hardware/max_storage_temp_tutorial.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Tutorial: Extracting Maximum Storage Temperatures for Transistors from PDF Datasheets"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Introduction\n",
    "\n",
    "We will walk through the process of using `Fonduer` to extract relations from [**richly formatted** data](https://hazyresearch.github.io/snorkel/blog/fonduer.html), where information is conveyed via combinations of textual, structural, tabular, and visual expressions, as seen in webpages, business reports, product specifications, and scientific literature.\n",
    "\n",
    "In this tutorial, we use `Fonduer` to identify mentions of the maximum storage temperature of transistors (e.g. `150°C`) in a corpus of transistor datasheets from [Digikey.com](https://www.digikey.com/products/en/discrete-semiconductor-products/transistors-bipolar-bjt-single/276).\n",
    "\n",
    "The tutorial is broken into several parts, each covering a phase of the `Fonduer` pipeline (as outlined in the [paper](https://arxiv.org/abs/1703.05028)), and the iterative KBC process:\n",
    "\n",
    "1. KBC Initialization\n",
    "2. Candidate Generation and Multimodal Featurization\n",
    "3. Probabilistic Relation Classification\n",
    "4. Error Analysis and Iterative KBC\n",
    "\n",
    "In addition, we show how users can iteratively improve labeling functions to improve relation extraction quality.\n",
    "\n",
    "# Phase 1: KBC Initialization\n",
    "\n",
    "In this first phase of `Fonduer`'s pipeline, `Fonduer` uses a user-specified _schema_ to initialize a relational database where the output KB will be stored. Furthermore, `Fonduer` iterates over its input _corpus_ and transforms each document into a unified data model, which captures the variability and multimodality of richly formatted data. This unified data model then servers as an intermediate representation used in the rest of the phases.\n",
    "\n",
    "This preprocessed data is saved to a database. The connection string to the database is provided to the `Meta` object, which will initialize a PostgreSQL database for parallel execution.\n",
    "\n",
    "We initialize several variables for convenience that define what the database should be called and what level of parallelization the `Fonduer` pipeline will be run with.\n",
    "\n",
    "Before you continue, please make sure that you have PostgreSQL installed and have created a new database named `stg_temp_max`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "%matplotlib inline\n",
    "\n",
    "import os\n",
    "import sys\n",
    "import logging\n",
    "\n",
    "# Configure logging for Fonduer\n",
    "logging.basicConfig(stream=sys.stdout, format='[%(levelname)s] %(name)s - %(message)s')\n",
    "log = logging.getLogger('fonduer')\n",
    "log.setLevel(logging.INFO)\n",
    "\n",
    "PARALLEL = 4 # assuming a quad-core machine\n",
    "ATTRIBUTE = \"stg_temp_max\"\n",
    "conn_string = 'postgres://localhost:5432/' + ATTRIBUTE"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1.1 Defining a Candidate Schema\n",
    "\n",
    "We first initialize a `Meta` object, which manages the connection to the database automatically, and enables us to save intermediate results."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[INFO] fonduer.meta - Connecting None to stg_temp_max:5432\n",
      "[INFO] fonduer.meta - Initializing the storage schema\n"
     ]
    }
   ],
   "source": [
    "from fonduer import Meta\n",
    "\n",
    "session = Meta.init(conn_string).Session()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, we define the _schema_ of the relation we want to extract. This must be a subclass of Candidate, and we define it using a helper function. Here, we define a binary relation which connects two Span objects of text. This is what creates the relation's database table if it does not already exist."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "from fonduer import candidate_subclass\n",
    "\n",
    "Part_Attr = candidate_subclass('Part_Attr', ['part','attr'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "scrolled": false
   },
   "source": [
    "## 1.2 Parsing and Transforming the Input Documents into Unified Data Models\n",
    "\n",
    "Next, we load the corpus of datasheets and transform them into the unified data model. Each datasheet has a PDF and HTML representation (in this example, the HTML is created using Adobe Acrobat). Both representations are used in conjunction to create a robust unified data model with textual, structural, tabular, and visual modality information. Note that since each document is independent of each other, we can parse the documents in parallel. We depend on PostgreSQL for this functionality.\n",
    "\n",
    "### Configuring an `HTMLDocPreprocessor`\n",
    "We start by setting the paths to where our documents are stored, and defining a `HTMLDocPreprocessor` to read in the documents found in the specified paths. `max_docs` specified the number of documents to parse. For the sake of this tutorial, we only look at 100 documents.\n",
    "\n",
    "**Note that you need to have run `download_data.sh` before executing these next steps or you won't have the documents needed for the tutorial.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "from fonduer.parser.preprocessors import HTMLDocPreprocessor\n",
    "from fonduer.parser import Parser\n",
    "\n",
    "docs_path = 'data/html/'\n",
    "pdf_path = 'data/pdf/'\n",
    "\n",
    "max_docs = 100\n",
    "doc_preprocessor = HTMLDocPreprocessor(docs_path, max_docs=max_docs)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Configuring a `Parser`\n",
    "Next, we configure an `OmniParser`, which serves as our `CorpusParser` for PDF documents. We use [spaCy](https://spacy.io/) as a preprocessing tool to split our documents into sentences and tokens, and to provide annotations such as part-of-speech tags and dependency parse structures for these sentences. In addition, we can specify which modality information to include in the unified data model for each document. Below, we enable all modality information."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[INFO] fonduer.utils.udf - Clearing existing...\n",
      "[INFO] fonduer.utils.udf - Running UDF...\n",
      "CPU times: user 7.98 s, sys: 104 ms, total: 8.08 s\n",
      "Wall time: 28min 46s\n"
     ]
    }
   ],
   "source": [
    "corpus_parser = Parser(structural=True, lingual=True, visual=True, pdf_path=pdf_path)\n",
    "%time corpus_parser.apply(doc_preprocessor, parallelism=PARALLEL)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can then use simple database queries (written in the syntax of [SQLAlchemy](http://www.sqlalchemy.org/), which `Fonduer` uses) to check how many documents and sentences were parsed, or even check how many sentences are contained in each document."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Documents: 100\n",
      "Sentences: 46414\n"
     ]
    }
   ],
   "source": [
    "from fonduer import Document, Sentence\n",
    "\n",
    "print(\"Documents:\", session.query(Document).count())\n",
    "print(\"Sentences:\", session.query(Sentence).count())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1.3 Dividing the Corpus into Test and Train\n",
    "\n",
    "We'll split the documents 80/10/10 into train/dev/test splits. Note that here we do this in a non-random order to preverse the consistency in the tutorial, and we reference the splits by 0/1/2 respectively."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['BournsInc_BD246BS',\n",
      " '2N4123-D',\n",
      " 'LTSCS02920-1',\n",
      " 'Infineon-BC817KSERIES_BC818KSERIES-DS-v01_01-en',\n",
      " 'BournsInc_TIP152S',\n",
      " 'MOTOS03189-1',\n",
      " '2N4124',\n",
      " 'MCCCS08610-1',\n",
      " 'CentralSemiconductorCorp_2N4013',\n",
      " '2N6426-D',\n",
      " 'LITES00690-1',\n",
      " 'FAIRS25065-1',\n",
      " 'MOTOS04676-1',\n",
      " 'LTSCS02912-1',\n",
      " 'CentralSemiconductorCorp_CENU45',\n",
      " 'MCCCS08818-1',\n",
      " '2N6427',\n",
      " 'CentralSemiconductorCorp_CMPT5401ETR',\n",
      " 'MOTOS04796-1',\n",
      " 'AUKCS04635-1',\n",
      " 'LTSCS02910-1',\n",
      " 'FAIRS19194-1',\n",
      " 'MCCCS08984-1',\n",
      " 'CentralSemiconductorCorp_CXT4033TR',\n",
      " 'DISES00645-1',\n",
      " 'BC182',\n",
      " 'FairchildSemiconductor_KSC2310YTA',\n",
      " 'NXPUSAInc_PBSS5360PASX',\n",
      " 'MCCCS09540-1',\n",
      " 'CSEMS02742-1',\n",
      " 'BC182-D',\n",
      " 'CSEMS03485-1',\n",
      " 'ONSemiconductor_MMBT6521LT1',\n",
      " 'BC337',\n",
      " 'LITES00689-1',\n",
      " 'MCCCS09741-1',\n",
      " 'CSEMS05382-1',\n",
      " 'BC337-D',\n",
      " 'LITES00686-1',\n",
      " 'ONSMS04099-1',\n",
      " 'MicroCommercialCo_2N3904AP',\n",
      " 'CSEMS05383-1',\n",
      " 'BC546',\n",
      " 'DIODS00215-1',\n",
      " 'DISES00616-1',\n",
      " 'DiodesIncorporated_2DD26527',\n",
      " 'BC546A_Series_B14-521026',\n",
      " 'LITES00424-1',\n",
      " 'MicroCommercialCo_TIP29ABP',\n",
      " 'PHGLS19500-1',\n",
      " 'DiodesIncorporated_FCX491ATA',\n",
      " 'BC546-BC548C(TO-92)',\n",
      " 'DISES00490-1',\n",
      " 'PHGLS20267-1',\n",
      " 'MicrosemiCorporation_2N2484UB',\n",
      " 'DIODS13249-1',\n",
      " 'DiodesIncorporated_FZT651TC',\n",
      " 'KECCS05435-1',\n",
      " 'INFNS19372-1',\n",
      " 'DISES00242-1',\n",
      " 'DiodesIncorporated_ZTX688BSTZ',\n",
      " 'KECCS03676-1',\n",
      " 'MINDS00015-1',\n",
      " 'DiodesIncorporated_ZTX789ASTZ',\n",
      " 'BC547',\n",
      " 'DISES00192-1',\n",
      " 'JCSTS01155-1',\n",
      " '112823',\n",
      " 'DiodesIncorporated_ZTX953STZ',\n",
      " 'MMBT3904',\n",
      " '2N3906',\n",
      " 'DiodesIncorporated_ZXT690BKTC',\n",
      " 'BC818',\n",
      " 'MMMCS17742-1',\n",
      " 'DISES00189-1',\n",
      " 'MOTOS03160-1',\n",
      " '2N3906-D',\n",
      " 'BC818-40LT1-D',\n",
      " 'DISES00023-1',\n",
      " 'Infineon-BC857SERIES_BC858SERIES_BC859SERIES_BC860SERIES-DS-v01_01-en']\n"
     ]
    }
   ],
   "source": [
    "docs = session.query(Document).order_by(Document.name).all()\n",
    "ld   = len(docs)\n",
    "\n",
    "train_docs = set()\n",
    "dev_docs   = set()\n",
