Training_Data_Selection_Final.ipynb

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      "cell_type": "markdown",
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        "id": "hRf6PMBZhDbD"
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      "source": [
        "#Exploring methods to minimize training data size while maintaining maximum model accuracy."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_CsbbXiiii7a"
      },
      "source": [
        "I am using the MNIST dataset and sklearn's MLP classifier to easily test these methods before transfering to chemprop."
      ]
    },
    {
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        },
        "id": "fz_uibmOrpsJ",
        "outputId": "c7b0d189-75ca-4c39-c226-e185f3b16e45"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz\n",
            "Downloading http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz to ./MNIST/raw/train-images-idx3-ubyte.gz\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
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          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Extracting ./MNIST/raw/train-images-idx3-ubyte.gz to ./MNIST/raw\n",
            "\n",
            "Downloading http://yann.lecun.com/exdb/mnist/train-labels-idx1-ubyte.gz\n",
            "Downloading http://yann.lecun.com/exdb/mnist/train-labels-idx1-ubyte.gz to ./MNIST/raw/train-labels-idx1-ubyte.gz\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
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              "model_id": "d0e575d84d5746e7948fd6a7fd379a07"
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          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Extracting ./MNIST/raw/train-labels-idx1-ubyte.gz to ./MNIST/raw\n",
            "\n",
            "Downloading http://yann.lecun.com/exdb/mnist/t10k-images-idx3-ubyte.gz\n",
            "Downloading http://yann.lecun.com/exdb/mnist/t10k-images-idx3-ubyte.gz to ./MNIST/raw/t10k-images-idx3-ubyte.gz\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/1648877 [00:00<?, ?it/s]"
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          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Extracting ./MNIST/raw/t10k-images-idx3-ubyte.gz to ./MNIST/raw\n",
            "\n",
            "Downloading http://yann.lecun.com/exdb/mnist/t10k-labels-idx1-ubyte.gz\n",
            "Downloading http://yann.lecun.com/exdb/mnist/t10k-labels-idx1-ubyte.gz to ./MNIST/raw/t10k-labels-idx1-ubyte.gz\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/4542 [00:00<?, ?it/s]"
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            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "bfbada1747ca4b2a80b0b9d82ff1a3b0"
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          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Extracting ./MNIST/raw/t10k-labels-idx1-ubyte.gz to ./MNIST/raw\n",
            "\n"
          ]
        }
      ],
      "source": [
        "from torchvision import datasets\n",
        "\n",
        "training = datasets.MNIST(root = '.',\n",
        "                          train = True,\n",
        "                          download = True\n",
        "                          )\n",
        "data = training.data.numpy()\n",
        "labels = training.targets.numpy()\n",
        "\n",
        "data = data.reshape(60000, 784)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aZH9Vli9sSkQ"
      },
      "source": [
        "Of the 60k images, split into 48k training set and 12k testing set."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "fxJaB_JvrtVR"
      },
      "outputs": [],
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "x_train_full, x_test_full, y_train_full, y_test_full = train_test_split(data, labels, train_size = 0.80, random_state=0)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2DfjbAhBsjwa"
      },
      "source": [
        "Model training with full training data set to establish a baseline."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oXh6Hk8zrtx2",
        "outputId": "f082bfcb-9b00-4fed-e0cb-fadba4fdf199"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Full training set score 0.9615\n",
            "Runtime:  5m19s\n"
          ]
        }
      ],
      "source": [
        "import time\n",
        "from sklearn.neural_network import MLPClassifier\n",
        "\n",
        "start = time.time()\n",
        "clf = MLPClassifier(activation = 'logistic', hidden_layer_sizes = (100, 100), random_state = 0)\n",
        "clf.fit(x_train_full, y_train_full)\n",
        "full_training_score = clf.score(x_test_full, y_test_full)\n",
        "end = time.time()\n",
        "print(\"Full training set score\", full_training_score)\n",
        "seconds = end - start\n",
        "print(\"Runtime: %2dm%2ds\" % (seconds / 60, seconds % 60))"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Model training with first 10k training samples."
      ],
      "metadata": {
        "id": "tvEqS1ngXBFc"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import time\n",
        "from sklearn.neural_network import MLPClassifier\n",
        "\n",
        "start = time.time()\n",
        "clf = MLPClassifier(activation = 'logistic', hidden_layer_sizes = (100, 100), random_state = 0)\n",
        "clf.fit(x_train_full[:10000], y_train_full[:10000])\n",
        "random_10k_training_score = clf.score(x_test_full, y_test_full)\n",
        "end = time.time()\n",
        "print(\"Full training set score\", full_training_score)\n",
        "seconds = end - start\n",
        "print(\"Runtime: %2dm%2ds\" % (seconds / 60, seconds % 60))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "K4-gj3zJW_e3",
        "outputId": "8619fc58-8751-42c8-f140-5dfeb00f7f97"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Full training set score 0.9615\n",
            "Runtime:  0m38s\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-d3vbuFTujbC"
      },
      "source": [
        "#Training on the model's least confident samples\n",
        "\n",
        "Train on less data by training on small chunks and adding the least confident remaining training samples to the training set."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "6m_CutwbKpAN"
      },
      "outputs": [],
      "source": [
        "from sklearn.neural_network import MLPClassifier\n",
        "import numpy as np\n",
        "import time\n",
        "\n",
        "class Learner:\n",
        "  def __init__(self, confidence_measure, step_size):\n",
        "    if confidence_measure == 'max_proba':\n",
        "      self.confidence_function = np.max\n",
        "    elif confidence_measure == 'std':\n",
        "      self.confidence_function = np.std\n",
        "    elif confidence_measure == 'top_two':\n",
        "      def top_two(proba, axis):\n",
        "        sorted = np.sort(proba, axis = axis)[:,-2:]\n",
        "        return sorted[:, 1] - sorted[:, 0]\n",
        "      self.confidence_function = top_two\n",
        "\n",
        "    self.step_size = step_size\n",
        "\n",
        "  def selection(self, clf, x):\n",
        "    \"\"\"\n",
        "      Returns the indices of the most ambiguous samples that should be labeled\n",
        "    \"\"\"\n",
        "    probabilities = clf.predict_proba(x)\n",
        "    confidence = self.confidence_function(probabilities, axis = 1)\n",
        "    self.indices = np.argsort(confidence)[:self.step_size]\n",
        "\n",
        "    return self.indices\n",
        "\n",
        "  def update(self, labeled_x, labeled_y, unlabeled_x, unlabeled_y, labels):\n",
        "    \"\"\"\n",
        "      Rearranges the labeled and unlabeled datasets\n",
        "    \"\"\"\n",
        "    # adding to labeled dataset\n",
        "    labeled_x = np.concatenate([labeled_x, unlabeled_x[self.indices]])\n",
        "    labeled_y = np.concatenate([labeled_y, unlabeled_y[self.indices]])\n",
        "\n",
        "    # removing from unlabeled dataset\n",
        "    unlabeled_y = np.delete(unlabeled_y, self.indices)\n",
        "    unlabeled_x = np.delete(unlabeled_x, self.indices, axis = 0)\n",
        "\n",
        "    return labeled_x, labeled_y, unlabeled_x, unlabeled_y\n",
        "\n",
        "\"\"\"\n",
        "OptimizeTrain is a container for learner\n",
        "\"\"\"\n",
        "\n",
        "class OptimizeTrain:\n",
        "    def __init__(self, clfs = [MLPClassifier(activation='logistic', hidden_layer_sizes = (100, 100), random_state = 0)]):\n",
        "      self.clfs = clfs\n",
        "\n",
        "    def train_and_score(self, x_train, y_train, x_test, y_test, training_size = 10000, step_size = 1000, confidence_measure = \"max_proba\", print_output = False):\n",
        "      results_dict = {}\n",
        "      for clf in self.clfs:\n",
        "        start = time.time() #Time the runtime\n",
        "        clf_results_dict = {} #dictionary to populate with training size and accuracy\n",
        "\n",
        "        learner = Learner(confidence_measure, step_size)\n",
        "\n",
        "        iterations = int(training_size / step_size)\n",
        "\n",
        "        labeled_x, labeled_y = x_train[:step_size], y_train[:step_size]\n",
        "        unlabeled_x, unlabeled_y = x_train[step_size:], y_train[step_size:]\n",
        "\n",
        "        for i in range(iterations):\n",
        "          clf.fit(labeled_x, labeled_y)\n",
        "          score = clf.score(x_test, y_test)\n",
        "\n",
        "          if(print_output):\n",
        "            print(labeled_x.shape[0], score)\n",
        "\n",
        "          clf_results_dict[labeled_x.shape[0]] = score\n",
        "\n",
        "\n",
        "          indices = learner.selection(clf, unlabeled_x)\n",
        "          # labels = oracle.label(unlabeled_x[indices]) # when we incorporate our system into the docking system, we need to do something like this\n",
        "          labels = unlabeled_y[indices] # for now, we can simply use this since we know the truth\n",
        "          labeled_x, labeled_y, unlabeled_x, unlabeled_y = learner.update(labeled_x, labeled_y, unlabeled_x, unlabeled_y, labels)\n",
        "\n",
        "\n",
        "        end = time.time()\n",
        "        runtime_seconds = end - start\n",
        "        runtime_string = \"%2dm%2ds\" % (runtime_seconds / 60, runtime_seconds % 60)\n",
        "        print(\"Training complete in \" + runtime_string)\n",
        "\n",
        "        if(len(self.clfs) == 1): #if only one clf is given, return the results, otherwise add to dictionary to return later\n",
        "          return clf_results_dict\n",
        "\n",
        "        results_dict[clf] = clf_results_dict\n",
        "\n",
        "      return results_dict\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QbcDK183goMm",
        "outputId": "47d950e6-93a0-4218-9d76-1a5698efe715"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/neural_network/_multilayer_perceptron.py:696: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/neural_network/_multilayer_perceptron.py:696: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
            "  ConvergenceWarning,\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training complete in  5m45s\n"
          ]
        }
      ],
      "source": [
        "optimize_train = OptimizeTrain()\n",
        "least_confident_samples_results = optimize_train.train_and_score(x_train_full, y_train_full, x_test_full, y_test_full)"
      ]
    },
    {
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        "outputId": "c8be6cf6-1b24-4251-9f3d-672db931b1a7"
      },
      "outputs": [
        {
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              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "import matplotlib.pyplot as plt\n",
        "plt.title(\"Training Data\")\n",
        "plt.hlines(y=full_training_score, xmin=1000, xmax=10000, color = 'r', label = \"Full Training Data (48k)\")\n",
        "plt.hlines(y=random_10k_training_score, xmin=1000, xmax=10000, color = 'g', label = \"Random (10k)\")\n",
        "plt.plot(list(least_confident_samples_results.keys()), list(least_confident_samples_results.values()), label = \"Least confident (10k)\")\n",
        "plt.xlabel(\"Training Size\")\n",
        "plt.ylabel(\"Model Accuracy\")\n",
        "plt.legend()\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "optimize_train = OptimizeTrain(clfs = [MLPClassifier(hidden_layer_sizes=(100), activation='relu', random_state=0), MLPClassifier(hidden_layer_sizes=(100, 100, 100, 100), activation='relu', random_state=0)])\n",
        "optimize_train.train_and_score(x_train_full, y_train_full, x_test_full, y_test_full)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ondvS7RqiE4M",
        "outputId": "a44b93f6-5172-4358-d920-8e0d65236480"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training complete in  1m47s\n",
            "Training complete in  2m52s\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{MLPClassifier(hidden_layer_sizes=(100, 100, 100, 100), random_state=0): {1000: 0.7375,\n",
              "  2000: 0.8265833333333333,\n",
              "  3000: 0.8696666666666667,\n",
              "  4000: 0.8915,\n",
              "  5000: 0.9015,\n",
              "  6000: 0.9153333333333333,\n",
              "  7000: 0.93125,\n",
              "  8000: 0.9358333333333333,\n",
              "  9000: 0.9365,\n",
              "  10000: 0.9400833333333334},\n",
              " MLPClassifier(hidden_layer_sizes=100, random_state=0): {1000: 0.8035,\n",
              "  2000: 0.8599166666666667,\n",
              "  3000: 0.8860833333333333,\n",
              "  4000: 0.90575,\n",
              "  5000: 0.916,\n",
              "  6000: 0.9264166666666667,\n",
              "  7000: 0.9325833333333333,\n",
              "  8000: 0.9341666666666667,\n",
              "  9000: 0.9361666666666667,\n",
              "  10000: 0.9500833333333333}}"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1wRmhoV59gta",
        "outputId": "12686cb6-d894-43f7-fcb0-af423490c6c8"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/neural_network/_multilayer_perceptron.py:696: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
            "  ConvergenceWarning,\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Training complete in  8m20s\n"
          ]
        }
      ],
      "source": [
        "optimize_train = OptimizeTrain()\n",
        "least_confident_samples_results_2 = optimize_train.train_and_score(x_train_full, y_train_full, x_test_full, y_test_full, training_size = 20000, step_size = 2000)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "F-zCirKa9rPK",
        "outputId": "7b69695b-052d-4ce8-dfd6-c477fa3db535"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "import matplotlib.pyplot as plt\n",
        "plt.title(\"Training Data\")\n",
        "plt.hlines(y=full_training_score, xmin=1000, xmax=20000, color = 'r', label = \"Full Training Data (48k)\")\n",
        "plt.plot(list(least_confident_samples_results_2.keys()), list(least_confident_samples_results_2.values()), label = \"Least confident (20k)\")\n",
        "plt.xlabel(\"Training Size\")\n",
        "plt.ylabel(\"Model Accuracy\")\n",
        "plt.legend()\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DrKCm0lF83IK"
      },
      "source": [
        "Stepping through and systematically adding training data the model is least confident in yields a model with comparable accuracy to that of the full training set with only a fraction of the training data. Beyond a training size of about 10k, the accuracy plateaus."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "37JLV5HyEAOa"
      },
      "source": [
        "#Different methods of measuring the model's confidence on a training sample.\n",
        "\n",
        "\n",
        "clf.predict_proba returns an array with the probabity for each possible label (1-10 in this case)\n",
        "\n",
        "*   Maximum proba\n",
        "*   Standard deviation\n",
        "*   Difference in top two\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xpoORbl5EW4u",
        "outputId": "3185e045-40e6-4b7c-b49e-ad14eef617ec"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/neural_network/_multilayer_perceptron.py:696: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/neural_network/_multilayer_perceptron.py:696: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
            "  ConvergenceWarning,\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Training complete in  4m40s\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/neural_network/_multilayer_perceptron.py:696: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/neural_network/_multilayer_perceptron.py:696: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
            "  ConvergenceWarning,\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Training complete in  5m 8s\n"
          ]
        }
      ],
      "source": [
        "optimize_train = OptimizeTrain()\n",
        "cm_max_proba_results = least_confident_samples_results\n",
        "cm_std_results = optimize_train.train_and_score(x_train_full, y_train_full, x_test_full, y_test_full, confidence_measure = \"std\")\n",
        "cm_top_two_results = optimize_train.train_and_score(x_train_full, y_train_full, x_test_full, y_test_full, confidence_measure = \"top_two\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "LtW9YoVp2qTi",
        "outputId": "dc61cdf5-df40-43af-ddfb-794ce6143456"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "import matplotlib.pyplot as plt\n",
        "plt.hlines(y=full_training_score, xmin=1000, xmax=10000, color = 'r', label = \"Full Training Data (48k)\")\n",
        "plt.plot(list(cm_max_proba_results.keys()), list(cm_max_proba_results.values()), label = \"Max Proba\")\n",
        "plt.plot(list(cm_std_results.keys()), list(cm_std_results.values()), label = \"Standard Deviation\")\n",
        "plt.plot(list(cm_top_two_results.keys()), list(cm_top_two_results.values()), label = \"Top Two\")\n",
        "plt.title(\"Training Data\")\n",
        "plt.xlabel(\"Training Size\")\n",
        "plt.ylabel(\"Model Accuracy\")\n",
        "plt.legend()\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "While this graph is still relatively clear, heat maps can be a great way to represent lots of data."
