{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"name":"python","version":"3.10.12","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"},"kaggle":{"accelerator":"gpu","dataSources":[{"sourceId":4829,"databundleVersionId":44847,"sourceType":"competition"}],"dockerImageVersionId":30558,"isInternetEnabled":true,"language":"python","sourceType":"notebook","isGpuEnabled":true}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"markdown","source":"# **DSI206 Project: Kaggle**","metadata":{}},{"cell_type":"markdown","source":"Project Context\n-\nCompete in a Kaggle competition **[Yelp Restaurant Photo Classification](https://www.kaggle.com/competitions/yelp-restaurant-photo-classification)**, predict attribute labels for restaurants using user-submitted photos and submit our solution to the system.\n\nLabels\n-\n* 0: good_for_lunch \n* 1: good_for_dinner \n* 2: takes_reservations \n* 3: outdoor_seating \n* 4: restaurant_is_expensive \n* 5: has_alcohol \n* 6: has_table_service \n* 7: ambience_is_classy \n* 8: good_for_kids\n\nBrief Summary\n-\nPrepared our data in the Data Preparation part and train it on a VGGNet model to extract the image's feature vector in the Feature Extraction part and top it off by using a Random Forest and KNN model as our classification model to predict the labels and submit both models to see which performed better with the unseen data - That will be our final result.\n\nProject path way\n-\n* Data Preparation\n* Feature Extraction\n* Classification\n\nReferences\n-\n1. [VGG Very Deep Convolutional Networks (VGGNet) – What you need to know](https://viso.ai/deep-learning/vgg-very-deep-convolutional-networks/)\n2. [Transfer learning using VGG-16 with Deep Convolutional Neural Network for Classifying Images](https://www.ijsrp.org/research-paper-1019/ijsrp-p9420.pdf)\n3. [Encoding Categorical Features with MultiLabelBinarizer](https://www.kdnuggets.com/2023/01/encoding-categorical-features-multilabelbinarizer.html)\n4. [Random Forest Classifier Tutorial: How to Use Tree-Based Algorithms for Machine Learning](https://www.freecodecamp.org/news/how-to-use-the-tree-based-algorithm-for-machine-learning/)\n5. [K-Nearest Neighbor(KNN) Algorithm for Machine Learning](https://www.javatpoint.com/k-nearest-neighbor-algorithm-for-machine-learning)\n\nMembers\n-\n* 6524651038 ปุณณวิช ศิลปเสริฐ\n* 6524651244 ณิชพน รัถยาบัณฑิต\n* 6524651376 นางสาวลภัสรดา ตรึกตรองกิจ\n* 6524651400 นายวสันต์ อารัมภ์สกุล\n* 6524651418 นางสาวศรินภัสร์ ศุภลักษณ์เมธา","metadata":{}},{"cell_type":"code","source":"# Required Libraries\n\nimport time\nfrom tqdm import tqdm\nimport pandas as pd\nimport numpy as np\nimport os \n\n# Feature Extraction\nfrom tensorflow.keras.models import Model\nfrom tensorflow.keras.preprocessing import image\nfrom tensorflow.keras.applications.vgg16 import preprocess_input\nfrom tensorflow.keras.applications import VGG16\n\n# Data Preparation & Evalutaion\nfrom sklearn.model_selection import train_test_split, GridSearchCV\nfrom sklearn.preprocessing import MultiLabelBinarizer\nfrom sklearn.metrics import accuracy_score, classification_report, f1_score, make_scorer\nfrom sklearn.multioutput import MultiOutputClassifier\n\n# Classification Model\nfrom sklearn.ensemble import RandomForestClassifier\nfrom sklearn.neighbors import KNeighborsClassifier\n\n# Visualization\nimport matplotlib.pyplot as plt","metadata":{"execution":{"iopub.status.busy":"2023-11-30T15:58:53.139861Z","iopub.execute_input":"2023-11-30T15:58:53.140122Z","iopub.status.idle":"2023-11-30T15:59:01.960071Z","shell.execute_reply.started":"2023-11-30T15:58:53.140097Z","shell.execute_reply":"2023-11-30T15:59:01.959242Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# Extract files\nstart_time = time.time()\n\n!tar -xzf /kaggle/input/yelp-restaurant-photo-classification/sample_submission.csv.tgz\n!tar -xzf /kaggle/input/yelp-restaurant-photo-classification/test_photo_to_biz.csv.tgz\n!tar -xzf /kaggle/input/yelp-restaurant-photo-classification/test_photos.tgz\n!tar -xzf /kaggle/input/yelp-restaurant-photo-classification/train.csv.tgz\n!tar -xzf /kaggle/input/yelp-restaurant-photo-classification/train_photo_to_biz_ids.csv.tgz\n!tar -xzf /kaggle/input/yelp-restaurant-photo-classification/train_photos.tgz\n\nend_time = time.time()\nprint(\"Run time: {:.2f} seconds\".format(end_time - start_time))\nprint(\"Run