acquisition-experiment-batch-four.ipynb

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   "source": [
    "# Acquiring Minimalist Grammars using the Z3 SMT-solver.\n",
    "\n",
    "***by Sagar Indurkhya (indurks@mit.edu)***\n",
    "Copyright 2018-2023"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Prelude"
   ]
  },
  {
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   "source": [
    "# This cell should be uncommented and executed as needed to upgrade dependencies.\n",
    "#!pip -q install -r requirements.txt\n",
    "#!conda install -y pygraphviz"
   ]
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     "start_time": "2023-06-02T01:45:04.546152Z"
    }
   },
   "outputs": [],
   "source": [
    "from IPython.core.display import display, HTML\n",
    "display(HTML(\"<style>.container { width:100% !important; }</style>\"))\n",
    "%config InlineBackend.figure_format = 'retina'"
   ]
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   "outputs": [],
   "source": [
    "import itertools, json, pprint as pp, random, traceback\n",
    "\n",
    "import simplejson as json\n",
    "\n",
    "import mgsmt\n",
    "from mgsmt.experiments.inferenceexperiment import InferenceExperiment\n",
    "from mgsmt.experiments.experimentrunner import ParallelExperimentRunner"
   ]
  },
  {
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   },
   "source": [
    "## Acquisition Experiment for Batch Four of the PLD"
   ]
  },
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   "cell_type": "code",
   "execution_count": null,
   "metadata": {
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    "scrolled": false
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   "outputs": [],
   "source": [
    "e = InferenceExperiment(\n",
    "    config_filename=\"experiment-data/primary-linguistic-data.json\",\n",
    "    other_args={\n",
    "        \"display.jupyter-widgets\": True,\n",
    "        \"proc.id\": 0,\n",
    "        \"wisdom\": {},\n",
    "        \"model_values_serialization_filepath\": \"model-values-from-batch-four.json\",\n",
    "        \"selection_feature_labels\": [\"x0\"],\n",
    "        \"licensing_feature_labels\": [\"l\", \"z\"],\n",
    "        \"max_num_overt_pf_connections\": 3,\n",
    "        \"num_overt_lexical_entries_per_form\": 3,\n",
    "        \"num_covert_lexical_entries_per_form\": 5,\n",
    "        \"ics_to_use\": '37-39',\n",
    "        \"init_from_lexicon\": True,\n",
    "        \"init_lexicons\": [\"inferred-lexicon-from-batch-three.json\"],\n",
    "    },\n",
    "    verbose=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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   "outputs": [],
   "source": [
    "per = ParallelExperimentRunner(e)\n",
    "per.run(checkpoint_filepath=\"output-checkpoint-for-batch-four-test.json\", run_from_checkpoint=False)"
   ]
  },
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   "cell_type": "code",
   "execution_count": null,
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   },
   "outputs": [],
   "source": [
    "def serialize_inferred_lexicon(inf_exp, output_filename):\n",
    "    lexicon = json.loads(inf_exp.output['final_lexicon'])\n",
    "    with open(output_filename, 'w') as f_out:\n",
    "        json.dump(lexicon, f_out)\n",
    "\n",
    "serialize_inferred_lexicon(e, \"inferred-lexicon-from-batch-four.json\")"
   ]
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   "execution_count": null,
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     "start_time": "2023-06-02T09:29:44.034403Z"
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    "scrolled": false
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   "outputs": [],
   "source": [
    "import mgsmt.views.factored_lexicon_view\n",
    "\n",
    "flv = mgsmt.views.factored_lexicon_view.FactoredLexiconLaTeXView(e.grammar)\n",
    "lw_flv = mgsmt.views.LaTeXWidget(flv, \"Factored-Lexicon\", e.logging_img_dir)\n",
    "lw_flv.display()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## End of Notebook"
   ]
  }
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