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reds.py
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########################################
# REDS
# Mohammad Mahdavi
# March 2017
# Big Data Management Group
# TU Berlin
# All Rights Reserved
########################################
########################################
import os
import sys
import re
import time
import math
import string
import random
import operator
import pickle
import numpy
import nltk
import sklearn.neighbors
import sklearn.naive_bayes
import sklearn.tree
import sklearn.svm
import sklearn.neural_network
import sklearn.kernel_ridge
import sklearn.ensemble
import dataset
import data_cleaning_tool
########################################
########################################
class REDS:
"""
The main class.
"""
def __init__(self):
"""
The constructor does nothing.
"""
pass
def dataset_profiler(self, dataset_dictionary):
"""
This method profiles the dataset.
"""
d = dataset.Dataset(dataset_dictionary)
print "Profiling dataset {}...".format(d.name)
characters_unique_list = [0.0] * d.dataframe.shape[1]
characters_alphabet_list = [0.0] * d.dataframe.shape[1]
characters_numeric_list = [0.0] * d.dataframe.shape[1]
characters_punctuation_list = [0.0] * d.dataframe.shape[1]
characters_miscellaneous_list = [0.0] * d.dataframe.shape[1]
words_unique_list = [0.0] * d.dataframe.shape[1]
words_alphabet_list = [0.0] * d.dataframe.shape[1]
words_numeric_list = [0.0] * d.dataframe.shape[1]
words_punctuation_list = [0.0] * d.dataframe.shape[1]
words_miscellaneous_list = [0.0] * d.dataframe.shape[1]
words_length_list = [0.0] * d.dataframe.shape[1]
cells_unique_list = [0.0] * d.dataframe.shape[1]
cells_alphabet_list = [0.0] * d.dataframe.shape[1]
cells_numeric_list = [0.0] * d.dataframe.shape[1]
cells_punctuation_list = [0.0] * d.dataframe.shape[1]
cells_miscellaneous_list = [0.0] * d.dataframe.shape[1]
cells_length_list = [0.0] * d.dataframe.shape[1]
cells_null_list = [0.0] * d.dataframe.shape[1]
top_keywords_dictionary = {a.lower(): 1.0 for a in d.dataframe.columns}
stop_words_set = set(nltk.corpus.stopwords.words("english"))
for column, attribute in enumerate(d.dataframe.columns):
characters_dictionary = {}
words_dictionary = {}
cells_dictionary = {}
keywords_dictionary = {}
for cell in d.dataframe[attribute]:
for character in cell:
if character not in characters_dictionary:
characters_dictionary[character] = 0
characters_unique_list[column] += 1
characters_dictionary[character] += 1
if re.findall("^[a-zA-Z]$", character):
characters_alphabet_list[column] += 1
elif re.findall("^[0-9]$", character):
characters_numeric_list[column] += 1
elif re.findall("^[{}]$".format(string.punctuation), character):
characters_punctuation_list[column] += 1
else:
characters_miscellaneous_list[column] += 1
for word in nltk.word_tokenize(cell):
if word not in words_dictionary:
words_dictionary[word] = 0
words_unique_list[column] += 1
words_dictionary[word] += 1
if re.findall("^[a-zA-Z_-]+$", word):
words_alphabet_list[column] += 1
word = word.lower()
if word not in keywords_dictionary:
keywords_dictionary[word] = 0
keywords_dictionary[word] += 1
elif re.findall("^[0-9]+[.,][0-9]+$", word) or re.findall("^[0-9]+$", word):
words_numeric_list[column] += 1
elif re.findall("^[{}]+$".format(string.punctuation), word):
words_punctuation_list[column] += 1
else:
words_miscellaneous_list[column] += 1
words_length_list[column] += len(word)
if cell not in cells_dictionary:
cells_dictionary[cell] = 0
cells_unique_list[column] += 1
cells_dictionary[cell] += 1
if re.findall("^[a-zA-Z_ -]+$", cell):
cells_alphabet_list[column] += 1
elif re.findall("^[0-9]+[.,][0-9]+$", cell) or re.findall("^[0-9]+$", cell):
cells_numeric_list[column] += 1
elif re.findall("^[{}]+$".format(string.punctuation), cell, re.IGNORECASE):
cells_punctuation_list[column] += 1
else:
cells_miscellaneous_list[column] += 1
cells_length_list[column] += len(cell)
if cell == "":
cells_null_list[column] += 1
if sum(words_dictionary.values()) > 0:
words_length_list[column] /= sum(words_dictionary.values())
sorted_keywords_dictionary = sorted(keywords_dictionary.items(), key=operator.itemgetter(1), reverse=True)
for keyword, frequency in sorted_keywords_dictionary[:self.KEYWORDS_COUNT_PER_COLUMN]:
if keyword not in stop_words_set:
top_keywords_dictionary[keyword] = float(frequency) / d.dataframe.shape[0]
def f(columns_value_list):
return numpy.mean(numpy.array(columns_value_list).astype(numpy.float) / d.dataframe.shape[0])
def g(columns_value_list):
return numpy.var(numpy.array(columns_value_list).astype(numpy.float) / d.dataframe.shape[0])
dataset_profile = {
"dataset_top_keywords": top_keywords_dictionary,
"dataset_rules_count": len(self.DATASETS[d.name]["functions"]),
"dataset_patterns_count": len(self.DATASETS[d.name]["patterns"]),
"characters_unique_mean": f(characters_unique_list),
"characters_unique_variance": g(characters_unique_list),
"characters_alphabet_mean": f(characters_alphabet_list),
"characters_alphabet_variance": g(characters_alphabet_list),
"characters_numeric_mean": f(characters_numeric_list),
"characters_numeric_variance": g(characters_numeric_list),
"characters_punctuation_mean": f(characters_punctuation_list),
"characters_punctuation_variance": g(characters_punctuation_list),
"characters_miscellaneous_mean": f(characters_miscellaneous_list),
"characters_miscellaneous_variance": g(characters_miscellaneous_list),
"words_unique_mean": f(words_unique_list),
"words_unique_variance": g(words_unique_list),
"words_alphabet_mean": f(words_alphabet_list),
"words_alphabet_variance": g(words_alphabet_list),
"words_numeric_mean": f(words_numeric_list),
"words_numeric_variance": g(words_numeric_list),
"words_punctuation_mean": f(words_punctuation_list),
"words_punctuation_variance": g(words_punctuation_list),
"words_miscellaneous_mean": f(words_miscellaneous_list),
"words_miscellaneous_variance": g(words_miscellaneous_list),
"words_length_mean": f(words_length_list),
"words_length_variance": g(words_length_list),
"cells_unique_mean": f(cells_unique_list),
"cells_unique_variance": g(cells_unique_list),
"cells_alphabet_mean": f(cells_alphabet_list),
"cells_alphabet_variance": g(cells_alphabet_list),
"cells_numeric_mean": f(cells_numeric_list),
"cells_numeric_variance": g(cells_numeric_list),
"cells_punctuation_mean": f(cells_punctuation_list),
"cells_punctuation_variance": g(cells_punctuation_list),
"cells_miscellaneous_mean": f(cells_miscellaneous_list),
"cells_miscellaneous_variance": g(cells_miscellaneous_list),
"cells_length_mean": f(cells_length_list),
"cells_length_variance": g(cells_length_list),
"cells_null_mean": f(cells_null_list),
"cells_null_variance": g(cells_null_list)
}
pickle.dump(dataset_profile, open(os.path.join(self.RESULTS_FOLDER, d.name, "dataset_profile.dictionary"), "wb"))
def strategy_profiler(self, dataset_dictionary):
"""
This method profiles the output of error detection strategies.
"""
d = dataset.Dataset(dataset_dictionary)
strategies_profile = {}
for tn in self.ERROR_DETECTION_STRATEGIES:
print "Running error detection strategy {}...".format(tn)
if tn.startswith("histogram") or tn.startswith("gaussian") or tn.startswith("mixture") or \
tn.startswith("partitionedhistogram"):
td = {"name": "dboost", "configuration": tn.split("_")}
elif tn == "fd_checker":
td = {"name": "fd_checker", "configuration": self.DATASETS[d.name]["functions"]}
elif tn == "regex":
td = {"name": "regex", "configuration": self.DATASETS[d.name]["patterns"]}
elif tn == "katara":
td = {"name": "katara", "configuration": ["tools/KATARA/dominSpecific"]}
else:
sys.stderr.write("I do not know the data cleaning tool!\n")
return
start_time = time.time()
if not td["configuration"]:
detected_cells_dictionary = {}
else:
t = data_cleaning_tool.DataCleaningTool(td)
detected_cells_dictionary = t.run(d)
strategies_profile["{}|output".format(tn)] = detected_cells_dictionary.keys()
strategies_profile["{}|runtime".format(tn)] = time.time() - start_time
pickle.dump(strategies_profile, open(os.path.join(self.RESULTS_FOLDER, d.name, "strategies_profile.dictionary"), "wb"))
def evaluation_profiler(self, dataset_dictionary):
"""
This method evaluates the error detection strategies on dataset.
"""
d = dataset.Dataset(dataset_dictionary)
actual_errors = d.get_actual_errors_dictionary()
sp = pickle.load(open(os.path.join(self.RESULTS_FOLDER, d.name, "strategies_profile.dictionary"), "rb"))
evaluation_profile = {}
for tool_name in self.ERROR_DETECTION_STRATEGIES:
print "Evaluating error detection strategy {}...".format(tool_name)
tp = 0.0
for cell in sp["{}|output".format(tool_name)]:
if cell in actual_errors:
tp += 1.0
precision = 0.0 if len(sp["{}|output".format(tool_name)]) == 0 else tp / len(sp["{}|output".format(tool_name)])
recall = 0.0 if len(actual_errors) == 0 else tp / len(actual_errors)
f1 = 0.0 if (precision + recall) == 0 else (2 * precision * recall) / (precision + recall)
evaluation_profile[tool_name] = [precision, recall, f1]
pickle.dump(evaluation_profile, open(os.path.join(self.RESULTS_FOLDER, d.name, "evaluation_profile.dictionary"), "wb"))
def regression(self):
"""
This method extracts feature vectors, train, and test regression models.
"""
# --------------------Loading Dirtiness Profiles--------------------
dp = {}
sp = {}
ep = {}
keywords_dictionary = {}
for dataset_name in self.DATASETS:
print "Loading dirtiness profile for dataset {}...".format(dataset_name)
dp[dataset_name] = pickle.load(open(os.path.join(self.RESULTS_FOLDER, dataset_name, "dataset_profile.dictionary"), "rb"))
for keyword in dp[dataset_name]["dataset_top_keywords"]:
if keyword not in keywords_dictionary:
keywords_dictionary[keyword] = 1
sp[dataset_name] = pickle.load(open(os.path.join(self.RESULTS_FOLDER, dataset_name, "strategies_profile.dictionary"), "rb"))
ep[dataset_name] = pickle.load(open(os.path.join(self.RESULTS_FOLDER, dataset_name, "evaluation_profile.dictionary"), "rb"))
mean_squared_error_list = []
precision_at_k_list = []
for r in range(self.RUN_COUNT):
print "Run {}...".format(r)
x = []
y = []
for dataset_name in self.DATASETS:
print "Extracting feature vector for dataset {}...".format(dataset_name)
feature_vector = []
# --------------------Extracting Content Features--------------------
if "C" in self.SELECTED_FEATURE_GROUPS:
for keyword in keywords_dictionary.keys():
a = 0.0
if keyword in dp[dataset_name]["dataset_top_keywords"]:
a = dp[dataset_name]["dataset_top_keywords"][keyword]
feature_vector.append(a)
# --------------------Extracting Structure Features--------------------
if "S" in self.SELECTED_FEATURE_GROUPS:
for f, v in dp[dataset_name].items():
if f not in ["dataset_top_keywords"]:
feature_vector.append(v)
# --------------------Extracting Dirtiness Features--------------------
if "D" in self.SELECTED_FEATURE_GROUPS:
d = dataset.Dataset(self.DATASETS[dataset_name])
for tool_name in self.ERROR_DETECTION_STRATEGIES:
ndc = float(len(sp[dataset_name]["{}|output".format(tool_name)]))
ndc /= (d.dataframe.shape[0] * d.dataframe.shape[1])
feature_vector.append(ndc)
for tool_name_1 in self.ERROR_DETECTION_STRATEGIES:
a = set([tuple(z) for z in sp[dataset_name]["{}|output".format(tool_name_1)]])
for tool_name_2 in self.ERROR_DETECTION_STRATEGIES:
if tool_name_1 < tool_name_2:
b = set([tuple(z) for z in sp[dataset_name]["{}|output".format(tool_name_2)]])
no = float(len(list(a & b)))
de = len(list(a | b))
v = 0.0 if de == 0 else no / de
feature_vector.append(v)
if self.SAMPLING_RATE > 0.0:
d = dataset.Dataset(self.DATASETS[dataset_name])
actual_errors = d.get_actual_errors_dictionary()
selected_rows_dictionary = {r: 1 for r in random.sample(range(0, d.dataframe.shape[0]),
int(self.SAMPLING_RATE * d.dataframe.shape[0]))}
for tool_name in self.ERROR_DETECTION_STRATEGIES:
tp = 0.0
total = 0.0
for cell in sp[d.name]["{}|output".format(tool_name)]:
if cell[0] in selected_rows_dictionary:
