam')\n",
        "nn4_small2_train.fit(get_triplets_batch(128,train['text'],unique_train_label,map_train_label_indices,embed), epochs=100,steps_per_epoch=10)"
      ],
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/100\n",
            "10/10 [==============================] - 2s 182ms/step - loss: 45.2491\n",
            "Epoch 2/100\n",
            "10/10 [==============================] - 2s 178ms/step - loss: 28.8773\n",
            "Epoch 3/100\n",
            "10/10 [==============================] - 2s 181ms/step - loss: 13.7412\n",
            "Epoch 4/100\n",
            "10/10 [==============================] - 2s 179ms/step - loss: 7.0871\n",
            "Epoch 5/100\n",
            "10/10 [==============================] - 2s 182ms/step - loss: 3.3830\n",
            "Epoch 6/100\n",
            "10/10 [==============================] - 2s 182ms/step - loss: 1.9942\n",
            "Epoch 7/100\n",
            "10/10 [==============================] - 2s 181ms/step - loss: 0.9001\n",
            "Epoch 8/100\n",
            "10/10 [==============================] - 2s 180ms/step - loss: 0.9011\n",
            "Epoch 9/100\n",
            "10/10 [==============================] - 2s 179ms/step - loss: 1.1437\n",
            "Epoch 10/100\n",
            "10/10 [==============================] - 2s 177ms/step - loss: 0.8510\n",
            "Epoch 11/100\n",
            "10/10 [==============================] - 2s 180ms/step - loss: 0.7078\n",
            "Epoch 12/100\n",
            "10/10 [==============================] - 2s 179ms/step - loss: 0.4708\n",
            "Epoch 13/100\n",
            "10/10 [==============================] - 2s 180ms/step - loss: 0.7362\n",
            "Epoch 14/100\n",
            "10/10 [==============================] - 2s 181ms/step - loss: 0.5731\n",
            "Epoch 15/100\n",
            "10/10 [==============================] - 2s 180ms/step - loss: 0.3239\n",
            "Epoch 16/100\n",
            "10/10 [==============================] - 2s 179ms/step - loss: 0.5148\n",
            "Epoch 17/100\n",
            "10/10 [==============================] - 2s 180ms/step - loss: 0.1967\n",
            "Epoch 18/100\n",
            "10/10 [==============================] - 2s 181ms/step - loss: 0.3164\n",
            "Epoch 19/100\n",
            "10/10 [==============================] - 2s 180ms/step - loss: 0.4576\n",
            "Epoch 20/100\n",
            "10/10 [==============================] - 2s 181ms/step - loss: 0.3141\n",
            "Epoch 21/100\n",
            "10/10 [==============================] - 2s 183ms/step - loss: 0.5322\n",
            "Epoch 22/100\n",
            "10/10 [==============================] - 2s 184ms/step - loss: 0.2339\n",
            "Epoch 23/100\n",
            "10/10 [==============================] - 2s 179ms/step - loss: 0.2505\n",
            "Epoch 24/100\n",
            "10/10 [==============================] - 2s 179ms/step - loss: 0.2828\n",
            "Epoch 25/100\n",
            "10/10 [==============================] - 2s 180ms/step - loss: 0.2323\n",
            "Epoch 26/100\n",
            "10/10 [==============================] - 2s 180ms/step - loss: 0.1448\n",
            "Epoch 27/100\n",
            "10/10 [==============================] - 2s 180ms/step - loss: 0.4666\n",
            "Epoch 28/100\n",
            "10/10 [==============================] - 2s 177ms/step - loss: 0.2361\n",
            "Epoch 29/100\n",
            "10/10 [==============================] - 2s 180ms/step - loss: 0.3424\n",
            "Epoch 30/100\n",
            "10/10 [==============================] - 2s 179ms/step - loss: 0.1468\n",
            "Epoch 31/100\n",
            "10/10 [==============================] - 2s 181ms/step - loss: 0.2975\n",
            "Epoch 32/100\n",
            "10/10 [==============================] - 2s 184ms/step - loss: 0.1934\n",
            "Epoch 33/100\n",
            "10/10 [==============================] - 2s 183ms/step - loss: 0.2296\n",
            "Epoch 34/100\n",
            "10/10 [==============================] - 2s 182ms/step - loss: 0.2064\n",
            "Epoch 35/100\n",
            "10/10 [==============================] - 2s 182ms/step - loss: 0.1688\n",
            "Epoch 36/100\n",
            "10/10 [==============================] - 2s 183ms/step - loss: 0.2229\n",
            "Epoch 37/100\n",
            "10/10 [==============================] - 2s 180ms/step - loss: 0.2321\n",
            "Epoch 38/100\n",
            "10/10 [==============================] - 2s 186ms/step - loss: 0.1186\n",
            "Epoch 39/100\n",
            "10/10 [==============================] - 2s 178ms/step - loss: 0.1436\n",
            "Epoch 40/100\n",
            "10/10 [==============================] - 2s 183ms/step - loss: 0.1407\n",
            "Epoch 41/100\n",
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            "Epoch 42/100\n",
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            "Epoch 43/100\n",
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            "Epoch 44/100\n",
            "10/10 [==============================] - 2s 180ms/step - loss: 0.2673\n",
