ghana-crop-project-resampled.ipynb

{"cells":[{"cell_type":"code","execution_count":1,"metadata":{"execution":{"iopub.execute_input":"2024-10-17T16:59:42.827013Z","iopub.status.busy":"2024-10-17T16:59:42.826565Z","iopub.status.idle":"2024-10-17T16:59:43.762639Z","shell.execute_reply":"2024-10-17T16:59:43.761489Z","shell.execute_reply.started":"2024-10-17T16:59:42.826968Z"},"id":"wH79yW8O2mQK","trusted":true},"outputs":[{"name":"stdout","output_type":"stream","text":["Train Data:\n","        Image_ID  confidence                  class         ymin        xmin  \\\n","0  id_11543h.jpg         1.0  Pepper_Bacterial_Spot   194.649671  328.803454   \n","1  id_11543h.jpg         1.0  Pepper_Bacterial_Spot   149.632401  256.768914   \n","2  id_11543h.jpg         1.0  Pepper_Bacterial_Spot   234.046875  327.138158   \n","3  id_11543h.jpg         1.0  Pepper_Bacterial_Spot   221.277138  340.411184   \n","4  id_11ee1c.jpg         1.0        Pepper_Fusarium  2000.563598  989.588908   \n","\n","          ymax         xmax  \n","0   208.107730   341.967928  \n","1   162.910362   266.195724  \n","2   252.712993   338.876645  \n","3   238.593750   354.651316  \n","4  2184.252196  1401.748952  \n","\n","Test Data:\n","        Image_ID  confidence  class  ymin  xmin  ymax  xmax\n","0  id_128pxx.jpg         NaN    NaN   NaN   NaN   NaN   NaN\n","1  id_12jbci.jpg         NaN    NaN   NaN   NaN   NaN   NaN\n","2  id_143s4o.jpg         NaN    NaN   NaN   NaN   NaN   NaN\n","3  id_14tfmb.jpg         NaN    NaN   NaN   NaN   NaN   NaN\n","4  id_14tw4o.jpg         NaN    NaN   NaN   NaN   NaN   NaN\n","\n","Train Data Info:\n","<class 'pandas.core.frame.DataFrame'>\n","RangeIndex: 41029 entries, 0 to 41028\n","Data columns (total 7 columns):\n"," #   Column      Non-Null Count  Dtype  \n","---  ------      --------------  -----  \n"," 0   Image_ID    41029 non-null  object \n"," 1   confidence  41029 non-null  float64\n"," 2   class       41029 non-null  object \n"," 3   ymin        41029 non-null  float64\n"," 4   xmin        41029 non-null  float64\n"," 5   ymax        41029 non-null  float64\n"," 6   xmax        41029 non-null  float64\n","dtypes: float64(5), object(2)\n","memory usage: 2.2+ MB\n","None\n"]}],"source":["import pandas as pd\n","\n","# Charger les fichiers CSV\n","train = pd.read_csv('/kaggle/input/ghana-crop-disease/Train.csv')\n","test = pd.read_csv('/kaggle/input/ghana-crop-disease/Test.csv')\n","\n","# Afficher les premières lignes du fichier d'entraînement\n","print(\"Train Data:\")\n","print(train.head())\n","\n","# Afficher les premières lignes du fichier de test\n","print(\"\\nTest Data:\")\n","print(test.head())\n","\n","# Afficher les informations sur les colonnes\n","print(\"\\nTrain Data Info:\")\n","print(train.info())\n"]},{"cell_type":"code","execution_count":12,"metadata":{"execution":{"iopub.execute_input":"2024-10-17T17:45:36.113984Z","iopub.status.busy":"2024-10-17T17:45:36.113213Z","iopub.status.idle":"2024-10-17T17:45:37.262481Z","shell.execute_reply":"2024-10-17T17:45:37.260616Z","shell.execute_reply.started":"2024-10-17T17:45:36.113916Z"},"trusted":true},"outputs":[{"data":{"image/png":"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","text/plain":["<Figure size 1000x600 with 1 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["import pandas as pd\n","import matplotlib.pyplot as plt\n","import seaborn as sns\n","\n","# Charger les données\n","#train = pd.read_csv('/content/Train.csv')\n","\n","# Visualiser la distribution des classes\n","plt.figure(figsize=(10,6))\n","sns.countplot(x='class', data=train)\n","plt.title('Distribution des classes dans le dataset de formation')\n","plt.xticks(rotation=90)\n","plt.show()\n","\n","# Analyser les dimensions des images (si disponibles)\n","train['image_path'] = '/content/images/' + train['Image_ID']\n"]},{"cell_type":"code","execution_count":20,"metadata":{"execution":{"iopub.execute_input":"2024-10-17T18:05:52.460546Z","iopub.status.busy":"2024-10-17T18:05:52.460035Z","iopub.status.idle":"2024-10-17T18:05:52.976170Z","shell.execute_reply":"2024-10-17T18:05:52.974944Z","shell.execute_reply.started":"2024-10-17T18:05:52.460502Z"},"trusted":true},"outputs":[{"name":"stdout","output_type":"stream","text":["\n","Distribution des classes avant over-sampling:\n","class\n","Corn_Cercospora_Leaf_Spot    6538\n","Tomato_Septoria              6483\n","Tomato_Late_Blight           3918\n","Corn_Streak                  3180\n","Tomato_Healthy               2872\n","Pepper_Septoria              2295\n","Pepper_Leaf_Mosaic           2113\n","Tomato_Early_Blight          1946\n","Pepper_Bacterial_Spot        1943\n","Corn_Common_Rust             1682\n","Corn_Healthy                 1571\n","Pepper_Leaf_Curl             1530\n","Tomato_Fusarium               882\n","Pepper_Healthy                736\n","Pepper_Late_Blight            575\n","Pepper_Leaf_Blight            508\n","Tomato_Bacterial_Spot         484\n","Pepper_Cercospora             472\n","Pepper_Fusarium               472\n","Tomato_Leaf_Curl              437\n","Corn_Northern_Leaf_Blight     157\n","Tomato_Mosaic                 134\n","Pepper_Early_Blight           101\n","Name: count, dtype: int64\n","\n","Distribution des classes après over-sampling:\n","class\n","Pepper_Bacterial_Spot        6538\n","Tomato_Healthy               6538\n","Pepper_Early_Blight          6538\n","Tomato_Mosaic                6538\n","Tomato_Bacterial_Spot        6538\n","Tomato_Leaf_Curl             6538\n","Pepper_Leaf_Blight           6538\n","Tomato_Fusarium              6538\n","Pepper_Cercospora            6538\n","Tomato_Late_Blight           6538\n","Pepper_Late_Blight           6538\n","Pepper_Healthy               6538\n","Pepper_Fusarium              6538\n","Corn_Healthy                 6538\n","Corn_Streak                  6538\n","Pepper_Leaf_Mosaic           6538\n","Pepper_Leaf_Curl             6538\n","Tomato_Septoria              6538\n","Pepper_Septoria              6538\n","Tomato_Early_Blight          6538\n","Corn_Common_Rust             6538\n","Corn_Cercospora_Leaf_Spot    6538\n","Corn_Northern_Leaf_Blight    6538\n","Name: count, dtype: int64\n","\n","Train Data Resampled:\n","        Image_ID  confidence         ymin        xmin         ymax  \\\n","0  id_11543h.jpg         1.0   194.649671  328.803454   208.107730   \n","1  id_11543h.jpg         1.0   149.632401  256.768914   162.910362   \n","2  id_11543h.jpg         1.0   234.046875  327.138158   252.712993   \n","3  id_11543h.jpg         1.0   221.277138  340.411184   238.593750   \n","4  id_11ee1c.jpg         1.0  2000.563598  989.588908  2184.252196   \n","\n","          xmax                     image_path                  class  \n","0   341.967928  /content/images/id_11543h.jpg  Pepper_Bacterial_Spot  \n","1   266.195724  /content/images/id_11543h.jpg  Pepper_Bacterial_Spot  \n","2   338.876645  /content/images/id_11543h.jpg  Pepper_Bacterial_Spot  \n","3   354.651316  /content/images/id_11543h.jpg  Pepper_Bacterial_Spot  \n","4  1401.748952  /content/images/id_11ee1c.jpg        Pepper_Fusarium  \n"]}],"source":["import pandas as pd\n","from imblearn.over_sampling import RandomOverSampler\n","\n","\n","# Identifier les colonnes de caractéristiques et la cible\n","X = train.drop(columns=['class'])  # Ici, \"label\" est supposé être la colonne cible, ajuste selon ton dataset\n","y = train['class']  # Colonne des classes cibles\n","\n","# Vérifier la distribution des classes avant le sur-échantillonnage\n","print(\"\\nDistribution des classes avant over-sampling:\")\n","print(y.value_counts())\n","\n","# Appliquer le Random Over-Sampling pour équilibrer les classes\n","ros = RandomOverSampler(random_state=42)\n","X_resampled, y_resampled = ros.fit_resample(X, y)\n","\n","# Créer un nouveau DataFrame après over-sampling\n","train_resampled = pd.concat([pd.DataFrame(X_resampled, columns=X.columns), pd.DataFrame(y_resampled, columns=['class'])], axis=1)\n","\n","# Vérifier la distribution des classes après le sur-échantillonnage\n","print(\"\\nDistribution des classes après over-sampling:\")\n","print(train_resampled['class'].value_counts())\n","\n","# Afficher les premières lignes du nouveau dataset sur-échantillonné\n","print(\"\\nTrain Data Resampled:\")\n","print(train_resampled.head())\n"]},{"cell_type":"code","execution_count":null,"metadata":{"trusted":true},"outputs":[],"source":[]}],"metadata":{"accelerator":"GPU","colab":{"gpuType":"T4","machine_shape":"hm","provenance":[]},"kaggle":{"accelerator":"none","dataSources":[{"datasetId":5864929,"sourceId":9611596,"sourceType":"datasetVersion"},{"isSourceIdPinned":true,"modelId":140045,"modelInstanceId":116830,"sourceId":137982,"sourceType":"modelInstanceVersion"}],"dockerImageVersionId":30786,"isGpuEnabled":false,"isInternetEnabled":false,"language":"python","sourceType":"notebook"},"kernelspec":{"display_name":"Python 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