knn_classifier_grid_search.ipynb

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  {
   "source": [
    "# k-Nearest Neighbors Classifiers. Grid Search"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "source": [
    "## Libraries Import"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as pyplot\n",
    "import seaborn as sns\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.metrics import accuracy_score, confusion_matrix"
   ]
  },
  {
   "source": [
    "## Configuration"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "DATASET_FILE = \"datasets/mnist.csv\"\n",
    "FIRST_IMAGES_TO_DISPLAY = 5\n",
    "PIXELS_PER_DIMENSION = 28\n",
    "TRAIN_ENTRIES = 60000\n",
    "NEIGHBORS_COUNT = 5\n",
    "TESTED_INDEX = TRAIN_ENTRIES\n",
    "ERRORS_TO_SHOW = 3\n",
    "CLASS_ERRORS_TO_SHOW = 0\n",
    "NUMBERS_TO_TEST = [3, 5, 7]\n",
    "DISTANCES_TO_TEST = [\"euclidean\"]"
   ]
  },
  {
   "source": [
    "## Dataset Import"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "mnist_df = pd.read_csv(DATASET_FILE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "The shapes of the vectors are (70000, 784) and (70000,)\n"
     ]
    }
   ],
   "source": [
    "X = mnist_df.loc[:, mnist_df.columns != \"class\"]\n",
    "y = mnist_df[\"class\"]\n",
    "print(\"The shapes of the vectors are {} and {}\".format(X.shape, y.shape))"
   ]
  },
  {
   "source": [
    "## Dataset Exploration"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "source": [
    "def display_image(pixels: list) -> None:\n",
    "    pixels = pixels.reshape([PIXELS_PER_DIMENSION, PIXELS_PER_DIMENSION])\n",
    "\n",
    "    pyplot.gray()\n",
    "    pyplot.imshow(pixels)\n",
    "    pyplot.show()"
   ],
   "cell_type": "code",
   "metadata": {},
   "execution_count": 5,
   "outputs": []
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
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     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "for i in range(FIRST_IMAGES_TO_DISPLAY):\n",
    "    pixels = mnist_df.iloc[i].to_numpy()[:-1]\n",
    "    display_image(pixels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def generate_distribution_plot(dataframe: pd.DataFrame) -> None:\n",
    "    entries_per_class = dataframe.groupby([\"class\"])[\"pixel1\"].count()\n",
    "\n",
    "    classes = dataframe[\"class\"]\n",
    "    classes_names = [str(current_class) for current_class in np.sort(classes.unique())]\n",
    "\n",
    "    entries_per_class_df = pd.DataFrame({\n",
    "        \"names\": classes_names,\n",
    "        \"count\": entries_per_class.values\n",
    "    })\n",
    "\n",
    "    plot = sns.barplot(x=\"names\", y=\"count\", data=entries_per_class_df)\n",
    "    plot.set_xlabel(\"Class Name\")\n",
    "    plot.set_ylabel(\"Entries Count\")\n",
    "    plot.set_title(\"Distribution of Entries by Class\")\n",
    "    plot.legend([],[], frameon=False)\n",
    "    pyplot.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
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     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "generate_distribution_plot(mnist_df)"
   ]
  },
  {
   "source": [
    "## Dataset Splitting"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "source": [
    "X_train = X[:TRAIN_ENTRIES]\n",
    "y_train = y[:TRAIN_ENTRIES]\n",
    "X_test = X[TRAIN_ENTRIES:]\n",
    "y_test = y[TRAIN_ENTRIES:]"
   ],
   "cell_type": "code",
   "metadata": {},
   "execution_count": 9,
   "outputs": []
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
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     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "generate_distribution_plot(mnist_df[:TRAIN_ENTRIES])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "generate_distribution_plot(mnist_df[TRAIN_ENTRIES:])"
   ]
  },
  {
   "source": [
    "## Model Training"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "KNeighborsClassifier()"
      ]
     },
     "metadata": {},
     "execution_count": 12
    }
   ],
   "source": [
    "model = KNeighborsClassifier(n_neighbors=NEIGHBORS_COUNT)\n",
    "model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "The predicted probabilities are:\n    - 0.0 for letter 0\n    - 0.0 for letter 1\n    - 0.0 for letter 2\n    - 0.0 for letter 3\n    - 0.0 for letter 4\n    - 0.0 for letter 5\n    - 0.0 for letter 6\n    - 1.0 for letter 7\n    - 0.0 for letter 8\n    - 0.0 for letter 9\n\nThe image is:\n"
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     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "# Make a prediction\n",
    "random_image = X_test.loc[TRAIN_ENTRIES]\n",
    "probabilities = model.predict_proba([random_image])\n",
    "\n",
    "# Display the probabilities\n",
    "print(\"The predicted probabilities are:\")\n",
