goit_ds_hw_05.ipynb

{
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  "metadata": {
    "colab": {
      "provenance": [],
      "collapsed_sections": [
        "ff0th10_6aei"
      ],
      "authorship_tag": "ABX9TyOtXUOVVeqtZqSm9pGKxYAj",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/lexxai/goit_python_ds_hw_05/blob/main/goit_ds_hw_05.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## LOAD DATASET"
      ],
      "metadata": {
        "id": "AW7Ag9r3BfSV"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.pipeline import Pipeline\n",
        "from sklearn.compose import ColumnTransformer\n",
        "from sklearn.impute import SimpleImputer\n",
        "from sklearn.svm import SVC\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.model_selection import GridSearchCV\n",
        "from sklearn.metrics import classification_report, confusion_matrix\n",
        "from sklearn.pipeline import make_pipeline\n",
        "from sklearn.model_selection import cross_val_score\n",
        "from sklearn.metrics import f1_score, accuracy_score\n"
      ],
      "metadata": {
        "id": "1D0oT1i-ROzp"
      },
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from pathlib import Path\n",
        "\n",
        "DATASET_KEYS = (\"f0s0\", \"f0s1\", \"f1s0\", \"f1s1\")\n",
        "SAVED_DATAFRAME_BASE = Path(\"/content/\")\n",
        "URL = \"https://drive.usercontent.google.com/download?id=1nzrtQpfaHL0OgJ_eXzA7VuEj7XotrSWO&export=download&authuser=0\"\n",
        "OUTPUT = Path(\"/content/homework.zip\")\n",
        "CSV_DATA_PATH  = Path(\"/content/data\")"
      ],
      "metadata": {
        "id": "6PnBB155s5Pd"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "GzZEhfMLIgwU",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f8228fb4-c89d-4160-e984-f58b6609467f"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "--2024-01-31 18:02:48--  https://drive.usercontent.google.com/download?id=1nzrtQpfaHL0OgJ_eXzA7VuEj7XotrSWO\n",
            "Resolving drive.usercontent.google.com (drive.usercontent.google.com)... 173.194.216.132, 2607:f8b0:400c:c0c::84\n",
            "Connecting to drive.usercontent.google.com (drive.usercontent.google.com)|173.194.216.132|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 4211746 (4.0M) [application/octet-stream]\n",
            "Saving to: ‘/content/homework.zip’\n",
            "\n",
            "/content/homework.z 100%[===================>]   4.02M  --.-KB/s    in 0.1s    \n",
            "\n",
            "2024-01-31 18:03:18 (31.5 MB/s) - ‘/content/homework.zip’ saved [4211746/4211746]\n",
            "\n"
          ]
        }
      ],
      "source": [
        "if not OUTPUT.is_file():\n",
        "  !wget -O $OUTPUT $URL\n",
        "\n",
        "if OUTPUT.is_file() and not CSV_DATA_PATH.is_dir():\n",
        "  !unzip -q -o $OUTPUT\n",
        "  #!rm $OUTPUT"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "if CSV_DATA_PATH.is_dir():\n",
        "  class_path = list(CSV_DATA_PATH.iterdir())\n",
        "  class_list = list(d.name for d in class_path)\n",
        "  print(class_list)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JlsLFHAeMD-T",
        "outputId": "a9b1a9e9-d219-421a-b68d-878f33acc887"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['walking', 'stairs', 'idle', 'running']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### PRELOAD SAVED DATASETs"
      ],
      "metadata": {
        "id": "8K5C4W6HR59c"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def gen_saved_name(key):\n",
        "  return SAVED_DATAFRAME_BASE.joinpath(f\"data-{key}.father\")"
      ],
      "metadata": {
        "id": "e2OMuKwEQM59"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_set = {}\n",
        "X_set = {}\n",
        "y_set = {}\n",
        "reports = {}"
      ],
      "metadata": {
        "id": "49wi3Zu0LqnS"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "skip_load = False"
      ],
      "metadata": {
        "id": "3zO42cScqqCg"
      },
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "for key in DATASET_KEYS:\n",
        "  filename = gen_saved_name(key)\n",
        "  if not skip_load and filename.is_file():\n",
        "    df_set[key] = pd.read_feather(filename)\n",
        "    X_set[key] = df_set[key].iloc[:,:-1]\n",
        "    y_set[key] = df_set[key].iloc[:,-1]\n",
        "    print(f\"DATASET {key}. Loaded. shape: {df_set[key].shape}\")\n",
        "  else:\n",
        "    df_set[key] = pd.DataFrame()"
      ],
      "metadata": {
        "id": "k03po368tWcn"
      },
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## BUILD DataFrame"
      ],
      "metadata": {
        "id": "FmsQbzSxPLOt"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### functions"
      ],
      "metadata": {
        "id": "wxXLGdbuJ5wN"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def save_dataset(key,df):\n",
        "  filename = gen_saved_name(key)\n",
        "  if not df.empty and not filename.is_file():\n",
        "    df.to_feather(filename)"
      ],
      "metadata": {
        "id": "Z2c4UCMyTc4p"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def flatten_frame(frame):\n",
        "        columns=[f\"{col}_{i}\" for i in range(frame.shape[0]) for col in frame.columns]\n",
        "        return pd.DataFrame(frame.values.reshape(1, -1), columns=columns)"
      ],
      "metadata": {
        "id": "cwvTi6GrUNPb"
      },
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def add_stat_feature_frame(frame, rows):\n",
        "  features = []\n",
        "  for col_id in range(0,3):\n",
        "    col = frame.iloc[:,col_id]\n",
        "    features.append(pd.DataFrame([col.mean()] * rows,columns=[f'{col.name}_mean']))\n",
        "    features.append(pd.DataFrame([col.max()] * rows,columns=[f'{col.name}_max']))\n",
        "    features.append(pd.DataFrame([col.min()] * rows,columns=[f'{col.name}_min']))\n",
        "    features.append(pd.DataFrame([col.quantile(0.75)-col.quantile(0.25)] * rows,columns=[f'{col.name}_interquartile_range']))\n",
        "    features.append(pd.DataFrame([col.idxmin()] * rows,columns=[f'{col.name}_index_of_minimum_value']))\n",
        "    features.append(pd.DataFrame([np.mean(np.abs(col - col.mean()))] * rows,columns=[f'{col.name}_mean_of_absolute_deviation']))\n",
        "    features.append(pd.DataFrame([col.median()] * rows,columns=[f'{col.name}_median']))\n",
        "    # features.append(pd.DataFrame([df.skew(axis=0).iloc[:,col_id]] * rows,columns=[f'{col.name}_skewness']))\n",
        "    features.append(pd.DataFrame([col.std()] * rows,columns=[f'{col.name}_standard_deviation']))\n",
        "    features.append(pd.DataFrame([np.sqrt(np.mean(col)**2)] * rows,columns=[f'{col.name}_root_mean_square_error']))\n",
        "\n",
        "  result = pd.concat(features, axis=1)\n",
        "  # rint(f\"New stats features: {result.columns}\")\n",
        "  return result"
      ],
      "metadata": {
        "id": "0fgZZnp5VadH"
      },
