notebook1.ipynb

{
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    "tags": []
   },
   "source": [
    "# **Data Mining Notebook**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "51fa4d15",
   "metadata": {
    "_cell_guid": "b1076dfc-b9ad-4769-8c92-a6c4dae69d19",
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "import numpy as np \n",
    "import pandas as pd\n",
    "import os, re"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "8215a750",
   "metadata": {
    "execution": {
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    "tags": []
   },
   "outputs": [],
   "source": [
    "!git config --global user.name \"Crinmatic\"\n",
    "!git config --global user.email \"Oluseunalagbee@gmailcom\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "11ed8da1",
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Crinmatic\r\n",
      "Oluseunalagbee@gmailcom\r\n"
     ]
    }
   ],
   "source": [
    "!git config --global user.name\n",
    "!git config --global user.email"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "1938504e",
   "metadata": {
    "execution": {
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cloning into 'yoruba-text'...\r\n",
      "remote: Enumerating objects: 72729, done.\u001b[K\r\n",
      "remote: Counting objects: 100% (212/212), done.\u001b[K\r\n",
      "remote: Compressing objects: 100% (187/187), done.\u001b[K\r\n",
      "remote: Total 72729 (delta 44), reused 184 (delta 25), pack-reused 72517\u001b[K\r\n",
      "Receiving objects: 100% (72729/72729), 82.97 MiB | 10.22 MiB/s, done.\r\n",
      "Resolving deltas: 100% (137/137), done.\r\n",
      "Updating files: 100% (69550/69550), done.\r\n"
     ]
    }
   ],
   "source": [
    "!git clone https://Crinmatic: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@github.com/Niger-Volta-LTI/yoruba-text.git"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a36f0f02",
   "metadata": {
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     "duration": 0.038923,
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     "start_time": "2022-01-23T00:50:09.077318",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### **Extracting .txt files**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "292525a4",
   "metadata": {
    "_kg_hide-output": true,
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    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "all_folders = []\n",
    "fold = './'\n",
    "for folder in fold:\n",
    "  for root, dirs, files in os.walk(fold + folder, topdown=False):\n",
    "    all_folders.append(root)\n",
    "  all_folders = all_folders[:-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "3c851a01",
   "metadata": {
    "_kg_hide-output": true,
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   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4912\n"
     ]
    }
   ],
   "source": [
    "#Number of files\n",
    "print(len(all_folders))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "1447a63e",
   "metadata": {
    "execution": {
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   "outputs": [],
   "source": [
    "text_paths = []\n",
    "for dir in all_folders:\n",
    "  for file in os.listdir(dir):\n",
    "      if file.endswith(\".txt\"):\n",
    "          text_paths.append(os.path.join(dir, file))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "cfa1fe38",
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    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "138940\n"
     ]
    }
   ],
   "source": [
    "print(len(text_paths))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "fab1c314",
   "metadata": {
    "_kg_hide-output": true,
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   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "138939 text written...\n"
     ]
    }
   ],
   "source": [
    "cleaned_data = []\n",
    "\n",
    "for i, dir in enumerate(text_paths):\n",
    "    \n",
    "    line = open(dir, 'r').read()\n",
    "    if '\\n' in line: \n",
    "        for nl in line.split('\\n'):\n",
    "            if len(nl) > 3: \n",
    "                high = max([ord(i) for i in list(nl.lower())])\n",
    "                if (300 > high > 127) or (7935 > high > 7680):\n",
    "                        cleaned_data.append(nl.strip())\n",
    "            \n",
    "\n",
    "    else: \n",
    "        high = max([ord(i) for i in list(line.lower())])\n",
    "        if (300 > high > 127) or (7935 > high > 7680):\n",
    "            cleaned_data.append(line.strip())\n",
    "print(f'{i} text written...')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "a6de5fd3",
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    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1185126"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(cleaned_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "09511b2e",
   "metadata": {
    "execution": {
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   },
   "outputs": [
    {
     "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>labels</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A kì í ṣíwájú ẹlẹ́èẹ́dẹ́.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Èpè-é pọ̀ ju ohun tó nù lọ; abẹ́rẹ́ sọnù a gbé...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Bí a kò bá ṣe bí ẹlẹ́dẹ̀ lọ́nà Ìkòròdú, a ò lè...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>A kì í fi idà pa ìgbín.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Ọkùnrin kì í ké, akọ igi kì í ṣoje.</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                              labels\n",
       "0                          A kì í ṣíwájú ẹlẹ́èẹ́dẹ́.\n",
       "1  Èpè-é pọ̀ ju ohun tó nù lọ; abẹ́rẹ́ sọnù a gbé...\n",
       "2  Bí a kò bá ṣe bí ẹlẹ́dẹ̀ lọ́nà Ìkòròdú, a ò lè...\n",
       "3                            A kì í fi idà pa ìgbín.\n",
       "4                Ọkùnrin kì í ké, akọ igi kì í ṣoje."
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame(cleaned_data, columns = ['labels'])\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "733bd645",
   "metadata": {
    "execution": {
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     "exception": false,
     "start_time": "2022-01-23T00:50:45.948594",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "df.to_csv('train.csv', index = False, compression = 'zip')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1ca574ab",
   "metadata": {
    "papermill": {
     "duration": 0.041093,
     "end_time": "2022-01-23T00:51:05.610573",
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     "start_time": "2022-01-23T00:51:05.569480",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### **Extract other text files into csv**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "f1885968",
   "metadata": {
    "execution": {
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "def print_dir_files(tree):\n",
    "  for dirname, _, filenames in os.walk(tree):\n",
    "    for filename in filenames:\n",
    "        print(os.path.join(dirname, filename))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "36e276e9",
   "metadata": {
    "execution": {
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    "tags": []
   },
   "outputs": [],
   "source": [
    "#print_dir_files('./yoruba-text')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "ede1b16f",
   "metadata": {
    "execution": {
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    "jupyter": {
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    "tags": []
   },
   "outputs": [],
   "source": [
    "ext, file_lst = set(), []\n",
    "for dirname, _, filenames in os.walk('./yoruba-text'):\n",
    "    for filename in filenames:\n",
    "        if '.git' in filename: continue\n",
    "        if filename.endswith('LICENSE'): continue\n",
    "        if filename.endswith('.py'): continue\n",
    "        if filename.endswith('.pdf'): continue\n",
    "        if filename.endswith('.xml'): continue\n",
    "        if filename.endswith('.txt'): continue\n",
    "        if filename.endswith('.data'): continue\n",
    "        if filename.endswith('.md'): continue\n",
    "        \n",
    "        file_dir = os.path.join(dirname, filename)\n",
    "        tail = filename[filename.rfind('.'):]\n",
    "        \n",
    "        file_lst.append(file_dir)\n",
    "        ext.add(tail)\n",
    "\n",
    "#Unique set of extensions\n",
    "#ext"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "b4ccaeea",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-01-23T00:51:06.187579Z",
     "iopub.status.busy": "2022-01-23T00:51:06.186677Z",
     "iopub.status.idle": "2022-01-23T00:51:06.188921Z",
     "shell.execute_reply": "2022-01-23T00:51:06.189525Z",
     "shell.execute_reply.started": "2022-01-23T00:31:05.455001Z"
    },
    "papermill": {
     "duration": 0.048194,
     "end_time": "2022-01-23T00:51:06.189691",
     "exception": false,
     "start_time": "2022-01-23T00:51:06.141497",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "sen_lst = []"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1d76c503",
   "metadata": {
    "papermill": {
     "duration": 0.040859,
     "end_time": "2022-01-23T00:51:06.271927",
     "exception": false,
     "start_time": "2022-01-23T00:51:06.231068",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### **Extract .ORIG to .CSV**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "850b0f62",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-01-23T00:51:06.358743Z",
     "iopub.status.busy": "2022-01-23T00:51:06.357690Z",
     "iopub.status.idle": "2022-01-23T00:51:06.367620Z",
     "shell.execute_reply": "2022-01-23T00:51:06.368173Z",
     "shell.execute_reply.started": "2022-01-23T00:31:05.460432Z"
    },
    "papermill": {
     "duration": 0.055241,
     "end_time": "2022-01-23T00:51:06.368364",
     "exception": false,
     "start_time": "2022-01-23T00:51:06.313123",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "for fil in file_lst:\n",
    "  if not fil.endswith('.orig'): continue\n",
    "  \n",
    "  fr = open(fil, 'r').read()\n",
    "  sen_lst += re.findall('\"(.*)\"', fr)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b07a616d",
   "metadata": {
    "papermill": {
     "duration": 0.041002,
     "end_time": "2022-01-23T00:51:06.450463",
     "exception": false,