total += 1.0
if cell in actual_errors:
tp += 1.0
precision = 0.0 if total == 0 else tp / total
feature_vector.append(precision)
x.append(feature_vector)
# --------------------Extracting Target Vector--------------------
target_vector = []
for tool_name in self.ERROR_DETECTION_STRATEGIES:
target_vector.append(ep[dataset_name][tool_name][2])
y.append(target_vector)
# --------------------Removing Useless Features--------------------
x_matrix = numpy.array(x, dtype=numpy.float)
non_identical_columns = numpy.any(x_matrix != x_matrix[0, :], axis=0)
x = x_matrix[:, non_identical_columns].tolist()
selected_rows = random.sample(range(0, len(self.DATASETS)), self.DIRTINESS_PROFILES_COUNT)
x = numpy.array(x, dtype=numpy.float)[selected_rows, :].tolist()
y = numpy.array(y, dtype=numpy.float)[selected_rows, :].tolist()
# --------------------Training and Testing Regression Models--------------------
print "Training regression models..."
estimated_f1_list_list = []
for i in range(len(x)):
x_train = x[:i] + x[i + 1:]
y_train = y[:i] + y[i + 1:]
x_test = [x[i]]
y_test = [y[i]]
estimated_f1_list = []
for j in range(len(self.ERROR_DETECTION_STRATEGIES)):
model = ""
if self.REGRESSION_MODEL == "LR":
model = sklearn.linear_model.LinearRegression(normalize=True)
elif self.REGRESSION_MODEL == "KNR":
model = sklearn.neighbors.KNeighborsRegressor(n_neighbors=5)
elif self.REGRESSION_MODEL == "RR":
model = sklearn.linear_model.Ridge(alpha=0.04, normalize=True)
elif self.REGRESSION_MODEL == "BRR":
model = sklearn.linear_model.BayesianRidge(normalize=False)
elif self.REGRESSION_MODEL == "DTR":
model = sklearn.tree.DecisionTreeRegressor(criterion="mae")
elif self.REGRESSION_MODEL == "SVR":
model = sklearn.svm.SVR(kernel="rbf")
elif self.REGRESSION_MODEL == "GBR":
model = sklearn.ensemble.GradientBoostingRegressor(loss="lad", n_estimators=100)
y_temp = [z[j] for z in y_train]
model = model.fit(x_train, y_temp)
y_estimated = model.predict(x_test)[0]
if math.isnan(y_estimated) or y_estimated < 0.0:
y_estimated = 0.0
if y_estimated > 1.0:
y_estimated = 1.0
estimated_f1_list.append(y_estimated)
estimated_f1_list_list.append(estimated_f1_list)
actual_f1_list_list = y
mse = numpy.mean(numpy.power(numpy.array(actual_f1_list_list) - numpy.array(estimated_f1_list_list), 2))
mean_squared_error_list.append(mse)
pak = numpy.array([0.0] * self.PRECISION_AT_K)
for i in range(len(x)):
estimated_f1 = [round(z, 2) for z in estimated_f1_list_list[i]]
actual_f1 = [round(z, 2) for z in actual_f1_list_list[i]]
estimated_index_list = []
actual_index_list = []
precision_at_list = [0.0] * self.PRECISION_AT_K
for k in range(self.PRECISION_AT_K):
estimated_max_f1 = max(estimated_f1)
actual_max_f1 = max(actual_f1)
estimated_index_list += [j for j, z in enumerate(estimated_f1) if z == estimated_max_f1]
actual_index_list += [j for j, z in enumerate(actual_f1) if z == actual_max_f1]
precision_at_list[k] += float(
len(list(set(estimated_index_list) & set(actual_index_list)))) / len(
list(set(estimated_index_list)))
estimated_f1 = [z if z != estimated_max_f1 else -1000.0 for z in estimated_f1]
actual_f1 = [z if z != actual_max_f1 else -1000.0 for z in actual_f1]
pak += numpy.array(precision_at_list) / len(x)
precision_at_k_list.append(pak)
print "REDS,", self.SAMPLING_RATE
print "MSE = {:.3f} +- {:.3f}".format(numpy.mean(mean_squared_error_list), numpy.std(mean_squared_error_list))
for k in range(self.PRECISION_AT_K):
temp_array = numpy.array(precision_at_k_list)[:, k]
print "P@{} = {:.3f} +- {:.3f}".format(k + 1, numpy.mean(temp_array), numpy.std(temp_array))
def baseline(self):
"""
The method provide maximum entropy-based baseline.
"""
actual_f1_list_list = []
sp = {}
for dataset_name in self.DATASETS:
ep = pickle.load(open(os.path.join(self.RESULTS_FOLDER, dataset_name, "evaluation_profile.dictionary"), "rb"))
actual_f1_list_list.append([ep[sn][2] for sn in self.ERROR_DETECTION_STRATEGIES])
sp[dataset_name] = pickle.load(open(os.path.join(self.RESULTS_FOLDER, dataset_name, "strategies_profile.dictionary"), "rb"))
mean_squared_error_list = []
precision_at_k_list = []
for r in range(self.RUN_COUNT):
print "Run {}...".format(r)
estimated_f1_list_list = []
for dataset_name in self.DATASETS:
print "Evaluating error detection strategies on a sample of dataset {}...".format(dataset_name)
estimated_precision_list = []
d = dataset.Dataset(self.DATASETS[dataset_name])
actual_errors = d.get_actual_errors_dictionary()
selected_rows_dictionary = {r: 1 for r in random.sample(range(0, d.dataframe.shape[0]),
int(self.SAMPLING_RATE * d.dataframe.shape[0]))}
for tool_name in self.ERROR_DETECTION_STRATEGIES:
tp = 0.0
total = 0.0
for cell in sp[dataset_name]["{}|output".format(tool_name)]:
if cell[0] in selected_rows_dictionary:
total += 1.0
if cell in actual_errors:
tp += 1.0
precision = 0.0 if total == 0 else tp / total
estimated_precision_list.append(precision)
estimated_f1_list_list.append(estimated_precision_list)
mse = numpy.mean(numpy.power(numpy.array(actual_f1_list_list) - numpy.array(estimated_f1_list_list), 2))
mean_squared_error_list.append(mse)
pak = numpy.array([0.0] * self.PRECISION_AT_K)
for i in range(len(self.DATASETS)):
estimated_f1 = [round(z, 2) for z in estimated_f1_list_list[i]]
actual_f1 = [round(z, 2) for z in actual_f1_list_list[i]]
estimated_index_list = []
actual_index_list = []
precision_at_list = [0.0] * self.PRECISION_AT_K
for k in range(self.PRECISION_AT_K):
estimated_max_f1 = max(estimated_f1)
actual_max_f1 = max(actual_f1)
estimated_index_list += [j for j, z in enumerate(estimated_f1) if z == estimated_max_f1]
actual_index_list += [j for j, z in enumerate(actual_f1) if z == actual_max_f1]
precision_at_list[k] += float(
len(list(set(estimated_index_list) & set(actual_index_list)))) / len(
list(set(estimated_index_list)))
estimated_f1 = [z if z != estimated_max_f1 else -1000.0 for z in estimated_f1]
actual_f1 = [z if z != actual_max_f1 else -1000.0 for z in actual_f1]
pak += numpy.array(precision_at_list) / len(self.DATASETS)
precision_at_k_list.append(pak)
print "Baseline,", self.SAMPLING_RATE
print "MSE = {:.3f} +- {:.3f}".format(numpy.mean(mean_squared_error_list), numpy.std(mean_squared_error_list))
for k in range(self.PRECISION_AT_K):
temp_array = numpy.array(precision_at_k_list)[:, k]
print "P@{} = {:.3f} +- {:.3f}".format(k + 1, numpy.mean(temp_array), numpy.std(temp_array))
########################################
########################################
if __name__ == "__main__":
# ----------------------------------------
application = REDS()
application.DATASETS_FOLDER = "datasets"
application.RESULTS_FOLDER = "results"
application.DATASETS = {
"hospital": {
"name": "hospital",
"path": os.path.join(application.DATASETS_FOLDER, "hospital", "dirty.csv"),
"clean_path": os.path.join(application.DATASETS_FOLDER, "hospital", "clean.csv"),
"functions": [["zip", "city"], ["zip", "state"], ["zip", "county"], ["zip", "type"],
["zip", "emergency_service"],
["state_average", "type"], ["state_average", "state"], ["state_average", "condition"],
["state_average", "measure_code"], ["state_average", "measure_name"],
["state", "type"],
["score", "type"],
["sample", "type"],
["provider_number", "name"], ["provider_number", "address_1"], ["provider_number", "city"],
["provider_number", "state"], ["provider_number", "zip"],
["provider_number", "county"], ["provider_number", "phone"], ["provider_number", "type"],
["provider_number", "owner"], ["provider_number", "emergency_service"],
["phone", "provider_number"], ["phone", "name"], ["phone", "address_1"], ["phone", "city"],
["phone", "state"], ["phone", "county"], ["phone", "zip"], ["phone", "type"],
["phone", "owner"], ["phone", "emergency_service"],
["measure_name", "type"], ["measure_name", "condition"], ["measure_name", "measure_code"],
["measure_code", "type"], ["measure_code", "condition"], ["measure_code", "measure_name"],
["owner", "type"],
["name", "provider_number"], ["name", "address_1"], ["name", "city"],
["name", "state"], ["name", "zip"],
["name", "county"], ["name", "phone"], ["name", "type"],
["name", "owner"], ["name", "emergency_service"],
["emergency_service", "type"],
["county", "state"], ["county", "type"], ["county", "emergency_service"],
["condition", "type"],
["city", "state"], ["city", "county"], ["city", "type"], ["city", "emergency_service"],
["address_1", "provider_number"], ["address_1", "name"], ["address_1", "city"],
["address_1", "state"], ["address_1", "zip"], ["address_1", "county"], ["address_1", "phone"],
["address_1", "type"], ["address_1", "owner"], ["address_1", "emergency_service"]],
"patterns": [["provider_number", "^[\d]+$", "ONM"], ["zip", "^[\d]{5}$", "ONM"],
["state", "^[a-z]{2}$", "ONM"], ["phone", "^[\d]+$", "ONM"],
["emergency_service", "^(yes|no)$", "ONM"]],
},
"flights": {
"name": "flights",
"path": os.path.join(application.DATASETS_FOLDER, "flights", "dirty.csv"),
"clean_path": os.path.join(application.DATASETS_FOLDER, "flights", "clean.csv"),
"functions": [["flight", "act_dep_time"], ["flight", "sched_arr_time"], ["flight", "act_arr_time"],
["flight", "sched_dep_time"]],
"patterns": [["sched_dep_time", "^[\d]{1,2}[:][\d]{1,2}[ ][ap].m.$", "ONM"],
["act_dep_time", "^[\d]{1,2}[:][\d]{1,2}[ ][ap].m.$", "ONM"],
["sched_arr_time", "^[\d]{1,2}[:][\d]{1,2}[ ][ap].m.$", "ONM"],
["act_arr_time", "^[\d]{1,2}[:][\d]{1,2}[ ][ap].m.$", "ONM"]],
},
"address": {
"name": "address",
"path": os.path.join(application.DATASETS_FOLDER, "address", "dirty.csv"),
"clean_path": os.path.join(application.DATASETS_FOLDER, "address", "clean.csv"),
"functions": [["address", "state"], ["zip", "state"]], # ["zip", "city"], ["city", "state"]
"patterns": [["state", "^[A-Z]{2}$", "ONM"], ["zip", "^[\d]+$", "ONM"], ["ssn", "^[\d]*$", "ONM"]],
},
"beers": {
"name": "beers",
"path": os.path.join(application.DATASETS_FOLDER, "beers", "dirty.csv"),
"clean_path": os.path.join(application.DATASETS_FOLDER, "beers", "clean.csv"),
"functions": [["brewery_id", "brewery_name"], ["brewery_id", "city"], ["brewery_id", "state"]],
"patterns": [["state", "^[A-Z]{2}$", "ONM"], ["brewery_id", "^[\d]+$", "ONM"]],
},
"rayyan": {
"name": "rayyan",
"path": os.path.join(application.DATASETS_FOLDER, "rayyan", "dirty.csv"),
"clean_path": os.path.join(application.DATASETS_FOLDER, "rayyan", "clean.csv"),
"functions": [], # ["jounral_abbreviation", "journal_title"], ["jounral_abbreviation", "journal_issn"], ["journal_issn", "journal_title"]
"patterns": [["article_jvolumn", "^[\d]+$|^[-][1]$", "ONM"], ["article_jissue", "^[\d]+$|^[-][1]$", "ONM"],
["article_jcreated_at", "^[\d]+[/][\d]+[/][\d]+$|^$", "ONM"]],
# ["article_jvolumn", "^$", "OM"], ["article_jissue", "^$", "OM"],
# ["article_jcreated_at", "^[\d]+[/][\d]+[/][\d]+$|^$", "OM"], ["journal_issn", "^$", "OM"],
# ["journal_title", "^$", "OM"], ["article_language", "^$", "OM"], ["article_title", "^$", "OM"],
# ["jounral_abbreviation", "^$", "OM"], ["article_pagination", "^$", "OM"],
# ["author_list", "^$", "OM"]
},
"movies_1": {
"name": "movies_1",
"path": os.path.join(application.DATASETS_FOLDER, "movies_1", "dirty.csv"),
"clean_path": os.path.join(application.DATASETS_FOLDER, "movies_1", "clean.csv"),
"functions": [],
"patterns": [["id", "^tt[\d]+$", "ONM"], ["year", "^[\d]{4}$", "ONM"], ["rating_value", "^[\d.]*$", "ONM"],
["rating_count", "^[\d]*$", "ONM"], ["duration", "^([\d]+[ ]min)*$", "ONM"]],
},
"merck": {
"name": "merck",
"path": os.path.join(application.DATASETS_FOLDER, "merck", "dirty.csv"),
"clean_path": os.path.join(application.DATASETS_FOLDER, "merck", "clean.csv"),
"functions": [],
"patterns": [["support_level", "^$", "OM"], ["app_status", "^$", "OM"], ["curr_status", "^$", "OM"],
["tower", "^$", "OM"], ["end_users", "^$", "OM"], ["account_manager", "^$", "OM"],
["decomm_dt", "^$", "OM"], ["decomm_start", "^$", "OM"], ["decomm_end", "^$", "OM"],
["end_users", "^(0)$", "OM"],
["retirement", "^(2010|2011|2012|2013|2014|2015|2016|2017|2018)$", "ONM"],
["emp_dta", "^(n|N|y|Y|n/a|N/A|n/A|N/a)$", "ONM"],
["retire_plan", "^(true|True|TRUE|false|False|FALSE|n/a|N/A|n/A|N/a)$", "ONM"],
["bus_import", "^(important|n/a|IP Strategy)$", "OM"],
["division", "^(Merck Research Laboratories|Merck Consumer Health Care)$", "OM"]],
},
"restaurants_4": {
"name": "restaurants_4",
"path": os.path.join(application.DATASETS_FOLDER, "restaurants_4", "dirty.csv"),
"clean_path": os.path.join(application.DATASETS_FOLDER, "restaurants_4", "clean.csv"),
"functions": [["zip_code", "state"]],
"patterns": [["state", "^[A-Z]{2}$|^$", "ONM"], ["zip_code", "^[\d]*$", "ONM"],
["phone", "^[(][\d]{3}[)][ ][\d]{3}[-][\d]{4}$|^$", "ONM"]],
},
"salaries": {
"name": "salaries",
"path": os.path.join(application.DATASETS_FOLDER, "salaries", "dirty.csv"),
"clean_path": os.path.join(application.DATASETS_FOLDER, "salaries", "clean.csv"),
"functions": [],
"patterns": [["year", "^[\d]{4}$", "ONM"], ["base_pay", "^([\d]+)([.][\d]+)?$", "ONM"],
["overtime_pay", "^([\d]+)([.][\d]+)?$", "ONM"], ["other_pay", "^([\d]+)([.][\d]+)?$", "ONM"],
["benefits", "^([\d]+)([.][\d]+)?$|^$", "ONM"], ["total_pay", "^([\d]+)([.][\d]+)?$", "ONM"],
["total_pay_benefits", "^([\d]+)([.][\d]+)?$", "ONM"]],
},
"soccer": {
"name": "soccer",
"path": os.path.join(application.DATASETS_FOLDER, "soccer", "dirty.csv"),
"clean_path": os.path.join(application.DATASETS_FOLDER, "soccer", "clean.csv"),
"functions": [["name", "surname"], ["name", "birth_year"], ["name", "birth_place"]],
"patterns": [["birth_year", "^[\d]{4}$", "ONM"], ["season", "^[\d]{4}$", "ONM"]],
},
"tax": {
"name": "tax",
"path": os.path.join(application.DATASETS_FOLDER, "tax", "dirty.csv"),
"clean_path": os.path.join(application.DATASETS_FOLDER, "tax", "clean.csv"),
"functions": [["area_code", "state"], ["f_name", "gender"], ["zip", "city"], ["zip", "state"]],
"patterns": [["gender", "^(F|M)$", "ONM"], ["area_code", "^[\d]{3}$", "ONM"],
["phone", "^[\d]{3}[-][\d]{4}$", "ONM"], ["state", "^[A-Z]{2}$", "ONM"],
["zip", "^[\d]+$", "ONM"], ["marital_status", "^(M|S)$", "ONM"],
["has_child", "^(N|Y)$", "ONM"], ["salary", "^[\d]+$", "ONM"],
["rate", "^([\d])([.][\d]+)?$", "ONM"], ["single_exemp", "^[\d]+$", "ONM"],
["married_exemp", "^[\d]+$", "ONM"], ["child_exemp", "^[\d]+$", "ONM"]],
}
# ----------------------------------------------------
# "toy": {
# "name": "toy",
# "path": os.path.join(application.DATASETS_FOLDER, "toy", "dirty.csv"),
# "clean_path": os.path.join(application.DATASETS_FOLDER, "toy", "clean.csv"),
# "functions": [["city", "country"]],
# "patterns": [["age", "^[\d]+$", "ONM"]],
# },
}
application.ERROR_DETECTION_STRATEGIES = ["histogram_0.8_0.1", "histogram_0.8_0.2", "histogram_0.9_0.2",
"gaussian_1.0", "gaussian_1.5", "gaussian_2.0",
"mixture_2_0.005", "mixture_2_0.01", "mixture_2_0.05",
"partitionedhistogram_5_0.8_0.1", "partitionedhistogram_10_0.8_0.1",
"partitionedhistogram_15_0.8_0.1",
"fd_checker", "regex", "katara"]
application.RUN_COUNT = 10
application.PRECISION_AT_K = 3
application.KEYWORDS_COUNT_PER_COLUMN = 10
application.SAMPLING_RATE = 0.01 # [0.0, 0.01, 0.02, 0.03, 0.04, 0.05]
application.SELECTED_FEATURE_GROUPS = ["C", "S", "D"] # ["C", "S", "D"]
application.REGRESSION_MODEL = "GBR" # ["LR", "KNR", "RR", "BRR", "DTR", "SVR", "GBR"]
application.DIRTINESS_PROFILES_COUNT = len(application.DATASETS) # [2,..., len(application.DATASETS)]
# ----------------------------------------
for dd in application.DATASETS.values():
print "===================== Dataset: {} =====================".format(dd["name"])
if not os.path.exists(os.path.join(application.RESULTS_FOLDER, dd["name"])):
os.mkdir(os.path.join(application.RESULTS_FOLDER, dd["name"]))
# application.dataset_profiler(dd)
# application.strategy_profiler(dd)
# application.evaluation_profiler(dd)
# ----------------------------------------
# application.regression()
# ----------------------------------------
# application.baseline()
# ----------------------------------------
########################################