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def prepare_dataset(class_path: list[Path], flatten = True, stat_feture = True, limit_frames = None):\n",
        "  dfws = []\n",
        "  for class_id, work_class_path in enumerate(class_path):\n",
        "    list_files = list(sorted(work_class_path.glob('*.csv'), key=lambda path: int(path.stem.rsplit(\"-\", 1)[1])))\n",
        "    print(f\"Importing class '{work_class_path.name:7}' : {class_id}. Frames: {len(list_files)}\")\n",
        "    for i, filename in enumerate(list_files):\n",
        "      # READ FRAME CSV FILE\n",
        "      df_w: pd.DataFrame = pd.read_csv(filename)\n",
        "      addon_features = [df_w]\n",
        "      if flatten:\n",
        "        addon_features = [flatten_frame(df_w)]\n",
        "      if stat_feture:\n",
        "        addon_features.append(add_stat_feature_frame(df_w,addon_features[0].shape[0]))\n",
        "      df_w = pd.concat(addon_features, axis=1)\n",
        "      df_w['class'] = class_id\n",
        "      dfws.append(df_w)\n",
        "      if limit_frames and (i > limit_frames):\n",
        "        break\n",
        "  df = pd.concat(dfws, axis=0, ignore_index=True)\n",
        "  # print(df.info())\n",
        "  print(df.shape)\n",
        "  return df"
      ],
      "metadata": {
        "id": "ULUpYPrXqw5o"
      },
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### DATASETs  (f0s0, f0s1, f1s0, f1s1)"
      ],
      "metadata": {
        "id": "_0ib1L70Gzgo"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "limit_frames = None\n",
        "for f in range(2):\n",
        "  for s in range(2):\n",
        "    key_set = f\"f{f}s{s}\"\n",
        "    print(f\"\\nDATASET {key_set}. Where flatten={bool(f)}, stat_feture={bool(s)}\")\n",
        "    if df_set.get(key_set) is not None and (not df_set[key_set].empty):\n",
        "      print(\"Alredy loaded, skipped\")\n",
        "      continue\n",
        "    df_set[key_set] = prepare_dataset(class_path, flatten = f, stat_feture = s, limit_frames = limit_frames)\n",
        "    save_dataset(key_set, df_set[key_set])\n",
        "    # print(df_set[key_set].sample(4))\n",
        "    X_set[key_set] = df_set[key_set].iloc[:,:-1]\n",
        "    y_set[key_set] = df_set[key_set].iloc[:,-1]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ab909afc-1290-42a4-b262-bafb9d8c2010",
        "id": "KSgYfYz6Gzgo"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "DATASET f0s0. Where flatten=False, stat_feture=False\n",
            "Importing class 'walking' : 0. Frames: 1850\n",
            "Importing class 'stairs ' : 1. Frames: 165\n",
            "Importing class 'idle   ' : 2. Frames: 1039\n",
            "Importing class 'running' : 3. Frames: 3408\n",
            "(193860, 4)\n",
            "\n",
            "DATASET f0s1. Where flatten=False, stat_feture=True\n",
            "Importing class 'walking' : 0. Frames: 1850\n",
            "Importing class 'stairs ' : 1. Frames: 165\n",
            "Importing class 'idle   ' : 2. Frames: 1039\n",
            "Importing class 'running' : 3. Frames: 3408\n",
            "(193860, 31)\n",
            "\n",
            "DATASET f1s0. Where flatten=True, stat_feture=False\n",
            "Importing class 'walking' : 0. Frames: 1850\n",
            "Importing class 'stairs ' : 1. Frames: 165\n",
            "Importing class 'idle   ' : 2. Frames: 1039\n",
            "Importing class 'running' : 3. Frames: 3408\n",
            "(6462, 91)\n",
            "\n",
            "DATASET f1s1. Where flatten=True, stat_feture=True\n",
            "Importing class 'walking' : 0. Frames: 1850\n",
            "Importing class 'stairs ' : 1. Frames: 165\n",
            "Importing class 'idle   ' : 2. Frames: 1039\n",
            "Importing class 'running' : 3. Frames: 3408\n",
            "(6462, 118)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "key_set = list(df_set.keys())[0]\n",
        "for x in df_set[key_set]['class'].unique():\n",
        "  count = df_set[key_set]['class'][df_set[key_set]['class'] == x].count()\n",
        "  prop = count / df_set[key_set].shape[0]\n",
        "  print(f\"class: {x}, rows: {count:7}, {class_list[x]:7}, prop: {prop:.4}\" )"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f11202b0-c328-457a-8c28-7d116ba3304f",
        "id": "z6mVG5UtGzgr"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "class: 0, rows:   55500, walking, prop: 0.2863\n",
            "class: 1, rows:    4950, stairs , prop: 0.02553\n",
            "class: 2, rows:   31170, idle   , prop: 0.1608\n",
            "class: 3, rows:  102240, running, prop: 0.5274\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## COMPARE MODELS"
      ],
      "metadata": {
        "id": "3wsHkSh0ZwmP"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "models = {\"SVC\": lambda: SVC(),\n",
        "          \"SVC_Linear\": lambda: SVC(kernel=\"linear\"),\n",
        "          \"RandomForestClassifier\": lambda: RandomForestClassifier()}\n",
        "reports = {}\n",
        "skip_models = {\n",
        "    \"SVC_Linear\": [\"f0s0\"]\n",
        "}"
      ],
      "metadata": {
        "id": "Fzunb__6b62C"
      },
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "SEED = 42\n",
        "print(\"Models fit and prepare report\")\n",
        "for key in X_set.keys():\n",
        "  print(\"-\"*80)\n",
        "  print(f\"DATASET {key}. shape: {df_set[key].shape}\")\n",
        "  X = X_set[key]\n",
        "  y = y_set[key]\n",
        "  X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=SEED, stratify=y)\n",
        "  if reports.get(key) is None:\n",
        "    reports[key] = {}\n",
        "  for model, classification in models.items():\n",
        "    print(f\"\\n- classification: {model}\")\n",
        "    if reports[key].get(model):\n",
        "      print(\"   alredy fit, skipped\")\n",
        "      continue\n",
        "    if key in skip_models.get(model,[]):\n",
        "      print(\"   skip this model\")\n",
        "      continue\n",
        "    clf = classification()\n",
        "    %time clf.fit(X_train, y_train)\n",
        "    %time y_test_predict = clf.predict(X_test)\n",
        "    reports[key][model] = classification_report(y_test, y_test_predict, digits=4, target_names=class_list)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WbKsWF1HZ7Qx",
        "outputId": "c6e466c6-3bc2-4f9a-9e49-eeba34b9c04b"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Models fit and prepare report\n",
            "--------------------------------------------------------------------------------\n",
            "DATASET f0s0. shape: (193860, 4)\n",
            "\n",
            "- classification: SVC\n",
            "CPU times: user 3min 57s, sys: 461 ms, total: 3min 58s\n",
            "Wall time: 3min 58s\n",
            "CPU times: user 1min 49s, sys: 99.9 ms, total: 1min 49s\n",
            "Wall time: 1min 50s\n",
            "\n",
            "- classification: SVC_Linear\n",
            "   skip this model\n",
            "\n",
