diff --git "a/training/src/.ipynb_checkpoints/MSML-602-Final-Project-checkpoint.ipynb" "b/training/src/.ipynb_checkpoints/MSML-602-Final-Project-checkpoint.ipynb"
new file mode 100644--- /dev/null
+++ "b/training/src/.ipynb_checkpoints/MSML-602-Final-Project-checkpoint.ipynb"
@@ -0,0 +1,1085 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": 16,
+ "id": "fb7daf67-de7e-4626-a194-417aa210c959",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "%%capture\n",
+ "!pip install keras tensorflow seaborn requests-cache"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 17,
+ "id": "c005096b-eaca-4244-998d-a92338d22902",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import os\n",
+ "import sqlite3\n",
+ "import IPython\n",
+ "import IPython.display\n",
+ "import matplotlib as mpl\n",
+ "import matplotlib.pyplot as plt\n",
+ "import numpy as np\n",
+ "import pandas as pd\n",
+ "import seaborn as sns\n",
+ "import tensorflow as tf\n",
+ "import requests\n",
+ "import requests_cache\n",
+ "from requests_cache.backends.sqlite import SQLiteCache\n",
+ "import sqlite3\n",
+ "import datetime\n",
+ "from datetime import date, timedelta, timezone\n",
+ "import time\n",
+ "import pytz\n",
+ "\n",
+ "local_tz = pytz.timezone('America/New_York')\n",
+ "\n",
+ "\n",
+ "mpl.rcParams['figure.figsize'] = (8, 6)\n",
+ "mpl.rcParams['axes.grid'] = False\n",
+ "\n",
+ "# initialize cache\n",
+ "requests_cache.install_cache('./data/weather_api_cache')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 21,
+ "id": "2e054aef-ce49-47a8-ba20-dc2e6928600e",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Starting Date: 2022-12-07\n",
+ "--------------------------------\n",
+ "Already up to date!\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "
\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " | \n",
+ " temp | \n",
+ " obs_timestamp | \n",
+ " pressure | \n",
+ " wspd | \n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ " 0 | \n",
+ " 50.0 | \n",
+ " 2000-01-02 00:51:00 | \n",
+ " 30.22 | \n",
+ " NaN | \n",
+ "
\n",
+ " \n",
+ " 1 | \n",
+ " 45.0 | \n",
+ " 2000-01-02 01:51:00 | \n",
+ " 30.21 | \n",
+ " 5.0 | \n",
+ "
\n",
+ " \n",
+ " 2 | \n",
+ " 47.0 | \n",
+ " 2000-01-02 02:51:00 | \n",
+ " 30.21 | \n",
+ " 5.0 | \n",
+ "
\n",
+ " \n",
+ " 3 | \n",
+ " 46.0 | \n",
+ " 2000-01-02 03:51:00 | \n",
+ " 30.21 | \n",
+ " NaN | \n",
+ "
\n",
+ " \n",
+ " 4 | \n",
+ " 47.0 | \n",
+ " 2000-01-02 04:51:00 | \n",
+ " 30.20 | \n",
+ " 5.0 | \n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " temp obs_timestamp pressure wspd\n",
+ "0 50.0 2000-01-02 00:51:00 30.22 NaN\n",
+ "1 45.0 2000-01-02 01:51:00 30.21 5.0\n",
+ "2 47.0 2000-01-02 02:51:00 30.21 5.0\n",
+ "3 46.0 2000-01-02 03:51:00 30.21 NaN\n",
+ "4 47.0 2000-01-02 04:51:00 30.20 5.0"
+ ]
+ },
+ "execution_count": 21,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "\n",
+ "\n",
+ "# API_KEY = os.env.get(\"API_KEY\")\n",
+ "DATE_FORMAT = \"%Y-%m-%d %H:%M:%S\"\n",
+ "API_KEY = \"e1f10a1e78da46f5b10a1e78da96f525\"\n",
+ "BASE_URL = \"https://api.weather.com/v1/location/KDCA:9:US/observations/historical.json?apiKey={api_key}&units=e&startDate={start_date}&endDate={end_date}\"\n",
+ "\n",
+ "# Field descriptions here\n",
