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{
"cells": [
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Using custom data configuration tappyness1--causion-f0c7d8cb97d796fc\n",
"Found cached dataset parquet (C:/Users/neoce/.cache/huggingface/datasets/tappyness1___parquet/tappyness1--causion-f0c7d8cb97d796fc/0.0.0/2a3b91fbd88a2c90d1dbbb32b460cf621d31bd5b05b934492fdef7d8d6f236ec)\n",
"100%|██████████| 1/1 [00:00<00:00, 144.77it/s]\n"
]
}
],
"source": [
"from datasets import load_dataset\n",
"import pandas as pd\n",
"import os\n",
"import yaml\n",
"import sys\n",
"sys.path.append(\"..\")\n",
"from src.pred_plot import prep_data_pred_plot, data_split\n",
"%load_ext autoreload\n",
"%autoreload 2\n",
"\n",
"token_file = open(\"../token_secret.yaml\")\n",
"token_obj = yaml.load(token_file, Loader=yaml.FullLoader)\n",
"dataset = load_dataset(\"tappyness1/causion\", use_auth_token=token_obj['TOKEN'])\n",
"counts_df = pd.DataFrame(dataset['train'])"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>date</th>\n",
" <th>time</th>\n",
" <th>view</th>\n",
" <th>car</th>\n",
" <th>motorcycle</th>\n",
" <th>large_vehicle</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>2023-02-14</td>\n",
" <td>22:36:03</td>\n",
" <td>View_from_Second_Link_at_Tuas</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2023-02-14</td>\n",
" <td>22:36:03</td>\n",
" <td>View_from_Tuas_Checkpoint</td>\n",
" <td>2</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>2023-02-14</td>\n",
" <td>22:36:03</td>\n",
" <td>View_from_Woodlands_Causeway_Towards_Johor</td>\n",
" <td>2</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>2023-02-14</td>\n",
" <td>22:36:03</td>\n",
" <td>View_from_Woodlands_Checkpoint_Towards_BKE</td>\n",
" <td>3</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>2023-02-14</td>\n",
" <td>23:14:34</td>\n",
" <td>View_from_Second_Link_at_Tuas</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>6</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" date time view car \\\n",
"0 2023-02-14 22:36:03 View_from_Second_Link_at_Tuas 0 \n",
"1 2023-02-14 22:36:03 View_from_Tuas_Checkpoint 2 \n",
"2 2023-02-14 22:36:03 View_from_Woodlands_Causeway_Towards_Johor 2 \n",
"3 2023-02-14 22:36:03 View_from_Woodlands_Checkpoint_Towards_BKE 3 \n",
"4 2023-02-14 23:14:34 View_from_Second_Link_at_Tuas 0 \n",
"\n",
" motorcycle large_vehicle \n",
"0 0 1 \n",
"1 0 0 \n",
"2 0 0 \n",
"3 0 1 \n",
"4 0 6 "
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"counts_df.head()"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"g:\\My Drive\\DataScience\\OMSA GaTech\\08_CSE6242\\Project\\Org_Space\\Causion\\notebooks\\..\\src\\pred_plot.py:117: SettingWithCopyWarning:\n",
"\n",
"\n",
"A value is trying to be set on a copy of a slice from a DataFrame.\n",
"Try using .loc[row_indexer,col_indexer] = value instead\n",
"\n",
"See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
"\n",
"g:\\My Drive\\DataScience\\OMSA GaTech\\08_CSE6242\\Project\\Org_Space\\Causion\\notebooks\\..\\src\\pred_plot.py:118: SettingWithCopyWarning:\n",
"\n",
"\n",
"A value is trying to be set on a copy of a slice from a DataFrame.\n",
"Try using .loc[row_indexer,col_indexer] = value instead\n",
"\n",
"See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
"\n",
"g:\\My Drive\\DataScience\\OMSA GaTech\\08_CSE6242\\Project\\Org_Space\\Causion\\notebooks\\..\\src\\pred_plot.py:120: FutureWarning:\n",
"\n",
"The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
"\n"
]
}
],
"source": [
"from src.pred_plot import prep_data_pred_plot, data_split, train_model, predicted_figure, get_today, convert_date, gen_fig\n",
