3.Data_Analysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## PubMed all"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Pubmed Data without filtering, contains all articles\n",
    "x = pd.read_csv('./Dataset/data_pubmed_all.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['pubmed_id', 'title', 'keywords', 'journal', 'abstract', 'conclusions',\n",
       "       'methods', 'results', 'copyrights', 'doi', 'publication_date',\n",
       "       'authors', 'AKE_pubmed_id', 'AKE_pubmed_title', 'AKE_abstract',\n",
       "       'AKE_keywords', 'File_Name'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Dataframe\n",
    "x.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8335"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Number of unique journals\n",
    "x['journal'].nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "830978"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Number of articles\n",
    "x.shape[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Count the number of articles per journal\n",
    "article_count_per_journal = x['journal'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count     8335.000000\n",
       "mean        99.697421\n",
       "std        416.964437\n",
       "min          1.000000\n",
       "25%          2.000000\n",
       "50%         10.000000\n",
       "75%         49.000000\n",
       "max      17236.000000\n",
       "Name: journal, dtype: float64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "article_count_per_journal.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Your existing histogram data preparation code\n",
    "hist, bin_edges = np.histogram(article_count_per_journal, bins=10)\n",
    "first_bin_range = (bin_edges[0], bin_edges[1])\n",
    "articles_in_first_bin = article_count_per_journal[\n",
    "    (article_count_per_journal >= first_bin_range[0]) & (article_count_per_journal <= first_bin_range[1])\n",
    "]\n",
    "\n",
    "# Set the global font size for all plots (optional)\n",
    "plt.rcParams.update({'font.size': 18})  # You can adjust the size as needed\n",
    "\n",
    "# Plotting\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.hist(articles_in_first_bin, bins=10, edgecolor='black', log=True)\n",
    "\n",
    "# Increase font size for title and labels\n",
    "plt.title('Log-Scaled Histogram of Articles per Journal in 10 bins', fontsize=16)\n",
    "plt.xlabel('Number of Articles', fontsize=16)\n",
    "plt.ylabel('Number of Journals (Log Scale)', fontsize=16)\n",
    "\n",
    "# Increase font size for tick labels\n",
    "plt.xticks(fontsize=14)\n",
    "plt.yticks(fontsize=14)\n",
    "\n",
    "plt.savefig(f'Histogram of Articles per Journal.pdf', format='pdf')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "# Your existing code for computing the histogram data\n",
    "hist, bin_edges = np.histogram(articles_in_first_bin, bins=10)\n",
    "second_bin_range = (bin_edges[0], bin_edges[1])\n",
    "articles_in_second_bin = article_count_per_journal[\n",
    "    (article_count_per_journal >= second_bin_range[0]) & (article_count_per_journal <= second_bin_range[1])\n",
    "]\n",
    "\n",
    "# Set the global font size for all plots (optional)\n",
    "plt.rcParams.update({'font.size': 14})  # You can adjust the size as needed\n",
    "\n",
    "# Plotting\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.hist(articles_in_second_bin, bins=10, edgecolor='black')\n",
    "\n",
    "# Increase font size for title and labels\n",
    "plt.title('Histogram of Articles per Journal in the First Bin Range', fontsize=16)\n",
    "plt.xlabel('Number of Articles', fontsize=14)\n",
    "plt.ylabel('Number of Journals', fontsize=14)\n",
    "\n",
    "# Increase font size for tick labels\n",
    "plt.xticks(fontsize=12)\n",
    "plt.yticks(fontsize=12)\n",
    "\n",
    "plt.savefig(f'Histogram of Articles per Journal2.pdf', format='pdf')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## PubMed filtered"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Filtered PubMed Data, contains \n",
    "x = pd.read_csv('./Dataset/data_pubmed.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['publication_date', 'AKE_pubmed_id', 'AKE_pubmed_title', 'AKE_abstract',\n",
       "       'AKE_keywords', 'journal'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Dataframe\n",
    "x.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "469"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Number of unique journals\n",
    "x['journal'].nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "262870"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Number of articles\n",