time: {:.2f} minutes\".format((end_time - start_time)/60))","metadata":{"execution":{"iopub.status.busy":"2023-11-30T13:04:54.685124Z","iopub.execute_input":"2023-11-30T13:04:54.686143Z","iopub.status.idle":"2023-11-30T13:10:05.645901Z","shell.execute_reply.started":"2023-11-30T13:04:54.686095Z","shell.execute_reply":"2023-11-30T13:10:05.644493Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## Data Preparation","metadata":{}},{"cell_type":"code","source":"# Verify images in folder \"train_photos\" and collect validated images in \"valid\" for furthur usage\nvalid = []\nfor dirname, _, filenames in os.walk('/kaggle/working/train_photos'):\n for filename in filenames:\n if filename.startswith(\".\"):\n pass\n else: valid.append(filename)\n \nlabel_dict = {\n 0: 'good_for_lunch',\n 1: 'good_for_dinner',\n 2: 'takes_reservations',\n 3: 'outdoor_seating',\n 4: 'restaurant_is_expensive',\n 5: 'has_alcohol',\n 6: 'has_table_service',\n 7: 'ambience_is_classy',\n 8: 'good_for_kids'\n}\n\nt = \"\"\"\n0: good_for_lunch\n1: good_for_dinner\n2: takes_reservations\n3: outdoor_seating\n4: restaurant_is_expensive\n5: has_alcohol\n6: has_table_service\n7: ambience_is_classy\n8: good_for_kids\n\"\"\"\n\n# Extract business attribute from \"t\" to \"class_labels\" e.g. good_for_lunch, outdoor_seating\nclass_lab = [x.split(\":\")[1].strip() for x in t.split(\"\\n\") if x.strip()]\nclass_labels = class_lab\n\n# Generate Meta data of y\nmeta_data = pd.DataFrame(list(label_dict.items()), columns=['labid', 'lab'])\n\nbiz2lab = pd.read_csv(\"train.csv\") # business id & label \nimg2biz = pd.read_csv(\"train_photo_to_biz_ids.csv\") # photo_id & business id\n\n# Feature engineering; create new column \"lab\", each sample contains list of string from \"labels\"\nimg2lab = img2biz.merge(biz2lab, on='business_id')\nimg2lab[\"lab\"] = img2lab['labels'].str.split(\" \")\n\n# Get Image Function\ndef get_img(photo_id:str):\n '''\n input : photo_id (str)\n output : showing image\n \n Showing image in train photos folder given photo_id\n '''\n img_path = 'train_photos/' + str(photo_id) + '.jpg'\n img = image.load_img(img_path, target_size=(200, 200, 3))\n img = image.img_to_array(img)\n img = img / 255.0 \n \n return img\n\n# Drop duplicates \nbizimg = img2lab.drop_duplicates(subset=\"business_id\")[['business_id', 'lab']].reset_index(drop=True).copy()\n\n# Prepare 5 new columns for inputing \"photo_id\" from each 5 images of each business_id;\nfor i in range(1, 6):\n bizimg[f'photo_{i}'] = np.nan\n\n# For each business_id pick 5 images, then input \"photo_id\" of each image to the prepared columns.\nfor ind,row in bizimg.iterrows():\n try:\n mask = img2lab['business_id'] == row['business_id']\n photo_ids = img2lab.loc[mask, 'photo_id'].sample(n=5).values.tolist()\n for i in range(1, 6):\n bizimg.at[ind, f'photo_{i}'] =f\"{photo_ids[i - 1]:.0f}\"\n except Exception as e:print(e)\n \n# Drop missing values\nbizimg = bizimg.dropna().reset_index(drop=True)\ndisplay(bizimg)","metadata":{"_kg_hide-output":true,"execution":{"iopub.status.busy":"2023-11-30T13:10:05.647751Z","iopub.execute_input":"2023-11-30T13:10:05.648139Z","iopub.status.idle":"2023-11-30T13:10:09.217966Z","shell.execute_reply.started":"2023-11-30T13:10:05.648105Z","shell.execute_reply":"2023-11-30T13:10:09.216618Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## Feature Extraction using VGGNet\nWe tried using Resnet50 as our first approach but it turned out that it's too much time-consuming. Instead, we found a different approach that is VGG-16 form VGGNet \n\n### VGGNet\n\nVGG stands for Visual Geometry Group; it is a standard deep Convolutional Neural Network (CNN) architecture with multiple layers. The “deep” refers to the number of layers with VGG-16 or VGG-19 consisting of 16 and 19 convolutional layers.\n\n#### VGG Architecture\nVGGNets are based on the most essential features of convolutional neural networks (CNN). The following graphic shows the basic concept of how a CNN works:\n\n\n\nThe architecture of a Convolutional Neural Network: Image data is the input of the CNN; the model output provides prediction categories for input images. - [Source](https://icmlviz.github.io/icmlviz2016/assets/papers/4.pdf)\n\nThe VGG network is constructed with very small convolutional filters. The VGG-16 consists of 13 convolutional layers and three fully connected layers.