            "- classification: RandomForestClassifier\n",
            "CPU times: user 13.9 s, sys: 19 ms, total: 13.9 s\n",
            "Wall time: 13.9 s\n",
            "CPU times: user 963 ms, sys: 2.01 ms, total: 965 ms\n",
            "Wall time: 963 ms\n",
            "--------------------------------------------------------------------------------\n",
            "DATASET f0s1. shape: (193860, 31)\n",
            "\n",
            "- classification: SVC\n",
            "CPU times: user 22.5 s, sys: 115 ms, total: 22.6 s\n",
            "Wall time: 22.7 s\n",
            "CPU times: user 17.5 s, sys: 18 ms, total: 17.5 s\n",
            "Wall time: 17.6 s\n",
            "\n",
            "- classification: SVC_Linear\n",
            "CPU times: user 2min 2s, sys: 234 ms, total: 2min 2s\n",
            "Wall time: 2min 2s\n",
            "CPU times: user 7.26 s, sys: 6.98 ms, total: 7.26 s\n",
            "Wall time: 7.3 s\n",
            "\n",
            "- classification: RandomForestClassifier\n",
            "CPU times: user 15.3 s, sys: 35 ms, total: 15.3 s\n",
            "Wall time: 15.3 s\n",
            "CPU times: user 463 ms, sys: 1e+03 µs, total: 464 ms\n",
            "Wall time: 461 ms\n",
            "--------------------------------------------------------------------------------\n",
            "DATASET f1s0. shape: (6462, 91)\n",
            "\n",
            "- classification: SVC\n",
            "CPU times: user 189 ms, sys: 6 µs, total: 189 ms\n",
            "Wall time: 188 ms\n",
            "CPU times: user 140 ms, sys: 1.93 ms, total: 142 ms\n",
            "Wall time: 142 ms\n",
            "\n",
            "- classification: SVC_Linear\n",
            "CPU times: user 13.9 s, sys: 13 ms, total: 13.9 s\n",
            "Wall time: 14 s\n",
            "CPU times: user 45.3 ms, sys: 0 ns, total: 45.3 ms\n",
            "Wall time: 44.7 ms\n",
            "\n",
            "- classification: RandomForestClassifier\n",
            "CPU times: user 2.86 s, sys: 4.01 ms, total: 2.86 s\n",
            "Wall time: 2.88 s\n",
            "CPU times: user 43.3 ms, sys: 0 ns, total: 43.3 ms\n",
            "Wall time: 44.4 ms\n",
            "--------------------------------------------------------------------------------\n",
            "DATASET f1s1. shape: (6462, 118)\n",
            "\n",
            "- classification: SVC\n",
            "CPU times: user 243 ms, sys: 993 µs, total: 244 ms\n",
            "Wall time: 249 ms\n",
            "CPU times: user 346 ms, sys: 900 µs, total: 347 ms\n",
            "Wall time: 363 ms\n",
            "\n",
            "- classification: SVC_Linear\n",
            "CPU times: user 942 ms, sys: 1.98 ms, total: 944 ms\n",
            "Wall time: 962 ms\n",
            "CPU times: user 28.1 ms, sys: 0 ns, total: 28.1 ms\n",
            "Wall time: 27.4 ms\n",
            "\n",
            "- classification: RandomForestClassifier\n",
            "CPU times: user 2.01 s, sys: 2.02 ms, total: 2.02 s\n",
            "Wall time: 2.02 s\n",
            "CPU times: user 27.7 ms, sys: 0 ns, total: 27.7 ms\n",
            "Wall time: 27.8 ms\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Classification report"
      ],
      "metadata": {
        "id": "dHPChmT0gwRT"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print(f\"{limit_frames=}\")\n",
        "for dset in reports.keys():\n",
        "  for model in reports[dset].keys():\n",
        "    print(\"-\"*80)\n",
        "    print(f\"Data set: {dset}, shape: {df_set[dset].shape}, model: {model}\")\n",
        "    print(reports[dset][model])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uU6QAI4Cg07r",
        "outputId": "9d30fa77-a5bf-4d92-825a-b7a67e891c81"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "limit_frames=None\n",
            "--------------------------------------------------------------------------------\n",
            "Data set: f0s0, shape: (193860, 4), model: SVC\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "     walking     0.8010    0.9013    0.8482     16650\n",
            "      stairs     1.0000    0.0034    0.0067      1485\n",
            "        idle     0.9585    0.9863    0.9722      9351\n",
            "     running     0.9301    0.9035    0.9166     30672\n",
            "\n",
            "    accuracy                         0.8932     58158\n",
            "   macro avg     0.9224    0.6986    0.6859     58158\n",
            "weighted avg     0.8995    0.8932    0.8827     58158\n",
            "\n",
            "--------------------------------------------------------------------------------\n",
            "Data set: f0s0, shape: (193860, 4), model: RandomForestClassifier\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "     walking     0.9994    0.9998    0.9996     16650\n",
            "      stairs     1.0000    0.9865    0.9932      1485\n",
            "        idle     1.0000    0.9997    0.9998      9351\n",
            "     running     0.9994    1.0000    0.9997     30672\n",
            "\n",
            "    accuracy                         0.9995     58158\n",
            "   macro avg     0.9997    0.9965    0.9981     58158\n",
            "weighted avg     0.9995    0.9995    0.9995     58158\n",
            "\n",
            "--------------------------------------------------------------------------------\n",
            "Data set: f0s1, shape: (193860, 31), model: SVC\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "     walking     0.9887    0.9991    0.9939     16650\n",
            "      stairs     0.9885    0.8721    0.9267      1485\n",
            "        idle     1.0000    1.0000    1.0000      9351\n",
            "     running     1.0000    1.0000    1.0000     30672\n",
            "\n",
            "    accuracy                         0.9965     58158\n",
            "   macro avg     0.9943    0.9678    0.9801     58158\n",
            "weighted avg     0.9965    0.9965    0.9964     58158\n",
            "\n",
            "--------------------------------------------------------------------------------\n",
            "Data set: f0s1, shape: (193860, 31), model: SVC_Linear\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "     walking     0.9855    0.9888    0.9871     16650\n",
            "      stairs     0.8692    0.8370    0.8528      1485\n",
            "        idle     1.0000    1.0000    1.0000      9351\n",
            "     running     1.0000    1.0000    1.0000     30672\n",
            "\n",
            "    accuracy                         0.9926     58158\n",
            "   macro avg     0.9637    0.9565    0.9600     58158\n",
            "weighted avg     0.9925    0.9926    0.9926     58158\n",
            "\n",
            "--------------------------------------------------------------------------------\n",
            "Data set: f0s1, shape: (193860, 31), model: RandomForestClassifier\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "     walking     1.0000    1.0000    1.0000     16650\n",
