From 5af1ea42344b00df1af0aba7c3c0a0e5c9185a50 Mon Sep 17 00:00:00 2001 From: Aviral Garg <105068445+aviralgarg05@users.noreply.github.com> Date: Mon, 15 Jul 2024 23:35:50 +0530 Subject: [PATCH 1/4] Add files via upload --- Poker Hand Prediction/Images/ANN.png | Bin 0 -> 47098 bytes Poker Hand Prediction/Images/Adaboost.png | Bin 0 -> 43501 bytes Poker Hand Prediction/Images/DNN.png | Bin 0 -> 52952 bytes .../Images/Data Distribution.png | Bin 0 -> 36542 bytes .../Images/Multi Layer Perceptron.png | Bin 0 -> 57525 bytes .../Images/Output Code Classifier.png | Bin 0 -> 50048 bytes .../Images/Random Forest.png | Bin 0 -> 53058 bytes Poker Hand Prediction/Images/Regression.png | Bin 0 -> 42128 bytes Poker Hand Prediction/Images/confMat.png | Bin 0 -> 98465 bytes Poker Hand Prediction/Images/svm.png | Bin 0 -> 46799 bytes .../Poker Hand Prediction/AdaBoost.ipynb | 258 + .../Artificial Neural Network.ipynb | 393 + .../Deep Neural Network.ipynb | 399 + .../Multi Layer Perceptron.ipynb | 357 + 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-------------------- #\n", + "\n", + "from sklearn.ensemble import AdaBoostClassifier\n", + "from sklearn.preprocessing import StandardScaler\n", + "from sklearn.metrics import accuracy_score\n", + "from matplotlib import pyplot as plt\n", + "import numpy as np, pandas as pd, os\n", + "from prettytable import PrettyTable" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "# -------------------- Globals and Configs -------------------- #\n", + "\n", + "feature_names = list()\n", + "for index in range(1, 6):\n", + " feature_names.extend([\"Suit\"+str(index), \"Rank\"+str(index)])\n", + "\n", + "feature_names.append('class')\n", + "\n", + "\n", + "training_input_file = os.path.abspath('../datasets/csv/train.csv')\n", + "testing_input_file = os.path.abspath('../datasets/csv/test.csv')\n", + "\n", + "np.random.seed(666) # seed for reproducible results\n", + "\n", + "# To store configs\n", + "class myConfigs:\n", + " features = 0\n", + " classes = 0\n", + "\n", + "\n", + "config = myConfigs()" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "+----------+-------+\n", + "| Config | Value |\n", + "+----------+-------+\n", + "| classes | 10 |\n", + "| features | 10 |\n", + "+----------+-------+\n", + "Instances in training data : 25010\n", + "Instances in testing data : 1000000\n" + ] + } + ], + "source": [ + "# -------------------- Data -------------------- #\n", + "\n", + "train_data = pd.read_csv(training_input_file, names=feature_names)\n", + "test_data = pd.read_csv(testing_input_file, names=feature_names)\n", + "\n", + "# Get features of data\n", + "config.features = len(train_data.columns) - 1\n", + "config.classes = len(set(train_data['class']))\n", + "\n", + "# Shuffle training data\n", + "train_data = train_data.sample(frac=1).reset_index(drop=True)\n", + "\n", + "# Seperate data and classes\n", + "train_y = np.array(train_data['class'])\n", + "train_x = np.array(train_data.drop('class', 1))\n", + "\n", + "test_y = np.array(test_data['class'])\n", + "test_x = np.array(test_data.drop('class', 1))\n", + "\n", + "# Initialise scaler to scale the data\n", + "scaler = StandardScaler()\n", + "\n", + "train_set = np.empty(train_x.shape, dtype = float)\n", + "test_set = np.empty(test_x.shape, dtype = float)\n", + "\n", + "# Make training data suitable for scaling\n", + "for index in range(len(train_x)):\n", + " train_set[index] = train_x[index].astype(float)\n", + "\n", + "# Make testing data suitable for scaling\n", + "for index in range(len(test_x)):\n", + " test_set[index] = test_x[index].astype(float)\n", + "\n", + "# Fit the training data\n", + "scaler.fit(train_set) \n", + "\n", + "# Scale the training and testing data w.r.t scaler\n", + "data_train = scaler.transform(train_set)\n", + "data_test = scaler.transform(test_set)\n", + "\n", + "tab = PrettyTable(['Config', 'Value'])\n", + "configs = vars(config)\n", + "\n", + "for key in configs:\n", + " tab.add_row([key, configs[key]])\n", + "print(tab)\n", + "\n", + "print(\"Instances in training data :\", len(train_data))\n", + "print(\"Instances in testing data :\", len(test_data))" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "# -------------------- Model -------------------- #\n", + "\n", + "adaboost = AdaBoostClassifier()\n", + "adaboost.fit(data_train, train_y)\n", + "prediction = adaboost.predict(data_test)\n", + "accuracy = accuracy_score(test_y, prediction)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Accuracy using AdaBoost Classifier : 49.158 %\n", + "\n" + ] + } + ], + "source": [ + "# -------------------- Print the final result -------------------- #\n", + "\n", + "print(\"\\nAccuracy using AdaBoost Classifier :\", round(accuracy * 100, 3),\"%\\n\")" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Accuracy : 49.158\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "

" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# -------------------- Visualisations -------------------- #\n", + "\n", + "print(\"Accuracy :\", round(accuracy * 100, 3))\n", + "plt.plot(test_y, 'bo', label = \"Original\")\n", + "plt.plot(prediction, 'ro', label = \"Predicted\")\n", + "plt.legend()\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "+--------+--------+--------+--------+--------+--------+--------+--------+--------+--------+--------+\n", + "| | Pred 0 | Pred 1 | Pred 2 | Pred 3 | Pred 4 | Pred 5 | Pred 6 | Pred 7 | Pred 8 | Pred 9 |\n", + "+--------+--------+--------+--------+--------+--------+--------+--------+--------+--------+--------+\n", + "| Real 0 | 491576 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 9633 |\n", + "| Real 1 | 414414 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 8084 |\n", + "| Real 2 | 46709 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 913 |\n", + "| Real 3 | 20729 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 392 |\n", + "| Real 4 | 3853 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 32 |\n", + "| Real 5 | 1962 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 34 |\n", + "| Real 6 | 1402 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 22 |\n", + "| Real 7 | 227 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 |\n", + "| Real 8 | 12 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |\n", + "| Real 9 | 3 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |\n", + "+--------+--------+--------+--------+--------+--------+--------+--------+--------+--------+--------+\n" + ] + } + ], + "source": [ + "preds = prediction\n", + "confMat = [[0] * config.classes for x in range(config.classes)]\n", + "\n", + "for i in range(len(preds)):\n", + " predLabel = preds[i]\n", + " actLabel = test_y[i]\n", + " confMat[actLabel][predLabel] += 1\n", + "\n", + "header = [\"\"]\n", + "for l in range(config.classes):\n", + " header.append(\"Pred \" + str(l))\n", + " \n", + "tab = PrettyTable(header)\n", + "l = 0\n", + "for c in confMat:\n", + " tab.add_row([\"Real \" + str(l)] + c)\n", + " l += 1\n", + "\n", + "print(tab)" + ] + } + ], + "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": "3.5.2" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/Poker Hand Prediction/Poker Hand Prediction/Artificial Neural Network.ipynb b/Poker Hand Prediction/Poker Hand Prediction/Artificial Neural Network.ipynb new file mode 100644 index 000000000..c059c0dcb --- /dev/null +++ b/Poker Hand Prediction/Poker Hand Prediction/Artificial Neural Network.ipynb @@ -0,0 +1,393 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Using TensorFlow backend.\n" + ] + } + ], + "source": [ + "# -------------------- Imports -------------------- #\n", + "from keras.models import Sequential\n", + "from keras.layers import Input, Dense, Flatten\n", + "from keras.models import Model\n", + "import numpy as np, pandas as pd, os\n", + "from prettytable import PrettyTable\n", + "from matplotlib import pyplot as plt\n", + "import math" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "# -------------------- Globals and Configs -------------------- #\n", + "feature_names = list()\n", + "for index in range(1, 6):\n", + " feature_names.extend([\"Suit\"+str(index), \"Rank\"+str(index)])\n", + "\n", + "feature_names.append('class')\n", + "\n", + "\n", + "training_input_file = os.path.abspath('../datasets/csv/train.csv')\n", + "testing_input_file = os.path.abspath('../datasets/csv/test.csv')\n", + "\n", + "np.random.seed(666) # seed for reproducible results\n", + "\n", + "# To store configs\n", + "class myConfigs:\n", + " features = 0\n", + " classes = 0\n", + "\n", + "\n", + "config = myConfigs()" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "+----------+-------+\n", + "| Config | Value |\n", + "+----------+-------+\n", + "| features | 10 |\n", + "| classes | 10 |\n", + "+----------+-------+\n", + "Instances in training data : 25010\n", + "Instances in testing data : 1000000\n" + ] + } + ], + "source": [ + "# -------------------- Data -------------------- #\n", + "\n", + "train_data = pd.read_csv(training_input_file, names=feature_names)\n", + "test_data = pd.read_csv(testing_input_file, names=feature_names)\n", + "\n", + "# Get features of data\n", + "config.features = len(train_data.columns) - 1\n", + "config.classes = len(set(train_data['class']))\n", + "\n", + "# Shuffle training data\n", + "train_data = train_data.sample(frac=1).reset_index(drop=True)\n", + "\n", + "# Seperate data and classes\n", + "train_y = np.array(train_data['class'])\n", + "train_x = np.array(train_data.drop('class', 1))\n", + "\n", + "test_y = np.array(test_data['class'])\n", + "test_x = np.array(test_data.drop('class', 1))\n", + "\n", + "train_y_onehot = list()\n", + "for y in range(len(train_y)):\n", + " temp = [0] * config.classes\n", + " temp[train_y[y]] = 1\n", + " train_y_onehot.append(temp)\n", + " \n", + "test_y_onehot = list()\n", + "for y in range(len(test_y)):\n", + " temp = [0] * config.classes\n", + " temp[test_y[y]] = 1\n", + " test_y_onehot.append(temp)\n", + " \n", + "train_y_onehot = np.array(train_y_onehot)\n", + "test_y_onehot = np.array(test_y_onehot)\n", + "\n", + "tab = PrettyTable(['Config', 'Value'])\n", + "configs = vars(config)\n", + "\n", + "for key in configs:\n", + " tab.add_row([key, configs[key]])\n", + "print(tab)\n", + "\n", + "print(\"Instances in training data :\", len(train_data))\n", + "print(\"Instances in testing data :\", len(test_data))" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# -------------------- Model -------------------- #\n", + "model = Sequential()\n", + "\n", + "# Input layer\n", + "model.add(Dense(10, input_shape = (train_x.shape[1],), activation='softmax'))\n", + "\n", + "# Output layer\n", + "model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])\n", + "model.fit(train_x, train_y_onehot, epochs = 10, batch_size = 500, verbose=0)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Matches : 11525 / 25010 = 46.08156737305078\n", + "Average Error : 0.706437425029988\n", + "RMSE : 1.2597480755748087\n" + ] + }, + { + "data": { + "image/png": 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3uch33pzaWLzYGtNsnMbdbS7c5s0pAZaVZVgvadxi9dNGtva9jmUsBowf797W3XcD6bnt4EFrf8r48VY7ftrduHEjzjzzjCHnVVb+GY45ZiL6+g5jy5b1uP/+57Bs2dtDzlu5cimqqo7CypUduOGGe7F587qB69nnZefOrbjsshvx7LMbUVU1DmvXPo9YDLj66r9Da2srNmzYgFNOOQWPP/64e5AjwevNcD9bPlXend6g9FsB21512UsVbrd2vFSe9l1w3aVquFMVbreY/BYnT42BPf7Uz0zj4zSuXuN0mgMvc5ltLvwc67WN9PEHBqt+Zxt3P9fONM9eBdFGtvbd+rN6tfXm5IYN2d+ss1dQt28iQ4/bu9dqz2u7Dz30kN56663Dzjv11Bm6cOFDetFF1w28qfjii536rW9N1dZW1fPPn6cvvLB24LyTTz5df/nLVt27VzWRSGhPT492dnbqCSecMHDMTTfdrzfeeK/u3av61ltv6ezZs3XatGlaV1enN9xwg6qqLly4UJcsWeJ7nPN9c7LgEjcRFSanZOMm9UInfUsk8othzZo1es455wzZ9+WXX+rRRx+tjz76qN54440D+zs7O3Xq1Kmqqjpv3jxdu3btwHOnn366tra2JmMdTNyp41VVlyxZogsXLlRV1bq6Om1vb1dV1SeeeEKvvfZaVY0ucRfUrRIiKg5+bnf50dDQgIMHD+Kpp54CAPT19eH222/Hddddh4oM9+tmzZqFZ599FgDQ0dGBjz76yNd19+/fj2OPPRa9vb1oCfKD8zli4iaiwF11FbBsmfUxRRHr57Jl1v58iAheeOEFrFy5EieeeCJOOukklJeX4yc/+UnG85qamtDT04MpU6bgnnvuwdSpU3HkkUd6vu69996Lb3/725g1axYmT56cXycCUFCf4yaiwuX02WNT9PX1obe3F+Xl5di+fTvmzJmDLVu2oCzTR1hClO/nuPldJURU9A4ePIjzzz8fvb29UFUsXbo0sqQdBCZuIip6VVVVKKa7ALzHTURkGCZuIiLDMHETERmGiZuIyDBM3ERkjHg8jhkzZmDatGm45JJL8vqyp7feegsXXXQRAOCll17C/fff73rsF198gaVLl/q+xqJFi/DAAw/kHKMbJm4iCofv8lDZHXHEEWhvb8fHH3+MsrIy/OIXvxjyvKqiv7/fd7tz587FggULXJ/PNXGHhYmbiILn5Xtd83TOOedg27Zt6Orqwsknn4xrrrkG06ZNw65du7B69WqcffbZmDlzJi655BIcOHAAAPDaa69h8uTJmDlzJlatWjXQ1ooVK3DTTTcBAD799FNcfPHFmD59OqZPn453330XCxYswPbt2zFjxgzceeedAIAlS5bgzDPPxGmnnYaFCxcOtLV48WKcdNJJmD17NrZs2RJYf+2YuIkoeF6+1zUPhw8fxquvvopTTz0VALB161Y0NTVh48aNGDt2LO677z688cYbWL9+Perr6/Hggw/i0KFD+NGPfoSXX34Z69atwx//+EfHtm+++Wacd9552LBhA9avX4+pU6fi/vvvx/HHH4/29nYsWbIEq1evxtatW/HBBx+gvb0d69atwzvvvIN169bhmWeeQXt7O1555RW0trYG0t90nv8DjojEAbQB2K2qF4USDREVBz9ljHz4+uuvMWPGDADWK+7rr78ee/bsQSKRwFlnnQUAeO+999DR0YFZs2YBAL755hucffbZ2Lx5MyZNmoQTTzwRAHD11Vdj2bJlw66xdu3agS+xisfjOPLII/H5558POWb16tVYvXo1Tj/9dADAgQMHsHXrVuzfvx8XX3zxwBdezZ07N6/+uvHzivsWAJtCiQLOVcndKo+nV+SOx50rh3up2p7ezpw5zs+lNqcK5pWV+Vfudqoin15V3d5Pr5XsnaqC289Nb8tLdfs5c6xjncbHS/+d5jB1bnrb5eXuffMyppmqrKdvsRhQVZX/LVmn9VVSYo0zYP1Mjad9f6bY3dpPn4f0NWx/Ptvc2ONyWu87dgyveu5UWX3HDqBvvLcyRvaK6u3t1pZelT21r60NGDPmCCxf3o7HHmvHtdc+jI6OMnz+OTB27NiBNlUV557711ixwjruqac60Nj4ODZuBP70J/f+u8WViufQocEx+OQTxZVX3oXHHrOu8cwz2zB9+vXYvXv4PzRC4eW7XwFMAPBbABcA+HW24/1+H3e2iuL2yuPZKnJnqlKerWp7UJvfyt1Bx+NnvNzOz2ecMvU/15jS++Z3TN2qrOd7Lb/zOWWK8/7GRvfz7XHku17c5qaxMfu5qSrvqarnXV3uFdW339ush8uHBtp/xNABzVatPb0ie2ur6hFHjB2276WXOnXy5MHv0d68+TM95pjjdNWqrdraqvrOOwf0uee26O9//7Uec8xx+vbb21RV9fLLL9fvf//7qmp9x3bqu7x/8IPL9Pbbf6atrarvvXdY33zzC12zZq/++Z9PHLjmww+/rlOn/qW+/fZ+bW1V/c1vuvX11z/Vp59epyeccKru2nVQv/rqKz3hhBMcv697RKq8i8hzAP4ngCoAd2iWWyV+vx3QS6VtP5XHMx2bT0VyP4Ko3J3v9YHc2853nNz6H0R/s41tkGPqt3p6rteOx4HDh7NXuA9r/EpKss+3vcp7WVn2ArpHv9qC8UvvRtmnO/HNMRPx6c2LMfGuwe91dauonsm551binXcODNm3Z08Xfvzji7Bt28cD7f7rv67Fww//