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get_data.py
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import os
import pandas as pd
import numpy as np
import glob
import json
import socket
import warnings
from sklearn.preprocessing import LabelEncoder
from constants import sample_seed
import datasets.src
CE_HOME = os.environ.get('CE_HOME')
dataset_config_file = os.path.join(CE_HOME, 'python', 'categorical_encoding',
'datasets_config.json')
with open(dataset_config_file, 'r') as f:
DATASET_CONFIG = json.load(f)
def preprocess_data(df, cols):
def string_normalize(s):
res = str(s).lower()
# res = ' ' + res + ' '
return res
for col in cols:
print('Preprocessing column: %s' % col)
df[col] = [string_normalize(str(r)) for r in df[col]]
return df
def get_data_path():
hostname = socket.gethostname()
if hostname in ['drago2', 'drago3', 'drago4']:
path = '/storage/inria/pcerda/data'
elif hostname in ['paradox', 'paradigm', 'parametric', 'parabolic']:
path = '/storage/local/pcerda/data'
elif hostname in ['drago']:
path = '/storage/workspace/pcerda/data'
else:
path = os.path.join(CE_HOME, 'data')
return path
def create_folder(path, folder):
if not os.path.exists(os.path.join(path, folder)):
os.makedirs(os.path.join(path, folder))
return
def print_unique_values(df):
for col in df.columns:
print(col, df[col].unique().shape)
print(df[col].unique())
print('\n')
def check_nan_percentage(df, col_name):
threshold = .15
missing_fraction = df[col_name].isnull().sum()/df.shape[0]
if missing_fraction > threshold:
warnings.warn(
"Fraction of missing values for column '%s' "
"(%.3f) is higher than %.3f"
% (col_name, missing_fraction, threshold))
class Data:
''' Given the dataset name, return the respective dataframe as other
relevant information.'''
def __init__(self, name):
self.name = name
self.configs = None
self.xcols, self.ycol = None, None
self.col_action = DATASET_CONFIG[self.name]["col_action"]
self.clf_type = DATASET_CONFIG[self.name]["clf_type"]
self.dirtiness_type = DATASET_CONFIG[self.name]["dirtiness_type"]
self.datasets_with_downloader = [
'employee_salaries',
'journal_influence',
'met_objects',
'colleges',
'beer_reviews',
'midwest_survey',
'medical_charge',
'traffic_violations',
'crime_data',
'adult',
'public_procurement',
'intrusion_detection',
'emobank',
'text_emotion',
]
self.has_downloader = False
if self.name in self.datasets_with_downloader:
self.has_downloader = True
if name == 'indultos_espana':
'''Source: '''
data_path = os.path.join(get_data_path(),
'bigml/Indultos_en_Espana_1996-2013')
create_folder(data_path, 'output/results')
data_file = os.path.join(data_path, 'raw',
'Indultos_en_Espana_1996-2013.csv')
if name == 'open_payments':
'''Source: '''
data_path = os.path.join(get_data_path(), 'docs_payments')
data_file = os.path.join(data_path, 'output', 'DfD.csv')
if name == 'road_safety':
'''Source: https://data.gov.uk/dataset/road-accidents-safety-data
'''
data_path = os.path.join(get_data_path(), 'road_safety')
create_folder(data_path, 'output/results')
data_file = [os.path.join(data_path, 'raw', '2015_Make_Model.csv'),
os.path.join(data_path, 'raw', 'Accidents_2015.csv'),
os.path.join(data_path, 'raw', 'Casualties_2015.csv'),
os.path.join(data_path, 'raw', 'Vehicles_2015.csv')]
if name == 'consumer_complaints':
'''Source: https://catalog.data.gov/dataset/
consumer-complaint-database
Documentation: https://cfpb.github.io/api/ccdb//fields.html'''
