{"metadata":{"kernelspec":{"display_name":"Pytorch_Kernel","language":"python","name":"torch_env"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.10.18"},"kaggle":{"accelerator":"none","dataSources":[{"sourceId":112509,"databundleVersionId":14254895,"isSourceIdPinned":false,"sourceType":"competition"},{"sourceId":14288415,"sourceType":"datasetVersion","datasetId":9120226}],"dockerImageVersionId":31192,"isInternetEnabled":false,"language":"python","sourceType":"notebook","isGpuEnabled":false}},"nbformat_minor":4,"nbformat":4,"cells":[{"id":"66e7fae7","cell_type":"markdown","source":"# Library Import and Data Import","metadata":{}},{"id":"0068c567","cell_type":"code","source":"import numpy as np\nimport pandas as pd\nimport matplotlib.pyplot as plt\nimport seaborn as sns ","metadata":{},"outputs":[],"execution_count":null},{"id":"c2ea1be8","cell_type":"code","source":"import os\n\n# Check if running in Kaggle (where /kaggle/input exists)\nif os.path.exists('/kaggle/input/csiro-biomass/'):\n    DATA_DIR = '/kaggle/input/csiro-biomass/'\nelse:\n    # Fallback to local relative path\n    DATA_DIR = 'kaggle/input/csiro-biomass/'\n\nprint(f\"Using data directory: {DATA_DIR}\")","metadata":{},"outputs":[],"execution_count":null},{"id":"4dd6b8d0","cell_type":"code","source":"train_path = DATA_DIR + 'train.csv'\ndf = pd.read_csv(train_path)\ndf.head()","metadata":{},"outputs":[],"execution_count":null},{"id":"c72b1270","cell_type":"code","source":"# Pivot data to wide format\ndf_wide = df.pivot(index = 'image_path', columns = 'target_name', values = 'target').reset_index()\nmetadata = df.groupby('image_path')[['Sampling_Date','State','Species','Pre_GSHH_NDVI','Height_Ave_cm']].first().reset_index()\ndf = df_wide.merge(metadata, on = 'image_path', how = 'left')\ndf.head()","metadata":{},"outputs":[],"execution_count":null},{"id":"14a2b2ec","cell_type":"code","source":"# Update image_path to match kaggle dataset path\ndf['image_path'] =  DATA_DIR + df['image_path']","metadata":{},"outputs":[],"execution_count":null},{"id":"6e5c31cb","cell_type":"code","source":"df.head()","metadata":{},"outputs":[],"execution_count":null},{"id":"cffa207e","cell_type":"markdown","source":"# EDA","metadata":{}},{"id":"c6caa5c5","cell_type":"markdown","source":"Feature explanation:\n\n![image.png](attachment:image.png)","metadata":{},"attachments":{"image.png":{"image/png":"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"}}},{"id":"c1a969cb","cell_type":"markdown","source":"## Data Integrity Check\n- Check data shape\n- Check for missing values\n- Check for duplicate values\n- Check for invalid values\n- Check for data type consistency\n","metadata":{}},{"id":"d9503fe1","cell_type":"markdown","source":"#### *Code*","metadata":{}},{"id":"ec690761","cell_type":"code","source":"df.head()","metadata":{},"outputs":[],"execution_count":null},{"id":"77e26cc0","cell_type":"code","source":"print('Data shape:')\nprint(f'{df.shape}')\nprint('')\nprint('Missing values:')\nprint(f'{df.isnull().sum()}')\nprint('')\nprint('Duplicate instances:')\nprint(f'{df.duplicated().sum()}')\nprint('')\nprint('Data overview')\nprint(df.info())","metadata":{},"outputs":[],"execution_count":null},{"id":"d565ad18","cell_type":"markdown","source":"#### *Key Findings*\n1. Shape is (357,11), only 357 unique images, insufficient for training, consider to do image augmentation.\n2. No missing values and duplicate instance, which is good.