Kaggle competition: Predict Future Sales
<SOS> metric: rmse Kaggle data source: predict-future-sales<save_to_csv>
import numpy as np, pandas as pd
from glob import glob
import shutil, os
import matplotlib.pyplot as plt
from sklearn.model_selection import GroupKFold
from tqdm.notebook import tqdm
import seaborn as sns
sub = pd.DataFrame()
sub['seg_id'] = test_df.seg_id.values
sub['time_to_failure'] =.5*GPI(alldata[mfcc_ttf_df.shape[0]:] ).values+.5*GPII(alldata[mfcc_ttf_df.shape[0]:] ).values
sub.to_csv('gpmfccsubmean.csv',index=False)
 sub.head()<import_modules>
dim = 512
test_dir = f'/kaggle/input/vinbigdata-{dim}-image-dataset/vinbigdata/test'
weights_dir = '/kaggle/input/chest-x-ray-abnormality-detection-with-yolo-v3/yolov3/runs/train/exp/weights/best.pt'
warnings.filterwarnings("ignore")
 np.seterr(divide='ignore', invalid='ignore')
 np.seterr(divide='ignore')
 print(os.listdir(".. /input"))<save_to_csv>
test_df = pd.read_csv(f'/kaggle/input/vinbigdata-{dim}-image-dataset/vinbigdata/test.csv')
 test_df.head()
def progressbar(it, prefix="", size=60, file=sys.stdout):
count = len(it)
 def show(j):
x = int(size*j/count)
 file.write("%s[%s%s] %i/%i\r" %(prefix, "
file.flush()
show(0)
 for i, item in enumerate(it):
yield item
show(i+1)
 file.write("
")
 file.flush()<load_from_csv>
shutil.copytree('/kaggle/input/yolov5-official-v31-dataset/yolov5', '/kaggle/working/yolov5')
 os.chdir('/kaggle/working/yolov5')
 clear_output()
print('Setup complete.Using torch %s %s' %(torch.__version__, torch.cuda.get_device_properties(0)if torch.cuda.is_available() else 'CPU'))
%matplotlib inline
start = time.time()
train = pd.read_csv('.. /input/train.csv',dtype={'acoustic_data': np.int16, 'time_to_failure': np.float32})
 end = time.time()
print(end - start )<init_hyperparams>
!python detect.py --weights $weights_dir\
--img 512\
--conf 0.15\
--iou 0.4\
--source $test_dir\
--save-txt --save-conf --exist-ok
settings={'variance_larger_than_standard_deviation': None,
'has_duplicate_max': None,
'has_duplicate_min': None,
'has_duplicate': None,
'sum_values': None,
'abs_energy': None,
'mean_abs_change': None,
'mean_change': None,
'mean_second_derivative_central': None,
'median': None,
'mean': None,
'length': None,
'standard_deviation': None,
'variance': None,
'skewness': None,
'kurtosis': None,
'absolute_sum_of_changes': None,
'longest_strike_below_mean': None,
'longest_strike_above_mean': None,
'count_above_mean': None,
'count_below_mean': None,
'last_location_of_maximum': None,
'first_location_of_maximum': None,
'last_location_of_minimum': None,
'first_location_of_minimum': None,