    "test_docs  = set()\n",
    "splits = (0.8, 0.9)\n",
    "data = [(doc.name, doc) for doc in docs]\n",
    "data.sort(key=lambda x: x[0])\n",
    "for i, (doc_name, doc) in enumerate(data):\n",
    "    if i < splits[0] * ld:\n",
    "        train_docs.add(doc)\n",
    "    elif i < splits[1] * ld:\n",
    "        dev_docs.add(doc)\n",
    "    else:\n",
    "        test_docs.add(doc)\n",
    "from pprint import pprint\n",
    "pprint([x.name for x in train_docs])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Phase 2: Candidate Extraction & Multimodal Featurization\n",
    "Given the unified data model from Phase 1, `Fonduer` extracts relation candidates based on user-provided **matchers** and **throttlers**. Then, `Fonduer` leverages the multimodality information captured in the unified data model to provide multimodal features for each candidate.\n",
    "\n",
    "## 2.1 Candidate Extraction\n",
    "\n",
    "The next step is to extract **candidates** from our corpus. A `candidate` is the object for which we want to make predictions. In this case, the candidates are pairs of transistor part numbers and their corresponding maximum storage temperatures as found in their datasheets. Our task is to predict which pairs are true in the associated document.\n",
    "\n",
    "To do so, we write **matchers** to define which spans of text in the corpus are instances of each entity. Matchers can leverage a variety of information from regular expressions, to dictionaries, to user-defined functions. Furthermore, different techniques can be combined to form higher quality matchers. In general, matchers should seek to be as precise as possible while maintaining complete recall.\n",
    "\n",
    "In our case, we need to write a matcher that defines a transistor part number and a matcher to define a valid temperature value.\n",
    "\n",
    "### Writing a simple temperature matcher\n",
    "\n",
    "Our maximum storage temperature matcher can be a very simple regular expression since we know that we are looking for integers, and by inspecting a portion of our corpus, we see that maximum storage temperatures fall within a fairly narrow range."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "from fonduer import RegexMatchSpan, DictionaryMatch, LambdaFunctionMatcher, Intersect, Union\n",
    "\n",
    "attr_matcher = RegexMatchSpan(rgx=r'(?:[1][5-9]|20)[05]', longest_match_only=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Writing an advanced transistor part matcher\n",
    "\n",
    "In contrast, transistor part numbers are complex expressions. Here, we show how transistor part numbers can leverage [naming conventions](https://en.wikipedia.org/wiki/Transistor#Part_numbering_standards.2Fspecifications) as regular expressions, and use a dictionary of known part numbers, and use user-defined functions together. First, we create a regular expression matcher for standard transistor naming conventions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "### Transistor Naming Conventions as Regular Expressions ###\n",
    "eeca_rgx = r'([ABC][A-Z][WXYZ]?[0-9]{3,5}(?:[A-Z]){0,5}[0-9]?[A-Z]?(?:-[A-Z0-9]{1,7})?(?:[-][A-Z0-9]{1,2})?(?:\\/DG)?)'\n",
    "jedec_rgx = r'(2N\\d{3,4}[A-Z]{0,5}[0-9]?[A-Z]?)'\n",
    "jis_rgx = r'(2S[ABCDEFGHJKMQRSTVZ]{1}[\\d]{2,4})'\n",
    "others_rgx = r'((?:NSVBC|SMBT|MJ|MJE|MPS|MRF|RCA|TIP|ZTX|ZT|ZXT|TIS|TIPL|DTC|MMBT|SMMBT|PZT|FZT|STD|BUV|PBSS|KSC|CXT|FCX|CMPT){1}[\\d]{2,4}[A-Z]{0,5}(?:-[A-Z0-9]{0,6})?(?:[-][A-Z0-9]{0,1})?)'\n",
    "\n",
    "part_rgx = '|'.join([eeca_rgx, jedec_rgx, jis_rgx, others_rgx])\n",
    "part_rgx_matcher = RegexMatchSpan(rgx=part_rgx, longest_match_only=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, we can create a matcher from a dictionary of known part numbers:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "import csv\n",
    "\n",
    "def get_digikey_parts_set(path):\n",
    "    \"\"\"\n",
    "    Reads in the digikey part dictionary and yeilds each part.\n",
    "    \"\"\"\n",
    "    all_parts = set()\n",
    "    with open(path, \"r\") as csvinput:\n",
    "        reader = csv.reader(csvinput)\n",
    "        for line in reader:\n",
    "            (part, url) = line\n",
    "            all_parts.add(part)\n",
    "    return all_parts\n",
    "\n",
    "### Dictionary of known transistor parts ###\n",
    "dict_path = 'data/digikey_part_dictionary.csv'\n",
    "part_dict_matcher = DictionaryMatch(d=get_digikey_parts_set(dict_path))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also use user-defined functions to further improve our matchers. For example, here we use patterns in the document filenames as a signal for whether a span of text in a document is a valid transistor part number."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "from builtins import range\n",
    "\n",
    "def common_prefix_length_diff(str1, str2):\n",
    "    for i in range(min(len(str1), len(str2))):\n",
    "        if str1[i] != str2[i]:\n",
    "            return min(len(str1), len(str2)) - i\n",
    "    return 0\n",
    "\n",
    "def part_file_name_conditions(attr):\n",
    "    file_name = attr.sentence.document.name\n",
    "    if len(file_name.split('_')) != 2: return False\n",
    "    if attr.get_span()[0] == '-': return False\n",
    "    name = attr.get_span().replace('-', '')\n",
    "    return any(char.isdigit() for char in name) and any(char.isalpha() for char in name) and common_prefix_length_diff(file_name.split('_')[1], name) <= 2\n",
    "\n",
    "add_rgx = '^[A-Z0-9\\-]{5,15}$'\n",
    "\n",
    "part_file_name_lambda_matcher = LambdaFunctionMatcher(func=part_file_name_conditions)\n",
    "part_file_name_matcher = Intersect(RegexMatchSpan(rgx=add_rgx, longest_match_only=True), part_file_name_lambda_matcher)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Then, we can union all of these matchers together to form our final part matcher."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "part_matcher = Union(part_rgx_matcher, part_dict_matcher, part_file_name_matcher)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "These two matchers define each entity in our relation schema."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Define a relation's `ContextSpaces`\n",
    "\n",
    "Next, in order to define the \"space\" of all candidates that are even considered from the document, we need to define a `ContextSpace` for each component of the relation we wish to extract.\n",
    "\n",
    "In the case of transistor part numbers, the `ContextSpace` can be quite complex due to the need to handle implicit part numbers that are implied in text like \"BC546A/B/C...BC548A/B/C\", which refers to 9 unique part numbers. To handle these, we consider all n-grams up to 3 words long.\n",
    "\n",
    "In contrast, the `ContextSpace` for temperature values is simpler: we only need to process different unicode representations of a (`-`), and don't need to look at more than two words at a time.\n",
    "\n",
    "When no special preproessing like this is needed, we could have used the default `OmniNgrams` class provided by `fonduer`. For example, if we were looking to match polarities, which only take the form of \"NPN\" or \"PNP\", we could've used `attr_ngrams = OmniNgrams(n_max=1)`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "from hardware_spaces import OmniNgramsPart, OmniNgramsTemp\n",
    "    \n",
    "part_ngrams = OmniNgramsPart(parts_by_doc=None, n_max=3)\n",
    "attr_ngrams = OmniNgramsTemp(n_max=2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Defining candidate `Throttlers`\n",
    "\n",
    "Next, we need to define **throttlers**, which allow us to further prune excess candidates and avoid unnecessarily materializing invalid candidates. Throttlers, like matchers, act as hard filters, and should be created to have high precision while maintaining complete recall, if possible.\n",
    "\n",
    "Here, we create a throttler that discards candidates if they are in the same table, but the part and storage temperature are not vertically or horizontally aligned."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "from fonduer.supervision.lf_helpers import *\n",
    "import re\n",
    "\n",
    "def stg_temp_filter(c):\n",
    "    (part, attr) = c\n",
    "    if same_table((part, attr)):\n",
    "        return (is_horz_aligned((part, attr)) or is_vert_aligned((part, attr)))\n",
    "    return True\n",
    "\n",
    "candidate_filter = stg_temp_filter"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Running the `CandidateExtractor`\n",
    "\n",
    "Now, we have all the component necessary to perform candidate extraction. We have defined the \"space\" of things to consider for each candidate, provided matchers that signal when a valid mention is seen, and a throttler to prunes away excess candidates. We now can define the `CandidateExtractor` with the contexts to extract from, the matchers, and the throttler to use. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[INFO] fonduer.utils.udf - Clearing existing...\n",
      "[INFO] fonduer.utils.udf - Running UDF...\n",
      "CPU times: user 61 ms, sys: 88.6 ms, total: 150 ms\n",
      "Wall time: 1min 27s\n"
     ]
    }
   ],
   "source": [
    "from fonduer import CandidateExtractor\n",
    "\n",
    "\n",
    "candidate_extractor = CandidateExtractor(Part_Attr, \n",
    "                        [part_ngrams, attr_ngrams], \n",
    "                        [part_matcher, attr_matcher], \n",
    "                        candidate_filter=candidate_filter)\n",
    "\n",
    "%time candidate_extractor.apply(train_docs, split=0, parallelism=PARALLEL)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here we specified that these `Candidates` belong to the training set by specifying `split=0`; recall that we're referring to train/dev/test as splits 0/1/2."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of candidates: 29789\n"
     ]
    }
   ],
   "source": [
    "train_cands = session.query(Part_Attr).filter(Part_Attr.split == 0).all()\n",
    "print(\"Number of candidates:\", len(train_cands))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Repeating for development and test splits\n",
