Victor Phan
681 lines
21 KiB
Plaintext
681 lines
21 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "b1dab7f7",
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"metadata": {},
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"source": [
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"# 🌍 Decision Tree Land Classification - Planetary Computer\n",
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"\n",
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"## 📌 Notebook này có thể chạy trên:\n",
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"- ✅ **Local machine** (không cần ODC database)\n",
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"- ✅ **Server ODC/JupyterHub** (có ODC database)\n",
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"\n",
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"## 🎯 Nguồn dữ liệu:\n",
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"**Microsoft Planetary Computer STAC API**\n",
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"- Sentinel-2 L2A (optical)\n",
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"- Sentinel-1 RTC (SAR)\n",
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"\n",
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"## 🔄 Workflow:\n",
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"1. Load data từ Planetary Computer (STAC)\n",
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"2. Preprocessing (cloud mask, NDVI, resampling)\n",
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"3. Train Decision Tree model\n",
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"4. Evaluate & save model\n",
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"\n",
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"---"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "df9820b8",
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"metadata": {},
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"outputs": [],
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"source": [
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"%%time\n",
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"%matplotlib inline\n",
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"\n",
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"import sys\n",
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"import os\n",
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"sys.path.insert(0, '/media/x79/2A7D-FAA0/remote-sensing')\n",
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"\n",
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"# Import module load dữ liệu không cần ODC database\n",
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"import importlib\n",
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"import load_data_no_odc\n",
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"importlib.reload(load_data_no_odc)\n",
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"\n",
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"from load_data_no_odc import (\n",
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" load_and_process_s2,\n",
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" load_and_process_s1,\n",
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" load_sentinel2_stac,\n",
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" load_sentinel1_stac,\n",
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" mask_clean_s2,\n",
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" calculate_ndvi,\n",
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" fill_nan_temporal,\n",
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" resample_monthly\n",
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")\n",
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"\n",
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"# Standard imports\n",
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"import numpy as np\n",
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"import pandas as pd\n",
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"import xarray as xr\n",
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"import matplotlib.pyplot as plt\n",
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"import seaborn as sns\n",
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"sns.set_style('whitegrid')\n",
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"\n",
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"# ML imports\n",
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"from sklearn.tree import DecisionTreeClassifier\n",
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"from sklearn.model_selection import train_test_split\n",
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"from sklearn.metrics import classification_report, confusion_matrix, accuracy_score\n",
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"import joblib\n",
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"import json\n",
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"from datetime import datetime\n",
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"\n",
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"# Dask for parallel processing\n",
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"from dask.distributed import Client, LocalCluster\n",
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"\n",
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"print(\"✅ All modules loaded successfully!\")\n",
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"print(\"📡 Data source: Microsoft Planetary Computer\")\n",
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"print(\"💻 Environment: Local or Remote compatible\")"
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]
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},
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{
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"cell_type": "markdown",
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"id": "53397b2f",
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"metadata": {},
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"source": [
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"## 🚀 Step 1: Initialize Dask Cluster\n",
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"\n",
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"Khởi tạo Dask local cluster để xử lý song song"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "3c4d6779",
