mimi-iii-time-series-data-preprocessing.ipynb

{"cells":[{"source":"<a href=\"https://www.kaggle.com/code/wangyuweikiwi/mimi-iii-time-series-data-preprocessing?scriptVersionId=198599877\" target=\"_blank\"><img align=\"left\" alt=\"Kaggle\" title=\"Open in Kaggle\" src=\"https://kaggle.com/static/images/open-in-kaggle.svg\"></a>","metadata":{},"cell_type":"markdown"},{"cell_type":"markdown","id":"f840299c","metadata":{"papermill":{"duration":0.010101,"end_time":"2024-09-28T05:22:18.592341","exception":false,"start_time":"2024-09-28T05:22:18.58224","status":"completed"},"tags":[]},"source":["# Load data from dataset\n","\n","As a demo, only use some of the values in vitals_records, not all"]},{"cell_type":"code","execution_count":1,"id":"a836fc46","metadata":{"execution":{"iopub.execute_input":"2024-09-28T05:22:18.613419Z","iopub.status.busy":"2024-09-28T05:22:18.612068Z","iopub.status.idle":"2024-09-28T05:22:22.064389Z","shell.execute_reply":"2024-09-28T05:22:22.062805Z"},"papermill":{"duration":3.465521,"end_time":"2024-09-28T05:22:22.067026","exception":false,"start_time":"2024-09-28T05:22:18.601505","status":"completed"},"tags":[]},"outputs":[{"name":"stdout","output_type":"stream","text":["5000\n"]}],"source":["import pickle\n","import copy\n","import numpy as np\n","\n","\n","with open('/kaggle/input/mimic-iii-time-series-filters-by-measurement-ids/vitals_records_10000.p', 'rb') as file:\n","    vitals = pickle.load(file)\n","print(len(vitals))"]},{"cell_type":"code","execution_count":2,"id":"398e6f34","metadata":{"execution":{"iopub.execute_input":"2024-09-28T05:22:22.086496Z","iopub.status.busy":"2024-09-28T05:22:22.085653Z","iopub.status.idle":"2024-09-28T05:22:22.092126Z","shell.execute_reply":"2024-09-28T05:22:22.090955Z"},"papermill":{"duration":0.019143,"end_time":"2024-09-28T05:22:22.095002","exception":false,"start_time":"2024-09-28T05:22:22.075859","status":"completed"},"tags":[]},"outputs":[{"name":"stdout","output_type":"stream","text":["<class 'list'>\n","The admissions: 5000\n","the vital signs (or categories of measurements): 16\n","Time-series data points: 130\n"]}],"source":["def list_info(vitals: list):\n","    print(type(vitals))\n","\n","    print(f\"The admissions: {len(vitals)}\")\n","    print(f\"the vital signs (or categories of measurements): {len(vitals[0])}\")\n","    print(f\"Time-series data points: {len(vitals[0][0])}\")\n","    \n","list_info(vitals)"]},{"cell_type":"markdown","id":"e3503d4b","metadata":{"papermill":{"duration":0.008182,"end_time":"2024-09-28T05:22:22.111818","exception":false,"start_time":"2024-09-28T05:22:22.103636","status":"completed"},"tags":[]},"source":["# Description of data\n","\n","* **First dimension(5000):** The outermost list contains 5000 elements, it corresponding to the number of admissions or records we are processing.\n","* **Second dimension (16):** Each of the 5000 elements contains a list with 16 sub-elements. This represent different vital signs or measurement categories (e.g., SpO2, HR, SBP, etc.) for each admission.\n","* **Third dimension (130):** Each of the 16 sub-elements contains a list of 130 time-series measurements, which likely corresponds to the number of timestamped data points collected for each vital sign."]},{"cell_type":"code","execution_count":3,"id":"0ce98c26","metadata":{"execution":{"iopub.execute_input":"2024-09-28T05:22:22.130606Z","iopub.status.busy":"2024-09-28T05:22:22.130199Z","iopub.status.idle":"2024-09-28T05:22:22.136948Z","shell.execute_reply":"2024-09-28T05:22:22.13578Z"},"papermill":{"duration":0.019742,"end_time":"2024-09-28T05:22:22.140199","exception":false,"start_time":"2024-09-28T05:22:22.120457","status":"completed"},"tags":[]},"outputs":[{"name":"stdout","output_type":"stream","text":["Dimensions of the list: [5000, 16, 130]\n"]}],"source":["def get_list_dimensions(lst):\n","    if isinstance(lst, list):\n","        return [len(lst)] + get_list_dimensions(lst[0]) if lst else [0]\n","    else:\n","        return []\n","\n","\n","dimensions = get_list_dimensions(vitals)\n","print(f\"Dimensions of the list: {dimensions}\")"]},{"cell_type":"code","execution_count":4,"id":"c73227c2","metadata":{"execution":{"iopub.execute_input":"2024-09-28T05:22:22.15979Z","iopub.status.busy":"2024-09-28T05:22:22.159385Z","iopub.status.idle":"2024-09-28T05:22:22.246215Z","shell.execute_reply":"2024-09-28T05:22:22.245057Z"},"papermill":{"duration":0.099653,"end_time":"2024-09-28T05:22:22.24864","exception":false,"start_time":"2024-09-28T05:22:22.148987","status":"completed"},"tags":[]},"outputs":[{"name":"stdout","output_type":"stream","text":["5000\n"]}],"source":["with open('/kaggle/input/mimic-iii-time-series-filters-by-measurement-ids/adm_type_los_mortality.p', 'rb') as file:\n","    adm_info = pickle.load(file)\n","    \n","adm_info_5000=adm_info[:5000]\n","print(len(adm_info_5000))"]},{"cell_type":"code","execution_count":5,"id":"a6ec6d17","metadata":{"execution":{"iopub.execute_input":"2024-09-28T05:22:22.268618Z","iopub.status.busy":"2024-09-28T05:22:22.267713Z","iopub.status.idle":"2024-09-28T05:22:22.273448Z","shell.execute_reply":"2024-09-28T05:22:22.27235Z"},"papermill":{"duration":0.018355,"end_time":"2024-09-28T05:22:22.276024","exception":false,"start_time":"2024-09-28T05:22:22.257669","status":"completed"},"tags":[]},"outputs":[{"name":"stdout","output_type":"stream","text":["(165315, 'EMERGENCY', Decimal('27'), 