feature-engineering-and-ensemble.ipynb

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   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "from catboost import CatBoostClassifier\n",
    "from scipy.signal import savgol_filter, medfilt, find_peaks\n",
    "from scipy.ndimage import binary_closing, binary_dilation\n",
    "from scipy.signal.windows import triang\n",
    "from scipy.ndimage import convolve1d\n",
    "from tqdm.notebook import tqdm\n",
    "from typing import List"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e0c8c4d8",
   "metadata": {
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    "tags": []
   },
   "source": [
    "### Preprocessing functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "5b6866ba",
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   "source": [
    "def preprocess_timeseries(ts_df: pd.DataFrame) -> pd.DataFrame:\n",
    "    \"\"\"Preprocess timeseries data to correct timestamp format and more memory-efficient\n",
    "    data types\"\"\"\n",
    "    ts_df[\"timestamp\"] = ts_df[\"timestamp\"].str[:-5]  # remove tzinfo\n",
    "    ts_df[\"timestamp\"] = pd.to_datetime(ts_df[\"timestamp\"], format=\"ISO8601\")\n",
    "    return ts_df\n",
    "\n",
    "\n",
    "def preprocess_events(events_df: pd.DataFrame) -> pd.DataFrame:\n",
    "    \"\"\"Preprocess events data to correct timestamp format and more memory-efficient\n",
    "    data types\"\"\"\n",
    "    events_df = events_df.dropna().reset_index(drop=True)\n",
    "    events_df[\"timestamp\"] = events_df[\"timestamp\"].str[:-5]  # remove tzinfo\n",
    "    events_df[\"timestamp\"] = pd.to_datetime(events_df[\"timestamp\"], format=\"ISO8601\")\n",
    "    events_df[\"step\"] = events_df[\"step\"].astype(int)\n",
    "    return events_df\n",
    "\n",
    "\n",
    "def single_series_1min_resampling(ts_df: pd.DataFrame) -> pd.DataFrame:\n",
    "    \"\"\"Resample a single raw time series (5sec) data to 1min intervals\"\"\"\n",
    "    sid = ts_df.series_id.iloc[0]\n",
    "    ts_5sec_df = ts_df.copy()\n",
    "    ts_5sec_df = ts_5sec_df.set_index(\"timestamp\")\n",
    "    ts_1min_df = ts_5sec_df.resample(\"1min\").agg(\n",
    "        step=(\"step\", \"min\"),\n",
    "        anglez_1min_mean=(\"anglez\", \"mean\"),\n",
    "        enmo_1min_mean=(\"enmo\", \"mean\"),\n",
    "    )\n",
    "    contains_nan = ts_1min_df.isnull().values.any()\n",
    "    if contains_nan:\n",
    "        prev_len = len(ts_1min_df)\n",
    "        ts_1min_df = ts_1min_df.dropna()\n",
    "        new_len = len(ts_1min_df)\n",
    "        print(f\"Dropped {prev_len - new_len} entries in {sid}\")\n",
    "    ts_1min_df = ts_1min_df.reset_index()\n",
    "    ts_1min_df[\"series_id\"] = sid\n",
    "    ts_1min_df[\"series_id\"] = ts_1min_df[\"series_id\"].astype(\"large_string[pyarrow]\")\n",
    "    ts_1min_df[\"hour\"] = ts_1min_df[\"timestamp\"].dt.hour.astype(\"uint8[pyarrow]\")\n",
    "    return ts_1min_df\n",
    "\n",
    "\n",
    "def resample_timeseries_data_to_1min(series_df: pd.DataFrame) -> pd.DataFrame:\n",
    "    \"\"\"Iterate over each series in the data and resample to 1min interval\"\"\"\n",
    "    series_grps = series_df.groupby(by=\"series_id\")\n",
    "    one_min_dfs = []\n",
    "    for series_id, ts_df in tqdm(series_grps):\n",
    "        ts_5sec_df = ts_df.copy()\n",
    "        ts_5sec_df = preprocess_timeseries(ts_5sec_df)\n",
    "        ts_1min_df = single_series_1min_resampling(ts_5sec_df)\n",
    "        one_min_dfs.append(ts_1min_df)\n",
    "\n",
    "    full_1min_df = pd.concat(one_min_dfs).reset_index(drop=True)\n",
    "\n",
    "    return full_1min_df\n",
    "\n",
    "\n",
    "def create_asleep_column(ts_df: pd.DataFrame, events_df: pd.DataFrame) -> pd.DataFrame:\n",
    "    \"\"\"Create a new binary column in the timeseries data indicating whether the person\n",
    "    is asleep at the specified timestamp or not. This column can be derived from the\n",
    "    onset and wakeup events in the events dataframe\"\"\"\n",
    "    ts_df = ts_df.copy()\n",
    "    ts_df[\"asleep\"] = 0\n",
    "    ts_df = ts_df.reset_index(drop=True)\n",
    "    for night_id, night_df in events_df.groupby(by=\"night\"):\n",
    "        if len(night_df) != 2:\n",
    "            continue\n",
    "        onset_step = night_df[night_df[\"event\"] == \"onset\"].step.item()\n",
    "        wakeup_step = night_df[night_df[\"event\"] == \"wakeup\"].step.item()\n",
    "        step_list = ts_df[\"step\"].tolist()\n",
    "        onset_1min_idx = step_list.index(onset_step)\n",
    "        wake_1min_idx = step_list.index(wakeup_step)\n",
    "        ts_df.loc[onset_1min_idx:wake_1min_idx, \"asleep\"] = 1\n",
    "    ts_df[\"asleep\"] = ts_df[\"asleep\"].astype(\"uint8[pyarrow]\")\n",
    "    return ts_df\n",
    "\n",
    "\n",
    "def event_minmax_prune_timeseries(\n",
    "    ts_df: pd.DataFrame, events_df: pd.DataFrame, hour_buffer: int\n",
    ") -> pd.DataFrame:\n",
    "    \"\"\"Prune timeseries dataframe to only include timestamps in the min and max\n",
    "    intervals of the event dataframe (some timeseries have long stretches of unannotated\n",
    "    data)\"\"\"\n",
    "    time_buffer = dt.timedelta(hours=hour_buffer)\n",
    "    min_event_dt = events_df[\"timestamp\"].min() - time_buffer\n",
    "    max_event_dt = events_df[\"timestamp\"].max() + time_buffer\n",
    "    prune_idx = (ts_df[\"timestamp\"] >= min_event_dt) & (\n",
    "        ts_df[\"timestamp\"] <= max_event_dt\n",
    "    )\n",
    "    ts_df = ts_df[prune_idx].reset_index(drop=True)\n",
    "    return ts_df\n",
    "\n",
    "\n",
    "def remove_no_annotation_periods(\n",
    "    ts_df: pd.DataFrame, min_hours: int = 24\n",
    ") -> pd.DataFrame:\n",
    "    \"\"\"Remove any sequences in the time series where no sleep has been detected for\n",
    "    'min_hours'. This most likely indicates that data was not annotated in this\n",
    "     period.\"\"\"\n",
    "    no_sleep_24h = (1 - maximum_filter1d(ts_df[\"asleep\"], size=60 * min_hours)).astype(\n",
    "        bool\n",
    "    )\n",
    "    ts_df = ts_df[~no_sleep_24h]\n",
    "    return ts_df\n",
    "\n",
    "\n",
    "def add_asleep_target_to_timeseries_data(\n",
    "    series_df: pd.DataFrame, events_df: pd.DataFrame, remove_unannotated: bool = False\n",
    "):\n",
    "    \"\"\"For each series in the data add a column which indicates whether the person is\n",
    "    asleep or not. This is based on the onset and wakeup times in the events data.\"\"\"\n",
    "    series_grps = series_df.groupby(by=\"series_id\")\n",
    "    event_grps = events_df.groupby(by=\"series_id\")\n",
    "    series_grp_keys = set(series_grps.groups.keys())\n",
    "    event_grp_keys = set(event_grps.groups.keys())\n",
    "    # remove keys that are not present in both events and series data\n",
    "    grp_keys = series_grp_keys.intersection(event_grp_keys)\n",
    "\n",
    "    ts_asleep_dataframes = []\n",
    "    for series_id in tqdm(grp_keys):\n",
    "        event_df = event_grps.get_group(series_id)\n",
    "        ts_df = series_grps.get_group(series_id)\n",
    "        ts_df = create_asleep_column(ts_df, event_df)\n",
    "        if remove_unannotated:\n",
    "            ts_df = remove_no_annotation_periods(ts_df)\n",
    "        ts_asleep_dataframes.append(ts_df)\n",
    "    ts_asleep_df = pd.concat(ts_asleep_dataframes).reset_index(drop=True)\n",
    "    return ts_asleep_df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "051fa646",
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     "start_time": "2023-11-07T23:00:47.539613",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### Feature_extraction functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "94b63dbb",
   "metadata": {
    "execution": {
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   "outputs": [],
   "source": [
    "def iqr90(x):\n",
    "    return np.quantile(x, q=0.95) - np.quantile(x, q=0.05)\n",
    "\n",
    "\n",
    "def rolled_timeseries_features(\n",
    "    ts_df: pd.DataFrame,\n",
    "    agg_dict: dict,\n",
    "    roll_freqs: List[str],\n",
    ") -> pd.DataFrame:\n",
    "    \"\"\"Generate time series features based on rolling windows. In this case the\n",
    "    mean and standard deviation of each 'roll_freqs' frequency will be calculated\"\"\"\n",
    "    rolled_dfs = []\n",
    "    for freq in roll_freqs:\n",
    "        ts_roll_df = ts_df.rolling(freq).agg(agg_dict)\n",
    "        feat_cols = [\n",
    "            \"_\".join(name_parts) + f\"_{freq}\"\n",
    "            for name_parts in ts_roll_df.columns.to_flat_index()\n",
    "        ]\n",
    "        ts_roll_df.columns = feat_cols\n",
    "        ts_roll_df[feat_cols] = ts_roll_df[feat_cols].astype(np.float32)\n",
    "        rolled_dfs.append(ts_roll_df)\n",
    "    roll_df = pd.concat(rolled_dfs, axis=1)\n",
    "    return roll_df\n",
    "\n",
    "\n",
    "def extract_rolling_features(\n",
    "    ts_1min_df: pd.DataFrame, agg_dict, roll_freqs\n",
    ") -> pd.DataFrame:\n",
    "    series_grps = ts_1min_df.groupby(by=\"series_id\")\n",
    "    feature_dataframes = []\n",
    "    for series_id, ts_df in tqdm(series_grps):\n",
    "        ts_df = ts_df.set_index(\"timestamp\")\n",
    "        roll_feat_df = rolled_timeseries_features(ts_df, agg_dict, roll_freqs)\n",
    "        feat_df = pd.concat([ts_df, roll_feat_df], axis=1)\n",
    "        feat_df = feat_df.dropna()\n",
    "        feature_dataframes.append(feat_df)\n",
    "\n",
    "    full_feat_df = pd.concat(feature_dataframes).reset_index(names=\"timestamp\")\n",
    "    full_feat_df[\"step\"] = full_feat_df[\"step\"].astype(np.uint32)\n",
    "\n",
    "    return full_feat_df"
   ]
  },
  {
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   "id": "4a783342",
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     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### Load train and test data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "9cbc8225",
   "metadata": {
    "execution": {
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   "outputs": [],
   "source": [
    "train_datapath = \"/kaggle/input/child-mind-institute-detect-sleep-states/train_series.parquet\"\n",
    "raw_train_df = pd.read_parquet(train_datapath)"
   ]
  },
  {
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   "execution_count": 5,
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    "tags": []
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   "outputs": [],
   "source": [
    "test_datapath = \"/kaggle/input/child-mind-institute-detect-sleep-states/test_series.parquet\"\n",
    "raw_test_df = pd.read_parquet(test_datapath)"
   ]
  },
  {
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    "tags": []
   },
   "source": [
    "### Resample data to 1min instead of 5sec frequency"
   ]
  },
  {
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   "outputs": [
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     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dropped 60 entries in 1087d7b0ff2e\n",
      "Dropped 60 entries in 18a0ca03431d\n",
      "Dropped 60 entries in 292a75c0b94e\n",
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      "Dropped 60 entries in c75b4b207bea\n",
      "Dropped 60 entries in dfc3ccebfdc9\n"
     ]
    }
   ],
   "source": [
    "raw_train_df[\"enmo\"] = raw_train_df[\"enmo\"].clip( 0.0, 3.0)\n",
    "ts_train_1min_df = resample_timeseries_data_to_1min(raw_train_df)"
   ]
  },
  {
   "cell_type": "code",
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    "execution": {
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    "tags": []
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   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "e33077816116497bab1e5797d129f28d",
       "version_major": 2,
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     "metadata": {},
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    }
   ],
   "source": [
    "raw_test_df[\"enmo\"] = raw_test_df[\"enmo\"].clip( 0.0, 3.0)\n",
    "ts_test_1min_df = resample_timeseries_data_to_1min(raw_test_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3443eb75",
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     "start_time": "2023-11-07T23:04:29.095546",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### Add binary target column that indicates whether people are asleep"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "4291db56",
   "metadata": {
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    "tags": []
   },
   "outputs": [],
   "source": [
    "events_path = \"/kaggle/input/child-mind-institute-detect-sleep-states/train_events.csv\"\n",
    "raw_events_df = pd.read_csv(events_path)"
   ]
  },
  {
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     "iopub.status.busy": "2023-11-07T23:04:29.208199Z",
     "iopub.status.idle": "2023-11-07T23:04:29.232517Z",
     "shell.execute_reply": "2023-11-07T23:04:29.231063Z"
    },
    "papermill": {
     "duration": 0.042438,
     "end_time": "2023-11-07T23:04:29.235822",
     "exception": false,
     "start_time": "2023-11-07T23:04:29.193384",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "events_df = raw_events_df.copy()\n",
    "events_df = preprocess_events(events_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "03124dbe",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-07T23:04:29.265383Z",
     "iopub.status.busy": "2023-11-07T23:04:29.264982Z",
     "iopub.status.idle": "2023-11-07T23:05:50.990846Z",
     "shell.execute_reply": "2023-11-07T23:05:50.989267Z"
    },
    "papermill": {
     "duration": 81.743624,
     "end_time": "2023-11-07T23:05:50.993818",
     "exception": false,
     "start_time": "2023-11-07T23:04:29.250194",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "abe700c6e2d7472e93af559a2aad672c",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/269 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ts_train_1min_df = add_asleep_target_to_timeseries_data(ts_train_1min_df, events_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "54c95b1b",
   "metadata": {
    "papermill": {
     "duration": 0.013296,
     "end_time": "2023-11-07T23:05:51.021135",
     "exception": false,
     "start_time": "2023-11-07T23:05:51.007839",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### Extract rolling features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "6a9fba45",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-07T23:05:51.050783Z",
     "iopub.status.busy": "2023-11-07T23:05:51.050362Z",
     "iopub.status.idle": "2023-11-07T23:05:51.056477Z",
     "shell.execute_reply": "2023-11-07T23:05:51.055294Z"
    },
    "papermill": {
     "duration": 0.02435,
     "end_time": "2023-11-07T23:05:51.058922",
     "exception": false,
     "start_time": "2023-11-07T23:05:51.034572",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "def calculate_first_order_variation(ts_1min_df, var_col):\n",
    "    first_order_var = ts_1min_df.groupby(by=\"series_id\")[var_col].diff().abs()\n",
    "    first_order_var[first_order_var.isna()] = 0\n",
    "    return first_order_var"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "ee94a583",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-07T23:05:51.159660Z",
     "iopub.status.busy": "2023-11-07T23:05:51.159253Z",
     "iopub.status.idle": "2023-11-07T23:05:52.525458Z",
     "shell.execute_reply": "2023-11-07T23:05:52.524161Z"
    },
    "papermill": {
     "duration": 1.455991,
     "end_time": "2023-11-07T23:05:52.528372",
     "exception": false,
     "start_time": "2023-11-07T23:05:51.072381",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "ts_train_1min_df[\"anglez_1min_1v\"] = calculate_first_order_variation(ts_train_1min_df, \"anglez_1min_mean\")\n",
    "ts_train_1min_df[\"enmo_1min_1v\"] = calculate_first_order_variation(ts_train_1min_df, \"enmo_1min_mean\")\n",
    "ts_test_1min_df[\"anglez_1min_1v\"] = calculate_first_order_variation(ts_test_1min_df, \"anglez_1min_mean\")\n",
    "ts_test_1min_df[\"enmo_1min_1v\"] = calculate_first_order_variation(ts_test_1min_df, \"enmo_1min_mean\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "44388b0f",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-07T23:05:52.558230Z",
