2. train the model.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "from imblearn.over_sampling import SMOTE\n",
    "import numpy as np\n",
    "\n",
    "products = ['BANANAS', 'PEARLS']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('trainingsets.pkl', 'rb') as f:\n",
    "    trainingsets = pickle.load(f) # deserialize using load()\n",
    "\n",
    "with open('traininggts.pkl', 'rb') as f:\n",
    "    traininggts = pickle.load(f) # deserialize using load()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# compute the stats\n",
    "stats = {\n",
    "    product:[\n",
    "        np.mean([trainingsets[i][0][product][0] for i in range(3)], axis=0).tolist(),\n",
    "        np.mean([trainingsets[i][0][product][1] for i in range(3)], axis=0).tolist(),\n",
    "    ]\n",
    "    for product in products\n",
    "}\n",
    "\n",
    "import json\n",
    "with open('stats.json', 'w') as f:\n",
    "    json.dump(stats, f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "0.7022269034005417\n",
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      "0.5408891671884783\n",
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     ]
    }
   ],
   "source": [
    "from src.logreg import LogisticRegression, serialize_logreg, deserialize_logreg\n",
    "\n",
    "hardcode = {}\n",
    "for product in products:\n",
    "    hardcode[product] = []\n",
    "    for i in range(9):\n",
    "        logreg = LogisticRegression(num_iter=10000, fit_intercept=False)\n",
    "        sm = SMOTE(random_state=42)\n",
    "        for (_, xss, _), (_, gts) in zip(trainingsets, traininggts):\n",
    "            X_res, y_res = sm.fit_resample(xss[product], np.array(gts[product])[:,i])\n",
    "            logreg.train(X_res, y_res)\n",
    "        print(logreg.eval(X_res, y_res))\n",
    "        hardcode[product].append(\n",
    "            serialize_logreg(logreg)\n",
    "        )\n",
    "print(hardcode)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "import json\n",
    "with open('logreg_params_less.json', 'w') as f:\n",
    "    json.dump(hardcode, f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "from src.dectree import DecisionTreeClassifier, serialize_decision_tree, deserialize_decision_tree\n",
    "\n",
    "xss = {\n",
    "    product: np.concatenate([xss[product] for (_,xss,_) in trainingsets])\n",
    "    for product in products\n",
    "}\n",
    "\n",
    "gts = {\n",
    "    product: np.concatenate([gts[product] for (_,gts) in traininggts])\n",
    "    for product in products\n",
    "}\n",
    "\n",
    "hardcode = {}\n",
    "for product in products:\n",
    "    hardcode[product] = []\n",
    "    for i in range(9):\n",
    "        clf = DecisionTreeClassifier(max_depth=4)\n",
    "        sm = SMOTE(random_state=42)\n",
    "        X_res, y_res = sm.fit_resample(xss[product], np.array(gts[product])[:,i])\n",
    "        clf.train(X_res, y_res)\n",
    "        print(clf.eval(X_res, y_res))\n",
    "        hardcode[product].append(\n",
    "            serialize_decision_tree(clf)\n",
    "        )\n",
    "print(hardcode)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import json\n",
    "with open('dectree_params_less.json', 'w') as f:\n",
    "    json.dump(hardcode, f)"
   ]
  }
 ],
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