'percentage_of_reoccurring_datapoints_to_all_datapoints': None,
'percentage_of_reoccurring_values_to_all_values': None,
'sum_of_reoccurring_values': None,
'sum_of_reoccurring_data_points': None,
'ratio_value_number_to_time_series_length': None,
'sample_entropy': None,
'maximum': None,
'minimum': None,
'time_reversal_asymmetry_statistic': [{'lag': 1}, {'lag': 2}, {'lag': 3}],
'c3': [{'lag': 1}, {'lag': 2}, {'lag': 3}],
'cid_ce': [{'normalize': True}, {'normalize': False}],
'symmetry_looking': [{'r': 0.0},
{'r': 0.05},
{'r': 0.1},
{'r': 0.15000000000000002},
{'r': 0.2},
{'r': 0.25},
{'r': 0.30000000000000004},
{'r': 0.35000000000000003},
{'r': 0.4},
{'r': 0.45},
{'r': 0.5},
{'r': 0.55},
{'r': 0.6000000000000001},
{'r': 0.65},
{'r': 0.7000000000000001},
{'r': 0.75},
{'r': 0.8},
{'r': 0.8500000000000001},
{'r': 0.9},
{'r': 0.9500000000000001}],
'large_standard_deviation': [{'r': 0.05},
{'r': 0.1},
{'r': 0.15000000000000002},
{'r': 0.2},
{'r': 0.25},
{'r': 0.30000000000000004},
{'r': 0.35000000000000003},
{'r': 0.4},
{'r': 0.45},
{'r': 0.5},
{'r': 0.55},
{'r': 0.6000000000000001},
{'r': 0.65},
{'r': 0.7000000000000001},
{'r': 0.75},
{'r': 0.8},
{'r': 0.8500000000000001},
{'r': 0.9},
{'r': 0.9500000000000001}],
'quantile': [{'q': 0.1},
{'q': 0.2},
{'q': 0.3},
{'q': 0.4},
{'q': 0.6},
{'q': 0.7},
{'q': 0.8},
{'q': 0.9}],
'autocorrelation': [{'lag': 0},
{'lag': 1},
{'lag': 2},
{'lag': 3},
{'lag': 4},
{'lag': 5},
{'lag': 6},
{'lag': 7},
{'lag': 8},
{'lag': 9}],
'agg_autocorrelation': [{'f_agg': 'mean', 'maxlag': 40},
{'f_agg': 'median', 'maxlag': 40},
{'f_agg': 'var', 'maxlag': 40}],
'partial_autocorrelation': [{'lag': 0},
{'lag': 1},
{'lag': 2},
{'lag': 3},
{'lag': 4},
{'lag': 5},
{'lag': 6},
{'lag': 7},
{'lag': 8},
{'lag': 9}],
'number_cwt_peaks': [{'n': 1}, {'n': 5}],
'number_peaks': [{'n': 1}, {'n': 3}, {'n': 5}, {'n': 10}, {'n': 50}],
'binned_entropy': [{'max_bins': 10}],
'index_mass_quantile': [{'q': 0.1},
{'q': 0.2},
{'q': 0.3},
{'q': 0.4},
{'q': 0.6},
{'q': 0.7},
{'q': 0.8},
{'q': 0.9}],
'cwt_coefficients': [{'widths':(2, 5, 10, 20), 'coeff': 0, 'w': 2},
{'widths':(2, 5, 10, 20), 'coeff': 0, 'w': 5},
{'widths':(2, 5, 10, 20), 'coeff': 0, 'w': 10},
{'widths':(2, 5, 10, 20), 'coeff': 0, 'w': 20},
{'widths':(2, 5, 10, 20), 'coeff': 1, 'w': 2},
{'widths':(2, 5, 10, 20), 'coeff': 1, 'w': 5},
{'widths':(2, 5, 10, 20), 'coeff': 1, 'w': 10},
{'widths':(2, 5, 10, 20), 'coeff': 1, 'w': 20},
{'widths':(2, 5, 10, 20), 'coeff': 2, 'w': 2},
{'widths':(2, 5, 10, 20), 'coeff': 2, 'w': 5},
{'widths':(2, 5, 10, 20), 'coeff': 2, 'w': 10},
{'widths':(2, 5, 10, 20), 'coeff': 2, 'w': 20},
{'widths':(2, 5, 10, 20), 'coeff': 3, 'w': 2},
{'widths':(2, 5, 10, 20), 'coeff': 3, 'w': 5},
{'widths':(2, 5, 10, 20), 'coeff': 3, 'w': 10},
{'widths':(2, 5, 10, 20), 'coeff': 3, 'w': 20},
{'widths':(2, 5, 10, 20), 'coeff': 4, 'w': 2},
{'widths':(2, 5, 10, 20), 'coeff': 4, 'w': 5},
{'widths':(2, 5, 10, 20), 'coeff': 4, 'w': 10},
{'widths':(2, 5, 10, 20), 'coeff': 4, 'w': 20},
{'widths':(2, 5, 10, 20), 'coeff': 5, 'w': 2},
{'widths':(2, 5, 10, 20), 'coeff': 5, 'w': 5},
{'widths':(2, 5, 10, 20), 'coeff': 5, 'w': 10},
{'widths':(2, 5, 10, 20), 'coeff': 5, 'w': 20},
{'widths':(2, 5, 10, 20), 'coeff': 6, 'w': 2},
{'widths':(2, 5, 10, 20), 'coeff': 6, 'w': 5},
{'widths':(2, 5, 10, 20), 'coeff': 6, 'w': 10},
{'widths':(2, 5, 10, 20), 'coeff': 6, 'w': 20},
{'widths':(2, 5, 10, 20), 'coeff': 7, 'w': 2},
{'widths':(2, 5, 10, 20), 'coeff': 7, 'w': 5},
{'widths':(2, 5, 10, 20), 'coeff': 7, 'w': 10},
{'widths':(2, 5, 10, 20), 'coeff': 7, 'w': 20},
{'widths':(2, 5, 10, 20), 'coeff': 8, 'w': 2},
{'widths':(2, 5, 10, 20), 'coeff': 8, 'w': 5},
{'widths':(2, 5, 10, 20), 'coeff': 8, 'w': 10},
{'widths':(2, 5, 10, 20), 'coeff': 8, 'w': 20},
{'widths':(2, 5, 10, 20), 'coeff': 9, 'w': 2},
{'widths':(2, 5, 10, 20), 'coeff': 9, 'w': 5},
{'widths':(2, 5, 10, 20), 'coeff': 9, 'w': 10},
{'widths':(2, 5, 10, 20), 'coeff': 9, 'w': 20},
{'widths':(2, 5, 10, 20), 'coeff': 10, 'w': 2},
{'widths':(2, 5, 10, 20), 'coeff': 10, 'w': 5},
{'widths':(2, 5, 10, 20), 'coeff': 10, 'w': 10},
{'widths':(2, 5, 10, 20), 'coeff': 10, 'w': 20},
{'widths':(2, 5, 10, 20), 'coeff': 11, 'w': 2},
{'widths':(2, 5, 10, 20), 'coeff': 11, 'w': 5},
{'widths':(2, 5, 10, 20), 'coeff': 11, 'w': 10},
{'widths':(2, 5, 10, 20), 'coeff': 11, 'w': 20},
{'widths':(2, 5, 10, 20), 'coeff': 12, 'w': 2},
{'widths':(2, 5, 10, 20), 'coeff': 12, 'w': 5},
{'widths':(2, 5, 10, 20), 'coeff': 12, 'w': 10},
{'widths':(2, 5, 10, 20), 'coeff': 12, 'w': 20},
{'widths':(2, 5, 10, 20), 'coeff': 13, 'w': 2},
{'widths':(2, 5, 10, 20), 'coeff': 13, 'w': 5},
{'widths':(2, 5, 10, 20), 'coeff': 13, 'w': 10},
{'widths':(2, 5, 10, 20), 'coeff': 13, 'w': 20},
{'widths':(2, 5, 10, 20), 'coeff': 14, 'w': 2},
{'widths':(2, 5, 10, 20), 'coeff': 14, 'w': 5},
{'widths':(2, 5, 10, 20), 'coeff': 14, 'w': 10},
{'widths':(2, 5, 10, 20), 'coeff': 14, 'w': 20}],
'spkt_welch_density': [{'coeff': 2}, {'coeff': 5}, {'coeff': 8}],
'ar_coefficient': [{'coeff': 0, 'k': 10},
{'coeff': 1, 'k': 10},
{'coeff': 2, 'k': 10},
{'coeff': 3, 'k': 10},
{'coeff': 4, 'k': 10}],
'change_quantiles': [{'ql': 0.0, 'qh': 0.2, 'isabs': False, 'f_agg': 'mean'},
{'ql': 0.0, 'qh': 0.2, 'isabs': False, 'f_agg': 'var'},
{'ql': 0.0, 'qh': 0.2, 'isabs': True, 'f_agg': 'mean'},
{'ql': 0.0, 'qh': 0.2, 'isabs': True, 'f_agg': 'var'},
{'ql': 0.0, 'qh': 0.4, 'isabs': False, 'f_agg': 'mean'},
{'ql': 0.0, 'qh': 0.4, 'isabs': False, 'f_agg': 'var'},
{'ql': 0.0, 'qh': 0.4, 'isabs': True, 'f_agg': 'mean'},
{'ql': 0.0, 'qh': 0.4, 'isabs': True, 'f_agg': 'var'},
{'ql': 0.0, 'qh': 0.6, 'isabs': False, 'f_agg': 'mean'},
{'ql': 0.0, 'qh': 0.6, 'isabs': False, 'f_agg': 'var'},
{'ql': 0.0, 'qh': 0.6, 'isabs': True, 'f_agg': 'mean'},
{'ql': 0.0, 'qh': 0.6, 'isabs': True, 'f_agg': 'var'},
{'ql': 0.0, 'qh': 0.8, 'isabs': False, 'f_agg': 'mean'},
{'ql': 0.0, 'qh': 0.8, 'isabs': False, 'f_agg': 'var'},
{'ql': 0.0, 'qh': 0.8, 'isabs': True, 'f_agg': 'mean'},
{'ql': 0.0, 'qh': 0.8, 'isabs': True, 'f_agg': 'var'},
{'ql': 0.0, 'qh': 1.0, 'isabs': False, 'f_agg': 'mean'},
{'ql': 0.0, 'qh': 1.0, 'isabs': False, 'f_agg': 'var'},
{'ql': 0.0, 'qh': 1.0, 'isabs': True, 'f_agg': 'mean'},
{'ql': 0.0, 'qh': 1.0, 'isabs': True, 'f_agg': 'var'},
{'ql': 0.2, 'qh': 0.2, 'isabs': False, 'f_agg': 'mean'},
{'ql': 0.2, 'qh': 0.2, 'isabs': False, 'f_agg': 'var'},
{'ql': 0.2, 'qh': 0.2, 'isabs': True, 'f_agg': 'mean'},
{'ql': 0.2, 'qh': 0.2, 'isabs': True, 'f_agg': 'var'},
{'ql': 0.2, 'qh': 0.4, 'isabs': False, 'f_agg': 'mean'},
{'ql': 0.2, 'qh': 0.4, 'isabs': False, 'f_agg': 'var'},
{'ql': 0.2, 'qh': 0.4, 'isabs': True, 'f_agg': 'mean'},
{'ql': 0.2, 'qh': 0.4, 'isabs': True, 'f_agg': 'var'},
{'ql': 0.2, 'qh': 0.6, 'isabs': False, 'f_agg': 'mean'},
{'ql': 0.2, 'qh': 0.6, 'isabs': False, 'f_agg': 'var'},
{'ql': 0.2, 'qh': 0.6, 'isabs': True, 'f_agg': 'mean'},
{'ql': 0.2, 'qh': 0.6, 'isabs': True, 'f_agg': 'var'},
{'ql': 0.2, 'qh': 0.8, 'isabs': False, 'f_agg': 'mean'},
{'ql': 0.2, 'qh': 0.8, 'isabs': False, 'f_agg': 'var'},
{'ql': 0.2, 'qh': 0.8, 'isabs': True, 'f_agg': 'mean'},
{'ql': 0.2, 'qh': 0.8, 'isabs': True, 'f_agg': 'var'},
{'ql': 0.2, 'qh': 1.0, 'isabs': False, 'f_agg': 'mean'},
{'ql': 0.2, 'qh': 1.0, 'isabs': False, 'f_agg': 'var'},
{'ql': 0.2, 'qh': 1.0, 'isabs': True, 'f_agg': 'mean'},
{'ql': 0.2, 'qh': 1.0, 'isabs': True, 'f_agg': 'var'},
{'ql': 0.4, 'qh': 0.2, 'isabs': False, 'f_agg': 'mean'},
{'ql': 0.4, 'qh': 0.2, 'isabs': False, 'f_agg': 'var'},
{'ql': 0.4, 'qh': 0.2, 'isabs': True, 'f_agg': 'mean'},
{'ql': 0.4, 'qh': 0.2, 'isabs': True, 'f_agg': 'var'},
{'ql': 0.4, 'qh': 0.4, 'isabs': False, 'f_agg': 'mean'},
{'ql': 0.4, 'qh': 0.4, 'isabs': False, 'f_agg': 'var'},
{'ql': 0.4, 'qh': 0.4, 'isabs': True, 'f_agg': 'mean'},
{'ql': 0.4, 'qh': 0.4, 'isabs': True, 'f_agg': 'var'},
{'ql': 0.4, 'qh': 0.6, 'isabs': False, 'f_agg': 'mean'},
{'ql': 0.4, 'qh': 0.6, 'isabs': False, 'f_agg': 'var'},
{'ql': 0.4, 'qh': 0.6, 'isabs': True, 'f_agg': 'mean'},
{'ql': 0.4, 'qh': 0.6, 'isabs': True, 'f_agg': 'var'},
{'ql': 0.4, 'qh': 0.8, 'isabs': False, 'f_agg': 'mean'},
{'ql': 0.4, 'qh': 0.8, 'isabs': False, 'f_agg': 'var'},
{'ql': 0.4, 'qh': 0.8, 'isabs': True, 'f_agg': 'mean'},
{'ql': 0.4, 'qh': 0.8, 'isabs': True, 'f_agg': 'var'},
{'ql': 0.4, 'qh': 1.0, 'isabs': False, 'f_agg': 'mean'},
{'ql': 0.4, 'qh': 1.0, 'isabs': False, 'f_agg': 'var'},
{'ql': 0.4, 'qh': 1.0, 'isabs': True, 'f_agg': 'mean'},
{'ql': 0.4, 'qh': 1.0, 'isabs': True, 'f_agg': 'var'},
{'ql': 0.6, 'qh': 0.2, 'isabs': False, 'f_agg': 'mean'},
{'ql': 0.6, 'qh': 0.2, 'isabs': False, 'f_agg': 'var'},
{'ql': 0.6, 'qh': 0.2, 'isabs': True, 'f_agg': 'mean'},
{'ql': 0.6, 'qh': 0.2, 'isabs': True, 'f_agg': 'var'},
{'ql': 0.6, 'qh': 0.4, 'isabs': False, 'f_agg': 'mean'},
{'ql': 0.6, 'qh': 0.4, 'isabs': False, 'f_agg': 'var'},
{'ql': 0.6, 'qh': 0.4, 'isabs': True, 'f_agg': 'mean'},
{'ql': 0.6, 'qh': 0.4, 'isabs': True, 'f_agg': 'var'},
{'ql': 0.6, 'qh': 0.6, 'isabs': False, 'f_agg': 'mean'},
{'ql': 0.6, 'qh': 0.6, 'isabs': False, 'f_agg': 'var'},
{'ql': 0.6, 'qh': 0.6, 'isabs': True, 'f_agg': 'mean'},
{'ql': 0.6, 'qh': 0.6, 'isabs': True, 'f_agg': 'var'},
{'ql': 0.6, 'qh': 0.8, 'isabs': False, 'f_agg': 'mean'},
{'ql': 0.6, 'qh': 0.8, 'isabs': False, 'f_agg': 'var'},
{'ql': 0.6, 'qh': 0.8, 'isabs': True, 'f_agg': 'mean'},
{'ql': 0.6, 'qh': 0.8, 'isabs': True, 'f_agg': 'var'},
{'ql': 0.6, 'qh': 1.0, 'isabs': False, 'f_agg': 'mean'},
{'ql': 0.6, 'qh': 1.0, 'isabs': False, 'f_agg': 'var'},
{'ql': 0.6, 'qh': 1.0, 'isabs': True, 'f_agg': 'mean'},
{'ql': 0.6, 'qh': 1.0, 'isabs': True, 'f_agg': 'var'},
{'ql': 0.8, 'qh': 0.2, 'isabs': False, 'f_agg': 'mean'},
{'ql': 0.8, 'qh': 0.2, 'isabs': False, 'f_agg': 'var'},
{'ql': 0.8, 'qh': 0.2, 'isabs': True, 'f_agg': 'mean'},
{'ql': 0.8, 'qh': 0.2, 'isabs': True, 'f_agg': 'var'},
{'ql': 0.8, 'qh': 0.4, 'isabs': False, 'f_agg': 'mean'},
{'ql': 0.8, 'qh': 0.4, 'isabs': False, 'f_agg': 'var'},
{'ql': 0.8, 'qh': 0.4, 'isabs': True, 'f_agg': 'mean'},
{'ql': 0.8, 'qh': 0.4, 'isabs': True, 'f_agg': 'var'},
{'ql': 0.8, 'qh': 0.6, 'isabs': False, 'f_agg': 'mean'},
{'ql': 0.8, 'qh': 0.6, 'isabs': False, 'f_agg': 'var'},
{'ql': 0.8, 'qh': 0.6, 'isabs': True, 'f_agg': 'mean'},
{'ql': 0.8, 'qh': 0.6, 'isabs': True, 'f_agg': 'var'},
{'ql': 0.8, 'qh': 0.8, 'isabs': False, 'f_agg': 'mean'},
{'ql': 0.8, 'qh': 0.8, 'isabs': False, 'f_agg': 'var'},
{'ql': 0.8, 'qh': 0.8, 'isabs': True, 'f_agg': 'mean'},
{'ql': 0.8, 'qh': 0.8, 'isabs': True, 'f_agg': 'var'},
{'ql': 0.8, 'qh': 1.0, 'isabs': False, 'f_agg': 'mean'},
{'ql': 0.8, 'qh': 1.0, 'isabs': False, 'f_agg': 'var'},
{'ql': 0.8, 'qh': 1.0, 'isabs': True, 'f_agg': 'mean'},
{'ql': 0.8, 'qh': 1.0, 'isabs': True, 'f_agg': 'var'}],
'fft_coefficient': [{'coeff': 0, 'attr': 'real'},
{'coeff': 1, 'attr': 'real'},
{'coeff': 2, 'attr': 'real'},
{'coeff': 3, 'attr': 'real'},
{'coeff': 4, 'attr': 'real'},
{'coeff': 5, 'attr': 'real'},
{'coeff': 6, 'attr': 'real'},
{'coeff': 7, 'attr': 'real'},
{'coeff': 8, 'attr': 'real'},
{'coeff': 9, 'attr': 'real'},
{'coeff': 10, 'attr': 'real'},
{'coeff': 11, 'attr': 'real'},
{'coeff': 12, 'attr': 'real'},
{'coeff': 13, 'attr': 'real'},
{'coeff': 14, 'attr': 'real'},
{'coeff': 15, 'attr': 'real'},
{'coeff': 16, 'attr': 'real'},
{'coeff': 17, 'attr': 'real'},
{'coeff': 18, 'attr': 'real'},
{'coeff': 19, 'attr': 'real'},
{'coeff': 20, 'attr': 'real'},
{'coeff': 21, 'attr': 'real'},
{'coeff': 22, 'attr': 'real'},
{'coeff': 23, 'attr': 'real'},
{'coeff': 24, 'attr': 'real'},
{'coeff': 25, 'attr': 'real'},
{'coeff': 26, 'attr': 'real'},
{'coeff': 27, 'attr': 'real'},
{'coeff': 28, 'attr': 'real'},
{'coeff': 29, 'attr': 'real'},
{'coeff': 30, 'attr': 'real'},
{'coeff': 31, 'attr': 'real'},
{'coeff': 32, 'attr': 'real'},
{'coeff': 33, 'attr': 'real'},
{'coeff': 34, 'attr': 'real'},
{'coeff': 35, 'attr': 'real'},
{'coeff': 36, 'attr': 'real'},
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{'coeff': 40, 'attr': 'real'},
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{'coeff': 44, 'attr': 'real'},
{'coeff': 45, 'attr': 'real'},
{'coeff': 46, 'attr': 'real'},
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{'coeff': 48, 'attr': 'real'},
{'coeff': 49, 'attr': 'real'},
{'coeff': 50, 'attr': 'real'},
{'coeff': 51, 'attr': 'real'},
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{'coeff': 55, 'attr': 'real'},
{'coeff': 56, 'attr': 'real'},
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{'coeff': 64, 'attr': 'real'},
{'coeff': 65, 'attr': 'real'},
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{'coeff': 70, 'attr': 'real'},
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{'coeff': 72, 'attr': 'real'},
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{'coeff': 76, 'attr': 'real'},
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{'coeff': 80, 'attr': 'real'},
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{'coeff': 82, 'attr': 'real'},
{'coeff': 83, 'attr': 'real'},
{'coeff': 84, 'attr': 'real'},
{'coeff': 85, 'attr': 'real'},
{'coeff': 86, 'attr': 'real'},
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'value_count': [{'value': 0}, {'value': 1}, {'value': -1}],
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{'attr': 'pvalue'},
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'energy_ratio_by_chunks': [{'num_segments': 10, 'segment_focus': 0},
{'num_segments': 10, 'segment_focus': 1},
{'num_segments': 10, 'segment_focus': 2},
{'num_segments': 10, 'segment_focus': 3},
{'num_segments': 10, 'segment_focus': 4},
{'num_segments': 10, 'segment_focus': 5},
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'ratio_beyond_r_sigma': [{'r': 0.5},
{'r': 1},
{'r': 1.5},
{'r': 2},
{'r': 2.5},
{'r': 3},
{'r': 5},
{'r': 6},
{'r': 7},
{'r': 10}]}<init_hyperparams>
def yolo2voc(image_height, image_width, bboxes):

 bboxes = bboxes.copy().astype(float)
 bboxes[..., [0, 2]] = bboxes[..., [0, 2]]* image_width
bboxes[..., [1, 3]] = bboxes[..., [1, 3]]* image_height
bboxes[..., [0, 1]] = bboxes[..., [0, 1]] - bboxes[..., [2, 3]]/2
bboxes[..., [2, 3]] = bboxes[..., [0, 1]] + bboxes[..., [2, 3]]
return bboxes
settings={
'standard_deviation': None,
'variance': None,
'skewness': None,
'kurtosis': None,
'absolute_sum_of_changes': None,
'last_location_of_maximum': None,
'first_location_of_maximum': None,
'last_location_of_minimum': None,
'first_location_of_minimum': None,
'count_above_mean': None,
'count_below_mean': None,
'maximum': None,
'minimum': None,
'c3': [{'lag': 100}, {'lag': 2000}, {'lag': 3000},{'lag': 10000}],
'number_peaks': [{'n': 1}, {'n': 5},{'n': 100},{'n': 1000}],
'fft_coefficient':
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],
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'index_mass_quantile': [{'q': 0.1},
{'q': 0.2},
{'q': 0.3},
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{'q': 0.6},
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{'q': 0.8},
{'q': 0.9}],
'spkt_welch_density': [{'coeff': 2}, {'coeff': 5}, {'coeff': 8}],
'ar_coefficient': [{'coeff': 0, 'k': 10},
{'coeff': 1, 'k': 10},
{'coeff': 2, 'k': 10},
{'coeff': 3, 'k': 10},
{'coeff': 4, 'k': 10}],
'energy_ratio_by_chunks': [{'num_segments': 10, 'segment_focus': 0},
{'num_segments': 10, 'segment_focus': 1},
{'num_segments': 10, 'segment_focus': 2},
{'num_segments': 10, 'segment_focus': 3},
{'num_segments': 10, 'segment_focus': 4},
{'num_segments': 10, 'segment_focus': 5},
{'num_segments': 10, 'segment_focus': 6},
{'num_segments': 10, 'segment_focus': 7},
{'num_segments': 10, 'segment_focus': 8},
{'num_segments': 10, 'segment_focus': 9}],
'ratio_beyond_r_sigma': [{'r': 0.5},
{'r': 1},
{'r': 1.5},
{'r': 2},
{'r': 2.5},
{'r': 3},
{'r': 5},
{'r': 6},
{'r': 7},
{'r': 10}],
'max_langevin_fixed_point': [{'m': 3, 'r': 30}],
'change_quantiles': [{'ql': 0.0, 'qh': 0.2, 'isabs': False, 'f_agg': 'mean'},
{'ql': 0.0, 'qh': 0.2, 'isabs': False, 'f_agg': 'var'},
{'ql': 0.0, 'qh': 0.2, 'isabs': True, 'f_agg': 'mean'},
{'ql': 0.0, 'qh': 0.2, 'isabs': True, 'f_agg': 'var'},
{'ql': 0.0, 'qh': 0.4, 'isabs': False, 'f_agg': 'mean'},
{'ql': 0.0, 'qh': 0.4, 'isabs': False, 'f_agg': 'var'},
{'ql': 0.0, 'qh': 0.4, 'isabs': True, 'f_agg': 'mean'},
{'ql': 0.0, 'qh': 0.4, 'isabs': True, 'f_agg': 'var'},
{'ql': 0.0, 'qh': 0.6, 'isabs': False, 'f_agg': 'mean'},
{'ql': 0.0, 'qh': 0.6, 'isabs': False, 'f_agg': 'var'},
{'ql': 0.0, 'qh': 0.6, 'isabs': True, 'f_agg': 'mean'},
{'ql': 0.0, 'qh': 0.6, 'isabs': True, 'f_agg': 'var'},
{'ql': 0.0, 'qh': 0.8, 'isabs': False, 'f_agg': 'mean'},
{'ql': 0.0, 'qh': 0.8, 'isabs': False, 'f_agg': 'var'},
{'ql': 0.0, 'qh': 0.8, 'isabs': True, 'f_agg': 'mean'},
{'ql': 0.0, 'qh': 0.8, 'isabs': True, 'f_agg': 'var'},
{'ql': 0.0, 'qh': 1.0, 'isabs': False, 'f_agg': 'mean'}]
}<feature_engineering>
image_ids = []
PredictionStrings = []
for file_path in tqdm(glob('runs/detect/exp/labels/*txt')) :
image_id = file_path.split('/')[-1].split('.')[0]
w, h = test_df.loc[test_df.image_id==image_id,['width', 'height']].values[0]
f = open(file_path, 'r')
 data = np.array(f.read().replace('
', ' ' ).strip().split(' ')).astype(np.float32 ).reshape(-1, 6)
 data = data[:, [0, 5, 1, 2, 3, 4]]
bboxes = list(np.round(np.concatenate(( data[:, :2], np.round(yolo2voc(h, w, data[:, 2:]))), axis =1 ).reshape(-1), 1 ).astype(str))
for idx in range(len(bboxes)) :
bboxes[idx] = str(int(float(bboxes[idx])))if idx%6!=1 else bboxes[idx]
image_ids.append(image_id)
 PredictionStrings.append(' '.join(bboxes))
res = pd.DataFrame()
n_ts = 20
l_ts = 1000
n_ti = 3
def tsfresh_time(sett):
for f, param in tqdm(sett.items()):
res.loc[f, "feature"] = f
res.loc[f, "n_samp"] = n_ts
res.loc[f, "length"] = l_ts
fc_dict = {f:param}
t = timeit.timeit(lambda : extract_features(train_t,
column_id='id',
column_sort='time',
column_value="acoustic_data",
n_jobs=1,
default_fc_parameters=fc_dict,
disable_progressbar=True),
number=n_ti)
 n_fs = 1
res.loc[f, "n_fs"] = n_fs
res.loc[f, "t_abs"] = t * 1.0/n_fs
res.loc[f, "t_1ts"] = t*1.0/(n_ts*n_fs)
 return res 
<init_hyperparams>
pred_df = pd.DataFrame({'image_id':image_ids,
'PredictionString':PredictionStrings})
 sub_df = pd.merge(test_df, pred_df, on = 'image_id', how = 'left' ).fillna("14 1 0 0 1 1")
 sub_df = sub_df[['image_id', 'PredictionString']]
sub_df.to_csv('/kaggle/working/submission.csv',index = False)
 sub_df.tail()
<prepare_x_and_y><EOS> 
shutil.rmtree('/kaggle/working/yolov5' )