{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"pygments_lexer":"ipython3","nbconvert_exporter":"python","version":"3.6.4","file_extension":".py","codemirror_mode":{"name":"ipython","version":3},"name":"python","mimetype":"text/x-python"}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"markdown","source":"<h1><center>Indoor Location & Navigation</h1></center>\n<h2><center>Identify the position of a smartphone in a shopping mall</h2></center>","metadata":{}},{"cell_type":"markdown","source":"<b>In this notebook I have tried my best to explain all the topics/aspects, related to this competition, at easiest form. </b>","metadata":{}},{"cell_type":"markdown","source":"<center><img src =\"http://static.dist10.cn/Fh7Zz-ipJRPZRM9bE1p7Dsu7YLpJ/section3_bg.png?imageView2/5/w/1440/h/600 \" width = \"800\" height = \"800\"/></center>   ","metadata":{}},{"cell_type":"markdown","source":"<center><h2>Understand The Competition</center></h2>\nOur smartphone goes everywhere with us and with permission, apps can use our location to provide contextual information. We get driving directions, find a store, or receive alerts for nearby promotions. These handy features are enabled by GPS, which requires outdoor exposure for the best accuracy.\n\nCurrent positioning solutions have poor accuracy, particularly in multi-level buildings.\n\n<b>In this competition, our task is to predict the indoor position of smartphones based on real-time sensor data.</b>","metadata":{}},{"cell_type":"code","source":"# Libraries\nimport numpy as np # linear algebra\nimport pandas as pd # data processing\nimport os\nimport json\nimport random\nimport glob\nimport seaborn as sns\nfrom pathlib import Path\nfrom PIL import Image, ImageOps\nimport matplotlib.pyplot as plt\nimport scipy.stats as stats\nfrom sklearn.model_selection import KFold\nimport lightgbm as lgb\nimport psutil\nimport random\nimport os\nimport time\nimport sys\nimport math\nfrom contextlib import contextmanager","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"<h2><center>Data Exploration and Data Understanding (EDA)</h2></center>\n<center><img src =\"https://luminousmen.com/media/exploratory-data-analysis.jpg \" width = \"400\" height = \"400\"/></center>   \n\n\n\n<b>In this section, I will drive you through all the important aspects of Data so that we can understand the Data which is the most important thing to ace the competition.</b>","metadata":{}},{"cell_type":"markdown","source":"<h2>Understand the Data</h2>\nFirst, we need to understand the input data. The folder \"indoor-location-navigation\" contains 3 subdirectories, \"metadata\", \"test\", and \"train\", respectively.\nThe following schematic shows the data structure in the \"metadata\" folder, as it is given in the \"Data Description\" tab.","metadata":{}},{"cell_type":"markdown","source":"![Kaggle Indoor 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"}}},{"cell_type":"markdown","source":"We are going to use the images in the .png format to display the locations. The other two files associated with each floor are in .json format. The floor_info.json describes the size of the location (height and width), while the geojson_map.json is a file for geospatial visualization (it is possible to reproduce a raster image in .png format using the *geopandas* library applied to this .json file).","metadata":{}},{"cell_type":"markdown","source":"<h2>Data Organization</h2>\n\n<h4>Training Dataset</h4>","metadata":{}},{"cell_type":"code","source":"dataset_path = Path('../input/indoor-location-navigation')\nos.listdir(dataset_path)","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"<b>Note that the data is organized by the sites and floors. Let's see how many sites do we have in train dataset?