data_path = os.path.join(get_data_path(), 'consumer_complaints')
create_folder(data_path, 'output/results')
data_file = os.path.join(data_path, 'raw',
'Consumer_Complaints.csv')
if name == 'product_relevance':
'''Source: '''
data_path = os.path.join(get_data_path(), 'product_relevance')
create_folder(data_path, 'output/results')
data_file = os.path.join(data_path, 'raw', 'train.csv')
if name == 'federal_election':
'''Source: https://classic.fec.gov/finance/disclosure/
ftpdet.shtml#a2011_2012'''
data_path = os.path.join(get_data_path(), 'federal_election')
create_folder(data_path, 'output/results')
data_file = os.path.join(data_path, 'raw', 'itcont.txt')
self.data_dict_file = os.path.join(data_path, 'data_dict.csv')
if name == 'drug_directory':
'''Source:
https://www.fda.gov/Drugs/InformationOnDrugs/ucm142438.htm'''
data_path = os.path.join(get_data_path(), 'drug_directory')
create_folder(data_path, 'output/results')
data_file = os.path.join(data_path, 'raw', 'product.txt')
if name == 'french_companies':
'''Source: '''
data_path = os.path.join(get_data_path(), 'french_companies')
create_folder(data_path, 'output/results')
data_file = [os.path.join(data_path, 'raw',
'datasets', 'chiffres-cles-2017.csv'),
os.path.join(data_path, 'raw',
'datasets', 'avis_attribution_2017.csv')]
if name == 'dating_profiles':
'''Source: https://github.com/rudeboybert/JSE_OkCupid'''
data_path = os.path.join(get_data_path(), 'dating_profiles')
create_folder(data_path, 'output/results')
data_file = os.path.join(data_path, 'raw', 'profiles.csv')
if name == 'cacao_flavors':
'''Source: https://www.kaggle.com/rtatman/chocolate-bar-ratings/'''
data_path = os.path.join(get_data_path(), 'cacao_flavors')
create_folder(data_path, 'output/results')
data_file = os.path.join(data_path, 'raw', 'flavors_of_cacao.csv')
if name == 'wine_reviews':
'''Source: https://www.kaggle.com/zynicide/wine-reviews/home'''
data_path = os.path.join(get_data_path(), 'wine_reviews')
create_folder(data_path, 'output/results')
data_file = os.path.join(
data_path, 'raw', 'winemag-data_first150k.csv')
if name == 'house_prices':
'''Source: https://www.kaggle.com/c/
house-prices-advanced-regression-techniques/data'''
data_path = os.path.join(get_data_path(), 'house_prices')
create_folder(data_path, 'output/results')
data_file = os.path.join(data_path, 'raw', 'train.csv')
if name == 'kickstarter_projects':
'''Source: https://www.kaggle.com/kemical/kickstarter-projects'''
data_path = os.path.join(get_data_path(), 'kickstarter_projects')
create_folder(data_path, 'output/results')
data_file = os.path.join(
data_path, 'raw', 'ks-projects-201612.csv')
if name == 'building_permits':
'''Source:
https://www.kaggle.com/chicago/chicago-building-permits'''
data_path = os.path.join(get_data_path(), 'building_permits')
create_folder(data_path, 'output/results')
data_file = os.path.join(data_path, 'raw', 'building-permits.csv')
if name == 'california_housing':
'''Source:
https://github.com/ageron/handson-ml/tree/master/datasets/housing
'''
data_path = os.path.join(get_data_path(), 'california_housing')
create_folder(data_path, 'output/results')
data_file = os.path.join(data_path, 'raw', 'housing.csv')
if name == 'house_sales':
'''Source: https://www.kaggle.com/harlfoxem/housesalesprediction'''
data_path = os.path.join(get_data_path(), 'house_sales')
create_folder(data_path, 'output/results')
data_file = os.path.join(data_path, 'raw', 'kc_house_data.csv')
if name == 'vancouver_employee':
'''Source: https://data.vancouver.ca/datacatalogue/
employeeRemunerationExpensesOver75k.htm
Remuneration and Expenses for Employees Earning over $75,000
'''
data_path = os.path.join(get_data_path(), 'vancouver_employee')
create_folder(data_path, 'output/results')
data_file = os.path.join(
data_path, 'raw',
'2017StaffRemunerationOver75KWithExpenses.csv')
if name == 'firefighter_interventions':
'''Source:
https://www.data.gouv.fr/fr/datasets/interventions-des-pompiers/
'''
data_path = os.path.join(
get_data_path(), 'firefighter_interventions')
create_folder(data_path, 'output/results')
data_file = os.path.join(
data_path, 'raw', 'interventions-hebdo-2010-2017.csv')
if name == 'criteo':
'''Source:
https://labs.criteo.com/2014/08/criteo-release-public-datasets/
'''
data_path = os.path.join(
get_data_path(), 'criteo')
data_file = os.path.join(
data_path, 'raw', 'data.txt')
# add here the path to a new dataset ##################################
# if name == 'new_dataset':
# '''Source: '''
# data_path = os.path.join(get_data_path(), 'new_dataset')
# create_folder(data_path, 'output/results')
# data_file = os.path.join(data_path, 'raw', 'data_file.csv')
#######################################################################
if self.name not in self.datasets_with_downloader:
self.file = data_file
self.path = data_path
def preprocess(self, n_rows=-1, str_preprocess=True,
clf_type=None):
self.col_action = {k: v for k, v in self.col_action.items()
if v != 'Delete'}
self.xcols = [key for key in self.col_action
if self.col_action[key] is not 'y']
self.ycol = [key for key in self.col_action
if self.col_action[key] is 'y'][0]
for col in self.col_action:
check_nan_percentage(self.df, col)
if self.col_action[col] in ['OneHotEncoderDense', 'OneHotEncoder',
'Special', 'OneHotEncoderDense-1']:
self.df[col] = self.df[col].astype(str)
self.df = self.df.fillna(value={col: 'na'})
self.df = self.df.dropna(
axis=0, subset=[c for c in self.xcols if self.col_action[c]
is not 'Delete'] + [self.ycol])
if n_rows == -1:
self.df = self.df.sample(
frac=1, random_state=sample_seed).reset_index(drop=True)
else:
self.df = self.df.sample(
n=n_rows, random_state=sample_seed).reset_index(drop=True)
if str_preprocess:
self.df = preprocess_data(
self.df, [key for key in self.col_action
if self.col_action[key] == 'Special'])
# label encoder for the target variable
if self.clf_type in ['binary', 'multiclass']:
value_counts = self.df[self.ycol].value_counts()
le = LabelEncoder()
le.fit(value_counts.index)
self.df['target_count'] = self.df[self.ycol].apply(
lambda x: value_counts.loc[x]).astype(int)
self.df[self.ycol] = le.transform(self.df[self.ycol])
self.df = self.df[self.df['target_count'] > 20]
self.df = self.df.drop(columns=['target_count'])
# because n_splits=20 for us
# self.df = self.df[self.df[self.ycol]]
assert np.all(np.in1d(list(self.col_action.keys()), self.df.columns))
return self
def get_df(self):
if self.name in self.datasets_with_downloader:
self.df = getattr(
datasets.src, self.name
).get_df(directory=os.path.split(get_data_path())[0])
if self.name == 'indultos_espana':
self.df = pd.read_csv(self.file)
if self.name == 'open_payments':
# Variable names in Dollars for Docs dataset ######################
pi_specialty = ['Physician_Specialty']
drug_nm = ['Name_of_Associated_Covered_Drug_or_Biological1']
dev_nm = ['Name_of_Associated_Covered_Device_or_Medical_Supply1']
