{"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":"# Dynamic Time Warping Snap-To-Grid\n\nThis notebook demonstrates a snap-to-grid incorporating dynamic time warping (DTW). \n\n## A problem in snap-to-grid\nEspecially in downtown area where GNSS-based localization tend to be noisy, it is very effective to snap the estimated points to the ground truth positions. A simple snap-to-grid algorithm, however, does not consider time series information, which induces some problems such as:\n- a point is snapped to different road, especially in crossroads\n- a point is snapped to the left lane, in spite of the right lane rule in the US\n\n\n## Dynamic Time Warping (DTW)\nDynamic time warping (DTW) is an algorithm for calculating the similarity between two time series data. Unlike other distance methods like Euclid, DTW is able to calculate the similarity of the shape of the data. The implementation can be simply done using dynamic programming.\n\n![dtw_sample.png](attachment:76f83eaa-c84b-4ab0-9adf-de1f85614fc5.png)\n\n## DTW-based snap-to-grid\nThe algorithm is as follows: for every sub-trajectory query $(x_i)$ in the estimated data, \n1. find the closest sub-trajectory $(y_i)$ in ground truth data based on DTW\n2. snap the query $(x_i)$ to the grid $(y_i)$\n\nIn our implementation, the length of $x_i$ is $60 [\\rm{s}]$. Using this algorithm, you can avoid snapping a point to an opposite lane or to the different roads. \n\nIn this competition, the method works pretty well on downtown area, where the multi-path errors are significant and plenty of ground truth data is available. For example on 2021-04-22-US-SJC-1, the score reduces from $18.4 [\\rm{m}]$ to $11.7 [\\rm{m}]$.\n\n![dtw-snap.png](attachment:ca45c260-6f37-4a87-8c92-eadd57456d4a.png)\n\n## Some tips\nOne of the drawbacks of DTW is the computational complexity. When calculating the distance between two time-series data whose length is $N$ and $M$ each, the complexity is $\\mathcal{O}(MN)$. This is highly complex compared to other distance method such as Euclidean distance. Indeed, it took too much time to calculate between a query trajectory and all the possible sub-trajectory in ground truth data.\n\nTo overcome this issue, for each query trajectory $(x_i) = (x_0, x_1, \\cdots, x_{n-1})$, we first pick up the points which is close enough to $x_0$ and $x_{n-1}$ each. Then, among the possible set of trajectory, we choose the trajectory with the closest DTW distance.\n\nWe also tried to apply this algorithm to highway and tree area, but did not work very well. 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"},"76f83eaa-c84b-4ab0-9adf-de1f85614fc5.png":{"image/png":"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"}}},{"cell_type":"code","source":"from pathlib import Path\nimport numpy as np\nimport pandas as pd\nimport copy\nimport plotly.express as px\nimport plotly.graph_objects as go\nimport pyproj\nimport json\nimport bisect\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom matplotlib.collections import LineCollection\nfrom matplotlib.colors import ListedColormap, BoundaryNorm\nimport pickle\nimport random\nfrom tqdm.notebook import tqdm\n\nimport warnings\nwarnings.simplefilter('ignore')\npd.set_option('display.max_rows',30)\npd.set_option('display.max_columns',None)","metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","_kg_hide-input":true,"execution":{"iopub.status.busy":"2021-08-03T02:38:30.030508Z","iopub.execute_input":"2021-08-03T02:38:30.030938Z","iopub.status.idle":"2021-08-03T02:38:30.039536Z","shell.execute_reply.started":"2021-08-03T02:38:30.030901Z","shell.execute_reply":"2021-08-03T02:38:30.038152Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"def