\n3. Data type is consistent, except for 'Sampling_Date' which is str type, will convert to datetime.","metadata":{}},{"id":"bf19ce97","cell_type":"code","source":"# Convert Sampling_Date to datetime format\ndf['Sampling_Date'] = pd.to_datetime(df['Sampling_Date'])\ntype(df['Sampling_Date'].values[0])","metadata":{},"outputs":[],"execution_count":null},{"id":"8dbeae4e","cell_type":"markdown","source":"## Univariate Analysis\n- Target distribution\n- Image metadata (if any)\n- Feature Distribution\n- Categorical Balance","metadata":{}},{"id":"df391028","cell_type":"markdown","source":"### **Target Distribution**","metadata":{}},{"id":"5771c5e2","cell_type":"markdown","source":"#### *Target Distribution - Code*","metadata":{}},{"id":"7347c8b7","cell_type":"code","source":"target_names = ['Dry_Green_g', 'Dry_Dead_g', 'Dry_Clover_g', 'GDM_g', 'Dry_Total_g']\ndf[target_names].describe()","metadata":{},"outputs":[],"execution_count":null},{"id":"fc94790c","cell_type":"code","source":"df[target_names].hist(figsize=(12, 8))\nplt.show()","metadata":{},"outputs":[],"execution_count":null},{"id":"10e82bc8","cell_type":"markdown","source":"#### *Target Distribution - Key Findings*\n1. All targets are right skewed, with a long tail of high values, i.e. target mean values are larger than median values. \n2. Skewed data might lead to confusion to the model, so we will apply log1p transformation to the target values if necessary.\n","metadata":{}},{"id":"fffc9506","cell_type":"markdown","source":"### **Image Metadata**","metadata":{}},{"id":"7730fcef","cell_type":"markdown","source":"#### *Image metadata - Code*","metadata":{}},{"id":"b858a0ff","cell_type":"code","source":"from PIL.ExifTags import TAGS\nfrom PIL import Image\n\ndef get_exif_data(image_path):\n    with Image.open(image_path) as img:\n        # _getexif is specific to JPEGs. Returns None if no EXIF data.\n        exif_data = img._getexif()\n        \n        if exif_data:\n            # Use .get(tag, tag) to fallback to the ID if the name is unknown\n            return {TAGS.get(tag, tag): value for tag, value in exif_data.items()}\n            \n        return None\n\ndef get_basic_meta_data(image_path):\n    with Image.open(image_path) as img:\n        return {\n            \"format\": img.format,\n            \"size\": img.size,\n            \"mode\": img.mode\n        }","metadata":{},"outputs":[],"execution_count":null},{"id":"17061481","cell_type":"code","source":"exit_data_collections = []\nbasic_meta_data_collections = []\nfor image_path in df['image_path']:\n    exif_data = get_exif_data(image_path)\n    basic_meta_data = get_basic_meta_data(image_path)\n    if exif_data:\n        exit_data_collections.append(exif_data)\n    if basic_meta_data:\n        basic_meta_data_collections.append(basic_meta_data)\n\nif exit_data_collections:\n    exit_data_df = pd.DataFrame(exit_data_collections)\n    print(exit_data_df.value_counts())\nelse:\n    print(\"No EXIF data found.\")\n\nprint(\"=\"*60)\n\nif basic_meta_data_collections:\n    basic_meta_data_df = pd.DataFrame(basic_meta_data_collections)\n    print(\"Basic Meta Data\")\n    print(basic_meta_data_df.value_counts())\nelse:\n    print(\"No basic meta data found.\")","metadata":{},"outputs":[],"execution_count":null},{"id":"73d138b5","cell_type":"markdown","source":"#### *Image metadata - Key Findings*\n1. No EXIF data found (e.g. GPS Info)\n2. Metadata is consistent across all 357 images -> JPEG format; size (2000,1000); RGB;","metadata":{}},{"id":"ddc326df","cell_type":"markdown","source":"### **Feature Distribution**","metadata":{}},{"id":"c490258f","cell_type":"markdown","source":"#### *Feature Distribution - Code*","metadata":{}},{"id":"424185b0","cell_type":"code","source":"num_df = df.drop(columns=target_names).select_dtypes(include=['int64', 'float64'])\nnum_df.describe()","metadata":{},"outputs":[],"execution_count":null},{"id":"4a9be6fc","cell_type":"code","source":"num_df.hist(bins = 