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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"objc[49355]: Class CaptureDelegate is implemented in both /Users/fuixlabsdev1/Programming/PP/graduation-thesis/env/lib/python3.8/site-packages/mediapipe/.dylibs/libopencv_videoio.3.4.16.dylib (0x108688860) and /Users/fuixlabsdev1/Programming/PP/graduation-thesis/env/lib/python3.8/site-packages/cv2/cv2.abi3.so (0x160ece480). One of the two will be used. Which one is undefined.\n",
"objc[49355]: Class CVWindow is implemented in both /Users/fuixlabsdev1/Programming/PP/graduation-thesis/env/lib/python3.8/site-packages/mediapipe/.dylibs/libopencv_highgui.3.4.16.dylib (0x103440a68) and /Users/fuixlabsdev1/Programming/PP/graduation-thesis/env/lib/python3.8/site-packages/cv2/cv2.abi3.so (0x160ece4d0). One of the two will be used. Which one is undefined.\n",
"objc[49355]: Class CVView is implemented in both /Users/fuixlabsdev1/Programming/PP/graduation-thesis/env/lib/python3.8/site-packages/mediapipe/.dylibs/libopencv_highgui.3.4.16.dylib (0x103440a90) and /Users/fuixlabsdev1/Programming/PP/graduation-thesis/env/lib/python3.8/site-packages/cv2/cv2.abi3.so (0x160ece4f8). One of the two will be used. Which one is undefined.\n",
"objc[49355]: Class CVSlider is implemented in both /Users/fuixlabsdev1/Programming/PP/graduation-thesis/env/lib/python3.8/site-packages/mediapipe/.dylibs/libopencv_highgui.3.4.16.dylib (0x103440ab8) and /Users/fuixlabsdev1/Programming/PP/graduation-thesis/env/lib/python3.8/site-packages/cv2/cv2.abi3.so (0x160ece520). One of the two will be used. Which one is undefined.\n"
]
}
],
"source": [
"import mediapipe as mp\n",
"import cv2\n",
"import pandas as pd\n",
"import pickle\n",
"\n",
"from sklearn.model_selection import train_test_split\n",
"from sklearn.linear_model import LogisticRegression, SGDClassifier\n",
"from sklearn.svm import SVC\n",
"from sklearn.neighbors import KNeighborsClassifier\n",
"from sklearn.tree import DecisionTreeClassifier\n",
"from sklearn.ensemble import RandomForestClassifier\n",
"from sklearn.naive_bayes import GaussianNB\n",
"from sklearn.metrics import precision_score, accuracy_score, f1_score, recall_score, confusion_matrix\n",
"from sklearn.preprocessing import StandardScaler\n",
"from sklearn.calibration import CalibratedClassifierCV\n",
"\n",
"import warnings\n",
"warnings.filterwarnings('ignore')\n",
"\n",
"# Drawing helpers\n",
"mp_drawing = mp.solutions.drawing_utils\n",
"mp_pose = mp.solutions.pose"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 1. Train model"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### 1.1. Describe data and split dataset"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"def rescale_frame(frame, percent=50):\n",
" '''\n",
" Rescale a frame to a certain percentage compare to its original frame\n",
" '''\n",
" width = int(frame.shape[1] * percent/ 100)\n",
" height = int(frame.shape[0] * percent/ 100)\n",
" dim = (width, height)\n",
" return cv2.resize(frame, dim, interpolation = cv2.INTER_AREA)\n",
"\n",
"\n",
"def describe_dataset(dataset_path: str):\n",
" '''\n",
" Describe dataset\n",
" '''\n",
"\n",
" data = pd.read_csv(dataset_path)\n",
" print(f\"Headers: {list(data.columns.values)}\")\n",
" print(f'Number of rows: {data.shape[0]} \\nNumber of columns: {data.shape[1]}\\n')\n",
" print(f\"Labels: \\n{data['label'].value_counts()}\\n\")\n",
" print(f\"Missing values: {data.isnull().values.any()}\\n\")\n",
" \n",
" duplicate = data[data.duplicated()]\n",
" print(f\"Duplicate Rows : {len(duplicate.sum(axis=1))}\")\n",
"\n",
" return data\n",
"\n",
"\n",
"def round_up_metric_results(results) -> list:\n",
" '''Round up metrics results such as precision score, recall score, ...'''\n",
" return list(map(lambda el: round(el, 3), results))"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Headers: ['label', 'nose_x', 'nose_y', 'nose_z', 'nose_v', 'left_shoulder_x', 'left_shoulder_y', 'left_shoulder_z', 'left_shoulder_v', 'right_shoulder_x', 'right_shoulder_y', 'right_shoulder_z', 'right_shoulder_v', 'right_elbow_x', 'right_elbow_y', 'right_elbow_z', 'right_elbow_v', 'left_elbow_x', 'left_elbow_y', 'left_elbow_z', 'left_elbow_v', 'right_wrist_x', 'right_wrist_y', 'right_wrist_z', 'right_wrist_v', 'left_wrist_x', 'left_wrist_y', 'left_wrist_z', 'left_wrist_v', 'left_hip_x', 'left_hip_y', 'left_hip_z', 'left_hip_v', 'right_hip_x', 'right_hip_y', 'right_hip_z', 'right_hip_v']\n",
"Number of rows: 15372 \n",
"Number of columns: 37\n",
"\n",
"Labels: \n",
"C 8238\n",
"L 7134\n",
"Name: label, dtype: int64\n",
"\n",
"Missing values: False\n",
"\n",
"Duplicate Rows : 0\n"
]
}
],
"source": [
"# load dataset\n",
"df = describe_dataset(\"./train.csv\")\n",
"\n",
"# Categorizing label\n",
"df.loc[df[\"label\"] == \"C\", \"label\"] = 0\n",
"df.loc[df[\"label\"] == \"L\", \"label\"] = 1"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"sc = StandardScaler()"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"with open(\"./model/input_scaler.pkl\", \"rb\") as f:\n",
" sc = pickle.load(f)"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [],
"source": [
"# Standard Scaling of features\n",
"x = df.drop(\"label\", axis = 1)\n",
"x = pd.DataFrame(sc.transform(x))\n",
"\n",
"y = df[\"label\"].astype('int')"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"9465 1\n",
"8833 0\n",
"6190 0\n",
"7645 0\n",
"13890 1\n",
" ..\n",
"11468 1\n",
"7221 1\n",
"1318 1\n",
"8915 1\n",
"11055 1\n",
"Name: label, Length: 12297, dtype: int64"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"X_train, X_test, y_train, y_test = train_test_split(x, y, test_size=0.2, random_state=1234)\n",
"y_train"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### 1.2. Train model using Scikit-learn"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Model</th>\n",
" <th>Precision Score</th>\n",
" <th>Accuracy score</th>\n",
" <th>Recall Score</th>\n",
" <th>F1 score</th>\n",
" <th>Confusion Matrix</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>RF</td>\n",
" <td>[0.999, 0.999]</td>\n",
" <td>0.999024</td>\n",
" <td>[0.999, 0.999]</td>\n",
" <td>[0.999, 0.999]</td>\n",
" <td>[[1677, 2], [1, 1395]]</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>KNN</td>\n",
" <td>[0.997, 0.999]</td>\n",
" <td>0.998049</td>\n",
" <td>[0.999, 0.996]</td>\n",
" <td>[0.998, 0.998]</td>\n",
" <td>[[1678, 1], [5, 1391]]</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>SVC</td>\n",
" <td>[0.997, 0.995]</td>\n",
" <td>0.996098</td>\n",
" <td>[0.996, 0.996]</td>\n",
" <td>[0.996, 0.996]</td>\n",
" <td>[[1672, 7], [5, 1391]]</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>DTC</td>\n",
" <td>[0.997, 0.991]</td>\n",
" <td>0.994146</td>\n",
" <td>[0.992, 0.996]</td>\n",