    "Finally, we rerun the same operation for the other two document divisions: dev and test. For each, we simply load the `Corpus` object and run them through the `CandidateExtractor`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[INFO] fonduer.utils.udf - Clearing existing...\n",
      "[INFO] fonduer.utils.udf - Running UDF...\n",
      "[========================================] 100%\n",
      "Number of candidates: 2697\n",
      "[INFO] fonduer.utils.udf - Clearing existing...\n",
      "[INFO] fonduer.utils.udf - Running UDF...\n",
      "[========================================] 100%\n",
      "Number of candidates: 1862\n",
      "CPU times: user 14.2 s, sys: 831 ms, total: 15 s\n",
      "Wall time: 39.8 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "for i, docs in enumerate([dev_docs, test_docs]):\n",
    "    candidate_extractor.apply(docs, split=i+1)\n",
    "    print(\"Number of candidates:\", session.query(Part_Attr).filter(Part_Attr.split == i+1).count())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2.2 Multimodal Featurization\n",
    "Unlike dealing with plain unstructured text, `Fonduer` deals with richly formatted data, and consequently featurizes each candidate with a baseline library of multimodal features. \n",
    "\n",
    "### Featurize with `Fonduer`'s optimized Postgres Feature Annotator\n",
    "We now annotate the candidates in our training, dev, and test sets with features. The `BatchFeatureAnnotator` provided by `Fonduer` allows this to be done in parallel to improve performance.\n",
    "\n",
    "`featurizer.apply` takes three important arguments.\n",
    "* `split` defines which candidate set wer are dealing with. For example, `split=0` is the training set.\n",
    "* `replace_key_set` determine whether or not replace, or reinitialize, the set of features to apply to candidates. That is, when `replace_key_set` is true, key set of features will be replaced with the key set of the features found in the split that is being processed.\n",
    "* `parallelism` determines how many processes to run in parallel.\n",
    "\n",
    "Notices that `replace_key_set=True` only for the first call to `featurizer.apply`, while the other calls have this parameter set to `False`. This is because we want to have the set of features we label candidates with defined by the features found in the set of training documents only. If a later call to `featurizer.apply` replaced the key set, then only the features of that particular split would be considered later in the pipeline."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[INFO] fonduer.utils.udf - Clearing existing...\n",
      "[INFO] fonduer.utils.udf - Running UDF...\n",
      "[INFO] fonduer.supervision.async_annotations - Copying part_attr_feature_updates to postgres\n",
      "[INFO] fonduer.supervision.async_annotations - b'COPY 29789\\n'\n",
      "CPU times: user 41.3 s, sys: 934 ms, total: 42.2 s\n",
      "Wall time: 15min 57s\n",
      "(29789, 26392)\n",
      "[INFO] fonduer.utils.udf - Clearing existing...\n",
      "[INFO] fonduer.utils.udf - Running UDF...\n",
      "[INFO] fonduer.supervision.async_annotations - Copying part_attr_feature_updates to postgres\n",
      "[INFO] fonduer.supervision.async_annotations - b'COPY 2697\\n'\n",
      "CPU times: user 4.38 s, sys: 275 ms, total: 4.66 s\n",
      "Wall time: 1min 26s\n",
      "(2697, 26392)\n",
      "[INFO] fonduer.utils.udf - Clearing existing...\n",
      "[INFO] fonduer.utils.udf - Running UDF...\n",
      "[INFO] fonduer.supervision.async_annotations - Copying part_attr_feature_updates to postgres\n",
      "[INFO] fonduer.supervision.async_annotations - b'COPY 1862\\n'\n",
      "CPU times: user 3.16 s, sys: 251 ms, total: 3.41 s\n",
      "Wall time: 52.7 s\n",
      "(1862, 26392)\n"
     ]
    }
   ],
   "source": [
    "from fonduer import BatchFeatureAnnotator\n",
    "\n",
    "featurizer = BatchFeatureAnnotator(Part_Attr)\n",
    "%time F_train = featurizer.apply(split=0, replace_key_set=True, parallelism=PARALLEL)\n",
    "print(F_train.shape)\n",
    "%time F_dev = featurizer.apply(split=1, replace_key_set=False, parallelism=PARALLEL)\n",
    "print(F_dev.shape)\n",
    "%time F_test = featurizer.apply(split=2, replace_key_set=False, parallelism=PARALLEL)\n",
    "print(F_test.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "At the end of this phase, `Fonduer` has generated the set of candidates and the feature matrix. Note that Phase 1 and 2 are relatively static and typically are only executed once during the KBC process.\n",
    "\n",
    "# Phase 3: Probabilistic Relation Classification\n",
    "In this phase, `Fonduer` applies user-defined **labeling functions**, which express various heuristics, patterns, and [weak supervision](http://hazyresearch.github.io/snorkel/blog/weak_supervision.html) strategies to label our data, to each of the candidates to create a label matrix that is used by our data programming engine.\n",
    "\n",
    "In the wild, hand-labeled training data is rare and expensive. A common scenario is to have access to tons of unlabeled training data, and have some idea of how to label them programmatically. For example:\n",
    "* We may be able to think of text patterns that would indicate a part and polarity mention are related, for example the word \"temperature\" appearing between them.\n",
    "* We may have access to an external knowledge base that lists some pairs of parts and polarities, and can use these to noisily label some of our mention pairs.\n",
    "Our labeling functions will capture these types of strategies. We know that these labeling functions will not be perfect, and some may be quite low-quality, so we will model their accuracies with a generative model, which `Fonduer` will help us easily apply.\n",
    "\n",
    "Using data programming, we can then train machine learning models to learn which features are the most important in classifying candidates.\n",
    "\n",
    "### Loading Gold Data\n",
    "For convenience in error analysis and evaluation, we have already annotated the dev and test set for this tutorial, and we'll now load it using an externally-defined helper function.\n",
    "\n",
    "Loading and saving external \"gold\" labels can be a bit messy, but is often a critical part of development, especially when gold labels are expensive and/or time-consuming to obtain. `Fonduer` stores all labels that are manually annotated in a **stable** format (called StableLabels), which is somewhat independent from the rest of `Fonduers`'s data model, does not get deleted when you delete the candidates, corpus, or any other objects, and can be recovered even if the rest of the data changes or is deleted.\n",
    "\n",
    "Our general procedure with external labels is to load them into the `StableLabel` table, then use `Fonduer`'s helpers to load them into the main data model from there. If interested in example implementation details, please see the script we now load:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loading 34348 candidate labels\n",
      "[========================================] 100%\n",
      "AnnotatorLabels created: 34348\n"
     ]
    }
   ],
   "source": [
    "from hardware_utils import load_hardware_labels\n",
    "\n",
    "gold_file = 'data/hardware_tutorial_gold.csv'\n",
    "load_hardware_labels(session, Part_Attr, gold_file, ATTRIBUTE ,annotator_name='gold')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Creating Labeling Functions\n",
    "\n",
    "In `Fonduer`, our primary interface through which we provide training signal to the end extraction model we are training is by writing labeling functions (**LFs**) (as opposed to hand-labeling massive training sets).\n",
    "\n",
    "A labeling function isn't anything special. It's just a Python function that accepts a `Candidate` as the input argument and returns `1` if it says the Candidate should be marked as true, `-1` if it says the `Candidate` should be marked as false, and `0` if it doesn't know how to vote and abstains. In practice, many labeling functions are unipolar: it labels only 1s and 0s, or it labels only -1s and 0s.\n",
    "\n",
    "Recall that our goal is ultimately to train a high-performance classification model that predicts which of our Candidates are true mentions of maximum storage temperature relations. It turns out that we can do this by writing potentially low-quality labeling functions!\n",
    "\n",
    "With `Fonduer`, labeling functions can be written using intuitive patterns discovered by inspecting the target corpus. A library of labeling function helpers can be found in `fonduer.lf_helpers`. \n",
    "\n",
    "For example, inspecting several documents may reveal that storage temperatures are typically listed inside a table where the row header contains the word \"storage\". This intuitive pattern can be directly expressed as a labeling function. Similarly, the word \"temperature\" is an obvious positive signal."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "from fonduer.supervision.lf_helpers import *\n",
    "import re\n",
    "\n",
    "def LF_storage_row(c):\n",
    "    return 1 if 'storage' in get_row_ngrams(c.attr) else 0\n",
    "\n",
    "def LF_temperature_row(c):\n",
    "    return 1 if 'temperature' in get_row_ngrams(c.attr) else 0"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We express several of these simple patterns below as a set of labeling functions:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "def LF_operating_row(c):\n",
    "    return 1 if 'operating' in get_row_ngrams(c.attr) else 0\n",
    "\n",
    "def LF_tstg_row(c):\n",
    "    return 1 if overlap(\n",
    "        ['tstg','stg','ts'], \n",
    "        list(get_row_ngrams(c.attr))) else 0\n",
    "\n",
    "\n",
    "def LF_to_left(c):\n",
    "    return 1 if 'to' in get_left_ngrams(c.attr, window=2) else 0\n",
    "\n",
    "def LF_negative_number_left(c):\n",
    "    return 1 if any([re.match(r'-\\s*\\d+', ngram) for ngram in get_left_ngrams(c.attr, window=4)]) else 0"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Then, we collect all of the labeling function we would like to use into a single list, which is provided as input to the `LabelAnnotator`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "stg_temp_lfs = [\n",
    "    LF_storage_row,\n",
    "    LF_operating_row,\n",
    "    LF_temperature_row,\n",
    "    LF_tstg_row,\n",
    "    LF_to_left,\n",
    "    LF_negative_number_left\n",
    "]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Applying the Labeling Functions\n",
    "\n",
    "Next, we need to actually run the LFs over all of our training candidates, producing a set of `Labels` and `LabelKeys` (just the names of the LFs) in the database. We'll do this using the `LabelAnnotator` class, a `UDF` which we will again run with `UDFRunner`. Note that this will delete any existing `Labels` and `LabelKeys` for this candidate set. Also note that we are using `Fonduer`'s optimized batch label annotator, which runs in parallel and depends on having Postgres as the backend database. \n",
    "\n",
    "By default, `labeler.apply` will drop the existing table of labeling functions and the label values for each candidate. However, this behavior can be controlled by three parameters to the function to imperove iteration performance and reduce redundant computation:\n",
    "- `split` defines which set to operate on (e.g. train, dev, or test)\n",
    "- `clear` can be `True` or `False`, and is `True` by default. When set to `False`, the labeling functioni table is not dropped, and the behavior of `labeler.apply` is defined by the following two parameters.\n",
    "- `update_keys` can be `True` or `False`. When `True`, the keys (which are each labeling function) are updated according to the set of labeling functions provided to the function. This should be set to `True` if new labeling functions are added. When `False`, no new LFs are evaluated and the keys of existing LFs remain the same.\n",
    "- `update_values` can be `True` or `False`. This defines how to resolve conflicts. When `True`, the values assigned to each candiate is updated to the new values when in conflict. This should be set to `True` if labeling function logic is edited, even though the name of the labeling function remains the same. When `False`, the existing labels assigned to each candidate are used, and newly computed labels are ignored.\n",
    "- `parallelism` is the amount of parallelism to use when labeling.\n",
    "\n",
    "With this in mind, we set `clear=True` when we first apply our labeling functions, and this ensures that the table is created and intialized with proper keys and values.\n",
    "\n",
    "In future iterations, we would typically set `clear=False, update_keys=True, update_values=True` so that we can simply update the set of LFs and their values without recreating the entire table. We will see how this is used later in the tutorial."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[INFO] fonduer.utils.udf - Clearing existing...\n",
      "[INFO] fonduer.utils.udf - Running UDF...\n",
      "[INFO] fonduer.supervision.async_annotations - Copying part_attr_label_updates to postgres\n",
      "[INFO] fonduer.supervision.async_annotations - b'COPY 29789\\n'\n",
      "CPU times: user 1.25 s, sys: 252 ms, total: 1.5 s\n",
      "Wall time: 7min 25s\n",
      "(29789, 16)\n"
     ]
    }
   ],
   "source": [
    "from fonduer import BatchLabelAnnotator\n",
    "\n",
    "labeler = BatchLabelAnnotator(Part_Attr, lfs = stg_temp_lfs)\n",
    "%time L_train = labeler.apply(split=0, clear=True, parallelism=PARALLEL)\n",
    "print(L_train.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that the returned matrix is a special subclass of the scipy.sparse.csr_matrix class, with some special features which we demonstrate below:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Part_Attr(Span(\"BC857\", sentence=213105, chars=[0,4], words=[0,0]), ImplicitSpan(\"150\", sentence=273721, words=[0,0], position=[0]))"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "L_train.get_candidate(session, 0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also view statistics about the resulting label matrix.\n",
    "* **Coverage** is the fraction of candidates that the labeling function emits a non-zero label for.\n",
    "* **Overlap** is the fraction candidates that the labeling function emits a non-zero label for and that another labeling function emits a non-zero label for.\n",
    "* **Conflict** is the fraction candidates that the labeling function emits a non-zero label for and that another labeling function emits a conflicting non-zero label for.\n",
    "\n",
    "In addition, because we have already loaded the gold labels, we can view the emperical accuracy of these labeling functions when compared to our gold labels:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 2.78 s, sys: 0 ns, total: 2.78 s\n",
      "Wall time: 2.83 s\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>j</th>\n",
       "      <th>Coverage</th>\n",
       "      <th>Overlaps</th>\n",
       "      <th>Conflicts</th>\n",
       "      <th>TP</th>\n",
       "      <th>FP</th>\n",
       "      <th>FN</th>\n",
       "      <th>TN</th>\n",
       "      <th>Empirical Acc.</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>LF_temperature_row</th>\n",
       "      <td>0</td>\n",
       "      <td>0.138105</td>\n",
       "      <td>0.118131</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3233</td>\n",
       "      <td>881</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.785853</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LF_not_temp_relevant</th>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LF_current_aligned</th>\n",
       "      <td>2</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LF_collector_aligned</th>\n",
       "      <td>3</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LF_negative_number_left</th>\n",
       "      <td>4</td>\n",
       "      <td>0.075028</td>\n",
       "      <td>0.075028</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2049</td>\n",
       "      <td>186</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.916779</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LF_voltage_row_temp</th>\n",
       "      <td>5</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LF_test_condition_aligned</th>\n",
       "      <td>6</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LF_typ_row</th>\n",
       "      <td>7</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LF_operating_row</th>\n",
       "      <td>8</td>\n",
       "      <td>0.060861</td>\n",
       "      <td>0.060861</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1151</td>\n",
       "      <td>662</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.634859</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LF_temp_outside_table</th>\n",
       "      <td>9</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LF_complement_left_row</th>\n",
       "      <td>10</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LF_voltage_row_part</th>\n",
       "      <td>11</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LF_tstg_row</th>\n",
       "      <td>12</td>\n",
       "      <td>0.105744</td>\n",
       "      <td>0.105341</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2258</td>\n",
       "      <td>892</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.716825</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LF_temp_on_high_page_num</th>\n",
       "      <td>13</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LF_to_left</th>\n",
       "      <td>14</td>\n",
       "      <td>0.103125</td>\n",
       "      <td>0.101917</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2285</td>\n",
       "      <td>787</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.743815</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LF_storage_row</th>\n",
       "      <td>15</td>\n",
       "      <td>0.105341</td>\n",
       "      <td>0.105341</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2258</td>\n",
       "      <td>880</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.719567</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                            j  Coverage  Overlaps  Conflicts    TP   FP  FN  \\\n",
       "LF_temperature_row          0  0.138105  0.118131        0.0  3233  881   0   \n",
       "LF_not_temp_relevant        1  0.000000  0.000000        0.0     0    0   0   \n",
       "LF_current_aligned          2  0.000000  0.000000        0.0     0    0   0   \n",
       "LF_collector_aligned        3  0.000000  0.000000        0.0     0    0   0   \n",
       "LF_negative_number_left     4  0.075028  0.075028        0.0  2049  186   0   \n",
       "LF_voltage_row_temp         5  0.000000  0.000000        0.0     0    0   0   \n",
       "LF_test_condition_aligned   6  0.000000  0.000000        0.0     0    0   0   \n",
       "LF_typ_row                  7  0.000000  0.000000        0.0     0    0   0   \n",
       "LF_operating_row            8  0.060861  0.060861        0.0  1151  662   0   \n",
       "LF_temp_outside_table       9  0.000000  0.000000        0.0     0    0   0   \n",
       "LF_complement_left_row     10  0.000000  0.000000        0.0     0    0   0   \n",
       "LF_voltage_row_part        11  0.000000  0.000000        0.0     0    0   0   \n",
       "LF_tstg_row                12  0.105744  0.105341        0.0  2258  892   0   \n",
       "LF_temp_on_high_page_num   13  0.000000  0.000000        0.0     0    0   0   \n",
       "LF_to_left                 14  0.103125  0.101917        0.0  2285  787   0   \n",
       "LF_storage_row             15  0.105341  0.105341        0.0  2258  880   0   \n",
       "\n",
       "                           TN  Empirical Acc.  \n",
       "LF_temperature_row          0        0.785853  \n",
       "LF_not_temp_relevant        0             NaN  \n",
       "LF_current_aligned          0             NaN  \n",
       "LF_collector_aligned        0             NaN  \n",
       "LF_negative_number_left     0        0.916779  \n",
       "LF_voltage_row_temp         0             NaN  \n",
       "LF_test_condition_aligned   0             NaN  \n",
       "LF_typ_row                  0             NaN  \n",
       "LF_operating_row            0        0.634859  \n",
       "LF_temp_outside_table       0             NaN  \n",
       "LF_complement_left_row      0             NaN  \n",
       "LF_voltage_row_part         0             NaN  \n",
       "LF_tstg_row                 0        0.716825  \n",
       "LF_temp_on_high_page_num    0             NaN  \n",
       "LF_to_left                  0        0.743815  \n",