            "Epoch 45/100\n",
            "10/10 [==============================] - 2s 178ms/step - loss: 0.3383\n",
            "Epoch 46/100\n",
            "10/10 [==============================] - 2s 177ms/step - loss: 0.1617\n",
            "Epoch 47/100\n",
            "10/10 [==============================] - 2s 178ms/step - loss: 0.3550\n",
            "Epoch 48/100\n",
            "10/10 [==============================] - 2s 179ms/step - loss: 0.1915\n",
            "Epoch 49/100\n",
            "10/10 [==============================] - 2s 180ms/step - loss: 0.1539\n",
            "Epoch 50/100\n",
            "10/10 [==============================] - 2s 181ms/step - loss: 0.1604\n",
            "Epoch 51/100\n",
            "10/10 [==============================] - 2s 176ms/step - loss: 0.1526\n",
            "Epoch 52/100\n",
            "10/10 [==============================] - 2s 179ms/step - loss: 0.1078\n",
            "Epoch 53/100\n",
            "10/10 [==============================] - 2s 184ms/step - loss: 0.1233\n",
            "Epoch 54/100\n",
            "10/10 [==============================] - 2s 182ms/step - loss: 0.2574\n",
            "Epoch 55/100\n",
            "10/10 [==============================] - 2s 180ms/step - loss: 0.2132\n",
            "Epoch 56/100\n",
            "10/10 [==============================] - 2s 182ms/step - loss: 0.2019\n",
            "Epoch 57/100\n",
            "10/10 [==============================] - 2s 177ms/step - loss: 0.1629\n",
            "Epoch 58/100\n",
            "10/10 [==============================] - 2s 177ms/step - loss: 0.1766\n",
            "Epoch 59/100\n",
            "10/10 [==============================] - 2s 182ms/step - loss: 0.1408\n",
            "Epoch 60/100\n",
            "10/10 [==============================] - 2s 182ms/step - loss: 0.2452\n",
            "Epoch 61/100\n",
            "10/10 [==============================] - 2s 181ms/step - loss: 0.1134\n",
            "Epoch 62/100\n",
            "10/10 [==============================] - 2s 183ms/step - loss: 0.2006\n",
            "Epoch 63/100\n",
            "10/10 [==============================] - 2s 181ms/step - loss: 0.1100\n",
            "Epoch 64/100\n",
            "10/10 [==============================] - 2s 178ms/step - loss: 0.1024\n",
            "Epoch 65/100\n",
            "10/10 [==============================] - 2s 184ms/step - loss: 0.1655\n",
            "Epoch 66/100\n",
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            "Epoch 67/100\n",
            "10/10 [==============================] - 2s 180ms/step - loss: 0.2032\n",
            "Epoch 68/100\n",
            "10/10 [==============================] - 2s 185ms/step - loss: 0.2170\n",
            "Epoch 69/100\n",
            "10/10 [==============================] - 2s 180ms/step - loss: 0.1200\n",
            "Epoch 70/100\n",
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            "Epoch 71/100\n",
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            "Epoch 72/100\n",
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            "Epoch 73/100\n",
            "10/10 [==============================] - 2s 181ms/step - loss: 0.1113\n",
            "Epoch 74/100\n",
            "10/10 [==============================] - 2s 179ms/step - loss: 0.1310\n",
            "Epoch 75/100\n",
            "10/10 [==============================] - 2s 178ms/step - loss: 0.1020\n",
            "Epoch 76/100\n",
            "10/10 [==============================] - 2s 181ms/step - loss: 0.1331\n",
            "Epoch 77/100\n",
            "10/10 [==============================] - 2s 176ms/step - loss: 0.1416\n",
            "Epoch 78/100\n",
            "10/10 [==============================] - 2s 180ms/step - loss: 0.1216\n",
            "Epoch 79/100\n",
            "10/10 [==============================] - 2s 183ms/step - loss: 0.1880\n",
            "Epoch 80/100\n",
            "10/10 [==============================] - 2s 182ms/step - loss: 0.1289\n",
            "Epoch 81/100\n",
            "10/10 [==============================] - 2s 182ms/step - loss: 0.1647\n",
            "Epoch 82/100\n",
            "10/10 [==============================] - 2s 181ms/step - loss: 0.2704\n",
            "Epoch 83/100\n",
            "10/10 [==============================] - 2s 180ms/step - loss: 0.1016\n",
            "Epoch 84/100\n",
            "10/10 [==============================] - 2s 181ms/step - loss: 0.1016\n",
            "Epoch 85/100\n",
            "10/10 [==============================] - 2s 184ms/step - loss: 0.1084\n",
            "Epoch 86/100\n",
            "10/10 [==============================] - 2s 176ms/step - loss: 0.1015\n",
            "Epoch 87/100\n",
            "10/10 [==============================] - 2s 178ms/step - loss: 0.1368\n",
            "Epoch 88/100\n",
            "10/10 [==============================] - 2s 177ms/step - loss: 0.1084\n",
            "Epoch 89/100\n",
            "10/10 [==============================] - 2s 178ms/step - loss: 0.1449\n",
            "Epoch 90/100\n",
            "10/10 [==============================] - 2s 185ms/step - loss: 0.1132\n",
            "Epoch 91/100\n",
            "10/10 [==============================] - 2s 179ms/step - loss: 0.1305\n",
            "Epoch 92/100\n",
            "10/10 [==============================] - 2s 179ms/step - loss: 0.1596\n",
            "Epoch 93/100\n",
            "10/10 [==============================] - 2s 179ms/step - loss: 0.1011\n",
            "Epoch 94/100\n",
            "10/10 [==============================] - 2s 183ms/step - loss: 0.1199\n",
            "Epoch 95/100\n",
            "10/10 [==============================] - 2s 185ms/step - loss: 0.1335\n",
            "Epoch 96/100\n",
            "10/10 [==============================] - 2s 179ms/step - loss: 0.1330\n",
            "Epoch 97/100\n",
            "10/10 [==============================] - 2s 178ms/step - loss: 0.1243\n",
            "Epoch 98/100\n",
            "10/10 [==============================] - 2s 180ms/step - loss: 0.1980\n",
            "Epoch 99/100\n",
            "10/10 [==============================] - 2s 178ms/step - loss: 0.1081\n",
            "Epoch 100/100\n",
            "10/10 [==============================] - 2s 182ms/step - loss: 0.1008\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tensorflow.python.keras.callbacks.History at 0x7f1591ec1128>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 46
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "G3K0iR2-lE7h"
      },
      "source": [
        "\n",
        "X_train = model.predict(embed(np.array(train['text'].values.tolist())))\n",
        "X_test = model.predict(embed(np.array(test['text'].values.tolist())))\n",
        "\n",
        "y_train = np.array(train['class'].values.tolist())\n",
        "y_test = np.array(test['class'].values.tolist())"
      ],
      "execution_count": 47,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7MFx_xCclE9Z",
        "outputId": "a0a2f7a5-81d8-4f26-c643-b02982b836a0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "from sklearn.preprocessing import LabelEncoder\n",
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "from sklearn.svm import LinearSVC\n",
        "\n",
        "knn = KNeighborsClassifier(n_neighbors=5, metric='euclidean')\n",
        "svc = LinearSVC()\n",
        "\n",
        "knn.fit(X_train, y_train)\n",
        "svc.fit(X_train, y_train)\n",
        "\n",
        "y_pred_knn = knn.predict(X_test)\n",
        "acc_knn = accuracy_score(y_test, y_pred_knn)\n",
        "y_pred_svc = svc.predict(X_test)\n",
        "acc_svc = accuracy_score(y_test, y_pred_svc)\n",
        "\n",
        "print(f'KNN accuracy = {acc_knn}, SVM accuracy = {acc_svc}')"
      ],
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "KNN accuracy = 0.8764113518462008, SVM accuracy = 0.8748855660665242\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rjJwsUVvtl1R",
        "outputId": "43e8b1cf-3669-4b52-b279-3a9f8e983818",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 221
        }
      },
      "source": [
        "from sklearn import metrics\n",
        "print(metrics.classification_report(list(y_test), list(y_pred_knn)))"
      ],
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           1       0.93      0.91      0.92      1072\n",
            "           2       0.58      0.87      0.70       262\n",
            "           3       0.91      0.76      0.83       425\n",
            "           4       0.85      0.86      0.86       532\n",
            "           5       0.94      0.90      0.92       986\n",
            "\n",
            "    accuracy                           0.88      3277\n",
            "   macro avg       0.84      0.86      0.84      3277\n",
            "weighted avg       0.89      0.88      0.88      3277\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YczOJtb6tqVT",
        "outputId": "8f2e174b-6b14-4afa-c4a8-8fecd36ed6c3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 595
        }
      },
      "source": [
        "from sklearn.manifold import TSNE\n",
        "\n",
        "X_embedded = TSNE(n_components=2).fit_transform(X_test)\n",
        "\n",
        "plt.figure(figsize=(10,10))\n",
        "\n",
        "for i, t in enumerate(set(y_test)):\n",
        "    idx = y_test == t\n",
        "    plt.scatter(X_embedded[idx, 0], X_embedded[idx, 1], label=t)   \n",
        "\n",
        "plt.legend(bbox_to_anchor=(1, 1));"
      ],
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4ZFEAMaytqX2",
        "outputId": "a841abf9-3aee-43c3-e16d-d207fa14cf6c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 592
        }
      },
      "source": [
        "from sklearn.manifold import TSNE\n",
        "\n",
        "X_embedded = TSNE(n_components=2).fit_transform(X_train)\n",
        "\n",
        "plt.figure(figsize=(10,10))\n",
        "\n",
        "for i, t in enumerate(set(y_train)):\n",
        "    idx = y_train == t\n",
        "    plt.scatter(X_embedded[idx, 0], X_embedded[idx, 1], label=t)   \n",
        "\n",
        "plt.legend(bbox_to_anchor=(1, 1));"
      ],
      "execution_count": 51,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}