Muhammad al-xorazmiy nomidagi toshkent axborot texnalogiyalari universiteti kiberxavfsizlik fakulteti
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O’zbekiston Respublikasi Raqamli texnologiyalar vazirligi MUHAMMAD AL-XORAZMIY NOMIDAGI TOSHKENT AXBOROT TEXNALOGIYALARI UNIVERSITETI KIBERXAVFSIZLIK FAKULTETI Mashinali o’qitishga kirish Mustaqil ish Guruh: 715-21 Bajarildi: Sultonov Diyor Fan o’qituvchisi: Ochilov Mannon Toshkent 2023 MAVZU: AHOLI QATLAMLARINI SINFLASH import numpy as np import pandas as pd import matplotlib.pyplot as plt import cv2 import os from random import shuffle from tqdm import tqdm from PIL import Image import pandas as pd # загружаем данные data = pd.read_csv('population.csv') X = data.drop(['Class'], axis=1) # удаляем столбец с метками из данных для обучения y = data['Class'] # извлекаем столбец с метками # разделяем данные на обучающую и тестовую выборки seed = 1 np.random.seed(seed) X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=seed) # обучаем модель clf = RandomForestClassifier(n_estimators=100, random_state=seed) # устанавливаем количество деревьев в лесу равным 100 clf.fit(X_train, y_train) # # /content/drive/MyDrive/Room/images/images from google.colab import drive drive.mount('/content/drive') Mounted at /content/drive train_messy = "/content/drive/MyDrive/Room/images/images/train/messy" train_clean= "/content/drive/MyDrive/Room/images/images/train/clean" test_messy= "/content/drive/MyDrive/Room/images/images/val/messy" test_clean= "/content/drive/MyDrive/Room/images/images/val/clean" image_size = 128 Image.open("/content/drive/MyDrive/Room/images/images/train/messy/76.png") Image.open("/content/drive/MyDrive/Room/images/images/train/clean/76.png") for image in tqdm(os.listdir(train_messy)): path = os.path.join(train_messy, image) img = cv2.imread(path, cv2.IMREAD_GRAYSCALE) img = cv2.resize(img, (image_size, image_size)).flatten() np_img=np.asarray(img) for image2 in tqdm(os.listdir(train_clean)): path = os.path.join(train_clean, image2) img2 = cv2.imread(path, cv2.IMREAD_GRAYSCALE) img2 = cv2.resize(img2, (image_size, image_size)).flatten() np_img2=np.asarray(img2) plt.figure(figsize=(10,10)) plt.subplot(1, 2, 1) plt.imshow(np_img.reshape(image_size, image_size)) plt.axis('off') plt.subplot(1, 2, 2) plt.imshow(np_img2.reshape(image_size, image_size)) plt.axis('off') plt.title("Messy and Clean Rooms in GrayScale") 100%|██████████| 96/96 [01:15<00:00, 1.27it/s] 100%|██████████| 96/96 [01:12<00:00, 1.33it/s] Text(0.5, 1.0, 'Messy and Clean Rooms in GrayScale') def train_data(): train_data_messy = [] train_data_clean=[] for image1 in tqdm(os.listdir(train_messy)): path = os.path.join(train_messy, image) img1 = cv2.imread(path, cv2.IMREAD_GRAYSCALE) img1 = cv2.resize(img1, (image_size, image_size)) train_data_messy.append(img1) for image2 in tqdm(os.listdir(train_clean)): path = os.path.join(train_clean, image) img2 = cv2.imread(path, cv2.IMREAD_GRAYSCALE) img2 = cv2.resize(img2, (image_size, image_size)) train_data_clean.append(img2) train_data= np.concatenate((np.asarray(train_data_messy),np.asarray(train_data_clean)),axis=0) return train_data def test_data(): test_data_messy = [] test_data_clean=[] for image1 in tqdm(os.listdir(test_messy)): path = os.path.join(test_messy, image1) img1 = cv2.imread(path, cv2.IMREAD_GRAYSCALE) img1 = cv2.resize(img1, (image_size, image_size)) test_data_messy.append(img1) for image2 in tqdm(os.listdir(test_clean)): path = os.path.join(test_clean, image2) img2 = cv2.imread(path, cv2.IMREAD_GRAYSCALE) img2 = cv2.resize(img2, (image_size, image_size)) test_data_clean.append(img2) test_data= np.concatenate((np.asarray(test_data_messy),np.asarray(test_data_clean)),axis=0) return test_data train_data = train_data() test_data = test_data() 100%|██████████| 96/96 [00:00<00:00, 162.29it/s] 100%|██████████| 96/96 [00:00<00:00, 161.09it/s] 100%|██████████| 10/10 [00:07<00:00, 1.29it/s] 100%|██████████| 10/10 [00:07<00:00, 1.30it/s] train_data array([[[219, 220, 228, ..., 145, 145, 145], [202, 201, 221, ..., 148, 149, 149], [190, 190, 201, ..., 149, 148, 143], ..., [ 43, 43, 45, ..., 19, 18, 18], [ 43, 43, 49, ..., 20, 22, 18], [ 44, 45, 53, ..., 