{"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":"code","source":"# This Python 3 environment comes with many helpful analytics libraries installed\n# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python\n# For example, here's several helpful packages to load\n\nimport numpy as np # linear algebra\nimport pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n\n# Input data files are available in the read-only \"../input/\" directory\n# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\n\nimport os\nfor dirname, _, filenames in os.walk('/kaggle/input'):\n    for filename in filenames:\n        print(os.path.join(dirname, filename))\n\n# You can write up to 20GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using \"Save & Run All\" \n# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session","metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","_kg_hide-input":true,"_kg_hide-output":true,"execution":{"iopub.status.busy":"2023-10-25T17:24:14.622744Z","iopub.execute_input":"2023-10-25T17:24:14.624258Z","iopub.status.idle":"2023-10-25T17:24:15.130983Z","shell.execute_reply.started":"2023-10-25T17:24:14.624200Z","shell.execute_reply":"2023-10-25T17:24:15.129751Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# Exploratory data analysis of train_series \n\n**Table of contents:**\n\n* [Download the data](#section-one)\n* [Inspect the train_series dataframe](#section-two)    \n    - [STEPS data analysis](#subsection_one)\n        - [Descriptive statistics for 'step' column](#subsubsection_one)\n        - [Check the consistensy of 'step' values](#subsubsection_two)\n        - [Summary on 'step' values analysis](#subsubsection_three)\n    - [ENMO data analysis](#subsubsection_four)\n        - [ENMO definition](#subsubsection_def)\n        - [Descriptive statistics for ENMO column (original train_series)](#subsubsection_five)\n        - [ENMO standards](#subsubsection_stand)\n        - [Descriptive statistics for updated ENMO column (outliers were fixed)](#updated_enmo)\n        - [Visualizing ENMO values for several random series_id](#vis_e)\n        - [Check the stationarity of ENMO values](#vis_stationarity)\n        - [Summary on 'ENMO' metric analysis](#subsubsection_six)\n    - [ANGLEZ metric data analysis](#subsection_anglez)\n        - [Definition of 'anglez'](#subsubsection_seven)\n        - [Descriptive statistics for 'anglez' column](#subsubsection_eight)\n        - [Visualizing ANGLEZ (accompanied with ENMO) values for several random series_id](#vis_ae)\n        - [Check the stationarity of ANGLEZ values](#vis_astationarity)\n        - [Summary on 'ANGLEZ' metric analysis](#anglez_summary)\n* [Inspect the train_events dataframe](#train_event)\n","metadata":{}},{"cell_type":"code","source":"import matplotlib.pyplot as plt\nimport seaborn as sns\nimport polars as pl\nfrom datetime import datetime","metadata":{"_kg_hide-input":true,"execution":{"iopub.status.busy":"2023-10-25T17:39:29.576861Z","iopub.execute_input":"2023-10-25T17:39:29.578427Z","iopub.status.idle":"2023-10-25T17:39:30.810406Z","shell.execute_reply.started":"2023-10-25T17:39:29.578368Z","shell.execute_reply":"2023-10-25T17:39:30.809207Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"<a id=\"section-one\"></a>\n# Download the data","metadata":{}},{"cell_type":"code","source":"# this code chunk was inspired by notebook: https://www.kaggle.com/code/zulqarnainali/explained-baseline-solution\n\n# create a list 'dt_transforms' with transformations \n# to be applied to a timestamp column in the data\ndt_transforms = [\n      pl.col('timestamp').str.to_datetime(), \n    (pl.col('timestamp').str.to_datetime().dt.date()).alias('date'), \n    pl.col('timestamp').str.to_datetime().dt.time().alias('time')\n    ]","metadata":{"_kg_hide-input":true,"execution":{"iopub.status.busy":"2023-10-25T17:39:33.997881Z","iopub.execute_input":"2023-10-25T17:39:33.998297Z","iopub.status.idle":"2023-10-25T17:39:34.021426Z","shell.execute_reply.started":"2023-10-25T17:39:33.998263Z","shell.execute_reply":"2023-10-25T17:39:34.020397Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# download the train_series with polars and then convert to pandas dataframe\n\ntrain_series = (pl.scan_parquet('/kaggle/input/child-mind-institute-detect-sleep-states/train_series.parquet')\n                .with_columns(\n    (dt_transforms)\n    )\n                .collect()\n                .to_pandas()\n               )               ","metadata":{"_kg_hide-input":true,"execution":{"iopub.status.busy":"2023-10-25T17:39:37.503756Z","iopub.execute_input":"2023-10-25T17:39:37.504699Z","iopub.status.idle":"2023-10-25T17:42:14.817678Z","shell.execute_reply.started":"2023-10-25T17:39:37.504651Z","shell.execute_reply":"2023-10-25T17:42:14.816127Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"train_events = (pl.scan_csv('/kaggle/input/child-mind-institute-detect-sleep-states/train_events.csv')\n                .with_columns(\n                    (dt_transforms)\n                    )\n                .collect()\n                .to_pandas()\n               )","metadata":{"_kg_hide-input":true,"execution":{"iopub.status.busy":"2023-10-25T17:43:39.195315Z","iopub.execute_input":"2023-10-25T17:43:39.195838Z","iopub.status.idle":"2023-10-25T17:43:39.254252Z","shell.execute_reply.started":"2023-10-25T17:43:39.195801Z","shell.execute_reply":"2023-10-25T17:43:39.253113Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# download test_series data\ntest_series = (pl.scan_parquet('/kaggle/input/child-mind-institute-detect-sleep-states/test_series.parquet')\n                .with_columns(\n                    (dt_transforms)\n                    )\n                .collect()\n                .to_pandas()\n               )","metadata":{"_kg_hide-input":true,"execution":{"iopub.status.busy":"2023-10-25T17:43:46.213374Z","iopub.execute_input":"2023-10-25T17:43:46.213950Z","iopub.status.idle":"2023-10-25T17:43:46.229663Z","shell.execute_reply.started":"2023-10-25T17:43:46.213907Z","shell.execute_reply":"2023-10-25T17:43:46.228798Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# Memory reduction for train_series\n#The following function has been taken from the notebook\n# https://www.kaggle.com/code/renatoreggiani/zzzs-feat-eng-ideas-60-memory-reduction?scriptVersionId=143987308\n\nfrom pandas.api.types import is_datetime64_ns_dtype\nimport gc\n\nimport warnings\nwarnings.filterwarnings(\"ignore\")\n\ndef reduce_mem_usage(df):\n    \n    \"\"\" \n    Iterate through all numeric columns of a dataframe and modify the data type\n    to reduce memory usage.        \n    \"\"\"\n    \n    start_mem = df.memory_usage().sum() / 1024**2\n    print(f'Memory usage of dataframe is {start_mem:.2f} MB')\n    \n    for col in df.columns:\n        col_type = df[col].dtype\n\n        if col_type != object and not is_datetime64_ns_dtype(df[col]):\n            c_min = df[col].min()\n            c_max = df[col].max()\n            if str(col_type)[:3] == 'int':\n                if c_min > np.iinfo(np.int8).min and c_max < np.iinfo(np.int8).max:\n                    df[col] = df[col].astype(np.int8)\n                elif c_min > np.iinfo(np.int16).min and c_max < np.iinfo(np.int16).max:\n                    df[col] = df[col].astype(np.int16)\n                elif c_min > np.iinfo(np.int32).min and c_max < np.iinfo(np.int32).max:\n                    df[col] = df[col].astype(np.int32)\n                elif c_min > np.iinfo(np.int64).min and c_max < np.iinfo(np.int64).max:\n                    df[col] = df[col].astype(np.int64)  \n            else:\n                if c_min > np.finfo(np.float16).min and c_max < np.finfo(np.float16).max:\n                    df[col] = df[col].astype(np.float32)\n                elif c_min > np.finfo(np.float32).min and c_max < np.finfo(np.float32).max:\n                    df[col] = df[col].astype(np.float32)\n                else:\n                    df[col] = df[col].astype(np.float64)\n        \n\n    df['series_id'] = df['series_id'].astype('category')\n\n    end_mem = df.memory_usage().sum() / 1024**2\n    print(f'Memory usage after optimization is: {end_mem:.2f} MB')\n    decrease = 100 * (start_mem - end_mem) / start_mem\n    print(f'Decreased by {decrease:.2f}%')\n    \n    return df","metadata":{"_kg_hide-input":true,"execution":{"iopub.status.busy":"2023-10-25T17:43:52.552036Z","iopub.execute_input":"2023-10-25T17:43:52.552434Z","iopub.status.idle":"2023-10-25T17:43:52.577392Z","shell.execute_reply.started":"2023-10-25T17:43:52.552403Z","shell.execute_reply":"2023-10-25T17:43:52.576315Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# reduction of train_series memory usage \nreduce_mem_usage(train_series)","metadata":{"_kg_hide-input":true,"_kg_hide-output":true,"execution":{"iopub.status.busy":"2023-10-25T17:44:15.871240Z","iopub.execute_input":"2023-10-25T17:44:15.871673Z","iopub.status.idle":"2023-10-25T17:44:34.468549Z","shell.execute_reply.started":"2023-10-25T17:44:15.871638Z","shell.execute_reply":"2023-10-25T17:44:34.466975Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# Memory reduction for test_series\nreduce_mem_usage(test_series)","metadata":{"_kg_hide-input":true,"_kg_hide-output":true,"execution":{"iopub.status.busy":"2023-10-25T17:47:02.564400Z","iopub.execute_input":"2023-10-25T17:47:02.564976Z","iopub.status.idle":"2023-10-25T17:47:02.600881Z","shell.execute_reply.started":"2023-10-25T17:47:02.564934Z","shell.execute_reply":"2023-10-25T17:47:02.599146Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"<a id=\"section-two\"></a>\n# Inspect the train_series dataframe","metadata":{}},{"cell_type":"code","source":"# checkthe shape of train_series\nsh = train_series.shape\nprint(f'The shape of train_series is: {sh}')","metadata":{"_kg_hide-input":true,"execution":{"iopub.status.busy":"2023-10-25T17:47:07.660623Z","iopub.execute_input":"2023-10-25T17:47:07.661114Z","iopub.status.idle":"2023-10-25T17:47:07.668243Z","shell.execute_reply.started":"2023-10-25T17:47:07.661077Z","shell.execute_reply":"2023-10-25T17:47:07.666508Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"Look at first & last 5 rows of the train_series: ","metadata":{}},{"cell_type":"code","source":"train_series","metadata":{"_kg_hide-output":false,"execution":{"iopub.status.busy":"2023-10-25T17:47:21.395150Z","iopub.execute_input":"2023-10-25T17:47:21.395591Z","iopub.status.idle":"2023-10-25T17:47:21.423089Z","shell.execute_reply.started":"2023-10-25T17:47:21.395560Z","shell.execute_reply":"2023-10-25T17:47:21.421674Z"},"_kg_hide-input":true,"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# lets look at train data\ntrain_series.info()","metadata":{"_kg_hide-input":true,"execution":{"iopub.status.busy":"2023-10-25T17:47:35.111836Z","iopub.execute_input":"2023-10-25T17:47:35.112270Z","iopub.status.idle":"2023-10-25T17:47:35.133538Z","shell.execute_reply.started":"2023-10-25T17:47:35.112241Z","shell.execute_reply":"2023-10-25T17:47:35.132084Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# count the number of unique values in train_series\nk = train_series.nunique()\nprint(f'Unique values in train_series are: {k}')","metadata":{"_kg_hide-input":true,"execution":{"iopub.status.busy":"2023-10-25T17:47:54.307435Z","iopub.execute_input":"2023-10-25T17:47:54.307894Z","iopub.status.idle":"2023-10-25T17:48:41.291580Z","shell.execute_reply.started":"2023-10-25T17:47:54.307861Z","shell.execute_reply":"2023-10-25T17:48:41.290404Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"We see that there are 705 unique dates in train_series. ","metadata":{}},{"cell_type":"code","source":"# check the earliest date of train_series\ndate_max_tr = train_series.date.max()\nprint(f'The most recent date of the train_series data is {date_max_tr}')","metadata":{"_kg_hide-input":true,"execution":{"iopub.status.busy":"2023-10-25T13:12:58.911222Z","iopub.execute_input":"2023-10-25T13:12:58.911661Z","iopub.status.idle":"2023-10-25T13:12:59.414832Z","shell.execute_reply.started":"2023-10-25T13:12:58.911629Z","shell.execute_reply":"2023-10-25T13:12:59.413086Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# check the latest date of train_series\ndate_min_tr = train_series.date.min()\nprint(f'The earliest date of the train_series data is {date_min_tr}')","metadata":{"_kg_hide-input":true,"execution":{"iopub.status.busy":"2023-10-25T17:56:37.949941Z","iopub.execute_input":"2023-10-25T17:56:37.952385Z","iopub.status.idle":"2023-10-25T17:56:39.182167Z","shell.execute_reply.started":"2023-10-25T17:56:37.952314Z","shell.execute_reply":"2023-10-25T17:56:39.180836Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"<a id=\"subsection_one\"></a>\n### STEPS data analysis","metadata":{}},{"cell_type":"markdown","source":"From the competition data description it is known that:\n\n* Step - An integer timestep for each observation within a series.\n* Each series is a continuous recording.\n* Each data series represents this continuous (multi-day/event) recording for a unique experimental subject.\n\nFrom the first 5 rows of the train_series dataframe it is seen that one step = 5 seconds. Let's check if all the records have a step = 5 sec.","metadata":{}},{"cell_type":"code","source":"# make a column with difference between timestamps of neighbour rows \ntrain_series['time_diff'] = train_series['timestamp'].diff()","metadata":{"_kg_hide-input":true,"execution":{"iopub.status.busy":"2023-10-25T17:57:21.311431Z","iopub.execute_input":"2023-10-25T17:57:21.311919Z","iopub.status.idle":"2023-10-25T17:57:25.972916Z","shell.execute_reply.started":"2023-10-25T17:57:21.311882Z","shell.execute_reply":"2023-10-25T17:57:25.971499Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# check if the column 'time_diff' was added\ntrain_series.head()","metadata":{"_kg_hide-input":true,"_kg_hide-output":true,"execution":{"iopub.status.busy":"2023-10-25T17:57:30.639987Z","iopub.execute_input":"2023-10-25T17:57:30.640754Z","iopub.status.idle":"2023-10-25T17:57:30.668551Z","shell.execute_reply.started":"2023-10-25T17:57:30.640717Z","shell.execute_reply":"2023-10-25T17:57:30.667081Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"Let's count values in the column with timestamp difference.","metadata":{}},{"cell_type":"code","source":"# count values in the column with timestamp difference\ntrain_series['time_diff'].value_counts()\n","metadata":{"_kg_hide-input":true,"_kg_hide-output":false,"execution":{"iopub.status.busy":"2023-10-25T17:57:34.401409Z","iopub.execute_input":"2023-10-25T17:57:34.401853Z","iopub.status.idle":"2023-10-25T17:57:35.623551Z","shell.execute_reply.started":"2023-10-25T17:57:34.401819Z","shell.execute_reply":"2023-10-25T17:57:35.622255Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# count number of unique values of time difference between neighbour rows grouped by series_id\nunique_diff = train_series.groupby('series_id', as_index = False).time_diff.nunique()\n\nunique_diff","metadata":{"_kg_hide-input":true,"_kg_hide-output":true,"execution":{"iopub.status.busy":"2023-10-25T17:57:39.224405Z","iopub.execute_input":"2023-10-25T17:57:39.225017Z","iopub.status.idle":"2023-10-25T17:57:44.789581Z","shell.execute_reply.started":"2023-10-25T17:57:39.224980Z","shell.execute_reply":"2023-10-25T17:57:44.787884Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# count number of unique values of time difference between neighbour rows\nunique_diff.time_diff.value_counts()","metadata":{"_kg_hide-input":true,"_kg_hide-output":false,"execution":{"iopub.status.busy":"2023-10-25T17:58:11.639561Z","iopub.execute_input":"2023-10-25T17:58:11.640065Z","iopub.status.idle":"2023-10-25T17:58:11.651486Z","shell.execute_reply.started":"2023-10-25T17:58:11.640032Z","shell.execute_reply":"2023-10-25T17:58:11.650107Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"We see that most part of time difference values are 5 seconds.  