       "LF_storage_row              0        0.719567  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from fonduer import load_gold_labels\n",
    "L_gold_train = load_gold_labels(session, annotator_name='gold', split=0)\n",
    "%time L_train.lf_stats(L_gold_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Fitting the Generative Model\n",
    "\n",
    "Now, we'll train a model of the LFs to estimate their accuracies. Once the model is trained, we can combine the outputs of the LFs into a single, noise-aware training label set for our extractor. Intuitively, we'll model the LFs by observing how they overlap and conflict with each other."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[INFO] fonduer.learning.gen_learning - Inferred cardinality: 2\n",
      "CPU times: user 13min 46s, sys: 442 ms, total: 13min 47s\n",
      "Wall time: 13min 56s\n"
     ]
    }
   ],
   "source": [
    "from fonduer import GenerativeModel\n",
    "\n",
    "gen_model = GenerativeModel()\n",
    "%time gen_model.train(L_train, epochs=500, decay=0.9, step_size=0.001/L_train.shape[0], reg_param=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We now apply the generative model to the training candidates to get the noise-aware training label set. We'll refer to these as the training marginals:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_marginals = gen_model.marginals(L_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We'll look at the distribution of the training marginals:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "plt.hist(train_marginals, bins=20)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can view the learned accuracy parameters as well."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.99118405, 0.98854235, 0.98853689, 0.98854481, 0.99006455,\n",
       "       0.98851745, 0.98854854, 0.98854239, 0.98975953, 0.98854176,\n",
       "       0.98855174, 0.98854105, 0.99065967, 0.98853992, 0.99058715,\n",
       "       0.99067107])"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gen_model.weights.lf_accuracy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "scrolled": true
   },
   "source": [
    "### Using the Model to Iterate on Labeling Functions\n",
    "\n",
    "Now that we have learned the generative model, we can stop here and use this to potentially debug and/or improve our labeling function set. First, we apply the LFs to our development set:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[INFO] fonduer.utils.udf - Clearing existing...\n",
      "[INFO] fonduer.utils.udf - Running UDF...\n",
      "[========================================] 100%\n",
      "[INFO] fonduer.supervision.async_annotations - Copying part_attr_label_updates to postgres\n",
      "[INFO] fonduer.supervision.async_annotations - b'COPY 2697\\n'\n"
     ]
    }
   ],
   "source": [
    "L_dev = labeler.apply_existing(split=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2697, 16)"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "L_dev.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Then, we get the score of the generative model:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "from fonduer import load_gold_labels\n",
    "L_gold_dev = load_gold_labels(session, annotator_name='gold', split=1)\n",
    "prec, rec, f1 = gen_model.score(L_dev, L_gold_dev)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also view statistics about the labeling function's learned accuracy and compare them to the emperical accuracy."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>j</th>\n",
       "      <th>Coverage</th>\n",
       "      <th>Overlaps</th>\n",
       "      <th>Conflicts</th>\n",
       "      <th>TP</th>\n",
       "      <th>FP</th>\n",
       "      <th>FN</th>\n",
       "      <th>TN</th>\n",
       "      <th>Empirical Acc.</th>\n",
       "      <th>Learned Acc.</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>LF_temperature_row</th>\n",
       "      <td>0</td>\n",
       "      <td>0.195773</td>\n",
       "      <td>0.136819</td>\n",
       "      <td>0.0</td>\n",
       "      <td>509</td>\n",
       "      <td>19</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.964015</td>\n",
       "      <td>0.991184</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LF_not_temp_relevant</th>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.988542</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LF_current_aligned</th>\n",
       "      <td>2</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.988537</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LF_collector_aligned</th>\n",
       "      <td>3</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.988545</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LF_negative_number_left</th>\n",
       "      <td>4</td>\n",
       "      <td>0.143122</td>\n",
       "      <td>0.142010</td>\n",
       "      <td>0.0</td>\n",
       "      <td>370</td>\n",
       "      <td>16</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.958549</td>\n",
       "      <td>0.990065</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LF_voltage_row_temp</th>\n",
       "      <td>5</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.988517</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LF_test_condition_aligned</th>\n",
       "      <td>6</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.988549</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LF_typ_row</th>\n",
       "      <td>7</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.988542</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LF_operating_row</th>\n",
       "      <td>8</td>\n",
       "      <td>0.008899</td>\n",
       "      <td>0.008899</td>\n",
       "      <td>0.0</td>\n",
       "      <td>24</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.989760</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LF_temp_outside_table</th>\n",
       "      <td>9</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.988542</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LF_complement_left_row</th>\n",
       "      <td>10</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.988552</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LF_voltage_row_part</th>\n",
       "      <td>11</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.988541</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LF_tstg_row</th>\n",
       "      <td>12</td>\n",
       "      <td>0.121987</td>\n",
       "      <td>0.121987</td>\n",
       "      <td>0.0</td>\n",
       "      <td>320</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.972644</td>\n",
       "      <td>0.990660</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LF_temp_on_high_page_num</th>\n",
       "      <td>13</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.988540</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LF_to_left</th>\n",
       "      <td>14</td>\n",
       "      <td>0.107898</td>\n",
       "      <td>0.107898</td>\n",
       "      <td>0.0</td>\n",
       "      <td>288</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.989691</td>\n",
       "      <td>0.990587</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LF_storage_row</th>\n",
       "      <td>15</td>\n",
       "      <td>0.035224</td>\n",
       "      <td>0.035224</td>\n",
       "      <td>0.0</td>\n",
       "      <td>88</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.926316</td>\n",
       "      <td>0.990671</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                            j  Coverage  Overlaps  Conflicts   TP  FP  FN  TN  \\\n",
       "LF_temperature_row          0  0.195773  0.136819        0.0  509  19   0   0   \n",
       "LF_not_temp_relevant        1  0.000000  0.000000        0.0    0   0   0   0   \n",
       "LF_current_aligned          2  0.000000  0.000000        0.0    0   0   0   0   \n",
       "LF_collector_aligned        3  0.000000  0.000000        0.0    0   0   0   0   \n",
       "LF_negative_number_left     4  0.143122  0.142010        0.0  370  16   0   0   \n",
       "LF_voltage_row_temp         5  0.000000  0.000000        0.0    0   0   0   0   \n",
       "LF_test_condition_aligned   6  0.000000  0.000000        0.0    0   0   0   0   \n",
       "LF_typ_row                  7  0.000000  0.000000        0.0    0   0   0   0   \n",
       "LF_operating_row            8  0.008899  0.008899        0.0   24   0   0   0   \n",
       "LF_temp_outside_table       9  0.000000  0.000000        0.0    0   0   0   0   \n",
       "LF_complement_left_row     10  0.000000  0.000000        0.0    0   0   0   0   \n",
       "LF_voltage_row_part        11  0.000000  0.000000        0.0    0   0   0   0   \n",
       "LF_tstg_row                12  0.121987  0.121987        0.0  320   9   0   0   \n",
       "LF_temp_on_high_page_num   13  0.000000  0.000000        0.0    0   0   0   0   \n",
       "LF_to_left                 14  0.107898  0.107898        0.0  288   3   0   0   \n",
       "LF_storage_row             15  0.035224  0.035224        0.0   88   7   0   0   \n",
       "\n",
       "                           Empirical Acc.  Learned Acc.  \n",
       "LF_temperature_row               0.964015      0.991184  \n",
       "LF_not_temp_relevant                  NaN      0.988542  \n",
       "LF_current_aligned                    NaN      0.988537  \n",
       "LF_collector_aligned                  NaN      0.988545  \n",
       "LF_negative_number_left          0.958549      0.990065  \n",
       "LF_voltage_row_temp                   NaN      0.988517  \n",
       "LF_test_condition_aligned             NaN      0.988549  \n",
       "LF_typ_row                            NaN      0.988542  \n",
       "LF_operating_row                 1.000000      0.989760  \n",
       "LF_temp_outside_table                 NaN      0.988542  \n",
       "LF_complement_left_row                NaN      0.988552  \n",
       "LF_voltage_row_part                   NaN      0.988541  \n",
       "LF_tstg_row                      0.972644      0.990660  \n",
       "LF_temp_on_high_page_num              NaN      0.988540  \n",
       "LF_to_left                       0.989691      0.990587  \n",
       "LF_storage_row                   0.926316      0.990671  "
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "L_dev.lf_stats(L_gold_dev, gen_model.weights.lf_accuracy)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Interpreting Generative Model Performance\n",
    "\n",
    "At this point, we should be getting an F1 score of around 0.6 to 0.7 on the development set, which is pretty good! However, we should be very careful in interpreting this. Since we developed our labeling functions using this development set as a guide, and our generative model is composed of these labeling functions, we expect it to score very well here!\n",
    "\n",
    "In fact, it is probably somewhat overfit to this set. However this is fine, since in the next, we'll train a more powerful end extraction model which will generalize beyond the development set, and which we will evaluate on a blind test set (i.e. one we never looked at during development).\n",
    "\n",
    "\n",
    "### Training the Discriminative Model\n",
    "\n",
    "Now, we'll use the noisy training labels we generated in the last part to train our end extraction model. For this tutorial, we will be training a simple--but fairly effective--logistic regression model.\n",
    "\n",
    "We use the training marginals to train a discriminative model that classifies each Candidate as a true or false mention. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[INFO] fonduer.learning.disc_learning - [SparseLogisticRegression] Training model\n",
      "[INFO] fonduer.learning.disc_learning - [SparseLogisticRegression] n_train=4246  #epochs=200  batch size=256\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[INFO] fonduer.learning.disc_learning - [SparseLogisticRegression] Epoch 0 (1.16s)\tAverage loss=0.468044\n",
      "[INFO] fonduer.learning.disc_learning - [SparseLogisticRegression] Epoch 5 (6.47s)\tAverage loss=0.085634\n",
      "[INFO] fonduer.learning.disc_learning - [SparseLogisticRegression] Epoch 10 (12.30s)\tAverage loss=0.075464\n",
      "[INFO] fonduer.learning.disc_learning - [SparseLogisticRegression] Epoch 15 (17.64s)\tAverage loss=0.070831\n",
      "[INFO] fonduer.learning.disc_learning - [SparseLogisticRegression] Epoch 20 (22.69s)\tAverage loss=0.068582\n",
      "[INFO] fonduer.learning.disc_learning - [SparseLogisticRegression] Epoch 25 (27.98s)\tAverage loss=0.067460\n",
      "[INFO] fonduer.learning.disc_learning - [SparseLogisticRegression] Epoch 30 (32.88s)\tAverage loss=0.066406\n",
      "[INFO] fonduer.learning.disc_learning - [SparseLogisticRegression] Epoch 35 (38.03s)\tAverage loss=0.066409\n",
      "[INFO] fonduer.learning.disc_learning - [SparseLogisticRegression] Epoch 40 (43.29s)\tAverage loss=0.065593\n",
      "[INFO] fonduer.learning.disc_learning - [SparseLogisticRegression] Epoch 45 (48.54s)\tAverage loss=0.064933\n",
      "[INFO] fonduer.learning.disc_learning - [SparseLogisticRegression] Epoch 50 (53.77s)\tAverage loss=0.064860\n",
      "[INFO] fonduer.learning.disc_learning - [SparseLogisticRegression] Epoch 55 (59.08s)\tAverage loss=0.064672\n",
      "[INFO] fonduer.learning.disc_learning - [SparseLogisticRegression] Epoch 60 (64.59s)\tAverage loss=0.064714\n",
      "[INFO] fonduer.learning.disc_learning - [SparseLogisticRegression] Epoch 65 (69.72s)\tAverage loss=0.064329\n",
      "[INFO] fonduer.learning.disc_learning - [SparseLogisticRegression] Epoch 70 (75.14s)\tAverage loss=0.064024\n",
      "[INFO] fonduer.learning.disc_learning - [SparseLogisticRegression] Epoch 75 (80.56s)\tAverage loss=0.063655\n",
      "[INFO] fonduer.learning.disc_learning - [SparseLogisticRegression] Epoch 80 (86.12s)\tAverage loss=0.064159\n",
      "[INFO] fonduer.learning.disc_learning - [SparseLogisticRegression] Epoch 85 (91.55s)\tAverage loss=0.063659\n",
      "[INFO] fonduer.learning.disc_learning - [SparseLogisticRegression] Epoch 90 (97.94s)\tAverage loss=0.063869\n",
      "[INFO] fonduer.learning.disc_learning - [SparseLogisticRegression] Epoch 95 (104.07s)\tAverage loss=0.063626\n",
      "[INFO] fonduer.learning.disc_learning - [SparseLogisticRegression] Epoch 100 (110.23s)\tAverage loss=0.063963\n",
      "[INFO] fonduer.learning.disc_learning - [SparseLogisticRegression] Epoch 105 (115.51s)\tAverage loss=0.063662\n",
      "[INFO] fonduer.learning.disc_learning - [SparseLogisticRegression] Epoch 110 (121.45s)\tAverage loss=0.063680\n",
      "[INFO] fonduer.learning.disc_learning - [SparseLogisticRegression] Epoch 115 (127.02s)\tAverage loss=0.064051\n",
      "[INFO] fonduer.learning.disc_learning - [SparseLogisticRegression] Epoch 120 (132.56s)\tAverage loss=0.063635\n",
      "[INFO] fonduer.learning.disc_learning - [SparseLogisticRegression] Epoch 125 (137.66s)\tAverage loss=0.063313\n",
      "[INFO] fonduer.learning.disc_learning - [SparseLogisticRegression] Epoch 130 (142.77s)\tAverage loss=0.063259\n",
      "[INFO] fonduer.learning.disc_learning - [SparseLogisticRegression] Epoch 135 (148.36s)\tAverage loss=0.063718\n",
      "[INFO] fonduer.learning.disc_learning - [SparseLogisticRegression] Epoch 140 (153.85s)\tAverage loss=0.063076\n",
      "[INFO] fonduer.learning.disc_learning - [SparseLogisticRegression] Epoch 145 (159.32s)\tAverage loss=0.063530\n",
      "[INFO] fonduer.learning.disc_learning - [SparseLogisticRegression] Epoch 150 (164.55s)\tAverage loss=0.063759\n",
      "[INFO] fonduer.learning.disc_learning - [SparseLogisticRegression] Epoch 155 (170.09s)\tAverage loss=0.063792\n",
      "[INFO] fonduer.learning.disc_learning - [SparseLogisticRegression] Epoch 160 (175.45s)\tAverage loss=0.063877\n",
      "[INFO] fonduer.learning.disc_learning - [SparseLogisticRegression] Epoch 165 (180.77s)\tAverage loss=0.063601\n",
      "[INFO] fonduer.learning.disc_learning - [SparseLogisticRegression] Epoch 170 (186.77s)\tAverage loss=0.063049\n",
      "[INFO] fonduer.learning.disc_learning - [SparseLogisticRegression] Epoch 175 (191.20s)\tAverage loss=0.063578\n",
      "[INFO] fonduer.learning.disc_learning - [SparseLogisticRegression] Epoch 180 (194.82s)\tAverage loss=0.063825\n",
      "[INFO] fonduer.learning.disc_learning - [SparseLogisticRegression] Epoch 185 (198.63s)\tAverage loss=0.063509\n",
      "[INFO] fonduer.learning.disc_learning - [SparseLogisticRegression] Epoch 190 (202.37s)\tAverage loss=0.063040\n",
      "[INFO] fonduer.learning.disc_learning - [SparseLogisticRegression] Epoch 195 (205.97s)\tAverage loss=0.063677\n",
      "[INFO] fonduer.learning.disc_learning - [SparseLogisticRegression] Epoch 199 (209.65s)\tAverage loss=0.063442\n",
      "[INFO] fonduer.learning.disc_learning - [SparseLogisticRegression] Training done (209.65s)\n",
      "CPU times: user 3min 14s, sys: 805 ms, total: 3min 14s\n",
      "Wall time: 3min 30s\n"
     ]
    }
   ],
   "source": [
    "from fonduer import SparseLogisticRegression\n",
    "\n",
    "disc_model = SparseLogisticRegression()\n",
    "%time disc_model.train(F_train, train_marginals, n_epochs=200, lr=0.001)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Evaluating on the Test Set\n",
    "In this final section, we'll get the score we've been after: the performance of the extraction model on the blind test set (split 2). First, we load the test set labels and gold candidates from earlier:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "from fonduer import load_gold_labels\n",
    "L_gold_test = load_gold_labels(session, annotator_name='gold', split=2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "scrolled": true
   },
   "source": [
    "Now, we score using the discriminitive model:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "\n",
    "test_candidates = [F_test.get_candidate(session, i) for i in range(F_test.shape[0])]\n",
    "test_score = disc_model.predictions(F_test)\n",
    "true_pred = [test_candidates[_] for _ in np.nditer(np.where(test_score > 0))]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Preparing candidates...\n",
      "[========================================] 100%\n",
      "========================================\n",
      "Scoring on Entity-Level Gold Data\n",
      "========================================\n",
      "Corpus Precision 0.333\n",
      "Corpus Recall    0.892\n",
      "Corpus F1        0.485\n",
      "----------------------------------------\n",
      "TP: 74 | FP: 148 | FN: 9\n",
      "========================================\n",
      "\n",
      "CPU times: user 3.47 s, sys: 128 ms, total: 3.59 s\n",
      "Wall time: 4.17 s\n"
     ]
    }
   ],
   "source": [
    "from hardware_utils import entity_level_f1\n",
    "import os\n",
    "\n",
    "import pickle\n",
    "pickle_file = 'data/parts_by_doc_dict.pkl'\n",
    "with open(pickle_file, 'rb') as f:\n",
    "    parts_by_doc = pickle.load(f)\n",
    "\n",
    "%time (TP, FP, FN) = entity_level_f1(true_pred, gold_file, ATTRIBUTE, test_docs, parts_by_doc=parts_by_doc)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Phase 4:  Error Analysis & Iterative KBC\n",
    "\n",
    "During the development process, we can iteratively improve the quality of our labeling functions through error analysis, without executing the full pipeline as in previous techniques. \n",
    "\n",
    "You may have noticed that our final score is about 50 F1 points. To remedy this and improve our quality, we can perform error analysis to understand what kinds of patterns we may have missed, or what issues exist with our labeling functions. Then, we can edit our set of labeling functions and rerun Phase 3, Probabilistic Relation Classification. \n",
    "\n",
    "## Error Analysis\n",
    "For example, notice that our `entity_level_f1` returns `TP`, `FP`, `FN` sets. We can also see that our recall is high, but we have low precision, so let's look at our false positivies, `FP`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('BC546-D', 'BC546', '180'),\n",
       " ('BC546-D', 'BC546', '200'),\n",
       " ('BC546-D', 'BC546B', '180'),\n",
       " ('BC546-D', 'BC546B', '200'),\n",
       " ('BC546-D', 'BC546BG', '180'),\n",
       " ('BC546-D', 'BC546BG', '200'),\n",
       " ('BC546-D', 'BC546BRL1', '180'),\n",
       " ('BC546-D', 'BC546BRL1', '200'),\n",
       " ('BC546-D', 'BC546BRL1G', '180'),\n",
       " ('BC546-D', 'BC546BRL1G', '200'),\n",
       " ('BC546-D', 'BC546BZL1G', '180'),\n",
       " ('BC546-D', 'BC546BZL1G', '200'),\n",
       " ('BC546-D', 'BC547', '180'),\n",
       " ('BC546-D', 'BC547', '200'),\n",
