tet.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      1949-01-01\n",
       "1      1949-02-01\n",
       "2      1949-03-01\n",
       "3      1949-04-01\n",
       "4      1949-05-01\n",
       "          ...    \n",
       "139    1960-08-01\n",
       "140    1960-09-01\n",
       "141    1960-10-01\n",
       "142    1960-11-01\n",
       "143    1960-12-01\n",
       "Name: date, Length: 144, dtype: object"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.date"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1280x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot the Airpassengers data\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "df = pd.read_csv(\"https://raw.githubusercontent.com/selva86/datasets/master/AirPassengers.csv\")\n",
    "df['date'] = pd.to_datetime(df['date'], format='%Y-%m-%d')\n",
    "df.set_index('date', inplace=True)\n",
    "\n",
    "# Draw high quality plot\n",
    "plt.figure(figsize=(16,10), dpi= 80)\n",
    "plt.plot(df.index, df['value'], color='tab:red')\n",
    "\n",
    "# Decoration\n",
    "plt.ylim(50,750)\n",
    "xtick_location = df.index[::12]\n",
    "xtick_labels = [x.strftime('%Y-%m') for x in xtick_location]\n",
    "plt.xticks(ticks=xtick_location, labels=xtick_labels, rotation=0, fontsize=12, horizontalalignment='center', alpha=.7)\n",
    "plt.yticks(fontsize=12, alpha=.7)\n",
    "plt.title(\"Air Passengers Traffic (1949 - 1960)\", fontsize=22)\n",
    "plt.grid(axis='both', alpha=.3)\n",
    "\n",
    "# Remove borders\n",
    "plt.gca().spines[\"top\"].set_alpha(0.0)\n",
    "plt.gca().spines[\"bottom\"].set_alpha(0.3)\n",
    "plt.gca().spines[\"right\"].set_alpha(0.0)\n",
    "plt.gca().spines[\"left\"].set_alpha(0.3)\n",
    "# plt.show()\n",
    "plt.tight_layout()\n",
    "# save the plot\n",
    "plt.savefig('AirPassengers.png')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['1949-01-01', '1950-01-01', '1951-01-01', '1952-01-01',\n",
       "               '1953-01-01', '1954-01-01', '1955-01-01', '1956-01-01',\n",
       "               '1957-01-01', '1958-01-01', '1959-01-01', '1960-01-01'],\n",
       "              dtype='datetime64[ns]', name='date', freq=None)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xtick_location + ['1961-01-01']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['1949-01-01', '1949-02-01', '1949-03-01', '1949-04-01',\n",
       "               '1949-05-01', '1949-06-01', '1949-07-01', '1949-08-01',\n",
       "               '1949-09-01', '1949-10-01',\n",
       "               ...\n",
       "               '1960-03-01', '1960-04-01', '1960-05-01', '1960-06-01',\n",
       "               '1960-07-01', '1960-08-01', '1960-09-01', '1960-10-01',\n",
       "               '1960-11-01', '1960-12-01'],\n",
       "              dtype='datetime64[ns]', name='date', length=144, freq=None)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import yaml\n",
    "\n",
    "def update_env_yml(conda_install_str, yml_file_path):\n",
    "    # Remove 'conda install' from the string and split by '-c' to separate channels\n",
    "    conda_install_str = conda_install_str.replace('conda install', '').strip()\n",
    "    parts = conda_install_str.split(' -c ')\n",
    "    \n",
    "    # The first part contains packages, the rest are channels\n",
    "    packages_part = parts[0].strip()\n",
    "    packages = packages_part.split()\n",
    "    \n",
    "    channels = [channel.strip() for channel in parts[1:]]\n",
    "    \n",
    "    # Load the existing environment.yml file\n",
    "    with open(yml_file_path, 'r') as file:\n",
    "        env_data = yaml.safe_load(file)\n",
    "    \n",
    "    # Update the dependencies and channels\n",
    "    if 'dependencies' not in env_data:\n",
    "        env_data['dependencies'] = []\n",
    "    \n",
    "    for package in packages:\n",
    "        if package not in env_data['dependencies']:\n",
    "            env_data['dependencies'].append(package)\n",
    "    \n",
    "    if 'channels' not in env_data:\n",
    "        env_data['channels'] = []\n",
    "    \n",
    "    for channel in channels:\n",
    "        if channel not in env_data['channels']:\n",
    "            env_data['channels'].append(channel)\n",
    "    \n",
    "    # Save the updated environment.yml file\n",
    "    new_yml_file_path = yml_file_path.replace('.yml', '_updated.yml')\n",
    "    with open(new_yml_file_path, 'w') as file:\n",
    "        yaml.safe_dump(env_data, file, default_flow_style=False)\n",
    "    \n",
    "    return new_yml_file_path"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Example usage:\n",
    "conda_install_str = \"conda install pytorch torchvision torchaudio pytorch-cuda=11.8 -c pytorch -c nvidia\"\n",
    "yml_file_path = \"anaconda_env.yml\"\n",
    "new_yml_file_path = update_env_yml(conda_install_str, yml_file_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "import torch.nn as nn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "context_window=5\n",
    "forecast_horizon = 3\n",
    "n_timeseries = 10\n",
    "batch_size = 32\n",
    "\n",
    "sample_data = torch.randn(batch_size, context_window, n_time_series)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Declare nn.Linear for each channel\n",
    "layers = nn.ModuleList([nn.Linear(context_window, forecast_horizon) for _ in range(n_timeseries)])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "forecast = [layers[i](sample_data[:,:,i]) for i in range(n_timeseries)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "trend_layers = nn.ModuleList([nn.Linear(context_window, forecast_horizon) for _ in range(n_timeseries)])\n",
    "seasonality_layers = nn.ModuleList([nn.Linear(context_window, forecast_horizon) for _ in range(n_timeseries)])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "trend, seasonality = torch.randn(batch_size, context_window, n_time_series), torch.randn(batch_size, context_window, n_time_series)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "trend_forecast = [trend_layers[i]( trend[:,:,i]) for i in range(n_timeseries)]\n",
    "seasonality_forecast = [seasonality_layers[i]( seasonality [:,:,i]) for i in range(n_timeseries)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "forecast = [trend_forecast[i] + seasonality_forecast[i] for i in range(n_timeseries)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([32, 3])"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "forecast[0].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "layers = nn.ModuleList([nn.Linear(context_window, forecast_horizon) for _ in range(n_timeseries)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "last_value = sample_data[:,-1:,:]\n",
    "norm_ts = sample_data - last_value\n",
    "output = [layers[i](norm_ts[:,:,i]) for i in range(n_timeseries)]\n",
    "forecast = [o + last_value[:,:,i] for i, o in enumerate(output)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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