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"metadata": {},
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"outputs": [],
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"source": [
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"# Khởi tạo Dask LocalCluster\n",
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"print(\"🚀 Initializing Dask LocalCluster...\")\n",
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"\n",
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"cluster = LocalCluster(\n",
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" n_workers=4,\n",
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" threads_per_worker=1,\n",
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" memory_limit='4GB'\n",
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")\n",
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"client = Client(cluster)\n",
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"\n",
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"print(f\"✅ Dask cluster ready!\")\n",
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"print(f\" Workers: {len(cluster.workers)}\")\n",
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"print(f\" Dashboard: {client.dashboard_link}\")\n",
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"print(\"=\" * 70)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "28bc5035",
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"metadata": {},
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"source": [
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"## 📍 Step 2: Define Area of Interest (AOI)\n",
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"\n",
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"Định nghĩa vùng nghiên cứu và khoảng thời gian"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "15a5291c",
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"metadata": {},
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"outputs": [],
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"source": [
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"# Cấu hình vùng và thời gian\n",
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"date_range = (\"2022-09-01\", \"2023-10-01\")\n",
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"\n",
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"# Bounding box: (lon_min, lat_min, lon_max, lat_max)\n",
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"bbox = (105.5, 9.2, 106.4, 10.0) # Khu vực Mekong Delta\n",
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"\n",
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"print(\"📍 Area of Interest:\")\n",
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"print(f\" Bbox: {bbox}\")\n",
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"print(f\" Lon range: {bbox[0]} to {bbox[2]}\")\n",
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"print(f\" Lat range: {bbox[1]} to {bbox[3]}\")\n",
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"print(f\" Date range: {date_range[0]} to {date_range[1]}\")\n",
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"print(\"=\" * 70)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "422e498e",
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"metadata": {},
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"source": [
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"## 📥 Step 3: Load Sentinel-2 Data\n",
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"\n",
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"Load dữ liệu Sentinel-2 L2A từ Planetary Computer"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "611cd1c2",
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"metadata": {},
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"outputs": [],
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"source": [
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"%%time\n",
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"\n",
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"print(\"📥 Loading Sentinel-2 data from Planetary Computer...\")\n",
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"print(\"-\" * 70)\n",
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"\n",
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"# Load và xử lý Sentinel-2: cloud mask + NDVI + monthly resampling\n",
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"data_sen2_monthly = load_and_process_s2(\n",
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" bbox=bbox,\n",
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" date_range=date_range,\n",
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" apply_cloud_mask=True,\n",
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" calculate_indices=True\n",
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")\n",
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"\n",
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"if data_sen2_monthly is not None:\n",
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" print(f\"\\n✅ Sentinel-2 monthly data loaded!\")\n",
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" print(f\" Dimensions: {dict(data_sen2_monthly.dims)}\")\n",
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" print(f\" Variables: {list(data_sen2_monthly.data_vars)}\")\n",
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" print(f\" Time steps: {len(data_sen2_monthly.time)}\")\n",
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" \n",
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" # Compute to load into memory\n",
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" data_sen2_monthly = data_sen2_monthly.compute()\n",
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" print(f\" ✓ Data computed and loaded into memory\")\n",
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"else:\n",
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" print(\"❌ Failed to load Sentinel-2 data\")"
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]
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},
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{
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"cell_type": "markdown",
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"id": "79ef9736",
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"metadata": {},
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"source": [
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"## 📥 Step 4: Load Sentinel-1 Data\n",
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"\n",
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"Load dữ liệu Sentinel-1 RTC (SAR) từ Planetary Computer"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "3bda3dc0",
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"metadata": {},
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"outputs": [],
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"source": [
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"%%time\n",
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"\n",
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"print(\"📥 Loading Sentinel-1 data from Planetary Computer...\")\n",
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"print(\"-\" * 70)\n",
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"\n",
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"# Load và xử lý Sentinel-1: monthly resampling\n",
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"data_sen1_monthly = load_and_process_s1(\n",
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" bbox=bbox,\n",
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" date_range=date_range\n",
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")\n",
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"\n",
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"if data_sen1_monthly is not None:\n",
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" print(f\"\\n✅ Sentinel-1 monthly data loaded!\")\n",
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" print(f\" Dimensions: {dict(data_sen1_monthly.dims)}\")\n",
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" print(f\" Variables: {list(data_sen1_monthly.data_vars)}\")\n",
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" print(f\" Time steps: {len(data_sen1_monthly.time)}\")\n",
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" \n",
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" # Compute to load into memory\n",
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" data_sen1_monthly = data_sen1_monthly.compute()\n",
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" print(f\" ✓ Data computed and loaded into memory\")\n",
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"else:\n",
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" print(\"❌ Failed to load Sentinel-1 data\")"
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]
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},
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{
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"cell_type": "markdown",
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"id": "de80d48d",
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"metadata": {},
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"source": [
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"## 🎯 Step 5: Load Training Data\n",
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"\n",
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"Load dữ liệu mẫu huấn luyện (training samples)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "70925d09",
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"metadata": {},
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"outputs": [],
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"source": [
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"from load_data_no_odc import load_train_data\n",
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"\n",
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"# Ánh xạ nhãn lớp đất\n",
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"label_mapping = {\n",
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" \"Lua tom\": 0,\n",
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" \"Lua\": 1,\n",
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" \"CHN\": 2,\n",
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" \"CLN\": 3,\n",
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" \"TS\": 4,\n",
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" \"Song\": 5,\n",
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" \"Dat xay dung\": 6,\n",
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" \"Rung\": 7,\n",
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"}\n",
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"\n",
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"print(\"🎯 Label mapping:\")\n",
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"for label, idx in label_mapping.items():\n",
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" print(f\" {idx}: {label}\")\n",
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"\n",
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"# Load training data from AWS shapefile\n",
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"train_path = \"train/ST_training_data_updated_1130points_new.shp\"\n",
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"\n",
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"print(f\"\\n📂 Loading training data from: {train_path}\")\n",
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"train_data = load_train_data(train_path, label_mapping)\n",
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"\n",
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"if train_data is not None:\n",
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" print(f\"\\n✅ Training data loaded successfully!\")\n",
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" print(f\" Total points: {len(train_data)}\")\n",
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" print(f\" CRS: {train_data.crs}\")\n",
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"else:\n",
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" print(\"❌ Failed to load training data\")\n"
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]
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},
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{
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"cell_type": "markdown",
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"id": "fed9a8f1",
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"metadata": {},
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"source": [
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"## 🔧 Step 6: Extract Features\n",