      ],
      "metadata": {
        "id": "j907zCwZPfRf"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "Zr6TxkOhNlBU",
        "outputId": "9904a9b7-dba0-49df-f2c0-4c49cc2005c9"
      },
      "outputs": [
        {
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Key: (0: Max Proba) (1: Standard Deviation) (2: Top Two)\n"
          ]
        }
      ],
      "source": [
        "result_table = np.array([list(cm_max_proba_results.values()), list(cm_std_results.values()), list(cm_top_two_results.values())])\n",
        "plt.imshow(result_table, cmap = 'jet')\n",
        "plt.colorbar()\n",
        "plt.show()\n",
        "print(\"Key: (0: Max Proba) (1: Standard Deviation) (2: Top Two)\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xE4IuCdy05EF"
      },
      "source": [
        "All three methods of measuring confidence are comparable."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sgVbepzI70Xs"
      },
      "source": [
        "#Different step sizes\n",
        "The amount of new least confident training data added and retrained on at each step."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "WVFFcEIM5PTt"
      },
      "outputs": [],
      "source": [
        "optimize_train = OptimizeTrain()\n",
        "step_sizes = [2000, 1000, 500, 200, 100]\n",
        "\n",
        "step_size_results_dict = {}\n",
        "\n",
        "for step_size in step_sizes:\n",
        "  print(str(step_size) + \": \", end = \"\\t\")\n",
        "  step_size_results_dict[step_size] = optimize_train.train_and_score(x_train_full, y_train_full, x_test_full, y_test_full, step_size = step_size)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "o1fYmbXdrkBi",
        "outputId": "8f300651-30e7-40f4-ff21-6d2c7edd3f5e"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "for step_size in step_size_results_dict.keys():\n",
        "  results_dict = step_size_results_dict[step_size]\n",
        "  plt.plot(list(results_dict.keys()), list(results_dict.values()), label = step_size)\n",
        "\n",
        "plt.ylim(0.91, 0.97)\n",
        "plt.title(\"Training Data\")\n",
        "plt.title(\"Training Performance\")\n",
        "plt.xlabel(\"Training Size\")\n",
        "plt.ylabel(\"Model Accuracy\")\n",
        "plt.legend()\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def showHeatMap(results_dict_dict, step_size = 1000, full_size = 10000): #assumes first index is at step_size\n",
        "  results_table = []\n",
        "  results_dict_list = list(results_dict_dict.values())\n",
        "\n",
        "  iterations = int(full_size / step_size)\n",
        "\n",
        "  for results_dict in results_dict_list:\n",
        "    row = list()\n",
        "    for i in range(iterations):\n",
        "      key = (i + 1) * step_size\n",
        "      if key in results_dict.keys():\n",
        "        row.append(results_dict[key])\n",
        "      elif len(row) > 0:\n",
        "        row.append(row[-1])\n",
        "      else:\n",
        "        row.append(0)\n",
        "    results_table.append(row)\n",
        "\n",
        "\n",
        "  plt.imshow(np.array(results_table), cmap = 'jet')\n",
        "  plt.colorbar()\n",
        "  plt.show()\n",
        "\n",
        "  print(\"Key: \", end=\"\")\n",
        "  for i in range(len(results_dict_dict.keys())):\n",
        "    print(\"(\" + str(i) + \": \" + str(list(results_dict_dict.keys())[i]), end=\") \")\n"
      ],
      "metadata": {
        "id": "rGQVWuThNpV6"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "results_table = []\n",
        "results_dict_list = list(step_size_results_dict.values())\n",
        "\n",
        "for results_dict in results_dict_list:\n",
        "  row = list()\n",
        "  for i in range(10):\n",
        "    key = i * 1000 + 1000\n",
        "    if key in results_dict.keys():\n",
        "      row.append(results_dict[key])\n",
        "    else:\n",
        "      row.append(results_dict[key+1000])\n",
        "  results_table.append(row)\n",
        "\n",
        "\n",
        "plt.imshow(np.array(results_table), cmap = 'jet')\n",
        "plt.colorbar()\n",
        "plt.title(\"\")\n",
        "plt.xlabel(\"Sample Size\")\n",
        "# plt.xticks(np.arange(1000, 11000, 1000))\n",
        "# plt.gca().set_xticks(np.arange(1000, 11000, 1000))\n",
        "# plt.ylabel(\"See Key\")\n",
        "plt.show()\n",
        "\n",
        "print(\"Key: \", end=\"\")\n",
        "for i in range(len(step_size_results_dict.keys())):\n",
        "  print(\"(\" + str(i) + \": \" + str(list(step_size_results_dict.keys())[i]), end=\") \")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "iU7GKtyQwp1j",
        "outputId": "981aaa19-32a3-43da-9649-c274059ef0fa"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Key: (0: 2000) (1: 1000) (2: 500) (3: 200) (4: 100) "
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GvdYFc5rggMr"
      },
      "source": [
        "#Dimensionality reduction through principal component anaysis\n",
        "\n",
        "The more features our data has, the more data we need to train it. PCA can remove insignificant features from our entire dataset,"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "RaOnMVC3YbXk"
      },
      "outputs": [],
      "source": [
        "from sklearn.decomposition import PCA\n",
        "\n",
        "pca = PCA(n_components = 24, random_state = 0)\n",
        "compressed = pca.fit_transform(data)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "EJ6m5LvxiqBy"
      },
      "outputs": [],
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "x_train_pca, x_test_pca, y_train_pca, y_test_pca = train_test_split(compressed, labels, train_size = 0.80, random_state=0)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bdr4M-WUwi6J",
        "outputId": "495a5156-6da4-4536-de85-254afdee4d4b"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "0.96375\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/neural_network/_multilayer_perceptron.py:696: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
            "  ConvergenceWarning,\n"
          ]
        }
      ],
      "source": [
        "from sklearn.neural_network import MLPClassifier\n",
        "\n",
        "clf = MLPClassifier(hidden_layer_sizes = (100, 100), activation = \"logistic\", random_state = 0)\n",
        "clf.fit(x_train_pca, y_train_pca)\n",
        "\n",
        "pca_full_training = clf.score(x_test_pca, y_test_pca)\n",
        "print(pca_full_training)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uB4KRzSTVs7V",
        "outputId": "daac0926-64b2-4e34-a2e3-77892a8a0d7f"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "0.96375\n"
          ]
        }
      ],
      "source": [
        "print(pca_full_training)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NcGepJvcYirb"
      },
      "source": [
        "Training on the full training set with only 24 features (instead of 784) yields a better model accuracy."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "c8LNWXDcnrJ4"
      },
      "outputs": [],
      "source": [
        "n_components_list = [8, 16, 32, 64, 128, 256, 512, 12, 24, 49, 98, 196, 392, 784]\n",
        "optimize_train = OptimizeTrain()\n",
        "pca_n_components_results_dict = {}\n",
        "\n",
        "for n_components in n_components_list:\n",
        "  pca = PCA(n_components = n_components, random_state = 0)\n",
        "  compressed = pca.fit_transform(data)\n",
        "  x_train_pca, x_test_pca, y_train_pca, y_test_pca = train_test_split(compressed, labels, train_size = 0.80, random_state=0)\n",
        "  pca_n_components_results_dict[n_components] = optimize_train.train_and_score(x_train_pca, y_train_pca, x_test_pca, y_test_pca)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "sBIb6gJd0DBk",
        "outputId": "70cb940e-a0c7-4805-d2a6-c5a74f9ee419"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "for n_components in pca_n_components_results_dict.keys():\n",
        "  results_dict = pca_n_components_results_dict[n_components]\n",
        "  plt.plot(list(results_dict.keys()), list(results_dict.values()), label = n_components)\n",
        "\n",
        "# plt.ylim(0.91, 0.97)\n",
        "plt.title(\"Training Data\")\n",
        "plt.title(\"Training Performance\")\n",
        "plt.xlabel(\"Training Size\")\n",
        "plt.ylabel(\"Model Accuracy\")\n",
        "plt.legend()\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "showHeatMap(pca_n_components_results_dict, 1000, 10000)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "rxgPT5zAP_K8",
        "outputId": "ca04c5b9-2cfb-40f4-838f-c8a110fe8580"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Key: (0: 8) (1: 12) (2: 16) (3: 24) (4: 32) (5: 49) (6: 64) (7: 98) (8: 128) (9: 196) (10: 256) (11: 392) (12: 512) (13: 784) "
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "XpprjfvtXo2s",
        "outputId": "4ecc75e9-9416-4579-c76c-1e08b4bda5ab"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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8uSH1WNdQpYvpTmBRbUTSJpJ2BYiImc0KzMzMOleVBHElsLQwvjSXmZlZF1YlQSifpAZS1xL+BbaZWZdXJUHMkfQVST3z6xRgTrMDMzOzzlXlSOBE4AfAWUAA9wAnNDMoM7M1yY7X79iQeqYfM70h9awubSaIiHgROGI1xGJmZmuQKr+DWA84HtgeWK9WHhHHNTEuM7Mub+b7t2tIPds91ZwLSqucg7gR2BL4OPB7oD/walOiMTOzNUaVcxDvjYhPSRoVEddL+inwQLMD6zIacX9635vezDpBlQTxRv67RNIOpPsxbdG8kMysqxt0xh0NqWfuBQc1pB4rVyVBXJ2fB3EWcDuwEfDtpkZlZmadrtVzEPmGfK9ExOKIuD8itomILSLiR1Uqz8+PmCVptqQzSqZvLekeSdMkTZTUP5cPl/SQpBl52r936N2ZmVmHtZog8q+mO3S3VkndgSuAA4ChwJGShtbNdjFwQ0QMA8YC5+fy14CjI2J7YH/gMkm9OhKHmZl1TJWrmH4n6XRJAyRtXntVWG4kMDsi5kTE68CtwKi6eYYC9+bh+2rTI+LpiPhTHl4AvAj0rbBOMzNrkCrnIGrdO18ulAWwTRvLbQU8VxifB+xaN89U4DDg+8ChwMaSekfEy7UZJI0E1gFWug+xpBPIv+oeOHBgm2/EzMyqq/JL6sFNXP/pwOWSjgXuB+YDy2sTJb2b9DuMY3J3V31sVwNXA4wYMSLqp5uZWcdV+SX10WXlEXFDG4vOBwYUxvvnsmIdC0hHEEjaCDg8Ipbk8U2AO4BvRcQf24rTzMwaq0oX0y6F4fWAfYHHgLYSxKPAtpIGkxLDEcBRxRkk9QEW5aODM4Frcvk6wC9JJ7BvqxCjmZk1WJUuppOL4/lqolsrLLdM0knABKA7cE1EzJA0FpgUEbcDewHnSwpSF1PtPMe/AR8BeufuJ4BjI2JKpXdlZmarrCMP/vkbUOm8RESMB8bXlZ1dGL4NWOkIISJuAm7qQGxmZtYgVc5B/Jp01RKky2KHAuOaGZSZmXW+KkcQFxeGlwHPRMS8JsVjZmZriCoJ4lng+Yj4B4Ck9SUNioi5TY3MzMw6VZVfUv8cKP4GYXkuMzOzLqxKguiRb5UBQB5ep3khmZnZmqBKglgo6ZDaiKRRwEvNC8nMzNYEVc5BnAjcLOnyPD4PKP11tZmZdR1Vfij3Z+BD+VYYRMTSpkdlZmadrs0uJknnSeoVEUsjYqmkzST939URnJmZdZ4q5yAOqN1ADyAiFgMHNi8kMzNbE1RJEN0lrVsbkbQ+sG4r85uZWRdQ5ST1zcA9kq7N45+l7Tu5mpnZWq7KServSZoKfDQXfTciJjQ3LDMz62yV7uYaEXcBd0naEDhM0h0RcVBzQzMzs85U5SqmdSQdKunnwPPAPsBVTY/MzMw6VYtHEJL2A44E9gPuI5132CUiPruaYjMzs07U2hHEXcA2wO4RMToifs3bb9pnZmZdWGvnIHYmPUf6d5LmkB4z2n21RGVmZp2uxSOIiJgSEWdExHuAc4DhQE9Jd0o6YbVFaGZmnaLKD+WIiAcj4mSgP3Ap8KGmRmVmZp2u0mWuNRHxJnB3fpmZWRdW6QjCzMzeeZwgzMysVGu/g9i8tQUjYlHjwzEzszVFa+cgJgMBqGRakH4jYWZmXVSLCSIiBq/OQMzMbM1S5V5MkjRa0rfz+EBJI5sfmpmZdaYqJ6l/COwGHJXHXwWuqFK5pP0lzZI0W9IZJdO3lnSPpGmSJkrqX5h2jKQ/5dcxVdZnZmaNUyVB7BoRXwb+AW89cnSdthaS1J2USA4AhgJHShpaN9vFwA0RMQwYC5yfl92c9OvtXYGRwDmSNqv0jszMrCGqJIg38pd9AEjqS7Wb9o0EZkfEnIh4nXQvp1F18wwF7s3D9xWmfxz4bUQsygnpt8D+FdZpZmYNUiVB/AD4JbCFpHOBPwDnVVhuK+C5wvi8XFY0FTgsDx8KbCypd8VlkXSCpEmSJi1cuLBCSGZmVlWVR47eLGkysC/pktd/jYiZDVr/6cDlko4F7gfmA8urLhwRVwNXA4wYMSIaFJOZmVH9h3IvArcUp1X4odx8YEBhvH8ue0tELCAfQUjaCDg8IpZImg/sVbfsxDbWZ2ZmDVT1h3IDgcV5uBfwLNDW7yQeBbaVNJiUGI5gxZVQAEjqAyzKNwE8E7gmT5oAnFc4Mb1fnm5mZqtJa8+DGBwR2wC/Az4REX0iojdwMBXu5hoRy4CTSF/2M4FxETFD0lhJh+TZ9gJmSXoaeBdwbl52EfBdUpJ5FBjrW3uYma1eVW73/aGI+HxtJCLulHRhlcojYjwwvq7s7MLwbcBtLSx7DSuOKMzMbDWrkiAWSDoLuCmPfxpY0LyQzMxsTVDlMtcjgb6kS11/CWyRy8zMrAurcpnrIuAUSRun0Vja/LDMzKyzVblZ346SHgeeAGZImixph+aHZmZmnalKF9OPgK9FxNYRsTVwGvnHaWZm1nVVSRAbRsR9tZGImAhs2LSIzMxsjVDlKqY5+VkQN+bx0cCc5oVkZmZrgipHEMeRrmL6RX71zWVmZtaFVbmKaTHwldUQi5mZrUFau1nf7a0tGBGHtDbdzMzWbq0dQexGeibDLcDDpBv1mZnZO0RrCWJL4GOkX00fBdwB3BIRM1ZHYGZm1rlau5vr8oi4KyKOAT4EzAYmSjpptUVnZmadptWT1JLWBQ4iHUUMYsXjR83MrItr7ST1DcAOpNt1fycinlhtUZmZWadr7QhiNPA34BTgK9Jb56hFumnfJk2OzczMOlGLCSIiqvyIzszMuignATMzK+UEYWZmpZwgzMyslBOEmZmVcoIwM7NSThBmZlbKCcLMzEo5QZiZWSknCDMzK+UEYWZmpZqaICTtL2mWpNmSziiZPlDSfZIelzRN0oG5vKek6yVNlzRT0pnNjNPMzFbWtAQhqTtwBXAAMBQ4UtLQutnOAsZFxAeAI4Af5vJPAetGxI7AB4EvSBrUrFjNzGxlzTyCGAnMjog5EfE6cCswqm6eAGp3hd0UWFAo31BSD2B94HXglSbGamZmdZqZILYiPdO6Zl4uKxoDjJY0j/TciZNz+W2kW40/DzwLXBwRi+pXIOkESZMkTVq4cGGDwzcze2fr7JPURwLXRUR/4EDgRkndSEcfy4F+wGDgNEnb1C8cEVdHxIiIGNG3b9/VGbeZWZfXzAQxHxhQGO+fy4qOB8YBRMRDwHpAH+Ao4K6IeCMiXgT+FxjRxFjNzKxOq8+kXkWPAttKGkxKDEeQvviLngX2Ba6TtB0pQSzM5fuQjig2BD4EXNbEWBl0xh2rXMfcCw5qQCRmZmuGph1BRMQy4CRgAjCTdLXSDEljJR2SZzsN+LykqcAtwLEREaSrnzaSNIOUaK6NiGnNitXMzFbWzCMIImI86eRzsezswvCTwIdLlltKutTVzMw6SWefpDYzszWUE4SZmZVygjAzs1JOEGZmVsoJwszMSjlBmJlZKScIMzMr5QRhZmalnCDMzKyUE4SZmZVygjAzs1JOEGZmVsoJwszMSjlBmJlZKScIMzMr5QRhZmalnCDMzKyUE4SZmZVygjAzs1JOEGZmVsoJwszMSjlBmJlZKScIMzMr5QRhZmalnCDMzKyUE4SZmZVygjAzs1JOEGZmVqqpCULS/pJmSZot6YyS6QMl3SfpcUnTJB1YmDZM0kOSZkiaLmm9ZsZqZmZv16NZFUvqDlwBfAyYBzwq6faIeLIw21nAuIi4UtJQYDwwSFIP4CbgMxExVVJv4I1mxWpmZitr5hHESGB2RMyJiNeBW4FRdfMEsEke3hRYkIf3A6ZFxFSAiHg5IpY3MVYzM6vTzASxFfBcYXxeLisaA4yWNI909HByLh8ChKQJkh6T9I2yFUg6QdIkSZMWLlzY2OjNzN7hOvsk9ZHAdRHRHzgQuFFSN1LX1+7Ap/PfQyXtW79wRFwdESMiYkTfvn1XZ9xmZl1eMxPEfGBAYbx/Lis6HhgHEBEPAesBfUhHG/dHxEsR8Rrp6GLnJsZqZmZ1mpkgHgW2lTRY0jrAEcDtdfM8C+wLIGk7UoJYCEwAdpS0QT5hvSfwJGZmtto07SqmiFgm6STSl3134JqImCFpLDApIm4HTgP+S9JXSSesj42IABZL+g9SkglgfETc0axYzcxsZU1LEAARMZ7UPVQsO7sw/CTw4RaWvYl0qauZmXWCzj5JbWZmaygnCDMzK+UEYWZmpZwgzMyslBOEmZmVcoIwM7NSThBmZlbKCcLMzEo5QZiZWSknCDMzK+UEYWZmpZwgzMyslNLNU9d+khYCzzRxFX2Al9ayul2v63W9rrctW0dE6RPXukyCaDZJkyJixNpUt+t1va7X9a4KdzGZmVkpJwgzMyvlBFHd1Wth3a7X9bpe19thPgdhZmalfARhZmalnCDMzKyUE0QFkr4qaYakJyTdImm9DtZzjaQXJT1RKLtI0lOSpkn6paRejag3l5+c654h6cIO1DtA0n2Snsx1nFI3/TRJIalPO+tdT9Ijkqbmer+Ty2+WNCtv52sk9WxvzLme7pIel/SbPL6PpMdyvddL6tGBOntJui1vz5mSditM69B2yMuekuOaIenUXDZc0h8lTZE0SdLIinVVbl+SeuZtMT2/nzPbWe9Okh7Ky/9a0iaFacPytBl5+kr/Ly21LUljJM3P732KpAPbWW9Lbau0DUj6dN420yU9KGmnVrbD3DzfFEmTctmn8nrelDSiMO/HJE3O80+WtE8r9b6v8H6nSHpF0qlttQNJu0haJumTLdW9yiLCr1ZewFbA/wPWz+PjgGM7WNdHgJ2BJwpl+wE98vD3gO81qN69gd8B6+bxLTpQ77uBnfPwxsDTwNA8PgCYQPpxYp921itgozzcE3gY+BBwYJ4m4Bbgix3czl8Dfgr8hrQT9BwwJE8bCxzfgTqvBz6Xh9cBejVgO+wAPAFsAPTIn9d7gbuBA/I8BwITG92+gKOAW/PwBsBcYFA76n0U2DMPHwd8Nw/3AKYBO+Xx3kD3qm0LGAOcXjJ/1XrL2ta/tNQG8rTN8vABwMOtbN+59Z8xsB3wPmAiMKJQ/gGgX+Fznl/xM+wOvABs3Vo7yPPdC4wHPtmR/5MqLx9BVNMDWD/vdWwALOhIJRFxP7CoruzuiFiWR/8I9G9EvcAXgQsi4p95nhc7UO/zEfFYHn4VmElKmACXAt8A2n2VQyRL82jP/IqIGJ+nBfAIHdgWkvoDBwE/zkW9gdcj4uk8/lvg8HbWuSnpS/InOf7XI2JJntzh7UD6cnk4Il7LbeD3wGG5rtoe+aZUbG/tbF8BbJjb9PrA68ArVesFhgD35+HiNt0PmBYRU/OyL0fE8pI6W2tbZarWW9a2ltNCG4iIByNicS5v9/9fRMyMiFkl5Y9HRO1zm0H6/li3QpX7An+OiGdovR2cDPw30O7/6/ZwgmhDRMwHLgaeBZ4H/hoRdzdpdccBdzaoriHAHpIelvR7SbusSmWSBpH2ih6WNIq0RzR1FerrLmkKqYH/NiIeLkzrCXwGuKsDVV9G+sJ+M4+/BPQoHP5/krTX3x6DgYXAtbnr6seSNmzAdniC9Bn1lrQBaS9xAHAqcJGk50htr8Xun3Yqtq/bgL+R2vSzwMURUZ8EWjMDGJWHP8WKbToECEkTcpfON9qqqNi2ctFJudvnGkmbtbfe+rZF2tmo0gaOp/X/vwDuzl1GJ7T1vgoOBx6r7ay14QjS0TO00A4kbQUcClzZjhg6xAmiDbmBjiJ9SfQj7XWNbsJ6vgUsA25uUJU9gM1JXTdfB8ZJUgdj24i0t3JqjvGbwNmrElxELI+I4aQ9tpGSdihM/iFwf0Q80M44DwZejIjJhfUE6Z/uUkmPAK+S9ijbowepi+XKiPgA6Yt1DKu4HSJiJqnb525SMpySY/si8NWIGAB8lXzksipK2tfIvK5+pLZ9mqRt2lHlccCXJE0mdRG9nst7ALsDn85/D5W0bytxvdW2IuIV0pfee4DhpOR1SXvrrW9bwPa00QYk7U1KEP+nlfe8e0TsTOqK+rKkj7Qyb63e7Umf8RcqzLsOcAjw81zUUju4DPg/EfHmyrU0WLP6rrrKi7R39ALJkOQAAAXASURBVJPC+NHAD1ehvkEU+nJz2bHAQ8AGjaqX9IWzd2H8z0DfDtTbk9TH/rU8viNpz2xufi0j7YFuuQqxn03udwbOAX4FdOtAPecD83JcLwCvATfVzbMfMK6d9W4JzC2M7wHc04TtcB7wJeCvrPiNkoBXGt2+gCuAzxTGrwH+rT31FqYNAR7Jw0cA1xemfRv4epW21do621NvS22rpTYADMv/H0PasZ3HFOul7hxELutPOrfy4Yp1jgLuLoyXtgPSOdFau1ua2+G/drTdtRpTMyrtSi9gV9Lh9Ab5Q7oeOHkV6nvbPxqwP/AkHfjybqPeE4GxeXgI6SSd2lmngBuAy1qZZy7tPznblxUnedcHHgAOBj4HPEi+IGAVt8dewG/y8Bb577qkL/Z9OlDfA8D78vAY4KJV3Q51sQ0EngJ6kfrj98rl+wKTG92+SHvK1+bhDfM8w9pRby3ubrmNHJfHNwMe4+0n3g+q2raAdxeGv8qKE+lV622pbZW2gbzdZwP/0sZ23RDYuDD8ILB/YfpE3n6SuhcwFTisHZ/drcBnC+NttgPgOpp4kroplXa1F/Cd/M/7BHAj+cqgDtRzC+mw+Q3Snu7xuXE+R+pemAJc1aB61wFuyjE/Rse+FHcn9btOK8R3YN08c2l/ghgGPJ7rfQI4O5cvI+3J1dZ19ip8ZnuxIkFclP/ZZpG6MjpS33BgUo75V+QrX1ZlO+TlHiB9OU8F9i1s98m57GHgg41uX8BGpK6MGXn9Le6Nt1DvKaS946eBCyjsfACjc71PABe2p23l/6/pufx23p4wqtTbUtsqbQOkixkWF2KY1EK92+TPY2qO4Vu5/NC8Tf4J/AWYkMvPInVFTim8WrySkJR0XgY2rdtGrbYDmpwgfKsNMzMr5ZPUZmZWygnCzMxKOUGYmVkpJwgzMyvlBGFmZqWcIKxLUrq76iWF8dMljWlh3gPy3TKfzLfSuKRsvrVJvhvoBp0dh63dnCCsq/oncJjauAV3vsXH5cDoiBgKjCD9dmBtdyrpR2VmHeYEYV3VMtKzfL/axnzfAM6NiKfgrfv4XAnpJnKS7s03jrtH0sBcfp2kK/O9+udI2ivfWG6mpOtqFUtaKunS/LyAeyT1zeW1+/zXntGwWS6fKOl7Ss8zeFrSHrm8u9JzHR7Ny3whl++Vl6k9p+JmJV8h3WPpPqVnLnTPMT+h9HyCtraJGeAEYV3bFcCn8+26W7ID6deqZf6TdP+fYaSb3P2gMG0zYDdSArqddNvv7YEdJQ3P82xI+mXu9qRbeZ+Ty28g3WxtGOlXw+cU6u0RESNJRwC18uNJdxHeBdgF+LykwXnaB/K8Q0m/9v1wRPyAdGvovSNib9KvwLeKiB0iYkfg2la2h9lbnCCsy4p0d9AbgK90sIrdSA8egnQLiN0L034d6TYE04G/RMT0SHfXnEG6bxGkW47/LA/fBOyek1WviPh9Lr+e9KyJml/kv5ML9ewHHJ1vYf0w6RkX2+Zpj0TEvLzuKYVliuYA20j6T0n708JzH8zqOUFYV3cZaQ98wxamzwA+2IF6a/f2f7MwXBtv6ZGmVe5rU6treaEekW4QOTy/BseKZ5IU111cZsVK0wNxdiLdUO5EVjxMyaxVThDWpUV6CM44UpIocxHwTUlDACR1k3RinvYg6TbTkJ5D0K7nU5D+v2rPCz4K+ENE/BVYXDu/QHow0u/LFi6YAHwxP0gJSUMktZTwal4lPaeBfKK+W0T8N+kmcju3833YO1S7H95utha6BDipbEJETJN0KnBLviw0SM+yhvRYx2slfZ30RLnPtnO9fyM9DOks0j37/z2XHwNcldc3p0K9PyZ1HT2WH/q0EPjXNpa5GrhL0gLSOYprJdV2CBv1hDrr4nw3V7MmkbQ0Ijbq7DjMOspdTGZmVspHEGZmVspHEGZmVsoJwszMSjlBmJlZKScIMzMr5QRhZmal/j/sVTM5Lqs33QAAAABJRU5ErkJggg==\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import collections\n",
        "\n",
        "pca_final_accuracy = {}\n",
        "\n",
        "pca_n_components_results_dict = collections.OrderedDict(sorted(pca_n_components_results_dict.items()))\n",
        "\n",
        "for n_components in pca_n_components_results_dict.keys():\n",
        "  # print(list(pca_n_components_results_dict[n_components].values())[-1])\n",
        "  # plt.plot(n_components, list(pca_n_components_results_dict[n_components].values())[-1])\n",
        "  plt.bar(str(n_components), list(pca_n_components_results_dict[n_components].values())[-1])\n",
        "\n",
        "plt.title(\"n_components vs. Final Model Accuracy\")\n",
        "plt.xlabel(\"N Components\")\n",
        "plt.ylabel(\"Model Accuracy\")\n",
        "plt.ylim(0.85, 0.97)\n",
        "# plt.legend()\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "r8YGEfCspUI_",
        "outputId": "7773092a-9230-412e-8924-0efebe4bb249"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/neural_network/_multilayer_perceptron.py:696: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/neural_network/_multilayer_perceptron.py:696: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/neural_network/_multilayer_perceptron.py:696: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/neural_network/_multilayer_perceptron.py:696: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/neural_network/_multilayer_perceptron.py:696: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/neural_network/_multilayer_perceptron.py:696: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
            "  ConvergenceWarning,\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training complete in  3m33s\n"
          ]
        }
      ],
      "source": [
        "from sklearn.decomposition import PCA\n",
        "optimize_train = OptimizeTrain()\n",
        "\n",
        "pca = PCA(n_components = 24, random_state = 0)\n",
        "compressed = pca.fit_transform(data)\n",
        "x_train_pca, x_test_pca, y_train_pca, y_test_pca = train_test_split(compressed, labels, train_size = 0.80, random_state=0)\n",
        "pca_24_results_dict = optimize_train.train_and_score(x_train_pca, y_train_pca, x_test_pca, y_test_pca)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "plt.hlines(y=full_training_score, xmin=1000, xmax=10000, color = 'r', label = \"Full Training Data (48k)\")\n",
        "plt.plot(list(least_confident_samples_results.keys()), list(least_confident_samples_results.values()), label = \"Least confident (10k)\")\n",
        "plt.plot(list(pca_24_results_dict.keys()), list(pca_24_results_dict.values()), label = \"PCA 24 (10k)\")\n",
        "plt.title(\"Training Data\")\n",
        "plt.xlabel(\"Training Size\")\n",
        "plt.ylabel(\"Model Accuracy\")\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "aHd0a0xySrnq",
        "outputId": "87d6c25b-76c1-4c2b-a891-c030541bb4ad"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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DbQlCRLyA94DhQBywQUQWG2O2Ox02HZhpjJkhIkOBV4FbnPa/BPzkrhiV5xxKyWBxbDyLYhLYcyydmjWEQR0a8viVnRjetTF1fLVxq84jLwf2/KClqeXAnf8X9gX2GmP2A4jIHGAMVosgX1fgEfvjVcDC/B0i0htoDCwDItwYpyonx05l8t3mIyyKSSDm8AkA+rZuwMvXdOOq7k1pUMfHwxGqCu10Mmz8HDZ8CqcStDS1HLgzQTQHDjs9jwP6FTomFhiH1Q01FvAXkSDgOPBP4Gbg8vO9gIhMBCYCtGzZsswCV2UnLTOHZVsTWRyTwK/7krEZ6No0gKdGdmZUj2Y012ktVEkSNsHvH1ldSXlZ0DYSoqZDhyu0NNXNPN2Ofwx4V0Rux+pKigfygPuA740xccUNSBpjPgI+AoiIiLjAlWSVu2Tm5PHjzmMsioln1a4ksnNttAqqzf2XtWd0eDPal2Y+alW95OVYFUjrP4LDv4N3Heh1i9ViCO7k6eiqDXcmiHighdPzEPs2B2NMAlYLAhGpC1xrjDkhIpcAg0TkPqAu4CMi6caYSW6MV12E3Dwba/elsCgmnh+2HSU9K5eGdX2Z0K8lY8Kb0yMkUKuPVMnSj53tRkpPhPpt4MpXoecE8Av0dHTVjjsTxAagg4i0wUoM44GbnA8QkYZAqjHGBjyFVdGEMWaC0zG3AxGaHCoeYwx/HDrO4pgEvttyhOT0bPz9anJV9yaMCW9O/7ZBOr2Fck3cRlj/IWxbYK281v5y6PuO9VvLUz3GbQnCGJMrIvcDy7HKXD8zxmwTkSlAtDFmMRAJvCoiBquL6e/uikeVnV2Jp1gUE8/i2ATijp/Bt6ZVljo6vBmRnYJ1/QTlmtxs2L4Qfv8A4jdaU2r3vgP63gUNO3g6OgWIKbxieiUVERFhoqOjPR1GlXU4NYPFsQksjklg19FTeNUQBrZvyOgezbgitLHeq6BcdyoRoj+D6P/A6WMQ1N5ejXQj+AV4OrpqR0Q2GmOKrBT19CC1qsCS07PsZanx/HHIKkuNaFWfKWNCuap7UxrqZHjKVcZA3Ab4/UOr1WDLs6qQ+k2EtkO1G6mC0gShCsjOtfFtbAKLYhNYuzeZPJuhcxN/nhjRiavDmtGiQfVbNEVdhNws2Drf6kY6EmNNf9F3IvT5m86JVAloglAONpvhwdmbWLYtkZD6tbhnSFtG92hOpyZalqouUFqCVYm08XPISIaGnSDqnxA2Hnzrejo65SJNEMrhjRW7WbYtkUkjO3P34LZalqoujDFwaJ1VjbR9MRgbdBpptRjaRuqdzpWQJggFwKKYeN5dtZe/RLTQ5KAuTM4Z2PK1lRgSt1j3K/S/1+pGatDG09Gpi6AJQrHp0HEe/3ozfds04KVrumlyUK45cRiiP4WNM+BMKgR3sRbiCbsBfOp4OjpVBjRBVHMJJ85w18yNNAnw44Obe+NTU6tJVDGMgQO/WK2Fnd9Z2zpdBf3uhtaDtBupitEEUY1lZOfytxnRZObk8eVd/XQ2VVVQTiak7rMW4knZA8l7rYnzkndBrfpw6YPQ505rtTZVJWmCqKZsNsMjX8WyMzGNT2/vQ8fGWqlULRkDp44UTAIpe6znJw4BTjfSBjS37nC+9H5r3QVdjKfK0wRRTeVXLD0b1YXLOjXydDjK3bIzIGXv2SSQvNt6nLIPstPPHuddx7o/ISTCurO5YQfrTueg9lqeWg1pgqiG8iuWxvdpwZ0DtcqkyrDZIC3e/uG/t2CrIC3O6UCBwBbQsL21VnPDDvZE0AECmuk4gnLQBFHN5Fcs9WvTgCljtGKpUso6Zf/wL5QEUvZC7pmzx/kGWN/8Ww+wPvwbtrd+B7XT7iHlEk0Q1YhzxdK/tWKpcsjLhYNrYfcy6x6D5D3WOgn5pAbUa2W1ANoMPpsEGnaAuo21NaAuiiaIaiK/YikrJ4/ZWrFUseVkwv5VsGMJ7PreusegZi1o0g3aDS3YJdSgDdTUSROVe2iCqAZsNsPDX8U4KpY6aMVSxZN1Cvb8ADu+hT0rrIFj30DoNAK6XA3thoGPTpSoypcmiGrgjRW7Wb7tKM+N6qoVSxXJ6WSrhbBjidViyMuGOo2sEtIuV1s3ntXUlp7ynBIThIg8AHxhjDleDvGoMuZcsfTXAa09HY46GWfdgbzjW2tswdisG836TrSSQkgfqKEr8qmKwZUWRGNgg4j8gbVm9HJTVZahq+K0YqmCSN5jJYQd30LCH9a24C4w6DErKTTproPJqkIqMUEYY54VkeeAK4A7gHdFZC7wqTFmn7sDVKWjFUseZAwciYWdS6ykkLTT2t68N1z+InS+2qo2UqqCc2kMwhhjRCQRSARygfrA1yKywhjzhDsDVBdOK5Y8wJYHh3+3xhN2fAsnD4F4QatLIeJO6BwFgc09HaVSF8SVMYiHgFuBZOAT4HFjTI6I1AD2AJogKhCtWCpHudnw50+w81trXOF0Enj5QrvLIPJJ6DgS6gR5OkqlSs2VFkQDYJwx5qDzRmOMTURGuScsVVpaseRm2adh70qrpbB7OWSdBJ+60OEKazyhw3Dw1aSsqgZXEsRSIDX/iYgEAF2MMb8bY3a4LTJ1wRZusiqWbuyrFUtl6sxx2LXMGlPYuxJyM6FWA+h6NXQZDW2GgLefp6NUqsy5kiD+DfRyep5exDblYZsOHeeJb6yKpcmjtWKpTJw6CsuetMYUbLnWdNe9brNaCi0vAS+9jUhVba78CxfnslZ715L+n1GB6KpwZcwY2DIPvn/cai30vxdCx0KzXlqOqqoVVz7o94vIg1itBoD7gP3uC0ldiMIVS/W1YunipB+DJQ9b3UkhfeCaf1vzHilVDbnyVfMe4FIgHogD+gET3RmUco1zxdI7N/XUiqWLYQxs+Rre62fNhTT8Jfjrck0Oqlpz5Ua5Y8D4cohFXaB/rtjF8m1HeX5UVyK1Yqn00pPgu4etsYbmEVarIbijp6NSyuNcuQ/CD7gTCAUcpRrGmL+6cO4I4F+AF/CJMWZaof2tsKbvCMaqlLrZGBMnIuFYXVoBQB4w1RjzlatvqjpYuCme91bt48a+LbhDK5ZKb+t8+P4xazbVyyfDJffr4LNSdq50Mf0XaAJcCawBQoBTJZ0kIl7Ae8BIoCtwo4h0LXTYdGCmMSYMmAK8at+eAdxqjAkFRgBviUg9F2KtFv7QiqWLdzoZ5t4KX99hLbhz988w8B+aHJRy4kqCaG+MeQ44bYyZAURhjUOUpC+w1xiz3xiTDcwBxhQ6pivwo/3xqvz9xpjdxpg99scJwDGsVka1F3/iDBNnbqRpoFYsldq2hdZYw66lMOwFuHMFNOrs6aiUqnBc+XTJsf8+ISLdgEDAlQ7v5sBhp+dx9m3OYoFx9sdjAX8RKTA3gYj0BXyAcyYGFJGJIhItItFJSUkuhFS5nc46W7H06W0RWrF0oU6nwLw7YN5tEBgCE9fAoEe01aDUebiSID4SkfrAs8BiYDvwWhm9/mPAEBHZBAzBqpTKy98pIk2xurjuMMbYCp9sjPnIGBNhjIkIDq7aDYz8iqVd9oql9o20YumCbF8M7/ezBqKHPgd/+x80LtzjqZRyVuxXJ/uEfGn2xYJ+AtpewLXjgRZOz0Ps2xzs3Ufj7K9VF7jWGHPC/jwA+A54xhiz7gJet0r654pd/LBdK5YuWEaqdcPb1q+haQ+4dRE0DvV0VEpVCsW2IOzf2ks7W+sGoIOItBERH6xS2cXOB4hIQ3sSAngKq6IJ+/ELsAawvy7l61cZWrFUSju/s8Yati+Cy56xtxo0OSjlKle6mFaKyGMi0kJEGuT/lHSSMSYXuB9YDuwA5hpjtonIFBEZbT8sEtglIruxVq6bat9+AzAYuF1EYuw/4Rf43qqE/Iql/m21YsllGanwzV0w5ybwbwwTV8OQJ8DL29ORKVWpSEmrh4rIn0VsNsaYC+lucruIiAgTHR3t6TDKVPyJM4x5dy11fL1YeN8AHZR2xc7vYck/ICMFBj8Ogx7VxKBUMURkozEmoqh9rtxJ3absQ1Ilca5YmjNR51gq0ZnjsHQSbJ4DjbvBhK+haZino1KqUnPlTupbi9pujJlZ9uEoKFix9NntfbRiqSS7l8O3D1kT7Q15EgY9BjU1oSp1sVwpAO/j9NgPGAb8AWiCcJP8iqUXrtaKpWKdOQHLnoLYL6FRKNw4B5pVy6EqpdzClS6mB5yf26e8mOO2iKq5BZvi7BVLLbn90taeDqfi2rMCFj8I6UetsYbBT2irQakyVppbSE8DOi7hBn8cOs6T32yhf9sGTBkTqhVLRck8CcuehpgvILgLjJ8FzXVxQ6XcwZUxiG+B/FKnGljzJ811Z1DVUeLJTMccS/+e0BtvL51j6Rx7V1qthlNHrOqkIU9CTV9PR6VUleVKC2K60+Nc4KAxJs5N8VRLeTbDP77aREZ2rlYsFSUzDX54Bv6YCcGd4S//hea9PR2VUlWeKwniEHDEGJMJICK1RKS1MeaAWyOrRj5Ys491+1P5v+vCtGKpsH0/wqIH4FQCDHwYhkwCb7+Sz1NKXTRX+jHmAc4T5eXZt6kysOnQcd5YsZurezTjut4hng6n4sg6ZZWu/ncs+NS2puS+/EVNDkqVI1daEDXt6zkAYIzJts+VpC7SqcwcHpoTQ5MAP16+RqfRcMg8CTNGQ+JmuPRBax4lTQxKlTtXEkSSiIw2xiwGEJExQLJ7w6oeXli0jbjjGcy75xICa+l0EADknIHZN8LRrTB+NnQa4emIlKq2XEkQ9wCzRORd+/M4oMi7q5XrFm6KZ/6meB6+vCO9W5U492H1kJcDc2+Dg7/CtZ9oclDKw1y5UW4f0N++XgPGmHS3R1XFHUrJ4NmFW+nTuj5/v6ydp8OpGGx5sOBu2LMcRr0F3a/zdERKVXslDlKLyCsiUs8Yk26MSReR+iLycnkEVxXl5Nl4cM4mROCt8T2pqfc7gDHw3aOw9RtrIDriDk9HpJTCtSqmkfmrvAHYV5e7yn0hVW3/WrmHmMMneHVcd5rXq+XpcCqG/02Gjf+xylgHPuzpaJRSdq4kCC8RcdyuKiK1AL19tRTW7U/hvdV7uSEihFFhzTwdTsXwy5vWT8RfYdgLno5GKeXElUHqWcD/ROQ/9ud3oDO5XrATGdk8/FUMbYLq8MLVuuwlANGfwcoXodt1cNV00DJfpSoUVwapXxORWOBy+6aXjDHL3RtW1WKMYdI3W0hOz2LBfQOo41uaORKrmC1fw5JHoMOVMPYDqOHl6YiUUoW49ElljFkGLBOROsA4EfnOGBPl3tCqjtnrD7NsWyLPXNWFbs0DPR2O5+1eblUstboUbpihS4IqVUG5UsXkIyJjRWQecAQYCnzg9siqiL3HTjFlyTYGdWjInQN1lnQOrIW5t1rLgt44B7x1oF6piuq8LQgRuQK4EbgCWIU17tDHGKM1iC7Kys3jgdkx1PapyT+v70GNGtW8jz1hE3z5F6jXEm6eD34Bno5IKVWM4rqYlgE/AwONMX8CiMi/yiWqKuK1pbvYcSSNz26PoFFANZ9LKGkXfHEt1KoPtyyEOkGejkgpVYLiEkQvYDywUkT2Yy0zqiOJLlq16xifrf2T2y9tzdDOjT0djmcdPwgzrwHxglsXQmBzT0eklHLBeccgjDExxphJxph2wAtAOOAtIktFZGK5RVgJJZ3K4vF5sXRu4s+kkZ09HY5nnToK/70Gck7DLQsgSKcWUaqycGmeB2PMr8aYB4AQ4E2gv1ujqsRsNsNj82I5lZnL2zf2xM+7Gje6zhyHL8bBqUSY8DU06ebpiJRSF+CCCvKNMTbgB/uPKsJna/9kze4kXrqmGx0bV+PV4bJPw6wbIHk33PQVtOjr6YiUUhdI79gqQ1vjT/Lasp0M79qYm/u19HQ4npObBXMmQHw0XD8D2g31dERKqVJw61SiIjJCRHaJyF4RmVTE/lYi8j8R2Swiq0UkxGnfbSKyx/5zmzvjLAsZ2bk8NGcTDer48Nq1YdV3dbi8XPjmTti/Cka/C11HezoipVQpFXcfRLGr2BhjUovbLyJewHvAcKxFhjaIyGJjzHanw6YDM40xM0RkKPAqcIv9tV8AIgADbLSfe9yVN+UJL16ncfMAACAASURBVC3Zzv7k08y6sx8N6lTTFVltNmsd6R3fwpWvQs8Jno5IKXURiuti2oj14VzUV2EDtC3h2n2BvcaY/QAiMgcYAzgniK7AI/bHq4CF9sdXAivyk5CIrABGALNLeE2PWLrlCLPXH+beyHZc2r6hp8PxDGPgh2cg5gsYMgkuuc/TESmlLtJ5E4Qx5mLnhWgOHHZ6Hgf0K3RMLDAO+BcwFvAXkaDznHtO8by93HYiQMuWnunzTzhxhknzt9AjJJBHhnf0SAwVwprXYd370O8eiDynN1EpVQm5MheTiMjNIvKc/XlLESmrkpTHgCEisgkYAsQDea6ebIz5yBgTYYyJCA4OLqOQXJdnM/zjqxhy82z8a3xPvKvr6nDrPoDVr0CPm6yupeo6/qJUFePKJ9r7wCXATfbnp7DGFkoSD7Rweh5i3+ZgjEkwxowzxvQEnrFvO+HKuRXBv1fvZf2fqUwZ043WDet4OhzPiPkSlj0JnUfB6HegRjVNkkpVQa7839zPGPN3IBMcS466Mgq7AeggIm1ExAdr2o7FzgeISEMRyY/hKeAz++PlwBX29a/rY00YWKHWoNh48DhvrtzDmPBmjOtVTaeO2LEEFt0PbYbAtZ+Cl1ZNK1WVuJIgcuwVSQZARIIBW0knGWNygfuxPth3AHONMdtEZIqI5Nc+RgK7RGQ30BiYaj83FXgJK8lsAKaUVDVVntIyc3hoziaaBvrx0jXdqmdJ6/7V8PUd0KwnjP8SvKv5ZIRKVUGufOV7G1gANBKRqcB1wLOuXNwY8z3wfaFtzzs9/hr4+jznfsbZFkWFYYzhuYVbOXIyk7l3X0KAXzVc7ObwBph9EwS1hwnzwLeupyNSSrmBK0uOzhKRjcAwrJLXa4wxO9weWQW1YFM8i2ISeHR4R3q3qu/pcMrf0W0w6zqo28iafK92sbfLKKUqMVdvlDuG0z0IItKgInX5lJeDKad5buFW+rZuwH2Xtfd0OOUvZR/8d6y1Ctyti8C/iacjUkq5kas3yrUEjtsf1wMOAdVq/cycPBsPzonBq4bw5vhwvKrb6nBpCda03Xk5cMdSqN/K0xEppdysuPUg2hhj2gIrgauNMQ2NMUHAKKrhbK5vrthN7OETTLs2jOb1qtk6yqdTrJZDxnG4+RtoVM3XuFCqmnCliqm/fbAZAGPMUuBS94VU8fy6N5l/r9nH+D4tuKp7U0+HU74y02DWtZD6J9w4G5r38nRESqly4koVU4KIPAt8YX8+AUhwX0gVy/HT2Tw8N4Y2Devw/NVdPR1O+co5A7NvhMQt8JdZ0GaQpyNSSpUjV1oQNwLBWKWuC4BG9m1VnjGGJ77ZTOrpbN4e35PaPtXoRrC8HJh3OxxcC9d8AJ1GeDoipVQ5c6XMNRV4SET8racm3f1hVQyzfj/Eiu1HeTaqC92aB3o6nPJjs8HCe2H3Moj6J4Rd7+mIlFIe4Mpkfd3tk+ltBbaJyEYRqfKLC+85eoqXlmxncMdg/jqgGhVsGQNLH4ct82DY89Dnb56OSCnlIa70mXwIPGKMWQUgIpHAR1ThgerMnDwemL0Jf7+aTL8+jBrVpaT1dDJ8/xhsWwCXPggDHyn5HKVUleVKgqiTnxwAjDGrRaRKT106belOdiae4j+396GRfzWYY8gY2PI1LH0CstNh6HMw6FGdtlupas6VBLHfvhbEf+3Pbwb2uy8kz/px51E+//UAdwxozWWdG3k6HPc7GQ/fPWKNNzSPgDHv6X0OSinAtQTxV2AyMN/+/Gf7tirnWFomj83bTJemATw5oop/SBoDGz+HFc9bFUtXvmKtBlfDy9ORKaUqCFeqmI4DD5ZDLB5lsxkenRdLRnYub48Px8+7Cn9Qpu6HxQ/CgZ+hzWC4+m1oUI0G4pVSLilusr7F59sHYIwZXdz+yubTX/7k5z3JTB3bjQ6N/T0djnvY8mDdv+HHl8HLG67+F/S6TccalFJFKq4FcQlwGGsW19+xJuqrkrbGn+T15Tu5MrQxN/Vt6elw3OPYDmv1t/ho6DgCot6AwGq6Ep5SyiXFJYgmwHCsu6ZvAr4DZhtjtpVHYOXldFYuD87eRFAdX6aNC6t6q8PlZsPat2DN6+AXYC0N2u1abTUopUp03gRhjMkDlgHLRMQXK1GsFpHJxph3yytAd0vLzMG/ljcvj+1G/TquLLVdiSRssloNR7daSWHk61CnoaejUkpVEsUOUtsTQxRWcmjN2eVHq4ymgbVYeN+lVavlkHMGVk+DX9+xVn4bPxs6X+XpqJRSlUxxg9QzgW5Ya0pPNsZsLbeoylmVSg4Hf4XFD0DKXuh1Kwx/CWrV83RUSqlKqLgWxM3AaeAh4EGnD1HBmrQvwM2xqQuRdQpWToYNH0O9VtaSoG0jPR2VUqoSK24MwpWpwFVFsHclfPsPOBkH/e+Doc+CT5WeDUUpVQ6q0QIHVVBGKix/BmK/hIad4M4foEVfT0ellKoiNEFUVtsXw3ePwplUGPy49VPT19NRKaWqEE0Qlc2po9aU3DsWQ5MwuPkbaBrm6aiUUlWQJojKwhiInQPLJlllrMNegEsfsKbMUEopN9AEURmcOAxL/mENRrfoD6PfgeCOno5KKVXFaYKoyGw2iP4UVr5otSBGvg597oIaWmCmlHI/t37SiMgIEdklIntFZFIR+1uKyCoR2SQim0XkKvt2bxGZISJbRGSHiDzlzjgrpJR9MGOUNd4Q0gfu+w363a3JQSlVbtzWghARL+A9rAn/4oANIrLYGLPd6bBngbnGmH+LSFesu7ZbA9cDvsaY7iJSG9guIrONMQfcFW+FkZcL696DVa9YVUlj3oPwCTq5nlKq3Lmzi6kvsNcYsx9AROYAYwDnBGGA/DuyA4EEp+11RKQmUAvIBtLcGGvFcHQbLPq7Ncle51EQ9U/wb+LpqJRS1ZQ7E0RzrPUk8sUB/Qod8yLwg4g8ANQBLrdv/xormRwBagMPG2NSC7+AiEwEJgK0bFnJ13HYvxq+uM6aN+n6z6HrNdpqUEp5lKc7tG8EPjfGhABXAf8VkRpYrY88oBnQBnhURNoWPtkY85ExJsIYExEcHFyecZetjFRYcI+17Off10PoWE0OSimPc2eCiAdaOD0PsW9zdicwF8AY8xvgBzTEWqBomTEmxxhzDFgLRLgxVs8xBpY8DKeTYNzHULuBpyNSSinAvQliA9BBRNqIiA8wHii8zvUhYBiAiHTBShBJ9u1D7dvrAP2BnW6M1XM2fwXbF8Jlz0CzcE9Ho5RSDm5LEMaYXOB+YDmwA6taaZuITBGR0fbDHgXuEpFYrLWvbzfGGKzqp7oisg0r0fzHGLPZXbF6zPGD8N1j0PJSGPCQp6NRSqkC3HqjnDHme6zSVedtzzs93g4MKOK8dKxS16rLlgcL7rYej/0Aanh5Nh6llCpE76T2lLX/gkO/wdgPoX4rT0ejKoicnBzi4uLIzMz0dCiqivHz8yMkJARvb9fnb9ME4QkJMbBqqlWtFPYXT0ejKpC4uDj8/f1p3bp11VoKV3mUMYaUlBTi4uJo06aNy+d5usy1+snOgPl3QZ1GEPWGlrOqAjIzMwkKCtLkoMqUiBAUFHTBLVNtQZS3Fc9D8m64ZaGWtKoiaXJQ7lCaf1fagihPe1bAho+h/9+h3WWejkYppYqlCaK8nE625llq1BWGPV/y8Up5SN26dd1y3QMHDvDll1+65drObrzxRsLCwnjzzTd5/vnnWbly5TnHrF69mlGjRpX6NV555ZXz7jPGMHToUNLSrOnj/vrXv9KoUSO6detW4LjU1FSGDx9Ohw4dGD58OMePHwfgxRdfZPr06edc97HHHuPHH38sdcyloQmiPBgD3z4EZ45bd0t7+3k6IqXKXXkkiMTERDZs2MDmzZt5+OGHmTJlCpdffnnJJ16g4hLE999/T48ePQgIsOYhvf3221m2bNk5x02bNo1hw4axZ88ehg0bxrRp04p9zQceeKDEY8qajkGUh01fwM4lcMXL0KRbyccrBUz+dhvbE8p2EuOuzQJ44erQCz5v3759/P3vfycpKYnatWvz8ccf07lzZ7799ltefvllsrOzCQoKYtasWTRu3Jg1a9bw0EPWzZ8iwk8//cSkSZPYsWMH4eHh3HbbbTz88MMFXuO1117jiy++oEaNGowcOZJp06YRExPDPffcQ0ZGBu3ateOzzz6jfv36REZG0q9fP1atWsWJEyf49NNPGTRoEFdccQXx8fGEh4fzzjvv8OmnnzJq1Ciuu+46li1bxj/+8Q9q167NwIEDHa97+vRpHnjgAbZu3UpOTg4vvvgiY8aM4fPPP2fx4sVkZGSwb98+xo4dy+uvv86kSZM4c+YM4eHhhIaGMmvWrALvY9asWUycONHxfPDgwRw4cOCcv+miRYtYvXo1ALfddhuRkZG89tprBY75+OOPmT9/PvPnz6dVq1akpKSQmJhIkyblM8uztiDcLXU/LH0SWg+yxh6UqoQmTpzIO++8w8aNG5k+fTr33XcfAAMHDmTdunVs2rSJ8ePH8/rrrwMwffp03nvvPWJiYvj555+pVasW06ZNY9CgQcTExJyTHJYuXcqiRYv4/fffiY2N5YknngDg1ltv5bXXXmPz5s10796dyZMnO87Jzc1l/fr1vPXWW47tixcvpl27dsTExDBo0CDHsZmZmdx11118++23bNy4kcTERMe+qVOnMnToUNavX8+qVat4/PHHOX36NAAxMTF89dVXbNmyha+++orDhw8zbdo0atWqRUxMzDnJAWDt2rX07t27xL/p0aNHadq0KQBNmjTh6NGjBfa/++67LFmyhIULF1KrVi0AevXqxdq1a0u8dlnRFoQ75eXC/IngVdN+t7TmY+W60nzTd4f09HR+/fVXrr/+7OQGWVlZgHXfxl/+8heOHDlCdna2o8Z+wIABPPLII0yYMIFx48YREhJS7GusXLmSO+64g9q1awPQoEEDTp48yYkTJxgyZAhgfct2jmHcuHEA9O7du8hv6M527txJmzZt6NChAwA333wzH330EQA//PADixcvdvT7Z2ZmcujQIQCGDRtGYGAgAF27duXgwYO0aNGiiFc4KzU1FX9//2KPKUxEClQZzZw5kxYtWrBw4cICN7Y1atSIhISEoi7hFvqJ5U4//xPiNlj3OwQW/z+IUhWVzWajXr16xMTEOH527NgBWP3i999/P1u2bOHDDz901NlPmjSJTz75hDNnzjBgwAB27iz7uTZ9fX0B8PLyIjc3t9TXMcbwzTffON7boUOH6NKlS4HXuJDXqVmzJjabrcTjGjduzJEjRwA4cuQIjRo1cuzr3r07Bw4cIC4ursA5mZmZjtZEedAE4S5xG2HNa9D9Buh+naejUarUAgICaNOmDfPmzQOsD9TY2FgATp48SfPmzQGYMWOG45x9+/bRvXt3nnzySfr06cPOnTvx9/fn1KlTRb7G8OHD+c9//kNGRgZgfQsPDAykfv36/PzzzwD897//dbQmLlTnzp05cOAA+/btA2D27NmOfVdeeSXvvPMO1jyhsGnTphKv5+3tTU5OTpH7OnXqxP79+0u8xujRox1/sxkzZjBmzBjHvp49e/Lhhx8yevToAi2G3bt3n1MN5U6aINwhKx3m/w0CmsFV/+fpaJS6IBkZGYSEhDh+3njjDWbNmsWnn35Kjx49CA0NZdGiRYBVknn99dfTu3dvGjZs6LjGW2+9Rbdu3QgLC8Pb25uRI0cSFhaGl5cXPXr04M033yzwmiNGjGD06NFEREQQHh7u6O6ZMWMGjz/+OGFhYcTExPD886UrEffz8+Ojjz4iKiqKXr16Ffi2/txzz5GTk0NYWBihoaE899xzJV5v4sSJhIWFMWHChHP2RUVFOQafwSq7veSSS9i1axchISF8+umngNXKWrFiBR06dGDlypVMmjSpwHUGDhzI9OnTiYqKIjk5mZycHPbu3UtERPktjSP5WbOyi4iIMNHR0Z4Ow/LtQ7BxBty+BFoPLPl4pex27Njh6N5QldORI0e49dZbWbFiRZled8GCBfzxxx+89NJLpb5GUf++RGSjMabIrKMtiLK283vY+DkMeFCTg1LVUNOmTbnrrrscN8qVldzcXB599NEyvWZJtIqpLKUfg8UPQJPu1gpxSqlq6YYbbijzazpXcJUXbUGUFWNg0f2QnQ7jPoGaviWfo5RSFZi2IMpK9GewZzmMeA0adfZ0NEopddG0BVEWkvfA8meg3VDoO7Hk45VSqhLQBHGx8nKsBYC8/WDM+3q3tFKqytBPs4u15jVI2ARXvw0BTT0djVIXzcvLi/DwcLp168b111/vuHktMTGR8ePH065dO3r37s1VV13F7t27Hee99dZb+Pn5cfLkySKvGxMTwyWXXEJoaChhYWF89dVX5xzz4IMPFjvd+MKFC5kyZQoAP/30E7169aJmzZp8/fXXBY6bMWMGHTp0oEOHDgVu4Cvq2klJSYwYMaKYv0j1pQniYhxaZ02nET4Buo72dDRKlYn8iei2bt2Kj48PH3zwAcYYxo4dS2RkJPv27WPjxo28+uqrBSaYmz17Nn369GH+/PlFXrd27drMnDmTbdu2OWZWPXHihGN/dHS0Y02E83n99dcdEwW2bNmSzz//nJtuuqnAMampqUyePJnff/+d9evXM3ny5GKvGxwcTNOmTct1ErzKQgepSyszzZqIL7AFjCjfOdpVNbF0EiRuKdtrNukOI13/9zpo0CA2b97MqlWr8Pb25p577nHs69Gjh+Pxvn37SE9P5/3332fq1Knccccd51yrY8eOjsfNmjWjUaNGJCUlUa9ePfLy8nj88cf58ssvWbBgQZGx7N69G19fX8cd261btwagRqFu3eXLlzN8+HAaNLCW9B0+fDjLli3jxhtvdByTnJzM1VdfzbPPPktUVBTXXHMNs2bNYsCAAS7/baoDbUGU1rKn4ORhGPcR+AV4Ohqlylxubi5Lly6le/fubN26tdgprOfMmcP48eMZNGgQu3btOmfq6sLWr19PdnY27dq1A6yprUePHu2Y/rooa9eupVevXiXGHR8fX2DG1ZCQEOLj4x3Pjx49SlRUFFOmTCEqKgqAiIgIx5xP6ixtQZTG9kUQ8wUMfhxa9vd0NKqquoBv+mUpfzEcsFoQd955Jx988EGx58yePZsFCxZQo0YNrr32WubNm8f9999f5LFHjhzhlltuYcaMGdSoUYOEhATmzZtXYP6i850XHBxcqveULycnh2HDhvHee+8VmPivvKfRriw0QVyotCPWXEvNesKQJz0djVJlLn8MwlloaOg5A8H5tmzZwp49exg+fDiAY12IohJEWloaUVFRTJ06lf79rS9XmzZtYu/evbRv3x6wJgts3749e/fuPSeu8w2AO2vevHmBZBMXF0dkZCRgTcXdu3dvli9fXiBBlPc02pWFdjFdCJsNFt0HOZnW2tJe3iWfo1QVMHToULKyshyL7ABs3ryZn3/+mdmzZ/Piiy9y4MABDhw4QEJCAgkJCRw8eLDANbKzsxk7diy33nor1113dgr8qKgoEhMTHefXrl37nOQA0KVLlyK3F3bllVfyww8/cPz4cY4fP84PP/zAlVdeCVgL83z22Wfs3LmzwPKe5T2NdmXh1gQhIiNEZJeI7BWRSUXsbykiq0Rkk4hsFpGrnPaFichvIrJNRLaIiJ87Y3XJho9h349w5VRo2MHT0ShVbkSEBQsWsHLlStq1a0doaChPPfUUTZo0Yc6cOYwdO7bA8WPHjmXOnDkFts2dO5effvqJzz//nPDwcMLDw89pqRRn8ODBbNq0ybFuw4YNGwgJCWHevHncfffdhIZaK/A1aNCA5557jj59+tCnTx+ef/55x4A1WGW8s2fP5scff+T9998HYNWqVY7xCOXEGOOWH8AL2Ae0BXyAWKBroWM+Au61P+4KHLA/rglsBnrYnwcBXsW9Xu/evY1bHd1uzEuNjPniemNsNve+lqq2tm/f7ukQKrQHH3zQrFixosyvO2jQIJOamlrm161oivr3BUSb83yuurMF0RfYa4zZb4zJBuYAYwodY4D8EqBAIH+U6ApgszEmFsAYk2KMyXNjrMXLzbbulvapC2PeBae1Y5VS5efpp5923LhXVpKSknjkkUeoX79+mV63KnBngmgOHHZ6Hmff5uxF4GYRiQO+Bx6wb+8IGBFZLiJ/iMgTRb2AiEwUkWgRiU5KSirb6J2tmmrVo49+B+o2Kvl4pZRbNG7cmNGjy/am1ODgYK655poyvWZV4elB6huBz40xIcBVwH9FpAZWF9NAYIL991gRGVb4ZGPMR8aYCGNMxMWWv53XgV9g7b+g9+3Q+aoSD1dKqarCnQkiHmjh9DzEvs3ZncBcAGPMb4Af0BCrtfGTMSbZGJOB1boo+Q6ZsnbmBCy4Bxq0gSumlvvLK6WUJ7kzQWwAOohIGxHxAcYDiwsdcwgYBiAiXbASRBKwHOguIrVFpCYwBNjuxliL9v3jkJZglbT6nn8CMaWUqorcdqOcMSZXRO7H+rD3Aj4zxmwTkSlYo+aLgUeBj0XkYawB69vto+rHReQNrCRjgO+NMd+5K9YibfkatsyFyKchpMj1vJVSqkpz6xiEMeZ7Y0xHY0w7Y8xU+7bn7ckBY8x2Y8wAY0wPY0y4MeYHp3O/MMaEGmO6GWOKHKR2m5Nx8N0jENIHBpXvIuFKeVr+dN/5PwcOHDjvsZ9//rnjjukXX3yR6dOnF9g/depUx3Wcr/v222+7FMvf/vY3tm8vvvPggw8+YObMmS5drySRkZF06tSJsLAwOnfuzP33319gxtnzeeWVV0r1etdddx379+8vsG306NEFbtqLiYmhf//+hIeHExERwfr164Gi/94Ajz32GD/++GOp4ilMp9oozGazxh3ycq2J+Lz0T6Sql6Km2iitZ555hmeeeQaw1mIofF1Hvf15Ftr65JNPSnwN5xlmy8KsWbOIiIggOzubp556ijFjxrBmzZpiz3nllVd4+umnL+h1tm3bRl5eHm3btnVsmz9//jlrVjzxxBO88MILjBw5ku+//54nnnii2HmrHnjgAe666y6GDh16QfEUxdNVTBXPuvfgwM/WRGkN2pZ8vFLuFBlZtj+l1Lp1a5KTkwFr3YbIi7jWgQMH6NSpE7feeivdunXj8OHD3HvvvURERBAaGsoLL7zgODYyMpLo6GjASjDPPPMMPXr0oH///o4ZY52/SUdGRvLkk0/St29fOnbs6JihNSMjgxtuuIGuXbsyduxY+vXr57ju+fj4+PD6669z6NAhYmNjAbjmmmvo3bs3oaGhjmlHJk2a5JjgcMKECec9rrBZs2YxZszZW8PS09N54403ePbZZwscJyKkpaUBcPLkSZo1a3bOtT7++GNGjhzJmTNnaNWqFSkpKSQmJhb7/lyhCcJZ4lb43xToPAp63uLpaJTyiPwPu/Dw8HOm0Cgre/bs4b777mPbtm20atWKqVOnEh0dzebNm1mzZg2bN28+55zTp0/Tv39/YmNjGTx4MB9//HGR187NzWX9+vW89dZbTJ48GYD333+f+vXrs337dl566SU2btzoUpxeXl706NGDnTt3AvDZZ5+xceNGoqOjefvtt0lJSWHatGmOVtesWbPOe1xha9euLTCF+nPPPcejjz5K7dq1Cxz31ltv8fjjj9OiRQsee+wxXn311QL73333XZYsWcLChQsdEw726tWrTBZA0v6TfDmZ1t3Stepby4fq3dKqIihhCmx3KMsupvNp1aqVYzZXsOZp+uijj8jNzeXIkSNs376dsLCwAuf4+PgwatQoAHr37s2KFSuKvPa4ceMcx+SPn/zyyy889NBDAHTr1u2caxfH2Od+Anj77bcdCxodPnyYPXv2EBQUdM45rhznPH15TEwM+/bt48033zxnzOff//43b775Jtdeey1z587lzjvvZOXKlQDMnDmTFi1asHDhQry9z04eWlbTl2sLIt//psCx7TDmPahz7n9wpaqzmjVrYrPZAGtq7ItVp04dx+M///yT6dOn87///Y/NmzcTFRVV5Gt4e3sj9i9uXl5e5ObmFnltX1/fEo9xVV5eHlu2bKFLly6sXr2alStX8ttvvxEbG0vPnj2LjNPV42rVquXY/ttvvxEdHU3r1q0ZOHAgu3fvdnTjzZgxw5H0rr/+escgNUD37t05cOAAcXFxBa5dVtOXa4IA2L/aGnvocxd0GO7paJSqcFq3bu3olvnmm2/K9NppaWnUqVOHwMBAjh49ytKlS8v0+gADBgxg7ty5AGzfvp0tW0peyjUnJ4ennnqKFi1aEBYWxsmTJ6lfvz61a9dm586drFu3znGst7c3OTk5AMUe58x5+vJ7772XhIQEDhw4wC+//ELHjh0dA9HNmjVzDJL/+OOPdOhwdibpnj178uGHHzJ69OgCLYaymr5cE0RGKiy4Fxp2hOFTPB2NUhXSCy+8wEMPPURERAReXl5leu0ePXrQs2dPOnfuzE033eSWdaHvu+8+kpKS6Nq1K88++yyhoaEEBgYWeeyECRMICwujW7dunD59mkWLFgEwYsQIcnNz6dKlC5MmTSrQRTZx4kTCwsKYMGFCscc5i4qKKnEVPbAGoB999FF69OjB008/fc6g98CBA5k+fTpRUVEkJyeTk5PD3r17iYi4+Pu3xLl/rTKLiIgwJVUlFCk9Cb590Fodrll42Qem1AXYsWMHXbp08XQYVU5eXh45OTn4+fmxb98+Lr/8cnbt2oWPj4/HYjpz5gyXXXYZa9euLdOku2DBAv744w9eeumlc/YV9e9LRDYaY4rMJjpIXTcYbpzt6SiUUm6UkZHBZZddRk5ODsYY3n//fY8mB7DGICZPnkx8fDwtW7Yss+vm5uby6KNlc4OvJgilVJXn7+9f4n0PnpC/FGpZuv7668vsWjoGoVQFU1W6fVXF33jWyQAAB6VJREFUUpp/V5oglKpA/Pz8SElJ0SShypQxhpSUFPz8/C7oPO1iUqoCCQkJIS4uDreukKiqJT8/P0JCQi7oHE0QSlUg3t7etGnTxtNhKAVoF5NSSqnz0ATx/+3de4wVZxnH8e8vrEAvhkvFZgtNAdNU+1e5pAGxptFaLfESE5LS1JR6TTSmVv7QbjQVq9FImqqkjZBokShibUvUYBQrgompAUtA2AIrFHqBFKEaW2lMU+njH/MczuzJnELh7B525/dJJvvO874zZ+bd95z3zOW8Y2ZmldxBmJlZpVHzS2pJx4Fnur0d5+gtwAvd3ojziOtjMNdHk+tisHOpjysiYkpVxqjpIEYDSU+0+8l7Hbk+BnN9NLkuBhuq+vApJjMzq+QOwszMKrmDOL9UP7y2vlwfg7k+mlwXgw1JffgahJmZVfIRhJmZVXIHYWZmldxBDDFJl0vaLGmPpCclfSHjkyU9Jml//p2UcUlaIemApF2SZpfWtSTL75e0pFv7dK4kjZG0Q9KGnJ8haWvu80OSxmZ8XM4fyPzppXX0ZXxAUucH1R8mkiZKekTSPkl7Jc2vedv4Yr5P+iWtkzS+Lu1D0oOSjknqL8U61hYkzZG0O5dZIUmn3aiI8DSEE9ALzM70m4G/A1cDy4G7Mn4X8J1MLwR+CwiYB2zN+GTgYP6dlOlJ3d6/s6yTpcDPgA05/wtgcaZXAp/N9OeAlZleDDyU6auBvwHjgBnAU8CYbu/XWdbFGuBTmR4LTKxr2wCmAoeAC0rt4va6tA/g3cBsoL8U61hbALZlWeWyN512m7pdKXWbgF8B7wMGgN6M9QIDmV4F3FIqP5D5twCrSvFB5UbKBEwDNgHvATZkY30B6Mn8+cDGTG8E5me6J8sJ6AP6Sus8VW4kTcCE/EBUS7yubWMq8Fx+uPVk+3h/ndoHML2lg+hIW8i8faX4oHLtJp9iGkZ5CDwL2ApcGhHPZ9ZR4NJMN94kDYcz1i4+0nwP+BLwWs5fAvw7Iv6X8+X9OrXPmf9ilh8tdTEDOA6szlNuP5R0ETVtGxFxBLgXeBZ4nuL/vZ36tg/oXFuYmunW+OtyBzFMJF0MPArcGREvlfOi6NJH/f3Gkj4IHIuI7d3elvNED8UphR9ExCzgZYrTCKfUpW0A5Pn1j1B0nJcBFwEf6OpGnUe60RbcQQwDSW+i6BzWRsT6DP9DUm/m9wLHMn4EuLy0+LSMtYuPJAuAD0t6Gvg5xWmm7wMTJTUeXlXer1P7nPkTgH8yOuoCim9xhyNia84/QtFh1LFtANwAHIqI4xHxKrCeos3UtX1A59rCkUy3xl+XO4ghlncK/AjYGxH3lbJ+DTTuMFhCcW2iEb8t71KYB7yYh5gbgRslTcpvWjdmbMSIiL6ImBYR0ykuKv4xIm4FNgOLslhrXTTqaFGWj4wvzrtYZgBXUlyAG1Ei4ijwnKSrMvReYA81bBvpWWCepAvzfdOoj1q2j9SRtpB5L0mal3V7W2ld7XX7osxon4B3URwW7gJ25rSQ4lzpJmA/8AdgcpYX8ADFnRe7gbmldX0COJDTx7u9b+dYL9fTvItpJsUb+ADwMDAu4+Nz/kDmzywt/5WsowHO4G6M83UCrgGeyPbxS4o7T2rbNoCvA/uAfuAnFHci1aJ9AOsorr28SnF0+clOtgVgbtbrU8D9tNwcUTV5qA0zM6vkU0xmZlbJHYSZmVVyB2FmZpXcQZiZWSV3EGZmVskdhNWCpEsk7czpqKQjpfmxp1l2rqQVZ/Aaj3doWy+UtDZH3uyX9Of8JX7HXsPsTPg2V6sdScuAExFxbynWE83xfrpKUh8wJSKW5vxVwNMR8Up3t8zqxkcQVluSfixppaStwHJJ10r6Sw6c93jjF86Srlfz2RXLctz+LZIOSrqjtL4TpfJb1HzOw9rG2PuSFmZse47Jv6Fi03opDYMQEQONzqH0GveUjoCOSFqd8Y9J2pbxVZLGDEnlWS24g7C6mwa8M7+t7wOui2LgvLuBb7VZ5u0Uw1BfC3wtx9pqNQu4k+LZBDOBBZLGUwy/fFNEzAGmtFn/g8CXs7P6pqQrWwtExN0RcQ3FL9L/Bdwv6R3AzcCCzDsJ3HraGjBro+f0RcxGtYcj4mSmJwBr8gM5gKoPfoDf5Df6VyQdoxiC+XBLmW0RcRhA0k6Kcf5PAAcj4lCWWQd8pnXlEbFT0kyKcXRuAP4qaX5E7C2Xy6OSnwL3RcR2SZ8H5mR5gAtoDu5m9oa5g7C6e7mU/gawOSI+quLZHVvaLFO+FnCS6vfRmZRpKyJOUIxmul7SaxTjd+1tKbaMYjTY1TkvYE1E9L2R1zJrx6eYzJom0Dz3f/sQrH8AmKnms5NvriokaYGazx4eS3Ga6pmWMh+iOLq4oxTeBCyS9NYsM1nSFZ3cAasXdxBmTcuBb0vawRAcXUfEfymeo/w7SduB/1A8Ba3V24A/SdoN7KAY7fXRljJLKZ4I1rggfU9E7AG+Cvxe0i7gMYoL3mZnxbe5mg0jSRdHxIm8fvAAsD8ivtvt7TKr4iMIs+H16bxo/STFKa1VXd4es7Z8BGFmZpV8BGFmZpXcQZiZWSV3EGZmVskdhJmZVXIHYWZmlf4Ppz+ZsiPnjnYAAAAASUVORK5CYII=\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "1NS4n88dw-4W",
        "outputId": "064fcfed-40b2-4575-f1c5-f010ed342d97"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7f7e4840c810>"
            ]
          },
          "metadata": {},
          "execution_count": 110
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "plt.hlines(y=full_training_score, xmin=1000, xmax=10000, color = 'r', label = \"Full Training Data (48k)\")\n",
        "plt.plot(list(least_confident_samples_results.keys()), list(least_confident_samples_results.values()), label = \"Least confident (10k)\")\n",
        "plt.plot(list(pca_24_results_dict.keys()), list(pca_24_results_dict.values()), label = \"PCA (10k)\")\n",
        "plt.title(\"Training Data\")\n",
        "plt.xlabel(\"Training Size\")\n",
        "plt.ylabel(\"Model Accuracy\")\n",
        "plt.legend()\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "R07X2eVsRTjC"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 132
        },
        "id": "OdVAEYqY6_65",
        "outputId": "af6f2c8e-c538-4bb8-f673-2c033affdab6"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7f7e481de950>"
            ]
          },
          "metadata": {},
          "execution_count": 111
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "plt.imshow(np.array([list(least_confident_samples_results.values()), list(pca_24_results_dict.values())]), cmap = 'jet')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0pqs0ZB07ptk",
        "outputId": "54732dd7-f549-488d-d335-2fda05803904"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0.88758333, 0.91333333, 0.93558333, 0.94208333, 0.94733333,\n",
              "        0.94916667, 0.948     , 0.94991667, 0.95041667, 0.95508333],\n",
              "       [0.85308333, 0.8795    , 0.89158333, 0.91141667, 0.92383333,\n",
              "        0.92975   , 0.93258333, 0.9405    , 0.94733333, 0.94966667]])"
            ]
          },
          "metadata": {},
          "execution_count": 112
        }
      ],
      "source": [
        "np.array([list(least_confident_samples_results.values()), list(pca_24_results_dict.values())])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QuGiNaOWqjPR"
      },
      "source": [
        "Reached comparable accuracy and trained faster (5m13s vs 3m35s). Unfortunately, PCA doesn't seem be able to surpass accuracy of traing with all the features."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "lTy6k7sMxElm"
      },
      "outputs": [],
      "source": [
        "from sklearn.discriminant_analysis import LinearDiscriminantAnalysis\n",
        "lda = LinearDiscriminantAnalysis(n_components=8)\n",
        "compressed = lda.fit_transform(data, labels)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "p8f6ptihr5JR"
      },
      "outputs": [],
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "x_train_lda, x_test_lda, y_train_lda, y_test_lda = train_test_split(compressed, labels, train_size = 0.80, random_state=0)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MBpzQyouszFt",
        "outputId": "8a582195-d08a-420c-9a7e-51f24ffe9faa"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.9254166666666667\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/neural_network/_multilayer_perceptron.py:696: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
            "  ConvergenceWarning,\n"
          ]
        }
      ],
      "source": [
        "from sklearn.neural_network import MLPClassifier\n",
        "\n",
        "clf = MLPClassifier(hidden_layer_sizes = (100, 100), activation = \"logistic\", random_state = 0)\n",
        "clf.fit(x_train_lda, y_train_lda)\n",
        "\n",
        "lda_full_training = clf.score(x_test_lda, y_test_lda)\n",
        "print(lda_full_training)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "phFU2_U3s-E1",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "6e81c6f1-5444-41e6-a7a8-aa0e9d3f8eaa"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/neural_network/_multilayer_perceptron.py:696: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/neural_network/_multilayer_perceptron.py:696: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/neural_network/_multilayer_perceptron.py:696: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/neural_network/_multilayer_perceptron.py:696: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/neural_network/_multilayer_perceptron.py:696: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/neural_network/_multilayer_perceptron.py:696: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/neural_network/_multilayer_perceptron.py:696: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/neural_network/_multilayer_perceptron.py:696: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/neural_network/_multilayer_perceptron.py:696: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/neural_network/_multilayer_perceptron.py:696: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
            "  ConvergenceWarning,\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training complete in  3m59s\n"
          ]
        }
      ],
      "source": [
        "optimize_train = OptimizeTrain()\n",
        "lda_results_dict = optimize_train.train_and_score(x_train_lda, y_train_lda, x_test_lda, y_test_lda)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "plt.hlines(y=full_training_score, xmin=1000, xmax=10000, color = 'r', label = \"Full Training Data (48k)\")\n",
        "plt.plot(list(least_confident_samples_results.keys()), list(least_confident_samples_results.values()), label = \"Least confident (10k)\")\n",
        "plt.plot(list(pca_24_results_dict.keys()), list(pca_24_results_dict.values()), label = \"PCA (10k)\")\n",
        "plt.plot(list(lda_results_dict.keys()), list(lda_results_dict.values()), label = \"LDA (10k)\")\n",
        "plt.title(\"Training Data\")\n",
        "plt.xlabel(\"Training Size\")\n",
        "plt.ylabel(\"Model Accuracy\")\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "gYyfbKBUVCZy",
        "outputId": "cce5ea13-1990-45df-997d-dfdfe91e2e55"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "QDGrXack2feo",
        "outputId": "1302ecf9-cc33-4118-fc75-973f617c7891"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7f7e48092f50>"
            ]
          },
          "metadata": {},
          "execution_count": 117
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "plt.hlines(y=full_training_score, xmin=1000, xmax=10000, color = 'r', label = \"Full Training Data (48k)\")\n",
        "plt.plot(list(least_confident_samples_results.keys()), list(least_confident_samples_results.values()), label = \"Least confident (10k)\")\n",
        "plt.plot(list(pca_24_results_dict.keys()), list(pca_24_results_dict.values()), label = \"PCA (10k)\")\n",
        "plt.plot(list(lda_results_dict.keys()), list(lda_results_dict.values()), label = \"LDA (10k)\")\n",
        "plt.legend()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "xaum8RZT7l9w",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "39fd9b90-e102-4ca2-d99c-4aa65f3773ec"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/manifold/_t_sne.py:783: FutureWarning: The default initialization in TSNE will change from 'random' to 'pca' in 1.2.\n",
            "  FutureWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/manifold/_t_sne.py:793: FutureWarning: The default learning rate in TSNE will change from 200.0 to 'auto' in 1.2.\n",
            "  FutureWarning,\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[t-SNE] Computing 91 nearest neighbors...\n",
            "[t-SNE] Indexed 60000 samples in 0.050s...\n",
            "[t-SNE] Computed neighbors for 60000 samples in 259.191s...\n",
            "[t-SNE] Computed conditional probabilities for sample 1000 / 60000\n",
            "[t-SNE] Computed conditional probabilities for sample 2000 / 60000\n",
            "[t-SNE] Computed conditional probabilities for sample 3000 / 60000\n",
            "[t-SNE] Computed conditional probabilities for sample 4000 / 60000\n",
            "[t-SNE] Computed conditional probabilities for sample 5000 / 60000\n",
            "[t-SNE] Computed conditional probabilities for sample 6000 / 60000\n",
            "[t-SNE] Computed conditional probabilities for sample 7000 / 60000\n",
            "[t-SNE] Computed conditional probabilities for sample 8000 / 60000\n",
            "[t-SNE] Computed conditional probabilities for sample 9000 / 60000\n",
            "[t-SNE] Computed conditional probabilities for sample 10000 / 60000\n",
            "[t-SNE] Computed conditional probabilities for sample 11000 / 60000\n",
            "[t-SNE] Computed conditional probabilities for sample 12000 / 60000\n",
            "[t-SNE] Computed conditional probabilities for sample 13000 / 60000\n",
            "[t-SNE] Computed conditional probabilities for sample 14000 / 60000\n",
            "[t-SNE] Computed conditional probabilities for sample 15000 / 60000\n",
            "[t-SNE] Computed conditional probabilities for sample 16000 / 60000\n",
            "[t-SNE] Computed conditional probabilities for sample 17000 / 60000\n",
            "[t-SNE] Computed conditional probabilities for sample 18000 / 60000\n",
            "[t-SNE] Computed conditional probabilities for sample 19000 / 60000\n",
            "[t-SNE] Computed conditional probabilities for sample 20000 / 60000\n",
            "[t-SNE] Computed conditional probabilities for sample 21000 / 60000\n",
            "[t-SNE] Computed conditional probabilities for sample 22000 / 60000\n",
            "[t-SNE] Computed conditional probabilities for sample 23000 / 60000\n",
            "[t-SNE] Computed conditional probabilities for sample 24000 / 60000\n",
            "[t-SNE] Computed conditional probabilities for sample 25000 / 60000\n",
            "[t-SNE] Computed conditional probabilities for sample 26000 / 60000\n",
            "[t-SNE] Computed conditional probabilities for sample 27000 / 60000\n",
            "[t-SNE] Computed conditional probabilities for sample 28000 / 60000\n",
            "[t-SNE] Computed conditional probabilities for sample 29000 / 60000\n",
            "[t-SNE] Computed conditional probabilities for sample 30000 / 60000\n",
            "[t-SNE] Computed conditional probabilities for sample 31000 / 60000\n",
            "[t-SNE] Computed conditional probabilities for sample 32000 / 60000\n",