+ "# https://www.research-collection.ethz.ch/bitstream/handle/20.500.11850/511371/1/LapamonpinyoEtAl_engrXiv_2021.pdf \n",
+ "# Sample\n",
+ "# {\n",
+ "# 'key': 'KDCA', 'class': 'observation', 'expire_time_gmt': 946709460, 'obs_id': 'KDCA', 'obs_name': 'Washington/Natl', \n",
+ "# 'valid_time_gmt': 946702260, 'day_ind': 'N', 'temp': 41, 'wx_icon': 33, 'icon_extd': 3300, 'wx_phrase': 'Fair', 'pressure_tend': None,\n",
+ "# 'pressure_desc': None, 'dewPt': 34, 'heat_index': 41, 'rh': 76, 'pressure': 30.19, 'vis': 5, 'wc': 41, 'wdir': None, \n",
+ "# 'wdir_cardinal': 'CALM', 'gust': None, 'wspd': None, 'max_temp': 57, 'min_temp': 41, 'precip_total': None, \n",
+ "# 'precip_hrly': None, 'snow_hrly': None, 'uv_desc': 'Low', 'feels_like': 41, 'uv_index': 0, 'qualifier': None, 'qualifier_svrty': None,\n",
+ "# 'blunt_phrase': None, 'terse_phrase': None, 'clds': 'CLR', 'water_temp': None, 'primary_wave_period': None, 'primary_wave_height': None, \n",
+ "# 'primary_swell_period': None, 'primary_swell_height': None, 'primary_swell_direction': None, 'secondary_swell_period': None, \n",
+ "# 'secondary_swell_height': None, 'secondary_swell_direction': None\n",
+ "# }\n",
+ "\n",
+ "conn = sqlite3.connect(\"./data/weather-raw.db\")\n",
+ "cur = conn.cursor()\n",
+ "\n",
+ "\n",
+ "def create_weather_table(cur, table_name):\n",
+ " cur.execute(\"\"\"\n",
+ " CREATE TABLE IF NOT EXISTS {table_name}(\n",
+ " key, class, expire_time_gmt, obs_id, obs_name, valid_time_gmt INTEGER NOT NULL PRIMARY KEY, day_ind, temp, wx_icon, icon_extd, \n",
+ " wx_phrase, pressure_tend, pressure_desc, dewPt, heat_index, rh, pressure, vis, wc, wdir, wdir_cardinal, \n",
+ " gust, wspd, max_temp, min_temp, precip_total, precip_hourly, snow_hrly, uv_desc, feels_like, uv_index,\n",
+ " qualifier, qualifier_svrty, blunt_phrase, terse_phrase, clds, water_temp, primary_wave_period, \n",
+ " primary_wave_height, primary_swell_period, primary_swell_height, primary_swell_direction, \n",
+ " secondary_swell_period, secondary_swell_height, secondary_swell_direction, obs_timestamp)\n",
+ " \"\"\".format(table_name=table_name))\n",
+ " cur.execute(\"\"\"\n",
+ " CREATE INDEX idx_obs_timestamp ON weather_raw(obs_timestamp);\n",
+ " \"\"\")\n",
+ " cur.execute(\"\"\"\n",
+ " CREATE INDEX idx_obs_timestamp_month ON weather_raw(STRFTIME('%M', obs_timestamp));\n",
+ " \"\"\")\n",
+ " cur.execute(\"\"\"\n",
+ " CREATE INDEX idx_obs_timestamp_date ON weather_raw(STRFTIME('%Y-%m-%d', wr.obs_timestamp));\n",
+ " \"\"\")\n",
+ " \n",
+ "# Create tables for raw & cleaned data respectively (if they don't exist already)\n",
+ "create_weather_table(cur, \"weather_raw\")\n",
+ "\n",
+ "\n",
+ "# Get the latest date that data has been downloaded for and start downloading new data from that timestamp\n",
+ "cur.execute(\"SELECT MAX(obs_timestamp) FROM weather_raw\")\n",
+ "max_date = cur.fetchone()[0]\n",
+ "target_date = datetime.datetime.strptime(max_date, DATE_FORMAT).date() if max_date else date(2000, 1, 1)\n",
+ "\n",
+ "print(f\"Starting Date: {target_date}\") \n",
+ "print(\"--------------------------------\")\n",
+ "defaults = {\n",
+ " 'key': None,'class': None,'expire_time_gmt': None,'obs_id': None,'obs_name': None,'valid_time_gmt': None,\n",
+ " 'day_ind': None,'temp': None,'wx_icon': None,'icon_extd': None,'wx_phrase': None,'pressure_tend': None,\n",
+ " 'pressure_desc': None,'dewPt': None,'heat_index': None,'rh': None,'pressure': None,'vis': None,'wc': None,\n",
+ " 'wdir': None,'wdir_cardinal': None,'gust': None,'wspd': None,'max_temp': None,'min_temp': None,'precip_total': None,\n",
+ " 'precip_hrly': None,'snow_hrly': None,'uv_desc': None,'feels_like': None,'uv_index': None,'qualifier': None,\n",
+ " 'qualifier_svrty': None,'blunt_phrase': None,'terse_phrase': None,'clds': None,'water_temp': None,\n",
+ " 'primary_wave_period': None,'primary_wave_height': None,'primary_swell_period': None,'primary_swell_height': None,\n",
+ " 'primary_swell_direction': None,'secondary_swell_period': None,'secondary_swell_height': None,'secondary_swell_direction': None\n",
+ "}\n",
+ "\n",
+ "\n",
+ "INSERT_SQL = \"\"\"\n",
+ "INSERT OR IGNORE INTO weather_raw VALUES (\n",
+ " :key, :class, :expire_time_gmt, :obs_id, :obs_name, :valid_time_gmt, :day_ind, :temp, :wx_icon, :icon_extd, :wx_phrase,\n",
+ " :pressure_tend, :pressure_desc, :dewPt, :heat_index, :rh, :pressure, :vis, :wc, :wdir, :wdir_cardinal,\n",
+ " :gust, :wspd, :max_temp, :min_temp, :precip_total, :precip_hrly, :snow_hrly, :uv_desc, :feels_like, :uv_index,\n",
+ " :qualifier, :qualifier_svrty, :blunt_phrase, :terse_phrase, :clds, :water_temp, :primary_wave_period,\n",
+ " :primary_wave_height, :primary_swell_period, :primary_swell_height, :primary_swell_direction,\n",
+ " :secondary_swell_period, :secondary_swell_height, :secondary_swell_direction, :obs_timestamp\n",
+ ")\n",
+ "\"\"\"\n",
+ "\n",
+ "def augment_data(rec):\n",
+ " rec[\"obs_timestamp\"] = datetime.datetime.fromtimestamp(rec[\"valid_time_gmt\"]).strftime(DATE_FORMAT)\n",
+ " return rec\n",
+ "\n",
+ "today = datetime.date.today()\n",
+ "if target_date == today:\n",
+ " print(\"Already up to date!\")\n",
+ " \n",
+ "while target_date != today:\n",
+ " end_date = target_date + timedelta(days=1) \n",
+ " start_date_str = target_date.strftime(\"%Y%m%d\")\n",
+ " end_date_str = end_date.strftime(\"%Y%m%d\")\n",
+ " target_url = BASE_URL.format(api_key=API_KEY, start_date=start_date_str, end_date=start_date_str)\n",
+ " res = requests.get(target_url)\n",
+ " target_date = end_date\n",
+ "\n",
+ " data = res.json()\n",
+ " if not \"observations\" in data:\n",
+ " print(f\"Date: {target_date} NF\", end=\"\\r\")\n",
+ " continue\n",
+ " params = ({k: d.get(k, defaults[k]) for k in defaults} for d in data[\"observations\"])\n",
+ " params = list(map(augment_data, params))\n",
+ "\n",
+ " cur.executemany(INSERT_SQL, params)\n",
+ " conn.commit()\n",
+ " # time.sleep(0.05)\n",
+ " # was_cached = \"Cache HIT\" if res.from_cache else \"Cache MISS\"\n",
+ " print(f\"Date: {target_date} OK\", end=\"\\r\")\n",
+ " target_date = end_date\n",
+ "\n",
+ "SQL_CLEANED_DATA = \"\"\"\n",
+ " WITH RECURSIVE generate_series(x) AS (\n",
+ " SELECT 0\n",
+ " UNION ALL\n",
+ " SELECT x+1 FROM generate_series LIMIT 24\n",
+ " ), distinct_dates AS (\n",
+ " SELECT DISTINCT DATE(obs_timestamp) AS obs_date\n",
+ " FROM weather_raw wr \n",
+ " WHERE obs_timestamp >= '2000-01-01'\n",
+ " ), hours AS (\n",