            "      stairs     1.0000    1.0000    1.0000      1485\n",
            "        idle     1.0000    1.0000    1.0000      9351\n",
            "     running     1.0000    1.0000    1.0000     30672\n",
            "\n",
            "    accuracy                         1.0000     58158\n",
            "   macro avg     1.0000    1.0000    1.0000     58158\n",
            "weighted avg     1.0000    1.0000    1.0000     58158\n",
            "\n",
            "--------------------------------------------------------------------------------\n",
            "Data set: f1s0, shape: (6462, 91), model: SVC\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "     walking     0.9406    0.9982    0.9685       555\n",
            "      stairs     0.9333    0.2857    0.4375        49\n",
            "        idle     1.0000    1.0000    1.0000       312\n",
            "     running     1.0000    1.0000    1.0000      1023\n",
            "\n",
            "    accuracy                         0.9814      1939\n",
            "   macro avg     0.9685    0.8210    0.8515      1939\n",
            "weighted avg     0.9813    0.9814    0.9768      1939\n",
            "\n",
            "--------------------------------------------------------------------------------\n",
            "Data set: f1s0, shape: (6462, 91), model: SVC_Linear\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "     walking     0.9392    0.9748    0.9567       555\n",
            "      stairs     0.5000    0.2857    0.3636        49\n",
            "        idle     0.9873    1.0000    0.9936       312\n",
            "     running     1.0000    0.9961    0.9980      1023\n",
            "\n",
            "    accuracy                         0.9727      1939\n",
            "   macro avg     0.8566    0.8141    0.8280      1939\n",
            "weighted avg     0.9679    0.9727    0.9695      1939\n",
            "\n",
            "--------------------------------------------------------------------------------\n",
            "Data set: f1s0, shape: (6462, 91), model: RandomForestClassifier\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "     walking     0.9204    1.0000    0.9585       555\n",
            "      stairs     1.0000    0.0204    0.0400        49\n",
            "        idle     1.0000    1.0000    1.0000       312\n",
            "     running     1.0000    1.0000    1.0000      1023\n",
            "\n",
            "    accuracy                         0.9752      1939\n",
            "   macro avg     0.9801    0.7551    0.7496      1939\n",
            "weighted avg     0.9772    0.9752    0.9639      1939\n",
            "\n",
            "--------------------------------------------------------------------------------\n",
            "Data set: f1s1, shape: (6462, 118), model: SVC\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "     walking     0.9619    1.0000    0.9806       555\n",
            "      stairs     1.0000    0.5510    0.7105        49\n",
            "        idle     1.0000    1.0000    1.0000       312\n",
            "     running     1.0000    1.0000    1.0000      1023\n",
            "\n",
            "    accuracy                         0.9887      1939\n",
            "   macro avg     0.9905    0.8878    0.9228      1939\n",
            "weighted avg     0.9891    0.9887    0.9871      1939\n",
            "\n",
            "--------------------------------------------------------------------------------\n",
            "Data set: f1s1, shape: (6462, 118), model: SVC_Linear\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "     walking     0.9779    0.9586    0.9682       555\n",
            "      stairs     0.6167    0.7551    0.6789        49\n",
            "        idle     1.0000    1.0000    1.0000       312\n",
            "     running     1.0000    1.0000    1.0000      1023\n",
            "\n",
            "    accuracy                         0.9819      1939\n",
            "   macro avg     0.8987    0.9284    0.9118      1939\n",
            "weighted avg     0.9840    0.9819    0.9828      1939\n",
            "\n",
            "--------------------------------------------------------------------------------\n",
            "Data set: f1s1, shape: (6462, 118), model: RandomForestClassifier\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "     walking     0.9875    1.0000    0.9937       555\n",
            "      stairs     1.0000    0.8571    0.9231        49\n",
            "        idle     1.0000    1.0000    1.0000       312\n",
            "     running     1.0000    1.0000    1.0000      1023\n",
            "\n",
            "    accuracy                         0.9964      1939\n",
            "   macro avg     0.9969    0.9643    0.9792      1939\n",
            "weighted avg     0.9964    0.9964    0.9963      1939\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## RESULTS:"
      ],
      "metadata": {
        "id": "fKoHHUh33rkg"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Different datasets were created from CSV source files downloaded from various folders, each named according to the related activity.\n",
        "\n",
        "* Dataset f0s0: Contains data with 4 features.\n",
        "* Dataset f0s1: Contains data with 31 features.\n",
        "* Dataset f1s0: Contains data with 91 features.\n",
        "* Dataset f1s1: Contains data with 118 features.\n",
        "\n",
        "In the 'f0s0' dataset, the SVC-Linear model was skipped during log-time operation, potentially exceeding one hour.\n",
        "\n",
        "The models used for analysis included SVC, SVC-Linear, and RandomForestClassifier.\n",
        "\n",
        "* For the dataset with 4 features (f0s0), the RandomForestClassifier performs better.\n",
        "* For the dataset with 31 features (f0s1), the RandomForestClassifier performs better.\n",
        "* For the dataset with 91 features (f1s0), the RandomForestClassifier performs better.\n",
        "* For the dataset with 118 features (f1s1), the RandomForestClassifier performs better.\n",
        "\n",
        "This dataset 'f0s1' achieved a perfect accuracy of 1.0000, making it the highest among the provided datasets.\n",
        "\n",
        "The dataset 'f0s1' has not been flattened and statistical features have been added.\n",
        "\n",
        "Winner is datast 'f0s1' with with 31 features when using the RandomForestClassifier model."
      ],
      "metadata": {
        "id": "WDpiNovf3vJF"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## TEST на minimuм данних аккселерометра"
      ],
      "metadata": {
        "id": "ff0th10_6aei"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "rows = 10\n",
        "df_one = df_set['f0s0'].iloc[:rows,:3]\n",
        "df_one_class = df_set['f0s0'].iloc[:rows,3]\n",
        "df_one_class"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "n6TVrDsU3rjw",
        "outputId": "86fa33bb-ba98-4f3f-b591-eba32c4e722b"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    0\n",
              "1    0\n",
              "2    0\n",