visualize_trafic(df, center={\"lat\":37.423576, \"lon\":-122.094132}, zoom=9):\n    fig = px.scatter_mapbox(df,\n                            \n                            # Here, plotly gets, (x,y) coordinates\n                            lat=\"latDeg\",\n                            lon=\"lngDeg\",\n                            \n                            #Here, plotly detects color of series\n                            color=\"phoneName\",\n                            labels=\"phoneName\",\n                            \n                            zoom=zoom,\n                            center=center,\n                            height=300,\n                            width=500)\n    fig.update_layout(mapbox_style='stamen-terrain')\n    fig.update_layout(margin={\"r\": 0, \"t\": 0, \"l\": 0, \"b\": 0})\n    fig.update_layout(title_text=\"GPS trafic\")\n    fig.show()\n    \ndef visualize_collection(df, collection, center={\"lat\":37.423576, \"lon\":-122.094132}, zoom=9):\n    df_traj = df[df['collectionName'] == collection]\n    visualize_trafic(df_traj, center, zoom)","metadata":{"_kg_hide-input":true,"execution":{"iopub.status.busy":"2021-08-03T02:40:48.999018Z","iopub.execute_input":"2021-08-03T02:40:48.999416Z","iopub.status.idle":"2021-08-03T02:40:49.008899Z","shell.execute_reply.started":"2021-08-03T02:40:48.999382Z","shell.execute_reply":"2021-08-03T02:40:49.00749Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"def calc_haversine(lat1, lon1, lat2, lon2):\n    \"\"\"Calculates the great circle distance between two points\n    on the earth. Inputs are array-like and specified in decimal degrees.\n    \"\"\"\n    RADIUS = 6_367_000\n    lat1, lon1, lat2, lon2 = map(np.radians, [lat1, lon1, lat2, lon2])\n    dlat = lat2 - lat1\n    dlon = lon2 - lon1\n    a = np.sin(dlat/2)**2 + \\\n        np.cos(lat1) * np.cos(lat2) * np.sin(dlon/2)**2\n    dist = 2 * RADIUS * np.arcsin(a**0.5)\n    return dist","metadata":{"_kg_hide-input":true,"execution":{"iopub.status.busy":"2021-08-03T02:38:30.366897Z","iopub.execute_input":"2021-08-03T02:38:30.367275Z","iopub.status.idle":"2021-08-03T02:38:30.374601Z","shell.execute_reply.started":"2021-08-03T02:38:30.367226Z","shell.execute_reply":"2021-08-03T02:38:30.373447Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"def percentile50(x):\n    return np.percentile(x, 50)\ndef percentile95(x):\n    return np.percentile(x, 95)\n\ndef get_train_score(df, gt):\n    gt = gt.rename(columns={'latDeg':'latDeg_gt', 'lngDeg':'lngDeg_gt'})\n    df = df.merge(gt, on=['collectionName', 'phoneName', 'millisSinceGpsEpoch'], how='inner')\n    # calc_distance_error\n    df['err'] = calc_haversine(df['latDeg_gt'], df['lngDeg_gt'], df['latDeg'], df['lngDeg'])\n    # calc_evaluate_score\n    df['phone'] = df['collectionName'] + '_' + df['phoneName']\n    res = df.groupby('phone')['err'].agg([percentile50, percentile95])\n    res['p50_p90_mean'] = (res['percentile50'] + res['percentile95']) / 2 \n    score = res['p50_p90_mean'].mean()\n    return score","metadata":{"_kg_hide-input":true,"execution":{"iopub.status.busy":"2021-08-03T02:38:30.536033Z","iopub.execute_input":"2021-08-03T02:38:30.53654Z","iopub.status.idle":"2021-08-03T02:38:30.54423Z","shell.execute_reply.started":"2021-08-03T02:38:30.536506Z","shell.execute_reply":"2021-08-03T02:38:30.543368Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"def eval_all(df_pred, df_gt):\n    scores = []\n    compared_cols = [\"latDeg_truth\",\"lngDeg_truth\",\"latDeg_pred\",\"lngDeg_pred\"]\n    collections = sorted(df_gt['collectionName'].unique())\n    for collection in collections:\n        df_pred_col = df_pred[df_pred['collectionName'] == collection]\n        df_gt_col = df_gt[df_gt['collectionName'] == collection]\n        \n        score = get_train_score(df_pred_col, df_gt_col)\n        \n        df_merged = pd.merge_asof(df_gt_col.sort_values('millisSinceGpsEpoch'), df_pred_col.sort_values('millisSinceGpsEpoch'), \n                                  on=\"millisSinceGpsEpoch\", by=[\"collectionName\", \"phoneName\"], \n                                  direction='nearest',tolerance=100000, suffixes=('_truth', '_pred'))\n        df_merged = df_merged.sort_values(by=[\"collectionName\", \"phoneName\", \"millisSinceGpsEpoch\"], ignore_index=True)\n\n        haversine = calc_haversine(*df_merged[compared_cols].to_numpy().transpose()).mean()\n        scores.append([collection, haversine, score])\n    \n    score = get_train_score(df_pred, df_gt)\n    df_merged = pd.merge_asof(df_gt.sort_values('millisSinceGpsEpoch'), df_pred.sort_values('millisSinceGpsEpoch'), \n                              on=\"millisSinceGpsEpoch\", by=[\"collectionName\", \"phoneName\"], \n                              direction='nearest',tolerance=100000, suffixes=('_truth', '_pred'))\n    haversine = calc_haversine(*df_merged[compared_cols].to_numpy().transpose()).mean()\n    scores.append(['all', haversine, score])\n    \n    df_scores = pd.DataFrame(scores, columns=['collection', 'haversine', 'score'])\n    return df_scores","metadata":{"_kg_hide-input":true,"execution":{"iopub.status.busy":"2021-08-03T02:38:30.804054Z","iopub.execute_input":"2021-08-03T02:38:30.804414Z","iopub.status.idle":"2021-08-03T02:38:30.815099Z","shell.execute_reply.started":"2021-08-03T02:38:30.804385Z","shell.execute_reply":"2021-08-03T02:38:30.814309Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"def apply_dtw_snap_to_grid(df_input, df_gt, collections_to_snap = None, th_dtw=30.0 / 100_000, th_snap=10.0 / 100_000):\n    df_snapped = df_input.copy()\n    collections = df_snapped['collectionName'].unique()\n    for collection in tqdm(collections, desc = 'Apply DTW snap-to-grid (for {} trajs)'.format(\"whole\" if collections_to_snap is None else len(collections_to_snap))):\n        if collections_to_snap is not None:\n            if collection not in collections_to_snap:\n                continue\n        cond_col = df_snapped['collectionName'] == collection\n        phones = df_snapped[cond_col]['phoneName'].unique()\n        for phone in phones:\n            cond_traj = cond_col & (df_snapped['phoneName'] == phone)\n            time_traj = df_snapped[cond_traj]['millisSinceGpsEpoch'].values\n            latlng_traj = df_snapped[cond_traj][['latDeg', 'lngDeg']].values\n\n            segment_ids_list = get_segment_ids(latlng_traj, base_len=60, stride=30)\n            \n            snapped_count_list = np.zeros_like(time_traj)\n            snapped_results_list = np.zeros((len(time_traj), 2))\n            \n            for ids in segment_ids_list:\n                cond_seg = np.arange(0, latlng_traj.shape[0], 1)\n                cond_seg = (cond_seg >= ids[0]) & (cond_seg < ids[1])\n                latlng_seg = latlng_traj[cond_seg]\n                snapped_count_list += cond_seg\n                subtraj_opt, _ = search_closest_subtraj(latlng_seg, df_gt, threshold=th_dtw)\n                if subtraj_opt is not None: # もし良い感じのが見つかったら、、、\n                    grids = increase_points_array(subtraj_opt)\n                    snapped_latlng = snap_to_grid_array(latlng_seg, grids, threshold=th_snap)\n                    snapped_results_list[ids[0]:ids[1]] += snapped_latlng\n                else:\n                    snapped_results_list[ids[0]:ids[1]] += latlng_seg\n            \n            # Add original value if never counted.