reproduce_experiments_Confident_Sinkhorn_Allocation.ipynb

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      "name": "reproduce_experiments_Confident_Sinkhorn_Allocation.ipynb",
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        "<a href=\"https://colab.research.google.com/drive/1Miq659eCRpbH0qzLjGWxbSbhZQmwx_lM#scrollTo=tubZzNQc3EFg\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
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            "      Successfully uninstalled scipy-1.4.1\n",
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            "    Found existing installation: joblib 1.1.0\n",
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            "      Successfully uninstalled joblib-1.1.0\n",
            "  Attempting uninstall: cycler\n",
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            "      Successfully uninstalled cycler-0.11.0\n",
            "  Attempting uninstall: xgboost\n",
            "    Found existing installation: xgboost 0.90\n",
            "    Uninstalling xgboost-0.90:\n",
            "      Successfully uninstalled xgboost-0.90\n",
            "  Attempting uninstall: tqdm\n",
            "    Found existing installation: tqdm 4.64.0\n",
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            "      Successfully uninstalled tqdm-4.64.0\n",
            "  Attempting uninstall: scikit-learn\n",
            "    Found existing installation: scikit-learn 1.0.2\n",
            "    Uninstalling scikit-learn-1.0.2:\n",
            "      Successfully uninstalled scikit-learn-1.0.2\n",
            "  Attempting uninstall: pandas\n",
            "    Found existing installation: pandas 1.3.5\n",
            "    Uninstalling pandas-1.3.5:\n",
            "      Successfully uninstalled pandas-1.3.5\n",
            "  Attempting uninstall: matplotlib\n",
            "    Found existing installation: matplotlib 3.2.2\n",
            "    Uninstalling matplotlib-3.2.2:\n",
            "      Successfully uninstalled matplotlib-3.2.2\n",
            "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
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            "google-colab 1.0.0 requires six~=1.15.0, but you have six 1.16.0 which is incompatible.\n",
            "datascience 0.10.6 requires folium==0.2.1, but you have folium 0.8.3 which is incompatible.\n",
            "albumentations 0.1.12 requires imgaug<0.2.7,>=0.2.5, but you have imgaug 0.2.9 which is incompatible.\u001b[0m\n",
            "Successfully installed Pillow-9.2.0 colorama-0.4.5 csa-1.0 cycler-0.11.0 fonttools-4.34.4 joblib-1.1.0 kiwisolver-1.4.3 matplotlib-3.5.2 numpy-1.21.6 packaging-21.3 pandas-1.3.5 pyparsing-3.0.9 python-dateutil-2.8.2 pytz-2022.1 scikit-learn-1.0.2 scipy-1.7.3 six-1.16.0 threadpoolctl-3.1.0 tqdm-4.64.0 typing-extensions-4.3.0 xgboost-1.6.1\n"
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                  "mpl_toolkits",
                  "numpy",
                  "six"
                ]
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      ],
      "source": [
        "!pip install --upgrade --force-reinstall git+https://ntienvu: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@github.com/ntienvu/confident_sinkhorn_allocation"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!git clone https://ntienvu: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@github.com/ntienvu/confident_sinkhorn_allocation\n",
        "\n",
        "!python -m pip uninstall matplotlib\n",
        "!pip install matplotlib==3.1.3"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wWqPgmS_M0s9",
        "outputId": "f1e2e9ca-16f4-4428-cbfe-d3bb71f24232"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Cloning into 'confident_sinkhorn_allocation'...\n",
            "remote: Enumerating objects: 1925, done.\u001b[K\n",
            "remote: Counting objects: 100% (359/359), done.\u001b[K\n",
            "remote: Compressing objects: 100% (181/181), done.\u001b[K\n",
            "remote: Total 1925 (delta 218), reused 298 (delta 173), pack-reused 1566\u001b[K\n",
            "Receiving objects: 100% (1925/1925), 69.16 MiB | 13.84 MiB/s, done.\n",
            "Resolving deltas: 100% (577/577), done.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import os\n",
        "import argparse\n",
        "import logging\n",
        "import pickle\n",
        "from tqdm import tqdm\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "from algorithm.pseudo_labeling import Pseudo_Labeling\n",
        "from algorithm.flexmatch import FlexMatch\n",
        "from algorithm.ups import UPS\n",
        "from algorithm.csa import CSA\n",
        "from xgboost import XGBClassifier\n",
        "from sklearn.metrics import accuracy_score\n",
        "\n",
        "from utilities.utils import get_train_test_unlabeled,append_acc_early_termination\n",
        "from utilities.utils import get_train_test_unlabeled_for_multilabel\n",
        "\n",
        "import warnings\n",
        "warnings.filterwarnings('ignore')"
      ],
      "metadata": {
        "id": "HkUXA22N3Oj1"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# We run the experiments using 10 repeated trials (in the paper we run over 30 trials)\n",
        "# We compare the results with 5 baselines: Supervised learning, Pseudo-labeling, FlexMatch, UPS, SLA and CSA\n",
        "# There are multiple datasets, we pick three of them for this report."
