{"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":"
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Snap to Grid - Part(3)

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Identify the position of a smartphone in a shopping mall

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By: Somayyeh Gholami & Mehran Kazeminia

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","metadata":{}},{"cell_type":"markdown","source":"
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","metadata":{}},{"cell_type":"markdown","source":"# Description:","metadata":{}},{"cell_type":"markdown","source":"### - In this notebook, we want to improve the score of our previous notebook (No. 2). We chose \"generated6\", which has a score of \"5.265\". The address of our previous notebook is as follows:\n\nhttps://www.kaggle.com/mehrankazeminia/2-3-indoor-navigation-comparative-method\n\n### - We have used the following notebook codes in this notebook. Thanks again for sharing this great notebook.\n\nhttps://www.kaggle.com/robikscube/indoor-navigation-snap-to-grid-post-processing\n\n### - \"Data Visualization\" is of particular importance in this challenge. Because the location of the corridors is important :)\n\n\n## >>> Good Luck <<<\n\n","metadata":{}},{"cell_type":"markdown","source":"
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","metadata":{}},{"cell_type":"markdown","source":"# If you find this work useful, please don't forget upvoting :)","metadata":{}},{"cell_type":"markdown","source":"
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","metadata":{}},{"cell_type":"markdown","source":"# Import ","metadata":{}},{"cell_type":"code","source":"import json\nimport numpy as np\nimport pandas as pd \nimport matplotlib.pylab as plt\n\nfrom scipy.spatial.distance import cdist\n\n%matplotlib inline","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"
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","metadata":{}},{"cell_type":"markdown","source":"# Helper Functions","metadata":{}},{"cell_type":"code","source":"def split_col(df):\n df = pd.concat([\n df['site_path_timestamp'].str.split('_', expand=True) \\\n .rename(columns={0:'site',\n 1:'path',\n 2:'timestamp'}),\n df\n ], axis=1).copy()\n return df\n\nfloor_map = {\"B2\":-2, \"B1\":-1, \"F1\":0, \"F2\": 1, \"F3\":2,\n \"F4\":3, \"F5\":4, \"F6\":5, \"F7\":6,\"F8\":7,\"F9\":8,\n \"1F\":0, \"2F\":1, \"3F\":2, \"4F\":3, \"5F\":4, \"6F\":5,\n \"7F\":6, \"8F\": 7, \"9F\":8}\n\n\ndef plot_preds(\n site,\n floorNo,\n sub=None,\n true_locs=None,\n base=\"../input/indoor-location-navigation\",\n show_train=True,\n show_preds=True,\n fix_labels=True,\n map_floor=None\n):\n \"\"\"\n Plots predictions on floorplan map.\n \n map_floor : use a different floor's map\n \"\"\"\n if map_floor is None:\n map_floor = floorNo\n # Prepare width_meter & height_meter (taken from the .json file)\n floor_plan_filename = f\"{base}/metadata/{site}/{map_floor}/floor_image.png\"\n json_plan_filename = f\"{base}/metadata/{site}/{map_floor}/floor_info.json\"\n with open(json_plan_filename) as json_file:\n json_data = json.load(json_file)\n\n width_meter = json_data[\"map_info\"][\"width\"]\n height_meter = json_data[\"map_info\"][\"height\"]\n\n floor_img = plt.imread(f\"{base}/metadata/{site}/{map_floor}/floor_image.png\")\n\n fig, ax = plt.subplots(figsize=(12, 12))\n plt.imshow(floor_img)\n\n if show_train:\n true_locs = true_locs.query('site == @site and floorNo == @map_floor').copy()\n true_locs[\"x_\"] = true_locs[\"x\"] * floor_img.shape[0] / height_meter\n true_locs[\"y_\"] = (\n true_locs[\"y\"] * -1 * floor_img.shape[1] / width_meter\n ) + floor_img.shape[0]\n true_locs.query(\"site == @site and floorNo == @map_floor\").groupby(\"path\").plot(\n x=\"x_\",\n y=\"y_\",\n style=\"+\",\n ax=ax,\n label=\"train waypoint location\",\n color=\"grey\",\n alpha=0.5,\n )\n\n if show_preds:\n sub = sub.query('site == @site and floorNo == @floorNo').copy()\n sub[\"x_\"] = sub[\"x\"] * floor_img.shape[0] / height_meter\n sub[\"y_\"] = (\n sub[\"y\"] * -1 * floor_img.shape[1] / width_meter\n ) + floor_img.shape[0]\n for path, path_data in sub.query(\n \"site == @site and floorNo == @floorNo\"\n ).groupby(\"path\"):\n path_data.plot(\n x=\"x_\",\n y=\"y_\",\n style=\".