corp = ['Applicable_Manufacturer_or_Applicable_GPO_Making_' +
'Payment_Name']
amount = ['Total_Amount_of_Payment_USDollars']
dispute = ['Dispute_Status_for_Publication']
###################################################################
if os.path.exists(self.file):
df = pd.read_csv(self.file)
# print('Loading DataFrame from:\n\t%s' % self.file)
else:
csv_files = glob.glob(os.path.join(self.path, 'raw', '*.csv'))
csv_files_ = []
for file_ in csv_files:
if 'RSRCH_PGYR2013' in file_:
csv_files_.append(file_)
if 'GNRL_PGYR2013' in file_:
csv_files_.append(file_)
dfd_cols = pi_specialty + drug_nm + dev_nm + corp + amount + \
dispute
df_dfd = pd.DataFrame(columns=dfd_cols)
for csv_file in csv_files_:
if 'RSRCH' in csv_file:
df = pd.read_csv(csv_file)
df = df[dfd_cols]
df['status'] = 'allowed'
df = df.drop_duplicates(keep='first')
df_dfd = pd.concat([df_dfd, df],
ignore_index=True)
print('size: %d, %d' % tuple(df_dfd.shape))
unique_vals = {}
for col in df_dfd.columns:
unique_vals[col] = set(list(df_dfd[col].unique()))
for csv_file in csv_files_:
if 'GNRL' in csv_file:
df = pd.read_csv(csv_file)
df = df[dfd_cols]
df['status'] = 'disallowed'
df = df.drop_duplicates(keep='first')
df_dfd = pd.concat([df_dfd, df],
ignore_index=True)
print('size: %d, %d' % tuple(df_dfd.shape))
df_dfd = df_dfd.drop_duplicates(keep='first')
df_dfd.to_csv(self.file)
df = df_dfd
df['status'] = (df['status'] == 'allowed')
self.df = df
# print_unique_values(df)
self.col_action = {
pi_specialty[0]: 'Delete',
drug_nm[0]: 'Delete',
dev_nm[0]: 'Delete',
corp[0]: 'Special',
amount[0]: 'Numerical',
dispute[0]: 'OneHotEncoderDense-1',
'status': 'y'}
self.dirtiness_type = {
corp[0]: 'Synonyms; Overlap'
}
self.clf_type = 'binary'
if self.name == 'road_safety':
files = self.file
for filename in files:
if filename.split('/')[-1] == '2015_Make_Model.csv':
df_mod = pd.read_csv(filename, low_memory=False)
df_mod['Vehicle_Reference'] = (df_mod['Vehicle_Reference']
.map(str))
df_mod['Vehicle_Index'] = (df_mod['Accident_Index'] +
df_mod['Vehicle_Reference'])
df_mod = df_mod.set_index('Vehicle_Index')
df_mod = df_mod.dropna(axis=0, how='any', subset=['make'])
# for filename in files:
# if filename.split('/')[-1] == 'Accidents_2015.csv':
# df_acc = pd.read_csv(filename).set_index('Accident_Index')
for filename in files:
if filename.split('/')[-1] == 'Vehicles_2015.csv':
df_veh = pd.read_csv(filename)
df_veh['Vehicle_Reference'] = (df_veh['Vehicle_Reference']
.map(str))
df_veh['Vehicle_Index'] = (df_veh['Accident_Index'] +
df_veh['Vehicle_Reference'])
df_veh = df_veh.set_index('Vehicle_Index')
for filename in files:
if filename.split('/')[-1] == 'Casualties_2015.csv':
df_cas = pd.read_csv(filename)
df_cas['Vehicle_Reference'] = (df_cas['Vehicle_Reference']
.map(str))
df_cas['Vehicle_Index'] = (df_cas['Accident_Index'] +
df_cas['Vehicle_Reference'])
df_cas = df_cas.set_index('Vehicle_Index')
df = df_cas.join(df_mod, how='left', lsuffix='_cas',
rsuffix='_model')
df = df.dropna(axis=0, how='any', subset=['make'])
df = df[df['Sex_of_Driver'] != 3]
df = df[df['Sex_of_Driver'] != -1]
df['Sex_of_Driver'] = df['Sex_of_Driver'] - 1
self.df = df
# col_action = {'Casualty_Severity': 'y',
# 'Casualty_Class': 'Numerical',
# 'make': 'OneHotEncoderDense',
# 'model': 'Special'}
self.file = self.file[0]
if self.name == 'consumer_complaints':
self.df = pd.read_csv(self.file)
self.df = self.df.dropna(
axis=0, how='any', subset=['Consumer disputed?'])