276 cases have unique time differences which can be explaind by switching from one series_id to another. ","metadata":{}},{"cell_type":"code","source":"# drop column with time difference\ntrain_series = train_series.drop('time_diff', axis = 1)","metadata":{"_kg_hide-input":true,"execution":{"iopub.status.busy":"2023-10-25T17:58:15.753539Z","iopub.execute_input":"2023-10-25T17:58:15.754888Z","iopub.status.idle":"2023-10-25T17:58:23.285666Z","shell.execute_reply.started":"2023-10-25T17:58:15.754830Z","shell.execute_reply":"2023-10-25T17:58:23.284210Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"Each data series represents continuous (multi-day/event) recording for a unique experimental subject. It means that **for each series_id, steps enumeration should start from '0'**. \nLet's check it. ","metadata":{}},{"cell_type":"markdown","source":"Make a subset with first rows for every series_id.","metadata":{}},{"cell_type":"code","source":"# check values of the first rows for each series_id\nid_first_rows = train_series.groupby('series_id').first()\nid_first_rows","metadata":{"_kg_hide-input":true,"execution":{"iopub.status.busy":"2023-10-25T17:58:59.194327Z","iopub.execute_input":"2023-10-25T17:58:59.194767Z","iopub.status.idle":"2023-10-25T17:59:19.155821Z","shell.execute_reply.started":"2023-10-25T17:58:59.194730Z","shell.execute_reply":"2023-10-25T17:59:19.154405Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# count the number of unique values in the 'step' column \n# for the first rows of every unique series_id\nfirst_un = id_first_rows.step.value_counts()\nprint(f'Unique values of the \"step\" column for the first rows of each series_id: {first_un}')","metadata":{"_kg_hide-input":true,"execution":{"iopub.status.busy":"2023-10-25T17:59:24.789175Z","iopub.execute_input":"2023-10-25T17:59:24.789841Z","iopub.status.idle":"2023-10-25T17:59:24.800082Z","shell.execute_reply.started":"2023-10-25T17:59:24.789788Z","shell.execute_reply":"2023-10-25T17:59:24.798792Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"We see that different series_id's steps start with zero. ","metadata":{}},{"cell_type":"markdown","source":"<a id=\"subsubsection_one\"></a>\n#### Descriptive statistics for 'step' column","metadata":{}},{"cell_type":"code","source":"mxs = train_series.step.max()","metadata":{"_kg_hide-input":true,"execution":{"iopub.status.busy":"2023-10-25T17:59:31.411647Z","iopub.execute_input":"2023-10-25T17:59:31.412088Z","iopub.status.idle":"2023-10-25T17:59:31.729989Z","shell.execute_reply.started":"2023-10-25T17:59:31.412057Z","shell.execute_reply":"2023-10-25T17:59:31.728331Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"mns = train_series.step.min()","metadata":{"_kg_hide-input":true,"execution":{"iopub.status.busy":"2023-10-25T17:59:34.371152Z","iopub.execute_input":"2023-10-25T17:59:34.371560Z","iopub.status.idle":"2023-10-25T17:59:34.676575Z","shell.execute_reply.started":"2023-10-25T17:59:34.371530Z","shell.execute_reply":"2023-10-25T17:59:34.675148Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"tr_means = train_series.step.mean()","metadata":{"_kg_hide-input":true,"execution":{"iopub.status.busy":"2023-10-25T17:59:41.691161Z","iopub.execute_input":"2023-10-25T17:59:41.691624Z","iopub.status.idle":"2023-10-25T17:59:41.956535Z","shell.execute_reply.started":"2023-10-25T17:59:41.691593Z","shell.execute_reply":"2023-10-25T17:59:41.955256Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"sts = train_series.step.std()","metadata":{"_kg_hide-input":true,"execution":{"iopub.status.busy":"2023-10-25T17:59:45.704842Z","iopub.execute_input":"2023-10-25T17:59:45.705269Z","iopub.status.idle":"2023-10-25T17:59:46.726386Z","shell.execute_reply.started":"2023-10-25T17:59:45.705239Z","shell.execute_reply":"2023-10-25T17:59:46.725057Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"mds = train_series.step.median()","metadata":{"_kg_hide-input":true,"execution":{"iopub.status.busy":"2023-10-25T17:59:50.151032Z","iopub.execute_input":"2023-10-25T17:59:50.151518Z","iopub.status.idle":"2023-10-25T17:59:51.285537Z","shell.execute_reply.started":"2023-10-25T17:59:50.151486Z","shell.execute_reply":"2023-10-25T17:59:51.284149Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"perc25s = train_series.step.quantile(0.25)","metadata":{"_kg_hide-input":true,"execution":{"iopub.status.busy":"2023-10-25T17:59:55.229427Z","iopub.execute_input":"2023-10-25T17:59:55.229983Z","iopub.status.idle":"2023-10-25T17:59:56.318342Z","shell.execute_reply.started":"2023-10-25T17:59:55.229932Z","shell.execute_reply":"2023-10-25T17:59:56.317040Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"perc75s = train_series.step.quantile(0.75)","metadata":{"_kg_hide-input":true,"execution":{"iopub.status.busy":"2023-10-25T18:00:00.286023Z","iopub.execute_input":"2023-10-25T18:00:00.286568Z","iopub.status.idle":"2023-10-25T18:00:01.352833Z","shell.execute_reply.started":"2023-10-25T18:00:00.286530Z","shell.execute_reply":"2023-10-25T18:00:01.351506Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"print(f'step max is {mxs}')\nprint(f'step min is {mns}')\nprint(f'step mean is {tr_means}')\nprint(f'step standard deviation is {sts}')\nprint(f'step median is {mds}')\nprint(f'step first quantile is {perc25s}')\nprint(f'step third quantile is {perc75s}')","metadata":{"_kg_hide-input":true,"execution":{"iopub.status.busy":"2023-10-25T18:00:06.832590Z","iopub.execute_input":"2023-10-25T18:00:06.833099Z","iopub.status.idle":"2023-10-25T18:00:06.842214Z","shell.execute_reply.started":"2023-10-25T18:00:06.833058Z","shell.execute_reply":"2023-10-25T18:00:06.840361Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"We see that max value differs from the third quantile several times. Let's find the series_id belonged to max number of steps.","metadata":{}},{"cell_type":"code","source":"# find the series_id belonged to max number of steps\nmxs=train_series.step.max()\nim=train_series.loc[train_series['step'] == mxs]\n\nprint(im)","metadata":{"_kg_hide-input":true,"execution":{"iopub.status.busy":"2023-10-25T18:00:40.210014Z","iopub.execute_input":"2023-10-25T18:00:40.210502Z","iopub.status.idle":"2023-10-25T18:00:41.122577Z","shell.execute_reply.started":"2023-10-25T18:00:40.210467Z","shell.execute_reply":"2023-10-25T18:00:41.121166Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"Look at all the data of series_id = '78569a801a38':","metadata":{}},{"cell_type":"code","source":"# make a subset of the data for series_id = '78569a801a38' only\nid_78569a801a38 = train_series[train_series['series_id']=='78569a801a38']\nid_78569a801a38.head()","metadata":{"_kg_hide-input":true,"execution":{"iopub.status.busy":"2023-10-25T18:00:45.541120Z","iopub.execute_input":"2023-10-25T18:00:45.541619Z","iopub.status.idle":"2023-10-25T18:00:45.824151Z","shell.execute_reply.started":"2023-10-25T18:00:45.541587Z","shell.execute_reply":"2023-10-25T18:00:45.822660Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# look all the unique values for series_id = '78569a801a38'\nnun = id_78569a801a38.nunique()\nprint(f'Unique values for the data belonged to series_id = \"78569a801a38\": {nun}')","metadata":{"_kg_hide-input":true,"execution":{"iopub.status.busy":"2023-10-25T18:00:49.988336Z","iopub.execute_input":"2023-10-25T18:00:49.988788Z","iopub.status.idle":"2023-10-25T18:00:50.513696Z","shell.execute_reply.started":"2023-10-25T18:00:49.988753Z","shell.execute_reply":"2023-10-25T18:00:50.512472Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"We see that the number of unique steps and unique timestamps are the same. It corresponds to the train_series description data ('Each series is a continuous recording'). Let's check the first rows for every date recorded for series_id = '78569a801a38'.","metadata":{}},{"cell_type":"code","source":"# check the starting steps for every day recorded for series_id = '78569a801a38'\nfirst_rows_per_day = id_78569a801a38.groupby('date').first()\nfirst_rows_per_day","metadata":{"_kg_hide-input":true,"execution":{"iopub.status.busy":"2023-10-25T18:01:11.946851Z","iopub.execute_input":"2023-10-25T18:01:11.947238Z","iopub.status.idle":"2023-10-25T18:01:12.279202Z","shell.execute_reply.started":"2023-10-25T18:01:11.947211Z","shell.execute_reply":"2023-10-25T18:01:12.277657Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"We see that data are continuous and recorded period for series_id = '78569a801a38' is 84 days. Let's look how many steps were recorded for every day.","metadata":{}},{"cell_type":"code","source":"# look how many steps were recorded per day for series_id = '78569a801a38'\nsteps_outl = id_78569a801a38.groupby('date').step.count()\nsteps_outl = steps_outl.to_frame()\nsteps_outl","metadata":{"_kg_hide-input":true,"execution":{"iopub.status.busy":"2023-10-25T18:01:21.694761Z","iopub.execute_input":"2023-10-25T18:01:21.695218Z","iopub.status.idle":"2023-10-25T18:01:21.740279Z","shell.execute_reply.started":"2023-10-25T18:01:21.695183Z","shell.execute_reply":"2023-10-25T18:01:21.738792Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# count number of unique values for steps of series_id = '78569a801a38'\nunst_outl = steps_outl['step'].value_counts()\nprint(f'Number of unique values of steps for series_id = \"78569a801a3\" is {unst_outl}')","metadata":{"_kg_hide-input":true,"execution":{"iopub.status.busy":"2023-10-25T18:01:25.107576Z","iopub.execute_input":"2023-10-25T18:01:25.108439Z","iopub.status.idle":"2023-10-25T18:01:25.118261Z","shell.execute_reply.started":"2023-10-25T18:01:25.108388Z","shell.execute_reply":"2023-10-25T18:01:25.116480Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# let's count how many hours in 17280 steps. One step = 5 seconds\nnum_hours = 17280*5/60/60\nprint(f'17280 steps = {num_hours} hours')","metadata":{"_kg_hide-input":true,"execution":{"iopub.status.busy":"2023-10-25T18:01:28.247082Z","iopub.execute_input":"2023-10-25T18:01:28.247575Z","iopub.status.idle":"2023-10-25T18:01:28.254281Z","shell.execute_reply.started":"2023-10-25T18:01:28.247538Z","shell.execute_reply":"2023-10-25T18:01:28.253284Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"We see that most of time  (82 days) the data for series_id = '78569a801a38' were recorded during 24 hours. Except the first and last days of observations. Let's check **if the steps for all other train sries_id have the same structure: records during 24-hours period except the first and the last dates.**","metadata":{}},{"cell_type":"markdown","source":"<a id=\"subsubsection_two\"></a>\n#### Check the consistensy of 'step' values","metadata":{}},{"cell_type":"markdown","source":"Let's count the number of dates with not full 24-hour recorded period for every series_id.","metadata":{}},{"cell_type":"code","source":"# count number of steps for every series_id in train_series data frame grouped by date\ntrain_series_steps_per_day = train_series.groupby(['series_id', 'date'], as_index = False).step.count()","metadata":{"_kg_hide-input":true,"execution":{"iopub.status.busy":"2023-10-25T18:04:57.759046Z","iopub.execute_input":"2023-10-25T18:04:57.759623Z","iopub.status.idle":"2023-10-25T18:05:04.380387Z","shell.execute_reply.started":"2023-10-25T18:04:57.759585Z","shell.execute_reply":"2023-10-25T18:05:04.378801Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"train_series_steps_per_day","metadata":{"_kg_hide-input":true,"_kg_hide-output":true,"execution":{"iopub.status.busy":"2023-10-23T10:48:33.263782Z","iopub.execute_input":"2023-10-23T10:48:33.264217Z","iopub.status.idle":"2023-10-23T10:48:33.285715Z","shell.execute_reply.started":"2023-10-23T10:48:33.264184Z","shell.execute_reply":"2023-10-23T10:48:33.284001Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# count unique values of 'step' column for every 'series_id'\nsteps2 = train_series_steps_per_day.groupby(['series_id'], as_index=False).step.value_counts()\nsteps2","metadata":{"_kg_hide-input":true,"_kg_hide-output":true,"execution":{"iopub.status.busy":"2023-10-25T18:08:02.927347Z","iopub.execute_input":"2023-10-25T18:08:02.927881Z","iopub.status.idle":"2023-10-25T18:08:02.978267Z","shell.execute_reply.started":"2023-10-25T18:08:02.927841Z","shell.execute_reply":"2023-10-25T18:08:02.976810Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# select from steps1 rows with not zero number of steps for each series_id\nno_zero_steps = steps2[(steps2['step'] !=0) & (steps2['count'] !=0)]\nno_zero_steps","metadata":{"_kg_hide-input":true,"_kg_hide-output":true,"execution":{"iopub.status.busy":"2023-10-25T18:08:06.712668Z","iopub.execute_input":"2023-10-25T18:08:06.713213Z","iopub.status.idle":"2023-10-25T18:08:06.734101Z","shell.execute_reply.started":"2023-10-25T18:08:06.713174Z","shell.execute_reply":"2023-10-25T18:08:06.732529Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# count the sum of observed days for each series_id\ndays_per_id = no_zero_steps.groupby('series_id')['count'].sum()\ndays_per_id = days_per_id.reset_index()\ndays_per_id.columns = ['series_id', 'total_days']\ndays_per_id","metadata":{"_kg_hide-output":true,"_kg_hide-input":true,"execution":{"iopub.status.busy":"2023-10-25T18:08:10.276049Z","iopub.execute_input":"2023-10-25T18:08:10.276498Z","iopub.status.idle":"2023-10-25T18:08:10.298529Z","shell.execute_reply.started":"2023-10-25T18:08:10.276464Z","shell.execute_reply":"2023-10-25T18:08:10.296853Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# make a subset of no_zero_steps with steps = 17280 (24-hour recorded days)\nfull_day = steps2[steps2['step'] == 17280 ]\nfull_day.columns = ['series_id', 'step', 'full_day_steps_count']\nfull_day","metadata":{"_kg_hide-input":true,"_kg_hide-output":true,"execution":{"iopub.status.busy":"2023-10-25T18:08:13.999099Z","iopub.execute_input":"2023-10-25T18:08:13.999650Z","iopub.status.idle":"2023-10-25T18:08:14.028442Z","shell.execute_reply.started":"2023-10-25T18:08:13.999605Z","shell.execute_reply":"2023-10-25T18:08:14.026876Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# merge days_per_id and full_day on series_id\n\nmerged_steps = pd.merge(full_day, days_per_id, how = 'left', on = 'series_id')\nmerged_steps","metadata":{"_kg_hide-input":true,"_kg_hide-output":true,"execution":{"iopub.status.busy":"2023-10-25T18:08:18.448121Z","iopub.execute_input":"2023-10-25T18:08:18.448506Z","iopub.status.idle":"2023-10-25T18:08:18.482130Z","shell.execute_reply.started":"2023-10-25T18:08:18.448477Z","shell.execute_reply":"2023-10-25T18:08:18.480231Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# add column with difference between total_days and full_day_steps_count\nmerged_steps['diff'] = merged_steps.total_days - merged_steps.full_day_steps_count\nmerged_steps","metadata":{"_kg_hide-input":true,"_kg_hide-output":true,"execution":{"iopub.status.busy":"2023-10-25T18:08:23.915494Z","iopub.execute_input":"2023-10-25T18:08:23.915943Z","iopub.status.idle":"2023-10-25T18:08:23.936674Z","shell.execute_reply.started":"2023-10-25T18:08:23.915910Z","shell.execute_reply":"2023-10-25T18:08:23.935317Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# look how many series_id have complete and incomplete number of steps \n#complete number of steps is the number with difference =< 2 days (last and first of observed days)\ns = merged_steps['diff'].value_counts()\ns = s.to_frame().reset_index()\ns.columns= ['days_less_24', 'count_series_id']\ns","metadata":{"_kg_hide-input":true,"_kg_hide-output":false,"execution":{"iopub.status.busy":"2023-10-25T18:08:27.284175Z","iopub.execute_input":"2023-10-25T18:08:27.284676Z","iopub.status.idle":"2023-10-25T18:08:27.299977Z","shell.execute_reply.started":"2023-10-25T18:08:27.284638Z","shell.execute_reply":"2023-10-25T18:08:27.298800Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"We see that two series_id have one date with records less than 24 hours and 275 series_id have two dates with records less than 24 hours. Hence **steps for all the train sries_id have the same structure: records during 24-hours period except one or two dates (the first and / or the last one).**","metadata":{}},{"cell_type":"code","source":"# convert steps statistica to number of days\n# count median for 24-hour recorded periods \n\nthird_quant_steps_days = (perc75s* 5 / 60 / 60 / 24).round(1)\nmean_steps_days = (tr_means* 5 / 60 / 60 / 24).round(1)\n\n\nprint(f'* Up to 75% of the data has observed period equals to {third_quant_steps_days} days')\nprint(f'* In average the data has observed period equals to {mean_steps_days} days')","metadata":{"_kg_hide-input":true,"execution":{"iopub.status.busy":"2023-10-25T18:08:31.106327Z","iopub.execute_input":"2023-10-25T18:08:31.106783Z","iopub.status.idle":"2023-10-25T18:08:31.115823Z","shell.execute_reply.started":"2023-10-25T18:08:31.106745Z","shell.execute_reply":"2023-10-25T18:08:31.114265Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"Count number of Null values in train_series:","metadata":{}},{"cell_type":"code","source":"train_series.isnull().sum()","metadata":{"_kg_hide-input":true,"execution":{"iopub.status.busy":"2023-10-25T18:08:36.510126Z","iopub.execute_input":"2023-10-25T18:08:36.510536Z","iopub.status.idle":"2023-10-25T18:08:51.020169Z","shell.execute_reply.started":"2023-10-25T18:08:36.510505Z","shell.execute_reply":"2023-10-25T18:08:51.018526Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"Count number of NaN values in train_series:","metadata":{}},{"cell_type":"code","source":"train_series.isna().sum()","metadata":{"_kg_hide-input":true,"execution":{"iopub.status.busy":"2023-10-25T18:08:56.685845Z","iopub.execute_input":"2023-10-25T18:08:56.686281Z","iopub.status.idle":"2023-10-25T18:09:11.121443Z","shell.execute_reply.started":"2023-10-25T18:08:56.686247Z","shell.execute_reply":"2023-10-25T18:09:11.119811Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"<a id=\"subsubsection_three\"></a>\n#### Summary on 'step' values analysis\n\n* Step is an integer timestep for each observation within a series.