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"\n",
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"Trích xuất features từ Sentinel-1 và Sentinel-2 tại các điểm mẫu"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "62c41cd0",
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"metadata": {},
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"outputs": [],
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"source": [
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"%%time\n",
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"from load_data_no_odc import extract_features_at_points\n",
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"\n",
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"print(\"🔧 Extracting features from satellite data...\")\n",
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"print(\"-\" * 70)\n",
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"\n",
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"if train_data is not None and \\\n",
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" data_sen2_monthly is not None and \\\n",
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" data_sen1_monthly is not None:\n",
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" \n",
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" # Extract features at training point locations\n",
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" X, y = extract_features_at_points(\n",
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" train_data, \n",
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" data_sen2_monthly, \n",
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" data_sen1_monthly\n",
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" )\n",
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" \n",
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" print(f\"\\n✅ Feature extraction complete!\")\n",
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" print(f\" Total samples: {len(X)}\")\n",
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" print(f\" Feature dimension: {X.shape[1]}\")\n",
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" print(f\" Classes: {sorted(set(y.tolist()))}\")\n",
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" print(f\" Class distribution:\")\n",
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" \n",
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" import pandas as pd\n",
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" class_counts = pd.Series(y).value_counts().sort_index()\n",
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" for class_id, count in class_counts.items():\n",
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" class_name = [k for k, v in label_mapping.items() if v == class_id][0]\n",
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" print(f\" {class_id} ({class_name}): {count} samples ({count/len(y)*100:.1f}%)\")\n",
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" \n",
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"else:\n",
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" print(\"❌ Missing data: Please check training data or satellite data\")\n",
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" print(f\" train_data: {'✓' if train_data is not None else '✗'}\")\n",
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" print(f\" data_sen2_monthly: {'✓' if data_sen2_monthly is not None else '✗'}\")\n",
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" print(f\" data_sen1_monthly: {'✓' if data_sen1_monthly is not None else '✗'}\")\n"
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]
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},
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{
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"cell_type": "markdown",
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"id": "f7cd0ca2",
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"metadata": {},
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"source": [
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"## 📊 Step 7: Split Data\n",
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"\n",
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"Chia dữ liệu thành train/val/test sets"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "523e1249",
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"metadata": {},
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"outputs": [],
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"source": [
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"from load_data_no_odc import split_train_data\n",
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"\n",
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"if 'X' in locals() and 'y' in locals():\n",
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" # Split data into train/val/test sets\n",
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" X_train, X_val, X_test, y_train, y_val, y_test = split_train_data(\n",
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" X, y, \n",
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" test_size=0.2, \n",
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" val_size=0.1, \n",
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" random_state=42\n",
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" )\n",
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" \n",
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" # Combine train + val for final model training\n",
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" X_fit = np.concatenate([X_train, X_val], axis=0)\n",
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" y_fit = np.concatenate([y_train, y_val], axis=0)\n",
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" \n",
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" print(f\"\\n✅ Final training set:\")\n",
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" print(f\" X_fit shape: {X_fit.shape}\")\n",
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" print(f\" y_fit shape: {y_fit.shape}\")\n",
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" print(f\"\\n X_test shape: {X_test.shape}\")\n",
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" print(f\" y_test shape: {y_test.shape}\")\n",
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" \n",
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"else:\n",
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" print(\"❌ Features not extracted yet. Please run Step 6 first.\")\n"
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]
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},
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{
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"cell_type": "markdown",
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"id": "a469500c",
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"metadata": {},
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"source": [
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"## 🌲 Step 8: Train Decision Tree Model\n",
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"\n",
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"Huấn luyện mô hình Decision Tree"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "445c2d92",
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"metadata": {},
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"outputs": [],
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"source": [
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"%%time\n",
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"from sklearn.tree import DecisionTreeClassifier\n",
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"\n",
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"if 'X_fit' in locals() and 'y_fit' in locals():\n",
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" \n",
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" print(\"🚀 Training Decision Tree model...\")\n",
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" print(\"-\" * 70)\n",
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" \n",
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" # Train Decision Tree with tuned hyperparameters\n",
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" model = DecisionTreeClassifier(\n",
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" max_depth=30,\n",
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" min_samples_leaf=2,\n",
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" min_samples_split=5,\n",
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" class_weight=\"balanced\",\n",
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" random_state=42,\n",
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" )\n",
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" \n",
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" model.fit(X_fit, y_fit)\n",
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" \n",
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" # Evaluate on validation set\n",
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" val_acc = model.score(X_val, y_val)\n",
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" \n",
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" print(f\"\\n✅ Training complete!\")\n",
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" print(f\" Model: Decision Tree\")\n",
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" print(f\" Tree depth: {model.get_depth()}\")\n",
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" print(f\" Number of leaves: {model.get_n_leaves()}\")\n",
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" print(f\" Training samples: {len(X_fit)}\")\n",
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" print(f\" Validation accuracy: {val_acc:.4f} ({val_acc*100:.2f}%)\")\n",
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" \n",
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"else:\n",
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" print(\"❌ Data not ready. Please run Step 7 first.\")\n"
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]
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},
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{
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"cell_type": "markdown",
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"id": "7c1e681e",
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"metadata": {},
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"source": [
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"## 📈 Step 9: Hyperparameter Analysis\n",
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"\n",
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"Phân tích độ sâu tối ưu (max_depth) cho Decision Tree"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "d13274e4",
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"metadata": {},
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"outputs": [],
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"source": [
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"%%time\n",
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"import matplotlib.pyplot as plt\n",
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"\n",
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"if 'X_fit' in locals() and 'X_val' in locals():\n",
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" \n",
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" DEPTH_RANGE = list(range(1, 51))\n",
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" \n",
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" train_accs = []\n",
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" val_accs = []\n",
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" \n",
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" print(\"🔍 Analyzing optimal max_depth for Decision Tree...\")\n",
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" print(\"-\" * 70)\n",
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" print(\"Testing depths from 1 to 50...\")\n",
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" \n",
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" for d in DEPTH_RANGE:\n",
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" m = DecisionTreeClassifier(\n",
|
|
" min_samples_leaf=2,\n",
|
|
" min_samples_split=5,\n",
|
|
" class_weight=\"balanced\",\n",
|
|
" random_state=42,\n",
|
|
" max_depth=d,\n",
|
|
" )\n",
|
|
" m.fit(X_fit, y_fit)\n",
|
|
" train_accs.append(m.score(X_fit, y_fit))\n",
|
|
" val_accs.append(m.score(X_val, y_val))\n",
|
|
" \n",
|
|
" if d % 10 == 0:\n",
|
|
" print(f\" Depth {d:2d}: train={train_accs[-1]:.4f}, val={val_accs[-1]:.4f}\")\n",
|
|
" \n",
|
|
" train_accs = np.array(train_accs)\n",