       " ('BC546-D', 'BC547A', '180'),\n",
       " ('BC546-D', 'BC547A', '200'),\n",
       " ('BC546-D', 'BC547ARL', '180'),\n",
       " ('BC546-D', 'BC547ARL', '200'),\n",
       " ('BC546-D', 'BC547ARLG', '180'),\n",
       " ('BC546-D', 'BC547ARLG', '200'),\n",
       " ('BC546-D', 'BC547AZL1G', '180'),\n",
       " ('BC546-D', 'BC547AZL1G', '200'),\n",
       " ('BC546-D', 'BC547B', '180'),\n",
       " ('BC546-D', 'BC547B', '200'),\n",
       " ('BC546-D', 'BC547BG', '180'),\n",
       " ('BC546-D', 'BC547BG', '200'),\n",
       " ('BC546-D', 'BC547BRL1G', '180'),\n",
       " ('BC546-D', 'BC547BRL1G', '200'),\n",
       " ('BC546-D', 'BC547BZL1G', '180'),\n",
       " ('BC546-D', 'BC547BZL1G', '200'),\n",
       " ('BC546-D', 'BC547C', '180'),\n",
       " ('BC546-D', 'BC547C', '200'),\n",
       " ('BC546-D', 'BC547CG', '180'),\n",
       " ('BC546-D', 'BC547CG', '200'),\n",
       " ('BC546-D', 'BC547CZL1G', '180'),\n",
       " ('BC546-D', 'BC547CZL1G', '200'),\n",
       " ('BC546-D', 'BC548', '180'),\n",
       " ('BC546-D', 'BC548', '200'),\n",
       " ('BC546-D', 'BC548B', '180'),\n",
       " ('BC546-D', 'BC548B', '200'),\n",
       " ('BC546-D', 'BC548BG', '180'),\n",
       " ('BC546-D', 'BC548BG', '200'),\n",
       " ('BC546-D', 'BC548BRL1G', '180'),\n",
       " ('BC546-D', 'BC548BRL1G', '200'),\n",
       " ('BC546-D', 'BC548BZL1G', '180'),\n",
       " ('BC546-D', 'BC548BZL1G', '200'),\n",
       " ('BC546-D', 'BC548C', '180'),\n",
       " ('BC546-D', 'BC548C', '200'),\n",
       " ('BC546-D', 'BC548CG', '180'),\n",
       " ('BC546-D', 'BC548CG', '200'),\n",
       " ('BC546-D', 'BC548CZL1G', '180'),\n",
       " ('BC546-D', 'BC548CZL1G', '200'),\n",
       " ('BC546_DIOTEC', 'BC546', '200'),\n",
       " ('BC546_DIOTEC', 'BC546A', '200'),\n",
       " ('BC546_DIOTEC', 'BC546B', '200'),\n",
       " ('BC546_DIOTEC', 'BC546C', '150'),\n",
       " ('BC546_DIOTEC', 'BC546C', '200'),\n",
       " ('BC546_DIOTEC', 'BC547', '200'),\n",
       " ('BC546_DIOTEC', 'BC547A', '200'),\n",
       " ('BC546_DIOTEC', 'BC547B', '200'),\n",
       " ('BC546_DIOTEC', 'BC547C', '200'),\n",
       " ('BC546_DIOTEC', 'BC548', '200'),\n",
       " ('BC546_DIOTEC', 'BC548A', '200'),\n",
       " ('BC546_DIOTEC', 'BC548B', '200'),\n",
       " ('BC546_DIOTEC', 'BC548C', '200'),\n",
       " ('BC546_DIOTEC', 'BC549', '200'),\n",
       " ('BC546_DIOTEC', 'BC549A', '150'),\n",
       " ('BC546_DIOTEC', 'BC549A', '200'),\n",
       " ('BC546_DIOTEC', 'BC549B', '200'),\n",
       " ('BC546_DIOTEC', 'BC549C', '200'),\n",
       " ('BC546_DIOTEC', 'BC556', '150'),\n",
       " ('BC546_DIOTEC', 'BC556', '200'),\n",
       " ('BC550', 'BC546', '200'),\n",
       " ('BC550', 'BC546A', '200'),\n",
       " ('BC550', 'BC546B', '200'),\n",
       " ('BC550', 'BC546C', '200'),\n",
       " ('BC550', 'BC549', '200'),\n",
       " ('BC550', 'BC549A', '200'),\n",
       " ('BC550', 'BC549B', '200'),\n",
       " ('BC550', 'BC549C', '200'),\n",
       " ('BC550', 'BC550', '200'),\n",
       " ('BC550', 'BC550A', '200'),\n",
       " ('BC550', 'BC550B', '200'),\n",
       " ('BC550', 'BC550C', '200'),\n",
       " ('BC550', 'BC556', '150'),\n",
       " ('BC550', 'BC556', '200'),\n",
       " ('BC550', 'BC560', '150'),\n",
       " ('BC550', 'BC560', '200'),\n",
       " ('DTC114W', 'DTC114W', '180'),\n",
       " ('DTC114W', 'DTC114W', '200'),\n",
       " ('DTC114W', 'DTC114WE', '180'),\n",
       " ('DTC114W', 'DTC114WE', '200'),\n",
       " ('DTC114W', 'DTC114WKA', '180'),\n",
       " ('DTC114W', 'DTC114WKA', '200'),\n",
       " ('DTC114W', 'DTC114WUA', '180'),\n",
       " ('DTC114W', 'DTC114WUA', '200'),\n",
       " ('RECTS01214-1', 'DTC114EUA', '200'),\n",
       " ('RECTS01325-1', 'DTC114YUA', '200'),\n",
       " ('UTCLS01324-1', 'MMBT3904', '200'),\n",
       " ('UTCLS01324-1', 'MMBT3904-E', '150'),\n",
       " ('UTCLS01324-1', 'MMBT3904-E', '200'),\n",
       " ('UTCLS01324-1', 'MMBT3904G-AE3-R', '200'),\n",
       " ('UTCLS01324-1', 'MMBT3904G-AL3-R', '200'),\n",
       " ('UTCLS01324-1', 'MMBT3904L-AE3-R', '200'),\n",
       " ('UTCLS01324-1', 'MMBT3904L-AL3-R', '200'),\n",
       " ('UTCLS01324-1', 'MMBT3906', '150'),\n",
       " ('UTCLS01324-1', 'MMBT3906', '200'),\n",
       " ('VSMIS00373-1', 'BC327', '150'),\n",
       " ('VSMIS00373-1', 'BC327', '160'),\n",
       " ('VSMIS00373-1', 'BC327', '170'),\n",
       " ('VSMIS00373-1', 'BC327', '200'),\n",
       " ('VSMIS00373-1', 'BC328', '150'),\n",
       " ('VSMIS00373-1', 'BC328', '160'),\n",
       " ('VSMIS00373-1', 'BC328', '170'),\n",
       " ('VSMIS00373-1', 'BC328', '200'),\n",
       " ('VSMIS00373-1', 'BC337', '160'),\n",
       " ('VSMIS00373-1', 'BC337', '170'),\n",
       " ('VSMIS00373-1', 'BC337', '200'),\n",
       " ('VSMIS00373-1', 'BC337-16', '160'),\n",
       " ('VSMIS00373-1', 'BC337-16', '170'),\n",
       " ('VSMIS00373-1', 'BC337-16', '200'),\n",
       " ('VSMIS00373-1', 'BC337-25', '160'),\n",
       " ('VSMIS00373-1', 'BC337-25', '170'),\n",
       " ('VSMIS00373-1', 'BC337-25', '200'),\n",
       " ('VSMIS00373-1', 'BC337-40', '160'),\n",
       " ('VSMIS00373-1', 'BC337-40', '170'),\n",
       " ('VSMIS00373-1', 'BC337-40', '200'),\n",
       " ('VSMIS00373-1', 'BC338', '160'),\n",
       " ('VSMIS00373-1', 'BC338', '170'),\n",
       " ('VSMIS00373-1', 'BC338', '200'),\n",
       " ('VSMIS00373-1', 'BC338-16', '160'),\n",
       " ('VSMIS00373-1', 'BC338-16', '170'),\n",
       " ('VSMIS00373-1', 'BC338-16', '200'),\n",
       " ('VSMIS00373-1', 'BC338-25', '160'),\n",
       " ('VSMIS00373-1', 'BC338-25', '170'),\n",
       " ('VSMIS00373-1', 'BC338-25', '200'),\n",
       " ('VSMIS00373-1', 'BC338-40', '160'),\n",
       " ('VSMIS00373-1', 'BC338-40', '170'),\n",
       " ('VSMIS00373-1', 'BC338-40', '200'),\n",
       " ('WEITS00252-1', 'BC807', '150'),\n",
       " ('WEITS00252-1', 'BC807', '160'),\n",
       " ('WEITS00252-1', 'BC807-1', '150'),\n",
       " ('WEITS00252-1', 'BC807-1', '160'),\n",
       " ('WEITS00252-1', 'BC807-16', '160'),\n",
       " ('WEITS00252-1', 'BC807-17', '150'),\n",
       " ('WEITS00252-1', 'BC807-17', '160'),\n",
       " ('WEITS00252-1', 'BC807-25', '160'),\n",
       " ('WEITS00252-1', 'BC807-40', '160')]"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "FP"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see that there are actually only a few documents that are causing us problems. In particular, we see that `BC546-D` is giving us many false positives. So, let's inspect one of those candidates. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<wand.image.Image: 98f0b5d 'PDF' (612x792)>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<wand.image.Image: fa447d5 'PDF' (612x792)>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from fonduer.utils.visualizer import Visualizer\n",
    "from hardware_utils import entity_to_candidates\n",
    "vis = Visualizer(pdf_path)\n",
    "\n",
    "# Get a list of candidates that match the FN[10] entity\n",
    "test_cands = session.query(Part_Attr).filter(Part_Attr.split == 2).all()\n",
    "fp_cands = entity_to_candidates(FP[10], test_cands)\n",
    "# Display a candidate\n",
    "vis.display_candidates([fp_cands[0]])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here, the candidates are boxed in blue. We see that the temperature falls within the range of numbers that our matcher for storage temperature allows to match. By inspecting candidates like this, or just by examining the problematic PDFs directly, we can notice some patterns that we can exploit as new labeling functions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<wand.image.Image: a699cbe 'PDF' (612x792)>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<wand.image.Image: fa447d5 'PDF' (612x792)>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Get a list of candidates that match the FN[10] entity\n",
    "test_cands = session.query(Part_Attr).filter(Part_Attr.split == 2).all()\n",
    "fp_cands = entity_to_candidates(FP[40], test_cands)\n",
    "\n",
    "# # Display this candidate\n",
    "vis.display_candidates([fp_cands[0]])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Iteratively Improving Labeling Functions\n",
    "\n",
    "From this error analysis, we may notice two important things. First, our original set of labeling functions had no labeling functions that labeled candidates a negative. This resulted in most skewing the models to accept most candidates, and hurt our precision. Second, we have now noticed that we need to focus on negatively labeling numbers that pass through our storage temperature matchers, but are not related to storage temperature.\n",
    "\n",
    "Below are a set of negative labeling functions that capture some of these patterns. For example, we label candidates an negative if the number is aligned with attributes that are not related to storage temperature, if a candidate represents a typical value, rather than a maximum value, if a temperature value is found outside of a table, and other intuitive patterns we noticed when carefully inspecting our false positives."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "from itertools import chain\n",
    "\n",
    "def LF_test_condition_aligned(c):\n",
    "    return -1 if overlap(\n",
    "        ['test', 'condition'],\n",
    "        list(get_aligned_ngrams(c.attr))) else 0\n",
    "\n",
    "def LF_collector_aligned(c):\n",
    "    return -1 if overlap(\n",
    "        ['collector', 'collector-current', 'collector-base', 'collector-emitter'],\n",
    "        list(get_aligned_ngrams(c.attr))) else 0\n",
    "\n",
    "def LF_current_aligned(c):\n",
    "    ngrams = get_aligned_ngrams(c.attr)\n",
    "    return -1 if overlap(\n",
    "        ['current', 'dc', 'ic'],\n",
    "        list(get_aligned_ngrams(c.attr))) else 0\n",
    "\n",
    "def LF_voltage_row_temp(c):\n",
    "    ngrams = get_aligned_ngrams(c.attr)\n",
    "    return -1 if overlap(\n",
    "        ['voltage', 'cbo', 'ceo', 'ebo', 'v'],\n",
    "        list(get_aligned_ngrams(c.attr))) else 0\n",
    "\n",
    "def LF_voltage_row_part(c):\n",
    "    ngrams = get_aligned_ngrams(c.part)\n",
    "    return -1 if overlap(\n",
    "        ['voltage', 'cbo', 'ceo', 'ebo', 'v'],\n",
    "        list(get_aligned_ngrams(c.attr))) else 0\n",
    "\n",
    "def LF_typ_row(c):\n",