\n\n* Data for each series_id is  equidistant (each step = 5 seconds).\n\n* Step data for each series_id is continuous and has the following characteristics:\n\n1. Steps enumeration for each series_id starts with zero.\n2. 1st and / or last dates of recordings don't cover full 24-hour period;\n3. All the other observed dates of recordings cover 24-hour period.\n\n* Maximum number of observed dates (84) belongs to series_id = '78569a801a3'\n\n* In average the data has observed period equals to 14.7 day\n\n* Up to 75% of the data has observed period equals to 20.7 days","metadata":{}},{"cell_type":"markdown","source":"<a id=\"subsection_two\"></a>\n### ENMO data analysis","metadata":{}},{"cell_type":"markdown","source":"<a id=\"subsubsection_def\"></a>\n#### ENMO definition","metadata":{}},{"cell_type":"markdown","source":"In the data description of the competition ENMO is defined as:\n\n* **enmo** - As calculated and described by the [GGIR package](https://cran.r-project.org/web/packages/GGIR/vignettes/GGIR.html#4_Inspecting_the_results), ENMO is the **Euclidean Norm Minus One** of all accelerometer signals, with negative values rounded to zero. While no standard measure of acceleration exists in this space, this is one of the several commonly computed features.","metadata":{}},{"cell_type":"markdown","source":"Thanks to [Bhargav Kowshik](https://www.kaggle.com/bkowshik) for [providing the link](https://www.kaggle.com/competitions/child-mind-institute-detect-sleep-states/discussion/438238) on GGIR documentation [Package ‘GGIR’](https://cran.r-project.org/web/packages/GGIR/GGIR.pdf) where we find the following description of ENMO metric:\n\n![ggir_enmo.png](attachment:ed0793d0-28bd-4044-b14c-ad83add965fc.png)","metadata":{},"attachments":{"ed0793d0-28bd-4044-b14c-ad83add965fc.png":{"image/png":"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"}}},{"cell_type":"markdown","source":"Thanks to  [MARÍLIA PRATA notebook](https://www.kaggle.com/code/mpwolke/the-zzzs-anglez-aka-z-angle) for providing the link to the article: van Hees, V.T., Sabia, S., Jones, S.E. et al. Estimating sleep parameters using an accelerometer without sleep diary. Sci Rep 8, 12975 (2018). https://doi.org/10.1038/s41598-018-31266-z. In this article we find description of the ENMO formula:\n\n> with accx, accy, and accz referring to the three orthogonal acceleration axes pointing in the lateral, distal, and ventral directions, respectively. Metric ENMO has previously been demonstrated to be correlated with magnitude of acceleration as well as human energy expenditure in the present generation of wearable acceleration sensors.","metadata":{}},{"cell_type":"markdown","source":"To understand ENMO-measure better we reffered to some scientific papers. In the article [Accelerometry data in health research: challenges and opportunities](https://www.biorxiv.org/content/10.1101/276154v1.full) we find the following description of ENMO:\n> Euclidean Norm Minus One (ENMO) was first introduced as a summary metric for accelerometry data in van Hees et al (2013). It is directly based on the Euclidean norm of x(t), defined as\n\n![enmo_1.png](attachment:64f460dc-d3e5-430b-96b2-498759d16da3.png)\n\n> The ENMO at time t is defined as r(t) −1 when r(t) −1 ≥ 0 and 0 otherwise, or notationally, max [r(t) −1, 0]. Further, the ENMO in a window of size H is defined as the average ENMO across the time points in that window. Formally,\n\n![enmo_2.png](attachment:96e93e92-dbb4-47ee-9ea5-326099bd13e3.png)","metadata":{},"attachments":{"64f460dc-d3e5-430b-96b2-498759d16da3.png":{"image/png":"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"},"96e93e92-dbb4-47ee-9ea5-326099bd13e3.png":{"image/png":"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"}}},{"cell_type":"markdown","source":" In the article [Intensity Thresholds on Raw Acceleration Data: Euclidean Norm Minus One (ENMO) and Mean Amplitude Deviation (MAD) Approaches](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5051724/) we read:\n \n > 1000 milligravitational units = 1 gravitational unit <...> The ENMO subtracts a fixed offset value of 1 gravitational unit at each time point to correct for gravity <...> Negative ENMO values are rounded up to zero to reduce any bias and error.","metadata":{}},{"cell_type":"markdown","source":"<a id=\"subsubsection_five\"></a>\n#### Descriptive statistics for ENMO column (original train_series)","metadata":{}},{"cell_type":"code","source":"enmo_mx= train_series.enmo.max()","metadata":{"_kg_hide-input":true,"execution":{"iopub.status.busy":"2023-10-25T18:11:05.071225Z","iopub.execute_input":"2023-10-25T18:11:05.071694Z","iopub.status.idle":"2023-10-25T18:11:05.376308Z","shell.execute_reply.started":"2023-10-25T18:11:05.071662Z","shell.execute_reply":"2023-10-25T18:11:05.375060Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"enmo_mn = train_series.enmo.min()","metadata":{"_kg_hide-input":true,"execution":{"iopub.status.busy":"2023-10-25T18:11:09.124896Z","iopub.execute_input":"2023-10-25T18:11:09.125366Z","iopub.status.idle":"2023-10-25T18:11:09.386898Z","shell.execute_reply.started":"2023-10-25T18:11:09.125333Z","shell.execute_reply":"2023-10-25T18:11:09.385665Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"enmo_md = train_series.enmo.median()\n","metadata":{"_kg_hide-input":true,"execution":{"iopub.status.busy":"2023-10-25T18:11:12.730912Z","iopub.execute_input":"2023-10-25T18:11:12.731399Z","iopub.status.idle":"2023-10-25T18:11:14.357501Z","shell.execute_reply.started":"2023-10-25T18:11:12.731366Z","shell.execute_reply":"2023-10-25T18:11:14.356127Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"enmo_st = train_series.enmo.std()\n","metadata":{"_kg_hide-input":true,"execution":{"iopub.status.busy":"2023-10-25T18:11:16.868557Z","iopub.execute_input":"2023-10-25T18:11:16.869042Z","iopub.status.idle":"2023-10-25T18:11:17.827037Z","shell.execute_reply.started":"2023-10-25T18:11:16.869005Z","shell.execute_reply":"2023-10-25T18:11:17.825479Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"enmo_perc25 = train_series.enmo.quantile(0.25)","metadata":{"_kg_hide-input":true,"execution":{"iopub.status.busy":"2023-10-25T18:11:21.252905Z","iopub.execute_input":"2023-10-25T18:11:21.253340Z","iopub.status.idle":"2023-10-25T18:11:22.909124Z","shell.execute_reply.started":"2023-10-25T18:11:21.253305Z","shell.execute_reply":"2023-10-25T18:11:22.907625Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"enmo_perc75 = train_series.enmo.quantile(0.75)\n","metadata":{"_kg_hide-input":true,"execution":{"iopub.status.busy":"2023-10-25T18:11:26.134756Z","iopub.execute_input":"2023-10-25T18:11:26.135249Z","iopub.status.idle":"2023-10-25T18:11:27.785860Z","shell.execute_reply.started":"2023-10-25T18:11:26.135215Z","shell.execute_reply":"2023-10-25T18:11:27.784414Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"enmo_perc99 = train_series.enmo.quantile(0.99)","metadata":{"_kg_hide-input":true,"execution":{"iopub.status.busy":"2023-10-25T18:11:30.450338Z","iopub.execute_input":"2023-10-25T18:11:30.450811Z","iopub.status.idle":"2023-10-25T18:11:31.995017Z","shell.execute_reply.started":"2023-10-25T18:11:30.450777Z","shell.execute_reply":"2023-10-25T18:11:31.993510Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"enmo_tr_mean = train_series.enmo.mean()","metadata":{"_kg_hide-input":true,"execution":{"iopub.status.busy":"2023-10-25T18:11:34.626950Z","iopub.execute_input":"2023-10-25T18:11:34.627355Z","iopub.status.idle":"2023-10-25T18:11:34.924601Z","shell.execute_reply.started":"2023-10-25T18:11:34.627326Z","shell.execute_reply":"2023-10-25T18:11:34.923137Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"print(f'enmo max is {enmo_mx}')\nprint(f'enmo min is {enmo_mn}')\nprint(f'enmo mean is {enmo_tr_mean}')\nprint(f'enmo standard deviation is {enmo_st}')\nprint(f'enmo first quantile is {enmo_perc25}')\nprint(f'enmo median is {enmo_md}')\nprint(f'enmo third quantile is {enmo_perc75}')\nprint(f'enmo 99 percentile is {enmo_perc99}')","metadata":{"_kg_hide-input":true,"execution":{"iopub.status.busy":"2023-10-25T18:11:38.096136Z","iopub.execute_input":"2023-10-25T18:11:38.096557Z","iopub.status.idle":"2023-10-25T18:11:38.105058Z","shell.execute_reply.started":"2023-10-25T18:11:38.096528Z","shell.execute_reply":"2023-10-25T18:11:38.103482Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"Let's look at the distribution of the ENMO values in new_train_series.","metadata":{}},{"cell_type":"code","source":"# plot the distribution of enmo-values\nplt.figure(figsize=(10, 6))\nax = sns.distplot(train_series.enmo)\nplt.title('Distribution of ENMO values in train_series')\nplt.show()","metadata":{"_kg_hide-input":true,"execution":{"iopub.status.busy":"2023-10-25T18:11:41.492082Z","iopub.execute_input":"2023-10-25T18:11:41.493294Z","iopub.status.idle":"2023-10-25T18:20:37.736047Z","shell.execute_reply.started":"2023-10-25T18:11:41.493254Z","shell.execute_reply":"2023-10-25T18:20:37.735010Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"From the plot above we see that the most part of enmo values is between 0 and 1. From descriptive statistics we define the upper outlier: max value = 11.433699607849121 (while 99% of the data are under 0.39430001378059387). Let's look closer to this outlier.","metadata":{}},{"cell_type":"code","source":"# find the row with outlier enmo_mx\n\nenmo_outlier = train_series.loc[train_series['enmo'] == enmo_mx]\nprint(enmo_outlier)","metadata":{"_kg_hide-input":true,"execution":{"iopub.status.busy":"2023-10-25T18:21:57.859392Z","iopub.execute_input":"2023-10-25T18:21:57.860657Z","iopub.status.idle":"2023-10-25T18:21:57.995106Z","shell.execute_reply.started":"2023-10-25T18:21:57.860613Z","shell.execute_reply":"2023-10-25T18:21:57.993794Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# make a subset of data for series_id = '4a31811f3558'\nid_4a31811f3558 = train_series[train_series['series_id'] == '4a31811f3558']\nid_4a31811f3558","metadata":{"_kg_hide-input":true,"_kg_hide-output":true,"execution":{"iopub.status.busy":"2023-10-25T18:22:01.727986Z","iopub.execute_input":"2023-10-25T18:22:01.728405Z","iopub.status.idle":"2023-10-25T18:22:01.921521Z","shell.execute_reply.started":"2023-10-25T18:22:01.728374Z","shell.execute_reply":"2023-10-25T18:22:01.920096Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"Descriptive statistics for series_id data with outlier enmo = '11.433699607849121':","metadata":{}},{"cell_type":"code","source":"id_4a31811f3558['enmo'].describe()","metadata":{"_kg_hide-input":true,"execution":{"iopub.status.busy":"2023-10-25T18:22:05.294458Z","iopub.execute_input":"2023-10-25T18:22:05.294917Z","iopub.status.idle":"2023-10-25T18:22:05.325355Z","shell.execute_reply.started":"2023-10-25T18:22:05.294854Z","shell.execute_reply":"2023-10-25T18:22:05.323957Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"<a id=\"subsubsection_stand\"></a>\n#### ENMO standards","metadata":{}},{"cell_type":"markdown","source":"We see that even for series_id = '4a31811f3558' value of enmo = 11.433700 is extremely large. How deal with this outlier? **What are the norms for ENMO metric?** To answer this question we reffered to a scientific paper [Accelerometry data in health research: challenges and opportunities](https://www.biorxiv.org/content/10.1101/276154v1.full#T1):\n\n>  Ideally, an accelerometer resting on a flat surface, with one measurement axis oriented perpendicularly to the ground, should measure constant acceleration of 1g for that axis and 0g for the other two orthogonal axes. In practice, measurements may deviate slightly due to their imprecision or to the quality of assembly, which may, for example, misalign the accelerometer axes with the monitors’ casing. In theory, the vector magnitude <...> for resting state (no movement) is equal to 1g, as the earth’s gravity is the only force acting on the accelerometer. In practice, however, the vector magnitude at rest can be slightly different from 1g.","metadata":{}},{"cell_type":"markdown","source":"In the article [Intensity Thresholds on Raw Acceleration Data: Euclidean Norm Minus One (ENMO) and Mean Amplitude Deviation (MAD) Approaches](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5051724/) we find example of ENMO means for differents activities stratified by accelerometer brand and monitor placement:\n\n![enmo_norms.png](attachment:863762f0-9624-4861-8f8e-d542cbc25cb9.png)","metadata":{},"attachments":{"863762f0-9624-4861-8f8e-d542cbc25cb9.png":{"image/png":"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"}}},{"cell_type":"markdown","source":"Take inaccording that in this example ENMO values measured in milligravity unites we can transfer them into gravity unites. So for sedentary behaviours mean = 9.3/1000 = 0.0093 gravity unites; for sweeping floor = 107.5 / 1000 = 0.1075 gravity unites. Train_series data description doesn't tell us which measurement units were used for ENMO. But according to the  mean ENMO values from the article above **we can suppose that in this competition dataset ENMO was measured in gravity unites**. Could be **extremely huge values of ENMO** (e.g. ENMO = 11)  categorized like a device mistake? May be. But it **can be explained by some extreme activities** ([see the discussion thread](https://www.kaggle.com/competitions/child-mind-institute-detect-sleep-states/discussion/445166#2468724)). ","metadata":{}},{"cell_type":"markdown","source":"**What is the best way to deal with huge enmo values?**\n\n* We can fix all the extreme values at the 99 percentile level or little bit higher than it ([see the discussion thread](https://www.kaggle.com/competitions/child-mind-institute-detect-sleep-states/discussion/445166#2468724)). \n\n* Another way is to use the QuantileTransformer in scikit-learn ([see the discussion thread](https://www.kaggle.com/competitions/child-mind-institute-detect-sleep-states/discussion/445166#2469561)). \n\nDue to 99 percentile (0.39430001378059387) we can consider as huge all the ENMO data larger than this value. Let's find all the data with ENMO > 0.39430001378059387.","metadata":{}},{"cell_type":"code","source":"# make a subset of new_train_series with ENMO > 0.39430001378059387\n\nhuge_enmo99 = train_series[train_series['enmo'] > enmo_perc99]\nhuge_enmo99","metadata":{"_kg_hide-input":true,"execution":{"iopub.status.busy":"2023-10-25T18:22:11.507758Z","iopub.execute_input":"2023-10-25T18:22:11.508180Z","iopub.status.idle":"2023-10-25T18:22:13.130276Z","shell.execute_reply.started":"2023-10-25T18:22:11.508149Z","shell.execute_reply":"2023-10-25T18:22:13.129064Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"There are too much records. Try to choose threshold > 3.","metadata":{}},{"cell_type":"code","source":"# make a subset of new_train_series with ENMO > 3\n\nhuge_enmo3 = train_series[train_series['enmo'] > 3]\nhuge_enmo3","metadata":{"_kg_hide-input":true,"execution":{"iopub.status.busy":"2023-10-25T18:22:18.485752Z","iopub.execute_input":"2023-10-25T18:22:18.486210Z","iopub.status.idle":"2023-10-25T18:22:18.723616Z","shell.execute_reply.started":"2023-10-25T18:22:18.486176Z","shell.execute_reply":"2023-10-25T18:22:18.722129Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"There are also a lot of data. Let's try ENMO > 6.","metadata":{}},{"cell_type":"code","source":"# make a subset of new_train_series with ENMO > 6\n\nhuge_enmo6 = train_series[train_series['enmo'] > 6]\nhuge_enmo6","metadata":{"_kg_hide-input":true,"_kg_hide-output":true,"execution":{"iopub.status.busy":"2023-10-25T18:22:23.699556Z","iopub.execute_input":"2023-10-25T18:22:23.699974Z","iopub.status.idle":"2023-10-25T18:22:23.940004Z","shell.execute_reply.started":"2023-10-25T18:22:23.699943Z","shell.execute_reply":"2023-10-25T18:22:23.938828Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# look at the subset of new_train_series with ENMO > 6\nhuge_enmo6.head(42)","metadata":{"_kg_hide-input":true,"execution":{"iopub.status.busy":"2023-10-25T18:22:27.517931Z","iopub.execute_input":"2023-10-25T18:22:27.518395Z","iopub.status.idle":"2023-10-25T18:22:27.554990Z","shell.execute_reply.started":"2023-10-25T18:22:27.518362Z","shell.execute_reply":"2023-10-25T18:22:27.553770Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"huge_enmo6.tail(55)","metadata":{"_kg_hide-input":true,"execution":{"iopub.status.busy":"2023-10-25T18:22:31.624816Z","iopub.execute_input":"2023-10-25T18:22:31.625376Z","iopub.status.idle":"2023-10-25T18:22:31.672458Z","shell.execute_reply.started":"2023-10-25T18:22:31.625325Z","shell.execute_reply":"2023-10-25T18:22:31.671116Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"We