     "iopub.status.busy": "2023-11-07T23:05:52.557776Z",
     "iopub.status.idle": "2023-11-07T23:05:53.596447Z",
     "shell.execute_reply": "2023-11-07T23:05:53.595496Z"
    },
    "papermill": {
     "duration": 1.056422,
     "end_time": "2023-11-07T23:05:53.599124",
     "exception": false,
     "start_time": "2023-11-07T23:05:52.542702",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "awake_df = ts_train_1min_df[ts_train_1min_df[\"asleep\"]==0]\n",
    "awake_enmo_median = awake_df[\"enmo_1min_mean\"].median()\n",
    "ts_train_1min_df[\"awake_count\"] = ts_train_1min_df[\"enmo_1min_mean\"]>awake_enmo_median\n",
    "ts_test_1min_df[\"awake_count\"] = ts_test_1min_df[\"enmo_1min_mean\"]>awake_enmo_median"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "f2e1c0c0",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-07T23:05:53.630826Z",
     "iopub.status.busy": "2023-11-07T23:05:53.629350Z",
     "iopub.status.idle": "2023-11-07T23:05:53.636293Z",
     "shell.execute_reply": "2023-11-07T23:05:53.634952Z"
    },
    "papermill": {
     "duration": 0.025828,
     "end_time": "2023-11-07T23:05:53.639374",
     "exception": false,
     "start_time": "2023-11-07T23:05:53.613546",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "roll_freqs = [\"15min\", \"30min\", \"1H\"]\n",
    "agg_funcs = [\"mean\", \"std\", \"max\"]\n",
    "agg_dict = {\n",
    "    \"enmo_1min_mean\": agg_funcs,\n",
    "    \"enmo_1min_1v\": agg_funcs,\n",
    "    \"anglez_1min_mean\": agg_funcs,\n",
    "    \"anglez_1min_1v\": agg_funcs,\n",
    "    \"awake_count\": \"mean\"\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "c3da1070",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-07T23:05:53.669229Z",
     "iopub.status.busy": "2023-11-07T23:05:53.668471Z",
     "iopub.status.idle": "2023-11-07T23:06:42.397623Z",
     "shell.execute_reply": "2023-11-07T23:06:42.396493Z"
    },
    "papermill": {
     "duration": 48.74873,
     "end_time": "2023-11-07T23:06:42.402079",
     "exception": false,
     "start_time": "2023-11-07T23:05:53.653349",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "9fcb2fccfce04e66a0590ec495f0d3bb",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/269 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ts_train_feat_df = extract_rolling_features(ts_train_1min_df, agg_dict, roll_freqs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "8a147f2b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-07T23:06:42.431166Z",
     "iopub.status.busy": "2023-11-07T23:06:42.430755Z",
     "iopub.status.idle": "2023-11-07T23:06:42.661958Z",
     "shell.execute_reply": "2023-11-07T23:06:42.660566Z"
    },
    "papermill": {
     "duration": 0.249043,
     "end_time": "2023-11-07T23:06:42.664929",
     "exception": false,
     "start_time": "2023-11-07T23:06:42.415886",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "b9e0c23120bc45cbbabf4151b2c0cb54",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/3 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ts_test_feat_df = extract_rolling_features(ts_test_1min_df, agg_dict, roll_freqs)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b24b14fa",
   "metadata": {
    "papermill": {
     "duration": 0.013381,
     "end_time": "2023-11-07T23:06:42.692225",
     "exception": false,
     "start_time": "2023-11-07T23:06:42.678844",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### Put extra emphasis on transition periods"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "289d8427",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-07T23:06:42.721919Z",
     "iopub.status.busy": "2023-11-07T23:06:42.721031Z",
     "iopub.status.idle": "2023-11-07T23:06:42.727127Z",
     "shell.execute_reply": "2023-11-07T23:06:42.726111Z"
    },
    "papermill": {
     "duration": 0.023605,
     "end_time": "2023-11-07T23:06:42.729600",
     "exception": false,
     "start_time": "2023-11-07T23:06:42.705995",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "def rectangular_transition_sample_weights(sample_weights, transition_idxs, transition_weight, window_size=31):\n",
    "    transition_filter = np.zeros_like(sample_weights)\n",
    "    transition_filter[transition_idxs] = 1\n",
    "    transition_filter = binary_dilation(transition_filter, structure=np.ones(window_size))\n",
    "    transition_filter = transition_filter*transition_weight\n",
    "    sample_weights += transition_filter\n",
    "    return sample_weights"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "651696d5",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-07T23:06:42.759655Z",
     "iopub.status.busy": "2023-11-07T23:06:42.758854Z",
     "iopub.status.idle": "2023-11-07T23:06:42.766149Z",
     "shell.execute_reply": "2023-11-07T23:06:42.764535Z"
    },
    "papermill": {
     "duration": 0.025252,
     "end_time": "2023-11-07T23:06:42.768742",
     "exception": false,
     "start_time": "2023-11-07T23:06:42.743490",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "def triangular_transition_sample_weights(sample_weights, transition_idxs, transition_weight, window_size=31):\n",
    "    transition_filter = np.zeros_like(sample_weights)\n",
    "    transition_filter[transition_idxs] = 1\n",
    "    tri_window = triang(window_size)*transition_weight\n",
    "    transition_filter = convolve1d(transition_filter, tri_window)\n",
    "    sample_weights += transition_filter\n",
    "    return sample_weights"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "1363a472",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-07T23:06:42.799427Z",
     "iopub.status.busy": "2023-11-07T23:06:42.798701Z",
     "iopub.status.idle": "2023-11-07T23:06:54.226385Z",
     "shell.execute_reply": "2023-11-07T23:06:54.225040Z"
    },
    "papermill": {
     "duration": 11.446122,
     "end_time": "2023-11-07T23:06:54.229223",
     "exception": false,
     "start_time": "2023-11-07T23:06:42.783101",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "transition_idxs = ts_train_feat_df.reset_index().merge(events_df, on=[\"step\",\"series_id\"])[\"index\"].values\n",
    "sample_weights = np.ones(len(ts_train_feat_df))\n",
    "sample_weights = triangular_transition_sample_weights(sample_weights, transition_idxs, transition_weight=10, window_size=31)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a894678f",
   "metadata": {
    "papermill": {
     "duration": 0.013819,
     "end_time": "2023-11-07T23:06:54.257711",
     "exception": false,
     "start_time": "2023-11-07T23:06:54.243892",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### Train model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "e59c7066",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-07T23:06:54.288776Z",
     "iopub.status.busy": "2023-11-07T23:06:54.287733Z",
     "iopub.status.idle": "2023-11-07T23:06:55.631114Z",
     "shell.execute_reply": "2023-11-07T23:06:55.630045Z"
    },
    "papermill": {
     "duration": 1.362235,
     "end_time": "2023-11-07T23:06:55.633940",
     "exception": false,
     "start_time": "2023-11-07T23:06:54.271705",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "feat_cols = ts_train_feat_df.columns.drop([\"timestamp\", \"series_id\", \"step\", \"asleep\", \"awake_count\"]).tolist()\n",
    "target_col = \"asleep\"\n",
    "X_train = ts_train_feat_df[feat_cols].copy()\n",
    "y_train = ts_train_feat_df[target_col].copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "6db612ca",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-07T23:06:55.663380Z",
     "iopub.status.busy": "2023-11-07T23:06:55.662944Z",
     "iopub.status.idle": "2023-11-07T23:58:52.225887Z",
     "shell.execute_reply": "2023-11-07T23:58:52.223163Z"
    },
    "papermill": {
     "duration": 3116.581862,
     "end_time": "2023-11-07T23:58:52.229629",
     "exception": false,
     "start_time": "2023-11-07T23:06:55.647767",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Learning rate set to 0.5\n",
      "0:\tlearn: 0.3048957\ttotal: 7.37s\tremaining: 1h 1m 16s\n",
      "10:\tlearn: 0.2065311\ttotal: 1m 9s\tremaining: 51m 44s\n",