"from datetime import date, datetime, timedelta\n",
"from jupyter_dash import JupyterDash\n",
"import dash\n",
"from dash import Dash, html, dcc, Input, Output\n",
"final_table = prep_data_pred_plot(counts_df)\n",
"x_train, x_test, y_train, y_test = data_split(final_table)\n",
"clf = train_model(x_train, y_train)"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {},
"outputs": [],
"source": [
"starter_variables = [x_train, str(date.today()), \"07:00\", \"Tuas-Johor\"]\n",
"figs = gen_fig()\n",
"fig = predicted_figure(clf, starter_variables, figs)\n",
"today = get_today()"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {},
"outputs": [],
"source": [
"app = JupyterDash(__name__)\n",
"\n",
"app.title = 'CSE6242 Dashboard'\n",
"app.layout = html.Div([\n",
" html.Div([\n",
" dcc.DatePickerSingle(\n",
" id='my_date_picker_single',\n",
" min_date_allowed=date(2023, 4, 11),\n",
" max_date_allowed=date(2024, 4, 11),\n",
" initial_visible_month=date(today[0],today[1], today[2]),\n",
" date=date(today[0],today[1], today[2])\n",
" ),\n",
" html.Div(id='output-container-date-picker-single')],\n",
" style={'width':'20%','height':'0px', 'padding-left':'2%',\n",
" 'display':'inline-block'}),\n",
" html.Div([dcc.Dropdown(id='hours_dropdown_id',\n",
" options=['00:00', '01:00', '02:00', '03:00', '04:00', '05:00', '06:00', '07:00', '08:00', '09:00', \n",
" '10:00', '11:00', '12:00', '13:00', '14:00', '15:00', '16:00', '17:00', '18:00','19:00',\n",
" '20:00', '21:00', '22:00', '23:00'],\n",
" value='07:00', clearable=False),html.Label(\"Estimated Leave Time\")],\n",
" style={'width':'20%','height':'60px', 'padding-left':'2%',\n",
" 'display':'inline-block'}),\n",
" html.Div([dcc.Dropdown(id='direction_id',\n",
" options=['Johor-Tuas','Johor-Woodlands',\n",
" 'Tuas-Johor', 'Woodlands-Johor'],\n",
" value=\"Tuas-Johor\", clearable=False),html.Label(\"Direction\")],\n",
" style={'width':'20%','height':'60px', 'padding-left':'2%',\n",
" 'display':'inline-block'}),\n",
" html.Div(dcc.Graph(id='final_output', figure=fig))])\n",
"\n",
"\n",
"@app.callback(Output('output-container-date-picker-single', 'children'),\n",
" Input('my_date_picker_single', 'date'))\n",
"def update_output(date_value):\n",
" string_prefix = 'Travel Day: '\n",
" if date_value is not None:\n",
" date_object = date.fromisoformat(date_value)\n",
" date_string = convert_date(date_value)\n",
" return string_prefix + date_string\n",
" \n",
"@app.callback(Output('final_output', \"figure\"),\n",
" Input('my_date_picker_single', 'date'),\n",
" Input('hours_dropdown_id', 'value'),\n",
" Input('direction_id', 'value'))\n",
"def update_final_output_hour(my_date_picker_single, hours_dropdown_id, direction_id):\n",
" starter_variables[0] = x_train\n",
" starter_variables[1] = str(my_date_picker_single)\n",
" starter_variables[2] = str(hours_dropdown_id)\n",
" starter_variables[3] = str(direction_id)\n",
" fig = predicted_figure(clf, starter_variables, figs)\n",
" return fig"
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Dash is running on http://127.0.0.1:8050/\n",
"\n"
]
}
],
"source": [
"app.run_server(mode='inline')"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'2023-04-14'"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from datetime import date\n",
"\n",
"str(date.today())"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "causion",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.8.16"
},
"orig_nbformat": 4
},
"nbformat": 4,
"nbformat_minor": 2
}
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