def compute_dtw(data, dataarray, w):
click.echo('--- Compute DTW ---')
timeseries, timeseries_label = load_labelled(dataarray)
timeserie_1 = load_test(data)
click.echo(' - data : %s ' % data)
click.echo(' - dataarray : %s ' % dataarray)
click.echo(' - w : %d ' % w)
click.echo('\nRunning...')
unsorted_dtws = get_distances(timeserie_1[0], data_array=timeseries, max_warping_window=w)
# Save plots
dtw_plots(unsorted_dtws)
click.echo('Done. Plots have been saved.')
click.echo('Choose a maximum number for labelling good and bad data based on DTW values.')
click.echo('Check the plots to take better decsision.')
click.echo(' Example: If value for Good is 150, all data with DTW 0-150 will be labelled "Good".')
# Enter limit for 'Good'
good_value = raw_input(' > Enter a value for "Good" (Ex: 150) : ')
# Enter limit for 'Bad'
bad_value = raw_input(' > Enter a value for "Bad" (Ex: 350) : ')
# Print and save results to CSV
fileName = raw_input(' > Enter a file name (add .csv at the end) : ')
label_dtws(unsorted_dtws, int(good_value), int(bad_value), fileName)
click.echo('\nDone.')
def predict(k, w, train, test):
click.echo('--- Predicting a label ---')
#click.echo('Predicting with k=%d and w=%d.' % (k,w))
train_data, train_label = load_labelled(train)
test_data = load_test(test)
click.echo(' - k : %d ' % k)
click.echo(' - w : %d ' % w)
click.echo(' - train : %s ' % train)
click.echo(' - test : %s ' % test)
click.echo('\nRunning...')
model = KnnDtw(k_neighbours = k, max_warping_window = w)
model.fit(train_data, train_label)
predicted_label, probability = model.predict(test_data)
click.echo('\nPredicted label : %s ' % str(predicted_label))
click.echo('\nDone.')
print "Best average score =", best[0]
print "Average threshold =", threshold
print "Best params =", params
print "Save fold predictions for stacking..."
decisions = best[5]
for i, d in enumerate(decisions):
np.save("stack/%s-fold%d.npy" % (prefix, i), decisions[i])
# Retrain on the training set
print "Retrain on the full training set..."
clf = Classifier(**params)
w = rescale(w)
w = rebalance(y, w)
try:
clf.fit(X, y, sample_weight=w)
except:
clf.fit(X, y)
print "Save test predictions for stacking..."
X_test, _, _, ids = load_test()
#X_test = tf.transform(X_test.astype(np.float32))
d = clf.predict_proba(X_test)[:, 0]
d = d.flatten()
np.save("stack/%s-test.npy" % prefix, d)
utils.start(__file__)
#==============================================================================
PREF = 'f332_'
KEY = 'SK_ID_CURR'
ins_start = 0 # 1~277
ins_end = 1 # 1~277
os.system(f'rm ../feature/t*_{PREF}*')
# =============================================================================
#
# =============================================================================
train = utils.load_train([KEY])
test = utils.load_test([KEY])
# =============================================================================
#
# =============================================================================
def aggregate(args):
path, pref = args
df = utils.read_pickles(path)
df = df[df['NUM_INSTALMENT_NUMBER'].between(ins_start, ins_end)]
del df['SK_ID_PREV']
df_agg = df.groupby(KEY).agg({**utils_agg.ins_num_aggregations})
df_agg.columns = pd.Index(
[e[0] + "_" + e[1] for e in df_agg.columns.tolist()])
li = only_target1(c)
feature[f'{PREF}_{c}'] = (df[c].isin(li)) * 1
feature[f'{PREF}_sum'] = feature.sum(1)
feature.iloc[:200000].to_pickle(f'../data/train_{PREF}.pkl')
feature.iloc[200000:].reset_index(
drop=True).to_pickle(f'../data/test_{PREF}.pkl')
return
# =============================================================================
# main
# =============================================================================
if __name__ == "__main__":
utils.start(__file__)
tr = utils.load_train().drop(['ID_code', 'target'], axis=1)
y_train = utils.load_target()['target']
te = utils.load_test().drop(['ID_code'], axis=1)
tr0 = tr[y_train == 0]
tr1 = tr[y_train == 1]
trte = pd.concat([tr, te], ignore_index=True)[tr.columns]
fe(trte)
utils.end(__file__)
# as any of the known words - so the default idf is the max of
# known idf's
max_idf = max(tfidf.idf_)
self.word2weight = defaultdict(
lambda: max_idf,
[(w, tfidf.idf_[i]) for w, i in tfidf.vocabulary_.items()])
return self
def transform(self, X):
return np.array([
np.mean([self.word2vec[w] * self.word2weight[w]
for w in words if w in self.word2vec] or
[np.zeros(self.dim)], axis=0)
for words in X
])
etree_w2v = Pipeline([
("word2vec vectorizer", MeanEmbeddingVectorizer(w2v)),
("extra trees", ExtraTreesClassifier(n_estimators=200))])
etree_w2v_tfidf = Pipeline([
("word2vec vectorizer", TfidfEmbeddingVectorizer(w2v)),
("extra trees", ExtraTreesClassifier(n_estimators=200))])
res = utils.load_train()
etree_w2v.fit(res[0], res[1])
test = utils.load_test()
preds = etree_w2v.predict(test[0])
print(metrics.classification_report(test[1], preds))
print(metrics.confusion_matrix(test[1], preds))
loop = 1
param = {
'max_depth': 15,
'eta': 0.1,
'colsample_bytree': 0.6,
'subsample': 0.5,
'silent': 1,
# 'scale_pos_weight':1.707, # neg/pos
'eval_metric': 'auc',
'objective': 'binary:logistic'
}
train = utils.load_train(file_in=file_in)
test = utils.load_test(file_in=file_in)
#==============================================================================
# logloss NO sampling
#==============================================================================
def get_valid_col(col):
return [
c for c in col if c.count(',') > 0 or c.count('[') > 0
or c.count(']') > 0 or c.count('>') > 0
]
col = ['qid1', 'qid2', 'question1', 'question2', 'is_duplicate']
y_train = train.is_duplicate
train_sub = train[['id', 'is_duplicate']]
def classify(train, examples):
cv_res = {
"PP": 0,
"PN": 0,
"NP": 0,
"NN": 0,
"contradictory": 0,
}
plus = train["plus"]
minus = train["minus"]
l = len(examples)
i = 0
for elem in examples:
i += 1
print "%i/%i" % (i, l)
result = check_hypothesis(plus, minus, elem)
cv_res[result] += 1
return cv_res
if __name__ == "__main__":
index = int(sys.argv[1])
train = utils.load_train(index)
test = utils.load_test(index)
res = classify(train, test)
print res
print utils.summary(res)
models.append(model)
model.save_model('../model/xgb{}.model'.format(i))
train_col = dtrain.feature_names
del train, dtrain, y_train
gc.collect()
imp = ex.getImp(models)
imp.to_csv('../output/imp-{}.csv'.format(date), index=0)
#==============================================================================
# test
#==============================================================================
test1 = utils.load_test(file_in, file_remove)
col = ['test_id', 'question1', 'question2']
sub = test1[col]
test1.drop(col, axis=1, inplace=1)
if is_mirror:
print('q1_to_q2!')
test2 = utils.q1_to_q2(test1)
dtest1 = xgb.DMatrix(test1[train_col])
dtest2 = xgb.DMatrix(test2[train_col])
del test1, test2
gc.collect()
sub['is_duplicate'] = 0
def get_ppr10():
p = utils.load_test()
ppr10 = simple_extract(p)
return ppr10[0]
def gae_for(args, iter='0.txt'):
# print("Using {} dataset".format(args.ds))
# adj_cd, features = load_data(args.ds)
'Load features!'
if args.ds.startswith('tf'):
if args.labels == 'y':
adj_cd, adj_dd, features, tags_nodes = my_load_data_tfidf_semi(
args.wmd)
else:
adj_cd, adj_dd, features = my_load_data_tfidf(args.wmd)
else:
#
if args.labels == 'y':
adj_cd, adj_dd, features, tags_nodes = my_load_data_p2v_semi(
args.wmd)
else:
adj_cd, adj_dd, features = my_load_data_p2v(args.wmd)
# adj_cd, adj_dd, features = my_load_data_p2v()
'Load test adjacency matrix'
adj_test = load_test()
# adj_test = load_test_10_percent(iter)
n_nodes, feat_dim = features.shape
# Store original adjacency matrix (without diagonal entries) for later
adj_orig_cd = adj_cd
'do again for adj_dd'
adj_orig_dd = adj_dd
adj_train_cd, train_edges, val_edges, val_edges_false = mask_train_edges(
adj_cd)
'do again for adj_dd'
adj_train_dd, train_edges_dd, _, _ = mask_train_edges(adj_dd) #
adj_cd = adj_train_cd
adj_dd = adj_train_dd
test_edges, test_edges_false = make_test_edges(adj_train_cd, adj_test)
# Some preprocessing: calculate norm
adj_norm_cd = preprocess_graph(adj_cd)
'For loss function: add diag values'
adj_label_cd = adj_train_cd + sp.eye(adj_train_cd.shape[0])
adj_label_cd = torch.FloatTensor(adj_label_cd.toarray())
pos_weight_cd = float(adj_cd.shape[0] * adj_cd.shape[0] -
adj_cd.sum()) / adj_cd.sum()
norm_cd = adj_cd.shape[0] * adj_cd.shape[0] / float(
(adj_cd.shape[0] * adj_cd.shape[0] - adj_cd.sum()) * 2)
'do it again for adj_dd'
adj_norm_dd = preprocess_graph(adj_dd)
adj_label_dd = adj_train_dd + sp.eye(adj_train_dd.shape[0])
adj_label_dd = torch.FloatTensor(adj_label_dd.toarray())
pos_weight_dd = float(adj_dd.shape[0] * adj_dd.shape[0] -
adj_dd.sum()) / adj_dd.sum()
norm_dd = adj_dd.shape[0] * adj_dd.shape[0] / float(
(adj_dd.shape[0] * adj_dd.shape[0] - adj_dd.sum()) * 2)
if args.labels == 'y':
model = GCNModelVAE_Semi(feat_dim, args.hidden1, args.hidden2,
args.dropout, args.class_dim)
else:
model = GCNModelVAE(feat_dim, args.hidden1, args.hidden2, args.dropout)
optimizer = optim.Adam(model.parameters(), lr=args.lr)
print('Now start training...')
hidden_emb = None
for epoch in range(args.epochs):
t = time.time()
model.train()
optimizer.zero_grad()
# import pdb;pdb.set_trace
if args.labels == 'y':
recovered, mu, logvar, pred_nodes = model(
features, [adj_norm_cd, adj_norm_dd])
loss = loss_function_relation_semi(preds=recovered,
labels=(adj_label_cd,
adj_label_dd),
mu=mu,
logvar=logvar,
n_nodes=n_nodes,
norm=(norm_cd, norm_dd),
pos_weight=(pos_weight_cd,
pos_weight_dd),
pred_nodes=pred_nodes,
tags_nodes=tags_nodes)
else:
recovered, mu, logvar = model(features, [adj_norm_cd, adj_norm_dd])
loss = loss_function_relation(preds=recovered,
labels=(adj_label_cd, adj_label_dd),
mu=mu,
logvar=logvar,
n_nodes=n_nodes,
norm=(norm_cd, norm_dd),
pos_weight=(pos_weight_cd,
pos_weight_dd))
loss.backward()
cur_loss = loss.item()
optimizer.step()
hidden_emb = mu.data.numpy()
acc_curr, p, r, f1, map_curr, roc_curr = my_eval(
hidden_emb, (adj_orig_cd, adj_orig_dd), val_edges, val_edges_false)
print("Epoch:", '%04d' % (epoch + 1), "train_loss=",
"{:.5f}".format(cur_loss), "val_ap=", "{:.5f}".format(map_curr),
"val_ac=", "{:.5f}".format(acc_curr), "time=",
"{:.5f}".format(time.time() - t))
print("Optimization Finished!")
acc_score, p, r, f1, map_score, roc_score = my_eval_test(
hidden_emb, (adj_orig_cd, adj_orig_dd), test_edges, test_edges_false)
# print('Test ROC score: ' + str(roc_score))
# print('Test AP score: ' + str(ap_score))
# print ("Test accuracy ", "{:.5f}".format(acc_score))
# print ('P {:.5f}, R {:.5f}, F1 {:.5f}'.format(p,r,f1))
print('Acc, P, R, F1, MAP, AUC')
print('{:5f},{:5f},{:5f},{:5f},{:5f},{:5f}'.format(acc_score, p, r, f1,
map_score, roc_score))
return acc_score, p, r, f1, map_score, roc_score
"""
import pandas as pd
import os
import utils
utils.start(__file__)
# =============================================================================
folders = ['../feature_prev', '../feature_prev_unused']
for fol in folders:
os.system(f'rm -rf {fol}')
os.system(f'mkdir {fol}')
train = utils.load_train(['SK_ID_CURR', 'TARGET'])
test = utils.load_test(['SK_ID_CURR'])
prev = utils.read_pickles('../data/previous_application')
prev_train = pd.merge(prev, train, on='SK_ID_CURR', how='inner')
prev_test = pd.merge(prev, test, on='SK_ID_CURR', how='inner')
utils.to_pickles(prev_train, '../data/prev_train', utils.SPLIT_SIZE)
utils.to_pickles(prev_test, '../data/prev_test', utils.SPLIT_SIZE)
utils.to_pickles(prev_train[['TARGET']], '../data/prev_label',
utils.SPLIT_SIZE)
"""
prev_train = utils.read_pickles('../data/prev_train')
prev_test = utils.read_pickles('../data/prev_test')
from alpaca import Alpaca
from utils import load_test, split_df, TimeSeriesResampler, confusion_matrix
import time
from sklearn.model_selection import train_test_split
from sklearn.utils import shuffle
from sklearn.pipeline import Pipeline
import numpy as np
import pandas as pd
if __name__ == '__main__':
X, y = load_test()
# Length of timeseries for resampler and cnn
sz = 230
# Number of channels for cnn
num_channels = X.shape[-1]
# Number of classes for cnn
num_classes = np.unique(y).shape[0]
classes = np.array(["0", "1", "2", "3", "4", "?"])
repetitions = 1
results = []
outliers = np.empty((0, 230 * 3 + 5))
for r in range(repetitions):
print("Repetition #", r)
X, y = shuffle(X, y, random_state=r)
# Turn y to numpy array
y = np.array(y)
def predict():
model = RL(istrained=True, name='rl_noun')
te_X, _ = load_test(size='_t')
te_X = norm4d_per_sample(te_X)
return model.predict(te_X)
import numpy as np
from run_knn import run_knn
import matplotlib.pyplot as plt
"""
CSC 2515 - Assignment 1
Tausif Sharif
Notes:
- Runs the run_knn.py functions from here
- Will show and save relevant plots
"""
trainInputs, trainTargets = load_train()
smallInputs, smallTargets = load_train_small()
validInputs, validTargets = load_valid()
testInputs, testTargets = load_test()
kList = [1, 3, 5, 7, 9]
classRates = range(0, len(kList))
classRatesT = range(0, len(kList))
listCount = 0
for k in kList:
correctCount = 0
validLables = run_knn(k, trainInputs, trainTargets, validInputs)
for i in xrange(len(validLables)):
if validLables[i] == validTargets[i]:
correctCount += 1
classRates[listCount] = (correctCount / float(len(validLables)))
listCount += 1
import utils as utl
import tensorflow as tf
import numpy as np
cuisine_list, ingredients_list, xs, ys = utl.load_train('vector')
ts, ids = utl.load_test(ingredients_list)
cuisine_count = len(cuisine_list)
ingredients_count = len(ingredients_list)
x = tf.placeholder(tf.float32, [None, ingredients_count])
W = tf.Variable(tf.zeros([ingredients_count, cuisine_count]))
b = tf.Variable(tf.zeros([cuisine_count]))
y = tf.nn.softmax(tf.matmul(x, W) + b)
y_ = tf.placeholder(tf.float32, [None, cuisine_count])
t = tf.placeholder(tf.float32, [None, ingredients_count])
p = tf.nn.softmax(tf.matmul(t, W) + b)
cross_entropy = -tf.reduce_sum(y_*tf.log(y))
train_step = tf.train.GradientDescentOptimizer(0.001).minimize(cross_entropy)
# train_step = tf.train.AdamOptimizer(1e-4).minimize(cross_entropy)
init = tf.initialize_all_variables()
sess = tf.Session(config=tf.ConfigProto(allow_soft_placement=True))
sess.run(init)
'date_time_dow', 'date_time_hour', 'date_time_month', 'srch_ci_dow', 'srch_ci_month', \
'srch_co_dow', 'srch_co_month', 'booking_window', 'length_of_stay']], \
site_name_encoding, posa_continent_encoding, user_location_country_encoding, user_location_region_encoding, \
channel_encoding, srch_destination_type_id_encoding, hotel_continent_encoding, hotel_country_encoding], axis=1)
train_is_booking_features.to_csv(utils.processed_data_path +
'_'.join(['train_is_booking_baseline', 'year', utils.train_year]) + '.csv',
header=True, index=False)
del train_is_booking
#############################################################
#################### test dataset ####################
#############################################################
test = utils.load_test('baseline')
print 'generate test time features...'
time_features_enricher(test)
print 'generate test one hot encoding features...'
site_name_encoding, posa_continent_encoding, user_location_country_encoding, user_location_region_encoding, \
channel_encoding, srch_destination_type_id_encoding, hotel_continent_encoding, hotel_country_encoding = \
gen_all_top_one_hot_encoding_columns(test)
print 'fill test na features...'
fill_na_features(test)
print 'concat all test baseline features...'
test_features = pd.concat([test[['date_time', 'orig_destination_distance', \
'is_mobile', 'is_package', 'srch_adults_cnt', 'srch_children_cnt', 'srch_rm_cnt', \
def predict():
model = PlainCNN(istrained=True)
te_X, _ = load_test(size='_t')
te_X = norm4d_per_sample(te_X)
return model.predict(te_X)
'NAME_TYPE_SUITE',
'NAME_INCOME_TYPE',
'NAME_EDUCATION_TYPE',
'NAME_FAMILY_STATUS',
'NAME_HOUSING_TYPE',
'OCCUPATION_TYPE',
'WEEKDAY_APPR_PROCESS_START',
'ORGANIZATION_TYPE',
'FONDKAPREMONT_MODE',
'HOUSETYPE_MODE',
'WALLSMATERIAL_MODE',
# 'EMERGENCYSTATE_MODE'
]
train = utils.load_train(categorical_features)
test = utils.load_test(categorical_features)
le = LabelEncoder()
for c in categorical_features:
train[c].fillna('na dayo', inplace=True)
test[c].fillna('na dayo', inplace=True)
le.fit(train[c].append(test[c]))
train[c] = le.transform(train[c])
test[c] = le.transform(test[c])
utils.to_feature(train.add_prefix(PREF), '../feature/train')
utils.to_feature(test.add_prefix(PREF), '../feature/test')
#==============================================================================
utils.end(__file__)
import pandas as pd
import os
import utils
utils.start(__file__)
#==============================================================================
PREF = 'f110_'
KEY = 'SK_ID_CURR'
os.system(f'rm ../feature/t*_{PREF}*')
# =============================================================================
# load
# =============================================================================
train = utils.load_train(['SK_ID_CURR']).set_index('SK_ID_CURR')
test = utils.load_test(['SK_ID_CURR']).set_index('SK_ID_CURR')
prev = utils.read_pickles('../data/previous_application',
['SK_ID_CURR', 'SK_ID_PREV'])
# =============================================================================
# prev
# =============================================================================
gr = prev.groupby('SK_ID_CURR')
train['SK_ID_PREV_min'] = gr.SK_ID_PREV.min()
train['SK_ID_PREV_mean'] = gr.SK_ID_PREV.mean()
train['SK_ID_PREV_max'] = gr.SK_ID_PREV.max()
train['SK_ID_PREV_median'] = gr.SK_ID_PREV.median()
train['SK_ID_PREV_std'] = gr.SK_ID_PREV.std()
train['SK_ID_PREV_std-d-mean'] = train['SK_ID_PREV_std'] / train[
'SK_ID_PREV_mean']
def _conditional_change(dictionary, target, value):
dictionary[target] = test.get(value, dictionary[target])
def _conditional_remove(dictionary, key):
if key in test:
if not test[key]:
dictionary.pop(key)
_conditional_change(settings["input"], "shape", "shape")
_conditional_change(settings["input"], "type", "input_type")
_conditional_change(settings["normalize"], "means", "means")
_conditional_change(settings["normalize"], "stddevs", "stddevs")
_conditional_change(settings["return"]["result"], "operations",
"operations")
_conditional_change(settings["return"]["result"], "arguments", "arguments")
_conditional_change(settings["return"]["result"], "type", "return_type")
_conditional_change(settings["return"]["result"], "item", "item")
# Remove if no output or result should be returned
_conditional_remove(settings, "normalize")
_conditional_remove(settings["return"], "output")
_conditional_remove(settings["return"], "result")
if __name__ == "__main__":
args = utils.parse_args()
test = utils.load_test(args)
settings = load_settings()
imput_arguments(settings, test)
save(settings)
"""
Created on Feb 27 2017
Author: Weiping Song
"""
import sys
import numpy as np
import argparse
import tensorflow as tf
from model import GRU4Rec
from utils import load_test
unfold_max = 20
cut_off = 20
test_x, test_y, n_items = load_test(unfold_max)
class Args():
is_training = False
layers = 1
rnn_size = 100
n_epochs = 10
batch_size = 50
keep_prob = 1
learning_rate = 0.002
decay = 0.98
decay_steps = 1e3 * 5
sigma = 0.0005
init_as_normal = False
grad_cap = 0
# Leo Woiceshyn, Student Number 998082159, for CSC2515 Assignment 1
import numpy as np
import utils as ut
import matplotlib.pyplot as plt
from run_knn import run_knn
#Load data
train_data, train_labels = ut.load_train()
valid_data, valid_labels = ut.load_valid()
test_data, test_labels = ut.load_test()
#Create empty arrays for accuracy values
validation_accuracies = []
test_accuracies = []
#List for k
k_values = [1,3,5,7,9]
#Validation Set
for k in k_values:
correct_predictions = 0
total_predictions = 0
predicted_valid_labels = run_knn(k, train_data, train_labels, valid_data)
#Iterate through the predicted labels and compare them to the true labels to determine validation accuracy
for index, value in enumerate(predicted_valid_labels):
if predicted_valid_labels[index] == valid_labels[index]:
correct_predictions += 1
total_predictions += 1
if FLAGS.is_train:
train_data, valid_data = load_data(FLAGS.train_data)
model = Model(FLAGS)
model.print_parameters()
if tf.train.get_checkpoint_state(FLAGS.train_dir):
model.saver.restore(sess, tf.train.latest_checkpoint(FLAGS.train_dir))
else:
sess.run(tf.global_variables_initializer())
epoch = 0
pre_loss = 1000000.0;
while epoch < FLAGS.epoch:
#train data
start_time = time.time()
train_acc, train_loss = train(sess, model, train_data, FLAGS.batch_size, trainable = True)
epoch_time = time.time() - start_time
lr = model.learning_rate.eval()
print("epoch %d time: %.4f seconds, learning_rate: %.6f\n train loss: %.6f, train accuracy: %.6f" % (epoch, epoch_time, lr, train_loss, train_acc))
valid_acc, valid_loss = train(sess, model, valid_data, FLAGS.batch_size, trainable = False)
print("valid loss: %.6f, valid accuracy: %.6f" % (valid_loss, valid_acc))
if valid_loss < pre_loss:
pre_loss = valid_loss
model.saver.save(sess, '%s/ckp' % FLAGS.train_dir, global_step = epoch)
sess.run(model.learning_rate_decay_op)
epoch += 1
else:
model = Model(FLAGS)
model.saver.restore(sess, tf.train.latest_checkpoint(FLAGS.train_dir))
test_data = load_test(FLAGS.test_data)
get_test_label(sess, model, test_data, )
# 'CODE_GENDER',
# 'FLAG_OWN_CAR',
# 'FLAG_OWN_REALTY',
'NAME_TYPE_SUITE',
'NAME_INCOME_TYPE',
'NAME_EDUCATION_TYPE',
'NAME_FAMILY_STATUS',
'NAME_HOUSING_TYPE',
'OCCUPATION_TYPE',
'WEEKDAY_APPR_PROCESS_START',
'ORGANIZATION_TYPE',
'FONDKAPREMONT_MODE',
'HOUSETYPE_MODE',
'WALLSMATERIAL_MODE',
# 'EMERGENCYSTATE_MODE'
]
# =============================================================================
#
# =============================================================================
train = utils.load_train().drop(['SK_ID_CURR', 'TARGET']+categorical_features, axis=1)
test = utils.load_test().drop(['SK_ID_CURR']+categorical_features, axis=1)
utils.to_feature(train.add_prefix(PREF), '../feature/train')
utils.to_feature(test.add_prefix(PREF), '../feature/test')
#==============================================================================
utils.end(__file__)
import os
import utils
utils.start(__file__)
#==============================================================================
PREF = 'f705_'
KEY = 'SK_ID_CURR'
os.system(f'rm ../feature/t*_{PREF}*')
# =============================================================================
# load
# =============================================================================
train = utils.load_train([KEY]).set_index(KEY)
test = utils.load_test([KEY]).set_index(KEY)
prev_train = pd.read_feather('../data/prev_train_imputation_f705.f')
prev_test = pd.read_feather('../data/prev_test_imputation_f705.f')
# =============================================================================
# feature
# =============================================================================
# train
gr = prev_train.groupby(KEY)
train['prev_y_min'] = gr.y_pred.min()
train['prev_y_mean'] = gr.y_pred.mean()
train['prev_y_max'] = gr.y_pred.max()
train['prev_y_var'] = gr.y_pred.var()
train['prev_y_median'] = gr.y_pred.median()
train['prev_y_q25'] = gr.y_pred.quantile(.25)
from sklearn.multiclass import OneVsRestClassifier
from sklearn.svm import LinearSVC
import utils as utl
cuisine_list, ingredients_list, x, y = utl.load_train('number')
classifier = OneVsRestClassifier(LinearSVC(random_state=0)).fit(x, y)
p = classifier.predict(x)
precision = 0
for i in range(len(y)):
if y[i] == p[i]:
precision += 1
accuracy = (1.0 * precision) / len(y)
print('Training Set Accuracy:', accuracy)
t, ids = utl.load_test(ingredients_list)
p = classifier.predict(t)
utl.save_result('sk_svm', cuisine_list, p, ids, 'number')
import numpy as np
import pandas as pd
import sys
import os
from sklearn.externals import joblib
scriptpath = os.path.dirname(os.path.realpath(sys.argv[0])) + '/../'
sys.path.append(os.path.abspath(scriptpath))
import utils
test = utils.load_test('group_by')
def predict_group_by_model(group_by_field):
"""
Use group by model to predict the top 5 hotel clusters for the test data
:param group_by_field: group by field to get the related group by model
:return: the dataframe with the submission format according to the model
"""
group_by_model = joblib.load(utils.model_path +
'_'.join(['top', str(utils.k), 'cw', str(utils.click_weight), 'group', group_by_field, 'year', utils.train_year]) + '.pkl')
merged_df = test.merge(group_by_model, how='left',left_on= group_by_field, right_index=True)
merged_df.reset_index(inplace = True)
result = merged_df[['index', 'hotel_cluster']]
result.columns = ['id', 'hotel_cluster']
return result
print 'predict with orig_destination_distance model...'
result = predict_group_by_model('orig_destination_distance')
print 'predict with srch_destination_id model...'
def svm(training_file, development_file, test_file, counts):
twords, tlabels_true = hs.load_file(training_file)
dwords, dlabels_true = hs.load_file(development_file)
test_words = utils.load_test(test_file)
## Length
tlength_feature = hs.length_feature(twords)
tlength_normalized, tl_mean, tl_std = utils.normalize(tlength_feature)
dlength_feature = hs.length_feature(dwords)
dlength_normalized = utils.normalize_with_params(dlength_feature, tl_mean,
tl_std)
## Frequency
tfrequency_feature = hs.frequency_feature(twords, counts)
tfrequency_normalized, tf_mean, tf_std = utils.normalize(
tfrequency_feature)
dfrequency_feature = hs.frequency_feature(dwords, counts)
dfrequency_normalized = utils.normalize_with_params(
dfrequency_feature, tf_mean, tf_std)
## Syllables
tsyllables_feature = features.syllables_feature(twords)
tsyllables_normalized, tsy_mean, tsy_std = utils.normalize(
tsyllables_feature)
dsyllables_feature = features.syllables_feature(dwords)
dsyllables_normalized = utils.normalize_with_params(
dsyllables_feature, tsy_mean, tsy_std)
## Vowels
tvowels_feature = features.vowels_feature(twords)
tvowels_normalized, tv_mean, tv_std = utils.normalize(tvowels_feature)
dvowels_feature = features.vowels_feature(dwords)
dvowels_normalized = utils.normalize_with_params(dvowels_feature, tv_mean,
tv_std)
## Consonants
tconsonant_feature = features.vowels_feature(twords)
tconsonant_normalized, tc_mean, tc_std = utils.normalize(
tconsonant_feature)
dconsonant_feature = features.vowels_feature(dwords)
dconsonant_normalized = utils.normalize_with_params(
dconsonant_feature, tc_mean, tc_std)
## Senses
tsenses_feature = features.senses_feature(twords)
tsenses_normalized, tse_mean, tse_std = utils.normalize(tsenses_feature)
dsenses_feature = features.senses_feature(dwords)
dsenses_normalized = utils.normalize_with_params(dsenses_feature, tse_mean,
tse_std)
## Hypernyms
thypernyms_feature = features.hypernyms_feature(twords)
thypernyms_normalized, th_mean, th_std = utils.normalize(
thypernyms_feature)
dhypernyms_feature = features.hypernyms_feature(dwords)
dhypernyms_normalized = utils.normalize_with_params(
dhypernyms_feature, th_mean, th_std)
x_train = np.column_stack((tlength_normalized, tfrequency_normalized,
tsyllables_normalized, tsenses_normalized))
y = tlabels_true
x_dev = np.column_stack((dlength_normalized, dfrequency_normalized,
dsyllables_normalized, dsenses_normalized))
clf = SVC(C=48,
cache_size=200,
class_weight=None,
coef0=0.0,
decision_function_shape='ovr',
degree=3,
gamma='scale',
kernel='rbf',
max_iter=-1,
probability=False,
random_state=None,
shrinking=True,
tol=0.001,
verbose=False)
clf.fit(x_train, y)
y_pred = clf.predict(x_dev)
daccuracy = hs.get_accuracy(y_pred, dlabels_true)
dprecision = hs.get_precision(y_pred, dlabels_true)
drecall = hs.get_recall(y_pred, dlabels_true)
dfscore = hs.get_fscore(y_pred, dlabels_true)
# Test Set
# test_length_feature = hs.length_feature(test_words)
# test_frequency_feature = hs.frequency_feature(test_words, counts)
# test_syllables_feature = features.syllables_feature(test_words)
# test_senses_feature = features.senses_feature(test_words)
#
# test_length_normalized = utils.normalize_with_params(test_length_feature, tl_mean, tl_std)
# test_frequency_normalized = utils.normalize_with_params(test_frequency_feature, tf_mean, tf_std)
# test_syllables_normalized = utils.normalize_with_params(test_syllables_feature, tsy_mean, tsy_std)
# test_senses_normalized = utils.normalize_with_params(test_senses_feature, tse_mean, tse_std)
#
# x_test = np.column_stack((test_length_normalized, test_frequency_normalized,
# test_syllables_normalized, test_senses_normalized))
# y_pred_test = clf.predict(x_test)
#
# f = open('test_labels.txt', 'w')
# for item in y_pred_test:
# print(item, file=f)
# f.close()
# training_performance = (tprecision, trecall, tfscore)
development_performance = (daccuracy, dprecision, drecall, dfscore)
return development_performance
def random_forest(training_file, development_file, test_file, counts):
twords, tlabels_true = hs.load_file(training_file)
dwords, dlabels_true = hs.load_file(development_file)
test_words = utils.load_test(test_file)
## Length
tlength_feature = hs.length_feature(twords)
tlength_normalized, tl_mean, tl_std = utils.normalize(tlength_feature)
dlength_feature = hs.length_feature(dwords)
dlength_normalized = utils.normalize_with_params(dlength_feature, tl_mean,
tl_std)
## Frequency
tfrequency_feature = hs.frequency_feature(twords, counts)
tfrequency_normalized, tf_mean, tf_std = utils.normalize(
tfrequency_feature)
dfrequency_feature = hs.frequency_feature(dwords, counts)
dfrequency_normalized = utils.normalize_with_params(
dfrequency_feature, tf_mean, tf_std)
## Syllables
tsyllables_feature = features.syllables_feature(twords)
tsyllables_normalized, tsy_mean, tsy_std = utils.normalize(
tsyllables_feature)
dsyllables_feature = features.syllables_feature(dwords)
dsyllables_normalized = utils.normalize_with_params(
dsyllables_feature, tsy_mean, tsy_std)
## Vowels
tvowels_feature = features.vowels_feature(twords)
tvowels_normalized, tv_mean, tv_std = utils.normalize(tvowels_feature)
dvowels_feature = features.vowels_feature(dwords)
dvowels_normalized = utils.normalize_with_params(dvowels_feature, tv_mean,
tv_std)
## Consonants
tconsonant_feature = features.vowels_feature(twords)
tconsonant_normalized, tc_mean, tc_std = utils.normalize(
tconsonant_feature)
dconsonant_feature = features.vowels_feature(dwords)
dconsonant_normalized = utils.normalize_with_params(
dconsonant_feature, tc_mean, tc_std)
## Senses
tsenses_feature = features.senses_feature(twords)
tsenses_normalized, tse_mean, tse_std = utils.normalize(tsenses_feature)
dsenses_feature = features.senses_feature(dwords)
dsenses_normalized = utils.normalize_with_params(dsenses_feature, tse_mean,
tse_std)
## Hypernyms
thypernyms_feature = features.hypernyms_feature(twords)
thypernyms_normalized, th_mean, th_std = utils.normalize(
thypernyms_feature)
dhypernyms_feature = features.hypernyms_feature(dwords)
dhypernyms_normalized = utils.normalize_with_params(
dhypernyms_feature, th_mean, th_std)
x_train = np.column_stack(
(tlength_normalized, tfrequency_normalized, tsyllables_normalized,
tsenses_normalized, thypernyms_normalized))
y = tlabels_true
x_dev = np.column_stack(
(dlength_normalized, dfrequency_normalized, dsyllables_normalized,
dsenses_normalized, dhypernyms_normalized))
clf = RandomForestClassifier(bootstrap=True,
class_weight=None,
criterion='gini',
max_depth=7,
max_features=3,
max_leaf_nodes=None,
min_impurity_decrease=0.0,
min_impurity_split=None,
min_samples_leaf=8,
min_samples_split=50,
min_weight_fraction_leaf=0.0,
n_estimators=70,
n_jobs=None,
oob_score=False,
random_state=0,
verbose=0,
warm_start=False)
clf.fit(x_train, y)
y_pred = clf.predict(x_dev)
daccuracy = hs.get_accuracy(y_pred, dlabels_true)
dprecision = hs.get_precision(y_pred, dlabels_true)
drecall = hs.get_recall(y_pred, dlabels_true)
dfscore = hs.get_fscore(y_pred, dlabels_true)
# Test Set
test_length_feature = hs.length_feature(test_words)
test_frequency_feature = hs.frequency_feature(test_words, counts)
test_syllables_feature = features.syllables_feature(test_words)
test_vowels_feature = features.vowels_feature(test_words)
test_consonants_feature = features.consonants_feature(test_words)
test_senses_feature = features.senses_feature(test_words)
test_hypernyms_feature = features.hypernyms_feature(test_words)
test_length_normalized = utils.normalize_with_params(
test_length_feature, tl_mean, tl_std)
test_frequency_normalized = utils.normalize_with_params(
test_frequency_feature, tf_mean, tf_std)
test_syllables_normalized = utils.normalize_with_params(
test_syllables_feature, tsy_mean, tsy_std)
test_vowels_normalized = utils.normalize_with_params(
test_vowels_feature, tv_mean, tv_std)
test_consonants_normalized = utils.normalize_with_params(
test_consonants_feature, tc_mean, tc_std)
test_senses_normalized = utils.normalize_with_params(
test_senses_feature, tse_mean, tse_std)
test_hypernyms_normalized = utils.normalize_with_params(
test_hypernyms_feature, th_mean, th_std)
x_test = np.column_stack(
(test_length_normalized, test_frequency_normalized,
test_syllables_normalized, test_senses_normalized,
test_hypernyms_normalized))
y_pred_test = clf.predict(x_test)
f = open('test_labels.txt', 'w')
for item in y_pred_test:
print(item, file=f)
f.close()
# training_performance = (tprecision, trecall, tfscore)
development_performance = (daccuracy, dprecision, drecall, dfscore)
return development_performance
def get_ppr10(): p = utils.load_test() ppr10 = simple_extract(p) return ppr10[0]
def evaluate_nonepisode(data, config, model, loss_fn, eval):
x_te, y_te, te_len, te_mask, text_te = utils.load_test(data, eval)
x_te, y_te, te_len, te_mask, text_te = utils.shuffle_data(
x_te, y_te, te_len, te_mask, text_te)
y_te_ind = utils.create_index(y_te)
reverse_dict = data['reverse_dict']
num_class = np.unique(y_te)
num_test_query = config['num_query_per_class'] * num_class.shape[0]
x_support, y_support, x_len_support, support_m, support_text = utils.load_support(
data, False)
y_support_ind = utils.create_index(y_support)
test_batch = int(math.ceil(x_te.shape[0] / float(num_test_query)))
total_prediction = np.array([], dtype=np.int64)
total_y_test = np.array([], dtype=np.int64)
cum_loss = 0.0
kl_loss = torch.nn.KLDivLoss(reduction='batchmean').to(config['device'])
with torch.no_grad():
for batch in range(test_batch):
support_feature, support_class, support_len, support_ind, support_mask = utils.init_support_query(
config['num_samples_per_class'], x_te.shape[1],
num_class.shape[0])
query_feature, query_class, query_len, query_ind, query_mask = utils.init_support_query(
config['num_query_per_class'], x_te.shape[1],
num_class.shape[0])
begin_index = batch * (num_test_query)
end_index = min((batch + 1) * num_test_query, x_te.shape[0])
query_feature = x_te[begin_index:end_index]
query_len = te_len[begin_index:end_index]
query_class = y_te[begin_index:end_index]
query_mask = te_mask[begin_index:end_index]
query_text = text_te[begin_index:end_index]
support_idx = 0
num_class = np.unique(y_support)
for counter in range(num_class.shape[0]):
class_index = np.where(y_support == num_class[counter])[0]
old_support_idx = support_idx
support_idx = support_idx + config['num_samples_per_class']
support_feature[old_support_idx:support_idx] = x_support[
class_index]
support_class[old_support_idx:support_idx] = y_support[
class_index]
support_len[old_support_idx:support_idx] = x_len_support[
class_index]
support_mask[old_support_idx:support_idx] = support_m[
class_index]
support_text[old_support_idx:support_idx] = support_text[
class_index]
cs = np.unique(query_class)
#Obtain indexes
q_ind_key = {}
s_ind_key = {}
for i in range(len(cs)):
q_index = np.where(query_class == cs[i])[0]
s_index = np.where(support_class == cs[i])[0]
q_ind_key[cs[i]] = q_index
s_ind_key[cs[i]] = s_index
# Changet values
for i in range(len(cs)):
query_class[q_ind_key[cs[i]]] = i
support_class[s_ind_key[cs[i]]] = i
support_ind = utils.create_index(support_class)
query_ind = utils.create_index(query_class)
support_feature, support_id, support_ind, support_len, support_mask = convert_to_tensor(
support_feature, support_class, support_ind, support_len,
support_mask, config['device'])
query_feature, query_id, query_ind, query_len, query_mask = convert_to_tensor(
query_feature, query_class, query_ind, query_len, query_mask,
config['device'])
prediction, _, support_attn, query_attn, _, _ = model.forward(
support_feature, support_len, support_mask, query_feature,
query_len, query_mask)
pred = np.argmax(prediction.cpu().detach().numpy(), 1)
total_prediction = np.concatenate((total_prediction, pred))
total_y_test = np.concatenate((total_y_test, query_class))
acc = accuracy_score(total_y_test, total_prediction)
cnf = confusion_matrix(total_y_test, total_prediction)
print("Confusion matrix:")
print(cnf)
return acc
X_fold = np.hstack((X_fold, X_pred))
all_X.append(X_fold)
all_y.append(y_fold)
all_w.append(w_fold)
X = np.vstack(all_X)
y = np.concatenate(all_y)
w = np.concatenate(all_w)
clf = Classifier(**params)
w = rescale(w)
w = rebalance(y, w)
try:
clf.fit(X, y, sample_weight=w)
except:
clf.fit(X, y)
# And make a submussion
print "Making submission..."
X_test, _, _, _ = load_test()
X_pred = load_predictions("stack/*-test.npy")
X_test = np.hstack((X_test, X_pred))
make_submission(clf, threshold, "output-stacking.csv", X_test=X_test)
import IPython; IPython.embed()
# main
# =============================================================================
if __name__ == "__main__":
utils.start(__file__)
# train
tr = utils.load_train(['object_id'])
df = pd.read_pickle(
'../FROM_MYTEAM/LCfit_feature_allSN_i_train_v3_20181215.pkl.gz')
df = pd.merge(tr, df, on='object_id', how='left')
df.reset_index(drop=True, inplace=True)
get_feature(df)
del df['object_id']
df.add_prefix(PREF + '_').to_pickle(f'../data/train_{PREF}.pkl')
# test
te = utils.load_test(['object_id'])
df = pd.read_pickle(
'../FROM_MYTEAM/LCfit_feature_allSN_i_test_v3_20181215.pkl.gz')
df = pd.merge(te, df, on='object_id', how='left')
df.reset_index(drop=True, inplace=True)
get_feature(df)
del df['object_id']
df = df.add_prefix(PREF + '_')
utils.to_pkl_gzip(df, f'../data/test_{PREF}.pkl')
utils.end(__file__)
import lasagne
import utils
from lasagne.layers import *
from nolearn.lasagne import NeuralNet
from sklearn.metrics import classification_report
from sklearn.metrics import confusion_matrix
from sklearn import cross_validation
from nolearn.lasagne import TrainSplit
from nolearn.lasagne import objective
from lasagne.nonlinearities import softmax
from lasagne.updates import momentum
X, y=utils.load_train()
X_test=utils.load_test()
X=X.reshape([X.shape[0],3,32,32])
y=np.array(y,dtype="int32")
X_test=X_test.reshape([X_test.shape[0],3,32,32])
layers = [
# layer dealing with the input data
(InputLayer, {'shape': (None, 3, 32, 32)}),
# first stage of our convolutional layers
# second stage of our convolutional layers
(Conv2DLayer, {'pad':2,'num_filters': 32, 'filter_size': 5,'W':lasagne.init.Normal(std=0.01)}),
(ParametricRectifierLayer, {'alpha':lasagne.init.Constant(0)}),
(Pool2DLayer, {'pool_size': 2,'stride':2,'mode':'max'}),
'''
X = T.tensor4('X')
Y = T.ivector('y')
# set up theano functions to generate output by feeding data through network, any test outputs should be deterministic
output_layer = ResNet_FullPre(X, n=5)
output_test = lasagne.layers.get_output(output_layer, deterministic=True)
# set up training and prediction functions
predict_proba = theano.function(inputs=[X], outputs=output_test)
'''
Load data and make predictions
'''
# load data
X_test, X_test_id = load_test(cache=True)
nn_count = 1
for ensb in range(19):
# load network weights
f = gzip.open('data/weights/resnet32_fullpre_' + str(nn_count) + '.pklz', 'rb')
all_params = pickle.load(f)
f.close()
helper.set_all_param_values(output_layer, all_params)
'''
# make regular predictions
predictions = []
for j in range((X_test.shape[0] + BATCHSIZE - 1) // BATCHSIZE):
sl = slice(j * BATCHSIZE, (j + 1) * BATCHSIZE)
X_batch = X_test[sl]
result = utils.fill_all_top_5(train_is_booking, result, 'hotel_market', 'train')
print 'hotel clusters to ranking features...'
new_result = result.apply(lambda row: hotel_clusters_to_ranking_features(row), axis=1)
new_result.columns = ['_'.join(['hotel_cluster', str(hotel_cluster_id), 'rank']) for hotel_cluster_id in range(100)]
new_result = pd.concat([train_is_booking['date_time'], new_result], axis=1)
new_result.to_csv(utils.processed_data_path +
'_'.join(['train_is_booking_group_by', 'top', str(utils.k), 'cw', str(utils.click_weight), 'year', utils.train_year]) +
'.csv', header=True, index=False)
del train_is_booking
#############################################################
#################### test dataset ####################
#############################################################
test = utils.load_test('group_by')
print 'generate top k hotel clusters with orig_destination_distance model...'
result = gen_top_k_hotel_cluster(test, 'orig_destination_distance')
print 'generate top k hotel clusters with srch_destination_id model...'
result = utils.fill_all_top_5(test, result, 'srch_destination_id')
print 'generate top k hotel clusters with user_id model...'
result = utils.fill_all_top_5(test, result, 'user_id')
print 'generate top k hotel clusters with hotel_market model...'
result = utils.fill_all_top_5(test, result, 'hotel_market')
print 'hotel clusters to ranking features...'
new_result = result.apply(lambda row: hotel_clusters_to_ranking_features(row), axis=1)
new_result.columns = ['_'.join(['hotel_cluster', str(hotel_cluster_id), 'rank']) for hotel_cluster_id in range(100)]
new_result.to_csv(utils.processed_data_path +
'_'.join(['test_groupb_by', 'top', str(utils.k), 'cw', str(utils.click_weight), 'year', utils.train_year]) +
from keras import metrics
import tensorflow as tf
import numpy as np
from utils import load_test, PSNR
import scipy.misc
import argparse
parser = argparse.ArgumentParser(description='Test function')
parser.add_argument('--test', metavar='test', type=str, help='test directory')
parser.add_argument('--network',
metavar='network',
type=str,
help='network weight')
args = parser.parse_args()
val_in, val_out = load_test(directory=args.test)
model = load_model(args.network)
prediction = model.predict(val_in, batch_size=1, verbose=1)
Result = PSNR(val_out, prediction)
sess = tf.Session()
RR = sess.run(Result)
print(RR)
for img_count in range(prediction.shape[0]):
img_in = val_in[img_count, :, :, :]
img_out = prediction[img_count, :, :, :]
img_gt = val_out[img_count, :, :, :]
scipy.misc.imsave('./Result/LR' + '{0:03d}'.format(img_count) + '.png',