0)\n"]}],"source":["print(adm_info_5000[0])"]},{"cell_type":"markdown","id":"5780e899","metadata":{"papermill":{"duration":0.0086,"end_time":"2024-09-28T05:22:22.293948","exception":false,"start_time":"2024-09-28T05:22:22.285348","status":"completed"},"tags":[]},"source":["# Preprocessing data\n","\n","\n","## 1.The length of stay should >=48H\n","\n","`adm_info_5000[x][2>=48]`"]},{"cell_type":"code","execution_count":6,"id":"ab699ff0","metadata":{"execution":{"iopub.execute_input":"2024-09-28T05:22:22.313894Z","iopub.status.busy":"2024-09-28T05:22:22.312916Z","iopub.status.idle":"2024-09-28T05:22:22.343214Z","shell.execute_reply":"2024-09-28T05:22:22.342053Z"},"papermill":{"duration":0.043171,"end_time":"2024-09-28T05:22:22.345884","exception":false,"start_time":"2024-09-28T05:22:22.302713","status":"completed"},"tags":[]},"outputs":[{"name":"stdout","output_type":"stream","text":["0\n","4531\n","4531\n"]}],"source":["# This step is about to filter vitals result by choose adm_info's 4th value larger than 48.  Only keep the corresponding vitals values\n","# Here is the original codes, correct me if I messed the logic. adm_id_needed is confusing\n","\n","# adm_id = [record[0] for record in adm_info]\n","# adm_id_needed = [record[0] for record in adm_info if record[2] >= 48]\n","# vitals_dict = {}\n","# for i in range(len(adm_id)):\n","#     vitals_dict[adm_id[i]] = vitals[i]\n","# vitals = [vitals_dict[x] for x in adm_id_needed]\n","# label = [rec[3] for x in adm_id_needed for rec in adm_info if x == rec[0]]\n","\n","# batch_size is number of records within each vital file\n","batch_size = 10000\n","# batch_idx is index of the vital file.  \n","# Please note, vital files should be loaded in order\n","batch_idx = 1\n","\n","start_point= batch_size*(batch_idx-1)\n","\n","print(start_point)\n","\n","label = []\n","for record in adm_info_5000:\n","    if record[2] >= 48:\n","        label.append(record[3])\n","\n","print(len(label))\n","\n","vitals_new = []\n","for i in range(len(vitals)):\n","    if adm_info_5000[start_point+i][2] >= 48:\n","        vitals_new.append(vitals[i])\n","print(len(vitals_new))\n","\n","vitals = vitals_new"]},{"cell_type":"code","execution_count":7,"id":"e21a3729","metadata":{"execution":{"iopub.execute_input":"2024-09-28T05:22:22.36585Z","iopub.status.busy":"2024-09-28T05:22:22.365402Z","iopub.status.idle":"2024-09-28T05:22:22.371384Z","shell.execute_reply":"2024-09-28T05:22:22.370199Z"},"papermill":{"duration":0.019,"end_time":"2024-09-28T05:22:22.373984","exception":false,"start_time":"2024-09-28T05:22:22.354984","status":"completed"},"tags":[]},"outputs":[{"name":"stdout","output_type":"stream","text":["<class 'list'>\n","The admissions: 4531\n","the vital signs (or categories of measurements): 16\n","Time-series data points: 84\n"]}],"source":["list_info(vitals)"]},{"cell_type":"markdown","id":"d5c6422f","metadata":{"papermill":{"duration":0.009187,"end_time":"2024-09-28T05:22:22.392226","exception":false,"start_time":"2024-09-28T05:22:22.383039","status":"completed"},"tags":[]},"source":["# Visualize the sample's time series length"]},{"cell_type":"code","execution_count":8,"id":"7077f402","metadata":{"execution":{"iopub.execute_input":"2024-09-28T05:22:22.412423Z","iopub.status.busy":"2024-09-28T05:22:22.411965Z","iopub.status.idle":"2024-09-28T05:22:23.254984Z","shell.execute_reply":"2024-09-28T05:22:23.253809Z"},"papermill":{"duration":0.856364,"end_time":"2024-09-28T05:22:23.257681","exception":false,"start_time":"2024-09-28T05:22:22.401317","status":"completed"},"tags":[]},"outputs":[{"data":{"text/html":["        <script type=\"text/javascript\">\n","        window.PlotlyConfig = {MathJaxConfig: 'local'};\n","        if (window.MathJax && window.MathJax.Hub && window.MathJax.Hub.Config) 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num_samples=4):\n","    \"\"\"\n","    Interactive visualization of the lengths of the time-series data for the top 'num_samples' Samples using Plotly.\n","    \n","    vitals: 2D list where the first dimension is Samples and the second dimension is the time-series data.\n","    num_samples: Number of samples (Samples) to visualize (default is 4).\n","    \"\"\"\n","    # Create subplots: 2 rows and 2 columns\n","    fig = make_subplots(rows=2, cols=2, subplot_titles=[f\"Sample {i+1}\" for i in range(num_samples)])\n","\n","    # Plot for each Sample in their respective subplot\n","    for i in range(num_samples):\n","        Sample_data = vitals[i]  # Get data for the ith Sample\n","        time_series_lengths = [len(vital_sign) for vital_sign in Sample_data]  # Get the lengths of each vital sign\n","        \n","        # Determine row and column for the subplot\n","        row = (i // 2) + 1\n","        col = (i % 2) + 1\n","        \n","        # Add bar chart to the appropriate subplot\n","        fig.add_trace(\n","            go.Bar(\n","                x=[f'Vital Sign {j+1}' for j in range(len(time_series_lengths))],\n","                y=time_series_lengths,\n","                marker=dict(color='skyblue'),\n","                name=f'Sample {i+1}'\n","            ),\n","            row=row, col=col\n","        )\n","\n","    # Update layout for the entire figure\n","    fig.update_layout(\n","        title_text=\"Time-Series Lengths for Top 4 Samples\",\n","        showlegend=False,  # Disable legend since titles indicate the Samples\n","        height=800,\n","        hovermode=\"x unified\"\n","    )\n","    \n","    # Set axis labels for each subplot\n","    fig.update_xaxes(title_text='Vital Sign Index', tickangle=-45)\n","    fig.update_yaxes(title_text='Length