              "3    0\n",
              "4    0\n",
              "5    0\n",
              "6    0\n",
              "7    0\n",
              "8    0\n",
              "9    0\n",
              "Name: class, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_one_stat = add_stat_feature_frame(df_one,df_one.shape[0])\n",
        "df_one_stat"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 412
        },
        "id": "K8F9TunT3M7n",
        "outputId": "273e0533-fade-4c06-c1e1-ec784ff95aca"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   accelerometer_X_mean  accelerometer_X_max  accelerometer_X_min  \\\n",
              "0              6.501215            11.219229             1.781286   \n",
              "1              6.501215            11.219229             1.781286   \n",
              "2              6.501215            11.219229             1.781286   \n",
              "3              6.501215            11.219229             1.781286   \n",
              "4              6.501215            11.219229             1.781286   \n",
              "5              6.501215            11.219229             1.781286   \n",
              "6              6.501215            11.219229             1.781286   \n",
              "7              6.501215            11.219229             1.781286   \n",
              "8              6.501215            11.219229             1.781286   \n",
              "9              6.501215            11.219229             1.781286   \n",
              "\n",
              "   accelerometer_X_interquartile_range  \\\n",
              "0                             6.412869   \n",
              "1                             6.412869   \n",
              "2                             6.412869   \n",
              "3                             6.412869   \n",
              "4                             6.412869   \n",
              "5                             6.412869   \n",
              "6                             6.412869   \n",
              "7                             6.412869   \n",
              "8                             6.412869   \n",
              "9                             6.412869   \n",
              "\n",
              "   accelerometer_X_index_of_minimum_value  \\\n",
              "0                                       1   \n",
              "1                                       1   \n",
              "2                                       1   \n",
              "3                                       1   \n",
              "4                                       1   \n",
              "5                                       1   \n",
              "6                                       1   \n",
              "7                                       1   \n",
              "8                                       1   \n",
              "9                                       1   \n",
              "\n",
              "   accelerometer_X_mean_of_absolute_deviation  accelerometer_X_median  \\\n",
              "0                                    3.112941                 6.80911   \n",
              "1                                    3.112941                 6.80911   \n",
              "2                                    3.112941                 6.80911   \n",
              "3                                    3.112941                 6.80911   \n",
              "4                                    3.112941                 6.80911   \n",
              "5                                    3.112941                 6.80911   \n",
              "6                                    3.112941                 6.80911   \n",
              "7                                    3.112941                 6.80911   \n",
              "8                                    3.112941                 6.80911   \n",
              "9                                    3.112941                 6.80911   \n",
              "\n",
              "   accelerometer_X_standard_deviation  accelerometer_X_root_mean_square_error  \\\n",
              "0                            3.655132                                6.501215   \n",
              "1                            3.655132                                6.501215   \n",
              "2                            3.655132                                6.501215   \n",
              "3                            3.655132                                6.501215   \n",
              "4                            3.655132                                6.501215   \n",
              "5                            3.655132                                6.501215   \n",
              "6                            3.655132                                6.501215   \n",
              "7                            3.655132                                6.501215   \n",
              "8                            3.655132                                6.501215   \n",
              "9                            3.655132                                6.501215   \n",
              "\n",
              "   accelerometer_Y_mean  ...  accelerometer_Y_root_mean_square_error  \\\n",
              "0             -7.608294  ...                                7.608294   \n",
              "1             -7.608294  ...                                7.608294   \n",
              "2             -7.608294  ...                                7.608294   \n",
              "3             -7.608294  ...                                7.608294   \n",
              "4             -7.608294  ...                                7.608294   \n",
              "5             -7.608294  ...                                7.608294   \n",
              "6             -7.608294  ...                                7.608294   \n",
              "7             -7.608294  ...                                7.608294   \n",
              "8             -7.608294  ...                                7.608294   \n",
              "9             -7.608294  ...                                7.608294   \n",
              "\n",
              "   accelerometer_Z_mean  accelerometer_Z_max  accelerometer_Z_min  \\\n",
              "0              1.600285            11.827356            -8.087613   \n",
              "1              1.600285            11.827356            -8.087613   \n",
              "2              1.600285            11.827356            -8.087613   \n",
              "3              1.600285            11.827356            -8.087613   \n",
              "4              1.600285            11.827356            -8.087613   \n",
              "5              1.600285            11.827356            -8.087613   \n",
              "6              1.600285            11.827356            -8.087613   \n",
              "7              1.600285            11.827356            -8.087613   \n",
              "8              1.600285            11.827356            -8.087613   \n",
              "9              1.600285            11.827356            -8.087613   \n",