</b>","metadata":{}},{"cell_type":"code","source":"train_sites = os.listdir(dataset_path/\"train\")\nprint(f'There are {len(train_sites)} sites in the training set')","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"<b>Now each site is organized by Floors</b>","metadata":{}},{"cell_type":"code","source":"example_site = os.listdir(dataset_path/\"train\")[10]\nexample_site_path = dataset_path/\"train\"/example_site\nprint('Floors for example site:')\nprint(os.listdir(example_site_path))","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"floors_per_site = []\nfor i in os.listdir(dataset_path/\"train\"): floors_per_site.append(len(os.listdir(dataset_path/\"train\"/i)))\nprint(f'There are a total of {sum(floors_per_site)} floors. On average, each site has {np.mean(floors_per_site)} floors')","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"<b>So every site has about 5 floors. In each floor are the path trace text files with the data</b>","metadata":{}},{"cell_type":"markdown","source":"<b>Path File</b>","metadata":{}},{"cell_type":"code","source":"print('Path text files for example floor:')\nprint(os.listdir(example_site_path/'B1'))","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"<h4>Train Data path files count</h4>","metadata":{}},{"cell_type":"code","source":"print(f\"There are {len(list((dataset_path/'train').rglob('*.txt')))} path text files in the training set\")","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"<b>To summarize, this is how the training data is structured:</b>","metadata":{}},{"cell_type":"markdown","source":"\n\n```\n└───train                                                        //raw data from two sites\n      └───site1\n      |     └───B1                                               //traces from one floor\n      |     |   └───5dda14a2c5b77e0006b17533.txt                 //trace file                             \n      |     |   | ...\n      |     |\n      |     |\n      |     └───F1\n      |     | ...\n      |\n      └───site2\n```","metadata":{}},{"cell_type":"markdown","source":"<h4>Test Dataset</h4>","metadata":{}},{"cell_type":"code","source":"print(f\"There are {len(os.listdir(dataset_path/'test'))} path text files in the test set\")","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"<h4>Metdata Organization</h4>","metadata":{}},{"cell_type":"code","source":"print(f'There are {len(os.listdir(dataset_path/\"metadata\"))} sites in the metadata, just like the training set')","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"<b>Let's look at a single floor of a single site</b>","metadata":{}},{"cell_type":"code","source":"metadata_example_site = os.listdir(dataset_path/\"metadata\")[10]\nmetadata_example_site_path = dataset_path/\"metadata\"/metadata_example_site\nmetadata_example_floor_path = dataset_path/\"metadata\"/metadata_example_site/os.listdir(metadata_example_site_path)[0]\nprint(os.listdir(metadata_example_floor_path))","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"<b>Explore 'floor_image.png' file</b>","metadata":{}},{"cell_type":"code","source":"Image.open(metadata_example_floor_path/'floor_image.png')","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"<b>Explore 'geojson_map.json' file</b>","metadata":{}},{"cell_type":"code","source":"##################### In comment because Large Output Data ########################\n\n\n# with open(metadata_example_floor_path/'geojson_map.json') as geojson_map:\n#     data = json.load(geojson_map)\n#     geojson_map.close()\n# print(data)","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"<b>Explore 'floor_info.json' file</b>","metadata":{}},{"cell_type":"code","source":"with open(metadata_example_floor_path/'floor_info.json') as floor_info:\n    data = json.load(floor_info)\n    floor_info.close()\nprint(data)","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"\n<h3>Path text files</h3>\n<b>Brief here. Each Column is described in detail in session (Deep Dive) below</b>\n<p></p>\nLet's look more closely at the path text files and how to process them.