l3wrapper vs AL3.jar comparison.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Compare `l3wrapper` and `AL3.jar`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Setup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/home/andrea/Documents/Politecnico/tirocinio/src/../\n"
     ]
    }
   ],
   "source": [
    "import random\n",
    "from os.path import join\n",
    "from typing import Tuple, List\n",
    "\n",
    "import arff\n",
    "from snapshottest import TestCase\n",
    "\n",
    "from src import DEFAULT_DIR\n",
    "from src.XPLAIN_explainer import XPLAIN_explainer\n",
    "from src.dataset import Dataset\n",
    "\n",
    "\n",
    "def load_arff(f) -> Dataset:\n",
    "    a = arff.load(f)\n",
    "    dataset = Dataset(a['data'], a['attributes'])\n",
    "\n",
    "    return dataset\n",
    "\n",
    "\n",
    "def import_dataset_arff(f, explain_indices: List[int],\n",
    "                        random_explain_dataset: bool) -> Tuple[Dataset, Dataset, List[str]]:\n",
    "    dataset = load_arff(f)\n",
    "\n",
    "    dataset_len = len(dataset)\n",
    "    train_indices = list(range(dataset_len))\n",
    "\n",
    "    if random_explain_dataset:\n",
    "        random.seed(1)\n",
    "        # small dataset\n",
    "        MAX_SAMPLE_COUNT = 100\n",
    "        if dataset_len < (2 * MAX_SAMPLE_COUNT):\n",
    "            samples = int(0.2 * dataset_len)\n",
    "        else:\n",
    "            samples = MAX_SAMPLE_COUNT\n",
    "\n",
    "        # Randomly pick some instances to remove from the training dataset and use in the\n",
    "        # explain dataset\n",
    "        explain_indices = list(random.sample(train_indices, samples))\n",
    "    for i in explain_indices:\n",
    "        train_indices.remove(i)\n",
    "\n",
    "    train_dataset = Dataset.from_indices(train_indices, dataset)\n",
    "    explain_dataset = Dataset.from_indices(explain_indices, dataset)\n",
    "\n",
    "    return train_dataset, explain_dataset, [str(i) for i in explain_indices]\n",
    "\n",
    "\n",
    "def import_datasets_arff(f, f_explain, explain_indices: List[int],\n",
    "                         random_explain_dataset: bool) -> Tuple[Dataset, Dataset, List[str]]:\n",
    "    train_dataset = load_arff(f)\n",
    "    explain_dataset = load_arff(f_explain)\n",
    "\n",
    "    len_explain_dataset = len(explain_dataset)\n",
    "\n",
    "    if random_explain_dataset:\n",
    "        random.seed(7)\n",
    "        explain_indices = list(random.sample(range(len_explain_dataset), 300))\n",
    "        explain_dataset = Dataset.from_indices(explain_indices, explain_dataset)\n",
    "\n",
    "    return train_dataset, explain_dataset, [str(i) for i in explain_indices]\n",
    "\n",
    "\n",
    "def get_classifier(classifier_name: str):\n",
    "    if classifier_name == \"sklearn_nb\":\n",
    "        from sklearn.naive_bayes import MultinomialNB\n",
    "\n",
    "        skl_clf = MultinomialNB()\n",
    "\n",
    "        return skl_clf\n",
    "\n",
    "    elif classifier_name == \"sklearn_rf\":\n",
    "        from sklearn.ensemble import RandomForestClassifier\n",
    "        from sklearn.pipeline import make_pipeline\n",
    "        from sklearn.preprocessing import OneHotEncoder\n",
    "\n",
    "        pipe = make_pipeline(OneHotEncoder(), RandomForestClassifier(random_state=42))\n",
    "        skl_clf = pipe\n",
    "\n",
    "        return skl_clf\n",
    "\n",
    "    else:\n",
    "        raise ValueError(\"Classifier not available\")\n",
    "\n",
    "\n",
    "def get_explanation(dataset_name: str, classifier_name: str):\n",
    "    explain_dataset_indices = []\n",
    "    if dataset_name in [join(DEFAULT_DIR, \"datasets/adult_d.arff\"),\n",
    "                        join(DEFAULT_DIR, \"datasets/compas-scores-two-years_d.arff\")]:\n",
    "        with open(dataset_name) as f, open(dataset_name[:-5] + \"_explain.arff\") as f_explain:\n",
    "            train_dataset, explain_dataset, explain_indices = import_datasets_arff(f, f_explain,\n",
    "                                                                                   explain_dataset_indices,\n",
    "                                                                                   True)\n",
    "    else:\n",
    "        with open(dataset_name) as f:\n",
    "            train_dataset, explain_dataset, explain_indices = import_dataset_arff(\n",
    "                f, explain_dataset_indices, True)\n",
    "\n",
    "    clf = get_classifier(classifier_name).fit(train_dataset.X_numpy(),\n",
    "                                              train_dataset.Y_numpy())\n",
    "    explainer = XPLAIN_explainer(clf, train_dataset)\n",
    "\n",
    "    instance = explain_dataset.get_decoded(0)\n",
    "\n",
    "    cc = explain_dataset.class_column_name()\n",
    "    target_class_index = instance[cc]\n",
    "\n",
    "    return explainer.explain_instance(explain_dataset[0], target_class_index)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Monks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open(join(DEFAULT_DIR, \"datasets/monks.arff\")) as monks_f:\n",
    "    monks_train, monks_explain, monks_explain_indices = import_dataset_arff(monks_f, [], True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "def l3wrapper(instance_ix, train):\n",
    "    from l3wrapper.l3wrapper import L3Classifier\n",
    "    \n",
    "    clf = L3Classifier(min_sup=0.01, min_conf=0.50)\n",
    "    clf.fit(train.X_decoded(),\n",
    "            train.Y_decoded(),\n",
    "            column_names=train.X_decoded().columns.to_list())\n",
    "    \n",
    "    def get_rule_attrs_and_values(r, clf):\n",
    "            r_attr_ixs_and_values = sorted([clf._item_id_to_item[i] for i in r.item_ids])\n",
    "            r_attrs_and_values = [(clf._column_id_to_name[c], v) for c, v in r_attr_ixs_and_values]\n",
    "            return r_attrs_and_values\n",
    "\n",
    "\n",
    "    # Perform matching: remove all rules thta use an attibute value not present in the instance to\n",
    "    # explain\n",
    "    \n",
    "    # For each rule\n",
    "    # Remove rules that use item_ids greater that instance attributes\n",
    "    rules = [list(sorted(r.item_ids)) for r in clf.lvl1_rules_ if\n",
    "                 all(i < (len(train.X_decoded().iloc[0]) + 1) for i in r.item_ids)]\n",
    "\n",
    "    return rules\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import subprocess  \n",
    "import os\n",
    "from shutil import rmtree\n",
    "import tempfile\n",
    "\n",
    "def AL3(instance_ix, train):\n",
    "    with tempfile.TemporaryDirectory() as d:\n",
    "        with open(join(d, 'Knnres.arff'), \"w\") as knnres:\n",
    "            arff.dump(\n",
    "                train.to_arff_obj(),\n",
    "                knnres)\n",
    "\n",
    "        with open(join(d, 'Filetest.arff'), \"w\") as filetest:\n",
    "            arff.dump(\n",
    "                Dataset(\n",
    "                    [train.inverse_transform_instance(train[instance_ix])],\n",
    "                    train.columns\n",
    "                ).to_arff_obj(),\n",
    "                filetest)\n",
    "\n",
    "        subprocess.call(['java', '-jar', 'AL3.jar',\n",
    "                         '-no-cv',\n",
    "                         '-t', knnres.name,\n",
    "                         '-T', filetest.name,\n",
    "                         '-S', '1.0', # minimum support\n",
    "                         '-C', '50.0', # minimum confidence\n",
    "                         '-PN', d, # temporary files folder\n",
    "                         '-SP', '10', # measure threshold\n",
    "                         '-NRUL','1']) #  maximum number of rules to classify a transaction\n",
    "\n",
    "        with open(join(d, 'impo_rules.txt'), \"r\") as impo_rules:\n",
    "            rules = impo_rules.readlines()\n",
    "\n",
    "        def parse_rules(rules_lines):\n",
    "            union_rule = []\n",
    "            rules = []\n",
    "\n",
    "            for rule_line in rules_lines:\n",
    "                rule = []\n",
    "\n",
    "                for attribute_str in rule_line.split(\",\"):\n",
    "                    attribute = int(attribute_str)\n",
    "                    rule.append(attribute)\n",
    "                    union_rule.append(attribute)\n",
    "\n",
    "                rules.append(rule)\n",
    "\n",
    "            # Remove duplicates\n",
    "            union_rule = list(sorted(set(union_rule)))\n",
    "\n",
    "            if union_rule not in rules:\n",
    "                rules.append(union_rule)\n",
    "\n",
    "            return rules\n",
    "\n",
    "        return parse_rules(rules)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
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      "455\n",
      "[[1, 2]]\n",
      "[[1, 2]]\n"
     ]
    }
   ],
   "source": [
    "for i in range(len(monks_train)):\n",
    "    print(i)\n",
    "    print(AL3(i, monks_train))\n",
    "    print(l3wrapper(i, monks_train))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Zoo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open(join(DEFAULT_DIR, \"datasets/zoo.arff\")) as zoo_f:\n",
    "    zoo_train, zoo_explain, zoo_explain_indices = import_dataset_arff(zoo_f, [], True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "[[2, 3, 4, 8, 9, 10, 11]]\n"
     ]
    }
   ],
   "source": [
    "for i in range(max(len(zoo_train), 50)):\n",
    "    print(i)\n",
    "    print(AL3(i, zoo_train))\n",
    "    print(l3wrapper(i, zoo_train))"
   ]
  }
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