of Time-Series Data (Number of Time Steps)')\n","\n","    # Show the interactive plot\n","    fig.show()\n","\n","# Visualize the time-series lengths for the top 4 Samples\n","interactive_visualize_top_4_samples(vitals, num_samples=4)\n"]},{"cell_type":"markdown","id":"0ed8ddae","metadata":{"papermill":{"duration":0.009021,"end_time":"2024-09-28T05:22:23.276061","exception":false,"start_time":"2024-09-28T05:22:23.26704","status":"completed"},"tags":[]},"source":["## 2.Filter the vital signs(or categories of measurements)\n","\n","```\n","# Item - item_id\n","\n","# SpO2 - 646, 220277\n","# HR - 211, 220045\n","# RR - 618, 615, 220210, 224690\n","# SBP - 51,442,455,6701,220179,220050\n","# DBP - 8368,8440,8441,8555,220180,220051\n","# EtCO2 - 1817, 228640\n","# Temp(F) - 223761,678\n","# Temp(C) - 223762,676\n","# TGCS - 198, 226755, 227013\n","# CRR - 3348\n","# Urine Output - 43647, 43053, 43171, 43173, 43333, 43347,\n","# 43348, 43355, 43365, 43373, 43374, 43379, 43380, 43431,\n","# 43519, 43522, 43537, 43576, 43583, 43589, 43638, 43654,\n","# 43811, 43812, 43856, 44706, 45304, 227519,\n","# FiO2 - 2981, 3420, 3422, 223835,\n","# Glucose - 807,811,1529,3745,3744,225664,220621,226537\n","# pH - 780, 860, 1126, 1673, 3839, 4202, 4753, 6003, 220274, 220734, 223830, 228243,\n","\n","\n","# vitals[0] - SpO2\n","# vitals[1] - HR\n","```\n","\n","### Remove the lossing data.\n","\n","* num_features=12 because the measurement value is blank, like vitals[0][5].\n","\n","\n","### 2880 for 48 hours of minute-level data\n","\n","* max_length=2881\n","\n","The collected data at 1-minute intervals for 48 hours, you'd get 48 hours * 60 minutes = 2880 timestamps. The extra 1 might be padding or for safety against slight variations in timestamp intervals."]},{"cell_type":"code","execution_count":9,"id":"139bdc0e","metadata":{"execution":{"iopub.execute_input":"2024-09-28T05:22:23.296848Z","iopub.status.busy":"2024-09-28T05:22:23.29644Z","iopub.status.idle":"2024-09-28T05:22:37.619706Z","shell.execute_reply":"2024-09-28T05:22:37.618414Z"},"papermill":{"duration":14.337215,"end_time":"2024-09-28T05:22:37.622536","exception":false,"start_time":"2024-09-28T05:22:23.285321","status":"completed"},"tags":[]},"outputs":[],"source":["# Original code: https://github.com/mlds-lab/interp-net/blob/master/src/mimic_preprocessing.py#L25\n","\n","import numpy as np\n","from concurrent.futures import ProcessPoolExecutor\n","\n","def trim_los_parallel(data_chunk, length_of_stay):\n","    num_features = 12  # final features (excluding EtCO2) because the measurement value is blank: vitals[0][5]\n","    max_length = 2881  # maximum length of time stamp\n","    a = np.full((len(data_chunk), num_features, max_length), -100, dtype=float)  # initialize array with -100 (missing data)\n","    timestamps = []\n","\n","    for i in range(len(data_chunk)):\n","        # Process temperature conversion in a vectorized way\n","        if data_chunk[i][7]:\n","            temp_array = np.array([elem[1] for elem in data_chunk[i][7] if elem[1] is not None])\n","            data_chunk[i][6] += [(elem[0], temp * 1.8 + 32) for elem, temp in zip(data_chunk[i][7], temp_array)]\n","\n","        # Combine data[9] with data[10] and data[11]\n","        data_chunk[i][9].extend(data_chunk[i][10] + data_chunk[i][11])\n","\n","        # Remove unwanted elements (EtCO2 data)\n","        del data_chunk[i][5:7]\n","        del data_chunk[i][8]\n","\n","        # Collect unique timestamps across all features\n","        all_timestamps = sorted(set([elem[0] for j in range(num_features) for elem in data_chunk[i][j]]))\n","\n","        # Extract first 48-hour data\n","        first_ts = all_timestamps[0] if all_timestamps else None\n","        TS = [ts for ts in all_timestamps if (ts - first_ts).total_seconds() / 3600 <= length_of_stay]\n","\n","        timestamps.append(TS)\n","\n","        for j in range(num_features):\n","            feature_data = data_chunk[i][j]\n","            feature_dict = {entry[0]: entry[1] for entry in feature_data}  # Convert list to dictionary for fast lookup\n","\n","            for k, ts in enumerate(TS):\n","                if ts in feature_dict:\n","                    value = feature_dict[ts]\n","                    if value is None or value in ('Other/Remarks', 'Comment'):\n","                        a[i, j, k] = -100\n","                    elif value in ('Normal <3 secs', 'Normal <3 Seconds', 'Brisk'):\n","                        a[i, j, k] = 1\n","                    elif value in ('Abnormal >3 secs', 'Abnormal >3 Seconds', 'Delayed'):\n","                        a[i, j, k] = 2\n","                    else:\n","                        a[i, j, k] = value\n","                else:\n","                    a[i, j, k] = -100  # missing data\n","\n","    return a, timestamps\n","\n","# Function to split the data and run in parallel\n","def run_trim_los_in_parallel(data, length_of_stay, num_workers=4):\n","    # Split data into chunks for parallel processing\n","    chunk_size = len(data) // num_workers\n","    data_chunks = [data[i:i + chunk_size] for i in range(0, len(data), chunk_size)]\n","\n","    results = []\n","    with ProcessPoolExecutor(max_workers=num_workers) as executor:\n","        futures = [executor.submit(trim_los_parallel, chunk, length_of_stay) for chunk in data_chunks]\n","        for future in futures:\n","            results.append(future.result())\n","\n","    # Combine results from all chunks\n","    all_a = np.concatenate([result[0] for result in results], axis=0)\n","    all_timestamps = sum([result[1] for result in results], [])\n","    \n","    return all_a, all_timestamps\n","\n","# Example usage\n","hours_look_ahead = 48\n","num_workers = 15  # Use the available 15 cores for parallel processing\n","vitals, timestamps = run_trim_los_in_parallel(vitals, hours_look_ahead, num_workers)"]},{"cell_type":"code","execution_count":10,"id":"6139fa57","metadata":{"execution":{"iopub.execute_input":"2024-09-28T05:22:37.643858Z","iopub.status.busy":"2024-09-28T05:22:37.643393Z","iopub.status.idle":"2024-09-28T05:22:37.649886Z","shell.execute_reply":"2024-09-28T05:22:37.648576Z"},"papermill":{"duration":0.020026,"end_time":"2024-09-28T05:22:37.652485","exception":false,"start_time":"2024-09-28T05:22:37.632459","status":"completed"},"tags":[]},"outputs":[{"name":"stdout","output_type":"stream","text":["4531\n","4531\n"]}],"source":["print(len(vitals))\n","print(len(timestamps))"]},{"cell_type":"markdown","id":"762a0436","metadata":{"papermill":{"duration":0.009242,"end_time":"2024-09-28T05:22:37.671577","exception":false,"start_time":"2024-09-28T05:22:37.662335","status":"completed"},"tags":[]},"source":["# Visualization top 100 samples's timestamps data"]},{"cell_type":"code","execution_count":11,"id":"b1765faf","metadata":{"execution":{"iopub.execute_input":"2024-09-28T05:22:37.692831Z","iopub.status.busy":"2024-09-28T05:22:37.692412Z","iopub.status.idle":"2024-09-28T05:22:37.85734Z","shell.execute_reply":"2024-09-28T05:22:37.85611Z"},"papermill":{"duration":0.179756,"end_time":"2024-09-28T05:22:37.861377","exception":false,"start_time":"2024-09-28T05:22:37.681621","status":"completed"},"tags":[]},"outputs":[{"data":{"text/html":["<div>                            <div id=\"a9f6a3a6-a238-4d3d-a70c-93659607300a\" class=\"plotly-graph-div\" style=\"height:525px; width:100%;\"></div>            <script type=\"text/javascript\">                require([\"plotly\"], function(Plotly) {                    window.PLOTLYENV=window.PLOTLYENV || {};                                    if (document.getElementById(\"a9f6a3a6-a238-4d3d-a70c-93659607300a\")) {                    Plotly.newPlot(                        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x=list(range(len(sample_timestamps))), \n","            y=sample_timestamps,\n","            mode='lines+markers', \n","            name=f'Sample {i+1}'\n","        ))\n","    \n","    # Set labels and title\n","    fig.update_layout(\n","        title=f'Interactive Time-Series Data for First {num_samples} Samples',\n","        xaxis_title='Time Step Index',\n","        yaxis_title='Time (Hours)',\n","        hovermode='x unified'\n","    )\n","    \n","    # Show the interactive plot\n","    fig.show()\n","\n","# Call the function to visualize the first 4 samples interactively\n","interactive_visualize_multiple_samples(timestamps, num_samples=100)\n"]},{"cell_type":"code","execution_count":12,"id":"06a1caac","metadata":{"execution":{"iopub.execute_input":"2024-09-28T05:22:37.886046Z","iopub.status.busy":"2024-09-28T05:22:37.88561Z","iopub.status.idle":"2024-09-28T05:22:37.89135Z","shell.execute_reply":"2024-09-28T05:22:37.890196Z"},"papermill":{"duration":0.020823,"end_time":"2024-09-28T05:22:37.893914","exception":false,"start_time":"2024-09-28T05:22:37.873091","status":"completed"},"tags":[]},"outputs":[{"name":"stdout","output_type":"stream","text":["<class 'numpy.ndarray'>\n","The admissions: 4531\n","the vital signs (or categories of measurements): 12\n","Time-series data points: 2881\n"]}],"source":["list_info(vitals)"]},{"cell_type":"code","execution_count":13,"id":"9af081e6","metadata":{"execution":{"iopub.execute_input":"2024-09-28T05:22:37.91831Z","iopub.status.busy":"2024-09-28T05:22:37.917832Z","iopub.status.idle":"2024-09-28T05:22:37.977366Z","shell.execute_reply":"2024-09-28T05:22:37.976178Z"},"papermill":{"duration":0.074527,"end_time":"2024-09-28T05:22:37.980009","exception":false,"start_time":"2024-09-28T05:22:37.905482","status":"completed"},"tags":[]},"outputs":[{"data":{"text/html":["<div>                            <div id=\"1849d7a7-e668-49a3-abbe-ad36c2aee8d7\" class=\"plotly-graph-div\" style=\"height:800px; width:100%;\"></div>            <script type=\"text/javascript\">                require([\"plotly\"], function(Plotly) {                    window.PLOTLYENV=window.PLOTLYENV || {};                                    if (document.getElementById(\"1849d7a7-e668-49a3-abbe-ad36c2aee8d7\")) {                    Plotly.newPlot(                 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data for the top 'num_samples' Samples using Plotly.\n","    \n","    vitals: 2D list where the first dimension is Samples and the second dimension is the time-series data.\n","    num_samples: Number of samples (Samples) to visualize (default is 4).