\n\nThe [GitHub README](https://github.com/location-competition/indoor-location-competition-20) provides more information about the text file format:\n\n> The first column is Unix Time in millisecond. In specific, we use SensorEvent.timestamp for sensor data and system time for WiFi and Bluetooth scans.\n> \n>The second column is the data type (ten in total).\n>\n>TYPE_ACCELEROMETER\n>\n>TYPE_MAGNETIC_FIELD\n>\n>TYPE_GYROSCOPE\n>\n>TYPE_ROTATION_VECTOR\n>\n>TYPE_MAGNETIC_FIELD_UNCALIBRATED\n>TYPE_GYROSCOPE_UNCALIBRATED\n>\n>TYPE_ACCELEROMETER_UNCALIBRATED\n>\n>TYPE_WIFI\n>\n>TYPE_BEACON\n>\n>TYPE_WAYPOINT: ground truth location labeled by the surveyor\n>\n>Data values start from the third column.\n>\n>Column 3-5 of TYPE_ACCELEROMETER、TYPE_ACCELEROMETER、TYPE_GYROSCOPE、TYPE_ROTATION_VECTOR are SensorEvent.values[0-2] from the callback function onSensorChanged(). Column 6 is SensorEvent.accuracy.\n>\n>Column 3-8 of TYPE_ACCELEROMETER_UNCALIBRATED、TYPE_GYROSCOPE_UNCALIBRATED、TYPE_MAGNETIC_FIELD_UNCALIBRATED are SensorEvent.values[0-5] from the callback function onSensorChanged(). Column 9 is SensorEvent.accuracy.","metadata":{}},{"cell_type":"markdown","source":"<b>Let's Explore Text File</b>","metadata":{}},{"cell_type":"code","source":"example_floor_path = example_site_path/'B1'\nexample_txt_path = example_floor_path/os.listdir(example_floor_path)[0]\nwith open(example_txt_path) as example_txt:\n    data = example_txt.read()\n    example_txt.close()\n\n##################### In comment because Large Output Data ########################\n# print(data)","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"<h2><center>Deep Dive into EDA</h2>","metadata":{}},{"cell_type":"markdown","source":"<center><img src =\"https://encrypted-tbn0.gstatic.com/images?q=tbn:ANd9GcRSq7tFWVYNmxhjebh_havbaBkI5UmrewSEKw&usqp=CAU \" width = \"400\" height = \"400\"/></center>  ","metadata":{}},{"cell_type":"markdown","source":"<h4>Define some Functions/Tools that we will use for EDA</h4>","metadata":{}},{"cell_type":"code","source":"def load_trace_as_dataframe(filepath):\n    # Returns trace dataframe sorted by timestamp\n    \n    names = ['time', 'type'] + [f'col_{i}' for i in range(1, 9)]\n    \n    trace_df = pd.read_csv(\n        filepath, sep='\\t', comment='#', header=None, names=names\n    )\n    \n    trace_df.sort_values(by='time', inplace=True)\n    trace_df.reset_index(drop=True, inplace=True)\n    return trace_df","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"def extract_feature_df(trace_df, \n                       feature_name, \n                       col_names=('x', 'y', 'z', 'accuracy')):\n    \n    \n#     Extracts feature dataframe from trace dataframe by feature name.\n    \n#     Suitable for features: \n#     ----------------------\n#         TYPE_WAYPOINT, if set col_names=('x', 'y'),\n#         TYPE_ACCELEROMETER,\n#         TYPE_GYROSCOPE,\n#         TYPE_MAGNETIC_FIELD, \n#         TYPE_ROTATION_VECTOR,\n        \n#         TYPE_ACCELEROMETER_UNCALIBRATED, \n#                     if set col_names=('x', 'y', 'z', 'x_2', 'y_2', 'z_2', 'accuracy'),\n                    \n#         TYPE_GYROSCOPE_UNCALIBRATED, \n#                     if set col_names=('x', 'y', 'z', 'x_2', 'y_2', 'z_2', 'accuracy'),\n                    \n#         TYPE_MAGNETIC_FIELD_UNCALIBRATED, \n#                     if set col_names=('x', 'y', 'z', 'x_2', 'y_2', 'z_2', 'accuracy')\n    \n    \n    feature_df = trace_df[trace_df['type'] == feature_name].copy()\n    for i, col in enumerate(col_names, start=1):\n        feature_df[col] = feature_df[f'col_{i}'].astype('float64')\n        \n    feature_df.drop(columns=[f'col_{i}' for i in range(1, 9)], inplace=True)\n    feature_df.drop(columns=['type'], inplace=True)\n    feature_df.reset_index(drop=True, inplace=True)\n    \n    return feature_df","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"def load_points(filepath):\n    # Takes the path to the trace file.\n    # Returns pandas dataframe which consists of device locations \n    # as x and y coordinates (values from TYPE_WAYPOINT) and their timestamps.