              "\n",
              "   accelerometer_Z_interquartile_range  \\\n",
              "0                             4.721366   \n",
              "1                             4.721366   \n",
              "2                             4.721366   \n",
              "3                             4.721366   \n",
              "4                             4.721366   \n",
              "5                             4.721366   \n",
              "6                             4.721366   \n",
              "7                             4.721366   \n",
              "8                             4.721366   \n",
              "9                             4.721366   \n",
              "\n",
              "   accelerometer_Z_index_of_minimum_value  \\\n",
              "0                                       5   \n",
              "1                                       5   \n",
              "2                                       5   \n",
              "3                                       5   \n",
              "4                                       5   \n",
              "5                                       5   \n",
              "6                                       5   \n",
              "7                                       5   \n",
              "8                                       5   \n",
              "9                                       5   \n",
              "\n",
              "   accelerometer_Z_mean_of_absolute_deviation  accelerometer_Z_median  \\\n",
              "0                                    3.572532                1.261744   \n",
              "1                                    3.572532                1.261744   \n",
              "2                                    3.572532                1.261744   \n",
              "3                                    3.572532                1.261744   \n",
              "4                                    3.572532                1.261744   \n",
              "5                                    3.572532                1.261744   \n",
              "6                                    3.572532                1.261744   \n",
              "7                                    3.572532                1.261744   \n",
              "8                                    3.572532                1.261744   \n",
              "9                                    3.572532                1.261744   \n",
              "\n",
              "   accelerometer_Z_standard_deviation  accelerometer_Z_root_mean_square_error  \n",
              "0                            5.243697                                1.600285  \n",
              "1                            5.243697                                1.600285  \n",
              "2                            5.243697                                1.600285  \n",
              "3                            5.243697                                1.600285  \n",
              "4                            5.243697                                1.600285  \n",
              "5                            5.243697                                1.600285  \n",
              "6                            5.243697                                1.600285  \n",
              "7                            5.243697                                1.600285  \n",
              "8                            5.243697                                1.600285  \n",
              "9                            5.243697                                1.600285  \n",
              "\n",
              "[10 rows x 27 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-69cbc7ed-e646-4148-965c-8209fc9d0d6d\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>accelerometer_X_mean</th>\n",
              "      <th>accelerometer_X_max</th>\n",
              "      <th>accelerometer_X_min</th>\n",
              "      <th>accelerometer_X_interquartile_range</th>\n",
              "      <th>accelerometer_X_index_of_minimum_value</th>\n",
              "      <th>accelerometer_X_mean_of_absolute_deviation</th>\n",
              "      <th>accelerometer_X_median</th>\n",
              "      <th>accelerometer_X_standard_deviation</th>\n",
              "      <th>accelerometer_X_root_mean_square_error</th>\n",
              "      <th>accelerometer_Y_mean</th>\n",
              "      <th>...</th>\n",
              "      <th>accelerometer_Y_root_mean_square_error</th>\n",
              "      <th>accelerometer_Z_mean</th>\n",
              "      <th>accelerometer_Z_max</th>\n",
              "      <th>accelerometer_Z_min</th>\n",
              "      <th>accelerometer_Z_interquartile_range</th>\n",
              "      <th>accelerometer_Z_index_of_minimum_value</th>\n",
              "      <th>accelerometer_Z_mean_of_absolute_deviation</th>\n",
              "      <th>accelerometer_Z_median</th>\n",
              "      <th>accelerometer_Z_standard_deviation</th>\n",
              "      <th>accelerometer_Z_root_mean_square_error</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>6.501215</td>\n",
              "      <td>11.219229</td>\n",
              "      <td>1.781286</td>\n",
              "      <td>6.412869</td>\n",
              "      <td>1</td>\n",
              "      <td>3.112941</td>\n",
              "      <td>6.80911</td>\n",
              "      <td>3.655132</td>\n",
              "      <td>6.501215</td>\n",
              "      <td>-7.608294</td>\n",
              "      <td>...</td>\n",
              "      <td>7.608294</td>\n",
              "      <td>1.600285</td>\n",
              "      <td>11.827356</td>\n",
              "      <td>-8.087613</td>\n",
              "      <td>4.721366</td>\n",
              "      <td>5</td>\n",
              "      <td>3.572532</td>\n",
              "      <td>1.261744</td>\n",
              "      <td>5.243697</td>\n",
              "      <td>1.600285</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>6.501215</td>\n",
              "      <td>11.219229</td>\n",
              "      <td>1.781286</td>\n",
              "      <td>6.412869</td>\n",
              "      <td>1</td>\n",
              "      <td>3.112941</td>\n",
              "      <td>6.80911</td>\n",
              "      <td>3.655132</td>\n",
              "      <td>6.501215</td>\n",
              "      <td>-7.608294</td>\n",
              "      <td>...</td>\n",
              "      <td>7.608294</td>\n",
              "      <td>1.600285</td>\n",
              "      <td>11.827356</td>\n",
              "      <td>-8.087613</td>\n",
              "      <td>4.721366</td>\n",
              "      <td>5</td>\n",
              "      <td>3.572532</td>\n",
              "      <td>1.261744</td>\n",
              "      <td>5.243697</td>\n",
              "      <td>1.600285</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>6.501215</td>\n",
              "      <td>11.219229</td>\n",
              "      <td>1.781286</td>\n",
              "      <td>6.412869</td>\n",
