deploy_models.ipynb

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 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Deploy Models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import sys\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import pickle\n",
    "import sklearn\n",
    "import gc\n",
    "from sklearn.linear_model import SGDClassifier\n",
    "from IPython.display import clear_output"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.set_option('display.max_columns', 500)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_path = 'C:\\\\Users\\\\zking\\\\Documents\\\\Kaggle\\\\MS Malware\\\\data\\\\'\n",
    "model_path = 'C:\\\\Users\\\\zking\\\\Documents\\\\Kaggle\\\\MS Malware\\\\MS-Malware-Kaggle\\\\models\\\\'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open(data_path+'model_cols', 'rb') as f:\n",
    "    model_cols = pickle.load(f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open(data_path+'x_test', 'rb') as f:\n",
    "    x_test = pickle.load(f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sample = pd.read_csv(data_path + 'sample_submission.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_test_min = x_test.loc[:,model_cols]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "del x_test\n",
    "gc.collect()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# SGD Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open(model_path + 'clf_sgd_elast_' + str(len(model_cols)), 'rb') as c:\n",
    "    clf = pickle.load(c)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Generate predicted probabilites\n",
    "batch_size = 100000\n",
    "clf_probs = []\n",
    "batch_range = range(0,len(x_test_min)+1, batch_size)\n",
    "for b in batch_range:\n",
    "    b_stop = b + batch_size\n",
    "    test_batch = x_test_min.iloc[b:b_stop,]\n",
    "    clf_probs = clf_probs + clf.predict_proba(test_batch)[:,1].tolist()\n",
    "    clear_output()\n",
    "    print(f'Finished batch {b} to {b_stop-1}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sample['HasDetections'] = clf_probs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sample.to_csv(data_path +'clf_sgd_elast_' + str(len(model_cols)) + '.csv', index = False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Model 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open(model_path + 'clf_rf', 'wb') as c:\n",
    "    clf = pickle.load(c)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Generate predicted probabilites\n",
    "clf_probs = clf.predict_proba(x_test_min)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sample['HasDetections'] = clf_probs[:,1]\n",
    "sample.to_csv(data_path + \"prob_rf.csv\", index = False)"
   ]
  }
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