\n    \n    trace_df = load_trace_as_dataframe(filepath)\n    points_df = extract_feature_df(\n        trace_df, 'TYPE_WAYPOINT', col_names=('x', 'y')\n    )\n    \n    return points_df","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"def visualize_many_traces_on_the_map(traces_dataframes, map_image, width, height, \n                                     traces_filenames=None, \n                                     figsize=None):\n    \n\n#     Draws traces on the floor map.\n    \n#     Parameters\n#     ----------\n#         traces_dataframes: list of pandas DataFrames\n#             Each DataFrame should consist of device locations as x and y \n#             coordinates and their timestamps.\n\n#         map_image : numpy.array\n#             Image of floor map.\n\n#         width : float,\n#             Width of floor. Should be taken from floor_info.json\n\n#         height : float, \n#             Height of floor. Should be taken from floor_info.json\n\n#         traces_filenames : list of strings, optional, default: None\n#             List of filenames. Used to display the legend. \n#             There will be no legend if you pass traces_filenames=None\n\n#         figsize : (float, float), optional, default: None\n#             Size of the result image in terms of matplotlib.\n    \n\n    \n    fig = plt.figure(figsize=figsize)\n    ax = plt.subplot(111)\n\n    plt.imshow(map_image, extent=[0, width, 0, height])\n\n    if traces_filenames:\n        \n        for filename, points in zip(traces_filenames, traces_dataframes):\n            plt.scatter(points['x'], points['y'], label=filename)\n            plt.plot(points['x'], points['y'])\n            \n        ax.legend(loc='center left', bbox_to_anchor=(1, 0.5))\n    else:\n        for points in traces_dataframes:\n            plt.scatter(points['x'], points['y'])\n            plt.plot(points['x'], points['y'])\n\n\n    plt.show()","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"def visualize_single_trace_on_the_map(points_df, map_image, width, height, \n                                      scaling_coef=0.3, figsize=None):\n    \n    \n#     Draws single trace on the floor map.\n    \n#     Parameters\n#     ----------\n#         points_df: pandas DataFrame\n#             Should consist of device locations as x and y \n#             coordinates and their timestamps.\n\n#         map_image : numpy.array\n#             Image of floor map.\n\n#         width : float,\n#             Width of floor. Should be taken from floor_info.json\n\n#         height : float, \n#             Height of floor. Should be taken from floor_info.json\n\n#         scaling_coef : float\n#             Scaling Coefficient. \n\n#         figsize : (float, float), optional, default: None\n#             Size of the result image in terms of matplotlib.\n    \n    \n    \n    fig = plt.figure(figsize=figsize)\n    ax = plt.subplot(111)\n\n    plt.imshow(map_image, extent=[0, width, 0, height])\n    plt.plot(points_df['x'], points_df['y'], linewidth=5, linestyle='-', color='blue')\n\n    for i in range(len(points_df)):\n        ax.text(\n            points_df.loc[i, 'x'], points_df.loc[i, 'y'], i, \n            ha=\"center\", size=15, \n            bbox=dict(boxstyle=\"circle, pad=0.3\", \n                      fc=\"cyan\", lw=2)\n        )\n \n    x_min, x_max = points_df['x'].min(), points_df['x'].max()\n    y_min, y_max = points_df['y'].min(), points_df['y'].max()\n\n    ax.set_xlim(x_min - scaling_coef*(x_max - x_min), x_max + scaling_coef*(x_max - x_min))\n    ax.set_ylim(y_min - scaling_coef*(y_max - y_min), y_max + scaling_coef*(y_max - y_min))\n\n    plt.show()\n","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"def plot_trace_features(feature_df, timestamps=None, figsize=None):\n    \n\n#     Plots the trace features.