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How to use test_submit_id method in lettuce_webdriver
Best Python code snippet using lettuce_webdriver_python
genetic-algorithm-for-parameterisation.py
Source:genetic-algorithm-for-parameterisation.py 
1import random2import numpy as np3import xgboost as xgb4import pandas as pd5from sklearn.preprocessing import LabelEncoder6from sklearn.metrics import r2_score7from deap import base8from deap import creator9from deap import tools10#######################11# Load & process data12#######################13def get_data():14 #################15 # read datasets16 #################17 train = pd.read_csv('../input/train.csv')18 test_submit = pd.read_csv('../input/test.csv')19 # Get y and ID20 train = train[train.y < 250] # Optional: Drop y outliers21 y_train = train['y']22 train = train.drop('y', 1)23 test_submit_id = test_submit['ID']24 #########################25 # Create data26 #########################27 features = ['X0',28 'X5',29 'X118',30 'X127',31 'X47',32 'X315',33 'X311',34 'X179',35 'X314',36 'X232',37 'X29',38 'X263',39 'X261']40 # Build a new dataset using key parameters, lots of drops41 train = train[features]42 test_submit = test_submit[features]43 # Label encoder44 for c in train.columns:45 if train[c].dtype == 'object':46 lbl = LabelEncoder()47 lbl.fit(list(train[c].values) + list(test_submit[c].values))48 train[c] = lbl.transform(list(train[c].values))49 test_submit[c] = lbl.transform(list(test_submit[c].values))50 # Convert to matrix51 train = train.as_matrix()52 y_train = np.transpose([y_train.as_matrix()])53 test_submit = test_submit.as_matrix()54 test_submit_id = test_submit_id.as_matrix()55 return train, y_train, test_submit, test_submit_id56#########################57# XGBoost Model58#########################59def gradient_boost(train, y_train, params):60 y_mean_train = np.mean(y_train)61 # prepare dict of params for xgboost to run with62 xgb_params = {63 'n_trees': params[0],64 'eta': params[1],65 'max_depth': params[2],66 'subsample': params[3],67 'objective': 'reg:linear',68 'eval_metric': 'rmse',69 'base_score': y_mean_train,70 'seed': 123456789,71 'silent': 172 }73 # form DMatrices for Xgboost training74 dtrain = xgb.DMatrix(train, y_train)75 # xgboost, cross-validation76 cv_result = xgb.cv(xgb_params,77 dtrain,78 nfold = 10,79 num_boost_round=5000,80 early_stopping_rounds=100,81 verbose_eval=False,82 show_stdv=False83 )84 num_boost_rounds = len(cv_result)85 # train model86 model = xgb.train(dict(xgb_params), dtrain, num_boost_round=num_boost_rounds)87 # get model accuracy88 accuracy = r2_score(dtrain.get_label(), model.predict(dtrain))89 return model, accuracy90######################91# Genetic algorithm92######################93creator.create("FitnessMax", base.Fitness, weights=(1.0,))94creator.create("Individual", list, fitness=creator.FitnessMax)95toolbox = base.Toolbox()96# Get data97train, y_train, test_submit, test_submit_id = get_data()98# Attribute generator99toolbox.register("n_trees", random.randint, 100, 10000)100toolbox.register("eta", random.uniform, 0.0001, 0.01)101toolbox.register("max_depth", random.randint, 1, 10)102toolbox.register("subsample", random.uniform, 0, 1)103# Structure initializers104toolbox.register("individual", tools.initCycle, creator.Individual,105 (toolbox.n_trees, toolbox.eta, toolbox.max_depth, toolbox.subsample), n=1)106# define the population to be a list of individuals107toolbox.register("population", tools.initRepeat, list, toolbox.individual)108# the goal ('fitness') function to be maximized109def evalOneMax(individual):110 