+ " SELECT x AS hrs FROM generate_series\n",
+ " )\n",
+ " SELECT w.temp, w.obs_timestamp, w.pressure, w.wspd\n",
+ " FROM weather_raw w \n",
+ " JOIN (\n",
+ " SELECT d.obs_date, COUNT(*)\n",
+ " FROM distinct_dates d \n",
+ " CROSS JOIN hours h \n",
+ " INNER JOIN (SELECT * FROM weather_raw WHERE STRFTIME('%M', obs_timestamp) in ('51', '52')) wr \n",
+ " ON DATE(STRFTIME('%Y-%m-%d', wr.obs_timestamp)) = d.obs_date \n",
+ " AND CAST(STRFTIME('%H', obs_timestamp) AS INTEGER) = h.hrs\n",
+ " GROUP BY d.obs_date\n",
+ " HAVING COUNT(*) = 24\n",
+ " ) d ON d.obs_date = STRFTIME('%Y-%m-%d', w.obs_timestamp) \n",
+ " WHERE STRFTIME('%M', w.obs_timestamp) in ('51', '52');\n",
+ "\"\"\"\n",
+ "df = pd.read_sql(SQL_CLEANED_DATA, conn, parse_dates=[\"obs_timestamp\"]) \n",
+ "df.head() "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 22,
+ "id": "7233dd6b-241b-46c6-a206-184b33d43792",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " | \n",
+ " temp | \n",
+ " pressure | \n",
+ " wspd | \n",
+ " day_sin | \n",
+ " day_cos | \n",
+ " year_sin | \n",
+ " year_cos | \n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ " 1 | \n",
+ " 45.0 | \n",
+ " 30.21 | \n",
+ " 5.0 | \n",
+ " 0.465615 | \n",
+ " 0.884988 | \n",
+ " 0.013798 | \n",
+ " 0.999905 | \n",
+ "
\n",
+ " \n",
+ " 2 | \n",
+ " 47.0 | \n",
+ " 30.21 | \n",
+ " 5.0 | \n",
+ " 0.678801 | \n",
+ " 0.734323 | \n",
+ " 0.014514 | \n",
+ " 0.999895 | \n",
+ "
\n",
+ " \n",
+ " 4 | \n",
+ " 47.0 | \n",
+ " 30.20 | \n",
+ " 5.0 | \n",
+ " 0.955020 | \n",
+ " 0.296542 | \n",
+ " 0.015948 | \n",
+ " 0.999873 | \n",
+ "
\n",
+ " \n",
+ " 5 | \n",
+ " 44.0 | \n",
+ " 30.19 | \n",
+ " 3.0 | \n",
+ " 0.999229 | \n",
+ " 0.039260 | \n",
+ " 0.016664 | \n",
+ " 0.999861 | \n",
+ "
\n",
+ " \n",
+ " 6 | \n",
+ " 48.0 | \n",
+ " 30.18 | \n",
+ " 12.0 | \n",
+ " 0.975342 | \n",
+ " -0.220697 | \n",
+ " 0.017381 | \n",
+ " 0.999849 | \n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " temp pressure wspd day_sin day_cos year_sin year_cos\n",
+ "1 45.0 30.21 5.0 0.465615 0.884988 0.013798 0.999905\n",
+ "2 47.0 30.21 5.0 0.678801 0.734323 0.014514 0.999895\n",
+ "4 47.0 30.20 5.0 0.955020 0.296542 0.015948 0.999873\n",
+ "5 44.0 30.19 3.0 0.999229 0.039260 0.016664 0.999861\n",
+ "6 48.0 30.18 12.0 0.975342 -0.220697 0.017381 0.999849"
+ ]
+ },
+ "execution_count": 22,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "def prepare_dataframe(_df):\n",
+ " _df = _df.astype(\n",
+ " {\n",
+ " 'temp': 'float',\n",
+ " 'pressure': 'float',\n",
+ " 'wspd': 'float'\n",
+ " },\n",
+ " )\n",
+ " _df = _df.dropna()\n",
+ " _df = _df.sort_values(by=['obs_timestamp'])\n",
+ " date_time = _df.pop('obs_timestamp')\n",
+ " timestamp_s = date_time.map(pd.Timestamp.timestamp)\n",
+ " day = 24*60*60\n",
+ " year = (365.2425)*day\n",
+ " \n",
+ " _df['day_sin'] = np.sin(timestamp_s * (2 * np.pi / day))\n",
+ " _df['day_cos'] = np.cos(timestamp_s * (2 * np.pi / day))\n",
+ " _df['year_sin'] = np.sin(timestamp_s * (2 * np.pi / year))\n",
+ " _df['year_cos'] = np.cos(timestamp_s * (2 * np.pi / year))\n",
+ "\n",
+ " \n",
+ " return _df, date_time\n",
+ "\n",
+ "# Field descriptions here\n",