\n","    \"\"\"\n","    # Create subplots: 2 rows and 2 columns\n","    fig = make_subplots(rows=2, cols=2, subplot_titles=[f\"Sample {i+1}\" for i in range(num_samples)])\n","\n","    # Plot for each Sample in their respective subplot\n","    for i in range(num_samples):\n","        Sample_data = vitals[i]  # Get data for the ith Sample\n","        time_series_lengths = [len(vital_sign) for vital_sign in Sample_data]  # Get the lengths of each vital sign\n","        \n","        # Determine row and column for the subplot\n","        row = (i // 2) + 1\n","        col = (i % 2) + 1\n","        \n","        # Add bar chart to the appropriate subplot\n","        fig.add_trace(\n","            go.Bar(\n","                x=[f'Vital Sign {j+1}' for j in range(len(time_series_lengths))],\n","                y=time_series_lengths,\n","                marker=dict(color='skyblue'),\n","                name=f'Sample {i+1}'\n","            ),\n","            row=row, col=col\n","        )\n","\n","    # Update layout for the entire figure\n","    fig.update_layout(\n","        title_text=\"Time-Series Lengths for Top 4 Samples\",\n","        showlegend=False,  # Disable legend since titles indicate the Samples\n","        height=800,\n","        hovermode=\"x unified\"\n","    )\n","    \n","    # Set axis labels for each subplot\n","    fig.update_xaxes(title_text='Vital Sign Index', tickangle=-45)\n","    fig.update_yaxes(title_text='Length of Time-Series Data (Number of Time Steps)')\n","\n","    # Show the interactive plot\n","    fig.show()\n","\n","# Visualize the time-series lengths for the top 4 Samples\n","interactive_visualize_top_4_samples(vitals, num_samples=4)"]},{"cell_type":"code","execution_count":14,"id":"9c514b89","metadata":{"execution":{"iopub.execute_input":"2024-09-28T05:22:38.005081Z","iopub.status.busy":"2024-09-28T05:22:38.004026Z","iopub.status.idle":"2024-09-28T05:22:38.010436Z","shell.execute_reply":"2024-09-28T05:22:38.008989Z"},"papermill":{"duration":0.021519,"end_time":"2024-09-28T05:22:38.012949","exception":false,"start_time":"2024-09-28T05:22:37.99143","status":"completed"},"tags":[]},"outputs":[],"source":["normal_values=[i for i in vitals[0][0] if i!=-100]"]},{"cell_type":"code","execution_count":15,"id":"a30e3d77","metadata":{"execution":{"iopub.execute_input":"2024-09-28T05:22:38.037744Z","iopub.status.busy":"2024-09-28T05:22:38.037338Z","iopub.status.idle":"2024-09-28T05:22:38.042896Z","shell.execute_reply":"2024-09-28T05:22:38.041873Z"},"papermill":{"duration":0.021388,"end_time":"2024-09-28T05:22:38.04579","exception":false,"start_time":"2024-09-28T05:22:38.024402","status":"completed"},"tags":[]},"outputs":[{"name":"stdout","output_type":"stream","text":["4531\n","85\n"]}],"source":["print(len(timestamps))\n","print(len(timestamps[0]))"]},{"cell_type":"markdown","id":"62f92b57","metadata":{"papermill":{"duration":0.011118,"end_time":"2024-09-28T05:22:38.068376","exception":false,"start_time":"2024-09-28T05:22:38.057258","status":"completed"},"tags":[]},"source":["## 3.Fixing input format(Trim time stamps higher than 200)\n","\n","Return the input in the proper format\n","\n","* x: observed values\n","* M: masking, 0 indicates missing values\n","* delta: time points of observation\n","\n","\n","Here we set a **consistent length** of time streps across all samples by `timestamp=200`, the code ensure that all sample records have exactly 200 time steps. (No sure the reason use 200 time-steps.)"]},{"cell_type":"code","execution_count":16,"id":"1013c312","metadata":{"execution":{"iopub.execute_input":"2024-09-28T05:22:38.093051Z","iopub.status.busy":"2024-09-28T05:22:38.092657Z","iopub.status.idle":"2024-09-28T05:22:39.129222Z","shell.execute_reply":"2024-09-28T05:22:39.12821Z"},"papermill":{"duration":1.052038,"end_time":"2024-09-28T05:22:39.132028","exception":false,"start_time":"2024-09-28T05:22:38.07999","status":"completed"},"tags":[]},"outputs":[{"name":"stdout","output_type":"stream","text":["(4531, 12, 200) 4531\n"]}],"source":["# Original https://github.com/mlds-lab/interp-net/blob/master/src/multivariate_example.py#L45\n","# Adapted by Micost, Aisuko, Yuwei Wang\n","\n","def fix_input_format(x, T):\n","    \"\"\"\n","    The code aims to standardize time-series data across Samples and remove outliers, missing values, and negative values.\n","    \n","    x: observed values\n","    M: masking, 0 indicates missing values\n","    delta: time points of observation\n","    \"\"\"\n","    timestamp = 200\n","    num_features = 12\n","\n","    # trim time stamps higher than 200\n","    for i in range(len(T)):\n","        if len(T[i]) > timestamp:\n","            T[i] = T[i][:timestamp]\n","\n","    x = x[:, :, :timestamp]\n","    M = np.zeros_like(x)\n","    delta = np.zeros_like(x)\n","    print(x.shape, len(T))\n","\n","    for t in T:\n","        for i in range(1, len(t)):\n","            t[i] = (t[i] - t[0]).total_seconds()/3600.0\n","        if len(t) != 0:\n","            t[0] = 0\n","\n","    # count outliers and negative values as missing values\n","    # M = 0 indicates missing value\n","    # M = 1 indicates observed value\n","    # now since we have mask variable, we don't need -100\n","    M[x > 500] = 0\n","    x[x > 500] = 0.0\n","    M[x < 0] = 0\n","    x[x < 0] = 0.0\n","    M[x > 0] = 1\n","\n","    for i in range(num_features):\n","        for j in range(x.shape[0]):\n","            for k in range(len(T[j])):\n","                delta[j, i, k] = T[j][k]\n","\n","    return x, M, delta\n","\n","# Vitials: 3D array (Samples, features, timestamps) containing observed values for vital signs.\n","# Timestamp: List of timestamp lists corresponding to each Sample.