\n    \n#     Parameters\n#     ----------\n#         feature_df : pandas DataFrame\n#             Can be exctracted from trace dataframe \n#             using extract_feature_df function.\n        \n#         timestamps : array-like, optional, default: None\n#             Array of timestamps. \n#             Used to mark timestamps on the chart in the form of vertical lines.\n#             Pass timestamps=None if you don't want to use this feature.\n\n#         figsize : (float, float), optional, default: None\n#             Size of the result image in terms of matplotlib.\n    \n#     Suitable for features: \n#     ----------------------\n#         TYPE_ACCELEROMETER \n#         TYPE_GYROSCOPE \n#         TYPE_MAGNETIC_FIELD \n#         TYPE_ROTATION_VECTOR \n#         TYPE_ACCELEROMETER_UNCALIBRATED \n#         TYPE_GYROSCOPE_UNCALIBRATED \n#         TYPE_MAGNETIC_FIELD_UNCALIBRATED\n   \n    \n    fig = plt.figure(figsize=figsize)\n    ax = plt.subplot(111)\n\n    for col in ['x', 'y', 'z']:\n        plt.plot(feature_df['time'], feature_df[col], label=col)\n\n\n    if points_df is not None:\n        xmin, xmax, ymin, ymax = plt.axis()\n\n        for i, timestamp in enumerate(timestamps):\n            plt.axvline(x=timestamp, c='k', ls='--')\n\n            ax.text(\n            timestamp, ymax, i, \n            ha=\"center\", size=15, \n            bbox=dict(boxstyle=\"circle, pad=0.3\", \n                      fc=\"white\", lw=2)\n            )\n    ax.legend(loc='center left', bbox_to_anchor=(1, 0.5))\n    plt.show()","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"data_path = '/kaggle/input/indoor-location-navigation'\nfloor = '5a0546857ecc773753327266/F1'\nfloor_metadata_dir = os.path.join(data_path, 'metadata', floor)\nfloor_train_dir = os.path.join(data_path, 'train', floor)","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"<h4>Loading coordinates of paths points</h4>","metadata":{}},{"cell_type":"code","source":"paths = []\nfor filename in os.listdir(floor_train_dir):\n    if '.txt' not in filename:\n        continue\n    points = load_points(os.path.join(floor_train_dir, filename))\n    paths.append((filename, points))\n\npaths = sorted(paths, key=lambda path: len(path[1]), reverse=True)\npaths = paths[:20]\n\n\ntraces_dataframes = [trace for filename, trace in paths]\ntraces_filenames = [filename for filename, trace in paths]","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"<h4>Loading floor map and its size</h4>","metadata":{}},{"cell_type":"code","source":"MAP_IMAGE = plt.imread(\n    os.path.join(floor_metadata_dir, 'floor_image.png')\n)\n\nwith open(os.path.join(floor_metadata_dir, 'floor_info.json')) as f:\n    content = f.read()\n    floor_info = json.loads(content)\n\nMAP_HEIGHT = float(floor_info['map_info']['height'])\nMAP_WIDTH = float(floor_info['map_info']['width'])\n    \nfloor_info","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"<h4>Visualization of Traces (Paths) on the Floor Map</h4>","metadata":{}},{"cell_type":"code","source":"visualize_many_traces_on_the_map(\n    traces_dataframes, MAP_IMAGE, MAP_WIDTH, MAP_HEIGHT, \n    traces_filenames=traces_filenames, \n    figsize=(15, 12)\n)","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"<h4>Features Visualization</h4>\nLet's take a closer look at single trace (path) and its attributes.","metadata":{}},{"cell_type":"code","source":"filename='5e15b0171506f2000638fe49.txt'\ntrace_filepath = os.path.join(floor_train_dir, filename)\n\ntrace_df = load_trace_as_dataframe(trace_filepath)\ntrace_df","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"Here is a table of the attributes we need to deal with in this competition. \n\nAs you can see it contains feature names and its params. We will describe some of these features in more detail below.\n\nEach feature can be extracted from trace as dataframe with params as columns. For this we are using function `extract_feature_df` which is suitable for all features except TYPE_WIFI and TYPE_BEACON.