+ "# https://www.research-collection.ethz.ch/bitstream/handle/20.500.11850/511371/1/LapamonpinyoEtAl_engrXiv_2021.pdf \n",
+ "df, date_time = prepare_dataframe(df)\n",
+ "df.head()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 24,
+ "id": "049cf91d-30f9-4c42-b705-fdfb48f0d1e2",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "/tmp/ipykernel_64/4039997715.py:7: FutureWarning: The behavior of `series[i:j]` with an integer-dtype index is deprecated. In a future version, this will be treated as *label-based* indexing, consistent with e.g. `series[i]` lookups. To retain the old behavior, use `series.iloc[i:j]`. To get the future behavior, use `series.loc[i:j]`.\n",
+ " plot_features.index = date_time[-480:]\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " | \n",
+ " count | \n",
+ " mean | \n",
+ " std | \n",
+ " min | \n",
+ " 25% | \n",
+ " 50% | \n",
+ " 75% | \n",
+ " max | \n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ " temp | \n",
+ " 179231.0 | \n",
+ " 59.138720 | \n",
+ " 17.768787 | \n",
+ " 6.000000 | \n",
+ " 45.000000 | \n",
+ " 60.000000 | \n",
+ " 74.000000 | \n",
+ " 118.000000 | \n",
+ "
\n",
+ " \n",
+ " pressure | \n",
+ " 179231.0 | \n",
+ " 30.034228 | \n",
+ " 0.216623 | \n",
+ " 28.610000 | \n",
+ " 29.900000 | \n",
+ " 30.030000 | \n",
+ " 30.170000 | \n",
+ " 30.860000 | \n",
+ "
\n",
+ " \n",
+ " wspd | \n",
+ " 179231.0 | \n",
+ " 8.984768 | \n",
+ " 4.745124 | \n",
+ " 0.000000 | \n",
+ " 6.000000 | \n",
+ " 8.000000 | \n",
+ " 12.000000 | \n",
+ " 45.000000 | \n",
+ "
\n",
+ " \n",
+ " day_sin | \n",
+ " 179231.0 | \n",
+ " -0.015076 | \n",
+ " 0.706559 | \n",
+ " -0.999391 | \n",
+ " -0.731354 | \n",
+ " -0.034899 | \n",
+ " 0.681998 | \n",
+ " 0.999391 | \n",
+ "
\n",
+ " \n",
+ " day_cos | \n",
+ " 179231.0 | \n",
+ " 0.007189 | \n",
+ " 0.707461 | \n",
+ " -0.999391 | \n",
+ " -0.681998 | \n",
+ " 0.034899 | \n",
+ " 0.731354 | \n",
+ " 0.999391 | \n",
+ "
\n",
+ " \n",
+ " year_sin | \n",
+ " 179231.0 | \n",
+ " 0.004124 | \n",
+ " 0.709370 | \n",
+ " -1.000000 | \n",
+ " -0.705328 | \n",
+ " 0.007787 | \n",
+ " 0.717489 | \n",
+ " 1.000000 | \n",
+ "
\n",
+ " \n",
+ " year_cos | \n",
+ " 179231.0 | \n",
+ " -0.003987 | \n",
+ " 0.704817 | \n",
+ " -1.000000 | \n",
+ " -0.708857 | \n",
+ " -0.004902 | \n",
+ " 0.696913 | \n",
+ " 1.000000 | \n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " count mean std min 25% 50% \\\n",
+ "temp 179231.0 59.138720 17.768787 6.000000 45.000000 60.000000 \n",
+ "pressure 179231.0 30.034228 0.216623 28.610000 29.900000 30.030000 \n",
+ "wspd 179231.0 8.984768 4.745124 0.000000 6.000000 8.000000 \n",
+ "day_sin 179231.0 -0.015076 0.706559 -0.999391 -0.731354 -0.034899 \n",
+ "day_cos 179231.0 0.007189 0.707461 -0.999391 -0.681998 0.034899 \n",
+ "year_sin 179231.0 0.004124 0.709370 -1.000000 -0.705328 0.007787 \n",
+ "year_cos 179231.0 -0.003987 0.704817 -1.000000 -0.708857 -0.004902 \n",
+ "\n",
+ " 75% max \n",
+ "temp 74.000000 118.000000 \n",
+ "pressure 30.170000 30.860000 \n",
+ "wspd 12.000000 45.000000 \n",
+ "day_sin 0.681998 0.999391 \n",
+ "day_cos 0.731354 0.999391 \n",
+ "year_sin 0.717489 1.000000 \n",
+ "year_cos 0.696913 1.000000 "
+ ]
+ },
+ "execution_count": 24,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "