diff --git a/classification.ipynb b/classification.ipynb new file mode 100644 index 0000000..406014f --- /dev/null +++ b/classification.ipynb @@ -0,0 +1,970 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "id": "corrected-wholesale", + "metadata": {}, + "outputs": [], + "source": [ + "!kaggle datasets download -d yasserh/breast-cancer-dataset" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ranging-police", + "metadata": {}, + "outputs": [], + "source": [ + "!unzip -o breast-cancer-dataset.zip" + ] + }, + { + "cell_type": "code", + "execution_count": 109, + "id": "ideal-spouse", + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import torch\n", + "from torch import nn\n", + "from torch.autograd import Variable\n", + "from sklearn.datasets import load_iris\n", + "from sklearn.model_selection import train_test_split\n", + "from sklearn.metrics import accuracy_score\n", + "from sklearn.preprocessing import LabelEncoder\n", + "from tensorflow.keras.utils import to_categorical\n", + "import torch.nn.functional as F\n", + "import pandas as pd" + ] + }, + { + "cell_type": "code", + "execution_count": 110, + "id": "major-compromise", + "metadata": {}, + "outputs": [], + "source": [ + "class Model(nn.Module):\n", + " def __init__(self, input_dim):\n", + " super(Model, self).__init__()\n", + " self.layer1 = nn.Linear(input_dim,50)\n", + " self.layer2 = nn.Linear(50, 20)\n", + " self.layer3 = nn.Linear(20, 3)\n", + " \n", + " def forward(self, x):\n", + " x = F.relu(self.layer1(x))\n", + " x = F.relu(self.layer2(x))\n", + " x = F.softmax(self.layer3(x)) # To check with the loss function\n", + " return x" + ] + }, + { + "cell_type": "code", + "execution_count": 111, + "id": "czech-regular", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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"outputs": [], + "source": [ + "lb = LabelEncoder()\n", + "data['diagnosis'] = lb.fit_transform(data['diagnosis'])\n", + "features = data.iloc[:, 1:32].values\n", + "labels = np.array(data['diagnosis'])" + ] + }, + { + "cell_type": "code", + "execution_count": 113, + "id": "buried-community", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[2.057e+01, 1.777e+01, 1.329e+02, 1.326e+03, 8.474e-02, 7.864e-02,\n", + " 8.690e-02, 7.017e-02, 1.812e-01, 5.667e-02, 5.435e-01, 7.339e-01,\n", + " 3.398e+00, 7.408e+01, 5.225e-03, 1.308e-02, 1.860e-02, 1.340e-02,\n", + " 1.389e-02, 3.532e-03, 2.499e+01, 2.341e+01, 1.588e+02, 1.956e+03,\n", + " 1.238e-01, 1.866e-01, 2.416e-01, 1.860e-01, 2.750e-01, 8.902e-02],\n", + " [1.969e+01, 2.125e+01, 1.300e+02, 1.203e+03, 1.096e-01, 1.599e-01,\n", + " 1.974e-01, 1.279e-01, 2.069e-01, 5.999e-02, 7.456e-01, 7.869e-01,\n", + " 4.585e+00, 9.403e+01, 6.150e-03, 4.006e-02, 3.832e-02, 2.058e-02,\n", + " 2.250e-02, 4.571e-03, 2.357e+01, 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2.182e+01, 8.750e+01, 5.198e+02, 1.273e-01, 1.932e-01,\n", + " 1.859e-01, 9.353e-02, 2.350e-01, 7.389e-02, 3.063e-01, 1.002e+00,\n", + " 2.406e+00, 2.432e+01, 5.731e-03, 3.502e-02, 3.553e-02, 1.226e-02,\n", + " 2.143e-02, 3.749e-03, 1.549e+01, 3.073e+01, 1.062e+02, 7.393e+02,\n", + " 1.703e-01, 5.401e-01, 5.390e-01, 2.060e-01, 4.378e-01, 1.072e-01],\n", + " [1.246e+01, 2.404e+01, 8.397e+01, 4.759e+02, 1.186e-01, 2.396e-01,\n", + " 2.273e-01, 8.543e-02, 2.030e-01, 8.243e-02, 2.976e-01, 1.599e+00,\n", + " 2.039e+00, 2.394e+01, 7.149e-03, 7.217e-02, 7.743e-02, 1.432e-02,\n", + " 1.789e-02, 1.008e-02, 1.509e+01, 4.068e+01, 9.765e+01, 7.114e+02,\n", + " 1.853e-01, 1.058e+00, 1.105e+00, 2.210e-01, 4.366e-01, 2.075e-01]])" + ] + }, + "execution_count": 113, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "features[1:10]" + ] + }, + { + "cell_type": "code", + "execution_count": 114, + "id": "incredible-quantum", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([1, 1, 1, 1, 1, 1, 1, 1, 1])" + ] + }, + "execution_count": 114, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "labels[1:10]" + ] + }, + { + "cell_type": "code", + "execution_count": 115, + "id": "brazilian-butler", + "metadata": {}, + "outputs": [], + "source": [ + "features_train, features_test, labels_train, labels_test = train_test_split(features, labels, random_state=42, shuffle=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 116, + "id": "exotic-method", + "metadata": {}, + "outputs": [], + "source": [ + "# Training\n", + "model = Model(features_train.shape[1])\n", + "optimizer = torch.optim.Adam(model.parameters(), lr=0.01)\n", + "loss_fn = nn.CrossEntropyLoss()\n", + "epochs = 100\n", + "\n", + "def print_(loss):\n", + " print (\"The loss calculated: \", loss)" + ] + }, + { + "cell_type": "code", + "execution_count": 117, + "id": "sharp-month", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch # 1\n", + "The loss calculated: 0.922476053237915\n", + "Epoch # 2\n", + "The loss calculated: 0.9223369359970093\n", + "Epoch # 3\n", + "The loss calculated: 0.9223369359970093\n", + "Epoch # 4\n", + "The loss calculated: 0.9223369359970093\n", + "Epoch # 5\n", + "The loss calculated: 0.9223369359970093\n", + "Epoch # 6\n", + "The loss calculated: 0.9223369359970093\n", + "Epoch # 7\n", + "The loss calculated: 0.9223369359970093\n", + "Epoch # 8\n", + "The loss calculated: 0.9223369359970093\n", + "Epoch # 9\n", + "The loss calculated: 0.9223369359970093\n", + "Epoch # 10\n", + "The loss calculated: 0.9223369359970093\n", + "Epoch # 11\n", + "The loss calculated: 0.9223369359970093\n", + "Epoch # 12\n", + "The loss calculated: 0.9223369359970093\n", + "Epoch # 13\n", + "The loss calculated: 0.9223369359970093\n", + "Epoch # 14\n", + "The loss calculated: 0.9223369359970093\n", + "Epoch # 15\n", + "The loss calculated: 0.9223369359970093\n", + "Epoch # 16\n", + "The loss calculated: 0.9223369359970093\n", + "Epoch # 17\n", + "The loss calculated: 0.9223369359970093\n", + "Epoch # 18\n", + "The loss calculated: 0.9223369359970093\n", + "Epoch # 19\n", + "The loss calculated: 0.9223369359970093\n", + "Epoch # 20\n", + "The loss calculated: 0.9223369359970093\n", + "Epoch # 21\n", + "The loss calculated: 0.9223369359970093\n", + "Epoch # 22\n", + "The loss calculated: 0.9223369359970093\n", + "Epoch # 23\n", + "The loss calculated: 0.9223369359970093\n", + "Epoch # 24\n", + "The loss calculated: 0.9223369359970093\n", + "Epoch # 25\n", + "The loss calculated: 0.9223369359970093\n", + "Epoch # 26\n", + "The loss calculated: 0.9223369359970093\n", + "Epoch # 27\n", + "The loss calculated: 0.9223369359970093\n", + "Epoch # 28\n", + "The loss calculated: 0.9223369359970093\n", + "Epoch # 29\n", + "The loss calculated: 0.9223369359970093\n", + "Epoch # 30\n", + "The loss calculated: 0.9223369359970093\n", + "Epoch # 31\n", + "The loss calculated: 0.9223369359970093\n", + "Epoch # 32\n", + "The loss calculated: 0.9223369359970093\n", + "Epoch # 33\n", + "The loss calculated: 0.9223369359970093\n", + "Epoch # 34\n", + "The loss calculated: 0.9223369359970093\n", + "Epoch # 35\n", + "The loss calculated: 0.9223369359970093\n", + "Epoch # 36\n", + "The loss calculated: 0.9223369359970093\n", + "Epoch # 37\n", + "The loss calculated: 0.9223369359970093\n", + "Epoch # 38\n", + "The loss calculated: 0.9223369359970093\n", + "Epoch # 39\n", + "The loss calculated: 0.9223369359970093\n", + "Epoch # 40\n", + "The loss calculated: 0.9223369359970093\n", + "Epoch # 41\n", + "The loss calculated: 0.9223369359970093\n", + "Epoch # 42\n", + "The loss calculated: 0.9223369359970093\n", + "Epoch # 43\n", + "The loss calculated: 0.9223369359970093\n", + "Epoch # 44\n", + "The loss calculated: 0.9223369359970093\n", + "Epoch # 45\n", + "The loss calculated: 0.9223369359970093\n", + "Epoch # 46\n", + "The 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"Epoch # 77\n", + "The loss calculated: 0.9223369359970093\n", + "Epoch # 78\n", + "The loss calculated: 0.9223369359970093\n", + "Epoch # 79\n", + "The loss calculated: 0.9223369359970093\n", + "Epoch # 80\n", + "The loss calculated: 0.9223369359970093\n", + "Epoch # 81\n", + "The loss calculated: 0.9223369359970093\n", + "Epoch # 82\n", + "The loss calculated: 0.9223369359970093\n", + "Epoch # 83\n", + "The loss calculated: 0.9223369359970093\n", + "Epoch # 84\n", + "The loss calculated: 0.9223369359970093\n", + "Epoch # 85\n", + "The loss calculated: 0.9223369359970093\n", + "Epoch # 86\n", + "The loss calculated: 0.9223369359970093\n", + "Epoch # 87\n", + "The loss calculated: 