-\",\n ax=ax,\n title=f\"{site} - floor - {floorNo}\",\n alpha=1,\n label=path,\n )\n if fix_labels:\n handles, labels = ax.get_legend_handles_labels()\n by_label = dict(zip(labels, handles))\n plt.legend(\n by_label.values(), by_label.keys(), loc=\"center left\", bbox_to_anchor=(1, 0.5)\n )\n return fig, ax\n\ndef sub_process(sub, train_waypoints):\n train_waypoints['isTrainWaypoint'] = True\n sub = split_col(sub[['site_path_timestamp','floor','x','y']]).copy()\n sub = sub.merge(train_waypoints[['site','floorNo','floor']].drop_duplicates(), how='left')\n sub = sub.merge(\n train_waypoints[['x','y','site','floor','isTrainWaypoint']].drop_duplicates(),\n how='left',\n on=['site','x','y','floor']\n )\n sub['isTrainWaypoint'] = sub['isTrainWaypoint'].fillna(False)\n return sub.copy()\n","metadata":{"_kg_hide-input":true,"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"
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","metadata":{}},{"cell_type":"markdown","source":"# Data Set","metadata":{}},{"cell_type":"code","source":"train_waypoints = pd.read_csv('../input/indoor-location-train-waypoints/train_waypoints.csv')\nsub = sub_process(pd.read_csv('../input/2-3-indoor-navigation-comparative-method/generated6.csv'),\n train_waypoints)\n","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"
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","metadata":{}},{"cell_type":"markdown","source":"# Find the closest \"grid\" point for each prediction.","metadata":{}},{"cell_type":"code","source":"def add_xy(df):\n df['xy'] = [(x, y) for x,y in zip(df['x'], df['y'])]\n return df\n\ndef closest_point(point, points):\n \"\"\" Find closest point from a list of points. \"\"\"\n return points[cdist([point], points).argmin()]\n\nsub = add_xy(sub)\ntrain_waypoints = add_xy(train_waypoints)\n\nds = []\nfor (site, myfloor), d in sub.groupby(['site','floor']):\n true_floor_locs = train_waypoints.loc[(train_waypoints['floor'] == myfloor) &\n (train_waypoints['site'] == site)] \\\n .reset_index(drop=True)\n if len(true_floor_locs) == 0:\n print(f'Skipping {site} {myfloor}')\n continue\n d['matched_point'] = [closest_point(x, list(true_floor_locs['xy'])) for x in d['xy']]\n d['x_'] = d['matched_point'].apply(lambda x: x[0])\n d['y_'] = d['matched_point'].apply(lambda x: x[1])\n ds.append(d)\n\nsub = pd.concat(ds)\n","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"
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","metadata":{}},{"cell_type":"markdown","source":"# Apply a Threshold and \"Snap to Grid\"","metadata":{}},{"cell_type":"code","source":"def snap_to_grid(sub, threshold):\n \"\"\"\n Snap to grid if within a threshold.\n \n x, y are the predicted points.\n x_, y_ are the closest grid points.\n _x_, _y_ are the new predictions after post processing.\n \"\"\"\n sub['_x_'] = sub['x']\n sub['_y_'] = sub['y']\n sub.loc[sub['dist'] < threshold, '_x_'] = sub.loc[sub['dist'] < threshold]['x_']\n sub.loc[sub['dist'] < threshold, '_y_'] = sub.loc[sub['dist'] < threshold]['y_']\n return sub.copy()\n\n# Calculate the distances\nsub['dist'] = np.sqrt( (sub.x-sub.x_)**2 + (sub.y-sub.y_)**2 )\n\n#sub_pp = snap_to_grid(sub, threshold=8) #4.792\n\n#sub_pp = snap_to_grid(sub, threshold=7) #4.809\n#sub_pp = snap_to_grid(sub, threshold=9) #4.824\n\n#sub_pp = snap_to_grid(sub, threshold=7.5) #4.784\n \n#sub_pp = snap_to_grid(sub, threshold=7.3) #4.794\n\n#sub_pp = snap_to_grid(sub, threshold=7.6) #4.788\n\n#sub_pp = snap_to_grid(sub, threshold=7.4) #4.796\n\n#sub_pp = snap_to_grid(sub, threshold=7.55) #4.782\n\n#sub_pp = snap_to_grid(sub, threshold=7.52) #4.782 \n\nsub_pp = snap_to_grid(sub, threshold=7.57) \n\nsub_pp = sub_pp[['site_path_timestamp','floor','_x_','_y_','site','path','floorNo']] \\\n .rename(columns={'_x_':'x', '_y_':'y'})","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# Save Post Processed Submission.","metadata":{}},{"cell_type":"markdown","source":"
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","metadata":{}},{"cell_type":"code","source":"sub_pp[['site_path_timestamp','floor','x','y']] \\\n .to_csv('submission_snap_to_grid.csv', index=False)","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"
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","metadata":{}},{"cell_type":"markdown","source":"# Data Visualization","metadata":{}},{"cell_type":"code","source":"def split_col(df):\n \"\"\"\n Split submission site/path/timestamp into individual columns.\n \"\"\"\n df = pd.concat(\n [\n df[\"site_path_timestamp\"]\n .str.split(\"_\", expand=True)\n .rename(columns={0: \"site\", 1: \"path\", 2: \"timestamp\"}),\n df,\n ],\n axis=1,\n ).copy()\n return df\n\n\ndef plot_preds(\n site,\n floorNo,\n sub=None,\n true_locs=None,\n base=\"../input/indoor-location-navigation\",\n show_train=True,\n show_preds=True,\n):\n \"\"\"\n Plots predictions on floorplan map.\n \"\"\"\n # Prepare width_meter & height_meter (taken from the .json file)\n floor_plan_filename = f\"{base}/metadata/{site}/{floorNo}/floor_image.png\"\n json_plan_filename = f\"{base}/metadata/{site}/{floorNo}/floor_info.json\"\n with open(json_plan_filename) as json_file:\n json_data = json.load(json_file)\n\n width_meter = json_data[\"map_info\"][\"width\"]\n height_meter = json_data[\"map_info\"][\"height\"]\n\n floor_img = plt.imread(f\"{base}/metadata/{site}/{floorNo}/floor_image.png\")\n\n fig, ax = plt.subplots(figsize=(12, 12))\n plt.imshow(floor_img)\n\n if show_train:\n true_locs[\"x_\"] = true_locs[\"x\"] * floor_img.shape[0] / height_meter\n true_locs[\"y_\"] = (\n true_locs[\"y\"] * -1 * floor_img.shape[1] / width_meter\n ) + floor_img.shape[0]\n true_locs.query(\"site == @site and floorNo == @floorNo\").groupby(\"path\").plot(\n x=\"x_\",\n y=\"y_\",\n style=\"+\",\n ax=ax,\n label=\"train waypoint location\",\n color=\"grey\",\n alpha=0.5,\n )\n\n if show_preds:\n sub[\"x_\"] = sub[\"x\"] * floor_img.shape[0] / height_meter\n sub[\"y_\"] = (\n sub[\"y\"] * -1 * floor_img.shape[1] / width_meter\n ) + floor_img.shape[0]\n for path, path_data in sub.query(\n \"site == @site and floorNo == @floorNo\"\n ).groupby(\"path\"):\n path_data.plot(\n x=\"x_\",\n y=\"y_\",\n style=\".-\",\n ax=ax,\n title=f\"{site} - floor - {floorNo}\",\n alpha=1,\n label=path,\n )\n return fig, ax\n","metadata":{"_kg_hide-input":true,"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"sub = split_col(sub_pp[['site_path_timestamp','floor','x','y']])\n\ntrue_locs = pd.read_csv(\"../input/indoor-location-train-waypoints/train_waypoints.csv\")\n\n# Add floor No to sub file\nsub = sub.merge(true_locs[[\"site\", \"floor\", \"floorNo\"]].drop_duplicates())\n\n\nfor (site, floorNo), d in sub.groupby([\"site\", \"floorNo\"]):\n fig, ax = plot_preds(site, floorNo, sub, true_locs)\n # Remove duplicate labels\n handles, labels = ax.get_legend_handles_labels()\n by_label = dict(zip(labels, handles))\n plt.legend(\n by_label.values(), by_label.keys(), loc=\"center left\", bbox_to_anchor=(1, 0.5)\n )\n plt.show()\n ","metadata":{"_kg_hide-output":false,"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"
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","metadata":{}}]}