      ],
      "metadata": {
        "id": "fE9mhjas4cQP"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Specify hyperparameters"
      ],
      "metadata": {
        "id": "N0ZtejZ74chA"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "numTrials=10\n",
        "numIters=5\n",
        "confidence_choice='ttest'\n",
        "num_XGB_models=10\n",
        "upper_threshold=0.8\n",
        "lower_threshold=0.2 # for UPS\n",
        "dataset_list=['analcatdata_authorship','synthetic_control_6c','madelon_no','digits']\n",
        "algorithm_list=['Supervised_Learning','Pseudo_Labeling','FlexMatch','UPS','SLA','CSA']\n",
        "\n",
        "path_to_file='confident_sinkhorn_allocation/all_data.pickle'\n"
      ],
      "metadata": {
        "id": "iVk2Ugdx-0Ol"
      },
      "execution_count": 35,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def supervised_learning(x_train, y_train, x_test, y_test):\n",
        "  param = {}\n",
        "  param['booster'] = 'gbtree'\n",
        "  param['objective'] = 'binary:logistic'\n",
        "  param['verbosity'] = 0\n",
        "  param['silent'] = 1\n",
        "  param['seed'] = 0\n",
        "\n",
        "  # create XGBoost instance with default hyper-parameters\n",
        "  xgb=XGBClassifier(**param,use_label_encoder=False)\n",
        "\n",
        "  xgb.fit(x_train, y_train)\n",
        "\n",
        "  # evaluate the performance on the test set\n",
        "  y_test_pred = xgb.predict(x_test)      \n",
        "  supervised_learning_accuracy= np.round( accuracy_score(y_test_pred, y_test)*100, 2)# round to 2 digits xx.yy %\n",
        "  return supervised_learning_accuracy"
      ],
      "metadata": {
        "id": "XCIZlW5rCDXm"
      },
      "execution_count": 36,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# a wrapper function to plot the comparison\n",
        "\n",
        "def get_mean_std(Accuracy_Matrix):\n",
        "  \"\"\"\n",
        "  Args:\n",
        "    Accuracy_Matrix: [nRepeats x nIterations]\n",
        "  Out:\n",
        "    mean [1 x nIterations]\n",
        "    std  [1 x nIterations]\n",
        "  \"\"\"\n",
        "  return np.mean(Accuracy_Matrix,axis=0),np.std(Accuracy_Matrix,axis=0)\n",
        "\n",
        "def plot_result_comparison(numIters,Accuracy_Supervised_Learning,Accuracy_Pseudo_Labeling,\\\n",
        "                        Accuracy_FlexMatch,Accuracy_UPS,Accuracy_SLA, Accuracy_CSA,  dataset_name):\n",
        "\n",
        "  plt.figure(figsize=(8,5))\n",
        "\n",
        "  # Supervised Learning\n",
        "  mean,std=get_mean_std(Accuracy_Supervised_Learning)\n",
        "  plt.errorbar(np.arange(numIters+1),mean,yerr=0.1*std,fmt='m:',linewidth=4,label=\"Supervised Learning\") \n",
        "\n",
        "  # Pseudo Labeling\n",
        "  mean,std=get_mean_std(Accuracy_Pseudo_Labeling)\n",
        "  plt.errorbar(np.arange(numIters+1),mean,yerr=0.1*std,fmt='k-.',linewidth=4,label='Pseudo-labeling')\n",
        "\n",
        "  # FlexMatch\n",
        "  mean,std=get_mean_std(Accuracy_FlexMatch)\n",
        "  plt.errorbar(np.arange(numIters+1),mean,yerr=0.1*std,fmt='g--*',linewidth=4,label='FlexMatch')\n",
        "\n",
        "  # UPS\n",
        "  mean,std=get_mean_std(Accuracy_UPS)\n",
        "  plt.errorbar(np.arange(numIters+1),mean,yerr=0.1*std,fmt='c:v',linewidth=4,label='UPS')\n",
        "\n",
        "  # SLA\n",
        "  mean,std=get_mean_std(Accuracy_SLA)\n",
        "  plt.errorbar(np.arange(numIters+1),mean,yerr=0.1*std,fmt='b:s',linewidth=4,label='SLA')\n",
        "\n",
        "  # CSA\n",
        "  mean,std=get_mean_std(Accuracy_CSA)\n",
        "  plt.errorbar(np.arange(numIters+1),mean,yerr=0.1*std,fmt='r-s',linewidth=4,label='CSA')\n",
        "\n",
        "  plt.xlabel(\"Pseudo-labeling Iteration\",fontsize=14)\n",
        "  plt.ylabel(\"Test Accuracy\",fontsize=14)\n",
        "\n",
        "  plt.legend(fontsize=12,ncol=3)\n",
        "\n",
        "  plt.title(\"Dataset = \" + dataset_name,fontsize=14 )\n",
        "\n",
        "\n",
        "def run_algorithm(dataset_name,path_to_file, algorithm_name,**kwargs):\n",
        "  numIters=kwargs['num_iters']\n",
        "\n",
        "  Accuracy_list=[]\n",
        "  for tt in tqdm(range(numTrials)):\n",
        "    np.random.seed(tt)\n",
        "    \n",
        "    # load the data for multiclassification\n",
        "    x_train,y_train, x_test, y_test, x_unlabeled=get_train_test_unlabeled(dataset_name,path_to_file,random_state=tt)\n",
        "\n",
        "    #============================= supervised learning\n",
        "    if algorithm_name==\"Supervised_Learning\":\n",
        "      accuracy=supervised_learning(x_train, y_train, x_test, y_test)\n",
        "      accuracy=append_acc_early_termination([accuracy],numIters)\n",
        "      Accuracy_list.append( accuracy)\n",
        "      continue\n",
        "    \n",
        "    #======================== semi-supervised learning\n",
        "    elif algorithm_name==\"Pseudo_Labeling\":\n",
        "      pseudo_labeller = Pseudo_Labeling(x_unlabeled,x_test,y_test, **kwargs)\n",
        "    elif algorithm_name==\"FlexMatch\":\n",
        "      pseudo_labeller = FlexMatch(x_unlabeled,x_test,y_test,**kwargs)\n",
        "    elif algorithm_name==\"UPS\":\n",
        "      pseudo_labeller = UPS(x_unlabeled,x_test,y_test, **kwargs)\n",
        "    elif algorithm_name==\"SLA\":\n",
        "      pseudo_labeller = CSA(x_unlabeled,x_test,y_test, **kwargs)\n",
        "    elif algorithm_name==\"CSA\":\n",
        "      pseudo_labeller = CSA(x_unlabeled,x_test,y_test,**kwargs)\n",
        "\n",
        "    pseudo_labeller.fit(x_train, y_train)\n",
        "\n",
        "    #  append_acc_early_termination: if early termination happens, this function will copy the result to be in the same dimension\n",
        "\n",
        "    Accuracy_list.append( append_acc_early_termination(pseudo_labeller.test_acc,numIters) )\n",
        "\n",
        "  return Accuracy_list\n",
        "    "
      ],
      "metadata": {
        "id": "efMR8vCrtk1B"
      },
      "execution_count": 38,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "dataset_name='analcatdata_authorship'\n",
        "\n",
        "# Supervised-learning =========================================================\n",
        "Accuracy_Supervised_Learning=run_algorithm(dataset_name,path_to_file, algorithm_name=\"Supervised_Learning\",\\\n",
        "                                       num_iters=numIters, verbose = 0)\n",
        "\n",
        "# Pseudo-labeling==============================================================\n",
        "Accuracy_Pseudo_Labeling=run_algorithm(dataset_name,path_to_file, algorithm_name=\"Pseudo_Labeling\",\\\n",
        "                                       num_iters=numIters, upper_threshold=upper_threshold,verbose = 0)\n",
        "\n",
        "# FlexMatch====================================================================\n",
        "Accuracy_FlexMatch=run_algorithm(dataset_name,path_to_file, algorithm_name=\"FlexMatch\",\\\n",
        "                                num_iters=numIters,upper_threshold=upper_threshold,verbose = 0)\n",
        "\n",
        "# UPS====================================================================\n",
        "Accuracy_UPS=run_algorithm(dataset_name,path_to_file, algorithm_name=\"UPS\",\\\n",
