live-ex-1.ipynb

{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"name":"live-ex-1.ipynb","provenance":[],"collapsed_sections":[],"machine_shape":"hm"},"kernelspec":{"name":"python3","display_name":"Python 3"},"language_info":{"name":"python"}},"cells":[{"cell_type":"markdown","source":["##  Võ Thành Hoàng Sơn - 19DH110660\n"],"metadata":{"id":"4CZv3wikIy4C"}},{"cell_type":"markdown","source":["# MLPClassifier with digits dataset\n"," "],"metadata":{"id":"pl7sZlO7I8uv"}},{"cell_type":"code","source":["##Import lib\n","\n","\n","import numpy as np\n","import pandas as pd\n","import matplotlib.pyplot as plt\n","\n","!pip install pipelinehelper -q\n","from sklearn.model_selection import train_test_split, cross_val_score, GridSearchCV, KFold\n","from sklearn.neural_network import MLPClassifier\n","from sklearn.metrics import classification_report\n","\n","from sklearn.datasets import load_digits\n","\n","\n","from sklearn.pipeline import Pipeline\n","from pipelinehelper import PipelineHelper\n","\n","import warnings\n","warnings.filterwarnings(\"ignore\")"],"metadata":{"id":"E5E9TKUiJIxm"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["##Load dataset\n","imgs = load_digits().images\n","data,target = load_digits(return_X_y = True)\n","print(f\"Data shape: {data.shape}\")\n","print(f\"Output shape: {target.shape}\")\n"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"Rf1khR9rJpI0","executionInfo":{"status":"ok","timestamp":1651906542726,"user_tz":-420,"elapsed":352,"user":{"displayName":"Hoàng Sơn Võ Thành","userId":"13162457009128917605"}},"outputId":"f5eeb596-1dd4-408b-9a94-e9fe36794d9e"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Data shape: (1797, 64)\n","Output shape: (1797,)\n"]}]},{"cell_type":"markdown","source":["- Dataset gồm 1797 mẫu với 64 đặc trưng tương ứng với 64 điểm ảnh (8*8)\n","- Output gồm 1797 mẫu tương ứng kết quả của Dataset"],"metadata":{"id":"l5zaN3mETZzf"}},{"cell_type":"code","source":["print(f\"Số class: \\n{pd.DataFrame(target).value_counts()}\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"3SyQccgOTwJ6","executionInfo":{"status":"ok","timestamp":1651906851812,"user_tz":-420,"elapsed":367,"user":{"displayName":"Hoàng Sơn Võ Thành","userId":"13162457009128917605"}},"outputId":"2ad04ab2-0916-4a13-ffe1-4d40e697681e"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Số class: \n","3    183\n","1    182\n","5    182\n","4    181\n","6    181\n","9    180\n","7    179\n","0    178\n","2    177\n","8    174\n","dtype: int64\n"]}]},{"cell_type":"markdown","source":["## Plot some data"],"metadata":{"id":"mxDfrf7MJ5_j"}},{"cell_type":"code","source":["plt.figure(figsize=(20,4))\n","for i in range(1,11):\n","  plt.gray()\n","  plt.subplot(1,11,i)\n","  plt.imshow(imgs[i])\n","  plt.title(f\"target:{target[i]}\")\n","plt.show()\n"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":150},"id":"7CpYi2aXJ8ym","executionInfo":{"status":"ok","timestamp":1651906441047,"user_tz":-420,"elapsed":1285,"user":{"displayName":"Hoàng Sơn Võ Thành","userId":"13162457009128917605"}},"outputId":"3dcf7707-62fb-4ecb-c025-3798a12f4757"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 1440x288 with 10 Axes>"],"image/png":"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GridSearch"],"metadata":{"id":"mvhkBjfFKJv2"}},{"cell_type":"code","source":["## Custom transform\n","from sklearn.preprocessing import FunctionTransformer\n","\n","from sklearn.preprocessing import Normalizer,StandardScaler"],"metadata":{"id":"kO_NBMVDK4OU"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["## Pipeline params\n","\n","\n","params = {}\n","params[\"data_split_train\"] = 0.7\n","params['random_state'] = 54\n","params['k_fold'] = 3\n"," \n"],"metadata":{"id":"EkAP0UbzLFli"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["pipe = Pipeline([\n","    ('scaler', PipelineHelper([\n","        ('std', StandardScaler()),\n","        ('normal', Normalizer()),\n","        ('non', FunctionTransformer(None,validate=False)),\n","    ])),\n","    ('classifier', PipelineHelper([\n","        ('mlp', MLPClassifier()),\n","\n","    ])),\n","])\n","\n","# Các cấu hình thử nghiệm\n","parameters = {\n","\n","    'scaler__selected_model': pipe.named_steps['scaler'].generate({\n","      'std__with_mean': [True],\n","      'std__with_std': [False],\n","\n","      'normal__norm': ['l1','l2','max']\n","    }),\n","    'classifier__selected_model': pipe.named_steps['classifier'].generate({\n","      'mlp__activation': ['logistic','tanh', 'relu'],\n","      'mlp__hidden_layer_sizes': [(100,),(32,64,),(64,32,),(64,32,10,)],\n","\n","    })\n","}"],"metadata":{"id":"__fsSIX3KfeZ"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["X_train, X_test, y_train, y_test = train_test_split(data,target,random_state=params[\"random_state\"],train_size=params['data_split_train'])"],"metadata":{"id":"e2ssD3TOLGZ_"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["import os\n","n_cpus= os.cpu_count()\n","print(f\"Số nhân cpu hiện có: {n_cpus}\")\n","# Lựa chọn cấu hình: kiểm thử chéo \n","grid = GridSearchCV(pipe, parameters, cv=params['k_fold'], scoring='accuracy', refit=True,n_jobs=n_cpus,verbose=10)\n","#Fit and train model\n","grid.fit(X_train, y_train)\n","\n","\n","print(\"-\"*80)\n","print(\"Cấu hình tốt nhất: \", grid.best_params_)\n","print(\"Độ chính xác: {:6.2f}\".format(grid.score(X_test, y_test)))"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"I9Xl5uvJLLS5","executionInfo":{"status":"ok","timestamp":1651905442854,"user_tz":-420,"elapsed":113346,"user":{"displayName":"Hoàng Sơn Võ Thành","userId":"13162457009128917605"}},"outputId":"999f13a0-a47f-46c8-d9ae-6addd2311ecf"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Số nhân cpu hiện có: 4\n","Fitting 3 folds for each of 60 candidates, totalling 180 fits\n","--------------------------------------------------------------------------------\n","Cấu hình tốt nhất:  {'classifier__selected_model': ('mlp', {'activation': 'tanh', 'hidden_layer_sizes': (100,)}), 'scaler__selected_model': ('non', {})}\n","Độ chính xác:   0.98\n"]}]},{"cell_type":"markdown","source":["## Define model with best params"],"metadata":{"id":"r37IYHHWNuL0"}},{"cell_type":"code","source":["X_train, X_test, y_train, y_test = train_test_split(data,target,random_state=params[\"random_state\"],train_size=params['data_split_train'])\n","model = MLPClassifier(activation= 'tanh',hidden_layer_sizes= (100,))\n","model.fit(X_train,y_train)\n","print(\"Train done!\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"OW_pac03NkP1","executionInfo":{"status":"ok","timestamp":1651905466006,"user_tz":-420,"elapsed":5010,"user":{"displayName":"Hoàng Sơn Võ Thành","userId":"13162457009128917605"}},"outputId":"371a4797-b072-4619-df95-2081a8281496"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Train done!