\n\n* Input: The VGGNet takes in an image input size of 224×224. For the ImageNet competition, the creators of the model cropped out the center 224×224 patch in each image to keep the input size of the image consistent. \n* Convolutional Layers: VGG’s convolutional layers leverage a minimal receptive field, i.e., 3×3, the smallest possible size that still captures up/down and left/right. Moreover, there are also 1×1 convolution filters acting as a linear transformation of the input. This is followed by a ReLU unit, which is a huge innovation from AlexNet that reduces training time. ReLU stands for rectified linear unit activation function; it is a piecewise linear function that will output the input if positive; otherwise, the output is zero. The convolution stride is fixed at 1 pixel to keep the spatial resolution preserved after convolution (stride is the number of pixel shifts over the input matrix).\n* Hidden Layers: All the hidden layers in the VGG network use ReLU. VGG does not usually leverage Local Response Normalization (LRN) as it increases memory consumption and training time. Moreover, it makes no improvements to overall accuracy.\n* Fully-Connected Layers: The VGGNet has three fully connected layers. Out of the three layers, the first two have 4096 channels each, and the third has 1000 channels, 1 for each class.\n\n### VGG16\nThe VGG model, or VGGNet, that supports 16 layers is also referred to as VGG16, which is a convolutional neural network model proposed by A. Zisserman and K. Simonyan from the University of Oxford. These researchers published their model in the research paper titled, “[Very Deep Convolutional Networks for Large-Scale Image Recognition](https://icmlviz.github.io/icmlviz2016/assets/papers/4.pdf).”\n\n#### VGG16 Architecture\nThe number 16 in the name VGG refers to the fact that it is 16 layers deep neural network (VGGnet). This means that VGG16 is a pretty extensive network and has a total of around 138 million parameters. Even according to modern standards, it is a huge network. However, VGGNet16 architecture’s simplicity is what makes the network more appealing. Just by looking at its architecture, it can be said that it is quite uniform. There are a few convolution layers followed by a pooling layer that reduces the height and the width. If we look at the number of filters that we can use, around 64 filters are available that we can double to about 128 and then to 256 filters. In the last layers, we can use 512 filters.\n\n\n\nVGG-16 Architecture of a VGG16 model. - [source](https://www.ijsrp.org/research-paper-1019/ijsrp-p9420.pdf)\n\nVGG networks have a simple architecture and can be effective for multi-class classification. They are relatively straightforward to implement and less time-consuming compared to other models. With that, VGG-16 with pre-trained weights will be used for the Feature Extraction part. In other word, we are just going to use only the 13 Convolutional Layers of VGG-16\n\n### Transfer Learning\nPre-trained models leverage knowledge gained from large datasets during their initial training. This knowledge includes general features and representations that are useful for a wide range of tasks. When you use a pre-trained model as a starting point, you're essentially benefiting from the learning that has already taken place, saving time and computational resources.\n\nInstead of training the model from scratch, you can use pre-trained models as feature extractors. The early layers of the network, which capture low-level features and patterns, can be frozen, and only the final layers are fine-tuned on your specific task. This approach is faster than training the entire model. Thus, will help a lot to our solution.\n\n### Pretrained Convolutional neural network model as a feature extractor with image augmentation\n– Here we will implement VGG-16 pretrained model which is trained on the imagenet weights to extract features and feed this output to new classifier to classify images.\n\n\n\nBlock diagram shows the architecture of VGG-16. To extract feature vectors from VGG-16 model, weights of all 5 convolutional blocks are frozen and resulted output is given to new classifier. - [source](https://www.ijsrp.org/research-paper-1019/ijsrp-p9420.pdf)","metadata":{},"attachments":{"e8a6d3cd-c02b-4c62-9878-737a2d104b90.jpg":{"image/jpeg":"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"},"28533943-76d4-4db4-a64a-c644a0ae0725.jpg":{"image/jpeg":"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"}}},{"cell_type":"code","source":"# Load pre-trained VGG16 model\nbase_model = VGG16(weights='imagenet', include_top=False)\n\n# Create a model that includes only the convolutional layers\nmodel = Model(inputs=base_model.input, outputs=base_model.output)\n\n# Extract Feature !!\ndef extract_features(photo_id:str):\n '''\n input : photo_id(str)\n output : vector feature of feature (array)\n \n Extract feature from image using VGG16 CNN Architecture\n '''\n image_path = 'train_photos/' + str(photo_id) + '.jpg'\n img = image.load_img(image_path, target_size=(224, 224))\n img_array = image.img_to_array(img)\n img_array = np.expand_dims(img_array, axis=0)\n img_array = preprocess_input(img_array)\n features = model.predict(img_array,verbose=0)\n return features.flatten()","metadata":{"execution":{"iopub.status.busy":"2023-11-30T13:10:09.221425Z","iopub.execute_input":"2023-11-30T13:10:09.221798Z","iopub.status.idle":"2023-11-30T13:10:10.518603Z","shell.execute_reply.started":"2023-11-30T13:10:09.221765Z","shell.execute_reply":"2023-11-30T13:10:10.517527Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"Above is the code to call VGG-16 pretrained model. We need to include weights = ‘imagenet’ to fetch VGG-16 model which is trained on the imagenet dataset. It is important to set include_top = False to avoid downloading the fully connected layers of the pretrained model.","metadata":{}},{"cell_type":"code","source":"# Extract Feature vectors (X) and get Labels (y) to train classification model\n\nstart_time = time.time()\n\nX = []\ny = []\n\nfor i in tqdm(range(len(bizimg)), \"Getting Feature\"):\n label = bizimg.iloc[i, 1]\n feature_1 = extract_features(bizimg.iloc[i, 2])\n feature_2 = extract_features(bizimg.iloc[i, 3])\n feature_3 = extract_features(bizimg.iloc[i, 4])\n feature_4 = extract_features(bizimg.iloc[i, 5])\n feature_5 = extract_features(bizimg.iloc[i, 6])\n \n X.extend([feature_1, feature_2, feature_3, feature_4, feature_5])\n y.extend([label]*5) # Repeat the label for each feature\n \nend_time = time.time()\nprint(\"Run time: {:.2f} seconds\".format(end_time - start_time))\nprint(\"Run time: {:.2f} minutes\".format((end_time - start_time)/60))","metadata":{"execution":{"iopub.status.busy":"2023-11-30T13:10:10.520383Z","iopub.execute_input":"2023-11-30T13:10:10.520902Z","iopub.status.idle":"2023-11-30T14:09:23.603751Z","shell.execute_reply.started":"2023-11-30T13:10:10.520851Z","shell.execute_reply":"2023-11-30T14:09:23.602342Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# Each class's amount of apperances\nmlb = MultiLabelBinarizer()\ny_binary = mlb.fit_transform(y)\ny_bin_df = pd.DataFrame(y_binary)\n\nclass_count = y_bin_df.sum(axis=0)\nclass_count.index = class_count.index.map(label_dict)\n\nclass_count.plot(kind='barh',width = 0.7)\nplt.title(\"Class Count\")\nfor i, val in enumerate(class_count):\n pos_x = val * 0.85\n plt.text(pos_x, i, f\"{val:,.0f}\", va='center', color='white')\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2023-11-30T14:09:23.606071Z","iopub.execute_input":"2023-11-30T14:09:23.606525Z","iopub.status.idle":"2023-11-30T14:09:24.056140Z","shell.execute_reply.started":"2023-11-30T14:09:23.606488Z","shell.execute_reply":"2023-11-30T14:09:24.054770Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## **Classification**\nPreviously, we got a feature vectors from the Feature Extraction part and it's ready to be train. In this Classification part, we'll keep it simple by using two popular classifiers, **Random Forest Classifier** and **KNN** (K-Nearest Neighbors Algorithm) to predict the outcomes and compare to see which model performed better in this task.","metadata":{}},{"cell_type":"code","source":"# Feature vectors sample size\nprint(f\"Sample size: {len(X)}\")","metadata":{"execution":{"iopub.status.busy":"2023-11-30T14:09:24.058030Z","iopub.execute_input":"2023-11-30T14:09:24.058481Z","iopub.status.idle":"2023-11-30T14:09:24.064529Z","shell.execute_reply.started":"2023-11-30T14:09:24.058446Z","shell.execute_reply":"2023-11-30T14:09:24.063328Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# Split data train:test = 80:20\nX_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n\n# Convert y_train and y_test to a binary matrix using MultiLabelBinarizer encoding \nmlb = MultiLabelBinarizer()\ny_train_binary = mlb.fit_transform(y_train)\ny_test_binary = mlb.transform(y_test)","metadata":{"execution":{"iopub.status.busy":"2023-11-30T14:09:24.068101Z","iopub.execute_input":"2023-11-30T14:09:24.068507Z","iopub.status.idle":"2023-11-30T14:09:24.118311Z","shell.execute_reply.started":"2023-11-30T14:09:24.068475Z","shell.execute_reply":"2023-11-30T14:09:24.117349Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"#### One-hot encoding\n– is a technique used in machine learning and data preprocessing to represent categorical variables as binary vectors, in a format suitable for machine learning models. It is particularly useful when dealing with categorical features in a dataset. The process involves converting categorical values into a binary matrix, where each category is represented by a unique column, and the presence or absence of a category is indicated by a binary value (1 or 0).\n\n\n\nOne-hot encoding works with data that have only one label per sample. In this project we need to deal with samples with multiple labels. Instead, we use MultiLabelBinarizer\n\n#### MultiLabelBinarizer\n– converts iterable of iterables and multilabel targets into binary encoding which is transforming multiple labels into a binary matrix representation. It is particularly useful when dealing with multi-label classification problems.\n\n","metadata":{},"attachments":{"8233fd51-ad69-41a8-a4f7-42c3704982ea.png":{"image/png":"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"},"1cd92a37-9f3b-495d-9dcf-741957130345.png":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAQ8AAACOCAIAAACg1hqgAAAOfUlEQVR4Ae2dS66rvBKFmWGkSHs4UaSIeaTFGUbSI3PYjXR2IyNImyO/bXAZbGwIYaVxA9gul7+q5Qf597lVhw8IgMA0AtW0aqgFAiDQQS1IAhCYSgBqmUoK9UAAakEOgMBUAlDLVFKoBwJQC3IABKYSgFqmkkI9EIBakAMgMJUA1DKVFOqBANSCHACBqQSglqmkUA8EllPL69+xqqr60bUX9t162f81rNKl7R61qOyt1X84bMWfHP+9nJrDak4xebOo57YXGsIHem77WeLaG0HdUbhUV+u6bnpNuxVxvbBajs3fuFpYlutEIfx2HmsiupV+YtfTD3U1u5S+5mpZynPbDe3nB3pu+1niWg/ZazxcajeZXtNuRVwvpxaucraksOT7acS0z9eZin/EavNqftj6wyuzBJ32GbQSjC41W6mqSnU3qDbNutdzoWduXfiZZtzTSixlbGm96AXWU22S74xDn7m2PxfLJA9SK9lZzq9FJOV+wR/frutahox/WBb11hbLjiyN9G5BtQw9e9QyYGIYF//ubNhu/IkwKDTJZ+j+rmzcRLAGt8+JszxWaRdsMrHQQiGmkrS4kr3p9UokVkbmZJdJBZwDj5qaKTqxK+Fzkz++JhZ6O2DtxExp6CwQdHZVtXDPzPKSMXKGddd1BlMQRUQhDwZx9Iow46lqwtx1cdtRjzHPIztR2LVa5D1V133kRFAeP/iaYdSiZkAVX7sJv2YTjXnIq5mNRsrw1lSL3BIwkfAFdDtqsXMuhTrdprBaVMbI3Qr9uoX2cKESk+Vic8VEYuCYUms25MumGVlVMTnZNe0KSdPEimpRUwLDvzG1mLDlzh0xg8jdl9k1ZeumnM6zuSgM6Sy3IBjsupRVVonkPFTueB7Ks41amlTNCd8rqsW8HBMpwl4c5/pwRvI9NcedgCbkixWDzPknPOcouGX+ziPkSmSZSb7cM1SkIyPVNWF9IU/wZic2iK+SjXiTVJmaPPrWeA2EES96xWuqRaySYjN6LHBWPup3Yhl1qPlx4mYnrZ/Pv1CWrXdi840aC3JumreDN+YKXRmRiFmVkT7+yJ/s5P7KE1+5bpgf6yw7Vr7x3/TiPV9VLfHuogUIrEgAalkRPrreGAGoZWMBg7srEoBaVoSPrjdGAGrZWMDg7ooEoJYV4aPrjRGAWjYWMLi7IgGoZUX46HpjBKCWjQUM7q5IIKQW+z9QwzUI7IFAWIohtbzxAYGdEYBadhZwDHcGAahlBjw03RkBqGVnAcdwZxCAWmbAQ9OdEYBadhZwDHcGAahlBjw03RkBqGVnAcdwZxDIrpbbWf5Mdbj+zvAr0PT3ejhen4EKc4oKGf+9HtSvd4fsvhc1LmBGY3le+T9teLg+b6fqfH8/r4fqdPNGJq6Cx5PbuTrf3qzH893fQ0wF6bnXVGa13E5VxVOZXbAx5P6IzCiklmLGRUIwFqyLzFiKGlc+y7BODqfJXS0GapqIqxBSCzVBazlNrWCQugPOqxa+sIgp5M7WGK9AXQdi7pjNw/VeZm0paFxES46UikQMCLtuUePvdyIWo5bn9cx2GfezSAa2yKiPeuJUsMcmrh0LXC1XNhdXLBnY/uV5PbH1+naipqF+BeODXO76FagYZVULn5vlFGJfDwHMeeKZXeaYc9sWMd5PaGqWdV2ZeFfUuPIhGotRizLBv62llaVswh6B55Wche/nFAtM/1JXt1PljQXU4kSNvIlOC9KSKbBS5M2mQH+ETP2oq6LGtSfRWAi1aINvvmqlqcW0el6P1ObK7sm5dvgT44JaHGTkDYGPrD+poOj0X9S4Gl40loBa9Hug2LMQd8bxJFEtaicovj37t0XU8i58bhGxc3ipcOb6LmK8n9BZj3NFjSus0VgItYhTkHhZmraPcjy5neXRRfk54ZtSgt2UqpP13MK3GWIryQ9SHtXaPiVeO7wSbZDNyhi36KcEmPSWFxQ1LruOxuJXC8sKfbA+pq4t6u1R4snHOrewdxhmX2cwW0jNw/f7nVkt7AWF/P8Qit5QOn4FbqIjF7A1KCpknEVIfrzHyoEfMQ+KGheORGPxq+Utdh+MBH+3qU7b1mjHZhPuiXon5puO3YOcZdlc8qlchMNngU/63vU/u1qMT7jaMQFKLeNI5Pvi8YpkDfpVMtmkV7DY2tLrF7f7JCC3GPELqfzpYwa125n4jwam2ZSeY22Zhgu1QIAggJ0YAQaPQWBAAGoZIMEDECAIQC0EGDwGgQEBqGWABA9AgCAAtRBg8BgEBgSglgESPAABgkC6WsItUQoCeyMQ+pdd98YC4wWBMAGoJcwHpSBgCEAthgWuQCBMAGoJ80EpCBgCUIthgSsQCBOAWsJ8UAoChgDUYljgCgTCBBLV8vrH/oq0+Qsbjy/9a+TfZVbV8d8rvn2wRVHjoue/5vjT5Pa767breTAg3aPmf99Yt5JbW/uTqq2ruu1ezU9VP7wWoyowO1VFmfLalw9T1NJexN9w5ldLe1FjYPlRtyHPo8uKGmfeiJwuoJbtej4SpEddCVxGLd6gazFQKRddwSAdcdEpjlaLkkpVYG0RY5b+pY3HGZxzU9R4x6fJY/MosbZs13MnAJ4brZaurS9t172ai1iZW7HoVOxhp5+3F6+WPBX43ofP6YSFtOxKUAvTN9cMJXQPlmmP+mmRdTNW1LgaX5Gd2HY9V1iob6MWp0Z7EfvwV/OTlGNsgyd1pUw59ruOJTCxqevXtO+j1SIaF1GLu/uixml7H3Fd1Lj2o4Ratuu5xkJd+NVCnV4oK/3nTuYQEdm+Wrqik2hR4ypgRGxUcdr3dj0fGy+pFmrHNWaQl1uHBXHA9lj7QrUkrJU0zn7OZTWuul1ELdvxXGGhvkm1JG3AVC9TlDCljrJnvj9pJ+bsJucux2aI6soClN+47KSIWuxN9sY8V+yJb79a2Hj5kTXwypgwKB5b5xb29sX3ltJKhqApt/Cz1KJewLMFNOsRnw9avt0vY1xgLaOWsliKeu5mW/+OSOWO7cn5R77Rctu5Bzm3TN6Zd2LquN+rtqhaen3jFgRSCJBqGTFGv0oeaaiLoRaNAhcbIaB/y4/zV/w4E9fGqr3sb/lWx7gEgR0RSDy37IgQhgoCigDUokjgGwTGCEAtY4RQDgKKANSiSOAbBMYIQC1jhFAOAooA1KJI4BsExgiE1EL845d4DAJfSyCsF6jlawOPgSUQgFoSoKHJTglALTsNPIadQABqSYCGJjslALXsNPAYdgIBqCUBGprslADUstPAY9gJBHKr5fd6EH/XVlWH6zPBofEmv9fDsYDpop7D+Hhcs9e4nXkqnu+3c3W+vZ/XY3W+e3uxK7BqVeWvmVctvCeRyvcz08uv17kZD0XaFVDL7aQAsS7Otxk+DpvC+JBJ+Se3s8xALQYqIfsVTLxcL/OqxbLN0zrz8iIUeC+xtghe0n8KljW8qEsYj8KVq7JWy/N6YpuR20lOgreT2v9IOXkqeFehUmoRDnm7nAujyE6sn9BZdQ7jc2Oe1F6rxW19P1dqb8Ky9OTZRlDTZRG1POXhJfN+Rg66hFrc3dftlPXQBeNuui51R6jF6p4l6spqYfsl8pxkuZp6WUItb0z/3nAUxeLtMeNDWi38mCB3Y2uqRfmRdSfjElxELVn3kP2cg3E3ooXu/GrhGx+561l3bZFv37yrWzYkRdTytraqfspz/IfxOfRS2/rjaG2z+Svm1dYWtbDINa7QTy5l1PJWG8iqhNswnpryM9r51fI2WXq+WSd+uyNrdrMfv4uc8p0ecAMC6xAg1DLBGahlAiRU+SoC+rf8qFEt91t+lFuoDAIbI4Cd2MYCBndXJAC1rAgfXW+MANSysYDB3RUJQC0rwkfXGyMAtWwsYHB3RQJQy4rw0fXGCKSrJdwSpSCwNwKhf6tybywwXhAIE4BawnxQCgKGANRiWOAKBMIEoJYwH5SCgCEAtRgWuAKBMAGoJcwHpSBgCEAthgWuQCBMIF4tfw3/l/7YX0/Wj7Dx+FLL+PHfK779hBZ/zfGnKWO66woZ/0wsj5r/CW3ddm1d1W33an54Sjzq6tK6kXAruGXsTnJr6+rY/A2Lx5r3HBgacCowP9OyN1YtvCeebe2F9dij4vEz5lF7UQpk+ZHZOHNEpF0htRQz/qFYHnUlSeps5rkeUotXDLZavEF37XsyKrqCQeqxRj6KVYsxVEAtYsyyi7TxGP+GV2wuPDaPMmtLQeOfisWo5dVc2HLdXniuM7U0Yv5W86lbYRiarq3ZciSrdWwp4B+5Ro0271d4/VMbIMJCWnalqcWsMJ6Bpz/qp0WRzVihzZIYdRHjn4rFqMUNuZg4+IaKZW1/V+ZW9t21l4qH/tX8EGuRr5V5xhyQa5QyZQrF1ZJqYT1y+SYNpu+5und3X9Q4Ve3U7yIJrZwpYfxjsQTUYhTiSF1hCn9Tp5dwK1PqZA4RkaXVIs4AWad/h6wzZoNi9hWBb7ZdbqCI8U/FUlAt3tPL1BDxM4LYyYn/9VhbXC1svVOH8qkDCdfrp0X+d27m9UvYk9TSRdTyKVimqMVdGKdhzbC2jCJaRi38+MXX2QKn/M4aw1xkZGCKJLTqrYzxD8USUIt6EcySxOzKFKWxb7WtUK+kx+r3y61zS0c4aSHttw7cx5/y+ZLCV7ishxbhozEuznkBz1OLyiS09KaQ8c/EQiQiS1D9Tsz7sn58wem9E3NjPd5cHKrJbVjHXt+lbIvi1eJ6jrv9EqDUMoGIfNc8oaa3yszmUIuXKh6WJCBXPM8ZeqxX8evKWC2yfGbz5X7LJ0eAAhD4egLYiX19iDHAbASglmwoYejrCUAtXx9iDDAbAaglG0oY+noCUMvXhxgDzEYAasmGEoa+ngDU8vUhxgCzEYBasqGEoa8nALV8fYgxwGwEoJZsKGHo6wlALV8fYgwwGwGoJRtKGPp6AlDL14cYA8xG4D+q55PM+DLg2wAAAABJRU5ErkJggg=="}}},{"cell_type":"code","source":"pd.DataFrame(y_train_binary, columns=mlb.classes_)","metadata":{"execution":{"iopub.status.busy":"2023-11-30T14:09:24.119581Z","iopub.execute_input":"2023-11-30T14:09:24.119904Z","iopub.status.idle":"2023-11-30T14:09:24.139944Z","shell.execute_reply.started":"2023-11-30T14:09:24.119876Z","shell.execute_reply":"2023-11-30T14:09:24.138773Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"### **1. Random Forest Classifier**\nThe Random Forest Classifier is an ensemble learning algorithm that leverages the power of decision trees to achieve robust and accurate predictions. It constructs a multitude of decision trees during training, each trained on a different subset of the dataset using bootstrapped sampling. To introduce diversity among the trees and prevent overfitting, the algorithm randomly selects a subset of features at each split node. During prediction, the individual trees \"vote\" for a class in classification tasks, and the class with the majority of votes becomes the final prediction.\n\n#### The random forest algorithm works by completing the following steps:\n1. The algorithm select random samples from the dataset provided.\n2. The algorithm will create a decision tree for each sample selected. Then it will get a prediction result from each decision tree created.\n3. Voting will then be performed for every predicted result. For a classification problem, it will use mode, and for a regression problem, it will use mean.\n4. And finally, the algorithm will select the most voted prediction result as the final prediction.\n\n\n\n how Random Forest Classifier works - [source](https://www.freecodecamp.org/news/how-to-use-the-tree-based-algorithm-for-machine-learning/)","metadata":{},"attachments":{"cf204ec4-afa5-4459-b877-718effb56d19.png":{"image/png":"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"}}},{"cell_type":"code","source":"# Random Forest Classifier\nparam_grid = {\n 'n_estimators': [200],\n 'max_depth': [20]\n}\n\ngrid_search = GridSearchCV(RandomForestClassifier(random_state=42), param_grid, cv=2, scoring = make_scorer(f1_score, average='weighted'), verbose=2)\ngrid_search.fit(X_train, y_train_binary)\n\nbest_params = grid_search.best_params_\nbest_model_rdf = grid_search.best_estimator_\n\ny_pred_binary = best_model_rdf.predict(X_test)\n\naccuracy = accuracy_score(y_test_binary, y_pred_binary)\nclassification_report_output = classification_report(y_test_binary, y_pred_binary)\n\nprint(\"Best Parameters:\", best_params)\nprint(f\"Accuracy: {accuracy:.2f}\")\nprint(\"Classification Report:\")\nprint(classification_report_output)","metadata":{"execution":{"iopub.status.busy":"2023-11-30T14:09:24.144250Z","iopub.execute_input":"2023-11-30T14:09:24.144672Z","iopub.status.idle":"2023-11-30T14:14:57.255566Z","shell.execute_reply.started":"2023-11-30T14:09:24.144637Z","shell.execute_reply":"2023-11-30T14:14:57.254145Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"Above is the code to train Random Forest Classifier. We set the list of Hyperparameter \"n_estimator\"(number of trees) to 200 and \"max_depth\"(tree's depth) to 20 which has the highest score among other set of parameters.","metadata":{}},{"cell_type":"markdown","source":"### **2. KNN**\nK-Nearest Neighbors (KNN) is a simple and intuitive machine learning algorithm used for both classification and regression tasks. It belongs to the family of instance-based or lazy learning algorithms. Instead of learning a model during the training phase, KNN classifies or predicts new data points based on the similarity to the training data.\n\n\n\n how KNN works - [source](https://www.javatpoint.com/k-nearest-neighbor-algorithm-for-machine-learning)\n\n#### The K-NN working can be explained on the basis of the below algorithm:\n1. Select the number K of the neighbors\n2. Calculate the Euclidean distance of K number of neighbors\n3. Take the K nearest neighbors as per the calculated Euclidean distance.\n4. Among these k neighbors, count the number of the data points in each category.\n5. Assign the new data points to that category for which the number of the neighbor is maximum.\n6. Our model is ready.\n\n$$\\text{distance} = \\sqrt{\\sum_{i=1}^d (\\text{x}_\\text{new,i} - \\text{x}_{\\text{train,i}})^2}$$\n\nWhile KNN is typically designed for single-label tasks, where each instance belongs to a single class or has a single target value. However, KNN can be adapted for multi-label tasks too.\n\n#### How KNN algorithm works in a multilabel problem?\n– One of the approachs is to use Multi-Output KNN or to use MultiOutputClassifier() with KNeighborsClassifier() that provide a multi-output version of KNN, where the algorithm considers multiple labels simultaneously during training and prediction. This allows the model to capture dependencies between labels. It is show in the code below:","metadata":{},"attachments":{"60ad2fea-ee66-40e6-8d56-90e749f022ca.png":{"image/png":"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"}}},{"cell_type":"code","source":"# K-Nearest Neighbors\nparam_grid_knn = {'estimator__n_neighbors': [8,9,10],\n 'estimator__weights': ['distance'],\n 'estimator__metric': ['euclidean', 'cosine']}\n\nbase_knn_model = KNeighborsClassifier()\n\nknn_model = MultiOutputClassifier(base_knn_model)\n\ngrid_search_knn = GridSearchCV(knn_model, param_grid_knn, cv=2,\n scoring=make_scorer(f1_score, average='weighted'), verbose=2)\n\ngrid_search_knn.fit(X_train, y_train_binary)\n\nbest_params_knn = grid_search_knn.best_params_\nbest_model_knn = grid_search_knn.best_estimator_\n\ny_pred_knn = best_model_knn.predict(X_test)\n\nf1_knn = f1_score(y_test_binary, y_pred_knn, average='weighted')\naccuracy_knn = accuracy_score(y_test_binary, y_pred_knn)\n\n# Print the results\nprint(\"Best parameters for KNN:\", best_params_knn)\nprint(\"Macro-average F1 Score (KNN):\", f1_knn)\nprint(\"Accuracy (KNN):\", accuracy_knn)\n\nreport = classification_report(y_test_binary, y_pred_knn)\nprint(report)","metadata":{"execution":{"iopub.status.busy":"2023-11-30T14:14:57.257125Z","iopub.execute_input":"2023-11-30T14:14:57.257549Z","iopub.status.idle":"2023-11-30T14:37:30.632010Z","shell.execute_reply.started":"2023-11-30T14:14:57.257513Z","shell.execute_reply":"2023-11-30T14:37:30.630668Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"Above is the code to train KNN. We