\n\nThe table is taken from [competition's official github repo](https://github.com/location-competition/indoor-location-competition-20).","metadata":{}},{"cell_type":"markdown","source":"| Feature                           | Values |       |        |        |                   |        |           |                                   |\n|:----------------------------------|:------:|:-----:|:------:|:-------|:-----------------:|:------:|:---------:|:---------------------------------:|\n|TYPE_WAYPOINT                      |X axis  |Y axis |       |         |                   |        |           |                                   |\n|TYPE_ACCELEROMETER                 |X axis  |Y axis |Z axis |accuracy |                   |        |           |                                   |\n|TYPE_GYROSCOPE                     |X axis  |Y axis |Z axis |accuracy |                   |        |           |                                   | \n|TYPE_MAGNETIC_FIELD                |X axis  |Y axis |Z axis |accuracy |                   |        |           |                                   | \n|TYPE_ROTATION_VECTOR               |X axis  |Y axis |Z axis |accuracy |                   |        |           |                                   |\n|TYPE_ACCELEROMETER_UNCALIBRATED    |X axis  |Y axis |Z axis |X axis   |Y axis             |Z axis  |accuracy   |                                   |\n|TYPE_GYROSCOPE_UNCALIBRATED        |X axis  |Y axis |Z axis |X axis   |Y axis             |Z axis  |accuracy   |                                   |\n|TYPE_MAGNETIC_FIELD_UNCALIBRATED   |X axis  |Y axis |Z axis |X axis   |Y axis             |Z axis  |accuracy   |                                   |\n|TYPE_WIFI                          |ssid    |bssid  |RSSI   |frequency|last seen timestamp|        |           |                                   |\n|TYPE_BEACON                        |UUID    |MajorID|MinorID|Tx Power |RSSI               |Distance|MAC Address|same with Unix time, padding data  |\n​","metadata":{}},{"cell_type":"markdown","source":"<b>TYPE_WAYPOINT</b>","metadata":{}},{"cell_type":"code","source":"points_df = extract_feature_df(trace_df, 'TYPE_WAYPOINT', col_names=('x', 'y'))\npoints_df.head()","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"visualize_single_trace_on_the_map(\n    points_df, MAP_IMAGE, MAP_WIDTH, MAP_HEIGHT, figsize=(10, 8)\n)","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"<b>TYPE_ACCELEROMETER</b>","metadata":{}},{"cell_type":"code","source":"acc_df = extract_feature_df(trace_df, 'TYPE_ACCELEROMETER')\nacc_df.head()","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"sns.pairplot(acc_df[['x', 'y', 'z']])\nplt.show()","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"<b>TYPE_GYROSCOPE</b>","metadata":{}},{"cell_type":"code","source":"gyro_df = extract_feature_df(trace_df, 'TYPE_GYROSCOPE')\ngyro_df.head()","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"<b>TYPE_MAGNETIC_FIELD</b>","metadata":{}},{"cell_type":"code","source":"magn_df = extract_feature_df(trace_df, 'TYPE_MAGNETIC_FIELD')\nmagn_df.head()","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"<b>TYPE_ROTATION_VECTOR</b>","metadata":{}},{"cell_type":"code","source":"rot_df = extract_feature_df(trace_df, 'TYPE_ROTATION_VECTOR')\nrot_df.head()","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"sns.pairplot(rot_df[['x', 'y', 'z']])\nplt.show()","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"<h2><center>Baseline Model 1</h2></center>\n<center><img src =\"https://lightgbm.readthedocs.io/en/latest/_images/LightGBM_logo_black_text.svg \" width = \"400\" height = \"400\"/></center> ","metadata":{}},{"cell_type":"code","source":"import numpy as np\nimport pandas as pd\nimport scipy.stats as stats\nfrom pathlib import Path\nimport glob\n\nfrom sklearn.model_selection import KFold\nimport lightgbm as lgb\n\nimport psutil\nimport random\nimport os\nimport time\nimport sys\nimport math\nfrom contextlib import