        "                            num_iters=numIters,upper_threshold=upper_threshold,\\\n",
        "                            lower_threshold=lower_threshold,num_XGB_models=num_XGB_models,verbose = 0)\n",
        "\n",
        "# SLA====================================================================\n",
        "Accuracy_SLA=run_algorithm(dataset_name,path_to_file, algorithm_name=\"SLA\",\\\n",
        "                        num_iters=numIters,confidence_choice=None,# when setting confidence_choice=None => this is equivalent to SLA \n",
        "                        num_XGB_models=num_XGB_models,verbose = 0)\n",
        "\n",
        "# CSA====================================================================\n",
        "Accuracy_CSA=run_algorithm(dataset_name,path_to_file, algorithm_name=\"CSA\",\\\n",
        "                          num_iters=numIters,confidence_choice='ttest',num_XGB_models=num_XGB_models,verbose = 0)\n",
        "     "
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PnWZENzL4cop",
        "outputId": "24c32833-497f-4372-c8f1-9c81fa1ad9c7"
      },
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 10/10 [00:08<00:00,  1.22it/s]\n",
            "  0%|          | 0/10 [00:00<?, ?it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "===== Pseudo_Labeling\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\r 10%|█         | 1/10 [00:09<01:22,  9.17s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "===== Pseudo_Labeling\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\r 20%|██        | 2/10 [00:21<01:27, 10.90s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "===== Pseudo_Labeling\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\r 30%|███       | 3/10 [00:31<01:15, 10.78s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "===== Pseudo_Labeling\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\r 40%|████      | 4/10 [00:44<01:09, 11.58s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "===== Pseudo_Labeling\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\r 50%|█████     | 5/10 [00:51<00:49,  9.83s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "===== Pseudo_Labeling\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\r 60%|██████    | 6/10 [00:56<00:32,  8.15s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "===== Pseudo_Labeling\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\r 70%|███████   | 7/10 [01:01<00:21,  7.17s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "===== Pseudo_Labeling\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\r 80%|████████  | 8/10 [01:06<00:13,  6.52s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "===== Pseudo_Labeling\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\r 90%|█████████ | 9/10 [01:11<00:06,  6.08s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "===== Pseudo_Labeling\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 10/10 [01:16<00:00,  7.68s/it]\n",
            "  0%|          | 0/10 [00:00<?, ?it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "===== FlexMatch\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\r 10%|█         | 1/10 [00:05<00:46,  5.14s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "===== FlexMatch\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\r 20%|██        | 2/10 [00:10<00:40,  5.10s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "===== FlexMatch\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\r 30%|███       | 3/10 [00:15<00:36,  5.16s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "===== FlexMatch\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\r 40%|████      | 4/10 [00:20<00:30,  5.14s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "===== FlexMatch\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\r 50%|█████     | 5/10 [00:25<00:26,  5.24s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "===== FlexMatch\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\r 60%|██████    | 6/10 [00:31<00:20,  5.23s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "===== FlexMatch\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\r 70%|███████   | 7/10 [00:36<00:15,  5.21s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "===== FlexMatch\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\r 80%|████████  | 8/10 [00:41<00:10,  5.20s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "===== FlexMatch\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\r 90%|█████████ | 9/10 [00:49<00:05,  5.96s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "===== FlexMatch\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 10/10 [00:54<00:00,  5.43s/it]\n",
            "  0%|          | 0/10 [00:00<?, ?it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "===== UPS\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\r 10%|█         | 1/10 [03:05<27:51, 185.76s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "===== UPS\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\r 20%|██        | 2/10 [06:36<26:44, 200.53s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "===== UPS\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\r 30%|███       | 3/10 [09:15<21:10, 181.56s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "===== UPS\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\r 40%|████      | 4/10 [13:09<20:13, 202.25s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "===== UPS\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\r 50%|█████     | 5/10 [14:29<13:10, 158.13s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "===== UPS\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\r 60%|██████    | 6/10 [19:01<13:06, 196.74s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "===== UPS\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\r 70%|███████   | 7/10 [22:31<10:03, 201.24s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "===== UPS\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\r 80%|████████  | 8/10 [25:29<06:27, 193.82s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "===== UPS\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\r 90%|█████████ | 9/10 [28:00<03:00, 180.27s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "===== UPS\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 10/10 [31:29<00:00, 188.91s/it]\n",
            "  0%|          | 0/10 [00:00<?, ?it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "===== SLA\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\r 10%|█         | 1/10 [02:53<26:02, 173.62s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "===== SLA\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\r 20%|██        | 2/10 [06:04<24:30, 183.76s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "===== SLA\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\r 30%|███       | 3/10 [08:37<19:49, 169.95s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "===== SLA\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\r 40%|████      | 4/10 [12:20<19:05, 190.86s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "===== SLA\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\r 50%|█████     | 5/10 [13:40<12:33, 150.69s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "===== SLA\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\r 60%|██████    | 6/10 [18:03<12:35, 188.80s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "===== SLA\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\r 70%|███████   | 7/10 [21:19<09:34, 191.43s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "===== SLA\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\r 80%|████████  | 8/10 [24:01<06:03, 181.78s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "===== SLA\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\r 90%|█████████ | 9/10 [26:23<02:49, 169.59s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "===== SLA\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 10/10 [29:40<00:00, 178.01s/it]\n",