    "x.shape[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "from transformers import AutoTokenizer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "tokenizer = AutoTokenizer.from_pretrained('allenai/cs_roberta_base', do_lower_case=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "import data_loader.CustomTokenize as ct"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "docs = ct.process_samples2(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "docs = pd.DataFrame(docs, columns=['Input'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Laser Application to the Root Surface Increases the Bonding Strength of Surface-Treated Prefabricated Glass-Fiber Posts in Teeth with Excessive Substance Loss\\nThis study examined the effect of roughening of the root surface using an erbium-doped yttrium aluminum garnet (Er: YAG) laser on the binding strength of teeth undergoing root canal treatment. Ninety single-rooted teeth were used and assigned randomly to 9 groups (n=10 each). Root canals were prepared using the FlexMaster rotary system. An Er: YAG laser was applied to the root canals in Group 1, with no surface treatment of the glass-fiber post. In Group 2, aluminum oxide particles were applied. In Group 3, the laser was applied to the root canals, with Cojet treatment. Group 4 received laser treatment and Clearfil Ceramic Primer. In Group 5, Clearfil Ceramic Primer silane coupling was performed on post surfaces without laser treatment. In Group 6, hydrofluoric acid (HF) application was followed by Clearfil Ceramic Primer cementing of the glass post surfaces with laser application. In Group 7, HF acid treatment was performed without laser. In Group 8, the laser was applied, followed by sanding of post surfaces using Korox 50, and silane coupling with Clearfil Ceramic Primer. In Group 9, the post surfaces were sanded using Korox 50 with laser application to the root canals. The samples were subjected to a push-out experiment. The data were analyzed using Friedman’s test and the Wilcoxon signed-rank test. A significant difference in bonding strength was found among the groups (p<0.005). Use of an Er: YAG laser in the root canal may be beneficial prior to bonding of glass posts.\\nLasers, Solid-State, Photoacoustic Techniques, Post and Core Technique, Smear Layer'"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "docs['Input'][0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Token indices sequence length is longer than the specified maximum sequence length for this model (567 > 512). Running this sequence through the model will result in indexing errors\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Minimum Token Length: 21\n",
      "Mean Token Length: 399.9529349107924\n",
      "Maximum Token Length: 3448\n"
     ]
    }
   ],
   "source": [
    "# Define a function to calculate the token length\n",
    "def get_token_length(text):\n",
    "    tokens = tokenizer.tokenize(text)\n",
    "    return len(tokens)\n",
    "\n",
    "# Apply the function to a DataFrame column and create a new column for token lengths\n",
    "docs['token_length'] = docs['Input'].apply(get_token_length)\n",
    "\n",
    "# Calculate min, mean, and max token lengths\n",
    "min_token_length = docs['token_length'].min()\n",
    "mean_token_length = docs['token_length'].mean()\n",
    "max_token_length = docs['token_length'].max()\n",
    "\n",
    "# Print the results\n",
    "print(f\"Minimum Token Length: {min_token_length}\")\n",
    "print(f\"Mean Token Length: {mean_token_length}\")\n",
    "print(f\"Maximum Token Length: {max_token_length}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create bins for token length ranges\n",
    "bins = [0, 256, 512, 768, float('inf')]  # You can adjust these bins as needed\n",
    "\n",
    "# Use pd.cut to categorize token lengths into bins\n",
    "category_labels = ['<256', '256-512', '512-768', '>768']\n",
    "docs['token_length_category'] = pd.cut(docs['token_length'], bins=bins, labels=category_labels)\n",
    "\n",
    "# Calculate the count of items in each category\n",
    "category_counts = docs['token_length_category'].value_counts().reindex(category_labels, fill_value=0)\n",
    "\n",
    "# Set the global font size for all plots (optional)\n",
    "plt.rcParams.update({'font.size': 14})  # Adjust the size as needed\n",
    "\n",
    "# Create a bar plot\n",
    "category_counts.plot(kind='bar', rot=0)\n",
    "\n",
    "# Increase font size for title and labels\n",
    "plt.title('Number of Articles by Token Length Range', fontsize=16)\n",
    "plt.xlabel('Token Length Range', fontsize=14)\n",
    "plt.ylabel('Number of Articles', fontsize=14)\n",
    "\n",
    "# Increase font size for tick labels\n",
    "plt.xticks(fontsize=12)\n",
    "plt.yticks(fontsize=12)\n",
    "\n",
    "plt.savefig(f'Number of Articles by Token Length Range.pdf', format='pdf')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## PubMed Filtered Train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Filtered PubMed Data, contains \n",
    "x = pd.read_csv('./Dataset/data_pubmed_train.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['publication_date', 'AKE_pubmed_id', 'AKE_pubmed_title', 'AKE_abstract',\n",
       "       'AKE_keywords', 'journal'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Dataframe\n",
    "x.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "469"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Number of unique journals\n",
    "x['journal'].nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "157540"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Number of articles\n",
    "x.shape[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## PubMed Filtered Validation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Filtered PubMed Data, contains \n",
    "x = pd.read_csv('./Dataset/data_pubmed_val.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['publication_date', 'AKE_pubmed_id', 'AKE_pubmed_title', 'AKE_abstract',\n",
       "       'AKE_keywords', 'journal'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Dataframe\n",
    "x.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "469"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Number of unique journals\n",
    "x['journal'].nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "52571"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Number of articles\n",
    "x.shape[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## PubMed filtered Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Filtered PubMed Data, contains \n",
    "x = pd.read_csv('./Dataset/data_pubmed_test.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['publication_date', 'AKE_pubmed_id', 'AKE_pubmed_title', 'AKE_abstract',\n",
       "       'AKE_keywords', 'journal'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Dataframe\n",
    "x.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "469"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Number of unique journals\n",
    "x['journal'].nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "52759"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Number of articles\n",
    "x.shape[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Extract Labels"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Extract labels to use later."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from sklearn.preprocessing import LabelEncoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = pd.read_csv('./Dataset/data_pubmed_test.csv')\n",
    "label_encoder = LabelEncoder()\n",
    "label_encoder = label_encoder.fit(x['journal'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['label_encoder.pkl']"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import joblib\n",
    "\n",
    "# Save the label_encoder object\n",
    "joblib.dump(label_encoder, 'label_encoder.pkl')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Random Paper"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = pd.read_csv('./Dataset/data_pubmed_test.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "a = x[x['AKE_pubmed_title'] == 'Environment-dependent attack rates of cryptic and aposematic butterflies']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "a.to_csv('RandomArticle.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "unique_journals = set(x['journal'])\n",
    "\n",
    "# Convert the set back to a list if needed\n",
    "unique_journals_list = list(unique_journals)\n",
    "\n",
    "# Save the unique journal names to a file\n",
    "with open('unique_journals.txt', 'w') as file:\n",
    "    for journal in unique_journals_list:\n",
    "        file.write(journal + '\\n')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Random Journal"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "import random"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = pd.read_csv('./Dataset/data_pubmed_test.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Neural regeneration research\n"
     ]
    }
   ],
   "source": [
    "# Get the list of journal entries\n",
    "journal_entries = x['journal']\n",
    "\n",
    "# Choose a random index\n",
    "random_index = random.randint(0, len(journal_entries) - 1)\n",
    "\n",
    "# Retrieve the random journal entry\n",
    "random_journal = journal_entries[random_index]\n",
    "\n",
    "# Print the random journal entry\n",
    "print(random_journal)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Search in https://pubmed.ncbi.nlm.nih.gov/"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  }
 ],
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