from __future__ import print_function # This Python 3 environment comes with many helpful analytics libraries installed # It is defined by the kaggle/python docker image: https://github.com/kaggle/docker-python # For example, here's several helpful packages to load in import numpy as np # linear algebra import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv) # Input data files are available in the "../input/" directory. # For example, running this (by clicking run or pressing Shift+Enter) will list the files in the input directory from subprocess import check_output print(check_output(["ls", "../input"]).decode("utf8")) # Any results you write to the current directory are saved as output. import math #import time #import warnings #from skimage import data, io, filters import cv2 from sklearn.neural_network import MLPRegressor from sklearn.neural_network import MLPClassifier from sklearn.metrics import classification_report from sklearn.model_selection import KFold from sklearn.model_selection import cross_val_score as cv from sklearn.model_selection import cross_val_predict from sklearn import datasets from sklearn.metrics import r2_score from sklearn.metrics import roc_auc_score from matplotlib import pyplot as plt def load_dataset(): url = '/train/' def load_train_data(): X_train = [] X_train_id = [] y_train = [] start_time = time.time() print('Read train images') folders = ['ALB', 'BET', 'DOL', 'LAG', 'NoF', 'OTHER', 'SHARK', 'YFT'] for fld in folders: index = folders.index(fld) print('Load folder {} (Index: {})'.format(fld, index)) path = os.path.join('..', 'input', 'train', fld, '*.jpg') files = glob.glob(path) for fl in files: flbase = os.path.basename(fl) img = get_im_cv2(fl) X_train.append(img) X_train_id.append(flbase) y_train.append(index) print('Read train data time: {} seconds'.format(round(time.time() - start_time, 2))) return X_train, y_train, X_train_id def load_test_data(): path = os.path.join('..') X_test = [] X_test_id = [] for fl in files: flbase = os.path.basename(fl) img = get_im_cv2(fl) X_test.append(img) X_test_id.append(flbase) return X_test, X_test_id #create the CSV file ready for transmission with all the predictions def create_submission(predictions, test_id, info): result1 = pd.DataFrame(predictions, columns=['ALB', 'BET', 'DOL', 'LAG', 'NoF', 'OTHER', 'SHARK', 'YFT']) result1.loc[:, 'image'] = pd.Series(test_id, index=result1.index) now = datetime.datetime.now() sub_file = 'submission_' + info + '_' + str(now.strftime("%Y-%m-%d-%H-%M")) + '.csv' result1.to_csv(sub_file, index=False) def read_and_normalize_train_data(): train_data, train_target, train_id = load_train_data() print('Convert to numpy...') train_data = np.array(train_data, dtype=np.uint8) train_target = np.array(train_target, dtype=np.uint8) print('Reshape...') train_data = train_data.transpose((0, 3, 1, 2)) print('Convert to float...') train_data = train_data.astype('float32') train_data = train_data / 255 train_target = np_utils.to_categorical(train_target, 8) print('Train shape:', train_data.shape) print(train_data.shape[0], 'train samples') return train_data, train_target, train_id def create_model(): model = Sequential() model.add(ZeroPadding2D((1, 1), input_shape=(3, 32, 32), dim_ordering='th')) model.add(Convolution2D(4, 3, 3, activation='relu', dim_ordering='th')) model.add(ZeroPadding2D((1, 1), dim_ordering='th')) model.add(Convolution2D(4, 3, 3, activation='relu', dim_ordering='th')) model.add(MaxPooling2D(pool_size=(2, 2), strides=(2, 2), dim_ordering='th')) model.add(ZeroPadding2D((1, 1), dim_ordering='th')) model.add(Convolution2D(8, 3, 3, activation='relu', dim_ordering='th')) model.add(ZeroPadding2D((1, 1), dim_ordering='th')) model.add(Convolution2D(8, 3, 3, activation='relu', dim_ordering='th')) model.add(MaxPooling2D(pool_size=(2, 2), strides=(2, 2), dim_ordering='th')) model.add(Flatten()) model.add(Dense(32, activation='relu')) model.add(Dropout(0.5)) model.add(Dense(32, activation='relu')) model.add(Dropout(0.5)) model.add(Dense(8, activation='softmax')) sgd = SGD(lr=1e-2, decay=1e-6, momentum=0.9, nesterov=True) model.compile(optimizer=sgd, loss='categorical_crossentropy') return model #---------------- def read_and_normalize_test_data(): start_time = time.time() test_data, test_id = load_test() test_data = np.array(test_data, dtype=np.uint8) test_data = test_data.transpose((0, 3, 1, 2)) test_data = test_data.astype('float32') test_data = test_data / 255 print('Test shape:', test_data.shape) print(test_data.shape[0], 'test samples') print('Read and process test data time: {} seconds'.format(round(time.time() - start_time, 2))) return test_data, test_id def dict_to_list(d): ret = [] for i in d.items(): ret.append(i[1]) return ret def merge_several_folds_mean(data, nfolds): a = np.array(data[0]) for i in range(1, nfolds): a += np.array(data[i]) a /= nfolds return a.tolist() def predictTNA(df,tLabel): mlp = MLPRegressor(hidden_layer_sizes=(10,10)) #other parameters -- ,activation = 'logistic') Y = df[tLabel] X = df.drop(tLabel,axis=1).copy() #k_fold = KFold(n_splits=10) #scores = [] #for train,test in k_fold.split(X): # mlp = mlp.fit(X.iloc[train],Y.iloc[train]) # s = mlp.score(X.iloc[test],Y.iloc[test]) # scores.append(s) #print scores predicted = cross_val_predict(mlp,X,Y,cv=10) print (r2_score(Y,predicted)) def predictClass(df,tLabel): mlp = MLPClassifier(hidden_layer_sizes=(10,10)) Y = df[tLabel] X = df.drop(tLabel,axis=1).copy() predicted = cross_val_predict(mlp,X,Y,cv=10) print (roc_auc_score(Y,predicted)) def predictKerasClass(df,tLabel): Y = df[tLabel] X = df.drop(tLabel,axis=1).copy() #create model with 2 hidden layers of 10 units, and an output layer model = Sequential() model.add(Dense(10,input_dim=13,init='uniform',activation='relu')) model.add(Dropout(0.2)) #this randomly sets some input units to 0 - prevents overfitting model.add(Dense(10,init='uniform')) model.add(Activation('relu')) model.add(Dense(1,init='normal')) model.compile(loss='mean_squared_error',optimizer='adam') k_fold = KFold(n_splits=10) scores = [] #just one iteration of kFold CV for train,test in k_fold.split(X): model.fit(X.iloc[train].as_matrix(),Y.iloc[train].as_matrix(),nb_epoch=20, batch_size=100) s = model.evaluate(X.iloc[test].as_matrix(),Y.iloc[test].as_matrix(),batch_size=100) scores.append(s) break print (scores) predictKerasClass(url, '/test_stg1')