\n"]}]},{"cell_type":"markdown","source":["## Predict"],"metadata":{"id":"DxA5wdSDOBuV"}},{"cell_type":"code","source":["y_pred = model.predict(X_test)"],"metadata":{"id":"fAYDcLpBOIJM"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["from sklearn import metrics\n","from sklearn.metrics import confusion_matrix\n","import seaborn as sns\n","\n","# Tính các độ đo\n","cmatrix = confusion_matrix(y_pred,y_test)\n","plt.figure(figsize=(10,5))\n","sns.heatmap(cmatrix,cmap='coolwarm',alpha=0.8)\n","\n","print(\"Ma trận nhầm lẫn: \")\n","print(cmatrix)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":507},"id":"cMGqvTcwOKaG","executionInfo":{"status":"ok","timestamp":1651905466008,"user_tz":-420,"elapsed":25,"user":{"displayName":"Hoàng Sơn Võ Thành","userId":"13162457009128917605"}},"outputId":"59e5cfbd-5d4f-4e38-d18c-faa4cd521561"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Ma trận nhầm lẫn: \n","[[48  0  0  0  0  0  0  0  0  0]\n"," [ 0 51  0  0  0  0  0  0  3  0]\n"," [ 0  0 56  0  0  0  0  0  0  0]\n"," [ 0  0  0 48  0  0  0  0  0  0]\n"," [ 0  0  0  0 63  1  0  0  0  0]\n"," [ 0  0  0  0  0 57  0  1  0  0]\n"," [ 0  0  0  0  0  1 55  0  0  0]\n"," [ 0  0  0  0  1  0  0 51  0  0]\n"," [ 0  0  1  0  1  0  0  1 46  0]\n"," [ 0  0  0  1  0  1  0  0  0 53]]\n"]},{"output_type":"display_data","data":{"text/plain":["<Figure size 720x360 with 2 Axes>"],"image/png":"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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","source":["# Tổng quan kết quả\n","metric_score = classification_report(y_pred,y_test)\n","print(metric_score)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"A529CD9bOMkK","executionInfo":{"status":"ok","timestamp":1651905466011,"user_tz":-420,"elapsed":22,"user":{"displayName":"Hoàng Sơn Võ Thành","userId":"13162457009128917605"}},"outputId":"59766501-cb79-47da-fe33-4ecf55742214"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["              precision    recall  f1-score   support\n","\n","           0       1.00      1.00      1.00        48\n","           1       1.00      0.94      0.97        54\n","           2       0.98      1.00      0.99        56\n","           3       0.98      1.00      0.99        48\n","           4       0.97      0.98      0.98        64\n","           5       0.95      0.98      0.97        58\n","           6       1.00      0.98      0.99        56\n","           7       0.96      0.98      0.97        52\n","           8       0.94      0.94      0.94        49\n","           9       1.00      0.96      0.98        55\n","\n","    accuracy                           0.98       540\n","   macro avg       0.98      0.98      0.98       540\n","weighted avg       0.98      0.98      0.98       540\n","\n"]}]},{"cell_type":"code","source":["from sklearn.metrics import precision_recall_fscore_support\n","scores = precision_recall_fscore_support(y_test, y_pred, average='macro')\n","accuracy = metrics.accuracy_score(y_test, y_pred, normalize=True)\n","# Kết quả\n","print(\"Độ chính xác (precision): {:7.2f}%\".format(scores[0]*100))\n","print(\"Độ triệu hồi (recall): {:7.2f}%\".format(scores[1]*100))\n","print(\"Độ đo F1 (F1-measure): {:7.2f}%\".format(scores[2]*100))\n","print(\"Độ chính xác (accuracy): {:7.2f}%\".format(accuracy*100))"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"WHugZceQOQV6","executionInfo":{"status":"ok","timestamp":1651905466012,"user_tz":-420,"elapsed":20,"user":{"displayName":"Hoàng Sơn Võ Thành","userId":"13162457009128917605"}},"outputId":"c1165abe-aa8b-4210-9c40-f1c43f340b08"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Độ chính xác (precision):   97.77%\n","Độ triệu hồi (recall):   97.82%\n","Độ đo F1 (F1-measure):   97.78%\n","Độ chính xác (accuracy):   97.78%\n"]}]},{"cell_type":"code","source":[""],"metadata":{"id":"1Acup2P_Ohkz"},"execution_count":null,"outputs":[]}]}