CNN_multiinput.ipynb

{
 "cells": [
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   "execution_count": 59,
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   "source": [
    "import pandas as pd\n",
    "from tensorflow import keras\n",
    "from textaugment import EDA\n",
    "from sklearn.model_selection import train_test_split\n",
    "from tensorflow.keras.layers import Dropout, Dense,Input,Embedding,Flatten\n",
    "from tensorflow.keras.models import Model\n",
    "import numpy as np\n",
    "from sklearn import metrics\n",
    "from tensorflow.keras.preprocessing.text import Tokenizer\n",
    "from tensorflow.keras.preprocessing.sequence import pad_sequences\n",
    "from tensorflow.keras.utils import to_categorical"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import FunctionTransformer\n",
    "columns = ['学号', '性别', '生源地', '总分', '幻觉、妄想症状', '自杀意图', '焦虑指标总分', '抑郁指标总分', '偏执指标总分', '自卑指标总分',\n",
    "               '敏感指标总分', '社交恐惧指标总分', '躯体化指标总分', '依赖指标总分', '敌对攻击指标总分', '冲动指标总分', '强迫指标总分',\n",
    "               '网络成瘾指标总分', '自伤行为指标总分', '进食问题指标总分', '睡眠困扰指标总分', '学校适应困难指标总分', '人际关系困扰指标总分',\n",
    "               '学业压力指标总分', '就业压力指标总分', '恋爱困扰指标总分']\n",
    "data = pd.read_csv('student_data.csv', encoding='utf-8')\n",
    "data.drop(columns=columns, inplace=True)\n",
    "train_df, test_df = train_test_split(data, test_size=0.2, random_state=8)"
   ],
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  {
   "cell_type": "code",
   "execution_count": 61,
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\hamburger_sct\\Anaconda3\\envs\\nlp_env\\lib\\site-packages\\ipykernel_launcher.py:13: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  del sys.path[0]\n",
      "C:\\Users\\hamburger_sct\\Anaconda3\\envs\\nlp_env\\lib\\site-packages\\ipykernel_launcher.py:17: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "C:\\Users\\hamburger_sct\\Anaconda3\\envs\\nlp_env\\lib\\site-packages\\ipykernel_launcher.py:21: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"
     ]
    },
    {
     "data": {
      "text/plain": "0    84\n1    62\n2    28\n3    26\nName: 可能问题, dtype: int64"
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# augment training data\n",
    "train_class_0 = train_df[train_df['可能问题']==0]\n",
    "train_class_1 = train_df[train_df['可能问题']==1]\n",
    "train_class_2 = train_df[train_df['可能问题']==2]\n",
    "train_class_3 = train_df[train_df['可能问题']==3]\n",
    "\n",
    "t = EDA(random_state=8)\n",
    "new_text_1 = [t.random_swap(t.random_insertion(t.random_deletion(t.synonym_replacement(sent), p=0.4))) for sent in train_class_1['text']]\n",
    "new_text_2 = [t.random_swap(t.random_insertion(t.random_deletion(t.synonym_replacement(sent), p=0.4))) for sent in train_class_2['text']]\n",
    "new_text_3 = [t.random_swap(t.random_insertion(t.random_deletion(t.synonym_replacement(sent), p=0.4))) for sent in train_class_3['text']]\n",
    "\n",
    "new_class_1 = train_class_1\n",
    "new_class_1['text'] = new_text_1\n",
    "train_1 = pd.concat([train_class_1, new_class_1])\n",
    "\n",
    "new_class_2 = train_class_2\n",
    "new_class_2['text'] = new_text_2\n",
    "train_2 = pd.concat([train_class_2, new_class_2])\n",
    "\n",
    "new_class_3 = train_class_3\n",
    "new_class_3['text'] = new_text_3\n",
    "train_3 = pd.concat([train_class_3, new_class_3])\n",
    "\n",
    "train = pd.concat([train_class_0, train_1, train_2, train_3])\n",
    "train['可能问题'].value_counts()"