    "for index, probability in enumerate(probabilities[0]):\n",
    "    print(\"    - {} for letter {}\".format(probability, index))\n",
    "print()\n",
    "\n",
    "# Display the image\n",
    "print(\"The image is:\")\n",
    "display_image(random_image.to_numpy())"
   ]
  },
  {
   "source": [
    "## Model Evaluation"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Model's accuracy is 0.97.\n"
     ]
    }
   ],
   "source": [
    "accuracy = accuracy_score(y_test, y_pred)\n",
    "print(\"Model's accuracy is {:.2f}.\".format(accuracy))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "text/plain": "<Figure size 432x288 with 2 Axes>",
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "matrix = confusion_matrix(y_test, y_pred).round(1)\n",
    "\n",
    "plot = sns.heatmap(matrix, annot=True, fmt=\"d\")\n",
    "plot.set_xlabel(\"True Labels\")\n",
    "plot.set_ylabel(\"Predicted Labels\")\n",
    "plot.set_title(\"Confusion Matrix\")\n",
    "plot.legend([],[], frameon=False)\n",
    "pyplot.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "The predicted labels for this image (with a 0) was 2.\n"
     ]
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
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     "text": [
      "The predicted labels for this image (with a 0) was 7.\n"
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    },
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     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "shown_images_count = 0\n",
    "for index, (real_label, predicted_label) in enumerate(zip(y_test, y_pred)):\n",
    "    if (real_label == CLASS_ERRORS_TO_SHOW and real_label != predicted_label):\n",
    "        print(\"The predicted labels for this image (with a {}) was {}.\".format(CLASS_ERRORS_TO_SHOW, predicted_label))\n",
    "        display_image(X_test.loc[TRAIN_ENTRIES + index].to_numpy())\n",
    "\n",
    "        shown_images_count += 1\n",
    "        if (shown_images_count == ERRORS_TO_SHOW):\n",
    "            break"
   ]
  },
  {
   "source": [
    "## Model Training via Grid Search"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Fitting 5 folds for each of 3 candidates, totalling 15 fits\n",
      "[CV 1/5] END ................metric=euclidean, n_neighbors=3; total time=  37.1s\n",
      "[CV 2/5] END ................metric=euclidean, n_neighbors=3; total time=  33.3s\n",
      "[CV 3/5] END ................metric=euclidean, n_neighbors=3; total time=  34.4s\n",
      "[CV 4/5] END ................metric=euclidean, n_neighbors=3; total time=  34.6s\n",
      "[CV 5/5] END ................metric=euclidean, n_neighbors=3; total time=  33.0s\n",
      "[CV 1/5] END ................metric=euclidean, n_neighbors=5; total time=  40.7s\n",
      "[CV 2/5] END ................metric=euclidean, n_neighbors=5; total time=  38.7s\n",
      "[CV 3/5] END ................metric=euclidean, n_neighbors=5; total time=  38.1s\n",
      "[CV 4/5] END ................metric=euclidean, n_neighbors=5; total time=  38.2s\n",
      "[CV 5/5] END ................metric=euclidean, n_neighbors=5; total time=  38.7s\n",
      "[CV 1/5] END ................metric=euclidean, n_neighbors=7; total time=  40.1s\n",
      "[CV 2/5] END ................metric=euclidean, n_neighbors=7; total time=  41.2s\n",
      "[CV 3/5] END ................metric=euclidean, n_neighbors=7; total time=  40.0s\n",
      "[CV 4/5] END ................metric=euclidean, n_neighbors=7; total time=  39.6s\n",
      "[CV 5/5] END ................metric=euclidean, n_neighbors=7; total time=  39.7s\n"
     ]
    },
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "GridSearchCV(estimator=KNeighborsClassifier(),\n",
       "             param_grid={'metric': ['euclidean'], 'n_neighbors': [3, 5, 7]},\n",
       "             verbose=4)"
      ]
     },
     "metadata": {},
     "execution_count": 18
    }
   ],
   "source": [
    "parameters = {\n",
    "    \"n_neighbors\": NUMBERS_TO_TEST,\n",
    "    \"metric\": DISTANCES_TO_TEST\n",
    "}\n",
    "model = KNeighborsClassifier()\n",
    "model_search = GridSearchCV(model, parameters, verbose=4)\n",
    "model_search.fit(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "The scores of the models are:\n    - 0.97 for parameters ('euclidean', 3)\n    - 0.97 for parameters ('euclidean', 5)\n    - 0.97 for parameters ('euclidean', 7)\n"
     ]
    }
   ],
   "source": [
    "results = model_search.cv_results_\n",
    "print(\"The scores of the models are:\")\n",
    "for parameters, score in zip(results[\"params\"], results[\"mean_test_score\"]):\n",
    "    print(\"    - {:.2f} for parameters ('{}', {})\".format(score.round(2), parameters[\"metric\"], parameters[\"n_neighbors\"]))"
   ]
  }
 ]
}