Display_LDA_CountVect_parameters.ipynb

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   "cell_type": "code",
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   "source": [
    "import os\n",
    "import pickle"
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   "source": [
    "## INPUT PARAMETERS"
   ]
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   "source": [
    "# parameters to select the topic2vec model to load\n",
    "n_docs = 11314\n",
    "n_window_t2v = 2\n",
    "n_topics_LDA = 8"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## DIR WHERE THE MODEL IS SAVED"
   ]
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    "cwd =  os.getcwd() # Prints the working directory\n",
    "results_dir_path = cwd + '/results/DBOW/20NG_lemmatiz_DBOW_win5_n_topics' + str (n_topics_LDA) +'_n_doc' + str(n_docs) + '_n_win' + str(n_window_t2v)"
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   "cell_type": "code",
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   "source": [
    "output = open(results_dir_path + '/CountVect_LDA_param.pkl', 'r')\n",
    "t2v_parameters = pickle.load(output) \n",
    "output.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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   "source": [
    "t2v_parameters['lda_param']"
   ]
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   "cell_type": "code",
   "execution_count": null,
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   "source": [
    "t2v_parameters['CountVectorizer_param']"
   ]
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    "t2v_parameters['top_words_per_topic']"
   ]
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   "cell_type": "code",
   "execution_count": null,
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    "collapsed": false
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   "source": [
    "t2v_parameters['n_epoch_t2v']"
   ]
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   "execution_count": null,
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