   ],
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  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train_metadata shape: (200, 22)\n",
      "test_metadata shape: (36, 22)\n"
     ]
    }
   ],
   "source": [
    "train_metadata = np.array(train.iloc[:, 0:-2])\n",
    "test_metadata = np.array(test_df.iloc[:, 0:-2])\n",
    "print('train_metadata shape:', train_metadata.shape)\n",
    "print('test_metadata shape:', test_metadata.shape)"
   ],
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    "pycharm": {
     "name": "#%%\n"
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  {
   "cell_type": "code",
   "execution_count": 63,
   "outputs": [
    {
     "data": {
      "text/plain": "236"
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "texts = np.array(pd.concat([train['text'], test_df['text']]))\n",
    "len(texts)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
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  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\hamburger_sct\\Anaconda3\\envs\\nlp_env\\lib\\site-packages\\keras_preprocessing\\text.py:180: UserWarning: The `nb_words` argument in `Tokenizer` has been renamed `num_words`.\n",
      "  warnings.warn('The `nb_words` argument in `Tokenizer` '\n"
     ]
    },
    {
     "data": {
      "text/plain": "236"
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "MAX_NB_WORDS = 5000\n",
    "tokenizer = Tokenizer(nb_words=MAX_NB_WORDS)\n",
    "tokenizer.fit_on_texts(texts)\n",
    "sequences = tokenizer.texts_to_sequences(texts)\n",
    "len(sequences)\n",
    "# sequences即将每个句子中的每个单词使用词典序表示的形式"
   ],
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     "name": "#%%\n"
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   }
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 5982 unique tokens\n"
     ]
    }
   ],
   "source": [
    "word_index = tokenizer.word_index\n",
    "print('Found %s unique tokens' % len(word_index))"
   ],
   "metadata": {
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    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "outputs": [],
   "source": [
    "MAX_SEQUENCE_LENGTH = 500\n",
    "data = pad_sequences(sequences, maxlen=MAX_SEQUENCE_LENGTH)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "outputs": [],
   "source": [
    "labels = to_categorical(np.array(pd.concat([train['可能问题'], test_df['可能问题']])))"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of data tensor: (236, 500)\n",
      "Shape of label tensor: (236, 4)\n"
     ]
    }
   ],
   "source": [
    "print('Shape of data tensor:', data.shape)\n",
    "print('Shape of label tensor:', labels.shape)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "number of sequences in X_train:  200\n",
      "number of sequences in X_test:  36\n",
      "200\n",
      "36\n"
     ]
    }
   ],
   "source": [
    "indices = np.arange(data.shape[0])\n",
    "data = data[indices]\n",
    "labels = labels[indices]\n",
    "\n",
    "X_train = data[:len(train)]\n",
    "X_test = data[len(train):]\n",
    "y_train = np.array(train['可能问题'])\n",
    "y_test = np.array(test_df['可能问题'])\n",
    "\n",
    "print('number of sequences in X_train: ', len(X_train))\n",
    "print('number of sequences in X_test: ', len(X_test))\n",
    "print(len(y_train))\n",
    "print(len(y_test))"
   ],
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     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 400000 word vectors.\n"
     ]
    }
   ],
   "source": [
    "# Preparing the Embedding Layer\n",
    "embedding_index = {}\n",
    "f = open('./glove.6B.100d.txt', encoding='utf-8')\n",
    "for line in f:\n",
    "    values = line.split()\n",
    "    word = values[0]\n",
    "    coefs = np.array(values[1:], dtype='float32')\n",
    "    embedding_index[word] = coefs\n",
    "f.close()\n",
    "\n",
    "print('Found %s word vectors.' % len(embedding_index))"
   ],
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    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "outputs": [],
   "source": [
    "# embedding matrix\n",
    "EMBEDDING_DIM = 100\n",
    "embedding_matrix = np.zeros((len(word_index) + 1, EMBEDDING_DIM))\n",
    "for word, index in word_index.items():\n",
    "    embedding_vector = embedding_index.get(word)\n",
    "    if embedding_vector is not None:\n",
    "        # words not found in embedding index will be all-zeros.\n",
    "            if len(embedding_matrix[index]) != len(embedding_vector):\n",
    "                print(\"could not broadcast input array from shape\",str(len(embedding_matrix[index])),\n",
    "                                 \"into shape\",str(len(embedding_vector)),\" Please make sure your\"\n",
    "                                 \" EMBEDDING_DIM is equal to embedding_vector file ,GloVe,\")\n",
    "                exit(1)\n",
    "\n",
    "            embedding_matrix[index] = embedding_vector"
   ],
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     "name": "#%%\n"
    }
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  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "outputs": [],
   "source": [
    "# Load embedding matrix into an Embedding Layer\n",
    "from tensorflow.keras.layers import Embedding\n",
    "embedding_layer = Embedding(len(word_index)+1,\n",
    "                            EMBEDDING_DIM,\n",
    "                            weights=[embedding_matrix],\n",
    "                            input_length=MAX_SEQUENCE_LENGTH,\n",
    "                            trainable=False)"
   ],
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    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "outputs": [],
   "source": [
    "convs = []\n",
    "filter_sizes = []\n",
    "layer = 3\n",
    "for fl in range(0,layer):\n",
    "    filter_sizes.append((fl+2))"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "outputs": [],
   "source": [
    "dropout = 0.5\n",
    "node = 128\n",
    "sequence_input = Input(shape=(MAX_SEQUENCE_LENGTH,), dtype='int32')\n",
    "metadata_input = Input(shape=(train_metadata.shape[1],), dtype='float32')\n",
    "embedded_sequences = embedding_layer(sequence_input)\n",
    "# emb = Reshape((MAX_SEQUENCE_LENGTH,10,10), input_shape=(MAX_SEQUENCE_LENGTH,100))(embedded_sequences)\n",
    "\n",
    "for fsz in filter_sizes:\n",
    "    l_conv = keras.layers.Conv1D(node, kernel_size=fsz, activation='relu')(embedded_sequences)\n",
    "    l_pool = keras.layers.MaxPooling1D(3)(l_conv)\n",
    "    # l_pool = Dropout(0.25)(l_pool)\n",
    "    convs.append(l_pool)\n",
    "\n",
    "l_merge = keras.layers.Concatenate(axis=1)(convs)\n",
    "l_cov1 = keras.layers.Conv1D(node, 5, activation='relu')(l_merge)\n",
    "l_pool1 = keras.layers.MaxPooling1D(3)(l_cov1)\n",
    "l_cov2 = keras.layers.Conv1D(node, 5, activation='relu')(l_pool1)\n",
    "l_pool2 = keras.layers.MaxPooling1D(20)(l_cov2)\n",
    "l_cov2 = Dropout(dropout)(l_pool2)\n",
