use_da_rnn_from_csv_module.ipynb

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 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Use da_rnn_from_csv.py"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sys,os\n",
    "import pandas as pd\n",
    "import da_rnn_from_csv as drfc"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Run the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "fname_no_ext = 'nasdaq100_padding'\n",
    "rows_to_use = 40000\n",
    "return_dict = drfc.main(FILE_NAME_NO_EXTENSION=fname_no_ext,subset_rows=rows_to_use)\n",
    "df_partial = return_dict['dataframe']\n",
    "m = return_dict['model']\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### What does the model return?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(f'The model returns a dictionary with keys {list(return_dict.keys())}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plot a sub-section of the output to zoom in on the differences between y_act and y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_size = return_dict['test_size']\n",
    "y_act = m.y[-1*test_size:]\n",
    "y_pred = return_dict['y_pred'][-1 * test_size:]\n",
    "print(test_size,len(y_act),len(y_pred))\n",
    "df_review = pd.DataFrame({'y_act':y_act,'y_pred':y_pred})\n",
    "\n",
    "num_to_view = 200\n",
    "\n",
    "beg_index = 1\n",
    "end_index = beg_index+num_to_view\n",
    "df_review.iloc[beg_index:end_index].plot(y=['y_act','y_pred'],figsize=(12,10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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