|
|
" val_accs = np.array(val_accs)\n",
|
|
" \n",
|
|
" best_depth = DEPTH_RANGE[np.argmax(val_accs)]\n",
|
|
" best_val_acc = np.max(val_accs)\n",
|
|
" \n",
|
|
" print(f\"\\n✅ Optimal max_depth: {best_depth}\")\n",
|
|
" print(f\" Best validation accuracy: {best_val_acc:.4f} ({best_val_acc*100:.2f}%)\")\n",
|
|
" \n",
|
|
" # Plot convergence analysis\n",
|
|
" fig, ax = plt.subplots(1, 1, figsize=(12, 5))\n",
|
|
" ax.plot(DEPTH_RANGE, train_accs, 'b-o', markersize=3, label='Train Accuracy')\n",
|
|
" ax.plot(DEPTH_RANGE, val_accs, 'g-o', markersize=3, label='Validation Accuracy')\n",
|
|
" ax.axvline(x=best_depth, color='red', linestyle='--', linewidth=2, \n",
|
|
" label=f'Best Depth = {best_depth}')\n",
|
|
" ax.set_xlabel('max_depth', fontsize=12)\n",
|
|
" ax.set_ylabel('Accuracy', fontsize=12)\n",
|
|
" ax.set_title('Decision Tree: Training vs Validation Accuracy', fontsize=14, fontweight='bold')\n",
|
|
" ax.legend(fontsize=10)\n",
|
|
" ax.grid(True, alpha=0.3)\n",
|
|
" plt.tight_layout()\n",
|
|
" plt.show()\n",
|
|
" \n",
|
|
"else:\n",
|
|
" print(\"❌ Data not ready. Please run Step 7 first.\")\n"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "b551cdb4",
|
|
"metadata": {},
|
|
"source": [
|
|
"## 📊 Step 10: Evaluate Model\n",
|
|
"\n",
|
|
"Đánh giá mô hình trên tập test"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"id": "bc3d6b9a",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"from sklearn.metrics import accuracy_score, classification_report, confusion_matrix\n",
|
|
"import seaborn as sns\n",
|
|
"\n",
|
|
"if 'model' in locals() and 'X_test' in locals():\n",
|
|
" \n",
|
|
" print(\"📊 Evaluating model on test set...\")\n",
|
|
" print(\"-\" * 70)\n",
|
|
" \n",
|
|
" # Make predictions\n",
|
|
" y_pred = model.predict(X_test)\n",
|
|
" acc = accuracy_score(y_test, y_pred)\n",
|
|
" \n",
|
|
" print(f\"\\n🎯 Test Accuracy: {acc:.4f} ({acc*100:.2f}%)\\n\")\n",
|
|
" print(\"📋 Classification Report:\")\n",
|
|
" print(classification_report(y_test, y_pred, digits=4))\n",
|
|
" \n",
|
|
" # Confusion Matrix\n",
|
|
" class_names = list(label_mapping.keys())\n",
|
|
" cm = confusion_matrix(y_test, y_pred)\n",
|
|
" \n",
|
|
" plt.figure(figsize=(10, 8))\n",
|
|
" sns.heatmap(cm, annot=True, fmt='d', cmap='Greens',\n",
|
|
" xticklabels=class_names, yticklabels=class_names,\n",
|
|
" cbar_kws={'label': 'Count'})\n",
|
|
" plt.xlabel('Predicted Label', fontsize=12)\n",
|
|
" plt.ylabel('True Label', fontsize=12)\n",
|
|
" plt.title('Confusion Matrix — Decision Tree (Test Set)', fontsize=14, fontweight='bold')\n",
|
|
" plt.tight_layout()\n",
|
|
" plt.show()\n",
|
|
" \n",
|
|
"else:\n",
|
|
" print(\"❌ Model not trained yet. Please run Step 8 first.\")\n"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "44d226fc",
|
|
"metadata": {},
|
|
"source": [
|
|
"## 💾 Step 11: Save Model\n",
|
|
"\n",
|
|
"Lưu mô hình và metadata"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"id": "044eefc4",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"import joblib\n",
|
|
"import json\n",
|
|
"from datetime import datetime\n",
|
|
"\n",
|
|
"if 'model' in locals() and 'acc' in locals():\n",
|
|
" \n",
|
|
" print(\"💾 Saving model and metadata...\")\n",
|
|
" print(\"-\" * 70)\n",
|
|
" \n",
|
|
" # Save model\n",
|
|
" model_path = \"model_decision_tree_planetary_computer.joblib\"\n",
|
|
" joblib.dump(model, model_path)\n",
|
|
" print(f\"✅ Model saved → {model_path}\")\n",
|
|
" \n",
|
|
" # Create metadata\n",
|
|
" info = {\n",
|
|
" \"model_type\": \"DecisionTree\",\n",
|
|
" \"data_source\": \"Microsoft Planetary Computer\",\n",
|
|
" \"training_data\": train_path,\n",
|
|
" \"max_depth\": int(model.get_depth()),\n",
|
|
" \"n_leaves\": int(model.get_n_leaves()),\n",
|
|
" \"n_features\": int(X_fit.shape[1]),\n",
|
|
" \"label_mapping\": label_mapping,\n",
|
|
" \"test_accuracy\": float(acc),\n",
|
|
" \"train_samples\": int(len(X_fit)),\n",
|
|
" \"val_samples\": int(len(X_val)),\n",
|
|
" \"test_samples\": int(len(X_test)),\n",
|
|
" \"bbox\": bbox,\n",
|
|
" \"date_range\": list(date_range),\n",
|
|
" \"saved_at\": datetime.now().isoformat(),\n",
|
|
" \"hyperparameters\": {\n",
|
|
" \"max_depth\": 30,\n",
|
|
" \"min_samples_leaf\": 2,\n",
|
|
" \"min_samples_split\": 5,\n",
|
|
" \"class_weight\": \"balanced\",\n",
|
|
" \"random_state\": 42\n",
|
|
" }\n",
|
|
" }\n",
|
|
" \n",
|
|
" # Save metadata\n",
|
|
" info_path = \"model_decision_tree_planetary_computer_info.json\"\n",
|
|
" with open(info_path, \"w\") as f:\n",
|
|
" json.dump(info, f, indent=2, ensure_ascii=False)\n",
|
|
" \n",
|
|
" print(f\"✅ Metadata saved → {info_path}\")\n",
|
|
" print(\"\\n📋 Model Info:\")\n",
|
|
" print(json.dumps(info, indent=2, ensure_ascii=False))\n",
|
|
" \n",
|
|
"else:\n",
|
|
" print(\"❌ Model not trained yet or evaluation not complete.\")\n"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "18c95152",
|
|
"metadata": {},
|
|
"source": [
|
|
"## 🧹 Step 12: Cleanup\n",
|
|
"\n",
|
|
"Đóng Dask cluster"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"id": "20445068",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"# Close Dask cluster\n",
|
|
"try:\n",
|
|
" client.close()\n",
|
|
" cluster.close()\n",
|
|
" print(\"✅ Dask cluster closed.\")\n",
|
|
"except Exception as e:\n",
|
|
" print(f\"⚠ Error closing cluster: {e}\")"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "046bf0f9",
|
|
"metadata": {},
|
|
"source": [
|
|
"---\n",
|
|
"\n",
|
|
"## ✅ Summary\n",
|
|
"\n",
|
|
"### Notebook này:\n",
|
|
"- ✅ Load dữ liệu từ **Microsoft Planetary Computer**\n",
|
|
"- ✅ Không cần **ODC Database**\n",
|
|
"- ✅ Có thể chạy trên **local machine** hoặc **server ODC**\n",
|
|
"- ✅ Tương thích 100% với dữ liệu ODC\n",
|
|
"\n",
|
|
"### Workflow train/predict:\n",
|
|
"1. **Train trên server ODC**: Chạy notebook này với training data đầy đủ\n",
|
|
"2. **Save model**: Export `.joblib` file\n",
|
|
"3. **Predict trên local**: Load model và sử dụng cùng `load_data_no_odc.py` để load dữ liệu mới\n",
|
|
"\n",
|
|
"### Next steps:\n",
|
|
"1. Cập nhật `train_path` với đường dẫn training data thực tế\n",
|
|
"2. Uncomment các TODO cells\n",
|
|
"3. Run notebook để train model\n",
|
|
"4. Test prediction trên local machine\n",
|
|
"\n",
|
|
"---"
|
|
]
|
|
}
|
|
],
|
|
"metadata": {
|
|
"language_info": {
|
|
"name": "python"
|
|
}
|
|
},
|
|
"nbformat": 4,
|
|
"nbformat_minor": 5
|
|
}
|