Using_RESSPECT_Walkthrough.ipynb

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   "source": [
    "# https://resspect.readthedocs.io/en/latest/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "dab55b9a-e3dd-4e87-b849-fa5a62b36989",
   "metadata": {},
   "outputs": [],
   "source": [
    "from resspect import fit_snpcc"
   ]
  },
  {
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   "execution_count": null,
   "id": "88c74cf5-bfe5-4685-9335-bdfe8c63f051",
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    "path_to_data_dir = 'data/SIMGEN_PUBLIC_DES/'            # raw data directory \n",
    "features_file = 'results/Malanchev.csv'                     # output file \n",
    "feature_extractor = 'malanchev'\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "360ecf08-9c52-4075-8617-4deb1959bb94",
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   "source": [
    "fit_snpcc(path_to_data_dir=path_to_data_dir, features_file=features_file, feature_extractor=feature_extractor)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9df62bef-2588-4fd4-8039-41cfd9bb91bc",
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   "outputs": [],
   "source": [
    "from resspect.learn_loop import  learn_loop\n",
    "\n",
    "nloops = 1000                                  # number of iterations\n",
    "method = 'malanchev'                               # only option in v1.0\n",
    "ml = 'RandomForest'                            # classifier\n",
    "strategy = 'UncSampling'                       # learning strategy\n",
    "input_file = 'results/Malanchev.csv'               # input features file\n",
    "metric = 'results/metrics.csv'                 # output metrics file\n",
    "queried = 'results/queried.csv'                # output query file\n",
    "train = 'original'                             # initial training\n",
    "batch = 1                                      # size of batch\n",
    "\n",
    "learn_loop(nloops=nloops, features_method=method, classifier=ml,\n",
    "           strategy=strategy, path_to_features=input_file, output_metrics_file=metric,\n",
    "           output_queried_file=queried, training=train, batch=batch)\n"
   ]
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  {
   "cell_type": "code",
   "execution_count": null,
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   "source": [
    "nloops = 1000                                  # number of iterations\n",
    "method = 'malanchev'                               # only option in v1.0\n",
    "ml = 'RandomForest'                            # classifier\n",
    "strategy = 'RandomSampling'                       # learning strategy\n",
    "input_file = 'results/Malanchev.csv'               # input features file\n",
    "metric = 'results/metrics_random.csv'                 # output metrics file\n",
    "queried = 'results/queried_random.csv'                # output query file\n",
    "train = 'original'                             # initial training\n",
    "batch = 1                                      # size of batch\n",
    "\n",
    "learn_loop(nloops=nloops, features_method=method, classifier=ml,\n",
    "           strategy=strategy, path_to_features=input_file, output_metrics_file=metric,\n",
    "           output_queried_file=queried, training=train, batch=batch)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3ffa34f1-64f5-46ea-93f7-f8935cba1cc6",
   "metadata": {},
   "outputs": [],
   "source": [
    "from resspect.plot_results import Canvas\n",
    "\n",
    "# define parameters\n",
    "path_to_files = ['results/metrics.csv','results/metrics_random.csv']\n",
    "strategies_list = ['UncSampling', 'RandomSampling']\n",
    "output_plot = 'plots/metrics.png'\n",
    "\n",
    "#Initiate the Canvas object, read and plot the results for\n",
    "# each metric and strategy.\n",
    "cv = Canvas()\n",
    "cv.load_metrics(path_to_files=path_to_files,\n",
    "                   strategies_list=strategies_list)\n",
    "cv.set_plot_dimensions()\n",
    "cv.plot_metrics(output_plot_file=output_plot,\n",
    "                   strategies_list=strategies_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1f88d53b-1591-4aed-8ee1-35904d8b23b2",
   "metadata": {},
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   "source": []
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