18, 21, 17]], [[219, 220, 228, ..., 145, 145, 145], [202, 201, 221, ..., 148, 149, 149], [190, 190, 201, ..., 149, 148, 143], ..., [ 43, 43, 45, ..., 19, 18, 18], [ 43, 43, 49, ..., 20, 22, 18], [ 44, 45, 53, ..., 18, 21, 17]], [[219, 220, 228, ..., 145, 145, 145], [202, 201, 221, ..., 148, 149, 149], [190, 190, 201, ..., 149, 148, 143], ..., [ 43, 43, 45, ..., 19, 18, 18], [ 43, 43, 49, ..., 20, 22, 18], [ 44, 45, 53, ..., 18, 21, 17]], ..., [[171, 173, 173, ..., 131, 149, 154], [172, 173, 173, ..., 126, 157, 151], [173, 174, 175, ..., 52, 53, 67], ..., [ 47, 47, 64, ..., 109, 110, 107], [ 47, 46, 85, ..., 111, 98, 107], [ 48, 48, 97, ..., 107, 111, 117]], [[171, 173, 173, ..., 131, 149, 154], [172, 173, 173, ..., 126, 157, 151], [173, 174, 175, ..., 52, 53, 67], ..., [ 47, 47, 64, ..., 109, 110, 107], [ 47, 46, 85, ..., 111, 98, 107], [ 48, 48, 97, ..., 107, 111, 117]], [[171, 173, 173, ..., 131, 149, 154], [172, 173, 173, ..., 126, 157, 151], [173, 174, 175, ..., 52, 53, 67], ..., [ 47, 47, 64, ..., 109, 110, 107], [ 47, 46, 85, ..., 111, 98, 107], [ 48, 48, 97, ..., 107, 111, 117]]], dtype=uint8) x_data=np.concatenate((train_data,test_data),axis=0) norm_max=np.max(x_data) norm_min=np.min(x_data) x_data = (x_data-norm_min)/(norm_max-norm_min) test_data.shape (20, 128, 128) z1 = np.zeros(96) o1 = np.ones(96) Y_train = np.concatenate((o1, z1), axis=0) z = np.zeros(10) o = np.ones(10) Y_test = np.concatenate((o, z), axis=0) Y_test array([1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.]) y_data=np.concatenate((Y_train,Y_test),axis=0).reshape(x_data.shape[0],1) print("X shape: " , x_data.shape) print("Y shape: " , y_data.shape) X shape: (212, 128, 128) Y shape: (212, 1) from sklearn.model_selection import train_test_split x_train, x_test, y_train, y_test = train_test_split(x_data, y_data, test_size=0.15, random_state=42) number_of_train = x_train.shape[0] number_of_test = x_test.shape[0] x_train_flatten = x_train.reshape(number_of_train,x_train.shape[1]*x_train.shape[2]) x_test_flatten = x_test .reshape(number_of_test,x_test.shape[1]*x_test.shape[2]) print("X train flatten",x_train_flatten.shape) print("X test flatten",x_test_flatten.shape) X train flatten (180, 16384) X test flatten (32, 16384) x_train = x_train_flatten.T x_test = x_test_flatten.T y_test = y_test.T y_train = y_train.T print("x train: ",x_train.shape) print("x test: ",x_test.shape) print("y train: ",y_train.shape) print("y test: ",y_test.shape) x train: (16384, 180) x test: (16384, 32) y train: (1, 180) y test: (1, 32) LOGISTIC REGRESSION def initialize_weights_and_bias(dimension): w = np.full((dimension,1),0.01) b = 0.0 return w, b def sigmoid(z): y_head = 1/(1+np.exp(-z)) return y_head def forward_backward_propagation(w,b,x_train,y_train): # forward propagation z = np.dot(w.T,x_train) + b y_head = sigmoid(z) loss = -y_train*np.log(y_head)-(1-y_train)*np.log(1-y_head) cost = (np.sum(loss))/x_train.shape[1] # backward propagation derivative_weight = (np.dot(x_train,((y_head-y_train).T)))/x_train.shape[1] derivative_bias = np.sum(y_head-y_train)/x_train.shape[1] gradients = {"derivative_weight": derivative_weight,"derivative_bias": derivative_bias} return cost,gradients def update(w, b, x_train, y_train, learning_rate,number_of_iterarion): cost_list = [] cost_list2 = [] index = [] for i in range(number_of_iterarion): cost,gradients = forward_backward_propagation(w,b,x_train,y_train) cost_list.append(cost) w = w - learning_rate * gradients["derivative_weight"] b = b - learning_rate * gradients["derivative_bias"] if i % 100 == 0: cost_list2.append(cost) index.append(i) print ("Cost after iteration %i: %f" %(i, cost)) parameters = {"weight": w,"bias": b} plt.plot(index,cost_list2) plt.xticks(index,rotation='vertical') plt.xlabel("Number of Iterarion") plt.ylabel("Cost") plt.show() return parameters, gradients, cost_list def predict(w,b,x_test): z = sigmoid(np.dot(w.T,x_test)+b) Y_prediction = np.zeros((1,x_test.shape[1])) for i in range(z.shape[1]): if z[0,i]<= 0.5: Y_prediction[0,i] = 0 else: Y_prediction[0,i] = 1 return Y_prediction def logistic_regression(x_train, y_train, x_test, y_test, learning_rate , num_iterations): dimension = x_train.shape[0] w,b = initialize_weights_and_bias(dimension) parameters, gradients, cost_list = update(w, b, x_train, y_train, learning_rate,num_iterations) y_prediction_test = predict(parameters["weight"],parameters["bias"],x_test) y_prediction_train = predict(parameters["weight"],parameters["bias"],x_train) print("Test Accuracy: {} %".format(round(100 - np.mean(np.abs(y_prediction_test - y_test)) * 100,2))) print("Train Accuracy: {} %".format(round(100 - np.mean(np.abs(y_prediction_train - y_train)) * 100,2))) logistic_regression(x_train, y_train, x_test, y_test,learning_rate = 0.01, num_iterations = 1500) loss = -y_train*np.log(y_head)-(1-y_train)*np.log(1-y_head) loss = -y_train*np.log(y_head)-(1-y_train)*np.log(1-y_head) Cost after iteration 0: nan Cost after iteration 100: 0.023990 Cost after iteration 200: 0.013663 Cost after iteration 300: 0.009604 Cost after iteration 400: 0.007400 Cost after iteration 500: 0.006012 Cost after iteration 600: 0.005057 Cost after iteration 700: 0.004361 Cost after iteration 800: 0.003831 Cost after iteration 900: 0.003415 Cost after iteration 1000: 0.003079 Cost after iteration 1100: 0.002803 Cost after iteration 1200: 0.002571 Cost after iteration 1300: 0.002375 Cost after iteration 1400: 0.002206 Test Accuracy: 90.62 % Train Accuracy: 100.0 % from sklearn.linear_model import LogisticRegression from sklearn.model_selection import GridSearchCV grid={"C":np.logspace(-3,3,7),"penalty":["l1","l2"]}, logistic_regression=LogisticRegression(random_state=42) log_reg_cv=GridSearchCV(logistic_regression,grid,cv=10) log_reg_cv.fit(x_train.T,y_train.T) /usr/local/lib/python3.10/dist-packages/sklearn/utils/validation.py:1143: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel(). y = column_or_1d(y, warn=True) /usr/local/lib/python3.10/dist-packages/sklearn/utils/validation.py:1143: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel(). y = column_or_1d(y, warn=True) /usr/local/lib/python3.10/dist-packages/sklearn/utils/validation.py:1143: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel(). y = column_or_1d(y, warn=True) /usr/local/lib/python3.10/dist-packages/sklearn/utils/validation.py:1143: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel(). y = column_or_1d(y, warn=True) /usr/local/lib/python3.10/dist-packages/sklearn/utils/validation.py:1143: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel(). y = column_or_1d(y, warn=True) /usr/local/lib/python3.10/dist-packages/sklearn/utils/validation.py:1143: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel(). y = column_or_1d(y, warn=True) /usr/local/lib/python3.10/dist-packages/sklearn/utils/validation.py:1143: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel(). y = column_or_1d(y, warn=True) /usr/local/lib/python3.10/dist-packages/sklearn/utils/validation.py:1143: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel(). y = column_or_1d(y, warn=True) /usr/local/lib/python3.10/dist-packages/sklearn/utils/validation.py:1143: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel(). y = column_or_1d(y, warn=True) /usr/local/lib/python3.10/dist-packages/sklearn/utils/validation.py:1143: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel(). y = column_or_1d(y, warn=True) /usr/local/lib/python3.10/dist-packages/sklearn/utils/validation.py:1143: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel(). y = column_or_1d(y, warn=True) /usr/local/lib/python3.10/dist-packages/sklearn/utils/validation.py:1143: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel(). y = column_or_1d(y, warn=True) /usr/local/lib/python3.10/dist-packages/sklearn/utils/validation.py:1143: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel(). y = column_or_1d(y, warn=True) /usr/local/lib/python3.10/dist-packages/sklearn/utils/validation.py:1143: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel(). y = column_or_1d(y, warn=True) /usr/local/lib/python3.10/dist-packages/sklearn/utils/validation.py:1143: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel(). y = column_or_1d(y, warn=True) /usr/local/lib/python3.10/dist-packages/sklearn/utils/validation.py:1143: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel(). y = column_or_1d(y, warn=True) /usr/local/lib/python3.10/dist-packages/sklearn/utils/validation.py:1143: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel(). y = column_or_1d(y, warn=True) /usr/local/lib/python3.10/dist-packages/sklearn/utils/validation.py:1143: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel(). y = column_or_1d(y, warn=True) /usr/local/lib/python3.10/dist-packages/sklearn/utils/validation.py:1143: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel(). y = column_or_1d(y, warn=True) /usr/local/lib/python3.10/dist-packages/sklearn/utils/validation.py:1143: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel(). y = column_or_1d(y, warn=True) /usr/local/lib/python3.10/dist-packages/sklearn/utils/validation.py:1143: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel(). y = column_or_1d(y, warn=True) /usr/local/lib/python3.10/dist-packages/sklearn/utils/validation.py:1143: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel(). y = column_or_1d(y, warn=True) /usr/local/lib/python3.10/dist-packages/sklearn/utils/validation.py:1143: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel(). y = column_or_1d(y, warn=True) /usr/local/lib/python3.10/dist-packages/sklearn/utils/validation.py:1143: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel(). y = column_or_1d(y, warn=True) /usr/local/lib/python3.10/dist-packages/sklearn/utils/validation.py:1143: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel(). y = column_or_1d(y, warn=True) /usr/local/lib/python3.10/dist-packages/sklearn/utils/validation.py:1143: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel(). y = column_or_1d(y, warn=True) /usr/local/lib/python3.10/dist-packages/sklearn/utils/validation.py:1143: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel(). y = column_or_1d(y, warn=True) /usr/local/lib/python3.10/dist-packages/sklearn/utils/validation.py:1143: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel(). y = column_or_1d(y, warn=True) /usr/local/lib/python3.10/dist-packages/sklearn/utils/validation.py:1143: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel(). y = column_or_1d(y, warn=True) /usr/local/lib/python3.10/dist-packages/sklearn/utils/validation.py:1143: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel(). y = column_or_1d(y, warn=True) /usr/local/lib/python3.10/dist-packages/sklearn/utils/validation.py:1143: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel(). y = column_or_1d(y, warn=True) /usr/local/lib/python3.10/dist-packages/sklearn/utils/validation.py:1143: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel(). y = column_or_1d(y, warn=True) /usr/local/lib/python3.10/dist-packages/sklearn/utils/validation.py:1143: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel(). y = column_or_1d(y, warn=True) /usr/local/lib/python3.10/dist-packages/sklearn/utils/validation.py:1143: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel(). y = column_or_1d(y, warn=True) /usr/local/lib/python3.10/dist-packages/sklearn/utils/validation.py:1143: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel(). y = column_or_1d(y, warn=True) /usr/local/lib/python3.10/dist-packages/sklearn/utils/validation.py:1143: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel(). y = column_or_1d(y, warn=True) /usr/local/lib/python3.10/dist-packages/sklearn/utils/validation.py:1143: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel(). y = column_or_1d(y, warn=True) /usr/local/lib/python3.10/dist-packages/sklearn/utils/validation.py:1143: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel(). y = column_or_1d(y, warn=True) /usr/local/lib/python3.10/dist-packages/sklearn/utils/validation.py:1143: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel(). y = column_or_1d(y, warn=True) /usr/local/lib/python3.10/dist-packages/sklearn/utils/validation.py:1143: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel(). y = column_or_1d(y, warn=True) /usr/local/lib/python3.10/dist-packages/sklearn/utils/validation.py:1143: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel(). y = column_or_1d(y, warn=True) /usr/local/lib/python3.10/dist-packages/sklearn/utils/validation.py:1143: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel(). y = column_or_1d(y, warn=True) /usr/local/lib/python3.10/dist-packages/sklearn/utils/validation.py:1143: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel(). y = column_or_1d(y, warn=True) /usr/local/lib/python3.10/dist-packages/sklearn/utils/validation.py:1143: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel(). y = column_or_1d(y, warn=True) /usr/local/lib/python3.10/dist-packages/sklearn/utils/validation.py:1143: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel(). y = column_or_1d(y, warn=True) /usr/local/lib/python3.10/dist-packages/sklearn/utils/validation.py:1143: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel(). y = column_or_1d(y, warn=True) /usr/local/lib/python3.10/dist-packages/sklearn/utils/validation.py:1143: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel(). y = column_or_1d(y, warn=True) /usr/local/lib/python3.10/dist-packages/sklearn/utils/validation.py:1143: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel(). y = column_or_1d(y, warn=True) /usr/local/lib/python3.10/dist-packages/sklearn/utils/validation.py:1143: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel(). y = column_or_1d(y, warn=True) /usr/local/lib/python3.10/dist-packages/sklearn/utils/validation.py:1143: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel(). y = column_or_1d(y, warn=True) /usr/local/lib/python3.10/dist-packages/sklearn/utils/validation.py:1143: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel(). y = column_or_1d(y, warn=True) /usr/local/lib/python3.10/dist-packages/sklearn/utils/validation.py:1143: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel(). y = column_or_1d(y, warn=True) /usr/local/lib/python3.10/dist-packages/sklearn/utils/validation.py:1143: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel(). y = column_or_1d(y, warn=True) /usr/local/lib/python3.10/dist-packages/sklearn/utils/validation.py:1143: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel(). y = column_or_1d(y, warn=True) /usr/local/lib/python3.10/dist-packages/sklearn/utils/validation.py:1143: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel(). y = column_or_1d(y, warn=True) /usr/local/lib/python3.10/dist-packages/sklearn/utils/validation.py:1143: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel(). y = column_or_1d(y, warn=True) /usr/local/lib/python3.10/dist-packages/sklearn/utils/validation.py:1143: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel(). y = column_or_1d(y, warn=True) /usr/local/lib/python3.10/dist-packages/sklearn/utils/validation.py:1143: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel(). y = column_or_1d(y, warn=True) /usr/local/lib/python3.10/dist-packages/sklearn/utils/validation.py:1143: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel(). y = column_or_1d(y, warn=True) /usr/local/lib/python3.10/dist-packages/sklearn/utils/validation.py:1143: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel(). y = column_or_1d(y, warn=True) /usr/local/lib/python3.10/dist-packages/sklearn/utils/validation.py:1143: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel(). y = column_or_1d(y, warn=True) /usr/local/lib/python3.10/dist-packages/sklearn/utils/validation.py:1143: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel(). y = column_or_1d(y, warn=True) /usr/local/lib/python3.10/dist-packages/sklearn/utils/validation.py:1143: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel(). y = column_or_1d(y, warn=True) /usr/local/lib/python3.10/dist-packages/sklearn/utils/validation.py:1143: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel(). y = column_or_1d(y, warn=True) /usr/local/lib/python3.10/dist-packages/sklearn/utils/validation.py:1143: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel(). y = column_or_1d(y, warn=True) /usr/local/lib/python3.10/dist-packages/sklearn/utils/validation.py:1143: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel(). y = column_or_1d(y, warn=True) /usr/local/lib/python3.10/dist-packages/sklearn/utils/validation.py:1143: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel(). y = column_or_1d(y, warn=True) /usr/local/lib/python3.10/dist-packages/sklearn/utils/validation.py:1143: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel(). y = column_or_1d(y, warn=True) /usr/local/lib/python3.10/dist-packages/sklearn/utils/validation.py:1143: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel(). y = column_or_1d(y, warn=True) /usr/local/lib/python3.10/dist-packages/sklearn/utils/validation.py:1143: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel(). y = column_or_1d(y, warn=True) /usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:378: FitFailedWarning: 70 fits failed out of a total of 140. The score on these train-test partitions for these parameters will be set to nan. If these failures are not expected, you can try to debug them by setting error_score='raise'. Below are more details about the failures: -------------------------------------------------------------------------------- 70 fits failed with the following error: Traceback (most recent call last): File "/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py", line 686, in _fit_and_score estimator.fit(X_train, y_train, **fit_params) File "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_logistic.py", line 1162, in fit solver = _check_solver(self.solver, self.penalty, self.dual) File "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_logistic.py", line 54, in _check_solver raise ValueError( ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got l1 penalty. warnings.warn(some_fits_failed_message, FitFailedWarning) /usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_search.py:952: UserWarning: One or more of the test scores are non-finite: [ nan 0.96111111 nan 0.95 nan 0.94444444 nan 0.95555556 nan 0.95555556 nan 0.93888889 nan 0.94444444] warnings.warn( /usr/local/lib/python3.10/dist-packages/sklearn/utils/validation.py:1143: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel(). y = column_or_1d(y, warn=True) GridSearchCV estimator: LogisticRegression LogisticRegression print("best hyperparameters: ", log_reg_cv.best_params_) print("accuracy: ", log_reg_cv.best_score_) best hyperparameters: {'C': 0.001, 'penalty': 'l2'} accuracy: 0.961111111111111 log_reg= LogisticRegression(C=0.001,penalty="l2") log_reg.fit(x_train.T,y_train.T) /usr/local/lib/python3.10/dist-packages/sklearn/utils/validation.py:1143: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel(). y = column_or_1d(y, warn=True) LogisticRegression LogisticRegression(C=0.001) print("test accuracy: {} ".format(log_reg.fit(x_test.T, y_test.T).score(x_test.T, y_test.T))) print("train accuracy: {} ".format(log_reg.fit(x_train.T, y_train.T).score(x_train.T, y_train.T))) /usr/local/lib/python3.10/dist-packages/sklearn/utils/validation.py:1143: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel(). y = column_or_1d(y, warn=True) /usr/local/lib/python3.10/dist-packages/sklearn/utils/validation.py:1143: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel(). y = column_or_1d(y, warn=True) test accuracy: 0.96875 train accuracy: 0.9666666666666667 [ ] log_reg.predict(x_test.T[0].reshape(1,-1)) account_circle array([1.]) [ ] from sklearn.metrics import classification_report, confusion_matrix, accuracy_score y_pred=log_reg.predict(x_train.T) result = confusion_matrix(y_train[0], y_pred) print(result) account_circle [[87 1] [ 5 87]] [ ] new_test_path="/content/drive/MyDrive/Room/images/images/test/8.png" Image.open(new_test_path) account_circle [ ] test_img = cv2.imread(new_test_path, cv2.IMREAD_GRAYSCALE) test_img = cv2.resize(test_img, (image_size, image_size)) test_img_1 = (test_img-norm_min)/(norm_max-norm_min) log_reg.predict(test_img_1.reshape(1,image_size*image_size)) account_circle array([1.]) [ ] from sklearn.neighbors import KNeighborsClassifier [ ] classifier = KNeighborsClassifier(n_neighbors = 127) classifier.fit(x_train.T,y_train.T) y_pred=classifier.predict(x_test.T) result = confusion_matrix(y_test[0], y_pred) print(result) account_circle [[18 0] [ 1 13]] /usr/local/lib/python3.10/dist-packages/sklearn/neighbors/_classification.py:215: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel(). return self._fit(X, y) Download 199.52 Kb. Do'stlaringiz bilan baham: 
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