    "l_flat = Flatten()(l_cov2)\n",
    "l_dense = Dense(512, activation='relu')(l_flat)\n",
    "l_dense = Dropout(dropout)(l_dense)\n",
    "l_dense = Dense(256, activation='relu')(l_dense)\n",
    "l_dense = Dropout(dropout)(l_dense)\n",
    "l_dense = Dense(128, activation='relu')(l_dense)\n",
    "l_dense = Dropout(dropout)(l_dense)\n",
    "\n",
    "r_dense = keras.layers.Dense(256, activation='relu')(metadata_input)\n",
    "r_dense = keras.layers.Dropout(dropout)(r_dense)\n",
    "r_dense = keras.layers.Dense(128, activation='relu')(r_dense)\n",
    "r_dense = keras.layers.Dropout(dropout)(r_dense)\n",
    "\n",
    "c_merge = keras.layers.Concatenate(axis=1)([l_dense, r_dense])\n",
    "# c_flat = Flatten()(c_merge)\n",
    "preds = Dense(4, activation='softmax')(c_merge)\n",
    "model = Model([sequence_input, metadata_input], preds)\n",
    "\n",
    "model.compile(loss='sparse_categorical_crossentropy',\n",
    "            optimizer='adam',\n",
    "            metrics=['accuracy'])"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
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    }
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  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "outputs": [
    {
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\n",
      "text/plain": "<IPython.core.display.Image object>"
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "keras.utils.plot_model(model, \"CNN_metadata_model.png\", show_shapes=True)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 200 samples, validate on 36 samples\n",
      "Epoch 1/30\n",
      "200/200 - 5s - loss: 1.8174 - accuracy: 0.2250 - val_loss: 0.9699 - val_accuracy: 0.6389\n",
      "Epoch 2/30\n",
      "200/200 - 1s - loss: 1.3024 - accuracy: 0.4900 - val_loss: 0.8773 - val_accuracy: 0.6111\n",
      "Epoch 3/30\n",
      "200/200 - 2s - loss: 1.2947 - accuracy: 0.4700 - val_loss: 0.9013 - val_accuracy: 0.6389\n",
      "Epoch 4/30\n",
      "200/200 - 2s - loss: 1.1503 - accuracy: 0.5150 - val_loss: 0.9271 - val_accuracy: 0.7222\n",
      "Epoch 5/30\n",
      "200/200 - 2s - loss: 1.0473 - accuracy: 0.5550 - val_loss: 0.9392 - val_accuracy: 0.7222\n",
      "Epoch 6/30\n",
      "200/200 - 2s - loss: 1.0244 - accuracy: 0.5900 - val_loss: 0.9296 - val_accuracy: 0.6944\n",
      "Epoch 7/30\n",
      "200/200 - 2s - loss: 0.9043 - accuracy: 0.6200 - val_loss: 0.9209 - val_accuracy: 0.6667\n",
      "Epoch 8/30\n",
      "200/200 - 2s - loss: 0.9397 - accuracy: 0.6100 - val_loss: 0.9203 - val_accuracy: 0.6944\n",
      "Epoch 9/30\n",
      "200/200 - 2s - loss: 0.9216 - accuracy: 0.6150 - val_loss: 0.8981 - val_accuracy: 0.6667\n",
      "Epoch 10/30\n",
      "200/200 - 1s - loss: 0.8867 - accuracy: 0.6500 - val_loss: 0.8489 - val_accuracy: 0.6944\n",
      "Epoch 11/30\n",
      "200/200 - 2s - loss: 0.8183 - accuracy: 0.6700 - val_loss: 0.8020 - val_accuracy: 0.7222\n",
      "Epoch 12/30\n",
      "200/200 - 2s - loss: 0.7793 - accuracy: 0.6500 - val_loss: 0.7716 - val_accuracy: 0.6944\n",
      "Epoch 13/30\n",
      "200/200 - 2s - loss: 0.7601 - accuracy: 0.6750 - val_loss: 0.7733 - val_accuracy: 0.7222\n",
      "Epoch 14/30\n",
      "200/200 - 2s - loss: 0.7356 - accuracy: 0.7100 - val_loss: 0.7925 - val_accuracy: 0.6944\n",
      "Epoch 15/30\n",
      "200/200 - 2s - loss: 0.6954 - accuracy: 0.7500 - val_loss: 0.8080 - val_accuracy: 0.6944\n",
      "Epoch 16/30\n",
      "200/200 - 2s - loss: 0.6730 - accuracy: 0.7150 - val_loss: 0.8190 - val_accuracy: 0.7222\n",
      "Epoch 17/30\n",
      "200/200 - 2s - loss: 0.6912 - accuracy: 0.7100 - val_loss: 0.8474 - val_accuracy: 0.6667\n",
      "Epoch 18/30\n",
      "200/200 - 2s - loss: 0.6274 - accuracy: 0.7700 - val_loss: 0.8556 - val_accuracy: 0.6667\n",
      "Epoch 19/30\n",
      "200/200 - 2s - loss: 0.6126 - accuracy: 0.7750 - val_loss: 0.8593 - val_accuracy: 0.6667\n",
      "Epoch 20/30\n",
      "200/200 - 2s - loss: 0.6103 - accuracy: 0.7500 - val_loss: 0.8438 - val_accuracy: 0.6389\n",
      "Epoch 21/30\n",
      "200/200 - 1s - loss: 0.5917 - accuracy: 0.7550 - val_loss: 0.8454 - val_accuracy: 0.6944\n",
      "Epoch 22/30\n",
      "200/200 - 1s - loss: 0.5982 - accuracy: 0.7550 - val_loss: 0.8618 - val_accuracy: 0.6389\n",
      "Epoch 23/30\n",
      "200/200 - 1s - loss: 0.5964 - accuracy: 0.7550 - val_loss: 0.8935 - val_accuracy: 0.6667\n",
      "Epoch 24/30\n",
      "200/200 - 1s - loss: 0.5000 - accuracy: 0.8200 - val_loss: 0.8997 - val_accuracy: 0.6389\n",
      "Epoch 25/30\n",
      "200/200 - 1s - loss: 0.5589 - accuracy: 0.8100 - val_loss: 0.8477 - val_accuracy: 0.6944\n",
      "Epoch 26/30\n",
      "200/200 - 1s - loss: 0.5451 - accuracy: 0.7900 - val_loss: 0.8254 - val_accuracy: 0.6667\n",
      "Epoch 27/30\n",
      "200/200 - 1s - loss: 0.5301 - accuracy: 0.8200 - val_loss: 0.8399 - val_accuracy: 0.6667\n",
      "Epoch 28/30\n",
      "200/200 - 1s - loss: 0.4811 - accuracy: 0.7850 - val_loss: 0.8782 - val_accuracy: 0.6667\n",
      "Epoch 29/30\n",
      "200/200 - 1s - loss: 0.4137 - accuracy: 0.8350 - val_loss: 0.8906 - val_accuracy: 0.6389\n",
      "Epoch 30/30\n",
      "200/200 - 1s - loss: 0.3711 - accuracy: 0.8950 - val_loss: 0.9242 - val_accuracy: 0.7222\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.72      0.95      0.82        22\n",
      "           1       0.80      0.31      0.44        13\n",
      "           2       0.50      1.00      0.67         1\n",
      "\n",
      "    accuracy                           0.72        36\n",
      "   macro avg       0.67      0.75      0.64        36\n",
      "weighted avg       0.75      0.72      0.68        36\n",
      "\n",
      "[[21  1  0]\n",
      " [ 8  4  1]\n",
      " [ 0  0  1]]\n",
      "precision:  0.674712643678161\n",
      "accuracy:  0.7222222222222222\n",
      "F1 score:  0.6448801742919389\n",
      "recall:  0.7540792540792541\n"
     ]
    }
   ],
   "source": [
    "model.fit([X_train, train_metadata], y_train,\n",
    "          validation_data=([X_test, test_metadata], y_test),\n",
    "          epochs=30,\n",
    "          batch_size=128,\n",
    "          verbose=2)\n",
    "predicted = model.predict([X_test, test_metadata])\n",
    "predicted = np.argmax(predicted, axis=1)\n",
    "\n",
    "print(metrics.classification_report(y_test, predicted))\n",
    "print(metrics.confusion_matrix(y_test, predicted))\n",
    "print(\"precision: \", str(metrics.precision_score(y_test, predicted, average='macro')))\n",
    "print(\"accuracy: \", str(metrics.accuracy_score(y_test, predicted)))\n",
    "print(\"F1 score: \", str(metrics.f1_score(y_test, predicted, average='macro')))\n",
    "print(\"recall: \", str(metrics.recall_score(y_test, predicted, average='macro')))"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "outputs": [],
   "source": [],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  }
 ],
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