see that the longest periods of high ENMO values belong to series_id = '5c55a5e717d6' and lasts 4 min 40 sec. The second place belongs to series_id = '3664fe9233f9' and lasts 1 min 55 sec. We see that extreme activiteis last not very long periods. Due to the purperses of this competition (predict sleep states) we decide to fix huge enmo values at the level = 1.5.","metadata":{}},{"cell_type":"code","source":"# fix huge enmo values (enmo > 99th percentile) at the level = 1.5\n\ntrain_series.loc[train_series.enmo > enmo_perc99, 'enmo'] = 1.5","metadata":{"_kg_hide-input":true,"execution":{"iopub.status.busy":"2023-10-25T18:22:38.414961Z","iopub.execute_input":"2023-10-25T18:22:38.415384Z","iopub.status.idle":"2023-10-25T18:22:38.717766Z","shell.execute_reply.started":"2023-10-25T18:22:38.415353Z","shell.execute_reply":"2023-10-25T18:22:38.716521Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"enmo_mx= train_series.enmo.max()","metadata":{"_kg_hide-input":true,"execution":{"iopub.status.busy":"2023-10-25T18:22:49.869898Z","iopub.execute_input":"2023-10-25T18:22:49.870310Z","iopub.status.idle":"2023-10-25T18:22:50.155531Z","shell.execute_reply.started":"2023-10-25T18:22:49.870279Z","shell.execute_reply":"2023-10-25T18:22:50.153850Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"enmo_mn = train_series.enmo.min()","metadata":{"_kg_hide-input":true,"execution":{"iopub.status.busy":"2023-10-25T18:22:54.471580Z","iopub.execute_input":"2023-10-25T18:22:54.471997Z","iopub.status.idle":"2023-10-25T18:22:54.757163Z","shell.execute_reply.started":"2023-10-25T18:22:54.471967Z","shell.execute_reply":"2023-10-25T18:22:54.755850Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"enmo_md = train_series.enmo.median()","metadata":{"_kg_hide-input":true,"execution":{"iopub.status.busy":"2023-10-25T18:22:58.065072Z","iopub.execute_input":"2023-10-25T18:22:58.065478Z","iopub.status.idle":"2023-10-25T18:23:00.090319Z","shell.execute_reply.started":"2023-10-25T18:22:58.065447Z","shell.execute_reply":"2023-10-25T18:23:00.088808Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"enmo_st = train_series.enmo.std()","metadata":{"_kg_hide-input":true,"execution":{"iopub.status.busy":"2023-10-25T18:23:03.257307Z","iopub.execute_input":"2023-10-25T18:23:03.257767Z","iopub.status.idle":"2023-10-25T18:23:04.304617Z","shell.execute_reply.started":"2023-10-25T18:23:03.257701Z","shell.execute_reply":"2023-10-25T18:23:04.303324Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"enmo_perc25 = train_series.enmo.quantile(0.25)","metadata":{"_kg_hide-input":true,"execution":{"iopub.status.busy":"2023-10-25T18:23:07.970951Z","iopub.execute_input":"2023-10-25T18:23:07.971479Z","iopub.status.idle":"2023-10-25T18:23:09.616391Z","shell.execute_reply.started":"2023-10-25T18:23:07.971438Z","shell.execute_reply":"2023-10-25T18:23:09.615096Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"enmo_perc75 = train_series.enmo.quantile(0.75)","metadata":{"_kg_hide-input":true,"execution":{"iopub.status.busy":"2023-10-25T18:23:13.725643Z","iopub.execute_input":"2023-10-25T18:23:13.726079Z","iopub.status.idle":"2023-10-25T18:23:15.587475Z","shell.execute_reply.started":"2023-10-25T18:23:13.726049Z","shell.execute_reply":"2023-10-25T18:23:15.586164Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"enmo_perc99 = train_series.enmo.quantile(0.99)","metadata":{"_kg_hide-input":true,"execution":{"iopub.status.busy":"2023-10-25T13:32:56.276234Z","iopub.execute_input":"2023-10-25T13:32:56.276717Z","iopub.status.idle":"2023-10-25T13:32:58.116776Z","shell.execute_reply.started":"2023-10-25T13:32:56.276679Z","shell.execute_reply":"2023-10-25T13:32:58.115388Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"enmo_tr_mean = train_series.enmo.mean()","metadata":{"_kg_hide-input":true,"execution":{"iopub.status.busy":"2023-10-25T18:23:32.027282Z","iopub.execute_input":"2023-10-25T18:23:32.027677Z","iopub.status.idle":"2023-10-25T18:23:32.318787Z","shell.execute_reply.started":"2023-10-25T18:23:32.027649Z","shell.execute_reply":"2023-10-25T18:23:32.317297Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"<a id=\"updated_enmo\"></a>\n#### Descriptive statistics for updated ENMO column (outliers were fixed)","metadata":{}},{"cell_type":"code","source":"print(f'enmo max is {enmo_mx}')\nprint(f'enmo min is {enmo_mn}')\nprint(f'enmo mean is {enmo_tr_mean}')\nprint(f'enmo standard deviation is {enmo_st}')\nprint(f'enmo first quantile is {enmo_perc25}')\nprint(f'enmo median is {enmo_md}')\nprint(f'enmo third quantile is {enmo_perc75}')\nprint(f'enmo 99 percentile is {enmo_perc99}')","metadata":{"_kg_hide-input":true,"execution":{"iopub.status.busy":"2023-10-25T18:23:46.007489Z","iopub.execute_input":"2023-10-25T18:23:46.007937Z","iopub.status.idle":"2023-10-25T18:23:46.017288Z","shell.execute_reply.started":"2023-10-25T18:23:46.007906Z","shell.execute_reply":"2023-10-25T18:23:46.015258Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# plot the distribution of updated enmo-values\nplt.figure(figsize=(10, 6))\nax = sns.distplot(train_series.enmo)\nplt.title('Distribution of updated ENMO values in train_series')\nplt.show()","metadata":{"_kg_hide-input":true,"execution":{"iopub.status.busy":"2023-10-25T18:23:51.640099Z","iopub.execute_input":"2023-10-25T18:23:51.640585Z","iopub.status.idle":"2023-10-25T18:33:06.670034Z","shell.execute_reply.started":"2023-10-25T18:23:51.640549Z","shell.execute_reply":"2023-10-25T18:33:06.668269Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"<a id=\"vis_e\"></a> \n### Visualizing ENMO values for several random series_id","metadata":{}},{"cell_type":"markdown","source":"#### Visualizing anglez values for series_id = '18b61dd5aae8'","metadata":{}},{"cell_type":"code","source":"# make a subset of data for series_id = '18b61dd5aae8'\n\nid_18b61dd5aae8 = train_series[train_series['series_id'] == '18b61dd5aae8']\nid_18b61dd5aae8","metadata":{"_kg_hide-input":true,"_kg_hide-output":true,"execution":{"iopub.status.busy":"2023-10-25T18:33:16.969230Z","iopub.execute_input":"2023-10-25T18:33:16.969776Z","iopub.status.idle":"2023-10-25T18:33:17.200894Z","shell.execute_reply.started":"2023-10-25T18:33:16.969722Z","shell.execute_reply":"2023-10-25T18:33:17.199543Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# make a subset of data for series_id = '18b61dd5aae8' on 2017-12-23 & 2017-12-24\n\nid_18b61dd5aae8_23 = id_18b61dd5aae8[id_18b61dd5aae8['date'].isin(['2017-12-23', '2017-12-24'])]\nid_18b61dd5aae8_23","metadata":{"_kg_hide-input":true,"_kg_hide-output":true,"execution":{"iopub.status.busy":"2023-10-25T18:33:20.669214Z","iopub.execute_input":"2023-10-25T18:33:20.669671Z","iopub.status.idle":"2023-10-25T18:33:20.704599Z","shell.execute_reply.started":"2023-10-25T18:33:20.669636Z","shell.execute_reply":"2023-10-25T18:33:20.703053Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# plot the anglez data for series_id = '18b61dd5aae8' on 2017-12-23 & 2017-12-24\n\nplt.figure(figsize = (20, 6))\nax = sns.lineplot(x = id_18b61dd5aae8_23.timestamp, y = id_18b61dd5aae8_23.enmo, color = '#FF7F50')\nplt.title('ENMO values for series_id \"18b61dd5aae8\" on 2017-12-23 & 2017-12-24')\nplt.show()","metadata":{"_kg_hide-input":true,"execution":{"iopub.status.busy":"2023-10-25T18:33:23.910329Z","iopub.execute_input":"2023-10-25T18:33:23.910833Z","iopub.status.idle":"2023-10-25T18:33:27.224344Z","shell.execute_reply.started":"2023-10-25T18:33:23.910794Z","shell.execute_reply":"2023-10-25T18:33:27.222995Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"#### Visualizing anglez values for series_id = '2b0a1fa8eba8'","metadata":{}},{"cell_type":"code","source":"# make a subset of data for series_id = '2b0a1fa8eba8'\n\nid_2b0a1fa8eba8 = train_series[train_series['series_id'] == '2b0a1fa8eba8']\nid_2b0a1fa8eba8","metadata":{"_kg_hide-input":true,"_kg_hide-output":true,"execution":{"iopub.status.busy":"2023-10-25T18:33:30.281431Z","iopub.execute_input":"2023-10-25T18:33:30.282481Z","iopub.status.idle":"2023-10-25T18:33:30.501100Z","shell.execute_reply.started":"2023-10-25T18:33:30.282432Z","shell.execute_reply":"2023-10-25T18:33:30.499815Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# make a subset of data for series_id = '2b0a1fa8eba8' on 2018-11-13 & 2018-11-14\n\nid_2b0a1fa8eba8_14 = id_2b0a1fa8eba8[id_2b0a1fa8eba8['date'].isin(['2018-11-13', '2018-11-14'])]\nid_2b0a1fa8eba8_14","metadata":{"_kg_hide-input":true,"_kg_hide-output":true,"execution":{"iopub.status.busy":"2023-10-25T18:33:33.682695Z","iopub.execute_input":"2023-10-25T18:33:33.683894Z","iopub.status.idle":"2023-10-25T18:33:33.714979Z","shell.execute_reply.started":"2023-10-25T18:33:33.683851Z","shell.execute_reply":"2023-10-25T18:33:33.713771Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# plot the anglez data for series_id = '2b0a1fa8eba8' on 2018-11-13 & 2018-11-14\n\nplt.figure(figsize = (20, 6))\nax = sns.lineplot(x = id_2b0a1fa8eba8_14.timestamp, y = id_2b0a1fa8eba8_14.enmo, color = '#FF7F50')\nplt.title('ENMO values for series_id \"2b0a1fa8eba8\" on 2018-11-13 & 2018-11-14')\nplt.show()","metadata":{"_kg_hide-input":true,"execution":{"iopub.status.busy":"2023-10-25T18:33:36.993036Z","iopub.execute_input":"2023-10-25T18:33:36.993515Z","iopub.status.idle":"2023-10-25T18:33:39.154452Z","shell.execute_reply.started":"2023-10-25T18:33:36.993479Z","shell.execute_reply":"2023-10-25T18:33:39.153019Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"#### Visualizing anglez values for series_id = 'e0686434d029'","metadata":{}},{"cell_type":"code","source":"# make a subset of data for series_id = 'e0686434d029'\n\nid_e0686434d029 = train_series[train_series['series_id'] == 'e0686434d029']\nid_e0686434d029","metadata":{"_kg_hide-input":true,"_kg_hide-output":true,"execution":{"iopub.status.busy":"2023-10-25T18:33:41.957485Z","iopub.execute_input":"2023-10-25T18:33:41.958099Z","iopub.status.idle":"2023-10-25T18:33:42.212559Z","shell.execute_reply.started":"2023-10-25T18:33:41.958043Z","shell.execute_reply":"2023-10-25T18:33:42.211122Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# make a subset of data for series_id = 'e0686434d029' on 2018-04-07 & 2018-04-08\n\nid_e0686434d029_07 = id_e0686434d029[id_e0686434d029['date'].isin(['2018-04-07', '2018-04-08'])]\nid_e0686434d029_07","metadata":{"_kg_hide-input":true,"_kg_hide-output":true,"execution":{"iopub.status.busy":"2023-10-25T18:33:45.474433Z","iopub.execute_input":"2023-10-25T18:33:45.474895Z","iopub.status.idle":"2023-10-25T18:33:45.509945Z","shell.execute_reply.started":"2023-10-25T18:33:45.474858Z","shell.execute_reply":"2023-10-25T18:33:45.508560Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# plot the anglez data for series_id = 'e0686434d029' on 2018-04-07 & 2018-04-08\n\nplt.figure(figsize = (20, 6))\nax = sns.lineplot(x = id_e0686434d029_07.timestamp, y = id_e0686434d029_07.enmo, color = '#FF7F50')\nstart2, end2 = ax.get_xlim()\nplt.xticks(np.arange(start2, end2, 0.5), rotation = 45)\nplt.title('ENMO values for series_id \"e0686434d029_07\" on 2018-04-07 & 2018-04-08')\nplt.show()","metadata":{"_kg_hide-input":true,"execution":{"iopub.status.busy":"2023-10-25T18:33:48.181593Z","iopub.execute_input":"2023-10-25T18:33:48.182076Z","iopub.status.idle":"2023-10-25T18:33:50.097425Z","shell.execute_reply.started":"2023-10-25T18:33:48.182041Z","shell.execute_reply":"2023-10-25T18:33:50.095786Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"Visualizing enmo for several series_id shows that enmo values are higher during daytime. Let's resample enmo values to reach some insights.","metadata":{}},{"cell_type":"markdown","source":"#### Visualizing resampled ENMO values for random series_id for 2 week period","metadata":{}},{"cell_type":"code","source":"# make a subset of data for series_id = '18b61dd5aae8' for 2 weeks period (2017-12-23 & 2018-01-05)\n\nid_18b61dd5aae8_2w = id_18b61dd5aae8[id_18b61dd5aae8['date'].isin(['2017-12-23', '2017-12-24', '2017-12-25', '2017-12-26', '2017-12-27', '2017-12-28', '2017-12-29', '2017-12-30', '2017-12-31', '2018-01-01', '2018-01-02', '2018-01-03', '2018-01-04', '2018-01-05'])]\nid_18b61dd5aae8_2w","metadata":{"_kg_hide-input":true,"_kg_hide-output":true,"execution":{"iopub.status.busy":"2023-10-25T18:33:53.943194Z","iopub.execute_input":"2023-10-25T18:33:53.943628Z","iopub.status.idle":"2023-10-25T18:33:53.991985Z","shell.execute_reply.started":"2023-10-25T18:33:53.943596Z","shell.execute_reply":"2023-10-25T18:33:53.990455Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# plot resampled enmo & anglez values for series_id = '18b61dd5aae8' during 2 weeks (2017-12-23 & 2018-01-05)\n\ncolour_enmo = '#FF7F50'\n\nfig, ax = plt.subplots(ncols = 1, nrows = 7, figsize = (20, 16))\n\nax1 = sns.lineplot(x = id_18b61dd5aae8_2w.timestamp, y = id_18b61dd5aae8_2w.enmo, color = colour_enmo, ax=ax[0])\nax[0].set_title('ENMO values for series_id \"18b61dd5aae8\" (5 sec.)', fontsize = 16)\nax1.set(xlabel = None)\nax1.tick_params('x', labelbottom=False)\n\nresampled_enmo_1min = id_18b61dd5aae8_2w[['timestamp', 'enmo']].resample('min', on='timestamp').mean().reset_index(drop=False)\nsns.lineplot(x = resampled_enmo_1min.timestamp, y = resampled_enmo_1min.enmo, color = colour_enmo, ax=ax[1])\nax[1].set_title('ENMO values for series_id \"18b61dd5aae8\" (resampled 1 min.)', fontsize = 16)\nax[1].set(xlabel = None)\nax[1].tick_params('x', labelbottom=False)\n\nresampled_enmo_15min = id_18b61dd5aae8_2w[['timestamp', 'enmo']].resample('15min', on='timestamp').mean().reset_index(drop=False)\nsns.lineplot(x = resampled_enmo_15min.timestamp, y = resampled_enmo_15min.enmo, color = colour_enmo, ax=ax[2])\nax[2].set_title('ENMO values for series_id \"18b61dd5aae8\" (resampled 15 min.)', fontsize = 16)\nax[2].set(xlabel = None)\nax[2].tick_params('x', labelbottom=False)\n\nresampled_enmo_30 = id_18b61dd5aae8_2w[['timestamp', 'enmo']].resample('30min', on='timestamp').mean().reset_index(drop=False)\nsns.lineplot(x = resampled_enmo_30.timestamp, y = resampled_enmo_30.enmo, color = colour_enmo, ax=ax[3])\nax[3].set_title('ENMO values for series_id \"18b61dd5aae8\" (resampled 30 min.)', fontsize = 16)\nax[3].set(xlabel = None)\nax[3].tick_params('x', labelbottom=False)\n\n\nresampled_enmo_1h = id_18b61dd5aae8_2w[['timestamp', 'enmo']].resample('H', on='timestamp').mean().reset_index(drop=False)\nsns.lineplot(x = resampled_enmo_1h.timestamp, y = resampled_enmo_1h.enmo, color = colour_enmo, ax=ax[4])\nax[4].set_title('ENMO values for series_id \"18b61dd5aae8\" (resampled 1 hour)', fontsize = 16)\nax[4].set(xlabel = None)\nax[4].tick_params('x', labelbottom=False)\n\n\nresampled_enmo_90min = id_18b61dd5aae8_2w[['timestamp', 'enmo']].resample('90min', on='timestamp').mean().reset_index(drop=False)\nsns.lineplot(x = resampled_enmo_90min.timestamp, y = resampled_enmo_90min.enmo, color = colour_enmo, ax=ax[5])\nax[5].set_title('ENMO values for series_id \"18b61dd5aae8\" (resampled 1.5 hour)', fontsize = 16)\nax[5].set(xlabel = None)\nax[5].tick_params('x', labelbottom=False)\n\n\nresampled_enmo_2h = id_18b61dd5aae8_2w[['timestamp', 'enmo']].resample('2H', on='timestamp').mean().reset_index(drop=False)\nsns.lineplot(x = resampled_enmo_2h.timestamp, y = resampled_enmo_2h.enmo, color = colour_enmo, ax=ax[6])\nax[6].set_title('ENMO values for series_id \"18b61dd5aae8\" (resampled 2 hour)', fontsize = 16)\n\nfig.suptitle('Resampled ENMO values for series_id \"18b61dd5aae8\" during 2 weeks', fontsize = 20)\n\n\nstart1, end1 = ax[6].get_xlim()\nplt.xticks(np.arange(start1, end1, 1), rotation = 45)\nplt.show()","metadata":{"_kg_hide-input":true,"_kg_hide-output":false,"execution":{"iopub.status.busy":"2023-10-25T18:33:57.235855Z","iopub.execute_input":"2023-10-25T18:33:57.236326Z","iopub.status.idle":"2023-10-25T18:34:11.381233Z","shell.execute_reply.started":"2023-10-25T18:33:57.236291Z","shell.execute_reply":"2023-10-25T18:34:11.379553Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"From the plot above we see that ENMO values are higher in daytime and lower during the night time (begining of every day) - seasonality of the data. Values with strong seasonality are not stationary. But stationarity could be checked in other ways.","metadata":{}},{"cell_type":"markdown","source":"<a id=\"vis_stationarity\"></a> \n\n### Check the stationarity of ENMO values\n\nWe can check the stationary by checking the **underlying statistics**. At first we'll split train_series into 2 parts and then check the mean & variance for every part. ","metadata":{}},{"cell_type":"code","source":"# assign enmo values of all the train_series to the variable enmo_val\n\nenmo_val = train_series.enmo","metadata":{"_kg_hide-input":true,"execution":{"iopub.status.busy":"2023-10-25T18:35:22.447728Z","iopub.execute_input":"2023-10-25T18:35:22.449806Z","iopub.status.idle":"2023-10-25T18:35:22.457445Z","shell.execute_reply.started":"2023-10-25T18:35:22.449732Z","shell.execute_reply":"2023-10-25T18:35:22.455640Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# split train_series into 2 parts \nnum_partitions = 2\nsplit = int(len(enmo_val) / num_partitions)\nenmo_val1, enmo_val2 = enmo_val[0:split], enmo_val[split:]","metadata":{"_kg_hide-input":true,"execution":{"iopub.status.busy":"2023-10-25T18:35:26.287376Z","iopub.execute_input":"2023-10-25T18:35:26.287886Z","iopub.status.idle":"2023-10-25T18:35:26.296586Z","shell.execute_reply.started":"2023-10-25T18:35:26.287846Z","shell.execute_reply":"2023-10-25T18:35:26.294768Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# calculate variance & mean for every part of the splitted dataset\nmean1 = enmo_val1.mean()\nmean2 = enmo_val2.mean()\nvar1 = enmo_val1.var()\nvar2 = enmo_val2.var()","metadata":{"_kg_hide-input":true,"execution":{"iopub.status.busy":"2023-10-25T18:35:30.036070Z","iopub.execute_input":"2023-10-25T18:35:30.036536Z","iopub.status.idle":"2023-10-25T18:35:31.794001Z","shell.execute_reply.started":"2023-10-25T18:35:30.036503Z","shell.execute_reply":"2023-10-25T18:35:31.792523Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"print('mean1 = %f, mean2 = %f' % (mean1, mean2))\nprint('variance1 = %f, variance2 = %f' % (var1, var2))","metadata":{"_kg_hide-input":true,"execution":{"iopub.status.busy":"2023-10-25T18:35:34.954671Z","iopub.execute_input":"2023-10-25T18:35:34.955208Z","iopub.status.idle":"2023-10-25T18:35:34.963156Z","shell.execute_reply.started":"2023-10-25T18:35:34.955168Z","shell.execute_reply":"2023-10-25T18:35:34.961397Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"We see that ENMO values show constant mean & variance. It is one of the characteristics of stationary data. To control this result we'll use Augmented Dickey-Fuller (ADF) test. **Due to huge size of the train_series we'll apply ADF test to the subsets of several randomly selected series_id.