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      "499:\tlearn: 0.1439557\ttotal: 51m 18s\tremaining: 0us\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.9661553653941106"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf = CatBoostClassifier(n_estimators=500, cat_features=[\"hour\"], verbose=10)\n",
    "clf.fit(X_train, y_train, sample_weight=sample_weights)\n",
    "clf.score(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fd2de9ca",
   "metadata": {
    "papermill": {
     "duration": 0.018247,
     "end_time": "2023-11-07T23:58:52.266628",
     "exception": false,
     "start_time": "2023-11-07T23:58:52.248381",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### Make predictions"
   ]
  },
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   "source": [
    "def filter_predictions(y_proba, window_size):\n",
    "    y_proba_smooth = savgol_filter(y_proba, window_length=window_size, polyorder=2)\n",
    "    y_pred = (y_proba_smooth > 0.5)*1\n",
    "    if (window_size%2)==0: window_size+=1 \n",
    "    y_pred = binary_closing(y_pred, structure=np.ones(window_size))\n",
    "    return y_pred"
   ]
  },
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   "execution_count": 23,
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    "def score_sleep_periods(y_proba, onset_idxs, wakeup_idxs):\n",
    "    onset_scores = []\n",
    "    wakeup_scores = []\n",
    "    for start_idx, end_idx in zip(onset_idxs, wakeup_idxs):\n",
    "        yp = y_proba[start_idx:end_idx]\n",
    "        lin_weight = np.linspace(0, 1, num=len(yp))\n",
    "        onset_score_ = np.sum(yp*lin_weight[::-1])/np.sum(lin_weight)\n",
    "        wakeup_score_ = np.sum(yp*lin_weight)/np.sum(lin_weight)\n",
    "        onset_scores.append(onset_score_)\n",
    "        wakeup_scores.append(wakeup_score_)\n",
    "    return np.array(onset_scores), np.array(wakeup_scores)"
   ]
  },
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   "source": [
    "def create_event_dataframe(feat_df, event_idxs, event_scores, event_type):\n",
    "    event_df = feat_df.loc[event_idxs, [\"step\", \"series_id\"]]\n",
    "    event_df[\"event\"] = event_type\n",
    "    event_df[\"score\"] = event_scores\n",
    "    return event_df"
   ]
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    "window_size = 12 # mins\n",
    "plateau_size = 120 # mins\n",
    "test_event_dfs = []\n",
    "series_grps = ts_test_feat_df.groupby(by=\"series_id\")\n",
    "for series_id, test_feat_df in tqdm(series_grps):\n",
    "    test_feat_df = test_feat_df.reset_index(drop=True)\n",
    "    X_test = test_feat_df[feat_cols].copy()\n",
    "    y_proba = clf.predict_proba(X_test)\n",
    "    y_proba = y_proba[:, 1]\n",
    "    y_pred = filter_predictions(y_proba, window_size)\n",
    "    peaks, plateaus = find_peaks(y_pred, plateau_size=plateau_size)\n",
    "\n",
    "    # get indices of onset and wake ups\n",
    "    onset_idxs = plateaus[\"left_edges\"]\n",
    "    wakeup_idxs = plateaus[\"right_edges\"]\n",
    "\n",
    "    # score each sleeping event and create event dataframe\n",
    "    onset_scores, wakeup_scores = score_sleep_periods(y_proba, onset_idxs, wakeup_idxs)\n",
    "    onset_df = create_event_dataframe(\n",
    "        test_feat_df, onset_idxs, onset_scores, event_type=\"onset\"\n",
    "    )\n",
    "    wakeup_df = create_event_dataframe(\n",
    "        test_feat_df, wakeup_idxs, wakeup_scores, event_type=\"wakeup\"\n",
    "    )\n",
    "    event_df = pd.concat([onset_df, wakeup_df])\n",
    "    event_df = event_df.sort_values(by=\"step\")\n",
    "    test_event_dfs.append(event_df)\n",
    "\n",
    "submission_df = (\n",
    "    pd.concat(test_event_dfs).reset_index(drop=True).reset_index(names=\"row_id\")\n",
    ")"
   ]
  },
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    "submission_df = pd.concat(test_event_dfs).reset_index(drop=True).reset_index(names=\"row_id\")\n",
    "submission_df.to_csv(\"submission.csv\", index=False)"
   ]
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