Machine Learning - 0.89.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "fd1e12e3",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import torch\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "50dc8113",
   "metadata": {},
   "outputs": [],
   "source": [
    "arr = np.load('X.npy')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "16908073",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(70578, 1024)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "cd7b9459",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f07ab85e3a0>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(arr[450].reshape(32,32))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "aa09b5fc",
   "metadata": {},
   "outputs": [],
   "source": [
    "y = np.load('Y.npy')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "34a2bc5a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(70578,)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "a86b1aa9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RidgeClassifier()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RidgeClassifier</label><div class=\"sk-toggleable__content\"><pre>RidgeClassifier()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "RidgeClassifier()"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import RidgeClassifier\n",
    "clf = RidgeClassifier()\n",
    "clf.fit(arr,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "294e0574",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = clf.predict(arr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "9c4dcc8d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6103318314488935"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import classification_report, f1_score\n",
    "\n",
    "f1_score(y_pred, y,average='micro')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "8aa25176",
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "from torch import nn\n",
    "import torch.nn.functional as F"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "d8bc3f9b",
   "metadata": {},
   "outputs": [],
   "source": [
    "device = 'cuda' if torch.cuda.is_available() else 'cpu'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "375665c9",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = arr/255.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "978389c0",
   "metadata": {},
   "outputs": [],
   "source": [
    "Y = y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "3df09580",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((70578,), (70578, 1024))"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y.shape, X.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "baa4191f",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "db767187",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "fcae501c",
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_example(X, y):\n",
    "    \"\"\"Plot the first 5 images and their labels in a row.\"\"\"\n",
    "    for i, (img, y) in enumerate(zip(X[:5].reshape(5, 32, 32), y[:5])):\n",
    "        plt.subplot(151 + i)\n",
    "        plt.imshow(img)\n",
    "        plt.xticks([])\n",
    "        plt.yticks([])\n",
    "        plt.title(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "720ceb05",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_example(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "93b5a6ea",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.naive_bayes import BernoulliNB\n",
    "clf = RandomForestClassifier()\n",
    "#clf = KNeighborsClassifier(n_neighbors = 6)\n",
    "#clf = LogisticRegression()\n",
    "#clf = BernoulliNB()\n",
    "#clf = SVC()\n",
    "clf.fit(X_train, y_train)\n",
    "\n",
    "y_pred = clf.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "dd2988ed",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.8925843561213566, 0.8924341173136866, 0.8919617919448789)"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f1_score(y_pred, y_test, average='weighted' ), f1_score(y_pred, y_test, average='micro' ),f1_score(y_pred, y_test, average='macro' )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "3cde56a3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'              precision    recall  f1-score   support\\n\\n           1       0.91      0.93      0.92       507\\n           2       0.95      0.92      0.94       426\\n           3       0.93      0.91      0.92       516\\n           4       0.89      0.87      0.88       420\\n           5       0.95      0.91      0.93       515\\n           6       0.93      0.89      0.91       527\\n           7       0.96      0.94      0.95       513\\n           8       0.85      0.87      0.86       477\\n           9       0.85      0.86      0.86       408\\n          10       0.85      0.88      0.87       491\\n          11       0.93      0.92      0.92       488\\n          12       0.92      0.85      0.88       551\\n          13       0.92      0.80      0.86       610\\n          14       0.95      0.88      0.91       480\\n          15       0.79      0.91      0.85       427\\n          16       0.88      0.92      0.90       483\\n          17       0.83      0.88      0.85       477\\n          18       0.84      0.81      0.83       495\\n          19       0.95      0.92      0.94       532\\n          20       0.93      0.94      0.93       529\\n          21       0.84      0.85      0.84       487\\n          22       0.87      0.92      0.89       482\\n          23       0.88      0.88      0.88       488\\n          24       0.91      0.81      0.86       515\\n          25       0.81      0.88      0.84       465\\n          26       0.82      0.92      0.87       353\\n          27       0.89      0.89      0.89       510\\n          28       0.91      0.90      0.90       517\\n          29       0.92      0.92      0.92       519\\n          30       0.90      0.92      0.91       450\\n          31       0.83      0.87      0.85       508\\n          32       0.87      0.92      0.90       487\\n          33       0.95      0.85      0.90       540\\n          34       0.85      0.90      0.87       466\\n          35       0.92      0.95      0.94       510\\n          36       0.93      0.95      0.94       476\\n\\n    accuracy                           0.89     17645\\n   macro avg       0.89      0.89      0.89     17645\\nweighted avg       0.89      0.89      0.89     17645\\n'"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import classification_report, f1_score\n",
    "y_pred = clf.predict(X_test)\n",
    "\n",
    "target_names = [ '1',  '2',  '3',  '4',  '5',  '6',  '7',  '8',  '9', '10', '11', '12', '13', '14', '15', '16', '17',\n",
    "       '18', '19', '20', '21', '22', '23', '24', '25', '26', '27', '28', '29', '30', '31', '32', '33', '34',\n",
    "       '35', '36']\n",
    "classification_report(y_pred,y_test,target_names=target_names)\n",
    "#print(f1_score(y_pred, y_test ,average='micro'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "222a7de3",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.decomposition import PCA\n",
    "X_pca = PCA(n_components=300).fit_transform(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "4ee5120e",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X_pca, y, test_size=0.25, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "40e2e38a",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.naive_bayes import BernoulliNB\n",
    "#clf = RandomForestClassifier()\n",
    "clf = KNeighborsClassifier(n_neighbors = 3)\n",
    "#clf = LogisticRegression()\n",
    "#clf = BernoulliNB()\n",
    "#clf = SVC()\n",
    "clf.fit(X_train, y_train)\n",
    "\n",
    "y_pred = clf.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "67ab6449",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.8914043989365603, 0.8930008500991783, 0.8939090974845807)"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f1_score(y_pred, y_test, average='weighted' ), f1_score(y_pred, y_test, average='micro' ),f1_score(y_pred, y_test, average='macro' )\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}