**","metadata":{}},{"cell_type":"markdown","source":"**Check stationarity of enmo values for series_id = '18b61dd5aae8'**","metadata":{}},{"cell_type":"code","source":"# make a subset of data for series_id = '18b61dd5aae8'\nadfl = train_series[train_series['series_id'].isin(['18b61dd5aae8'])]","metadata":{"_kg_hide-input":true,"execution":{"iopub.status.busy":"2023-10-25T18:37:39.110445Z","iopub.execute_input":"2023-10-25T18:37:39.111043Z","iopub.status.idle":"2023-10-25T18:37:39.564307Z","shell.execute_reply.started":"2023-10-25T18:37:39.111005Z","shell.execute_reply":"2023-10-25T18:37:39.562855Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# check stationarity of enmo values for selected series_id with Augmented Dickey-Fuller (ADF) test\n\nfrom statsmodels.tsa.stattools import adfuller","metadata":{"_kg_hide-input":true,"execution":{"iopub.status.busy":"2023-10-25T18:37:54.814673Z","iopub.execute_input":"2023-10-25T18:37:54.815206Z","iopub.status.idle":"2023-10-25T18:37:54.821762Z","shell.execute_reply.started":"2023-10-25T18:37:54.815169Z","shell.execute_reply":"2023-10-25T18:37:54.820259Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# assign enmo values of the subset to a new variable\nad = adfl.enmo","metadata":{"_kg_hide-input":true,"execution":{"iopub.status.busy":"2023-10-25T18:37:47.339865Z","iopub.execute_input":"2023-10-25T18:37:47.341352Z","iopub.status.idle":"2023-10-25T18:37:47.347431Z","shell.execute_reply.started":"2023-10-25T18:37:47.341298Z","shell.execute_reply":"2023-10-25T18:37:47.345831Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# split enmo values of a subset into several parts\nnum_partitions3 = 3\nsplit3 = int(len(ad) / num_partitions3)","metadata":{"_kg_hide-input":true,"execution":{"iopub.status.busy":"2023-10-25T18:37:57.134401Z","iopub.execute_input":"2023-10-25T18:37:57.135190Z","iopub.status.idle":"2023-10-25T18:37:57.142417Z","shell.execute_reply.started":"2023-10-25T18:37:57.135139Z","shell.execute_reply":"2023-10-25T18:37:57.141029Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# assign every part of the splitted dataset to different variable\nenmo_val_1, enmo_val_2, enmo_val_3  = ad[0:split3], ad[(split3*1):(split3*2)], ad[(split3*2):(split3*3)]\n","metadata":{"_kg_hide-input":true,"execution":{"iopub.status.busy":"2023-10-25T18:38:00.695661Z","iopub.execute_input":"2023-10-25T18:38:00.696179Z","iopub.status.idle":"2023-10-25T18:38:00.704165Z","shell.execute_reply.started":"2023-10-25T18:38:00.696140Z","shell.execute_reply":"2023-10-25T18:38:00.702751Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# calculate ADF test for the first part of the splitted subset\nresult1 = adfuller(enmo_val_1)","metadata":{"_kg_hide-input":true,"execution":{"iopub.status.busy":"2023-10-25T18:38:03.727238Z","iopub.execute_input":"2023-10-25T18:38:03.727679Z","iopub.status.idle":"2023-10-25T18:39:31.303796Z","shell.execute_reply.started":"2023-10-25T18:38:03.727644Z","shell.execute_reply":"2023-10-25T18:39:31.301771Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# calculate ADF test for the second part of the splitted subset\nresult2 = adfuller(enmo_val_2)","metadata":{"_kg_hide-input":true,"execution":{"iopub.status.busy":"2023-10-25T18:40:14.848174Z","iopub.execute_input":"2023-10-25T18:40:14.848683Z","iopub.status.idle":"2023-10-25T18:41:39.438187Z","shell.execute_reply.started":"2023-10-25T18:40:14.848643Z","shell.execute_reply":"2023-10-25T18:41:39.436339Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# calculate ADF test for the third part of the splitted subset\nresult3 = adfuller(enmo_val_3)","metadata":{"_kg_hide-input":true,"execution":{"iopub.status.busy":"2023-10-25T18:41:54.422896Z","iopub.execute_input":"2023-10-25T18:41:54.423328Z","iopub.status.idle":"2023-10-25T18:43:28.785647Z","shell.execute_reply.started":"2023-10-25T18:41:54.423296Z","shell.execute_reply":"2023-10-25T18:43:28.783259Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# print the results of the ADF test for part1\nprint('ADF Statistic for part 1: %f' % result1[0])\nprint('ADF p_value: %f' % (result1[1]))\nprint('Critical values:')\n\nfor key,value in result1[4].items():\n    print('\\t%s: %.3f' % (key, value))","metadata":{"_kg_hide-input":true,"execution":{"iopub.status.busy":"2023-10-25T18:44:12.683093Z","iopub.execute_input":"2023-10-25T18:44:12.683586Z","iopub.status.idle":"2023-10-25T18:44:12.693906Z","shell.execute_reply.started":"2023-10-25T18:44:12.683550Z","shell.execute_reply":"2023-10-25T18:44:12.692075Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# print the results of the ADF test for part2\nprint('ADF Statistic for part 2: %f' % result2[0])\nprint('ADF p_value: %f' % (result2[1]))\nprint('Critical values:')\n\nfor key,value in result2[4].items():\n    print('\\t%s: %.3f' % (key, value))","metadata":{"_kg_hide-input":true,"execution":{"iopub.status.busy":"2023-10-25T18:44:15.802289Z","iopub.execute_input":"2023-10-25T18:44:15.802742Z","iopub.status.idle":"2023-10-25T18:44:15.809287Z","shell.execute_reply.started":"2023-10-25T18:44:15.802694Z","shell.execute_reply":"2023-10-25T18:44:15.808431Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# print the results of the ADF test for part3\nprint('ADF Statistic for part 3: %f' % result3[0])\nprint('ADF p_value: %f' % (result3[1]))\nprint('Critical values:')\n\nfor key,value in result3[4].items():\n    print('\\t%s: %.3f' % (key, value))","metadata":{"_kg_hide-input":true,"execution":{"iopub.status.busy":"2023-10-25T18:44:19.164829Z","iopub.execute_input":"2023-10-25T18:44:19.165971Z","iopub.status.idle":"2023-10-25T18:44:19.172204Z","shell.execute_reply.started":"2023-10-25T18:44:19.165925Z","shell.execute_reply":"2023-10-25T18:44:19.171344Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"According to the results we can say that enmo valuesfor series_id = '18b61dd5aae8' are stationary (due to all the results are less than critical values).","metadata":{}},{"cell_type":"markdown","source":"**Check stationarity of enmo values for series_id = 'a9a2f7fac455'**\n\n\n\nLet's check stationarity of enmo values for several random selected series_id. ","metadata":{}},{"cell_type":"code","source":"# make a subset of data for series_id = 'a9a2f7fac455'\nadfl2 = train_series[train_series['series_id'] == 'a9a2f7fac455']","metadata":{"_kg_hide-input":true,"execution":{"iopub.status.busy":"2023-10-25T18:44:24.704971Z","iopub.execute_input":"2023-10-25T18:44:24.705409Z","iopub.status.idle":"2023-10-25T18:44:24.940074Z","shell.execute_reply.started":"2023-10-25T18:44:24.705380Z","shell.execute_reply":"2023-10-25T18:44:24.938391Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# assign enmo values of the subset to a new variable\nad2 = adfl2.enmo","metadata":{"_kg_hide-input":true,"execution":{"iopub.status.busy":"2023-10-25T18:44:28.867578Z","iopub.execute_input":"2023-10-25T18:44:28.868046Z","iopub.status.idle":"2023-10-25T18:44:28.874911Z","shell.execute_reply.started":"2023-10-25T18:44:28.868010Z","shell.execute_reply":"2023-10-25T18:44:28.873465Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# split enmo values of a subset into several parts\nnum_partitions3 = 3\nsplit4 = int(len(ad2) / num_partitions3)","metadata":{"_kg_hide-input":true,"execution":{"iopub.status.busy":"2023-10-25T18:44:33.213864Z","iopub.execute_input":"2023-10-25T18:44:33.214311Z","iopub.status.idle":"2023-10-25T18:44:33.221596Z","shell.execute_reply.started":"2023-10-25T18:44:33.214278Z","shell.execute_reply":"2023-10-25T18:44:33.219900Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# assign every part of the splitted dataset to different variables\nenmo2_val_1, enmo2_val_2, enmo2_val_3  = ad2[0:split4], ad2[(split4*1):(split4*2)], ad2[(split4*2):(split4*3)]","metadata":{"_kg_hide-input":true,"execution":{"iopub.status.busy":"2023-10-25T18:44:39.454226Z","iopub.execute_input":"2023-10-25T18:44:39.454916Z","iopub.status.idle":"2023-10-25T18:44:39.462897Z","shell.execute_reply.started":"2023-10-25T18:44:39.454853Z","shell.execute_reply":"2023-10-25T18:44:39.461264Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# calculate ADF test for the first part of the splitted subset\nresult1_2 = adfuller(enmo2_val_1)","metadata":{"_kg_hide-input":true,"execution":{"iopub.status.busy":"2023-10-25T18:44:43.128920Z","iopub.execute_input":"2023-10-25T18:44:43.129312Z","iopub.status.idle":"2023-10-25T18:46:55.414159Z","shell.execute_reply.started":"2023-10-25T18:44:43.129283Z","shell.execute_reply":"2023-10-25T18:46:55.412318Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# calculate ADF test for the second part of the splitted subset\nresult2_2 = adfuller(enmo2_val_2)","metadata":{"_kg_hide-input":true,"execution":{"iopub.status.busy":"2023-10-25T18:48:06.807350Z","iopub.execute_input":"2023-10-25T18:48:06.807841Z","iopub.status.idle":"2023-10-25T18:50:17.147968Z","shell.execute_reply.started":"2023-10-25T18:48:06.807804Z","shell.execute_reply":"2023-10-25T18:50:17.146124Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# calculate ADF test for the third part of the splitted subset\nresult3_2 = adfuller(enmo2_val_3)","metadata":{"_kg_hide-input":true,"execution":{"iopub.status.busy":"2023-10-25T18:50:24.349735Z","iopub.execute_input":"2023-10-25T18:50:24.350313Z","iopub.status.idle":"2023-10-25T18:52:32.114620Z","shell.execute_reply.started":"2023-10-25T18:50:24.350278Z","shell.execute_reply":"2023-10-25T18:52:32.112069Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# print the results of the ADF test for part1\nprint('ADF Statistic for part 1: %f' % result1_2[0])\nprint('ADF p_value: %f' % (result1_2[1]))\nprint('Critical values:')\n\nfor key,value in result1_2[4].items():\n    print('\\t%s: %.3f' % (key, value))","metadata":{"_kg_hide-input":true,"execution":{"iopub.status.busy":"2023-10-25T18:53:11.596143Z","iopub.execute_input":"2023-10-25T18:53:11.596621Z","iopub.status.idle":"2023-10-25T18:53:11.605289Z","shell.execute_reply.started":"2023-10-25T18:53:11.596589Z","shell.execute_reply":"2023-10-25T18:53:11.603681Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# print the results of the ADF test for part2\nprint('ADF Statistic for part 2: %f' % result2_2[0])\nprint('ADF p_value: %f' % (result2_2[1]))\nprint('Critical values:')\n\nfor key,value in result2_2[4].items():\n    print('\\t%s: %.3f' % (key, value))","metadata":{"_kg_hide-input":true,"execution":{"iopub.status.busy":"2023-10-25T18:53:15.803870Z","iopub.execute_input":"2023-10-25T18:53:15.804366Z","iopub.status.idle":"2023-10-25T18:53:15.813612Z","shell.execute_reply.started":"2023-10-25T18:53:15.804329Z","shell.execute_reply":"2023-10-25T18:53:15.812543Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# print the results of the ADF test for part3\nprint('ADF Statistic for part 3: %f' % result3_2[0])\nprint('ADF p_value: %f' % (result3_2[1]))\nprint('Critical values:')\n\nfor key,value in result3_2[4].items():\n    print('\\t%s: %.3f' % (key, value))","metadata":{"_kg_hide-input":true,"execution":{"iopub.status.busy":"2023-10-25T18:53:18.951172Z","iopub.execute_input":"2023-10-25T18:53:18.951659Z","iopub.status.idle":"2023-10-25T18:53:18.960753Z","shell.execute_reply.started":"2023-10-25T18:53:18.951625Z","shell.execute_reply":"2023-10-25T18:53:18.959283Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"We see that ADF test results are much less than critical values. So enmo values for series_id= 'a9a2f7fac455' are stationary.","metadata":{}},{"cell_type":"markdown","source":"**Check stationarity of enmo values for series_id = '60e51cad2ffb'**","metadata":{}},{"cell_type":"code","source":"# make a subset of data for series_id = '60e51cad2ffb'\nadfl3 = train_series[train_series['series_id'] == '60e51cad2ffb']","metadata":{"_kg_hide-input":true,"execution":{"iopub.status.busy":"2023-10-25T18:53:22.671379Z","iopub.execute_input":"2023-10-25T18:53:22.671894Z","iopub.status.idle":"2023-10-25T18:53:22.822596Z","shell.execute_reply.started":"2023-10-25T18:53:22.671851Z","shell.execute_reply":"2023-10-25T18:53:22.819174Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# assign enmo values of the subset to a new variable\nad3 = adfl3.enmo","metadata":{"_kg_hide-input":true,"execution":{"iopub.status.busy":"2023-10-25T18:53:24.992849Z","iopub.execute_input":"2023-10-25T18:53:24.993592Z","iopub.status.idle":"2023-10-25T18:53:25.012830Z","shell.execute_reply.started":"2023-10-25T18:53:24.993543Z","shell.execute_reply":"2023-10-25T18:53:25.008768Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# split enmo values of a subset into several parts\nnum_partitions3 = 3\nsplit5 = int(len(ad3) / num_partitions3)","metadata":{"_kg_hide-input":true,"execution":{"iopub.status.busy":"2023-10-25T18:53:29.470658Z","iopub.execute_input":"2023-10-25T18:53:29.471170Z","iopub.status.idle":"2023-10-25T18:53:29.485369Z","shell.execute_reply.started":"2023-10-25T18:53:29.471133Z","shell.execute_reply":"2023-10-25T18:53:29.482272Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# assign every part of the splitted dataset to different variables\nenmo3_val_1, enmo3_val_2, enmo3_val_3  = ad3[0:split5], ad3[(split5*1):(split5*2)], ad3[(split5*2):(split5*3)]","metadata":{"_kg_hide-input":true,"execution":{"iopub.status.busy":"2023-10-25T18:53:36.884865Z","iopub.execute_input":"2023-10-25T18:53:36.885382Z","iopub.status.idle":"2023-10-25T18:53:36.892797Z","shell.execute_reply.started":"2023-10-25T18:53:36.885345Z","shell.execute_reply":"2023-10-25T18:53:36.891207Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# calculate ADF test for the first part of the splitted subset\nresult1_3 = adfuller(enmo3_val_1)","metadata":{"_kg_hide-input":true,"execution":{"iopub.status.busy":"2023-10-25T18:53:42.927089Z","iopub.execute_input":"2023-10-25T18:53:42.927490Z","iopub.status.idle":"2023-10-25T18:53:52.142989Z","shell.execute_reply.started":"2023-10-25T18:53:42.927461Z","shell.execute_reply":"2023-10-25T18:53:52.141159Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# calculate ADF test for the second part of the splitted subset\nresult2_3 = adfuller(enmo3_val_2)","metadata":{"_kg_hide-input":true,"execution":{"iopub.status.busy":"2023-10-25T18:54:30.938013Z","iopub.execute_input":"2023-10-25T18:54:30.938522Z","iopub.status.idle":"2023-10-25T18:54:37.846021Z","shell.execute_reply.started":"2023-10-25T18:54:30.938486Z","shell.execute_reply":"2023-10-25T18:54:37.843833Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# calculate ADF test for the third part of the splitted subset\nresult3_3 = adfuller(enmo3_val_3)","metadata":{"_kg_hide-input":true,"execution":{"iopub.status.busy":"2023-10-25T18:55:25.828799Z","iopub.execute_input":"2023-10-25T18:55:25.829304Z","iopub.status.idle":"2023-10-25T18:55:32.908595Z","shell.execute_reply.started":"2023-10-25T18:55:25.829267Z","shell.execute_reply":"2023-10-25T18:55:32.906472Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# print the results of the ADF test for part1\nprint('ADF Statistic for part 1: %f' % result1_3[0])\nprint('ADF p_value: %f' % (result1_3[1]))\nprint('Critical values:')\n\nfor key,value in result1_3[4].items():\n    print('\\t%s: %.3f' % (key, value))","metadata":{"_kg_hide-input":true,"execution":{"iopub.status.busy":"2023-10-25T18:56:12.847540Z","iopub.execute_input":"2023-10-25T18:56:12.848057Z","iopub.status.idle":"2023-10-25T18:56:12.856444Z","shell.execute_reply.started":"2023-10-25T18:56:12.848019Z","shell.execute_reply":"2023-10-25T18:56:12.855278Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# print the results of the ADF test for part2\nprint('ADF Statistic for part 2: %f' % result2_3[0])\nprint('ADF p_value: %f' % (result2_3[1]))\nprint('Critical values:')\n\nfor key,value in result2_3[4].items():\n    print('\\t%s: %.3f' % (key, value))","metadata":{"_kg_hide-input":true,"execution":{"iopub.status.busy":"2023-10-25T18:56:16.143951Z","iopub.execute_input":"2023-10-25T18:56:16.144472Z","iopub.status.idle":"2023-10-25T18:56:16.154372Z","shell.execute_reply.started":"2023-10-25T18:56:16.144433Z","shell.execute_reply":"2023-10-25T18:56:16.152312Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# print the results of the ADF test for part3\nprint('ADF Statistic for part 3: %f' % result3_3[0])\nprint('ADF p_value: %f' % (result3_3[1]))\nprint('Critical values:')\n\nfor key,value in result3_3[4].items():\n    print('\\t%s: %.3f' % (key, value))","metadata":{"_kg_hide-input":true,"execution":{"iopub.status.busy":"2023-10-25T18:56:19.585118Z","iopub.execute_input":"2023-10-25T18:56:19.585558Z","iopub.status.idle":"2023-10-25T18:56:19.596077Z","shell.execute_reply.started":"2023-10-25T18:56:19.585525Z","shell.execute_reply":"2023-10-25T18:56:19.594210Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"It is seen that ADF test results are much less than critical values. So enmo values for series_id = '60e51cad2ffb' are stationary. Due to the underlying statistics for enmo values of all dataset and due to results of checking stationarity of enmo values for several random series_id **we suppose ENMO values to be stationary**.","metadata":{}},{"cell_type":"markdown","source":"<a id=\"subsubsection_six\"></a>\n#### Summary on 'ENMO' metric analysis\n\n* Competition dataset doesn't specify the enmo metric measurements. Breif analysis of some scientific papers let to suppose that in the competition dataset **ENMO was measured in gravity unites (g)**.\n\n* According to ENMO values of some scientific papers we can suppose that **mean ENMO values should be about 0.0093 gravity unites for sedentary behaviours; and around 0.1075 gravity unites for active periods.** These data is consistent with train_series data where ENMO mean = 0.041315 and 99% of ENMO values are up to 0.39430001378059387. \n\n* Extreme enmo values can be explained by extreme activities (e.g. pounding on a table) and they don't last very long periods.\n\n* ENMO outliers were fixed at the level = 1.5.\n\n* ENMO values are stationary.","metadata":{}},{"cell_type":"markdown","source":"<a id=\"subsection_anglez\"></a>\n### ANGLEZ metric data analysis","metadata":{}},{"cell_type":"markdown","source":"<a id=\"subsubsection_seven\"></a>\n#### Definition of 'anglez' ","metadata":{}},{"cell_type":"markdown","source":"Competition data description provides the following information:\n\n* **anglez** - As calculated and described by the [GGIR package](https://cran.r-project.org/web/packages/GGIR/vignettes/GGIR.html#4_Inspecting_the_results), z-angle is a metric derived from individual accelerometer components that is commonly used in sleep detection, and refers to the **angle of the arm relative to the vertical axis of the body**.","metadata":{}},{"cell_type":"markdown","source":"GGIR package provides an example of what ANGLEZ-records might look 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image above shows the naturally expected changes in the arm angle data during periods of sleep and activity. It is naturally to observe more frequent changes of the anglez values during non-sleeping period when a someone's arm position changes more often than during sleep period.","metadata":{}},{"cell_type":"markdown","source":"GGIR documentation [Package ‘GGIR’](https://cran.r-project.org/web/packages/GGIR/GGIR.pdf) provides following formula for calculating anglez:\n\n![anglez.png](attachment:ac5c6013-a611-439a-bf51-fbe599797a36.png)","metadata":{},"attachments":{"ac5c6013-a611-439a-bf51-fbe599797a36.png":{"image/png":"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"}}},{"cell_type":"markdown","source":"In the article mentioned earlier [van Hees, V.T., Sabia, S., Jones, S.E. et al. Estimating sleep parameters using an accelerometer without sleep diary. Sci Rep 8, 12975 (2018)](https://doi.org/10.1038/s41598-018-31266-z)  we find explanation of the anglez metric formula:\n\n> where ax, ay, and az are the median values of the three orthogonally positioned raw acceleration sensors in gravitational (g) units (1 g = 1000 mg) derived based on a rolling five second time window. Here, the z-axis corresponds to the axis positioned perpendicular to the skin surface (dorsal-ventral direction when the wrist is in the anatomical position). ","metadata":{}},{"cell_type":"markdown","source":"<a id=\"subsubsection_eight\"></a>\n#### Descriptive statistics for 'anglez' column","metadata":{}},{"cell_type":"code","source":"mxa= train_series.anglez.max()","metadata":{"_kg_hide-input":true,"execution":{"iopub.status.busy":"2023-10-25T18:56:28.284269Z","iopub.execute_input":"2023-10-25T18:56:28.284691Z","iopub.status.idle":"2023-10-25T18:56:29.140739Z","shell.execute_reply.started":"2023-10-25T18:56:28.284662Z","shell.execute_reply":"2023-10-25T18:56:29.139103Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"mna = train_series.anglez.min()","metadata":{"_kg_hide-input":true,"execution":{"iopub.status.busy":"2023-10-25T18:56:32.062548Z","iopub.execute_input":"2023-10-25T18:56:32.063063Z","iopub.status.idle":"2023-10-25T18:56:32.338465Z","shell.execute_reply.started":"2023-10-25T18:56:32.063024Z","shell.execute_reply":"2023-10-25T18:56:32.337147Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"meana = train_series.anglez.mean()","metadata":{"_kg_hide-input":true,"execution":{"iopub.status.busy":"2023-10-25T18:56:35.901854Z","iopub.execute_input":"2023-10-25T18:56:35.902362Z","iopub.status.idle":"2023-10-25T18:56:36.178102Z","shell.execute_reply.started":"2023-10-25T18:56:35.902323Z","shell.execute_reply":"2023-10-25T18:56:36.176721Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"mda = train_series.anglez.median()","metadata":{"_kg_hide-input":true,"execution":{"iopub.status.busy":"2023-10-25T18:56:39.508404Z","iopub.execute_input":"2023-10-25T18:56:39.508907Z","iopub.status.idle":"2023-10-25T18:56:42.715993Z","shell.execute_reply.started":"2023-10-25T18:56:39.508870Z","shell.execute_reply":"2023-10-25T18:56:42.714616Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"sta = train_series.anglez.std()","metadata":{"_kg_hide-input":true,"execution":{"iopub.status.busy":"2023-10-25T18:56:44.165471Z","iopub.execute_input":"2023-10-25T18:56:44.165975Z","iopub.status.idle":"2023-10-25T18:56:45.455203Z","shell.execute_reply.started":"2023-10-25T18:56:44.165937Z","shell.execute_reply":"2023-10-25T18:56:45.453724Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"perc25a = train_series.anglez.quantile(0.25)","metadata":{"_kg_hide-input":true,"execution":{"iopub.status.busy":"2023-10-25T18:56:49.087448Z","iopub.execute_input":"2023-10-25T18:56:49.088800Z","iopub.status.idle":"2023-10-25T18:56:50.396751Z","shell.execute_reply.started":"2023-10-25T18:56:49.088729Z","shell.execute_reply":"2023-10-25T18:56:50.395313Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"perc75a = train_series.anglez.quantile(0.75)","metadata":{"_kg_hide-input":true,"execution":{"iopub.status.busy":"2023-10-25T18:56:53.635021Z","iopub.execute_input":"2023-10-25T18:56:53.635496Z","iopub.status.idle":"2023-10-25T18:56:55.142289Z","shell.execute_reply.started":"2023-10-25T18:56:53.635464Z","shell.execute_reply":"2023-10-25T18:56:55.140792Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"perc99a = train_series.anglez.quantile(0.99)","metadata":{"_kg_hide-input":true,"execution":{"iopub.status.busy":"2023-10-25T18:56:58.806022Z","iopub.execute_input":"2023-10-25T18:56:58.806508Z","iopub.status.idle":"2023-10-25T18:57:00.219963Z","shell.execute_reply.started":"2023-10-25T18:56:58.806476Z","shell.execute_reply":"2023-10-25T18:57:00.218289Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"perc1a = train_series.anglez.quantile(0.01)","metadata":{"_kg_hide-input":true,"execution":{"iopub.status.busy":"2023-10-25T18:57:03.974930Z","iopub.execute_input":"2023-10-25T18:57:03.975398Z","iopub.status.idle":"2023-10-25T18:57:05.467315Z","shell.execute_reply.started":"2023-10-25T18:57:03.975365Z","shell.execute_reply":"2023-10-25T18:57:05.465839Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"print(f'anglez max is {mxa}')\nprint(f'anglez min is {mna}')\nprint(f'anglez mean is {meana}')\nprint(f'anglez standard deviation is {sta}')\nprint(f'anglez first quantile is {perc25a}')\nprint(f'anglez median is {mda}')\nprint(f'anglez third quantile is {perc75a}')\nprint(f'anglez 99 percentile is {perc99a}')\nprint(f'anglez 1 percentile is {perc1a}')","metadata":{"_kg_hide-input":true,"execution":{"iopub.status.busy":"2023-10-25T18:57:08.855098Z","iopub.execute_input":"2023-10-25T18:57:08.855577Z","iopub.status.idle":"2023-10-25T18:57:08.865001Z","shell.execute_reply.started":"2023-10-25T18:57:08.855543Z","shell.execute_reply":"2023-10-25T18:57:08.863320Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"From the descriptiv statistics it is seen that anglez-values distribution is similar to the Normal distribution. Let's plot the distribution of 'anglez' values.","metadata":{}},{"cell_type":"code","source":"# plot the distribution of anglez-values\nplt.figure(figsize=(10, 6))\nax = sns.distplot(train_series.anglez)\nplt.title('Distribution of anglez values in train_series')\nplt.show()","metadata":{"_kg_hide-input":true,"execution":{"iopub.status.busy":"2023-10-25T18:57:23.144875Z","iopub.execute_input":"2023-10-25T18:57:23.145366Z","iopub.status.idle":"2023-10-25T19:07:05.267485Z","shell.execute_reply.started":"2023-10-25T18:57:23.145331Z","shell.execute_reply":"2023-10-25T19:07:05.266127Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"Let's look example of anglez values for one series_id during some period on a selected date.","metadata":{}},{"cell_type":"markdown","source":"<a id=\"vis_ae\"></a> \n### Visualizing ANGLEZ (accompanied with ENMO) values for several random series_id","metadata":{}},{"cell_type":"markdown","source":"#### Visualizing anglez values for series_id = '0402a003dae9'","metadata":{}},{"cell_type":"code","source":"# make a subset of data for series_id = '0402a003dae9'\n\nid_0402a003dae9 = train_series[train_series['series_id'] == '0402a003dae9']\nid_0402a003dae9","metadata":{"_kg_hide-input":true,"_kg_hide-output":true,"execution":{"iopub.status.busy":"2023-10-25T19:21:03.252426Z","iopub.execute_input":"2023-10-25T19:21:03.253091Z","iopub.status.idle":"2023-10-25T19:21:03.592627Z","shell.execute_reply.started":"2023-10-25T19:21:03.253048Z","shell.execute_reply":"2023-10-25T19:21:03.590851Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# make a subset of data for series_id = '0402a003dae9' on 2018-12-20 & 2018-12-21\n\nid_0402a003dae9_21 = id_0402a003dae9[id_0402a003dae9['date'].isin(['2018-12-20', '2018-12-21'])]\nid_0402a003dae9_21","metadata":{"_kg_hide-input":true,"_kg_hide-output":true,"execution":{"iopub.status.busy":"2023-10-25T19:21:09.372908Z","iopub.execute_input":"2023-10-25T19:21:09.373399Z","iopub.status.idle":"2023-10-25T19:21:09.409369Z","shell.execute_reply.started":"2023-10-25T19:21:09.373363Z","shell.execute_reply":"2023-10-25T19:21:09.407999Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# plot the anglez data for series_id = '0402a003dae9' on 2018-12-19\n\npl_id_0402a003dae9_21 = id_0402a003dae9_21.loc[(id_0402a003dae9_21['step'] >= 21780.0) & (id_0402a003dae9_21['step'] <= 56339.0)]\nplt.figure(figsize=(20, 6))\nax = sns.lineplot(x=id_0402a003dae9_21.step, y=id_0402a003dae9_21.anglez, color='blue')\nplt.title('Anglez values for series_id \"0402a003dae9\" on 2018-12-20 & 2018-12-21')\nplt.show()","metadata":{"_kg_hide-input":true,"execution":{"iopub.status.busy":"2023-10-25T19:21:12.579442Z","iopub.execute_input":"2023-10-25T19:21:12.579915Z","iopub.status.idle":"2023-10-25T19:21:13.965430Z","shell.execute_reply.started":"2023-10-25T19:21:12.579882Z","shell.execute_reply":"2023-10-25T19:21:13.963810Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"We see that this visual corresponds to the expected picture from GGIR package: **anglez values change with following pattern - switching bitween periods with small & large range of changes in angles values**. Less changes should belong to sleep periods. We can check it after adding the data about sleep and active periods from the train_events data. For mow we can *add ENMO values to the graph with anglez values.*","metadata":{}},{"cell_type":"code","source":"# compare changes in anglez values with changes of enmo values at the same period \n# of the selected series_id = '0402a003dae9'\n\ncolour_enmo = '#FF7F50'\ncolour_anglez = '#0000FF'\n\nfig, ax1 = plt.subplots(figsize=(20, 6))\nax2 = ax1.twinx()\nax1.plot(id_0402a003dae9_21.step, id_0402a003dae9_21.anglez, color= colour_anglez)\nax2.plot(id_0402a003dae9_21.step, id_0402a003dae9_21.enmo, color= colour_enmo)\n\nax1.set_xlabel('Step')\nax1.set_ylabel('Anglez', color = colour_anglez, fontsize = 14)\n\n\nax2.set_ylabel('ENMO', color = colour_enmo, fontsize = 14)\n\n\nfig.suptitle('ENMO & ANGLEZ values for series_id \"0402a003dae9\" on 2018-12-20 & 2018-12-21', fontsize = 20)\nfig.autofmt_xdate()","metadata":{"_kg_hide-input":true,"execution":{"iopub.status.busy":"2023-10-25T19:21:18.015746Z","iopub.execute_input":"2023-10-25T19:21:18.016247Z","iopub.status.idle":"2023-10-25T19:21:18.893250Z","shell.execute_reply.started":"2023-10-25T19:21:18.016209Z","shell.execute_reply":"2023-10-25T19:21:18.892317Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"#### Visualizing anglez values for series_id = '04f547b8017d'","metadata":{}},{"cell_type":"code","source":"# make a subset of data for series_id = '04f547b8017d'\n\nid_04f547b8017d = train_series[train_series['series_id'] == '04f547b8017d']\nid_04f547b8017d.head(50)","metadata":{"_kg_hide-input":true,"_kg_hide-output":true,"execution":{"iopub.status.busy":"2023-10-25T19:21:22.663419Z","iopub.execute_input":"2023-10-25T19:21:22.664410Z","iopub.status.idle":"2023-10-25T19:21:22.914821Z","shell.execute_reply.started":"2023-10-25T19:21:22.664355Z","shell.execute_reply":"2023-10-25T19:21:22.913211Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# make a subset of data for series_id = '04f547b8017d' on '2018-11-29' & '2018-11-30'\n\nid_04f547b8017d_30 = id_04f547b8017d[id_04f547b8017d['date'].isin(['2018-11-29', '2018-11-30'])]\nid_04f547b8017d_30","metadata":{"_kg_hide-input":true,"_kg_hide-output":true,"execution":{"iopub.status.busy":"2023-10-25T19:21:27.707084Z","iopub.execute_input":"2023-10-25T19:21:27.707516Z","iopub.status.idle":"2023-10-25T19:21:27.741261Z","shell.execute_reply.started":"2023-10-25T19:21:27.707484Z","shell.execute_reply":"2023-10-25T19:21:27.739818Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# visualizing anglez & enmo values at the same period \n# of the selected series_id = '04f547b8017d'\n\ncolour_enmo = '#FF7F50'\ncolour_anglez = '#0000FF'\n\nfig, ax1 = plt.subplots(figsize=(20, 6))\nax2 = ax1.twinx()\nax1.plot(id_04f547b8017d_30.step, id_04f547b8017d_30.anglez, color= colour_anglez)\nax2.plot(id_04f547b8017d_30.step, id_04f547b8017d_30.enmo, color= colour_enmo)\n\nax1.set_xlabel('Step')\nax1.set_ylabel('Anglez', color = colour_anglez, fontsize = 14)\n\n\nax2.set_ylabel('ENMO', color = colour_enmo, fontsize = 14)\n\n\nfig.suptitle('ENMO & ANGLEZ values for series_id \"04f547b8017d\" on 2018-11-29 & 2018-11-30', fontsize = 20)\nfig.autofmt_xdate()","metadata":{"_kg_hide-input":true,"execution":{"iopub.status.busy":"2023-10-25T19:21:30.923553Z","iopub.execute_input":"2023-10-25T19:21:30.923980Z","iopub.status.idle":"2023-10-25T19:21:31.875534Z","shell.execute_reply.started":"2023-10-25T19:21:30.923949Z","shell.execute_reply":"2023-10-25T19:21:31.873762Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"#### Visualizing anglez values for series_id = '25e2b3dd9c3b'","metadata":{}},{"cell_type":"code","source":"# make a subset of data for series_id = '25e2b3dd9c3b'\n\nid_25e2b3dd9c3b = train_series[train_series['series_id'] == '25e2b3dd9c3b']\nid_25e2b3dd9c3b.head(50)","metadata":{"_kg_hide-input":true,"_kg_hide-output":true,"execution":{"iopub.status.busy":"2023-10-25T19:21:35.528814Z","iopub.execute_input":"2023-10-25T19:21:35.529290Z","iopub.status.idle":"2023-10-25T19:21:35.788355Z","shell.execute_reply.started":"2023-10-25T19:21:35.529256Z","shell.execute_reply":"2023-10-25T19:21:35.786808Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# make a subset of data for series_id = '25e2b3dd9c3b' on 2018-12-15 & 2018-12-16\n\nid_25e2b3dd9c3b_16 = id_25e2b3dd9c3b[id_25e2b3dd9c3b['date'].isin(['2018-12-15', '2018-12-16'])]\nid_25e2b3dd9c3b_16","metadata":{"_kg_hide-input":true,"_kg_hide-output":true,"execution":{"iopub.status.busy":"2023-10-25T19:21:39.997184Z","iopub.execute_input":"2023-10-25T19:21:39.997636Z","iopub.status.idle":"2023-10-25T19:21:40.033127Z","shell.execute_reply.started":"2023-10-25T19:21:39.997602Z","shell.execute_reply":"2023-10-25T19:21:40.031597Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# visualizing anglez & enmo values at the same period \n# of the selected series_id = '25e2b3dd9c3b'\n\ncolour_enmo = '#FF7F50'\ncolour_anglez = '#0000FF'\n\nfig, ax1 = plt.subplots(figsize=(20, 6))\nax2 = ax1.twinx()\nax1.plot(id_25e2b3dd9c3b_16.step, id_25e2b3dd9c3b_16.anglez, color= colour_anglez)\nax2.plot(id_25e2b3dd9c3b_16.step, id_25e2b3dd9c3b_16.enmo, color= colour_enmo)\n\nax1.set_xlabel('Step')\nax1.set_ylabel('Anglez', color = colour_anglez, fontsize = 14)\n\n\nax2.set_ylabel('ENMO', color = colour_enmo, fontsize = 14)\n\n\nfig.suptitle('ENMO & ANGLEZ values for series_id \"25e2b3dd9c3b\" on 2018-12-15 & 2018-12-16', fontsize = 20)\nfig.autofmt_xdate()","metadata":{"_kg_hide-input":true,"execution":{"iopub.status.busy":"2023-10-25T19:21:43.317779Z","iopub.execute_input":"2023-10-25T19:21:43.318234Z","iopub.status.idle":"2023-10-25T19:21:44.227446Z","shell.execute_reply.started":"2023-10-25T19:21:43.318202Z","shell.execute_reply":"2023-10-25T19:21:44.225799Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"It is seen that enmo values for selected series_id change with similar to anglez pattern: **enmo values switching bitween periods with small & large range of changes. We suppose that periods with small range of changes belong to sleep periods.** We can check it after adding data from train_events dataset. ","metadata":{}},{"cell_type":"markdown","source":"It is seen also that during supposed sleep periods the changes are in some small diapazone which doesn't change some period. **We should understand this period and this small diapazone of changes.** Periods should be like 400 - 800 steps. **One metric should be derived: not more than N anglez switches inside definit range of steps**.  We can try to discover the valuable time period by resampling. Let's make resampling for several intervals (1 min, 15 min, 30 min, 1 hour, 1.5 hour & 2 hour)  for series_id '0402a003dae9' during 2 weeks (from 2018-12-21 till 2018-01-02).","metadata":{}},{"cell_type":"code","source":"# make a subset of data for series_id = '0402a003dae9' during 2 weeks (2018-12-20 & 2019-01-02)\n\nid_0402a003dae9_2w = id_0402a003dae9[id_0402a003dae9['date'].isin(['2018-12-20', '2018-12-21', '2018-12-22', '2018-12-23', '2018-12-24', '2018-12-25', '2018-12-26', '2018-12-27', '2018-12-28', '2018-12-29', '2018-12-30', '2018-12-31', '2019-01-01', '2019-01-02'])]\nid_0402a003dae9_2w","metadata":{"_kg_hide-input":true,"_kg_hide-output":true,"execution":{"iopub.status.busy":"2023-10-25T19:22:08.809397Z","iopub.execute_input":"2023-10-25T19:22:08.809880Z","iopub.status.idle":"2023-10-25T19:22:08.856641Z","shell.execute_reply.started":"2023-10-25T19:22:08.809844Z","shell.execute_reply":"2023-10-25T19:22:08.855111Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# plot resampled enmo & anglez values for series_id = '0402a003dae9' during 2 weeks (2018-12-20 & 2019-01-02)\n# this code was inspired by notebook\n# https://www.kaggle.com/code/iamleonie/intro-to-time-series-forecasting\n\nfig, ax = plt.subplots(ncols = 2, nrows = 7, sharex = True, figsize = (20, 16))\n\nsns.lineplot(x = id_0402a003dae9_2w.timestamp, y = id_0402a003dae9_2w.anglez, color = colour_anglez, ax=ax[0,0])\nax[0, 0].set_title('ANGLEZ values (5 sec.)', fontsize = 16)\n\nresampled_ang1min = id_0402a003dae9_2w[['timestamp', 'anglez']].resample('min', on='timestamp').mean().reset_index(drop=False)\nsns.lineplot(x = resampled_ang1min.timestamp, y = resampled_ang1min.anglez, color = colour_anglez, ax=ax[1,0])\nax[1, 0].set_title('ANGLEZ values (resampled 1 min.)', fontsize = 16)\n\nresampled_ang15min = id_0402a003dae9_2w[['timestamp', 'anglez']].resample('15min', on='timestamp').mean().reset_index(drop=False)\nsns.lineplot(x = resampled_ang15min.timestamp, y = resampled_ang15min.anglez, color = colour_anglez, ax=ax[2,0])\nax[2, 0].set_title('ANGLEZ values (resampled 15 min.)', fontsize = 16)\n\nresampled_ang30 = id_0402a003dae9_2w[['timestamp', 'anglez']].resample('30min', on='timestamp').mean().reset_index(drop=False)\nsns.lineplot(x = resampled_ang30.timestamp, y = resampled_ang30.anglez, color = colour_anglez, ax=ax[3,0])\nax[3, 0].set_title('ANGLEZ values (resampled 30 min.)', fontsize = 16)\n\nresampled_ang = id_0402a003dae9_2w[['timestamp', 'anglez']].resample('H', on='timestamp').mean().reset_index(drop=False)\nsns.lineplot(x = resampled_ang.timestamp, y = resampled_ang.anglez, color = colour_anglez, ax=ax[4,0])\nax[4, 0].set_title('ANGLEZ values (resampled 1 hour)', fontsize = 16)\n\nresampled_ang90min = id_0402a003dae9_2w[['timestamp', 'anglez']].resample('90min', on='timestamp').mean().reset_index(drop=False)\nsns.lineplot(x = resampled_ang90min.timestamp, y = resampled_ang90min.anglez, color = colour_anglez, ax=ax[5,0])\nax[5, 0].set_title('ANGLEZ values (resampled 1.5 hour)', fontsize = 16)\n\nresampled_ang2h = id_0402a003dae9_2w[['timestamp', 'anglez']].resample('2H', on='timestamp').mean().reset_index(drop=False)\nsns.lineplot(x = resampled_ang2h.timestamp, y = resampled_ang2h.anglez, color = colour_anglez, ax=ax[6,0])\nax[6, 0].set_title('ANGLEZ values (resampled 2 hour)', fontsize = 16)\n\n\n#\n\nsns.lineplot(x = id_0402a003dae9_2w.timestamp, y = id_0402a003dae9_2w.enmo, color = colour_enmo, ax=ax[0,1])\nax[0, 1].set_title('ENMO values (5 sec.)', fontsize = 16)\n\nresampled_enmo1min = id_0402a003dae9_2w[['timestamp', 'enmo']].resample('min', on='timestamp').mean().reset_index(drop=False)\nsns.lineplot(x = resampled_enmo1min.timestamp, y = resampled_enmo1min.enmo, color = colour_enmo, ax=ax[1,1])\nax[1, 1].set_title('ENMO values (resampled 1 min.)', fontsize = 16)\n\nresampled_enmo15min = id_0402a003dae9_2w[['timestamp', 'enmo']].resample('15min', on='timestamp').mean().reset_index(drop=False)\nsns.lineplot(x = resampled_enmo15min.timestamp, y = resampled_enmo15min.enmo, color = colour_enmo, ax=ax[2, 1])\nax[2, 1].set_title('ENMO values (resampled 15 min.)', fontsize = 16)\n\nresampled_enmo30 = id_0402a003dae9_2w[['timestamp', 'enmo']].resample('30min', on='timestamp').mean().reset_index(drop=False)\nsns.lineplot(x = resampled_enmo30.timestamp, y = resampled_enmo30.enmo, color = colour_enmo, ax=ax[3,1])\nax[3, 1].set_title('ENMO values (resampled 30 min.)', fontsize = 16)\n\nresampled_enmo = id_0402a003dae9_2w[['timestamp', 'enmo']].resample('H', on='timestamp').mean().reset_index(drop=False)\nsns.lineplot(x = resampled_enmo.timestamp, y = resampled_enmo.enmo, color = colour_enmo, ax=ax[4,1])\nax[4, 1].set_title('ENMO values (resampled 1 hour)', fontsize = 16)\n\nresampled_enmo90min = id_0402a003dae9_2w[['timestamp', 'enmo']].resample('90min', on='timestamp').mean().reset_index(drop=False)\nsns.lineplot(x = resampled_enmo90min.timestamp, y = resampled_enmo90min.enmo, color = colour_enmo, ax=ax[5,1])\nax[5, 1].set_title('ENMO values (resampled 1.5 hour)', fontsize = 16)\n\nresampled_enmo2h = id_0402a003dae9_2w[['timestamp', 'enmo']].resample('2H', on='timestamp').mean().reset_index(drop=False)\nsns.lineplot(x = resampled_enmo2h.timestamp, y = resampled_enmo2h.enmo, color = colour_enmo, ax=ax[6,1])\nax[6, 1].set_title('ENMO values (resampled 2 hour)', fontsize = 16)\n\nfor i in range(2):\n    ax[6, i].set_xticklabels(['2018-12-20', '2018-12-21', '2018-12-22', '2018-12-23', '2018-12-24', '2018-12-25', '2018-12-26', '2018-12-27', '2018-12-28', '2018-12-29', '2018-12-30', '2018-12-31', '2019-01-01', '2019-01-02'], rotation = 45)\n\nfig.suptitle('Resampled ANGLEZ & ENMO values for series_id \"0402a003dae9\" during 2 weeks', fontsize = 20)\nplt.show()\n","metadata":{"_kg_hide-input":true,"execution":{"iopub.status.busy":"2023-10-25T19:22:15.126122Z","iopub.execute_input":"2023-10-25T19:22:15.126567Z","iopub.status.idle":"2023-10-25T19:22:42.940700Z","shell.execute_reply.started":"2023-10-25T19:22:15.126534Z","shell.execute_reply":"2023-10-25T19:22:42.939141Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"<a id=\"vis_astationarity\"></a> \n\n### Check the stationarity of ANGLEZ values\n\nLet's check the stationary of ANGLEZ values with help of **underlying statistics**. At first we'll split train_series into 2 parts and then check the mean & variance for every part. ","metadata":{}},{"cell_type":"code","source":"# assign anglez values of all the train_series to the variable anglez_val\n\nanglez_val = train_series.anglez","metadata":{"_kg_hide-input":true,"execution":{"iopub.status.busy":"2023-10-25T19:23:56.398013Z","iopub.execute_input":"2023-10-25T19:23:56.398488Z","iopub.status.idle":"2023-10-25T19:23:56.406067Z","shell.execute_reply.started":"2023-10-25T19:23:56.398455Z","shell.execute_reply":"2023-10-25T19:23:56.404371Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# split train_series into 2 parts \nnum_part = 2\nsplit_ang = int(len(anglez_val) / num_part)\nanglez_val1, anglez_val2 = anglez_val[0:split_ang], anglez_val[split_ang:]","metadata":{"_kg_hide-input":true,"execution":{"iopub.status.busy":"2023-10-25T19:24:02.853493Z","iopub.execute_input":"2023-10-25T19:24:02.854601Z","iopub.status.idle":"2023-10-25T19:24:02.862070Z","shell.execute_reply.started":"2023-10-25T19:24:02.854552Z","shell.execute_reply":"2023-10-25T19:24:02.860558Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# calculate variance & mean for every part of the splitted dataset\nmean1a = anglez_val1.mean()\nmean2a = anglez_val2.mean()\nvar1a = anglez_val1.var()\nvar2a = anglez_val2.var()","metadata":{"_kg_hide-input":true,"execution":{"iopub.status.busy":"2023-10-25T19:24:07.439211Z","iopub.execute_input":"2023-10-25T19:24:07.439642Z","iopub.status.idle":"2023-10-25T19:24:09.079078Z","shell.execute_reply.started":"2023-10-25T19:24:07.439608Z","shell.execute_reply":"2023-10-25T19:24:09.077325Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"print('mean1 = %f, mean2 = %f' % (mean1a, mean2a))\nprint('variance1 = %f, variance2 = %f' % (var1a, var2a))","metadata":{"_kg_hide-input":true,"execution":{"iopub.status.busy":"2023-10-25T19:24:15.508514Z","iopub.execute_input":"2023-10-25T19:24:15.508958Z","iopub.status.idle":"2023-10-25T19:24:15.515609Z","shell.execute_reply.started":"2023-10-25T19:24:15.508925Z","shell.execute_reply":"2023-10-25T19:24:15.514133Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"It is seen that variance is very similar, while mean differs in tow parts. Let's check the stationarity with ADF test. Due to huge size of the train_series we'll aaply ADF test for subset of train_series with data of several series_id.","metadata":{}},{"cell_type":"markdown","source":"**Check stationarity of ANGLEZ values for series_id = 'b364205aba43'**","metadata":{}},{"cell_type":"code","source":"# make a subset of data for series_id = 'b364205aba43'\nang_b364205aba43 = train_series[train_series['series_id'] == 'b364205aba43']","metadata":{"_kg_hide-input":true,"execution":{"iopub.status.busy":"2023-10-25T19:24:19.365868Z","iopub.execute_input":"2023-10-25T19:24:19.366340Z","iopub.status.idle":"2023-10-25T19:24:19.533498Z","shell.execute_reply.started":"2023-10-25T19:24:19.366306Z","shell.execute_reply":"2023-10-25T19:24:19.532247Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# assign enmo values of the subset to a new variable\nang_ad1 = ang_b364205aba43.anglez","metadata":{"_kg_hide-input":true,"execution":{"iopub.status.busy":"2023-10-25T19:24:23.171286Z","iopub.execute_input":"2023-10-25T19:24:23.171809Z","iopub.status.idle":"2023-10-25T19:24:23.178168Z","shell.execute_reply.started":"2023-10-25T19:24:23.171769Z","shell.execute_reply":"2023-10-25T19:24:23.176670Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# split anglez values of a subset into several parts\nnum_par = 3\ndiv1 = int(len(ang_ad1) / num_par)","metadata":{"_kg_hide-input":true,"execution":{"iopub.status.busy":"2023-10-25T19:24:27.143785Z","iopub.execute_input":"2023-10-25T19:24:27.144303Z","iopub.status.idle":"2023-10-25T19:24:27.151068Z","shell.execute_reply.started":"2023-10-25T19:24:27.144270Z","shell.execute_reply":"2023-10-25T19:24:27.149415Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# assign every part of the splitted dataset to different variables\nang1_val_1, ang1_val_2, ang1_val_3  = ang_ad1[0:div1], ang_ad1[(div1*1):(div1*2)], ang_ad1[(div1*2):(div1*3)]","metadata":{"_kg_hide-input":true,"execution":{"iopub.status.busy":"2023-10-25T19:24:33.670406Z","iopub.execute_input":"2023-10-25T19:24:33.670918Z","iopub.status.idle":"2023-10-25T19:24:33.678361Z","shell.execute_reply.started":"2023-10-25T19:24:33.670883Z","shell.execute_reply":"2023-10-25T19:24:33.677326Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# calculate ADF test for the first part of the splitted subset\nang_result1_1 = adfuller(ang1_val_1)","metadata":{"_kg_hide-input":true,"execution":{"iopub.status.busy":"2023-10-25T19:24:37.483304Z","iopub.execute_input":"2023-10-25T19:24:37.483947Z","iopub.status.idle":"2023-10-25T19:25:38.475064Z","shell.execute_reply.started":"2023-10-25T19:24:37.483873Z","shell.execute_reply":"2023-10-25T19:25:38.473249Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# calculate ADF test for the second part of the splitted subset\nang_result2_1 = adfuller(ang1_val_2)","metadata":{"_kg_hide-input":true,"execution":{"iopub.status.busy":"2023-10-25T19:29:13.109118Z","iopub.execute_input":"2023-10-25T19:29:13.109634Z","iopub.status.idle":"2023-10-25T19:30:12.210212Z","shell.execute_reply.started":"2023-10-25T19:29:13.109602Z","shell.execute_reply":"2023-10-25T19:30:12.208534Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# calculate ADF test for the third part of the splitted subset\nang_result3_1 = adfuller(ang1_val_3)","metadata":{"_kg_hide-input":true,"execution":{"iopub.status.busy":"2023-10-25T19:30:27.214342Z","iopub.execute_input":"2023-10-25T19:30:27.214854Z","iopub.status.idle":"2023-10-25T19:31:27.207092Z","shell.execute_reply.started":"2023-10-25T19:30:27.214818Z","shell.execute_reply":"2023-10-25T19:31:27.205215Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# print the results of the ADF test for part1\nprint('ADF Statistic for part 1: %f' % ang_result1_1[0])\nprint('ADF p_value: %f' % (ang_result1_1[1]))\nprint('Critical values:')\n\nfor key,value in ang_result1_1[4].items():\n    print('\\t%s: %.3f' % (key, value))","metadata":{"_kg_hide-input":true,"execution":{"iopub.status.busy":"2023-10-25T19:31:47.484687Z","iopub.execute_input":"2023-10-25T19:31:47.485149Z","iopub.status.idle":"2023-10-25T19:31:47.494185Z","shell.execute_reply.started":"2023-10-25T19:31:47.485117Z","shell.execute_reply":"2023-10-25T19:31:47.492593Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# print the results of the ADF test for part2\nprint('ADF Statistic for part 2: %f' % ang_result2_1[0])\nprint('ADF p_value: %f' % (ang_result2_1[1]))\nprint('Critical values:')\n\nfor key,value in ang_result2_1[4].items():\n    print('\\t%s: %.3f' % (key, value))","metadata":{"_kg_hide-input":true,"execution":{"iopub.status.busy":"2023-10-25T19:31:51.395642Z","iopub.execute_input":"2023-10-25T19:31:51.396115Z","iopub.status.idle":"2023-10-25T19:31:51.403958Z","shell.execute_reply.started":"2023-10-25T19:31:51.396082Z","shell.execute_reply":"2023-10-25T19:31:51.402909Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# print the results of the ADF test for part3\nprint('ADF Statistic for part 3: %f' % ang_result3_1[0])\nprint('ADF p_value: %f' % (ang_result3_1[1]))\nprint('Critical values:')\n\nfor key,value in ang_result3_1[4].items():\n    print('\\t%s: %.3f' % (key, value))","metadata":{"_kg_hide-input":true,"execution":{"iopub.status.busy":"2023-10-25T19:31:54.873343Z","iopub.execute_input":"2023-10-25T19:31:54.874001Z","iopub.status.idle":"2023-10-25T19:31:54.881360Z","shell.execute_reply.started":"2023-10-25T19:31:54.873966Z","shell.execute_reply":"2023-10-25T19:31:54.879966Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"ADF test results for all three parts are much less than critical values hence anglez data for series_id = 'b364205aba43' is stationary.","metadata":{}},{"cell_type":"markdown","source":"**Check stationarity of ANGLEZ values for series_id = '6ee4ade1f2bd'**","metadata":{}},{"cell_type":"code","source":"# make a subset of data for series_id = '6ee4ade1f2bd'\nang_6ee4ade1f2bd = train_series[train_series['series_id'] == '6ee4ade1f2bd']","metadata":{"_kg_hide-input":true,"execution":{"iopub.status.busy":"2023-10-25T19:31:58.310654Z","iopub.execute_input":"2023-10-25T19:31:58.311088Z","iopub.status.idle":"2023-10-25T19:31:58.526043Z","shell.execute_reply.started":"2023-10-25T19:31:58.311059Z","shell.execute_reply":"2023-10-25T19:31:58.524717Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# assign enmo values of the subset to a new variable\nang_ad2 = ang_6ee4ade1f2bd.anglez","metadata":{"_kg_hide-input":true,"execution":{"iopub.status.busy":"2023-10-25T19:32:02.454248Z","iopub.execute_input":"2023-10-25T19:32:02.455040Z","iopub.status.idle":"2023-10-25T19:32:02.460332Z","shell.execute_reply.started":"2023-10-25T19:32:02.454999Z","shell.execute_reply":"2023-10-25T19:32:02.459102Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# split anglez values of a subset into several parts\nnum_par = 3\ndiv2 = int(len(ang_ad2) / num_par)","metadata":{"_kg_hide-input":true,"execution":{"iopub.status.busy":"2023-10-25T19:32:05.308055Z","iopub.execute_input":"2023-10-25T19:32:05.308547Z","iopub.status.idle":"2023-10-25T19:32:05.315306Z","shell.execute_reply.started":"2023-10-25T19:32:05.308512Z","shell.execute_reply":"2023-10-25T19:32:05.313321Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# assign every part of the splitted dataset to different variables\nang2_val_1, ang2_val_2, ang2_val_3  = ang_ad2[0:div2], ang_ad2[(div2*1):(div2*2)], ang_ad2[(div2*2):(div2*3)]","metadata":{"_kg_hide-input":true,"execution":{"iopub.status.busy":"2023-10-25T19:32:11.796735Z","iopub.execute_input":"2023-10-25T19:32:11.797167Z","iopub.status.idle":"2023-10-25T19:32:11.804532Z","shell.execute_reply.started":"2023-10-25T19:32:11.797136Z","shell.execute_reply":"2023-10-25T19:32:11.802778Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# calculate ADF test for the first part of the splitted subset\nang_result1_2 = adfuller(ang2_val_1)","metadata":{"_kg_hide-input":true,"execution":{"iopub.status.busy":"2023-10-25T19:32:14.848683Z","iopub.execute_input":"2023-10-25T19:32:14.849124Z","iopub.status.idle":"2023-10-25T19:33:21.239075Z","shell.execute_reply.started":"2023-10-25T19:32:14.849093Z","shell.execute_reply":"2023-10-25T19:33:21.236991Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# calculate ADF test for the second part of the splitted subset\nang_result2_2 = adfuller(ang2_val_2)","metadata":{"_kg_hide-input":true,"execution":{"iopub.status.busy":"2023-10-25T19:35:01.393064Z","iopub.execute_input":"2023-10-25T19:35:01.393481Z","iopub.status.idle":"2023-10-25T19:36:05.022048Z","shell.execute_reply.started":"2023-10-25T19:35:01.393449Z","shell.execute_reply":"2023-10-25T19:36:05.020150Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# calculate ADF test for the second part of the splitted subset\nang_result3_2 = adfuller(ang2_val_3)","metadata":{"_kg_hide-input":true,"execution":{"iopub.status.busy":"2023-10-25T19:33:36.110135Z","iopub.execute_input":"2023-10-25T19:33:36.110647Z","iopub.status.idle":"2023-10-25T19:34:40.343729Z","shell.execute_reply.started":"2023-10-25T19:33:36.110611Z","shell.execute_reply":"2023-10-25T19:34:40.341774Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# print the results of the ADF test for part1\nprint('ADF Statistic for part 1: %f' % ang_result1_2[0])\nprint('ADF p_value: %f' % (ang_result1_2[1]))\nprint('Critical values:')\n\nfor key,value in ang_result1_2[4].items():\n    print('\\t%s: %.3f' % (key, value))","metadata":{"_kg_hide-input":true,"execution":{"iopub.status.busy":"2023-10-25T19:34:53.743169Z","iopub.execute_input":"2023-10-25T19:34:53.743608Z","iopub.status.idle":"2023-10-25T19:34:53.753286Z","shell.execute_reply.started":"2023-10-25T19:34:53.743576Z","shell.execute_reply":"2023-10-25T19:34:53.751513Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# print the results of the ADF test for part2\nprint('ADF Statistic for part 2: %f' % ang_result2_2[0])\nprint('ADF p_value: %f' % (ang_result2_2[1]))\nprint('Critical values:')\n\nfor key,value in ang_result2_2[4].items():\n    print('\\t%s: %.3f' % (key, value))","metadata":{"_kg_hide-input":true,"execution":{"iopub.status.busy":"2023-10-25T19:36:12.683840Z","iopub.execute_input":"2023-10-25T19:36:12.684515Z","iopub.status.idle":"2023-10-25T19:36:12.698412Z","shell.execute_reply.started":"2023-10-25T19:36:12.684471Z","shell.execute_reply":"2023-10-25T19:36:12.695520Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# print the results of the ADF test for part3\nprint('ADF Statistic for part 3: %f' % ang_result3_2[0])\nprint('ADF p_value: %f' % (ang_result3_2[1]))\nprint('Critical values:')\n\nfor key,value in ang_result3_2[4].items():\n    print('\\t%s: %.3f' % (key, value))","metadata":{"_kg_hide-input":true,"execution":{"iopub.status.busy":"2023-10-25T19:36:15.684109Z","iopub.execute_input":"2023-10-25T19:36:15.684849Z","iopub.status.idle":"2023-10-25T19:36:15.694924Z","shell.execute_reply.started":"2023-10-25T19:36:15.684791Z","shell.execute_reply":"2023-10-25T19:36:15.693317Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"ADF test results for all three parts are much less than critical values hence anglez data for series_id = 'b364205aba43' is stationary.","metadata":{}},{"cell_type":"markdown","source":"**Check stationarity of ANGLEZ values for series_id = '939932f1822d'**","metadata":{}},{"cell_type":"code","source":"# make a subset of data for series_id = '939932f1822d'\nang_939932f1822d = train_series[train_series['series_id'] == '939932f1822d']","metadata":{"_kg_hide-input":true,"execution":{"iopub.status.busy":"2023-10-25T19:36:18.894348Z","iopub.execute_input":"2023-10-25T19:36:18.894982Z","iopub.status.idle":"2023-10-25T19:36:19.148420Z","shell.execute_reply.started":"2023-10-25T19:36:18.894942Z","shell.execute_reply":"2023-10-25T19:36:19.146648Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# assign enmo values of the subset to a new variable\nang_ad3 = ang_939932f1822d.anglez","metadata":{"_kg_hide-input":true,"execution":{"iopub.status.busy":"2023-10-25T19:36:21.680065Z","iopub.execute_input":"2023-10-25T19:36:21.680576Z","iopub.status.idle":"2023-10-25T19:36:21.687236Z","shell.execute_reply.started":"2023-10-25T19:36:21.680539Z","shell.execute_reply":"2023-10-25T19:36:21.685766Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# split anglez values of a subset into several parts\nnum_par = 3\ndiv3 = int(len(ang_ad3) / num_par)","metadata":{"_kg_hide-input":true,"execution":{"iopub.status.busy":"2023-10-25T19:36:25.352084Z","iopub.execute_input":"2023-10-25T19:36:25.352616Z","iopub.status.idle":"2023-10-25T19:36:25.358747Z","shell.execute_reply.started":"2023-10-25T19:36:25.352576Z","shell.execute_reply":"2023-10-25T19:36:25.357469Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# assign every part of the splitted dataset to different variables\nang3_val_1, ang3_val_2, ang3_val_3  = ang_ad3[0:div3], ang_ad3[(div3*1):(div3*2)], ang_ad3[(div3*2):(div3*3)]","metadata":{"_kg_hide-input":true,"execution":{"iopub.status.busy":"2023-10-25T19:36:33.511410Z","iopub.execute_input":"2023-10-25T19:36:33.511940Z","iopub.status.idle":"2023-10-25T19:36:33.520843Z","shell.execute_reply.started":"2023-10-25T19:36:33.511903Z","shell.execute_reply":"2023-10-25T19:36:33.519257Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# calculate ADF test for the first part of the splitted subset\nang_result1_3 = adfuller(ang3_val_1)","metadata":{"_kg_hide-input":true,"execution":{"iopub.status.busy":"2023-10-25T19:36:36.998196Z","iopub.execute_input":"2023-10-25T19:36:36.998771Z","iopub.status.idle":"2023-10-25T19:37:42.011386Z","shell.execute_reply.started":"2023-10-25T19:36:36.998725Z","shell.execute_reply":"2023-10-25T19:37:42.008677Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# calculate ADF test for the second part of the splitted subset\nang_result2_3 = adfuller(ang3_val_2)","metadata":{"_kg_hide-input":true,"execution":{"iopub.status.busy":"2023-10-25T19:37:55.539498Z","iopub.execute_input":"2023-10-25T19:37:55.539912Z","iopub.status.idle":"2023-10-25T19:38:58.364763Z","shell.execute_reply.started":"2023-10-25T19:37:55.539882Z","shell.execute_reply":"2023-10-25T19:38:58.362758Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# calculate ADF test for the second part of the splitted subset\nang_result3_3 = adfuller(ang3_val_3)","metadata":{"_kg_hide-input":true,"execution":{"iopub.status.busy":"2023-10-25T19:39:27.326357Z","iopub.execute_input":"2023-10-25T19:39:27.326883Z","iopub.status.idle":"2023-10-25T19:40:30.158454Z","shell.execute_reply.started":"2023-10-25T19:39:27.326848Z","shell.execute_reply":"2023-10-25T19:40:30.156591Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# print the results of the ADF test for part1\nprint('ADF Statistic for part 1: %f' % ang_result1_3[0])\nprint('ADF p_value: %f' % (ang_result1_3[1]))\nprint('Critical values:')\n\nfor key,value in ang_result1_3[4].items():\n    print('\\t%s: %.3f' % (key, value))","metadata":{"_kg_hide-input":true,"execution":{"iopub.status.busy":"2023-10-25T19:41:51.806639Z","iopub.execute_input":"2023-10-25T19:41:51.807171Z","iopub.status.idle":"2023-10-25T19:41:51.816060Z","shell.execute_reply.started":"2023-10-25T19:41:51.807136Z","shell.execute_reply":"2023-10-25T19:41:51.814338Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# print the results of the ADF test for part2\nprint('ADF Statistic for part 2: %f' % ang_result2_3[0])\nprint('ADF p_value: %f' % (ang_result2_3[1]))\nprint('Critical values:')\n\nfor key,value in ang_result2_3[4].items():\n    print('\\t%s: %.3f' % (key, value))","metadata":{"_kg_hide-input":true,"execution":{"iopub.status.busy":"2023-10-25T19:41:55.823682Z","iopub.execute_input":"2023-10-25T19:41:55.824159Z","iopub.status.idle":"2023-10-25T19:41:55.832432Z","shell.execute_reply.started":"2023-10-25T19:41:55.824124Z","shell.execute_reply":"2023-10-25T19:41:55.830825Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# print the results of the ADF test for part3\nprint('ADF Statistic for part 3: %f' % ang_result3_3[0])\nprint('ADF p_value: %f' % (ang_result3_3[1]))\nprint('Critical values:')\n\nfor key,value in ang_result3_3[4].items():\n    print('\\t%s: %.3f' % (key, value))","metadata":{"_kg_hide-input":true,"execution":{"iopub.status.busy":"2023-10-25T19:41:58.715152Z","iopub.execute_input":"2023-10-25T19:41:58.715630Z","iopub.status.idle":"2023-10-25T19:41:58.723417Z","shell.execute_reply.started":"2023-10-25T19:41:58.715596Z","shell.execute_reply":"2023-10-25T19:41:58.721998Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"ADF test results for all three parts are much less than critical values hence anglez data for series_id = '939932f1822d' is stationary.","metadata":{}},{"cell_type":"markdown","source":"Due to the underlying statistics for andlez values of all dataset and due to the results of checking stationarity of anglez values for several random series_id we suppose **ANGLEZ values to be stationary**.","metadata":{}},{"cell_type":"markdown","source":"<a id=\"anglez_summary\"></a>\n#### Summary on 'ANGLEZ' metric analysis\n\n* 'Anglez' metric refers to the angle of the arm relative to the vertical axis of the body.\n* Anglez values have normal distribution with max = 90.0, min = -90.0, mean = -8.81 and standard deviation = 35.52.\n* Anglez values changes with following pattern: periods with more frequent changes in values are switched to periods with less frequent changes in values.\n* Periods with less frequent changes in anglez values correspond to lower enmo-values.\n* Anglez values are stationary.","metadata":{}},{"cell_type":"markdown","source":"So **features for predicting sleep states are:**\n- step\n- timestamp\n- enmo\n- anglez\n\n**Target** is event (wakeup or onset). Target values are introduced in train_events dataset. Let's look at it closer.","metadata":{}},{"cell_type":"markdown","source":"<a id=\"train_event\"></a>\n# Inspect the train_event dataframe","metadata":{}},{"cell_type":"code","source":"# check the shape of train_events\ntes = train_events.shape\nprint(f'The shape of train_events is: {tes}')","metadata":{"_kg_hide-input":true,"execution":{"iopub.status.busy":"2023-10-25T19:42:03.298121Z","iopub.execute_input":"2023-10-25T19:42:03.298588Z","iopub.status.idle":"2023-10-25T19:42:03.306461Z","shell.execute_reply.started":"2023-10-25T19:42:03.298555Z","shell.execute_reply":"2023-10-25T19:42:03.304735Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"Look at 5 first & last rows of train_events","metadata":{}},{"cell_type":"code","source":"train_events","metadata":{"_kg_hide-input":true,"execution":{"iopub.status.busy":"2023-10-25T19:42:05.683857Z","iopub.execute_input":"2023-10-25T19:42:05.684346Z","iopub.status.idle":"2023-10-25T19:42:05.714307Z","shell.execute_reply.started":"2023-10-25T19:42:05.684312Z","shell.execute_reply":"2023-10-25T19:42:05.712817Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"train_events.info()","metadata":{"_kg_hide-input":true,"execution":{"iopub.status.busy":"2023-10-25T19:42:10.404794Z","iopub.execute_input":"2023-10-25T19:42:10.405232Z","iopub.status.idle":"2023-10-25T19:42:10.428601Z","shell.execute_reply.started":"2023-10-25T19:42:10.405199Z","shell.execute_reply":"2023-10-25T19:42:10.427085Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"From the info chunk we see that the train_events dataframe has incomplete data for some series_id in 'step', 'timestamp', 'date' & 'time' columns. Let's check the how many lost data in each column with incomplete data.","metadata":{}},{"cell_type":"code","source":"# count the sum of nan values for every column\ntrain_events.isna().sum()","metadata":{"_kg_hide-input":true,"execution":{"iopub.status.busy":"2023-10-25T19:42:14.579020Z","iopub.execute_input":"2023-10-25T19:42:14.579487Z","iopub.status.idle":"2023-10-25T19:42:14.596691Z","shell.execute_reply.started":"2023-10-25T19:42:14.579453Z","shell.execute_reply":"2023-10-25T19:42:14.594884Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# find series_id with no missing values\n\nseries_full = train_events[~train_events.isnull().any(axis=1)]\nseries_full\n","metadata":{"_kg_hide-output":true,"_kg_hide-input":true,"execution":{"iopub.status.busy":"2023-10-25T19:42:17.931631Z","iopub.execute_input":"2023-10-25T19:42:17.932051Z","iopub.status.idle":"2023-10-25T19:42:17.968038Z","shell.execute_reply.started":"2023-10-25T19:42:17.932020Z","shell.execute_reply":"2023-10-25T19:42:17.966788Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# count number of series_id with complete data\n\nseries_full.nunique()","metadata":{"_kg_hide-input":true,"execution":{"iopub.status.busy":"2023-10-25T19:42:21.058282Z","iopub.execute_input":"2023-10-25T19:42:21.058822Z","iopub.status.idle":"2023-10-25T19:42:21.076148Z","shell.execute_reply.started":"2023-10-25T19:42:21.058767Z","shell.execute_reply":"2023-10-25T19:42:21.074382Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"series_full.info()","metadata":{"_kg_hide-input":true,"execution":{"iopub.status.busy":"2023-10-25T19:42:23.888067Z","iopub.execute_input":"2023-10-25T19:42:23.889043Z","iopub.status.idle":"2023-10-25T19:42:23.906511Z","shell.execute_reply.started":"2023-10-25T19:42:23.889002Z","shell.execute_reply":"2023-10-25T19:42:23.905296Z"},"trusted":true},"execution_count":null,"outputs":[]}]}