10, figsize=(10,5))\nplt.show()","metadata":{},"outputs":[],"execution_count":null},{"id":"b4ad6484","cell_type":"code","source":"df['Month_Year'] = df['Sampling_Date'].dt.to_period('M').astype(str)\ndf.head()","metadata":{},"outputs":[],"execution_count":null},{"id":"a4be9839","cell_type":"code","source":"plt.figure(figsize=(12,6))\nsns.countplot(data=df.sort_values(by='Sampling_Date'), x='Month_Year')\nplt.show()","metadata":{},"outputs":[],"execution_count":null},{"id":"85ad342e","cell_type":"code","source":"df.describe(include='object')","metadata":{},"outputs":[],"execution_count":null},{"id":"530a4ec2","cell_type":"code","source":"# Visualize categorical feature, excluding id, path and date\ncat_features = df.drop(columns=['image_path','Month_Year']).select_dtypes(include=['object']).columns\n\nfor i,feature in enumerate(cat_features):\n    ax = df[feature].value_counts().plot(kind='barh')\n    ax.bar_label(ax.containers[0])\n    plt.show()\n\n","metadata":{},"outputs":[],"execution_count":null},{"id":"3fd39e53","cell_type":"markdown","source":"#### *Feature Distribution - Key Findings*\n\n1. Both Pre_GSHH_NDVI,Height_Ave_cm are skewed, scaling is required\n2. Most samples found in Tas (138), followed by Vic (112), NSW (75), WA (32)\n3. Imbalanced species distribution, range from 2 -> 98\n\n","metadata":{}},{"id":"aadbc7f1","cell_type":"markdown","source":"## Multivariate Analysis","metadata":{}},{"id":"6ef4d0b1","cell_type":"markdown","source":"#### *Multivariate Analysis - Code*","metadata":{}},{"id":"2b0b7e86","cell_type":"code","source":"cor_matrix = df.select_dtypes(include=['float64', 'int64']).corr()\nsns.heatmap(cor_matrix, annot=True)","metadata":{},"outputs":[],"execution_count":null},{"id":"738ad8fc","cell_type":"code","source":"sns.barplot(data = df, x = 'State', y='Dry_Total_g' )\nplt.show()","metadata":{},"outputs":[],"execution_count":null},{"id":"aa6cb0ca","cell_type":"code","source":"sns.scatterplot(data = df, x='Pre_GSHH_NDVI', y='Dry_Total_g', hue='State')\nplt.show()","metadata":{},"outputs":[],"execution_count":null},{"id":"98bf60d7","cell_type":"code","source":"sns.scatterplot(data = df, x='Height_Ave_cm', y='Dry_Total_g', hue='State')\nplt.show()","metadata":{},"outputs":[],"execution_count":null},{"id":"d13a1fda","cell_type":"markdown","source":"Interestingly, we can notice there are higher Dry_Total_g captured in NSW.","metadata":{}},{"id":"ff16bc9f","cell_type":"code","source":"plt.figure(figsize=(12, 6))\nax = sns.boxenplot(data=df, x=\"Species\", y=\"Dry_Total_g\")\nax.set_xticklabels(ax.get_xticklabels(), rotation=90)\nplt.show()","metadata":{},"outputs":[],"execution_count":null},{"id":"965bd1a1","cell_type":"code","source":"species_state_table = pd.crosstab(df['Species'], df['State'])\nspecies_state_table","metadata":{},"outputs":[],"execution_count":null},{"id":"36007910","cell_type":"markdown","source":"#### *Multivariate - Key Findings*\n\n1. Both Pre_GSHH_NDVI,Height_Ave_cm have positive correlation with Biomass, except for Dry_Dead_g which has negative correlation\n2. NSW has highest average biomass compared to other State, and upon further investigation, it's because of several species with high biomass that only exist in NSW, e.g. Fescue, Phalaris and etc","metadata":{}},{"id":"3c45b1e9","cell_type":"markdown","source":"","metadata":{}},{"id":"0925a172","cell_type":"code","source":"","metadata":{},"outputs":[],"execution_count":null},{"id":"1ae283bf","cell_type":"code","source":"","metadata":{},"outputs":[],"execution_count":null},{"id":"e1a8b86c","cell_type":"code","source":"","metadata":{},"outputs":[],"execution_count":null},{"id":"6d9d54a9","cell_type":"code","source":"","metadata":{},"outputs":[],"execution_count":null}]}