model, accuracy = gradient_boost(train, y_train, individual)111 return [accuracy]112# the model for a specified individual113def getModel(individual):114 model, accuracy = gradient_boost(train, y_train, individual)115 return model116# register the goal / fitness function117toolbox.register("evaluate", evalOneMax)118# register the crossover operator119toolbox.register("mate", tools.cxTwoPoint)120# register a mutation operator121toolbox.register("mutate", tools.mutFlipBit, indpb=0.05)122# operator for selecting individuals for breeding the next123# generation124toolbox.register("select", tools.selTournament, tournsize=3)125def main():126 random.seed(12345)127 # create an initial population128 pop = toolbox.population(n=20)129 # CXPB is the probability with which two individuals130 # are crossed131 #132 # MUTPB is the probability for mutating an individual133 CXPB, MUTPB = 0.5, 0.2134 print("Start of evolution")135 # Evaluate the entire population136 fitnesses = list(map(toolbox.evaluate, pop))137 for ind, fit in zip(pop, fitnesses):138 ind.fitness.values = fit139 print(" Evaluated %i individuals" % len(pop))140 # Extracting all the fitnesses of141 fits = [ind.fitness.values[0] for ind in pop]142 # Variable keeping track of the number of generations143 g = 0144 # Begin the evolution145 while max(fits) < 100 and g < 1000:146 # A new generation147 g = g + 1148 print("-- Generation %i --" % g)149 # Select the next generation individuals150 offspring = toolbox.select(pop, len(pop))151 # Clone the selected individuals152 offspring = list(map(toolbox.clone, offspring))153 # Apply crossover and mutation on the offspring154 for child1, child2 in zip(offspring[::2], offspring[1::2]):155 # cross two individuals with probability CXPB156 if random.random() < CXPB:157 toolbox.mate(child1, child2)158 # fitness values of the children159 # must be recalculated later160 del child1.fitness.values161 del child2.fitness.values162 for mutant in offspring:163 # mutate an individual with probability MUTPB164 if random.random() < MUTPB:165 toolbox.mutate(mutant)166 del mutant.fitness.values167 # Evaluate the individuals with an invalid fitness168 invalid_ind = [ind for ind in offspring if not ind.fitness.valid]169 fitnesses = map(toolbox.evaluate, invalid_ind)170 for ind, fit in zip(invalid_ind, fitnesses):171 ind.fitness.values = fit172 print(" Evaluated %i individuals" % len(invalid_ind))173 # The population is entirely replaced by the offspring174 pop[:] = offspring175 # Gather all the fitnesses in one list and print the stats176 fits = [ind.fitness.values[0] for ind in pop]177 length = len(pop)178 mean = sum(fits) / length179 sum2 = sum(x * x for x in fits)180 std = abs(sum2 / length - mean ** 2) ** 0.5181 print(" Min %s" % min(fits))182 print(" Max %s" % max(fits))183 print(" Avg %s" % mean)184 print(" Std %s" % std)185 best_ind = tools.selBest(pop, 1)[0]186 print("Best individual so far is %s, %s" % (best_ind, best_ind.fitness.values))187 print("-- End of (successful) evolution --")188 best_ind = tools.selBest(pop, 1)[0]189 print("Best individual is %s, %s" % (best_ind, best_ind.fitness.values))190if __name__ == "__main__":...
deep-neural-network-using-tensorflow.py
Source:deep-neural-network-using-tensorflow.py
1import numpy as np2import pandas as pd3from sklearn.preprocessing import LabelEncoder4import tensorflow as tf5from sklearn.metrics import r2_score6from sklearn.model_selection import KFold7###################8# Load & process data9###################10def get_data():11 #################12 # read datasets13 #################14 train = pd.read_csv('../input/train.csv')15 test_submit = pd.read_csv('../input/test.csv')16 # Get y and ID17 train = train[train.y < 250] # Optional: Drop y outliers18 y_train = train['y']19 train = train.drop('y', 1)20 test_submit_id = test_submit['ID']21 #########################22 # Create data23 #########################24 features = ['X0',25 'X5',26 'X118',27 'X127',28 'X47',29 'X315',30 'X311',31 'X179',32 'X314',33 'X232',34 'X29',35 'X263',36 'X261']37 # Build a new dataset using key parameters, lots of drops38 train = train[features]39 test_submit = test_submit[features]40 # Label encoder41 for c in train.columns:42 if train[c].dtype == 'object':43 lbl = LabelEncoder()44 lbl.fit(list(train[c].values) + list(test_submit[c].values))45 train[c] = lbl.transform(list(train[c].values))46 test_submit[c] = lbl.transform(list(test_submit[c].values))47 # Convert to matrix48 train = train.as_matrix()49 y_train = np.transpose([y_train.as_matrix()])50 test_submit = test_submit.as_matrix()51 test_submit_id = test_submit_id.as_matrix()52 #print(train.shape)53 #print(test_submit.shape)54 return train, y_train, test_submit, test_submit_id55#####################56# Neural Network57#####################58# Training steps59STEPS = 50060LEARNING_RATE = 0.000161BETA = 0.0162DROPOUT = 0.563RANDOM_SEED = 1234564MAX_Y = 25065RESTORE = True66START = 067# Training variables68IN_DIM = 1369# Network Parameters - Hidden layers70n_hidden_1 = 10071n_hidden_2 = 5072def weight_variable(shape):73 initial = tf.truncated_normal(shape, stddev=0.01)74 return tf.Variable(initial)75def bias_variable(shape):76 initial = tf.constant(0.03, shape=shape)77 return tf.Variable(initial)78def deep_network(inputs, keep_prob):79 # Input -> Hidden Layer80 w1 = weight_variable([IN_DIM, n_hidden_1])81 b1 = bias_variable([n_hidden_1])82 # Hidden Layer -> Hidden Layer83 w2 = weight_variable([n_hidden_1, n_hidden_2])84 b2 = bias_variable([n_hidden_2])85 # Hidden Layer -> Output86 w3 = weight_variable([n_hidden_2, 1])87 b3 = bias_variable([1])88 # 1st Hidden layer with dropout89 h1 = tf.nn.relu(tf.matmul(inputs, w1) + b1)90 h1_dropout = tf.nn.dropout(h1, keep_prob)91 # 2nd Hidden layer with dropout92 h2 = tf.nn.relu(tf.matmul(h1_dropout, w2) + b2)93 h2_dropout = tf.nn.dropout(h2, keep_prob)94 # Run sigmoid on output to get 0 to 195 out = tf.nn.sigmoid(tf.matmul(h2_dropout, w3) + b3)96 # Loss function with L2 Regularization97 regularizers = tf.nn.l2_loss(w1) + tf.nn.l2_loss(w2) + tf.nn.l2_loss(w3)98 scaled_out = tf.multiply(out, MAX_Y) # Scale output99 return inputs, out, scaled_out, regularizers100def main(_):101 tf.set_random_seed(RANDOM_SEED)102 # Create the model103 x = tf.placeholder(tf.float32, [None, IN_DIM])104 # Define loss and optimizer105 y_ = tf.placeholder(tf.float32, [None, 1])106 # Dropout on hidden layers107 keep_prob = tf.placeholder("float")108 # Build the graph for the deep net109 inputs, out, scaled_out, regularizers = deep_network(x, keep_prob)110 # Normal loss function (RMSE)111 loss = tf.sqrt(tf.reduce_mean(tf.square(tf.subtract(y_, scaled_out))))112 # Loss function with L2 Regularization113 loss = tf.reduce_mean(loss + BETA * regularizers)114 # Optimizer115 train_step = tf.train.AdamOptimizer(LEARNING_RATE).minimize(loss)116 total_error = tf.reduce_sum(tf.square(tf.subtract(y_, tf.reduce_mean(y_))))117 unexplained_error = tf.reduce_sum(tf.square(tf.subtract(y_, scaled_out)))118 accuracy = tf.subtract(1.0, tf.divide(unexplained_error, total_error))119 # Save model120 saver = tf.train.Saver(max_to_keep=5)121 with tf.Session() as sess:122 #if RESTORE:123 # print('Loading Model...')124 # ckpt = tf.train.get_checkpoint_state('./models/neural/')125 # saver.restore(sess, ckpt.model_checkpoint_path)126 #else:127 sess.run(tf.global_variables_initializer())128 train, y_train, test_submit, test_submit_id = get_data()129 # Train until maximum steps reached or interrupted130 for i in range(START, STEPS):131 k_fold = KFold(n_splits=10, shuffle=True)132 #if i % 100 == 0:133 # saver.save(sess, './models/neural/step_' + str(i) + '.cptk')134 for k, (ktrain, ktest) in enumerate(k_fold.split(train, y_train)):135 train_step.run(feed_dict={x: train[ktrain], y_: y_train[ktrain], keep_prob: DROPOUT})136 # Show test score every 10 iterations137 if i % 10 == 0:138 # Tensorflow R2139 #train_accuracy = accuracy.eval(feed_dict={140 # x: train[ktest], y_: y_train[ktest]})141 # SkLearn metrics R2142 train_accuracy = r2_score(y_train[ktest],143 sess.run(scaled_out, feed_dict={x: train[ktest], keep_prob: 1.0}))144 print('Step: %d, Fold: %d, R2 Score: %g' % (i, k, train_accuracy))145 ####################146 # CV (repeat 5 times)147 ####################148 CV = []149 for i in range(5):150 k_fold = KFold(n_splits=10, shuffle=True)151 for k, (ktrain, ktest) in enumerate(k_fold.split(train, y_train)):152 # Tensorflow R2153 #accuracy = accuracy.eval(feed_dict={154 # x: train[ktest], y_: y_train[ktest]})155 # SkLearn metrics R2156 accuracy = r2_score(y_train[ktest],157 sess.run(scaled_out, feed_dict={x: train[ktest], keep_prob: 1.0}))158 print('Step: %d, Fold: %d, R2 Score: %g' % (i, k, accuracy))159 CV.append(accuracy)160 print('Mean R2: %g' % (np.mean(CV)))161if __name__ == '__main__':...
deepNN-Liam.py
Source:deepNN-Liam.py
1import numpy as np2import pandas as pd3from sklearn.preprocessing import LabelEncoder4import tensorflow as tf5from sklearn.metrics import r2_score6from sklearn.model_selection import KFold7###################8# Load & process data9###################10def get_data():11 #################12 # read datasets13 #################14 train = pd.read_csv('../input/train.csv')15 test_submit = pd.read_csv('../input/test.csv')16 # Get y and ID17 train = train[train.y < 250] # Optional: Drop y outliers18 y_train = train['y']19 train = train.drop('y', 1)20 test_submit_id = test_submit['ID']21 #########################22 # Create data23 #########################24 features = ['X0',25 'X5',26 'X118',27 'X127',28 'X47',29 'X315',30 'X311',31 'X179',32 'X314',33 'X232',34 'X29',35 'X263',36 'X261']37 # Build a new dataset using key parameters, lots of drops38 train = train[features]39 test_submit = test_submit[features]40 # Label encoder41 for c in train.columns:42 if train[c].dtype == 'object':43 lbl = LabelEncoder()44 lbl.fit(list(train[c].values) + list(test_submit[c].values))45 train[c] = lbl.transform(list(train[c].values))46 test_submit[c] = lbl.transform(list(test_submit[c].values))47 # Convert to matrix48 train = train.as_matrix()49 y_train = np.transpose([y_train.as_matrix()])50 test_submit = test_submit.as_matrix()51 test_submit_id = test_submit_id.as_matrix()52 #print(train.shape)53 #print(test_submit.shape)54 return train, y_train, test_submit, test_submit_id55#####################56# Neural Network57#####################58# Training steps59STEPS = 50060LEARNING_RATE = 0.000161BETA = 0.0162DROPOUT = 0.563RANDOM_SEED = 1234564MAX_Y = 25065RESTORE = True66START = 067# Training variables68IN_DIM = 1369# Network Parameters - Hidden layers70n_hidden_1 = 10071n_hidden_2 = 5072def weight_variable(shape):73 initial = tf.truncated_normal(shape, stddev=0.01)74 return tf.Variable(initial)75def bias_variable(shape):76 initial = tf.constant(0.03, shape=shape)77 return tf.Variable(initial)78def deep_network(inputs, keep_prob):79 # Input -> Hidden Layer80 w1 = weight_variable([IN_DIM, n_hidden_1])81 b1 = bias_variable([n_hidden_1])82 # Hidden Layer -> Hidden Layer83 w2 = weight_variable([n_hidden_1, n_hidden_2])84 b2 = bias_variable([n_hidden_2])85 # Hidden Layer -> Output86 w3 = weight_variable([n_hidden_2, 1])87 b3 = bias_variable([1])88 # 1st Hidden layer with dropout89 h1 = tf.nn.relu(tf.matmul(inputs, w1) + b1)90 h1_dropout = tf.nn.dropout(h1, keep_prob)91 # 2nd Hidden layer with dropout92 h2 = tf.nn.relu(tf.matmul(h1_dropout, w2) + b2)93 h2_dropout = tf.nn.dropout(h2, keep_prob)94 # Run sigmoid on output to get 0 to 195 out = tf.nn.sigmoid(tf.matmul(h2_dropout, w3) + b3)96 # Loss function with L2 Regularization97 regularizers = tf.nn.l2_loss(w1) + tf.nn.l2_loss(w2) + tf.nn.l2_loss(w3)98 scaled_out = tf.multiply(out, MAX_Y) # Scale output99 return inputs, out, scaled_out, regularizers100def main(_):101 tf.set_random_seed(RANDOM_SEED)102 # Create the model103 x = tf.placeholder(tf.float32, [None, IN_DIM])104 # Define loss and optimizer105 y_ = tf.placeholder(tf.float32, [None, 1])106 # Dropout on hidden layers107 keep_prob = tf.placeholder("float")108 # Build the graph for the deep net109 inputs, out, scaled_out, regularizers = deep_network(x, keep_prob)110 # Normal loss function (RMSE)111 loss = tf.sqrt(tf.reduce_mean(tf.square(tf.subtract(y_, scaled_out))))112 # Loss function with L2 Regularization113 loss = tf.reduce_mean(loss + BETA * regularizers)114 # Optimizer115 train_step = tf.train.AdamOptimizer(LEARNING_RATE).minimize(loss)116 total_error = tf.reduce_sum(tf.square(tf.subtract(y_, tf.reduce_mean(y_))))117 unexplained_error = tf.reduce_sum(tf.square(tf.subtract(y_, scaled_out)))118 accuracy = tf.subtract(1.0, tf.divide(unexplained_error, total_error))119 # Save model120 saver = tf.train.Saver(max_to_keep=5)121 with tf.Session() as sess:122 #if RESTORE:123 # print('Loading Model...')124 # ckpt = tf.train.get_checkpoint_state('./models/neural/')125 # saver.restore(sess, ckpt.model_checkpoint_path)126 #else:127 sess.run(tf.global_variables_initializer())128 train, y_train, test_submit, test_submit_id = get_data()129 # Train until maximum steps reached or interrupted130 for i in range(START, STEPS):131 k_fold = KFold(n_splits=10, shuffle=True)132 #if i % 100 == 0:133 # saver.save(sess, './models/neural/step_' + str(i) + '.cptk')134 for k, (ktrain, ktest) in enumerate(k_fold.split(train, y_train)):135 train_step.run(feed_dict={x: train[ktrain], y_: y_train[ktrain], keep_prob: DROPOUT})136 # Show test score every 10 iterations137 if i % 10 == 0:138 # Tensorflow R2139 #train_accuracy = accuracy.eval(feed_dict={140 # x: train[ktest], y_: y_train[ktest]})141 # SkLearn metrics R2142 train_accuracy = r2_score(y_train[ktest],143 sess.run(scaled_out, feed_dict={x: train[ktest], keep_prob: 1.0}))144 print('Step: %d, Fold: %d, R2 Score: %g' % (i, k, train_accuracy))145 ####################146 # CV (repeat 5 times)147 ####################148 CV = []149 for i in range(5):150 k_fold = KFold(n_splits=10, shuffle=True)151 for k, (ktrain, ktest) in enumerate(k_fold.split(train, y_train)):152 # Tensorflow R2153 #accuracy = accuracy.eval(feed_dict={154 # x: train[ktest], y_: y_train[ktest]})155 # SkLearn metrics R2156 accuracy = r2_score(y_train[ktest],157 sess.run(scaled_out, feed_dict={x: train[ktest], keep_prob: 1.0}))158 print('Step: %d, Fold: %d, R2 Score: %g' % (i, k, accuracy))159 CV.append(accuracy)160 print('Mean R2: %g' % (np.mean(CV)))161if __name__ == '__main__':...
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