\n            snapped_results_list[snapped_count_list == 0] += latlng_traj[snapped_count_list == 0]\n            snapped_count_list[snapped_count_list == 0] += 1\n\n            df_snapped.loc[cond_traj, ['latDeg', 'lngDeg']] = snapped_results_list / snapped_count_list.reshape(-1, 1)\n    return df_snapped\n\n\ndef search_closest_subtraj(latlng, df_gt, threshold=30/100_000, expand_idx_len=10):\n    \n    dist_opt = 1e9\n    subtraj_opt = None\n    for collection, df_col in df_gt.groupby('collectionName'):\n        # Phoneは1つだけ使えばOK\n        phone_here = df_col['phoneName'].unique()[0]\n        df_traj = df_col[df_col['phoneName'] == phone_here]\n        latlng_traj = df_traj[['latDeg', 'lngDeg']].values\n        \n        dist_start = np.linalg.norm(latlng_traj - latlng[0, :], axis=1)\n        dist_end = np.linalg.norm(latlng_traj - latlng[-1, :], axis=1)\n        start_cand_idx = np.where(dist_start < threshold)[0]\n        end_cand_idx = np.where(dist_end < threshold)[0]\n\n        for start_idx in reduce_array(start_cand_idx):\n            for end_idx in reduce_array(end_cand_idx):\n                if start_idx >= end_idx:\n                    continue\n                dist = calc_dtw(latlng[::20], latlng_traj[start_idx: end_idx][::20])[-1][-1][0]\n                # try:\n                #     dist = calc_dtw(latlng[::20], latlng_traj[start_idx: end_idx][::20])[-1][-1][0]\n                # except IndexError as e:\n                #     print(start_idx, end_idx)\n                if dist_opt > dist:\n                    dist_opt = dist\n                    subtraj_opt = latlng_traj[max(start_idx-expand_idx_len, 0): end_idx+expand_idx_len]\n\n    return subtraj_opt, dist_opt\n\n\ndef increase_points_array(data, min_dist=0.2/100_000):\n    data_increased = []\n    for i, point in enumerate(data):\n        if i == data.shape[0] - 1:\n            continue\n        data_increased.append(point)\n        if np.array_equal(data[i, 1:], data[i + 1, 1:]):\n            continue\n        latDeg0 = data[i, 0]\n        lngDeg0 = data[i, 1]\n        latDeg1 = data[i+1, 0]\n        lngDeg1 = data[i+1, 1]\n        num_to_increase = np.ceil(np.linalg.norm([latDeg0 - latDeg1, lngDeg0 - lngDeg1]) / min_dist)\n        for j in range(int(num_to_increase)):\n            latDeg = ((num_to_increase-j) * latDeg0 + j * latDeg1) / num_to_increase\n            lngDeg = ((num_to_increase-j) * lngDeg0 + j * lngDeg1) / num_to_increase\n            data_increased.append([latDeg, lngDeg])\n    data_increased = np.array(data_increased)\n    return data_increased\n\n\ndef snap_to_grid_array(query, grids, threshold):\n    snapped_query = copy.deepcopy(query)\n    grids_sorted = np.array(sorted(grids, key=lambda x: x[0]))\n    for i, point in enumerate(query):\n        left_lat_idx = bisect.bisect_left(grids_sorted[:, 0], point[0] - threshold)\n        right_lat_idx = bisect.bisect_left(grids_sorted[:, 0], point[0] + threshold)\n        grids_of_interest = grids_sorted[left_lat_idx : right_lat_idx, :]\n        if len(grids_of_interest) == 0:\n            continue\n\n        dist = np.linalg.norm(grids_of_interest - point, axis=1)        \n        if np.min(dist) > threshold:\n             continue\n        idx_opt = np.argmin(dist)\n        snapped_query[i] = grids_of_interest[idx_opt]\n    return snapped_query\n\n\ndef reduce_array(array, length=50):\n    reduced = []\n    for idx in array:\n        if len(reduced) == 0:\n            reduced.append(idx)\n        else:\n            if reduced[-1] + length <= idx:\n                reduced.append(idx)\n    return reduced\n\n\ndef get_segment_ids(latlng, base_len=60, stride=30):\n    segment_ids = []\n    start = 0\n    end = start + base_len\n    while True:\n        if np.linalg.norm(latlng[min(end, latlng.shape[0]-1), :] - latlng[start, :]) > 