def save_pred_as_submit_format(pred_path, output_file, col_name=('ID', "TARGET")):
print ('writing prediction as submission format')
print ('read prediction <{}>'.format(pred_path))
pred = paratext.load_csv_to_pandas(pred_path, allow_quoted_newlines=True).values
#(((test.mean(1) - test.mean(1).mean())/test.mean(1).std()/100. + 0.5).values + pred)/2.0
submission = pd.read_csv(INPUT_PATH+SUBMIT_FORMAT)
submission[col_name[1]] = pred
submission.to_csv( output_file, columns = col_name, index = None )
print ('done writing')
return
def save_pred_as_submit_format(pred_path, output_file, col_name=('ID', "TARGET")):
print 'writing prediction as submission format'
print 'read prediction <{}>'.format(pred_path)
pred = paratext.load_csv_to_pandas(pred_path, allow_quoted_newlines=True).values
#(((test.mean(1) - test.mean(1).mean())/test.mean(1).std()/100. + 0.5).values + pred)/2.0
submission = pd.read_csv(INPUT_PATH+SUBMIT_FORMAT)
submission[col_name[1]] = pred
submission.to_csv( output_file, columns = col_name, index = None )
print 'done writing'
return
def run(parallel=False, pool_size=-1, base_parser=BaseParser.pandas):
file_name = '../../../ATTIC/data/lineitem.csv'
expected_row_count = 6001216
# file_name = '../../../ATTIC/data/customer.csv'
# expected_row_count = 150001
f = open(file_name)
file_size = os.path.getsize(file_name)
if base_parser is not BaseParser.paratext:
parser = CSVParser(callback, parallel, pool_size, base_parser)
start_time = timeit.default_timer()
while True:
chunk = f.read(READ_CHUNK_SIZE)
if chunk:
parser.pump(chunk)
else:
break
parser.close()
row_count = parser.line_count
stop_time = timeit.default_timer()
else:
start_time = timeit.default_timer()
df = paratext.load_csv_to_pandas(file_name, num_threads=pool_size)
stop_time = timeit.default_timer()
row_count = len(df) + 1
time = stop_time - start_time
print("Time: {}, Rows: {}, Size: {}, MB/Sec {}".format(
time, row_count, file_size, (file_size / time) / 1000 / 1000))
assert expected_row_count == row_count
def load_data(flist, drop_duplicates=False):
'''
Usage: set train, target, and test key and feature files.
FEATURE_LIST_stage2 = {
'train':(
TEMP_PATH + 'v1_stage1_all_fold.csv',
TEMP_PATH + 'v2_stage1_all_fold.csv',
TEMP_PATH + 'v3_stage1_all_fold.csv',
),#target is not in 'train'
'target':(
INPUT_PATH + 'target.csv',
),#target is in 'target'
'test':(
TEMP_PATH + 'v1_stage1_test.csv',
TEMP_PATH + 'v2_stage1_test.csv',
TEMP_PATH + 'v3_stage1_test.csv',
),
}
'''
if (len(flist['train'])==0) or (len(flist['target'])==0) or (len(flist['test'])==0):
raise Exception('train, target, and test must be set at \
least one file, respectively.')
X_train = pd.DataFrame()
test = pd.DataFrame()
print ('Reading train dataset')
for i in flist['train']: # why need many train data?
X_train = pd.concat([X_train, paratext.load_csv_to_pandas(PATH+i, allow_quoted_newlines=True)],axis=1)
print ('train dataset is created')
print ('Reading target data')
y_train = paratext.load_csv_to_pandas(PATH+flist['target'][0], allow_quoted_newlines=True)['target']
print ('Reading train dataset')
for i in flist['test']:
test = pd.concat([test, paratext.load_csv_to_pandas(PATH+i, allow_quoted_newlines=True)],axis=1)
#del test['t_id']
#print X_train.columns
#print test.columns
assert( (False in X_train.columns == test.columns) == False)
print ('train shape :{}'.format(X_train.shape))
if drop_duplicates == True:
#delete identical columns
unique_col = X_train.T.drop_duplicates().T.columns
X_train = X_train[unique_col]
test = test[unique_col]
assert( all(X_train.columns == test.columns))
print ('train shape after concat and drop_duplicates :{}'.format(X_train.shape))
# drop constant features
#X_train = X_train.loc[:, (X_train != X_train.ix[0]).any()]
#test = test.loc[:, (test != test.ix[0]).any()]
#common_col = list(set(X_train.columns.tolist()) and set(test.columns.tolist()))
#X_train = X_train[common_col]
#test = test[common_col]
#print 'shape after dropping constant features: {}'.format(X_train.shape)
return X_train, y_train, test
# Scikit learn
import sklearn as sk
from sklearn import metrics
from sklearn.model_selection import train_test_split
from sklearn.metrics import precision_score, recall_score, confusion_matrix, classification_report, accuracy_score, f1_score
# IO
import paratext
# ## Read Data
# In[125]:
print('Reading data')
#df_all = pd.read_csv('./casted_data_norm.csv', encoding='utf-8')
df_all = paratext.load_csv_to_pandas('data/casted_data_norm.csv',
in_encoding='utf-8')
print('(Read data) End')
# In[3]:
df_all = df_all.assign(RSP_FLG_N=1 - df_all.RSP_FLG)
print(df_all.shape)
df_all.head()
# In[4]:
print(len(df_all.columns.values) - 4)
# 600 = 12 * 50
# In[5]:
def load_data(flist, drop_duplicates=False):
'''
Usage: set train, target, and test key and feature files.
FEATURE_LIST_stage2 = {
'train':(
TEMP_PATH + 'v1_stage1_all_fold.csv',
TEMP_PATH + 'v2_stage1_all_fold.csv',
TEMP_PATH + 'v3_stage1_all_fold.csv',
),#target is not in 'train'
'target':(
INPUT_PATH + 'target.csv',
),#target is in 'target'
'test':(
TEMP_PATH + 'v1_stage1_test.csv',
TEMP_PATH + 'v2_stage1_test.csv',
TEMP_PATH + 'v3_stage1_test.csv',
),
}
'''
if (len(flist['train'])==0) or (len(flist['target'])==0) or (len(flist['test'])==0):
raise Exception('train, target, and test must be set at \
least one file, respectively.')
X_train = pd.DataFrame()
test = pd.DataFrame()
print 'Reading train dataset'
for i in flist['train']:
X_train = pd.concat([X_train, paratext.load_csv_to_pandas(PATH+i, allow_quoted_newlines=True)],axis=1)
print 'train dataset is created'
print 'Reading target data'
y_train = paratext.load_csv_to_pandas(PATH+flist['target'][0], allow_quoted_newlines=True)['target']
print 'Reading train dataset'
for i in flist['test']:
test = pd.concat([test, paratext.load_csv_to_pandas(PATH+i, allow_quoted_newlines=True)],axis=1)
#del test['t_id']
#print X_train.columns
#print test.columns
assert( (False in X_train.columns == test.columns) == False)
print 'train shape :{}'.format(X_train.shape)
if drop_duplicates == True:
#delete identical columns
unique_col = X_train.T.drop_duplicates().T.columns
X_train = X_train[unique_col]
test = test[unique_col]
assert( all(X_train.columns == test.columns))
print 'train shape after concat and drop_duplicates :{}'.format(X_train.shape)
# drop constant features
#X_train = X_train.loc[:, (X_train != X_train.ix[0]).any()]
#test = test.loc[:, (test != test.ix[0]).any()]
#common_col = list(set(X_train.columns.tolist()) and set(test.columns.tolist()))
#X_train = X_train[common_col]
#test = test[common_col]
#print 'shape after dropping constant features: {}'.format(X_train.shape)
return X_train, y_train, test