            "[t-SNE] Computed conditional probabilities for sample 33000 / 60000\n",
            "[t-SNE] Computed conditional probabilities for sample 34000 / 60000\n",
            "[t-SNE] Computed conditional probabilities for sample 35000 / 60000\n",
            "[t-SNE] Computed conditional probabilities for sample 36000 / 60000\n",
            "[t-SNE] Computed conditional probabilities for sample 37000 / 60000\n",
            "[t-SNE] Computed conditional probabilities for sample 38000 / 60000\n",
            "[t-SNE] Computed conditional probabilities for sample 39000 / 60000\n",
            "[t-SNE] Computed conditional probabilities for sample 40000 / 60000\n",
            "[t-SNE] Computed conditional probabilities for sample 41000 / 60000\n",
            "[t-SNE] Computed conditional probabilities for sample 42000 / 60000\n",
            "[t-SNE] Computed conditional probabilities for sample 43000 / 60000\n",
            "[t-SNE] Computed conditional probabilities for sample 44000 / 60000\n",
            "[t-SNE] Computed conditional probabilities for sample 45000 / 60000\n",
            "[t-SNE] Computed conditional probabilities for sample 46000 / 60000\n",
            "[t-SNE] Computed conditional probabilities for sample 47000 / 60000\n",
            "[t-SNE] Computed conditional probabilities for sample 48000 / 60000\n",
            "[t-SNE] Computed conditional probabilities for sample 49000 / 60000\n",
            "[t-SNE] Computed conditional probabilities for sample 50000 / 60000\n",
            "[t-SNE] Computed conditional probabilities for sample 51000 / 60000\n",
            "[t-SNE] Computed conditional probabilities for sample 52000 / 60000\n",
            "[t-SNE] Computed conditional probabilities for sample 53000 / 60000\n",
            "[t-SNE] Computed conditional probabilities for sample 54000 / 60000\n",
            "[t-SNE] Computed conditional probabilities for sample 55000 / 60000\n",
            "[t-SNE] Computed conditional probabilities for sample 56000 / 60000\n",
            "[t-SNE] Computed conditional probabilities for sample 57000 / 60000\n",
            "[t-SNE] Computed conditional probabilities for sample 58000 / 60000\n",
            "[t-SNE] Computed conditional probabilities for sample 59000 / 60000\n",
            "[t-SNE] Computed conditional probabilities for sample 60000 / 60000\n",
            "[t-SNE] Mean sigma: 419.405861\n",
            "[t-SNE] KL divergence after 250 iterations with early exaggeration: 97.264595\n",
            "[t-SNE] KL divergence after 1000 iterations: 3.040395\n",
            "0.9674166666666667\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/neural_network/_multilayer_perceptron.py:696: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
            "  ConvergenceWarning,\n"
          ]
        }
      ],
      "source": [
        "from sklearn.manifold import TSNE\n",
        "from sklearn.neural_network import MLPClassifier\n",
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "tsne = TSNE(n_components=2, verbose=1, random_state=0)\n",
        "\n",
        "compressed = tsne.fit_transform(data, labels)\n",
        "\n",
        "x_train_tsne, x_test_tsne, y_train_tsne, y_test_tsne = train_test_split(compressed, labels, train_size = 0.80, random_state=0)\n",
        "\n",
        "clf = MLPClassifier(hidden_layer_sizes = (100, 100), activation = \"logistic\", random_state = 0)\n",
        "clf.fit(x_train_tsne, y_train_tsne)\n",
        "\n",
        "tsne_full_training = clf.score(x_test_tsne, y_test_tsne)\n",
        "print(tsne_full_training)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "54yLjAp77mRA",
        "outputId": "202e376e-35af-4043-e6e0-0ac4e3e91de2"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/neural_network/_multilayer_perceptron.py:696: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/neural_network/_multilayer_perceptron.py:696: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/neural_network/_multilayer_perceptron.py:696: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/neural_network/_multilayer_perceptron.py:696: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/neural_network/_multilayer_perceptron.py:696: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/neural_network/_multilayer_perceptron.py:696: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/neural_network/_multilayer_perceptron.py:696: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/neural_network/_multilayer_perceptron.py:696: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/neural_network/_multilayer_perceptron.py:696: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/neural_network/_multilayer_perceptron.py:696: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
            "  ConvergenceWarning,\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training complete in  2m20s\n"
          ]
        }
      ],
      "source": [
        "optimize_train = OptimizeTrain()\n",
        "tsne_results_dict = optimize_train.train_and_score(x_train_tsne, y_train_tsne, x_test_tsne, y_test_tsne)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "X1T26UK3HQcH",
        "outputId": "60be07c1-1d46-4b26-f7a7-da6da37c0b23"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "plt.hlines(y=full_training_score, xmin=1000, xmax=10000, color = 'r', label = \"Full Training Data (48k)\")\n",
        "plt.plot(list(least_confident_samples_results.keys()), list(least_confident_samples_results.values()), label = \"Least confident (10k)\")\n",
        "plt.plot(list(pca_24_results_dict.keys()), list(pca_24_results_dict.values()), label = \"PCA (10k)\")\n",
        "plt.plot(list(lda_results_dict.keys()), list(lda_results_dict.values()), label = \"LDA (10k)\")\n",
        "plt.plot(list(tsne_results_dict.keys()), list(tsne_results_dict.values()), label = \"TSNE (10k)\")\n",
        "plt.title(\"Training Data\")\n",
        "plt.xlabel(\"Training Size\")\n",
        "plt.ylabel(\"Model Accuracy\")\n",
        "plt.legend()\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gx7XRoHaJ0fQ"
      },
      "source": [
        "t-distributed Stochastic Neighbourhood Embedding (TSNE) dimensionality reduction is able to reduce our features and training time (2m28s), and surpasses the full training data accuracy with less than 4k training data!"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "_5nMIpX1IKSD"
      },
      "outputs": [],
      "source": [
        "optimize_train = OptimizeTrain()\n",
        "confidence_measures = [\"max_proba\", \"std\", \"top_two\"]\n",
        "step_sizes = [2000, 1000, 500, 100]\n",
        "\n",
        "tsne_results_dict = {}\n",
        "\n",
        "for confidence_measure in confidence_measures:\n",
        "  for step_size in step_sizes:\n",
        "    tsne_results_dict[(confidence_measure, step_size)] = optimize_train.train_and_score(x_train_tsne, y_train_tsne, x_test_tsne, y_test_tsne, confidence_measure = confidence_measure, step_size = step_size)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 286
        },
        "id": "odKgtIyjJwWJ",
        "outputId": "debf1238-94bd-490d-81fb-8bf385466983"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(0.85, 0.98)"
            ]
          },
          "metadata": {},
          "execution_count": 121
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "plt.hlines(y=full_training_score, xmin=1000, xmax=10000, color = 'black', label = \"Full Training Data (48k)\")\n",
        "plt.plot(list(least_confident_samples_results.keys()), list(least_confident_samples_results.values()), label = \"Least confident (10k)\")\n",
        "\n",
        "colors = {'max_proba': 'green', 'std': 'blue', 'top_two': 'red'}\n",
        "\n",
        "for key in tsne_results_dict:\n",
        "  results_dict = tsne_results_dict[key]\n",
        "  plt.plot(list(results_dict.keys()), list(results_dict.values()), color = colors[key[0]])\n",
        "\n",
        "plt.ylim(0.85, 0.98)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "showHeatMap(tsne_results_dict, 100, 3000)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 291
        },
        "id": "zvOV9HpDwFJ7",
        "outputId": "81b07ebb-9268-47aa-9bd3-ac87daadc960"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Key: (0: ('max_proba', 2000)) (1: ('max_proba', 1000)) (2: ('max_proba', 500)) (3: ('max_proba', 100)) (4: ('std', 2000)) (5: ('std', 1000)) (6: ('std', 500)) (7: ('std', 100)) (8: ('top_two', 2000)) (9: ('top_two', 1000)) (10: ('top_two', 500)) (11: ('top_two', 100)) "
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "r_qz_aTG-roR"
      },
      "source": [
        "Smaller step sizes here with top two confidence measuring are promising, achieving peak accuracy in around 1k samples."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "t4_GWqUU-ffF",
        "outputId": "c83f8ee2-5387-4a7d-afec-b467e2219cd5"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7f11e8d29790>]"
            ]
          },
          "execution_count": 37,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "results_dict = tsne_results_dict[('top_two', 100)]\n",
        "plt.plot(list(results_dict.keys()), list(results_dict.values()), color = colors[key[0]])\n",
        "plt.ylim()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "hKCiMemW7f7n"
      },
      "outputs": [],
      "source": [
        "optimize_train = OptimizeTrain()\n",
        "\n",
        "tsne_2k_results = optimize_train.train_and_score(x_train_tsne, y_train_tsne, x_test_tsne, y_test_tsne, confidence_measure=\"top_two\", step_size=10, training_size=1000)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "6Ti4nDQEDTBB",
        "outputId": "c8a026fe-f8ff-4449-9082-eb45cbb46a2e"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7f11e8d68090>"
            ]
          },
          "execution_count": 66,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "plt.hlines(y=full_training_score, xmin=50, xmax=2000, color = 'r', label = \"Full Training Data (48k)\")\n",
        "# plt.plot(list(least_confident_samples_results.keys()), list(least_confident_samples_results.values()), label = \"Least confident (10k)\")\n",
        "plt.plot(list(tsne_2k_results.keys()), list(tsne_2k_results.values()), label = \"TSNE (2k, 50ss)\")\n",
        "plt.legend()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lFgD62gBEy5E",
        "outputId": "0cf029a7-6b0d-46f0-a223-ae01a88714f8"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/neural_network/_multilayer_perceptron.py:696: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/neural_network/_multilayer_perceptron.py:696: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/neural_network/_multilayer_perceptron.py:696: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/neural_network/_multilayer_perceptron.py:696: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/neural_network/_multilayer_perceptron.py:696: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/neural_network/_multilayer_perceptron.py:696: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/neural_network/_multilayer_perceptron.py:696: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/neural_network/_multilayer_perceptron.py:696: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/neural_network/_multilayer_perceptron.py:696: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/neural_network/_multilayer_perceptron.py:696: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/neural_network/_multilayer_perceptron.py:696: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/neural_network/_multilayer_perceptron.py:696: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
            "  ConvergenceWarning,\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Training complete in  0m16s\n"
          ]
        }
      ],
      "source": [
        "optimize_train = OptimizeTrain()\n",
        "\n",
        "tsne_1k_results = optimize_train.train_and_score(x_train_tsne, y_train_tsne, x_test_tsne, y_test_tsne, confidence_measure=\"top_two\", step_size=50, training_size=600)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "ghZd2GivDhEt",
        "outputId": "159b2b31-f3e6-4546-e651-04a37d71f235"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7f11e8999f50>"
            ]
          },
          "execution_count": 80,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "plt.hlines(y=full_training_score, xmin=50, xmax=600, color = 'r', label = \"Full Training Data (48k)\")\n",
        "# plt.plot(list(least_confident_samples_results.keys()), list(least_confident_samples_results.values()), label = \"Least confident (10k)\")\n",
        "plt.plot(list(tsne_1k_results.keys()), list(tsne_1k_results.values()), label = \"TSNE (600, 50ss)\")\n",
        "plt.legend()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iQZe6s3yGsj6",
        "outputId": "28677c27-b30a-41b1-bbdd-a1ad88be5be7"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(48000, 2)"
            ]
          },
          "execution_count": 81,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "x_train_tsne.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ph18os9dIxtC"
      },
      "outputs": [],
      "source": [
        "from sklearn.manifold import TSNE\n",
        "from sklearn.neural_network import MLPClassifier\n",
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "tsne2 = TSNE(n_components=3, verbose=1, random_state=0)\n",
        "\n",
        "compressed = tsne2.fit_transform(data, labels)\n",
        "\n",
        "x_train_tsne2, x_test_tsne2, y_train_tsne2, y_test_tsne2 = train_test_split(compressed, labels, train_size = 0.80, random_state=0)\n",
        "\n",
        "clf = MLPClassifier(hidden_layer_sizes = (100, 100), activation = \"logistic\", random_state = 0)\n",
        "clf.fit(x_train_tsne2, y_train_tsne2)\n",
        "\n",
        "print(clf.score(x_test_tsne2, y_test_tsne2))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rqLcZHAVJIr_",
        "outputId": "6e306714-e0cf-4b16-8184-09bea29e7612"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/neural_network/_multilayer_perceptron.py:696: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/neural_network/_multilayer_perceptron.py:696: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/neural_network/_multilayer_perceptron.py:696: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/neural_network/_multilayer_perceptron.py:696: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/neural_network/_multilayer_perceptron.py:696: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/neural_network/_multilayer_perceptron.py:696: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/neural_network/_multilayer_perceptron.py:696: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/neural_network/_multilayer_perceptron.py:696: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/neural_network/_multilayer_perceptron.py:696: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
            "  ConvergenceWarning,\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Training complete in  0m12s\n"
          ]
        }
      ],
      "source": [
        "optimize_train = OptimizeTrain()\n",
        "\n",
        "tsne2_results = optimize_train.train_and_score(x_train_tsne2, y_train_tsne2, x_test_tsne2, y_test_tsne2, confidence_measure=\"top_two\", step_size=50, training_size=450)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "h9xdBqNQibg8",
        "outputId": "9f8c1fc3-3705-4682-eea6-565e722c4399"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7f11e86bbf50>"
            ]
          },
          "execution_count": 93,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "plt.hlines(y=full_training_score, xmin=50, xmax=450, color = 'r', label = \"Full Training Data (48k)\")\n",
        "# plt.plot(list(least_confident_samples_results.keys()), list(least_confident_samples_results.values()), label = \"Least confident (10k)\")\n",
        "plt.plot(list(tsne2_results.keys()), list(tsne2_results.values()), label = \"TSNE (600, 50ss)\")\n",
        "plt.legend()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "LUd-dElRifOJ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "4f47fd00-d854-424c-c144-e95759a85d83"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/manifold/_t_sne.py:783: FutureWarning: The default initialization in TSNE will change from 'random' to 'pca' in 1.2.\n",
            "  FutureWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/manifold/_t_sne.py:793: FutureWarning: The default learning rate in TSNE will change from 200.0 to 'auto' in 1.2.\n",
            "  FutureWarning,\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[t-SNE] Computing 91 nearest neighbors...\n",
            "[t-SNE] Indexed 60000 samples in 0.068s...\n",
            "[t-SNE] Computed neighbors for 60000 samples in 250.533s...\n",
            "[t-SNE] Computed conditional probabilities for sample 1000 / 60000\n",
            "[t-SNE] Computed conditional probabilities for sample 2000 / 60000\n",
            "[t-SNE] Computed conditional probabilities for sample 3000 / 60000\n",
            "[t-SNE] Computed conditional probabilities for sample 4000 / 60000\n",
            "[t-SNE] Computed conditional probabilities for sample 5000 / 60000\n",
            "[t-SNE] Computed conditional probabilities for sample 6000 / 60000\n",
            "[t-SNE] Computed conditional probabilities for sample 7000 / 60000\n",
            "[t-SNE] Computed conditional probabilities for sample 8000 / 60000\n",
            "[t-SNE] Computed conditional probabilities for sample 9000 / 60000\n",
            "[t-SNE] Computed conditional probabilities for sample 10000 / 60000\n",
            "[t-SNE] Computed conditional probabilities for sample 11000 / 60000\n",
            "[t-SNE] Computed conditional probabilities for sample 12000 / 60000\n",
            "[t-SNE] Computed conditional probabilities for sample 13000 / 60000\n",
            "[t-SNE] Computed conditional probabilities for sample 14000 / 60000\n",
            "[t-SNE] Computed conditional probabilities for sample 15000 / 60000\n",
            "[t-SNE] Computed conditional probabilities for sample 16000 / 60000\n",
            "[t-SNE] Computed conditional probabilities for sample 17000 / 60000\n",
            "[t-SNE] Computed conditional probabilities for sample 18000 / 60000\n",
            "[t-SNE] Computed conditional probabilities for sample 19000 / 60000\n",
            "[t-SNE] Computed conditional probabilities for sample 20000 / 60000\n",
            "[t-SNE] Computed conditional probabilities for sample 21000 / 60000\n",
            "[t-SNE] Computed conditional probabilities for sample 22000 / 60000\n",
            "[t-SNE] Computed conditional probabilities for sample 23000 / 60000\n",
            "[t-SNE] Computed conditional probabilities for sample 24000 / 60000\n",
            "[t-SNE] Computed conditional probabilities for sample 25000 / 60000\n",
            "[t-SNE] Computed conditional probabilities for sample 26000 / 60000\n",
            "[t-SNE] Computed conditional probabilities for sample 27000 / 60000\n",
            "[t-SNE] Computed conditional probabilities for sample 28000 / 60000\n",
            "[t-SNE] Computed conditional probabilities for sample 29000 / 60000\n",
            "[t-SNE] Computed conditional probabilities for sample 30000 / 60000\n",
            "[t-SNE] Computed conditional probabilities for sample 31000 / 60000\n",
            "[t-SNE] Computed conditional probabilities for sample 32000 / 60000\n",
            "[t-SNE] Computed conditional probabilities for sample 33000 / 60000\n",
            "[t-SNE] Computed conditional probabilities for sample 34000 / 60000\n",
            "[t-SNE] Computed conditional probabilities for sample 35000 / 60000\n",
            "[t-SNE] Computed conditional probabilities for sample 36000 / 60000\n",
            "[t-SNE] Computed conditional probabilities for sample 37000 / 60000\n",
            "[t-SNE] Computed conditional probabilities for sample 38000 / 60000\n",
            "[t-SNE] Computed conditional probabilities for sample 39000 / 60000\n",
            "[t-SNE] Computed conditional probabilities for sample 40000 / 60000\n",
            "[t-SNE] Computed conditional probabilities for sample 41000 / 60000\n",
            "[t-SNE] Computed conditional probabilities for sample 42000 / 60000\n",
            "[t-SNE] Computed conditional probabilities for sample 43000 / 60000\n",
            "[t-SNE] Computed conditional probabilities for sample 44000 / 60000\n",
            "[t-SNE] Computed conditional probabilities for sample 45000 / 60000\n",
            "[t-SNE] Computed conditional probabilities for sample 46000 / 60000\n",
            "[t-SNE] Computed conditional probabilities for sample 47000 / 60000\n",
            "[t-SNE] Computed conditional probabilities for sample 48000 / 60000\n",
            "[t-SNE] Computed conditional probabilities for sample 49000 / 60000\n",
            "[t-SNE] Computed conditional probabilities for sample 50000 / 60000\n",
            "[t-SNE] Computed conditional probabilities for sample 51000 / 60000\n",
            "[t-SNE] Computed conditional probabilities for sample 52000 / 60000\n",
            "[t-SNE] Computed conditional probabilities for sample 53000 / 60000\n",
            "[t-SNE] Computed conditional probabilities for sample 54000 / 60000\n",
            "[t-SNE] Computed conditional probabilities for sample 55000 / 60000\n",
            "[t-SNE] Computed conditional probabilities for sample 56000 / 60000\n",
            "[t-SNE] Computed conditional probabilities for sample 57000 / 60000\n",
            "[t-SNE] Computed conditional probabilities for sample 58000 / 60000\n",
            "[t-SNE] Computed conditional probabilities for sample 59000 / 60000\n",
            "[t-SNE] Computed conditional probabilities for sample 60000 / 60000\n",
            "[t-SNE] Mean sigma: 419.405861\n",
            "[t-SNE] KL divergence after 250 iterations with early exaggeration: 103.028069\n",
            "[t-SNE] KL divergence after 1000 iterations: 4.196221\n",
            "0.9575833333333333\n"
          ]
        }
      ],
      "source": [
        "from sklearn.manifold import TSNE\n",
        "from sklearn.neural_network import MLPClassifier\n",
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "tsne3 = TSNE(n_components=1, verbose=1, random_state=0)\n",
        "\n",
        "compressed = tsne3.fit_transform(data, labels)\n",
        "\n",
        "x_train_tsne3, x_test_tsne3, y_train_tsne3, y_test_tsne3 = train_test_split(compressed, labels, train_size = 0.80, random_state=0)\n",
        "\n",
        "clf = MLPClassifier(hidden_layer_sizes = (100, 100), activation = \"logistic\", random_state = 0)\n",
        "clf.fit(x_train_tsne3, y_train_tsne3)\n",
        "\n",
        "print(clf.score(x_test_tsne3, y_test_tsne3))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Q2CwX7RQmml0",
        "outputId": "d5b7369e-e6f4-47ef-9b32-8e1773022075"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/neural_network/_multilayer_perceptron.py:696: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/neural_network/_multilayer_perceptron.py:696: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/neural_network/_multilayer_perceptron.py:696: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/neural_network/_multilayer_perceptron.py:696: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/neural_network/_multilayer_perceptron.py:696: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/neural_network/_multilayer_perceptron.py:696: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/neural_network/_multilayer_perceptron.py:696: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/neural_network/_multilayer_perceptron.py:696: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/neural_network/_multilayer_perceptron.py:696: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/neural_network/_multilayer_perceptron.py:696: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/neural_network/_multilayer_perceptron.py:696: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/neural_network/_multilayer_perceptron.py:696: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/neural_network/_multilayer_perceptron.py:696: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/neural_network/_multilayer_perceptron.py:696: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/neural_network/_multilayer_perceptron.py:696: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/neural_network/_multilayer_perceptron.py:696: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/neural_network/_multilayer_perceptron.py:696: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/neural_network/_multilayer_perceptron.py:696: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/neural_network/_multilayer_perceptron.py:696: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/neural_network/_multilayer_perceptron.py:696: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/neural_network/_multilayer_perceptron.py:696: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/neural_network/_multilayer_perceptron.py:696: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/neural_network/_multilayer_perceptron.py:696: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/neural_network/_multilayer_perceptron.py:696: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/neural_network/_multilayer_perceptron.py:696: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/neural_network/_multilayer_perceptron.py:696: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/neural_network/_multilayer_perceptron.py:696: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/neural_network/_multilayer_perceptron.py:696: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/neural_network/_multilayer_perceptron.py:696: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/neural_network/_multilayer_perceptron.py:696: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/neural_network/_multilayer_perceptron.py:696: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/neural_network/_multilayer_perceptron.py:696: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/neural_network/_multilayer_perceptron.py:696: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/neural_network/_multilayer_perceptron.py:696: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/neural_network/_multilayer_perceptron.py:696: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/neural_network/_multilayer_perceptron.py:696: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/neural_network/_multilayer_perceptron.py:696: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/neural_network/_multilayer_perceptron.py:696: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/neural_network/_multilayer_perceptron.py:696: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/neural_network/_multilayer_perceptron.py:696: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/neural_network/_multilayer_perceptron.py:696: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/neural_network/_multilayer_perceptron.py:696: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/neural_network/_multilayer_perceptron.py:696: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/neural_network/_multilayer_perceptron.py:696: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/neural_network/_multilayer_perceptron.py:696: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
            "  ConvergenceWarning,\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training complete in  1m 6s\n"
          ]
        }
      ],
      "source": [
        "optimize_train = OptimizeTrain()\n",
        "\n",
        "tsne_results_dict = optimize_train.train_and_score(x_train_tsne, y_train_tsne, x_test_tsne, y_test_tsne, training_size = 450, step_size = 10)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        },
        "id": "UEumkdEcm0xe",
        "outputId": "d48880cf-0b1f-4c0b-b140-0f1cb09b51ef"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7fdf9096b250>"
            ]
          },
          "metadata": {},
          "execution_count": 31
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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n690WG1pZWRmlpaXExsby/fffM3r0aNauXRv8rtJwKCoq4oILLmDx4sX1+iL53nvvsXz58mO+4Qka5m6LHYG8kPl8YHilNiuAK4A/ApcDCWaW5JwrrFTIFGAKQOfOnWuxaxE5HR0+fJgLLriA0tJSnHM899xzYQ1z8I+hP/roo2zZsqVe88vn83HPPffUy7bq66TovcAzZjYZ+BTYApRVbuScexF4EfxH6PW0bxHxmISEhBo/dx4OF110Ub1v86qrrqq3bdUm0LcAnULmUwPLgpxzW/EfoWNmLYAJzrmar78V8QDn3El9+bFIVU7ky4dq8ymXpUAvM+tmZjHAJGBuaAMza2NmR7f1IPBKnSsRaYJiY2MpLCw8of98IsfjnKOwsJDY2Ng6Pa7GI3TnnM/MpgIfApHAK8651Wb2GJDlnJsLZAK/NTOHf8jlP+v6B4g0RampqeTn57Nz585wlyIeExsbS2pq3T4Bru8UFRFpQqr7lIsu/RcR8QgFuoiIRyjQRUQ8QoEuIuIRCnQREY9QoIuIeIQCXUTEIxToIiIeoUAXEfEIBbqIiEco0EVEPEKBLiLiEQp0ERGPUKCLiHiEAl1ExCMU6CIiHqFAFxHxCAW6iIhHKNBFRDxCgS4i4hEKdBERj1Cgi4h4hAJdRMQjFOgiIh6hQBcR8QgFuoiIRyjQRUQ8QoEuIuIRCnQREY+oVaCb2RgzW2tmuWY2rYr1nc3sYzP7xsxWmtnF9V+qiIhUp8ZAN7NI4FlgLNAPuMbM+lVqNh14yzk3BJgEPFffhYqISPVqc4Q+DMh1zm1wzpUAs4Hxldo4IDEw3RLYWn8liohIbdQm0DsCeSHz+YFloWYA15tZPjAPuKOqDZnZFDPLMrOsnTt3nkC5IiJyPPV1UvQa4FXnXCpwMfC6mR2zbefci865DOdcRnJycj3tWkREoHaBvgXoFDKfGlgW6mbgLQDn3JdALNCmPgoUEZHaqU2gLwV6mVk3M4vBf9JzbqU2m4ELAcysL/5A15iKiEgjqjHQnXM+YCrwIfAd/k+zrDazx8xsXKDZPcCtZrYCeAOY7JxzDVW0iIgcK6o2jZxz8/Cf7Axd9suQ6Rzg3PotTURE6kJXioqIeIQCXUTEIxToIiIeoUAXEfEIBbqIiEco0EVEPEKBLiLiEQp0ERGPUKCLiHiEAl1ExCMU6CIiHqFAFxHxCAW6iIhHKNBFRDxCgS4i4hEKdBERj1Cgi4h4hAJdRMQjFOgiIh6hQBcR8QgFuoiIRyjQRUQ8QoEuIuIRCnQREY9QoIuIeIQCXUTEIxToIiIeoUAXEfEIBbqIiEfUKtDNbIyZrTWzXDObVsX6p80sO/Czzsz21n+pIiJSnaiaGphZJPAs8GMgH1hqZnOdczlH2zjn7g5pfwcwpAFqFRGRatTmCH0YkOuc2+CcKwFmA+OraX8N8EZ9FCciIrVXm0DvCOSFzOcHlh3DzLoA3YCFx1k/xcyyzCxr586dda1VRESqUd8nRScBbzvnyqpa6Zx70TmX4ZzLSE5Oruddi4ic3moT6FuATiHzqYFlVZmEhltERMKiNoG+FOhlZt3MLAZ/aM+t3MjM+gCtgC/rt0QREamNGgPdOecDpgIfAt8BbznnVpvZY2Y2LqTpJGC2c841TKkiIlKdGj+2COCcmwfMq7Tsl5XmZ9RfWSIiUle6UlRExCMU6CIiHqFAFxHxCAW6iIhHKNBFRDxCgS4i4hEKdBERj1Cgi4h4hAJdRMQjFOgiIh6hQBcR8QgFuoiIRyjQRUQ8QoEuIuIRCnQREY9QoIuIeIQCXUTEIxToIiIeoUAXEfEIBbqIiEco0EVEPEKBLiLiEQp0ERGPUKCLiHiEAl1ExCMU6CIiHqFAFxHxCAW6iIhHKNBFRDxCgS4i4hG1CnQzG2Nma80s18ymHafNRDPLMbPVZvb3+i1TRKRpcs5xuMTH9n3FrCs4QNam3RTsL26QfUXV1MDMIoFngR8D+cBSM5vrnMsJadMLeBA41zm3x8xSGqRaEZFG5pzjUEkZ+4tK2V9cyv4iX3D6QLGv4vLiStNF/ja+cldhm7++bADXj+hS77XWGOjAMCDXObcBwMxmA+OBnJA2twLPOuf2ADjndtR3oSIiJ6K83HGwJBC8IUG7v9jHgeLKyyoG89HArpTHx4iLjiQxLorE2GgS46Jp0yKG7snNA/P+5Qkh02ltExrkb61NoHcE8kLm84HhldqkAZjZYiASmOGcm195Q2Y2BZgC0Llz5xOpV0SkWlv2FvHW0jzeX7mVnQeOcOCID1dDIDePiSQxLjoYwO0SY+mV0qLCsqNhXXk+ITaK6MhT43RkbQK9ttvpBWQCqcCnZjbQObc3tJFz7kXgRYCMjIwaulhEpHZKy8r513cFvLEkj0/X78Q5OLdnEuf3SiYxNqpCECfERlc6co4i6hQJ5JNVm0DfAnQKmU8NLAuVD3ztnCsFNprZOvwBv7ReqhQRqcKGnQd5MyuPd5bls+tgCW0TmzH1gp5MzOhEp9bx4S6v0dUm0JcCvcysG/4gnwRcW6nNP4FrgL+ZWRv8QzAb6rNQERGA4tIyPli1jdlL8vh6424iI4xRfVK4ZlgnftQr2TNH2yeixkB3zvnMbCrwIf7x8Vecc6vN7DEgyzk3N7DuJ2aWA5QB9znnChuycBE5veRs3c+bSzfz3jdb2F/so3PreO67qDdXDU0lJTE23OWdEszVdLaggWRkZLisrKyw7FtEmoaDR3zMzd7Km0s3syJ/HzGREYwZ0I5JZ3ViRPckIiIs3CU2OjNb5pzLqGpdfZ0UFRGpF845vsnby+wlm3l/5TYOl5SR1rYFv7y0H5cP6Uir5jHhLvGUpUAXkVPCnkMlvPfNFt5cmsfaggPEx0TyH4M6cPWwTgzpdAZmp9/ReF0p0EUkbMrLHV9tKGT20jzmr95Oia+c9NSW/PaKgVw6qD0JsdHhLrFJUaCLSKPbsb+YfyzL562sPP5deJjE2CiuOasTV5/VmX4dEsNdXpOlQBeRRlFW7vhk3Q5mL8njX2t2UFbuGNatNT8f3YuxA9oTGx0Z7hKbPAW6iDSovN2H+UdWHm9l5bN9fzFJzWO45bxuXH1WJ7ontwh3eZ6iQBeRelfiK2dBTgGzl27m89xdAPyoVzIzxvVjVJ+2xESdvhf/NCQFuojUm9wdB3lz6WbeWb6F3YdK6NAyljtH9WLiWZ3oeEZcuMvzPAW6iJyUopIy5n27jdlLN7N00x6iIozRfdtydeBS/MjT8OKfcFGgi0idbdtXxKfrdrJo7U4+W7+Lg0d8dGvTnGlj+3DFmR1JSdCl+OGgQBeRGpX4ysn6924+WbuTT9btZM32AwC0S4zl0kHtuWxIR4Z3a62Lf8JMgS4iVcrfc5hFgQD/IncXh0rKiI40zuramgfH9iGzdwppbVsoxE8hCnQRAfy3pV26aTeL1u5k0dodfL/zEAAdz4jjsiEdGZmWzDk929CimWLjVKV/GZHT2L8LDwWPwr/8vpCi0jJioiIY3q011wzrTGbvFHokN9dReBOhQBc5jRSVlPHVxkI+CRyFbyo8DECXpHgmZqQysncyI7onER+jaGiK9K8m4mHOOTbs+uEo/OsNhRzxldMsKoKzeyQx+ZyuZPZOoWub5uEuVeqBAl3EYw4d8fHl94UsWreDT9btJG93EQDdk5tz3fAujOydzPBurXXvFA9SoIs0cc451u84yKK1/gBfunEPJWXlxMdEck6PJKb8qAeZacmn5Zcmn24U6CIhNu46xMufb8A5iIuOJC4mktjoyOB0XLR/PjY64phlodMNfXXkgeJSFucW8sm6HXyydidb9xUDkNa2BZPP7crItGQyuraiWZSOwk8nCnSRgPe+yWf6e6soc47mMVEUlZZRVFrGiXztbkxUBLFREVUGflx0JLExkcRGRRIXE1FhWVzIi0dsdMiLSXQkvvJyvtzgP6G57N978JU7WjSL4tyeSdxxYS9GpiXTQfdLOa0p0OW0d+iIj1/MWcW7y7cwrGtr/jBpcDAYnXMc8ZVTHAj3ohL/7+LSwLLAvH+Zf764tLzCfOX1+4tLK7Q72qa2+rZP5NYfdWdkWjJDu7QiOlJ3LhQ/Bbqc1lZt2cedb3zDxsJD3HlhL+4c1ZOokIA0s+CR8hkNWMfRF46ikjKKfaEvHGUUlfiDv6y8nCGdW9E2UfdJkaop0OW05Jxj5heb+M28NbRqHs3fbxnB2T2SwlZP6AuHyIlSoMtpZ8+hEu5/ZyULcgoY1SeFp65Kp3XzmHCXJXLSFOhyWlmycTd3zf6GXQePMP2Svtx8Xjdd1i6eoUCX00JZuePZj3P5w0fr6NQ6nndvO5eBqS3DXZZIvVKgi+dt31fMz9/8hq827Gb84A78+rIBJMRGh7sskXqnQBdPW7imgHv/sZKikjL+35WDuHJoqoZYxLMU6OJJJb5ynpy/hr9+vpG+7RP58zVD6JnSItxliTSoWl2RYGZjzGytmeWa2bQq1k82s51mlh34uaX+SxWpnU27DjHh+S/46+cb+enZXXjv9nMU5nJaqPEI3cwigWeBHwP5wFIzm+ucy6nU9E3n3NQGqFGk1uZkb+Ghd78lKjKCv9wwlIv6twt3SSKNpjZDLsOAXOfcBgAzmw2MByoHukjYHC7x8cic1fxjWT4ZXVrxx2uG0FH3NZHTTG0CvSOQFzKfDwyvot0EM/sRsA642zmXV7mBmU0BpgB07ty57tWKVCFn636mvrGcjbsOcceontx1Ya8Kl++LnC7q61n/P0BX59wgYAEws6pGzrkXnXMZzrmM5OTketq1nK6cc7z+5SYue24xB4t9zLp5OPf8pLfCXE5btTlC3wJ0CplPDSwLcs4Vhsz+FXjy5EsTOb69h0t44J2VfLi6gMzeyfzXVekktWgW7rJEwqo2gb4U6GVm3fAH+STg2tAGZtbeObctMDsO+K5eqxQJkbVpN3e+8Q07A5fv/59zuxHRwF8oIdIU1BjozjmfmU0FPgQigVecc6vN7DEgyzk3F7jTzMYBPmA3MLkBa5bTVFm54/lFuTz90Xo6nhHH2z87h/RODXlTW5GmxdyJfB1LPcjIyHBZWVlh2bc0PTv2F/PzN7P54vtCxqV34PHLdfm+nJ7MbJlzLqOqdbpSVE55H6/dwb1vreBQiY8nJwziqgxdvi9SFQW6xznn2HO4lFbx0U0uBEt85Tz1v2t58dMN9GmXwJvXjqBnSkK4yxI5ZSnQPai83LF88x7mr9rO/NXbyd9TREKzKHq2bUFaSgK92ragd7sE0tomkJLQ7JQM+s2Fh7njjeWsyN/HDSO68PAlffVtPiI1UKB7hK+snK837mb+qu18uHo7Ow4cITrSOK9nG64f0YUte4pYV3CABd8V8GbWD9d8JcZGkdY2gV5tE0hr24Legek2LWLCFvRzV2zloXe/JcLghevPZMyA9mGpQ6SpUaA3YUd8ZSzO3cX8VdtZkFPAnsOlxEZHkJmWwtiB7bigTwqJVZw43HXwCOsKDrC+4CBrCw6wvuAA877dxhtLSoNtWsVHHxPyaW0TGvSr2g6X+Hh0bg5vZuUxtEsr/jhpMKmt4htsfyJeo0BvYg6X+Phk7U7mr97Owu92cOCIj4RmUVzYN4UxA9oxMi2FuJjqhybatGhGmxbNOKdHm+Ay5xw7DxxhXcFB1hUcCP7M+WYrB474Qh4bQ68Uf9CnBYZt0lISaBl/cp84WbN9P1P//g3f7zzI1At68vPRunxfpK4U6E3A/uJSFn63gw9WbeOTdTspLi2nVXw0Fw9sz5gB7TinZxLNok5ufNnMSEmMJSUxlvN6VQz67fuLWVdwkPUFB1i7/QDrdhzk7WX5HCopC7ZLSWgWGLr54Yi+V9sWVb5DCOWcY9bXm/nV+zkkxkXz3zcP59yebap9jIhUTYF+itp9qIQFOdv5YNV2FufuorTMkZLQjIkZnRjTvx3DurVulCNYM6N9yzjat4xjZNoP999xzrFlbxHrg0f0/t+zl+RRVPpD0LdvGesfrkn54Yi+V0oLmjeLYt/hUqa9u5IPVm3nR2nJ/H5iOm10+b7ICVOgn0IK9hfz4ertzF+1na837qas3JHaKo7J53RlzIB2DFiJb5MAAAdLSURBVOnU6pS5xN3MSG0VT2qreC7okxJcXl7uyA+cgF23IzBOv/0AX28o5IivPNiu4xlxlJSVs+dQCQ9d3Idbzut+yvxtIk2VAj3M8nYfDn68cNm/9wDQI7k5t43swZgB7ejfIfGU/Fjh8UREGJ2T4umcFM/ofm2Dy8vKHZt3Hw6cjPUf0e8tKuX//jiNwbp8X6ReKNDDIHfHQeav2sb81dtZtWU/AP3aJ3LPj9MYO7CdJy+eiYwwurVpTrc2zfUtQiINRIHeCJxz5Gzbz/xV/jHx3B0HARjS+QweurgPY/q3p3OSPp4nIidHgd5Ayssd2fl7/cMpq7azefdhIgyGdWvNDSP6c1H/drRrGRvuMkXEQ5pcoH+ybifzV20nOtKIioggOtKIjowg6ujvCCMqMqLK9UfnoyIjiI4woqP87atcH2lER1TcbmSEVTueXVbuWLJxN/NXbePD1QVs319MdKRxTo823J7Zgx/3a6svYRCRBtPkAn1z4SEW5BTgKy/HV+YoLSuntKyc8ka6C3B0SMBXfiHYW1TK3sOlNIuKILN3Mg8M6M2oPm1pGafbvIpIw2tygX7D2V254eyuxywvL3eUBkLeV/bD9NHA95X7p31lDl95OSU+V+FF4ej60jKHr6yc0nL/72O3FVgesr3SQPvYqAgu6JNCZu9k4mOaXNeKSBPnmdSJiDCaRUTSzDN/kYhI3ehmGSIiHqFAFxHxCAW6iIhHKNBFRDxCgS4i4hEKdBERj1Cgi4h4hAJdRMQjzLlGuma+8o7NdgL/PsGHtwF21WM59UV11Y3qqrtTtTbVVTcnU1cX51xyVSvCFugnw8yynHMZ4a6jMtVVN6qr7k7V2lRX3TRUXRpyERHxCAW6iIhHNNVAfzHcBRyH6qob1VV3p2ptqqtuGqSuJjmGLiIix2qqR+giIlKJAl1ExCOaRKCb2SYz+9bMss0sK7CstZktMLP1gd+tGqGOV8xsh5mtCllWZR3m9yczyzWzlWZ2ZiPXNcPMtgT6LNvMLg5Z92CgrrVmdlED1tXJzD42sxwzW21mdwWWh7XPqqkrrH1mZrFmtsTMVgTqejSwvJuZfR3Y/5tmFhNY3iwwnxtY37WR63rVzDaG9NfgwPJGe+4H9hdpZt+Y2fuB+bD2VzV1NXx/OedO+R9gE9Cm0rIngWmB6WnA7xqhjh8BZwKraqoDuBj4ADBgBPB1I9c1A7i3irb9gBVAM6Ab8D0Q2UB1tQfODEwnAOsC+w9rn1VTV1j7LPB3twhMRwNfB/rhLWBSYPkLwG2B6duBFwLTk4A3G6i/jlfXq8CVVbRvtOd+YH//F/g78H5gPqz9VU1dDd5fTeII/TjGAzMD0zOByxp6h865T4HdtaxjPPCa8/sKOMPM2jdiXcczHpjtnDvinNsI5ALDGqiubc655YHpA8B3QEfC3GfV1HU8jdJngb/7YGA2OvDjgFHA24HllfvraD++DVxoZtaIdR1Poz33zSwVuAT4a2DeCHN/VVVXDeqtv5pKoDvgf81smZlNCSxr65zbFpjeDrQNT2nHraMjkBfSLp/qQ6MhTA28hXvFfhiSCktdgbe3Q/Af3Z0yfVapLghznwXepmcDO4AF+N8N7HXO+arYd7CuwPp9QFJj1OWcO9pfjwf662kza1a5ripqrm9/AO4HygPzSZwC/VVFXUc1aH81lUA/zzl3JjAW+E8z+1HoSud/3xL2z1+eKnUEPA/0AAYD24D/ClchZtYCeAf4uXNuf+i6cPZZFXWFvc+cc2XOucFAKv53AX0au4aqVK7LzAYAD+Kv7yygNfBAY9ZkZpcCO5xzyxpzvzWppq4G768mEejOuS2B3zuA9/A/0QuOvi0J/N4RpvKOV8cWoFNIu9TAskbhnCsI/CcsB17ihyGCRq3LzKLxh+Ys59y7gcVh77Oq6jpV+ixQy17gY+Bs/G/Bo6rYd7CuwPqWQGEj1TUmMHTlnHNHgL/R+P11LjDOzDYBs/EPtfyR8PfXMXWZ2X83Rn+d8oFuZs3NLOHoNPATYBUwF/hpoNlPgTnhqfC4dcwFbgycwR4B7AsZZmhwlcbgLsffZ0frmhQ4498N6AUsaaAaDHgZ+M459/uQVWHts+PVFe4+M7NkMzsjMB0H/Bj/+P7HwJWBZpX762g/XgksDLzjaYy61oS8KBv+cerQ/mrwf0fn3IPOuVTnXFf8JzkXOueuI8z9dZy6rm+U/jrRs6mN9QN0x/8JgxXAauDhwPIk4F/AeuAjoHUj1PIG/rfipfjHuW4+Xh34z1g/i38M9Fsgo5Hrej2w35WBJ0z7kPYPB+paC4xtwLrOwz+cshLIDvxcHO4+q6ausPYZMAj4JrD/VcAvQ/4PLMF/MvYfQLPA8tjAfG5gffdGrmthoL9WAf/ND5+EabTnfkiNmfzwaZKw9lc1dTV4f+nSfxERjzjlh1xERKR2FOgiIh6hQBcR8QgFuoiIRyjQRUQ8QoEuIuIRCnQREY/4/2sJB71cljc+AAAAAElFTkSuQmCC\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "plt.hlines(y=full_training_score, xmin=50, xmax=450, color = 'r', label = \"Full Training Data (48k)\")\n",
        "# plt.plot(list(least_confident_samples_results.keys()), list(least_confident_samples_results.values()), label = \"Least confident (10k)\")\n",
        "plt.plot(list(tsne3_results.keys()), list(tsne3_results.values()), label = \"TSNE (600, 50ss)\")\n",
        "plt.legend()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Mxr1tergm7JA"
      },
      "outputs": [],
      "source": []
    }
  ],
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
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      "name": "python"
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