contextmanager","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"N_SPLITS = 20\nSEED = 1234","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"Get file path from kaggle ","metadata":{}},{"cell_type":"code","source":"LOG_PATH = Path(\"./log/\")\nLOG_PATH.mkdir(parents=True, exist_ok=True)","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"Some utility code\n\n1. A timer\n2. A seed setter\n3. A intermediate metric finder\n4. A score logger","metadata":{}},{"cell_type":"code","source":"@contextmanager\ndef timer(name: str):\n    t0 = time.time()\n    p = psutil.Process(os.getpid())\n    m0 = p.memory_info()[0] / 2. ** 30\n    try:\n        yield\n    finally:\n        m1 = p.memory_info()[0] / 2. ** 30\n        delta = m1 - m0\n        sign = '+' if delta >= 0 else '-'\n        delta = math.fabs(delta)\n        print(f\"[{m1:.1f}GB({sign}{delta:.1f}GB): {time.time() - t0:.3f}sec] {name}\", file=sys.stderr)\n\n\ndef set_seed(seed=1234):\n    random.seed(seed)\n    os.environ[\"PYTHONHASHSEED\"] = str(seed)\n    np.random.seed(seed)\n\n    \ndef comp_metric(xhat, yhat, fhat, x, y, f):\n    intermediate = np.sqrt(np.power(xhat-x, 2) + np.power(yhat-y, 2)) + 15 * np.abs(fhat-f)\n    return intermediate.sum()/xhat.shape[0]\n\n\ndef score_log(df: pd.DataFrame, num_files: int, nam_file: str, data_shape: tuple, n_fold: int, seed: int, mpe: float):\n    score_dict = {'n_files': num_files, 'file_name': nam_file, 'shape': data_shape, 'fold': n_fold, 'seed': seed, 'score': mpe}\n    # noinspection PyTypeChecker\n    df = pd.concat([df, pd.DataFrame.from_dict([score_dict])])\n    df.to_csv(LOG_PATH / f\"log_score.csv\", index=False)\n    return df\n","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"Get all the data in our nb env","metadata":{}},{"cell_type":"code","source":"set_seed(SEED)\nfeature_dir = \"../input/indoor-navigation-and-location-wifi-features\"\ntrain_files = sorted(glob.glob(os.path.join(feature_dir, '*_train.csv')))\ntest_files = sorted(glob.glob(os.path.join(feature_dir, '*_test.csv')))\nsubm = pd.read_csv('../input/indoor-location-navigation/sample_submission.csv', index_col=0)","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"Create some tuned paratmeters for the LGB ( These will be tweaked to get the best performance)","metadata":{}},{"cell_type":"code","source":"lgb_params = {'objective': 'root_mean_squared_error',\n              'boosting_type': 'gbdt',\n              'n_estimators': 50000,\n              'learning_rate': 0.1,\n              'num_leaves': 90,\n              'colsample_bytree': 0.4,\n              'subsample': 0.6,\n              'subsample_freq': 2,\n              'bagging_seed': SEED,\n              'reg_alpha': 8,\n              'reg_lambda': 2,\n              'random_state': SEED,\n              'n_jobs': -1\n              }\n\nlgb_f_params = {'objective': 'multiclass',\n                'boosting_type': 'gbdt',\n                'n_estimators': 50000,\n                'learning_rate': 0.1,\n                'num_leaves': 90,\n                'colsample_bytree': 0.4,\n                'subsample': 0.6,\n                'subsample_freq': 2,\n                'bagging_seed': SEED,\n                'reg_alpha': 10,\n                'reg_lambda': 2,\n                'random_state': SEED,\n                'n_jobs': -1\n                }","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"Splitting the data into train and validation and passed them through LGBm and then ssaving the predicitions into pred","metadata":{}},{"cell_type":"markdown","source":"<b>Below is Light GBM code. Commented because very large output.