Mitigated_text_tag.ipynb

{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Bayesian Optimizarion _ M-MTT"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from Utils import load_data, preprocessing, model_performances\n",
    "import os\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import tensorflow as tf\n",
    "from sklearn.model_selection import KFold\n",
    "import matplotlib.pyplot as plt\n",
    "import json\n",
    "import keras\n",
    "from functools import partial\n",
    "from sklearn.metrics import classification_report\n",
    "import time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import skopt\n",
    "from skopt import gp_minimize, forest_minimize\n",
    "from skopt.space import Real, Categorical, Integer\n",
    "from skopt import plots\n",
    "from skopt.utils import use_named_args\n",
    "from collections import namedtuple"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Utils"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "label_column = \"misogynous\"\n",
    "input_columns = ['text_USE', 'tags_USE']\n",
    "threshold = 0.5\n",
    "\n",
    "embed_size = 512  # 512-length array with Universal Sentence Encoder algorithm\n",
    "input_shape = embed_size*2\n",
    "batch_size = 64\n",
    "epochs = 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# ________________________________________Utils ___________________________________________________\n",
    "MODELNAMES = ['multimodal_text_tags_BO_v{}'.format(i) for i in range(10)]\n",
    "path_models = \"./Mitigated/models/\"\n",
    "path_predictions= \"./Mitigated/predictions/\"\n",
    "path_performances = \"./Mitigated/performances\"\n",
    "model_name = 'multimodal_text_tags'\n",
    "\n",
    "results_txt_path = path_performances + 'BO_results.txt'\n",
    "\n",
    "for path in [path_models, path_predictions, path_performances]:\n",
    "    if not os.path.exists(path):\n",
    "        os.makedirs(path)\n",
    "\n",
    "file = open(results_txt_path, 'a+')\n",
    "file.truncate(0)  # erase file content\n",
    "file.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# ________________________________________load training data ___________________________________________________\n",
    "meme_df = load_data.load_clarifai_training_data()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "meme_df['text_USE'] = preprocessing.use_preprocessing(meme_df, 'Text Transcription')\n",
    "meme_df['tags_USE'] = preprocessing.meme_tag_embedding(meme_df, 'clarifai')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# ________________________________________load test data ___________________________________________________\n",
    "test_df = load_data.load_clarifai_test_data()\n",
    "test_df['text_USE'] = preprocessing.use_preprocessing(test_df, 'Text Transcription')\n",
    "test_df['tags_USE'] = preprocessing.meme_tag_embedding(test_df, 'clarifai')\n",
    "\n",
    "x_test, y_test = preprocessing.elaborate_input(test_df, input_columns, label_column)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# ________________________________________load synthetic data ___________________________________________________\n",
    "syn_df = load_data.load_clarifai_syn_data()\n",
    "syn_df = load_data.shuffle_syn(syn_df, 'file_name')\n",
    "\n",
    "syn_df['text_USE'] = preprocessing.use_preprocessing(syn_df, 'Text Transcription')\n",
    "syn_df['tags_USE'] = preprocessing.meme_tag_embedding(syn_df, 'clarifai')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "res_test = test_df[['file_name', 'misogynous']].copy()\n",
    "res_syn = syn_df[['file_name', 'misogynous']].copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "Identity_Terms = load_data.read_clear_identity_terms()\n",
    "Identity_Tags = load_data.read_clear_identity_tags()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "syn_df = syn_df.merge(load_data.load_syn_identity_data().drop(columns=['misogynous', 'Text Transcription']),\n",
    "                      how='inner', on=\"file_name\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Bayesian Optimization _ Utils"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# define a search space as a List\n",
    "search_space = list()\n",
    "search_space.append(Real(0.00001, 0.1, 'log-uniform', name='lr'))\n",
    "search_space.append(Real(1e-8, 1, 'log-uniform', name='epsilon'))\n",
    "search_space.append(Integer(64, input_shape, name='neurons'))\n",
    "search_space.append(Categorical(['sigmoid', 'relu', 'tanh', 'LeakyReLU'], name='activation_function'))\n",
    "search_space.append(Real(0.1, 0.6, 'log-uniform', name='dropout'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "default_parameters={\"lr\":0.00001,\n",
    "        \"epsilon\": 1e-7,\n",
    "        \"neurons\": 512, \n",
    "        \"activation_function\": 'LeakyReLU',\n",
    "        \"dropout\": 0.2\n",
    "    }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# distinguish variable parameter from fixed parameters in 10Fold execution\n",
    "VariableParams = namedtuple('VariableParams', 'lr epsilon neurons activation_function dropout')\n",
    "FixedParams = namedtuple('FixedParams', 'iteration dataset syn_data train_index test_index')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "def fitness(v_args, f_args):\n",
    "    \"\"\"  A function that will be called by the search procedure. \n",
    "    This is a function expected by the optimization procedure later and takes a model and set of specific\n",
    "    hyperparameters for the model, evaluates it, and returns a score for the set of hyperparameters ( the value to maximize).\n",
    "\n",
    "    This is the function that creates and trains a neural network with the given hyper-parameters, and then evaluates its\n",
    "    performance on the validation-set. The function then returns the so-called fitness value (aka. objective value),\n",
    "    which is the negative final_multimodal_Score on the validation-set. \n",
    "    It is negative because skopt performs minimization instead of maximization.\n",
    "\n",
    "    It creates a model and train that with the parameters (lr and epsilon) passed\n",
    "    as input. The trained model is used to make predictions on test and on syn\n",
    "    and finally the predictions are used to compute the multimodal bias metric\n",
    "    (value to maximize).\n",
    "    \"\"\"\n",
    "    iteration = f_args.iteration\n",
    "    syn_data = f_args.syn_data\n",
    "\t\n",
    "    saving_folder = path_models + f'Iteration_{iteration}/'.format(iteration=iteration)\n",
    "    if not os.path.exists(saving_folder):\n",
    "        os.makedirs(saving_folder)\n",
    "\n",
    "    # configure the data\n",
    "    x_train, y_train, x_val, y_val, x_test, y_test = preprocessing.elaborate_data_10fold(f_args.dataset, f_args.train_index,\n",
    "                                                                                            f_args.test_index, iteration,\n",
    "                                                                                            input_columns, label_column)\n",
    "\n",
    "\n",
    "    # configure the model with specific hyperparameters\n",
    "    model, _ = model_performances.get_trained_model(x_train, \n",
    "                            y_train, \n",
    "                            x_val, \n",
    "                            y_val,\n",
    "                            input_shape=input_shape, \n",
    "                            activation_function=v_args.activation_function, \n",
    "                            neurons=v_args.neurons, \n",
    "                            dropout=v_args.dropout, \n",
    "                            lr=v_args.lr, \n",
    "                            epsilon=v_args.epsilon, \n",
    "                            epochs=epochs,\n",
    "                            batch_size=batch_size)\n",
    "\n",
    "\n",
    "    # save the model\n",
    "    model.save(path_models + 'BO_model_multimodal_text_tags_' + str(iteration))\n",
    "\n",
    "    # make prediction on the test fold of Training data\n",
    "    predict_values = model.predict(x_test, batch_size=batch_size)\n",
    "    test_data = f_args.dataset.iloc[f_args.test_index, :].copy()\n",
    "    test_data['pred_perc'] = predict_values\n",
    "\n",
    "    csv_name = saving_folder + model_name + '_' + 'Train' + '_' + str(iteration) + '_' + str(int(time.time())) + '.csv'\n",
    "    test_data.to_csv(csv_name, index=False, sep='\\t')\n",
    "\n",
    "    # make prediction on syn\n",
    "    x_syn_BO, y_syn_BO = preprocessing.elaborate_input(syn_data, input_columns, label_column)\n",
    "\n",
    "    predict_values = model.predict(x_syn_BO, batch_size=batch_size)\n",
    "    syn_data['pred_perc'] = predict_values\n",
    "\n",
    "    csv_name = saving_folder + 'multimodal_text_tags_BO' + '_' + 'SYN' + '_' + str(iteration) + '_' + str(int(time.time())) + '.csv'\n",
    "    syn_data.to_csv(csv_name, index=False, sep='\\t')\n",
    "\n",
    "    # BIAS:\n",
    "    identity_terms_present = model_performances.identity_element_presence(syn_data, Identity_Terms, label_column)\n",
    "    tags_present = model_performances.identity_element_presence_OR(syn_data, Identity_Tags, label_column)\n",
    "\n",
    "\n",
    "    bias_metrics_text = model_performances.compute_bias_metrics_for_model(syn_data, identity_terms_present, 'pred_perc',\n",
    "                                                                            label_column)\n",
    "\n",
    "    bias_metrics_image = model_performances.compute_bias_metrics_for_model(syn_data, tags_present, 'pred_perc',\n",
    "                                                                            label_column)\n",
    "\n",
    "    overall_auc_metrics = model_performances.calculate_overall_auc(test_data, 'pred_perc')\n",
    "\n",
    "    final_multimodal_scores = model_performances.get_final_multimodal_metric_nan(bias_metrics_text, bias_metrics_image,\n",
    "                                                                                overall_auc_metrics, 'pred_perc')\n",
    "\n",
    "    model_performances.write_performance_on_file(saving_folder + 'results_{it}.txt'.format(it=iteration), iteration,\n",
    "                                                    bias_metrics_text, bias_metrics_image, overall_auc_metrics,\n",
    "                                                    final_multimodal_scores)\n",
    "\n",
    "    # Because we are interested in the HIGHEST classification\n",
    "    # metric, we need to negate this number so it can be minimized\n",
    "    return -final_multimodal_scores  # Metric to maximize"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# new objective passed to the optimizer\n",
    "# sub-version of the black_box_function (fitness) with blocked value that need to stay stable during optimization (e.g. iteration)\n",
    "@use_named_args(dimensions=search_space)\n",
    "def objective(lr, epsilon, neurons, activation_function, dropout):\n",
    "    variable_args = VariableParams(lr, epsilon, neurons, activation_function, dropout)\n",
    "    return fitness(variable_args, fixed_args) #fixed_args as global variable"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Bayesian Optimization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "iteration = 0\n",
    "kf = KFold(n_splits=10, shuffle=False)\n",
    "syn_folds = kf.split(syn_df)\n",
    "syn_pred = []\n",
    "syn_10_df=pd.DataFrame()\n",
    "\n",
    "for train_index, val_index in kf.split(meme_df):\n",
    "    preprocessing.set_seed(iteration)\n",
    "    print('___ITERATION {it}___'.format(it=iteration))\n",
    "    MODELNAME = MODELNAMES[iteration]\n",
    "    # clear session\n",
    "    tf.keras.backend.clear_session()\n",
    "\n",
    "    BO_test_syn, test_syn = next(syn_folds)\n",
    "    BO_syn_data = syn_df.iloc[BO_test_syn, :]\n",
    "    syn_data = syn_df.iloc[test_syn, :]\n",
    "    x_syn, y_syn = preprocessing.elaborate_input(syn_df.iloc[test_syn, :], input_columns, label_column)\n",
    "\n",
    "    fixed_args = FixedParams(iteration=iteration, dataset=meme_df, syn_data=BO_syn_data, train_index=train_index, test_index=val_index)\n",
    "    search_result = gp_minimize(func=objective,\n",
    "                                dimensions=search_space,\n",
    "                                acq_func='EI', # Expected Improvement.\n",
    "                                n_calls=40,\n",
    "                                x0=list(default_parameters.values()),\n",
    "                                n_random_starts=5)\n",
    "                        \n",
    "    # TRAIN OF A NEW MODEL ON TRAINING DATA (9fold train-1fold val) TO COMPUTE FINAL METRICS\n",
    "    x_train, y_train = preprocessing.elaborate_input(meme_df.iloc[train_index, :], input_columns, label_column)\n",
    "    x_val, y_val = preprocessing.elaborate_input(meme_df.iloc[val_index, :], input_columns, label_column)\n",
    "\n",
    "    optimal_config = dict(zip(list(default_parameters.keys()), search_result.x)) #uses the same keys in default_parameters\n",
    "\n",
    "    model, _ = model_performances.get_trained_model(x_train, \n",
    "                                y_train, \n",
    "                                x_val, \n",
    "                                y_val,\n",
    "                                input_shape=input_shape, \n",
    "                                activation_function=optimal_config['activation_function'], \n",
    "                                neurons=optimal_config['neurons'], \n",
    "                                dropout=optimal_config['dropout'], \n",
    "                                lr=optimal_config['lr'], \n",
    "                                epsilon=optimal_config['epsilon'], \n",
    "                                epochs=epochs,\n",
    "                                batch_size=batch_size)\n",
    "\n",
    "    # make prediction on test\n",
    "    predict_values_test = model.predict(x_test, batch_size=batch_size)\n",
    "\n",
    "    res_test[MODELNAME] = pd.DataFrame(predict_values_test)[0]\n",
    "    res_test[['file_name', 'misogynous', MODELNAME]].to_csv(\n",
    "        path_predictions + 'BO_Final_Test_' + str(iteration) + '_' + str(int(time.time())) + '.csv', index=False, sep='\\t')\n",
    "\n",
    "    # make prediction on syn\n",
    "    predict_values_test = model.predict(x_syn, batch_size=batch_size)\n",
    "\n",
    "    \"\"\" dataframe: for each model, three columns are added:\n",
    "    - 'label_'+MODELNAME: real values\n",
    "    - MODELNAME: predictions\n",
    "    -'file_name_'+MODELNAME: ids of the memes\n",
    "\n",
    "    \"\"\"\n",
    "    syn_10_df['label_'+MODELNAME]=list(syn_df[label_column][test_syn].values)\n",
    "    syn_10_df[MODELNAME]= predict_values_test\n",
    "    syn_10_df['file_name_'+MODELNAME]=list(syn_df['file_name'][test_syn].values)\n",
    "\n",
    "    syn_data[MODELNAME] = predict_values_test #pd.DataFrame(predict_values_test)[0]\n",
    "    syn_data[['file_name', 'misogynous', MODELNAME]].to_csv(\n",
    "        path_predictions + 'BO_Final_Syn_' + str(iteration) + '_' + str(int(time.time())) + '.csv', index=False, sep='\\t')\n",
    "\n",
    "    # BIAS:\n",
    "    bias_metrics_text = model_performances.compute_bias_metrics_for_model(syn_data, Identity_Terms, MODELNAME,\n",
    "                                                                        label_column)\n",
    "    bias_metrics_image = model_performances.compute_bias_metrics_for_model(syn_data, Identity_Tags, MODELNAME,\n",
    "                                                                        label_column)\n",
    "    overall_auc_metrics = model_performances.calculate_overall_auc(res_test, MODELNAME)\n",
    "\n",
    "    final_multimodal_scores = model_performances.get_final_multimodal_metric_nan(bias_metrics_text, bias_metrics_image,\n",
    "                                                                            overall_auc_metrics, MODELNAME)\n",
    "\n",
    "    model_performances.write_performance_on_file(results_txt_path, iteration, bias_metrics_text, bias_metrics_image,\n",
    "                                                overall_auc_metrics, final_multimodal_scores)\n",
    "    iteration = iteration + 1\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Performance Syn 10Fold split\n",
    "model_performances.multimodal_bias_metrics_on_file_10Fold(results_txt_path, res_test, syn_10_df, Identity_Terms, Identity_Tags,\n",
    "                                                   MODELNAMES, label_column)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: title={'center': 'Convergence plot'}, xlabel='Number of calls $n$', ylabel='$\\\\min f(x)$ after $n$ calls'>"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plots.plot_convergence(search_result)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x1000 with 25 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig=plots.plot_objective(result=search_result)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "syn_10_df.to_csv(path_predictions + 'MTT_syn_10_df.csv', index=False, sep='\\t')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# dataframe with complete Syn\n",
    "syn_df[model_name] = syn_pred\n",
    "syn_df[['file_name', 'misogynous', model_name]].to_csv(\n",
    "        path_predictions + 'BO_Final_SYN_' + str(iteration) + '_' + str(int(time.time())) + '.csv', index=False, sep='\\t')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3.8.13 ('ProjEnv')",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.13 (default, Mar 28 2022, 06:59:08) [MSC v.1916 64 bit (AMD64)]"
  },
  "orig_nbformat": 4,
  "vscode": {
   "interpreter": {
    "hash": "036d5e79b2f89acf0b2080b6b489e4897d66fffe68690462bc44e5a56924deac"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}