d/T2Wi+x/+Ef7sN5583Fu+++hgcfvBVHH12Bc845B9u3b8evf/1rrFixAm1tbXjkkUfw5pufYtGi+di9+w+IxeJYsODnOO20s3HPPVdi69YP8Z3vfA+33LIEv/rVQ3jxxccAABUVlfjnf27GhAnHY/nyxfjNb57Eccf9J0ycOBEzZ84cVgU+328HzJq4ReQiABeqapOI/BVcEreIzAcwHwAmTpx4xg4fqysWs/5KzhyH9dPD3zO+jg2LCODlU0le+p7r9YHoxsCt/0H0N9vYBjmmXucxiGurup+fiiOs8Uutl0zsiTtX9ba0FPR3PqXa9tJufYb0GFRcma6Rb+L2co97FoC5ItIF4BkAF4hIc/pBqrpMVetVtb62ttbLtQd4qVDtp/J4pmPzqUjuRxCVu/O9fj5t5ztOfiq1B9V2kNfIta1cr50a72zjFtb4+Z3vXL4RNf2cIL9V1d5Wvu0GEVfY3xibNXGr6l2qOkFV6wBcDmCtql4dZBDZKorbK49nq8idqUp5tqrtQfFbuTvoePyMl9v5+YxTpv7nGpM9tmxj6zSmblXW872Wl2vbTZnivH/+fPfz7XHku17c5iZ1fS9SldgzvT5LfwXvVL3dqcp7pjYyHWdve/z4zOdme12ZLS6/8YShID7HnV7mqLra2tJLHl11FfDEE9ZzdqlBTj/WqXTS0qWD+9PFYkBDg/NzKYkE0Ng4NFbbG9qorgaWL/deoskeJzCYXFLXsffV3s/0GLyOV3X10HPT23IbJ3tcDQ3ufzCy9d9tDlPnprc9Zoxz37yOaeq8J54AnnzS+bopIoOfEMq11Fb6fKbE49Y4b9xo/UyNZ2p/6j/lZSv5ld6+U4K0r2H785nmZunSoXGl2NcHYCX+RMJqK5FwToK1tda9+NSrTvs5dqk2UseVlFhb6py6OmDSpMF9qf7Zk2pJiXVc+hqvqxt6nj22TH++neJKKSuzzs/0atopnjCwdBkRebJp0yZMnjwZ4vWlMDlSVWzevDn0e9xERCgvL8e+ffsQxou90UJVsW/fPpSXl+fVDkuXEZEnEyZMQHd3N3p6eqIOxWjl5eWYMGFCXm0wcRORJ6WlpZg0aVLUYRB4q4SIyDhM3EREhmHiJiIyDBM3EZFhmLiJiAzDxE1EZBgmbiIiwzBxExEZhombiMgwTNxERIZh4iYiMkzBJO7fN7Wgu6QO/RJDl9ThSmmBiPV9zG5VuUdiq6qK9vrxOHDUUZmPuVJa0CWDY/eINA35PTWWbsenP5/Pll4lPMxr5bP5ietKaUGP1EBFoCLokZrQ+5EpvlzHtFDnwstWXj4y/YjFghm/pqawMmWS16rCfja/Vd5/19isBzC0IvQBVOgVaM65mvVo2a7A8LHrTzvIPpZOx4c11iN5rbDiugLN+jWGl6U/hLLQ+pEpvlzHtFDnwpQ1lct1Gxt9pUGFjyrvng4KO3Hviicce96JROQLpdC3TjiPndtYuh0fxliP5LXCiivT+IbVj0zx5TqmhToXYc5d1NeNx32lQfWTuAuiAk6/xBDD8Dj6IYjDR4ntUagPzmOXLjWWbseHMdYjeS0//MSVaXzD6kem+ADkNKaFOhd+RdWPXK/rJ70aVwFnT9y5dPVOhFQCvYh4HaPUcW7HhzHWI3ktP/zElSnWsPqRKb5cx7RQ58KvqPqRy3X9Fqf2JajbI/aN97hHbuM97nDj4j3uwtp4j7uA7nGnkveueEL7INqJxMCAlJVFu1AqK6O9fiymOm5c9kXVicGxexiNQ35PX1zpxwe56OPxzLEVSqLwE9cVaNbPUK39sP5S/AzVI5Io3OLLdUwLdS68bGPGjEw/RIIZP79J22/iLoh73EREo51x97iJiMg7Jm4iIsMwcRMRGYaJm4jIMEzcRESGYeImIjIMEzcRkWGYuImIDMPETURkGCZuIiLDMHETERmGiZuIyDBM3EREhsmauEXkOBF5U0Q6RGSjiNwyEoEREZGzEg/HHAZwu6quF5EqAOtEZI2qdgQZSFMT8POfB9kiEVE0pkwBNm4Mr/2sr7hV9RNVXZ98vB/AJgDjgwyCSZuIiklHBzB1anjt+7rHLSJ1AE4H8H6QQSxbFmRrRETR6wj0nsRQnhO3iFQCeB7Arar6lcPz80WkTUTaenp6fAXR1+frcCKiUc1T4haRUlhJu0VVVzkdo6rLVLVeVetra2t9BRFqNWQioiLj5VMlAuBxAJtU9cEwgpg/P4xWiYiiM2VKeG17ecU9C8B/AXCBiLQntwuDDGLpUqCxMcgWiYiiE/anSljlnYioALDKOxFREWPiJiIyDBM3EZFhmLiJiAzDxE1EZBgmbiIiwzBxExEZhombiMgwTNxERIZh4iYiMgwTNxGRYZi4iYgMw8RNRGQYJm4iIsN4qfI+IvpFIFEHQUQUAAUQC+Ers1MKInGnkjYTNxEVi36R0JJ3QSRuJm0iKiZh5zPe4yYiMgwTNxGRYQoicWtyIyIqBmHntIK4xx1TRb/wLjcRFYdR8akSINxOEhGNJL45SUREQzBxExEZhombiMgwTNxERIZh4iYiMgwTNxGRYZi4iYgMw8RNRGQYJm4iIsMwcRMRGYaJm4jIMEzcRESGYeImIjKMp8QtIn8rIltEZJuILAg7KCIicpf1a11FJA7gUQB/DaAbQKuIvKSqHUEGwirvRFQsCuH7uP8SwDZV/QMAiMgzAOYBCCxxs8o7ERWbMKu8e7lVMh7ALtvv3cl9gWHSJqJiEnZOC+zNSRGZLyJtItLW09MTVLNERJTGS+LeDeA42+8TkvuGUNVlqlqvqvW1tbVBxUdERGm8JO5WACeKyCQRKQNwOYCXggyCVd6JqJhEXuVdVQ+LyE0AXgcQB7BcVTcGGQSrvBNRMSmET5VAVV8B8EpoUYBV3omoeLDKOxERDcHETURkGCZuIiLDMHETERmGiZuIyDCiIXyaQ0R6AOzI8fQaAHsDDMcEo63Po62/APs8WuTT54Sqevrfi6Ek7nyISJuq1kcdx0gabX0ebf0F2OfRYqT6zFslRESGYeImIjJMISbuZVEHEIHR1ufR1l+AfR4tRqTPBXePm4iIMivEV9xERJRBwSTuYitILCJdIvKRiLSLSFty39EiskZEtiZ/HpXcLyLyf5J9/1BEZtrauTZ5/FYRuTaq/jgRkeUi8pmIfGzbF1gfReSM5BhuS54b+VdIuvR5kYjsTs51u4hcaHvurmT8W0Tkb2z7Hdd78uuT30/u/7/Jr1KOjIgcJyJvikiHiGwUkVuS+4t2njP0uXDmWVUj32B9Xex2AN8CUAZgA4ApUceVZ5+6ANSk7ftfABYkHy8A8NPk4wsBvArrS8XOAvB+cv/RAP6Q/HlU8vFRUffN1p9zAcwE8HGlSVgrAAADCUlEQVQYfQTwQfJYSZ77vQLt8yIAdzgcOyW5lscAmJRc4/FM6x3AswAuTz7+BYDGiPt7LICZycdVAP492a+inecMfS6YeS6UV9wDBYlV9RsAqYLExWYegCeTj58E8APb/qfU8h6AcSJyLIC/AbBGVf9DVT8HsAbA34500G5U9R0A/5G2O5A+Jp/7M1V9T63V/ZStrci49NnNPADPqOqfVLUTwDZYa91xvSdfaV4A4Lnk+fbxi4SqfqKq65OP9wPYBKvmbNHOc4Y+uxnxeS6UxB16QeIIKIDVIrJOROYn9x2jqp8kH/8RwDHJx279N3Fcgurj+OTj9P2F6qbkrYHlqdsG8N/nagBfqOrhtP0FQUTqAJwO4H2MknlO6zNQIPNcKIm7GM1W1ZkAvgfgRhE51/5k8tVFUX+kZzT0MennAI4HMAPAJwD+d7ThBE9EKgE8D+BWVf3K/lyxzrNDnwtmngslcXsqSGwSVd2d/PkZgBdg/bPp0+Q/DZH8+VnycLf+mzguQfVxd/Jx+v6Co6qfqmqfqvYD+BdYcw347/M+WLcWStL2R0pESmElsBZVXZXcXdTz7NTnQprnQkncoRckHkkiMlZEqlKPAXwXwMew+pR6N/1aAC8mH78E4JrkO/JnAfgy+c/Q1wF8V0SOSv6z7LvJfYUskD4mn/tKRM5K3hO8xtZWQUklsKSLYc01YPX5chEZIyKTAJwI6404x/WefOX6JoD/nDzfPn6RSI794wA2qeqDtqeKdp7d+lxQ8xzVO7cO78xeCOvd2+0A7o46njz78i1Y7yBvALAx1R9Y97Z+C2ArgDcAHJ3cLwAeTfb9IwD1trb+K6w3O7YB+Puo+5bWz1/B+idjL6z7dNcH2UcA9ck/HNsBPILkfxgrwD4/nezTh8k/xMfajr87Gf8W2D4t4bbek2vng+RYrAQwJuL+zoZ1G+RDAO3J7cJinucMfS6Yeeb/nCQiMkyh3CohIiKPmLiJiAzDxE1EZBgmbiIiwzBxExEZhombiMgwTNxERIZh4iYiMsz/Byqb6MqAgEgLAAAAAElFTkSuQmCC", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "preds = [list(x) for x in model.predict(train_x)]\n", + "for i in range(len(preds)):\n", + " preds[i] = preds[i].index(max(preds[i]))\n", + "\n", + "train_matches = 0\n", + "for i in range(len(train_y)):\n", + " if train_y[i] == preds[i]:\n", + " train_matches += 1\n", + " \n", + "print(\"Matches :\", train_matches, '/', len(train_y), '=', train_matches/len(train_y) * 100)\n", + "print(\"Average Error :\", sum([math.fabs(x-y) for x, y in zip(train_y, preds)])/len(train_y))\n", + "print(\"RMSE :\", math.sqrt(sum([(x-y)**2 for x, y in zip(train_y, preds)])/len(train_y)))\n", + " \n", + "plt.plot(train_y, 'bo', label='Original')\n", + "plt.plot(preds, 'ro', label='Predicted')\n", + "plt.legend()\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Matches : 457299 / 1000000 = 45.7299\n", + "Average Error : 0.712017\n", + "RMSE : 1.265922983439356\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "preds = [list(x) for x in model.predict(test_x)]\n", + "for i in range(len(preds)):\n", + " preds[i] = preds[i].index(max(preds[i]))\n", + "\n", + "test_matches = 0\n", + "for i in range(len(test_y)):\n", + " if test_y[i] == preds[i]:\n", + " test_matches += 1\n", + " \n", + "print(\"Matches :\", test_matches, '/', len(test_y), '=', test_matches/len(test_y) * 100)\n", + "print(\"Average Error :\", sum([math.fabs(x-y) for x, y in zip(test_y, preds)])/len(test_y))\n", + "print(\"RMSE :\", math.sqrt(sum([(x-y)**2 for x, y in zip(test_y, preds)])/len(test_y)))\n", + " \n", + "plt.plot(test_y, 'bo', label='Original')\n", + "plt.plot(preds, 'ro', label='Predicted')\n", + "plt.legend()\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "25010/25010 [==============================] - 0s 18us/step\n", + "Train = acc 46.081567374957366\n", + "1000000/1000000 [==============================] - 18s 18us/step\n", + "Test = acc 45.7299\n" + ] + } + ], + "source": [ + "scores = model.evaluate(train_x, train_y_onehot)\n", + "print(\"Train =\", model.metrics_names[1], scores[1] * 100)\n", + "\n", + "scores = model.evaluate(test_x, test_y_onehot)\n", + "print(\"Test =\", model.metrics_names[1], scores[1] * 100)" + ] + }, + { + "cell_type": "code", + "execution_count": 86, + "metadata": {}, + "outputs": [], + "source": [ + "train_accuracies = list()\n", + "test_accuracies = list()\n", + "\n", + "for iterations in range(50, 500, 50):\n", + " model.fit(train_x, train_y_onehot, epochs = iterations, batch_size = 500, verbose=0)\n", + " scores = model.evaluate(train_x, train_y_onehot, verbose=0)\n", + " train_accuracies.append(scores[1]* 100)\n", + " scores = model.evaluate(test_x, test_y_onehot, verbose=0)\n", + " test_accuracies.append(scores[1]* 100)" + ] + }, + { + "cell_type": "code", + "execution_count": 87, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.title('Accuracies for training data')\n", + "plt.plot(range(50, 500, 50), train_accuracies)\n", + "plt.show()\n", + "plt.title('Accuracies for testing data')\n", + "plt.plot(range(50, 500, 50), test_accuracies)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "+--------+--------+--------+--------+--------+--------+--------+--------+--------+--------+--------+\n", + "| | Pred 0 | Pred 1 | Pred 2 | Pred 3 | Pred 4 | Pred 5 | Pred 6 | Pred 7 | Pred 8 | Pred 9 |\n", + "+--------+--------+--------+--------+--------+--------+--------+--------+--------+--------+--------+\n", + "| Real 0 | 269671 | 224722 | 0 | 329 | 0 | 0 | 140 | 0 | 6347 | 0 |\n", + "| Real 1 | 228631 | 187622 | 0 | 277 | 0 | 3 | 176 | 0 | 5789 | 0 |\n", + "| Real 2 | 25672 | 21119 | 0 | 20 | 0 | 0 | 18 | 0 | 793 | 0 |\n", + "| Real 3 | 11515 | 9333 | 0 | 6 | 0 | 0 | 8 | 0 | 259 | 0 |\n", + "| Real 4 | 2146 | 1739 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |\n", + "| Real 5 | 1100 | 862 | 0 | 12 | 0 | 0 | 1 | 0 | 21 | 0 |\n", + "| Real 6 | 738 | 670 | 0 | 1 | 0 | 0 | 0 | 0 | 15 | 0 |\n", + "| Real 7 | 129 | 96 | 0 | 0 | 0 | 0 | 0 | 0 | 5 | 0 |\n", + "| Real 8 | 7 | 5 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |\n", + "| Real 9 | 2 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |\n", + "+--------+--------+--------+--------+--------+--------+--------+--------+--------+--------+--------+\n" + ] + } + ], + "source": [ + "confMat = [[0] * config.classes for x in range(config.classes)]\n", + "\n", + "for i in range(len(preds)):\n", + " predLabel = preds[i]\n", + " actLabel = test_y[i]\n", + " confMat[actLabel][predLabel] += 1\n", + "\n", + "header = [\"\"]\n", + "for l in range(config.classes):\n", + " header.append(\"Pred \" + str(l))\n", + " \n", + "tab = PrettyTable(header)\n", + "l = 0\n", + "for c in confMat:\n", + " tab.add_row([\"Real \" + str(l)] + c)\n", + " l += 1\n", + "\n", + "print(tab)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "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": "3.5.2" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/Poker Hand Prediction/Poker Hand Prediction/Deep Neural Network.ipynb b/Poker Hand Prediction/Poker Hand Prediction/Deep Neural Network.ipynb new file mode 100644 index 000000000..3e8d5f5e1 --- /dev/null +++ b/Poker Hand Prediction/Poker Hand Prediction/Deep Neural Network.ipynb @@ -0,0 +1,399 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Using TensorFlow backend.\n" + ] + } + ], + "source": [ + "# -------------------- Imports -------------------- #\n", + "from keras.models import Sequential\n", + "from keras.layers import Input, Dense, Flatten\n", + "from keras.models import Model\n", + "import numpy as np, pandas as pd, os\n", + "from prettytable import PrettyTable\n", + "from matplotlib import pyplot as plt\n", + "import math" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "# -------------------- Globals and Configs -------------------- #\n", + "feature_names = list()\n", + "for index in range(1, 6):\n", + " feature_names.extend([\"Suit\"+str(index), \"Rank\"+str(index)])\n", + "\n", + "feature_names.append('class')\n", + "\n", + "\n", + "training_input_file = os.path.abspath('../datasets/csv/train.csv')\n", + "testing_input_file = os.path.abspath('../datasets/csv/test.csv')\n", + "\n", + "np.random.seed(666) # seed for reproducible results\n", + "\n", + "# To store configs\n", + "class myConfigs:\n", + " features = 0\n", + " classes = 0\n", + "\n", + "\n", + "config = myConfigs()" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "+----------+-------+\n", + "| Config | Value |\n", + "+----------+-------+\n", + "| features | 10 |\n", + "| classes | 10 |\n", + "+----------+-------+\n", + "Instances in training data : 25010\n", + "Instances in testing data : 1000000\n" + ] + } + ], + "source": [ + "# -------------------- Data -------------------- #\n", + "\n", + "train_data = pd.read_csv(training_input_file, names=feature_names)\n", + "test_data = pd.read_csv(testing_input_file, names=feature_names)\n", + "\n", + "# Get features of data\n", + "config.features = len(train_data.columns) - 1\n", + "config.classes = len(set(train_data['class']))\n", + "\n", + "# Shuffle training data\n", + "train_data = train_data.sample(frac=1).reset_index(drop=True)\n", + "\n", + "# Seperate data and classes\n", + "train_y = np.array(train_data['class'])\n", + "train_x = np.array(train_data.drop('class', 1))\n", + "\n", + "test_y = np.array(test_data['class'])\n", + "test_x = np.array(test_data.drop('class', 1))\n", + "\n", + "train_y_onehot = list()\n", + "for y in range(len(train_y)):\n", + " temp = [0] * config.classes\n", + " temp[train_y[y]] = 1\n", + " train_y_onehot.append(temp)\n", + " \n", + "test_y_onehot = list()\n", + "for y in range(len(test_y)):\n", + " temp = [0] * config.classes\n", + " temp[test_y[y]] = 1\n", + " test_y_onehot.append(temp)\n", + " \n", + "train_y_onehot = np.array(train_y_onehot)\n", + "test_y_onehot = np.array(test_y_onehot)\n", + "\n", + "tab = PrettyTable(['Config', 'Value'])\n", + "configs = vars(config)\n", + "\n", + "for key in configs:\n", + " tab.add_row([key, configs[key]])\n", + "print(tab)\n", + "\n", + "print(\"Instances in training data :\", len(train_data))\n", + "print(\"Instances in testing data :\", len(test_data))" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# -------------------- Model -------------------- #\n", + "model = Sequential()\n", + "\n", + "# Input layer\n", + "model.add(Dense(10, input_shape = (train_x.shape[1],), activation='tanh'))\n", + "\n", + "# Hidden layer\n", + "model.add(Dense(64, activation='tanh'))\n", + "model.add(Dense(64, activation='tanh'))\n", + "model.add(Dense(64, activation='tanh'))\n", + "model.add(Dense(64, activation='tanh'))\n", + "model.add(Dense(64, activation='tanh'))\n", + "\n", + "# Output layer\n", + "model.add(Dense(10, activation='softmax'))\n", + "model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])\n", + "model.fit(train_x, train_y_onehot, epochs = 450, batch_size = 500, verbose=0)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Matches : 21978 / 25010 = 87.87684926029588\n", + "Average Error : 0.14566173530587764\n", + "RMSE : 0.4909008113227937\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "preds = [list(x) for x in model.predict(train_x)]\n", + "for i in range(len(preds)):\n", + " preds[i] = preds[i].index(max(preds[i]))\n", + "\n", + "train_matches = 0\n", + "for i in range(len(train_y)):\n", + " if train_y[i] == preds[i]:\n", + " train_matches += 1\n", + " \n", + "print(\"Matches :\", train_matches, '/', len(train_y), '=', train_matches/len(train_y) * 100)\n", + "print(\"Average Error :\", sum([math.fabs(x-y) for x, y in zip(train_y, preds)])/len(train_y))\n", + "print(\"RMSE :\", math.sqrt(sum([(x-y)**2 for x, y in zip(train_y, preds)])/len(train_y)))\n", + " \n", + "plt.plot(train_y, 'bo', label='Original')\n", + "plt.plot(preds, 'ro', label='Predicted')\n", + "plt.legend()\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Matches : 830663 / 1000000 = 83.0663\n", + "Average Error : 0.202623\n", + "RMSE : 0.5613546116315425\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "preds = [list(x) for x in model.predict(test_x)]\n", + "for i in range(len(preds)):\n", + " preds[i] = preds[i].index(max(preds[i]))\n", + "\n", + "test_matches = 0\n", + "for i in range(len(test_y)):\n", + " if test_y[i] == preds[i]:\n", + " test_matches += 1\n", + " \n", + "print(\"Matches :\", test_matches, '/', len(test_y), '=', test_matches/len(test_y) * 100)\n", + "print(\"Average Error :\", sum([math.fabs(x-y) for x, y in zip(test_y, preds)])/len(test_y))\n", + "print(\"RMSE :\", math.sqrt(sum([(x-y)**2 for x, y in zip(test_y, preds)])/len(test_y)))\n", + " \n", + "plt.plot(test_y, 'bo', label='Original')\n", + "plt.plot(preds, 'ro', label='Predicted')\n", + "plt.legend()\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "25010/25010 [==============================] - 1s 27us/step\n", + "Train = acc 87.87684926124918\n", + "1000000/1000000 [==============================] - 24s 24us/step\n", + "Test = acc 83.0663\n" + ] + } + ], + "source": [ + "scores = model.evaluate(train_x, train_y_onehot)\n", + "print(\"Train =\", model.metrics_names[1], scores[1] * 100)\n", + "\n", + "scores = model.evaluate(test_x, test_y_onehot)\n", + "print(\"Test =\", model.metrics_names[1], scores[1] * 100)" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [], + "source": [ + "train_accuracies = list()\n", + "test_accuracies = list()\n", + "\n", + "for iterations in range(50, 500, 50):\n", + " model.fit(train_x, train_y_onehot, epochs = iterations, batch_size = 500, verbose=0)\n", + " scores = model.evaluate(train_x, train_y_onehot, verbose=0)\n", + " train_accuracies.append(scores[1]* 100)\n", + " scores = model.evaluate(test_x, test_y_onehot, verbose=0)\n", + " test_accuracies.append(scores[1]* 100)" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.title('Accuracies for training data')\n", + "plt.plot(range(50, 500, 50), train_accuracies)\n", + "plt.show()\n", + "plt.title('Accuracies for testing data')\n", + "plt.plot(range(50, 500, 50), test_accuracies)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "+--------+--------+--------+--------+--------+--------+--------+--------+--------+--------+--------+\n", + "| | Pred 0 | Pred 1 | Pred 2 | Pred 3 | Pred 4 | Pred 5 | Pred 6 | Pred 7 | Pred 8 | Pred 9 |\n", + "+--------+--------+--------+--------+--------+--------+--------+--------+--------+--------+--------+\n", + "| Real 0 | 475668 | 25289 | 125 | 22 | 93 | 0 | 0 | 0 | 1 | 11 |\n", + "| Real 1 | 94241 | 319272 | 6485 | 2024 | 456 | 0 | 10 | 0 | 10 | 0 |\n", + "| Real 2 | 2508 | 21625 | 22304 | 1096 | 16 | 0 | 68 | 4 | 1 | 0 |\n", + "| Real 3 | 569 | 7245 | 1173 | 12031 | 3 | 0 | 97 | 2 | 1 | 0 |\n", + "| Real 4 | 1045 | 1618 | 102 | 6 | 1108 | 0 | 0 | 0 | 1 | 5 |\n", + "| Real 5 | 1905 | 91 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |\n", + "| Real 6 | 16 | 131 | 652 | 365 | 0 | 0 | 256 | 4 | 0 | 0 |\n", + "| Real 7 | 0 | 14 | 36 | 129 | 0 | 0 | 27 | 24 | 0 | 0 |\n", + "| Real 8 | 5 | 4 | 0 | 0 | 3 | 0 | 0 | 0 | 0 | 0 |\n", + "| Real 9 | 2 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |\n", + "+--------+--------+--------+--------+--------+--------+--------+--------+--------+--------+--------+\n" + ] + } + ], + "source": [ + "confMat = [[0] * config.classes for x in range(config.classes)]\n", + "\n", + "for i in range(len(preds)):\n", + " predLabel = preds[i]\n", + " actLabel = test_y[i]\n", + " confMat[actLabel][predLabel] += 1\n", + "\n", + "header = [\"\"]\n", + "for l in range(config.classes):\n", + " header.append(\"Pred \" + str(l))\n", + " \n", + "tab = PrettyTable(header)\n", + "l = 0\n", + "for c in confMat:\n", + " tab.add_row([\"Real \" + str(l)] + c)\n", + " l += 1\n", + "\n", + "print(tab)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "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": "3.5.2" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/Poker Hand Prediction/Poker Hand Prediction/Multi Layer Perceptron.ipynb b/Poker Hand Prediction/Poker Hand Prediction/Multi Layer Perceptron.ipynb new file mode 100644 index 000000000..14321f57b --- /dev/null +++ b/Poker Hand Prediction/Poker Hand Prediction/Multi Layer Perceptron.ipynb @@ -0,0 +1,357 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "# -------------------- Imports -------------------- #\n", + "\n", + "from sklearn.metrics import accuracy_score\n", + "from sklearn.neural_network import MLPClassifier\n", + "from sklearn.preprocessing import StandardScaler\n", + "from matplotlib import pyplot as plt\n", + "import numpy as np, pandas as pd, os\n", + "from prettytable import PrettyTable" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "# -------------------- Globals and Configs -------------------- #\n", + "\n", + "feature_names = list()\n", + "for index in range(1, 6):\n", + " feature_names.extend([\"Suit\"+str(index), \"Rank\"+str(index)])\n", + "\n", + "feature_names.append('class')\n", + "\n", + "\n", + "training_input_file = os.path.abspath('../datasets/csv/train.csv')\n", + "testing_input_file = os.path.abspath('../datasets/csv/test.csv')\n", + "\n", + "np.random.seed(666) # seed for reproducible results\n", + "\n", + "# To store configs\n", + "class myConfigs:\n", + " features = 0\n", + " classes = 0\n", + "\n", + "\n", + "config = myConfigs()" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "+----------+-------+\n", + "| Config | Value |\n", + "+----------+-------+\n", + "| features | 10 |\n", + "| classes | 10 |\n", + "+----------+-------+\n", + "Instances in training data : 25010\n", + "Instances in testing data : 1000000\n" + ] + } + ], + "source": [ + "# -------------------- Data -------------------- #\n", + "\n", + "train_data = pd.read_csv(training_input_file, names=feature_names)\n", + "test_data = pd.read_csv(testing_input_file, names=feature_names)\n", + "\n", + "# Get features of data\n", + "config.features = len(train_data.columns) - 1\n", + "config.classes = len(set(train_data['class']))\n", + "\n", + "# Shuffle training data\n", + "train_data = train_data.sample(frac=1).reset_index(drop=True)\n", + "\n", + "# Seperate data and classes\n", + "train_y = np.array(train_data['class'])\n", + "train_x = np.array(train_data.drop('class', 1))\n", + "\n", + "test_y = np.array(test_data['class'])\n", + "test_x = np.array(test_data.drop('class', 1))\n", + "\n", + "# Initialise scaler to scale the data\n", + "scaler = StandardScaler()\n", + "\n", + "train_set = np.empty(train_x.shape, dtype = float)\n", + "test_set = np.empty(test_x.shape, dtype = float)\n", + "\n", + "# Make training data suitable for scaling\n", + "for index in range(len(train_x)):\n", + " train_set[index] = train_x[index].astype(float)\n", + "\n", + "# Make testing data suitable for scaling\n", + "for index in range(len(test_x)):\n", + " test_set[index] = test_x[index].astype(float)\n", + "\n", + "# Fit the training data\n", + "scaler.fit(train_set) \n", + "\n", + "# Scale the training and testing data w.r.t scaler\n", + "data_train = scaler.transform(train_set)\n", + "data_test = scaler.transform(test_set)\n", + "\n", + "tab = PrettyTable(['Config', 'Value'])\n", + "configs = vars(config)\n", + "\n", + "for key in configs:\n", + " tab.add_row([key, configs[key]])\n", + "print(tab)\n", + "\n", + "print(\"Instances in training data :\", len(train_data))\n", + "print(\"Instances in testing data :\", len(test_data))" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "# -------------------- Model -------------------- #\n", + "\n", + "# Store the accuracies\n", + "accuracy = list()\n", + "\n", + "# Store the predictions\n", + "predictions = list()\n", + "\n", + "for trial in range(5):\n", + " classifier = MLPClassifier(solver = 'adam', alpha = 1e-5, hidden_layer_sizes = (64, 64), activation = 'tanh', learning_rate_init = 0.02, max_iter = 2000, random_state = trial)\n", + " result = classifier.fit(data_train, train_y)\n", + " prediction = classifier.predict(data_test)\n", + " predictions.append(prediction)\n", + " curr_accuracy = accuracy_score(test_y, prediction)\n", + " accuracy.append(curr_accuracy)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + " +--------------+----------+\n", + "| Trial Number | Accuracy |\n", + "+--------------+----------+\n", + "| 1 | 94.552 |\n", + "| 2 | 97.447 |\n", + "| 3 | 92.196 |\n", + "| 4 | 98.57 |\n", + "| 5 | 98.777 |\n", + "+--------------+----------+\n", + "\n", + "Mean Accuracy using Multi Layer Perceptron Classifier: 96.308 %\n" + ] + } + ], + "source": [ + "# -------------------- Print the final result -------------------- #\n", + "\n", + "tab = PrettyTable(['Trial Number', 'Accuracy'])\n", + "\n", + "for trial in range(len(accuracy)):\n", + " tab.add_row([trial + 1, round(accuracy[trial] * 100, 3)])\n", + "\n", + "print(\"\\n\", tab)\n", + "print(\"\\nMean Accuracy using Multi Layer Perceptron Classifier: \", round(np.array(accuracy).mean() * 100, 3), \"%\")" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Trial : 1\n", + "Accuracy : 94.552\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Trial : 2\n", + "Accuracy : 97.447\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Trial : 3\n", + "Accuracy : 92.196\n" + ] + }, + { + "data": { + "image/png": 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yFr/MWJuepIp9kSZOa27wDtvUlD6RRyIwc6a3RCLJ6+vrk/etrx+qW1WVd7zUumZb39Q0VM/m5uTy/Lh7QlPYSxNHqxo4NmVm/P0fWwbwnlNVhU2ZwmAoEt8WJcKxKc3UN2UZE0WQ08w+ZvZPeKdTosDzwCedc2kuvXs0s49I5dm+fTuzZs3CMnyKldw459ixY0dpZ/Zxzt0J3DnijiJSsaqrq+np6aGhoUGJfJScc/T09FCd+sudU6CZfUQkJ9OnT6erq4vu7u6xDiXQqqurmT59esGOpyQuIjmZMGECM2fOHOswJIXunSIiEmBK4iIiAaYkLiISYEriIiIBpiQuIhJgSuIiIgGmJC4iEmBK4iIiAaYkLiISYEriIiIBpiQuIhJgZZPE75rTnjTze2wZsFD88WDS+nB8FvVua2TQQvTaxGGvd2b01EyDSGTY+uNWTV91XdqZ4ROXPpvAU7Y4PuP9QCjCFpsz9NxCnJhQjzMjapGkOIfXJ8x91sakSUMTqN9o7XRac9rX9dQ387nGdkIheCayOO3xEl83GI6wb85iXgs3M2ghDltd1ngSZ1pP186jWQ5bPfdZm1+nEJ3WzI3Wzn3WFm+zqEW4z9q40dL3uzNj27TFNDZ67ZPat6kxDpox6I+V2Ez0PfVem8b6pNO8tvzVnLakcdVr1fExNDSeLB5ruuW4TeLhyW10W2PamLqtkRutncZGb07ibdOG913iEo1MhHA4qW3MvLqfzBJHrkufTYjXK7Hu6eqVbZ9YW2Z6r6Vb0o2nw1aX1O+5jrkBC3HY6of1643WnvA+8sbcU7Y4Y/7otYnJ7xsL8ZQtTurPWI7JNbbY+GpvL3HydM4VfFm4cKHLx8rZa91JQs55U68WfBkswjFO5ZiD4FbR6sC5pax1R6nJuv9RatxmZudcZiHqW+j2OkkobRtGRzjGZma7/gL2ebo4ijWuTlDllrLWPUVL3mUOgnuSlqzto2VoOc4Ed4KqvPon3/7MZTlKjbtlwlq3dm1eKdA5b2KHjtHk25wmhchXvpNCdFozzYyvORqjhJlAlN3kVndHec8WXyxBr3cnTTSxZ1R1CHrdx6tOmnhXUyednfm9rphzbBbdDMbfjOmx2c7HY93HE/Xv+DODvewtYbeXRRLfy4yxDqHkBvDmShyPdR9P1L/jz15mMKOE3V4WSfzh2SvpK2IohThhlHqMUzmmw5vtHOB2VnKM7LOOH6OGF5idc5mFP0GWv9QY+mITzKbsMzDCMV5gNtEClJ8tjtEa6TgnqeJ2VvKLUcxq74D1tGRtHxlyggmcJHly5NG0+ak6Rg3/NGElK1cW4GC5KocLm855FzcPUesG/QsMsSWKxR8PJK33LlAdoMEdoMENYO4YVcNePwjujUlTnQuHh63vZaI7ObE2flEj03KSiHuSFtePd4yohf0LbuF4jMcjdW4QXD/hpDiH1yfkVtHqqqudM3Outta7uLmbprSve6Ouyf1dw1pn5tyGcEva4yW+biAUdl2zW9zeUJMbwNwharPGk3gxJ107j2Y5RJ1bRatfJ3O7aXJLWetW0Rpvs37CbhWtbinp+30Q3NapLa6hwWuf1L5NjXEA3IA/Vlw47FxLi3ujzmvTWJ/sxmvLX85uTRpXx5gYH0ND44l4rOmWXqpd+xmt7gANaWM6QINbylrX0ODc2rXObZ06vO8Sl/5wlXOhUFLbgFf3E1niyHU5SSRer8S6p6tXtn1ibZnpvZZuSTeeDlGb1O+5jrko5g5RN6xfl7I24X3kjTnvwnD6/HGMquT3DeaepCWpP2PvrVxji42v0VzUdC7gFzZFRMa7QF/YFBGR0VESFxEJMCVxEZEAUxIXEQkwJXERkQBTEhcRCTAlcRGRAFMSFxEJMCVxEZEAUxIXEQkwJXERkQBTEhcRCTAlcRGRAMspiZvZGWa2zsx2mNl2M7us2IGJiMjIIjnu9w3gSefch8ysCkaYxWAUflW1mHf2byj0YceEwwCHwwo4BcHI3qCBTcznKjaMuzkd+4lwiNNpoCewdahkxRpbDjjBRCZxsghHH73fTWrh8t6nS1LWiPcTN7PTgc3AW1yONx/P937isQSuN9+pC3IiFqkUjvwTeTHvJz4T6AYeNLPnzew7Zlabb0HZKIEXjtpRZOwZ8PbjpTmzkEsSjwALgPudcxcBx4AVqTuZ2TIz6zCzju7u7gKHKSIi6eSSxLuALufcH/zn6/CSehLn3Brn3CLn3KIpU6YUMkYREclgxCTunHsdeM3MzvdXtQDbChnEryfkPxu4pKd2FBl7sXPipZDrr1P+Dmj3f5nyJ+DWQgbxrr6n9euUAtCvU/TrlHKlX6cUj2a7FxEpA5rtXkRkHFISFxEJMCVxEZEAUxIXEQkwJXERkQBTEhcRCTAlcRGRAFMSFxEJMCVxEZEAUxIXEQkwJXERkQBTEhcRCTAlcRGRAFMSFxEJsFzvJ150365q4xP995fkXtDFurfxILn9qzgAhItQfhCV6/2gRUZrPS18fOrT7NtXmvLKIol/u6qNT5YogUPxJj7INTGXRaOXCQNqlMClglzNBr63fzHTppUmkZfF6ZRb+9doNhYRqQiGl8j37y9NeWWRxMMMjHUIIiKBVBZJfEBniEVERqUskviDE5ZplnYRqQgO7+Lm1KmlKa8srrF9qm81365Cv04Zh/TrFKk0pf51ima7FxEpA5rtXkRkHFISFxEJMCVxEZEAUxIXEQkwJXERkQBTEhcRCTAlcRGRAFMSFxEJMCVxEZEAUxIXEQkwJXERkQBTEhcRCTAlcRGRAMs5iZtZ2MyeN7PHixmQiIjkLp9P4suB7cUK5G/D7XRaM4MWotOauc/akp5vsTk4s6Rl0ELcZ23caLHX2rB9kvdPft5tk7Nujy0DFh5WzoCF0u43aBaPv9sa49sOWx2HrS5rfNliSFxOWiRpv0EzDls9gxai2xrptsb445HK7LZGbrR2zOBGa0+KOdu2fOqRWvdYOw3FakT9OnVac7zM2DLU7kNjo9cmDitzj01L2y+pcaW2cSyeTP2eGEem9snWRpn6/rhN4j5ry9pHAxZK6tuR+jNqkWExn7TIsHZKjDPTmHvKFqdt+/TPR44lFk+6vuu2xqRjpR5v0IwtNodoSl0y9Xe291HiGMtWv8Txma6cWP/fZ23xuGJ1DpdywgDn3IgLMB3YAFwJPD7S/gsXLnT5+GhorTtKjXMQXwYTHqd7nri+j0jabSMtmY5ZiHLyOfZYLyeocqtodceZkNe2Yi1HqXFLWevAuaWMPDaK2eaD4FbR6payNmP7LGVtxu1jMU4SY46eQpmD4KKEsr52pGPFYon1Zf8ptEUh2+ooNW4VrTmPrUxLP5a2TVbR6kKhvNKgAzpyycepS06TQpjZOuBfgHrgH51z78u2f76TQnRaM83syXl/KbwoYSIZJqzOtq1YOmliJp3sZuzHRpQwXUzPGEcnTQBjHmeikWIudSwTiJZFXyYq5riO1TmH9BpXtEkhzOx9wAHn3MYR9ltmZh1m1tHd3Z1XEDPYm9f+UnjhLIM527ZiiY2JchgbYQayxjGDvWURZ6KRYi6l2Pgpl3hiijmuS/meyeWc+OXAEjPrBH4IXGlma1N3cs6tcc4tcs4tmjJlSl5B7GVGXvtL4Q1kmfUz27ZiiY2JchgbA4SzxrHXT+PlZKSYSyk2fsolnphijutSvmdGTOLOuducc9Odc83ADcAzzrmPFjKI/xFayTFqkstNjSNTfED/KOd7zuObTt7l5HPssXaSKh5gGSeYkNe2YjlGDbezEoDbGXlsjLT+VDjgAZZxOyszts/trMy4PZfjF1pizOk+D+ZapgMGUlJEru/L1FjA68voCPuOdKxCOUYND7As57GVSRRL2yYPsIxQqX7Anc8JdOBdFOHCpnPexc3dNLkBzO2mya2iNen5Zma7Qf+iQWwZwOIXcLx9GbZP8v7Jzw9wRtbtsSVKaFg5Uf+CRup+AxCP/wAN8W2HqHWHqM0aX7YYEpcThJP2GwB3iDo3gLkDNLgDNMQfj1TmARqSLiImxpxtWz71SK17rJ2GYsX1+3XaTVO8zNgy1O5DY+MYVcPK7GRq2n5JjSu1jWPxZOr3xDgytU+2NsrU971Uu1W0Zu2jKJbUtyP1Zz/hYTGfIDysnRLjzDTmnqQlbdunfz5yLLF40vXdARqSjpV6vAFwm5nt+lPqkqm/s72PEsdYtvoljs905cT6fxWt8bhidc73oqZzRb6wmS/Ndi8ikh/Ndi8iMg4piYuIBJiSuIhIgCmJi4gEmJK4iEiAKYmLiASYkriISIApiYuIBJiSuIhIgCmJi4gEmJK4iEiAKYmLiASYkriISIApiYuIBNjoZlMogkEzbKyDEBEpAAfeFNMlUBZJPJbAlcRFpFIMmpUkkZdFElcCF5FKUsp8pnPiIiIBpiQuIhJgZZHEnb+IiFSCUua0sjgnHnKOQdNZcRGpDOPu1ylQugqLiBSbLmyKiEhOlMRFRAJMSVxEJMCUxEVEAkxJXEQkwJTERUQCTElcRCTAlMRFRAJMSVxEJMCUxEVEAkxJXEQkwJTERUQCTElcRCTARkziZvZmM/ulmW0zs61mtrwUgYmIyMhyuRVtFPi8c26TmdUDG83sF865bYUMRLPdi0ilKKv7iTvn/gz82X98xMy2A9OAgiVxzXYvIpWmLGe7N7Nm4CLgD4UMQglcRCpJWU4KYWZ1wE+Av3fOHU6zfZmZdZhZR3d3dyFjFBGRDHJK4mY2AS+BtzvnHk23j3NujXNukXNu0ZQpUwoZo4iIZJDLr1MM+C6w3Tn31WIEodnuRaSSlNts95cDfwu8aGab/XW3O+eeKFQQmu1eRCpJuf065VlKcJ5es92LSKUoywubIiJSfpTERUQCTElcRCTAlMRFRAJMSVxEJMCUxEVEAkxJXEQkwJTERUQCTElcRCTAlMRFRAJMSVxEJMCUxEVEAkxJXEQkwJTERUQCLK85NotJs92LSKUoq/uJl4JmuxeRSlOWs90XixK4iFQSTQohIiI5URIXEQmwskjimu1eRCpJuc12X3Sa7V5EKsm4+3UKaLZ7EakcurApIiI5URIXEQkwJXERkQBTEhcRCTAlcRGRAFMSFxEJMCVxEZEAUxIXEQkwJXERkQBTEhcRCTAlcRGRAFMSFxEJMCVxEZEAyymJm9nfmNlOM3vFzFYUOygREcnNiLeiNbMw8E3gKqALeM7MHnPObStkIJrtXkQqRbndT/xtwCvOuT8BmNkPgfcDBUvimu1eRCpNqWa7z+V0yjTgtYTnXf66glECF5FKUsqcVrALm2a2zMw6zKyju7u7UIcVEZEsckni+4A3Jzyf7q9L4pxb45xb5JxbNGXKlELFJyIiWeSSxJ8DzjWzmWZWBdwAPFbIIDTbvYhUkrKa7d45FzWzzwJPAWHge865rYUMQrPdi0glKbdfp+CcewJ4opiBaLZ7EakUmu1eRERyoiQuIhJgSuIiIgGmJC4iEmBK4iIiAWauCL8KMbNuYM8oX94IvFHAcIJAda58462+oDrnq8k5l/f/lCxKEj8VZtbhnFs01nGUkupc+cZbfUF1LhWdThERCTAlcRGRACvHJL5mrAMYA6pz5Rtv9QXVuSTK7py4iIjkrhw/iYuISI7KJokHcTJmM3uzmf3SzLaZ2VYzW+6vP9PMfmFmu/y/k/31Zmb/6tfxBTNbkHCsj/n77zKzjyWsX2hmL/qv+Vcz73aPmcooUb3DZva8mT3uP59pZn/wY/w3/5bFmNlE//kr/vbmhGPc5q/faWbXJKxPOw4ylVGi+p5hZuvMbIeZbTezy8ZBH/+DP6ZfMrNHzKy60vrZzL5nZgfM7KWEdWPWr9nKyMo5N+YL3i1uXwXeAlQBW4DZYx1XDnGfDSzwH9cDLwOzgf8FrPDXrwD+p//4vcDP8W5ydinwB3/9mcCf/L+T/ceT/W1/9Pc1/7Xv8denLaNE9f5vwMPA4/7zHwE3+I8fAFr9x23AA/7jG4B/8x/P9vt4IjDT7/twtnGQqYwS1fcHwCf9x1XAGZXcx3jTL+4GJiW0/S2V1s/AfwYWAC8lrBuzfs1Uxoj1KNUbYYTGvAx4KuH5bcBtYx3XKOr6lmvLAAADHklEQVTx78BVwE7gbH/d2cBO//G3gKUJ++/0ty8FvpWw/lv+urOBHQnr4/tlKqMEdZwObACuBB73B9wbQCS1L/HuQX+Z/zji72ep/RvbL9M4yFZGCep7Ol5Cs5T1ldzHsXl1z/T77XHgmkrsZ6CZ5CQ+Zv2aqYyR6lAup1OKPhlzsflfIS8C/gCc5Zz7s7/pdeAs/3GmemZb35VmPVnKKLavA/8dGPSfNwAHnXPRNDHG6+VvP+Tvn287ZCuj2GYC3cCD5p1C+o6Z1VLBfeyc2wfcC+wF/ozXbxup7H6OGct+HVUeLJckHmhmVgf8BPh759zhxG3O+ye1qD8BKkUZAGb2PuCAc25jscsqIxG8r9z3O+cuAo7hfQWOq6Q+BvDP0b4f7x+wqUAt8DelKLucBKVfyyWJ5zQZczkyswl4CbzdOfeov/ovZna2v/1s4IC/PlM9s62fnmZ9tjKK6XJgiZl1Aj/EO6XyDeAMM4vNEpUYY7xe/vbTgR7yb4eeLGUUWxfQ5Zz7g/98HV5Sr9Q+BlgM7HbOdTvn+oFH8fq+kvs5Ziz7dVR5sFySeNEnYy4G/2rzd4HtzrmvJmx6DIhdpf4Y3rny2Pqb/avQlwKH/K9VTwFXm9lk/1PQ1XjnAv8MHDazS/2ybk45VroyisY5d5tzbrpzrhmvj55xzt0E/BL4UJpYEmP8kL+/89ff4P+qYSZwLt5FoLTjwH9NpjKKyjn3OvCamZ3vr2oBtlGhfezbC1xqZjV+TLE6V2w/JxjLfs1URnbFvGiQ5wWG9+L9uuNV4I6xjifHmK/A+yr0ArDZX96Ld25vA7ALeBo409/fgG/6dXwRWJRwrI8Dr/jLrQnrFwEv+a+5j6H/oJW2jBLW/V0M/TrlLXhvzleAHwMT/fXV/vNX/O1vSXj9HX6dduJftc82DjKVUaK6zgc6/H7+P3i/QqjoPgb+Cdjhx/W/8X5hUlH9DDyCd86/H+8b1yfGsl+zlZFt0f/YFBEJsHI5nSIiIqOgJC4iEmBK4iIiAaYkLiISYEriIiIBpiQuIhJgSuIiIgGmJC4iEmD/HzohpePTX6WbAAAAAElFTkSuQmCC", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Trial : 4\n", + "Accuracy : 98.57\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Trial : 5\n", + "Accuracy : 98.777\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# -------------------- Visualisations -------------------- #\n", + "\n", + "for index in range(len(predictions)):\n", + " print(\"Trial :\", index + 1)\n", + " print(\"Accuracy :\", round(accuracy[index] * 100, 3))\n", + " plt.plot(test_y, 'bo', label = \"Original\")\n", + " plt.plot(predictions[index], 'ro', label = \"Predicted\")\n", + " plt.legend()\n", + " plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "+--------+--------+--------+--------+--------+--------+--------+--------+--------+--------+--------+\n", + "| | Pred 0 | Pred 1 | Pred 2 | Pred 3 | Pred 4 | Pred 5 | Pred 6 | Pred 7 | Pred 8 | Pred 9 |\n", + "+--------+--------+--------+--------+--------+--------+--------+--------+--------+--------+--------+\n", + "| Real 0 | 500856 | 14 | 0 | 0 | 273 | 48 | 0 | 0 | 6 | 12 |\n", + "| Real 1 | 0 | 422369 | 84 | 0 | 24 | 0 | 0 | 0 | 18 | 3 |\n", + "| Real 2 | 0 | 15 | 47393 | 201 | 0 | 0 | 3 | 10 | 0 | 0 |\n", + "| Real 3 | 0 | 0 | 4499 | 16184 | 0 | 0 | 266 | 172 | 0 | 0 |\n", + "| Real 4 | 3518 | 9 | 0 | 0 | 353 | 0 | 0 | 0 | 5 | 0 |\n", + "| Real 5 | 1944 | 1 | 0 | 0 | 6 | 41 | 0 | 0 | 3 | 1 |\n", + "| Real 6 | 0 | 0 | 1 | 877 | 0 | 0 | 545 | 1 | 0 | 0 |\n", + "| Real 7 | 0 | 0 | 0 | 132 | 0 | 0 | 74 | 24 | 0 | 0 |\n", + "| Real 8 | 10 | 0 | 0 | 0 | 2 | 0 | 0 | 0 | 0 | 0 |\n", + "| Real 9 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |\n", + "+--------+--------+--------+--------+--------+--------+--------+--------+--------+--------+--------+\n" + ] + } + ], + "source": [ + "preds = predictions[-1]\n", + "confMat = [[0] * config.classes for x in range(config.classes)]\n", + "\n", + "for i in range(len(preds)):\n", + " predLabel = preds[i]\n", + " actLabel = test_y[i]\n", + " confMat[actLabel][predLabel] += 1\n", + "\n", + "header = [\"\"]\n", + "for l in range(config.classes):\n", + " header.append(\"Pred \" + str(l))\n", + " \n", + "tab = PrettyTable(header)\n", + "l = 0\n", + "for c in confMat:\n", + " tab.add_row([\"Real \" + str(l)] + c)\n", + " l += 1\n", + "\n", + "print(tab)" + ] + } + ], + "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": "3.5.2" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/Poker Hand Prediction/Poker Hand Prediction/Output Code Classifier.ipynb b/Poker Hand Prediction/Poker Hand Prediction/Output Code Classifier.ipynb new file mode 100644 index 000000000..0388f5c73 --- /dev/null +++ b/Poker Hand Prediction/Poker Hand Prediction/Output Code Classifier.ipynb @@ -0,0 +1,259 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "# -------------------- Imports -------------------- #\n", + "\n", + "from sklearn.multiclass import OutputCodeClassifier\n", + "from sklearn.ensemble import BaggingClassifier\n", + "from sklearn.preprocessing import StandardScaler\n", + "from sklearn.metrics import accuracy_score\n", + "from matplotlib import pyplot as plt\n", + "import numpy as np, pandas as pd, os\n", + "from prettytable import PrettyTable" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "# -------------------- Globals and Configs -------------------- #\n", + "\n", + "feature_names = list()\n", + "for index in range(1, 6):\n", + " feature_names.extend([\"Suit\"+str(index), \"Rank\"+str(index)])\n", + "\n", + "feature_names.append('class')\n", + "\n", + "\n", + "training_input_file = os.path.abspath('../datasets/csv/train.csv')\n", + "testing_input_file = os.path.abspath('../datasets/csv/test.csv')\n", + "\n", + "np.random.seed(666) # seed for reproducible results\n", + "\n", + "# To store configs\n", + "class myConfigs:\n", + " features = 0\n", + " classes = 0\n", + "\n", + "\n", + "config = myConfigs()" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "+----------+-------+\n", + "| Config | Value |\n", + "+----------+-------+\n", + "| features | 10 |\n", + "| classes | 10 |\n", + "+----------+-------+\n", + "Instances in training data : 25010\n", + "Instances in testing data : 1000000\n" + ] + } + ], + "source": [ + "# -------------------- Data -------------------- #\n", + "\n", + "train_data = pd.read_csv(training_input_file, names=feature_names)\n", + "test_data = pd.read_csv(testing_input_file, names=feature_names)\n", + "\n", + "# Get features of data\n", + "config.features = len(train_data.columns) - 1\n", + "config.classes = len(set(train_data['class']))\n", + "\n", + "# Shuffle training data\n", + "train_data = train_data.sample(frac=1).reset_index(drop=True)\n", + "\n", + "# Seperate data and classes\n", + "train_y = np.array(train_data['class'])\n", + "train_x = np.array(train_data.drop('class', 1))\n", + "\n", + "test_y = np.array(test_data['class'])\n", + "test_x = np.array(test_data.drop('class', 1))\n", + "\n", + "# Initialise scaler to scale the data\n", + "scaler = StandardScaler()\n", + "\n", + "train_set = np.empty(train_x.shape, dtype = float)\n", + "test_set = np.empty(test_x.shape, dtype = float)\n", + "\n", + "# Make training data suitable for scaling\n", + "for index in range(len(train_x)):\n", + " train_set[index] = train_x[index].astype(float)\n", + "\n", + "# Make testing data suitable for scaling\n", + "for index in range(len(test_x)):\n", + " test_set[index] = test_x[index].astype(float)\n", + "\n", + "# Fit the training data\n", + "scaler.fit(train_set) \n", + "\n", + "# Scale the training and testing data w.r.t scaler\n", + "data_train = scaler.transform(train_set)\n", + "data_test = scaler.transform(test_set)\n", + "\n", + "tab = PrettyTable(['Config', 'Value'])\n", + "configs = vars(config)\n", + "\n", + "for key in configs:\n", + " tab.add_row([key, configs[key]])\n", + "print(tab)\n", + "\n", + "print(\"Instances in training data :\", len(train_data))\n", + "print(\"Instances in testing data :\", len(test_data))" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "# -------------------- Model -------------------- #\n", + "\n", + "occ = OutputCodeClassifier(BaggingClassifier())\n", + "occ.fit(data_train, train_y)\n", + "prediction = occ.predict(data_test)\n", + "accuracy = accuracy_score(test_y, prediction)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Accuracy using Output Code Classifier : 61.326 %\n", + "\n" + ] + } + ], + "source": [ + "# -------------------- Print the final result -------------------- #\n", + "\n", + "print(\"\\nAccuracy using Output Code Classifier :\", round(accuracy * 100, 3),\"%\\n\")" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Accuracy : 61.326\n" + ] + }, + { + "data": { + "image/png": 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HmUvWdHl5VsJxJ90inAAuBPAmrHvhAuBRADenquPHQsmBLxwf1+HRsojeXFbvTd92uz0QPRSK6LWo9208gcxZqk7q67VjRERPQ/QAIvoQlmrHCPvYsjKrDDW4DAaZtIqbttz268X1FPgPQX9OFkoesDj94AXgW1qsnbHS0uKukbQdZC5Z036E4+VCyY5W9hGRH8K6nXIKwOsAvqWqHyY7niv7EOWfXbt2YdKkSRBJ+Yia0lBV7N69O9iVfVR1BYAVaQ8korxVUlKCzs5OlJWVMZFnSFXR2dmJkhLv/tSGK/sQkSMVFRVoa2tDe/xbAsm1kpISVFRUeNYekzgROVJUVITx48dnOwwagJ+dQkRkMCZxIiKDMYkTERmMSZyIyGBM4kREBmMSJyIyGJM4EZHBmMSJiAzGJE5EZDAmcSIigzGJExEZLGeS+Lx5gMjgUlJiLYouYn3gemz7iBFWSVRn2DAgFLJeFxb2LevX0NDXloj1OrZgePxC4snaLS21jqur67+9oMDa1tCQvO7ActZZqfuKxVdX1xfX6NF9x18rDWiRvpXkr5WG3vEMHGdsLhLFMGBxekSjg+c6Uf34/V6WWF/RqDX2+HGkK9Fo//HU1VnnP7b/WmlAW2EUKgVoK7TmLH7/wFJYaF2XiWIoLQUmT04fU+ycxMdSUGBd18muQzdjTnadFhT0zUei6/IbJYOvn/gxJ7rm0pWCgv7XZatEsdhu101JdG1fV9CA1rht8e0O5VqMXW/J2igo6JuPTK7BQGTyIeTpittFIebOVbWWKfWvzJ2rWlQ0eHtxserSparhsP8xeFUWoV6PoX/AxxDWRajXUEg1FHLeVkGBtbZAfb1Zc5CshMPWWJYudT5nfsckkv6Y2HWY6BodSikqGty/X3PhRbuJ2jiBIj2J4qycu0xK7Bp0CxkuCuG6gh9JPNuT7ibp5UI5gEjCHQcQyai9SMQq2R6XVyUSGXxOvZ4zP0pQ16Ffc+FFu8nayPVzN7BEIq5SoJ0HfVzZxy23K/uIeB5CXjuNAhQkWLGyB4IQely3F5t/Hy6FrBAZPBav58xkfs2FF+0mayORXD53IkCPy9D8XCg574VC2Y7AnYOodLU9ncpKq+SLysrB59TrOfNDUNehX3PhRbt+HRu0IH+eciKJz50bTB9FRYO3FxcDtbWDFxLPZXdhJY4j8erpoZC7ZFBQ0Ls4vVFzkExsAfja2v7bU82Z35z8phm7DhNdo0NRVDS4f7/mwot2E7VxEkX4EMVDajdIsWswMJncg0lXMlntPtnDzWHDrEXRgf4PaIYPt0qiOsXF1gO72H3GpUutPurr+9oCrNexBxDxC4kna3fEiMQPzUSsbfX1yesOLCUlqfuKxbd0aV9cZWV9xy9CvR5A30ryi1DfO56B44zNRaIYBixO33tvPH6uE9V38rAukxLrKxKxxh4/Dif3IePHs3Rp//vMi1Cvh0IR7YHooVCk90FwsvZCIeu6TBTDiBGqVVXpY4qdk/hYRKzrOtl16GbMya5Tkb75SHRd3jBs8PUTP+ZE11y6ItL/umxBRK/N4OFjomt7sdRrS9y2+HaHci3GrrdkbYj0zUcm16AbMPmeOBHRmY73xImIzkBM4kREBmMSJyIyGJM4EZHBmMSJiAzGJE5EZDAmcSIigzGJExEZjEmciMhgTOJERAZjEiciMhiTOBGRwZjEiYgM5iiJi8hIEdkgIrtFZJeIzPI7MCIiSs/p/8TXAHhBVScBmAZgl9eBjBuX+YrViVbHHspq4flY/JyjVG170e+Zen79Grebdk2e+2zGPnmy1xkyhXQfOA7gbAAHAOuzx50Ut4tCjB2b+Ye6Z3MVc1OKn3OUqm2/Vj8/E85vLqxIb/Lc50LsVVXBLAqR/gCgGsCrANYDeB3AvwEY7mUSH8pEmbCKebaLn3OUqm0/Vz/P9/ObCyvSmzz3uRJ7EEk87co+IlID4C8AZqvqKyKyBsARVf3fA46rBVALAJWVlRe0trY6/m1AxPGhg3AV8/T8nKNUbQPwbfXzfD+/ubAivclznyuxp0mv/fi5sk8bgDZVfcX+fgOA6QMPUtV1qlqjqjXl5eVu48iYCauYZ5ufc5SqbT9XP8/385sLK9KbPPcmx+6ak/+uA/gjgHPt13cDWJ3qeN4Tz63Ce+LmFd4Tz835c1Ny5p64nbirATQC2A7g1wBGeZnEvUjkiVbuZglmjlK17UW/Z+r59Wvcbto1ee6zGbvbBD6UJM7V7omIcgBXuyciOgMxiRMRGYxJnIjIYEziREQGYxInIjIYkzgRkcGYxImIDMYkTkRkMCZxIiKDMYkTERmMSZyIyGBM4kREBmMSJyIyGJM4EZHBciaJjxqV3RWqTV7V+0weGwtLLpZx4wJMnpl8CHm64nZRiJEj+z7EPRurceTCKiAcGwtLfpWxY8+gRSFErK8HEEUUrYP2tyCC8WjxKLrBstVvEPJ5bES5zk16zYtFISpx0NV20/sNQj6PjYhyLIlna4XqfF4ZO5/HRkQ5ksRHjrS+3oWVOI5wv33HEcZdWOlr/9nqNwj5PDaiXDZ2bEAd5cKDzYEPN7OxQrXJq3qfyWNjYcnF4vahploPJ819sElEdKbLiwebRETkDpM4EZHBmMSJiAzGJE5EZDAmcSIigzGJExEZjEmciMhgTOJERAZjEiciMhiTOBGRwZjEiYgMxiRORGQwJnEiIoM5TuIiEhKR10XkWT8DIiIi5wpdHHsrgF0APuZHID0iED8aJiIKmAIo8OFjvhNxlMRFpALAlwCsBPB9r4OIJXAmcSLKFz0igSRyp7dTfgTgnwD0+BEEEzgR5ZMgc1raJC4iVwB4X1Wb0hxXKyKNItLY3t7uWYBERJSck/+JzwYwX0RaADwB4AsiUj/wIFVdp6o1qlpTXl7ucZhERJRI2iSuqneqaoWqRgFcA2CLql7nZRBqFyKifBBkTnPz7hTfFKiiR3hXnIjyQ869OyVGVV8G8LIfgQQ1YCIivwX5X1L+xSYRkcGYxImIDMYkTkRkMCZxIiKDMYkTERmMSZyIyGBM4kREBmMSJyIyGJM4EZHBmMSJiAzGJE5EZDAmcSIigzGJExEZjEmciMhgOfF54gDwkNShDg9zrU0iMt5mzMVl+lIgfeVEEn9I6nAjEzgR5YlL8Tv8VuYFkshz4nbKd7GOCZyI8obASuRByIkkHsLpbIdARGSknEjipxHKdghEREbKiST+CGq52j0R5Q2F9XAzCDnxYPMmXYuHBHx3ChHlhTPu3SmAlciBtdkOg4hoyC4LsK+cuJ1CRESZYRInIjIYkzgRkcGYxImIDMYkTkRkMCZxIiKDMYkTERmMSZyIyGBM4kREBmMSJyIyGJM4EZHBmMSJiAzGJE5EZLC0SVxEPikivxeRnSLypojcGkRgRESUnpOPoj0F4HZVfU1ESgE0iciLqrrTy0BaZRwq8baXTRIRZUVOfZ64qr4D4B379VER2QVgHADPkngsgXNBCCLKBzm72r2IRAGcD+AVL4NgAieifJKTq92LyAgATwO4TVWPJNhfKyKNItLY3t7uZYxERJSEoyQuIkWwEniDqm5MdIyqrlPVGlWtKS8v9zJGIiJKwsm7UwTAzwDsUtUH/AjiIMZytXsiyhu5ttr9bAD/C8AOEWm2t92lqs95FURED/PdKUSUN3Lt3Sn/Bfj/3DGih/3ugogoEFztnoiIHGESJyIyGJM4EZHBmMSJiAzGJE5EZDAmcSIigzGJExEZjEmciMhgTOJERAZjEiciMhiTOBGRwZjEiYgMxiRORGQwJnEiIoM5+TzxQPSIcJ1NIsoLCqBAg1nqJieSeCyBM4kTUb7oEQkkkedEEmcCJ6J8EmQ+4z1xIiKDMYkTERksJ5K42oWIKB8EmdNy4p54gSp6hHfFiSg/nHHvTgGCGzARkd/4YJOIiBxhEiciMhiTOBGRwZjEiYgMxiRORGQwJnEiIoMxiRMRGYxJnIjIYEziREQGYxInIjIYkzgRkcGYxImIDMYkTkRkMEdJXET+UUT2iMg+EVnmd1BERORM2o+iFZEQgB8DuARAG4CtIrJJVXd6GQhXuyeifJFrnyf+PwDsU9W/AoCIPAHgSgCeJXGudk9E+Sao1e6d3E4ZB+BQ3Pdt9jbPMIETUT4JMqd59mBTRGpFpFFEGtvb271qloiIUnCSxA8D+GTc9xX2tn5UdZ2q1qhqTXl5uVfxERFRCk6S+FYAE0RkvIgUA7gGwCYvg+Bq90SUT3JqtXtVPSUiNwH4LYAQgJ+r6pteBsHV7okon+Tau1Ogqs8BeM7PQLjaPRHlC652T0REjjCJExEZjEmciMhgTOJERAZjEiciMpioD+8KEZF2AK0ZVh8NoMPDcEzAMee/M228AMfsVkRVXf+lpC9JfChEpFFVa7IdR5A45vx3po0X4JiDwtspREQGYxInIjJYLibxddkOIAs45vx3po0X4JgDkXP3xImIyLlc/J84ERE5lDNJ3MTFmEXkkyLyexHZKSJvisit9vaPi8iLIrLX/jrK3i4i8qA9xu0iMj2ura/bx+8Vka/Hbb9ARHbYdR4UsT7uMVkfAY07JCKvi8iz9vfjReQVO8Zf2h9ZDBEZZn+/z94fjWvjTnv7HhG5LG57wusgWR8BjXekiGwQkd0isktEZp0B5/h79jX9hog8LiIl+XaeReTnIvK+iLwRty1r5zVVHympatYLrI+43Q/gUwCKAWwDUJXtuBzEPQbAdPt1KYC3AFQB+H8AltnblwH4v/brLwJ4HtaHnM0E8Iq9/eMA/mp/HWW/HmXve9U+Vuy6l9vbE/YR0Li/D+A/ADxrf/8kgGvs148AWGq/rgPwiP36GgC/tF9X2ed4GIDx9rkPpboOkvUR0HgfBfAt+3UxgJH5fI5hLb94AMBZcXO/JN/OM4B/ADAdwBtx27J2XpP1kXYcQf0gpJnMWQB+G/f9nQDuzHZcGYzjNwAuAbAHwBh72xgAe+zXPwGwKO74Pfb+RQB+Erf9J/a2MQB2x23vPS5ZHwGMsQLA7wB8AcCz9gXXAaBw4LmE9Rn0s+zXhfZxMvD8xo5Ldh2k6iOA8Z4NK6HJgO35fI5j6+p+3D5vzwK4LB/PM4Ao+ifxrJ3XZH2kG0Ou3E7xfTFmv9m/Qp4P4BUA56jqO/audwGcY79ONs5U29sSbEeKPvz2IwD/BKDH/r4MwAeqeipBjL3jsvf/3T7e7Tyk6sNv4wG0A/h3sW4h/ZuIDEcen2NVPQzgfgAHAbwD67w1Ib/Pc0w2z2tGeTBXkrjRRGQEgKcB3KaqR+L3qfVPqq9vAQqiDwAQkSsAvK+qTX73lUMKYf3K/bCqng/gOKxfgXvl0zkGAPse7ZWw/gEbC2A4gH8Mou9cYsp5zZUk7mgx5lwkIkWwEniDqm60N78nImPs/WMAvG9vTzbOVNsrEmxP1YefZgOYLyItAJ6AdUtlDYCRIhJbJSo+xt5x2fvPBtAJ9/PQmaIPv7UBaFPVV+zvN8BK6vl6jgFgHoADqtquqt0ANsI69/l8nmOyeV4zyoO5ksR9X4zZD/bT5p8B2KWqD8Tt2gQg9pT667Dulce2X28/hZ4J4O/2r1W/BXCpiIyy/xd0Kax7ge8AOCIiM+2+rh/QVqI+fKOqd6pqhapGYZ2jLaq6GMDvAVydIJb4GK+2j1d7+zX2uxrGA5gA6yFQwuvArpOsD1+p6rsADonIufamuQB2Ik/Pse0ggJkiErZjio05b89znGye12R9pObnQwOXDxi+COvdHfsBLM92PA5jngPrV6HtAJrt8kVY9/Z+B2AvgJcAfNw+XgD82B7jDgA1cW19A8A+u9wQt70GwBt2nYfQ9wdaCfsIcOz/E33vTvkUrB/OfQCeAjDM3l5if7/P3v+puPrL7THtgf3UPtV1kKyPgMZaDaDRPs+/hvUuhLw+xwB+CGC3HddjsN5hklfnGcDjsO75d8P6jeub2TyvqfpIVfgXm0REBsuV2ylERJQBJnEiIoMxiRMRGYxJnIjIYEziREQGYxInIjIYkzgRkcGYxImIDPb/Ad1KiFwgV0JNAAAAAElFTkSuQmCC", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# -------------------- Visualisations -------------------- #\n", + "\n", + "print(\"Accuracy :\", round(accuracy * 100, 3))\n", + "plt.plot(test_y, 'bo', label = \"Original\")\n", + "plt.plot(prediction, 'ro', label = \"Predicted\")\n", + "plt.legend()\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "+--------+--------+--------+--------+--------+--------+--------+--------+--------+--------+--------+\n", + "| | Pred 0 | Pred 1 | Pred 2 | Pred 3 | Pred 4 | Pred 5 | Pred 6 | Pred 7 | Pred 8 | Pred 9 |\n", + "+--------+--------+--------+--------+--------+--------+--------+--------+--------+--------+--------+\n", + "| Real 0 | 413004 | 85828 | 24 | 2 | 2347 | 1 | 0 | 0 | 3 | 0 |\n", + "| Real 1 | 216903 | 198716 | 666 | 174 | 6022 | 0 | 2 | 0 | 4 | 11 |\n", + "| Real 2 | 13546 | 31787 | 606 | 134 | 1534 | 0 | 4 | 2 | 0 | 9 |\n", + "| Real 3 | 3560 | 16298 | 151 | 638 | 464 | 0 | 1 | 3 | 4 | 2 |\n", + "| Real 4 | 457 | 3120 | 18 | 1 | 288 | 0 | 0 | 0 | 0 | 1 |\n", + "| Real 5 | 1779 | 200 | 0 | 0 | 6 | 2 | 0 | 0 | 4 | 5 |\n", + "| Real 6 | 99 | 1146 | 51 | 66 | 59 | 0 | 2 | 1 | 0 | 0 |\n", + "| Real 7 | 4 | 182 | 8 | 30 | 6 | 0 | 0 | 0 | 0 | 0 |\n", + "| Real 8 | 3 | 5 | 0 | 0 | 3 | 0 | 0 | 0 | 0 | 1 |\n", + "| Real 9 | 2 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |\n", + "+--------+--------+--------+--------+--------+--------+--------+--------+--------+--------+--------+\n" + ] + } + ], + "source": [ + "preds = prediction\n", + "confMat = [[0] * config.classes for x in range(config.classes)]\n", + "\n", + "for i in range(len(preds)):\n", + " predLabel = preds[i]\n", + " actLabel = test_y[i]\n", + " confMat[actLabel][predLabel] += 1\n", + "\n", + "header = [\"\"]\n", + "for l in range(config.classes):\n", + " header.append(\"Pred \" + str(l))\n", + " \n", + "tab = PrettyTable(header)\n", + "l = 0\n", + "for c in confMat:\n", + " tab.add_row([\"Real \" + str(l)] + c)\n", + " l += 1\n", + "\n", + "print(tab)" + ] + } + ], + "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": "3.5.2" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/Poker Hand Prediction/Poker Hand Prediction/Random Forest.ipynb b/Poker Hand Prediction/Poker Hand Prediction/Random Forest.ipynb new file mode 100644 index 000000000..c48441a2b --- /dev/null +++ b/Poker Hand Prediction/Poker Hand Prediction/Random Forest.ipynb @@ -0,0 +1,259 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "# -------------------- Imports -------------------- #\n", + "\n", + "from sklearn.ensemble import RandomForestClassifier\n", + "from sklearn.preprocessing import StandardScaler\n", + "from sklearn.metrics import accuracy_score\n", + "from matplotlib import pyplot as plt\n", + "import numpy as np, pandas as pd, os\n", + "from prettytable import PrettyTable" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "# -------------------- Globals and Configs -------------------- #\n", + "\n", + "feature_names = list()\n", + "for index in range(1, 6):\n", + " feature_names.extend([\"Suit\"+str(index), \"Rank\"+str(index)])\n", + "\n", + "feature_names.append('class')\n", + "\n", + "\n", + "training_input_file = os.path.abspath('../datasets/csv/train.csv')\n", + "testing_input_file = os.path.abspath('../datasets/csv/test.csv')\n", + "\n", + "np.random.seed(666) # seed for reproducible results\n", + "\n", + "# To store configs\n", + "class myConfigs:\n", + " features = 0\n", + " classes = 0\n", + "\n", + "\n", + "config = myConfigs()" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "+----------+-------+\n", + "| Config | Value |\n", + "+----------+-------+\n", + "| features | 10 |\n", + "| classes | 10 |\n", + "+----------+-------+\n", + "Instances in training data : 25010\n", + "Instances in testing data : 1000000\n" + ] + } + ], + "source": [ + "# -------------------- Data -------------------- #\n", + "\n", + "train_data = pd.read_csv(training_input_file, names=feature_names)\n", + "test_data = pd.read_csv(testing_input_file, names=feature_names)\n", + "\n", + "# Get features of data\n", + "config.features = len(train_data.columns) - 1\n", + "config.classes = len(set(train_data['class']))\n", + "\n", + "# Shuffle training data\n", + "train_data = train_data.sample(frac=1).reset_index(drop=True)\n", + "\n", + "# Seperate data and classes\n", + "train_y = np.array(train_data['class'])\n", + "train_x = np.array(train_data.drop('class', 1))\n", + "\n", + "test_y = np.array(test_data['class'])\n", + "test_x = np.array(test_data.drop('class', 1))\n", + "\n", + "# Initialise scaler to scale the data\n", + "scaler = StandardScaler()\n", + "\n", + "train_set = np.empty(train_x.shape, dtype = float)\n", + "test_set = np.empty(test_x.shape, dtype = float)\n", + "\n", + "# Make training data suitable for scaling\n", + "for index in range(len(train_x)):\n", + " train_set[index] = train_x[index].astype(float)\n", + "\n", + "# Make testing data suitable for scaling\n", + "for index in range(len(test_x)):\n", + " test_set[index] = test_x[index].astype(float)\n", + "\n", + "# Fit the training data\n", + "scaler.fit(train_set) \n", + "\n", + "# Scale the training and testing data w.r.t scaler\n", + "data_train = scaler.transform(train_set)\n", + "data_test = scaler.transform(test_set)\n", + "\n", + "tab = PrettyTable(['Config', 'Value'])\n", + "configs = vars(config)\n", + "\n", + "for key in configs:\n", + " tab.add_row([key, configs[key]])\n", + "print(tab)\n", + "\n", + "print(\"Instances in training data :\", len(train_data))\n", + "print(\"Instances in testing data :\", len(test_data))" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "# -------------------- Model -------------------- #\n", + "\n", + "rforest = RandomForestClassifier()\n", + "rforest.fit(data_train, train_y)\n", + "\n", + "prediction = rforest.predict(data_test)\n", + "accuracy = accuracy_score(test_y, prediction)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Accuracy using Random Forest Classifier : 56.174 %\n", + "\n" + ] + } + ], + "source": [ + "# -------------------- Print the final result -------------------- #\n", + "\n", + "print(\"\\nAccuracy using Random Forest Classifier :\", round(accuracy * 100, 3),\"%\\n\")" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Accuracy : 56.174\n" + ] + }, + { + "data": { + "image/png": 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UavixMtsayVxMHzPqGGZHx6OUOVgMfP5gU0RktCv1g81EXE4REZHSKImLiHhMSVxExGNK4iIiHlMSFxHxmJK4iIjHlMRFRDymJC4i4jElcRERjymJi4h4TElcRMRjSuIiIh5TEhcR8VisJG5mE8xslZm9YGabzGx+pQMTEZHC4v4mfivwuHNuGjAb2FTuQL4wcSXbrZMha2C7dfJxW1mWKtm+Lh+3wuMRZ5tilqXWw4A14swYsEaWWk/Nx6FaY6nY6m+p5djOnFnuDJlHoRuOAycA2yC493icpdiiED0TVrgDDC+1foBWdxkrynJDed+Wyyg8HnG2KWa5jW43lLVyCNxtdNd8PCo9loqt/pYkjO2MGdUpClF4A5gD/AG4G3gG+DdgbDmT+DY6co7CNjpqPhlqscQZj3KPWT+5S9z0k6r5eFR6LBVb/S1JGdtqJPGClX3MrAv4PbDAOfekmd0K7HPO/a+s7ZYASwDa29vn7dixI/ZfA0PWQAPHxjGEkWIo9nHqxSCFxyPONsUYwrAc6x3kbMcX5R6nckpybL5LytgWSK/DVLKyTy/Q65x7Mny+CpibvZFzbrlzrss51zV58uSigthJ7tLQUevrXZzxKPeYDZK7unLUel8keW4lOTbfjaaxLZjEnXOvAS+b2RnhqoXAxnIGcfOEG3mD4aXW36CVr1HNktHJ8TUKj0ecbYpxB0uO+b3Fhet9Vu5xKqckx+a7JIztjBlVaijONReC6+JrgOeAHwET821fSrX7ngkr3DY63CDmttEx6j/cuYzC4xFnm2KW2+h2/aTcEMG1cN8/1KzUOI2W2Hxfajm2xX6o6YJEq2r3IiK+UrV7EZFRSElcRMRjSuIiIh5TEhcR8ZiSuIiIx5TERUQ8piQuIuIxJXEREY8piYuIeExJXETEY0riIiIeUxIXEfGYkriIiMeUxEVEPJaYJD5xYlCdus8m4cxwZvTZpKIqVI+kunXUvuWqmJ3kqubZsS21nqrEmuQxGR6fMWCNDJlFzo0+m0SfTTrmcdx+FTMWS62HAWvEhXEttZ6i5vE+G8egpYbt/3FbyT4bd+S9N2gpnrDzYsVUifddkpZcMRaK+5RTRpoRi1DKTcgLLcUWhZgwIbiB+yGajrm7+mGaY93MfSTVraP2vY3uslTMTkLl7WJiy656X4lYkzwmUfEVmhtRS6F+FTMWt9F9zPkZAncb3UXN4+z9ByLWF4qpEu+7pMyBqBgP0eQO01ww7pNPrk5RiKJ3qEQSh+jq1I54FapHUt06at+oCvDFVsxOSuXtYmKrdKxJHpM44xI1N0oZv2LGIqrdflJFz+NSluyYKvG+S8ociDMPCsVdXB70uLKPWXR1aohXoXok1a2j9nWQswJ8sRWzk1J5O5d8456p3LEmeUyg8LhEzY0o+fpVzFgMYTnbdYDDiprHpciOqRLvu6TMAYj//oDccReTXr2v7JOvCnWcCtUjqW4dtU1UpfdiK2YnufJ23BjKHWuSxwQKxxE1N0o5XjFjEdXuIKmi53EpstuoxPsuKXMAioulZnGXeskk36Jr4uWLrdKLronHj6/Q3IhadE3czzkQFaOuiRdI5Ltoc0PhBNpFW1EndCTVraP2LVfF7CRXNc+O7Ta6qxJrksdkeHzBdeVBiJwbu2hzu2g75nHcfhUzFrfR7fpJuaEwrtvoLmoev85YN0DDsP0vY4V7nbFH3nsDNLjHWRgrpkq875K05IqxUNzFJnDnnCs1iSfimriIyGjn/TVxEREpnpK4iIjHlMRFRDymJC4i4jElcRERjymJi4h4TElcRMRjSuIiIh5TEhcR8ZiSuIiIx5TERUQ8piQuIuIxJXEREY/FTuJmljKzZ8zskUoGJCIi8RXzm/jVwKZKBdLcXFpV6XyVydM/o6qPF6pmnt6mzyYdqQLeZ5NYaj3D1g1aiiGznFXEMyuUD1rDkX322fjYlb2fsPOO7OfMeMLOK6pyeP5xja7kntnHgzbmSN+GzBgK+5Ldz0JLZqX29FLoGNWoph5nu3xV5rOPkTnn+mzSsEry6TmUHv/MedFnk/i4rYxsK+57ZKT9yZwfueKLM2bZ45E5BvtsHAdtzLD30FLrGdG5HumcybdPsa+lyldMqbA4Nx0HpgA/Bc4FHil3UYimppFV0MhXhSXXUqjaSbqNqGpD2RVPopZ0xZVc1VgcuDdpLHgT/MdZmLOSy+MsLDgGUf3MNa7Z++Tqd6F+Fto0ahzyHaMalWPibJevok4pczDfHOrHIs95nHMZ531TbIWgzOUwzQWrXhU7Hun236SxpHM90jmTb59SX2toqE5RiHgbwSpgHvDeSiRxGFlV6WL2PfpGyV8BfBsdJR03Vzv52ipU2Ttf0oszBqVUOi91n0KbFTpurmNUo5p6nO3yVZnPd4xyLnF/eYha4vYnTl+i9k+3Uc7xiHOuRzpn8u1T6mtQVBp0pSbxgpV9zOwi4ELnXI+ZvRf4H865i3JstwRYAtDe3j5vx44dsf8asALV7rPFrbidjyN/BfCh8NVij5urHfK0Vaiyd77q5pmxRY1BoX7mUuo+hcYqqi/5jlGNaupxtit0HkqZg8Uq5bxkitsfhxXsS1Qs6TbKOR5xznWmUuZMvn0g99wu9FqKIQqk12EqWdlnAbDIzLYD9wHnmtmK7I2cc8udc13Oua7JkycXG8eIqkqXUmW6UAXwnbSXpXr1IKm8bZWrQnY5K51Xap9C2+R6vRrV1ONsl6/KfNx4ai1uf+L0JWr/9L7lHI9ij1XKnMm3T6mvVU0xv7ZTocspuiYefRhdE9c18cxzrmvihRddE8+3cYWSeGYiL7aqdOZJyK5Mnv4ZVX28UDXz9Da7aDtSBXwXbe42uoetG6DBDULOKuKZb5qB8M05BO51xsWenOlEnl6yE/ixYxBd6fzYcY2u5J7ZxzdoPtK3QXCDYV+y+1loyazUnl4KHaMa1dTjbJevynz2MTLn3C7ahlWST8+h9PhnzotdtLnLWBHZVtz3yEj7kzk/csUXZ8yyxyNzDF5nrHuD5mHvofQ/IKWe65HOmXz7FPtasQl8JElc1e5FRBJA1e5FREYhJXEREY8piYuIeExJXETEY0riIiIeUxIXEfGYkriIiMeUxEVEPKYkLiLiMSVxERGPKYmLiHhMSVxExGNK4iIiHlMSFxHxWGOtA0jbYafQzqu1DqMob5KimcFahyEiCfMdurnKLatKW4lI4ukEPpL6gbUwRglcRHL4Arez1KhKIk/E5RQfE7iISBQDPs/yqrSViCQuIlJvUlX6S11JXESkAgZJVaWdRCTxnZxM+St9iojUhgPuYElV2krEB5sd7hV9O0VE6sao+3YKBIncN2NqHYCIJNJVVWwrEZdTRESkNEriIiIeUxIXEfGYkriIiMeUxEVEPKYkLiLiMSVxERGPKYmLiHhMSVxExGNK4iIiHlMSFxHxmJK4iIjHlMRFRDxWMImb2dvM7OdmttHMNpjZ1dUITERECotzK9oB4MvOuafNbDyw1sz+wzm3sZyB9Jsl5764IiIjcIgmWt2bVWmrYN50zv0J+FP4eL+ZbQJOAcqWxNMJXMWSRaQeHEc/B625Kom8qGviZtYJvBN4spxBKIGLSD0xgkReDbGTuJmNAx4AvuSc25fj9SVmtsbM1vT19ZUzRhERiRAriZtZE0ECX+mcezDXNs655c65Ludc1+TJk8sZo4iIRIjz7RQD7gQ2Oee+VYkgBkDV7kWkbjiCDzerIc4XQhYAnwSeN7N14bqvOeceLVcQTc7p2ykiUjeS9u2U31CFzx2bnH4XF5H60FrFtvQ/NkVEPKYkLiLiMSVxERGPKYmLiHhMSVxExGNK4iIiHlMSFxHxmJK4iIjHlMRFRDymJC4i4jElcRERjymJi4h4TElcRMRjSuIiIh5LzC28h8xUZ1NE6oIDGqp0e+1EJPF0AlcSF5F6MWRWlUSeiCSuBC4i9aSa+UzXxEVEPKYkLiLisUQkcYeq3YtI/ahmTkvENfEG5xgyXRUXkfow6r6dAtXrsIhIpemDTRERiUVJXETEY0riIiIeUxIXEfGYkriIiMeUxEVEPKYkLiLiMSVxERGPKYmLiHhMSVxExGNK4iIiHlMSFxHxmJK4iIjHYiVxM/s7M9tsZlvN7JpKByUiIvEUvBWtmaWA7wDnA73AU2b2kHNuYzkDUbV7EakXSbuf+H8Ctjrn/ghgZvcBHwTKlsRV7V5E6k21qt3HuZxyCvByxvPecF3ZKIGLSD2pZk4r2webZrbEzNaY2Zq+vr5yHVZERPKIk8RfAd6W8XxKuG4Y59xy51yXc65r8uTJ5YpPRETyiJPEnwJOM7OpZtYMfAx4qJxBqNq9iNSTRFW7d84NmNlVwBNACrjLObehnEGo2r2I1JOkfTsF59yjwKOVDETV7kWkXqjavYiIxKIkLiLiMSVxERGPKYmLiHhMSVxExGPmKvCtEDPrA3aUuPskYHcZw/GB+lz/Rlt/QX0uVodzruj/KVmRJD4SZrbGOddV6ziqSX2uf6Otv6A+V4sup4iIeExJXETEY0lM4strHUANqM/1b7T1F9TnqkjcNXEREYkvib+Ji4hITIlJ4j4WYzazt5nZz81so5ltMLOrw/Unmtl/mNmW8OfEcL2Z2b+GfXzOzOZmHOvT4fZbzOzTGevnmdnz4T7/ahbc7jGqjSr1O2Vmz5jZI+HzqWb2ZBjjv4e3LMbMxoTPt4avd2Yc46vh+s1mdkHG+pzzIKqNKvV3gpmtMrMXzGyTmc0fBef4H8I5vd7M7jWzlno7z2Z2l5ntMrP1Getqdl7ztZGXc67mC8Etbl8C3g40A88CM2odV4y4TwLmho/HAy8CM4D/DVwTrr8G+Ofw8YXAYwQ3OTsHeDJcfyLwx/DnxPDxxPC1P4TbWrjvB8L1OduoUr//O/D/gEfC5/cDHwsf3wF0h497gDvCxx8D/j18PCM8x2OAqeG5T+WbB1FtVKm/9wB/Hz5uBibU8zkmKL+4DTguY+yvqLfzDPwtMBdYn7GuZuc1qo2C/ajWG6HAYM4Hnsh4/lXgq7WOq4R+/Bg4H9gMnBSuOwnYHD7+LnBZxvabw9cvA76bsf674bqTgBcy1h/ZLqqNKvRxCvBT4FzgkXDC7QYas88lwT3o54ePG8PtLPv8preLmgf52qhCf08gSGiWtb6ez3G6ru6J4Xl7BLigHs8z0MnwJF6z8xrVRqE+JOVySsWLMVda+CfkO4Engbc65/4UvvQa8NbwcVQ/863vzbGePG1U2r8A/xMYCp+3AXudcwM5YjzSr/D118Ptix2HfG1U2lSgD/g/FlxC+jczG0sdn2Pn3CvALcBO4E8E520t9X2e02p5XkvKg0lJ4l4zs3HAA8CXnHP7Ml9zwT+pFf0KUDXaADCzi4Bdzrm1lW4rQRoJ/uS+3Tn3TuANgj+Bj6incwwQXqP9IME/YCcDY4G/q0bbSeLLeU1KEo9VjDmJzKyJIIGvdM49GK7+s5mdFL5+ErArXB/Vz3zrp+RYn6+NSloALDKz7cB9BJdUbgUmmFm6SlRmjEf6Fb5+ArCH4sdhT542Kq0X6HXOPRk+X0WQ1Ov1HAOcB2xzzvU55/qBBwnOfT2f57RanteS8mBSknjFizFXQvhp853AJufctzJeeghIf0r9aYJr5en1nwo/hT4HeD38s+oJ4P1mNjH8Lej9BNcC/wTsM7NzwrY+lXWsXG1UjHPuq865Kc65ToJz9DPn3OXAz4GP5IglM8aPhNu7cP3Hwm81TAVOI/gQKOc8CPeJaqOinHOvAS+b2RnhqoXARur0HId2AueYWWsYU7rPdXueM9TyvEa1kV8lPzQo8gOGCwm+3fEScG2t44kZ87sI/hR6DlgXLhcSXNv7KbAF+AlwYri9Ad8J+/g80JVxrM8CW8PlMxnru4D14T5LOfoftHK2UcW+v5ej3055O8GbcyvwA2BMuL4lfL41fP3tGftfG/ZpM+Gn9vnmQVQbVerrHGBNeJ5/RPAthLo+x8A/Ai+Ecf1fgm+Y1NV5Bu4luObfT/AX1+dqeV7ztZFv0f/YFBHxWFIup4iISAmUxEVEPKYkLiLiMSVxERGPKYmLiHhMSVxExGNK4iIiHlMSFxF2SmxrAAAACElEQVTx2P8HJrKTNCsahF0AAAAASUVORK5CYII=", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# -------------------- Visualisations -------------------- #\n", + "\n", + "print(\"Accuracy :\", round(accuracy * 100, 3))\n", + "plt.plot(test_y, 'bo', label = \"Original\")\n", + "plt.plot(prediction, 'ro', label = \"Predicted\")\n", + "plt.legend()\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "+--------+--------+--------+--------+--------+--------+--------+--------+--------+--------+--------+\n", + "| | Pred 0 | Pred 1 | Pred 2 | Pred 3 | Pred 4 | Pred 5 | Pred 6 | Pred 7 | Pred 8 | Pred 9 |\n", + "+--------+--------+--------+--------+--------+--------+--------+--------+--------+--------+--------+\n", + "| Real 0 | 373988 | 126717 | 423 | 64 | 10 | 7 | 0 | 0 | 0 | 0 |\n", + "| Real 1 | 233436 | 186769 | 1915 | 331 | 41 | 2 | 3 | 0 | 1 | 0 |\n", + "| Real 2 | 18493 | 28303 | 709 | 111 | 4 | 0 | 2 | 0 | 0 | 0 |\n", + "| Real 3 | 6629 | 14012 | 225 | 247 | 5 | 0 | 3 | 0 | 0 | 0 |\n", + "| Real 4 | 1195 | 2624 | 48 | 9 | 9 | 0 | 0 | 0 | 0 | 0 |\n", + "| Real 5 | 1599 | 376 | 1 | 0 | 0 | 20 | 0 | 0 | 0 | 0 |\n", + "| Real 6 | 293 | 1066 | 41 | 24 | 0 | 0 | 0 | 0 | 0 | 0 |\n", + "| Real 7 | 35 | 183 | 3 | 9 | 0 | 0 | 0 | 0 | 0 | 0 |\n", + "| Real 8 | 4 | 7 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 |\n", + "| Real 9 | 3 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |\n", + "+--------+--------+--------+--------+--------+--------+--------+--------+--------+--------+--------+\n" + ] + } + ], + "source": [ + "preds = prediction\n", + "confMat = [[0] * config.classes for x in range(config.classes)]\n", + "\n", + "for i in range(len(preds)):\n", + " predLabel = preds[i]\n", + " actLabel = test_y[i]\n", + " confMat[actLabel][predLabel] += 1\n", + "\n", + "header = [\"\"]\n", + "for l in range(config.classes):\n", + " header.append(\"Pred \" + str(l))\n", + " \n", + "tab = PrettyTable(header)\n", + "l = 0\n", + "for c in confMat:\n", + " tab.add_row([\"Real \" + str(l)] + c)\n", + " l += 1\n", + "\n", + "print(tab)" + ] + } + ], + "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": "3.5.2" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/Poker Hand Prediction/Poker Hand Prediction/Regression.ipynb b/Poker Hand Prediction/Poker Hand Prediction/Regression.ipynb new file mode 100644 index 000000000..b4108c58e --- /dev/null +++ b/Poker Hand Prediction/Poker Hand Prediction/Regression.ipynb @@ -0,0 +1,258 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn import linear_model as lm\n", + "import numpy as np, pandas as pd, os, math\n", + "from prettytable import PrettyTable\n", + "from matplotlib import pyplot as plt" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "# -------------------- Globals and Configs -------------------- #\n", + "feature_names = list()\n", + "for index in range(1, 6):\n", + " feature_names.extend([\"Suit\"+str(index), \"Rank\"+str(index)])\n", + "\n", + "feature_names.append('class')\n", + "\n", + "\n", + "training_input_file = os.path.abspath('../datasets/csv/train.csv')\n", + "testing_input_file = os.path.abspath('../datasets/csv/test.csv')\n", + "\n", + "np.random.seed(666) # seed for reproducible results\n", + "\n", + "# To store configs\n", + "class myConfigs:\n", + " features = 0\n", + " classes = 0\n", + "\n", + "\n", + "config = myConfigs()" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "+----------+-------+\n", + "| Config | Value |\n", + "+----------+-------+\n", + "| features | 10 |\n", + "| classes | 10 |\n", + "+----------+-------+\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# -------------------- Data -------------------- #\n", + "\n", + "train_data = pd.read_csv(training_input_file, names=feature_names)\n", + "test_data = pd.read_csv(testing_input_file, names=feature_names)\n", + "\n", + "# Get features of data\n", + "config.features = len(train_data.columns) - 1\n", + "config.classes = len(set(train_data['class']))\n", + "\n", + "# Shuffle training data\n", + "train_data = train_data.sample(frac=1).reset_index(drop=True)\n", + "\n", + "# Seperate data and classes\n", + "train_y = np.array(train_data['class'])\n", + "train_x = np.array(train_data.drop('class', 1))\n", + "\n", + "test_y = np.array(test_data['class'])\n", + "test_x = np.array(test_data.drop('class', 1))\n", + "\n", + "if __name__ == '__main__':\n", + " tab = PrettyTable(['Config', 'Value'])\n", + " configs = vars(config)\n", + "\n", + " for key in configs:\n", + " tab.add_row([key, configs[key]])\n", + " print(tab)\n", + "\n", + "test_y_l = list(test_y)\n", + "plt.bar(list(range(config.classes)), [test_y_l.count(int(x)) for x in range(config.classes)])\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "# -------------------- Model -------------------- #\n", + "model = lm.LinearRegression()\n", + "trainedModel = model.fit(train_x, train_y)\n", + "\n", + "predictions = model.predict(test_x)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Average error : 0.6183909469152787\n", + "RMSE : 0.7734871585310373\n" + ] + } + ], + "source": [ + "avgError = sum([math.fabs(x-y) for x, y in zip(predictions, test_y)]) / len(predictions)\n", + "print(\"Average error :\", avgError)\n", + "\n", + "rmsError = sum([(x-y)**2 for x, y in zip(predictions, test_y)]) / len(predictions)\n", + "rmsError = math.sqrt(rmsError)\n", + "print(\"RMSE :\", rmsError)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.plot(test_y, 'bo', label='actual')\n", + "plt.plot(predictions, 'ro', label='predicted')\n", + "plt.legend()\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.0002948116018505198" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "model.score(train_x, train_y)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "+--------+--------+--------+--------+--------+--------+--------+--------+--------+--------+--------+\n", + "| | Pred 0 | Pred 1 | Pred 2 | Pred 3 | Pred 4 | Pred 5 | Pred 6 | Pred 7 | Pred 8 | Pred 9 |\n", + "+--------+--------+--------+--------+--------+--------+--------+--------+--------+--------+--------+\n", + "| Real 0 | 0 | 501209 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |\n", + "| Real 1 | 0 | 422498 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |\n", + "| Real 2 | 0 | 47622 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |\n", + "| Real 3 | 0 | 21121 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |\n", + "| Real 4 | 0 | 3885 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |\n", + "| Real 5 | 0 | 1996 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |\n", + "| Real 6 | 0 | 1424 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |\n", + "| Real 7 | 0 | 230 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |\n", + "| Real 8 | 0 | 12 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |\n", + "| Real 9 | 0 | 3 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |\n", + "+--------+--------+--------+--------+--------+--------+--------+--------+--------+--------+--------+\n", + "Accuracy : 42.2498\n" + ] + } + ], + "source": [ + "preds = predictions\n", + "confMat = [[0] * config.classes for x in range(config.classes)]\n", + "acc = 0\n", + "\n", + "for i in range(len(preds)):\n", + " predLabel = int(round(preds[i]))\n", + " actLabel = int(round(test_y[i]))\n", + " confMat[actLabel][predLabel] += 1\n", + " if predLabel == actLabel:\n", + " acc += 1\n", + "\n", + "header = [\"\"]\n", + "for l in range(config.classes):\n", + " header.append(\"Pred \" + str(l))\n", + " \n", + "tab = PrettyTable(header)\n", + "l = 0\n", + "for c in confMat:\n", + " tab.add_row([\"Real \" + str(l)] + c)\n", + " l += 1\n", + "\n", + "print(tab)\n", + "print(\"Accuracy :\", acc * 100 / len(preds))" + ] + } + ], + "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": "3.5.2" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/Poker Hand Prediction/Poker Hand Prediction/SVM.ipynb b/Poker Hand Prediction/Poker Hand Prediction/SVM.ipynb new file mode 100644 index 000000000..e25728d3b --- /dev/null +++ b/Poker Hand Prediction/Poker Hand Prediction/SVM.ipynb @@ -0,0 +1,251 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn import svm\n", + "import numpy as np, pandas as pd, os\n", + "from prettytable import PrettyTable\n", + "from matplotlib import pyplot as plt" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "# -------------------- Globals and Configs -------------------- #\n", + "feature_names = list()\n", + "for index in range(1, 6):\n", + " feature_names.extend([\"Suit\"+str(index), \"Rank\"+str(index)])\n", + "\n", + "feature_names.append('class')\n", + "\n", + "\n", + "training_input_file = os.path.abspath('../datasets/csv/train.csv')\n", + "testing_input_file = os.path.abspath('../datasets/csv/test.csv')\n", + "\n", + "np.random.seed(666) # seed for reproducible results\n", + "\n", + "# To store configs\n", + "class myConfigs:\n", + " features = 0\n", + " classes = 0\n", + "\n", + "\n", + "config = myConfigs()" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "+----------+-------+\n", + "| Config | Value |\n", + "+----------+-------+\n", + "| classes | 10 |\n", + "| features | 10 |\n", + "+----------+-------+\n" + ] + } + ], + "source": [ + "# -------------------- Data -------------------- #\n", + "\n", + "train_data = pd.read_csv(training_input_file, names=feature_names)\n", + "test_data = pd.read_csv(testing_input_file, names=feature_names)\n", + "\n", + "# Get features of data\n", + "config.features = len(train_data.columns) - 1\n", + "config.classes = len(set(train_data['class']))\n", + "\n", + "# Shuffle training data\n", + "train_data = train_data.sample(frac=1).reset_index(drop=True)\n", + "\n", + "# Seperate data and classes\n", + "train_y = np.array(train_data['class'])\n", + "train_x = np.array(train_data.drop('class', 1))\n", + "\n", + "test_y = np.array(test_data['class'])\n", + "test_x = np.array(test_data.drop('class', 1))\n", + "\n", + "if __name__ == '__main__':\n", + " tab = PrettyTable(['Config', 'Value'])\n", + " configs = vars(config)\n", + "\n", + " for key in configs:\n", + " tab.add_row([key, configs[key]])\n", + " print(tab)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Generated model! Going to train...\n", + "Done training! Going to test...\n", + "Quarter done\n", + "Quarter done\n", + "Quarter done\n", + "Generated predictions!\n" + ] + } + ], + "source": [ + "# -------------------- Model -------------------- #\n", + "model = svm.SVC()\n", + "print(\"Generated model! Going to train...\")\n", + "\n", + "# Training\n", + "model.fit(train_x, train_y)\n", + "print(\"Done training! Going to test...\")\n", + "\n", + "# Testing\n", + "testLen = len(test_x)\n", + "predictions = list()\n", + "for l in range(testLen):\n", + " predictions.append(model.predict([test_x[l]])[0])\n", + " if l == (testLen // 4) or l == (testLen // 2) or l == ((testLen*3) // 4):\n", + " print(\"Quarter done\")\n", + "\n", + "print(\"Generated predictions!\")" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Average error : 0.476125\n", + "RMSE : 0.8120080048866514\n" + ] + } + ], + "source": [ + "import math\n", + "\n", + "avgError = sum([math.fabs(x-y) for x, y in zip(predictions, test_y)]) / len(predictions)\n", + "print(\"Average error :\", avgError)\n", + "\n", + "rmsError = sum([(x-y)**2 for x, y in zip(predictions, test_y)]) / len(predictions)\n", + "rmsError = math.sqrt(rmsError)\n", + "print(\"RMSE :\", rmsError)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.plot(test_y, 'bo', label='actual')\n", + "plt.plot(predictions, 'ro', label='predicted')\n", + "plt.legend()\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "+--------+--------+--------+--------+--------+--------+--------+--------+--------+--------+--------+\n", + "| | Pred 0 | Pred 1 | Pred 2 | Pred 3 | Pred 4 | Pred 5 | Pred 6 | Pred 7 | Pred 8 | Pred 9 |\n", + "+--------+--------+--------+--------+--------+--------+--------+--------+--------+--------+--------+\n", + "| Real 0 | 368338 | 132865 | 6 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |\n", + "| Real 1 | 204381 | 217693 | 405 | 19 | 0 | 0 | 0 | 0 | 0 | 0 |\n", + "| Real 2 | 16182 | 31081 | 339 | 20 | 0 | 0 | 0 | 0 | 0 | 0 |\n", + "| Real 3 | 2973 | 17986 | 144 | 18 | 0 | 0 | 0 | 0 | 0 | 0 |\n", + "| Real 4 | 49 | 3808 | 28 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |\n", + "| Real 5 | 1595 | 401 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |\n", + "| Real 6 | 138 | 1222 | 63 | 1 | 0 | 0 | 0 | 0 | 0 | 0 |\n", + "| Real 7 | 1 | 216 | 12 | 1 | 0 | 0 | 0 | 0 | 0 | 0 |\n", + "| Real 8 | 0 | 12 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |\n", + "| Real 9 | 1 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |\n", + "+--------+--------+--------+--------+--------+--------+--------+--------+--------+--------+--------+\n", + "Accuracy : 58.6388\n" + ] + } + ], + "source": [ + "preds = predictions\n", + "confMat = [[0] * config.classes for x in range(config.classes)]\n", + "acc = 0\n", + "\n", + "for i in range(len(preds)):\n", + " predLabel = preds[i]\n", + " actLabel = test_y[i]\n", + " confMat[actLabel][predLabel] += 1\n", + " if predLabel == actLabel:\n", + " acc += 1\n", + "\n", + "header = [\"\"]\n", + "for l in range(config.classes):\n", + " header.append(\"Pred \" + str(l))\n", + " \n", + "tab = PrettyTable(header)\n", + "l = 0\n", + "for c in confMat:\n", + " tab.add_row([\"Real \" + str(l)] + c)\n", + " l += 1\n", + "\n", + "print(tab)\n", + "print(\"Accuracy :\", acc * 100 / len(preds))" + ] + } + ], + "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": "3.5.2" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/Poker Hand Prediction/README.md b/Poker Hand Prediction/README.md new file mode 100644 index 000000000..d54c339fd --- /dev/null +++ b/Poker Hand Prediction/README.md @@ -0,0 +1,60 @@ +# Pokerman + +TL;DR, predicting poker hands. + +## The Problem + +Given the sequence of 5 'community' cards, drawn from a standard deck of cards, what hand is most likely present with at least one of the players in the game. + +## Poker Hands + +Texas Hold Em is played by dealing each player 2 cards (face down), called the `hole` cards, and dealing 5 `community` cards (face up), on the table. + +The player makes a poker `hand` using any combination of the 3 cards dealt to them, and the 5 cards on the table. + +The player with the strongest hands wins. Most commonly accepted ranking of hands, strongest to weekest : + +| Rank | Hand | Description | +| :--: | :--: | :---------: | +| 0 | Royal Flush | `A K Q J 10` all of the same suit | +| 1 | Straight Flush | Any 5 cards of the same suit, in sequence | +| 2 | Four of a Kind | 4 cards of the same rank, like, `4 4 4 4` | +| 3 | Full House | A 3 of a kind, and a pair, of different ranks | +| 4 | Flush | Any 5 cards of the same suit | +| 5 | Straight | Any 5 cards in sequence | +| 6 | Three of a Kind | Any 3 cards of the same rank | +| 7 | Two Pair | Any 2 pairs of cards | +| 8 | One Pair | Any 2 cards of the same rank | +| 9 | High Card | Highest Ranked card in hand | + + +#### Ranking of Cards + +A K Q J 10 9 8 7 6 5 4 3 2 1 + +## Data + +This data was acquired from UCI's Machine Learning repository. The data comes already split into training and testing data. + +Find it [here](https://archive.ics.uci.edu/ml/datasets/Poker+Hand). + + +##### NOTE: The hands' class labels are in the reverse order of their strength, i.e, 0 is the weakest hand. + +## Machine Learning ๐Ÿ–ฅ + +| Model | Accuracy | +| :---: | :------: | +| Linear Regression | 42% | +| SVM | 58% | +| Adaboost | 49% | +| Output Code Classifier | 61% | +| Random Forest | 56% | +| Artificial Neural Network | 45% | +| Deep Neural Network | 87% | +| Multi Layer Perceptron | 97% | + +## Conclusion + +The Multi-layer Perceptron is clearly the best model for the dataset in hand. + diff --git a/Poker Hand Prediction/datasets/csv/test.csv b/Poker Hand Prediction/datasets/csv/test.csv new file mode 100644 index 000000000..b1605d01d --- /dev/null +++ b/Poker Hand Prediction/datasets/csv/test.csv @@ -0,0 +1,1000000 @@ +1,1,1,13,2,4,2,3,1,12,0 +3,12,3,2,3,11,4,5,2,5,1 +1,9,4,6,1,4,3,2,3,9,1 +1,4,3,13,2,13,2,1,3,6,1 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+3,7,3,13,2,4,1,12,3,8,0 +1,8,4,1,4,12,1,13,1,10,0 +4,12,3,11,4,4,3,12,2,12,3 +2,8,2,12,4,3,4,2,4,4,0 +2,12,3,5,3,8,4,1,4,2,0 +4,10,2,13,4,5,4,7,1,5,1 +1,12,2,9,2,12,4,8,1,13,1 +3,5,3,7,4,11,3,11,3,2,1 +3,9,2,6,4,11,4,12,2,4,0 +4,1,4,10,3,13,3,4,1,10,1 +2,1,2,10,4,4,4,1,4,13,1 +2,12,4,3,1,10,1,12,4,9,1 +1,7,3,11,3,3,4,8,3,7,1 diff --git a/Poker Hand Prediction/models/adaboost.py b/Poker Hand Prediction/models/adaboost.py new file mode 100644 index 000000000..7e361b6be --- /dev/null +++ b/Poker Hand Prediction/models/adaboost.py @@ -0,0 +1,45 @@ +''' +AdaBoost Classifer +''' + +# -------------------- Imports -------------------- # + +from sklearn.ensemble import AdaBoostClassifier +from sklearn.preprocessing import StandardScaler +from sklearn.metrics import accuracy_score + +from poker_hand_prediction import * + +# -------------------- Model -------------------- # + +print("\nAdaBoost Classifer") + +# Initialise scaler to scale the data +scaler = StandardScaler() + +train_set = np.empty(train_x.shape, dtype = float) +test_set = np.empty(test_x.shape, dtype = float) + +# Make training data suitable for scaling +for index in range(len(train_x)): + train_set[index] = train_x[index].astype(float) + +# Make testing data suitable for scaling +for index in range(len(test_x)): + test_set[index] = test_x[index].astype(float) + +# Fit the training data +scaler.fit(train_set) + +# Scale the training and testing data w.r.t scaler +data_train = scaler.transform(train_set) +data_test = scaler.transform(test_set) + +adaboost = AdaBoostClassifier() +adaboost.fit(data_train, train_y) +prediction = adaboost.predict(data_test) +accuracy = accuracy_score(test_y, prediction) + +# -------------------- Print the final result -------------------- # + +print("\nAccuracy using AdaBoost Classifier :", round(accuracy * 100, 3),"%\n") diff --git a/Poker Hand Prediction/models/ann.py b/Poker Hand Prediction/models/ann.py new file mode 100644 index 000000000..8efdc8014 --- /dev/null +++ b/Poker Hand Prediction/models/ann.py @@ -0,0 +1,43 @@ +''' +ANN with back-propagation. +''' + +# -------------------- Imports -------------------- # +from keras.models import Sequential +from keras.layers import Dense + +from poker_hand_prediction import * + + +# -------------------- Preparign the Data -------------------- # +train_y_onehot = list() +for y in range(len(train_y)): + temp = [0] * config.classes + temp[train_y[y]] = 1 + train_y_onehot.append(temp) + +test_y_onehot = list() +for y in range(len(test_y)): + temp = [0] * config.classes + temp[test_y[y]] = 1 + test_y_onehot.append(temp) + +train_y_onehot = np.array(train_y_onehot) +test_y_onehot = np.array(test_y_onehot) + + +# -------------------- Model -------------------- # +model = Sequential() + +# Input layer +model.add(Dense(config.features, input_shape = (train_x.shape[1],), activation='softmax')) + + +model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy']) +model.fit(train_x, train_y_onehot, epochs = 500, batch_size = 500, verbose=0) + +scores = model.evaluate(train_x, train_y) +print("Train =", model.metrics_names[1], scores[1] * 100) + +scores = model.evaluate(test_x, test_y) +print("Test =", model.metrics_names[1], scores[1] * 100) \ No newline at end of file diff --git a/Poker Hand Prediction/models/dnn.py b/Poker Hand Prediction/models/dnn.py new file mode 100644 index 000000000..b3cdaa21a --- /dev/null +++ b/Poker Hand Prediction/models/dnn.py @@ -0,0 +1,51 @@ +''' +Deep Neural Net. +''' + +# -------------------- Imports -------------------- # +from keras.models import Sequential +from keras.layers import Dense + +from poker_hand_prediction import * + + +# -------------------- Preparign the Data -------------------- # +train_y_onehot = list() +for y in range(len(train_y)): + temp = [0] * config.classes + temp[train_y[y]] = 1 + train_y_onehot.append(temp) + +test_y_onehot = list() +for y in range(len(test_y)): + temp = [0] * config.classes + temp[test_y[y]] = 1 + test_y_onehot.append(temp) + +train_y_onehot = np.array(train_y_onehot) +test_y_onehot = np.array(test_y_onehot) + + +# -------------------- Model -------------------- # +model = Sequential() + +# Input layer +model.add(Dense(config.features, input_shape = (train_x.shape[1],), activation='tanh')) + +# Hidden Layers +model.add(Dense(64, activation='tanh')) +model.add(Dense(64, activation='tanh')) +model.add(Dense(64, activation='tanh')) +model.add(Dense(64, activation='tanh')) +model.add(Dense(64, activation='tanh')) + +# Output layer +model.add(Dense(10, activation='softmax')) +model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy']) +model.fit(train_x, train_y_onehot, epochs = 500, batch_size = 500, verbose=0) + +scores = model.evaluate(train_x, train_y) +print("Train =", model.metrics_names[1], scores[1] * 100) + +scores = model.evaluate(test_x, test_y) +print("Test =", model.metrics_names[1], scores[1] * 100) \ No newline at end of file diff --git a/Poker Hand Prediction/models/occ.py b/Poker Hand Prediction/models/occ.py new file mode 100644 index 000000000..607d55975 --- /dev/null +++ b/Poker Hand Prediction/models/occ.py @@ -0,0 +1,46 @@ +''' +Output Code Classifer +''' + +# -------------------- Imports -------------------- # + +from sklearn.multiclass import OutputCodeClassifier +from sklearn.ensemble import BaggingClassifier +from sklearn.preprocessing import StandardScaler +from sklearn.metrics import accuracy_score + +from poker_hand_prediction import * + +# -------------------- Model -------------------- # + +print("\nOutput Code Classifer") + +# Initialise scaler to scale the data +scaler = StandardScaler() + +train_set = np.empty(train_x.shape, dtype = float) +test_set = np.empty(test_x.shape, dtype = float) + +# Make training data suitable for scaling +for index in range(len(train_x)): + train_set[index] = train_x[index].astype(float) + +# Make testing data suitable for scaling +for index in range(len(test_x)): + test_set[index] = test_x[index].astype(float) + +# Fit the training data +scaler.fit(train_set) + +# Scale the training and testing data w.r.t scaler +data_train = scaler.transform(train_set) +data_test = scaler.transform(test_set) + +occ = OutputCodeClassifier(BaggingClassifier()) +occ.fit(data_train, train_y) +prediction = occ.predict(data_test) +accuracy = accuracy_score(test_y, prediction) + +# -------------------- Print the final result -------------------- # + +print("\nAccuracy using Output Code Classifier :", round(accuracy * 100, 3),"%\n") diff --git a/Poker Hand Prediction/models/perceptron.py b/Poker Hand Prediction/models/perceptron.py new file mode 100644 index 000000000..5f6b86c86 --- /dev/null +++ b/Poker Hand Prediction/models/perceptron.py @@ -0,0 +1,55 @@ +''' +Multi-layer perceptron with 2 hidden layers and stochastic gradient descent +''' + +# -------------------- Imports -------------------- # +from sklearn.metrics import accuracy_score +from sklearn.neural_network import MLPClassifier +from sklearn.preprocessing import StandardScaler + +from poker_hand_prediction import * + +# -------------------- Model -------------------- # + +print("\nMulti Layer Perceptron with 2 hidden layers and stochastic gradient descent") + +# Initialise scaler to scale the data +scaler = StandardScaler() + +train_set = np.empty(train_x.shape, dtype = float) +test_set = np.empty(test_x.shape, dtype = float) + +# Make training data suitable for scaling +for index in range(len(train_x)): + train_set[index] = train_x[index].astype(float) + +# Make testing data suitable for scaling +for index in range(len(test_x)): + test_set[index] = test_x[index].astype(float) + +# Fit the training data +scaler.fit(train_set) + +# Scale the training and testing data w.r.t scaler +data_train = scaler.transform(train_set) +data_test = scaler.transform(test_set) + +# Store the accuracies +accuracy = list() + +for trial in range(5): + classifier = MLPClassifier(solver = 'adam', alpha = 1e-5, hidden_layer_sizes = (64, 64), activation = 'tanh', learning_rate_init = 0.02, max_iter = 2000, random_state = trial) + result = classifier.fit(data_train, train_y) + prediction = classifier.predict(data_test) + curr_accuracy = accuracy_score(test_y, prediction) + accuracy.append(curr_accuracy) + +# -------------------- Print the final result -------------------- # + +tab = PrettyTable(['Trial Number', 'Accuracy']) + +for trial in range(len(accuracy)): + tab.add_row([trial + 1, round(accuracy[trial] * 100, 3)]) + +print("\n", tab) +print("\nMean Accuracy using Multi Layer Perceptron Classifier: ", round(np.array(accuracy).mean() * 100, 3), "%") diff --git a/Poker Hand Prediction/models/poker_hand_prediction.py b/Poker Hand Prediction/models/poker_hand_prediction.py new file mode 100644 index 000000000..8b19f1ec4 --- /dev/null +++ b/Poker Hand Prediction/models/poker_hand_prediction.py @@ -0,0 +1,57 @@ +''' +Contains things common to all models implemented here; such as imports, configs, etc. +''' + +# -------------------- Imports -------------------- # +import numpy as np, pandas as pd, os +from prettytable import PrettyTable + + +# -------------------- Globals and Configs -------------------- # +feature_names = list() +for index in range(1, 6): + feature_names.extend(["Suit"+str(index), "Rank"+str(index)]) + +feature_names.append('class') + + +training_input_file = os.path.abspath('../datasets/csv/train.csv') +testing_input_file = os.path.abspath('../datasets/csv/test.csv') + +np.random.seed(666) # seed for reproducible results + +# To store configs +class myConfigs: + features = 0 + classes = 0 + + +config = myConfigs() + + +# -------------------- Data -------------------- # + +train_data = pd.read_csv(training_input_file, names=feature_names) +test_data = pd.read_csv(testing_input_file, names=feature_names) + +# Get features of data +config.features = len(train_data.columns) - 1 +config.classes = len(set(train_data['class'])) + +# Shuffle training data +train_data = train_data.sample(frac=1).reset_index(drop=True) + +# Seperate data and classes +train_y = np.array(train_data['class']) +train_x = np.array(train_data.drop('class', 1)) + +test_y = np.array(test_data['class']) +test_x = np.array(test_data.drop('class', 1)) + +if __name__ == '__main__': + tab = PrettyTable(['Config', 'Value']) + configs = vars(config) + + for key in configs: + tab.add_row([key, configs[key]]) + print(tab) diff --git a/Poker Hand Prediction/models/random_forest.py b/Poker Hand Prediction/models/random_forest.py new file mode 100644 index 000000000..b7de6b441 --- /dev/null +++ b/Poker Hand Prediction/models/random_forest.py @@ -0,0 +1,45 @@ +''' +Random Forest Classifer +''' + +# -------------------- Imports -------------------- # + +from sklearn.ensemble import RandomForestClassifier +from sklearn.preprocessing import StandardScaler +from sklearn.metrics import accuracy_score + +from poker_hand_prediction import * + +# -------------------- Model -------------------- # + +print("\nRandom Forest Classifier") + +# Initialise scaler to scale the data +scaler = StandardScaler() + +train_set = np.empty(train_x.shape, dtype = float) +test_set = np.empty(test_x.shape, dtype = float) + +# Make training data suitable for scaling +for index in range(len(train_x)): + train_set[index] = train_x[index].astype(float) + +# Make testing data suitable for scaling +for index in range(len(test_x)): + test_set[index] = test_x[index].astype(float) + +# Fit the training data +scaler.fit(train_set) + +# Scale the training and testing data w.r.t scaler +data_train = scaler.transform(train_set) +data_test = scaler.transform(test_set) + +rforest = RandomForestClassifier() +rforest.fit(data_train, train_y) +prediction = rforest.predict(data_test) +accuracy = accuracy_score(test_y, prediction) + +# -------------------- Print the final result -------------------- # + +print("\nAccuracy using Random Forest Classifier :", round(accuracy * 100, 3),"%\n") diff --git a/Poker Hand Prediction/models/regression.py b/Poker Hand Prediction/models/regression.py new file mode 100644 index 000000000..855141a7a --- /dev/null +++ b/Poker Hand Prediction/models/regression.py @@ -0,0 +1,28 @@ +''' +Multi-linear regression. +''' + +# -------------------- Imports -------------------- # +from sklearn import linear_model as lm +import math +from poker_hand_prediction import * + + +# -------------------- Model -------------------- # +model = lm.LinearRegression() + +# FIT (i.e train) +trainedModel = model.fit(train_x, train_y) + +# TEST (i.e predict) +predictions = model.predict(test_x) + +# Errors +avgError = sum([math.fabs(x-y) for x, y in zip(predictions, test_y)]) / len(predictions) +print("Average error :", avgError) + +rmsError = sum([(x-y)**2 for x, y in zip(predictions, test_y)]) / len(predictions) +rmsError = math.sqrt(rmsError) +print("RMSE :", rmsError) + +print("Model R2 score :", model.score(train_x, train_y)) \ No newline at end of file diff --git a/Poker Hand Prediction/models/svm.py b/Poker Hand Prediction/models/svm.py new file mode 100644 index 000000000..153628219 --- /dev/null +++ b/Poker Hand Prediction/models/svm.py @@ -0,0 +1,25 @@ +''' +SVMs for multi-class classification. +''' + +# -------------------- Imports -------------------- # +import sys +from sklearn import svm +from poker_hand_prediction import * + + +# -------------------- Model -------------------- # +model = svm.SVC() +print("Generated model! Going to train...") + +# Training +model.fit(train_x, train_y) +print("Done training! Going to test...") + +# Testing + +predictions = list() +for l in range(len(test_x)): + predictions.append(model.predict([test_x[i]])[0]) + +print("Generated predictions!") \ No newline at end of file diff --git a/Poker Hand Prediction/requirements.txt b/Poker Hand Prediction/requirements.txt new file mode 100644 index 000000000..758eef7d6 --- /dev/null +++ b/Poker Hand Prediction/requirements.txt @@ -0,0 +1,7 @@ +tensorflow +keras +pandas +numpy +prettytable +sklearn +dill From fe79d587f1f4ea6781acbc953349d04c4abdc051 Mon Sep 17 00:00:00 2001 From: Aviral Garg <105068445+aviralgarg05@users.noreply.github.com> Date: Tue, 16 Jul 2024 16:35:35 +0000 Subject: [PATCH 2/4] changes have been made --- Poker Hand Prediction/datasets/{csv => }/test.csv | 0 Poker Hand Prediction/datasets/{csv => }/train.csv | 0 2 files changed, 0 insertions(+), 0 deletions(-) rename Poker Hand Prediction/datasets/{csv => }/test.csv (100%) rename Poker Hand Prediction/datasets/{csv => }/train.csv (100%) diff --git a/Poker Hand Prediction/datasets/csv/test.csv b/Poker Hand Prediction/datasets/test.csv similarity index 100% rename from Poker Hand Prediction/datasets/csv/test.csv rename to Poker Hand Prediction/datasets/test.csv diff --git a/Poker Hand Prediction/datasets/csv/train.csv b/Poker Hand Prediction/datasets/train.csv similarity index 100% rename from Poker Hand Prediction/datasets/csv/train.csv rename to Poker Hand Prediction/datasets/train.csv From bcc45d876ecc912379bf0e454325b381c047bbd7 Mon Sep 17 00:00:00 2001 From: Aviral Garg <105068445+aviralgarg05@users.noreply.github.com> Date: Fri, 19 Jul 2024 15:29:03 +0530 Subject: [PATCH 3/4] Update README.md --- Poker Hand Prediction/README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/Poker Hand Prediction/README.md b/Poker Hand Prediction/README.md index d54c339fd..f2283c273 100644 --- a/Poker Hand Prediction/README.md +++ b/Poker Hand Prediction/README.md @@ -1,4 +1,4 @@ -# Pokerman +# Poker Hand Prediction TL;DR, predicting poker hands. From a336f4abfe071e73ae836a11487af49189701be9 Mon Sep 17 00:00:00 2001 From: Aviral Garg <105068445+aviralgarg05@users.noreply.github.com> Date: Sun, 21 Jul 2024 08:27:26 +0000 Subject: [PATCH 4/4] Changes Updated --- .../AdaBoost.ipynb | 0 .../Artificial Neural Network.ipynb | 0 .../Deep Neural Network.ipynb | 0 .../Multi Layer Perceptron.ipynb | 0 .../Output Code Classifier.ipynb | 0 .../Random Forest.ipynb | 0 .../Regression.ipynb | 0 .../SVM.ipynb | 0 Poker Hand Prediction/README.md | 74 +++++++++++-------- 9 files changed, 42 insertions(+), 32 deletions(-) rename Poker Hand Prediction/{Poker Hand Prediction => Notebooks}/AdaBoost.ipynb (100%) rename Poker Hand Prediction/{Poker Hand Prediction => Notebooks}/Artificial Neural Network.ipynb (100%) rename Poker Hand Prediction/{Poker Hand Prediction => Notebooks}/Deep Neural Network.ipynb (100%) rename Poker Hand Prediction/{Poker Hand Prediction => Notebooks}/Multi Layer Perceptron.ipynb (100%) rename Poker Hand Prediction/{Poker Hand Prediction => Notebooks}/Output Code Classifier.ipynb (100%) rename Poker Hand Prediction/{Poker Hand Prediction => Notebooks}/Random Forest.ipynb (100%) rename Poker Hand Prediction/{Poker Hand Prediction => Notebooks}/Regression.ipynb (100%) rename Poker Hand Prediction/{Poker Hand Prediction => Notebooks}/SVM.ipynb (100%) diff --git a/Poker Hand Prediction/Poker Hand Prediction/AdaBoost.ipynb b/Poker Hand Prediction/Notebooks/AdaBoost.ipynb similarity index 100% rename from Poker Hand Prediction/Poker Hand Prediction/AdaBoost.ipynb rename to Poker Hand Prediction/Notebooks/AdaBoost.ipynb diff --git a/Poker Hand Prediction/Poker Hand Prediction/Artificial Neural Network.ipynb b/Poker Hand Prediction/Notebooks/Artificial Neural Network.ipynb similarity index 100% rename from Poker Hand Prediction/Poker Hand Prediction/Artificial Neural Network.ipynb rename to Poker Hand Prediction/Notebooks/Artificial Neural Network.ipynb diff --git a/Poker Hand Prediction/Poker Hand Prediction/Deep Neural Network.ipynb b/Poker Hand Prediction/Notebooks/Deep Neural Network.ipynb similarity index 100% rename from Poker Hand Prediction/Poker Hand Prediction/Deep Neural Network.ipynb rename to Poker Hand Prediction/Notebooks/Deep Neural Network.ipynb diff --git a/Poker Hand Prediction/Poker Hand Prediction/Multi Layer Perceptron.ipynb b/Poker Hand Prediction/Notebooks/Multi Layer Perceptron.ipynb similarity index 100% rename from Poker Hand Prediction/Poker Hand Prediction/Multi Layer Perceptron.ipynb rename to Poker Hand Prediction/Notebooks/Multi Layer Perceptron.ipynb diff --git a/Poker Hand Prediction/Poker Hand Prediction/Output Code Classifier.ipynb b/Poker Hand Prediction/Notebooks/Output Code Classifier.ipynb similarity index 100% rename from Poker Hand Prediction/Poker Hand Prediction/Output Code Classifier.ipynb rename to Poker Hand Prediction/Notebooks/Output Code Classifier.ipynb diff --git a/Poker Hand Prediction/Poker Hand Prediction/Random Forest.ipynb b/Poker Hand Prediction/Notebooks/Random Forest.ipynb similarity index 100% rename from Poker Hand Prediction/Poker Hand Prediction/Random Forest.ipynb rename to Poker Hand Prediction/Notebooks/Random Forest.ipynb diff --git a/Poker Hand Prediction/Poker Hand Prediction/Regression.ipynb b/Poker Hand Prediction/Notebooks/Regression.ipynb similarity index 100% rename from Poker Hand Prediction/Poker Hand Prediction/Regression.ipynb rename to Poker Hand Prediction/Notebooks/Regression.ipynb diff --git a/Poker Hand Prediction/Poker Hand Prediction/SVM.ipynb b/Poker Hand Prediction/Notebooks/SVM.ipynb similarity index 100% rename from Poker Hand Prediction/Poker Hand Prediction/SVM.ipynb rename to Poker Hand Prediction/Notebooks/SVM.ipynb diff --git a/Poker Hand Prediction/README.md b/Poker Hand Prediction/README.md index f2283c273..0ffd707bd 100644 --- a/Poker Hand Prediction/README.md +++ b/Poker Hand Prediction/README.md @@ -1,47 +1,52 @@ -# Poker Hand Prediction +## **Poker Hand Prediction** -TL;DR, predicting poker hands. +### ๐ŸŽฏ **Goal** -## The Problem +To predict the most likely poker hand present with at least one of the players in the game, given the sequence of 5 'community' cards drawn from a standard deck. -Given the sequence of 5 'community' cards, drawn from a standard deck of cards, what hand is most likely present with at least one of the players in the game. +### ๐Ÿงต **Dataset** -## Poker Hands +The dataset is acquired from UCI's Machine Learning repository. Find it [here](https://archive.ics.uci.edu/ml/datasets/Poker+Hand). -Texas Hold Em is played by dealing each player 2 cards (face down), called the `hole` cards, and dealing 5 `community` cards (face up), on the table. +### ๐Ÿงพ **Description** -The player makes a poker `hand` using any combination of the 3 cards dealt to them, and the 5 cards on the table. +Texas Hold 'Em is played by dealing each player 2 'hole' cards (face down) and 5 'community' cards (face up) on the table. The player makes a poker hand using any combination of the 2 cards dealt to them and the 5 cards on the table. The objective is to predict the rank of the poker hand that is most likely to be present among the players, given the 5 community cards. -The player with the strongest hands wins. Most commonly accepted ranking of hands, strongest to weekest : +### ๐Ÿงฎ **What I had done!** -| Rank | Hand | Description | -| :--: | :--: | :---------: | -| 0 | Royal Flush | `A K Q J 10` all of the same suit | -| 1 | Straight Flush | Any 5 cards of the same suit, in sequence | -| 2 | Four of a Kind | 4 cards of the same rank, like, `4 4 4 4` | -| 3 | Full House | A 3 of a kind, and a pair, of different ranks | -| 4 | Flush | Any 5 cards of the same suit | -| 5 | Straight | Any 5 cards in sequence | -| 6 | Three of a Kind | Any 3 cards of the same rank | -| 7 | Two Pair | Any 2 pairs of cards | -| 8 | One Pair | Any 2 cards of the same rank | -| 9 | High Card | Highest Ranked card in hand | +1. **Data Acquisition**: Downloaded the dataset from UCI's Machine Learning repository. +2. **Data Preprocessing**: Cleaned and prepared the data for analysis. +3. **Model Selection**: Implemented various machine learning models to predict poker hands. +4. **Model Training**: Trained the models on the training dataset. +5. **Model Evaluation**: Evaluated the models on the testing dataset. +6. **Performance Comparison**: Compared the accuracy of different models to determine the best one. +### ๐Ÿš€ **Models Implemented** -#### Ranking of Cards +1. **Linear Regression**: Basic model to establish a baseline. +2. **Support Vector Machine (SVM)**: Chosen for its effectiveness in classification tasks. +3. **Adaboost**: Implemented to improve model performance through boosting. +4. **Output Code Classifier**: Used for multiclass classification. +5. **Random Forest**: Chosen for its robustness and ensemble learning capabilities. +6. **Artificial Neural Network (ANN)**: Implemented for its potential in capturing complex patterns. +7. **Deep Neural Network (DNN)**: Used for its ability to learn from large datasets. +8. **Multi-Layer Perceptron (MLP)**: Chosen for its superior performance in the dataset. -A K Q J 10 9 8 7 6 5 4 3 2 1 +### ๐Ÿ“š **Libraries Needed** -## Data +- numpy +- pandas +- scikit-learn +- tensorflow +- matplotlib +- seaborn -This data was acquired from UCI's Machine Learning repository. The data comes already split into training and testing data. +### ๐Ÿ“Š **Exploratory Data Analysis Results** -Find it [here](https://archive.ics.uci.edu/ml/datasets/Poker+Hand). +![EDA Result 1](https://github.com/aviralgarg05/ML-Crate/blob/main/Poker%20Hand%20Prediction/Images/ANN.png) +![EDA Result 2](https://github.com/aviralgarg05/ML-Crate/blob/main/Poker%20Hand%20Prediction/Images/DNN.png) - -##### NOTE: The hands' class labels are in the reverse order of their strength, i.e, 0 is the weakest hand. - -## Machine Learning ๐Ÿ–ฅ +### ๐Ÿ“ˆ **Performance of the Models based on the Accuracy Scores** | Model | Accuracy | | :---: | :------: | @@ -52,9 +57,14 @@ Find it [here](https://archive.ics.uci.edu/ml/datasets/Poker+Hand). | Random Forest | 56% | | Artificial Neural Network | 45% | | Deep Neural Network | 87% | -| Multi Layer Perceptron | 97% | +| Multi-Layer Perceptron | 97% | + +### ๐Ÿ“ข **Conclusion** + +The Multi-layer Perceptron (MLP) is clearly the best model for the dataset in hand, achieving an accuracy of 97%. This indicates that MLP is highly effective in predicting the poker hands given the community cards. -## Conclusion +### โœ’๏ธ **Your Signature** -The Multi-layer Perceptron is clearly the best model for the dataset in hand. +**Aviral Garg** +[LinkedIn](https://www.linkedin.com/in/aviral-garg-b7b053280/) \ No newline at end of file

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