     "start_time": "2022-01-23T00:51:06.409461",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### **Extract .TSV to .CSV**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "a6993eb0",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-01-23T00:51:06.536124Z",
     "iopub.status.busy": "2022-01-23T00:51:06.535424Z",
     "iopub.status.idle": "2022-01-23T00:51:06.541637Z",
     "shell.execute_reply": "2022-01-23T00:51:06.542219Z",
     "shell.execute_reply.started": "2022-01-23T00:31:05.477998Z"
    },
    "papermill": {
     "duration": 0.050884,
     "end_time": "2022-01-23T00:51:06.542398",
     "exception": false,
     "start_time": "2022-01-23T00:51:06.491514",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "for fil in file_lst:\n",
    "  if not fil.endswith('.tsv'): continue\n",
    "  \n",
    "  fr = open(fil, 'r').read()\n",
    "  sen_lst += re.findall('  (.*)', fr)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "444b5e25",
   "metadata": {
    "papermill": {
     "duration": 0.041242,
     "end_time": "2022-01-23T00:51:06.625170",
     "exception": false,
     "start_time": "2022-01-23T00:51:06.583928",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### **Display Result**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "478bd710",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-01-23T00:51:06.712131Z",
     "iopub.status.busy": "2022-01-23T00:51:06.711098Z",
     "iopub.status.idle": "2022-01-23T00:51:06.717052Z",
     "shell.execute_reply": "2022-01-23T00:51:06.717560Z",
     "shell.execute_reply.started": "2022-01-23T00:31:05.485563Z"
    },
    "papermill": {
     "duration": 0.05065,
     "end_time": "2022-01-23T00:51:06.717766",
     "exception": false,
     "start_time": "2022-01-23T00:51:06.667116",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4334"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(sen_lst)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "a4ce1799",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-01-23T00:51:06.813081Z",
     "iopub.status.busy": "2022-01-23T00:51:06.812083Z",
     "iopub.status.idle": "2022-01-23T00:51:06.817107Z",
     "shell.execute_reply": "2022-01-23T00:51:06.816546Z",
     "shell.execute_reply.started": "2022-01-23T00:31:05.495693Z"
    },
    "papermill": {
     "duration": 0.058102,
     "end_time": "2022-01-23T00:51:06.817250",
     "exception": false,
     "start_time": "2022-01-23T00:51:06.759148",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "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>labels</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ṣùgbọ́n ọlọpáá alábòójútó ìhámọ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ẹ̀bẹ̀ mo bẹ̀ ọ́ olúwa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>áfíríkà nínú ìgbìmọ̀ ààbò àjọ</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ó kà nítòrì nípa lílọ</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>jáde ilé ìwé àgbà yunifásítì</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                              labels\n",
       "0  ṣùgbọ́n ọlọpáá alábòójútó ìhámọ...\n",
       "1                         ẹ̀bẹ̀ mo bẹ̀ ọ́ olúwa\n",
       "2        áfíríkà nínú ìgbìmọ̀ ààbò àjọ\n",
       "3                      ó kà nítòrì nípa lílọ\n",
       "4              jáde ilé ìwé àgbà yunifásítì"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_df = pd.DataFrame(sen_lst, columns = ['labels'])\n",
    "test_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "cd280e45",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-01-23T00:51:06.904814Z",
     "iopub.status.busy": "2022-01-23T00:51:06.904149Z",
     "iopub.status.idle": "2022-01-23T00:51:06.923406Z",
     "shell.execute_reply": "2022-01-23T00:51:06.923919Z",
     "shell.execute_reply.started": "2022-01-23T00:31:05.511471Z"
    },
    "papermill": {
     "duration": 0.06473,
     "end_time": "2022-01-23T00:51:06.924111",
     "exception": false,
     "start_time": "2022-01-23T00:51:06.859381",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "test_df.to_csv('test.csv', index = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5554f877",
   "metadata": {
    "papermill": {
     "duration": 0.041403,
     "end_time": "2022-01-23T00:51:07.007368",
     "exception": false,
     "start_time": "2022-01-23T00:51:06.965965",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": []
  }
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