13_zApr22Adv_Code_GBDT.ipynb

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   "execution_count": 1,
   "metadata": {
    "id": "xQIfyjyXk3a2"
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import os\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "ZSYBQfVcmbRe",
    "outputId": "7007efa2-59f5-4578-b238-8c66bdda2ed2"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Downloading...\n",
      "From: https://drive.google.com/uc?id=1h86M8si2YT-aI4Zec1MeMP_mPYsLPy5F\n",
      "To: C:\\Users\\sunny\\Data Science Studies\\GitHub_Desktop\\DSML Classical Machine Learning 1\\emg.rar\n",
      "\n",
      "  0%|          | 0.00/18.6M [00:00<?, ?B/s]\n",
      " 14%|#4        | 2.62M/18.6M [00:00<00:00, 24.5MB/s]\n",
      " 28%|##8       | 5.24M/18.6M [00:00<00:00, 14.1MB/s]\n",
      " 48%|####7     | 8.91M/18.6M [00:00<00:00, 12.6MB/s]\n",
      " 85%|########4 | 15.7M/18.6M [00:00<00:00, 23.8MB/s]\n",
      "100%|##########| 18.6M/18.6M [00:00<00:00, 20.1MB/s]\n"
     ]
    }
   ],
   "source": [
    "!gdown 1h86M8si2YT-aI4Zec1MeMP_mPYsLPy5F"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "id": "cVHL7dZtmeqv"
   },
   "outputs": [],
   "source": [
    "# !pip install unrar\n",
    "# !pip install unrar"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "_0wQvtI-mdSA",
    "outputId": "8e6764c1-c2cf-4786-e6a7-4da05ca83051"
   },
   "outputs": [],
   "source": [
    "# x is extract\n",
    "\n",
    "# !unrar x \"./emg.rar\" \"./\" "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "Wauf-oFTmhMk",
    "outputId": "296da380-e594-4793-d72d-c109bab808a9"
   },
   "outputs": [],
   "source": [
    "# list files\n",
    "# !ls ./EMG\\ Physical\\ Action\\ Data\\ Set/sub1/Aggressive/txt/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "neHOQZp_mjtO",
    "outputId": "d26ab27a-fd9b-48eb-dde5-179a8efe214a"
   },
   "outputs": [],
   "source": [
    "# !cat ./EMG\\ Physical\\ Action\\ Data\\ Set/sub1/Aggressive/txt/Slapping.txt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "id": "Ei1ASFBWmlyy"
   },
   "outputs": [],
   "source": [
    "\n",
    "# actions = {}\n",
    "\n",
    "# data_dirs = [\"./EMG Physical Action Data Set/sub1/Aggressive/txt\",\n",
    "#              \"./EMG Physical Action Data Set/sub1/Normal/txt\"]\n",
    "\n",
    "# ind = 0\n",
    "# data = pd.DataFrame()\n",
    "\n",
    "# for dirs in data_dirs :\n",
    "\n",
    "#   for files in os.listdir(dirs):\n",
    "\n",
    "#     with open(os.path.join(dirs, files), \"r\") as f:\n",
    "\n",
    "#       temp = pd.read_csv(f.name,\n",
    "#                         sep = \"\\t\",\n",
    "#                         header = None, \n",
    "#                         names = [\"ch\" + str(i) for i in range(1, 9)] # 8 input channels\n",
    "#                         )\n",
    "      \n",
    "#       # chunking using Max of every 10 sequential values.\n",
    "#       temp_chunked = pd.DataFrame()\n",
    "\n",
    "#       for i in range(0, len(temp), 10):\n",
    "#         temp_chunked = temp_chunked.append(temp.iloc[i:i+10].max(), ignore_index = True)\n",
    "\n",
    "#       labels = [files[:-4] for i in range(len(temp_chunked))] # remove the last 4 characters=\".txt\" from the filename\n",
    "#       actions[files[:-4]] = ind\n",
    "\n",
    "#       temp_chunked[\"Action\"] = labels\n",
    "\n",
    "#       data = pd.concat([data, temp_chunked])\n",
    "\n",
    "#       ind+=1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "XvZtQGn52EEQ",
    "outputId": "84012390-0a43-4df8-f0d1-099ee2eddc0c"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'Slapping': 0, 'Kneeing': 1, 'Elbowing': 2, 'Sidekicking': 3, 'Headering': 4, 'Frontkicking': 5, 'Pushing': 6, 'Pulling': 7, 'Punching': 8, 'Hamering': 9, 'Seating': 10, 'Hugging': 11, 'Running': 12, 'Standing': 13, 'Handshaking': 14, 'Waving': 15, 'Bowing': 16, 'Jumping': 17, 'Clapping': 18, 'Walking': 19}\n"
     ]
    }
   ],
   "source": [
    "print(actions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {
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    "id": "yHcLJZBlmuM9",
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       "      <td>180.0</td>\n",
       "      <td>238.0</td>\n",
       "      <td>251.0</td>\n",
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       "      ch1     ch2    ch3    ch4     ch5    ch6     ch7     ch8    Action\n",
       "0     2.0   113.0  129.0   95.0 -1042.0  845.0   -94.0  -407.0  Slapping\n",
       "1  4000.0   250.0  -54.0  142.0 -2550.0  303.0 -1458.0  1765.0  Slapping\n",
       "2  2410.0  1516.0  803.0    8.0  3284.0  309.0 -1877.0  1199.0  Slapping\n",
       "3 -1556.0   876.0  710.0   85.0  4000.0  213.0 -1517.0   646.0  Slapping\n",
       "4  4000.0   275.0  304.0  137.0  4000.0  180.0   238.0   251.0  Slapping"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
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   "execution_count": 7,
   "metadata": {
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    {
     "data": {
      "text/plain": [
       "(19711, 9)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
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   "execution_count": 8,
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     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 19711 entries, 0 to 999\n",
      "Data columns (total 9 columns):\n",
      " #   Column  Non-Null Count  Dtype  \n",
      "---  ------  --------------  -----  \n",
      " 0   ch1     19711 non-null  float64\n",
      " 1   ch2     19711 non-null  float64\n",
      " 2   ch3     19711 non-null  float64\n",
      " 3   ch4     19711 non-null  float64\n",
      " 4   ch5     19711 non-null  float64\n",
      " 5   ch6     19711 non-null  float64\n",
      " 6   ch7     19711 non-null  float64\n",
      " 7   ch8     19711 non-null  float64\n",
      " 8   Action  19711 non-null  object \n",
      "dtypes: float64(8), object(1)\n",
      "memory usage: 1.5+ MB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "id": "Mvd1jm23m1Iy"
   },
   "outputs": [],
   "source": [
    "Y = data[\"Action\"]\n",
    "X = data.drop(columns = [\"Action\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "kVnPWop4m3Cq",
    "outputId": "c410ad3a-ef9c-45f9-bd0f-e13bef292163"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Slapping' 'Kneeing' 'Elbowing' 'Sidekicking' 'Headering' 'Frontkicking'\n",
      " 'Pushing' 'Pulling' 'Punching' 'Hamering' 'Seating' 'Hugging' 'Running'\n",
      " 'Standing' 'Handshaking' 'Waving' 'Bowing' 'Jumping' 'Clapping' 'Walking']\n"
     ]
    }
   ],
   "source": [
    "print(Y.unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "3TSbuSncm_iR",
    "outputId": "0ac40b36-222a-4386-d9a3-6f7af6d91658"
   },
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    {
     "data": {
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       "0    0\n",
       "1    0\n",
       "2    0\n",
       "3    0\n",
       "4    0\n",
       "Name: Action, dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y = Y.map(actions)\n",
    "Y.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "HXSW4LZH2pf2",
    "outputId": "d098319c-27e2-447f-cc7d-2fef1b91918f"
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    {
     "data": {
      "text/plain": [
       "10    1000\n",
       "9     1000\n",
       "18    1000\n",
       "17    1000\n",
       "15    1000\n",
       "1     1000\n",
       "19    1000\n",
       "4     1000\n",
       "12     997\n",
       "16     983\n",
       "3      983\n",
       "5      982\n",
       "0      979\n",
       "2      978\n",
       "11     976\n",
       "13     973\n",
       "6      968\n",
       "7      966\n",
       "8      964\n",
       "14     962\n",
       "Name: Action, dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y.value_counts()"
   ]
  },
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   "execution_count": 15,
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       "      <td>845.000000</td>\n",
       "      <td>94.000000</td>\n",
       "      <td>407.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2096.190476</td>\n",
       "      <td>184.761905</td>\n",
       "      <td>33.142857</td>\n",
       "      <td>119.619048</td>\n",
       "      <td>1831.904762</td>\n",
       "      <td>561.095238</td>\n",
       "      <td>808.476190</td>\n",
       "      <td>730.714286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2210.906344</td>\n",
       "      <td>671.407855</td>\n",
       "      <td>314.570997</td>\n",
       "      <td>78.815710</td>\n",
       "      <td>38.259819</td>\n",
       "      <td>468.939577</td>\n",
       "      <td>1199.084592</td>\n",
       "      <td>901.900302</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1130.589097</td>\n",
       "      <td>730.083172</td>\n",
       "      <td>427.976729</td>\n",
       "      <td>80.589313</td>\n",
       "      <td>1174.453782</td>\n",
       "      <td>395.538246</td>\n",
       "      <td>1290.260073</td>\n",
       "      <td>828.510235</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1818.719431</td>\n",
       "      <td>620.946995</td>\n",
       "      <td>398.245139</td>\n",
       "      <td>94.117492</td>\n",
       "      <td>1852.064667</td>\n",
       "      <td>343.848749</td>\n",
       "      <td>923.759021</td>\n",
       "      <td>690.014103</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>\n",
       "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-f4aed3a0-5fd9-4230-bb89-597af095553a')\"\n",
       "              title=\"Convert this dataframe to an interactive table.\"\n",
       "              style=\"display:none;\">\n",
       "        \n",
       "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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       "  </svg>\n",
       "      </button>\n",
       "      \n",
       "  <style>\n",
       "    .colab-df-container {\n",
       "      display:flex;\n",
       "      flex-wrap:wrap;\n",
       "      gap: 12px;\n",
       "    }\n",
       "\n",
       "    .colab-df-convert {\n",
       "      background-color: #E8F0FE;\n",
       "      border: none;\n",
       "      border-radius: 50%;\n",
       "      cursor: pointer;\n",
       "      display: none;\n",
       "      fill: #1967D2;\n",
       "      height: 32px;\n",
       "      padding: 0 0 0 0;\n",
       "      width: 32px;\n",
       "    }\n",
       "\n",
       "    .colab-df-convert:hover {\n",
       "      background-color: #E2EBFA;\n",
       "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
       "      fill: #174EA6;\n",
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       "\n",
       "    [theme=dark] .colab-df-convert {\n",
       "      background-color: #3B4455;\n",
       "      fill: #D2E3FC;\n",
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       "\n",
       "    [theme=dark] .colab-df-convert:hover {\n",
       "      background-color: #434B5C;\n",
       "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
       "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
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       "\n",
       "      <script>\n",
       "        const buttonEl =\n",
       "          document.querySelector('#df-f4aed3a0-5fd9-4230-bb89-597af095553a button.colab-df-convert');\n",
       "        buttonEl.style.display =\n",
       "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
       "\n",
       "        async function convertToInteractive(key) {\n",
       "          const element = document.querySelector('#df-f4aed3a0-5fd9-4230-bb89-597af095553a');\n",
       "          const dataTable =\n",
       "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
       "                                                     [key], {});\n",
       "          if (!dataTable) return;\n",
       "\n",
       "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
       "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
       "            + ' to learn more about interactive tables.';\n",
       "          element.innerHTML = '';\n",
       "          dataTable['output_type'] = 'display_data';\n",
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       "      </script>\n",
       "    </div>\n",
       "  </div>\n",
       "  "
      ],
      "text/plain": [
       "           ch1         ch2         ch3         ch4          ch5         ch6  \\\n",
       "0     2.000000  113.000000  129.000000   95.000000  1042.000000  845.000000   \n",
       "1  2096.190476  184.761905   33.142857  119.619048  1831.904762  561.095238   \n",
       "2  2210.906344  671.407855  314.570997   78.815710    38.259819  468.939577   \n",
       "3  1130.589097  730.083172  427.976729   80.589313  1174.453782  395.538246   \n",
       "4  1818.719431  620.946995  398.245139   94.117492  1852.064667  343.848749   \n",
       "\n",
       "           ch7         ch8  \n",
       "0    94.000000  407.000000  \n",
       "1   808.476190  730.714286  \n",
       "2  1199.084592  901.900302  \n",
       "3  1290.260073  828.510235  \n",
       "4   923.759021  690.014103  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = abs(X)\n",
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "id": "iBnE44aFnI0q"
   },
   "outputs": [],
   "source": [
    "# moving average. \n",
    "\n",
    "X = X.ewm(10).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "DzNhnjLwnsy5",
    "outputId": "3fba48cb-59bc-430a-938e-635878462bf1"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sizes of the sets created are:\n",
      "Training set: 15768\n",
      "Test set: 3943\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "X = np.array(X.values)\n",
    "Y = np.array(Y.values)\n",
    "\n",
    "X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size = 0.2, shuffle = True, random_state=3)\n",
    "\n",
    "print(f\"Sizes of the sets created are:\\nTraining set: {X_train.shape[0]}\\nTest set: {X_test.shape[0]}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "y39mIiLjlK-c"
   },
   "source": [
    "# Decision Tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "B5MjVrkan2ED",
    "outputId": "541cf80e-7d0c-4b5a-99d2-91730606c6a2"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "K-Fold Accuracy Mean: Train: 74.1 Validation: 73.6\n"
     ]
    }
   ],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.model_selection import cross_validate\n",
    "\n",
    "tree_clf = DecisionTreeClassifier(random_state=7, max_depth = 7, )\n",
    "cv_acc_results = cross_validate(tree_clf, X_train, Y_train, cv = 5, scoring = 'accuracy', return_train_score = True)\n",
    "\n",
    "print(f\"K-Fold Accuracy Mean: Train: {cv_acc_results['train_score'].mean().round(3)*100} Validation: {cv_acc_results['test_score'].mean().round(3)*100}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "id": "aKPUSJnhlV9i"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "iVKlmhXQmZaP"
   },
   "source": [
    "# RandomForest "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "soYT4wGtmX5M",
    "outputId": "1c7ae78e-eac9-4451-d2e3-3bb8217c8c3e"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "K-Fold Accuracy Mean: Train: 83.2 Validation: 82.1\n"
     ]
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.model_selection import cross_validate\n",
    "\n",
    "tree_clf = RandomForestClassifier(random_state=7, max_depth = 7, n_estimators= 100 )\n",
    "cv_acc_results = cross_validate(tree_clf, X_train, Y_train, cv = 5, scoring = 'accuracy', return_train_score = True)\n",
    "\n",
    "print(f\"K-Fold Accuracy Mean: Train: {cv_acc_results['train_score'].mean().round(3)*100} Validation: {cv_acc_results['test_score'].mean().round(3)*100}\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "b85QV_rNnAuP"
   },
   "source": [
    "# GBDT"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "nNXOrMRhmtDd",
    "outputId": "ad665085-0b6e-4def-942b-d012ef4d2cae"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "K-Fold Accuracy Mean: Train: 100.0 Validation: 94.5\n"
     ]
    }
   ],
   "source": [
    "from sklearn.ensemble import GradientBoostingClassifier\n",
    "\n",
    "\n",
    "tree_clf = GradientBoostingClassifier(random_state=7, max_depth = 4, n_estimators= 150, learning_rate = 0.1 )\n",
    "cv_acc_results = cross_validate(tree_clf, X_train, Y_train, cv = 3, scoring = 'accuracy', return_train_score = True)\n",
    "\n",
    "print(f\"K-Fold Accuracy Mean: Train: {cv_acc_results['train_score'].mean().round(3)*100} Validation: {cv_acc_results['test_score'].mean().round(3)*100}\")\n"
   ]
  }
 ],
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