0.9223369359970093\n", + "Epoch # 88\n", + "The loss calculated: 0.9223369359970093\n", + "Epoch # 89\n", + "The loss calculated: 0.9223369359970093\n", + "Epoch # 90\n", + "The loss calculated: 0.9223369359970093\n", + "Epoch # 91\n", + "The loss calculated: 0.9223369359970093\n", + "Epoch # 92\n", + "The loss calculated: 0.9223369359970093\n", + "Epoch # 93\n", + "The loss calculated: 0.9223369359970093\n", + "Epoch # 94\n", + "The loss calculated: 0.9223369359970093\n", + "Epoch # 95\n", + "The loss calculated: 0.9223369359970093\n", + "Epoch # 96\n", + "The loss calculated: 0.9223369359970093\n", + "Epoch # 97\n", + "The loss calculated: 0.9223369359970093\n", + "Epoch # 98\n", + "The loss calculated: 0.9223369359970093\n", + "Epoch # 99\n", + "The loss calculated: 0.9223369359970093\n", + "Epoch # 100\n", + "The loss calculated: 0.9223369359970093\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:11: UserWarning: Implicit dimension choice for softmax has been deprecated. Change the call to include dim=X as an argument.\n", + " # This is added back by InteractiveShellApp.init_path()\n" + ] + } + ], + "source": [ + "# Not using dataloader\n", + "x_train, y_train = Variable(torch.from_numpy(features_train)).float(), Variable(torch.from_numpy(labels_train)).long()\n", + "for epoch in range(1, epochs+1):\n", + " print (\"Epoch #\",epoch)\n", + " y_pred = model(x_train)\n", + " loss = loss_fn(y_pred, y_train)\n", + " print_(loss.item())\n", + " \n", + " # Zero gradients\n", + " optimizer.zero_grad()\n", + " loss.backward() # Gradients\n", + " optimizer.step() # Update" + ] + }, + { + "cell_type": "code", + "execution_count": 118, + "id": "mechanical-humidity", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:11: UserWarning: Implicit dimension choice for softmax has been deprecated. Change the call to include dim=X as an argument.\n", + " # This is added back by InteractiveShellApp.init_path()\n" + ] + } + ], + "source": [ + "# Prediction\n", + "x_test = Variable(torch.from_numpy(features_test)).float()\n", + "pred = model(x_test)" + ] + }, + { + "cell_type": "code", + "execution_count": 119, + "id": "based-charleston", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[1., 0., 0.],\n", + " [1., 0., 0.],\n", + " [1., 0., 0.],\n", + " [1., 0., 0.],\n", + " [1., 0., 0.],\n", + " [1., 0., 0.],\n", + " [1., 0., 0.],\n", + " [1., 0., 0.],\n", + " [1., 0., 0.]], dtype=float32)" + ] + }, + "execution_count": 119, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pred = pred.detach().numpy()\n", + "pred[1:10]" + ] + }, + { + "cell_type": "code", + "execution_count": 120, + "id": "dried-accessory", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The accuracy is 0.6223776223776224\n" + ] + } + ], + "source": [ + "print (\"The accuracy is\", accuracy_score(labels_test, np.argmax(pred, axis=1)))" + ] + }, + { + "cell_type": "code", + "execution_count": 121, + "id": "effective-characterization", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0" + ] + }, + "execution_count": 121, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "labels_test[0]" + ] + }, + { + "cell_type": "code", + "execution_count": 122, + "id": "oriented-determination", + "metadata": {}, + "outputs": [], + "source": [ + "torch.save(model, \"travel_insurance-pytorch.pkl\")" + ] + }, + { + "cell_type": "code", + "execution_count": 123, + "id": "infectious-wagon", + "metadata": {}, + "outputs": [], + "source": [ + "saved_model = torch.load(\"travel_insurance-pytorch.pkl\")" + ] + }, + { + "cell_type": "code", + "execution_count": 124, + "id": "built-contributor", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:11: UserWarning: Implicit dimension choice for softmax has been deprecated. Change the call to include dim=X as an argument.\n", + " # This is added back by InteractiveShellApp.init_path()\n" + ] + }, + { + "data": { + "text/plain": [ + "0" + ] + }, + "execution_count": 124, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.argmax(saved_model(x_test[0]).detach().numpy(), axis=0)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "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": "3.7.3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +}