'latitude', 'longitude', 'out_of_state', 'luxury_make', 'domestic_make'
]
if __name__ == '__main__':
use_project_path()
logger = Logger('Data_Scratch/preprocess.txt')
raw_record_count = -1
processed_records = 0
missing_data = {}
for column in column_names:
missing_data[column] = 0
logger.time_log('Starting Data Pre-Processing...')
with open('data_scratch/Parking_Citations.csv') as input_file:
with open('data_scratch/preprocessed_citations.csv', 'w',
newline='') as output_file:
reader = csv.reader(input_file)
writer = csv.writer(output_file)
writer.writerow(column_names)
for i, line in enumerate(reader):
raw_record_count += 1
if raw_record_count == 0:
continue
violation_code = line[14]
from utility import use_project_path, Logger
from data import column_names
if __name__ == "__main__":
use_project_path()
logger = Logger('data_scratch/sample_cleaned.txt')
sample_seed = 1029
sample_rate = 0.10
raw_record_count = -1
processed_records = 0
random.seed(sample_seed)
logger.time_log('Starting Data Sampling.')
with open('data_scratch/cleaned_crimes.csv') as input_file:
with open('data_scratch/sampled_cleaned_crimes.csv', 'w',
newline='') as output_file:
reader = csv.reader(input_file)
writer = csv.writer(output_file)
writer.writerow(column_names)
for i, line in enumerate(reader):
raw_record_count += 1
if raw_record_count == 0:
continue
if random.random() < sample_rate:
if __name__ == '__main__':
use_project_path()
logger = Logger('Data_Scratch/categories.txt')
raw_record_count = -1
iucr_codes = {}
type_codes = {}
description_codes = {}
location_codes = {}
fbi_codes = {}
logger.time_log('Starting Category Transformation.')
with open('data_scratch/Crimes_-_2001_to_present.csv') as input_file:
reader = csv.reader(input_file)
for i, line in enumerate(reader):
raw_record_count += 1
if raw_record_count == 0:
continue
iucr = line[4]
type = line[5]
description = line[6]
location = line[7]
fbi_code = line[14]
iucr_codes[iucr] = 1
logger = Logger('Data_Scratch/preprocess.txt')
iucr_codes = load_codes('data_scratch/iucr_codes.json')
type_codes = load_codes('data_scratch/type_codes.json')
description_codes = load_codes('data_scratch/description_codes.json')
location_codes = load_codes('data_scratch/location_codes.json')
fbi_codes = load_codes('data_scratch/fbi_codes.json')
missing_columns = {}
for column in column_names:
missing_columns[column] = 0
raw_record_count = -1
processed_records = 0
logger.time_log('Starting Pre-Processing.')
with open('data_scratch/Crimes_-_2001_to_present.csv') as input_file:
with open('data_scratch/preprocessed_crimes.csv', 'w',
newline='') as output_file:
reader = csv.reader(input_file)
writer = csv.writer(output_file)
writer.writerow(column_names)
for i, line in enumerate(reader):
raw_record_count += 1
if raw_record_count == 0:
continue
arrest = line[8]
date = line[2]
def run_classification_search_experiment(
self,
scoring,
sample=None,
random_state=None,
test_size=0.25,
n_jobs=-1,
n_iter=2,
cv=5,
verbose=3,
multiclass=False,
record_predict_proba=False):
"""
The classification search makes use of a bayesian search to find the best hyper-parameters.
"""
use_project_path()
logger = Logger('%s.txt' % self.name)
search = BayesSearchCV(
self.estimator,
self.hyper_parameters.search_space,
n_jobs=n_jobs,
n_iter=n_iter,
cv=cv,
verbose=verbose,
scoring=scoring,
return_train_score=True
)
data_frame = self.df
if sample is not None:
data_frame = data_frame.sample(n=sample, random_state=random_state)
x_train, x_test, y_train, y_test = train_test_split(data_frame, data_frame[self.target], test_size=test_size)
logger.time_log('Starting HyperParameter Search...')
results = search.fit(x_train, y_train)
logger.time_log('Search Complete.\n')
logger.time_log('Testing Training Partition...')
y_train_predict = batch_predict(results.best_estimator_, x_train)
logger.time_log('Testing Complete.\n')
train_evaluation_frame = EvaluationFrame(y_train, y_train_predict)
logger.time_log('Testing Holdout Partition...')
y_test_predict = batch_predict(results.best_estimator_, x_test)
logger.time_log('Testing Complete.\n')
test_evaluation_frame = EvaluationFrame(y_test, y_test_predict)
test_evaluation_frame.save('%s_predict.p' % self.name)
test_proba_evaluation_frame = None
if record_predict_proba:
logger.time_log('Testing Holdout Partition (probability)...')
y_test_predict_proba = batch_predict_proba(results.best_estimator_, x_test)
test_proba_evaluation_frame = EvaluationFrame(y_test, y_test_predict_proba)
test_proba_evaluation_frame.save('%s_predict_proba.p' % self.name)
logger.time_log('Testing Complete.\n')
evaluator = Evaluator(logger)
evaluator.evaluate_classifier_result(
results,
test_evaluation_frame,
train=train_evaluation_frame,
test_proba=test_proba_evaluation_frame,
multiclass=multiclass
)
logger.close()
self.hyper_parameters.params = results.best_params_
self.hyper_parameters.save('%s_params.p' % self.name)
self.trained_estimator = results.best_estimator_
def run_classification_experiment(
self,
sample=None,
random_state=None,
test_size=0.25,
multiclass=False,
record_predict_proba=False):
"""
Running a classification experiment is used when only a single model run and fit is necessary.
"""
use_project_path()
logger = Logger('%s.txt' % self.name)
data_frame = self.df
if sample is not None:
data_frame = data_frame.sample(n=sample, random_state=random_state)
x_train, x_test, y_train, y_test = train_test_split(data_frame, data_frame[self.target], test_size=test_size)
if self.hyper_parameters is not None:
self.estimator.set_params(**self.hyper_parameters.params)
logger.time_log('Training Model...')
self.estimator.fit(x_train, y_train)
logger.time_log('Training Complete.\n')
logger.time_log('Testing Training Partition...')
y_train_predict = batch_predict(self.estimator, x_train)
logger.time_log('Testing Complete.\n')
train_evaluation_frame = EvaluationFrame(y_train, y_train_predict)
logger.time_log('Testing Holdout Partition...')
y_test_predict = batch_predict(self.estimator, x_test)
logger.time_log('Testing Complete.\n')
test_evaluation_frame = EvaluationFrame(y_test, y_test_predict)
test_evaluation_frame.save('%s_predict.p' % self.name)
test_proba_evaluation_frame = None
if record_predict_proba:
logger.time_log('Testing Holdout Partition (probability)...')
y_test_predict_proba = batch_predict_proba(self.estimator, x_test)
test_proba_evaluation_frame = EvaluationFrame(y_test, y_test_predict_proba)
test_proba_evaluation_frame.save('%s_predict_proba.p' % self.name)
logger.time_log('Testing Complete.\n')
evaluator = Evaluator(logger)
evaluator.evaluate_classifier_result(
self.estimator,
test_evaluation_frame,
train=train_evaluation_frame,
test_proba=test_proba_evaluation_frame,
multiclass=multiclass
)
logger.close()
if self.hyper_parameters is not None:
self.hyper_parameters.save('%s_params.p' % self.name)
def run_classification_experiment(self,
sample=None,
random_state=None,
test_size=0.20,
multiclass=False,
record_predict_proba=False,
sampling=None,
cv=5,
verbose=True,
transformer=None,
fit_increment=None,
warm_start=False,
max_iters=None,
n_jobs=-1):
use_project_path()
logger = Logger('%s.txt' % self.name)
evaluator = Evaluator(logger)
data_frame = self.df
if sample is not None:
data_frame = data_frame.sample(n=sample, random_state=random_state)
x_train, x_test, y_train, y_test = train_test_split(
data_frame, data_frame[self.target], test_size=test_size)
if transformer is not None:
logger.time_log('Fitting Transformer...')
transformer.fit(x_train)
logger.time_log('Transformer Fit Complete.\n')
if sampling is not None:
logger.time_log('Starting Data Re-Sampling...')
logger.log('Original Training Shape is %s' % Counter(y_train))
x_new, y_new = sampling.fit_resample(x_train, y_train)
logger.log('Balanced Training Shape is %s' % Counter(y_new))
if hasattr(x_train, 'columns'):
x_new = pd.DataFrame(x_new, columns=x_train.columns)
x_train, y_train = x_new, y_new
logger.time_log('Re-Sampling Complete.\n')
logger.time_log('Shuffling Re-Sampled Data.\n')
x_train, y_train = shuffle(x_train,
y_train,
random_state=random_state)
logger.time_log('Shuffling Complete.\n')
if self.hyper_parameters is not None:
self.estimator.set_params(**self.hyper_parameters.params)
if cv is not None:
kfold = StratifiedKFold(n_splits=cv, random_state=random_state)
logger.time_log('Cross Validating Model...')
fold_scores = Parallel(n_jobs=n_jobs, verbose=3)(
delayed(crossfold_classifier)
(clone(self.estimator), transformer, x_train, y_train,
train_index, test_index, record_predict_proba, verbose,
fit_increment, warm_start, max_iters, random_state)
for train_index, test_index in kfold.split(x_train, y_train))
logger.time_log('Cross Validation Complete.\n')
logger.time_log('Training Model...')
if fit_increment is not None:
if max_iters is not None:
for iter in range(max_iters):
x_iter_train, y_iter_train = shuffle(
x_train, y_train, random_state=random_state)
batch_fit_classifier(self.estimator,
x_iter_train,
y_iter_train,
transformer=transformer,
increment=fit_increment,
verbose=verbose)
else:
batch_fit_classifier(self.estimator,
x_train,
y_train,
transformer=transformer,
increment=fit_increment,
verbose=verbose)
else:
if transformer is not None:
x_train_transformed = transformer.transform(x_train)
self.estimator.fit(x_train_transformed, y_train)
else:
self.estimator.fit(x_train, y_train)
logger.time_log('Training Complete.\n')
logger.time_log('Testing Training Partition...')
y_train_predict = batch_predict(self.estimator,
x_train,
transformer=transformer,
verbose=verbose)
logger.time_log('Testing Complete.\n')
train_evaluation_frame = EvaluationFrame(y_train, y_train_predict)
logger.time_log('Testing Holdout Partition...')
y_test_predict = batch_predict(self.estimator,
x_test,
transformer=transformer,
verbose=verbose)
logger.time_log('Testing Complete.\n')
test_evaluation_frame = EvaluationFrame(y_test, y_test_predict)
test_evaluation_frame.save('%s_predict.p' % self.name)
test_proba_evaluation_frame = None
if record_predict_proba:
logger.time_log('Testing Holdout Partition (probability)...')
y_test_predict_proba = batch_predict_proba(self.estimator,
x_test,
transformer=transformer,
verbose=verbose)
test_proba_evaluation_frame = EvaluationFrame(
y_test, y_test_predict_proba)
test_proba_evaluation_frame.save('%s_predict_proba.p' % self.name)
logger.time_log('Testing Complete.\n')
if cv is not None:
evaluator.evaluate_fold_scores(fold_scores)
evaluator.evaluate_classifier_result(
self.estimator,
test_evaluation_frame,
train=train_evaluation_frame,
test_proba=test_proba_evaluation_frame,
multiclass=multiclass)
logger.close()
if self.hyper_parameters is not None:
self.hyper_parameters.save('%s_params.p' % self.name)
self.trained_estimator = self.estimator
def run_classification_search_experiment(self,
scoring,
sample=None,
random_state=None,
test_size=0.20,
n_jobs=-1,
n_iter=2,
cv=5,
verbose=3,
multiclass=False,
record_predict_proba=False,
sampling=None):
use_project_path()
logger = Logger('%s.txt' % self.name)
search = BayesSearchCV(self.estimator,
self.hyper_parameters.search_space,
n_jobs=n_jobs,
n_iter=n_iter,
cv=cv,
verbose=verbose,
scoring=scoring,
return_train_score=True)
data_frame = self.df
if sample is not None:
data_frame = data_frame.sample(n=sample, random_state=random_state)
x_train, x_test, y_train, y_test = train_test_split(
data_frame, data_frame[self.target], test_size=test_size)
if sampling is not None:
logger.time_log('Starting Data Re-Sampling...')
logger.log('Original Training Shape is %s' % Counter(y_train))
x_new, y_new = sampling.fit_resample(x_train, y_train)
logger.log('Balanced Training Shape is %s' % Counter(y_new))
if hasattr(x_train, 'columns'):
x_new = pd.DataFrame(x_new, columns=x_train.columns)
x_train, y_train = x_new, y_new
logger.time_log('Re-Sampling Complete.\n')
logger.time_log('Shuffling Re-Sampled Data.\n')
x_train, y_train = shuffle(x_train,
y_train,
random_state=random_state)
logger.time_log('Shuffling Complete.\n')
logger.time_log('Starting HyperParameter Search...')
results = search.fit(x_train, y_train)
logger.time_log('Search Complete.\n')
logger.time_log('Testing Training Partition...')
y_train_predict = batch_predict(results.best_estimator_, x_train)
logger.time_log('Testing Complete.\n')
train_evaluation_frame = EvaluationFrame(y_train, y_train_predict)
logger.time_log('Testing Holdout Partition...')
y_test_predict = batch_predict(results.best_estimator_, x_test)
logger.time_log('Testing Complete.\n')
test_evaluation_frame = EvaluationFrame(y_test, y_test_predict)
test_evaluation_frame.save('%s_predict.p' % self.name)
test_proba_evaluation_frame = None
if record_predict_proba:
logger.time_log('Testing Holdout Partition (probability)...')
y_test_predict_proba = batch_predict_proba(results.best_estimator_,
x_test)
test_proba_evaluation_frame = EvaluationFrame(
y_test, y_test_predict_proba)
test_proba_evaluation_frame.save('%s_predict_proba.p' % self.name)
logger.time_log('Testing Complete.\n')
evaluator = Evaluator(logger)
evaluator.evaluate_classifier_result(
results,
test_evaluation_frame,
train=train_evaluation_frame,
test_proba=test_proba_evaluation_frame,
multiclass=multiclass)
logger.close()
self.hyper_parameters.params = results.best_params_
self.hyper_parameters.save('%s_params.p' % self.name)
self.trained_estimator = results.best_estimator_