</b>","metadata":{}},{"cell_type":"code","source":"# score_df = pd.DataFrame()\n# oof = list()\n# predictions = list()\n# for n_files, file in enumerate(train_files):\n#     data = pd.read_csv(file, index_col=0)\n#     test_data = pd.read_csv(test_files[n_files], index_col=0)\n\n#     oof_x, oof_y, oof_f = np.zeros(data.shape[0]), np.zeros(data.shape[0]), np.zeros(data.shape[0])\n#     preds_x, preds_y = 0, 0\n#     preds_f_arr = np.zeros((test_data.shape[0], N_SPLITS))\n\n#     kf = KFold(n_splits=N_SPLITS, shuffle=True, random_state=SEED)\n#     for fold, (trn_idx, val_idx) in enumerate(kf.split(data.iloc[:, :-4])):\n#         X_train = data.iloc[trn_idx, :-4]\n#         y_trainx = data.iloc[trn_idx, -4]\n#         y_trainy = data.iloc[trn_idx, -3]\n#         y_trainf = data.iloc[trn_idx, -2]\n\n#         X_valid = data.iloc[val_idx, :-4]\n#         y_validx = data.iloc[val_idx, -4]\n#         y_validy = data.iloc[val_idx, -3]\n#         y_validf = data.iloc[val_idx, -2]\n\n#         modelx = lgb.LGBMRegressor(**lgb_params)\n#         with timer(\"fit X\"):\n#             modelx.fit(X_train, y_trainx,\n#                        eval_set=[(X_valid, y_validx)],\n#                        eval_metric='rmse',\n#                        verbose=True,\n#                        early_stopping_rounds=5\n#                        )\n\n#         modely = lgb.LGBMRegressor(**lgb_params)\n#         with timer(\"fit Y\"):\n#             modely.fit(X_train, y_trainy,\n#                        eval_set=[(X_valid, y_validy)],\n#                        eval_metric='rmse',\n#                        verbose=True,\n#                        early_stopping_rounds=5\n#                        )\n#         modelf = lgb.LGBMClassifier(**lgb_f_params)\n#         with timer(\"fit F\"):\n#             modelf.fit(X_train, y_trainf,\n#                        eval_set=[(X_valid, y_validf)],\n#                        eval_metric='multi_logloss',\n#                        verbose=True,\n#                        early_stopping_rounds=5\n#                        )\n\n#         oof_x[val_idx] = modelx.predict(X_valid)\n#         oof_y[val_idx] = modely.predict(X_valid)\n#         oof_f[val_idx] = modelf.predict(X_valid).astype(int)\n\n#         preds_x += modelx.predict(test_data.iloc[:, :-1]) / N_SPLITS\n#         preds_y += modely.predict(test_data.iloc[:, :-1]) / N_SPLITS\n#         preds_f_arr[:, fold] = modelf.predict(test_data.iloc[:, :-1]).astype(int)\n\n#         score = comp_metric(oof_x[val_idx], oof_y[val_idx], oof_f[val_idx],\n#                             y_validx.to_numpy(), y_validy.to_numpy(), y_validf.to_numpy())\n#         print(f\"fold {fold}: mean position error {score}\")\n#         score_df = score_log(score_df, n_files, os.path.basename(file), data.shape, fold, SEED, score)\n\n#     print(\"*+\"*40)\n#     print(f\"file #{n_files}, shape={data.shape}, name={os.path.basename(file)}\")\n#     score = comp_metric(oof_x, oof_y, oof_f,\n#                         data.iloc[:, -4].to_numpy(), data.iloc[:, -3].to_numpy(), data.iloc[:, -2].to_numpy())\n#     oof.append(score)\n#     print(f\"mean position error {score}\")\n#     print(\"*+\"*40)\n#     score_df = score_log(score_df, n_files, os.path.basename(file), data.shape, 999, SEED, score)\n\n#     preds_f_mode = stats.mode(preds_f_arr, axis=1)\n#     preds_f = preds_f_mode[0].astype(int).reshape(-1)\n#     test_preds = pd.DataFrame(np.stack((preds_f, preds_x, preds_y))).T\n#     test_preds.columns = subm.columns\n#     test_preds.index = test_data[\"site_path_timestamp\"]\n#     test_preds[\"floor\"] = test_preds[\"floor\"].astype(int)\n#     predictions.append(test_preds)","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"Save the predictions into the same format as required ","metadata":{}},{"cell_type":"code","source":"# all_preds = pd.concat(predictions)\n# all_preds = all_preds.reindex(subm.index)\n# all_preds.to_csv('submission.csv')\n# all_preds.head(20)","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"<h3>Refrences</h3>\n\n\nhttps://www.kaggle.com/harshsharma511/indoor-location-navigation-eda\n\nhttps://www.kaggle.com/harshsharma511/basic-eda-traces-and-features-visualization\n\nhttps://www.kaggle.com/harshsharma511/lightgbm-regressor","metadata":{}},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]}]}