\n","x, M, delta = fix_input_format(vitals, timestamps)"]},{"cell_type":"code","execution_count":17,"id":"9450bd10","metadata":{"execution":{"iopub.execute_input":"2024-09-28T05:22:39.15683Z","iopub.status.busy":"2024-09-28T05:22:39.156392Z","iopub.status.idle":"2024-09-28T05:22:39.162365Z","shell.execute_reply":"2024-09-28T05:22:39.161223Z"},"papermill":{"duration":0.022056,"end_time":"2024-09-28T05:22:39.165664","exception":false,"start_time":"2024-09-28T05:22:39.143608","status":"completed"},"tags":[]},"outputs":[{"name":"stdout","output_type":"stream","text":["<class 'numpy.ndarray'>\n","The admissions: 4531\n","the vital signs (or categories of measurements): 12\n","Time-series data points: 200\n","<class 'numpy.ndarray'>\n","The admissions: 4531\n","the vital signs (or categories of measurements): 12\n","Time-series data points: 200\n","<class 'numpy.ndarray'>\n","The admissions: 4531\n","the vital signs (or categories of measurements): 12\n","Time-series data points: 200\n"]}],"source":["list_info(x)\n","list_info(M)\n","list_info(delta)"]},{"cell_type":"code","execution_count":18,"id":"701d3999","metadata":{"execution":{"iopub.execute_input":"2024-09-28T05:22:39.190941Z","iopub.status.busy":"2024-09-28T05:22:39.190502Z","iopub.status.idle":"2024-09-28T05:22:39.784521Z","shell.execute_reply":"2024-09-28T05:22:39.783075Z"},"papermill":{"duration":0.609757,"end_time":"2024-09-28T05:22:39.787323","exception":false,"start_time":"2024-09-28T05:22:39.177566","status":"completed"},"tags":[]},"outputs":[],"source":["np.save('x.npy', x)\n","np.save('M.npy', M)\n","np.save('delta.npy', delta)"]},{"cell_type":"code","execution_count":19,"id":"7465e829","metadata":{"execution":{"iopub.execute_input":"2024-09-28T05:22:39.813142Z","iopub.status.busy":"2024-09-28T05:22:39.812126Z","iopub.status.idle":"2024-09-28T05:22:39.905713Z","shell.execute_reply":"2024-09-28T05:22:39.904572Z"},"papermill":{"duration":0.109119,"end_time":"2024-09-28T05:22:39.908233","exception":false,"start_time":"2024-09-28T05:22:39.799114","status":"completed"},"tags":[]},"outputs":[{"data":{"text/html":["<div>                            <div id=\"190c3992-0078-4ec5-b8b9-4672c5c585ca\" class=\"plotly-graph-div\" style=\"height:525px; width:100%;\"></div>            <script type=\"text/javascript\">                require([\"plotly\"], function(Plotly) {                    window.PLOTLYENV=window.PLOTLYENV || {};                                    if (document.getElementById(\"190c3992-0078-4ec5-b8b9-4672c5c585ca\")) {                    Plotly.newPlot(                        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Time-Series Data for First 100 Samples\"},\"xaxis\":{\"title\":{\"text\":\"Time Step Index\"}},\"yaxis\":{\"title\":{\"text\":\"Time (Hours)\"}},\"hovermode\":\"x unified\"},                        {\"responsive\": true}                    ).then(function(){\n","                            \n","var gd = document.getElementById('190c3992-0078-4ec5-b8b9-4672c5c585ca');\n","var x = new MutationObserver(function (mutations, observer) {{\n","        var display = window.getComputedStyle(gd).display;\n","        if (!display || display === 'none') {{\n","            console.log([gd, 'removed!']);\n","            Plotly.purge(gd);\n","            observer.disconnect();\n","        }}\n","}});\n","\n","// Listen for the removal of the full notebook cells\n","var notebookContainer = gd.closest('#notebook-container');\n","if (notebookContainer) {{\n","    x.observe(notebookContainer, {childList: true});\n","}}\n","\n","// Listen for the clearing of the current output cell\n","var outputEl = 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number of features, number of timestamps].\n","* mask: A 3D numpy array (M) representing the observed values mask, where:\n","  * M[i, j, k] = 1 indicates that the value vitals[i, j, k] is observed.\n","  * M[i, j, k] = 0 indicates that the value vitals[i, j, k] is missing."]},{"cell_type":"code","execution_count":20,"id":"26d819e2","metadata":{"_kg_hide-input":true,"execution":{"iopub.execute_input":"2024-09-28T05:22:39.961688Z","iopub.status.busy":"2024-09-28T05:22:39.961251Z","iopub.status.idle":"2024-09-28T05:22:39.968894Z","shell.execute_reply":"2024-09-28T05:22:39.967656Z"},"papermill":{"duration":0.024679,"end_time":"2024-09-28T05:22:39.971565","exception":false,"start_time":"2024-09-28T05:22:39.946886","status":"completed"},"tags":[]},"outputs":[],"source":["# original https://github.com/mlds-lab/interp-net/blob/master/src/multivariate_example.py#L45\n","\n","def mean_imputation(vitals, mask):\n","    \"\"\"For the time series missing entirely, our interpolation network \n","    assigns the starting point (time t=0) value of the time series to \n","    the global mean before applying the two-layer interpolation network.\n","    In such cases, the first interpolation layer just outputs the global\n","    mean for that channel, but the second interpolation layer performs \n","    a more meaningful interpolation using the learned correlations from\n","    other channels.\"\"\"\n","    counts = np.sum(np.sum(mask, axis=2), axis=0)\n","    mean_values = np.sum(np.sum(vitals*mask, axis=2), axis=0)/counts\n","    for i in range(mask.shape[0]):\n","        for j in range(mask.shape[1]):\n","            if np.sum(mask[i, j]) == 0:\n","                mask[i, j, 0] = 1\n","                vitals[i, j, 0] = mean_values[j]\n","    return"]},{"cell_type":"markdown","id":"50a1cadc","metadata":{"papermill":{"duration":0.012182,"end_time":"2024-09-28T05:22:39.996388","exception":false,"start_time":"2024-09-28T05:22:39.984206","status":"completed"},"tags":[]},"source":["# Simulating missing values that the autoencoder must reconstruct\n","\n","\n","The autoencoder's learning mechanism. No useful for our case."]