set the list of Hyperparameter \"estimator__n_neighbors\"(number of neighbors) to [8,9,10], \"estimator__weights\"(weight function) to ['distance'] and \"estimator__metric\"(distance metrics) to ['euclidean', 'cosine'] and let grid_search_knn.best_params_ find the best parameter. The same goes for grid_search_knn.best_estimator_ to find the best KNN model and get the most score out of it.","metadata":{}},{"cell_type":"markdown","source":"## **Submit to competition**","metadata":{}},{"cell_type":"code","source":"test_df = pd.read_csv(\"test_photo_to_biz.csv\")\ntest_df = test_df.drop_duplicates(subset='business_id', keep='first').reset_index(drop=True)","metadata":{"execution":{"iopub.status.busy":"2023-11-30T14:37:30.633377Z","iopub.execute_input":"2023-11-30T14:37:30.633746Z","iopub.status.idle":"2023-11-30T14:37:31.156048Z","shell.execute_reply.started":"2023-11-30T14:37:30.633716Z","shell.execute_reply":"2023-11-30T14:37:31.154719Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"def extract_test_features(photo_id:str):\n '''\n input : photo_id(str)\n output : vector feature of feature (array)\n \n Extract feature from image using VGG16 CNN Architecture\n '''\n image_path = 'test_photos/' + str(photo_id) + '.jpg'\n img = image.load_img(image_path, target_size=(224, 224))\n img_array = image.img_to_array(img)\n img_array = np.expand_dims(img_array, axis=0)\n img_array = preprocess_input(img_array)\n features = model.predict(img_array,verbose=0)\n return features.flatten()","metadata":{"execution":{"iopub.status.busy":"2023-11-30T14:37:31.157756Z","iopub.execute_input":"2023-11-30T14:37:31.158244Z","iopub.status.idle":"2023-11-30T14:37:31.166516Z","shell.execute_reply.started":"2023-11-30T14:37:31.158209Z","shell.execute_reply":"2023-11-30T14:37:31.164862Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# Extract test image feature\ntest_biz = []\ntest_X = []\nfor ind,row in tqdm(test_df.iterrows(), \"Extract Feature\"):\n photo_id = row['photo_id']\n test_X.append(extract_test_features(photo_id))\n test_biz.append(row['business_id'])","metadata":{"execution":{"iopub.status.busy":"2023-11-30T14:37:31.168504Z","iopub.execute_input":"2023-11-30T14:37:31.168999Z","iopub.status.idle":"2023-11-30T15:37:10.371976Z","shell.execute_reply.started":"2023-11-30T14:37:31.168961Z","shell.execute_reply":"2023-11-30T15:37:10.370643Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# Random Forest\ntest_pred = best_model_rdf.predict(test_X)\nreal_pred = [' '.join(x) for x in mlb.inverse_transform(test_pred)]\nsubmiss = pd.DataFrame(data = [test_biz, real_pred]).T\nsubmiss.columns = ['business_id', 'labels']\nsubmiss.to_csv(\"submission_rdf.csv\", index=False)","metadata":{"execution":{"iopub.status.busy":"2023-11-30T15:37:10.373725Z","iopub.execute_input":"2023-11-30T15:37:10.374108Z","iopub.status.idle":"2023-11-30T15:37:15.157161Z","shell.execute_reply.started":"2023-11-30T15:37:10.374075Z","shell.execute_reply":"2023-11-30T15:37:15.155791Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# K-Nearest Neighbors\ntest_pred = best_model_knn.predict(test_X)\nreal_pred = [' '.join(x) for x in mlb.inverse_transform(test_pred)]\nsubmiss = pd.DataFrame(data = [test_biz, real_pred]).T\nsubmiss.columns = ['business_id', 'labels']\nsubmiss.to_csv(\"submission_knn.csv\", index=False)","metadata":{"execution":{"iopub.status.busy":"2023-11-30T15:37:15.159302Z","iopub.execute_input":"2023-11-30T15:37:15.159681Z","iopub.status.idle":"2023-11-30T15:42:21.430097Z","shell.execute_reply.started":"2023-11-30T15:37:15.159648Z","shell.execute_reply":"2023-11-30T15:42:21.428582Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# Remove files/directories that aren't relevant for submitting\n!rm -r /kaggle/working/test_photos\n!rm -r /kaggle/working/train_photos\n!rm -r /kaggle/working/test_photo_to_biz.csv\n!rm -r /kaggle/working/train_photo_to_biz_ids.csv\n!rm -r /kaggle/working/sample_submission.csv\n!rm -r /kaggle/working/train.csv","metadata":{"execution":{"iopub.status.busy":"2023-11-30T15:42:21.432156Z","iopub.execute_input":"2023-11-30T15:42:21.432710Z","iopub.status.idle":"2023-11-30T15:43:11.060572Z","shell.execute_reply.started":"2023-11-30T15:42:21.432662Z","shell.execute_reply":"2023-11-30T15:43:11.058482Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"#### Thanks for reading.","metadata":{}}]}