            "  0%|          | 0/10 [00:00<?, ?it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "===== CSA_ttest\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\r 10%|█         | 1/10 [03:40<33:02, 220.24s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "===== CSA_ttest\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\r 20%|██        | 2/10 [07:23<29:35, 221.96s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "===== CSA_ttest\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\r 30%|███       | 3/10 [10:08<22:51, 195.94s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "===== CSA_ttest\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\r 40%|████      | 4/10 [14:08<21:20, 213.48s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "===== CSA_ttest\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\r 50%|█████     | 5/10 [15:34<13:56, 167.35s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "===== CSA_ttest\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\r 60%|██████    | 6/10 [20:22<13:53, 208.48s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "===== CSA_ttest\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\r 70%|███████   | 7/10 [23:57<10:31, 210.60s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "===== CSA_ttest\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\r 80%|████████  | 8/10 [26:50<06:37, 198.50s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "===== CSA_ttest\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\r 90%|█████████ | 9/10 [29:13<03:01, 181.15s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "===== CSA_ttest\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 10/10 [33:01<00:00, 198.18s/it]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#!python -m pip uninstall matplotlib\n",
        "#!pip install matplotlib==3.1.3\n",
        "\n",
        "# Plot the performance\n",
        "\n",
        "plot_result_comparison(numIters,Accuracy_Supervised_Learning,Accuracy_Pseudo_Labeling,\\\n",
        "                        Accuracy_FlexMatch,Accuracy_UPS,Accuracy_SLA, Accuracy_CSA,  dataset_name)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 355
        },
        "id": "JtduXoXpSHzm",
        "outputId": "e10811f1-0c63-4e18-ed9a-90e1c1eed089"
      },
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 576x360 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Next let use another dataset: 'synthetic_control_6c'\n",
        "\n",
        "---\n",
        "\n"
      ],
      "metadata": {
        "id": "feveEvmzl5ww"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "dataset_name='synthetic_control_6c'\n",
        "\n",
        "\n",
        "# Supervised-learning =========================================================\n",
        "Accuracy_Supervised_Learning=run_algorithm(dataset_name,path_to_file, algorithm_name=\"Supervised_Learning\",\\\n",
        "                                       num_iters=numIters, verbose = 0)\n",
        "\n",
        "# Pseudo-labeling==============================================================\n",
        "Accuracy_Pseudo_Labeling=run_algorithm(dataset_name,path_to_file, algorithm_name=\"Pseudo_Labeling\",\\\n",
        "                                       num_iters=numIters, upper_threshold=upper_threshold,verbose = 0)\n",
        "\n",
        "# FlexMatch====================================================================\n",
        "Accuracy_FlexMatch=run_algorithm(dataset_name,path_to_file, algorithm_name=\"FlexMatch\",\\\n",
        "                                num_iters=numIters,upper_threshold=upper_threshold,verbose = 0)\n",
        "\n",
        "# UPS====================================================================\n",
        "Accuracy_UPS=run_algorithm(dataset_name,path_to_file, algorithm_name=\"UPS\",\\\n",
        "                            num_iters=numIters,upper_threshold=upper_threshold,\\\n",
        "                            lower_threshold=lower_threshold,num_XGB_models=num_XGB_models,verbose = 0)\n",
        "\n",
        "# SLA====================================================================\n",
        "Accuracy_SLA=run_algorithm(dataset_name,path_to_file, algorithm_name=\"SLA\",\\\n",
        "                        num_iters=numIters,confidence_choice=None,# when setting confidence_choice=None => this is equivalent to SLA \n",
        "                        num_XGB_models=num_XGB_models,verbose = 0)\n",
        "\n",
        "# CSA====================================================================\n",
        "Accuracy_CSA=run_algorithm(dataset_name,path_to_file, algorithm_name=\"CSA\",\\\n",
        "                          num_iters=numIters,confidence_choice='ttest',num_XGB_models=num_XGB_models,verbose = 0)\n",
        "\n",
        "\n",
        "plot_result_comparison(numIters,Accuracy_Supervised_Learning,Accuracy_Pseudo_Labeling,\\\n",
        "                        Accuracy_FlexMatch,Accuracy_UPS,Accuracy_SLA, Accuracy_CSA,  dataset_name)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "kWuGRIBJ9SpK",
        "outputId": "8d54db4a-7a56-49bc-9f5a-10e97e98c08b"
      },
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 10/10 [00:07<00:00,  1.31it/s]\n",
            "  0%|          | 0/10 [00:00<?, ?it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "===== Pseudo_Labeling\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\r 10%|█         | 1/10 [00:04<00:44,  4.92s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "===== Pseudo_Labeling\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\r 20%|██        | 2/10 [00:09<00:38,  4.85s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "===== Pseudo_Labeling\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\r 30%|███       | 3/10 [00:14<00:33,  4.83s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "===== Pseudo_Labeling\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\r 40%|████      | 4/10 [00:19<00:29,  4.84s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "===== Pseudo_Labeling\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\r 50%|█████     | 5/10 [00:24<00:24,  4.87s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "===== Pseudo_Labeling\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\r 60%|██████    | 6/10 [00:31<00:22,  5.53s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "===== Pseudo_Labeling\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\r 70%|███████   | 7/10 [00:36<00:16,  5.34s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "===== Pseudo_Labeling\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\r 80%|████████  | 8/10 [00:41<00:10,  5.22s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "===== Pseudo_Labeling\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\r 90%|█████████ | 9/10 [00:45<00:05,  5.08s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "===== Pseudo_Labeling\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 10/10 [00:50<00:00,  5.07s/it]\n",
            "  0%|          | 0/10 [00:00<?, ?it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "===== FlexMatch\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\r 10%|█         | 1/10 [00:05<00:48,  5.33s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "===== FlexMatch\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\r 20%|██        | 2/10 [00:10<00:41,  5.24s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "===== FlexMatch\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\r 30%|███       | 3/10 [00:15<00:36,  5.19s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "===== FlexMatch\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\r 40%|████      | 4/10 [00:20<00:30,  5.16s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "===== FlexMatch\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\r 50%|█████     | 5/10 [00:26<00:26,  5.24s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "===== FlexMatch\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\r 60%|██████    | 6/10 [00:31<00:20,  5.19s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "===== FlexMatch\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\r 70%|███████   | 7/10 [00:36<00:15,  5.18s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "===== FlexMatch\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\r 80%|████████  | 8/10 [00:41<00:10,  5.18s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "===== FlexMatch\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\r 90%|█████████ | 9/10 [00:46<00:05,  5.24s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "===== FlexMatch\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 10/10 [00:52<00:00,  5.22s/it]\n",
            "  0%|          | 0/10 [00:00<?, ?it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "===== UPS\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\r 10%|█         | 1/10 [02:44<24:41, 164.58s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "===== UPS\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\r 20%|██        | 2/10 [06:10<25:10, 188.82s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "===== UPS\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\r 30%|███       | 3/10 [10:17<25:07, 215.41s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "===== UPS\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\r 40%|████      | 4/10 [13:06<19:41, 196.94s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "===== UPS\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\r 50%|█████     | 5/10 [16:56<17:25, 209.05s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "===== UPS\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\r 60%|██████    | 6/10 [18:53<11:51, 177.80s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "===== UPS\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\r 70%|███████   | 7/10 [22:35<09:36, 192.30s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "===== UPS\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\r 80%|████████  | 8/10 [25:05<05:57, 178.72s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "===== UPS\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\r 90%|█████████ | 9/10 [28:15<03:02, 182.23s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "===== UPS\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 10/10 [31:36<00:00, 189.63s/it]\n",
            "  0%|          | 0/10 [00:00<?, ?it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "===== SLA\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\r 10%|█         | 1/10 [02:32<22:49, 152.19s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "===== SLA\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\r 20%|██        | 2/10 [05:37<22:53, 171.71s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "===== SLA\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\r 30%|███       | 3/10 [09:22<22:51, 195.95s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "===== SLA\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\r 40%|████      | 4/10 [11:59<18:04, 180.74s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "===== SLA\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\r 50%|█████     | 5/10 [15:27<15:52, 190.60s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "===== SLA\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\r 60%|██████    | 6/10 [17:15<10:49, 162.37s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "===== SLA\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\r 70%|███████   | 7/10 [20:39<08:47, 175.84s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "===== SLA\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\r 80%|████████  | 8/10 [22:47<05:21, 160.77s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "===== SLA\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\r 90%|█████████ | 9/10 [25:39<02:44, 164.30s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "===== SLA\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 10/10 [28:41<00:00, 172.18s/it]\n",
            "  0%|          | 0/10 [00:00<?, ?it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "===== CSA_ttest\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\r 10%|█         | 1/10 [02:50<25:33, 170.41s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "===== CSA_ttest\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\r 20%|██        | 2/10 [06:18<25:42, 192.85s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "===== CSA_ttest\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\r 30%|███       | 3/10 [10:35<25:53, 221.95s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "===== CSA_ttest\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\r 40%|████      | 4/10 [13:28<20:14, 202.42s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "===== CSA_ttest\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\r 50%|█████     | 5/10 [17:20<17:46, 213.38s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "===== CSA_ttest\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\r 60%|██████    | 6/10 [19:25<12:12, 183.09s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "===== CSA_ttest\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\r 70%|███████   | 7/10 [23:07<09:47, 195.99s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "===== CSA_ttest\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\r 80%|████████  | 8/10 [25:40<06:04, 182.24s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "===== CSA_ttest\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\r 90%|█████████ | 9/10 [29:02<03:08, 188.50s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "===== CSA_ttest\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 10/10 [32:29<00:00, 194.99s/it]\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 576x360 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dataset_name='madelon_no'\n",
        "\n",
        "\n",
        "# Supervised-learning =========================================================\n",
        "Accuracy_Supervised_Learning=run_algorithm(dataset_name,path_to_file, algorithm_name=\"Supervised_Learning\",\\\n",
        "                                       num_iters=numIters, verbose = 0)\n",
        "\n",
        "# Pseudo-labeling==============================================================\n",
        "Accuracy_Pseudo_Labeling=run_algorithm(dataset_name,path_to_file, algorithm_name=\"Pseudo_Labeling\",\\\n",
        "                                       num_iters=numIters, upper_threshold=upper_threshold,verbose = 0)\n",
        "\n",
        "# FlexMatch====================================================================\n",
        "Accuracy_FlexMatch=run_algorithm(dataset_name,path_to_file, algorithm_name=\"FlexMatch\",\\\n",
        "                                num_iters=numIters,upper_threshold=upper_threshold,verbose = 0)\n",
        "\n",
        "# UPS====================================================================\n",
        "Accuracy_UPS=run_algorithm(dataset_name,path_to_file, algorithm_name=\"UPS\",\\\n",
        "                            num_iters=numIters,upper_threshold=upper_threshold,\\\n",
        "                            lower_threshold=lower_threshold,num_XGB_models=num_XGB_models,verbose = 0)\n",
        "\n",
        "# SLA====================================================================\n",
        "Accuracy_SLA=run_algorithm(dataset_name,path_to_file, algorithm_name=\"SLA\",\\\n",
        "                        num_iters=numIters,confidence_choice=None,# when setting confidence_choice=None => this is equivalent to SLA \n",
        "                        num_XGB_models=num_XGB_models,verbose = 0)\n",
        "\n",
        "# CSA====================================================================\n",
        "Accuracy_CSA=run_algorithm(dataset_name,path_to_file, algorithm_name=\"CSA\",\\\n",
        "                          num_iters=numIters,confidence_choice='ttest',num_XGB_models=num_XGB_models,verbose = 0)\n",
        "\n",
        "# Make the plot =========================================================\n",
        "plot_result_comparison(numIters,Accuracy_Supervised_Learning,Accuracy_Pseudo_Labeling,\\\n",
        "                        Accuracy_FlexMatch,Accuracy_UPS,Accuracy_SLA, Accuracy_CSA,  dataset_name)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "t47luIMmcDiV",
        "outputId": "d36c0427-f4d7-40cb-d2dd-2b95b6074405"
      },
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 10/10 [00:09<00:00,  1.03it/s]\n",
            "  0%|          | 0/10 [00:00<?, ?it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "===== Pseudo_Labeling\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\r 10%|█         | 1/10 [00:17<02:34, 17.22s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "===== Pseudo_Labeling\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\r 20%|██        | 2/10 [00:33<02:13, 16.70s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "===== Pseudo_Labeling\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\r 30%|███       | 3/10 [00:46<01:45, 15.14s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "===== Pseudo_Labeling\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\r 40%|████      | 4/10 [01:02<01:32, 15.36s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "===== Pseudo_Labeling\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\r 50%|█████     | 5/10 [01:18<01:18, 15.71s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "===== Pseudo_Labeling\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\r 60%|██████    | 6/10 [01:34<01:02, 15.64s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "===== Pseudo_Labeling\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\r 70%|███████   | 7/10 [01:49<00:46, 15.48s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "===== Pseudo_Labeling\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\r 80%|████████  | 8/10 [02:03<00:29, 14.97s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "===== Pseudo_Labeling\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\r 90%|█████████ | 9/10 [02:18<00:14, 14.86s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "===== Pseudo_Labeling\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 10/10 [02:32<00:00, 15.23s/it]\n",
            "  0%|          | 0/10 [00:00<?, ?it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "===== FlexMatch\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\r 10%|█         | 1/10 [00:18<02:50, 19.00s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "===== FlexMatch\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\r 20%|██        | 2/10 [00:38<02:33, 19.15s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "===== FlexMatch\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\r 30%|███       | 3/10 [00:54<02:03, 17.66s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "===== FlexMatch\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\r 40%|████      | 4/10 [01:12<01:46, 17.76s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "===== FlexMatch\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\r 50%|█████     | 5/10 [01:33<01:34, 18.93s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "===== FlexMatch\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\r 60%|██████    | 6/10 [01:50<01:13, 18.48s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "===== FlexMatch\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\r 70%|███████   | 7/10 [02:08<00:54, 18.19s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "===== FlexMatch\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\r 80%|████████  | 8/10 [02:23<00:34, 17.34s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "===== FlexMatch\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\r 90%|█████████ | 9/10 [02:40<00:17, 17.09s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "===== FlexMatch\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 10/10 [02:56<00:00, 17.65s/it]\n",
            "  0%|          | 0/10 [00:00<?, ?it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "===== UPS\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\r 10%|█         | 1/10 [05:33<50:00, 333.44s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "===== UPS\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\r 20%|██        | 2/10 [09:40<37:42, 282.79s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "===== UPS\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\r 30%|███       | 3/10 [15:13<35:39, 305.58s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "===== UPS\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\r 40%|████      | 4/10 [19:24<28:23, 283.92s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "===== UPS\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\r 50%|█████     | 5/10 [24:15<23:52, 286.55s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "===== UPS\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\r 60%|██████    | 6/10 [29:30<19:45, 296.40s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "===== UPS\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\r 70%|███████   | 7/10 [35:16<15:37, 312.60s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "===== UPS\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\r 80%|████████  | 8/10 [40:09<10:12, 306.23s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "===== UPS\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\r 90%|█████████ | 9/10 [44:12<04:46, 286.60s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "===== UPS\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 10/10 [47:33<00:00, 285.36s/it]\n",
            "  0%|          | 0/10 [00:00<?, ?it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "===== SLA\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\r 10%|█         | 1/10 [03:06<27:57, 186.37s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "===== SLA\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\r 20%|██        | 2/10 [05:33<21:45, 163.21s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "===== SLA\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\r 30%|███       | 3/10 [08:46<20:38, 176.88s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "===== SLA\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\r 40%|████      | 4/10 [11:17<16:40, 166.69s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "===== SLA\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\r 50%|█████     | 5/10 [14:18<14:19, 171.95s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "===== SLA\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\r 60%|██████    | 6/10 [17:23<11:44, 176.24s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "===== SLA\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\r 70%|███████   | 7/10 [20:38<09:06, 182.27s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "===== SLA\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\r 80%|████████  | 8/10 [23:21<05:52, 176.10s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "===== SLA\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\r 90%|█████████ | 9/10 [25:53<02:48, 168.64s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "===== SLA\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 10/10 [27:45<00:00, 166.53s/it]\n",
            "  0%|          | 0/10 [00:00<?, ?it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "===== CSA_ttest\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\r 10%|█         | 1/10 [08:11<1:13:41, 491.23s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "===== CSA_ttest\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\r 20%|██        | 2/10 [13:05<50:01, 375.18s/it]  "
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "===== CSA_ttest\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\r 30%|███       | 3/10 [19:02<42:48, 366.94s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "===== CSA_ttest\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\r 40%|████      | 4/10 [23:47<33:28, 334.68s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "===== CSA_ttest\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\r 50%|█████     | 5/10 [29:20<27:50, 334.09s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "===== CSA_ttest\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\r 60%|██████    | 6/10 [35:01<22:25, 336.36s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "===== CSA_ttest\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\r 70%|███████   | 7/10 [42:41<18:50, 376.69s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "===== CSA_ttest\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\r 80%|████████  | 8/10 [47:59<11:56, 358.19s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "===== CSA_ttest\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\r 90%|█████████ | 9/10 [52:23<05:28, 328.76s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "===== CSA_ttest\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 10/10 [56:09<00:00, 336.95s/it]\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 576x360 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dataset_name='digits'\n",
        "\n",
        "\n",
        "# Supervised-learning =========================================================\n",
        "Accuracy_Supervised_Learning=run_algorithm(dataset_name,path_to_file, algorithm_name=\"Supervised_Learning\",\\\n",
        "                                       num_iters=numIters, verbose = 0)\n",
        "\n",
        "# Pseudo-labeling==============================================================\n",
        "Accuracy_Pseudo_Labeling=run_algorithm(dataset_name,path_to_file, algorithm_name=\"Pseudo_Labeling\",\\\n",
        "                                       num_iters=numIters, upper_threshold=upper_threshold,verbose = 0)\n",
        "\n",
        "# FlexMatch====================================================================\n",
        "Accuracy_FlexMatch=run_algorithm(dataset_name,path_to_file, algorithm_name=\"FlexMatch\",\\\n",
        "                                num_iters=numIters,upper_threshold=upper_threshold,verbose = 0)\n",
        "\n",
        "# UPS====================================================================\n",
        "Accuracy_UPS=run_algorithm(dataset_name,path_to_file, algorithm_name=\"UPS\",\\\n",
        "                            num_iters=numIters,upper_threshold=upper_threshold,\\\n",
        "                            lower_threshold=lower_threshold,num_XGB_models=num_XGB_models,verbose = 0)\n",
        "\n",
        "# SLA====================================================================\n",
        "Accuracy_SLA=run_algorithm(dataset_name,path_to_file, algorithm_name=\"SLA\",\\\n",
        "                        num_iters=numIters,confidence_choice=None,# when setting confidence_choice=None => this is equivalent to SLA \n",
        "                        num_XGB_models=num_XGB_models,verbose = 0)\n",
        "\n",
        "# CSA====================================================================\n",
        "Accuracy_CSA=run_algorithm(dataset_name,path_to_file, algorithm_name=\"CSA\",\\\n",
        "                          num_iters=numIters,confidence_choice='ttest',num_XGB_models=num_XGB_models,verbose = 0)\n",
        "\n",
        "# Make the plot =========================================================\n",
        "plot_result_comparison(numIters,Accuracy_Supervised_Learning,Accuracy_Pseudo_Labeling,\\\n",
        "                        Accuracy_FlexMatch,Accuracy_UPS,Accuracy_SLA, Accuracy_CSA,  dataset_name)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "pMEHQlQycEWy",
        "outputId": "89ea8c19-e9c7-46d6-e822-6999379a34b9"
      },
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 10/10 [00:08<00:00,  1.16it/s]\n",
            "  0%|          | 0/10 [00:00<?, ?it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "===== Pseudo_Labeling\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\r 10%|█         | 1/10 [00:07<01:06,  7.41s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "===== Pseudo_Labeling\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\r 20%|██        | 2/10 [00:15<01:02,  7.80s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "===== Pseudo_Labeling\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\r 30%|███       | 3/10 [00:23<00:54,  7.81s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "===== Pseudo_Labeling\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\r 40%|████      | 4/10 [00:31<00:46,  7.83s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "===== Pseudo_Labeling\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\r 50%|█████     | 5/10 [00:39<00:39,  7.88s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "===== Pseudo_Labeling\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\r 60%|██████    | 6/10 [01:03<00:53, 13.36s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "===== Pseudo_Labeling\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\r 70%|███████   | 7/10 [01:11<00:34, 11.64s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "===== Pseudo_Labeling\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\r 80%|████████  | 8/10 [01:21<00:22, 11.26s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "===== Pseudo_Labeling\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\r 90%|█████████ | 9/10 [01:29<00:10, 10.25s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "===== Pseudo_Labeling\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 10/10 [01:37<00:00,  9.72s/it]\n",
            "  0%|          | 0/10 [00:00<?, ?it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "===== FlexMatch\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\r 10%|█         | 1/10 [00:11<01:47, 11.94s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "===== FlexMatch\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\r 20%|██        | 2/10 [00:21<01:23, 10.42s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "===== FlexMatch\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\r 30%|███       | 3/10 [00:30<01:07,  9.69s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "===== FlexMatch\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\r 40%|████      | 4/10 [00:39<00:56,  9.43s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "===== FlexMatch\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\r 50%|█████     | 5/10 [00:48<00:46,  9.31s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "===== FlexMatch\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\r 60%|██████    | 6/10 [00:57<00:37,  9.28s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "===== FlexMatch\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\r 70%|███████   | 7/10 [01:06<00:27,  9.33s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "===== FlexMatch\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\r 80%|████████  | 8/10 [01:16<00:18,  9.32s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "===== FlexMatch\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\r 90%|█████████ | 9/10 [01:25<00:09,  9.31s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "===== FlexMatch\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 10/10 [01:34<00:00,  9.43s/it]\n",
            "  0%|          | 0/10 [00:00<?, ?it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "===== UPS\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\r 10%|█         | 1/10 [04:49<43:27, 289.72s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "===== UPS\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\r 20%|██        | 2/10 [09:44<38:59, 292.43s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "===== UPS\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\r 30%|███       | 3/10 [13:07<29:22, 251.82s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "===== UPS\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\r 40%|████      | 4/10 [16:40<23:38, 236.47s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "===== UPS\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\r 50%|█████     | 5/10 [20:42<19:52, 238.50s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "===== UPS\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\r 60%|██████    | 6/10 [21:21<11:21, 170.48s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "===== UPS\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\r 70%|███████   | 7/10 [26:03<10:21, 207.09s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "===== UPS\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\r 80%|████████  | 8/10 [31:16<08:01, 240.85s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "===== UPS\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\r 90%|█████████ | 9/10 [33:34<03:28, 208.76s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "===== UPS\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 10/10 [37:24<00:00, 224.49s/it]\n",
            "  0%|          | 0/10 [00:00<?, ?it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "===== SLA\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\r 10%|█         | 1/10 [03:33<32:03, 213.67s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "===== SLA\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\r 20%|██        | 2/10 [07:27<30:04, 225.59s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "===== SLA\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\r 30%|███       | 3/10 [10:07<22:47, 195.42s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "===== SLA\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\r 40%|████      | 4/10 [13:00<18:41, 186.85s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "===== SLA\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\r 50%|█████     | 5/10 [16:03<15:27, 185.43s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "===== SLA\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\r 60%|██████    | 6/10 [19:18<12:34, 188.54s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "===== SLA\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\r 70%|███████   | 7/10 [22:49<09:47, 195.95s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "===== SLA\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\r 80%|████████  | 8/10 [26:31<06:48, 204.35s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "===== SLA\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\r 90%|█████████ | 9/10 [28:18<02:53, 173.91s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "===== SLA\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 10/10 [31:16<00:00, 187.62s/it]\n",
            "  0%|          | 0/10 [00:00<?, ?it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "===== CSA_ttest\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\r 10%|█         | 1/10 [04:56<44:32, 296.97s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "===== CSA_ttest\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\r 20%|██        | 2/10 [09:52<39:29, 296.15s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "===== CSA_ttest\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\r 30%|███       | 3/10 [13:17<29:41, 254.49s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "===== CSA_ttest\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\r 40%|████      | 4/10 [17:26<25:13, 252.22s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "===== CSA_ttest\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\r 50%|█████     | 5/10 [21:29<20:45, 249.12s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "===== CSA_ttest\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\r 60%|██████    | 6/10 [26:06<17:14, 258.61s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "===== CSA_ttest\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\r 70%|███████   | 7/10 [31:01<13:30, 270.24s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "===== CSA_ttest\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\r 80%|████████  | 8/10 [36:17<09:29, 284.98s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "===== CSA_ttest\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\r 90%|█████████ | 9/10 [38:41<04:00, 241.00s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "===== CSA_ttest\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 10/10 [42:30<00:00, 255.00s/it]\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 576x360 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}