" <td>[0.995, 0.994]</td>\n",
" <td>[[1666, 13], [5, 1391]]</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>SGDC</td>\n",
" <td>[0.987, 0.974]</td>\n",
" <td>0.981463</td>\n",
" <td>[0.979, 0.985]</td>\n",
" <td>[0.983, 0.98]</td>\n",
" <td>[[1643, 36], [21, 1375]]</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>LR</td>\n",
" <td>[0.986, 0.975]</td>\n",
" <td>0.980813</td>\n",
" <td>[0.979, 0.983]</td>\n",
" <td>[0.982, 0.979]</td>\n",
" <td>[[1644, 35], [24, 1372]]</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>NB</td>\n",
" <td>[0.927, 0.842]</td>\n",
" <td>0.884878</td>\n",
" <td>[0.857, 0.918]</td>\n",
" <td>[0.89, 0.879]</td>\n",
" <td>[[1439, 240], [114, 1282]]</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Model Precision Score Accuracy score Recall Score F1 score \\\n",
"0 RF [0.999, 0.999] 0.999024 [0.999, 0.999] [0.999, 0.999] \n",
"1 KNN [0.997, 0.999] 0.998049 [0.999, 0.996] [0.998, 0.998] \n",
"2 SVC [0.997, 0.995] 0.996098 [0.996, 0.996] [0.996, 0.996] \n",
"3 DTC [0.997, 0.991] 0.994146 [0.992, 0.996] [0.995, 0.994] \n",
"4 SGDC [0.987, 0.974] 0.981463 [0.979, 0.985] [0.983, 0.98] \n",
"5 LR [0.986, 0.975] 0.980813 [0.979, 0.983] [0.982, 0.979] \n",
"6 NB [0.927, 0.842] 0.884878 [0.857, 0.918] [0.89, 0.879] \n",
"\n",
" Confusion Matrix \n",
"0 [[1677, 2], [1, 1395]] \n",
"1 [[1678, 1], [5, 1391]] \n",
"2 [[1672, 7], [5, 1391]] \n",
"3 [[1666, 13], [5, 1391]] \n",
"4 [[1643, 36], [21, 1375]] \n",
"5 [[1644, 35], [24, 1372]] \n",
"6 [[1439, 240], [114, 1282]] "
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"algorithms =[(\"LR\", LogisticRegression()),\n",
" (\"SVC\", SVC(probability=True)),\n",
" ('KNN',KNeighborsClassifier()),\n",
" (\"DTC\", DecisionTreeClassifier()),\n",
" (\"SGDC\", CalibratedClassifierCV(SGDClassifier())),\n",
" (\"NB\", GaussianNB()),\n",
" ('RF', RandomForestClassifier()),]\n",
"\n",
"models = {}\n",
"final_results = []\n",
"\n",
"for name, model in algorithms:\n",
" trained_model = model.fit(X_train, y_train)\n",
" models[name] = trained_model\n",
"\n",
" # Evaluate model\n",
" model_results = model.predict(X_test)\n",
"\n",
" p_score = precision_score(y_test, model_results, average=None, labels=[0, 1])\n",
" a_score = accuracy_score(y_test, model_results)\n",
" r_score = recall_score(y_test, model_results, average=None, labels=[0, 1])\n",
" f1_score_result = f1_score(y_test, model_results, average=None, labels=[0, 1])\n",
" cm = confusion_matrix(y_test, model_results, labels=[0, 1])\n",
" final_results.append(( name, round_up_metric_results(p_score), a_score, round_up_metric_results(r_score), round_up_metric_results(f1_score_result), cm))\n",
"\n",
"# Sort results by F1 score\n",
"final_results.sort(key=lambda k: sum(k[4]), reverse=True)\n",
"pd.DataFrame(final_results, columns=[\"Model\", \"Precision Score\", \"Accuracy score\", \"Recall Score\", \"F1 score\", \"Confusion Matrix\"])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### 1.3. Dump models pickle"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [],
"source": [
"with open(\"./model/all_sklearn.pkl\", \"wb\") as f:\n",
" pickle.dump(models, f)"
]
},
{
"cell_type": "code",
"execution_count": 43,
"metadata": {},
"outputs": [],
"source": [
"with open(\"./model/input_scaler.pkl\", \"wb\") as f:\n",
" pickle.dump(sc, f)"
]
}
],
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