              "      <td>1</td>\n",
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              "      <td>6.501215</td>\n",
              "      <td>-7.608294</td>\n",
              "      <td>...</td>\n",
              "      <td>7.608294</td>\n",
              "      <td>1.600285</td>\n",
              "      <td>11.827356</td>\n",
              "      <td>-8.087613</td>\n",
              "      <td>4.721366</td>\n",
              "      <td>5</td>\n",
              "      <td>3.572532</td>\n",
              "      <td>1.261744</td>\n",
              "      <td>5.243697</td>\n",
              "      <td>1.600285</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>6.501215</td>\n",
              "      <td>11.219229</td>\n",
              "      <td>1.781286</td>\n",
              "      <td>6.412869</td>\n",
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              "      <td>3.112941</td>\n",
              "      <td>6.80911</td>\n",
              "      <td>3.655132</td>\n",
              "      <td>6.501215</td>\n",
              "      <td>-7.608294</td>\n",
              "      <td>...</td>\n",
              "      <td>7.608294</td>\n",
              "      <td>1.600285</td>\n",
              "      <td>11.827356</td>\n",
              "      <td>-8.087613</td>\n",
              "      <td>4.721366</td>\n",
              "      <td>5</td>\n",
              "      <td>3.572532</td>\n",
              "      <td>1.261744</td>\n",
              "      <td>5.243697</td>\n",
              "      <td>1.600285</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>6.501215</td>\n",
              "      <td>11.219229</td>\n",
              "      <td>1.781286</td>\n",
              "      <td>6.412869</td>\n",
              "      <td>1</td>\n",
              "      <td>3.112941</td>\n",
              "      <td>6.80911</td>\n",
              "      <td>3.655132</td>\n",
              "      <td>6.501215</td>\n",
              "      <td>-7.608294</td>\n",
              "      <td>...</td>\n",
              "      <td>7.608294</td>\n",
              "      <td>1.600285</td>\n",
              "      <td>11.827356</td>\n",
              "      <td>-8.087613</td>\n",
              "      <td>4.721366</td>\n",
              "      <td>5</td>\n",
              "      <td>3.572532</td>\n",
              "      <td>1.261744</td>\n",
              "      <td>5.243697</td>\n",
              "      <td>1.600285</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>6.501215</td>\n",
              "      <td>11.219229</td>\n",
              "      <td>1.781286</td>\n",
              "      <td>6.412869</td>\n",
              "      <td>1</td>\n",
              "      <td>3.112941</td>\n",
              "      <td>6.80911</td>\n",
              "      <td>3.655132</td>\n",
              "      <td>6.501215</td>\n",
              "      <td>-7.608294</td>\n",
              "      <td>...</td>\n",
              "      <td>7.608294</td>\n",
              "      <td>1.600285</td>\n",
              "      <td>11.827356</td>\n",
              "      <td>-8.087613</td>\n",
              "      <td>4.721366</td>\n",
              "      <td>5</td>\n",
              "      <td>3.572532</td>\n",
              "      <td>1.261744</td>\n",
              "      <td>5.243697</td>\n",
              "      <td>1.600285</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>6.501215</td>\n",
              "      <td>11.219229</td>\n",
              "      <td>1.781286</td>\n",
              "      <td>6.412869</td>\n",
              "      <td>1</td>\n",
              "      <td>3.112941</td>\n",
              "      <td>6.80911</td>\n",
              "      <td>3.655132</td>\n",
              "      <td>6.501215</td>\n",
              "      <td>-7.608294</td>\n",
              "      <td>...</td>\n",
              "      <td>7.608294</td>\n",
              "      <td>1.600285</td>\n",
              "      <td>11.827356</td>\n",
              "      <td>-8.087613</td>\n",
              "      <td>4.721366</td>\n",
              "      <td>5</td>\n",
              "      <td>3.572532</td>\n",
              "      <td>1.261744</td>\n",
              "      <td>5.243697</td>\n",
              "      <td>1.600285</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>6.501215</td>\n",
              "      <td>11.219229</td>\n",
              "      <td>1.781286</td>\n",
              "      <td>6.412869</td>\n",
              "      <td>1</td>\n",
              "      <td>3.112941</td>\n",
              "      <td>6.80911</td>\n",
              "      <td>3.655132</td>\n",
              "      <td>6.501215</td>\n",
              "      <td>-7.608294</td>\n",
              "      <td>...</td>\n",
              "      <td>7.608294</td>\n",
              "      <td>1.600285</td>\n",
              "      <td>11.827356</td>\n",
              "      <td>-8.087613</td>\n",
              "      <td>4.721366</td>\n",
              "      <td>5</td>\n",
              "      <td>3.572532</td>\n",
              "      <td>1.261744</td>\n",
              "      <td>5.243697</td>\n",
              "      <td>1.600285</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>6.501215</td>\n",
              "      <td>11.219229</td>\n",
              "      <td>1.781286</td>\n",
              "      <td>6.412869</td>\n",
              "      <td>1</td>\n",
              "      <td>3.112941</td>\n",
              "      <td>6.80911</td>\n",
              "      <td>3.655132</td>\n",
              "      <td>6.501215</td>\n",
              "      <td>-7.608294</td>\n",
              "      <td>...</td>\n",
              "      <td>7.608294</td>\n",
              "      <td>1.600285</td>\n",
              "      <td>11.827356</td>\n",
              "      <td>-8.087613</td>\n",
              "      <td>4.721366</td>\n",
              "      <td>5</td>\n",
              "      <td>3.572532</td>\n",
              "      <td>1.261744</td>\n",
              "      <td>5.243697</td>\n",
              "      <td>1.600285</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>6.501215</td>\n",
              "      <td>11.219229</td>\n",
              "      <td>1.781286</td>\n",
              "      <td>6.412869</td>\n",
              "      <td>1</td>\n",
              "      <td>3.112941</td>\n",
              "      <td>6.80911</td>\n",
              "      <td>3.655132</td>\n",
              "      <td>6.501215</td>\n",
              "      <td>-7.608294</td>\n",
              "      <td>...</td>\n",
              "      <td>7.608294</td>\n",
              "      <td>1.600285</td>\n",
              "      <td>11.827356</td>\n",
              "      <td>-8.087613</td>\n",
              "      <td>4.721366</td>\n",
              "      <td>5</td>\n",
              "      <td>3.572532</td>\n",
              "      <td>1.261744</td>\n",
              "      <td>5.243697</td>\n",
              "      <td>1.600285</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>10 rows × 27 columns</p>\n",
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              "      const quickchartButtonEl =\n",
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              "            'suggestCharts', [key], {});\n",
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              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
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              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_test_one = pd.concat([df_one, df_one_stat], axis=1)\n",
        "X_test_one.fillna(0, inplace=True)\n",
        "X_test_one[:1]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 130
        },
        "id": "qKSdQSpi6Aml",
        "outputId": "c8842e0d-b3a8-428d-b08f-574c1eb2403e"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   accelerometer_X  accelerometer_Y  accelerometer_Z  accelerometer_X_mean  \\\n",
              "0         2.322376         3.643975        11.827356              6.501215   \n",
              "\n",
              "   accelerometer_X_max  accelerometer_X_min  \\\n",
              "0            11.219229             1.781286   \n",
              "\n",
              "   accelerometer_X_interquartile_range  \\\n",
              "0                             6.412869   \n",
              "\n",
              "   accelerometer_X_index_of_minimum_value  \\\n",
              "0                                       1   \n",
              "\n",
              "   accelerometer_X_mean_of_absolute_deviation  accelerometer_X_median  ...  \\\n",
              "0                                    3.112941                 6.80911  ...   \n",
              "\n",
              "   accelerometer_Y_root_mean_square_error  accelerometer_Z_mean  \\\n",
              "0                                7.608294              1.600285   \n",
              "\n",
              "   accelerometer_Z_max  accelerometer_Z_min  \\\n",
              "0            11.827356            -8.087613   \n",
              "\n",
              "   accelerometer_Z_interquartile_range  \\\n",
              "0                             4.721366   \n",
              "\n",
              "   accelerometer_Z_index_of_minimum_value  \\\n",
              "0                                       5   \n",
              "\n",
              "   accelerometer_Z_mean_of_absolute_deviation  accelerometer_Z_median  \\\n",
              "0                                    3.572532                1.261744   \n",
              "\n",
              "   accelerometer_Z_standard_deviation  accelerometer_Z_root_mean_square_error  \n",
              "0                            5.243697                                1.600285  \n",
              "\n",
              "[1 rows x 30 columns]"
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              "      <th></th>\n",
              "      <th>accelerometer_X</th>\n",
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              "      <th>accelerometer_Z_max</th>\n",
              "      <th>accelerometer_Z_min</th>\n",
              "      <th>accelerometer_Z_interquartile_range</th>\n",
              "      <th>accelerometer_Z_index_of_minimum_value</th>\n",
              "      <th>accelerometer_Z_mean_of_absolute_deviation</th>\n",
              "      <th>accelerometer_Z_median</th>\n",
              "      <th>accelerometer_Z_standard_deviation</th>\n",
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              "      <th>0</th>\n",
              "      <td>2.322376</td>\n",
              "      <td>3.643975</td>\n",
              "      <td>11.827356</td>\n",
              "      <td>6.501215</td>\n",
              "      <td>11.219229</td>\n",
              "      <td>1.781286</td>\n",
              "      <td>6.412869</td>\n",
              "      <td>1</td>\n",
              "      <td>3.112941</td>\n",
              "      <td>6.80911</td>\n",
              "      <td>...</td>\n",
              "      <td>7.608294</td>\n",
              "      <td>1.600285</td>\n",
              "      <td>11.827356</td>\n",
              "      <td>-8.087613</td>\n",
              "      <td>4.721366</td>\n",
              "      <td>5</td>\n",
              "      <td>3.572532</td>\n",
              "      <td>1.261744</td>\n",
              "      <td>5.243697</td>\n",
              "      <td>1.600285</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1 rows × 30 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-87921e20-8583-4474-8543-fe2ed0730223')\"\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 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\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",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\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",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-87921e20-8583-4474-8543-fe2ed0730223 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-87921e20-8583-4474-8543-fe2ed0730223');\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",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "key = 'f0s1'\n",
        "X = X_set[key]\n",
        "y = y_set[key]\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=SEED, stratify=y)\n",
        "# clf = SVC(kernel=\"linear\")\n",
        "clf = RandomForestClassifier()\n",
        "%time clf.fit(X_train, y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 129
        },
        "id": "c8o-dr379p2V",
        "outputId": "f71c2f69-288a-4889-b48d-7b709324796e"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "CPU times: user 15.7 s, sys: 25 ms, total: 15.7 s\n",
            "Wall time: 15.7 s\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RandomForestClassifier()"
            ],
            "text/html": [
              "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestClassifier()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier()</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_test_predict_one = clf.predict(X_test_one[:1])\n",
        "y_test_predict_one"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8g0OAcM281kP",
        "outputId": "d8cd10eb-64dd-4925-a6c2-7939c3f0d923"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0])"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(classification_report(df_one_class[:y_test_predict_one.shape[0]], y_test_predict_one, digits=4))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tFIJcB1HA94V",
        "outputId": "be4c8b2a-164b-45e4-a931-83ce8bd04ffc"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0     1.0000    1.0000    1.0000         1\n",
            "\n",
            "    accuracy                         1.0000         1\n",
            "   macro avg     1.0000    1.0000    1.0000         1\n",
            "weighted avg     1.0000    1.0000    1.0000         1\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "тести показали мінімум 10 записів акселерометра треба для формування (статистики),\n",
        " щоб прогноз сходився"
      ],
      "metadata": {
        "id": "DnSjtWCJDYBE"
      }
    }
  ]
}