},{"cell_type":"code","execution_count":21,"id":"a0006141","metadata":{"_kg_hide-input":true,"execution":{"iopub.execute_input":"2024-09-28T05:22:40.023399Z","iopub.status.busy":"2024-09-28T05:22:40.022948Z","iopub.status.idle":"2024-09-28T05:22:40.031434Z","shell.execute_reply":"2024-09-28T05:22:40.03028Z"},"papermill":{"duration":0.024851,"end_time":"2024-09-28T05:22:40.03387","exception":false,"start_time":"2024-09-28T05:22:40.009019","status":"completed"},"tags":[]},"outputs":[],"source":["def hold_out(mask, perc=0.2):\n","    \"\"\"To implement the autoencoder component of the loss, we introduce a set\n","    of masking variables mr (and mr1) for each data point. If drop_mask = 0,\n","    then we removecthe data point as an input to the interpolation network,\n","    and includecthe predicted value at this time point when assessing\n","    the autoencoder loss. In practice, we randomly select 20% of the\n","    observed data points to hold out from\n","    every input time series.\"\"\"\n","    drop_mask = np.ones_like(mask)\n","    drop_mask *= mask\n","    for i in range(mask.shape[0]):\n","        for j in range(mask.shape[1]):\n","            count = np.sum(mask[i, j], dtype='int')\n","            if int(0.20*count) > 1:\n","                index = 0\n","                r = np.ones((count, 1))\n","                b = np.random.choice(count, int(0.20*count), replace=False)\n","                r[b] = 0\n","                for k in range(mask.shape[2]):\n","                    if mask[i, j, k] > 0:\n","                        drop_mask[i, j, k] = r[index]\n","                        index += 1\n","    return drop_mask\n"]},{"cell_type":"markdown","id":"108e5758","metadata":{"papermill":{"duration":0.012023,"end_time":"2024-09-28T05:22:40.05856","exception":false,"start_time":"2024-09-28T05:22:40.046537","status":"completed"},"tags":[]},"source":["# How we going to handle missing values effectively?\n","\n","There are two ways to do that.\n","\n","###  Using GluonTS for Time-Series Modeling\n","\n","GluonTS is a powerful toolkit for probabilistic time-series modeling. It can model temporal dependencies and handle missing values effectively by learning patterns from the observed time-series.\n","\n","\n","### Using Amazon Chronos (Transformer-based Forecasting Model):\n","\n","Chronos is Amazon’s deep learning-based forecasting tool built on transformer architectures, specifically designed for time-series forecasting.\n","\n","\n","I already familiar with transformer models, Chronos might be a better fit for our project. The Chronos model is only support univariate forcasting, see https://github.com/amazon-science/chronos-forecasting/issues/13\n","\n","\n","# Visualizing Time-Series data\n","\n","Let's visualize the time-seris of one sample across different features.\n","\n","* **x (observed values):** This array contains the actual time-series data of vital signs (with missing values and outliers handled). This is the array we would most likely want to visualize, as it represents the vital sign measurements over time. It shows how the data changes across time steps for each feature and Sample.\n","\n","* **M (masking):** This is a mask that indicates whether a value is observed (1) or missing (0). Visualizing this could be useful to understand where missing data points occur, but it won't give insights into the actual values of the vital signs. It would mainly show the distribution of missing or invalid data over time.\n","\n","* **delta (time points of observation):** This array represents the time intervals between observations, indicating how time progresses for each Sample and feature. Visualizing delta can help we understand the gaps between observations, which might be relevant in understanding irregular sampling patterns.\n","\n","\n","## Visualizing `x` of a single Sample for all 12 features\n"]},{"cell_type":"code","execution_count":22,"id":"3143325e","metadata":{"execution":{"iopub.execute_input":"2024-09-28T05:22:40.085186Z","iopub.status.busy":"2024-09-28T05:22:40.084728Z","iopub.status.idle":"2024-09-28T05:22:40.119407Z","shell.execute_reply":"2024-09-28T05:22:40.11826Z"},"papermill":{"duration":0.050719,"end_time":"2024-09-28T05:22:40.121845","exception":false,"start_time":"2024-09-28T05:22:40.071126","status":"completed"},"tags":[]},"outputs":[{"data":{"text/html":["<div>                            <div id=\"2ead98e7-fc21-4455-b851-31b20d2bd549\" class=\"plotly-graph-div\" style=\"height:525px; width:100%;\"></div>            <script type=\"text/javascript\">                require([\"plotly\"], function(Plotly) {                    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each feature's time-series data for the selected sample\n","    for i in range(num_features):\n","        fig.add_trace(go.Scatter(\n","            x=time_steps, \n","            y=x[sample_index, i, :], \n","            mode='lines', \n","            name=f'Feature {i+1}'\n","        ))\n","    \n","    # Set labels and title\n","    fig.update_layout(\n","        title=f'Observed Values for Sample {sample_index + 1}',\n","        xaxis_title='Time Steps',\n","        yaxis_title='Observed Values',\n","        hovermode='x unified'\n","    )\n","\n","    # Show the interactive plot\n","    fig.show()\n","\n","# Example usage to visualize time-series data for sample 0\n","interactive_visualize_sample_time_series(x, sample_index=0)\n","\n"]},{"cell_type":"code","execution_count":23,"id":"9a106fc7","metadata":{"execution":{"iopub.execute_input":"2024-09-28T05:22:40.149357Z","iopub.status.busy":"2024-09-28T05:22:40.148896Z","iopub.status.idle":"2024-09-28T05:22:40.244456Z","shell.execute_reply":"2024-09-28T05:22:40.24306Z"},"papermill":{"duration":0.112202,"end_time":"2024-09-28T05:22:40.247047","exception":false,"start_time":"2024-09-28T05:22:40.134845","status":"completed"},"tags":[]},"outputs":[{"data":{"text/html":["<div>                            <div id=\"a4718b1e-1b6d-4e68-ac99-db1ef0759864\" class=\"plotly-graph-div\" style=\"height:525px; width:100%;\"></div>            <script type=\"text/javascript\">                require([\"plotly\"], function(Plotly) {                    window.PLOTLYENV=window.PLOTLYENV || {};                                    if (document.getElementById(\"a4718b1e-1b6d-4e68-ac99-db1ef0759864\")) {                    Plotly.newPlot(                        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selected sample\n","        x=list(range(num_time_steps)),  # Time steps\n","        y=[f'Feature {i+1}' for i in range(num_features)],  # Features\n","        colorscale='Viridis',  # Use the 'viridis' colorscale\n","        colorbar=dict(title=\"Mask Value\"),  # Add a color bar\n","        showscale=True\n","    ))\n","\n","    # Set layout properties\n","    fig.update_layout(\n","        title=f'Heatmap of Mask for Sample {sample_index + 1} (1 = Observed, 0 = Missing)',\n","        xaxis_title='Time Steps',\n","        yaxis_title='Features',\n","        hovermode='closest'\n","    )\n","\n","    # Show the interactive heatmap\n","    fig.show()\n","\n","# Example usage to visualize the mask for sample 0\n","interactive_visualize_mask_heatmap(M, sample_index=0)\n","\n"]},{"cell_type":"code","execution_count":24,"id":"d649386a","metadata":{"execution":{"iopub.execute_input":"2024-09-28T05:22:40.275981Z","iopub.status.busy":"2024-09-28T05:22:40.275592Z","iopub.status.idle":"2024-09-28T05:22:40.308476Z","shell.execute_reply":"2024-09-28T05:22:40.307312Z"},"papermill":{"duration":0.050162,"end_time":"2024-09-28T05:22:40.311061","exception":false,"start_time":"2024-09-28T05:22:40.260899","status":"completed"},"tags":[]},"outputs":[{"data":{"text/html":["<div>                            <div id=\"01b57639-84d9-497e-8570-a2d8885067ec\" class=\"plotly-graph-div\" style=\"height:525px; width:100%;\"></div>            <script type=\"text/javascript\">                require([\"plotly\"], function(Plotly) {                    window.PLOTLYENV=window.PLOTLYENV || {};                                    if (document.getElementById(\"01b57639-84d9-497e-8570-a2d8885067ec\")) {                    Plotly.newPlot(                        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  fig = go.Figure()\n","\n","    # Plot each feature's time intervals for the selected sample\n","    for i in range(num_features):\n","        fig.add_trace(go.Scatter(\n","            x=time_steps, \n","            y=delta[sample_index, i, :], \n","            mode='lines', \n","            name=f'Feature {i+1}'\n","        ))\n","\n","    # Set labels and title\n","    fig.update_layout(\n","        title=f'Time Intervals Between Observations for Sample {sample_index + 1}',\n","        xaxis_title='Time Steps',\n","        yaxis_title='Time Intervals (Hours)',\n","        hovermode='x unified'  # Unified hover mode\n","    )\n","\n","    # Show the interactive plot\n","    fig.show()\n","\n","# Example usage to visualize time intervals for sample 0\n","interactive_visualize_time_intervals(delta, sample_index=0)\n"]},{"cell_type":"code","execution_count":25,"id":"412600a2","metadata":{"execution":{"iopub.execute_input":"2024-09-28T05:22:40.341867Z","iopub.status.busy":"2024-09-28T05:22:40.340814Z","iopub.status.idle":"2024-09-28T05:22:40.345743Z","shell.execute_reply":"2024-09-28T05:22:40.344646Z"},"papermill":{"duration":0.023187,"end_time":"2024-09-28T05:22:40.348402","exception":false,"start_time":"2024-09-28T05:22:40.325215","status":"completed"},"tags":[]},"outputs":[],"source":["# x = np.concatenate((x, M, delta), axis=1)\n","# print(x.shape)\n","# y= np.array(label)\n","# print(y.shape)"]},{"cell_type":"code","execution_count":26,"id":"981e849c","metadata":{"execution":{"iopub.execute_input":"2024-09-28T05:22:40.378143Z","iopub.status.busy":"2024-09-28T05:22:40.377651Z","iopub.status.idle":"2024-09-28T05:22:40.382927Z","shell.execute_reply":"2024-09-28T05:22:40.381698Z"},"papermill":{"duration":0.023034,"end_time":"2024-09-28T05:22:40.38551","exception":false,"start_time":"2024-09-28T05:22:40.362476","status":"completed"},"tags":[]},"outputs":[],"source":["# np.savez('preprocessed_5000.npz', array1=x, array2=y)"]},{"cell_type":"markdown","id":"af7ce59d","metadata":{"papermill":{"duration":0.013789,"end_time":"2024-09-28T05:22:40.413278","exception":false,"start_time":"2024-09-28T05:22:40.399489","status":"completed"},"tags":[]},"source":["# Reference\n","\n","* https://github.com/amazon-science/chronos-forecasting/issues/13"]}],"metadata":{"kaggle":{"accelerator":"none","dataSources":[{"datasetId":5761873,"sourceId":9474187,"sourceType":"datasetVersion"}],"dockerImageVersionId":30761,"isGpuEnabled":false,"isInternetEnabled":true,"language":"python","sourceType":"notebook"},"kernelspec":{"display_name":"Python 3","language":"python","name":"python3"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.10.14"},"papermill":{"default_parameters":{},"duration":25.724681,"end_time":"2024-09-28T05:22:41.350131","environment_variables":{},"exception":null,"input_path":"__notebook__.ipynb","output_path":"__notebook__.ipynb","parameters":{},"start_time":"2024-09-28T05:22:15.62545","version":"2.6.0"}},"nbformat":4,"nbformat_minor":5}