self.df.loc[:, 'Consumer disputed?'] = (
self.df['Consumer disputed?'] == 'Yes')
if self.name in ['dating_profiles',
'wine_reviews', 'california_housing']:
self.df = pd.read_csv(self.file)
if self.name in ['drug_directory']:
self.df = pd.read_csv(self.file, sep='\t', encoding='latin1')
if self.name == 'product_relevance':
self.df = pd.read_csv(self.file, encoding='latin1')
if self.name == 'federal_election':
df_dict = pd.read_csv(self.data_dict_file)
self.df = pd.read_csv(self.file, sep='|', encoding='latin1',
header=None, names=df_dict['Column Name'])
# Some donations are negative
self.df['TRANSACTION_AMT'] = self.df['TRANSACTION_AMT'].abs()
# Predicting the log of the donation
self.df['TRANSACTION_AMT'] = self.df[
'TRANSACTION_AMT'].apply(np.log)
self.df = self.df[self.df['TRANSACTION_AMT'] > 0]
if self.name == 'cacao_flavors':
self.df = pd.read_csv(self.file)
self.df['Cocoa\nPercent'] = self.df[
'Cocoa\nPercent'].astype(str).str[:-1].astype(float)
if self.name == 'house_prices':
self.df = pd.read_csv(self.file, index_col=0)
# Identifies the type of dwelling involved in the sale.
MSSubClass = {
20: '1-STORY 1946 & NEWER ALL STYLES',
30: '1-STORY 1945 & OLDER',
40: '1-STORY W/FINISHED ATTIC ALL AGES',
45: '1-1/2 STORY - UNFINISHED ALL AGES',
50: '1-1/2 STORY FINISHED ALL AGES',
60: '2-STORY 1946 & NEWER',
70: '2-STORY 1945 & OLDER',
75: '2-1/2 STORY ALL AGES',
80: 'SPLIT OR MULTI-LEVEL',
85: 'SPLIT FOYER',
90: 'DUPLEX - ALL STYLES AND AGES',
120: '1-STORY PUD (Planned Unit Development) - 1946 & NEWER',
150: '1-1/2 STORY PUD - ALL AGES',
160: '2-STORY PUD - 1946 & NEWER',
180: 'PUD - MULTILEVEL - INCL SPLIT LEV/FOYER',
190: '2 FAMILY CONVERSION - ALL STYLES AND AGES',
}
for key, value in MSSubClass.items():
self.df.replace({'MSSubClass': key}, value, inplace=True)
if self.name == 'kickstarter_projects':
self.df = pd.read_csv(self.file, encoding='latin1', index_col=0)
self.df = self.df[self.df['state '].isin(['failed', 'successful'])]
self.df['state '] = (self.df['state '] == 'successful')
self.df['usd pledged '] = (
self.df['usd pledged '].astype(float) + 1E-10).apply(np.log)
if self.name == 'building_permits':
self.df = pd.read_csv(self.file)
self.df.columns = self.df.columns.str.strip()
self.df['ESTIMATED_COST'] = (
self.df['ESTIMATED_COST'].astype(float) + 1E-10).apply(np.log)
if self.name == 'house_sales':
self.df = pd.read_csv(self.file, index_col=0)
if self.name == 'vancouver_employee':
self.df = pd.read_csv(self.file, header=3)
self.df['Remuneration'] = self.df[
'Remuneration'].apply(
lambda x: np.log(float(''.join(str(x).split(',')))))
if self.name == 'firefighter_interventions':
self.df = pd.read_csv(self.file, sep=';')
if self.name == 'criteo':
self.df = pd.read_csv(self.file, sep='\t', header=None)
self.df.columns = self.df.columns.astype(str)
self.dirty_col = [col for col in self.col_action
if self.col_action[col] == 'Special']
# self.df = self.df[list(self.col_action)]
return self