200/100_000: \n            segment_ids.append([start, end])\n            start = end - stride\n            end = start + base_len\n        else:\n            end += stride\n        if end >= len(latlng):\n            segment_ids.append([latlng.shape[0]-1 - 250, latlng.shape[0]-1])\n            break\n    return segment_ids\n\n\ndelta = lambda a, b: np.linalg.norm(a - b)\nfirst = lambda x: x[0]\nsecond = lambda x: x[1]\n\ndef minVal(v1, v2, v3):\n    if first(v1) <= min(first(v2), first(v3)):\n        return v1, 0\n    elif first(v2) <= first(v3):\n        return v2, 1\n    else:\n        return v3, 2 \n\ndef calc_dtw(A, B):\n    S = len(A)\n    T = len(B)\n\n    m = [[0 for j in range(T)] for i in range(S)]\n    m[0][0] = (delta(A[0],B[0]), (-1,-1))\n    for i in range(1,S):\n        m[i][0] = (m[i-1][0][0] + delta(A[i], B[0]), (i-1,0))\n    for j in range(1,T):\n        m[0][j] = (m[0][j-1][0] + delta(A[0], B[j]), (0,j-1))\n\n    for i in range(1,S):\n        for j in range(1,T):\n            minimum, index = minVal(m[i-1][j], m[i][j-1], m[i-1][j-1])\n            indexes = [(i-1,j), (i,j-1), (i-1,j-1)]\n            m[i][j] = (first(minimum)+delta(A[i], B[j]), indexes[index])\n    return m","metadata":{"_kg_hide-input":true,"execution":{"iopub.status.busy":"2021-08-03T02:38:31.238764Z","iopub.execute_input":"2021-08-03T02:38:31.239455Z","iopub.status.idle":"2021-08-03T02:38:31.282751Z","shell.execute_reply.started":"2021-08-03T02:38:31.239407Z","shell.execute_reply":"2021-08-03T02:38:31.281751Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# Train","metadata":{}},{"cell_type":"code","source":"datapath = Path(\"../input/google-smartphone-decimeter-challenge/\")\nground_truths = (datapath / \"train\").rglob(\"ground_truth.csv\")\ndf_gt = pd.concat([pd.read_csv(filepath) for filepath in tqdm(ground_truths, total=73, desc=\"Reading ground truth data\")], ignore_index=True)\n\ndf_pred_train = pd.read_csv('../input/k/minomonter/gnss-ensembled/train_submission_filtered.csv')\ndf_pred_train['collectionName'] = df_pred_train['phone'].apply(lambda x: x.split('_')[0])\ndf_pred_train['phoneName'] = df_pred_train['phone'].apply(lambda x: x.split('_')[1])\n\ndf_pred_test = pd.read_csv('../input/k/minomonter/gnss-ensembled/submission_filtered.csv')\ndf_pred_test['collectionName'] = df_pred_test['phone'].apply(lambda x: x.split('_')[0])\ndf_pred_test['phoneName'] = df_pred_test['phone'].apply(lambda x: x.split('_')[1])","metadata":{"execution":{"iopub.status.busy":"2021-08-03T02:38:32.266823Z","iopub.execute_input":"2021-08-03T02:38:32.267535Z","iopub.status.idle":"2021-08-03T02:38:34.642142Z","shell.execute_reply.started":"2021-08-03T02:38:32.267495Z","shell.execute_reply":"2021-08-03T02:38:34.641371Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"with open('../input/region-classification/region_type_train.json') as f:\n    region_type_train = json.load(f)\nwith open('../input/region-classification/region_type_test.json') as f:\n    region_type_test = json.load(f)\n\ndowntowns = [*[key for (key, val) in region_type_train.items() if 'downtown' in val], *[key for (key, val) in region_type_test.items() if 'downtown' in val]]\ntrees = [*[key for (key, val) in region_type_train.items() if 'tree' in val], *[key for (key, val) in region_type_test.items() if 'tree' in val]]\nhighways = [*[key for (key, val) in region_type_train.items() if 'tree' not in val and 'downtown' not in val],\n          *[key for (key, val) in region_type_test.items() if 'tree' not in val and 'downtown' not in val]]\n\nprocess_snap_to_grid_dtw_downtown_train = lambda df: apply_dtw_snap_to_grid(\n    df,\n    df_gt,\n    collections_to_snap = downtowns,\n    th_dtw=30.0 / 100_000,\n    th_snap=50.0 / 100_000\n)\nprocess_snap_to_grid_dtw_downtown_test = lambda df: apply_dtw_snap_to_grid(\n    df,\n    df_gt,\n    collections_to_snap = downtowns,\n    th_dtw=30.0 / 100_000,\n    th_snap=50.0 / 100_000\n)","metadata":{"execution":{"iopub.status.busy":"2021-08-03T02:38:34.643341Z","iopub.execute_input":"2021-08-03T02:38:34.643743Z","iopub.status.idle":"2021-08-03T02:38:34.669938Z","shell.execute_reply.started":"2021-08-03T02:38:34.643712Z","shell.execute_reply":"2021-08-03T02:38:34.668664Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## Visualize baseline","metadata":{}},{"cell_type":"code","source":"eval_all(df_gt, df_pred_train)","metadata":{"execution":{"iopub.status.busy":"2021-08-03T02:44:38.887921Z","iopub.execute_input":"2021-08-03T02:44:38.888341Z","iopub.status.idle":"2021-08-03T02:44:42.622633Z","shell.execute_reply.started":"2021-08-03T02:44:38.888303Z","shell.execute_reply":"2021-08-03T02:44:42.621818Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"visualize_collection(df_pred_train, '2021-04-29-US-SJC-2', center={\"lat\":37.333929, \"lon\":-121.888997}, zoom=14)","metadata":{"execution":{"iopub.status.busy":"2021-08-03T02:45:42.701279Z","iopub.execute_input":"2021-08-03T02:45:42.701867Z","iopub.status.idle":"2021-08-03T02:45:42.821802Z","shell.execute_reply.started":"2021-08-03T02:45:42.701829Z","shell.execute_reply":"2021-08-03T02:45:42.820781Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## Visualize DTW snap-to-grid results","metadata":{}},{"cell_type":"code","source":"best_funcs = [\n    process_snap_to_grid_dtw_downtown_train,\n]\n\ndf_pred_train_processed = df_pred_train.copy()\nfor func in best_funcs:\n    df_pred_train_processed = func(df_pred_train_processed)\n    print(func.__name__, get_train_score(df_pred_train_processed, df_gt))\n\neval_all(df_pred_train_processed, df_gt)","metadata":{"execution":{"iopub.status.busy":"2021-08-03T02:41:05.672457Z","iopub.execute_input":"2021-08-03T02:41:05.672816Z","iopub.status.idle":"2021-08-03T02:41:57.758511Z","shell.execute_reply.started":"2021-08-03T02:41:05.672788Z","shell.execute_reply":"2021-08-03T02:41:57.757528Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"visualize_collection(df_pred_train_processed, '2021-04-29-US-SJC-2', center={\"lat\":37.333929, \"lon\":-121.888997}, zoom=14)","metadata":{"execution":{"iopub.status.busy":"2021-08-03T02:42:58.599442Z","iopub.execute_input":"2021-08-03T02:42:58.59986Z","iopub.status.idle":"2021-08-03T02:42:58.71751Z","shell.execute_reply.started":"2021-08-03T02:42:58.599824Z","shell.execute_reply":"2021-08-03T02:42:58.7165Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"df_pred_train_processed[['phone', 'millisSinceGpsEpoch', 'latDeg', 'lngDeg']].to_csv('train_submission.csv', index=False)","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# Test","metadata":{}},{"cell_type":"code","source":"visualize_trafic(df_pred_test)","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"best_funcs = [\n    process_snap_to_grid_dtw_downtown_test,\n]\n\ndf_pred_test_processed = df_pred_test.copy()\nfor func in best_funcs:\n    df_pred_test_processed = func(df_pred_test_processed)","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# Visualize","metadata":{}},{"cell_type":"code","source":"visualize_trafic(df_pred_test_processed)","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# Submit","metadata":{}},{"cell_type":"code","source":"sub = pd.read_csv('../input/google-smartphone-decimeter-challenge/sample_submission.csv')\nsub = sub.assign(\n    latDeg = df_pred_test_processed.latDeg,\n    lngDeg = df_pred_test_processed.lngDeg\n)\nsub.to_csv('submission.csv', index=False)","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]}]}