Feat: Code Update

Dockerfile

@@ -2,6 +2,15 @@ FROM ubuntu
 
 RUN apt-get update && apt-get install -y python3 python3-pip unzip
 
-RUN pip3 install kaggle pandas
+RUN python3 -m pip install kaggle numpy pandas torchvision torch
+ 
+COPY ium_DL.py
+COPY ./olympics-124-years-datasettill-2020/Athletes_winter_games.csv
+
+RUN chmod +r ./ium_DL.py
+RUN chmod +r ./Athletes_winter_games.csv
+RUN chmod +r ./olympics-124-years-datasettill-2020/Athletes_winter_games.csv
+
+RUN python3 ./ium_DL.py
 
 WORKDIR /app

ium_DL.py

@@ -0,0 +1,34 @@
+import tensorflow as tf
+import pandas as pd
+
+train_data = pd.read_csv('olympics-124-years-datasettill-2020/Athletes_winter_games.csv')
+
+X_train = train_data[['Sex']]
+y_train = train_data['Medal']
+
+X_train.loc[:, 'Sex'] = X_train['Sex'].map({'M': 0, 'F': 1})
+y_train = y_train.map({'Bronze': 0, 'Silver': 1, 'Gold': 1}).fillna(0).astype('float32')
+
+X_train = X_train.astype('float32')
+y_train = y_train.astype('float32')
+
+model = tf.keras.Sequential([
+    tf.keras.layers.Dense(16, activation='relu', input_shape=(X_train.shape[1],)),
+    tf.keras.layers.Dense(1, activation='sigmoid')
+])
+
+model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])
+
+model.fit(X_train, y_train, epochs=10)
+
+model.save('model.h5')
+
+
+test_data = pd.read_csv('olympics-124-years-datasettill-2020/Athletes_winter_games.csv')
+
+test_data.loc[:, 'Sex'] = test_data['Sex'].map({'M': 0, 'F': 1})
+test_data = test_data[['Sex']].astype('float32')
+
+predictions = model.predict(test_data)
+
+pd.DataFrame(predictions).to_csv('predictions.csv', index=False, header=False)

lab5.ipynb

@@ -0,0 +1,144 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 1/10\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\kmjay\\AppData\\Local\\Temp\\ipykernel_17164\\3575846689.py:9: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
+      "Try using .loc[row_indexer,col_indexer] = value instead\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  X_train.loc[:, 'Sex'] = X_train['Sex'].map({'M': 0, 'F': 1})\n",
+      "C:\\Users\\kmjay\\AppData\\Local\\Temp\\ipykernel_17164\\3575846689.py:9: DeprecationWarning: In a future version, `df.iloc[:, i] = newvals` will attempt to set the values inplace instead of always setting a new array. To retain the old behavior, use either `df[df.columns[i]] = newvals` or, if columns are non-unique, `df.isetitem(i, newvals)`\n",
+      "  X_train.loc[:, 'Sex'] = X_train['Sex'].map({'M': 0, 'F': 1})\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1518/1518 [==============================] - 2s 758us/step - loss: 0.3609 - accuracy: 0.9112\n",
+      "Epoch 2/10\n",
+      "1518/1518 [==============================] - 1s 726us/step - loss: 0.2763 - accuracy: 0.9216\n",
+      "Epoch 3/10\n",
+      "1518/1518 [==============================] - 1s 731us/step - loss: 0.2751 - accuracy: 0.9216\n",
+      "Epoch 4/10\n",
+      "1518/1518 [==============================] - 1s 725us/step - loss: 0.2750 - accuracy: 0.9216\n",
+      "Epoch 5/10\n",
+      "1518/1518 [==============================] - 1s 733us/step - loss: 0.2750 - accuracy: 0.9216\n",
+      "Epoch 6/10\n",
+      "1518/1518 [==============================] - 1s 733us/step - loss: 0.2750 - accuracy: 0.9216\n",
+      "Epoch 7/10\n",
+      "1518/1518 [==============================] - 1s 729us/step - loss: 0.2750 - accuracy: 0.9216\n",
+      "Epoch 8/10\n",
+      "1518/1518 [==============================] - 1s 728us/step - loss: 0.2750 - accuracy: 0.9216\n",
+      "Epoch 9/10\n",
+      "1518/1518 [==============================] - 1s 727us/step - loss: 0.2750 - accuracy: 0.9216\n",
+      "Epoch 10/10\n",
+      "1518/1518 [==============================] - 1s 755us/step - loss: 0.2750 - accuracy: 0.9216\n"
+     ]
+    }
+   ],
+   "source": [
+    "import tensorflow as tf\n",
+    "import pandas as pd\n",
+    "\n",
+    "train_data = pd.read_csv('olympics-124-years-datasettill-2020/Athletes_winter_games.csv')\n",
+    "\n",
+    "X_train = train_data[['Sex']]\n",
+    "y_train = train_data['Medal']\n",
+    "\n",
+    "X_train.loc[:, 'Sex'] = X_train['Sex'].map({'M': 0, 'F': 1})\n",
+    "y_train = y_train.map({'Bronze': 0, 'Silver': 1, 'Gold': 1}).fillna(0).astype('float32')\n",
+    "\n",
+    "X_train = X_train.astype('float32')\n",
+    "y_train = y_train.astype('float32')\n",
+    "\n",
+    "model = tf.keras.Sequential([\n",
+    "    tf.keras.layers.Dense(16, activation='relu', input_shape=(X_train.shape[1],)),\n",
+    "    tf.keras.layers.Dense(1, activation='sigmoid')\n",
+    "])\n",
+    "\n",
+    "model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])\n",
+    "\n",
+    "model.fit(X_train, y_train, epochs=10)\n",
+    "\n",
+    "model.save('model.h5')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      " 170/1518 [==>...........................] - ETA: 0s"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\kmjay\\AppData\\Local\\Temp\\ipykernel_17164\\2746302769.py:3: DeprecationWarning: In a future version, `df.iloc[:, i] = newvals` will attempt to set the values inplace instead of always setting a new array. To retain the old behavior, use either `df[df.columns[i]] = newvals` or, if columns are non-unique, `df.isetitem(i, newvals)`\n",
+      "  test_data.loc[:, 'Sex'] = test_data['Sex'].map({'M': 0, 'F': 1})\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1518/1518 [==============================] - 1s 574us/step\n"
+     ]
+    }
+   ],
+   "source": [
+    "test_data = pd.read_csv('olympics-124-years-datasettill-2020/Athletes_winter_games.csv')\n",
+    "\n",
+    "test_data.loc[:, 'Sex'] = test_data['Sex'].map({'M': 0, 'F': 1})\n",
+    "test_data = test_data[['Sex']].astype('float32')\n",
+    "\n",
+    "predictions = model.predict(test_data)\n",
+    "\n",
+    "pd.DataFrame(predictions).to_csv('predictions.csv', index=False, header=False)"
+   ]
+  }
+ ],
+ "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": "3.11.3"
+  },
+  "orig_nbformat": 4
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}