    "    return -1 if overlap(\n",
    "        ['typ', 'typ.'],\n",
    "        list(get_row_ngrams(c.attr))) else 0\n",
    "\n",
    "def LF_complement_left_row(c):\n",
    "    return -1 if (\n",
    "        overlap(['complement','complementary'], \n",
    "        chain.from_iterable([get_row_ngrams(c.part), get_left_ngrams(c.part, window=10)]))) else 0\n",
    "\n",
    "\n",
    "def LF_too_many_numbers_row(c):\n",
    "    num_numbers = list(get_row_ngrams(c.attr, attrib=\"ner_tags\")).count('number')\n",
    "    return -1 if num_numbers >= 3 else 0\n",
    "\n",
    "def LF_temp_on_high_page_num(c):\n",
    "    return -1 if c.attr.get_attrib_tokens('page')[0] > 2 else 0\n",
    "\n",
    "def LF_temp_outside_table(c):\n",
    "    return -1 if not c.attr.sentence.is_tabular() is None else 0\n",
    "\n",
    "def LF_not_temp_relevant(c):\n",
    "    return -1 if not overlap(\n",
    "        ['storage','temperature','tstg','stg', 'ts'],\n",
    "        list(get_aligned_ngrams(c.attr))) else 0"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "Then, we can add these to our list of labeling functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "stg_temp_lfs_2 = [LF_test_condition_aligned,\n",
    "                 LF_collector_aligned,\n",
    "                 LF_current_aligned,\n",
    "                 LF_voltage_row_temp,\n",
    "                 LF_voltage_row_part,\n",
    "                 LF_typ_row,\n",
    "                 LF_complement_left_row,\n",
    "                 LF_too_many_numbers_row,\n",
    "                 LF_temp_on_high_page_num,\n",
    "                 LF_temp_outside_table,\n",
    "                 LF_not_temp_relevant\n",
    "                ]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And rerun labeling. Importantly, this time we set `clear=False`, `update_keys=True` and `update_values=True` to reflect the fact that we are adding new labeling functions, but do not want to throw away the computations already performed in the previous iteration."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[INFO] fonduer.utils.udf - Running UDF...\n",
      "[INFO] fonduer.supervision.async_annotations - Copying part_attr_label_updates to postgres\n",
      "[INFO] fonduer.supervision.async_annotations - b'COPY 29789\\n'\n",
      "CPU times: user 1.41 s, sys: 231 ms, total: 1.64 s\n",
      "Wall time: 9min 52s\n",
      "(29789, 16)\n"
     ]
    }
   ],
   "source": [
    "labeler = BatchLabelAnnotator(Part_Attr, lfs = stg_temp_lfs_2)\n",
    "%time L_train = labeler.apply(split=0, clear=False, update_keys=True, update_values=True, parallelism=PARALLEL)\n",
    "print(L_train.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, we can rerun probablistic relation classification, the same way we did above. We start with the generative model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[INFO] fonduer.learning.gen_learning - Inferred cardinality: 2\n",
      "CPU times: user 10min 43s, sys: 191 ms, total: 10min 43s\n",
      "Wall time: 10min 43s\n"
     ]
    }
   ],
   "source": [
    "gen_model = GenerativeModel()\n",
    "%time gen_model.train(L_train, epochs=500, decay=0.9, step_size=0.001/L_train.shape[0], reg_param=0)\n",
    "train_marginals = gen_model.marginals(L_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, we rerun the discriminitive model and see that our score has improved significantly to about 80 F1 points."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[INFO] fonduer.learning.disc_learning - [SparseLogisticRegression] Training model\n",
      "[INFO] fonduer.learning.disc_learning - [SparseLogisticRegression] n_train=29789  #epochs=200  batch size=256\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[INFO] fonduer.learning.disc_learning - [SparseLogisticRegression] Epoch 0 (3.60s)\tAverage loss=0.228042\n",
      "[INFO] fonduer.learning.disc_learning - [SparseLogisticRegression] Epoch 5 (21.71s)\tAverage loss=0.055171\n",
      "[INFO] fonduer.learning.disc_learning - [SparseLogisticRegression] Epoch 10 (40.14s)\tAverage loss=0.052588\n",
      "[INFO] fonduer.learning.disc_learning - [SparseLogisticRegression] Epoch 15 (58.76s)\tAverage loss=0.051935\n",
      "[INFO] fonduer.learning.disc_learning - [SparseLogisticRegression] Epoch 20 (77.42s)\tAverage loss=0.051755\n",
      "[INFO] fonduer.learning.disc_learning - [SparseLogisticRegression] Epoch 25 (95.98s)\tAverage loss=0.051545\n",
      "[INFO] fonduer.learning.disc_learning - [SparseLogisticRegression] Epoch 30 (114.67s)\tAverage loss=0.051525\n",
      "[INFO] fonduer.learning.disc_learning - [SparseLogisticRegression] Epoch 35 (133.33s)\tAverage loss=0.051487\n",
      "[INFO] fonduer.learning.disc_learning - [SparseLogisticRegression] Epoch 40 (152.07s)\tAverage loss=0.051486\n",
      "[INFO] fonduer.learning.disc_learning - [SparseLogisticRegression] Epoch 45 (170.72s)\tAverage loss=0.051344\n",
      "[INFO] fonduer.learning.disc_learning - [SparseLogisticRegression] Epoch 50 (189.42s)\tAverage loss=0.051365\n",
      "[INFO] fonduer.learning.disc_learning - [SparseLogisticRegression] Epoch 55 (208.03s)\tAverage loss=0.051404\n",
      "[INFO] fonduer.learning.disc_learning - [SparseLogisticRegression] Epoch 60 (226.73s)\tAverage loss=0.051372\n",
      "[INFO] fonduer.learning.disc_learning - [SparseLogisticRegression] Epoch 65 (245.36s)\tAverage loss=0.051468\n",
      "[INFO] fonduer.learning.disc_learning - [SparseLogisticRegression] Epoch 70 (264.93s)\tAverage loss=0.051335\n",
      "[INFO] fonduer.learning.disc_learning - [SparseLogisticRegression] Epoch 75 (283.56s)\tAverage loss=0.051445\n",
      "[INFO] fonduer.learning.disc_learning - [SparseLogisticRegression] Epoch 80 (302.39s)\tAverage loss=0.051299\n",
      "[INFO] fonduer.learning.disc_learning - [SparseLogisticRegression] Epoch 85 (321.21s)\tAverage loss=0.051312\n",
      "[INFO] fonduer.learning.disc_learning - [SparseLogisticRegression] Epoch 90 (339.87s)\tAverage loss=0.051429\n",
      "[INFO] fonduer.learning.disc_learning - [SparseLogisticRegression] Epoch 95 (358.46s)\tAverage loss=0.051315\n",
      "[INFO] fonduer.learning.disc_learning - [SparseLogisticRegression] Epoch 100 (377.06s)\tAverage loss=0.051466\n",
      "[INFO] fonduer.learning.disc_learning - [SparseLogisticRegression] Epoch 105 (395.71s)\tAverage loss=0.051280\n",
      "[INFO] fonduer.learning.disc_learning - [SparseLogisticRegression] Epoch 110 (414.80s)\tAverage loss=0.051421\n",
      "[INFO] fonduer.learning.disc_learning - [SparseLogisticRegression] Epoch 115 (433.39s)\tAverage loss=0.051365\n",
      "[INFO] fonduer.learning.disc_learning - [SparseLogisticRegression] Epoch 120 (452.05s)\tAverage loss=0.051343\n",
      "[INFO] fonduer.learning.disc_learning - [SparseLogisticRegression] Epoch 125 (470.70s)\tAverage loss=0.051543\n",
      "[INFO] fonduer.learning.disc_learning - [SparseLogisticRegression] Epoch 130 (489.49s)\tAverage loss=0.051514\n",
      "[INFO] fonduer.learning.disc_learning - [SparseLogisticRegression] Epoch 135 (508.41s)\tAverage loss=0.051322\n",
      "[INFO] fonduer.learning.disc_learning - [SparseLogisticRegression] Epoch 140 (527.20s)\tAverage loss=0.051358\n",
      "[INFO] fonduer.learning.disc_learning - [SparseLogisticRegression] Epoch 145 (545.92s)\tAverage loss=0.051466\n",
      "[INFO] fonduer.learning.disc_learning - [SparseLogisticRegression] Epoch 150 (564.56s)\tAverage loss=0.051368\n",
      "[INFO] fonduer.learning.disc_learning - [SparseLogisticRegression] Epoch 155 (583.18s)\tAverage loss=0.051279\n",
      "[INFO] fonduer.learning.disc_learning - [SparseLogisticRegression] Epoch 160 (602.17s)\tAverage loss=0.051339\n",
      "[INFO] fonduer.learning.disc_learning - [SparseLogisticRegression] Epoch 165 (620.79s)\tAverage loss=0.051313\n",
      "[INFO] fonduer.learning.disc_learning - [SparseLogisticRegression] Epoch 170 (639.44s)\tAverage loss=0.051238\n",
      "[INFO] fonduer.learning.disc_learning - [SparseLogisticRegression] Epoch 175 (658.30s)\tAverage loss=0.051383\n",
      "[INFO] fonduer.learning.disc_learning - [SparseLogisticRegression] Epoch 180 (676.93s)\tAverage loss=0.051568\n",
      "[INFO] fonduer.learning.disc_learning - [SparseLogisticRegression] Epoch 185 (695.57s)\tAverage loss=0.051281\n",
      "[INFO] fonduer.learning.disc_learning - [SparseLogisticRegression] Epoch 190 (714.24s)\tAverage loss=0.051385\n",
      "[INFO] fonduer.learning.disc_learning - [SparseLogisticRegression] Epoch 195 (732.84s)\tAverage loss=0.051382\n",
      "[INFO] fonduer.learning.disc_learning - [SparseLogisticRegression] Epoch 199 (750.34s)\tAverage loss=0.051301\n",
      "[INFO] fonduer.learning.disc_learning - [SparseLogisticRegression] Training done (750.34s)\n",
      "CPU times: user 13min 29s, sys: 3.45 s, total: 13min 32s\n",
      "Wall time: 12min 30s\n"
     ]
    }
   ],
   "source": [
    "disc_model = SparseLogisticRegression()\n",
    "%time disc_model.train(F_train, train_marginals, n_epochs=200, lr=0.001)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Preparing candidates...\n",
      "[========================================] 100%\n",
      "========================================\n",
      "Scoring on Entity-Level Gold Data\n",
      "========================================\n",
      "Corpus Precision 0.857\n",
      "Corpus Recall    0.867\n",
      "Corpus F1        0.862\n",
      "----------------------------------------\n",
      "TP: 72 | FP: 12 | FN: 11\n",
      "========================================\n",
      "\n",
      "CPU times: user 107 ms, sys: 24 ms, total: 132 ms\n",
      "Wall time: 130 ms\n"
     ]
    }
   ],
   "source": [
    "test_candidates = [F_test.get_candidate(session, i) for i in range(F_test.shape[0])]\n",
    "test_score = disc_model.predictions(F_test)\n",
    "true_pred = [test_candidates[_] for _ in np.nditer(np.where(test_score > 0))]\n",
    "# tp, fp, tn, fn = disc_model.score(session, F_test, L_gold_test)\n",
    "%time (TP, FP, FN) = entity_level_f1(true_pred, gold_file, ATTRIBUTE, test_docs, parts_by_doc=parts_by_doc)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using these new LFs, we've significantly improved precision and lowered our number of false positives for an F1 score of about 80."
   ]
  }
 ],
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